diff --git a/.gitignore b/.gitignore
index 064e8ff..fdedbdb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,6 +7,9 @@ dataplot/
 *.bmp
 *.gif
 
+# OS
+.DS_Store
+
 # IDE file
 .idea/
 
diff --git a/Nov_27/Full/Arduino/timer/._timer.ino b/Nov_27/Full/Arduino/timer/._timer.ino
new file mode 100644
index 0000000..94deaab
Binary files /dev/null and b/Nov_27/Full/Arduino/timer/._timer.ino differ
diff --git a/Nov_27/Full/Arduino/timer/timer.ino b/Nov_27/Full/Arduino/timer/timer.ino
new file mode 100644
index 0000000..5f8e178
--- /dev/null
+++ b/Nov_27/Full/Arduino/timer/timer.ino
@@ -0,0 +1,111 @@
+// Global Variables
+int timer = 0;
+int on = 0;
+int minInterval = 5;
+float val;
+float time2;
+float time3;
+
+void setup() {
+  // Setup pins for pin activated interrupts: p8, p7
+  pinMode(3,INPUT);//digitalWrite(8,HIGH);
+  pinMode(2,INPUT);//digitalWrite(7,HIGH);
+  //pinMode(2, INPUT_PULLUP);
+  
+  // Setup pin for timer activated interrupts: p13
+  pinMode(13,OUTPUT);//`******digitalWrite(13,LOW);
+  pinMode(12,OUTPUT); // Open the relay to shut of RPi
+
+  
+  // Setup the timer interrupt configuration for 1 Hz
+  /*
+  cli();
+  TCCR1A = 0;// set entire TCCR1A register to 0
+  TCCR1B = 0;// same for TCCR1B
+  TCNT1  = 0;//initialize counter value to 0
+  // set compare match register for 1hz increments
+  OCR1A = 15624;// = (16*10^6) / (1*1024) - 1 (must be <65536)
+  // Set CS12 and CS10 bits for 1024 prescaler
+  TCCR1B |= (1 << CS12) | (1 << CS10);  
+  // enable timer compare interrupt
+  TIMSK1 |= (1 << OCIE1A);
+  // Normally would turn on, but this is left for the p8 interrupt to do:
+  // turn on CTC mode
+  // TCCR1B |= (1 << WGM12);
+  //
+  sei();
+  */
+
+  // Setup p3 and p2 interrupt code
+  attachInterrupt(digitalPinToInterrupt(2),ISR_p2,FALLING);
+  //attachInterrupt(digitalPinToInterrupt(2),ISR_p2,RISING);
+  attachInterrupt(digitalPinToInterrupt(3),ISR_p3,RISING);
+
+  // Turn on the RPi pin to start the flow of energy to the RPi so it 
+  // can be configured to run the system and eventually activate p8 and
+  // p9 interrupt ISR's which will begin the total system operation.
+  digitalWrite(13,HIGH); // this flips a relay energizing the RPi
+  digitalWrite(12,LOW);
+
+  Serial.begin(9600);
+  Serial.println("Running...");
+}
+
+// timer1 interrupt service routine
+ISR(TIMER1_COMPA_vect){
+  // turn on the RPi, as the time interval has elapsed
+  if(timer == 60*minInterval){
+  //if(timer == 10){
+    digitalWrite(12,LOW);
+    digitalWrite(13,HIGH);
+    timer = 1;
+    Serial.println(timer);
+  } else {
+    timer += 1;
+    Serial.println(timer);
+  }
+}
+
+// pin2 interrupt service routine
+void ISR_p2(){
+  // turn off RPi, as the signal pin has lifted saying it is shutdown
+  //if (timer > 1){
+  //if (timer > 5){
+  //     digitalWrite(13,LOW);
+  //     digitalWrite(12,HIGH);
+  //     Serial.println("pin 2 lifted");
+  delay(10000);
+  digitalWrite(13,LOW);
+  digitalWrite(12,HIGH);
+  Serial.println("pin 2 lifted");
+  //}
+}
+
+// pin3 interrup service routine
+void ISR_p3(){
+  // start the timer1, as the signal pin has been lifted saying the clocks are synchronized
+  cli();
+  TCCR1A = 0;// set entire TCCR1A register to 0
+  TCCR1B = 0;// same for TCCR1B
+  TCNT1  = 0;//initialize counter value to 0
+  // set compare match register for 1hz increments
+  OCR1A = 15624;// = (16*10^6) / (1*1024) - 1 (must be <65536)
+  // Set CS12 and CS10 bits for 1024 prescaler
+  TCCR1B |= (1 << CS12) | (1 << CS10);  
+  // enable timer compare interrupt
+  TIMSK1 |= (1 << OCIE1A);
+  TCCR1B |= (1 << WGM12); 
+  sei();
+  Serial.println("pin 3 lifted");
+  detachInterrupt(3);
+}
+
+
+
+void loop() {
+  // put your main code here, to run repeatedly:
+  //int pinstate = digitalRead(2);
+  //Serial.println(pinstate);
+  //delay(1);
+
+}
diff --git a/Nov_27/Full/Arduino/timer_polls/timer_polls.ino b/Nov_27/Full/Arduino/timer_polls/timer_polls.ino
new file mode 100644
index 0000000..44d358e
--- /dev/null
+++ b/Nov_27/Full/Arduino/timer_polls/timer_polls.ino
@@ -0,0 +1,112 @@
+// Global Variables
+int timer = 0;
+int minInterval = 5;
+int syncInterval = 1;
+int flag_sync = 0;
+//int timer_sync = 0;
+
+int CurrentStatePin2 = LOW;
+int PrevStatePin2 = LOW;
+int CurrentStatePin3 = LOW;
+int PrevStatePin3 = LOW;
+
+void setup() {
+  // Setup pins for pin activated interrupts: p8, p7
+  pinMode(3,INPUT);//digitalWrite(8,HIGH);
+  pinMode(2,INPUT);//digitalWrite(7,HIGH);
+  
+  // Setup pin for timer activated interrupts: p13
+  pinMode(13,OUTPUT);//`******digitalWrite(13,LOW);
+  pinMode(12,OUTPUT); // Open the relay to shut of RPi
+
+  // Turn on the RPi pin to start the flow of energy to the RPi so it 
+  // can be configured to run the system and eventually activate p8 and
+  // p9 interrupt ISR's which will begin the total system operation.
+  digitalWrite(13,HIGH); // this flips a relay energizing the RPi
+  digitalWrite(12,LOW);
+
+  Serial.begin(9600);
+  Serial.println("Running...");
+}
+
+// timer1 interrupt service routine
+ISR(TIMER1_COMPA_vect){
+  // turn on the RPi, as the time interval has elapsed
+  if(timer == 60*minInterval && flag_sync == 0){
+    digitalWrite(12,LOW);
+    digitalWrite(13,HIGH);
+    timer = 1;
+    Serial.println(timer);
+  } else if (timer == 60*syncInterval && flag_sync == 1){
+    digitalWrite(12,LOW);
+    digitalWrite(13,HIGH);
+    timer = 1;
+    Serial.println(timer);
+    flag_sync = 0;
+    Serial.println("flag_sync off");
+  //} else if  (timer_sync == 1) {
+  //  timer = 1;
+  //  timer_sync = 0;
+  //  Serial.println("timer_sync off");
+  } else {
+    timer += 1;
+    Serial.println(timer);
+  }
+}
+
+// pin2 interrupt service routine
+void ISR_p2(){
+  // turn off RPi, as the signal pin has lifted saying it is shutdown
+  delay(1000);
+  digitalWrite(13,LOW);
+  digitalWrite(12,HIGH);
+  Serial.println("pin 2 lifted");
+}
+
+// pin3 interrup service routine
+void ISR_p3(){
+  // start the timer1, as the signal pin has been lifted saying the clocks are synchronized
+  cli();
+  Serial.println("l1");
+  TCCR1A = 0;// set entire TCCR1A register to 0
+  TCCR1B = 0;// same for TCCR1B
+  TCNT1  = 0;//initialize counter value to 0
+  Serial.println("l2");
+  // set compare match register for 1hz increments
+  OCR1A = 15624;// = (16*10^6) / (1*1024) - 1 (must be <65536)
+  //OCR1A = 15614;// for Laser Module
+  Serial.println("l3");
+  // Set CS12 and CS10 bits for 1024 prescaler
+  TCCR1B |= (1 << CS12) | (1 << CS10);  
+  // enable timer compare interrupt
+  TIMSK1 |= (1 << OCIE1A);
+  TCCR1B |= (1 << WGM12); 
+  Serial.println("l4");
+  sei();
+  Serial.println("l5");
+  timer = 0; // initialize or reset timer (first execution, or re-sync)
+  Serial.println("pin 3 lifted");
+}
+
+
+void loop() {
+  
+  PrevStatePin2 = CurrentStatePin2;
+  PrevStatePin3 = CurrentStatePin3;
+
+  delay (10);
+  
+  CurrentStatePin2 = digitalRead(2);
+  CurrentStatePin3 = digitalRead(3);
+
+  if ((PrevStatePin2 == HIGH) && (CurrentStatePin2 == LOW)){
+    ISR_p2();
+  }
+  if ((PrevStatePin3 == LOW) && (CurrentStatePin3 == HIGH)){
+    ISR_p3();
+    flag_sync = 1;
+    //timer_sync = 1;
+    Serial.println("flag_sync on");
+    //Serial.println("timer_sync on");
+  }
+}
diff --git a/Nov_27/Full/Camera/RPiCam/._RPiCM.py b/Nov_27/Full/Camera/RPiCam/._RPiCM.py
new file mode 100644
index 0000000..540a5a9
Binary files /dev/null and b/Nov_27/Full/Camera/RPiCam/._RPiCM.py differ
diff --git a/Nov_27/Full/Camera/RPiCam/._img_capture.py b/Nov_27/Full/Camera/RPiCam/._img_capture.py
new file mode 100644
index 0000000..cdc2139
Binary files /dev/null and b/Nov_27/Full/Camera/RPiCam/._img_capture.py differ
diff --git a/Nov_27/Full/Camera/RPiCam/RPiCM.py b/Nov_27/Full/Camera/RPiCam/RPiCM.py
new file mode 100644
index 0000000..ca837ac
--- /dev/null
+++ b/Nov_27/Full/Camera/RPiCam/RPiCM.py
@@ -0,0 +1,174 @@
+# Author:  Alex R. Mead
+# Date: May 2016
+# Description:
+# This code will run on the Raspberry Pi 2 with Camera Module (RPiCM) to interact with the MacBookPro (MBP). It will
+# wait for the MBP to request a measurement, take a measurement, then wait for the MBP to request the latest
+# measurement, at which time it will send the measurement to the MBP.
+
+# Same as the MBP this system is designed to run like finite state machine.
+
+from __future__ import (
+    unicode_literals,
+    absolute_import,
+    print_function,
+    division,
+    )
+
+import sys
+import socket
+import io
+import subprocess
+import picamera
+import numpy as np
+import gc
+from time import sleep
+
+# Constants
+#IP = '127.0.0.1' # Testing on a single machine
+IP = '0.0.0.0' # Actual run time.
+port1 = 1234
+port2 = 2345
+marker = "?"
+
+MAX_PIXEL_VALUE = 1023 # 2^10 = 1024
+PIXEL_HEIGHT = 1944
+PIXEL_WIDTH = 2592
+
+# Takes in the Camera object instantiated at the beginning of the run and takes the desired SS expsoures with it.
+def HDRI(SS,camera):
+
+    size = len(SS)
+    print("Taking %i measurements.\n",size)
+    names = []
+
+    # Loop through for each shutter speed requested
+    for index, shutterSpeed in enumerate(SS):
+        fileName = str(shutterSpeed) + ".data"
+        camera.shutter_speed = shutterSpeed
+        sleep(1)
+        print(str(camera.shutter_speed))
+        camera.capture(fileName,format='jpeg',bayer=True)
+        names.append(fileName)
+
+    # Return the list of filenames to the calling function.
+    return names
+
+def main():
+
+    STATE = "idle"
+    RUNNING = [True,0]
+
+    # First declare the camera object and configure it as needed
+    camera = picamera.PiCamera()
+    camera.framerate=1
+    sleep(1.0)
+
+    camera.led = False
+    camera.iso = 100
+    sleep(10.0)
+    # This longer sleep is needed so analog_gain and digital_gains settle so when I turn them off in the next step
+    # they will have settled and not be in their initial 'low' values.
+
+    camera.exposure_mode = 'off' # Fixes analog_gain and digital_gain values
+    g = camera.awb_gains
+    camera.aws_mode = 'off'
+    camera.awb_gains = [1.0,1.0] # typical values 0.9-1.9 according to documentation, I picked these for consistency.
+    camera.rotation = 180
+    sleep(2.0)
+
+    while(RUNNING[0]):
+
+        if("idle" == STATE):
+            print("We're in idle state waiting to hear from the MacBookPro...")
+
+            # Start up a connection listening for the MBP.
+            sock = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
+            sock.setsockopt(socket.SOL_SOCKET,socket.SO_REUSEADDR,1)
+            sock.bind((IP,port1))
+            sock.listen(1)
+            connection, client_address = sock.accept()
+
+            rcv_msg = []
+            while True:
+                data = connection.recv(1)
+                if marker in data:
+                    rcv_msg.append(data[:data.find(marker)])
+                    break
+                rcv_msg.append(data)
+
+            # Check the message received from the MBP, if correct, continue, else exit.
+            rpl = ''.join(rcv_msg)
+            if rpl == "True":
+                print("Now going to acknowledge measurement.")
+
+            # elif used to teminate remotely.
+            elif rpl == "c":
+                print("Big John called it for me...")
+                sys.exit(0)
+
+            # Acknowledge the MBP so it knows a measurement will be taking place.
+            connection.send("True?")
+            connection.close()
+
+            # Change state: Continue on and take measurement
+            STATE ="Measuring"
+
+        elif ("Measuring" == STATE):
+            print("Engaging the Camera Module to measure the output of the CFS under test...")
+            # Here is the function call to take the measurement of the CFS inside CUBE2.0
+
+            # List of the shutter speeds in microseconds to be used
+            #SS = [1000000, 100000, 10000, 1000, 100, 10];
+            SS = [1000000, 100000, 10000];
+
+            # Take the measurements with the above stated shutter speeds
+            files = HDRI(SS,camera)
+
+            print("...Measurement taken of CFS.\n")
+
+            # Continue to the next state and wait for MBP to query for the 145 measurements.
+            STATE = "waitToSend"
+
+        elif("waitToSend" == STATE):
+            print("Waiting to send to MBP...")
+            # Setup connection to wait for the request from the MBP
+            # This maybe a short wait as the MBP may be trying to connect already if the Measurememt was a long time.
+            sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+            sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+            sock.bind((IP, port2))
+            sock.listen(1)
+            connection, client_address = sock.accept()
+
+            rcv_msg = []
+            while True:
+                data = connection.recv(1)
+                if marker in data:
+                    rcv_msg.append(data[:data.find(marker)])
+                    break
+                rcv_msg.append(data)
+
+            # Check the message received from the MBP, if correct, continue, else exit.
+            rpl = ''.join(rcv_msg)
+            if rpl == "True":
+                print("Send measurement.")
+
+            # Now use the same connection to let the MBP know it can grab the .jpg+RAW
+            # files and run RPiCM_C.c locally over there, which is MUCH faster.
+            connection.sendall("True?")
+            connection.close()
+
+            print("...measurment has been sent.\n")
+
+            # Change state: measurement has been sent, change state back to idle
+            STATE = "idle"
+
+        # Cycle counter for the finite state machine
+        RUNNING[1] = RUNNING[1] + 1
+        print("Cycle number: " + str(RUNNING[1]))
+
+    print("Little John called it...")
+    sys.exit(0)
+
+if __name__ == "__main__":
+    main()
+
diff --git a/Nov_27/Full/Camera/RPiCam/cam.py b/Nov_27/Full/Camera/RPiCam/cam.py
new file mode 100644
index 0000000..c91e666
--- /dev/null
+++ b/Nov_27/Full/Camera/RPiCam/cam.py
@@ -0,0 +1,32 @@
+import picamera
+from fractions import Fraction
+
+cam = picamera.PiCamera(resolution=(640, 480))
+cam.exposure_mode = 'off'
+DEBUG_DIR = 'debug/'
+filename = DEBUG_DIR + 'shutter_{0}_iso_{1}_expo_{2}.png'
+cam.capture("base.png")
+cam.framerate = Fraction(1, 6)
+while True:
+    try:
+        exp = input("Run experiment?[y/n] ")
+        if exp == 'y':
+            ss = input("shutter speed:  ")
+            iso = input("iso:  ")
+            options = {'a': 'off', 'b': 'auto', 'c': 'night', 'd': 'night_preview', 'e': 'sports'}
+            expo = input("exposure mode: " + str(options) + "   ")
+            #bright = input("brightness:  ")
+            ss, iso, opt = int(ss), int(iso), options[expo]
+            cam.shutter_speed = ss
+            cam.iso = iso
+            cam.exposure_mode = options[expo]
+            #cam.brightness = bright
+            cam.capture(filename.format(ss, iso, opt))
+        else:
+            cam.close()
+            break
+    except ValueError:
+        pass
+
+
+
diff --git a/Nov_27/Full/Camera/RPiCam/img_capture.py b/Nov_27/Full/Camera/RPiCam/img_capture.py
new file mode 100644
index 0000000..86fd14a
--- /dev/null
+++ b/Nov_27/Full/Camera/RPiCam/img_capture.py
@@ -0,0 +1,186 @@
+import picamera
+import os
+from fractions import Fraction
+import sys, traceback
+import time
+
+
+class ImgCollector:
+
+    def __init__(self, dir='', ns='img', form='png', raw=False, num=1, serialize=True):
+        if dir:
+            if not os.path.exists(dir):
+                os.mkdir(dir)
+            if dir[len(dir)-1] != '/':
+                dir += '/'
+
+        self.name_scheme = dir + ns + '_{0}.' + form
+        self._dir = dir
+        self._ns = ns
+        self._form = form
+        self._raw = raw
+        self._num = num
+        self.init_cam()
+        if serialize:
+            if os.path.exists("img_log.txt"):
+                rfile = open("img_log.txt", "r")
+                try:
+                    self.counter = int(rfile.read())
+                except ValueError:
+                    self.counter = 1
+            else:
+                rfile = open("img_log.txt", "w")
+                self.counter = 1
+            rfile.close()
+        else:
+            self.counter = 1
+
+
+    def change_ns(self, ns):
+        self._ns = ns
+        self.name_scheme = self._dir + self._ns + '_{0}.' + self._form
+
+    def change_format(self, form):
+        self._form = form
+        self.name_scheme = self._dir + self._ns + '_{0}.' + self._form
+
+    def change_dir(self, dir):
+        self._dir = dir
+        self.name_scheme = self._dir + self._ns + '_{0}.' + self._form
+
+    def change_num(self, num):
+        self._num = num
+        if self._num == 1:
+            self.capture = self.uni_capture
+        else:
+            self.capture = self.multi_capture
+
+    def init_cam(self):
+        self.cam = picamera.PiCamera(resolution=(640, 480))
+        time.sleep(2)
+        self.cam.led = False
+        self.cam.framerate = Fraction(1, 6)
+        self.cam.rotation = 180
+        self.cam.shutter_speed = 800000
+        self.cam.iso = 100
+        self.cam.exposure_mode = 'off'
+        time.sleep(3)
+        if self._num == 1:
+            self.capture = self.uni_capture
+        else:
+            self.capture = self.multi_capture
+
+    def get_last_meas(self):
+        if self._num == 1:
+            return self.name_scheme.format(self.counter - 1)
+        else:
+            return self._dir + self._ns + '_%d_{0}.' % (self.counter - 1) + self._form
+
+    def shutdown(self):
+        self.cam.close()
+        wfile = open("img_log.txt", "w")
+        wfile.write(str(self.counter))
+        wfile.close()
+
+    def uni_capture(self):
+        self.cam.capture(self.name_scheme.format(self.counter), bayer=self._raw)
+        self.counter += 1
+
+    def multi_capture(self):
+        file_list = [self.name_scheme.format("%d_%d" % (self.counter, i)) for i in range(1, self._num+1)]
+        self.cam.capture_sequence(file_list, bayer=self._raw)
+        self.counter += 1
+
+
+def main():
+    prompt = input("Welcome to the RPiCam Module. Type q for quick test, d for debug, or f / [other inputs] for full test.\n")
+    global recur
+    if prompt == 'q':
+        directory = 'quick_test'
+        name_pattern = 'img'
+        pic_format = 'png'
+        raw_image = False
+        num_meas = 1
+        recur = True
+    elif prompt == 'd':
+        camera_debug()
+        sys.exit()
+    else:
+        directory = input("Input a directory:\n")
+        name_pattern = input("Input a name pattern:\n")
+        pic_format = input("Input a picture format:\n")
+        raw_image = input("Raw image?[y/n]\n") in ['y', 'yes']
+        num_meas = int(input("Number of image samples for one measurement?\n"))
+        recur = False
+
+    while True:
+        try:
+            ic = ImgCollector(dir=directory, ns=name_pattern, form=pic_format, raw=raw_image, num=num_meas)
+            break
+        except:
+            directory = input("Ill-formated directory, type in another one: ")
+            traceback.print_exc(file=sys.stdout)
+
+    while True:
+        if recur:
+            ic.shutdown()
+            break
+
+        option = input("Type in an action or h for help:\n")
+        if option == 'h':
+            print("m: take measurement\n" +
+                  "r: show raw image status\n" +
+                  "cr: change raw image status\n" +
+                  "cf: change image format\n" +
+                  "cd: change directory\n" +
+                  "cn: change name\n" +
+                  "cm: change number of measurement\n" +
+                  "e: end the program")
+        elif option == 'm':
+            ic.capture()
+        elif option == 'r':
+            print(ic._raw)
+        elif option == 'cr':
+            ic._raw = input("Raw image?[y/n]\n") in ['y', 'yes']
+        elif option == 'cf':
+            ic.change_format(input("Input a picture format:\n"))
+        elif option == 'cd':
+            ic.change_dir(input("Input a directory:\n"))
+        elif option == 'cn':
+            ic.change_ns(input("Input a name pattern:\n"))
+        elif option == 'cm':
+            ic.change_num(int(input("Number of image samples for one measurement?\n")))
+        elif option == 'e':
+            ic.shutdown()
+            break
+
+
+def camera_debug():
+    DEBUG_DIR = 'debug/'
+    filename = DEBUG_DIR + 'shutter_{0}_iso_{1}_bright_{2}.jpeg'
+    analog = 'analog_gain: '
+    awb_gain = 'awb_gain: '
+    expo_modes = ['night', 'night_preview', 'very_long']
+    #flash_modes = ['off', 'redeye']
+    flash_modes = ['off']
+    shutter = [600000, 800000, 1000000]
+    with picamera.PiCamera(resolution=(640, 480)) as cam:
+        time.sleep(1)
+        print(analog + str(cam.analog_gain))
+        print(awb_gain + str(cam.awb_gains))
+        cam.framerate = Fraction(1, 6)
+        for ss in shutter:
+            #for br in brightness:
+            time.sleep(0.1)
+            cam.shutter_speed = ss
+            cam.brightness = 50
+            cam.capture(filename.format(ss), bayer=False)
+
+
+if __name__ == "__main__":
+    recur = True
+    while recur:
+        main()
+
+
+
diff --git a/Nov_27/Full/Camera/accel_correct_config/LICENSE b/Nov_27/Full/Camera/accel_correct_config/LICENSE
new file mode 100644
index 0000000..9cecc1d
--- /dev/null
+++ b/Nov_27/Full/Camera/accel_correct_config/LICENSE
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
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+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    {one line to give the program's name and a brief idea of what it does.}
+    Copyright (C) {year}  {name of author}
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    {project}  Copyright (C) {year}  {fullname}
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.
diff --git a/Nov_27/Full/Camera/accel_correct_config/accel.py b/Nov_27/Full/Camera/accel_correct_config/accel.py
new file mode 100644
index 0000000..1c04871
--- /dev/null
+++ b/Nov_27/Full/Camera/accel_correct_config/accel.py
@@ -0,0 +1,739 @@
+#!/usr/bin/env python
+# -*- coding:utf-8, indent=tab, tabstop=4 -*-
+#
+# See 'LICENSE'  for copying
+#
+# Revision history
+# Date			Author					Version		Details
+# ----------------------------------------------------------------------------------
+# 2016-12-31	Massimo Di Primio		V.0.04		Fixed some basic functionality
+#
+# 2017-01-03	Massimo Di Primio		0.05		Added Interrut handler
+#
+# 2018-01-10    Massimo Di Primio       0.06        Added config file parser
+
+"""Simple code example for Adafruit MMA8452 3-axis Accelerometer
+
+This experimental code is intended for measuring gravity acceleration trough Adafruit(c) MMA8451, connected
+to a Raspberry Pi Model 2A, 2B, 2B+ or 3 (not yet tested with RPi Zero).
+Through this code we will demonstrate the ability of the 3-axis sensor MMA8451 to efficiently measure
+gravity acceleration, so that we can identify the spatial orientation of the device.
+Further and even more useful application can start from this minimal basic code.
+"""
+
+import smbus
+import time
+import datetime
+import os
+import sys
+import logging
+import threading
+import RPi.GPIO as GPIO
+import rss_cli_config as clicfg
+from collections import deque
+
+__author__ = "Massimo Di Primio"
+__copyright__ = "Copyright 2016, dpmiictc"
+__credits__ = ["Massimo Di Primio", "Dario Dalla Libera"]
+__license__ = "GNU GENERAL PUBLIC LICENSE Version 3"
+__version__ = "0.0.1"
+__deprecated__ = "None so far"
+__date__ = "2017-01-03"
+__maintainer__ = "Massimo Di Primio"
+__email__ = "massimo@diprimio.com"
+__status__ = "Testing"
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# User Configuration Options (UCO)
+# This section will be repaced soon by an external configuration file
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+CFG_INTERRUPT = 1
+# MMA8451_RANGE		= {}
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Application Definition Constants (ADC)
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+# Useful definitions
+EARTH_GRAVITY_MS2 = 9.80665
+
+# Range values
+RANGE_8_G = 0b10  # +/- 8g
+RANGE_4_G = 0b01  # +/- 4g
+RANGE_2_G = 0b00  # +/- 2g (default value)
+
+RANGE_DIVIDER = {
+    RANGE_2_G: 4096 / EARTH_GRAVITY_MS2,
+    RANGE_4_G: 2048 / EARTH_GRAVITY_MS2,
+    RANGE_8_G: 1024 / EARTH_GRAVITY_MS2,
+}
+
+# Some static values
+deviceName = 0x1a
+
+# Various addresses
+i2caddr = 0x1D
+#
+# Useful Register Address
+REG_STATUS = 0x00  # Read-Only
+REG_WHOAMI = 0x0d  # Read-Only
+REG_DEVID = 0x1A  # Read-Only
+REG_OUT_X_MSB = 0x01  # Read-Only
+REG_OUT_X_LSB = 0x02  # Read-Only
+REG_OUT_Y_MSB = 0x03  # Read-Only
+REG_OUT_Y_LSB = 0x04  # Read-Only
+REG_OUT_Z_MSB = 0x05  # Read-Only
+REG_OUT_Z_LSB = 0x06  # Read-Only
+REG_F_SETUP = 0x09  # Read/Write
+REG_XYZ_DATA_CFG = 0x0e  # Read/Write
+REG_PL_STATUS = 0x10  # Read-Only
+REG_PL_CFG = 0x11  # Read/Write
+REG_CTRL_REG1 = 0x2A  # Read/Write
+REG_CTRL_REG2 = 0x2B  # Read/Write
+REG_CTRL_REG3 = 0x2C  # Read/Write
+REG_CTRL_REG4 = 0x2D  # Read/Write
+REG_CTRL_REG5 = 0x2E  # Read/Write
+
+REDUCED_NOISE_MODE = 0
+OVERSAMPLING_MODE = 1
+HIGH_RES_MODE = {
+    REDUCED_NOISE_MODE: [REG_CTRL_REG1, 0x4],
+    OVERSAMPLING_MODE: [REG_CTRL_REG2, 0x2],
+}
+
+# Auto-Wake Sample Frequencies for Register CTRL_REG1 (0x2A) (Read/Write)
+# sample frequency when the device is in SLEEP Mode. Default value: 00.
+ASLP_RATE_FREQ_50_HZ = 0x00
+ASLP_RATE_FREQ_12_5_HZ = 0x40
+ASLP_RATE_FREQ_6_25HZ = 0x80
+ASLP_RATE_FREQ_1_56_HZ = 0xc0
+
+# Data rate values
+DATARATE_800_HZ = 0x00  # 800Hz
+DATARATE_400_HZ = 0x08  # 400Hz
+DATARATE_200_HZ = 0x10  # 200Hz
+DATARATE_100_HZ = 0x18  # 100Hz
+DATARATE_50_HZ = 0x20  # 50Hz
+DATARATE_12_5_HZ = 0x28  # 12.5Hz
+DATARATE_6_25HZ = 0x30  # 6.25Hz
+DATARATE_1_56_HZ = 0x38  # 1.56Hz
+
+# Orientation labeling 
+PL_PUF = 0
+PL_PUB = 1
+PL_PDF = 2
+PL_PDB = 3
+PL_LRF = 4
+PL_LRB = 5
+PL_LLF = 6
+PL_LLB = 7
+
+# Precision
+PRECISION_14_BIT = 14
+PRECISION_08_BIT = 8
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define Register Flags
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Register CTRL_REG1 (0x2a) R/W - System Control 1 Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |  ASLPRATE1   |  ASLPRATE0   |     DR2      |    DR1       |    DR0       |  LNOISE      |  F_READ      |  ACTIVE      |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# Auto-Wake Sample frequency Selection
+FLAG_ASLPRATE_50_HZ = 0x00  # Auto-Wake Sample frequency (Sleep Mode Rate Detection) 50 Hz
+FLAG_ASLPRATE_12_5_HZ = 0x40  # Auto-Wake Sample frequency (Sleep Mode Rate Detection) 12.5 Hz
+FLAG_ASLPRATE_6_25_HZ = 0x80  # Auto-Wake Sample frequency (Sleep Mode Rate Detection) 6.25 Hz
+FLAG_ASLPRATE_1_56_HZ = 0xc0  # Auto-Wake Sample frequency (Sleep Mode Rate Detection) 1.56 Hz
+# System Output Data Rates Selection
+FLAG_ODR_800_HZ = 0x00  # System Output Data Rate 800 Hz
+FLAG_ODR_400_HZ = 0x08  # System Output Data Rate 400 Hz
+FLAG_ODR_200_HZ = 0x10  # System Output Data Rate 200 Hz
+FLAG_ODR_100_HZ = 0x18  # System Output Data Rate 100 Hz
+FLAG_ODR_50_HZ = 0x20  # System Output Data Rate 50 Hz
+FLAG_ODR_12_5_HZ = 0x28  # System Output Data Rate 12.5 Hz
+FLAG_ODR_6_25_HZ = 0x30  # System Output Data Rate 6.25 Hz
+FLAG_ODR_1_56_HZ = 0x38  # System Output Data Rate 1.56 Hz
+# Other Flags
+FLAG_LNOISE = 0x04  # Low Noise (1: Reduced Noise, 0: Normal Mode)
+FLAG_F_READ = 0x02  # Fast Read  (1: 8 bit sample, 0: 14 bit Sample)
+FLAG_ACTIVE = 0x01  # Active (1: ACTIVE Mode, 0: STANDBY Mode)
+
+# Register CTRL_REG2 (0x2b) R/W - System Control 2 Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   ST         |   RST        |     0        |  SMODS1      |  SMODS0      |   SLPE       |   MODS1      |   MODS0      |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# Other Flags
+FLAG_STEST = 0x80  # Self Test (1: Self-Test enabled, 0: Self-Test disabled)
+FLAG_RESET = 0x40  # Reset (1: Reset enabled, 0: Reset disabled)
+# Sleep Mode Power Scheme Selection
+FLAG_SMODS_NORM = 0x00  # Sleep Mode Power Scheme Selection: Normal
+FLAG_SMODS_LNLP = 0x0a  # Sleep Mode Power Scheme Selection: Low-Noise Low Power
+FLAG_SMODS_HR = 0x12  # Sleep Mode Power Scheme Selection: High Resolution
+FLAG_SMODS_LP = 0x1b  # Sleep Mode Power Scheme Selection: Low Power
+# Other Flags
+FLAG_SLPE = 0x04  # Auto-Sleep (1: Auto-Sleep enabled, 0: Auto-Sleep Disabled)
+# Active Mode Power Scheme Selection (for both: Sleep and Active mode)
+FLAG_MODS_NORM = 0x00  # Active Mode Power Scheme Selection: Normal
+FLAG_MODS_LNLP = 0x09  # Active Mode Power Scheme Selection: Low-Noise Low Power
+FLAG_MODS_HR = 0x12  # Active Mode Power Scheme Selection: High Resolution
+FLAG_MODS_LP = 0x1b  # Active Mode Power Scheme Selection: Low Power
+
+# Register CTRL_REG4 (0x2d) R/W - Interrupt Enable Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# | INT_EN_ASLP  | INT_EN_FIFO  |INT_EN_TRANS  |INT_EN_LNDPR  |INT_EN_PULSE  |INT_EN_FF_MT  |       -      | INT_EN_DRDY  |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_INT_EN_ASLP = 0x80  # Interrupt Auto SLEEP/WAKE (0: Disabled, 1: Enabled)
+FLAG_INT_EN_FIFO = 0x40  # Interrupt FIFO (0: Disabled, 1: Enabled)
+FLAG_INT_EN_TRANS = 0x20  # Interrupt Transient (0: Disabled, 1: Enabled)
+FLAG_INT_EN_LNDPRT = 0x10  # Interrupt Orientation (0: Disabled, 1: Enabled)
+FLAG_INT_EN_PULSE = 0x08  # Interrupt Pulse Detection (0: Disabled, 1: Enabled)
+FLAG_INT_EN_FF_MT = 0x04  # Interrupt Freefall/Motion (0: Disabled, 1: Enabled)
+FLAG_INT_EN_BIT1 = 0x00  # Not Used
+FLAG_INT_EN_DRDY = 0x01  # Interrupt Data Ready (0: Disabled, 1: Enabled)
+
+# Register CTRL_REG5 (0x2e) R/W - Interrupt Configuration Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# | INT_CFG_ASLP | INT_CFG_FIFO |INT_CFG_TRANS |INT_CFG_LNDPRT|INT_CFG_PULSE |INT_CFG_FF_MT |       -      | INT_CFG_DRDY |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_INT_CFG_ASLP = 0x80  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_FIFO = 0x40  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_TRANS = 0x20  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_LNDPRT = 0x10  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_PULSE = 0x08  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_FF_MT = 0x04  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_BIT1 = 0x00  # Not Used
+FLAG_INT_CFG_DRDY = 0x01  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+
+# Register XYZ_DATA_CFG (0x0e) R/W
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |     0        |     0        |     0        |  HPF_OUT     |     0        |     0        |    FS1       |    FS0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# Other Flags
+FLAG_XYZ_DATA_BIT_7 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_6 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_5 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_HPF_OUT = 0x00  # High-Pass Filter (1: output data High-pass filtered, 0: output data High-pass NOT filtered)
+FLAG_XYZ_DATA_BIT_3 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_2 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_FS_2G = 0x00  # Full Scale Range 2g
+FLAG_XYZ_DATA_BIT_FS_4G = 0x01  # Full Scale Range 4g
+FLAG_XYZ_DATA_BIT_FS_8G = 0x02  # Full Scale Range 8g
+FLAG_XYZ_DATA_BIT_FS_RSVD = 0x03  # Reserved
+
+# Register F_SETUP (0x09) R/W - FIFO Setup Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |  F_MODE1     |  F_MODE0     |  F_WMRK5     |  F_WMRK4     |  F_WMRK3     |  F_WMRK2     |  F_WMRK1     |  F_WMRK0     |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_F_MODE_FIFO_NO = 0x00  # FIFO is disabled.
+FLAG_F_MODE_FIFO_RECNT = 0x40  # FIFO contains the most recent samples when overflowed (circular buffer)
+FLAG_F_MODE_FIFO_STOP = 0x80  # FIFO stops accepting new samples when overflowed.
+FLAG_F_MODE_FIFO_TRIGGER = 0xc0  # FIFO Trigger mode
+
+# Register PL_STATUS (0x010) R/O - Portrait/Landscape Status Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |    NEWLP     |    LO        |      -       |      -       |     -        |  LAPO[1]     |  LAPO[0]     |  BAFRO       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_PL_NEWLP = 0x80  # Landscape/Portrait status change flag.
+FLAG_PL_LO = 0x40  # Z-Tilt Angle Lockout.
+FLAG_PL_LAPO_PU = 0x00  # 00: Portrait Up: Equipment standing vertically in the normal orientation
+FLAG_PL_LAPO_PD = 0x02  # 01: Portrait Down: Equipment standing vertically in the inverted orientation
+FLAG_PL_LAPO_LR = 0x04  # 10: Landscape Right: Equipment is in landscape mode to the right
+FLAG_PL_LAPO_LL = 0x06  # 11: Landscape Left: Equipment is in landscape mode to the left.
+FLAG_PL_BAFRO = 0x01  # Back or Front orientation. (0: Front: Equipment is in the front facing orientation, 1: Back)
+
+# Register PL_CFG (0x011) R/W - Portrait/Landscape Configuration Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   DBCNTM     |  PL_EN       |      -       |      -       |     -        |      -       |      -       |     -        |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_PL_CFG_DBCNTM = 0x80  # Debounce counter mode selection (0: Decrements debounce, 1: Clears counter)
+FLAG_PL_CFG_PL_EN = 0x40  # Portrait/Landscape Detection Enable (0: P/L Detection Disabled, 1: P/L Detection Enabled)
+
+# Register TRANSIENT_CFG (0x1d) R/W - Transient_CFG Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |      -       |      -       |      -       |     ELE      |   ZTEFE      |   YTEFE      |   XTEFE      |  HPF_BYP     |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_TRANSIENT_CFG_ELE = 0x10      # Transient event flags (0: Event flag latch disabled; 1: Event flag latch enabled)
+FLAG_TRANSIENT_CFG_ZTEFE = 0x08    # Event flag enable on Z (0: Event detection disabled; 1: Raise event flag)
+FLAG_TRANSIENT_CFG_YTEFE = 0x04    # Event flag enable on Y (0: Event detection disabled; 1: Raise event flag)
+FLAG_TRANSIENT_CFG_XTEFE = 0x02    # Event flag enable on X (0: Event detection disabled; 1: Raise event flag)
+FLAG_TRANSIENT_CFG_HPF_BYP = 0x01  # Bypass High-Pass filter/Motion Detection
+
+# Register TRANSIENT_SCR (0x01e) R/O - TRANSIENT_SRC Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |       -      |      EA      |   ZTRANSE    | Z_Trans_Pol  |   YTRANSE    | Y_Trans_Pol  |   XTRANSE    | X_Trans_Pol  |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_TRANSIENT_SCR_EA = 0x40       # Event Active Flag (0: no event flag has been asserted; 1: one or more event flag has been asserted)
+FLAG_TRANSIENT_SCR_ZTRANSE = 0x20  # Z transient event (0: no interrupt, 1: Z Transient acceleration > than TRANSIENT_THS event has occurred
+FLAG_TRANSIENT_SCR_ZTR_POL = 0x10  # Polarity of Z Transient Event that triggered interrupt (0: Z event Positive g, 1: Z event Negative g)
+FLAG_TRANSIENT_SCR_YTRANSE = 0x08  # Y transient event (0: no interrupt, 1: Y Transient acceleration > than TRANSIENT_THS event has occurred
+FLAG_TRANSIENT_SCR_YTR_POL = 0x04  # Polarity of Y Transient Event that triggered interrupt (0: Y event Positive g, 1: Y event Negative g)
+FLAG_TRANSIENT_SCR_XTRANSE = 0x02  # X transient event (0: no interrupt, 1: X Transient acceleration > than TRANSIENT_THS event has occurred
+FLAG_TRANSIENT_SCR_XTR_POL = 0x01  # Polarity of X Transient Event that triggered interrupt (0: X event Positive g, 1: X event Negative g)
+
+# Register FF_MT_THS (0x017) R/W - Freefall and Motion Threshold Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   DBCNTM     |     THS6     |    THS5      |    THS4      |    THS3      |    THS2      |    THS1      |    THS0      |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define SOME GLOBAL VARIABLES
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define the acceleration FIFO buffer
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# The acceleration FIFO buffer is a list of records containing all meaningful acceleration data plus some
+# other useful information, whose format il as described below
+#
+#   1.  curTime     as returned by: datetime.datetime.now().  Format is: 'YYYY-MM-DD hh:mi:ss.uuuuuuu'
+#   2.  xAccel      Current X acceleration value in row format
+#   3.  yAccel      Current Y acceleration value in row format
+#   4.  xAccel      Current Z acceleration value in row format
+#   5.  plo         Current Portrait/Landscape orientation
+#accelBuffer = [0, 0, 0, 0, 0]
+accelBuffer = []
+#accelBuffer.append([0, 0, 0, 0, 0])
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define the threaded interrupt vector
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+def my_callback(channel):
+    """
+    my_callback is the threaded callback functions for interrupt events.
+    These will run in another thread when our events are detected
+
+    :param channel: The GPIO channel where the interrupt event was risen
+    :return: None
+    """
+    # Please  nte that, for performance reasons, axis data are not convertd in m/s2,
+    # Although, all 6 registers containing acceleration data are read and formatted appropriately
+    bus = smbus.SMBus(1)
+    axisData = bus.read_i2c_block_data(i2caddr, REG_OUT_X_MSB, 6)
+    #
+    #print ("!"),  #print("Falling edge detected on GPIO channel: " + str(channel))
+    #
+    runTimeConfigObject.NumInterrupts = runTimeConfigObject.NumInterrupts + 1
+    #
+    xAccel = ((axisData[0] << 8) | axisData[1]) >> 2
+    yAccel = ((axisData[2] << 8) | axisData[3]) >> 2
+    zAccel = ((axisData[4] << 8) | axisData[5]) >> 2
+    plo = bus.read_byte_data(i2caddr, REG_PL_STATUS) & 0x7
+    # Append data to the accelBuffer
+    accelBuffer.append([str(datetime.datetime.now()), xAccel, yAccel, zAccel, plo])
+    pass
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define a class called Accel
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+import ConfigParser
+class Accel():
+    raspiBus = -1               # The Raspberry Pi Bus (dpends on hardware model)
+    raspiIntEnabled = 0         # 0 = Interrupt routine was not enabled after initialization, 1 = Interrupt routine enabled successfully
+    raspiInfo = ""              # Raspberry Pi Info
+
+    def __init__(self):
+
+        #
+        # Setup RPI specific bus
+        #
+        myBus = ""
+        if GPIO.RPI_INFO['P1_REVISION'] == 1:
+            myBus = 0
+        else:
+            myBus = 1
+        #print('myBus=' + str(myBus))
+        self.raspiBus = myBus
+
+        self.b = smbus.SMBus(myBus)  # 0 = /dev/i2c-0 (port I2C0), 1 = /dev/i2c-1 (port I2C1)
+        self.a = i2caddr
+        self.high_res_mode = OVERSAMPLING_MODE
+        self.sensor_range = RANGE_4_G
+        self.raspiInfo = GPIO.RPI_INFO
+
+
+    def whoAmI(self):
+        return self.b.read_byte_data(i2caddr, REG_WHOAMI)
+
+    def init(self):
+        # Preliminary actions
+        # sudo chmod 666 /sys/module/i2c_bcm2708/parameters/combined
+        # sudo echo -n 1 > /sys/module/i2c_bcm2708/parameters/combined
+        #
+        # the above 2 sh commands can be replaced with the following statements, in the case this program is ran as root (sudo)
+        # (For more information, please see: http://raspberrypi.znix.com/hipidocs/topic_i2c_rs_and_cs.htm)
+        #
+        # BCM2708_COMBINED_PARAM_PATH = '/sys/module/i2c_bcm2708/parameters/combined'
+        # os.chmod(BCM2708_COMBINED_PARAM_PATH, 666)
+        # os.system('echo -n 1 > {!s}'.format(BCM2708_COMBINED_PARAM_PATH))
+        # sudo i2cdetect -y 1               # this sh cmmand will search /dev/i2c-1 for all address
+        # sudo i2cget -y 1 0x1d 0x0d		# This sh command should return 0x1a for MMA8451
+        #
+        # Setup all registers appropriately
+        self.writeRegister(REG_CTRL_REG2, self.readRegister(REG_CTRL_REG2) | FLAG_RESET)  # Reset
+        # self.writeRegister(REG_CTRL_REG2,	 self.readRegister(REG_CTRL_REG2) | FLAG_STEST)						# SelfTest
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) & ~FLAG_ACTIVE)  # Put the device in Standby
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) & ~FLAG_F_READ)  # No Fast-Read (14-bits), Fast-Read (8-Bits)
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) | FLAG_ODR_50_HZ)  # Data Rate
+        self.writeRegister(REG_XYZ_DATA_CFG, self.readRegister(REG_XYZ_DATA_CFG) | FLAG_XYZ_DATA_BIT_FS_4G)  # Full Scale Range 2g, 4g or 8g
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) | FLAG_LNOISE)  # Low Noise
+        self.writeRegister(REG_CTRL_REG2, self.readRegister(REG_CTRL_REG2) & ~FLAG_SLPE)  # No Auto-Sleep
+        self.writeRegister(REG_CTRL_REG2, self.readRegister(REG_CTRL_REG2) | FLAG_SMODS_HR)  # High Resolution
+        self.writeRegister(REG_PL_CFG, self.readRegister(REG_PL_CFG) | FLAG_PL_CFG_PL_EN)  # P/L Detection Enabled
+
+        # Setup interrupts
+        if CFG_INTERRUPT == 1:
+            GPIO.setmode(GPIO.BCM)
+            # GPIO 23 & 17 set up as inputs, pulled up to avoid false detection.
+            # Both ports are wired to connect to GND on button press.
+            # So we'll be setting up falling edge detection for both
+            GPIO.setup(17, GPIO.IN, pull_up_down=GPIO.PUD_UP)
+            # when a falling edge is detected on port 17, regardless of whatever
+            # else is happening in the program, the function my_callback will be run
+            # GPIO.add_event_detect(17, GPIO.FALLING, callback=my_callback, bouncetime=300)
+            GPIO.add_event_detect(17, GPIO.FALLING, callback=my_callback)
+            #print("Interrupt OK")
+            self.raspiIntEnabled = 1    # Interrupt enabled successfully
+            # Force 1st sensor read
+            my_callback(0)
+
+
+            # Configure register for interrupt
+            self.writeRegister(REG_CTRL_REG4, 0x00)  # Reset all interrupt enabled flags
+            self.writeRegister(REG_CTRL_REG4, self.readRegister(REG_CTRL_REG4) | FLAG_INT_EN_DRDY)  # Data Ready Interrupt Enabled
+            self.writeRegister(REG_CTRL_REG5, 0x00)  # Reset all interrupt config flags
+            self.writeRegister(REG_CTRL_REG5, self.readRegister(REG_CTRL_REG5) | FLAG_INT_CFG_DRDY)  # Data Ready Interrupt is routed to INT1 pin
+
+            # Initialize the accelBuffer
+            accelBuffer = deque()
+
+
+        # Finally, Activate the sensor
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) | FLAG_ACTIVE)  # Activate the device
+
+    def writeRegister(self, regNumber, regData):
+        """
+        Writes one byte (8-bts) of data passed in 'regData', into the register 'regNumber'
+        """
+        try:
+            self.b.write_byte_data(self.a, regNumber, regData)
+            time.sleep(0.01)
+        except IOError:
+            print("Error detected in function writeRegister() [IOError = " + str(IOError) + "]")
+            sys.exit()
+
+    def readRegister(self, regNumber):
+        """
+        Retrieves one byte (8-bits) of data from register 'regNumber' returning to the caller
+        """
+        try:
+            return self.b.read_byte_data(self.a, regNumber)
+        except IOError:
+            print("Error detected in function readRegister() [IOError = " + str(IOError) + "]")
+            sys.exit()
+
+    def block_read(self, offset, length):
+        """
+        Performs a burst-read on the device registers retrieving the requested amount of data
+        Read a block of <length> bytes from  offset <offset>
+        """
+        try:
+            return self.b.read_i2c_block_data(i2caddr, offset, length)
+        except IOError:
+            print("Error detected in function block_read() [IOError = " + str(IOError) + "]")
+            sys.exit()
+
+    def get_orientation(self):
+        """
+        Get current orientation of the sensor.
+        :return: orientation. Orientation number for the sensor.
+        """
+        orientation = self.b.read_byte_data(self.a, REG_PL_STATUS) & 0x7
+        return orientation
+
+    def getAxisValue(self):
+        """
+        Retrieves axis values and converts into a readable format (i.e. m/s2)
+        :return:	None
+        """
+        # Make sure F_READ and F_MODE are disabled.
+        f_read = self.b.read_byte_data(self.a, REG_CTRL_REG1) & FLAG_F_READ
+        assert f_read == 0, 'F_READ mode is not disabled. : %s' % (f_read)
+        f_mode = self.b.read_byte_data(self.a, REG_F_SETUP) & FLAG_F_MODE_FIFO_TRIGGER
+        assert f_mode == 0, 'F_MODE mode is not disabled. : %s' % (f_mode)
+
+        #
+        self.xyzdata = self.block_read(REG_OUT_X_MSB, 6)
+        if self.high_res_mode is not None:
+            x = ((self.xyzdata[0] << 8) | self.xyzdata[1]) >> 2
+            y = ((self.xyzdata[2] << 8) | self.xyzdata[3]) >> 2
+            z = ((self.xyzdata[4] << 8) | self.xyzdata[5]) >> 2
+            precision = PRECISION_14_BIT  # Precision 14 bit data
+        else:
+            x = (self.xyzdata[0] << 8)
+            y = (self.xyzdata[1] << 8)
+            z = (self.xyzdata[2] << 8)
+            precision = PRECISION_08_BIT  # Precision 08 bit data
+        max_val = 2 ** (precision - 1) - 1
+        signed_max = 2 ** precision
+        #
+        x -= signed_max if x > max_val else 0
+        y -= signed_max if y > max_val else 0
+        z -= signed_max if z > max_val else 0
+        #
+        x = round((float(x)) / RANGE_DIVIDER[self.sensor_range], 3)
+        y = round((float(y)) / RANGE_DIVIDER[self.sensor_range], 3)
+        z = round((float(z)) / RANGE_DIVIDER[self.sensor_range], 3)
+
+        return {"x": x, "y": y, "z": z}
+
+    def debugShowRpiInfo(self):
+        #print("Raspberry Info      = " + str(GPIO.RPI_INFO))
+        print("Raspberry Info      = " + str(self.raspiInfo))
+
+    def debugShowRegisters(self):
+        print("REG_STATUS       (0x00):" + str(format(self.readRegister(REG_STATUS), '#04x')) + " | Binary: " + format(self.readRegister(REG_STATUS), 'b').zfill(8))
+        print("REG_WHOAMI       (0x0d):" + str(format(self.readRegister(REG_WHOAMI), '#04x')) + " | Binary: " + format(self.readRegister(REG_WHOAMI), 'b').zfill(8))
+        print("REG_F_SETUP      (0x09):" + str(format(self.readRegister(REG_F_SETUP), '#04x')) + " | Binary: " + format(self.readRegister(REG_F_SETUP), 'b').zfill(8))
+        print("REG_XYZ_DATA_CFG (0x0e):" + str(format(self.readRegister(REG_XYZ_DATA_CFG), '#04x')) + " | Binary: " + format(self.readRegister(REG_XYZ_DATA_CFG), 'b').zfill(8))
+        print("REG_CTRL_REG1    (0x2a):" + str(format(self.readRegister(REG_CTRL_REG1), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG1), 'b').zfill(8))
+        print("REG_CTRL_REG2    (0x2b):" + str(format(self.readRegister(REG_CTRL_REG2), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG2), 'b').zfill(8))
+        print("REG_CTRL_REG3    (0x2c):" + str(format(self.readRegister(REG_CTRL_REG3), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG3), 'b').zfill(8))
+        print("REG_CTRL_REG4    (0x2d):" + str(format(self.readRegister(REG_CTRL_REG4), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG4), 'b').zfill(8))
+        print("REG_CTRL_REG5    (0x2e):" + str(format(self.readRegister(REG_CTRL_REG5), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG5), 'b').zfill(8))
+        print("REG_PL_STATUS    (0x10):" + str(format(self.readRegister(REG_PL_STATUS), '#04x')) + " | Binary: " + format(self.readRegister(REG_PL_STATUS), 'b').zfill(8))
+        print ("debugRealTime    " + str(runTimeConfigObject.debugRealTime))
+        print ("NumInterrupts    " + str(runTimeConfigObject.NumInterrupts))
+
+    def debugShowOrientation(self):
+        print("Position = %d" % (self.get_orientation()))
+
+    def debugShowAxisAcceleration(self, xaccel, yaccel, zaccel):
+        print("   x (m/s2)= %+.3f" % (xaccel))
+        print("   y (m/s2)= %+.3f" % (yaccel))
+        print("   z (m/s2)= %+.3f" % (zaccel))
+
+    def debugRealTimeBuffer(self):
+        n = 0
+        for elements in accelBuffer:
+            myData = accelBuffer.pop()
+            n += 1
+            print ("N=" + str(n) + " myData=" + str(myData))    # + "Element=" + str(elements))
+        try:
+            print("End of printout\n")
+            #time.sleep(1.0)
+        # os.system("clear")
+        except KeyboardInterrupt:
+            print("Program Termination Requested")
+            sys.exit()
+        
+###############################################################################
+#   Threading functions
+###############################################################################
+#def rssClient():
+#    """Manage data shipping over th network, in a separate thread."""
+#    #logger.debug('Thread Starting')
+#    while True:
+#        time.sleep(1.0)
+#        print ("This is thread rssClient()")
+#
+###############################################################################
+#   Useful functions
+###############################################################################
+def printHelp():
+    print ("\n")
+    print ("usage: accel.py [options]")
+    print ("Available options:")
+    print (" -h \t\t Print this help and exit")
+    print (" -d \t\t Show debug realtime interrupt data")
+    print (" -s \t\t Execute silently (no screen output)")
+    print (" -L <lvl>\t Set Log level. where <lvl> is the log level (0 = NONE - 8 = DEBUG)") 
+    print ("")
+
+
+def main(argv):
+    import sys, getopt, logging
+    #
+    try:
+        opts, args = getopt.getopt(argv,"hdsL:")
+    except getopt.GetoptError:
+        print ("\nInvalid option requested on command line")
+        printHelp()
+        sys.exit(2)
+
+    for opt, arg in opts:
+        if opt == '-h':
+            printHelp()
+            sys.exit()
+        elif opt == '-d':
+            runTimeConfigObject.debugRealTime = 1
+        elif opt == '-s':
+            runTimeConfigObject.executeSilently = 1
+        elif opt == '-L':
+            if (int(arg) == 0) or (int(arg) > 5):
+                pass
+                #logger.setLevel(logger.NOTSET)            # Same as value 0
+            elif int(arg) == 1:
+                logger.setLevel(logging.CRITICAL)          # Same as value 50
+            elif int(arg) == 2:
+                logger.setLevel(logging.ERROR)             # Same as value 40
+            elif int(arg) == 3:
+                logger.setLevel(logging.WARNING)           # Same as value 30
+            elif int(arg) == 4:
+                logger.setLevel(logging.INFO)              # Same as value 20
+            elif int(arg) == 5:
+                logger.setLevel(logging.DEBUG)             # Same as value 10
+
+
+#####################################################################################
+#   M A I N 
+#####################################################################################
+class runTimeConfigObject(object):
+    pass
+#if __name__ == "__main__":
+def extract_meas():
+    
+    #class runTimeConfigObject(object):
+        #pass
+    #
+    # Setup Logger
+    #
+    #logger = logging.basicConfig(level=logging.DEBUG, format='[%(asctime)15s].%(levelname)s] (%(threadName)-10s) %(message)s', )
+    #logger.basicConfig(level=logging.DEBUG,format='[%(asctime)15s].%(levelname)s] (%(threadName)-10s) %(message)s',)
+    logger = logging.getLogger()
+    logger.setLevel(logging.DEBUG)
+    # create console handler and set level to debug
+    ch = logging.StreamHandler()
+    ch.setLevel(logging.DEBUG)
+    # create formatter
+    formatter = logging.Formatter('[%(asctime)s.%(levelname)s] (%(name)s.%(threadName)-10s) : %(message)s')
+    # add formatter to ch
+    ch.setFormatter(formatter)
+    # add ch to logger
+    logger.addHandler(ch)
+
+    #
+    # Set some default command line options
+    #
+    runTimeConfig = runTimeConfigObject()
+    runTimeConfigObject.debugRealTime = 0       # 1 = Show debug realtime interrupt data
+    runTimeConfigObject.executeSilently = 0     # 1 = Execute silently (no sceen output)
+    runTimeConfigObject.NumInterrupts = 0       # keep Nbr of sensor interrupts withi the main loop
+    
+    main(sys.argv[1:])
+
+    #
+    # Read configuration file
+    #
+    configFile = "./rss_config.dat"
+    #logger.debug('Reading Config file: ' + configFile) #Commented out by Henry
+    sections = {'GeoData', 'DeviceInfo', 'Networking'}
+    #configParameters = {}
+    #Config = ConfigParser.ConfigParser()
+    #Config.read(configFile)
+    #for section in sections:
+    #    try:
+    #        options = Config.options(section)
+    #    except:
+    #        print ("ERROR: Section '" + section + "' Not found in config file: '" + configFile + "'.")
+    #        sys.exit()
+    #    for option in options:
+    #        try:
+    #            configParameters[option] = Config.get(section, option)
+    #        except:
+    #            configParameters[option] = None
+    #        logger.debug("Config Section: " + section + " / Option: " + option + " => " + configParameters[option])
+
+    MMA8451 = Accel()
+    #os.system("clear")
+    MMA8451.init()
+
+    if MMA8451.whoAmI() != deviceName:
+        print("Error! Device not recognized! (" + str(deviceName) + ")")
+        sys.exit()
+
+    #
+    # Thread client start
+    # Commented out by Henry
+    '''
+    import rss_client
+    pill2kill = threading.Event()
+    #threadClient = threading.Thread(name='netClientWorker', target=rss_client.cli_worker, args=(pill2kill, configParameters, accelBuffer))
+    threadClient = threading.Thread(name='netClientWorker', target=rss_client.cli_worker, args=(pill2kill, accelBuffer))
+    threadClient.setDaemon(False)         #threadClient.daemon = False
+    threadClient.start()
+    myThread = []
+    myThread.append('netClientWorker')
+    '''
+    
+    #Added by Henry:
+    count_samples = 5 #num of samples we want
+    count_taken = 0
+    xvals = []
+    yvals = []
+    zvals = []
+    while count_taken < count_samples:
+        if runTimeConfigObject.executeSilently == 0:
+            axes = MMA8451.getAxisValue()
+            print("X: " + str(axes['x']) + ", Y: " + str(axes['y']) + ", Z: " + str(axes['z']))
+            xvals.append(axes['x'])
+            yvals.append(axes['y'])
+            zvals.append(axes['z'])
+            count_taken = count_taken + 1
+    return xvals,yvals,zvals
+    
+    #Commented Out by Henry:
+    '''
+    while True:  # forever loop
+        if runTimeConfigObject.executeSilently == 0:
+            print ("\nCurrent Date-Time: " + str(datetime.datetime.now()))
+            print ("Raspberry Bus       = " + str(MMA8451.raspiBus))
+            print ("Raspberry Interrupt = " + str(MMA8451.raspiIntEnabled))
+            print ("Number of elemets   = " + str(len(accelBuffer)))
+            MMA8451.debugShowRpiInfo()
+            MMA8451.debugShowRegisters()
+            MMA8451.debugShowOrientation()
+            axes = MMA8451.getAxisValue()
+            MMA8451.debugShowAxisAcceleration(axes['x'], axes['y'], axes['z'])
+            #
+            if runTimeConfigObject.debugRealTime != 0:
+                MMA8451.debugRealTimeBuffer()
+
+        runTimeConfigObject.NumInterrupts = 0
+        try:
+            time.sleep(1.0)
+        except KeyboardInterrupt:
+            logger.debug ("Killing threads...")
+            pill2kill.set()
+            threadClient.join()
+            time.sleep(1.0)
+
+            logger.debug("\nUser termination requested!\n")
+            sys.exit()
+            
+    sys.exit()
+    '''
+
diff --git a/Nov_27/Full/Camera/accel_correct_config/raspidata.py b/Nov_27/Full/Camera/accel_correct_config/raspidata.py
new file mode 100644
index 0000000..fb99cbb
--- /dev/null
+++ b/Nov_27/Full/Camera/accel_correct_config/raspidata.py
@@ -0,0 +1,48 @@
+""" A Raspberry Pi class.
+To access some of the most useful hardware information
+"""
+
+
+#class RaspiData():
+
+
+    # Get Raspberry Serial Number
+    # You can do it in bash:
+    #   cat /proc/cpuinfo | perl -n -e '/^Serial[ ]*: ([0-9a-f]{16})$/ && print "$1\n"'
+    # Or with perl:
+    #   cat cpuinfo | perl -n -e '/^Serial[ ]*: ([0-9a-f]{16})$/ && print "$1\n"'
+    #
+    #
+
+
+#    def __init__(self):
+#       pass
+from uuid import getnode as get_mac
+
+#    @staticmethod
+# def get_serial(self):
+def get_serial():
+    """Extract serial from cpuinfo file"""
+    cpu_serial= "0000000000000000"
+    try:
+        f = open('/proc/cpuinfo','r')
+        for line in f:
+            if line[0:6]=='Serial':
+                cpu_serial = line[10:26]
+        f.close()
+    except:
+        cpu_serial = "ERROR000000000"
+    return cpu_serial
+
+
+def mac_address():
+    """Extract rth0 MAC address"""
+    # mac = '00:00:00:00:00:00'
+    # mac = '00:00:00:00:00:00'
+    try:
+        t = get_mac()
+    except:
+        t = "000000000000"
+    mac = ':'.join(("%012X" % t)[i:i+2] for i in range(0, 12, 2))
+    return mac
+
diff --git a/Nov_27/Full/Camera/accel_correct_config/rss_cli_config.py b/Nov_27/Full/Camera/accel_correct_config/rss_cli_config.py
new file mode 100644
index 0000000..e10aa53
--- /dev/null
+++ b/Nov_27/Full/Camera/accel_correct_config/rss_cli_config.py
@@ -0,0 +1,21 @@
+# [GeoData]
+City = 'Rome'
+cityname =  'Rome'
+latitude = 41.947550
+longitude = 12.469794
+deviceorientation = 'L'
+
+# [DeviceInfo]
+devicetype = 'raspberry'
+devicename = 'rssd0001'
+sensorname = 'mma8451'
+sensortheshold = 20
+logfile = './rssd.log'
+
+# [Networking]
+#serveraddress = 'www.diprimio.com'
+serveraddress = 'localhost'
+serverprotocol = 'tcp'
+servertcpport = 15000
+serverhearthbit = 100
+receivebuffersize = 1024
diff --git a/Nov_27/Full/Camera/accel_correct_config/rss_client.py b/Nov_27/Full/Camera/accel_correct_config/rss_client.py
new file mode 100644
index 0000000..a2a4d1b
--- /dev/null
+++ b/Nov_27/Full/Camera/accel_correct_config/rss_client.py
@@ -0,0 +1,159 @@
+#!/usr/bin/env python
+# -*- coding:utf-8, indent=tab, tabstop=4 -*-
+#
+# See 'LICENSE'  for copying
+#
+# This file contains the code for the client module of 'accel.py'
+#
+# Revision history
+# Date          Author                  Version     Details
+# ----------------------------------------------------------------------------------
+# 2018-01-18    Massimo Di Primio       0.06        1st file implementation
+
+"""Client thread worker - This is a simple client code example for 'accel'.py' program"""
+
+import logging
+import time
+import datetime
+import socket
+import json
+import rss_client_messages as climsg
+import rss_cli_config as ccfg
+import raspidata
+
+
+#def cli_connect(params):
+def cli_connect():
+    """Open connection to the server"""
+    server_address = (str(ccfg.serveraddress), int(ccfg.servertcpport))
+    logging.debug('Trying to connect to server ' + str(server_address))
+    # Create a TCP/IP socket
+    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    try:
+        # Connect the socket to the port where the server is listening
+        s.connect(server_address)
+        logging.debug('Connection Established to server ' + str(server_address))
+    except:
+        logging.debug(
+            "Failed to open connection: " + str(ccfg.serverprotocol) +
+            ", to IP: " + str(ccfg.serveraddress) +
+            ", on port: " + str(ccfg.servertcpport)
+            )
+        return(-1)
+    return(s)
+
+
+def cli_close(s):
+    """Close the server connection"""
+    if s > -1:
+        s.close()
+
+
+# def cli_worker(stopEvent, config, accelBuffer):
+def cli_worker(stopEvent, accelBuffer):
+    """A client worker as thread"""
+    logging.debug('Thread Starting')
+    s = cli_connect()     # s = cli_connect(config)
+    send_client_hello(s)
+    time.sleep(0.5)
+    send_config_affirm_message(s)
+
+    ts = int(time.time())
+    te = ts
+    while not stopEvent.wait(0.3):
+        if len(accelBuffer) > 0:
+            send_accel_data(s, accelBuffer)
+            te = int(time.time())
+        if (te - ts) > 10:
+            send_client_heartbit(s)
+            ts = int(time.time())
+
+        time.sleep(0.5)
+    send_zap_message(s)
+    cli_close(s)
+    logging.debug("Thread cliWorker is terminating as per your request.")
+
+    
+def send_accel_data(s, accelBuffer):
+    """Send acceleration data to the server"""
+    #msg = dict(cmd='ADM', timestamp=str(datetime.datetime.now()), clid=raspidata.get_serial())
+    #a = 123 #str(datetime.datetime.now())
+    pbuf = parse_accel_data(accelBuffer)
+    msg = dict(cmd = 'ADM', timestamp = str(datetime.datetime.now()), clid = raspidata.get_serial(), data = pbuf)
+    # if len(pbuf) > 0: # this sometimes returns error (when buf is empty, it has None type)
+    if (pbuf is not None) and (len(pbuf) > 0):
+        #str = climsg.accel_data_message(pbuf)
+        try:
+            logging.debug("Sending Acceleration data to the server")
+            s.sendall(str(json.dumps(msg)) + "\n")  #s.sendall(json.dumps(str) + "\n")
+        except:
+            logging.debug("Failed to send Acceleration-Data to the server")
+
+
+def send_client_hello(s):
+    """Send Hello message to the server"""
+    msg = dict(cmd = 'CHM', timestamp = str(datetime.datetime.now()), clid = raspidata.get_serial())
+    try:
+        logging.debug("Sending Hello to the server")
+        s.sendall(str(json.dumps(msg)) + "\n")  #s.sendall(json.dumps(climsg.hello_message()) + "\n")
+    except:
+        logging.debug("Failed to send Hello to the server")
+
+
+def send_zap_message(s):
+    """Send Zap message to the server"""
+    msg = dict(cmd = 'CZM', timestamp = str(datetime.datetime.now()), clid = raspidata.get_serial())
+    try:
+        logging.debug("Sending Zap to the server")
+        s.sendall(str(json.dumps(msg)) + "\n")  #s.sendall(json.dumps(climsg.zap_message()) + "\n")
+    except:
+        logging.debug("Failed to send Zap to the server")
+
+
+def send_config_affirm_message(s):      #def send_config_affirm_message(s, config):
+    msg_data = dict(city = ccfg.cityname, latitude = ccfg.latitude,longitude = ccfg.longitude)
+    msg = dict(cmd = 'CCA', timestamp = str(datetime.datetime.now()), clid = raspidata.get_serial(), config = msg_data)
+    try:
+        logging.debug("Sending client configuration to the server")
+        s.sendall(str(json.dumps(msg)) + "\n")    #s.sendall(climsg.config_affirm_message(cfg_data))
+    except:
+        logging.debug("Failed to send client configuration to the server")
+
+
+def send_client_heartbit(s):
+    """Send Heartbit to the server"""
+    msg = dict(cmd = 'CHB', timestamp = str(datetime.datetime.now()), clid=raspidata.get_serial())
+    try:
+        logging.debug("Sending Heartbit to the server")
+        s.sendall(str(json.dumps(msg)) + "\n")  #s.sendall(json.dumps(climsg.heart_bit()) + "\n")
+    except:
+        logging.debug("Failed to send Heartbit to the server")
+
+
+def parse_accel_data(b):
+    """Parse acceleration data to make sure we only send meaningfull data to the server"""
+    tsh = 10
+    tbuf = []
+    # tbuf.append([0, 0, 0, 0, 0])
+    # bLength = len(b)
+    # logging.debug("parseAccelData(b) # of elements   = " + str(len(b)))
+    if len(b) > 1:
+        logging.debug("parseAccelData: In  AccelData/BufLen: " + str(len(b)) + "/" +str(len(tbuf)))
+        firstTime = 1
+        prow = None
+        for row in b:
+            crow = b.pop(0)     # Get the oldest record
+            if firstTime == 1:
+                prow = crow
+                firstTime = 0
+            if ( (abs(abs(int(crow[1])) - abs(int(prow[1]))) > tsh) or
+                 (abs(abs(int(crow[2])) - abs(int(prow[2]))) > tsh) or
+                 (abs(abs(int(crow[3])) - abs(int(prow[3]))) > tsh)
+                ):
+                tbuf.append(crow)
+                prow = crow
+                print ("Again PROW/CROW/TBUFLEN:" + str(prow) + " / " + str(crow) + " / " +  str(len(tbuf)))
+        
+        logging.debug("parseAccelData: Out AccelData/BufLen: " + str(len(b)) + "/" +str(len(tbuf)))
+        return(tbuf)
+
diff --git a/Nov_27/Full/Camera/accel_correct_config/rss_client_messages.py b/Nov_27/Full/Camera/accel_correct_config/rss_client_messages.py
new file mode 100644
index 0000000..4a507d7
--- /dev/null
+++ b/Nov_27/Full/Camera/accel_correct_config/rss_client_messages.py
@@ -0,0 +1,113 @@
+
+import datetime
+import raspidata
+import json
+import rss_cli_config as ccfg
+
+
+#
+# All client messages are handled in here.
+#
+# Message Formats
+# ---------------
+# 1 - Timestamp
+# 2 - Device ID
+# 3 - Message Type
+#
+# Client message types
+# --------------------
+# 0001  CHB - Client Heart Bit (Client is alive)
+# 0002  CHM - Client Hello Message (Client is online)
+# 0003  CZM - Client Zap Message (Client is shut down)
+# 0004  ADM - Acceleration Data Message (Acceleration data)
+# 0005  CCA - Client Config Affirm
+# ... (other to come)
+#
+# Data message Format for all Client Message Types
+# ------------------------------------------------
+#
+# # 0001 - CHB - Client Heart Bit
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0001         |  Var  | Literally 'CHB'                                        |
+# # +-------------------------------------------------------------------------------+
+#
+# 0002 CHM - Client Hello Message
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0002         |  Var  | Literally 'CHM'                                        |
+# +--------------+-------+--------------------------------------------------------+
+#
+# 0003  CZM - Client Zap Message (Client is shut down)
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0003         |  Var  | Literally 'CZM'                                        |
+# +--------------+-------+--------------------------------------------------------+
+#
+# 0004  ADM - Acceleration Data Message (Acceleration data)
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0004         |  Var  | Literally 'ADM'                                        |
+# | Accel Data   |  Var  | Array containing acceleration data (X,Y,Z.O)           |
+# +--------------+-------+--------------------------------------------------------+
+#
+# 0005  CCA - Client Configuration Affirm
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0004         |  Var  | Literally 'CCA'                                        |
+# | Config Data  |  Var  | Array containing client configuration                  |
+# +--------------+-------+--------------------------------------------------------+
+
+###########################################################################
+#   WARNIG ! THIS FILE IS NOT CURRENTLY USED !
+#   Its presence it is for debugging/testing only
+#   Althought, it will be used in some future version
+###########################################################################
+
+#def heart_bit():
+#    """Client Heart Bit"""
+#    msg = {'cmd': 'CHB', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial()}
+#    return msg
+#
+#
+#def hello_message():
+#    """Client Hello Message"""
+#    msg = {'cmd': 'CHM', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial()}
+#    return msg
+#
+#
+#def zap_message():
+#    """Client Zap Message"""
+#    msg = {'cmd': 'CZM', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial()}
+#    return msg
+#
+#
+#def accel_data_message(data):
+#    """Acceleration Data Message"""
+#    msg = {'cmd': 'ADM', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial(), 'acceldata': str(data)}
+#    return msg
+#
+#
+#def config_affirm_message(data):    #def config_affirm_message(data):
+#    """Client Configuration Affirm"""
+#    ###
+#    pkt_hdr = {'cmd': 'CCA', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial(), 'data' : data}
+#
+#    msg = dict(city = ccfg.cityname, latitude = ccfg.latitude, longitude = ccfg.longitude, config = data)
+#    return msg
+
diff --git a/Nov_27/Full/Camera/communication_module/._communication.py b/Nov_27/Full/Camera/communication_module/._communication.py
new file mode 100644
index 0000000..f2e25e8
Binary files /dev/null and b/Nov_27/Full/Camera/communication_module/._communication.py differ
diff --git a/Nov_27/Full/Camera/communication_module/com_test.py b/Nov_27/Full/Camera/communication_module/com_test.py
new file mode 100644
index 0000000..a99d3ec
--- /dev/null
+++ b/Nov_27/Full/Camera/communication_module/com_test.py
@@ -0,0 +1,35 @@
+import requests
+from requests import Session
+import json
+from datetime import datetime
+from dateutil import parser
+from random import randint
+
+BRIDGE_NAME = 'dummy'
+dest = "http://127.0.0.1:8000/sensors/"+BRIDGE_NAME+"/update/"
+PASSWORD = "djioewfj34jod2jdoi3jr0jl983jsa"
+
+
+def send_data_to_server(x=0.0, y=0.0, z=0.0, theta=0.0, phi=0.0, psi=0.0):
+    payload = {'x': x, 'y': y, 'z': z, 'theta': theta, 'phi': phi, 'psi': psi}
+    dt = datetime.now()
+    cookies = {'csrftoken': encode(dt), 'time': str(dt)}
+    r = requests.post(dest, data=payload, cookies=cookies)
+    print(r.status_code)
+    if r.status_code == 403:
+        raise RuntimeWarning("CSRF ALERT ON, INSPECT YOUR SYSTEM")
+    else:
+        return r.text
+
+
+def encode(time_sign):
+    len_pw = len(PASSWORD)
+    index = (time_sign.year + time_sign.month * 100 + time_sign.day + time_sign.hour * time_sign.minute
+             * time_sign.second) % len_pw
+    repl = chr(randint(97, 122))
+    return PASSWORD[:index] + repl + PASSWORD[index+1:]
+
+
+print(send_data_to_server(0.5, 1.5, 1.5))
+#send_data_form(1.0, 2.0, 3.0)
+#cookies = {'csrftoken': "djioewfj34jod2jdoi3jr0jl983jsa", 'TIME': datetime.now()}
diff --git a/Nov_27/Full/Camera/communication_module/communication.py b/Nov_27/Full/Camera/communication_module/communication.py
new file mode 100644
index 0000000..8c8b9a4
--- /dev/null
+++ b/Nov_27/Full/Camera/communication_module/communication.py
@@ -0,0 +1,28 @@
+import requests, sys
+from requests import Session
+import json
+from datetime import datetime
+from dateutil import parser
+from random import randint
+sys.path.insert(0,"/home/pi/Desktop/Full_Demo/transfer_test_correct_config")
+from SETTINGS import PASSWORD, SERVER
+
+dest = SERVER
+
+def send_data_to_server(x=0.0, y=0.0, z=0.0, theta=0.0, phi=0.0, psi=0.0):
+    payload = {'x': x, 'y': y, 'z': z, 'theta': theta, 'phi': phi, 'psi': psi}
+    dt = datetime.now()
+    cookies = {'csrftoken': encode(dt), 'time': str(dt)}
+    r = requests.post(dest, data=payload, cookies=cookies)
+    if r.status_code == 403:
+        raise RuntimeWarning("CSRF ALERT ON, INSPECT YOUR SYSTEM")
+    else:
+        return r.text
+
+
+def encode(time_sign):
+    len_pw = len(PASSWORD)
+    index = (time_sign.year + time_sign.month * 100 + time_sign.day + time_sign.hour * time_sign.minute
+             * time_sign.second) % len_pw
+    repl = chr(randint(97, 122))
+    return PASSWORD[:index] + repl + PASSWORD[index+1:]
\ No newline at end of file
diff --git a/Nov_27/Full/Camera/img_rec_module/._img_1_1new_meas.txt b/Nov_27/Full/Camera/img_rec_module/._img_1_1new_meas.txt
new file mode 100644
index 0000000..6ccf872
Binary files /dev/null and b/Nov_27/Full/Camera/img_rec_module/._img_1_1new_meas.txt differ
diff --git a/Nov_27/Full/Camera/img_rec_module/FastDataMatrixTest.py b/Nov_27/Full/Camera/img_rec_module/FastDataMatrixTest.py
new file mode 100644
index 0000000..21cdf8d
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/FastDataMatrixTest.py
@@ -0,0 +1,45 @@
+from img_rec_module.sig_proc_test import FM
+import numpy as np
+from random import randint
+import traceback
+import sys
+
+matrix = np.random.rand(20, 30)
+
+
+def segmentation_test(sample):
+    ax = randint(0, 1)
+    target = randint(0, 19) if ax == 1 else randint(0, 29)
+    fmat = FM(matrix, ax, target)
+    assert fmat.irange() == 20 if ax == 1 else fmat.irange() == 30
+    cap = len(fmat)
+    for i in range(sample):
+        start = randint(0, cap - 1)
+        end = randint(start + 1, cap)
+        fmat.segmentize(start, end)
+        assert len(fmat.extract_array()) == end - start, "start: {0}, end: {1}".format(start, end)
+        elem = randint(0, len(fmat) - 1)
+        assert fmat[elem] == matrix.item(target, elem + start) if ax == 1 \
+            else fmat[elem] == matrix.item(elem + start, target)
+    print("Test 1 passed!")
+    return fmat.copy()
+
+
+def shape_change_test(sample):
+    fmat = mat
+    print(len(fmat), "start: {0}, end: {1}, ax: {2}, index: {3}".format(fmat.start, fmat.end, fmat._ax, fmat._index))
+    for i in range(sample):
+        ax = randint(0, 1)
+        target = randint(0, 19) if ax == 1 else randint(0, 29)
+        try:
+            fmat = fmat.copy(ax, target)
+            elem = randint(0, len(fmat) - 1)
+            assert fmat[elem] == matrix.item(target, elem) if ax == 1 else fmat[elem] == matrix.item(elem, target), "start: {0}, end: {1}, ax: {2}, index: {3}, changed:{4}, elem: {5}".format(fmat.start, fmat.end, ax, fmat._index, target, elem)
+        except IndexError:
+            print("Index Error, start: {0}, end: {1}, ax: {2}, index: {3}".format(fmat.start, fmat.end, fmat._ax, fmat._index))
+            traceback.print_exc(file=sys.stderr)
+            sys.exit(0)
+    print("Test 2 passed!")
+
+mat = segmentation_test(1000000)
+shape_change_test(1000000)
\ No newline at end of file
diff --git a/Nov_27/Full/Camera/img_rec_module/HoughLineTest.py b/Nov_27/Full/Camera/img_rec_module/HoughLineTest.py
new file mode 100644
index 0000000..a338b2c
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/HoughLineTest.py
@@ -0,0 +1,70 @@
+from img_rec_module.sig_proc_test import HoughLine, angle_interp, normalize_angle
+import numpy as np
+from random import uniform
+from math import sin, cos, asin, acos, degrees
+
+
+def angle_interp_test(sample):
+    for t in range(4):
+        angle = np.pi * t / 2
+        angle_test(angle)
+    for i in range(sample):
+        angle = uniform(0, 2 * np.pi)
+        angle_test(angle)
+    print("All tests passed!")
+
+
+def angle_test(angle):
+    s, c = sin(angle), cos(angle)
+    aint = angle_interp(s, c)
+    e = abs(aint - angle)
+    assert e < 10 ** (-8), "angle: {0} aint: {3} sin: {1}, cos: {2}".format(degrees(angle), s, c, degrees(aint))
+
+
+data1 = [(50, 93.58985799408744), (100, 94.83752561512769), (150, 93.46476622047003), (200, 92.12757475618413), (250, 91.21899822237681), (300, 94.66610143151713), (350, 93.39700158152765), (400, 93.55400856320203), (450, 94.81488837189904)]
+data2 = [(50, 220.68898677024384), (150, 207.0369850844262), (200, 219.1873113806197), (250, 218.4221525438996), (300, 215.83141259108666), (350, 216.1097540779057), (400, 214.30630223604018), (450, 211.84789180370862), (500, 211.03929058602466), (550, 208.37556376057634), (600, 208.1358916890448)]
+
+
+def diagnose():
+    x1 = np.array([d[1] for d in data1])
+    y1 = np.array([d[0] for d in data1])
+    line1 = HoughLine(x=x1, data=y1)
+    x2 = np.array([d[0] for d in data2])
+    y2 = np.array([d[1] for d in data2])
+    line2 = HoughLine(x=x2, data=y2)
+    xmean = sum(x1) / len(x1)
+    ymean = sum(y2) / len(y2)
+    print(line1._r, line1._t)
+    print(line2._r, line2._t)
+    print((xmean, ymean), HoughLine.intersect(line1, line2))
+
+
+def normalize_angle_test(sample):
+    for t in range(4):
+        angle = np.pi * t / 2
+        test_normalize(angle)
+    for i in range(sample):
+        angle = uniform(0, 1000000)
+        test_normalize(angle)
+    print('All tests passed!')
+
+
+def test_normalize(angle):
+    s, c = sin(angle), cos(angle)
+    norm_ang = normalize_angle(angle)
+    ns, nc = sin(norm_ang), cos(norm_ang)
+    case1 = equal_within_error(s, ns) and equal_within_error(c, nc)
+    case2 = equal_within_error(s, -ns) and equal_within_error(c, -nc)
+    assert case1 or case2, 's:{0} c:{1}, ns:{2}, nc:{3}, angle:{4}, norm:{5}'.format(s, c, ns, nc, angle, norm_ang)
+
+
+
+def equal_within_error(v1, v2):
+    e = abs(v1 - v2)
+    return e < 10 ** (-8)
+
+
+if __name__ == '__main__':
+    #angle_interp_test(100000000)
+    #diagnose()
+    normalize_angle_test(1000000)
\ No newline at end of file
diff --git a/Nov_27/Full/Camera/img_rec_module/__init__.py b/Nov_27/Full/Camera/img_rec_module/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Full/Camera/img_rec_module/centers.txt b/Nov_27/Full/Camera/img_rec_module/centers.txt
new file mode 100644
index 0000000..ea1e64b
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/centers.txt
@@ -0,0 +1,640 @@
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diff --git a/Nov_27/Full/Camera/img_rec_module/flags.txt b/Nov_27/Full/Camera/img_rec_module/flags.txt
new file mode 100644
index 0000000..b15d479
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/flags.txt
@@ -0,0 +1,274 @@
+COLOR_BAYER_BG2BGR
+COLOR_BAYER_BG2BGRA
+COLOR_BAYER_BG2BGR_EA
+COLOR_BAYER_BG2BGR_VNG
+COLOR_BAYER_BG2GRAY
+COLOR_BAYER_BG2RGB
+COLOR_BAYER_BG2RGBA
+COLOR_BAYER_BG2RGB_EA
+COLOR_BAYER_BG2RGB_VNG
+COLOR_BAYER_GB2BGR
+COLOR_BAYER_GB2BGRA
+COLOR_BAYER_GB2BGR_EA
+COLOR_BAYER_GB2BGR_VNG
+COLOR_BAYER_GB2GRAY
+COLOR_BAYER_GB2RGB
+COLOR_BAYER_GB2RGBA
+COLOR_BAYER_GB2RGB_EA
+COLOR_BAYER_GB2RGB_VNG
+COLOR_BAYER_GR2BGR
+COLOR_BAYER_GR2BGRA
+COLOR_BAYER_GR2BGR_EA
+COLOR_BAYER_GR2BGR_VNG
+COLOR_BAYER_GR2GRAY
+COLOR_BAYER_GR2RGB
+COLOR_BAYER_GR2RGBA
+COLOR_BAYER_GR2RGB_EA
+COLOR_BAYER_GR2RGB_VNG
+COLOR_BAYER_RG2BGR
+COLOR_BAYER_RG2BGRA
+COLOR_BAYER_RG2BGR_EA
+COLOR_BAYER_RG2BGR_VNG
+COLOR_BAYER_RG2GRAY
+COLOR_BAYER_RG2RGB
+COLOR_BAYER_RG2RGBA
+COLOR_BAYER_RG2RGB_EA
+COLOR_BAYER_RG2RGB_VNG
+COLOR_BGR2BGR555
+COLOR_BGR2BGR565
+COLOR_BGR2BGRA
+COLOR_BGR2GRAY
+COLOR_BGR2HLS
+COLOR_BGR2HLS_FULL
+COLOR_BGR2HSV
+COLOR_BGR2HSV_FULL
+COLOR_BGR2LAB
+COLOR_BGR2LUV
+COLOR_BGR2Lab
+COLOR_BGR2Luv
+COLOR_BGR2RGB
+COLOR_BGR2RGBA
+COLOR_BGR2XYZ
+COLOR_BGR2YCR_CB
+COLOR_BGR2YCrCb
+COLOR_BGR2YUV
+COLOR_BGR2YUV_I420
+COLOR_BGR2YUV_IYUV
+COLOR_BGR2YUV_YV12
+COLOR_BGR5552BGR
+COLOR_BGR5552BGRA
+COLOR_BGR5552GRAY
+COLOR_BGR5552RGB
+COLOR_BGR5552RGBA
+COLOR_BGR5652BGR
+COLOR_BGR5652BGRA
+COLOR_BGR5652GRAY
+COLOR_BGR5652RGB
+COLOR_BGR5652RGBA
+COLOR_BGRA2BGR
+COLOR_BGRA2BGR555
+COLOR_BGRA2BGR565
+COLOR_BGRA2GRAY
+COLOR_BGRA2RGB
+COLOR_BGRA2RGBA
+COLOR_BGRA2YUV_I420
+COLOR_BGRA2YUV_IYUV
+COLOR_BGRA2YUV_YV12
+COLOR_BayerBG2BGR
+COLOR_BayerBG2BGRA
+COLOR_BayerBG2BGR_EA
+COLOR_BayerBG2BGR_VNG
+COLOR_BayerBG2GRAY
+COLOR_BayerBG2RGB
+COLOR_BayerBG2RGBA
+COLOR_BayerBG2RGB_EA
+COLOR_BayerBG2RGB_VNG
+COLOR_BayerGB2BGR
+COLOR_BayerGB2BGRA
+COLOR_BayerGB2BGR_EA
+COLOR_BayerGB2BGR_VNG
+COLOR_BayerGB2GRAY
+COLOR_BayerGB2RGB
+COLOR_BayerGB2RGBA
+COLOR_BayerGB2RGB_EA
+COLOR_BayerGB2RGB_VNG
+COLOR_BayerGR2BGR
+COLOR_BayerGR2BGRA
+COLOR_BayerGR2BGR_EA
+COLOR_BayerGR2BGR_VNG
+COLOR_BayerGR2GRAY
+COLOR_BayerGR2RGB
+COLOR_BayerGR2RGBA
+COLOR_BayerGR2RGB_EA
+COLOR_BayerGR2RGB_VNG
+COLOR_BayerRG2BGR
+COLOR_BayerRG2BGRA
+COLOR_BayerRG2BGR_EA
+COLOR_BayerRG2BGR_VNG
+COLOR_BayerRG2GRAY
+COLOR_BayerRG2RGB
+COLOR_BayerRG2RGBA
+COLOR_BayerRG2RGB_EA
+COLOR_BayerRG2RGB_VNG
+COLOR_COLORCVT_MAX
+COLOR_GRAY2BGR
+COLOR_GRAY2BGR555
+COLOR_GRAY2BGR565
+COLOR_GRAY2BGRA
+COLOR_GRAY2RGB
+COLOR_GRAY2RGBA
+COLOR_HLS2BGR
+COLOR_HLS2BGR_FULL
+COLOR_HLS2RGB
+COLOR_HLS2RGB_FULL
+COLOR_HSV2BGR
+COLOR_HSV2BGR_FULL
+COLOR_HSV2RGB
+COLOR_HSV2RGB_FULL
+COLOR_LAB2BGR
+COLOR_LAB2LBGR
+COLOR_LAB2LRGB
+COLOR_LAB2RGB
+COLOR_LBGR2LAB
+COLOR_LBGR2LUV
+COLOR_LBGR2Lab
+COLOR_LBGR2Luv
+COLOR_LRGB2LAB
+COLOR_LRGB2LUV
+COLOR_LRGB2Lab
+COLOR_LRGB2Luv
+COLOR_LUV2BGR
+COLOR_LUV2LBGR
+COLOR_LUV2LRGB
+COLOR_LUV2RGB
+COLOR_Lab2BGR
+COLOR_Lab2LBGR
+COLOR_Lab2LRGB
+COLOR_Lab2RGB
+COLOR_Luv2BGR
+COLOR_Luv2LBGR
+COLOR_Luv2LRGB
+COLOR_Luv2RGB
+COLOR_M_RGBA2RGBA
+COLOR_RGB2BGR
+COLOR_RGB2BGR555
+COLOR_RGB2BGR565
+COLOR_RGB2BGRA
+COLOR_RGB2GRAY
+COLOR_RGB2HLS
+COLOR_RGB2HLS_FULL
+COLOR_RGB2HSV
+COLOR_RGB2HSV_FULL
+COLOR_RGB2LAB
+COLOR_RGB2LUV
+COLOR_RGB2Lab
+COLOR_RGB2Luv
+COLOR_RGB2RGBA
+COLOR_RGB2XYZ
+COLOR_RGB2YCR_CB
+COLOR_RGB2YCrCb
+COLOR_RGB2YUV
+COLOR_RGB2YUV_I420
+COLOR_RGB2YUV_IYUV
+COLOR_RGB2YUV_YV12
+COLOR_RGBA2BGR
+COLOR_RGBA2BGR555
+COLOR_RGBA2BGR565
+COLOR_RGBA2BGRA
+COLOR_RGBA2GRAY
+COLOR_RGBA2M_RGBA
+COLOR_RGBA2RGB
+COLOR_RGBA2YUV_I420
+COLOR_RGBA2YUV_IYUV
+COLOR_RGBA2YUV_YV12
+COLOR_RGBA2mRGBA
+COLOR_XYZ2BGR
+COLOR_XYZ2RGB
+COLOR_YCR_CB2BGR
+COLOR_YCR_CB2RGB
+COLOR_YCrCb2BGR
+COLOR_YCrCb2RGB
+COLOR_YUV2BGR
+COLOR_YUV2BGRA_I420
+COLOR_YUV2BGRA_IYUV
+COLOR_YUV2BGRA_NV12
+COLOR_YUV2BGRA_NV21
+COLOR_YUV2BGRA_UYNV
+COLOR_YUV2BGRA_UYVY
+COLOR_YUV2BGRA_Y422
+COLOR_YUV2BGRA_YUNV
+COLOR_YUV2BGRA_YUY2
+COLOR_YUV2BGRA_YUYV
+COLOR_YUV2BGRA_YV12
+COLOR_YUV2BGRA_YVYU
+COLOR_YUV2BGR_I420
+COLOR_YUV2BGR_IYUV
+COLOR_YUV2BGR_NV12
+COLOR_YUV2BGR_NV21
+COLOR_YUV2BGR_UYNV
+COLOR_YUV2BGR_UYVY
+COLOR_YUV2BGR_Y422
+COLOR_YUV2BGR_YUNV
+COLOR_YUV2BGR_YUY2
+COLOR_YUV2BGR_YUYV
+COLOR_YUV2BGR_YV12
+COLOR_YUV2BGR_YVYU
+COLOR_YUV2GRAY_420
+COLOR_YUV2GRAY_I420
+COLOR_YUV2GRAY_IYUV
+COLOR_YUV2GRAY_NV12
+COLOR_YUV2GRAY_NV21
+COLOR_YUV2GRAY_UYNV
+COLOR_YUV2GRAY_UYVY
+COLOR_YUV2GRAY_Y422
+COLOR_YUV2GRAY_YUNV
+COLOR_YUV2GRAY_YUY2
+COLOR_YUV2GRAY_YUYV
+COLOR_YUV2GRAY_YV12
+COLOR_YUV2GRAY_YVYU
+COLOR_YUV2RGB
+COLOR_YUV2RGBA_I420
+COLOR_YUV2RGBA_IYUV
+COLOR_YUV2RGBA_NV12
+COLOR_YUV2RGBA_NV21
+COLOR_YUV2RGBA_UYNV
+COLOR_YUV2RGBA_UYVY
+COLOR_YUV2RGBA_Y422
+COLOR_YUV2RGBA_YUNV
+COLOR_YUV2RGBA_YUY2
+COLOR_YUV2RGBA_YUYV
+COLOR_YUV2RGBA_YV12
+COLOR_YUV2RGBA_YVYU
+COLOR_YUV2RGB_I420
+COLOR_YUV2RGB_IYUV
+COLOR_YUV2RGB_NV12
+COLOR_YUV2RGB_NV21
+COLOR_YUV2RGB_UYNV
+COLOR_YUV2RGB_UYVY
+COLOR_YUV2RGB_Y422
+COLOR_YUV2RGB_YUNV
+COLOR_YUV2RGB_YUY2
+COLOR_YUV2RGB_YUYV
+COLOR_YUV2RGB_YV12
+COLOR_YUV2RGB_YVYU
+COLOR_YUV420P2BGR
+COLOR_YUV420P2BGRA
+COLOR_YUV420P2GRAY
+COLOR_YUV420P2RGB
+COLOR_YUV420P2RGBA
+COLOR_YUV420SP2BGR
+COLOR_YUV420SP2BGRA
+COLOR_YUV420SP2GRAY
+COLOR_YUV420SP2RGB
+COLOR_YUV420SP2RGBA
+COLOR_YUV420p2BGR
+COLOR_YUV420p2BGRA
+COLOR_YUV420p2GRAY
+COLOR_YUV420p2RGB
+COLOR_YUV420p2RGBA
+COLOR_YUV420sp2BGR
+COLOR_YUV420sp2BGRA
+COLOR_YUV420sp2GRAY
+COLOR_YUV420sp2RGB
+COLOR_YUV420sp2RGBA
+COLOR_mRGBA2RGBA
diff --git a/Nov_27/Full/Camera/img_rec_module/img_1_1_meas.txt b/Nov_27/Full/Camera/img_rec_module/img_1_1_meas.txt
new file mode 100644
index 0000000..97ed4c5
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/img_1_1_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 384.9492753623189; 
+y: zero_crossing: 128.7986577181208, edge_converge: 124.79086538461539
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 385.07291666666663; 
+y: zero_crossing: 129.5, edge_converge: 125.5
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 383.90625, edge_converge: 379.9741379310345; 
+y: zero_crossing: 129.52727272727273, edge_converge: 130.70366132723112
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 379.9741379310345; 
+y: zero_crossing: 129.6, edge_converge: 130.70366132723112
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 379.9741379310345; 
+y: zero_crossing: 129.68627450980392, edge_converge: 130.70366132723112
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 383.6551724137931, edge_converge: 379.9741379310345; 
+y: zero_crossing: 129.55172413793105, edge_converge: 130.70366132723112
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 383.55172413793105, edge_converge: 379.9741379310345; 
+y: zero_crossing: 129.5, edge_converge: 130.70366132723112
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 383.90625, edge_converge: 379.9741379310345; 
+y: zero_crossing: 129.5, edge_converge: 130.70366132723112
diff --git a/Nov_27/Full/Camera/img_rec_module/img_1_1new_meas.txt b/Nov_27/Full/Camera/img_rec_module/img_1_1new_meas.txt
new file mode 100644
index 0000000..70d4297
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/img_1_1new_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 323.66566716641677; 
+y: zero_crossing: -1, edge_converge: 828.3849673202615
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 597.0; 
+y: zero_crossing: -1, edge_converge: 792.34375
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 880.0; 
+y: zero_crossing: -1, edge_converge: 796.6236413043478
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 716.546875; 
+y: zero_crossing: -1, edge_converge: 832.172697368421
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 308.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 308.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 308.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 321.5, edge_converge: 308.0; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Full/Camera/img_rec_module/img_20_1_meas.txt b/Nov_27/Full/Camera/img_rec_module/img_20_1_meas.txt
new file mode 100644
index 0000000..8f3d333
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/img_20_1_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: 225.33333333333334, edge_converge: 222.40969899665552; 
+y: zero_crossing: 222.44086021505376, edge_converge: 232.5
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 224.90625, edge_converge: 212.425; 
+y: zero_crossing: 222.7012987012987, edge_converge: 244.8653846153846
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 224.55172413793105, edge_converge: 225.7096153846154; 
+y: zero_crossing: 222.8507462686567, edge_converge: 233.3653846153846
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 224.86363636363637, edge_converge: 207.6159420289855; 
+y: zero_crossing: 223.22413793103448, edge_converge: 215.5216346153846
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 224.8421052631579, edge_converge: 198.0; 
+y: zero_crossing: 223.37254901960785, edge_converge: 238.5
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 224.8421052631579, edge_converge: 198.0; 
+y: zero_crossing: 223.33333333333334, edge_converge: 238.5
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 224.8421052631579, edge_converge: 198.0; 
+y: zero_crossing: 223.46341463414635, edge_converge: 238.5
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 224.56521739130434, edge_converge: 198.0; 
+y: zero_crossing: 223.57894736842104, edge_converge: 238.5
diff --git a/Nov_27/Full/Camera/img_rec_module/img_20_2_meas.txt b/Nov_27/Full/Camera/img_rec_module/img_20_2_meas.txt
new file mode 100644
index 0000000..4e85aab
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/img_20_2_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 225.58333333333331; 
+y: zero_crossing: -1, edge_converge: 182.35897435897436
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 227.5; 
+y: zero_crossing: -1, edge_converge: 224.0
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 218.89655172413794, edge_converge: 212.9415292353823; 
+y: zero_crossing: -1, edge_converge: 232.0
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 218.86363636363637, edge_converge: 266.84375; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 218.5, edge_converge: 213.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 213.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 213.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 213.0; 
+y: zero_crossing: 191.76923076923077, edge_converge: -1
diff --git a/Nov_27/Full/Camera/img_rec_module/img_20_3_meas.txt b/Nov_27/Full/Camera/img_rec_module/img_20_3_meas.txt
new file mode 100644
index 0000000..b228fdb
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/img_20_3_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: 43.1578947368421, edge_converge: 27.907894736842106; 
+y: zero_crossing: 222.41739130434783, edge_converge: 224.8991379310345
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 222.0; 
+y: zero_crossing: 222.53191489361703, edge_converge: 228.6346153846154
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 225.0; 
+y: zero_crossing: 222.6338028169014, edge_converge: 213.0
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 222.75, edge_converge: 291.0
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 222.79166666666666, edge_converge: 200.0
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 222.7450980392157, edge_converge: 200.0
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 222.92105263157896, edge_converge: 200.0
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 222.9268292682927, edge_converge: 200.0
diff --git a/Nov_27/Full/Camera/img_rec_module/img_2_1new_meas.txt b/Nov_27/Full/Camera/img_rec_module/img_2_1new_meas.txt
new file mode 100644
index 0000000..a8b0bc8
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/img_2_1new_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 1178.6311475409836; 
+y: zero_crossing: -1, edge_converge: 376.4716202270382
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: 635.5
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 1582.0; 
+y: zero_crossing: -1, edge_converge: 723.0
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 1682.4423076923076; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 1081.9783653846152; 
+y: zero_crossing: -1, edge_converge: 558.15625
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 72.5, edge_converge: 1081.9783653846152; 
+y: zero_crossing: -1, edge_converge: 558.15625
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 1081.9783653846152; 
+y: zero_crossing: -1, edge_converge: 558.15625
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 1081.9783653846152; 
+y: zero_crossing: -1, edge_converge: 558.15625
diff --git a/Nov_27/Full/Camera/img_rec_module/img_3_1new_meas.txt b/Nov_27/Full/Camera/img_rec_module/img_3_1new_meas.txt
new file mode 100644
index 0000000..8cf1d3c
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/img_3_1new_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 485.2365079365079; 
+y: zero_crossing: -1, edge_converge: 463.47727272727275
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 484.8125; 
+y: zero_crossing: -1, edge_converge: 464.0
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 482.4; 
+y: zero_crossing: 462.8421052631579, edge_converge: 464.13461538461536
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 480.5, edge_converge: 475.3696120689655; 
+y: zero_crossing: 462.5, edge_converge: 478.50674662668666
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 485.2307692307692, edge_converge: 493.35480943738656; 
+y: zero_crossing: 462.1875, edge_converge: 448.98519736842104
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 493.35480943738656; 
+y: zero_crossing: 462.5, edge_converge: 448.98519736842104
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 487.5, edge_converge: 493.35480943738656; 
+y: zero_crossing: 462.1875, edge_converge: 448.98519736842104
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 493.35480943738656; 
+y: zero_crossing: 462.4347826086956, edge_converge: 448.98519736842104
diff --git a/Nov_27/Full/Camera/img_rec_module/img_filter.py b/Nov_27/Full/Camera/img_rec_module/img_filter.py
new file mode 100644
index 0000000..3b8b0dc
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/img_filter.py
@@ -0,0 +1,23 @@
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+img = cv2.imread('../testpic/img_20_1.png',0)
+blur = cv2.GaussianBlur(img,(5,5),0)
+cv2.imshow("ok", blur)
+img = blur
+
+laplacian = cv2.Laplacian(img,cv2.CV_64F)
+sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)
+sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=5)
+
+plt.subplot(2,2,1),plt.imshow(img,cmap = 'gray')
+plt.title('Original'), plt.xticks([]), plt.yticks([])
+plt.subplot(2,2,2),plt.imshow(laplacian,cmap = 'gray')
+plt.title('Laplacian'), plt.xticks([]), plt.yticks([])
+plt.subplot(2,2,3),plt.imshow(sobelx,cmap = 'gray')
+plt.title('Sobel X'), plt.xticks([]), plt.yticks([])
+plt.subplot(2,2,4),plt.imshow(sobely,cmap = 'gray')
+plt.title('Sobel Y'), plt.xticks([]), plt.yticks([])
+
+plt.show()
\ No newline at end of file
diff --git a/Nov_27/Full/Camera/img_rec_module/img_rec.py b/Nov_27/Full/Camera/img_rec_module/img_rec.py
new file mode 100644
index 0000000..eac36ff
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/img_rec.py
@@ -0,0 +1,176 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+from img_rec_module.sig_proc_test import FM, compare_data_plots, compare_images
+
+
+def identity(img):
+    return img
+
+
+def grayscale(img):
+    return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+
+def grad_detect(img, n, ori, func):
+    #img = np.array(img)
+    if ori == 'h':
+        grad = [func(img, n, i+1) - func(img, n, i) for i in range(img.shape[1]-1)]
+    else:
+        grad = [func(img, i+1, n) - func(img, i, n) for i in range(img.shape[0]-1)]
+    return grad
+
+
+def abs_detect(img, n, ori, func):
+    if ori == 'h':
+        return [func(img, n, i) for i in range(img.shape[1])]
+    else:
+        return [func(img, i, n) for i in range(img.shape[0])]
+
+
+def visualize_data(folder, oname, sample, orient, func, data_p=grad_detect, img_p=identity):
+    dirs = os.listdir(folder)
+    for f in dirs:
+        try:
+            img = img_p(cv2.imread(folder + f))
+            interval = int(img.shape[1 if orient == 'v' else 0] / sample)
+            for j in range(sample):
+                n = j * interval
+                data = data_p(img, n, orient, func)
+                plt_save_data(data, oname + 'data_{0}{1}_'.format(orient, n) + f)
+        except:
+            print(f)
+
+
+def max_entry(img):
+    max_e = 0
+    for row in img:
+        temp = max(row)
+        if temp > max_e:
+            max_e = temp
+
+
+def record_file(vals, fname):
+    f = open(fname, 'w')
+    for v in vals:
+        f.write(str(v) + '\n')
+    f.close()
+
+
+def rel_lumin(img, r, c):
+    b = img.item(r, c, 0)
+    g = img.item(r, c, 1)
+    r = img.item(r, c, 2)
+    return 0.2126 * r + 0.7152 * g + 0.0722 * b
+
+
+def max_method(img):
+    index = []
+    s = 0
+    for i in range(img.shape[1]):
+        val = np.argmax(img[:, i])
+        index.append(val)
+        s += val
+    return s / len(index)
+
+
+def plot_show_data(data):
+    plt.figure(figsize=(16, 8))
+    #plt.xticks(range(0, img.shape[0], 20), range(0, img.shape[0], 20))
+    plt.plot(data, 'b-')
+    plt.show()
+    plt.close()
+
+
+def plt_save_data(data, fname):
+    plt.figure(figsize=(16, 8))
+    plt.plot(data, 'b-')
+    plt.savefig(fname)
+    plt.close()
+
+
+def centroid(img, func):
+    r = img.shape[0]
+    c = img.shape[1]
+    aggr_i = 0
+    aggr_j = 0
+    aggr = 0
+    for i in range(r):
+        for j in range(c):
+            lum = func(img, i, j)
+            aggr_i += lum * i
+            aggr_j += lum * j
+            aggr += lum
+    dim = r * c
+    return aggr_i / aggr, aggr_j / aggr
+    # return aggr_i / dim, aggr_j / dim
+
+
+def centroid_test(fname, n, trials, func, img_p=identity):
+    for i in range(1, n+1):
+        for j in range(1, trials+1):
+            cf = fname.format(i, j)
+            print(cf)
+            im = img_p(cv2.imread(cf))
+            print(centroid(im, func))
+
+
+def brightness_test():
+    BASE_DIR = '../calib/'
+    image_name = 'shutter_{0}_iso_{1}_bright_{2}.jpeg'
+    img_path = BASE_DIR + image_name
+    shutter = [100, 1000, 5000, 10000]
+    isos = [100, 400, 800]
+    brightness = [0, 50, 100]
+    expo_modes = ['auto', 'very_long']
+    for ss in shutter[0:]:
+        comp = []
+        plts = []
+        print(ss)
+        for iso in isos:
+            name = img_path.format(ss, iso, 'very_long')
+            imgr = cv2.imread(name)
+            tp = (imgr, name)
+            comp.append(tp)
+            img = cv2.cvtColor(imgr, cv2.COLOR_BGR2GRAY)
+            plts.append((FM(img, FM.HOR, img.shape[0] // 2).extract_array(), name))
+        compare_images(ilist=comp)
+        compare_data_plots(ilist=plts)
+
+
+gray_p = lambda img, r, c: img.item(r, c)
+
+if __name__ == '__main__':
+    #centroid_test("../testpic/img_{0}_{1}.png", 25, 3, gray_p, img_p=grayscale)
+    # centroid_test("../testpic/img_{0}_{1}.png", 25, 3, rel_lumin)
+
+    """img = cv2.imread("../testpic/png_1_1.png")
+    plot_show_data(abs_detect(img, 0, 'v', rel_lumin))"""
+
+    """img = cv2.imread("../testpic/png_1_1.png")
+    print(img.shape)
+    cv2.imshow('all', img)
+    gs = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    cv2.imshow('gray', gs)
+    print(sum(centers) / len(centers))
+    print(max_method(gs))
+    record_file(centers, 'centers.txt')
+    
+    while True:
+        prompt = input("type in an index to draw or q to quit")
+        if prompt == 'q':
+            break
+        else:
+            plot_i(int(prompt))
+    
+    centers = []
+    for i in range(gs.shape[1]):
+        grad = grad_detect(gs, i, 'v', rel_lumin)
+        max_v = max(range(len(grad)), key=lambda i: grad[i])
+        min_v = min(range(len(grad)), key=lambda i: grad[i])
+        centers.append((max_v + min_v) / 2)"""
+
+    #visualize_data("../testpic/", "../absdataplot/", 10, 'h', rel_lumin, data_p=abs_detect)
+    brightness_test()
+
diff --git a/Nov_27/Full/Camera/img_rec_module/log.docx b/Nov_27/Full/Camera/img_rec_module/log.docx
new file mode 100644
index 0000000..b574f98
Binary files /dev/null and b/Nov_27/Full/Camera/img_rec_module/log.docx differ
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/convergence.csv b/Nov_27/Full/Camera/img_rec_module/meas/convergence.csv
new file mode 100644
index 0000000..418d98a
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/convergence.csv
@@ -0,0 +1,373 @@
+Image Number,Center X,Center Y,StdDev Horizontal,Std Dev Vertical
+0
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/convergence_naive_std1.5.csv b/Nov_27/Full/Camera/img_rec_module/meas/convergence_naive_std1.5.csv
new file mode 100644
index 0000000..cd215cf
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/convergence_naive_std1.5.csv
@@ -0,0 +1,373 @@
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/convergence_naive_std2.csv b/Nov_27/Full/Camera/img_rec_module/meas/convergence_naive_std2.csv
new file mode 100644
index 0000000..9c454aa
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/convergence_naive_std2.csv
@@ -0,0 +1,373 @@
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/convergence_naive_std2_p20.csv b/Nov_27/Full/Camera/img_rec_module/meas/convergence_naive_std2_p20.csv
new file mode 100644
index 0000000..2961aa4
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/convergence_naive_std2_p20.csv
@@ -0,0 +1,373 @@
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/convergence_naive_std_1.2.csv b/Nov_27/Full/Camera/img_rec_module/meas/convergence_naive_std_1.2.csv
new file mode 100644
index 0000000..5f851e0
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/convergence_naive_std_1.2.csv
@@ -0,0 +1,373 @@
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\ No newline at end of file
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/convergence_prime.csv b/Nov_27/Full/Camera/img_rec_module/meas/convergence_prime.csv
new file mode 100644
index 0000000..54b1f8f
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/convergence_prime.csv
@@ -0,0 +1,373 @@
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(100, 200).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(100, 200).txt
new file mode 100644
index 0000000..06ac13b
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(100, 200).txt	
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(100, 300).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(100, 300).txt
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index 0000000..c66127c
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(100, 300).txt	
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(120, 300).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(120, 300).txt
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(490, 50).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(490, 50).txt
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_10_{0}/img_10_{0}_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_10_{0}/img_10_{0}_(240, 320).txt
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index 0000000..0aa7bd9
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+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_10_{0}/img_10_{0}_(240, 320).txt	
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(100, 150).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(100, 150).txt
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(150, 200).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(150, 200).txt
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(20, 300).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(20, 300).txt
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(200, 250).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(200, 250).txt
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(250, 300).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(250, 300).txt
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(30, 100).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(30, 100).txt
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(300, 350).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(300, 350).txt
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index 0000000..9fcb0bd
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(300, 400).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(300, 400).txt
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(350, 400).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(350, 400).txt
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(400, 450).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(400, 450).txt
new file mode 100644
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diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(450, 550).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(450, 550).txt
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+y: edge_centroid: 316.68044077134977, poly: 313.73273273273276, gaussian: 315.5095095095095
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(50, 100).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(50, 100).txt
new file mode 100644
index 0000000..c0a15fe
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(50, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 474.3070001323277, poly: 472.996996996997, gaussian: 472.81081081081084; 
+y: edge_centroid: 312.8755548978987, poly: 312.6666666666667, gaussian: 313.2162162162162
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_133_{0}/img_133_{0}.png_(250, 100).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_133_{0}/img_133_{0}.png_(250, 100).txt
new file mode 100644
index 0000000..f2af609
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_133_{0}/img_133_{0}.png_(250, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 277.76777028087247, poly: 281.1461461461461, gaussian: 278.5765765765766; 
+y: edge_centroid: 302.41199200532964, poly: 296.71771771771773, gaussian: 299.7837837837838
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_133_{0}/img_133_{0}.png_(250, 50).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_133_{0}/img_133_{0}.png_(250, 50).txt
new file mode 100644
index 0000000..b79b4f9
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_133_{0}/img_133_{0}.png_(250, 50).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 277.76777028087247, poly: 281.1461461461461, gaussian: 278.5765765765766; 
+y: edge_centroid: 266.9218650877565, poly: 284.07207207207205, gaussian: 279.6546546546547
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_133_{0}/img_133_{0}.png_(300, 550).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_133_{0}/img_133_{0}.png_(300, 550).txt
new file mode 100644
index 0000000..84b1934
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_133_{0}/img_133_{0}.png_(300, 550).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 81.92717111682785, poly: 81.3933933933934, gaussian: 81.24724724724724; 
+y: edge_centroid: 289.13216374268995, poly: 286.6216216216216, gaussian: 288.1381381381381
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_134_{0}/img_134_{0}.png_(200, 150).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_134_{0}/img_134_{0}.png_(200, 150).txt
new file mode 100644
index 0000000..0d980c5
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_134_{0}/img_134_{0}.png_(200, 150).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 58.87142186766776, poly: 57.72372372372372, gaussian: 58.56256256256256; 
+y: edge_centroid: 298.5141452126122, poly: 298.53453453453454, gaussian: 299.05505505505505
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_13_{0}/img_13_{0}_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_13_{0}/img_13_{0}_(240, 320).txt
new file mode 100644
index 0000000..f932d1e
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_13_{0}/img_13_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 52.44813278008298, poly: 53.77977977977978, gaussian: 53.35935935935936; 
+y: edge_centroid: 215.0489725113424, poly: 217.44444444444446, gaussian: 216.44444444444446
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_157_{0}/img_157_{0}.png_(250, 450).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_157_{0}/img_157_{0}.png_(250, 450).txt
new file mode 100644
index 0000000..bc8985f
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_157_{0}/img_157_{0}.png_(250, 450).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 231.9349172144786, poly: 229.16516516516518, gaussian: 230.4204204204204; 
+y: edge_centroid: 324.7964368509638, poly: 320.92092092092093, gaussian: 322.7027027027027
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_160_{0}/img_160_{0}_(100, 500).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_160_{0}/img_160_{0}_(100, 500).txt
new file mode 100644
index 0000000..7b9d1b7
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_160_{0}/img_160_{0}_(100, 500).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 225.6382858488344, poly: 219.85385385385385, gaussian: 223.6846846846847; 
+y: edge_centroid: 326.9082845283447, poly: 329.64864864864865, gaussian: 329.45245245245246
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_160_{0}/img_160_{0}_(300, 400).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_160_{0}/img_160_{0}_(300, 400).txt
new file mode 100644
index 0000000..a009916
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_160_{0}/img_160_{0}_(300, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 218.77561608300925, poly: 224.71471471471472, gaussian: 223.88388388388387; 
+y: edge_centroid: 325.48699770378875, poly: 323.76576576576576, gaussian: 321.72972972972974
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_160_{0}/img_160_{0}_(350, 450).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_160_{0}/img_160_{0}_(350, 450).txt
new file mode 100644
index 0000000..7dca8cd
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_160_{0}/img_160_{0}_(350, 450).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 221.86800998243837, poly: 223.86086086086087, gaussian: 222.33833833833833; 
+y: edge_centroid: 324.95901767558263, poly: 319.3243243243243, gaussian: 320.1621621621622
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_170_{0}/img_170_{0}_(200, 350).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_170_{0}/img_170_{0}_(200, 350).txt
new file mode 100644
index 0000000..deecda3
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_170_{0}/img_170_{0}_(200, 350).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 392.6319532908705, poly: 390.93993993993996, gaussian: 391.22222222222223; 
+y: edge_centroid: 332.7505637119807, poly: 333.94494494494495, gaussian: 333.13613613613614
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_170_{0}/img_170_{0}_(200, 450).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_170_{0}/img_170_{0}_(200, 450).txt
new file mode 100644
index 0000000..b69498b
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_170_{0}/img_170_{0}_(200, 450).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 392.6319532908705, poly: 390.93993993993996, gaussian: 391.22222222222223; 
+y: edge_centroid: 340.36645473811694, poly: 333.6066066066066, gaussian: 333.86686686686687
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(100, 150).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(100, 150).txt
new file mode 100644
index 0000000..0564307
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(100, 150).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 220.7910065139735, poly: 217.15515515515517, gaussian: 220.11711711711712; 
+y: edge_centroid: 160.24814595075648, poly: 158.1981981981982, gaussian: 155.2112112112112
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(150, 200).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(150, 200).txt
new file mode 100644
index 0000000..1f815cd
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(150, 200).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 226.56943727413525, poly: 233.0, gaussian: 231.61461461461462; 
+y: edge_centroid: -1, poly: 157.03003003003002, gaussian: 159.97997997998
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(200, 250).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(200, 250).txt
new file mode 100644
index 0000000..7084079
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(200, 250).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 224.38829323735553, poly: 221.4204204204204, gaussian: 222.997997997998; 
+y: edge_centroid: -1, poly: 136.21621621621622, gaussian: 153.43543543543544
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(200, 50).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(200, 50).txt
new file mode 100644
index 0000000..751271c
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(200, 50).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 224.38829323735553, poly: 221.4204204204204, gaussian: 222.997997997998; 
+y: edge_centroid: 159.31560235197594, poly: 147.12512512512512, gaussian: 138.0
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(250, 300).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(250, 300).txt
new file mode 100644
index 0000000..e2f1dfc
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(250, 300).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 228.60011869116025, poly: 215.4144144144144, gaussian: 201.0; 
+y: edge_centroid: 155.03382867757378, poly: 157.1791791791792, gaussian: 158.998998998999
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(300, 350).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(300, 350).txt
new file mode 100644
index 0000000..238ae5c
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(300, 350).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 228.2786542650262, poly: 231.36236236236238, gaussian: 224.34534534534535; 
+y: edge_centroid: -1, poly: 139.21621621621622, gaussian: 139.81581581581582
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(350, 400).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(350, 400).txt
new file mode 100644
index 0000000..c508eef
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(350, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 218.11615628299896, poly: 221.07207207207207, gaussian: 219.3013013013013; 
+y: edge_centroid: 155.50516848445383, poly: 152.59459459459458, gaussian: 152.37137137137137
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(400, 450).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(400, 450).txt
new file mode 100644
index 0000000..4c33dde
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(400, 450).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 228.5702052689802, poly: 220.84284284284286, gaussian: 200.0; 
+y: edge_centroid: 148.5988915951851, poly: 134.5095095095095, gaussian: 135.5895895895896
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(50, 100).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(50, 100).txt
new file mode 100644
index 0000000..45a7482
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(50, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 225.69889181411875, poly: 219.86186186186185, gaussian: 228.002002002002; 
+y: edge_centroid: 156.60606647930996, poly: 154.3803803803804, gaussian: 151.83083083083082
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_19_3/img_19_3_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_19_3/img_19_3_(240, 320).txt
new file mode 100644
index 0000000..5d569df
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_19_3/img_19_3_(240, 320).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 354.17105263157896; 
+y: zero_crossing: 264.9651162790698, edge_converge: 252.34375
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 355.72727272727275, edge_converge: 353.32894736842104; 
+y: zero_crossing: 265.5, edge_converge: 269.3763586956522
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 355.76190476190476, edge_converge: 349.5; 
+y: zero_crossing: 265.5, edge_converge: 239.0
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 356.1034482758621, edge_converge: 371.0; 
+y: zero_crossing: 265.37254901960785, edge_converge: 300.0
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 346.65625; 
+y: zero_crossing: 265.4313725490196, edge_converge: 238.5
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 356.40625, edge_converge: 422.36538461538464; 
+y: zero_crossing: 265.40845070422534, edge_converge: 294.36538461538464
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 356.3095238095238, edge_converge: 256.4673913043478; 
+y: zero_crossing: 265.37254901960785, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 356.09375, edge_converge: 317.0; 
+y: zero_crossing: 265.3333333333333, edge_converge: -1
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_19_{0}/img_19_{0}_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_19_{0}/img_19_{0}_(240, 320).txt
new file mode 100644
index 0000000..84c72a9
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_19_{0}/img_19_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 356.46369710467707, poly: 356.40440440440443, gaussian: 356.67667667667666; 
+y: edge_centroid: 264.30495510475555, poly: 263.84384384384384, gaussian: 264.1141141141141
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_1_1/img_1_1_(600, 960).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_1_1/img_1_1_(600, 960).txt
new file mode 100644
index 0000000..1495c39
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_1_1/img_1_1_(600, 960).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 1033.5062801932368; 
+y: zero_crossing: -1, edge_converge: 671.6582417582417
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 285.5, edge_converge: 286.10200668896323; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 285.5, edge_converge: 276.20913461538464; 
+y: zero_crossing: -1, edge_converge: 771.828947368421
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 278.8111413043478; 
+y: zero_crossing: -1, edge_converge: 607.5
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 285.5, edge_converge: 284.02163461538464; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 284.5, edge_converge: 279.5; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 283.8615560640732; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 306.5; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_1_{0}/img_1_{0}_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_1_{0}/img_1_{0}_(240, 320).txt
new file mode 100644
index 0000000..d00a799
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_1_{0}/img_1_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 383.30516277540283, poly: 383.17317317317315, gaussian: 383.2852852852853; 
+y: edge_centroid: 129.78768233387356, poly: 129.64064064064064, gaussian: 129.4164164164164
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_1_{0}/img_1_{0}_(600, 960).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_1_{0}/img_1_{0}_(600, 960).txt
new file mode 100644
index 0000000..1df54e1
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_1_{0}/img_1_{0}_(600, 960).txt	
@@ -0,0 +1,6 @@
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 282.80022496724126; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 277.11846281807; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_20_1/img_20_1_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_20_1/img_20_1_(240, 320).txt
new file mode 100644
index 0000000..7f8ccc7
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_20_1/img_20_1_(240, 320).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: 225.33333333333334, edge_converge: 222.40969899665552; 
+y: zero_crossing: 222.44086021505376, edge_converge: 232.5
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 224.90625, edge_converge: 212.425; 
+y: zero_crossing: 222.7012987012987, edge_converge: 244.8653846153846
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 224.55172413793105, edge_converge: 225.7096153846154; 
+y: zero_crossing: 222.8507462686567, edge_converge: 233.3653846153846
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 224.86363636363637, edge_converge: 207.6159420289855; 
+y: zero_crossing: 223.22413793103448, edge_converge: 215.5216346153846
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 224.8421052631579, edge_converge: 198.0; 
+y: zero_crossing: 223.37254901960785, edge_converge: 238.5
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 224.8421052631579, edge_converge: -1; 
+y: zero_crossing: 223.33333333333334, edge_converge: 309.4673913043478
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 224.8421052631579, edge_converge: 220.5; 
+y: zero_crossing: 223.46341463414635, edge_converge: 210.0
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 224.56521739130434, edge_converge: -1; 
+y: zero_crossing: 223.57894736842104, edge_converge: -1
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_20_1/img_20_1_(900, 1400).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_20_1/img_20_1_(900, 1400).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_2_1/img_2_1_(200, 500).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_2_1/img_2_1_(200, 500).txt
new file mode 100644
index 0000000..b5bbd3d
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_2_1/img_2_1_(200, 500).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 1178.6311475409836; 
+y: zero_crossing: -1, edge_converge: 376.4716202270382
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: 635.5
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 1582.0; 
+y: zero_crossing: -1, edge_converge: 723.0
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 1682.4423076923076; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 1081.9783653846152; 
+y: zero_crossing: -1, edge_converge: 558.15625
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 72.5, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: 600.4166666666666
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_2_1/img_2_1_(400, 700).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_2_1/img_2_1_(400, 700).txt
new file mode 100644
index 0000000..5799b85
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_2_1/img_2_1_(400, 700).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 1114.8653846153848; 
+y: zero_crossing: -1, edge_converge: 920.8071348940914
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 1120.671052631579; 
+y: zero_crossing: -1, edge_converge: 604.5
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 886.5258620689655; 
+y: zero_crossing: -1, edge_converge: 614.671052631579
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 847.671052631579; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: 1025.9558823529412
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 94.1875, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_2_1/img_2_1_(600, 960).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_2_1/img_2_1_(600, 960).txt
new file mode 100644
index 0000000..9b33d97
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_2_1/img_2_1_(600, 960).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 316.76911544227886; 
+y: zero_crossing: -1, edge_converge: 646.7335664335665
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 360.91666666666663; 
+y: zero_crossing: -1, edge_converge: 910.05625
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 919.5; 
+y: zero_crossing: -1, edge_converge: 568.328947368421
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 422.0326086956522; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 782.0326086956521; 
+y: zero_crossing: -1, edge_converge: 1111.5
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 231.65625; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_2_1/img_2_1_(800, 900).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_2_1/img_2_1_(800, 900).txt
new file mode 100644
index 0000000..85e0c4d
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_2_1/img_2_1_(800, 900).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 94.49201474201475; 
+y: zero_crossing: -1, edge_converge: 612.9423076923077
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 97.05847076461768; 
+y: zero_crossing: -1, edge_converge: 615.672697368421
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 95.59030100334448; 
+y: zero_crossing: -1, edge_converge: 626.328947368421
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 96.23076923076923, edge_converge: 104.08333333333333; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 72.65625; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 105.76923076923077, edge_converge: 135.17105263157896; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 106.5, edge_converge: 264.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_2_1/img_2_1_(900, 1700).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_2_1/img_2_1_(900, 1700).txt
new file mode 100644
index 0000000..51550d1
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_2_1/img_2_1_(900, 1700).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 992.733709273183; 
+y: zero_crossing: -1, edge_converge: 779.3782051282051
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 968.26875; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 953.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 198.43478260869566, edge_converge: 189.65625; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 197.5, edge_converge: 207.5; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 200.55172413793105, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 207.40969899665552; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_2_1/img_2_1_(900, 900).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_2_1/img_2_1_(900, 900).txt
new file mode 100644
index 0000000..4e5269e
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_2_1/img_2_1_(900, 900).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 992.733709273183; 
+y: zero_crossing: -1, edge_converge: 612.9423076923077
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 968.26875; 
+y: zero_crossing: -1, edge_converge: 615.672697368421
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 953.0; 
+y: zero_crossing: -1, edge_converge: 626.328947368421
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 198.43478260869566, edge_converge: 189.65625; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 197.5, edge_converge: 207.5; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 200.55172413793105, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 207.40969899665552; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_2_4/img_2_4_(1400, 900).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_2_4/img_2_4_(1400, 900).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_2_4/img_2_4_(600, 960).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_2_4/img_2_4_(600, 960).txt
new file mode 100644
index 0000000..725c811
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_2_4/img_2_4_(600, 960).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 1100.8305555555555; 
+y: zero_crossing: 468.88853503184714, edge_converge: 468.9624718174986
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 1284.6410256410256; 
+y: zero_crossing: -1, edge_converge: 472.02868852459017
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 1086.828947368421; 
+y: zero_crossing: 468.9056603773585, edge_converge: 469.513210927004
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 1086.6; 
+y: zero_crossing: -1, edge_converge: 526.828947368421
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 1092.3333333333333, edge_converge: 1189.2003676470588; 
+y: zero_crossing: -1, edge_converge: 445.3327759197324
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 1092.157894736842, edge_converge: 1190.0; 
+y: zero_crossing: -1, edge_converge: 439.0
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 1046.8653846153848; 
+y: zero_crossing: -1, edge_converge: 503.0326086956522
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 1072.84375; 
+y: zero_crossing: -1, edge_converge: 517.84375
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_2_4/img_2_4_(900, 1400).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_2_4/img_2_4_(900, 1400).txt
new file mode 100644
index 0000000..b979bb5
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_2_4/img_2_4_(900, 1400).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 1296.632183836245; 
+y: zero_crossing: -1, edge_converge: 683.452135931303
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 1295.6363633216238; 
+y: zero_crossing: 690.1150168914178, edge_converge: 693.2398839220738
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 1295.3653844696842; 
+y: zero_crossing: -1, edge_converge: 693.9883644021885
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 1278.906249850798; 
+y: zero_crossing: 688.6590191104578, edge_converge: 700.2655993429721
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 1174.4999998716144; 
+y: zero_crossing: -1, edge_converge: 964.4428415672992
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 1161.4999997817608; 
+y: zero_crossing: -1, edge_converge: 940.947694754178
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 1160.9999998929734; 
+y: zero_crossing: -1, edge_converge: 941.7896120081671
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 1186.0000001232456, edge_converge: 1174.4999996450306; 
+y: zero_crossing: -1, edge_converge: 962.456256071665
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_2_5/img_2_5_(600, 960).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_2_5/img_2_5_(600, 960).txt
new file mode 100644
index 0000000..d1de3a8
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_2_5/img_2_5_(600, 960).txt	
@@ -0,0 +1,21 @@
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 1158.301886792453, edge_converge: 1158.0; 
+y: zero_crossing: 423.6167664670659, edge_converge: 425.51180555555555
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 1158.3440860215053, edge_converge: 1166.740170940171; 
+y: zero_crossing: -1, edge_converge: 385.2810276679842
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 1158.134328358209, edge_converge: 1188.467391304348; 
+y: zero_crossing: -1, edge_converge: 326.3781954887218
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 1158.2131147540983, edge_converge: 1194.753947368421; 
+y: zero_crossing: 423.44827586206895, edge_converge: 374.91666666666663
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 1170.5; 
+y: zero_crossing: 423.1578947368421, edge_converge: 351.5
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 1255.0; 
+y: zero_crossing: -1, edge_converge: 700.0326086956521
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 1218.532608695652; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_2_{0}/img_2_{0}_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_2_{0}/img_2_{0}_(240, 320).txt
new file mode 100644
index 0000000..d77b164
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_2_{0}/img_2_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 383.569200132758, poly: 382.83083083083085, gaussian: 383.4114114114114; 
+y: edge_centroid: 128.77362969356932, poly: 129.4084084084084, gaussian: 129.21021021021022
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_2_{0}/img_2_{0}_(600, 960).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_2_{0}/img_2_{0}_(600, 960).txt
new file mode 100644
index 0000000..d22680c
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_2_{0}/img_2_{0}_(600, 960).txt	
@@ -0,0 +1 @@
+ksize:(7, 7), sigmax:0
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_3_3/img_3_3_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_3_3/img_3_3_(240, 320).txt
new file mode 100644
index 0000000..15cad33
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_3_3/img_3_3_(240, 320).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 388.0; 
+y: zero_crossing: 129.02597402597402, edge_converge: 125.57692307692308
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 384.1578947368421, edge_converge: 389.5364372469636; 
+y: zero_crossing: 129.47297297297297, edge_converge: 129.40969899665552
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 384.23809523809524, edge_converge: 371.13461538461536; 
+y: zero_crossing: 129.62337662337663, edge_converge: 129.21517027863777
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 401.32894736842104; 
+y: zero_crossing: 129.66666666666666, edge_converge: 199.6346153846154
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 405.3327759197324; 
+y: zero_crossing: 129.72916666666666, edge_converge: 123.56414473684211
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 129.60416666666666, edge_converge: 125.0
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 383.8421052631579, edge_converge: -1; 
+y: zero_crossing: 129.5, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 383.59375, edge_converge: -1; 
+y: zero_crossing: 129.46341463414635, edge_converge: 105.46739130434783
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_3_3/img_3_3_new_(600, 960).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_3_3/img_3_3_new_(600, 960).txt
new file mode 100644
index 0000000..85506ff
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_3_3/img_3_3_new_(600, 960).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 627.2624342255776; 
+y: zero_crossing: 282.9597701149425, edge_converge: 280.3083778966132
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 626.3650177462289; 
+y: zero_crossing: -1, edge_converge: 283.4423076923077
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 608.9765275707898; 
+y: zero_crossing: 287.3333333333333, edge_converge: 284.86538461538464
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 650.8565573770492; 
+y: zero_crossing: -1, edge_converge: 296.0326086956522
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 652.203125; 
+y: zero_crossing: -1, edge_converge: 689.3056680161943
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 659.8423913043478; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 631.6904761904761, edge_converge: 597.0326086956522; 
+y: zero_crossing: -1, edge_converge: 683.5
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 645.0; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_3_{0}/img_3_{0}_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_3_{0}/img_3_{0}_(240, 320).txt
new file mode 100644
index 0000000..056b176
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_3_{0}/img_3_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 383.6794588625068, poly: 383.2082082082082, gaussian: 383.5855855855856; 
+y: edge_centroid: 128.72770467836258, poly: 129.44144144144144, gaussian: 129.17717717717719
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_3_{0}/img_3_{0}_(300, 573).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_3_{0}/img_3_{0}_(300, 573).txt
new file mode 100644
index 0000000..6a9be15
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_3_{0}/img_3_{0}_(300, 573).txt	
@@ -0,0 +1,21 @@
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 551.1085016945578; 
+y: zero_crossing: -1, edge_converge: 401.2614005098476
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 551.0790923034685; 
+y: zero_crossing: -1, edge_converge: 401.1959898455841
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 551.3164486248141; 
+y: zero_crossing: -1, edge_converge: 396.65866001765227
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 553.6140783045154; 
+y: zero_crossing: 385.44129158264957, edge_converge: 391.39592471066015
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 554.2854352720954, edge_converge: 553.3228366624788; 
+y: zero_crossing: 385.28612080628454, edge_converge: 391.30816089061875
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 554.0283816103325, edge_converge: 552.9026715626205; 
+y: zero_crossing: 385.12690508006284, edge_converge: 391.2389474584604
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 553.7215989908605, edge_converge: 552.4769129699359; 
+y: zero_crossing: 384.9703803392806, edge_converge: 391.0686695466003
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_3_{0}/img_3_{0}_(600, 960).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_3_{0}/img_3_{0}_(600, 960).txt
new file mode 100644
index 0000000..eebe206
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_3_{0}/img_3_{0}_(600, 960).txt	
@@ -0,0 +1,6 @@
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 629.1857205127992; 
+y: zero_crossing: -1, edge_converge: 303.2178917674522
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 626.2193034291377; 
+y: zero_crossing: -1, edge_converge: 279.055693080456
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_4_1/img_4_1_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_4_1/img_4_1_(240, 320).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_4_1/img_4_1_(500, 1200).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_4_1/img_4_1_(500, 1200).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_4_{0}/img_4_{0}_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_4_{0}/img_4_{0}_(240, 320).txt
new file mode 100644
index 0000000..7808d12
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_4_{0}/img_4_{0}_(240, 320).txt	
@@ -0,0 +1,6 @@
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 236.92774193101968, edge_converge: 237.5228648141686; 
+y: zero_crossing: 325.7458333221428, edge_converge: 321.11252693875815
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 237.21044902427207, edge_converge: 234.45920213005442; 
+y: zero_crossing: 325.6826399612562, edge_converge: 321.13169019760346
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(200, 50).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(200, 50).txt
new file mode 100644
index 0000000..cb9440f
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(200, 50).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.21657196969699, poly: 93.01701701701703, gaussian: 92.54954954954955; 
+y: edge_centroid: 220.67972613160907, poly: 205.44544544544544, gaussian: 220.13513513513513
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 100).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 100).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 150).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 150).txt
new file mode 100644
index 0000000..4e7e731
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 150).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 91.0, poly: 90.61461461461461, gaussian: 90.76176176176176; 
+y: edge_centroid: 207.25707213578505, poly: 211.72972972972974, gaussian: 210.05605605605606
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 250).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 250).txt
new file mode 100644
index 0000000..0b0c80e
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 250).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 91.0, poly: 90.61461461461461, gaussian: 90.76176176176176; 
+y: edge_centroid: 218.612658674189, poly: 210.37037037037038, gaussian: 216.64864864864865
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 550).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 550).txt
new file mode 100644
index 0000000..5d716b8
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 550).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 91.0, poly: 90.61461461461461, gaussian: 90.76176176176176; 
+y: edge_centroid: 208.3091801113311, poly: 194.96096096096096, gaussian: 207.03603603603602
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(300, 500).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(300, 500).txt
new file mode 100644
index 0000000..7669241
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(300, 500).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 94.67445813854653, poly: 94.85585585585585, gaussian: 94.37537537537537; 
+y: edge_centroid: 211.19331646152077, poly: 195.86786786786786, gaussian: 209.42642642642642
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(100, 10).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(100, 10).txt
new file mode 100644
index 0000000..c7c4711
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(100, 10).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 94.53349610865254, poly: 90.78078078078079, gaussian: 90.11711711711712; 
+y: edge_centroid: 221.0741458686698, poly: 244.62762762762765, gaussian: 258.0
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(100, 400).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(100, 400).txt
new file mode 100644
index 0000000..e9008a5
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(100, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 94.53349610865254, poly: 92.03003003003003, gaussian: 92.93793793793793; 
+y: edge_centroid: 214.17900156837212, poly: 209.03203203203202, gaussian: 214.83483483483485
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 100).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 100).txt
new file mode 100644
index 0000000..34f75b7
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.21657196969699, poly: 92.74474474474475, gaussian: 92.64864864864865; 
+y: edge_centroid: -1, poly: 196.7027027027027, gaussian: 220.20220220220222
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 300).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 300).txt
new file mode 100644
index 0000000..b1e658b
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 300).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.21657196969699, poly: 92.74474474474475, gaussian: 92.64264264264264; 
+y: edge_centroid: 215.8282069593912, poly: 200.8108108108108, gaussian: 203.97297297297297
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 350).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 350).txt
new file mode 100644
index 0000000..ae51c4c
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 350).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.21657196969699, poly: 92.74474474474475, gaussian: 92.64264264264264; 
+y: edge_centroid: 215.93902685241267, poly: 199.14814814814815, gaussian: 201.96296296296296
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 40).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 40).txt
new file mode 100644
index 0000000..35d04b6
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 40).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.21657196969699, poly: 92.74474474474475, gaussian: 92.64264264264264; 
+y: edge_centroid: 213.46588200238375, poly: 187.52852852852854, gaussian: 250.0
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 400).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 400).txt
new file mode 100644
index 0000000..dd9188e
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.21657196969699, poly: 93.01701701701703, gaussian: 92.54954954954955; 
+y: edge_centroid: 214.17900156837212, poly: 209.03203203203202, gaussian: 214.83483483483485
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(240, 320).txt
new file mode 100644
index 0000000..beaeadc
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.95545171339562, poly: 92.43843843843844, gaussian: 92.54054054054055; 
+y: edge_centroid: 216.32595947660937, poly: 197.66066066066065, gaussian: 203.43843843843842
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(250, 100).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(250, 100).txt
new file mode 100644
index 0000000..d73fc36
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(250, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 91.0, poly: 90.61461461461461, gaussian: 90.76176176176176; 
+y: edge_centroid: -1, poly: 198.59459459459458, gaussian: 236.03003003003005
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(30, 300).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(30, 300).txt
new file mode 100644
index 0000000..8c9068f
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(30, 300).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 106.90241734802697, poly: 130.08608608608608, gaussian: 137.0; 
+y: edge_centroid: 215.8282069593912, poly: 200.8108108108108, gaussian: 203.94794794794794
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(300, 150).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(300, 150).txt
new file mode 100644
index 0000000..00f639b
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(300, 150).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 94.67445813854653, poly: 94.85585585585585, gaussian: 94.37537537537537; 
+y: edge_centroid: 207.25707213578505, poly: 211.72972972972974, gaussian: 210.05605605605606
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(300, 400).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(300, 400).txt
new file mode 100644
index 0000000..07a7d5e
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(300, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 94.67445813854653, poly: 94.72372372372372, gaussian: 94.26126126126127; 
+y: edge_centroid: 214.17900156837212, poly: 194.24424424424424, gaussian: 197.8028028028028
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(350, 200).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(350, 200).txt
new file mode 100644
index 0000000..031f3ef
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(350, 200).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 93.30997090716743, poly: 92.7907907907908, gaussian: 93.55555555555556; 
+y: edge_centroid: 219.13322627053438, poly: 210.43043043043042, gaussian: 218.1951951951952
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(400, 250).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(400, 250).txt
new file mode 100644
index 0000000..525a96e
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(400, 250).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 93.80265120132562, poly: 93.7977977977978, gaussian: 93.90190190190191; 
+y: edge_centroid: 218.612658674189, poly: 210.37037037037038, gaussian: 216.64864864864865
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(450, 300).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(450, 300).txt
new file mode 100644
index 0000000..013cc84
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(450, 300).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 94.75074524191695, poly: 94.30530530530531, gaussian: 94.26426426426427; 
+y: edge_centroid: 215.8282069593912, poly: 211.35135135135135, gaussian: 215.15915915915917
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(50, 550).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(50, 550).txt
new file mode 100644
index 0000000..7db67f4
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(50, 550).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 93.89583756345176, poly: 92.57657657657657, gaussian: 92.48648648648648; 
+y: edge_centroid: 208.3091801113311, poly: 189.6096096096096, gaussian: 209.03303303303304
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_5_1/img_5_1_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_5_1/img_5_1_(240, 320).txt
new file mode 100644
index 0000000..aed0952
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_5_1/img_5_1_(240, 320).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: 556.6842105263158, edge_converge: -1; 
+y: zero_crossing: 301.9344262295082, edge_converge: 308.5
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 555.8125, edge_converge: -1; 
+y: zero_crossing: 301.94444444444446, edge_converge: 301.5
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 555.4347826086956, edge_converge: -1; 
+y: zero_crossing: 301.84375, edge_converge: 293.0
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 555.5, edge_converge: -1; 
+y: zero_crossing: 301.75, edge_converge: 318.4673913043478
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 555.5, edge_converge: -1; 
+y: zero_crossing: 301.6862745098039, edge_converge: 280.9673913043478
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 446.17105263157896; 
+y: zero_crossing: 301.7291666666667, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 301.7291666666667, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 301.6666666666667, edge_converge: -1
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_5_1/img_5_1_(900, 1400).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_5_1/img_5_1_(900, 1400).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_5_{0}/img_5_{0}_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_5_{0}/img_5_{0}_(240, 320).txt
new file mode 100644
index 0000000..65e1696
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_5_{0}/img_5_{0}_(240, 320).txt	
@@ -0,0 +1,21 @@
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 555.6464323051268, edge_converge: 568.4630895970322; 
+y: zero_crossing: 302.34291829038295, edge_converge: 304.5181313275549
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 555.4526325366787, edge_converge: 566.8934642695242; 
+y: zero_crossing: 302.34965401891816, edge_converge: 304.4386691292941
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 555.2960595115023, edge_converge: 563.5356471927093; 
+y: zero_crossing: 302.33803803307313, edge_converge: 304.27810760651005
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 555.1849293811231, edge_converge: 563.5416332228492; 
+y: zero_crossing: 302.3176065339906, edge_converge: 300.31359690803674
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 555.1016003888399, edge_converge: 563.6004921726214; 
+y: zero_crossing: 302.29189598827344, edge_converge: 300.16238390552815
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 555.0360469257112, edge_converge: 563.6160702558284; 
+y: zero_crossing: 302.26525876715635, edge_converge: 296.30288208863885
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 554.9834021944725, edge_converge: 554.5573053432411; 
+y: zero_crossing: 302.24049538378574, edge_converge: 308.2023326126082
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}.png_(200, 600).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}.png_(200, 600).txt
new file mode 100644
index 0000000..ed07d5f
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}.png_(200, 600).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.32846573208725, poly: 92.23423423423424, gaussian: 92.21921921921921; 
+y: edge_centroid: 196.4742684157417, poly: 195.8928928928929, gaussian: 196.3783783783784
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(200, 500).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(200, 500).txt
new file mode 100644
index 0000000..a483322
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(200, 500).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.32846573208725, poly: 92.03003003003003, gaussian: 92.08708708708708; 
+y: edge_centroid: 210.0317535296279, poly: 189.0990990990991, gaussian: 212.75375375375376
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(240, 320).txt
new file mode 100644
index 0000000..302af08
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.0877174261018, poly: 92.41741741741741, gaussian: 92.28528528528528; 
+y: edge_centroid: 221.40502732240432, poly: 205.07807807807808, gaussian: 206.8998998998999
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(400, 100).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(400, 100).txt
new file mode 100644
index 0000000..b340acb
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(400, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 93.30595634480204, poly: 94.14414414414415, gaussian: 93.81681681681681; 
+y: edge_centroid: 307.8291557805356, poly: 354.0, gaussian: 308.5155155155155
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(400, 400).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(400, 400).txt
new file mode 100644
index 0000000..461ddb4
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(400, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 93.30595634480204, poly: 94.14414414414415, gaussian: 93.81681681681681; 
+y: edge_centroid: 212.21240264432728, poly: 190.33833833833833, gaussian: 193.35135135135135
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_61_{0}/img_61_{0}_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_61_{0}/img_61_{0}_(240, 320).txt
new file mode 100644
index 0000000..750c5f3
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_61_{0}/img_61_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 91.9518581931715, poly: 92.18618618618618, gaussian: 91.98798798798799; 
+y: edge_centroid: 218.40512573697507, poly: 201.44444444444446, gaussian: 206.24924924924926
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_61_{0}/img_61_{0}_(400, 400).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_61_{0}/img_61_{0}_(400, 400).txt
new file mode 100644
index 0000000..7978897
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_61_{0}/img_61_{0}_(400, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 93.19376913380157, poly: 94.17417417417417, gaussian: 94.03603603603604; 
+y: edge_centroid: 212.52264302981456, poly: 190.97897897897897, gaussian: 194.45845845845847
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_62_{0}/img_62_{0}_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_62_{0}/img_62_{0}_(240, 320).txt
new file mode 100644
index 0000000..d58a228
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_62_{0}/img_62_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 178.91803535934721, poly: 178.44144144144144, gaussian: 178.33333333333334; 
+y: edge_centroid: -1, poly: 184.47747747747746, gaussian: 265.93293293293294
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_63_{0}/img_63_{0}_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_63_{0}/img_63_{0}_(240, 320).txt
new file mode 100644
index 0000000..52cbf69
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_63_{0}/img_63_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 178.97878718007834, poly: 178.87387387387386, gaussian: 178.72972972972974; 
+y: edge_centroid: -1, poly: 182.97297297297297, gaussian: 227.4954954954955
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_6_{0}/img_6_{0}_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_6_{0}/img_6_{0}_(240, 320).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_73_{0}/img_73_{0}_(400, 400).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_73_{0}/img_73_{0}_(400, 400).txt
new file mode 100644
index 0000000..3c97a4b
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_73_{0}/img_73_{0}_(400, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 283.485064401206, poly: 323.0, gaussian: 286.4864864864865; 
+y: edge_centroid: 195.9362609551483, poly: 189.6016016016016, gaussian: 190.22222222222223
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(100, 300).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(100, 300).txt
new file mode 100644
index 0000000..462740a
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(100, 300).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 278.09682467236087, poly: 268.2682682682683, gaussian: 274.5575575575576; 
+y: edge_centroid: 174.44361763022326, poly: 175.0990990990991, gaussian: 174.0
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(150, 350).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(150, 350).txt
new file mode 100644
index 0000000..3abe0ab
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(150, 350).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 264.6934011476264, poly: 267.4674674674675, gaussian: 265.3213213213213; 
+y: edge_centroid: 212.65002063557583, poly: 193.16216216216216, gaussian: 205.15315315315314
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(200, 100).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(200, 100).txt
new file mode 100644
index 0000000..ec7eba6
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(200, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 299.2048811389324, poly: 298.0, gaussian: 299.0900900900901; 
+y: edge_centroid: 212.06545603775513, poly: 211.82282282282281, gaussian: 212.06006006006007
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(200, 400).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(200, 400).txt
new file mode 100644
index 0000000..0fc7e6b
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(200, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 299.2048811389324, poly: 298.0, gaussian: 299.0900900900901; 
+y: edge_centroid: 216.82302078726215, poly: 193.26126126126127, gaussian: 186.53453453453454
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(250, 150).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(250, 150).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(250, 450).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(250, 450).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_78_{0}/img_78_{0}_(400, 260).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_78_{0}/img_78_{0}_(400, 260).txt
new file mode 100644
index 0000000..6892c16
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_78_{0}/img_78_{0}_(400, 260).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 366.0705646776132, poly: 403.0, gaussian: 402.0; 
+y: edge_centroid: 206.59396835595538, poly: 198.7157157157157, gaussian: 201.34434434434434
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_79_{0}/img_79_{0}_(100, 200).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_79_{0}/img_79_{0}_(100, 200).txt
new file mode 100644
index 0000000..de27310
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_79_{0}/img_79_{0}_(100, 200).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 361.17140024783157, poly: 351.23423423423424, gaussian: 355.06006006006004; 
+y: edge_centroid: 206.05061553818015, poly: 197.32232232232232, gaussian: 204.25025025025025
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_79_{0}/img_79_{0}_(200, 200).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_79_{0}/img_79_{0}_(200, 200).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_7_{0}/img_7_{0}_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_7_{0}/img_7_{0}_(240, 320).txt
new file mode 100644
index 0000000..43ad63f
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_7_{0}/img_7_{0}_(240, 320).txt	
@@ -0,0 +1,21 @@
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 157.8389116826965, edge_converge: 158.45130962399327; 
+y: zero_crossing: -1, edge_converge: 411.79259030140565
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 157.75410480327545, edge_converge: 158.32429639754088; 
+y: zero_crossing: -1, edge_converge: 411.6718763916268
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 157.7245284134464, edge_converge: 155.54913028965618; 
+y: zero_crossing: -1, edge_converge: 411.593647090896
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 157.76000829765138, edge_converge: 158.15343806061153; 
+y: zero_crossing: -1, edge_converge: 411.5797965685118
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 157.83060827808885, edge_converge: 157.98016047257528; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 157.90853193451255, edge_converge: 157.6948902708295; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 157.98721999268648, edge_converge: 157.47066779723983; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_80_{0}/img_80_{0}_(200, 300).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_80_{0}/img_80_{0}_(200, 300).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_80_{0}/img_80_{0}_(300, 280).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_80_{0}/img_80_{0}_(300, 280).txt
new file mode 100644
index 0000000..5df82f5
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_80_{0}/img_80_{0}_(300, 280).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 311.3298049296839, poly: 319.7637637637638, gaussian: 319.5735735735736; 
+y: edge_centroid: -1, poly: 0, gaussian: 0
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_86_{0}/img_86_{0}.png_(150, 100).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_86_{0}/img_86_{0}.png_(150, 100).txt
new file mode 100644
index 0000000..639b64b
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_86_{0}/img_86_{0}.png_(150, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 122.42161046111492, poly: 124.84684684684684, gaussian: 124.58358358358359; 
+y: edge_centroid: 268.99951226309923, poly: 263.5865865865866, gaussian: 263.9189189189189
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_86_{0}/img_86_{0}.png_(200, 400).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_86_{0}/img_86_{0}.png_(200, 400).txt
new file mode 100644
index 0000000..34cfc9d
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_86_{0}/img_86_{0}.png_(200, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 326.7772293517605, poly: 326.3703703703704, gaussian: 326.84684684684686; 
+y: edge_centroid: 247.36609869795853, poly: 246.3783783783784, gaussian: 244.63563563563565
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_97_{0}/img_97_{0}_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_97_{0}/img_97_{0}_(240, 320).txt
new file mode 100644
index 0000000..5d771cc
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_97_{0}/img_97_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 249.44900531810134, poly: 248.6956956956957, gaussian: 249.67667667667666; 
+y: edge_centroid: 259.66796296933614, poly: 261.015015015015, gaussian: 261.4074074074074
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_99_{0}/img_99_{0}_(200, 500).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_99_{0}/img_99_{0}_(200, 500).txt
new file mode 100644
index 0000000..653e98a
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_99_{0}/img_99_{0}_(200, 500).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 248.7235796541028, poly: 252.58858858858858, gaussian: 249.82882882882882; 
+y: edge_centroid: 262.84957891453524, poly: 258.80680680680683, gaussian: 260.3963963963964
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_99_{0}/img_99_{0}_(373, 234).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_99_{0}/img_99_{0}_(373, 234).txt
new file mode 100644
index 0000000..550694b
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_99_{0}/img_99_{0}_(373, 234).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 246.9395417318486, poly: 256.96296296296293, gaussian: 253.03203203203202; 
+y: edge_centroid: 280.6590576754352, poly: 258.1631631631632, gaussian: 257.65765765765764
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_99_{0}/img_99_{0}_(400, 400).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_99_{0}/img_99_{0}_(400, 400).txt
new file mode 100644
index 0000000..ceb5b0e
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_99_{0}/img_99_{0}_(400, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 263.7749063060128, poly: 262.6166166166166, gaussian: 263.40440440440443; 
+y: edge_centroid: 255.54966073622904, poly: 253.15915915915917, gaussian: 254.85185185185185
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/img_9_1/img_9_1_(240, 320).txt b/Nov_27/Full/Camera/img_rec_module/meas/img_9_1/img_9_1_(240, 320).txt
new file mode 100644
index 0000000..501f1b1
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/img_9_1/img_9_1_(240, 320).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 243.84375; 
+y: zero_crossing: 211.51459854014598, edge_converge: 204.6346153846154
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 241.55172413793105, edge_converge: 250.5; 
+y: zero_crossing: 211.45945945945945, edge_converge: 205.6346153846154
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 241.59375, edge_converge: 243.8653846153846; 
+y: zero_crossing: 211.30208333333334, edge_converge: 233.08333333333334
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 241.76190476190476, edge_converge: 252.2848297213622; 
+y: zero_crossing: -1, edge_converge: 145.15625
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 241.86363636363637, edge_converge: 252.4673913043478; 
+y: zero_crossing: 210.77586206896552, edge_converge: 218.0
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 252.5; 
+y: zero_crossing: 210.64814814814815, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 241.63888888888889, edge_converge: 217.0; 
+y: zero_crossing: 210.5, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 249.52586206896552; 
+y: zero_crossing: 210.52727272727273, edge_converge: -1
diff --git a/Nov_27/Full/Camera/img_rec_module/meas/truth.txt b/Nov_27/Full/Camera/img_rec_module/meas/truth.txt
new file mode 100644
index 0000000..dcf364f
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/meas/truth.txt
@@ -0,0 +1,32 @@
+new_test
+img_4  237, 325
+
+calib
+img_29  140, 203
+
+testpic
+img_1  385, 130  E: (2, 1)
+img_2  385, 130  E: (2, 1)
+img_3  385, 130  E: (2, 1)
+img_4  -2, -2    175, 113  E: (4, 7) (DATA)
+img_5  176, 52   E: (1, 3)
+img_6  174, -1   E: (1, 0)
+img_7  158, -1   E: (0, 0)
+img_8  -2, -2    159, -1   E: (F, 0) (DATA)
+img_9  242, 212  E: (0, 2)
+img_10  278, 215 E: (1, 3)
+img_11  -2, -2   325, 218 E: (1, 4) (DATA)
+img_12  143, 215 E: (0, 1)
+img_13  53, 219  E: (0, 4)
+img_14  -1, 17   E: (0, 0)
+img_15  138, 19  E: (0, 0)
+img_16  355, 14  E: (1, 0)
+img_17  288, -1  E: (1, 0)
+img_18  369, 180 E: (1, 0)
+img_19  357, 265 E: (0, 1)
+img_20  225, 224 E: (3, 1)
+img_21  41, 224  E: (0, 1)
+img_22  -1, 59   E: (0, 3)
+img_23  -2, -2   -1, -1   E: (0, T)  (DATA)
+img_24  -1, 160  E: (0, 1)
+img_25  445, 166 E: (2, 4)
diff --git a/Nov_27/Full/Camera/img_rec_module/sig_proc.py b/Nov_27/Full/Camera/img_rec_module/sig_proc.py
new file mode 100644
index 0000000..9640c3b
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/sig_proc.py
@@ -0,0 +1,958 @@
+import cv2
+import numpy as np
+import csv
+from scipy.optimize import curve_fit
+from math import atan, sqrt, tan, radians, acos, asin, degrees, sin, cos
+
+""" ===================================
+========== ANALYTIC GEOMETRY ==========
+======================================= """
+
+
+class Line:
+    inf = float('inf')
+
+    def __init__(self, a=inf, b=inf, p1=None, p2=None, data=None):
+        if p1 and p2:
+            self.point(p1, p2)
+        elif data:
+            self.reg(data)
+        else:
+            self.a = a
+            self.b = b
+
+    def reg(self, data):
+        D = np.array([[d[0], 1] for d in data])
+        y = np.array([d[1] for d in data])
+        self.a, self.b = leastSquares(D, y)
+
+    def point(self, p1, p2):
+        self.a = 1
+
+    @staticmethod
+    def intersect(l1, l2):
+        x = (l2.b - l1.b) / (l1.a - l2.a)
+        y = l1.a * x + l1.b
+        return x, y
+
+
+class HoughLine:
+    def __init__(self, rho_rad=0, theta=0, x=None, data=None):
+        if data is not None and x is not None:
+            self.reg(x, data)
+        else:
+            self._r = degrees(rho_rad)
+            self._t = theta
+            self._s = sin(rho_rad)
+            self._c = cos(rho_rad)
+
+    def reg(self, x, data):
+        """D = hough_data_matrix(x, data)
+        print(D)
+        y = np.zeros(len(data))
+        cosine, sine, self._r = leastSquares(D, y)
+        self._t = angle_interp(sine, cosine)
+        self._s = sine
+        self._c = cosine"""
+        x1, x2 = x[0], x[-1]
+        y1, y2 = data[0], data[-1]
+        theta0 = theta_pred(x1, y1, x2, y2)
+        # print(degrees(theta0))
+        p0 = [theta0, x1 * cos(theta0) + y1 * sin(theta0)]
+        pm, vm = curve_fit(hough_line, x, data, p0=p0)
+        # data_pred = hough_line(x, *pm)
+        # print('PRED', data_pred)
+        # res = data_pred - data
+        # print("RES", res)
+        # stderr = np.linalg.norm(res, 2)
+        # print("ERR", stderr)
+        # rankerr = res / stderr
+        # print("Z", rankerr)
+        if pm[1] < 0:
+            pm[1] = -pm[1]
+            pm[0] -= HC
+            # pm[0] = 2 * np.pi
+        angle = normalize_angle(pm[0])
+        self._t = angle
+        self._r = pm[1]
+        self._s = sin(angle)
+        self._c = cos(angle)
+
+    def extract_points(self, x_input):
+        x1 = int(x_input[0])
+        x2 = int(x_input[-1])
+        y1 = int(self.fit_x(x1))
+        y2 = int(self.fit_x(x2))
+        return (x1, y1), (x2, y2)
+
+    def fit_x(self, x):
+        # print(self._r, self._t)
+        return (self._r - x * self._c) / self._s
+
+    def __str__(self):
+        return 'hough line with cos:{0}, sin:{1}, rho:{2}, theta:{3}'.format(self._c, self._s,
+                                                                             self._r, degrees(self._t))
+
+    @staticmethod
+    def intersect(l1, l2):
+        x = (l2._r / l2._s - l1._r / l1._s) / (l2._c / l2._s - l1._c / l1._s)
+        y = l1.fit_x(x)
+        return x, y
+
+
+# QC: QUARTER_CYCLE, HC: HALF_CYCLE, TQC: THIRD_QUARTER_CYCLE, FC: FULL_CYCLE
+QC = np.pi / 2
+HC = np.pi
+TQC = 3 * np.pi / 2
+FC = 2 * np.pi
+
+
+def angle_interp(s, c):
+    if c == 1.0:
+        return 0
+    elif s == 1.0:
+        return np.pi / 2
+    elif c == -1.0:
+        return np.pi
+    elif s == -1.0:
+        return np.pi * 3 / 2
+    elif c < 0 < s:
+        return acos(c)
+    elif s < 0 and c < 0:
+        return 2 * np.pi - acos(c)
+    elif s < 0 < c:
+        return asin(s) + 2 * np.pi
+    else:
+        return asin(s)
+
+
+def normalize_angle(angle):
+    res = angle - (angle // FC) * FC
+    return res
+    """
+    if QC < res < HC:
+        return res + HC
+    elif HC <= res <= TQC:
+        return res - HC
+    else:
+        return res"""
+
+
+def theta_pred(x1, y1, x2, y2):
+    dx = x2 - x1
+    dy = y2 - y1
+    angle = sin_angle_from_points(dx, dy)
+    if dx * dy <= 0:
+        return np.pi / 2 - angle
+    else:
+        root = root_finding(x1, x2, y1, y2)
+        if root < 0:
+            return np.pi / 2 + angle
+        else:
+            return angle + 3 * np.pi / 2
+
+
+def sin_angle_from_points(dx, dy):
+    return asin(abs(dy) / sqrt(dx ** 2 + dy ** 2))
+
+
+def poly_curve(params, x_input):
+    # params contains the coefficients that multiply the polynomial terms, in degree of lowest degree to highest degree
+    degree = len(params) - 1
+    x_range = [x_input[1], x_input[-1]]
+    x = np.linspace(x_range[0], x_range[1], 1000)
+    y = x * 0
+
+    for k in range(0, degree + 1):
+        coeff = params[k]
+        y = y + list(map(lambda z: coeff * z ** k, x))
+    return x, y
+
+
+def gauss_2d(x, y, a, b1, c_s1, b2, c_s2):
+    return a * np.exp(- ((x - b1) ** 2 / (2 * c_s1) + (y - b2) ** 2) / (2 * c_s2))
+
+
+def gauss_hat(x, a, b, c_s):
+    return a * np.exp(- (x - b) ** 2 / (2 * c_s))
+
+
+def gaussian_curve(x_input, a, b, c_s):
+    x_range = [x_input[1], x_input[-1]]
+    x = np.linspace(x_range[0], x_range[1], 1000)
+    y = a * np.exp(- (x - b) ** 2 / (2 * c_s))
+    return x, y
+
+
+def hough_line(x, theta, rho):
+    return (rho - x * cos(theta)) / sin(theta)
+
+
+""" ===================================
+========== MATRIX-ARRAY UTIL ==========
+======================================= """
+
+
+class FastDataMatrix2D:
+    # TODO: HANDLE THE CASE WHEN IT WRAPS 1D
+
+    HOR = 1
+    VER = 0
+
+    def __init__(self, data, ax, index):
+        self._data = data
+        self._ax = ax
+        assert 0 <= index < self.irange(), "start: {0}, end: {1}, ax: {2}, index: {3}".format(self.start, self.end,
+                                                                                              self._ax, index)
+        self._index = index
+        self.itr = 0
+        self.initialize()
+
+    def segmentize(self, start, end):
+        assert 0 <= start < end <= self._data.shape[self._ax], "start: {0}, end: {1}, ax: {2}, index: {3}".format(start,
+                                                                                                                  end,
+                                                                                                                  self._ax,
+                                                                                                                  self._index)
+        self.start = start
+        self.end = end
+
+    def initialize(self):
+        self.segmentize(0, self._data.shape[self._ax])
+
+    def irange(self):
+        # index range of the fast matrix array
+        return self._data.shape[1 - self._ax]
+
+    def set_axis(self, ax):
+        """USE copy when trying to switch axis and index"""
+        self._ax = ax
+        if self._index >= self.irange():
+            raise IndexError("Old index {0} is too large for the new axis".format(self._index))
+        self.initialize()
+
+    def set_index(self, index):
+        assert 0 <= index < self.irange(), "start: {0}, end: {1}, ax: {2}, index: {3}".format(self.start, self.end,
+                                                                                              self._ax, index)
+        self._index = index
+
+    def extract_array(self):
+        """Optimize later for better performance"""
+        arr = self._data[self._index, self.start:self.end] if self._ax == FastDataMatrix2D.HOR \
+            else self._data[self.start:self.end, self._index]
+        return np.array(arr)
+
+    def copy(self, ax=None, index=None):
+        if ax is not None and index is not None:
+            return FastDataMatrix2D(self._data, ax, index)
+        else:
+            return FastDataMatrix2D(self._data, self._ax, self._index)
+
+    def __iter__(self):
+        raise RuntimeError("You need the ITER method!")
+
+    def __next__(self):
+        raise RuntimeError("You need the NEXT method!")
+
+    def __getitem__(self, item):
+        return self._data[self._index, item + self.start] if self._ax == FastDataMatrix2D.HOR else self._data[item + self.start, self._index]
+
+    def __setitem__(self, key, value):
+        if self._ax == FastDataMatrix2D.HOR:
+            self._data[self._index, key + self.start] = value
+        else:
+            self._data[key + self.start, self._index] = value
+
+    def __len__(self):
+        return self.end - self.start
+
+
+""" ===================================
+=========== REGRESSION UTILS ==========
+======================================= """
+
+
+# Generic Regression Tools 128-133
+def leastSquares(D, y):
+    return np.linalg.lstsq(D, y, rcond=None)[0]
+
+
+def MSE(y, y_hat, N):
+    return np.linalg.norm(y - y_hat) ** 2 / N
+
+
+def std_dev(data):
+    std_dev = 0
+    len_data = len(data)
+    mean = sum(data) / len_data
+    for i in range(len_data):
+        std_dev += (data[i] - mean) ** 2
+    return sqrt(std_dev / len_data)
+
+
+def reg_pre_debias(ind, data):
+    sd = np.std(data)
+    miu = np.mean(data)
+    debiased_data = []
+    debiased_ind = []
+    for i in range(len(data)):
+        dev = abs((data[i] - miu) / sd)
+        if dev < 2:
+            debiased_data.append(data[i])
+            debiased_ind.append(ind[i])
+    return debiased_ind, debiased_data
+
+
+# Polynomial Regression Tools 137-171
+def poly_data_matrix(input_data, degree):
+    # degree is the degree of the polynomial you plan to fit the data with
+    Data = np.zeros((len(input_data), degree + 1))
+
+    for k in range(0, degree + 1):
+        Data[:, k] = (list(map(lambda x: x ** k, input_data)))
+
+    return Data
+
+
+def poly_error(params, D_a, y_a):
+    '''degree=len(params)-1
+    y=x_a*0
+
+    for k in range(0,degree+1):
+        coeff=params[k]
+        y=y+list(map(lambda z:coeff*z**k,x_a))'''
+    y = np.dot(D_a, params)
+    return np.linalg.norm(y - y_a) ** 2
+
+
+def improvedCost(x, y, x_test, y_test, start, end):
+    """Given a set of x and y points training points,
+    this function calculates polynomial approximations of varying
+    degrees from start to end. Then it returns the cost, with
+    the polynomial tested on test points of each degree in an array"""
+    c = []
+    degrees = range(start, end)
+    ps = []
+    for degree in degrees:
+        # YOUR CODE HERE
+        D = poly_data_matrix(x, degree)
+        p = leastSquares(D, y)
+        ps.append(p)
+        D_t = poly_data_matrix(x_test, degree)
+        y_hat = np.dot(D_t, p)
+        c.append(MSE(y_test, y_hat, len(x_test)))
+    return np.array(degrees), ps, np.array(c)
+
+
+# Gaussian Regression Tools 184-210
+def gauss_data_matrix(data):
+    return np.array([[1, x ** 2, x, 1] for x in data])
+
+
+def gauss_reg(x, y, p0):
+    """Given a set of x and y training points, this function
+    calculates the gaussian approximation function"""
+    """skip_x = np.array([x[i] for i in range(len(y)) if y[i] != 0])
+    y_prime = np.array([y[i] for i in range(len(y)) if y[i] != 0])
+    logy = np.log(y_prime)
+
+    sol = leastSquares(gauss_data_matrix(skip_x), logy)
+    alp, bet, gam, lam = sol
+    a = np.e ** alp
+    c_s = - 1 / (2 * bet)
+    b = gam * c_s
+    maxi, mini = max_min(y)
+    pred = gauss_hat(maxi, a, b, c_s)
+    real = y[maxi]
+    a = real / pred // 1000000000 * 10"""
+    # timeb = datetime.now()
+    param, vm = curve_fit(gauss_hat, x, y, p0=p0)
+    # timea = datetime.now()
+    # print(timea - timeb)
+    return param
+
+
+def gauss_mat(shape, a, b1, c_s1, b2, c_s2):
+    mat = np.zeros(shape)
+    for r in range(mat.shape[0]):
+        for c in range(mat.shape[1]):
+            mat[r, c] = gauss_2d(c, r, a, b1, c_s1, b2, c_s2)
+
+    return mat
+
+
+# Hough Transform Regression Tools
+def hough_data_matrix(x, y):
+    dim = len(x)
+    mat = np.full((dim, 3), -1.0)
+    for i in range(dim):
+        mat[i][0] = x[i]
+        mat[i][1] = y[i]
+    return mat
+
+
+""" =======================================
+========= EDGE DETECTION UTILS ============
+=========================================== """
+
+
+# Generic Helper
+def max_min(data):
+    # Returns the maximum and minimum for the data
+    maxi = data[0]
+    max_ind = 0
+    mini = data[0]
+    min_ind = 0
+    for i in range(1, len(data)):
+        target = data[i]
+        if target > maxi:
+            maxi = target
+            max_ind = i
+        if target < mini:
+            mini = target
+            min_ind = i
+    return max_ind, min_ind
+
+
+def edge_max_min(data):
+    # Returns a *safe* edge maxi, mini for the data
+    # TODO: OPTIMIZE THE WIDTH AND VALUE THRESHOLD
+    width_thres = 90
+    value_thres = 20
+    maxi = data[0]
+    max_ind = 0
+    mini = data[0]
+    min_ind = 0
+    for i in range(1, len(data)):
+        target = data[i]
+        if target > maxi:
+            maxi = target
+            max_ind = i
+        if target < mini:
+            mini = target
+            min_ind = i
+    maxi, mini = max_ind, min_ind
+    assert data[maxi] >= value_thres and mini - maxi <= width_thres and maxi < mini
+    return maxi, mini
+
+
+def min_max(data, max, min):
+    """ Converts the data to min max space. """
+    g_diff = max - min
+    return [(d - min) / g_diff for d in data]
+
+
+def root_finding(x1, x2, y1, y2):
+    """Given two points on a line, finds its zero crossing root."""
+    return - y1 * (x2 - x1) / (y2 - y1) + x1
+
+
+def check_cross(a, b):
+    # Checks whether two points are of opposite signs
+    return a * b < 0
+
+
+def round_up(num):
+    down = int(num)
+    if num - down > 0:
+        return num + 1
+    else:
+        return num
+
+
+def smart_interval(start, end, data):
+    start = 0 if start < 0 else start
+    end = len(data) if end > len(data) else end
+    return start, end
+
+
+def edge_preprocess(data, padding):
+    maxi, mini = edge_max_min(data)
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    return start, end
+
+
+# ZERO CROSSING (PEAK FINDING)
+def zero_crossing(data):
+    # Yields the center with zero-crossing method
+    maxi, mini = max_min(data)
+    cross = -1
+    cross_count = 0
+    for j in range(maxi, mini):
+        y1 = data[j]
+        x2 = j + 1
+        y2 = data[x2]
+        if check_cross(y1, y2):
+            cross = root_finding(j, x2, y1, y2)  # change to function later
+            cross_count += 1
+    return -1 if cross_count > 1 else cross
+
+
+# AVERAGE METHOD FOR EDGE CENTER FINDING
+def edge_converge_base(data):
+    maxi, mini = max_min(data)
+    width_thres = 70
+    if mini - maxi > width_thres:
+        return -1
+    else:
+        return (maxi + mini) / 2
+
+
+# PSEUDO EDGE (closest noise gradient change) CENTER FINDING
+def edge_converge_extreme(data):
+    maxi, mini = max_min(data)
+    width_thres = 70
+    if mini - maxi > width_thres:
+        return -1
+    emax = -1
+    emin = -1
+    maxflag = True
+    minflag = True
+    cmax = maxi
+    cmin = mini
+    while cmax > 0 and cmin < len(data) - 1 and (maxflag or minflag):
+        max2 = cmax - 1
+        maxy1, maxy2 = data[cmax], data[max2]
+        min2 = cmin + 1
+        miny1, miny2 = data[cmin], data[min2]
+        if check_cross(maxy1, maxy2) and maxflag:
+            emax = root_finding(cmax, max2, maxy1, maxy2)
+            maxflag = False
+        if check_cross(miny1, miny2) and minflag:
+            emin = root_finding(cmin, min2, miny1, miny2)
+            minflag = False
+        if maxflag:
+            cmax -= 1
+        if minflag:
+            cmin += 1
+    return -1 if (emax == -1 or emin == -1) else (emax + emin) / 2
+
+
+def extract_extrema(data):
+    len_data = len(data)
+    mean = sum(data) / len_data
+    std_dev = 0
+    for x in data:
+        std_dev += (x - mean) ** 2
+    std_dev = sqrt(std_dev / len_data)
+    signifmax = []
+    signifmin = []
+    coeff = 3
+    for i, x in enumerate(data):
+        diff = (x - mean) / std_dev
+        if diff >= coeff:
+            signifmax.append(i)
+        elif diff <= -coeff:
+            signifmin.append(i)
+    return signifmax, signifmin
+
+
+# CENTROID METHOD FOR EDGE CENTER FINDING
+def edge_centroid(data, img_data, padding=20):
+    # With Gaussian Blur might achieve the best performance
+    try:
+        start, end = edge_preprocess(data, padding)
+    except AssertionError:
+        return -1
+    isums = 0
+    total = 0
+    for i in range(start, end):
+        isums += img_data[i] * i
+        total += img_data[i]
+    return isums / total if total else -1
+
+
+def centroid_seg(data, start, end):
+    """ Given a segment (start, end) of the data,
+    find the centroid. """
+    isums = 0
+    total = 0
+    start = 0 if start < 0 else start
+    end = len(data) if end > len(data) else end
+    for i in range(start, end):
+        isums += data[i] * i
+        total += data[i]
+    return isums / total if total else -1
+
+
+# POLYNOMIAL FITTING
+def poly_fitting(data, img_data, padding=20):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING AND THE EXTRACT_ARRAY
+    try:
+        start, end = edge_preprocess(data, padding)
+    except AssertionError:
+        return -1
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        y = img_data.extract_array()
+    else:
+        #y = np.array(img_data[start:end])
+        idata = np.zeros(end - start)
+        for i in range(start, end):
+            idata[i - start] = img_data[i]
+    degrees, params, cost = improvedCost(x, y, x, y, 1, 7)
+    ind = np.argmin(cost)
+    degree = degrees[ind]
+    param = params[ind]
+    degree_register(degree)
+    curve_x, curve_y = poly_curve(param, x)
+    center_id = np.argmax(curve_y)
+    return curve_x[center_id]
+
+
+def poly_fitting_params(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING AND THE EXTRACT_ARRAY
+    maxi, mini = max_min(data)
+    width_thres = 90
+    value_thres = 20
+    if data[maxi] < value_thres or mini - maxi > width_thres:
+        print(data[maxi], mini, maxi)
+        raise AssertionError("Bad column or row")
+    start = maxi - padding
+    end = mini + padding + 1
+    start, end = smart_interval(start, end, data)
+    if start > end:
+        try:
+            print(maxi, mini, img_data._ax, start, end)
+        except:
+            print("else", maxi, mini, start, end)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        y = img_data.extract_array()
+    else:
+        idata = np.zeros(end - start)
+        for i in range(start, end):
+            idata[i - start] = img_data[i]
+    degrees, params, cost = improvedCost(x, y, x, y, 1, 7)
+    ind = np.argmin(cost)
+    degree = degrees[ind]
+    param = params[ind]
+    degree_register(degree)
+    return param, x
+
+
+DEGREES = {}
+
+
+def degree_register(elem):
+    if elem in DEGREES:
+        DEGREES[elem] += 1
+    else:
+        DEGREES[elem] = 1
+
+
+# GAUSSIAN FITTING
+def gaussian_fitting(data, img_data, padding=20):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING, ALONG WITH THE WIDTH THRESHOLD
+    try:
+        maxi, mini = edge_max_min(data)
+    except AssertionError:
+        return -1
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.zeros(end-start)
+        for i in range(start, end):
+            idata[i-start] = img_data[i]
+    try:
+        param = gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)])
+    except RuntimeError:
+        return -1
+    return param[1]
+
+
+def gaussian_fitting_params(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING, ALONG WITH THE WIDTH THRESHOLD
+    maxi, mini = max_min(data)
+    width_thres = 90
+    value_thres = 20  # TODO: CONSOLIDATE THIS VALUE
+    if data[maxi] < value_thres or mini - maxi > width_thres:
+        print(data[maxi], mini, maxi)
+        raise AssertionError("Bad column or row")
+    start = maxi - padding
+    end = mini + padding
+    start, end = smart_interval(start, end, data)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.zeros(end - start)
+        for i in range(start, end):
+            idata[i-start] = img_data[i]
+    return gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)]), x
+
+
+"""======================================
+======== IMAGE PROCESSING UTIL ==========
+========================================= """
+
+
+def gauss_bg_deduce(x, img_data):
+    # TODO: OPTIMIZE PERFORMANCE
+    idata = img_data.extract_array()
+    p0 = [1, len(img_data) / 2, std_dev(idata)]
+    a, b, c_s = gauss_reg(x, idata, p0=p0)
+    rem_gauss = gauss_hat(x, a, b, c_s)
+    new_y = idata - rem_gauss
+    return x, rem_gauss, new_y
+
+
+def significance_test(data, val):
+    """Outputs whether the maximum or minimum value is a significant value."""
+    return val in extract_extrema(data)[0] or val in extract_extrema(data)[1]
+
+
+def edge_detect_expr(edges, original):
+    """
+    TODO: 1. SIMPLE Approach: pairwise mask over original image, then compute total
+          2. Traverse the image to mark the image.
+    """
+    se = 0
+    row_sum = 0
+    col_sum = 0
+    tot_sum = 0
+    for i in range(edges.shape[0]):
+        for j in range(edges.shape[1]):
+            if edges[i][j] > 0:
+                row_sum += i * original[i][j]
+                col_sum += j * original[i][j]
+                tot_sum += original[i][j]
+                se += 1
+    print(se)
+    if tot_sum != 0:
+        print(row_sum / tot_sum)
+        print(col_sum / tot_sum)
+
+
+def hori(img, i, j, d):
+    dest = j + d
+    if dest < 0 or dest >= img.shape[1]:
+        return img.item(i, j)
+    return img.item(i, dest)
+
+
+def verti(img, i, j, d):
+    dest = i + d
+    if dest < 0 or dest >= img.shape[0]:
+        return img.item(i, j)
+    return img.item(dest, j)
+
+
+def test_blur_then_nr(iname):
+    imgs = iname
+    numIMG = 5
+    imgr = None
+    for i in range(1, numIMG + 1):
+        target = cv2.imread(imgs.format(i), 0)
+        target = cv2.GaussianBlur(target, (9, 9), 0)
+        if i == 1:
+            imgr = np.uint16(target)
+        else:
+            imgr = imgr + target
+    return imgr / numIMG
+
+
+def get_center_val(img):
+    return img.item(img.shape[0] // 2, img.shape[1] // 2)
+
+
+def test_noise_reduce(iname, numIMG=5):
+    imgs = iname
+    imgr = None
+    original = None
+    for i in range(1, numIMG + 1):
+        target = cv2.imread(imgs.format(i), 0)
+        if target is None:
+            raise AttributeError("File {0} not found".format(iname))
+        if i == 1:
+            imgr = np.uint16(target)
+            original = target
+        else:
+            imgr = imgr + target
+    return imgr / numIMG, original
+
+
+def img_add(dest, src):
+    """Destructive!"""
+    row, col = dest.shape
+    for i in range(row):
+        for j in range(col):
+            dest[i][j] += src[i][j]
+
+
+FM = FastDataMatrix2D
+
+
+def folder_to_imgs(img_name_scheme, num_sample):
+    """This function takes img files and return cv imgs"""
+    return [cv2.imread(img_name_scheme.format(i)) for i in range(1, num_sample + 1)]
+
+
+# The image taken is flipped horizontally, result x should be img.shape[1] - x
+# The image sometimes has two peaks, try experimenting with different gaussian kernels
+def center_detect(img_name_scheme, num_sample, sample_int=50, gk=9, ks=-1, m=0, p=20,
+                       b=1, c=0):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair
+    hs: HORIZONTAL SLICE!  vs: VERTICAL SLICE!"""
+    imgr = test_noise_reduce(img_name_scheme, num_sample)[0]
+    dimr = imgr.shape[0]
+    dimc = imgr.shape[1]
+    # Image Processing
+    gksize = (gk, gk)
+    sigmaX = 0
+    img = cv2.GaussianBlur(imgr, gksize, sigmaX)
+    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=ks)
+    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=ks)
+    # ------------------------------------------------------------
+    # Parameter Setting
+    METHODS = {0: gaussian_fitting, 1: poly_fitting, 2: edge_centroid,
+               3: zero_crossing, 4: edge_converge_base, 5: edge_converge_extreme}
+    edge_method = METHODS[m]
+    nr = sample_int
+    r_thresh = dimr / (sample_int * 3.0)
+    nc = sample_int
+    c_thresh = dimc / (sample_int * 3.0)
+    # ------------------------------------------------------------
+    # Gathering Data
+    hs = []
+    vs = []
+    while nr < dimr:
+        data_x = FM(sobelx, FM.HOR, nr)
+        if m < 3:
+            ec_x = edge_method(data_x, FM(img, FM.HOR, nr), p)
+        else:
+            ec_x = edge_method(data_x)
+        nr += sample_int
+        if ec_x == -1:
+            continue
+        else:
+            hs.append((nr - sample_int, ec_x))
+
+    while nc < dimc:
+        data_y = FM(sobely, FM.VER, nc)
+        if m < 3:
+            ec_y = edge_method(data_y, FM(img, FM.VER, nc), p)
+        else:
+            ec_y = edge_method(data_y)
+        nc += sample_int
+        if ec_y == -1:
+            continue
+        else:
+            vs.append((nc - sample_int, ec_y))
+    len_hs = len(hs)
+    len_vs = len(vs)
+    # ------------------------------------------------------------
+    # --------------- PRE-CHECK DATA VALUES ----------------------
+    hxs = np.zeros(len_hs)
+    hys = np.zeros(len_hs)
+    for i in range(len_hs):
+        hxs[i] = hs[i][1]
+        hys[i] = hs[i][0]
+    vxs = np.zeros(len_vs)
+    vys = np.zeros(len_vs)
+    for i in range(len_vs):
+        vxs[i] = vs[i][0]
+        vys[i] = vs[i][1]
+    x_valid = False
+    y_valid = False
+    # OUTLIER DETECTION TODO: OPTIMIZE, THIS IS NAIVE
+    if len_hs >= r_thresh:
+        x_valid = True
+        hys, hxs = reg_pre_debias(hys, hxs)
+        line_a = HoughLine(x=hxs, data=hys)
+    if len_vs >= c_thresh:
+        y_valid = True
+        vxs, vys = reg_pre_debias(vxs, vys)
+        line_b = HoughLine(x=vxs, data=vys)
+    # ------------------------------------------------------------
+    # ----- Following Modules Handles Hough Line Drawing ---------
+    # ------------------------------------------------------------
+    # DATA RECORDING AND PROCESSING
+    if c == 1:
+        center_x = sum(hxs) / len_hs if x_valid else -1
+        center_y = sum(vys) / len_vs if y_valid else -1
+    else:
+        if x_valid and y_valid:
+            center_x, center_y = HoughLine.intersect(line_a, line_b)
+        else:
+            center_x = sum(hxs) / len_hs if x_valid else -1
+            center_y = sum(vys) / len_vs if y_valid else -1
+    # ---------------------------------------------------------
+    return center_x, center_y
+
+
+
+def convergence_test_final(folder, ns):
+    offset = '../'
+    convergence = {}
+    variations = []
+    startNP = 59
+    startP = 80
+    endP = 193
+    ms = range(3)
+    fwrite = open('meas/convergence.csv', 'w')
+    cwriter = csv.writer(fwrite)
+    cwriter.writerow(['Image Number', 'Center X', 'Center Y', 'StdDev Horizontal', 'Std Dev Vertical'])
+    for m in ms:
+        cwriter.writerow([str(m)])
+        # Consistency Cycled
+        pv = 0
+        pvs = 0
+        rcount = 0
+        cvx = np.zeros(4)
+        cvy = np.zeros(4)
+        # print(g)
+        for i in range(startNP, startP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                x, y = center_detect(fpath + imgfile, 5, m=m)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y)])
+                # CONVERGENCE
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+        for i in range(startP, endP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                x, y = center_detect(fpath + imgfile, 5, m=m)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y)])
+                # CONVERGENCE
+                # Record x, y, check rcount, refresh CONSISTENCY
+                cvx[rcount] = x
+                cvy[rcount] = y
+                rcount += 1
+                if rcount == 4:
+                    pv += np.var(cvx) + np.var(cvy)
+                    pvs += 1
+                    rcount = 0
+                    cvx = np.zeros(4)
+                    cvy = np.zeros(4)
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+                # print(str(i), val)
+        msepv = sqrt(pv / pvs)
+        cvg = {'PicConsistency': msepv}
+        convergence[m] = cvg
+        variations.append(msepv)
+    print(convergence)
+    fwrite.close()
+
+if __name__ == '__main__':
+    convergence_test_final('calib4/', 'img_{0}')
+
+#print(center_detect('../calib4/img_59_{0}.png', 5))
+
diff --git a/Nov_27/Full/Camera/img_rec_module/sig_proc_test.py b/Nov_27/Full/Camera/img_rec_module/sig_proc_test.py
new file mode 100644
index 0000000..459fec9
--- /dev/null
+++ b/Nov_27/Full/Camera/img_rec_module/sig_proc_test.py
@@ -0,0 +1,2371 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.optimize import curve_fit
+#from img_rec_module import img_rec
+from random import randint
+import os
+import csv
+from datetime import datetime
+from math import atan, sqrt, tan, radians, acos, asin, degrees, sin, cos
+
+
+""" ===================================
+========== ANALYTIC GEOMETRY ==========
+======================================= """
+
+
+class Line:
+
+    inf = float('inf')
+
+    def __init__(self, a=inf, b=inf, p1=None, p2=None, data=None):
+        if p1 and p2:
+            self.point(p1, p2)
+        elif data:
+            self.reg(data)
+        else:
+            self.a = a
+            self.b = b
+
+    def reg(self, data):
+        D = np.array([[d[0], 1] for d in data])
+        y = np.array([d[1] for d in data])
+        self.a, self.b = leastSquares(D, y)
+
+    def point(self, p1, p2):
+        self.a = 1
+
+    @staticmethod
+    def intersect(l1, l2):
+        x = (l2.b - l1.b) / (l1.a - l2.a)
+        y = l1.a * x + l1.b
+        return x, y
+
+
+class HoughLine:
+
+    def __init__(self, rho_rad=0, theta=0, x=None, data=None):
+        if data is not None and x is not None:
+            self.reg(x, data)
+        else:
+            self._r = degrees(rho_rad)
+            self._t = theta
+            self._s = sin(rho_rad)
+            self._c = cos(rho_rad)
+
+    def reg(self, x, data):
+        """D = hough_data_matrix(x, data)
+        print(D)
+        y = np.zeros(len(data))
+        cosine, sine, self._r = leastSquares(D, y)
+        self._t = angle_interp(sine, cosine)
+        self._s = sine
+        self._c = cosine"""
+        x1, x2 = x[0], x[-1]
+        y1, y2 = data[0], data[-1]
+        theta0 = theta_pred(x1, y1, x2, y2)
+        #print(degrees(theta0))
+        p0 = [theta0, x1 * cos(theta0) + y1 * sin(theta0)]
+        pm, vm = curve_fit(hough_line, x, data, p0=p0)
+        #data_pred = hough_line(x, *pm)
+        #print('PRED', data_pred)
+        #res = data_pred - data
+        #print("RES", res)
+        #stderr = np.linalg.norm(res, 2)
+        #print("ERR", stderr)
+        #rankerr = res / stderr
+        #print("Z", rankerr)
+        if pm[1] < 0:
+            pm[1] = -pm[1]
+            pm[0] -= HC
+            #pm[0] = 2 * np.pi
+        angle = normalize_angle(pm[0])
+        self._t = angle
+        self._r = pm[1]
+        self._s = sin(angle)
+        self._c = cos(angle)
+
+    def extract_points(self, x_input):
+        x1 = int(x_input[0])
+        x2 = int(x_input[-1])
+        y1 = int(self.fit_x(x1))
+        y2 = int(self.fit_x(x2))
+        return (x1, y1), (x2, y2)
+
+    def fit_x(self, x):
+        #print(self._r, self._t)
+        return (self._r - x * self._c) / self._s
+
+    def __str__(self):
+        return 'hough line with cos:{0}, sin:{1}, rho:{2}, theta:{3}'.format(self._c, self._s,
+                                                                             self._r, degrees(self._t))
+
+    @staticmethod
+    def intersect(l1, l2):
+        x = (l2._r / l2._s - l1._r / l1._s) / (l2._c / l2._s - l1._c / l1._s)
+        y = l1.fit_x(x)
+        return x, y
+
+
+# QC: QUARTER_CYCLE, HC: HALF_CYCLE, TQC: THIRD_QUARTER_CYCLE, FC: FULL_CYCLE
+QC = np.pi / 2
+HC = np.pi
+TQC = 3 * np.pi / 2
+FC = 2 * np.pi
+
+
+def angle_interp(s, c):
+    if c == 1.0:
+        return 0
+    elif s == 1.0:
+        return np.pi / 2
+    elif c == -1.0:
+        return np.pi
+    elif s == -1.0:
+        return np.pi * 3 / 2
+    elif c < 0 < s:
+        return acos(c)
+    elif s < 0 and c < 0:
+        return 2 * np.pi - acos(c)
+    elif s < 0 < c:
+        return asin(s) + 2 * np.pi
+    else:
+        return asin(s)
+
+
+def normalize_angle(angle):
+    res = angle - (angle // FC) * FC
+    return res
+    """
+    if QC < res < HC:
+        return res + HC
+    elif HC <= res <= TQC:
+        return res - HC
+    else:
+        return res"""
+
+
+def theta_pred(x1, y1, x2, y2):
+    dx = x2 - x1
+    dy = y2 - y1
+    angle = sin_angle_from_points(dx, dy)
+    if dx * dy <= 0:
+        return np.pi / 2 - angle
+    else:
+        root = root_finding(x1, x2, y1, y2)
+        if root < 0:
+            return np.pi / 2 + angle
+        else:
+            return angle + 3 * np.pi / 2
+
+
+def sin_angle_from_points(dx, dy):
+    return asin(abs(dy) / sqrt(dx ** 2 + dy ** 2))
+
+
+def poly_curve(params, x_input):
+    # params contains the coefficients that multiply the polynomial terms, in degree of lowest degree to highest degree
+    degree = len(params) - 1
+    x_range = [x_input[1], x_input[-1]]
+    x = np.linspace(x_range[0], x_range[1], 1000)
+    y = x * 0
+
+    for k in range(0, degree + 1):
+        coeff = params[k]
+        y = y + list(map(lambda z: coeff * z ** k, x))
+    return x, y
+
+
+def gauss_2d(x, y, a, b1, c_s1, b2, c_s2):
+    return a * np.exp(- ((x - b1) ** 2  / (2 * c_s1) + (y - b2) ** 2) / (2 * c_s2))
+
+
+def gauss_hat(x, a, b, c_s):
+    return a * np.exp(- (x - b) ** 2 / (2 * c_s))
+
+
+def gaussian_curve(x_input, a,  b, c_s):
+    x_range = [x_input[1], x_input[-1]]
+    x = np.linspace(x_range[0], x_range[1], 1000)
+    y = a * np.exp(- (x - b) ** 2 / (2 * c_s))
+    return x, y
+
+
+def hough_line(x, theta, rho):
+    return (rho - x * cos(theta)) / sin(theta)
+
+
+""" ===================================
+========== MATRIX-ARRAY UTIL ==========
+======================================= """
+
+
+class FastDataMatrix2D:
+    # TODO: HANDLE THE CASE WHEN IT WRAPS 1D
+
+    HOR = 1
+    VER = 0
+
+    def __init__(self, data, ax, index):
+        self._data = data
+        self._ax = ax
+        assert 0 <= index < self.irange(), "start: {0}, end: {1}, ax: {2}, index: {3}".format(self.start, self.end,
+                                                                                              self._ax, index)
+        self._index = index
+        self.itr = 0
+        self.initialize()
+
+    def segmentize(self, start, end):
+        assert 0 <= start < end <= self._data.shape[self._ax], "start: {0}, end: {1}, ax: {2}, index: {3}".format(start, end, self._ax, self._index)
+        self.start = start
+        self.end = end
+
+    def initialize(self):
+        self.segmentize(0, self._data.shape[self._ax])
+
+    def irange(self):
+        # index range of the fast matrix array
+        return self._data.shape[1 - self._ax]
+
+    def set_axis(self, ax):
+        """USE copy when trying to switch axis and index"""
+        self._ax = ax
+        if self._index >= self.irange():
+            raise IndexError("Old index {0} is too large for the new axis".format(self._index))
+        self.initialize()
+
+    def set_index(self, index):
+        assert 0 <= index < self.irange(), "start: {0}, end: {1}, ax: {2}, index: {3}".format(self.start, self.end, self._ax, index)
+        self._index = index
+
+    def extract_array(self):
+        """Optimize later for better performance"""
+        arr = self._data[self._index, self.start:self.end] if self._ax == FastDataMatrix2D.HOR \
+            else self._data[self.start:self.end, self._index]
+        return np.array(arr)
+
+    def copy(self, ax=None, index=None):
+        if ax is not None and index is not None:
+            return FastDataMatrix2D(self._data, ax, index)
+        else:
+            return FastDataMatrix2D(self._data, self._ax, self._index)
+
+    def __iter__(self):
+        raise RuntimeError("You need the ITER method!")
+
+    def __next__(self):
+        raise RuntimeError("You need the NEXT method!")
+
+    def __getitem__(self, item):
+        return self._data[self._index, item + self.start] if self._ax == FastDataMatrix2D.HOR \
+            else self._data[item + self.start, self._index]
+
+    def __setitem__(self, key, value):
+        if self._ax == FastDataMatrix2D.HOR:
+            self._data[self._index, key + self.start] = value
+        else:
+            self._data[key + self.start, self._index] = value
+
+    def __len__(self):
+        return self.end - self.start
+
+
+""" ===================================
+=========== REGRESSION UTILS ==========
+======================================= """
+
+
+# Generic Regression Tools 128-133
+def leastSquares(D, y):
+    return np.linalg.lstsq(D, y, rcond=None)[0]
+
+
+def MSE(y, y_hat, N):
+    return np.linalg.norm(y - y_hat) ** 2 / N
+
+
+def std_dev(data):
+    std_dev = 0
+    len_data = len(data)
+    mean = sum(data) / len_data
+    for i in range(len_data):
+        std_dev += (data[i] - mean) ** 2
+    return sqrt(std_dev / len_data)
+
+
+def reg_pre_debias(ind, data):
+    sd = np.std(data)
+    miu = np.mean(data)
+    debiased_data = []
+    debiased_ind = []
+    for i in range(len(data)):
+        dev = abs((data[i] - miu) / sd)
+        if dev < 2:
+            debiased_data.append(data[i])
+            debiased_ind.append(ind[i])
+    return debiased_ind, debiased_data
+
+
+# Polynomial Regression Tools 137-171
+def poly_data_matrix(input_data, degree):
+    # degree is the degree of the polynomial you plan to fit the data with
+    Data = np.zeros((len(input_data), degree + 1))
+
+    for k in range(0, degree + 1):
+        Data[:, k] = (list(map(lambda x: x ** k, input_data)))
+
+    return Data
+
+
+def poly_error(params, D_a, y_a):
+    '''degree=len(params)-1
+    y=x_a*0
+
+    for k in range(0,degree+1):
+        coeff=params[k]
+        y=y+list(map(lambda z:coeff*z**k,x_a))'''
+    y = np.dot(D_a, params)
+    return np.linalg.norm(y - y_a) ** 2
+
+
+def improvedCost(x, y, x_test, y_test, start, end):
+    """Given a set of x and y points training points,
+    this function calculates polynomial approximations of varying
+    degrees from start to end. Then it returns the cost, with
+    the polynomial tested on test points of each degree in an array"""
+    c = []
+    degrees = range(start, end)
+    ps = []
+    for degree in degrees:
+        # YOUR CODE HERE
+        D = poly_data_matrix(x, degree)
+        p = leastSquares(D, y)
+        ps.append(p)
+        D_t = poly_data_matrix(x_test, degree)
+        y_hat = np.dot(D_t, p)
+        c.append(MSE(y_test, y_hat, len(x_test)))
+    return np.array(degrees), ps,  np.array(c)
+
+
+# Gaussian Regression Tools 184-210
+def gauss_data_matrix(data):
+    return np.array([[1, x ** 2, x, 1] for x in data])
+
+
+def gauss_reg(x, y, p0):
+    """Given a set of x and y training points, this function
+    calculates the gaussian approximation function"""
+    """skip_x = np.array([x[i] for i in range(len(y)) if y[i] != 0])
+    y_prime = np.array([y[i] for i in range(len(y)) if y[i] != 0])
+    logy = np.log(y_prime)
+
+    sol = leastSquares(gauss_data_matrix(skip_x), logy)
+    alp, bet, gam, lam = sol
+    a = np.e ** alp
+    c_s = - 1 / (2 * bet)
+    b = gam * c_s
+    maxi, mini = max_min(y)
+    pred = gauss_hat(maxi, a, b, c_s)
+    real = y[maxi]
+    a = real / pred // 1000000000 * 10"""
+    #timeb = datetime.now()
+    param, vm = curve_fit(gauss_hat, x, y, p0=p0)
+    #timea = datetime.now()
+    #print(timea - timeb)
+    return param
+
+
+def gauss_mat(shape, a, b1, c_s1, b2, c_s2):
+    mat = np.zeros(shape)
+    for r in range(mat.shape[0]):
+        for c in range(mat.shape[1]):
+            mat[r, c] = gauss_2d(c, r, a, b1, c_s1, b2, c_s2)
+
+    return mat
+
+
+# Hough Transform Regression Tools
+def hough_data_matrix(x, y):
+    dim = len(x)
+    mat = np.full((dim, 3), -1.0)
+    for i in range(dim):
+        mat[i][0] = x[i]
+        mat[i][1] = y[i]
+    return mat
+
+
+""" =======================================
+========= EDGE DETECTION UTILS ============
+=========================================== """
+
+
+# Generic Helper
+def max_min(data):
+    # Returns the maximum and minimum for the data
+    maxi = data[0]
+    max_ind = 0
+    mini = data[0]
+    min_ind = 0
+    for i in range(1, len(data)):
+        target = data[i]
+        if target > maxi:
+            maxi = target
+            max_ind = i
+        if target < mini:
+            mini = target
+            min_ind = i
+    return max_ind, min_ind
+
+
+def edge_max_min(data):
+    # Returns a *safe* edge maxi, mini for the data
+    # TODO: OPTIMIZE THE WIDTH AND VALUE THRESHOLD
+    width_thres = 90
+    value_thres = 20
+    maxi = data[0]
+    max_ind = 0
+    mini = data[0]
+    min_ind = 0
+    for i in range(1, len(data)):
+        target = data[i]
+        if target > maxi:
+            maxi = target
+            max_ind = i
+        if target < mini:
+            mini = target
+            min_ind = i
+    maxi, mini = max_ind, min_ind
+    assert data[maxi] >= value_thres and mini - maxi <= width_thres and maxi < mini
+    return maxi, mini
+
+
+def min_max(data, max, min):
+    """ Converts the data to min max space. """
+    g_diff = max - min
+    return [(d - min) / g_diff for d in data]
+
+
+def root_finding(x1, x2, y1, y2):
+    """Given two points on a line, finds its zero crossing root."""
+    return - y1 * (x2 - x1) / (y2 - y1) + x1
+
+
+def check_cross(a, b):
+    # Checks whether two points are of opposite signs
+    return a * b < 0
+
+
+def round_up(num):
+    down = int(num)
+    if num - down > 0:
+        return num + 1
+    else:
+        return num
+
+
+def smart_interval(start, end, data):
+    start = 0 if start < 0 else start
+    end = len(data) if end > len(data) else end
+    return start, end
+
+
+def edge_preprocess(data, padding):
+    maxi, mini = edge_max_min(data)
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    return start, end
+
+
+# ZERO CROSSING (PEAK FINDING)
+def zero_crossing(data):
+    # Yields the center with zero-crossing method
+    maxi, mini = max_min(data)
+    cross = -1
+    cross_count = 0
+    for j in range(maxi, mini):
+        y1 = data[j]
+        x2 = j + 1
+        y2 = data[x2]
+        if check_cross(y1, y2):
+            cross = root_finding(j, x2, y1, y2)  # change to function later
+            cross_count += 1
+    return -1 if cross_count > 1 else cross
+
+
+# AVERAGE METHOD FOR EDGE CENTER FINDING
+def edge_converge_base(data):
+    maxi, mini = max_min(data)
+    width_thres = 70
+    if mini - maxi > width_thres:
+        return -1
+    else:
+        return (maxi + mini) / 2
+
+
+# PSEUDO EDGE (closest noise gradient change) CENTER FINDING
+def edge_converge_extreme(data):
+    maxi, mini = max_min(data)
+    width_thres = 70
+    if mini - maxi > width_thres:
+        return -1
+    emax = -1
+    emin = -1
+    maxflag = True
+    minflag = True
+    cmax = maxi
+    cmin = mini
+    while cmax > 0 and cmin < len(data) - 1 and (maxflag or minflag):
+        max2 = cmax - 1
+        maxy1, maxy2 = data[cmax], data[max2]
+        min2 = cmin + 1
+        miny1, miny2 = data[cmin], data[min2]
+        if check_cross(maxy1, maxy2) and maxflag:
+            emax = root_finding(cmax, max2, maxy1, maxy2)
+            maxflag = False
+        if check_cross(miny1, miny2) and minflag:
+            emin = root_finding(cmin, min2, miny1, miny2)
+            minflag = False
+        if maxflag:
+            cmax -= 1
+        if minflag:
+            cmin += 1
+    return -1 if (emax == -1 or emin == -1) else (emax + emin) / 2
+
+
+def extract_extrema(data):
+    len_data = len(data)
+    mean = sum(data) / len_data
+    std_dev = 0
+    for x in data:
+        std_dev += (x - mean) ** 2
+    std_dev = sqrt(std_dev / len_data)
+    signifmax = []
+    signifmin = []
+    coeff = 3
+    for i, x in enumerate(data):
+        diff = (x - mean) / std_dev
+        if diff >= coeff:
+            signifmax.append(i)
+        elif diff <= -coeff:
+            signifmin.append(i)
+    return signifmax, signifmin
+
+
+# CENTROID METHOD FOR EDGE CENTER FINDING
+def edge_centroid(data, img_data, padding=10):
+    # With Gaussian Blur might achieve the best performance
+    try:
+        start, end = edge_preprocess(data, padding)
+    except AssertionError:
+        return -1
+    isums = 0
+    total = 0
+    for i in range(start, end):
+        isums += img_data[i] * i
+        total += img_data[i]
+    return isums / total if total else -1
+
+
+def centroid_seg(data, start, end):
+    """ Given a segment (start, end) of the data,
+    find the centroid. """
+    isums = 0
+    total = 0
+    start = 0 if start < 0 else start
+    end = len(data) if end > len(data) else end
+    for i in range(start, end):
+        isums += data[i] * i
+        total += data[i]
+    return isums / total if total else -1
+
+
+# POLYNOMIAL FITTING
+def poly_fitting(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING AND THE EXTRACT_ARRAY
+    try:
+        start, end = edge_preprocess(data, padding)
+    except AssertionError:
+        return -1
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        y = img_data.extract_array()
+    else:
+        y = np.array(img_data[start:end])
+    degrees, params, cost = improvedCost(x, y, x, y, 1, 7)
+    ind = np.argmin(cost)
+    degree = degrees[ind]
+    param = params[ind]
+    degree_register(degree)
+    curve_x, curve_y = poly_curve(param, x)
+    center_id = np.argmax(curve_y)
+    return curve_x[center_id]
+
+
+def poly_fitting_params(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING AND THE EXTRACT_ARRAY
+    maxi, mini = max_min(data)
+    width_thres = 90
+    value_thres = 20
+    if data[maxi] < value_thres or mini - maxi > width_thres:
+        print(data[maxi], mini, maxi)
+        raise AssertionError("Bad column or row")
+    start = maxi - padding
+    end = mini + padding + 1
+    start, end = smart_interval(start, end, data)
+    if start > end:
+        try:
+            print(maxi, mini, img_data._ax, start, end)
+        except:
+            print("else", maxi, mini, start, end)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        y = img_data.extract_array()
+    else:
+        y = np.array(img_data[start:end])
+    degrees, params, cost = improvedCost(x, y, x, y, 1, 7)
+    ind = np.argmin(cost)
+    degree = degrees[ind]
+    param = params[ind]
+    degree_register(degree)
+    return param, x
+
+
+DEGREES = {}
+
+
+def degree_register(elem):
+    if elem in DEGREES:
+        DEGREES[elem] += 1
+    else:
+        DEGREES[elem] = 1
+
+
+# GAUSSIAN FITTING
+def gaussian_fitting(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING, ALONG WITH THE WIDTH THRESHOLD
+    try:
+        maxi, mini = edge_max_min(data)
+    except AssertionError:
+        return -1
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.array(img_data[start:end])
+    try:
+        param = gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)])
+    except RuntimeError:
+        return -1
+    return param[1]
+
+
+def gaussian_fitting_params(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING, ALONG WITH THE WIDTH THRESHOLD
+    maxi, mini = max_min(data)
+    width_thres = 90
+    value_thres = 20  # TODO: CONSOLIDATE THIS VALUE
+    if data[maxi] < value_thres or mini - maxi > width_thres:
+        print(data[maxi], mini, maxi)
+        raise AssertionError("Bad column or row")
+    start = maxi - padding
+    end = mini + padding
+    start, end = smart_interval(start, end, data)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.array(img_data[start:end])
+    return gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)]), x
+
+
+"""======================================
+======== IMAGE PROCESSING UTIL ==========
+========================================= """
+
+
+def gauss_bg_deduce(x, img_data):
+    # TODO: OPTIMIZE PERFORMANCE
+    idata = img_data.extract_array()
+    p0 = [1, len(img_data) / 2, std_dev(idata)]
+    a, b, c_s = gauss_reg(x, idata, p0=p0)
+    rem_gauss = gauss_hat(x, a, b, c_s)
+    new_y = idata - rem_gauss
+    return x, rem_gauss, new_y
+
+
+def sobel_process(imgr, gks, sig):
+    gksize = (gks, gks)
+    sigmaX = sig
+    blur = cv2.GaussianBlur(imgr, gksize, sigmaX)
+    # cv2.imshow("blurred", blur)                        # REMOVES TO SHOW BLURRED IMAGE
+    if len(imgr.shape) == 3:
+        img = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
+    else:
+        img = blur
+    # GAUSSIAN PROCESSING                                 # UNCOMMENT TO SHOW GAUSSIAN PROCESSING
+    """x = np.array(range(dimc))
+    #y = np.array([img_rec.rel_lumin(img, 0, c) for c in x])
+    y = np.array([img.item(dimr // 2, c) for c in x])
+    a1, b1, c_s1 = gauss_reg(x, y)
+
+    x2 = np.array(range(dimr))
+    y2 = np.array([img.item(r, dimc // 2) for r in x2])
+    a2, b2, c_s2 = gauss_reg(x2, y2)
+    rem_gauss = gauss_mat(img.shape, (a1+a2) / 2, b1, c_s1, b2, c_s2)
+    img = img - rem_gauss
+    cv2.imshow("denoise", img)
+    y_hat = gauss_hat(x, a1, b1, c_s1)
+    plt.figure(figsize=(16, 8))
+    plt.plot(x, y, 'b-', x, y_hat, 'r-')
+    plt.show()
+    plt.close()
+    plt.figure(figsize=(16,8))
+    plt.plot(x, y-y_hat, 'b-')
+    plt.show()"""
+
+    # IMAGE PROCESSING
+    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=-1)
+    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=-1)
+    return img, sobelx, sobely
+
+
+def test_canny_detect():
+    imgn = "img_89_{0}.png"
+    IMGDIR = "../calib4/"
+    NAME = IMGDIR + imgn
+
+    while True:
+        lowp = input("Type the Lower Bound or c to cancel:    ")
+        if lowp == 'c':
+            break
+        while True:
+            try:
+                low = int(lowp)
+                break
+            except ValueError:
+                lowp = input("Bad input Try again! ")
+        highp = input("Type the Upper Bound or c to cancel:   ")
+        if highp == 'c':
+            break
+        while True:
+            try:
+                high = int(highp)
+                break
+            except ValueError:
+                highp = input("Bad input Try again! ")
+
+        denoised, original = test_noise_reduce(NAME)
+        print(get_center_val(denoised))
+        NL_denoised = cv2.fastNlMeansDenoising(original)
+        bdenoise = test_blur_then_nr(NAME)
+
+        dblur, sobelx, sobely = sobel_process(denoised, 9, 0)
+        nldblur = sobel_process(NL_denoised, 9, 0)[0]
+        pblur, sobelx, sobely = sobel_process(original, 9, 0)
+
+        de_edges = canny_detect(denoised, low, high)
+        db_edges = canny_detect(dblur, low, high)
+        nld_edges = canny_detect(nldblur, low, high)
+        blur_denoise = canny_detect(bdenoise, low, high)
+        blur_edges = canny_detect(pblur, low, high)
+        #edge_detect_expr(db_edges, original)
+        """compare_images((imgr, 'Original'), (edges, 'Canny Edge'),
+                       (sobelx, 'Sobel X'), (sobely, 'Sobel Y'))"""
+        compare_images((original, 'Original'), (NL_denoised, "NLMEANS"), (de_edges, 'DENOISE Edges'),
+                       (blur_edges, 'Plain Blur Edges'), (denoised, 'Denoised'), (nld_edges, 'NIDE-BLUR Edges'),
+                       (db_edges, 'DE-BLUR Edges'), (blur_denoise, "BDENOISE Edges"), color_map='gray')
+
+
+def canny_detect(src, low=4, high=10):
+    return cv2.Canny(np.uint8(src), 4, 10, L2gradient=True)
+
+
+def extract_verge(edges, i, j, max_blank, dir, axis):
+    """
+    :param edges: img MAT resulting from canny edge procedure
+    :param i: starting row i
+    :param j: starting col j
+    :param max_blank: maximum number of blanks to rule out the existence of a verge outside of the current verge (in which case would be a fake verge)
+    :param dir: Directions of movement, 1 for right or down, -1 for left or up
+    :param axis:
+    :return: (r, c) index of the verge on the given 'axis' in the given 'dir' from the starting point (i, j)
+    """
+
+
+def significance_test(data, val):
+    """Outputs whether the maximum or minimum value is a significant value."""
+    return val in extract_extrema(data)[0] or val in extract_extrema(data)[1]
+
+
+def edge_detect_expr(edges, original):
+    """
+    TODO: 1. SIMPLE Approach: pairwise mask over original image, then compute total
+          2. Traverse the image to mark the image.
+    """
+    se = 0
+    row_sum = 0
+    col_sum = 0
+    tot_sum = 0
+    for i in range(edges.shape[0]):
+        for j in range(edges.shape[1]):
+            if edges[i][j] > 0:
+                row_sum += i * original[i][j]
+                col_sum += j * original[i][j]
+                tot_sum += original[i][j]
+                se += 1
+    print(se)
+    if tot_sum != 0:
+        print(row_sum / tot_sum)
+        print(col_sum / tot_sum)
+
+
+def hori(img, i, j, d):
+    dest = j + d
+    if dest < 0 or dest >= img.shape[1]:
+        return img.item(i, j)
+    return img.item(i, dest)
+
+
+def verti(img, i, j, d):
+    dest = i + d
+    if dest < 0 or dest >= img.shape[0]:
+        return img.item(i, j)
+    return img.item(dest, j)
+
+
+def test_blur_then_nr(iname):
+    imgs = iname
+    numIMG = 5
+    imgr = None
+    for i in range(1, numIMG + 1):
+        target = cv2.imread(imgs.format(i), 0)
+        target = cv2.GaussianBlur(target, (9, 9), 0)
+        if i == 1:
+            imgr = np.uint16(target)
+        else:
+            imgr = imgr + target
+    return imgr / numIMG
+
+
+def get_center_val(img):
+    return img.item(img.shape[0] // 2, img.shape[1] // 2)
+
+
+def test_noise_reduce(iname, numIMG=5):
+    imgs = iname
+    imgr = None
+    original = None
+    for i in range(1, numIMG+1):
+        target = cv2.imread(imgs.format(i), 0)
+        if target is None:
+            raise AttributeError("File {0} not found".format(iname))
+        if i == 1:
+            imgr = np.uint16(target)
+            original = target
+        else:
+            imgr = imgr + target
+    return imgr / numIMG, original
+
+
+def img_add(dest, src):
+    """Destructive!"""
+    row, col = dest.shape
+    for i in range(row):
+        for j in range(col):
+            dest[i][j] += src[i][j]
+
+
+""" ===================================
+    ============ PLOTTING =============
+    ============= HELPER ==============
+    =================================== """
+
+
+def quick_plot(data, xs=None):
+    plt.figure()
+    if xs:
+        plt.plot(xs, data, 'bo-')
+    else:
+        plt.plot(data, 'bo-')
+    plt.show()
+    plt.close()
+
+
+def compare_images(*args, ilist=None, color_map=None):
+    # Takes in a sequence of IMG(Gray) and TITLE pairs and plot them side by side
+    if ilist:
+        args = ilist
+    graph_amount = len(args)
+    row = int(sqrt(graph_amount))
+    col = round_up(float(graph_amount) / row)
+    plt.figure(figsize=(16, 8))
+    for i, pair in enumerate(args):
+        plt.subplot(row, col, i+1)
+        if color_map:
+            plt.imshow(pair[0], cmap=color_map)
+        plt.xticks([]), plt.yticks([])
+        plt.title(pair[1])
+    plt.show()
+
+
+def compare_data_plots(*args, ilist=None, suptitle=None):
+    # Takes in a sequence of tuples with data, xs, and title pairs
+    if ilist:
+        args = ilist
+    graph_amount = len(args)
+    row = int(sqrt(graph_amount))
+    col = round_up(float(graph_amount) / row)
+    plt.figure(figsize=(16, 8))
+    if suptitle:
+        plt.suptitle(suptitle, fontsize=14)
+    for i, pair in enumerate(args):
+        plt.subplot(row, col, i + 1)
+        if len(pair) == 2:
+            plt.plot(pair[0], 'bo-')
+            plt.title(pair[1])
+        else:
+            plt.plot(pair[1], pair[0], 'bo-')
+            plt.title(pair[2])
+    plt.show()
+
+
+def line_graph_contrast(img, xs, ys):
+    line = HoughLine(x=xs, data=ys)
+    #print(xs)
+    x0 = line._c * line._r
+    y0 = line._s * line._r
+
+    print('x0:{0}, y0:{1}, cos:{2}, sin:{3}, rho:{4}, theta:{5}'.format(x0, y0, line._c, line._s, line._r, degrees(line._t)))
+    x1 = int(x0 + 1000 * (-line._s))
+    y1 = int(y0 + 1000 * (line._c))
+    x2 = int(x0 - 1000 * (-line._s))
+    y2 = int(y0 - 1000 * (line._c))
+    p1, p2 = (x1, y1), (x2, y2)
+    cv2.line(img, p1, p2, (255, 0, 0), 1)
+    return p1, p2, line
+
+
+""" ===================================
+    ====== DATA RECORDING HELPER ======
+    =================================== """
+
+# 1. Try Noise Reduction:
+# ---- 1.1 Reduction By Averaging, without FastNIMEANS  ~~ GOOD
+# ---- 1.2 Reduction By FastNIMEANS                     ~~ INFERIOR
+# ---- 1.3 Reduction By Combination                     ~~ INFERIOR
+# ---- 1 NOTE: USE PLOTTING OF ORIGINAL GRAPH TO SEE RESULT
+# 2. Try Di-Axial Scharr Kernel
+# ---- 2.1 Add To Name Scheme, record graphs and compare them
+# ---- 2 NOTE: GRAPH TO SAVE: 4-CONTRAST, TRANSITION PLOTS
+# 3. Optimizing Data Processing:
+# ---- 3.1 Marking point as edge maxima and minima according to STD Dev values
+# ---- 3.2 Taking Sequential Samples
+# ---- 3.3 Explore Thresholding Techniques
+
+
+def register(img):
+    lval = []
+    for i in range(img.shape[0]):
+        for j in range(img.shape[1]):
+            if img.item(i, j) > 250:
+                lval.append((i, j, img.item(i, j)))
+    print(lval)
+
+
+""" TEST GAUSSIAN BLUR'S influence on noise distribution:
+    Method:
+        1. Preliminary
+            1. Generate a random noise matrix with n * n, change shape to a vector
+            2. Gaussian blur applied, change shape to a vector
+            3. Compare noise level
+        2. Secondary
+            1. Generate a Gaussian / Cosine Matrix and add random noise matrix
+            2. Gaussian blur applied
+            3. Compare a randomly selected row of each one
+"""
+
+
+def test_old():
+    # SETTINGS
+    imgn = "img_4_{0}"
+    IMGDIR = "../new_test/"
+    ROOTMEAS = "meas/"
+    SAVEDIR = ROOTMEAS+imgn + "/"
+    #imgr = cv2.imread(IMGDIR + imgn + ".png")
+    imgr, ori = test_noise_reduce(IMGDIR + imgn + ".png")
+    dimc = imgr.shape[1]
+    dimr = imgr.shape[0]
+    r_int = dimr // 2
+    c_int = dimc // 2
+    start = 7
+    end = 10
+    sig = 0
+    ins = range(start, end, 2)
+    name_scheme = imgn + ("_({0}, {1})").format(r_int, c_int)
+    NAME_HEADER = SAVEDIR + name_scheme
+    if not os.path.exists(SAVEDIR):
+        os.mkdir(SAVEDIR)
+    fwrite = open(NAME_HEADER + ".txt", "w")
+    plot_save = NAME_HEADER + ".png"
+
+
+    # INITIALIZATION
+    cenvalsx = []
+    cenvalsy = []
+    edgvalsx = []
+    edgvalsy = []
+    ebvalsx = []
+    ebvalsy = []
+    ecvalsx = []
+    ecvalsy = []
+    ecbvalsx = []
+    ecbvalsy = []
+    #cv2.imshow("denoised", np.uint8(imgr))                                 # REMOVES TO SHOW IMAGE
+    x = np.array(range(dimc))
+    x2 = np.array(range(dimr))
+
+    for i in ins:
+        # INITIALIZATION
+        gksize = (i, i)
+        sigmaX = sig
+        blur = cv2.GaussianBlur(imgr,gksize,sigmaX)
+        #cv2.imshow("blurred", blur)                        # REMOVES TO SHOW BLURRED IMAGE
+        if len(blur.shape) == 3:
+            img = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
+        else:
+            img = blur
+
+        # GAUSSIAN PROCESSING                                 # UNCOMMENT TO SHOW GAUSSIAN PROCESSING
+        """x = np.array(range(dimc))
+        #y = np.array([img_rec.rel_lumin(img, 0, c) for c in x])
+        y = np.array([img.item(dimr // 2, c) for c in x])
+        a1, b1, c_s1 = gauss_reg(x, y)
+        x2 = np.array(range(dimr))
+        y2 = np.array([img.item(r, dimc // 2) for r in x2])
+        a2, b2, c_s2 = gauss_reg(x2, y2)
+        rem_gauss = gauss_mat(img.shape, (a1+a2) / 2, b1, c_s1, b2, c_s2)
+        img = img - rem_gauss
+        cv2.imshow("denoise", img)
+        y_hat = gauss_hat(x, a1, b1, c_s1)
+        plt.figure(figsize=(16, 8))
+        plt.plot(x, y, 'b-', x, y_hat, 'r-')
+        plt.show()
+        plt.close()
+        plt.figure(figsize=(16,8))
+        plt.plot(x, y-y_hat, 'b-')
+        plt.show()"""
+
+        # IMAGE PROCESSING
+        sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=-1)
+        sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=-1)
+        """kernel = np.array([[-1, -1, -1],
+                           [-1, 8, -1],
+                           [-1, -1, -1]])
+        kerneled = cv2.filter2D(img, -1, kernel)
+        cv2.imshow("kerneled", kerneled)"""                 # KERNEL EXPERIMENT
+        #laplacian = cv2.Laplacian(img,cv2.CV_64F)          # LAPLACIAN FILTERING
+        """plt.subplot(2,2,1),plt.imshow(cv2.cvtColor(imgr, cv2.COLOR_BGR2GRAY),cmap = 'gray')
+        plt.title('Original'), plt.xticks([]), plt.yticks([])
+        plt.subplot(2,2,2),plt.imshow(img,cmap = 'gray')
+        plt.title('Gaussian Filter'), plt.xticks([]), plt.yticks([])
+        plt.subplot(2,2,3),plt.imshow(sobelx,cmap = 'gray')
+        plt.title('Scharr X'), plt.xticks([]), plt.yticks([])
+        plt.subplot(2,2,4),plt.imshow(sobely,cmap = 'gray')
+        plt.title('Scharr Y'), plt.xticks([]), plt.yticks([])"""
+        #plt.savefig(NAME_HEADER + "filters.png")
+        #plt.savefig(NAME_HEADER + "2D_filters.png")
+        #plt.close()                                         # REMOVES TO SHOW CONTRAST OF FILTERS
+
+
+        # DETECTION INIT
+        gk_setting = "ksize:{0}, sigmax:{1}".format(gksize, sigmaX)
+        y_s_x = np.array([sobelx.item(r_int, c) for c in x])
+        y_s_y = np.array([sobely.item(r, c_int) for r in x2])
+        imgx = [imgr.item(r_int, c) for c in x]
+        imgy = [imgr.item(r, c_int) for r in x2]
+        blurimgx = [img.item(r_int, c) for c in x]
+        blurimgy = [img.item(r, c_int) for r in x2]
+        print(gk_setting)
+        print(extract_extrema(y_s_x))
+        print(extract_extrema(y_s_y))
+        fwrite.write(gk_setting)
+        fwrite.write('\n')
+
+        # EDGE DETECTION
+        zx = zero_crossing(y_s_x)
+        cenvalsx.append(zx)
+        ex = edge_converge_extreme(y_s_x)
+        edgvalsx.append(ex)
+        ebx = edge_converge_base(y_s_x)
+        ebvalsx.append(ebx)
+        ecx = edge_centroid(y_s_x, imgx)
+        ecvalsx.append(ecx)
+        ecbx = edge_centroid(y_s_x, blurimgx)
+        ecbvalsx.append(ecbx)
+
+        zy = zero_crossing(y_s_y)
+        cenvalsy.append(zy)
+        ey = edge_converge_extreme(y_s_y)
+        edgvalsy.append(ey)
+        eby = edge_converge_base(y_s_y)
+        ebvalsy.append(eby)
+        ecy = edge_centroid(y_s_y, imgy)
+        ecvalsy.append(ecy)
+        ecby = edge_centroid(y_s_y, blurimgy)
+        ecbvalsy.append(ecby)
+
+        # DATA RECORDING
+        fwrite.write("x: zero_crossing: {0}, edge_converge: {1}; ".format(zx, ex))
+        fwrite.write('\n')
+        fwrite.write("y: zero_crossing: {0}, edge_converge: {1}\n".format(zy, ey))
+
+        # GK_PLOT
+        """if i == 9:
+            fig = plt.figure(figsize=(16, 8))
+            fig.canvas.set_window_title(gk_setting)
+            plt.subplot(211)
+            plt.plot(y_s_x, 'b-')
+            plt.ylabel("sobel_x")
+            plt.subplot(212)
+            plt.plot(y_s_y, 'b-')
+            plt.ylabel("sobel_y")
+            #plt.savefig(NAME_HEADER + "_" + gk_setting + ".png")"""
+
+        #if i >= 13:
+        fig = plt.figure(figsize=(16, 8))
+        fig.canvas.set_window_title(gk_setting)
+        plt.subplot(211)
+        plt.plot(y_s_x, 'b-')
+        plt.ylabel("sobel_x")
+        plt.subplot(212)
+        plt.plot(y_s_y, 'b-')
+        plt.ylabel("sobel_y")
+        plt.show()
+        # plt.savefig(NAME_HEADER + "_" + gk_setting + ".png") # UNCOMMENT WHEN SAVING PLOTS
+
+        # USING MIN-MAX
+        fig = plt.figure(figsize=(16, 8))
+        fig.canvas.set_window_title(gk_setting)
+        plt.subplot(221)
+        plt.plot(y_s_x, 'b-')
+        plt.ylabel("sobel_x")
+        plt.subplot(222)
+        plt.plot(min_max(y_s_x, max(y_s_x), min(y_s_x)), 'b-')
+        plt.subplot(223)
+        plt.plot(y_s_y, 'b-')
+        plt.ylabel("sobel_y")
+        plt.xlabel("Plain")
+        plt.subplot(224)
+        plt.plot(min_max(y_s_y, max(y_s_y), min(y_s_y)), 'b-')
+        plt.xlabel("MINMAX")
+        plt.show()
+
+
+    fig = plt.figure(figsize=(16, 8))
+    plt.subplot(2, 1, 1)
+    #plt.plot(ins, cenvalsx, 'b-', ins, edgvalsx, 'r-', ins, ebvalsx, 'g-', ins, ecvalsx, 'c-', ins, ecbvalsx, 'm-')
+    #plt.legend(['zero crossing', 'edge_converging_extreme', 'edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'], loc="lower left")
+    #plt.plot(ins, cenvalsx, 'b-', ins, ebvalsx, 'g-', ins, ecvalsx, 'c-', ins, ecbvalsx, 'm-')
+    #plt.legend(['zero crossing', 'edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'],loc="lower left")
+    plt.plot(ins, ebvalsx, 'g-', ins, ecvalsx, 'c-', ins, ecbvalsx, 'm-')
+    plt.legend(['edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'],loc="lower left")
+    plt.ylabel("x_meas")
+    plt.subplot(2, 1, 2)
+    #plt.plot(ins, cenvalsy, 'b-', ins, ebvalsy, 'g-', ins, ecvalsy, 'c-', ins, ecbvalsy, 'm-')
+    #plt.legend(['zero crossing', 'edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'], loc="lower left")
+    plt.plot(ins, ebvalsy, 'g-', ins, ecvalsy, 'c-', ins, ecbvalsy, 'm-')
+    plt.legend(['edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'], loc="lower left")
+    plt.ylabel("y_meas")
+    plt.xlabel("kernel size")
+    plt.show()
+    #fig.savefig(plot_save)
+    #plt.savefig(plot_save)
+
+
+def test_interactive():
+    # TODO: TEST OUT FACET MODEL AND CONDITIONAL CONVOLUTIONAL KERNEL
+    # SETTINGS
+    while True:
+        number = input("type the picture number u wanna test or c to cancel:    ")
+        if number == 'c':
+            break
+        imgn = "img_%d_{0}" % int(number)
+        print(imgn)
+        IMGDIR = "../testpic/"
+        IMGDIR = "../calib4/"
+        ROOTMEAS = "meas/"
+        SAVEDIR = ROOTMEAS + imgn + "/"
+        # imgr = cv2.imread(IMGDIR + imgn + ".png")
+
+        imgr, ori = test_noise_reduce(IMGDIR + imgn + ".png", numIMG=5)
+        dimc = imgr.shape[1]
+        dimr = imgr.shape[0]
+        r_prompt = input("Type in the index for horizontal slice: (range: [0, {0}])   ".format(dimr - 1)) #dimr // 2
+        c_prompt = input("Type in the index for vertical slice: (range: [0, {0}])   ".format(dimc - 1)) #dimc // 2
+        while True:
+            try:
+                r_int = int(r_prompt)
+                assert 0 <= r_int < dimr
+                break
+            except ValueError:
+                r_prompt = input("Bad Input, try again: (range: [0, {0}])    ".format(dimr - 1))  # dimr // 2
+            except AssertionError:
+                r_prompt = input("Out of Bound, try again: (range: [0, {0}])    ".format(dimr - 1))
+        while True:
+            try:
+                c_int = int(c_prompt)
+                assert 0 <= c_int < dimc
+                break
+            except ValueError:
+                c_prompt = input("Bad Input, try again: (range: [0, {0}])    ".format(dimc - 1))  # dimc // 2
+            except AssertionError:
+                c_prompt = input("Out of Bound, try again: (range: [0, {0}])    ".format(dimc - 1))  # dimc // 2
+        #start = 7
+        #end = 10
+        sig = 0
+        gk = 9
+        name_scheme = imgn + ("_({0}, {1})").format(r_int, c_int)
+        NAME_HEADER = SAVEDIR + name_scheme
+        if not os.path.exists(SAVEDIR):
+            os.mkdir(SAVEDIR)
+        fwrite = open(NAME_HEADER + ".txt", "w")
+        plot_save = NAME_HEADER + ".png"
+
+        # INITIALIZATION
+        # cv2.imshow("denoised", np.uint8(imgr))                                 # REMOVES TO SHOW IMAGE
+        #x = np.array(range(dimc))
+        #x2 = np.array(range(dimr))
+
+        # INITIALIZATION
+        gksize = (gk, gk)
+        sigmaX = sig
+        blur = cv2.GaussianBlur(imgr, gksize, sigmaX)
+        # cv2.imshow("blurred", blur)                        # REMOVES TO SHOW BLURRED IMAGE
+        if len(blur.shape) == 3:
+            img = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
+        else:
+            img = blur
+
+        # GAUSSIAN PROCESSING                                 # UNCOMMENT TO SHOW GAUSSIAN PROCESSING
+        """x = np.array(range(dimc))
+        #y = np.array([img_rec.rel_lumin(img, 0, c) for c in x])
+        y = np.array([img.item(dimr // 2, c) for c in x])
+        a1, b1, c_s1 = gauss_reg(x, y)
+    
+        x2 = np.array(range(dimr))
+        y2 = np.array([img.item(r, dimc // 2) for r in x2])
+        a2, b2, c_s2 = gauss_reg(x2, y2)
+        rem_gauss = gauss_mat(img.shape, (a1+a2) / 2, b1, c_s1, b2, c_s2)
+        img = img - rem_gauss
+        cv2.imshow("denoise", img)
+        y_hat = gauss_hat(x, a1, b1, c_s1)
+        plt.figure(figsize=(16, 8))
+        plt.plot(x, y, 'b-', x, y_hat, 'r-')
+        plt.show()
+        plt.close()
+        plt.figure(figsize=(16,8))
+        plt.plot(x, y-y_hat, 'b-')
+        plt.show()"""
+
+        # IMAGE PROCESSING
+        sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=-1)
+        sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=-1)
+        """kernel = np.array([[-1, -1, -1],
+                           [-1, 8, -1],
+                           [-1, -1, -1]])
+        kerneled = cv2.filter2D(img, -1, kernel)
+        cv2.imshow("kerneled", kerneled)"""  # KERNEL EXPERIMENT
+        # laplacian = cv2.Laplacian(img,cv2.CV_64F)          # LAPLACIAN FILTERING
+        """plt.subplot(2,2,1),plt.imshow(cv2.cvtColor(imgr, cv2.COLOR_BGR2GRAY),cmap = 'gray')
+        plt.title('Original'), plt.xticks([]), plt.yticks([])
+        plt.subplot(2,2,2),plt.imshow(img,cmap = 'gray')
+        plt.title('Gaussian Filter'), plt.xticks([]), plt.yticks([])
+        plt.subplot(2,2,3),plt.imshow(sobelx,cmap = 'gray')
+        plt.title('Scharr X'), plt.xticks([]), plt.yticks([])
+        plt.subplot(2,2,4),plt.imshow(sobely,cmap = 'gray')
+        plt.title('Scharr Y'), plt.xticks([]), plt.yticks([])"""
+        # plt.savefig(NAME_HEADER + "filters.png")
+        # plt.savefig(NAME_HEADER + "2D_filters.png")
+        # plt.close()                                         # REMOVES TO SHOW CONTRAST OF FILTERS
+
+
+        # DETECTION INIT
+        xh = np.array(range(dimc))
+        xv = np.array(range(dimr))
+        zeroh = np.zeros(dimc)
+        zerov = np.zeros(dimr)
+        y_s_x = FM(sobelx, FM.HOR, r_int)
+        y_s_y = FM(sobely, FM.VER, c_int)
+        imgx = FM(imgr, FM.HOR, r_int)
+        imgy = FM(imgr, FM.VER, c_int)
+        blurimgx = FM(img, FM.HOR, r_int)
+        blurimgy = FM(img, FM.VER, c_int)
+        gx, bgx, deducedx = gauss_bg_deduce(xh, imgx)
+        gy, bgy, deducedy = gauss_bg_deduce(xv, imgy)
+
+        #print(extract_extrema(y_s_x))
+        #print(extract_extrema(y_s_y))
+
+        # EDGE DETECTION
+        zx = zero_crossing(y_s_x)
+        ex = edge_converge_extreme(y_s_x)
+        ebx = edge_converge_base(y_s_x)
+        ecbx = edge_centroid(y_s_x, blurimgx)
+        ecx = edge_centroid(y_s_x, imgx)
+        try:
+            #paramhp, xp1 = poly_fitting_params(y_s_x, deducedx)
+            #paramhg, xg1 = gaussian_fitting_params(y_s_x, deducedx)
+            paramhp, xp1 = poly_fitting_params(y_s_x, imgx)
+            paramhg, xg1 = gaussian_fitting_params(y_s_x, imgx)
+            xp_h_plot, yp_h_plot = poly_curve(paramhp, xp1)
+            xg_h_plot, yg_h_plot = gaussian_curve(xg1, paramhg[0], paramhg[1], paramhg[2])
+        except AssertionError:
+            print("No image data for horizontal slice!")
+            xp_h_plot, yp_h_plot = xh, zeroh
+            xg_h_plot, yg_h_plot = xh, zeroh
+
+        px, gx = xp_h_plot[np.argmax(yp_h_plot)], xg_h_plot[np.argmax(yg_h_plot)]
+        print("edge: {0}, poly:{1}, gaussian:{2}".format(ecx, px, gx))
+
+        zy = zero_crossing(y_s_y)
+        ey = edge_converge_extreme(y_s_y)
+        eby = edge_converge_base(y_s_y)
+        ecby = edge_centroid(y_s_y, blurimgy)
+        ecy = edge_centroid(y_s_y, imgy)
+        try:
+            paramvp, xp2 = poly_fitting_params(y_s_y, imgy)
+            paramvg, xg2 = gaussian_fitting_params(y_s_y, imgy)
+            xp_v_plot, yp_v_plot = poly_curve(paramvp, xp2)
+            xg_v_plot, yg_v_plot = gaussian_curve(xg2, paramvg[0], paramvg[1], paramvg[2])
+        except AssertionError:
+            print("No image data for vertical slice!")
+            xp_v_plot, yp_v_plot = xv, zerov
+            xg_v_plot, yg_v_plot = xv, zerov
+        py, gy = xp_v_plot[np.argmax(yp_v_plot)], xg_v_plot[np.argmax(yg_v_plot)]
+        print("edge: {0} poly:{1}, gaussian:{2}".format(ecy, py, gy))
+
+
+        # DATA RECORDING
+        fwrite.write("x: edge_centroid: {0}, poly: {1}, gaussian: {2}; ".format(ecx, px, gx))
+        fwrite.write('\n')
+        fwrite.write("y: edge_centroid: {0}, poly: {1}, gaussian: {2}\n".format(ecy, py, gy))
+
+        # GK_PLOT
+        """if i == 9:
+            fig = plt.figure(figsize=(16, 8))
+            fig.canvas.set_window_title(gk_setting)
+            plt.subplot(211)
+            plt.plot(y_s_x, 'b-')
+            plt.ylabel("sobel_x")
+            plt.subplot(212)
+            plt.plot(y_s_y, 'b-')
+            plt.ylabel("sobel_y")
+            #plt.savefig(NAME_HEADER + "_" + gk_setting + ".png")"""
+
+        # if i >= 13:
+        fig = plt.figure(figsize=(16, 8))
+        plt.subplot(211)
+        imgx.initialize()
+        plt.plot(xh, imgx.extract_array(), 'b-', xh, bgx, 'm-', xh, deducedx, 'c-', xp_h_plot, yp_h_plot, 'g-', xg_h_plot, yg_h_plot, 'r-')
+        plt.ylabel("Horizontal Slice, ECX: {0}".format(ecx))
+        plt.legend(['Raw image data', 'gaussian background', 'remnant', 'polynomial', 'gaussian'], loc="upper right")
+        plt.subplot(212)
+        imgy.initialize()
+        plt.plot(xv, imgy.extract_array(), 'b-', xv, bgy, 'm-', xv, deducedy, 'c-', xp_v_plot, yp_v_plot, 'g-', xg_v_plot, yg_v_plot, 'r-')
+        plt.ylabel("Vertical Slice, ECY: {0}".format(ecy))
+        plt.legend(['Raw image data', 'gaussian background', 'remnant', 'polynomial', 'gaussian'], loc="upper right")
+        plt.show()
+        # plt.savefig(NAME_HEADER + "_" + gk_setting + ".png") # UNCOMMENT WHEN SAVING PLOTS
+
+        """fig = plt.figure(figsize=(16, 8))
+        plt.subplot(2, 1, 1)"""
+        # plt.plot(ins, cenvalsx, 'b-', ins, edgvalsx, 'r-', ins, ebvalsx, 'g-', ins, ecvalsx, 'c-', ins, ecbvalsx, 'm-')
+        # plt.legend(['zero crossing', 'edge_converging_extreme', 'edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'], loc="lower left")
+        # plt.plot(ins, cenvalsx, 'b-', ins, ebvalsx, 'g-', ins, ecvalsx, 'c-', ins, ecbvalsx, 'm-')
+        # plt.legend(['zero crossing', 'edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'],loc="lower left")
+        """plt.plot(ins, ebvalsx, 'g-', ins, ecvalsx, 'c-', ins, ecbvalsx, 'm-')
+        plt.legend(['edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'], loc="lower left")
+        plt.ylabel("x_meas")
+        plt.subplot(2, 1, 2)
+        # plt.plot(ins, cenvalsy, 'b-', ins, ebvalsy, 'g-', ins, ecvalsy, 'c-', ins, ecbvalsy, 'm-')
+        # plt.legend(['zero crossing', 'edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'], loc="lower left")
+        plt.plot(ins, ebvalsy, 'g-', ins, ecvalsy, 'c-', ins, ecbvalsy, 'm-')
+        plt.legend(['edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'], loc="lower left")
+        plt.ylabel("y_meas")
+        plt.xlabel("kernel size")
+        plt.show()
+        # fig.savefig(plot_save)
+        # plt.savefig(plot_save)"""
+
+
+def test(folder, imgn):
+    # TODO: TEST OUT FACET MODEL AND CONDITIONAL CONVOLUTIONAL KERNEL
+    # SETTINGS
+    ROOTMEAS = "meas/"
+    SAVEDIR = ROOTMEAS + imgn[:-4] + "/"
+    # imgr = cv2.imread(IMGDIR + imgn + ".png")
+
+    imgr, ori = test_noise_reduce(folder + imgn, numIMG=5)
+    dimc = imgr.shape[1]
+    dimr = imgr.shape[0]
+    while True:
+        r_prompt = input("Type in the index for horizontal slice or c to cancel: (range: [0, {0}])   ".format(dimr - 1))  # dimr // 2
+        c_prompt = input("Type in the index for vertical slice: (range: [0, {0}])   ".format(dimc - 1))  # dimc // 2
+        if r_prompt == 'c':
+            break
+        while True:
+            try:
+                r_int = int(r_prompt)
+                assert 0 <= r_int < dimr
+                break
+            except ValueError:
+                r_prompt = input("Bad Input, try again: (range: [0, {0}])    ".format(dimr - 1))  # dimr // 2
+            except AssertionError:
+                r_prompt = input("Out of Bound, try again: (range: [0, {0}])    ".format(dimr - 1))
+        while True:
+            try:
+                c_int = int(c_prompt)
+                assert 0 <= c_int < dimc
+                break
+            except ValueError:
+                c_prompt = input("Bad Input, try again: (range: [0, {0}])    ".format(dimc - 1))  # dimc // 2
+            except AssertionError:
+                c_prompt = input("Out of Bound, try again: (range: [0, {0}])    ".format(dimc - 1))  # dimc // 2
+        # start = 7
+        # end = 10
+        sig = 0
+        gk = 9
+        name_scheme = imgn + ("_({0}, {1})").format(r_int, c_int)
+        NAME_HEADER = SAVEDIR + name_scheme
+        if not os.path.exists(SAVEDIR):
+            os.mkdir(SAVEDIR)
+        fwrite = open(NAME_HEADER + ".txt", "w")
+        plot_save = NAME_HEADER + ".png"
+
+        # INITIALIZATION
+        # cv2.imshow("denoised", np.uint8(imgr))                                 # REMOVES TO SHOW IMAGE
+        # x = np.array(range(dimc))
+        # x2 = np.array(range(dimr))
+
+        # INITIALIZATION
+        gksize = (gk, gk)
+        sigmaX = sig
+        blur = cv2.GaussianBlur(imgr, gksize, sigmaX)
+        # cv2.imshow("blurred", blur)                        # REMOVES TO SHOW BLURRED IMAGE
+        if len(blur.shape) == 3:
+            img = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
+        else:
+            img = blur
+
+        # GAUSSIAN PROCESSING                                 # UNCOMMENT TO SHOW GAUSSIAN PROCESSING
+        """x = np.array(range(dimc))
+        #y = np.array([img_rec.rel_lumin(img, 0, c) for c in x])
+        y = np.array([img.item(dimr // 2, c) for c in x])
+        a1, b1, c_s1 = gauss_reg(x, y)
+
+        x2 = np.array(range(dimr))
+        y2 = np.array([img.item(r, dimc // 2) for r in x2])
+        a2, b2, c_s2 = gauss_reg(x2, y2)
+        rem_gauss = gauss_mat(img.shape, (a1+a2) / 2, b1, c_s1, b2, c_s2)
+        img = img - rem_gauss
+        cv2.imshow("denoise", img)
+        y_hat = gauss_hat(x, a1, b1, c_s1)
+        plt.figure(figsize=(16, 8))
+        plt.plot(x, y, 'b-', x, y_hat, 'r-')
+        plt.show()
+        plt.close()
+        plt.figure(figsize=(16,8))
+        plt.plot(x, y-y_hat, 'b-')
+        plt.show()"""
+
+        # IMAGE PROCESSING
+        sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=-1)
+        sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=-1)
+
+
+        # DETECTION INIT
+        xh = np.array(range(dimc))
+        xv = np.array(range(dimr))
+        zeroh = np.zeros(dimc)
+        zerov = np.zeros(dimr)
+        y_s_x = FM(sobelx, FM.HOR, r_int)
+        y_s_y = FM(sobely, FM.VER, c_int)
+        imgx = FM(imgr, FM.HOR, r_int)
+        imgy = FM(imgr, FM.VER, c_int)
+        blurimgx = FM(img, FM.HOR, r_int)
+        blurimgy = FM(img, FM.VER, c_int)
+        gx, bgx, deducedx = gauss_bg_deduce(xh, imgx)
+        gy, bgy, deducedy = gauss_bg_deduce(xv, imgy)
+        # print(extract_extrema(y_s_x))
+        # print(extract_extrema(y_s_y))
+        # EDGE DETECTION
+        ecx = edge_centroid(y_s_x, imgx)
+        try:
+            # paramhp, xp1 = poly_fitting_params(y_s_x, deducedx)
+            # paramhg, xg1 = gaussian_fitting_params(y_s_x, deducedx)
+            paramhp, xp1 = poly_fitting_params(y_s_x, imgx)
+            paramhg, xg1 = gaussian_fitting_params(y_s_x, imgx)
+            xp_h_plot, yp_h_plot = poly_curve(paramhp, xp1)
+            xg_h_plot, yg_h_plot = gaussian_curve(xg1, paramhg[0], paramhg[1], paramhg[2])
+        except AssertionError:
+            print("No image data for horizontal slice!")
+            xp_h_plot, yp_h_plot = xh, zeroh
+            xg_h_plot, yg_h_plot = xh, zeroh
+
+        px, gx = xp_h_plot[np.argmax(yp_h_plot)], xg_h_plot[np.argmax(yg_h_plot)]
+        print("edge: {0}, poly:{1}, gaussian:{2}".format(ecx, px, gx))
+
+        ecy = edge_centroid(y_s_y, imgy)
+        try:
+            paramvp, xp2 = poly_fitting_params(y_s_y, imgy)
+            paramvg, xg2 = gaussian_fitting_params(y_s_y, imgy)
+            xp_v_plot, yp_v_plot = poly_curve(paramvp, xp2)
+            xg_v_plot, yg_v_plot = gaussian_curve(xg2, paramvg[0], paramvg[1], paramvg[2])
+        except AssertionError:
+            print("No image data for vertical slice!")
+            xp_v_plot, yp_v_plot = xv, zerov
+            xg_v_plot, yg_v_plot = xv, zerov
+        py, gy = xp_v_plot[np.argmax(yp_v_plot)], xg_v_plot[np.argmax(yg_v_plot)]
+        print("edge: {0} poly:{1}, gaussian:{2}".format(ecy, py, gy))
+
+        # DATA RECORDING
+        fwrite.write("x: edge_centroid: {0}, poly: {1}, gaussian: {2}; ".format(ecx, px, gx))
+        fwrite.write('\n')
+        fwrite.write("y: edge_centroid: {0}, poly: {1}, gaussian: {2}\n".format(ecy, py, gy))
+
+        # if i >= 13:
+        fig = plt.figure(figsize=(16, 8))
+        plt.subplot(411)
+        plt.plot(y_s_x.extract_array(), 'b-')
+        plt.ylabel("sobel_x")
+        plt.subplot(412)
+        imgx.initialize()
+        plt.plot(xh, imgx.extract_array(), 'b-', xh, bgx, 'm-', xh, deducedx, 'c-', xp_h_plot, yp_h_plot, 'g-',
+                 xg_h_plot, yg_h_plot, 'r-')
+        plt.ylabel("Horizontal Slice, ECX: {0}".format(ecx))
+        plt.legend(['Raw image data', 'gaussian background', 'remnant', 'polynomial', 'gaussian'], loc="upper right")
+        plt.subplot(413)
+        plt.plot(y_s_y.extract_array(), 'b-')
+        plt.ylabel("sobel_y")
+        plt.subplot(414)
+        imgy.initialize()
+        plt.plot(xv, imgy.extract_array(), 'b-', xv, bgy, 'm-', xv, deducedy, 'c-', xp_v_plot, yp_v_plot, 'g-',
+                 xg_v_plot, yg_v_plot, 'r-')
+        plt.ylabel("Vertical Slice, ECY: {0}".format(ecy))
+        plt.legend(['Raw image data', 'gaussian background', 'remnant', 'polynomial', 'gaussian'], loc="upper right")
+        plt.show()
+        # plt.savefig(NAME_HEADER + "_" + gk_setting + ".png") # UNCOMMENT WHEN SAVING PLOTS
+
+
+FM = FastDataMatrix2D
+
+
+def folder_to_imgs(img_name_scheme, num_sample):
+    """This function takes img files and return cv imgs"""
+    return [cv2.imread(img_name_scheme.format(i)) for i in range(1, num_sample+1)]
+
+
+def center_detect_old(img_name_scheme, num_sample, sample_int=50):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair"""
+    imgs = folder_to_imgs(img_name_scheme, num_sample)
+    num_imgs = len(imgs)
+    dimr = imgs[0].shape[0]
+    dimc = imgs[0].shape[1]
+    xnum_imgs = num_imgs
+    ynum_imgs = num_imgs
+    xsum = 0
+    ysum = 0
+    zw = 0
+    ew = 1
+    gk = 9
+    count = 1
+    for imgr in imgs:
+        if count == num_sample:
+            imgshow = imgr
+        count += 1
+        #print(imgr.shape)
+        # Image Processing
+        gksize = (gk, gk)
+        sigmaX = 0
+        img = cv2.GaussianBlur(imgr, gksize, sigmaX)
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=-1)
+        sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=-1)
+        # Gathering Data
+        nr = sample_int
+        r_thresh = dimr * 2.0 / (sample_int * 3)
+        xs = []
+        xdummies = []
+        ys = []
+        ydummies = []
+        while nr < dimr:
+            data_x = sobelx[nr, :]
+            zc_x = zero_crossing(data_x)
+            print("row:", nr)
+            ed_x = edge_centroid(data_x, FM(img, FM.HOR, nr))
+            print("At {0}: {1}".format(nr, ed_x))
+            nr += sample_int
+            xdummies.append(zc_x * zw + ed_x * ew)
+            if zc_x == -1:
+                continue
+            else:
+                xs.append(zc_x * zw + ed_x * ew)
+        nc = sample_int
+        c_thresh = dimc * 2.0 / (sample_int * 3)
+        while nc < dimc:
+            data_y = sobely[:, nc]
+            zc_y = zero_crossing(data_y)
+            ed_y = edge_centroid(data_y, FM(img, FM.VER, nc))
+            nc += sample_int
+            ydummies.append(zc_y * zw + ed_y * ew)
+            if zc_y == -1:
+                continue
+            else:
+                ys.append(zc_y * zw + ed_y * ew)
+        #print("x")
+        #print(xdummies)
+        #print(xs)
+        #print("y")
+        #print(ydummies)
+        #print(ys)
+        len_xs = len(xs)
+        len_ys = len(ys)
+        print("img {0}: {1}".format(count - 1, xs))
+        if len_xs < r_thresh:
+            xnum_imgs -= 1
+        else:
+            xsum += sum(xs) / len(xs)
+        if len_ys < c_thresh:
+            ynum_imgs -= 1
+        else:
+            ysum += sum(ys) / len(ys)
+
+    plt.show()
+
+    center_x = -1 if xnum_imgs == 0 else xsum / xnum_imgs
+    center_y = -1 if ynum_imgs == 0 else ysum / ynum_imgs
+    return center_x, center_y
+
+# The image taken is flipped horizontally, result x should be img.shape[1] - x
+# The image sometimes has two peaks, try experimenting with different gaussian kernels
+
+
+def center_detect_test(folder_path, img_name_scheme, num_sample, sample_int=50, debug=False, gk=9, ks=-1, m=0, p=20, b=1, c=0, hough=True):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair
+    hs: HORIZONTAL SLICE!  vs: VERTICAL SLICE!"""
+    imgr = test_noise_reduce(folder_path + img_name_scheme, num_sample)[0]
+    dimr = imgr.shape[0]
+    dimc = imgr.shape[1]
+    # Image Processing
+    gksize = (gk, gk)
+    sigmaX = 0
+    img = cv2.GaussianBlur(imgr, gksize, sigmaX)
+    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=ks)
+    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=ks)
+    # ------------------------------------------------------------
+    # Parameter Setting
+    METHODS = {0: gaussian_fitting, 1: poly_fitting, 2: edge_centroid,
+               3: zero_crossing, 4: edge_converge_base, 5: edge_converge_extreme}
+    edge_method = METHODS[m]
+    nr = sample_int
+    r_thresh = dimr / (sample_int * 3.0)
+    nc = sample_int
+    c_thresh = dimc / (sample_int * 3.0)
+    # ------------------------------------------------------------
+    # Gathering Data
+    hs = []
+    vs = []
+    while nr < dimr:
+        data_x = FM(sobelx, FM.HOR, nr)
+        if m < 3:
+            ec_x = edge_method(data_x, FM(img, FM.HOR, nr), p)
+        else:
+            ec_x = edge_method(data_x)
+        nr += sample_int
+        if ec_x == -1:
+            continue
+        else:
+            hs.append((nr - sample_int, ec_x))
+
+    while nc < dimc:
+        data_y = FM(sobely, FM.VER, nc)
+        if m < 3:
+            ec_y = edge_method(data_y, FM(img, FM.VER, nc), p)
+        else:
+            ec_y = edge_method(data_y)
+        nc += sample_int
+        if ec_y == -1:
+            continue
+        else:
+            vs.append((nc - sample_int, ec_y))
+    len_hs = len(hs)
+    len_vs = len(vs)
+    # --------------- PRE-CHECK DATA VALUES ----------------------
+    hxs = np.zeros(len_hs)
+    hys = np.zeros(len_hs)
+    for i in range(len_hs):
+        hxs[i] = hs[i][1]
+        hys[i] = hs[i][0]
+    vxs = np.zeros(len_vs)
+    vys = np.zeros(len_vs)
+    for i in range(len_vs):
+        vxs[i] = vs[i][0]
+        vys[i] = vs[i][1]
+    x_valid = False
+    y_valid = False
+    stdh = -1
+    stdv = -1
+    #valuesH = [d[1] for d in hs]
+    #valuesV = [d[1] for d in vs]
+    if len_hs >= r_thresh:
+        x_valid = True
+        stdh = std_dev(hxs)
+    if len_vs >= c_thresh:
+        y_valid = True
+        stdv = std_dev(vys)
+    #hough_img = img_name_scheme.format(1)
+
+    # OUTLIER DETECTION TODO: OPTIMIZE, THIS IS NAIVE
+    if x_valid:  # NAIVE
+        hys, hxs = reg_pre_debias(hys, hxs)
+    if y_valid:
+        vxs, vys = reg_pre_debias(vxs, vys)
+    # ---------------------------------------------------------
+    # ----- Following Modules Handles Hough Line Drawing ---------
+    if hough:
+        hough_img = folder_path + img_name_scheme.format(1)
+        print(hough_img)
+        img_h = cv2.imread(hough_img)
+        if len_hs:
+            hp1, hp2, line_a = line_graph_contrast(img_h, hxs, hys)
+            print('Drawn H')
+        if len_vs:
+            vp1, vp2, line_b = line_graph_contrast(img_h, vxs, vys)
+            print('Drawn V')
+        namespace = 'houghs/hough_{0}{1}_{2}'.format(folder_path[3:], m, img_name_scheme.format(1))
+        if len_hs:
+            print(hp1, hp2)
+        if len_vs:
+            print(vp1, vp2)
+        print(namespace)
+        cv2.imwrite(namespace, img_h)
+    else:
+        #print("MEAS", hys)
+        line_a = HoughLine(x=hxs, data=hys)
+        #print("MEAS", vys)
+        line_b = HoughLine(x=vxs, data=vys)
+    # ------------------------------------------------------------
+    # DEBUG MODULE
+    if debug:
+        print((dimr, dimc))
+        print("imgX(BEFORE):", hs)
+        print("imgX:", list(zip(hys, hxs)))
+        print("imgY(BEFORE):", vs)
+        print("imgY:", list(zip(vxs, vys)))
+        compare_images((sobelx, 'Sobel X'), (sobely, 'Sobel Y'), color_map='gray')
+        test(folder_path, img_name_scheme)
+    # --------------------------------------------------------
+    # DATA RECORDING AND PROCESSING
+    if len_hs >= r_thresh:
+        x_valid = True
+        stdh = std_dev(hxs)
+    if len_vs >= c_thresh:
+        y_valid = True
+        stdv = std_dev(vys)
+    if c == 1:
+        center_x = sum(hxs) / len_hs if x_valid else -1
+        center_y = sum(vys) / len_vs if y_valid else -1
+    else:
+        if x_valid and y_valid:
+            center_x, center_y = HoughLine.intersect(line_a, line_b)
+        else:
+            center_x = sum(hxs) / len_hs if x_valid else -1
+            center_y = sum(vys) / len_vs if y_valid else -1
+    # ---------------------------------------------------------
+    return center_x, center_y, stdh, stdv
+
+
+def center_detect(img_name_scheme, num_sample, sample_int=50, debug=False, gk=9, ks=-1, m=1, p=10, b=1):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair
+    hs: HORIZONTAL SLICE!  vs: VERTICAL SLICE!"""
+    imgr = test_noise_reduce(img_name_scheme, num_sample)[0]
+    dimr = imgr.shape[0]
+    dimc = imgr.shape[1]
+    zw = 0
+    ew = 1
+    # Image Processing
+    gksize = (gk, gk)
+    sigmaX = 0
+    img = cv2.GaussianBlur(imgr, gksize, sigmaX)
+    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=ks)
+    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=ks)
+    # Gathering Data
+    nr = sample_int
+    r_thresh = dimr / (sample_int * 3.0)
+    hs = []
+    vs = []
+    while nr < dimr:
+        data_x = FM(sobelx, FM.HOR, nr)
+        zc_x = zero_crossing(data_x)
+        ec_x = edge_centroid(data_x, FM(img, FM.HOR, nr))
+        nr += sample_int
+        if ec_x == -1:
+            continue
+        else:
+            hs.append((nr - sample_int, zc_x * zw + ec_x * ew))
+    nc = sample_int
+    c_thresh = dimc / (sample_int * 3.0)
+    while nc < dimc:
+        data_y = FM(sobely, FM.VER, nc)
+        zc_y = zero_crossing(data_y)
+        ec_y = edge_centroid(data_y, FM(img, FM.VER, nc))
+        nc += sample_int
+        if ec_y == -1:
+            continue
+        else:
+            vs.append((nc - sample_int, zc_y * zw + ec_y * ew))
+    len_hs = len(hs)
+    len_vs = len(vs)
+    if debug:
+        print((dimr, dimc))
+        print("imgX:", hs)
+        print("imgY:", vs)
+        compare_images((sobelx, 'Sobel X'), (sobely, 'Sobel Y'), color_map='gray')
+        while True:
+            ax = input("Input the axis you wanna debug, h for horizontal, v for vertical, or c to cancel:  ")
+            if ax == 'c':
+                break
+            inds = input("Input the index you wanna debug:  ")
+            while True:
+                try:
+                    index = int(inds)
+                    axis = FM.HOR if ax == 'h' else FM.VER
+                    assert 0 <= index < imgr.shape[1 - axis]
+                    break
+                except ValueError:
+                    inds = input("Invalid input, do it again:  ")
+                except AssertionError:
+                    inds = input("Index Out of Bound, do it again: {0}  ".format(imgr.shape))
+            sobelop = sobelx if axis else sobely
+            quick_plot(FM(sobelop, axis, index).extract_array())
+
+    # ----- Following Modules Handles Hough Line Drawing -----
+    hxs = np.zeros(len_hs)
+    hys = np.zeros(len_hs)
+    for i in range(len_hs):
+        hxs[i] = hs[i][1]
+        hys[i] = hs[i][0]
+    vxs = np.zeros(len_vs)
+    vys = np.zeros(len_vs)
+    for i in range(len_vs):
+        vxs[i] = vs[i][0]
+        vys[i] = vs[i][1]
+
+    hough_img = img_name_scheme.format(1)
+    img_h = cv2.imread(hough_img)
+    if len_hs:
+        hp1, hp2, lina = line_graph_contrast(img_h, hxs, hys)
+        print('Drawn H')
+    if len_vs:
+        vp1, vp2, linb = line_graph_contrast(img_h, vxs, vys)
+        print('Drawn V')
+    namespace = 'houghs/hough_{0}'.format(hough_img[3:])
+    if len_hs:
+        print(hp1, hp2)
+    if len_vs:
+        print(vp1, vp2)
+    print(namespace)
+    cv2.imwrite(namespace, img_h)
+    # --------------------------------------------------------
+    x_invalid = False
+    y_invalid = False
+    if len_hs < r_thresh:
+        x_invalid = True
+    if len_vs < c_thresh:
+        y_invalid = True
+
+    center_x = -1 if x_invalid else sum([d[1] for d in hs]) / len_hs
+    center_y = -1 if y_invalid else sum([d[1] for d in vs]) / len_vs
+    return center_x, center_y
+
+
+def random_test():
+    data = [27, 21, 22, 21, 21, 18, 41, 69, 83, 62, 38, 16, 21, 20, 18, 17]
+    print(len(data))
+    ids = 0
+    gs = 0
+    for i, d in enumerate(data):
+        if d == 83:
+            print(i)
+        ids += i * d
+        gs += d
+    print(ids / gs)
+
+
+TEST_PIC_TRUTH = [(385, 130),
+                  (385, 130),
+                  (385, 130),
+                  (-2, -2),
+                  (176, 52),
+                  (174, -1),
+                  (158, -1),
+                  (159, -1),
+                  (242, 212),
+                  (278, 215),
+                  (325, 218),
+                  (143, 215),
+                  (53, 219),
+                  (-1, 17),
+                  (138, 19),
+                  (355, 14),
+                  (288, -1),
+                  (369, 180),
+                  (357, 265),
+                  (225, 224),
+                  (-1, 195),
+                  (-1, 59),
+                  (-1, -1),
+                  (-1, 160),
+                  (445, 166)]
+
+
+def read_truth(filename):
+    with open(filename) as truth:
+        TRUTH = {}
+        active = None
+        for line in truth:
+            meas = line.replace(',', ' ').split()
+            if len(meas) == 0:
+                pass
+            elif len(meas) == 1:
+                active = {}
+                TRUTH[meas[0]] = active
+
+            else:
+                tp = (int(meas[1]), int(meas[2]))
+                active[meas[0]] = tp
+    return TRUTH
+
+
+def error_calc(meas, truth):
+    E = 0
+    T = 0
+    F = 0
+    terms = 0
+    #ls = ['img_5', 'img_6', 'img_7', 'img_12', 'img_13', 'img_14', 'img_15', 'img_16', 'img_17', 'img_18', 'img_20', 'img_21', 'img_22', 'img_25']
+    #ms = []
+    #es = []
+    for m, vals in meas.items():
+        """if m in ls:
+            continue"""
+        mx, my = vals
+        tx, ty = truth[m]
+        if tx == -2 or ty == -2:
+            pass
+        elif (mx == -1 and tx != -1) or (my == -1 and ty != -1):
+            F += 1
+            #ms.append(m)
+        elif (mx != -1 and tx == -1) or (my != -1 and ty == -1):
+            T += 1
+        else:
+            dx = mx - tx
+            dy = my - ty
+            E += (dx) ** 2 + (dy) ** 2
+            #es.append((dx, dy))
+            terms += 2
+    # print(ms)
+    # print(es)
+    error = sqrt(E / terms) if terms else float('inf')
+    return {'E': error, 'T': T, 'F': F}
+
+
+TRUTH_FILE = "meas/truth.txt"
+TRUTH = read_truth(TRUTH_FILE)
+
+
+""" =================================
+============= Parameter =============
+=========== Optimization ============
+===================================== """
+
+
+# TODO: OPTIMIZE ON THE THE GAUSSIAN KERNEL PARAMETER. WHAT HAPPENED THAT MAKES THE SMALL KERNEL BETTER???
+def gaussian_kernel_expr(folder, ns):
+    offset = '../'
+    truth_doc = TRUTH[folder]
+    error = {}
+    gs = range(1, 20, 2)
+    Es = []
+    Ts = []
+    Fs = []
+    for g in gs:
+        meas = {}
+        #print(g)
+        for i in range(1, 26):
+            img_name = ns.format(i)
+            imgfile = offset + folder + "/%s_{0}.png" % img_name
+            # if i in [11, 13, 21, 23, 25]:
+            if i == 0:
+                val = center_detect(imgfile, 3, debug=True, gk=g)
+            else:
+                val = center_detect(imgfile, 3, gk=g)
+            #print(str(i), val)
+            meas[img_name] = val
+            # print(str(i), val)
+
+        ed = error_calc(meas, truth_doc)
+        Es.append(ed['E'])
+        Ts.append(ed['T'])
+        Fs.append(ed['F'])
+        error[g] = ed
+    print(error)
+    compare_data_plots((Es, gs, 'Error'), (Ts, gs, 'False Positive'), (Fs, gs, 'False Negative'), suptitle="Gaussian Blur Test")
+
+
+def sobel_kernel_expr(folder, ns):
+    offset = '../'
+    truth_doc = TRUTH[folder]
+    error = {}
+    #ks = [-1]
+    #ks.extend(list(range(3, 8, 2)))
+    ks = range(-1, 8, 2)
+    Es = []
+    Ts = []
+    Fs = []
+    for k in ks:
+        # print(k)
+        meas = {}
+        for i in range(1, 26):
+            img_name = ns.format(i)
+            imgfile = offset + folder + "/%s_{0}.png" % img_name
+            # if i in [11, 13, 21, 23, 25]:
+            if i == 0:
+                val = center_detect(imgfile, 3, debug=True, ks=k)
+            else:
+                val = center_detect(imgfile, 3, ks=k)
+            #print(str(i), val)
+            meas[img_name] = val
+            # print(str(i), val)
+
+        ed = error_calc(meas, truth_doc)
+        Es.append(ed['E'])
+        Ts.append(ed['T'])
+        Fs.append(ed['F'])
+        error[k] = ed
+    print(error)
+    compare_data_plots((Es, ks, 'Error'), (Ts, ks, 'False Positive'), (Fs, ks, 'False Negative'), suptitle="Sobel Kernel Test")
+
+
+def convergence_test(folder, ns):
+    offset = '../'
+    convergence = {}
+    line_stdh = []
+    line_stdv = []
+    variations = []
+    startNP = 59
+    startP = 80
+    endP = 193
+    ms = range(3)
+    fwrite = open('meas/convergence.csv', 'w')
+    cwriter = csv.writer(fwrite)
+    cwriter.writerow(['Image Number', 'Center X', 'Center Y', 'StdDev Horizontal', 'Std Dev Vertical'])
+    for m in ms:
+        cwriter.writerow([str(m)])
+        # Convergence
+        lrh = 0
+        lrhs = 0
+        lrv = 0
+        lrvs = 0
+        # Consistency Cycled
+        pv = 0
+        pvs = 0
+        rcount = 0
+        cvx = np.zeros(4)
+        cvy = np.zeros(4)
+        #print(g)
+        for i in range(startNP, startP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                x, y, stdh, stdv = center_detect_test(fpath, imgfile, 5, debug=False, m=m)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y), str(stdh), str(stdv)])
+                # CONVERGENCE
+                if x != -1:
+                    lrh += stdh ** 2
+                    lrhs += 1
+                if y != -1:
+                    lrv += stdv ** 2
+                    lrvs += 1
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+        for i in range(startP, endP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                x, y, stdh, stdv = center_detect_test(fpath, imgfile, 5, debug=False, m=m)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y), str(stdh), str(stdv)])
+                # CONVERGENCE
+                if x != -1:
+                    lrh += stdh ** 2
+                    lrhs += 1
+                if y != -1:
+                    lrv += stdv ** 2
+                    lrvs += 1
+                # Record x, y, check rcount, refresh CONSISTENCY
+                cvx[rcount] = x
+                cvy[rcount] = y
+                rcount += 1
+                if rcount == 4:
+                    pv += np.var(cvx) + np.var(cvy)
+                    pvs += 1
+                    rcount = 0
+                    cvx = np.zeros(4)
+                    cvy = np.zeros(4)
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+            # print(str(i), val)
+        mselrh = sqrt(lrh / lrhs)
+        mselrv = sqrt(lrv / lrvs)
+        msepv = sqrt(pv / pvs)
+        cvg = {'LineConvergenceH': mselrh if lrhs else float('inf'),
+               'LineConvergenceV': mselrv if lrvs else float('inf'),
+               'PicConsistency': msepv}
+        convergence[m] = cvg
+        line_stdh.append(mselrh)
+        line_stdv.append(mselrv)
+        variations.append(msepv)
+    print(convergence)
+    compare_data_plots((line_stdh, ms, 'Line Convergence Hor'), (line_stdv, ms, 'Line Convergence Ver'),
+                       (variations, ms, 'Reading Consistency'), suptitle="Convergence/Consistency Test")
+    fwrite.close()
+
+
+def convergence_test_final(folder, ns):
+    offset = '../'
+    convergence = {}
+    variations = []
+    startNP = 59
+    startP = 80
+    endP = 193
+    ms = range(3)
+    fwrite = open('meas/convergence.csv', 'w')
+    cwriter = csv.writer(fwrite)
+    cwriter.writerow(['Image Number', 'Center X', 'Center Y', 'StdDev Horizontal', 'Std Dev Vertical'])
+    for m in ms:
+        cwriter.writerow([str(m)])
+        # Consistency Cycled
+        pv = 0
+        pvs = 0
+        rcount = 0
+        cvx = np.zeros(4)
+        cvy = np.zeros(4)
+        # print(g)
+        for i in range(startNP, startP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                x, y = center_detect(fpath + imgfile, 5, m=m)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y)])
+                # CONVERGENCE
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+        for i in range(startP, endP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                x, y = center_detect(fpath + imgfile, 5, m=m)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y)])
+                # CONVERGENCE
+                # Record x, y, check rcount, refresh CONSISTENCY
+                cvx[rcount] = x
+                cvy[rcount] = y
+                rcount += 1
+                if rcount == 4:
+                    pv += np.var(cvx) + np.var(cvy)
+                    pvs += 1
+                    rcount = 0
+                    cvx = np.zeros(4)
+                    cvy = np.zeros(4)
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+                # print(str(i), val)
+        msepv = sqrt(pv / pvs)
+        cvg = {'PicConsistency': msepv}
+        convergence[m] = cvg
+        variations.append(msepv)
+    print(convergence)
+    fwrite.close()
+""""""
+
+
+def parameter_convert(method, padding, blur):
+    return method + 2 * padding + blur * 100
+
+
+def main():
+    # TODO: HANDLE THE MAXI MINI LEFT AND RIGHT PROBLEM (2 PEAKS)
+    folder = "../calib4/"
+    img_name = "img_59_{0}.jpg"
+    ns = "img_%d_{0}.png"
+    # print("Center Detection yields: ")
+    # gaussian_kernel_expr('testpic', 'img_{0}')
+    #sobel_kernel_expr('testpic', 'img_{0}')
+    convergence_test('calib4/', 'img_{0}')
+    """for i in range(59, 193):
+        #if i in [29]:
+        try:
+            if i in [133, 134, 157, 158, 159, 160]:
+                print(i)
+                val = center_detect_test(folder, ns % i, 5, debug=True, hough=False)
+                print(str(i), val)
+            #val = center_detect_test(folder, ns % i, 5, hough=False)
+            #print(str(i), val)
+        except AttributeError:
+            print('No image {0}!'.format(i))
+        print('------------------------------------------------------')"""
+    # convergence_test_final('calib4/', 'img_{0}')
+
+
+def case_calc():
+    hori_lower = 53.50 - 0.13
+    hori_upper = 53.50 + 0.13
+    verti_lower = 41.41 - 0.11
+    verti_upper = 41.41 + 0.11
+    height = 3
+    width = 4
+    print('hori_lower: {0}'.format(distance(width / 2, hori_lower / 2)))
+    print('hori_upper: {0}'.format(distance(width / 2, hori_upper / 2)))
+    print('verti_lower: {0}'.format(distance(height / 2, verti_lower / 2)))
+    print('verti_upper: {0}'.format(distance(height / 2, verti_upper / 2)))
+
+
+def distance(h, angle):
+    return 2.54 * (h / tan(radians(angle)))
+
+
+    #print(center_detect(folder + img_name, 10, debug=True))
+
+
+
+    #print("Center Detection yields: ")
+    #print(center_detect("../new_test/img_3_{0}.png", 5))
+    #2.5 + 13  = 15.5 / 2 = 7.75
+
+    """plt.figure()
+    plt.plot([1,2 ,3])
+    plt.show()"""
+
+    #print(atan(56 / 200) * 180 / 3.1415926535)
+    #print(atan(134 / 537) * 180 / 3.1415926535)
+
+    #test()
+    #test_canny_detect()
+    #random_test()
+
+
+if __name__ == '__main__':
+    main()
+    #print(DEGREES)
+    #test_interactive()
+    #test_canny_detect()
+
+
+
+# INSIGHT:
+"""Might be able to solve the problem by auto-thresholding and converting to binary.
+Alternative:
+    1. Mark the maximum and minimum that are certain multitudes of standard deviation above
+
+===========================================
+========= THE FOLLOWING FUNCTIONS =========
+============== ARE DEPRECATED =============
+==========================================-
+
+def OMP(imDims, sparsity, measurements, A):
+    display = None
+    numPixels = 0
+    r = measurements.copy()
+    indices = []
+
+    # Threshold to check error. If error is below this value, stop.
+    THRESHOLD = 0.1
+
+    # For iterating to recover all signal
+    i = 0
+
+    while i < sparsity and np.linalg.norm(r) > THRESHOLD:
+        # Calculate the correlations
+        print('%d - ' % i, end="", flush=True)
+        corrs = A.T.dot(r)
+
+        # Choose highest-correlated pixel location and add to collection
+        # COMPLETE THE LINE BELOW
+        best_index = np.argmax(np.abs(corrs))
+        indices.append(best_index)
+
+        # Build the matrix made up of selected indices so far
+        # COMPLETE THE LINE BELOW
+        Atrunc = A[:, indices]
+
+        # Find orthogonal projection of measurements to subspace
+        # spanned by recovered codewords
+        b = measurements
+        # COMPLETE THE LINE BELOW
+        xhat = np.linalg.lstsq(Atrunc, b)[0]
+
+        # Find component orthogonal to subspace to use for next measurement
+        # COMPLETE THE LINE BELOW
+        r = b - Atrunc.dot(xhat)
+
+        # This is for viewing the recovery process
+        if i % 10 == 0 or i == sparsity - 1 or np.linalg.norm(r) <= THRESHOLD:
+            recovered_signal = np.zeros(numPixels)
+            for j, x in zip(indices, xhat):
+                recovered_signal[j] = x
+            Ihat = recovered_signal.reshape(imDims)
+            plt.title('estimated image')
+            plt.imshow(Ihat, cmap=plt.cm.gray, interpolation='nearest')
+            display.clear_output(wait=True)
+            display.display(plt.gcf())
+
+        i = i + 1
+
+    display.clear_output(wait=True)
+
+    # Fill in the recovered signal
+    recovered_signal = np.zeros(numPixels)
+    for i, x in zip(indices, xhat):
+        recovered_signal[i] = x
+
+    return recovered_signal
+
+if ori == 'h':
+    x = np.array(range(img.shape[1]))
+    y = np.array([img_rec.rel_lumin(img, p, c) for c in x])
+else:
+    x = np.array(range(img.shape[0]))
+    y = np.array([img_rec.rel_lumin(img, r, p) for r in x])
+"""
+
+CALK = 166.1861
diff --git a/Nov_27/Full/Camera/img_rec_module/~$log.docx b/Nov_27/Full/Camera/img_rec_module/~$log.docx
new file mode 100644
index 0000000..0430bb4
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diff --git a/Nov_27/Full/Camera/transfer_test_correct_config/._RPI_C.py b/Nov_27/Full/Camera/transfer_test_correct_config/._RPI_C.py
new file mode 100644
index 0000000..304e0fd
Binary files /dev/null and b/Nov_27/Full/Camera/transfer_test_correct_config/._RPI_C.py differ
diff --git a/Nov_27/Full/Camera/transfer_test_correct_config/._Read_Accel.py b/Nov_27/Full/Camera/transfer_test_correct_config/._Read_Accel.py
new file mode 100644
index 0000000..af4016e
Binary files /dev/null and b/Nov_27/Full/Camera/transfer_test_correct_config/._Read_Accel.py differ
diff --git a/Nov_27/Full/Camera/transfer_test_correct_config/RPI_C.py b/Nov_27/Full/Camera/transfer_test_correct_config/RPI_C.py
new file mode 100644
index 0000000..78342b9
--- /dev/null
+++ b/Nov_27/Full/Camera/transfer_test_correct_config/RPI_C.py
@@ -0,0 +1,212 @@
+#Code for communications, sensor actions, and calculations for RPi on camera module
+#Assume that RPI_C and RPI_L codes are implemented at same time (synced)
+#Below code will execute at startup of RPI_C
+
+from SETTINGS import *
+import subprocess
+import RPi.GPIO as GPIO
+import sys
+import socket
+import time
+from Read_Accel import read_accel
+sys.path.insert(0,IMG_REC)
+from sig_proc import center_detect
+sys.path.insert(0,IMG_CAM)
+from img_capture import ImgCollector
+sys.path.insert(0,COMM)
+from communication import send_data_to_server
+from server_socket_connect_new import server_socket
+from pinMasterSleep import pinMasterSleep
+import traceback
+
+#Givens
+
+#See SETTINGS.py
+
+time.sleep(10) #to avoid pins being set during flickering
+
+def main():
+
+    running = True
+
+    print("run_log_path: " + RUN_LOG_PATH)
+    if os.path.exists(RUN_LOG_PATH):
+        rfile = open(RUN_LOG_PATH, "r")
+        try:
+            run_counter = int(rfile.read())
+        except ValueError:
+            run_counter = -1
+    else:
+        rfile = open(RUN_LOG_PATH, "w")
+        run_counter = 0
+        rfile.write(str(run_counter))
+    rfile.close()
+
+    print("run _counter: " + str(run_counter))
+    if run_counter > 0 and run_counter % re_sync != 0:
+        #Power Pins (The same actions would be performed in pinMasterSleep if run_counter = 0)
+        GPIO.setmode(GPIO.BCM)
+        GPIO.setup(pMap[12], GPIO.OUT)
+        GPIO.output(pMap[12], GPIO.HIGH)
+        print("RPICam: 12 High")
+
+        #Other initializations
+        pos = []
+        pitch_1 = []
+        pitch_2 = []
+        ds = []
+        state = "BASELINE"
+    elif run_counter == -1:
+        print("RPICam: Issue with Run_File Read, Proceeding to Shutdown")
+        state = "SHUTDOWN"
+    else:
+        state = "SYNC"
+
+    while running == True:
+
+        try:
+            if state == "BASELINE":
+                #Listen for Connection from RPI_L
+                print("RPICam: Waiting for RPI_L to Wake")
+                try:
+                    rsv_msg_L = server_socket("RPI_C_AWAKE?")
+                    if rsv_msg_L == '':
+                        print("RPICam: Connection with RPI_L Not Successful")
+                        print("RPICam: Proceeding to Safe Shutdown")
+                        state = "SHUTDOWN"
+                    joined_msg = ''.join(rsv_msg_L)
+                    print("RPICam: Received: " + joined_msg)
+                    if joined_msg == "LaserOn":
+                        print("RPICam: Laser is On, Continuing to Accel Acquisition")
+                        state = "ACCEL"
+                    else:
+                        print("RPICam: ERROR: Laser Apparently Not On")
+                        print("RPICam: Will Proceed to Safe Shutdown")
+                        state = "SHUTDOWN"
+                except:
+                    print("RPICam: BASELINE ERROR")
+                    state = "SHUTDOWN"
+
+            elif state == "SYNC":
+                #Run Master or Slave code
+                print("RPICam: Performing Time Sync with RPI_L")
+                try:
+                    t_sleep_sec = pinMasterSleep(pMap)
+                except:
+                    print("RPICam: SYNC ERROR")
+                    t_sleep_sec = -1
+
+                state = "SYNC_SHUTDOWN"
+
+            elif state == "ACCEL":
+                try:
+                    print("RPICam: Taking Acceleration 1 Readings at Camera Module")
+                    pitch_1 = read_accel()
+                    print("pitch_1: " + str(pitch_1*180/pi))
+                    print("RPICam: Continue to Image Acquisition")
+                    state = "IMAGE"
+                except:
+                    print("RPICam: ACCEL ERROR")
+                    state = "SHUTDOWN"
+
+            elif state == "IMAGE":
+                try:
+                    img_collector = ImgCollector(IMG_DIR, NAME_SCHEME, FILE_FORMAT, RAW_IMAGE, NUM_SAMPLES)
+                    print("RPICam: Taking Camera Picture of Laser Beam")
+                    img_collector.capture()
+                    time.sleep(2)
+                    img_collector.shutdown()
+
+                    rsv_msg_L = server_socket("TURN_OFF_LASER?")
+                    joined_msg = ''.join(rsv_msg_L)
+                    if joined_msg == "RPI_L_WAITING":
+                        print("RPICam: Performing Image Processing of Image")
+                        pos = center_detect(img_collector.get_last_meas(), NUM_SAMPLES)
+                        print("pos = [" + str(pos[0]) + "," + str(pos[1]) + "]")
+                        print("RPICam: Proceeding to Settlement Estimation")
+                        state = "ESTIMATE"
+                    else:
+                        print("RPICam: RPI_L Communication Error")
+                        state = "SHUTDOWN"
+                except:
+                    traceback.print_exc(file = sys.stdout)
+                    print("RPICam: IMAGE ERROR")
+                    state = "SHUTDOWN"
+
+            elif state == "ESTIMATE":
+                print("RPICam: Retrieving Acceleration Value from Laser Module")
+                try:
+                    accel_2_rsv = server_socket("GOT_ACCEL_2?")
+                    joined_msg = ''.join(accel_2_rsv)
+                    print("RPICam: Received: " + joined_msg)
+                    if joined_msg == "ERROR_ACCEL":
+                        print("RPICam: ERROR: Accel_2 Not Valid")
+                        state = "SHUTDOWN"
+                    else:
+                        pitch_2 = float(''.join(accel_2_rsv))
+                        print("pitch_2: " + str(pitch_2 * 180 / pi))
+
+                        print("RPI_Cam: Performing Settlement Estimation")
+                        ds = pos[1]
+                        print("Settlement = " + str(ds) + " " + units)
+                        print("RPI_Cam: Proceeding to Server Connection")
+                        state = "SERVER"
+                except:
+                    traceback.print_exc(file = sys.stdout)
+                    print("RPICam: ESTIMATE ERROR")
+                    state = "SHUTDOWN"
+
+
+            elif state == "SERVER":
+                print("Sending Data to Server")
+                try:
+                    print(send_data_to_server(x = pos[0], y = ds, phi = pitch_1))
+                    print("RPI_Cam: Value pushed, proceeding to safe shutdown")
+                except RuntimeWarning as e:
+                    print(e)
+                    print("RPI_Cam: RunTime Warning: Value Not Pushed to Server")
+                except:
+                    print("RPI_Cam: Value Not Pushed to Server")
+                time.sleep(1)
+                state = "SHUTDOWN"
+
+            elif state == "SHUTDOWN":
+                run_counter = run_counter + 1
+                wfile = open(RUN_LOG_PATH, "w")
+                wfile.write(str(run_counter))
+                wfile.close()
+
+                img_collector.shutdown()
+                print("RPI_Cam: Defer to Arduino for Power Control")
+                time.sleep(10)
+                subprocess.call(["shutdown", "-h", "now"])
+
+            elif state == "SYNC_SHUTDOWN":
+                if t_sleep_sec == -1:
+                    print("RPICam: Will Reattempt Sync")
+                else:
+                    run_counter = run_counter + 1
+                    wfile = open(RUN_LOG_PATH, "w")
+                    wfile.write(str(run_counter))
+                    wfile.close()
+                    time.sleep(t_sleep_sec)
+                GPIO.output(pMap[16], GPIO.HIGH)
+                print("RPI_Cam: 16 high")
+                time.sleep(1)
+                subprocess.call(['shutdown', '-h', 'now'])
+
+        except KeyboardInterrupt:
+            img_collector.shutdown()
+            print('User KeyBoard Interrupt, Proceeding to Shutdown')
+            time.sleep(10)
+            subprocess.call(["shutdown", "-h", "now"])
+
+if __name__ == "__main__":
+    main()
+
+
+
+
+
+
+
diff --git a/Nov_27/Full/Camera/transfer_test_correct_config/Read_Accel.py b/Nov_27/Full/Camera/transfer_test_correct_config/Read_Accel.py
new file mode 100644
index 0000000..ae2388e
--- /dev/null
+++ b/Nov_27/Full/Camera/transfer_test_correct_config/Read_Accel.py
@@ -0,0 +1,19 @@
+from SETTINGS import *
+import sys
+from  math import atan,sin,cos,sqrt
+sys.path.insert(0,ACCELEROMETER)
+from accel import extract_meas
+
+grav = 9.81
+
+def read_accel():
+    x_vec,y_vec,z_vec = extract_meas()
+    x = float(sum(x_vec))/float(len(x_vec))
+    y = float(sum(y_vec))/float(len(y_vec))
+    z = float(sum(z_vec))/float(len(z_vec))
+    pitch = atan(x/(sqrt(z**2 + y**2))) #angle forward from vert [x,y flat, z up]
+    #roll = atan(y/(sqrt(x**2 + z**2))) #angle side from vert [x,y flat, z up]
+    return pitch
+
+
+
diff --git a/Nov_27/Full/Camera/transfer_test_correct_config/SETTINGS.py b/Nov_27/Full/Camera/transfer_test_correct_config/SETTINGS.py
new file mode 100644
index 0000000..6cb09e7
--- /dev/null
+++ b/Nov_27/Full/Camera/transfer_test_correct_config/SETTINGS.py
@@ -0,0 +1,34 @@
+import os
+
+BASE_DIR = "/home/pi/Desktop/Full/"
+
+IMG_DIR = os.path.join(BASE_DIR, "images")
+ACCELEROMETER = os.path.join(BASE_DIR, "accel_correct_config")
+IMG_REC = os.path.join(BASE_DIR, "img_rec_module")
+IMG_CAM = os.path.join(BASE_DIR, "RPiCam")
+COMM = os.path.join(BASE_DIR,"communication_module")
+RUN_LOG_PATH = os.path.join(BASE_DIR, "transfer_test_correct_config/run_log.txt")
+NAME_SCHEME = "img"
+FILE_FORMAT = "png"
+RAW_IMAGE = False
+NUM_SAMPLES = 5
+NAME_FORMAT = IMG_DIR + NAME_SCHEME + "_{0}" + FILE_FORMAT
+
+#------- IMPORT THESE THREE ----
+BRIDGE_NAME = 'dummy'
+SERVER = "http://192.168.15.101:8000/sensors/"+BRIDGE_NAME+"/update/"
+PASSWORD = "djioewfj34jod2jdoi3jr0jl983jsa"
+#-------- ABOVE ------
+#For RPI_C.py
+
+RPI_L_IP = "192.168.15.103" #when hooked to Netgear Switch (Phil)
+RPI_C_IP = "0.0.0.0"
+RPI_C_Port = 1234
+marker = "?"
+units = "mm"
+span = 10*1000
+pi = 3.1415926535897932
+query_time = 5
+re_sync = 3
+
+pMap = {3:2,5:3,7:4,8:14,10:15,11:17,12:18,13:27,15:22,16:23,18:24,19:10,21:9,22:25,23:11,24:8,26:7}
diff --git a/Nov_27/Full/Camera/transfer_test_correct_config/pinMasterSleep.py b/Nov_27/Full/Camera/transfer_test_correct_config/pinMasterSleep.py
new file mode 100644
index 0000000..a48ff3f
--- /dev/null
+++ b/Nov_27/Full/Camera/transfer_test_correct_config/pinMasterSleep.py
@@ -0,0 +1,60 @@
+import socket, time
+import RPi.GPIO as GPIO
+from SETTINGS import *
+
+def pinMasterSleep(pMap):
+    max_count = 30
+    # Configure board pin numbering system
+    GPIO.setmode(GPIO.BCM)
+    # Setup pin 12 to high so upon shutdown the pin drops and notifies the Arduino to cut power
+    GPIO.setup(pMap[12],GPIO.OUT)
+    GPIO.output(pMap[12],GPIO.HIGH)
+    # Set up pin 16 as clock output
+    GPIO.setup(pMap[16],GPIO.OUT)
+    GPIO.setup(pMap[16],GPIO.LOW)
+    print("RPI_Cam: 16 Low, 12 High")
+
+    # Set up the server socket
+    server = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
+    server.settimeout(query_time*max_count)
+    server.setsockopt(socket.SOL_SOCKET,socket.SO_REUSEADDR,1)
+    server.bind((RPI_C_IP, RPI_C_Port))
+
+    # Listen to the socket for client connection
+    #server.bind(('localhost',1234))
+    #server.bind(('0.0.0.0',1234))
+    server.listen(1)
+    toClient, client_address = server.accept()
+
+
+    # Send time t1 to slave
+    toClient.send(str.encode(str(int(time.time()*10**6))))
+
+    # Get msg from Slave, delivered at t4
+    BURN = toClient.recv(16)
+    t4 = time.time()
+    t4 = str.encode(str(int(t4*10**6)))
+
+    # Send t4 to slave
+    toClient.send(t4)
+
+    print('Done with the code')
+
+    # Now determine the clock coordination code.
+
+    t_now = int(time.time()*10**6)
+
+    #t_awake = t_now + 0.25*60*10**6
+    t_awake = t_now + 15*10**6
+
+    toClient.send(str.encode(str(t_awake)))
+
+    t_sleep = t_awake - int(time.time()*10**6)
+
+    t_sleep_sec = t_sleep / 10.0**6
+
+    print('sleep time: ' + str(t_sleep_sec))
+
+    server.close()
+
+    return t_sleep_sec
diff --git a/Nov_27/Full/Camera/transfer_test_correct_config/server_socket_connect_new.py b/Nov_27/Full/Camera/transfer_test_correct_config/server_socket_connect_new.py
new file mode 100644
index 0000000..13c264a
--- /dev/null
+++ b/Nov_27/Full/Camera/transfer_test_correct_config/server_socket_connect_new.py
@@ -0,0 +1,76 @@
+from SETTINGS import *
+import socket, time, sys, select, traceback
+
+#connects (one client only), and reads any data (one message anticipated), then sends out a response (one)
+max_count = 30
+
+def server_socket(send = "" ):
+
+    recv = []
+    print("RPICam: Establishing Server Socket...")
+    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+    sock.bind((RPI_C_IP, RPI_C_Port))
+    sock.listen(1)
+    sock.setblocking(0)
+    print("RPICam: Waiting for RPI_L to Connect")
+    inputs = [sock]
+    connect = False
+    try:
+        while inputs:
+                readable, writable, errored = select.select(inputs, [], [], query_time*max_count)
+                #print readable
+                #print writable
+                #print errored
+                if not readable:
+                    break #case of no connection
+                for s in readable:
+                    if s is sock and connect == False: #the socket itself is ready to accept a connection
+                        print("RPICam: Connected")
+                        connection, server_address = s.accept()
+                        connection.setblocking(0)
+                        inputs.append(connection)
+                        connect = True
+                    else: #the socket has something that needs to be read
+                        print("RPICam: Reading Data")
+                        msg = s.recv(1)
+                        rsv_msg_L = []
+                        if msg: #rea d in the message if the queue not empty
+                            while True:
+                                if marker in msg:
+                                    rsv_msg_L.append(msg[:msg.find(marker)])
+                                    break
+                                rsv_msg_L.append(msg)
+                                msg = s.recv(1)
+                            print("RPICam: Data Read Successfully")
+                            print("received: " + str(rsv_msg_L))
+                            recv = rsv_msg_L
+                            s.send(send) #immediately send back message response
+                            print("RPICam: Data Sent")
+                        else: #no data, client disconnected, close socket
+                            inputs.remove(s) #remove socket from inputs
+                            s.close() #close socket
+                            print("RPICam: Accept Socket Closed")
+                            print(inputs)
+                            if connect == True:
+                                inputs.remove(sock)
+                                sock.close()
+                                print("RPICam: Listen Socket Closed")
+
+    except KeyboardInterrupt:
+        if sock:
+            sock.close
+    except:
+        traceback.print_exc(file=sys.stdout)
+
+    return recv
+
+
+
+
+
+
+
+
+
+
diff --git a/Nov_27/Full/Camera/transfer_test_correct_config/server_socket_connect_new_backup.py b/Nov_27/Full/Camera/transfer_test_correct_config/server_socket_connect_new_backup.py
new file mode 100644
index 0000000..828551b
--- /dev/null
+++ b/Nov_27/Full/Camera/transfer_test_correct_config/server_socket_connect_new_backup.py
@@ -0,0 +1,92 @@
+from SETTINGS import *
+import socket, time, sys, select, traceback, Queue
+
+#connects (one client only), and reads any data (one message anticipated), then sends out a response (one)
+max_count = 30
+
+def server_socket(send = "" ):
+
+    recv = []
+    print("RPICam: Establishing Server Socket...")
+    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+    sock.bind((RPI_C_IP, RPI_C_Port))
+    sock.listen(1)
+    sock.setblocking(0)
+    print("RPICam: Waiting for RPI_L to Connect")
+    message_queues = {}
+    inputs = [sock]
+    outputs = []
+
+    try:
+        while inputs:
+                readable, writable, errored = select.select(inputs, [], [], query_time*max_count)
+                print readable
+                print writable
+                print errored
+                for s in readable:
+                    if s is sock: #the socket itself is ready to accept a connection
+                        print("RPICam: Connected")
+                        connection, server_address = s.accept()
+                        connection.setblocking(0)
+                        inputs.append(connection)
+                        message_queues[connection] = Queue.Queue()
+                    else: #the socket has something that needs to be read
+                        print("RPICam: Reading Data")
+                        msg = s.recv(1)
+                        rsv_msg_L = []
+                        if msg: #read in the message if the queue not empty
+                            while True:
+                                if marker in msg:
+                                    rsv_msg_L.append(msg[:msg.find(marker)])
+                                    break
+                                rsv_msg_L.append(msg)
+                                msg = s.recv(1)
+                            message_queues[s].put(send)
+                            print("RPICam: Data Read Successfully")
+                            print("received: " + str(rsv_msg_L))
+                            recv = rsv_msg_L
+                            if s not in outputs:
+                                outputs.append(s)
+                                print("RPICam: Data to be Sent")
+                            s.send(send)
+                            #print(inputs)
+                            #print(outputs)
+                        else: #no data, client disconnected, close socket
+                            if s in outputs:
+                                outputs.remove(s)
+                            inputs.remove(s)
+                            s.close()
+                            print("RPICam: Socket Closed")
+                            del message_queues[s]
+                #for s in writable:
+                #    try: #retrieve output queue message and send it
+                #        next_msg = message_queues[s].get_nowait()
+                #        s.send(next_msg)
+                #        print("RPICam: Data Sent Successfully")
+                #    except Queue.Empty:
+                #        outputs.remove(s)
+                #        print("RPICam: All Data Sent")
+                #for s in errored:
+                #    inputs.remove(s)
+                #    if s in outputs:
+                #        outputs.remove(s)
+                #    s.close()
+                #    del message_queues[s]
+    except KeyboardInterrupt:
+        if sock:
+            sock.close
+    except:
+        traceback.print_exc(file=sys.stdout)
+
+    return recv
+
+
+
+
+
+
+
+
+
+
diff --git a/Nov_27/Full/Laser/accel_correct_config/._accel.py b/Nov_27/Full/Laser/accel_correct_config/._accel.py
new file mode 100644
index 0000000..e00addf
Binary files /dev/null and b/Nov_27/Full/Laser/accel_correct_config/._accel.py differ
diff --git a/Nov_27/Full/Laser/accel_correct_config/LICENSE b/Nov_27/Full/Laser/accel_correct_config/LICENSE
new file mode 100644
index 0000000..9cecc1d
--- /dev/null
+++ b/Nov_27/Full/Laser/accel_correct_config/LICENSE
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
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+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
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+price.  Our General Public Licenses are designed to make sure that you
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+you modify it: responsibilities to respect the freedom of others.
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+
+  Developers that use the GNU GPL protect your rights with two steps:
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+authors' sake, the GPL requires that modified versions be marked as
+changed, so that their problems will not be attributed erroneously to
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diff --git a/Nov_27/Full/Laser/accel_correct_config/accel.py b/Nov_27/Full/Laser/accel_correct_config/accel.py
new file mode 100644
index 0000000..1c04871
--- /dev/null
+++ b/Nov_27/Full/Laser/accel_correct_config/accel.py
@@ -0,0 +1,739 @@
+#!/usr/bin/env python
+# -*- coding:utf-8, indent=tab, tabstop=4 -*-
+#
+# See 'LICENSE'  for copying
+#
+# Revision history
+# Date			Author					Version		Details
+# ----------------------------------------------------------------------------------
+# 2016-12-31	Massimo Di Primio		V.0.04		Fixed some basic functionality
+#
+# 2017-01-03	Massimo Di Primio		0.05		Added Interrut handler
+#
+# 2018-01-10    Massimo Di Primio       0.06        Added config file parser
+
+"""Simple code example for Adafruit MMA8452 3-axis Accelerometer
+
+This experimental code is intended for measuring gravity acceleration trough Adafruit(c) MMA8451, connected
+to a Raspberry Pi Model 2A, 2B, 2B+ or 3 (not yet tested with RPi Zero).
+Through this code we will demonstrate the ability of the 3-axis sensor MMA8451 to efficiently measure
+gravity acceleration, so that we can identify the spatial orientation of the device.
+Further and even more useful application can start from this minimal basic code.
+"""
+
+import smbus
+import time
+import datetime
+import os
+import sys
+import logging
+import threading
+import RPi.GPIO as GPIO
+import rss_cli_config as clicfg
+from collections import deque
+
+__author__ = "Massimo Di Primio"
+__copyright__ = "Copyright 2016, dpmiictc"
+__credits__ = ["Massimo Di Primio", "Dario Dalla Libera"]
+__license__ = "GNU GENERAL PUBLIC LICENSE Version 3"
+__version__ = "0.0.1"
+__deprecated__ = "None so far"
+__date__ = "2017-01-03"
+__maintainer__ = "Massimo Di Primio"
+__email__ = "massimo@diprimio.com"
+__status__ = "Testing"
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# User Configuration Options (UCO)
+# This section will be repaced soon by an external configuration file
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+CFG_INTERRUPT = 1
+# MMA8451_RANGE		= {}
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Application Definition Constants (ADC)
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+# Useful definitions
+EARTH_GRAVITY_MS2 = 9.80665
+
+# Range values
+RANGE_8_G = 0b10  # +/- 8g
+RANGE_4_G = 0b01  # +/- 4g
+RANGE_2_G = 0b00  # +/- 2g (default value)
+
+RANGE_DIVIDER = {
+    RANGE_2_G: 4096 / EARTH_GRAVITY_MS2,
+    RANGE_4_G: 2048 / EARTH_GRAVITY_MS2,
+    RANGE_8_G: 1024 / EARTH_GRAVITY_MS2,
+}
+
+# Some static values
+deviceName = 0x1a
+
+# Various addresses
+i2caddr = 0x1D
+#
+# Useful Register Address
+REG_STATUS = 0x00  # Read-Only
+REG_WHOAMI = 0x0d  # Read-Only
+REG_DEVID = 0x1A  # Read-Only
+REG_OUT_X_MSB = 0x01  # Read-Only
+REG_OUT_X_LSB = 0x02  # Read-Only
+REG_OUT_Y_MSB = 0x03  # Read-Only
+REG_OUT_Y_LSB = 0x04  # Read-Only
+REG_OUT_Z_MSB = 0x05  # Read-Only
+REG_OUT_Z_LSB = 0x06  # Read-Only
+REG_F_SETUP = 0x09  # Read/Write
+REG_XYZ_DATA_CFG = 0x0e  # Read/Write
+REG_PL_STATUS = 0x10  # Read-Only
+REG_PL_CFG = 0x11  # Read/Write
+REG_CTRL_REG1 = 0x2A  # Read/Write
+REG_CTRL_REG2 = 0x2B  # Read/Write
+REG_CTRL_REG3 = 0x2C  # Read/Write
+REG_CTRL_REG4 = 0x2D  # Read/Write
+REG_CTRL_REG5 = 0x2E  # Read/Write
+
+REDUCED_NOISE_MODE = 0
+OVERSAMPLING_MODE = 1
+HIGH_RES_MODE = {
+    REDUCED_NOISE_MODE: [REG_CTRL_REG1, 0x4],
+    OVERSAMPLING_MODE: [REG_CTRL_REG2, 0x2],
+}
+
+# Auto-Wake Sample Frequencies for Register CTRL_REG1 (0x2A) (Read/Write)
+# sample frequency when the device is in SLEEP Mode. Default value: 00.
+ASLP_RATE_FREQ_50_HZ = 0x00
+ASLP_RATE_FREQ_12_5_HZ = 0x40
+ASLP_RATE_FREQ_6_25HZ = 0x80
+ASLP_RATE_FREQ_1_56_HZ = 0xc0
+
+# Data rate values
+DATARATE_800_HZ = 0x00  # 800Hz
+DATARATE_400_HZ = 0x08  # 400Hz
+DATARATE_200_HZ = 0x10  # 200Hz
+DATARATE_100_HZ = 0x18  # 100Hz
+DATARATE_50_HZ = 0x20  # 50Hz
+DATARATE_12_5_HZ = 0x28  # 12.5Hz
+DATARATE_6_25HZ = 0x30  # 6.25Hz
+DATARATE_1_56_HZ = 0x38  # 1.56Hz
+
+# Orientation labeling 
+PL_PUF = 0
+PL_PUB = 1
+PL_PDF = 2
+PL_PDB = 3
+PL_LRF = 4
+PL_LRB = 5
+PL_LLF = 6
+PL_LLB = 7
+
+# Precision
+PRECISION_14_BIT = 14
+PRECISION_08_BIT = 8
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define Register Flags
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Register CTRL_REG1 (0x2a) R/W - System Control 1 Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |  ASLPRATE1   |  ASLPRATE0   |     DR2      |    DR1       |    DR0       |  LNOISE      |  F_READ      |  ACTIVE      |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# Auto-Wake Sample frequency Selection
+FLAG_ASLPRATE_50_HZ = 0x00  # Auto-Wake Sample frequency (Sleep Mode Rate Detection) 50 Hz
+FLAG_ASLPRATE_12_5_HZ = 0x40  # Auto-Wake Sample frequency (Sleep Mode Rate Detection) 12.5 Hz
+FLAG_ASLPRATE_6_25_HZ = 0x80  # Auto-Wake Sample frequency (Sleep Mode Rate Detection) 6.25 Hz
+FLAG_ASLPRATE_1_56_HZ = 0xc0  # Auto-Wake Sample frequency (Sleep Mode Rate Detection) 1.56 Hz
+# System Output Data Rates Selection
+FLAG_ODR_800_HZ = 0x00  # System Output Data Rate 800 Hz
+FLAG_ODR_400_HZ = 0x08  # System Output Data Rate 400 Hz
+FLAG_ODR_200_HZ = 0x10  # System Output Data Rate 200 Hz
+FLAG_ODR_100_HZ = 0x18  # System Output Data Rate 100 Hz
+FLAG_ODR_50_HZ = 0x20  # System Output Data Rate 50 Hz
+FLAG_ODR_12_5_HZ = 0x28  # System Output Data Rate 12.5 Hz
+FLAG_ODR_6_25_HZ = 0x30  # System Output Data Rate 6.25 Hz
+FLAG_ODR_1_56_HZ = 0x38  # System Output Data Rate 1.56 Hz
+# Other Flags
+FLAG_LNOISE = 0x04  # Low Noise (1: Reduced Noise, 0: Normal Mode)
+FLAG_F_READ = 0x02  # Fast Read  (1: 8 bit sample, 0: 14 bit Sample)
+FLAG_ACTIVE = 0x01  # Active (1: ACTIVE Mode, 0: STANDBY Mode)
+
+# Register CTRL_REG2 (0x2b) R/W - System Control 2 Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   ST         |   RST        |     0        |  SMODS1      |  SMODS0      |   SLPE       |   MODS1      |   MODS0      |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# Other Flags
+FLAG_STEST = 0x80  # Self Test (1: Self-Test enabled, 0: Self-Test disabled)
+FLAG_RESET = 0x40  # Reset (1: Reset enabled, 0: Reset disabled)
+# Sleep Mode Power Scheme Selection
+FLAG_SMODS_NORM = 0x00  # Sleep Mode Power Scheme Selection: Normal
+FLAG_SMODS_LNLP = 0x0a  # Sleep Mode Power Scheme Selection: Low-Noise Low Power
+FLAG_SMODS_HR = 0x12  # Sleep Mode Power Scheme Selection: High Resolution
+FLAG_SMODS_LP = 0x1b  # Sleep Mode Power Scheme Selection: Low Power
+# Other Flags
+FLAG_SLPE = 0x04  # Auto-Sleep (1: Auto-Sleep enabled, 0: Auto-Sleep Disabled)
+# Active Mode Power Scheme Selection (for both: Sleep and Active mode)
+FLAG_MODS_NORM = 0x00  # Active Mode Power Scheme Selection: Normal
+FLAG_MODS_LNLP = 0x09  # Active Mode Power Scheme Selection: Low-Noise Low Power
+FLAG_MODS_HR = 0x12  # Active Mode Power Scheme Selection: High Resolution
+FLAG_MODS_LP = 0x1b  # Active Mode Power Scheme Selection: Low Power
+
+# Register CTRL_REG4 (0x2d) R/W - Interrupt Enable Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# | INT_EN_ASLP  | INT_EN_FIFO  |INT_EN_TRANS  |INT_EN_LNDPR  |INT_EN_PULSE  |INT_EN_FF_MT  |       -      | INT_EN_DRDY  |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_INT_EN_ASLP = 0x80  # Interrupt Auto SLEEP/WAKE (0: Disabled, 1: Enabled)
+FLAG_INT_EN_FIFO = 0x40  # Interrupt FIFO (0: Disabled, 1: Enabled)
+FLAG_INT_EN_TRANS = 0x20  # Interrupt Transient (0: Disabled, 1: Enabled)
+FLAG_INT_EN_LNDPRT = 0x10  # Interrupt Orientation (0: Disabled, 1: Enabled)
+FLAG_INT_EN_PULSE = 0x08  # Interrupt Pulse Detection (0: Disabled, 1: Enabled)
+FLAG_INT_EN_FF_MT = 0x04  # Interrupt Freefall/Motion (0: Disabled, 1: Enabled)
+FLAG_INT_EN_BIT1 = 0x00  # Not Used
+FLAG_INT_EN_DRDY = 0x01  # Interrupt Data Ready (0: Disabled, 1: Enabled)
+
+# Register CTRL_REG5 (0x2e) R/W - Interrupt Configuration Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# | INT_CFG_ASLP | INT_CFG_FIFO |INT_CFG_TRANS |INT_CFG_LNDPRT|INT_CFG_PULSE |INT_CFG_FF_MT |       -      | INT_CFG_DRDY |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_INT_CFG_ASLP = 0x80  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_FIFO = 0x40  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_TRANS = 0x20  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_LNDPRT = 0x10  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_PULSE = 0x08  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_FF_MT = 0x04  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_BIT1 = 0x00  # Not Used
+FLAG_INT_CFG_DRDY = 0x01  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+
+# Register XYZ_DATA_CFG (0x0e) R/W
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |     0        |     0        |     0        |  HPF_OUT     |     0        |     0        |    FS1       |    FS0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# Other Flags
+FLAG_XYZ_DATA_BIT_7 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_6 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_5 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_HPF_OUT = 0x00  # High-Pass Filter (1: output data High-pass filtered, 0: output data High-pass NOT filtered)
+FLAG_XYZ_DATA_BIT_3 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_2 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_FS_2G = 0x00  # Full Scale Range 2g
+FLAG_XYZ_DATA_BIT_FS_4G = 0x01  # Full Scale Range 4g
+FLAG_XYZ_DATA_BIT_FS_8G = 0x02  # Full Scale Range 8g
+FLAG_XYZ_DATA_BIT_FS_RSVD = 0x03  # Reserved
+
+# Register F_SETUP (0x09) R/W - FIFO Setup Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |  F_MODE1     |  F_MODE0     |  F_WMRK5     |  F_WMRK4     |  F_WMRK3     |  F_WMRK2     |  F_WMRK1     |  F_WMRK0     |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_F_MODE_FIFO_NO = 0x00  # FIFO is disabled.
+FLAG_F_MODE_FIFO_RECNT = 0x40  # FIFO contains the most recent samples when overflowed (circular buffer)
+FLAG_F_MODE_FIFO_STOP = 0x80  # FIFO stops accepting new samples when overflowed.
+FLAG_F_MODE_FIFO_TRIGGER = 0xc0  # FIFO Trigger mode
+
+# Register PL_STATUS (0x010) R/O - Portrait/Landscape Status Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |    NEWLP     |    LO        |      -       |      -       |     -        |  LAPO[1]     |  LAPO[0]     |  BAFRO       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_PL_NEWLP = 0x80  # Landscape/Portrait status change flag.
+FLAG_PL_LO = 0x40  # Z-Tilt Angle Lockout.
+FLAG_PL_LAPO_PU = 0x00  # 00: Portrait Up: Equipment standing vertically in the normal orientation
+FLAG_PL_LAPO_PD = 0x02  # 01: Portrait Down: Equipment standing vertically in the inverted orientation
+FLAG_PL_LAPO_LR = 0x04  # 10: Landscape Right: Equipment is in landscape mode to the right
+FLAG_PL_LAPO_LL = 0x06  # 11: Landscape Left: Equipment is in landscape mode to the left.
+FLAG_PL_BAFRO = 0x01  # Back or Front orientation. (0: Front: Equipment is in the front facing orientation, 1: Back)
+
+# Register PL_CFG (0x011) R/W - Portrait/Landscape Configuration Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   DBCNTM     |  PL_EN       |      -       |      -       |     -        |      -       |      -       |     -        |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_PL_CFG_DBCNTM = 0x80  # Debounce counter mode selection (0: Decrements debounce, 1: Clears counter)
+FLAG_PL_CFG_PL_EN = 0x40  # Portrait/Landscape Detection Enable (0: P/L Detection Disabled, 1: P/L Detection Enabled)
+
+# Register TRANSIENT_CFG (0x1d) R/W - Transient_CFG Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |      -       |      -       |      -       |     ELE      |   ZTEFE      |   YTEFE      |   XTEFE      |  HPF_BYP     |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_TRANSIENT_CFG_ELE = 0x10      # Transient event flags (0: Event flag latch disabled; 1: Event flag latch enabled)
+FLAG_TRANSIENT_CFG_ZTEFE = 0x08    # Event flag enable on Z (0: Event detection disabled; 1: Raise event flag)
+FLAG_TRANSIENT_CFG_YTEFE = 0x04    # Event flag enable on Y (0: Event detection disabled; 1: Raise event flag)
+FLAG_TRANSIENT_CFG_XTEFE = 0x02    # Event flag enable on X (0: Event detection disabled; 1: Raise event flag)
+FLAG_TRANSIENT_CFG_HPF_BYP = 0x01  # Bypass High-Pass filter/Motion Detection
+
+# Register TRANSIENT_SCR (0x01e) R/O - TRANSIENT_SRC Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |       -      |      EA      |   ZTRANSE    | Z_Trans_Pol  |   YTRANSE    | Y_Trans_Pol  |   XTRANSE    | X_Trans_Pol  |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_TRANSIENT_SCR_EA = 0x40       # Event Active Flag (0: no event flag has been asserted; 1: one or more event flag has been asserted)
+FLAG_TRANSIENT_SCR_ZTRANSE = 0x20  # Z transient event (0: no interrupt, 1: Z Transient acceleration > than TRANSIENT_THS event has occurred
+FLAG_TRANSIENT_SCR_ZTR_POL = 0x10  # Polarity of Z Transient Event that triggered interrupt (0: Z event Positive g, 1: Z event Negative g)
+FLAG_TRANSIENT_SCR_YTRANSE = 0x08  # Y transient event (0: no interrupt, 1: Y Transient acceleration > than TRANSIENT_THS event has occurred
+FLAG_TRANSIENT_SCR_YTR_POL = 0x04  # Polarity of Y Transient Event that triggered interrupt (0: Y event Positive g, 1: Y event Negative g)
+FLAG_TRANSIENT_SCR_XTRANSE = 0x02  # X transient event (0: no interrupt, 1: X Transient acceleration > than TRANSIENT_THS event has occurred
+FLAG_TRANSIENT_SCR_XTR_POL = 0x01  # Polarity of X Transient Event that triggered interrupt (0: X event Positive g, 1: X event Negative g)
+
+# Register FF_MT_THS (0x017) R/W - Freefall and Motion Threshold Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   DBCNTM     |     THS6     |    THS5      |    THS4      |    THS3      |    THS2      |    THS1      |    THS0      |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define SOME GLOBAL VARIABLES
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define the acceleration FIFO buffer
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# The acceleration FIFO buffer is a list of records containing all meaningful acceleration data plus some
+# other useful information, whose format il as described below
+#
+#   1.  curTime     as returned by: datetime.datetime.now().  Format is: 'YYYY-MM-DD hh:mi:ss.uuuuuuu'
+#   2.  xAccel      Current X acceleration value in row format
+#   3.  yAccel      Current Y acceleration value in row format
+#   4.  xAccel      Current Z acceleration value in row format
+#   5.  plo         Current Portrait/Landscape orientation
+#accelBuffer = [0, 0, 0, 0, 0]
+accelBuffer = []
+#accelBuffer.append([0, 0, 0, 0, 0])
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define the threaded interrupt vector
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+def my_callback(channel):
+    """
+    my_callback is the threaded callback functions for interrupt events.
+    These will run in another thread when our events are detected
+
+    :param channel: The GPIO channel where the interrupt event was risen
+    :return: None
+    """
+    # Please  nte that, for performance reasons, axis data are not convertd in m/s2,
+    # Although, all 6 registers containing acceleration data are read and formatted appropriately
+    bus = smbus.SMBus(1)
+    axisData = bus.read_i2c_block_data(i2caddr, REG_OUT_X_MSB, 6)
+    #
+    #print ("!"),  #print("Falling edge detected on GPIO channel: " + str(channel))
+    #
+    runTimeConfigObject.NumInterrupts = runTimeConfigObject.NumInterrupts + 1
+    #
+    xAccel = ((axisData[0] << 8) | axisData[1]) >> 2
+    yAccel = ((axisData[2] << 8) | axisData[3]) >> 2
+    zAccel = ((axisData[4] << 8) | axisData[5]) >> 2
+    plo = bus.read_byte_data(i2caddr, REG_PL_STATUS) & 0x7
+    # Append data to the accelBuffer
+    accelBuffer.append([str(datetime.datetime.now()), xAccel, yAccel, zAccel, plo])
+    pass
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define a class called Accel
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+import ConfigParser
+class Accel():
+    raspiBus = -1               # The Raspberry Pi Bus (dpends on hardware model)
+    raspiIntEnabled = 0         # 0 = Interrupt routine was not enabled after initialization, 1 = Interrupt routine enabled successfully
+    raspiInfo = ""              # Raspberry Pi Info
+
+    def __init__(self):
+
+        #
+        # Setup RPI specific bus
+        #
+        myBus = ""
+        if GPIO.RPI_INFO['P1_REVISION'] == 1:
+            myBus = 0
+        else:
+            myBus = 1
+        #print('myBus=' + str(myBus))
+        self.raspiBus = myBus
+
+        self.b = smbus.SMBus(myBus)  # 0 = /dev/i2c-0 (port I2C0), 1 = /dev/i2c-1 (port I2C1)
+        self.a = i2caddr
+        self.high_res_mode = OVERSAMPLING_MODE
+        self.sensor_range = RANGE_4_G
+        self.raspiInfo = GPIO.RPI_INFO
+
+
+    def whoAmI(self):
+        return self.b.read_byte_data(i2caddr, REG_WHOAMI)
+
+    def init(self):
+        # Preliminary actions
+        # sudo chmod 666 /sys/module/i2c_bcm2708/parameters/combined
+        # sudo echo -n 1 > /sys/module/i2c_bcm2708/parameters/combined
+        #
+        # the above 2 sh commands can be replaced with the following statements, in the case this program is ran as root (sudo)
+        # (For more information, please see: http://raspberrypi.znix.com/hipidocs/topic_i2c_rs_and_cs.htm)
+        #
+        # BCM2708_COMBINED_PARAM_PATH = '/sys/module/i2c_bcm2708/parameters/combined'
+        # os.chmod(BCM2708_COMBINED_PARAM_PATH, 666)
+        # os.system('echo -n 1 > {!s}'.format(BCM2708_COMBINED_PARAM_PATH))
+        # sudo i2cdetect -y 1               # this sh cmmand will search /dev/i2c-1 for all address
+        # sudo i2cget -y 1 0x1d 0x0d		# This sh command should return 0x1a for MMA8451
+        #
+        # Setup all registers appropriately
+        self.writeRegister(REG_CTRL_REG2, self.readRegister(REG_CTRL_REG2) | FLAG_RESET)  # Reset
+        # self.writeRegister(REG_CTRL_REG2,	 self.readRegister(REG_CTRL_REG2) | FLAG_STEST)						# SelfTest
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) & ~FLAG_ACTIVE)  # Put the device in Standby
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) & ~FLAG_F_READ)  # No Fast-Read (14-bits), Fast-Read (8-Bits)
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) | FLAG_ODR_50_HZ)  # Data Rate
+        self.writeRegister(REG_XYZ_DATA_CFG, self.readRegister(REG_XYZ_DATA_CFG) | FLAG_XYZ_DATA_BIT_FS_4G)  # Full Scale Range 2g, 4g or 8g
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) | FLAG_LNOISE)  # Low Noise
+        self.writeRegister(REG_CTRL_REG2, self.readRegister(REG_CTRL_REG2) & ~FLAG_SLPE)  # No Auto-Sleep
+        self.writeRegister(REG_CTRL_REG2, self.readRegister(REG_CTRL_REG2) | FLAG_SMODS_HR)  # High Resolution
+        self.writeRegister(REG_PL_CFG, self.readRegister(REG_PL_CFG) | FLAG_PL_CFG_PL_EN)  # P/L Detection Enabled
+
+        # Setup interrupts
+        if CFG_INTERRUPT == 1:
+            GPIO.setmode(GPIO.BCM)
+            # GPIO 23 & 17 set up as inputs, pulled up to avoid false detection.
+            # Both ports are wired to connect to GND on button press.
+            # So we'll be setting up falling edge detection for both
+            GPIO.setup(17, GPIO.IN, pull_up_down=GPIO.PUD_UP)
+            # when a falling edge is detected on port 17, regardless of whatever
+            # else is happening in the program, the function my_callback will be run
+            # GPIO.add_event_detect(17, GPIO.FALLING, callback=my_callback, bouncetime=300)
+            GPIO.add_event_detect(17, GPIO.FALLING, callback=my_callback)
+            #print("Interrupt OK")
+            self.raspiIntEnabled = 1    # Interrupt enabled successfully
+            # Force 1st sensor read
+            my_callback(0)
+
+
+            # Configure register for interrupt
+            self.writeRegister(REG_CTRL_REG4, 0x00)  # Reset all interrupt enabled flags
+            self.writeRegister(REG_CTRL_REG4, self.readRegister(REG_CTRL_REG4) | FLAG_INT_EN_DRDY)  # Data Ready Interrupt Enabled
+            self.writeRegister(REG_CTRL_REG5, 0x00)  # Reset all interrupt config flags
+            self.writeRegister(REG_CTRL_REG5, self.readRegister(REG_CTRL_REG5) | FLAG_INT_CFG_DRDY)  # Data Ready Interrupt is routed to INT1 pin
+
+            # Initialize the accelBuffer
+            accelBuffer = deque()
+
+
+        # Finally, Activate the sensor
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) | FLAG_ACTIVE)  # Activate the device
+
+    def writeRegister(self, regNumber, regData):
+        """
+        Writes one byte (8-bts) of data passed in 'regData', into the register 'regNumber'
+        """
+        try:
+            self.b.write_byte_data(self.a, regNumber, regData)
+            time.sleep(0.01)
+        except IOError:
+            print("Error detected in function writeRegister() [IOError = " + str(IOError) + "]")
+            sys.exit()
+
+    def readRegister(self, regNumber):
+        """
+        Retrieves one byte (8-bits) of data from register 'regNumber' returning to the caller
+        """
+        try:
+            return self.b.read_byte_data(self.a, regNumber)
+        except IOError:
+            print("Error detected in function readRegister() [IOError = " + str(IOError) + "]")
+            sys.exit()
+
+    def block_read(self, offset, length):
+        """
+        Performs a burst-read on the device registers retrieving the requested amount of data
+        Read a block of <length> bytes from  offset <offset>
+        """
+        try:
+            return self.b.read_i2c_block_data(i2caddr, offset, length)
+        except IOError:
+            print("Error detected in function block_read() [IOError = " + str(IOError) + "]")
+            sys.exit()
+
+    def get_orientation(self):
+        """
+        Get current orientation of the sensor.
+        :return: orientation. Orientation number for the sensor.
+        """
+        orientation = self.b.read_byte_data(self.a, REG_PL_STATUS) & 0x7
+        return orientation
+
+    def getAxisValue(self):
+        """
+        Retrieves axis values and converts into a readable format (i.e. m/s2)
+        :return:	None
+        """
+        # Make sure F_READ and F_MODE are disabled.
+        f_read = self.b.read_byte_data(self.a, REG_CTRL_REG1) & FLAG_F_READ
+        assert f_read == 0, 'F_READ mode is not disabled. : %s' % (f_read)
+        f_mode = self.b.read_byte_data(self.a, REG_F_SETUP) & FLAG_F_MODE_FIFO_TRIGGER
+        assert f_mode == 0, 'F_MODE mode is not disabled. : %s' % (f_mode)
+
+        #
+        self.xyzdata = self.block_read(REG_OUT_X_MSB, 6)
+        if self.high_res_mode is not None:
+            x = ((self.xyzdata[0] << 8) | self.xyzdata[1]) >> 2
+            y = ((self.xyzdata[2] << 8) | self.xyzdata[3]) >> 2
+            z = ((self.xyzdata[4] << 8) | self.xyzdata[5]) >> 2
+            precision = PRECISION_14_BIT  # Precision 14 bit data
+        else:
+            x = (self.xyzdata[0] << 8)
+            y = (self.xyzdata[1] << 8)
+            z = (self.xyzdata[2] << 8)
+            precision = PRECISION_08_BIT  # Precision 08 bit data
+        max_val = 2 ** (precision - 1) - 1
+        signed_max = 2 ** precision
+        #
+        x -= signed_max if x > max_val else 0
+        y -= signed_max if y > max_val else 0
+        z -= signed_max if z > max_val else 0
+        #
+        x = round((float(x)) / RANGE_DIVIDER[self.sensor_range], 3)
+        y = round((float(y)) / RANGE_DIVIDER[self.sensor_range], 3)
+        z = round((float(z)) / RANGE_DIVIDER[self.sensor_range], 3)
+
+        return {"x": x, "y": y, "z": z}
+
+    def debugShowRpiInfo(self):
+        #print("Raspberry Info      = " + str(GPIO.RPI_INFO))
+        print("Raspberry Info      = " + str(self.raspiInfo))
+
+    def debugShowRegisters(self):
+        print("REG_STATUS       (0x00):" + str(format(self.readRegister(REG_STATUS), '#04x')) + " | Binary: " + format(self.readRegister(REG_STATUS), 'b').zfill(8))
+        print("REG_WHOAMI       (0x0d):" + str(format(self.readRegister(REG_WHOAMI), '#04x')) + " | Binary: " + format(self.readRegister(REG_WHOAMI), 'b').zfill(8))
+        print("REG_F_SETUP      (0x09):" + str(format(self.readRegister(REG_F_SETUP), '#04x')) + " | Binary: " + format(self.readRegister(REG_F_SETUP), 'b').zfill(8))
+        print("REG_XYZ_DATA_CFG (0x0e):" + str(format(self.readRegister(REG_XYZ_DATA_CFG), '#04x')) + " | Binary: " + format(self.readRegister(REG_XYZ_DATA_CFG), 'b').zfill(8))
+        print("REG_CTRL_REG1    (0x2a):" + str(format(self.readRegister(REG_CTRL_REG1), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG1), 'b').zfill(8))
+        print("REG_CTRL_REG2    (0x2b):" + str(format(self.readRegister(REG_CTRL_REG2), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG2), 'b').zfill(8))
+        print("REG_CTRL_REG3    (0x2c):" + str(format(self.readRegister(REG_CTRL_REG3), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG3), 'b').zfill(8))
+        print("REG_CTRL_REG4    (0x2d):" + str(format(self.readRegister(REG_CTRL_REG4), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG4), 'b').zfill(8))
+        print("REG_CTRL_REG5    (0x2e):" + str(format(self.readRegister(REG_CTRL_REG5), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG5), 'b').zfill(8))
+        print("REG_PL_STATUS    (0x10):" + str(format(self.readRegister(REG_PL_STATUS), '#04x')) + " | Binary: " + format(self.readRegister(REG_PL_STATUS), 'b').zfill(8))
+        print ("debugRealTime    " + str(runTimeConfigObject.debugRealTime))
+        print ("NumInterrupts    " + str(runTimeConfigObject.NumInterrupts))
+
+    def debugShowOrientation(self):
+        print("Position = %d" % (self.get_orientation()))
+
+    def debugShowAxisAcceleration(self, xaccel, yaccel, zaccel):
+        print("   x (m/s2)= %+.3f" % (xaccel))
+        print("   y (m/s2)= %+.3f" % (yaccel))
+        print("   z (m/s2)= %+.3f" % (zaccel))
+
+    def debugRealTimeBuffer(self):
+        n = 0
+        for elements in accelBuffer:
+            myData = accelBuffer.pop()
+            n += 1
+            print ("N=" + str(n) + " myData=" + str(myData))    # + "Element=" + str(elements))
+        try:
+            print("End of printout\n")
+            #time.sleep(1.0)
+        # os.system("clear")
+        except KeyboardInterrupt:
+            print("Program Termination Requested")
+            sys.exit()
+        
+###############################################################################
+#   Threading functions
+###############################################################################
+#def rssClient():
+#    """Manage data shipping over th network, in a separate thread."""
+#    #logger.debug('Thread Starting')
+#    while True:
+#        time.sleep(1.0)
+#        print ("This is thread rssClient()")
+#
+###############################################################################
+#   Useful functions
+###############################################################################
+def printHelp():
+    print ("\n")
+    print ("usage: accel.py [options]")
+    print ("Available options:")
+    print (" -h \t\t Print this help and exit")
+    print (" -d \t\t Show debug realtime interrupt data")
+    print (" -s \t\t Execute silently (no screen output)")
+    print (" -L <lvl>\t Set Log level. where <lvl> is the log level (0 = NONE - 8 = DEBUG)") 
+    print ("")
+
+
+def main(argv):
+    import sys, getopt, logging
+    #
+    try:
+        opts, args = getopt.getopt(argv,"hdsL:")
+    except getopt.GetoptError:
+        print ("\nInvalid option requested on command line")
+        printHelp()
+        sys.exit(2)
+
+    for opt, arg in opts:
+        if opt == '-h':
+            printHelp()
+            sys.exit()
+        elif opt == '-d':
+            runTimeConfigObject.debugRealTime = 1
+        elif opt == '-s':
+            runTimeConfigObject.executeSilently = 1
+        elif opt == '-L':
+            if (int(arg) == 0) or (int(arg) > 5):
+                pass
+                #logger.setLevel(logger.NOTSET)            # Same as value 0
+            elif int(arg) == 1:
+                logger.setLevel(logging.CRITICAL)          # Same as value 50
+            elif int(arg) == 2:
+                logger.setLevel(logging.ERROR)             # Same as value 40
+            elif int(arg) == 3:
+                logger.setLevel(logging.WARNING)           # Same as value 30
+            elif int(arg) == 4:
+                logger.setLevel(logging.INFO)              # Same as value 20
+            elif int(arg) == 5:
+                logger.setLevel(logging.DEBUG)             # Same as value 10
+
+
+#####################################################################################
+#   M A I N 
+#####################################################################################
+class runTimeConfigObject(object):
+    pass
+#if __name__ == "__main__":
+def extract_meas():
+    
+    #class runTimeConfigObject(object):
+        #pass
+    #
+    # Setup Logger
+    #
+    #logger = logging.basicConfig(level=logging.DEBUG, format='[%(asctime)15s].%(levelname)s] (%(threadName)-10s) %(message)s', )
+    #logger.basicConfig(level=logging.DEBUG,format='[%(asctime)15s].%(levelname)s] (%(threadName)-10s) %(message)s',)
+    logger = logging.getLogger()
+    logger.setLevel(logging.DEBUG)
+    # create console handler and set level to debug
+    ch = logging.StreamHandler()
+    ch.setLevel(logging.DEBUG)
+    # create formatter
+    formatter = logging.Formatter('[%(asctime)s.%(levelname)s] (%(name)s.%(threadName)-10s) : %(message)s')
+    # add formatter to ch
+    ch.setFormatter(formatter)
+    # add ch to logger
+    logger.addHandler(ch)
+
+    #
+    # Set some default command line options
+    #
+    runTimeConfig = runTimeConfigObject()
+    runTimeConfigObject.debugRealTime = 0       # 1 = Show debug realtime interrupt data
+    runTimeConfigObject.executeSilently = 0     # 1 = Execute silently (no sceen output)
+    runTimeConfigObject.NumInterrupts = 0       # keep Nbr of sensor interrupts withi the main loop
+    
+    main(sys.argv[1:])
+
+    #
+    # Read configuration file
+    #
+    configFile = "./rss_config.dat"
+    #logger.debug('Reading Config file: ' + configFile) #Commented out by Henry
+    sections = {'GeoData', 'DeviceInfo', 'Networking'}
+    #configParameters = {}
+    #Config = ConfigParser.ConfigParser()
+    #Config.read(configFile)
+    #for section in sections:
+    #    try:
+    #        options = Config.options(section)
+    #    except:
+    #        print ("ERROR: Section '" + section + "' Not found in config file: '" + configFile + "'.")
+    #        sys.exit()
+    #    for option in options:
+    #        try:
+    #            configParameters[option] = Config.get(section, option)
+    #        except:
+    #            configParameters[option] = None
+    #        logger.debug("Config Section: " + section + " / Option: " + option + " => " + configParameters[option])
+
+    MMA8451 = Accel()
+    #os.system("clear")
+    MMA8451.init()
+
+    if MMA8451.whoAmI() != deviceName:
+        print("Error! Device not recognized! (" + str(deviceName) + ")")
+        sys.exit()
+
+    #
+    # Thread client start
+    # Commented out by Henry
+    '''
+    import rss_client
+    pill2kill = threading.Event()
+    #threadClient = threading.Thread(name='netClientWorker', target=rss_client.cli_worker, args=(pill2kill, configParameters, accelBuffer))
+    threadClient = threading.Thread(name='netClientWorker', target=rss_client.cli_worker, args=(pill2kill, accelBuffer))
+    threadClient.setDaemon(False)         #threadClient.daemon = False
+    threadClient.start()
+    myThread = []
+    myThread.append('netClientWorker')
+    '''
+    
+    #Added by Henry:
+    count_samples = 5 #num of samples we want
+    count_taken = 0
+    xvals = []
+    yvals = []
+    zvals = []
+    while count_taken < count_samples:
+        if runTimeConfigObject.executeSilently == 0:
+            axes = MMA8451.getAxisValue()
+            print("X: " + str(axes['x']) + ", Y: " + str(axes['y']) + ", Z: " + str(axes['z']))
+            xvals.append(axes['x'])
+            yvals.append(axes['y'])
+            zvals.append(axes['z'])
+            count_taken = count_taken + 1
+    return xvals,yvals,zvals
+    
+    #Commented Out by Henry:
+    '''
+    while True:  # forever loop
+        if runTimeConfigObject.executeSilently == 0:
+            print ("\nCurrent Date-Time: " + str(datetime.datetime.now()))
+            print ("Raspberry Bus       = " + str(MMA8451.raspiBus))
+            print ("Raspberry Interrupt = " + str(MMA8451.raspiIntEnabled))
+            print ("Number of elemets   = " + str(len(accelBuffer)))
+            MMA8451.debugShowRpiInfo()
+            MMA8451.debugShowRegisters()
+            MMA8451.debugShowOrientation()
+            axes = MMA8451.getAxisValue()
+            MMA8451.debugShowAxisAcceleration(axes['x'], axes['y'], axes['z'])
+            #
+            if runTimeConfigObject.debugRealTime != 0:
+                MMA8451.debugRealTimeBuffer()
+
+        runTimeConfigObject.NumInterrupts = 0
+        try:
+            time.sleep(1.0)
+        except KeyboardInterrupt:
+            logger.debug ("Killing threads...")
+            pill2kill.set()
+            threadClient.join()
+            time.sleep(1.0)
+
+            logger.debug("\nUser termination requested!\n")
+            sys.exit()
+            
+    sys.exit()
+    '''
+
diff --git a/Nov_27/Full/Laser/accel_correct_config/raspidata.py b/Nov_27/Full/Laser/accel_correct_config/raspidata.py
new file mode 100644
index 0000000..fb99cbb
--- /dev/null
+++ b/Nov_27/Full/Laser/accel_correct_config/raspidata.py
@@ -0,0 +1,48 @@
+""" A Raspberry Pi class.
+To access some of the most useful hardware information
+"""
+
+
+#class RaspiData():
+
+
+    # Get Raspberry Serial Number
+    # You can do it in bash:
+    #   cat /proc/cpuinfo | perl -n -e '/^Serial[ ]*: ([0-9a-f]{16})$/ && print "$1\n"'
+    # Or with perl:
+    #   cat cpuinfo | perl -n -e '/^Serial[ ]*: ([0-9a-f]{16})$/ && print "$1\n"'
+    #
+    #
+
+
+#    def __init__(self):
+#       pass
+from uuid import getnode as get_mac
+
+#    @staticmethod
+# def get_serial(self):
+def get_serial():
+    """Extract serial from cpuinfo file"""
+    cpu_serial= "0000000000000000"
+    try:
+        f = open('/proc/cpuinfo','r')
+        for line in f:
+            if line[0:6]=='Serial':
+                cpu_serial = line[10:26]
+        f.close()
+    except:
+        cpu_serial = "ERROR000000000"
+    return cpu_serial
+
+
+def mac_address():
+    """Extract rth0 MAC address"""
+    # mac = '00:00:00:00:00:00'
+    # mac = '00:00:00:00:00:00'
+    try:
+        t = get_mac()
+    except:
+        t = "000000000000"
+    mac = ':'.join(("%012X" % t)[i:i+2] for i in range(0, 12, 2))
+    return mac
+
diff --git a/Nov_27/Full/Laser/accel_correct_config/rss_cli_config.py b/Nov_27/Full/Laser/accel_correct_config/rss_cli_config.py
new file mode 100644
index 0000000..e10aa53
--- /dev/null
+++ b/Nov_27/Full/Laser/accel_correct_config/rss_cli_config.py
@@ -0,0 +1,21 @@
+# [GeoData]
+City = 'Rome'
+cityname =  'Rome'
+latitude = 41.947550
+longitude = 12.469794
+deviceorientation = 'L'
+
+# [DeviceInfo]
+devicetype = 'raspberry'
+devicename = 'rssd0001'
+sensorname = 'mma8451'
+sensortheshold = 20
+logfile = './rssd.log'
+
+# [Networking]
+#serveraddress = 'www.diprimio.com'
+serveraddress = 'localhost'
+serverprotocol = 'tcp'
+servertcpport = 15000
+serverhearthbit = 100
+receivebuffersize = 1024
diff --git a/Nov_27/Full/Laser/accel_correct_config/rss_client.py b/Nov_27/Full/Laser/accel_correct_config/rss_client.py
new file mode 100644
index 0000000..a2a4d1b
--- /dev/null
+++ b/Nov_27/Full/Laser/accel_correct_config/rss_client.py
@@ -0,0 +1,159 @@
+#!/usr/bin/env python
+# -*- coding:utf-8, indent=tab, tabstop=4 -*-
+#
+# See 'LICENSE'  for copying
+#
+# This file contains the code for the client module of 'accel.py'
+#
+# Revision history
+# Date          Author                  Version     Details
+# ----------------------------------------------------------------------------------
+# 2018-01-18    Massimo Di Primio       0.06        1st file implementation
+
+"""Client thread worker - This is a simple client code example for 'accel'.py' program"""
+
+import logging
+import time
+import datetime
+import socket
+import json
+import rss_client_messages as climsg
+import rss_cli_config as ccfg
+import raspidata
+
+
+#def cli_connect(params):
+def cli_connect():
+    """Open connection to the server"""
+    server_address = (str(ccfg.serveraddress), int(ccfg.servertcpport))
+    logging.debug('Trying to connect to server ' + str(server_address))
+    # Create a TCP/IP socket
+    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    try:
+        # Connect the socket to the port where the server is listening
+        s.connect(server_address)
+        logging.debug('Connection Established to server ' + str(server_address))
+    except:
+        logging.debug(
+            "Failed to open connection: " + str(ccfg.serverprotocol) +
+            ", to IP: " + str(ccfg.serveraddress) +
+            ", on port: " + str(ccfg.servertcpport)
+            )
+        return(-1)
+    return(s)
+
+
+def cli_close(s):
+    """Close the server connection"""
+    if s > -1:
+        s.close()
+
+
+# def cli_worker(stopEvent, config, accelBuffer):
+def cli_worker(stopEvent, accelBuffer):
+    """A client worker as thread"""
+    logging.debug('Thread Starting')
+    s = cli_connect()     # s = cli_connect(config)
+    send_client_hello(s)
+    time.sleep(0.5)
+    send_config_affirm_message(s)
+
+    ts = int(time.time())
+    te = ts
+    while not stopEvent.wait(0.3):
+        if len(accelBuffer) > 0:
+            send_accel_data(s, accelBuffer)
+            te = int(time.time())
+        if (te - ts) > 10:
+            send_client_heartbit(s)
+            ts = int(time.time())
+
+        time.sleep(0.5)
+    send_zap_message(s)
+    cli_close(s)
+    logging.debug("Thread cliWorker is terminating as per your request.")
+
+    
+def send_accel_data(s, accelBuffer):
+    """Send acceleration data to the server"""
+    #msg = dict(cmd='ADM', timestamp=str(datetime.datetime.now()), clid=raspidata.get_serial())
+    #a = 123 #str(datetime.datetime.now())
+    pbuf = parse_accel_data(accelBuffer)
+    msg = dict(cmd = 'ADM', timestamp = str(datetime.datetime.now()), clid = raspidata.get_serial(), data = pbuf)
+    # if len(pbuf) > 0: # this sometimes returns error (when buf is empty, it has None type)
+    if (pbuf is not None) and (len(pbuf) > 0):
+        #str = climsg.accel_data_message(pbuf)
+        try:
+            logging.debug("Sending Acceleration data to the server")
+            s.sendall(str(json.dumps(msg)) + "\n")  #s.sendall(json.dumps(str) + "\n")
+        except:
+            logging.debug("Failed to send Acceleration-Data to the server")
+
+
+def send_client_hello(s):
+    """Send Hello message to the server"""
+    msg = dict(cmd = 'CHM', timestamp = str(datetime.datetime.now()), clid = raspidata.get_serial())
+    try:
+        logging.debug("Sending Hello to the server")
+        s.sendall(str(json.dumps(msg)) + "\n")  #s.sendall(json.dumps(climsg.hello_message()) + "\n")
+    except:
+        logging.debug("Failed to send Hello to the server")
+
+
+def send_zap_message(s):
+    """Send Zap message to the server"""
+    msg = dict(cmd = 'CZM', timestamp = str(datetime.datetime.now()), clid = raspidata.get_serial())
+    try:
+        logging.debug("Sending Zap to the server")
+        s.sendall(str(json.dumps(msg)) + "\n")  #s.sendall(json.dumps(climsg.zap_message()) + "\n")
+    except:
+        logging.debug("Failed to send Zap to the server")
+
+
+def send_config_affirm_message(s):      #def send_config_affirm_message(s, config):
+    msg_data = dict(city = ccfg.cityname, latitude = ccfg.latitude,longitude = ccfg.longitude)
+    msg = dict(cmd = 'CCA', timestamp = str(datetime.datetime.now()), clid = raspidata.get_serial(), config = msg_data)
+    try:
+        logging.debug("Sending client configuration to the server")
+        s.sendall(str(json.dumps(msg)) + "\n")    #s.sendall(climsg.config_affirm_message(cfg_data))
+    except:
+        logging.debug("Failed to send client configuration to the server")
+
+
+def send_client_heartbit(s):
+    """Send Heartbit to the server"""
+    msg = dict(cmd = 'CHB', timestamp = str(datetime.datetime.now()), clid=raspidata.get_serial())
+    try:
+        logging.debug("Sending Heartbit to the server")
+        s.sendall(str(json.dumps(msg)) + "\n")  #s.sendall(json.dumps(climsg.heart_bit()) + "\n")
+    except:
+        logging.debug("Failed to send Heartbit to the server")
+
+
+def parse_accel_data(b):
+    """Parse acceleration data to make sure we only send meaningfull data to the server"""
+    tsh = 10
+    tbuf = []
+    # tbuf.append([0, 0, 0, 0, 0])
+    # bLength = len(b)
+    # logging.debug("parseAccelData(b) # of elements   = " + str(len(b)))
+    if len(b) > 1:
+        logging.debug("parseAccelData: In  AccelData/BufLen: " + str(len(b)) + "/" +str(len(tbuf)))
+        firstTime = 1
+        prow = None
+        for row in b:
+            crow = b.pop(0)     # Get the oldest record
+            if firstTime == 1:
+                prow = crow
+                firstTime = 0
+            if ( (abs(abs(int(crow[1])) - abs(int(prow[1]))) > tsh) or
+                 (abs(abs(int(crow[2])) - abs(int(prow[2]))) > tsh) or
+                 (abs(abs(int(crow[3])) - abs(int(prow[3]))) > tsh)
+                ):
+                tbuf.append(crow)
+                prow = crow
+                print ("Again PROW/CROW/TBUFLEN:" + str(prow) + " / " + str(crow) + " / " +  str(len(tbuf)))
+        
+        logging.debug("parseAccelData: Out AccelData/BufLen: " + str(len(b)) + "/" +str(len(tbuf)))
+        return(tbuf)
+
diff --git a/Nov_27/Full/Laser/accel_correct_config/rss_client_messages.py b/Nov_27/Full/Laser/accel_correct_config/rss_client_messages.py
new file mode 100644
index 0000000..4a507d7
--- /dev/null
+++ b/Nov_27/Full/Laser/accel_correct_config/rss_client_messages.py
@@ -0,0 +1,113 @@
+
+import datetime
+import raspidata
+import json
+import rss_cli_config as ccfg
+
+
+#
+# All client messages are handled in here.
+#
+# Message Formats
+# ---------------
+# 1 - Timestamp
+# 2 - Device ID
+# 3 - Message Type
+#
+# Client message types
+# --------------------
+# 0001  CHB - Client Heart Bit (Client is alive)
+# 0002  CHM - Client Hello Message (Client is online)
+# 0003  CZM - Client Zap Message (Client is shut down)
+# 0004  ADM - Acceleration Data Message (Acceleration data)
+# 0005  CCA - Client Config Affirm
+# ... (other to come)
+#
+# Data message Format for all Client Message Types
+# ------------------------------------------------
+#
+# # 0001 - CHB - Client Heart Bit
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0001         |  Var  | Literally 'CHB'                                        |
+# # +-------------------------------------------------------------------------------+
+#
+# 0002 CHM - Client Hello Message
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0002         |  Var  | Literally 'CHM'                                        |
+# +--------------+-------+--------------------------------------------------------+
+#
+# 0003  CZM - Client Zap Message (Client is shut down)
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0003         |  Var  | Literally 'CZM'                                        |
+# +--------------+-------+--------------------------------------------------------+
+#
+# 0004  ADM - Acceleration Data Message (Acceleration data)
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0004         |  Var  | Literally 'ADM'                                        |
+# | Accel Data   |  Var  | Array containing acceleration data (X,Y,Z.O)           |
+# +--------------+-------+--------------------------------------------------------+
+#
+# 0005  CCA - Client Configuration Affirm
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0004         |  Var  | Literally 'CCA'                                        |
+# | Config Data  |  Var  | Array containing client configuration                  |
+# +--------------+-------+--------------------------------------------------------+
+
+###########################################################################
+#   WARNIG ! THIS FILE IS NOT CURRENTLY USED !
+#   Its presence it is for debugging/testing only
+#   Althought, it will be used in some future version
+###########################################################################
+
+#def heart_bit():
+#    """Client Heart Bit"""
+#    msg = {'cmd': 'CHB', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial()}
+#    return msg
+#
+#
+#def hello_message():
+#    """Client Hello Message"""
+#    msg = {'cmd': 'CHM', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial()}
+#    return msg
+#
+#
+#def zap_message():
+#    """Client Zap Message"""
+#    msg = {'cmd': 'CZM', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial()}
+#    return msg
+#
+#
+#def accel_data_message(data):
+#    """Acceleration Data Message"""
+#    msg = {'cmd': 'ADM', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial(), 'acceldata': str(data)}
+#    return msg
+#
+#
+#def config_affirm_message(data):    #def config_affirm_message(data):
+#    """Client Configuration Affirm"""
+#    ###
+#    pkt_hdr = {'cmd': 'CCA', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial(), 'data' : data}
+#
+#    msg = dict(city = ccfg.cityname, latitude = ccfg.latitude, longitude = ccfg.longitude, config = data)
+#    return msg
+
diff --git a/Nov_27/Full/Laser/transfer_test_correct_config/._Read_Accel.py b/Nov_27/Full/Laser/transfer_test_correct_config/._Read_Accel.py
new file mode 100644
index 0000000..bb564a5
Binary files /dev/null and b/Nov_27/Full/Laser/transfer_test_correct_config/._Read_Accel.py differ
diff --git a/Nov_27/Full/Laser/transfer_test_correct_config/RPI_L.py b/Nov_27/Full/Laser/transfer_test_correct_config/RPI_L.py
new file mode 100644
index 0000000..8e9ce36
--- /dev/null
+++ b/Nov_27/Full/Laser/transfer_test_correct_config/RPI_L.py
@@ -0,0 +1,185 @@
+#Code for communications, sensor actions, and calculations for RPi on laser module
+#Assume that RPI_C and RPI_L codes are implemented at same time (synced)
+#Below code will execute at startup of RPI_L
+
+from SETTINGS import *
+import sys
+import subprocess
+import socket
+import RPi.GPIO as GPIO
+import time
+from Read_Accel import read_accel
+from client_socket_connect_new import client_socket
+from slaveSleep import slaveSleep
+
+#Givens
+
+# See: SETTINGS.py
+
+time.sleep(10) #to avoid setting pins when pins are flickering
+ 
+def main():
+    running = True
+
+    print("run_log_path: " + RUN_LOG_PATH)
+    if os.path.exists(RUN_LOG_PATH):
+        rfile = open(RUN_LOG_PATH, "r")
+        try:
+            run_counter = int(rfile.read())
+        except ValueError:
+            run_counter = -1
+    else:
+        rfile = open(RUN_LOG_PATH, "w")
+        run_counter = 0
+        rfile.write(str(run_counter))
+    rfile.close()
+    print("run_counter: " +  str(run_counter))
+    if run_counter > 0 and run_counter % re_sync != 0:
+        #Power Pins (The same actions would be performed in slaveSleep if run_counter = 0)
+        GPIO.setmode(GPIO.BCM)
+        GPIO.setup(pMap[12], GPIO.OUT)
+        GPIO.output(pMap[12], GPIO.HIGH)
+        print("RPI_L: 12 High")
+
+        #Other initializations
+        pitch_2 = []
+        state = "BASELINE"
+    elif run_counter == -1:
+        print("RPI_L: Issue with Run_File Read, Proceeding to Shutdown")
+        state = "SHUTDOWN"
+    else:
+        state = "SYNC"
+
+    while running == True:
+
+        try:
+            if state == "BASELINE":
+
+                print("RPI_L: Turning on Laser")
+                GPIO.setmode(GPIO.BCM)
+                GPIO.setup(pMap[Laser_Port], GPIO.OUT)
+                GPIO.output(pMap[Laser_Port], True)
+                print("RPI_L: Waiting for RPI_Cam to Connect")
+
+                try:
+                    rsv_msg_C = client_socket("LaserOn?")
+                    joined_msg = ''.join(rsv_msg_C)
+                    print("RPI_L: Received: " + joined_msg)
+                    if joined_msg == '':
+                        print("RPI_L: Connection with RPI_C Not Successful")
+                        print("RPI_L: Will Proceed to Safe Shutdown")
+                        GPIO.output(pMap(Laser_Port),False)
+                        state = "SHUTDOWN"
+                    if joined_msg == "RPI_C_AWAKE":
+                        print("RPI_L: RPICam Awake, Will Turn off Laser Upon Notice")
+                        state = "WAIT"
+                    else:
+                        print("RPI_L: ERROR: RPICam Apparently Not On")
+                        print("RPI_L: Will Proceed to Safe Shutdown")
+                        GPIO.output(pMap(Laser_Port),False)
+                        state = "SHUTDOWN"
+                except:
+                    print("RPI_L: BASELINE ERROR")
+                    state = "SHUTDOWN"
+
+            elif state == "WAIT":
+                try:
+                    time.sleep(query_time * 5) # give RPI_C a chance to close its socket
+                    rsv_msg_C = client_socket("RPI_L_WAITING?")
+                    joined_msg = ''.join(rsv_msg_C)
+                    print("RPI_L: Received: " + joined_msg)
+
+                    if joined_msg == "TURN_OFF_LASER":
+                        print("RPI_L: RPICam Took Photo, Will Turn Off Laser")
+                        try:
+                            GPIO.output(pMap[Laser_Port], False)
+                            state = "ACCEL"
+                            time.sleep(2)
+                        except:
+                            print("RPI_L: ERROR: Cannot Turn off Laser, Proceed to Shutdown")
+                            state = "SHUTDOWN"
+                    else:
+                        print("RPI_L: ERROR: RPICam Apparently Did Not Take Photo. Exiting Program")
+                        state = "SHUTDOWN"
+                except:
+                    print("RPI_L: WAIT ERROR")
+                    state = "SHUTDOWN"
+
+            elif state == "SYNC":
+                #Run Master or Slave code
+                print("RPI_L: Performing Time Sync with RPICam")
+                try:
+                    t_sleep_sec = slaveSleep(pMap)
+                except:
+                    print("RPI_L: SYNC ERROR")
+                    t_sleep_sec = -1
+
+                state = "SYNC_SHUTDOWN"
+
+            elif state == "ACCEL":
+                print("RPI_L: Taking Acceleration Reading at Laser Module")
+                try:
+                    pitch_2 = read_accel() #[rads]
+                    print("pitch_2: " + str(pitch_2*180/pi))
+                    state = "SEND_ACCEL"
+                except:
+                    print("RPI_L: ACCEL ERROR")
+                    state = "SHUTDOWN"
+
+            elif state == "SEND_ACCEL":
+                    try:
+                        rsv_msg_C = client_socket(str(pitch_2) + "?")
+                        joined_msg = ''.join(rsv_msg_C)
+
+                        if joined_msg == "GOT_ACCEL_2":
+                            print("RPI_L: RPICam Received Accel_2, Proceed to Safe Shutdown")
+                            state = "SHUTDOWN"
+                        else:
+                            print("RPI_L: ERROR: Accel_2 Not Acquired, Proceed to Safe Shutdown")
+                            state = "SHUTDOWN"
+                    except:
+                        print("RPI_L: ACCEL ERROR")
+                        state = "SHUTDOWN"
+
+            elif state == "SHUTDOWN":
+                run_counter = run_counter + 1
+                wfile = open(RUN_LOG_PATH, "w")
+                wfile.write(str(run_counter))
+                wfile.close()
+
+                print("RPI_L: Defer to Arduino for Power Control")
+                time.sleep(10)
+                subprocess.call(["shutdown", "-h", "now"])
+
+            elif state == "SYNC_SHUTDOWN":
+                if t_sleep_sec == -1:
+                    print("RPI_L: Will Reattempt Sync")
+                else:
+                    run_counter = run_counter + 1
+                    wfile = open(RUN_LOG_PATH, "w")
+                    wfile.write(str(run_counter))
+                    wfile.close()
+                    time.sleep(t_sleep_sec)
+                GPIO.output(pMap[16], GPIO.HIGH)
+                print("RPI_L: pin 16 HIGH")
+                time.sleep(1)
+                subprocess.call(['shutdown', '-h', 'now'])
+
+        except KeyboardInterrupt:
+            print('User KeyBoard Interrupt, Proceeding to Shutdown')
+            time.sleep(10)
+            subprocess.call(["shutdown", "-h", "now"])
+
+
+if __name__ == "__main__":
+    main()
+
+
+
+
+
+
+
+
+
+
diff --git a/Nov_27/Full/Laser/transfer_test_correct_config/Read_Accel.py b/Nov_27/Full/Laser/transfer_test_correct_config/Read_Accel.py
new file mode 100644
index 0000000..0e9ccf4
--- /dev/null
+++ b/Nov_27/Full/Laser/transfer_test_correct_config/Read_Accel.py
@@ -0,0 +1,19 @@
+from SETTINGS import *
+import sys
+from math import atan,sqrt,sin,cos
+sys.path.insert(0,ACCELEROMETER)
+from accel import extract_meas
+
+grav = 9.81
+
+def read_accel():
+    x_vec,y_vec,z_vec = extract_meas()
+    x = float(sum(x_vec))/float(len(x_vec))
+    y = float(sum(y_vec))/float(len(y_vec))
+    z = float(sum(z_vec))/float(len(z_vec))
+    pitch = atan(x/(sqrt(z**2 + y**2)))  #angle forward from vert [x,y flat, z vert]
+    #roll = atan(y/(sqrt(x**2 + z**2))) #angle side from vert [x,y flat, z vert]
+    return pitch
+
+
+
diff --git a/Nov_27/Full/Laser/transfer_test_correct_config/SETTINGS.py b/Nov_27/Full/Laser/transfer_test_correct_config/SETTINGS.py
new file mode 100644
index 0000000..210b3ba
--- /dev/null
+++ b/Nov_27/Full/Laser/transfer_test_correct_config/SETTINGS.py
@@ -0,0 +1,21 @@
+import os
+
+BASE_DIR = '/home/pi/Desktop/Full/'
+
+ACCELEROMETER = os.path.join(BASE_DIR, "accel_correct_config")
+RUN_LOG_PATH = os.path.join(BASE_DIR, "transfer_test_correct_config/run_log.txt")
+
+#For RPI_L.py
+
+#RPI_L_IP = "192.168.3.3" #address of cam module (client)
+RPI_C_IP = "192.168.15.104" #when hooked to Netgear Switch (Phil)
+RPI_L_IP = "192.168.15.103" #when hooked to Netgear Switch (Phil)
+RPI_L_Port = 1234
+RPI_C_Port = 1234
+Laser_Port = 11
+marker = "?"
+query_time = 5
+re_sync = 3
+pi = 3.1415926535897932
+
+pMap = {3:2,5:3,7:4,8:14,10:15,11:17,12:18,13:27,15:22,16:23,18:24,19:10,21:9,22:25,23:11,24:8,26:7}
diff --git a/Nov_27/Full/Laser/transfer_test_correct_config/client_socket_connect_new.py b/Nov_27/Full/Laser/transfer_test_correct_config/client_socket_connect_new.py
new file mode 100644
index 0000000..6ce00d4
--- /dev/null
+++ b/Nov_27/Full/Laser/transfer_test_correct_config/client_socket_connect_new.py
@@ -0,0 +1,73 @@
+from SETTINGS import *
+import socket, time, sys, select, traceback, Queue
+
+max_count = 40
+
+def client_socket(send_msg = ''):
+
+    rcv = []
+    print("RPI_L: Establishing Client Socket...")
+    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    sock.setblocking(0)
+    sock.settimeout(max_count * query_time)
+
+    try:
+        for i in range(0,max_count-1):
+            try:
+                print("RPI_L: Waiting for RPICam to Come On..." + str(i) + "/" + str(max_count-1))
+                sock.connect((RPI_C_IP, RPI_C_Port))
+                print("RPI_L: Link Established")
+                break
+            except:
+                time.sleep(query_time)
+    except KeyboardInterrupt:
+        if sock:
+            sock.close()
+    except:
+        traceback.print_exc(file=sys.stdout)
+
+    try:
+        try:
+            print("RPI_L: Sending Data")
+            sock.send(send_msg)
+            print("RPI_L: Data Sent")
+            count = 0
+            while True:
+                print("RPI_L: Reading Data...")
+                if count < max_count:
+                    try:
+                        #print("trying...")
+                        msg = sock.recv(1)
+                        #print("msg: " + str(msg))
+                        if marker in msg:
+                            rcv.append(msg[:msg.find(marker)])
+                            break
+                        rcv.append(msg)
+                        count = count + 1
+                    except KeyboardInterrupt:
+                        if sock:
+                            sock.close()
+                        break
+                    except:
+                        print("RPI_L: ERROR: Reading Interrupted")
+                        break
+            sock.close()
+        except KeyboardInterrupt:
+            if sock:
+                sock.close()
+        except:
+            traceback.print_exc(file=sys.stdout)
+            print("RPI_L: Connect Attempt Failed")
+            if sock:
+                sock.close()
+                sock = -1
+    except KeyboardInterrupt:
+        sock.close()
+    except:
+        if i == max_count - 1:
+            print("RPI_L: RPI_C Never Connected")
+        traceback.print_exc(file=sys.stdout)
+
+    return rcv
+
+
diff --git a/Nov_27/Full/Laser/transfer_test_correct_config/slaveSleep.py b/Nov_27/Full/Laser/transfer_test_correct_config/slaveSleep.py
new file mode 100644
index 0000000..9d0b0c8
--- /dev/null
+++ b/Nov_27/Full/Laser/transfer_test_correct_config/slaveSleep.py
@@ -0,0 +1,77 @@
+import socket, time, sys, subprocess
+import RPi.GPIO as GPIO
+from SETTINGS import *
+
+def slaveSleep(pMap):
+    max_count = 30
+
+    # Configure board pin numbering system
+    GPIO.setmode(GPIO.BCM)
+    # Setup pin 12 to high so upon shutdown the pin drops and notifies the Arduino to cut power
+    GPIO.setup(pMap[12],GPIO.OUT)
+    GPIO.output(pMap[12],GPIO.HIGH)
+    # Set up pin 16 as clock output
+    GPIO.setup(pMap[16],GPIO.OUT)
+    GPIO.output(pMap[16],GPIO.LOW)
+    print("RPI_L: 16 Low, 12 High")
+
+    # Set up socket and connect to the server
+    client = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
+    client.setblocking(0)
+    client.settimeout(query_time*max_count)
+    #client.connect(('192.168.15.104',1234))
+
+    for i in range(0, max_count - 1):
+        try:
+            client.connect((RPI_C_IP, RPI_C_Port))
+            break
+        except:
+            time.sleep(query_time)
+
+    # Receive t1 from Master, at t1p
+    t1b = client.recv(16) # Receive t1 value
+    t2p = time.time() # Get time of delivery of t1, which is t2p
+
+    t3p = time.time() # Get time t3p, send notification to Master
+    client.send(str.encode(str(int(time.time()*10**6))))
+
+    t4b = client.recv(16)
+
+    # Now we have all times, t1, t2p, t3p, t4
+
+    t1 = int(t1b.decode())
+    t2p = int(t2p*10**6)
+    t3p = int(t3p*10**6)
+    t4 = int(t4b)
+
+
+    print('t1: ' + str(t1))
+    print('t2p: ' + str(t2p))
+    print('t3p: ' + str(t3p))
+    print('t4: ' + str(t4))
+
+    r = 30 # transmission time, microseconds
+
+    e = t2p - t1 - (r / 2)
+
+    print('Error Estimate: ' + str(e))
+
+
+    #print(str(t1 - t2p))
+
+    # This is the synchronization code for the CalTrans bridge start up
+    t_awake = int(client.recv(16))
+
+    t_sleep = t_awake - int(time.time()*10**6) + e
+
+    print('t_sleep: ' + str(t_sleep))
+
+    t_sleep_sec = t_sleep / 10.0**6
+
+    print('sleep time: ' + str(t_sleep_sec))
+
+    client.close()
+
+    return t_sleep_sec
+
+
diff --git a/Nov_27/Manual/Camera/RPiCam/._RPiCM.py b/Nov_27/Manual/Camera/RPiCam/._RPiCM.py
new file mode 100644
index 0000000..540a5a9
Binary files /dev/null and b/Nov_27/Manual/Camera/RPiCam/._RPiCM.py differ
diff --git a/Nov_27/Manual/Camera/RPiCam/._img_capture.py b/Nov_27/Manual/Camera/RPiCam/._img_capture.py
new file mode 100644
index 0000000..cdc2139
Binary files /dev/null and b/Nov_27/Manual/Camera/RPiCam/._img_capture.py differ
diff --git a/Nov_27/Manual/Camera/RPiCam/RPiCM.py b/Nov_27/Manual/Camera/RPiCam/RPiCM.py
new file mode 100644
index 0000000..ca837ac
--- /dev/null
+++ b/Nov_27/Manual/Camera/RPiCam/RPiCM.py
@@ -0,0 +1,174 @@
+# Author:  Alex R. Mead
+# Date: May 2016
+# Description:
+# This code will run on the Raspberry Pi 2 with Camera Module (RPiCM) to interact with the MacBookPro (MBP). It will
+# wait for the MBP to request a measurement, take a measurement, then wait for the MBP to request the latest
+# measurement, at which time it will send the measurement to the MBP.
+
+# Same as the MBP this system is designed to run like finite state machine.
+
+from __future__ import (
+    unicode_literals,
+    absolute_import,
+    print_function,
+    division,
+    )
+
+import sys
+import socket
+import io
+import subprocess
+import picamera
+import numpy as np
+import gc
+from time import sleep
+
+# Constants
+#IP = '127.0.0.1' # Testing on a single machine
+IP = '0.0.0.0' # Actual run time.
+port1 = 1234
+port2 = 2345
+marker = "?"
+
+MAX_PIXEL_VALUE = 1023 # 2^10 = 1024
+PIXEL_HEIGHT = 1944
+PIXEL_WIDTH = 2592
+
+# Takes in the Camera object instantiated at the beginning of the run and takes the desired SS expsoures with it.
+def HDRI(SS,camera):
+
+    size = len(SS)
+    print("Taking %i measurements.\n",size)
+    names = []
+
+    # Loop through for each shutter speed requested
+    for index, shutterSpeed in enumerate(SS):
+        fileName = str(shutterSpeed) + ".data"
+        camera.shutter_speed = shutterSpeed
+        sleep(1)
+        print(str(camera.shutter_speed))
+        camera.capture(fileName,format='jpeg',bayer=True)
+        names.append(fileName)
+
+    # Return the list of filenames to the calling function.
+    return names
+
+def main():
+
+    STATE = "idle"
+    RUNNING = [True,0]
+
+    # First declare the camera object and configure it as needed
+    camera = picamera.PiCamera()
+    camera.framerate=1
+    sleep(1.0)
+
+    camera.led = False
+    camera.iso = 100
+    sleep(10.0)
+    # This longer sleep is needed so analog_gain and digital_gains settle so when I turn them off in the next step
+    # they will have settled and not be in their initial 'low' values.
+
+    camera.exposure_mode = 'off' # Fixes analog_gain and digital_gain values
+    g = camera.awb_gains
+    camera.aws_mode = 'off'
+    camera.awb_gains = [1.0,1.0] # typical values 0.9-1.9 according to documentation, I picked these for consistency.
+    camera.rotation = 180
+    sleep(2.0)
+
+    while(RUNNING[0]):
+
+        if("idle" == STATE):
+            print("We're in idle state waiting to hear from the MacBookPro...")
+
+            # Start up a connection listening for the MBP.
+            sock = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
+            sock.setsockopt(socket.SOL_SOCKET,socket.SO_REUSEADDR,1)
+            sock.bind((IP,port1))
+            sock.listen(1)
+            connection, client_address = sock.accept()
+
+            rcv_msg = []
+            while True:
+                data = connection.recv(1)
+                if marker in data:
+                    rcv_msg.append(data[:data.find(marker)])
+                    break
+                rcv_msg.append(data)
+
+            # Check the message received from the MBP, if correct, continue, else exit.
+            rpl = ''.join(rcv_msg)
+            if rpl == "True":
+                print("Now going to acknowledge measurement.")
+
+            # elif used to teminate remotely.
+            elif rpl == "c":
+                print("Big John called it for me...")
+                sys.exit(0)
+
+            # Acknowledge the MBP so it knows a measurement will be taking place.
+            connection.send("True?")
+            connection.close()
+
+            # Change state: Continue on and take measurement
+            STATE ="Measuring"
+
+        elif ("Measuring" == STATE):
+            print("Engaging the Camera Module to measure the output of the CFS under test...")
+            # Here is the function call to take the measurement of the CFS inside CUBE2.0
+
+            # List of the shutter speeds in microseconds to be used
+            #SS = [1000000, 100000, 10000, 1000, 100, 10];
+            SS = [1000000, 100000, 10000];
+
+            # Take the measurements with the above stated shutter speeds
+            files = HDRI(SS,camera)
+
+            print("...Measurement taken of CFS.\n")
+
+            # Continue to the next state and wait for MBP to query for the 145 measurements.
+            STATE = "waitToSend"
+
+        elif("waitToSend" == STATE):
+            print("Waiting to send to MBP...")
+            # Setup connection to wait for the request from the MBP
+            # This maybe a short wait as the MBP may be trying to connect already if the Measurememt was a long time.
+            sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+            sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+            sock.bind((IP, port2))
+            sock.listen(1)
+            connection, client_address = sock.accept()
+
+            rcv_msg = []
+            while True:
+                data = connection.recv(1)
+                if marker in data:
+                    rcv_msg.append(data[:data.find(marker)])
+                    break
+                rcv_msg.append(data)
+
+            # Check the message received from the MBP, if correct, continue, else exit.
+            rpl = ''.join(rcv_msg)
+            if rpl == "True":
+                print("Send measurement.")
+
+            # Now use the same connection to let the MBP know it can grab the .jpg+RAW
+            # files and run RPiCM_C.c locally over there, which is MUCH faster.
+            connection.sendall("True?")
+            connection.close()
+
+            print("...measurment has been sent.\n")
+
+            # Change state: measurement has been sent, change state back to idle
+            STATE = "idle"
+
+        # Cycle counter for the finite state machine
+        RUNNING[1] = RUNNING[1] + 1
+        print("Cycle number: " + str(RUNNING[1]))
+
+    print("Little John called it...")
+    sys.exit(0)
+
+if __name__ == "__main__":
+    main()
+
diff --git a/Nov_27/Manual/Camera/RPiCam/cam.py b/Nov_27/Manual/Camera/RPiCam/cam.py
new file mode 100644
index 0000000..c91e666
--- /dev/null
+++ b/Nov_27/Manual/Camera/RPiCam/cam.py
@@ -0,0 +1,32 @@
+import picamera
+from fractions import Fraction
+
+cam = picamera.PiCamera(resolution=(640, 480))
+cam.exposure_mode = 'off'
+DEBUG_DIR = 'debug/'
+filename = DEBUG_DIR + 'shutter_{0}_iso_{1}_expo_{2}.png'
+cam.capture("base.png")
+cam.framerate = Fraction(1, 6)
+while True:
+    try:
+        exp = input("Run experiment?[y/n] ")
+        if exp == 'y':
+            ss = input("shutter speed:  ")
+            iso = input("iso:  ")
+            options = {'a': 'off', 'b': 'auto', 'c': 'night', 'd': 'night_preview', 'e': 'sports'}
+            expo = input("exposure mode: " + str(options) + "   ")
+            #bright = input("brightness:  ")
+            ss, iso, opt = int(ss), int(iso), options[expo]
+            cam.shutter_speed = ss
+            cam.iso = iso
+            cam.exposure_mode = options[expo]
+            #cam.brightness = bright
+            cam.capture(filename.format(ss, iso, opt))
+        else:
+            cam.close()
+            break
+    except ValueError:
+        pass
+
+
+
diff --git a/Nov_27/Manual/Camera/RPiCam/img_capture.py b/Nov_27/Manual/Camera/RPiCam/img_capture.py
new file mode 100644
index 0000000..86fd14a
--- /dev/null
+++ b/Nov_27/Manual/Camera/RPiCam/img_capture.py
@@ -0,0 +1,186 @@
+import picamera
+import os
+from fractions import Fraction
+import sys, traceback
+import time
+
+
+class ImgCollector:
+
+    def __init__(self, dir='', ns='img', form='png', raw=False, num=1, serialize=True):
+        if dir:
+            if not os.path.exists(dir):
+                os.mkdir(dir)
+            if dir[len(dir)-1] != '/':
+                dir += '/'
+
+        self.name_scheme = dir + ns + '_{0}.' + form
+        self._dir = dir
+        self._ns = ns
+        self._form = form
+        self._raw = raw
+        self._num = num
+        self.init_cam()
+        if serialize:
+            if os.path.exists("img_log.txt"):
+                rfile = open("img_log.txt", "r")
+                try:
+                    self.counter = int(rfile.read())
+                except ValueError:
+                    self.counter = 1
+            else:
+                rfile = open("img_log.txt", "w")
+                self.counter = 1
+            rfile.close()
+        else:
+            self.counter = 1
+
+
+    def change_ns(self, ns):
+        self._ns = ns
+        self.name_scheme = self._dir + self._ns + '_{0}.' + self._form
+
+    def change_format(self, form):
+        self._form = form
+        self.name_scheme = self._dir + self._ns + '_{0}.' + self._form
+
+    def change_dir(self, dir):
+        self._dir = dir
+        self.name_scheme = self._dir + self._ns + '_{0}.' + self._form
+
+    def change_num(self, num):
+        self._num = num
+        if self._num == 1:
+            self.capture = self.uni_capture
+        else:
+            self.capture = self.multi_capture
+
+    def init_cam(self):
+        self.cam = picamera.PiCamera(resolution=(640, 480))
+        time.sleep(2)
+        self.cam.led = False
+        self.cam.framerate = Fraction(1, 6)
+        self.cam.rotation = 180
+        self.cam.shutter_speed = 800000
+        self.cam.iso = 100
+        self.cam.exposure_mode = 'off'
+        time.sleep(3)
+        if self._num == 1:
+            self.capture = self.uni_capture
+        else:
+            self.capture = self.multi_capture
+
+    def get_last_meas(self):
+        if self._num == 1:
+            return self.name_scheme.format(self.counter - 1)
+        else:
+            return self._dir + self._ns + '_%d_{0}.' % (self.counter - 1) + self._form
+
+    def shutdown(self):
+        self.cam.close()
+        wfile = open("img_log.txt", "w")
+        wfile.write(str(self.counter))
+        wfile.close()
+
+    def uni_capture(self):
+        self.cam.capture(self.name_scheme.format(self.counter), bayer=self._raw)
+        self.counter += 1
+
+    def multi_capture(self):
+        file_list = [self.name_scheme.format("%d_%d" % (self.counter, i)) for i in range(1, self._num+1)]
+        self.cam.capture_sequence(file_list, bayer=self._raw)
+        self.counter += 1
+
+
+def main():
+    prompt = input("Welcome to the RPiCam Module. Type q for quick test, d for debug, or f / [other inputs] for full test.\n")
+    global recur
+    if prompt == 'q':
+        directory = 'quick_test'
+        name_pattern = 'img'
+        pic_format = 'png'
+        raw_image = False
+        num_meas = 1
+        recur = True
+    elif prompt == 'd':
+        camera_debug()
+        sys.exit()
+    else:
+        directory = input("Input a directory:\n")
+        name_pattern = input("Input a name pattern:\n")
+        pic_format = input("Input a picture format:\n")
+        raw_image = input("Raw image?[y/n]\n") in ['y', 'yes']
+        num_meas = int(input("Number of image samples for one measurement?\n"))
+        recur = False
+
+    while True:
+        try:
+            ic = ImgCollector(dir=directory, ns=name_pattern, form=pic_format, raw=raw_image, num=num_meas)
+            break
+        except:
+            directory = input("Ill-formated directory, type in another one: ")
+            traceback.print_exc(file=sys.stdout)
+
+    while True:
+        if recur:
+            ic.shutdown()
+            break
+
+        option = input("Type in an action or h for help:\n")
+        if option == 'h':
+            print("m: take measurement\n" +
+                  "r: show raw image status\n" +
+                  "cr: change raw image status\n" +
+                  "cf: change image format\n" +
+                  "cd: change directory\n" +
+                  "cn: change name\n" +
+                  "cm: change number of measurement\n" +
+                  "e: end the program")
+        elif option == 'm':
+            ic.capture()
+        elif option == 'r':
+            print(ic._raw)
+        elif option == 'cr':
+            ic._raw = input("Raw image?[y/n]\n") in ['y', 'yes']
+        elif option == 'cf':
+            ic.change_format(input("Input a picture format:\n"))
+        elif option == 'cd':
+            ic.change_dir(input("Input a directory:\n"))
+        elif option == 'cn':
+            ic.change_ns(input("Input a name pattern:\n"))
+        elif option == 'cm':
+            ic.change_num(int(input("Number of image samples for one measurement?\n")))
+        elif option == 'e':
+            ic.shutdown()
+            break
+
+
+def camera_debug():
+    DEBUG_DIR = 'debug/'
+    filename = DEBUG_DIR + 'shutter_{0}_iso_{1}_bright_{2}.jpeg'
+    analog = 'analog_gain: '
+    awb_gain = 'awb_gain: '
+    expo_modes = ['night', 'night_preview', 'very_long']
+    #flash_modes = ['off', 'redeye']
+    flash_modes = ['off']
+    shutter = [600000, 800000, 1000000]
+    with picamera.PiCamera(resolution=(640, 480)) as cam:
+        time.sleep(1)
+        print(analog + str(cam.analog_gain))
+        print(awb_gain + str(cam.awb_gains))
+        cam.framerate = Fraction(1, 6)
+        for ss in shutter:
+            #for br in brightness:
+            time.sleep(0.1)
+            cam.shutter_speed = ss
+            cam.brightness = 50
+            cam.capture(filename.format(ss), bayer=False)
+
+
+if __name__ == "__main__":
+    recur = True
+    while recur:
+        main()
+
+
+
diff --git a/Nov_27/Manual/Camera/accel_correct_config/LICENSE b/Nov_27/Manual/Camera/accel_correct_config/LICENSE
new file mode 100644
index 0000000..9cecc1d
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+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    {one line to give the program's name and a brief idea of what it does.}
+    Copyright (C) {year}  {name of author}
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    {project}  Copyright (C) {year}  {fullname}
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.
diff --git a/Nov_27/Manual/Camera/accel_correct_config/accel.py b/Nov_27/Manual/Camera/accel_correct_config/accel.py
new file mode 100644
index 0000000..331c0c6
--- /dev/null
+++ b/Nov_27/Manual/Camera/accel_correct_config/accel.py
@@ -0,0 +1,739 @@
+#!/usr/bin/env python
+# -*- coding:utf-8, indent=tab, tabstop=4 -*-
+#
+# See 'LICENSE'  for copying
+#
+# Revision history
+# Date			Author					Version		Details
+# ----------------------------------------------------------------------------------
+# 2016-12-31	Massimo Di Primio		V.0.04		Fixed some basic functionality
+#
+# 2017-01-03	Massimo Di Primio		0.05		Added Interrut handler
+#
+# 2018-01-10    Massimo Di Primio       0.06        Added config file parser
+
+"""Simple code example for Adafruit MMA8452 3-axis Accelerometer
+
+This experimental code is intended for measuring gravity acceleration trough Adafruit(c) MMA8451, connected
+to a Raspberry Pi Model 2A, 2B, 2B+ or 3 (not yet tested with RPi Zero).
+Through this code we will demonstrate the ability of the 3-axis sensor MMA8451 to efficiently measure
+gravity acceleration, so that we can identify the spatial orientation of the device.
+Further and even more useful application can start from this minimal basic code.
+"""
+
+import smbus
+import time
+import datetime
+import os
+import sys
+import logging
+import threading
+import RPi.GPIO as GPIO
+import rss_cli_config as clicfg
+from collections import deque
+
+__author__ = "Massimo Di Primio"
+__copyright__ = "Copyright 2016, dpmiictc"
+__credits__ = ["Massimo Di Primio", "Dario Dalla Libera"]
+__license__ = "GNU GENERAL PUBLIC LICENSE Version 3"
+__version__ = "0.0.1"
+__deprecated__ = "None so far"
+__date__ = "2017-01-03"
+__maintainer__ = "Massimo Di Primio"
+__email__ = "massimo@diprimio.com"
+__status__ = "Testing"
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# User Configuration Options (UCO)
+# This section will be repaced soon by an external configuration file
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+CFG_INTERRUPT = 1
+# MMA8451_RANGE		= {}
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Application Definition Constants (ADC)
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+# Useful definitions
+EARTH_GRAVITY_MS2 = 9.80665
+
+# Range values
+RANGE_8_G = 0b10  # +/- 8g
+RANGE_4_G = 0b01  # +/- 4g
+RANGE_2_G = 0b00  # +/- 2g (default value)
+
+RANGE_DIVIDER = {
+    RANGE_2_G: 4096 / EARTH_GRAVITY_MS2,
+    RANGE_4_G: 2048 / EARTH_GRAVITY_MS2,
+    RANGE_8_G: 1024 / EARTH_GRAVITY_MS2,
+}
+
+# Some static values
+deviceName = 0x1a
+
+# Various addresses
+i2caddr = 0x1D
+#
+# Useful Register Address
+REG_STATUS = 0x00  # Read-Only
+REG_WHOAMI = 0x0d  # Read-Only
+REG_DEVID = 0x1A  # Read-Only
+REG_OUT_X_MSB = 0x01  # Read-Only
+REG_OUT_X_LSB = 0x02  # Read-Only
+REG_OUT_Y_MSB = 0x03  # Read-Only
+REG_OUT_Y_LSB = 0x04  # Read-Only
+REG_OUT_Z_MSB = 0x05  # Read-Only
+REG_OUT_Z_LSB = 0x06  # Read-Only
+REG_F_SETUP = 0x09  # Read/Write
+REG_XYZ_DATA_CFG = 0x0e  # Read/Write
+REG_PL_STATUS = 0x10  # Read-Only
+REG_PL_CFG = 0x11  # Read/Write
+REG_CTRL_REG1 = 0x2A  # Read/Write
+REG_CTRL_REG2 = 0x2B  # Read/Write
+REG_CTRL_REG3 = 0x2C  # Read/Write
+REG_CTRL_REG4 = 0x2D  # Read/Write
+REG_CTRL_REG5 = 0x2E  # Read/Write
+
+REDUCED_NOISE_MODE = 0
+OVERSAMPLING_MODE = 1
+HIGH_RES_MODE = {
+    REDUCED_NOISE_MODE: [REG_CTRL_REG1, 0x4],
+    OVERSAMPLING_MODE: [REG_CTRL_REG2, 0x2],
+}
+
+# Auto-Wake Sample Frequencies for Register CTRL_REG1 (0x2A) (Read/Write)
+# sample frequency when the device is in SLEEP Mode. Default value: 00.
+ASLP_RATE_FREQ_50_HZ = 0x00
+ASLP_RATE_FREQ_12_5_HZ = 0x40
+ASLP_RATE_FREQ_6_25HZ = 0x80
+ASLP_RATE_FREQ_1_56_HZ = 0xc0
+
+# Data rate values
+DATARATE_800_HZ = 0x00  # 800Hz
+DATARATE_400_HZ = 0x08  # 400Hz
+DATARATE_200_HZ = 0x10  # 200Hz
+DATARATE_100_HZ = 0x18  # 100Hz
+DATARATE_50_HZ = 0x20  # 50Hz
+DATARATE_12_5_HZ = 0x28  # 12.5Hz
+DATARATE_6_25HZ = 0x30  # 6.25Hz
+DATARATE_1_56_HZ = 0x38  # 1.56Hz
+
+# Orientation labeling 
+PL_PUF = 0
+PL_PUB = 1
+PL_PDF = 2
+PL_PDB = 3
+PL_LRF = 4
+PL_LRB = 5
+PL_LLF = 6
+PL_LLB = 7
+
+# Precision
+PRECISION_14_BIT = 14
+PRECISION_08_BIT = 8
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define Register Flags
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Register CTRL_REG1 (0x2a) R/W - System Control 1 Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |  ASLPRATE1   |  ASLPRATE0   |     DR2      |    DR1       |    DR0       |  LNOISE      |  F_READ      |  ACTIVE      |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# Auto-Wake Sample frequency Selection
+FLAG_ASLPRATE_50_HZ = 0x00  # Auto-Wake Sample frequency (Sleep Mode Rate Detection) 50 Hz
+FLAG_ASLPRATE_12_5_HZ = 0x40  # Auto-Wake Sample frequency (Sleep Mode Rate Detection) 12.5 Hz
+FLAG_ASLPRATE_6_25_HZ = 0x80  # Auto-Wake Sample frequency (Sleep Mode Rate Detection) 6.25 Hz
+FLAG_ASLPRATE_1_56_HZ = 0xc0  # Auto-Wake Sample frequency (Sleep Mode Rate Detection) 1.56 Hz
+# System Output Data Rates Selection
+FLAG_ODR_800_HZ = 0x00  # System Output Data Rate 800 Hz
+FLAG_ODR_400_HZ = 0x08  # System Output Data Rate 400 Hz
+FLAG_ODR_200_HZ = 0x10  # System Output Data Rate 200 Hz
+FLAG_ODR_100_HZ = 0x18  # System Output Data Rate 100 Hz
+FLAG_ODR_50_HZ = 0x20  # System Output Data Rate 50 Hz
+FLAG_ODR_12_5_HZ = 0x28  # System Output Data Rate 12.5 Hz
+FLAG_ODR_6_25_HZ = 0x30  # System Output Data Rate 6.25 Hz
+FLAG_ODR_1_56_HZ = 0x38  # System Output Data Rate 1.56 Hz
+# Other Flags
+FLAG_LNOISE = 0x04  # Low Noise (1: Reduced Noise, 0: Normal Mode)
+FLAG_F_READ = 0x02  # Fast Read  (1: 8 bit sample, 0: 14 bit Sample)
+FLAG_ACTIVE = 0x01  # Active (1: ACTIVE Mode, 0: STANDBY Mode)
+
+# Register CTRL_REG2 (0x2b) R/W - System Control 2 Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   ST         |   RST        |     0        |  SMODS1      |  SMODS0      |   SLPE       |   MODS1      |   MODS0      |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# Other Flags
+FLAG_STEST = 0x80  # Self Test (1: Self-Test enabled, 0: Self-Test disabled)
+FLAG_RESET = 0x40  # Reset (1: Reset enabled, 0: Reset disabled)
+# Sleep Mode Power Scheme Selection
+FLAG_SMODS_NORM = 0x00  # Sleep Mode Power Scheme Selection: Normal
+FLAG_SMODS_LNLP = 0x0a  # Sleep Mode Power Scheme Selection: Low-Noise Low Power
+FLAG_SMODS_HR = 0x12  # Sleep Mode Power Scheme Selection: High Resolution
+FLAG_SMODS_LP = 0x1b  # Sleep Mode Power Scheme Selection: Low Power
+# Other Flags
+FLAG_SLPE = 0x04  # Auto-Sleep (1: Auto-Sleep enabled, 0: Auto-Sleep Disabled)
+# Active Mode Power Scheme Selection (for both: Sleep and Active mode)
+FLAG_MODS_NORM = 0x00  # Active Mode Power Scheme Selection: Normal
+FLAG_MODS_LNLP = 0x09  # Active Mode Power Scheme Selection: Low-Noise Low Power
+FLAG_MODS_HR = 0x12  # Active Mode Power Scheme Selection: High Resolution
+FLAG_MODS_LP = 0x1b  # Active Mode Power Scheme Selection: Low Power
+
+# Register CTRL_REG4 (0x2d) R/W - Interrupt Enable Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# | INT_EN_ASLP  | INT_EN_FIFO  |INT_EN_TRANS  |INT_EN_LNDPR  |INT_EN_PULSE  |INT_EN_FF_MT  |       -      | INT_EN_DRDY  |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_INT_EN_ASLP = 0x80  # Interrupt Auto SLEEP/WAKE (0: Disabled, 1: Enabled)
+FLAG_INT_EN_FIFO = 0x40  # Interrupt FIFO (0: Disabled, 1: Enabled)
+FLAG_INT_EN_TRANS = 0x20  # Interrupt Transient (0: Disabled, 1: Enabled)
+FLAG_INT_EN_LNDPRT = 0x10  # Interrupt Orientation (0: Disabled, 1: Enabled)
+FLAG_INT_EN_PULSE = 0x08  # Interrupt Pulse Detection (0: Disabled, 1: Enabled)
+FLAG_INT_EN_FF_MT = 0x04  # Interrupt Freefall/Motion (0: Disabled, 1: Enabled)
+FLAG_INT_EN_BIT1 = 0x00  # Not Used
+FLAG_INT_EN_DRDY = 0x01  # Interrupt Data Ready (0: Disabled, 1: Enabled)
+
+# Register CTRL_REG5 (0x2e) R/W - Interrupt Configuration Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# | INT_CFG_ASLP | INT_CFG_FIFO |INT_CFG_TRANS |INT_CFG_LNDPRT|INT_CFG_PULSE |INT_CFG_FF_MT |       -      | INT_CFG_DRDY |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_INT_CFG_ASLP = 0x80  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_FIFO = 0x40  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_TRANS = 0x20  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_LNDPRT = 0x10  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_PULSE = 0x08  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_FF_MT = 0x04  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_BIT1 = 0x00  # Not Used
+FLAG_INT_CFG_DRDY = 0x01  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+
+# Register XYZ_DATA_CFG (0x0e) R/W
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |     0        |     0        |     0        |  HPF_OUT     |     0        |     0        |    FS1       |    FS0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# Other Flags
+FLAG_XYZ_DATA_BIT_7 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_6 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_5 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_HPF_OUT = 0x00  # High-Pass Filter (1: output data High-pass filtered, 0: output data High-pass NOT filtered)
+FLAG_XYZ_DATA_BIT_3 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_2 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_FS_2G = 0x00  # Full Scale Range 2g
+FLAG_XYZ_DATA_BIT_FS_4G = 0x01  # Full Scale Range 4g
+FLAG_XYZ_DATA_BIT_FS_8G = 0x02  # Full Scale Range 8g
+FLAG_XYZ_DATA_BIT_FS_RSVD = 0x03  # Reserved
+
+# Register F_SETUP (0x09) R/W - FIFO Setup Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |  F_MODE1     |  F_MODE0     |  F_WMRK5     |  F_WMRK4     |  F_WMRK3     |  F_WMRK2     |  F_WMRK1     |  F_WMRK0     |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_F_MODE_FIFO_NO = 0x00  # FIFO is disabled.
+FLAG_F_MODE_FIFO_RECNT = 0x40  # FIFO contains the most recent samples when overflowed (circular buffer)
+FLAG_F_MODE_FIFO_STOP = 0x80  # FIFO stops accepting new samples when overflowed.
+FLAG_F_MODE_FIFO_TRIGGER = 0xc0  # FIFO Trigger mode
+
+# Register PL_STATUS (0x010) R/O - Portrait/Landscape Status Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |    NEWLP     |    LO        |      -       |      -       |     -        |  LAPO[1]     |  LAPO[0]     |  BAFRO       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_PL_NEWLP = 0x80  # Landscape/Portrait status change flag.
+FLAG_PL_LO = 0x40  # Z-Tilt Angle Lockout.
+FLAG_PL_LAPO_PU = 0x00  # 00: Portrait Up: Equipment standing vertically in the normal orientation
+FLAG_PL_LAPO_PD = 0x02  # 01: Portrait Down: Equipment standing vertically in the inverted orientation
+FLAG_PL_LAPO_LR = 0x04  # 10: Landscape Right: Equipment is in landscape mode to the right
+FLAG_PL_LAPO_LL = 0x06  # 11: Landscape Left: Equipment is in landscape mode to the left.
+FLAG_PL_BAFRO = 0x01  # Back or Front orientation. (0: Front: Equipment is in the front facing orientation, 1: Back)
+
+# Register PL_CFG (0x011) R/W - Portrait/Landscape Configuration Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   DBCNTM     |  PL_EN       |      -       |      -       |     -        |      -       |      -       |     -        |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_PL_CFG_DBCNTM = 0x80  # Debounce counter mode selection (0: Decrements debounce, 1: Clears counter)
+FLAG_PL_CFG_PL_EN = 0x40  # Portrait/Landscape Detection Enable (0: P/L Detection Disabled, 1: P/L Detection Enabled)
+
+# Register TRANSIENT_CFG (0x1d) R/W - Transient_CFG Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |      -       |      -       |      -       |     ELE      |   ZTEFE      |   YTEFE      |   XTEFE      |  HPF_BYP     |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_TRANSIENT_CFG_ELE = 0x10      # Transient event flags (0: Event flag latch disabled; 1: Event flag latch enabled)
+FLAG_TRANSIENT_CFG_ZTEFE = 0x08    # Event flag enable on Z (0: Event detection disabled; 1: Raise event flag)
+FLAG_TRANSIENT_CFG_YTEFE = 0x04    # Event flag enable on Y (0: Event detection disabled; 1: Raise event flag)
+FLAG_TRANSIENT_CFG_XTEFE = 0x02    # Event flag enable on X (0: Event detection disabled; 1: Raise event flag)
+FLAG_TRANSIENT_CFG_HPF_BYP = 0x01  # Bypass High-Pass filter/Motion Detection
+
+# Register TRANSIENT_SCR (0x01e) R/O - TRANSIENT_SRC Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |       -      |      EA      |   ZTRANSE    | Z_Trans_Pol  |   YTRANSE    | Y_Trans_Pol  |   XTRANSE    | X_Trans_Pol  |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_TRANSIENT_SCR_EA = 0x40       # Event Active Flag (0: no event flag has been asserted; 1: one or more event flag has been asserted)
+FLAG_TRANSIENT_SCR_ZTRANSE = 0x20  # Z transient event (0: no interrupt, 1: Z Transient acceleration > than TRANSIENT_THS event has occurred
+FLAG_TRANSIENT_SCR_ZTR_POL = 0x10  # Polarity of Z Transient Event that triggered interrupt (0: Z event Positive g, 1: Z event Negative g)
+FLAG_TRANSIENT_SCR_YTRANSE = 0x08  # Y transient event (0: no interrupt, 1: Y Transient acceleration > than TRANSIENT_THS event has occurred
+FLAG_TRANSIENT_SCR_YTR_POL = 0x04  # Polarity of Y Transient Event that triggered interrupt (0: Y event Positive g, 1: Y event Negative g)
+FLAG_TRANSIENT_SCR_XTRANSE = 0x02  # X transient event (0: no interrupt, 1: X Transient acceleration > than TRANSIENT_THS event has occurred
+FLAG_TRANSIENT_SCR_XTR_POL = 0x01  # Polarity of X Transient Event that triggered interrupt (0: X event Positive g, 1: X event Negative g)
+
+# Register FF_MT_THS (0x017) R/W - Freefall and Motion Threshold Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   DBCNTM     |     THS6     |    THS5      |    THS4      |    THS3      |    THS2      |    THS1      |    THS0      |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define SOME GLOBAL VARIABLES
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define the acceleration FIFO buffer
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# The acceleration FIFO buffer is a list of records containing all meaningful acceleration data plus some
+# other useful information, whose format il as described below
+#
+#   1.  curTime     as returned by: datetime.datetime.now().  Format is: 'YYYY-MM-DD hh:mi:ss.uuuuuuu'
+#   2.  xAccel      Current X acceleration value in row format
+#   3.  yAccel      Current Y acceleration value in row format
+#   4.  xAccel      Current Z acceleration value in row format
+#   5.  plo         Current Portrait/Landscape orientation
+#accelBuffer = [0, 0, 0, 0, 0]
+accelBuffer = []
+#accelBuffer.append([0, 0, 0, 0, 0])
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define the threaded interrupt vector
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+def my_callback(channel):
+    """
+    my_callback is the threaded callback functions for interrupt events.
+    These will run in another thread when our events are detected
+
+    :param channel: The GPIO channel where the interrupt event was risen
+    :return: None
+    """
+    # Please  nte that, for performance reasons, axis data are not convertd in m/s2,
+    # Although, all 6 registers containing acceleration data are read and formatted appropriately
+    bus = smbus.SMBus(1)
+    axisData = bus.read_i2c_block_data(i2caddr, REG_OUT_X_MSB, 6)
+    #
+    #print ("!"),  #print("Falling edge detected on GPIO channel: " + str(channel))
+    #
+    runTimeConfigObject.NumInterrupts = runTimeConfigObject.NumInterrupts + 1
+    #
+    xAccel = ((axisData[0] << 8) | axisData[1]) >> 2
+    yAccel = ((axisData[2] << 8) | axisData[3]) >> 2
+    zAccel = ((axisData[4] << 8) | axisData[5]) >> 2
+    plo = bus.read_byte_data(i2caddr, REG_PL_STATUS) & 0x7
+    # Append data to the accelBuffer
+    accelBuffer.append([str(datetime.datetime.now()), xAccel, yAccel, zAccel, plo])
+    pass
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define a class called Accel
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+import ConfigParser
+class Accel():
+    raspiBus = -1               # The Raspberry Pi Bus (dpends on hardware model)
+    raspiIntEnabled = 0         # 0 = Interrupt routine was not enabled after initialization, 1 = Interrupt routine enabled successfully
+    raspiInfo = ""              # Raspberry Pi Info
+
+    def __init__(self):
+
+        #
+        # Setup RPI specific bus
+        #
+        myBus = ""
+        if GPIO.RPI_INFO['P1_REVISION'] == 1:
+            myBus = 0
+        else:
+            myBus = 1
+        #print('myBus=' + str(myBus))
+        self.raspiBus = myBus
+
+        self.b = smbus.SMBus(myBus)  # 0 = /dev/i2c-0 (port I2C0), 1 = /dev/i2c-1 (port I2C1)
+        self.a = i2caddr
+        self.high_res_mode = OVERSAMPLING_MODE
+        self.sensor_range = RANGE_4_G
+        self.raspiInfo = GPIO.RPI_INFO
+
+
+    def whoAmI(self):
+        return self.b.read_byte_data(i2caddr, REG_WHOAMI)
+
+    def init(self):
+        # Preliminary actions
+        # sudo chmod 666 /sys/module/i2c_bcm2708/parameters/combined
+        # sudo echo -n 1 > /sys/module/i2c_bcm2708/parameters/combined
+        #
+        # the above 2 sh commands can be replaced with the following statements, in the case this program is ran as root (sudo)
+        # (For more information, please see: http://raspberrypi.znix.com/hipidocs/topic_i2c_rs_and_cs.htm)
+        #
+        # BCM2708_COMBINED_PARAM_PATH = '/sys/module/i2c_bcm2708/parameters/combined'
+        # os.chmod(BCM2708_COMBINED_PARAM_PATH, 666)
+        # os.system('echo -n 1 > {!s}'.format(BCM2708_COMBINED_PARAM_PATH))
+        # sudo i2cdetect -y 1               # this sh cmmand will search /dev/i2c-1 for all address
+        # sudo i2cget -y 1 0x1d 0x0d		# This sh command should return 0x1a for MMA8451
+        #
+        # Setup all registers appropriately
+        self.writeRegister(REG_CTRL_REG2, self.readRegister(REG_CTRL_REG2) | FLAG_RESET)  # Reset
+        # self.writeRegister(REG_CTRL_REG2,	 self.readRegister(REG_CTRL_REG2) | FLAG_STEST)						# SelfTest
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) & ~FLAG_ACTIVE)  # Put the device in Standby
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) & ~FLAG_F_READ)  # No Fast-Read (14-bits), Fast-Read (8-Bits)
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) | FLAG_ODR_50_HZ)  # Data Rate
+        self.writeRegister(REG_XYZ_DATA_CFG, self.readRegister(REG_XYZ_DATA_CFG) | FLAG_XYZ_DATA_BIT_FS_4G)  # Full Scale Range 2g, 4g or 8g
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) | FLAG_LNOISE)  # Low Noise
+        self.writeRegister(REG_CTRL_REG2, self.readRegister(REG_CTRL_REG2) & ~FLAG_SLPE)  # No Auto-Sleep
+        self.writeRegister(REG_CTRL_REG2, self.readRegister(REG_CTRL_REG2) | FLAG_SMODS_HR)  # High Resolution
+        self.writeRegister(REG_PL_CFG, self.readRegister(REG_PL_CFG) | FLAG_PL_CFG_PL_EN)  # P/L Detection Enabled
+
+        # Setup interrupts
+        if CFG_INTERRUPT == 1:
+            GPIO.setmode(GPIO.BCM)
+            # GPIO 23 & 17 set up as inputs, pulled up to avoid false detection.
+            # Both ports are wired to connect to GND on button press.
+            # So we'll be setting up falling edge detection for both
+            GPIO.setup(17, GPIO.IN, pull_up_down=GPIO.PUD_UP)
+            # when a falling edge is detected on port 17, regardless of whatever
+            # else is happening in the program, the function my_callback will be run
+            # GPIO.add_event_detect(17, GPIO.FALLING, callback=my_callback, bouncetime=300)
+            GPIO.add_event_detect(17, GPIO.FALLING, callback=my_callback)
+            #print("Interrupt OK")
+            self.raspiIntEnabled = 1    # Interrupt enabled successfully
+            # Force 1st sensor read
+            my_callback(0)
+
+
+            # Configure register for interrupt
+            self.writeRegister(REG_CTRL_REG4, 0x00)  # Reset all interrupt enabled flags
+            self.writeRegister(REG_CTRL_REG4, self.readRegister(REG_CTRL_REG4) | FLAG_INT_EN_DRDY)  # Data Ready Interrupt Enabled
+            self.writeRegister(REG_CTRL_REG5, 0x00)  # Reset all interrupt config flags
+            self.writeRegister(REG_CTRL_REG5, self.readRegister(REG_CTRL_REG5) | FLAG_INT_CFG_DRDY)  # Data Ready Interrupt is routed to INT1 pin
+
+            # Initialize the accelBuffer
+            accelBuffer = deque()
+
+
+        # Finally, Activate the sensor
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) | FLAG_ACTIVE)  # Activate the device
+
+    def writeRegister(self, regNumber, regData):
+        """
+        Writes one byte (8-bts) of data passed in 'regData', into the register 'regNumber'
+        """
+        try:
+            self.b.write_byte_data(self.a, regNumber, regData)
+            time.sleep(0.01)
+        except IOError:
+            print("Error detected in function writeRegister() [IOError = " + str(IOError) + "]")
+            sys.exit()
+
+    def readRegister(self, regNumber):
+        """
+        Retrieves one byte (8-bits) of data from register 'regNumber' returning to the caller
+        """
+        try:
+            return self.b.read_byte_data(self.a, regNumber)
+        except IOError:
+            print("Error detected in function readRegister() [IOError = " + str(IOError) + "]")
+            sys.exit()
+
+    def block_read(self, offset, length):
+        """
+        Performs a burst-read on the device registers retrieving the requested amount of data
+        Read a block of <length> bytes from  offset <offset>
+        """
+        try:
+            return self.b.read_i2c_block_data(i2caddr, offset, length)
+        except IOError:
+            print("Error detected in function block_read() [IOError = " + str(IOError) + "]")
+            sys.exit()
+
+    def get_orientation(self):
+        """
+        Get current orientation of the sensor.
+        :return: orientation. Orientation number for the sensor.
+        """
+        orientation = self.b.read_byte_data(self.a, REG_PL_STATUS) & 0x7
+        return orientation
+
+    def getAxisValue(self):
+        """
+        Retrieves axis values and converts into a readable format (i.e. m/s2)
+        :return:	None
+        """
+        # Make sure F_READ and F_MODE are disabled.
+        f_read = self.b.read_byte_data(self.a, REG_CTRL_REG1) & FLAG_F_READ
+        assert f_read == 0, 'F_READ mode is not disabled. : %s' % (f_read)
+        f_mode = self.b.read_byte_data(self.a, REG_F_SETUP) & FLAG_F_MODE_FIFO_TRIGGER
+        assert f_mode == 0, 'F_MODE mode is not disabled. : %s' % (f_mode)
+
+        #
+        self.xyzdata = self.block_read(REG_OUT_X_MSB, 6)
+        if self.high_res_mode is not None:
+            x = ((self.xyzdata[0] << 8) | self.xyzdata[1]) >> 2
+            y = ((self.xyzdata[2] << 8) | self.xyzdata[3]) >> 2
+            z = ((self.xyzdata[4] << 8) | self.xyzdata[5]) >> 2
+            precision = PRECISION_14_BIT  # Precision 14 bit data
+        else:
+            x = (self.xyzdata[0] << 8)
+            y = (self.xyzdata[1] << 8)
+            z = (self.xyzdata[2] << 8)
+            precision = PRECISION_08_BIT  # Precision 08 bit data
+        max_val = 2 ** (precision - 1) - 1
+        signed_max = 2 ** precision
+        #
+        x -= signed_max if x > max_val else 0
+        y -= signed_max if y > max_val else 0
+        z -= signed_max if z > max_val else 0
+        #
+        x = round((float(x)) / RANGE_DIVIDER[self.sensor_range], 3)
+        y = round((float(y)) / RANGE_DIVIDER[self.sensor_range], 3)
+        z = round((float(z)) / RANGE_DIVIDER[self.sensor_range], 3)
+
+        return {"x": x, "y": y, "z": z}
+
+    def debugShowRpiInfo(self):
+        #print("Raspberry Info      = " + str(GPIO.RPI_INFO))
+        print("Raspberry Info      = " + str(self.raspiInfo))
+
+    def debugShowRegisters(self):
+        print("REG_STATUS       (0x00):" + str(format(self.readRegister(REG_STATUS), '#04x')) + " | Binary: " + format(self.readRegister(REG_STATUS), 'b').zfill(8))
+        print("REG_WHOAMI       (0x0d):" + str(format(self.readRegister(REG_WHOAMI), '#04x')) + " | Binary: " + format(self.readRegister(REG_WHOAMI), 'b').zfill(8))
+        print("REG_F_SETUP      (0x09):" + str(format(self.readRegister(REG_F_SETUP), '#04x')) + " | Binary: " + format(self.readRegister(REG_F_SETUP), 'b').zfill(8))
+        print("REG_XYZ_DATA_CFG (0x0e):" + str(format(self.readRegister(REG_XYZ_DATA_CFG), '#04x')) + " | Binary: " + format(self.readRegister(REG_XYZ_DATA_CFG), 'b').zfill(8))
+        print("REG_CTRL_REG1    (0x2a):" + str(format(self.readRegister(REG_CTRL_REG1), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG1), 'b').zfill(8))
+        print("REG_CTRL_REG2    (0x2b):" + str(format(self.readRegister(REG_CTRL_REG2), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG2), 'b').zfill(8))
+        print("REG_CTRL_REG3    (0x2c):" + str(format(self.readRegister(REG_CTRL_REG3), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG3), 'b').zfill(8))
+        print("REG_CTRL_REG4    (0x2d):" + str(format(self.readRegister(REG_CTRL_REG4), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG4), 'b').zfill(8))
+        print("REG_CTRL_REG5    (0x2e):" + str(format(self.readRegister(REG_CTRL_REG5), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG5), 'b').zfill(8))
+        print("REG_PL_STATUS    (0x10):" + str(format(self.readRegister(REG_PL_STATUS), '#04x')) + " | Binary: " + format(self.readRegister(REG_PL_STATUS), 'b').zfill(8))
+        print ("debugRealTime    " + str(runTimeConfigObject.debugRealTime))
+        print ("NumInterrupts    " + str(runTimeConfigObject.NumInterrupts))
+
+    def debugShowOrientation(self):
+        print("Position = %d" % (self.get_orientation()))
+
+    def debugShowAxisAcceleration(self, xaccel, yaccel, zaccel):
+        print("   x (m/s2)= %+.3f" % (xaccel))
+        print("   y (m/s2)= %+.3f" % (yaccel))
+        print("   z (m/s2)= %+.3f" % (zaccel))
+
+    def debugRealTimeBuffer(self):
+        n = 0
+        for elements in accelBuffer:
+            myData = accelBuffer.pop()
+            n += 1
+            print ("N=" + str(n) + " myData=" + str(myData))    # + "Element=" + str(elements))
+        try:
+            print("End of printout\n")
+            #time.sleep(1.0)
+        # os.system("clear")
+        except KeyboardInterrupt:
+            print("Program Termination Requested")
+            sys.exit()
+        
+###############################################################################
+#   Threading functions
+###############################################################################
+#def rssClient():
+#    """Manage data shipping over th network, in a separate thread."""
+#    #logger.debug('Thread Starting')
+#    while True:
+#        time.sleep(1.0)
+#        print ("This is thread rssClient()")
+#
+###############################################################################
+#   Useful functions
+###############################################################################
+def printHelp():
+    print ("\n")
+    print ("usage: accel.py [options]")
+    print ("Available options:")
+    print (" -h \t\t Print this help and exit")
+    print (" -d \t\t Show debug realtime interrupt data")
+    print (" -s \t\t Execute silently (no screen output)")
+    print (" -L <lvl>\t Set Log level. where <lvl> is the log level (0 = NONE - 8 = DEBUG)") 
+    print ("")
+
+
+def main(argv):
+    import sys, getopt, logging
+    #
+    try:
+        opts, args = getopt.getopt(argv,"hdsL:")
+    except getopt.GetoptError:
+        print ("\nInvalid option requested on command line")
+        printHelp()
+        sys.exit(2)
+
+    for opt, arg in opts:
+        if opt == '-h':
+            printHelp()
+            sys.exit()
+        elif opt == '-d':
+            runTimeConfigObject.debugRealTime = 1
+        elif opt == '-s':
+            runTimeConfigObject.executeSilently = 1
+        elif opt == '-L':
+            if (int(arg) == 0) or (int(arg) > 5):
+                pass
+                #logger.setLevel(logger.NOTSET)            # Same as value 0
+            elif int(arg) == 1:
+                logger.setLevel(logging.CRITICAL)          # Same as value 50
+            elif int(arg) == 2:
+                logger.setLevel(logging.ERROR)             # Same as value 40
+            elif int(arg) == 3:
+                logger.setLevel(logging.WARNING)           # Same as value 30
+            elif int(arg) == 4:
+                logger.setLevel(logging.INFO)              # Same as value 20
+            elif int(arg) == 5:
+                logger.setLevel(logging.DEBUG)             # Same as value 10
+
+
+#####################################################################################
+#   M A I N 
+#####################################################################################
+class runTimeConfigObject(object):
+    pass
+#if __name__ == "__main__":
+def extract_meas():
+    
+    #class runTimeConfigObject(object):
+        #pass
+    #
+    # Setup Logger
+    #
+    #logger = logging.basicConfig(level=logging.DEBUG, format='[%(asctime)15s].%(levelname)s] (%(threadName)-10s) %(message)s', )
+    #logger.basicConfig(level=logging.DEBUG,format='[%(asctime)15s].%(levelname)s] (%(threadName)-10s) %(message)s',)
+    logger = logging.getLogger()
+    logger.setLevel(logging.DEBUG)
+    # create console handler and set level to debug
+    ch = logging.StreamHandler()
+    ch.setLevel(logging.DEBUG)
+    # create formatter
+    formatter = logging.Formatter('[%(asctime)s.%(levelname)s] (%(name)s.%(threadName)-10s) : %(message)s')
+    # add formatter to ch
+    ch.setFormatter(formatter)
+    # add ch to logger
+    logger.addHandler(ch)
+
+    #
+    # Set some default command line options
+    #
+    runTimeConfig = runTimeConfigObject()
+    runTimeConfigObject.debugRealTime = 0       # 1 = Show debug realtime interrupt data
+    runTimeConfigObject.executeSilently = 0     # 1 = Execute silently (no sceen output)
+    runTimeConfigObject.NumInterrupts = 0       # keep Nbr of sensor interrupts withi the main loop
+    
+    main(sys.argv[1:])
+
+    #
+    # Read configuration file
+    #
+    configFile = "./rss_config.dat"
+    #logger.debug('Reading Config file: ' + configFile) #Commented out by Henry
+    sections = {'GeoData', 'DeviceInfo', 'Networking'}
+    #configParameters = {}
+    #Config = ConfigParser.ConfigParser()
+    #Config.read(configFile)
+    #for section in sections:
+    #    try:
+    #        options = Config.options(section)
+    #    except:
+    #        print ("ERROR: Section '" + section + "' Not found in config file: '" + configFile + "'.")
+    #        sys.exit()
+    #    for option in options:
+    #        try:
+    #            configParameters[option] = Config.get(section, option)
+    #        except:
+    #            configParameters[option] = None
+    #        logger.debug("Config Section: " + section + " / Option: " + option + " => " + configParameters[option])
+
+    MMA8451 = Accel()
+    #os.system("clear")
+    MMA8451.init()
+
+    if MMA8451.whoAmI() != deviceName:
+        print("Error! Device not recognized! (" + str(deviceName) + ")")
+        sys.exit()
+
+    #
+    # Thread client start
+    # Commented out by Henry
+    '''
+    import rss_client
+    pill2kill = threading.Event()
+    #threadClient = threading.Thread(name='netClientWorker', target=rss_client.cli_worker, args=(pill2kill, configParameters, accelBuffer))
+    threadClient = threading.Thread(name='netClientWorker', target=rss_client.cli_worker, args=(pill2kill, accelBuffer))
+    threadClient.setDaemon(False)         #threadClient.daemon = False
+    threadClient.start()
+    myThread = []
+    myThread.append('netClientWorker')
+    '''
+    
+    #Added by Henry:
+    count_samples = 8 #num of samples we want
+    count_taken = 0
+    xvals = []
+    yvals = []
+    zvals = []
+    while count_taken < count_samples:
+        if runTimeConfigObject.executeSilently == 0:
+            axes = MMA8451.getAxisValue()
+            print("X: " + str(axes['x']) + ", Y: " + str(axes['y']) + ", Z: " + str(axes['z']))
+            xvals.append(axes['x'])
+            yvals.append(axes['y'])
+            zvals.append(axes['z'])
+            count_taken = count_taken + 1
+    return xvals,yvals,zvals
+    
+    #Commented Out by Henry:
+    '''
+    while True:  # forever loop
+        if runTimeConfigObject.executeSilently == 0:
+            print ("\nCurrent Date-Time: " + str(datetime.datetime.now()))
+            print ("Raspberry Bus       = " + str(MMA8451.raspiBus))
+            print ("Raspberry Interrupt = " + str(MMA8451.raspiIntEnabled))
+            print ("Number of elemets   = " + str(len(accelBuffer)))
+            MMA8451.debugShowRpiInfo()
+            MMA8451.debugShowRegisters()
+            MMA8451.debugShowOrientation()
+            axes = MMA8451.getAxisValue()
+            MMA8451.debugShowAxisAcceleration(axes['x'], axes['y'], axes['z'])
+            #
+            if runTimeConfigObject.debugRealTime != 0:
+                MMA8451.debugRealTimeBuffer()
+
+        runTimeConfigObject.NumInterrupts = 0
+        try:
+            time.sleep(1.0)
+        except KeyboardInterrupt:
+            logger.debug ("Killing threads...")
+            pill2kill.set()
+            threadClient.join()
+            time.sleep(1.0)
+
+            logger.debug("\nUser termination requested!\n")
+            sys.exit()
+            
+    sys.exit()
+    '''
+
diff --git a/Nov_27/Manual/Camera/accel_correct_config/raspidata.py b/Nov_27/Manual/Camera/accel_correct_config/raspidata.py
new file mode 100644
index 0000000..fb99cbb
--- /dev/null
+++ b/Nov_27/Manual/Camera/accel_correct_config/raspidata.py
@@ -0,0 +1,48 @@
+""" A Raspberry Pi class.
+To access some of the most useful hardware information
+"""
+
+
+#class RaspiData():
+
+
+    # Get Raspberry Serial Number
+    # You can do it in bash:
+    #   cat /proc/cpuinfo | perl -n -e '/^Serial[ ]*: ([0-9a-f]{16})$/ && print "$1\n"'
+    # Or with perl:
+    #   cat cpuinfo | perl -n -e '/^Serial[ ]*: ([0-9a-f]{16})$/ && print "$1\n"'
+    #
+    #
+
+
+#    def __init__(self):
+#       pass
+from uuid import getnode as get_mac
+
+#    @staticmethod
+# def get_serial(self):
+def get_serial():
+    """Extract serial from cpuinfo file"""
+    cpu_serial= "0000000000000000"
+    try:
+        f = open('/proc/cpuinfo','r')
+        for line in f:
+            if line[0:6]=='Serial':
+                cpu_serial = line[10:26]
+        f.close()
+    except:
+        cpu_serial = "ERROR000000000"
+    return cpu_serial
+
+
+def mac_address():
+    """Extract rth0 MAC address"""
+    # mac = '00:00:00:00:00:00'
+    # mac = '00:00:00:00:00:00'
+    try:
+        t = get_mac()
+    except:
+        t = "000000000000"
+    mac = ':'.join(("%012X" % t)[i:i+2] for i in range(0, 12, 2))
+    return mac
+
diff --git a/Nov_27/Manual/Camera/accel_correct_config/rss_cli_config.py b/Nov_27/Manual/Camera/accel_correct_config/rss_cli_config.py
new file mode 100644
index 0000000..e10aa53
--- /dev/null
+++ b/Nov_27/Manual/Camera/accel_correct_config/rss_cli_config.py
@@ -0,0 +1,21 @@
+# [GeoData]
+City = 'Rome'
+cityname =  'Rome'
+latitude = 41.947550
+longitude = 12.469794
+deviceorientation = 'L'
+
+# [DeviceInfo]
+devicetype = 'raspberry'
+devicename = 'rssd0001'
+sensorname = 'mma8451'
+sensortheshold = 20
+logfile = './rssd.log'
+
+# [Networking]
+#serveraddress = 'www.diprimio.com'
+serveraddress = 'localhost'
+serverprotocol = 'tcp'
+servertcpport = 15000
+serverhearthbit = 100
+receivebuffersize = 1024
diff --git a/Nov_27/Manual/Camera/accel_correct_config/rss_client.py b/Nov_27/Manual/Camera/accel_correct_config/rss_client.py
new file mode 100644
index 0000000..a2a4d1b
--- /dev/null
+++ b/Nov_27/Manual/Camera/accel_correct_config/rss_client.py
@@ -0,0 +1,159 @@
+#!/usr/bin/env python
+# -*- coding:utf-8, indent=tab, tabstop=4 -*-
+#
+# See 'LICENSE'  for copying
+#
+# This file contains the code for the client module of 'accel.py'
+#
+# Revision history
+# Date          Author                  Version     Details
+# ----------------------------------------------------------------------------------
+# 2018-01-18    Massimo Di Primio       0.06        1st file implementation
+
+"""Client thread worker - This is a simple client code example for 'accel'.py' program"""
+
+import logging
+import time
+import datetime
+import socket
+import json
+import rss_client_messages as climsg
+import rss_cli_config as ccfg
+import raspidata
+
+
+#def cli_connect(params):
+def cli_connect():
+    """Open connection to the server"""
+    server_address = (str(ccfg.serveraddress), int(ccfg.servertcpport))
+    logging.debug('Trying to connect to server ' + str(server_address))
+    # Create a TCP/IP socket
+    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    try:
+        # Connect the socket to the port where the server is listening
+        s.connect(server_address)
+        logging.debug('Connection Established to server ' + str(server_address))
+    except:
+        logging.debug(
+            "Failed to open connection: " + str(ccfg.serverprotocol) +
+            ", to IP: " + str(ccfg.serveraddress) +
+            ", on port: " + str(ccfg.servertcpport)
+            )
+        return(-1)
+    return(s)
+
+
+def cli_close(s):
+    """Close the server connection"""
+    if s > -1:
+        s.close()
+
+
+# def cli_worker(stopEvent, config, accelBuffer):
+def cli_worker(stopEvent, accelBuffer):
+    """A client worker as thread"""
+    logging.debug('Thread Starting')
+    s = cli_connect()     # s = cli_connect(config)
+    send_client_hello(s)
+    time.sleep(0.5)
+    send_config_affirm_message(s)
+
+    ts = int(time.time())
+    te = ts
+    while not stopEvent.wait(0.3):
+        if len(accelBuffer) > 0:
+            send_accel_data(s, accelBuffer)
+            te = int(time.time())
+        if (te - ts) > 10:
+            send_client_heartbit(s)
+            ts = int(time.time())
+
+        time.sleep(0.5)
+    send_zap_message(s)
+    cli_close(s)
+    logging.debug("Thread cliWorker is terminating as per your request.")
+
+    
+def send_accel_data(s, accelBuffer):
+    """Send acceleration data to the server"""
+    #msg = dict(cmd='ADM', timestamp=str(datetime.datetime.now()), clid=raspidata.get_serial())
+    #a = 123 #str(datetime.datetime.now())
+    pbuf = parse_accel_data(accelBuffer)
+    msg = dict(cmd = 'ADM', timestamp = str(datetime.datetime.now()), clid = raspidata.get_serial(), data = pbuf)
+    # if len(pbuf) > 0: # this sometimes returns error (when buf is empty, it has None type)
+    if (pbuf is not None) and (len(pbuf) > 0):
+        #str = climsg.accel_data_message(pbuf)
+        try:
+            logging.debug("Sending Acceleration data to the server")
+            s.sendall(str(json.dumps(msg)) + "\n")  #s.sendall(json.dumps(str) + "\n")
+        except:
+            logging.debug("Failed to send Acceleration-Data to the server")
+
+
+def send_client_hello(s):
+    """Send Hello message to the server"""
+    msg = dict(cmd = 'CHM', timestamp = str(datetime.datetime.now()), clid = raspidata.get_serial())
+    try:
+        logging.debug("Sending Hello to the server")
+        s.sendall(str(json.dumps(msg)) + "\n")  #s.sendall(json.dumps(climsg.hello_message()) + "\n")
+    except:
+        logging.debug("Failed to send Hello to the server")
+
+
+def send_zap_message(s):
+    """Send Zap message to the server"""
+    msg = dict(cmd = 'CZM', timestamp = str(datetime.datetime.now()), clid = raspidata.get_serial())
+    try:
+        logging.debug("Sending Zap to the server")
+        s.sendall(str(json.dumps(msg)) + "\n")  #s.sendall(json.dumps(climsg.zap_message()) + "\n")
+    except:
+        logging.debug("Failed to send Zap to the server")
+
+
+def send_config_affirm_message(s):      #def send_config_affirm_message(s, config):
+    msg_data = dict(city = ccfg.cityname, latitude = ccfg.latitude,longitude = ccfg.longitude)
+    msg = dict(cmd = 'CCA', timestamp = str(datetime.datetime.now()), clid = raspidata.get_serial(), config = msg_data)
+    try:
+        logging.debug("Sending client configuration to the server")
+        s.sendall(str(json.dumps(msg)) + "\n")    #s.sendall(climsg.config_affirm_message(cfg_data))
+    except:
+        logging.debug("Failed to send client configuration to the server")
+
+
+def send_client_heartbit(s):
+    """Send Heartbit to the server"""
+    msg = dict(cmd = 'CHB', timestamp = str(datetime.datetime.now()), clid=raspidata.get_serial())
+    try:
+        logging.debug("Sending Heartbit to the server")
+        s.sendall(str(json.dumps(msg)) + "\n")  #s.sendall(json.dumps(climsg.heart_bit()) + "\n")
+    except:
+        logging.debug("Failed to send Heartbit to the server")
+
+
+def parse_accel_data(b):
+    """Parse acceleration data to make sure we only send meaningfull data to the server"""
+    tsh = 10
+    tbuf = []
+    # tbuf.append([0, 0, 0, 0, 0])
+    # bLength = len(b)
+    # logging.debug("parseAccelData(b) # of elements   = " + str(len(b)))
+    if len(b) > 1:
+        logging.debug("parseAccelData: In  AccelData/BufLen: " + str(len(b)) + "/" +str(len(tbuf)))
+        firstTime = 1
+        prow = None
+        for row in b:
+            crow = b.pop(0)     # Get the oldest record
+            if firstTime == 1:
+                prow = crow
+                firstTime = 0
+            if ( (abs(abs(int(crow[1])) - abs(int(prow[1]))) > tsh) or
+                 (abs(abs(int(crow[2])) - abs(int(prow[2]))) > tsh) or
+                 (abs(abs(int(crow[3])) - abs(int(prow[3]))) > tsh)
+                ):
+                tbuf.append(crow)
+                prow = crow
+                print ("Again PROW/CROW/TBUFLEN:" + str(prow) + " / " + str(crow) + " / " +  str(len(tbuf)))
+        
+        logging.debug("parseAccelData: Out AccelData/BufLen: " + str(len(b)) + "/" +str(len(tbuf)))
+        return(tbuf)
+
diff --git a/Nov_27/Manual/Camera/accel_correct_config/rss_client_messages.py b/Nov_27/Manual/Camera/accel_correct_config/rss_client_messages.py
new file mode 100644
index 0000000..4a507d7
--- /dev/null
+++ b/Nov_27/Manual/Camera/accel_correct_config/rss_client_messages.py
@@ -0,0 +1,113 @@
+
+import datetime
+import raspidata
+import json
+import rss_cli_config as ccfg
+
+
+#
+# All client messages are handled in here.
+#
+# Message Formats
+# ---------------
+# 1 - Timestamp
+# 2 - Device ID
+# 3 - Message Type
+#
+# Client message types
+# --------------------
+# 0001  CHB - Client Heart Bit (Client is alive)
+# 0002  CHM - Client Hello Message (Client is online)
+# 0003  CZM - Client Zap Message (Client is shut down)
+# 0004  ADM - Acceleration Data Message (Acceleration data)
+# 0005  CCA - Client Config Affirm
+# ... (other to come)
+#
+# Data message Format for all Client Message Types
+# ------------------------------------------------
+#
+# # 0001 - CHB - Client Heart Bit
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0001         |  Var  | Literally 'CHB'                                        |
+# # +-------------------------------------------------------------------------------+
+#
+# 0002 CHM - Client Hello Message
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0002         |  Var  | Literally 'CHM'                                        |
+# +--------------+-------+--------------------------------------------------------+
+#
+# 0003  CZM - Client Zap Message (Client is shut down)
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0003         |  Var  | Literally 'CZM'                                        |
+# +--------------+-------+--------------------------------------------------------+
+#
+# 0004  ADM - Acceleration Data Message (Acceleration data)
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0004         |  Var  | Literally 'ADM'                                        |
+# | Accel Data   |  Var  | Array containing acceleration data (X,Y,Z.O)           |
+# +--------------+-------+--------------------------------------------------------+
+#
+# 0005  CCA - Client Configuration Affirm
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0004         |  Var  | Literally 'CCA'                                        |
+# | Config Data  |  Var  | Array containing client configuration                  |
+# +--------------+-------+--------------------------------------------------------+
+
+###########################################################################
+#   WARNIG ! THIS FILE IS NOT CURRENTLY USED !
+#   Its presence it is for debugging/testing only
+#   Althought, it will be used in some future version
+###########################################################################
+
+#def heart_bit():
+#    """Client Heart Bit"""
+#    msg = {'cmd': 'CHB', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial()}
+#    return msg
+#
+#
+#def hello_message():
+#    """Client Hello Message"""
+#    msg = {'cmd': 'CHM', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial()}
+#    return msg
+#
+#
+#def zap_message():
+#    """Client Zap Message"""
+#    msg = {'cmd': 'CZM', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial()}
+#    return msg
+#
+#
+#def accel_data_message(data):
+#    """Acceleration Data Message"""
+#    msg = {'cmd': 'ADM', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial(), 'acceldata': str(data)}
+#    return msg
+#
+#
+#def config_affirm_message(data):    #def config_affirm_message(data):
+#    """Client Configuration Affirm"""
+#    ###
+#    pkt_hdr = {'cmd': 'CCA', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial(), 'data' : data}
+#
+#    msg = dict(city = ccfg.cityname, latitude = ccfg.latitude, longitude = ccfg.longitude, config = data)
+#    return msg
+
diff --git a/Nov_27/Manual/Camera/communication_module/._communication.py b/Nov_27/Manual/Camera/communication_module/._communication.py
new file mode 100644
index 0000000..f2e25e8
Binary files /dev/null and b/Nov_27/Manual/Camera/communication_module/._communication.py differ
diff --git a/Nov_27/Manual/Camera/communication_module/com_test.py b/Nov_27/Manual/Camera/communication_module/com_test.py
new file mode 100644
index 0000000..a99d3ec
--- /dev/null
+++ b/Nov_27/Manual/Camera/communication_module/com_test.py
@@ -0,0 +1,35 @@
+import requests
+from requests import Session
+import json
+from datetime import datetime
+from dateutil import parser
+from random import randint
+
+BRIDGE_NAME = 'dummy'
+dest = "http://127.0.0.1:8000/sensors/"+BRIDGE_NAME+"/update/"
+PASSWORD = "djioewfj34jod2jdoi3jr0jl983jsa"
+
+
+def send_data_to_server(x=0.0, y=0.0, z=0.0, theta=0.0, phi=0.0, psi=0.0):
+    payload = {'x': x, 'y': y, 'z': z, 'theta': theta, 'phi': phi, 'psi': psi}
+    dt = datetime.now()
+    cookies = {'csrftoken': encode(dt), 'time': str(dt)}
+    r = requests.post(dest, data=payload, cookies=cookies)
+    print(r.status_code)
+    if r.status_code == 403:
+        raise RuntimeWarning("CSRF ALERT ON, INSPECT YOUR SYSTEM")
+    else:
+        return r.text
+
+
+def encode(time_sign):
+    len_pw = len(PASSWORD)
+    index = (time_sign.year + time_sign.month * 100 + time_sign.day + time_sign.hour * time_sign.minute
+             * time_sign.second) % len_pw
+    repl = chr(randint(97, 122))
+    return PASSWORD[:index] + repl + PASSWORD[index+1:]
+
+
+print(send_data_to_server(0.5, 1.5, 1.5))
+#send_data_form(1.0, 2.0, 3.0)
+#cookies = {'csrftoken': "djioewfj34jod2jdoi3jr0jl983jsa", 'TIME': datetime.now()}
diff --git a/Nov_27/Manual/Camera/communication_module/communication.py b/Nov_27/Manual/Camera/communication_module/communication.py
new file mode 100644
index 0000000..8c8b9a4
--- /dev/null
+++ b/Nov_27/Manual/Camera/communication_module/communication.py
@@ -0,0 +1,28 @@
+import requests, sys
+from requests import Session
+import json
+from datetime import datetime
+from dateutil import parser
+from random import randint
+sys.path.insert(0,"/home/pi/Desktop/Full_Demo/transfer_test_correct_config")
+from SETTINGS import PASSWORD, SERVER
+
+dest = SERVER
+
+def send_data_to_server(x=0.0, y=0.0, z=0.0, theta=0.0, phi=0.0, psi=0.0):
+    payload = {'x': x, 'y': y, 'z': z, 'theta': theta, 'phi': phi, 'psi': psi}
+    dt = datetime.now()
+    cookies = {'csrftoken': encode(dt), 'time': str(dt)}
+    r = requests.post(dest, data=payload, cookies=cookies)
+    if r.status_code == 403:
+        raise RuntimeWarning("CSRF ALERT ON, INSPECT YOUR SYSTEM")
+    else:
+        return r.text
+
+
+def encode(time_sign):
+    len_pw = len(PASSWORD)
+    index = (time_sign.year + time_sign.month * 100 + time_sign.day + time_sign.hour * time_sign.minute
+             * time_sign.second) % len_pw
+    repl = chr(randint(97, 122))
+    return PASSWORD[:index] + repl + PASSWORD[index+1:]
\ No newline at end of file
diff --git a/Nov_27/Manual/Camera/img_rec_module/FastDataMatrixTest.py b/Nov_27/Manual/Camera/img_rec_module/FastDataMatrixTest.py
new file mode 100644
index 0000000..21cdf8d
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/FastDataMatrixTest.py
@@ -0,0 +1,45 @@
+from img_rec_module.sig_proc_test import FM
+import numpy as np
+from random import randint
+import traceback
+import sys
+
+matrix = np.random.rand(20, 30)
+
+
+def segmentation_test(sample):
+    ax = randint(0, 1)
+    target = randint(0, 19) if ax == 1 else randint(0, 29)
+    fmat = FM(matrix, ax, target)
+    assert fmat.irange() == 20 if ax == 1 else fmat.irange() == 30
+    cap = len(fmat)
+    for i in range(sample):
+        start = randint(0, cap - 1)
+        end = randint(start + 1, cap)
+        fmat.segmentize(start, end)
+        assert len(fmat.extract_array()) == end - start, "start: {0}, end: {1}".format(start, end)
+        elem = randint(0, len(fmat) - 1)
+        assert fmat[elem] == matrix.item(target, elem + start) if ax == 1 \
+            else fmat[elem] == matrix.item(elem + start, target)
+    print("Test 1 passed!")
+    return fmat.copy()
+
+
+def shape_change_test(sample):
+    fmat = mat
+    print(len(fmat), "start: {0}, end: {1}, ax: {2}, index: {3}".format(fmat.start, fmat.end, fmat._ax, fmat._index))
+    for i in range(sample):
+        ax = randint(0, 1)
+        target = randint(0, 19) if ax == 1 else randint(0, 29)
+        try:
+            fmat = fmat.copy(ax, target)
+            elem = randint(0, len(fmat) - 1)
+            assert fmat[elem] == matrix.item(target, elem) if ax == 1 else fmat[elem] == matrix.item(elem, target), "start: {0}, end: {1}, ax: {2}, index: {3}, changed:{4}, elem: {5}".format(fmat.start, fmat.end, ax, fmat._index, target, elem)
+        except IndexError:
+            print("Index Error, start: {0}, end: {1}, ax: {2}, index: {3}".format(fmat.start, fmat.end, fmat._ax, fmat._index))
+            traceback.print_exc(file=sys.stderr)
+            sys.exit(0)
+    print("Test 2 passed!")
+
+mat = segmentation_test(1000000)
+shape_change_test(1000000)
\ No newline at end of file
diff --git a/Nov_27/Manual/Camera/img_rec_module/HoughLineTest.py b/Nov_27/Manual/Camera/img_rec_module/HoughLineTest.py
new file mode 100644
index 0000000..a338b2c
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/HoughLineTest.py
@@ -0,0 +1,70 @@
+from img_rec_module.sig_proc_test import HoughLine, angle_interp, normalize_angle
+import numpy as np
+from random import uniform
+from math import sin, cos, asin, acos, degrees
+
+
+def angle_interp_test(sample):
+    for t in range(4):
+        angle = np.pi * t / 2
+        angle_test(angle)
+    for i in range(sample):
+        angle = uniform(0, 2 * np.pi)
+        angle_test(angle)
+    print("All tests passed!")
+
+
+def angle_test(angle):
+    s, c = sin(angle), cos(angle)
+    aint = angle_interp(s, c)
+    e = abs(aint - angle)
+    assert e < 10 ** (-8), "angle: {0} aint: {3} sin: {1}, cos: {2}".format(degrees(angle), s, c, degrees(aint))
+
+
+data1 = [(50, 93.58985799408744), (100, 94.83752561512769), (150, 93.46476622047003), (200, 92.12757475618413), (250, 91.21899822237681), (300, 94.66610143151713), (350, 93.39700158152765), (400, 93.55400856320203), (450, 94.81488837189904)]
+data2 = [(50, 220.68898677024384), (150, 207.0369850844262), (200, 219.1873113806197), (250, 218.4221525438996), (300, 215.83141259108666), (350, 216.1097540779057), (400, 214.30630223604018), (450, 211.84789180370862), (500, 211.03929058602466), (550, 208.37556376057634), (600, 208.1358916890448)]
+
+
+def diagnose():
+    x1 = np.array([d[1] for d in data1])
+    y1 = np.array([d[0] for d in data1])
+    line1 = HoughLine(x=x1, data=y1)
+    x2 = np.array([d[0] for d in data2])
+    y2 = np.array([d[1] for d in data2])
+    line2 = HoughLine(x=x2, data=y2)
+    xmean = sum(x1) / len(x1)
+    ymean = sum(y2) / len(y2)
+    print(line1._r, line1._t)
+    print(line2._r, line2._t)
+    print((xmean, ymean), HoughLine.intersect(line1, line2))
+
+
+def normalize_angle_test(sample):
+    for t in range(4):
+        angle = np.pi * t / 2
+        test_normalize(angle)
+    for i in range(sample):
+        angle = uniform(0, 1000000)
+        test_normalize(angle)
+    print('All tests passed!')
+
+
+def test_normalize(angle):
+    s, c = sin(angle), cos(angle)
+    norm_ang = normalize_angle(angle)
+    ns, nc = sin(norm_ang), cos(norm_ang)
+    case1 = equal_within_error(s, ns) and equal_within_error(c, nc)
+    case2 = equal_within_error(s, -ns) and equal_within_error(c, -nc)
+    assert case1 or case2, 's:{0} c:{1}, ns:{2}, nc:{3}, angle:{4}, norm:{5}'.format(s, c, ns, nc, angle, norm_ang)
+
+
+
+def equal_within_error(v1, v2):
+    e = abs(v1 - v2)
+    return e < 10 ** (-8)
+
+
+if __name__ == '__main__':
+    #angle_interp_test(100000000)
+    #diagnose()
+    normalize_angle_test(1000000)
\ No newline at end of file
diff --git a/Nov_27/Manual/Camera/img_rec_module/__init__.py b/Nov_27/Manual/Camera/img_rec_module/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Manual/Camera/img_rec_module/centers.txt b/Nov_27/Manual/Camera/img_rec_module/centers.txt
new file mode 100644
index 0000000..ea1e64b
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/centers.txt
@@ -0,0 +1,640 @@
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diff --git a/Nov_27/Manual/Camera/img_rec_module/flags.txt b/Nov_27/Manual/Camera/img_rec_module/flags.txt
new file mode 100644
index 0000000..b15d479
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/flags.txt
@@ -0,0 +1,274 @@
+COLOR_BAYER_BG2BGR
+COLOR_BAYER_BG2BGRA
+COLOR_BAYER_BG2BGR_EA
+COLOR_BAYER_BG2BGR_VNG
+COLOR_BAYER_BG2GRAY
+COLOR_BAYER_BG2RGB
+COLOR_BAYER_BG2RGBA
+COLOR_BAYER_BG2RGB_EA
+COLOR_BAYER_BG2RGB_VNG
+COLOR_BAYER_GB2BGR
+COLOR_BAYER_GB2BGRA
+COLOR_BAYER_GB2BGR_EA
+COLOR_BAYER_GB2BGR_VNG
+COLOR_BAYER_GB2GRAY
+COLOR_BAYER_GB2RGB
+COLOR_BAYER_GB2RGBA
+COLOR_BAYER_GB2RGB_EA
+COLOR_BAYER_GB2RGB_VNG
+COLOR_BAYER_GR2BGR
+COLOR_BAYER_GR2BGRA
+COLOR_BAYER_GR2BGR_EA
+COLOR_BAYER_GR2BGR_VNG
+COLOR_BAYER_GR2GRAY
+COLOR_BAYER_GR2RGB
+COLOR_BAYER_GR2RGBA
+COLOR_BAYER_GR2RGB_EA
+COLOR_BAYER_GR2RGB_VNG
+COLOR_BAYER_RG2BGR
+COLOR_BAYER_RG2BGRA
+COLOR_BAYER_RG2BGR_EA
+COLOR_BAYER_RG2BGR_VNG
+COLOR_BAYER_RG2GRAY
+COLOR_BAYER_RG2RGB
+COLOR_BAYER_RG2RGBA
+COLOR_BAYER_RG2RGB_EA
+COLOR_BAYER_RG2RGB_VNG
+COLOR_BGR2BGR555
+COLOR_BGR2BGR565
+COLOR_BGR2BGRA
+COLOR_BGR2GRAY
+COLOR_BGR2HLS
+COLOR_BGR2HLS_FULL
+COLOR_BGR2HSV
+COLOR_BGR2HSV_FULL
+COLOR_BGR2LAB
+COLOR_BGR2LUV
+COLOR_BGR2Lab
+COLOR_BGR2Luv
+COLOR_BGR2RGB
+COLOR_BGR2RGBA
+COLOR_BGR2XYZ
+COLOR_BGR2YCR_CB
+COLOR_BGR2YCrCb
+COLOR_BGR2YUV
+COLOR_BGR2YUV_I420
+COLOR_BGR2YUV_IYUV
+COLOR_BGR2YUV_YV12
+COLOR_BGR5552BGR
+COLOR_BGR5552BGRA
+COLOR_BGR5552GRAY
+COLOR_BGR5552RGB
+COLOR_BGR5552RGBA
+COLOR_BGR5652BGR
+COLOR_BGR5652BGRA
+COLOR_BGR5652GRAY
+COLOR_BGR5652RGB
+COLOR_BGR5652RGBA
+COLOR_BGRA2BGR
+COLOR_BGRA2BGR555
+COLOR_BGRA2BGR565
+COLOR_BGRA2GRAY
+COLOR_BGRA2RGB
+COLOR_BGRA2RGBA
+COLOR_BGRA2YUV_I420
+COLOR_BGRA2YUV_IYUV
+COLOR_BGRA2YUV_YV12
+COLOR_BayerBG2BGR
+COLOR_BayerBG2BGRA
+COLOR_BayerBG2BGR_EA
+COLOR_BayerBG2BGR_VNG
+COLOR_BayerBG2GRAY
+COLOR_BayerBG2RGB
+COLOR_BayerBG2RGBA
+COLOR_BayerBG2RGB_EA
+COLOR_BayerBG2RGB_VNG
+COLOR_BayerGB2BGR
+COLOR_BayerGB2BGRA
+COLOR_BayerGB2BGR_EA
+COLOR_BayerGB2BGR_VNG
+COLOR_BayerGB2GRAY
+COLOR_BayerGB2RGB
+COLOR_BayerGB2RGBA
+COLOR_BayerGB2RGB_EA
+COLOR_BayerGB2RGB_VNG
+COLOR_BayerGR2BGR
+COLOR_BayerGR2BGRA
+COLOR_BayerGR2BGR_EA
+COLOR_BayerGR2BGR_VNG
+COLOR_BayerGR2GRAY
+COLOR_BayerGR2RGB
+COLOR_BayerGR2RGBA
+COLOR_BayerGR2RGB_EA
+COLOR_BayerGR2RGB_VNG
+COLOR_BayerRG2BGR
+COLOR_BayerRG2BGRA
+COLOR_BayerRG2BGR_EA
+COLOR_BayerRG2BGR_VNG
+COLOR_BayerRG2GRAY
+COLOR_BayerRG2RGB
+COLOR_BayerRG2RGBA
+COLOR_BayerRG2RGB_EA
+COLOR_BayerRG2RGB_VNG
+COLOR_COLORCVT_MAX
+COLOR_GRAY2BGR
+COLOR_GRAY2BGR555
+COLOR_GRAY2BGR565
+COLOR_GRAY2BGRA
+COLOR_GRAY2RGB
+COLOR_GRAY2RGBA
+COLOR_HLS2BGR
+COLOR_HLS2BGR_FULL
+COLOR_HLS2RGB
+COLOR_HLS2RGB_FULL
+COLOR_HSV2BGR
+COLOR_HSV2BGR_FULL
+COLOR_HSV2RGB
+COLOR_HSV2RGB_FULL
+COLOR_LAB2BGR
+COLOR_LAB2LBGR
+COLOR_LAB2LRGB
+COLOR_LAB2RGB
+COLOR_LBGR2LAB
+COLOR_LBGR2LUV
+COLOR_LBGR2Lab
+COLOR_LBGR2Luv
+COLOR_LRGB2LAB
+COLOR_LRGB2LUV
+COLOR_LRGB2Lab
+COLOR_LRGB2Luv
+COLOR_LUV2BGR
+COLOR_LUV2LBGR
+COLOR_LUV2LRGB
+COLOR_LUV2RGB
+COLOR_Lab2BGR
+COLOR_Lab2LBGR
+COLOR_Lab2LRGB
+COLOR_Lab2RGB
+COLOR_Luv2BGR
+COLOR_Luv2LBGR
+COLOR_Luv2LRGB
+COLOR_Luv2RGB
+COLOR_M_RGBA2RGBA
+COLOR_RGB2BGR
+COLOR_RGB2BGR555
+COLOR_RGB2BGR565
+COLOR_RGB2BGRA
+COLOR_RGB2GRAY
+COLOR_RGB2HLS
+COLOR_RGB2HLS_FULL
+COLOR_RGB2HSV
+COLOR_RGB2HSV_FULL
+COLOR_RGB2LAB
+COLOR_RGB2LUV
+COLOR_RGB2Lab
+COLOR_RGB2Luv
+COLOR_RGB2RGBA
+COLOR_RGB2XYZ
+COLOR_RGB2YCR_CB
+COLOR_RGB2YCrCb
+COLOR_RGB2YUV
+COLOR_RGB2YUV_I420
+COLOR_RGB2YUV_IYUV
+COLOR_RGB2YUV_YV12
+COLOR_RGBA2BGR
+COLOR_RGBA2BGR555
+COLOR_RGBA2BGR565
+COLOR_RGBA2BGRA
+COLOR_RGBA2GRAY
+COLOR_RGBA2M_RGBA
+COLOR_RGBA2RGB
+COLOR_RGBA2YUV_I420
+COLOR_RGBA2YUV_IYUV
+COLOR_RGBA2YUV_YV12
+COLOR_RGBA2mRGBA
+COLOR_XYZ2BGR
+COLOR_XYZ2RGB
+COLOR_YCR_CB2BGR
+COLOR_YCR_CB2RGB
+COLOR_YCrCb2BGR
+COLOR_YCrCb2RGB
+COLOR_YUV2BGR
+COLOR_YUV2BGRA_I420
+COLOR_YUV2BGRA_IYUV
+COLOR_YUV2BGRA_NV12
+COLOR_YUV2BGRA_NV21
+COLOR_YUV2BGRA_UYNV
+COLOR_YUV2BGRA_UYVY
+COLOR_YUV2BGRA_Y422
+COLOR_YUV2BGRA_YUNV
+COLOR_YUV2BGRA_YUY2
+COLOR_YUV2BGRA_YUYV
+COLOR_YUV2BGRA_YV12
+COLOR_YUV2BGRA_YVYU
+COLOR_YUV2BGR_I420
+COLOR_YUV2BGR_IYUV
+COLOR_YUV2BGR_NV12
+COLOR_YUV2BGR_NV21
+COLOR_YUV2BGR_UYNV
+COLOR_YUV2BGR_UYVY
+COLOR_YUV2BGR_Y422
+COLOR_YUV2BGR_YUNV
+COLOR_YUV2BGR_YUY2
+COLOR_YUV2BGR_YUYV
+COLOR_YUV2BGR_YV12
+COLOR_YUV2BGR_YVYU
+COLOR_YUV2GRAY_420
+COLOR_YUV2GRAY_I420
+COLOR_YUV2GRAY_IYUV
+COLOR_YUV2GRAY_NV12
+COLOR_YUV2GRAY_NV21
+COLOR_YUV2GRAY_UYNV
+COLOR_YUV2GRAY_UYVY
+COLOR_YUV2GRAY_Y422
+COLOR_YUV2GRAY_YUNV
+COLOR_YUV2GRAY_YUY2
+COLOR_YUV2GRAY_YUYV
+COLOR_YUV2GRAY_YV12
+COLOR_YUV2GRAY_YVYU
+COLOR_YUV2RGB
+COLOR_YUV2RGBA_I420
+COLOR_YUV2RGBA_IYUV
+COLOR_YUV2RGBA_NV12
+COLOR_YUV2RGBA_NV21
+COLOR_YUV2RGBA_UYNV
+COLOR_YUV2RGBA_UYVY
+COLOR_YUV2RGBA_Y422
+COLOR_YUV2RGBA_YUNV
+COLOR_YUV2RGBA_YUY2
+COLOR_YUV2RGBA_YUYV
+COLOR_YUV2RGBA_YV12
+COLOR_YUV2RGBA_YVYU
+COLOR_YUV2RGB_I420
+COLOR_YUV2RGB_IYUV
+COLOR_YUV2RGB_NV12
+COLOR_YUV2RGB_NV21
+COLOR_YUV2RGB_UYNV
+COLOR_YUV2RGB_UYVY
+COLOR_YUV2RGB_Y422
+COLOR_YUV2RGB_YUNV
+COLOR_YUV2RGB_YUY2
+COLOR_YUV2RGB_YUYV
+COLOR_YUV2RGB_YV12
+COLOR_YUV2RGB_YVYU
+COLOR_YUV420P2BGR
+COLOR_YUV420P2BGRA
+COLOR_YUV420P2GRAY
+COLOR_YUV420P2RGB
+COLOR_YUV420P2RGBA
+COLOR_YUV420SP2BGR
+COLOR_YUV420SP2BGRA
+COLOR_YUV420SP2GRAY
+COLOR_YUV420SP2RGB
+COLOR_YUV420SP2RGBA
+COLOR_YUV420p2BGR
+COLOR_YUV420p2BGRA
+COLOR_YUV420p2GRAY
+COLOR_YUV420p2RGB
+COLOR_YUV420p2RGBA
+COLOR_YUV420sp2BGR
+COLOR_YUV420sp2BGRA
+COLOR_YUV420sp2GRAY
+COLOR_YUV420sp2RGB
+COLOR_YUV420sp2RGBA
+COLOR_mRGBA2RGBA
diff --git a/Nov_27/Manual/Camera/img_rec_module/img_1_1_meas.txt b/Nov_27/Manual/Camera/img_rec_module/img_1_1_meas.txt
new file mode 100644
index 0000000..97ed4c5
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/img_1_1_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 384.9492753623189; 
+y: zero_crossing: 128.7986577181208, edge_converge: 124.79086538461539
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 385.07291666666663; 
+y: zero_crossing: 129.5, edge_converge: 125.5
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 383.90625, edge_converge: 379.9741379310345; 
+y: zero_crossing: 129.52727272727273, edge_converge: 130.70366132723112
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 379.9741379310345; 
+y: zero_crossing: 129.6, edge_converge: 130.70366132723112
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 379.9741379310345; 
+y: zero_crossing: 129.68627450980392, edge_converge: 130.70366132723112
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 383.6551724137931, edge_converge: 379.9741379310345; 
+y: zero_crossing: 129.55172413793105, edge_converge: 130.70366132723112
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 383.55172413793105, edge_converge: 379.9741379310345; 
+y: zero_crossing: 129.5, edge_converge: 130.70366132723112
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 383.90625, edge_converge: 379.9741379310345; 
+y: zero_crossing: 129.5, edge_converge: 130.70366132723112
diff --git a/Nov_27/Manual/Camera/img_rec_module/img_1_1new_meas.txt b/Nov_27/Manual/Camera/img_rec_module/img_1_1new_meas.txt
new file mode 100644
index 0000000..70d4297
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/img_1_1new_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 323.66566716641677; 
+y: zero_crossing: -1, edge_converge: 828.3849673202615
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 597.0; 
+y: zero_crossing: -1, edge_converge: 792.34375
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 880.0; 
+y: zero_crossing: -1, edge_converge: 796.6236413043478
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 716.546875; 
+y: zero_crossing: -1, edge_converge: 832.172697368421
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 308.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 308.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 308.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 321.5, edge_converge: 308.0; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Manual/Camera/img_rec_module/img_20_1_meas.txt b/Nov_27/Manual/Camera/img_rec_module/img_20_1_meas.txt
new file mode 100644
index 0000000..8f3d333
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/img_20_1_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: 225.33333333333334, edge_converge: 222.40969899665552; 
+y: zero_crossing: 222.44086021505376, edge_converge: 232.5
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 224.90625, edge_converge: 212.425; 
+y: zero_crossing: 222.7012987012987, edge_converge: 244.8653846153846
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 224.55172413793105, edge_converge: 225.7096153846154; 
+y: zero_crossing: 222.8507462686567, edge_converge: 233.3653846153846
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 224.86363636363637, edge_converge: 207.6159420289855; 
+y: zero_crossing: 223.22413793103448, edge_converge: 215.5216346153846
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 224.8421052631579, edge_converge: 198.0; 
+y: zero_crossing: 223.37254901960785, edge_converge: 238.5
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 224.8421052631579, edge_converge: 198.0; 
+y: zero_crossing: 223.33333333333334, edge_converge: 238.5
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 224.8421052631579, edge_converge: 198.0; 
+y: zero_crossing: 223.46341463414635, edge_converge: 238.5
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 224.56521739130434, edge_converge: 198.0; 
+y: zero_crossing: 223.57894736842104, edge_converge: 238.5
diff --git a/Nov_27/Manual/Camera/img_rec_module/img_20_2_meas.txt b/Nov_27/Manual/Camera/img_rec_module/img_20_2_meas.txt
new file mode 100644
index 0000000..4e85aab
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/img_20_2_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 225.58333333333331; 
+y: zero_crossing: -1, edge_converge: 182.35897435897436
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 227.5; 
+y: zero_crossing: -1, edge_converge: 224.0
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 218.89655172413794, edge_converge: 212.9415292353823; 
+y: zero_crossing: -1, edge_converge: 232.0
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 218.86363636363637, edge_converge: 266.84375; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 218.5, edge_converge: 213.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 213.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 213.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 213.0; 
+y: zero_crossing: 191.76923076923077, edge_converge: -1
diff --git a/Nov_27/Manual/Camera/img_rec_module/img_20_3_meas.txt b/Nov_27/Manual/Camera/img_rec_module/img_20_3_meas.txt
new file mode 100644
index 0000000..b228fdb
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/img_20_3_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: 43.1578947368421, edge_converge: 27.907894736842106; 
+y: zero_crossing: 222.41739130434783, edge_converge: 224.8991379310345
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 222.0; 
+y: zero_crossing: 222.53191489361703, edge_converge: 228.6346153846154
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 225.0; 
+y: zero_crossing: 222.6338028169014, edge_converge: 213.0
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 222.75, edge_converge: 291.0
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 222.79166666666666, edge_converge: 200.0
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 222.7450980392157, edge_converge: 200.0
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 222.92105263157896, edge_converge: 200.0
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 222.9268292682927, edge_converge: 200.0
diff --git a/Nov_27/Manual/Camera/img_rec_module/img_2_1new_meas.txt b/Nov_27/Manual/Camera/img_rec_module/img_2_1new_meas.txt
new file mode 100644
index 0000000..a8b0bc8
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/img_2_1new_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 1178.6311475409836; 
+y: zero_crossing: -1, edge_converge: 376.4716202270382
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: 635.5
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 1582.0; 
+y: zero_crossing: -1, edge_converge: 723.0
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 1682.4423076923076; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 1081.9783653846152; 
+y: zero_crossing: -1, edge_converge: 558.15625
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 72.5, edge_converge: 1081.9783653846152; 
+y: zero_crossing: -1, edge_converge: 558.15625
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 1081.9783653846152; 
+y: zero_crossing: -1, edge_converge: 558.15625
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 1081.9783653846152; 
+y: zero_crossing: -1, edge_converge: 558.15625
diff --git a/Nov_27/Manual/Camera/img_rec_module/img_3_1new_meas.txt b/Nov_27/Manual/Camera/img_rec_module/img_3_1new_meas.txt
new file mode 100644
index 0000000..8cf1d3c
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/img_3_1new_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 485.2365079365079; 
+y: zero_crossing: -1, edge_converge: 463.47727272727275
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 484.8125; 
+y: zero_crossing: -1, edge_converge: 464.0
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 482.4; 
+y: zero_crossing: 462.8421052631579, edge_converge: 464.13461538461536
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 480.5, edge_converge: 475.3696120689655; 
+y: zero_crossing: 462.5, edge_converge: 478.50674662668666
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 485.2307692307692, edge_converge: 493.35480943738656; 
+y: zero_crossing: 462.1875, edge_converge: 448.98519736842104
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 493.35480943738656; 
+y: zero_crossing: 462.5, edge_converge: 448.98519736842104
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 487.5, edge_converge: 493.35480943738656; 
+y: zero_crossing: 462.1875, edge_converge: 448.98519736842104
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 493.35480943738656; 
+y: zero_crossing: 462.4347826086956, edge_converge: 448.98519736842104
diff --git a/Nov_27/Manual/Camera/img_rec_module/img_filter.py b/Nov_27/Manual/Camera/img_rec_module/img_filter.py
new file mode 100644
index 0000000..3b8b0dc
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/img_filter.py
@@ -0,0 +1,23 @@
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+img = cv2.imread('../testpic/img_20_1.png',0)
+blur = cv2.GaussianBlur(img,(5,5),0)
+cv2.imshow("ok", blur)
+img = blur
+
+laplacian = cv2.Laplacian(img,cv2.CV_64F)
+sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)
+sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=5)
+
+plt.subplot(2,2,1),plt.imshow(img,cmap = 'gray')
+plt.title('Original'), plt.xticks([]), plt.yticks([])
+plt.subplot(2,2,2),plt.imshow(laplacian,cmap = 'gray')
+plt.title('Laplacian'), plt.xticks([]), plt.yticks([])
+plt.subplot(2,2,3),plt.imshow(sobelx,cmap = 'gray')
+plt.title('Sobel X'), plt.xticks([]), plt.yticks([])
+plt.subplot(2,2,4),plt.imshow(sobely,cmap = 'gray')
+plt.title('Sobel Y'), plt.xticks([]), plt.yticks([])
+
+plt.show()
\ No newline at end of file
diff --git a/Nov_27/Manual/Camera/img_rec_module/img_rec.py b/Nov_27/Manual/Camera/img_rec_module/img_rec.py
new file mode 100644
index 0000000..eac36ff
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/img_rec.py
@@ -0,0 +1,176 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+from img_rec_module.sig_proc_test import FM, compare_data_plots, compare_images
+
+
+def identity(img):
+    return img
+
+
+def grayscale(img):
+    return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+
+def grad_detect(img, n, ori, func):
+    #img = np.array(img)
+    if ori == 'h':
+        grad = [func(img, n, i+1) - func(img, n, i) for i in range(img.shape[1]-1)]
+    else:
+        grad = [func(img, i+1, n) - func(img, i, n) for i in range(img.shape[0]-1)]
+    return grad
+
+
+def abs_detect(img, n, ori, func):
+    if ori == 'h':
+        return [func(img, n, i) for i in range(img.shape[1])]
+    else:
+        return [func(img, i, n) for i in range(img.shape[0])]
+
+
+def visualize_data(folder, oname, sample, orient, func, data_p=grad_detect, img_p=identity):
+    dirs = os.listdir(folder)
+    for f in dirs:
+        try:
+            img = img_p(cv2.imread(folder + f))
+            interval = int(img.shape[1 if orient == 'v' else 0] / sample)
+            for j in range(sample):
+                n = j * interval
+                data = data_p(img, n, orient, func)
+                plt_save_data(data, oname + 'data_{0}{1}_'.format(orient, n) + f)
+        except:
+            print(f)
+
+
+def max_entry(img):
+    max_e = 0
+    for row in img:
+        temp = max(row)
+        if temp > max_e:
+            max_e = temp
+
+
+def record_file(vals, fname):
+    f = open(fname, 'w')
+    for v in vals:
+        f.write(str(v) + '\n')
+    f.close()
+
+
+def rel_lumin(img, r, c):
+    b = img.item(r, c, 0)
+    g = img.item(r, c, 1)
+    r = img.item(r, c, 2)
+    return 0.2126 * r + 0.7152 * g + 0.0722 * b
+
+
+def max_method(img):
+    index = []
+    s = 0
+    for i in range(img.shape[1]):
+        val = np.argmax(img[:, i])
+        index.append(val)
+        s += val
+    return s / len(index)
+
+
+def plot_show_data(data):
+    plt.figure(figsize=(16, 8))
+    #plt.xticks(range(0, img.shape[0], 20), range(0, img.shape[0], 20))
+    plt.plot(data, 'b-')
+    plt.show()
+    plt.close()
+
+
+def plt_save_data(data, fname):
+    plt.figure(figsize=(16, 8))
+    plt.plot(data, 'b-')
+    plt.savefig(fname)
+    plt.close()
+
+
+def centroid(img, func):
+    r = img.shape[0]
+    c = img.shape[1]
+    aggr_i = 0
+    aggr_j = 0
+    aggr = 0
+    for i in range(r):
+        for j in range(c):
+            lum = func(img, i, j)
+            aggr_i += lum * i
+            aggr_j += lum * j
+            aggr += lum
+    dim = r * c
+    return aggr_i / aggr, aggr_j / aggr
+    # return aggr_i / dim, aggr_j / dim
+
+
+def centroid_test(fname, n, trials, func, img_p=identity):
+    for i in range(1, n+1):
+        for j in range(1, trials+1):
+            cf = fname.format(i, j)
+            print(cf)
+            im = img_p(cv2.imread(cf))
+            print(centroid(im, func))
+
+
+def brightness_test():
+    BASE_DIR = '../calib/'
+    image_name = 'shutter_{0}_iso_{1}_bright_{2}.jpeg'
+    img_path = BASE_DIR + image_name
+    shutter = [100, 1000, 5000, 10000]
+    isos = [100, 400, 800]
+    brightness = [0, 50, 100]
+    expo_modes = ['auto', 'very_long']
+    for ss in shutter[0:]:
+        comp = []
+        plts = []
+        print(ss)
+        for iso in isos:
+            name = img_path.format(ss, iso, 'very_long')
+            imgr = cv2.imread(name)
+            tp = (imgr, name)
+            comp.append(tp)
+            img = cv2.cvtColor(imgr, cv2.COLOR_BGR2GRAY)
+            plts.append((FM(img, FM.HOR, img.shape[0] // 2).extract_array(), name))
+        compare_images(ilist=comp)
+        compare_data_plots(ilist=plts)
+
+
+gray_p = lambda img, r, c: img.item(r, c)
+
+if __name__ == '__main__':
+    #centroid_test("../testpic/img_{0}_{1}.png", 25, 3, gray_p, img_p=grayscale)
+    # centroid_test("../testpic/img_{0}_{1}.png", 25, 3, rel_lumin)
+
+    """img = cv2.imread("../testpic/png_1_1.png")
+    plot_show_data(abs_detect(img, 0, 'v', rel_lumin))"""
+
+    """img = cv2.imread("../testpic/png_1_1.png")
+    print(img.shape)
+    cv2.imshow('all', img)
+    gs = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    cv2.imshow('gray', gs)
+    print(sum(centers) / len(centers))
+    print(max_method(gs))
+    record_file(centers, 'centers.txt')
+    
+    while True:
+        prompt = input("type in an index to draw or q to quit")
+        if prompt == 'q':
+            break
+        else:
+            plot_i(int(prompt))
+    
+    centers = []
+    for i in range(gs.shape[1]):
+        grad = grad_detect(gs, i, 'v', rel_lumin)
+        max_v = max(range(len(grad)), key=lambda i: grad[i])
+        min_v = min(range(len(grad)), key=lambda i: grad[i])
+        centers.append((max_v + min_v) / 2)"""
+
+    #visualize_data("../testpic/", "../absdataplot/", 10, 'h', rel_lumin, data_p=abs_detect)
+    brightness_test()
+
diff --git a/Nov_27/Manual/Camera/img_rec_module/log.docx b/Nov_27/Manual/Camera/img_rec_module/log.docx
new file mode 100644
index 0000000..b574f98
Binary files /dev/null and b/Nov_27/Manual/Camera/img_rec_module/log.docx differ
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/convergence.csv b/Nov_27/Manual/Camera/img_rec_module/meas/convergence.csv
new file mode 100644
index 0000000..418d98a
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/convergence.csv
@@ -0,0 +1,373 @@
+Image Number,Center X,Center Y,StdDev Horizontal,Std Dev Vertical
+0
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diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/convergence_naive_std1.5.csv b/Nov_27/Manual/Camera/img_rec_module/meas/convergence_naive_std1.5.csv
new file mode 100644
index 0000000..cd215cf
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/convergence_naive_std1.5.csv
@@ -0,0 +1,373 @@
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diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/convergence_naive_std2.csv b/Nov_27/Manual/Camera/img_rec_module/meas/convergence_naive_std2.csv
new file mode 100644
index 0000000..9c454aa
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/convergence_naive_std2.csv
@@ -0,0 +1,373 @@
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diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/convergence_naive_std2_p20.csv b/Nov_27/Manual/Camera/img_rec_module/meas/convergence_naive_std2_p20.csv
new file mode 100644
index 0000000..2961aa4
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/convergence_naive_std2_p20.csv
@@ -0,0 +1,373 @@
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diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/convergence_naive_std_1.2.csv b/Nov_27/Manual/Camera/img_rec_module/meas/convergence_naive_std_1.2.csv
new file mode 100644
index 0000000..5f851e0
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/convergence_naive_std_1.2.csv
@@ -0,0 +1,373 @@
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\ No newline at end of file
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/convergence_prime.csv b/Nov_27/Manual/Camera/img_rec_module/meas/convergence_prime.csv
new file mode 100644
index 0000000..54b1f8f
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/convergence_prime.csv
@@ -0,0 +1,373 @@
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diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(100, 200).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(100, 200).txt
new file mode 100644
index 0000000..06ac13b
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(100, 200).txt	
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diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(100, 300).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(100, 300).txt
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--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(100, 300).txt	
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diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(120, 300).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(120, 300).txt
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diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(490, 50).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_100_{0}/img_100_{0}_(490, 50).txt
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diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_10_{0}/img_10_{0}_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_10_{0}/img_10_{0}_(240, 320).txt
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index 0000000..0aa7bd9
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diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(100, 150).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(100, 150).txt
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diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(150, 200).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(150, 200).txt
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diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(30, 100).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(30, 100).txt
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diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(350, 400).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(350, 400).txt
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diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(400, 450).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(400, 450).txt
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diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(450, 550).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(450, 550).txt
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+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(450, 550).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 480.90588235294115, poly: 479.13613613613614, gaussian: 479.12312312312315; 
+y: edge_centroid: 316.68044077134977, poly: 313.73273273273276, gaussian: 315.5095095095095
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(50, 100).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(50, 100).txt
new file mode 100644
index 0000000..c0a15fe
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_130_{0}/img_130_{0}_(50, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 474.3070001323277, poly: 472.996996996997, gaussian: 472.81081081081084; 
+y: edge_centroid: 312.8755548978987, poly: 312.6666666666667, gaussian: 313.2162162162162
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_133_{0}/img_133_{0}.png_(250, 100).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_133_{0}/img_133_{0}.png_(250, 100).txt
new file mode 100644
index 0000000..f2af609
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_133_{0}/img_133_{0}.png_(250, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 277.76777028087247, poly: 281.1461461461461, gaussian: 278.5765765765766; 
+y: edge_centroid: 302.41199200532964, poly: 296.71771771771773, gaussian: 299.7837837837838
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_133_{0}/img_133_{0}.png_(250, 50).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_133_{0}/img_133_{0}.png_(250, 50).txt
new file mode 100644
index 0000000..b79b4f9
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_133_{0}/img_133_{0}.png_(250, 50).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 277.76777028087247, poly: 281.1461461461461, gaussian: 278.5765765765766; 
+y: edge_centroid: 266.9218650877565, poly: 284.07207207207205, gaussian: 279.6546546546547
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_133_{0}/img_133_{0}.png_(300, 550).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_133_{0}/img_133_{0}.png_(300, 550).txt
new file mode 100644
index 0000000..84b1934
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_133_{0}/img_133_{0}.png_(300, 550).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 81.92717111682785, poly: 81.3933933933934, gaussian: 81.24724724724724; 
+y: edge_centroid: 289.13216374268995, poly: 286.6216216216216, gaussian: 288.1381381381381
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_134_{0}/img_134_{0}.png_(200, 150).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_134_{0}/img_134_{0}.png_(200, 150).txt
new file mode 100644
index 0000000..0d980c5
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_134_{0}/img_134_{0}.png_(200, 150).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 58.87142186766776, poly: 57.72372372372372, gaussian: 58.56256256256256; 
+y: edge_centroid: 298.5141452126122, poly: 298.53453453453454, gaussian: 299.05505505505505
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_13_{0}/img_13_{0}_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_13_{0}/img_13_{0}_(240, 320).txt
new file mode 100644
index 0000000..f932d1e
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_13_{0}/img_13_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 52.44813278008298, poly: 53.77977977977978, gaussian: 53.35935935935936; 
+y: edge_centroid: 215.0489725113424, poly: 217.44444444444446, gaussian: 216.44444444444446
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_157_{0}/img_157_{0}.png_(250, 450).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_157_{0}/img_157_{0}.png_(250, 450).txt
new file mode 100644
index 0000000..bc8985f
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_157_{0}/img_157_{0}.png_(250, 450).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 231.9349172144786, poly: 229.16516516516518, gaussian: 230.4204204204204; 
+y: edge_centroid: 324.7964368509638, poly: 320.92092092092093, gaussian: 322.7027027027027
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_160_{0}/img_160_{0}_(100, 500).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_160_{0}/img_160_{0}_(100, 500).txt
new file mode 100644
index 0000000..7b9d1b7
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_160_{0}/img_160_{0}_(100, 500).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 225.6382858488344, poly: 219.85385385385385, gaussian: 223.6846846846847; 
+y: edge_centroid: 326.9082845283447, poly: 329.64864864864865, gaussian: 329.45245245245246
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_160_{0}/img_160_{0}_(300, 400).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_160_{0}/img_160_{0}_(300, 400).txt
new file mode 100644
index 0000000..a009916
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_160_{0}/img_160_{0}_(300, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 218.77561608300925, poly: 224.71471471471472, gaussian: 223.88388388388387; 
+y: edge_centroid: 325.48699770378875, poly: 323.76576576576576, gaussian: 321.72972972972974
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_160_{0}/img_160_{0}_(350, 450).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_160_{0}/img_160_{0}_(350, 450).txt
new file mode 100644
index 0000000..7dca8cd
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_160_{0}/img_160_{0}_(350, 450).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 221.86800998243837, poly: 223.86086086086087, gaussian: 222.33833833833833; 
+y: edge_centroid: 324.95901767558263, poly: 319.3243243243243, gaussian: 320.1621621621622
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_170_{0}/img_170_{0}_(200, 350).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_170_{0}/img_170_{0}_(200, 350).txt
new file mode 100644
index 0000000..deecda3
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_170_{0}/img_170_{0}_(200, 350).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 392.6319532908705, poly: 390.93993993993996, gaussian: 391.22222222222223; 
+y: edge_centroid: 332.7505637119807, poly: 333.94494494494495, gaussian: 333.13613613613614
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_170_{0}/img_170_{0}_(200, 450).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_170_{0}/img_170_{0}_(200, 450).txt
new file mode 100644
index 0000000..b69498b
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_170_{0}/img_170_{0}_(200, 450).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 392.6319532908705, poly: 390.93993993993996, gaussian: 391.22222222222223; 
+y: edge_centroid: 340.36645473811694, poly: 333.6066066066066, gaussian: 333.86686686686687
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(100, 150).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(100, 150).txt
new file mode 100644
index 0000000..0564307
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(100, 150).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 220.7910065139735, poly: 217.15515515515517, gaussian: 220.11711711711712; 
+y: edge_centroid: 160.24814595075648, poly: 158.1981981981982, gaussian: 155.2112112112112
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(150, 200).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(150, 200).txt
new file mode 100644
index 0000000..1f815cd
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(150, 200).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 226.56943727413525, poly: 233.0, gaussian: 231.61461461461462; 
+y: edge_centroid: -1, poly: 157.03003003003002, gaussian: 159.97997997998
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(200, 250).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(200, 250).txt
new file mode 100644
index 0000000..7084079
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(200, 250).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 224.38829323735553, poly: 221.4204204204204, gaussian: 222.997997997998; 
+y: edge_centroid: -1, poly: 136.21621621621622, gaussian: 153.43543543543544
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(200, 50).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(200, 50).txt
new file mode 100644
index 0000000..751271c
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(200, 50).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 224.38829323735553, poly: 221.4204204204204, gaussian: 222.997997997998; 
+y: edge_centroid: 159.31560235197594, poly: 147.12512512512512, gaussian: 138.0
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(250, 300).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(250, 300).txt
new file mode 100644
index 0000000..e2f1dfc
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(250, 300).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 228.60011869116025, poly: 215.4144144144144, gaussian: 201.0; 
+y: edge_centroid: 155.03382867757378, poly: 157.1791791791792, gaussian: 158.998998998999
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(300, 350).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(300, 350).txt
new file mode 100644
index 0000000..238ae5c
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(300, 350).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 228.2786542650262, poly: 231.36236236236238, gaussian: 224.34534534534535; 
+y: edge_centroid: -1, poly: 139.21621621621622, gaussian: 139.81581581581582
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(350, 400).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(350, 400).txt
new file mode 100644
index 0000000..c508eef
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(350, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 218.11615628299896, poly: 221.07207207207207, gaussian: 219.3013013013013; 
+y: edge_centroid: 155.50516848445383, poly: 152.59459459459458, gaussian: 152.37137137137137
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(400, 450).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(400, 450).txt
new file mode 100644
index 0000000..4c33dde
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(400, 450).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 228.5702052689802, poly: 220.84284284284286, gaussian: 200.0; 
+y: edge_centroid: 148.5988915951851, poly: 134.5095095095095, gaussian: 135.5895895895896
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(50, 100).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(50, 100).txt
new file mode 100644
index 0000000..45a7482
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_180_{0}/img_180_{0}_(50, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 225.69889181411875, poly: 219.86186186186185, gaussian: 228.002002002002; 
+y: edge_centroid: 156.60606647930996, poly: 154.3803803803804, gaussian: 151.83083083083082
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_19_3/img_19_3_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_19_3/img_19_3_(240, 320).txt
new file mode 100644
index 0000000..5d569df
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_19_3/img_19_3_(240, 320).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 354.17105263157896; 
+y: zero_crossing: 264.9651162790698, edge_converge: 252.34375
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 355.72727272727275, edge_converge: 353.32894736842104; 
+y: zero_crossing: 265.5, edge_converge: 269.3763586956522
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 355.76190476190476, edge_converge: 349.5; 
+y: zero_crossing: 265.5, edge_converge: 239.0
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 356.1034482758621, edge_converge: 371.0; 
+y: zero_crossing: 265.37254901960785, edge_converge: 300.0
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 346.65625; 
+y: zero_crossing: 265.4313725490196, edge_converge: 238.5
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 356.40625, edge_converge: 422.36538461538464; 
+y: zero_crossing: 265.40845070422534, edge_converge: 294.36538461538464
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 356.3095238095238, edge_converge: 256.4673913043478; 
+y: zero_crossing: 265.37254901960785, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 356.09375, edge_converge: 317.0; 
+y: zero_crossing: 265.3333333333333, edge_converge: -1
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_19_{0}/img_19_{0}_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_19_{0}/img_19_{0}_(240, 320).txt
new file mode 100644
index 0000000..84c72a9
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_19_{0}/img_19_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 356.46369710467707, poly: 356.40440440440443, gaussian: 356.67667667667666; 
+y: edge_centroid: 264.30495510475555, poly: 263.84384384384384, gaussian: 264.1141141141141
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_1_1/img_1_1_(600, 960).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_1_1/img_1_1_(600, 960).txt
new file mode 100644
index 0000000..1495c39
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_1_1/img_1_1_(600, 960).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 1033.5062801932368; 
+y: zero_crossing: -1, edge_converge: 671.6582417582417
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 285.5, edge_converge: 286.10200668896323; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 285.5, edge_converge: 276.20913461538464; 
+y: zero_crossing: -1, edge_converge: 771.828947368421
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 278.8111413043478; 
+y: zero_crossing: -1, edge_converge: 607.5
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 285.5, edge_converge: 284.02163461538464; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 284.5, edge_converge: 279.5; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 283.8615560640732; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 306.5; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_1_{0}/img_1_{0}_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_1_{0}/img_1_{0}_(240, 320).txt
new file mode 100644
index 0000000..d00a799
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_1_{0}/img_1_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 383.30516277540283, poly: 383.17317317317315, gaussian: 383.2852852852853; 
+y: edge_centroid: 129.78768233387356, poly: 129.64064064064064, gaussian: 129.4164164164164
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_1_{0}/img_1_{0}_(600, 960).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_1_{0}/img_1_{0}_(600, 960).txt
new file mode 100644
index 0000000..1df54e1
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_1_{0}/img_1_{0}_(600, 960).txt	
@@ -0,0 +1,6 @@
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 282.80022496724126; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 277.11846281807; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_20_1/img_20_1_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_20_1/img_20_1_(240, 320).txt
new file mode 100644
index 0000000..7f8ccc7
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_20_1/img_20_1_(240, 320).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: 225.33333333333334, edge_converge: 222.40969899665552; 
+y: zero_crossing: 222.44086021505376, edge_converge: 232.5
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 224.90625, edge_converge: 212.425; 
+y: zero_crossing: 222.7012987012987, edge_converge: 244.8653846153846
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 224.55172413793105, edge_converge: 225.7096153846154; 
+y: zero_crossing: 222.8507462686567, edge_converge: 233.3653846153846
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 224.86363636363637, edge_converge: 207.6159420289855; 
+y: zero_crossing: 223.22413793103448, edge_converge: 215.5216346153846
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 224.8421052631579, edge_converge: 198.0; 
+y: zero_crossing: 223.37254901960785, edge_converge: 238.5
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 224.8421052631579, edge_converge: -1; 
+y: zero_crossing: 223.33333333333334, edge_converge: 309.4673913043478
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 224.8421052631579, edge_converge: 220.5; 
+y: zero_crossing: 223.46341463414635, edge_converge: 210.0
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 224.56521739130434, edge_converge: -1; 
+y: zero_crossing: 223.57894736842104, edge_converge: -1
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_20_1/img_20_1_(900, 1400).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_20_1/img_20_1_(900, 1400).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_2_1/img_2_1_(200, 500).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_1/img_2_1_(200, 500).txt
new file mode 100644
index 0000000..b5bbd3d
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_1/img_2_1_(200, 500).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 1178.6311475409836; 
+y: zero_crossing: -1, edge_converge: 376.4716202270382
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: 635.5
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 1582.0; 
+y: zero_crossing: -1, edge_converge: 723.0
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 1682.4423076923076; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 1081.9783653846152; 
+y: zero_crossing: -1, edge_converge: 558.15625
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 72.5, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: 600.4166666666666
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_2_1/img_2_1_(400, 700).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_1/img_2_1_(400, 700).txt
new file mode 100644
index 0000000..5799b85
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_1/img_2_1_(400, 700).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 1114.8653846153848; 
+y: zero_crossing: -1, edge_converge: 920.8071348940914
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 1120.671052631579; 
+y: zero_crossing: -1, edge_converge: 604.5
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 886.5258620689655; 
+y: zero_crossing: -1, edge_converge: 614.671052631579
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 847.671052631579; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: 1025.9558823529412
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 94.1875, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_2_1/img_2_1_(600, 960).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_1/img_2_1_(600, 960).txt
new file mode 100644
index 0000000..9b33d97
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_1/img_2_1_(600, 960).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 316.76911544227886; 
+y: zero_crossing: -1, edge_converge: 646.7335664335665
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 360.91666666666663; 
+y: zero_crossing: -1, edge_converge: 910.05625
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 919.5; 
+y: zero_crossing: -1, edge_converge: 568.328947368421
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 422.0326086956522; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 782.0326086956521; 
+y: zero_crossing: -1, edge_converge: 1111.5
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 231.65625; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_2_1/img_2_1_(800, 900).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_1/img_2_1_(800, 900).txt
new file mode 100644
index 0000000..85e0c4d
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_1/img_2_1_(800, 900).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 94.49201474201475; 
+y: zero_crossing: -1, edge_converge: 612.9423076923077
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 97.05847076461768; 
+y: zero_crossing: -1, edge_converge: 615.672697368421
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 95.59030100334448; 
+y: zero_crossing: -1, edge_converge: 626.328947368421
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 96.23076923076923, edge_converge: 104.08333333333333; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 72.65625; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 105.76923076923077, edge_converge: 135.17105263157896; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 106.5, edge_converge: 264.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_2_1/img_2_1_(900, 1700).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_1/img_2_1_(900, 1700).txt
new file mode 100644
index 0000000..51550d1
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_1/img_2_1_(900, 1700).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 992.733709273183; 
+y: zero_crossing: -1, edge_converge: 779.3782051282051
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 968.26875; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 953.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 198.43478260869566, edge_converge: 189.65625; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 197.5, edge_converge: 207.5; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 200.55172413793105, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 207.40969899665552; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_2_1/img_2_1_(900, 900).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_1/img_2_1_(900, 900).txt
new file mode 100644
index 0000000..4e5269e
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_1/img_2_1_(900, 900).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 992.733709273183; 
+y: zero_crossing: -1, edge_converge: 612.9423076923077
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 968.26875; 
+y: zero_crossing: -1, edge_converge: 615.672697368421
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 953.0; 
+y: zero_crossing: -1, edge_converge: 626.328947368421
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 198.43478260869566, edge_converge: 189.65625; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 197.5, edge_converge: 207.5; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 200.55172413793105, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 207.40969899665552; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_2_4/img_2_4_(1400, 900).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_4/img_2_4_(1400, 900).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_2_4/img_2_4_(600, 960).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_4/img_2_4_(600, 960).txt
new file mode 100644
index 0000000..725c811
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_4/img_2_4_(600, 960).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 1100.8305555555555; 
+y: zero_crossing: 468.88853503184714, edge_converge: 468.9624718174986
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 1284.6410256410256; 
+y: zero_crossing: -1, edge_converge: 472.02868852459017
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 1086.828947368421; 
+y: zero_crossing: 468.9056603773585, edge_converge: 469.513210927004
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 1086.6; 
+y: zero_crossing: -1, edge_converge: 526.828947368421
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 1092.3333333333333, edge_converge: 1189.2003676470588; 
+y: zero_crossing: -1, edge_converge: 445.3327759197324
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 1092.157894736842, edge_converge: 1190.0; 
+y: zero_crossing: -1, edge_converge: 439.0
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 1046.8653846153848; 
+y: zero_crossing: -1, edge_converge: 503.0326086956522
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 1072.84375; 
+y: zero_crossing: -1, edge_converge: 517.84375
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_2_4/img_2_4_(900, 1400).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_4/img_2_4_(900, 1400).txt
new file mode 100644
index 0000000..b979bb5
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_4/img_2_4_(900, 1400).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 1296.632183836245; 
+y: zero_crossing: -1, edge_converge: 683.452135931303
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 1295.6363633216238; 
+y: zero_crossing: 690.1150168914178, edge_converge: 693.2398839220738
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 1295.3653844696842; 
+y: zero_crossing: -1, edge_converge: 693.9883644021885
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 1278.906249850798; 
+y: zero_crossing: 688.6590191104578, edge_converge: 700.2655993429721
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 1174.4999998716144; 
+y: zero_crossing: -1, edge_converge: 964.4428415672992
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 1161.4999997817608; 
+y: zero_crossing: -1, edge_converge: 940.947694754178
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 1160.9999998929734; 
+y: zero_crossing: -1, edge_converge: 941.7896120081671
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 1186.0000001232456, edge_converge: 1174.4999996450306; 
+y: zero_crossing: -1, edge_converge: 962.456256071665
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_2_5/img_2_5_(600, 960).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_5/img_2_5_(600, 960).txt
new file mode 100644
index 0000000..d1de3a8
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_5/img_2_5_(600, 960).txt	
@@ -0,0 +1,21 @@
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 1158.301886792453, edge_converge: 1158.0; 
+y: zero_crossing: 423.6167664670659, edge_converge: 425.51180555555555
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 1158.3440860215053, edge_converge: 1166.740170940171; 
+y: zero_crossing: -1, edge_converge: 385.2810276679842
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 1158.134328358209, edge_converge: 1188.467391304348; 
+y: zero_crossing: -1, edge_converge: 326.3781954887218
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 1158.2131147540983, edge_converge: 1194.753947368421; 
+y: zero_crossing: 423.44827586206895, edge_converge: 374.91666666666663
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 1170.5; 
+y: zero_crossing: 423.1578947368421, edge_converge: 351.5
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 1255.0; 
+y: zero_crossing: -1, edge_converge: 700.0326086956521
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 1218.532608695652; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_2_{0}/img_2_{0}_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_{0}/img_2_{0}_(240, 320).txt
new file mode 100644
index 0000000..d77b164
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_{0}/img_2_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 383.569200132758, poly: 382.83083083083085, gaussian: 383.4114114114114; 
+y: edge_centroid: 128.77362969356932, poly: 129.4084084084084, gaussian: 129.21021021021022
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_2_{0}/img_2_{0}_(600, 960).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_{0}/img_2_{0}_(600, 960).txt
new file mode 100644
index 0000000..d22680c
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_2_{0}/img_2_{0}_(600, 960).txt	
@@ -0,0 +1 @@
+ksize:(7, 7), sigmax:0
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_3_3/img_3_3_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_3_3/img_3_3_(240, 320).txt
new file mode 100644
index 0000000..15cad33
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_3_3/img_3_3_(240, 320).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 388.0; 
+y: zero_crossing: 129.02597402597402, edge_converge: 125.57692307692308
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 384.1578947368421, edge_converge: 389.5364372469636; 
+y: zero_crossing: 129.47297297297297, edge_converge: 129.40969899665552
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 384.23809523809524, edge_converge: 371.13461538461536; 
+y: zero_crossing: 129.62337662337663, edge_converge: 129.21517027863777
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 401.32894736842104; 
+y: zero_crossing: 129.66666666666666, edge_converge: 199.6346153846154
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 405.3327759197324; 
+y: zero_crossing: 129.72916666666666, edge_converge: 123.56414473684211
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 129.60416666666666, edge_converge: 125.0
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 383.8421052631579, edge_converge: -1; 
+y: zero_crossing: 129.5, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 383.59375, edge_converge: -1; 
+y: zero_crossing: 129.46341463414635, edge_converge: 105.46739130434783
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_3_3/img_3_3_new_(600, 960).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_3_3/img_3_3_new_(600, 960).txt
new file mode 100644
index 0000000..85506ff
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_3_3/img_3_3_new_(600, 960).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 627.2624342255776; 
+y: zero_crossing: 282.9597701149425, edge_converge: 280.3083778966132
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 626.3650177462289; 
+y: zero_crossing: -1, edge_converge: 283.4423076923077
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 608.9765275707898; 
+y: zero_crossing: 287.3333333333333, edge_converge: 284.86538461538464
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 650.8565573770492; 
+y: zero_crossing: -1, edge_converge: 296.0326086956522
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 652.203125; 
+y: zero_crossing: -1, edge_converge: 689.3056680161943
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 659.8423913043478; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 631.6904761904761, edge_converge: 597.0326086956522; 
+y: zero_crossing: -1, edge_converge: 683.5
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 645.0; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_3_{0}/img_3_{0}_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_3_{0}/img_3_{0}_(240, 320).txt
new file mode 100644
index 0000000..056b176
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_3_{0}/img_3_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 383.6794588625068, poly: 383.2082082082082, gaussian: 383.5855855855856; 
+y: edge_centroid: 128.72770467836258, poly: 129.44144144144144, gaussian: 129.17717717717719
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_3_{0}/img_3_{0}_(300, 573).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_3_{0}/img_3_{0}_(300, 573).txt
new file mode 100644
index 0000000..6a9be15
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_3_{0}/img_3_{0}_(300, 573).txt	
@@ -0,0 +1,21 @@
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 551.1085016945578; 
+y: zero_crossing: -1, edge_converge: 401.2614005098476
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 551.0790923034685; 
+y: zero_crossing: -1, edge_converge: 401.1959898455841
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 551.3164486248141; 
+y: zero_crossing: -1, edge_converge: 396.65866001765227
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 553.6140783045154; 
+y: zero_crossing: 385.44129158264957, edge_converge: 391.39592471066015
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 554.2854352720954, edge_converge: 553.3228366624788; 
+y: zero_crossing: 385.28612080628454, edge_converge: 391.30816089061875
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 554.0283816103325, edge_converge: 552.9026715626205; 
+y: zero_crossing: 385.12690508006284, edge_converge: 391.2389474584604
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 553.7215989908605, edge_converge: 552.4769129699359; 
+y: zero_crossing: 384.9703803392806, edge_converge: 391.0686695466003
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_3_{0}/img_3_{0}_(600, 960).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_3_{0}/img_3_{0}_(600, 960).txt
new file mode 100644
index 0000000..eebe206
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_3_{0}/img_3_{0}_(600, 960).txt	
@@ -0,0 +1,6 @@
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 629.1857205127992; 
+y: zero_crossing: -1, edge_converge: 303.2178917674522
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 626.2193034291377; 
+y: zero_crossing: -1, edge_converge: 279.055693080456
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_4_1/img_4_1_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_4_1/img_4_1_(240, 320).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_4_1/img_4_1_(500, 1200).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_4_1/img_4_1_(500, 1200).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_4_{0}/img_4_{0}_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_4_{0}/img_4_{0}_(240, 320).txt
new file mode 100644
index 0000000..7808d12
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_4_{0}/img_4_{0}_(240, 320).txt	
@@ -0,0 +1,6 @@
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 236.92774193101968, edge_converge: 237.5228648141686; 
+y: zero_crossing: 325.7458333221428, edge_converge: 321.11252693875815
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 237.21044902427207, edge_converge: 234.45920213005442; 
+y: zero_crossing: 325.6826399612562, edge_converge: 321.13169019760346
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(200, 50).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(200, 50).txt
new file mode 100644
index 0000000..cb9440f
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(200, 50).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.21657196969699, poly: 93.01701701701703, gaussian: 92.54954954954955; 
+y: edge_centroid: 220.67972613160907, poly: 205.44544544544544, gaussian: 220.13513513513513
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 100).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 100).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 150).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 150).txt
new file mode 100644
index 0000000..4e7e731
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 150).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 91.0, poly: 90.61461461461461, gaussian: 90.76176176176176; 
+y: edge_centroid: 207.25707213578505, poly: 211.72972972972974, gaussian: 210.05605605605606
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 250).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 250).txt
new file mode 100644
index 0000000..0b0c80e
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 250).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 91.0, poly: 90.61461461461461, gaussian: 90.76176176176176; 
+y: edge_centroid: 218.612658674189, poly: 210.37037037037038, gaussian: 216.64864864864865
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 550).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 550).txt
new file mode 100644
index 0000000..5d716b8
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(250, 550).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 91.0, poly: 90.61461461461461, gaussian: 90.76176176176176; 
+y: edge_centroid: 208.3091801113311, poly: 194.96096096096096, gaussian: 207.03603603603602
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(300, 500).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(300, 500).txt
new file mode 100644
index 0000000..7669241
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}.png_(300, 500).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 94.67445813854653, poly: 94.85585585585585, gaussian: 94.37537537537537; 
+y: edge_centroid: 211.19331646152077, poly: 195.86786786786786, gaussian: 209.42642642642642
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(100, 10).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(100, 10).txt
new file mode 100644
index 0000000..c7c4711
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(100, 10).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 94.53349610865254, poly: 90.78078078078079, gaussian: 90.11711711711712; 
+y: edge_centroid: 221.0741458686698, poly: 244.62762762762765, gaussian: 258.0
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(100, 400).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(100, 400).txt
new file mode 100644
index 0000000..e9008a5
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(100, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 94.53349610865254, poly: 92.03003003003003, gaussian: 92.93793793793793; 
+y: edge_centroid: 214.17900156837212, poly: 209.03203203203202, gaussian: 214.83483483483485
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 100).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 100).txt
new file mode 100644
index 0000000..34f75b7
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.21657196969699, poly: 92.74474474474475, gaussian: 92.64864864864865; 
+y: edge_centroid: -1, poly: 196.7027027027027, gaussian: 220.20220220220222
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 300).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 300).txt
new file mode 100644
index 0000000..b1e658b
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 300).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.21657196969699, poly: 92.74474474474475, gaussian: 92.64264264264264; 
+y: edge_centroid: 215.8282069593912, poly: 200.8108108108108, gaussian: 203.97297297297297
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 350).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 350).txt
new file mode 100644
index 0000000..ae51c4c
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 350).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.21657196969699, poly: 92.74474474474475, gaussian: 92.64264264264264; 
+y: edge_centroid: 215.93902685241267, poly: 199.14814814814815, gaussian: 201.96296296296296
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 40).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 40).txt
new file mode 100644
index 0000000..35d04b6
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 40).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.21657196969699, poly: 92.74474474474475, gaussian: 92.64264264264264; 
+y: edge_centroid: 213.46588200238375, poly: 187.52852852852854, gaussian: 250.0
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 400).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 400).txt
new file mode 100644
index 0000000..dd9188e
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(200, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.21657196969699, poly: 93.01701701701703, gaussian: 92.54954954954955; 
+y: edge_centroid: 214.17900156837212, poly: 209.03203203203202, gaussian: 214.83483483483485
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(240, 320).txt
new file mode 100644
index 0000000..beaeadc
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.95545171339562, poly: 92.43843843843844, gaussian: 92.54054054054055; 
+y: edge_centroid: 216.32595947660937, poly: 197.66066066066065, gaussian: 203.43843843843842
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(250, 100).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(250, 100).txt
new file mode 100644
index 0000000..d73fc36
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(250, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 91.0, poly: 90.61461461461461, gaussian: 90.76176176176176; 
+y: edge_centroid: -1, poly: 198.59459459459458, gaussian: 236.03003003003005
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(30, 300).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(30, 300).txt
new file mode 100644
index 0000000..8c9068f
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(30, 300).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 106.90241734802697, poly: 130.08608608608608, gaussian: 137.0; 
+y: edge_centroid: 215.8282069593912, poly: 200.8108108108108, gaussian: 203.94794794794794
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(300, 150).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(300, 150).txt
new file mode 100644
index 0000000..00f639b
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(300, 150).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 94.67445813854653, poly: 94.85585585585585, gaussian: 94.37537537537537; 
+y: edge_centroid: 207.25707213578505, poly: 211.72972972972974, gaussian: 210.05605605605606
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(300, 400).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(300, 400).txt
new file mode 100644
index 0000000..07a7d5e
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(300, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 94.67445813854653, poly: 94.72372372372372, gaussian: 94.26126126126127; 
+y: edge_centroid: 214.17900156837212, poly: 194.24424424424424, gaussian: 197.8028028028028
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(350, 200).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(350, 200).txt
new file mode 100644
index 0000000..031f3ef
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(350, 200).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 93.30997090716743, poly: 92.7907907907908, gaussian: 93.55555555555556; 
+y: edge_centroid: 219.13322627053438, poly: 210.43043043043042, gaussian: 218.1951951951952
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(400, 250).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(400, 250).txt
new file mode 100644
index 0000000..525a96e
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(400, 250).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 93.80265120132562, poly: 93.7977977977978, gaussian: 93.90190190190191; 
+y: edge_centroid: 218.612658674189, poly: 210.37037037037038, gaussian: 216.64864864864865
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(450, 300).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(450, 300).txt
new file mode 100644
index 0000000..013cc84
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(450, 300).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 94.75074524191695, poly: 94.30530530530531, gaussian: 94.26426426426427; 
+y: edge_centroid: 215.8282069593912, poly: 211.35135135135135, gaussian: 215.15915915915917
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(50, 550).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(50, 550).txt
new file mode 100644
index 0000000..7db67f4
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_59_{0}/img_59_{0}_(50, 550).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 93.89583756345176, poly: 92.57657657657657, gaussian: 92.48648648648648; 
+y: edge_centroid: 208.3091801113311, poly: 189.6096096096096, gaussian: 209.03303303303304
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_5_1/img_5_1_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_5_1/img_5_1_(240, 320).txt
new file mode 100644
index 0000000..aed0952
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_5_1/img_5_1_(240, 320).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: 556.6842105263158, edge_converge: -1; 
+y: zero_crossing: 301.9344262295082, edge_converge: 308.5
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 555.8125, edge_converge: -1; 
+y: zero_crossing: 301.94444444444446, edge_converge: 301.5
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 555.4347826086956, edge_converge: -1; 
+y: zero_crossing: 301.84375, edge_converge: 293.0
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 555.5, edge_converge: -1; 
+y: zero_crossing: 301.75, edge_converge: 318.4673913043478
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 555.5, edge_converge: -1; 
+y: zero_crossing: 301.6862745098039, edge_converge: 280.9673913043478
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 446.17105263157896; 
+y: zero_crossing: 301.7291666666667, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 301.7291666666667, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 301.6666666666667, edge_converge: -1
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_5_1/img_5_1_(900, 1400).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_5_1/img_5_1_(900, 1400).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_5_{0}/img_5_{0}_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_5_{0}/img_5_{0}_(240, 320).txt
new file mode 100644
index 0000000..65e1696
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_5_{0}/img_5_{0}_(240, 320).txt	
@@ -0,0 +1,21 @@
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 555.6464323051268, edge_converge: 568.4630895970322; 
+y: zero_crossing: 302.34291829038295, edge_converge: 304.5181313275549
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 555.4526325366787, edge_converge: 566.8934642695242; 
+y: zero_crossing: 302.34965401891816, edge_converge: 304.4386691292941
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 555.2960595115023, edge_converge: 563.5356471927093; 
+y: zero_crossing: 302.33803803307313, edge_converge: 304.27810760651005
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 555.1849293811231, edge_converge: 563.5416332228492; 
+y: zero_crossing: 302.3176065339906, edge_converge: 300.31359690803674
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 555.1016003888399, edge_converge: 563.6004921726214; 
+y: zero_crossing: 302.29189598827344, edge_converge: 300.16238390552815
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 555.0360469257112, edge_converge: 563.6160702558284; 
+y: zero_crossing: 302.26525876715635, edge_converge: 296.30288208863885
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 554.9834021944725, edge_converge: 554.5573053432411; 
+y: zero_crossing: 302.24049538378574, edge_converge: 308.2023326126082
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}.png_(200, 600).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}.png_(200, 600).txt
new file mode 100644
index 0000000..ed07d5f
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}.png_(200, 600).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.32846573208725, poly: 92.23423423423424, gaussian: 92.21921921921921; 
+y: edge_centroid: 196.4742684157417, poly: 195.8928928928929, gaussian: 196.3783783783784
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(200, 500).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(200, 500).txt
new file mode 100644
index 0000000..a483322
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(200, 500).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.32846573208725, poly: 92.03003003003003, gaussian: 92.08708708708708; 
+y: edge_centroid: 210.0317535296279, poly: 189.0990990990991, gaussian: 212.75375375375376
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(240, 320).txt
new file mode 100644
index 0000000..302af08
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.0877174261018, poly: 92.41741741741741, gaussian: 92.28528528528528; 
+y: edge_centroid: 221.40502732240432, poly: 205.07807807807808, gaussian: 206.8998998998999
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(400, 100).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(400, 100).txt
new file mode 100644
index 0000000..b340acb
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(400, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 93.30595634480204, poly: 94.14414414414415, gaussian: 93.81681681681681; 
+y: edge_centroid: 307.8291557805356, poly: 354.0, gaussian: 308.5155155155155
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(400, 400).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(400, 400).txt
new file mode 100644
index 0000000..461ddb4
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_60_{0}/img_60_{0}_(400, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 93.30595634480204, poly: 94.14414414414415, gaussian: 93.81681681681681; 
+y: edge_centroid: 212.21240264432728, poly: 190.33833833833833, gaussian: 193.35135135135135
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_61_{0}/img_61_{0}_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_61_{0}/img_61_{0}_(240, 320).txt
new file mode 100644
index 0000000..750c5f3
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_61_{0}/img_61_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 91.9518581931715, poly: 92.18618618618618, gaussian: 91.98798798798799; 
+y: edge_centroid: 218.40512573697507, poly: 201.44444444444446, gaussian: 206.24924924924926
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_61_{0}/img_61_{0}_(400, 400).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_61_{0}/img_61_{0}_(400, 400).txt
new file mode 100644
index 0000000..7978897
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_61_{0}/img_61_{0}_(400, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 93.19376913380157, poly: 94.17417417417417, gaussian: 94.03603603603604; 
+y: edge_centroid: 212.52264302981456, poly: 190.97897897897897, gaussian: 194.45845845845847
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_62_{0}/img_62_{0}_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_62_{0}/img_62_{0}_(240, 320).txt
new file mode 100644
index 0000000..d58a228
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_62_{0}/img_62_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 178.91803535934721, poly: 178.44144144144144, gaussian: 178.33333333333334; 
+y: edge_centroid: -1, poly: 184.47747747747746, gaussian: 265.93293293293294
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_63_{0}/img_63_{0}_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_63_{0}/img_63_{0}_(240, 320).txt
new file mode 100644
index 0000000..52cbf69
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_63_{0}/img_63_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 178.97878718007834, poly: 178.87387387387386, gaussian: 178.72972972972974; 
+y: edge_centroid: -1, poly: 182.97297297297297, gaussian: 227.4954954954955
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_6_{0}/img_6_{0}_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_6_{0}/img_6_{0}_(240, 320).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_73_{0}/img_73_{0}_(400, 400).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_73_{0}/img_73_{0}_(400, 400).txt
new file mode 100644
index 0000000..3c97a4b
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_73_{0}/img_73_{0}_(400, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 283.485064401206, poly: 323.0, gaussian: 286.4864864864865; 
+y: edge_centroid: 195.9362609551483, poly: 189.6016016016016, gaussian: 190.22222222222223
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(100, 300).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(100, 300).txt
new file mode 100644
index 0000000..462740a
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(100, 300).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 278.09682467236087, poly: 268.2682682682683, gaussian: 274.5575575575576; 
+y: edge_centroid: 174.44361763022326, poly: 175.0990990990991, gaussian: 174.0
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(150, 350).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(150, 350).txt
new file mode 100644
index 0000000..3abe0ab
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(150, 350).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 264.6934011476264, poly: 267.4674674674675, gaussian: 265.3213213213213; 
+y: edge_centroid: 212.65002063557583, poly: 193.16216216216216, gaussian: 205.15315315315314
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(200, 100).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(200, 100).txt
new file mode 100644
index 0000000..ec7eba6
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(200, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 299.2048811389324, poly: 298.0, gaussian: 299.0900900900901; 
+y: edge_centroid: 212.06545603775513, poly: 211.82282282282281, gaussian: 212.06006006006007
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(200, 400).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(200, 400).txt
new file mode 100644
index 0000000..0fc7e6b
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(200, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 299.2048811389324, poly: 298.0, gaussian: 299.0900900900901; 
+y: edge_centroid: 216.82302078726215, poly: 193.26126126126127, gaussian: 186.53453453453454
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(250, 150).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(250, 150).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(250, 450).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_74_{0}/img_74_{0}_(250, 450).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_78_{0}/img_78_{0}_(400, 260).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_78_{0}/img_78_{0}_(400, 260).txt
new file mode 100644
index 0000000..6892c16
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_78_{0}/img_78_{0}_(400, 260).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 366.0705646776132, poly: 403.0, gaussian: 402.0; 
+y: edge_centroid: 206.59396835595538, poly: 198.7157157157157, gaussian: 201.34434434434434
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_79_{0}/img_79_{0}_(100, 200).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_79_{0}/img_79_{0}_(100, 200).txt
new file mode 100644
index 0000000..de27310
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_79_{0}/img_79_{0}_(100, 200).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 361.17140024783157, poly: 351.23423423423424, gaussian: 355.06006006006004; 
+y: edge_centroid: 206.05061553818015, poly: 197.32232232232232, gaussian: 204.25025025025025
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_79_{0}/img_79_{0}_(200, 200).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_79_{0}/img_79_{0}_(200, 200).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_7_{0}/img_7_{0}_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_7_{0}/img_7_{0}_(240, 320).txt
new file mode 100644
index 0000000..43ad63f
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_7_{0}/img_7_{0}_(240, 320).txt	
@@ -0,0 +1,21 @@
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 157.8389116826965, edge_converge: 158.45130962399327; 
+y: zero_crossing: -1, edge_converge: 411.79259030140565
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 157.75410480327545, edge_converge: 158.32429639754088; 
+y: zero_crossing: -1, edge_converge: 411.6718763916268
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 157.7245284134464, edge_converge: 155.54913028965618; 
+y: zero_crossing: -1, edge_converge: 411.593647090896
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 157.76000829765138, edge_converge: 158.15343806061153; 
+y: zero_crossing: -1, edge_converge: 411.5797965685118
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 157.83060827808885, edge_converge: 157.98016047257528; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 157.90853193451255, edge_converge: 157.6948902708295; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 157.98721999268648, edge_converge: 157.47066779723983; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_80_{0}/img_80_{0}_(200, 300).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_80_{0}/img_80_{0}_(200, 300).txt
new file mode 100644
index 0000000..e69de29
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_80_{0}/img_80_{0}_(300, 280).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_80_{0}/img_80_{0}_(300, 280).txt
new file mode 100644
index 0000000..5df82f5
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_80_{0}/img_80_{0}_(300, 280).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 311.3298049296839, poly: 319.7637637637638, gaussian: 319.5735735735736; 
+y: edge_centroid: -1, poly: 0, gaussian: 0
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_86_{0}/img_86_{0}.png_(150, 100).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_86_{0}/img_86_{0}.png_(150, 100).txt
new file mode 100644
index 0000000..639b64b
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_86_{0}/img_86_{0}.png_(150, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 122.42161046111492, poly: 124.84684684684684, gaussian: 124.58358358358359; 
+y: edge_centroid: 268.99951226309923, poly: 263.5865865865866, gaussian: 263.9189189189189
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_86_{0}/img_86_{0}.png_(200, 400).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_86_{0}/img_86_{0}.png_(200, 400).txt
new file mode 100644
index 0000000..34cfc9d
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_86_{0}/img_86_{0}.png_(200, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 326.7772293517605, poly: 326.3703703703704, gaussian: 326.84684684684686; 
+y: edge_centroid: 247.36609869795853, poly: 246.3783783783784, gaussian: 244.63563563563565
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_97_{0}/img_97_{0}_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_97_{0}/img_97_{0}_(240, 320).txt
new file mode 100644
index 0000000..5d771cc
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_97_{0}/img_97_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 249.44900531810134, poly: 248.6956956956957, gaussian: 249.67667667667666; 
+y: edge_centroid: 259.66796296933614, poly: 261.015015015015, gaussian: 261.4074074074074
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_99_{0}/img_99_{0}_(200, 500).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_99_{0}/img_99_{0}_(200, 500).txt
new file mode 100644
index 0000000..653e98a
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_99_{0}/img_99_{0}_(200, 500).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 248.7235796541028, poly: 252.58858858858858, gaussian: 249.82882882882882; 
+y: edge_centroid: 262.84957891453524, poly: 258.80680680680683, gaussian: 260.3963963963964
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_99_{0}/img_99_{0}_(373, 234).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_99_{0}/img_99_{0}_(373, 234).txt
new file mode 100644
index 0000000..550694b
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_99_{0}/img_99_{0}_(373, 234).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 246.9395417318486, poly: 256.96296296296293, gaussian: 253.03203203203202; 
+y: edge_centroid: 280.6590576754352, poly: 258.1631631631632, gaussian: 257.65765765765764
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_99_{0}/img_99_{0}_(400, 400).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_99_{0}/img_99_{0}_(400, 400).txt
new file mode 100644
index 0000000..ceb5b0e
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_99_{0}/img_99_{0}_(400, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 263.7749063060128, poly: 262.6166166166166, gaussian: 263.40440440440443; 
+y: edge_centroid: 255.54966073622904, poly: 253.15915915915917, gaussian: 254.85185185185185
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/img_9_1/img_9_1_(240, 320).txt b/Nov_27/Manual/Camera/img_rec_module/meas/img_9_1/img_9_1_(240, 320).txt
new file mode 100644
index 0000000..501f1b1
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/img_9_1/img_9_1_(240, 320).txt	
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 243.84375; 
+y: zero_crossing: 211.51459854014598, edge_converge: 204.6346153846154
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 241.55172413793105, edge_converge: 250.5; 
+y: zero_crossing: 211.45945945945945, edge_converge: 205.6346153846154
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 241.59375, edge_converge: 243.8653846153846; 
+y: zero_crossing: 211.30208333333334, edge_converge: 233.08333333333334
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 241.76190476190476, edge_converge: 252.2848297213622; 
+y: zero_crossing: -1, edge_converge: 145.15625
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 241.86363636363637, edge_converge: 252.4673913043478; 
+y: zero_crossing: 210.77586206896552, edge_converge: 218.0
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 252.5; 
+y: zero_crossing: 210.64814814814815, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 241.63888888888889, edge_converge: 217.0; 
+y: zero_crossing: 210.5, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 249.52586206896552; 
+y: zero_crossing: 210.52727272727273, edge_converge: -1
diff --git a/Nov_27/Manual/Camera/img_rec_module/meas/truth.txt b/Nov_27/Manual/Camera/img_rec_module/meas/truth.txt
new file mode 100644
index 0000000..dcf364f
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/meas/truth.txt
@@ -0,0 +1,32 @@
+new_test
+img_4  237, 325
+
+calib
+img_29  140, 203
+
+testpic
+img_1  385, 130  E: (2, 1)
+img_2  385, 130  E: (2, 1)
+img_3  385, 130  E: (2, 1)
+img_4  -2, -2    175, 113  E: (4, 7) (DATA)
+img_5  176, 52   E: (1, 3)
+img_6  174, -1   E: (1, 0)
+img_7  158, -1   E: (0, 0)
+img_8  -2, -2    159, -1   E: (F, 0) (DATA)
+img_9  242, 212  E: (0, 2)
+img_10  278, 215 E: (1, 3)
+img_11  -2, -2   325, 218 E: (1, 4) (DATA)
+img_12  143, 215 E: (0, 1)
+img_13  53, 219  E: (0, 4)
+img_14  -1, 17   E: (0, 0)
+img_15  138, 19  E: (0, 0)
+img_16  355, 14  E: (1, 0)
+img_17  288, -1  E: (1, 0)
+img_18  369, 180 E: (1, 0)
+img_19  357, 265 E: (0, 1)
+img_20  225, 224 E: (3, 1)
+img_21  41, 224  E: (0, 1)
+img_22  -1, 59   E: (0, 3)
+img_23  -2, -2   -1, -1   E: (0, T)  (DATA)
+img_24  -1, 160  E: (0, 1)
+img_25  445, 166 E: (2, 4)
diff --git a/Nov_27/Manual/Camera/img_rec_module/sig_proc.py b/Nov_27/Manual/Camera/img_rec_module/sig_proc.py
new file mode 100644
index 0000000..9640c3b
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/sig_proc.py
@@ -0,0 +1,958 @@
+import cv2
+import numpy as np
+import csv
+from scipy.optimize import curve_fit
+from math import atan, sqrt, tan, radians, acos, asin, degrees, sin, cos
+
+""" ===================================
+========== ANALYTIC GEOMETRY ==========
+======================================= """
+
+
+class Line:
+    inf = float('inf')
+
+    def __init__(self, a=inf, b=inf, p1=None, p2=None, data=None):
+        if p1 and p2:
+            self.point(p1, p2)
+        elif data:
+            self.reg(data)
+        else:
+            self.a = a
+            self.b = b
+
+    def reg(self, data):
+        D = np.array([[d[0], 1] for d in data])
+        y = np.array([d[1] for d in data])
+        self.a, self.b = leastSquares(D, y)
+
+    def point(self, p1, p2):
+        self.a = 1
+
+    @staticmethod
+    def intersect(l1, l2):
+        x = (l2.b - l1.b) / (l1.a - l2.a)
+        y = l1.a * x + l1.b
+        return x, y
+
+
+class HoughLine:
+    def __init__(self, rho_rad=0, theta=0, x=None, data=None):
+        if data is not None and x is not None:
+            self.reg(x, data)
+        else:
+            self._r = degrees(rho_rad)
+            self._t = theta
+            self._s = sin(rho_rad)
+            self._c = cos(rho_rad)
+
+    def reg(self, x, data):
+        """D = hough_data_matrix(x, data)
+        print(D)
+        y = np.zeros(len(data))
+        cosine, sine, self._r = leastSquares(D, y)
+        self._t = angle_interp(sine, cosine)
+        self._s = sine
+        self._c = cosine"""
+        x1, x2 = x[0], x[-1]
+        y1, y2 = data[0], data[-1]
+        theta0 = theta_pred(x1, y1, x2, y2)
+        # print(degrees(theta0))
+        p0 = [theta0, x1 * cos(theta0) + y1 * sin(theta0)]
+        pm, vm = curve_fit(hough_line, x, data, p0=p0)
+        # data_pred = hough_line(x, *pm)
+        # print('PRED', data_pred)
+        # res = data_pred - data
+        # print("RES", res)
+        # stderr = np.linalg.norm(res, 2)
+        # print("ERR", stderr)
+        # rankerr = res / stderr
+        # print("Z", rankerr)
+        if pm[1] < 0:
+            pm[1] = -pm[1]
+            pm[0] -= HC
+            # pm[0] = 2 * np.pi
+        angle = normalize_angle(pm[0])
+        self._t = angle
+        self._r = pm[1]
+        self._s = sin(angle)
+        self._c = cos(angle)
+
+    def extract_points(self, x_input):
+        x1 = int(x_input[0])
+        x2 = int(x_input[-1])
+        y1 = int(self.fit_x(x1))
+        y2 = int(self.fit_x(x2))
+        return (x1, y1), (x2, y2)
+
+    def fit_x(self, x):
+        # print(self._r, self._t)
+        return (self._r - x * self._c) / self._s
+
+    def __str__(self):
+        return 'hough line with cos:{0}, sin:{1}, rho:{2}, theta:{3}'.format(self._c, self._s,
+                                                                             self._r, degrees(self._t))
+
+    @staticmethod
+    def intersect(l1, l2):
+        x = (l2._r / l2._s - l1._r / l1._s) / (l2._c / l2._s - l1._c / l1._s)
+        y = l1.fit_x(x)
+        return x, y
+
+
+# QC: QUARTER_CYCLE, HC: HALF_CYCLE, TQC: THIRD_QUARTER_CYCLE, FC: FULL_CYCLE
+QC = np.pi / 2
+HC = np.pi
+TQC = 3 * np.pi / 2
+FC = 2 * np.pi
+
+
+def angle_interp(s, c):
+    if c == 1.0:
+        return 0
+    elif s == 1.0:
+        return np.pi / 2
+    elif c == -1.0:
+        return np.pi
+    elif s == -1.0:
+        return np.pi * 3 / 2
+    elif c < 0 < s:
+        return acos(c)
+    elif s < 0 and c < 0:
+        return 2 * np.pi - acos(c)
+    elif s < 0 < c:
+        return asin(s) + 2 * np.pi
+    else:
+        return asin(s)
+
+
+def normalize_angle(angle):
+    res = angle - (angle // FC) * FC
+    return res
+    """
+    if QC < res < HC:
+        return res + HC
+    elif HC <= res <= TQC:
+        return res - HC
+    else:
+        return res"""
+
+
+def theta_pred(x1, y1, x2, y2):
+    dx = x2 - x1
+    dy = y2 - y1
+    angle = sin_angle_from_points(dx, dy)
+    if dx * dy <= 0:
+        return np.pi / 2 - angle
+    else:
+        root = root_finding(x1, x2, y1, y2)
+        if root < 0:
+            return np.pi / 2 + angle
+        else:
+            return angle + 3 * np.pi / 2
+
+
+def sin_angle_from_points(dx, dy):
+    return asin(abs(dy) / sqrt(dx ** 2 + dy ** 2))
+
+
+def poly_curve(params, x_input):
+    # params contains the coefficients that multiply the polynomial terms, in degree of lowest degree to highest degree
+    degree = len(params) - 1
+    x_range = [x_input[1], x_input[-1]]
+    x = np.linspace(x_range[0], x_range[1], 1000)
+    y = x * 0
+
+    for k in range(0, degree + 1):
+        coeff = params[k]
+        y = y + list(map(lambda z: coeff * z ** k, x))
+    return x, y
+
+
+def gauss_2d(x, y, a, b1, c_s1, b2, c_s2):
+    return a * np.exp(- ((x - b1) ** 2 / (2 * c_s1) + (y - b2) ** 2) / (2 * c_s2))
+
+
+def gauss_hat(x, a, b, c_s):
+    return a * np.exp(- (x - b) ** 2 / (2 * c_s))
+
+
+def gaussian_curve(x_input, a, b, c_s):
+    x_range = [x_input[1], x_input[-1]]
+    x = np.linspace(x_range[0], x_range[1], 1000)
+    y = a * np.exp(- (x - b) ** 2 / (2 * c_s))
+    return x, y
+
+
+def hough_line(x, theta, rho):
+    return (rho - x * cos(theta)) / sin(theta)
+
+
+""" ===================================
+========== MATRIX-ARRAY UTIL ==========
+======================================= """
+
+
+class FastDataMatrix2D:
+    # TODO: HANDLE THE CASE WHEN IT WRAPS 1D
+
+    HOR = 1
+    VER = 0
+
+    def __init__(self, data, ax, index):
+        self._data = data
+        self._ax = ax
+        assert 0 <= index < self.irange(), "start: {0}, end: {1}, ax: {2}, index: {3}".format(self.start, self.end,
+                                                                                              self._ax, index)
+        self._index = index
+        self.itr = 0
+        self.initialize()
+
+    def segmentize(self, start, end):
+        assert 0 <= start < end <= self._data.shape[self._ax], "start: {0}, end: {1}, ax: {2}, index: {3}".format(start,
+                                                                                                                  end,
+                                                                                                                  self._ax,
+                                                                                                                  self._index)
+        self.start = start
+        self.end = end
+
+    def initialize(self):
+        self.segmentize(0, self._data.shape[self._ax])
+
+    def irange(self):
+        # index range of the fast matrix array
+        return self._data.shape[1 - self._ax]
+
+    def set_axis(self, ax):
+        """USE copy when trying to switch axis and index"""
+        self._ax = ax
+        if self._index >= self.irange():
+            raise IndexError("Old index {0} is too large for the new axis".format(self._index))
+        self.initialize()
+
+    def set_index(self, index):
+        assert 0 <= index < self.irange(), "start: {0}, end: {1}, ax: {2}, index: {3}".format(self.start, self.end,
+                                                                                              self._ax, index)
+        self._index = index
+
+    def extract_array(self):
+        """Optimize later for better performance"""
+        arr = self._data[self._index, self.start:self.end] if self._ax == FastDataMatrix2D.HOR \
+            else self._data[self.start:self.end, self._index]
+        return np.array(arr)
+
+    def copy(self, ax=None, index=None):
+        if ax is not None and index is not None:
+            return FastDataMatrix2D(self._data, ax, index)
+        else:
+            return FastDataMatrix2D(self._data, self._ax, self._index)
+
+    def __iter__(self):
+        raise RuntimeError("You need the ITER method!")
+
+    def __next__(self):
+        raise RuntimeError("You need the NEXT method!")
+
+    def __getitem__(self, item):
+        return self._data[self._index, item + self.start] if self._ax == FastDataMatrix2D.HOR else self._data[item + self.start, self._index]
+
+    def __setitem__(self, key, value):
+        if self._ax == FastDataMatrix2D.HOR:
+            self._data[self._index, key + self.start] = value
+        else:
+            self._data[key + self.start, self._index] = value
+
+    def __len__(self):
+        return self.end - self.start
+
+
+""" ===================================
+=========== REGRESSION UTILS ==========
+======================================= """
+
+
+# Generic Regression Tools 128-133
+def leastSquares(D, y):
+    return np.linalg.lstsq(D, y, rcond=None)[0]
+
+
+def MSE(y, y_hat, N):
+    return np.linalg.norm(y - y_hat) ** 2 / N
+
+
+def std_dev(data):
+    std_dev = 0
+    len_data = len(data)
+    mean = sum(data) / len_data
+    for i in range(len_data):
+        std_dev += (data[i] - mean) ** 2
+    return sqrt(std_dev / len_data)
+
+
+def reg_pre_debias(ind, data):
+    sd = np.std(data)
+    miu = np.mean(data)
+    debiased_data = []
+    debiased_ind = []
+    for i in range(len(data)):
+        dev = abs((data[i] - miu) / sd)
+        if dev < 2:
+            debiased_data.append(data[i])
+            debiased_ind.append(ind[i])
+    return debiased_ind, debiased_data
+
+
+# Polynomial Regression Tools 137-171
+def poly_data_matrix(input_data, degree):
+    # degree is the degree of the polynomial you plan to fit the data with
+    Data = np.zeros((len(input_data), degree + 1))
+
+    for k in range(0, degree + 1):
+        Data[:, k] = (list(map(lambda x: x ** k, input_data)))
+
+    return Data
+
+
+def poly_error(params, D_a, y_a):
+    '''degree=len(params)-1
+    y=x_a*0
+
+    for k in range(0,degree+1):
+        coeff=params[k]
+        y=y+list(map(lambda z:coeff*z**k,x_a))'''
+    y = np.dot(D_a, params)
+    return np.linalg.norm(y - y_a) ** 2
+
+
+def improvedCost(x, y, x_test, y_test, start, end):
+    """Given a set of x and y points training points,
+    this function calculates polynomial approximations of varying
+    degrees from start to end. Then it returns the cost, with
+    the polynomial tested on test points of each degree in an array"""
+    c = []
+    degrees = range(start, end)
+    ps = []
+    for degree in degrees:
+        # YOUR CODE HERE
+        D = poly_data_matrix(x, degree)
+        p = leastSquares(D, y)
+        ps.append(p)
+        D_t = poly_data_matrix(x_test, degree)
+        y_hat = np.dot(D_t, p)
+        c.append(MSE(y_test, y_hat, len(x_test)))
+    return np.array(degrees), ps, np.array(c)
+
+
+# Gaussian Regression Tools 184-210
+def gauss_data_matrix(data):
+    return np.array([[1, x ** 2, x, 1] for x in data])
+
+
+def gauss_reg(x, y, p0):
+    """Given a set of x and y training points, this function
+    calculates the gaussian approximation function"""
+    """skip_x = np.array([x[i] for i in range(len(y)) if y[i] != 0])
+    y_prime = np.array([y[i] for i in range(len(y)) if y[i] != 0])
+    logy = np.log(y_prime)
+
+    sol = leastSquares(gauss_data_matrix(skip_x), logy)
+    alp, bet, gam, lam = sol
+    a = np.e ** alp
+    c_s = - 1 / (2 * bet)
+    b = gam * c_s
+    maxi, mini = max_min(y)
+    pred = gauss_hat(maxi, a, b, c_s)
+    real = y[maxi]
+    a = real / pred // 1000000000 * 10"""
+    # timeb = datetime.now()
+    param, vm = curve_fit(gauss_hat, x, y, p0=p0)
+    # timea = datetime.now()
+    # print(timea - timeb)
+    return param
+
+
+def gauss_mat(shape, a, b1, c_s1, b2, c_s2):
+    mat = np.zeros(shape)
+    for r in range(mat.shape[0]):
+        for c in range(mat.shape[1]):
+            mat[r, c] = gauss_2d(c, r, a, b1, c_s1, b2, c_s2)
+
+    return mat
+
+
+# Hough Transform Regression Tools
+def hough_data_matrix(x, y):
+    dim = len(x)
+    mat = np.full((dim, 3), -1.0)
+    for i in range(dim):
+        mat[i][0] = x[i]
+        mat[i][1] = y[i]
+    return mat
+
+
+""" =======================================
+========= EDGE DETECTION UTILS ============
+=========================================== """
+
+
+# Generic Helper
+def max_min(data):
+    # Returns the maximum and minimum for the data
+    maxi = data[0]
+    max_ind = 0
+    mini = data[0]
+    min_ind = 0
+    for i in range(1, len(data)):
+        target = data[i]
+        if target > maxi:
+            maxi = target
+            max_ind = i
+        if target < mini:
+            mini = target
+            min_ind = i
+    return max_ind, min_ind
+
+
+def edge_max_min(data):
+    # Returns a *safe* edge maxi, mini for the data
+    # TODO: OPTIMIZE THE WIDTH AND VALUE THRESHOLD
+    width_thres = 90
+    value_thres = 20
+    maxi = data[0]
+    max_ind = 0
+    mini = data[0]
+    min_ind = 0
+    for i in range(1, len(data)):
+        target = data[i]
+        if target > maxi:
+            maxi = target
+            max_ind = i
+        if target < mini:
+            mini = target
+            min_ind = i
+    maxi, mini = max_ind, min_ind
+    assert data[maxi] >= value_thres and mini - maxi <= width_thres and maxi < mini
+    return maxi, mini
+
+
+def min_max(data, max, min):
+    """ Converts the data to min max space. """
+    g_diff = max - min
+    return [(d - min) / g_diff for d in data]
+
+
+def root_finding(x1, x2, y1, y2):
+    """Given two points on a line, finds its zero crossing root."""
+    return - y1 * (x2 - x1) / (y2 - y1) + x1
+
+
+def check_cross(a, b):
+    # Checks whether two points are of opposite signs
+    return a * b < 0
+
+
+def round_up(num):
+    down = int(num)
+    if num - down > 0:
+        return num + 1
+    else:
+        return num
+
+
+def smart_interval(start, end, data):
+    start = 0 if start < 0 else start
+    end = len(data) if end > len(data) else end
+    return start, end
+
+
+def edge_preprocess(data, padding):
+    maxi, mini = edge_max_min(data)
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    return start, end
+
+
+# ZERO CROSSING (PEAK FINDING)
+def zero_crossing(data):
+    # Yields the center with zero-crossing method
+    maxi, mini = max_min(data)
+    cross = -1
+    cross_count = 0
+    for j in range(maxi, mini):
+        y1 = data[j]
+        x2 = j + 1
+        y2 = data[x2]
+        if check_cross(y1, y2):
+            cross = root_finding(j, x2, y1, y2)  # change to function later
+            cross_count += 1
+    return -1 if cross_count > 1 else cross
+
+
+# AVERAGE METHOD FOR EDGE CENTER FINDING
+def edge_converge_base(data):
+    maxi, mini = max_min(data)
+    width_thres = 70
+    if mini - maxi > width_thres:
+        return -1
+    else:
+        return (maxi + mini) / 2
+
+
+# PSEUDO EDGE (closest noise gradient change) CENTER FINDING
+def edge_converge_extreme(data):
+    maxi, mini = max_min(data)
+    width_thres = 70
+    if mini - maxi > width_thres:
+        return -1
+    emax = -1
+    emin = -1
+    maxflag = True
+    minflag = True
+    cmax = maxi
+    cmin = mini
+    while cmax > 0 and cmin < len(data) - 1 and (maxflag or minflag):
+        max2 = cmax - 1
+        maxy1, maxy2 = data[cmax], data[max2]
+        min2 = cmin + 1
+        miny1, miny2 = data[cmin], data[min2]
+        if check_cross(maxy1, maxy2) and maxflag:
+            emax = root_finding(cmax, max2, maxy1, maxy2)
+            maxflag = False
+        if check_cross(miny1, miny2) and minflag:
+            emin = root_finding(cmin, min2, miny1, miny2)
+            minflag = False
+        if maxflag:
+            cmax -= 1
+        if minflag:
+            cmin += 1
+    return -1 if (emax == -1 or emin == -1) else (emax + emin) / 2
+
+
+def extract_extrema(data):
+    len_data = len(data)
+    mean = sum(data) / len_data
+    std_dev = 0
+    for x in data:
+        std_dev += (x - mean) ** 2
+    std_dev = sqrt(std_dev / len_data)
+    signifmax = []
+    signifmin = []
+    coeff = 3
+    for i, x in enumerate(data):
+        diff = (x - mean) / std_dev
+        if diff >= coeff:
+            signifmax.append(i)
+        elif diff <= -coeff:
+            signifmin.append(i)
+    return signifmax, signifmin
+
+
+# CENTROID METHOD FOR EDGE CENTER FINDING
+def edge_centroid(data, img_data, padding=20):
+    # With Gaussian Blur might achieve the best performance
+    try:
+        start, end = edge_preprocess(data, padding)
+    except AssertionError:
+        return -1
+    isums = 0
+    total = 0
+    for i in range(start, end):
+        isums += img_data[i] * i
+        total += img_data[i]
+    return isums / total if total else -1
+
+
+def centroid_seg(data, start, end):
+    """ Given a segment (start, end) of the data,
+    find the centroid. """
+    isums = 0
+    total = 0
+    start = 0 if start < 0 else start
+    end = len(data) if end > len(data) else end
+    for i in range(start, end):
+        isums += data[i] * i
+        total += data[i]
+    return isums / total if total else -1
+
+
+# POLYNOMIAL FITTING
+def poly_fitting(data, img_data, padding=20):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING AND THE EXTRACT_ARRAY
+    try:
+        start, end = edge_preprocess(data, padding)
+    except AssertionError:
+        return -1
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        y = img_data.extract_array()
+    else:
+        #y = np.array(img_data[start:end])
+        idata = np.zeros(end - start)
+        for i in range(start, end):
+            idata[i - start] = img_data[i]
+    degrees, params, cost = improvedCost(x, y, x, y, 1, 7)
+    ind = np.argmin(cost)
+    degree = degrees[ind]
+    param = params[ind]
+    degree_register(degree)
+    curve_x, curve_y = poly_curve(param, x)
+    center_id = np.argmax(curve_y)
+    return curve_x[center_id]
+
+
+def poly_fitting_params(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING AND THE EXTRACT_ARRAY
+    maxi, mini = max_min(data)
+    width_thres = 90
+    value_thres = 20
+    if data[maxi] < value_thres or mini - maxi > width_thres:
+        print(data[maxi], mini, maxi)
+        raise AssertionError("Bad column or row")
+    start = maxi - padding
+    end = mini + padding + 1
+    start, end = smart_interval(start, end, data)
+    if start > end:
+        try:
+            print(maxi, mini, img_data._ax, start, end)
+        except:
+            print("else", maxi, mini, start, end)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        y = img_data.extract_array()
+    else:
+        idata = np.zeros(end - start)
+        for i in range(start, end):
+            idata[i - start] = img_data[i]
+    degrees, params, cost = improvedCost(x, y, x, y, 1, 7)
+    ind = np.argmin(cost)
+    degree = degrees[ind]
+    param = params[ind]
+    degree_register(degree)
+    return param, x
+
+
+DEGREES = {}
+
+
+def degree_register(elem):
+    if elem in DEGREES:
+        DEGREES[elem] += 1
+    else:
+        DEGREES[elem] = 1
+
+
+# GAUSSIAN FITTING
+def gaussian_fitting(data, img_data, padding=20):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING, ALONG WITH THE WIDTH THRESHOLD
+    try:
+        maxi, mini = edge_max_min(data)
+    except AssertionError:
+        return -1
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.zeros(end-start)
+        for i in range(start, end):
+            idata[i-start] = img_data[i]
+    try:
+        param = gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)])
+    except RuntimeError:
+        return -1
+    return param[1]
+
+
+def gaussian_fitting_params(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING, ALONG WITH THE WIDTH THRESHOLD
+    maxi, mini = max_min(data)
+    width_thres = 90
+    value_thres = 20  # TODO: CONSOLIDATE THIS VALUE
+    if data[maxi] < value_thres or mini - maxi > width_thres:
+        print(data[maxi], mini, maxi)
+        raise AssertionError("Bad column or row")
+    start = maxi - padding
+    end = mini + padding
+    start, end = smart_interval(start, end, data)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.zeros(end - start)
+        for i in range(start, end):
+            idata[i-start] = img_data[i]
+    return gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)]), x
+
+
+"""======================================
+======== IMAGE PROCESSING UTIL ==========
+========================================= """
+
+
+def gauss_bg_deduce(x, img_data):
+    # TODO: OPTIMIZE PERFORMANCE
+    idata = img_data.extract_array()
+    p0 = [1, len(img_data) / 2, std_dev(idata)]
+    a, b, c_s = gauss_reg(x, idata, p0=p0)
+    rem_gauss = gauss_hat(x, a, b, c_s)
+    new_y = idata - rem_gauss
+    return x, rem_gauss, new_y
+
+
+def significance_test(data, val):
+    """Outputs whether the maximum or minimum value is a significant value."""
+    return val in extract_extrema(data)[0] or val in extract_extrema(data)[1]
+
+
+def edge_detect_expr(edges, original):
+    """
+    TODO: 1. SIMPLE Approach: pairwise mask over original image, then compute total
+          2. Traverse the image to mark the image.
+    """
+    se = 0
+    row_sum = 0
+    col_sum = 0
+    tot_sum = 0
+    for i in range(edges.shape[0]):
+        for j in range(edges.shape[1]):
+            if edges[i][j] > 0:
+                row_sum += i * original[i][j]
+                col_sum += j * original[i][j]
+                tot_sum += original[i][j]
+                se += 1
+    print(se)
+    if tot_sum != 0:
+        print(row_sum / tot_sum)
+        print(col_sum / tot_sum)
+
+
+def hori(img, i, j, d):
+    dest = j + d
+    if dest < 0 or dest >= img.shape[1]:
+        return img.item(i, j)
+    return img.item(i, dest)
+
+
+def verti(img, i, j, d):
+    dest = i + d
+    if dest < 0 or dest >= img.shape[0]:
+        return img.item(i, j)
+    return img.item(dest, j)
+
+
+def test_blur_then_nr(iname):
+    imgs = iname
+    numIMG = 5
+    imgr = None
+    for i in range(1, numIMG + 1):
+        target = cv2.imread(imgs.format(i), 0)
+        target = cv2.GaussianBlur(target, (9, 9), 0)
+        if i == 1:
+            imgr = np.uint16(target)
+        else:
+            imgr = imgr + target
+    return imgr / numIMG
+
+
+def get_center_val(img):
+    return img.item(img.shape[0] // 2, img.shape[1] // 2)
+
+
+def test_noise_reduce(iname, numIMG=5):
+    imgs = iname
+    imgr = None
+    original = None
+    for i in range(1, numIMG + 1):
+        target = cv2.imread(imgs.format(i), 0)
+        if target is None:
+            raise AttributeError("File {0} not found".format(iname))
+        if i == 1:
+            imgr = np.uint16(target)
+            original = target
+        else:
+            imgr = imgr + target
+    return imgr / numIMG, original
+
+
+def img_add(dest, src):
+    """Destructive!"""
+    row, col = dest.shape
+    for i in range(row):
+        for j in range(col):
+            dest[i][j] += src[i][j]
+
+
+FM = FastDataMatrix2D
+
+
+def folder_to_imgs(img_name_scheme, num_sample):
+    """This function takes img files and return cv imgs"""
+    return [cv2.imread(img_name_scheme.format(i)) for i in range(1, num_sample + 1)]
+
+
+# The image taken is flipped horizontally, result x should be img.shape[1] - x
+# The image sometimes has two peaks, try experimenting with different gaussian kernels
+def center_detect(img_name_scheme, num_sample, sample_int=50, gk=9, ks=-1, m=0, p=20,
+                       b=1, c=0):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair
+    hs: HORIZONTAL SLICE!  vs: VERTICAL SLICE!"""
+    imgr = test_noise_reduce(img_name_scheme, num_sample)[0]
+    dimr = imgr.shape[0]
+    dimc = imgr.shape[1]
+    # Image Processing
+    gksize = (gk, gk)
+    sigmaX = 0
+    img = cv2.GaussianBlur(imgr, gksize, sigmaX)
+    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=ks)
+    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=ks)
+    # ------------------------------------------------------------
+    # Parameter Setting
+    METHODS = {0: gaussian_fitting, 1: poly_fitting, 2: edge_centroid,
+               3: zero_crossing, 4: edge_converge_base, 5: edge_converge_extreme}
+    edge_method = METHODS[m]
+    nr = sample_int
+    r_thresh = dimr / (sample_int * 3.0)
+    nc = sample_int
+    c_thresh = dimc / (sample_int * 3.0)
+    # ------------------------------------------------------------
+    # Gathering Data
+    hs = []
+    vs = []
+    while nr < dimr:
+        data_x = FM(sobelx, FM.HOR, nr)
+        if m < 3:
+            ec_x = edge_method(data_x, FM(img, FM.HOR, nr), p)
+        else:
+            ec_x = edge_method(data_x)
+        nr += sample_int
+        if ec_x == -1:
+            continue
+        else:
+            hs.append((nr - sample_int, ec_x))
+
+    while nc < dimc:
+        data_y = FM(sobely, FM.VER, nc)
+        if m < 3:
+            ec_y = edge_method(data_y, FM(img, FM.VER, nc), p)
+        else:
+            ec_y = edge_method(data_y)
+        nc += sample_int
+        if ec_y == -1:
+            continue
+        else:
+            vs.append((nc - sample_int, ec_y))
+    len_hs = len(hs)
+    len_vs = len(vs)
+    # ------------------------------------------------------------
+    # --------------- PRE-CHECK DATA VALUES ----------------------
+    hxs = np.zeros(len_hs)
+    hys = np.zeros(len_hs)
+    for i in range(len_hs):
+        hxs[i] = hs[i][1]
+        hys[i] = hs[i][0]
+    vxs = np.zeros(len_vs)
+    vys = np.zeros(len_vs)
+    for i in range(len_vs):
+        vxs[i] = vs[i][0]
+        vys[i] = vs[i][1]
+    x_valid = False
+    y_valid = False
+    # OUTLIER DETECTION TODO: OPTIMIZE, THIS IS NAIVE
+    if len_hs >= r_thresh:
+        x_valid = True
+        hys, hxs = reg_pre_debias(hys, hxs)
+        line_a = HoughLine(x=hxs, data=hys)
+    if len_vs >= c_thresh:
+        y_valid = True
+        vxs, vys = reg_pre_debias(vxs, vys)
+        line_b = HoughLine(x=vxs, data=vys)
+    # ------------------------------------------------------------
+    # ----- Following Modules Handles Hough Line Drawing ---------
+    # ------------------------------------------------------------
+    # DATA RECORDING AND PROCESSING
+    if c == 1:
+        center_x = sum(hxs) / len_hs if x_valid else -1
+        center_y = sum(vys) / len_vs if y_valid else -1
+    else:
+        if x_valid and y_valid:
+            center_x, center_y = HoughLine.intersect(line_a, line_b)
+        else:
+            center_x = sum(hxs) / len_hs if x_valid else -1
+            center_y = sum(vys) / len_vs if y_valid else -1
+    # ---------------------------------------------------------
+    return center_x, center_y
+
+
+
+def convergence_test_final(folder, ns):
+    offset = '../'
+    convergence = {}
+    variations = []
+    startNP = 59
+    startP = 80
+    endP = 193
+    ms = range(3)
+    fwrite = open('meas/convergence.csv', 'w')
+    cwriter = csv.writer(fwrite)
+    cwriter.writerow(['Image Number', 'Center X', 'Center Y', 'StdDev Horizontal', 'Std Dev Vertical'])
+    for m in ms:
+        cwriter.writerow([str(m)])
+        # Consistency Cycled
+        pv = 0
+        pvs = 0
+        rcount = 0
+        cvx = np.zeros(4)
+        cvy = np.zeros(4)
+        # print(g)
+        for i in range(startNP, startP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                x, y = center_detect(fpath + imgfile, 5, m=m)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y)])
+                # CONVERGENCE
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+        for i in range(startP, endP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                x, y = center_detect(fpath + imgfile, 5, m=m)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y)])
+                # CONVERGENCE
+                # Record x, y, check rcount, refresh CONSISTENCY
+                cvx[rcount] = x
+                cvy[rcount] = y
+                rcount += 1
+                if rcount == 4:
+                    pv += np.var(cvx) + np.var(cvy)
+                    pvs += 1
+                    rcount = 0
+                    cvx = np.zeros(4)
+                    cvy = np.zeros(4)
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+                # print(str(i), val)
+        msepv = sqrt(pv / pvs)
+        cvg = {'PicConsistency': msepv}
+        convergence[m] = cvg
+        variations.append(msepv)
+    print(convergence)
+    fwrite.close()
+
+if __name__ == '__main__':
+    convergence_test_final('calib4/', 'img_{0}')
+
+#print(center_detect('../calib4/img_59_{0}.png', 5))
+
diff --git a/Nov_27/Manual/Camera/img_rec_module/sig_proc_test.py b/Nov_27/Manual/Camera/img_rec_module/sig_proc_test.py
new file mode 100644
index 0000000..459fec9
--- /dev/null
+++ b/Nov_27/Manual/Camera/img_rec_module/sig_proc_test.py
@@ -0,0 +1,2371 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.optimize import curve_fit
+#from img_rec_module import img_rec
+from random import randint
+import os
+import csv
+from datetime import datetime
+from math import atan, sqrt, tan, radians, acos, asin, degrees, sin, cos
+
+
+""" ===================================
+========== ANALYTIC GEOMETRY ==========
+======================================= """
+
+
+class Line:
+
+    inf = float('inf')
+
+    def __init__(self, a=inf, b=inf, p1=None, p2=None, data=None):
+        if p1 and p2:
+            self.point(p1, p2)
+        elif data:
+            self.reg(data)
+        else:
+            self.a = a
+            self.b = b
+
+    def reg(self, data):
+        D = np.array([[d[0], 1] for d in data])
+        y = np.array([d[1] for d in data])
+        self.a, self.b = leastSquares(D, y)
+
+    def point(self, p1, p2):
+        self.a = 1
+
+    @staticmethod
+    def intersect(l1, l2):
+        x = (l2.b - l1.b) / (l1.a - l2.a)
+        y = l1.a * x + l1.b
+        return x, y
+
+
+class HoughLine:
+
+    def __init__(self, rho_rad=0, theta=0, x=None, data=None):
+        if data is not None and x is not None:
+            self.reg(x, data)
+        else:
+            self._r = degrees(rho_rad)
+            self._t = theta
+            self._s = sin(rho_rad)
+            self._c = cos(rho_rad)
+
+    def reg(self, x, data):
+        """D = hough_data_matrix(x, data)
+        print(D)
+        y = np.zeros(len(data))
+        cosine, sine, self._r = leastSquares(D, y)
+        self._t = angle_interp(sine, cosine)
+        self._s = sine
+        self._c = cosine"""
+        x1, x2 = x[0], x[-1]
+        y1, y2 = data[0], data[-1]
+        theta0 = theta_pred(x1, y1, x2, y2)
+        #print(degrees(theta0))
+        p0 = [theta0, x1 * cos(theta0) + y1 * sin(theta0)]
+        pm, vm = curve_fit(hough_line, x, data, p0=p0)
+        #data_pred = hough_line(x, *pm)
+        #print('PRED', data_pred)
+        #res = data_pred - data
+        #print("RES", res)
+        #stderr = np.linalg.norm(res, 2)
+        #print("ERR", stderr)
+        #rankerr = res / stderr
+        #print("Z", rankerr)
+        if pm[1] < 0:
+            pm[1] = -pm[1]
+            pm[0] -= HC
+            #pm[0] = 2 * np.pi
+        angle = normalize_angle(pm[0])
+        self._t = angle
+        self._r = pm[1]
+        self._s = sin(angle)
+        self._c = cos(angle)
+
+    def extract_points(self, x_input):
+        x1 = int(x_input[0])
+        x2 = int(x_input[-1])
+        y1 = int(self.fit_x(x1))
+        y2 = int(self.fit_x(x2))
+        return (x1, y1), (x2, y2)
+
+    def fit_x(self, x):
+        #print(self._r, self._t)
+        return (self._r - x * self._c) / self._s
+
+    def __str__(self):
+        return 'hough line with cos:{0}, sin:{1}, rho:{2}, theta:{3}'.format(self._c, self._s,
+                                                                             self._r, degrees(self._t))
+
+    @staticmethod
+    def intersect(l1, l2):
+        x = (l2._r / l2._s - l1._r / l1._s) / (l2._c / l2._s - l1._c / l1._s)
+        y = l1.fit_x(x)
+        return x, y
+
+
+# QC: QUARTER_CYCLE, HC: HALF_CYCLE, TQC: THIRD_QUARTER_CYCLE, FC: FULL_CYCLE
+QC = np.pi / 2
+HC = np.pi
+TQC = 3 * np.pi / 2
+FC = 2 * np.pi
+
+
+def angle_interp(s, c):
+    if c == 1.0:
+        return 0
+    elif s == 1.0:
+        return np.pi / 2
+    elif c == -1.0:
+        return np.pi
+    elif s == -1.0:
+        return np.pi * 3 / 2
+    elif c < 0 < s:
+        return acos(c)
+    elif s < 0 and c < 0:
+        return 2 * np.pi - acos(c)
+    elif s < 0 < c:
+        return asin(s) + 2 * np.pi
+    else:
+        return asin(s)
+
+
+def normalize_angle(angle):
+    res = angle - (angle // FC) * FC
+    return res
+    """
+    if QC < res < HC:
+        return res + HC
+    elif HC <= res <= TQC:
+        return res - HC
+    else:
+        return res"""
+
+
+def theta_pred(x1, y1, x2, y2):
+    dx = x2 - x1
+    dy = y2 - y1
+    angle = sin_angle_from_points(dx, dy)
+    if dx * dy <= 0:
+        return np.pi / 2 - angle
+    else:
+        root = root_finding(x1, x2, y1, y2)
+        if root < 0:
+            return np.pi / 2 + angle
+        else:
+            return angle + 3 * np.pi / 2
+
+
+def sin_angle_from_points(dx, dy):
+    return asin(abs(dy) / sqrt(dx ** 2 + dy ** 2))
+
+
+def poly_curve(params, x_input):
+    # params contains the coefficients that multiply the polynomial terms, in degree of lowest degree to highest degree
+    degree = len(params) - 1
+    x_range = [x_input[1], x_input[-1]]
+    x = np.linspace(x_range[0], x_range[1], 1000)
+    y = x * 0
+
+    for k in range(0, degree + 1):
+        coeff = params[k]
+        y = y + list(map(lambda z: coeff * z ** k, x))
+    return x, y
+
+
+def gauss_2d(x, y, a, b1, c_s1, b2, c_s2):
+    return a * np.exp(- ((x - b1) ** 2  / (2 * c_s1) + (y - b2) ** 2) / (2 * c_s2))
+
+
+def gauss_hat(x, a, b, c_s):
+    return a * np.exp(- (x - b) ** 2 / (2 * c_s))
+
+
+def gaussian_curve(x_input, a,  b, c_s):
+    x_range = [x_input[1], x_input[-1]]
+    x = np.linspace(x_range[0], x_range[1], 1000)
+    y = a * np.exp(- (x - b) ** 2 / (2 * c_s))
+    return x, y
+
+
+def hough_line(x, theta, rho):
+    return (rho - x * cos(theta)) / sin(theta)
+
+
+""" ===================================
+========== MATRIX-ARRAY UTIL ==========
+======================================= """
+
+
+class FastDataMatrix2D:
+    # TODO: HANDLE THE CASE WHEN IT WRAPS 1D
+
+    HOR = 1
+    VER = 0
+
+    def __init__(self, data, ax, index):
+        self._data = data
+        self._ax = ax
+        assert 0 <= index < self.irange(), "start: {0}, end: {1}, ax: {2}, index: {3}".format(self.start, self.end,
+                                                                                              self._ax, index)
+        self._index = index
+        self.itr = 0
+        self.initialize()
+
+    def segmentize(self, start, end):
+        assert 0 <= start < end <= self._data.shape[self._ax], "start: {0}, end: {1}, ax: {2}, index: {3}".format(start, end, self._ax, self._index)
+        self.start = start
+        self.end = end
+
+    def initialize(self):
+        self.segmentize(0, self._data.shape[self._ax])
+
+    def irange(self):
+        # index range of the fast matrix array
+        return self._data.shape[1 - self._ax]
+
+    def set_axis(self, ax):
+        """USE copy when trying to switch axis and index"""
+        self._ax = ax
+        if self._index >= self.irange():
+            raise IndexError("Old index {0} is too large for the new axis".format(self._index))
+        self.initialize()
+
+    def set_index(self, index):
+        assert 0 <= index < self.irange(), "start: {0}, end: {1}, ax: {2}, index: {3}".format(self.start, self.end, self._ax, index)
+        self._index = index
+
+    def extract_array(self):
+        """Optimize later for better performance"""
+        arr = self._data[self._index, self.start:self.end] if self._ax == FastDataMatrix2D.HOR \
+            else self._data[self.start:self.end, self._index]
+        return np.array(arr)
+
+    def copy(self, ax=None, index=None):
+        if ax is not None and index is not None:
+            return FastDataMatrix2D(self._data, ax, index)
+        else:
+            return FastDataMatrix2D(self._data, self._ax, self._index)
+
+    def __iter__(self):
+        raise RuntimeError("You need the ITER method!")
+
+    def __next__(self):
+        raise RuntimeError("You need the NEXT method!")
+
+    def __getitem__(self, item):
+        return self._data[self._index, item + self.start] if self._ax == FastDataMatrix2D.HOR \
+            else self._data[item + self.start, self._index]
+
+    def __setitem__(self, key, value):
+        if self._ax == FastDataMatrix2D.HOR:
+            self._data[self._index, key + self.start] = value
+        else:
+            self._data[key + self.start, self._index] = value
+
+    def __len__(self):
+        return self.end - self.start
+
+
+""" ===================================
+=========== REGRESSION UTILS ==========
+======================================= """
+
+
+# Generic Regression Tools 128-133
+def leastSquares(D, y):
+    return np.linalg.lstsq(D, y, rcond=None)[0]
+
+
+def MSE(y, y_hat, N):
+    return np.linalg.norm(y - y_hat) ** 2 / N
+
+
+def std_dev(data):
+    std_dev = 0
+    len_data = len(data)
+    mean = sum(data) / len_data
+    for i in range(len_data):
+        std_dev += (data[i] - mean) ** 2
+    return sqrt(std_dev / len_data)
+
+
+def reg_pre_debias(ind, data):
+    sd = np.std(data)
+    miu = np.mean(data)
+    debiased_data = []
+    debiased_ind = []
+    for i in range(len(data)):
+        dev = abs((data[i] - miu) / sd)
+        if dev < 2:
+            debiased_data.append(data[i])
+            debiased_ind.append(ind[i])
+    return debiased_ind, debiased_data
+
+
+# Polynomial Regression Tools 137-171
+def poly_data_matrix(input_data, degree):
+    # degree is the degree of the polynomial you plan to fit the data with
+    Data = np.zeros((len(input_data), degree + 1))
+
+    for k in range(0, degree + 1):
+        Data[:, k] = (list(map(lambda x: x ** k, input_data)))
+
+    return Data
+
+
+def poly_error(params, D_a, y_a):
+    '''degree=len(params)-1
+    y=x_a*0
+
+    for k in range(0,degree+1):
+        coeff=params[k]
+        y=y+list(map(lambda z:coeff*z**k,x_a))'''
+    y = np.dot(D_a, params)
+    return np.linalg.norm(y - y_a) ** 2
+
+
+def improvedCost(x, y, x_test, y_test, start, end):
+    """Given a set of x and y points training points,
+    this function calculates polynomial approximations of varying
+    degrees from start to end. Then it returns the cost, with
+    the polynomial tested on test points of each degree in an array"""
+    c = []
+    degrees = range(start, end)
+    ps = []
+    for degree in degrees:
+        # YOUR CODE HERE
+        D = poly_data_matrix(x, degree)
+        p = leastSquares(D, y)
+        ps.append(p)
+        D_t = poly_data_matrix(x_test, degree)
+        y_hat = np.dot(D_t, p)
+        c.append(MSE(y_test, y_hat, len(x_test)))
+    return np.array(degrees), ps,  np.array(c)
+
+
+# Gaussian Regression Tools 184-210
+def gauss_data_matrix(data):
+    return np.array([[1, x ** 2, x, 1] for x in data])
+
+
+def gauss_reg(x, y, p0):
+    """Given a set of x and y training points, this function
+    calculates the gaussian approximation function"""
+    """skip_x = np.array([x[i] for i in range(len(y)) if y[i] != 0])
+    y_prime = np.array([y[i] for i in range(len(y)) if y[i] != 0])
+    logy = np.log(y_prime)
+
+    sol = leastSquares(gauss_data_matrix(skip_x), logy)
+    alp, bet, gam, lam = sol
+    a = np.e ** alp
+    c_s = - 1 / (2 * bet)
+    b = gam * c_s
+    maxi, mini = max_min(y)
+    pred = gauss_hat(maxi, a, b, c_s)
+    real = y[maxi]
+    a = real / pred // 1000000000 * 10"""
+    #timeb = datetime.now()
+    param, vm = curve_fit(gauss_hat, x, y, p0=p0)
+    #timea = datetime.now()
+    #print(timea - timeb)
+    return param
+
+
+def gauss_mat(shape, a, b1, c_s1, b2, c_s2):
+    mat = np.zeros(shape)
+    for r in range(mat.shape[0]):
+        for c in range(mat.shape[1]):
+            mat[r, c] = gauss_2d(c, r, a, b1, c_s1, b2, c_s2)
+
+    return mat
+
+
+# Hough Transform Regression Tools
+def hough_data_matrix(x, y):
+    dim = len(x)
+    mat = np.full((dim, 3), -1.0)
+    for i in range(dim):
+        mat[i][0] = x[i]
+        mat[i][1] = y[i]
+    return mat
+
+
+""" =======================================
+========= EDGE DETECTION UTILS ============
+=========================================== """
+
+
+# Generic Helper
+def max_min(data):
+    # Returns the maximum and minimum for the data
+    maxi = data[0]
+    max_ind = 0
+    mini = data[0]
+    min_ind = 0
+    for i in range(1, len(data)):
+        target = data[i]
+        if target > maxi:
+            maxi = target
+            max_ind = i
+        if target < mini:
+            mini = target
+            min_ind = i
+    return max_ind, min_ind
+
+
+def edge_max_min(data):
+    # Returns a *safe* edge maxi, mini for the data
+    # TODO: OPTIMIZE THE WIDTH AND VALUE THRESHOLD
+    width_thres = 90
+    value_thres = 20
+    maxi = data[0]
+    max_ind = 0
+    mini = data[0]
+    min_ind = 0
+    for i in range(1, len(data)):
+        target = data[i]
+        if target > maxi:
+            maxi = target
+            max_ind = i
+        if target < mini:
+            mini = target
+            min_ind = i
+    maxi, mini = max_ind, min_ind
+    assert data[maxi] >= value_thres and mini - maxi <= width_thres and maxi < mini
+    return maxi, mini
+
+
+def min_max(data, max, min):
+    """ Converts the data to min max space. """
+    g_diff = max - min
+    return [(d - min) / g_diff for d in data]
+
+
+def root_finding(x1, x2, y1, y2):
+    """Given two points on a line, finds its zero crossing root."""
+    return - y1 * (x2 - x1) / (y2 - y1) + x1
+
+
+def check_cross(a, b):
+    # Checks whether two points are of opposite signs
+    return a * b < 0
+
+
+def round_up(num):
+    down = int(num)
+    if num - down > 0:
+        return num + 1
+    else:
+        return num
+
+
+def smart_interval(start, end, data):
+    start = 0 if start < 0 else start
+    end = len(data) if end > len(data) else end
+    return start, end
+
+
+def edge_preprocess(data, padding):
+    maxi, mini = edge_max_min(data)
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    return start, end
+
+
+# ZERO CROSSING (PEAK FINDING)
+def zero_crossing(data):
+    # Yields the center with zero-crossing method
+    maxi, mini = max_min(data)
+    cross = -1
+    cross_count = 0
+    for j in range(maxi, mini):
+        y1 = data[j]
+        x2 = j + 1
+        y2 = data[x2]
+        if check_cross(y1, y2):
+            cross = root_finding(j, x2, y1, y2)  # change to function later
+            cross_count += 1
+    return -1 if cross_count > 1 else cross
+
+
+# AVERAGE METHOD FOR EDGE CENTER FINDING
+def edge_converge_base(data):
+    maxi, mini = max_min(data)
+    width_thres = 70
+    if mini - maxi > width_thres:
+        return -1
+    else:
+        return (maxi + mini) / 2
+
+
+# PSEUDO EDGE (closest noise gradient change) CENTER FINDING
+def edge_converge_extreme(data):
+    maxi, mini = max_min(data)
+    width_thres = 70
+    if mini - maxi > width_thres:
+        return -1
+    emax = -1
+    emin = -1
+    maxflag = True
+    minflag = True
+    cmax = maxi
+    cmin = mini
+    while cmax > 0 and cmin < len(data) - 1 and (maxflag or minflag):
+        max2 = cmax - 1
+        maxy1, maxy2 = data[cmax], data[max2]
+        min2 = cmin + 1
+        miny1, miny2 = data[cmin], data[min2]
+        if check_cross(maxy1, maxy2) and maxflag:
+            emax = root_finding(cmax, max2, maxy1, maxy2)
+            maxflag = False
+        if check_cross(miny1, miny2) and minflag:
+            emin = root_finding(cmin, min2, miny1, miny2)
+            minflag = False
+        if maxflag:
+            cmax -= 1
+        if minflag:
+            cmin += 1
+    return -1 if (emax == -1 or emin == -1) else (emax + emin) / 2
+
+
+def extract_extrema(data):
+    len_data = len(data)
+    mean = sum(data) / len_data
+    std_dev = 0
+    for x in data:
+        std_dev += (x - mean) ** 2
+    std_dev = sqrt(std_dev / len_data)
+    signifmax = []
+    signifmin = []
+    coeff = 3
+    for i, x in enumerate(data):
+        diff = (x - mean) / std_dev
+        if diff >= coeff:
+            signifmax.append(i)
+        elif diff <= -coeff:
+            signifmin.append(i)
+    return signifmax, signifmin
+
+
+# CENTROID METHOD FOR EDGE CENTER FINDING
+def edge_centroid(data, img_data, padding=10):
+    # With Gaussian Blur might achieve the best performance
+    try:
+        start, end = edge_preprocess(data, padding)
+    except AssertionError:
+        return -1
+    isums = 0
+    total = 0
+    for i in range(start, end):
+        isums += img_data[i] * i
+        total += img_data[i]
+    return isums / total if total else -1
+
+
+def centroid_seg(data, start, end):
+    """ Given a segment (start, end) of the data,
+    find the centroid. """
+    isums = 0
+    total = 0
+    start = 0 if start < 0 else start
+    end = len(data) if end > len(data) else end
+    for i in range(start, end):
+        isums += data[i] * i
+        total += data[i]
+    return isums / total if total else -1
+
+
+# POLYNOMIAL FITTING
+def poly_fitting(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING AND THE EXTRACT_ARRAY
+    try:
+        start, end = edge_preprocess(data, padding)
+    except AssertionError:
+        return -1
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        y = img_data.extract_array()
+    else:
+        y = np.array(img_data[start:end])
+    degrees, params, cost = improvedCost(x, y, x, y, 1, 7)
+    ind = np.argmin(cost)
+    degree = degrees[ind]
+    param = params[ind]
+    degree_register(degree)
+    curve_x, curve_y = poly_curve(param, x)
+    center_id = np.argmax(curve_y)
+    return curve_x[center_id]
+
+
+def poly_fitting_params(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING AND THE EXTRACT_ARRAY
+    maxi, mini = max_min(data)
+    width_thres = 90
+    value_thres = 20
+    if data[maxi] < value_thres or mini - maxi > width_thres:
+        print(data[maxi], mini, maxi)
+        raise AssertionError("Bad column or row")
+    start = maxi - padding
+    end = mini + padding + 1
+    start, end = smart_interval(start, end, data)
+    if start > end:
+        try:
+            print(maxi, mini, img_data._ax, start, end)
+        except:
+            print("else", maxi, mini, start, end)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        y = img_data.extract_array()
+    else:
+        y = np.array(img_data[start:end])
+    degrees, params, cost = improvedCost(x, y, x, y, 1, 7)
+    ind = np.argmin(cost)
+    degree = degrees[ind]
+    param = params[ind]
+    degree_register(degree)
+    return param, x
+
+
+DEGREES = {}
+
+
+def degree_register(elem):
+    if elem in DEGREES:
+        DEGREES[elem] += 1
+    else:
+        DEGREES[elem] = 1
+
+
+# GAUSSIAN FITTING
+def gaussian_fitting(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING, ALONG WITH THE WIDTH THRESHOLD
+    try:
+        maxi, mini = edge_max_min(data)
+    except AssertionError:
+        return -1
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.array(img_data[start:end])
+    try:
+        param = gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)])
+    except RuntimeError:
+        return -1
+    return param[1]
+
+
+def gaussian_fitting_params(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING, ALONG WITH THE WIDTH THRESHOLD
+    maxi, mini = max_min(data)
+    width_thres = 90
+    value_thres = 20  # TODO: CONSOLIDATE THIS VALUE
+    if data[maxi] < value_thres or mini - maxi > width_thres:
+        print(data[maxi], mini, maxi)
+        raise AssertionError("Bad column or row")
+    start = maxi - padding
+    end = mini + padding
+    start, end = smart_interval(start, end, data)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.array(img_data[start:end])
+    return gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)]), x
+
+
+"""======================================
+======== IMAGE PROCESSING UTIL ==========
+========================================= """
+
+
+def gauss_bg_deduce(x, img_data):
+    # TODO: OPTIMIZE PERFORMANCE
+    idata = img_data.extract_array()
+    p0 = [1, len(img_data) / 2, std_dev(idata)]
+    a, b, c_s = gauss_reg(x, idata, p0=p0)
+    rem_gauss = gauss_hat(x, a, b, c_s)
+    new_y = idata - rem_gauss
+    return x, rem_gauss, new_y
+
+
+def sobel_process(imgr, gks, sig):
+    gksize = (gks, gks)
+    sigmaX = sig
+    blur = cv2.GaussianBlur(imgr, gksize, sigmaX)
+    # cv2.imshow("blurred", blur)                        # REMOVES TO SHOW BLURRED IMAGE
+    if len(imgr.shape) == 3:
+        img = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
+    else:
+        img = blur
+    # GAUSSIAN PROCESSING                                 # UNCOMMENT TO SHOW GAUSSIAN PROCESSING
+    """x = np.array(range(dimc))
+    #y = np.array([img_rec.rel_lumin(img, 0, c) for c in x])
+    y = np.array([img.item(dimr // 2, c) for c in x])
+    a1, b1, c_s1 = gauss_reg(x, y)
+
+    x2 = np.array(range(dimr))
+    y2 = np.array([img.item(r, dimc // 2) for r in x2])
+    a2, b2, c_s2 = gauss_reg(x2, y2)
+    rem_gauss = gauss_mat(img.shape, (a1+a2) / 2, b1, c_s1, b2, c_s2)
+    img = img - rem_gauss
+    cv2.imshow("denoise", img)
+    y_hat = gauss_hat(x, a1, b1, c_s1)
+    plt.figure(figsize=(16, 8))
+    plt.plot(x, y, 'b-', x, y_hat, 'r-')
+    plt.show()
+    plt.close()
+    plt.figure(figsize=(16,8))
+    plt.plot(x, y-y_hat, 'b-')
+    plt.show()"""
+
+    # IMAGE PROCESSING
+    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=-1)
+    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=-1)
+    return img, sobelx, sobely
+
+
+def test_canny_detect():
+    imgn = "img_89_{0}.png"
+    IMGDIR = "../calib4/"
+    NAME = IMGDIR + imgn
+
+    while True:
+        lowp = input("Type the Lower Bound or c to cancel:    ")
+        if lowp == 'c':
+            break
+        while True:
+            try:
+                low = int(lowp)
+                break
+            except ValueError:
+                lowp = input("Bad input Try again! ")
+        highp = input("Type the Upper Bound or c to cancel:   ")
+        if highp == 'c':
+            break
+        while True:
+            try:
+                high = int(highp)
+                break
+            except ValueError:
+                highp = input("Bad input Try again! ")
+
+        denoised, original = test_noise_reduce(NAME)
+        print(get_center_val(denoised))
+        NL_denoised = cv2.fastNlMeansDenoising(original)
+        bdenoise = test_blur_then_nr(NAME)
+
+        dblur, sobelx, sobely = sobel_process(denoised, 9, 0)
+        nldblur = sobel_process(NL_denoised, 9, 0)[0]
+        pblur, sobelx, sobely = sobel_process(original, 9, 0)
+
+        de_edges = canny_detect(denoised, low, high)
+        db_edges = canny_detect(dblur, low, high)
+        nld_edges = canny_detect(nldblur, low, high)
+        blur_denoise = canny_detect(bdenoise, low, high)
+        blur_edges = canny_detect(pblur, low, high)
+        #edge_detect_expr(db_edges, original)
+        """compare_images((imgr, 'Original'), (edges, 'Canny Edge'),
+                       (sobelx, 'Sobel X'), (sobely, 'Sobel Y'))"""
+        compare_images((original, 'Original'), (NL_denoised, "NLMEANS"), (de_edges, 'DENOISE Edges'),
+                       (blur_edges, 'Plain Blur Edges'), (denoised, 'Denoised'), (nld_edges, 'NIDE-BLUR Edges'),
+                       (db_edges, 'DE-BLUR Edges'), (blur_denoise, "BDENOISE Edges"), color_map='gray')
+
+
+def canny_detect(src, low=4, high=10):
+    return cv2.Canny(np.uint8(src), 4, 10, L2gradient=True)
+
+
+def extract_verge(edges, i, j, max_blank, dir, axis):
+    """
+    :param edges: img MAT resulting from canny edge procedure
+    :param i: starting row i
+    :param j: starting col j
+    :param max_blank: maximum number of blanks to rule out the existence of a verge outside of the current verge (in which case would be a fake verge)
+    :param dir: Directions of movement, 1 for right or down, -1 for left or up
+    :param axis:
+    :return: (r, c) index of the verge on the given 'axis' in the given 'dir' from the starting point (i, j)
+    """
+
+
+def significance_test(data, val):
+    """Outputs whether the maximum or minimum value is a significant value."""
+    return val in extract_extrema(data)[0] or val in extract_extrema(data)[1]
+
+
+def edge_detect_expr(edges, original):
+    """
+    TODO: 1. SIMPLE Approach: pairwise mask over original image, then compute total
+          2. Traverse the image to mark the image.
+    """
+    se = 0
+    row_sum = 0
+    col_sum = 0
+    tot_sum = 0
+    for i in range(edges.shape[0]):
+        for j in range(edges.shape[1]):
+            if edges[i][j] > 0:
+                row_sum += i * original[i][j]
+                col_sum += j * original[i][j]
+                tot_sum += original[i][j]
+                se += 1
+    print(se)
+    if tot_sum != 0:
+        print(row_sum / tot_sum)
+        print(col_sum / tot_sum)
+
+
+def hori(img, i, j, d):
+    dest = j + d
+    if dest < 0 or dest >= img.shape[1]:
+        return img.item(i, j)
+    return img.item(i, dest)
+
+
+def verti(img, i, j, d):
+    dest = i + d
+    if dest < 0 or dest >= img.shape[0]:
+        return img.item(i, j)
+    return img.item(dest, j)
+
+
+def test_blur_then_nr(iname):
+    imgs = iname
+    numIMG = 5
+    imgr = None
+    for i in range(1, numIMG + 1):
+        target = cv2.imread(imgs.format(i), 0)
+        target = cv2.GaussianBlur(target, (9, 9), 0)
+        if i == 1:
+            imgr = np.uint16(target)
+        else:
+            imgr = imgr + target
+    return imgr / numIMG
+
+
+def get_center_val(img):
+    return img.item(img.shape[0] // 2, img.shape[1] // 2)
+
+
+def test_noise_reduce(iname, numIMG=5):
+    imgs = iname
+    imgr = None
+    original = None
+    for i in range(1, numIMG+1):
+        target = cv2.imread(imgs.format(i), 0)
+        if target is None:
+            raise AttributeError("File {0} not found".format(iname))
+        if i == 1:
+            imgr = np.uint16(target)
+            original = target
+        else:
+            imgr = imgr + target
+    return imgr / numIMG, original
+
+
+def img_add(dest, src):
+    """Destructive!"""
+    row, col = dest.shape
+    for i in range(row):
+        for j in range(col):
+            dest[i][j] += src[i][j]
+
+
+""" ===================================
+    ============ PLOTTING =============
+    ============= HELPER ==============
+    =================================== """
+
+
+def quick_plot(data, xs=None):
+    plt.figure()
+    if xs:
+        plt.plot(xs, data, 'bo-')
+    else:
+        plt.plot(data, 'bo-')
+    plt.show()
+    plt.close()
+
+
+def compare_images(*args, ilist=None, color_map=None):
+    # Takes in a sequence of IMG(Gray) and TITLE pairs and plot them side by side
+    if ilist:
+        args = ilist
+    graph_amount = len(args)
+    row = int(sqrt(graph_amount))
+    col = round_up(float(graph_amount) / row)
+    plt.figure(figsize=(16, 8))
+    for i, pair in enumerate(args):
+        plt.subplot(row, col, i+1)
+        if color_map:
+            plt.imshow(pair[0], cmap=color_map)
+        plt.xticks([]), plt.yticks([])
+        plt.title(pair[1])
+    plt.show()
+
+
+def compare_data_plots(*args, ilist=None, suptitle=None):
+    # Takes in a sequence of tuples with data, xs, and title pairs
+    if ilist:
+        args = ilist
+    graph_amount = len(args)
+    row = int(sqrt(graph_amount))
+    col = round_up(float(graph_amount) / row)
+    plt.figure(figsize=(16, 8))
+    if suptitle:
+        plt.suptitle(suptitle, fontsize=14)
+    for i, pair in enumerate(args):
+        plt.subplot(row, col, i + 1)
+        if len(pair) == 2:
+            plt.plot(pair[0], 'bo-')
+            plt.title(pair[1])
+        else:
+            plt.plot(pair[1], pair[0], 'bo-')
+            plt.title(pair[2])
+    plt.show()
+
+
+def line_graph_contrast(img, xs, ys):
+    line = HoughLine(x=xs, data=ys)
+    #print(xs)
+    x0 = line._c * line._r
+    y0 = line._s * line._r
+
+    print('x0:{0}, y0:{1}, cos:{2}, sin:{3}, rho:{4}, theta:{5}'.format(x0, y0, line._c, line._s, line._r, degrees(line._t)))
+    x1 = int(x0 + 1000 * (-line._s))
+    y1 = int(y0 + 1000 * (line._c))
+    x2 = int(x0 - 1000 * (-line._s))
+    y2 = int(y0 - 1000 * (line._c))
+    p1, p2 = (x1, y1), (x2, y2)
+    cv2.line(img, p1, p2, (255, 0, 0), 1)
+    return p1, p2, line
+
+
+""" ===================================
+    ====== DATA RECORDING HELPER ======
+    =================================== """
+
+# 1. Try Noise Reduction:
+# ---- 1.1 Reduction By Averaging, without FastNIMEANS  ~~ GOOD
+# ---- 1.2 Reduction By FastNIMEANS                     ~~ INFERIOR
+# ---- 1.3 Reduction By Combination                     ~~ INFERIOR
+# ---- 1 NOTE: USE PLOTTING OF ORIGINAL GRAPH TO SEE RESULT
+# 2. Try Di-Axial Scharr Kernel
+# ---- 2.1 Add To Name Scheme, record graphs and compare them
+# ---- 2 NOTE: GRAPH TO SAVE: 4-CONTRAST, TRANSITION PLOTS
+# 3. Optimizing Data Processing:
+# ---- 3.1 Marking point as edge maxima and minima according to STD Dev values
+# ---- 3.2 Taking Sequential Samples
+# ---- 3.3 Explore Thresholding Techniques
+
+
+def register(img):
+    lval = []
+    for i in range(img.shape[0]):
+        for j in range(img.shape[1]):
+            if img.item(i, j) > 250:
+                lval.append((i, j, img.item(i, j)))
+    print(lval)
+
+
+""" TEST GAUSSIAN BLUR'S influence on noise distribution:
+    Method:
+        1. Preliminary
+            1. Generate a random noise matrix with n * n, change shape to a vector
+            2. Gaussian blur applied, change shape to a vector
+            3. Compare noise level
+        2. Secondary
+            1. Generate a Gaussian / Cosine Matrix and add random noise matrix
+            2. Gaussian blur applied
+            3. Compare a randomly selected row of each one
+"""
+
+
+def test_old():
+    # SETTINGS
+    imgn = "img_4_{0}"
+    IMGDIR = "../new_test/"
+    ROOTMEAS = "meas/"
+    SAVEDIR = ROOTMEAS+imgn + "/"
+    #imgr = cv2.imread(IMGDIR + imgn + ".png")
+    imgr, ori = test_noise_reduce(IMGDIR + imgn + ".png")
+    dimc = imgr.shape[1]
+    dimr = imgr.shape[0]
+    r_int = dimr // 2
+    c_int = dimc // 2
+    start = 7
+    end = 10
+    sig = 0
+    ins = range(start, end, 2)
+    name_scheme = imgn + ("_({0}, {1})").format(r_int, c_int)
+    NAME_HEADER = SAVEDIR + name_scheme
+    if not os.path.exists(SAVEDIR):
+        os.mkdir(SAVEDIR)
+    fwrite = open(NAME_HEADER + ".txt", "w")
+    plot_save = NAME_HEADER + ".png"
+
+
+    # INITIALIZATION
+    cenvalsx = []
+    cenvalsy = []
+    edgvalsx = []
+    edgvalsy = []
+    ebvalsx = []
+    ebvalsy = []
+    ecvalsx = []
+    ecvalsy = []
+    ecbvalsx = []
+    ecbvalsy = []
+    #cv2.imshow("denoised", np.uint8(imgr))                                 # REMOVES TO SHOW IMAGE
+    x = np.array(range(dimc))
+    x2 = np.array(range(dimr))
+
+    for i in ins:
+        # INITIALIZATION
+        gksize = (i, i)
+        sigmaX = sig
+        blur = cv2.GaussianBlur(imgr,gksize,sigmaX)
+        #cv2.imshow("blurred", blur)                        # REMOVES TO SHOW BLURRED IMAGE
+        if len(blur.shape) == 3:
+            img = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
+        else:
+            img = blur
+
+        # GAUSSIAN PROCESSING                                 # UNCOMMENT TO SHOW GAUSSIAN PROCESSING
+        """x = np.array(range(dimc))
+        #y = np.array([img_rec.rel_lumin(img, 0, c) for c in x])
+        y = np.array([img.item(dimr // 2, c) for c in x])
+        a1, b1, c_s1 = gauss_reg(x, y)
+        x2 = np.array(range(dimr))
+        y2 = np.array([img.item(r, dimc // 2) for r in x2])
+        a2, b2, c_s2 = gauss_reg(x2, y2)
+        rem_gauss = gauss_mat(img.shape, (a1+a2) / 2, b1, c_s1, b2, c_s2)
+        img = img - rem_gauss
+        cv2.imshow("denoise", img)
+        y_hat = gauss_hat(x, a1, b1, c_s1)
+        plt.figure(figsize=(16, 8))
+        plt.plot(x, y, 'b-', x, y_hat, 'r-')
+        plt.show()
+        plt.close()
+        plt.figure(figsize=(16,8))
+        plt.plot(x, y-y_hat, 'b-')
+        plt.show()"""
+
+        # IMAGE PROCESSING
+        sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=-1)
+        sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=-1)
+        """kernel = np.array([[-1, -1, -1],
+                           [-1, 8, -1],
+                           [-1, -1, -1]])
+        kerneled = cv2.filter2D(img, -1, kernel)
+        cv2.imshow("kerneled", kerneled)"""                 # KERNEL EXPERIMENT
+        #laplacian = cv2.Laplacian(img,cv2.CV_64F)          # LAPLACIAN FILTERING
+        """plt.subplot(2,2,1),plt.imshow(cv2.cvtColor(imgr, cv2.COLOR_BGR2GRAY),cmap = 'gray')
+        plt.title('Original'), plt.xticks([]), plt.yticks([])
+        plt.subplot(2,2,2),plt.imshow(img,cmap = 'gray')
+        plt.title('Gaussian Filter'), plt.xticks([]), plt.yticks([])
+        plt.subplot(2,2,3),plt.imshow(sobelx,cmap = 'gray')
+        plt.title('Scharr X'), plt.xticks([]), plt.yticks([])
+        plt.subplot(2,2,4),plt.imshow(sobely,cmap = 'gray')
+        plt.title('Scharr Y'), plt.xticks([]), plt.yticks([])"""
+        #plt.savefig(NAME_HEADER + "filters.png")
+        #plt.savefig(NAME_HEADER + "2D_filters.png")
+        #plt.close()                                         # REMOVES TO SHOW CONTRAST OF FILTERS
+
+
+        # DETECTION INIT
+        gk_setting = "ksize:{0}, sigmax:{1}".format(gksize, sigmaX)
+        y_s_x = np.array([sobelx.item(r_int, c) for c in x])
+        y_s_y = np.array([sobely.item(r, c_int) for r in x2])
+        imgx = [imgr.item(r_int, c) for c in x]
+        imgy = [imgr.item(r, c_int) for r in x2]
+        blurimgx = [img.item(r_int, c) for c in x]
+        blurimgy = [img.item(r, c_int) for r in x2]
+        print(gk_setting)
+        print(extract_extrema(y_s_x))
+        print(extract_extrema(y_s_y))
+        fwrite.write(gk_setting)
+        fwrite.write('\n')
+
+        # EDGE DETECTION
+        zx = zero_crossing(y_s_x)
+        cenvalsx.append(zx)
+        ex = edge_converge_extreme(y_s_x)
+        edgvalsx.append(ex)
+        ebx = edge_converge_base(y_s_x)
+        ebvalsx.append(ebx)
+        ecx = edge_centroid(y_s_x, imgx)
+        ecvalsx.append(ecx)
+        ecbx = edge_centroid(y_s_x, blurimgx)
+        ecbvalsx.append(ecbx)
+
+        zy = zero_crossing(y_s_y)
+        cenvalsy.append(zy)
+        ey = edge_converge_extreme(y_s_y)
+        edgvalsy.append(ey)
+        eby = edge_converge_base(y_s_y)
+        ebvalsy.append(eby)
+        ecy = edge_centroid(y_s_y, imgy)
+        ecvalsy.append(ecy)
+        ecby = edge_centroid(y_s_y, blurimgy)
+        ecbvalsy.append(ecby)
+
+        # DATA RECORDING
+        fwrite.write("x: zero_crossing: {0}, edge_converge: {1}; ".format(zx, ex))
+        fwrite.write('\n')
+        fwrite.write("y: zero_crossing: {0}, edge_converge: {1}\n".format(zy, ey))
+
+        # GK_PLOT
+        """if i == 9:
+            fig = plt.figure(figsize=(16, 8))
+            fig.canvas.set_window_title(gk_setting)
+            plt.subplot(211)
+            plt.plot(y_s_x, 'b-')
+            plt.ylabel("sobel_x")
+            plt.subplot(212)
+            plt.plot(y_s_y, 'b-')
+            plt.ylabel("sobel_y")
+            #plt.savefig(NAME_HEADER + "_" + gk_setting + ".png")"""
+
+        #if i >= 13:
+        fig = plt.figure(figsize=(16, 8))
+        fig.canvas.set_window_title(gk_setting)
+        plt.subplot(211)
+        plt.plot(y_s_x, 'b-')
+        plt.ylabel("sobel_x")
+        plt.subplot(212)
+        plt.plot(y_s_y, 'b-')
+        plt.ylabel("sobel_y")
+        plt.show()
+        # plt.savefig(NAME_HEADER + "_" + gk_setting + ".png") # UNCOMMENT WHEN SAVING PLOTS
+
+        # USING MIN-MAX
+        fig = plt.figure(figsize=(16, 8))
+        fig.canvas.set_window_title(gk_setting)
+        plt.subplot(221)
+        plt.plot(y_s_x, 'b-')
+        plt.ylabel("sobel_x")
+        plt.subplot(222)
+        plt.plot(min_max(y_s_x, max(y_s_x), min(y_s_x)), 'b-')
+        plt.subplot(223)
+        plt.plot(y_s_y, 'b-')
+        plt.ylabel("sobel_y")
+        plt.xlabel("Plain")
+        plt.subplot(224)
+        plt.plot(min_max(y_s_y, max(y_s_y), min(y_s_y)), 'b-')
+        plt.xlabel("MINMAX")
+        plt.show()
+
+
+    fig = plt.figure(figsize=(16, 8))
+    plt.subplot(2, 1, 1)
+    #plt.plot(ins, cenvalsx, 'b-', ins, edgvalsx, 'r-', ins, ebvalsx, 'g-', ins, ecvalsx, 'c-', ins, ecbvalsx, 'm-')
+    #plt.legend(['zero crossing', 'edge_converging_extreme', 'edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'], loc="lower left")
+    #plt.plot(ins, cenvalsx, 'b-', ins, ebvalsx, 'g-', ins, ecvalsx, 'c-', ins, ecbvalsx, 'm-')
+    #plt.legend(['zero crossing', 'edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'],loc="lower left")
+    plt.plot(ins, ebvalsx, 'g-', ins, ecvalsx, 'c-', ins, ecbvalsx, 'm-')
+    plt.legend(['edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'],loc="lower left")
+    plt.ylabel("x_meas")
+    plt.subplot(2, 1, 2)
+    #plt.plot(ins, cenvalsy, 'b-', ins, ebvalsy, 'g-', ins, ecvalsy, 'c-', ins, ecbvalsy, 'm-')
+    #plt.legend(['zero crossing', 'edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'], loc="lower left")
+    plt.plot(ins, ebvalsy, 'g-', ins, ecvalsy, 'c-', ins, ecbvalsy, 'm-')
+    plt.legend(['edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'], loc="lower left")
+    plt.ylabel("y_meas")
+    plt.xlabel("kernel size")
+    plt.show()
+    #fig.savefig(plot_save)
+    #plt.savefig(plot_save)
+
+
+def test_interactive():
+    # TODO: TEST OUT FACET MODEL AND CONDITIONAL CONVOLUTIONAL KERNEL
+    # SETTINGS
+    while True:
+        number = input("type the picture number u wanna test or c to cancel:    ")
+        if number == 'c':
+            break
+        imgn = "img_%d_{0}" % int(number)
+        print(imgn)
+        IMGDIR = "../testpic/"
+        IMGDIR = "../calib4/"
+        ROOTMEAS = "meas/"
+        SAVEDIR = ROOTMEAS + imgn + "/"
+        # imgr = cv2.imread(IMGDIR + imgn + ".png")
+
+        imgr, ori = test_noise_reduce(IMGDIR + imgn + ".png", numIMG=5)
+        dimc = imgr.shape[1]
+        dimr = imgr.shape[0]
+        r_prompt = input("Type in the index for horizontal slice: (range: [0, {0}])   ".format(dimr - 1)) #dimr // 2
+        c_prompt = input("Type in the index for vertical slice: (range: [0, {0}])   ".format(dimc - 1)) #dimc // 2
+        while True:
+            try:
+                r_int = int(r_prompt)
+                assert 0 <= r_int < dimr
+                break
+            except ValueError:
+                r_prompt = input("Bad Input, try again: (range: [0, {0}])    ".format(dimr - 1))  # dimr // 2
+            except AssertionError:
+                r_prompt = input("Out of Bound, try again: (range: [0, {0}])    ".format(dimr - 1))
+        while True:
+            try:
+                c_int = int(c_prompt)
+                assert 0 <= c_int < dimc
+                break
+            except ValueError:
+                c_prompt = input("Bad Input, try again: (range: [0, {0}])    ".format(dimc - 1))  # dimc // 2
+            except AssertionError:
+                c_prompt = input("Out of Bound, try again: (range: [0, {0}])    ".format(dimc - 1))  # dimc // 2
+        #start = 7
+        #end = 10
+        sig = 0
+        gk = 9
+        name_scheme = imgn + ("_({0}, {1})").format(r_int, c_int)
+        NAME_HEADER = SAVEDIR + name_scheme
+        if not os.path.exists(SAVEDIR):
+            os.mkdir(SAVEDIR)
+        fwrite = open(NAME_HEADER + ".txt", "w")
+        plot_save = NAME_HEADER + ".png"
+
+        # INITIALIZATION
+        # cv2.imshow("denoised", np.uint8(imgr))                                 # REMOVES TO SHOW IMAGE
+        #x = np.array(range(dimc))
+        #x2 = np.array(range(dimr))
+
+        # INITIALIZATION
+        gksize = (gk, gk)
+        sigmaX = sig
+        blur = cv2.GaussianBlur(imgr, gksize, sigmaX)
+        # cv2.imshow("blurred", blur)                        # REMOVES TO SHOW BLURRED IMAGE
+        if len(blur.shape) == 3:
+            img = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
+        else:
+            img = blur
+
+        # GAUSSIAN PROCESSING                                 # UNCOMMENT TO SHOW GAUSSIAN PROCESSING
+        """x = np.array(range(dimc))
+        #y = np.array([img_rec.rel_lumin(img, 0, c) for c in x])
+        y = np.array([img.item(dimr // 2, c) for c in x])
+        a1, b1, c_s1 = gauss_reg(x, y)
+    
+        x2 = np.array(range(dimr))
+        y2 = np.array([img.item(r, dimc // 2) for r in x2])
+        a2, b2, c_s2 = gauss_reg(x2, y2)
+        rem_gauss = gauss_mat(img.shape, (a1+a2) / 2, b1, c_s1, b2, c_s2)
+        img = img - rem_gauss
+        cv2.imshow("denoise", img)
+        y_hat = gauss_hat(x, a1, b1, c_s1)
+        plt.figure(figsize=(16, 8))
+        plt.plot(x, y, 'b-', x, y_hat, 'r-')
+        plt.show()
+        plt.close()
+        plt.figure(figsize=(16,8))
+        plt.plot(x, y-y_hat, 'b-')
+        plt.show()"""
+
+        # IMAGE PROCESSING
+        sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=-1)
+        sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=-1)
+        """kernel = np.array([[-1, -1, -1],
+                           [-1, 8, -1],
+                           [-1, -1, -1]])
+        kerneled = cv2.filter2D(img, -1, kernel)
+        cv2.imshow("kerneled", kerneled)"""  # KERNEL EXPERIMENT
+        # laplacian = cv2.Laplacian(img,cv2.CV_64F)          # LAPLACIAN FILTERING
+        """plt.subplot(2,2,1),plt.imshow(cv2.cvtColor(imgr, cv2.COLOR_BGR2GRAY),cmap = 'gray')
+        plt.title('Original'), plt.xticks([]), plt.yticks([])
+        plt.subplot(2,2,2),plt.imshow(img,cmap = 'gray')
+        plt.title('Gaussian Filter'), plt.xticks([]), plt.yticks([])
+        plt.subplot(2,2,3),plt.imshow(sobelx,cmap = 'gray')
+        plt.title('Scharr X'), plt.xticks([]), plt.yticks([])
+        plt.subplot(2,2,4),plt.imshow(sobely,cmap = 'gray')
+        plt.title('Scharr Y'), plt.xticks([]), plt.yticks([])"""
+        # plt.savefig(NAME_HEADER + "filters.png")
+        # plt.savefig(NAME_HEADER + "2D_filters.png")
+        # plt.close()                                         # REMOVES TO SHOW CONTRAST OF FILTERS
+
+
+        # DETECTION INIT
+        xh = np.array(range(dimc))
+        xv = np.array(range(dimr))
+        zeroh = np.zeros(dimc)
+        zerov = np.zeros(dimr)
+        y_s_x = FM(sobelx, FM.HOR, r_int)
+        y_s_y = FM(sobely, FM.VER, c_int)
+        imgx = FM(imgr, FM.HOR, r_int)
+        imgy = FM(imgr, FM.VER, c_int)
+        blurimgx = FM(img, FM.HOR, r_int)
+        blurimgy = FM(img, FM.VER, c_int)
+        gx, bgx, deducedx = gauss_bg_deduce(xh, imgx)
+        gy, bgy, deducedy = gauss_bg_deduce(xv, imgy)
+
+        #print(extract_extrema(y_s_x))
+        #print(extract_extrema(y_s_y))
+
+        # EDGE DETECTION
+        zx = zero_crossing(y_s_x)
+        ex = edge_converge_extreme(y_s_x)
+        ebx = edge_converge_base(y_s_x)
+        ecbx = edge_centroid(y_s_x, blurimgx)
+        ecx = edge_centroid(y_s_x, imgx)
+        try:
+            #paramhp, xp1 = poly_fitting_params(y_s_x, deducedx)
+            #paramhg, xg1 = gaussian_fitting_params(y_s_x, deducedx)
+            paramhp, xp1 = poly_fitting_params(y_s_x, imgx)
+            paramhg, xg1 = gaussian_fitting_params(y_s_x, imgx)
+            xp_h_plot, yp_h_plot = poly_curve(paramhp, xp1)
+            xg_h_plot, yg_h_plot = gaussian_curve(xg1, paramhg[0], paramhg[1], paramhg[2])
+        except AssertionError:
+            print("No image data for horizontal slice!")
+            xp_h_plot, yp_h_plot = xh, zeroh
+            xg_h_plot, yg_h_plot = xh, zeroh
+
+        px, gx = xp_h_plot[np.argmax(yp_h_plot)], xg_h_plot[np.argmax(yg_h_plot)]
+        print("edge: {0}, poly:{1}, gaussian:{2}".format(ecx, px, gx))
+
+        zy = zero_crossing(y_s_y)
+        ey = edge_converge_extreme(y_s_y)
+        eby = edge_converge_base(y_s_y)
+        ecby = edge_centroid(y_s_y, blurimgy)
+        ecy = edge_centroid(y_s_y, imgy)
+        try:
+            paramvp, xp2 = poly_fitting_params(y_s_y, imgy)
+            paramvg, xg2 = gaussian_fitting_params(y_s_y, imgy)
+            xp_v_plot, yp_v_plot = poly_curve(paramvp, xp2)
+            xg_v_plot, yg_v_plot = gaussian_curve(xg2, paramvg[0], paramvg[1], paramvg[2])
+        except AssertionError:
+            print("No image data for vertical slice!")
+            xp_v_plot, yp_v_plot = xv, zerov
+            xg_v_plot, yg_v_plot = xv, zerov
+        py, gy = xp_v_plot[np.argmax(yp_v_plot)], xg_v_plot[np.argmax(yg_v_plot)]
+        print("edge: {0} poly:{1}, gaussian:{2}".format(ecy, py, gy))
+
+
+        # DATA RECORDING
+        fwrite.write("x: edge_centroid: {0}, poly: {1}, gaussian: {2}; ".format(ecx, px, gx))
+        fwrite.write('\n')
+        fwrite.write("y: edge_centroid: {0}, poly: {1}, gaussian: {2}\n".format(ecy, py, gy))
+
+        # GK_PLOT
+        """if i == 9:
+            fig = plt.figure(figsize=(16, 8))
+            fig.canvas.set_window_title(gk_setting)
+            plt.subplot(211)
+            plt.plot(y_s_x, 'b-')
+            plt.ylabel("sobel_x")
+            plt.subplot(212)
+            plt.plot(y_s_y, 'b-')
+            plt.ylabel("sobel_y")
+            #plt.savefig(NAME_HEADER + "_" + gk_setting + ".png")"""
+
+        # if i >= 13:
+        fig = plt.figure(figsize=(16, 8))
+        plt.subplot(211)
+        imgx.initialize()
+        plt.plot(xh, imgx.extract_array(), 'b-', xh, bgx, 'm-', xh, deducedx, 'c-', xp_h_plot, yp_h_plot, 'g-', xg_h_plot, yg_h_plot, 'r-')
+        plt.ylabel("Horizontal Slice, ECX: {0}".format(ecx))
+        plt.legend(['Raw image data', 'gaussian background', 'remnant', 'polynomial', 'gaussian'], loc="upper right")
+        plt.subplot(212)
+        imgy.initialize()
+        plt.plot(xv, imgy.extract_array(), 'b-', xv, bgy, 'm-', xv, deducedy, 'c-', xp_v_plot, yp_v_plot, 'g-', xg_v_plot, yg_v_plot, 'r-')
+        plt.ylabel("Vertical Slice, ECY: {0}".format(ecy))
+        plt.legend(['Raw image data', 'gaussian background', 'remnant', 'polynomial', 'gaussian'], loc="upper right")
+        plt.show()
+        # plt.savefig(NAME_HEADER + "_" + gk_setting + ".png") # UNCOMMENT WHEN SAVING PLOTS
+
+        """fig = plt.figure(figsize=(16, 8))
+        plt.subplot(2, 1, 1)"""
+        # plt.plot(ins, cenvalsx, 'b-', ins, edgvalsx, 'r-', ins, ebvalsx, 'g-', ins, ecvalsx, 'c-', ins, ecbvalsx, 'm-')
+        # plt.legend(['zero crossing', 'edge_converging_extreme', 'edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'], loc="lower left")
+        # plt.plot(ins, cenvalsx, 'b-', ins, ebvalsx, 'g-', ins, ecvalsx, 'c-', ins, ecbvalsx, 'm-')
+        # plt.legend(['zero crossing', 'edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'],loc="lower left")
+        """plt.plot(ins, ebvalsx, 'g-', ins, ecvalsx, 'c-', ins, ecbvalsx, 'm-')
+        plt.legend(['edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'], loc="lower left")
+        plt.ylabel("x_meas")
+        plt.subplot(2, 1, 2)
+        # plt.plot(ins, cenvalsy, 'b-', ins, ebvalsy, 'g-', ins, ecvalsy, 'c-', ins, ecbvalsy, 'm-')
+        # plt.legend(['zero crossing', 'edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'], loc="lower left")
+        plt.plot(ins, ebvalsy, 'g-', ins, ecvalsy, 'c-', ins, ecbvalsy, 'm-')
+        plt.legend(['edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'], loc="lower left")
+        plt.ylabel("y_meas")
+        plt.xlabel("kernel size")
+        plt.show()
+        # fig.savefig(plot_save)
+        # plt.savefig(plot_save)"""
+
+
+def test(folder, imgn):
+    # TODO: TEST OUT FACET MODEL AND CONDITIONAL CONVOLUTIONAL KERNEL
+    # SETTINGS
+    ROOTMEAS = "meas/"
+    SAVEDIR = ROOTMEAS + imgn[:-4] + "/"
+    # imgr = cv2.imread(IMGDIR + imgn + ".png")
+
+    imgr, ori = test_noise_reduce(folder + imgn, numIMG=5)
+    dimc = imgr.shape[1]
+    dimr = imgr.shape[0]
+    while True:
+        r_prompt = input("Type in the index for horizontal slice or c to cancel: (range: [0, {0}])   ".format(dimr - 1))  # dimr // 2
+        c_prompt = input("Type in the index for vertical slice: (range: [0, {0}])   ".format(dimc - 1))  # dimc // 2
+        if r_prompt == 'c':
+            break
+        while True:
+            try:
+                r_int = int(r_prompt)
+                assert 0 <= r_int < dimr
+                break
+            except ValueError:
+                r_prompt = input("Bad Input, try again: (range: [0, {0}])    ".format(dimr - 1))  # dimr // 2
+            except AssertionError:
+                r_prompt = input("Out of Bound, try again: (range: [0, {0}])    ".format(dimr - 1))
+        while True:
+            try:
+                c_int = int(c_prompt)
+                assert 0 <= c_int < dimc
+                break
+            except ValueError:
+                c_prompt = input("Bad Input, try again: (range: [0, {0}])    ".format(dimc - 1))  # dimc // 2
+            except AssertionError:
+                c_prompt = input("Out of Bound, try again: (range: [0, {0}])    ".format(dimc - 1))  # dimc // 2
+        # start = 7
+        # end = 10
+        sig = 0
+        gk = 9
+        name_scheme = imgn + ("_({0}, {1})").format(r_int, c_int)
+        NAME_HEADER = SAVEDIR + name_scheme
+        if not os.path.exists(SAVEDIR):
+            os.mkdir(SAVEDIR)
+        fwrite = open(NAME_HEADER + ".txt", "w")
+        plot_save = NAME_HEADER + ".png"
+
+        # INITIALIZATION
+        # cv2.imshow("denoised", np.uint8(imgr))                                 # REMOVES TO SHOW IMAGE
+        # x = np.array(range(dimc))
+        # x2 = np.array(range(dimr))
+
+        # INITIALIZATION
+        gksize = (gk, gk)
+        sigmaX = sig
+        blur = cv2.GaussianBlur(imgr, gksize, sigmaX)
+        # cv2.imshow("blurred", blur)                        # REMOVES TO SHOW BLURRED IMAGE
+        if len(blur.shape) == 3:
+            img = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
+        else:
+            img = blur
+
+        # GAUSSIAN PROCESSING                                 # UNCOMMENT TO SHOW GAUSSIAN PROCESSING
+        """x = np.array(range(dimc))
+        #y = np.array([img_rec.rel_lumin(img, 0, c) for c in x])
+        y = np.array([img.item(dimr // 2, c) for c in x])
+        a1, b1, c_s1 = gauss_reg(x, y)
+
+        x2 = np.array(range(dimr))
+        y2 = np.array([img.item(r, dimc // 2) for r in x2])
+        a2, b2, c_s2 = gauss_reg(x2, y2)
+        rem_gauss = gauss_mat(img.shape, (a1+a2) / 2, b1, c_s1, b2, c_s2)
+        img = img - rem_gauss
+        cv2.imshow("denoise", img)
+        y_hat = gauss_hat(x, a1, b1, c_s1)
+        plt.figure(figsize=(16, 8))
+        plt.plot(x, y, 'b-', x, y_hat, 'r-')
+        plt.show()
+        plt.close()
+        plt.figure(figsize=(16,8))
+        plt.plot(x, y-y_hat, 'b-')
+        plt.show()"""
+
+        # IMAGE PROCESSING
+        sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=-1)
+        sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=-1)
+
+
+        # DETECTION INIT
+        xh = np.array(range(dimc))
+        xv = np.array(range(dimr))
+        zeroh = np.zeros(dimc)
+        zerov = np.zeros(dimr)
+        y_s_x = FM(sobelx, FM.HOR, r_int)
+        y_s_y = FM(sobely, FM.VER, c_int)
+        imgx = FM(imgr, FM.HOR, r_int)
+        imgy = FM(imgr, FM.VER, c_int)
+        blurimgx = FM(img, FM.HOR, r_int)
+        blurimgy = FM(img, FM.VER, c_int)
+        gx, bgx, deducedx = gauss_bg_deduce(xh, imgx)
+        gy, bgy, deducedy = gauss_bg_deduce(xv, imgy)
+        # print(extract_extrema(y_s_x))
+        # print(extract_extrema(y_s_y))
+        # EDGE DETECTION
+        ecx = edge_centroid(y_s_x, imgx)
+        try:
+            # paramhp, xp1 = poly_fitting_params(y_s_x, deducedx)
+            # paramhg, xg1 = gaussian_fitting_params(y_s_x, deducedx)
+            paramhp, xp1 = poly_fitting_params(y_s_x, imgx)
+            paramhg, xg1 = gaussian_fitting_params(y_s_x, imgx)
+            xp_h_plot, yp_h_plot = poly_curve(paramhp, xp1)
+            xg_h_plot, yg_h_plot = gaussian_curve(xg1, paramhg[0], paramhg[1], paramhg[2])
+        except AssertionError:
+            print("No image data for horizontal slice!")
+            xp_h_plot, yp_h_plot = xh, zeroh
+            xg_h_plot, yg_h_plot = xh, zeroh
+
+        px, gx = xp_h_plot[np.argmax(yp_h_plot)], xg_h_plot[np.argmax(yg_h_plot)]
+        print("edge: {0}, poly:{1}, gaussian:{2}".format(ecx, px, gx))
+
+        ecy = edge_centroid(y_s_y, imgy)
+        try:
+            paramvp, xp2 = poly_fitting_params(y_s_y, imgy)
+            paramvg, xg2 = gaussian_fitting_params(y_s_y, imgy)
+            xp_v_plot, yp_v_plot = poly_curve(paramvp, xp2)
+            xg_v_plot, yg_v_plot = gaussian_curve(xg2, paramvg[0], paramvg[1], paramvg[2])
+        except AssertionError:
+            print("No image data for vertical slice!")
+            xp_v_plot, yp_v_plot = xv, zerov
+            xg_v_plot, yg_v_plot = xv, zerov
+        py, gy = xp_v_plot[np.argmax(yp_v_plot)], xg_v_plot[np.argmax(yg_v_plot)]
+        print("edge: {0} poly:{1}, gaussian:{2}".format(ecy, py, gy))
+
+        # DATA RECORDING
+        fwrite.write("x: edge_centroid: {0}, poly: {1}, gaussian: {2}; ".format(ecx, px, gx))
+        fwrite.write('\n')
+        fwrite.write("y: edge_centroid: {0}, poly: {1}, gaussian: {2}\n".format(ecy, py, gy))
+
+        # if i >= 13:
+        fig = plt.figure(figsize=(16, 8))
+        plt.subplot(411)
+        plt.plot(y_s_x.extract_array(), 'b-')
+        plt.ylabel("sobel_x")
+        plt.subplot(412)
+        imgx.initialize()
+        plt.plot(xh, imgx.extract_array(), 'b-', xh, bgx, 'm-', xh, deducedx, 'c-', xp_h_plot, yp_h_plot, 'g-',
+                 xg_h_plot, yg_h_plot, 'r-')
+        plt.ylabel("Horizontal Slice, ECX: {0}".format(ecx))
+        plt.legend(['Raw image data', 'gaussian background', 'remnant', 'polynomial', 'gaussian'], loc="upper right")
+        plt.subplot(413)
+        plt.plot(y_s_y.extract_array(), 'b-')
+        plt.ylabel("sobel_y")
+        plt.subplot(414)
+        imgy.initialize()
+        plt.plot(xv, imgy.extract_array(), 'b-', xv, bgy, 'm-', xv, deducedy, 'c-', xp_v_plot, yp_v_plot, 'g-',
+                 xg_v_plot, yg_v_plot, 'r-')
+        plt.ylabel("Vertical Slice, ECY: {0}".format(ecy))
+        plt.legend(['Raw image data', 'gaussian background', 'remnant', 'polynomial', 'gaussian'], loc="upper right")
+        plt.show()
+        # plt.savefig(NAME_HEADER + "_" + gk_setting + ".png") # UNCOMMENT WHEN SAVING PLOTS
+
+
+FM = FastDataMatrix2D
+
+
+def folder_to_imgs(img_name_scheme, num_sample):
+    """This function takes img files and return cv imgs"""
+    return [cv2.imread(img_name_scheme.format(i)) for i in range(1, num_sample+1)]
+
+
+def center_detect_old(img_name_scheme, num_sample, sample_int=50):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair"""
+    imgs = folder_to_imgs(img_name_scheme, num_sample)
+    num_imgs = len(imgs)
+    dimr = imgs[0].shape[0]
+    dimc = imgs[0].shape[1]
+    xnum_imgs = num_imgs
+    ynum_imgs = num_imgs
+    xsum = 0
+    ysum = 0
+    zw = 0
+    ew = 1
+    gk = 9
+    count = 1
+    for imgr in imgs:
+        if count == num_sample:
+            imgshow = imgr
+        count += 1
+        #print(imgr.shape)
+        # Image Processing
+        gksize = (gk, gk)
+        sigmaX = 0
+        img = cv2.GaussianBlur(imgr, gksize, sigmaX)
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=-1)
+        sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=-1)
+        # Gathering Data
+        nr = sample_int
+        r_thresh = dimr * 2.0 / (sample_int * 3)
+        xs = []
+        xdummies = []
+        ys = []
+        ydummies = []
+        while nr < dimr:
+            data_x = sobelx[nr, :]
+            zc_x = zero_crossing(data_x)
+            print("row:", nr)
+            ed_x = edge_centroid(data_x, FM(img, FM.HOR, nr))
+            print("At {0}: {1}".format(nr, ed_x))
+            nr += sample_int
+            xdummies.append(zc_x * zw + ed_x * ew)
+            if zc_x == -1:
+                continue
+            else:
+                xs.append(zc_x * zw + ed_x * ew)
+        nc = sample_int
+        c_thresh = dimc * 2.0 / (sample_int * 3)
+        while nc < dimc:
+            data_y = sobely[:, nc]
+            zc_y = zero_crossing(data_y)
+            ed_y = edge_centroid(data_y, FM(img, FM.VER, nc))
+            nc += sample_int
+            ydummies.append(zc_y * zw + ed_y * ew)
+            if zc_y == -1:
+                continue
+            else:
+                ys.append(zc_y * zw + ed_y * ew)
+        #print("x")
+        #print(xdummies)
+        #print(xs)
+        #print("y")
+        #print(ydummies)
+        #print(ys)
+        len_xs = len(xs)
+        len_ys = len(ys)
+        print("img {0}: {1}".format(count - 1, xs))
+        if len_xs < r_thresh:
+            xnum_imgs -= 1
+        else:
+            xsum += sum(xs) / len(xs)
+        if len_ys < c_thresh:
+            ynum_imgs -= 1
+        else:
+            ysum += sum(ys) / len(ys)
+
+    plt.show()
+
+    center_x = -1 if xnum_imgs == 0 else xsum / xnum_imgs
+    center_y = -1 if ynum_imgs == 0 else ysum / ynum_imgs
+    return center_x, center_y
+
+# The image taken is flipped horizontally, result x should be img.shape[1] - x
+# The image sometimes has two peaks, try experimenting with different gaussian kernels
+
+
+def center_detect_test(folder_path, img_name_scheme, num_sample, sample_int=50, debug=False, gk=9, ks=-1, m=0, p=20, b=1, c=0, hough=True):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair
+    hs: HORIZONTAL SLICE!  vs: VERTICAL SLICE!"""
+    imgr = test_noise_reduce(folder_path + img_name_scheme, num_sample)[0]
+    dimr = imgr.shape[0]
+    dimc = imgr.shape[1]
+    # Image Processing
+    gksize = (gk, gk)
+    sigmaX = 0
+    img = cv2.GaussianBlur(imgr, gksize, sigmaX)
+    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=ks)
+    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=ks)
+    # ------------------------------------------------------------
+    # Parameter Setting
+    METHODS = {0: gaussian_fitting, 1: poly_fitting, 2: edge_centroid,
+               3: zero_crossing, 4: edge_converge_base, 5: edge_converge_extreme}
+    edge_method = METHODS[m]
+    nr = sample_int
+    r_thresh = dimr / (sample_int * 3.0)
+    nc = sample_int
+    c_thresh = dimc / (sample_int * 3.0)
+    # ------------------------------------------------------------
+    # Gathering Data
+    hs = []
+    vs = []
+    while nr < dimr:
+        data_x = FM(sobelx, FM.HOR, nr)
+        if m < 3:
+            ec_x = edge_method(data_x, FM(img, FM.HOR, nr), p)
+        else:
+            ec_x = edge_method(data_x)
+        nr += sample_int
+        if ec_x == -1:
+            continue
+        else:
+            hs.append((nr - sample_int, ec_x))
+
+    while nc < dimc:
+        data_y = FM(sobely, FM.VER, nc)
+        if m < 3:
+            ec_y = edge_method(data_y, FM(img, FM.VER, nc), p)
+        else:
+            ec_y = edge_method(data_y)
+        nc += sample_int
+        if ec_y == -1:
+            continue
+        else:
+            vs.append((nc - sample_int, ec_y))
+    len_hs = len(hs)
+    len_vs = len(vs)
+    # --------------- PRE-CHECK DATA VALUES ----------------------
+    hxs = np.zeros(len_hs)
+    hys = np.zeros(len_hs)
+    for i in range(len_hs):
+        hxs[i] = hs[i][1]
+        hys[i] = hs[i][0]
+    vxs = np.zeros(len_vs)
+    vys = np.zeros(len_vs)
+    for i in range(len_vs):
+        vxs[i] = vs[i][0]
+        vys[i] = vs[i][1]
+    x_valid = False
+    y_valid = False
+    stdh = -1
+    stdv = -1
+    #valuesH = [d[1] for d in hs]
+    #valuesV = [d[1] for d in vs]
+    if len_hs >= r_thresh:
+        x_valid = True
+        stdh = std_dev(hxs)
+    if len_vs >= c_thresh:
+        y_valid = True
+        stdv = std_dev(vys)
+    #hough_img = img_name_scheme.format(1)
+
+    # OUTLIER DETECTION TODO: OPTIMIZE, THIS IS NAIVE
+    if x_valid:  # NAIVE
+        hys, hxs = reg_pre_debias(hys, hxs)
+    if y_valid:
+        vxs, vys = reg_pre_debias(vxs, vys)
+    # ---------------------------------------------------------
+    # ----- Following Modules Handles Hough Line Drawing ---------
+    if hough:
+        hough_img = folder_path + img_name_scheme.format(1)
+        print(hough_img)
+        img_h = cv2.imread(hough_img)
+        if len_hs:
+            hp1, hp2, line_a = line_graph_contrast(img_h, hxs, hys)
+            print('Drawn H')
+        if len_vs:
+            vp1, vp2, line_b = line_graph_contrast(img_h, vxs, vys)
+            print('Drawn V')
+        namespace = 'houghs/hough_{0}{1}_{2}'.format(folder_path[3:], m, img_name_scheme.format(1))
+        if len_hs:
+            print(hp1, hp2)
+        if len_vs:
+            print(vp1, vp2)
+        print(namespace)
+        cv2.imwrite(namespace, img_h)
+    else:
+        #print("MEAS", hys)
+        line_a = HoughLine(x=hxs, data=hys)
+        #print("MEAS", vys)
+        line_b = HoughLine(x=vxs, data=vys)
+    # ------------------------------------------------------------
+    # DEBUG MODULE
+    if debug:
+        print((dimr, dimc))
+        print("imgX(BEFORE):", hs)
+        print("imgX:", list(zip(hys, hxs)))
+        print("imgY(BEFORE):", vs)
+        print("imgY:", list(zip(vxs, vys)))
+        compare_images((sobelx, 'Sobel X'), (sobely, 'Sobel Y'), color_map='gray')
+        test(folder_path, img_name_scheme)
+    # --------------------------------------------------------
+    # DATA RECORDING AND PROCESSING
+    if len_hs >= r_thresh:
+        x_valid = True
+        stdh = std_dev(hxs)
+    if len_vs >= c_thresh:
+        y_valid = True
+        stdv = std_dev(vys)
+    if c == 1:
+        center_x = sum(hxs) / len_hs if x_valid else -1
+        center_y = sum(vys) / len_vs if y_valid else -1
+    else:
+        if x_valid and y_valid:
+            center_x, center_y = HoughLine.intersect(line_a, line_b)
+        else:
+            center_x = sum(hxs) / len_hs if x_valid else -1
+            center_y = sum(vys) / len_vs if y_valid else -1
+    # ---------------------------------------------------------
+    return center_x, center_y, stdh, stdv
+
+
+def center_detect(img_name_scheme, num_sample, sample_int=50, debug=False, gk=9, ks=-1, m=1, p=10, b=1):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair
+    hs: HORIZONTAL SLICE!  vs: VERTICAL SLICE!"""
+    imgr = test_noise_reduce(img_name_scheme, num_sample)[0]
+    dimr = imgr.shape[0]
+    dimc = imgr.shape[1]
+    zw = 0
+    ew = 1
+    # Image Processing
+    gksize = (gk, gk)
+    sigmaX = 0
+    img = cv2.GaussianBlur(imgr, gksize, sigmaX)
+    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=ks)
+    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=ks)
+    # Gathering Data
+    nr = sample_int
+    r_thresh = dimr / (sample_int * 3.0)
+    hs = []
+    vs = []
+    while nr < dimr:
+        data_x = FM(sobelx, FM.HOR, nr)
+        zc_x = zero_crossing(data_x)
+        ec_x = edge_centroid(data_x, FM(img, FM.HOR, nr))
+        nr += sample_int
+        if ec_x == -1:
+            continue
+        else:
+            hs.append((nr - sample_int, zc_x * zw + ec_x * ew))
+    nc = sample_int
+    c_thresh = dimc / (sample_int * 3.0)
+    while nc < dimc:
+        data_y = FM(sobely, FM.VER, nc)
+        zc_y = zero_crossing(data_y)
+        ec_y = edge_centroid(data_y, FM(img, FM.VER, nc))
+        nc += sample_int
+        if ec_y == -1:
+            continue
+        else:
+            vs.append((nc - sample_int, zc_y * zw + ec_y * ew))
+    len_hs = len(hs)
+    len_vs = len(vs)
+    if debug:
+        print((dimr, dimc))
+        print("imgX:", hs)
+        print("imgY:", vs)
+        compare_images((sobelx, 'Sobel X'), (sobely, 'Sobel Y'), color_map='gray')
+        while True:
+            ax = input("Input the axis you wanna debug, h for horizontal, v for vertical, or c to cancel:  ")
+            if ax == 'c':
+                break
+            inds = input("Input the index you wanna debug:  ")
+            while True:
+                try:
+                    index = int(inds)
+                    axis = FM.HOR if ax == 'h' else FM.VER
+                    assert 0 <= index < imgr.shape[1 - axis]
+                    break
+                except ValueError:
+                    inds = input("Invalid input, do it again:  ")
+                except AssertionError:
+                    inds = input("Index Out of Bound, do it again: {0}  ".format(imgr.shape))
+            sobelop = sobelx if axis else sobely
+            quick_plot(FM(sobelop, axis, index).extract_array())
+
+    # ----- Following Modules Handles Hough Line Drawing -----
+    hxs = np.zeros(len_hs)
+    hys = np.zeros(len_hs)
+    for i in range(len_hs):
+        hxs[i] = hs[i][1]
+        hys[i] = hs[i][0]
+    vxs = np.zeros(len_vs)
+    vys = np.zeros(len_vs)
+    for i in range(len_vs):
+        vxs[i] = vs[i][0]
+        vys[i] = vs[i][1]
+
+    hough_img = img_name_scheme.format(1)
+    img_h = cv2.imread(hough_img)
+    if len_hs:
+        hp1, hp2, lina = line_graph_contrast(img_h, hxs, hys)
+        print('Drawn H')
+    if len_vs:
+        vp1, vp2, linb = line_graph_contrast(img_h, vxs, vys)
+        print('Drawn V')
+    namespace = 'houghs/hough_{0}'.format(hough_img[3:])
+    if len_hs:
+        print(hp1, hp2)
+    if len_vs:
+        print(vp1, vp2)
+    print(namespace)
+    cv2.imwrite(namespace, img_h)
+    # --------------------------------------------------------
+    x_invalid = False
+    y_invalid = False
+    if len_hs < r_thresh:
+        x_invalid = True
+    if len_vs < c_thresh:
+        y_invalid = True
+
+    center_x = -1 if x_invalid else sum([d[1] for d in hs]) / len_hs
+    center_y = -1 if y_invalid else sum([d[1] for d in vs]) / len_vs
+    return center_x, center_y
+
+
+def random_test():
+    data = [27, 21, 22, 21, 21, 18, 41, 69, 83, 62, 38, 16, 21, 20, 18, 17]
+    print(len(data))
+    ids = 0
+    gs = 0
+    for i, d in enumerate(data):
+        if d == 83:
+            print(i)
+        ids += i * d
+        gs += d
+    print(ids / gs)
+
+
+TEST_PIC_TRUTH = [(385, 130),
+                  (385, 130),
+                  (385, 130),
+                  (-2, -2),
+                  (176, 52),
+                  (174, -1),
+                  (158, -1),
+                  (159, -1),
+                  (242, 212),
+                  (278, 215),
+                  (325, 218),
+                  (143, 215),
+                  (53, 219),
+                  (-1, 17),
+                  (138, 19),
+                  (355, 14),
+                  (288, -1),
+                  (369, 180),
+                  (357, 265),
+                  (225, 224),
+                  (-1, 195),
+                  (-1, 59),
+                  (-1, -1),
+                  (-1, 160),
+                  (445, 166)]
+
+
+def read_truth(filename):
+    with open(filename) as truth:
+        TRUTH = {}
+        active = None
+        for line in truth:
+            meas = line.replace(',', ' ').split()
+            if len(meas) == 0:
+                pass
+            elif len(meas) == 1:
+                active = {}
+                TRUTH[meas[0]] = active
+
+            else:
+                tp = (int(meas[1]), int(meas[2]))
+                active[meas[0]] = tp
+    return TRUTH
+
+
+def error_calc(meas, truth):
+    E = 0
+    T = 0
+    F = 0
+    terms = 0
+    #ls = ['img_5', 'img_6', 'img_7', 'img_12', 'img_13', 'img_14', 'img_15', 'img_16', 'img_17', 'img_18', 'img_20', 'img_21', 'img_22', 'img_25']
+    #ms = []
+    #es = []
+    for m, vals in meas.items():
+        """if m in ls:
+            continue"""
+        mx, my = vals
+        tx, ty = truth[m]
+        if tx == -2 or ty == -2:
+            pass
+        elif (mx == -1 and tx != -1) or (my == -1 and ty != -1):
+            F += 1
+            #ms.append(m)
+        elif (mx != -1 and tx == -1) or (my != -1 and ty == -1):
+            T += 1
+        else:
+            dx = mx - tx
+            dy = my - ty
+            E += (dx) ** 2 + (dy) ** 2
+            #es.append((dx, dy))
+            terms += 2
+    # print(ms)
+    # print(es)
+    error = sqrt(E / terms) if terms else float('inf')
+    return {'E': error, 'T': T, 'F': F}
+
+
+TRUTH_FILE = "meas/truth.txt"
+TRUTH = read_truth(TRUTH_FILE)
+
+
+""" =================================
+============= Parameter =============
+=========== Optimization ============
+===================================== """
+
+
+# TODO: OPTIMIZE ON THE THE GAUSSIAN KERNEL PARAMETER. WHAT HAPPENED THAT MAKES THE SMALL KERNEL BETTER???
+def gaussian_kernel_expr(folder, ns):
+    offset = '../'
+    truth_doc = TRUTH[folder]
+    error = {}
+    gs = range(1, 20, 2)
+    Es = []
+    Ts = []
+    Fs = []
+    for g in gs:
+        meas = {}
+        #print(g)
+        for i in range(1, 26):
+            img_name = ns.format(i)
+            imgfile = offset + folder + "/%s_{0}.png" % img_name
+            # if i in [11, 13, 21, 23, 25]:
+            if i == 0:
+                val = center_detect(imgfile, 3, debug=True, gk=g)
+            else:
+                val = center_detect(imgfile, 3, gk=g)
+            #print(str(i), val)
+            meas[img_name] = val
+            # print(str(i), val)
+
+        ed = error_calc(meas, truth_doc)
+        Es.append(ed['E'])
+        Ts.append(ed['T'])
+        Fs.append(ed['F'])
+        error[g] = ed
+    print(error)
+    compare_data_plots((Es, gs, 'Error'), (Ts, gs, 'False Positive'), (Fs, gs, 'False Negative'), suptitle="Gaussian Blur Test")
+
+
+def sobel_kernel_expr(folder, ns):
+    offset = '../'
+    truth_doc = TRUTH[folder]
+    error = {}
+    #ks = [-1]
+    #ks.extend(list(range(3, 8, 2)))
+    ks = range(-1, 8, 2)
+    Es = []
+    Ts = []
+    Fs = []
+    for k in ks:
+        # print(k)
+        meas = {}
+        for i in range(1, 26):
+            img_name = ns.format(i)
+            imgfile = offset + folder + "/%s_{0}.png" % img_name
+            # if i in [11, 13, 21, 23, 25]:
+            if i == 0:
+                val = center_detect(imgfile, 3, debug=True, ks=k)
+            else:
+                val = center_detect(imgfile, 3, ks=k)
+            #print(str(i), val)
+            meas[img_name] = val
+            # print(str(i), val)
+
+        ed = error_calc(meas, truth_doc)
+        Es.append(ed['E'])
+        Ts.append(ed['T'])
+        Fs.append(ed['F'])
+        error[k] = ed
+    print(error)
+    compare_data_plots((Es, ks, 'Error'), (Ts, ks, 'False Positive'), (Fs, ks, 'False Negative'), suptitle="Sobel Kernel Test")
+
+
+def convergence_test(folder, ns):
+    offset = '../'
+    convergence = {}
+    line_stdh = []
+    line_stdv = []
+    variations = []
+    startNP = 59
+    startP = 80
+    endP = 193
+    ms = range(3)
+    fwrite = open('meas/convergence.csv', 'w')
+    cwriter = csv.writer(fwrite)
+    cwriter.writerow(['Image Number', 'Center X', 'Center Y', 'StdDev Horizontal', 'Std Dev Vertical'])
+    for m in ms:
+        cwriter.writerow([str(m)])
+        # Convergence
+        lrh = 0
+        lrhs = 0
+        lrv = 0
+        lrvs = 0
+        # Consistency Cycled
+        pv = 0
+        pvs = 0
+        rcount = 0
+        cvx = np.zeros(4)
+        cvy = np.zeros(4)
+        #print(g)
+        for i in range(startNP, startP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                x, y, stdh, stdv = center_detect_test(fpath, imgfile, 5, debug=False, m=m)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y), str(stdh), str(stdv)])
+                # CONVERGENCE
+                if x != -1:
+                    lrh += stdh ** 2
+                    lrhs += 1
+                if y != -1:
+                    lrv += stdv ** 2
+                    lrvs += 1
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+        for i in range(startP, endP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                x, y, stdh, stdv = center_detect_test(fpath, imgfile, 5, debug=False, m=m)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y), str(stdh), str(stdv)])
+                # CONVERGENCE
+                if x != -1:
+                    lrh += stdh ** 2
+                    lrhs += 1
+                if y != -1:
+                    lrv += stdv ** 2
+                    lrvs += 1
+                # Record x, y, check rcount, refresh CONSISTENCY
+                cvx[rcount] = x
+                cvy[rcount] = y
+                rcount += 1
+                if rcount == 4:
+                    pv += np.var(cvx) + np.var(cvy)
+                    pvs += 1
+                    rcount = 0
+                    cvx = np.zeros(4)
+                    cvy = np.zeros(4)
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+            # print(str(i), val)
+        mselrh = sqrt(lrh / lrhs)
+        mselrv = sqrt(lrv / lrvs)
+        msepv = sqrt(pv / pvs)
+        cvg = {'LineConvergenceH': mselrh if lrhs else float('inf'),
+               'LineConvergenceV': mselrv if lrvs else float('inf'),
+               'PicConsistency': msepv}
+        convergence[m] = cvg
+        line_stdh.append(mselrh)
+        line_stdv.append(mselrv)
+        variations.append(msepv)
+    print(convergence)
+    compare_data_plots((line_stdh, ms, 'Line Convergence Hor'), (line_stdv, ms, 'Line Convergence Ver'),
+                       (variations, ms, 'Reading Consistency'), suptitle="Convergence/Consistency Test")
+    fwrite.close()
+
+
+def convergence_test_final(folder, ns):
+    offset = '../'
+    convergence = {}
+    variations = []
+    startNP = 59
+    startP = 80
+    endP = 193
+    ms = range(3)
+    fwrite = open('meas/convergence.csv', 'w')
+    cwriter = csv.writer(fwrite)
+    cwriter.writerow(['Image Number', 'Center X', 'Center Y', 'StdDev Horizontal', 'Std Dev Vertical'])
+    for m in ms:
+        cwriter.writerow([str(m)])
+        # Consistency Cycled
+        pv = 0
+        pvs = 0
+        rcount = 0
+        cvx = np.zeros(4)
+        cvy = np.zeros(4)
+        # print(g)
+        for i in range(startNP, startP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                x, y = center_detect(fpath + imgfile, 5, m=m)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y)])
+                # CONVERGENCE
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+        for i in range(startP, endP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                x, y = center_detect(fpath + imgfile, 5, m=m)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y)])
+                # CONVERGENCE
+                # Record x, y, check rcount, refresh CONSISTENCY
+                cvx[rcount] = x
+                cvy[rcount] = y
+                rcount += 1
+                if rcount == 4:
+                    pv += np.var(cvx) + np.var(cvy)
+                    pvs += 1
+                    rcount = 0
+                    cvx = np.zeros(4)
+                    cvy = np.zeros(4)
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+                # print(str(i), val)
+        msepv = sqrt(pv / pvs)
+        cvg = {'PicConsistency': msepv}
+        convergence[m] = cvg
+        variations.append(msepv)
+    print(convergence)
+    fwrite.close()
+""""""
+
+
+def parameter_convert(method, padding, blur):
+    return method + 2 * padding + blur * 100
+
+
+def main():
+    # TODO: HANDLE THE MAXI MINI LEFT AND RIGHT PROBLEM (2 PEAKS)
+    folder = "../calib4/"
+    img_name = "img_59_{0}.jpg"
+    ns = "img_%d_{0}.png"
+    # print("Center Detection yields: ")
+    # gaussian_kernel_expr('testpic', 'img_{0}')
+    #sobel_kernel_expr('testpic', 'img_{0}')
+    convergence_test('calib4/', 'img_{0}')
+    """for i in range(59, 193):
+        #if i in [29]:
+        try:
+            if i in [133, 134, 157, 158, 159, 160]:
+                print(i)
+                val = center_detect_test(folder, ns % i, 5, debug=True, hough=False)
+                print(str(i), val)
+            #val = center_detect_test(folder, ns % i, 5, hough=False)
+            #print(str(i), val)
+        except AttributeError:
+            print('No image {0}!'.format(i))
+        print('------------------------------------------------------')"""
+    # convergence_test_final('calib4/', 'img_{0}')
+
+
+def case_calc():
+    hori_lower = 53.50 - 0.13
+    hori_upper = 53.50 + 0.13
+    verti_lower = 41.41 - 0.11
+    verti_upper = 41.41 + 0.11
+    height = 3
+    width = 4
+    print('hori_lower: {0}'.format(distance(width / 2, hori_lower / 2)))
+    print('hori_upper: {0}'.format(distance(width / 2, hori_upper / 2)))
+    print('verti_lower: {0}'.format(distance(height / 2, verti_lower / 2)))
+    print('verti_upper: {0}'.format(distance(height / 2, verti_upper / 2)))
+
+
+def distance(h, angle):
+    return 2.54 * (h / tan(radians(angle)))
+
+
+    #print(center_detect(folder + img_name, 10, debug=True))
+
+
+
+    #print("Center Detection yields: ")
+    #print(center_detect("../new_test/img_3_{0}.png", 5))
+    #2.5 + 13  = 15.5 / 2 = 7.75
+
+    """plt.figure()
+    plt.plot([1,2 ,3])
+    plt.show()"""
+
+    #print(atan(56 / 200) * 180 / 3.1415926535)
+    #print(atan(134 / 537) * 180 / 3.1415926535)
+
+    #test()
+    #test_canny_detect()
+    #random_test()
+
+
+if __name__ == '__main__':
+    main()
+    #print(DEGREES)
+    #test_interactive()
+    #test_canny_detect()
+
+
+
+# INSIGHT:
+"""Might be able to solve the problem by auto-thresholding and converting to binary.
+Alternative:
+    1. Mark the maximum and minimum that are certain multitudes of standard deviation above
+
+===========================================
+========= THE FOLLOWING FUNCTIONS =========
+============== ARE DEPRECATED =============
+==========================================-
+
+def OMP(imDims, sparsity, measurements, A):
+    display = None
+    numPixels = 0
+    r = measurements.copy()
+    indices = []
+
+    # Threshold to check error. If error is below this value, stop.
+    THRESHOLD = 0.1
+
+    # For iterating to recover all signal
+    i = 0
+
+    while i < sparsity and np.linalg.norm(r) > THRESHOLD:
+        # Calculate the correlations
+        print('%d - ' % i, end="", flush=True)
+        corrs = A.T.dot(r)
+
+        # Choose highest-correlated pixel location and add to collection
+        # COMPLETE THE LINE BELOW
+        best_index = np.argmax(np.abs(corrs))
+        indices.append(best_index)
+
+        # Build the matrix made up of selected indices so far
+        # COMPLETE THE LINE BELOW
+        Atrunc = A[:, indices]
+
+        # Find orthogonal projection of measurements to subspace
+        # spanned by recovered codewords
+        b = measurements
+        # COMPLETE THE LINE BELOW
+        xhat = np.linalg.lstsq(Atrunc, b)[0]
+
+        # Find component orthogonal to subspace to use for next measurement
+        # COMPLETE THE LINE BELOW
+        r = b - Atrunc.dot(xhat)
+
+        # This is for viewing the recovery process
+        if i % 10 == 0 or i == sparsity - 1 or np.linalg.norm(r) <= THRESHOLD:
+            recovered_signal = np.zeros(numPixels)
+            for j, x in zip(indices, xhat):
+                recovered_signal[j] = x
+            Ihat = recovered_signal.reshape(imDims)
+            plt.title('estimated image')
+            plt.imshow(Ihat, cmap=plt.cm.gray, interpolation='nearest')
+            display.clear_output(wait=True)
+            display.display(plt.gcf())
+
+        i = i + 1
+
+    display.clear_output(wait=True)
+
+    # Fill in the recovered signal
+    recovered_signal = np.zeros(numPixels)
+    for i, x in zip(indices, xhat):
+        recovered_signal[i] = x
+
+    return recovered_signal
+
+if ori == 'h':
+    x = np.array(range(img.shape[1]))
+    y = np.array([img_rec.rel_lumin(img, p, c) for c in x])
+else:
+    x = np.array(range(img.shape[0]))
+    y = np.array([img_rec.rel_lumin(img, r, p) for r in x])
+"""
+
+CALK = 166.1861
diff --git a/Nov_27/Manual/Camera/img_rec_module/~$log.docx b/Nov_27/Manual/Camera/img_rec_module/~$log.docx
new file mode 100644
index 0000000..0430bb4
Binary files /dev/null and b/Nov_27/Manual/Camera/img_rec_module/~$log.docx differ
diff --git a/Nov_27/Manual/Camera/transfer_test_correct_config/._RPI_C.py b/Nov_27/Manual/Camera/transfer_test_correct_config/._RPI_C.py
new file mode 100644
index 0000000..dbdfc51
Binary files /dev/null and b/Nov_27/Manual/Camera/transfer_test_correct_config/._RPI_C.py differ
diff --git a/Nov_27/Manual/Camera/transfer_test_correct_config/RPI_C.py b/Nov_27/Manual/Camera/transfer_test_correct_config/RPI_C.py
new file mode 100644
index 0000000..85d7fcb
--- /dev/null
+++ b/Nov_27/Manual/Camera/transfer_test_correct_config/RPI_C.py
@@ -0,0 +1,85 @@
+#Code for communications, sensor actions, and calculations for RPi on camera module
+#Assume that RPI_C and RPI_L codes are implemented at same time (synced)
+#Below code will execute at startup of RPI_C
+
+from SETTINGS import *
+import RPi.GPIO as GPIO
+import sys, traceback
+import time
+from Read_Accel import read_accel
+sys.path.insert(0,IMG_REC)
+from sig_proc import center_detect
+sys.path.insert(0,IMG_CAM)
+from img_capture import ImgCollector
+sys.path.insert(0,COMM)
+from communication import send_data_to_server
+
+#See SETTINGS.py
+
+def main():
+
+    try:
+        try:
+            img_collector = ImgCollector(IMG_DIR, NAME_SCHEME, FILE_FORMAT, RAW_IMAGE, NUM_SAMPLES)
+        except:
+            print('Error: Camera Not Setup')
+        while True:
+            pic = raw_input('Press [y] to continue to picture taking [Ambient]: ')
+            if pic == 'y':
+                try:
+                    print('Taking Picture...')
+                    img_collector.capture()
+                    time.sleep(2)
+                    print('Picture Taken, img_collector shutdown')
+                except:
+                    print('Error Taking Photo...')
+                    traceback.print_exc(file=sys.stdout)
+                finally:
+                    break
+            else:
+                print('Please try again')
+
+        while True:
+            pic = raw_input('Press [y] to continue to picture taking [Laser]: ')
+            if pic == 'y':
+                try:
+                    print('Taking Picture...')
+                    img_collector.capture()
+                    time.sleep(2)
+                    img_collector.shutdown()
+                    print('Picture Taken, img_collector shutdown')
+                except:
+                    print('Error Taking Photo...')
+                    traceback.print_exc(file=sys.stdout)
+                finally:
+                    break
+            else:
+                print('Please try again')
+
+        while True:
+            accel = raw_input('Press [y] to continue to accel_1 taking: ')
+            if accel == 'y':
+                try:
+                    print('Taking Accel_1 Value...')
+                    Ax,Ay,Az,pitch = read_accel()
+                    print("Ax: " + str(Ax) + " Ay: " + str(Ay) + "Az: " + str(Az) + "Pitch [deg]: " + str(pitch*180/pi))
+                except:
+                    print('Error Taking Accel_1 Value...')
+                finally:
+                    break
+            else:
+                print('Please try again')
+
+        print('Program Done, Proceeding to Program Exit...')
+        GPIO.cleanup()
+        sys.exit(0)
+
+    except KeyboardInterrupt:
+        img_collector.shutdown()
+        GPIO.cleanup()
+        print('User KeyBoard Interrupt, Proceeding to Program Exit')
+        time.sleep(2)
+        sys.exit(0)
+
+if __name__ == "__main__":
+    main()
diff --git a/Nov_27/Manual/Camera/transfer_test_correct_config/Read_Accel.py b/Nov_27/Manual/Camera/transfer_test_correct_config/Read_Accel.py
new file mode 100644
index 0000000..826ded4
--- /dev/null
+++ b/Nov_27/Manual/Camera/transfer_test_correct_config/Read_Accel.py
@@ -0,0 +1,19 @@
+from SETTINGS import *
+import sys
+from  math import atan,sin,cos,sqrt
+sys.path.insert(0,ACCELEROMETER)
+from accel import extract_meas
+
+grav = 9.81
+
+def read_accel():
+    x_vec,y_vec,z_vec = extract_meas()
+    x = float(sum(x_vec[2:]))/float(len(x_vec[2:]))
+    y = float(sum(y_vec[2:]))/float(len(y_vec[2:]))
+    z = float(sum(z_vec[2:]))/float(len(z_vec[2:]))
+    pitch = atan(x/(sqrt(z**2 + y**2))) #angle forward from vert [x,y flat, z up]
+    #roll = atan(y/(sqrt(x**2 + z**2))) #angle side from vert [x,y flat, z up]
+    return x,y,z,pitch
+
+
+
diff --git a/Nov_27/Manual/Camera/transfer_test_correct_config/SETTINGS.py b/Nov_27/Manual/Camera/transfer_test_correct_config/SETTINGS.py
new file mode 100644
index 0000000..86726e8
--- /dev/null
+++ b/Nov_27/Manual/Camera/transfer_test_correct_config/SETTINGS.py
@@ -0,0 +1,31 @@
+import os
+
+BASE_DIR = "/home/pi/Desktop/Manual/"
+
+IMG_DIR = os.path.join(BASE_DIR, "images")
+ACCELEROMETER = os.path.join(BASE_DIR, "accel_correct_config")
+IMG_REC = os.path.join(BASE_DIR, "img_rec_module")
+IMG_CAM = os.path.join(BASE_DIR, "RPiCam")
+COMM = os.path.join(BASE_DIR,"communication_module")
+NAME_SCHEME = "img"
+FILE_FORMAT = "png"
+RAW_IMAGE = False
+NUM_SAMPLES = 1
+NAME_FORMAT = IMG_DIR + NAME_SCHEME + "_{0}" + FILE_FORMAT
+
+BRIDGE_NAME = 'dummy'
+SERVER = "http://192.168.15.101:8000/sensors/"+BRIDGE_NAME+"/update/"
+PASSWORD = "djioewfj34jod2jdoi3jr0jl983jsa"
+
+#For RPI_C.py
+
+RPI_L_IP = "192.168.15.103" #when hooked to Netgear Switch (Phil)
+RPI_C_IP = "0.0.0.0"
+RPI_C_Port = 1234
+marker = "?"
+units = "mm"
+span = 10*1000
+pi = 3.1415926535897932
+query_time = 2
+
+pMap = {3:2,5:3,7:4,8:14,10:15,11:17,12:18,13:27,15:22,16:23,18:24,19:10,21:9,22:25,23:11,24:8,26:7}
diff --git a/Nov_27/Manual/Camera/transfer_test_correct_config/accel_test.py b/Nov_27/Manual/Camera/transfer_test_correct_config/accel_test.py
new file mode 100644
index 0000000..fb6ff49
--- /dev/null
+++ b/Nov_27/Manual/Camera/transfer_test_correct_config/accel_test.py
@@ -0,0 +1,15 @@
+from Read_Accel import read_accel
+
+while True:
+    accel = raw_input('Press [y] to continue to acceL_test taking: ')
+    if accel == 'y':
+        try:
+            print('Taking Accel Value...')
+            Ax, Ay, Az = read_accel()
+            print("Ax: " + str(Ax) + " Ay: " + str(Ay) + "Az: " + str(Az))
+        except:
+            print('Error Taking Accel Value...')
+        finally:
+            break
+    else:
+        print('Please try again')
\ No newline at end of file
diff --git a/Nov_27/Manual/Laser/accel_correct_config/LICENSE b/Nov_27/Manual/Laser/accel_correct_config/LICENSE
new file mode 100644
index 0000000..9cecc1d
--- /dev/null
+++ b/Nov_27/Manual/Laser/accel_correct_config/LICENSE
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  To protect your rights, we need to prevent others from denying you
+these rights or asking you to surrender the rights.  Therefore, you have
+certain responsibilities if you distribute copies of the software, or if
+you modify it: responsibilities to respect the freedom of others.
+
+  For example, if you distribute copies of such a program, whether
+gratis or for a fee, you must pass on to the recipients the same
+freedoms that you received.  You must make sure that they, too, receive
+or can get the source code.  And you must show them these terms so they
+know their rights.
+
+  Developers that use the GNU GPL protect your rights with two steps:
+(1) assert copyright on the software, and (2) offer you this License
+giving you legal permission to copy, distribute and/or modify it.
+
+  For the developers' and authors' protection, the GPL clearly explains
+that there is no warranty for this free software.  For both users' and
+authors' sake, the GPL requires that modified versions be marked as
+changed, so that their problems will not be attributed erroneously to
+authors of previous versions.
+
+  Some devices are designed to deny users access to install or run
+modified versions of the software inside them, although the manufacturer
+can do so.  This is fundamentally incompatible with the aim of
+protecting users' freedom to change the software.  The systematic
+pattern of such abuse occurs in the area of products for individuals to
+use, which is precisely where it is most unacceptable.  Therefore, we
+have designed this version of the GPL to prohibit the practice for those
+products.  If such problems arise substantially in other domains, we
+stand ready to extend this provision to those domains in future versions
+of the GPL, as needed to protect the freedom of users.
+
+  Finally, every program is threatened constantly by software patents.
+States should not allow patents to restrict development and use of
+software on general-purpose computers, but in those that do, we wish to
+avoid the special danger that patents applied to a free program could
+make it effectively proprietary.  To prevent this, the GPL assures that
+patents cannot be used to render the program non-free.
+
+  The precise terms and conditions for copying, distribution and
+modification follow.
+
+                       TERMS AND CONDITIONS
+
+  0. Definitions.
+
+  "This License" refers to version 3 of the GNU General Public License.
+
+  "Copyright" also means copyright-like laws that apply to other kinds of
+works, such as semiconductor masks.
+
+  "The Program" refers to any copyrightable work licensed under this
+License.  Each licensee is addressed as "you".  "Licensees" and
+"recipients" may be individuals or organizations.
+
+  To "modify" a work means to copy from or adapt all or part of the work
+in a fashion requiring copyright permission, other than the making of an
+exact copy.  The resulting work is called a "modified version" of the
+earlier work or a work "based on" the earlier work.
+
+  A "covered work" means either the unmodified Program or a work based
+on the Program.
+
+  To "propagate" a work means to do anything with it that, without
+permission, would make you directly or secondarily liable for
+infringement under applicable copyright law, except executing it on a
+computer or modifying a private copy.  Propagation includes copying,
+distribution (with or without modification), making available to the
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diff --git a/Nov_27/Manual/Laser/accel_correct_config/accel.py b/Nov_27/Manual/Laser/accel_correct_config/accel.py
new file mode 100644
index 0000000..331c0c6
--- /dev/null
+++ b/Nov_27/Manual/Laser/accel_correct_config/accel.py
@@ -0,0 +1,739 @@
+#!/usr/bin/env python
+# -*- coding:utf-8, indent=tab, tabstop=4 -*-
+#
+# See 'LICENSE'  for copying
+#
+# Revision history
+# Date			Author					Version		Details
+# ----------------------------------------------------------------------------------
+# 2016-12-31	Massimo Di Primio		V.0.04		Fixed some basic functionality
+#
+# 2017-01-03	Massimo Di Primio		0.05		Added Interrut handler
+#
+# 2018-01-10    Massimo Di Primio       0.06        Added config file parser
+
+"""Simple code example for Adafruit MMA8452 3-axis Accelerometer
+
+This experimental code is intended for measuring gravity acceleration trough Adafruit(c) MMA8451, connected
+to a Raspberry Pi Model 2A, 2B, 2B+ or 3 (not yet tested with RPi Zero).
+Through this code we will demonstrate the ability of the 3-axis sensor MMA8451 to efficiently measure
+gravity acceleration, so that we can identify the spatial orientation of the device.
+Further and even more useful application can start from this minimal basic code.
+"""
+
+import smbus
+import time
+import datetime
+import os
+import sys
+import logging
+import threading
+import RPi.GPIO as GPIO
+import rss_cli_config as clicfg
+from collections import deque
+
+__author__ = "Massimo Di Primio"
+__copyright__ = "Copyright 2016, dpmiictc"
+__credits__ = ["Massimo Di Primio", "Dario Dalla Libera"]
+__license__ = "GNU GENERAL PUBLIC LICENSE Version 3"
+__version__ = "0.0.1"
+__deprecated__ = "None so far"
+__date__ = "2017-01-03"
+__maintainer__ = "Massimo Di Primio"
+__email__ = "massimo@diprimio.com"
+__status__ = "Testing"
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# User Configuration Options (UCO)
+# This section will be repaced soon by an external configuration file
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+CFG_INTERRUPT = 1
+# MMA8451_RANGE		= {}
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Application Definition Constants (ADC)
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+# Useful definitions
+EARTH_GRAVITY_MS2 = 9.80665
+
+# Range values
+RANGE_8_G = 0b10  # +/- 8g
+RANGE_4_G = 0b01  # +/- 4g
+RANGE_2_G = 0b00  # +/- 2g (default value)
+
+RANGE_DIVIDER = {
+    RANGE_2_G: 4096 / EARTH_GRAVITY_MS2,
+    RANGE_4_G: 2048 / EARTH_GRAVITY_MS2,
+    RANGE_8_G: 1024 / EARTH_GRAVITY_MS2,
+}
+
+# Some static values
+deviceName = 0x1a
+
+# Various addresses
+i2caddr = 0x1D
+#
+# Useful Register Address
+REG_STATUS = 0x00  # Read-Only
+REG_WHOAMI = 0x0d  # Read-Only
+REG_DEVID = 0x1A  # Read-Only
+REG_OUT_X_MSB = 0x01  # Read-Only
+REG_OUT_X_LSB = 0x02  # Read-Only
+REG_OUT_Y_MSB = 0x03  # Read-Only
+REG_OUT_Y_LSB = 0x04  # Read-Only
+REG_OUT_Z_MSB = 0x05  # Read-Only
+REG_OUT_Z_LSB = 0x06  # Read-Only
+REG_F_SETUP = 0x09  # Read/Write
+REG_XYZ_DATA_CFG = 0x0e  # Read/Write
+REG_PL_STATUS = 0x10  # Read-Only
+REG_PL_CFG = 0x11  # Read/Write
+REG_CTRL_REG1 = 0x2A  # Read/Write
+REG_CTRL_REG2 = 0x2B  # Read/Write
+REG_CTRL_REG3 = 0x2C  # Read/Write
+REG_CTRL_REG4 = 0x2D  # Read/Write
+REG_CTRL_REG5 = 0x2E  # Read/Write
+
+REDUCED_NOISE_MODE = 0
+OVERSAMPLING_MODE = 1
+HIGH_RES_MODE = {
+    REDUCED_NOISE_MODE: [REG_CTRL_REG1, 0x4],
+    OVERSAMPLING_MODE: [REG_CTRL_REG2, 0x2],
+}
+
+# Auto-Wake Sample Frequencies for Register CTRL_REG1 (0x2A) (Read/Write)
+# sample frequency when the device is in SLEEP Mode. Default value: 00.
+ASLP_RATE_FREQ_50_HZ = 0x00
+ASLP_RATE_FREQ_12_5_HZ = 0x40
+ASLP_RATE_FREQ_6_25HZ = 0x80
+ASLP_RATE_FREQ_1_56_HZ = 0xc0
+
+# Data rate values
+DATARATE_800_HZ = 0x00  # 800Hz
+DATARATE_400_HZ = 0x08  # 400Hz
+DATARATE_200_HZ = 0x10  # 200Hz
+DATARATE_100_HZ = 0x18  # 100Hz
+DATARATE_50_HZ = 0x20  # 50Hz
+DATARATE_12_5_HZ = 0x28  # 12.5Hz
+DATARATE_6_25HZ = 0x30  # 6.25Hz
+DATARATE_1_56_HZ = 0x38  # 1.56Hz
+
+# Orientation labeling 
+PL_PUF = 0
+PL_PUB = 1
+PL_PDF = 2
+PL_PDB = 3
+PL_LRF = 4
+PL_LRB = 5
+PL_LLF = 6
+PL_LLB = 7
+
+# Precision
+PRECISION_14_BIT = 14
+PRECISION_08_BIT = 8
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define Register Flags
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Register CTRL_REG1 (0x2a) R/W - System Control 1 Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |  ASLPRATE1   |  ASLPRATE0   |     DR2      |    DR1       |    DR0       |  LNOISE      |  F_READ      |  ACTIVE      |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# Auto-Wake Sample frequency Selection
+FLAG_ASLPRATE_50_HZ = 0x00  # Auto-Wake Sample frequency (Sleep Mode Rate Detection) 50 Hz
+FLAG_ASLPRATE_12_5_HZ = 0x40  # Auto-Wake Sample frequency (Sleep Mode Rate Detection) 12.5 Hz
+FLAG_ASLPRATE_6_25_HZ = 0x80  # Auto-Wake Sample frequency (Sleep Mode Rate Detection) 6.25 Hz
+FLAG_ASLPRATE_1_56_HZ = 0xc0  # Auto-Wake Sample frequency (Sleep Mode Rate Detection) 1.56 Hz
+# System Output Data Rates Selection
+FLAG_ODR_800_HZ = 0x00  # System Output Data Rate 800 Hz
+FLAG_ODR_400_HZ = 0x08  # System Output Data Rate 400 Hz
+FLAG_ODR_200_HZ = 0x10  # System Output Data Rate 200 Hz
+FLAG_ODR_100_HZ = 0x18  # System Output Data Rate 100 Hz
+FLAG_ODR_50_HZ = 0x20  # System Output Data Rate 50 Hz
+FLAG_ODR_12_5_HZ = 0x28  # System Output Data Rate 12.5 Hz
+FLAG_ODR_6_25_HZ = 0x30  # System Output Data Rate 6.25 Hz
+FLAG_ODR_1_56_HZ = 0x38  # System Output Data Rate 1.56 Hz
+# Other Flags
+FLAG_LNOISE = 0x04  # Low Noise (1: Reduced Noise, 0: Normal Mode)
+FLAG_F_READ = 0x02  # Fast Read  (1: 8 bit sample, 0: 14 bit Sample)
+FLAG_ACTIVE = 0x01  # Active (1: ACTIVE Mode, 0: STANDBY Mode)
+
+# Register CTRL_REG2 (0x2b) R/W - System Control 2 Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   ST         |   RST        |     0        |  SMODS1      |  SMODS0      |   SLPE       |   MODS1      |   MODS0      |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# Other Flags
+FLAG_STEST = 0x80  # Self Test (1: Self-Test enabled, 0: Self-Test disabled)
+FLAG_RESET = 0x40  # Reset (1: Reset enabled, 0: Reset disabled)
+# Sleep Mode Power Scheme Selection
+FLAG_SMODS_NORM = 0x00  # Sleep Mode Power Scheme Selection: Normal
+FLAG_SMODS_LNLP = 0x0a  # Sleep Mode Power Scheme Selection: Low-Noise Low Power
+FLAG_SMODS_HR = 0x12  # Sleep Mode Power Scheme Selection: High Resolution
+FLAG_SMODS_LP = 0x1b  # Sleep Mode Power Scheme Selection: Low Power
+# Other Flags
+FLAG_SLPE = 0x04  # Auto-Sleep (1: Auto-Sleep enabled, 0: Auto-Sleep Disabled)
+# Active Mode Power Scheme Selection (for both: Sleep and Active mode)
+FLAG_MODS_NORM = 0x00  # Active Mode Power Scheme Selection: Normal
+FLAG_MODS_LNLP = 0x09  # Active Mode Power Scheme Selection: Low-Noise Low Power
+FLAG_MODS_HR = 0x12  # Active Mode Power Scheme Selection: High Resolution
+FLAG_MODS_LP = 0x1b  # Active Mode Power Scheme Selection: Low Power
+
+# Register CTRL_REG4 (0x2d) R/W - Interrupt Enable Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# | INT_EN_ASLP  | INT_EN_FIFO  |INT_EN_TRANS  |INT_EN_LNDPR  |INT_EN_PULSE  |INT_EN_FF_MT  |       -      | INT_EN_DRDY  |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_INT_EN_ASLP = 0x80  # Interrupt Auto SLEEP/WAKE (0: Disabled, 1: Enabled)
+FLAG_INT_EN_FIFO = 0x40  # Interrupt FIFO (0: Disabled, 1: Enabled)
+FLAG_INT_EN_TRANS = 0x20  # Interrupt Transient (0: Disabled, 1: Enabled)
+FLAG_INT_EN_LNDPRT = 0x10  # Interrupt Orientation (0: Disabled, 1: Enabled)
+FLAG_INT_EN_PULSE = 0x08  # Interrupt Pulse Detection (0: Disabled, 1: Enabled)
+FLAG_INT_EN_FF_MT = 0x04  # Interrupt Freefall/Motion (0: Disabled, 1: Enabled)
+FLAG_INT_EN_BIT1 = 0x00  # Not Used
+FLAG_INT_EN_DRDY = 0x01  # Interrupt Data Ready (0: Disabled, 1: Enabled)
+
+# Register CTRL_REG5 (0x2e) R/W - Interrupt Configuration Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# | INT_CFG_ASLP | INT_CFG_FIFO |INT_CFG_TRANS |INT_CFG_LNDPRT|INT_CFG_PULSE |INT_CFG_FF_MT |       -      | INT_CFG_DRDY |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_INT_CFG_ASLP = 0x80  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_FIFO = 0x40  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_TRANS = 0x20  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_LNDPRT = 0x10  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_PULSE = 0x08  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_FF_MT = 0x04  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+FLAG_INT_CFG_BIT1 = 0x00  # Not Used
+FLAG_INT_CFG_DRDY = 0x01  # INT1/INT2 Configuration (0: Interrupt is routed to INT2 pin; 1: Interrupt is routed to INT1 pin)
+
+# Register XYZ_DATA_CFG (0x0e) R/W
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |     0        |     0        |     0        |  HPF_OUT     |     0        |     0        |    FS1       |    FS0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# Other Flags
+FLAG_XYZ_DATA_BIT_7 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_6 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_5 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_HPF_OUT = 0x00  # High-Pass Filter (1: output data High-pass filtered, 0: output data High-pass NOT filtered)
+FLAG_XYZ_DATA_BIT_3 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_2 = 0x00  # 0 (Zero): Not Used
+FLAG_XYZ_DATA_BIT_FS_2G = 0x00  # Full Scale Range 2g
+FLAG_XYZ_DATA_BIT_FS_4G = 0x01  # Full Scale Range 4g
+FLAG_XYZ_DATA_BIT_FS_8G = 0x02  # Full Scale Range 8g
+FLAG_XYZ_DATA_BIT_FS_RSVD = 0x03  # Reserved
+
+# Register F_SETUP (0x09) R/W - FIFO Setup Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |  F_MODE1     |  F_MODE0     |  F_WMRK5     |  F_WMRK4     |  F_WMRK3     |  F_WMRK2     |  F_WMRK1     |  F_WMRK0     |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_F_MODE_FIFO_NO = 0x00  # FIFO is disabled.
+FLAG_F_MODE_FIFO_RECNT = 0x40  # FIFO contains the most recent samples when overflowed (circular buffer)
+FLAG_F_MODE_FIFO_STOP = 0x80  # FIFO stops accepting new samples when overflowed.
+FLAG_F_MODE_FIFO_TRIGGER = 0xc0  # FIFO Trigger mode
+
+# Register PL_STATUS (0x010) R/O - Portrait/Landscape Status Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |    NEWLP     |    LO        |      -       |      -       |     -        |  LAPO[1]     |  LAPO[0]     |  BAFRO       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_PL_NEWLP = 0x80  # Landscape/Portrait status change flag.
+FLAG_PL_LO = 0x40  # Z-Tilt Angle Lockout.
+FLAG_PL_LAPO_PU = 0x00  # 00: Portrait Up: Equipment standing vertically in the normal orientation
+FLAG_PL_LAPO_PD = 0x02  # 01: Portrait Down: Equipment standing vertically in the inverted orientation
+FLAG_PL_LAPO_LR = 0x04  # 10: Landscape Right: Equipment is in landscape mode to the right
+FLAG_PL_LAPO_LL = 0x06  # 11: Landscape Left: Equipment is in landscape mode to the left.
+FLAG_PL_BAFRO = 0x01  # Back or Front orientation. (0: Front: Equipment is in the front facing orientation, 1: Back)
+
+# Register PL_CFG (0x011) R/W - Portrait/Landscape Configuration Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   DBCNTM     |  PL_EN       |      -       |      -       |     -        |      -       |      -       |     -        |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_PL_CFG_DBCNTM = 0x80  # Debounce counter mode selection (0: Decrements debounce, 1: Clears counter)
+FLAG_PL_CFG_PL_EN = 0x40  # Portrait/Landscape Detection Enable (0: P/L Detection Disabled, 1: P/L Detection Enabled)
+
+# Register TRANSIENT_CFG (0x1d) R/W - Transient_CFG Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |      -       |      -       |      -       |     ELE      |   ZTEFE      |   YTEFE      |   XTEFE      |  HPF_BYP     |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_TRANSIENT_CFG_ELE = 0x10      # Transient event flags (0: Event flag latch disabled; 1: Event flag latch enabled)
+FLAG_TRANSIENT_CFG_ZTEFE = 0x08    # Event flag enable on Z (0: Event detection disabled; 1: Raise event flag)
+FLAG_TRANSIENT_CFG_YTEFE = 0x04    # Event flag enable on Y (0: Event detection disabled; 1: Raise event flag)
+FLAG_TRANSIENT_CFG_XTEFE = 0x02    # Event flag enable on X (0: Event detection disabled; 1: Raise event flag)
+FLAG_TRANSIENT_CFG_HPF_BYP = 0x01  # Bypass High-Pass filter/Motion Detection
+
+# Register TRANSIENT_SCR (0x01e) R/O - TRANSIENT_SRC Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |       -      |      EA      |   ZTRANSE    | Z_Trans_Pol  |   YTRANSE    | Y_Trans_Pol  |   XTRANSE    | X_Trans_Pol  |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+FLAG_TRANSIENT_SCR_EA = 0x40       # Event Active Flag (0: no event flag has been asserted; 1: one or more event flag has been asserted)
+FLAG_TRANSIENT_SCR_ZTRANSE = 0x20  # Z transient event (0: no interrupt, 1: Z Transient acceleration > than TRANSIENT_THS event has occurred
+FLAG_TRANSIENT_SCR_ZTR_POL = 0x10  # Polarity of Z Transient Event that triggered interrupt (0: Z event Positive g, 1: Z event Negative g)
+FLAG_TRANSIENT_SCR_YTRANSE = 0x08  # Y transient event (0: no interrupt, 1: Y Transient acceleration > than TRANSIENT_THS event has occurred
+FLAG_TRANSIENT_SCR_YTR_POL = 0x04  # Polarity of Y Transient Event that triggered interrupt (0: Y event Positive g, 1: Y event Negative g)
+FLAG_TRANSIENT_SCR_XTRANSE = 0x02  # X transient event (0: no interrupt, 1: X Transient acceleration > than TRANSIENT_THS event has occurred
+FLAG_TRANSIENT_SCR_XTR_POL = 0x01  # Polarity of X Transient Event that triggered interrupt (0: X event Positive g, 1: X event Negative g)
+
+# Register FF_MT_THS (0x017) R/W - Freefall and Motion Threshold Register
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   Bit 7      |   Bit 6      |  Bit 5       |  Bit 4       |   Bit 3      |  Bit 2       |  Bit 1       |  Bit 0       |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+# |   DBCNTM     |     THS6     |    THS5      |    THS4      |    THS3      |    THS2      |    THS1      |    THS0      |
+# +--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define SOME GLOBAL VARIABLES
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define the acceleration FIFO buffer
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# The acceleration FIFO buffer is a list of records containing all meaningful acceleration data plus some
+# other useful information, whose format il as described below
+#
+#   1.  curTime     as returned by: datetime.datetime.now().  Format is: 'YYYY-MM-DD hh:mi:ss.uuuuuuu'
+#   2.  xAccel      Current X acceleration value in row format
+#   3.  yAccel      Current Y acceleration value in row format
+#   4.  xAccel      Current Z acceleration value in row format
+#   5.  plo         Current Portrait/Landscape orientation
+#accelBuffer = [0, 0, 0, 0, 0]
+accelBuffer = []
+#accelBuffer.append([0, 0, 0, 0, 0])
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define the threaded interrupt vector
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+def my_callback(channel):
+    """
+    my_callback is the threaded callback functions for interrupt events.
+    These will run in another thread when our events are detected
+
+    :param channel: The GPIO channel where the interrupt event was risen
+    :return: None
+    """
+    # Please  nte that, for performance reasons, axis data are not convertd in m/s2,
+    # Although, all 6 registers containing acceleration data are read and formatted appropriately
+    bus = smbus.SMBus(1)
+    axisData = bus.read_i2c_block_data(i2caddr, REG_OUT_X_MSB, 6)
+    #
+    #print ("!"),  #print("Falling edge detected on GPIO channel: " + str(channel))
+    #
+    runTimeConfigObject.NumInterrupts = runTimeConfigObject.NumInterrupts + 1
+    #
+    xAccel = ((axisData[0] << 8) | axisData[1]) >> 2
+    yAccel = ((axisData[2] << 8) | axisData[3]) >> 2
+    zAccel = ((axisData[4] << 8) | axisData[5]) >> 2
+    plo = bus.read_byte_data(i2caddr, REG_PL_STATUS) & 0x7
+    # Append data to the accelBuffer
+    accelBuffer.append([str(datetime.datetime.now()), xAccel, yAccel, zAccel, plo])
+    pass
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Define a class called Accel
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+import ConfigParser
+class Accel():
+    raspiBus = -1               # The Raspberry Pi Bus (dpends on hardware model)
+    raspiIntEnabled = 0         # 0 = Interrupt routine was not enabled after initialization, 1 = Interrupt routine enabled successfully
+    raspiInfo = ""              # Raspberry Pi Info
+
+    def __init__(self):
+
+        #
+        # Setup RPI specific bus
+        #
+        myBus = ""
+        if GPIO.RPI_INFO['P1_REVISION'] == 1:
+            myBus = 0
+        else:
+            myBus = 1
+        #print('myBus=' + str(myBus))
+        self.raspiBus = myBus
+
+        self.b = smbus.SMBus(myBus)  # 0 = /dev/i2c-0 (port I2C0), 1 = /dev/i2c-1 (port I2C1)
+        self.a = i2caddr
+        self.high_res_mode = OVERSAMPLING_MODE
+        self.sensor_range = RANGE_4_G
+        self.raspiInfo = GPIO.RPI_INFO
+
+
+    def whoAmI(self):
+        return self.b.read_byte_data(i2caddr, REG_WHOAMI)
+
+    def init(self):
+        # Preliminary actions
+        # sudo chmod 666 /sys/module/i2c_bcm2708/parameters/combined
+        # sudo echo -n 1 > /sys/module/i2c_bcm2708/parameters/combined
+        #
+        # the above 2 sh commands can be replaced with the following statements, in the case this program is ran as root (sudo)
+        # (For more information, please see: http://raspberrypi.znix.com/hipidocs/topic_i2c_rs_and_cs.htm)
+        #
+        # BCM2708_COMBINED_PARAM_PATH = '/sys/module/i2c_bcm2708/parameters/combined'
+        # os.chmod(BCM2708_COMBINED_PARAM_PATH, 666)
+        # os.system('echo -n 1 > {!s}'.format(BCM2708_COMBINED_PARAM_PATH))
+        # sudo i2cdetect -y 1               # this sh cmmand will search /dev/i2c-1 for all address
+        # sudo i2cget -y 1 0x1d 0x0d		# This sh command should return 0x1a for MMA8451
+        #
+        # Setup all registers appropriately
+        self.writeRegister(REG_CTRL_REG2, self.readRegister(REG_CTRL_REG2) | FLAG_RESET)  # Reset
+        # self.writeRegister(REG_CTRL_REG2,	 self.readRegister(REG_CTRL_REG2) | FLAG_STEST)						# SelfTest
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) & ~FLAG_ACTIVE)  # Put the device in Standby
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) & ~FLAG_F_READ)  # No Fast-Read (14-bits), Fast-Read (8-Bits)
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) | FLAG_ODR_50_HZ)  # Data Rate
+        self.writeRegister(REG_XYZ_DATA_CFG, self.readRegister(REG_XYZ_DATA_CFG) | FLAG_XYZ_DATA_BIT_FS_4G)  # Full Scale Range 2g, 4g or 8g
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) | FLAG_LNOISE)  # Low Noise
+        self.writeRegister(REG_CTRL_REG2, self.readRegister(REG_CTRL_REG2) & ~FLAG_SLPE)  # No Auto-Sleep
+        self.writeRegister(REG_CTRL_REG2, self.readRegister(REG_CTRL_REG2) | FLAG_SMODS_HR)  # High Resolution
+        self.writeRegister(REG_PL_CFG, self.readRegister(REG_PL_CFG) | FLAG_PL_CFG_PL_EN)  # P/L Detection Enabled
+
+        # Setup interrupts
+        if CFG_INTERRUPT == 1:
+            GPIO.setmode(GPIO.BCM)
+            # GPIO 23 & 17 set up as inputs, pulled up to avoid false detection.
+            # Both ports are wired to connect to GND on button press.
+            # So we'll be setting up falling edge detection for both
+            GPIO.setup(17, GPIO.IN, pull_up_down=GPIO.PUD_UP)
+            # when a falling edge is detected on port 17, regardless of whatever
+            # else is happening in the program, the function my_callback will be run
+            # GPIO.add_event_detect(17, GPIO.FALLING, callback=my_callback, bouncetime=300)
+            GPIO.add_event_detect(17, GPIO.FALLING, callback=my_callback)
+            #print("Interrupt OK")
+            self.raspiIntEnabled = 1    # Interrupt enabled successfully
+            # Force 1st sensor read
+            my_callback(0)
+
+
+            # Configure register for interrupt
+            self.writeRegister(REG_CTRL_REG4, 0x00)  # Reset all interrupt enabled flags
+            self.writeRegister(REG_CTRL_REG4, self.readRegister(REG_CTRL_REG4) | FLAG_INT_EN_DRDY)  # Data Ready Interrupt Enabled
+            self.writeRegister(REG_CTRL_REG5, 0x00)  # Reset all interrupt config flags
+            self.writeRegister(REG_CTRL_REG5, self.readRegister(REG_CTRL_REG5) | FLAG_INT_CFG_DRDY)  # Data Ready Interrupt is routed to INT1 pin
+
+            # Initialize the accelBuffer
+            accelBuffer = deque()
+
+
+        # Finally, Activate the sensor
+        self.writeRegister(REG_CTRL_REG1, self.readRegister(REG_CTRL_REG1) | FLAG_ACTIVE)  # Activate the device
+
+    def writeRegister(self, regNumber, regData):
+        """
+        Writes one byte (8-bts) of data passed in 'regData', into the register 'regNumber'
+        """
+        try:
+            self.b.write_byte_data(self.a, regNumber, regData)
+            time.sleep(0.01)
+        except IOError:
+            print("Error detected in function writeRegister() [IOError = " + str(IOError) + "]")
+            sys.exit()
+
+    def readRegister(self, regNumber):
+        """
+        Retrieves one byte (8-bits) of data from register 'regNumber' returning to the caller
+        """
+        try:
+            return self.b.read_byte_data(self.a, regNumber)
+        except IOError:
+            print("Error detected in function readRegister() [IOError = " + str(IOError) + "]")
+            sys.exit()
+
+    def block_read(self, offset, length):
+        """
+        Performs a burst-read on the device registers retrieving the requested amount of data
+        Read a block of <length> bytes from  offset <offset>
+        """
+        try:
+            return self.b.read_i2c_block_data(i2caddr, offset, length)
+        except IOError:
+            print("Error detected in function block_read() [IOError = " + str(IOError) + "]")
+            sys.exit()
+
+    def get_orientation(self):
+        """
+        Get current orientation of the sensor.
+        :return: orientation. Orientation number for the sensor.
+        """
+        orientation = self.b.read_byte_data(self.a, REG_PL_STATUS) & 0x7
+        return orientation
+
+    def getAxisValue(self):
+        """
+        Retrieves axis values and converts into a readable format (i.e. m/s2)
+        :return:	None
+        """
+        # Make sure F_READ and F_MODE are disabled.
+        f_read = self.b.read_byte_data(self.a, REG_CTRL_REG1) & FLAG_F_READ
+        assert f_read == 0, 'F_READ mode is not disabled. : %s' % (f_read)
+        f_mode = self.b.read_byte_data(self.a, REG_F_SETUP) & FLAG_F_MODE_FIFO_TRIGGER
+        assert f_mode == 0, 'F_MODE mode is not disabled. : %s' % (f_mode)
+
+        #
+        self.xyzdata = self.block_read(REG_OUT_X_MSB, 6)
+        if self.high_res_mode is not None:
+            x = ((self.xyzdata[0] << 8) | self.xyzdata[1]) >> 2
+            y = ((self.xyzdata[2] << 8) | self.xyzdata[3]) >> 2
+            z = ((self.xyzdata[4] << 8) | self.xyzdata[5]) >> 2
+            precision = PRECISION_14_BIT  # Precision 14 bit data
+        else:
+            x = (self.xyzdata[0] << 8)
+            y = (self.xyzdata[1] << 8)
+            z = (self.xyzdata[2] << 8)
+            precision = PRECISION_08_BIT  # Precision 08 bit data
+        max_val = 2 ** (precision - 1) - 1
+        signed_max = 2 ** precision
+        #
+        x -= signed_max if x > max_val else 0
+        y -= signed_max if y > max_val else 0
+        z -= signed_max if z > max_val else 0
+        #
+        x = round((float(x)) / RANGE_DIVIDER[self.sensor_range], 3)
+        y = round((float(y)) / RANGE_DIVIDER[self.sensor_range], 3)
+        z = round((float(z)) / RANGE_DIVIDER[self.sensor_range], 3)
+
+        return {"x": x, "y": y, "z": z}
+
+    def debugShowRpiInfo(self):
+        #print("Raspberry Info      = " + str(GPIO.RPI_INFO))
+        print("Raspberry Info      = " + str(self.raspiInfo))
+
+    def debugShowRegisters(self):
+        print("REG_STATUS       (0x00):" + str(format(self.readRegister(REG_STATUS), '#04x')) + " | Binary: " + format(self.readRegister(REG_STATUS), 'b').zfill(8))
+        print("REG_WHOAMI       (0x0d):" + str(format(self.readRegister(REG_WHOAMI), '#04x')) + " | Binary: " + format(self.readRegister(REG_WHOAMI), 'b').zfill(8))
+        print("REG_F_SETUP      (0x09):" + str(format(self.readRegister(REG_F_SETUP), '#04x')) + " | Binary: " + format(self.readRegister(REG_F_SETUP), 'b').zfill(8))
+        print("REG_XYZ_DATA_CFG (0x0e):" + str(format(self.readRegister(REG_XYZ_DATA_CFG), '#04x')) + " | Binary: " + format(self.readRegister(REG_XYZ_DATA_CFG), 'b').zfill(8))
+        print("REG_CTRL_REG1    (0x2a):" + str(format(self.readRegister(REG_CTRL_REG1), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG1), 'b').zfill(8))
+        print("REG_CTRL_REG2    (0x2b):" + str(format(self.readRegister(REG_CTRL_REG2), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG2), 'b').zfill(8))
+        print("REG_CTRL_REG3    (0x2c):" + str(format(self.readRegister(REG_CTRL_REG3), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG3), 'b').zfill(8))
+        print("REG_CTRL_REG4    (0x2d):" + str(format(self.readRegister(REG_CTRL_REG4), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG4), 'b').zfill(8))
+        print("REG_CTRL_REG5    (0x2e):" + str(format(self.readRegister(REG_CTRL_REG5), '#04x')) + " | Binary: " + format(self.readRegister(REG_CTRL_REG5), 'b').zfill(8))
+        print("REG_PL_STATUS    (0x10):" + str(format(self.readRegister(REG_PL_STATUS), '#04x')) + " | Binary: " + format(self.readRegister(REG_PL_STATUS), 'b').zfill(8))
+        print ("debugRealTime    " + str(runTimeConfigObject.debugRealTime))
+        print ("NumInterrupts    " + str(runTimeConfigObject.NumInterrupts))
+
+    def debugShowOrientation(self):
+        print("Position = %d" % (self.get_orientation()))
+
+    def debugShowAxisAcceleration(self, xaccel, yaccel, zaccel):
+        print("   x (m/s2)= %+.3f" % (xaccel))
+        print("   y (m/s2)= %+.3f" % (yaccel))
+        print("   z (m/s2)= %+.3f" % (zaccel))
+
+    def debugRealTimeBuffer(self):
+        n = 0
+        for elements in accelBuffer:
+            myData = accelBuffer.pop()
+            n += 1
+            print ("N=" + str(n) + " myData=" + str(myData))    # + "Element=" + str(elements))
+        try:
+            print("End of printout\n")
+            #time.sleep(1.0)
+        # os.system("clear")
+        except KeyboardInterrupt:
+            print("Program Termination Requested")
+            sys.exit()
+        
+###############################################################################
+#   Threading functions
+###############################################################################
+#def rssClient():
+#    """Manage data shipping over th network, in a separate thread."""
+#    #logger.debug('Thread Starting')
+#    while True:
+#        time.sleep(1.0)
+#        print ("This is thread rssClient()")
+#
+###############################################################################
+#   Useful functions
+###############################################################################
+def printHelp():
+    print ("\n")
+    print ("usage: accel.py [options]")
+    print ("Available options:")
+    print (" -h \t\t Print this help and exit")
+    print (" -d \t\t Show debug realtime interrupt data")
+    print (" -s \t\t Execute silently (no screen output)")
+    print (" -L <lvl>\t Set Log level. where <lvl> is the log level (0 = NONE - 8 = DEBUG)") 
+    print ("")
+
+
+def main(argv):
+    import sys, getopt, logging
+    #
+    try:
+        opts, args = getopt.getopt(argv,"hdsL:")
+    except getopt.GetoptError:
+        print ("\nInvalid option requested on command line")
+        printHelp()
+        sys.exit(2)
+
+    for opt, arg in opts:
+        if opt == '-h':
+            printHelp()
+            sys.exit()
+        elif opt == '-d':
+            runTimeConfigObject.debugRealTime = 1
+        elif opt == '-s':
+            runTimeConfigObject.executeSilently = 1
+        elif opt == '-L':
+            if (int(arg) == 0) or (int(arg) > 5):
+                pass
+                #logger.setLevel(logger.NOTSET)            # Same as value 0
+            elif int(arg) == 1:
+                logger.setLevel(logging.CRITICAL)          # Same as value 50
+            elif int(arg) == 2:
+                logger.setLevel(logging.ERROR)             # Same as value 40
+            elif int(arg) == 3:
+                logger.setLevel(logging.WARNING)           # Same as value 30
+            elif int(arg) == 4:
+                logger.setLevel(logging.INFO)              # Same as value 20
+            elif int(arg) == 5:
+                logger.setLevel(logging.DEBUG)             # Same as value 10
+
+
+#####################################################################################
+#   M A I N 
+#####################################################################################
+class runTimeConfigObject(object):
+    pass
+#if __name__ == "__main__":
+def extract_meas():
+    
+    #class runTimeConfigObject(object):
+        #pass
+    #
+    # Setup Logger
+    #
+    #logger = logging.basicConfig(level=logging.DEBUG, format='[%(asctime)15s].%(levelname)s] (%(threadName)-10s) %(message)s', )
+    #logger.basicConfig(level=logging.DEBUG,format='[%(asctime)15s].%(levelname)s] (%(threadName)-10s) %(message)s',)
+    logger = logging.getLogger()
+    logger.setLevel(logging.DEBUG)
+    # create console handler and set level to debug
+    ch = logging.StreamHandler()
+    ch.setLevel(logging.DEBUG)
+    # create formatter
+    formatter = logging.Formatter('[%(asctime)s.%(levelname)s] (%(name)s.%(threadName)-10s) : %(message)s')
+    # add formatter to ch
+    ch.setFormatter(formatter)
+    # add ch to logger
+    logger.addHandler(ch)
+
+    #
+    # Set some default command line options
+    #
+    runTimeConfig = runTimeConfigObject()
+    runTimeConfigObject.debugRealTime = 0       # 1 = Show debug realtime interrupt data
+    runTimeConfigObject.executeSilently = 0     # 1 = Execute silently (no sceen output)
+    runTimeConfigObject.NumInterrupts = 0       # keep Nbr of sensor interrupts withi the main loop
+    
+    main(sys.argv[1:])
+
+    #
+    # Read configuration file
+    #
+    configFile = "./rss_config.dat"
+    #logger.debug('Reading Config file: ' + configFile) #Commented out by Henry
+    sections = {'GeoData', 'DeviceInfo', 'Networking'}
+    #configParameters = {}
+    #Config = ConfigParser.ConfigParser()
+    #Config.read(configFile)
+    #for section in sections:
+    #    try:
+    #        options = Config.options(section)
+    #    except:
+    #        print ("ERROR: Section '" + section + "' Not found in config file: '" + configFile + "'.")
+    #        sys.exit()
+    #    for option in options:
+    #        try:
+    #            configParameters[option] = Config.get(section, option)
+    #        except:
+    #            configParameters[option] = None
+    #        logger.debug("Config Section: " + section + " / Option: " + option + " => " + configParameters[option])
+
+    MMA8451 = Accel()
+    #os.system("clear")
+    MMA8451.init()
+
+    if MMA8451.whoAmI() != deviceName:
+        print("Error! Device not recognized! (" + str(deviceName) + ")")
+        sys.exit()
+
+    #
+    # Thread client start
+    # Commented out by Henry
+    '''
+    import rss_client
+    pill2kill = threading.Event()
+    #threadClient = threading.Thread(name='netClientWorker', target=rss_client.cli_worker, args=(pill2kill, configParameters, accelBuffer))
+    threadClient = threading.Thread(name='netClientWorker', target=rss_client.cli_worker, args=(pill2kill, accelBuffer))
+    threadClient.setDaemon(False)         #threadClient.daemon = False
+    threadClient.start()
+    myThread = []
+    myThread.append('netClientWorker')
+    '''
+    
+    #Added by Henry:
+    count_samples = 8 #num of samples we want
+    count_taken = 0
+    xvals = []
+    yvals = []
+    zvals = []
+    while count_taken < count_samples:
+        if runTimeConfigObject.executeSilently == 0:
+            axes = MMA8451.getAxisValue()
+            print("X: " + str(axes['x']) + ", Y: " + str(axes['y']) + ", Z: " + str(axes['z']))
+            xvals.append(axes['x'])
+            yvals.append(axes['y'])
+            zvals.append(axes['z'])
+            count_taken = count_taken + 1
+    return xvals,yvals,zvals
+    
+    #Commented Out by Henry:
+    '''
+    while True:  # forever loop
+        if runTimeConfigObject.executeSilently == 0:
+            print ("\nCurrent Date-Time: " + str(datetime.datetime.now()))
+            print ("Raspberry Bus       = " + str(MMA8451.raspiBus))
+            print ("Raspberry Interrupt = " + str(MMA8451.raspiIntEnabled))
+            print ("Number of elemets   = " + str(len(accelBuffer)))
+            MMA8451.debugShowRpiInfo()
+            MMA8451.debugShowRegisters()
+            MMA8451.debugShowOrientation()
+            axes = MMA8451.getAxisValue()
+            MMA8451.debugShowAxisAcceleration(axes['x'], axes['y'], axes['z'])
+            #
+            if runTimeConfigObject.debugRealTime != 0:
+                MMA8451.debugRealTimeBuffer()
+
+        runTimeConfigObject.NumInterrupts = 0
+        try:
+            time.sleep(1.0)
+        except KeyboardInterrupt:
+            logger.debug ("Killing threads...")
+            pill2kill.set()
+            threadClient.join()
+            time.sleep(1.0)
+
+            logger.debug("\nUser termination requested!\n")
+            sys.exit()
+            
+    sys.exit()
+    '''
+
diff --git a/Nov_27/Manual/Laser/accel_correct_config/raspidata.py b/Nov_27/Manual/Laser/accel_correct_config/raspidata.py
new file mode 100644
index 0000000..fb99cbb
--- /dev/null
+++ b/Nov_27/Manual/Laser/accel_correct_config/raspidata.py
@@ -0,0 +1,48 @@
+""" A Raspberry Pi class.
+To access some of the most useful hardware information
+"""
+
+
+#class RaspiData():
+
+
+    # Get Raspberry Serial Number
+    # You can do it in bash:
+    #   cat /proc/cpuinfo | perl -n -e '/^Serial[ ]*: ([0-9a-f]{16})$/ && print "$1\n"'
+    # Or with perl:
+    #   cat cpuinfo | perl -n -e '/^Serial[ ]*: ([0-9a-f]{16})$/ && print "$1\n"'
+    #
+    #
+
+
+#    def __init__(self):
+#       pass
+from uuid import getnode as get_mac
+
+#    @staticmethod
+# def get_serial(self):
+def get_serial():
+    """Extract serial from cpuinfo file"""
+    cpu_serial= "0000000000000000"
+    try:
+        f = open('/proc/cpuinfo','r')
+        for line in f:
+            if line[0:6]=='Serial':
+                cpu_serial = line[10:26]
+        f.close()
+    except:
+        cpu_serial = "ERROR000000000"
+    return cpu_serial
+
+
+def mac_address():
+    """Extract rth0 MAC address"""
+    # mac = '00:00:00:00:00:00'
+    # mac = '00:00:00:00:00:00'
+    try:
+        t = get_mac()
+    except:
+        t = "000000000000"
+    mac = ':'.join(("%012X" % t)[i:i+2] for i in range(0, 12, 2))
+    return mac
+
diff --git a/Nov_27/Manual/Laser/accel_correct_config/rss_cli_config.py b/Nov_27/Manual/Laser/accel_correct_config/rss_cli_config.py
new file mode 100644
index 0000000..e10aa53
--- /dev/null
+++ b/Nov_27/Manual/Laser/accel_correct_config/rss_cli_config.py
@@ -0,0 +1,21 @@
+# [GeoData]
+City = 'Rome'
+cityname =  'Rome'
+latitude = 41.947550
+longitude = 12.469794
+deviceorientation = 'L'
+
+# [DeviceInfo]
+devicetype = 'raspberry'
+devicename = 'rssd0001'
+sensorname = 'mma8451'
+sensortheshold = 20
+logfile = './rssd.log'
+
+# [Networking]
+#serveraddress = 'www.diprimio.com'
+serveraddress = 'localhost'
+serverprotocol = 'tcp'
+servertcpport = 15000
+serverhearthbit = 100
+receivebuffersize = 1024
diff --git a/Nov_27/Manual/Laser/accel_correct_config/rss_client.py b/Nov_27/Manual/Laser/accel_correct_config/rss_client.py
new file mode 100644
index 0000000..a2a4d1b
--- /dev/null
+++ b/Nov_27/Manual/Laser/accel_correct_config/rss_client.py
@@ -0,0 +1,159 @@
+#!/usr/bin/env python
+# -*- coding:utf-8, indent=tab, tabstop=4 -*-
+#
+# See 'LICENSE'  for copying
+#
+# This file contains the code for the client module of 'accel.py'
+#
+# Revision history
+# Date          Author                  Version     Details
+# ----------------------------------------------------------------------------------
+# 2018-01-18    Massimo Di Primio       0.06        1st file implementation
+
+"""Client thread worker - This is a simple client code example for 'accel'.py' program"""
+
+import logging
+import time
+import datetime
+import socket
+import json
+import rss_client_messages as climsg
+import rss_cli_config as ccfg
+import raspidata
+
+
+#def cli_connect(params):
+def cli_connect():
+    """Open connection to the server"""
+    server_address = (str(ccfg.serveraddress), int(ccfg.servertcpport))
+    logging.debug('Trying to connect to server ' + str(server_address))
+    # Create a TCP/IP socket
+    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    try:
+        # Connect the socket to the port where the server is listening
+        s.connect(server_address)
+        logging.debug('Connection Established to server ' + str(server_address))
+    except:
+        logging.debug(
+            "Failed to open connection: " + str(ccfg.serverprotocol) +
+            ", to IP: " + str(ccfg.serveraddress) +
+            ", on port: " + str(ccfg.servertcpport)
+            )
+        return(-1)
+    return(s)
+
+
+def cli_close(s):
+    """Close the server connection"""
+    if s > -1:
+        s.close()
+
+
+# def cli_worker(stopEvent, config, accelBuffer):
+def cli_worker(stopEvent, accelBuffer):
+    """A client worker as thread"""
+    logging.debug('Thread Starting')
+    s = cli_connect()     # s = cli_connect(config)
+    send_client_hello(s)
+    time.sleep(0.5)
+    send_config_affirm_message(s)
+
+    ts = int(time.time())
+    te = ts
+    while not stopEvent.wait(0.3):
+        if len(accelBuffer) > 0:
+            send_accel_data(s, accelBuffer)
+            te = int(time.time())
+        if (te - ts) > 10:
+            send_client_heartbit(s)
+            ts = int(time.time())
+
+        time.sleep(0.5)
+    send_zap_message(s)
+    cli_close(s)
+    logging.debug("Thread cliWorker is terminating as per your request.")
+
+    
+def send_accel_data(s, accelBuffer):
+    """Send acceleration data to the server"""
+    #msg = dict(cmd='ADM', timestamp=str(datetime.datetime.now()), clid=raspidata.get_serial())
+    #a = 123 #str(datetime.datetime.now())
+    pbuf = parse_accel_data(accelBuffer)
+    msg = dict(cmd = 'ADM', timestamp = str(datetime.datetime.now()), clid = raspidata.get_serial(), data = pbuf)
+    # if len(pbuf) > 0: # this sometimes returns error (when buf is empty, it has None type)
+    if (pbuf is not None) and (len(pbuf) > 0):
+        #str = climsg.accel_data_message(pbuf)
+        try:
+            logging.debug("Sending Acceleration data to the server")
+            s.sendall(str(json.dumps(msg)) + "\n")  #s.sendall(json.dumps(str) + "\n")
+        except:
+            logging.debug("Failed to send Acceleration-Data to the server")
+
+
+def send_client_hello(s):
+    """Send Hello message to the server"""
+    msg = dict(cmd = 'CHM', timestamp = str(datetime.datetime.now()), clid = raspidata.get_serial())
+    try:
+        logging.debug("Sending Hello to the server")
+        s.sendall(str(json.dumps(msg)) + "\n")  #s.sendall(json.dumps(climsg.hello_message()) + "\n")
+    except:
+        logging.debug("Failed to send Hello to the server")
+
+
+def send_zap_message(s):
+    """Send Zap message to the server"""
+    msg = dict(cmd = 'CZM', timestamp = str(datetime.datetime.now()), clid = raspidata.get_serial())
+    try:
+        logging.debug("Sending Zap to the server")
+        s.sendall(str(json.dumps(msg)) + "\n")  #s.sendall(json.dumps(climsg.zap_message()) + "\n")
+    except:
+        logging.debug("Failed to send Zap to the server")
+
+
+def send_config_affirm_message(s):      #def send_config_affirm_message(s, config):
+    msg_data = dict(city = ccfg.cityname, latitude = ccfg.latitude,longitude = ccfg.longitude)
+    msg = dict(cmd = 'CCA', timestamp = str(datetime.datetime.now()), clid = raspidata.get_serial(), config = msg_data)
+    try:
+        logging.debug("Sending client configuration to the server")
+        s.sendall(str(json.dumps(msg)) + "\n")    #s.sendall(climsg.config_affirm_message(cfg_data))
+    except:
+        logging.debug("Failed to send client configuration to the server")
+
+
+def send_client_heartbit(s):
+    """Send Heartbit to the server"""
+    msg = dict(cmd = 'CHB', timestamp = str(datetime.datetime.now()), clid=raspidata.get_serial())
+    try:
+        logging.debug("Sending Heartbit to the server")
+        s.sendall(str(json.dumps(msg)) + "\n")  #s.sendall(json.dumps(climsg.heart_bit()) + "\n")
+    except:
+        logging.debug("Failed to send Heartbit to the server")
+
+
+def parse_accel_data(b):
+    """Parse acceleration data to make sure we only send meaningfull data to the server"""
+    tsh = 10
+    tbuf = []
+    # tbuf.append([0, 0, 0, 0, 0])
+    # bLength = len(b)
+    # logging.debug("parseAccelData(b) # of elements   = " + str(len(b)))
+    if len(b) > 1:
+        logging.debug("parseAccelData: In  AccelData/BufLen: " + str(len(b)) + "/" +str(len(tbuf)))
+        firstTime = 1
+        prow = None
+        for row in b:
+            crow = b.pop(0)     # Get the oldest record
+            if firstTime == 1:
+                prow = crow
+                firstTime = 0
+            if ( (abs(abs(int(crow[1])) - abs(int(prow[1]))) > tsh) or
+                 (abs(abs(int(crow[2])) - abs(int(prow[2]))) > tsh) or
+                 (abs(abs(int(crow[3])) - abs(int(prow[3]))) > tsh)
+                ):
+                tbuf.append(crow)
+                prow = crow
+                print ("Again PROW/CROW/TBUFLEN:" + str(prow) + " / " + str(crow) + " / " +  str(len(tbuf)))
+        
+        logging.debug("parseAccelData: Out AccelData/BufLen: " + str(len(b)) + "/" +str(len(tbuf)))
+        return(tbuf)
+
diff --git a/Nov_27/Manual/Laser/accel_correct_config/rss_client_messages.py b/Nov_27/Manual/Laser/accel_correct_config/rss_client_messages.py
new file mode 100644
index 0000000..4a507d7
--- /dev/null
+++ b/Nov_27/Manual/Laser/accel_correct_config/rss_client_messages.py
@@ -0,0 +1,113 @@
+
+import datetime
+import raspidata
+import json
+import rss_cli_config as ccfg
+
+
+#
+# All client messages are handled in here.
+#
+# Message Formats
+# ---------------
+# 1 - Timestamp
+# 2 - Device ID
+# 3 - Message Type
+#
+# Client message types
+# --------------------
+# 0001  CHB - Client Heart Bit (Client is alive)
+# 0002  CHM - Client Hello Message (Client is online)
+# 0003  CZM - Client Zap Message (Client is shut down)
+# 0004  ADM - Acceleration Data Message (Acceleration data)
+# 0005  CCA - Client Config Affirm
+# ... (other to come)
+#
+# Data message Format for all Client Message Types
+# ------------------------------------------------
+#
+# # 0001 - CHB - Client Heart Bit
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0001         |  Var  | Literally 'CHB'                                        |
+# # +-------------------------------------------------------------------------------+
+#
+# 0002 CHM - Client Hello Message
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0002         |  Var  | Literally 'CHM'                                        |
+# +--------------+-------+--------------------------------------------------------+
+#
+# 0003  CZM - Client Zap Message (Client is shut down)
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0003         |  Var  | Literally 'CZM'                                        |
+# +--------------+-------+--------------------------------------------------------+
+#
+# 0004  ADM - Acceleration Data Message (Acceleration data)
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0004         |  Var  | Literally 'ADM'                                        |
+# | Accel Data   |  Var  | Array containing acceleration data (X,Y,Z.O)           |
+# +--------------+-------+--------------------------------------------------------+
+#
+# 0005  CCA - Client Configuration Affirm
+# +--------------+-------+--------------------------------------------------------+
+# | Field        |Length | Description                                            |
+# +--------------+-------+--------------------------------------------------------+
+# | Datetime     |  16   | 'HH/MM/DD hh:mi:ss.uuuuuu'                             |
+# | Device ID    |  16   | 16 byte device ID                                      |
+# | 0004         |  Var  | Literally 'CCA'                                        |
+# | Config Data  |  Var  | Array containing client configuration                  |
+# +--------------+-------+--------------------------------------------------------+
+
+###########################################################################
+#   WARNIG ! THIS FILE IS NOT CURRENTLY USED !
+#   Its presence it is for debugging/testing only
+#   Althought, it will be used in some future version
+###########################################################################
+
+#def heart_bit():
+#    """Client Heart Bit"""
+#    msg = {'cmd': 'CHB', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial()}
+#    return msg
+#
+#
+#def hello_message():
+#    """Client Hello Message"""
+#    msg = {'cmd': 'CHM', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial()}
+#    return msg
+#
+#
+#def zap_message():
+#    """Client Zap Message"""
+#    msg = {'cmd': 'CZM', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial()}
+#    return msg
+#
+#
+#def accel_data_message(data):
+#    """Acceleration Data Message"""
+#    msg = {'cmd': 'ADM', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial(), 'acceldata': str(data)}
+#    return msg
+#
+#
+#def config_affirm_message(data):    #def config_affirm_message(data):
+#    """Client Configuration Affirm"""
+#    ###
+#    pkt_hdr = {'cmd': 'CCA', 'timestamp': str(datetime.datetime.now()), 'clid': raspidata.get_serial(), 'data' : data}
+#
+#    msg = dict(city = ccfg.cityname, latitude = ccfg.latitude, longitude = ccfg.longitude, config = data)
+#    return msg
+
diff --git a/Nov_27/Manual/Laser/transfer_test_correct_config/._RPI_L.py b/Nov_27/Manual/Laser/transfer_test_correct_config/._RPI_L.py
new file mode 100644
index 0000000..a34d731
Binary files /dev/null and b/Nov_27/Manual/Laser/transfer_test_correct_config/._RPI_L.py differ
diff --git a/Nov_27/Manual/Laser/transfer_test_correct_config/._Read_Accel.py b/Nov_27/Manual/Laser/transfer_test_correct_config/._Read_Accel.py
new file mode 100644
index 0000000..4f96332
Binary files /dev/null and b/Nov_27/Manual/Laser/transfer_test_correct_config/._Read_Accel.py differ
diff --git a/Nov_27/Manual/Laser/transfer_test_correct_config/RPI_L.py b/Nov_27/Manual/Laser/transfer_test_correct_config/RPI_L.py
new file mode 100644
index 0000000..076e061
--- /dev/null
+++ b/Nov_27/Manual/Laser/transfer_test_correct_config/RPI_L.py
@@ -0,0 +1,74 @@
+#Code for communications, sensor actions, and calculations for RPi on laser module
+#Assume that RPI_C and RPI_L codes are implemented at same time (synced)
+#Below code will execute at startup of RPI_L
+
+from SETTINGS import *
+import sys
+import RPi.GPIO as GPIO
+import time
+from Read_Accel import read_accel
+
+#Givens
+
+# See: SETTINGS.py
+
+def main():
+
+    try:
+        while True:
+            las_o = raw_input('Press [y] to turn on laser: ')
+            if las_o == 'y':
+                try:
+                    print('Turning on laser...')
+                    GPIO.setmode(GPIO.BCM)
+                    GPIO.setup(pMap[Laser_Port], GPIO.OUT)
+                    GPIO.output(pMap[Laser_Port], True)
+                except:
+                    print('Error turning on laser')
+                finally:
+                    break
+            else:
+                print('Please try again')
+
+
+        while True:
+            las = raw_input('Press [y] to turn off laser: ')
+            if las == "y":
+                try:
+                    print('Turning off laser...')
+                    GPIO.output(pMap[Laser_Port],False)
+                    GPIO.cleanup()
+                except:
+                    print('Error turning off laser')
+                finally:
+                    break
+            else:
+                print('Please try again')
+
+        while True:
+            accel = raw_input('Press [y] to continue to accel_2 taking: ')
+            if accel == 'y':
+                try:
+                    print('Taking Accel_1 Value...')
+                    Ax, Ay, Az, Pitch = read_accel()
+                    print("Ax: " + str(Ax) + " Ay: " + str(Ay) + "Az: " + str(Az) + "Pitch: " + str(Pitch*180/pi))
+                except:
+                    print('Error taking Accel_1 value')
+                finally:
+                    break
+            else:
+                print('Please try again')
+
+        print('Program Done, Proceeding to Program Exit...')
+        GPIO.cleanup()
+        sys.exit(0)
+
+    except KeyboardInterrupt:
+        print('User KeyBoard Interrupt, Proceeding to Program Exit')
+        GPIO.cleanup()
+        time.sleep(2)
+        sys.exit(0)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Nov_27/Manual/Laser/transfer_test_correct_config/Read_Accel.py b/Nov_27/Manual/Laser/transfer_test_correct_config/Read_Accel.py
new file mode 100644
index 0000000..4a50ada
--- /dev/null
+++ b/Nov_27/Manual/Laser/transfer_test_correct_config/Read_Accel.py
@@ -0,0 +1,16 @@
+from SETTINGS import *
+import sys
+from math import atan,sqrt,sin,cos
+sys.path.insert(0,ACCELEROMETER)
+from accel import extract_meas
+
+grav = 9.81
+
+def read_accel():
+    x_vec,y_vec,z_vec = extract_meas()
+    x = float(sum(x_vec[2:]))/float(len(x_vec[2:]))
+    y = float(sum(y_vec[2:]))/float(len(y_vec[2:]))
+    z = float(sum(z_vec[2:]))/float(len(z_vec[2:]))
+    pitch = atan(x/(sqrt(z**2 + y**2)))  #angle forward from vert [x,y flat, z vert]
+    #roll = atan(y/(sqrt(x**2 + z**2))) #angle side from vert [x,y flat, z vert]
+    return x,y,z,pitch
diff --git a/Nov_27/Manual/Laser/transfer_test_correct_config/SETTINGS.py b/Nov_27/Manual/Laser/transfer_test_correct_config/SETTINGS.py
new file mode 100644
index 0000000..7764aa7
--- /dev/null
+++ b/Nov_27/Manual/Laser/transfer_test_correct_config/SETTINGS.py
@@ -0,0 +1,19 @@
+import os
+
+BASE_DIR = '/home/pi/Desktop/Manual/'
+
+ACCELEROMETER = os.path.join(BASE_DIR, "accel_correct_config")
+
+#For RPI_L.py
+
+#RPI_L_IP = "192.168.3.3" #address of cam module (client)
+RPI_C_IP = "192.168.15.104" #when hooked to Netgear Switch (Phil)
+RPI_L_IP = "192.168.15.103" #when hooked to Netgear Switch (Phil)
+RPI_L_Port = 1234
+RPI_C_Port = 1234
+Laser_Port = 11
+marker = "?"
+query_time = 3
+pi = 3.1415926535897932
+
+pMap = {3:2,5:3,7:4,8:14,10:15,11:17,12:18,13:27,15:22,16:23,18:24,19:10,21:9,22:25,23:11,24:8,26:7}
diff --git a/Nov_27/Manual/Laser/transfer_test_correct_config/accel_test.py b/Nov_27/Manual/Laser/transfer_test_correct_config/accel_test.py
new file mode 100644
index 0000000..fb6ff49
--- /dev/null
+++ b/Nov_27/Manual/Laser/transfer_test_correct_config/accel_test.py
@@ -0,0 +1,15 @@
+from Read_Accel import read_accel
+
+while True:
+    accel = raw_input('Press [y] to continue to acceL_test taking: ')
+    if accel == 'y':
+        try:
+            print('Taking Accel Value...')
+            Ax, Ay, Az = read_accel()
+            print("Ax: " + str(Ax) + " Ay: " + str(Ay) + "Az: " + str(Az))
+        except:
+            print('Error Taking Accel Value...')
+        finally:
+            break
+    else:
+        print('Please try again')
\ No newline at end of file
diff --git a/RPiCam/RPiCM.py b/RPiCam/RPiCM.py
new file mode 100644
index 0000000..ca837ac
--- /dev/null
+++ b/RPiCam/RPiCM.py
@@ -0,0 +1,174 @@
+# Author:  Alex R. Mead
+# Date: May 2016
+# Description:
+# This code will run on the Raspberry Pi 2 with Camera Module (RPiCM) to interact with the MacBookPro (MBP). It will
+# wait for the MBP to request a measurement, take a measurement, then wait for the MBP to request the latest
+# measurement, at which time it will send the measurement to the MBP.
+
+# Same as the MBP this system is designed to run like finite state machine.
+
+from __future__ import (
+    unicode_literals,
+    absolute_import,
+    print_function,
+    division,
+    )
+
+import sys
+import socket
+import io
+import subprocess
+import picamera
+import numpy as np
+import gc
+from time import sleep
+
+# Constants
+#IP = '127.0.0.1' # Testing on a single machine
+IP = '0.0.0.0' # Actual run time.
+port1 = 1234
+port2 = 2345
+marker = "?"
+
+MAX_PIXEL_VALUE = 1023 # 2^10 = 1024
+PIXEL_HEIGHT = 1944
+PIXEL_WIDTH = 2592
+
+# Takes in the Camera object instantiated at the beginning of the run and takes the desired SS expsoures with it.
+def HDRI(SS,camera):
+
+    size = len(SS)
+    print("Taking %i measurements.\n",size)
+    names = []
+
+    # Loop through for each shutter speed requested
+    for index, shutterSpeed in enumerate(SS):
+        fileName = str(shutterSpeed) + ".data"
+        camera.shutter_speed = shutterSpeed
+        sleep(1)
+        print(str(camera.shutter_speed))
+        camera.capture(fileName,format='jpeg',bayer=True)
+        names.append(fileName)
+
+    # Return the list of filenames to the calling function.
+    return names
+
+def main():
+
+    STATE = "idle"
+    RUNNING = [True,0]
+
+    # First declare the camera object and configure it as needed
+    camera = picamera.PiCamera()
+    camera.framerate=1
+    sleep(1.0)
+
+    camera.led = False
+    camera.iso = 100
+    sleep(10.0)
+    # This longer sleep is needed so analog_gain and digital_gains settle so when I turn them off in the next step
+    # they will have settled and not be in their initial 'low' values.
+
+    camera.exposure_mode = 'off' # Fixes analog_gain and digital_gain values
+    g = camera.awb_gains
+    camera.aws_mode = 'off'
+    camera.awb_gains = [1.0,1.0] # typical values 0.9-1.9 according to documentation, I picked these for consistency.
+    camera.rotation = 180
+    sleep(2.0)
+
+    while(RUNNING[0]):
+
+        if("idle" == STATE):
+            print("We're in idle state waiting to hear from the MacBookPro...")
+
+            # Start up a connection listening for the MBP.
+            sock = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
+            sock.setsockopt(socket.SOL_SOCKET,socket.SO_REUSEADDR,1)
+            sock.bind((IP,port1))
+            sock.listen(1)
+            connection, client_address = sock.accept()
+
+            rcv_msg = []
+            while True:
+                data = connection.recv(1)
+                if marker in data:
+                    rcv_msg.append(data[:data.find(marker)])
+                    break
+                rcv_msg.append(data)
+
+            # Check the message received from the MBP, if correct, continue, else exit.
+            rpl = ''.join(rcv_msg)
+            if rpl == "True":
+                print("Now going to acknowledge measurement.")
+
+            # elif used to teminate remotely.
+            elif rpl == "c":
+                print("Big John called it for me...")
+                sys.exit(0)
+
+            # Acknowledge the MBP so it knows a measurement will be taking place.
+            connection.send("True?")
+            connection.close()
+
+            # Change state: Continue on and take measurement
+            STATE ="Measuring"
+
+        elif ("Measuring" == STATE):
+            print("Engaging the Camera Module to measure the output of the CFS under test...")
+            # Here is the function call to take the measurement of the CFS inside CUBE2.0
+
+            # List of the shutter speeds in microseconds to be used
+            #SS = [1000000, 100000, 10000, 1000, 100, 10];
+            SS = [1000000, 100000, 10000];
+
+            # Take the measurements with the above stated shutter speeds
+            files = HDRI(SS,camera)
+
+            print("...Measurement taken of CFS.\n")
+
+            # Continue to the next state and wait for MBP to query for the 145 measurements.
+            STATE = "waitToSend"
+
+        elif("waitToSend" == STATE):
+            print("Waiting to send to MBP...")
+            # Setup connection to wait for the request from the MBP
+            # This maybe a short wait as the MBP may be trying to connect already if the Measurememt was a long time.
+            sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+            sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+            sock.bind((IP, port2))
+            sock.listen(1)
+            connection, client_address = sock.accept()
+
+            rcv_msg = []
+            while True:
+                data = connection.recv(1)
+                if marker in data:
+                    rcv_msg.append(data[:data.find(marker)])
+                    break
+                rcv_msg.append(data)
+
+            # Check the message received from the MBP, if correct, continue, else exit.
+            rpl = ''.join(rcv_msg)
+            if rpl == "True":
+                print("Send measurement.")
+
+            # Now use the same connection to let the MBP know it can grab the .jpg+RAW
+            # files and run RPiCM_C.c locally over there, which is MUCH faster.
+            connection.sendall("True?")
+            connection.close()
+
+            print("...measurment has been sent.\n")
+
+            # Change state: measurement has been sent, change state back to idle
+            STATE = "idle"
+
+        # Cycle counter for the finite state machine
+        RUNNING[1] = RUNNING[1] + 1
+        print("Cycle number: " + str(RUNNING[1]))
+
+    print("Little John called it...")
+    sys.exit(0)
+
+if __name__ == "__main__":
+    main()
+
diff --git a/RPiCam/cam.py b/RPiCam/cam.py
new file mode 100644
index 0000000..c91e666
--- /dev/null
+++ b/RPiCam/cam.py
@@ -0,0 +1,32 @@
+import picamera
+from fractions import Fraction
+
+cam = picamera.PiCamera(resolution=(640, 480))
+cam.exposure_mode = 'off'
+DEBUG_DIR = 'debug/'
+filename = DEBUG_DIR + 'shutter_{0}_iso_{1}_expo_{2}.png'
+cam.capture("base.png")
+cam.framerate = Fraction(1, 6)
+while True:
+    try:
+        exp = input("Run experiment?[y/n] ")
+        if exp == 'y':
+            ss = input("shutter speed:  ")
+            iso = input("iso:  ")
+            options = {'a': 'off', 'b': 'auto', 'c': 'night', 'd': 'night_preview', 'e': 'sports'}
+            expo = input("exposure mode: " + str(options) + "   ")
+            #bright = input("brightness:  ")
+            ss, iso, opt = int(ss), int(iso), options[expo]
+            cam.shutter_speed = ss
+            cam.iso = iso
+            cam.exposure_mode = options[expo]
+            #cam.brightness = bright
+            cam.capture(filename.format(ss, iso, opt))
+        else:
+            cam.close()
+            break
+    except ValueError:
+        pass
+
+
+
diff --git a/RPiCam/img_capture.py b/RPiCam/img_capture.py
new file mode 100644
index 0000000..86fd14a
--- /dev/null
+++ b/RPiCam/img_capture.py
@@ -0,0 +1,186 @@
+import picamera
+import os
+from fractions import Fraction
+import sys, traceback
+import time
+
+
+class ImgCollector:
+
+    def __init__(self, dir='', ns='img', form='png', raw=False, num=1, serialize=True):
+        if dir:
+            if not os.path.exists(dir):
+                os.mkdir(dir)
+            if dir[len(dir)-1] != '/':
+                dir += '/'
+
+        self.name_scheme = dir + ns + '_{0}.' + form
+        self._dir = dir
+        self._ns = ns
+        self._form = form
+        self._raw = raw
+        self._num = num
+        self.init_cam()
+        if serialize:
+            if os.path.exists("img_log.txt"):
+                rfile = open("img_log.txt", "r")
+                try:
+                    self.counter = int(rfile.read())
+                except ValueError:
+                    self.counter = 1
+            else:
+                rfile = open("img_log.txt", "w")
+                self.counter = 1
+            rfile.close()
+        else:
+            self.counter = 1
+
+
+    def change_ns(self, ns):
+        self._ns = ns
+        self.name_scheme = self._dir + self._ns + '_{0}.' + self._form
+
+    def change_format(self, form):
+        self._form = form
+        self.name_scheme = self._dir + self._ns + '_{0}.' + self._form
+
+    def change_dir(self, dir):
+        self._dir = dir
+        self.name_scheme = self._dir + self._ns + '_{0}.' + self._form
+
+    def change_num(self, num):
+        self._num = num
+        if self._num == 1:
+            self.capture = self.uni_capture
+        else:
+            self.capture = self.multi_capture
+
+    def init_cam(self):
+        self.cam = picamera.PiCamera(resolution=(640, 480))
+        time.sleep(2)
+        self.cam.led = False
+        self.cam.framerate = Fraction(1, 6)
+        self.cam.rotation = 180
+        self.cam.shutter_speed = 800000
+        self.cam.iso = 100
+        self.cam.exposure_mode = 'off'
+        time.sleep(3)
+        if self._num == 1:
+            self.capture = self.uni_capture
+        else:
+            self.capture = self.multi_capture
+
+    def get_last_meas(self):
+        if self._num == 1:
+            return self.name_scheme.format(self.counter - 1)
+        else:
+            return self._dir + self._ns + '_%d_{0}.' % (self.counter - 1) + self._form
+
+    def shutdown(self):
+        self.cam.close()
+        wfile = open("img_log.txt", "w")
+        wfile.write(str(self.counter))
+        wfile.close()
+
+    def uni_capture(self):
+        self.cam.capture(self.name_scheme.format(self.counter), bayer=self._raw)
+        self.counter += 1
+
+    def multi_capture(self):
+        file_list = [self.name_scheme.format("%d_%d" % (self.counter, i)) for i in range(1, self._num+1)]
+        self.cam.capture_sequence(file_list, bayer=self._raw)
+        self.counter += 1
+
+
+def main():
+    prompt = input("Welcome to the RPiCam Module. Type q for quick test, d for debug, or f / [other inputs] for full test.\n")
+    global recur
+    if prompt == 'q':
+        directory = 'quick_test'
+        name_pattern = 'img'
+        pic_format = 'png'
+        raw_image = False
+        num_meas = 1
+        recur = True
+    elif prompt == 'd':
+        camera_debug()
+        sys.exit()
+    else:
+        directory = input("Input a directory:\n")
+        name_pattern = input("Input a name pattern:\n")
+        pic_format = input("Input a picture format:\n")
+        raw_image = input("Raw image?[y/n]\n") in ['y', 'yes']
+        num_meas = int(input("Number of image samples for one measurement?\n"))
+        recur = False
+
+    while True:
+        try:
+            ic = ImgCollector(dir=directory, ns=name_pattern, form=pic_format, raw=raw_image, num=num_meas)
+            break
+        except:
+            directory = input("Ill-formated directory, type in another one: ")
+            traceback.print_exc(file=sys.stdout)
+
+    while True:
+        if recur:
+            ic.shutdown()
+            break
+
+        option = input("Type in an action or h for help:\n")
+        if option == 'h':
+            print("m: take measurement\n" +
+                  "r: show raw image status\n" +
+                  "cr: change raw image status\n" +
+                  "cf: change image format\n" +
+                  "cd: change directory\n" +
+                  "cn: change name\n" +
+                  "cm: change number of measurement\n" +
+                  "e: end the program")
+        elif option == 'm':
+            ic.capture()
+        elif option == 'r':
+            print(ic._raw)
+        elif option == 'cr':
+            ic._raw = input("Raw image?[y/n]\n") in ['y', 'yes']
+        elif option == 'cf':
+            ic.change_format(input("Input a picture format:\n"))
+        elif option == 'cd':
+            ic.change_dir(input("Input a directory:\n"))
+        elif option == 'cn':
+            ic.change_ns(input("Input a name pattern:\n"))
+        elif option == 'cm':
+            ic.change_num(int(input("Number of image samples for one measurement?\n")))
+        elif option == 'e':
+            ic.shutdown()
+            break
+
+
+def camera_debug():
+    DEBUG_DIR = 'debug/'
+    filename = DEBUG_DIR + 'shutter_{0}_iso_{1}_bright_{2}.jpeg'
+    analog = 'analog_gain: '
+    awb_gain = 'awb_gain: '
+    expo_modes = ['night', 'night_preview', 'very_long']
+    #flash_modes = ['off', 'redeye']
+    flash_modes = ['off']
+    shutter = [600000, 800000, 1000000]
+    with picamera.PiCamera(resolution=(640, 480)) as cam:
+        time.sleep(1)
+        print(analog + str(cam.analog_gain))
+        print(awb_gain + str(cam.awb_gains))
+        cam.framerate = Fraction(1, 6)
+        for ss in shutter:
+            #for br in brightness:
+            time.sleep(0.1)
+            cam.shutter_speed = ss
+            cam.brightness = 50
+            cam.capture(filename.format(ss), bayer=False)
+
+
+if __name__ == "__main__":
+    recur = True
+    while recur:
+        main()
+
+
+
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..88b97b5
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1,2 @@
+# Test
+
diff --git a/calbridgesensors/calbridgesensors/__init__.py b/calbridgesensors/calbridgesensors/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/calbridgesensors/calbridgesensors/settings.py b/calbridgesensors/calbridgesensors/settings.py
new file mode 100644
index 0000000..b089ea7
--- /dev/null
+++ b/calbridgesensors/calbridgesensors/settings.py
@@ -0,0 +1,137 @@
+"""
+Django settings for calbridgesensors project.
+
+Generated by 'django-admin startproject' using Django 2.0.3.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/2.0/topics/settings/
+
+For the full list of settings and their values, see
+https://docs.djangoproject.com/en/2.0/ref/settings/
+"""
+
+import os
+
+
+# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+# Quick-start development settings - unsuitable for production
+# See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/
+
+# SECURITY WARNING: keep the secret key used in production secret!
+SECRET_KEY = '&vuf$!t_6n7_z^0pfj-gdmk2k^8(^4%thu&&i-a)5=na_klw!!'
+
+# SECURITY WARNING: don't run with debug turned on in production!
+DEBUG = True
+
+ALLOWED_HOSTS = ["127.0.0.1", "192.168.15.101", "10.142.156.122", "192.168.0.12"]
+
+
+# Application definition
+
+INSTALLED_APPS = [
+    'django.contrib.admin',
+    'django.contrib.auth',
+    'django.contrib.contenttypes',
+    'django.contrib.sessions',
+    'django.contrib.messages',
+    'django.contrib.staticfiles',
+    "sensors.apps.SensorsConfig"
+]
+
+MIDDLEWARE = [
+    'django.middleware.security.SecurityMiddleware',
+    'django.contrib.sessions.middleware.SessionMiddleware',
+    'django.middleware.common.CommonMiddleware',
+    'django.middleware.csrf.CsrfViewMiddleware',
+    'django.contrib.auth.middleware.AuthenticationMiddleware',
+    'django.contrib.messages.middleware.MessageMiddleware',
+    'django.middleware.clickjacking.XFrameOptionsMiddleware',
+]
+
+ROOT_URLCONF = 'calbridgesensors.urls'
+
+TEMPLATES = [
+    {
+        'BACKEND': 'django.template.backends.django.DjangoTemplates',
+        'DIRS': [os.path.join(BASE_DIR, "templates")],
+        'APP_DIRS': True,
+        'OPTIONS': {
+            'context_processors': [
+                'django.template.context_processors.debug',
+                'django.template.context_processors.request',
+                'django.contrib.auth.context_processors.auth',
+                'django.contrib.messages.context_processors.messages',
+            ],
+        },
+    },
+]
+
+WSGI_APPLICATION = 'calbridgesensors.wsgi.application'
+
+
+# Database
+# https://docs.djangoproject.com/en/2.0/ref/settings/#databases
+
+DATABASES = {
+    'default': {
+        'ENGINE': 'django.db.backends.sqlite3',
+        'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
+    }
+}
+
+
+# Password validation
+# https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators
+
+AUTH_PASSWORD_VALIDATORS = [
+    {
+        'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
+    },
+]
+
+
+# Internationalization
+# https://docs.djangoproject.com/en/2.0/topics/i18n/
+
+LANGUAGE_CODE = 'en-us'
+
+TIME_ZONE = 'America/Los_Angeles'
+
+USE_I18N = True
+
+USE_L10N = True
+
+USE_TZ = True
+
+
+# Static files (CSS, JavaScript, Images)
+# https://docs.djangoproject.com/en/2.0/howto/static-files/
+
+STATIC_URL = '/static/'
+STATIC_ROOT = os.path.join(BASE_DIR, 'var/static/')
+STATICFILES_DIRS = [
+    os.path.join(BASE_DIR, 'static/')
+]
+STATICFILE_FINDERS = [
+    'django.contrib.staticfiles.finders.FileSystemFinder',
+    'django.contrib.staticfiles.finders.AppDirectoriesFinder'
+]
+
+
+# AUTHENTICATION URL Dispatchers
+
+LOGIN_URL = 'login'
+LOGIN_REDIRECT_URL = 'sensors:index'
+LOGOUT_REDIRECT_URL = 'home'
diff --git a/calbridgesensors/calbridgesensors/urls.py b/calbridgesensors/calbridgesensors/urls.py
new file mode 100644
index 0000000..88c765e
--- /dev/null
+++ b/calbridgesensors/calbridgesensors/urls.py
@@ -0,0 +1,29 @@
+"""calbridgesensors URL Configuration
+
+The `urlpatterns` list routes URLs to views. For more information please see:
+    https://docs.djangoproject.com/en/2.0/topics/http/urls/
+Examples:
+Function views
+    1. Add an import:  from my_app import views
+    2. Add a URL to urlpatterns:  path('', views.home, name='home')
+Class-based views
+    1. Add an import:  from other_app.views import Home
+    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')
+Including another URLconf
+    1. Import the include() function: from django.urls import include, path
+    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))
+"""
+from django.contrib import admin
+from django.views.generic.base import TemplateView
+from django.urls import path, include
+from django.conf import settings
+from django.conf.urls.static import static
+
+
+urlpatterns = [
+    path('', TemplateView.as_view(template_name="home.html"), name='home'),
+    path('admin/', admin.site.urls),
+    path('accounts/', include('django.contrib.auth.urls')),
+    path('sensors/', include("sensors.urls"))
+]
+
diff --git a/calbridgesensors/calbridgesensors/wsgi.py b/calbridgesensors/calbridgesensors/wsgi.py
new file mode 100644
index 0000000..cca3138
--- /dev/null
+++ b/calbridgesensors/calbridgesensors/wsgi.py
@@ -0,0 +1,16 @@
+"""
+WSGI config for calbridgesensors project.
+
+It exposes the WSGI callable as a module-level variable named ``application``.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/2.0/howto/deployment/wsgi/
+"""
+
+import os
+
+from django.core.wsgi import get_wsgi_application
+
+os.environ.setdefault("DJANGO_SETTINGS_MODULE", "calbridgesensors.settings")
+
+application = get_wsgi_application()
diff --git a/calbridgesensors/manage.py b/calbridgesensors/manage.py
new file mode 100755
index 0000000..12bb361
--- /dev/null
+++ b/calbridgesensors/manage.py
@@ -0,0 +1,15 @@
+#!/usr/bin/env python
+import os
+import sys
+
+if __name__ == "__main__":
+    os.environ.setdefault("DJANGO_SETTINGS_MODULE", "calbridgesensors.settings")
+    try:
+        from django.core.management import execute_from_command_line
+    except ImportError as exc:
+        raise ImportError(
+            "Couldn't import Django. Are you sure it's installed and "
+            "available on your PYTHONPATH environment variable? Did you "
+            "forget to activate a virtual environment?"
+        ) from exc
+    execute_from_command_line(sys.argv)
diff --git a/calbridgesensors/runserver.sh b/calbridgesensors/runserver.sh
new file mode 100755
index 0000000..155f968
--- /dev/null
+++ b/calbridgesensors/runserver.sh
@@ -0,0 +1,9 @@
+#!/bin/bash/
+
+line=`ifconfig |grep broadcast`
+echo $line
+set -- $line
+ip=$2
+echo $ip
+python manage.py runserver $ip:8000
+
diff --git a/calbridgesensors/sensors/__init__.py b/calbridgesensors/sensors/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/calbridgesensors/sensors/admin.py b/calbridgesensors/sensors/admin.py
new file mode 100644
index 0000000..ca58d9b
--- /dev/null
+++ b/calbridgesensors/sensors/admin.py
@@ -0,0 +1,7 @@
+from django.contrib import admin
+from .models import Bridge, BridgeLog, BrokenFlag
+
+# Register your models here.
+admin.site.register(Bridge)
+admin.site.register(BridgeLog)
+admin.site.register(BrokenFlag)
\ No newline at end of file
diff --git a/calbridgesensors/sensors/apps.py b/calbridgesensors/sensors/apps.py
new file mode 100644
index 0000000..41b5c2e
--- /dev/null
+++ b/calbridgesensors/sensors/apps.py
@@ -0,0 +1,13 @@
+from django.apps import AppConfig
+from decimal import Decimal
+
+
+class SensorsConfig(AppConfig):
+    name = 'sensors'
+
+CONNECTION_PASSWORD = "djioewfj34jod2jdoi3jr0jl983jsa"
+BUFFER_TIME = 3
+RECENT_PERIOD = 10
+THRESHOLD_DIS = 1
+THRESHOLD_ROT = 1
+CALIB_VAL = Decimal(166.1861)
diff --git a/calbridgesensors/sensors/converters.py b/calbridgesensors/sensors/converters.py
new file mode 100644
index 0000000..ff311d1
--- /dev/null
+++ b/calbridgesensors/sensors/converters.py
@@ -0,0 +1,8 @@
+class BridgeNameConverter:
+    regex = '[\w-]+'
+
+    def to_python(self, value):
+        return value.replace("-", " ").title()
+    
+    def to_url(self, value):
+        return str(value).replace(" ", "-").lower()
\ No newline at end of file
diff --git a/calbridgesensors/sensors/migrations/0001_initial.py b/calbridgesensors/sensors/migrations/0001_initial.py
new file mode 100644
index 0000000..faceee4
--- /dev/null
+++ b/calbridgesensors/sensors/migrations/0001_initial.py
@@ -0,0 +1,65 @@
+# Generated by Django 2.0.5 on 2018-06-23 01:15
+
+from django.db import migrations, models
+import django.db.models.deletion
+
+
+class Migration(migrations.Migration):
+
+    initial = True
+
+    dependencies = [
+    ]
+
+    operations = [
+        migrations.CreateModel(
+            name='Bridge',
+            fields=[
+                ('name', models.CharField(max_length=40, primary_key=True, serialize=False)),
+                ('anomaly', models.BooleanField(default=False)),
+                ('validated', models.BooleanField(default=False)),
+            ],
+            options={
+                'ordering': ['name'],
+            },
+        ),
+        migrations.CreateModel(
+            name='BridgeLog',
+            fields=[
+                ('log_type', models.CharField(choices=[('D', 'Damage Record'), ('R', 'Repair Record')], max_length=1)),
+                ('log_time', models.DateTimeField(auto_now_add=True, primary_key=True, serialize=False)),
+                ('bridge', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='sensors.Bridge', unique_for_date='log_time')),
+            ],
+            options={
+                'ordering': [],
+            },
+        ),
+        migrations.CreateModel(
+            name='BrokenFlag',
+            fields=[
+                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+                ('bridge', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to='sensors.Bridge')),
+                ('record', models.OneToOneField(on_delete=django.db.models.deletion.PROTECT, to='sensors.BridgeLog')),
+            ],
+            options={
+                'ordering': [],
+            },
+        ),
+        migrations.CreateModel(
+            name='Reading',
+            fields=[
+                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+                ('x', models.DecimalField(decimal_places=2, max_digits=4)),
+                ('y', models.DecimalField(decimal_places=2, max_digits=4)),
+                ('z', models.DecimalField(decimal_places=2, max_digits=4)),
+                ('theta', models.DecimalField(decimal_places=2, max_digits=4)),
+                ('phi', models.DecimalField(decimal_places=2, max_digits=4)),
+                ('psi', models.DecimalField(decimal_places=2, max_digits=4)),
+                ('time_taken', models.DateTimeField(auto_now_add=True)),
+                ('bridge', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='sensors.Bridge', unique_for_date='time_taken')),
+            ],
+            options={
+                'verbose_name': 'Bridge sensor reading',
+            },
+        ),
+    ]
diff --git a/calbridgesensors/sensors/migrations/0002_auto_20180623_0133.py b/calbridgesensors/sensors/migrations/0002_auto_20180623_0133.py
new file mode 100644
index 0000000..d923aed
--- /dev/null
+++ b/calbridgesensors/sensors/migrations/0002_auto_20180623_0133.py
@@ -0,0 +1,17 @@
+# Generated by Django 2.0.5 on 2018-06-23 01:33
+
+from django.db import migrations
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('sensors', '0001_initial'),
+    ]
+
+    operations = [
+        migrations.AlterModelOptions(
+            name='reading',
+            options={'ordering': ['-time_taken'], 'verbose_name': 'Bridge sensor reading'},
+        ),
+    ]
diff --git a/calbridgesensors/sensors/migrations/0003_auto_20180624_0658.py b/calbridgesensors/sensors/migrations/0003_auto_20180624_0658.py
new file mode 100644
index 0000000..fda13cd
--- /dev/null
+++ b/calbridgesensors/sensors/migrations/0003_auto_20180624_0658.py
@@ -0,0 +1,33 @@
+# Generated by Django 2.0.5 on 2018-06-24 06:58
+
+from django.db import migrations
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('sensors', '0002_auto_20180623_0133'),
+    ]
+
+    operations = [
+        migrations.AlterModelOptions(
+            name='bridgelog',
+            options={'ordering': ['-log_time', 'log_type', 'bridge__name']},
+        ),
+        migrations.AlterModelOptions(
+            name='brokenflag',
+            options={'ordering': ['bridge__name']},
+        ),
+        migrations.AlterModelOptions(
+            name='reading',
+            options={'ordering': ['bridge__name', '-time_taken'], 'verbose_name': 'Bridge Sensor Reading'},
+        ),
+        migrations.RemoveField(
+            model_name='bridge',
+            name='anomaly',
+        ),
+        migrations.RemoveField(
+            model_name='bridge',
+            name='validated',
+        ),
+    ]
diff --git a/calbridgesensors/sensors/migrations/0004_auto_20180626_0607.py b/calbridgesensors/sensors/migrations/0004_auto_20180626_0607.py
new file mode 100644
index 0000000..9aead8a
--- /dev/null
+++ b/calbridgesensors/sensors/migrations/0004_auto_20180626_0607.py
@@ -0,0 +1,22 @@
+# Generated by Django 2.0.5 on 2018-06-26 06:07
+
+from django.db import migrations
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('sensors', '0003_auto_20180624_0658'),
+    ]
+
+    operations = [
+        migrations.AlterModelOptions(
+            name='bridgelog',
+            options={'ordering': ['bridge__name', 'log_type', '-log_time']},
+        ),
+        migrations.RenameField(
+            model_name='brokenflag',
+            old_name='record',
+            new_name='first_broken_record',
+        ),
+    ]
diff --git a/calbridgesensors/sensors/migrations/0005_auto_20180628_1815.py b/calbridgesensors/sensors/migrations/0005_auto_20180628_1815.py
new file mode 100644
index 0000000..f916970
--- /dev/null
+++ b/calbridgesensors/sensors/migrations/0005_auto_20180628_1815.py
@@ -0,0 +1,18 @@
+# Generated by Django 2.0.5 on 2018-06-28 18:15
+
+from django.db import migrations
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('sensors', '0004_auto_20180626_0607'),
+    ]
+
+    operations = [
+        migrations.RenameField(
+            model_name='brokenflag',
+            old_name='first_broken_record',
+            new_name='damage_record',
+        ),
+    ]
diff --git a/calbridgesensors/sensors/migrations/__init__.py b/calbridgesensors/sensors/migrations/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/calbridgesensors/sensors/models.py b/calbridgesensors/sensors/models.py
new file mode 100644
index 0000000..15f1cec
--- /dev/null
+++ b/calbridgesensors/sensors/models.py
@@ -0,0 +1,131 @@
+from django.db import models
+from django.utils import timezone
+from .utils import name_validate, max_val, succinct_time_str
+from .apps import THRESHOLD_DIS, THRESHOLD_ROT, BUFFER_TIME, RECENT_PERIOD
+
+
+class Bridge(models.Model):
+    # Field: reading
+    name = models.CharField(max_length=40, primary_key=True)
+
+    class Meta:
+        ordering = ["name"]
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.name = name_validate(self.name)
+
+    def __str__(self):
+        return self.name
+
+    def latest_reading(self):
+        r_set = self.reading_set.all()
+        return r_set[0] if r_set else None
+
+    def get_damage_records(self):
+        return self.bridgelog_set.filter(log_type="D")
+
+    def get_repair_records(self):
+        return self.bridgelog_set.filter(log_type="R")
+
+    def is_broken(self):
+        try:
+            return self.brokenflag
+        except AttributeError:
+            return False
+
+    def update(self, data):
+        """ Creates new reading, checks anomaly;
+        --> Raises Broken Flag if showing anomaly for longer than BUFFER_TIME;
+        --> Marks bridge as repaired when repaired. """
+        new_reading = self.reading_set.create(x=float(data['x']), y=float(data['y']), z=float(data['z']),
+                                              theta=float(data['theta']), phi=float(data['phi']),
+                                              psi=float(data['psi']), bridge=self)
+        if new_reading.shows_anomaly():
+            # Check whether it has a broken flag, whether it is new anomaly
+            if not self.is_broken():
+                # Means it's new. Checks latest damage record, don't raise alarm if it is within buffer time
+                damage_recs = self.get_damage_records()
+                if damage_recs:
+                    latest_damage = damage_recs[0]
+                    offset = latest_damage.time_elapsed().days
+                    if offset > BUFFER_TIME:
+                        # Latest Damage Record not created yet
+                        self.bridgelog_set.create(log_type='D', bridge=self)
+                    elif offset == BUFFER_TIME:
+                        # Already over the buffer time, time to raise alarm
+                        self.mark_broken(latest_damage)
+                    # Else it is within the buffer time, wait for sometime
+                else:
+                    # No Damage Record Yet
+                    self.bridgelog_set.create(log_type='D', bridge=self)
+            # Else it's old. No need to create new records. Also no need to raise alarm again
+        else:
+            # Now it seems normal. Three scenerios: 1) Repaired, 2) False Alarm Previously, 3) Good In the First Place
+            if self.is_broken():
+                self.mark_repaired()
+
+    def mark_repaired(self):
+        self.bridgelog_set.create(log_type='R', bridge=self)
+        self.brokenflag.delete()
+
+    def mark_broken(self, damage_rec):
+        BrokenFlag.objects.create(bridge=self, damage_record=damage_rec)
+
+
+class Reading(models.Model):
+    x = models.DecimalField(max_digits=4, decimal_places=2)
+    y = models.DecimalField(max_digits=4, decimal_places=2)
+    z = models.DecimalField(max_digits=4, decimal_places=2)
+    theta = models.DecimalField(max_digits=4, decimal_places=2)
+    phi = models.DecimalField(max_digits=4, decimal_places=2)
+    psi = models.DecimalField(max_digits=4, decimal_places=2)
+    bridge = models.ForeignKey(Bridge, on_delete=models.CASCADE, unique_for_date="time_taken")
+    time_taken = models.DateTimeField(auto_now_add=True)
+
+    class Meta:
+        verbose_name = "Bridge Sensor Reading"
+        ordering = ["bridge__name", "-time_taken"]
+
+    def __str__(self):
+        return "Reading for " + self.bridge.name + " at " + succinct_time_str(self.time_taken)
+
+    def shows_anomaly(self):
+        return max_val(self.x, self.y, self.z) > THRESHOLD_DIS \
+               or max_val(self.theta, self.phi, self.psi) > THRESHOLD_ROT
+
+
+class BridgeLog(models.Model):
+    DAMAGE = "D"
+    REPAIR = "R"
+    LOG_TYPES = ((DAMAGE, "Damage Record"),
+                 (REPAIR, "Repair Record"))
+    log_type = models.CharField(max_length=1, choices=LOG_TYPES)
+    log_time = models.DateTimeField(auto_now_add=True, primary_key=True)
+    bridge = models.ForeignKey(Bridge, on_delete=models.CASCADE, unique_for_date="log_time")
+
+    class Meta:
+        ordering = ["bridge__name", "log_type", "-log_time"]
+
+    def __str__(self):
+        return self.get_log_type_display() + " for " + self.bridge.name + " at " + succinct_time_str(self.log_time)
+
+    def description(self):
+        return self.get_log_type_display() + " at " + str(self.log_time)
+
+    def time_elapsed(self):
+        return timezone.now() - self.log_time
+
+    def is_recent(self):
+        return self.time_elapsed() <= RECENT_PERIOD
+
+
+class BrokenFlag(models.Model):
+    bridge = models.OneToOneField(Bridge, on_delete=models.CASCADE)
+    damage_record = models.OneToOneField(BridgeLog, on_delete=models.PROTECT)
+
+    class Meta:
+        ordering = ["bridge__name"]
+
+    def broken_time(self):
+        return self.damage_record.time_elapsed()
diff --git a/calbridgesensors/sensors/signals.py b/calbridgesensors/sensors/signals.py
new file mode 100644
index 0000000..e69de29
diff --git a/calbridgesensors/sensors/templatetags/__init__.py b/calbridgesensors/sensors/templatetags/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/calbridgesensors/sensors/templatetags/sensors_filters.py b/calbridgesensors/sensors/templatetags/sensors_filters.py
new file mode 100644
index 0000000..0fee682
--- /dev/null
+++ b/calbridgesensors/sensors/templatetags/sensors_filters.py
@@ -0,0 +1,10 @@
+from django import template
+from django.template.defaultfilters import stringfilter
+
+register = template.Library()
+
+
+@stringfilter
+@register.filter(name='tourl')
+def name_to_url(value):
+    return value.replace(" ", "-").lower()
diff --git a/calbridgesensors/sensors/tests.py b/calbridgesensors/sensors/tests.py
new file mode 100644
index 0000000..7ce503c
--- /dev/null
+++ b/calbridgesensors/sensors/tests.py
@@ -0,0 +1,3 @@
+from django.test import TestCase
+
+# Create your tests here.
diff --git a/calbridgesensors/sensors/urls.py b/calbridgesensors/sensors/urls.py
new file mode 100644
index 0000000..4703d90
--- /dev/null
+++ b/calbridgesensors/sensors/urls.py
@@ -0,0 +1,14 @@
+from django.urls import path, re_path, register_converter
+from .converters import BridgeNameConverter
+from . import views
+
+register_converter(BridgeNameConverter, 'brn')
+
+app_name = "sensors"
+
+urlpatterns = [
+    path('', views.SensorsHomeView.as_view(), name='index'),
+    path('<brn:pk>/', views.bridge_view, name='detail'),
+    #re_path(r'^(?P<pk>[\w-]+)/$', views.BridgeView.as_view(), name='detail'),
+    path('<brn:pk>/update/', views.bridge_update)
+]
diff --git a/calbridgesensors/sensors/utils.py b/calbridgesensors/sensors/utils.py
new file mode 100644
index 0000000..ac24743
--- /dev/null
+++ b/calbridgesensors/sensors/utils.py
@@ -0,0 +1,72 @@
+import re
+import json
+from .apps import CONNECTION_PASSWORD, CALIB_VAL
+from dateutil import parser
+from datetime import datetime
+
+
+def name_validate(s):
+    target = r'[^ A-Za-z0-9]'
+    return re.sub(target, " ", s).title()
+
+
+def check_abnomaly(r1, r2):
+    return True
+
+
+def minv(a, b):
+    return a if a < b else b
+
+
+def maxv(a, b):
+    return a if a > b else b
+
+
+def min_val(a, b, c):
+    return minv(a, minv(b, c))
+
+
+def max_val(a, b, c):
+    return maxv(a, maxv(b, c))
+
+
+def verify_request(ck):
+    time_sign = parser.parse(ck['time'])
+    len_pw = len(CONNECTION_PASSWORD)
+    quest = ck['csrftoken']
+    index = code_expr(time_sign) % len_pw
+    for i in range(len_pw):
+        if i != index and quest[i] != CONNECTION_PASSWORD[i]:
+            return False
+    return True
+
+
+def code_expr(time_sign):
+    return time_sign.year + time_sign.month * 100 + time_sign.day \
+           + time_sign.hour * time_sign.minute * time_sign.second
+
+
+def succinct_time_str(dt):
+    return str(dt)[:19]
+
+
+def calib_dp_to_di(dp):
+    return dp / CALIB_VAL
+
+
+def decimal_rep(n):
+    return "%.4f" % n
+
+
+def parse_db_time_string(time_string):
+    date = datetime.strptime(time_string.split('.')[0], '%Y-%m-%d %H:%M:%S')  # String to object
+    return datetime.strftime(date, '%d %b %Y %H:%M:%S')
+
+
+def parse_db_time(db_time):
+    date = datetime.strftime(db_time, '%Y-%m-%d %H:%M:%S')
+    return date
+
+
+
+
diff --git a/calbridgesensors/sensors/views.py b/calbridgesensors/sensors/views.py
new file mode 100644
index 0000000..fd6f91c
--- /dev/null
+++ b/calbridgesensors/sensors/views.py
@@ -0,0 +1,97 @@
+from django.shortcuts import render, get_object_or_404
+from django.contrib.auth.mixins import LoginRequiredMixin
+from django.contrib.auth.decorators import login_required
+from django.views.generic import DetailView
+from django.http import HttpResponse
+from django.views.generic.base import TemplateView
+from .models import Bridge, BrokenFlag, Reading
+from .utils import verify_request, calib_dp_to_di, decimal_rep, parse_db_time
+from django.views.decorators.csrf import csrf_exempt, csrf_protect
+import json
+
+
+class SensorsHomeView(LoginRequiredMixin, TemplateView):
+    """View for home page"""
+    template_name = "sensors/index.html"
+    login_url = "/accounts/login/"
+
+    def get_context_data(self, **kwargs):
+        broken_bridges = BrokenFlag.objects.all()
+        return {"user": self.request.user, "broken_bridges": broken_bridges, "bridges": Bridge.objects.all()}
+
+
+"""class BridgeView(LoginRequiredMixin, DetailView):
+    model = Bridge
+    template_name = "sensors/detail.html"
+    login_url = "/accounts/login/"
+    context_object_name = "bridge"
+
+    def get_context_data(self, **kwargs):
+        return {"user": self.request.user}.update(super().get_context_data(**kwargs))"""
+
+
+@login_required(login_url='/accounts/login/')
+def bridge_view(request, pk):
+    bridge = get_object_or_404(Bridge, pk=pk)
+    readings = bridge.reading_set.all()
+    len_reading = len(readings)
+    ground_zero = readings[len_reading - 1]
+    ground_zero_x = ground_zero.x
+    ground_zero_y = ground_zero.y
+    calibrated = [(0.0, 0.0, None)] * len_reading
+    calibratedx = [0.0] * len_reading
+    calibratedy = [0.0] * len_reading
+    dates = [None] * len_reading
+    for i in range(len_reading):
+        curr = readings[i]
+        dx = decimal_rep(calib_dp_to_di(curr.x - ground_zero_x))
+        dy = decimal_rep(calib_dp_to_di(curr.y - ground_zero_y))
+        dt = curr.time_taken
+        calibrated[len_reading - i - 1] = (dx, dy, parse_db_time(dt))
+        calibratedx[i] = dx
+        calibratedy[i] = dy
+    calib_json = json.dumps(calibrated)
+    #print(calibrated[-10:])
+    #json_readings = json.dumps(readings)
+    context = {"user": request.user,
+               "damage_recs": bridge.get_damage_records(),
+               "repair_recs": bridge.get_repair_records(),
+               "bridge": bridge,
+               "reading": bridge.latest_reading(),
+               "readings": calib_json,
+               "readingsx": calibratedx,
+               "readingsy": calibratedy
+               }
+    return render(request, "sensors/detail.html", context=context)
+
+
+@csrf_exempt
+def bridge_update(request, pk):
+    """JSON"""
+    def update_procedure():
+        # Connects the front-end request with backend database process
+        try:
+            br = Bridge.objects.get(pk=pk)
+        except Bridge.DoesNotExist:
+            return HttpResponse(content='Bridge Unregistered', status=412)
+        br.update(request.POST)  # Should resolve all sorts of issues by passing a dictionary in
+        return HttpResponse('BONJOUR')
+
+    @csrf_protect
+    def protected_update(r, p):
+        return update_procedure()
+
+    if verify_request(request.COOKIES):
+        return update_procedure()
+    else:
+        return protected_update(request, pk)
+
+
+
+
+
+
+
+
+
+
diff --git a/calbridgesensors/static/plotly.js b/calbridgesensors/static/plotly.js
new file mode 100644
index 0000000..6753b8d
--- /dev/null
+++ b/calbridgesensors/static/plotly.js
@@ -0,0 +1,7 @@
+/**
+* plotly.js v1.39.3
+* Copyright 2012-2018, Plotly, Inc.
+* All rights reserved.
+* Licensed under the MIT license
+*/
+!function(t){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=t();else if("function"==typeof define&&define.amd)define([],t);else{("undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:this).Plotly=t()}}(function(){return function(){return function t(e,r,n){function i(o,s){if(!r[o]){if(!e[o]){var l="function"==typeof require&&require;if(!s&&l)return l(o,!0);if(a)return a(o,!0);var c=new Error("Cannot find module '"+o+"'");throw c.code="MODULE_NOT_FOUND",c}var u=r[o]={exports:{}};e[o][0].call(u.exports,function(t){var r=e[o][1][t];return i(r||t)},u,u.exports,t,e,r,n)}return r[o].exports}for(var a="function"==typeof require&&require,o=0;o<n.length;o++)i(n[o]);return i}}()({1:[function(t,e,r){"use strict";var n=t("../src/lib"),i={"X,X div":"direction:ltr;font-family:'Open Sans', verdana, arial, sans-serif;margin:0;padding:0;","X input,X button":"font-family:'Open Sans', verdana, arial, sans-serif;","X input:focus,X 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255.;\n\t\t\t}",frag:"\n\t\t\tprecision highp float;\n\t\t\tuniform sampler2D atlas;\n\t\t\tuniform float fontSize, charStep, opacity;\n\t\t\tuniform vec2 atlasSize;\n\t\t\tuniform vec4 viewport;\n\t\t\tvarying vec4 fontColor;\n\t\t\tvarying vec2 charCoord, charId;\n\t\t\tvarying float charWidth;\n\n\t\t\tfloat lightness(vec4 color) {\n\t\t\t\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\n\t\t\t}\n\n\t\t\tvoid main () {\n\t\t\t\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\n\t\t\t\tfloat halfCharStep = floor(charStep * .5 + .5);\n\n\t\t\t\t// invert y and shift by 1px (FF expecially needs that)\n\t\t\t\tuv.y = charStep - uv.y;\n\n\t\t\t\t// ignore points outside of character bounding box\n\t\t\t\tfloat halfCharWidth = ceil(charWidth * .5);\n\t\t\t\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\n\t\t\t\t\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\n\n\t\t\t\tuv += charId * charStep;\n\t\t\t\tuv = uv / atlasSize;\n\n\t\t\t\tvec4 color = fontColor;\n\t\t\t\tvec4 mask = texture2D(atlas, uv);\n\n\t\t\t\tfloat maskY = lightness(mask);\n\t\t\t\t// float colorY = lightness(color);\n\t\t\t\tcolor.a *= maskY;\n\t\t\t\tcolor.a *= opacity;\n\n\t\t\t\t// color.a += .1;\n\n\t\t\t\t// antialiasing, see yiq color space y-channel formula\n\t\t\t\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\n\n\t\t\t\tgl_FragColor = color;\n\t\t\t}"});return{regl:t,draw:e,atlas:{}}},w.prototype.update=function(t){var e=this;if("string"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:"position positions coord coords coordinates",font:"font fontFace fontface typeface cssFont css-font family fontFamily",fontSize:"fontSize fontsize size font-size",text:"text texts chars characters value values symbols",align:"align alignment textAlign textbaseline",baseline:"baseline textBaseline textbaseline",direction:"dir direction textDirection",color:"color colour fill fill-color fillColor textColor textcolor",kerning:"kerning kern",range:"range dataBox",viewport:"vp viewport viewBox viewbox viewPort",opacity:"opacity alpha transparency visible visibility opaque",offset:"offset positionOffset padding shift indent indentation"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map(function(t){return parseFloat(t)}):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=h(t.viewport),w.normalViewport&&(this.viewport.y=this.canvas.height-this.viewport.y-this.viewport.height),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&("number"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=w.baseFontSize+"px sans-serif");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach(function(t,r){if("string"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(w.baseFontSize+"px "+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:w.baseFontSize,family:t.family,stretch:b?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=w.fonts[i],e.font[r]))){var c=t.family.join(", "),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),b&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:"top",fontSize:w.baseFontSize,fontStyle:u.join(" ")})},w.fonts[i]=e.font[r]}}),(a||o)&&this.font.forEach(function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:b?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)}),"string"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),f=0;f<s.length;f++)s[f]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var x=1;x<t.text.length;x++)e.textOffsets[x]=e.textOffsets[x-1]+t.text[x-1].length,e.count+=t.text[x].length,e.counts.push(t.text[x].length);this.text=t.text.join("")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach(function(t,n){w.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=w.atlasContext.measureText(o).width/w.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);m(t.kerning,g(t.family,{pairs:s}))}}})}if(t.position)if(t.position.length>2){for(var _=!t.position[0].length,k=u.mallocFloat(2*this.count),M=0,A=0;M<this.counts.length;M++){var T=e.counts[M];if(_)for(var S=0;S<T;S++)k[A++]=t.position[2*M],k[A++]=t.position[2*M+1];else for(var E=0;E<T;E++)k[A++]=t.position[M][0],k[A++]=t.position[M][1]}this.position.call?this.position({type:"float",data:k}):this.position=this.regl.buffer({type:"float",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=u.mallocUint8(this.count),L=u.mallocFloat(2*this.count);this.textWidth=[];for(var z=0,P=0;z<this.counts.length;z++){for(var I=e.counts[z],O=e.font[z]||e.font[0],D=e.fontAtlas[z]||e.fontAtlas[0],R=0;R<I;R++){var B=e.text.charAt(P),F=e.text.charAt(P-1);if(C[P]=D.ids[B],L[2*P]=O.width[B],R){var N=L[2*P-2],j=L[2*P],V=L[2*P-1]+.5*N+.5*j;if(e.kerning){var U=O.kerning[F+B];U&&(V+=.001*U)}L[2*P+1]=V}else 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e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n("number"==typeof t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n("number"==typeof t&&t>=0);var r=t/26|0,i=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var p=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],h=8191&u,f=u>>>13,p=0|o[1],d=8191&p,g=p>>>13,m=0|o[2],v=8191&m,y=m>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],k=8191&w,M=w>>>13,A=0|o[5],T=8191&A,S=A>>>13,E=0|o[6],C=8191&E,L=E>>>13,z=0|o[7],P=8191&z,I=z>>>13,O=0|o[8],D=8191&O,R=O>>>13,B=0|o[9],F=8191&B,N=B>>>13,j=0|s[0],V=8191&j,U=j>>>13,q=0|s[1],H=8191&q,G=q>>>13,W=0|s[2],Y=8191&W,X=W>>>13,Z=0|s[3],$=8191&Z,J=Z>>>13,K=0|s[4],Q=8191&K,tt=K>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ht=8191&ut,ft=ut>>>13,pt=0|s[9],dt=8191&pt,gt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var mt=(c+(n=Math.imul(h,V))|0)+((8191&(i=(i=Math.imul(h,U))+Math.imul(f,V)|0))<<13)|0;c=((a=Math.imul(f,U))+(i>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(d,V),i=(i=Math.imul(d,U))+Math.imul(g,V)|0,a=Math.imul(g,U);var vt=(c+(n=n+Math.imul(h,H)|0)|0)+((8191&(i=(i=i+Math.imul(h,G)|0)+Math.imul(f,H)|0))<<13)|0;c=((a=a+Math.imul(f,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,V),i=(i=Math.imul(v,U))+Math.imul(y,V)|0,a=Math.imul(y,U),n=n+Math.imul(d,H)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(g,H)|0,a=a+Math.imul(g,G)|0;var yt=(c+(n=n+Math.imul(h,Y)|0)|0)+((8191&(i=(i=i+Math.imul(h,X)|0)+Math.imul(f,Y)|0))<<13)|0;c=((a=a+Math.imul(f,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,V),i=(i=Math.imul(b,U))+Math.imul(_,V)|0,a=Math.imul(_,U),n=n+Math.imul(v,H)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,H)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,Y)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(g,Y)|0,a=a+Math.imul(g,X)|0;var xt=(c+(n=n+Math.imul(h,$)|0)|0)+((8191&(i=(i=i+Math.imul(h,J)|0)+Math.imul(f,$)|0))<<13)|0;c=((a=a+Math.imul(f,J)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(k,V),i=(i=Math.imul(k,U))+Math.imul(M,V)|0,a=Math.imul(M,U),n=n+Math.imul(b,H)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,H)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,Y)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,Y)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,J)|0)+Math.imul(g,$)|0,a=a+Math.imul(g,J)|0;var bt=(c+(n=n+Math.imul(h,Q)|0)|0)+((8191&(i=(i=i+Math.imul(h,tt)|0)+Math.imul(f,Q)|0))<<13)|0;c=((a=a+Math.imul(f,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(T,V),i=(i=Math.imul(T,U))+Math.imul(S,V)|0,a=Math.imul(S,U),n=n+Math.imul(k,H)|0,i=(i=i+Math.imul(k,G)|0)+Math.imul(M,H)|0,a=a+Math.imul(M,G)|0,n=n+Math.imul(b,Y)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,Y)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,J)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,J)|0,n=n+Math.imul(d,Q)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(g,Q)|0,a=a+Math.imul(g,tt)|0;var _t=(c+(n=n+Math.imul(h,rt)|0)|0)+((8191&(i=(i=i+Math.imul(h,nt)|0)+Math.imul(f,rt)|0))<<13)|0;c=((a=a+Math.imul(f,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(C,V),i=(i=Math.imul(C,U))+Math.imul(L,V)|0,a=Math.imul(L,U),n=n+Math.imul(T,H)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(S,H)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(k,Y)|0,i=(i=i+Math.imul(k,X)|0)+Math.imul(M,Y)|0,a=a+Math.imul(M,X)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,J)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,J)|0,n=n+Math.imul(v,Q)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,Q)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(g,rt)|0,a=a+Math.imul(g,nt)|0;var wt=(c+(n=n+Math.imul(h,at)|0)|0)+((8191&(i=(i=i+Math.imul(h,ot)|0)+Math.imul(f,at)|0))<<13)|0;c=((a=a+Math.imul(f,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(P,V),i=(i=Math.imul(P,U))+Math.imul(I,V)|0,a=Math.imul(I,U),n=n+Math.imul(C,H)|0,i=(i=i+Math.imul(C,G)|0)+Math.imul(L,H)|0,a=a+Math.imul(L,G)|0,n=n+Math.imul(T,Y)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(S,Y)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(k,$)|0,i=(i=i+Math.imul(k,J)|0)+Math.imul(M,$)|0,a=a+Math.imul(M,J)|0,n=n+Math.imul(b,Q)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,Q)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(g,at)|0,a=a+Math.imul(g,ot)|0;var kt=(c+(n=n+Math.imul(h,lt)|0)|0)+((8191&(i=(i=i+Math.imul(h,ct)|0)+Math.imul(f,lt)|0))<<13)|0;c=((a=a+Math.imul(f,ct)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(D,V),i=(i=Math.imul(D,U))+Math.imul(R,V)|0,a=Math.imul(R,U),n=n+Math.imul(P,H)|0,i=(i=i+Math.imul(P,G)|0)+Math.imul(I,H)|0,a=a+Math.imul(I,G)|0,n=n+Math.imul(C,Y)|0,i=(i=i+Math.imul(C,X)|0)+Math.imul(L,Y)|0,a=a+Math.imul(L,X)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,J)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,J)|0,n=n+Math.imul(k,Q)|0,i=(i=i+Math.imul(k,tt)|0)+Math.imul(M,Q)|0,a=a+Math.imul(M,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(g,lt)|0,a=a+Math.imul(g,ct)|0;var Mt=(c+(n=n+Math.imul(h,ht)|0)|0)+((8191&(i=(i=i+Math.imul(h,ft)|0)+Math.imul(f,ht)|0))<<13)|0;c=((a=a+Math.imul(f,ft)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(F,V),i=(i=Math.imul(F,U))+Math.imul(N,V)|0,a=Math.imul(N,U),n=n+Math.imul(D,H)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,H)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(P,Y)|0,i=(i=i+Math.imul(P,X)|0)+Math.imul(I,Y)|0,a=a+Math.imul(I,X)|0,n=n+Math.imul(C,$)|0,i=(i=i+Math.imul(C,J)|0)+Math.imul(L,$)|0,a=a+Math.imul(L,J)|0,n=n+Math.imul(T,Q)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(S,Q)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(k,rt)|0,i=(i=i+Math.imul(k,nt)|0)+Math.imul(M,rt)|0,a=a+Math.imul(M,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ht)|0,i=(i=i+Math.imul(d,ft)|0)+Math.imul(g,ht)|0,a=a+Math.imul(g,ft)|0;var At=(c+(n=n+Math.imul(h,dt)|0)|0)+((8191&(i=(i=i+Math.imul(h,gt)|0)+Math.imul(f,dt)|0))<<13)|0;c=((a=a+Math.imul(f,gt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(F,H),i=(i=Math.imul(F,G))+Math.imul(N,H)|0,a=Math.imul(N,G),n=n+Math.imul(D,Y)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,Y)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(P,$)|0,i=(i=i+Math.imul(P,J)|0)+Math.imul(I,$)|0,a=a+Math.imul(I,J)|0,n=n+Math.imul(C,Q)|0,i=(i=i+Math.imul(C,tt)|0)+Math.imul(L,Q)|0,a=a+Math.imul(L,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(k,at)|0,i=(i=i+Math.imul(k,ot)|0)+Math.imul(M,at)|0,a=a+Math.imul(M,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ht)|0,i=(i=i+Math.imul(v,ft)|0)+Math.imul(y,ht)|0,a=a+Math.imul(y,ft)|0;var Tt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,gt)|0)+Math.imul(g,dt)|0))<<13)|0;c=((a=a+Math.imul(g,gt)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(F,Y),i=(i=Math.imul(F,X))+Math.imul(N,Y)|0,a=Math.imul(N,X),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,J)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,J)|0,n=n+Math.imul(P,Q)|0,i=(i=i+Math.imul(P,tt)|0)+Math.imul(I,Q)|0,a=a+Math.imul(I,tt)|0,n=n+Math.imul(C,rt)|0,i=(i=i+Math.imul(C,nt)|0)+Math.imul(L,rt)|0,a=a+Math.imul(L,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(k,lt)|0,i=(i=i+Math.imul(k,ct)|0)+Math.imul(M,lt)|0,a=a+Math.imul(M,ct)|0,n=n+Math.imul(b,ht)|0,i=(i=i+Math.imul(b,ft)|0)+Math.imul(_,ht)|0,a=a+Math.imul(_,ft)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,gt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,gt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(F,$),i=(i=Math.imul(F,J))+Math.imul(N,$)|0,a=Math.imul(N,J),n=n+Math.imul(D,Q)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,Q)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(P,rt)|0,i=(i=i+Math.imul(P,nt)|0)+Math.imul(I,rt)|0,a=a+Math.imul(I,nt)|0,n=n+Math.imul(C,at)|0,i=(i=i+Math.imul(C,ot)|0)+Math.imul(L,at)|0,a=a+Math.imul(L,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(k,ht)|0,i=(i=i+Math.imul(k,ft)|0)+Math.imul(M,ht)|0,a=a+Math.imul(M,ft)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,gt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,gt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(F,Q),i=(i=Math.imul(F,tt))+Math.imul(N,Q)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(P,at)|0,i=(i=i+Math.imul(P,ot)|0)+Math.imul(I,at)|0,a=a+Math.imul(I,ot)|0,n=n+Math.imul(C,lt)|0,i=(i=i+Math.imul(C,ct)|0)+Math.imul(L,lt)|0,a=a+Math.imul(L,ct)|0,n=n+Math.imul(T,ht)|0,i=(i=i+Math.imul(T,ft)|0)+Math.imul(S,ht)|0,a=a+Math.imul(S,ft)|0;var Ct=(c+(n=n+Math.imul(k,dt)|0)|0)+((8191&(i=(i=i+Math.imul(k,gt)|0)+Math.imul(M,dt)|0))<<13)|0;c=((a=a+Math.imul(M,gt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(F,rt),i=(i=Math.imul(F,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(P,lt)|0,i=(i=i+Math.imul(P,ct)|0)+Math.imul(I,lt)|0,a=a+Math.imul(I,ct)|0,n=n+Math.imul(C,ht)|0,i=(i=i+Math.imul(C,ft)|0)+Math.imul(L,ht)|0,a=a+Math.imul(L,ft)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,gt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,gt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(F,at),i=(i=Math.imul(F,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(P,ht)|0,i=(i=i+Math.imul(P,ft)|0)+Math.imul(I,ht)|0,a=a+Math.imul(I,ft)|0;var zt=(c+(n=n+Math.imul(C,dt)|0)|0)+((8191&(i=(i=i+Math.imul(C,gt)|0)+Math.imul(L,dt)|0))<<13)|0;c=((a=a+Math.imul(L,gt)|0)+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,n=Math.imul(F,lt),i=(i=Math.imul(F,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ht)|0,i=(i=i+Math.imul(D,ft)|0)+Math.imul(R,ht)|0,a=a+Math.imul(R,ft)|0;var Pt=(c+(n=n+Math.imul(P,dt)|0)|0)+((8191&(i=(i=i+Math.imul(P,gt)|0)+Math.imul(I,dt)|0))<<13)|0;c=((a=a+Math.imul(I,gt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(F,ht),i=(i=Math.imul(F,ft))+Math.imul(N,ht)|0,a=Math.imul(N,ft);var It=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,gt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,gt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863;var Ot=(c+(n=Math.imul(F,dt))|0)+((8191&(i=(i=Math.imul(F,gt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,gt))+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,l[0]=mt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=kt,l[8]=Mt,l[9]=At,l[10]=Tt,l[11]=St,l[12]=Et,l[13]=Ct,l[14]=Lt,l[15]=zt,l[16]=Pt,l[17]=It,l[18]=Ot,0!==c&&(l[19]=c,r.length++),r};function d(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(p=f),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?p(this,t,e):r<63?f(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,h=(0|t.words[u])*(0|e.words[c]),f=67108863&h;s=67108863&(f=f+s|0),i+=(o=(o=o+(h/67108864|0)|0)+(f>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):d(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var h=l,f=c,p=0;p<o;p++){var d=r[u+p],g=n[u+p],m=r[u+p+o],v=n[u+p+o],y=h*m-f*v;v=h*v+f*m,m=y,r[u+p]=d+m,n[u+p]=g+v,r[u+p+o]=d-m,n[u+p+o]=g-v,p!==s&&(y=l*h-c*f,f=l*f+c*h,h=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),h=new Array(n),f=r.words;f.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,h,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*h[p];l[p]=s[p]*h[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,f,a,n,i),this.conjugate(f,a,n),this.normalize13b(f,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),d(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n("number"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n("number"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n("number"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var h=0|this.words[c];this.words[c]=u<<26-a|h>>>a,u=h&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,"imaskn works only with positive numbers"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n("number"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n("number"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if("mod"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var h=l-1;h>=0;h--){var f=67108864*(0|n.words[i.length+h])+(0|n.words[i.length+h-1]);for(f=Math.min(f/o|0,67108863),n._ishlnsubmul(i,f,h);0!==n.negative;)f--,n.negative=0,n._ishlnsubmul(i,1,h),n.isZero()||(n.negative^=1);s&&(s.words[h]=f)}return s&&s.strip(),n.strip(),"div"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),"mod"!==e&&(i=s.div.neg()),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),"mod"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?"div"===e?{div:this.divn(t.words[0]),mod:null}:"mod"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,"div",!1).div},a.prototype.mod=function(t){return this.divmod(t,"mod",!1).mod},a.prototype.umod=function(t){return this.divmod(t,"mod",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),h=e.clone();!e.isZero();){for(var f=0,p=1;0==(e.words[0]&p)&&f<26;++f,p<<=1);if(f>0)for(e.iushrn(f);f-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(h)),i.iushrn(1),o.iushrn(1);for(var d=0,g=1;0==(r.words[0]&g)&&d<26;++d,g<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(h)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- 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e=t.height,r=t.data,n=3;n<r.length;n+=4)if(0!==r[n])return Math.floor(.25*(n-3)/e)}function o(t){for(var e=t.height,r=t.data,n=r.length-1;n>0;n-=4)if(0!==r[n])return Math.floor(.25*(n-3)/e)}e.exports=n,n.canvas=document.createElement("canvas"),n.cache={}},{}],219:[function(t,e,r){"use strict";e.exports=function(t){return new c(t||d,null)};var n=0,i=1;function a(t,e,r,n,i,a){this._color=t,this.key=e,this.value=r,this.left=n,this.right=i,this._count=a}function o(t){return new a(t._color,t.key,t.value,t.left,t.right,t._count)}function s(t,e){return new a(t,e.key,e.value,e.left,e.right,e._count)}function l(t){t._count=1+(t.left?t.left._count:0)+(t.right?t.right._count:0)}function c(t,e){this._compare=t,this.root=e}var u=c.prototype;function h(t,e){this.tree=t,this._stack=e}Object.defineProperty(u,"keys",{get:function(){var t=[];return this.forEach(function(e,r){t.push(e)}),t}}),Object.defineProperty(u,"values",{get:function(){var t=[];return 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c=(a=r.right=o(a)).right=o(a.right),r.right=a.left,a.left=r,a.right=c,a._color=r._color,e._color=i,r._color=i,c._color=i,l(r),l(a),u>1&&((h=t[u-2]).left===r?h.left=a:h.right=a),void(t[u-1]=a);if(a.left&&a.left._color===n)return c=(a=r.right=o(a)).left=o(a.left),r.right=c.left,a.left=c.right,c.left=r,c.right=a,c._color=r._color,r._color=i,a._color=i,e._color=i,l(r),l(a),l(c),u>1&&((h=t[u-2]).left===r?h.left=c:h.right=c),void(t[u-1]=c);if(a._color===i){if(r._color===n)return r._color=i,void(r.right=s(n,a));r.right=s(n,a);continue}a=o(a),r.right=a.left,a.left=r,a._color=r._color,r._color=n,l(r),l(a),u>1&&((h=t[u-2]).left===r?h.left=a:h.right=a),t[u-1]=a,t[u]=r,u+1<t.length?t[u+1]=e:t.push(e),u+=2}else{if((a=r.left).left&&a.left._color===n)return c=(a=r.left=o(a)).left=o(a.left),r.left=a.right,a.right=r,a.left=c,a._color=r._color,e._color=i,r._color=i,c._color=i,l(r),l(a),u>1&&((h=t[u-2]).right===r?h.right=a:h.left=a),void(t[u-1]=a);if(a.right&&a.right._color===n)return 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r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),f.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(f,"hasNext",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),f.update=function(t){var e=this._stack;if(0===e.length)throw new Error("Can't update empty node!");var r=new Array(e.length),n=e[e.length-1];r[r.length-1]=new a(n._color,n.key,t,n.left,n.right,n._count);for(var i=e.length-2;i>=0;--i)(n=e[i]).left===e[i+1]?r[i]=new a(n._color,n.key,n.value,r[i+1],n.right,n._count):r[i]=new a(n._color,n.key,n.value,n.left,r[i+1],n._count);return new c(this.tree._compare,r[0])},f.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(f,"hasPrev",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],220:[function(t,e,r){var n=[.9999999999998099,676.5203681218851,-1259.1392167224028,771.3234287776531,-176.6150291621406,12.507343278686905,-.13857109526572012,9984369578019572e-21,1.5056327351493116e-7],i=607/128,a=[.9999999999999971,57.15623566586292,-59.59796035547549,14.136097974741746,-.4919138160976202,3399464998481189e-20,4652362892704858e-20,-9837447530487956e-20,.0001580887032249125,-.00021026444172410488,.00021743961811521265,-.0001643181065367639,8441822398385275e-20,-26190838401581408e-21,36899182659531625e-22];function o(t){if(t<0)return Number("0/0");for(var e=a[0],r=a.length-1;r>0;--r)e+=a[r]/(t+r);var n=t+i+.5;return.5*Math.log(2*Math.PI)+(t+.5)*Math.log(n)-n+Math.log(e)-Math.log(t)}e.exports=function t(e){if(e<.5)return Math.PI/(Math.sin(Math.PI*e)*t(1-e));if(e>100)return Math.exp(o(e));e-=1;for(var r=n[0],i=1;i<9;i++)r+=n[i]/(e+i);var a=e+7+.5;return Math.sqrt(2*Math.PI)*Math.pow(a,e+.5)*Math.exp(-a)*r},e.exports.log=o},{}],221:[function(t,e,r){e.exports=function(t,e){if("string"!=typeof t)throw new TypeError("must specify type string");if(e=e||{},"undefined"==typeof document&&!e.canvas)return null;var r=e.canvas||document.createElement("canvas");"number"==typeof e.width&&(r.width=e.width);"number"==typeof e.height&&(r.height=e.height);var n,i=e;try{var a=[t];0===t.indexOf("webgl")&&a.push("experimental-"+t);for(var o=0;o<a.length;o++)if(n=r.getContext(a[o],i))return n}catch(t){n=null}return n||null}},{}],222:[function(t,e,r){"use strict";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t("./lib/text.js"),i=t("./lib/lines.js"),a=t("./lib/background.js"),o=t("./lib/cube.js"),s=t("./lib/ticks.js"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont="sans-serif",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var h=u.prototype;function f(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}h.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]}),u=!1,h=!1;if("bounds"in t)for(var f=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)f[p][d]!==this.bounds[p][d]&&(h=!0),this.bounds[p][d]=f[p][d];if("ticks"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a("tickSpacing")&&(this.autoTicks=!0,h=!0);if(this._firstInit&&("ticks"in t||"tickSpacing"in t||(this.autoTicks=!0),h=!0,u=!0,this._firstInit=!1),h&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort(function(t,e){return t.x-e.x});s.equal(r,this.ticks)?u=!1:this.ticks=r}o("tickEnable"),l("tickFont")&&(u=!0),a("tickSize"),a("tickAngle"),a("tickPad"),c("tickColor");var g=l("labels");l("labelFont")&&(g=!0),o("labelEnable"),a("labelSize"),a("labelPad"),c("labelColor"),o("lineEnable"),o("lineMirror"),a("lineWidth"),c("lineColor"),o("lineTickEnable"),o("lineTickMirror"),a("lineTickLength"),a("lineTickWidth"),c("lineTickColor"),o("gridEnable"),a("gridWidth"),c("gridColor"),o("zeroEnable"),c("zeroLineColor"),a("zeroLineWidth"),o("backgroundEnable"),c("backgroundColor"),this._text?this._text&&(g||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new f,new f,new f];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var h=a,f=s,p=o,d=l;c&1<<u&&(h=s,f=a,p=l,d=o),h[u]=r[0][u],f[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var g=[0,0,0],m={model:l,view:l,projection:l};h.isOpaque=function(){return!0},h.isTransparent=function(){return!1},h.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];h.draw=function(t){t=t||m;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=o(r,n,i,a),u=s.cubeEdges,h=s.axis,f=n[12],b=n[13],_=n[14],w=n[15],k=this.pixelRatio*(i[3]*f+i[7]*b+i[11]*_+i[15]*w)/e.drawingBufferHeight,M=0;M<3;++M)this.lastCubeProps.cubeEdges[M]=u[M],this.lastCubeProps.axis[M]=h[M];var A=p;for(M=0;M<3;++M)d(p[M],M,this.bounds,u,h);e=this.gl;var T=g;for(M=0;M<3;++M)this.backgroundEnable[M]?T[M]=h[M]:T[M]=0;this._background.draw(r,n,i,a,T,this.backgroundColor),this._lines.bind(r,n,i,this);for(M=0;M<3;++M){var S=[0,0,0];h[M]>0?S[M]=a[1][M]:S[M]=a[0][M];for(var E=0;E<2;++E){var C=(M+1+E)%3,L=(M+1+(1^E))%3;this.gridEnable[C]&&this._lines.drawGrid(C,L,this.bounds,S,this.gridColor[C],this.gridWidth[C]*this.pixelRatio)}for(E=0;E<2;++E){C=(M+1+E)%3,L=(M+1+(1^E))%3;this.zeroEnable[L]&&a[0][L]<=0&&a[1][L]>=0&&this._lines.drawZero(C,L,this.bounds,S,this.zeroLineColor[L],this.zeroLineWidth[L]*this.pixelRatio)}}for(M=0;M<3;++M){this.lineEnable[M]&&this._lines.drawAxisLine(M,this.bounds,A[M].primalOffset,this.lineColor[M],this.lineWidth[M]*this.pixelRatio),this.lineMirror[M]&&this._lines.drawAxisLine(M,this.bounds,A[M].mirrorOffset,this.lineColor[M],this.lineWidth[M]*this.pixelRatio);var z=c(v,A[M].primalMinor),P=c(y,A[M].mirrorMinor),I=this.lineTickLength;for(E=0;E<3;++E){var O=k/r[5*E];z[E]*=I[E]*O,P[E]*=I[E]*O}this.lineTickEnable[M]&&this._lines.drawAxisTicks(M,A[M].primalOffset,z,this.lineTickColor[M],this.lineTickWidth[M]*this.pixelRatio),this.lineTickMirror[M]&&this._lines.drawAxisTicks(M,A[M].mirrorOffset,P,this.lineTickColor[M],this.lineTickWidth[M]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);for(M=0;M<3;++M){var D=A[M].primalMinor,R=c(x,A[M].primalOffset);for(E=0;E<3;++E)this.lineTickEnable[M]&&(R[E]+=k*D[E]*Math.max(this.lineTickLength[E],0)/r[5*E]);if(this.tickEnable[M]){for(E=0;E<3;++E)R[E]+=k*D[E]*this.tickPad[E]/r[5*E];this._text.drawTicks(M,this.tickSize[M],this.tickAngle[M],R,this.tickColor[M])}if(this.labelEnable[M]){for(E=0;E<3;++E)R[E]+=k*D[E]*this.labelPad[E]/r[5*E];R[M]+=.5*(a[0][M]+a[1][M]),this._text.drawLabel(M,this.labelSize[M],this.labelAngle[M],R,this.labelColor[M])}}this._text.unbind()},h.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{"./lib/background.js":223,"./lib/cube.js":224,"./lib/lines.js":225,"./lib/text.js":227,"./lib/ticks.js":228}],223:[function(t,e,r){"use strict";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,h=[0,0,0],f=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),h[l]=p,f[l]=p;for(var d=-1;d<=1;d+=2){h[c]=d;for(var g=-1;g<=1;g+=2)h[u]=g,e.push(h[0],h[1],h[2],f[0],f[1],f[2]),s+=1}var m=c;c=u,u=m}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders":226,"gl-buffer":230,"gl-vao":308}],224:[function(t,e,r){"use strict";e.exports=function(t,e,r,a){i(s,e,t),i(s,r,s);for(var p=0,y=0;y<2;++y){u[2]=a[y][2];for(var x=0;x<2;++x){u[1]=a[x][1];for(var b=0;b<2;++b)u[0]=a[b][0],f(l[p],u,s),p+=1}}for(var _=-1,y=0;y<8;++y){for(var w=l[y][3],k=0;k<3;++k)c[y][k]=l[y][k]/w;w<0&&(_<0?_=y:c[y][2]<c[_][2]&&(_=y))}if(_<0){_=0;for(var M=0;M<3;++M){for(var A=(M+2)%3,T=(M+1)%3,S=-1,E=-1,C=0;C<2;++C){var L=C<<M,z=L+(C<<A)+(1-C<<T),P=L+(1-C<<A)+(C<<T);o(c[L],c[z],c[P],h)<0||(C?S=1:E=1)}if(S<0||E<0)E>S&&(_|=1<<M);else{for(var C=0;C<2;++C){var L=C<<M,z=L+(C<<A)+(1-C<<T),P=L+(1-C<<A)+(C<<T),I=d([l[L],l[z],l[P],l[L+(1<<A)+(1<<T)]]);C?S=I:E=I}E>S&&(_|=1<<M)}}}for(var O=7^_,D=-1,y=0;y<8;++y)y!==_&&y!==O&&(D<0?D=y:c[D][1]>c[y][1]&&(D=y));for(var R=-1,y=0;y<3;++y){var B=D^1<<y;if(B!==_&&B!==O){R<0&&(R=B);var T=c[B];T[0]<c[R][0]&&(R=B)}}for(var F=-1,y=0;y<3;++y){var B=D^1<<y;if(B!==_&&B!==O&&B!==R){F<0&&(F=B);var T=c[B];T[0]>c[F][0]&&(F=B)}}var N=g;N[0]=N[1]=N[2]=0,N[n.log2(R^D)]=D&R,N[n.log2(D^F)]=D&F;var j=7^F;j===_||j===O?(j=7^R,N[n.log2(F^j)]=j&F):N[n.log2(R^j)]=j&R;for(var V=m,U=_,M=0;M<3;++M)V[M]=U&1<<M?-1:1;return v};var n=t("bit-twiddle"),i=t("gl-mat4/multiply"),a=(t("gl-mat4/invert"),t("split-polygon")),o=t("robust-orientation"),s=new Array(16),l=(new Array(16),new Array(8)),c=new Array(8),u=new Array(3),h=[0,0,0];function f(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],c[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,h=l[0]/l[3]-n,f=l[1]/l[3]-i;o+=Math.abs(c*f-u*h)}return o}var g=[1,1,1],m=[0,0,0],v={cubeEdges:g,axis:m}},{"bit-twiddle":80,"gl-mat4/invert":254,"gl-mat4/multiply":256,"robust-orientation":471,"split-polygon":488}],225:[function(t,e,r){"use strict";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var h=0;h<3;++h){for(var f=o.length/3|0,d=0;d<r[h].length;++d){var g=+r[h][d].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var m=o.length/3|0;s[h]=f,l[h]=m-f;for(var f=o.length/3|0,v=0;v<r[h].length;++v){var g=+r[h][v].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var m=o.length/3|0;c[h]=f,u[h]=m-f}var y=n(t,new 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p=[0,0,0];u.drawLabel=function(t,e,r,n,i){this.labelCount[t]&&(this.shader.uniforms.axis=p,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this,t("_process"))},{"./shaders":226,_process:449,"gl-buffer":230,"gl-vao":308,"vectorize-text":515}],228:[function(t,e,r){"use strict";function n(t,e){var r=t+"",n=r.indexOf("."),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+"";if(s.indexOf("e")>=0)return s;var l=o/a,c=o%a;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=""+l;if(o<0&&(u="-"+u),i){for(var h=""+c;h.length<i;)h="0"+h;return u+"."+h}return u}r.create=function(t,e){for(var r=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:n(e[i],o)});for(var 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A=S[E],L=d(m,u,A,r,l),C=0;C<3;++C)b[C].lo=Math.min(b[C].lo,A[C]),b[C].hi=Math.max(b[C].hi,A[C]),C!==k&&(b[C].pixelsPerDataUnit=Math.min(b[C].pixelsPerDataUnit,Math.abs(L[C])))}}}return b};var n=t("extract-frustum-planes"),i=t("split-polygon"),a=t("./lib/cube.js"),o=t("gl-mat4/multiply"),s=t("gl-mat4/transpose"),l=t("gl-vec4/transformMat4"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function h(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var f=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=f,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,h=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+h*h)}return t}var g=[new h(1/0,-1/0,1/0),new h(1/0,-1/0,1/0),new h(1/0,-1/0,1/0)],m=[0,0,0]},{"./lib/cube.js":224,"extract-frustum-planes":213,"gl-mat4/multiply":256,"gl-mat4/transpose":264,"gl-vec4/transformMat4":366,"split-polygon":488}],230:[function(t,e,r){"use strict";var n=t("typedarray-pool"),i=t("ndarray-ops"),a=t("ndarray"),o=["uint8","uint8_clamped","uint16","uint32","int8","int16","int32","float32"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error("gl-buffer: If resizing buffer, must not specify offset");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if("number"!=typeof e&&(e=-1),this.bind(),"object"==typeof t&&"undefined"!=typeof t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r="float32"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension("OES_element_index_uint")&&"uint16"!==r?"uint32":"uint16";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var h;h=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,"uint16"):u(t,"float32"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?h:h.subarray(0,t.length),e),n.free(h)}else if("object"==typeof t&&"number"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if("number"!=typeof t&&void 0!==t)throw new Error("gl-buffer: Invalid data type");if(e>=0)throw new Error("gl-buffer: Cannot specify offset when resizing buffer");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error("gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error("gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:417,"ndarray-ops":411,"typedarray-pool":507}],231:[function(t,e,r){"use strict";var n=t("gl-vec3"),i=(t("gl-vec4"),function(t,e){for(var r=0;r<t.length;r++)if(t[r]>=e)return r-1;return r}),a=n.create(),o=n.create(),s=function(t,e,r){return t<e?e:t>r?r:t},l=function(t,e,r,l){var c=t[0],u=t[1],h=t[2],f=r[0].length,p=r[1].length,d=r[2].length,g=i(r[0],c),m=i(r[1],u),v=i(r[2],h),y=g+1,x=m+1,b=v+1;if(l&&(g=s(g,0,f-1),y=s(y,0,f-1),m=s(m,0,p-1),x=s(x,0,p-1),v=s(v,0,d-1),b=s(b,0,d-1)),g<0||m<0||v<0||y>=f||x>=p||b>=d)return n.create();var _=(c-r[0][g])/(r[0][y]-r[0][g]),w=(u-r[1][m])/(r[1][x]-r[1][m]),k=(h-r[2][v])/(r[2][b]-r[2][v]);(_<0||_>1||isNaN(_))&&(_=0),(w<0||w>1||isNaN(w))&&(w=0),(k<0||k>1||isNaN(k))&&(k=0);var M=v*f*p,A=b*f*p,T=m*f,S=x*f,E=g,C=y,L=e[T+M+E],z=e[T+M+C],P=e[S+M+E],I=e[S+M+C],O=e[T+A+E],D=e[T+A+C],R=e[S+A+E],B=e[S+A+C],F=n.create();return n.lerp(F,L,z,_),n.lerp(a,P,I,_),n.lerp(F,F,a,w),n.lerp(a,O,D,_),n.lerp(o,R,B,_),n.lerp(a,a,o,w),n.lerp(F,F,a,k),F};e.exports=function(t,e){var r;r=t.positions?t.positions:function(t){for(var e=t[0],r=t[1],n=t[2],i=[],a=0;a<n.length;a++)for(var o=0;o<r.length;o++)for(var s=0;s<e.length;s++)i.push([n[a],r[o],e[s]]);return i}(t.meshgrid);var i=t.meshgrid,a=t.vectors,o={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vertexNormals:[],vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),o;for(var s=0,c=1/0,u=-1/0,h=1/0,f=-1/0,p=1/0,d=-1/0,g=null,m=null,v=[],y=1/0,x=0;x<r.length;x++){var b,_=r[x];c=Math.min(_[0],c),u=Math.max(_[0],u),h=Math.min(_[1],h),f=Math.max(_[1],f),p=Math.min(_[2],p),d=Math.max(_[2],d),b=i?l(_,a,i,!0):a[x],n.length(b)>s&&(s=n.length(b)),x&&(y=Math.min(y,2*n.distance(g,_)/(n.length(m)+n.length(b)))),g=_,m=b,v.push(b)}var w=[c,h,p],k=[u,f,d];e&&(e[0]=w,e[1]=k),0===s&&(s=1);var M=1/s;isFinite(y)&&!isNaN(y)||(y=1),o.vectorScale=y;var A=function(t,e,r){var i=n.create();return void 0!==t&&n.set(i,t,e,r),i}(0,1,0),T=t.coneSize||.5;t.absoluteConeSize&&(T=t.absoluteConeSize*M),o.coneScale=T;x=0;for(var S=0;x<r.length;x++)for(var E=(_=r[x])[0],C=_[1],L=_[2],z=v[x],P=n.length(z)*M,I=0;I<8;I++){o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.vectors.push(z),o.vectors.push(z),o.vectors.push(z),o.vectors.push(z),o.vectors.push(z),o.vectors.push(z),o.vertexIntensity.push(P,P,P),o.vertexIntensity.push(P,P,P),o.vertexNormals.push(A,A,A),o.vertexNormals.push(A,A,A);var O=o.positions.length;o.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return o},e.exports.createConeMesh=t("./lib/conemesh")},{"./lib/conemesh":233,"gl-vec3":320,"gl-vec4":350}],232:[function(t,e,r){"use strict";var n=t("barycentric"),i=t("polytope-closest-point/lib/closest_point_2d.js");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}e.exports=function(t,e,r,a,s,l){if(1===t.length)return[0,t[0].slice()];for(var c=new Array(t.length),u=0;u<t.length;++u)c[u]=o(t[u],r,a,s,l);for(var h=0,f=1/0,u=0;u<c.length;++u){for(var p=0,d=0;d<2;++d)p+=Math.pow(c[u][d]-e[d],2);p<f&&(f=p,h=u)}for(var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(c,e),m=0,u=0;u<3;++u){if(g[u]<-.001||g[u]>1.0001)return null;m+=g[u]}if(Math.abs(m-1)>.001)return null;return[h,function(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}(t,g),g]}},{barycentric:61,"polytope-closest-point/lib/closest_point_2d.js":448}],233:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("normals"),l=t("gl-mat4/multiply"),c=t("gl-mat4/invert"),u=t("ndarray"),h=t("colormap"),f=t("simplicial-complex-contour"),p=t("typedarray-pool"),d=t("./shaders"),g=(t("./closest-point"),d.meshShader),m=d.pickShader,v=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function y(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,g,m,y,x,b,_,w,k,M,A,T,S,E){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleVectors=c,this.triangleColors=h,this.triangleNormals=p,this.triangleUVs=f,this.triangleIds=u,this.triangleVAO=d,this.triangleCount=0,this.lineWidth=1,this.edgePositions=g,this.edgeColors=y,this.edgeUVs=x,this.edgeIds=m,this.edgeVAO=b,this.edgeCount=0,this.pointPositions=_,this.pointColors=k,this.pointUVs=M,this.pointSizes=A,this.pointIds=w,this.pointVAO=T,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=S,this.contourVAO=E,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=v,this._view=v,this._projection=v,this._resolution=[1,1]}var x=y.prototype;function b(t){var e=n(t,m.vertex,m.fragment,null,m.attributes);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.vector.location=5,e}x.isOpaque=function(){return this.opacity>=1},x.isTransparent=function(){return this.opacity<1},x.pickSlots=1,x.setPickBase=function(t){this.pickId=t},x.highlight=function(t){if(t&&this.contourEnable){for(var e=f(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var h=c[0];2===c.length&&(h=c[u]);for(var d=n[h][0],g=n[h][1],m=i[h],v=1-m,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=m*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},x.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=h({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],l=[],c=[],f=[],p=[],d=[],g=[],m=[],v=[],y=[],x=[],b=[],_=[],w=[],k=[];this.cells=r,this.positions=n;var M=t.vertexNormals,A=t.cellNormals,T=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,S=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!A&&(A=s.faceNormals(r,n,S)),A||M||(M=s.vertexNormals(r,n,T));var E=t.vertexColors,C=t.cellColors,L=t.meshColor||[1,1,1,1],z=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,D=1/0,R=-1/0;if(!z&&!I)if(P)if(t.vertexIntensityBounds)D=+t.vertexIntensityBounds[0],R=+t.vertexIntensityBounds[1];else for(var B=0;B<P.length;++B){var F=P[B];D=Math.min(D,F),R=Math.max(R,F)}else if(O)for(B=0;B<O.length;++B){F=O[B];D=Math.min(D,F),R=Math.max(R,F)}else for(B=0;B<n.length;++B){F=n[B][2];D=Math.min(D,F),R=Math.max(R,F)}this.intensity=P||(O?function(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;var a=t.length;for(i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}(r,n.length,O):function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n));var N=t.pointSizes,j=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(B=0;B<n.length;++B)for(var V=n[B],U=0;U<3;++U)!isNaN(V[U])&&isFinite(V[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],V[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],V[U]));var q=0,H=0,G=0;t:for(B=0;B<r.length;++B){var W=r[B];switch(W.length){case 1:for(V=n[X=W[0]],U=0;U<3;++U)if(isNaN(V[U])||!isFinite(V[U]))continue t;x.push(V[0],V[1],V[2],V[3]),3===(Z=E?E[X]:C?C[B]:L).length?b.push(Z[0],Z[1],Z[2],1):b.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:P?[(P[X]-D)/(R-D),0]:I?I[B]:O?[(O[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],_.push($[0],$[1]),N?w.push(N[X]):w.push(j),k.push(B),G+=1;break;case 2:for(U=0;U<2;++U){V=n[X=W[U]];for(var Y=0;Y<3;++Y)if(isNaN(V[Y])||!isFinite(V[Y]))continue t}for(U=0;U<2;++U){V=n[X=W[U]];g.push(V[0],V[1],V[2]),3===(Z=E?E[X]:C?C[B]:L).length?m.push(Z[0],Z[1],Z[2],1):m.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:P?[(P[X]-D)/(R-D),0]:I?I[B]:O?[(O[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],v.push($[0],$[1]),y.push(B)}H+=1;break;case 3:for(U=0;U<3;++U)for(V=n[X=W[U]],Y=0;Y<3;++Y)if(isNaN(V[Y])||!isFinite(V[Y]))continue t;for(U=0;U<3;++U){var X;V=n[X=W[U]];a.push(V[0],V[1],V[2],V[3]);var Z,$,J,K=i[X];l.push(K[0],K[1],K[2]),3===(Z=E?E[X]:C?C[B]:L).length?c.push(Z[0],Z[1],Z[2],1):c.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:P?[(P[X]-D)/(R-D),0]:I?I[B]:O?[(O[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],p.push($[0],$[1]),J=M?M[X]:A[B],f.push(J[0],J[1],J[2]),d.push(B)}q+=1}}this.pointCount=G,this.edgeCount=H,this.triangleCount=q,this.pointPositions.update(x),this.pointColors.update(b),this.pointUVs.update(_),this.pointSizes.update(w),this.pointIds.update(new Uint32Array(k)),this.edgePositions.update(g),this.edgeColors.update(m),this.edgeUVs.update(v),this.edgeIds.update(new Uint32Array(y)),this.trianglePositions.update(a),this.triangleVectors.update(l),this.triangleColors.update(c),this.triangleUVs.update(p),this.triangleNormals.update(f),this.triangleIds.update(new Uint32Array(d))}},x.drawTransparent=x.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||v,n=t.view||v,i=t.projection||v,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,contourColor:this.contourColor,texture:0};this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var h,f=u[15];for(o=0;o<3;++o)f+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/f}this.triangleCount>0&&((h=this.triShader).bind(),h.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((h=this.lineShader).bind(),h.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((h=this.pointShader).bind(),h.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((h=this.contourShader).bind(),h.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},x.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||v,n=t.view||v,i=t.projection||v,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},x.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3);return{index:Math.floor(r[1]/48),position:n,dataCoordinate:n}},x.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=e.triShader||function(t){var e=n(t,g.vertex,g.fragment,null,g.attributes);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.vector.location=5,e}(t),s=b(t),l=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));l.generateMipmap(),l.minFilter=t.LINEAR_MIPMAP_LINEAR,l.magFilter=t.LINEAR;var c=i(t),h=i(t),f=i(t),p=i(t),d=i(t),m=i(t),v=a(t,[{buffer:c,type:t.FLOAT,size:4},{buffer:m,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:f,type:t.FLOAT,size:4},{buffer:p,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:3},{buffer:h,type:t.FLOAT,size:3}]),x=i(t),_=i(t),w=i(t),k=i(t),M=a(t,[{buffer:x,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),A=i(t),T=i(t),S=i(t),E=i(t),C=i(t),L=a(t,[{buffer:A,type:t.FLOAT,size:3},{buffer:C,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:T,type:t.FLOAT,size:4},{buffer:S,type:t.FLOAT,size:2},{buffer:E,type:t.FLOAT,size:1}]),z=i(t),P=new y(t,l,r,null,null,s,null,null,c,h,m,f,p,d,v,x,k,_,w,M,A,C,T,S,E,L,z,a(t,[{buffer:z,type:t.FLOAT,size:3}]));return P.update(e),P}},{"./closest-point":232,"./shaders":234,colormap:113,"gl-buffer":230,"gl-mat4/invert":254,"gl-mat4/multiply":256,"gl-shader":287,"gl-texture2d":303,"gl-vao":308,ndarray:417,normals:420,"simplicial-complex-contour":479,"typedarray-pool":507}],234:[function(t,e,r){var n=t("glslify"),i=n(["precision mediump float;\n#define GLSLIFY 1\n\nfloat inverse(float m) {\n  return 1.0 / m;\n}\n\nmat2 inverse(mat2 m) {\n  return mat2(m[1][1],-m[0][1],\n             -m[1][0], m[0][0]) / (m[0][0]*m[1][1] - m[0][1]*m[1][0]);\n}\n\nmat3 inverse(mat3 m) {\n  float a00 = m[0][0], a01 = m[0][1], a02 = m[0][2];\n  float a10 = m[1][0], a11 = m[1][1], a12 = m[1][2];\n  float a20 = m[2][0], a21 = m[2][1], a22 = m[2][2];\n\n  float b01 = a22 * a11 - a12 * a21;\n  float b11 = -a22 * a10 + a12 * a20;\n  float b21 = a21 * a10 - a11 * a20;\n\n  float det = a00 * b01 + a01 * b11 + a02 * b21;\n\n  return mat3(b01, (-a22 * a01 + a02 * a21), (a12 * a01 - a02 * a11),\n              b11, (a22 * a00 - a02 * a20), (-a12 * a00 + a02 * a10),\n              b21, (-a21 * a00 + a01 * a20), (a11 * a00 - a01 * a10)) / det;\n}\n\nmat4 inverse(mat4 m) {\n  float\n      a00 = m[0][0], a01 = m[0][1], a02 = m[0][2], a03 = m[0][3],\n      a10 = m[1][0], a11 = m[1][1], a12 = m[1][2], a13 = m[1][3],\n      a20 = m[2][0], a21 = m[2][1], a22 = m[2][2], a23 = m[2][3],\n      a30 = m[3][0], a31 = m[3][1], a32 = m[3][2], a33 = m[3][3],\n\n      b00 = a00 * a11 - a01 * a10,\n      b01 = a00 * a12 - a02 * a10,\n      b02 = a00 * a13 - a03 * a10,\n      b03 = a01 * a12 - a02 * a11,\n      b04 = a01 * a13 - a03 * a11,\n      b05 = a02 * a13 - a03 * a12,\n      b06 = a20 * a31 - a21 * a30,\n      b07 = a20 * a32 - a22 * a30,\n      b08 = a20 * a33 - a23 * a30,\n      b09 = a21 * a32 - a22 * a31,\n      b10 = a21 * a33 - a23 * a31,\n      b11 = a22 * a33 - a23 * a32,\n\n      det = b00 * b11 - b01 * b10 + b02 * b09 + b03 * b08 - b04 * b07 + b05 * b06;\n\n  return mat4(\n      a11 * b11 - a12 * b10 + a13 * b09,\n      a02 * b10 - a01 * b11 - a03 * b09,\n      a31 * b05 - a32 * b04 + a33 * b03,\n      a22 * b04 - a21 * b05 - a23 * b03,\n      a12 * b08 - a10 * b11 - a13 * b07,\n      a00 * b11 - a02 * b08 + a03 * b07,\n      a32 * b02 - a30 * b05 - a33 * b01,\n      a20 * b05 - a22 * b02 + a23 * b01,\n      a10 * b10 - a11 * b08 + a13 * b06,\n      a01 * b08 - a00 * b10 - a03 * b06,\n      a30 * b04 - a31 * b02 + a33 * b00,\n      a21 * b02 - a20 * b04 - a23 * b00,\n      a11 * b07 - a10 * b09 - a12 * b06,\n      a00 * b09 - a01 * b07 + a02 * b06,\n      a31 * b01 - a30 * b03 - a32 * b00,\n      a20 * b03 - a21 * b01 + a22 * b00) / det;\n}\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float index, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  index = mod(index, segmentCount * 6.0);\n\n  float segment = floor(index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex == 3.0) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  // angle = 2pi * ((segment + ((segmentIndex == 1.0 || segmentIndex == 5.0) ? 1.0 : 0.0)) / segmentCount)\n  float nextAngle = float(segmentIndex == 1.0 || segmentIndex == 5.0);\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex <= 2.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec3 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\nuniform float vectorScale;\nuniform float coneScale;\n\nuniform float coneOffset;\n\nuniform mat4 model\n           , view\n           , projection;\nuniform vec3 eyePosition\n           , lightPosition;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal), 0.0);\n  normal = normalize(normal * inverse(mat3(model)));\n\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\n  vec4 t_position  = view * conePosition;\n  gl_Position      = projection * t_position;\n  f_color          = color; //vec4(position.w, color.r, 0, 0);\n  f_normal         = normal;\n  f_data           = conePosition.xyz;\n  f_eyeDirection   = eyePosition   - conePosition.xyz;\n  f_lightDirection = lightPosition - conePosition.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["precision mediump float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular\n            , opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  //if(any(lessThan(f_data, clipBounds[0])) || \n  //   any(greaterThan(f_data, clipBounds[1]))) {\n  //  discard;\n  //}\n\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n  \n  if(!gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = cookTorranceSpecular(L, V, N, roughness, fresnel);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor =  texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float index, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  index = mod(index, segmentCount * 6.0);\n\n  float segment = floor(index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex == 3.0) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  // angle = 2pi * ((segment + ((segmentIndex == 1.0 || segmentIndex == 5.0) ? 1.0 : 0.0)) / segmentCount)\n  float nextAngle = float(segmentIndex == 1.0 || segmentIndex == 5.0);\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex <= 2.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec3 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nuniform float vectorScale;\nuniform float coneScale;\nuniform float coneOffset;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal), 0.0);\n  gl_Position = projection * view * conePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if(any(lessThan(f_position, clipBounds[0])) || \n     any(greaterThan(f_position, clipBounds[1]))) {\n    discard;\n  }\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec3"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec3"}]}},{glslify:377}],235:[function(t,e,r){e.exports={0:"NONE",1:"ONE",2:"LINE_LOOP",3:"LINE_STRIP",4:"TRIANGLES",5:"TRIANGLE_STRIP",6:"TRIANGLE_FAN",256:"DEPTH_BUFFER_BIT",512:"NEVER",513:"LESS",514:"EQUAL",515:"LEQUAL",516:"GREATER",517:"NOTEQUAL",518:"GEQUAL",519:"ALWAYS",768:"SRC_COLOR",769:"ONE_MINUS_SRC_COLOR",770:"SRC_ALPHA",771:"ONE_MINUS_SRC_ALPHA",772:"DST_ALPHA",773:"ONE_MINUS_DST_ALPHA",774:"DST_COLOR",775:"ONE_MINUS_DST_COLOR",776:"SRC_ALPHA_SATURATE",1024:"STENCIL_BUFFER_BIT",1028:"FRONT",1029:"BACK",1032:"FRONT_AND_BACK",1280:"INVALID_ENUM",1281:"INVALID_VALUE",1282:"INVALID_OPERATION",1285:"OUT_OF_MEMORY",1286:"INVALID_FRAMEBUFFER_OPERATION",2304:"CW",2305:"CCW",2849:"LINE_WIDTH",2884:"CULL_FACE",2885:"CULL_FACE_MODE",2886:"FRONT_FACE",2928:"DEPTH_RANGE",2929:"DEPTH_TEST",2930:"DEPTH_WRITEMASK",2931:"DEPTH_CLEAR_VALUE",2932:"DEPTH_FUNC",2960:"STENCIL_TEST",2961:"STENCIL_CLEAR_VALUE",2962:"STENCIL_FUNC",2963:"STENCIL_VALUE_MASK",2964:"STENCIL_FAIL",2965:"STENCIL_PASS_DEPTH_FAIL",2966:"STENCIL_PASS_DEPTH_PASS",2967:"STENCIL_REF",2968:"STENCIL_WRITEMASK",2978:"VIEWPORT",3024:"DITHER",3042:"BLEND",3088:"SCISSOR_BOX",3089:"SCISSOR_TEST",3106:"COLOR_CLEAR_VALUE",3107:"COLOR_WRITEMASK",3317:"UNPACK_ALIGNMENT",3333:"PACK_ALIGNMENT",3379:"MAX_TEXTURE_SIZE",3386:"MAX_VIEWPORT_DIMS",3408:"SUBPIXEL_BITS",3410:"RED_BITS",3411:"GREEN_BITS",3412:"BLUE_BITS",3413:"ALPHA_BITS",3414:"DEPTH_BITS",3415:"STENCIL_BITS",3553:"TEXTURE_2D",4352:"DONT_CARE",4353:"FASTEST",4354:"NICEST",5120:"BYTE",5121:"UNSIGNED_BYTE",5122:"SHORT",5123:"UNSIGNED_SHORT",5124:"INT",5125:"UNSIGNED_INT",5126:"FLOAT",5386:"INVERT",5890:"TEXTURE",6401:"STENCIL_INDEX",6402:"DEPTH_COMPONENT",6406:"ALPHA",6407:"RGB",6408:"RGBA",6409:"LUMINANCE",6410:"LUMINANCE_ALPHA",7680:"KEEP",7681:"REPLACE",7682:"INCR",7683:"DECR",7936:"VENDOR",7937:"RENDERER",7938:"VERSION",9728:"NEAREST",9729:"LINEAR",9984:"NEAREST_MIPMAP_NEAREST",9985:"LINEAR_MIPMAP_NEAREST",9986:"NEAREST_MIPMAP_LINEAR",9987:"LINEAR_MIPMAP_LINEAR",10240:"TEXTURE_MAG_FILTER",10241:"TEXTURE_MIN_FILTER",10242:"TEXTURE_WRAP_S",10243:"TEXTURE_WRAP_T",10497:"REPEAT",10752:"POLYGON_OFFSET_UNITS",16384:"COLOR_BUFFER_BIT",32769:"CONSTANT_COLOR",32770:"ONE_MINUS_CONSTANT_COLOR",32771:"CONSTANT_ALPHA",32772:"ONE_MINUS_CONSTANT_ALPHA",32773:"BLEND_COLOR",32774:"FUNC_ADD",32777:"BLEND_EQUATION_RGB",32778:"FUNC_SUBTRACT",32779:"FUNC_REVERSE_SUBTRACT",32819:"UNSIGNED_SHORT_4_4_4_4",32820:"UNSIGNED_SHORT_5_5_5_1",32823:"POLYGON_OFFSET_FILL",32824:"POLYGON_OFFSET_FACTOR",32854:"RGBA4",32855:"RGB5_A1",32873:"TEXTURE_BINDING_2D",32926:"SAMPLE_ALPHA_TO_COVERAGE",32928:"SAMPLE_COVERAGE",32936:"SAMPLE_BUFFERS",32937:"SAMPLES",32938:"SAMPLE_COVERAGE_VALUE",32939:"SAMPLE_COVERAGE_INVERT",32968:"BLEND_DST_RGB",32969:"BLEND_SRC_RGB",32970:"BLEND_DST_ALPHA",32971:"BLEND_SRC_ALPHA",33071:"CLAMP_TO_EDGE",33170:"GENERATE_MIPMAP_HINT",33189:"DEPTH_COMPONENT16",33306:"DEPTH_STENCIL_ATTACHMENT",33635:"UNSIGNED_SHORT_5_6_5",33648:"MIRRORED_REPEAT",33901:"ALIASED_POINT_SIZE_RANGE",33902:"ALIASED_LINE_WIDTH_RANGE",33984:"TEXTURE0",33985:"TEXTURE1",33986:"TEXTURE2",33987:"TEXTURE3",33988:"TEXTURE4",33989:"TEXTURE5",33990:"TEXTURE6",33991:"TEXTURE7",33992:"TEXTURE8",33993:"TEXTURE9",33994:"TEXTURE10",33995:"TEXTURE11",33996:"TEXTURE12",33997:"TEXTURE13",33998:"TEXTURE14",33999:"TEXTURE15",34000:"TEXTURE16",34001:"TEXTURE17",34002:"TEXTURE18",34003:"TEXTURE19",34004:"TEXTURE20",34005:"TEXTURE21",34006:"TEXTURE22",34007:"TEXTURE23",34008:"TEXTURE24",34009:"TEXTURE25",34010:"TEXTURE26",34011:"TEXTURE27",34012:"TEXTURE28",34013:"TEXTURE29",34014:"TEXTURE30",34015:"TEXTURE31",34016:"ACTIVE_TEXTURE",34024:"MAX_RENDERBUFFER_SIZE",34041:"DEPTH_STENCIL",34055:"INCR_WRAP",34056:"DECR_WRAP",34067:"TEXTURE_CUBE_MAP",34068:"TEXTURE_BINDING_CUBE_MAP",34069:"TEXTURE_CUBE_MAP_POSITIVE_X",34070:"TEXTURE_CUBE_MAP_NEGATIVE_X",34071:"TEXTURE_CUBE_MAP_POSITIVE_Y",34072:"TEXTURE_CUBE_MAP_NEGATIVE_Y",34073:"TEXTURE_CUBE_MAP_POSITIVE_Z",34074:"TEXTURE_CUBE_MAP_NEGATIVE_Z",34076:"MAX_CUBE_MAP_TEXTURE_SIZE",34338:"VERTEX_ATTRIB_ARRAY_ENABLED",34339:"VERTEX_ATTRIB_ARRAY_SIZE",34340:"VERTEX_ATTRIB_ARRAY_STRIDE",34341:"VERTEX_ATTRIB_ARRAY_TYPE",34342:"CURRENT_VERTEX_ATTRIB",34373:"VERTEX_ATTRIB_ARRAY_POINTER",34466:"NUM_COMPRESSED_TEXTURE_FORMATS",34467:"COMPRESSED_TEXTURE_FORMATS",34660:"BUFFER_SIZE",34661:"BUFFER_USAGE",34816:"STENCIL_BACK_FUNC",34817:"STENCIL_BACK_FAIL",34818:"STENCIL_BACK_PASS_DEPTH_FAIL",34819:"STENCIL_BACK_PASS_DEPTH_PASS",34877:"BLEND_EQUATION_ALPHA",34921:"MAX_VERTEX_ATTRIBS",34922:"VERTEX_ATTRIB_ARRAY_NORMALIZED",34930:"MAX_TEXTURE_IMAGE_UNITS",34962:"ARRAY_BUFFER",34963:"ELEMENT_ARRAY_BUFFER",34964:"ARRAY_BUFFER_BINDING",34965:"ELEMENT_ARRAY_BUFFER_BINDING",34975:"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING",35040:"STREAM_DRAW",35044:"STATIC_DRAW",35048:"DYNAMIC_DRAW",35632:"FRAGMENT_SHADER",35633:"VERTEX_SHADER",35660:"MAX_VERTEX_TEXTURE_IMAGE_UNITS",35661:"MAX_COMBINED_TEXTURE_IMAGE_UNITS",35663:"SHADER_TYPE",35664:"FLOAT_VEC2",35665:"FLOAT_VEC3",35666:"FLOAT_VEC4",35667:"INT_VEC2",35668:"INT_VEC3",35669:"INT_VEC4",35670:"BOOL",35671:"BOOL_VEC2",35672:"BOOL_VEC3",35673:"BOOL_VEC4",35674:"FLOAT_MAT2",35675:"FLOAT_MAT3",35676:"FLOAT_MAT4",35678:"SAMPLER_2D",35680:"SAMPLER_CUBE",35712:"DELETE_STATUS",35713:"COMPILE_STATUS",35714:"LINK_STATUS",35715:"VALIDATE_STATUS",35716:"INFO_LOG_LENGTH",35717:"ATTACHED_SHADERS",35718:"ACTIVE_UNIFORMS",35719:"ACTIVE_UNIFORM_MAX_LENGTH",35720:"SHADER_SOURCE_LENGTH",35721:"ACTIVE_ATTRIBUTES",35722:"ACTIVE_ATTRIBUTE_MAX_LENGTH",35724:"SHADING_LANGUAGE_VERSION",35725:"CURRENT_PROGRAM",36003:"STENCIL_BACK_REF",36004:"STENCIL_BACK_VALUE_MASK",36005:"STENCIL_BACK_WRITEMASK",36006:"FRAMEBUFFER_BINDING",36007:"RENDERBUFFER_BINDING",36048:"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE",36049:"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME",36050:"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL",36051:"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE",36053:"FRAMEBUFFER_COMPLETE",36054:"FRAMEBUFFER_INCOMPLETE_ATTACHMENT",36055:"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT",36057:"FRAMEBUFFER_INCOMPLETE_DIMENSIONS",36061:"FRAMEBUFFER_UNSUPPORTED",36064:"COLOR_ATTACHMENT0",36096:"DEPTH_ATTACHMENT",36128:"STENCIL_ATTACHMENT",36160:"FRAMEBUFFER",36161:"RENDERBUFFER",36162:"RENDERBUFFER_WIDTH",36163:"RENDERBUFFER_HEIGHT",36164:"RENDERBUFFER_INTERNAL_FORMAT",36168:"STENCIL_INDEX8",36176:"RENDERBUFFER_RED_SIZE",36177:"RENDERBUFFER_GREEN_SIZE",36178:"RENDERBUFFER_BLUE_SIZE",36179:"RENDERBUFFER_ALPHA_SIZE",36180:"RENDERBUFFER_DEPTH_SIZE",36181:"RENDERBUFFER_STENCIL_SIZE",36194:"RGB565",36336:"LOW_FLOAT",36337:"MEDIUM_FLOAT",36338:"HIGH_FLOAT",36339:"LOW_INT",36340:"MEDIUM_INT",36341:"HIGH_INT",36346:"SHADER_COMPILER",36347:"MAX_VERTEX_UNIFORM_VECTORS",36348:"MAX_VARYING_VECTORS",36349:"MAX_FRAGMENT_UNIFORM_VECTORS",37440:"UNPACK_FLIP_Y_WEBGL",37441:"UNPACK_PREMULTIPLY_ALPHA_WEBGL",37442:"CONTEXT_LOST_WEBGL",37443:"UNPACK_COLORSPACE_CONVERSION_WEBGL",37444:"BROWSER_DEFAULT_WEBGL"}},{}],236:[function(t,e,r){var n=t("./1.0/numbers");e.exports=function(t){return n[t]}},{"./1.0/numbers":235}],237:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var c=new s(e,r,o,l);return c.update(t),c};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1}var l=s.prototype;function c(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return this.opacity>=1},l.isTransparent=function(){return this.opacity<1},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],c=n[15],u=this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*c)/e.drawingBufferHeight;this.vao.bind();for(var h=0;h<3;++h)e.lineWidth(this.lineWidth[h]),r.capSize=this.capSize[h]*u,this.lineCount[h]&&e.drawArrays(e.LINES,this.lineOffset[h],this.lineCount[h]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function h(t,e,r,n){for(var i=u[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){"lineWidth"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),"capSize"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),"opacity"in t&&(this.opacity=t.opacity);var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var u=r[l],f=0;f<3;++f)if(isNaN(u[f])||!isFinite(u[f]))continue t;var p=n[l],d=e[s];if(Array.isArray(d[0])&&(d=e[l]),3===d.length&&(d=[d[0],d[1],d[2],1]),!isNaN(p[0][s])&&!isNaN(p[1][s])){var g;if(p[0][s]<0)(g=u.slice())[s]+=p[0][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,g[0],g[1],g[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,g),o+=2+h(i,g,d,s);if(p[1][s]>0)(g=u.slice())[s]+=p[1][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,g[0],g[1],g[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,g),o+=2+h(i,g,d,s)}}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{"./shaders/index":238,"gl-buffer":230,"gl-vao":308}],238:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position, offset;\nattribute vec4 color;\nuniform mat4 model, view, projection;\nuniform float capSize;\nvarying vec4 fragColor;\nvarying vec3 fragPosition;\n\nvoid main() {\n  vec4 worldPosition  = model * vec4(position, 1.0);\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\n  gl_Position         = projection * view * worldPosition;\n  fragColor           = color;\n  fragPosition        = position;\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\nuniform vec3 clipBounds[2];\nuniform float opacity;\nvarying vec3 fragPosition;\nvarying vec4 fragColor;\n\nvoid main() {\n  if(any(lessThan(fragPosition, clipBounds[0])) || any(greaterThan(fragPosition, clipBounds[1]))) {\n    discard;\n  }\n  gl_FragColor = opacity * fragColor;\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"offset",type:"vec3"}])}},{"gl-shader":287,glslify:377}],239:[function(t,e,r){"use strict";var n=t("gl-texture2d");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension("WEBGL_draw_buffers");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(var a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,c);Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]);if("number"!=typeof e)throw new Error("gl-fbo: Missing shape parameter");var u=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>u||r<0||r>u)throw new Error("gl-fbo: Parameters are too large for FBO");var h=1;if("color"in(n=n||{})){if((h=Math.max(0|n.color,0))<0)throw new Error("gl-fbo: Must specify a nonnegative number of colors");if(h>1){if(!c)throw new Error("gl-fbo: Multiple draw buffer extension not supported");if(h>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error("gl-fbo: Context does not support "+h+" draw buffers")}}var f=t.UNSIGNED_BYTE,p=t.getExtension("OES_texture_float");if(n.float&&h>0){if(!p)throw new Error("gl-fbo: Context does not support floating point textures");f=t.FLOAT}else n.preferFloat&&h>0&&p&&(f=t.FLOAT);var g=!0;"depth"in n&&(g=!!n.depth);var m=!1;"stencil"in n&&(m=!!n.stencil);return new d(t,e,r,f,h,g,m,c)};var i,a,o,s,l=null;function c(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function u(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function h(t){switch(t){case i:throw new Error("gl-fbo: Framebuffer unsupported");case a:throw new Error("gl-fbo: Framebuffer incomplete attachment");case o:throw new Error("gl-fbo: Framebuffer incomplete dimensions");case s:throw new Error("gl-fbo: Framebuffer incomplete missing attachment");default:throw new Error("gl-fbo: Framebuffer failed for unspecified reason")}}function f(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var g=this,m=[0|e,0|r];Object.defineProperties(m,{0:{get:function(){return g._shape[0]},set:function(t){return g.width=t}},1:{get:function(){return g._shape[1]},set:function(t){return g.height=t}}}),this._shapeVector=m,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,g=t._useDepth,m=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=f(r,i,a,m,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension("WEBGL_depth_texture");y?d?t.depth=f(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g&&(t.depth=f(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):g&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){for(t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null),v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),h(x)}u(r,e)}(this)}var g=d.prototype;function m(t,e,r){if(t._destroyed)throw new Error("gl-fbo: Can't resize destroyed FBO");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-fbo: Can't resize FBO, invalid dimensions");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),h(s)),u(n,a)}}Object.defineProperties(g,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error("gl-fbo: Shape vector must be length 2");var e=0|t[0],r=0|t[1];return m(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return m(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,m(this,this._shape[0],t),t},enumerable:!1}}),g.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},g.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{"gl-texture2d":303}],240:[function(t,e,r){var n=t("sprintf-js").sprintf,i=t("gl-constants/lookup"),a=t("glsl-shader-name"),o=t("add-line-numbers");e.exports=function(t,e,r){"use strict";var s=a(e)||"of unknown name (see npm glsl-shader-name)",l="unknown type";void 0!==r&&(l=r===i.FRAGMENT_SHADER?"fragment":"vertex");for(var c=n("Error compiling %s shader %s:\n",l,s),u=n("%s%s",c,t),h=t.split("\n"),f={},p=0;p<h.length;p++){var d=h[p];if(""!==d&&"\0"!==d){var g=parseInt(d.split(":")[2]);if(isNaN(g))throw new Error(n("Could not parse error: %s",d));f[g]=d}}for(var m=o(e).split("\n"),p=0;p<m.length;p++)if(f[p+3]||f[p+2]||f[p+1]){var v=m[p];if(c+=v+"\n",f[p+1]){var y=f[p+1];y=y.substr(y.split(":",3).join(":").length+1).trim(),c+=n("^^^ %s\n\n",y)}}return{long:c.trim(),short:u.trim()}}},{"add-line-numbers":52,"gl-constants/lookup":236,"glsl-shader-name":369,"sprintf-js":489}],241:[function(t,e,r){"use strict";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),h=s(r),f=s(r),p=new c(t,n,i,a,u,h,f);return p.update(e),t.addObject(p),p};var n=t("binary-search-bounds"),i=t("iota-array"),a=t("typedarray-pool"),o=t("gl-shader"),s=t("gl-buffer"),l=t("./lib/shaders");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,h=c.prototype,f=[0,0,1,0,0,1,1,0,1,1,0,1];h.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var h=e.uniforms;h.viewTransform=u,h.shape=this.shape;var f=e.attributes;this.positionBuffer.bind(),f.position.pointer(),this.weightBuffer.bind(),f.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),f.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),h.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],h=l[2]-l[0],f=l[3]-l[1];t[0]=2*c/h,t[4]=2*u/f,t[6]=2*(a[0]-l[0])/h-1,t[7]=2*(a[1]-l[1])/f-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var g=i.attributes;return this.positionBuffer.bind(),g.position.pointer(),this.weightBuffer.bind(),g.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),g.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),h.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},h.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]);this.xData=r,this.yData=o;var l=t.colorLevels||[0],c=t.colorValues||[0,0,0,1],u=l.length,h=this.bounds,p=h[0]=r[0],d=h[1]=o[0],g=1/((h[2]=r[r.length-1])-p),m=1/((h[3]=o[o.length-1])-d),v=e[0],y=e[1];this.shape=[v,y];var x=(v-1)*(y-1)*(f.length>>>1);this.numVertices=x;for(var b=a.mallocUint8(4*x),_=a.mallocFloat32(2*x),w=a.mallocUint8(2*x),k=a.mallocUint32(x),M=0,A=0;A<y-1;++A)for(var T=m*(o[A]-d),S=m*(o[A+1]-d),E=0;E<v-1;++E)for(var C=g*(r[E]-p),L=g*(r[E+1]-p),z=0;z<f.length;z+=2){var P,I,O,D,R=f[z],B=f[z+1],F=s[(A+B)*v+(E+R)],N=n.le(l,F);if(N<0)P=c[0],I=c[1],O=c[2],D=c[3];else if(N===u-1)P=c[4*u-4],I=c[4*u-3],O=c[4*u-2],D=c[4*u-1];else{var j=(F-l[N])/(l[N+1]-l[N]),V=1-j,U=4*N,q=4*(N+1);P=V*c[U]+j*c[q],I=V*c[U+1]+j*c[q+1],O=V*c[U+2]+j*c[q+2],D=V*c[U+3]+j*c[q+3]}b[4*M]=255*P,b[4*M+1]=255*I,b[4*M+2]=255*O,b[4*M+3]=255*D,_[2*M]=.5*C+.5*L,_[2*M+1]=.5*T+.5*S,w[2*M]=R,w[2*M+1]=B,k[M]=A*v+E,M+=1}this.positionBuffer.update(_),this.weightBuffer.update(w),this.colorBuffer.update(b),this.idBuffer.update(k),a.free(_),a.free(b),a.free(w),a.free(k)},h.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{"./lib/shaders":242,"binary-search-bounds":243,"gl-buffer":230,"gl-shader":287,"iota-array":384,"typedarray-pool":507}],242:[function(t,e,r){"use strict";var n=t("glslify");e.exports={fragment:n(["precision lowp float;\n#define GLSLIFY 1\nvarying vec4 fragColor;\nvoid main() {\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\n}\n"]),vertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 color;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  fragColor = color;\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"]),pickFragment:n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nuniform vec2 shape;\nuniform vec4 pickOffset;\n\nvoid main() {\n  vec2 d = step(.5, vWeight);\n  vec4 id = fragId + pickOffset;\n  id.x += d.x + d.y*shape.x;\n\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  gl_FragColor = id/255.;\n}\n"]),pickVertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nvoid main() {\n  vWeight = weight;\n\n  fragId = pickId;\n\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"])}},{glslify:377}],243:[function(t,e,r){arguments[4][98][0].apply(r,arguments)},{dup:98}],244:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position, nextPosition;\nattribute float arcLength, lineWidth;\nattribute vec4 color;\n\nuniform vec2 screenShape;\nuniform float pixelRatio;\nuniform mat4 model, view, projection;\n\nvarying vec4 fragColor;\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\n\nvoid main() {\n  vec4 projected = projection * view * model * vec4(position, 1.0);\n  vec4 tangentClip = projection * view * model * vec4(nextPosition - position, 0.0);\n  vec2 tangent = normalize(screenShape * tangentClip.xy);\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(tangent.y, -tangent.x) / screenShape;\n\n  gl_Position = vec4(projected.xy + projected.w * offset, projected.zw);\n\n  worldPosition = position;\n  pixelArcLength = arcLength;\n  fragColor = color;\n}\n"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3      clipBounds[2];\nuniform sampler2D dashTexture;\nuniform float     dashScale;\nuniform float     opacity;\n\nvarying vec3    worldPosition;\nvarying float   pixelArcLength;\nvarying vec4    fragColor;\n\nvoid main() {\n  if(any(lessThan(worldPosition, clipBounds[0])) || any(greaterThan(worldPosition, clipBounds[1]))) {\n    discard;\n  }\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\n  if(dashWeight < 0.5) {\n    discard;\n  }\n  gl_FragColor = fragColor * opacity;\n}\n"]),s=n(["precision mediump float;\n#define GLSLIFY 1\n\n#define FLOAT_MAX  1.70141184e38\n#define FLOAT_MIN  1.17549435e-38\n\nlowp vec4 encode_float_1540259130(highp float v) {\n  highp float av = abs(v);\n\n  //Handle special cases\n  if(av < FLOAT_MIN) {\n    return vec4(0.0, 0.0, 0.0, 0.0);\n  } else if(v > FLOAT_MAX) {\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\n  } else if(v < -FLOAT_MAX) {\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\n  }\n\n  highp vec4 c = vec4(0,0,0,0);\n\n  //Compute exponent and mantissa\n  highp float e = floor(log2(av));\n  highp float m = av * pow(2.0, -e) - 1.0;\n  \n  //Unpack mantissa\n  c[1] = floor(128.0 * m);\n  m -= c[1] / 128.0;\n  c[2] = floor(32768.0 * m);\n  m -= c[2] / 32768.0;\n  c[3] = floor(8388608.0 * m);\n  \n  //Unpack exponent\n  highp float ebias = e + 127.0;\n  c[0] = floor(ebias / 2.0);\n  ebias -= c[0] * 2.0;\n  c[1] += floor(ebias) * 128.0; \n\n  //Unpack sign bit\n  c[0] += 128.0 * step(0.0, -v);\n\n  //Scale back to range\n  return c / 255.0;\n}\n\nuniform float pickId;\nuniform vec3 clipBounds[2];\n\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\nvarying vec4 fragColor;\n\nvoid main() {\n  if(any(lessThan(worldPosition, clipBounds[0])) || any(greaterThan(worldPosition, clipBounds[1]))) {\n    discard;\n  }\n  gl_FragColor = vec4(pickId/255.0, encode_float_1540259130(pixelArcLength).xyz);\n}"]),l=[{name:"position",type:"vec3"},{name:"nextPosition",type:"vec3"},{name:"arcLength",type:"float"},{name:"lineWidth",type:"float"},{name:"color",type:"vec4"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{"gl-shader":287,glslify:377}],245:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=u(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),c=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),f=l(new Array(1024),[256,1,4]),p=0;p<1024;++p)f.data[p]=255;var d=a(e,f);d.wrap=e.REPEAT;var g=new m(e,r,o,s,c,d);return g.update(t),g};var n=t("gl-buffer"),i=t("gl-vao"),a=t("gl-texture2d"),o=t("glsl-read-float"),s=t("binary-search-bounds"),l=t("ndarray"),c=t("./lib/shaders"),u=c.createShader,h=c.createPickShader,f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function p(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function d(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function m(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.dirty=!0,this.pixelRatio=1}var v=m.prototype;v.isTransparent=function(){return this.opacity<1},v.isOpaque=function(){return this.opacity>=1},v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.drawTransparent=v.draw=function(t){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||f,view:t.view||f,projection:t.projection||f,clipBounds:d(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()},v.drawPick=function(t){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||f,view:t.view||f,projection:t.projection||f,pickId:this.pickId,clipBounds:d(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()},v.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;"dashScale"in t&&(this.dashScale=t.dashScale),"opacity"in t&&(this.opacity=+t.opacity);var i=t.position||t.positions;if(i){var a=t.color||t.colors||[0,0,0,1],o=t.lineWidth||1,c=[],u=[],h=[],f=0,d=0,g=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],m=!1;t:for(e=1;e<i.length;++e){var v,y,x,b=i[e-1],_=i[e];for(u.push(f),h.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&c.length>0){for(var w=0;w<24;++w)c.push(c[c.length-12]);d+=2,m=!0}continue t}g[0][r]=Math.min(g[0][r],b[r],_[r]),g[1][r]=Math.max(g[1][r],b[r],_[r])}Array.isArray(a[0])?(v=a[e-1],y=a[e]):v=y=a,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),x=Array.isArray(o)?o[e-1]:o;var k=f;if(f+=p(b,_),m){for(r=0;r<2;++r)c.push(b[0],b[1],b[2],_[0],_[1],_[2],k,x,v[0],v[1],v[2],v[3]);d+=2,m=!1}c.push(b[0],b[1],b[2],_[0],_[1],_[2],k,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],k,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],f,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],f,x,y[0],y[1],y[2],y[3]),d+=4}if(this.buffer.update(c),u.push(f),h.push(i[i.length-1].slice()),this.bounds=g,this.vertexCount=d,this.points=h,this.arcLength=u,"dashes"in t){var M=t.dashes.slice();for(M.unshift(0),e=1;e<M.length;++e)M[e]=M[e-1]+M[e];var A=l(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&s.le(M,M[M.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}}},v.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=o(t.value[0],t.value[1],t.value[2],0),r=s.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),l=1-a,c=[0,0,0],u=0;u<3;++u)c[u]=l*n[u]+a*i[u];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,c,h,this.points[h])}},{"./lib/shaders":244,"binary-search-bounds":79,"gl-buffer":230,"gl-texture2d":303,"gl-vao":308,"glsl-read-float":368,ndarray:417}],246:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r*a-i*n;return o?(o=1/o,t[0]=a*o,t[1]=-n*o,t[2]=-i*o,t[3]=r*o,t):null}},{}],247:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],h=u*o-s*c,f=-u*a+s*l,p=c*a-o*l,d=r*h+n*f+i*p;return d?(d=1/d,t[0]=h*d,t[1]=(-u*n+i*c)*d,t[2]=(s*n-i*o)*d,t[3]=f*d,t[4]=(u*r-i*l)*d,t[5]=(-s*r+i*a)*d,t[6]=p*d,t[7]=(-c*r+n*l)*d,t[8]=(o*r-n*a)*d,t):null}},{}],248:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],249:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],250:[function(t,e,r){e.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],c=t[8],u=t[9],h=t[10],f=t[11],p=t[12],d=t[13],g=t[14],m=t[15];return(e*o-r*a)*(h*m-f*g)-(e*s-n*a)*(u*m-f*d)+(e*l-i*a)*(u*g-h*d)+(r*s-n*o)*(c*m-f*p)-(r*l-i*o)*(c*g-h*p)+(n*l-i*s)*(c*d-u*p)}},{}],251:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,c=r*o,u=n*o,h=n*s,f=i*o,p=i*s,d=i*l,g=a*o,m=a*s,v=a*l;return t[0]=1-h-d,t[1]=u+v,t[2]=f-m,t[3]=0,t[4]=u-v,t[5]=1-c-d,t[6]=p+g,t[7]=0,t[8]=f+m,t[9]=p-g,t[10]=1-c-h,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],252:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,c=a+a,u=n*s,h=n*l,f=n*c,p=i*l,d=i*c,g=a*c,m=o*s,v=o*l,y=o*c;return t[0]=1-(p+g),t[1]=h+y,t[2]=f-v,t[3]=0,t[4]=h-y,t[5]=1-(u+g),t[6]=d+m,t[7]=0,t[8]=f+v,t[9]=d-m,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],253:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],254:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],h=e[9],f=e[10],p=e[11],d=e[12],g=e[13],m=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,k=i*c-a*l,M=u*g-h*d,A=u*m-f*d,T=u*v-p*d,S=h*m-f*g,E=h*v-p*g,C=f*v-p*m,L=y*C-x*E+b*S+_*T-w*A+k*M;if(!L)return null;return L=1/L,t[0]=(s*C-l*E+c*S)*L,t[1]=(i*E-n*C-a*S)*L,t[2]=(g*k-m*w+v*_)*L,t[3]=(f*w-h*k-p*_)*L,t[4]=(l*T-o*C-c*A)*L,t[5]=(r*C-i*T+a*A)*L,t[6]=(m*b-d*k-v*x)*L,t[7]=(u*k-f*b+p*x)*L,t[8]=(o*E-s*T+c*M)*L,t[9]=(n*T-r*E-a*M)*L,t[10]=(d*w-g*b+v*y)*L,t[11]=(h*b-u*w-p*y)*L,t[12]=(s*A-o*S-l*M)*L,t[13]=(r*S-n*A+i*M)*L,t[14]=(g*x-d*_-m*y)*L,t[15]=(u*_-h*x+f*y)*L,t}},{}],255:[function(t,e,r){var n=t("./identity");e.exports=function(t,e,r,i){var a,o,s,l,c,u,h,f,p,d,g=e[0],m=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],k=r[2];if(Math.abs(g-_)<1e-6&&Math.abs(m-w)<1e-6&&Math.abs(v-k)<1e-6)return n(t);h=g-_,f=m-w,p=v-k,d=1/Math.sqrt(h*h+f*f+p*p),a=x*(p*=d)-b*(f*=d),o=b*(h*=d)-y*p,s=y*f-x*h,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=f*s-p*o,c=p*a-h*s,u=h*o-f*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=h,t[3]=0,t[4]=o,t[5]=c,t[6]=f,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*g+o*m+s*v),t[13]=-(l*g+c*m+u*v),t[14]=-(h*g+f*m+p*v),t[15]=1,t}},{"./identity":253}],256:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],h=e[8],f=e[9],p=e[10],d=e[11],g=e[12],m=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*h+w*g,t[1]=x*i+b*l+_*f+w*m,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*h+w*g,t[5]=x*i+b*l+_*f+w*m,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*h+w*g,t[9]=x*i+b*l+_*f+w*m,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*h+w*g,t[13]=x*i+b*l+_*f+w*m,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],257:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],258:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,h,f,p,d,g,m,v,y,x,b,_,w,k,M,A,T,S,E=n[0],C=n[1],L=n[2],z=Math.sqrt(E*E+C*C+L*L);if(Math.abs(z)<1e-6)return null;E*=z=1/z,C*=z,L*=z,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],h=e[4],f=e[5],p=e[6],d=e[7],g=e[8],m=e[9],v=e[10],y=e[11],x=E*E*o+a,b=C*E*o+L*i,_=L*E*o-C*i,w=E*C*o-L*i,k=C*C*o+a,M=L*C*o+E*i,A=E*L*o+C*i,T=C*L*o-E*i,S=L*L*o+a,t[0]=s*x+h*b+g*_,t[1]=l*x+f*b+m*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+h*k+g*M,t[5]=l*w+f*k+m*M,t[6]=c*w+p*k+v*M,t[7]=u*w+d*k+y*M,t[8]=s*A+h*T+g*S,t[9]=l*A+f*T+m*S,t[10]=c*A+p*T+v*S,t[11]=u*A+d*T+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],259:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],h=e[10],f=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+h*n,t[7]=l*i+f*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=h*i-s*n,t[11]=f*i-l*n,t}},{}],260:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],h=e[10],f=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-h*n,t[3]=l*i-f*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+h*i,t[11]=l*n+f*i,t}},{}],261:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],h=e[6],f=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+h*n,t[3]=l*i+f*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=h*i-s*n,t[7]=f*i-l*n,t}},{}],262:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],263:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,h,f,p,d,g=r[0],m=r[1],v=r[2];e===t?(t[12]=e[0]*g+e[4]*m+e[8]*v+e[12],t[13]=e[1]*g+e[5]*m+e[9]*v+e[13],t[14]=e[2]*g+e[6]*m+e[10]*v+e[14],t[15]=e[3]*g+e[7]*m+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],h=e[8],f=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=h,t[9]=f,t[10]=p,t[11]=d,t[12]=n*g+s*m+h*v+e[12],t[13]=i*g+l*m+f*v+e[13],t[14]=a*g+c*m+p*v+e[14],t[15]=o*g+u*m+d*v+e[15]);return t}},{}],264:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],265:[function(t,e,r){"use strict";e.exports=function(t,e){switch(e.length){case 0:break;case 1:t[0]=1/e[0];break;case 4:n(t,e);break;case 9:i(t,e);break;case 16:a(t,e);break;default:throw new Error("currently supports matrices up to 4x4")}return t};var n=t("gl-mat2/invert"),i=t("gl-mat3/invert"),a=t("gl-mat4/invert")},{"gl-mat2/invert":246,"gl-mat3/invert":247,"gl-mat4/invert":254}],266:[function(t,e,r){"use strict";var n=t("barycentric"),i=t("polytope-closest-point/lib/closest_point_2d.js");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}e.exports=function(t,e,r,a,s,l){if(1===t.length)return[0,t[0].slice()];for(var c=new Array(t.length),u=0;u<t.length;++u)c[u]=o(t[u],r,a,s,l);for(var h=0,f=1/0,u=0;u<c.length;++u){for(var p=0,d=0;d<2;++d)p+=Math.pow(c[u][d]-e[d],2);p<f&&(f=p,h=u)}for(var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(c,e),m=0,u=0;u<3;++u){if(g[u]<-.001||g[u]>1.0001)return null;m+=g[u]}if(Math.abs(m-1)>.001)return null;return[h,function(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}(t,g),g]}},{barycentric:61,"polytope-closest-point/lib/closest_point_2d.js":448}],267:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, normal;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model\n           , view\n           , projection;\nuniform vec3 eyePosition\n           , lightPosition;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec4 m_position  = model * vec4(position, 1.0);\n  vec4 t_position  = view * m_position;\n  gl_Position      = projection * t_position;\n  f_color          = color;\n  f_normal         = normal;\n  f_data           = position;\n  f_eyeDirection   = eyePosition   - position;\n  f_lightDirection = lightPosition - position;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nvec3 normals(vec3 pos) {\n  vec3 fdx = dFdx(pos);\n  vec3 fdy = dFdy(pos);\n  return normalize(cross(fdx, fdy));\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular\n            , opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if(any(lessThan(f_data, clipBounds[0])) ||\n     any(greaterThan(f_data, clipBounds[1]))) {\n    discard;\n  }\n\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  vec3 normal = normals(f_data);\n\n  if (\n    dot(N, normal) < 0.0\n    ) {\n      N = -N;\n  }\n\n  float specular = cookTorranceSpecular(L, V, N, roughness, fresnel);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model, view, projection;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_color = color;\n  f_data  = position;\n  f_uv    = uv;\n}"]),s=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3 clipBounds[2];\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  if(any(lessThan(f_data, clipBounds[0])) || \n     any(greaterThan(f_data, clipBounds[1]))) {\n    discard;\n  }\n\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),l=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\nattribute float pointSize;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if(any(lessThan(position, clipBounds[0])) || \n     any(greaterThan(position, clipBounds[1]))) {\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    gl_Position = projection * view * model * vec4(position, 1.0);\n  }\n  gl_PointSize = pointSize;\n  f_color = color;\n  f_uv = uv;\n}"]),c=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5,0.5);\n  if(dot(pointR, pointR) > 0.25) {\n    discard;\n  }\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),u=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_id        = id;\n  f_position  = position;\n}"]),h=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if(any(lessThan(f_position, clipBounds[0])) || \n     any(greaterThan(f_position, clipBounds[1]))) {\n    discard;\n  }\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]),f=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3  position;\nattribute float pointSize;\nattribute vec4  id;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if(any(lessThan(position, clipBounds[0])) || \n     any(greaterThan(position, clipBounds[1]))) {\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    gl_Position  = projection * view * model * vec4(position, 1.0);\n    gl_PointSize = pointSize;\n  }\n  f_id         = id;\n  f_position   = position;\n}"]),p=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n}"]),d=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3 contourColor;\n\nvoid main() {\n  gl_FragColor = vec4(contourColor,1);\n}\n"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec3"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"pointSize",type:"float"}]},r.pickShader={vertex:u,fragment:h,attributes:[{name:"position",type:"vec3"},{name:"id",type:"vec4"}]},r.pointPickShader={vertex:f,fragment:h,attributes:[{name:"position",type:"vec3"},{name:"pointSize",type:"float"},{name:"id",type:"vec4"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:"position",type:"vec3"}]}},{glslify:377}],268:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("normals"),l=t("gl-mat4/multiply"),c=t("gl-mat4/invert"),u=t("ndarray"),h=t("colormap"),f=t("simplicial-complex-contour"),p=t("typedarray-pool"),d=t("./lib/shaders"),g=t("./lib/closest-point"),m=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function k(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,g,m,v,y,x,b,_,k,M,A,T,S){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=f,this.triangleUVs=h,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=m,this.edgeUVs=v,this.edgeIds=g,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=k,this.pointSizes=M,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=T,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var M=k.prototype;function A(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function T(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function S(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function E(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}M.isOpaque=function(){return this.opacity>=1},M.isTransparent=function(){return this.opacity<1},M.pickSlots=1,M.setPickBase=function(t){this.pickId=t},M.highlight=function(t){if(t&&this.contourEnable){for(var e=f(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var h=c[0];2===c.length&&(h=c[u]);for(var d=n[h][0],g=n[h][1],m=i[h],v=1-m,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=m*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},M.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=h({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],f=[],p=[],d=[],g=[],m=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,k=t.cellNormals,M=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,A=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!k&&(k=s.faceNormals(r,n,A)),k||w||(w=s.vertexNormals(r,n,M));var T=t.vertexColors,S=t.cellColors,E=t.meshColor||[1,1,1,1],C=t.vertexUVs,L=t.vertexIntensity,z=t.cellUVs,P=t.cellIntensity,I=1/0,O=-1/0;if(!C&&!z)if(L)if(t.vertexIntensityBounds)I=+t.vertexIntensityBounds[0],O=+t.vertexIntensityBounds[1];else for(var D=0;D<L.length;++D){var R=L[D];I=Math.min(I,R),O=Math.max(O,R)}else if(P)for(D=0;D<P.length;++D){R=P[D];I=Math.min(I,R),O=Math.max(O,R)}else for(D=0;D<n.length;++D){R=n[D][2];I=Math.min(I,R),O=Math.max(O,R)}this.intensity=L||(P?function(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;var a=t.length;for(i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}(r,n.length,P):function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n));var B=t.pointSizes,F=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(D=0;D<n.length;++D)for(var N=n[D],j=0;j<3;++j)!isNaN(N[j])&&isFinite(N[j])&&(this.bounds[0][j]=Math.min(this.bounds[0][j],N[j]),this.bounds[1][j]=Math.max(this.bounds[1][j],N[j]));var V=0,U=0,q=0;t:for(D=0;D<r.length;++D){var H=r[D];switch(H.length){case 1:for(N=n[W=H[0]],j=0;j<3;++j)if(isNaN(N[j])||!isFinite(N[j]))continue t;v.push(N[0],N[1],N[2]),3===(Y=T?T[W]:S?S[D]:E).length?y.push(Y[0],Y[1],Y[2],1):y.push(Y[0],Y[1],Y[2],Y[3]),X=C?C[W]:L?[(L[W]-I)/(O-I),0]:z?z[D]:P?[(P[D]-I)/(O-I),0]:[(N[2]-I)/(O-I),0],x.push(X[0],X[1]),B?b.push(B[W]):b.push(F),_.push(D),q+=1;break;case 2:for(j=0;j<2;++j){N=n[W=H[j]];for(var G=0;G<3;++G)if(isNaN(N[G])||!isFinite(N[G]))continue t}for(j=0;j<2;++j){N=n[W=H[j]];p.push(N[0],N[1],N[2]),3===(Y=T?T[W]:S?S[D]:E).length?d.push(Y[0],Y[1],Y[2],1):d.push(Y[0],Y[1],Y[2],Y[3]),X=C?C[W]:L?[(L[W]-I)/(O-I),0]:z?z[D]:P?[(P[D]-I)/(O-I),0]:[(N[2]-I)/(O-I),0],g.push(X[0],X[1]),m.push(D)}U+=1;break;case 3:for(j=0;j<3;++j)for(N=n[W=H[j]],G=0;G<3;++G)if(isNaN(N[G])||!isFinite(N[G]))continue t;for(j=0;j<3;++j){var W,Y,X,Z;N=n[W=H[j]];i.push(N[0],N[1],N[2]),3===(Y=T?T[W]:S?S[D]:E).length?a.push(Y[0],Y[1],Y[2],1):a.push(Y[0],Y[1],Y[2],Y[3]),X=C?C[W]:L?[(L[W]-I)/(O-I),0]:z?z[D]:P?[(P[D]-I)/(O-I),0]:[(N[2]-I)/(O-I),0],c.push(X[0],X[1]),Z=w?w[W]:k[D],l.push(Z[0],Z[1],Z[2]),f.push(D)}V+=1}}this.pointCount=q,this.edgeCount=U,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(g),this.edgeIds.update(new Uint32Array(m)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(f))}},M.drawTransparent=M.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,contourColor:this.contourColor,texture:0};this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var h,f=u[15];for(o=0;o<3;++o)f+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/f}this.triangleCount>0&&((h=this.triShader).bind(),h.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((h=this.lineShader).bind(),h.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((h=this.pointShader).bind(),h.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((h=this.contourShader).bind(),h.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},M.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},M.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=g(i,[t.coord[0],this._resolution[1]-t.coord[1]],this._model,this._view,this._projection,this._resolution);if(!o)return null;var s=o[2],l=0;for(a=0;a<r.length;++a)l+=s[a]*this.intensity[r[a]];return{position:o[1],index:r[o[0]],cell:r,cellId:e,intensity:l,dataCoordinate:this.positions[r[o[0]]]}},M.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){if(1===arguments.length&&(t=(e=t).gl),!(t.getExtension("OES_standard_derivatives")||t.getExtension("MOZ_OES_standard_derivatives")||t.getExtension("WEBKIT_OES_standard_derivatives")))throw new Error("derivatives not supported");var r=function(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}(t),s=function(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}(t),l=A(t),c=T(t),h=S(t),f=E(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),g=i(t),y=i(t),x=i(t),b=i(t),_=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:b,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:y,type:t.FLOAT,size:2},{buffer:x,type:t.FLOAT,size:3}]),w=i(t),M=i(t),C=i(t),L=i(t),z=a(t,[{buffer:w,type:t.FLOAT,size:3},{buffer:L,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:M,type:t.FLOAT,size:4},{buffer:C,type:t.FLOAT,size:2}]),P=i(t),I=i(t),O=i(t),D=i(t),R=i(t),B=a(t,[{buffer:P,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:I,type:t.FLOAT,size:4},{buffer:O,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),F=i(t),N=new k(t,p,r,s,l,c,h,f,d,b,g,y,x,_,w,L,M,C,z,P,R,I,O,D,B,F,a(t,[{buffer:F,type:t.FLOAT,size:3}]));return N.update(e),N}},{"./lib/closest-point":266,"./lib/shaders":267,colormap:113,"gl-buffer":230,"gl-mat4/invert":254,"gl-mat4/multiply":256,"gl-shader":287,"gl-texture2d":303,"gl-vao":308,ndarray:417,normals:420,"simplicial-complex-contour":479,"typedarray-pool":507}],269:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("./shaders");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":272,"gl-buffer":230,"gl-shader":287}],270:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("binary-search-bounds"),o=t("./shaders");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,h,f,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],h=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,f=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,g=0;g<2;++g){var m=a[g],v=a[g+2]-m,y=.5*(o[g+2]+o[g]),x=o[g+2]-o[g];u[g]=2*v/x,c[g]=2*(m-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(g=0;g<2;++g){h[0]=h[1]=0,h[g]=1,r.uniforms.dataAxis=h,r.uniforms.lineWidth=l[g]/(s[g+2]-s[g])*d,r.uniforms.color=f[g];var _=6*n[g].length;p[g]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,h=this.ticks,f=s.gl,p=s._tickBounds,d=s.dataBox,g=s.viewBox,m=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=g[2]-g[0],_=g[3]-g[1],w=0;w<2;++w){var k=p[w],M=p[w+2]-k,A=.5*(d[w+2]+d[w]),T=d[w+2]-d[w];e[w]=2*M/T,t[w]=2*(k-A)/T}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,C=s.tickMarkWidth,L=s.tickMarkColor,z=6*h[0].length,P=Math.min(a.ge(h[0],(d[0]-p[0])/(p[2]-p[0]),l),h[0].length),I=Math.min(a.gt(h[0],(d[2]-p[0])/(p[2]-p[0]),l),h[0].length),O=0+6*P,D=6*Math.max(0,I-P),R=Math.min(a.ge(h[1],(d[1]-p[1])/(p[3]-p[1]),l),h[1].length),B=Math.min(a.gt(h[1],(d[3]-p[1])/(p[3]-p[1]),l),h[1].length),F=z+6*R,N=6*Math.max(0,B-R);i[0]=2*(g[0]-E[1])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[1]*m/y,o[1]=C[1]*m/x,N&&(S.color=L[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,f.drawArrays(f.TRIANGLES,F,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2*(g[1]-E[0])/x-1,o[0]=C[0]*m/y,o[1]=E[0]*m/x,D&&(S.color=L[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,f.drawArrays(f.TRIANGLES,O,D)),i[0]=2*(g[2]+E[3])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[3]*m/y,o[1]=C[3]*m/x,N&&(S.color=L[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,f.drawArrays(f.TRIANGLES,F,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2*(g[3]+E[2])/x-1,o[0]=C[2]*m/y,o[1]=E[2]*m/x,D&&(S.color=L[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,f.drawArrays(f.TRIANGLES,O,D))}}(),d.update=(f=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],h=0;h<l.length;++h){var d=(l[h].x-c)/(u-c);s.push(d);for(var g=0;g<6;++g)n[i++]=d,n[i++]=f[g],n[i++]=p[g]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{"./shaders":272,"binary-search-bounds":274,"gl-buffer":230,"gl-shader":287}],271:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("./shaders");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":272,"gl-buffer":230,"gl-shader":287}],272:[function(t,e,r){"use strict";var n=t("glslify"),i=n(["precision lowp float;\n#define GLSLIFY 1\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\n}\n"]);e.exports={lineVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 start, end;\nuniform float width;\n\nvec2 perp(vec2 v) {\n  return vec2(v.y, -v.x);\n}\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  vec2 delta = normalize(perp(start - end));\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\n}\n"]),lineFrag:i,textVert:n(["#define GLSLIFY 1\nattribute vec3 textCoordinate;\n\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\nuniform float angle;\n\nvoid main() {\n  float dataOffset  = textCoordinate.z;\n  vec2 glyphOffset  = textCoordinate.xy;\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\n    glyphMatrix * glyphOffset * textScale + screenOffset;\n  gl_Position = vec4(screenCoordinate, 0, 1);\n}\n"]),textFrag:i,gridVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale;\nuniform float lineWidth;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\n  gl_Position = vec4(pos, 0, 1);\n}\n"]),gridFrag:i,boxVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 lo, hi;\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\n}\n"]),tickVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\n}\n"])}},{glslify:377}],273:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("text-cache"),o=t("binary-search-bounds"),s=t("./shaders");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,h,f,p,d,g=l.prototype;g.drawTicks=(c=[0,0],u=[0,0],h=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,f=e.screenBox,p=e.pixelRatio,d=e.tickEnable,g=e.tickPad,m=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],k=this.labelCount[t],M=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(f[2+t]-f[t])-1;var T=2/f[2+(1^t)]-f[1^t];u[1^t]=T*s[1^t]-1,d[t]&&(u[1^t]-=T*p*g[t],M<A&&i[A]>i[M]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[M],i[A]-i[M]))),y[t]&&k&&(u[1^t]-=T*p*x[t],r.uniforms.dataAxis=h,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,k)),u[1^t]=T*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=T*p*g[t+2],M<A&&i[A]>i[M]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[M],i[A]-i[M]))),y[t+2]&&k&&(u[1^t]+=T*p*x[t+2],r.uniforms.dataAxis=h,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,k))}),g.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),g.bind=(f=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],h=a[o],g=a[o+2]-h,m=i[o],v=i[o+2]-m;p[o]=2*l/u*g/v,f[o]=2*(s-c)/u*g/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=f,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),g.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],h=[-1/0],f=l[o];for(e=0;e<f.length;++e){var p=f[e],d=p.x,g=p.text,m=p.font||"sans-serif";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=g.split("\n"),b=0;b<x.length;b++)for(n=a(m,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),h.push(d)}this.tickOffset[o]=u,this.tickX[o]=h}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:"center"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},g.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":272,"binary-search-bounds":274,"gl-buffer":230,"gl-shader":287,"text-cache":498}],274:[function(t,e,r){arguments[4][98][0].apply(r,arguments)},{dup:98}],275:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t("gl-select-static"),i=t("./lib/grid"),a=t("./lib/text"),o=t("./lib/line"),s=t("./lib/box");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function h(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var h=this.zeroLineEnable,f=this.zeroLineColor,p=this.zeroLineWidth;if(h[0]||h[1]){o.bind();for(var d=0;d<2;++d)if(h[d]&&n[d]<=0&&n[d+2]>=0){var g=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(g,e[1],g,e[3],p[d],f[d]):o.drawLine(e[0],g,e[2],g,p[d],f[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var m=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(m[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),m[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),m[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),m[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl;this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var h=c[u].pick(a,o,l);if(h)return h}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!("titleEnable"in t&&!t.titleEnable),this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(h),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||["x","y"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||["sans-serif","sans-serif"],title:t.title||"",titleSize:t.titleSize||18,titleFont:t.titleFont||"sans-serif"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{"./lib/box":269,"./lib/grid":270,"./lib/line":271,"./lib/text":273,"gl-select-static":286}],276:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\nattribute vec2 position;\nvarying vec2 uv;\nvoid main() {\n  uv = position;\n  gl_Position = vec4(position, 0, 1);\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform sampler2D accumBuffer;\nvarying vec2 uv;\n\nvoid main() {\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\n  gl_FragColor = min(vec4(1,1,1,1), accum);\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec2"}])}},{"gl-shader":287,glslify:377}],277:[function(t,e,r){"use strict";e.exports=function(t){var e=!1,r=((t=t||{}).pixelRatio||parseFloat(window.devicePixelRatio),t.canvas);if(!r)if(r=document.createElement("canvas"),t.container){var v=t.container;v.appendChild(r)}else document.body.appendChild(r);var y=t.gl;y||(y=function(t,e){var r=null;try{(r=t.getContext("webgl",e))||(r=t.getContext("experimental-webgl",e))}catch(t){return null}return r}(r,t.glOptions||{premultipliedAlpha:!0,antialias:!0}));if(!y)throw new Error("webgl not supported");var x=t.bounds||[[-10,-10,-10],[10,10,10]],b=new d,_=l(y,[y.drawingBufferWidth,y.drawingBufferHeight],{preferFloat:!p}),w=f(y),k=t.camera||{eye:[2,0,0],center:[0,0,0],up:[0,1,0],zoomMin:.1,zoomMax:100,mode:"turntable"},M=t.axes||{},A=i(y,M);A.enable=!M.disable;var T=t.spikes||{},S=o(y,T),E=[],C=[],L=[],z=[],P=!0,I=!0,O=new Array(16),D=new Array(16),R={view:null,projection:O,model:D},I=!0,B=[y.drawingBufferWidth,y.drawingBufferHeight],F={gl:y,contextLost:!1,pixelRatio:t.pixelRatio||parseFloat(window.devicePixelRatio),canvas:r,selection:b,camera:n(r,k),axes:A,axesPixels:null,spikes:S,bounds:x,objects:E,shape:B,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:m(t.autoResize),autoBounds:m(t.autoBounds),autoScale:!!t.autoScale,autoCenter:m(t.autoCenter),clipToBounds:m(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null},N=[y.drawingBufferWidth/F.pixelRatio|0,y.drawingBufferHeight/F.pixelRatio|0];function j(){if(!e&&F.autoResize){var t=r.parentNode,n=1,i=1;t&&t!==document.body?(n=t.clientWidth,i=t.clientHeight):(n=window.innerWidth,i=window.innerHeight);var a=0|Math.ceil(n*F.pixelRatio),o=0|Math.ceil(i*F.pixelRatio);if(a!==r.width||o!==r.height){r.width=a,r.height=o;var s=r.style;s.position=s.position||"absolute",s.left="0px",s.top="0px",s.width=n+"px",s.height=i+"px",P=!0}}}F.autoResize&&j();function V(){for(var t=E.length,e=z.length,r=0;r<e;++r)L[r]=0;t:for(var r=0;r<t;++r){var n=E[r],i=n.pickSlots;if(i){for(var a=0;a<e;++a)if(L[a]+i<255){C[r]=a,n.setPickBase(L[a]+1),L[a]+=i;continue t}var o=s(y,B);C[r]=e,z.push(o),L.push(i),n.setPickBase(1),e+=1}else C[r]=-1}for(;e>0&&0===L[e-1];)L.pop(),z.pop().dispose()}window.addEventListener("resize",j),F.update=function(t){e||(t=t||{},P=!0,I=!0)},F.add=function(t){e||(t.axes=A,E.push(t),C.push(-1),P=!0,I=!0,V())},F.remove=function(t){if(!e){var r=E.indexOf(t);r<0||(E.splice(r,1),C.pop(),P=!0,I=!0,V())}},F.dispose=function(){if(!e&&(e=!0,window.removeEventListener("resize",j),r.removeEventListener("webglcontextlost",H),F.mouseListener.enabled=!1,!F.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();_.dispose();for(var t=0;t<z.length;++t)z[t].dispose();w.dispose(),y=null,A=null,S=null,E=[]}};var U=!1,q=0;function H(){if(F.contextLost)return!0;y.isContextLost()&&(F.contextLost=!0,F.mouseListener.enabled=!1,F.selection.object=null,F.oncontextloss&&F.oncontextloss())}F.mouseListener=u(r,function(t,r,n){if(!e){var i=z.length,a=E.length,o=b.object;b.distance=1/0,b.mouse[0]=r,b.mouse[1]=n,b.object=null,b.screen=null,b.dataCoordinate=b.dataPosition=null;var s=!1;if(t&&q)U=!0;else{U&&(I=!0),U=!1;for(var l=0;l<i;++l){var c=z[l].query(r,N[1]-n-1,F.pickRadius);if(c){if(c.distance>b.distance)continue;for(var u=0;u<a;++u){var h=E[u];if(C[u]===l){var f=h.pick(c);f&&(b.buttons=t,b.screen=c.coord,b.distance=c.distance,b.object=h,b.index=f.distance,b.dataPosition=f.position,b.dataCoordinate=f.dataCoordinate,b.data=f,s=!0)}}}}}o&&o!==b.object&&(o.highlight&&o.highlight(null),P=!0),b.object&&(b.object.highlight&&b.object.highlight(b.data),P=!0),(s=s||b.object!==o)&&F.onselect&&F.onselect(b),1&t&&!(1&q)&&F.onclick&&F.onclick(b),q=t}}),r.addEventListener("webglcontextlost",H);var G=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],W=[G[0].slice(),G[1].slice()];function Y(){if(!H()){j();var t=F.camera.tick();R.view=F.camera.matrix,P=P||t,I=I||t,A.pixelRatio=F.pixelRatio,S.pixelRatio=F.pixelRatio;var e=E.length,r=G[0],n=G[1];r[0]=r[1]=r[2]=1/0,n[0]=n[1]=n[2]=-1/0;for(var i=0;i<e;++i){var o=E[i];o.pixelRatio=F.pixelRatio,o.axes=F.axes,P=P||!!o.dirty,I=I||!!o.dirty;var s=o.bounds;if(s)for(var l=s[0],u=s[1],f=0;f<3;++f)r[f]=Math.min(r[f],l[f]),n[f]=Math.max(n[f],u[f])}var p=F.bounds;if(F.autoBounds)for(var f=0;f<3;++f){if(n[f]<r[f])r[f]=-1,n[f]=1;else{r[f]===n[f]&&(r[f]-=1,n[f]+=1);var d=.05*(n[f]-r[f]);r[f]=r[f]-d,n[f]=n[f]+d}p[0][f]=r[f],p[1][f]=n[f]}for(var m=!1,f=0;f<3;++f)m=m||W[0][f]!==p[0][f]||W[1][f]!==p[1][f],W[0][f]=p[0][f],W[1][f]=p[1][f];if(I=I||m,P=P||m){if(m){for(var v=[0,0,0],i=0;i<3;++i)v[i]=g((p[1][i]-p[0][i])/10);A.autoTicks?A.update({bounds:p,tickSpacing:v}):A.update({bounds:p})}var x=y.drawingBufferWidth,k=y.drawingBufferHeight;B[0]=x,B[1]=k,N[0]=0|Math.max(x/F.pixelRatio,1),N[1]=0|Math.max(k/F.pixelRatio,1),h(O,F.fovy,x/k,F.zNear,F.zFar);for(var i=0;i<16;++i)D[i]=0;D[15]=1;for(var M=0,i=0;i<3;++i)M=Math.max(M,p[1][i]-p[0][i]);for(var i=0;i<3;++i)F.autoScale?D[5*i]=F.aspect[i]/(p[1][i]-p[0][i]):D[5*i]=1/M,F.autoCenter&&(D[12+i]=.5*-D[5*i]*(p[0][i]+p[1][i]));for(var i=0;i<e;++i){var o=E[i];o.axesBounds=p,F.clipToBounds&&(o.clipBounds=p)}b.object&&(F.snapToData?S.position=b.dataCoordinate:S.position=b.dataPosition,S.bounds=p),I&&(I=!1,function(){if(H())return;y.colorMask(!0,!0,!0,!0),y.depthMask(!0),y.disable(y.BLEND),y.enable(y.DEPTH_TEST);for(var t=E.length,e=z.length,r=0;r<e;++r){var n=z[r];n.shape=N,n.begin();for(var i=0;i<t;++i)if(C[i]===r){var a=E[i];a.drawPick&&(a.pixelRatio=1,a.drawPick(R))}n.end()}}()),F.axesPixels=a(F.axes,R,x,k),F.onrender&&F.onrender(),y.bindFramebuffer(y.FRAMEBUFFER,null),y.viewport(0,0,x,k);var T=F.clearColor;y.clearColor(T[0],T[1],T[2],T[3]),y.clear(y.COLOR_BUFFER_BIT|y.DEPTH_BUFFER_BIT),y.depthMask(!0),y.colorMask(!0,!0,!0,!0),y.enable(y.DEPTH_TEST),y.depthFunc(y.LEQUAL),y.disable(y.BLEND),y.disable(y.CULL_FACE);var L=!1;A.enable&&(L=L||A.isTransparent(),A.draw(R)),S.axes=A,b.object&&S.draw(R),y.disable(y.CULL_FACE);for(var i=0;i<e;++i){var o=E[i];o.axes=A,o.pixelRatio=F.pixelRatio,o.isOpaque&&o.isOpaque()&&o.draw(R),o.isTransparent&&o.isTransparent()&&(L=!0)}if(L){_.shape=B,_.bind(),y.clear(y.DEPTH_BUFFER_BIT),y.colorMask(!1,!1,!1,!1),y.depthMask(!0),y.depthFunc(y.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(var i=0;i<e;++i){var o=E[i];o.isOpaque&&o.isOpaque()&&o.draw(R)}y.enable(y.BLEND),y.blendEquation(y.FUNC_ADD),y.blendFunc(y.ONE,y.ONE_MINUS_SRC_ALPHA),y.colorMask(!0,!0,!0,!0),y.depthMask(!1),y.clearColor(0,0,0,0),y.clear(y.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(var i=0;i<e;++i){var o=E[i];o.isTransparent&&o.isTransparent()&&o.drawTransparent(R)}y.bindFramebuffer(y.FRAMEBUFFER,null),y.blendFunc(y.ONE,y.ONE_MINUS_SRC_ALPHA),y.disable(y.DEPTH_TEST),w.bind(),_.color[0].bind(0),w.uniforms.accumBuffer=0,c(y),y.disable(y.BLEND)}P=!1;for(var i=0;i<e;++i)E[i].dirty=!1}}}return function t(){e||F.contextLost||(requestAnimationFrame(t),Y())}(),F.redraw=function(){e||(P=!0,Y())},F};var n=t("3d-view-controls"),i=t("gl-axes3d"),a=t("gl-axes3d/properties"),o=t("gl-spikes3d"),s=t("gl-select-static"),l=t("gl-fbo"),c=t("a-big-triangle"),u=t("mouse-change"),h=t("gl-mat4/perspective"),f=t("./lib/shader"),p=t("is-mobile")();function d(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function m(t){return"boolean"!=typeof t||t}},{"./lib/shader":276,"3d-view-controls":42,"a-big-triangle":50,"gl-axes3d":222,"gl-axes3d/properties":229,"gl-fbo":239,"gl-mat4/perspective":257,"gl-select-static":286,"gl-spikes3d":296,"is-mobile":388,"mouse-change":402}],278:[function(t,e,r){var n=t("glslify");r.pointVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform float pointCloud;\n\nhighp float rand(vec2 co) {\n  highp float a = 12.9898;\n  highp float b = 78.233;\n  highp float c = 43758.5453;\n  highp float d = dot(co.xy, vec2(a, b));\n  highp float e = mod(d, 3.14);\n  return fract(sin(e) * c);\n}\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n    // if we don't jitter the point size a bit, overall point cloud\n    // saturation 'jumps' on zooming, which is disturbing and confusing\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    // get the same square surface as circle would be\n    gl_PointSize *= 0.886;\n  }\n}"]),r.pointFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color, borderColor;\nuniform float centerFraction;\nuniform float pointCloud;\n\nvoid main() {\n  float radius;\n  vec4 baseColor;\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    if(centerFraction == 1.0) {\n      gl_FragColor = color;\n    } else {\n      gl_FragColor = mix(borderColor, color, centerFraction);\n    }\n  } else {\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\n    if(radius > 1.0) {\n      discard;\n    }\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\n  }\n}\n"]),r.pickVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform vec4 pickOffset;\n\nvarying vec4 fragId;\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n  gl_PointSize = pointSize;\n\n  vec4 id = pickId + pickOffset;\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  fragId = id;\n}\n"]),r.pickFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\n\nvoid main() {\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\n  if(radius > 1.0) {\n    discard;\n  }\n  gl_FragColor = fragId / 255.0;\n}\n"])},{glslify:377}],279:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("typedarray-pool"),o=t("./lib/shader");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),h=new s(t,a,l,c,u);return h.update(e),t.addObject(h),h};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r("sizeMin",.5),this.sizeMax=r("sizeMax",20),this.color=r("color",[1,0,0,1]).slice(),this.areaRatio=r("areaRatio",1),this.borderColor=r("borderColor",[0,0,0,1]).slice(),this.blend=r("blend",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var h=n.getParameter(n.BLEND),f=n.getParameter(n.DITHER);return h&&!this.blend&&n.disable(n.BLEND),f&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),h&&!this.blend&&n.enable(n.BLEND),f&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{"./lib/shader":278,"gl-buffer":230,"gl-shader":287,"typedarray-pool":507}],280:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],h=e[2],f=e[3],p=r[0],d=r[1],g=r[2],m=r[3];(a=c*p+u*d+h*g+f*m)<0&&(a=-a,p=-p,d=-d,g=-g,m=-m);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*h+l*g,t[3]=s*f+l*m,t}},{}],281:[function(t,e,r){"use strict";var n=t("vectorize-text");e.exports=function(t,e){var r=i[e];r||(r=i[e]={});if(t in r)return r[t];for(var a=n(t,{textAlign:"center",textBaseline:"middle",lineHeight:1,font:e}),o=n(t,{triangles:!0,textAlign:"center",textBaseline:"middle",lineHeight:1,font:e}),s=[[1/0,1/0],[-1/0,-1/0]],l=0;l<a.positions.length;++l)for(var c=a.positions[l],u=0;u<2;++u)s[0][u]=Math.min(s[0][u],c[u]),s[1][u]=Math.max(s[1][u],c[u]);return r[t]=[o,a,s]};var i={}},{"vectorize-text":515}],282:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform vec4 highlightId;\nuniform float highlightScale;\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if(any(lessThan(position, clipBounds[0]))   || \n     any(greaterThan(position, clipBounds[1])) ) {\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = 1.0;\n    if(distance(highlightId, id) < 0.0001) {\n      scale = highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1);\n    vec4 viewPosition = view * worldPosition;\n    viewPosition = viewPosition / viewPosition.w;\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\n    \n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),o=i(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float highlightScale, pixelRatio;\nuniform vec4 highlightId;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if(any(lessThan(position, clipBounds[0])) || any(greaterThan(position, clipBounds[1]))) {\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = pixelRatio;\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\n      scale *= highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1.0);\n    vec4 viewPosition = view * worldPosition;\n    vec4 clipPosition = projection * viewPosition;\n    clipPosition /= clipPosition.w;\n    \n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),s=i(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform float highlightScale;\nuniform vec4 highlightId;\nuniform vec3 axes[2];\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float scale, pixelRatio;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if(any(lessThan(position, clipBounds[0]))   ||\n     any(greaterThan(position, clipBounds[1])) ) {\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float lscale = pixelRatio * scale;\n    if(distance(highlightId, id) < 0.0001) {\n      lscale *= highlightScale;\n    }\n\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = dataPosition;\n  }\n}\n"]),l=i(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3 fragClipBounds[2];\nuniform float opacity;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if(any(lessThan(dataCoordinate, fragClipBounds[0]))   ||\n     any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\n    discard;\n  } else {\n    gl_FragColor = interpColor * opacity;\n  }\n}\n"]),c=i(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3 fragClipBounds[2];\nuniform float pickGroup;\n\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if(any(lessThan(dataCoordinate, fragClipBounds[0]))   || \n     any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\n    discard;\n  } else {\n    gl_FragColor = vec4(pickGroup, pickId.bgr);\n  }\n}"]),u=[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"glyph",type:"vec2"},{name:"id",type:"vec4"}],h={vertex:a,fragment:l,attributes:u},f={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},g={vertex:o,fragment:c,attributes:u},m={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,h)},r.createOrtho=function(t){return v(t,f)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,g)},r.createPickProject=function(t){return v(t,m)}},{"gl-shader":287,glslify:377}],283:[function(t,e,r){"use strict";var n=t("gl-buffer"),i=t("gl-vao"),a=t("typedarray-pool"),o=t("gl-mat4/multiply"),s=t("./lib/shaders"),l=t("./lib/glyphs"),c=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function u(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function h(t,e,r,n){return u(n,n),u(n,n),u(n,n)}function f(t,e){this.index=t,this.dataCoordinate=this.position=e}function p(t,e,r,n,i,a,o,s,l,c,u,h){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=h,this.points=[],this._selectResult=new f(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=s.createPerspective(e),a=s.createOrtho(e),o=s.createProject(e),l=s.createPickPerspective(e),c=s.createPickOrtho(e),u=s.createPickProject(e),h=n(e),f=n(e),d=n(e),g=n(e),m=i(e,[{buffer:h,size:3,type:e.FLOAT},{buffer:f,size:4,type:e.FLOAT},{buffer:d,size:2,type:e.FLOAT},{buffer:g,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new p(e,r,a,o,h,f,d,g,m,l,c,u);return v.update(t),v};var d=p.prototype;d.pickSlots=1,d.setPickBase=function(t){this.pickId=t},d.isTransparent=function(){if(this.opacity<1)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectOpacity[t]<1)return!0;return!1},d.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectOpacity[t]>=1)return!0;return!1};var g=[0,0],m=[0,0,0],v=[0,0,0],y=[0,0,0,1],x=[0,0,0,1],b=c.slice(),_=[0,0,0],w=[[0,0,0],[0,0,0]];function k(t){return t[0]=t[1]=t[2]=0,t}function M(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function A(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function T(t,e,r,n,i){var a,s=e.axesProject,l=e.gl,u=t.uniforms,f=r.model||c,p=r.view||c,d=r.projection||c,T=e.axesBounds,S=function(t){for(var e=w,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);a=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],g[0]=2/l.drawingBufferWidth,g[1]=2/l.drawingBufferHeight,t.bind(),u.view=p,u.projection=d,u.screenSize=g,u.highlightId=e.highlightId,u.highlightScale=e.highlightScale,u.clipBounds=S,u.pickGroup=e.pickId/255,u.pixelRatio=e.pixelRatio;for(var E=0;E<3;++E)if(s[E]&&e.projectOpacity[E]<1===n){u.scale=e.projectScale[E],u.opacity=e.projectOpacity[E];for(var C=b,L=0;L<16;++L)C[L]=0;for(L=0;L<4;++L)C[5*L]=1;C[5*E]=0,a[E]<0?C[12+E]=T[0][E]:C[12+E]=T[1][E],o(C,f,C),u.model=C;var z=(E+1)%3,P=(E+2)%3,I=k(m),O=k(v);I[z]=1,O[P]=1;var D=h(0,0,0,M(y,I)),R=h(0,0,0,M(x,O));if(Math.abs(D[1])>Math.abs(R[1])){var B=D;D=R,R=B,B=I,I=O,O=B;var F=z;z=P,P=F}D[0]<0&&(I[z]=-1),R[1]>0&&(O[P]=-1);var N=0,j=0;for(L=0;L<4;++L)N+=Math.pow(f[4*z+L],2),j+=Math.pow(f[4*P+L],2);I[z]/=Math.sqrt(N),O[P]/=Math.sqrt(j),u.axes[0]=I,u.axes[1]=O,u.fragClipBounds[0]=A(_,S[0],E,-1e8),u.fragClipBounds[1]=A(_,S[1],E,1e8),e.vao.draw(l.TRIANGLES,e.vertexCount),e.lineWidth>0&&(l.lineWidth(e.lineWidth),e.vao.draw(l.LINES,e.lineVertexCount,e.vertexCount))}}var S=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function E(t,e,r,n,i,a){var o=r.gl;if(r.vao.bind(),i===r.opacity<1||a){t.bind();var s=t.uniforms;s.model=n.model||c,s.view=n.view||c,s.projection=n.projection||c,g[0]=2/o.drawingBufferWidth,g[1]=2/o.drawingBufferHeight,s.screenSize=g,s.highlightId=r.highlightId,s.highlightScale=r.highlightScale,s.fragClipBounds=S,s.clipBounds=r.axes.bounds,s.opacity=r.opacity,s.pickGroup=r.pickId/255,s.pixelRatio=r.pixelRatio,r.vao.draw(o.TRIANGLES,r.vertexCount),r.lineWidth>0&&(o.lineWidth(r.lineWidth),r.vao.draw(o.LINES,r.lineVertexCount,r.vertexCount))}T(e,r,n,i),r.vao.unbind()}d.draw=function(t){E(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,!1,!1)},d.drawTransparent=function(t){E(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,!0,!1)},d.drawPick=function(t){E(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,!1,!0)},d.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},d.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},d.update=function(t){if("perspective"in(t=t||{})&&(this.useOrtho=!t.perspective),"orthographic"in t&&(this.useOrtho=!!t.orthographic),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"project"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if("projectScale"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if("projectOpacity"in t)if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}"opacity"in t&&(this.opacity=t.opacity),this.dirty=!0;var n=t.position;if(n){var i=t.font||"normal",o=t.alignment||[0,0],s=[1/0,1/0,1/0],c=[-1/0,-1/0,-1/0],u=t.glyph,h=t.color,f=t.size,p=t.angle,d=t.lineColor,g=0,m=0,v=0,y=n.length;t:for(var x=0;x<y;++x){for(var b=n[x],_=0;_<3;++_)if(isNaN(b[_])||!isFinite(b[_]))continue t;var w=(B=Array.isArray(u)?l(u[x],i):l(u||"\u25cf",i))[0],k=B[1],M=B[2];m+=3*w.cells.length,v+=2*k.edges.length}var A=m+v,T=a.mallocFloat(3*A),S=a.mallocFloat(4*A),E=a.mallocFloat(2*A),C=a.mallocUint32(A),L=[0,o[1]],z=0,P=m,I=[0,0,0,1],O=[0,0,0,1],D=Array.isArray(h)&&Array.isArray(h[0]),R=Array.isArray(d)&&Array.isArray(d[0]);t:for(x=0;x<y;++x){var B;for(b=n[x],_=0;_<3;++_){if(isNaN(b[_])||!isFinite(b[_])){g+=1;continue t}c[_]=Math.max(c[_],b[_]),s[_]=Math.min(s[_],b[_])}var F;w=(B=Array.isArray(u)?l(u[x],i):l(u||"\u25cf",i))[0],k=B[1],M=B[2];if(Array.isArray(h)){if(3===(F=D?h[x]:h).length){for(_=0;_<3;++_)I[_]=F[_];I[3]=1}else if(4===F.length)for(_=0;_<4;++_)I[_]=F[_]}else 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X=H[G[W]];E[2*z]=N*(V*X[0]-U*X[1]+L[0]),E[2*z+1]=N*(U*X[0]+V*X[1]+L[1]),z+=1}for(q=k.edges,H=k.positions,_=0;_<q.length;++_)for(G=q[_],W=0;W<2;++W){for(Y=0;Y<3;++Y)T[3*P+Y]=b[Y];for(Y=0;Y<4;++Y)S[4*P+Y]=O[Y];C[P]=g;X=H[G[W]];E[2*P]=N*(V*X[0]-U*X[1]+L[0]),E[2*P+1]=N*(U*X[0]+V*X[1]+L[1]),P+=1}g+=1}this.vertexCount=m,this.lineVertexCount=v,this.pointBuffer.update(T),this.colorBuffer.update(S),this.glyphBuffer.update(E),this.idBuffer.update(new Uint32Array(C)),a.free(T),a.free(S),a.free(E),a.free(C),this.bounds=[s,c],this.points=n,this.pointCount=n.length}},d.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{"./lib/glyphs":281,"./lib/shaders":282,"gl-buffer":230,"gl-mat4/multiply":256,"gl-vao":308,"typedarray-pool":507}],284:[function(t,e,r){"use strict";var n=t("glslify");r.boxVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 vertex;\n\nuniform vec2 cornerA, cornerB;\n\nvoid main() {\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\n}\n"]),r.boxFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color;\n\nvoid main() {\n  gl_FragColor = color;\n}\n"])},{glslify:377}],285:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("./lib/shaders");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,h=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],f=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(h=Math.max(h,c[0]),f=Math.max(f,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<h||d<f)){o.bind();var g=s[2]-s[0],m=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,g,f,i),o.drawBox(0,f,h,d,i),o.drawBox(0,d,g,m,i),o.drawBox(p,f,g,d,i)),this.innerFill&&o.drawBox(h,f,p,d,n),r>0){var v=r*u;o.drawBox(h-v,f-v,p+v,f+v,a),o.drawBox(h-v,d-v,p+v,d+v,a),o.drawBox(h-v,f-v,h+v,d+v,a),o.drawBox(p-v,f-v,p+v,d+v,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{"./lib/shaders":284,"gl-buffer":230,"gl-shader":287}],286:[function(t,e,r){"use strict";e.exports=function(t,e){var r=n(t,e),a=i.mallocUint8(e[0]*e[1]*4);return new c(t,r,a)};var n=t("gl-fbo"),i=t("typedarray-pool"),a=t("ndarray"),o=t("bit-twiddle").nextPow2,s=t("cwise/lib/wrapper")({args:["array",{offset:[0,0,1],array:0},{offset:[0,0,2],array:0},{offset:[0,0,3],array:0},"scalar","scalar","index"],pre:{body:"{this_closestD2=1e8,this_closestX=-1,this_closestY=-1}",args:[],thisVars:["this_closestD2","this_closestX","this_closestY"],localVars:[]},body:{body:"{if(_inline_16_arg0_<255||_inline_16_arg1_<255||_inline_16_arg2_<255||_inline_16_arg3_<255){var 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l(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function c(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}var u=c.prototype;Object.defineProperty(u,"shape",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){i.free(this.buffer);for(var n=this.buffer=i.mallocUint8(o(r*e*4)),a=0;a<r*e*4;++a)n[a]=255}return t}}}),u.begin=function(){var t=this.gl;this.shape;t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT))},u.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},u.query=function(t,e,r){if(!this.gl)return null;var n=this.fbo.shape.slice();t|=0,e|=0,"number"!=typeof r&&(r=1);var i=0|Math.min(Math.max(t-r,0),n[0]),o=0|Math.min(Math.max(t+r,0),n[0]),c=0|Math.min(Math.max(e-r,0),n[1]),u=0|Math.min(Math.max(e+r,0),n[1]);if(o<=i||u<=c)return null;var h=[o-i,u-c],f=a(this.buffer,[h[0],h[1],4],[4,4*n[0],1],4*(i+n[0]*c)),p=s(f.hi(h[0],h[1],1),r,r),d=p[0],g=p[1];return d<0||Math.pow(this.radius,2)<p[2]?null:new l(d+i|0,g+c|0,f.get(d,g,0),[f.get(d,g,1),f.get(d,g,2),f.get(d,g,3)],Math.sqrt(p[2]))},u.dispose=function(){this.gl&&(this.fbo.dispose(),i.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},{"bit-twiddle":80,"cwise/lib/wrapper":136,"gl-fbo":239,ndarray:417,"typedarray-pool":507}],287:[function(t,e,r){"use strict";var n=t("./lib/create-uniforms"),i=t("./lib/create-attributes"),a=t("./lib/reflect"),o=t("./lib/shader-cache"),s=t("./lib/runtime-reflect"),l=t("./lib/GLError");function 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f=this,p=f.gl,d=f._vref;f._vref=o.shader(p,p.VERTEX_SHADER,t),d&&d.dispose(),f.vertShader=f._vref.shader;var g=this._fref;if(f._fref=o.shader(p,p.FRAGMENT_SHADER,e),g&&g.dispose(),f.fragShader=f._fref.shader,!r||!c){var m=p.createProgram();if(p.attachShader(m,f.fragShader),p.attachShader(m,f.vertShader),p.linkProgram(m),!p.getProgramParameter(m,p.LINK_STATUS)){var v=p.getProgramInfoLog(m);throw new l(v,"Error linking program:"+v)}r=r||s.uniforms(p,m),c=c||s.attributes(p,m),p.deleteProgram(m)}(c=c.slice()).sort(h);var y,x=[],b=[],_=[];for(y=0;y<c.length;++y){var w=c[y];if(w.type.indexOf("mat")>=0){for(var k=0|w.type.charAt(w.type.length-1),M=new Array(k),A=0;A<k;++A)M[A]=_.length,b.push(w.name+"["+A+"]"),"number"==typeof w.location?_.push(w.location+A):Array.isArray(w.location)&&w.location.length===k&&"number"==typeof w.location[A]?_.push(0|w.location[A]):_.push(-1);x.push({name:w.name,type:w.type,locations:M})}else x.push({name:w.name,type:w.type,locations:[_.length]}),b.push(w.name),"number"==typeof w.location?_.push(0|w.location):_.push(-1)}var T=0;for(y=0;y<_.length;++y)if(_[y]<0){for(;_.indexOf(T)>=0;)T+=1;_[y]=T}var S=new Array(r.length);function E(){f.program=o.program(p,f._vref,f._fref,b,_);for(var t=0;t<r.length;++t)S[t]=p.getUniformLocation(f.program,r[t].name)}E(),f._relink=E,f.types={uniforms:a(r),attributes:a(c)},f.attributes=i(p,f,x,_),Object.defineProperty(f,"uniforms",n(p,f,r,S))},e.exports=function(t,e,r,n,i){var a=new c(t);return a.update(e,r,n,i),a}},{"./lib/GLError":288,"./lib/create-attributes":289,"./lib/create-uniforms":290,"./lib/reflect":291,"./lib/runtime-reflect":292,"./lib/shader-cache":293}],288:[function(t,e,r){function n(t,e,r){this.shortMessage=e||"",this.longMessage=r||"",this.rawError=t||"",this.message="gl-shader: "+(e||t||"")+(r?"\n"+r:""),this.stack=(new Error).stack}n.prototype=new 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l=s[0]+(n[0]-o[0])/(o[2]-o[0])*(s[2]-s[0]),c=s[1]+(n[1]-o[1])/(o[3]-o[1])*(s[3]-s[1]);t[0]&&a.drawLine(l,c,s[0],c,e[0],r[0]),t[1]&&a.drawLine(l,c,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,c,s[2],c,e[2],r[2]),t[3]&&a.drawLine(l,c,l,s[3],e[3],r[3])}},i.dispose=function(){this.plot.removeOverlay(this)}},{}],295:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position, color;\nattribute float weight;\n\nuniform mat4 model, view, projection;\nuniform vec3 coordinates[3];\nuniform vec4 colors[3];\nuniform vec2 screenShape;\nuniform float lineWidth;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vertexPosition = mix(coordinates[0],\n    mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));\n\n  vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\n  vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\n  vec2 delta = weight * clipOffset * screenShape;\n  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\n\n  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\n  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\n}\n"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n  gl_FragColor = fragColor;\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec3"},{name:"weight",type:"float"}])}},{"gl-shader":287,glslify:377}],296:[function(t,e,r){"use strict";var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index");e.exports=function(t,e){var r=[];function o(t,e,n,i,a,o){var 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l=s.prototype,c=[0,0,0],u=[0,0,0],h=[0,0];l.isTransparent=function(){return!1},l.drawTransparent=function(t){},l.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||o,s=t.view||o,l=t.projection||o;this.axes&&(i=this.axes.lastCubeProps.axis);for(var f=c,p=u,d=0;d<3;++d)i&&i[d]<0?(f[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(f[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);h[0]=e.drawingBufferWidth,h[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,f,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=h;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&("bounds"in t&&(this.bounds=t.bounds),"position"in t&&(this.position=t.position),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"colors"in t&&(this.colors=t.colors),"enabled"in t&&(this.enabled=t.enabled),"drawSides"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders/index":295,"gl-buffer":230,"gl-vao":308}],297:[function(t,e,r){arguments[4][232][0].apply(r,arguments)},{barycentric:61,dup:232,"polytope-closest-point/lib/closest_point_2d.js":448}],298:[function(t,e,r){var n=t("glslify"),i=n(["precision mediump float;\n#define GLSLIFY 1\n\nfloat inverse(float m) {\n  return 1.0 / m;\n}\n\nmat2 inverse(mat2 m) {\n  return mat2(m[1][1],-m[0][1],\n             -m[1][0], m[0][0]) / (m[0][0]*m[1][1] - m[0][1]*m[1][0]);\n}\n\nmat3 inverse(mat3 m) {\n  float a00 = m[0][0], a01 = m[0][1], a02 = m[0][2];\n  float a10 = m[1][0], a11 = m[1][1], a12 = m[1][2];\n  float a20 = m[2][0], a21 = m[2][1], a22 = m[2][2];\n\n  float b01 = a22 * a11 - a12 * a21;\n  float b11 = -a22 * a10 + a12 * a20;\n  float b21 = a21 * a10 - a11 * a20;\n\n  float det = a00 * b01 + a01 * b11 + a02 * b21;\n\n  return mat3(b01, (-a22 * a01 + a02 * a21), (a12 * a01 - a02 * a11),\n              b11, (a22 * a00 - a02 * a20), (-a12 * a00 + a02 * a10),\n              b21, (-a21 * a00 + a01 * a20), (a11 * a00 - a01 * a10)) / det;\n}\n\nmat4 inverse(mat4 m) {\n  float\n      a00 = m[0][0], a01 = m[0][1], a02 = m[0][2], a03 = m[0][3],\n      a10 = m[1][0], a11 = m[1][1], a12 = m[1][2], a13 = m[1][3],\n      a20 = m[2][0], a21 = m[2][1], a22 = m[2][2], a23 = m[2][3],\n      a30 = m[3][0], a31 = m[3][1], a32 = m[3][2], a33 = m[3][3],\n\n      b00 = a00 * a11 - a01 * a10,\n      b01 = a00 * a12 - a02 * a10,\n      b02 = a00 * a13 - a03 * a10,\n      b03 = a01 * a12 - a02 * a11,\n      b04 = a01 * a13 - a03 * a11,\n      b05 = a02 * a13 - a03 * a12,\n      b06 = a20 * a31 - a21 * a30,\n      b07 = a20 * a32 - a22 * a30,\n      b08 = a20 * a33 - a23 * a30,\n      b09 = a21 * a32 - a22 * a31,\n      b10 = a21 * a33 - a23 * a31,\n      b11 = a22 * a33 - a23 * a32,\n\n      det = b00 * b11 - b01 * b10 + b02 * b09 + b03 * b08 - b04 * b07 + b05 * b06;\n\n  return mat4(\n      a11 * b11 - a12 * b10 + a13 * b09,\n      a02 * b10 - a01 * b11 - a03 * b09,\n      a31 * b05 - a32 * b04 + a33 * b03,\n      a22 * b04 - a21 * b05 - a23 * b03,\n      a12 * b08 - a10 * b11 - a13 * b07,\n      a00 * b11 - a02 * b08 + a03 * b07,\n      a32 * b02 - a30 * b05 - a33 * b01,\n      a20 * b05 - a22 * b02 + a23 * b01,\n      a10 * b10 - a11 * b08 + a13 * b06,\n      a01 * b08 - a00 * b10 - a03 * b06,\n      a30 * b04 - a31 * b02 + a33 * b00,\n      a21 * b02 - a20 * b04 - a23 * b00,\n      a11 * b07 - a10 * b09 - a12 * b06,\n      a00 * b09 - a01 * b07 + a02 * b06,\n      a31 * b01 - a30 * b03 - a32 * b00,\n      a20 * b03 - a21 * b01 + a22 * b00) / det;\n}\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\nuniform float tubeScale;\n\nuniform mat4 model\n           , view\n           , projection;\nuniform vec3 eyePosition\n           , lightPosition;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal), 0.0);\n  normal = normalize(normal * inverse(mat3(model)));\n\n  vec4 t_position  = view * tubePosition;\n  gl_Position      = projection * t_position;\n  f_color          = color;\n  f_normal         = normal;\n  f_data           = tubePosition.xyz;\n  f_eyeDirection   = eyePosition   - tubePosition.xyz;\n  f_lightDirection = lightPosition - tubePosition.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["precision mediump float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular\n            , opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n  \n  if(!gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = cookTorranceSpecular(L, V, N, roughness, fresnel);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor =  texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float tubeScale;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal), 0.0);\n  gl_Position = projection * view * tubePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec4"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec4"}]}},{glslify:377}],299:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("normals"),l=t("gl-mat4/multiply"),c=t("gl-mat4/invert"),u=t("ndarray"),h=t("colormap"),f=t("simplicial-complex-contour"),p=t("typedarray-pool"),d=t("./shaders"),g=(t("./closest-point"),d.meshShader),m=d.pickShader,v=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function y(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,g,m,y,x,b,_,w,k,M,A,T,S,E){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleVectors=c,this.triangleColors=h,this.triangleNormals=p,this.triangleUVs=f,this.triangleIds=u,this.triangleVAO=d,this.triangleCount=0,this.lineWidth=1,this.edgePositions=g,this.edgeColors=y,this.edgeUVs=x,this.edgeIds=m,this.edgeVAO=b,this.edgeCount=0,this.pointPositions=_,this.pointColors=k,this.pointUVs=M,this.pointSizes=A,this.pointIds=w,this.pointVAO=T,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=S,this.contourVAO=E,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!1,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.tubeScale=1,this._model=v,this._view=v,this._projection=v,this._resolution=[1,1]}var x=y.prototype;function b(t){var e=n(t,m.vertex,m.fragment,null,m.attributes);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.vector.location=5,e}x.isOpaque=function(){return this.opacity>=1},x.isTransparent=function(){return this.opacity<1},x.pickSlots=1,x.setPickBase=function(t){this.pickId=t},x.highlight=function(t){if(t&&this.contourEnable){for(var e=f(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var h=c[0];2===c.length&&(h=c[u]);for(var d=n[h][0],g=n[h][1],m=i[h],v=1-m,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=m*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},x.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=h({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale);var a=[],l=[],c=[],f=[],p=[],d=[],g=[],m=[],v=[],y=[],x=[],b=[],_=[],w=[],k=[];this.cells=r,this.positions=n,this.vectors=i;var M=t.vertexNormals,A=t.cellNormals,T=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,S=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!A&&(A=s.faceNormals(r,n,S)),A||M||(M=s.vertexNormals(r,n,T));var E=t.vertexColors,C=t.cellColors,L=t.meshColor||[1,1,1,1],z=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,D=1/0,R=-1/0;if(!z&&!I)if(P)if(t.vertexIntensityBounds)D=+t.vertexIntensityBounds[0],R=+t.vertexIntensityBounds[1];else for(var B=0;B<P.length;++B){var F=P[B];D=Math.min(D,F),R=Math.max(R,F)}else if(O)for(B=0;B<O.length;++B){F=O[B];D=Math.min(D,F),R=Math.max(R,F)}else for(B=0;B<n.length;++B){F=n[B][2];D=Math.min(D,F),R=Math.max(R,F)}this.intensity=P||(O?function(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;var a=t.length;for(i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}(r,n.length,O):function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n));var N=t.pointSizes,j=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(B=0;B<n.length;++B)for(var V=n[B],U=0;U<3;++U)!isNaN(V[U])&&isFinite(V[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],V[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],V[U]));var q=0,H=0,G=0;t:for(B=0;B<r.length;++B){var W=r[B];switch(W.length){case 1:for(V=n[X=W[0]],U=0;U<3;++U)if(isNaN(V[U])||!isFinite(V[U]))continue t;x.push(V[0],V[1],V[2],V[3]),3===(Z=E?E[X]:C?C[B]:L).length?b.push(Z[0],Z[1],Z[2],1):b.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:P?[(P[X]-D)/(R-D),0]:I?I[B]:O?[(O[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],_.push($[0],$[1]),N?w.push(N[X]):w.push(j),k.push(B),G+=1;break;case 2:for(U=0;U<2;++U){V=n[X=W[U]];for(var Y=0;Y<3;++Y)if(isNaN(V[Y])||!isFinite(V[Y]))continue t}for(U=0;U<2;++U){V=n[X=W[U]];g.push(V[0],V[1],V[2]),3===(Z=E?E[X]:C?C[B]:L).length?m.push(Z[0],Z[1],Z[2],1):m.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:P?[(P[X]-D)/(R-D),0]:I?I[B]:O?[(O[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],v.push($[0],$[1]),y.push(B)}H+=1;break;case 3:for(U=0;U<3;++U)for(V=n[X=W[U]],Y=0;Y<3;++Y)if(isNaN(V[Y])||!isFinite(V[Y]))continue t;for(U=0;U<3;++U){var X;V=n[X=W[U]];a.push(V[0],V[1],V[2],V[3]);var Z,$,J,K=i[X];l.push(K[0],K[1],K[2],K[3]),3===(Z=E?E[X]:C?C[B]:L).length?c.push(Z[0],Z[1],Z[2],1):c.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:P?[(P[X]-D)/(R-D),0]:I?I[B]:O?[(O[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],p.push($[0],$[1]),J=M?M[X]:A[B],f.push(J[0],J[1],J[2]),d.push(B)}q+=1}}this.pointCount=G,this.edgeCount=H,this.triangleCount=q,this.pointPositions.update(x),this.pointColors.update(b),this.pointUVs.update(_),this.pointSizes.update(w),this.pointIds.update(new Uint32Array(k)),this.edgePositions.update(g),this.edgeColors.update(m),this.edgeUVs.update(v),this.edgeIds.update(new Uint32Array(y)),this.trianglePositions.update(a),this.triangleVectors.update(l),this.triangleColors.update(c),this.triangleUVs.update(p),this.triangleNormals.update(f),this.triangleIds.update(new Uint32Array(d))}},x.drawTransparent=x.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||v,n=t.view||v,i=t.projection||v,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,contourColor:this.contourColor,texture:0};this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var h,f=u[15];for(o=0;o<3;++o)f+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/f}this.triangleCount>0&&((h=this.triShader).bind(),h.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((h=this.lineShader).bind(),h.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((h=this.pointShader).bind(),h.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((h=this.contourShader).bind(),h.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},x.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||v,n=t.view||v,i=t.projection||v,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},x.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3);return{index:e,position:n,intensity:this.intensity[r[1]],velocity:this.vectors[r[1]].slice(0,3),divergence:this.vectors[r[1]][3],dataCoordinate:n}},x.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=e.triShader||function(t){var e=n(t,g.vertex,g.fragment,null,g.attributes);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.vector.location=5,e}(t),s=b(t),l=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));l.generateMipmap(),l.minFilter=t.LINEAR_MIPMAP_LINEAR,l.magFilter=t.LINEAR;var c=i(t),h=i(t),f=i(t),p=i(t),d=i(t),m=i(t),v=a(t,[{buffer:c,type:t.FLOAT,size:4},{buffer:m,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:f,type:t.FLOAT,size:4},{buffer:p,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:3},{buffer:h,type:t.FLOAT,size:4}]),x=i(t),_=i(t),w=i(t),k=i(t),M=a(t,[{buffer:x,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),A=i(t),T=i(t),S=i(t),E=i(t),C=i(t),L=a(t,[{buffer:A,type:t.FLOAT,size:3},{buffer:C,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:T,type:t.FLOAT,size:4},{buffer:S,type:t.FLOAT,size:2},{buffer:E,type:t.FLOAT,size:1}]),z=i(t),P=new y(t,l,r,null,null,s,null,null,c,h,m,f,p,d,v,x,k,_,w,M,A,C,T,S,E,L,z,a(t,[{buffer:z,type:t.FLOAT,size:3}]));return P.update(e),P}},{"./closest-point":297,"./shaders":298,colormap:113,"gl-buffer":230,"gl-mat4/invert":254,"gl-mat4/multiply":256,"gl-shader":287,"gl-texture2d":303,"gl-vao":308,ndarray:417,normals:420,"simplicial-complex-contour":479,"typedarray-pool":507}],300:[function(t,e,r){"use strict";var n=t("gl-vec3"),i=t("gl-vec4"),a=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++){var u=n.length(l[c]);u>o&&(o=u)}var h=t.map(function(t){return function(t,e,r,a){var o,s,l,c=t.points,u=t.velocities,h=t.divergences;n.set(n.create(),0,1,0),n.create(),n.create();n.create();for(var f=[],p=[],d=[],g=[],m=[],v=[],y=0,x=0,b=i.create(),_=i.create(),w=0;w<c.length;w++){o=c[w],s=u[w],l=h[w],0===e&&(l=.05*r),x=n.length(s)/a,b=i.create(),n.copy(b,s),b[3]=l;for(var k=0;k<8;k++)m[k]=[o[0],o[1],o[2],k];if(g.length>0)for(k=0;k<8;k++){var M=(k+1)%8;f.push(g[k],m[k],m[M],m[M],g[M],g[k]),d.push(_,b,b,b,_,_),v.push(y,x,x,x,y,y),p.push([f.length-6,f.length-5,f.length-4],[f.length-3,f.length-2,f.length-1])}var A=g;g=m,m=A,A=_,_=b,b=A,A=y,y=x,x=A}return{positions:f,cells:p,vectors:d,vertexIntensity:v}}(t,r,a,o)}),f=[],p=[],d=[],g=[];for(s=0;s<h.length;s++){var m=h[s],v=f.length;f=f.concat(m.positions),d=d.concat(m.vectors),g=g.concat(m.vertexIntensity);for(c=0;c<m.cells.length;c++){var y=m.cells[c],x=[];p.push(x);for(var b=0;b<y.length;b++)x.push(y[b]+v)}}return{positions:f,cells:p,vectors:d,vertexIntensity:g,colormap:e}},o=function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=this.getVelocity(r);n.subtract(a,a,e),n.scale(a,a,1e4),n.add(r,t,[0,i,0]);var o=this.getVelocity(r);n.subtract(o,o,e),n.scale(o,o,1e4),n.add(r,t,[0,0,i]);var s=this.getVelocity(r);return n.subtract(s,s,e),n.scale(s,s,1e4),n.add(r,a,o),n.add(r,r,s),r},s=function(t){return f(t,this.vectors,this.meshgrid,this.clampBorders)},l=function(t,e){for(var r=0;r<t.length;r++){var n=t[r];if(n===e)return r;if(n>e)return r-1}return r},c=n.create(),u=n.create(),h=function(t,e,r){return t<e?e:t>r?r:t},f=function(t,e,r,i){var a=t[0],o=t[1],s=t[2],f=r[0].length,p=r[1].length,d=r[2].length,g=l(r[0],a),m=l(r[1],o),v=l(r[2],s),y=g+1,x=m+1,b=v+1;if(r[0][g]===a&&(y=g),r[1][m]===o&&(x=m),r[2][v]===s&&(b=v),i&&(g=h(g,0,f-1),y=h(y,0,f-1),m=h(m,0,p-1),x=h(x,0,p-1),v=h(v,0,d-1),b=h(b,0,d-1)),g<0||m<0||v<0||y>=f||x>=p||b>=d)return n.create();var _=(a-r[0][g])/(r[0][y]-r[0][g]),w=(o-r[1][m])/(r[1][x]-r[1][m]),k=(s-r[2][v])/(r[2][b]-r[2][v]);(_<0||_>1||isNaN(_))&&(_=0),(w<0||w>1||isNaN(w))&&(w=0),(k<0||k>1||isNaN(k))&&(k=0);var M=v*f*p,A=b*f*p,T=m*f,S=x*f,E=g,C=y,L=e[T+M+E],z=e[T+M+C],P=e[S+M+E],I=e[S+M+C],O=e[T+A+E],D=e[T+A+C],R=e[S+A+E],B=e[S+A+C],F=n.create();return n.lerp(F,L,z,_),n.lerp(c,P,I,_),n.lerp(F,F,c,w),n.lerp(c,O,D,_),n.lerp(u,R,B,_),n.lerp(c,c,u,w),n.lerp(F,F,c,k),F},p=function(t){var e=1/0;t.sort(function(t,e){return t-e});for(var r=1;r<t.length;r++){var n=Math.abs(t[r]-t[r-1]);n<e&&(e=n)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,l=t.tubeSize||1,c=t.absoluteTubeSize;t.getDivergence||(t.getDivergence=o),t.getVelocity||(t.getVelocity=s),void 0===t.clampBorders&&(t.clampBorders=!0);var u=[],h=e[0][0],f=e[0][1],d=e[0][2],g=e[1][0],m=e[1][1],v=e[1][2],y=function(t,e){var r=e[0],n=e[1],i=e[2];return r>=h&&r<=g&&n>=f&&n<=m&&i>=d&&i<=v},x=10*n.distance(e[0],e[1])/i,b=x*x,_=1,w=0;n.create();r.length>=2&&(_=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=0;s<t.length;s++){var l=t[s],c=l[0],u=l[1],h=l[2];i[c]||(e.push(c),i[c]=!0),a[u]||(r.push(u),a[u]=!0),o[h]||(n.push(h),o[h]=!0)}var f=p(e),d=p(r),g=p(n),m=Math.min(f,d,g);return isFinite(m)?m:1}(r));for(var k=0;k<r.length;k++){var M=n.create();n.copy(M,r[k]);var A=[M],T=[],S=t.getVelocity(M),E=M;T.push(S);var C=[],L=t.getDivergence(M,S);(O=n.length(L))>w&&!isNaN(O)&&isFinite(O)&&(w=O),C.push(O),u.push({points:A,velocities:T,divergences:C});for(var z=0;z<100*i&&A.length<i&&y(0,M);){z++;var P=n.clone(S),I=n.squaredLength(P);if(0===I)break;if(I>b&&n.scale(P,P,x/Math.sqrt(I)),n.add(P,P,M),S=t.getVelocity(P),n.squaredDistance(E,P)-b>-1e-4*b){A.push(P),E=P,T.push(S);L=t.getDivergence(P,S);(O=n.length(L))>w&&!isNaN(O)&&isFinite(O)&&(w=O),C.push(O)}M=P}}for(k=0;k<C.length;k++){var O=C[k];!isNaN(O)&&isFinite(O)||(C[k]=w)}var D=a(u,t.colormap,w,_);return c?D.tubeScale=c:(0===w&&(w=1),D.tubeScale=.5*l*_/w),D},e.exports.createTubeMesh=t("./lib/tubemesh")},{"./lib/tubemesh":299,"gl-vec3":320,"gl-vec4":350}],301:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute vec3 f;\nattribute vec3 normal;\n\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 lightPosition, eyePosition;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  worldCoordinate = vec3(uv.zw, f.x);\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n  vec4 clipPosition = projection * view * worldPosition;\n  gl_Position = clipPosition;\n  kill = f.y;\n  value = f.z;\n  planeCoordinate = uv.xy;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * worldPosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  lightDirection = lightPosition - cameraCoordinate.xyz;\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\n}\n"]),o=i(["precision mediump float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat beckmannSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness) {\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\n}\n\nuniform vec3 lowerBound, upperBound;\nuniform float contourTint;\nuniform vec4 contourColor;\nuniform sampler2D colormap;\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform float vertexColor;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  if (kill > 0.0 ||\n    any(lessThan(worldCoordinate, clipBounds[0])) || any(greaterThan(worldCoordinate, clipBounds[1]))) {\n    discard;\n  }\n\n  vec3 N = normalize(surfaceNormal);\n  vec3 V = normalize(eyeDirection);\n  vec3 L = normalize(lightDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  //decide how to interpolate color \u2014 in vertex or in fragment\n  vec4 surfaceColor = step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) + step(.5, vertexColor) * vColor;\n\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\n}\n"]),s=i(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute float f;\n\nuniform mat3 permutation;\nuniform mat4 model, view, projection;\nuniform float height, zOffset;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\n  vec4 worldPosition = model * vec4(dataCoordinate, 1.0);\n\n  vec4 clipPosition = projection * view * worldPosition;\n  clipPosition.z = clipPosition.z + zOffset;\n\n  gl_Position = clipPosition;\n  value = f;\n  kill = -1.0;\n  worldCoordinate = dataCoordinate;\n  planeCoordinate = uv.zw;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Don't do lighting for contours\n  surfaceNormal   = vec3(1,0,0);\n  eyeDirection    = vec3(0,1,0);\n  lightDirection  = vec3(0,0,1);\n}\n"]),l=i(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec2 shape;\nuniform vec3 clipBounds[2];\nuniform float pickId;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 surfaceNormal;\n\nvec2 splitFloat(float v) {\n  float vh = 255.0 * v;\n  float upper = floor(vh);\n  float lower = fract(vh);\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\n}\n\nvoid main() {\n  if(kill > 0.0 ||\n    any(lessThan(worldCoordinate, clipBounds[0])) || any(greaterThan(worldCoordinate, clipBounds[1]))) {\n    discard;\n  }\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\n}\n"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{"gl-shader":287,glslify:377}],302:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:w,offset:0},{buffer:c,size:3,stride:w,offset:16},{buffer:c,size:3,stride:w,offset:28}]),h=i(e),f=a(e,[{buffer:h,size:4,stride:20,offset:0},{buffer:h,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),g=o(e,1,S,e.RGBA,e.UNSIGNED_BYTE);g.minFilter=e.LINEAR,g.magFilter=e.LINEAR;var m=new E(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,g,s,l,h,f,p,d),v={levels:[[],[],[]]};for(var k in t)v[k]=t[k];return v.colormap=v.colormap||"jet",m.update(v),m};var n=t("bit-twiddle"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("typedarray-pool"),l=t("colormap"),c=t("ndarray-ops"),u=t("ndarray-pack"),h=t("ndarray"),f=t("surface-nets"),p=t("gl-mat4/multiply"),d=t("gl-mat4/invert"),g=t("binary-search-bounds"),m=t("ndarray-gradient"),v=t("./lib/shaders"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=40,k=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],M=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],A=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function T(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=A[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();var S=256;function E(t,e,r,n,i,a,o,l,c,u,f,p,d,g){this.gl=t,this.shape=e,this.bounds=r,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=f,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new T([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=g,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[h(s.mallocFloat(1024),[0,0]),h(s.mallocFloat(1024),[0,0]),h(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var C=E.prototype;C.isTransparent=function(){return this.opacity<1},C.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this._contourCounts[t].length>0||this._dynamicCounts[t]>0)return!0;return!1},C.pickSlots=1,C.setPickBase=function(t){this.pickId=t};var L=[0,0,0],z={showSurface:!1,showContour:!1,projections:[k.slice(),k.slice(),k.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function P(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||L,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=z.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=z.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return z.showSurface=o,z.showContour=s,z}var I={model:k,view:k,projection:k,inverseModel:k.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},O=k.slice(),D=[1,0,0,0,1,0,0,0,1];function R(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=I;n.model=t.model||k,n.view=t.view||k,n.projection=t.projection||k,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=D,n.vertexColor=this.vertexColor;var s=O;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=P(n,this);if(u.showSurface&&e===this.opacity<1){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour&&!e){var h=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,h.bind(),h.uniforms=n;var f=this._contourVAO;for(f.bind(),i=0;i<3;++i)for(h.uniforms.permutation=A[i],r.lineWidth(this.contourWidth[i]),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(h.uniforms.contourColor=this.highlightColor[i],h.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(h.uniforms.contourColor=this.contourColor[i],h.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(h.uniforms.height=this.contourLevels[i][o],f.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(h.uniforms.model=u.projections[i],h.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){h.uniforms.permutation=A[o],r.lineWidth(this.contourWidth[o]);for(var g=0;g<this.contourLevels[o].length;++g)g===this.highlightLevel[o]?(h.uniforms.contourColor=this.highlightColor[o],h.uniforms.contourTint=this.highlightTint[o]):0!==g&&g-1!==this.highlightLevel[o]||(h.uniforms.contourColor=this.contourColor[o],h.uniforms.contourTint=this.contourTint[o]),h.uniforms.height=this.contourLevels[o][g],f.draw(r.LINES,this._contourCounts[o][g],this._contourOffsets[o][g])}for(f.unbind(),(f=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(h.uniforms.model=n.model,h.uniforms.clipBounds=n.clipBounds,h.uniforms.permutation=A[i],r.lineWidth(this.dynamicWidth[i]),h.uniforms.contourColor=this.dynamicColor[i],h.uniforms.contourTint=this.dynamicTint[i],h.uniforms.height=this.dynamicLevel[i],f.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(h.uniforms.model=u.projections[o],h.uniforms.clipBounds=u.clipBounds[o],f.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));f.unbind()}}C.draw=function(t){return R.call(this,t,!1)},C.drawTransparent=function(t){return R.call(this,t,!0)};var B={model:k,view:k,projection:k,inverseModel:k,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function F(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))}function N(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function j(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function V(t){if(Array.isArray(t)){if(Array.isArray(t))return[j(t[0]),j(t[1]),j(t[2])];var e=j(t);return[e.slice(),e.slice(),e.slice()]}}C.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=B;r.model=t.model||k,r.view=t.view||k,r.projection=t.projection||k,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.permutation=D;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=P(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]),s.uniforms.permutation=A[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=A[a],e.lineWidth(this.contourWidth[a]);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},C.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var h=u?a:1-a,f=0;f<2;++f)for(var p=i+u,d=s+f,m=h*(f?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*m;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=g.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},C.update=function(t){t=t||{},this.dirty=!0,"contourWidth"in t&&(this.contourWidth=N(t.contourWidth,Number)),"showContour"in t&&(this.showContour=N(t.showContour,Boolean)),"showSurface"in t&&(this.showSurface=!!t.showSurface),"contourTint"in t&&(this.contourTint=N(t.contourTint,Boolean)),"contourColor"in t&&(this.contourColor=V(t.contourColor)),"contourProject"in t&&(this.contourProject=N(t.contourProject,function(t){return N(t,Boolean)})),"surfaceProject"in t&&(this.surfaceProject=t.surfaceProject),"dynamicColor"in t&&(this.dynamicColor=V(t.dynamicColor)),"dynamicTint"in t&&(this.dynamicTint=N(t.dynamicTint,Number)),"dynamicWidth"in t&&(this.dynamicWidth=N(t.dynamicWidth,Number)),"opacity"in t&&(this.opacity=t.opacity),"colorBounds"in t&&(this.colorBounds=t.colorBounds),"vertexColor"in t&&(this.vertexColor=t.vertexColor?1:0);var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),"field"in t||"coords"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=h(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),F(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=h(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var p=t.coords;if(!Array.isArray(p)||3!==p.length)throw new Error("gl-surface: invalid coordinates for x/y");for(o=0;o<2;++o){var d=p[o];for(b=0;b<2;++b)if(d.shape[b]!==a[b])throw new Error("gl-surface: coords have incorrect shape");F(this._field[o],d)}}else if(t.ticks){var g=t.ticks;if(!Array.isArray(g)||2!==g.length)throw new Error("gl-surface: invalid ticks");for(o=0;o<2;++o){var v=g[o];if((Array.isArray(v)||v.length)&&(v=h(v)),v.shape[0]!==a[o])throw new Error("gl-surface: invalid tick length");var y=h(v.data,a);y.stride[o]=v.stride[0],y.stride[1^o]=0,F(this._field[o],y)}}else{for(o=0;o<2;++o){var x=[0,0];x[o]=1,this._field[o]=h(this._field[o].data,[a[0]+2,a[1]+2],x,0)}this._field[0].set(0,0,0);for(var b=0;b<a[0];++b)this._field[0].set(b+1,0,b);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),b=0;b<a[1];++b)this._field[1].set(0,b+1,b);this._field[1].set(0,a[1]+1,a[1]-1)}var _=this._field,w=h(s.mallocFloat(3*_[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)m(w.pick(o),_[o],"mirror");var k=h(s.mallocFloat(3*_[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(b=0;b<a[1]+2;++b){var A=w.get(0,o,b,0),T=w.get(0,o,b,1),E=w.get(1,o,b,0),C=w.get(1,o,b,1),L=w.get(2,o,b,0),z=w.get(2,o,b,1),P=E*z-C*L,I=L*T-z*A,O=A*C-T*E,D=Math.sqrt(P*P+I*I+O*O);D<1e-8?(D=Math.max(Math.abs(P),Math.abs(I),Math.abs(O)))<1e-8?(O=1,I=P=0,D=1):D=1/D:D=1/Math.sqrt(D),k.set(o,b,0,P*D),k.set(o,b,1,I*D),k.set(o,b,2,O*D)}s.free(w.data);var R=[1/0,1/0,1/0],B=[-1/0,-1/0,-1/0],j=1/0,U=-1/0,q=(a[0]-1)*(a[1]-1)*6,H=s.mallocFloat(n.nextPow2(10*q)),G=0,W=0;for(o=0;o<a[0]-1;++o)t:for(b=0;b<a[1]-1;++b){for(var Y=0;Y<2;++Y)for(var X=0;X<2;++X)for(var Z=0;Z<3;++Z){var $=this._field[Z].get(1+o+Y,1+b+X);if(isNaN($)||!isFinite($))continue t}for(Z=0;Z<6;++Z){var J=o+M[Z][0],K=b+M[Z][1],Q=this._field[0].get(J+1,K+1),tt=this._field[1].get(J+1,K+1),et=$=this._field[2].get(J+1,K+1);P=k.get(J+1,K+1,0),I=k.get(J+1,K+1,1),O=k.get(J+1,K+1,2),t.intensity&&(et=t.intensity.get(J,K)),H[G++]=J,H[G++]=K,H[G++]=Q,H[G++]=tt,H[G++]=$,H[G++]=0,H[G++]=et,H[G++]=P,H[G++]=I,H[G++]=O,R[0]=Math.min(R[0],Q),R[1]=Math.min(R[1],tt),R[2]=Math.min(R[2],$),j=Math.min(j,et),B[0]=Math.max(B[0],Q),B[1]=Math.max(B[1],tt),B[2]=Math.max(B[2],$),U=Math.max(U,et),W+=1}}for(t.intensityBounds&&(j=+t.intensityBounds[0],U=+t.intensityBounds[1]),o=6;o<G;o+=10)H[o]=(H[o]-j)/(U-j);this._vertexCount=W,this._coordinateBuffer.update(H.subarray(0,G)),s.freeFloat(H),s.free(k.data),this.bounds=[R,B],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===j&&this.intensityBounds[1]===U||(r=!0),this.intensityBounds=[j,U]}if("levels"in t){var rt=t.levels;for(rt=Array.isArray(rt[0])?rt.slice():[[],[],rt],o=0;o<3;++o)rt[o]=rt[o].slice(),rt.sort(function(t,e){return t-e});t:for(o=0;o<3;++o){if(rt[o].length!==this.contourLevels[o].length){r=!0;break}for(b=0;b<rt[o].length;++b)if(rt[o][b]!==this.contourLevels[o][b]){r=!0;break t}}this.contourLevels=rt}if(r){_=this._field,a=this.shape;for(var nt=[],it=0;it<3;++it){rt=this.contourLevels[it];var at=[],ot=[],st=[0,0,0];for(o=0;o<rt.length;++o){var lt=f(this._field[it],rt[o]);at.push(nt.length/5|0),W=0;t:for(b=0;b<lt.cells.length;++b){var ct=lt.cells[b];for(Z=0;Z<2;++Z){var ut=lt.positions[ct[Z]],ht=ut[0],ft=0|Math.floor(ht),pt=ht-ft,dt=ut[1],gt=0|Math.floor(dt),mt=dt-gt,vt=!1;e:for(var yt=0;yt<3;++yt){st[yt]=0;var xt=(it+yt+1)%3;for(Y=0;Y<2;++Y){var bt=Y?pt:1-pt;for(J=0|Math.min(Math.max(ft+Y,0),a[0]),X=0;X<2;++X){var _t=X?mt:1-mt;if(K=0|Math.min(Math.max(gt+X,0),a[1]),$=yt<2?this._field[xt].get(J,K):(this.intensity.get(J,K)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite($)||isNaN($)){vt=!0;break e}var wt=bt*_t;st[yt]+=wt*$}}}if(vt){if(Z>0){for(var kt=0;kt<5;++kt)nt.pop();W-=1}continue t}nt.push(st[0],st[1],ut[0],ut[1],st[2]),W+=1}}ot.push(W)}this._contourOffsets[it]=at,this._contourCounts[it]=ot}var Mt=s.mallocFloat(nt.length);for(o=0;o<nt.length;++o)Mt[o]=nt[o];this._contourBuffer.update(Mt),s.freeFloat(Mt)}t.colormap&&this._colorMap.setPixels(function(t){var e=u([l({colormap:t,nshades:S,format:"rgba"}).map(function(t){return[t[0],t[1],t[2],255*t[3]]})]);return c.divseq(e,255),e}(t.colormap))},C.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},C.highlight=function(t){if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(var e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;var r;if(r=this.snapToData?t.dataCoordinate:t.position,this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],h=this._field[l],p=this._field[c],d=(this.intensity,f(u,r[o])),g=d.cells,m=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<g.length;++e)for(var v=g[e],y=0;y<2;++y){var x=m[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),k=b-_,M=1-k,A=+x[1],T=0|A,S=0|Math.min(T+1,i[1]),E=A-T,C=1-E,L=M*C,z=M*E,P=k*C,I=k*E,O=L*h.get(_,T)+z*h.get(_,S)+P*h.get(w,T)+I*h.get(w,S),D=L*p.get(_,T)+z*p.get(_,S)+P*p.get(w,T)+I*p.get(w,S);if(isNaN(O)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=O,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{"./lib/shaders":301,"binary-search-bounds":79,"bit-twiddle":80,colormap:113,"gl-buffer":230,"gl-mat4/invert":254,"gl-mat4/multiply":256,"gl-texture2d":303,"gl-vao":308,ndarray:417,"ndarray-gradient":408,"ndarray-ops":411,"ndarray-pack":412,"surface-nets":493,"typedarray-pool":507}],303:[function(t,e,r){"use strict";var n=t("ndarray"),i=t("ndarray-ops"),a=t("typedarray-pool");e.exports=function(t){if(arguments.length<=1)throw new Error("gl-texture2d: Missing arguments for texture2d constructor");o||function(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}(t);if("number"==typeof arguments[1])return m(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return m(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if("object"==typeof arguments[1]){var e=arguments[1],r=c(e)?e:e.raw;if(r)return function(t,e,r,n,i,a){var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new f(t,o,r,n,i,a)}(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return function(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error("gl-texture2d: Invalid texture size");var l=d(o,e.stride.slice()),c=0;"float32"===r?c=t.FLOAT:"float64"===r?(c=t.FLOAT,l=!1,r="float32"):"uint8"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r="uint8");var h,p,m=0;if(2===o.length)m=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error("gl-texture2d: Invalid shape for texture");if(1===o[2])m=t.ALPHA;else if(2===o[2])m=t.LUMINANCE_ALPHA;else if(3===o[2])m=t.RGB;else{if(4!==o[2])throw new Error("gl-texture2d: Invalid shape for pixel coords");m=t.RGBA}}c!==t.FLOAT||t.getExtension("OES_texture_float")||(c=t.UNSIGNED_BYTE,l=!1);var v=e.size;if(l)h=0===e.offset&&e.data.length===v?e.data:e.data.subarray(e.offset,e.offset+v);else{var y=[o[2],o[2]*o[0],1];p=a.malloc(v,r);var x=n(p,o,y,0);"float32"!==r&&"float64"!==r||c!==t.UNSIGNED_BYTE?i.assign(x,e):u(x,e),h=p.subarray(0,v)}var b=g(t);t.texImage2D(t.TEXTURE_2D,0,m,o[0],o[1],0,m,c,h),l||a.free(p);return new f(t,b,o[0],o[1],m,c)}(t,e)}throw new Error("gl-texture2d: Invalid arguments for texture2d constructor")};var o=null,s=null,l=null;function c(t){return"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||"undefined"!=typeof ImageData&&t instanceof ImageData}var u=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-texture2d: Invalid texture size");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function 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Math.ceil(t/e)*e}Yr.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Yr.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Yr.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Yr.prototype.clear=function(){this.length=0},Yr.prototype.resize=function(t){this.reserve(t),this.length=t},Yr.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Yr.prototype._refreshViews=function(){throw new Error("_refreshViews() must be implemented by each concrete StructArray 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Kr=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;this.resize(o+1);var s=6*o;return this.int16[s+0]=t,this.int16[s+1]=e,this.int16[s+2]=r,this.int16[s+3]=n,this.int16[s+4]=i,this.int16[s+5]=a,o},e}(Yr);Kr.prototype.bytesPerElement=12,pr("StructArrayLayout2i4i12",Kr);var Qr=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s){var l=this.length;this.resize(l+1);var c=6*l,u=12*l;return this.int16[c+0]=t,this.int16[c+1]=e,this.int16[c+2]=r,this.int16[c+3]=n,this.uint8[u+8]=i,this.uint8[u+9]=a,this.uint8[u+10]=o,this.uint8[u+11]=s,l},e}(Yr);Qr.prototype.bytesPerElement=12,pr("StructArrayLayout4i4ub12",Qr);var tn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s){var l=this.length;this.resize(l+1);var c=8*l;return this.int16[c+0]=t,this.int16[c+1]=e,this.int16[c+2]=r,this.int16[c+3]=n,this.uint16[c+4]=i,this.uint16[c+5]=a,this.uint16[c+6]=o,this.uint16[c+7]=s,l},e}(Yr);tn.prototype.bytesPerElement=16,pr("StructArrayLayout4i4ui16",tn);var en=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;this.resize(n+1);var i=3*n;return this.float32[i+0]=t,this.float32[i+1]=e,this.float32[i+2]=r,n},e}(Yr);en.prototype.bytesPerElement=12,pr("StructArrayLayout3f12",en);var rn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;this.resize(e+1);var r=1*e;return this.uint32[r+0]=t,e},e}(Yr);rn.prototype.bytesPerElement=4,pr("StructArrayLayout1ul4",rn);var nn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u){var h=this.length;this.resize(h+1);var f=12*h,p=6*h;return this.int16[f+0]=t,this.int16[f+1]=e,this.int16[f+2]=r,this.int16[f+3]=n,this.int16[f+4]=i,this.int16[f+5]=a,this.uint32[p+3]=o,this.uint16[f+8]=s,this.uint16[f+9]=l,this.int16[f+10]=c,this.int16[f+11]=u,h},e}(Yr);nn.prototype.bytesPerElement=24,pr("StructArrayLayout6i1ul2ui2i24",nn);var an=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;this.resize(o+1);var s=6*o;return this.int16[s+0]=t,this.int16[s+1]=e,this.int16[s+2]=r,this.int16[s+3]=n,this.int16[s+4]=i,this.int16[s+5]=a,o},e}(Yr);an.prototype.bytesPerElement=12,pr("StructArrayLayout2i2i2i12",an);var on=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;this.resize(r+1);var n=4*r;return this.uint8[n+0]=t,this.uint8[n+1]=e,r},e}(Yr);on.prototype.bytesPerElement=4,pr("StructArrayLayout2ub4",on);var sn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,h,f,p){var d=this.length;this.resize(d+1);var g=20*d,m=10*d,v=40*d;return this.int16[g+0]=t,this.int16[g+1]=e,this.uint16[g+2]=r,this.uint16[g+3]=n,this.uint32[m+2]=i,this.uint32[m+3]=a,this.uint32[m+4]=o,this.uint16[g+10]=s,this.uint16[g+11]=l,this.uint16[g+12]=c,this.float32[m+7]=u,this.float32[m+8]=h,this.uint8[v+36]=f,this.uint8[v+37]=p,d},e}(Yr);sn.prototype.bytesPerElement=40,pr("StructArrayLayout2i2ui3ul3ui2f2ub40",sn);var ln=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;this.resize(e+1);var r=1*e;return this.float32[r+0]=t,e},e}(Yr);ln.prototype.bytesPerElement=4,pr("StructArrayLayout1f4",ln);var cn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;this.resize(n+1);var i=3*n;return this.int16[i+0]=t,this.int16[i+1]=e,this.int16[i+2]=r,n},e}(Yr);cn.prototype.bytesPerElement=6,pr("StructArrayLayout3i6",cn);var un=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;this.resize(n+1);var i=2*n,a=4*n;return this.uint32[i+0]=t,this.uint16[a+2]=e,this.uint16[a+3]=r,n},e}(Yr);un.prototype.bytesPerElement=8,pr("StructArrayLayout1ul2ui8",un);var hn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;this.resize(n+1);var i=3*n;return this.uint16[i+0]=t,this.uint16[i+1]=e,this.uint16[i+2]=r,n},e}(Yr);hn.prototype.bytesPerElement=6,pr("StructArrayLayout3ui6",hn);var fn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;this.resize(r+1);var n=2*r;return this.uint16[n+0]=t,this.uint16[n+1]=e,r},e}(Yr);fn.prototype.bytesPerElement=4,pr("StructArrayLayout2ui4",fn);var pn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;this.resize(r+1);var n=2*r;return this.float32[n+0]=t,this.float32[n+1]=e,r},e}(Yr);pn.prototype.bytesPerElement=8,pr("StructArrayLayout2f8",pn);var dn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;this.resize(i+1);var a=4*i;return this.float32[a+0]=t,this.float32[a+1]=e,this.float32[a+2]=r,this.float32[a+3]=n,i},e}(Yr);dn.prototype.bytesPerElement=16,pr("StructArrayLayout4f16",dn);var gn=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},radius:{configurable:!0},signedDistanceFromAnchor:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointX.set=function(t){this._structArray.int16[this._pos2+0]=t},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.anchorPointY.set=function(t){this._structArray.int16[this._pos2+1]=t},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.x1.set=function(t){this._structArray.int16[this._pos2+2]=t},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.y1.set=function(t){this._structArray.int16[this._pos2+3]=t},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.x2.set=function(t){this._structArray.int16[this._pos2+4]=t},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.y2.set=function(t){this._structArray.int16[this._pos2+5]=t},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.featureIndex.set=function(t){this._structArray.uint32[this._pos4+3]=t},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.sourceLayerIndex.set=function(t){this._structArray.uint16[this._pos2+8]=t},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.bucketIndex.set=function(t){this._structArray.uint16[this._pos2+9]=t},r.radius.get=function(){return this._structArray.int16[this._pos2+10]},r.radius.set=function(t){this._structArray.int16[this._pos2+10]=t},r.signedDistanceFromAnchor.get=function(){return this._structArray.int16[this._pos2+11]},r.signedDistanceFromAnchor.set=function(t){this._structArray.int16[this._pos2+11]=t},r.anchorPoint.get=function(){return new l(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Wr);gn.prototype.size=24;var mn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new gn(this,t)},e}(nn);pr("CollisionBoxArray",mn);var vn=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},hidden:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorX.set=function(t){this._structArray.int16[this._pos2+0]=t},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.anchorY.set=function(t){this._structArray.int16[this._pos2+1]=t},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.glyphStartIndex.set=function(t){this._structArray.uint16[this._pos2+2]=t},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.numGlyphs.set=function(t){this._structArray.uint16[this._pos2+3]=t},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.vertexStartIndex.set=function(t){this._structArray.uint32[this._pos4+2]=t},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineStartIndex.set=function(t){this._structArray.uint32[this._pos4+3]=t},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.lineLength.set=function(t){this._structArray.uint32[this._pos4+4]=t},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.segment.set=function(t){this._structArray.uint16[this._pos2+10]=t},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.lowerSize.set=function(t){this._structArray.uint16[this._pos2+11]=t},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.upperSize.set=function(t){this._structArray.uint16[this._pos2+12]=t},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetX.set=function(t){this._structArray.float32[this._pos4+7]=t},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.lineOffsetY.set=function(t){this._structArray.float32[this._pos4+8]=t},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.writingMode.set=function(t){this._structArray.uint8[this._pos1+36]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+37]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+37]=t},Object.defineProperties(e.prototype,r),e}(Wr);vn.prototype.size=40;var yn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new vn(this,t)},e}(sn);pr("PlacedSymbolArray",yn);var xn=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={offsetX:{configurable:!0}};return r.offsetX.get=function(){return this._structArray.float32[this._pos4+0]},r.offsetX.set=function(t){this._structArray.float32[this._pos4+0]=t},Object.defineProperties(e.prototype,r),e}(Wr);xn.prototype.size=4;var bn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getoffsetX=function(t){return this.float32[1*t+0]},e.prototype.get=function(t){return new xn(this,t)},e}(ln);pr("GlyphOffsetArray",bn);var _n=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={x:{configurable:!0},y:{configurable:!0},tileUnitDistanceFromAnchor:{configurable:!0}};return r.x.get=function(){return this._structArray.int16[this._pos2+0]},r.x.set=function(t){this._structArray.int16[this._pos2+0]=t},r.y.get=function(){return this._structArray.int16[this._pos2+1]},r.y.set=function(t){this._structArray.int16[this._pos2+1]=t},r.tileUnitDistanceFromAnchor.get=function(){return this._structArray.int16[this._pos2+2]},r.tileUnitDistanceFromAnchor.set=function(t){this._structArray.int16[this._pos2+2]=t},Object.defineProperties(e.prototype,r),e}(Wr);_n.prototype.size=6;var wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getx=function(t){return this.int16[3*t+0]},e.prototype.gety=function(t){return this.int16[3*t+1]},e.prototype.gettileUnitDistanceFromAnchor=function(t){return this.int16[3*t+2]},e.prototype.get=function(t){return new _n(this,t)},e}(cn);pr("SymbolLineVertexArray",wn);var kn=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0}};return r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+0]},r.featureIndex.set=function(t){this._structArray.uint32[this._pos4+0]=t},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.sourceLayerIndex.set=function(t){this._structArray.uint16[this._pos2+2]=t},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+3]},r.bucketIndex.set=function(t){this._structArray.uint16[this._pos2+3]=t},Object.defineProperties(e.prototype,r),e}(Wr);kn.prototype.size=8;var Mn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new kn(this,t)},e}(un);pr("FeatureIndexArray",Mn);var An=Xr([{name:"a_pos",components:2,type:"Int16"}],4).members,Tn=function(t){void 0===t&&(t=[]),this.segments=t};Tn.prototype.prepareSegment=function(t,e,r){var n=this.segments[this.segments.length-1];return t>Tn.MAX_VERTEX_ARRAY_LENGTH&&_("Max vertices per segment is "+Tn.MAX_VERTEX_ARRAY_LENGTH+": bucket requested "+t),(!n||n.vertexLength+t>Tn.MAX_VERTEX_ARRAY_LENGTH)&&(n={vertexOffset:e.length,primitiveOffset:r.length,vertexLength:0,primitiveLength:0},this.segments.push(n)),n},Tn.prototype.get=function(){return this.segments},Tn.prototype.destroy=function(){for(var t=0,e=this.segments;t<e.length;t+=1){var r=e[t];for(var n in r.vaos)r.vaos[n].destroy()}},Tn.MAX_VERTEX_ARRAY_LENGTH=Math.pow(2,16)-1,pr("SegmentVector",Tn);var Sn=function(t,e){return 256*(t=f(Math.floor(t),0,255))+f(Math.floor(e),0,255)};function En(t){return[Sn(255*t.r,255*t.g),Sn(255*t.b,255*t.a)]}var Cn=function(t,e,r){this.value=t,this.name=e,this.type=r,this.statistics={max:-1/0}};Cn.prototype.defines=function(){return["#define HAS_UNIFORM_u_"+this.name]},Cn.prototype.populatePaintArray=function(){},Cn.prototype.upload=function(){},Cn.prototype.destroy=function(){},Cn.prototype.setUniforms=function(t,e,r,n){var i=n.constantOr(this.value),a=t.gl;"color"===this.type?a.uniform4f(e.uniforms["u_"+this.name],i.r,i.g,i.b,i.a):a.uniform1f(e.uniforms["u_"+this.name],i)};var Ln=function(t,e,r){this.expression=t,this.name=e,this.type=r,this.statistics={max:-1/0};var n="color"===r?pn:ln;this.paintVertexAttributes=[{name:"a_"+e,type:"Float32",components:"color"===r?2:1,offset:0}],this.paintVertexArray=new n};Ln.prototype.defines=function(){return[]},Ln.prototype.populatePaintArray=function(t,e){var r=this.paintVertexArray,n=r.length;r.reserve(t);var i=this.expression.evaluate(new Lr(0),e);if("color"===this.type)for(var a=En(i),o=n;o<t;o++)r.emplaceBack(a[0],a[1]);else{for(var s=n;s<t;s++)r.emplaceBack(i);this.statistics.max=Math.max(this.statistics.max,i)}},Ln.prototype.upload=function(t){this.paintVertexArray&&(this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes))},Ln.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()},Ln.prototype.setUniforms=function(t,e){t.gl.uniform1f(e.uniforms["a_"+this.name+"_t"],0)};var zn=function(t,e,r,n,i){this.expression=t,this.name=e,this.type=r,this.useIntegerZoom=n,this.zoom=i,this.statistics={max:-1/0};var a="color"===r?dn:pn;this.paintVertexAttributes=[{name:"a_"+e,type:"Float32",components:"color"===r?4:2,offset:0}],this.paintVertexArray=new a};zn.prototype.defines=function(){return[]},zn.prototype.populatePaintArray=function(t,e){var r=this.paintVertexArray,n=r.length;r.reserve(t);var i=this.expression.evaluate(new Lr(this.zoom),e),a=this.expression.evaluate(new Lr(this.zoom+1),e);if("color"===this.type)for(var 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r=0;r<t.length-1;r++)for(var n=t[r],i=t[r+1],a=0;a<e.length-1;a++)if(Gn(n,i,e[a],e[a+1]))return!0;return!1}function Gn(t,e,r,n){return w(t,r,n)!==w(e,r,n)&&w(t,e,r)!==w(t,e,n)}function Wn(t,e,r){var n=r*r;if(1===e.length)return t.distSqr(e[0])<n;for(var i=1;i<e.length;i++)if(Yn(t,e[i-1],e[i])<n)return!0;return!1}function Yn(t,e,r){var n=e.distSqr(r);if(0===n)return t.distSqr(e);var i=((t.x-e.x)*(r.x-e.x)+(t.y-e.y)*(r.y-e.y))/n;return i<0?t.distSqr(e):i>1?t.distSqr(r):t.distSqr(r.sub(e)._mult(i)._add(e))}function Xn(t,e){for(var r,n,i,a=!1,o=0;o<t.length;o++)for(var s=0,l=(r=t[o]).length-1;s<r.length;l=s++)n=r[s],i=r[l],n.y>e.y!=i.y>e.y&&e.x<(i.x-n.x)*(e.y-n.y)/(i.y-n.y)+n.x&&(a=!a);return a}function Zn(t,e){for(var r=!1,n=0,i=t.length-1;n<t.length;i=n++){var a=t[n],o=t[i];a.y>e.y!=o.y>e.y&&e.x<(o.x-a.x)*(e.y-a.y)/(o.y-a.y)+a.x&&(r=!r)}return r}function $n(t,e,r){var 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o=null;e.properties&&e.properties.hasOwnProperty("mapbox_clip_start")&&e.properties.hasOwnProperty("mapbox_clip_end")&&(o={start:e.properties.mapbox_clip_start,end:e.properties.mapbox_clip_end,tileTotal:void 0});for(var s="Polygon"===ma[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){o&&(o.tileTotal=function(t,e,r){for(var n,i,a=0,o=c;o<r-1;o++)n=t[o],i=t[o+1],a+=n.dist(i);return a}(t,0,l)),"bevel"===r&&(i=1.05);var u=Dn/(512*this.overscaling)*15,h=t[c],f=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray);this.distance=0;var p,d,g,m=n,v=s?"butt":n,y=!0,x=void 0,b=void 0,_=void 0,w=void 0;this.e1=this.e2=this.e3=-1,s&&(p=t[l-2],w=h.sub(p)._unit()._perp());for(var k=c;k<l;k++)if(!(b=s&&k===l-1?t[c+1]:t[k+1])||!t[k].equals(b)){w&&(_=w),p&&(x=p),p=t[k],w=b?b.sub(p)._unit()._perp():_;var M=(_=_||w).add(w);0===M.x&&0===M.y||M._unit();var A=M.x*w.x+M.y*w.y,T=0!==A?1/A:1/0,S=A<ya&&x&&b;if(S&&k>c){var E=p.dist(x);if(E>2*u){var C=p.sub(p.sub(x)._mult(u/E)._round());this.distance+=C.dist(x),this.addCurrentVertex(C,this.distance,_.mult(1),0,0,!1,f,o),x=C}}var L=x&&b,z=L?r:b?m:v;if(L&&"round"===z&&(T<a?z="miter":T<=2&&(z="fakeround")),"miter"===z&&T>i&&(z="bevel"),"bevel"===z&&(T>2&&(z="flipbevel"),T<i&&(z="miter")),x&&(this.distance+=p.dist(x)),"miter"===z)M._mult(T),this.addCurrentVertex(p,this.distance,M,0,0,!1,f,o);else if("flipbevel"===z){if(T>100)M=w.clone().mult(-1);else{var P=_.x*w.y-_.y*w.x>0?-1:1,I=T*_.add(w).mag()/_.sub(w).mag();M._perp()._mult(I*P)}this.addCurrentVertex(p,this.distance,M,0,0,!1,f,o),this.addCurrentVertex(p,this.distance,M.mult(-1),0,0,!1,f,o)}else if("bevel"===z||"fakeround"===z){var O=_.x*w.y-_.y*w.x>0,D=-Math.sqrt(T*T-1);if(O?(g=0,d=D):(d=0,g=D),y||this.addCurrentVertex(p,this.distance,_,d,g,!1,f,o),"fakeround"===z){for(var R=Math.floor(8*(.5-(A-.5))),B=void 0,F=0;F<R;F++)B=w.mult((F+1)/(R+1))._add(_)._unit(),this.addPieSliceVertex(p,this.distance,B,O,f,o);this.addPieSliceVertex(p,this.distance,M,O,f,o);for(var N=R-1;N>=0;N--)B=_.mult((N+1)/(R+1))._add(w)._unit(),this.addPieSliceVertex(p,this.distance,B,O,f,o)}b&&this.addCurrentVertex(p,this.distance,w,-d,-g,!1,f,o)}else"butt"===z?(y||this.addCurrentVertex(p,this.distance,_,0,0,!1,f,o),b&&this.addCurrentVertex(p,this.distance,w,0,0,!1,f,o)):"square"===z?(y||(this.addCurrentVertex(p,this.distance,_,1,1,!1,f,o),this.e1=this.e2=-1),b&&this.addCurrentVertex(p,this.distance,w,-1,-1,!1,f,o)):"round"===z&&(y||(this.addCurrentVertex(p,this.distance,_,0,0,!1,f,o),this.addCurrentVertex(p,this.distance,_,1,1,!0,f,o),this.e1=this.e2=-1),b&&(this.addCurrentVertex(p,this.distance,w,-1,-1,!0,f,o),this.addCurrentVertex(p,this.distance,w,0,0,!1,f,o)));if(S&&k<l-1){var j=p.dist(b);if(j>2*u){var V=p.add(b.sub(p)._mult(u/j)._round());this.distance+=V.dist(p),this.addCurrentVertex(V,this.distance,w.mult(1),0,0,!1,f,o),p=V}}y=!1}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,e)}},wa.prototype.addCurrentVertex=function(t,e,r,n,i,a,o,s){var l,c=this.layoutVertexArray,u=this.indexArray;s&&(e=ka(e,s)),l=r.clone(),n&&l._sub(r.perp()._mult(n)),_a(c,t,l,a,!1,n,e),this.e3=o.vertexLength++,this.e1>=0&&this.e2>=0&&(u.emplaceBack(this.e1,this.e2,this.e3),o.primitiveLength++),this.e1=this.e2,this.e2=this.e3,l=r.mult(-1),i&&l._sub(r.perp()._mult(i)),_a(c,t,l,a,!0,-i,e),this.e3=o.vertexLength++,this.e1>=0&&this.e2>=0&&(u.emplaceBack(this.e1,this.e2,this.e3),o.primitiveLength++),this.e1=this.e2,this.e2=this.e3,e>ba/2&&!s&&(this.distance=0,this.addCurrentVertex(t,this.distance,r,n,i,a,o))},wa.prototype.addPieSliceVertex=function(t,e,r,n,i,a){r=r.mult(n?-1:1);var o=this.layoutVertexArray,s=this.indexArray;a&&(e=ka(e,a)),_a(o,t,r,!1,n,0,e),this.e3=i.vertexLength++,this.e1>=0&&this.e2>=0&&(s.emplaceBack(this.e1,this.e2,this.e3),i.primitiveLength++),n?this.e2=this.e3:this.e1=this.e3},pr("LineBucket",wa,{omit:["layers"]});var Ma=new qr({"line-cap":new Nr(I.layout_line["line-cap"]),"line-join":new jr(I.layout_line["line-join"]),"line-miter-limit":new Nr(I.layout_line["line-miter-limit"]),"line-round-limit":new Nr(I.layout_line["line-round-limit"])}),Aa={paint:new qr({"line-opacity":new jr(I.paint_line["line-opacity"]),"line-color":new jr(I.paint_line["line-color"]),"line-translate":new Nr(I.paint_line["line-translate"]),"line-translate-anchor":new Nr(I.paint_line["line-translate-anchor"]),"line-width":new jr(I.paint_line["line-width"]),"line-gap-width":new jr(I.paint_line["line-gap-width"]),"line-offset":new jr(I.paint_line["line-offset"]),"line-blur":new jr(I.paint_line["line-blur"]),"line-dasharray":new Vr(I.paint_line["line-dasharray"]),"line-pattern":new Vr(I.paint_line["line-pattern"]),"line-gradient":new Ur(I.paint_line["line-gradient"])}),layout:Ma},Ta=new(function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new Lr(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n){return r=p({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n)},e}(jr))(Aa.paint.properties["line-width"].specification);Ta.useIntegerZoom=!0;var Sa=function(t){function e(e){t.call(this,e,Aa)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.setPaintProperty=function(e,r,n){t.prototype.setPaintProperty.call(this,e,r,n),"line-gradient"===e&&this._updateGradient()},e.prototype._updateGradient=function(){var t=this._transitionablePaint._values["line-gradient"].value.expression;this.gradient=fi(t,"lineProgress"),this.gradientTexture=null},e.prototype.recalculate=function(e){t.prototype.recalculate.call(this,e),this.paint._values["line-floorwidth"]=Ta.possiblyEvaluate(this._transitioningPaint._values["line-width"].value,e)},e.prototype.createBucket=function(t){return new wa(t)},e.prototype.queryRadius=function(t){var e=t,r=Ea($n("line-width",this,e),$n("line-gap-width",this,e)),n=$n("line-offset",this,e);return r/2+Math.abs(n)+Jn(this.paint.get("line-translate"))},e.prototype.queryIntersectsFeature=function(t,e,r,n,i,a){var o=Kn(t,this.paint.get("line-translate"),this.paint.get("line-translate-anchor"),i.angle,a),s=a/2*Ea(this.paint.get("line-width").evaluate(e),this.paint.get("line-gap-width").evaluate(e)),c=this.paint.get("line-offset").evaluate(e);return c&&(r=function(t,e){for(var r=[],n=new l(0,0),i=0;i<t.length;i++){for(var a=t[i],o=[],s=0;s<a.length;s++){var c=a[s-1],u=a[s],h=a[s+1],f=0===s?n:u.sub(c)._unit()._perp(),p=s===a.length-1?n:h.sub(u)._unit()._perp(),d=f._add(p)._unit(),g=d.x*p.x+d.y*p.y;d._mult(1/g),o.push(d._mult(e)._add(u))}r.push(o)}return r}(r,c*a)),Un(o,r,s)},e}(Hr);function Ea(t,e){return e>0?e+2*t:t}var Ca=Xr([{name:"a_pos_offset",components:4,type:"Int16"},{name:"a_data",components:4,type:"Uint16"}]),La=Xr([{name:"a_projected_pos",components:3,type:"Float32"}],4),za=(Xr([{name:"a_fade_opacity",components:1,type:"Uint32"}],4),Xr([{name:"a_placed",components:2,type:"Uint8"}],4)),Pa=(Xr([{type:"Int16",name:"anchorPointX"},{type:"Int16",name:"anchorPointY"},{type:"Int16",name:"x1"},{type:"Int16",name:"y1"},{type:"Int16",name:"x2"},{type:"Int16",name:"y2"},{type:"Uint32",name:"featureIndex"},{type:"Uint16",name:"sourceLayerIndex"},{type:"Uint16",name:"bucketIndex"},{type:"Int16",name:"radius"},{type:"Int16",name:"signedDistanceFromAnchor"}]),Xr([{name:"a_pos",components:2,type:"Int16"},{name:"a_anchor_pos",components:2,type:"Int16"},{name:"a_extrude",components:2,type:"Int16"}],4)),Ia=Xr([{name:"a_pos",components:2,type:"Int16"},{name:"a_anchor_pos",components:2,type:"Int16"},{name:"a_extrude",components:2,type:"Int16"}],4);function Oa(t,e,r){var n=e.layout.get("text-transform").evaluate(r);return"uppercase"===n?t=t.toLocaleUpperCase():"lowercase"===n&&(t=t.toLocaleLowerCase()),Cr.applyArabicShaping&&(t=Cr.applyArabicShaping(t)),t}Xr([{type:"Int16",name:"anchorX"},{type:"Int16",name:"anchorY"},{type:"Uint16",name:"glyphStartIndex"},{type:"Uint16",name:"numGlyphs"},{type:"Uint32",name:"vertexStartIndex"},{type:"Uint32",name:"lineStartIndex"},{type:"Uint32",name:"lineLength"},{type:"Uint16",name:"segment"},{type:"Uint16",name:"lowerSize"},{type:"Uint16",name:"upperSize"},{type:"Float32",name:"lineOffsetX"},{type:"Float32",name:"lineOffsetY"},{type:"Uint8",name:"writingMode"},{type:"Uint8",name:"hidden"}]),Xr([{type:"Float32",name:"offsetX"}]),Xr([{type:"Int16",name:"x"},{type:"Int16",name:"y"},{type:"Int16",name:"tileUnitDistanceFromAnchor"}]);var Da={"!":"\ufe15","#":"\uff03",$:"\uff04","%":"\uff05","&":"\uff06","(":"\ufe35",")":"\ufe36","*":"\uff0a","+":"\uff0b",",":"\ufe10","-":"\ufe32",".":"\u30fb","/":"\uff0f",":":"\ufe13",";":"\ufe14","<":"\ufe3f","=":"\uff1d",">":"\ufe40","?":"\ufe16","@":"\uff20","[":"\ufe47","\\":"\uff3c","]":"\ufe48","^":"\uff3e",_:"\ufe33","`":"\uff40","{":"\ufe37","|":"\u2015","}":"\ufe38","~":"\uff5e","\xa2":"\uffe0","\xa3":"\uffe1","\xa5":"\uffe5","\xa6":"\uffe4","\xac":"\uffe2","\xaf":"\uffe3","\u2013":"\ufe32","\u2014":"\ufe31","\u2018":"\ufe43","\u2019":"\ufe44","\u201c":"\ufe41","\u201d":"\ufe42","\u2026":"\ufe19","\u2027":"\u30fb","\u20a9":"\uffe6","\u3001":"\ufe11","\u3002":"\ufe12","\u3008":"\ufe3f","\u3009":"\ufe40","\u300a":"\ufe3d","\u300b":"\ufe3e","\u300c":"\ufe41","\u300d":"\ufe42","\u300e":"\ufe43","\u300f":"\ufe44","\u3010":"\ufe3b","\u3011":"\ufe3c","\u3014":"\ufe39","\u3015":"\ufe3a","\u3016":"\ufe17","\u3017":"\ufe18","\uff01":"\ufe15","\uff08":"\ufe35","\uff09":"\ufe36","\uff0c":"\ufe10","\uff0d":"\ufe32","\uff0e":"\u30fb","\uff1a":"\ufe13","\uff1b":"\ufe14","\uff1c":"\ufe3f","\uff1e":"\ufe40","\uff1f":"\ufe16","\uff3b":"\ufe47","\uff3d":"\ufe48","\uff3f":"\ufe33","\uff5b":"\ufe37","\uff5c":"\u2015","\uff5d":"\ufe38","\uff5f":"\ufe35","\uff60":"\ufe36","\uff61":"\ufe12","\uff62":"\ufe41","\uff63":"\ufe42"},Ra=function(t){function e(e,r,n,i){t.call(this,e,r),this.angle=n,void 0!==i&&(this.segment=i)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.clone=function(){return new e(this.x,this.y,this.angle,this.segment)},e}(l);function Ba(t,e){var r=e.expression;if("constant"===r.kind)return{functionType:"constant",layoutSize:r.evaluate(new Lr(t+1))};if("source"===r.kind)return{functionType:"source"};for(var n=r.zoomStops,i=0;i<n.length&&n[i]<=t;)i++;for(var a=i=Math.max(0,i-1);a<n.length&&n[a]<t+1;)a++;a=Math.min(n.length-1,a);var o={min:n[i],max:n[a]};return"composite"===r.kind?{functionType:"composite",zoomRange:o,propertyValue:e.value}:{functionType:"camera",layoutSize:r.evaluate(new Lr(t+1)),zoomRange:o,sizeRange:{min:r.evaluate(new Lr(o.min)),max:r.evaluate(new Lr(o.max))},propertyValue:e.value}}pr("Anchor",Ra);var Fa=ga.VectorTileFeature.types,Na=[{name:"a_fade_opacity",components:1,type:"Uint8",offset:0}];function ja(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,s?s[0]:0,s?s[1]:0)}function Va(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}var Ua=function(t){this.layoutVertexArray=new tn,this.indexArray=new hn,this.programConfigurations=t,this.segments=new Tn,this.dynamicLayoutVertexArray=new en,this.opacityVertexArray=new rn,this.placedSymbolArray=new yn};Ua.prototype.upload=function(t,e){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Ca.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.programConfigurations.upload(t),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,La.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,Na,!0),this.opacityVertexBuffer.itemSize=1},Ua.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},pr("SymbolBuffers",Ua);var qa=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new Tn,this.collisionVertexArray=new on};qa.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,za.members,!0)},qa.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},pr("CollisionBuffers",qa);var Ha=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map(function(t){return t.id}),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex;var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Ba(this.zoom,e["text-size"]),this.iconSizeData=Ba(this.zoom,e["icon-size"]);var r=this.layers[0].layout;this.sortFeaturesByY=r.get("text-allow-overlap")||r.get("icon-allow-overlap")||r.get("text-ignore-placement")||r.get("icon-ignore-placement")};Ha.prototype.createArrays=function(){this.text=new Ua(new In(Ca.members,this.layers,this.zoom,function(t){return/^text/.test(t)})),this.icon=new Ua(new In(Ca.members,this.layers,this.zoom,function(t){return/^icon/.test(t)})),this.collisionBox=new qa(an,Pa.members,fn),this.collisionCircle=new qa(an,Ia.members,hn),this.glyphOffsetArray=new bn,this.lineVertexArray=new wn},Ha.prototype.populate=function(t,e){var r=this.layers[0],n=r.layout,i=n.get("text-font"),a=n.get("text-field"),o=n.get("icon-image"),s=("constant"!==a.value.kind||a.value.value.length>0)&&("constant"!==i.value.kind||i.value.value.length>0),l="constant"!==o.value.kind||o.value.value&&o.value.value.length>0;if(this.features=[],s||l){for(var c=e.iconDependencies,u=e.glyphDependencies,h=new Lr(this.zoom),f=0,p=t;f<p.length;f+=1){var d=p[f],g=d.feature,m=d.index,v=d.sourceLayerIndex;if(r._featureFilter(h,g)){var y=void 0;s&&(y=Oa(y=r.getValueAndResolveTokens("text-field",g),r,g));var x=void 0;if(l&&(x=r.getValueAndResolveTokens("icon-image",g)),y||x){var b={text:y,icon:x,index:m,sourceLayerIndex:v,geometry:Bn(g),properties:g.properties,type:Fa[g.type]};if(void 0!==g.id&&(b.id=g.id),this.features.push(b),x&&(c[x]=!0),y)for(var _=i.evaluate(g).join(","),w=u[_]=u[_]||{},k="map"===n.get("text-rotation-alignment")&&"line"===n.get("symbol-placement"),M=xr(y),A=0;A<y.length;A++)if(w[y.charCodeAt(A)]=!0,k&&M){var T=Da[y.charAt(A)];T&&(w[T.charCodeAt(0)]=!0)}}}}"line"===n.get("symbol-placement")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+":"+n.x+":"+n.y}for(var c=0;c<t.length;c++){var u=t[c],h=u.geometry,f=u.text;if(f){var p=l(f,h),d=l(f,h,!0);if(p in r&&d in e&&r[p]!==e[d]){var g=s(p,d,h),m=o(p,d,n[g].geometry);delete e[p],delete r[d],r[l(f,n[m].geometry,!0)]=m,n[g].geometry=null}else p in r?o(p,d,h):d in e?s(p,d,h):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter(function(t){return t.geometry})}(this.features))}},Ha.prototype.isEmpty=function(){return 0===this.symbolInstances.length},Ha.prototype.upload=function(t){this.text.upload(t,this.sortFeaturesByY),this.icon.upload(t,this.sortFeaturesByY),this.collisionBox.upload(t),this.collisionCircle.upload(t)},Ha.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.collisionBox.destroy(),this.collisionCircle.destroy()},Ha.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Ha.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c){for(var u=t.indexArray,h=t.layoutVertexArray,f=t.dynamicLayoutVertexArray,p=t.segments.prepareSegment(4*e.length,t.layoutVertexArray,t.indexArray),d=this.glyphOffsetArray.length,g=p.vertexLength,m=0,v=e;m<v.length;m+=1){var y=v[m],x=y.tl,b=y.tr,_=y.bl,w=y.br,k=y.tex,M=p.vertexLength,A=y.glyphOffset[1];ja(h,s.x,s.y,x.x,A+x.y,k.x,k.y,r),ja(h,s.x,s.y,b.x,A+b.y,k.x+k.w,k.y,r),ja(h,s.x,s.y,_.x,A+_.y,k.x,k.y+k.h,r),ja(h,s.x,s.y,w.x,A+w.y,k.x+k.w,k.y+k.h,r),Va(f,s,0),u.emplaceBack(M,M+1,M+2),u.emplaceBack(M+1,M+2,M+3),p.vertexLength+=4,p.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(y.glyphOffset[0])}t.placedSymbolArray.emplaceBack(s.x,s.y,d,this.glyphOffsetArray.length-d,g,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,!1),t.programConfigurations.populatePaintArrays(t.layoutVertexArray.length,a)},Ha.prototype._addCollisionDebugVertex=function(t,e,r,n,i){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n.x,n.y,Math.round(i.x),Math.round(i.y))},Ha.prototype.addCollisionDebugVertices=function(t,e,r,n,i,a,o,s){var c=i.segments.prepareSegment(4,i.layoutVertexArray,i.indexArray),u=c.vertexLength,h=i.layoutVertexArray,f=i.collisionVertexArray;if(this._addCollisionDebugVertex(h,f,a,o.anchor,new l(t,e)),this._addCollisionDebugVertex(h,f,a,o.anchor,new l(r,e)),this._addCollisionDebugVertex(h,f,a,o.anchor,new l(r,n)),this._addCollisionDebugVertex(h,f,a,o.anchor,new l(t,n)),c.vertexLength+=4,s){var p=i.indexArray;p.emplaceBack(u,u+1,u+2),p.emplaceBack(u,u+2,u+3),c.primitiveLength+=2}else{var d=i.indexArray;d.emplaceBack(u,u+1),d.emplaceBack(u+1,u+2),d.emplaceBack(u+2,u+3),d.emplaceBack(u+3,u),c.primitiveLength+=4}},Ha.prototype.generateCollisionDebugBuffers=function(){for(var t=0,e=this.symbolInstances;t<e.length;t+=1){var r=e[t];r.textCollisionFeature={boxStartIndex:r.textBoxStartIndex,boxEndIndex:r.textBoxEndIndex},r.iconCollisionFeature={boxStartIndex:r.iconBoxStartIndex,boxEndIndex:r.iconBoxEndIndex};for(var n=0;n<2;n++){var i=r[0===n?"textCollisionFeature":"iconCollisionFeature"];if(i)for(var a=i.boxStartIndex;a<i.boxEndIndex;a++){var o=this.collisionBoxArray.get(a),s=o.x1,l=o.y1,c=o.x2,u=o.y2,h=o.radius>0;this.addCollisionDebugVertices(s,l,c,u,h?this.collisionCircle:this.collisionBox,o.anchorPoint,r,h)}}}},Ha.prototype.deserializeCollisionBoxes=function(t,e,r,n,i){for(var a={},o=e;o<r;o++){var s=t.get(o);if(0===s.radius){a.textBox={x1:s.x1,y1:s.y1,x2:s.x2,y2:s.y2,anchorPointX:s.anchorPointX,anchorPointY:s.anchorPointY},a.textFeatureIndex=s.featureIndex;break}a.textCircles||(a.textCircles=[],a.textFeatureIndex=s.featureIndex),a.textCircles.push(s.anchorPointX,s.anchorPointY,s.radius,s.signedDistanceFromAnchor,1)}for(var l=n;l<i;l++){var c=t.get(l);if(0===c.radius){a.iconBox={x1:c.x1,y1:c.y1,x2:c.x2,y2:c.y2,anchorPointX:c.anchorPointX,anchorPointY:c.anchorPointY},a.iconFeatureIndex=c.featureIndex;break}}return a},Ha.prototype.hasTextData=function(){return this.text.segments.get().length>0},Ha.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Ha.prototype.hasCollisionBoxData=function(){return this.collisionBox.segments.get().length>0},Ha.prototype.hasCollisionCircleData=function(){return this.collisionCircle.segments.get().length>0},Ha.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&(this.sortedAngle=t,!(this.text.segments.get().length>1||this.icon.segments.get().length>1))){for(var r=[],n=0;n<this.symbolInstances.length;n++)r.push(n);var i=Math.sin(t),a=Math.cos(t);r.sort(function(t,r){var n=e.symbolInstances[t],o=e.symbolInstances[r];return(i*n.anchor.x+a*n.anchor.y|0)-(i*o.anchor.x+a*o.anchor.y|0)||o.featureIndex-n.featureIndex}),this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var o=0,s=r;o<s.length;o+=1){var l=s[o],c=e.symbolInstances[l];e.featureSortOrder.push(c.featureIndex);for(var u=0,h=c.placedTextSymbolIndices;u<h.length;u+=1)for(var f=h[u],p=e.text.placedSymbolArray.get(f),d=p.vertexStartIndex+4*p.numGlyphs,g=p.vertexStartIndex;g<d;g+=4)e.text.indexArray.emplaceBack(g,g+1,g+2),e.text.indexArray.emplaceBack(g+1,g+2,g+3);var m=e.icon.placedSymbolArray.get(l);if(m.numGlyphs){var v=m.vertexStartIndex;e.icon.indexArray.emplaceBack(v,v+1,v+2),e.icon.indexArray.emplaceBack(v+1,v+2,v+3)}}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},pr("SymbolBucket",Ha,{omit:["layers","collisionBoxArray","features","compareText"],shallow:["symbolInstances"]}),Ha.MAX_GLYPHS=65535,Ha.addDynamicAttributes=Va;var Ga=new qr({"symbol-placement":new Nr(I.layout_symbol["symbol-placement"]),"symbol-spacing":new Nr(I.layout_symbol["symbol-spacing"]),"symbol-avoid-edges":new Nr(I.layout_symbol["symbol-avoid-edges"]),"icon-allow-overlap":new Nr(I.layout_symbol["icon-allow-overlap"]),"icon-ignore-placement":new Nr(I.layout_symbol["icon-ignore-placement"]),"icon-optional":new Nr(I.layout_symbol["icon-optional"]),"icon-rotation-alignment":new Nr(I.layout_symbol["icon-rotation-alignment"]),"icon-size":new jr(I.layout_symbol["icon-size"]),"icon-text-fit":new Nr(I.layout_symbol["icon-text-fit"]),"icon-text-fit-padding":new Nr(I.layout_symbol["icon-text-fit-padding"]),"icon-image":new jr(I.layout_symbol["icon-image"]),"icon-rotate":new jr(I.layout_symbol["icon-rotate"]),"icon-padding":new Nr(I.layout_symbol["icon-padding"]),"icon-keep-upright":new Nr(I.layout_symbol["icon-keep-upright"]),"icon-offset":new jr(I.layout_symbol["icon-offset"]),"icon-anchor":new jr(I.layout_symbol["icon-anchor"]),"icon-pitch-alignment":new Nr(I.layout_symbol["icon-pitch-alignment"]),"text-pitch-alignment":new Nr(I.layout_symbol["text-pitch-alignment"]),"text-rotation-alignment":new Nr(I.layout_symbol["text-rotation-alignment"]),"text-field":new jr(I.layout_symbol["text-field"]),"text-font":new jr(I.layout_symbol["text-font"]),"text-size":new jr(I.layout_symbol["text-size"]),"text-max-width":new jr(I.layout_symbol["text-max-width"]),"text-line-height":new Nr(I.layout_symbol["text-line-height"]),"text-letter-spacing":new jr(I.layout_symbol["text-letter-spacing"]),"text-justify":new jr(I.layout_symbol["text-justify"]),"text-anchor":new jr(I.layout_symbol["text-anchor"]),"text-max-angle":new Nr(I.layout_symbol["text-max-angle"]),"text-rotate":new jr(I.layout_symbol["text-rotate"]),"text-padding":new Nr(I.layout_symbol["text-padding"]),"text-keep-upright":new Nr(I.layout_symbol["text-keep-upright"]),"text-transform":new jr(I.layout_symbol["text-transform"]),"text-offset":new jr(I.layout_symbol["text-offset"]),"text-allow-overlap":new Nr(I.layout_symbol["text-allow-overlap"]),"text-ignore-placement":new Nr(I.layout_symbol["text-ignore-placement"]),"text-optional":new Nr(I.layout_symbol["text-optional"])}),Wa={paint:new qr({"icon-opacity":new jr(I.paint_symbol["icon-opacity"]),"icon-color":new jr(I.paint_symbol["icon-color"]),"icon-halo-color":new jr(I.paint_symbol["icon-halo-color"]),"icon-halo-width":new jr(I.paint_symbol["icon-halo-width"]),"icon-halo-blur":new jr(I.paint_symbol["icon-halo-blur"]),"icon-translate":new Nr(I.paint_symbol["icon-translate"]),"icon-translate-anchor":new Nr(I.paint_symbol["icon-translate-anchor"]),"text-opacity":new jr(I.paint_symbol["text-opacity"]),"text-color":new jr(I.paint_symbol["text-color"]),"text-halo-color":new jr(I.paint_symbol["text-halo-color"]),"text-halo-width":new jr(I.paint_symbol["text-halo-width"]),"text-halo-blur":new jr(I.paint_symbol["text-halo-blur"]),"text-translate":new Nr(I.paint_symbol["text-translate"]),"text-translate-anchor":new Nr(I.paint_symbol["text-translate-anchor"])}),layout:Ga},Ya=function(t){function e(e){t.call(this,e,Wa)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e){t.prototype.recalculate.call(this,e),"auto"===this.layout.get("icon-rotation-alignment")&&("line"===this.layout.get("symbol-placement")?this.layout._values["icon-rotation-alignment"]="map":this.layout._values["icon-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-rotation-alignment")&&("line"===this.layout.get("symbol-placement")?this.layout._values["text-rotation-alignment"]="map":this.layout._values["text-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-pitch-alignment")&&(this.layout._values["text-pitch-alignment"]=this.layout.get("text-rotation-alignment")),"auto"===this.layout.get("icon-pitch-alignment")&&(this.layout._values["icon-pitch-alignment"]=this.layout.get("icon-rotation-alignment"))},e.prototype.getValueAndResolveTokens=function(t,e){var r,n=this.layout.get(t).evaluate(e),i=this._unevaluatedLayout._values[t];return i.isDataDriven()||_e(i.value)?n:(r=e.properties,n.replace(/{([^{}]+)}/g,function(t,e){return e in r?String(r[e]):""}))},e.prototype.createBucket=function(t){return new Ha(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e}(Hr),Xa={paint:new qr({"background-color":new Nr(I.paint_background["background-color"]),"background-pattern":new Vr(I.paint_background["background-pattern"]),"background-opacity":new Nr(I.paint_background["background-opacity"])})},Za=function(t){function e(e){t.call(this,e,Xa)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Hr),$a={paint:new qr({"raster-opacity":new Nr(I.paint_raster["raster-opacity"]),"raster-hue-rotate":new Nr(I.paint_raster["raster-hue-rotate"]),"raster-brightness-min":new Nr(I.paint_raster["raster-brightness-min"]),"raster-brightness-max":new Nr(I.paint_raster["raster-brightness-max"]),"raster-saturation":new Nr(I.paint_raster["raster-saturation"]),"raster-contrast":new Nr(I.paint_raster["raster-contrast"]),"raster-fade-duration":new Nr(I.paint_raster["raster-fade-duration"])})},Ja={circle:ni,heatmap:pi,hillshade:gi,fill:Ji,"fill-extrusion":aa,line:Sa,symbol:Ya,background:Za,raster:function(t){function e(e){t.call(this,e,$a)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Hr)},Ka=i(function(t,e){t.exports=function(){function t(t,e,r){r=r||{},this.w=t||64,this.h=e||64,this.autoResize=!!r.autoResize,this.shelves=[],this.freebins=[],this.stats={},this.bins={},this.maxId=0}function e(t,e,r){this.x=0,this.y=t,this.w=this.free=e,this.h=r}return t.prototype.pack=function(t,e){t=[].concat(t),e=e||{};for(var r,n,i,a,o=[],s=0;s<t.length;s++)if(r=t[s].w||t[s].width,n=t[s].h||t[s].height,i=t[s].id,r&&n){if(!(a=this.packOne(r,n,i)))continue;e.inPlace&&(t[s].x=a.x,t[s].y=a.y,t[s].id=a.id),o.push(a)}return this.shrink(),o},t.prototype.packOne=function(t,r,n){var i,a,o,s,l,c,u,h,f={freebin:-1,shelf:-1,waste:1/0},p=0;if("string"==typeof n||"number"==typeof n){if(i=this.getBin(n))return this.ref(i),i;"number"==typeof n&&(this.maxId=Math.max(n,this.maxId))}else n=++this.maxId;for(s=0;s<this.freebins.length;s++){if(r===(i=this.freebins[s]).maxh&&t===i.maxw)return this.allocFreebin(s,t,r,n);r>i.maxh||t>i.maxw||r<=i.maxh&&t<=i.maxw&&(o=i.maxw*i.maxh-t*r)<f.waste&&(f.waste=o,f.freebin=s)}for(s=0;s<this.shelves.length;s++)if(p+=(a=this.shelves[s]).h,!(t>a.free)){if(r===a.h)return this.allocShelf(s,t,r,n);r>a.h||r<a.h&&(o=(a.h-r)*t)<f.waste&&(f.freebin=-1,f.waste=o,f.shelf=s)}return-1!==f.freebin?this.allocFreebin(f.freebin,t,r,n):-1!==f.shelf?this.allocShelf(f.shelf,t,r,n):r<=this.h-p&&t<=this.w?(a=new e(p,this.w,r),this.allocShelf(this.shelves.push(a)-1,t,r,n)):this.autoResize?(l=c=this.h,((u=h=this.w)<=l||t>u)&&(h=2*Math.max(t,u)),(l<u||r>l)&&(c=2*Math.max(r,l)),this.resize(h,c),this.packOne(t,r,n)):null},t.prototype.allocFreebin=function(t,e,r,n){var i=this.freebins.splice(t,1)[0];return i.id=n,i.w=e,i.h=r,i.refcount=0,this.bins[n]=i,this.ref(i),i},t.prototype.allocShelf=function(t,e,r,n){var i=this.shelves[t].alloc(e,r,n);return this.bins[n]=i,this.ref(i),i},t.prototype.shrink=function(){if(this.shelves.length>0){for(var t=0,e=0,r=0;r<this.shelves.length;r++){var n=this.shelves[r];e+=n.h,t=Math.max(n.w-n.free,t)}this.resize(t,e)}},t.prototype.getBin=function(t){return this.bins[t]},t.prototype.ref=function(t){if(1==++t.refcount){var e=t.h;this.stats[e]=1+(0|this.stats[e])}return t.refcount},t.prototype.unref=function(t){return 0===t.refcount?0:(0==--t.refcount&&(this.stats[t.h]--,delete this.bins[t.id],this.freebins.push(t)),t.refcount)},t.prototype.clear=function(){this.shelves=[],this.freebins=[],this.stats={},this.bins={},this.maxId=0},t.prototype.resize=function(t,e){this.w=t,this.h=e;for(var r=0;r<this.shelves.length;r++)this.shelves[r].resize(t);return!0},e.prototype.alloc=function(t,e,r){if(t>this.free||e>this.h)return null;var n=this.x;return this.x+=t,this.free-=t,new function(t,e,r,n,i,a,o){this.id=t,this.x=e,this.y=r,this.w=n,this.h=i,this.maxw=a||n,this.maxh=o||i,this.refcount=0}(r,n,this.y,t,e,t,this.h)},e.prototype.resize=function(t){return this.free+=t-this.w,this.w=t,!0},t}()}),Qa=function(t,e){var r=e.pixelRatio;this.paddedRect=t,this.pixelRatio=r},to={tl:{configurable:!0},br:{configurable:!0},displaySize:{configurable:!0}};to.tl.get=function(){return[this.paddedRect.x+1,this.paddedRect.y+1]},to.br.get=function(){return[this.paddedRect.x+this.paddedRect.w-1,this.paddedRect.y+this.paddedRect.h-1]},to.displaySize.get=function(){return[(this.paddedRect.w-2)/this.pixelRatio,(this.paddedRect.h-2)/this.pixelRatio]},Object.defineProperties(Qa.prototype,to);var eo=function(t){var e=new ui({width:0,height:0}),r={},n=new Ka(0,0,{autoResize:!0});for(var i in t){var a=t[i],o=n.packOne(a.data.width+2,a.data.height+2);e.resize({width:n.w,height:n.h}),ui.copy(a.data,e,{x:0,y:0},{x:o.x+1,y:o.y+1},a.data),r[i]=new Qa(o,a)}n.shrink(),e.resize({width:n.w,height:n.h}),this.image=e,this.positions=r};pr("ImagePosition",Qa),pr("ImageAtlas",eo);var ro=function(t,e,r,n,i){var 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bo(t,e,r){t[r]=e,t[r+1]=e>>>8,t[r+2]=e>>>16,t[r+3]=e>>>24}function _o(t,e){return(t[e]|t[e+1]<<8|t[e+2]<<16)+(t[e+3]<<24)}ao.Varint=0,ao.Fixed64=1,ao.Bytes=2,ao.Fixed32=5,ao.prototype={destroy:function(){this.buf=null},readFields:function(t,e,r){for(r=r||this.length;this.pos<r;){var n=this.readVarint(),i=n>>3,a=this.pos;this.type=7&n,t(i,e,this),this.pos===a&&this.skip(n)}return e},readMessage:function(t,e){return this.readFields(t,e,this.readVarint()+this.pos)},readFixed32:function(){var t=xo(this.buf,this.pos);return this.pos+=4,t},readSFixed32:function(){var t=_o(this.buf,this.pos);return this.pos+=4,t},readFixed64:function(){var t=xo(this.buf,this.pos)+4294967296*xo(this.buf,this.pos+4);return this.pos+=8,t},readSFixed64:function(){var t=xo(this.buf,this.pos)+4294967296*_o(this.buf,this.pos+4);return this.pos+=8,t},readFloat:function(){var t=ro(this.buf,this.pos,!0,23,4);return this.pos+=4,t},readDouble:function(){var t=ro(this.buf,this.pos,!0,52,8);return 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e=oo(this);for(t=t||[];this.pos<e;)t.push(this.readBoolean());return t},readPackedFloat:function(t){var e=oo(this);for(t=t||[];this.pos<e;)t.push(this.readFloat());return t},readPackedDouble:function(t){var e=oo(this);for(t=t||[];this.pos<e;)t.push(this.readDouble());return t},readPackedFixed32:function(t){var e=oo(this);for(t=t||[];this.pos<e;)t.push(this.readFixed32());return t},readPackedSFixed32:function(t){var e=oo(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed32());return t},readPackedFixed64:function(t){var e=oo(this);for(t=t||[];this.pos<e;)t.push(this.readFixed64());return t},readPackedSFixed64:function(t){var e=oo(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed64());return t},skip:function(t){var e=7&t;if(e===ao.Varint)for(;this.buf[this.pos++]>127;);else if(e===ao.Bytes)this.pos=this.readVarint()+this.pos;else if(e===ao.Fixed32)this.pos+=4;else{if(e!==ao.Fixed64)throw new Error("Unimplemented type: "+e);this.pos+=8}},writeTag:function(t,e){this.writeVarint(t<<3|e)},realloc:function(t){for(var e=this.length||16;e<this.pos+t;)e*=2;if(e!==this.length){var r=new Uint8Array(e);r.set(this.buf),this.buf=r,this.length=e}},finish:function(){return this.length=this.pos,this.pos=0,this.buf.subarray(0,this.length)},writeFixed32:function(t){this.realloc(4),bo(this.buf,t,this.pos),this.pos+=4},writeSFixed32:function(t){this.realloc(4),bo(this.buf,t,this.pos),this.pos+=4},writeFixed64:function(t){this.realloc(8),bo(this.buf,-1&t,this.pos),bo(this.buf,Math.floor(t*(1/4294967296)),this.pos+4),this.pos+=8},writeSFixed64:function(t){this.realloc(8),bo(this.buf,-1&t,this.pos),bo(this.buf,Math.floor(t*(1/4294967296)),this.pos+4),this.pos+=8},writeVarint:function(t){(t=+t||0)>268435455||t<0?function(t,e){var r,n;if(t>=0?(r=t%4294967296|0,n=t/4294967296|0):(n=~(-t/4294967296),4294967295^(r=~(-t%4294967296))?r=r+1|0:(r=0,n=n+1|0)),t>=0x10000000000000000||t<-0x10000000000000000)throw new Error("Given varint doesn't fit into 10 bytes");e.realloc(10),function(t,e,r){r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos]=127&t}(r,0,e),function(t,e){var r=(7&t)<<4;e.buf[e.pos++]|=r|((t>>>=3)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t)))))}(n,e)}(t,this):(this.realloc(4),this.buf[this.pos++]=127&t|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=t>>>7&127))))},writeSVarint:function(t){this.writeVarint(t<0?2*-t-1:2*t)},writeBoolean:function(t){this.writeVarint(Boolean(t))},writeString:function(t){t=String(t),this.realloc(4*t.length),this.pos++;var e=this.pos;this.pos=function(t,e,r){for(var n,i,a=0;a<e.length;a++){if((n=e.charCodeAt(a))>55295&&n<57344){if(!i){n>56319||a+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):i=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,i=n;continue}n=i-55296<<10|n-56320|65536,i=null}else i&&(t[r++]=239,t[r++]=191,t[r++]=189,i=null);n<128?t[r++]=n:(n<2048?t[r++]=n>>6|192:(n<65536?t[r++]=n>>12|224:(t[r++]=n>>18|240,t[r++]=n>>12&63|128),t[r++]=n>>6&63|128),t[r++]=63&n|128)}return r}(this.buf,t,this.pos);var r=this.pos-e;r>=128&&lo(e,r,this),this.pos=e-1,this.writeVarint(r),this.pos+=r},writeFloat:function(t){this.realloc(4),no(this.buf,t,this.pos,!0,23,4),this.pos+=4},writeDouble:function(t){this.realloc(8),no(this.buf,t,this.pos,!0,52,8),this.pos+=8},writeBytes:function(t){var e=t.length;this.writeVarint(e),this.realloc(e);for(var r=0;r<e;r++)this.buf[this.pos++]=t[r]},writeRawMessage:function(t,e){this.pos++;var r=this.pos;t(e,this);var n=this.pos-r;n>=128&&lo(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,ao.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){this.writeMessage(t,co,e)},writePackedSVarint:function(t,e){this.writeMessage(t,uo,e)},writePackedBoolean:function(t,e){this.writeMessage(t,po,e)},writePackedFloat:function(t,e){this.writeMessage(t,ho,e)},writePackedDouble:function(t,e){this.writeMessage(t,fo,e)},writePackedFixed32:function(t,e){this.writeMessage(t,go,e)},writePackedSFixed32:function(t,e){this.writeMessage(t,mo,e)},writePackedFixed64:function(t,e){this.writeMessage(t,vo,e)},writePackedSFixed64:function(t,e){this.writeMessage(t,yo,e)},writeBytesField:function(t,e){this.writeTag(t,ao.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,ao.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,ao.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,ao.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,ao.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,ao.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,ao.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,ao.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,ao.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,ao.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};var wo=3;function ko(t,e,r){1===t&&r.readMessage(Mo,e)}function Mo(t,e,r){if(3===t){var n=r.readMessage(Ao,{}),i=n.id,a=n.bitmap,o=n.width,s=n.height,l=n.left,c=n.top,u=n.advance;e.push({id:i,bitmap:new ci({width:o+2*wo,height:s+2*wo},a),metrics:{width:o,height:s,left:l,top:c,advance:u}})}}function Ao(t,e,r){1===t?e.id=r.readVarint():2===t?e.bitmap=r.readBytes():3===t?e.width=r.readVarint():4===t?e.height=r.readVarint():5===t?e.left=r.readSVarint():6===t?e.top=r.readSVarint():7===t&&(e.advance=r.readVarint())}var To=wo,So=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.callbackID=0,g(["receive"],this),this.target.addEventListener("message",this.receive,!1)};So.prototype.send=function(t,e,r,n){var i=r?this.mapId+":"+this.callbackID++:null;r&&(this.callbacks[i]=r);var a=[];this.target.postMessage({targetMapId:n,sourceMapId:this.mapId,type:t,id:String(i),data:gr(e,a)},a)},So.prototype.receive=function(t){var e,r=this,n=t.data,i=n.id;if(!n.targetMapId||this.mapId===n.targetMapId){var a=function(t,e){var n=[];r.target.postMessage({sourceMapId:r.mapId,type:"<response>",id:String(i),error:t?gr(t):null,data:gr(e,n)},n)};if("<response>"===n.type)e=this.callbacks[n.id],delete this.callbacks[n.id],e&&n.error?e(mr(n.error)):e&&e(null,mr(n.data));else if(void 0!==n.id&&this.parent[n.type])this.parent[n.type](n.sourceMapId,mr(n.data),a);else if(void 0!==n.id&&this.parent.getWorkerSource){var o=n.type.split(".");this.parent.getWorkerSource(n.sourceMapId,o[0],o[1])[o[2]](mr(n.data),a)}else this.parent[n.type](mr(n.data))}},So.prototype.remove=function(){this.target.removeEventListener("message",this.receive,!1)};var Eo=n(i(function(t,e){!function(t){function e(t,e,n){var i=r(256*t,256*(e=Math.pow(2,n)-e-1),n),a=r(256*(t+1),256*(e+1),n);return i[0]+","+i[1]+","+a[0]+","+a[1]}function r(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}t.getURL=function(t,r,n,i,a,o){return o=o||{},t+"?"+["bbox="+e(n,i,a),"format="+(o.format||"image/png"),"service="+(o.service||"WMS"),"version="+(o.version||"1.1.1"),"request="+(o.request||"GetMap"),"srs="+(o.srs||"EPSG:3857"),"width="+(o.width||256),"height="+(o.height||256),"layers="+r].join("&")},t.getTileBBox=e,t.getMercCoords=r,Object.defineProperty(t,"__esModule",{value:!0})}(e)})),Co=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=Po(0,t,e,r)};Co.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},Co.prototype.url=function(t,e){var r=Eo.getTileBBox(this.x,this.y,this.z),n=function(t,e,r){for(var n,i="",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace("{prefix}",(this.x%16).toString(16)+(this.y%16).toString(16)).replace("{z}",String(this.z)).replace("{x}",String(this.x)).replace("{y}",String("tms"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace("{quadkey}",n).replace("{bbox-epsg-3857}",r)};var Lo=function(t,e){this.wrap=t,this.canonical=e,this.key=Po(t,e.z,e.x,e.y)},zo=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new Co(r,+n,+i),this.key=Po(e,t,n,i)};function Po(t,e,r,n){(t*=2)<0&&(t=-1*t-1);var i=1<<e;return 32*(i*i*t+i*n+r)+e}zo.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},zo.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new zo(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new zo(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},zo.prototype.isChildOf=function(t){var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},zo.prototype.children=function(t){if(this.overscaledZ>=t)return[new zo(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new zo(e,this.wrap,e,r,n),new zo(e,this.wrap,e,r+1,n),new zo(e,this.wrap,e,r,n+1),new zo(e,this.wrap,e,r+1,n+1)]},zo.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},zo.prototype.wrapped=function(){return new zo(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},zo.prototype.unwrapTo=function(t){return new zo(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},zo.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},zo.prototype.toUnwrapped=function(){return new Lo(this.wrap,this.canonical)},zo.prototype.toString=function(){return this.overscaledZ+"/"+this.canonical.x+"/"+this.canonical.y},zo.prototype.toCoordinate=function(){return new s(this.canonical.x+Math.pow(2,this.wrap),this.canonical.y,this.canonical.z)},pr("CanonicalTileID",Co),pr("OverscaledTileID",zo,{omit:["posMatrix"]});var Io=function(t,e,r){if(t<=0)throw new RangeError("Level must have positive dimension");this.dim=t,this.border=e,this.stride=this.dim+2*this.border,this.data=r||new Int32Array((this.dim+2*this.border)*(this.dim+2*this.border))};Io.prototype.set=function(t,e,r){this.data[this._idx(t,e)]=r+65536},Io.prototype.get=function(t,e){return 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t.default$1(d,g);var I=n.layout.get("icon-rotate").evaluate(o)*Math.PI/180;if(I){var O=Math.sin(I),D=Math.cos(I),R=[D,-O,O,D];s._matMult(R),l._matMult(R),u._matMult(R),c._matMult(R)}return[{tl:s,tr:l,bl:u,br:c,tex:h.paddedRect,writingMode:void 0,glyphOffset:[0,0]}]}(0,a,l,0,i.horizontal,w);T=new s(c,n,r,u,h,f,a,y,x,!1,e.overscaling),E=4*D.length;var R=e.iconSizeData,B=null;"source"===R.functionType?B=[10*l.layout.get("icon-size").evaluate(w)]:"composite"===R.functionType&&(B=[10*M.compositeIconSizes[0].evaluate(w),10*M.compositeIconSizes[1].evaluate(w)]),e.addSymbols(e.icon,D,B,_,b,w,!1,r,S.lineStartIndex,S.lineLength)}var F=T?T.boxStartIndex:e.collisionBoxArray.length,N=T?T.boxEndIndex:e.collisionBoxArray.length;return e.glyphOffsetArray.length>=t.default$14.MAX_GLYPHS&&t.warnOnce("Too many glyphs being rendered in a tile. 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Rt(t,e,r,n,i,a){return[Math.round(r*(t*n-i)),Math.round(r*(e*n-a))]}function Bt(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)*i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,Ft(o,t[s],a,i);var l=t[s].minX,c=t[s].minY,u=t[s].maxX,h=t[s].maxY;l<o.minX&&(o.minX=l),c<o.minY&&(o.minY=c),u>o.maxX&&(o.maxX=u),h>o.maxY&&(o.maxY=h)}return o}function Ft(t,e,r,n){var i=e.geometry,a=e.type,o=[];if("Point"===a||"MultiPoint"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if("LineString"===a)Nt(o,i,t,r,!1,!1);else if("MultiLineString"===a||"Polygon"===a)for(s=0;s<i.length;s++)Nt(o,i[s],t,r,"Polygon"===a,0===s);else if("MultiPolygon"===a)for(var l=0;l<i.length;l++){var c=i[l];for(s=0;s<c.length;s++)Nt(o,c[s],t,r,!0,0===s)}if(o.length){var u=e.tags||null;if("LineString"===a&&n.lineMetrics){for(var h in u={},e.tags)u[h]=e.tags[h];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var f={geometry:o,type:"Polygon"===a||"MultiPolygon"===a?3:"LineString"===a||"MultiLineString"===a?2:1,tags:u};null!==e.id&&(f.id=e.id),t.features.push(f)}}function Nt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function jt(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time("preprocess data"),e.maxZoom<0||e.maxZoom>24)throw new Error("maxZoom should be in the 0-24 range");var n=function(t,e){var r=[];if("FeatureCollection"===t.type)for(var n=0;n<t.features.length;n++)xt(r,t.features[n],e);else"Feature"===t.type?xt(r,t,e):xt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd("preprocess data"),console.log("index: maxZoom: %d, maxPoints: %d",e.indexMaxZoom,e.indexMaxPoints),console.time("generate tiles"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=At(t,1,-1-r,r,0,-1,2,e),a=At(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=At(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=It(i,1).concat(n)),a&&(n=n.concat(It(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log("features: %d, points: %d",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd("generate tiles"),console.log("tiles generated:",this.total,JSON.stringify(this.stats)))}function Vt(t,e,r){return 32*((1<<t)*r+e)+t}function Ut(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new R(n.features),a=V(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}ot.prototype={options:{minZoom:0,maxZoom:16,radius:40,extent:512,nodeSize:64,log:!1,reduce:null,initial:function(){return{}},map:function(t){return t}},load:function(t){var e=this.options.log;e&&console.time("total time");var r="prepare "+t.length+" points";e&&console.time(r),this.points=t;var n=t.map(lt);e&&console.timeEnd(r);for(var i=this.options.maxZoom;i>=this.options.minZoom;i--){var a=+Date.now();this.trees[i+1]=rt(n,dt,gt,this.options.nodeSize,Float32Array),n=this._cluster(n,i),e&&console.log("z%d: %d clusters in %dms",i,n.length,+Date.now()-a)}return this.trees[this.options.minZoom]=rt(n,dt,gt,this.options.nodeSize,Float32Array),e&&console.timeEnd("total time"),this},getClusters:function(t,e){for(var r=this.trees[this._limitZoom(e)],n=r.range(ht(t[0]),ft(t[3]),ht(t[2]),ft(t[1])),i=[],a=0;a<n.length;a++){var o=r.points[n[a]];i.push(o.numPoints?ct(o):this.points[o.id])}return i},getChildren:function(t,e){for(var r=this.trees[e+1].points[t],n=this.options.radius/(this.options.extent*Math.pow(2,e)),i=this.trees[e+1].within(r.x,r.y,n),a=[],o=0;o<i.length;o++){var s=this.trees[e+1].points[i[o]];s.parentId===t&&a.push(s.numPoints?ct(s):this.points[s.id])}return a},getLeaves:function(t,e,r,n){r=r||10,n=n||0;var i=[];return this._appendLeaves(i,t,e,r,n,0),i},getTile:function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options.extent,o=this.options.radius/a,s=(r-o)/i,l=(r+1+o)/i,c={features:[]};return this._addTileFeatures(n.range((e-o)/i,s,(e+1+o)/i,l),n.points,e,r,i,c),0===e&&this._addTileFeatures(n.range(1-o/i,s,1,l),n.points,i,r,i,c),e===i-1&&this._addTileFeatures(n.range(0,s,o/i,l),n.points,-1,r,i,c),c.features.length?c:null},getClusterExpansionZoom:function(t,e){for(;e<this.options.maxZoom;){var r=this.getChildren(t,e);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},_appendLeaves:function(t,e,r,n,i,a){for(var o=this.getChildren(e,r),s=0;s<o.length;s++){var l=o[s].properties;if(l.cluster?a+l.point_count<=i?a+=l.point_count:a=this._appendLeaves(t,l.cluster_id,r+1,n,i,a):a<i?a++:t.push(o[s]),t.length===n)break}return a},_addTileFeatures:function(t,e,r,n,i,a){for(var o=0;o<t.length;o++){var s=e[t[o]];a.features.push({type:1,geometry:[[Math.round(this.options.extent*(s.x*i-r)),Math.round(this.options.extent*(s.y*i-n))]],tags:s.numPoints?ut(s):this.points[s.id].properties})}},_limitZoom:function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},_cluster:function(t,e){for(var r=[],n=this.options.radius/(this.options.extent*Math.pow(2,e)),i=0;i<t.length;i++){var a=t[i];if(!(a.zoom<=e)){a.zoom=e;var o=this.trees[e+1],s=o.within(a.x,a.y,n),l=a.numPoints||1,c=a.x*l,u=a.y*l,h=null;this.options.reduce&&(h=this.options.initial(),this._accumulate(h,a));for(var f=0;f<s.length;f++){var p=o.points[s[f]];if(e<p.zoom){var d=p.numPoints||1;p.zoom=e,c+=p.x*d,u+=p.y*d,l+=d,p.parentId=i,this.options.reduce&&this._accumulate(h,p)}}1===l?r.push(a):(a.parentId=i,r.push(st(c/l,u/l,l,i,h)))}}return r},_accumulate:function(t,e){var r=e.numPoints?e.properties:this.options.map(this.points[e.id].properties);this.options.reduce(t,r)}},jt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,debug:0},jt.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,h=Vt(e,r,n),f=this.tiles[h];if(!f&&(c>1&&console.time("creation"),f=this.tiles[h]=Bt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log("tile z%d-%d-%d (features: %d, points: %d, simplified: %d)",e,r,n,f.numFeatures,f.numPoints,f.numSimplified),console.timeEnd("creation"));var p="z"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(f.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||f.numPoints<=l.indexMaxPoints)continue;if(f.source=null,0!==t.length){c>1&&console.time("clipping");var g,m,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,k=.5+_,M=1+_;g=m=v=y=null,x=At(t,u,r-_,r+k,0,f.minX,f.maxX,l),b=At(t,u,r+w,r+M,0,f.minX,f.maxX,l),t=null,x&&(g=At(x,u,n-_,n+k,1,f.minY,f.maxY,l),m=At(x,u,n+w,n+M,1,f.minY,f.maxY,l),x=null),b&&(v=At(b,u,n-_,n+k,1,f.minY,f.maxY,l),y=At(b,u,n+w,n+M,1,f.minY,f.maxY,l),b=null),c>1&&console.timeEnd("clipping"),s.push(g||[],e+1,2*r,2*n),s.push(m||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},jt.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=Vt(t,e=(e%o+o)%o,r);if(this.tiles[s])return Dt(this.tiles[s],i);a>1&&console.log("drilling down to z%d-%d-%d",t,e,r);for(var l,c=t,u=e,h=r;!l&&c>0;)c--,u=Math.floor(u/2),h=Math.floor(h/2),l=this.tiles[Vt(c,u,h)];return l&&l.source?(a>1&&console.log("found parent tile z%d-%d-%d",c,u,h),a>1&&console.time("drilling down"),this.splitTile(l.source,c,u,h,t,e,r),a>1&&console.timeEnd("drilling down"),this.tiles[s]?Dt(this.tiles[s],i):null):null};var qt=function(e){function r(t,r,n){e.call(this,t,r,Ut),n&&(this.loadGeoJSON=n)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&"Idle"!==this._state?this._state="NeedsLoadData":(this._state="Coalescing",this._loadData())},r.prototype._loadData=function(){var t=this;if(this._pendingCallback&&this._pendingLoadDataParams){var e=this._pendingCallback,r=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams,this.loadGeoJSON(r,function(n,i){if(n||!i)return e(n);if("object"!=typeof i)return e(new Error("Input data is not a valid GeoJSON object."));!function t(e,r){switch(e&&e.type||null){case"FeatureCollection":return e.features=e.features.map(z(t,r)),e;case"Feature":return e.geometry=t(e.geometry,r),e;case"Polygon":case"MultiPolygon":return function(t,e){return"Polygon"===t.type?t.coordinates=P(t.coordinates,e):"MultiPolygon"===t.type&&(t.coordinates=t.coordinates.map(z(P,e))),t}(e,r);default:return e}}(i,!0);try{t._geoJSONIndex=r.cluster?function(t){return new ot(t)}(r.superclusterOptions).load(i.features):new jt(i,r.geojsonVtOptions)}catch(n){return e(n)}t.loaded={};var a={};if(r.request&&r.request.collectResourceTiming){var o=w(r.request.url);o&&(a.resourceTiming={},a.resourceTiming[r.source]=JSON.parse(JSON.stringify(o)))}e(null,a)})}},r.prototype.coalesce=function(){"Coalescing"===this._state?this._state="Idle":"NeedsLoadData"===this._state&&(this._state="Coalescing",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if("string"!=typeof e.data)return r(new Error("Input data is not a valid GeoJSON object."));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error("Input data is not a valid GeoJSON object."))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r}(M),Ht=function(e){var r=this;this.self=e,this.actor=new 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t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},F.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i(function(t,e){if(!x(t))return t;var r=A(t);return r.path="/fonts/v1"+r.path,y(r,e)}(n).replace("{fontstack}",e).replace("{range}",o+"-"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.default$3(r.data);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}})},F.TinySDF=I;var N=function(){this.specification=t.default$5.light.position};N.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},N.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var j=new t.Properties({anchor:new t.DataConstantProperty(t.default$5.light.anchor),position:new N,color:new t.DataConstantProperty(t.default$5.light.color),intensity:new t.DataConstantProperty(t.default$5.light.intensity)}),V=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(j),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e){if(!this._validate(t.validateLight,e))for(var r in e){var n=e[r];t.endsWith(r,"-transition")?this._transitionable.setTransition(r.slice(0,-"-transition".length),n):this._transitionable.setValue(r,n)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r){return t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.default$5})))},r}(t.Evented),U=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.bytes=4,this.data=new Uint8Array(this.width*this.height*this.bytes),this.positions={}};U.prototype.getDash=function(t,e){var r=t.join(",")+String(e);return this.positions[r]||(this.positions[r]=this.addDash(t,e)),this.positions[r]},U.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce("LineAtlas out of space"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];for(var s=this.width/a,l=s/2,c=e.length%2==1,u=-n;u<=n;u++)for(var h=this.nextRow+n+u,f=this.width*h,p=c?-e[e.length-1]:0,d=e[0],g=1,m=0;m<this.width;m++){for(;d<m/s;)p=d,d+=e[g],c&&g===e.length-1&&(d+=e[0]),g++;var v=Math.abs(m-p*s),y=Math.abs(m-d*s),x=Math.min(v,y),b=g%2==1,_=void 0;if(r){var w=n?u/n*(l+1):0;if(b){var k=l-Math.abs(w);_=Math.sqrt(x*x+k*k)}else _=l-Math.sqrt(x*x+w*w)}else _=(b?1:-1)*x;this.data[3+4*(f+m)]=Math.max(0,Math.min(255,_+128))}var M={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,M},U.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.RGBA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.RGBA,this.width,this.height,0,e.RGBA,e.UNSIGNED_BYTE,this.data))};var q=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name="Worker "+a,this.actors.push(s)}};function H(e,r,n){var i=function(e,r){if(e)return n(e);if(r){var i=t.pick(r,["tiles","minzoom","maxzoom","attribution","mapbox_logo","bounds"]);r.vector_layers&&(i.vectorLayers=r.vector_layers,i.vectorLayerIds=i.vectorLayers.map(function(t){return t.id})),n(null,i)}};e.url?t.getJSON(r(b(e.url),t.ResourceType.Source),i):a.frame(function(){return i(null,e)})}q.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,function(t,n){t.send(e,r,n)},n)},q.prototype.send=function(t,e,r,n){return("number"!=typeof n||isNaN(n))&&(n=this.currentActor=(this.currentActor+1)%this.actors.length),this.actors[n].send(t,e,r),n},q.prototype.remove=function(){this.actors.forEach(function(t){t.remove()}),this.actors=[],this.workerPool.release(this.id)},q.Actor=t.default$7;var G=function(t,e){if(isNaN(t)||isNaN(e))throw new Error("Invalid LngLat object: ("+t+", "+e+")");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error("Invalid LngLat latitude value: must be between -90 and 90")};G.prototype.wrap=function(){return new G(t.wrap(this.lng,-180,180),this.lat)},G.prototype.toArray=function(){return[this.lng,this.lat]},G.prototype.toString=function(){return"LngLat("+this.lng+", "+this.lat+")"},G.prototype.toBounds=function(t){var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new W(new G(this.lng-r,this.lat-e),new G(this.lng+r,this.lat+e))},G.convert=function(t){if(t instanceof G)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new G(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&"object"==typeof t&&null!==t)return new G(Number(t.lng),Number(t.lat));throw new Error("`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]")};var W=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};W.prototype.setNorthEast=function(t){return this._ne=t instanceof G?new G(t.lng,t.lat):G.convert(t),this},W.prototype.setSouthWest=function(t){return this._sw=t instanceof G?new G(t.lng,t.lat):G.convert(t),this},W.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof G)e=t,r=t;else{if(!(t instanceof W))return Array.isArray(t)?t.every(Array.isArray)?this.extend(W.convert(t)):this.extend(G.convert(t)):this;if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new G(e.lng,e.lat),this._ne=new G(r.lng,r.lat)),this},W.prototype.getCenter=function(){return new G((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},W.prototype.getSouthWest=function(){return this._sw},W.prototype.getNorthEast=function(){return this._ne},W.prototype.getNorthWest=function(){return new G(this.getWest(),this.getNorth())},W.prototype.getSouthEast=function(){return new G(this.getEast(),this.getSouth())},W.prototype.getWest=function(){return this._sw.lng},W.prototype.getSouth=function(){return this._sw.lat},W.prototype.getEast=function(){return this._ne.lng},W.prototype.getNorth=function(){return this._ne.lat},W.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},W.prototype.toString=function(){return"LngLatBounds("+this._sw.toString()+", "+this._ne.toString()+")"},W.prototype.isEmpty=function(){return!(this._sw&&this._ne)},W.convert=function(t){return!t||t instanceof W?t:new W(t)};var Y=function(t,e,r){this.bounds=W.convert(this.validateBounds(t)),this.minzoom=e||0,this.maxzoom=r||24};Y.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},Y.prototype.contains=function(t){var e=Math.floor(this.lngX(this.bounds.getWest(),t.z)),r=Math.floor(this.latY(this.bounds.getNorth(),t.z)),n=Math.ceil(this.lngX(this.bounds.getEast(),t.z)),i=Math.ceil(this.latY(this.bounds.getSouth(),t.z));return t.x>=e&&t.x<n&&t.y>=r&&t.y<i},Y.prototype.lngX=function(t,e){return(t+180)*(Math.pow(2,e)/360)},Y.prototype.latY=function(e,r){var n=t.clamp(Math.sin(Math.PI/180*e),-.9999,.9999),i=Math.pow(2,r)/(2*Math.PI);return Math.pow(2,r-1)+.5*Math.log((1+n)/(1-n))*-i};var X=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type="vector",this.minzoom=0,this.maxzoom=22,this.scheme="xyz",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,t.extend(this,t.pick(n,["url","scheme","tileSize"])),this._options=t.extend({type:"vector"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error("vector tile sources must have a tileSize of 512");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event("dataloading",{dataType:"source"})),H(this._options,this.map._transformRequest,function(r,n){r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new Y(n.bounds,e.minzoom,e.maxzoom)),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))})},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=k(e.tileID.canonical.url(this.tiles,this.scheme),this.url),i={request:this.map._transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:a.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes};function o(t,n){return e.aborted?r(null):t?r(t):(n&&n.resourceTiming&&(e.resourceTiming=n.resourceTiming),this.map._refreshExpiredTiles&&e.setExpiryData(n),e.loadVectorData(n,this.map.painter),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,void 0===e.workerID||"expired"===e.state?e.workerID=this.dispatcher.send("loadTile",i,o.bind(this)):"loading"===e.state?e.reloadCallback=r:this.dispatcher.send("reloadTile",i,o.bind(this),e.workerID)},r.prototype.abortTile=function(t){this.dispatcher.send("abortTile",{uid:t.uid,type:this.type,source:this.id},void 0,t.workerID)},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.dispatcher.send("removeTile",{uid:t.uid,type:this.type,source:this.id},void 0,t.workerID)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),Z=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type="raster",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme="xyz",this.tileSize=512,this._loaded=!1,this._options=t.extend({},n),t.extend(this,t.pick(n,["url","scheme","tileSize"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event("dataloading",{dataType:"source"})),H(this._options,this.map._transformRequest,function(r,n){r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new Y(n.bounds,e.minzoom,e.maxzoom)),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))})},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=k(e.tileID.canonical.url(this.tiles,this.scheme),this.url,this.tileSize);e.request=t.getImage(this.map._transformRequest(i,t.ResourceType.Tile),function(t,i){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(t)e.state="errored",r(t);else if(i){n.map._refreshExpiredTiles&&e.setExpiryData(i),delete i.cacheControl,delete i.expires;var a=n.map.painter.context,o=a.gl;e.texture=n.map.painter.getTileTexture(i.width),e.texture?e.texture.update(i,{useMipmap:!0}):(e.texture=new z(a,i,o.RGBA,{useMipmap:!0}),e.texture.bind(o.LINEAR,o.CLAMP_TO_EDGE,o.LINEAR_MIPMAP_NEAREST),a.extTextureFilterAnisotropic&&o.texParameterf(o.TEXTURE_2D,a.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,a.extTextureFilterAnisotropicMax)),e.state="loaded",r(null)}})},r.prototype.abortTile=function(t,e){t.request&&(t.request.abort(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),$=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type="raster-dem",this.maxzoom=22,this._options=t.extend({},n),this.encoding=n.encoding||"mapbox"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:"raster-dem",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=k(e.tileID.canonical.url(this.tiles,this.scheme),this.url,this.tileSize);e.request=t.getImage(this.map._transformRequest(n,t.ResourceType.Tile),function(t,n){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(t)e.state="errored",r(t);else if(n){this.map._refreshExpiredTiles&&e.setExpiryData(n),delete n.cacheControl,delete n.expires;var i=a.getImageData(n),o={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:i,encoding:this.encoding};e.workerID&&"expired"!==e.state||(e.workerID=this.dispatcher.send("loadDEMTile",o,function(t,n){t&&(e.state="errored",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state="loaded",r(null))}.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state="unloaded",this.dispatcher.send("removeDEMTile",{uid:t.uid,source:this.id},void 0,t.workerID)},r}(Z),J=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type="geojson",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this.dispatcher=i,this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type);var o=t.default$8/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.default$8,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.default$8,radius:(n.clusterRadius||50)*o,log:!1}},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData(function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:"source",sourceDataType:"metadata"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event("data",n))}})},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData(function(e){if(e)return r.fire(new t.ErrorEvent(e));var n={dataType:"source",sourceDataType:"content"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event("data",n))}),this},r.prototype._updateWorkerData=function(e){var r,n,i=this,a=t.extend({},this.workerOptions),o=this._data;"string"==typeof o?(a.request=this.map._transformRequest((r=o,(n=t.default.document.createElement("a")).href=r,n.href),t.ResourceType.Source),a.request.collectResourceTiming=this._collectResourceTiming):a.data=JSON.stringify(o),this.workerID=this.dispatcher.send(this.type+"."+a.source+".loadData",a,function(t,r){i._removed||r&&r.abandoned||(i._loaded=!0,r&&r.resourceTiming&&r.resourceTiming[i.id]&&(i._resourceTiming=r.resourceTiming[i.id].slice(0)),i.dispatcher.send(i.type+"."+a.source+".coalesce",null,null,i.workerID),e(t))},this.workerID)},r.prototype.loadTile=function(t,e){var r=this,n=void 0===t.workerID?"loadTile":"reloadTile",i={type:this.type,uid:t.uid,tileID:t.tileID,zoom:t.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:a.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes};t.workerID=this.dispatcher.send(n,i,function(i,a){return t.unloadVectorData(),t.aborted?e(null):i?e(i):(t.loadVectorData(a,r.map.painter,"reloadTile"===n),e(null))},this.workerID)},r.prototype.abortTile=function(t){t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.dispatcher.send("removeTile",{uid:t.uid,type:this.type,source:this.id},null,t.workerID)},r.prototype.onRemove=function(){this._removed=!0,this.dispatcher.send("removeSource",{type:this.type,source:this.id},null,this.workerID)},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),K=t.createLayout([{name:"a_pos",type:"Int16",components:2},{name:"a_texture_pos",type:"Int16",components:2}]),Q=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};Q.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},Q.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var h=l;h<s;h++)u.disableVertexAttribArray(h)}e.enableAttributes(u,t);for(var f=0,p=r;f<p.length;f+=1)p[f].enableAttributes(u,t);a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,g=r;d<g.length;d+=1){var m=g[d];m.bind(),m.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},Q.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var tt=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type="image",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event("dataloading",{dataType:"source"})),this.url=this.options.url,t.getImage(this.map._transformRequest(this.url,t.ResourceType.Image),function(r,n){r?e.fire(new t.ErrorEvent(r)):n&&(e.image=a.getImageData(n),e._finishLoading())})},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){this.coordinates=e;var r=this.map,n=e.map(function(t){return r.transform.locationCoordinate(G.convert(t)).zoomTo(0)}),i=this.centerCoord=t.getCoordinatesCenter(n);i.column=Math.floor(i.column),i.row=Math.floor(i.row),this.tileID=new t.CanonicalTileID(i.zoom,i.column,i.row),this.minzoom=this.maxzoom=i.zoom;var a=n.map(function(e){var r=e.zoomTo(i.zoom);return new t.default$1(Math.round((r.column-i.column)*t.default$8),Math.round((r.row-i.row)*t.default$8))});return this._boundsArray=new t.RasterBoundsArray,this._boundsArray.emplaceBack(a[0].x,a[0].y,0,0),this._boundsArray.emplaceBack(a[1].x,a[1].y,t.default$8,0),this._boundsArray.emplaceBack(a[3].x,a[3].y,0,t.default$8),this._boundsArray.emplaceBack(a[2].x,a[2].y,t.default$8,t.default$8),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var t=this.map.painter.context,e=t.gl;for(var r in this.boundsBuffer||(this.boundsBuffer=t.createVertexBuffer(this._boundsArray,K.members)),this.boundsVAO||(this.boundsVAO=new Q),this.texture||(this.texture=new z(t,this.image,e.RGBA),this.texture.bind(e.LINEAR,e.CLAMP_TO_EDGE)),this.tiles){var n=this.tiles[r];"loaded"!==n.state&&(n.state="loaded",n.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state="errored",e(null))},r.prototype.serialize=function(){return{type:"image",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented),et=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type="video",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this,r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];e.urls.push(e.map._transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,function(r,n){r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener("playing",function(){e.map._rerender()}),e.map&&e.video.play(),e._finishLoading())})},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var t=this.map.painter.context,e=t.gl;for(var r in this.boundsBuffer||(this.boundsBuffer=t.createVertexBuffer(this._boundsArray,K.members)),this.boundsVAO||(this.boundsVAO=new Q),this.texture?this.video.paused||(this.texture.bind(e.LINEAR,e.CLAMP_TO_EDGE),e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,this.video)):(this.texture=new z(t,this.video,e.RGBA),this.texture.bind(e.LINEAR,e.CLAMP_TO_EDGE)),this.tiles){var n=this.tiles[r];"loaded"!==n.state&&(n.state="loaded",n.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"video",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(tt),rt=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some(function(t){return!Array.isArray(t)||2!==t.length||t.some(function(t){return"number"!=typeof t})})||this.fire(new t.ErrorEvent(new t.default$9("sources."+r,null,'"coordinates" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.default$9("sources."+r,null,'missing required property "coordinates"'))),n.animate&&"boolean"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.default$9("sources."+r,null,'optional "animate" property must be a boolean value'))),n.canvas?"string"==typeof n.canvas||n.canvas instanceof t.default.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.default$9("sources."+r,null,'"canvas" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.default$9("sources."+r,null,'missing required property "canvas"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this.canvas||(this.canvas=this.options.canvas instanceof t.default.HTMLCanvasElement?this.options.canvas:t.default.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error("Canvas dimensions cannot be less than or equal to zero."))):(this.play=function(){this._playing=!0,this.map._rerender()},this.pause=function(){this._playing=!1},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var t=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,t=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,t=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,K.members)),this.boundsVAO||(this.boundsVAO=new Q),this.texture?t?this.texture.update(this.canvas):this._playing&&(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.canvas)):(this.texture=new z(e,this.canvas,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"canvas",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(tt),nt={vector:X,raster:Z,"raster-dem":$,geojson:J,video:et,image:tt,canvas:rt},it=function(e,r,n,i){var a=new nt[r.type](e,r,n,i);if(a.id!==e)throw new Error("Expected Source id to be "+e+" instead of "+a.id);return t.bindAll(["load","abort","unload","serialize","prepare"],a),a};function at(t,e,r,n,i){var a=i.maxPitchScaleFactor(),o=t.tilesIn(r,a);o.sort(ot);for(var s=[],l=0,c=o;l<c.length;l+=1){var u=c[l];s.push({wrappedTileID:u.tileID.wrapped().key,queryResults:u.tile.queryRenderedFeatures(e,u.queryGeometry,u.scale,n,i,a,t.transform.calculatePosMatrix(u.tileID.toUnwrapped()))})}return function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var c in o)for(var u=o[c],h=l[c]=l[c]||{},f=e[c]=e[c]||[],p=0,d=u;p<d.length;p+=1){var g=d[p];h[g.featureIndex]||(h[g.featureIndex]=!0,f.push(g.feature))}}return e}(s)}function ot(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}var st=function(e,r){this.tileID=e,this.uid=t.uniqueId(),this.uses=0,this.tileSize=r,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.expiredRequestCount=0,this.state="loading"};st.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<a.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},st.prototype.wasRequested=function(){return"errored"===this.state||"loaded"===this.state||"reloading"===this.state},st.prototype.loadVectorData=function(e,r,n){if(this.hasData()&&this.unloadVectorData(),this.state="loaded",e){if(e.featureIndex&&(this.latestFeatureIndex=e.featureIndex,e.rawTileData?(this.latestRawTileData=e.rawTileData,this.latestFeatureIndex.rawTileData=e.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=e.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.layerIds.map(function(t){return e.getLayer(t)}).filter(Boolean);if(0!==o.length){a.layers=o;for(var s=0,l=o;s<l.length;s+=1)r[l[s].id]=a}}return r}(e.buckets,r.style),n)for(var i in this.buckets){var a=this.buckets[i];a instanceof t.default$14&&(a.justReloaded=!0)}for(var o in this.queryPadding=0,this.buckets){var s=this.buckets[o];this.queryPadding=Math.max(this.queryPadding,r.style.getLayer(s.layerIds[0]).queryRadius(s))}e.iconAtlasImage&&(this.iconAtlasImage=e.iconAtlasImage),e.glyphAtlasImage&&(this.glyphAtlasImage=e.glyphAtlasImage)}else this.collisionBoxArray=new t.CollisionBoxArray},st.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.iconAtlasTexture&&this.iconAtlasTexture.destroy(),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state="unloaded"},st.prototype.unloadDEMData=function(){this.dem=null,this.neighboringTiles=null,this.state="unloaded"},st.prototype.getBucket=function(t){return this.buckets[t.id]},st.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploaded||(r.upload(t),r.uploaded=!0)}var n=t.gl;this.iconAtlasImage&&(this.iconAtlasTexture=new z(t,this.iconAtlasImage,n.RGBA),this.iconAtlasImage=null),this.glyphAtlasImage&&(this.glyphAtlasTexture=new z(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},st.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:e,scale:r,tileSize:this.tileSize,posMatrix:o,transform:i,params:n,queryPadding:this.queryPadding*a},t):{}},st.prototype.querySourceFeatures=function(e,r){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData){var n=this.latestFeatureIndex.loadVTLayers(),i=r?r.sourceLayer:"",a=n._geojsonTileLayer||n[i];if(a)for(var o=t.default$13(r&&r.filter),s={z:this.tileID.overscaledZ,x:this.tileID.canonical.x,y:this.tileID.canonical.y},l=0;l<a.length;l++){var c=a.feature(l);if(o(new t.default$16(this.tileID.overscaledZ),c)){var u=new t.default$12(c,s.z,s.x,s.y);u.tile=s,e.push(u)}}}},st.prototype.clearMask=function(){this.segments&&(this.segments.destroy(),delete this.segments),this.maskedBoundsBuffer&&(this.maskedBoundsBuffer.destroy(),delete this.maskedBoundsBuffer),this.maskedIndexBuffer&&(this.maskedIndexBuffer.destroy(),delete this.maskedIndexBuffer)},st.prototype.setMask=function(e,r){if(!t.default$10(this.mask,e)&&(this.mask=e,this.clearMask(),!t.default$10(e,{0:!0}))){var n=new t.RasterBoundsArray,i=new t.TriangleIndexArray;this.segments=new t.default$15,this.segments.prepareSegment(0,n,i);for(var a=Object.keys(e),o=0;o<a.length;o++){var s=e[a[o]],l=t.default$8>>s.z,c=new t.default$1(s.x*l,s.y*l),u=new t.default$1(c.x+l,c.y+l),h=this.segments.prepareSegment(4,n,i);n.emplaceBack(c.x,c.y,c.x,c.y),n.emplaceBack(u.x,c.y,u.x,c.y),n.emplaceBack(c.x,u.y,c.x,u.y),n.emplaceBack(u.x,u.y,u.x,u.y);var f=h.vertexLength;i.emplaceBack(f,f+1,f+2),i.emplaceBack(f+1,f+2,f+3),h.vertexLength+=4,h.primitiveLength+=2}this.maskedBoundsBuffer=r.createVertexBuffer(n,K.members),this.maskedIndexBuffer=r.createIndexBuffer(i)}},st.prototype.hasData=function(){return"loaded"===this.state||"reloading"===this.state||"expired"===this.state},st.prototype.setExpiryData=function(e){var r=this.expirationTime;if(e.cacheControl){var n=t.parseCacheControl(e.cacheControl);n["max-age"]&&(this.expirationTime=Date.now()+1e3*n["max-age"])}else e.expires&&(this.expirationTime=new Date(e.expires).getTime());if(this.expirationTime){var i=Date.now(),a=!1;if(this.expirationTime>i)a=!1;else if(r)if(this.expirationTime<r)a=!0;else{var o=this.expirationTime-r;o?this.expirationTime=i+Math.max(o,3e4):a=!0}else a=!0;a?(this.expiredRequestCount++,this.state="expired"):this.expiredRequestCount=0}},st.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)};var lt=function(t,e){this.max=t,this.onRemove=e,this.reset()};lt.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},lt.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout(function(){n.remove(t,a)},r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},lt.prototype.has=function(t){return t.wrapped().key in this.data},lt.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},lt.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},lt.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},lt.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},lt.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this};var ct=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};ct.prototype.unbindVAO=function(){this.context.extVertexArrayObject&&this.context.bindVertexArrayOES.set(null)},ct.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},ct.prototype.updateData=function(t){var e=this.context.gl;this.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},ct.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var ut={Int8:"BYTE",Uint8:"UNSIGNED_BYTE",Int16:"SHORT",Uint16:"UNSIGNED_SHORT",Int32:"INT",Uint32:"UNSIGNED_INT",Float32:"FLOAT"},ht=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};ht.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},ht.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},ht.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=this.attributes[r],i=e.attributes[n.name];void 0!==i&&t.enableVertexAttribArray(i)}},ht.prototype.setVertexAttribPointers=function(t,e,r){for(var n=0;n<this.attributes.length;n++){var i=this.attributes[n],a=e.attributes[i.name];void 0!==a&&t.vertexAttribPointer(a,i.components,t[ut[i.type]],!1,this.itemSize,i.offset+this.itemSize*(r||0))}},ht.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var ft=function(e){this.context=e,this.current=t.default$6.transparent};ft.prototype.get=function(){return this.current},ft.prototype.set=function(t){var e=this.current;t.r===e.r&&t.g===e.g&&t.b===e.b&&t.a===e.a||(this.context.gl.clearColor(t.r,t.g,t.b,t.a),this.current=t)};var pt=function(t){this.context=t,this.current=1};pt.prototype.get=function(){return this.current},pt.prototype.set=function(t){this.current!==t&&(this.context.gl.clearDepth(t),this.current=t)};var dt=function(t){this.context=t,this.current=0};dt.prototype.get=function(){return this.current},dt.prototype.set=function(t){this.current!==t&&(this.context.gl.clearStencil(t),this.current=t)};var gt=function(t){this.context=t,this.current=[!0,!0,!0,!0]};gt.prototype.get=function(){return this.current},gt.prototype.set=function(t){var e=this.current;t[0]===e[0]&&t[1]===e[1]&&t[2]===e[2]&&t[3]===e[3]||(this.context.gl.colorMask(t[0],t[1],t[2],t[3]),this.current=t)};var mt=function(t){this.context=t,this.current=!0};mt.prototype.get=function(){return this.current},mt.prototype.set=function(t){this.current!==t&&(this.context.gl.depthMask(t),this.current=t)};var vt=function(t){this.context=t,this.current=255};vt.prototype.get=function(){return this.current},vt.prototype.set=function(t){this.current!==t&&(this.context.gl.stencilMask(t),this.current=t)};var yt=function(t){this.context=t,this.current={func:t.gl.ALWAYS,ref:0,mask:255}};yt.prototype.get=function(){return this.current},yt.prototype.set=function(t){var e=this.current;t.func===e.func&&t.ref===e.ref&&t.mask===e.mask||(this.context.gl.stencilFunc(t.func,t.ref,t.mask),this.current=t)};var xt=function(t){this.context=t;var e=this.context.gl;this.current=[e.KEEP,e.KEEP,e.KEEP]};xt.prototype.get=function(){return this.current},xt.prototype.set=function(t){var e=this.current;t[0]===e[0]&&t[1]===e[1]&&t[2]===e[2]||(this.context.gl.stencilOp(t[0],t[1],t[2]),this.current=t)};var bt=function(t){this.context=t,this.current=!1};bt.prototype.get=function(){return this.current},bt.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;t?e.enable(e.STENCIL_TEST):e.disable(e.STENCIL_TEST),this.current=t}};var _t=function(t){this.context=t,this.current=[0,1]};_t.prototype.get=function(){return this.current},_t.prototype.set=function(t){var e=this.current;t[0]===e[0]&&t[1]===e[1]||(this.context.gl.depthRange(t[0],t[1]),this.current=t)};var wt=function(t){this.context=t,this.current=!1};wt.prototype.get=function(){return this.current},wt.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;t?e.enable(e.DEPTH_TEST):e.disable(e.DEPTH_TEST),this.current=t}};var kt=function(t){this.context=t,this.current=t.gl.LESS};kt.prototype.get=function(){return this.current},kt.prototype.set=function(t){this.current!==t&&(this.context.gl.depthFunc(t),this.current=t)};var Mt=function(t){this.context=t,this.current=!1};Mt.prototype.get=function(){return this.current},Mt.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;t?e.enable(e.BLEND):e.disable(e.BLEND),this.current=t}};var At=function(t){this.context=t;var e=this.context.gl;this.current=[e.ONE,e.ZERO]};At.prototype.get=function(){return this.current},At.prototype.set=function(t){var e=this.current;t[0]===e[0]&&t[1]===e[1]||(this.context.gl.blendFunc(t[0],t[1]),this.current=t)};var Tt=function(e){this.context=e,this.current=t.default$6.transparent};Tt.prototype.get=function(){return this.current},Tt.prototype.set=function(t){var e=this.current;t.r===e.r&&t.g===e.g&&t.b===e.b&&t.a===e.a||(this.context.gl.blendColor(t.r,t.g,t.b,t.a),this.current=t)};var St=function(t){this.context=t,this.current=null};St.prototype.get=function(){return this.current},St.prototype.set=function(t){this.current!==t&&(this.context.gl.useProgram(t),this.current=t)};var Et=function(t){this.context=t,this.current=1};Et.prototype.get=function(){return this.current},Et.prototype.set=function(e){var r=this.context.lineWidthRange,n=t.clamp(e,r[0],r[1]);this.current!==n&&(this.context.gl.lineWidth(n),this.current=e)};var Ct=function(t){this.context=t,this.current=t.gl.TEXTURE0};Ct.prototype.get=function(){return this.current},Ct.prototype.set=function(t){this.current!==t&&(this.context.gl.activeTexture(t),this.current=t)};var Lt=function(t){this.context=t;var e=this.context.gl;this.current=[0,0,e.drawingBufferWidth,e.drawingBufferHeight]};Lt.prototype.get=function(){return this.current},Lt.prototype.set=function(t){var e=this.current;t[0]===e[0]&&t[1]===e[1]&&t[2]===e[2]&&t[3]===e[3]||(this.context.gl.viewport(t[0],t[1],t[2],t[3]),this.current=t)};var zt=function(t){this.context=t,this.current=null};zt.prototype.get=function(){return this.current},zt.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;e.bindFramebuffer(e.FRAMEBUFFER,t),this.current=t}};var Pt=function(t){this.context=t,this.current=null};Pt.prototype.get=function(){return this.current},Pt.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;e.bindRenderbuffer(e.RENDERBUFFER,t),this.current=t}};var It=function(t){this.context=t,this.current=null};It.prototype.get=function(){return this.current},It.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;e.bindTexture(e.TEXTURE_2D,t),this.current=t}};var Ot=function(t){this.context=t,this.current=null};Ot.prototype.get=function(){return this.current},Ot.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;e.bindBuffer(e.ARRAY_BUFFER,t),this.current=t}};var Dt=function(t){this.context=t,this.current=null};Dt.prototype.get=function(){return this.current},Dt.prototype.set=function(t){var e=this.context.gl;e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,t),this.current=t};var Rt=function(t){this.context=t,this.current=null};Rt.prototype.get=function(){return this.current},Rt.prototype.set=function(t){this.current!==t&&this.context.extVertexArrayObject&&(this.context.extVertexArrayObject.bindVertexArrayOES(t),this.current=t)};var Bt=function(t){this.context=t,this.current=4};Bt.prototype.get=function(){return this.current},Bt.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;e.pixelStorei(e.UNPACK_ALIGNMENT,t),this.current=t}};var Ft=function(t){this.context=t,this.current=!1};Ft.prototype.get=function(){return this.current},Ft.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;e.pixelStorei(e.UNPACK_PREMULTIPLY_ALPHA_WEBGL,t),this.current=t}};var Nt=function(t,e){this.context=t,this.current=null,this.parent=e};Nt.prototype.get=function(){return this.current};var jt=function(t){function e(e,r){t.call(this,e,r),this.dirty=!1}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(this.dirty||this.current!==t){var e=this.context.gl;this.context.bindFramebuffer.set(this.parent),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}},e.prototype.setDirty=function(){this.dirty=!0},e}(Nt),Vt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;this.context.bindFramebuffer.set(this.parent),e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t),this.current=t}},e}(Nt),Ut=function(t,e,r){this.context=t,this.width=e,this.height=r;var n=t.gl,i=this.framebuffer=n.createFramebuffer();this.colorAttachment=new jt(t,i),this.depthAttachment=new Vt(t,i)};Ut.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();e&&t.deleteTexture(e);var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r),t.deleteFramebuffer(this.framebuffer)};var qt=function(t,e,r){this.func=t,this.mask=e,this.range=r};qt.ReadOnly=!1,qt.ReadWrite=!0,qt.disabled=new qt(519,qt.ReadOnly,[0,1]);var Ht=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Ht.disabled=new Ht({func:519,mask:0},0,0,7680,7680,7680);var Gt=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};Gt.disabled=new Gt(Gt.Replace=[1,0],t.default$6.transparent,[!1,!1,!1,!1]),Gt.unblended=new Gt(Gt.Replace,t.default$6.transparent,[!0,!0,!0,!0]),Gt.alphaBlended=new Gt([1,771],t.default$6.transparent,[!0,!0,!0,!0]);var Wt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension("OES_vertex_array_object"),this.lineWidthRange=t.getParameter(t.ALIASED_LINE_WIDTH_RANGE),this.clearColor=new ft(this),this.clearDepth=new pt(this),this.clearStencil=new dt(this),this.colorMask=new gt(this),this.depthMask=new mt(this),this.stencilMask=new vt(this),this.stencilFunc=new yt(this),this.stencilOp=new xt(this),this.stencilTest=new bt(this),this.depthRange=new _t(this),this.depthTest=new wt(this),this.depthFunc=new kt(this),this.blend=new Mt(this),this.blendFunc=new At(this),this.blendColor=new Tt(this),this.program=new St(this),this.lineWidth=new Et(this),this.activeTexture=new Ct(this),this.viewport=new Lt(this),this.bindFramebuffer=new zt(this),this.bindRenderbuffer=new Pt(this),this.bindTexture=new It(this),this.bindVertexBuffer=new Ot(this),this.bindElementBuffer=new Dt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new Rt(this),this.pixelStoreUnpack=new Bt(this),this.pixelStoreUnpackPremultiplyAlpha=new Ft(this),this.extTextureFilterAnisotropic=t.getExtension("EXT_texture_filter_anisotropic")||t.getExtension("MOZ_EXT_texture_filter_anisotropic")||t.getExtension("WEBKIT_EXT_texture_filter_anisotropic"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension("OES_texture_half_float"),this.extTextureHalfFloat&&t.getExtension("OES_texture_half_float_linear")};Wt.prototype.createIndexBuffer=function(t,e){return new ct(this,t,e)},Wt.prototype.createVertexBuffer=function(t,e,r){return new ht(this,t,e,r)},Wt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Wt.prototype.createFramebuffer=function(t,e){return new Ut(this,t,e)},Wt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Wt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Wt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Wt.prototype.setColorMode=function(e){t.default$10(e.blendFunction,Gt.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)};var Yt=function(e){function r(t,r,n){var i=this;e.call(this),this.id=t,this.dispatcher=n,this.on("data",function(t){"source"===t.dataType&&"metadata"===t.sourceDataType&&(i._sourceLoaded=!0),i._sourceLoaded&&!i._paused&&"source"===t.dataType&&"content"===t.sourceDataType&&(i.reload(),i.transform&&i.update(i.transform))}),this.on("error",function(){i._sourceErrored=!0}),this._source=it(t,r,n,this),this._tiles={},this._cache=new lt(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._isIdRenderable=this._isIdRenderable.bind(this),this._coveredTiles={}}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;for(var t in this._tiles){var e=this._tiles[t];if("loaded"!==e.state&&"errored"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,function(){})},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,function(){})},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._tiles)this._tiles[e].upload(t)},r.prototype.getIds=function(){var e=this;return Object.keys(this._tiles).map(Number).sort(function(r,n){var i=e._tiles[r].tileID,a=e._tiles[n].tileID,o=new t.default$1(i.canonical.x,i.canonical.y).rotate(e.transform.angle),s=new t.default$1(a.canonical.x,a.canonical.y).rotate(e.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})},r.prototype.getRenderableIds=function(){return this.getIds().filter(this._isIdRenderable)},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0,{});return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)this._reloadTile(t,"reloading")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&("loading"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state="errored",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=a.now(),"expired"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),"raster-dem"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._source.fire(new t.Event("data",{dataType:"source",tile:e,coord:e.tileID})),this.map&&(this.map.painter.tileExtentVAO.vao=null)},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype.getZoom=function(t){return t.zoom+t.scaleZoom(t.tileSize/this._source.tileSize)},r.prototype._findLoadedChildren=function(t,e,r){var n=!1;for(var i in this._tiles){var a=this._tiles[i];if(!(r[i]||!a.hasData()||a.tileID.overscaledZ<=t.overscaledZ||a.tileID.overscaledZ>e)){var o=Math.pow(2,a.tileID.canonical.z-t.canonical.z);if(Math.floor(a.tileID.canonical.x/o)===t.canonical.x&&Math.floor(a.tileID.canonical.y/o)===t.canonical.y)for(r[i]=a.tileID,n=!0;a&&a.tileID.overscaledZ-1>t.overscaledZ;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);if(!s)break;(a=this._tiles[s.key])&&a.hasData()&&(delete r[i],r[s.key]=s)}}}return n},r.prototype.findLoadedParent=function(t,e,r){for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n);if(!i)return;var a=String(i.key),o=this._tiles[a];if(o&&o.hasData())return r[a]=i,o;if(this._cache.has(i))return r[a]=i,this._cache.get(i)}},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n="number"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map(function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)}):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter(function(t){return n._source.hasTile(t)}))):i=[];var o,s=(this._source.roundZoom?Math.round:Math.floor)(this.getZoom(e)),l=Math.max(s-r.maxOverzooming,this._source.minzoom),c=Math.max(s+r.maxUnderzooming,this._source.minzoom),u=this._updateRetainedTiles(i,s),h={};if(Zt(this._source.type))for(var f=Object.keys(u),p=0;p<f.length;p++){var d=f[p],g=u[d],m=n._tiles[d];if(m&&(void 0===m.fadeEndTime||m.fadeEndTime>=a.now())){n._findLoadedChildren(g,c,u)&&(u[d]=g);var v=n.findLoadedParent(g,l,h);v&&n._addTile(v.tileID)}}for(o in h)u[o]||(n._coveredTiles[o]=!0);for(o in h)u[o]=h[o];for(var y=t.keysDifference(this._tiles,u),x=0;x<y.length;x++)n._removeTile(y[x])}},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s=0;s<t.length;s++){var l=t[s],c=this._addTile(l),u=!1;if(c.hasData())n[l.key]=l;else{u=c.wasRequested(),n[l.key]=l;var h=!0;if(e+1>this._source.maxzoom){var f=l.children(this._source.maxzoom)[0],p=this.getTile(f);p&&p.hasData()?n[f.key]=f:h=!1}else{this._findLoadedChildren(l,o,n);for(var d=l.children(this._source.maxzoom),g=0;g<d.length;g++)if(!n[d[g].key]){h=!1;break}}if(!h)for(var m=l.overscaledZ-1;m>=a;--m){var v=l.scaledTo(m);if(i[v.key])break;if(i[v.key]=!0,!(c=this.getTile(v))&&u&&(c=this._addTile(v)),c&&(n[v.key]=v,u=c.wasRequested(),c.hasData()))break}}}return n},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e);var n=Boolean(r);return n||(r=new st(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event("dataloading",{tile:r,coord:r.tileID,dataType:"source"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout(function(){r._reloadTile(t,"expired"),delete r._timers[t]},n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r){for(var n=[],i=this.getIds(),a=1/0,o=1/0,s=-1/0,l=-1/0,c=e[0].zoom,u=0;u<e.length;u++){var h=e[u];a=Math.min(a,h.column),o=Math.min(o,h.row),s=Math.max(s,h.column),l=Math.max(l,h.row)}for(var f=0;f<i.length;f++){var p=this._tiles[i[f]],d=p.tileID,g=Math.pow(2,this.transform.zoom-p.tileID.overscaledZ),m=r*p.queryPadding*t.default$8/p.tileSize/g,v=[Xt(d,new t.default$17(a,o,c)),Xt(d,new t.default$17(s,l,c))];if(v[0].x-m<t.default$8&&v[0].y-m<t.default$8&&v[1].x+m>=0&&v[1].y+m>=0){for(var y=[],x=0;x<e.length;x++)y.push(Xt(d,e[x]));n.push({tile:p,tileID:d,queryGeometry:[y],scale:g})}}return n},r.prototype.getVisibleCoordinates=function(){for(var t=this,e=this.getRenderableIds().map(function(e){return t._tiles[e].tileID}),r=0,n=e;r<n.length;r+=1){var i=n[r];i.posMatrix=t.transform.calculatePosMatrix(i.toUnwrapped())}return e},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(Zt(this._source.type))for(var t in this._tiles){var e=this._tiles[t];if(void 0!==e.fadeEndTime&&e.fadeEndTime>=a.now())return!0}return!1},r}(t.Evented);function Xt(e,r){var n=r.zoomTo(e.canonical.z);return new t.default$1((n.column-(e.canonical.x+e.wrap*Math.pow(2,e.canonical.z)))*t.default$8,(n.row-e.canonical.y)*t.default$8)}function Zt(t){return"raster"===t||"image"===t||"video"===t}function $t(){return new t.default.Worker(En.workerUrl)}Yt.maxOverzooming=10,Yt.maxUnderzooming=3;var Jt,Kt=function(){this.active={}};function Qt(e,r){var n={};for(var i in e)"ref"!==i&&(n[i]=e[i]);return t.default$18.forEach(function(t){t in r&&(n[t]=r[t])}),n}function te(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)"ref"in t[n]&&(t[n]=Qt(t[n],e[t[n].ref]));return t}Kt.prototype.acquire=function(t){if(!this.workers){var e=En.workerCount;for(this.workers=[];this.workers.length<e;)this.workers.push(new $t)}return this.active[t]=!0,this.workers.slice()},Kt.prototype.release=function(t){delete this.active[t],0===Object.keys(this.active).length&&(this.workers.forEach(function(t){t.terminate()}),this.workers=null)};var ee={setStyle:"setStyle",addLayer:"addLayer",removeLayer:"removeLayer",setPaintProperty:"setPaintProperty",setLayoutProperty:"setLayoutProperty",setFilter:"setFilter",addSource:"addSource",removeSource:"removeSource",setGeoJSONSourceData:"setGeoJSONSourceData",setLayerZoomRange:"setLayerZoomRange",setLayerProperty:"setLayerProperty",setCenter:"setCenter",setZoom:"setZoom",setBearing:"setBearing",setPitch:"setPitch",setSprite:"setSprite",setGlyphs:"setGlyphs",setTransition:"setTransition",setLight:"setLight"};function re(t,e,r){r.push({command:ee.addSource,args:[t,e[t]]})}function ne(t,e,r){e.push({command:ee.removeSource,args:[t]}),r[t]=!0}function ie(t,e,r,n){ne(t,r,n),re(t,e,r)}function ae(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&"data"!==i&&!t.default$10(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&"data"!==i&&!t.default$10(e[n][i],r[n][i]))return!1;return!0}function oe(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.default$10(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.default$10(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function se(t){return t.id}function le(t,e){return t[e.id]=e,t}var ce=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};ce.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},ce.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},ce.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},ce.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},ce.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},ce.prototype._query=function(t,e,r,n,i){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var a=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var o=0;o<this.boxKeys.length;o++)a.push({key:this.boxKeys[o],x1:this.bboxes[4*o],y1:this.bboxes[4*o+1],x2:this.bboxes[4*o+2],y2:this.bboxes[4*o+3]});for(var s=0;s<this.circleKeys.length;s++){var l=this.circles[3*s],c=this.circles[3*s+1],u=this.circles[3*s+2];a.push({key:this.circleKeys[s],x1:l-u,y1:c-u,x2:l+u,y2:c+u})}}else{var h={hitTest:i,seenUids:{box:{},circle:{}}};this._forEachCell(t,e,r,n,this._queryCell,a,h)}return i?a.length>0:a},ce.prototype._queryCircle=function(t,e,r,n){var i=t-r,a=t+r,o=e-r,s=e+r;if(a<0||i>this.width||s<0||o>this.height)return!n&&[];var l=[],c={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(i,o,a,s,this._queryCellCircle,l,c),n?l.length>0:l},ce.prototype.query=function(t,e,r,n){return this._query(t,e,r,n,!1)},ce.prototype.hitTest=function(t,e,r,n){return this._query(t,e,r,n,!0)},ce.prototype.hitTestCircle=function(t,e,r){return this._queryCircle(t,e,r,!0)},ce.prototype._queryCell=function(t,e,r,n,i,a,o){var s=o.seenUids,l=this.boxCells[i];if(null!==l)for(var c=this.bboxes,u=0,h=l;u<h.length;u+=1){var f=h[u];if(!s.box[f]){s.box[f]=!0;var p=4*f;if(t<=c[p+2]&&e<=c[p+3]&&r>=c[p+0]&&n>=c[p+1]){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[f],x1:c[p],y1:c[p+1],x2:c[p+2],y2:c[p+3]})}}}var d=this.circleCells[i];if(null!==d)for(var g=this.circles,m=0,v=d;m<v.length;m+=1){var y=v[m];if(!s.circle[y]){s.circle[y]=!0;var x=3*y;if(this._circleAndRectCollide(g[x],g[x+1],g[x+2],t,e,r,n)){if(o.hitTest)return a.push(!0),!0;var b=g[x],_=g[x+1],w=g[x+2];a.push({key:this.circleKeys[y],x1:b-w,y1:_-w,x2:b+w,y2:_+w})}}}},ce.prototype._queryCellCircle=function(t,e,r,n,i,a,o){var s=o.circle,l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,h=0,f=c;h<f.length;h+=1){var p=f[h];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(this._circleAndRectCollide(s.x,s.y,s.radius,u[d+0],u[d+1],u[d+2],u[d+3]))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var m=this.circles,v=0,y=g;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circlesCollide(m[b],m[b+1],m[b+2],s.x,s.y,s.radius))return a.push(!0),!0}}},ce.prototype._forEachCell=function(t,e,r,n,i,a,o){for(var s=this._convertToXCellCoord(t),l=this._convertToYCellCoord(e),c=this._convertToXCellCoord(r),u=this._convertToYCellCoord(n),h=s;h<=c;h++)for(var f=l;f<=u;f++){var p=this.xCellCount*f+h;if(i.call(this,t,e,r,n,p,a,o))return}},ce.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},ce.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},ce.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},ce.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var h=l-s,f=u-c;return h*h+f*f<=r*r};var ue=t.default$19.layout;function he(e,r,n,i,a){var o=t.mat4.identity(new Float32Array(16));return r?(t.mat4.identity(o),t.mat4.scale(o,o,[1/a,1/a,1]),n||t.mat4.rotateZ(o,o,i.angle)):(t.mat4.scale(o,o,[i.width/2,-i.height/2,1]),t.mat4.translate(o,o,[1,-1,0]),t.mat4.multiply(o,o,e)),o}function fe(e,r,n,i,a){var o=t.mat4.identity(new Float32Array(16));return r?(t.mat4.multiply(o,o,e),t.mat4.scale(o,o,[a,a,1]),n||t.mat4.rotateZ(o,o,-i.angle)):(t.mat4.scale(o,o,[1,-1,1]),t.mat4.translate(o,o,[-1,-1,0]),t.mat4.scale(o,o,[2/i.width,2/i.height,1])),o}function pe(e,r){var n=[e.x,e.y,0,1];ke(n,n,r);var i=n[3];return{point:new t.default$1(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function de(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ge(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom,ue.properties[i?"text-size":"icon-size"]),h=[256/n.width*2+1,256/n.height*2+1],f=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;f.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,g=n.transform.width/n.transform.height,m=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!m)we(y.numGlyphs,f);else{m=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.vec4.transformMat4(x,x,r),de(x,h)){var b=.5+x[3]/n.transform.cameraToCenterDistance*.5,_=t.evaluateSizeForFeature(c,u,y),w=s?_*b:_/b,k=new t.default$1(y.anchorX,y.anchorY),M=pe(k,a).point,A={},T=ye(y,w,!1,l,r,a,o,e.glyphOffsetArray,p,f,M,k,A,g);m=T.useVertical,(T.notEnoughRoom||m||T.needsFlipping&&ye(y,w,!0,l,r,a,o,e.glyphOffsetArray,p,f,M,k,A,g).notEnoughRoom)&&we(y.numGlyphs,f)}else we(y.numGlyphs,f)}}i?e.text.dynamicLayoutVertexBuffer.updateData(f):e.icon.dynamicLayoutVertexBuffer.updateData(f)}function me(t,e,r,n,i,a,o,s,l,c,u,h){var f=s.glyphStartIndex+s.numGlyphs,p=s.lineStartIndex,d=s.lineStartIndex+s.lineLength,g=e.getoffsetX(s.glyphStartIndex),m=e.getoffsetX(f-1),v=be(t*g,r,n,i,a,o,s.segment,p,d,l,c,u,h);if(!v)return null;var y=be(t*m,r,n,i,a,o,s.segment,p,d,l,c,u,h);return y?{first:v,last:y}:null}function ve(e,r,n,i){return e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i?{useVertical:!0}:(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function ye(e,r,n,i,a,o,s,l,c,u,h,f,p,d){var g,m=r/24,v=e.lineOffsetX*r,y=e.lineOffsetY*r;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=me(m,l,v,y,n,h,f,e,c,o,p,!1);if(!w)return{notEnoughRoom:!0};var k=pe(w.first.point,s).point,M=pe(w.last.point,s).point;if(i&&!n){var A=ve(e.writingMode,k,M,d);if(A)return A}g=[w.first];for(var T=e.glyphStartIndex+1;T<x-1;T++)g.push(be(m*l.getoffsetX(T),v,y,n,h,f,e.segment,b,_,c,o,p,!1));g.push(w.last)}else{if(i&&!n){var S=pe(f,a).point,E=e.lineStartIndex+e.segment+1,C=new t.default$1(c.getx(E),c.gety(E)),L=pe(C,a),z=L.signedDistanceFromCamera>0?L.point:xe(f,C,S,1,a),P=ve(e.writingMode,S,z,d);if(P)return P}var I=be(m*l.getoffsetX(e.glyphStartIndex),v,y,n,h,f,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p,!1);if(!I)return{notEnoughRoom:!0};g=[I]}for(var O=0,D=g;O<D.length;O+=1){var R=D[O];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function xe(t,e,r,n,i){var a=pe(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function be(e,r,n,i,a,o,s,l,c,u,h,f,p){var d=i?e-r:e+r,g=d>0?1:-1,m=0;i&&(g*=-1,m=Math.PI),g<0&&(m+=Math.PI);for(var v=g>0?l+s:l+s+1,y=v,x=a,b=a,_=0,w=0,k=Math.abs(d);_+w<=k;){if((v+=g)<l||v>=c)return null;if(b=x,void 0===(x=f[v])){var M=new t.default$1(u.getx(v),u.gety(v)),A=pe(M,h);if(A.signedDistanceFromCamera>0)x=f[v]=A.point;else{var T=v-g;x=xe(0===_?o:new t.default$1(u.getx(T),u.gety(T)),M,b,k-_+1,h)}}_+=w,w=b.dist(x)}var S=(k-_)/w,E=x.sub(b),C=E.mult(S)._add(b);return C._add(E._unit()._perp()._mult(n*g)),{point:C,angle:m+Math.atan2(x.y-b.y,x.x-b.x),tileDistance:p?{prevTileDistance:v-g===y?0:u.gettileUnitDistanceFromAnchor(v-g),lastSegmentViewportDistance:k-_}:null}}var _e=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function we(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(_e,3*n)}}function ke(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}t.default$20.mat4;var Me=function(t,e,r){void 0===e&&(e=new ce(t.width+200,t.height+200,25)),void 0===r&&(r=new ce(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100};function Ae(t,e,r){t[e+4]=r?1:0}function Te(e,r,n){return r*(t.default$8/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}Me.prototype.placeCollisionBox=function(t,e,r,n){var i=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),a=r*i.perspectiveRatio,o=t.x1*a+i.point.x,s=t.y1*a+i.point.y,l=t.x2*a+i.point.x,c=t.y2*a+i.point.y;return!e&&this.grid.hitTest(o,s,l,c)?{box:[],offscreen:!1}:{box:[o,s,l,c],offscreen:this.isOffscreen(o,s,l,c)}},Me.prototype.approximateTileDistance=function(t,e,r,n,i){var a=i?1:n/this.pitchfactor,o=t.lastSegmentViewportDistance*r;return t.prevTileDistance+o+(a-1)*o*Math.abs(Math.sin(e))},Me.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,h,f,p){var d=[],g=this.projectAnchor(u,o.anchorX,o.anchorY),m=c/24,v=o.lineOffsetX*c,y=o.lineOffsetY*c,x=new t.default$1(o.anchorX,o.anchorY),b=me(m,l,v,y,!1,pe(x,h).point,x,o,s,h,{},!0),_=!1,w=!0,k=g.perspectiveRatio*i,M=1/(i*n),A=0,T=0;b&&(A=this.approximateTileDistance(b.first.tileDistance,b.first.angle,M,g.cameraDistance,p),T=this.approximateTileDistance(b.last.tileDistance,b.last.angle,M,g.cameraDistance,p));for(var S=0;S<e.length;S+=5){var E=e[S],C=e[S+1],L=e[S+2],z=e[S+3];if(!b||z<-A||z>T)Ae(e,S,!1);else{var P=this.projectPoint(u,E,C),I=L*k;if(d.length>0){var O=P.x-d[d.length-4],D=P.y-d[d.length-3];if(I*I*2>O*O+D*D&&S+8<e.length){var R=e[S+8];if(R>-A&&R<T){Ae(e,S,!1);continue}}}var B=S/5;if(d.push(P.x,P.y,I,B),Ae(e,S,!0),w=w&&this.isOffscreen(P.x-I,P.y-I,P.x+I,P.y+I),!r&&this.grid.hitTestCircle(P.x,P.y,I)){if(!f)return{circles:[],offscreen:!1};_=!0}}}return{circles:_?[]:d,offscreen:w}},Me.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.default$1(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var h={},f={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var g=d[p],m=g.key;if(void 0===h[m.bucketInstanceId]&&(h[m.bucketInstanceId]={}),!h[m.bucketInstanceId][m.featureIndex]){var v=[new t.default$1(g.x1,g.y1),new t.default$1(g.x2,g.y1),new t.default$1(g.x2,g.y2),new t.default$1(g.x1,g.y2)];t.polygonIntersectsPolygon(r,v)&&(h[m.bucketInstanceId][m.featureIndex]=!0,void 0===f[m.bucketInstanceId]&&(f[m.bucketInstanceId]=[]),f[m.bucketInstanceId].push(m.featureIndex))}}return f},Me.prototype.insertCollisionBox=function(t,e,r,n){var i={bucketInstanceId:r,featureIndex:n};(e?this.ignoredGrid:this.grid).insert(i,t[0],t[1],t[2],t[3])},Me.prototype.insertCollisionCircles=function(t,e,r,n){for(var i=e?this.ignoredGrid:this.grid,a={bucketInstanceId:r,featureIndex:n},o=0;o<t.length;o+=4)i.insertCircle(a,t[o],t[o+1],t[o+2])},Me.prototype.projectAnchor=function(t,e,r){var n=[e,r,0,1];return ke(n,n,t),{perspectiveRatio:.5+this.transform.cameraToCenterDistance/n[3]*.5,cameraDistance:n[3]}},Me.prototype.projectPoint=function(e,r,n){var i=[r,n,0,1];return ke(i,i,e),new t.default$1((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100)},Me.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return ke(i,i,e),{point:new t.default$1((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},Me.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary};var Se=t.default$19.layout,Ee=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};Ee.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var Ce=function(t,e,r,n,i){this.text=new Ee(t?t.text:null,e,r,i),this.icon=new Ee(t?t.icon:null,e,n,i)};Ce.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var Le=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ze=function(t,e){this.transform=t.clone(),this.collisionIndex=new Me(this.transform),this.placements={},this.opacities={},this.stale=!1,this.fadeDuration=e,this.retainedQueryData={}};function Pe(t,e,r){t.emplaceBack(e?1:0,r?1:0),t.emplaceBack(e?1:0,r?1:0),t.emplaceBack(e?1:0,r?1:0),t.emplaceBack(e?1:0,r?1:0)}ze.prototype.placeLayerTile=function(e,r,n,i){var a=r.getBucket(e),o=r.latestFeatureIndex;if(a&&o&&e.id===a.layerIds[0]){var s=r.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-r.tileID.overscaledZ),u=r.tileSize/t.default$8,h=this.transform.calculatePosMatrix(r.tileID.toUnwrapped()),f=he(h,"map"===l.get("text-pitch-alignment"),"map"===l.get("text-rotation-alignment"),this.transform,Te(r,1,this.transform.zoom)),p=he(h,"map"===l.get("icon-pitch-alignment"),"map"===l.get("icon-rotation-alignment"),this.transform,Te(r,1,this.transform.zoom));this.retainedQueryData[a.bucketInstanceId]=new function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i}(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,r.tileID),this.placeLayerBucket(a,h,f,p,c,u,n,i,s)}},ze.prototype.placeLayerBucket=function(e,r,n,i,a,o,s,l,c){for(var u=e.layers[0].layout,h=t.evaluateSizeForZoom(e.textSizeData,this.transform.zoom,Se.properties["text-size"]),f=!e.hasTextData()||u.get("text-optional"),p=!e.hasIconData()||u.get("icon-optional"),d=0,g=e.symbolInstances;d<g.length;d+=1){var m=g[d];if(!l[m.crossTileID]){var v=void 0!==m.feature.text,y=void 0!==m.feature.icon,x=!0,b=null,_=null,w=null,k=0,M=0;m.collisionArrays||(m.collisionArrays=e.deserializeCollisionBoxes(c,m.textBoxStartIndex,m.textBoxEndIndex,m.iconBoxStartIndex,m.iconBoxEndIndex)),m.collisionArrays.textFeatureIndex&&(k=m.collisionArrays.textFeatureIndex),m.collisionArrays.textBox&&(v=(b=this.collisionIndex.placeCollisionBox(m.collisionArrays.textBox,u.get("text-allow-overlap"),o,r)).box.length>0,x=x&&b.offscreen);var A=m.collisionArrays.textCircles;if(A){var T=e.text.placedSymbolArray.get(m.placedTextSymbolIndices[0]),S=t.evaluateSizeForFeature(e.textSizeData,h,T);_=this.collisionIndex.placeCollisionCircles(A,u.get("text-allow-overlap"),a,o,m.key,T,e.lineVertexArray,e.glyphOffsetArray,S,r,n,s,"map"===u.get("text-pitch-alignment")),v=u.get("text-allow-overlap")||_.circles.length>0,x=x&&_.offscreen}m.collisionArrays.iconFeatureIndex&&(M=m.collisionArrays.iconFeatureIndex),m.collisionArrays.iconBox&&(y=(w=this.collisionIndex.placeCollisionBox(m.collisionArrays.iconBox,u.get("icon-allow-overlap"),o,r)).box.length>0,x=x&&w.offscreen),f||p?p?f||(y=y&&v):v=y&&v:y=v=y&&v,v&&b&&this.collisionIndex.insertCollisionBox(b.box,u.get("text-ignore-placement"),e.bucketInstanceId,k),y&&w&&this.collisionIndex.insertCollisionBox(w.box,u.get("icon-ignore-placement"),e.bucketInstanceId,M),v&&_&&this.collisionIndex.insertCollisionCircles(_.circles,u.get("text-ignore-placement"),e.bucketInstanceId,k),this.placements[m.crossTileID]=new Le(v,y,x||e.justReloaded),l[m.crossTileID]=!0}}e.justReloaded=!1},ze.prototype.commit=function(t,e){this.commitTime=e;var r=!1,n=t&&0!==this.fadeDuration?(this.commitTime-t.commitTime)/this.fadeDuration:1,i=t?t.opacities:{};for(var a in this.placements){var o=this.placements[a],s=i[a];s?(this.opacities[a]=new Ce(s,n,o.text,o.icon),r=r||o.text!==s.text.placed||o.icon!==s.icon.placed):(this.opacities[a]=new Ce(null,n,o.text,o.icon,o.skipFade),r=r||o.text||o.icon)}for(var l in i){var c=i[l];if(!this.opacities[l]){var u=new Ce(c,n,!1,!1);u.isHidden()||(this.opacities[l]=u,r=r||c.text.placed||c.icon.placed)}}r?this.lastPlacementChangeTime=e:"number"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=t?t.lastPlacementChangeTime:e)},ze.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},ze.prototype.updateBucketOpacities=function(t,e,r){t.hasTextData()&&t.text.opacityVertexArray.clear(),t.hasIconData()&&t.icon.opacityVertexArray.clear(),t.hasCollisionBoxData()&&t.collisionBox.collisionVertexArray.clear(),t.hasCollisionCircleData()&&t.collisionCircle.collisionVertexArray.clear();for(var n=t.layers[0].layout,i=new Ce(null,0,!1,!1,!0),a=new Ce(null,0,n.get("text-allow-overlap"),n.get("icon-allow-overlap"),!0),o=0;o<t.symbolInstances.length;o++){var s=t.symbolInstances[o],l=e[s.crossTileID],c=this.opacities[s.crossTileID];l?c=i:c||(c=a,this.opacities[s.crossTileID]=c),e[s.crossTileID]=!0;var u=s.numGlyphVertices>0||s.numVerticalGlyphVertices>0,h=s.numIconVertices>0;if(u){for(var f=je(c.text),p=(s.numGlyphVertices+s.numVerticalGlyphVertices)/4,d=0;d<p;d++)t.text.opacityVertexArray.emplaceBack(f);for(var g=0,m=s.placedTextSymbolIndices;g<m.length;g+=1){var v=m[g];t.text.placedSymbolArray.get(v).hidden=c.text.isHidden()}}if(h){for(var y=je(c.icon),x=0;x<s.numIconVertices/4;x++)t.icon.opacityVertexArray.emplaceBack(y);t.icon.placedSymbolArray.get(o).hidden=c.icon.isHidden()}s.collisionArrays||(s.collisionArrays=t.deserializeCollisionBoxes(r,s.textBoxStartIndex,s.textBoxEndIndex,s.iconBoxStartIndex,s.iconBoxEndIndex));var b=s.collisionArrays;if(b){b.textBox&&t.hasCollisionBoxData()&&Pe(t.collisionBox.collisionVertexArray,c.text.placed,!1),b.iconBox&&t.hasCollisionBoxData()&&Pe(t.collisionBox.collisionVertexArray,c.icon.placed,!1);var _=b.textCircles;if(_&&t.hasCollisionCircleData())for(var w=0;w<_.length;w+=5){var k=l||0===_[w+4];Pe(t.collisionCircle.collisionVertexArray,c.text.placed,k)}}}t.sortFeatures(this.transform.angle),this.retainedQueryData[t.bucketInstanceId]&&(this.retainedQueryData[t.bucketInstanceId].featureSortOrder=t.featureSortOrder),t.hasTextData()&&t.text.opacityVertexBuffer&&t.text.opacityVertexBuffer.updateData(t.text.opacityVertexArray),t.hasIconData()&&t.icon.opacityVertexBuffer&&t.icon.opacityVertexBuffer.updateData(t.icon.opacityVertexArray),t.hasCollisionBoxData()&&t.collisionBox.collisionVertexBuffer&&t.collisionBox.collisionVertexBuffer.updateData(t.collisionBox.collisionVertexArray),t.hasCollisionCircleData()&&t.collisionCircle.collisionVertexBuffer&&t.collisionCircle.collisionVertexBuffer.updateData(t.collisionCircle.collisionVertexArray)},ze.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration},ze.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},ze.prototype.stillRecent=function(t){return"undefined"!==this.commitTime&&this.commitTime+this.fadeDuration>t},ze.prototype.setStale=function(){this.stale=!0};var Ie=Math.pow(2,25),Oe=Math.pow(2,24),De=Math.pow(2,17),Re=Math.pow(2,16),Be=Math.pow(2,9),Fe=Math.pow(2,8),Ne=Math.pow(2,1);function je(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*Ie+e*Oe+r*De+e*Re+r*Be+e*Fe+r*Ne+e}var Ve=function(){this._currentTileIndex=0,this._seenCrossTileIDs={}};Ve.prototype.continuePlacement=function(t,e,r,n,i){for(;this._currentTileIndex<t.length;){var a=t[this._currentTileIndex];if(e.placeLayerTile(n,a,r,this._seenCrossTileIDs),this._currentTileIndex++,i())return!0}};var Ue=function(t,e,r,n,i){this.placement=new ze(t,i),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};Ue.prototype.isDone=function(){return this._done},Ue.prototype.continuePlacement=function(t,e,r){for(var n=this,i=a.now(),o=function(){var t=a.now()-i;return!n._forceFullPlacement&&t>2};this._currentPlacementIndex>=0;){var s=e[t[n._currentPlacementIndex]],l=n.placement.collisionIndex.transform.zoom;if("symbol"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(n._inProgressLayer||(n._inProgressLayer=new Ve),n._inProgressLayer.continuePlacement(r[s.source],n.placement,n._showCollisionBoxes,s,o))return;delete n._inProgressLayer}n._currentPlacementIndex--}this._done=!0},Ue.prototype.commit=function(t,e){return this.placement.commit(t,e),this.placement};var qe=512/t.default$8/2,He=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.key;this.indexedSymbolInstances[o]||(this.indexedSymbolInstances[o]=[]),this.indexedSymbolInstances[o].push({crossTileID:a.crossTileID,coord:this.getScaledCoordinates(a,t)})}};He.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=qe/Math.pow(2,n),a=e.anchor;return{x:Math.floor((r.canonical.x*t.default$8+a.x)*i),y:Math.floor((r.canonical.y*t.default$8+a.y)*i)}},He.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0,a=t;i<a.length;i+=1){var o=a[i];if(!o.crossTileID){var s=this.indexedSymbolInstances[o.key];if(s)for(var l=this.getScaledCoordinates(o,e),c=0,u=s;c<u.length;c+=1){var h=u[c];if(Math.abs(h.coord.x-l.x)<=n&&Math.abs(h.coord.y-l.y)<=n&&!r[h.crossTileID]){r[h.crossTileID]=!0,o.crossTileID=h.crossTileID;break}}}}};var Ge=function(){this.maxCrossTileID=0};Ge.prototype.generate=function(){return++this.maxCrossTileID};var We=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};We.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},We.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0,i=e.symbolInstances;n<i.length;n+=1)i[n].crossTileID=0;this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var a=this.usedCrossTileIDs[t.overscaledZ];for(var o in this.indexes){var s=this.indexes[o];if(Number(o)>t.overscaledZ)for(var l in s){var c=s[l];c.tileID.isChildOf(t)&&c.findMatches(e.symbolInstances,t,a)}else{var u=s[t.scaledTo(Number(o)).key];u&&u.findMatches(e.symbolInstances,t,a)}}for(var h=0,f=e.symbolInstances;h<f.length;h+=1){var p=f[h];p.crossTileID||(p.crossTileID=r.generate(),a[p.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new He(t,e.symbolInstances,e.bucketInstanceId),!0},We.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},We.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ye=function(){this.layerIndexes={},this.crossTileIDs=new Ge,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ye.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new We);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ye.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach(function(t){e[t]=!0}),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var Xe=function(e,r){return t.emitValidationErrors(e,r&&r.filter(function(t){return"source.canvas"!==t.identifier}))},Ze=t.pick(ee,["addLayer","removeLayer","setPaintProperty","setLayoutProperty","setFilter","addSource","removeSource","setLayerZoomRange","setLight","setTransition","setGeoJSONSourceData"]),$e=t.pick(ee,["setCenter","setZoom","setBearing","setPitch"]),Je=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new q((Jt||(Jt=new Kt),Jt),this),this.imageManager=new P,this.glyphManager=new F(n._transformRequest,i.localIdeographFontFamily),this.lineAtlas=new U(256,512),this.crossTileSymbolIndex=new Ye,this._layers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.default$23,this._loaded=!1,this._resetUpdates();var o=this;this._rtlTextPluginCallback=r.registerForPluginAvailability(function(t){for(var e in o.dispatcher.broadcast("loadRTLTextPlugin",t.pluginURL,t.completionCallback),o.sourceCaches)o.sourceCaches[e].reload()}),this.on("data",function(t){if("source"===t.dataType&&"metadata"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}})}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"}));var i="boolean"==typeof r.validate?r.validate:!x(e);e=function(t,e){if(!x(t))return t;var r=A(t);return r.path="/styles/v1"+r.path,y(r,e)}(e,r.accessToken);var a=this.map._transformRequest(e,t.ResourceType.Style);t.getJSON(a,function(e,r){e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)})},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"})),a.frame(function(){n._load(e,!1!==r.validate)})},r.prototype._load=function(e,r){var n=this;if(!r||!Xe(this,t.validateStyle(e))){for(var i in this._loaded=!0,this.stylesheet=e,e.sources)n.addSource(i,e.sources[i],{validate:!1});e.sprite?function(e,r,n){var i,o,s,l=a.devicePixelRatio>1?"@2x":"";function c(){if(s)n(s);else if(i&&o){var e=a.getImageData(o),r={};for(var l in i){var c=i[l],u=c.width,h=c.height,f=c.x,p=c.y,d=c.sdf,g=c.pixelRatio,m=new t.RGBAImage({width:u,height:h});t.RGBAImage.copy(e,m,{x:f,y:p},{x:0,y:0},{width:u,height:h}),r[l]={data:m,pixelRatio:g,sdf:d}}n(null,r)}}t.getJSON(r(_(e,l,".json"),t.ResourceType.SpriteJSON),function(t,e){s||(s=t,i=e,c())}),t.getImage(r(_(e,l,".png"),t.ResourceType.SpriteImage),function(t,e){s||(s=t,o=e,c())})}(e.sprite,this.map._transformRequest,function(e,r){if(e)n.fire(new t.ErrorEvent(e));else if(r)for(var i in r)n.imageManager.addImage(i,r[i]);n.imageManager.setLoaded(!0),n.fire(new t.Event("data",{dataType:"style"}))}):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var o=te(this.stylesheet.layers);this._order=o.map(function(t){return t.id}),this._layers={};for(var s=0,l=o;s<l.length;s+=1){var c=l[s];(c=t.default$22(c)).setEventedParent(n,{layer:{id:c.id}}),n._layers[c.id]=c}this.dispatcher.broadcast("setLayers",this._serializeLayers(this._order)),this.light=new V(this.stylesheet.light),this.fire(new t.Event("data",{dataType:"style"})),this.fire(new t.Event("style.load"))}},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();("geojson"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer "'+n+'" does not exist on source "'+i.id+'" as specified by style layer "'+e.id+'"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){var e=this;return t.map(function(t){return e._layers[t].serialize()})},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error("Style is not done loading")},r.prototype.update=function(e){if(this._loaded){if(this._changed){var r=Object.keys(this._updatedLayers),n=Object.keys(this._removedLayers);for(var i in(r.length||n.length)&&this._updateWorkerLayers(r,n),this._updatedSources){var a=this._updatedSources[i];"reload"===a?this._reloadSource(i):"clear"===a&&this._clearSource(i)}for(var o in this._updatedPaintProps)this._layers[o].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates(),this.fire(new t.Event("data",{dataType:"style"}))}for(var s in this.sourceCaches)this.sourceCaches[s].used=!1;for(var l=0,c=this._order;l<c.length;l+=1){var u=c[l],h=this._layers[u];h.recalculate(e),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast("updateLayers",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),Xe(this,t.validateStyle(e)))return!1;(e=t.clone(e)).layers=te(e.layers);var n=function(e,r){if(!e)return[{command:ee.setStyle,args:[r]}];var n=[];try{if(!t.default$10(e.version,r.version))return[{command:ee.setStyle,args:[r]}];t.default$10(e.center,r.center)||n.push({command:ee.setCenter,args:[r.center]}),t.default$10(e.zoom,r.zoom)||n.push({command:ee.setZoom,args:[r.zoom]}),t.default$10(e.bearing,r.bearing)||n.push({command:ee.setBearing,args:[r.bearing]}),t.default$10(e.pitch,r.pitch)||n.push({command:ee.setPitch,args:[r.pitch]}),t.default$10(e.sprite,r.sprite)||n.push({command:ee.setSprite,args:[r.sprite]}),t.default$10(e.glyphs,r.glyphs)||n.push({command:ee.setGlyphs,args:[r.glyphs]}),t.default$10(e.transition,r.transition)||n.push({command:ee.setTransition,args:[r.transition]}),t.default$10(e.light,r.light)||n.push({command:ee.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||ne(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.default$10(e[a],r[a])||("geojson"===e[a].type&&"geojson"===r[a].type&&ae(e,r,a)?n.push({command:ee.setGeoJSONSourceData,args:[a,r[a].data]}):ie(a,r,n,i)):re(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach(function(t){i[t.source]?n.push({command:ee.removeLayer,args:[t.id]}):o.push(t)}),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,h=(e=e||[]).map(se),f=r.map(se),p=e.reduce(le,{}),d=r.reduce(le,{}),g=h.slice(),m=Object.create(null);for(i=0,a=0;i<h.length;i++)o=h[i],d.hasOwnProperty(o)?a++:(n.push({command:ee.removeLayer,args:[o]}),g.splice(g.indexOf(o,a),1));for(i=0,a=0;i<f.length;i++)o=f[f.length-1-i],g[g.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:ee.removeLayer,args:[o]}),g.splice(g.lastIndexOf(o,g.length-a),1)):a++,c=g[g.length-i],n.push({command:ee.addLayer,args:[d[o],c]}),g.splice(g.length-i,0,o),m[o]=!0);for(i=0;i<f.length;i++)if(s=p[o=f[i]],l=d[o],!m[o]&&!t.default$10(s,l))if(t.default$10(s.source,l.source)&&t.default$10(s["source-layer"],l["source-layer"])&&t.default$10(s.type,l.type)){for(u in oe(s.layout,l.layout,n,o,null,ee.setLayoutProperty),oe(s.paint,l.paint,n,o,null,ee.setPaintProperty),t.default$10(s.filter,l.filter)||n.push({command:ee.setFilter,args:[o,l.filter]}),t.default$10(s.minzoom,l.minzoom)&&t.default$10(s.maxzoom,l.maxzoom)||n.push({command:ee.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?oe(s[u],l[u],n,o,u.slice(6),ee.setPaintProperty):t.default$10(s[u],l[u])||n.push({command:ee.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?oe(s[u],l[u],n,o,u.slice(6),ee.setPaintProperty):t.default$10(s[u],l[u])||n.push({command:ee.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:ee.removeLayer,args:[o]}),c=g[g.lastIndexOf(o)+1],n.push({command:ee.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn("Unable to compute style diff:",t),n=[{command:ee.setStyle,args:[r]}]}return n}(this.serialize(),e).filter(function(t){return!(t.command in $e)});if(0===n.length)return!1;var i=n.filter(function(t){return!(t.command in Ze)});if(i.length>0)throw new Error("Unimplemented: "+i.map(function(t){return t.command}).join(", ")+".");return n.forEach(function(t){"setTransition"!==t.command&&r[t.command].apply(r,t.args)}),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error("An image with this name already exists.")));this.imageManager.addImage(e,r),this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error("No image with this name exists.")));this.imageManager.removeImage(e),this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.addSource=function(e,r,n){var i=this;if(this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error("There is already a source with this ID");if(!r.type)throw new Error("The type property must be defined, but the only the following properties were given: "+Object.keys(r).join(", ")+".");if(!(["vector","raster","geojson","video","image"].indexOf(r.type)>=0&&this._validate(t.validateStyle.source,"sources."+e,r,null,n))){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Yt(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}}),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error("There is no source with this ID");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source "'+e+'" cannot be removed while layer "'+r+'" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event("data",{sourceDataType:"metadata",dataType:"source",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id "'+i+'" already exists on this map')));else if("object"==typeof e.source&&(this.addSource(i,e.source),e=t.clone(e),e=t.extend(e,{source:i})),!this._validate(t.validateStyle.layer,"layers."+i,e,{arrayIndex:-1},n)){var a=t.default$22(e);this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}});var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]="clear":(this._updatedSources[a.source]="reload",this.sourceCaches[a.source].pause())}this._updateLayer(a)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be moved.")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e]}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be removed.")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot have zoom extent.")))},r.prototype.setFilter=function(e,r){this._checkLoaded();var n=this.getLayer(e);if(n){if(!t.default$10(n.filter,r))return null==r?(n.filter=void 0,void this._updateLayer(n)):void(this._validate(t.validateStyle.filter,"layers."+n.id+".filter",r)||(n.filter=t.clone(r),this._updateLayer(n)))}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be filtered.")))},r.prototype.getFilter=function(e){return t.clone(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?t.default$10(i.getLayoutProperty(r),n)||(i.setLayoutProperty(r,n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getLayoutProperty=function(t,e){return this.getLayer(t).getLayoutProperty(e)},r.prototype.setPaintProperty=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.default$10(i.getPaintProperty(r),n)){var a=i._transitionablePaint._values[r].value.isDataDriven();i.setPaintProperty(r,n),(i._transitionablePaint._values[r].value.isDataDriven()||a)&&this._updateLayer(i),this._changed=!0,this._updatedPaintProps[e]=!0}}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){var e=this;return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,function(t){return t.serialize()}),layers:this._order.map(function(t){return e._layers[t].serialize()})},function(t){return void 0!==t})},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&(this._updatedSources[t.source]="reload",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenRenderedFeatures=function(t){for(var e=[],r=this._order.length-1;r>=0;r--)for(var n=this._order[r],i=0,a=t;i<a.length;i+=1){var o=a[i][n];if(o)for(var s=0,l=o;s<l.length;s+=1){var c=l[s];e.push(c)}}return e},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,"queryRenderedFeatures.filter",r.filter);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error("parameters.layers must be an Array."))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error("The layer '"+s+"' does not exist in the map's style and cannot be queried for features."))),[];i[l.source]=!0}}var c=[];for(var u in this.sourceCaches)r.layers&&!i[u]||c.push(at(this.sourceCaches[u],this._layers,e.worldCoordinate,r,n));return this.placement&&c.push(function(t,e,r,n,i){for(var a={},o=n.queryRenderedSymbols(e),s=[],l=0,c=Object.keys(o).map(Number);l<c.length;l+=1){var u=c[l];s.push(i[u])}s.sort(ot);for(var h=function(){var e=p[f],n=e.featureIndex.lookupSymbolFeatures(o[e.bucketInstanceId],e.bucketIndex,e.sourceLayerIndex,r.filter,r.layers,t);for(var i in n){var s=a[i]=a[i]||[],l=n[i];l.sort(function(t,r){var n=e.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(r.featureIndex)-i}return r.featureIndex-t.featureIndex});for(var c=0,u=l;c<u.length;c+=1){var h=u[c];s.push(h.feature)}}},f=0,p=s;f<p.length;f+=1)h();return a}(this._layers,e.viewport,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,"querySourceFeatures.filter",r.filter);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map(function(e){return t.getTileByID(e)}),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called "'+t+'" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast("loadWorkerSource",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e){this._checkLoaded();var r=this.light.getLight(),n=!1;for(var i in e)if(!t.default$10(e[i],r[i])){n=!0;break}if(n){var o={now:a.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return(!a||!1!==a.validate)&&Xe(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.default$5},i)))},r.prototype._remove=function(){for(var e in t.evented.off("pluginAvailable",this._rtlTextPluginCallback),this.sourceCaches)this.sourceCaches[e].clearTiles();this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(t,e,r){for(var n=!1,i=!1,o={},s=0,l=this._order;s<l.length;s+=1){var c=l[s],u=this._layers[c];if("symbol"===u.type){if(!o[u.source]){var h=this.sourceCaches[u.source];o[u.source]=h.getRenderableIds().map(function(t){return h.getTileByID(t)}).sort(function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)})}var f=this.crossTileSymbolIndex.addLayer(u,o[u.source],t.center.lng);n=n||f}}this.crossTileSymbolIndex.pruneUnusedLayers(this._order);var p=this._layerOrderChanged;if((p||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(a.now()))&&(this.pauseablePlacement=new Ue(t,this._order,p,e,r),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,o),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(this.placement,a.now()),i=!0),n&&this.pauseablePlacement.placement.setStale()),i||n)for(var d=0,g=this._order;d<g.length;d+=1){var m=g[d],v=this._layers[m];"symbol"===v.type&&this.placement.updateLayerOpacities(v,o[v.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(a.now())},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r}(t.Evented);Je.getSourceType=function(t){return nt[t]},Je.setSourceType=function(t,e){nt[t]=e},Je.registerForPluginAvailability=t.registerForPluginAvailability;var Ke=t.createLayout([{name:"a_pos",type:"Int16",components:2}]),Qe={prelude:{fragmentSource:"#ifdef GL_ES\nprecision mediump float;\n#else\n\n#if !defined(lowp)\n#define lowp\n#endif\n\n#if !defined(mediump)\n#define mediump\n#endif\n\n#if !defined(highp)\n#define highp\n#endif\n\n#endif\n",vertexSource:"#ifdef GL_ES\nprecision highp float;\n#else\n\n#if !defined(lowp)\n#define lowp\n#endif\n\n#if !defined(mediump)\n#define mediump\n#endif\n\n#if !defined(highp)\n#define highp\n#endif\n\n#endif\n\n// Unpack a pair of values that have been packed into a single float.\n// The packed values are assumed to be 8-bit unsigned integers, and are\n// packed like so:\n// packedValue = floor(input[0]) * 256 + input[1],\nvec2 unpack_float(const float packedValue) {\n    int packedIntValue = int(packedValue);\n    int v0 = packedIntValue / 256;\n    return vec2(v0, packedIntValue - v0 * 256);\n}\n\nvec2 unpack_opacity(const float packedOpacity) {\n    int intOpacity = int(packedOpacity) / 2;\n    return vec2(float(intOpacity) / 127.0, mod(packedOpacity, 2.0));\n}\n\n// To minimize the number of attributes needed, we encode a 4-component\n// color into a pair of floats (i.e. a vec2) as follows:\n// [ floor(color.r * 255) * 256 + color.g * 255,\n//   floor(color.b * 255) * 256 + color.g * 255 ]\nvec4 decode_color(const vec2 encodedColor) {\n    return vec4(\n        unpack_float(encodedColor[0]) / 255.0,\n        unpack_float(encodedColor[1]) / 255.0\n    );\n}\n\n// Unpack a pair of paint values and interpolate between them.\nfloat unpack_mix_vec2(const vec2 packedValue, const float t) {\n    return mix(packedValue[0], packedValue[1], t);\n}\n\n// Unpack a pair of paint values and interpolate between them.\nvec4 unpack_mix_vec4(const vec4 packedColors, const float t) {\n    vec4 minColor = decode_color(vec2(packedColors[0], packedColors[1]));\n    vec4 maxColor = decode_color(vec2(packedColors[2], packedColors[3]));\n    return mix(minColor, maxColor, t);\n}\n\n// The offset depends on how many pixels are between the world origin and the edge of the tile:\n// vec2 offset = mod(pixel_coord, size)\n//\n// At high zoom levels there are a ton of pixels between the world origin and the edge of the tile.\n// The glsl spec only guarantees 16 bits of precision for highp floats. We need more than that.\n//\n// The pixel_coord is passed in as two 16 bit values:\n// pixel_coord_upper = floor(pixel_coord / 2^16)\n// pixel_coord_lower = mod(pixel_coord, 2^16)\n//\n// The offset is calculated in a series of steps that should preserve this precision:\nvec2 get_pattern_pos(const vec2 pixel_coord_upper, const vec2 pixel_coord_lower,\n    const vec2 pattern_size, const float tile_units_to_pixels, const vec2 pos) {\n\n    vec2 offset = mod(mod(mod(pixel_coord_upper, pattern_size) * 256.0, pattern_size) * 256.0 + pixel_coord_lower, pattern_size);\n    return (tile_units_to_pixels * pos + offset) / pattern_size;\n}\n"},background:{fragmentSource:"uniform vec4 u_color;\nuniform float u_opacity;\n\nvoid main() {\n    gl_FragColor = u_color * u_opacity;\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"attribute vec2 a_pos;\n\nuniform mat4 u_matrix;\n\nvoid main() {\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\n}\n"},backgroundPattern:{fragmentSource:"uniform vec2 u_pattern_tl_a;\nuniform vec2 u_pattern_br_a;\nuniform vec2 u_pattern_tl_b;\nuniform vec2 u_pattern_br_b;\nuniform vec2 u_texsize;\nuniform float u_mix;\nuniform float u_opacity;\n\nuniform sampler2D u_image;\n\nvarying vec2 v_pos_a;\nvarying vec2 v_pos_b;\n\nvoid main() {\n    vec2 imagecoord = mod(v_pos_a, 1.0);\n    vec2 pos = mix(u_pattern_tl_a / u_texsize, u_pattern_br_a / u_texsize, imagecoord);\n    vec4 color1 = texture2D(u_image, pos);\n\n    vec2 imagecoord_b = mod(v_pos_b, 1.0);\n    vec2 pos2 = mix(u_pattern_tl_b / u_texsize, u_pattern_br_b / u_texsize, imagecoord_b);\n    vec4 color2 = texture2D(u_image, pos2);\n\n    gl_FragColor = mix(color1, color2, u_mix) * u_opacity;\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"uniform mat4 u_matrix;\nuniform vec2 u_pattern_size_a;\nuniform vec2 u_pattern_size_b;\nuniform vec2 u_pixel_coord_upper;\nuniform vec2 u_pixel_coord_lower;\nuniform float u_scale_a;\nuniform float u_scale_b;\nuniform float u_tile_units_to_pixels;\n\nattribute vec2 a_pos;\n\nvarying vec2 v_pos_a;\nvarying vec2 v_pos_b;\n\nvoid main() {\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\n\n    v_pos_a = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_a * u_pattern_size_a, u_tile_units_to_pixels, a_pos);\n    v_pos_b = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_b * u_pattern_size_b, u_tile_units_to_pixels, a_pos);\n}\n"},circle:{fragmentSource:"#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\n\nvarying vec3 v_data;\n\nvoid main() {\n    #pragma mapbox: initialize highp vec4 color\n    #pragma mapbox: initialize mediump float radius\n    #pragma mapbox: initialize lowp float blur\n    #pragma mapbox: initialize lowp float opacity\n    #pragma mapbox: initialize highp vec4 stroke_color\n    #pragma mapbox: initialize mediump float stroke_width\n    #pragma mapbox: initialize lowp float stroke_opacity\n\n    vec2 extrude = v_data.xy;\n    float extrude_length = length(extrude);\n\n    lowp float antialiasblur = v_data.z;\n    float antialiased_blur = -max(blur, antialiasblur);\n\n    float opacity_t = smoothstep(0.0, antialiased_blur, extrude_length - 1.0);\n\n    float color_t = stroke_width < 0.01 ? 0.0 : smoothstep(\n        antialiased_blur,\n        0.0,\n        extrude_length - radius / (radius + stroke_width)\n    );\n\n    gl_FragColor = opacity_t * mix(color * opacity, stroke_color * stroke_opacity, color_t);\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"uniform mat4 u_matrix;\nuniform bool u_scale_with_map;\nuniform bool u_pitch_with_map;\nuniform vec2 u_extrude_scale;\nuniform highp float u_camera_to_center_distance;\n\nattribute vec2 a_pos;\n\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\n\nvarying vec3 v_data;\n\nvoid main(void) {\n    #pragma mapbox: initialize highp vec4 color\n    #pragma mapbox: initialize mediump float radius\n    #pragma mapbox: initialize lowp float blur\n    #pragma mapbox: initialize lowp float opacity\n    #pragma mapbox: initialize highp vec4 stroke_color\n    #pragma mapbox: initialize mediump float stroke_width\n    #pragma mapbox: initialize lowp float stroke_opacity\n\n    // unencode the extrusion vector that we snuck into the a_pos vector\n    vec2 extrude = vec2(mod(a_pos, 2.0) * 2.0 - 1.0);\n\n    // multiply a_pos by 0.5, since we had it * 2 in order to sneak\n    // in extrusion data\n    vec2 circle_center = floor(a_pos * 0.5);\n    if (u_pitch_with_map) {\n        vec2 corner_position = circle_center;\n        if (u_scale_with_map) {\n            corner_position += extrude * (radius + stroke_width) * u_extrude_scale;\n        } else {\n            // Pitching the circle with the map effectively scales it with the map\n            // To counteract the effect for pitch-scale: viewport, we rescale the\n            // whole circle based on the pitch scaling effect at its central point\n            vec4 projected_center = u_matrix * vec4(circle_center, 0, 1);\n            corner_position += extrude * (radius + stroke_width) * u_extrude_scale * (projected_center.w / u_camera_to_center_distance);\n        }\n\n        gl_Position = u_matrix * vec4(corner_position, 0, 1);\n    } else {\n        gl_Position = u_matrix * vec4(circle_center, 0, 1);\n\n        if (u_scale_with_map) {\n            gl_Position.xy += extrude * (radius + stroke_width) * u_extrude_scale * u_camera_to_center_distance;\n        } else {\n            gl_Position.xy += extrude * (radius + stroke_width) * u_extrude_scale * gl_Position.w;\n        }\n    }\n\n    // This is a minimum blur distance that serves as a faux-antialiasing for\n    // the circle. since blur is a ratio of the circle's size and the intent is\n    // to keep the blur at roughly 1px, the two are inversely related.\n    lowp float antialiasblur = 1.0 / DEVICE_PIXEL_RATIO / (radius + stroke_width);\n\n    v_data = vec3(extrude.x, extrude.y, antialiasblur);\n}\n"},clippingMask:{fragmentSource:"void main() {\n    gl_FragColor = vec4(1.0);\n}\n",vertexSource:"attribute vec2 a_pos;\n\nuniform mat4 u_matrix;\n\nvoid main() {\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\n}\n"},heatmap:{fragmentSource:"#pragma mapbox: define highp float weight\n\nuniform highp float u_intensity;\nvarying vec2 v_extrude;\n\n// Gaussian kernel coefficient: 1 / sqrt(2 * PI)\n#define GAUSS_COEF 0.3989422804014327\n\nvoid main() {\n    #pragma mapbox: initialize highp float weight\n\n    // Kernel density estimation with a Gaussian kernel of size 5x5\n    float d = -0.5 * 3.0 * 3.0 * dot(v_extrude, v_extrude);\n    float val = weight * u_intensity * GAUSS_COEF * exp(d);\n\n    gl_FragColor = vec4(val, 1.0, 1.0, 1.0);\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"#pragma mapbox: define highp float weight\n#pragma mapbox: define mediump float radius\n\nuniform mat4 u_matrix;\nuniform float u_extrude_scale;\nuniform float u_opacity;\nuniform float u_intensity;\n\nattribute vec2 a_pos;\n\nvarying vec2 v_extrude;\n\n// Effective \"0\" in the kernel density texture to adjust the kernel size to;\n// this empirically chosen number minimizes artifacts on overlapping kernels\n// for typical heatmap cases (assuming clustered source)\nconst highp float ZERO = 1.0 / 255.0 / 16.0;\n\n// Gaussian kernel coefficient: 1 / sqrt(2 * PI)\n#define GAUSS_COEF 0.3989422804014327\n\nvoid main(void) {\n    #pragma mapbox: initialize highp float weight\n    #pragma mapbox: initialize mediump float radius\n\n    // unencode the extrusion vector that we snuck into the a_pos vector\n    vec2 unscaled_extrude = vec2(mod(a_pos, 2.0) * 2.0 - 1.0);\n\n    // This 'extrude' comes in ranging from [-1, -1], to [1, 1].  We'll use\n    // it to produce the vertices of a square mesh framing the point feature\n    // we're adding to the kernel density texture.  We'll also pass it as\n    // a varying, so that the fragment shader can determine the distance of\n    // each fragment from the point feature.\n    // Before we do so, we need to scale it up sufficiently so that the\n    // kernel falls effectively to zero at the edge of the mesh.\n    // That is, we want to know S such that\n    // weight * u_intensity * GAUSS_COEF * exp(-0.5 * 3.0^2 * S^2) == ZERO\n    // Which solves to:\n    // S = sqrt(-2.0 * log(ZERO / (weight * u_intensity * GAUSS_COEF))) / 3.0\n    float S = sqrt(-2.0 * log(ZERO / weight / u_intensity / GAUSS_COEF)) / 3.0;\n\n    // Pass the varying in units of radius\n    v_extrude = S * unscaled_extrude;\n\n    // Scale by radius and the zoom-based scale factor to produce actual\n    // mesh position\n    vec2 extrude = v_extrude * radius * u_extrude_scale;\n\n    // multiply a_pos by 0.5, since we had it * 2 in order to sneak\n    // in extrusion data\n    vec4 pos = vec4(floor(a_pos * 0.5) + extrude, 0, 1);\n\n    gl_Position = u_matrix * pos;\n}\n"},heatmapTexture:{fragmentSource:"uniform sampler2D u_image;\nuniform sampler2D u_color_ramp;\nuniform float u_opacity;\nvarying vec2 v_pos;\n\nvoid main() {\n    float t = texture2D(u_image, v_pos).r;\n    vec4 color = texture2D(u_color_ramp, vec2(t, 0.5));\n    gl_FragColor = color * u_opacity;\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(0.0);\n#endif\n}\n",vertexSource:"uniform mat4 u_matrix;\nuniform vec2 u_world;\nattribute vec2 a_pos;\nvarying vec2 v_pos;\n\nvoid main() {\n    gl_Position = u_matrix * vec4(a_pos * u_world, 0, 1);\n\n    v_pos.x = a_pos.x;\n    v_pos.y = 1.0 - a_pos.y;\n}\n"},collisionBox:{fragmentSource:"\nvarying float v_placed;\nvarying float v_notUsed;\n\nvoid main() {\n\n    float alpha = 0.5;\n\n    // Red = collision, hide label\n    gl_FragColor = vec4(1.0, 0.0, 0.0, 1.0) * alpha;\n\n    // Blue = no collision, label is showing\n    if (v_placed > 0.5) {\n        gl_FragColor = vec4(0.0, 0.0, 1.0, 0.5) * alpha;\n    }\n\n    if (v_notUsed > 0.5) {\n        // This box not used, fade it out\n        gl_FragColor *= .1;\n    }\n}",vertexSource:"attribute vec2 a_pos;\nattribute vec2 a_anchor_pos;\nattribute vec2 a_extrude;\nattribute vec2 a_placed;\n\nuniform mat4 u_matrix;\nuniform vec2 u_extrude_scale;\nuniform float u_camera_to_center_distance;\n\nvarying float v_placed;\nvarying float v_notUsed;\n\nvoid main() {\n    vec4 projectedPoint = u_matrix * vec4(a_anchor_pos, 0, 1);\n    highp float camera_to_anchor_distance = projectedPoint.w;\n    highp float collision_perspective_ratio = clamp(\n        0.5 + 0.5 * (u_camera_to_center_distance / camera_to_anchor_distance),\n        0.0, // Prevents oversized near-field boxes in pitched/overzoomed tiles\n        4.0);\n\n    gl_Position = u_matrix * vec4(a_pos, 0.0, 1.0);\n    gl_Position.xy += a_extrude * u_extrude_scale * gl_Position.w * collision_perspective_ratio;\n\n    v_placed = a_placed.x;\n    v_notUsed = a_placed.y;\n}\n"},collisionCircle:{fragmentSource:"uniform float u_overscale_factor;\n\nvarying float v_placed;\nvarying float v_notUsed;\nvarying float v_radius;\nvarying vec2 v_extrude;\nvarying vec2 v_extrude_scale;\n\nvoid main() {\n    float alpha = 0.5;\n\n    // Red = collision, hide label\n    vec4 color = vec4(1.0, 0.0, 0.0, 1.0) * alpha;\n\n    // Blue = no collision, label is showing\n    if (v_placed > 0.5) {\n        color = vec4(0.0, 0.0, 1.0, 0.5) * alpha;\n    }\n\n    if (v_notUsed > 0.5) {\n        // This box not used, fade it out\n        color *= .2;\n    }\n\n    float extrude_scale_length = length(v_extrude_scale);\n    float extrude_length = length(v_extrude) * extrude_scale_length;\n    float stroke_width = 15.0 * extrude_scale_length / u_overscale_factor;\n    float radius = v_radius * extrude_scale_length;\n\n    float distance_to_edge = abs(extrude_length - radius);\n    float opacity_t = smoothstep(-stroke_width, 0.0, -distance_to_edge);\n\n    gl_FragColor = opacity_t * color;\n}\n",vertexSource:"attribute vec2 a_pos;\nattribute vec2 a_anchor_pos;\nattribute vec2 a_extrude;\nattribute vec2 a_placed;\n\nuniform mat4 u_matrix;\nuniform vec2 u_extrude_scale;\nuniform float u_camera_to_center_distance;\n\nvarying float v_placed;\nvarying float v_notUsed;\nvarying float v_radius;\n\nvarying vec2 v_extrude;\nvarying vec2 v_extrude_scale;\n\nvoid main() {\n    vec4 projectedPoint = u_matrix * vec4(a_anchor_pos, 0, 1);\n    highp float camera_to_anchor_distance = projectedPoint.w;\n    highp float collision_perspective_ratio = clamp(\n        0.5 + 0.5 * (u_camera_to_center_distance / camera_to_anchor_distance),\n        0.0, // Prevents oversized near-field circles in pitched/overzoomed tiles\n        4.0);\n\n    gl_Position = u_matrix * vec4(a_pos, 0.0, 1.0);\n\n    highp float padding_factor = 1.2; // Pad the vertices slightly to make room for anti-alias blur\n    gl_Position.xy += a_extrude * u_extrude_scale * padding_factor * gl_Position.w * collision_perspective_ratio;\n\n    v_placed = a_placed.x;\n    v_notUsed = a_placed.y;\n    v_radius = abs(a_extrude.y); // We don't pitch the circles, so both units of the extrusion vector are equal in magnitude to the radius\n\n    v_extrude = a_extrude * padding_factor;\n    v_extrude_scale = u_extrude_scale * u_camera_to_center_distance * collision_perspective_ratio;\n}\n"},debug:{fragmentSource:"uniform highp vec4 u_color;\n\nvoid main() {\n    gl_FragColor = u_color;\n}\n",vertexSource:"attribute vec2 a_pos;\n\nuniform mat4 u_matrix;\n\nvoid main() {\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\n}\n"},fill:{fragmentSource:"#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\n\nvoid main() {\n    #pragma mapbox: initialize highp vec4 color\n    #pragma mapbox: initialize lowp float opacity\n\n    gl_FragColor = color * opacity;\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"attribute vec2 a_pos;\n\nuniform mat4 u_matrix;\n\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\n\nvoid main() {\n    #pragma mapbox: initialize highp vec4 color\n    #pragma mapbox: initialize lowp float opacity\n\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\n}\n"},fillOutline:{fragmentSource:"#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\n\nvarying vec2 v_pos;\n\nvoid main() {\n    #pragma mapbox: initialize highp vec4 outline_color\n    #pragma mapbox: initialize lowp float opacity\n\n    float dist = length(v_pos - gl_FragCoord.xy);\n    float alpha = 1.0 - smoothstep(0.0, 1.0, dist);\n    gl_FragColor = outline_color * (alpha * opacity);\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"attribute vec2 a_pos;\n\nuniform mat4 u_matrix;\nuniform vec2 u_world;\n\nvarying vec2 v_pos;\n\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\n\nvoid main() {\n    #pragma mapbox: initialize highp vec4 outline_color\n    #pragma mapbox: initialize lowp float opacity\n\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\n    v_pos = (gl_Position.xy / gl_Position.w + 1.0) / 2.0 * u_world;\n}\n"},fillOutlinePattern:{fragmentSource:"uniform vec2 u_pattern_tl_a;\nuniform vec2 u_pattern_br_a;\nuniform vec2 u_pattern_tl_b;\nuniform vec2 u_pattern_br_b;\nuniform vec2 u_texsize;\nuniform float u_mix;\n\nuniform sampler2D u_image;\n\nvarying vec2 v_pos_a;\nvarying vec2 v_pos_b;\nvarying vec2 v_pos;\n\n#pragma mapbox: define lowp float opacity\n\nvoid main() {\n    #pragma mapbox: initialize lowp float opacity\n\n    vec2 imagecoord = mod(v_pos_a, 1.0);\n    vec2 pos = mix(u_pattern_tl_a / u_texsize, u_pattern_br_a / u_texsize, imagecoord);\n    vec4 color1 = texture2D(u_image, pos);\n\n    vec2 imagecoord_b = mod(v_pos_b, 1.0);\n    vec2 pos2 = mix(u_pattern_tl_b / u_texsize, u_pattern_br_b / u_texsize, imagecoord_b);\n    vec4 color2 = texture2D(u_image, pos2);\n\n    // find distance to outline for alpha interpolation\n\n    float dist = length(v_pos - gl_FragCoord.xy);\n    float alpha = 1.0 - smoothstep(0.0, 1.0, dist);\n\n\n    gl_FragColor = mix(color1, color2, u_mix) * alpha * opacity;\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"uniform mat4 u_matrix;\nuniform vec2 u_world;\nuniform vec2 u_pattern_size_a;\nuniform vec2 u_pattern_size_b;\nuniform vec2 u_pixel_coord_upper;\nuniform vec2 u_pixel_coord_lower;\nuniform float u_scale_a;\nuniform float u_scale_b;\nuniform float u_tile_units_to_pixels;\n\nattribute vec2 a_pos;\n\nvarying vec2 v_pos_a;\nvarying vec2 v_pos_b;\nvarying vec2 v_pos;\n\n#pragma mapbox: define lowp float opacity\n\nvoid main() {\n    #pragma mapbox: initialize lowp float opacity\n\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\n\n    v_pos_a = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_a * u_pattern_size_a, u_tile_units_to_pixels, a_pos);\n    v_pos_b = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_b * u_pattern_size_b, u_tile_units_to_pixels, a_pos);\n\n    v_pos = (gl_Position.xy / gl_Position.w + 1.0) / 2.0 * u_world;\n}\n"},fillPattern:{fragmentSource:"uniform vec2 u_pattern_tl_a;\nuniform vec2 u_pattern_br_a;\nuniform vec2 u_pattern_tl_b;\nuniform vec2 u_pattern_br_b;\nuniform vec2 u_texsize;\nuniform float u_mix;\n\nuniform sampler2D u_image;\n\nvarying vec2 v_pos_a;\nvarying vec2 v_pos_b;\n\n#pragma mapbox: define lowp float opacity\n\nvoid main() {\n    #pragma mapbox: initialize lowp float opacity\n\n    vec2 imagecoord = mod(v_pos_a, 1.0);\n    vec2 pos = mix(u_pattern_tl_a / u_texsize, u_pattern_br_a / u_texsize, imagecoord);\n    vec4 color1 = texture2D(u_image, pos);\n\n    vec2 imagecoord_b = mod(v_pos_b, 1.0);\n    vec2 pos2 = mix(u_pattern_tl_b / u_texsize, u_pattern_br_b / u_texsize, imagecoord_b);\n    vec4 color2 = texture2D(u_image, pos2);\n\n    gl_FragColor = mix(color1, color2, u_mix) * opacity;\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"uniform mat4 u_matrix;\nuniform vec2 u_pattern_size_a;\nuniform vec2 u_pattern_size_b;\nuniform vec2 u_pixel_coord_upper;\nuniform vec2 u_pixel_coord_lower;\nuniform float u_scale_a;\nuniform float u_scale_b;\nuniform float u_tile_units_to_pixels;\n\nattribute vec2 a_pos;\n\nvarying vec2 v_pos_a;\nvarying vec2 v_pos_b;\n\n#pragma mapbox: define lowp float opacity\n\nvoid main() {\n    #pragma mapbox: initialize lowp float opacity\n\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\n\n    v_pos_a = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_a * u_pattern_size_a, u_tile_units_to_pixels, a_pos);\n    v_pos_b = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_b * u_pattern_size_b, u_tile_units_to_pixels, a_pos);\n}\n"},fillExtrusion:{fragmentSource:"varying vec4 v_color;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define highp vec4 color\n\nvoid main() {\n    #pragma mapbox: initialize lowp float base\n    #pragma mapbox: initialize lowp float height\n    #pragma mapbox: initialize highp vec4 color\n\n    gl_FragColor = v_color;\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"uniform mat4 u_matrix;\nuniform vec3 u_lightcolor;\nuniform lowp vec3 u_lightpos;\nuniform lowp float u_lightintensity;\n\nattribute vec2 a_pos;\nattribute vec4 a_normal_ed;\n\nvarying vec4 v_color;\n\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n\n#pragma mapbox: define highp vec4 color\n\nvoid main() {\n    #pragma mapbox: initialize lowp float base\n    #pragma mapbox: initialize lowp float height\n    #pragma mapbox: initialize highp vec4 color\n\n    vec3 normal = a_normal_ed.xyz;\n\n    base = max(0.0, base);\n    height = max(0.0, height);\n\n    float t = mod(normal.x, 2.0);\n\n    gl_Position = u_matrix * vec4(a_pos, t > 0.0 ? height : base, 1);\n\n    // Relative luminance (how dark/bright is the surface color?)\n    float colorvalue = color.r * 0.2126 + color.g * 0.7152 + color.b * 0.0722;\n\n    v_color = vec4(0.0, 0.0, 0.0, 1.0);\n\n    // Add slight ambient lighting so no extrusions are totally black\n    vec4 ambientlight = vec4(0.03, 0.03, 0.03, 1.0);\n    color += ambientlight;\n\n    // Calculate cos(theta), where theta is the angle between surface normal and diffuse light ray\n    float directional = clamp(dot(normal / 16384.0, u_lightpos), 0.0, 1.0);\n\n    // Adjust directional so that\n    // the range of values for highlight/shading is narrower\n    // with lower light intensity\n    // and with lighter/brighter surface colors\n    directional = mix((1.0 - u_lightintensity), max((1.0 - colorvalue + u_lightintensity), 1.0), directional);\n\n    // Add gradient along z axis of side surfaces\n    if (normal.y != 0.0) {\n        directional *= clamp((t + base) * pow(height / 150.0, 0.5), mix(0.7, 0.98, 1.0 - u_lightintensity), 1.0);\n    }\n\n    // Assign final color based on surface + ambient light color, diffuse light directional, and light color\n    // with lower bounds adjusted to hue of light\n    // so that shading is tinted with the complementary (opposite) color to the light color\n    v_color.r += clamp(color.r * directional * u_lightcolor.r, mix(0.0, 0.3, 1.0 - u_lightcolor.r), 1.0);\n    v_color.g += clamp(color.g * directional * u_lightcolor.g, mix(0.0, 0.3, 1.0 - u_lightcolor.g), 1.0);\n    v_color.b += clamp(color.b * directional * u_lightcolor.b, mix(0.0, 0.3, 1.0 - u_lightcolor.b), 1.0);\n}\n"},fillExtrusionPattern:{fragmentSource:"uniform vec2 u_pattern_tl_a;\nuniform vec2 u_pattern_br_a;\nuniform vec2 u_pattern_tl_b;\nuniform vec2 u_pattern_br_b;\nuniform vec2 u_texsize;\nuniform float u_mix;\n\nuniform sampler2D u_image;\n\nvarying vec2 v_pos_a;\nvarying vec2 v_pos_b;\nvarying vec4 v_lighting;\n\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n\nvoid main() {\n    #pragma mapbox: initialize lowp float base\n    #pragma mapbox: initialize lowp float height\n\n    vec2 imagecoord = mod(v_pos_a, 1.0);\n    vec2 pos = mix(u_pattern_tl_a / u_texsize, u_pattern_br_a / u_texsize, imagecoord);\n    vec4 color1 = texture2D(u_image, pos);\n\n    vec2 imagecoord_b = mod(v_pos_b, 1.0);\n    vec2 pos2 = mix(u_pattern_tl_b / u_texsize, u_pattern_br_b / u_texsize, imagecoord_b);\n    vec4 color2 = texture2D(u_image, pos2);\n\n    vec4 mixedColor = mix(color1, color2, u_mix);\n\n    gl_FragColor = mixedColor * v_lighting;\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"uniform mat4 u_matrix;\nuniform vec2 u_pattern_size_a;\nuniform vec2 u_pattern_size_b;\nuniform vec2 u_pixel_coord_upper;\nuniform vec2 u_pixel_coord_lower;\nuniform float u_scale_a;\nuniform float u_scale_b;\nuniform float u_tile_units_to_pixels;\nuniform float u_height_factor;\n\nuniform vec3 u_lightcolor;\nuniform lowp vec3 u_lightpos;\nuniform lowp float u_lightintensity;\n\nattribute vec2 a_pos;\nattribute vec4 a_normal_ed;\n\nvarying vec2 v_pos_a;\nvarying vec2 v_pos_b;\nvarying vec4 v_lighting;\nvarying float v_directional;\n\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n\nvoid main() {\n    #pragma mapbox: initialize lowp float base\n    #pragma mapbox: initialize lowp float height\n\n    vec3 normal = a_normal_ed.xyz;\n    float edgedistance = a_normal_ed.w;\n\n    base = max(0.0, base);\n    height = max(0.0, height);\n\n    float t = mod(normal.x, 2.0);\n    float z = t > 0.0 ? height : base;\n\n    gl_Position = u_matrix * vec4(a_pos, z, 1);\n\n    vec2 pos = normal.x == 1.0 && normal.y == 0.0 && normal.z == 16384.0\n        ? a_pos // extrusion top\n        : vec2(edgedistance, z * u_height_factor); // extrusion side\n\n    v_pos_a = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_a * u_pattern_size_a, u_tile_units_to_pixels, pos);\n    v_pos_b = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_b * u_pattern_size_b, u_tile_units_to_pixels, pos);\n\n    v_lighting = vec4(0.0, 0.0, 0.0, 1.0);\n    float directional = clamp(dot(normal / 16383.0, u_lightpos), 0.0, 1.0);\n    directional = mix((1.0 - u_lightintensity), max((0.5 + u_lightintensity), 1.0), directional);\n\n    if (normal.y != 0.0) {\n        directional *= clamp((t + base) * pow(height / 150.0, 0.5), mix(0.7, 0.98, 1.0 - u_lightintensity), 1.0);\n    }\n\n    v_lighting.rgb += clamp(directional * u_lightcolor, mix(vec3(0.0), vec3(0.3), 1.0 - u_lightcolor), vec3(1.0));\n}\n"},extrusionTexture:{fragmentSource:"uniform sampler2D u_image;\nuniform float u_opacity;\nvarying vec2 v_pos;\n\nvoid main() {\n    gl_FragColor = texture2D(u_image, v_pos) * u_opacity;\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(0.0);\n#endif\n}\n",vertexSource:"uniform mat4 u_matrix;\nuniform vec2 u_world;\nattribute vec2 a_pos;\nvarying vec2 v_pos;\n\nvoid main() {\n    gl_Position = u_matrix * vec4(a_pos * u_world, 0, 1);\n\n    v_pos.x = a_pos.x;\n    v_pos.y = 1.0 - a_pos.y;\n}\n"},hillshadePrepare:{fragmentSource:"#ifdef GL_ES\nprecision highp float;\n#endif\n\nuniform sampler2D u_image;\nvarying vec2 v_pos;\nuniform vec2 u_dimension;\nuniform float u_zoom;\nuniform float u_maxzoom;\n\nfloat getElevation(vec2 coord, float bias) {\n    // Convert encoded elevation value to meters\n    vec4 data = texture2D(u_image, coord) * 255.0;\n    return (data.r + data.g * 256.0 + data.b * 256.0 * 256.0) / 4.0;\n}\n\nvoid main() {\n    vec2 epsilon = 1.0 / u_dimension;\n\n    // queried pixels:\n    // +-----------+\n    // |   |   |   |\n    // | a | b | c |\n    // |   |   |   |\n    // +-----------+\n    // |   |   |   |\n    // | d | e | f |\n    // |   |   |   |\n    // +-----------+\n    // |   |   |   |\n    // | g | h | i |\n    // |   |   |   |\n    // +-----------+\n\n    float a = getElevation(v_pos + vec2(-epsilon.x, -epsilon.y), 0.0);\n    float b = getElevation(v_pos + vec2(0, -epsilon.y), 0.0);\n    float c = getElevation(v_pos + vec2(epsilon.x, -epsilon.y), 0.0);\n    float d = getElevation(v_pos + vec2(-epsilon.x, 0), 0.0);\n    float e = getElevation(v_pos, 0.0);\n    float f = getElevation(v_pos + vec2(epsilon.x, 0), 0.0);\n    float g = getElevation(v_pos + vec2(-epsilon.x, epsilon.y), 0.0);\n    float h = getElevation(v_pos + vec2(0, epsilon.y), 0.0);\n    float i = getElevation(v_pos + vec2(epsilon.x, epsilon.y), 0.0);\n\n    // here we divide the x and y slopes by 8 * pixel size\n    // where pixel size (aka meters/pixel) is:\n    // circumference of the world / (pixels per tile * number of tiles)\n    // which is equivalent to: 8 * 40075016.6855785 / (512 * pow(2, u_zoom))\n    // which can be reduced to: pow(2, 19.25619978527 - u_zoom)\n    // we want to vertically exaggerate the hillshading though, because otherwise\n    // it is barely noticeable at low zooms. to do this, we multiply this by some\n    // scale factor pow(2, (u_zoom - u_maxzoom) * a) where a is an arbitrary value\n    // Here we use a=0.3 which works out to the expression below. see \n    // nickidlugash's awesome breakdown for more info\n    // https://github.com/mapbox/mapbox-gl-js/pull/5286#discussion_r148419556\n    float exaggeration = u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;\n\n    vec2 deriv = vec2(\n        (c + f + f + i) - (a + d + d + g),\n        (g + h + h + i) - (a + b + b + c)\n    ) /  pow(2.0, (u_zoom - u_maxzoom) * exaggeration + 19.2562 - u_zoom);\n\n    gl_FragColor = clamp(vec4(\n        deriv.x / 2.0 + 0.5,\n        deriv.y / 2.0 + 0.5,\n        1.0,\n        1.0), 0.0, 1.0);\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"uniform mat4 u_matrix;\n\nattribute vec2 a_pos;\nattribute vec2 a_texture_pos;\n\nvarying vec2 v_pos;\n\nvoid main() {\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\n    v_pos = (a_texture_pos / 8192.0) / 2.0 + 0.25;\n}\n"},hillshade:{fragmentSource:"uniform sampler2D u_image;\nvarying vec2 v_pos;\n\nuniform vec2 u_latrange;\nuniform vec2 u_light;\nuniform vec4 u_shadow;\nuniform vec4 u_highlight;\nuniform vec4 u_accent;\n\n#define PI 3.141592653589793\n\nvoid main() {\n    vec4 pixel = texture2D(u_image, v_pos);\n\n    vec2 deriv = ((pixel.rg * 2.0) - 1.0);\n\n    // We divide the slope by a scale factor based on the cosin of the pixel's approximate latitude\n    // to account for mercator projection distortion. see #4807 for details\n    float scaleFactor = cos(radians((u_latrange[0] - u_latrange[1]) * (1.0 - v_pos.y) + u_latrange[1]));\n    // We also multiply the slope by an arbitrary z-factor of 1.25\n    float slope = atan(1.25 * length(deriv) / scaleFactor);\n    float aspect = deriv.x != 0.0 ? atan(deriv.y, -deriv.x) : PI / 2.0 * (deriv.y > 0.0 ? 1.0 : -1.0);\n\n    float intensity = u_light.x;\n    // We add PI to make this property match the global light object, which adds PI/2 to the light's azimuthal\n    // position property to account for 0deg corresponding to north/the top of the viewport in the style spec\n    // and the original shader was written to accept (-illuminationDirection - 90) as the azimuthal.\n    float azimuth = u_light.y + PI;\n\n    // We scale the slope exponentially based on intensity, using a calculation similar to\n    // the exponential interpolation function in the style spec:\n    // https://github.com/mapbox/mapbox-gl-js/blob/master/src/style-spec/expression/definitions/interpolate.js#L217-L228\n    // so that higher intensity values create more opaque hillshading.\n    float base = 1.875 - intensity * 1.75;\n    float maxValue = 0.5 * PI;\n    float scaledSlope = intensity != 0.5 ? ((pow(base, slope) - 1.0) / (pow(base, maxValue) - 1.0)) * maxValue : slope;\n\n    // The accent color is calculated with the cosine of the slope while the shade color is calculated with the sine\n    // so that the accent color's rate of change eases in while the shade color's eases out.\n    float accent = cos(scaledSlope);\n    // We multiply both the accent and shade color by a clamped intensity value\n    // so that intensities >= 0.5 do not additionally affect the color values\n    // while intensity values < 0.5 make the overall color more transparent.\n    vec4 accent_color = (1.0 - accent) * u_accent * clamp(intensity * 2.0, 0.0, 1.0);\n    float shade = abs(mod((aspect + azimuth) / PI + 0.5, 2.0) - 1.0);\n    vec4 shade_color = mix(u_shadow, u_highlight, shade) * sin(scaledSlope) * clamp(intensity * 2.0, 0.0, 1.0);\n    gl_FragColor = accent_color * (1.0 - shade_color.a) + shade_color;\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"uniform mat4 u_matrix;\n\nattribute vec2 a_pos;\nattribute vec2 a_texture_pos;\n\nvarying vec2 v_pos;\n\nvoid main() {\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\n    v_pos = a_texture_pos / 8192.0;\n}\n"},line:{fragmentSource:"#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n\nvarying vec2 v_width2;\nvarying vec2 v_normal;\nvarying float v_gamma_scale;\n\nvoid main() {\n    #pragma mapbox: initialize highp vec4 color\n    #pragma mapbox: initialize lowp float blur\n    #pragma mapbox: initialize lowp float opacity\n\n    // Calculate the distance of the pixel from the line in pixels.\n    float dist = length(v_normal) * v_width2.s;\n\n    // Calculate the antialiasing fade factor. This is either when fading in\n    // the line in case of an offset line (v_width2.t) or when fading out\n    // (v_width2.s)\n    float blur2 = (blur + 1.0 / DEVICE_PIXEL_RATIO) * v_gamma_scale;\n    float alpha = clamp(min(dist - (v_width2.t - blur2), v_width2.s - dist) / blur2, 0.0, 1.0);\n\n    gl_FragColor = color * (alpha * opacity);\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"\n\n// the distance over which the line edge fades out.\n// Retina devices need a smaller distance to avoid aliasing.\n#define ANTIALIASING 1.0 / DEVICE_PIXEL_RATIO / 2.0\n\n// floor(127 / 2) == 63.0\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\n// there are also \"special\" normals that have a bigger length (of up to 126 in\n// this case).\n// #define scale 63.0\n#define scale 0.015873016\n\nattribute vec4 a_pos_normal;\nattribute vec4 a_data;\n\nuniform mat4 u_matrix;\nuniform mediump float u_ratio;\nuniform vec2 u_gl_units_to_pixels;\n\nvarying vec2 v_normal;\nvarying vec2 v_width2;\nvarying float v_gamma_scale;\nvarying highp float v_linesofar;\n\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\n\nvoid main() {\n    #pragma mapbox: initialize highp vec4 color\n    #pragma mapbox: initialize lowp float blur\n    #pragma mapbox: initialize lowp float opacity\n    #pragma mapbox: initialize mediump float gapwidth\n    #pragma mapbox: initialize lowp float offset\n    #pragma mapbox: initialize mediump float width\n\n    vec2 a_extrude = a_data.xy - 128.0;\n    float a_direction = mod(a_data.z, 4.0) - 1.0;\n\n    v_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * 2.0;\n\n    vec2 pos = a_pos_normal.xy;\n\n    // x is 1 if it's a round cap, 0 otherwise\n    // y is 1 if the normal points up, and -1 if it points down\n    mediump vec2 normal = a_pos_normal.zw;\n    v_normal = normal;\n\n    // these transformations used to be applied in the JS and native code bases.\n    // moved them into the shader for clarity and simplicity.\n    gapwidth = gapwidth / 2.0;\n    float halfwidth = width / 2.0;\n    offset = -1.0 * offset;\n\n    float inset = gapwidth + (gapwidth > 0.0 ? ANTIALIASING : 0.0);\n    float outset = gapwidth + halfwidth * (gapwidth > 0.0 ? 2.0 : 1.0) + ANTIALIASING;\n\n    // Scale the extrusion vector down to a normal and then up by the line width\n    // of this vertex.\n    mediump vec2 dist = outset * a_extrude * scale;\n\n    // Calculate the offset when drawing a line that is to the side of the actual line.\n    // We do this by creating a vector that points towards the extrude, but rotate\n    // it when we're drawing round end points (a_direction = -1 or 1) since their\n    // extrude vector points in another direction.\n    mediump float u = 0.5 * a_direction;\n    mediump float t = 1.0 - abs(u);\n    mediump vec2 offset2 = offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\n\n    vec4 projected_extrude = u_matrix * vec4(dist / u_ratio, 0.0, 0.0);\n    gl_Position = u_matrix * vec4(pos + offset2 / u_ratio, 0.0, 1.0) + projected_extrude;\n\n    // calculate how much the perspective view squishes or stretches the extrude\n    float extrude_length_without_perspective = length(dist);\n    float extrude_length_with_perspective = length(projected_extrude.xy / gl_Position.w * u_gl_units_to_pixels);\n    v_gamma_scale = extrude_length_without_perspective / extrude_length_with_perspective;\n\n    v_width2 = vec2(outset, inset);\n}\n"},lineGradient:{fragmentSource:"\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n\nuniform sampler2D u_image;\n\nvarying vec2 v_width2;\nvarying vec2 v_normal;\nvarying float v_gamma_scale;\nvarying highp float v_lineprogress;\n\nvoid main() {\n    #pragma mapbox: initialize lowp float blur\n    #pragma mapbox: initialize lowp float opacity\n\n    // Calculate the distance of the pixel from the line in pixels.\n    float dist = length(v_normal) * v_width2.s;\n\n    // Calculate the antialiasing fade factor. This is either when fading in\n    // the line in case of an offset line (v_width2.t) or when fading out\n    // (v_width2.s)\n    float blur2 = (blur + 1.0 / DEVICE_PIXEL_RATIO) * v_gamma_scale;\n    float alpha = clamp(min(dist - (v_width2.t - blur2), v_width2.s - dist) / blur2, 0.0, 1.0);\n\n    // For gradient lines, v_lineprogress is the ratio along the entire line,\n    // scaled to [0, 2^15), and the gradient ramp is stored in a texture.\n    vec4 color = texture2D(u_image, vec2(v_lineprogress, 0.5));\n\n    gl_FragColor = color * (alpha * opacity);\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"\n// the attribute conveying progress along a line is scaled to [0, 2^15)\n#define MAX_LINE_DISTANCE 32767.0\n\n// the distance over which the line edge fades out.\n// Retina devices need a smaller distance to avoid aliasing.\n#define ANTIALIASING 1.0 / DEVICE_PIXEL_RATIO / 2.0\n\n// floor(127 / 2) == 63.0\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\n// there are also \"special\" normals that have a bigger length (of up to 126 in\n// this case).\n// #define scale 63.0\n#define scale 0.015873016\n\nattribute vec4 a_pos_normal;\nattribute vec4 a_data;\n\nuniform mat4 u_matrix;\nuniform mediump float u_ratio;\nuniform vec2 u_gl_units_to_pixels;\n\nvarying vec2 v_normal;\nvarying vec2 v_width2;\nvarying float v_gamma_scale;\nvarying highp float v_lineprogress;\n\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\n\nvoid main() {\n    #pragma mapbox: initialize lowp float blur\n    #pragma mapbox: initialize lowp float opacity\n    #pragma mapbox: initialize mediump float gapwidth\n    #pragma mapbox: initialize lowp float offset\n    #pragma mapbox: initialize mediump float width\n\n    vec2 a_extrude = a_data.xy - 128.0;\n    float a_direction = mod(a_data.z, 4.0) - 1.0;\n\n    v_lineprogress = (floor(a_data.z / 4.0) + a_data.w * 64.0) * 2.0 / MAX_LINE_DISTANCE;\n\n    vec2 pos = a_pos_normal.xy;\n\n    // x is 1 if it's a round cap, 0 otherwise\n    // y is 1 if the normal points up, and -1 if it points down\n    mediump vec2 normal = a_pos_normal.zw;\n    v_normal = normal;\n\n    // these transformations used to be applied in the JS and native code bases.\n    // moved them into the shader for clarity and simplicity.\n    gapwidth = gapwidth / 2.0;\n    float halfwidth = width / 2.0;\n    offset = -1.0 * offset;\n\n    float inset = gapwidth + (gapwidth > 0.0 ? ANTIALIASING : 0.0);\n    float outset = gapwidth + halfwidth * (gapwidth > 0.0 ? 2.0 : 1.0) + ANTIALIASING;\n\n    // Scale the extrusion vector down to a normal and then up by the line width\n    // of this vertex.\n    mediump vec2 dist = outset * a_extrude * scale;\n\n    // Calculate the offset when drawing a line that is to the side of the actual line.\n    // We do this by creating a vector that points towards the extrude, but rotate\n    // it when we're drawing round end points (a_direction = -1 or 1) since their\n    // extrude vector points in another direction.\n    mediump float u = 0.5 * a_direction;\n    mediump float t = 1.0 - abs(u);\n    mediump vec2 offset2 = offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\n\n    vec4 projected_extrude = u_matrix * vec4(dist / u_ratio, 0.0, 0.0);\n    gl_Position = u_matrix * vec4(pos + offset2 / u_ratio, 0.0, 1.0) + projected_extrude;\n\n    // calculate how much the perspective view squishes or stretches the extrude\n    float extrude_length_without_perspective = length(dist);\n    float extrude_length_with_perspective = length(projected_extrude.xy / gl_Position.w * u_gl_units_to_pixels);\n    v_gamma_scale = extrude_length_without_perspective / extrude_length_with_perspective;\n\n    v_width2 = vec2(outset, inset);\n}\n"},linePattern:{fragmentSource:"uniform vec2 u_pattern_size_a;\nuniform vec2 u_pattern_size_b;\nuniform vec2 u_pattern_tl_a;\nuniform vec2 u_pattern_br_a;\nuniform vec2 u_pattern_tl_b;\nuniform vec2 u_pattern_br_b;\nuniform vec2 u_texsize;\nuniform float u_fade;\n\nuniform sampler2D u_image;\n\nvarying vec2 v_normal;\nvarying vec2 v_width2;\nvarying float v_linesofar;\nvarying float v_gamma_scale;\n\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n\nvoid main() {\n    #pragma mapbox: initialize lowp float blur\n    #pragma mapbox: initialize lowp float opacity\n\n    // Calculate the distance of the pixel from the line in pixels.\n    float dist = length(v_normal) * v_width2.s;\n\n    // Calculate the antialiasing fade factor. This is either when fading in\n    // the line in case of an offset line (v_width2.t) or when fading out\n    // (v_width2.s)\n    float blur2 = (blur + 1.0 / DEVICE_PIXEL_RATIO) * v_gamma_scale;\n    float alpha = clamp(min(dist - (v_width2.t - blur2), v_width2.s - dist) / blur2, 0.0, 1.0);\n\n    float x_a = mod(v_linesofar / u_pattern_size_a.x, 1.0);\n    float x_b = mod(v_linesofar / u_pattern_size_b.x, 1.0);\n\n    // v_normal.y is 0 at the midpoint of the line, -1 at the lower edge, 1 at the upper edge\n    // we clamp the line width outset to be between 0 and half the pattern height plus padding (2.0)\n    // to ensure we don't sample outside the designated symbol on the sprite sheet.\n    // 0.5 is added to shift the component to be bounded between 0 and 1 for interpolation of\n    // the texture coordinate\n    float y_a = 0.5 + (v_normal.y * clamp(v_width2.s, 0.0, (u_pattern_size_a.y + 2.0) / 2.0) / u_pattern_size_a.y);\n    float y_b = 0.5 + (v_normal.y * clamp(v_width2.s, 0.0, (u_pattern_size_b.y + 2.0) / 2.0) / u_pattern_size_b.y);\n    vec2 pos_a = mix(u_pattern_tl_a / u_texsize, u_pattern_br_a / u_texsize, vec2(x_a, y_a));\n    vec2 pos_b = mix(u_pattern_tl_b / u_texsize, u_pattern_br_b / u_texsize, vec2(x_b, y_b));\n\n    vec4 color = mix(texture2D(u_image, pos_a), texture2D(u_image, pos_b), u_fade);\n\n    gl_FragColor = color * alpha * opacity;\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"// floor(127 / 2) == 63.0\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\n// there are also \"special\" normals that have a bigger length (of up to 126 in\n// this case).\n// #define scale 63.0\n#define scale 0.015873016\n\n// We scale the distance before adding it to the buffers so that we can store\n// long distances for long segments. Use this value to unscale the distance.\n#define LINE_DISTANCE_SCALE 2.0\n\n// the distance over which the line edge fades out.\n// Retina devices need a smaller distance to avoid aliasing.\n#define ANTIALIASING 1.0 / DEVICE_PIXEL_RATIO / 2.0\n\nattribute vec4 a_pos_normal;\nattribute vec4 a_data;\n\nuniform mat4 u_matrix;\nuniform mediump float u_ratio;\nuniform vec2 u_gl_units_to_pixels;\n\nvarying vec2 v_normal;\nvarying vec2 v_width2;\nvarying float v_linesofar;\nvarying float v_gamma_scale;\n\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define mediump float width\n\nvoid main() {\n    #pragma mapbox: initialize lowp float blur\n    #pragma mapbox: initialize lowp float opacity\n    #pragma mapbox: initialize lowp float offset\n    #pragma mapbox: initialize mediump float gapwidth\n    #pragma mapbox: initialize mediump float width\n\n    vec2 a_extrude = a_data.xy - 128.0;\n    float a_direction = mod(a_data.z, 4.0) - 1.0;\n    float a_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * LINE_DISTANCE_SCALE;\n\n    vec2 pos = a_pos_normal.xy;\n\n    // x is 1 if it's a round cap, 0 otherwise\n    // y is 1 if the normal points up, and -1 if it points down\n    mediump vec2 normal = a_pos_normal.zw;\n    v_normal = normal;\n\n    // these transformations used to be applied in the JS and native code bases.\n    // moved them into the shader for clarity and simplicity.\n    gapwidth = gapwidth / 2.0;\n    float halfwidth = width / 2.0;\n    offset = -1.0 * offset;\n\n    float inset = gapwidth + (gapwidth > 0.0 ? ANTIALIASING : 0.0);\n    float outset = gapwidth + halfwidth * (gapwidth > 0.0 ? 2.0 : 1.0) + ANTIALIASING;\n\n    // Scale the extrusion vector down to a normal and then up by the line width\n    // of this vertex.\n    mediump vec2 dist = outset * a_extrude * scale;\n\n    // Calculate the offset when drawing a line that is to the side of the actual line.\n    // We do this by creating a vector that points towards the extrude, but rotate\n    // it when we're drawing round end points (a_direction = -1 or 1) since their\n    // extrude vector points in another direction.\n    mediump float u = 0.5 * a_direction;\n    mediump float t = 1.0 - abs(u);\n    mediump vec2 offset2 = offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\n\n    vec4 projected_extrude = u_matrix * vec4(dist / u_ratio, 0.0, 0.0);\n    gl_Position = u_matrix * vec4(pos + offset2 / u_ratio, 0.0, 1.0) + projected_extrude;\n\n    // calculate how much the perspective view squishes or stretches the extrude\n    float extrude_length_without_perspective = length(dist);\n    float extrude_length_with_perspective = length(projected_extrude.xy / gl_Position.w * u_gl_units_to_pixels);\n    v_gamma_scale = extrude_length_without_perspective / extrude_length_with_perspective;\n\n    v_linesofar = a_linesofar;\n    v_width2 = vec2(outset, inset);\n}\n"},lineSDF:{fragmentSource:"\nuniform sampler2D u_image;\nuniform float u_sdfgamma;\nuniform float u_mix;\n\nvarying vec2 v_normal;\nvarying vec2 v_width2;\nvarying vec2 v_tex_a;\nvarying vec2 v_tex_b;\nvarying float v_gamma_scale;\n\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\n\nvoid main() {\n    #pragma mapbox: initialize highp vec4 color\n    #pragma mapbox: initialize lowp float blur\n    #pragma mapbox: initialize lowp float opacity\n    #pragma mapbox: initialize mediump float width\n    #pragma mapbox: initialize lowp float floorwidth\n\n    // Calculate the distance of the pixel from the line in pixels.\n    float dist = length(v_normal) * v_width2.s;\n\n    // Calculate the antialiasing fade factor. This is either when fading in\n    // the line in case of an offset line (v_width2.t) or when fading out\n    // (v_width2.s)\n    float blur2 = (blur + 1.0 / DEVICE_PIXEL_RATIO) * v_gamma_scale;\n    float alpha = clamp(min(dist - (v_width2.t - blur2), v_width2.s - dist) / blur2, 0.0, 1.0);\n\n    float sdfdist_a = texture2D(u_image, v_tex_a).a;\n    float sdfdist_b = texture2D(u_image, v_tex_b).a;\n    float sdfdist = mix(sdfdist_a, sdfdist_b, u_mix);\n    alpha *= smoothstep(0.5 - u_sdfgamma / floorwidth, 0.5 + u_sdfgamma / floorwidth, sdfdist);\n\n    gl_FragColor = color * (alpha * opacity);\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"// floor(127 / 2) == 63.0\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\n// there are also \"special\" normals that have a bigger length (of up to 126 in\n// this case).\n// #define scale 63.0\n#define scale 0.015873016\n\n// We scale the distance before adding it to the buffers so that we can store\n// long distances for long segments. Use this value to unscale the distance.\n#define LINE_DISTANCE_SCALE 2.0\n\n// the distance over which the line edge fades out.\n// Retina devices need a smaller distance to avoid aliasing.\n#define ANTIALIASING 1.0 / DEVICE_PIXEL_RATIO / 2.0\n\nattribute vec4 a_pos_normal;\nattribute vec4 a_data;\n\nuniform mat4 u_matrix;\nuniform mediump float u_ratio;\nuniform vec2 u_patternscale_a;\nuniform float u_tex_y_a;\nuniform vec2 u_patternscale_b;\nuniform float u_tex_y_b;\nuniform vec2 u_gl_units_to_pixels;\n\nvarying vec2 v_normal;\nvarying vec2 v_width2;\nvarying vec2 v_tex_a;\nvarying vec2 v_tex_b;\nvarying float v_gamma_scale;\n\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\n\nvoid main() {\n    #pragma mapbox: initialize highp vec4 color\n    #pragma mapbox: initialize lowp float blur\n    #pragma mapbox: initialize lowp float opacity\n    #pragma mapbox: initialize mediump float gapwidth\n    #pragma mapbox: initialize lowp float offset\n    #pragma mapbox: initialize mediump float width\n    #pragma mapbox: initialize lowp float floorwidth\n\n    vec2 a_extrude = a_data.xy - 128.0;\n    float a_direction = mod(a_data.z, 4.0) - 1.0;\n    float a_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * LINE_DISTANCE_SCALE;\n\n    vec2 pos = a_pos_normal.xy;\n\n    // x is 1 if it's a round cap, 0 otherwise\n    // y is 1 if the normal points up, and -1 if it points down\n    mediump vec2 normal = a_pos_normal.zw;\n    v_normal = normal;\n\n    // these transformations used to be applied in the JS and native code bases.\n    // moved them into the shader for clarity and simplicity.\n    gapwidth = gapwidth / 2.0;\n    float halfwidth = width / 2.0;\n    offset = -1.0 * offset;\n\n    float inset = gapwidth + (gapwidth > 0.0 ? ANTIALIASING : 0.0);\n    float outset = gapwidth + halfwidth * (gapwidth > 0.0 ? 2.0 : 1.0) + ANTIALIASING;\n\n    // Scale the extrusion vector down to a normal and then up by the line width\n    // of this vertex.\n    mediump vec2 dist =outset * a_extrude * scale;\n\n    // Calculate the offset when drawing a line that is to the side of the actual line.\n    // We do this by creating a vector that points towards the extrude, but rotate\n    // it when we're drawing round end points (a_direction = -1 or 1) since their\n    // extrude vector points in another direction.\n    mediump float u = 0.5 * a_direction;\n    mediump float t = 1.0 - abs(u);\n    mediump vec2 offset2 = offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\n\n    vec4 projected_extrude = u_matrix * vec4(dist / u_ratio, 0.0, 0.0);\n    gl_Position = u_matrix * vec4(pos + offset2 / u_ratio, 0.0, 1.0) + projected_extrude;\n\n    // calculate how much the perspective view squishes or stretches the extrude\n    float extrude_length_without_perspective = length(dist);\n    float extrude_length_with_perspective = length(projected_extrude.xy / gl_Position.w * u_gl_units_to_pixels);\n    v_gamma_scale = extrude_length_without_perspective / extrude_length_with_perspective;\n\n    v_tex_a = vec2(a_linesofar * u_patternscale_a.x / floorwidth, normal.y * u_patternscale_a.y + u_tex_y_a);\n    v_tex_b = vec2(a_linesofar * u_patternscale_b.x / floorwidth, normal.y * u_patternscale_b.y + u_tex_y_b);\n\n    v_width2 = vec2(outset, inset);\n}\n"},raster:{fragmentSource:"uniform float u_fade_t;\nuniform float u_opacity;\nuniform sampler2D u_image0;\nuniform sampler2D u_image1;\nvarying vec2 v_pos0;\nvarying vec2 v_pos1;\n\nuniform float u_brightness_low;\nuniform float u_brightness_high;\n\nuniform float u_saturation_factor;\nuniform float u_contrast_factor;\nuniform vec3 u_spin_weights;\n\nvoid main() {\n\n    // read and cross-fade colors from the main and parent tiles\n    vec4 color0 = texture2D(u_image0, v_pos0);\n    vec4 color1 = texture2D(u_image1, v_pos1);\n    if (color0.a > 0.0) {\n        color0.rgb = color0.rgb / color0.a;\n    }\n    if (color1.a > 0.0) {\n        color1.rgb = color1.rgb / color1.a;\n    }\n    vec4 color = mix(color0, color1, u_fade_t);\n    color.a *= u_opacity;\n    vec3 rgb = color.rgb;\n\n    // spin\n    rgb = vec3(\n        dot(rgb, u_spin_weights.xyz),\n        dot(rgb, u_spin_weights.zxy),\n        dot(rgb, u_spin_weights.yzx));\n\n    // saturation\n    float average = (color.r + color.g + color.b) / 3.0;\n    rgb += (average - rgb) * u_saturation_factor;\n\n    // contrast\n    rgb = (rgb - 0.5) * u_contrast_factor + 0.5;\n\n    // brightness\n    vec3 u_high_vec = vec3(u_brightness_low, u_brightness_low, u_brightness_low);\n    vec3 u_low_vec = vec3(u_brightness_high, u_brightness_high, u_brightness_high);\n\n    gl_FragColor = vec4(mix(u_high_vec, u_low_vec, rgb) * color.a, color.a);\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"uniform mat4 u_matrix;\nuniform vec2 u_tl_parent;\nuniform float u_scale_parent;\nuniform float u_buffer_scale;\n\nattribute vec2 a_pos;\nattribute vec2 a_texture_pos;\n\nvarying vec2 v_pos0;\nvarying vec2 v_pos1;\n\nvoid main() {\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\n    // We are using Int16 for texture position coordinates to give us enough precision for\n    // fractional coordinates. We use 8192 to scale the texture coordinates in the buffer\n    // as an arbitrarily high number to preserve adequate precision when rendering.\n    // This is also the same value as the EXTENT we are using for our tile buffer pos coordinates,\n    // so math for modifying either is consistent.\n    v_pos0 = (((a_texture_pos / 8192.0) - 0.5) / u_buffer_scale ) + 0.5;\n    v_pos1 = (v_pos0 * u_scale_parent) + u_tl_parent;\n}\n"},symbolIcon:{fragmentSource:"uniform sampler2D u_texture;\n\n#pragma mapbox: define lowp float opacity\n\nvarying vec2 v_tex;\nvarying float v_fade_opacity;\n\nvoid main() {\n    #pragma mapbox: initialize lowp float opacity\n\n    lowp float alpha = opacity * v_fade_opacity;\n    gl_FragColor = texture2D(u_texture, v_tex) * alpha;\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"const float PI = 3.141592653589793;\n\nattribute vec4 a_pos_offset;\nattribute vec4 a_data;\nattribute vec3 a_projected_pos;\nattribute float a_fade_opacity;\n\nuniform bool u_is_size_zoom_constant;\nuniform bool u_is_size_feature_constant;\nuniform highp float u_size_t; // used to interpolate between zoom stops when size is a composite function\nuniform highp float u_size; // used when size is both zoom and feature constant\nuniform highp float u_camera_to_center_distance;\nuniform highp float u_pitch;\nuniform bool u_rotate_symbol;\nuniform highp float u_aspect_ratio;\nuniform float u_fade_change;\n\n#pragma mapbox: define lowp float opacity\n\nuniform mat4 u_matrix;\nuniform mat4 u_label_plane_matrix;\nuniform mat4 u_gl_coord_matrix;\n\nuniform bool u_is_text;\nuniform bool u_pitch_with_map;\n\nuniform vec2 u_texsize;\n\nvarying vec2 v_tex;\nvarying float v_fade_opacity;\n\nvoid main() {\n    #pragma mapbox: initialize lowp float opacity\n\n    vec2 a_pos = a_pos_offset.xy;\n    vec2 a_offset = a_pos_offset.zw;\n\n    vec2 a_tex = a_data.xy;\n    vec2 a_size = a_data.zw;\n\n    highp float segment_angle = -a_projected_pos[2];\n\n    float size;\n    if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {\n        size = mix(a_size[0], a_size[1], u_size_t) / 10.0;\n    } else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {\n        size = a_size[0] / 10.0;\n    } else if (!u_is_size_zoom_constant && u_is_size_feature_constant) {\n        size = u_size;\n    } else {\n        size = u_size;\n    }\n\n    vec4 projectedPoint = u_matrix * vec4(a_pos, 0, 1);\n    highp float camera_to_anchor_distance = projectedPoint.w;\n    // See comments in symbol_sdf.vertex\n    highp float distance_ratio = u_pitch_with_map ?\n        camera_to_anchor_distance / u_camera_to_center_distance :\n        u_camera_to_center_distance / camera_to_anchor_distance;\n    highp float perspective_ratio = clamp(\n            0.5 + 0.5 * distance_ratio,\n            0.0, // Prevents oversized near-field symbols in pitched/overzoomed tiles\n            4.0);\n\n    size *= perspective_ratio;\n\n    float fontScale = u_is_text ? size / 24.0 : size;\n\n    highp float symbol_rotation = 0.0;\n    if (u_rotate_symbol) {\n        // See comments in symbol_sdf.vertex\n        vec4 offsetProjectedPoint = u_matrix * vec4(a_pos + vec2(1, 0), 0, 1);\n\n        vec2 a = projectedPoint.xy / projectedPoint.w;\n        vec2 b = offsetProjectedPoint.xy / offsetProjectedPoint.w;\n\n        symbol_rotation = atan((b.y - a.y) / u_aspect_ratio, b.x - a.x);\n    }\n\n    highp float angle_sin = sin(segment_angle + symbol_rotation);\n    highp float angle_cos = cos(segment_angle + symbol_rotation);\n    mat2 rotation_matrix = mat2(angle_cos, -1.0 * angle_sin, angle_sin, angle_cos);\n\n    vec4 projected_pos = u_label_plane_matrix * vec4(a_projected_pos.xy, 0.0, 1.0);\n    gl_Position = u_gl_coord_matrix * vec4(projected_pos.xy / projected_pos.w + rotation_matrix * (a_offset / 32.0 * fontScale), 0.0, 1.0);\n\n    v_tex = a_tex / u_texsize;\n    vec2 fade_opacity = unpack_opacity(a_fade_opacity);\n    float fade_change = fade_opacity[1] > 0.5 ? u_fade_change : -u_fade_change;\n    v_fade_opacity = max(0.0, min(1.0, fade_opacity[0] + fade_change));\n}\n"},symbolSDF:{fragmentSource:"#define SDF_PX 8.0\n#define EDGE_GAMMA 0.105/DEVICE_PIXEL_RATIO\n\nuniform bool u_is_halo;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\n\nuniform sampler2D u_texture;\nuniform highp float u_gamma_scale;\nuniform bool u_is_text;\n\nvarying vec2 v_data0;\nvarying vec3 v_data1;\n\nvoid main() {\n    #pragma mapbox: initialize highp vec4 fill_color\n    #pragma mapbox: initialize highp vec4 halo_color\n    #pragma mapbox: initialize lowp float opacity\n    #pragma mapbox: initialize lowp float halo_width\n    #pragma mapbox: initialize lowp float halo_blur\n\n    vec2 tex = v_data0.xy;\n    float gamma_scale = v_data1.x;\n    float size = v_data1.y;\n    float fade_opacity = v_data1[2];\n\n    float fontScale = u_is_text ? size / 24.0 : size;\n\n    lowp vec4 color = fill_color;\n    highp float gamma = EDGE_GAMMA / (fontScale * u_gamma_scale);\n    lowp float buff = (256.0 - 64.0) / 256.0;\n    if (u_is_halo) {\n        color = halo_color;\n        gamma = (halo_blur * 1.19 / SDF_PX + EDGE_GAMMA) / (fontScale * u_gamma_scale);\n        buff = (6.0 - halo_width / fontScale) / SDF_PX;\n    }\n\n    lowp float dist = texture2D(u_texture, tex).a;\n    highp float gamma_scaled = gamma * gamma_scale;\n    highp float alpha = smoothstep(buff - gamma_scaled, buff + gamma_scaled, dist);\n\n    gl_FragColor = color * (alpha * opacity * fade_opacity);\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"const float PI = 3.141592653589793;\n\nattribute vec4 a_pos_offset;\nattribute vec4 a_data;\nattribute vec3 a_projected_pos;\nattribute float a_fade_opacity;\n\n// contents of a_size vary based on the type of property value\n// used for {text,icon}-size.\n// For constants, a_size is disabled.\n// For source functions, we bind only one value per vertex: the value of {text,icon}-size evaluated for the current feature.\n// For composite functions:\n// [ text-size(lowerZoomStop, feature),\n//   text-size(upperZoomStop, feature) ]\nuniform bool u_is_size_zoom_constant;\nuniform bool u_is_size_feature_constant;\nuniform highp float u_size_t; // used to interpolate between zoom stops when size is a composite function\nuniform highp float u_size; // used when size is both zoom and feature constant\n\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\n\nuniform mat4 u_matrix;\nuniform mat4 u_label_plane_matrix;\nuniform mat4 u_gl_coord_matrix;\n\nuniform bool u_is_text;\nuniform bool u_pitch_with_map;\nuniform highp float u_pitch;\nuniform bool u_rotate_symbol;\nuniform highp float u_aspect_ratio;\nuniform highp float u_camera_to_center_distance;\nuniform float u_fade_change;\n\nuniform vec2 u_texsize;\n\nvarying vec2 v_data0;\nvarying vec3 v_data1;\n\nvoid main() {\n    #pragma mapbox: initialize highp vec4 fill_color\n    #pragma mapbox: initialize highp vec4 halo_color\n    #pragma mapbox: initialize lowp float opacity\n    #pragma mapbox: initialize lowp float halo_width\n    #pragma mapbox: initialize lowp float halo_blur\n\n    vec2 a_pos = a_pos_offset.xy;\n    vec2 a_offset = a_pos_offset.zw;\n\n    vec2 a_tex = a_data.xy;\n    vec2 a_size = a_data.zw;\n\n    highp float segment_angle = -a_projected_pos[2];\n    float size;\n\n    if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {\n        size = mix(a_size[0], a_size[1], u_size_t) / 10.0;\n    } else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {\n        size = a_size[0] / 10.0;\n    } else if (!u_is_size_zoom_constant && u_is_size_feature_constant) {\n        size = u_size;\n    } else {\n        size = u_size;\n    }\n\n    vec4 projectedPoint = u_matrix * vec4(a_pos, 0, 1);\n    highp float camera_to_anchor_distance = projectedPoint.w;\n    // If the label is pitched with the map, layout is done in pitched space,\n    // which makes labels in the distance smaller relative to viewport space.\n    // We counteract part of that effect by multiplying by the perspective ratio.\n    // If the label isn't pitched with the map, we do layout in viewport space,\n    // which makes labels in the distance larger relative to the features around\n    // them. We counteract part of that effect by dividing by the perspective ratio.\n    highp float distance_ratio = u_pitch_with_map ?\n        camera_to_anchor_distance / u_camera_to_center_distance :\n        u_camera_to_center_distance / camera_to_anchor_distance;\n    highp float perspective_ratio = clamp(\n        0.5 + 0.5 * distance_ratio,\n        0.0, // Prevents oversized near-field symbols in pitched/overzoomed tiles\n        4.0);\n\n    size *= perspective_ratio;\n\n    float fontScale = u_is_text ? size / 24.0 : size;\n\n    highp float symbol_rotation = 0.0;\n    if (u_rotate_symbol) {\n        // Point labels with 'rotation-alignment: map' are horizontal with respect to tile units\n        // To figure out that angle in projected space, we draw a short horizontal line in tile\n        // space, project it, and measure its angle in projected space.\n        vec4 offsetProjectedPoint = u_matrix * vec4(a_pos + vec2(1, 0), 0, 1);\n\n        vec2 a = projectedPoint.xy / projectedPoint.w;\n        vec2 b = offsetProjectedPoint.xy / offsetProjectedPoint.w;\n\n        symbol_rotation = atan((b.y - a.y) / u_aspect_ratio, b.x - a.x);\n    }\n\n    highp float angle_sin = sin(segment_angle + symbol_rotation);\n    highp float angle_cos = cos(segment_angle + symbol_rotation);\n    mat2 rotation_matrix = mat2(angle_cos, -1.0 * angle_sin, angle_sin, angle_cos);\n\n    vec4 projected_pos = u_label_plane_matrix * vec4(a_projected_pos.xy, 0.0, 1.0);\n    gl_Position = u_gl_coord_matrix * vec4(projected_pos.xy / projected_pos.w + rotation_matrix * (a_offset / 32.0 * fontScale), 0.0, 1.0);\n    float gamma_scale = gl_Position.w;\n\n    vec2 tex = a_tex / u_texsize;\n    vec2 fade_opacity = unpack_opacity(a_fade_opacity);\n    float fade_change = fade_opacity[1] > 0.5 ? u_fade_change : -u_fade_change;\n    float interpolated_fade_opacity = max(0.0, min(1.0, fade_opacity[0] + fade_change));\n\n    v_data0 = vec2(tex.x, tex.y);\n    v_data1 = vec3(gamma_scale, size, interpolated_fade_opacity);\n}\n"}},tr=/#pragma mapbox: ([\w]+) ([\w]+) ([\w]+) ([\w]+)/g,er=function(t){var e=Qe[t],r={};e.fragmentSource=e.fragmentSource.replace(tr,function(t,e,n,i,a){return r[a]=!0,"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nvarying "+n+" "+i+" "+a+";\n#else\nuniform "+n+" "+i+" u_"+a+";\n#endif\n":"\n#ifdef HAS_UNIFORM_u_"+a+"\n    "+n+" "+i+" "+a+" = u_"+a+";\n#endif\n"}),e.vertexSource=e.vertexSource.replace(tr,function(t,e,n,i,a){var o="float"===i?"vec2":"vec4";return r[a]?"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nuniform lowp float a_"+a+"_t;\nattribute "+n+" "+o+" a_"+a+";\nvarying "+n+" "+i+" "+a+";\n#else\nuniform "+n+" "+i+" u_"+a+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+a+" = unpack_mix_"+o+"(a_"+a+", a_"+a+"_t);\n#else\n    "+n+" "+i+" "+a+" = u_"+a+";\n#endif\n":"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nuniform lowp float a_"+a+"_t;\nattribute "+n+" "+o+" a_"+a+";\n#else\nuniform "+n+" "+i+" u_"+a+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+n+" "+i+" "+a+" = unpack_mix_"+o+"(a_"+a+", a_"+a+"_t);\n#else\n    "+n+" "+i+" "+a+" = u_"+a+";\n#endif\n"})};for(var rr in Qe)er(rr);var nr=Qe,ir=function(t,e,r,n){var i=t.gl;this.program=i.createProgram();var o=r.defines().concat("#define DEVICE_PIXEL_RATIO "+a.devicePixelRatio.toFixed(1));n&&o.push("#define OVERDRAW_INSPECTOR;");var s=o.concat(nr.prelude.fragmentSource,e.fragmentSource).join("\n"),l=o.concat(nr.prelude.vertexSource,e.vertexSource).join("\n"),c=i.createShader(i.FRAGMENT_SHADER);i.shaderSource(c,s),i.compileShader(c),i.attachShader(this.program,c);var u=i.createShader(i.VERTEX_SHADER);i.shaderSource(u,l),i.compileShader(u),i.attachShader(this.program,u);for(var h=r.layoutAttributes||[],f=0;f<h.length;f++)i.bindAttribLocation(this.program,f,h[f].name);i.linkProgram(this.program),this.numAttributes=i.getProgramParameter(this.program,i.ACTIVE_ATTRIBUTES),this.attributes={},this.uniforms={};for(var p=0;p<this.numAttributes;p++){var d=i.getActiveAttrib(this.program,p);d&&(this.attributes[d.name]=i.getAttribLocation(this.program,d.name))}for(var g=i.getProgramParameter(this.program,i.ACTIVE_UNIFORMS),m=0;m<g;m++){var v=i.getActiveUniform(this.program,m);v&&(this.uniforms[v.name]=i.getUniformLocation(this.program,v.name))}};function ar(e,r,n,i,a){for(var o=0;o<n.length;o++){var s=n[o];if(i.isLessThan(s.tileID))break;if(r.key===s.tileID.key)return;if(s.tileID.isChildOf(r)){for(var l=r.children(1/0),c=0;c<l.length;c++)ar(e,l[c],n.slice(o),i,a);return}}var u=r.overscaledZ-e.overscaledZ,h=new t.CanonicalTileID(u,r.canonical.x-(e.canonical.x<<u),r.canonical.y-(e.canonical.y<<u));a[h.key]=a[h.key]||h}function or(t,e,r,n,i){var a=t.context,o=a.gl,s=i?t.useProgram("collisionCircle"):t.useProgram("collisionBox");a.setDepthMode(qt.disabled),a.setStencilMode(Ht.disabled),a.setColorMode(t.colorModeForRenderPass());for(var l=0;l<n.length;l++){var c=n[l],u=e.getTile(c),h=u.getBucket(r);if(h){var f=i?h.collisionCircle:h.collisionBox;if(f){o.uniformMatrix4fv(s.uniforms.u_matrix,!1,c.posMatrix),i||a.lineWidth.set(1),o.uniform1f(s.uniforms.u_camera_to_center_distance,t.transform.cameraToCenterDistance);var p=Te(u,1,t.transform.zoom),d=Math.pow(2,t.transform.zoom-u.tileID.overscaledZ);o.uniform1f(s.uniforms.u_pixels_to_tile_units,p),o.uniform2f(s.uniforms.u_extrude_scale,t.transform.pixelsToGLUnits[0]/(p*d),t.transform.pixelsToGLUnits[1]/(p*d)),o.uniform1f(s.uniforms.u_overscale_factor,u.tileID.overscaleFactor()),s.draw(a,i?o.TRIANGLES:o.LINES,r.id,f.layoutVertexBuffer,f.indexBuffer,f.segments,null,f.collisionVertexBuffer,null)}}}}ir.prototype.draw=function(t,e,r,n,i,a,o,s,l){for(var c,u=t.gl,h=(c={},c[u.LINES]=2,c[u.TRIANGLES]=3,c)[e],f=0,p=a.get();f<p.length;f+=1){var d=p[f],g=d.vaos||(d.vaos={});(g[r]||(g[r]=new Q)).bind(t,this,n,o?o.getPaintVertexBuffers():[],i,d.vertexOffset,s,l),u.drawElements(e,d.primitiveLength*h,u.UNSIGNED_SHORT,d.primitiveOffset*h*2)}};var sr=t.mat4.identity(new Float32Array(16)),lr=t.default$19.layout;function cr(t,e,r,n,i,a,o,s,l,c){var u,h=t.context,f=h.gl,p=t.transform,d="map"===s,g="map"===l,m=d&&"line"===r.layout.get("symbol-placement"),v=d&&!g&&!m,y=g;h.setDepthMode(y?t.depthModeForSublayer(0,qt.ReadOnly):qt.disabled);for(var x=0,b=n;x<b.length;x+=1){var _=b[x],w=e.getTile(_),k=w.getBucket(r);if(k){var M=i?k.text:k.icon;if(M&&M.segments.get().length){var A=M.programConfigurations.get(r.id),T=i||k.sdfIcons,S=i?k.textSizeData:k.iconSizeData;if(u||(u=t.useProgram(T?"symbolSDF":"symbolIcon",A),A.setUniforms(t.context,u,r.paint,{zoom:t.transform.zoom}),ur(u,t,r,i,v,g,S)),h.activeTexture.set(f.TEXTURE0),f.uniform1i(u.uniforms.u_texture,0),i)w.glyphAtlasTexture.bind(f.LINEAR,f.CLAMP_TO_EDGE),f.uniform2fv(u.uniforms.u_texsize,w.glyphAtlasTexture.size);else{var E=1!==r.layout.get("icon-size").constantOr(0)||k.iconsNeedLinear,C=g||0!==p.pitch;w.iconAtlasTexture.bind(T||t.options.rotating||t.options.zooming||E||C?f.LINEAR:f.NEAREST,f.CLAMP_TO_EDGE),f.uniform2fv(u.uniforms.u_texsize,w.iconAtlasTexture.size)}f.uniformMatrix4fv(u.uniforms.u_matrix,!1,t.translatePosMatrix(_.posMatrix,w,a,o));var L=Te(w,1,t.transform.zoom),z=he(_.posMatrix,g,d,t.transform,L),P=fe(_.posMatrix,g,d,t.transform,L);f.uniformMatrix4fv(u.uniforms.u_gl_coord_matrix,!1,t.translatePosMatrix(P,w,a,o,!0)),m?(f.uniformMatrix4fv(u.uniforms.u_label_plane_matrix,!1,sr),ge(k,_.posMatrix,t,i,z,P,g,c)):f.uniformMatrix4fv(u.uniforms.u_label_plane_matrix,!1,z),f.uniform1f(u.uniforms.u_fade_change,t.options.fadeDuration?t.symbolFadeChange:1),hr(u,A,t,r,w,M,i,T,g)}}}}function ur(e,r,n,i,a,o,s){var l=r.context.gl,c=r.transform;l.uniform1i(e.uniforms.u_pitch_with_map,o?1:0),l.uniform1f(e.uniforms.u_is_text,i?1:0),l.uniform1f(e.uniforms.u_pitch,c.pitch/360*2*Math.PI);var u="constant"===s.functionType||"source"===s.functionType,h="constant"===s.functionType||"camera"===s.functionType;l.uniform1i(e.uniforms.u_is_size_zoom_constant,u?1:0),l.uniform1i(e.uniforms.u_is_size_feature_constant,h?1:0),l.uniform1f(e.uniforms.u_camera_to_center_distance,c.cameraToCenterDistance);var f=t.evaluateSizeForZoom(s,c.zoom,lr.properties[i?"text-size":"icon-size"]);void 0!==f.uSizeT&&l.uniform1f(e.uniforms.u_size_t,f.uSizeT),void 0!==f.uSize&&l.uniform1f(e.uniforms.u_size,f.uSize),l.uniform1f(e.uniforms.u_aspect_ratio,c.width/c.height),l.uniform1i(e.uniforms.u_rotate_symbol,a?1:0)}function hr(t,e,r,n,i,a,o,s,l){var c=r.context,u=c.gl,h=r.transform;if(s){var f=0!==n.paint.get(o?"text-halo-width":"icon-halo-width").constantOr(1),p=l?Math.cos(h._pitch)*h.cameraToCenterDistance:1;u.uniform1f(t.uniforms.u_gamma_scale,p),f&&(u.uniform1f(t.uniforms.u_is_halo,1),fr(a,n,c,t)),u.uniform1f(t.uniforms.u_is_halo,0)}fr(a,n,c,t)}function fr(t,e,r,n){n.draw(r,r.gl.TRIANGLES,e.id,t.layoutVertexBuffer,t.indexBuffer,t.segments,t.programConfigurations.get(e.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function pr(t,e,r,n,i,o,s,l,c){var u,h,f,p,d=e.context,g=d.gl,m=i.paint.get("line-dasharray"),v=i.paint.get("line-pattern");if(l||c){var y=1/Te(r,1,e.transform.tileZoom);if(m){u=e.lineAtlas.getDash(m.from,"round"===i.layout.get("line-cap")),h=e.lineAtlas.getDash(m.to,"round"===i.layout.get("line-cap"));var x=u.width*m.fromScale,b=h.width*m.toScale;g.uniform2f(t.uniforms.u_patternscale_a,y/x,-u.height/2),g.uniform2f(t.uniforms.u_patternscale_b,y/b,-h.height/2),g.uniform1f(t.uniforms.u_sdfgamma,e.lineAtlas.width/(256*Math.min(x,b)*a.devicePixelRatio)/2)}else if(v){if(f=e.imageManager.getPattern(v.from),p=e.imageManager.getPattern(v.to),!f||!p)return;g.uniform2f(t.uniforms.u_pattern_size_a,f.displaySize[0]*v.fromScale/y,f.displaySize[1]),g.uniform2f(t.uniforms.u_pattern_size_b,p.displaySize[0]*v.toScale/y,p.displaySize[1]);var _=e.imageManager.getPixelSize(),w=_.width,k=_.height;g.uniform2fv(t.uniforms.u_texsize,[w,k])}g.uniform2f(t.uniforms.u_gl_units_to_pixels,1/e.transform.pixelsToGLUnits[0],1/e.transform.pixelsToGLUnits[1])}l&&(m?(g.uniform1i(t.uniforms.u_image,0),d.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(d),g.uniform1f(t.uniforms.u_tex_y_a,u.y),g.uniform1f(t.uniforms.u_tex_y_b,h.y),g.uniform1f(t.uniforms.u_mix,m.t)):v&&(g.uniform1i(t.uniforms.u_image,0),d.activeTexture.set(g.TEXTURE0),e.imageManager.bind(d),g.uniform2fv(t.uniforms.u_pattern_tl_a,f.tl),g.uniform2fv(t.uniforms.u_pattern_br_a,f.br),g.uniform2fv(t.uniforms.u_pattern_tl_b,p.tl),g.uniform2fv(t.uniforms.u_pattern_br_b,p.br),g.uniform1f(t.uniforms.u_fade,v.t))),d.setStencilMode(e.stencilModeForClipping(o));var M=e.translatePosMatrix(o.posMatrix,r,i.paint.get("line-translate"),i.paint.get("line-translate-anchor"));if(g.uniformMatrix4fv(t.uniforms.u_matrix,!1,M),g.uniform1f(t.uniforms.u_ratio,1/Te(r,1,e.transform.zoom)),i.paint.get("line-gradient")){d.activeTexture.set(g.TEXTURE0);var A=i.gradientTexture;if(!i.gradient)return;A||(A=i.gradientTexture=new z(d,i.gradient,g.RGBA)),A.bind(g.LINEAR,g.CLAMP_TO_EDGE),g.uniform1i(t.uniforms.u_image,0)}t.draw(d,g.TRIANGLES,i.id,n.layoutVertexBuffer,n.indexBuffer,n.segments,s)}var dr=function(t,e){if(!t)return!1;var r=e.imageManager.getPattern(t.from),n=e.imageManager.getPattern(t.to);return!r||!n},gr=function(t,e,r){var n=e.context,i=n.gl,a=e.imageManager.getPattern(t.from),o=e.imageManager.getPattern(t.to);i.uniform1i(r.uniforms.u_image,0),i.uniform2fv(r.uniforms.u_pattern_tl_a,a.tl),i.uniform2fv(r.uniforms.u_pattern_br_a,a.br),i.uniform2fv(r.uniforms.u_pattern_tl_b,o.tl),i.uniform2fv(r.uniforms.u_pattern_br_b,o.br);var s=e.imageManager.getPixelSize(),l=s.width,c=s.height;i.uniform2fv(r.uniforms.u_texsize,[l,c]),i.uniform1f(r.uniforms.u_mix,t.t),i.uniform2fv(r.uniforms.u_pattern_size_a,a.displaySize),i.uniform2fv(r.uniforms.u_pattern_size_b,o.displaySize),i.uniform1f(r.uniforms.u_scale_a,t.fromScale),i.uniform1f(r.uniforms.u_scale_b,t.toScale),n.activeTexture.set(i.TEXTURE0),e.imageManager.bind(e.context)},mr=function(t,e,r){var n=e.context.gl;n.uniform1f(r.uniforms.u_tile_units_to_pixels,1/Te(t,1,e.transform.tileZoom));var i=Math.pow(2,t.tileID.overscaledZ),a=t.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(t.tileID.canonical.x+t.tileID.wrap*i),s=a*t.tileID.canonical.y;n.uniform2f(r.uniforms.u_pixel_coord_upper,o>>16,s>>16),n.uniform2f(r.uniforms.u_pixel_coord_lower,65535&o,65535&s)};function vr(t,e,r,n,i){if(!dr(r.paint.get("fill-pattern"),t))for(var a=!0,o=0,s=n;o<s.length;o+=1){var l=s[o],c=e.getTile(l),u=c.getBucket(r);u&&(t.context.setStencilMode(t.stencilModeForClipping(l)),i(t,e,r,c,l,u,a),a=!1)}}function yr(t,e,r,n,i,a,o){var s=t.context.gl,l=a.programConfigurations.get(r.id);br("fill",r.paint.get("fill-pattern"),t,l,r,n,i,o).draw(t.context,s.TRIANGLES,r.id,a.layoutVertexBuffer,a.indexBuffer,a.segments,l)}function xr(t,e,r,n,i,a,o){var s=t.context.gl,l=a.programConfigurations.get(r.id),c=br("fillOutline",r.getPaintProperty("fill-outline-color")?null:r.paint.get("fill-pattern"),t,l,r,n,i,o);s.uniform2f(c.uniforms.u_world,s.drawingBufferWidth,s.drawingBufferHeight),c.draw(t.context,s.LINES,r.id,a.layoutVertexBuffer,a.indexBuffer2,a.segments2,l)}function br(t,e,r,n,i,a,o,s){var l,c=r.context.program.get();return e?(l=r.useProgram(t+"Pattern",n),(s||l.program!==c)&&(n.setUniforms(r.context,l,i.paint,{zoom:r.transform.zoom}),gr(e,r,l)),mr(a,r,l)):(l=r.useProgram(t,n),(s||l.program!==c)&&n.setUniforms(r.context,l,i.paint,{zoom:r.transform.zoom})),r.context.gl.uniformMatrix4fv(l.uniforms.u_matrix,!1,r.translatePosMatrix(o.posMatrix,a,i.paint.get("fill-translate"),i.paint.get("fill-translate-anchor"))),l}var _r=t.default$20.mat3,wr=t.default$20.mat4,kr=t.default$20.vec3;function Mr(t,e,r,n,i,a,o){var s=t.context,l=s.gl,c=r.paint.get("fill-extrusion-pattern"),u=t.context.program.get(),h=a.programConfigurations.get(r.id),f=t.useProgram(c?"fillExtrusionPattern":"fillExtrusion",h);if((o||f.program!==u)&&h.setUniforms(s,f,r.paint,{zoom:t.transform.zoom}),c){if(dr(c,t))return;gr(c,t,f),mr(n,t,f),l.uniform1f(f.uniforms.u_height_factor,-Math.pow(2,i.overscaledZ)/n.tileSize/8)}t.context.gl.uniformMatrix4fv(f.uniforms.u_matrix,!1,t.translatePosMatrix(i.posMatrix,n,r.paint.get("fill-extrusion-translate"),r.paint.get("fill-extrusion-translate-anchor"))),function(t,e){var r=e.context.gl,n=e.style.light,i=n.properties.get("position"),a=[i.x,i.y,i.z],o=_r.create();"viewport"===n.properties.get("anchor")&&_r.fromRotation(o,-e.transform.angle),kr.transformMat3(a,a,o);var s=n.properties.get("color");r.uniform3fv(t.uniforms.u_lightpos,a),r.uniform1f(t.uniforms.u_lightintensity,n.properties.get("intensity")),r.uniform3f(t.uniforms.u_lightcolor,s.r,s.g,s.b)}(f,t),f.draw(s,l.TRIANGLES,r.id,a.layoutVertexBuffer,a.indexBuffer,a.segments,h)}function Ar(e,r,n){var i=e.context,a=i.gl,o=r.fbo;if(o){var s=e.useProgram("hillshade"),l=e.transform.calculatePosMatrix(r.tileID.toUnwrapped(),!0);!function(t,e,r){var n=r.paint.get("hillshade-illumination-direction")*(Math.PI/180);"viewport"===r.paint.get("hillshade-illumination-anchor")&&(n-=e.transform.angle),e.context.gl.uniform2f(t.uniforms.u_light,r.paint.get("hillshade-exaggeration"),n)}(s,e,n);var c=function(e,r){var n=r.toCoordinate(),i=new t.default$17(n.column,n.row+1,n.zoom);return[e.transform.coordinateLocation(n).lat,e.transform.coordinateLocation(i).lat]}(e,r.tileID);i.activeTexture.set(a.TEXTURE0),a.bindTexture(a.TEXTURE_2D,o.colorAttachment.get()),a.uniformMatrix4fv(s.uniforms.u_matrix,!1,l),a.uniform2fv(s.uniforms.u_latrange,c),a.uniform1i(s.uniforms.u_image,0);var u=n.paint.get("hillshade-shadow-color");a.uniform4f(s.uniforms.u_shadow,u.r,u.g,u.b,u.a);var h=n.paint.get("hillshade-highlight-color");a.uniform4f(s.uniforms.u_highlight,h.r,h.g,h.b,h.a);var f=n.paint.get("hillshade-accent-color");if(a.uniform4f(s.uniforms.u_accent,f.r,f.g,f.b,f.a),r.maskedBoundsBuffer&&r.maskedIndexBuffer&&r.segments)s.draw(i,a.TRIANGLES,n.id,r.maskedBoundsBuffer,r.maskedIndexBuffer,r.segments);else{var p=e.rasterBoundsBuffer;e.rasterBoundsVAO.bind(i,s,p,[]),a.drawArrays(a.TRIANGLE_STRIP,0,p.length)}}}function Tr(e,r,n){var i=e.context,a=i.gl;if(r.dem&&r.dem.level){var o=r.dem.level.dim,s=r.dem.getPixels();if(i.activeTexture.set(a.TEXTURE1),i.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(r.tileSize),r.demTexture){var l=r.demTexture;l.update(s,{premultiply:!1}),l.bind(a.NEAREST,a.CLAMP_TO_EDGE)}else r.demTexture=new z(i,s,a.RGBA,{premultiply:!1}),r.demTexture.bind(a.NEAREST,a.CLAMP_TO_EDGE);i.activeTexture.set(a.TEXTURE0);var c=r.fbo;if(!c){var u=new z(i,{width:o,height:o,data:null},a.RGBA);u.bind(a.LINEAR,a.CLAMP_TO_EDGE),(c=r.fbo=i.createFramebuffer(o,o)).colorAttachment.set(u.texture)}i.bindFramebuffer.set(c.framebuffer),i.viewport.set([0,0,o,o]);var h=t.mat4.create();t.mat4.ortho(h,0,t.default$8,-t.default$8,0,0,1),t.mat4.translate(h,h,[0,-t.default$8,0]);var f=e.useProgram("hillshadePrepare");a.uniformMatrix4fv(f.uniforms.u_matrix,!1,h),a.uniform1f(f.uniforms.u_zoom,r.tileID.overscaledZ),a.uniform2fv(f.uniforms.u_dimension,[2*o,2*o]),a.uniform1i(f.uniforms.u_image,1),a.uniform1f(f.uniforms.u_maxzoom,n);var p=e.rasterBoundsBuffer;e.rasterBoundsVAO.bind(i,f,p,[]),a.drawArrays(a.TRIANGLE_STRIP,0,p.length),r.needsHillshadePrepare=!1}}function Sr(e,r,n,i,o){var s=i.paint.get("raster-fade-duration");if(s>0){var l=a.now(),c=(l-e.timeAdded)/s,u=r?(l-r.timeAdded)/s:-1,h=n.getSource(),f=o.coveringZoomLevel({tileSize:h.tileSize,roundZoom:h.roundZoom}),p=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),d=p&&e.refreshedUponExpiration?1:t.clamp(p?c:1-u,0,1);return e.refreshedUponExpiration&&c>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-d}:{opacity:d,mix:0}}return{opacity:1,mix:0}}function Er(e,r,n){var i=e.context,o=i.gl;i.lineWidth.set(1*a.devicePixelRatio);var s=n.posMatrix,l=e.useProgram("debug");i.setDepthMode(qt.disabled),i.setStencilMode(Ht.disabled),i.setColorMode(e.colorModeForRenderPass()),o.uniformMatrix4fv(l.uniforms.u_matrix,!1,s),o.uniform4f(l.uniforms.u_color,1,0,0,1),e.debugVAO.bind(i,l,e.debugBuffer,[]),o.drawArrays(o.LINE_STRIP,0,e.debugBuffer.length);for(var c=function(t,e,r,n){n=n||1;var i,a,o,s,l,c,u,h,f=[];for(i=0,a=t.length;i<a;i++)if(l=Cr[t[i]]){for(h=null,o=0,s=l[1].length;o<s;o+=2)-1===l[1][o]&&-1===l[1][o+1]?h=null:(c=e+l[1][o]*n,u=200-l[1][o+1]*n,h&&f.push(h.x,h.y,c,u),h={x:c,y:u});e+=l[0]*n}return f}(n.toString(),50,0,5),u=new t.PosArray,h=0;h<c.length;h+=2)u.emplaceBack(c[h],c[h+1]);var f=i.createVertexBuffer(u,Ke.members);(new Q).bind(i,l,f,[]),o.uniform4f(l.uniforms.u_color,1,1,1,1);for(var p=r.getTile(n).tileSize,d=t.default$8/(Math.pow(2,e.transform.zoom-n.overscaledZ)*p),g=[[-1,-1],[-1,1],[1,-1],[1,1]],m=0;m<g.length;m++){var v=g[m];o.uniformMatrix4fv(l.uniforms.u_matrix,!1,t.mat4.translate([],s,[d*v[0],d*v[1],0])),o.drawArrays(o.LINES,0,f.length)}o.uniform4f(l.uniforms.u_color,0,0,0,1),o.uniformMatrix4fv(l.uniforms.u_matrix,!1,s),o.drawArrays(o.LINES,0,f.length)}var Cr={" 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f=e.context.program.get(),p=h.programConfigurations.get(n.id),d=e.useProgram("heatmap",p),g=e.transform.zoom;(s||d.program!==f)&&(p.setUniforms(e.context,d,n.paint,{zoom:g}),s=!1),o.uniform1f(d.uniforms.u_extrude_scale,Te(u,1,g)),o.uniform1f(d.uniforms.u_intensity,n.paint.get("heatmap-intensity")),o.uniformMatrix4fv(d.uniforms.u_matrix,!1,c.posMatrix),d.draw(a,o.TRIANGLES,n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,p)}}}a.viewport.set([0,0,e.width,e.height])}else"translucent"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(a){n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new z(n,r.colorRamp,i.RGBA)),o.bind(i.LINEAR,i.CLAMP_TO_EDGE),n.setDepthMode(qt.disabled);var s=e.useProgram("heatmapTexture"),l=r.paint.get("heatmap-opacity");i.uniform1f(s.uniforms.u_opacity,l),i.uniform1i(s.uniforms.u_image,0),i.uniform1i(s.uniforms.u_color_ramp,1);var c=t.mat4.create();t.mat4.ortho(c,0,e.width,e.height,0,0,1),i.uniformMatrix4fv(s.uniforms.u_matrix,!1,c),i.uniform2f(s.uniforms.u_world,i.drawingBufferWidth,i.drawingBufferHeight),e.viewportVAO.bind(e.context,s,e.viewportBuffer,[]),i.drawArrays(i.TRIANGLE_STRIP,0,4)}}(e,n))},line:function(t,e,r,n){if("translucent"===t.renderPass&&0!==r.paint.get("line-opacity").constantOr(1)){var i=t.context;i.setDepthMode(t.depthModeForSublayer(0,qt.ReadOnly)),i.setColorMode(t.colorModeForRenderPass());for(var a,o=r.paint.get("line-dasharray")?"lineSDF":r.paint.get("line-pattern")?"linePattern":r.paint.get("line-gradient")?"lineGradient":"line",s=!0,l=0,c=n;l<c.length;l+=1){var u=c[l],h=e.getTile(u),f=h.getBucket(r);if(f){var p=f.programConfigurations.get(r.id),d=t.context.program.get(),g=t.useProgram(o,p),m=s||g.program!==d,v=a!==h.tileID.overscaledZ;m&&p.setUniforms(t.context,g,r.paint,{zoom:t.transform.zoom}),pr(g,t,h,f,r,u,p,m,v),a=h.tileID.overscaledZ,s=!1}}}},fill:function(e,r,n,i){var a=n.paint.get("fill-color"),o=n.paint.get("fill-opacity");if(0!==o.constantOr(1)){var s=e.context;s.setColorMode(e.colorModeForRenderPass());var l=n.paint.get("fill-pattern")||1!==a.constantOr(t.default$6.transparent).a||1!==o.constantOr(0)?"translucent":"opaque";e.renderPass===l&&(s.setDepthMode(e.depthModeForSublayer(1,"opaque"===e.renderPass?qt.ReadWrite:qt.ReadOnly)),vr(e,r,n,i,yr)),"translucent"===e.renderPass&&n.paint.get("fill-antialias")&&(s.lineWidth.set(2),s.setDepthMode(e.depthModeForSublayer(n.getPaintProperty("fill-outline-color")?2:0,qt.ReadOnly)),vr(e,r,n,i,xr))}},"fill-extrusion":function(e,r,n,i){if(0!==n.paint.get("fill-extrusion-opacity"))if("offscreen"===e.renderPass){!function(e,r){var n=e.context,i=n.gl,a=r.viewportFrame;if(e.depthRboNeedsClear&&e.setupOffscreenDepthRenderbuffer(),!a){var o=new z(n,{width:e.width,height:e.height,data:null},i.RGBA);o.bind(i.LINEAR,i.CLAMP_TO_EDGE),(a=r.viewportFrame=n.createFramebuffer(e.width,e.height)).colorAttachment.set(o.texture)}n.bindFramebuffer.set(a.framebuffer),a.depthAttachment.set(e.depthRbo),e.depthRboNeedsClear&&(n.clear({depth:1}),e.depthRboNeedsClear=!1),n.clear({color:t.default$6.transparent}),n.setStencilMode(Ht.disabled),n.setDepthMode(new qt(i.LEQUAL,qt.ReadWrite,[0,1])),n.setColorMode(e.colorModeForRenderPass())}(e,n);for(var a=!0,o=0,s=i;o<s.length;o+=1){var l=s[o],c=r.getTile(l),u=c.getBucket(n);u&&(Mr(e,0,n,c,l,u,a),a=!1)}}else"translucent"===e.renderPass&&function(t,e){var r=e.viewportFrame;if(r){var n=t.context,i=n.gl,a=t.useProgram("extrusionTexture");n.setStencilMode(Ht.disabled),n.setDepthMode(qt.disabled),n.setColorMode(t.colorModeForRenderPass()),n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,r.colorAttachment.get()),i.uniform1f(a.uniforms.u_opacity,e.paint.get("fill-extrusion-opacity")),i.uniform1i(a.uniforms.u_image,0);var o=wr.create();wr.ortho(o,0,t.width,t.height,0,0,1),i.uniformMatrix4fv(a.uniforms.u_matrix,!1,o),i.uniform2f(a.uniforms.u_world,i.drawingBufferWidth,i.drawingBufferHeight),t.viewportVAO.bind(n,a,t.viewportBuffer,[]),i.drawArrays(i.TRIANGLE_STRIP,0,4)}}(e,n)},hillshade:function(t,e,r,n){if("offscreen"===t.renderPass||"translucent"===t.renderPass){var i=t.context,a=e.getSource().maxzoom;i.setDepthMode(t.depthModeForSublayer(0,qt.ReadOnly)),i.setStencilMode(Ht.disabled),i.setColorMode(t.colorModeForRenderPass());for(var o=0,s=n;o<s.length;o+=1){var l=s[o],c=e.getTile(l);c.needsHillshadePrepare&&"offscreen"===t.renderPass?Tr(t,c,a):"translucent"===t.renderPass&&Ar(t,c,r)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if("translucent"===t.renderPass&&0!==r.paint.get("raster-opacity")){var i,a,o=t.context,s=o.gl,l=e.getSource(),c=t.useProgram("raster");o.setStencilMode(Ht.disabled),o.setColorMode(t.colorModeForRenderPass()),s.uniform1f(c.uniforms.u_brightness_low,r.paint.get("raster-brightness-min")),s.uniform1f(c.uniforms.u_brightness_high,r.paint.get("raster-brightness-max")),s.uniform1f(c.uniforms.u_saturation_factor,(i=r.paint.get("raster-saturation"))>0?1-1/(1.001-i):-i),s.uniform1f(c.uniforms.u_contrast_factor,(a=r.paint.get("raster-contrast"))>0?1/(1-a):1+a),s.uniform3fv(c.uniforms.u_spin_weights,function(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}(r.paint.get("raster-hue-rotate"))),s.uniform1f(c.uniforms.u_buffer_scale,1),s.uniform1i(c.uniforms.u_image0,0),s.uniform1i(c.uniforms.u_image1,1);for(var u=n.length&&n[0].overscaledZ,h=0,f=n;h<f.length;h+=1){var p=f[h];o.setDepthMode(t.depthModeForSublayer(p.overscaledZ-u,1===r.paint.get("raster-opacity")?qt.ReadWrite:qt.ReadOnly,s.LESS));var d=e.getTile(p),g=t.transform.calculatePosMatrix(p.toUnwrapped(),!0);d.registerFadeDuration(r.paint.get("raster-fade-duration")),s.uniformMatrix4fv(c.uniforms.u_matrix,!1,g);var m=e.findLoadedParent(p,0,{}),v=Sr(d,m,e,r,t.transform),y=void 0,x=void 0;if(o.activeTexture.set(s.TEXTURE0),d.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.activeTexture.set(s.TEXTURE1),m?(m.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),y=Math.pow(2,m.tileID.overscaledZ-d.tileID.overscaledZ),x=[d.tileID.canonical.x*y%1,d.tileID.canonical.y*y%1]):d.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),s.uniform2fv(c.uniforms.u_tl_parent,x||[0,0]),s.uniform1f(c.uniforms.u_scale_parent,y||1),s.uniform1f(c.uniforms.u_fade_t,v.mix),s.uniform1f(c.uniforms.u_opacity,v.opacity*r.paint.get("raster-opacity")),l instanceof tt){var b=l.boundsBuffer;l.boundsVAO.bind(o,c,b,[]),s.drawArrays(s.TRIANGLE_STRIP,0,b.length)}else if(d.maskedBoundsBuffer&&d.maskedIndexBuffer&&d.segments)c.draw(o,s.TRIANGLES,r.id,d.maskedBoundsBuffer,d.maskedIndexBuffer,d.segments);else{var _=t.rasterBoundsBuffer;t.rasterBoundsVAO.bind(o,c,_,[]),s.drawArrays(s.TRIANGLE_STRIP,0,_.length)}}}},background:function(t,e,r){var n=r.paint.get("background-color"),i=r.paint.get("background-opacity");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get("background-pattern"),u=c||1!==n.a||1!==i?"translucent":"opaque";if(t.renderPass===u){var h;if(a.setStencilMode(Ht.disabled),a.setDepthMode(t.depthModeForSublayer(0,"opaque"===u?qt.ReadWrite:qt.ReadOnly)),a.setColorMode(t.colorModeForRenderPass()),c){if(dr(c,t))return;h=t.useProgram("backgroundPattern"),gr(c,t,h),t.tileExtentPatternVAO.bind(a,h,t.tileExtentBuffer,[])}else h=t.useProgram("background"),o.uniform4fv(h.uniforms.u_color,[n.r,n.g,n.b,n.a]),t.tileExtentVAO.bind(a,h,t.tileExtentBuffer,[]);o.uniform1f(h.uniforms.u_opacity,i);for(var f=0,p=s.coveringTiles({tileSize:l});f<p.length;f+=1){var d=p[f];c&&mr({tileID:d,tileSize:l},t,h),o.uniformMatrix4fv(h.uniforms.u_matrix,!1,t.transform.calculatePosMatrix(d.toUnwrapped())),o.drawArrays(o.TRIANGLE_STRIP,0,t.tileExtentBuffer.length)}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)Er(t,e,r[n])}},zr=function(e,r){this.context=new Wt(e),this.transform=r,this._tileTextures={},this.setup(),this.numSublayers=Yt.maxUnderzooming+Yt.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.depthRboNeedsClear=!0,this.emptyProgramConfiguration=new t.default$24,this.crossTileSymbolIndex=new Ye};function Pr(t,e){if(t.row>e.row){var r=t;t=e,e=r}return{x0:t.column,y0:t.row,x1:e.column,y1:e.row,dx:e.column-t.column,dy:e.row-t.row}}function Ir(t,e,r,n,i){var a=Math.max(r,Math.floor(e.y0)),o=Math.min(n,Math.ceil(e.y1));if(t.x0===e.x0&&t.y0===e.y0?t.x0+e.dy/t.dy*t.dx<e.x1:t.x1-e.dy/t.dy*t.dx<e.x0){var s=t;t=e,e=s}for(var l=t.dx/t.dy,c=e.dx/e.dy,u=t.dx>0,h=e.dx<0,f=a;f<o;f++){var p=l*Math.max(0,Math.min(t.dy,f+u-t.y0))+t.x0,d=c*Math.max(0,Math.min(e.dy,f+h-e.y0))+e.x0;i(Math.floor(d),Math.ceil(p),f)}}function Or(t,e,r,n,i,a){var o,s=Pr(t,e),l=Pr(e,r),c=Pr(r,t);s.dy>l.dy&&(o=s,s=l,l=o),s.dy>c.dy&&(o=s,s=c,c=o),l.dy>c.dy&&(o=l,l=c,c=o),s.dy&&Ir(c,s,n,i,a),l.dy&&Ir(c,l,n,i,a)}zr.prototype.resize=function(t,e){var r=this.context.gl;if(this.width=t*a.devicePixelRatio,this.height=e*a.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var o=i[n];this.style._layers[o].resize()}this.depthRbo&&(r.deleteRenderbuffer(this.depthRbo),this.depthRbo=null)},zr.prototype.setup=function(){var e=this.context,r=new t.PosArray;r.emplaceBack(0,0),r.emplaceBack(t.default$8,0),r.emplaceBack(0,t.default$8),r.emplaceBack(t.default$8,t.default$8),this.tileExtentBuffer=e.createVertexBuffer(r,Ke.members),this.tileExtentVAO=new Q,this.tileExtentPatternVAO=new Q;var n=new t.PosArray;n.emplaceBack(0,0),n.emplaceBack(t.default$8,0),n.emplaceBack(t.default$8,t.default$8),n.emplaceBack(0,t.default$8),n.emplaceBack(0,0),this.debugBuffer=e.createVertexBuffer(n,Ke.members),this.debugVAO=new Q;var i=new t.RasterBoundsArray;i.emplaceBack(0,0,0,0),i.emplaceBack(t.default$8,0,t.default$8,0),i.emplaceBack(0,t.default$8,0,t.default$8),i.emplaceBack(t.default$8,t.default$8,t.default$8,t.default$8),this.rasterBoundsBuffer=e.createVertexBuffer(i,K.members),this.rasterBoundsVAO=new Q;var a=new t.PosArray;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ke.members),this.viewportVAO=new Q},zr.prototype.clearStencil=function(){var e=this.context,r=e.gl;e.setColorMode(Gt.disabled),e.setDepthMode(qt.disabled),e.setStencilMode(new Ht({func:r.ALWAYS,mask:0},0,255,r.ZERO,r.ZERO,r.ZERO));var n=t.mat4.create();t.mat4.ortho(n,0,this.width,this.height,0,0,1),t.mat4.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]);var i=this.useProgram("clippingMask");r.uniformMatrix4fv(i.uniforms.u_matrix,!1,n),this.viewportVAO.bind(e,i,this.viewportBuffer,[]),r.drawArrays(r.TRIANGLE_STRIP,0,4)},zr.prototype._renderTileClippingMasks=function(t){var e=this.context,r=e.gl;e.setColorMode(Gt.disabled),e.setDepthMode(qt.disabled);var n=1;this._tileClippingMaskIDs={};for(var i=0,a=t;i<a.length;i+=1){var o=a[i],s=this._tileClippingMaskIDs[o.key]=n++;e.setStencilMode(new Ht({func:r.ALWAYS,mask:0},s,255,r.KEEP,r.KEEP,r.REPLACE));var l=this.useProgram("clippingMask");r.uniformMatrix4fv(l.uniforms.u_matrix,!1,o.posMatrix),this.tileExtentVAO.bind(this.context,l,this.tileExtentBuffer,[]),r.drawArrays(r.TRIANGLE_STRIP,0,this.tileExtentBuffer.length)}},zr.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Ht({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},zr.prototype.colorModeForRenderPass=function(){var e=this.context.gl;return this._showOverdrawInspector?new Gt([e.CONSTANT_COLOR,e.ONE],new t.default$6(1/8,1/8,1/8,0),[!0,!0,!0,!0]):"opaque"===this.renderPass?Gt.unblended:Gt.alphaBlended},zr.prototype.depthModeForSublayer=function(t,e,r){var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon,i=n-1+this.depthRange;return new qt(r||this.context.gl.LEQUAL,e,[i,n])},zr.prototype.render=function(e,r){var n=this;for(var i in this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(a.now()),e.sourceCaches){var o=n.style.sourceCaches[i];o.used&&o.prepare(n.context)}var s=this.style._order,l=t.filterObject(this.style.sourceCaches,function(t){return"raster"===t.getSource().type||"raster-dem"===t.getSource().type}),c=function(e){var r=l[e];!function(e,r){for(var n=e.sort(function(t,e){return t.tileID.isLessThan(e.tileID)?-1:e.tileID.isLessThan(t.tileID)?1:0}),i=0;i<n.length;i++){var a={},o=n[i],s=n.slice(i+1);ar(o.tileID.wrapped(),o.tileID,s,new t.OverscaledTileID(0,o.tileID.wrap+1,0,0,0),a),o.setMask(a,r)}}(r.getVisibleCoordinates().map(function(t){return r.getTile(t)}),n.context)};for(var u in l)c(u);this.renderPass="offscreen";var h,f=[];this.depthRboNeedsClear=!0;for(var p=0;p<s.length;p++){var d=n.style._layers[s[p]];d.hasOffscreenPass()&&!d.isHidden(n.transform.zoom)&&(d.source!==(h&&h.id)&&(f=[],(h=n.style.sourceCaches[d.source])&&(f=h.getVisibleCoordinates()).reverse()),f.length&&n.renderLayer(n,h,d,f))}this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.default$6.black:t.default$6.transparent,depth:1}),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRange=(e._order.length+2)*this.numSublayers*this.depthEpsilon,this.renderPass="opaque";var g,m=[];for(this.currentLayer=s.length-1,this.currentLayer;this.currentLayer>=0;this.currentLayer--){var v=n.style._layers[s[n.currentLayer]];v.source!==(g&&g.id)&&(m=[],(g=n.style.sourceCaches[v.source])&&(n.clearStencil(),m=g.getVisibleCoordinates(),g.getSource().isTileClipped&&n._renderTileClippingMasks(m))),n.renderLayer(n,g,v,m)}this.renderPass="translucent";var y,x=[];for(this.currentLayer=0,this.currentLayer;this.currentLayer<s.length;this.currentLayer++){var b=n.style._layers[s[n.currentLayer]];b.source!==(y&&y.id)&&(x=[],(y=n.style.sourceCaches[b.source])&&(n.clearStencil(),x=y.getVisibleCoordinates(),y.getSource().isTileClipped&&n._renderTileClippingMasks(x)),x.reverse()),n.renderLayer(n,y,b,x)}if(this.options.showTileBoundaries){var _=this.style.sourceCaches[Object.keys(this.style.sourceCaches)[0]];_&&Lr.debug(this,_,_.getVisibleCoordinates())}},zr.prototype.setupOffscreenDepthRenderbuffer=function(){var t=this.context;this.depthRbo||(this.depthRbo=t.createRenderbuffer(t.gl.DEPTH_COMPONENT16,this.width,this.height))},zr.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||("background"===r.type||n.length)&&(this.id=r.id,Lr[r.type](t,e,r,n))},zr.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?"map"===i?this.transform.angle:0:"viewport"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:Te(r,n[0],this.transform.zoom),a?n[1]:Te(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.mat4.translate(u,e,c),u},zr.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},zr.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},zr.prototype._createProgramCached=function(t,e){this.cache=this.cache||{};var r=""+t+(e.cacheKey||"")+(this._showOverdrawInspector?"/overdraw":"");return this.cache[r]||(this.cache[r]=new ir(this.context,nr[t],e,this._showOverdrawInspector)),this.cache[r]},zr.prototype.useProgram=function(t,e){var r=this._createProgramCached(t,e||this.emptyProgramConfiguration);return this.context.program.set(r.program),r};var Dr=t.default$20.vec4,Rr=t.default$20.mat4,Br=t.default$20.mat2,Fr=function(t,e,r){this.tileSize=512,this._renderWorldCopies=void 0===r||r,this._minZoom=t||0,this._maxZoom=e||22,this.latRange=[-85.05113,85.05113],this.width=0,this.height=0,this._center=new G(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._posMatrixCache={},this._alignedPosMatrixCache={}},Nr={minZoom:{configurable:!0},maxZoom:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerPoint:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},unmodified:{configurable:!0},x:{configurable:!0},y:{configurable:!0},point:{configurable:!0}};Fr.prototype.clone=function(){var t=new Fr(this._minZoom,this._maxZoom,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._calcMatrices(),t},Nr.minZoom.get=function(){return this._minZoom},Nr.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Nr.maxZoom.get=function(){return this._maxZoom},Nr.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Nr.renderWorldCopies.get=function(){return this._renderWorldCopies},Nr.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Nr.worldSize.get=function(){return this.tileSize*this.scale},Nr.centerPoint.get=function(){return this.size._div(2)},Nr.size.get=function(){return new t.default$1(this.width,this.height)},Nr.bearing.get=function(){return-this.angle/Math.PI*180},Nr.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=Br.create(),Br.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Nr.pitch.get=function(){return this._pitch/Math.PI*180},Nr.pitch.set=function(e){var r=t.clamp(e,0,60)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Nr.fov.get=function(){return this._fov/Math.PI*180},Nr.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Nr.zoom.get=function(){return this._zoom},Nr.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Nr.center.get=function(){return this._center},Nr.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Fr.prototype.coveringZoomLevel=function(t){return(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize))},Fr.prototype.getVisibleUnwrappedCoordinates=function(e){var r=this.pointCoordinate(new t.default$1(0,0),0),n=this.pointCoordinate(new t.default$1(this.width,0),0),i=Math.floor(r.column),a=Math.floor(n.column),o=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var s=i;s<=a;s++)0!==s&&o.push(new t.UnwrappedTileID(s,e));return o},Fr.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=this.pointCoordinate(this.centerPoint,r),a=new t.default$1(i.column-.5,i.row-.5);return function(e,r,n,i){void 0===i&&(i=!0);var a=1<<e,o={};function s(r,s,l){var c,u,h,f;if(l>=0&&l<=a)for(c=r;c<s;c++)u=Math.floor(c/a),h=(c%a+a)%a,0!==u&&!0!==i||(f=new t.OverscaledTileID(n,u,e,h,l),o[f.key]=f)}return Or(r[0],r[1],r[2],0,a,s),Or(r[2],r[3],r[0],0,a,s),Object.keys(o).map(function(t){return o[t]})}(r,[this.pointCoordinate(new t.default$1(0,0),r),this.pointCoordinate(new t.default$1(this.width,0),r),this.pointCoordinate(new t.default$1(this.width,this.height),r),this.pointCoordinate(new t.default$1(0,this.height),r)],e.reparseOverscaled?n:r,this._renderWorldCopies).sort(function(t,e){return a.dist(t.canonical)-a.dist(e.canonical)})},Fr.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Nr.unmodified.get=function(){return this._unmodified},Fr.prototype.zoomScale=function(t){return Math.pow(2,t)},Fr.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},Fr.prototype.project=function(e){return new t.default$1(this.lngX(e.lng),this.latY(e.lat))},Fr.prototype.unproject=function(t){return new G(this.xLng(t.x),this.yLat(t.y))},Nr.x.get=function(){return this.lngX(this.center.lng)},Nr.y.get=function(){return this.latY(this.center.lat)},Nr.point.get=function(){return new t.default$1(this.x,this.y)},Fr.prototype.lngX=function(t){return(180+t)*this.worldSize/360},Fr.prototype.latY=function(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))*this.worldSize/360},Fr.prototype.xLng=function(t){return 360*t/this.worldSize-180},Fr.prototype.yLat=function(t){var e=180-360*t/this.worldSize;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90},Fr.prototype.setLocationAtPoint=function(t,e){var r=this.pointCoordinate(e)._sub(this.pointCoordinate(this.centerPoint));this.center=this.coordinateLocation(this.locationCoordinate(t)._sub(r)),this._renderWorldCopies&&(this.center=this.center.wrap())},Fr.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},Fr.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},Fr.prototype.locationCoordinate=function(e){return new t.default$17(this.lngX(e.lng)/this.tileSize,this.latY(e.lat)/this.tileSize,this.zoom).zoomTo(this.tileZoom)},Fr.prototype.coordinateLocation=function(t){var e=t.zoomTo(this.zoom);return new G(this.xLng(e.column*this.tileSize),this.yLat(e.row*this.tileSize))},Fr.prototype.pointCoordinate=function(e,r){void 0===r&&(r=this.tileZoom);var n=[e.x,e.y,0,1],i=[e.x,e.y,1,1];Dr.transformMat4(n,n,this.pixelMatrixInverse),Dr.transformMat4(i,i,this.pixelMatrixInverse);var a=n[3],o=i[3],s=n[0]/a,l=i[0]/o,c=n[1]/a,u=i[1]/o,h=n[2]/a,f=i[2]/o,p=h===f?0:(0-h)/(f-h);return new t.default$17(t.number(s,l,p)/this.tileSize,t.number(c,u,p)/this.tileSize,this.zoom)._zoomTo(r)},Fr.prototype.coordinatePoint=function(e){var r=e.zoomTo(this.zoom),n=[r.column*this.tileSize,r.row*this.tileSize,0,1];return Dr.transformMat4(n,n,this.pixelMatrix),new t.default$1(n[0]/n[3],n[1]/n[3])},Fr.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=Rr.identity(new Float64Array(16));return Rr.translate(l,l,[s*o,a.y*o,0]),Rr.scale(l,l,[o/t.default$8,o/t.default$8,1]),Rr.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},Fr.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var h=this.latRange;a=this.latY(h[1]),e=(o=this.latY(h[0]))-a<c.y?c.y/(o-a):0}if(this.lngRange){var f=this.lngRange;s=this.lngX(f[0]),r=(l=this.lngX(f[1]))-s<c.x?c.x/(l-s):0}var p=Math.max(r||0,e||0);if(p)return this.center=this.unproject(new t.default$1(r?(l+s)/2:this.x,e?(o+a)/2:this.y)),this.zoom+=this.scaleZoom(p),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var d=this.y,g=c.y/2;d-g<a&&(i=a+g),d+g>o&&(i=o-g)}if(this.lngRange){var m=this.x,v=c.x/2;m-v<s&&(n=s+v),m+v>l&&(n=l-v)}void 0===n&&void 0===i||(this.center=this.unproject(new t.default$1(void 0!==n?n:this.x,void 0!==i?i:this.y))),this._unmodified=u,this._constraining=!1}},Fr.prototype._calcMatrices=function(){if(this.height){this.cameraToCenterDistance=.5/Math.tan(this._fov/2)*this.height;var t=this._fov/2,e=Math.PI/2+this._pitch,r=Math.sin(t)*this.cameraToCenterDistance/Math.sin(Math.PI-e-t),n=this.x,i=this.y,a=1.01*(Math.cos(Math.PI/2-this._pitch)*r+this.cameraToCenterDistance),o=new Float64Array(16);Rr.perspective(o,this._fov,this.width/this.height,1,a),Rr.scale(o,o,[1,-1,1]),Rr.translate(o,o,[0,0,-this.cameraToCenterDistance]),Rr.rotateX(o,o,this._pitch),Rr.rotateZ(o,o,this.angle),Rr.translate(o,o,[-n,-i,0]);var s=this.worldSize/(2*Math.PI*6378137*Math.abs(Math.cos(this.center.lat*(Math.PI/180))));Rr.scale(o,o,[1,1,s,1]),this.projMatrix=o;var l=this.width%2/2,c=this.height%2/2,u=Math.cos(this.angle),h=Math.sin(this.angle),f=n-Math.round(n)+u*l+h*c,p=i-Math.round(i)+u*c+h*l,d=new Float64Array(o);if(Rr.translate(d,d,[f>.5?f-1:f,p>.5?p-1:p,0]),this.alignedProjMatrix=d,o=Rr.create(),Rr.scale(o,o,[this.width/2,-this.height/2,1]),Rr.translate(o,o,[1,-1,0]),this.pixelMatrix=Rr.multiply(new Float64Array(16),o,this.projMatrix),!(o=Rr.invert(new Float64Array(16),this.pixelMatrix)))throw new Error("failed to invert matrix");this.pixelMatrixInverse=o,this._posMatrixCache={},this._alignedPosMatrixCache={}}},Fr.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.default$1(0,0)).zoomTo(this.zoom),r=[e.column*this.tileSize,e.row*this.tileSize,0,1];return Dr.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},Object.defineProperties(Fr.prototype,Nr);var jr=function(){var e,r,n,i;t.bindAll(["_onHashChange","_updateHash"],this),this._updateHash=(e=this._updateHashUnthrottled.bind(this),300,r=!1,n=0,i=function(){n=0,r&&(e(),n=setTimeout(i,300),r=!1)},function(){return r=!0,n||i(),n})};jr.prototype.addTo=function(e){return this._map=e,t.default.addEventListener("hashchange",this._onHashChange,!1),this._map.on("moveend",this._updateHash),this},jr.prototype.remove=function(){return t.default.removeEventListener("hashchange",this._onHashChange,!1),this._map.off("moveend",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},jr.prototype.getHashString=function(t){var e=this._map.getCenter(),r=Math.round(100*this._map.getZoom())/100,n=Math.ceil((r*Math.LN2+Math.log(512/360/.5))/Math.LN10),i=Math.pow(10,n),a=Math.round(e.lng*i)/i,o=Math.round(e.lat*i)/i,s=this._map.getBearing(),l=this._map.getPitch(),c="";return c+=t?"#/"+a+"/"+o+"/"+r:"#"+r+"/"+o+"/"+a,(s||l)&&(c+="/"+Math.round(10*s)/10),l&&(c+="/"+Math.round(l)),c},jr.prototype._onHashChange=function(){var e=t.default.location.hash.replace("#","").split("/");return e.length>=3&&(this._map.jumpTo({center:[+e[2],+e[1]],zoom:+e[0],bearing:+(e[3]||0),pitch:+(e[4]||0)}),!0)},jr.prototype._updateHashUnthrottled=function(){var e=this.getHashString();t.default.history.replaceState(t.default.history.state,"",e)};var Vr=function(e){function r(r,n,i,a){void 0===a&&(a={});var o=s.mousePos(n.getCanvasContainer(),i),l=n.unproject(o);e.call(this,r,t.extend({point:o,lngLat:l,originalEvent:i},a)),this._defaultPrevented=!1,this.target=n}e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r;var n={defaultPrevented:{configurable:!0}};return r.prototype.preventDefault=function(){this._defaultPrevented=!0},n.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(r.prototype,n),r}(t.Event),Ur=function(e){function r(r,n,i){var a=s.touchPos(n.getCanvasContainer(),i),o=a.map(function(t){return n.unproject(t)}),l=a.reduce(function(t,e,r,n){return t.add(e.div(n.length))},new t.default$1(0,0)),c=n.unproject(l);e.call(this,r,{points:a,point:l,lngLats:o,lngLat:c,originalEvent:i}),this._defaultPrevented=!1}e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r;var n={defaultPrevented:{configurable:!0}};return r.prototype.preventDefault=function(){this._defaultPrevented=!0},n.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(r.prototype,n),r}(t.Event),qr=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Hr=function(e){this._map=e,this._el=e.getCanvasContainer(),this._delta=0,t.bindAll(["_onWheel","_onTimeout","_onScrollFrame","_onScrollFinished"],this)};Hr.prototype.isEnabled=function(){return!!this._enabled},Hr.prototype.isActive=function(){return!!this._active},Hr.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&"center"===t.around)},Hr.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Hr.prototype.onWheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.default.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=a.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type="wheel":0!==r&&Math.abs(r)<4?this._type="trackpad":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?"trackpad":"wheel",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this.isActive()||this._start(e)),e.preventDefault()}},Hr.prototype._onTimeout=function(t){this._type="wheel",this._delta-=this._lastValue,this.isActive()||this._start(t)},Hr.prototype._start=function(e){if(this._delta){this._frameId&&(this._map._cancelRenderFrame(this._frameId),this._frameId=null),this._active=!0,this._map.fire(new t.Event("movestart",{originalEvent:e})),this._map.fire(new t.Event("zoomstart",{originalEvent:e})),this._finishTimeout&&clearTimeout(this._finishTimeout);var r=s.mousePos(this._el,e);this._around=G.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(r)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=this._map._requestRenderFrame(this._onScrollFrame))}},Hr.prototype._onScrollFrame=function(){var e=this;if(this._frameId=null,this.isActive()){var r=this._map.transform;if(0!==this._delta){var n="wheel"===this._type&&Math.abs(this._delta)>4.000244140625?1/450:.01,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var o="number"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(o*i))),"wheel"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var s=!1;if("wheel"===this._type){var l=Math.min((a.now()-this._lastWheelEventTime)/200,1),c=this._easing(l);r.zoom=t.number(this._startZoom,this._targetZoom,c),l<1?this._frameId||(this._frameId=this._map._requestRenderFrame(this._onScrollFrame)):s=!0}else r.zoom=this._targetZoom,s=!0;r.setLocationAtPoint(this._around,this._aroundPoint),this._map.fire(new t.Event("move",{originalEvent:this._lastWheelEvent})),this._map.fire(new t.Event("zoom",{originalEvent:this._lastWheelEvent})),s&&(this._active=!1,this._finishTimeout=setTimeout(function(){e._map.fire(new t.Event("zoomend",{originalEvent:e._lastWheelEvent})),e._map.fire(new t.Event("moveend",{originalEvent:e._lastWheelEvent})),delete e._targetZoom},200))}},Hr.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(a.now()-n.start)/n.duration,o=n.easing(i+.01)-n.easing(i),s=.27/Math.sqrt(o*o+1e-4)*.01,l=Math.sqrt(.0729-s*s);r=t.bezier(s,l,.25,1)}return this._prevEase={start:a.now(),duration:e,easing:r},r};var Gr=function(e){this._map=e,this._el=e.getCanvasContainer(),this._container=e.getContainer(),t.bindAll(["_onMouseMove","_onMouseUp","_onKeyDown"],this)};Gr.prototype.isEnabled=function(){return!!this._enabled},Gr.prototype.isActive=function(){return!!this._active},Gr.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Gr.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Gr.prototype.onMouseDown=function(e){this.isEnabled()&&e.shiftKey&&0===e.button&&(t.default.document.addEventListener("mousemove",this._onMouseMove,!1),t.default.document.addEventListener("keydown",this._onKeyDown,!1),t.default.document.addEventListener("mouseup",this._onMouseUp,!1),s.disableDrag(),this._startPos=s.mousePos(this._el,e),this._active=!0)},Gr.prototype._onMouseMove=function(t){var e=this._startPos,r=s.mousePos(this._el,t);this._box||(this._box=s.create("div","mapboxgl-boxzoom",this._container),this._container.classList.add("mapboxgl-crosshair"),this._fireEvent("boxzoomstart",t));var n=Math.min(e.x,r.x),i=Math.max(e.x,r.x),a=Math.min(e.y,r.y),o=Math.max(e.y,r.y);s.setTransform(this._box,"translate("+n+"px,"+a+"px)"),this._box.style.width=i-n+"px",this._box.style.height=o-a+"px"},Gr.prototype._onMouseUp=function(e){if(0===e.button){var r=this._startPos,n=s.mousePos(this._el,e),i=(new W).extend(this._map.unproject(r)).extend(this._map.unproject(n));this._finish(),s.suppressClick(),r.x===n.x&&r.y===n.y?this._fireEvent("boxzoomcancel",e):this._map.fitBounds(i,{linear:!0}).fire(new t.Event("boxzoomend",{originalEvent:e,boxZoomBounds:i}))}},Gr.prototype._onKeyDown=function(t){27===t.keyCode&&(this._finish(),this._fireEvent("boxzoomcancel",t))},Gr.prototype._finish=function(){this._active=!1,t.default.document.removeEventListener("mousemove",this._onMouseMove,!1),t.default.document.removeEventListener("keydown",this._onKeyDown,!1),t.default.document.removeEventListener("mouseup",this._onMouseUp,!1),this._container.classList.remove("mapboxgl-crosshair"),this._box&&(s.remove(this._box),this._box=null),s.enableDrag()},Gr.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Wr=t.bezier(0,0,.25,1),Yr=function(e,r){this._map=e,this._el=r.element||e.getCanvasContainer(),this._state="disabled",this._button=r.button||"right",this._bearingSnap=r.bearingSnap||0,this._pitchWithRotate=!1!==r.pitchWithRotate,t.bindAll(["_onMouseMove","_onMouseUp","_onBlur","_onDragFrame"],this)};Yr.prototype.isEnabled=function(){return"disabled"!==this._state},Yr.prototype.isActive=function(){return"active"===this._state},Yr.prototype.enable=function(){this.isEnabled()||(this._state="enabled")},Yr.prototype.disable=function(){if(this.isEnabled())switch(this._state){case"active":this._state="disabled",this._unbind(),this._deactivate(),this._fireEvent("rotateend"),this._pitchWithRotate&&this._fireEvent("pitchend"),this._fireEvent("moveend");break;case"pending":this._state="disabled",this._unbind();break;default:this._state="disabled"}},Yr.prototype.onMouseDown=function(e){if("enabled"===this._state){if("right"===this._button){if(this._eventButton=s.mouseButton(e),this._eventButton!==(e.ctrlKey?0:2))return}else{if(e.ctrlKey||0!==s.mouseButton(e))return;this._eventButton=0}s.disableDrag(),t.default.document.addEventListener("mousemove",this._onMouseMove,{capture:!0}),t.default.document.addEventListener("mouseup",this._onMouseUp),t.default.addEventListener("blur",this._onBlur),this._state="pending",this._inertia=[[a.now(),this._map.getBearing()]],this._previousPos=s.mousePos(this._el,e),this._center=this._map.transform.centerPoint,e.preventDefault()}},Yr.prototype._onMouseMove=function(t){this._lastMoveEvent=t,this._pos=s.mousePos(this._el,t),"pending"===this._state&&(this._state="active",this._fireEvent("rotatestart",t),this._fireEvent("movestart",t),this._pitchWithRotate&&this._fireEvent("pitchstart",t)),this._frameId||(this._frameId=this._map._requestRenderFrame(this._onDragFrame))},Yr.prototype._onDragFrame=function(){this._frameId=null;var t=this._lastMoveEvent;if(t){var e=this._map.transform,r=this._previousPos,n=this._pos,i=.8*(r.x-n.x),o=-.5*(r.y-n.y),s=e.bearing-i,l=e.pitch-o,c=this._inertia,u=c[c.length-1];this._drainInertiaBuffer(),c.push([a.now(),this._map._normalizeBearing(s,u[1])]),e.bearing=s,this._pitchWithRotate&&(this._fireEvent("pitch",t),e.pitch=l),this._fireEvent("rotate",t),this._fireEvent("move",t),delete this._lastMoveEvent,this._previousPos=this._pos}},Yr.prototype._onMouseUp=function(t){if(s.mouseButton(t)===this._eventButton)switch(this._state){case"active":this._state="enabled",s.suppressClick(),this._unbind(),this._deactivate(),this._inertialRotate(t);break;case"pending":this._state="enabled",this._unbind()}},Yr.prototype._onBlur=function(t){switch(this._state){case"active":this._state="enabled",this._unbind(),this._deactivate(),this._fireEvent("rotateend",t),this._pitchWithRotate&&this._fireEvent("pitchend",t),this._fireEvent("moveend",t);break;case"pending":this._state="enabled",this._unbind()}},Yr.prototype._unbind=function(){t.default.document.removeEventListener("mousemove",this._onMouseMove,{capture:!0}),t.default.document.removeEventListener("mouseup",this._onMouseUp),t.default.removeEventListener("blur",this._onBlur),s.enableDrag()},Yr.prototype._deactivate=function(){this._frameId&&(this._map._cancelRenderFrame(this._frameId),this._frameId=null),delete this._lastMoveEvent,delete this._previousPos},Yr.prototype._inertialRotate=function(t){var e=this;this._fireEvent("rotateend",t),this._drainInertiaBuffer();var r=this._map,n=r.getBearing(),i=this._inertia,a=function(){Math.abs(n)<e._bearingSnap?r.resetNorth({noMoveStart:!0},{originalEvent:t}):e._fireEvent("moveend",t),e._pitchWithRotate&&e._fireEvent("pitchend",t)};if(i.length<2)a();else{var o=i[0],s=i[i.length-1],l=i[i.length-2],c=r._normalizeBearing(n,l[1]),u=s[1]-o[1],h=u<0?-1:1,f=(s[0]-o[0])/1e3;if(0!==u&&0!==f){var p=Math.abs(u*(.25/f));p>180&&(p=180);var d=p/180;c+=h*p*(d/2),Math.abs(r._normalizeBearing(c,0))<this._bearingSnap&&(c=r._normalizeBearing(0,c)),r.rotateTo(c,{duration:1e3*d,easing:Wr,noMoveStart:!0},{originalEvent:t})}else a()}},Yr.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},Yr.prototype._drainInertiaBuffer=function(){for(var t=this._inertia,e=a.now();t.length>0&&e-t[0][0]>160;)t.shift()};var Xr=t.bezier(0,0,.3,1),Zr=function(e){this._map=e,this._el=e.getCanvasContainer(),this._state="disabled",t.bindAll(["_onMove","_onMouseUp","_onTouchEnd","_onBlur","_onDragFrame"],this)};Zr.prototype.isEnabled=function(){return"disabled"!==this._state},Zr.prototype.isActive=function(){return"active"===this._state},Zr.prototype.enable=function(){this.isEnabled()||(this._el.classList.add("mapboxgl-touch-drag-pan"),this._state="enabled")},Zr.prototype.disable=function(){if(this.isEnabled())switch(this._el.classList.remove("mapboxgl-touch-drag-pan"),this._state){case"active":this._state="disabled",this._unbind(),this._deactivate(),this._fireEvent("dragend"),this._fireEvent("moveend");break;case"pending":this._state="disabled",this._unbind();break;default:this._state="disabled"}},Zr.prototype.onMouseDown=function(e){"enabled"===this._state&&(e.ctrlKey||0!==s.mouseButton(e)||(s.addEventListener(t.default.document,"mousemove",this._onMove,{capture:!0}),s.addEventListener(t.default.document,"mouseup",this._onMouseUp),this._start(e)))},Zr.prototype.onTouchStart=function(e){"enabled"===this._state&&(e.touches.length>1||(s.addEventListener(t.default.document,"touchmove",this._onMove,{capture:!0,passive:!1}),s.addEventListener(t.default.document,"touchend",this._onTouchEnd),this._start(e)))},Zr.prototype._start=function(e){t.default.addEventListener("blur",this._onBlur),this._state="pending",this._previousPos=s.mousePos(this._el,e),this._inertia=[[a.now(),this._previousPos]]},Zr.prototype._onMove=function(t){this._lastMoveEvent=t,t.preventDefault(),this._pos=s.mousePos(this._el,t),this._drainInertiaBuffer(),this._inertia.push([a.now(),this._pos]),"pending"===this._state&&(this._state="active",this._fireEvent("dragstart",t),this._fireEvent("movestart",t)),this._frameId||(this._frameId=this._map._requestRenderFrame(this._onDragFrame))},Zr.prototype._onDragFrame=function(){this._frameId=null;var t=this._lastMoveEvent;if(t){var e=this._map.transform;e.setLocationAtPoint(e.pointLocation(this._previousPos),this._pos),this._fireEvent("drag",t),this._fireEvent("move",t),this._previousPos=this._pos,delete this._lastMoveEvent}},Zr.prototype._onMouseUp=function(t){if(0===s.mouseButton(t))switch(this._state){case"active":this._state="enabled",s.suppressClick(),this._unbind(),this._deactivate(),this._inertialPan(t);break;case"pending":this._state="enabled",this._unbind()}},Zr.prototype._onTouchEnd=function(t){switch(this._state){case"active":this._state="enabled",this._unbind(),this._deactivate(),this._inertialPan(t);break;case"pending":this._state="enabled",this._unbind()}},Zr.prototype._onBlur=function(t){switch(this._state){case"active":this._state="enabled",this._unbind(),this._deactivate(),this._fireEvent("dragend",t),this._fireEvent("moveend",t);break;case"pending":this._state="enabled",this._unbind()}},Zr.prototype._unbind=function(){s.removeEventListener(t.default.document,"touchmove",this._onMove,{capture:!0,passive:!1}),s.removeEventListener(t.default.document,"touchend",this._onTouchEnd),s.removeEventListener(t.default.document,"mousemove",this._onMove,{capture:!0}),s.removeEventListener(t.default.document,"mouseup",this._onMouseUp),s.removeEventListener(t.default,"blur",this._onBlur)},Zr.prototype._deactivate=function(){this._frameId&&(this._map._cancelRenderFrame(this._frameId),this._frameId=null),delete this._lastMoveEvent,delete this._previousPos,delete this._pos},Zr.prototype._inertialPan=function(t){this._fireEvent("dragend",t),this._drainInertiaBuffer();var e=this._inertia;if(e.length<2)this._fireEvent("moveend",t);else{var r=e[e.length-1],n=e[0],i=r[1].sub(n[1]),a=(r[0]-n[0])/1e3;if(0===a||r[1].equals(n[1]))this._fireEvent("moveend",t);else{var o=i.mult(.3/a),s=o.mag();s>1400&&(s=1400,o._unit()._mult(s));var l=s/750,c=o.mult(-l/2);this._map.panBy(c,{duration:1e3*l,easing:Xr,noMoveStart:!0},{originalEvent:t})}}},Zr.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},Zr.prototype._drainInertiaBuffer=function(){for(var t=this._inertia,e=a.now();t.length>0&&e-t[0][0]>160;)t.shift()};var $r=function(e){this._map=e,this._el=e.getCanvasContainer(),t.bindAll(["_onKeyDown"],this)};function Jr(t){return t*(2-t)}$r.prototype.isEnabled=function(){return!!this._enabled},$r.prototype.enable=function(){this.isEnabled()||(this._el.addEventListener("keydown",this._onKeyDown,!1),this._enabled=!0)},$r.prototype.disable=function(){this.isEnabled()&&(this._el.removeEventListener("keydown",this._onKeyDown),this._enabled=!1)},$r.prototype._onKeyDown=function(t){if(!(t.altKey||t.ctrlKey||t.metaKey)){var e=0,r=0,n=0,i=0,a=0;switch(t.keyCode){case 61:case 107:case 171:case 187:e=1;break;case 189:case 109:case 173:e=-1;break;case 37:t.shiftKey?r=-1:(t.preventDefault(),i=-1);break;case 39:t.shiftKey?r=1:(t.preventDefault(),i=1);break;case 38:t.shiftKey?n=1:(t.preventDefault(),a=-1);break;case 40:t.shiftKey?n=-1:(a=1,t.preventDefault());break;default:return}var o=this._map,s=o.getZoom(),l={duration:300,delayEndEvents:500,easing:Jr,zoom:e?Math.round(s)+e*(t.shiftKey?2:1):s,bearing:o.getBearing()+15*r,pitch:o.getPitch()+10*n,offset:[100*-i,100*-a],center:o.getCenter()};o.easeTo(l,{originalEvent:t})}};var Kr=function(e){this._map=e,t.bindAll(["_onDblClick","_onZoomEnd"],this)};Kr.prototype.isEnabled=function(){return!!this._enabled},Kr.prototype.isActive=function(){return!!this._active},Kr.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Kr.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Kr.prototype.onTouchStart=function(t){var e=this;this.isEnabled()&&(t.points.length>1||(this._tapped?(clearTimeout(this._tapped),this._tapped=null,this._zoom(t)):this._tapped=setTimeout(function(){e._tapped=null},300)))},Kr.prototype.onDblClick=function(t){this.isEnabled()&&(t.originalEvent.preventDefault(),this._zoom(t))},Kr.prototype._zoom=function(t){this._active=!0,this._map.on("zoomend",this._onZoomEnd),this._map.zoomTo(this._map.getZoom()+(t.originalEvent.shiftKey?-1:1),{around:t.lngLat},t)},Kr.prototype._onZoomEnd=function(){this._active=!1,this._map.off("zoomend",this._onZoomEnd)};var Qr=t.bezier(0,0,.15,1),tn=function(e){this._map=e,this._el=e.getCanvasContainer(),t.bindAll(["_onMove","_onEnd","_onTouchFrame"],this)};tn.prototype.isEnabled=function(){return!!this._enabled},tn.prototype.enable=function(t){this.isEnabled()||(this._el.classList.add("mapboxgl-touch-zoom-rotate"),this._enabled=!0,this._aroundCenter=!!t&&"center"===t.around)},tn.prototype.disable=function(){this.isEnabled()&&(this._el.classList.remove("mapboxgl-touch-zoom-rotate"),this._enabled=!1)},tn.prototype.disableRotation=function(){this._rotationDisabled=!0},tn.prototype.enableRotation=function(){this._rotationDisabled=!1},tn.prototype.onStart=function(e){if(this.isEnabled()&&2===e.touches.length){var r=s.mousePos(this._el,e.touches[0]),n=s.mousePos(this._el,e.touches[1]);this._startVec=r.sub(n),this._gestureIntent=void 0,this._inertia=[],s.addEventListener(t.default.document,"touchmove",this._onMove,{passive:!1}),s.addEventListener(t.default.document,"touchend",this._onEnd)}},tn.prototype._getTouchEventData=function(t){var e=s.mousePos(this._el,t.touches[0]),r=s.mousePos(this._el,t.touches[1]),n=e.sub(r);return{vec:n,center:e.add(r).div(2),scale:n.mag()/this._startVec.mag(),bearing:this._rotationDisabled?0:180*n.angleWith(this._startVec)/Math.PI}},tn.prototype._onMove=function(e){if(2===e.touches.length){var r=this._getTouchEventData(e),n=r.vec,i=r.scale,a=r.bearing;if(!this._gestureIntent){var o=Math.abs(1-i)>.15;Math.abs(a)>10?this._gestureIntent="rotate":o&&(this._gestureIntent="zoom"),this._gestureIntent&&(this._map.fire(new t.Event(this._gestureIntent+"start",{originalEvent:e})),this._map.fire(new t.Event("movestart",{originalEvent:e})),this._startVec=n)}this._lastTouchEvent=e,this._frameId||(this._frameId=this._map._requestRenderFrame(this._onTouchFrame)),e.preventDefault()}},tn.prototype._onTouchFrame=function(){this._frameId=null;var e=this._gestureIntent;if(e){var r=this._map.transform;this._startScale||(this._startScale=r.scale,this._startBearing=r.bearing);var n=this._getTouchEventData(this._lastTouchEvent),i=n.center,o=n.bearing,s=n.scale,l=r.pointLocation(i),c=r.locationPoint(l);"rotate"===e&&(r.bearing=this._startBearing+o),r.zoom=r.scaleZoom(this._startScale*s),r.setLocationAtPoint(l,c),this._map.fire(new t.Event(e,{originalEvent:this._lastTouchEvent})),this._map.fire(new t.Event("move",{originalEvent:this._lastTouchEvent})),this._drainInertiaBuffer(),this._inertia.push([a.now(),s,i])}},tn.prototype._onEnd=function(e){s.removeEventListener(t.default.document,"touchmove",this._onMove,{passive:!1}),s.removeEventListener(t.default.document,"touchend",this._onEnd);var r=this._gestureIntent,n=this._startScale;if(this._frameId&&(this._map._cancelRenderFrame(this._frameId),this._frameId=null),delete this._gestureIntent,delete this._startScale,delete this._startBearing,delete this._lastTouchEvent,r){this._map.fire(new t.Event(r+"end",{originalEvent:e})),this._drainInertiaBuffer();var i=this._inertia,a=this._map;if(i.length<2)a.snapToNorth({},{originalEvent:e});else{var o=i[i.length-1],l=i[0],c=a.transform.scaleZoom(n*o[1]),u=a.transform.scaleZoom(n*l[1]),h=c-u,f=(o[0]-l[0])/1e3,p=o[2];if(0!==f&&c!==u){var d=.15*h/f;Math.abs(d)>2.5&&(d=d>0?2.5:-2.5);var g=1e3*Math.abs(d/(12*.15)),m=c+d*g/2e3;m<0&&(m=0),a.easeTo({zoom:m,duration:g,easing:Qr,around:this._aroundCenter?a.getCenter():a.unproject(p),noMoveStart:!0},{originalEvent:e})}else a.snapToNorth({},{originalEvent:e})}}},tn.prototype._drainInertiaBuffer=function(){for(var t=this._inertia,e=a.now();t.length>2&&e-t[0][0]>160;)t.shift()};var en={scrollZoom:Hr,boxZoom:Gr,dragRotate:Yr,dragPan:Zr,keyboard:$r,doubleClickZoom:Kr,touchZoomRotate:tn},rn=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll(["_renderFrameCallback"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return this.transform.center},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.default$1.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.fitBounds=function(e,r,n){if("number"==typeof(r=t.extend({padding:{top:0,bottom:0,right:0,left:0},offset:[0,0],maxZoom:this.transform.maxZoom},r)).padding){var i=r.padding;r.padding={top:i,bottom:i,right:i,left:i}}if(!t.default$10(Object.keys(r.padding).sort(function(t,e){return t<e?-1:t>e?1:0}),["bottom","left","right","top"]))return t.warnOnce("options.padding must be a positive number, or an Object with keys 'bottom', 'left', 'right', 'top'"),this;e=W.convert(e);var a=[(r.padding.left-r.padding.right)/2,(r.padding.top-r.padding.bottom)/2],o=Math.min(r.padding.right,r.padding.left),s=Math.min(r.padding.top,r.padding.bottom);r.offset=[r.offset[0]+a[0],r.offset[1]+a[1]];var l=t.default$1.convert(r.offset),c=this.transform,u=c.project(e.getNorthWest()),h=c.project(e.getSouthEast()),f=h.sub(u),p=(c.width-2*o-2*Math.abs(l.x))/f.x,d=(c.height-2*s-2*Math.abs(l.y))/f.y;return d<0||p<0?(t.warnOnce("Map cannot fit within canvas with the given bounds, padding, and/or offset."),this):(r.center=c.unproject(u.add(h).div(2)),r.zoom=Math.min(c.scaleZoom(c.scale*Math.min(p,d)),r.maxZoom),r.bearing=0,r.linear?this.easeTo(r,n):this.flyTo(r,n))},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return"zoom"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=G.convert(e.center)),"bearing"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),"pitch"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),this.fire(new t.Event("movestart",r)).fire(new t.Event("move",r)),i&&this.fire(new t.Event("zoomstart",r)).fire(new t.Event("zoom",r)).fire(new t.Event("zoomend",r)),a&&this.fire(new t.Event("rotatestart",r)).fire(new t.Event("rotate",r)).fire(new t.Event("rotateend",r)),o&&this.fire(new t.Event("pitchstart",r)).fire(new t.Event("pitch",r)).fire(new t.Event("pitchend",r)),this.fire(new t.Event("moveend",r))},r.prototype.easeTo=function(e,r){var n=this;this.stop(),!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l="zoom"in e?+e.zoom:a,c="bearing"in e?this._normalizeBearing(e.bearing,o):o,u="pitch"in e?+e.pitch:s,h=i.centerPoint.add(t.default$1.convert(e.offset)),f=i.pointLocation(h),p=G.convert(e.center||f);this._normalizeCenter(p);var d,g,m=i.project(f),v=i.project(p).sub(m),y=i.zoomScale(l-a);return e.around&&(d=G.convert(e.around),g=i.locationPoint(d)),this._zooming=l!==a,this._rotating=o!==c,this._pitching=u!==s,this._prepareEase(r,e.noMoveStart),clearTimeout(this._easeEndTimeoutID),this._ease(function(e){if(n._zooming&&(i.zoom=t.number(a,l,e)),n._rotating&&(i.bearing=t.number(o,c,e)),n._pitching&&(i.pitch=t.number(s,u,e)),d)i.setLocationAtPoint(d,g);else{var f=i.zoomScale(i.zoom-a),p=l>a?Math.min(2,y):Math.max(.5,y),x=Math.pow(p,1-e),b=i.unproject(m.add(v.mult(e*x)).mult(f));i.setLocationAtPoint(i.renderWorldCopies?b.wrap():b,h)}n._fireMoveEvents(r)},function(){e.delayEndEvents?n._easeEndTimeoutID=setTimeout(function(){return n._afterEase(r)},e.delayEndEvents):n._afterEase(r)},e),this},r.prototype._prepareEase=function(e,r){this._moving=!0,r||this.fire(new t.Event("movestart",e)),this._zooming&&this.fire(new t.Event("zoomstart",e)),this._rotating&&this.fire(new t.Event("rotatestart",e)),this._pitching&&this.fire(new t.Event("pitchstart",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event("move",e)),this._zooming&&this.fire(new t.Event("zoom",e)),this._rotating&&this.fire(new t.Event("rotate",e)),this._pitching&&this.fire(new t.Event("pitch",e))},r.prototype._afterEase=function(e){var r=this._zooming,n=this._rotating,i=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,r&&this.fire(new t.Event("zoomend",e)),n&&this.fire(new t.Event("rotateend",e)),i&&this.fire(new t.Event("pitchend",e)),this.fire(new t.Event("moveend",e))},r.prototype.flyTo=function(e,r){var n=this;this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l="zoom"in e?t.clamp(+e.zoom,i.minZoom,i.maxZoom):a,c="bearing"in e?this._normalizeBearing(e.bearing,o):o,u="pitch"in e?+e.pitch:s,h=i.zoomScale(l-a),f=i.centerPoint.add(t.default$1.convert(e.offset)),p=i.pointLocation(f),d=G.convert(e.center||p);this._normalizeCenter(d);var g=i.project(p),m=i.project(d).sub(g),v=e.curve,y=Math.max(i.width,i.height),x=y/h,b=m.mag();if("minZoom"in e){var _=t.clamp(Math.min(e.minZoom,a,l),i.minZoom,i.maxZoom),w=y/i.zoomScale(_-a);v=Math.sqrt(w/b*2)}var k=v*v;function M(t){var e=(x*x-y*y+(t?-1:1)*k*k*b*b)/(2*(t?x:y)*k*b);return Math.log(Math.sqrt(e*e+1)-e)}function A(t){return(Math.exp(t)-Math.exp(-t))/2}function T(t){return(Math.exp(t)+Math.exp(-t))/2}var S=M(0),E=function(t){return T(S)/T(S+v*t)},C=function(t){return y*((T(S)*(A(e=S+v*t)/T(e))-A(S))/k)/b;var e},L=(M(1)-S)/v;if(Math.abs(b)<1e-6||!isFinite(L)){if(Math.abs(y-x)<1e-6)return this.easeTo(e,r);var z=x<y?-1:1;L=Math.abs(Math.log(x/y))/v,C=function(){return 0},E=function(t){return Math.exp(z*v*t)}}if("duration"in e)e.duration=+e.duration;else{var P="screenSpeed"in e?+e.screenSpeed/v:+e.speed;e.duration=1e3*L/P}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=o!==c,this._pitching=u!==s,this._prepareEase(r,!1),this._ease(function(e){var l=e*L,h=1/E(l);i.zoom=a+i.scaleZoom(h),n._rotating&&(i.bearing=t.number(o,c,e)),n._pitching&&(i.pitch=t.number(s,u,e));var p=i.unproject(g.add(m.mult(C(l))).mult(h));i.setLocationAtPoint(i.renderWorldCopies?p.wrap():p,f),n._fireMoveEvents(r)},function(){return n._afterEase(r)},e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var t=this._onEaseEnd;delete this._onEaseEnd,t.call(this)}return this},r.prototype._ease=function(t,e,r){!1===r.animate||0===r.duration?(t(1),e()):(this._easeStart=a.now(),this._easeOptions=r,this._onEaseFrame=t,this._onEaseEnd=e,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var t=Math.min((a.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(t)),t<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),nn=function(e){void 0===e&&(e={}),this.options=e,t.bindAll(["_updateEditLink","_updateData","_updateCompact"],this)};nn.prototype.getDefaultPosition=function(){return"bottom-right"},nn.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=s.create("div","mapboxgl-ctrl mapboxgl-ctrl-attrib"),e&&this._container.classList.add("mapboxgl-compact"),this._updateAttributions(),this._updateEditLink(),this._map.on("sourcedata",this._updateData),this._map.on("moveend",this._updateEditLink),void 0===e&&(this._map.on("resize",this._updateCompact),this._updateCompact()),this._container},nn.prototype.onRemove=function(){s.remove(this._container),this._map.off("sourcedata",this._updateData),this._map.off("moveend",this._updateEditLink),this._map.off("resize",this._updateCompact),this._map=void 0},nn.prototype._updateEditLink=function(){var t=this._editLink;t||(t=this._editLink=this._container.querySelector(".mapbox-improve-map"));var e=[{key:"owner",value:this.styleOwner},{key:"id",value:this.styleId},{key:"access_token",value:m.ACCESS_TOKEN}];if(t){var r=e.reduce(function(t,r,n){return r.value&&(t+=r.key+"="+r.value+(n<e.length-1?"&":"")),t},"?");t.href="https://www.mapbox.com/feedback/"+r+(this._map._hash?this._map._hash.getHashString(!0):"")}},nn.prototype._updateData=function(t){t&&"metadata"===t.sourceDataType&&(this._updateAttributions(),this._updateEditLink())},nn.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n].getSource();i.attribution&&t.indexOf(i.attribution)<0&&t.push(i.attribution)}t.sort(function(t,e){return t.length-e.length}),(t=t.filter(function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0})).length?(this._container.innerHTML=t.join(" | "),this._container.classList.remove("mapboxgl-attrib-empty")):this._container.classList.add("mapboxgl-attrib-empty"),this._editLink=null}},nn.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add("mapboxgl-compact"):this._container.classList.remove("mapboxgl-compact")};var an=function(){t.bindAll(["_updateLogo"],this)};an.prototype.onAdd=function(t){this._map=t,this._container=s.create("div","mapboxgl-ctrl");var e=s.create("a","mapboxgl-ctrl-logo");return e.target="_blank",e.href="https://www.mapbox.com/",e.setAttribute("aria-label","Mapbox logo"),this._container.appendChild(e),this._container.style.display="none",this._map.on("sourcedata",this._updateLogo),this._updateLogo(),this._container},an.prototype.onRemove=function(){s.remove(this._container),this._map.off("sourcedata",this._updateLogo)},an.prototype.getDefaultPosition=function(){return"bottom-left"},an.prototype._updateLogo=function(t){t&&"metadata"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?"block":"none")},an.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t)if(t[e].getSource().mapbox_logo)return!0;return!1}};var on=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};on.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},on.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},on.prototype.run=function(){var t=this._currentlyRunning=this._queue;this._queue=[];for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!n.cancelled&&(n.callback(),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},on.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var sn=t.default.HTMLImageElement,ln=t.default.HTMLElement,cn={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:0,maxZoom:22,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,bearingSnap:7,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,transformRequest:null,fadeDuration:300},un=function(r){function n(e){if(null!=(e=t.extend({},cn,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error("maxZoom must be greater than minZoom");var n=new Fr(e.minZoom,e.maxZoom,e.renderWorldCopies);r.call(this,n,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new on;var i=e.transformRequest;if(this._transformRequest=i?function(t,e){return i(t,e)||{url:t}}:function(t){return{url:t}},"string"==typeof e.container){var a=t.default.document.getElementById(e.container);if(!a)throw new Error("Container '"+e.container+"' not found.");this._container=a}else{if(!(e.container instanceof ln))throw new Error("Invalid type: 'container' must be a String or HTMLElement.");this._container=e.container}e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll(["_onWindowOnline","_onWindowResize","_contextLost","_contextRestored","_update","_render","_onData","_onDataLoading"],this),this._setupContainer(),this._setupPainter(),this.on("move",this._update.bind(this,!1)),this.on("zoom",this._update.bind(this,!0)),void 0!==t.default&&(t.default.addEventListener("online",this._onWindowOnline,!1),t.default.addEventListener("resize",this._onWindowResize,!1)),function(t,e){var r=t.getCanvasContainer(),n=null,i=!1;for(var a in en)t[a]=new en[a](t,e),e.interactive&&e[a]&&t[a].enable(e[a]);s.addEventListener(r,"mouseout",function(e){t.fire(new Vr("mouseout",t,e))}),s.addEventListener(r,"mousedown",function(r){i=!0;var n=new Vr("mousedown",t,r);t.fire(n),n.defaultPrevented||(e.interactive&&!t.doubleClickZoom.isActive()&&t.stop(),t.boxZoom.onMouseDown(r),t.boxZoom.isActive()||t.dragPan.isActive()||t.dragRotate.onMouseDown(r),t.boxZoom.isActive()||t.dragRotate.isActive()||t.dragPan.onMouseDown(r))}),s.addEventListener(r,"mouseup",function(e){var r=t.dragRotate.isActive();n&&!r&&t.fire(new Vr("contextmenu",t,n)),n=null,i=!1,t.fire(new Vr("mouseup",t,e))}),s.addEventListener(r,"mousemove",function(e){if(!t.dragPan.isActive()&&!t.dragRotate.isActive()){for(var n=e.toElement||e.target;n&&n!==r;)n=n.parentNode;n===r&&t.fire(new Vr("mousemove",t,e))}}),s.addEventListener(r,"mouseover",function(e){for(var n=e.toElement||e.target;n&&n!==r;)n=n.parentNode;n===r&&t.fire(new Vr("mouseover",t,e))}),s.addEventListener(r,"touchstart",function(r){var n=new Ur("touchstart",t,r);t.fire(n),n.defaultPrevented||(e.interactive&&t.stop(),t.boxZoom.isActive()||t.dragRotate.isActive()||t.dragPan.onTouchStart(r),t.touchZoomRotate.onStart(r),t.doubleClickZoom.onTouchStart(n))},{passive:!1}),s.addEventListener(r,"touchmove",function(e){t.fire(new Ur("touchmove",t,e))},{passive:!1}),s.addEventListener(r,"touchend",function(e){t.fire(new Ur("touchend",t,e))}),s.addEventListener(r,"touchcancel",function(e){t.fire(new Ur("touchcancel",t,e))}),s.addEventListener(r,"click",function(e){t.fire(new Vr("click",t,e))}),s.addEventListener(r,"dblclick",function(e){var r=new Vr("dblclick",t,e);t.fire(r),r.defaultPrevented||t.doubleClickZoom.onDblClick(r)}),s.addEventListener(r,"contextmenu",function(e){var r=t.dragRotate.isActive();i||r?i&&(n=e):t.fire(new Vr("contextmenu",t,e)),e.preventDefault()}),s.addEventListener(r,"wheel",function(e){var r=new qr("wheel",t,e);t.fire(r),r.defaultPrevented||t.scrollZoom.onWheel(e)},{passive:!1})}(this,e),this._hash=e.hash&&(new jr).addTo(this),this._hash&&this._hash._onHashChange()||this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),this.resize(),e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new nn),this.addControl(new an,e.logoPosition),this.on("style.load",function(){this.transform.unmodified&&this.jumpTo(this.style.stylesheet)}),this.on("data",this._onData),this.on("dataloading",this._onDataLoading)}r&&(n.__proto__=r),n.prototype=Object.create(r&&r.prototype),n.prototype.constructor=n;var i={showTileBoundaries:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0}};return n.prototype.addControl=function(t,e){void 0===e&&t.getDefaultPosition&&(e=t.getDefaultPosition()),void 0===e&&(e="top-right");var r=t.onAdd(this),n=this._controlPositions[e];return-1!==e.indexOf("bottom")?n.insertBefore(r,n.firstChild):n.appendChild(r),this},n.prototype.removeControl=function(t){return t.onRemove(this),this},n.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];return this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i),this.fire(new t.Event("movestart",e)).fire(new t.Event("move",e)).fire(new t.Event("resize",e)).fire(new t.Event("moveend",e))},n.prototype.getBounds=function(){var e=new W(this.transform.pointLocation(new t.default$1(0,this.transform.height)),this.transform.pointLocation(new t.default$1(this.transform.width,0)));return(this.transform.angle||this.transform.pitch)&&(e.extend(this.transform.pointLocation(new t.default$1(this.transform.size.x,0))),e.extend(this.transform.pointLocation(new t.default$1(0,this.transform.size.y)))),e},n.prototype.getMaxBounds=function(){return this.transform.latRange&&2===this.transform.latRange.length&&this.transform.lngRange&&2===this.transform.lngRange.length?new W([this.transform.lngRange[0],this.transform.latRange[0]],[this.transform.lngRange[1],this.transform.latRange[1]]):null},n.prototype.setMaxBounds=function(t){if(t){var e=W.convert(t);this.transform.lngRange=[e.getWest(),e.getEast()],this.transform.latRange=[e.getSouth(),e.getNorth()],this.transform._constrain(),this._update()}else null==t&&(this.transform.lngRange=null,this.transform.latRange=null,this._update());return this},n.prototype.setMinZoom=function(t){if((t=null==t?0:t)>=0&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error("minZoom must be between 0 and the current maxZoom, inclusive")},n.prototype.getMinZoom=function(){return this.transform.minZoom},n.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error("maxZoom must be greater than the current minZoom")},n.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},n.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update(),this},n.prototype.getMaxZoom=function(){return this.transform.maxZoom},n.prototype.project=function(t){return this.transform.locationPoint(G.convert(t))},n.prototype.unproject=function(e){return this.transform.pointLocation(t.default$1.convert(e))},n.prototype.isMoving=function(){return this._moving||this.dragPan.isActive()||this.dragRotate.isActive()||this.scrollZoom.isActive()},n.prototype.isZooming=function(){return this._zooming||this.scrollZoom.isActive()},n.prototype.isRotating=function(){return this._rotating||this.dragRotate.isActive()},n.prototype.on=function(t,e,n){var i,a=this;if(void 0===n)return r.prototype.on.call(this,t,e);var o=function(){if("mouseenter"===t||"mouseover"===t){var r=!1;return{layer:e,listener:n,delegates:{mousemove:function(i){var o=a.getLayer(e)?a.queryRenderedFeatures(i.point,{layers:[e]}):[];o.length?r||(r=!0,n.call(a,new Vr(t,a,i.originalEvent,{features:o}))):r=!1},mouseout:function(){r=!1}}}}if("mouseleave"===t||"mouseout"===t){var o=!1;return{layer:e,listener:n,delegates:{mousemove:function(r){(a.getLayer(e)?a.queryRenderedFeatures(r.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,n.call(a,new Vr(t,a,r.originalEvent)))},mouseout:function(e){o&&(o=!1,n.call(a,new Vr(t,a,e.originalEvent)))}}}}return{layer:e,listener:n,delegates:(i={},i[t]=function(t){var r=a.getLayer(e)?a.queryRenderedFeatures(t.point,{layers:[e]}):[];r.length&&(t.features=r,n.call(a,t),delete t.features)},i)}}();for(var s in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(o),o.delegates)a.on(s,o.delegates[s]);return this},n.prototype.off=function(t,e,n){if(void 0===n)return r.prototype.off.call(this,t,e);if(this._delegatedListeners&&this._delegatedListeners[t])for(var i=this._delegatedListeners[t],a=0;a<i.length;a++){var o=i[a];if(o.layer===e&&o.listener===n){for(var s in o.delegates)this.off(s,o.delegates[s]);return i.splice(a,1),this}}return this},n.prototype.queryRenderedFeatures=function(e,r){var n;return 2===arguments.length?(e=e,r=r):1===arguments.length&&((n=e)instanceof t.default$1||Array.isArray(n))?(e=e,r={}):1===arguments.length?r=e=void 0:(e=void 0,r={}),this.style?this.style.queryRenderedFeatures(this._makeQueryGeometry(e),r,this.transform):[]},n.prototype._makeQueryGeometry=function(e){var r,n=this;if(void 0===e&&(e=[t.default$1.convert([0,0]),t.default$1.convert([this.transform.width,this.transform.height])]),e instanceof t.default$1||"number"==typeof e[0])r=[t.default$1.convert(e)];else{var i=[t.default$1.convert(e[0]),t.default$1.convert(e[1])];r=[i[0],new t.default$1(i[1].x,i[0].y),i[1],new t.default$1(i[0].x,i[1].y),i[0]]}return{viewport:r,worldCoordinate:r.map(function(t){return n.transform.pointCoordinate(t)})}},n.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},n.prototype.setStyle=function(e,r){if((!r||!1!==r.diff&&!r.localIdeographFontFamily)&&this.style&&e&&"object"==typeof e)try{return this.style.setState(e)&&this._update(!0),this}catch(e){t.warnOnce("Unable to perform style diff: "+(e.message||e.error||e)+".  Rebuilding the style from scratch.")}return this.style&&(this.style.setEventedParent(null),this.style._remove()),e?(this.style=new Je(this,r||{}),this.style.setEventedParent(this,{style:this.style}),"string"==typeof e?this.style.loadURL(e):this.style.loadJSON(e),this):(delete this.style,this)},n.prototype.getStyle=function(){if(this.style)return this.style.serialize()},n.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce("There is no style added to the map.")},n.prototype.addSource=function(t,e){return this.style.addSource(t,e),this._update(!0),this},n.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error("There is no source with ID '"+e+"'")))},n.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if("loaded"!==i.state&&"errored"!==i.state)return!1}}return!0},n.prototype.addSourceType=function(t,e,r){return this.style.addSourceType(t,e,r)},n.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0),this},n.prototype.getSource=function(t){return this.style.getSource(t)},n.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var o=n.sdf;if(void 0===o&&(o=!1),r instanceof sn){var s=a.getImageData(r),l=s.width,c=s.height,u=s.data;this.style.addImage(e,{data:new t.RGBAImage({width:l,height:c},u),pixelRatio:i,sdf:o})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));var h=r.width,f=r.height,p=r.data;this.style.addImage(e,{data:new t.RGBAImage({width:h,height:f},p.slice(0)),pixelRatio:i,sdf:o})}},n.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error("Missing required image id"))),!1)},n.prototype.removeImage=function(t){this.style.removeImage(t)},n.prototype.loadImage=function(e,r){t.getImage(this._transformRequest(e,t.ResourceType.Image),r)},n.prototype.addLayer=function(t,e){return this.style.addLayer(t,e),this._update(!0),this},n.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0),this},n.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0),this},n.prototype.getLayer=function(t){return this.style.getLayer(t)},n.prototype.setFilter=function(t,e){return this.style.setFilter(t,e),this._update(!0),this},n.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0),this},n.prototype.getFilter=function(t){return this.style.getFilter(t)},n.prototype.setPaintProperty=function(t,e,r){return this.style.setPaintProperty(t,e,r),this._update(!0),this},n.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},n.prototype.setLayoutProperty=function(t,e,r){return this.style.setLayoutProperty(t,e,r),this._update(!0),this},n.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},n.prototype.setLight=function(t){return this.style.setLight(t),this._update(!0),this},n.prototype.getLight=function(){return this.style.getLight()},n.prototype.getContainer=function(){return this._container},n.prototype.getCanvasContainer=function(){return this._canvasContainer},n.prototype.getCanvas=function(){return this._canvas},n.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.offsetWidth||400,e=this._container.offsetHeight||300),[t,e]},n.prototype._setupContainer=function(){var t=this._container;t.classList.add("mapboxgl-map"),(this._missingCSSContainer=s.create("div","mapboxgl-missing-css",t)).innerHTML="Missing Mapbox GL JS CSS";var e=this._canvasContainer=s.create("div","mapboxgl-canvas-container",t);this._interactive&&e.classList.add("mapboxgl-interactive"),this._canvas=s.create("canvas","mapboxgl-canvas",e),this._canvas.style.position="absolute",this._canvas.addEventListener("webglcontextlost",this._contextLost,!1),this._canvas.addEventListener("webglcontextrestored",this._contextRestored,!1),this._canvas.setAttribute("tabindex","0"),this._canvas.setAttribute("aria-label","Map");var r=this._containerDimensions();this._resizeCanvas(r[0],r[1]);var n=this._controlContainer=s.create("div","mapboxgl-control-container",t),i=this._controlPositions={};["top-left","top-right","bottom-left","bottom-right"].forEach(function(t){i[t]=s.create("div","mapboxgl-ctrl-"+t,n)})},n.prototype._resizeCanvas=function(e,r){var n=t.default.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+"px",this._canvas.style.height=r+"px"},n.prototype._setupPainter=function(){var r=t.extend({failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer},e.webGLContextAttributes),n=this._canvas.getContext("webgl",r)||this._canvas.getContext("experimental-webgl",r);n?this.painter=new zr(n,this.transform):this.fire(new t.ErrorEvent(new Error("Failed to initialize WebGL")))},n.prototype._contextLost=function(e){e.preventDefault(),this._frameId&&(a.cancelFrame(this._frameId),this._frameId=null),this.fire(new t.Event("webglcontextlost",{originalEvent:e}))},n.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event("webglcontextrestored",{originalEvent:e}))},n.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!(!this.style||!this.style.loaded())},n.prototype._update=function(t){this.style&&(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this._rerender())},n.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},n.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},n.prototype._render=function(){this._renderTaskQueue.run();var e=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var r=this.transform.zoom,n=a.now();this.style.zoomHistory.update(r,n);var i=new t.default$16(r,{now:n,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),o=i.crossFadingFactor();1===o&&o===this._crossFadingFactor||(e=!0,this._crossFadingFactor=o),this.style.update(i)}return this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),fadeDuration:this._fadeDuration}),this.fire(new t.Event("render")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event("load"))),this.style&&(this.style.hasTransitions()||e)&&(this._styleDirty=!0),(this._sourcesDirty||this._repaint||this._styleDirty||this._placementDirty)&&this._rerender(),this},n.prototype.remove=function(){this._hash&&this._hash.remove(),a.cancelFrame(this._frameId),this._renderTaskQueue.clear(),this._frameId=null,this.setStyle(null),void 0!==t.default&&(t.default.removeEventListener("resize",this._onWindowResize,!1),t.default.removeEventListener("online",this._onWindowOnline,!1));var e=this.painter.context.gl.getExtension("WEBGL_lose_context");e&&e.loseContext(),hn(this._canvasContainer),hn(this._controlContainer),hn(this._missingCSSContainer),this._container.classList.remove("mapboxgl-map"),this.fire(new t.Event("remove"))},n.prototype._rerender=function(){var t=this;this.style&&!this._frameId&&(this._frameId=a.frame(function(){t._frameId=null,t._render()}))},n.prototype._onWindowOnline=function(){this._update()},n.prototype._onWindowResize=function(){this._trackResize&&this.stop().resize()._update()},i.showTileBoundaries.get=function(){return!!this._showTileBoundaries},i.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},i.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},i.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},i.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},i.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},i.repaint.get=function(){return!!this._repaint},i.repaint.set=function(t){this._repaint=t,this._update()},i.vertices.get=function(){return!!this._vertices},i.vertices.set=function(t){this._vertices=t,this._update()},n.prototype._onData=function(e){this._update("style"===e.dataType),this.fire(new t.Event(e.dataType+"data",e))},n.prototype._onDataLoading=function(e){this.fire(new t.Event(e.dataType+"dataloading",e))},Object.defineProperties(n.prototype,i),n}(rn);function hn(t){t.parentNode&&t.parentNode.removeChild(t)}var fn={showCompass:!0,showZoom:!0},pn=function(e){var r=this;this.options=t.extend({},fn,e),this._container=s.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._container.addEventListener("contextmenu",function(t){return t.preventDefault()}),this.options.showZoom&&(this._zoomInButton=this._createButton("mapboxgl-ctrl-icon mapboxgl-ctrl-zoom-in","Zoom In",function(){return r._map.zoomIn()}),this._zoomOutButton=this._createButton("mapboxgl-ctrl-icon mapboxgl-ctrl-zoom-out","Zoom Out",function(){return r._map.zoomOut()})),this.options.showCompass&&(t.bindAll(["_rotateCompassArrow"],this),this._compass=this._createButton("mapboxgl-ctrl-icon mapboxgl-ctrl-compass","Reset North",function(){return r._map.resetNorth()}),this._compassArrow=s.create("span","mapboxgl-ctrl-compass-arrow",this._compass))};function dn(t,e,r){if(t=new G(t.lng,t.lat),e){var n=new G(t.lng-360,t.lat),i=new G(t.lng+360,t.lat),a=r.locationPoint(t).distSqr(e);r.locationPoint(n).distSqr(e)<a?t=n:r.locationPoint(i).distSqr(e)<a&&(t=i)}for(;Math.abs(t.lng-r.center.lng)>180;){var o=r.locationPoint(t);if(o.x>=0&&o.y>=0&&o.x<=r.width&&o.y<=r.height)break;t.lng>r.center.lng?t.lng-=360:t.lng+=360}return t}pn.prototype._rotateCompassArrow=function(){var t="rotate("+this._map.transform.angle*(180/Math.PI)+"deg)";this._compassArrow.style.transform=t},pn.prototype.onAdd=function(t){return this._map=t,this.options.showCompass&&(this._map.on("rotate",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Yr(t,{button:"left",element:this._compass}),this._handler.enable()),this._container},pn.prototype.onRemove=function(){s.remove(this._container),this.options.showCompass&&(this._map.off("rotate",this._rotateCompassArrow),this._handler.disable(),delete this._handler),delete this._map},pn.prototype._createButton=function(t,e,r){var n=s.create("button",t,this._container);return n.type="button",n.setAttribute("aria-label",e),n.addEventListener("click",r),n};var gn={center:"translate(-50%,-50%)",top:"translate(-50%,0)","top-left":"translate(0,0)","top-right":"translate(-100%,0)",bottom:"translate(-50%,-100%)","bottom-left":"translate(0,-100%)","bottom-right":"translate(-100%,-100%)",left:"translate(0,-50%)",right:"translate(-100%,-50%)"};function mn(t,e,r){var n=t.classList;for(var i in gn)n.remove("mapboxgl-"+r+"-anchor-"+i);n.add("mapboxgl-"+r+"-anchor-"+e)}var vn=function(e){if((e instanceof t.default.HTMLElement||2===arguments.length)&&(e=t.extend({element:e},arguments[1])),t.bindAll(["_update","_onMapClick"],this),this._anchor=e&&e.anchor||"center",this._color=e&&e.color||"#3FB1CE",e&&e.element)this._element=e.element,this._offset=t.default$1.convert(e&&e.offset||[0,0]);else{this._defaultMarker=!0,this._element=s.create("div");var r=s.createNS("http://www.w3.org/2000/svg","svg");r.setAttributeNS(null,"height","41px"),r.setAttributeNS(null,"width","27px"),r.setAttributeNS(null,"viewBox","0 0 27 41");var n=s.createNS("http://www.w3.org/2000/svg","g");n.setAttributeNS(null,"stroke","none"),n.setAttributeNS(null,"stroke-width","1"),n.setAttributeNS(null,"fill","none"),n.setAttributeNS(null,"fill-rule","evenodd");var i=s.createNS("http://www.w3.org/2000/svg","g");i.setAttributeNS(null,"fill-rule","nonzero");var a=s.createNS("http://www.w3.org/2000/svg","g");a.setAttributeNS(null,"transform","translate(3.0, 29.0)"),a.setAttributeNS(null,"fill","#000000");for(var o=0,l=[{rx:"10.5",ry:"5.25002273"},{rx:"10.5",ry:"5.25002273"},{rx:"9.5",ry:"4.77275007"},{rx:"8.5",ry:"4.29549936"},{rx:"7.5",ry:"3.81822308"},{rx:"6.5",ry:"3.34094679"},{rx:"5.5",ry:"2.86367051"},{rx:"4.5",ry:"2.38636864"}];o<l.length;o+=1){var c=l[o],u=s.createNS("http://www.w3.org/2000/svg","ellipse");u.setAttributeNS(null,"opacity","0.04"),u.setAttributeNS(null,"cx","10.5"),u.setAttributeNS(null,"cy","5.80029008"),u.setAttributeNS(null,"rx",c.rx),u.setAttributeNS(null,"ry",c.ry),a.appendChild(u)}var h=s.createNS("http://www.w3.org/2000/svg","g");h.setAttributeNS(null,"fill",this._color);var f=s.createNS("http://www.w3.org/2000/svg","path");f.setAttributeNS(null,"d","M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z"),h.appendChild(f);var p=s.createNS("http://www.w3.org/2000/svg","g");p.setAttributeNS(null,"opacity","0.25"),p.setAttributeNS(null,"fill","#000000");var d=s.createNS("http://www.w3.org/2000/svg","path");d.setAttributeNS(null,"d","M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z"),p.appendChild(d);var g=s.createNS("http://www.w3.org/2000/svg","g");g.setAttributeNS(null,"transform","translate(6.0, 7.0)"),g.setAttributeNS(null,"fill","#FFFFFF");var m=s.createNS("http://www.w3.org/2000/svg","g");m.setAttributeNS(null,"transform","translate(8.0, 8.0)");var v=s.createNS("http://www.w3.org/2000/svg","circle");v.setAttributeNS(null,"fill","#000000"),v.setAttributeNS(null,"opacity","0.25"),v.setAttributeNS(null,"cx","5.5"),v.setAttributeNS(null,"cy","5.5"),v.setAttributeNS(null,"r","5.4999962");var y=s.createNS("http://www.w3.org/2000/svg","circle");y.setAttributeNS(null,"fill","#FFFFFF"),y.setAttributeNS(null,"cx","5.5"),y.setAttributeNS(null,"cy","5.5"),y.setAttributeNS(null,"r","5.4999962"),m.appendChild(v),m.appendChild(y),i.appendChild(a),i.appendChild(h),i.appendChild(p),i.appendChild(g),i.appendChild(m),r.appendChild(i),this._element.appendChild(r),this._offset=t.default$1.convert(e&&e.offset||[0,-14])}this._element.classList.add("mapboxgl-marker"),this._popup=null};vn.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on("move",this._update),t.on("moveend",this._update),this._update(),this._map.on("click",this._onMapClick),this},vn.prototype.remove=function(){return this._map&&(this._map.off("click",this._onMapClick),this._map.off("move",this._update),this._map.off("moveend",this._update),delete this._map),s.remove(this._element),this._popup&&this._popup.remove(),this},vn.prototype.getLngLat=function(){return this._lngLat},vn.prototype.setLngLat=function(t){return this._lngLat=G.convert(t),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},vn.prototype.getElement=function(){return this._element},vn.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null),t){if(!("offset"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],"top-left":[0,0],"top-right":[0,0],bottom:[0,-38.1],"bottom-left":[e,-1*(24.6+e)],"bottom-right":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat)}return this},vn.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},vn.prototype.getPopup=function(){return this._popup},vn.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},vn.prototype._update=function(t){this._map&&(this._map.transform.renderWorldCopies&&(this._lngLat=dn(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset),t&&"moveend"!==t.type||(this._pos=this._pos.round()),s.setTransform(this._element,gn[this._anchor]+" translate("+this._pos.x+"px, "+this._pos.y+"px)"),mn(this._element,this._anchor,"marker"))},vn.prototype.getOffset=function(){return this._offset},vn.prototype.setOffset=function(e){return this._offset=t.default$1.convert(e),this._update(),this};var yn,xn={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showUserLocation:!0},bn=function(e){function r(r){e.call(this),this.options=t.extend({},xn,r),t.bindAll(["_onSuccess","_onError","_finish","_setupUI","_updateCamera","_updateMarker"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(e){var r;return this._map=e,this._container=s.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),r=this._setupUI,void 0!==yn?r(yn):void 0!==t.default.navigator.permissions?t.default.navigator.permissions.query({name:"geolocation"}).then(function(t){yn="denied"!==t.state,r(yn)}):(yn=!!t.default.navigator.geolocation,r(yn)),this._container},r.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.default.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker.remove(),s.remove(this._container),this._map=void 0},r.prototype._onSuccess=function(e){if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"BACKGROUND":case"BACKGROUND_ERROR":this._watchState="BACKGROUND",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background")}this.options.showUserLocation&&"OFF"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&"ACTIVE_LOCK"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("geolocate",e)),this._finish()},r.prototype._updateCamera=function(t){var e=new G(t.coords.longitude,t.coords.latitude),r=t.coords.accuracy;this._map.fitBounds(e.toBounds(r),this.options.fitBoundsOptions,{geolocateSource:!0})},r.prototype._updateMarker=function(t){t?this._userLocationDotMarker.setLngLat([t.coords.longitude,t.coords.latitude]).addTo(this._map):this._userLocationDotMarker.remove()},r.prototype._onError=function(e){if(this.options.trackUserLocation)if(1===e.code)this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),void 0!==this._geolocationWatchID&&this._clearWatch();else switch(this._watchState){case"WAITING_ACTIVE":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"ACTIVE_LOCK":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting");break;case"BACKGROUND":this._watchState="BACKGROUND_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting")}"OFF"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("error",e)),this._finish()},r.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},r.prototype._setupUI=function(e){var r=this;!1!==e&&(this._container.addEventListener("contextmenu",function(t){return t.preventDefault()}),this._geolocateButton=s.create("button","mapboxgl-ctrl-icon mapboxgl-ctrl-geolocate",this._container),this._geolocateButton.type="button",this._geolocateButton.setAttribute("aria-label","Geolocate"),this.options.trackUserLocation&&(this._geolocateButton.setAttribute("aria-pressed","false"),this._watchState="OFF"),this.options.showUserLocation&&(this._dotElement=s.create("div","mapboxgl-user-location-dot"),this._userLocationDotMarker=new vn(this._dotElement),this.options.trackUserLocation&&(this._watchState="OFF")),this._geolocateButton.addEventListener("click",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on("movestart",function(e){e.geolocateSource||"ACTIVE_LOCK"!==r._watchState||(r._watchState="BACKGROUND",r._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background"),r._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),r.fire(new t.Event("trackuserlocationend")))}))},r.prototype.trigger=function(){if(!this._setup)return t.warnOnce("Geolocate control triggered before added to a map"),!1;if(this.options.trackUserLocation){switch(this._watchState){case"OFF":this._watchState="WAITING_ACTIVE",this.fire(new t.Event("trackuserlocationstart"));break;case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":case"BACKGROUND_ERROR":this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this.fire(new t.Event("trackuserlocationend"));break;case"BACKGROUND":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event("trackuserlocationstart"))}switch(this._watchState){case"WAITING_ACTIVE":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_LOCK":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"BACKGROUND":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background");break;case"BACKGROUND_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error")}"OFF"===this._watchState&&void 0!==this._geolocationWatchID?this._clearWatch():void 0===this._geolocationWatchID&&(this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","true"),this._geolocationWatchID=t.default.navigator.geolocation.watchPosition(this._onSuccess,this._onError,this.options.positionOptions))}else t.default.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},r.prototype._clearWatch=function(){t.default.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","false"),this.options.showUserLocation&&this._updateMarker(null)},r}(t.Evented),_n={maxWidth:100,unit:"metric"},wn=function(e){this.options=t.extend({},_n,e),t.bindAll(["_onMove","setUnit"],this)};function kn(t,e,r){var n,i,a,o,s,l,c=r&&r.maxWidth||100,u=t._container.clientHeight/2,h=(n=t.unproject([0,u]),i=t.unproject([c,u]),a=Math.PI/180,o=n.lat*a,s=i.lat*a,l=Math.sin(o)*Math.sin(s)+Math.cos(o)*Math.cos(s)*Math.cos((i.lng-n.lng)*a),6371e3*Math.acos(Math.min(l,1)));if(r&&"imperial"===r.unit){var f=3.2808*h;f>5280?Mn(e,c,f/5280,"mi"):Mn(e,c,f,"ft")}else r&&"nautical"===r.unit?Mn(e,c,h/1852,"nm"):Mn(e,c,h,"m")}function Mn(t,e,r,n){var i,a,o,s=(i=r,(a=Math.pow(10,(""+Math.floor(i)).length-1))*(o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:1)),l=s/r;"m"===n&&s>=1e3&&(s/=1e3,n="km"),t.style.width=e*l+"px",t.innerHTML=s+n}wn.prototype.getDefaultPosition=function(){return"bottom-left"},wn.prototype._onMove=function(){kn(this._map,this._container,this.options)},wn.prototype.onAdd=function(t){return this._map=t,this._container=s.create("div","mapboxgl-ctrl mapboxgl-ctrl-scale",t.getContainer()),this._map.on("move",this._onMove),this._onMove(),this._container},wn.prototype.onRemove=function(){s.remove(this._container),this._map.off("move",this._onMove),this._map=void 0},wn.prototype.setUnit=function(t){this.options.unit=t,kn(this._map,this._container,this.options)};var An=function(){this._fullscreen=!1,t.bindAll(["_onClickFullscreen","_changeIcon"],this),"onfullscreenchange"in t.default.document?this._fullscreenchange="fullscreenchange":"onmozfullscreenchange"in t.default.document?this._fullscreenchange="mozfullscreenchange":"onwebkitfullscreenchange"in t.default.document?this._fullscreenchange="webkitfullscreenchange":"onmsfullscreenchange"in t.default.document&&(this._fullscreenchange="MSFullscreenChange"),this._className="mapboxgl-ctrl"};An.prototype.onAdd=function(e){return this._map=e,this._mapContainer=this._map.getContainer(),this._container=s.create("div",this._className+" mapboxgl-ctrl-group"),this._checkFullscreenSupport()?this._setupUI():(this._container.style.display="none",t.warnOnce("This device does not support fullscreen mode.")),this._container},An.prototype.onRemove=function(){s.remove(this._container),this._map=null,t.default.document.removeEventListener(this._fullscreenchange,this._changeIcon)},An.prototype._checkFullscreenSupport=function(){return!!(t.default.document.fullscreenEnabled||t.default.document.mozFullScreenEnabled||t.default.document.msFullscreenEnabled||t.default.document.webkitFullscreenEnabled)},An.prototype._setupUI=function(){var e=this._fullscreenButton=s.create("button",this._className+"-icon "+this._className+"-fullscreen",this._container);e.setAttribute("aria-label","Toggle fullscreen"),e.type="button",this._fullscreenButton.addEventListener("click",this._onClickFullscreen),t.default.document.addEventListener(this._fullscreenchange,this._changeIcon)},An.prototype._isFullscreen=function(){return this._fullscreen},An.prototype._changeIcon=function(){(t.default.document.fullscreenElement||t.default.document.mozFullScreenElement||t.default.document.webkitFullscreenElement||t.default.document.msFullscreenElement)===this._mapContainer!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(this._className+"-shrink"),this._fullscreenButton.classList.toggle(this._className+"-fullscreen"))},An.prototype._onClickFullscreen=function(){this._isFullscreen()?t.default.document.exitFullscreen?t.default.document.exitFullscreen():t.default.document.mozCancelFullScreen?t.default.document.mozCancelFullScreen():t.default.document.msExitFullscreen?t.default.document.msExitFullscreen():t.default.document.webkitCancelFullScreen&&t.default.document.webkitCancelFullScreen():this._mapContainer.requestFullscreen?this._mapContainer.requestFullscreen():this._mapContainer.mozRequestFullScreen?this._mapContainer.mozRequestFullScreen():this._mapContainer.msRequestFullscreen?this._mapContainer.msRequestFullscreen():this._mapContainer.webkitRequestFullscreen&&this._mapContainer.webkitRequestFullscreen()};var Tn={closeButton:!0,closeOnClick:!0},Sn=function(e){function r(r){e.call(this),this.options=t.extend(Object.create(Tn),r),t.bindAll(["_update","_onClickClose"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.addTo=function(e){return this._map=e,this._map.on("move",this._update),this.options.closeOnClick&&this._map.on("click",this._onClickClose),this._update(),this.fire(new t.Event("open")),this},r.prototype.isOpen=function(){return!!this._map},r.prototype.remove=function(){return this._content&&s.remove(this._content),this._container&&(s.remove(this._container),delete this._container),this._map&&(this._map.off("move",this._update),this._map.off("click",this._onClickClose),delete this._map),this.fire(new t.Event("close")),this},r.prototype.getLngLat=function(){return this._lngLat},r.prototype.setLngLat=function(t){return this._lngLat=G.convert(t),this._pos=null,this._update(),this},r.prototype.setText=function(e){return this.setDOMContent(t.default.document.createTextNode(e))},r.prototype.setHTML=function(e){var r,n=t.default.document.createDocumentFragment(),i=t.default.document.createElement("body");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},r.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},r.prototype._createContent=function(){this._content&&s.remove(this._content),this._content=s.create("div","mapboxgl-popup-content",this._container),this.options.closeButton&&(this._closeButton=s.create("button","mapboxgl-popup-close-button",this._content),this._closeButton.type="button",this._closeButton.setAttribute("aria-label","Close popup"),this._closeButton.innerHTML="&#215;",this._closeButton.addEventListener("click",this._onClickClose))},r.prototype._update=function(){if(this._map&&this._lngLat&&this._content){this._container||(this._container=s.create("div","mapboxgl-popup",this._map.getContainer()),this._tip=s.create("div","mapboxgl-popup-tip",this._container),this._container.appendChild(this._content)),this._map.transform.renderWorldCopies&&(this._lngLat=dn(this._lngLat,this._pos,this._map.transform));var e=this._pos=this._map.project(this._lngLat),r=this.options.anchor,n=function e(r){if(r){if("number"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.default$1(0,0),top:new t.default$1(0,r),"top-left":new t.default$1(n,n),"top-right":new t.default$1(-n,n),bottom:new t.default$1(0,-r),"bottom-left":new t.default$1(n,-n),"bottom-right":new t.default$1(-n,-n),left:new t.default$1(r,0),right:new t.default$1(-r,0)}}if(r instanceof t.default$1||Array.isArray(r)){var i=t.default$1.convert(r);return{center:i,top:i,"top-left":i,"top-right":i,bottom:i,"bottom-left":i,"bottom-right":i,left:i,right:i}}return{center:t.default$1.convert(r.center||[0,0]),top:t.default$1.convert(r.top||[0,0]),"top-left":t.default$1.convert(r["top-left"]||[0,0]),"top-right":t.default$1.convert(r["top-right"]||[0,0]),bottom:t.default$1.convert(r.bottom||[0,0]),"bottom-left":t.default$1.convert(r["bottom-left"]||[0,0]),"bottom-right":t.default$1.convert(r["bottom-right"]||[0,0]),left:t.default$1.convert(r.left||[0,0]),right:t.default$1.convert(r.right||[0,0])}}return e(new t.default$1(0,0))}(this.options.offset);if(!r){var i,a=this._container.offsetWidth,o=this._container.offsetHeight;i=e.y+n.bottom.y<o?["top"]:e.y>this._map.transform.height-o?["bottom"]:[],e.x<a/2?i.push("left"):e.x>this._map.transform.width-a/2&&i.push("right"),r=0===i.length?"bottom":i.join("-")}var l=e.add(n[r]).round();s.setTransform(this._container,gn[r]+" translate("+l.x+"px,"+l.y+"px)"),mn(this._container,r,"popup")}},r.prototype._onClickClose=function(){this.remove()},r}(t.Evented),En={version:"0.45.0",supported:e,workerCount:Math.max(Math.floor(a.hardwareConcurrency/2),1),setRTLTextPlugin:t.setRTLTextPlugin,Map:un,NavigationControl:pn,GeolocateControl:bn,AttributionControl:nn,ScaleControl:wn,FullscreenControl:An,Popup:Sn,Marker:vn,Style:Je,LngLat:G,LngLatBounds:W,Point:t.default$1,Evented:t.Evented,config:m,get accessToken(){return m.ACCESS_TOKEN},set accessToken(t){m.ACCESS_TOKEN=t},workerUrl:""};return En}),n})}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{}],394:[function(t,e,r){"use strict";e.exports=function(t){for(var e=1<<t+1,r=new Array(e),n=0;n<e;++n)r[n]=a(t,n);return r};var n=t("convex-hull");function i(t,e,r){for(var n=new Array(t),i=0;i<t;++i)n[i]=0,i===e&&(n[i]+=.5),i===r&&(n[i]+=.5);return n}function a(t,e){if(0===e||e===(1<<t+1)-1)return[];for(var r=[],a=[],o=0;o<=t;++o)if(e&1<<o){r.push(i(t,o-1,o-1)),a.push(null);for(var s=0;s<=t;++s)~e&1<<s&&(r.push(i(t,o-1,s-1)),a.push([o,s]))}var l=n(r),c=[];t:for(o=0;o<l.length;++o){var u=l[o],h=[];for(s=0;s<u.length;++s){if(!a[u[s]])continue t;h.push(a[u[s]].slice())}c.push(h)}return c}},{"convex-hull":117}],395:[function(t,e,r){var n=t("./normalize"),i=t("gl-mat4/create"),a=t("gl-mat4/clone"),o=t("gl-mat4/determinant"),s=t("gl-mat4/invert"),l=t("gl-mat4/transpose"),c={length:t("gl-vec3/length"),normalize:t("gl-vec3/normalize"),dot:t("gl-vec3/dot"),cross:t("gl-vec3/cross")},u=i(),h=i(),f=[0,0,0,0],p=[[0,0,0],[0,0,0],[0,0,0]],d=[0,0,0];function g(t,e,r,n,i){t[0]=e[0]*n+r[0]*i,t[1]=e[1]*n+r[1]*i,t[2]=e[2]*n+r[2]*i}e.exports=function(t,e,r,i,m,v){if(e||(e=[0,0,0]),r||(r=[0,0,0]),i||(i=[0,0,0]),m||(m=[0,0,0,1]),v||(v=[0,0,0,1]),!n(u,t))return!1;if(a(h,u),h[3]=0,h[7]=0,h[11]=0,h[15]=1,Math.abs(o(h)<1e-8))return!1;var y,x,b,_,w,k,M,A=u[3],T=u[7],S=u[11],E=u[12],C=u[13],L=u[14],z=u[15];if(0!==A||0!==T||0!==S){if(f[0]=A,f[1]=T,f[2]=S,f[3]=z,!s(h,h))return!1;l(h,h),y=m,b=h,_=(x=f)[0],w=x[1],k=x[2],M=x[3],y[0]=b[0]*_+b[4]*w+b[8]*k+b[12]*M,y[1]=b[1]*_+b[5]*w+b[9]*k+b[13]*M,y[2]=b[2]*_+b[6]*w+b[10]*k+b[14]*M,y[3]=b[3]*_+b[7]*w+b[11]*k+b[15]*M}else m[0]=m[1]=m[2]=0,m[3]=1;if(e[0]=E,e[1]=C,e[2]=L,function(t,e){t[0][0]=e[0],t[0][1]=e[1],t[0][2]=e[2],t[1][0]=e[4],t[1][1]=e[5],t[1][2]=e[6],t[2][0]=e[8],t[2][1]=e[9],t[2][2]=e[10]}(p,u),r[0]=c.length(p[0]),c.normalize(p[0],p[0]),i[0]=c.dot(p[0],p[1]),g(p[1],p[1],p[0],1,-i[0]),r[1]=c.length(p[1]),c.normalize(p[1],p[1]),i[0]/=r[1],i[1]=c.dot(p[0],p[2]),g(p[2],p[2],p[0],1,-i[1]),i[2]=c.dot(p[1],p[2]),g(p[2],p[2],p[1],1,-i[2]),r[2]=c.length(p[2]),c.normalize(p[2],p[2]),i[1]/=r[2],i[2]/=r[2],c.cross(d,p[1],p[2]),c.dot(p[0],d)<0)for(var P=0;P<3;P++)r[P]*=-1,p[P][0]*=-1,p[P][1]*=-1,p[P][2]*=-1;return v[0]=.5*Math.sqrt(Math.max(1+p[0][0]-p[1][1]-p[2][2],0)),v[1]=.5*Math.sqrt(Math.max(1-p[0][0]+p[1][1]-p[2][2],0)),v[2]=.5*Math.sqrt(Math.max(1-p[0][0]-p[1][1]+p[2][2],0)),v[3]=.5*Math.sqrt(Math.max(1+p[0][0]+p[1][1]+p[2][2],0)),p[2][1]>p[1][2]&&(v[0]=-v[0]),p[0][2]>p[2][0]&&(v[1]=-v[1]),p[1][0]>p[0][1]&&(v[2]=-v[2]),!0}},{"./normalize":396,"gl-mat4/clone":248,"gl-mat4/create":249,"gl-mat4/determinant":250,"gl-mat4/invert":254,"gl-mat4/transpose":264,"gl-vec3/cross":314,"gl-vec3/dot":317,"gl-vec3/length":322,"gl-vec3/normalize":328}],396:[function(t,e,r){e.exports=function(t,e){var r=e[15];if(0===r)return!1;for(var n=1/r,i=0;i<16;i++)t[i]=e[i]*n;return!0}},{}],397:[function(t,e,r){var n=t("gl-vec3/lerp"),i=t("mat4-recompose"),a=t("mat4-decompose"),o=t("gl-mat4/determinant"),s=t("quat-slerp"),l=h(),c=h(),u=h();function h(){return{translate:f(),scale:f(1),skew:f(),perspective:[0,0,0,1],quaternion:[0,0,0,1]}}function f(t){return[t||0,t||0,t||0]}e.exports=function(t,e,r,h){if(0===o(e)||0===o(r))return!1;var f=a(e,l.translate,l.scale,l.skew,l.perspective,l.quaternion),p=a(r,c.translate,c.scale,c.skew,c.perspective,c.quaternion);return!(!f||!p||(n(u.translate,l.translate,c.translate,h),n(u.skew,l.skew,c.skew,h),n(u.scale,l.scale,c.scale,h),n(u.perspective,l.perspective,c.perspective,h),s(u.quaternion,l.quaternion,c.quaternion,h),i(t,u.translate,u.scale,u.skew,u.perspective,u.quaternion),0))}},{"gl-mat4/determinant":250,"gl-vec3/lerp":323,"mat4-decompose":395,"mat4-recompose":398,"quat-slerp":450}],398:[function(t,e,r){var n={identity:t("gl-mat4/identity"),translate:t("gl-mat4/translate"),multiply:t("gl-mat4/multiply"),create:t("gl-mat4/create"),scale:t("gl-mat4/scale"),fromRotationTranslation:t("gl-mat4/fromRotationTranslation")},i=(n.create(),n.create());e.exports=function(t,e,r,a,o,s){return n.identity(t),n.fromRotationTranslation(t,s,e),t[3]=o[0],t[7]=o[1],t[11]=o[2],t[15]=o[3],n.identity(i),0!==a[2]&&(i[9]=a[2],n.multiply(t,t,i)),0!==a[1]&&(i[9]=0,i[8]=a[1],n.multiply(t,t,i)),0!==a[0]&&(i[8]=0,i[4]=a[0],n.multiply(t,t,i)),n.scale(t,t,r),t}},{"gl-mat4/create":249,"gl-mat4/fromRotationTranslation":252,"gl-mat4/identity":253,"gl-mat4/multiply":256,"gl-mat4/scale":262,"gl-mat4/translate":263}],399:[function(t,e,r){"use strict";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],400:[function(t,e,r){"use strict";var n=t("binary-search-bounds"),i=t("mat4-interpolate"),a=t("gl-mat4/invert"),o=t("gl-mat4/rotateX"),s=t("gl-mat4/rotateY"),l=t("gl-mat4/rotateZ"),c=t("gl-mat4/lookAt"),u=t("gl-mat4/translate"),h=(t("gl-mat4/scale"),t("gl-vec3/normalize")),f=[0,0,0];function p(t){this._components=t.slice(),this._time=[0],this.prevMatrix=t.slice(),this.nextMatrix=t.slice(),this.computedMatrix=t.slice(),this.computedInverse=t.slice(),this.computedEye=[0,0,0],this.computedUp=[0,0,0],this.computedCenter=[0,0,0],this.computedRadius=[0],this._limits=[-1/0,1/0]}e.exports=function(t){return new p((t=t||{}).matrix||[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1])};var d=p.prototype;d.recalcMatrix=function(t){var e=this._time,r=n.le(e,t),o=this.computedMatrix;if(!(r<0)){var s=this._components;if(r===e.length-1)for(var l=16*r,c=0;c<16;++c)o[c]=s[l++];else{var u=e[r+1]-e[r],f=(l=16*r,this.prevMatrix),p=!0;for(c=0;c<16;++c)f[c]=s[l++];var d=this.nextMatrix;for(c=0;c<16;++c)d[c]=s[l++],p=p&&f[c]===d[c];if(u<1e-6||p)for(c=0;c<16;++c)o[c]=f[c];else i(o,f,d,(t-e[r])/u)}var g=this.computedUp;g[0]=o[1],g[1]=o[5],g[2]=o[9],h(g,g);var m=this.computedInverse;a(m,o);var v=this.computedEye,y=m[15];v[0]=m[12]/y,v[1]=m[13]/y,v[2]=m[14]/y;var 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thickness * normal * .5 * lineOffset / viewport.zw;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nuniform sampler2D dashPattern;\n\nuniform float dashSize, pixelRatio, thickness, opacity, id;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\n\nvoid main() {\n\tfloat alpha = 1.;\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashSize) * .5 + .25;\n\tfloat dash = texture2D(dashPattern, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * 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enableStartMiter, enableEndMiter;\n\nconst float REVERSE_THRESHOLD = -.875;\nconst float MIN_DIFF = 1e-6;\n\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\n// TODO: precalculate dot products, normalize things beforehead etc.\n// TODO: refactor to rectangular algorithm\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nbool isNaN( float val ){\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\n}\n\nvoid main() {\n\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\n\n\t// adjust scale for horizontal bars\n\tvec2 scale = max(scale, MIN_DIFF);\n\tvec2 scaleRatio = scale * viewport.zw;\n\n\tvec2 normalWidth = thickness / scaleRatio;\n\n\tfloat lineStart = 1. - lineEnd;\n\tfloat lineBot = 1. - lineTop;\n\n\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\n\n\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\n\n\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\n\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\n\n\tvec2 prevDiff = aCoord - prevCoord;\n\tvec2 currDiff = bCoord - aCoord;\n\tvec2 nextDiff = nextCoord - bCoord;\n\n\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\n\tvec2 currTangent = normalize(currDiff * scaleRatio);\n\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\n\n\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\n\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\n\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\n\n\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\n\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\n\n\t// collapsed/unidirectional segment cases\n\t// FIXME: there should be more elegant solution\n\tvec2 prevTanDiff = abs(prevTangent - currTangent);\n\tvec2 nextTanDiff = abs(nextTangent - currTangent);\n\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\n\t\tstartJoinDirection = currNormal;\n\t}\n\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\n\t\tendJoinDirection = currNormal;\n\t}\n\tif (aCoord == bCoord) {\n\t\tendJoinDirection = startJoinDirection;\n\t\tcurrNormal = prevNormal;\n\t\tcurrTangent = prevTangent;\n\t}\n\n\ttangent = currTangent;\n\n\t//calculate join shifts relative to normals\n\tfloat startJoinShift = dot(currNormal, startJoinDirection);\n\tfloat endJoinShift = dot(currNormal, endJoinDirection);\n\n\tfloat startMiterRatio = abs(1. / startJoinShift);\n\tfloat endMiterRatio = abs(1. / endJoinShift);\n\n\tvec2 startJoin = startJoinDirection * startMiterRatio;\n\tvec2 endJoin = endJoinDirection * endMiterRatio;\n\n\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\n\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\n\tstartBotJoin = -startTopJoin;\n\n\tendTopJoin = sign(endJoinShift) * endJoin * .5;\n\tendBotJoin = -endTopJoin;\n\n\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\n\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\n\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\n\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\n\n\t//miter anti-clipping\n\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\n\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\n\n\t//prevent close to reverse direction switch\n\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\n\tif (prevReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\n\t\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\n\t\t//handle miter clipping\n\t\tbTopCoord -= normalWidth * endTopJoin;\n\t\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\n\t}\n\n\tif (nextReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\n\t\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\n\t\t//handle miter clipping\n\t\taBotCoord -= normalWidth * startBotJoin;\n\t\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\n\t}\n\n\tvec2 aTopPosition = (aTopCoord) * scale + translate;\n\tvec2 aBotPosition = (aBotCoord) * scale + translate;\n\n\tvec2 bTopPosition = (bTopCoord) * scale + translate;\n\tvec2 bBotPosition = (bBotCoord) * scale + translate;\n\n\t//position is normalized 0..1 coord on the screen\n\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\n\n\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\n\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\n\n\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\n\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\n\n\t//bevel miter cutoffs\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n\n\t//round miter cutoffs\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nuniform sampler2D dashPattern;\nuniform float dashSize, pixelRatio, thickness, opacity, id, miterMode;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nvoid main() {\n\tfloat alpha = 1., distToStart, distToEnd;\n\tfloat cutoff = thickness * .5;\n\n\t//bevel miter\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToStart + 1., 0.), 1.);\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToEnd + 1., 0.), 1.);\n\t\t}\n\t}\n\n\t// round miter\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - startCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - endCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\t}\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashSize) * .5 + .25;\n\tfloat dash = texture2D(dashPattern, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * dash;\n}\n"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop("colorBuffer"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:"triangle",elements:function(t,e){return e.triangles},offset:0,vert:o(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec2 position, positionFract;\n\nuniform vec4 color;\nuniform vec2 scale, scaleFract, translate, translateFract;\nuniform float pixelRatio, id;\nuniform vec4 viewport;\nuniform float opacity;\n\nvarying vec4 fragColor;\n\nconst float MAX_LINES = 256.;\n\nvoid main() {\n\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\n\n\tvec2 position = position * scale + translate\n       + positionFract * scale + translateFract\n       + position * scaleFract\n       + positionFract * scaleFract;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n\tfragColor.a *= opacity;\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n\tgl_FragColor = fragColor;\n}\n"]),uniforms:{scale:t.prop("scale"),color:t.prop("fill"),scaleFract:t.prop("scaleFract"),translateFract:t.prop("translateFract"),translate:t.prop("translate"),opacity:t.prop("opacity"),pixelRatio:t.context("pixelRatio"),id:t.prop("id"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop("positionBuffer"),stride:8,offset:8},positionFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},m.defaults={dashes:null,join:"miter",miterLimit:1,thickness:10,cap:"square",color:"black",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},m.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},m.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach(function(e,r){if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);var n;("number"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity)&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>m.precisionThreshold||e.scale[1]*e.viewport.height>m.precisionThreshold?t.shaders.rect(e):"rect"===e.join||!e.join&&(e.thickness<=2||e.count>=m.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))}),this},m.prototype.update=function(t){var e=this;if(t){null!=t.length?"number"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach(function(t,h){var d=e.passes[h];if(void 0!==t)if(null!==t){if("number"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:"positions points data coords",thickness:"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth",join:"lineJoin linejoin join type mode",miterLimit:"miterlimit miterLimit",dashes:"dash dashes dasharray dash-array dashArray",color:"color colour stroke colors colours stroke-color strokeColor",fill:"fill fill-color fillColor",opacity:"alpha opacity",overlay:"overlay crease overlap intersect",close:"closed close closed-path closePath",range:"range dataBox",viewport:"viewport viewBox",hole:"holes hole hollow"}),d||(e.passes[h]=d={id:h,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:r.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:"linear",min:"linear"}),colorBuffer:r.buffer({usage:"dynamic",type:"uint8",data:new Uint8Array}),positionBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array}),positionFractBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array})},t=a({},m.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,h<m.maxLines&&(d.depth=2*(m.maxLines-1-h%m.maxLines)/m.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,"uint8"):null),null!=t.viewport&&(d.viewport=g(t.viewport)),d.viewport||(d.viewport=g([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var v,y;if(t.positions.x&&t.positions.y){var x=t.positions.x,b=t.positions.y;y=d.count=Math.max(x.length,b.length),v=new Float64Array(2*y);for(var _=0;_<y;_++)v[2*_]=x[_],v[2*_+1]=b[_]}else v=l(t.positions,"float64"),y=d.count=Math.floor(v.length/2);var w=d.bounds=i(v,2);if(d.fill){for(var k=[],M={},A=0,T=0,S=0,E=d.count;T<E;T++){var C=v[2*T],L=v[2*T+1];isNaN(C)||isNaN(L)||null==C||null==L?(C=v[2*A],L=v[2*A+1],M[T]=A):A=T,k[S++]=C,k[S++]=L}for(var z=c(k,d.hole||[]),P=0,I=z.length;P<I;P++)null!=M[z[P]]&&(z[P]=M[z[P]]);d.triangles=z}var O=new Float64Array(v);u(O,2,w);var D=new Float64Array(2*y+6);d.close?v[0]===v[2*y-2]&&v[1]===v[2*y-1]?(D[0]=O[2*y-4],D[1]=O[2*y-3]):(D[0]=O[2*y-2],D[1]=O[2*y-1]):(D[0]=O[0],D[1]=O[1]),D.set(O,2),d.close?v[0]===v[2*y-2]&&v[1]===v[2*y-1]?(D[2*y+2]=O[2],D[2*y+3]=O[3],d.count-=1):(D[2*y+2]=O[0],D[2*y+3]=O[1],D[2*y+4]=O[2],D[2*y+5]=O[3]):(D[2*y+2]=O[2*y-2],D[2*y+3]=O[2*y-1],D[2*y+4]=O[2*y-2],D[2*y+5]=O[2*y-1]),d.positionBuffer(f(D)),d.positionFractBuffer(p(D))}if(t.range?d.range=t.range:d.range||(d.range=d.bounds),(t.range||t.positions)&&d.count){var R=d.bounds,B=R[2]-R[0],F=R[3]-R[1],N=d.range[2]-d.range[0],j=d.range[3]-d.range[1];d.scale=[B/N,F/j],d.translate=[-d.range[0]/N+R[0]/N||0,-d.range[1]/j+R[1]/j||0],d.scaleFract=p(d.scale),d.translateFract=p(d.translate)}if(t.dashes){var V,U=0;if(!t.dashes||t.dashes.length<2)U=1,V=new Uint8Array([255,255,255,255,255,255,255,255]);else{U=0;for(var q=0;q<t.dashes.length;++q)U+=t.dashes[q];V=new Uint8Array(U*m.dashMult);for(var H=0,G=255,W=0;W<2;W++)for(var Y=0;Y<t.dashes.length;++Y){for(var X=0,Z=t.dashes[Y]*m.dashMult*.5;X<Z;++X)V[H++]=G;G^=255}}d.dashLength=U,d.dashTexture({channels:1,data:V,width:V.length,height:1,mag:"linear",min:"linear"},0,0)}if(t.color){var $=d.count,J=t.color;J||(J="transparent");var K=new Uint8Array(4*$+4);if(Array.isArray(J)&&"number"!=typeof J[0]){for(var Q=0;Q<$;Q++){var tt=n(J[Q],"uint8");K.set(tt,4*Q)}K.set(n(J[0],"uint8"),4*$)}else for(var et=n(J,"uint8"),rt=0;rt<$+1;rt++)K.set(et,4*rt);d.colorBuffer({usage:"dynamic",type:"uint8",data:K})}}else e.passes[h]=null}),t.length<this.passes.length){for(var h=t.length;h<this.passes.length;h++){var d=e.passes[h];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}return this.passes=this.passes.filter(Boolean),this}},m.prototype.destroy=function(){return this.passes.forEach(function(t){t.colorBuffer.destroy(),t.positionBuffer.destroy(),t.dashTexture.destroy()}),this.passes.length=0,this}},{"array-bounds":56,"array-normalize":57,"color-normalize":107,earcut:155,"es6-weak-map":208,"flatten-vertex-data":216,glslify:377,"object-assign":421,"parse-rect":426,"pick-by-alias":432,"to-float32":500}],459:[function(t,e,r){"use strict";var n=t("./scatter"),i=t("object-assign");e.exports=function(t,e){var r=new n(t,e),a=r.render.bind(r);return i(a,{render:a,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),a}},{"./scatter":460,"object-assign":421}],460:[function(t,e,r){"use strict";var n=t("color-normalize"),i=t("array-bounds"),a=t("color-id"),o=t("point-cluster"),s=t("object-assign"),l=t("glslify"),c=t("pick-by-alias"),u=t("update-diff"),h=t("flatten-vertex-data"),f=t("is-iexplorer"),p=t("to-float32"),d=p.float32,g=p.fract32,m=t("parse-rect");function v(t,e){var r=this;if(!(this instanceof v))return new v(t,e);"function"==typeof t?(e||(e={}),e.regl=t):(e=t,t=null),e&&e.length&&(e.positions=e);var n,i=(t=e.regl)._gl,a=[];this.tooManyColors=f,n=t.texture({data:new Uint8Array(1020),width:255,height:1,type:"uint8",format:"rgba",wrapS:"clamp",wrapT:"clamp",mag:"nearest",min:"nearest"}),s(this,{regl:t,gl:i,groups:[],markerCache:[null],markerTextures:[null],palette:a,paletteIds:{},paletteTexture:n,maxColors:255,maxSize:100,canvas:i.canvas}),this.update(e);var o={uniforms:{pixelRatio:t.context("pixelRatio"),palette:n,paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},scale:t.prop("scale"),scaleFract:t.prop("scaleFract"),translate:t.prop("translate"),translateFract:t.prop("translateFract"),opacity:t.prop("opacity"),marker:t.prop("markerTexture")},attributes:{x:function(t,e){return e.xAttr||{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return e.borderSize.length?{buffer:e.sizeBuffer,stride:2,offset:1}:{constant:[Math.round(255*e.borderSize/r.maxSize)]}},colorId:function(t,e){return e.color.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:0}:{constant:r.tooManyColors?a.slice(4*e.color,4*e.color+4):[e.color]}},borderColorId:function(t,e){return e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4*e.borderColor,4*e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},scissor:{enable:!0,box:t.prop("viewport")},viewport:t.prop("viewport"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop("elements"),count:t.prop("count"),offset:t.prop("offset"),primitive:"points"},c=s({},o);c.frag=l(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragWidth, fragBorderColorLevel, fragColorLevel;\n\nuniform sampler2D marker;\nuniform float pixelRatio, opacity;\n\nfloat smoothStep(float x, float y) {\n  return 1.0 / (1.0 + exp(50.0*(x - y)));\n}\n\nvoid main() {\n  float dist = texture2D(marker, gl_PointCoord).r, delta = fragWidth;\n\n  // max-distance alpha\n  if (dist < 0.003) discard;\n\n  // null-border case\n  if (fragBorderColorLevel == fragColorLevel || fragBorderColor.a == 0.) {\n    float colorAmt = smoothstep(.5 - delta, .5 + delta, dist);\n    gl_FragColor = vec4(fragColor.rgb, colorAmt * fragColor.a * opacity);\n    return;\n  }\n\n  float borderColorAmt = smoothstep(fragBorderColorLevel - delta, fragBorderColorLevel + delta, dist);\n  float colorAmt = smoothstep(fragColorLevel - delta, fragColorLevel + delta, dist);\n\n  vec4 color = fragBorderColor;\n  color.a *= borderColorAmt;\n  color = mix(color, fragColor, colorAmt);\n  color.a *= opacity;\n\n  gl_FragColor = color;\n}\n"]),c.vert=l(["precision mediump float;\n#define GLSLIFY 1\n\nattribute float x, y, xFract, yFract;\nattribute float size, borderSize;\nattribute vec4 colorId, borderColorId;\nattribute float isActive;\n\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\nuniform float pixelRatio;\nuniform sampler2D palette;\n\nconst float maxSize = 100.;\nconst float borderLevel = .5;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragPointSize, fragBorderRadius,\n    fragWidth, fragBorderColorLevel, fragColorLevel;\n\nvec2 paletteCoord(float id) {\n  return vec2(\n    (mod(id, paletteSize.x) + .5) / paletteSize.x,\n    (floor(id / paletteSize.x) + .5) / paletteSize.y\n  );\n}\nvec2 paletteCoord(vec2 id) {\n  return vec2(\n    (id.x + .5) / paletteSize.x,\n    (id.y + .5) / paletteSize.y\n  );\n}\nvec4 getColor(vec4 id) {\n  // zero-palette means we deal with direct buffer\n  if (paletteSize.x == 0.) return id / 255.;\n  return texture2D(palette, paletteCoord(id.xy));\n}\n\nvoid main() {\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = 2. * size * pixelRatio;\n  fragPointSize = size * pixelRatio;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0, 1);\n\n  fragColor = color;\n  fragBorderColor = borderColor;\n  fragWidth = 1. / gl_PointSize;\n\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\n}\n"]);try{this.drawMarker=t(c)}catch(t){}var u=s({},o);u.frag=l(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor, fragBorderColor;\n\nuniform float opacity;\nvarying float fragBorderRadius, fragWidth;\n\nfloat smoothStep(float edge0, float edge1, float x) {\n\tfloat t;\n\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\n\treturn t * t * (3.0 - 2.0 * t);\n}\n\nvoid main() {\n\tfloat radius, alpha = 1.0, delta = fragWidth;\n\n\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\n\n\tif (radius > 1.0 + delta) {\n\t\tdiscard;\n\t}\n\n\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\n\n\tfloat borderRadius = fragBorderRadius;\n\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\n\tvec4 color = mix(fragColor, fragBorderColor, ratio);\n\tcolor.a *= alpha * opacity;\n\tgl_FragColor = color;\n}\n"]),u.vert=l(["precision highp float;\n#define GLSLIFY 1\n\nattribute float x, y, xFract, yFract;\nattribute float size, borderSize;\nattribute vec4 colorId, borderColorId;\nattribute float isActive;\n\nuniform vec2 scale, scaleFract, translate, translateFract;\nuniform float pixelRatio;\nuniform sampler2D palette;\nuniform vec2 paletteSize;\n\nconst float maxSize = 100.;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragBorderRadius, fragWidth;\n\nvec2 paletteCoord(float id) {\n  return vec2(\n    (mod(id, paletteSize.x) + .5) / paletteSize.x,\n    (floor(id / paletteSize.x) + .5) / paletteSize.y\n  );\n}\nvec2 paletteCoord(vec2 id) {\n  return vec2(\n    (id.x + .5) / paletteSize.x,\n    (id.y + .5) / paletteSize.y\n  );\n}\n\nvec4 getColor(vec4 id) {\n  // zero-palette means we deal with direct buffer\n  if (paletteSize.x == 0.) return id / 255.;\n  return texture2D(palette, paletteCoord(id.xy));\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = (size + borderSize) * pixelRatio;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0, 1);\n\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\n  fragColor = color;\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\n  fragWidth = 1. / gl_PointSize;\n}\n"]),f&&(u.frag=u.frag.replace("smoothstep","smoothStep")),this.drawCircle=t(u)}e.exports=v,v.defaults={color:"black",borderColor:"transparent",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},v.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];return e.length&&(t=this).update.apply(t,e),this.draw(),this},v.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];var n=this.groups;if(1===e.length&&Array.isArray(e[0])&&(null===e[0][0]||Array.isArray(e[0][0]))&&(e=e[0]),this.regl._refresh(),e.length)for(var i=0;i<e.length;i++)t.drawItem(i,e[i]);else n.forEach(function(e,r){t.drawItem(r)});return this},v.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if("number"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var i=[],a=1;a<n.activation.length;a++)n.activation[a]&&(!0===n.activation[a]||n.activation[a].data.length)&&i.push.apply(i,this.getMarkerDrawOptions(a,n,e));i.length&&this.drawMarker(i)}},v.prototype.getMarkerDrawOptions=function(t,e,r){var n=e.range,i=e.tree,a=e.viewport,o=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!i)return r?[s({},e,{markerTexture:this.markerTextures[t],activation:o[t],count:r.length,elements:r,offset:0})]:[s({},e,{markerTexture:this.markerTextures[t],activation:o[t],offset:0})];var u=[],h=i.range(n,{lod:!0,px:[(n[2]-n[0])/a.width,(n[3]-n[1])/a.height]});if(r){for(var f=o[t].data,p=new Uint8Array(c),d=0;d<r.length;d++){var g=r[d];p[g]=f?f[g]:1}l.subdata(p)}for(var m=h.length;m--;){var v=h[m],y=v[0],x=v[1];u.push(s({},e,{markerTexture:this.markerTextures[t],activation:r?l:o[t],offset:y,count:x-y}))}return u},v.prototype.update=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){1===e.length&&Array.isArray(e[0])&&(e=e[0]);var n=this.groups,a=this.gl,l=this.regl,f=this.maxSize,p=this.maxColors,y=this.palette;this.groups=n=e.map(function(e,r){var x=n[r];if(void 0===e)return x;null===e?e={positions:null}:"function"==typeof e?e={ondraw:e}:"number"==typeof e[0]&&(e={positions:e}),null===(e=c(e,{positions:"positions data points",snap:"snap cluster lod tree",size:"sizes size radius",borderSize:"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline",color:"colors color fill fill-color fillColor",borderColor:"borderColors borderColor stroke stroke-color strokeColor",marker:"markers marker shape",range:"range dataBox databox",viewport:"viewport viewPort viewBox viewbox",opacity:"opacity alpha transparency",bounds:"bound bounds boundaries limits"})).positions&&(e.positions=[]),x||(n[r]=x={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:l.buffer({data:new Uint8Array(0),usage:"stream",type:"uint8"}),sizeBuffer:l.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),colorBuffer:l.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),positionBuffer:l.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"}),positionFractBuffer:l.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"})},e=s({},v.defaults,e)),!e.positions||"marker"in e||(e.marker=x.marker,delete x.marker),!e.marker||"positions"in e||(e.positions=x.positions,delete x.positions);var b=0,_=0;if(u(x,e,[{snap:!0,size:function(t,e){return null==t&&(t=v.defaults.size),b+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=v.defaults.borderSize),b+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=v.defaults.color),e=t.updateColor(e),_++,e},borderColor:function(e,r){return null==e&&(e=v.defaults.borderColor),e=t.updateColor(e),_++,e},bounds:function(t,e,r){return"range"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,a=e.positionBuffer,s=e.positionFractBuffer,c=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:l.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:l.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=h(t,"float64");var u=e.count=Math.floor(t.length/2),f=e.bounds=u?i(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||u>n)?e.tree=o(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var p={primitive:"points",usage:"static",data:e.tree,type:"uint32"};e.elements?e.elements(p):e.elements=l.elements(p)}return a({data:d(t),usage:"dynamic"}),s({data:g(t),usage:"dynamic"}),c({data:new Uint8Array(u),type:"uint8",usage:"stream"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach(function(t){return t&&t.destroy&&t.destroy()}),i.length=0,e&&"number"!=typeof e[0]){for(var a=[],o=0,s=Math.min(e.length,r.count);o<s;o++){var c=t.addMarker(e[o]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][o]=1}for(var u=0;u<a.length;u++)if(a[u]){var h={data:a[u],type:"uint8",usage:"static"};i[u]?i[u](h):i[u]=l.buffer(h),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=g(e.scale),e.translateFract=g(e.translate),t},viewport:function(t){return m(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),b){var w=x.count,k=x.size,M=x.borderSize,A=x.sizeBuffer,T=new Uint8Array(2*w);if(k.length||M.length)for(var S=0;S<w;S++)T[2*S]=Math.round(255*(null==k[S]?k:k[S])/f),T[2*S+1]=Math.round(255*(null==M[S]?M:M[S])/f);A({data:T,usage:"dynamic"})}if(_){var E,C=x.count,L=x.color,z=x.borderColor,P=x.colorBuffer;if(t.tooManyColors){if(L.length||z.length){E=new Uint8Array(8*C);for(var I=0;I<C;I++){var O=L[I];E[8*I]=y[4*O],E[8*I+1]=y[4*O+1],E[8*I+2]=y[4*O+2],E[8*I+3]=y[4*O+3];var D=z[I];E[8*I+4]=y[4*D],E[8*I+5]=y[4*D+1],E[8*I+6]=y[4*D+2],E[8*I+7]=y[4*D+3]}}}else if(L.length||z.length){E=new Uint8Array(4*C+2);for(var R=0;R<C;R++)null!=L[R]&&(E[4*R]=L[R]%p,E[4*R+1]=Math.floor(L[R]/p)),null!=z[R]&&(E[4*R+2]=z[R]%p,E[4*R+3]=Math.floor(z[R]/p))}P({data:E||new Uint8Array(0),type:"uint8",usage:"dynamic"})}return x})}},v.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:"linear",min:"linear"})),a},v.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,i=this.maxColors;Array.isArray(t)||(t=[t]);var o=[];if("number"==typeof t[0]){var s=[];if(Array.isArray(t))for(var l=0;l<t.length;l+=4)s.push(t.slice(l,l+4));else for(var c=0;c<t.length;c+=4)s.push(t.subarray(c,c+4));t=s}for(var u=0;u<t.length;u++){var h=t[u];h=n(h,"uint8");var f=a(h,!1);if(null==e[f]){var p=r.length;e[f]=Math.floor(p/4),r[p]=h[0],r[p+1]=h[1],r[p+2]=h[2],r[p+3]=h[3]}o[u]=e[f]}return!this.tooManyColors&&r.length>i*i*4&&(this.tooManyColors=!0),this.updatePalette(r),1===o.length?o[0]:o},v.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},v.prototype.destroy=function(){return this.groups.forEach(function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach(function(t){return t&&t.destroy&&t.destroy()}),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()}),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach(function(t){return t&&t.destroy&&t.destroy()}),this}},{"array-bounds":56,"color-id":105,"color-normalize":107,"flatten-vertex-data":216,glslify:377,"is-iexplorer":387,"object-assign":421,"parse-rect":426,"pick-by-alias":432,"point-cluster":436,"to-float32":500,"update-diff":511}],461:[function(t,e,r){"use strict";var n=t("regl-scatter2d/scatter"),i=t("pick-by-alias"),a=t("array-bounds"),o=t("raf"),s=t("array-range"),l=t("parse-rect"),c=t("flatten-vertex-data");function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function h(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function f(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x+s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y+l.height),[a,n,o,i]}function p(t){if("number"==typeof 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3||t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[""].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return 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t=i.year()+(i.year()<0?1:0),e=i.month(),(r=i.day())+(e>1?16:0)+(e>2?32*(e-2):0)+400*(t-1)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t+.5)-Math.floor(this.jdEpoch)-1;var e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:"Fruitbat",21:"Anchovy"};n.calendars.discworld=a},{"../main":536,"object-assign":421}],525:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new 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n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.ethiopian=a},{"../main":536,"object-assign":421}],526:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new 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a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.hebrew=a},{"../main":536,"object-assign":421}],527:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Islamic",jdEpoch:1948439.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Islamic",epochs:["BH","AH"],monthNames:["Muharram","Safar","Rabi' al-awwal","Rabi' al-thani","Jumada al-awwal","Jumada al-thani","Rajab","Sha'aban","Ramadan","Shawwal","Dhu al-Qi'dah","Dhu al-Hijjah"],monthNamesShort:["Muh","Saf","Rab1","Rab2","Jum1","Jum2","Raj","Sha'","Ram","Shaw","DhuQ","DhuH"],dayNames:["Yawm al-ahad","Yawm al-ithnayn","Yawm ath-thulaathaa'","Yawm al-arbi'aa'","Yawm al-kham\u012bs","Yawm al-jum'a","Yawm as-sabt"],dayNamesShort:["Aha","Ith","Thu","Arb","Kha","Jum","Sab"],dayNamesMin:["Ah","It","Th","Ar","Kh","Ju","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!1}},leapYear:function(t){return(11*this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year()+14)%30<11},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return this.leapYear(t)?355:354},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),t=t<=0?t+1:t,(r=i.day())+Math.ceil(29.5*(e-1))+354*(t-1)+Math.floor((3+11*t)/30)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},{"../main":536,"object-assign":421}],528:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new 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r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},{"../main":536,"object-assign":421}],529:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}function o(t,e){return t-e*Math.floor(t/e)}function s(t,e){return o(t-1,e)+1}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Mayan",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{"":{name:"Mayan",epochs:["",""],monthNames:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17"],monthNamesShort:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17"],dayNames:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],dayNamesShort:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],dayNamesMin:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],digits:null,dateFormat:"YYYY.m.d",firstDay:0,isRTL:!1,haabMonths:["Pop","Uo","Zip","Zotz","Tzec","Xul","Yaxkin","Mol","Chen","Yax","Zac","Ceh","Mac","Kankin","Muan","Pax","Kayab","Cumku","Uayeb"],tzolkinMonths:["Imix","Ik","Akbal","Kan","Chicchan","Cimi","Manik","Lamat","Muluc","Oc","Chuen","Eb","Ben","Ix","Men","Cib","Caban","Etznab","Cauac","Ahau"]}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+"."+Math.floor(t/20)+"."+t%20},forYear:function(t){if((t=t.split(".")).length<3)throw"Invalid Mayan year";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw"Invalid Mayan year";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{"../main":536,"object-assign":421}],530:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar;var o=n.instance("gregorian");i(a.prototype,{name:"Nanakshahi",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Nanakshahi",epochs:["BN","AN"],monthNames:["Chet","Vaisakh","Jeth","Harh","Sawan","Bhadon","Assu","Katak","Maghar","Poh","Magh","Phagun"],monthNamesShort:["Che","Vai","Jet","Har","Saw","Bha","Ass","Kat","Mgr","Poh","Mgh","Pha"],dayNames:["Somvaar","Mangalvar","Budhvaar","Veervaar","Shukarvaar","Sanicharvaar","Etvaar"],dayNamesShort:["Som","Mangal","Budh","Veer","Shukar","Sanichar","Et"],dayNamesMin:["So","Ma","Bu","Ve","Sh","Sa","Et"],digits:null,dateFormat:"dd-mm-yyyy",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[""].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s<i.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},{"../main":536,"object-assign":421}],531:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Nepali",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{"":{name:"Nepali",epochs:["BBS","ABS"],monthNames:["Baisakh","Jestha","Ashadh","Shrawan","Bhadra","Ashwin","Kartik","Mangsir","Paush","Mangh","Falgun","Chaitra"],monthNamesShort:["Bai","Je","As","Shra","Bha","Ash","Kar","Mang","Pau","Ma","Fal","Chai"],dayNames:["Aaitabaar","Sombaar","Manglbaar","Budhabaar","Bihibaar","Shukrabaar","Shanibaar"],dayNamesShort:["Aaita","Som","Mangl","Budha","Bihi","Shukra","Shani"],dayNamesMin:["Aai","So","Man","Bu","Bi","Shu","Sha"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return 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n.baseCalendar,i(a.prototype,{name:"UmmAlQura",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Umm al-Qura",epochs:["BH","AH"],monthNames:["Al-Muharram","Safar","Rabi' al-awwal","Rabi' Al-Thani","Jumada Al-Awwal","Jumada Al-Thani","Rajab","Sha'aban","Ramadan","Shawwal","Dhu al-Qi'dah","Dhu al-Hijjah"],monthNamesShort:["Muh","Saf","Rab1","Rab2","Jum1","Jum2","Raj","Sha'","Ram","Shaw","DhuQ","DhuH"],dayNames:["Yawm al-Ahad","Yawm al-Ithnain","Yawm al-Thal\u0101th\u0101\u2019","Yawm al-Arba\u2018\u0101\u2019","Yawm al-Kham\u012bs","Yawm al-Jum\u2018a","Yawm al-Sabt"],dayNamesMin:["Ah","Ith","Th","Ar","Kh","Ju","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\{0\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=t("object-assign");function i(){this.regionalOptions=[],this.regionalOptions[""]={invalidCalendar:"Calendar {0} not found",invalidDate:"Invalid {0} date",invalidMonth:"Invalid {0} month",invalidYear:"Invalid {0} year",differentCalendars:"Cannot mix {0} and {1} dates"},this.local=this.regionalOptions[""],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name)}function o(t,e){return"000000".substring(0,e-(t=""+t).length)+t}function s(){this.shortYearCutoff="+10"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[""]}n(i.prototype,{instance:function(t,e){t=(t||"gregorian").toLowerCase(),e=e||"";var r=this._localCals[t+"-"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+"-"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[""].invalidCalendar).replace(/\{0\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():"string"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+"").replace(/[0-9]/g,function(e){return t[e]})}},substituteChineseDigits:function(t,e){return function(r){for(var n="",i=0;r>0;){var a=r%10;n=(0===a?"":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,"y")},month:function(t){return 0===arguments.length?this._month:this.set(t,"m")},day:function(t){return 0===arguments.length?this._day:this.set(t,"d")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[""].differentCalendars).replace(/\{0\}/,this._calendar.local.name).replace(/\{1\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?"-":"")+o(Math.abs(this.year()),4)+"-"+o(this.month(),2)+"-"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return(e.year()<0?"-":"")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[""].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,"d"===r||"w"===r){var n=t.toJD()+e*("w"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+("y"===r?e:0),o=t.monthOfYear()+("m"===r?e:0);i=t.day();"y"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):"m"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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a=r.dayNamesShort||this.local.dayNamesShort,o=r.dayNames||this.local.dayNames,s=r.parseMonth||this.local.parseMonth,l=r.monthNumbers||this.local.monthNumbers,c=r.monthNamesShort||this.local.monthNamesShort,u=r.monthNames||this.local.monthNames,h=-1,f=-1,p=-1,d=-1,g=-1,m=!1,v=!1,y=function(e,r){for(var n=1;T+n<t.length&&t.charAt(T+n)===e;)n++;return T+=n-1,Math.floor(n/(r||1))>1},x=function(t,r){var n=y(t,r),a=[2,3,n?4:2,n?4:2,10,11,20]["oyYJ@!".indexOf(t)+1],o=new RegExp("^-?\\d{1,"+a+"}"),s=e.substring(A).match(o);if(!s)throw(i.local.missingNumberAt||i.regionalOptions[""].missingNumberAt).replace(/\{0\}/,A);return A+=s[0].length,parseInt(s[0],10)},b=this,_=function(){if("function"==typeof l){y("m");var t=l.call(b,e.substring(A));return A+=t.length,t}return x("m")},w=function(t,r,n,a){for(var o=y(t,a)?n:r,s=0;s<o.length;s++)if(e.substr(A,o[s].length).toLowerCase()===o[s].toLowerCase())return 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i.local.unexpectedText||i.regionalOptions[""].unexpectedText;if(-1===f?f=this.today().year():f<100&&m&&(f+=-1===n?1900:this.today().year()-this.today().year()%100-(f<=n?0:100)),"string"==typeof p&&(p=s.call(this,f,p)),g>-1){p=1,d=g;for(var E=this.daysInMonth(f,p);d>E;E=this.daysInMonth(f,p))p++,d-=E}return h>-1?this.fromJD(h):this.newDate(f,p,d)},determineDate:function(t,e,r,n,i){r&&"object"!=typeof r&&(i=n,n=r,r=null),"string"!=typeof n&&(i=n,n="");var a=this;return e=e?e.newDate():null,t=null==t?e:"string"==typeof t?function(t){try{return a.parseDate(n,t,i)}catch(t){}for(var e=((t=t.toLowerCase()).match(/^c/)&&r?r.newDate():null)||a.today(),o=/([+-]?[0-9]+)\s*(d|w|m|y)?/g,s=o.exec(t);s;)e.add(parseInt(s[1],10),s[2]||"d"),s=o.exec(t);return e}(t):"number"==typeof 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r,s,l=o(t,e),c=l.on,u=l.off.concat(l.explicitOff),h={},f=t._fullLayout.annotations;if(!c.length&&!u.length)return;for(r=0;r<c.length;r++)(s=a(t.layout,"annotations",f[c[r]])).modifyItem("visible",!0),n.extendFlat(h,s.getUpdateObj());for(r=0;r<u.length;r++)(s=a(t.layout,"annotations",f[u[r]])).modifyItem("visible",!1),n.extendFlat(h,s.getUpdateObj());return i.call("update",t,{},h)}}},{"../../lib":684,"../../plot_api/plot_template":722,"../../registry":817}],544:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../color");e.exports=function(t,e,r,a){a("opacity");var o=a("bgcolor"),s=a("bordercolor"),l=i.opacity(s);a("borderpad");var c=a("borderwidth"),u=a("showarrow");if(a("text",u?" 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l,c,u=t._fullLayout.annotations,h=e._id.charAt(0),f=0;f<u.length;f++)l=u[f],c="annotations["+f+"].",l[h+"ref"]===e._id&&p(h),l["a"+h+"ref"]===e._id&&p("a"+h);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{"../../lib/to_log_range":710,"fast-isnumeric":214}],546:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../plots/cartesian/axes"),a=t("../../plots/array_container_defaults"),o=t("./common_defaults"),s=t("./attributes");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a("visible"),c=a("clicktoshow");if(l||c){o(t,e,r,a);for(var u=e.showarrow,h=["x","y"],f=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var g=h[d],m=i.coerceRef(t,e,p,g,"","paper");if(i.coercePosition(e,p,a,m,g,.5),u){var v="a"+g,y=i.coerceRef(t,e,p,v,"pixel");"pixel"!==y&&y!==m&&(y=e[v]="pixel");var x="pixel"===y?f[d]:.4;i.coercePosition(e,p,a,y,v,x)}a(g+"anchor"),a(g+"shift")}if(n.noneOrAll(t,e,["x","y"]),u&&n.noneOrAll(t,e,["ax","ay"]),c){var 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w=d(t.layout,y,e),k=w.modifyBase,M=w.modifyItem,A=w.getUpdateObj;x._infolayer.selectAll("."+v+'[data-index="'+r+'"]').remove();var T="clip"+x._uid+"_ann"+r;if(e._input&&!1!==e.visible){var S={x:{},y:{}},E=+e.textangle||0,C=x._infolayer.append("g").classed(v,!0).attr("data-index",String(r)).style("opacity",e.opacity),L=C.append("g").classed("annotation-text-g",!0),z=_[e.showarrow?"annotationTail":"annotationPosition"],P=e.captureevents||_.annotationText||z,I=L.append("g").style("pointer-events",P?"all":null).call(f,"pointer").on("click",function(){t._dragging=!1;var i={index:r,annotation:e._input,fullAnnotation:e,event:n.event};a&&(i.subplotId=a),t.emit("plotly_clickannotation",i)});e.hovertext&&I.on("mouseover",function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();u.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:x._hoverlayer.node(),outerContainer:x._paper.node(),gd:t})}).on("mouseout",function(){u.loneUnhover(x._hoverlayer.node())});var O=e.borderwidth,D=e.borderpad,R=O+D,B=I.append("rect").attr("class","bg").style("stroke-width",O+"px").call(l.stroke,e.bordercolor).call(l.fill,e.bgcolor),F=e.width||e.height,N=x._topclips.selectAll("#"+T).data(F?[0]:[]);N.enter().append("clipPath").classed("annclip",!0).attr("id",T).append("rect"),N.exit().remove();var j=e.font,V=I.append("text").classed("annotation-text",!0).text(e.text);_.annotationText?V.call(h.makeEditable,{delegate:I,gd:t}).call(U).on("edit",function(r){e.text=r,this.call(U),M("text",r),s&&s.autorange&&k(s._name+".autorange",!0),m&&m.autorange&&k(m._name+".autorange",!0),i.call("relayout",t,A())}):V.call(U)}else n.selectAll("#"+T).remove();function U(r){return r.call(c.font,j).attr({"text-anchor":{left:"start",right:"end"}[e.align]||"middle"}),h.convertToTspans(r,t,q),r}function q(){var r=V.selectAll("a");1===r.size()&&r.text()===V.text()&&I.insert("a",":first-child").attr({"xlink:xlink:href":r.attr("xlink:href"),"xlink:xlink:show":r.attr("xlink:show")}).style({cursor:"pointer"}).node().appendChild(B.node());var n=I.select(".annotation-text-math-group"),u=!n.empty(),d=c.bBox((u?n:V).node()),v=d.width,y=d.height,w=e.width||v,P=e.height||y,D=Math.round(w+2*R),j=Math.round(P+2*R);function U(t,e){return"auto"===e&&(e=t<1/3?"left":t>2/3?"right":"center"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}e._w=w,e._h=P;for(var q=!1,H=["x","y"],G=0;G<H.length;G++){var W,Y,X,Z,$,J=H[G],K=e[J+"ref"]||J,Q=e["a"+J+"ref"],tt={x:s,y:m}[J],et=(E+("x"===J?0:-90))*Math.PI/180,rt=D*Math.cos(et),nt=j*Math.sin(et),it=Math.abs(rt)+Math.abs(nt),at=e[J+"anchor"],ot=e[J+"shift"]*("x"===J?1:-1),st=S[J];if(tt){var lt=tt.r2fraction(e[J]);if((t._dragging||!tt.autorange)&&(lt<0||lt>1)&&(Q===K?((lt=tt.r2fraction(e["a"+J]))<0||lt>1)&&(q=!0):q=!0,q))continue;W=tt._offset+tt.r2p(e[J]),Z=.5}else"x"===J?(X=e[J],W=b.l+b.w*X):(X=1-e[J],W=b.t+b.h*X),Z=e.showarrow?.5:X;if(e.showarrow){st.head=W;var ct=e["a"+J];$=rt*U(.5,e.xanchor)-nt*U(.5,e.yanchor),Q===K?(st.tail=tt._offset+tt.r2p(ct),Y=$):(st.tail=W+ct,Y=$+ct),st.text=st.tail+$;var ut=x["x"===J?"width":"height"];if("paper"===K&&(st.head=o.constrain(st.head,1,ut-1)),"pixel"===Q){var ht=-Math.max(st.tail-3,st.text),ft=Math.min(st.tail+3,st.text)-ut;ht>0?(st.tail+=ht,st.text+=ht):ft>0&&(st.tail-=ft,st.text-=ft)}st.tail+=ot,st.head+=ot}else Y=$=it*U(Z,at),st.text=W+$;st.text+=ot,$+=ot,Y+=ot,e["_"+J+"padplus"]=it/2+Y,e["_"+J+"padminus"]=it/2-Y,e["_"+J+"size"]=it,e["_"+J+"shift"]=$}if(q)I.remove();else{var pt=0,dt=0;if("left"!==e.align&&(pt=(w-v)*("center"===e.align?.5:1)),"top"!==e.valign&&(dt=(P-y)*("middle"===e.valign?.5:1)),u)n.select("svg").attr({x:R+pt-1,y:R+dt}).call(c.setClipUrl,F?T:null);else{var gt=R+dt-d.top,mt=R+pt-d.left;V.call(h.positionText,mt,gt).call(c.setClipUrl,F?T:null)}N.select("rect").call(c.setRect,R,R,w,P),B.call(c.setRect,O/2,O/2,D-O,j-O),I.call(c.setTranslate,Math.round(S.x.text-D/2),Math.round(S.y.text-j/2)),L.attr({transform:"rotate("+E+","+S.x.text+","+S.y.text+")"});var vt,yt=function(r,n){C.selectAll(".annotation-arrow-g").remove();var u=S.x.head,h=S.y.head,f=S.x.tail+r,d=S.y.tail+n,v=S.x.text+r,y=S.y.text+n,x=o.rotationXYMatrix(E,v,y),w=o.apply2DTransform(x),T=o.apply2DTransform2(x),z=+B.attr("width"),P=+B.attr("height"),O=v-.5*z,D=O+z,R=y-.5*P,F=R+P,N=[[O,R,O,F],[O,F,D,F],[D,F,D,R],[D,R,O,R]].map(T);if(!N.reduce(function(t,e){return t^!!o.segmentsIntersect(u,h,u+1e6,h+1e6,e[0],e[1],e[2],e[3])},!1)){N.forEach(function(t){var e=o.segmentsIntersect(f,d,u,h,t[0],t[1],t[2],t[3]);e&&(f=e.x,d=e.y)});var j=e.arrowwidth,V=e.arrowcolor,U=e.arrowside,q=C.append("g").style({opacity:l.opacity(V)}).classed("annotation-arrow-g",!0),H=q.append("path").attr("d","M"+f+","+d+"L"+u+","+h).style("stroke-width",j+"px").call(l.stroke,l.rgb(V));if(g(H,U,e),_.annotationPosition&&H.node().parentNode&&!a){var G=u,W=h;if(e.standoff){var Y=Math.sqrt(Math.pow(u-f,2)+Math.pow(h-d,2));G+=e.standoff*(f-u)/Y,W+=e.standoff*(d-h)/Y}var X,Z,$=q.append("path").classed("annotation-arrow",!0).classed("anndrag",!0).classed("cursor-move",!0).attr({d:"M3,3H-3V-3H3ZM0,0L"+(f-G)+","+(d-W),transform:"translate("+G+","+W+")"}).style("stroke-width",j+6+"px").call(l.stroke,"rgba(0,0,0,0)").call(l.fill,"rgba(0,0,0,0)");p.init({element:$.node(),gd:t,prepFn:function(){var t=c.getTranslate(I);X=t.x,Z=t.y,s&&s.autorange&&k(s._name+".autorange",!0),m&&m.autorange&&k(m._name+".autorange",!0)},moveFn:function(t,r){var n=w(X,Z),i=n[0]+t,a=n[1]+r;I.call(c.setTranslate,i,a),M("x",s?s.p2r(s.r2p(e.x)+t):e.x+t/b.w),M("y",m?m.p2r(m.r2p(e.y)+r):e.y-r/b.h),e.axref===e.xref&&M("ax",s.p2r(s.r2p(e.ax)+t)),e.ayref===e.yref&&M("ay",m.p2r(m.r2p(e.ay)+r)),q.attr("transform","translate("+t+","+r+")"),L.attr({transform:"rotate("+E+","+i+","+a+")"})},doneFn:function(){i.call("relayout",t,A());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}};if(e.showarrow&&yt(0,0),z)p.init({element:I.node(),gd:t,prepFn:function(){vt=L.attr("transform")},moveFn:function(t,r){var n="pointer";if(e.showarrow)e.axref===e.xref?M("ax",s.p2r(s.r2p(e.ax)+t)):M("ax",e.ax+t),e.ayref===e.yref?M("ay",m.p2r(m.r2p(e.ay)+r)):M("ay",e.ay+r),yt(t,r);else{if(a)return;var i,o;if(s)i=s.p2r(s.r2p(e.x)+t);else{var l=e._xsize/b.w,c=e.x+(e._xshift-e.xshift)/b.w-l/2;i=p.align(c+t/b.w,l,0,1,e.xanchor)}if(m)o=m.p2r(m.r2p(e.y)+r);else{var u=e._ysize/b.h,h=e.y-(e._yshift+e.yshift)/b.h-u/2;o=p.align(h-r/b.h,u,0,1,e.yanchor)}M("x",i),M("y",o),s&&m||(n=p.getCursor(s?.5:i,m?.5:o,e.xanchor,e.yanchor))}L.attr({transform:"translate("+t+","+r+")"+vt}),f(I,n)},doneFn:function(){f(I),i.call("relayout",t,A());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}}e.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(".annotation").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&m(t,r);return a.previousPromises(t)},drawOne:m,drawRaw:v}},{"../../lib":684,"../../lib/setcursor":704,"../../lib/svg_text_utils":708,"../../plot_api/plot_template":722,"../../plots/cartesian/axes":732,"../../plots/plots":795,"../../registry":817,"../color":558,"../dragelement":580,"../drawing":583,"../fx":600,"./draw_arrow_head":548,d3:147}],548:[function(t,e,r){"use strict";var n=t("d3"),i=t("../color"),a=t("./arrow_paths");e.exports=function(t,e,r){var o,s,l,c,u=t.node(),h=a[r.arrowhead||0],f=a[r.startarrowhead||0],p=(r.arrowwidth||1)*(r.arrowsize||1),d=(r.arrowwidth||1)*(r.startarrowsize||1),g=e.indexOf("start")>=0,m=e.indexOf("end")>=0,v=h.backoff*p+r.standoff,y=f.backoff*d+r.startstandoff;if("line"===u.nodeName){o={x:+t.attr("x1"),y:+t.attr("y1")},s={x:+t.attr("x2"),y:+t.attr("y2")};var x=o.x-s.x,b=o.y-s.y;if(c=(l=Math.atan2(b,x))+Math.PI,v&&y&&v+y>Math.sqrt(x*x+b*b))return void z();if(v){if(v*v>x*x+b*b)return void z();var 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n=t("../annotations/attributes"),i=t("../../plot_api/edit_types").overrideAll,a=t("../../plot_api/plot_template").templatedArray;e.exports=i(a("annotation",{visible:n.visible,x:{valType:"any"},y:{valType:"any"},z:{valType:"any"},ax:{valType:"number"},ay:{valType:"number"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),"calc","from-root")},{"../../plot_api/edit_types":715,"../../plot_api/plot_template":722,"../annotations/attributes":541}],551:[function(t,e,r){"use 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A,T,z=x.node().getBoundingClientRect(),P=t.xa._offset+(t.x0+t.x1)/2,I=t.ya._offset+(t.y0+t.y1)/2,O=Math.abs(t.x1-t.x0),D=Math.abs(t.y1-t.y0),R=z.width+w+k+_;t.ty0=S-z.top,t.bx=z.width+2*k,t.by=z.height+2*k,t.anchor="start",t.txwidth=z.width,t.tx2width=_,t.offset=0,a?(t.pos=P,A=I+D/2+R<=C,T=I-D/2-R>=0,"top"!==t.idealAlign&&A||!T?A?(I+=D/2,t.anchor="start"):t.anchor="middle":(I-=D/2,t.anchor="end")):(t.pos=I,A=P+O/2+R<=E,T=P-O/2-R>=0,"left"!==t.idealAlign&&A||!T?A?(P+=O/2,t.anchor="start"):t.anchor="middle":(P-=O/2,t.anchor="end")),x.attr("text-anchor",t.anchor),_&&b.attr("text-anchor",t.anchor),e.attr("transform","translate("+P+","+I+")"+(a?"rotate("+v+")":""))}),R}function A(t,e){t.each(function(t){var r=n.select(this);if(t.del)r.remove();else{var 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E(t,e){return!e||(e.vLinePoint!==t._spikepoints.vLinePoint||e.hLinePoint!==t._spikepoints.hLinePoint)}r.hover=function(t,e,r,a){t=o.getGraphDiv(t),o.throttle(t._fullLayout._uid+m.HOVERID,m.HOVERMINTIME,function(){!function(t,e,r,a){r||(r="xy");var l=Array.isArray(r)?r:[r],u=t._fullLayout,m=u._plots||[],v=m[r],y=u._has("cartesian");if(v){var b=v.overlays.map(function(t){return t.id});l=l.concat(b)}for(var _=l.length,w=new Array(_),k=new Array(_),C=!1,L=0;L<_;L++){var z=l[L],P=m[z];if(P)C=!0,w[L]=p.getFromId(t,P.xaxis._id),k[L]=p.getFromId(t,P.yaxis._id);else{var I=u[z]._subplot;w[L]=I.xaxis,k[L]=I.yaxis}}var O=e.hovermode||u.hovermode;O&&!C&&(O="closest");if(-1===["x","y","closest"].indexOf(O)||!t.calcdata||t.querySelector(".zoombox")||t._dragging)return f.unhoverRaw(t,e);var 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nt=1/0;for(F=0;F<J.length;F++)if((N=J[F])&&N[0]&&N[0].trace&&!0===N[0].trace.visible&&(j=N[0].trace,-1===["carpet","contourcarpet"].indexOf(j._module.name))){if("splom"===j.type?V=l[U=0]:(V=g.getSubplot(j),U=l.indexOf(V)),q=O,W={cd:N,trace:j,xa:w[U],ya:k[U],maxHoverDistance:X,maxSpikeDistance:Z,index:!1,distance:Math.min(nt,X),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:h.defaultLine,name:j.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},u[V]&&(W.subplot=u[V]._subplot),Y=$.length,"array"===q){var it=e[F];"pointNumber"in it?(W.index=it.pointNumber,q="closest"):(q="","xval"in it&&(H=it.xval,q="x"),"yval"in it&&(G=it.yval,q=q?"closest":"y"))}else H=D[U],G=R[U];if(0!==X)if(j._module&&j._module.hoverPoints){var at=j._module.hoverPoints(W,H,G,q,u._hoverlayer);if(at)for(var ot,st=0;st<at.length;st++)ot=at[st],i(ot.x0)&&i(ot.y0)&&$.push(T(ot,O))}else o.log("Unrecognized trace type in 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St="y"===O&&J.length>1,Et=h.combine(u.plot_bgcolor||h.background,u.paper_bgcolor),Ct={hovermode:O,rotateLabels:St,bgColor:Et,container:u._hoverlayer,outerContainer:u._paperdiv,commonLabelOpts:u.hoverlabel,hoverdistance:u.hoverdistance},Lt=M($,Ct,t);if(function(t,e,r){var n,i,a,o,s,l,c,u=0,h=t.map(function(t,n){var i=t[e];return[{i:n,dp:0,pos:t.pos,posref:t.posref,size:t.by*("x"===i._id.charAt(0)?x:1)/2,pmin:0,pmax:"x"===i._id.charAt(0)?r.width:r.height}]}).sort(function(t,e){return t[0].posref-e[0].posref});function f(t){var e=t[0],r=t[t.length-1];if(i=e.pmin-e.pos-e.dp+e.size,a=r.pos+r.dp+r.size-e.pmax,i>.01){for(s=t.length-1;s>=0;s--)t[s].dp+=i;n=!1}if(!(a<.01)){if(i<-.01){for(s=t.length-1;s>=0;s--)t[s].dp-=a;n=!1}if(n){var 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zt=d.getComponentMethod("annotations","hasClickToShow")(t,At);c(n.select(e.target),zt?"pointer":"")}if(!e.target||a||!function(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var i=r[n],a=t._hoverdata[n];if(i.curveNumber!==a.curveNumber||String(i.pointNumber)!==String(a.pointNumber))return!0}return!1}(t,0,Mt))return;Mt&&t.emit("plotly_unhover",{event:e,points:Mt});t.emit("plotly_hover",{event:e,points:t._hoverdata,xaxes:w,yaxes:k,xvals:D,yvals:R})}(t,e,r,a)})},r.loneHover=function(t,e){var r={color:t.color||h.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,trace:{index:0,hoverinfo:""},xa:{_offset:0},ya:{_offset:0},index:0},i=n.select(e.container),a=e.outerContainer?n.select(e.outerContainer):i,o={hovermode:"closest",rotateLabels:!1,bgColor:e.bgColor||h.background,container:i,outerContainer:a},s=M([r],o,e.gd);return A(s,o.rotateLabels),s.node()}},{"../../lib":684,"../../lib/events":672,"../../lib/override_cursor":695,"../../lib/svg_text_utils":708,"../../plots/cartesian/axes":732,"../../registry":817,"../color":558,"../dragelement":580,"../drawing":583,"./constants":595,"./helpers":597,d3:147,"fast-isnumeric":214,tinycolor2:499}],599:[function(t,e,r){"use strict";var n=t("../../lib");e.exports=function(t,e,r,i){r("hoverlabel.bgcolor",(i=i||{}).bgcolor),r("hoverlabel.bordercolor",i.bordercolor),r("hoverlabel.namelength",i.namelength),n.coerceFont(r,"hoverlabel.font",i.font)}},{"../../lib":684}],600:[function(t,e,r){"use strict";var n=t("d3"),i=t("../../lib"),a=t("../dragelement"),o=t("./helpers"),s=t("./layout_attributes");e.exports={moduleType:"component",name:"fx",constants:t("./constants"),schema:{layout:s},attributes:t("./attributes"),layoutAttributes:s,supplyLayoutGlobalDefaults:t("./layout_global_defaults"),supplyDefaults:t("./defaults"),supplyLayoutDefaults:t("./layout_defaults"),calc:t("./calc"),getDistanceFunction:o.getDistanceFunction,getClosest:o.getClosest,inbox:o.inbox,quadrature:o.quadrature,appendArrayPointValue:o.appendArrayPointValue,castHoverOption:function(t,e,r){return i.castOption(t,e,"hoverlabel."+r)},castHoverinfo:function(t,e,r){return i.castOption(t,r,"hoverinfo",function(r){return 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n=t("../../registry"),i=t("../../lib"),a=t("../../components/color"),o=t("../../plot_api/plot_template"),s=t("../layout_attributes"),l=t("./layout_attributes"),c=t("./type_defaults"),u=t("./axis_defaults"),h=t("./constraint_defaults"),f=t("./position_defaults"),p=t("./axis_ids");e.exports=function(t,e,r){var d,g={},m={},v={},y={};for(d=0;d<r.length;d++){var x=r[d];if(n.traceIs(x,"cartesian")||n.traceIs(x,"gl2d")){var b=p.id2name(x.xaxis),_=p.id2name(x.yaxis);if(n.traceIs(x,"carpet")&&("carpet"!==x.type||x._cheater)||b&&(m[b]=1),"carpet"===x.type&&x._cheater&&b&&(g[b]=1),n.traceIs(x,"2dMap")&&(v[b]=!0,v[_]=!0),n.traceIs(x,"oriented"))y["h"===x.orientation?_:b]=!0}}var w=e._subplots,k=w.xaxis,M=w.yaxis,A=i.simpleMap(k,p.id2name),T=i.simpleMap(M,p.id2name),S=A.concat(T),E=a.background;k.length&&M.length&&(E=i.coerce(t,e,s,"plot_bgcolor"));var C,L,z,P,I=a.combine(E,e.paper_bgcolor);function O(t,e){return i.coerce(z,P,l,t,e)}function D(t,e){return i.coerce2(z,P,l,t,e)}function 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n=t("../registry"),i=t("./cartesian/constants").SUBPLOT_PATTERN;r.getSubplotCalcData=function(t,e,r){var i=n.subplotsRegistry[e];if(!i)return[];for(var a=i.attr,o=[],s=0;s<t.length;s++){var l=t[s];l[0].trace[a]===r&&o.push(l)}return o},r.getModuleCalcData=function(t,e){var r,i=[],a=[];if(!(r="string"==typeof e?n.getModule(e).plot:"function"==typeof e?e:e.plot))return[i,t];for(var o=0;o<t.length;o++){var s=t[o],l=s[0].trace;!0===l.visible&&(l._module.plot===r?i.push(s):a.push(s))}return[i,a]},r.getSubplotData=function(t,e,r){if(!n.subplotsRegistry[e])return[];var a,o,s,l=n.subplotsRegistry[e].attr,c=[];if("gl2d"===e){var u=r.match(i);o="x"+u[1],s="y"+u[2]}for(var h=0;h<t.length;h++)a=t[h],"gl2d"===e&&n.traceIs(a,"gl2d")?a[l[0]]===o&&a[l[1]]===s&&c.push(a):a[l]===r&&c.push(a);return c},r.getUidsFromCalcData=function(t){for(var e={},r=0;r<t.length;r++){e[t[r][0].trace.uid]=1}return e}},{"../registry":817,"./cartesian/constants":737}],769:[function(t,e,r){"use strict";var n=t("mouse-change"),i=t("mouse-wheel"),a=t("mouse-event-offset"),o=t("../cartesian/constants"),s=t("has-passive-events");function l(t,e){this.element=t,this.plot=e,this.mouseListener=null,this.wheelListener=null,this.lastInputTime=Date.now(),this.lastPos=[0,0],this.boxEnabled=!1,this.boxInited=!1,this.boxStart=[0,0],this.boxEnd=[0,0],this.dragStart=[0,0]}e.exports=function(t){var e=t.mouseContainer,r=t.glplot,c=new l(e,r);function u(){t.xaxis.autorange=!1,t.yaxis.autorange=!1}function h(e,n,i){var a,s,l=t.calcDataBox(),h=r.viewBox,f=c.lastPos[0],p=c.lastPos[1],d=o.MINDRAG*r.pixelRatio,g=o.MINZOOM*r.pixelRatio;function m(e,r,n){var i=Math.min(r,n),a=Math.max(r,n);i!==a?(l[e]=i,l[e+2]=a,c.dataBox=l,t.setRanges(l)):(t.selectBox.selectBox=[0,0,1,1],t.glplot.setDirty())}switch(n*=r.pixelRatio,i*=r.pixelRatio,i=h[3]-h[1]-i,t.fullLayout.dragmode){case"zoom":if(e){var v=n/(h[2]-h[0])*(l[2]-l[0])+l[0],y=i/(h[3]-h[1])*(l[3]-l[1])+l[1];c.boxInited||(c.boxStart[0]=v,c.boxStart[1]=y,c.dragStart[0]=n,c.dragStart[1]=i),c.boxEnd[0]=v,c.boxEnd[1]=y,c.boxInited=!0,c.boxEnabled||c.boxStart[0]===c.boxEnd[0]&&c.boxStart[1]===c.boxEnd[1]||(c.boxEnabled=!0);var x=Math.abs(c.dragStart[0]-n)<g,b=Math.abs(c.dragStart[1]-i)<g;if(!function(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,i=0;i<e.length;i++)if(-1!==e[i][r]){if(-1!==e[i][n])return!0;break}return!1}()||x&&b)x&&(c.boxEnd[0]=c.boxStart[0]),b&&(c.boxEnd[1]=c.boxStart[1]);else{a=c.boxEnd[0]-c.boxStart[0],s=c.boxEnd[1]-c.boxStart[1];var _=(l[3]-l[1])/(l[2]-l[0]);Math.abs(a*_)>Math.abs(s)?(c.boxEnd[1]=c.boxStart[1]+Math.abs(a)*_*(s>=0?1:-1),c.boxEnd[1]<l[1]?(c.boxEnd[1]=l[1],c.boxEnd[0]=c.boxStart[0]+(l[1]-c.boxStart[1])/Math.abs(_)):c.boxEnd[1]>l[3]&&(c.boxEnd[1]=l[3],c.boxEnd[0]=c.boxStart[0]+(l[3]-c.boxStart[1])/Math.abs(_))):(c.boxEnd[0]=c.boxStart[0]+Math.abs(s)/_*(a>=0?1:-1),c.boxEnd[0]<l[0]?(c.boxEnd[0]=l[0],c.boxEnd[1]=c.boxStart[1]+(l[0]-c.boxStart[0])*Math.abs(_)):c.boxEnd[0]>l[2]&&(c.boxEnd[0]=l[2],c.boxEnd[1]=c.boxStart[1]+(l[2]-c.boxStart[0])*Math.abs(_)))}}else c.boxEnabled?(a=c.boxStart[0]!==c.boxEnd[0],s=c.boxStart[1]!==c.boxEnd[1],a||s?(a&&(m(0,c.boxStart[0],c.boxEnd[0]),t.xaxis.autorange=!1),s&&(m(1,c.boxStart[1],c.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),c.boxEnabled=!1,c.boxInited=!1):c.boxInited&&(c.boxInited=!1);break;case"pan":c.boxEnabled=!1,c.boxInited=!1,e?(c.panning||(c.dragStart[0]=n,c.dragStart[1]=i),Math.abs(c.dragStart[0]-n)<d&&(n=c.dragStart[0]),Math.abs(c.dragStart[1]-i)<d&&(i=c.dragStart[1]),a=(f-n)*(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)*(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),c.panning=!0,c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations()):c.panning&&(c.panning=!1,t.relayoutCallback())}c.lastPos[0]=n,c.lastPos[1]=i}return c.mouseListener=n(e,h),e.addEventListener("touchstart",function(t){var r=a(t.changedTouches[0],e);h(0,r[0],r[1]),h(1,r[0],r[1]),t.preventDefault()},!!s&&{passive:!1}),e.addEventListener("touchmove",function(t){t.preventDefault();var r=a(t.changedTouches[0],e);h(1,r[0],r[1]),t.preventDefault()},!!s&&{passive:!1}),e.addEventListener("touchend",function(t){h(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()},!!s&&{passive:!1}),c.wheelListener=i(e,function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),h=o/(a[2]-a[0])*(i[2]-i[0])+i[0],f=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-h)*l+h,i[2]=(i[2]-h)*l+h,i[1]=(i[1]-f)*l+f,i[3]=(i[3]-f)*l+f,t.setRanges(i),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0},!0),c}},{"../cartesian/constants":737,"has-passive-events":379,"mouse-change":402,"mouse-event-offset":403,"mouse-wheel":405}],770:[function(t,e,r){"use strict";var n=t("../cartesian/axes"),i=t("../../lib/html2unicode"),a=t("../../lib/str2rgbarray");function o(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=["x","y"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=["sans-serif","sans-serif"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title="",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont="sans-serif",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=!1,this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var s=o.prototype,l=["xaxis","yaxis"];s.merge=function(t){var e,r,n,o,s,c,u,h,f,p,d;for(this.titleEnable=!1,this.backgroundColor=a(t.plot_bgcolor),p=0;p<2;++p){var g=(e=l[p]).charAt(0);for(n=(r=t[this.scene[e]._name]).title===this.scene.fullLayout._dfltTitle[g]?"":r.title,d=0;d<=2;d+=2)this.labelEnable[p+d]=!1,this.labels[p+d]=i(n),this.labelColor[p+d]=a(r.titlefont.color),this.labelFont[p+d]=r.titlefont.family,this.labelSize[p+d]=r.titlefont.size,this.labelPad[p+d]=this.getLabelPad(e,r),this.tickEnable[p+d]=!1,this.tickColor[p+d]=a((r.tickfont||{}).color),this.tickAngle[p+d]="auto"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[p+d]=this.getTickPad(r),this.tickMarkLength[p+d]=0,this.tickMarkWidth[p+d]=r.tickwidth||0,this.tickMarkColor[p+d]=a(r.tickcolor),this.borderLineEnable[p+d]=!1,this.borderLineColor[p+d]=a(r.linecolor),this.borderLineWidth[p+d]=r.linewidth||0;u=this.hasSharedAxis(r),s=this.hasAxisInDfltPos(e,r)&&!u,c=this.hasAxisInAltrPos(e,r)&&!u,o=r.mirror||!1,h=u?-1!==String(o).indexOf("all"):!!o,f=u?"allticks"===o:-1!==String(o).indexOf("ticks"),s?this.labelEnable[p]=!0:c&&(this.labelEnable[p+2]=!0),s?this.tickEnable[p]=r.showticklabels:c&&(this.tickEnable[p+2]=r.showticklabels),(s||h)&&(this.borderLineEnable[p]=r.showline),(c||h)&&(this.borderLineEnable[p+2]=r.showline),(s||f)&&(this.tickMarkLength[p]=this.getTickMarkLength(r)),(c||f)&&(this.tickMarkLength[p+2]=this.getTickMarkLength(r)),this.gridLineEnable[p]=r.showgrid,this.gridLineColor[p]=a(r.gridcolor),this.gridLineWidth[p]=r.gridwidth,this.zeroLineEnable[p]=r.zeroline,this.zeroLineColor[p]=a(r.zerolinecolor),this.zeroLineWidth[p]=r.zerolinewidth}},s.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},s.hasAxisInDfltPos=function(t,e){var r=e.side;return"xaxis"===t?"bottom"===r:"yaxis"===t?"left"===r:void 0},s.hasAxisInAltrPos=function(t,e){var r=e.side;return"xaxis"===t?"top"===r:"yaxis"===t?"right"===r:void 0},s.getLabelPad=function(t,e){var r=e.titlefont.size,n=e.showticklabels;return"xaxis"===t?"top"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:"yaxis"===t?"right"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},s.getTickPad=function(t){return"outside"===t.ticks?10+t.ticklen:15},s.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return"inside"===t.ticks?-e:e},e.exports=function(t){return new o(t)}},{"../../lib/html2unicode":682,"../../lib/str2rgbarray":707,"../cartesian/axes":732}],771:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("./scene2d"),a=t("../layout_attributes"),o=t("../../constants/xmlns_namespaces"),s=t("../cartesian/constants"),l=t("../cartesian"),c=t("../../components/fx/layout_attributes"),u=t("../get_data").getSubplotData;r.name="gl2d",r.attr=["xaxis","yaxis"],r.idRoot=["x","y"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t("../cartesian/attributes"),r.supplyLayoutDefaults=function(t,e,r){e._has("cartesian")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,"plot","from-root"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},"plot","nested"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,"gl2d",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d)0===u(t,"gl2d",o).length&&(s._scene2d.destroy(),delete n._plots[o])}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage("png");e._glimages.append("svg:image").attr({xmlns:o.svg,"xlink:href":a,x:0,y:0,width:"100%",height:"100%",preserveAspectRatio:"none"}),i.destroy()}},r.updateFx=function(t){for(var e=t._subplots.gl2d,r=0;r<e.length;r++){t._plots[e[r]]._scene2d.updateFx(t.dragmode)}}},{"../../components/fx/layout_attributes":601,"../../constants/xmlns_namespaces":663,"../../plot_api/edit_types":715,"../cartesian":743,"../cartesian/attributes":730,"../cartesian/constants":737,"../get_data":768,"../layout_attributes":786,"./scene2d":772}],772:[function(t,e,r){"use strict";var n,i,a=t("../../registry"),o=t("../../plots/cartesian/axes"),s=t("../../components/fx"),l=t("gl-plot2d"),c=t("gl-spikes2d"),u=t("gl-select-box"),h=t("webgl-context"),f=t("./convert"),p=t("./camera"),d=t("../../lib/html2unicode"),g=t("../../lib/show_no_webgl_msg"),m=t("../cartesian/constraints"),v=m.enforce,y=m.clean,x=t("../cartesian/autorange").doAutoRange,b=["xaxis","yaxis"],_=t("../cartesian/constants").SUBPLOT_PATTERN;function w(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context.scrollZoom,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=f(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=w;var k=w.prototype;k.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement("canvas"),i=h({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error("Error creating static canvas/context for image server");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(".gl-canvas-focus"),e=h({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return g(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width="100%",r.style.height="100%",r.style.position="absolute",r.style.top="0px",r.style.left="0px",r.style["pointer-events"]="none",this.updateSize(r),r.className+=" user-select-none";var a=this.svgContainer=document.createElementNS("http://www.w3.org/2000/svg","svg");a.style.position="absolute",a.style.top=a.style.left="0px",a.style.width=a.style.height="100%",a.style["z-index"]=20,a.style["pointer-events"]="none";var o=this.mouseContainer=document.createElement("div");o.style.position="absolute",o.style["pointer-events"]="auto",this.pickCanvas=this.container.querySelector(".gl-canvas-pick");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener("mouseout",function(){l.isMouseOver=!1,l.unhover()}),o.addEventListener("mouseover",function(){l.isMouseOver=!0})},k.toImage=function(t){t||(t="png"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var h=document.createElement("canvas");h.width=r,h.height=i;var f,p=h.getContext("2d"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case"jpeg":f=h.toDataURL("image/jpeg");break;case"webp":f=h.toDataURL("image/webp");break;default:f=h.toDataURL("image/png")}return this.staticPlot&&this.container.removeChild(n),f},k.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},k.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=d(t[e][r].text+"");return t},k.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(_),r="xaxis"+e[1],n="yaxis"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},k.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout;n.xaxis.autorange=e.autorange,n.xaxis.range=e.range.slice(0),n.yaxis.autorange=r.autorange,n.yaxis.range=r.range.slice(0);var i={lastInputTime:this.camera.lastInputTime};i[e._name]=e.range.slice(0),i[r._name]=r.range.slice(0),t.emit("plotly_relayout",i)},k.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},k.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod("annotations","drawOne")(t,r)}},k.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map(function(e){t[e].dispose(),delete t[e]}),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener("wheel",this.camera.wheelListener),this.camera=null}},k.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:this.graphDiv._fullLayout._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis}};y(s,this.xaxis),y(s,this.yaxis);var l,c,u=r._size,h=this.xaxis.domain,f=this.yaxis.domain;for(o.viewBox=[u.l+h[0]*u.w,u.b+f[0]*u.h,i-u.r-(1-h[1])*u.w,a-u.t-(1-f[1])*u.h],this.mouseContainer.style.width=u.w*(h[1]-h[0])+"px",this.mouseContainer.style.height=u.h*(f[1]-f[0])+"px",this.mouseContainer.height=u.h*(f[1]-f[0]),this.mouseContainer.style.left=u.l+h[0]*u.w+"px",this.mouseContainer.style.top=u.t+(1-f[1])*u.h+"px",c=0;c<2;++c)(l=this[b[c]])._length=o.viewBox[c+2]-o.viewBox[c],x(l),l.setScale();v(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},k.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},k.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},k.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort(function(t,e){return t._trace.index-e._trace.index})},k.updateFx=function(t){"lasso"===t||"select"===t?(this.pickCanvas.style["pointer-events"]="none",this.mouseContainer.style["pointer-events"]="none"):(this.pickCanvas.style["pointer-events"]="auto",this.mouseContainer.style["pointer-events"]="auto"),this.mouseContainer.style.cursor="pan"===t?"move":"zoom"===t?"crosshair":null},k.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},k.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&"zoom"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var h=i._size,f=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+h.l+f[0]*h.w,l/t.pixelRatio-(h.t+(1-p[1])*h.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,"plotly_click"),a&&"skip"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var g=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),g.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,"plotly_hover");var m=this.fullData[g.trace.index]||{},v=g.pointIndex,y=s.castHoverinfo(m,i,v);if(y&&"all"!==y){var x=y.split("+");-1===x.indexOf("x")&&(g.traceCoord[0]=void 0),-1===x.indexOf("y")&&(g.traceCoord[1]=void 0),-1===x.indexOf("z")&&(g.traceCoord[2]=void 0),-1===x.indexOf("text")&&(g.textLabel=void 0),-1===x.indexOf("name")&&(g.name=void 0)}s.loneHover({x:g.screenCoord[0],y:g.screenCoord[1],xLabel:this.hoverFormatter("xaxis",g.traceCoord[0]),yLabel:this.hoverFormatter("yaxis",g.traceCoord[1]),zLabel:g.traceCoord[2],text:g.textLabel,name:g.name,color:s.castHoverOption(m,v,"bgcolor")||g.color,borderColor:s.castHoverOption(m,v,"bordercolor"),fontFamily:s.castHoverOption(m,v,"font.family"),fontSize:s.castHoverOption(m,v,"font.size"),fontColor:s.castHoverOption(m,v,"font.color")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},k.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit("plotly_unhover"),s.loneUnhover(this.svgContainer))},k.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),"hover").text}}},{"../../components/fx":600,"../../lib/html2unicode":682,"../../lib/show_no_webgl_msg":705,"../../plots/cartesian/axes":732,"../../registry":817,"../cartesian/autorange":731,"../cartesian/constants":737,"../cartesian/constraints":739,"./camera":769,"./convert":770,"gl-plot2d":275,"gl-select-box":285,"gl-spikes2d":294,"webgl-context":521}],773:[function(t,e,r){"use strict";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];"distanceLimits"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);"zoomMin"in e&&(r[0]=e.zoomMin);"zoomMax"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||"orbit",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],h=0,f=t.clientWidth,p=t.clientHeight,d={keyBindingMode:"rotate",view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===f&&t.clientHeight===p;return f=t.clientWidth,p=t.clientHeight,a?!l:(h=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),"turntable"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return h},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener("contextmenu",function(t){return t.preventDefault(),!1});var g=0,m=0,v={shift:!1,control:!1,alt:!1,meta:!1};function y(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s="rotate"===o,l="pan"===o,u="zoom"===o,f=!!a.control,p=!!a.alt,y=!!a.shift,x=!!(1&e),b=!!(2&e),_=!!(4&e),w=1/t.clientHeight,k=w*(r-g),M=w*(i-m),A=d.flipX?1:-1,T=d.flipY?1:-1,S=n(),E=Math.PI*d.rotateSpeed;if((s&&x&&!f&&!p&&!y||x&&!f&&!p&&y)&&c.rotate(S,A*E*k,-T*E*M,0),(l&&x&&!f&&!p&&!y||b||x&&f&&!p&&!y)&&c.pan(S,-d.translateSpeed*k*h,d.translateSpeed*M*h,0),u&&x&&!f&&!p&&!y||_||x&&!f&&p&&!y){var C=-d.zoomSpeed*M/window.innerHeight*(S-c.lastT())*100;c.pan(S,0,0,h*(Math.exp(C)-1))}return g=r,m=i,v=a,!0}}return d.mouseListener=a(t,y),t.addEventListener("touchstart",function(e){var r=s(e.changedTouches[0],t);y(0,r[0],r[1],v),y(1,r[0],r[1],v),e.preventDefault()},!!l&&{passive:!1}),t.addEventListener("touchmove",function(e){var r=s(e.changedTouches[0],t);y(1,r[0],r[1],v),e.preventDefault()},!!l&&{passive:!1}),t.addEventListener("touchend",function(t){y(0,g,m,v),t.preventDefault()},!!l&&{passive:!1}),d.wheelListener=o(t,function(t,e){if(!1!==d.keyBindingMode){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else{var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,h*(Math.exp(o)-1))}}},!0),d};var n=t("right-now"),i=t("3d-view"),a=t("mouse-change"),o=t("mouse-wheel"),s=t("mouse-event-offset"),l=t("has-passive-events")},{"3d-view":43,"has-passive-events":379,"mouse-change":402,"mouse-event-offset":403,"mouse-wheel":405,"right-now":465}],774:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("../../components/fx/layout_attributes"),a=t("./scene"),o=t("../get_data").getSubplotData,s=t("../../lib"),l=t("../../constants/xmlns_namespaces");r.name="gl3d",r.attr="scene",r.idRoot="scene",r.idRegex=r.attrRegex=s.counterRegex("scene"),r.attributes=t("./layout/attributes"),r.layoutAttributes=t("./layout/layout_attributes"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},"plot","nested"),r.supplyLayoutDefaults=t("./layout/defaults"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var l=n[i],c=o(r,"gl3d",l),u=e[l],h=u._scene;h||(h=new a({id:l,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),u._scene=h),h.cameraInitial||(h.cameraInitial=s.extendDeep({},u.camera)),h.plot(c,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(".annotation-"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage("png");e._glimages.append("svg:image").attr({xmlns:l.svg,"xlink:href":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:"none"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return"1"===e&&(e=""),"scene"+e}},r.updateFx=function(t){for(var e=t._subplots.gl3d,r=0;r<e.length;r++){t[e[r]]._scene.updateFx(t.dragmode,t.hovermode)}}},{"../../components/fx/layout_attributes":601,"../../constants/xmlns_namespaces":663,"../../lib":684,"../../plot_api/edit_types":715,"../get_data":768,"./layout/attributes":775,"./layout/defaults":779,"./layout/layout_attributes":780,"./scene":784}],775:[function(t,e,r){"use strict";e.exports={scene:{valType:"subplotid",dflt:"scene",editType:"calc+clearAxisTypes"}}},{}],776:[function(t,e,r){"use strict";var n=t("../../../components/color"),i=t("../../cartesian/layout_attributes"),a=t("../../../lib/extend").extendFlat,o=t("../../../plot_api/edit_types").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:"boolean",dflt:!0},spikesides:{valType:"boolean",dflt:!0},spikethickness:{valType:"number",min:0,dflt:2},spikecolor:{valType:"color",dflt:n.defaultLine},showbackground:{valType:"boolean",dflt:!1},backgroundcolor:{valType:"color",dflt:"rgba(204, 204, 204, 0.5)"},showaxeslabels:{valType:"boolean",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:i.title,titlefont:i.titlefont,type:i.type,autorange:i.autorange,rangemode:i.rangemode,range:i.range,tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:"rgb(204, 204, 204)"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth},"plot","from-root")},{"../../../components/color":558,"../../../lib/extend":673,"../../../plot_api/edit_types":715,"../../cartesian/layout_attributes":744}],777:[function(t,e,r){"use strict";var n=t("tinycolor2").mix,i=t("../../../lib"),a=t("../../../plot_api/plot_template"),o=t("./axis_attributes"),s=t("../../cartesian/type_defaults"),l=t("../../cartesian/axis_defaults"),c=["xaxis","yaxis","zaxis"];e.exports=function(t,e,r){var u,h;function f(t,e){return i.coerce(u,h,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(h=a.newContainer(e,d))._id=d[0]+r.scene,h._name=d,s(u,h,f,r.data),l(u,h,f,{font:r.font,letter:d[0],data:r.data,showGrid:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),f("gridcolor",n(h.color,r.bgColor,13600/187).toRgbString()),f("title",d[0]),h.setScale=i.noop,f("showspikes")&&(f("spikesides"),f("spikethickness"),f("spikecolor",h.color)),f("showaxeslabels"),f("showbackground")&&f("backgroundcolor")}}},{"../../../lib":684,"../../../plot_api/plot_template":722,"../../cartesian/axis_defaults":734,"../../cartesian/type_defaults":755,"./axis_attributes":776,tinycolor2:499}],778:[function(t,e,r){"use strict";var n=t("../../../lib/html2unicode"),i=t("../../../lib/str2rgbarray"),a=["xaxis","yaxis","zaxis"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont=["Open Sans","Open Sans","Open Sans"],this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[a[e]];r.visible?(this.labels[e]=n(r.title),"titlefont"in r&&(r.titlefont.color&&(this.labelColor[e]=i(r.titlefont.color)),r.titlefont.family&&(this.labelFont[e]=r.titlefont.family),r.titlefont.size&&(this.labelSize[e]=r.titlefont.size)),"showline"in r&&(this.lineEnable[e]=r.showline),"linecolor"in r&&(this.lineColor[e]=i(r.linecolor)),"linewidth"in r&&(this.lineWidth[e]=r.linewidth),"showgrid"in r&&(this.gridEnable[e]=r.showgrid),"gridcolor"in r&&(this.gridColor[e]=i(r.gridcolor)),"gridwidth"in r&&(this.gridWidth[e]=r.gridwidth),"log"===r.type?this.zeroEnable[e]=!1:"zeroline"in r&&(this.zeroEnable[e]=r.zeroline),"zerolinecolor"in r&&(this.zeroLineColor[e]=i(r.zerolinecolor)),"zerolinewidth"in r&&(this.zeroLineWidth[e]=r.zerolinewidth),"ticks"in r&&r.ticks?this.lineTickEnable[e]=!0:this.lineTickEnable[e]=!1,"ticklen"in r&&(this.lineTickLength[e]=this._defaultLineTickLength[e]=r.ticklen),"tickcolor"in r&&(this.lineTickColor[e]=i(r.tickcolor)),"tickwidth"in r&&(this.lineTickWidth[e]=r.tickwidth),"tickangle"in r&&(this.tickAngle[e]="auto"===r.tickangle?0:Math.PI*-r.tickangle/180),"showticklabels"in r&&(this.tickEnable[e]=r.showticklabels),"tickfont"in r&&(r.tickfont.color&&(this.tickColor[e]=i(r.tickfont.color)),r.tickfont.family&&(this.tickFont[e]=r.tickfont.family),r.tickfont.size&&(this.tickSize[e]=r.tickfont.size)),"mirror"in r?-1!==["ticks","all","allticks"].indexOf(r.mirror)?(this.lineTickMirror[e]=!0,this.lineMirror[e]=!0):!0===r.mirror?(this.lineTickMirror[e]=!1,this.lineMirror[e]=!0):(this.lineTickMirror[e]=!1,this.lineMirror[e]=!1):this.lineMirror[e]=!1,"showbackground"in r&&!1!==r.showbackground?(this.backgroundEnable[e]=!0,this.backgroundColor[e]=i(r.backgroundcolor)):this.backgroundEnable[e]=!1):(this.tickEnable[e]=!1,this.labelEnable[e]=!1,this.lineEnable[e]=!1,this.lineTickEnable[e]=!1,this.gridEnable[e]=!1,this.zeroEnable[e]=!1,this.backgroundEnable[e]=!1)}},e.exports=function(t){var e=new o;return e.merge(t),e}},{"../../../lib/html2unicode":682,"../../../lib/str2rgbarray":707}],779:[function(t,e,r){"use strict";var n=t("../../../lib"),i=t("../../../components/color"),a=t("../../../registry"),o=t("../../subplot_defaults"),s=t("./axis_defaults"),l=t("./layout_attributes");function c(t,e,r,n){for(var o=r("bgcolor"),l=i.combine(o,n.paper_bgcolor),c=["up","center","eye"],u=0;u<c.length;u++)r("camera."+c[u]+".x"),r("camera."+c[u]+".y"),r("camera."+c[u]+".z");var h=!!r("aspectratio.x")&&!!r("aspectratio.y")&&!!r("aspectratio.z"),f=r("aspectmode",h?"manual":"auto");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},"manual"===f&&(e.aspectmode="auto"),t.aspectmode=e.aspectmode),s(t,e,{font:n.font,scene:n.id,data:n.fullData,bgColor:l,calendar:n.calendar,fullLayout:n.fullLayout}),a.getComponentMethod("annotations3d","handleDefaults")(t,e,n),r("dragmode",n.getDfltFromLayout("dragmode")),r("hovermode",n.getDfltFromLayout("hovermode"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:"gl3d",attributes:l,handleDefaults:c,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{"../../../components/color":558,"../../../lib":684,"../../../registry":817,"../../subplot_defaults":809,"./axis_defaults":777,"./layout_attributes":780}],780:[function(t,e,r){"use strict";var n=t("./axis_attributes"),i=t("../../domain").attributes,a=t("../../../lib/extend").extendFlat,o=t("../../../lib").counterRegex;function s(t,e,r){return{x:{valType:"number",dflt:t,editType:"camera"},y:{valType:"number",dflt:e,editType:"camera"},z:{valType:"number",dflt:r,editType:"camera"},editType:"camera"}}e.exports={_arrayAttrRegexps:[o("scene",".annotations",!0)],bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"plot"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),editType:"camera"},domain:i({name:"scene",editType:"plot"}),aspectmode:{valType:"enumerated",values:["auto","cube","data","manual"],dflt:"auto",editType:"plot",impliedEdits:{"aspectratio.x":void 0,"aspectratio.y":void 0,"aspectratio.z":void 0}},aspectratio:{x:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},y:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},z:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},editType:"plot",impliedEdits:{aspectmode:"manual"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:"enumerated",values:["orbit","turntable","zoom","pan",!1],dflt:"turntable",editType:"plot"},hovermode:{valType:"enumerated",values:["closest",!1],dflt:"closest",editType:"modebar"},editType:"plot",_deprecated:{cameraposition:{valType:"info_array",editType:"camera"}}}},{"../../../lib":684,"../../../lib/extend":673,"../../domain":757,"./axis_attributes":776}],781:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=["xaxis","yaxis","zaxis"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{"../../../lib/str2rgbarray":707}],782:[function(t,e,r){"use strict";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,l=t.fullSceneLayout,c=[[],[],[]],u=0;u<3;++u){var h=l[o[u]];if(h._length=(r[u].hi-r[u].lo)*r[u].pixelsPerDataUnit/t.dataScale[u],Math.abs(h._length)===1/0)c[u]=[];else{h._input_range=h.range.slice(),h.range[0]=r[u].lo/t.dataScale[u],h.range[1]=r[u].hi/t.dataScale[u],h._m=1/(t.dataScale[u]*r[u].pixelsPerDataUnit),h.range[0]===h.range[1]&&(h.range[0]-=1,h.range[1]+=1);var f=h.tickmode;if("auto"===h.tickmode){h.tickmode="linear";var p=h.nticks||i.constrain(h._length/40,4,9);n.autoTicks(h,Math.abs(h.range[1]-h.range[0])/p)}for(var d=n.calcTicks(h),g=0;g<d.length;++g)d[g].x=d[g].x*t.dataScale[u],d[g].text=a(d[g].text);c[u]=d,h.tickmode=f}}e.ticks=c;for(var u=0;u<3;++u){s[u]=.5*(t.glplot.bounds[0][u]+t.glplot.bounds[1][u]);for(var g=0;g<2;++g)e.bounds[g][u]=t.glplot.bounds[g][u]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(c)};var n=t("../../cartesian/axes"),i=t("../../../lib"),a=t("../../../lib/html2unicode"),o=["xaxis","yaxis","zaxis"],s=[0,0,0]},{"../../../lib":684,"../../../lib/html2unicode":682,"../../cartesian/axes":732}],783:[function(t,e,r){"use strict";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],784:[function(t,e,r){"use strict";var n,i,a=t("gl-plot3d"),o=t("webgl-context"),s=t("has-passive-events"),l=t("../../registry"),c=t("../../lib"),u=t("../../plots/cartesian/axes"),h=t("../../components/fx"),f=t("../../lib/str2rgbarray"),p=t("../../lib/show_no_webgl_msg"),d=t("./camera"),g=t("./project"),m=t("./layout/convert"),v=t("./layout/spikes"),y=t("./layout/tick_marks");function x(t,e,r,l){var f={canvas:r,gl:l,container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1};if(t.staticMode){if(!(i||(n=document.createElement("canvas"),i=o({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error("error creating static canvas/context for image server");f.pixelRatio=t.pixelRatio,f.gl=i,f.canvas=n}try{t.glplot=a(f)}catch(e){return p(t)}var m=function(t){if(!1!==t.fullSceneLayout.dragmode){var e={};e[t.id+".camera"]=M(t.camera),t.saveCamera(t.graphDiv.layout),t.graphDiv.emit("plotly_relayout",e)}};if(t.glplot.canvas.addEventListener("mouseup",m.bind(null,t)),t.glplot.canvas.addEventListener("wheel",m.bind(null,t),!!s&&{passive:!1}),t.staticMode||t.glplot.canvas.addEventListener("webglcontextlost",function(t){c.warn("Lost WebGL context."),t.preventDefault()}),!t.camera){var v=t.fullSceneLayout.camera;t.camera=d(t.container,{center:[v.center.x,v.center.y,v.center.z],eye:[v.eye.x,v.eye.y,v.eye.z],up:[v.up.x,v.up.y,v.up.z],zoomMin:.1,zoomMax:100,mode:"orbit"})}return t.glplot.camera=t.camera,t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(t){var e,r=t.svgContainer,n=t.container.getBoundingClientRect(),i=n.width,a=n.height;r.setAttributeNS(null,"viewBox","0 0 "+i+" "+a),r.setAttributeNS(null,"width",i),r.setAttributeNS(null,"height",a),y(t),t.glplot.axes.update(t.axesOptions);for(var o,s=Object.keys(t.traces),l=null,c=t.glplot.selection,f=0;f<s.length;++f)"skip"!==(e=t.traces[s[f]]).data.hoverinfo&&e.handlePick(c)&&(l=e),e.setContourLevels&&e.setContourLevels();function p(e,r){var n=t.fullSceneLayout[e];return u.tickText(n,n.d2l(r),"hover").text}if(null!==l){var d=g(t.glplot.cameraParams,c.dataCoordinate);e=l.data;var m,v=c.index,x=h.castHoverinfo(e,t.fullLayout,v),b=x.split("+"),_="all"===x,w=p("xaxis",c.traceCoordinate[0]),k=p("yaxis",c.traceCoordinate[1]),M=p("zaxis",c.traceCoordinate[2]);if(_||(-1===b.indexOf("x")&&(w=void 0),-1===b.indexOf("y")&&(k=void 0),-1===b.indexOf("z")&&(M=void 0),-1===b.indexOf("text")&&(c.textLabel=void 0),-1===b.indexOf("name")&&(l.name=void 0)),"cone"===e.type||"streamtube"===e.type){var A=[];(_||-1!==b.indexOf("u"))&&A.push("u: "+p("xaxis",c.traceCoordinate[3])),(_||-1!==b.indexOf("v"))&&A.push("v: "+p("yaxis",c.traceCoordinate[4])),(_||-1!==b.indexOf("w"))&&A.push("w: "+p("zaxis",c.traceCoordinate[5])),(_||-1!==b.indexOf("norm"))&&A.push("norm: "+c.traceCoordinate[6].toPrecision(3)),"streamtube"!==e.type||!_&&-1===b.indexOf("divergence")||A.push("divergence: "+c.traceCoordinate[7].toPrecision(3)),c.textLabel&&A.push(c.textLabel),m=A.join("<br>")}else m=c.textLabel;t.fullSceneLayout.hovermode&&h.loneHover({x:(.5+.5*d[0]/d[3])*i,y:(.5-.5*d[1]/d[3])*a,xLabel:w,yLabel:k,zLabel:M,text:m,name:l.name,color:h.castHoverOption(e,v,"bgcolor")||l.color,borderColor:h.castHoverOption(e,v,"bordercolor"),fontFamily:h.castHoverOption(e,v,"font.family"),fontSize:h.castHoverOption(e,v,"font.size"),fontColor:h.castHoverOption(e,v,"font.color")},{container:r,gd:t.graphDiv});var T={x:c.traceCoordinate[0],y:c.traceCoordinate[1],z:c.traceCoordinate[2],data:e._input,fullData:e,curveNumber:e.index,pointNumber:v};e._module.eventData&&(T=e._module.eventData(T,c,e,{},v)),h.appendArrayPointValue(T,e,v);var S={points:[T]};c.buttons&&c.distance<5?t.graphDiv.emit("plotly_click",S):t.graphDiv.emit("plotly_hover",S),o=S}else h.loneUnhover(r),t.graphDiv.emit("plotly_unhover",o);t.drawAnnotations(t)}.bind(null,t),t.traces={},!0}function b(t,e){var r=document.createElement("div"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS("http://www.w3.org/2000/svg","svg");i.style.position="absolute",i.style.top=i.style.left="0px",i.style.width=i.style.height="100%",i.style["z-index"]=20,i.style["pointer-events"]="none",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position="absolute",r.style.top=r.style.left="0px",r.style.width=r.style.height="100%",n.appendChild(r),this.fullLayout=e,this.id=t.id||"scene",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=m(e[this.id]),this.spikeOptions=v(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=l.getComponentMethod("annotations3d","convert"),this.drawAnnotations=l.getComponentMethod("annotations3d","draw"),x(this)}var _=b.prototype;_.recoverContext=function(){var t=this,e=this.glplot.gl,r=this.glplot.canvas;this.glplot.dispose(),requestAnimationFrame(function n(){e.isContextLost()?requestAnimationFrame(n):x(t,t.fullLayout,r,e)?t.plot.apply(t,t.plotArgs):c.error("Catastrophic and unrecoverable WebGL error. Context lost.")})};var w=["xaxis","yaxis","zaxis"];function k(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=w[i],o=a.charAt(0),s=n[a],l=e[o],u=e[o+"calendar"],h=e["_"+o+"length"];if(c.isArrayOrTypedArray(l))for(var f,p=0;p<(h||l.length);p++)if(c.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)f=s.d2l(l[p][d],0,u),!isNaN(f)&&isFinite(f)&&(r[0][i]=Math.min(r[0][i],f),r[1][i]=Math.max(r[1][i],f));else f=s.d2l(l[p],0,u),!isNaN(f)&&isFinite(f)&&(r[0][i]=Math.min(r[0][i],f),r[1][i]=Math.max(r[1][i],f));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],h-1)}}function M(t){return{up:{x:t.up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]}}}_.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];c.bgcolor?this.glplot.clearColor=f(c.bgcolor):this.glplot.clearColor=[0,0,0,0],this.glplot.snapToData=!0,this.fullLayout=e,this.fullSceneLayout=c,this.glplotLayout=c,this.axesOptions.merge(c),this.spikeOptions.merge(c),this.setCamera(c.camera),this.updateFx(c.dragmode,c.hovermode),this.glplot.update({}),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var h=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&k(this,n,h);var p=[1,1,1];for(o=0;o<3;++o)h[0][o]>h[1][o]?p[o]=1:h[1][o]===h[0][o]?p[o]=1:p[o]=1/(h[1][o]-h[0][o]);for(this.dataScale=p,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var d=Object.keys(this.traces);t:for(a=0;a<d.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===d[a]&&!0===t[o].visible)continue t;(i=this.traces[d[a]]).dispose(),delete this.traces[d[a]]}this.glplot.objects.sort(function(t,e){return t._trace.data.index-e._trace.data.index});var g=[[0,0,0],[0,0,0]],m=[],v={};for(a=0;a<3;++a){if((l=(s=c[w[a]]).type)in v?(v[l].acc*=p[a],v[l].count+=1):v[l]={acc:p[a],count:1},s.autorange){g[0][a]=1/0,g[1][a]=-1/0;var y=this.glplot.objects,x=this.fullSceneLayout.annotations||[],b=s._name.charAt(0);for(o=0;o<y.length;o++){var _=y[o],M=_.bounds,A=_._trace.data._pad||0;g[0][a]=Math.min(g[0][a],M[0][a]/p[a]-A),g[1][a]=Math.max(g[1][a],M[1][a]/p[a]+A)}for(o=0;o<x.length;o++){var T=x[o];if(T.visible){var S=s.r2l(T[b]);g[0][a]=Math.min(g[0][a],S),g[1][a]=Math.max(g[1][a],S)}}if("rangemode"in s&&"tozero"===s.rangemode&&(g[0][a]=Math.min(g[0][a],0),g[1][a]=Math.max(g[1][a],0)),g[0][a]>g[1][a])g[0][a]=-1,g[1][a]=1;else{var E=g[1][a]-g[0][a];g[0][a]-=E/32,g[1][a]+=E/32}}else{var C=s.range;g[0][a]=s.r2l(C[0]),g[1][a]=s.r2l(C[1])}g[0][a]===g[1][a]&&(g[0][a]-=1,g[1][a]+=1),m[a]=g[1][a]-g[0][a],this.glplot.bounds[0][a]=g[0][a]*p[a],this.glplot.bounds[1][a]=g[1][a]*p[a]}var L=[1,1,1];for(a=0;a<3;++a){var z=v[l=(s=c[w[a]]).type];L[a]=Math.pow(z.acc,1/z.count)/p[a]}var P;if("auto"===c.aspectmode)P=Math.max.apply(null,L)/Math.min.apply(null,L)<=4?L:[1,1,1];else if("cube"===c.aspectmode)P=[1,1,1];else if("data"===c.aspectmode)P=L;else{if("manual"!==c.aspectmode)throw new Error("scene.js aspectRatio was not one of the enumerated types");var I=c.aspectratio;P=[I.x,I.y,I.z]}c.aspectratio.x=u.aspectratio.x=P[0],c.aspectratio.y=u.aspectratio.y=P[1],c.aspectratio.z=u.aspectratio.z=P[2],this.glplot.aspect=P;var O=c.domain||null,D=e._size||null;if(O&&D){var 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n=t("./font_attributes"),i=t("../components/color/attributes"),a=n({editType:"calc"});a.family.dflt='"Open Sans", verdana, arial, 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a=e[r[i]],s=a.domain,l=a._subplot,c=l.toImage("png");e._glimages.append("svg:image").attr({xmlns:o.svg,"xlink:href":c,x:n.l+n.w*s.x[0],y:n.t+n.h*(1-s.y[1]),width:n.w*(s.x[1]-s.x[0]),height:n.h*(s.y[1]-s.y[0]),preserveAspectRatio:"none"}),l.destroy()}},r.updateFx=function(t){for(var e=t._subplots.mapbox,r=0;r<e.length;r++){t[e[r]]._subplot.updateFx(t)}}},{"../../constants/xmlns_namespaces":663,"../../lib":684,"../../plots/get_data":768,"./constants":787,"./layout_attributes":791,"./layout_defaults":792,"./mapbox":793,"mapbox-gl":393}],790:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./convert_text_opts");function a(t,e){this.mapbox=t,this.map=t.map,this.uid=t.uid+"-layer"+e,this.idSource=this.uid+"-source",this.idLayer=this.uid+"-layer",this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}var o=a.prototype;function s(t){var e=t.source;return t.visible&&(n.isPlainObject(e)||"string"==typeof e&&e.length>0)}function l(t){var e={},r={};switch(t.type){case"circle":n.extendFlat(r,{"circle-radius":t.circle.radius,"circle-color":t.color,"circle-opacity":t.opacity});break;case"line":n.extendFlat(r,{"line-width":t.line.width,"line-color":t.color,"line-opacity":t.opacity});break;case"fill":n.extendFlat(r,{"fill-color":t.color,"fill-outline-color":t.fill.outlinecolor,"fill-opacity":t.opacity});break;case"symbol":var a=t.symbol,o=i(a.textposition,a.iconsize);n.extendFlat(e,{"icon-image":a.icon+"-15","icon-size":a.iconsize/10,"text-field":a.text,"text-size":a.textfont.size,"text-anchor":o.anchor,"text-offset":o.offset}),n.extendFlat(r,{"icon-color":t.color,"text-color":a.textfont.color,"text-opacity":t.opacity})}return{layout:e,paint:r}}o.update=function(t){this.visible?this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=s(t)},o.needsNewSource=function(t){return this.sourceType!==t.sourcetype||this.source!==t.source||this.layerType!==t.type},o.needsNewLayer=function(t){return this.layerType!==t.type||this.below!==t.below},o.updateSource=function(t){var e=this.map;if(e.getSource(this.idSource)&&e.removeSource(this.idSource),this.sourceType=t.sourcetype,this.source=t.source,s(t)){var r=function(t){var e,r=t.sourcetype,n=t.source,i={type:r};"geojson"===r?e="data":"vector"===r&&(e="string"==typeof n?"url":"tiles");return i[e]=n,i}(t);e.addSource(this.idSource,r)}},o.updateLayer=function(t){var e=this.map,r=l(t);this.removeLayer(),this.layerType=t.type,s(t)&&e.addLayer({id:this.idLayer,source:this.idSource,"source-layer":t.sourcelayer||"",type:t.type,layout:r.layout,paint:r.paint},t.below)},o.updateStyle=function(t){if(s(t)){var e=l(t);this.mapbox.setOptions(this.idLayer,"setLayoutProperty",e.layout),this.mapbox.setOptions(this.idLayer,"setPaintProperty",e.paint)}},o.removeLayer=function(){var t=this.map;t.getLayer(this.idLayer)&&t.removeLayer(this.idLayer)},o.dispose=function(){var t=this.map;t.removeLayer(this.idLayer),t.removeSource(this.idSource)},e.exports=function(t,e,r){var n=new a(t,e);return n.update(r),n}},{"../../lib":684,"./convert_text_opts":788}],791:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/color").defaultLine,a=t("../domain").attributes,o=t("../font_attributes"),s=t("../../traces/scatter/attributes").textposition,l=t("../../plot_api/edit_types").overrideAll,c=t("../../plot_api/plot_template").templatedArray,u=o({});u.family.dflt="Open Sans Regular, Arial Unicode MS 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n=t("mapbox-gl"),i=t("../../components/fx"),a=t("../../lib"),o=t("../../components/dragelement"),s=t("../cartesian/select").prepSelect,l=t("./constants"),c=t("./layout_attributes"),u=t("./layers");function h(t){this.id=t.id,this.gd=t.gd,this.container=t.container,this.isStatic=t.staticPlot;var e=t.fullLayout;this.uid=e._uid+"-"+this.id,this.opts=e[this.id],this.div=null,this.xaxis=null,this.yaxis=null,this.createFramework(e),this.map=null,this.accessToken=null,this.styleObj=null,this.traceHash={},this.layerList=[]}var f=h.prototype;function p(t){var e=c.style.values,r=c.style.dflt,n={};return a.isPlainObject(t)?(n.id=t.id,n.style=t):"string"==typeof t?(n.id=t,n.style=-1!==e.indexOf(t)?d(t):t):(n.id=r,n.style=d(r)),n.transition={duration:0,delay:0},n}function d(t){return l.styleUrlPrefix+t+"-"+l.styleUrlSuffix}function g(t){return[t.lon,t.lat]}e.exports=function(t){return new h(t)},f.plot=function(t,e,r){var n,i=this,a=i.opts=e[this.id];i.map&&a.accesstoken!==i.accessToken&&(i.map.remove(),i.map=null,i.styleObj=null,i.traceHash=[],i.layerList={}),n=i.map?new Promise(function(r,n){i.updateMap(t,e,r,n)}):new Promise(function(r,n){i.createMap(t,e,r,n)}),r.push(n)},f.createMap=function(t,e,r,a){var o=this,s=o.gd,c=o.opts,u=o.styleObj=p(c.style);o.accessToken=c.accesstoken;var h=o.map=new n.Map({container:o.div,style:u.style,center:g(c.center),zoom:c.zoom,bearing:c.bearing,pitch:c.pitch,interactive:!o.isStatic,preserveDrawingBuffer:o.isStatic,doubleClickZoom:!1,boxZoom:!1}),f=l.controlContainerClassName,d=o.div.getElementsByClassName(f)[0];if(o.div.removeChild(d),h._canvas.style.left="0px",h._canvas.style.top="0px",o.rejectOnError(a),h.once("load",function(){o.updateData(t),o.updateLayout(e),o.resolveOnRender(r)}),!o.isStatic){var m=!1;h.on("moveend",function(t){if(o.map){var e=o.getView();c._input.center=c.center=e.center,c._input.zoom=c.zoom=e.zoom,c._input.bearing=c.bearing=e.bearing,c._input.pitch=c.pitch=e.pitch,(t.originalEvent||m)&&y(e),m=!1}}),h.on("wheel",function(){m=!0}),h.on("mousemove",function(t){var e=o.div.getBoundingClientRect();t.clientX=t.point.x+e.left,t.clientY=t.point.y+e.top,t.target.getBoundingClientRect=function(){return e},o.xaxis.p2c=function(){return t.lngLat.lng},o.yaxis.p2c=function(){return t.lngLat.lat},i.hover(s,t,o.id)}),h.on("click",function(t){i.click(s,t.originalEvent)}),h.on("dragstart",v),h.on("zoomstart",v),h.on("dblclick",function(){s.emit("plotly_doubleclick",null);var t=o.viewInitial;h.setCenter(g(t.center)),h.setZoom(t.zoom),h.setBearing(t.bearing),h.setPitch(t.pitch);var 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y=f.select(".legend-marks").selectAll("path.legend-mark").data(o);y.enter().append("path").classed("legend-mark",!0),y.attr({transform:function(t,e){return"translate("+[c/2,m(e)+c/2]+")"},d:function(t,e){var r,i,a,o=t.symbol;return a=3*(i=c),"line"===(r=o)?"M"+[[-i/2,-i/12],[i/2,-i/12],[i/2,i/12],[-i/2,i/12]]+"Z":-1!=n.svg.symbolTypes.indexOf(r)?n.svg.symbol().type(r).size(a)():n.svg.symbol().type("square").size(a)()},fill:function(t,e){return g(e)}}),y.exit().remove()}var x=n.svg.axis().scale(m).orient("right"),b=f.select("g.legend-axis").attr({transform:"translate("+[u?e.colorBandWidth:c,c/2]+")"}).call(x);return b.selectAll(".domain").style({fill:"none",stroke:"none"}),b.selectAll("line").style({fill:"none",stroke:u?e.textColor:"none"}),b.selectAll("text").style({fill:e.textColor,"font-size":e.fontSize}).text(function(t,e){return o[e].name}),r}return r.config=function(e){return arguments.length?(i(t,e),this):t},n.rebind(r,e,"on"),r},o.Legend.defaultConfig=function(t,e){return{data:["a","b","c"],legendConfig:{elements:[{symbol:"line",color:"red"},{symbol:"square",color:"yellow"},{symbol:"diamond",color:"limegreen"}],height:150,colorBandWidth:30,fontSize:12,container:"body",isContinuous:null,textColor:"grey",reverseOrder:!1}}},o.tooltipPanel=function(){var t,e,r,a={container:null,hasTick:!1,fontSize:12,color:"white",padding:5},s="tooltip-"+o.tooltipPanel.uid++,l=10,c=function(){var n=(t=a.container.selectAll("g."+s).data([0])).enter().append("g").classed(s,!0).style({"pointer-events":"none",display:"none"});return r=n.append("path").style({fill:"white","fill-opacity":.9}).attr({d:"M0 0"}),e=n.append("text").attr({dx:a.padding+l,dy:.3*+a.fontSize}),c};return c.text=function(i){var o=n.hsl(a.color).l,s=o>=.5?"#aaa":"white",u=o>=.5?"black":"white",h=i||"";e.style({fill:u,"font-size":a.fontSize+"px"}).text(h);var f=a.padding,p=e.node().getBBox(),d={fill:a.color,stroke:s,"stroke-width":"2px"},g=p.width+2*f+l,m=p.height+2*f;return r.attr({d:"M"+[[l,-m/2],[l,-m/4],[a.hasTick?0:l,0],[l,m/4],[l,m/2],[g,m/2],[g,-m/2]].join("L")+"Z"}).style(d),t.attr({transform:"translate("+[l,-m/2+2*f]+")"}),t.style({display:"block"}),c},c.move=function(e){if(t)return t.attr({transform:"translate("+[e[0],e[1]]+")"}).style({display:"block"}),c},c.hide=function(){if(t)return t.style({display:"none"}),c},c.show=function(){if(t)return t.style({display:"block"}),c},c.config=function(t){return i(a,t),c},c},o.tooltipPanel.uid=1,o.adapter={},o.adapter.plotly=function(){var t={convert:function(t,e){var r={};if(t.data&&(r.data=t.data.map(function(t,r){var 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s=i({},t.layout);if([[s,["plot_bgcolor"],["backgroundColor"]],[s,["showlegend"],["showLegend"]],[s,["radialaxis"],["radialAxis"]],[s,["angularaxis"],["angularAxis"]],[s.angularaxis,["showline"],["gridLinesVisible"]],[s.angularaxis,["showticklabels"],["labelsVisible"]],[s.angularaxis,["nticks"],["ticksCount"]],[s.angularaxis,["tickorientation"],["tickOrientation"]],[s.angularaxis,["ticksuffix"],["ticksSuffix"]],[s.angularaxis,["range"],["domain"]],[s.angularaxis,["endpadding"],["endPadding"]],[s.radialaxis,["showline"],["gridLinesVisible"]],[s.radialaxis,["tickorientation"],["tickOrientation"]],[s.radialaxis,["ticksuffix"],["ticksSuffix"]],[s.radialaxis,["range"],["domain"]],[s.angularAxis,["showline"],["gridLinesVisible"]],[s.angularAxis,["showticklabels"],["labelsVisible"]],[s.angularAxis,["nticks"],["ticksCount"]],[s.angularAxis,["tickorientation"],["tickOrientation"]],[s.angularAxis,["ticksuffix"],["ticksSuffix"]],[s.angularAxis,["range"],["domain"]],[s.angularAxis,["endpadding"],["endPadding"]],[s.radialAxis,["showline"],["gridLinesVisible"]],[s.radialAxis,["tickorientation"],["tickOrientation"]],[s.radialAxis,["ticksuffix"],["ticksSuffix"]],[s.radialAxis,["range"],["domain"]],[s.font,["outlinecolor"],["outlineColor"]],[s.legend,["traceorder"],["reverseOrder"]],[s,["labeloffset"],["labelOffset"]],[s,["defaultcolorrange"],["defaultColorRange"]]].forEach(function(t,r){o.util.translator.apply(null,t.concat(e))}),e?("undefined"!=typeof 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L=Math.min(1,S/Math.abs(u.r2c(u.range[1])-u.r2c(u.range[0])));function z(t){return(d(t)+g(t))/2}v=t.maxHoverDistance-L,y=t.maxSpikeDistance-L;var P=a.getDistanceFunction(s,d,g,z);if(a.getClosest(b,P,t),!1===t.index)return[];var I=b[t.index],O=k.line.color,D=(k.marker||{}).color;o.opacity(O)&&k.line.width?t.color=O:o.opacity(D)&&k.boxpoints?t.color=D:t.color=k.fillcolor,t[l+"0"]=u.c2p(I.pos+M.bPos-S,!0),t[l+"1"]=u.c2p(I.pos+M.bPos+S,!0),t[l+"LabelVal"]=I.pos;var R=l+"Spike";t.spikeDistance=z(I)*y/v,t[R]=u.c2p(I.pos,!0);var B={},F=["med","min","q1","q3","max"];(k.boxmean||(k.meanline||{}).visible)&&F.push("mean"),(k.boxpoints||k.points)&&F.push("lf","uf");for(var N=0;N<F.length;N++){var j=F[N];if(j in I&&!(I[j]in B)){B[I[j]]=!0;var V=I[j],U=h.c2p(V,!0),q=i.extendFlat({},t);q[c+"0"]=q[c+"1"]=U,q[c+"LabelVal"]=V,q[c+"Label"]=(M.labels?M.labels[j]+" ":"")+n.hoverLabelText(h,V),"mean"===j&&"sd"in I&&"sd"===k.boxmean&&(q[c+"err"]=I.sd),t.name="",t.spikeDistance=void 0,t[R]=void 0,T.push(q)}}return T}function c(t,e,r){for(var n,o,l,c=t.cd,u=t.xa,h=t.ya,f=c[0].trace,p=u.c2p(e),d=h.c2p(r),g=a.quadrature(function(t){var e=Math.max(3,t.mrc||0);return Math.max(Math.abs(u.c2p(t.x)-p)-e,1-3/e)},function(t){var e=Math.max(3,t.mrc||0);return Math.max(Math.abs(h.c2p(t.y)-d)-e,1-3/e)}),m=!1,v=0;v<c.length;v++){o=c[v];for(var y=0;y<(o.pts||[]).length;y++){var x=g(l=o.pts[y]);x<=t.distance&&(t.distance=x,m=[v,y])}}if(!m)return!1;l=(o=c[m[0]]).pts[m[1]];var b=u.c2p(l.x,!0),_=h.c2p(l.y,!0),w=l.mrc||1;n=i.extendFlat({},t,{index:l.i,color:(f.marker||{}).color,name:f.name,x0:b-w,x1:b+w,xLabelVal:l.x,y0:_-w,y1:_+w,yLabelVal:l.y,spikeDistance:t.distance});var k="h"===f.orientation?"y":"x",M="h"===f.orientation?h:u;return n[k+"Spike"]=M.c2p(o.pos,!0),s(l,f,n),n}e.exports={hoverPoints:function(t,e,r,n){var i,a=t.cd[0].trace.hoveron,o=[];return-1!==a.indexOf("boxes")&&(o=o.concat(l(t,e,r,n))),-1!==a.indexOf("points")&&(i=c(t,e,r)),"closest"===n?i?[i]:o:i?(o.push(i),o):o},hoverOnBoxes:l,hoverOnPoints:c}},{"../../components/color":558,"../../components/fx":600,"../../lib":684,"../../plots/cartesian/axes":732,"../scatter/fill_hover_text":1022}],844:[function(t,e,r){"use strict";var n={};n.attributes=t("./attributes"),n.layoutAttributes=t("./layout_attributes"),n.supplyDefaults=t("./defaults").supplyDefaults,n.supplyLayoutDefaults=t("./layout_defaults").supplyLayoutDefaults,n.calc=t("./calc"),n.setPositions=t("./set_positions").setPositions,n.plot=t("./plot").plot,n.style=t("./style").style,n.styleOnSelect=t("./style").styleOnSelect,n.hoverPoints=t("./hover").hoverPoints,n.selectPoints=t("./select"),n.moduleType="trace",n.name="box",n.basePlotModule=t("../../plots/cartesian"),n.categories=["cartesian","svg","symbols","oriented","box-violin","showLegend","boxLayout","zoomScale"],n.meta={},e.exports=n},{"../../plots/cartesian":743,"./attributes":840,"./calc":841,"./defaults":842,"./hover":843,"./layout_attributes":845,"./layout_defaults":846,"./plot":847,"./select":848,"./set_positions":849,"./style":850}],845:[function(t,e,r){"use 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m=t.selectAll("path.box").data("violin"!==r.type||r.box?i.identity:[]);m.enter().append("path").style("vector-effect","non-scaling-stroke").attr("class","box"),m.exit().remove(),m.each(function(t){var e=t.pos,a=l.c2p(e+u,!0)+f,m=l.c2p(e+u-o,!0)+f,v=l.c2p(e+u+s,!0)+f,y=l.c2p(e+u-h,!0)+f,x=l.c2p(e+u+h,!0)+f,b=l.c2p(e+u-o*g,!0)+f,_=l.c2p(e+u+s*g,!0)+f,w=c.c2p(t.q1,!0),k=c.c2p(t.q3,!0),M=i.constrain(c.c2p(t.med,!0),Math.min(w,k)+1,Math.max(w,k)-1),A=void 0===t.lf||!1===r.boxpoints,T=c.c2p(A?t.min:t.lf,!0),S=c.c2p(A?t.max:t.uf,!0),E=c.c2p(t.ln,!0),C=c.c2p(t.un,!0);"h"===r.orientation?n.select(this).attr("d","M"+M+","+b+"V"+_+"M"+w+","+m+"V"+v+(d?"H"+E+"L"+M+","+_+"L"+C+","+v:"")+"H"+k+"V"+m+(d?"H"+C+"L"+M+","+b+"L"+E+","+m:"")+"ZM"+w+","+a+"H"+T+"M"+k+","+a+"H"+S+(0===p?"":"M"+T+","+y+"V"+x+"M"+S+","+y+"V"+x)):n.select(this).attr("d","M"+b+","+M+"H"+_+"M"+m+","+w+"H"+v+(d?"V"+E+"L"+_+","+M+"L"+v+","+C:"")+"V"+k+"H"+m+(d?"V"+C+"L"+b+","+M+"L"+m+","+E:"")+"ZM"+a+","+w+"V"+T+"M"+a+","+k+"V"+S+(0===p?"":"M"+y+","+T+"H"+x+"M"+y+","+S+"H"+x))})}function c(t,e,r,n){var l=e.x,c=e.y,u=n.bdPos,h=n.bPos,f=r.boxpoints||r.points;i.seedPseudoRandom();var p=t.selectAll("g.points").data(f?function(t){return t.forEach(function(t){t.t=n,t.trace=r}),t}:[]);p.enter().append("g").attr("class","points"),p.exit().remove();var d=p.selectAll("path").data(function(t){var e,n,a="all"===f?t.pts:t.pts.filter(function(e){return e.v<t.lf||e.v>t.uf}),l=Math.max((t.max-t.min)/10,t.q3-t.q1),c=1e-9*l,p=l*s,d=[],g=0;if(r.jitter){if(0===l)for(g=1,d=new Array(a.length),e=0;e<a.length;e++)d[e]=1;else for(e=0;e<a.length;e++){var m=Math.max(0,e-o),v=a[m].v,y=Math.min(a.length-1,e+o),x=a[y].v;"all"!==f&&(a[e].v<t.lf?x=Math.min(x,t.lf):v=Math.max(v,t.uf));var b=Math.sqrt(p*(y-m)/(x-v+c))||0;b=i.constrain(Math.abs(b),0,1),d.push(b),g=Math.max(b,g)}n=2*r.jitter/(g||1)}for(e=0;e<a.length;e++){var _=a[e],w=_.v,k=r.jitter?n*d[e]*(i.pseudoRandom()-.5):0,M=t.pos+h+u*(r.pointpos+k);"h"===r.orientation?(_.y=M,_.x=w):(_.x=M,_.y=w),"suspectedoutliers"===f&&w<t.uo&&w>t.lo&&(_.so=!0)}return a});d.enter().append("path").classed("point",!0),d.exit().remove(),d.call(a.translatePoints,l,c)}function u(t,e,r,a){var o,s,l=e.pos,c=e.val,u=a.bPos,h=a.bPosPxOffset||0,f=r.boxmean||(r.meanline||{}).visible;Array.isArray(a.bdPos)?(o=a.bdPos[0],s=a.bdPos[1]):(o=a.bdPos,s=a.bdPos);var p=t.selectAll("path.mean").data("box"===r.type&&r.boxmean||"violin"===r.type&&r.box&&r.meanline?i.identity:[]);p.enter().append("path").attr("class","mean").style({fill:"none","vector-effect":"non-scaling-stroke"}),p.exit().remove(),p.each(function(t){var e=l.c2p(t.pos+u,!0)+h,i=l.c2p(t.pos+u-o,!0)+h,a=l.c2p(t.pos+u+s,!0)+h,p=c.c2p(t.mean,!0),d=c.c2p(t.mean-t.sd,!0),g=c.c2p(t.mean+t.sd,!0);"h"===r.orientation?n.select(this).attr("d","M"+p+","+i+"V"+a+("sd"===f?"m0,0L"+d+","+e+"L"+p+","+i+"L"+g+","+e+"Z":"")):n.select(this).attr("d","M"+i+","+p+"H"+a+("sd"===f?"m0,0L"+e+","+d+"L"+i+","+p+"L"+e+","+g+"Z":""))})}e.exports={plot:function(t,e,r,i){var a=t._fullLayout,o=e.xaxis,s=e.yaxis,h=i.selectAll("g.trace.boxes").data(r,function(t){return t[0].trace.uid});h.enter().append("g").attr("class","trace boxes"),h.exit().remove(),h.order(),h.each(function(t){var r=t[0],i=r.t,h=r.trace,f=n.select(this);e.isRangePlot||(r.node3=f);var p,d,g=a._numBoxes,m=1-a.boxgap,v="group"===a.boxmode&&g>1,y=i.dPos*m*(1-a.boxgroupgap)/(v?g:1),x=v?2*i.dPos*((i.num+.5)/g-.5)*m:0,b=y*h.whiskerwidth;!0!==h.visible||i.empty?f.remove():("h"===h.orientation?(p=s,d=o):(p=o,d=s),i.bPos=x,i.bdPos=y,i.wdPos=b,i.wHover=i.dPos*(v?m/g:1),l(f,{pos:p,val:d},h,i),c(f,{x:o,y:s},h,i),u(f,{pos:p,val:d},h,i))})},plotBoxAndWhiskers:l,plotPoints:c,plotBoxMean:u}},{"../../components/drawing":583,"../../lib":684,d3:147}],848:[function(t,e,r){"use strict";e.exports=function(t,e){var r,n,i=t.cd,a=t.xaxis,o=t.yaxis,s=[];if(!1===e)for(r=0;r<i.length;r++)for(n=0;n<(i[r].pts||[]).length;n++)i[r].pts[n].selected=0;else for(r=0;r<i.length;r++)for(n=0;n<(i[r].pts||[]).length;n++){var l=i[r].pts[n],c=a.c2p(l.x),u=o.c2p(l.y);e.contains([c,u])?(s.push({pointNumber:l.i,x:a.c2d(l.x),y:o.c2d(l.y)}),l.selected=1):l.selected=0}return s}},{}],849:[function(t,e,r){"use strict";var n=t("../../plots/cartesian/axes"),i=t("../../lib"),a=["v","h"];function o(t,e,r,a,o){var s,l,c,u=e.calcdata,h=e._fullLayout,f=[],p="violin"===t?"_numViolins":"_numBoxes";for(s=0;s<r.length;s++)for(c=u[r[s]],l=0;l<c.length;l++)f.push(c[l].pos);if(f.length){var d=i.distinctVals(f),g=d.minDiff/2;for(f.length===d.vals.length&&(h[p]=1),n.minDtick(a,d.minDiff,d.vals[0],!0),s=0;s<r.length;s++)(c=u[r[s]])[0].t.dPos=g;var m=(1-h[t+"gap"])*(1-h[t+"groupgap"])*g/h[p];n.expand(a,d.vals,{vpadminus:g+o[0]*m,vpadplus:g+o[1]*m})}}e.exports={setPositions:function(t,e){for(var r=t.calcdata,n=e.xaxis,i=e.yaxis,s=0;s<a.length;s++){for(var l=a[s],c="h"===l?i:n,u=[],h=0,f=0,p=0;p<r.length;p++){var d=r[p],g=d[0].t,m=d[0].trace;!0!==m.visible||"box"!==m.type&&"candlestick"!==m.type||g.empty||(m.orientation||"v")!==l||m.xaxis!==n._id||m.yaxis!==i._id||(u.push(p),m.boxpoints&&(h=Math.max(h,m.jitter-m.pointpos-1),f=Math.max(f,m.jitter+m.pointpos-1)))}o("box",t,u,c,[h,f])}},setPositionOffset:o}},{"../../lib":684,"../../plots/cartesian/axes":732}],850:[function(t,e,r){"use strict";var n=t("d3"),i=t("../../components/color"),a=t("../../components/drawing");e.exports={style:function(t,e){var r=e?e[0].node3:n.select(t).selectAll("g.trace.boxes");r.style("opacity",function(t){return t[0].trace.opacity}),r.each(function(e){var r=n.select(this),o=e[0].trace,s=o.line.width;function l(t,e,r,n){t.style("stroke-width",e+"px").call(i.stroke,r).call(i.fill,n)}var c=r.selectAll("path.box");if("candlestick"===o.type)c.each(function(t){var e=n.select(this),r=o[t.dir];l(e,r.line.width,r.line.color,r.fillcolor),e.style("opacity",o.selectedpoints&&!t.selected?.3:1)});else{l(c,s,o.line.color,o.fillcolor),r.selectAll("path.mean").style({"stroke-width":s,"stroke-dasharray":2*s+"px,"+s+"px"}).call(i.stroke,o.line.color);var u=r.selectAll("path.point");a.pointStyle(u,o,t)}})},styleOnSelect:function(t,e){var r=e[0].node3,n=e[0].trace,i=r.selectAll("path.point");n.selectedpoints?a.selectedPointStyle(i,n):a.pointStyle(i,n,t)}}},{"../../components/color":558,"../../components/drawing":583,d3:147}],851:[function(t,e,r){"use strict";var n=t("../../lib").extendFlat,i=t("../ohlc/attributes"),a=t("../box/attributes");function o(t){return{line:{color:n({},a.line.color,{dflt:t}),width:a.line.width,editType:"style"},fillcolor:a.fillcolor,editType:"style"}}e.exports={x:i.x,open:i.open,high:i.high,low:i.low,close:i.close,line:{width:n({},a.line.width,{}),editType:"style"},increasing:o(i.increasing.line.color.dflt),decreasing:o(i.decreasing.line.color.dflt),text:i.text,whiskerwidth:n({},a.whiskerwidth,{dflt:0})}},{"../../lib":684,"../box/attributes":840,"../ohlc/attributes":970}],852:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../plots/cartesian/axes"),a=t("../ohlc/calc").calcCommon;function o(t,e,r,n){return{min:r,q1:Math.min(t,n),med:n,q3:Math.max(t,n),max:e}}e.exports=function(t,e){var r=t._fullLayout,s=i.getFromId(t,e.xaxis),l=i.getFromId(t,e.yaxis),c=s.makeCalcdata(e,"x"),u=a(t,e,c,l,o);return u.length?(n.extendFlat(u[0].t,{num:r._numBoxes,dPos:n.distinctVals(c).minDiff/2,posLetter:"x",valLetter:"y"}),r._numBoxes++,u):[{t:{empty:!0}}]}},{"../../lib":684,"../../plots/cartesian/axes":732,"../ohlc/calc":971}],853:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/color"),a=t("../ohlc/ohlc_defaults"),o=t("./attributes");function s(t,e,r,n){var a=r(n+".line.color");r(n+".line.width",e.line.width),r(n+".fillcolor",i.addOpacity(a,.5))}e.exports=function(t,e,r,i){function l(r,i){return n.coerce(t,e,o,r,i)}a(t,e,l,i)?(l("line.width"),s(t,e,l,"increasing"),s(t,e,l,"decreasing"),l("text"),l("whiskerwidth"),i._requestRangeslider[e.xaxis]=!0):e.visible=!1}},{"../../components/color":558,"../../lib":684,"../ohlc/ohlc_defaults":975,"./attributes":851}],854:[function(t,e,r){"use strict";e.exports={moduleType:"trace",name:"candlestick",basePlotModule:t("../../plots/cartesian"),categories:["cartesian","svg","showLegend","candlestick","boxLayout"],meta:{},attributes:t("./attributes"),layoutAttributes:t("../box/layout_attributes"),supplyLayoutDefaults:t("../box/layout_defaults").supplyLayoutDefaults,setPositions:t("../box/set_positions").setPositions,supplyDefaults:t("./defaults"),calc:t("./calc"),plot:t("../box/plot").plot,layerName:"boxlayer",style:t("../box/style").style,hoverPoints:t("../ohlc/hover"),selectPoints:t("../ohlc/select")}},{"../../plots/cartesian":743,"../box/layout_attributes":845,"../box/layout_defaults":846,"../box/plot":847,"../box/set_positions":849,"../box/style":850,"../ohlc/hover":973,"../ohlc/select":977,"./attributes":851,"./calc":852,"./defaults":853}],855:[function(t,e,r){"use strict";var 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a=Math.max(0,-r.newExtent[0]),o=Math.max(0,r.newExtent[1]-1);r.newExtent[0]+=a,r.newExtent[1]-=o,r.grabbingBar&&(r.newExtent[1]+=a,r.newExtent[0]-=o),t.brush.filterSpecified=!0,r.extent=r.stayingIntervals.concat([r.newExtent]),r.brushCallback(t),_(this.parentNode)}).on("dragend",function(t){i.event.sourceEvent.stopPropagation();var e=t.brush,r=e.filter,n=e.svgBrush,a=n.grabbingBar;if(n.grabbingBar=!1,n.grabLocation=void 0,t.parent.inBrushDrag=!1,x(),!n.wasDragged)return n.wasDragged=void 0,n.clickableOrdinalRange?e.filterSpecified&&t.multiselect?n.extent.push(n.clickableOrdinalRange):(n.extent=[n.clickableOrdinalRange],e.filterSpecified=!0):a?(n.extent=n.stayingIntervals,0===n.extent.length&&A(e)):A(e),n.brushCallback(t),_(this.parentNode),void n.brushEndCallback(e.filterSpecified?r.getConsolidated():[]);var o=function(){r.set(r.getConsolidated())};if(t.ordinal){var s=t.unitTickvals;s[s.length-1]<s[0]&&s.reverse(),n.newExtent=[f(s,n.newExtent[0],n.stayingIntervals),p(s,n.newExtent[1],n.stayingIntervals)];var l=n.newExtent[1]>n.newExtent[0];n.extent=n.stayingIntervals.concat(l?[n.newExtent]:[]),n.extent.length||A(e),n.brushCallback(t),l?_(this.parentNode,o):(o(),_(this.parentNode))}else o();n.brushEndCallback(e.filterSpecified?r.getConsolidated():[])}))}function M(t,e){return t[0]-e[0]}function A(t){t.filterSpecified=!1,t.svgBrush.extent=[[0,1]]}function T(t){for(var e,r=t.slice(),n=[],i=r.shift();i;){for(e=i.slice();(i=r.shift())&&i[0]<=e[1];)e[1]=Math.max(e[1],i[1]);n.push(e)}return n}e.exports={makeBrush:function(t,e,r,n,i,a){var o,l=function(){var t,e,r=[];return{set:function(n){r=n.map(function(t){return t.slice().sort(s)}).sort(M),t=T(r),e=r.reduce(function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]},[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return l.set(r),{filter:l,filterSpecified:e,svgBrush:{extent:[],brushStartCallback:n,brushCallback:(o=i,function(t){var e=t.brush,r=function(t){return t.svgBrush.extent.map(function(t){return t.slice()})}(e).slice();e.filter.set(r),o()}),brushEndCallback:a}}},ensureAxisBrush:function(t){var e=t.selectAll("."+n.cn.axisBrush).data(o,a);e.enter().append("g").classed(n.cn.axisBrush,!0),function(t){var e=t.selectAll(".background").data(o);e.enter().append("rect").classed("background",!0).call(g).call(m).style("pointer-events","auto").attr("transform","translate(0 "+n.verticalPadding+")"),e.call(k).attr("height",function(t){return t.height-n.verticalPadding});var r=t.selectAll(".highlight-shadow").data(o);r.enter().append("line").classed("highlight-shadow",!0).attr("x",-n.bar.width/2).attr("stroke-width",n.bar.width+n.bar.strokeWidth).attr("stroke",n.bar.strokeColor).attr("opacity",n.bar.strokeOpacity).attr("stroke-linecap","butt"),r.attr("y1",function(t){return t.height}).call(b);var i=t.selectAll(".highlight").data(o);i.enter().append("line").classed("highlight",!0).attr("x",-n.bar.width/2).attr("stroke-width",n.bar.width-n.bar.strokeWidth).attr("stroke",n.bar.fillColor).attr("opacity",n.bar.fillOpacity).attr("stroke-linecap","butt"),i.attr("y1",function(t){return t.height}).call(b)}(e)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map(function(t){return t.sort(s)}),t=e.multiselect?T(t.sort(M)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map(function(t){var e=[f(r,t[0],[]),p(r,t[1],[])];if(e[1]>e[0])return e}).filter(function(t){return t})).length)return}return t.length>1?t:t[0]}}},{"../../lib":684,"../../lib/gup":681,"./constants":983,d3:147}],981:[function(t,e,r){"use strict";var n=t("d3"),i=t("../../plots/get_data").getModuleCalcData,a=t("./plot"),o=t("../../constants/xmlns_namespaces");r.name="parcoords",r.plot=function(t){var e=i(t.calcdata,"parcoords")[0];e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has("parcoords"),a=e._has&&e._has("parcoords");i&&!a&&(n._paperdiv.selectAll(".parcoords").remove(),n._glimages.selectAll("*").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(".svg-container");r.filter(function(t,e){return e===r.size()-1}).selectAll(".gl-canvas-context, .gl-canvas-focus").each(function(){var t=this.toDataURL("image/png");e.append("svg:image").attr({xmlns:o.svg,"xlink:href":t,preserveAspectRatio:"none",x:0,y:0,width:this.width,height:this.height})}),window.setTimeout(function(){n.selectAll("#filterBarPattern").attr("id","filterBarPattern")},60)}},{"../../constants/xmlns_namespaces":663,"../../plots/get_data":768,"./plot":989,d3:147}],982:[function(t,e,r){"use strict";var n=t("../../components/colorscale/has_colorscale"),i=t("../../components/colorscale/calc"),a=t("../../lib"),o=t("../../lib/gup").wrap;e.exports=function(t,e){var 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n={};n.attributes=t("./attributes"),n.supplyDefaults=t("./defaults"),n.calc=t("./calc"),n.plot=t("./plot"),n.colorbar={container:"line",min:"cmin",max:"cmax"},n.moduleType="trace",n.name="parcoords",n.basePlotModule=t("./base_plot"),n.categories=["gl","regl","noOpacity"],n.meta={},e.exports=n},{"./attributes":979,"./base_plot":981,"./calc":982,"./defaults":984,"./plot":989}],986:[function(t,e,r){"use strict";var n=t("glslify"),i=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 p0, p1, p2, p3,\n               p4, p5, p6, p7,\n               p8, p9, pa, pb,\n               pc, pd, pe;\n\nattribute vec4 pf;\n\nuniform mat4 dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D,\n             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\n\nuniform vec2 resolution,\n             viewBoxPosition,\n             viewBoxSize;\n\nuniform sampler2D palette;\nuniform sampler2D mask;\nuniform float maskHeight;\n\nuniform vec2 colorClamp;\n\nvarying vec4 fragColor;\n\nvec4 unit_1 = vec4(1, 1, 1, 1);\n\nfloat val(mat4 p, mat4 v) {\n    return dot(matrixCompMult(p, v) * unit_1, unit_1);\n}\n\nfloat axisY(\n        float x,\n        mat4 d[4],\n        mat4 dim1A, mat4 dim2A, mat4 dim1B, mat4 dim2B, mat4 dim1C, mat4 dim2C, mat4 dim1D, mat4 dim2D\n    ) {\n\n    float y1 = val(d[0], dim1A) + val(d[1], dim1B) + val(d[2], dim1C) + val(d[3], dim1D);\n    float y2 = val(d[0], dim2A) + val(d[1], dim2B) + val(d[2], dim2C) + val(d[3], dim2D);\n    return y1 * (1.0 - x) + y2 * x;\n}\n\nconst int bitsPerByte = 8;\n\nint mod2(int a) {\n    return a - 2 * (a / 2);\n}\n\nint mod8(int a) {\n    return a - 8 * (a / 8);\n}\n\nvec4 zero = vec4(0, 0, 0, 0);\nvec4 unit_0 = vec4(1, 1, 1, 1);\nvec2 xyProjection = vec2(1, 1);\n\nmat4 mclamp(mat4 m, mat4 lo, mat4 hi) {\n    return mat4(clamp(m[0], lo[0], hi[0]),\n                clamp(m[1], lo[1], hi[1]),\n                clamp(m[2], lo[2], hi[2]),\n                clamp(m[3], lo[3], hi[3]));\n}\n\nbool mshow(mat4 p, mat4 lo, mat4 hi) {\n    return mclamp(p, lo, hi) == p;\n}\n\nbool withinBoundingBox(\n        mat4 d[4],\n        mat4 loA, mat4 hiA, mat4 loB, mat4 hiB, mat4 loC, mat4 hiC, mat4 loD, mat4 hiD\n    ) {\n\n    return mshow(d[0], loA, hiA) &&\n           mshow(d[1], loB, hiB) &&\n           mshow(d[2], loC, hiC) &&\n           mshow(d[3], loD, hiD);\n}\n\nbool withinRasterMask(mat4 d[4], sampler2D mask, float height) {\n    bool result = true;\n    int bitInByteStepper;\n    float valY, valueY, scaleX;\n    int hit, bitmask, valX;\n    for(int i = 0; i < 4; i++) {\n        for(int j = 0; j < 4; j++) {\n            for(int k = 0; k < 4; k++) {\n                bitInByteStepper = mod8(j * 4 + k);\n                valX = i * 2 + j / 2;\n                valY = d[i][j][k];\n                valueY = valY * (height - 1.0) + 0.5;\n                scaleX = (float(valX) + 0.5) / 8.0;\n                hit = int(texture2D(mask, vec2(scaleX, (valueY + 0.5) / height))[3] * 255.0) / int(pow(2.0, float(bitInByteStepper)));\n                result = result && mod2(hit) == 1;\n            }\n        }\n    }\n    return result;\n}\n\nvec4 position(\n        float depth,\n        vec2 resolution, vec2 viewBoxPosition, vec2 viewBoxSize,\n        mat4 dims[4],\n        float signum,\n        mat4 dim1A, mat4 dim2A, mat4 dim1B, mat4 dim2B, mat4 dim1C, mat4 dim2C, mat4 dim1D, mat4 dim2D,\n        mat4 loA, mat4 hiA, mat4 loB, mat4 hiB, mat4 loC, mat4 hiC, mat4 loD, mat4 hiD,\n        sampler2D mask, float maskHeight\n    ) {\n\n    float x = 0.5 * signum + 0.5;\n    float y = axisY(x, dims, dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D);\n\n    float show = float(\n                            withinBoundingBox(dims, loA, hiA, loB, hiB, loC, hiC, loD, hiD)\n                         && withinRasterMask(dims, mask, maskHeight)\n                      );\n\n    vec2 viewBoxXY = viewBoxPosition + viewBoxSize * vec2(x, y);\n    float depthOrHide = depth + 2.0 * (1.0 - show);\n\n    return vec4(\n        xyProjection * (2.0 * viewBoxXY / resolution - 1.0),\n        depthOrHide,\n        1.0\n    );\n}\n\nvoid main() {\n\n    float prominence = abs(pf[3]);\n\n    mat4 p[4];\n    p[0] = mat4(p0, p1, p2, p3);\n    p[1] = mat4(p4, p5, p6, p7);\n    p[2] = mat4(p8, p9, pa, pb);\n    p[3] = mat4(pc, pd, pe, abs(pf));\n\n    gl_Position = position(\n        1.0 - prominence,\n        resolution, viewBoxPosition, viewBoxSize,\n        p,\n        sign(pf[3]),\n        dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D,\n        loA, hiA, loB, hiB, loC, hiC, loD, hiD,\n        mask, maskHeight\n    );\n\n    float clampedColorIndex = clamp((prominence - colorClamp[0]) / (colorClamp[1] - colorClamp[0]), 0.0, 1.0);\n    fragColor = texture2D(palette, vec2((clampedColorIndex * 255.0 + 0.5) / 256.0, 0.5));\n}\n"]),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 p0, p1, p2, p3,\n               p4, p5, p6, p7,\n               p8, p9, pa, pb,\n               pc, pd, pe;\n\nattribute vec4 pf;\n\nuniform mat4 dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D;\n\nuniform vec2 resolution,\n             viewBoxPosition,\n             viewBoxSize;\n\nuniform sampler2D palette;\n\nuniform vec2 colorClamp;\n\nvarying vec4 fragColor;\n\nvec2 xyProjection = vec2(1, 1);\n\nvec4 unit = vec4(1, 1, 1, 1);\n\nfloat val(mat4 p, mat4 v) {\n    return dot(matrixCompMult(p, v) * unit, unit);\n}\n\nfloat axisY(\n        float x,\n        mat4 d[4],\n        mat4 dim1A, mat4 dim2A, mat4 dim1B, mat4 dim2B, mat4 dim1C, mat4 dim2C, mat4 dim1D, mat4 dim2D\n    ) {\n\n    float y1 = val(d[0], dim1A) + val(d[1], dim1B) + val(d[2], dim1C) + val(d[3], dim1D);\n    float y2 = val(d[0], dim2A) + val(d[1], dim2B) + val(d[2], dim2C) + val(d[3], dim2D);\n    return y1 * (1.0 - x) + y2 * x;\n}\n\nvec4 position(\n        float depth,\n        vec2 resolution, vec2 viewBoxPosition, vec2 viewBoxSize,\n        mat4 dims[4],\n        float signum,\n        mat4 dim1A, mat4 dim2A, mat4 dim1B, mat4 dim2B, mat4 dim1C, mat4 dim2C, mat4 dim1D, mat4 dim2D\n    ) {\n\n    float x = 0.5 * signum + 0.5;\n    float y = axisY(x, dims, dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D);\n\n    vec2 viewBoxXY = viewBoxPosition + viewBoxSize * vec2(x, y);\n\n    return vec4(\n        xyProjection * (2.0 * viewBoxXY / resolution - 1.0),\n        depth,\n        1.0\n    );\n}\n\nvoid main() {\n\n    float prominence = abs(pf[3]);\n\n    mat4 p[4];\n    p[0] = mat4(p0, p1, p2, p3);\n    p[1] = mat4(p4, p5, p6, p7);\n    p[2] = mat4(p8, p9, pa, pb);\n    p[3] = mat4(pc, pd, pe, abs(pf));\n\n    gl_Position = position(\n        1.0 - prominence,\n        resolution, viewBoxPosition, viewBoxSize,\n        p,\n        sign(pf[3]),\n        dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D\n    );\n\n    float clampedColorIndex = clamp((prominence - colorClamp[0]) / (colorClamp[1] - colorClamp[0]), 0.0, 1.0);\n    fragColor = texture2D(palette, vec2((clampedColorIndex * 255.0 + 0.5) / 256.0, 0.5));\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 p0, p1, p2, p3,\n               p4, p5, p6, p7,\n               p8, p9, pa, pb,\n               pc, pd, pe;\n\nattribute vec4 pf;\n\nuniform mat4 dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D,\n             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\n\nuniform vec2 resolution,\n             viewBoxPosition,\n             viewBoxSize;\n\nuniform sampler2D mask;\nuniform float maskHeight;\n\nuniform vec2 colorClamp;\n\nvarying vec4 fragColor;\n\nvec4 unit_1 = vec4(1, 1, 1, 1);\n\nfloat val(mat4 p, mat4 v) {\n    return dot(matrixCompMult(p, v) * unit_1, unit_1);\n}\n\nfloat axisY(\n        float x,\n        mat4 d[4],\n        mat4 dim1A, mat4 dim2A, mat4 dim1B, mat4 dim2B, mat4 dim1C, mat4 dim2C, mat4 dim1D, mat4 dim2D\n    ) {\n\n    float y1 = val(d[0], dim1A) + val(d[1], dim1B) + val(d[2], dim1C) + val(d[3], dim1D);\n    float y2 = val(d[0], dim2A) + val(d[1], dim2B) + val(d[2], dim2C) + val(d[3], dim2D);\n    return y1 * (1.0 - x) + y2 * x;\n}\n\nconst int bitsPerByte = 8;\n\nint mod2(int a) {\n    return a - 2 * (a / 2);\n}\n\nint mod8(int a) {\n    return a - 8 * (a / 8);\n}\n\nvec4 zero = vec4(0, 0, 0, 0);\nvec4 unit_0 = vec4(1, 1, 1, 1);\nvec2 xyProjection = vec2(1, 1);\n\nmat4 mclamp(mat4 m, mat4 lo, mat4 hi) {\n    return mat4(clamp(m[0], lo[0], hi[0]),\n                clamp(m[1], lo[1], hi[1]),\n                clamp(m[2], lo[2], hi[2]),\n                clamp(m[3], lo[3], hi[3]));\n}\n\nbool mshow(mat4 p, mat4 lo, mat4 hi) {\n    return mclamp(p, lo, hi) == p;\n}\n\nbool withinBoundingBox(\n        mat4 d[4],\n        mat4 loA, mat4 hiA, mat4 loB, mat4 hiB, mat4 loC, mat4 hiC, mat4 loD, mat4 hiD\n    ) {\n\n    return mshow(d[0], loA, hiA) &&\n           mshow(d[1], loB, hiB) &&\n           mshow(d[2], loC, hiC) &&\n           mshow(d[3], loD, hiD);\n}\n\nbool withinRasterMask(mat4 d[4], sampler2D mask, float height) {\n    bool result = true;\n    int bitInByteStepper;\n    float valY, valueY, scaleX;\n    int hit, bitmask, valX;\n    for(int i = 0; i < 4; i++) {\n        for(int j = 0; j < 4; j++) {\n            for(int k = 0; k < 4; k++) {\n                bitInByteStepper = mod8(j * 4 + k);\n                valX = i * 2 + j / 2;\n                valY = d[i][j][k];\n                valueY = valY * (height - 1.0) + 0.5;\n                scaleX = (float(valX) + 0.5) / 8.0;\n                hit = int(texture2D(mask, vec2(scaleX, (valueY + 0.5) / height))[3] * 255.0) / int(pow(2.0, float(bitInByteStepper)));\n                result = result && mod2(hit) == 1;\n            }\n        }\n    }\n    return result;\n}\n\nvec4 position(\n        float depth,\n        vec2 resolution, vec2 viewBoxPosition, vec2 viewBoxSize,\n        mat4 dims[4],\n        float signum,\n        mat4 dim1A, mat4 dim2A, mat4 dim1B, mat4 dim2B, mat4 dim1C, mat4 dim2C, mat4 dim1D, mat4 dim2D,\n        mat4 loA, mat4 hiA, mat4 loB, mat4 hiB, mat4 loC, mat4 hiC, mat4 loD, mat4 hiD,\n        sampler2D mask, float maskHeight\n    ) {\n\n    float x = 0.5 * signum + 0.5;\n    float y = axisY(x, dims, dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D);\n\n    float show = float(\n                            withinBoundingBox(dims, loA, hiA, loB, hiB, loC, hiC, loD, hiD)\n                         && withinRasterMask(dims, mask, maskHeight)\n                      );\n\n    vec2 viewBoxXY = viewBoxPosition + viewBoxSize * vec2(x, y);\n    float depthOrHide = depth + 2.0 * (1.0 - show);\n\n    return vec4(\n        xyProjection * (2.0 * viewBoxXY / resolution - 1.0),\n        depthOrHide,\n        1.0\n    );\n}\n\nvoid main() {\n\n    float prominence = abs(pf[3]);\n\n    mat4 p[4];\n    p[0] = mat4(p0, p1, p2, p3);\n    p[1] = mat4(p4, p5, p6, p7);\n    p[2] = mat4(p8, p9, pa, pb);\n    p[3] = mat4(pc, pd, pe, abs(pf));\n\n    gl_Position = position(\n        1.0 - prominence,\n        resolution, viewBoxPosition, viewBoxSize,\n        p,\n        sign(pf[3]),\n        dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D,\n        loA, hiA, loB, hiB, loC, hiC, loD, hiD,\n        mask, maskHeight\n    );\n\n    fragColor = vec4(pf.rgb, 1.0);\n}\n"]),s=n(["precision lowp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n    gl_FragColor = fragColor;\n}\n"]),l=t("../../lib"),c=1e-6,u=1e-7,h=2048,f=64,p=2,d=4,g=8,m=f/g,v=[119,119,119],y=new Uint8Array(4),x=new Uint8Array(4),b={shape:[256,1],format:"rgba",type:"uint8",mag:"nearest",min:"nearest"};function _(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function w(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(!function(t){t.read({x:0,y:0,width:1,height:1,data:y})}(t),r.drawCompleted=!0),function s(l){var c;c=Math.min(n,i-l*n),a.offset=p*l*n,a.count=p*c,0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete 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P(t){r=t.model,n=t.viewModel,p=n.dimensions.slice(),d=p[0]?p[0].values.length:0;var e=r.lines,i=T?e.color.map(function(t,r){return r/e.color.length}):e.color,a=Math.max(1/255,Math.pow(1/i.length,1/3)),o=function(t,e,r){for(var n,i=e.length,a=[],o=0;o<t;o++)for(var s=0;s<f;s++)a.push(s<i?e[s].paddedUnitValues[o]:s===f-1?(n=r[o],Math.max(c,Math.min(1-c,n))):s>=f-4?k(o,f-2-s):.5);return a}(d,p,i);!function(t,e,r){for(var n=0;n<16;n++)t["p"+n.toString(16)](M(e,r,n))}(C,d,o),L=S.texture(l.extendFlat({data:function(t,e,r){for(var n=[],i=0;i<256;i++){var a=t(i/255);n.push((e?v:a).concat(r))}return n}(r.unitToColor,A,Math.round(255*(A?a:1)))},b))}var I=[0,1];var O=[];function D(t,e,n,i,a,o,s,c,u,h,f){var p,d,g,m,v=[t,e],y=[0,1].map(function(){return[0,1,2,3].map(function(){return new Float32Array(16)})});for(p=0;p<2;p++)for(m=v[p],d=0;d<4;d++)for(g=0;g<16;g++)y[p][d][g]=g+16*d===m?1:0;var x=r.lines.canvasOverdrag,b=r.domain,_=r.canvasWidth,w=r.canvasHeight;return l.extendFlat({key:s,resolution:[_,w],viewBoxPosition:[n+x,i],viewBoxSize:[a,o],i:t,ii:e,dim1A:y[0][0],dim1B:y[0][1],dim1C:y[0][2],dim1D:y[0][3],dim2A:y[1][0],dim2B:y[1][1],dim2C:y[1][2],dim2D:y[1][3],colorClamp:I,scissorX:(c===u?0:n+x)+(r.pad.l-x)+r.layoutWidth*b.x[0],scissorWidth:(c===h?_-n+x:a+.5)+(c===u?n+x:0),scissorY:i+r.pad.b+r.layoutHeight*b.y[0],scissorHeight:o,viewportX:r.pad.l-x+r.layoutWidth*b.x[0],viewportY:r.pad.b+r.layoutHeight*b.y[0],viewportWidth:_,viewportHeight:w},f)}return{setColorDomain:function(t){I[0]=t[0],I[1]=t[1]},render:function(t,e,n){var i,a,o,s=t.length,l=1/0,c=-1/0;for(i=0;i<s;i++)t[i].dim2.canvasX>c&&(c=t[i].dim2.canvasX,o=i),t[i].dim1.canvasX<l&&(l=t[i].dim1.canvasX,a=i);0===s&&_(S,0,0,r.canvasWidth,r.canvasHeight);var f=A?{}:function(){var t,e,r,n=[0,1].map(function(){return[0,1,2,3].map(function(){return new Float32Array(16)})});for(t=0;t<2;t++)for(e=0;e<4;e++)for(r=0;r<16;r++){var 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1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),r}},{"../../components/fx/helpers":597}],995:[function(t,e,r){"use strict";var n=t("../../lib");r.formatPiePercent=function(t,e){var r=(100*t).toPrecision(3);return-1!==r.lastIndexOf(".")&&(r=r.replace(/[.]?0+$/,"")),n.numSeparate(r,e)+"%"},r.formatPieValue=function(t,e){var r=t.toPrecision(10);return-1!==r.lastIndexOf(".")&&(r=r.replace(/[.]?0+$/,"")),n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0}},{"../../lib":684}],996:[function(t,e,r){"use strict";var n={};n.attributes=t("./attributes"),n.supplyDefaults=t("./defaults"),n.supplyLayoutDefaults=t("./layout_defaults"),n.layoutAttributes=t("./layout_attributes"),n.calc=t("./calc"),n.plot=t("./plot"),n.style=t("./style"),n.styleOne=t("./style_one"),n.moduleType="trace",n.name="pie",n.basePlotModule=t("./base_plot"),n.categories=["pie","showLegend"],n.meta={},e.exports=n},{"./attributes":990,"./base_plot":991,"./calc":992,"./defaults":993,"./layout_attributes":997,"./layout_defaults":998,"./plot":999,"./style":1e3,"./style_one":1001}],997:[function(t,e,r){"use strict";e.exports={hiddenlabels:{valType:"data_array",editType:"calc"}}},{}],998:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./layout_attributes");e.exports=function(t,e){var r,a;r="hiddenlabels",n.coerce(t,e,i,r,a)}},{"../../lib":684,"./layout_attributes":997}],999:[function(t,e,r){"use strict";var n=t("d3"),i=t("../../components/fx"),a=t("../../components/color"),o=t("../../components/drawing"),s=t("../../lib"),l=t("../../lib/svg_text_utils"),c=t("./helpers"),u=t("./event_data");function h(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function f(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}e.exports=function(t,e){var r=t._fullLayout;!function(t,e){var r,n,i,a,o,s,l,c,u,h=[];for(i=0;i<t.length;i++){if(o=t[i][0],s=o.trace,r=e.w*(s.domain.x[1]-s.domain.x[0]),n=e.h*(s.domain.y[1]-s.domain.y[0]),l=s.pull,Array.isArray(l))for(l=0,a=0;a<s.pull.length;a++)s.pull[a]>l&&(l=s.pull[a]);o.r=Math.min(r,n)/(2+2*l),o.cx=e.l+e.w*(s.domain.x[1]+s.domain.x[0])/2,o.cy=e.t+e.h*(2-s.domain.y[1]-s.domain.y[0])/2,s.scalegroup&&-1===h.indexOf(s.scalegroup)&&h.push(s.scalegroup)}for(a=0;a<h.length;a++){for(u=1/0,c=h[a],i=0;i<t.length;i++)(o=t[i][0]).trace.scalegroup===c&&(u=Math.min(u,o.r*o.r/o.vTotal));for(i=0;i<t.length;i++)(o=t[i][0]).trace.scalegroup===c&&(o.r=Math.sqrt(u*o.vTotal))}}(e,r._size);var p=r._pielayer.selectAll("g.trace").data(e);p.enter().append("g").attr({"stroke-linejoin":"round",class:"trace"}),p.exit().remove(),p.order(),p.each(function(e){var p=n.select(this),d=e[0],g=d.trace;!function(t){var e,r,n,i=t[0],a=i.trace,o=a.rotation*Math.PI/180,s=2*Math.PI/i.vTotal,l="px0",c="px1";if("counterclockwise"===a.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;o+=s*t[e].v,s*=-1,l="px1",c="px0"}function u(t){return[i.r*Math.sin(t),-i.r*Math.cos(t)]}for(n=u(o),e=0;e<t.length;e++)(r=t[e]).hidden||(r[l]=n,o+=s*r.v/2,r.pxmid=u(o),r.midangle=o,o+=s*r.v/2,n=u(o),r[c]=n,r.largeArc=r.v>i.vTotal/2?1:0)}(e),p.each(function(){var p=n.select(this).selectAll("g.slice").data(e);p.enter().append("g").classed("slice",!0),p.exit().remove();var m=[[[],[]],[[],[]]],v=!1;p.each(function(e){if(e.hidden)n.select(this).selectAll("path,g").remove();else{e.pointNumber=e.i,e.curveNumber=g.index,m[e.pxmid[1]<0?0:1][e.pxmid[0]<0?0:1].push(e);var a=d.cx,p=d.cy,y=n.select(this),x=y.selectAll("path.surface").data([e]),b=!1,_=!1;if(x.enter().append("path").classed("surface",!0).style({"pointer-events":"all"}),y.select("path.textline").remove(),y.on("mouseover",function(){var o=t._fullLayout,s=t._fullData[g.index];if(!t._dragging&&!1!==o.hovermode){var l=s.hoverinfo;if(Array.isArray(l)&&(l=i.castHoverinfo({hoverinfo:[c.castOption(l,e.pts)],_module:g._module},o,0)),"all"===l&&(l="label+text+value+percent+name"),"none"!==l&&"skip"!==l&&l){var f=h(e,d),m=a+e.pxmid[0]*(1-f),v=p+e.pxmid[1]*(1-f),y=r.separators,x=[];if(-1!==l.indexOf("label")&&x.push(e.label),-1!==l.indexOf("text")){var w=c.castOption(s.hovertext||s.text,e.pts);w&&x.push(w)}-1!==l.indexOf("value")&&x.push(c.formatPieValue(e.v,y)),-1!==l.indexOf("percent")&&x.push(c.formatPiePercent(e.v/d.vTotal,y));var k=g.hoverlabel,M=k.font;i.loneHover({x0:m-f*d.r,x1:m+f*d.r,y:v,text:x.join("<br>"),name:-1!==l.indexOf("name")?s.name:void 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M="M"+(a+e.px0[0])+","+(p+e.px0[1])+C(e.px0,e.pxmid,!0,1)+C(e.pxmid,e.px0,!0,1)+"Z";k?x.attr("d","M"+(a+k*e.px0[0])+","+(p+k*e.px0[1])+C(e.px0,e.pxmid,!1,k)+C(e.pxmid,e.px0,!1,k)+"Z"+M):x.attr("d",M)}else{var A=C(e.px0,e.px1,!0,1);if(k){var T=1-k;x.attr("d","M"+(a+k*e.px1[0])+","+(p+k*e.px1[1])+C(e.px1,e.px0,!1,k)+"l"+T*e.px0[0]+","+T*e.px0[1]+A+"Z")}else x.attr("d","M"+a+","+p+"l"+e.px0[0]+","+e.px0[1]+A+"Z")}var S=c.castOption(g.textposition,e.pts),E=y.selectAll("g.slicetext").data(e.text&&"none"!==S?[0]:[]);E.enter().append("g").classed("slicetext",!0),E.exit().remove(),E.each(function(){var r=s.ensureSingle(n.select(this),"text","",function(t){t.attr("data-notex",1)});r.text(e.text).attr({class:"slicetext",transform:"","text-anchor":"middle"}).call(o.font,"outside"===S?g.outsidetextfont:g.insidetextfont).call(l.convertToTspans,t);var i,c=o.bBox(r.node());"outside"===S?i=f(c,e):(i=function(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),i=t.width/t.height,a=Math.PI*Math.min(e.v/r.vTotal,.5),o=1-r.trace.hole,s=h(e,r),l={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(l.scale>=1)return l;var c=i+1/(2*Math.tan(a)),u=r.r*Math.min(1/(Math.sqrt(c*c+.5)+c),o/(Math.sqrt(i*i+o/2)+i)),f={scale:2*u/t.height,rCenter:Math.cos(u/r.r)-u*i/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},p=1/i,d=p+1/(2*Math.tan(a)),g=r.r*Math.min(1/(Math.sqrt(d*d+.5)+d),o/(Math.sqrt(p*p+o/2)+p)),m={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/i/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},v=m.scale>f.scale?m:f;return l.scale<1&&v.scale>l.scale?v:l}(c,e,d),"auto"===S&&i.scale<1&&(r.call(o.font,g.outsidetextfont),g.outsidetextfont.family===g.insidetextfont.family&&g.outsidetextfont.size===g.insidetextfont.size||(c=o.bBox(r.node())),i=f(c,e)));var u=a+e.pxmid[0]*i.rCenter+(i.x||0),m=p+e.pxmid[1]*i.rCenter+(i.y||0);i.outside&&(e.yLabelMin=m-c.height/2,e.yLabelMid=m,e.yLabelMax=m+c.height/2,e.labelExtraX=0,e.labelExtraY=0,v=!0),r.attr("transform","translate("+u+","+m+")"+(i.scale<1?"scale("+i.scale+")":"")+(i.rotate?"rotate("+i.rotate+")":"")+"translate("+-(c.left+c.right)/2+","+-(c.top+c.bottom)/2+")")})}function C(t,r,n,i){return"a"+i*d.r+","+i*d.r+" 0 "+e.largeArc+(n?" 1 ":" 0 ")+i*(r[0]-t[0])+","+i*(r[1]-t[1])}}),v&&function(t,e){var r,n,i,a,o,s,l,u,h,f,p,d,g;function m(t,e){return t.pxmid[1]-e.pxmid[1]}function v(t,e){return e.pxmid[1]-t.pxmid[1]}function y(t,r){r||(r={});var i,u,h,p,d,g,m=r.labelExtraY+(n?r.yLabelMax:r.yLabelMin),v=n?t.yLabelMin:t.yLabelMax,y=n?t.yLabelMax:t.yLabelMin,x=t.cyFinal+o(t.px0[1],t.px1[1]),b=m-v;if(b*l>0&&(t.labelExtraY=b),Array.isArray(e.pull))for(u=0;u<f.length;u++)(h=f[u])===t||(c.castOption(e.pull,t.pts)||0)>=(c.castOption(e.pull,h.pts)||0)||((t.pxmid[1]-h.pxmid[1])*l>0?(p=h.cyFinal+o(h.px0[1],h.px1[1]),(b=p-v-t.labelExtraY)*l>0&&(t.labelExtraY+=b)):(y+t.labelExtraY-x)*l>0&&(i=3*s*Math.abs(u-f.indexOf(t)),d=h.cxFinal+a(h.px0[0],h.px1[0]),(g=d+i-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*s>0&&(t.labelExtraX+=g)))}for(n=0;n<2;n++)for(i=n?m:v,o=n?Math.max:Math.min,l=n?1:-1,r=0;r<2;r++){for(a=r?Math.max:Math.min,s=r?1:-1,(u=t[n][r]).sort(i),h=t[1-n][r],f=h.concat(u),d=[],p=0;p<u.length;p++)void 0!==u[p].yLabelMid&&d.push(u[p]);for(g=!1,p=0;n&&p<h.length;p++)if(void 0!==h[p].yLabelMid){g=h[p];break}for(p=0;p<d.length;p++){var x=p&&d[p-1];g&&!p&&(x=g),y(d[p],x)}}}(m,g),p.each(function(t){if(t.labelExtraX||t.labelExtraY){var e=n.select(this),r=e.select("g.slicetext text");r.attr("transform","translate("+t.labelExtraX+","+t.labelExtraY+")"+r.attr("transform"));var i=t.cxFinal+t.pxmid[0],o="M"+i+","+(t.cyFinal+t.pxmid[1]),s=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var l=t.labelExtraX*t.pxmid[1]/t.pxmid[0],c=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(l)>Math.abs(c)?o+="l"+c*t.pxmid[0]/t.pxmid[1]+","+c+"H"+(i+t.labelExtraX+s):o+="l"+t.labelExtraX+","+l+"v"+(c-l)+"h"+s}else o+="V"+(t.yLabelMid+t.labelExtraY)+"h"+s;e.append("path").classed("textline",!0).call(a.stroke,g.outsidetextfont.color).attr({"stroke-width":Math.min(2,g.outsidetextfont.size/8),d:o,fill:"none"})}})})}),setTimeout(function(){p.selectAll("tspan").each(function(){var t=n.select(this);t.attr("dy")&&t.attr("dy",t.attr("dy"))})},0)}},{"../../components/color":558,"../../components/drawing":583,"../../components/fx":600,"../../lib":684,"../../lib/svg_text_utils":708,"./event_data":994,"./helpers":995,d3:147}],1000:[function(t,e,r){"use strict";var n=t("d3"),i=t("./style_one");e.exports=function(t){t._fullLayout._pielayer.selectAll(".trace").each(function(t){var e=t[0].trace,r=n.select(this);r.style({opacity:e.opacity}),r.selectAll("path.surface").each(function(t){n.select(this).call(i,t,e)})})}},{"./style_one":1001,d3:147}],1001:[function(t,e,r){"use strict";var n=t("../../components/color"),i=t("./helpers").castOption;e.exports=function(t,e,r){var a=r.marker.line,o=i(a.color,e.pts)||n.defaultLine,s=i(a.width,e.pts)||0;t.style({"stroke-width":s}).call(n.fill,e.color).call(n.stroke,o)}},{"../../components/color":558,"./helpers":995}],1002:[function(t,e,r){"use strict";var n=t("../scatter/attributes");e.exports={x:n.x,y:n.y,xy:{valType:"data_array",editType:"calc"},indices:{valType:"data_array",editType:"calc"},xbounds:{valType:"data_array",editType:"calc"},ybounds:{valType:"data_array",editType:"calc"},text:n.text,marker:{color:{valType:"color",arrayOk:!1,editType:"calc"},opacity:{valType:"number",min:0,max:1,dflt:1,arrayOk:!1,editType:"calc"},blend:{valType:"boolean",dflt:null,editType:"calc"},sizemin:{valType:"number",min:.1,max:2,dflt:.5,editType:"calc"},sizemax:{valType:"number",min:.1,dflt:20,editType:"calc"},border:{color:{valType:"color",arrayOk:!1,editType:"calc"},arearatio:{valType:"number",min:0,max:1,dflt:0,editType:"calc"},editType:"calc"},editType:"calc"}}},{"../scatter/attributes":1015}],1003:[function(t,e,r){"use strict";var n=t("gl-pointcloud2d"),i=t("../../lib/str2rgbarray"),a=t("../../plots/cartesian/autorange").expand,o=t("../scatter/get_trace_color");function s(t,e){this.scene=t,this.uid=e,this.type="pointcloud",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color="rgb(0, 0, 0)",this.name="",this.hoverinfo="all",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=n(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}var l=s.prototype;l.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*e],this.pickXYData[2*e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:Array.isArray(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},l.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=o(t,{})},l.updateFast=function(t){var e,r,n,a,o,s,l=this.xData=this.pickXData=t.x,c=this.yData=this.pickYData=t.y,u=this.pickXYData=t.xy,h=t.xbounds&&t.ybounds,f=t.indices,p=this.bounds;if(u){if(n=u,e=u.length>>>1,h)p[0]=t.xbounds[0],p[2]=t.xbounds[1],p[1]=t.ybounds[0],p[3]=t.ybounds[1];else for(s=0;s<e;s++)a=n[2*s],o=n[2*s+1],a<p[0]&&(p[0]=a),a>p[2]&&(p[2]=a),o<p[1]&&(p[1]=o),o>p[3]&&(p[3]=o);if(f)r=f;else for(r=new Int32Array(e),s=0;s<e;s++)r[s]=s}else for(e=l.length,n=new Float32Array(2*e),r=new Int32Array(e),s=0;s<e;s++)a=l[s],o=c[s],r[s]=s,n[2*s]=a,n[2*s+1]=o,a<p[0]&&(p[0]=a),a>p[2]&&(p[2]=a),o<p[1]&&(p[1]=o),o>p[3]&&(p[3]=o);this.idToIndex=r,this.pointcloudOptions.idToIndex=r,this.pointcloudOptions.positions=n;var d=i(t.marker.color),g=i(t.marker.border.color),m=t.opacity*t.marker.opacity;d[3]*=m,this.pointcloudOptions.color=d;var v=t.marker.blend;if(null===v){v=l.length<100||c.length<100}this.pointcloudOptions.blend=v,g[3]*=m,this.pointcloudOptions.borderColor=g;var y=t.marker.sizemin,x=Math.max(t.marker.sizemax,t.marker.sizemin);this.pointcloudOptions.sizeMin=y,this.pointcloudOptions.sizeMax=x,this.pointcloudOptions.areaRatio=t.marker.border.arearatio,this.pointcloud.update(this.pointcloudOptions),this.expandAxesFast(p,x/2)},l.expandAxesFast=function(t,e){var r=e||.5;a(this.scene.xaxis,[t[0],t[2]],{ppad:r}),a(this.scene.yaxis,[t[1],t[3]],{ppad:r})},l.dispose=function(){this.pointcloud.dispose()},e.exports=function(t,e){var r=new s(t,e.uid);return r.update(e),r}},{"../../lib/str2rgbarray":707,"../../plots/cartesian/autorange":731,"../scatter/get_trace_color":1024,"gl-pointcloud2d":279}],1004:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./attributes");e.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}a("x"),a("y"),a("xbounds"),a("ybounds"),t.xy&&t.xy instanceof Float32Array&&(e.xy=t.xy),t.indices&&t.indices instanceof Int32Array&&(e.indices=t.indices),a("text"),a("marker.color",r),a("marker.opacity"),a("marker.blend"),a("marker.sizemin"),a("marker.sizemax"),a("marker.border.color",r),a("marker.border.arearatio"),e._length=null}},{"../../lib":684,"./attributes":1002}],1005:[function(t,e,r){"use strict";var n={};n.attributes=t("./attributes"),n.supplyDefaults=t("./defaults"),n.calc=t("../scatter3d/calc"),n.plot=t("./convert"),n.moduleType="trace",n.name="pointcloud",n.basePlotModule=t("../../plots/gl2d"),n.categories=["gl","gl2d","showLegend"],n.meta={},e.exports=n},{"../../plots/gl2d":771,"../scatter3d/calc":1041,"./attributes":1002,"./convert":1003,"./defaults":1004}],1006:[function(t,e,r){"use strict";var n=t("../../plots/font_attributes"),i=t("../../plots/attributes"),a=t("../../components/color/attributes"),o=t("../../components/fx/attributes"),s=t("../../plots/domain").attributes,l=t("../../lib/extend").extendFlat,c=t("../../plot_api/edit_types").overrideAll;e.exports=c({hoverinfo:l({},i.hoverinfo,{flags:["label","text","value","percent","name"]}),hoverlabel:o.hoverlabel,domain:s({name:"sankey",trace:!0}),orientation:{valType:"enumerated",values:["v","h"],dflt:"h"},valueformat:{valType:"string",dflt:".3s"},valuesuffix:{valType:"string",dflt:""},arrangement:{valType:"enumerated",values:["snap","perpendicular","freeform","fixed"],dflt:"snap"},textfont:n({}),node:{label:{valType:"data_array",dflt:[]},color:{valType:"color",arrayOk:!0},line:{color:{valType:"color",dflt:a.defaultLine,arrayOk:!0},width:{valType:"number",min:0,dflt:.5,arrayOk:!0}},pad:{valType:"number",arrayOk:!1,min:0,dflt:20},thickness:{valType:"number",arrayOk:!1,min:1,dflt:20}},link:{label:{valType:"data_array",dflt:[]},color:{valType:"color",arrayOk:!0},line:{color:{valType:"color",dflt:a.defaultLine,arrayOk:!0},width:{valType:"number",min:0,dflt:0,arrayOk:!0}},source:{valType:"data_array",dflt:[]},target:{valType:"data_array",dflt:[]},value:{valType:"data_array",dflt:[]}}},"calc","nested")},{"../../components/color/attributes":557,"../../components/fx/attributes":592,"../../lib/extend":673,"../../plot_api/edit_types":715,"../../plots/attributes":729,"../../plots/domain":757,"../../plots/font_attributes":758}],1007:[function(t,e,r){"use 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n={};n.attributes=t("./attributes"),n.supplyDefaults=t("./defaults"),n.calc=t("./calc"),n.plot=t("./plot"),n.moduleType="trace",n.name="sankey",n.basePlotModule=t("./base_plot"),n.categories=["noOpacity"],n.meta={},e.exports=n},{"./attributes":1006,"./base_plot":1007,"./calc":1008,"./defaults":1010,"./plot":1012}],1012:[function(t,e,r){"use strict";var n=t("d3"),i=t("./render"),a=t("../../components/fx"),o=t("../../components/color"),s=t("../../lib"),l=t("./constants").cn,c=s._;function u(t){return""!==t}function h(t,e){return t.filter(function(t){return t.key===e.traceId})}function f(t,e){n.select(t).select("path").style("fill-opacity",e),n.select(t).select("rect").style("fill-opacity",e)}function p(t){n.select(t).select("text.name").style("fill","black")}function d(t){return function(e){return-1!==t.node.sourceLinks.indexOf(e.link)||-1!==t.node.targetLinks.indexOf(e.link)}}function g(t){return 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s=i.link.trace,l=t._fullLayout._paperdiv.node().getBoundingClientRect(),c=e.getBoundingClientRect(),h=c.left+c.width/2,m=c.top+c.height/2,v=a.loneHover({x:h-l.left,y:m-l.top,name:n.format(i.valueFormat)(i.link.value)+i.valueSuffix,text:[i.link.label||"",d+i.link.source.label,g+i.link.target.label].filter(u).join("<br>"),color:b(s,"bgcolor")||o.addOpacity(i.tinyColorHue,1),borderColor:b(s,"bordercolor"),fontFamily:b(s,"font.family"),fontSize:b(s,"font.size"),fontColor:b(s,"font.color"),idealAlign:n.event.x<h?"right":"left"},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t});f(v,.65),p(v)},unhover:function(e,i,o){n.select(e).call(x.bind(0,i,o,!0)),t.emit("plotly_unhover",{event:n.event,points:[i.link]}),a.loneUnhover(r._hoverlayer.node())},select:function(e,r){var 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x(e),r}function w(t){t.call(b)}function k(t,e){t.call(w),e.attr("d",_)}function M(t){t.attr("width",function(t){return t.visibleWidth}).attr("height",function(t){return t.visibleHeight})}function A(t){return t.link.dy>1||t.linkLineWidth>0}function T(t){return"translate("+t.translateX+","+t.translateY+")"+(t.horizontal?"matrix(1 0 0 1 0 0)":"matrix(0 1 1 0 0 0)")}function S(t){return"translate("+(t.horizontal?0:t.labelY)+" "+(t.horizontal?t.labelY:0)+")"}function E(t){return i.svg.line()([[t.horizontal?t.left?-t.sizeAcross:t.visibleWidth+n.nodeTextOffsetHorizontal:n.nodeTextOffsetHorizontal,0],[t.horizontal?t.left?-n.nodeTextOffsetHorizontal:t.sizeAcross:t.visibleHeight-n.nodeTextOffsetHorizontal,0]])}function C(t){return t.horizontal?"matrix(1 0 0 1 0 0)":"matrix(0 1 1 0 0 0)"}function L(t){return t.horizontal?"scale(1 1)":"scale(-1 1)"}function z(t){return t.darkBackground&&!t.horizontal?"rgb(255,255,255)":"rgb(0,0,0)"}function P(t){return t.horizontal&&t.left?"100%":"0%"}function 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a=i.traceId+"|"+Math.floor(i.node.originalX);i.forceLayouts[a]?i.forceLayouts[a].alpha(1):function(t,e,r){var i=r.sankey.nodes().filter(function(t){return t.originalX===r.node.originalX});r.forceLayouts[e]=c.forceSimulation(i).alphaDecay(0).force("collide",c.forceCollide().radius(function(t){return t.dy/2+r.nodePad/2}).strength(1).iterations(n.forceIterations)).force("constrain",function(t,e,r,i){return function(){for(var t=0,a=0;a<r.length;a++){var o=r[a];o===i.interactionState.dragInProgress?(o.x=o.lastDraggedX,o.y=o.lastDraggedY):(o.vx=(o.originalX-o.x)/n.forceTicksPerFrame,o.y=Math.min(i.size-o.dy/2,Math.max(o.dy/2,o.y))),t=Math.max(t,Math.abs(o.vx),Math.abs(o.vy))}!i.interactionState.dragInProgress&&t<.1&&i.forceLayouts[e].alpha()>0&&i.forceLayouts[e].alpha(0)}}(0,e,i,r)).stop()}(0,a,i),function(t,e,r,i){window.requestAnimationFrame(function a(){for(var 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t.tinyColorAlpha}),p.transition().ease(n.ease).duration(n.duration).attr("d",_),p.exit().transition().ease(n.ease).duration(n.duration).style("opacity",0).remove();var v=c.selectAll("."+n.cn.sankeyNodeSet).data(g,d);v.enter().append("g").classed(n.cn.sankeyNodeSet,!0),v.style("cursor",function(t){switch(t.arrangement){case"fixed":return"default";case"perpendicular":return"ns-resize";default:return"move"}});var y=v.selectAll("."+n.cn.sankeyNode).data(function(t){var e=t.sankey.nodes();return function(t){var e,r=[];for(e=0;e<t.length;e++)t[e].originalX=t[e].x,t[e].originalY=t[e].y,-1===r.indexOf(t[e].x)&&r.push(t[e].x);for(r.sort(function(t,e){return t-e}),e=0;e<t.length;e++)t[e].originalLayerIndex=r.indexOf(t[e].originalX),t[e].originalLayer=t[e].originalLayerIndex/(r.length-1)}(e),e.filter(function(t){return t.value}).map(function(t,e,r){var i=a(r.color),s=n.nodePadAcross,l=e.nodePad/2,c=r.dx,u=Math.max(.5,r.dy),h=r.label,f=t[h];t[h]=(f||0)+1;var p=h+"__"+t[h];return 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w=y.selectAll("."+n.cn.nodeRect).data(g);w.enter().append("rect").classed(n.cn.nodeRect,!0).call(M),w.style("stroke-width",function(t){return t.nodeLineWidth}).style("stroke",function(t){return o.tinyRGB(a(t.nodeLineColor))}).style("stroke-opacity",function(t){return o.opacity(t.nodeLineColor)}).style("fill",function(t){return t.tinyColorHue}).style("fill-opacity",function(t){return t.tinyColorAlpha}),w.transition().ease(n.ease).duration(n.duration).call(M);var k=y.selectAll("."+n.cn.nodeCapture).data(g);k.enter().append("rect").classed(n.cn.nodeCapture,!0).style("fill-opacity",0),k.attr("x",function(t){return t.zoneX}).attr("y",function(t){return t.zoneY}).attr("width",function(t){return t.zoneWidth}).attr("height",function(t){return t.zoneHeight});var D=y.selectAll("."+n.cn.nodeCentered).data(g);D.enter().append("g").classed(n.cn.nodeCentered,!0).attr("transform",S),D.transition().ease(n.ease).duration(n.duration).attr("transform",S);var 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n=t("../../components/colorscale/attributes"),i=t("../../components/colorbar/attributes"),a=t("../../plots/font_attributes"),o=t("../../components/drawing/attributes").dash,s=t("../../components/drawing"),l=(t("./constants"),t("../../lib/extend").extendFlat);e.exports={x:{valType:"data_array",editType:"calc+clearAxisTypes"},x0:{valType:"any",dflt:0,editType:"calc+clearAxisTypes"},dx:{valType:"number",dflt:1,editType:"calc"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},y0:{valType:"any",dflt:0,editType:"calc+clearAxisTypes"},dy:{valType:"number",dflt:1,editType:"calc"},text:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},hovertext:{valType:"string",dflt:"",arrayOk:!0,editType:"style"},mode:{valType:"flaglist",flags:["lines","markers","text"],extras:["none"],editType:"calc"},hoveron:{valType:"flaglist",flags:["points","fills"],editType:"style"},line:{color:{valType:"color",editType:"style"},width:{valType:"number",min:0,dflt:2,editType:"style"},shape:{valType:"enumerated",values:["linear","spline","hv","vh","hvh","vhv"],dflt:"linear",editType:"plot"},smoothing:{valType:"number",min:0,max:1.3,dflt:1,editType:"plot"},dash:l({},o,{editType:"style"}),simplify:{valType:"boolean",dflt:!0,editType:"plot"},editType:"plot"},connectgaps:{valType:"boolean",dflt:!1,editType:"calc"},cliponaxis:{valType:"boolean",dflt:!0,editType:"plot"},fill:{valType:"enumerated",values:["none","tozeroy","tozerox","tonexty","tonextx","toself","tonext"],dflt:"none",editType:"calc"},fillcolor:{valType:"color",editType:"style"},marker:l({symbol:{valType:"enumerated",values:s.symbolList,dflt:"circle",arrayOk:!0,editType:"style"},opacity:{valType:"number",min:0,max:1,arrayOk:!0,editType:"style"},size:{valType:"number",min:0,dflt:6,arrayOk:!0,editType:"calcIfAutorange"},maxdisplayed:{valType:"number",min:0,dflt:0,editType:"plot"},sizeref:{valType:"number",dflt:1,editType:"calc"},sizemin:{valType:"number",min:0,dflt:0,editType:"calc"},sizemode:{valType:"enumerated",values:["diameter","area"],dflt:"diameter",editType:"calc"},colorbar:i,line:l({width:{valType:"number",min:0,arrayOk:!0,editType:"style"},editType:"calc"},n("marker.line")),gradient:{type:{valType:"enumerated",values:["radial","horizontal","vertical","none"],arrayOk:!0,dflt:"none",editType:"calc"},color:{valType:"color",arrayOk:!0,editType:"calc"},editType:"calc"},editType:"calc"},n("marker")),selected:{marker:{opacity:{valType:"number",min:0,max:1,editType:"style"},color:{valType:"color",editType:"style"},size:{valType:"number",min:0,editType:"style"},editType:"style"},textfont:{color:{valType:"color",editType:"style"},editType:"style"},editType:"style"},unselected:{marker:{opacity:{valType:"number",min:0,max:1,editType:"style"},color:{valType:"color",editType:"style"},size:{valType:"number",min:0,editType:"style"},editType:"style"},textfont:{color:{valType:"color",editType:"style"},editType:"style"},editType:"style"},textposition:{valType:"enumerated",values:["top 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u={padded:!0},h={padded:!0};l&&(u.ppad=h.ppad=l),!("tozerox"===e.fill||"tonextx"===e.fill&&t.firstscatter)||i[0]===i[c-1]&&o[0]===o[c-1]?(e.error_y||{}).visible||-1===["tonexty","tozeroy"].indexOf(e.fill)&&(s.hasMarkers(e)||s.hasText(e))||(u.padded=!1,u.ppad=0):u.tozero=!0,!("tozeroy"===e.fill||"tonexty"===e.fill&&t.firstscatter)||i[0]===i[c-1]&&o[0]===o[c-1]?-1!==["tonextx","tozerox"].indexOf(e.fill)&&(h.padded=!1):h.tozero=!0,a.expand(r,i,u),a.expand(n,o,h)}function f(t,e){if(s.hasMarkers(t)){var r,n=t.marker,o=1.6*(t.marker.sizeref||1);if(r="area"===t.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/o),3)}:function(t){return Math.max((t||0)/o,3)},i(n.size)){var l={type:"linear"};a.setConvert(l);for(var c=l.makeCalcdata(t.marker,"size"),u=new Array(e),h=0;h<e;h++)u[h]=r(c[h]);return u}return r(n.size)}}e.exports={calc:function(t,e){var r=a.getFromId(t,e.xaxis||"x"),i=a.getFromId(t,e.yaxis||"y"),s=r.makeCalcdata(e,"x"),p=i.makeCalcdata(e,"y"),d=e._length,g=new 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n=t("../../components/colorscale/has_colorscale"),i=t("../../components/colorscale/calc"),a=t("./subtypes");e.exports=function(t){a.hasLines(t)&&n(t,"line")&&i(t,t.line.color,"line","c"),a.hasMarkers(t)&&(n(t,"marker")&&i(t,t.marker.color,"marker","c"),n(t,"marker.line")&&i(t,t.marker.line.color,"marker.line","c"))}},{"../../components/colorscale/calc":566,"../../components/colorscale/has_colorscale":572,"./subtypes":1037}],1020:[function(t,e,r){"use strict";e.exports={PTS_LINESONLY:20,minTolerance:.2,toleranceGrowth:10,maxScreensAway:20}},{}],1021:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../registry"),a=t("./attributes"),o=t("./constants"),s=t("./subtypes"),l=t("./xy_defaults"),c=t("./marker_defaults"),u=t("./line_defaults"),h=t("./line_shape_defaults"),f=t("./text_defaults"),p=t("./fillcolor_defaults");e.exports=function(t,e,r,d){function g(r,i){return n.coerce(t,e,a,r,i)}var m=l(t,e,d,g),v=m<o.PTS_LINESONLY?"lines+markers":"lines";if(m){g("text"),g("hovertext"),g("mode",v),s.hasLines(e)&&(u(t,e,r,d,g),h(t,e,g),g("connectgaps"),g("line.simplify")),s.hasMarkers(e)&&c(t,e,r,d,g,{gradient:!0}),s.hasText(e)&&f(t,e,d,g);var y=[];(s.hasMarkers(e)||s.hasText(e))&&(g("cliponaxis"),g("marker.maxdisplayed"),y.push("points")),g("fill"),"none"!==e.fill&&(p(t,e,r,g),s.hasLines(e)||h(t,e,g)),"tonext"!==e.fill&&"toself"!==e.fill||y.push("fills"),g("hoveron",y.join("+")||"points");var x=i.getComponentMethod("errorbars","supplyDefaults");x(t,e,r,{axis:"y"}),x(t,e,r,{axis:"x",inherit:"y"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},{"../../lib":684,"../../registry":817,"./attributes":1015,"./constants":1020,"./fillcolor_defaults":1023,"./line_defaults":1027,"./line_shape_defaults":1029,"./marker_defaults":1033,"./subtypes":1037,"./text_defaults":1038,"./xy_defaults":1039}],1022:[function(t,e,r){"use strict";var n=t("../../lib");function i(t){return t||0===t}e.exports=function(t,e,r){var a=Array.isArray(r)?function(t){r.push(t)}:function(t){r.text=t},o=n.extractOption(t,e,"htx","hovertext");if(i(o))return a(o);var s=n.extractOption(t,e,"tx","text");return i(s)?a(s):void 0}},{"../../lib":684}],1023:[function(t,e,r){"use strict";var n=t("../../components/color"),i=t("../../lib").isArrayOrTypedArray;e.exports=function(t,e,r,a){var o=!1;if(e.marker){var s=e.marker.color,l=(e.marker.line||{}).color;s&&!i(s)?o=s:l&&!i(l)&&(o=l)}a("fillcolor",n.addOpacity((e.line||{}).color||o||r,.5))}},{"../../components/color":558,"../../lib":684}],1024:[function(t,e,r){"use strict";var n=t("../../components/color"),i=t("./subtypes");e.exports=function(t,e){var r,a;if("lines"===t.mode)return(r=t.line.color)&&n.opacity(r)?r:t.fillcolor;if("none"===t.mode)return t.fill?t.fillcolor:"";var o=e.mcc||(t.marker||{}).color,s=e.mlcc||((t.marker||{}).line||{}).color;return(a=o&&n.opacity(o)?o:s&&n.opacity(s)&&(e.mlw||((t.marker||{}).line||{}).width)?s:"")?n.opacity(a)<.3?n.addOpacity(a,.3):a:(r=(t.line||{}).color)&&n.opacity(r)&&i.hasLines(t)&&t.line.width?r:t.fillcolor}},{"../../components/color":558,"./subtypes":1037}],1025:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/fx"),a=t("../../registry"),o=t("./get_trace_color"),s=t("../../components/color"),l=t("./fill_hover_text");e.exports=function(t,e,r,c){var u=t.cd,h=u[0].trace,f=t.xa,p=t.ya,d=f.c2p(e),g=p.c2p(r),m=[d,g],v=h.hoveron||"",y=-1!==h.mode.indexOf("markers")?3:.5;if(-1!==v.indexOf("points")){var x=function(t){var e=Math.max(y,t.mrc||0),r=f.c2p(t.x)-d,n=p.c2p(t.y)-g;return Math.max(Math.sqrt(r*r+n*n)-e,1-y/e)},b=i.getDistanceFunction(c,function(t){var e=Math.max(3,t.mrc||0),r=1-1/e,n=Math.abs(f.c2p(t.x)-d);return n<e?r*n/e:n-e+r},function(t){var e=Math.max(3,t.mrc||0),r=1-1/e,n=Math.abs(p.c2p(t.y)-g);return n<e?r*n/e:n-e+r},x);if(i.getClosest(u,b,t),!1!==t.index){var _=u[t.index],w=f.c2p(_.x,!0),k=p.c2p(_.y,!0),M=_.mrc||1;return n.extendFlat(t,{color:o(h,_),x0:w-M,x1:w+M,xLabelVal:_.x,y0:k-M,y1:k+M,yLabelVal:_.y,spikeDistance:x(_)}),l(_,h,t),a.getComponentMethod("errorbars","hoverInfo")(_,h,t),[t]}}if(-1!==v.indexOf("fills")&&h._polygons){var A,T,S,E,C,L,z,P,I,O=h._polygons,D=[],R=!1,B=1/0,F=-1/0,N=1/0,j=-1/0;for(A=0;A<O.length;A++)(S=O[A]).contains(m)&&(R=!R,D.push(S),N=Math.min(N,S.ymin),j=Math.max(j,S.ymax));if(R){var V=((N=Math.max(N,0))+(j=Math.min(j,p._length)))/2;for(A=0;A<D.length;A++)for(E=D[A].pts,T=1;T<E.length;T++)(P=E[T-1][1])>V!=(I=E[T][1])>=V&&(L=E[T-1][0],z=E[T][0],I-P&&(C=L+(z-L)*(V-P)/(I-P),B=Math.min(B,C),F=Math.max(F,C)));B=Math.max(B,0),F=Math.min(F,f._length);var U=s.defaultLine;return s.opacity(h.fillcolor)?U=h.fillcolor:s.opacity((h.line||{}).color)&&(U=h.line.color),n.extendFlat(t,{distance:t.maxHoverDistance,x0:B,x1:F,y0:V,y1:V,color:U}),delete t.index,h.text&&!Array.isArray(h.text)?t.text=String(h.text):t.text=h.name,[t]}}}},{"../../components/color":558,"../../components/fx":600,"../../lib":684,"../../registry":817,"./fill_hover_text":1022,"./get_trace_color":1024}],1026:[function(t,e,r){"use strict";var n={},i=t("./subtypes");n.hasLines=i.hasLines,n.hasMarkers=i.hasMarkers,n.hasText=i.hasText,n.isBubble=i.isBubble,n.attributes=t("./attributes"),n.supplyDefaults=t("./defaults"),n.cleanData=t("./clean_data"),n.calc=t("./calc").calc,n.arraysToCalcdata=t("./arrays_to_calcdata"),n.plot=t("./plot"),n.colorbar=t("./marker_colorbar"),n.style=t("./style").style,n.styleOnSelect=t("./style").styleOnSelect,n.hoverPoints=t("./hover"),n.selectPoints=t("./select"),n.animatable=!0,n.moduleType="trace",n.name="scatter",n.basePlotModule=t("../../plots/cartesian"),n.categories=["cartesian","svg","symbols","errorBarsOK","showLegend","scatter-like","zoomScale"],n.meta={},e.exports=n},{"../../plots/cartesian":743,"./arrays_to_calcdata":1014,"./attributes":1015,"./calc":1016,"./clean_data":1018,"./defaults":1021,"./hover":1025,"./marker_colorbar":1032,"./plot":1034,"./select":1035,"./style":1036,"./subtypes":1037}],1027:[function(t,e,r){"use strict";var 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r=[i(t.x,t.error_x,e[0]),i(t.y,t.error_y,e[1]),i(t.z,t.error_z,e[2])],n=function(t){for(var e=0;e<t.length;e++)if(t[e])return t[e].length;return 0}(r);if(0===n)return null;for(var a=new Array(n),o=0;o<n;o++){for(var s=[[0,0,0],[0,0,0]],l=0;l<3;l++)if(r[l])for(var c=0;c<2;c++)s[c][l]=r[l][o][c];a[o]=s}return a}},{"../../registry":817}],1043:[function(t,e,r){"use strict";var n=t("gl-line3d"),i=t("gl-scatter3d"),a=t("gl-error3d"),o=t("gl-mesh3d"),s=t("delaunay-triangulate"),l=t("../../lib"),c=t("../../lib/str2rgbarray"),u=t("../../lib/gl_format_color").formatColor,h=t("../scatter/make_bubble_size_func"),f=t("../../constants/gl3d_dashes"),p=t("../../constants/gl3d_markers"),d=t("./calc_errors");function g(t,e){this.scene=t,this.uid=e,this.linePlot=null,this.scatterPlot=null,this.errorBars=null,this.textMarkers=null,this.delaunayMesh=null,this.color=null,this.mode="",this.dataPoints=[],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.textLabels=null,this.data=null}var m=g.prototype;function v(t,e){return e(4*t)}function y(t){return p[t]}function x(t,e,r,n,i){var a=null;if(l.isArrayOrTypedArray(t)){a=[];for(var o=0;o<e;o++)void 0===t[o]?a[o]=n:a[o]=r(t[o],i)}else a=r(t,l.identity);return a}function b(t,e){var r,n,i,a,o,s,f,p,g=[],m=t.fullSceneLayout,b=t.dataScale,_=m.xaxis,w=m.yaxis,k=m.zaxis,M=e.marker,A=e.line,T=e.x||[],S=e.y||[],E=e.z||[],C=T.length,L=e.xcalendar,z=e.ycalendar,P=e.zcalendar;for(n=0;n<C;n++)i=_.d2l(T[n],0,L)*b[0],a=w.d2l(S[n],0,z)*b[1],o=k.d2l(E[n],0,P)*b[2],g[n]=[i,a,o];if(Array.isArray(e.text))s=e.text;else if(void 0!==e.text)for(s=new Array(C),n=0;n<C;n++)s[n]=e.text;if(r={position:g,mode:e.mode,text:s},"line"in e&&(r.lineColor=u(A,1,C),r.lineWidth=A.width,r.lineDashes=A.dash),"marker"in e){var I=h(e);r.scatterColor=u(M,1,C),r.scatterSize=x(M.size,C,v,20,I),r.scatterMarker=x(M.symbol,C,y,"\u25cf"),r.scatterLineWidth=M.line.width,r.scatterLineColor=u(M.line,1,C),r.scatterAngle=0}"textposition"in 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e=t[0];return Array.isArray(e)&&(t=e),"rgb("+t.slice(0,3).map(function(t){return Math.round(255*t)})+")"}return null}m.handlePick=function(t){if(t.object&&(t.object===this.linePlot||t.object===this.delaunayMesh||t.object===this.textMarkers||t.object===this.scatterPlot)){var e=t.index=t.data.index;return t.object.highlight&&t.object.highlight(null),this.scatterPlot&&(t.object=this.scatterPlot,this.scatterPlot.highlight(t.data)),t.textLabel="",this.textLabels&&(Array.isArray(this.textLabels)?(this.textLabels[e]||0===this.textLabels[e])&&(t.textLabel=this.textLabels[e]):t.textLabel=this.textLabels),t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]],!0}},m.update=function(t){var e,r,l,c,u=this.scene.glplot.gl,h=f.solid;this.data=t;var p=b(this.scene,t);"mode"in p&&(this.mode=p.mode),"lineDashes"in p&&p.lineDashes in f&&(h=f[p.lineDashes]),this.color=_(p.scatterColor)||_(p.lineColor),this.dataPoints=p.position,e={gl:u,position:p.position,color:p.lineColor,lineWidth:p.lineWidth||1,dashes:h[0],dashScale:h[1],opacity:t.opacity,connectGaps:t.connectgaps},-1!==this.mode.indexOf("lines")?this.linePlot?this.linePlot.update(e):(this.linePlot=n(e),this.linePlot._trace=this,this.scene.glplot.add(this.linePlot)):this.linePlot&&(this.scene.glplot.remove(this.linePlot),this.linePlot.dispose(),this.linePlot=null);var d=t.opacity;if(t.marker&&t.marker.opacity&&(d*=t.marker.opacity),r={gl:u,position:p.position,color:p.scatterColor,size:p.scatterSize,glyph:p.scatterMarker,opacity:d,orthographic:!0,lineWidth:p.scatterLineWidth,lineColor:p.scatterLineColor,project:p.project,projectScale:p.projectScale,projectOpacity:p.projectOpacity},-1!==this.mode.indexOf("markers")?this.scatterPlot?this.scatterPlot.update(r):(this.scatterPlot=i(r),this.scatterPlot._trace=this,this.scatterPlot.highlightScale=1,this.scene.glplot.add(this.scatterPlot)):this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose(),this.scatterPlot=null),c={gl:u,position:p.position,glyph:p.text,color:p.textColor,size:p.textSize,angle:p.textAngle,alignment:p.textOffset,font:p.textFont,orthographic:!0,lineWidth:0,project:!1,opacity:t.opacity},this.textLabels=t.hovertext||t.text,-1!==this.mode.indexOf("text")?this.textMarkers?this.textMarkers.update(c):(this.textMarkers=i(c),this.textMarkers._trace=this,this.textMarkers.highlightScale=1,this.scene.glplot.add(this.textMarkers)):this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose(),this.textMarkers=null),l={gl:u,position:p.position,color:p.errorColor,error:p.errorBounds,lineWidth:p.errorLineWidth,capSize:p.errorCapSize,opacity:t.opacity},this.errorBars?p.errorBounds?this.errorBars.update(l):(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose(),this.errorBars=null):p.errorBounds&&(this.errorBars=a(l),this.errorBars._trace=this,this.scene.glplot.add(this.errorBars)),p.delaunayAxis>=0){var g=function(t,e,r){var n,i=(r+1)%3,a=(r+2)%3,o=[],l=[];for(n=0;n<t.length;++n){var c=t[n];!isNaN(c[i])&&isFinite(c[i])&&!isNaN(c[a])&&isFinite(c[a])&&(o.push([c[i],c[a]]),l.push(n))}var u=s(o);for(n=0;n<u.length;++n)for(var h=u[n],f=0;f<h.length;++f)h[f]=l[h[f]];return{positions:t,cells:u,meshColor:e}}(p.position,p.delaunayColor,p.delaunayAxis);g.opacity=t.opacity,this.delaunayMesh?this.delaunayMesh.update(g):(g.gl=u,this.delaunayMesh=o(g),this.delaunayMesh._trace=this,this.scene.glplot.add(this.delaunayMesh))}else 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n=t("../../registry"),i=t("../../lib"),a=t("../scatter/subtypes"),o=t("../scatter/marker_defaults"),s=t("../scatter/line_defaults"),l=t("../scatter/text_defaults"),c=t("./attributes");e.exports=function(t,e,r,u){function h(r,n){return i.coerce(t,e,c,r,n)}if(function(t,e,r,i){var a=0,o=r("x"),s=r("y"),l=r("z");n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],i),o&&s&&l&&(a=Math.min(o.length,s.length,l.length),e._length=e._xlength=e._ylength=e._zlength=a);return a}(t,e,h,u)){h("text"),h("hovertext"),h("mode"),a.hasLines(e)&&(h("connectgaps"),s(t,e,r,u,h)),a.hasMarkers(e)&&o(t,e,r,u,h,{noSelect:!0}),a.hasText(e)&&l(t,e,u,h,{noSelect:!0});var f=(e.line||{}).color,p=(e.marker||{}).color;h("surfaceaxis")>=0&&h("surfacecolor",f||p);for(var d=["x","y","z"],g=0;g<3;++g){var m="projection."+d[g];h(m+".show")&&(h(m+".opacity"),h(m+".scale"))}var v=n.getComponentMethod("errorbars","supplyDefaults");v(t,e,r,{axis:"z"}),v(t,e,r,{axis:"y",inherit:"z"}),v(t,e,r,{axis:"x",inherit:"z"})}else e.visible=!1}},{"../../lib":684,"../../registry":817,"../scatter/line_defaults":1027,"../scatter/marker_defaults":1033,"../scatter/subtypes":1037,"../scatter/text_defaults":1038,"./attributes":1040}],1045:[function(t,e,r){"use strict";var n={};n.plot=t("./convert"),n.attributes=t("./attributes"),n.markerSymbols=t("../../constants/gl3d_markers"),n.supplyDefaults=t("./defaults"),n.colorbar=t("../scatter/marker_colorbar"),n.calc=t("./calc"),n.moduleType="trace",n.name="scatter3d",n.basePlotModule=t("../../plots/gl3d"),n.categories=["gl3d","symbols","showLegend"],n.meta={},e.exports=n},{"../../constants/gl3d_markers":659,"../../plots/gl3d":774,"../scatter/marker_colorbar":1032,"./attributes":1040,"./calc":1041,"./convert":1043,"./defaults":1044}],1046:[function(t,e,r){"use strict";var n=t("../scatter/attributes"),i=t("../../plots/attributes"),a=t("../../components/colorscale/attributes"),o=t("../../components/colorbar/attributes"),s=t("../../lib/extend").extendFlat,l=n.marker,c=n.line,u=l.line;e.exports={carpet:{valType:"string",editType:"calc"},a:{valType:"data_array",editType:"calc"},b:{valType:"data_array",editType:"calc"},mode:s({},n.mode,{dflt:"markers"}),text:s({},n.text,{}),line:{color:c.color,width:c.width,dash:c.dash,shape:s({},c.shape,{values:["linear","spline"]}),smoothing:c.smoothing,editType:"calc"},connectgaps:n.connectgaps,fill:s({},n.fill,{values:["none","toself","tonext"]}),fillcolor:n.fillcolor,marker:s({symbol:l.symbol,opacity:l.opacity,maxdisplayed:l.maxdisplayed,size:l.size,sizeref:l.sizeref,sizemin:l.sizemin,sizemode:l.sizemode,line:s({width:u.width,editType:"calc"},a("marker.line")),gradient:l.gradient,editType:"calc"},a("marker"),{colorbar:o}),textfont:n.textfont,textposition:n.textposition,selected:n.selected,unselected:n.unselected,hoverinfo:s({},i.hoverinfo,{flags:["a","b","text","name"]}),hoveron:n.hoveron}},{"../../components/colorbar/attributes":559,"../../components/colorscale/attributes":565,"../../lib/extend":673,"../../plots/attributes":729,"../scatter/attributes":1015}],1047:[function(t,e,r){"use strict";var n=t("fast-isnumeric"),i=t("../scatter/colorscale_calc"),a=t("../scatter/arrays_to_calcdata"),o=t("../scatter/calc_selection"),s=t("../scatter/calc").calcMarkerSize,l=t("../carpet/lookup_carpetid");e.exports=function(t,e){var r=e._carpetTrace=l(t,e);if(r&&r.visible&&"legendonly"!==r.visible){var c;e.xaxis=r.xaxis,e.yaxis=r.yaxis;var u,h,f=e._length,p=new Array(f),d=!1;for(c=0;c<f;c++)if(u=e.a[c],h=e.b[c],n(u)&&n(h)){var g=r.ab2xy(+u,+h,!0),m=r.isVisible(+u,+h);m||(d=!0),p[c]={x:g[0],y:g[1],a:u,b:h,vis:m}}else p[c]={x:!1,y:!1};return e._needsCull=d,p[0].carpet=r,p[0].trace=e,s(e,f),i(e),a(p,e),o(p,e),p}}},{"../carpet/lookup_carpetid":874,"../scatter/arrays_to_calcdata":1014,"../scatter/calc":1016,"../scatter/calc_selection":1017,"../scatter/colorscale_calc":1019,"fast-isnumeric":214}],1048:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../scatter/constants"),a=t("../scatter/subtypes"),o=t("../scatter/marker_defaults"),s=t("../scatter/line_defaults"),l=t("../scatter/line_shape_defaults"),c=t("../scatter/text_defaults"),u=t("../scatter/fillcolor_defaults"),h=t("./attributes");e.exports=function(t,e,r,f){function p(r,i){return n.coerce(t,e,h,r,i)}p("carpet"),e.xaxis="x",e.yaxis="y";var d=p("a"),g=p("b"),m=Math.min(d.length,g.length);if(m){e._length=m,p("text"),p("mode",m<i.PTS_LINESONLY?"lines+markers":"lines"),a.hasLines(e)&&(s(t,e,r,f,p),l(t,e,p),p("connectgaps")),a.hasMarkers(e)&&o(t,e,r,f,p,{gradient:!0}),a.hasText(e)&&c(t,e,f,p);var v=[];(a.hasMarkers(e)||a.hasText(e))&&(p("marker.maxdisplayed"),v.push("points")),p("fill"),"none"!==e.fill&&(u(t,e,r,p),a.hasLines(e)||l(t,e,p)),"tonext"!==e.fill&&"toself"!==e.fill||v.push("fills"),p("hoveron",v.join("+")||"points"),n.coerceSelectionMarkerOpacity(e,p)}else e.visible=!1}},{"../../lib":684,"../scatter/constants":1020,"../scatter/fillcolor_defaults":1023,"../scatter/line_defaults":1027,"../scatter/line_shape_defaults":1029,"../scatter/marker_defaults":1033,"../scatter/subtypes":1037,"../scatter/text_defaults":1038,"./attributes":1046}],1049:[function(t,e,r){"use strict";e.exports=function(t,e,r,n,i){var a=n[i];return t.a=a.a,t.b=a.b,t}},{}],1050:[function(t,e,r){"use strict";var n=t("../scatter/hover");e.exports=function(t,e,r,i){var a=n(t,e,r,i);if(a&&!1!==a[0].index){var o=a[0];if(void 0===o.index){var s=1-o.y0/t.ya._length,l=t.xa._length,c=l*s/2,u=l-c;return o.x0=Math.max(Math.min(o.x0,u),c),o.x1=Math.max(Math.min(o.x1,u),c),a}var h=o.cd[o.index];o.a=h.a,o.b=h.b,o.xLabelVal=void 0,o.yLabelVal=void 0;var f=o.trace,p=f._carpet,d=(h.hi||f.hoverinfo).split("+"),g=[];-1!==d.indexOf("all")&&(d=["a","b"]),-1!==d.indexOf("a")&&w(p.aaxis,h.a),-1!==d.indexOf("b")&&w(p.baxis,h.b);var m=p.ab2ij([h.a,h.b]),v=Math.floor(m[0]),y=m[0]-v,x=Math.floor(m[1]),b=m[1]-x,_=p.evalxy([],v,x,y,b);return g.push("y: "+_[1].toFixed(3)),o.extraText=g.join("<br>"),a}function w(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,""):t._hovertitle,g.push(r+": "+e.toFixed(3)+t.labelsuffix)}}},{"../scatter/hover":1025}],1051:[function(t,e,r){"use strict";var n={};n.attributes=t("./attributes"),n.supplyDefaults=t("./defaults"),n.colorbar=t("../scatter/marker_colorbar"),n.calc=t("./calc"),n.plot=t("./plot"),n.style=t("../scatter/style").style,n.styleOnSelect=t("../scatter/style").styleOnSelect,n.hoverPoints=t("./hover"),n.selectPoints=t("../scatter/select"),n.eventData=t("./event_data"),n.moduleType="trace",n.name="scattercarpet",n.basePlotModule=t("../../plots/cartesian"),n.categories=["svg","carpet","symbols","showLegend","carpetDependent","zoomScale"],n.meta={},e.exports=n},{"../../plots/cartesian":743,"../scatter/marker_colorbar":1032,"../scatter/select":1035,"../scatter/style":1036,"./attributes":1046,"./calc":1047,"./defaults":1048,"./event_data":1049,"./hover":1050,"./plot":1052}],1052:[function(t,e,r){"use strict";var n=t("../scatter/plot"),i=t("../../plots/cartesian/axes"),a=t("../../components/drawing");e.exports=function(t,e,r,o){var s,l,c,u=r[0][0].carpet,h={xaxis:i.getFromId(t,u.xaxis||"x"),yaxis:i.getFromId(t,u.yaxis||"y"),plot:e.plot};for(n(t,h,r,o),s=0;s<r.length;s++)l=r[s][0].trace,c=o.selectAll("g.trace"+l.uid+" .js-line"),a.setClipUrl(c,u._clipPathId)}},{"../../components/drawing":583,"../../plots/cartesian/axes":732,"../scatter/plot":1034}],1053:[function(t,e,r){"use strict";var n=t("../scatter/attributes"),i=t("../../plots/attributes"),a=t("../../components/colorscale/attributes"),o=t("../../components/drawing/attributes").dash,s=t("../../lib/extend").extendFlat,l=t("../../plot_api/edit_types").overrideAll,c=n.marker,u=n.line,h=c.line;e.exports=l({lon:{valType:"data_array"},lat:{valType:"data_array"},locations:{valType:"data_array"},locationmode:{valType:"enumerated",values:["ISO-3","USA-states","country 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n={};n.attributes=t("./attributes"),n.supplyDefaults=t("./defaults"),n.colorbar=t("../scatter/marker_colorbar"),n.calc=t("./calc"),n.plot=t("./plot"),n.style=t("./style"),n.styleOnSelect=t("../scatter/style").styleOnSelect,n.hoverPoints=t("./hover"),n.eventData=t("./event_data"),n.selectPoints=t("./select"),n.moduleType="trace",n.name="scattergeo",n.basePlotModule=t("../../plots/geo"),n.categories=["geo","symbols","showLegend","scatter-like"],n.meta={},e.exports=n},{"../../plots/geo":762,"../scatter/marker_colorbar":1032,"../scatter/style":1036,"./attributes":1053,"./calc":1054,"./defaults":1055,"./event_data":1056,"./hover":1057,"./plot":1059,"./select":1060,"./style":1061}],1059:[function(t,e,r){"use strict";var n=t("d3"),i=t("../../lib"),a=t("../../constants/numerical").BADNUM,o=t("../../lib/topojson_utils").getTopojsonFeatures,s=t("../../lib/geo_location_utils").locationToFeature,l=t("../../lib/geojson_utils"),c=t("../scatter/subtypes"),u=t("./style");function h(t,e){var 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x=[];s.hasMarkers(e)&&(c(t,e,r,p,d),d("marker.line.width",g||m?1:0),x.push("points")),s.hasText(e)&&f(t,e,p,d),d("fill"),"none"!==e.fill&&h(t,e,r,d),"tonext"!==e.fill&&"toself"!==e.fill||x.push("fills"),d("hoveron",x.join("+")||"points");var b=i.getComponentMethod("errorbars","supplyDefaults");b(t,e,r,{axis:"y"}),b(t,e,r,{axis:"x",inherit:"y"}),n.coerceSelectionMarkerOpacity(e,d)}else e.visible=!1}},{"../../lib":684,"../../registry":817,"../scatter/constants":1020,"../scatter/fillcolor_defaults":1023,"../scatter/line_defaults":1027,"../scatter/marker_defaults":1033,"../scatter/subtypes":1037,"../scatter/text_defaults":1038,"../scatter/xy_defaults":1039,"./attributes":1062}],1066:[function(t,e,r){"use strict";var n=t("regl-scatter2d"),i=t("regl-line2d"),a=t("regl-error2d"),o=t("point-cluster"),s=t("array-range"),l=t("@etpinard/gl-text"),c=t("../../registry"),u=t("../../lib"),h=t("../../lib/prepare_regl"),f=t("../../plots/cartesian/axis_ids"),p=t("../../components/color"),d=t("../scatter/subtypes"),g=t("../scatter/calc").calcMarkerSize,m=t("../scatter/calc").calcAxisExpansion,v=t("../scatter/colorscale_calc"),y=t("../scatter/link_traces"),x=t("../scatter/get_trace_color"),b=t("../scatter/fill_hover_text"),_=t("./convert"),w=t("../../constants/numerical").BADNUM,k=t("./constants").TOO_MANY_POINTS,M=t("../../constants/interactions").DESELECTDIM;function A(t,e,r,n,i,a){var o=_.style(t,r);if(o.marker&&(o.marker.positions=n),o.line&&n.length>1&&u.extendFlat(o.line,_.linePositions(t,r,n)),o.errorX||o.errorY){var s=_.errorBarPositions(t,r,n,i,a);o.errorX&&u.extendFlat(o.errorX,s.x),o.errorY&&u.extendFlat(o.errorY,s.y)}return o.text&&(u.extendFlat(o.text,{positions:n},_.textPosition(t,r,o.text,o.marker)),u.extendFlat(o.textSel,{positions:n},_.textPosition(t,r,o.text,o.markerSel)),u.extendFlat(o.textUnsel,{positions:n},_.textPosition(t,r,o.text,o.markerUnsel))),o}function T(t,e){var r=e._scene,n={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[]},i={selectBatch:null,unselectBatch:null,fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:null};return e._scene||((r=e._scene={}).init=function(){u.extendFlat(r,i,n)},r.init(),r.update=function(t){var e,n=new Array(r.count);for(e=0;e<r.count;e++)n[e]=t;if(r.fill2d&&r.fill2d.update(n),r.scatter2d&&r.scatter2d.update(n),r.line2d&&r.line2d.update(n),r.error2d&&r.error2d.update(n.concat(n)),r.select2d&&r.select2d.update(n),r.glText)for(e=0;e<r.count;e++)r.glText[e].update(n[e]);r.draw()},r.draw=function(){var t;for(t=0;t<r.count;t++)r.fill2d&&r.fillOptions[t]&&r.fill2d.draw(t);for(t=0;t<r.count;t++)r.line2d&&r.lineOptions[t]&&r.line2d.draw(t),r.error2d&&r.errorXOptions[t]&&r.error2d.draw(t),r.error2d&&r.errorYOptions[t]&&r.error2d.draw(t+r.count),!r.scatter2d||!r.markerOptions[t]||r.selectBatch&&r.selectBatch[t]||r.scatter2d.draw(t);for(r.scatter2d&&r.select2d&&r.selectBatch&&(r.select2d.draw(r.selectBatch),r.scatter2d.draw(r.unselectBatch)),t=0;t<r.count;t++)r.glText[t]&&r.textOptions[t]&&r.glText[t].render();r.dirty=!1},r.clear=function(){var n=t._fullLayout,i=n._size,a=n.width,o=n.height,s=e.xaxis,l=e.yaxis,c=[i.l+s.domain[0]*i.w,i.b+l.domain[0]*i.h,a-i.r-(1-s.domain[1])*i.w,o-i.t-(1-l.domain[1])*i.h];r.select2d&&S(r.select2d,c),r.scatter2d?S(r.scatter2d,c):r.glText&&S(r.glText[0],c)},r.destroy=function(){r.fill2d&&r.fill2d.destroy(),r.scatter2d&&r.scatter2d.destroy(),r.error2d&&r.error2d.destroy(),r.line2d&&r.line2d.destroy(),r.select2d&&r.select2d.destroy(),r.glText&&r.glText.forEach(function(t){t.destroy()}),r.lineOptions=null,r.fillOptions=null,r.markerOptions=null,r.markerSelectedOptions=null,r.markerUnselectedOptions=null,r.errorXOptions=null,r.errorYOptions=null,r.textOptions=null,r.textSelectedOptions=null,r.textUnselectedOptions=null,r.selectBatch=null,r.unselectBatch=null,e._scene=null}),r.dirty||u.extendFlat(r,n),r}function S(t,e){var r=t.regl._gl;r.enable(r.SCISSOR_TEST),r.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT)}function E(t,e,r,n){var i=t.xa,a=t.ya,o=t.distance,s=t.dxy,l=t.index,h={pointNumber:l,x:e[l],y:r[l]};h.tx=Array.isArray(n.text)?n.text[l]:n.text,h.htx=Array.isArray(n.hovertext)?n.hovertext[l]:n.hovertext,h.data=Array.isArray(n.customdata)?n.customdata[l]:n.customdata,h.tp=Array.isArray(n.textposition)?n.textposition[l]:n.textposition;var f=n.textfont;f&&(h.ts=Array.isArray(f.size)?f.size[l]:f.size,h.tc=Array.isArray(f.color)?f.color[l]:f.color,h.tf=Array.isArray(f.family)?f.family[l]:f.family);var p=n.marker;p&&(h.ms=u.isArrayOrTypedArray(p.size)?p.size[l]:p.size,h.mo=u.isArrayOrTypedArray(p.opacity)?p.opacity[l]:p.opacity,h.mx=Array.isArray(p.symbol)?p.symbol[l]:p.symbol,h.mc=u.isArrayOrTypedArray(p.color)?p.color[l]:p.color);var d=p&&p.line;d&&(h.mlc=Array.isArray(d.color)?d.color[l]:d.color,h.mlw=u.isArrayOrTypedArray(d.width)?d.width[l]:d.width);var g=p&&p.gradient;g&&"none"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[l]:g.type,h.mgc=Array.isArray(g.color)?g.color[l]:g.color);var m=i.c2p(h.x,!0),v=a.c2p(h.y,!0),y=h.mrc||1,_=n.hoverlabel;_&&(h.hbg=Array.isArray(_.bgcolor)?_.bgcolor[l]:_.bgcolor,h.hbc=Array.isArray(_.bordercolor)?_.bordercolor[l]:_.bordercolor,h.hts=Array.isArray(_.font.size)?_.font.size[l]:_.font.size,h.htc=Array.isArray(_.font.color)?_.font.color[l]:_.font.color,h.htf=Array.isArray(_.font.family)?_.font.family[l]:_.font.family,h.hnl=Array.isArray(_.namelength)?_.namelength[l]:_.namelength);var w=n.hoverinfo;w&&(h.hi=Array.isArray(w)?w[l]:w);var k={};return k[t.index]=h,u.extendFlat(t,{color:x(n,h),x0:m-y,x1:m+y,xLabelVal:h.x,y0:v-y,y1:v+y,yLabelVal:h.y,cd:k,distance:o,spikeDistance:s}),h.htx?t.text=h.htx:h.tx?t.text=h.tx:n.text&&(t.text=n.text),b(h,n,t),c.getComponentMethod("errorbars","hoverInfo")(h,n,t),t}function C(t){var e,r,n=t[0].t,i=n._scene,a=n.index,o=i.selectBatch[a],s=i.unselectBatch[a],l=i.textOptions[a],c=i.textSelectedOptions[a]||{},h=i.textUnselectedOptions[a]||{},f=u.extendFlat({},l);if(o&&s){var d=c.color,g=h.color,m=l.color,v=Array.isArray(m);for(f.color=new Array(n.count),e=0;e<o.length;e++)r=o[e],f.color[r]=d||(v?m[r]:m);for(e=0;e<s.length;e++){r=s[e];var y=v?m[r]:m;f.color[r]=g||(d?y:p.addOpacity(y,M))}}i.glText[a].update(f)}e.exports={moduleType:"trace",name:"scattergl",basePlotModule:t("../../plots/cartesian"),categories:["gl","regl","cartesian","symbols","errorBarsOK","showLegend","scatter-like"],attributes:t("./attributes"),supplyDefaults:t("./defaults"),cleanData:t("../scatter/clean_data"),colorbar:t("../scatter/marker_colorbar"),calc:function(t,e){var r,n,i,a=t._fullLayout,s=f.getFromId(t,e.xaxis),l=f.getFromId(t,e.yaxis),c=a._plots[e.xaxis+e.yaxis],u=e._length,h=2*u,p={},d=s.makeCalcdata(e,"x"),y=l.makeCalcdata(e,"y"),x=new Array(h);for(r=0;r<u;r++)n=d[r],i=y[r],x[2*r]=n===w?NaN:n,x[2*r+1]=i===w?NaN:i;if("log"===s.type)for(r=0;r<h;r+=2)x[r]=s.c2l(x[r]);if("log"===l.type)for(r=1;r<h;r+=2)x[r]=l.c2l(x[r]);if("log"!==s.type&&"log"!==l.type)p.tree=o(x);else{var b=p.ids=new Array(u);for(r=0;r<u;r++)b[r]=r}v(e);var _,M=A(t,0,e,x,d,y),S=T(t,c);return u<k?_=g(e,u):M.marker&&(_=2*(M.marker.sizeAvg||Math.max(M.marker.size,3))),m(t,e,s,l,d,y,_),M.fill&&!S.fill2d&&(S.fill2d=!0),M.marker&&!S.scatter2d&&(S.scatter2d=!0),M.line&&!S.line2d&&(S.line2d=!0),!M.errorX&&!M.errorY||S.error2d||(S.error2d=!0),M.text&&!S.glText&&(S.glText=!0),M.marker&&u>=k&&(M.marker.cluster=p.tree),S.lineOptions.push(M.line),S.errorXOptions.push(M.errorX),S.errorYOptions.push(M.errorY),S.fillOptions.push(M.fill),S.markerOptions.push(M.marker),S.markerSelectedOptions.push(M.markerSel),S.markerUnselectedOptions.push(M.markerUnsel),S.textOptions.push(M.text),S.textSelectedOptions.push(M.textSel),S.textUnselectedOptions.push(M.textUnsel),S.count++,p._scene=S,p.index=S.count-1,p.x=d,p.y=y,p.positions=x,p.count=u,t.firstscatter=!1,[{x:!1,y:!1,t:p,trace:e}]},plot:function(t,e,r){if(r.length){var o,s=t._fullLayout,c=r[0][0].t._scene,p=s.dragmode;if(c){var d=s._size,g=s.width,m=s.height;if(h(t,["ANGLE_instanced_arrays","OES_element_index_uint"])){var v=s._glcanvas.data()[0].regl;if(y(t,e,r),c.dirty){if(!0===c.error2d&&(c.error2d=a(v)),!0===c.line2d&&(c.line2d=i(v)),!0===c.scatter2d&&(c.scatter2d=n(v)),!0===c.fill2d&&(c.fill2d=i(v)),!0===c.glText)for(c.glText=new Array(c.count),o=0;o<c.count;o++)c.glText[o]=new l(v);if(c.glText)for(o=0;o<c.count;o++)c.glText[o].update(c.textOptions[o]);if(c.line2d&&c.line2d.update(c.lineOptions),c.error2d){var x=(c.errorXOptions||[]).concat(c.errorYOptions||[]);c.error2d.update(x)}c.scatter2d&&c.scatter2d.update(c.markerOptions),c.fill2d&&(c.fillOptions=c.fillOptions.map(function(t,e){var n=r[e];if(!(t&&n&&n[0]&&n[0].trace))return null;var i,a,o=n[0],s=o.trace,l=o.t,u=c.lineOptions[e],h=[],f=u&&u.positions||l.positions;if("tozeroy"===s.fill)(h=(h=[f[0],0]).concat(f)).push(f[f.length-2]),h.push(0);else if("tozerox"===s.fill)(h=(h=[0,f[1]]).concat(f)).push(0),h.push(f[f.length-1]);else if("toself"===s.fill||"tonext"===s.fill){for(h=[],i=0,a=0;a<f.length;a+=2)(isNaN(f[a])||isNaN(f[a+1]))&&((h=h.concat(f.slice(i,a))).push(f[i],f[i+1]),i=a+2);h=h.concat(f.slice(i)),i&&h.push(f[i],f[i+1])}else{var p=s._nexttrace;if(p){var d=c.lineOptions[e+1];if(d){var g=d.positions;if("tonexty"===s.fill){for(h=f.slice(),e=Math.floor(g.length/2);e--;){var m=g[2*e],v=g[2*e+1];isNaN(m)||isNaN(v)||(h.push(m),h.push(v))}t.fill=p.fillcolor}}}}if(s._prevtrace&&"tonext"===s._prevtrace.fill){var y=c.lineOptions[e-1].positions,x=h.length/2,b=[i=x];for(a=0;a<y.length;a+=2)(isNaN(y[a])||isNaN(y[a+1]))&&(b.push(a/2+x+1),i=a+2);h=h.concat(y),t.hole=b}return t.opacity=s.opacity,t.positions=h,t}),c.fill2d.update(c.fillOptions))}var b="lasso"===p||"select"===p;c.selectBatch=null,c.unselectBatch=null;var _=r.map(function(r){if(r&&r[0]&&r[0].trace){var n,i=r[0],a=i.trace,o=i.t,s=o.index,l=o.x,h=o.y,p=e.xaxis||f.getFromId(t,a.xaxis||"x"),v=e.yaxis||f.getFromId(t,a.yaxis||"y"),y=[(p._rl||p.range)[0],(v._rl||v.range)[0],(p._rl||p.range)[1],(v._rl||v.range)[1]],x=[d.l+p.domain[0]*d.w,d.b+v.domain[0]*d.h,g-d.r-(1-p.domain[1])*d.w,m-d.t-(1-v.domain[1])*d.h];if(a.selectedpoints||b){if(b||(b=!0),c.selectBatch||(c.selectBatch=[],c.unselectBatch=[]),a.selectedpoints){var _=c.selectBatch[s]=u.selIndices2selPoints(a),w={};for(n=0;n<_.length;n++)w[_[n]]=1;var k=[];for(n=0;n<o.count;n++)w[n]||k.push(n);c.unselectBatch[s]=k}var M=new Array(o.count),A=new Array(o.count);for(n=0;n<o.count;n++)M[n]=p.c2p(l[n]),A[n]=v.c2p(h[n]);o.xpx=M,o.ypx=A}else o.xpx=o.ypx=null;return a.visible?{viewport:x,range:y}:null}});b&&(c.select2d||(c.select2d=n(s._glcanvas.data()[1].regl)),c.scatter2d&&c.selectBatch&&c.selectBatch.length&&c.scatter2d.update(c.markerUnselectedOptions.map(function(t,e){return c.selectBatch[e]?t:null})),c.select2d&&(c.select2d.update(c.markerOptions),c.select2d.update(c.markerSelectedOptions)),c.glText&&r.forEach(function(t){t&&t[0]&&t[0].trace&&C(t)})),c.fill2d&&c.fill2d.update(_),c.line2d&&c.line2d.update(_),c.error2d&&c.error2d.update(_.concat(_)),c.scatter2d&&c.scatter2d.update(_),c.select2d&&c.select2d.update(_),c.glText&&c.glText.forEach(function(t,e){t.update(_[e])}),c.draw()}else c.init()}}},hoverPoints:function(t,e,r,n){var i,a,o,s,l,c,u,h,f,p=t.cd,d=p[0].t,g=p[0].trace,m=t.xa,v=t.ya,y=d.x,x=d.y,b=m.c2p(e),_=v.c2p(r),w=t.distance;if(d.tree){var k=m.p2c(b-w),M=m.p2c(b+w),A=v.p2c(_-w),T=v.p2c(_+w);i="x"===n?d.tree.range(Math.min(k,M),Math.min(v._rl[0],v._rl[1]),Math.max(k,M),Math.max(v._rl[0],v._rl[1])):d.tree.range(Math.min(k,M),Math.min(A,T),Math.max(k,M),Math.max(A,T))}else{if(!d.ids)return[t];i=d.ids}var S=w;if("x"===n)for(l=0;l<i.length;l++)o=y[i[l]],(c=Math.abs(m.c2p(o)-b))<S&&(S=c,u=v.c2p(x[i[l]])-_,f=Math.sqrt(c*c+u*u),a=i[l]);else for(l=0;l<i.length;l++)o=y[i[l]],s=x[i[l]],c=m.c2p(o)-b,u=v.c2p(s)-_,(h=Math.sqrt(c*c+u*u))<S&&(S=f=h,a=i[l]);return t.index=a,t.distance=S,t.dxy=f,void 0===a?[t]:(E(t,y,x,g),[t])},style:function(t,e){if(e){var r=e[0][0].t._scene;t._fullLayout._has("splom")||r.clear(),r.draw()}},selectPoints:function(t,e){var r=t.cd,n=[],i=r[0].trace,a=r[0].t,o=a.x,l=a.y,c=a._scene;if(!c)return n;var u=d.hasText(i),h=d.hasMarkers(i),f=!h&&!u;if(!0!==i.visible||f)return n;var p,g=null,m=null;if(!1===e||e.degenerate)m=s(a.count);else for(g=[],m=[],p=0;p<a.count;p++)e.contains([a.xpx[p],a.ypx[p]])?(g.push(p),n.push({pointNumber:p,x:o[p],y:l[p]})):m.push(p);if(c.selectBatch||(c.selectBatch=[],c.unselectBatch=[]),!c.selectBatch[a.index]){for(p=0;p<c.count;p++)c.selectBatch[p]=[],c.unselectBatch[p]=[];h&&c.scatter2d.update(c.markerUnselectedOptions)}return 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a=r._plots[i];a._scene&&a._scene.draw()}}},meta:{}},a.register(o)},{"../../components/grid":604,"../../constants/numerical":661,"../../lib":684,"../../plots/cartesian/axis_ids":735,"../../registry":817,"../scatter/calc":1016,"../scatter/colorscale_calc":1019,"../scatter/marker_colorbar":1032,"../scatter/subtypes":1037,"../scattergl":1066,"../scattergl/constants":1063,"../scattergl/convert":1064,"./attributes":1091,"./base_plot":1092,"./defaults":1093,"array-range":58,"regl-splom":461}],1095:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../components/colorbar/attributes"),a=t("../mesh3d/attributes"),o=t("../../plots/attributes"),s=t("../../lib/extend").extendFlat,l={x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},u:{valType:"data_array",editType:"calc"},v:{valType:"data_array",editType:"calc"},w:{valType:"data_array",editType:"calc"},starts:{x:{valType:"data_array",editType:"calc"},y:{valType:"data_array",editType:"calc"},z:{valType:"data_array",editType:"calc"},editType:"calc"},maxdisplayed:{valType:"integer",min:0,dflt:1e3,editType:"calc"},sizeref:{valType:"number",editType:"calc",min:0,dflt:1},text:{valType:"string",dflt:"",editType:"calc"}};s(l,n("",{colorAttr:"u/v/w 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S=u[r];_=Math.max(_,S),w=Math.min(w,S);var E=h[r];k=Math.max(k,E),M=Math.min(M,E);var C=f[r];A=Math.max(A,C),T=Math.min(T,C)}for(r=0;r<d;r++){var L=i[r];_=Math.max(_,L),w=Math.min(w,L);var z=a[r];k=Math.max(k,z),M=Math.min(M,z);var P=o[r];A=Math.max(A,P),T=Math.min(T,P)}e._len=p,e._slen=d,e._normMax=g,e._xbnds=[w,_],e._ybnds=[M,k],e._zbnds=[T,A]}},{"../../components/colorscale/calc":566}],1097:[function(t,e,r){"use strict";var n=t("gl-streamtube3d"),i=n.createTubeMesh,a=t("../../lib"),o=t("../../lib/gl_format_color").parseColorScale,s=t("../../plots/gl3d/zip3"),l={xaxis:0,yaxis:1,zaxis:2};function c(t,e){this.scene=t,this.uid=e,this.mesh=null,this.data=null}var u=c.prototype;function h(t){return a.distinctVals(t).vals}function f(t){var e=t.length;return e>2?t.slice(1,e-1):2===e?[(t[0]+t[1])/2]:t}function p(t){var e=t.length;return 1===e?[.5,.5]:[t[1]-t[0],t[e-1]-t[e-2]]}function d(t,e){var r=t.fullSceneLayout,i=t.dataScale,c=e._len,u={};function d(t,e){var n=r[e],o=i[l[e]];return 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R.lightPosition=[B.x,B.y,B.z],R.ambient=e.lighting.ambient,R.diffuse=e.lighting.diffuse,R.specular=e.lighting.specular,R.roughness=e.lighting.roughness,R.fresnel=e.lighting.fresnel,R.opacity=e.opacity,e._pad=R.tubeScale*e.sizeref*2,R}u.handlePick=function(t){var e=this.scene.fullSceneLayout,r=this.scene.dataScale;function n(t,n){var i=e[n],a=r[l[n]];return i.l2c(t)/a}if(t.object===this.mesh){var i=t.data.position,a=t.data.velocity;return t.traceCoordinate=[n(i[0],"xaxis"),n(i[1],"yaxis"),n(i[2],"zaxis"),n(a[0],"xaxis"),n(a[1],"yaxis"),n(a[2],"zaxis"),t.data.intensity*this.data._normMax,t.data.divergence],t.textLabel=this.data.text,!0}},u.update=function(t){this.data=t;var e=d(this.scene,t);this.mesh.update(e)},u.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},e.exports=function(t,e){var r=t.glplot.gl,n=d(t,e),a=i(r,n),o=new c(t,e.uid);return 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c(t){return{show:{valType:"boolean",dflt:!1},project:{x:{valType:"boolean",dflt:!1},y:{valType:"boolean",dflt:!1},z:{valType:"boolean",dflt:!1}},color:{valType:"color",dflt:n.defaultLine},usecolormap:{valType:"boolean",dflt:!1},width:{valType:"number",min:1,max:16,dflt:2},highlight:{valType:"boolean",dflt:!0},highlightcolor:{valType:"color",dflt:n.defaultLine},highlightwidth:{valType:"number",min:1,max:16,dflt:2}}}var u=e.exports=l(s({z:{valType:"data_array"},x:{valType:"data_array"},y:{valType:"data_array"},text:{valType:"string",dflt:"",arrayOk:!0},surfacecolor:{valType:"data_array"}},i("",{colorAttr:"z or 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diff --git a/calbridgesensors/templates/404.html b/calbridgesensors/templates/404.html
new file mode 100644
index 0000000..7fa3adc
--- /dev/null
+++ b/calbridgesensors/templates/404.html
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+            color: white;
+            font-family: AppleGothic, "Arial Black", Georgia;
+            text-align: center;
+            margin-left: 10%;
+            margin-top: 10%;
+            margin-right: 10%;
+            width: 80%;
+        }
+
+    </style>
+</head>
+<body>
+    <h1>Some yahoo probably stole your data??!</h1>
+</body>
+</html>
\ No newline at end of file
diff --git a/calbridgesensors/templates/base.html b/calbridgesensors/templates/base.html
new file mode 100644
index 0000000..ac2a13f
--- /dev/null
+++ b/calbridgesensors/templates/base.html
@@ -0,0 +1,176 @@
+<!DOCTYPE html>
+<html lang="en">
+{% load static %}
+<head>
+    <meta charset="UTF-8">
+    <title>Cal Bridge Sensors</title>
+    <script src="{% static 'plotly.js' %}"></script>
+    <style>
+        #alert {
+            color: crimson;
+            font-family: Arial, sans-serif, Georgia, Helvetica;
+        }
+
+        div.header {
+            display: block;
+            width: 100%;
+            color: blue;
+            margin-right: 5px;
+        }
+
+        div li.right {
+            float: right;
+            border-left: 1px solid darkgrey;
+            padding: 5px 10px;
+        }
+
+        li {
+            list-style: none;
+        }
+
+        /* ----  href clickons --- */
+        a.button:link {
+            text-decoration: none;
+        }
+
+        a.button:visited {
+            text-decoration: none;
+        }
+
+        a.button:active {
+            text-decoration: underline;
+        }
+
+        a.button:hover {
+            text-decoration: underline;
+        }
+
+        li.right a.button:hover {
+            color: dodgerblue;
+        }
+
+        li.right a.button:active {
+            color: dodgerblue;
+        }
+
+        div.center {
+            text-align: center;
+            margin-top: 5%;
+            margin-bottom: auto;
+            border-top: 1px solid slategray;
+            display: flex;
+            justify-content: center;
+        }
+
+        div.center label {
+            padding-right: 20px;
+            margin-top: 10px;
+        }
+
+        div.center select {
+            margin-top: 10px;
+        }
+
+        .dropitem {
+            float: none;
+            color: black;
+            text-decoration: none;
+            display: block;
+            padding: 10px 74px;
+            text-align: center;
+            margin: 0px 1.5% 0px 1.5%;
+        }
+
+        .dropmenu {
+            display: none;
+            position: absolute;
+            background-color: aliceblue;
+            width: 12%;
+            border-radius: 10px;
+            box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
+            z-index: 1;
+            padding-left: 0px;
+            padding-right: 0px;
+        }
+
+        .dropbtn {
+            font-size: 16px;
+            outline: none;
+            color: black;
+            padding: 10px 8px;
+            background-color: aliceblue;
+            border-radius: 10px;
+            list-style: none;
+            font-family: inherit;
+            width: 133%;
+            border-color: aliceblue;
+        }
+
+        .dropdown {
+            float: left;
+            display: block;
+            margin: 3px 1% 3px 1%;
+            padding-left: 6px;
+            padding-right: 60px;
+            width: 10%;
+        }
+
+        .dropdown:hover .dropmenu {
+            display: block;
+        }
+
+        li.setview {
+            background-color: aliceblue;
+            margin: 3px 1.5% 3px 1.5%;
+            list-style: none;
+            border-radius: 10px;
+            padding: 10px 10px;
+            width: 12%;
+            height: 20px;
+            text-align: center;
+        }
+
+        .info-box {
+            display: block;
+            margin-top: 30px;
+        }
+
+        .bar {
+            display: table;
+            margin-top: 0px;
+            width: 90%;
+            margin-bottom: 0px;
+            margin-left: 40px;
+            margin-right: 40px;
+        }
+
+        .bar li {
+            float: left;
+        }
+
+        #myDiv {
+            display: block;
+            margin-top: 5%;
+        }
+
+    </style>
+</head>
+<body>
+    {% block header %}
+        <div class="header">
+            {% if user.is_authenticated %}
+                <li class="right"><a class="button" href={% url "logout" %}>logout</a></li>
+            {% endif %}
+            <li class="right"><a class="button" href="{% url "home" %}">home</a></li>
+        </div>
+    {% endblock %}
+
+
+    {% block alert %}
+    {% endblock %}
+
+
+    {% block content %}
+    {% endblock %}
+</body>
+</html>
\ No newline at end of file
diff --git a/calbridgesensors/templates/home.html b/calbridgesensors/templates/home.html
new file mode 100644
index 0000000..75b327a
--- /dev/null
+++ b/calbridgesensors/templates/home.html
@@ -0,0 +1,64 @@
+<!DOCTYPE html>
+<html lang="en">
+{% load static %}
+<head>
+    <meta charset="UTF-8">
+    <title>Cal Bridge Sensors</title>
+    <style>
+        body {
+            background-image: url("{% static 'img_2_5.png' %}");
+            text-align: center;
+        }
+        h1 {
+            font-family: "Fira Code Retina",Georgia, AppleGothic, Arial;
+            color: white;
+            text-align: center;
+            font-size: xx-large;
+            margin-top: 20%;
+
+        }
+
+        #HomeButton a.button:link {
+            text-decoration: black;
+        }
+
+        #HomeButton a.button:visited {
+            text-decoration: black;
+        }
+
+        #HomeButton a.button:active {
+            text-decoration: black;
+        }
+
+        #HomeButton a.button:hover {
+            text-decoration: black underline;
+        }
+
+        #HomeButton li {
+            background-color: aliceblue;
+            margin: auto 30px auto 30px;
+            list-style: none;
+            border-radius: 10px;
+            padding: 10px 20px;
+        }
+
+        #HomeButton {
+            display: flex;
+            justify-content: center;
+        }
+    
+    </style>
+</head>
+
+{% block content %}
+    <h1>Bridge Settlement</h1>
+    <div id="HomeButton">
+        {% if user.is_authenticated %}
+            <li><a class="button" href="{% url "sensors:index" %}">Manage</a></li>
+        {% else %}
+            <li><a class="button" href="{% url "login" %}">Log In</a></li>
+        {% endif %}
+        <li><a class="button" href="/admin/">Admin</a></li>
+    </div>
+{% endblock %}
+</html>
\ No newline at end of file
diff --git a/calbridgesensors/templates/registration/login.html b/calbridgesensors/templates/registration/login.html
new file mode 100644
index 0000000..aff6191
--- /dev/null
+++ b/calbridgesensors/templates/registration/login.html
@@ -0,0 +1,38 @@
+{% extends "base.html" %}
+
+{% block content %}
+
+{% if form.errors %}
+<p>Your username and password didn't match. Please try again.</p>
+{% endif %}
+
+{% if next %}
+    {% if user.is_authenticated %}
+    <p>Your account doesn't have access to this page. To proceed,
+    please login with an account that has access.</p>
+    {% else %}
+    <p>Please login to see this page.</p>
+    {% endif %}
+{% endif %}
+
+<form method="post" action="{% url 'login' %}">
+{% csrf_token %}
+<table>
+<tr>
+    <td>{{ form.username.label_tag }}</td>
+    <td>{{ form.username }}</td>
+</tr>
+<tr>
+    <td>{{ form.password.label_tag }}</td>
+    <td>{{ form.password }}</td>
+</tr>
+</table>
+
+<input type="submit" value="login" />
+<input type="hidden" name="next" value="{{ next }}" />
+</form>
+
+{# Assumes you setup the password_reset view in your URLconf #}
+<p><a href="{% url 'password_reset' %}">Lost password?</a></p>
+
+{% endblock %}
\ No newline at end of file
diff --git a/calbridgesensors/templates/sensors/detail.html b/calbridgesensors/templates/sensors/detail.html
new file mode 100644
index 0000000..fbd7576
--- /dev/null
+++ b/calbridgesensors/templates/sensors/detail.html
@@ -0,0 +1,125 @@
+{% extends 'base.html' %}
+
+{% block content %}
+    <div class="info-box">
+        <ul class="bar">
+            <li class="setview">Bridge Name</li>
+            <li class="setview">Bridge reading x</li>
+            <li class="setview">Bridge reading y</li>
+            <li class="setview">Broken Record</li>
+            <li class="setview">Repair Record</li>
+        </ul>
+        <br />
+        <ul class="bar">
+            <li class="setview">{{ bridge.name }}</li>
+            {% if reading %}
+            <!---<li class="setview">{{ reading.x }}</li>-->
+                <div class="dropdown">
+                {% for rx in readingsx %}
+                    {% if forloop.first %}
+                        <button class="setview dropbtn">{{ rx }}</button>
+                        <div class="dropmenu">
+                    {% elif forloop.last %}
+                        <li class="dropitem">{{ rx }}</li>
+                        </div>
+                    {% else %}
+                        <li class="dropitem">{{ rx }}</li>
+                    {% endif %}
+                {% endfor %}
+                </div>
+                <div class="dropdown">
+                {% for ry in readingsy %}
+                    {% if forloop.first %}
+                        <button class="setview dropbtn">{{ ry }}</button>
+                        <div class="dropmenu">
+                    {% elif forloop.last %}
+                        <li class="dropitem">{{ ry }}</li>
+                        </div>
+                    {% else %}
+                        <li class="dropitem">{{ ry }}</li>
+                    {% endif %}
+                {% endfor %}
+                </div>
+            <!----<li class="setview">{{ reading.y }}</li>-->
+            {% else %}
+            <li class="setview">{{ 0.0 }}</li>
+            <li class="setview">{{ 0.0 }}</li>
+            {% endif %}
+
+            {% if damage_recs %}
+                <div class="dropdown">
+                {% for rec in damage_recs %}
+                    {% if forloop.first %}
+                        <button class="setview dropbtn">{{ rec.log_time }}</button>
+                        <div class="dropmenu">
+                    {% elif forloop.last %}
+                        <li class="dropitem">{{ rec.log_time }}</li>
+                        </div>
+                    {% else %}
+                        <li class="dropitem">{{ rec.log_time }}</li>
+                    {% endif %}
+                {% endfor %}
+                </div>
+            {% else %}
+                <li class="setview">No Damages</li>
+            {% endif %}
+
+            {% if repair_recs %}
+                <div class="dropdown">
+                {% for rec in repair_recs %}
+                    {% if forloop.first %}
+                        <button class="setview dropbtn">{{ rec.log_time }}</button>
+                        <div class="dropmenu">
+                    {% elif forloop.last %}
+                        <li class="dropitem">{{ rec.log_time }}</li>
+                        </div>
+                    {% else %}
+                        <li class="dropitem">{{ rec.log_time }}</li>
+                    {% endif %}
+                {% endfor %}
+                </div>
+            {% else %}
+                <li class="setview">No Repairs</li>
+            {% endif %}
+        </ul>
+    </div>
+    <div id="myDiv"></div>
+    <script type='text/javascript'>
+        var calib_readings_json = '{{ readings|safe }}';
+        var calib_readings = JSON.parse(calib_readings_json);
+        var doc = document.getElementById('myDiv');
+        var i;
+        var xs = [];
+        var ys = [];
+        var dates = [];
+        for (i=0; i < calib_readings.length; i++) {
+            xs[i] = calib_readings[i][0];
+            ys[i] = calib_readings[i][1];
+            dates[i] = calib_readings[i][2]
+        }
+        var trace1 = {
+            type: "scatter",
+            mode: "lines",
+            name: 'X displacements',
+            x: dates,
+            y: xs,
+            line: {color: '#17BECF'}};
+
+        var trace2 = {
+            type: "scatter",
+            mode: "lines",
+            name: 'Y displacements',
+            x: dates,
+            y: ys,
+            line: {color: '#7F7F7F'}
+        };
+
+        var data = [trace1,trace2];
+
+        var layout = {
+            title: "Bridge DATA"
+        };
+
+        Plotly.newPlot('myDiv', data, layout);
+    </script>
+{% endblock %}
\ No newline at end of file
diff --git a/calbridgesensors/templates/sensors/index.html b/calbridgesensors/templates/sensors/index.html
new file mode 100644
index 0000000..d99b195
--- /dev/null
+++ b/calbridgesensors/templates/sensors/index.html
@@ -0,0 +1,28 @@
+{% extends "base.html" %}
+{% load sensors_filters %}
+
+{% block alert %}
+    <div id="alert" class="center">
+    {% if broken_bridges %}
+        <h2>Bridge In Danger!</h2>
+        {% for bb in broken_bridges %}
+            <li class="setview"><a href="{% url "sensors:detail" bb.bridge.name|tourl %}">{{ bb.bridge }}</a></li>
+            <li class="setview" style="width: auto">Time broken: {{ bb.broken_time }}</li>
+        {% endfor %}
+    {% else %}
+        <h2 style="color: lightgreen">All Bridges Are Healthy</h2>
+    {% endif %}
+    </div>
+{% endblock %}
+
+{% block content %}
+    <div class="center">
+        <label for="bselect">Choose a bridge for your query: </label>
+        <select name="bridges_select" id="bselect" onchange="window.location.assign(this.options[this.selectedIndex].value + '/')">
+            <option value="/sensors">--Select A Bridge--</option>
+            {% for b in bridges %}
+                <option value="{{ b.name|tourl }}">{{ b.name }}</option>
+            {% endfor %}
+        </select>
+    </div>
+{% endblock %}
\ No newline at end of file
diff --git a/calib3/Calibration_DX.xlsx b/calib3/Calibration_DX.xlsx
new file mode 100755
index 0000000..268ac9d
Binary files /dev/null and b/calib3/Calibration_DX.xlsx differ
diff --git a/calib4/Calibration_DX.csv b/calib4/Calibration_DX.csv
new file mode 100755
index 0000000..3719877
--- /dev/null
+++ b/calib4/Calibration_DX.csv
@@ -0,0 +1 @@
+"Camera module mounted on Albert's calibration mount, bolted to grid board. Grid board is not fixed to table. Table will not be moved due to impracticality of moving table."										
Laser module is mounted to rails. Distance between camera and laser can be changed. Rail and centerline of camera mount are aligned with laser										
"Imprecision in laesr mount and rail installation are significant in location of crosshairs. At beginning of each DX iteration, we will change location of camera module to capture crosshairs. "										
Rotation of either module is not changed. 										
										
Independent variable: Distance between modules										
		"Camera <Ax,Ay,Az> not of concern"								"Accelerometer axes are: x facing other module, z vertical, y accordingly determined"
DX	Img file	Phone deg (mount slab)	Novo 1 [in]	Novo 2 [in]	Novo 3 [in]	Novo 4 [in]	Pixel_X	Pixel_Y		Novo's represent differential displacements of camera module in Albert's mount. 
8.5	59	0.7	0	0	0	0	-1	-1		"Facing Laser module, behind camera module,  [LR, UR, UL, LL] = Novo [1,2,3,4]"
8.5	60	0.7	0	0	0	0	-1	-1		
8.5	61	0.7	0	0	0	0	-1	-1		
8.5	62	0.5	-0.501	-0.502	-0.501	-0.503	-1	-1		
8.5	63	0.5	-0.501	-0.502	-0.501	-0.503	-1	-1		
8.5	64	0.5	-0.501	-0.502	-0.501	-0.503	-1	-1		
8.5	65	0.7	-1.007	-1.005	-1.001	-1.008	-1	-1		
8.5	66	0.7	-1.007	-1.005	-1.001	-1.008	-1	-1		
8.5	67	0.7	-1.007	-1.005	-1.001	-1.008	-1	-1		
8.5	73	0.7	-1.007	-1.005	-1.001	-1.008	-1	-1		
8.5	74	0.7	-1.007	-1.005	-1.001	-1.008	-1	-1		
8.5	75	0.7	-1.007	-1.005	-1.001	-1.008	-1	-1		
8.5	77	0.8	-1.513	-1.509	-1.504	-1.512	-1	-1		
8.5	78	0.8	-1.513	-1.509	-1.504	-1.512	-1	-1		
8.5	79	0.8	-1.513	-1.509	-1.504	-1.512	-1	-1		
8.5	80	0.6	-1.256	-1.255	-1.253	-1.26	310.7142857	198.5714286		
8.5	81	0.6	-1.256	-1.255	-1.253	-1.26	313.7777778	199.6		
8.5	82	0.6	-1.256	-1.255	-1.253	-1.26	313	198.1		
8.5	83	0.6	-1.256	-1.255	-1.253	-1.26	313.333333	197.8		
8	84	0.3	0	0	0	0	130.777778	244		
8	85	0.3	0	0	0	0	129.33333	245.6363636		
8	86	0.3	0	0	0	0	129.2857143	249.4285714		
8	87	0.3	0	0	0	0	130.8888889	246.7272727		
8	88	0.4	-0.255	-0.251	-0.251	-0.252	174.625	249.375		
8	89	0.4	-0.255	-0.251	-0.251	-0.252	172.6666667	247.8181818		
8	90	0.4	-0.255	-0.251	-0.251	-0.252	172.8888889	249.2727273		
8	91	0.4	-0.255	-0.251	-0.251	-0.252	177.1428571	248		
8	92	0.5	-0.505	-0.5	-0.502	-0.506	213.8888889	249.1		
8	93	0.5	-0.505	-0.5	-0.502	-0.506	211	252.3636364		
8	94	0.5	-0.505	-0.5	-0.502	-0.506	210.7777778	259		
8	95	0.5	-0.505	-0.5	-0.502	-0.506	212.5	255.75		
8	96	0.2	-0.753	-0.755	-0.756	-0.752	255.5555556	253.7		
8	97	0.2	-0.753	-0.755	-0.756	-0.752	256.3333333	253.9		
8	98	0.2	-0.753	-0.755	-0.756	-0.752	256.1111111	254.1		
8	99	0.2	-0.753	-0.755	-0.756	-0.752	256.4444444	254.1		
8	100	0.3	-1.004	-1.007	-1.006	-1.001	297.8888889	252.8		
8	101	0.3	-1.004	-1.007	-1.006	-1.001	298.6666667	252.8		
8	102	0.3	-1.004	-1.007	-1.006	-1.001	298.1111111	253.4545455		
8	103	0.3	-1.004	-1.007	-1.006	-1.001	298	252.8		
8	104	0.4	-1.251	-1.251	-1.253	-1.254	334.4444444	247.75		
8	105	0.4	-1.251	-1.251	-1.253	-1.254	339.5555556	250.8181818		
8	107	0.4	-1.251	-1.251	-1.253	-1.254	338.7777778	250.1818182		
8	108	0.4	-1.251	-1.251	-1.253	-1.254	336.8888889	246.4166667		
7.5	113	0.1	0	0	0	0	270.8888889	306.8333333		
7.5	114	0.1	0	0	0	0	272	306.1666667		
7.5	115	0.1	0	0	0	0	272.8571429	308.8888889		
7.5	116	0.1	0	0	0	0	271.125	306		
7.5	117	0.3	-0.255	-0.251	-0.253	-0.25	313.5555556	307.4545455		
7.5	118	0.3	-0.255	-0.251	-0.253	-0.25	313.1111111	306.75		
7.5	119	0.3	-0.255	-0.251	-0.253	-0.25	312.8888889	307.9090909		
7.5	120	0.3	-0.255	-0.251	-0.253	-0.25	313.2222222	305.6666667		
7.5	121	0.3	-0.503	-0.5	-0.503	-0.501	350.6666667	310.7777778		
7.5	122	0.3	-0.503	-0.5	-0.503	-0.501	354.8888889	311.2727273		
7.5	123	0.3	-0.503	-0.5	-0.503	-0.501	355	307.7272727		
7.5	124	0.3	-0.503	-0.5	-0.503	-0.501	355.1111111	307.1818182		
7.5	125	0.2	-1.009	-1.006	-1.005	-1.002	442.8571429	314.8571429		
7.5	126	0.2	-1.009	-1.006	-1.005	-1.002	437.1111111	314.8333333		
7.5	127	0.2	-1.009	-1.006	-1.005	-1.002	436.1111111	316.8181818		
7.5	128	0.2	-1.009	-1.006	-1.005	-1.002	440.25	318.7		
7.5	129	0.3	-1.251	-1.252	-1.249	-1.252	477	315.9166667		
7.5	130	0.3	-1.251	-1.252	-1.249	-1.252	477.7777778	316.8333333		
7.5	131	0.3	-1.251	-1.252	-1.249	-1.252	477.1111111	317.5454545		
7.5	132	0.3	-1.251	-1.252	-1.249	-1.252	481.75	316.5555556		
7	133	0.2	-0.5	-0.5	-0.503	-0.502	63.375	292.4166667	Moved camera module horizontally to capture crosshairs	
7	134	0.2	-0.5	-0.5	-0.503	-0.502	91.85714286	290.4444444	Not beginning at 0 in order to capture crosshairs. Tared to 0 initially. 	
7	135	0.2	-0.5	-0.5	-0.503	-0.502	90.25	293.0909091		
7	136	0.2	-0.5	-0.5	-0.503	-0.502	62.75	291.6666667		
7	137	0.3	-0.752	-0.751	-0.756	-0.754	126.4444444	290.875		
7	138	0.3	-0.752	-0.751	-0.756	-0.754	103.2222222	294.9166667		
7	139	0.3	-0.752	-0.751	-0.756	-0.754	103.1111111	295.4166667		
7	140	0.3	-0.752	-0.751	-0.756	-0.754	103.75	291.3333333		
7	141	0.4	-0.852	-0.849	-0.854	-0.852	120.5714286	295.6666667		
7	142	0.4	-0.852	-0.849	-0.854	-0.852	123	294.9166667		
7	143	0.4	-0.852	-0.849	-0.854	-0.852	121	294.9166667		
7	144	0.4	-0.852	-0.849	-0.854	-0.852	121.1111111	295		
7	145	0.3	-1.001	-1	-1.004	-0.994	146.6666667	293.6		
7	146	0.3	-1.001	-1	-1.004	-0.994	148.375	292.2		
7	147	0.3	-1.001	-1	-1.004	-0.994	147.2222222	293.1		
7	148	0.3	-1.001	-1	-1.004	-0.994	146.7777778	293.5		
7	149	0.3	-1.251	-1.25	-1.253	-1.252	186	296.5454545		
7	150	0.3	-1.251	-1.25	-1.253	-1.252	186.8888889	296.0909091		
7	151	0.3	-1.251	-1.25	-1.253	-1.252	185.6666667	295.8181818		
7	152	0.3	-1.251	-1.25	-1.253	-1.252	185.7777778	295.5454545		
5	153	0.3	0	0	0	0	184.75	327.2727273		
5	154	0.3	0	0	0	0	184.7777778	327		
5	155	0.3	0	0	0	0	185.2222222	327		
5	156	0.3	0	0	0	0	184.7777778	327.75		
5	157	0.2	-0.251	-0.25	-0.254	-0.251	227.5555556	327.1666667		
5	158	0.2	-0.251	-0.25	-0.254	-0.251	226.3333333	327		
5	159	0.2	-0.251	-0.25	-0.254	-0.251	227.2222222	328.2727273		
5	160	0.2	-0.251	-0.25	-0.254	-0.251	226	327.0833333		
5	161	0.1	-0.503	-0.503	-0.506	-0.502	270.4444444	329.3636364		
5	162	0.1	-0.503	-0.503	-0.506	-0.502	269.2222222	328.5833333		
5	163	0.1	-0.503	-0.503	-0.506	-0.502	271	329		
5	164	0.1	-0.503	-0.503	-0.506	-0.502	270.3333333	330.1818182		
5	165	0.2	-0.75	-0.749	-0.753	-0.749	310.6666667	306.9166667		
5	166	0.2	-0.75	-0.749	-0.753	-0.749	309.8888889	329.9090909		
5	167	0.2	-0.75	-0.749	-0.753	-0.749	310.8888889	330		
5	168	0.2	-0.75	-0.749	-0.753	-0.749	310.2222222	329.9090909		
5	169	0.2	-1.253	-1.25	-1.253	-1.25	392.3333333	332.3636364		
5	170	0.2	-1.253	-1.25	-1.253	-1.25	393.3333333	330.0833333		
5	171	0.2	-1.253	-1.25	-1.253	-1.25	393.5	332.4545455		
5	172	0.2	-1.253	-1.25	-1.253	-1.25	393.25	332.3636364		
6	173	0.2	0	0	0	0	181	150.0909091		
6	174	0.2	0	0	0	0	179.1111111	149.4545455	stood in front of camera	
6	175	0.2	0	0	0	0	180.2222222	150.1818182		
6	176	0.2	0	0	0	0	179.4444444	149.2727273		
6	177	0.3	-0.253	-0.25	-0.253	-0.251	222.3333333	153		
6	178	0.3	-0.253	-0.25	-0.253	-0.251	222.6666667	152.4444444		
6	179	0.3	-0.253	-0.25	-0.253	-0.251	222.4444444	152.1111111		
6	180	0.3	-0.253	-0.25	-0.253	-0.251	223.8888889	152.2222222		
6	181	0	-0.5	-0.502	-0.507	-0.503	265	155.6363636		
6	182	0	-0.5	-0.502	-0.507	-0.503	264.8888889	141.9090909		
6	183	0	-0.5	-0.502	-0.507	-0.503	264.5555556	154.6363636		
6	184	0	-0.5	-0.502	-0.507	-0.503	263.8888889	154.5454545		
6	185	0.2	-0.751	-0.751	-0.75	-0.757	306.8888889	153.6363636		
6	186	0.2	-0.751	-0.751	-0.75	-0.757	307	153.1		
6	187	0.2	-0.751	-0.751	-0.75	-0.757	306	153.8181818		
6	188	0.2	-0.751	-0.751	-0.75	-0.757	306.5555556	154.5		
6	189	0.2	-1.253	-1.253	-1.255	-1.252	389	157.6363636		
6	190	0.2	-1.253	-1.253	-1.255	-1.252	390	156.0909091		
6	191	0.2	-1.253	-1.253	-1.255	-1.252	388.7777778	157.1		
6	192	0.2	-1.253	-1.253	-1.255	-1.252	388.8888889	157.0909091		
\ No newline at end of file
diff --git a/calib4/Calibration_DX_DONTUSE.xlsx b/calib4/Calibration_DX_DONTUSE.xlsx
new file mode 100755
index 0000000..d8325ba
Binary files /dev/null and b/calib4/Calibration_DX_DONTUSE.xlsx differ
diff --git a/calib4/DX_Reg.m b/calib4/DX_Reg.m
new file mode 100755
index 0000000..4b88f09
--- /dev/null
+++ b/calib4/DX_Reg.m
@@ -0,0 +1,46 @@
+%% Import & Parse Data
+filename = '/Volumes/HENRY_TENG/Research/Calibrations/DX/Calibration_DX.csv';
+delimiter = ',';
+startRow = 9;
+formatSpec = '%q%q%q%q%q%q%q%q%q%[^\n\r]';
+fileID = fopen(filename,'r','n','UTF-8');
+fseek(fileID, 3, 'bof');
+temp = textscan(fileID, formatSpec, 'Delimiter', delimiter, 'TextType', 'string', 'HeaderLines' ,startRow-1, 'ReturnOnError', false, 'EndOfLine', '\r\n');
+fclose(fileID);
+temp = [temp{:}];
+[m,n] = size(temp);
+for i = 1:m
+    for j = 1:n-1
+        dat(i,j) = str2num(temp(i,j));
+    end
+end
+        
+img = dat(:,2);
+DX = unique(dat(:,1));
+deg = dat(:,3);
+novo = dat(:,4:7);
+p_X = dat(:,8);
+p_Y = dat(:,9);
+
+%% Generate Values
+DZ = abs(mean(novo,2));
+for i = 1:length(DX)
+    ind = find(dat(:,1) == DX(i));
+    b = polyfit(DZ(ind),p_X(ind),1);
+    temp = [min(DZ(ind)):0.01:max(DZ(ind))];
+    figure(i)
+    plot(DZ(ind),p_X(ind),'b-o'), hold on
+    plot(temp,b(2) + b(1).*temp, '--r')
+    xlabel('DZ [in]')
+    ylabel('[Pixel]')
+    annotation('textbox',[0.2,0.5,0.3,0.3],'String',strcat('Slope: ',num2str(b(1))),'FitBoxToText','on');
+    legend('p_X', 'Regression');
+    title(strcat('DX: ', num2str(DX(i)), ' [m]'));
+end
+    
+
+    
+    
+
+
+
diff --git a/communication_module/com_test.py b/communication_module/com_test.py
new file mode 100644
index 0000000..a99d3ec
--- /dev/null
+++ b/communication_module/com_test.py
@@ -0,0 +1,35 @@
+import requests
+from requests import Session
+import json
+from datetime import datetime
+from dateutil import parser
+from random import randint
+
+BRIDGE_NAME = 'dummy'
+dest = "http://127.0.0.1:8000/sensors/"+BRIDGE_NAME+"/update/"
+PASSWORD = "djioewfj34jod2jdoi3jr0jl983jsa"
+
+
+def send_data_to_server(x=0.0, y=0.0, z=0.0, theta=0.0, phi=0.0, psi=0.0):
+    payload = {'x': x, 'y': y, 'z': z, 'theta': theta, 'phi': phi, 'psi': psi}
+    dt = datetime.now()
+    cookies = {'csrftoken': encode(dt), 'time': str(dt)}
+    r = requests.post(dest, data=payload, cookies=cookies)
+    print(r.status_code)
+    if r.status_code == 403:
+        raise RuntimeWarning("CSRF ALERT ON, INSPECT YOUR SYSTEM")
+    else:
+        return r.text
+
+
+def encode(time_sign):
+    len_pw = len(PASSWORD)
+    index = (time_sign.year + time_sign.month * 100 + time_sign.day + time_sign.hour * time_sign.minute
+             * time_sign.second) % len_pw
+    repl = chr(randint(97, 122))
+    return PASSWORD[:index] + repl + PASSWORD[index+1:]
+
+
+print(send_data_to_server(0.5, 1.5, 1.5))
+#send_data_form(1.0, 2.0, 3.0)
+#cookies = {'csrftoken': "djioewfj34jod2jdoi3jr0jl983jsa", 'TIME': datetime.now()}
diff --git a/communication_module/communication.py b/communication_module/communication.py
new file mode 100644
index 0000000..0891fbe
--- /dev/null
+++ b/communication_module/communication.py
@@ -0,0 +1,29 @@
+import requests
+from requests import Session
+import json
+from datetime import datetime
+from dateutil import parser
+from random import randint
+
+BRIDGE_NAME = 'dummy'
+dest = "http://192.168.15.101:8000/sensors/"+BRIDGE_NAME+"/update/"
+PASSWORD = "djioewfj34jod2jdoi3jr0jl983jsa"
+
+
+def send_data_to_server(x=0.0, y=0.0, z=0.0, theta=0.0, phi=0.0, psi=0.0):
+    payload = {'x': x, 'y': y, 'z': z, 'theta': theta, 'phi': phi, 'psi': psi}
+    dt = datetime.now()
+    cookies = {'csrftoken': encode(dt), 'time': str(dt)}
+    r = requests.post(dest, data=payload, cookies=cookies)
+    if r.status_code == 403:
+        raise RuntimeWarning("CSRF ALERT ON, INSPECT YOUR SYSTEM")
+    else:
+        return r.text
+
+
+def encode(time_sign):
+    len_pw = len(PASSWORD)
+    index = (time_sign.year + time_sign.month * 100 + time_sign.day + time_sign.hour * time_sign.minute
+             * time_sign.second) % len_pw
+    repl = chr(randint(97, 122))
+    return PASSWORD[:index] + repl + PASSWORD[index+1:]
diff --git a/expr.py b/expr.py
new file mode 100644
index 0000000..e69de29
diff --git a/img_rec_module/FastDataMatrixTest.py b/img_rec_module/FastDataMatrixTest.py
new file mode 100644
index 0000000..21cdf8d
--- /dev/null
+++ b/img_rec_module/FastDataMatrixTest.py
@@ -0,0 +1,45 @@
+from img_rec_module.sig_proc_test import FM
+import numpy as np
+from random import randint
+import traceback
+import sys
+
+matrix = np.random.rand(20, 30)
+
+
+def segmentation_test(sample):
+    ax = randint(0, 1)
+    target = randint(0, 19) if ax == 1 else randint(0, 29)
+    fmat = FM(matrix, ax, target)
+    assert fmat.irange() == 20 if ax == 1 else fmat.irange() == 30
+    cap = len(fmat)
+    for i in range(sample):
+        start = randint(0, cap - 1)
+        end = randint(start + 1, cap)
+        fmat.segmentize(start, end)
+        assert len(fmat.extract_array()) == end - start, "start: {0}, end: {1}".format(start, end)
+        elem = randint(0, len(fmat) - 1)
+        assert fmat[elem] == matrix.item(target, elem + start) if ax == 1 \
+            else fmat[elem] == matrix.item(elem + start, target)
+    print("Test 1 passed!")
+    return fmat.copy()
+
+
+def shape_change_test(sample):
+    fmat = mat
+    print(len(fmat), "start: {0}, end: {1}, ax: {2}, index: {3}".format(fmat.start, fmat.end, fmat._ax, fmat._index))
+    for i in range(sample):
+        ax = randint(0, 1)
+        target = randint(0, 19) if ax == 1 else randint(0, 29)
+        try:
+            fmat = fmat.copy(ax, target)
+            elem = randint(0, len(fmat) - 1)
+            assert fmat[elem] == matrix.item(target, elem) if ax == 1 else fmat[elem] == matrix.item(elem, target), "start: {0}, end: {1}, ax: {2}, index: {3}, changed:{4}, elem: {5}".format(fmat.start, fmat.end, ax, fmat._index, target, elem)
+        except IndexError:
+            print("Index Error, start: {0}, end: {1}, ax: {2}, index: {3}".format(fmat.start, fmat.end, fmat._ax, fmat._index))
+            traceback.print_exc(file=sys.stderr)
+            sys.exit(0)
+    print("Test 2 passed!")
+
+mat = segmentation_test(1000000)
+shape_change_test(1000000)
\ No newline at end of file
diff --git a/img_rec_module/HoughLineTest.py b/img_rec_module/HoughLineTest.py
new file mode 100644
index 0000000..a338b2c
--- /dev/null
+++ b/img_rec_module/HoughLineTest.py
@@ -0,0 +1,70 @@
+from img_rec_module.sig_proc_test import HoughLine, angle_interp, normalize_angle
+import numpy as np
+from random import uniform
+from math import sin, cos, asin, acos, degrees
+
+
+def angle_interp_test(sample):
+    for t in range(4):
+        angle = np.pi * t / 2
+        angle_test(angle)
+    for i in range(sample):
+        angle = uniform(0, 2 * np.pi)
+        angle_test(angle)
+    print("All tests passed!")
+
+
+def angle_test(angle):
+    s, c = sin(angle), cos(angle)
+    aint = angle_interp(s, c)
+    e = abs(aint - angle)
+    assert e < 10 ** (-8), "angle: {0} aint: {3} sin: {1}, cos: {2}".format(degrees(angle), s, c, degrees(aint))
+
+
+data1 = [(50, 93.58985799408744), (100, 94.83752561512769), (150, 93.46476622047003), (200, 92.12757475618413), (250, 91.21899822237681), (300, 94.66610143151713), (350, 93.39700158152765), (400, 93.55400856320203), (450, 94.81488837189904)]
+data2 = [(50, 220.68898677024384), (150, 207.0369850844262), (200, 219.1873113806197), (250, 218.4221525438996), (300, 215.83141259108666), (350, 216.1097540779057), (400, 214.30630223604018), (450, 211.84789180370862), (500, 211.03929058602466), (550, 208.37556376057634), (600, 208.1358916890448)]
+
+
+def diagnose():
+    x1 = np.array([d[1] for d in data1])
+    y1 = np.array([d[0] for d in data1])
+    line1 = HoughLine(x=x1, data=y1)
+    x2 = np.array([d[0] for d in data2])
+    y2 = np.array([d[1] for d in data2])
+    line2 = HoughLine(x=x2, data=y2)
+    xmean = sum(x1) / len(x1)
+    ymean = sum(y2) / len(y2)
+    print(line1._r, line1._t)
+    print(line2._r, line2._t)
+    print((xmean, ymean), HoughLine.intersect(line1, line2))
+
+
+def normalize_angle_test(sample):
+    for t in range(4):
+        angle = np.pi * t / 2
+        test_normalize(angle)
+    for i in range(sample):
+        angle = uniform(0, 1000000)
+        test_normalize(angle)
+    print('All tests passed!')
+
+
+def test_normalize(angle):
+    s, c = sin(angle), cos(angle)
+    norm_ang = normalize_angle(angle)
+    ns, nc = sin(norm_ang), cos(norm_ang)
+    case1 = equal_within_error(s, ns) and equal_within_error(c, nc)
+    case2 = equal_within_error(s, -ns) and equal_within_error(c, -nc)
+    assert case1 or case2, 's:{0} c:{1}, ns:{2}, nc:{3}, angle:{4}, norm:{5}'.format(s, c, ns, nc, angle, norm_ang)
+
+
+
+def equal_within_error(v1, v2):
+    e = abs(v1 - v2)
+    return e < 10 ** (-8)
+
+
+if __name__ == '__main__':
+    #angle_interp_test(100000000)
+    #diagnose()
+    normalize_angle_test(1000000)
\ No newline at end of file
diff --git a/img_rec_module/__init__.py b/img_rec_module/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/img_rec_module/centers.txt b/img_rec_module/centers.txt
new file mode 100644
index 0000000..ea1e64b
--- /dev/null
+++ b/img_rec_module/centers.txt
@@ -0,0 +1,640 @@
+135.5
+134.5
+134.0
+127.5
+131.0
+131.0
+133.5
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+134.0
+131.5
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+130.5
+1.0
+132.5
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+70.5
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diff --git a/img_rec_module/flags.txt b/img_rec_module/flags.txt
new file mode 100644
index 0000000..b15d479
--- /dev/null
+++ b/img_rec_module/flags.txt
@@ -0,0 +1,274 @@
+COLOR_BAYER_BG2BGR
+COLOR_BAYER_BG2BGRA
+COLOR_BAYER_BG2BGR_EA
+COLOR_BAYER_BG2BGR_VNG
+COLOR_BAYER_BG2GRAY
+COLOR_BAYER_BG2RGB
+COLOR_BAYER_BG2RGBA
+COLOR_BAYER_BG2RGB_EA
+COLOR_BAYER_BG2RGB_VNG
+COLOR_BAYER_GB2BGR
+COLOR_BAYER_GB2BGRA
+COLOR_BAYER_GB2BGR_EA
+COLOR_BAYER_GB2BGR_VNG
+COLOR_BAYER_GB2GRAY
+COLOR_BAYER_GB2RGB
+COLOR_BAYER_GB2RGBA
+COLOR_BAYER_GB2RGB_EA
+COLOR_BAYER_GB2RGB_VNG
+COLOR_BAYER_GR2BGR
+COLOR_BAYER_GR2BGRA
+COLOR_BAYER_GR2BGR_EA
+COLOR_BAYER_GR2BGR_VNG
+COLOR_BAYER_GR2GRAY
+COLOR_BAYER_GR2RGB
+COLOR_BAYER_GR2RGBA
+COLOR_BAYER_GR2RGB_EA
+COLOR_BAYER_GR2RGB_VNG
+COLOR_BAYER_RG2BGR
+COLOR_BAYER_RG2BGRA
+COLOR_BAYER_RG2BGR_EA
+COLOR_BAYER_RG2BGR_VNG
+COLOR_BAYER_RG2GRAY
+COLOR_BAYER_RG2RGB
+COLOR_BAYER_RG2RGBA
+COLOR_BAYER_RG2RGB_EA
+COLOR_BAYER_RG2RGB_VNG
+COLOR_BGR2BGR555
+COLOR_BGR2BGR565
+COLOR_BGR2BGRA
+COLOR_BGR2GRAY
+COLOR_BGR2HLS
+COLOR_BGR2HLS_FULL
+COLOR_BGR2HSV
+COLOR_BGR2HSV_FULL
+COLOR_BGR2LAB
+COLOR_BGR2LUV
+COLOR_BGR2Lab
+COLOR_BGR2Luv
+COLOR_BGR2RGB
+COLOR_BGR2RGBA
+COLOR_BGR2XYZ
+COLOR_BGR2YCR_CB
+COLOR_BGR2YCrCb
+COLOR_BGR2YUV
+COLOR_BGR2YUV_I420
+COLOR_BGR2YUV_IYUV
+COLOR_BGR2YUV_YV12
+COLOR_BGR5552BGR
+COLOR_BGR5552BGRA
+COLOR_BGR5552GRAY
+COLOR_BGR5552RGB
+COLOR_BGR5552RGBA
+COLOR_BGR5652BGR
+COLOR_BGR5652BGRA
+COLOR_BGR5652GRAY
+COLOR_BGR5652RGB
+COLOR_BGR5652RGBA
+COLOR_BGRA2BGR
+COLOR_BGRA2BGR555
+COLOR_BGRA2BGR565
+COLOR_BGRA2GRAY
+COLOR_BGRA2RGB
+COLOR_BGRA2RGBA
+COLOR_BGRA2YUV_I420
+COLOR_BGRA2YUV_IYUV
+COLOR_BGRA2YUV_YV12
+COLOR_BayerBG2BGR
+COLOR_BayerBG2BGRA
+COLOR_BayerBG2BGR_EA
+COLOR_BayerBG2BGR_VNG
+COLOR_BayerBG2GRAY
+COLOR_BayerBG2RGB
+COLOR_BayerBG2RGBA
+COLOR_BayerBG2RGB_EA
+COLOR_BayerBG2RGB_VNG
+COLOR_BayerGB2BGR
+COLOR_BayerGB2BGRA
+COLOR_BayerGB2BGR_EA
+COLOR_BayerGB2BGR_VNG
+COLOR_BayerGB2GRAY
+COLOR_BayerGB2RGB
+COLOR_BayerGB2RGBA
+COLOR_BayerGB2RGB_EA
+COLOR_BayerGB2RGB_VNG
+COLOR_BayerGR2BGR
+COLOR_BayerGR2BGRA
+COLOR_BayerGR2BGR_EA
+COLOR_BayerGR2BGR_VNG
+COLOR_BayerGR2GRAY
+COLOR_BayerGR2RGB
+COLOR_BayerGR2RGBA
+COLOR_BayerGR2RGB_EA
+COLOR_BayerGR2RGB_VNG
+COLOR_BayerRG2BGR
+COLOR_BayerRG2BGRA
+COLOR_BayerRG2BGR_EA
+COLOR_BayerRG2BGR_VNG
+COLOR_BayerRG2GRAY
+COLOR_BayerRG2RGB
+COLOR_BayerRG2RGBA
+COLOR_BayerRG2RGB_EA
+COLOR_BayerRG2RGB_VNG
+COLOR_COLORCVT_MAX
+COLOR_GRAY2BGR
+COLOR_GRAY2BGR555
+COLOR_GRAY2BGR565
+COLOR_GRAY2BGRA
+COLOR_GRAY2RGB
+COLOR_GRAY2RGBA
+COLOR_HLS2BGR
+COLOR_HLS2BGR_FULL
+COLOR_HLS2RGB
+COLOR_HLS2RGB_FULL
+COLOR_HSV2BGR
+COLOR_HSV2BGR_FULL
+COLOR_HSV2RGB
+COLOR_HSV2RGB_FULL
+COLOR_LAB2BGR
+COLOR_LAB2LBGR
+COLOR_LAB2LRGB
+COLOR_LAB2RGB
+COLOR_LBGR2LAB
+COLOR_LBGR2LUV
+COLOR_LBGR2Lab
+COLOR_LBGR2Luv
+COLOR_LRGB2LAB
+COLOR_LRGB2LUV
+COLOR_LRGB2Lab
+COLOR_LRGB2Luv
+COLOR_LUV2BGR
+COLOR_LUV2LBGR
+COLOR_LUV2LRGB
+COLOR_LUV2RGB
+COLOR_Lab2BGR
+COLOR_Lab2LBGR
+COLOR_Lab2LRGB
+COLOR_Lab2RGB
+COLOR_Luv2BGR
+COLOR_Luv2LBGR
+COLOR_Luv2LRGB
+COLOR_Luv2RGB
+COLOR_M_RGBA2RGBA
+COLOR_RGB2BGR
+COLOR_RGB2BGR555
+COLOR_RGB2BGR565
+COLOR_RGB2BGRA
+COLOR_RGB2GRAY
+COLOR_RGB2HLS
+COLOR_RGB2HLS_FULL
+COLOR_RGB2HSV
+COLOR_RGB2HSV_FULL
+COLOR_RGB2LAB
+COLOR_RGB2LUV
+COLOR_RGB2Lab
+COLOR_RGB2Luv
+COLOR_RGB2RGBA
+COLOR_RGB2XYZ
+COLOR_RGB2YCR_CB
+COLOR_RGB2YCrCb
+COLOR_RGB2YUV
+COLOR_RGB2YUV_I420
+COLOR_RGB2YUV_IYUV
+COLOR_RGB2YUV_YV12
+COLOR_RGBA2BGR
+COLOR_RGBA2BGR555
+COLOR_RGBA2BGR565
+COLOR_RGBA2BGRA
+COLOR_RGBA2GRAY
+COLOR_RGBA2M_RGBA
+COLOR_RGBA2RGB
+COLOR_RGBA2YUV_I420
+COLOR_RGBA2YUV_IYUV
+COLOR_RGBA2YUV_YV12
+COLOR_RGBA2mRGBA
+COLOR_XYZ2BGR
+COLOR_XYZ2RGB
+COLOR_YCR_CB2BGR
+COLOR_YCR_CB2RGB
+COLOR_YCrCb2BGR
+COLOR_YCrCb2RGB
+COLOR_YUV2BGR
+COLOR_YUV2BGRA_I420
+COLOR_YUV2BGRA_IYUV
+COLOR_YUV2BGRA_NV12
+COLOR_YUV2BGRA_NV21
+COLOR_YUV2BGRA_UYNV
+COLOR_YUV2BGRA_UYVY
+COLOR_YUV2BGRA_Y422
+COLOR_YUV2BGRA_YUNV
+COLOR_YUV2BGRA_YUY2
+COLOR_YUV2BGRA_YUYV
+COLOR_YUV2BGRA_YV12
+COLOR_YUV2BGRA_YVYU
+COLOR_YUV2BGR_I420
+COLOR_YUV2BGR_IYUV
+COLOR_YUV2BGR_NV12
+COLOR_YUV2BGR_NV21
+COLOR_YUV2BGR_UYNV
+COLOR_YUV2BGR_UYVY
+COLOR_YUV2BGR_Y422
+COLOR_YUV2BGR_YUNV
+COLOR_YUV2BGR_YUY2
+COLOR_YUV2BGR_YUYV
+COLOR_YUV2BGR_YV12
+COLOR_YUV2BGR_YVYU
+COLOR_YUV2GRAY_420
+COLOR_YUV2GRAY_I420
+COLOR_YUV2GRAY_IYUV
+COLOR_YUV2GRAY_NV12
+COLOR_YUV2GRAY_NV21
+COLOR_YUV2GRAY_UYNV
+COLOR_YUV2GRAY_UYVY
+COLOR_YUV2GRAY_Y422
+COLOR_YUV2GRAY_YUNV
+COLOR_YUV2GRAY_YUY2
+COLOR_YUV2GRAY_YUYV
+COLOR_YUV2GRAY_YV12
+COLOR_YUV2GRAY_YVYU
+COLOR_YUV2RGB
+COLOR_YUV2RGBA_I420
+COLOR_YUV2RGBA_IYUV
+COLOR_YUV2RGBA_NV12
+COLOR_YUV2RGBA_NV21
+COLOR_YUV2RGBA_UYNV
+COLOR_YUV2RGBA_UYVY
+COLOR_YUV2RGBA_Y422
+COLOR_YUV2RGBA_YUNV
+COLOR_YUV2RGBA_YUY2
+COLOR_YUV2RGBA_YUYV
+COLOR_YUV2RGBA_YV12
+COLOR_YUV2RGBA_YVYU
+COLOR_YUV2RGB_I420
+COLOR_YUV2RGB_IYUV
+COLOR_YUV2RGB_NV12
+COLOR_YUV2RGB_NV21
+COLOR_YUV2RGB_UYNV
+COLOR_YUV2RGB_UYVY
+COLOR_YUV2RGB_Y422
+COLOR_YUV2RGB_YUNV
+COLOR_YUV2RGB_YUY2
+COLOR_YUV2RGB_YUYV
+COLOR_YUV2RGB_YV12
+COLOR_YUV2RGB_YVYU
+COLOR_YUV420P2BGR
+COLOR_YUV420P2BGRA
+COLOR_YUV420P2GRAY
+COLOR_YUV420P2RGB
+COLOR_YUV420P2RGBA
+COLOR_YUV420SP2BGR
+COLOR_YUV420SP2BGRA
+COLOR_YUV420SP2GRAY
+COLOR_YUV420SP2RGB
+COLOR_YUV420SP2RGBA
+COLOR_YUV420p2BGR
+COLOR_YUV420p2BGRA
+COLOR_YUV420p2GRAY
+COLOR_YUV420p2RGB
+COLOR_YUV420p2RGBA
+COLOR_YUV420sp2BGR
+COLOR_YUV420sp2BGRA
+COLOR_YUV420sp2GRAY
+COLOR_YUV420sp2RGB
+COLOR_YUV420sp2RGBA
+COLOR_mRGBA2RGBA
diff --git a/img_rec_module/img_1_1_meas.txt b/img_rec_module/img_1_1_meas.txt
new file mode 100644
index 0000000..97ed4c5
--- /dev/null
+++ b/img_rec_module/img_1_1_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 384.9492753623189; 
+y: zero_crossing: 128.7986577181208, edge_converge: 124.79086538461539
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 385.07291666666663; 
+y: zero_crossing: 129.5, edge_converge: 125.5
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 383.90625, edge_converge: 379.9741379310345; 
+y: zero_crossing: 129.52727272727273, edge_converge: 130.70366132723112
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 379.9741379310345; 
+y: zero_crossing: 129.6, edge_converge: 130.70366132723112
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 379.9741379310345; 
+y: zero_crossing: 129.68627450980392, edge_converge: 130.70366132723112
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 383.6551724137931, edge_converge: 379.9741379310345; 
+y: zero_crossing: 129.55172413793105, edge_converge: 130.70366132723112
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 383.55172413793105, edge_converge: 379.9741379310345; 
+y: zero_crossing: 129.5, edge_converge: 130.70366132723112
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 383.90625, edge_converge: 379.9741379310345; 
+y: zero_crossing: 129.5, edge_converge: 130.70366132723112
diff --git a/img_rec_module/img_1_1new_meas.txt b/img_rec_module/img_1_1new_meas.txt
new file mode 100644
index 0000000..70d4297
--- /dev/null
+++ b/img_rec_module/img_1_1new_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 323.66566716641677; 
+y: zero_crossing: -1, edge_converge: 828.3849673202615
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 597.0; 
+y: zero_crossing: -1, edge_converge: 792.34375
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 880.0; 
+y: zero_crossing: -1, edge_converge: 796.6236413043478
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 716.546875; 
+y: zero_crossing: -1, edge_converge: 832.172697368421
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 308.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 308.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 308.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 321.5, edge_converge: 308.0; 
+y: zero_crossing: -1, edge_converge: -1
diff --git a/img_rec_module/img_20_1_meas.txt b/img_rec_module/img_20_1_meas.txt
new file mode 100644
index 0000000..8f3d333
--- /dev/null
+++ b/img_rec_module/img_20_1_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: 225.33333333333334, edge_converge: 222.40969899665552; 
+y: zero_crossing: 222.44086021505376, edge_converge: 232.5
+ksize:(7, 7), sigmax:0
+x: zero_crossing: 224.90625, edge_converge: 212.425; 
+y: zero_crossing: 222.7012987012987, edge_converge: 244.8653846153846
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 224.55172413793105, edge_converge: 225.7096153846154; 
+y: zero_crossing: 222.8507462686567, edge_converge: 233.3653846153846
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 224.86363636363637, edge_converge: 207.6159420289855; 
+y: zero_crossing: 223.22413793103448, edge_converge: 215.5216346153846
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 224.8421052631579, edge_converge: 198.0; 
+y: zero_crossing: 223.37254901960785, edge_converge: 238.5
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 224.8421052631579, edge_converge: 198.0; 
+y: zero_crossing: 223.33333333333334, edge_converge: 238.5
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 224.8421052631579, edge_converge: 198.0; 
+y: zero_crossing: 223.46341463414635, edge_converge: 238.5
+ksize:(19, 19), sigmax:0
+x: zero_crossing: 224.56521739130434, edge_converge: 198.0; 
+y: zero_crossing: 223.57894736842104, edge_converge: 238.5
diff --git a/img_rec_module/img_20_2_meas.txt b/img_rec_module/img_20_2_meas.txt
new file mode 100644
index 0000000..4e85aab
--- /dev/null
+++ b/img_rec_module/img_20_2_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 225.58333333333331; 
+y: zero_crossing: -1, edge_converge: 182.35897435897436
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 227.5; 
+y: zero_crossing: -1, edge_converge: 224.0
+ksize:(9, 9), sigmax:0
+x: zero_crossing: 218.89655172413794, edge_converge: 212.9415292353823; 
+y: zero_crossing: -1, edge_converge: 232.0
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 218.86363636363637, edge_converge: 266.84375; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 218.5, edge_converge: 213.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 213.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 213.0; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 213.0; 
+y: zero_crossing: 191.76923076923077, edge_converge: -1
diff --git a/img_rec_module/img_20_3_meas.txt b/img_rec_module/img_20_3_meas.txt
new file mode 100644
index 0000000..b228fdb
--- /dev/null
+++ b/img_rec_module/img_20_3_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: 43.1578947368421, edge_converge: 27.907894736842106; 
+y: zero_crossing: 222.41739130434783, edge_converge: 224.8991379310345
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 222.0; 
+y: zero_crossing: 222.53191489361703, edge_converge: 228.6346153846154
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 225.0; 
+y: zero_crossing: 222.6338028169014, edge_converge: 213.0
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 222.75, edge_converge: 291.0
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 222.79166666666666, edge_converge: 200.0
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 222.7450980392157, edge_converge: 200.0
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 222.92105263157896, edge_converge: 200.0
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: 222.9268292682927, edge_converge: 200.0
diff --git a/img_rec_module/img_2_1new_meas.txt b/img_rec_module/img_2_1new_meas.txt
new file mode 100644
index 0000000..a8b0bc8
--- /dev/null
+++ b/img_rec_module/img_2_1new_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 1178.6311475409836; 
+y: zero_crossing: -1, edge_converge: 376.4716202270382
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: -1; 
+y: zero_crossing: -1, edge_converge: 635.5
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 1582.0; 
+y: zero_crossing: -1, edge_converge: 723.0
+ksize:(11, 11), sigmax:0
+x: zero_crossing: -1, edge_converge: 1682.4423076923076; 
+y: zero_crossing: -1, edge_converge: -1
+ksize:(13, 13), sigmax:0
+x: zero_crossing: -1, edge_converge: 1081.9783653846152; 
+y: zero_crossing: -1, edge_converge: 558.15625
+ksize:(15, 15), sigmax:0
+x: zero_crossing: 72.5, edge_converge: 1081.9783653846152; 
+y: zero_crossing: -1, edge_converge: 558.15625
+ksize:(17, 17), sigmax:0
+x: zero_crossing: -1, edge_converge: 1081.9783653846152; 
+y: zero_crossing: -1, edge_converge: 558.15625
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 1081.9783653846152; 
+y: zero_crossing: -1, edge_converge: 558.15625
diff --git a/img_rec_module/img_3_1new_meas.txt b/img_rec_module/img_3_1new_meas.txt
new file mode 100644
index 0000000..8cf1d3c
--- /dev/null
+++ b/img_rec_module/img_3_1new_meas.txt
@@ -0,0 +1,24 @@
+ksize:(5, 5), sigmax:0
+x: zero_crossing: -1, edge_converge: 485.2365079365079; 
+y: zero_crossing: -1, edge_converge: 463.47727272727275
+ksize:(7, 7), sigmax:0
+x: zero_crossing: -1, edge_converge: 484.8125; 
+y: zero_crossing: -1, edge_converge: 464.0
+ksize:(9, 9), sigmax:0
+x: zero_crossing: -1, edge_converge: 482.4; 
+y: zero_crossing: 462.8421052631579, edge_converge: 464.13461538461536
+ksize:(11, 11), sigmax:0
+x: zero_crossing: 480.5, edge_converge: 475.3696120689655; 
+y: zero_crossing: 462.5, edge_converge: 478.50674662668666
+ksize:(13, 13), sigmax:0
+x: zero_crossing: 485.2307692307692, edge_converge: 493.35480943738656; 
+y: zero_crossing: 462.1875, edge_converge: 448.98519736842104
+ksize:(15, 15), sigmax:0
+x: zero_crossing: -1, edge_converge: 493.35480943738656; 
+y: zero_crossing: 462.5, edge_converge: 448.98519736842104
+ksize:(17, 17), sigmax:0
+x: zero_crossing: 487.5, edge_converge: 493.35480943738656; 
+y: zero_crossing: 462.1875, edge_converge: 448.98519736842104
+ksize:(19, 19), sigmax:0
+x: zero_crossing: -1, edge_converge: 493.35480943738656; 
+y: zero_crossing: 462.4347826086956, edge_converge: 448.98519736842104
diff --git a/img_rec_module/img_filter.py b/img_rec_module/img_filter.py
new file mode 100644
index 0000000..3b8b0dc
--- /dev/null
+++ b/img_rec_module/img_filter.py
@@ -0,0 +1,23 @@
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+img = cv2.imread('../testpic/img_20_1.png',0)
+blur = cv2.GaussianBlur(img,(5,5),0)
+cv2.imshow("ok", blur)
+img = blur
+
+laplacian = cv2.Laplacian(img,cv2.CV_64F)
+sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)
+sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=5)
+
+plt.subplot(2,2,1),plt.imshow(img,cmap = 'gray')
+plt.title('Original'), plt.xticks([]), plt.yticks([])
+plt.subplot(2,2,2),plt.imshow(laplacian,cmap = 'gray')
+plt.title('Laplacian'), plt.xticks([]), plt.yticks([])
+plt.subplot(2,2,3),plt.imshow(sobelx,cmap = 'gray')
+plt.title('Sobel X'), plt.xticks([]), plt.yticks([])
+plt.subplot(2,2,4),plt.imshow(sobely,cmap = 'gray')
+plt.title('Sobel Y'), plt.xticks([]), plt.yticks([])
+
+plt.show()
\ No newline at end of file
diff --git a/img_rec_module/img_rec.py b/img_rec_module/img_rec.py
new file mode 100644
index 0000000..eac36ff
--- /dev/null
+++ b/img_rec_module/img_rec.py
@@ -0,0 +1,176 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+from img_rec_module.sig_proc_test import FM, compare_data_plots, compare_images
+
+
+def identity(img):
+    return img
+
+
+def grayscale(img):
+    return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+
+def grad_detect(img, n, ori, func):
+    #img = np.array(img)
+    if ori == 'h':
+        grad = [func(img, n, i+1) - func(img, n, i) for i in range(img.shape[1]-1)]
+    else:
+        grad = [func(img, i+1, n) - func(img, i, n) for i in range(img.shape[0]-1)]
+    return grad
+
+
+def abs_detect(img, n, ori, func):
+    if ori == 'h':
+        return [func(img, n, i) for i in range(img.shape[1])]
+    else:
+        return [func(img, i, n) for i in range(img.shape[0])]
+
+
+def visualize_data(folder, oname, sample, orient, func, data_p=grad_detect, img_p=identity):
+    dirs = os.listdir(folder)
+    for f in dirs:
+        try:
+            img = img_p(cv2.imread(folder + f))
+            interval = int(img.shape[1 if orient == 'v' else 0] / sample)
+            for j in range(sample):
+                n = j * interval
+                data = data_p(img, n, orient, func)
+                plt_save_data(data, oname + 'data_{0}{1}_'.format(orient, n) + f)
+        except:
+            print(f)
+
+
+def max_entry(img):
+    max_e = 0
+    for row in img:
+        temp = max(row)
+        if temp > max_e:
+            max_e = temp
+
+
+def record_file(vals, fname):
+    f = open(fname, 'w')
+    for v in vals:
+        f.write(str(v) + '\n')
+    f.close()
+
+
+def rel_lumin(img, r, c):
+    b = img.item(r, c, 0)
+    g = img.item(r, c, 1)
+    r = img.item(r, c, 2)
+    return 0.2126 * r + 0.7152 * g + 0.0722 * b
+
+
+def max_method(img):
+    index = []
+    s = 0
+    for i in range(img.shape[1]):
+        val = np.argmax(img[:, i])
+        index.append(val)
+        s += val
+    return s / len(index)
+
+
+def plot_show_data(data):
+    plt.figure(figsize=(16, 8))
+    #plt.xticks(range(0, img.shape[0], 20), range(0, img.shape[0], 20))
+    plt.plot(data, 'b-')
+    plt.show()
+    plt.close()
+
+
+def plt_save_data(data, fname):
+    plt.figure(figsize=(16, 8))
+    plt.plot(data, 'b-')
+    plt.savefig(fname)
+    plt.close()
+
+
+def centroid(img, func):
+    r = img.shape[0]
+    c = img.shape[1]
+    aggr_i = 0
+    aggr_j = 0
+    aggr = 0
+    for i in range(r):
+        for j in range(c):
+            lum = func(img, i, j)
+            aggr_i += lum * i
+            aggr_j += lum * j
+            aggr += lum
+    dim = r * c
+    return aggr_i / aggr, aggr_j / aggr
+    # return aggr_i / dim, aggr_j / dim
+
+
+def centroid_test(fname, n, trials, func, img_p=identity):
+    for i in range(1, n+1):
+        for j in range(1, trials+1):
+            cf = fname.format(i, j)
+            print(cf)
+            im = img_p(cv2.imread(cf))
+            print(centroid(im, func))
+
+
+def brightness_test():
+    BASE_DIR = '../calib/'
+    image_name = 'shutter_{0}_iso_{1}_bright_{2}.jpeg'
+    img_path = BASE_DIR + image_name
+    shutter = [100, 1000, 5000, 10000]
+    isos = [100, 400, 800]
+    brightness = [0, 50, 100]
+    expo_modes = ['auto', 'very_long']
+    for ss in shutter[0:]:
+        comp = []
+        plts = []
+        print(ss)
+        for iso in isos:
+            name = img_path.format(ss, iso, 'very_long')
+            imgr = cv2.imread(name)
+            tp = (imgr, name)
+            comp.append(tp)
+            img = cv2.cvtColor(imgr, cv2.COLOR_BGR2GRAY)
+            plts.append((FM(img, FM.HOR, img.shape[0] // 2).extract_array(), name))
+        compare_images(ilist=comp)
+        compare_data_plots(ilist=plts)
+
+
+gray_p = lambda img, r, c: img.item(r, c)
+
+if __name__ == '__main__':
+    #centroid_test("../testpic/img_{0}_{1}.png", 25, 3, gray_p, img_p=grayscale)
+    # centroid_test("../testpic/img_{0}_{1}.png", 25, 3, rel_lumin)
+
+    """img = cv2.imread("../testpic/png_1_1.png")
+    plot_show_data(abs_detect(img, 0, 'v', rel_lumin))"""
+
+    """img = cv2.imread("../testpic/png_1_1.png")
+    print(img.shape)
+    cv2.imshow('all', img)
+    gs = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    cv2.imshow('gray', gs)
+    print(sum(centers) / len(centers))
+    print(max_method(gs))
+    record_file(centers, 'centers.txt')
+    
+    while True:
+        prompt = input("type in an index to draw or q to quit")
+        if prompt == 'q':
+            break
+        else:
+            plot_i(int(prompt))
+    
+    centers = []
+    for i in range(gs.shape[1]):
+        grad = grad_detect(gs, i, 'v', rel_lumin)
+        max_v = max(range(len(grad)), key=lambda i: grad[i])
+        min_v = min(range(len(grad)), key=lambda i: grad[i])
+        centers.append((max_v + min_v) / 2)"""
+
+    #visualize_data("../testpic/", "../absdataplot/", 10, 'h', rel_lumin, data_p=abs_detect)
+    brightness_test()
+
diff --git a/img_rec_module/log.docx b/img_rec_module/log.docx
new file mode 100644
index 0000000..b574f98
Binary files /dev/null and b/img_rec_module/log.docx differ
diff --git a/img_rec_module/meas/calib4/img_100_{0}/img_100_{0}_(100, 200).txt b/img_rec_module/meas/calib4/img_100_{0}/img_100_{0}_(100, 200).txt
new file mode 100644
index 0000000..06ac13b
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_100_{0}/img_100_{0}_(100, 200).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 291.03499068467625, poly: 289.2972972972973, gaussian: 289.8178178178178; 
+y: edge_centroid: 250.59977810650898, poly: 256.96096096096096, gaussian: 216.0
diff --git a/img_rec_module/meas/calib4/img_100_{0}/img_100_{0}_(100, 300).txt b/img_rec_module/meas/calib4/img_100_{0}/img_100_{0}_(100, 300).txt
new file mode 100644
index 0000000..c66127c
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_100_{0}/img_100_{0}_(100, 300).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 291.03499068467625, poly: 289.2972972972973, gaussian: 289.8408408408408; 
+y: edge_centroid: -1, poly: 0, gaussian: 0
diff --git a/img_rec_module/meas/calib4/img_100_{0}/img_100_{0}_(120, 300).txt b/img_rec_module/meas/calib4/img_100_{0}/img_100_{0}_(120, 300).txt
new file mode 100644
index 0000000..e69de29
diff --git a/img_rec_module/meas/calib4/img_100_{0}/img_100_{0}_(490, 50).txt b/img_rec_module/meas/calib4/img_100_{0}/img_100_{0}_(490, 50).txt
new file mode 100644
index 0000000..e69de29
diff --git a/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(100, 150).txt b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(100, 150).txt
new file mode 100644
index 0000000..5cc8360
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(100, 150).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 476.0200477822787, poly: 474.4554554554555, gaussian: 475.0960960960961; 
+y: edge_centroid: 319.75075987841956, poly: 352.0, gaussian: 317.5045045045045
diff --git a/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(150, 200).txt b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(150, 200).txt
new file mode 100644
index 0000000..c181231
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(150, 200).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 483.1316033979296, poly: 479.963963963964, gaussian: 472.96596596596595; 
+y: edge_centroid: 326.8176090597337, poly: 325.0900900900901, gaussian: 325.9259259259259
diff --git a/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(20, 300).txt b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(20, 300).txt
new file mode 100644
index 0000000..e69de29
diff --git a/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(200, 250).txt b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(200, 250).txt
new file mode 100644
index 0000000..8e70bbf
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(200, 250).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 477.7850568145749, poly: 483.3743743743744, gaussian: 481.3453453453453; 
+y: edge_centroid: 324.4145637296835, poly: 318.4034034034034, gaussian: 328.4984984984985
diff --git a/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(200, 300).txt b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(200, 300).txt
new file mode 100644
index 0000000..0858c20
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(200, 300).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 477.7850568145749, poly: 483.3743743743744, gaussian: 481.3453453453453; 
+y: edge_centroid: 315.5961538461538, poly: 301.93293293293294, gaussian: 314.3693693693694
diff --git a/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(200, 500).txt b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(200, 500).txt
new file mode 100644
index 0000000..f65380f
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(200, 500).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 477.7850568145749, poly: 483.3743743743744, gaussian: 481.3453453453453; 
+y: edge_centroid: 307.213521344408, poly: 323.22522522522524, gaussian: 322.5015015015015
diff --git a/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(250, 300).txt b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(250, 300).txt
new file mode 100644
index 0000000..41d732a
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(250, 300).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 476.7974399164053, poly: 480.7717717717718, gaussian: 478.4754754754755; 
+y: edge_centroid: 315.5961538461538, poly: 301.93293293293294, gaussian: 314.3693693693694
diff --git a/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(30, 100).txt b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(30, 100).txt
new file mode 100644
index 0000000..9a0a702
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(30, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 477.7408469342743, poly: 474.3133133133133, gaussian: 475.96296296296293; 
+y: edge_centroid: 312.8755548978987, poly: 312.6666666666667, gaussian: 313.2162162162162
diff --git a/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(300, 350).txt b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(300, 350).txt
new file mode 100644
index 0000000..9fcb0bd
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(300, 350).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 478.06332670022255, poly: 479.2022022022022, gaussian: 478.8808808808809; 
+y: edge_centroid: 315.1341000267452, poly: 314.23823823823824, gaussian: 347.0
diff --git a/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(300, 400).txt b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(300, 400).txt
new file mode 100644
index 0000000..002895d
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(300, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 478.06332670022255, poly: 479.2022022022022, gaussian: 478.8808808808809; 
+y: edge_centroid: 310.42203052422025, poly: 313.1111111111111, gaussian: 312.4864864864865
diff --git a/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(350, 400).txt b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(350, 400).txt
new file mode 100644
index 0000000..99ae642
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(350, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 476.5766195632225, poly: 479.4594594594595, gaussian: 477.3223223223223; 
+y: edge_centroid: 310.42203052422025, poly: 313.1111111111111, gaussian: 312.4864864864865
diff --git a/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(400, 450).txt b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(400, 450).txt
new file mode 100644
index 0000000..0101161
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(400, 450).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 481.25031779661015, poly: 481.04804804804803, gaussian: 480.36036036036035; 
+y: edge_centroid: 320.51106920874355, poly: 322.13113113113116, gaussian: 321.7687687687688
diff --git a/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(450, 550).txt b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(450, 550).txt
new file mode 100644
index 0000000..f859e85
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(450, 550).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 480.90588235294115, poly: 479.13613613613614, gaussian: 479.12312312312315; 
+y: edge_centroid: 316.68044077134977, poly: 313.73273273273276, gaussian: 315.5095095095095
diff --git a/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(50, 100).txt b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(50, 100).txt
new file mode 100644
index 0000000..c0a15fe
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_130_{0}/img_130_{0}_(50, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 474.3070001323277, poly: 472.996996996997, gaussian: 472.81081081081084; 
+y: edge_centroid: 312.8755548978987, poly: 312.6666666666667, gaussian: 313.2162162162162
diff --git a/img_rec_module/meas/calib4/img_133_{0}/img_133_{0}.png_(250, 100).txt b/img_rec_module/meas/calib4/img_133_{0}/img_133_{0}.png_(250, 100).txt
new file mode 100644
index 0000000..f2af609
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_133_{0}/img_133_{0}.png_(250, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 277.76777028087247, poly: 281.1461461461461, gaussian: 278.5765765765766; 
+y: edge_centroid: 302.41199200532964, poly: 296.71771771771773, gaussian: 299.7837837837838
diff --git a/img_rec_module/meas/calib4/img_133_{0}/img_133_{0}.png_(250, 50).txt b/img_rec_module/meas/calib4/img_133_{0}/img_133_{0}.png_(250, 50).txt
new file mode 100644
index 0000000..b79b4f9
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_133_{0}/img_133_{0}.png_(250, 50).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 277.76777028087247, poly: 281.1461461461461, gaussian: 278.5765765765766; 
+y: edge_centroid: 266.9218650877565, poly: 284.07207207207205, gaussian: 279.6546546546547
diff --git a/img_rec_module/meas/calib4/img_133_{0}/img_133_{0}.png_(300, 550).txt b/img_rec_module/meas/calib4/img_133_{0}/img_133_{0}.png_(300, 550).txt
new file mode 100644
index 0000000..84b1934
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_133_{0}/img_133_{0}.png_(300, 550).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 81.92717111682785, poly: 81.3933933933934, gaussian: 81.24724724724724; 
+y: edge_centroid: 289.13216374268995, poly: 286.6216216216216, gaussian: 288.1381381381381
diff --git a/img_rec_module/meas/calib4/img_134_{0}/img_134_{0}.png_(200, 150).txt b/img_rec_module/meas/calib4/img_134_{0}/img_134_{0}.png_(200, 150).txt
new file mode 100644
index 0000000..0d980c5
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_134_{0}/img_134_{0}.png_(200, 150).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 58.87142186766776, poly: 57.72372372372372, gaussian: 58.56256256256256; 
+y: edge_centroid: 298.5141452126122, poly: 298.53453453453454, gaussian: 299.05505505505505
diff --git a/img_rec_module/meas/calib4/img_157_{0}/img_157_{0}.png_(250, 450).txt b/img_rec_module/meas/calib4/img_157_{0}/img_157_{0}.png_(250, 450).txt
new file mode 100644
index 0000000..bc8985f
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_157_{0}/img_157_{0}.png_(250, 450).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 231.9349172144786, poly: 229.16516516516518, gaussian: 230.4204204204204; 
+y: edge_centroid: 324.7964368509638, poly: 320.92092092092093, gaussian: 322.7027027027027
diff --git a/img_rec_module/meas/calib4/img_160_{0}/img_160_{0}_(100, 500).txt b/img_rec_module/meas/calib4/img_160_{0}/img_160_{0}_(100, 500).txt
new file mode 100644
index 0000000..7b9d1b7
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_160_{0}/img_160_{0}_(100, 500).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 225.6382858488344, poly: 219.85385385385385, gaussian: 223.6846846846847; 
+y: edge_centroid: 326.9082845283447, poly: 329.64864864864865, gaussian: 329.45245245245246
diff --git a/img_rec_module/meas/calib4/img_160_{0}/img_160_{0}_(300, 400).txt b/img_rec_module/meas/calib4/img_160_{0}/img_160_{0}_(300, 400).txt
new file mode 100644
index 0000000..a009916
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_160_{0}/img_160_{0}_(300, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 218.77561608300925, poly: 224.71471471471472, gaussian: 223.88388388388387; 
+y: edge_centroid: 325.48699770378875, poly: 323.76576576576576, gaussian: 321.72972972972974
diff --git a/img_rec_module/meas/calib4/img_160_{0}/img_160_{0}_(350, 450).txt b/img_rec_module/meas/calib4/img_160_{0}/img_160_{0}_(350, 450).txt
new file mode 100644
index 0000000..7dca8cd
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_160_{0}/img_160_{0}_(350, 450).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 221.86800998243837, poly: 223.86086086086087, gaussian: 222.33833833833833; 
+y: edge_centroid: 324.95901767558263, poly: 319.3243243243243, gaussian: 320.1621621621622
diff --git a/img_rec_module/meas/calib4/img_170_{0}/img_170_{0}_(200, 350).txt b/img_rec_module/meas/calib4/img_170_{0}/img_170_{0}_(200, 350).txt
new file mode 100644
index 0000000..deecda3
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_170_{0}/img_170_{0}_(200, 350).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 392.6319532908705, poly: 390.93993993993996, gaussian: 391.22222222222223; 
+y: edge_centroid: 332.7505637119807, poly: 333.94494494494495, gaussian: 333.13613613613614
diff --git a/img_rec_module/meas/calib4/img_170_{0}/img_170_{0}_(200, 450).txt b/img_rec_module/meas/calib4/img_170_{0}/img_170_{0}_(200, 450).txt
new file mode 100644
index 0000000..b69498b
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_170_{0}/img_170_{0}_(200, 450).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 392.6319532908705, poly: 390.93993993993996, gaussian: 391.22222222222223; 
+y: edge_centroid: 340.36645473811694, poly: 333.6066066066066, gaussian: 333.86686686686687
diff --git a/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(100, 150).txt b/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(100, 150).txt
new file mode 100644
index 0000000..0564307
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(100, 150).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 220.7910065139735, poly: 217.15515515515517, gaussian: 220.11711711711712; 
+y: edge_centroid: 160.24814595075648, poly: 158.1981981981982, gaussian: 155.2112112112112
diff --git a/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(150, 200).txt b/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(150, 200).txt
new file mode 100644
index 0000000..1f815cd
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(150, 200).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 226.56943727413525, poly: 233.0, gaussian: 231.61461461461462; 
+y: edge_centroid: -1, poly: 157.03003003003002, gaussian: 159.97997997998
diff --git a/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(200, 250).txt b/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(200, 250).txt
new file mode 100644
index 0000000..7084079
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(200, 250).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 224.38829323735553, poly: 221.4204204204204, gaussian: 222.997997997998; 
+y: edge_centroid: -1, poly: 136.21621621621622, gaussian: 153.43543543543544
diff --git a/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(200, 50).txt b/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(200, 50).txt
new file mode 100644
index 0000000..751271c
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(200, 50).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 224.38829323735553, poly: 221.4204204204204, gaussian: 222.997997997998; 
+y: edge_centroid: 159.31560235197594, poly: 147.12512512512512, gaussian: 138.0
diff --git a/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(250, 300).txt b/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(250, 300).txt
new file mode 100644
index 0000000..e2f1dfc
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(250, 300).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 228.60011869116025, poly: 215.4144144144144, gaussian: 201.0; 
+y: edge_centroid: 155.03382867757378, poly: 157.1791791791792, gaussian: 158.998998998999
diff --git a/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(300, 350).txt b/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(300, 350).txt
new file mode 100644
index 0000000..238ae5c
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(300, 350).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 228.2786542650262, poly: 231.36236236236238, gaussian: 224.34534534534535; 
+y: edge_centroid: -1, poly: 139.21621621621622, gaussian: 139.81581581581582
diff --git a/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(350, 400).txt b/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(350, 400).txt
new file mode 100644
index 0000000..c508eef
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(350, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 218.11615628299896, poly: 221.07207207207207, gaussian: 219.3013013013013; 
+y: edge_centroid: 155.50516848445383, poly: 152.59459459459458, gaussian: 152.37137137137137
diff --git a/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(400, 450).txt b/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(400, 450).txt
new file mode 100644
index 0000000..4c33dde
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(400, 450).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 228.5702052689802, poly: 220.84284284284286, gaussian: 200.0; 
+y: edge_centroid: 148.5988915951851, poly: 134.5095095095095, gaussian: 135.5895895895896
diff --git a/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(50, 100).txt b/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(50, 100).txt
new file mode 100644
index 0000000..45a7482
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_180_{0}/img_180_{0}_(50, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 225.69889181411875, poly: 219.86186186186185, gaussian: 228.002002002002; 
+y: edge_centroid: 156.60606647930996, poly: 154.3803803803804, gaussian: 151.83083083083082
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}.png_(200, 50).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}.png_(200, 50).txt
new file mode 100644
index 0000000..cb9440f
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}.png_(200, 50).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.21657196969699, poly: 93.01701701701703, gaussian: 92.54954954954955; 
+y: edge_centroid: 220.67972613160907, poly: 205.44544544544544, gaussian: 220.13513513513513
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}.png_(250, 100).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}.png_(250, 100).txt
new file mode 100644
index 0000000..e69de29
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}.png_(250, 150).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}.png_(250, 150).txt
new file mode 100644
index 0000000..4e7e731
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}.png_(250, 150).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 91.0, poly: 90.61461461461461, gaussian: 90.76176176176176; 
+y: edge_centroid: 207.25707213578505, poly: 211.72972972972974, gaussian: 210.05605605605606
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}.png_(250, 250).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}.png_(250, 250).txt
new file mode 100644
index 0000000..0b0c80e
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}.png_(250, 250).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 91.0, poly: 90.61461461461461, gaussian: 90.76176176176176; 
+y: edge_centroid: 218.612658674189, poly: 210.37037037037038, gaussian: 216.64864864864865
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}.png_(250, 550).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}.png_(250, 550).txt
new file mode 100644
index 0000000..5d716b8
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}.png_(250, 550).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 91.0, poly: 90.61461461461461, gaussian: 90.76176176176176; 
+y: edge_centroid: 208.3091801113311, poly: 194.96096096096096, gaussian: 207.03603603603602
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}.png_(300, 500).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}.png_(300, 500).txt
new file mode 100644
index 0000000..7669241
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}.png_(300, 500).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 94.67445813854653, poly: 94.85585585585585, gaussian: 94.37537537537537; 
+y: edge_centroid: 211.19331646152077, poly: 195.86786786786786, gaussian: 209.42642642642642
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(100, 10).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(100, 10).txt
new file mode 100644
index 0000000..c7c4711
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(100, 10).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 94.53349610865254, poly: 90.78078078078079, gaussian: 90.11711711711712; 
+y: edge_centroid: 221.0741458686698, poly: 244.62762762762765, gaussian: 258.0
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(100, 400).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(100, 400).txt
new file mode 100644
index 0000000..e9008a5
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(100, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 94.53349610865254, poly: 92.03003003003003, gaussian: 92.93793793793793; 
+y: edge_centroid: 214.17900156837212, poly: 209.03203203203202, gaussian: 214.83483483483485
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(200, 100).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(200, 100).txt
new file mode 100644
index 0000000..34f75b7
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(200, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.21657196969699, poly: 92.74474474474475, gaussian: 92.64864864864865; 
+y: edge_centroid: -1, poly: 196.7027027027027, gaussian: 220.20220220220222
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(200, 300).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(200, 300).txt
new file mode 100644
index 0000000..b1e658b
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(200, 300).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.21657196969699, poly: 92.74474474474475, gaussian: 92.64264264264264; 
+y: edge_centroid: 215.8282069593912, poly: 200.8108108108108, gaussian: 203.97297297297297
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(200, 350).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(200, 350).txt
new file mode 100644
index 0000000..ae51c4c
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(200, 350).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.21657196969699, poly: 92.74474474474475, gaussian: 92.64264264264264; 
+y: edge_centroid: 215.93902685241267, poly: 199.14814814814815, gaussian: 201.96296296296296
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(200, 40).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(200, 40).txt
new file mode 100644
index 0000000..35d04b6
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(200, 40).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.21657196969699, poly: 92.74474474474475, gaussian: 92.64264264264264; 
+y: edge_centroid: 213.46588200238375, poly: 187.52852852852854, gaussian: 250.0
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(200, 400).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(200, 400).txt
new file mode 100644
index 0000000..dd9188e
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(200, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.21657196969699, poly: 93.01701701701703, gaussian: 92.54954954954955; 
+y: edge_centroid: 214.17900156837212, poly: 209.03203203203202, gaussian: 214.83483483483485
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(240, 320).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(240, 320).txt
new file mode 100644
index 0000000..beaeadc
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.95545171339562, poly: 92.43843843843844, gaussian: 92.54054054054055; 
+y: edge_centroid: 216.32595947660937, poly: 197.66066066066065, gaussian: 203.43843843843842
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(250, 100).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(250, 100).txt
new file mode 100644
index 0000000..d73fc36
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(250, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 91.0, poly: 90.61461461461461, gaussian: 90.76176176176176; 
+y: edge_centroid: -1, poly: 198.59459459459458, gaussian: 236.03003003003005
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(30, 300).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(30, 300).txt
new file mode 100644
index 0000000..8c9068f
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(30, 300).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 106.90241734802697, poly: 130.08608608608608, gaussian: 137.0; 
+y: edge_centroid: 215.8282069593912, poly: 200.8108108108108, gaussian: 203.94794794794794
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(300, 150).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(300, 150).txt
new file mode 100644
index 0000000..00f639b
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(300, 150).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 94.67445813854653, poly: 94.85585585585585, gaussian: 94.37537537537537; 
+y: edge_centroid: 207.25707213578505, poly: 211.72972972972974, gaussian: 210.05605605605606
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(300, 400).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(300, 400).txt
new file mode 100644
index 0000000..07a7d5e
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(300, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 94.67445813854653, poly: 94.72372372372372, gaussian: 94.26126126126127; 
+y: edge_centroid: 214.17900156837212, poly: 194.24424424424424, gaussian: 197.8028028028028
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(350, 200).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(350, 200).txt
new file mode 100644
index 0000000..031f3ef
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(350, 200).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 93.30997090716743, poly: 92.7907907907908, gaussian: 93.55555555555556; 
+y: edge_centroid: 219.13322627053438, poly: 210.43043043043042, gaussian: 218.1951951951952
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(400, 250).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(400, 250).txt
new file mode 100644
index 0000000..525a96e
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(400, 250).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 93.80265120132562, poly: 93.7977977977978, gaussian: 93.90190190190191; 
+y: edge_centroid: 218.612658674189, poly: 210.37037037037038, gaussian: 216.64864864864865
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(450, 300).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(450, 300).txt
new file mode 100644
index 0000000..013cc84
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(450, 300).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 94.75074524191695, poly: 94.30530530530531, gaussian: 94.26426426426427; 
+y: edge_centroid: 215.8282069593912, poly: 211.35135135135135, gaussian: 215.15915915915917
diff --git a/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(50, 550).txt b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(50, 550).txt
new file mode 100644
index 0000000..7db67f4
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_59_{0}/img_59_{0}_(50, 550).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 93.89583756345176, poly: 92.57657657657657, gaussian: 92.48648648648648; 
+y: edge_centroid: 208.3091801113311, poly: 189.6096096096096, gaussian: 209.03303303303304
diff --git a/img_rec_module/meas/calib4/img_60_{0}/img_60_{0}.png_(200, 600).txt b/img_rec_module/meas/calib4/img_60_{0}/img_60_{0}.png_(200, 600).txt
new file mode 100644
index 0000000..ed07d5f
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_60_{0}/img_60_{0}.png_(200, 600).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.32846573208725, poly: 92.23423423423424, gaussian: 92.21921921921921; 
+y: edge_centroid: 196.4742684157417, poly: 195.8928928928929, gaussian: 196.3783783783784
diff --git a/img_rec_module/meas/calib4/img_60_{0}/img_60_{0}_(200, 500).txt b/img_rec_module/meas/calib4/img_60_{0}/img_60_{0}_(200, 500).txt
new file mode 100644
index 0000000..a483322
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_60_{0}/img_60_{0}_(200, 500).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.32846573208725, poly: 92.03003003003003, gaussian: 92.08708708708708; 
+y: edge_centroid: 210.0317535296279, poly: 189.0990990990991, gaussian: 212.75375375375376
diff --git a/img_rec_module/meas/calib4/img_60_{0}/img_60_{0}_(240, 320).txt b/img_rec_module/meas/calib4/img_60_{0}/img_60_{0}_(240, 320).txt
new file mode 100644
index 0000000..302af08
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_60_{0}/img_60_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 92.0877174261018, poly: 92.41741741741741, gaussian: 92.28528528528528; 
+y: edge_centroid: 221.40502732240432, poly: 205.07807807807808, gaussian: 206.8998998998999
diff --git a/img_rec_module/meas/calib4/img_60_{0}/img_60_{0}_(400, 100).txt b/img_rec_module/meas/calib4/img_60_{0}/img_60_{0}_(400, 100).txt
new file mode 100644
index 0000000..b340acb
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_60_{0}/img_60_{0}_(400, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 93.30595634480204, poly: 94.14414414414415, gaussian: 93.81681681681681; 
+y: edge_centroid: 307.8291557805356, poly: 354.0, gaussian: 308.5155155155155
diff --git a/img_rec_module/meas/calib4/img_60_{0}/img_60_{0}_(400, 400).txt b/img_rec_module/meas/calib4/img_60_{0}/img_60_{0}_(400, 400).txt
new file mode 100644
index 0000000..461ddb4
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_60_{0}/img_60_{0}_(400, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 93.30595634480204, poly: 94.14414414414415, gaussian: 93.81681681681681; 
+y: edge_centroid: 212.21240264432728, poly: 190.33833833833833, gaussian: 193.35135135135135
diff --git a/img_rec_module/meas/calib4/img_61_{0}/img_61_{0}_(240, 320).txt b/img_rec_module/meas/calib4/img_61_{0}/img_61_{0}_(240, 320).txt
new file mode 100644
index 0000000..750c5f3
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_61_{0}/img_61_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 91.9518581931715, poly: 92.18618618618618, gaussian: 91.98798798798799; 
+y: edge_centroid: 218.40512573697507, poly: 201.44444444444446, gaussian: 206.24924924924926
diff --git a/img_rec_module/meas/calib4/img_61_{0}/img_61_{0}_(400, 400).txt b/img_rec_module/meas/calib4/img_61_{0}/img_61_{0}_(400, 400).txt
new file mode 100644
index 0000000..7978897
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_61_{0}/img_61_{0}_(400, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 93.19376913380157, poly: 94.17417417417417, gaussian: 94.03603603603604; 
+y: edge_centroid: 212.52264302981456, poly: 190.97897897897897, gaussian: 194.45845845845847
diff --git a/img_rec_module/meas/calib4/img_62_{0}/img_62_{0}_(240, 320).txt b/img_rec_module/meas/calib4/img_62_{0}/img_62_{0}_(240, 320).txt
new file mode 100644
index 0000000..d58a228
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_62_{0}/img_62_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 178.91803535934721, poly: 178.44144144144144, gaussian: 178.33333333333334; 
+y: edge_centroid: -1, poly: 184.47747747747746, gaussian: 265.93293293293294
diff --git a/img_rec_module/meas/calib4/img_63_{0}/img_63_{0}_(240, 320).txt b/img_rec_module/meas/calib4/img_63_{0}/img_63_{0}_(240, 320).txt
new file mode 100644
index 0000000..52cbf69
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_63_{0}/img_63_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 178.97878718007834, poly: 178.87387387387386, gaussian: 178.72972972972974; 
+y: edge_centroid: -1, poly: 182.97297297297297, gaussian: 227.4954954954955
diff --git a/img_rec_module/meas/calib4/img_73_{0}/img_73_{0}_(400, 400).txt b/img_rec_module/meas/calib4/img_73_{0}/img_73_{0}_(400, 400).txt
new file mode 100644
index 0000000..3c97a4b
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_73_{0}/img_73_{0}_(400, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 283.485064401206, poly: 323.0, gaussian: 286.4864864864865; 
+y: edge_centroid: 195.9362609551483, poly: 189.6016016016016, gaussian: 190.22222222222223
diff --git a/img_rec_module/meas/calib4/img_74_{0}/img_74_{0}_(100, 300).txt b/img_rec_module/meas/calib4/img_74_{0}/img_74_{0}_(100, 300).txt
new file mode 100644
index 0000000..462740a
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_74_{0}/img_74_{0}_(100, 300).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 278.09682467236087, poly: 268.2682682682683, gaussian: 274.5575575575576; 
+y: edge_centroid: 174.44361763022326, poly: 175.0990990990991, gaussian: 174.0
diff --git a/img_rec_module/meas/calib4/img_74_{0}/img_74_{0}_(150, 350).txt b/img_rec_module/meas/calib4/img_74_{0}/img_74_{0}_(150, 350).txt
new file mode 100644
index 0000000..3abe0ab
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_74_{0}/img_74_{0}_(150, 350).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 264.6934011476264, poly: 267.4674674674675, gaussian: 265.3213213213213; 
+y: edge_centroid: 212.65002063557583, poly: 193.16216216216216, gaussian: 205.15315315315314
diff --git a/img_rec_module/meas/calib4/img_74_{0}/img_74_{0}_(200, 100).txt b/img_rec_module/meas/calib4/img_74_{0}/img_74_{0}_(200, 100).txt
new file mode 100644
index 0000000..ec7eba6
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_74_{0}/img_74_{0}_(200, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 299.2048811389324, poly: 298.0, gaussian: 299.0900900900901; 
+y: edge_centroid: 212.06545603775513, poly: 211.82282282282281, gaussian: 212.06006006006007
diff --git a/img_rec_module/meas/calib4/img_74_{0}/img_74_{0}_(200, 400).txt b/img_rec_module/meas/calib4/img_74_{0}/img_74_{0}_(200, 400).txt
new file mode 100644
index 0000000..0fc7e6b
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_74_{0}/img_74_{0}_(200, 400).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 299.2048811389324, poly: 298.0, gaussian: 299.0900900900901; 
+y: edge_centroid: 216.82302078726215, poly: 193.26126126126127, gaussian: 186.53453453453454
diff --git a/img_rec_module/meas/calib4/img_74_{0}/img_74_{0}_(250, 150).txt b/img_rec_module/meas/calib4/img_74_{0}/img_74_{0}_(250, 150).txt
new file mode 100644
index 0000000..e69de29
diff --git a/img_rec_module/meas/calib4/img_74_{0}/img_74_{0}_(250, 450).txt b/img_rec_module/meas/calib4/img_74_{0}/img_74_{0}_(250, 450).txt
new file mode 100644
index 0000000..e69de29
diff --git a/img_rec_module/meas/calib4/img_78_{0}/img_78_{0}_(400, 260).txt b/img_rec_module/meas/calib4/img_78_{0}/img_78_{0}_(400, 260).txt
new file mode 100644
index 0000000..6892c16
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_78_{0}/img_78_{0}_(400, 260).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 366.0705646776132, poly: 403.0, gaussian: 402.0; 
+y: edge_centroid: 206.59396835595538, poly: 198.7157157157157, gaussian: 201.34434434434434
diff --git a/img_rec_module/meas/calib4/img_79_{0}/img_79_{0}_(100, 200).txt b/img_rec_module/meas/calib4/img_79_{0}/img_79_{0}_(100, 200).txt
new file mode 100644
index 0000000..de27310
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_79_{0}/img_79_{0}_(100, 200).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 361.17140024783157, poly: 351.23423423423424, gaussian: 355.06006006006004; 
+y: edge_centroid: 206.05061553818015, poly: 197.32232232232232, gaussian: 204.25025025025025
diff --git a/img_rec_module/meas/calib4/img_79_{0}/img_79_{0}_(200, 200).txt b/img_rec_module/meas/calib4/img_79_{0}/img_79_{0}_(200, 200).txt
new file mode 100644
index 0000000..e69de29
diff --git a/img_rec_module/meas/calib4/img_80_{0}/img_80_{0}_(200, 300).txt b/img_rec_module/meas/calib4/img_80_{0}/img_80_{0}_(200, 300).txt
new file mode 100644
index 0000000..e69de29
diff --git a/img_rec_module/meas/calib4/img_80_{0}/img_80_{0}_(300, 280).txt b/img_rec_module/meas/calib4/img_80_{0}/img_80_{0}_(300, 280).txt
new file mode 100644
index 0000000..5df82f5
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_80_{0}/img_80_{0}_(300, 280).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 311.3298049296839, poly: 319.7637637637638, gaussian: 319.5735735735736; 
+y: edge_centroid: -1, poly: 0, gaussian: 0
diff --git a/img_rec_module/meas/calib4/img_86_{0}/img_86_{0}.png_(150, 100).txt b/img_rec_module/meas/calib4/img_86_{0}/img_86_{0}.png_(150, 100).txt
new file mode 100644
index 0000000..639b64b
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_86_{0}/img_86_{0}.png_(150, 100).txt	
@@ -0,0 +1,2 @@
+x: edge_centroid: 122.42161046111492, poly: 124.84684684684684, gaussian: 124.58358358358359; 
+y: edge_centroid: 268.99951226309923, poly: 263.5865865865866, gaussian: 263.9189189189189
diff --git a/img_rec_module/meas/calib4/img_86_{0}/img_86_{0}.png_(200, 400).txt b/img_rec_module/meas/calib4/img_86_{0}/img_86_{0}.png_(200, 400).txt
new file mode 100644
index 0000000..34cfc9d
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_86_{0}/img_86_{0}.png_(200, 400).txt	
@@ -0,0 +1,2 @@
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diff --git a/img_rec_module/meas/calib4/img_97_{0}/img_97_{0}_(240, 320).txt b/img_rec_module/meas/calib4/img_97_{0}/img_97_{0}_(240, 320).txt
new file mode 100644
index 0000000..5d771cc
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_97_{0}/img_97_{0}_(240, 320).txt	
@@ -0,0 +1,2 @@
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+y: edge_centroid: 259.66796296933614, poly: 261.015015015015, gaussian: 261.4074074074074
diff --git a/img_rec_module/meas/calib4/img_99_{0}/img_99_{0}_(200, 500).txt b/img_rec_module/meas/calib4/img_99_{0}/img_99_{0}_(200, 500).txt
new file mode 100644
index 0000000..653e98a
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_99_{0}/img_99_{0}_(200, 500).txt	
@@ -0,0 +1,2 @@
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+y: edge_centroid: 262.84957891453524, poly: 258.80680680680683, gaussian: 260.3963963963964
diff --git a/img_rec_module/meas/calib4/img_99_{0}/img_99_{0}_(373, 234).txt b/img_rec_module/meas/calib4/img_99_{0}/img_99_{0}_(373, 234).txt
new file mode 100644
index 0000000..550694b
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_99_{0}/img_99_{0}_(373, 234).txt	
@@ -0,0 +1,2 @@
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+y: edge_centroid: 280.6590576754352, poly: 258.1631631631632, gaussian: 257.65765765765764
diff --git a/img_rec_module/meas/calib4/img_99_{0}/img_99_{0}_(400, 400).txt b/img_rec_module/meas/calib4/img_99_{0}/img_99_{0}_(400, 400).txt
new file mode 100644
index 0000000..ceb5b0e
--- /dev/null
+++ b/img_rec_module/meas/calib4/img_99_{0}/img_99_{0}_(400, 400).txt	
@@ -0,0 +1,2 @@
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+y: edge_centroid: 255.54966073622904, poly: 253.15915915915917, gaussian: 254.85185185185185
diff --git a/img_rec_module/meas/convergence_naive_std1.5.csv b/img_rec_module/meas/convergence_naive_std1.5.csv
new file mode 100644
index 0000000..cd215cf
--- /dev/null
+++ b/img_rec_module/meas/convergence_naive_std1.5.csv
@@ -0,0 +1,373 @@
+Image Number,Center X,Center Y,StdDev Horizontal,Std Dev Vertical
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diff --git a/img_rec_module/meas/convergence_naive_std2.csv b/img_rec_module/meas/convergence_naive_std2.csv
new file mode 100644
index 0000000..9c454aa
--- /dev/null
+++ b/img_rec_module/meas/convergence_naive_std2.csv
@@ -0,0 +1,373 @@
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diff --git a/img_rec_module/meas/convergence_naive_std2_p20.csv b/img_rec_module/meas/convergence_naive_std2_p20.csv
new file mode 100644
index 0000000..2961aa4
--- /dev/null
+++ b/img_rec_module/meas/convergence_naive_std2_p20.csv
@@ -0,0 +1,373 @@
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diff --git a/img_rec_module/meas/convergence_naive_std_1.2.csv b/img_rec_module/meas/convergence_naive_std_1.2.csv
new file mode 100644
index 0000000..5f851e0
--- /dev/null
+++ b/img_rec_module/meas/convergence_naive_std_1.2.csv
@@ -0,0 +1,373 @@
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\ No newline at end of file
diff --git a/img_rec_module/meas/convergence_prime.csv b/img_rec_module/meas/convergence_prime.csv
new file mode 100644
index 0000000..54b1f8f
--- /dev/null
+++ b/img_rec_module/meas/convergence_prime.csv
@@ -0,0 +1,373 @@
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diff --git a/img_rec_module/meas/convergence_v1.csv b/img_rec_module/meas/convergence_v1.csv
new file mode 100644
index 0000000..418d98a
--- /dev/null
+++ b/img_rec_module/meas/convergence_v1.csv
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diff --git a/img_rec_module/meas/truth.txt b/img_rec_module/meas/truth.txt
new file mode 100644
index 0000000..dcf364f
--- /dev/null
+++ b/img_rec_module/meas/truth.txt
@@ -0,0 +1,32 @@
+new_test
+img_4  237, 325
+
+calib
+img_29  140, 203
+
+testpic
+img_1  385, 130  E: (2, 1)
+img_2  385, 130  E: (2, 1)
+img_3  385, 130  E: (2, 1)
+img_4  -2, -2    175, 113  E: (4, 7) (DATA)
+img_5  176, 52   E: (1, 3)
+img_6  174, -1   E: (1, 0)
+img_7  158, -1   E: (0, 0)
+img_8  -2, -2    159, -1   E: (F, 0) (DATA)
+img_9  242, 212  E: (0, 2)
+img_10  278, 215 E: (1, 3)
+img_11  -2, -2   325, 218 E: (1, 4) (DATA)
+img_12  143, 215 E: (0, 1)
+img_13  53, 219  E: (0, 4)
+img_14  -1, 17   E: (0, 0)
+img_15  138, 19  E: (0, 0)
+img_16  355, 14  E: (1, 0)
+img_17  288, -1  E: (1, 0)
+img_18  369, 180 E: (1, 0)
+img_19  357, 265 E: (0, 1)
+img_20  225, 224 E: (3, 1)
+img_21  41, 224  E: (0, 1)
+img_22  -1, 59   E: (0, 3)
+img_23  -2, -2   -1, -1   E: (0, T)  (DATA)
+img_24  -1, 160  E: (0, 1)
+img_25  445, 166 E: (2, 4)
diff --git a/img_rec_module/robust_reg/calib4/convergence.txt b/img_rec_module/robust_reg/calib4/convergence.txt
new file mode 100644
index 0000000..e69de29
diff --git a/img_rec_module/sig_proc.py b/img_rec_module/sig_proc.py
new file mode 100644
index 0000000..c0dcf47
--- /dev/null
+++ b/img_rec_module/sig_proc.py
@@ -0,0 +1,1211 @@
+import cv2
+import numpy as np
+import csv
+from scipy.optimize import curve_fit
+from scipy.optimize import least_squares
+import heapq
+from decimal import Decimal
+from math import atan, sqrt, tan, radians, acos, asin, degrees, sin, cos
+
+""" ===================================
+========== ANALYTIC GEOMETRY ==========
+======================================= """
+
+
+class Line:
+    inf = float('inf')
+
+    def __init__(self, a=inf, b=inf, p1=None, p2=None, data=None):
+        if p1 and p2:
+            self.point(p1, p2)
+        elif data:
+            self.reg(data)
+        else:
+            self.a = a
+            self.b = b
+
+    def reg(self, data):
+        D = np.array([[d[0], 1] for d in data])
+        y = np.array([d[1] for d in data])
+        self.a, self.b = leastSquares(D, y)
+
+    def point(self, p1, p2):
+        self.a = 1
+
+    @staticmethod
+    def intersect(l1, l2):
+        x = (l2.b - l1.b) / (l1.a - l2.a)
+        y = l1.a * x + l1.b
+        return x, y
+
+
+class HoughLine:
+    def __init__(self, theta=0, rho=0, x=None, data=None, new=True, r_bound=np.inf, loss='soft_l1'):
+        if data is not None and x is not None:
+            self.x = x
+            self.data = data
+            self.r_bound = r_bound
+            self.loss = loss
+            if new:
+                self.reg = self.reg_new
+            else:
+                self.reg = self.reg_old
+            self.reg(x, data)
+        else:
+            self._r = rho
+            self._t = theta
+            self._s = sin(theta)
+            self._c = cos(theta)
+
+    def reg_old(self, x, data):
+        """D = hough_data_matrix(x, data)
+        print(D)
+        y = np.zeros(len(data))
+        cosine, sine, self._r = leastSquares(D, y)
+        self._t = angle_interp(sine, cosine)
+        self._s = sine
+        self._c = cosine"""
+
+        x1, x2 = np.mean(x[:int(len(x) / 2)]), np.mean(x[int(len(x) / 2):])
+        y1, y2 = np.mean(data[:int(len(data) / 2)]), np.mean(data[int(len(data) / 2):])
+        theta0 = theta_pred(x1, y1, x2, y2)
+        p0 = [theta0, np.mean(x) * np.cos(theta0) + np.mean(data) * np.sin(theta0)]
+        """
+        x1, x2 = x[0], x[-1]
+        y1, y2 = data[0], data[-1]
+        theta0 = theta_pred(x1, y1, x2, y2)
+        # print(degrees(theta0))
+        p0 = [theta0, x1 * cos(theta0) + y1 * sin(theta0)]"""
+        pm, vm = curve_fit(hough_line, x, data, p0=p0)
+        # data_pred = hough_line(x, *pm)
+        # print('PRED', data_pred)
+        # res = data_pred - data
+        # print("RES", res)
+        # stderr = np.linalg.norm(res, 2)
+        # print("ERR", stderr)
+        # rankerr = res / stderr
+        # print("Z", rankerr)
+        if pm[1] < 0:
+            pm[1] = -pm[1]
+            pm[0] -= HC
+            # pm[0] = 2 * np.pi
+        angle = normalize_angle(pm[0])
+        self._t = angle
+        self._r = pm[1]
+        self._s = sin(angle)
+        self._c = cos(angle)
+
+    def reg_new(self, x, data):
+        """D = hough_data_matrix(x, data)
+        print(D)
+        y = np.zeros(len(data))
+        cosine, sine, self._r = leastSquares(D, y)
+        self._t = angle_interp(sine, cosine)
+        self._s = sine
+        self._c = cosine"""
+        x1, x2 = np.mean(x[:int(len(x) / 2)]), np.mean(x[int(len(x) / 2):])
+        y1, y2 = np.mean(data[:int(len(data) / 2)]), np.mean(data[int(len(data) / 2):])
+        theta0 = theta_pred(x1, y1, x2, y2)
+        p0 = [theta0, np.mean(x) * np.cos(theta0) + np.mean(data) * np.sin(theta0)]
+        res = least_squares(self.get_err, p0, loss=self.loss, f_scale=3, args=(x, data))
+        # data_pred = hough_line(x, *pm)
+        # print('PRED', data_pred)
+        # res = data_pred - data
+        # print("RES", res)
+        # stderr = np.linalg.norm(res, 2)
+        # print("ERR", stderr)
+        # rankerr = res / stderr
+        # print("Z", rankerr)
+        angle = normalize_angle(res.x[0])
+        self._t = angle
+        self._r = res.x[1]
+        self._s = sin(angle)
+        self._c = cos(angle)
+
+    def get_err(self, vars, xs, ys):
+        return xs * np.cos(vars[0]) + ys * np.sin(vars[0]) - vars[1]
+
+    def fit_x(self, x):
+        # print(self._r, self._t)
+        return (self._r - x * self._c) / self._s if self._s != 0 else 'undef'
+
+    def debias(self, thres=1.0):
+        """ @:returns tuple with a) zerr_before: regression error before debias
+         b) zerr_after: regression error after debias"""
+        x, y = self.x, self.data
+        zero_hat_before = x * self._c + y * self._s - self._r
+        #y_hat_before = self.fit_x(x)
+        zerr_before = np.square(zero_hat_before)
+        #yerr_before = np.square(y_hat_before - y)
+        conds = (zerr_before - np.mean(zerr_before)) / np.std(zerr_before) <= thres
+        new_x, new_y = x[conds], y[conds]
+        self.x, self.data = new_x, new_y
+        self.reg(new_x, new_y)
+        zero_hat_after = new_x * self._c + new_y * self._s - self._r
+        #y_hat_after = self.fit_x(new_x)
+        zerr_after = np.square(zero_hat_after)
+        #yerr_after = np.square(y_hat_after - new_y)
+        return zerr_before, zerr_after
+
+    def point_gen(self):
+        x0 = self._c * self._r
+        y0 = self._s * self._r
+        x1 = int(x0 + 1000 * (-self._s))
+        y1 = int(y0 + 1000 * (self._c))
+        x2 = int(x0 - 1000 * (-self._s))
+        y2 = int(y0 - 1000 * (self._c))
+        return (x1, y1), (x2, y2)
+
+    def __str__(self):
+        return 'hough line with cos:{0}, sin:{1}, rho:{2}, theta:{3}'.format(self._c, self._s,
+                                                                             self._r, degrees(self._t))
+
+    @staticmethod
+    def intersect(l1, l2):
+        if l1._s == 0 and l2._s == 0:
+            return float('inf'), float('inf')
+        if l1._s == 0:
+            return l1._r, l2.fit_x(l1._r)
+        elif l2._s == 0:
+            return l2._r, l1.fit_x(l2._r)
+        x = (l2._r / l2._s - l1._r / l1._s) / (l2._c / l2._s - l1._c / l1._s)
+        y = l1.fit_x(x)
+        return x, y
+
+
+# QC: QUARTER_CYCLE, HC: HALF_CYCLE, TQC: THIRD_QUARTER_CYCLE, FC: FULL_CYCLE
+QC = np.pi / 2
+HC = np.pi
+TQC = 3 * np.pi / 2
+FC = 2 * np.pi
+
+
+def angle_interp(s, c):
+    if c == 1.0:
+        return 0
+    elif s == 1.0:
+        return np.pi / 2
+    elif c == -1.0:
+        return np.pi
+    elif s == -1.0:
+        return np.pi * 3 / 2
+    elif c < 0 < s:
+        return acos(c)
+    elif s < 0 and c < 0:
+        return 2 * np.pi - acos(c)
+    elif s < 0 < c:
+        return asin(s) + 2 * np.pi
+    else:
+        return asin(s)
+
+
+def normalize_angle(angle):
+    res = angle - (angle // FC) * FC
+    return res
+
+
+
+def theta_pred(x1, y1, x2, y2):
+    dx = x2 - x1
+    dy = y2 - y1
+    angle = sin_angle_from_points(dx, dy)
+    if dx * dy <= 0:
+        return np.pi / 2 - angle
+    else:
+        root = root_finding(x1, x2, y1, y2)
+        if root < 0:
+            return np.pi / 2 + angle
+        else:
+            return angle + 3 * np.pi / 2
+
+
+def sin_angle_from_points(dx, dy):
+    return asin(abs(dy) / sqrt(dx ** 2 + dy ** 2))
+
+
+def poly_curve(params, x_input):
+    # params contains the coefficients that multiply the polynomial terms, in degree of lowest degree to highest degree
+    degree = len(params) - 1
+    x_range = [x_input[1], x_input[-1]]
+    x = np.linspace(x_range[0], x_range[1], 1000)
+    y = x * 0
+
+    for k in range(0, degree + 1):
+        coeff = params[k]
+        y = y + list(map(lambda z: coeff * z ** k, x))
+    return x, y
+
+
+def gauss_2d(x, y, a, b1, c_s1, b2, c_s2):
+    return a * np.exp(- ((x - b1) ** 2 / (2 * c_s1) + (y - b2) ** 2) / (2 * c_s2))
+
+
+def gauss_hat(x, a, b, c_s):
+    return a * np.exp(- (x - b) ** 2 / (2 * c_s))
+
+
+def gaussian_curve(x_input, a, b, c_s):
+    x_range = [x_input[1], x_input[-1]]
+    x = np.linspace(x_range[0], x_range[1], 1000)
+    y = a * np.exp(- (x - b) ** 2 / (2 * c_s))
+    return x, y
+
+
+def hough_line(x, theta, rho):
+    return (rho - x * np.cos(theta)) / np.sin(theta)
+
+
+""" ===================================
+========== MATRIX-ARRAY UTIL ==========
+======================================= """
+
+
+class PseudoLL(list):
+    def push(self, obj):
+        self.append(obj)
+
+    def __getitem__(self, item):
+        return super().__getitem__(len(self) - 1 - item)
+
+    def __repr__(self):
+        rep = '['
+        ls = len(self)
+        for i in range(ls):
+            if i > 0:
+                rep += ', '
+            val = self[i]
+            rep += str(val)
+        rep += ']'
+        return rep
+
+
+class FastDataMatrix2D:
+    # TODO: HANDLE THE CASE WHEN IT WRAPS 1D
+
+    HOR = 1
+    VER = 0
+
+    def __init__(self, data, ax, index):
+        self._data = data
+        self._ax = ax
+        assert 0 <= index < self.irange(), "start: {0}, end: {1}, ax: {2}, index: {3}".format(self.start, self.end,
+                                                                                              self._ax, index)
+        self._index = index
+        self.itr = 0
+        self.initialize()
+
+    def segmentize(self, start, end):
+        assert 0 <= start < end <= self._data.shape[self._ax], \
+            "start: {0}, end: {1}, ax: {2}, index: {3}".format(start, end, self._ax, self._index)
+        self.start = start
+        self.end = end
+
+    def initialize(self):
+        self.segmentize(0, self._data.shape[self._ax])
+
+    def irange(self):
+        # index range of the fast matrix array
+        return self._data.shape[1 - self._ax]
+
+    def set_axis(self, ax):
+        """USE copy when trying to switch axis and index"""
+        self._ax = ax
+        if self._index >= self.irange():
+            raise IndexError("Old index {0} is too large for the new axis".format(self._index))
+        self.initialize()
+
+    def set_index(self, index):
+        assert 0 <= index < self.irange(), "start: {0}, end: {1}, ax: {2}, index: {3}".format(self.start, self.end,
+                                                                                              self._ax, index)
+        self._index = index
+
+    def extract_array(self):
+        """Optimize later for better performance"""
+        arr = self._data[self._index, self.start:self.end] if self._ax == FastDataMatrix2D.HOR \
+            else self._data[self.start:self.end, self._index]
+        return np.array(arr)
+
+    def copy(self, ax=None, index=None):
+        if ax is not None and index is not None:
+            return FastDataMatrix2D(self._data, ax, index)
+        else:
+            return FastDataMatrix2D(self._data, self._ax, self._index)
+
+    def __iter__(self):
+        raise RuntimeError("You need the ITER method!")
+
+    def __next__(self):
+        raise RuntimeError("You need the NEXT method!")
+
+    def __getitem__(self, item):
+        return self._data[self._index, item + self.start] if self._ax == FastDataMatrix2D.HOR else self._data[item + self.start, self._index]
+
+    def __setitem__(self, key, value):
+        if self._ax == FastDataMatrix2D.HOR:
+            self._data[self._index, key + self.start] = value
+        else:
+            self._data[key + self.start, self._index] = value
+
+    def __len__(self):
+        return self.end - self.start
+
+
+""" ===================================
+=========== REGRESSION UTILS ==========
+======================================= """
+
+
+# Generic Regression Tools 128-133
+def leastSquares(D, y):
+    return np.linalg.lstsq(D, y, rcond=None)[0]
+
+
+def MSE(y, y_hat, N):
+    return np.linalg.norm(y - y_hat) ** 2 / N
+
+
+def std_dev(data):
+    std_dev = 0
+    len_data = len(data)
+    mean = sum(data) / len_data
+    for i in range(len_data):
+        std_dev += (data[i] - mean) ** 2
+    return sqrt(std_dev / len_data)
+
+
+def reg_pre_debias(ind, data):
+    sd = np.std(data)
+    miu = np.mean(data)
+    debiased_data = []
+    debiased_ind = []
+    for i in range(len(data)):
+        dev = abs((data[i] - miu) / sd)
+        if dev < 2:
+            debiased_data.append(data[i])
+            debiased_ind.append(ind[i])
+    return debiased_ind, debiased_data
+
+
+# Polynomial Regression Tools 137-171
+def poly_data_matrix(input_data, degree):
+    # degree is the degree of the polynomial you plan to fit the data with
+    Data = np.zeros((len(input_data), degree + 1))
+
+    for k in range(0, degree + 1):
+        Data[:, k] = (list(map(lambda x: x ** k, input_data)))
+
+    return Data
+
+
+def poly_error(params, D_a, y_a):
+    '''degree=len(params)-1
+    y=x_a*0
+
+    for k in range(0,degree+1):
+        coeff=params[k]
+        y=y+list(map(lambda z:coeff*z**k,x_a))'''
+    y = np.dot(D_a, params)
+    return np.linalg.norm(y - y_a) ** 2
+
+
+def improvedCost(x, y, x_test, y_test, start, end):
+    """Given a set of x and y points training points,
+    this function calculates polynomial approximations of varying
+    degrees from start to end. Then it returns the cost, with
+    the polynomial tested on test points of each degree in an array"""
+    c = []
+    degrees = range(start, end)
+    ps = []
+    for degree in degrees:
+        # YOUR CODE HERE
+        D = poly_data_matrix(x, degree)
+        p = leastSquares(D, y)
+        ps.append(p)
+        D_t = poly_data_matrix(x_test, degree)
+        y_hat = np.dot(D_t, p)
+        c.append(MSE(y_test, y_hat, len(x_test)))
+    return np.array(degrees), ps, np.array(c)
+
+
+# Gaussian Regression Tools 184-210
+def gauss_data_matrix(data):
+    return np.array([[1, x ** 2, x, 1] for x in data])
+
+
+def gauss_reg(x, y, p0):
+    """Given a set of x and y training points, this function
+    calculates the gaussian approximation function"""
+    """skip_x = np.array([x[i] for i in range(len(y)) if y[i] != 0])
+    y_prime = np.array([y[i] for i in range(len(y)) if y[i] != 0])
+    logy = np.log(y_prime)
+
+    sol = leastSquares(gauss_data_matrix(skip_x), logy)
+    alp, bet, gam, lam = sol
+    a = np.e ** alp
+    c_s = - 1 / (2 * bet)
+    b = gam * c_s
+    maxi, mini = max_min(y)
+    pred = gauss_hat(maxi, a, b, c_s)
+    real = y[maxi]
+    a = real / pred // 1000000000 * 10"""
+    # timeb = datetime.now()
+    param, vm = curve_fit(gauss_hat, x, y, p0=p0)
+    # timea = datetime.now()
+    # print(timea - timeb)
+    return param
+
+
+def gauss_mat(shape, a, b1, c_s1, b2, c_s2):
+    mat = np.zeros(shape)
+    for r in range(mat.shape[0]):
+        for c in range(mat.shape[1]):
+            mat[r, c] = gauss_2d(c, r, a, b1, c_s1, b2, c_s2)
+
+    return mat
+
+
+# Hough Transform Regression Tools
+def hough_data_matrix(x, y):
+    dim = len(x)
+    mat = np.full((dim, 3), -1.0)
+    for i in range(dim):
+        mat[i][0] = x[i]
+        mat[i][1] = y[i]
+    return mat
+
+
+""" =======================================
+========= EDGE DETECTION UTILS ============
+=========================================== """
+
+
+def gather_all(imgr, sample_int=30, gk=9, ks=-1):
+    """ @:returns centers_v, centers_h, list of possible centers gathered by algorithm, represented as (row, col)
+    """
+    dimr = imgr.shape[0]
+    dimc = imgr.shape[1]
+    # Image Processing
+    gksize = (gk, gk)
+    sigmaX = 0
+    img = cv2.GaussianBlur(imgr, gksize, sigmaX)
+    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=ks)
+    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=ks)
+    # Parameter Setting
+    nr = sample_int
+    nc = sample_int
+    # Gathering Data
+    centers_v = []
+    centers_h = []
+    while nr < dimr:
+        data_x = sobelx[nr, :]
+        am = gather_centers(data_x, img[nr, :], centers_v, 0, nr, gaussian_center)
+        nr += sample_int
+    while nc < dimc:
+        data_y = sobely[:, nc]
+        raw_y = img[:, nc]
+        am = gather_centers(data_y, img[:, nc], centers_h, 1, nc, gaussian_center)
+        nc += sample_int
+    return centers_v, centers_h
+
+
+def gather_centers(grad, raw_data, reserve, ax, ax_n, center_method):
+    # Given grad and raw_data, insert the possible beam centers to reserves
+    max_grad, min_grad = get_maxi_mini(grad)
+    max_q, min_q, locs = bi_order_sort(max_grad, min_grad)
+    miu, sig = np.mean(raw_data), np.std(raw_data)
+    avant = lambda locs, i: locs[i - 1] if i - 1 >= 0 else None
+    apres = lambda locs, i: locs[i + 1] if i + 1 < len(locs) else None
+    i = 0
+    peaked = False
+    while (i < 2 or peaked) and max_q and min_q:
+        if peaked:
+            top = min_q.pop()
+            av = avant(locs, top[1])
+            if av in max_q and beam_bound(raw_data, miu, sig, av, top):
+                mid = center_method(grad, raw_data, av[0], top[0])
+                reserve.append((ax_n, mid) if ax == 0 else (mid, ax_n))
+                max_q.remove(av)
+                i += 1
+            peaked = False
+        else:
+            max_top = max_q.pop()
+            min_top = min_q[0]
+            if beam_bound(raw_data, miu, sig, max_top, min_top):
+                # print("MaxMin: ", max_top, min_top)
+                mid = center_method(grad, raw_data, max_top[0], min_top[0])
+                reserve.append((ax_n, mid) if ax == 0 else (mid, ax_n))
+                min_q.remove(min_top)
+                i += 1
+            else:
+                peaked = True
+                ap = apres(locs, max_top[1])
+                # QUICK FIX HERE, THINK MORE
+                if ap in min_q and beam_bound(raw_data, miu, sig, max_top, ap):
+                    # print("Max_apres: ", max_top, ap)
+                    mid = center_method(grad, raw_data, max_top[0], ap[0])
+                    reserve.append((ax_n, mid) if ax == 0 else (mid, ax_n))
+                    min_q.remove(ap)
+                    i += 1
+    return i
+
+
+def sup_tout(max_q, min_q, d, guess):
+    if guess == 0:
+        prem, deux = max_q, min_q
+    else:
+        prem, deux = min_q, max_q
+    if d in prem:
+        prem.remove(d)
+    elif d in prem:
+        deux.remove(d)
+    else:
+        raise RuntimeError("Tried to remove already removed term! {0}, {1}".format(d, guess))
+
+
+def get_maxi_mini(data, ceil=3):
+    # Given data, pluck the maxis and minis and store them in minpq and maxpq respectively
+    max_grad = []
+    min_grad = []
+    nat_select = lambda a, b: a if a[0] >= b[0] else b
+    maxer = lambda d: len(max_grad) < ceil or d > max_grad[0][0]
+    miner = lambda d: len(min_grad) < ceil or d > min_grad[0][0]
+    active_max, active_min = None, None
+
+    for i, d in enumerate(data):
+        if not active_max:
+            if maxer(d):
+                active_max = (d, i)
+        else:
+            curr = (d, i)
+            active_max = nat_select(curr, active_max)
+        if active_max and (check_crossing(data, i) or i == len(data) - 1):
+            heapq.heappush(max_grad, active_max)
+            if len(max_grad) > ceil:
+                heapq.heappop(max_grad)
+            active_max = None
+
+        if not active_min:
+            if miner(-d):
+                active_min = (-d, i)
+        else:
+            curr = (-d, i)
+            active_min = nat_select(curr, active_min)
+        if active_min and (check_crossing(data, i) or i == len(data) - 1):
+            heapq.heappush(min_grad, active_min)
+            if len(min_grad) > ceil:
+                heapq.heappop(min_grad)
+            active_min = None
+
+    return max_grad, min_grad
+
+
+def bi_order_sort(max_grad, min_grad):
+    # Takes in a max_grad and min_grad (heapq), returns a locational ordered array and a magnitude queue.
+    locs = []
+    max_q, min_q = PseudoLL(), PseudoLL()
+    while len(max_grad) and len(min_grad):
+        mat = heapq.heappop(max_grad)
+        mit = heapq.heappop(min_grad)
+        maxt, mint = [mat[1], None], [mit[1], None]
+        locs.append(maxt)
+        locs.append(mint)
+        max_q.push(maxt)
+        min_q.push(mint)
+    locs.sort(key=lambda pair: pair[0])
+    for i in range(len(locs)):
+        locs[i][1] = i
+    return max_q, min_q, locs
+
+
+def beam_bound(raw, miu, sig, a1, a2, thres=2):
+    # given raw data, determine if a1, a2 are beam bound entries, where a1, a2 are tuples [index, *(loc)]
+    return a1 and a2 and a2[1] == a1[1] + 1 and (raw[(a1[0] + a2[0]) // 2] - miu) / sig >= thres
+
+
+# Generic Helper
+def max_min(data):
+    # Returns the maximum and minimum for the data
+    maxi = data[0]
+    max_ind = 0
+    mini = data[0]
+    min_ind = 0
+    for i in range(1, len(data)):
+        target = data[i]
+        if target > maxi:
+            maxi = target
+            max_ind = i
+        if target < mini:
+            mini = target
+            min_ind = i
+    return max_ind, min_ind
+
+
+def edge_max_min(data):
+    # Returns a *safe* edge maxi, mini for the data
+    # TODO: OPTIMIZE THE WIDTH AND VALUE THRESHOLD
+    width_thres = 90
+    value_thres = 20
+    maxi = data[0]
+    max_ind = 0
+    mini = data[0]
+    min_ind = 0
+    for i in range(1, len(data)):
+        target = data[i]
+        if target > maxi:
+            maxi = target
+            max_ind = i
+        if target < mini:
+            mini = target
+            min_ind = i
+    maxi, mini = max_ind, min_ind
+    assert data[maxi] >= value_thres and mini - maxi <= width_thres and maxi < mini
+    return maxi, mini
+
+
+def min_max(data, max, min):
+    """ Converts the data to min max space. """
+    g_diff = max - min
+    return [(d - min) / g_diff for d in data]
+
+
+def root_finding(x1, x2, y1, y2):
+    """Given two points on a line, finds its zero crossing root."""
+    return - y1 * (x2 - x1) / (y2 - y1) + x1
+
+
+def check_crossing(data, i):
+    return i + 1 < len(data) and check_cross(data[i], data[i+1])
+
+
+def check_cross(a, b):
+    # Checks whether two points are of opposite signs
+    return a * b < 0
+
+
+def round_up(num):
+    down = int(num)
+    if num - down > 0:
+        return num + 1
+    else:
+        return num
+
+
+def smart_interval(start, end, data):
+    start = 0 if start < 0 else start
+    end = len(data) if end > len(data) else end
+    return start, end
+
+
+def edge_preprocess(data, padding):
+    maxi, mini = edge_max_min(data)
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    return start, end
+
+
+# ZERO CROSSING (PEAK FINDING)
+def zero_crossing(data):
+    # Yields the center with zero-crossing method
+    maxi, mini = max_min(data)
+    cross = -1
+    cross_count = 0
+    for j in range(maxi, mini):
+        y1 = data[j]
+        x2 = j + 1
+        y2 = data[x2]
+        if check_cross(y1, y2):
+            cross = root_finding(j, x2, y1, y2)  # change to function later
+            cross_count += 1
+    return -1 if cross_count > 1 else cross
+
+
+# AVERAGE METHOD FOR EDGE CENTER FINDING
+def edge_converge_base(data):
+    maxi, mini = max_min(data)
+    width_thres = 70
+    if mini - maxi > width_thres:
+        return -1
+    else:
+        return (maxi + mini) / 2
+
+
+# PSEUDO EDGE (closest noise gradient change) CENTER FINDING
+def edge_converge_extreme(data):
+    maxi, mini = max_min(data)
+    width_thres = 70
+    if mini - maxi > width_thres:
+        return -1
+    emax = -1
+    emin = -1
+    maxflag = True
+    minflag = True
+    cmax = maxi
+    cmin = mini
+    while cmax > 0 and cmin < len(data) - 1 and (maxflag or minflag):
+        max2 = cmax - 1
+        maxy1, maxy2 = data[cmax], data[max2]
+        min2 = cmin + 1
+        miny1, miny2 = data[cmin], data[min2]
+        if check_cross(maxy1, maxy2) and maxflag:
+            emax = root_finding(cmax, max2, maxy1, maxy2)
+            maxflag = False
+        if check_cross(miny1, miny2) and minflag:
+            emin = root_finding(cmin, min2, miny1, miny2)
+            minflag = False
+        if maxflag:
+            cmax -= 1
+        if minflag:
+            cmin += 1
+    return -1 if (emax == -1 or emin == -1) else (emax + emin) / 2
+
+
+def extract_extrema(data):
+    len_data = len(data)
+    mean = sum(data) / len_data
+    std_dev = 0
+    for x in data:
+        std_dev += (x - mean) ** 2
+    std_dev = sqrt(std_dev / len_data)
+    signifmax = []
+    signifmin = []
+    coeff = 3
+    for i, x in enumerate(data):
+        diff = (x - mean) / std_dev
+        if diff >= coeff:
+            signifmax.append(i)
+        elif diff <= -coeff:
+            signifmin.append(i)
+    return signifmax, signifmin
+
+
+# CENTROID METHOD FOR EDGE CENTER FINDING
+def edge_centroid(data, img_data, padding=20):
+    # With Gaussian Blur might achieve the best performance
+    try:
+        start, end = edge_preprocess(data, padding)
+    except AssertionError:
+        return -1
+    isums = 0
+    total = 0
+    for i in range(start, end):
+        isums += img_data[i] * i
+        total += img_data[i]
+    return isums / total if total else -1
+
+
+def centroid_seg(data, start, end):
+    """ Given a segment (start, end) of the data,
+    find the centroid. """
+    isums = 0
+    total = 0
+    start = 0 if start < 0 else start
+    end = len(data) if end > len(data) else end
+    for i in range(start, end):
+        isums += data[i] * i
+        total += data[i]
+    return isums / total if total else -1
+
+
+# POLYNOMIAL FITTING
+def poly_fitting(data, img_data, padding=20):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING AND THE EXTRACT_ARRAY
+    try:
+        start, end = edge_preprocess(data, padding)
+    except AssertionError:
+        return -1
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        y = img_data.extract_array()
+    else:
+        #y = np.array(img_data[start:end])
+        idata = np.zeros(end - start)
+        for i in range(start, end):
+            idata[i - start] = img_data[i]
+    degrees, params, cost = improvedCost(x, y, x, y, 1, 7)
+    ind = np.argmin(cost)
+    degree = degrees[ind]
+    param = params[ind]
+    degree_register(degree)
+    curve_x, curve_y = poly_curve(param, x)
+    center_id = np.argmax(curve_y)
+    return curve_x[center_id]
+
+
+def poly_fitting_params(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING AND THE EXTRACT_ARRAY
+    maxi, mini = max_min(data)
+    width_thres = 90
+    value_thres = 20
+    if data[maxi] < value_thres or mini - maxi > width_thres:
+        print(data[maxi], mini, maxi)
+        raise AssertionError("Bad column or row")
+    start = maxi - padding
+    end = mini + padding + 1
+    start, end = smart_interval(start, end, data)
+    if start > end:
+        try:
+            print(maxi, mini, img_data._ax, start, end)
+        except:
+            print("else", maxi, mini, start, end)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        y = img_data.extract_array()
+    else:
+        idata = np.zeros(end - start)
+        for i in range(start, end):
+            idata[i - start] = img_data[i]
+    degrees, params, cost = improvedCost(x, y, x, y, 1, 7)
+    ind = np.argmin(cost)
+    degree = degrees[ind]
+    param = params[ind]
+    degree_register(degree)
+    return param, x
+
+
+DEGREES = {}
+
+
+def degree_register(elem):
+    if elem in DEGREES:
+        DEGREES[elem] += 1
+    else:
+        DEGREES[elem] = 1
+
+
+def gaussian_center(data, img_data, maxi, mini, padding=20):
+    #padding = (mini - maxi) // 2
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.zeros(end - start)
+        for i in range(start, end):
+            idata[i - start] = img_data[i]
+    try:
+        param = gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)])
+    except RuntimeError:
+        return -1
+    return param[1]
+
+
+# GAUSSIAN FITTING
+def gaussian_fitting(data, img_data, padding=20):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING, ALONG WITH THE WIDTH THRESHOLD
+    try:
+        maxi, mini = edge_max_min(data)
+        print((mini - maxi) // 2)
+    except AssertionError:
+        return -1
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.zeros(end-start)
+        for i in range(start, end):
+            idata[i-start] = img_data[i]
+    try:
+        param = gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)])
+    except RuntimeError:
+        return -1
+    return param[1]
+
+
+def gaussian_fitting_params(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING, ALONG WITH THE WIDTH THRESHOLD
+    maxi, mini = max_min(data)
+    width_thres = 90
+    value_thres = 20  # TODO: CONSOLIDATE THIS VALUE
+    if data[maxi] < value_thres or mini - maxi > width_thres:
+        print(data[maxi], mini, maxi)
+        raise AssertionError("Bad column or row")
+    start = maxi - padding
+    end = mini + padding
+    start, end = smart_interval(start, end, data)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.zeros(end - start)
+        for i in range(start, end):
+            idata[i-start] = img_data[i]
+    return gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)]), x
+
+
+"""======================================
+======== IMAGE PROCESSING UTIL ==========
+========================================= """
+
+
+def image_diff(img1, img2):
+    i1 = img1.astype(np.int16)
+    i2 = img2.astype(np.int16)
+    res = i1 - i2
+    dst = np.zeros_like(res, dtype=np.int8)
+    return cv2.normalize(res, dst=dst, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=8)
+
+
+def gauss_bg_deduce(x, img_data):
+    # TODO: OPTIMIZE PERFORMANCE
+    idata = img_data.extract_array()
+    p0 = [1, len(img_data) / 2, std_dev(idata)]
+    a, b, c_s = gauss_reg(x, idata, p0=p0)
+    rem_gauss = gauss_hat(x, a, b, c_s)
+    new_y = idata - rem_gauss
+    return x, rem_gauss, new_y
+
+
+def significance_test(data, val):
+    """Outputs whether the maximum or minimum value is a significant value."""
+    return val in extract_extrema(data)[0] or val in extract_extrema(data)[1]
+
+
+def edge_detect_expr(edges, original):
+    """
+    TODO: 1. SIMPLE Approach: pairwise mask over original image, then compute total
+          2. Traverse the image to mark the image.
+    """
+    se = 0
+    row_sum = 0
+    col_sum = 0
+    tot_sum = 0
+    for i in range(edges.shape[0]):
+        for j in range(edges.shape[1]):
+            if edges[i][j] > 0:
+                row_sum += i * original[i][j]
+                col_sum += j * original[i][j]
+                tot_sum += original[i][j]
+                se += 1
+    print(se)
+    if tot_sum != 0:
+        print(row_sum / tot_sum)
+        print(col_sum / tot_sum)
+
+
+def hori(img, i, j, d):
+    dest = j + d
+    if dest < 0 or dest >= img.shape[1]:
+        return img.item(i, j)
+    return img.item(i, dest)
+
+
+def verti(img, i, j, d):
+    dest = i + d
+    if dest < 0 or dest >= img.shape[0]:
+        return img.item(i, j)
+    return img.item(dest, j)
+
+
+def test_blur_then_nr(iname):
+    imgs = iname
+    numIMG = 5
+    imgr = None
+    for i in range(1, numIMG + 1):
+        target = cv2.imread(imgs.format(i), 0)
+        target = cv2.GaussianBlur(target, (9, 9), 0)
+        if i == 1:
+            imgr = np.uint16(target)
+        else:
+            imgr = imgr + target
+    return imgr / numIMG
+
+
+def get_center_val(img):
+    return img.item(img.shape[0] // 2, img.shape[1] // 2)
+
+
+def test_noise_reduce(iname, numIMG=5):
+    imgs = iname
+    imgr = None
+    original = None
+    for i in range(1, numIMG + 1):
+        target = cv2.imread(imgs.format(i), 0)
+        if target is None:
+            raise AttributeError("File {0} not found".format(iname))
+        if i == 1:
+            imgr = np.uint16(target)
+            original = target
+        else:
+            imgr = imgr + target
+    return imgr / numIMG, original
+
+
+def img_add(dest, src):
+    """Destructive!"""
+    row, col = dest.shape
+    for i in range(row):
+        for j in range(col):
+            dest[i][j] += src[i][j]
+
+
+FM = FastDataMatrix2D
+
+
+def folder_to_imgs(img_name_scheme, num_sample):
+    """This function takes img files and return cv imgs"""
+    return [cv2.imread(img_name_scheme.format(i)) for i in range(1, num_sample + 1)]
+
+
+def center_detect_hough(img):
+    centers_v, centers_h = gather_all(img)
+    centpointsv = np.zeros_like(img, dtype=np.uint8)
+    centpointsh = np.zeros_like(img, dtype=np.uint8)
+    #print(centers_v+centers_h)
+    for r, c in centers_v:
+        try:
+            centpointsv[int(r)][int(c)] = 255
+        except:
+            continue
+    for r, c in centers_h:
+        try:
+            centpointsh[int(r)][int(c)] = 255
+        except:
+            continue
+    linesv = cv2.HoughLines(centpointsv, 0.5, 0.005, 1)
+    linesh = cv2.HoughLines(centpointsh, 0.5, 0.005, 1)
+    all_lines = []
+    try:
+        for rho, theta in linesv[0]:
+            #print(rho, theta)
+            all_lines.append(HoughLine(theta, rho))
+    except:
+        print(linesv)
+    for rho, theta in linesh[0]:
+        all_lines.append(HoughLine(theta, rho))
+    ct = HoughLine.intersect(all_lines[0], all_lines[-1])
+    return ct
+
+
+def center_detect(ins, sample_int=30, gk=9, ks=-1, l='soft_l1'):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair
+    hs: HORIZONTAL SLICE!  vs: VERTICAL SLICE!"""
+    ambi, laser = cv2.imread(ins.format(0), 0), cv2.imread(ins.format(1), 0)
+    res = image_diff(laser, ambi)
+    centers_v, centers_h = gather_all(res, sample_int, gk, ks)
+    centers_v, centers_h = np.array(centers_v), np.array(centers_h)
+    centers_vx, centers_vy = centers_v[:, 1], centers_v[:, 0]
+    centers_hx, centers_hy = centers_h[:, 1], centers_h[:, 0]
+    line_v, line_h = HoughLine(x=centers_vx, data=centers_vy), HoughLine(x=centers_hx, data=centers_hy, loss=l)
+    zv_err_av, zv_err_ap = line_v.debias()
+    zh_err_av, zh_err_ap = line_h.debias()
+    return HoughLine.intersect(line_h, line_v)
+
+
+def center_detect_test(imgr, sample_int=30, gk=9, ks=-1, l='soft_l1'):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair
+    hs: HORIZONTAL SLICE!  vs: VERTICAL SLICE!"""
+    centers_v, centers_h = gather_all(imgr, sample_int, gk, ks)
+    centers_v, centers_h = np.array(centers_v), np.array(centers_h)
+    centers_vx, centers_vy = centers_v[:, 1], centers_v[:, 0]
+    centers_hx, centers_hy = centers_h[:, 1], centers_h[:, 0]
+    line_v, line_h = HoughLine(x=centers_vx, data=centers_vy), HoughLine(x=centers_hx, data=centers_hy, loss=l)
+    zv_err_av, zv_err_ap = line_v.debias()
+    zh_err_av, zh_err_ap = line_h.debias()
+    return HoughLine.intersect(line_h, line_v)
+
+
+def expr2(series):
+    folder = '../calib5/'
+    ns = 'img_{0}.png'
+    ins = folder + ns
+    repertoire = []
+    i = 0
+    while i < len(series):
+        try:
+            ambi, laser = cv2.imread(ins.format(series[i]), 0), cv2.imread(ins.format(series[i+1]), 0)
+            res = image_diff(laser, ambi)
+        except:
+            print(ins.format(series[i]))
+        repertoire.append(center_detect_test(res))
+        i+= 2
+    for i, ct in enumerate(repertoire):
+        print((series[2*i], series[2*i+1]), ct)
+    CALIB_VAL = 118.6
+    print(repertoire)
+    i = 0
+    while i < len(repertoire):
+        dx, dy = (np.array(repertoire[i+1]) - np.array(repertoire[i])) / CALIB_VAL
+        print((series[2*i], series[2*i+1], series[2*i+2], series[2*i+3]), dx, dy)
+        i+=2
+
+
+def convergence_test_final(folder, ns):
+    offset = '../'
+    convergence = {}
+    variations = []
+    startNP = 59
+    startP = 80
+    endP = 193
+    ls = range(5)
+    fwrite = open('meas/convergence_rob.csv', 'w')
+    cwriter = csv.writer(fwrite)
+    cwriter.writerow(['Image Number', 'Center X', 'Center Y', 'StdDev Horizontal', 'Std Dev Vertical'])
+    loss_types = ['huber', 'soft_l1', 'arctan', 'linear', 'cauchy']
+    for l in loss_types:
+        cwriter.writerow([l])
+        # Consistency Cycled
+        pv = 0
+        pvs = 0
+        rcount = 0
+        cvx = np.zeros(4)
+        cvy = np.zeros(4)
+        # print(g)
+        for i in range(startNP, startP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                #x, y = center_detect(fpath + imgfile, 5, m=m) TODO: RESOLVE THE CHANGE
+                x, y = center_detect(fpath, imgfile.format(1), l=l)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y)])
+                # CONVERGENCE
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+        for i in range(startP, endP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                #x, y = center_detect(fpath + imgfile, 5, m=m) TODO: RESOLVE THE CHANGE
+                x, y = center_detect(fpath, imgfile.format(1), l=l)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y)])
+                # CONVERGENCE
+                # Record x, y, check rcount, refresh CONSISTENCY
+                cvx[rcount] = x
+                cvy[rcount] = y
+                rcount += 1
+                if rcount == 4:
+                    pv += np.var(cvx) + np.var(cvy)
+                    pvs += 1
+                    rcount = 0
+                    cvx = np.zeros(4)
+                    cvy = np.zeros(4)
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+                # print(str(i), val)
+        msepv = sqrt(pv / pvs)
+        cvg = {'PicConsistency': msepv}
+        convergence[l] = cvg
+        variations.append(msepv)
+    print(convergence)
+    fwrite.close()
+
+if __name__ == '__main__':
+    #print(center_detect('../calib4/img_59_{0}.png', 5))
+    #convergence_test_final('calib4/', 'img_{0}')
+    repertoire = expr2([10, 11, 15, 14, 18, 19, 22, 23])
+
+
+
+
diff --git a/img_rec_module/sig_proc_old.py b/img_rec_module/sig_proc_old.py
new file mode 100644
index 0000000..e416d0f
--- /dev/null
+++ b/img_rec_module/sig_proc_old.py
@@ -0,0 +1,988 @@
+import cv2
+import numpy as np
+import csv
+from scipy.optimize import curve_fit
+from math import atan, sqrt, tan, radians, acos, asin, degrees, sin, cos
+
+""" ===================================
+========== ANALYTIC GEOMETRY ==========
+======================================= """
+
+
+class Line:
+    inf = float('inf')
+
+    def __init__(self, a=inf, b=inf, p1=None, p2=None, data=None):
+        if p1 and p2:
+            self.point(p1, p2)
+        elif data:
+            self.reg(data)
+        else:
+            self.a = a
+            self.b = b
+
+    def reg(self, data):
+        D = np.array([[d[0], 1] for d in data])
+        y = np.array([d[1] for d in data])
+        self.a, self.b = leastSquares(D, y)
+
+    def point(self, p1, p2):
+        self.a = 1
+
+    @staticmethod
+    def intersect(l1, l2):
+        x = (l2.b - l1.b) / (l1.a - l2.a)
+        y = l1.a * x + l1.b
+        return x, y
+
+
+class HoughLine:
+    def __init__(self, theta=0, rho=0, x=None, data=None):
+        if data is not None and x is not None:
+            self.reg(x, data)
+        else:
+            self._r = rho
+            self._t = theta
+            self._s = sin(theta)
+            self._c = cos(theta)
+
+    def reg(self, x, data):
+        """D = hough_data_matrix(x, data)
+        print(D)
+        y = np.zeros(len(data))
+        cosine, sine, self._r = leastSquares(D, y)
+        self._t = angle_interp(sine, cosine)
+        self._s = sine
+        self._c = cosine"""
+        x1, x2 = x[0], x[-1]
+        y1, y2 = data[0], data[-1]
+        theta0 = theta_pred(x1, y1, x2, y2)
+        # print(degrees(theta0))
+        p0 = [theta0, x1 * cos(theta0) + y1 * sin(theta0)]
+        pm, vm = curve_fit(hough_line, x, data, p0=p0)
+        # data_pred = hough_line(x, *pm)
+        # print('PRED', data_pred)
+        # res = data_pred - data
+        # print("RES", res)
+        # stderr = np.linalg.norm(res, 2)
+        # print("ERR", stderr)
+        # rankerr = res / stderr
+        # print("Z", rankerr)
+        if pm[1] < 0:
+            pm[1] = -pm[1]
+            pm[0] -= HC
+            # pm[0] = 2 * np.pi
+        angle = normalize_angle(pm[0])
+        self._t = angle
+        self._r = pm[1]
+        self._s = sin(angle)
+        self._c = cos(angle)
+
+    def extract_points(self, x_input):
+        x1 = int(x_input[0])
+        x2 = int(x_input[-1])
+        y1 = int(self.fit_x(x1))
+        y2 = int(self.fit_x(x2))
+        return (x1, y1), (x2, y2)
+
+    def fit_x(self, x):
+        # print(self._r, self._t)
+        return (self._r - x * self._c) / self._s
+
+    def __str__(self):
+        return 'hough line with cos:{0}, sin:{1}, rho:{2}, theta:{3}'.format(self._c, self._s,
+                                                                             self._r, degrees(self._t))
+
+    @staticmethod
+    def intersect(l1, l2):
+        if l1._s == 0:
+            return l1._r, l2.fit_x(l1._r)
+        elif l2._s == 0:
+            return l2._r, l1.fit_x(l2._r)
+        x = (l2._r / l2._s - l1._r / l1._s) / (l2._c / l2._s - l1._c / l1._s)
+        y = l1.fit_x(x)
+        return x, y
+
+
+# QC: QUARTER_CYCLE, HC: HALF_CYCLE, TQC: THIRD_QUARTER_CYCLE, FC: FULL_CYCLE
+QC = np.pi / 2
+HC = np.pi
+TQC = 3 * np.pi / 2
+FC = 2 * np.pi
+
+
+def angle_interp(s, c):
+    if c == 1.0:
+        return 0
+    elif s == 1.0:
+        return np.pi / 2
+    elif c == -1.0:
+        return np.pi
+    elif s == -1.0:
+        return np.pi * 3 / 2
+    elif c < 0 < s:
+        return acos(c)
+    elif s < 0 and c < 0:
+        return 2 * np.pi - acos(c)
+    elif s < 0 < c:
+        return asin(s) + 2 * np.pi
+    else:
+        return asin(s)
+
+
+def normalize_angle(angle):
+    res = angle - (angle // FC) * FC
+    return res
+    """
+    if QC < res < HC:
+        return res + HC
+    elif HC <= res <= TQC:
+        return res - HC
+    else:
+        return res"""
+
+
+def theta_pred(x1, y1, x2, y2):
+    dx = x2 - x1
+    dy = y2 - y1
+    angle = sin_angle_from_points(dx, dy)
+    if dx * dy <= 0:
+        return np.pi / 2 - angle
+    else:
+        root = root_finding(x1, x2, y1, y2)
+        if root < 0:
+            return np.pi / 2 + angle
+        else:
+            return angle + 3 * np.pi / 2
+
+
+def sin_angle_from_points(dx, dy):
+    return asin(abs(dy) / sqrt(dx ** 2 + dy ** 2))
+
+
+def poly_curve(params, x_input):
+    # params contains the coefficients that multiply the polynomial terms, in degree of lowest degree to highest degree
+    degree = len(params) - 1
+    x_range = [x_input[1], x_input[-1]]
+    x = np.linspace(x_range[0], x_range[1], 1000)
+    y = x * 0
+
+    for k in range(0, degree + 1):
+        coeff = params[k]
+        y = y + list(map(lambda z: coeff * z ** k, x))
+    return x, y
+
+
+def gauss_2d(x, y, a, b1, c_s1, b2, c_s2):
+    return a * np.exp(- ((x - b1) ** 2 / (2 * c_s1) + (y - b2) ** 2) / (2 * c_s2))
+
+
+def gauss_hat(x, a, b, c_s):
+    return a * np.exp(- (x - b) ** 2 / (2 * c_s))
+
+
+def gaussian_curve(x_input, a, b, c_s):
+    x_range = [x_input[1], x_input[-1]]
+    x = np.linspace(x_range[0], x_range[1], 1000)
+    y = a * np.exp(- (x - b) ** 2 / (2 * c_s))
+    return x, y
+
+
+def hough_line(x, theta, rho):
+    return (rho - x * cos(theta)) / sin(theta)
+
+
+""" ===================================
+========== MATRIX-ARRAY UTIL ==========
+======================================= """
+
+
+class FastDataMatrix2D:
+    # TODO: HANDLE THE CASE WHEN IT WRAPS 1D
+
+    HOR = 1
+    VER = 0
+
+    def __init__(self, data, ax, index):
+        self._data = data
+        self._ax = ax
+        assert 0 <= index < self.irange(), "start: {0}, end: {1}, ax: {2}, index: {3}".format(self.start, self.end,
+                                                                                              self._ax, index)
+        self._index = index
+        self.itr = 0
+        self.initialize()
+
+    def segmentize(self, start, end):
+        assert 0 <= start < end <= self._data.shape[self._ax], "start: {0}, end: {1}, ax: {2}, index: {3}".format(start,
+                                                                                                                  end,
+                                                                                                                  self._ax,
+                                                                                                                  self._index)
+        self.start = start
+        self.end = end
+
+    def initialize(self):
+        self.segmentize(0, self._data.shape[self._ax])
+
+    def irange(self):
+        # index range of the fast matrix array
+        return self._data.shape[1 - self._ax]
+
+    def set_axis(self, ax):
+        """USE copy when trying to switch axis and index"""
+        self._ax = ax
+        if self._index >= self.irange():
+            raise IndexError("Old index {0} is too large for the new axis".format(self._index))
+        self.initialize()
+
+    def set_index(self, index):
+        assert 0 <= index < self.irange(), "start: {0}, end: {1}, ax: {2}, index: {3}".format(self.start, self.end,
+                                                                                              self._ax, index)
+        self._index = index
+
+    def extract_array(self):
+        """Optimize later for better performance"""
+        arr = self._data[self._index, self.start:self.end] if self._ax == FastDataMatrix2D.HOR \
+            else self._data[self.start:self.end, self._index]
+        return np.array(arr)
+
+    def copy(self, ax=None, index=None):
+        if ax is not None and index is not None:
+            return FastDataMatrix2D(self._data, ax, index)
+        else:
+            return FastDataMatrix2D(self._data, self._ax, self._index)
+
+    def __iter__(self):
+        raise RuntimeError("You need the ITER method!")
+
+    def __next__(self):
+        raise RuntimeError("You need the NEXT method!")
+
+    def __getitem__(self, item):
+        return self._data[self._index, item + self.start] if self._ax == FastDataMatrix2D.HOR else self._data[item + self.start, self._index]
+
+    def __setitem__(self, key, value):
+        if self._ax == FastDataMatrix2D.HOR:
+            self._data[self._index, key + self.start] = value
+        else:
+            self._data[key + self.start, self._index] = value
+
+    def __len__(self):
+        return self.end - self.start
+
+
+""" ===================================
+=========== REGRESSION UTILS ==========
+======================================= """
+
+
+# Generic Regression Tools 128-133
+def leastSquares(D, y):
+    return np.linalg.lstsq(D, y, rcond=None)[0]
+
+
+def MSE(y, y_hat, N):
+    return np.linalg.norm(y - y_hat) ** 2 / N
+
+
+def std_dev(data):
+    std_dev = 0
+    len_data = len(data)
+    mean = sum(data) / len_data
+    for i in range(len_data):
+        std_dev += (data[i] - mean) ** 2
+    return sqrt(std_dev / len_data)
+
+
+def reg_pre_debias(ind, data):
+    sd = np.std(data)
+    miu = np.mean(data)
+    debiased_data = []
+    debiased_ind = []
+    for i in range(len(data)):
+        dev = abs((data[i] - miu) / sd)
+        if dev < 2:
+            debiased_data.append(data[i])
+            debiased_ind.append(ind[i])
+    return debiased_ind, debiased_data
+
+
+
+
+# Polynomial Regression Tools 137-171
+def poly_data_matrix(input_data, degree):
+    # degree is the degree of the polynomial you plan to fit the data with
+    Data = np.zeros((len(input_data), degree + 1))
+
+    for k in range(0, degree + 1):
+        Data[:, k] = (list(map(lambda x: x ** k, input_data)))
+
+    return Data
+
+
+def poly_error(params, D_a, y_a):
+    '''degree=len(params)-1
+    y=x_a*0
+
+    for k in range(0,degree+1):
+        coeff=params[k]
+        y=y+list(map(lambda z:coeff*z**k,x_a))'''
+    y = np.dot(D_a, params)
+    return np.linalg.norm(y - y_a) ** 2
+
+
+def improvedCost(x, y, x_test, y_test, start, end):
+    """Given a set of x and y points training points,
+    this function calculates polynomial approximations of varying
+    degrees from start to end. Then it returns the cost, with
+    the polynomial tested on test points of each degree in an array"""
+    c = []
+    degrees = range(start, end)
+    ps = []
+    for degree in degrees:
+        # YOUR CODE HERE
+        D = poly_data_matrix(x, degree)
+        p = leastSquares(D, y)
+        ps.append(p)
+        D_t = poly_data_matrix(x_test, degree)
+        y_hat = np.dot(D_t, p)
+        c.append(MSE(y_test, y_hat, len(x_test)))
+    return np.array(degrees), ps, np.array(c)
+
+
+# Gaussian Regression Tools 184-210
+def gauss_data_matrix(data):
+    return np.array([[1, x ** 2, x, 1] for x in data])
+
+
+def gauss_reg(x, y, p0):
+    """Given a set of x and y training points, this function
+    calculates the gaussian approximation function"""
+    """skip_x = np.array([x[i] for i in range(len(y)) if y[i] != 0])
+    y_prime = np.array([y[i] for i in range(len(y)) if y[i] != 0])
+    logy = np.log(y_prime)
+
+    sol = leastSquares(gauss_data_matrix(skip_x), logy)
+    alp, bet, gam, lam = sol
+    a = np.e ** alp
+    c_s = - 1 / (2 * bet)
+    b = gam * c_s
+    maxi, mini = max_min(y)
+    pred = gauss_hat(maxi, a, b, c_s)
+    real = y[maxi]
+    a = real / pred // 1000000000 * 10"""
+    # timeb = datetime.now()
+    param, vm = curve_fit(gauss_hat, x, y, p0=p0)
+    # timea = datetime.now()
+    # print(timea - timeb)
+    return param
+
+
+def gauss_mat(shape, a, b1, c_s1, b2, c_s2):
+    mat = np.zeros(shape)
+    for r in range(mat.shape[0]):
+        for c in range(mat.shape[1]):
+            mat[r, c] = gauss_2d(c, r, a, b1, c_s1, b2, c_s2)
+
+    return mat
+
+
+# Hough Transform Regression Tools
+def hough_data_matrix(x, y):
+    dim = len(x)
+    mat = np.full((dim, 3), -1.0)
+    for i in range(dim):
+        mat[i][0] = x[i]
+        mat[i][1] = y[i]
+    return mat
+
+
+""" =======================================
+========= EDGE DETECTION UTILS ============
+=========================================== """
+
+
+# Generic Helper
+def max_min(data):
+    # Returns the maximum and minimum for the data
+    maxi = data[0]
+    max_ind = 0
+    mini = data[0]
+    min_ind = 0
+    for i in range(1, len(data)):
+        target = data[i]
+        if target > maxi:
+            maxi = target
+            max_ind = i
+        if target < mini:
+            mini = target
+            min_ind = i
+    return max_ind, min_ind
+
+
+def edge_max_min(data):
+    # Returns a *safe* edge maxi, mini for the data
+    # TODO: OPTIMIZE THE WIDTH AND VALUE THRESHOLD
+    width_thres = 90
+    value_thres = 20
+    maxi = data[0]
+    max_ind = 0
+    mini = data[0]
+    min_ind = 0
+    for i in range(1, len(data)):
+        target = data[i]
+        if target > maxi:
+            maxi = target
+            max_ind = i
+        if target < mini:
+            mini = target
+            min_ind = i
+    maxi, mini = max_ind, min_ind
+    assert data[maxi] >= value_thres and mini - maxi <= width_thres and maxi < mini
+    return maxi, mini
+
+
+def min_max(data, max, min):
+    """ Converts the data to min max space. """
+    g_diff = max - min
+    return [(d - min) / g_diff for d in data]
+
+
+def root_finding(x1, x2, y1, y2):
+    """Given two points on a line, finds its zero crossing root."""
+    return - y1 * (x2 - x1) / (y2 - y1) + x1
+
+
+def check_crossing(data, i):
+    return i + 1 < len(data) and check_cross(data[i], data[i+1])
+
+
+def check_cross(a, b):
+    # Checks whether two points are of opposite signs
+    return a * b < 0
+
+
+def round_up(num):
+    down = int(num)
+    if num - down > 0:
+        return num + 1
+    else:
+        return num
+
+
+def smart_interval(start, end, data):
+    start = 0 if start < 0 else start
+    end = len(data) if end > len(data) else end
+    return start, end
+
+
+def edge_preprocess(data, padding):
+    maxi, mini = edge_max_min(data)
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    return start, end
+
+
+# ZERO CROSSING (PEAK FINDING)
+def zero_crossing(data):
+    # Yields the center with zero-crossing method
+    maxi, mini = max_min(data)
+    cross = -1
+    cross_count = 0
+    for j in range(maxi, mini):
+        y1 = data[j]
+        x2 = j + 1
+        y2 = data[x2]
+        if check_cross(y1, y2):
+            cross = root_finding(j, x2, y1, y2)  # change to function later
+            cross_count += 1
+    return -1 if cross_count > 1 else cross
+
+
+# AVERAGE METHOD FOR EDGE CENTER FINDING
+def edge_converge_base(data):
+    maxi, mini = max_min(data)
+    width_thres = 70
+    if mini - maxi > width_thres:
+        return -1
+    else:
+        return (maxi + mini) / 2
+
+
+# PSEUDO EDGE (closest noise gradient change) CENTER FINDING
+def edge_converge_extreme(data):
+    maxi, mini = max_min(data)
+    width_thres = 70
+    if mini - maxi > width_thres:
+        return -1
+    emax = -1
+    emin = -1
+    maxflag = True
+    minflag = True
+    cmax = maxi
+    cmin = mini
+    while cmax > 0 and cmin < len(data) - 1 and (maxflag or minflag):
+        max2 = cmax - 1
+        maxy1, maxy2 = data[cmax], data[max2]
+        min2 = cmin + 1
+        miny1, miny2 = data[cmin], data[min2]
+        if check_cross(maxy1, maxy2) and maxflag:
+            emax = root_finding(cmax, max2, maxy1, maxy2)
+            maxflag = False
+        if check_cross(miny1, miny2) and minflag:
+            emin = root_finding(cmin, min2, miny1, miny2)
+            minflag = False
+        if maxflag:
+            cmax -= 1
+        if minflag:
+            cmin += 1
+    return -1 if (emax == -1 or emin == -1) else (emax + emin) / 2
+
+
+def extract_extrema(data):
+    len_data = len(data)
+    mean = sum(data) / len_data
+    std_dev = 0
+    for x in data:
+        std_dev += (x - mean) ** 2
+    std_dev = sqrt(std_dev / len_data)
+    signifmax = []
+    signifmin = []
+    coeff = 3
+    for i, x in enumerate(data):
+        diff = (x - mean) / std_dev
+        if diff >= coeff:
+            signifmax.append(i)
+        elif diff <= -coeff:
+            signifmin.append(i)
+    return signifmax, signifmin
+
+
+# CENTROID METHOD FOR EDGE CENTER FINDING
+def edge_centroid(data, img_data, padding=20):
+    # With Gaussian Blur might achieve the best performance
+    try:
+        start, end = edge_preprocess(data, padding)
+    except AssertionError:
+        return -1
+    isums = 0
+    total = 0
+    for i in range(start, end):
+        isums += img_data[i] * i
+        total += img_data[i]
+    return isums / total if total else -1
+
+
+def centroid_seg(data, start, end):
+    """ Given a segment (start, end) of the data,
+    find the centroid. """
+    isums = 0
+    total = 0
+    start = 0 if start < 0 else start
+    end = len(data) if end > len(data) else end
+    for i in range(start, end):
+        isums += data[i] * i
+        total += data[i]
+    return isums / total if total else -1
+
+
+# POLYNOMIAL FITTING
+def poly_fitting(data, img_data, padding=20):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING AND THE EXTRACT_ARRAY
+    try:
+        start, end = edge_preprocess(data, padding)
+    except AssertionError:
+        return -1
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        y = img_data.extract_array()
+    else:
+        #y = np.array(img_data[start:end])
+        idata = np.zeros(end - start)
+        for i in range(start, end):
+            idata[i - start] = img_data[i]
+    degrees, params, cost = improvedCost(x, y, x, y, 1, 7)
+    ind = np.argmin(cost)
+    degree = degrees[ind]
+    param = params[ind]
+    degree_register(degree)
+    curve_x, curve_y = poly_curve(param, x)
+    center_id = np.argmax(curve_y)
+    return curve_x[center_id]
+
+
+def poly_fitting_params(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING AND THE EXTRACT_ARRAY
+    maxi, mini = max_min(data)
+    width_thres = 90
+    value_thres = 20
+    if data[maxi] < value_thres or mini - maxi > width_thres:
+        print(data[maxi], mini, maxi)
+        raise AssertionError("Bad column or row")
+    start = maxi - padding
+    end = mini + padding + 1
+    start, end = smart_interval(start, end, data)
+    if start > end:
+        try:
+            print(maxi, mini, img_data._ax, start, end)
+        except:
+            print("else", maxi, mini, start, end)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        y = img_data.extract_array()
+    else:
+        idata = np.zeros(end - start)
+        for i in range(start, end):
+            idata[i - start] = img_data[i]
+    degrees, params, cost = improvedCost(x, y, x, y, 1, 7)
+    ind = np.argmin(cost)
+    degree = degrees[ind]
+    param = params[ind]
+    degree_register(degree)
+    return param, x
+
+
+DEGREES = {}
+
+
+def degree_register(elem):
+    if elem in DEGREES:
+        DEGREES[elem] += 1
+    else:
+        DEGREES[elem] = 1
+
+
+def gaussian_center(data, img_data, maxi, mini, padding=20):
+    #padding = (mini - maxi) // 2
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.zeros(end - start)
+        for i in range(start, end):
+            idata[i - start] = img_data[i]
+    try:
+        param = gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)])
+    except RuntimeError:
+        return -1
+    return param[1]
+
+
+# GAUSSIAN FITTING
+def gaussian_fitting(data, img_data, padding=20):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING, ALONG WITH THE WIDTH THRESHOLD
+    try:
+        maxi, mini = edge_max_min(data)
+        print((mini - maxi) // 2)
+    except AssertionError:
+        return -1
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.zeros(end-start)
+        for i in range(start, end):
+            idata[i-start] = img_data[i]
+    try:
+        param = gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)])
+    except RuntimeError:
+        return -1
+    return param[1]
+
+
+def gaussian_fitting_params(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING, ALONG WITH THE WIDTH THRESHOLD
+    maxi, mini = max_min(data)
+    width_thres = 90
+    value_thres = 20  # TODO: CONSOLIDATE THIS VALUE
+    if data[maxi] < value_thres or mini - maxi > width_thres:
+        print(data[maxi], mini, maxi)
+        raise AssertionError("Bad column or row")
+    start = maxi - padding
+    end = mini + padding
+    start, end = smart_interval(start, end, data)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.zeros(end - start)
+        for i in range(start, end):
+            idata[i-start] = img_data[i]
+    return gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)]), x
+
+
+"""======================================
+======== IMAGE PROCESSING UTIL ==========
+========================================= """
+
+
+def gauss_bg_deduce(x, img_data):
+    # TODO: OPTIMIZE PERFORMANCE
+    idata = img_data.extract_array()
+    p0 = [1, len(img_data) / 2, std_dev(idata)]
+    a, b, c_s = gauss_reg(x, idata, p0=p0)
+    rem_gauss = gauss_hat(x, a, b, c_s)
+    new_y = idata - rem_gauss
+    return x, rem_gauss, new_y
+
+
+def significance_test(data, val):
+    """Outputs whether the maximum or minimum value is a significant value."""
+    return val in extract_extrema(data)[0] or val in extract_extrema(data)[1]
+
+
+def edge_detect_expr(edges, original):
+    """
+    TODO: 1. SIMPLE Approach: pairwise mask over original image, then compute total
+          2. Traverse the image to mark the image.
+    """
+    se = 0
+    row_sum = 0
+    col_sum = 0
+    tot_sum = 0
+    for i in range(edges.shape[0]):
+        for j in range(edges.shape[1]):
+            if edges[i][j] > 0:
+                row_sum += i * original[i][j]
+                col_sum += j * original[i][j]
+                tot_sum += original[i][j]
+                se += 1
+    print(se)
+    if tot_sum != 0:
+        print(row_sum / tot_sum)
+        print(col_sum / tot_sum)
+
+
+def hori(img, i, j, d):
+    dest = j + d
+    if dest < 0 or dest >= img.shape[1]:
+        return img.item(i, j)
+    return img.item(i, dest)
+
+
+def verti(img, i, j, d):
+    dest = i + d
+    if dest < 0 or dest >= img.shape[0]:
+        return img.item(i, j)
+    return img.item(dest, j)
+
+
+def test_blur_then_nr(iname):
+    imgs = iname
+    numIMG = 5
+    imgr = None
+    for i in range(1, numIMG + 1):
+        target = cv2.imread(imgs.format(i), 0)
+        target = cv2.GaussianBlur(target, (9, 9), 0)
+        if i == 1:
+            imgr = np.uint16(target)
+        else:
+            imgr = imgr + target
+    return imgr / numIMG
+
+
+def get_center_val(img):
+    return img.item(img.shape[0] // 2, img.shape[1] // 2)
+
+
+def test_noise_reduce(iname, numIMG=5):
+    imgs = iname
+    imgr = None
+    original = None
+    for i in range(1, numIMG + 1):
+        target = cv2.imread(imgs.format(i), 0)
+        if target is None:
+            raise AttributeError("File {0} not found".format(iname))
+        if i == 1:
+            imgr = np.uint16(target)
+            original = target
+        else:
+            imgr = imgr + target
+    return imgr / numIMG, original
+
+
+def img_add(dest, src):
+    """Destructive!"""
+    row, col = dest.shape
+    for i in range(row):
+        for j in range(col):
+            dest[i][j] += src[i][j]
+
+
+FM = FastDataMatrix2D
+
+
+def folder_to_imgs(img_name_scheme, num_sample):
+    """This function takes img files and return cv imgs"""
+    return [cv2.imread(img_name_scheme.format(i)) for i in range(1, num_sample + 1)]
+
+
+# The image taken is flipped horizontally, result x should be img.shape[1] - x
+# The image sometimes has two peaks, try experimenting with different gaussian kernels
+def center_detect(img_name_scheme, num_sample, sample_int=50, gk=9, ks=-1, m=0, p=20,
+                       b=1, c=0):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair
+    hs: HORIZONTAL SLICE!  vs: VERTICAL SLICE!"""
+    imgr = test_noise_reduce(img_name_scheme, num_sample)[0]
+    dimr = imgr.shape[0]
+    dimc = imgr.shape[1]
+    # Image Processing
+    gksize = (gk, gk)
+    sigmaX = 0
+    img = cv2.GaussianBlur(imgr, gksize, sigmaX)
+    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=ks)
+    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=ks)
+    # ------------------------------------------------------------
+    # Parameter Setting
+    METHODS = {0: gaussian_fitting, 1: poly_fitting, 2: edge_centroid,
+               3: zero_crossing, 4: edge_converge_base, 5: edge_converge_extreme}
+    edge_method = METHODS[m]
+    nr = sample_int
+    r_thresh = dimr / (sample_int * 3.0)
+    nc = sample_int
+    c_thresh = dimc / (sample_int * 3.0)
+    # ------------------------------------------------------------
+    # Gathering Data
+    hs = []
+    vs = []
+    while nr < dimr:
+        data_x = FM(sobelx, FM.HOR, nr)
+        if m < 3:
+            ec_x = edge_method(data_x, FM(img, FM.HOR, nr), p)
+        else:
+            ec_x = edge_method(data_x)
+        nr += sample_int
+        if ec_x == -1:
+            continue
+        else:
+            hs.append((nr - sample_int, ec_x))
+
+    while nc < dimc:
+        data_y = FM(sobely, FM.VER, nc)
+        if m < 3:
+            ec_y = edge_method(data_y, FM(img, FM.VER, nc), p)
+        else:
+            ec_y = edge_method(data_y)
+        nc += sample_int
+        if ec_y == -1:
+            continue
+        else:
+            vs.append((nc - sample_int, ec_y))
+    len_hs = len(hs)
+    len_vs = len(vs)
+    # ------------------------------------------------------------
+    # --------------- PRE-CHECK DATA VALUES ----------------------
+    hxs = np.zeros(len_hs)
+    hys = np.zeros(len_hs)
+    for i in range(len_hs):
+        hxs[i] = hs[i][1]
+        hys[i] = hs[i][0]
+    vxs = np.zeros(len_vs)
+    vys = np.zeros(len_vs)
+    for i in range(len_vs):
+        vxs[i] = vs[i][0]
+        vys[i] = vs[i][1]
+    x_valid = False
+    y_valid = False
+    # OUTLIER DETECTION   TODO: OPTIMIZE, THIS IS NAIVE
+    if len_hs >= r_thresh:
+        x_valid = True
+        hys, hxs = reg_pre_debias(hys, hxs)
+        line_a = HoughLine(x=hxs, data=hys)
+    if len_vs >= c_thresh:
+        y_valid = True
+        vxs, vys = reg_pre_debias(vxs, vys)
+        line_b = HoughLine(x=vxs, data=vys)
+    # ------------------------------------------------------------
+    # ----- Following Modules Handles Hough Line Drawing ---------
+    # ------------------------------------------------------------
+    # DATA RECORDING AND PROCESSING
+    if c == 1:
+        center_x = sum(hxs) / len_hs if x_valid else -1
+        center_y = sum(vys) / len_vs if y_valid else -1
+    else:
+        if x_valid and y_valid:
+            center_x, center_y = HoughLine.intersect(line_a, line_b)
+        else:
+            center_x = sum(hxs) / len_hs if x_valid else -1
+            center_y = sum(vys) / len_vs if y_valid else -1
+    # ---------------------------------------------------------
+    return center_x, center_y
+
+
+
+def convergence_test_final(folder, ns):
+    offset = '../'
+    convergence = {}
+    variations = []
+    startNP = 59
+    startP = 80
+    endP = 193
+    ms = range(3)
+    fwrite = open('meas/convergence_v1.csv', 'w')
+    cwriter = csv.writer(fwrite)
+    cwriter.writerow(['Image Number', 'Center X', 'Center Y', 'StdDev Horizontal', 'Std Dev Vertical'])
+    for m in ms:
+        cwriter.writerow([str(m)])
+        # Consistency Cycled
+        pv = 0
+        pvs = 0
+        rcount = 0
+        cvx = np.zeros(4)
+        cvy = np.zeros(4)
+        # print(g)
+        for i in range(startNP, startP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                x, y = center_detect(fpath + imgfile, 5, m=m)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y)])
+                # CONVERGENCE
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+        for i in range(startP, endP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                x, y = center_detect(fpath + imgfile, 5, m=m)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y)])
+                # CONVERGENCE
+                # Record x, y, check rcount, refresh CONSISTENCY
+                cvx[rcount] = x
+                cvy[rcount] = y
+                rcount += 1
+                if rcount == 4:
+                    pv += np.var(cvx) + np.var(cvy)
+                    pvs += 1
+                    rcount = 0
+                    cvx = np.zeros(4)
+                    cvy = np.zeros(4)
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+                # print(str(i), val)
+        msepv = sqrt(pv / pvs)
+        cvg = {'PicConsistency': msepv}
+        convergence[m] = cvg
+        variations.append(msepv)
+    print(convergence)
+    fwrite.close()
+
+if __name__ == '__main__':
+    #print(center_detect('../calib4/img_59_{0}.png', 5))
+    convergence_test_final('calib4/', 'img_{0}')
+
+
+
diff --git a/img_rec_module/sig_proc_rob.py b/img_rec_module/sig_proc_rob.py
new file mode 100644
index 0000000..8a42d95
--- /dev/null
+++ b/img_rec_module/sig_proc_rob.py
@@ -0,0 +1,1485 @@
+import cv2
+import numpy as np
+import csv
+from scipy.optimize import curve_fit
+from scipy.optimize import least_squares
+import heapq
+from decimal import Decimal
+from math import atan, sqrt, tan, radians, acos, asin, degrees, sin, cos
+import matplotlib.pyplot as plt
+
+""" ===================================
+========== ANALYTIC GEOMETRY ==========
+======================================= """
+
+
+class Line:
+    inf = float('inf')
+
+    def __init__(self, a=inf, b=inf, p1=None, p2=None, data=None):
+        if p1 and p2:
+            self.point(p1, p2)
+        elif data:
+            self.reg(data)
+        else:
+            self.a = a
+            self.b = b
+
+    def reg(self, data):
+        D = np.array([[d[0], 1] for d in data])
+        y = np.array([d[1] for d in data])
+        self.a, self.b = leastSquares(D, y)
+
+    def point(self, p1, p2):
+        self.a = 1
+
+    @staticmethod
+    def intersect(l1, l2):
+        x = (l2.b - l1.b) / (l1.a - l2.a)
+        y = l1.a * x + l1.b
+        return x, y
+
+
+class HoughLine:
+    def __init__(self, theta=0, rho=0, x=None, data=None, new=True, r_bound=np.inf, loss='soft_l1'):
+        if data is not None and x is not None:
+            self.x = x
+            self.data = data
+            self.r_bound = r_bound
+            self.loss = loss
+            if new:
+                self.reg = self.reg_new
+            else:
+                self.reg = self.reg_old
+            self.reg(x, data)
+        else:
+            self._r = rho
+            self._t = theta
+            self._s = sin(theta)
+            self._c = cos(theta)
+
+    def reg_old(self, x, data):
+        """D = hough_data_matrix(x, data)
+        print(D)
+        y = np.zeros(len(data))
+        cosine, sine, self._r = leastSquares(D, y)
+        self._t = angle_interp(sine, cosine)
+        self._s = sine
+        self._c = cosine"""
+
+        x1, x2 = np.mean(x[:int(len(x) / 2)]), np.mean(x[int(len(x) / 2):])
+        y1, y2 = np.mean(data[:int(len(data) / 2)]), np.mean(data[int(len(data) / 2):])
+        theta0 = theta_pred(x1, y1, x2, y2)
+        p0 = [theta0, np.mean(x) * np.cos(theta0) + np.mean(data) * np.sin(theta0)]
+        """
+        x1, x2 = x[0], x[-1]
+        y1, y2 = data[0], data[-1]
+        theta0 = theta_pred(x1, y1, x2, y2)
+        # print(degrees(theta0))
+        p0 = [theta0, x1 * cos(theta0) + y1 * sin(theta0)]"""
+        pm, vm = curve_fit(hough_line, x, data, p0=p0)
+        # data_pred = hough_line(x, *pm)
+        # print('PRED', data_pred)
+        # res = data_pred - data
+        # print("RES", res)
+        # stderr = np.linalg.norm(res, 2)
+        # print("ERR", stderr)
+        # rankerr = res / stderr
+        # print("Z", rankerr)
+        if pm[1] < 0:
+            pm[1] = -pm[1]
+            pm[0] -= HC
+            # pm[0] = 2 * np.pi
+        angle = normalize_angle(pm[0])
+        self._t = angle
+        self._r = pm[1]
+        self._s = sin(angle)
+        self._c = cos(angle)
+
+    def reg_new(self, x, data):
+        """D = hough_data_matrix(x, data)
+        print(D)
+        y = np.zeros(len(data))
+        cosine, sine, self._r = leastSquares(D, y)
+        self._t = angle_interp(sine, cosine)
+        self._s = sine
+        self._c = cosine"""
+        x1, x2 = np.mean(x[:int(len(x) / 2)]), np.mean(x[int(len(x) / 2):])
+        y1, y2 = np.mean(data[:int(len(data) / 2)]), np.mean(data[int(len(data) / 2):])
+        theta0 = theta_pred(x1, y1, x2, y2)
+        p0 = [theta0, np.mean(x) * np.cos(theta0) + np.mean(data) * np.sin(theta0)]
+        res = least_squares(self.get_err, p0, loss=self.loss, f_scale=3, args=(x, data))
+        # data_pred = hough_line(x, *pm)
+        # print('PRED', data_pred)
+        # res = data_pred - data
+        # print("RES", res)
+        # stderr = np.linalg.norm(res, 2)
+        # print("ERR", stderr)
+        # rankerr = res / stderr
+        # print("Z", rankerr)
+        angle = normalize_angle(res.x[0])
+        self._t = angle
+        self._r = res.x[1]
+        self._s = sin(angle)
+        self._c = cos(angle)
+
+    def get_err(self, vars, xs, ys):
+        return xs * np.cos(vars[0]) + ys * np.sin(vars[0]) - vars[1]
+
+    def fit_x(self, x):
+        # print(self._r, self._t)
+        return (self._r - x * self._c) / self._s if self._s != 0 else 'undef'
+
+    def debias(self, thres=1.0):
+        """ @:returns tuple with a) zerr_before: regression error before debias
+         b) zerr_after: regression error after debias"""
+        x, y = self.x, self.data
+        zero_hat_before = x * self._c + y * self._s - self._r
+        #y_hat_before = self.fit_x(x)
+        zerr_before = np.square(zero_hat_before)
+        #yerr_before = np.square(y_hat_before - y)
+        conds = (zerr_before - np.mean(zerr_before)) / np.std(zerr_before) <= thres
+        new_x, new_y = x[conds], y[conds]
+        self.x, self.data = new_x, new_y
+        self.reg(new_x, new_y)
+        zero_hat_after = new_x * self._c + new_y * self._s - self._r
+        #y_hat_after = self.fit_x(new_x)
+        zerr_after = np.square(zero_hat_after)
+        #yerr_after = np.square(y_hat_after - new_y)
+        return zerr_before, zerr_after
+
+    def point_gen(self):
+        x0 = self._c * self._r
+        y0 = self._s * self._r
+        x1 = int(x0 + 1000 * (-self._s))
+        y1 = int(y0 + 1000 * (self._c))
+        x2 = int(x0 - 1000 * (-self._s))
+        y2 = int(y0 - 1000 * (self._c))
+        return (x1, y1), (x2, y2)
+
+    def __str__(self):
+        return 'hough line with cos:{0}, sin:{1}, rho:{2}, theta:{3}'.format(self._c, self._s,
+                                                                             self._r, degrees(self._t))
+
+    @staticmethod
+    def intersect(l1, l2):
+        if l1._s == 0 and l2._s == 0:
+            return float('inf'), float('inf')
+        if l1._s == 0:
+            return l1._r, l2.fit_x(l1._r)
+        elif l2._s == 0:
+            return l2._r, l1.fit_x(l2._r)
+        x = (l2._r / l2._s - l1._r / l1._s) / (l2._c / l2._s - l1._c / l1._s)
+        y = l1.fit_x(x)
+        return x, y
+
+
+# QC: QUARTER_CYCLE, HC: HALF_CYCLE, TQC: THIRD_QUARTER_CYCLE, FC: FULL_CYCLE
+QC = np.pi / 2
+HC = np.pi
+TQC = 3 * np.pi / 2
+FC = 2 * np.pi
+
+
+def angle_interp(s, c):
+    if c == 1.0:
+        return 0
+    elif s == 1.0:
+        return np.pi / 2
+    elif c == -1.0:
+        return np.pi
+    elif s == -1.0:
+        return np.pi * 3 / 2
+    elif c < 0 < s:
+        return acos(c)
+    elif s < 0 and c < 0:
+        return 2 * np.pi - acos(c)
+    elif s < 0 < c:
+        return asin(s) + 2 * np.pi
+    else:
+        return asin(s)
+
+
+def normalize_angle(angle):
+    res = angle - (angle // FC) * FC
+    return res
+    """
+    if QC < res < HC:
+        return res + HC
+    elif HC <= res <= TQC:
+        return res - HC
+    else:
+        return res"""
+
+
+def theta_pred(x1, y1, x2, y2):
+    dx = x2 - x1
+    dy = y2 - y1
+    angle = sin_angle_from_points(dx, dy)
+    if dx * dy <= 0:
+        return np.pi / 2 - angle
+    else:
+        root = root_finding(x1, x2, y1, y2)
+        if root < 0:
+            return np.pi / 2 + angle
+        else:
+            return angle + 3 * np.pi / 2
+
+
+def sin_angle_from_points(dx, dy):
+    return asin(abs(dy) / sqrt(dx ** 2 + dy ** 2))
+
+
+def poly_curve(params, x_input):
+    # params contains the coefficients that multiply the polynomial terms, in degree of lowest degree to highest degree
+    degree = len(params) - 1
+    x_range = [x_input[1], x_input[-1]]
+    x = np.linspace(x_range[0], x_range[1], 1000)
+    y = x * 0
+
+    for k in range(0, degree + 1):
+        coeff = params[k]
+        y = y + list(map(lambda z: coeff * z ** k, x))
+    return x, y
+
+
+def gauss_2d(x, y, a, b1, c_s1, b2, c_s2):
+    return a * np.exp(- ((x - b1) ** 2 / (2 * c_s1) + (y - b2) ** 2) / (2 * c_s2))
+
+
+def gauss_hat(x, a, b, c_s):
+    return a * np.exp(- (x - b) ** 2 / (2 * c_s))
+
+
+def gaussian_curve(x_input, a, b, c_s):
+    x_range = [x_input[1], x_input[-1]]
+    x = np.linspace(x_range[0], x_range[1], 1000)
+    y = a * np.exp(- (x - b) ** 2 / (2 * c_s))
+    return x, y
+
+
+def hough_line(x, theta, rho):
+    return (rho - x * np.cos(theta)) / np.sin(theta)
+
+
+""" ===================================
+========== MATRIX-ARRAY UTIL ==========
+======================================= """
+
+
+class PseudoLL(list):
+    def push(self, obj):
+        self.append(obj)
+
+    def __getitem__(self, item):
+        return super().__getitem__(len(self) - 1 - item)
+
+    def __repr__(self):
+        rep = '['
+        ls = len(self)
+        for i in range(ls):
+            if i > 0:
+                rep += ', '
+            val = self[i]
+            rep += str(val)
+        rep += ']'
+        return rep
+
+
+class FastDataMatrix2D:
+    # TODO: HANDLE THE CASE WHEN IT WRAPS 1D
+
+    HOR = 1
+    VER = 0
+
+    def __init__(self, data, ax, index):
+        self._data = data
+        self._ax = ax
+        assert 0 <= index < self.irange(), "start: {0}, end: {1}, ax: {2}, index: {3}".format(self.start, self.end,
+                                                                                              self._ax, index)
+        self._index = index
+        self.itr = 0
+        self.initialize()
+
+    def segmentize(self, start, end):
+        assert 0 <= start < end <= self._data.shape[self._ax], \
+            "start: {0}, end: {1}, ax: {2}, index: {3}".format(start, end, self._ax, self._index)
+        self.start = start
+        self.end = end
+
+    def initialize(self):
+        self.segmentize(0, self._data.shape[self._ax])
+
+    def irange(self):
+        # index range of the fast matrix array
+        return self._data.shape[1 - self._ax]
+
+    def set_axis(self, ax):
+        """USE copy when trying to switch axis and index"""
+        self._ax = ax
+        if self._index >= self.irange():
+            raise IndexError("Old index {0} is too large for the new axis".format(self._index))
+        self.initialize()
+
+    def set_index(self, index):
+        assert 0 <= index < self.irange(), "start: {0}, end: {1}, ax: {2}, index: {3}".format(self.start, self.end,
+                                                                                              self._ax, index)
+        self._index = index
+
+    def extract_array(self):
+        """Optimize later for better performance"""
+        arr = self._data[self._index, self.start:self.end] if self._ax == FastDataMatrix2D.HOR \
+            else self._data[self.start:self.end, self._index]
+        return np.array(arr)
+
+    def copy(self, ax=None, index=None):
+        if ax is not None and index is not None:
+            return FastDataMatrix2D(self._data, ax, index)
+        else:
+            return FastDataMatrix2D(self._data, self._ax, self._index)
+
+    def __iter__(self):
+        raise RuntimeError("You need the ITER method!")
+
+    def __next__(self):
+        raise RuntimeError("You need the NEXT method!")
+
+    def __getitem__(self, item):
+        return self._data[self._index, item + self.start] if self._ax == FastDataMatrix2D.HOR else self._data[item + self.start, self._index]
+
+    def __setitem__(self, key, value):
+        if self._ax == FastDataMatrix2D.HOR:
+            self._data[self._index, key + self.start] = value
+        else:
+            self._data[key + self.start, self._index] = value
+
+    def __len__(self):
+        return self.end - self.start
+
+
+""" ===================================
+=========== REGRESSION UTILS ==========
+======================================= """
+
+
+# Generic Regression Tools 128-133
+def leastSquares(D, y):
+    return np.linalg.lstsq(D, y, rcond=None)[0]
+
+
+def MSE(y, y_hat, N):
+    return np.linalg.norm(y - y_hat) ** 2 / N
+
+
+def std_dev(data):
+    std_dev = 0
+    len_data = len(data)
+    mean = sum(data) / len_data
+    for i in range(len_data):
+        std_dev += (data[i] - mean) ** 2
+    return sqrt(std_dev / len_data)
+
+
+def reg_pre_debias(ind, data):
+    sd = np.std(data)
+    miu = np.mean(data)
+    debiased_data = []
+    debiased_ind = []
+    for i in range(len(data)):
+        dev = abs((data[i] - miu) / sd)
+        if dev < 2:
+            debiased_data.append(data[i])
+            debiased_ind.append(ind[i])
+    return debiased_ind, debiased_data
+
+
+# Polynomial Regression Tools 137-171
+def poly_data_matrix(input_data, degree):
+    # degree is the degree of the polynomial you plan to fit the data with
+    Data = np.zeros((len(input_data), degree + 1))
+
+    for k in range(0, degree + 1):
+        Data[:, k] = (list(map(lambda x: x ** k, input_data)))
+
+    return Data
+
+
+def poly_error(params, D_a, y_a):
+    '''degree=len(params)-1
+    y=x_a*0
+
+    for k in range(0,degree+1):
+        coeff=params[k]
+        y=y+list(map(lambda z:coeff*z**k,x_a))'''
+    y = np.dot(D_a, params)
+    return np.linalg.norm(y - y_a) ** 2
+
+
+def improvedCost(x, y, x_test, y_test, start, end):
+    """Given a set of x and y points training points,
+    this function calculates polynomial approximations of varying
+    degrees from start to end. Then it returns the cost, with
+    the polynomial tested on test points of each degree in an array"""
+    c = []
+    degrees = range(start, end)
+    ps = []
+    for degree in degrees:
+        # YOUR CODE HERE
+        D = poly_data_matrix(x, degree)
+        p = leastSquares(D, y)
+        ps.append(p)
+        D_t = poly_data_matrix(x_test, degree)
+        y_hat = np.dot(D_t, p)
+        c.append(MSE(y_test, y_hat, len(x_test)))
+    return np.array(degrees), ps, np.array(c)
+
+
+# Gaussian Regression Tools 184-210
+def gauss_data_matrix(data):
+    return np.array([[1, x ** 2, x, 1] for x in data])
+
+
+def gauss_reg(x, y, p0):
+    """Given a set of x and y training points, this function
+    calculates the gaussian approximation function"""
+    """skip_x = np.array([x[i] for i in range(len(y)) if y[i] != 0])
+    y_prime = np.array([y[i] for i in range(len(y)) if y[i] != 0])
+    logy = np.log(y_prime)
+
+    sol = leastSquares(gauss_data_matrix(skip_x), logy)
+    alp, bet, gam, lam = sol
+    a = np.e ** alp
+    c_s = - 1 / (2 * bet)
+    b = gam * c_s
+    maxi, mini = max_min(y)
+    pred = gauss_hat(maxi, a, b, c_s)
+    real = y[maxi]
+    a = real / pred // 1000000000 * 10"""
+    # timeb = datetime.now()
+    param, vm = curve_fit(gauss_hat, x, y, p0=p0)
+    # timea = datetime.now()
+    # print(timea - timeb)
+    return param
+
+
+def gauss_mat(shape, a, b1, c_s1, b2, c_s2):
+    mat = np.zeros(shape)
+    for r in range(mat.shape[0]):
+        for c in range(mat.shape[1]):
+            mat[r, c] = gauss_2d(c, r, a, b1, c_s1, b2, c_s2)
+
+    return mat
+
+
+# Hough Transform Regression Tools
+def hough_data_matrix(x, y):
+    dim = len(x)
+    mat = np.full((dim, 3), -1.0)
+    for i in range(dim):
+        mat[i][0] = x[i]
+        mat[i][1] = y[i]
+    return mat
+
+
+""" =======================================
+========= EDGE DETECTION UTILS ============
+=========================================== """
+
+
+def gather_all(imgr, sample_int=30, gk=9, ks=-1):
+    """ @:returns centers_v, centers_h, list of possible centers gathered by algorithm, represented as (row, col)
+    """
+    dimr = imgr.shape[0]
+    dimc = imgr.shape[1]
+    # Image Processing
+    gksize = (gk, gk)
+    sigmaX = 0
+    img = cv2.GaussianBlur(imgr, gksize, sigmaX)
+    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=ks)
+    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=ks)
+    # Parameter Setting
+    nr = sample_int
+    nc = sample_int
+    # Gathering Data
+    centers_v = []
+    centers_h = []
+    while nr < dimr:
+        data_x = sobelx[nr, :]
+        am = gather_centers(data_x, img[nr, :], centers_v, 0, nr, gaussian_center)
+        nr += sample_int
+    while nc < dimc:
+        data_y = sobely[:, nc]
+        raw_y = img[:, nc]
+        am = gather_centers(data_y, img[:, nc], centers_h, 1, nc, gaussian_center)
+        nc += sample_int
+    return centers_v, centers_h
+
+
+def gather_centers(grad, raw_data, reserve, ax, ax_n, center_method):
+    # Given grad and raw_data, insert the possible beam centers to reserves
+    max_grad, min_grad = get_maxi_mini(grad)
+    max_q, min_q, locs = bi_order_sort(max_grad, min_grad)
+    miu, sig = np.mean(raw_data), np.std(raw_data)
+    avant = lambda locs, i: locs[i - 1] if i - 1 >= 0 else None
+    apres = lambda locs, i: locs[i + 1] if i + 1 < len(locs) else None
+    i = 0
+    peaked = False
+    while (i < 2 or peaked) and max_q and min_q:
+        if peaked:
+            top = min_q.pop()
+            av = avant(locs, top[1])
+            if av in max_q and beam_bound(raw_data, miu, sig, av, top):
+                mid = center_method(grad, raw_data, av[0], top[0])
+                reserve.append((ax_n, mid) if ax == 0 else (mid, ax_n))
+                max_q.remove(av)
+                i += 1
+            peaked = False
+        else:
+            max_top = max_q.pop()
+            min_top = min_q[0]
+            if beam_bound(raw_data, miu, sig, max_top, min_top):
+                # print("MaxMin: ", max_top, min_top)
+                mid = center_method(grad, raw_data, max_top[0], min_top[0])
+                reserve.append((ax_n, mid) if ax == 0 else (mid, ax_n))
+                min_q.remove(min_top)
+                i += 1
+            else:
+                peaked = True
+                ap = apres(locs, max_top[1])
+                # QUICK FIX HERE, THINK MORE
+                if ap in min_q and beam_bound(raw_data, miu, sig, max_top, ap):
+                    # print("Max_apres: ", max_top, ap)
+                    mid = center_method(grad, raw_data, max_top[0], ap[0])
+                    reserve.append((ax_n, mid) if ax == 0 else (mid, ax_n))
+                    min_q.remove(ap)
+                    i += 1
+    return i
+
+
+def sup_tout(max_q, min_q, d, guess):
+    if guess == 0:
+        prem, deux = max_q, min_q
+    else:
+        prem, deux = min_q, max_q
+    if d in prem:
+        prem.remove(d)
+    elif d in prem:
+        deux.remove(d)
+    else:
+        raise RuntimeError("Tried to remove already removed term! {0}, {1}".format(d, guess))
+
+
+def get_maxi_mini(data, ceil=3):
+    # Given data, pluck the maxis and minis and store them in minpq and maxpq respectively
+    max_grad = []
+    min_grad = []
+    nat_select = lambda a, b: a if a[0] >= b[0] else b
+    maxer = lambda d: len(max_grad) < ceil or d > max_grad[0][0]
+    miner = lambda d: len(min_grad) < ceil or d > min_grad[0][0]
+    active_max, active_min = None, None
+
+    for i, d in enumerate(data):
+        if not active_max:
+            if maxer(d):
+                active_max = (d, i)
+        else:
+            curr = (d, i)
+            active_max = nat_select(curr, active_max)
+        if active_max and (check_crossing(data, i) or i == len(data) - 1):
+            heapq.heappush(max_grad, active_max)
+            if len(max_grad) > ceil:
+                heapq.heappop(max_grad)
+            active_max = None
+
+        if not active_min:
+            if miner(-d):
+                active_min = (-d, i)
+        else:
+            curr = (-d, i)
+            active_min = nat_select(curr, active_min)
+        if active_min and (check_crossing(data, i) or i == len(data) - 1):
+            heapq.heappush(min_grad, active_min)
+            if len(min_grad) > ceil:
+                heapq.heappop(min_grad)
+            active_min = None
+
+    return max_grad, min_grad
+
+
+def bi_order_sort(max_grad, min_grad):
+    # Takes in a max_grad and min_grad (heapq), returns a locational ordered array and a magnitude queue.
+    locs = []
+    max_q, min_q = PseudoLL(), PseudoLL()
+    while len(max_grad) and len(min_grad):
+        mat = heapq.heappop(max_grad)
+        mit = heapq.heappop(min_grad)
+        maxt, mint = [mat[1], None], [mit[1], None]
+        locs.append(maxt)
+        locs.append(mint)
+        max_q.push(maxt)
+        min_q.push(mint)
+    locs.sort(key=lambda pair: pair[0])
+    for i in range(len(locs)):
+        locs[i][1] = i
+    return max_q, min_q, locs
+
+
+def beam_bound(raw, miu, sig, a1, a2, thres=2):
+    # given raw data, determine if a1, a2 are beam bound entries, where a1, a2 are tuples [index, *(loc)]
+    return a1 and a2 and a2[1] == a1[1] + 1 and (raw[(a1[0] + a2[0]) // 2] - miu) / sig >= thres
+
+
+# Generic Helper
+def max_min(data):
+    # Returns the maximum and minimum for the data
+    maxi = data[0]
+    max_ind = 0
+    mini = data[0]
+    min_ind = 0
+    for i in range(1, len(data)):
+        target = data[i]
+        if target > maxi:
+            maxi = target
+            max_ind = i
+        if target < mini:
+            mini = target
+            min_ind = i
+    return max_ind, min_ind
+
+
+def edge_max_min(data):
+    # Returns a *safe* edge maxi, mini for the data
+    # TODO: OPTIMIZE THE WIDTH AND VALUE THRESHOLD
+    width_thres = 90
+    value_thres = 20
+    maxi = data[0]
+    max_ind = 0
+    mini = data[0]
+    min_ind = 0
+    for i in range(1, len(data)):
+        target = data[i]
+        if target > maxi:
+            maxi = target
+            max_ind = i
+        if target < mini:
+            mini = target
+            min_ind = i
+    maxi, mini = max_ind, min_ind
+    assert data[maxi] >= value_thres and mini - maxi <= width_thres and maxi < mini
+    return maxi, mini
+
+
+def min_max(data, max, min):
+    """ Converts the data to min max space. """
+    g_diff = max - min
+    return [(d - min) / g_diff for d in data]
+
+
+def root_finding(x1, x2, y1, y2):
+    """Given two points on a line, finds its zero crossing root."""
+    return - y1 * (x2 - x1) / (y2 - y1) + x1
+
+
+def check_crossing(data, i):
+    return i + 1 < len(data) and check_cross(data[i], data[i+1])
+
+
+def check_cross(a, b):
+    # Checks whether two points are of opposite signs
+    return a * b < 0
+
+
+def round_up(num):
+    down = int(num)
+    if num - down > 0:
+        return num + 1
+    else:
+        return num
+
+
+def smart_interval(start, end, data):
+    start = 0 if start < 0 else start
+    end = len(data) if end > len(data) else end
+    return start, end
+
+
+def edge_preprocess(data, padding):
+    maxi, mini = edge_max_min(data)
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    return start, end
+
+
+# ZERO CROSSING (PEAK FINDING)
+def zero_crossing(data):
+    # Yields the center with zero-crossing method
+    maxi, mini = max_min(data)
+    cross = -1
+    cross_count = 0
+    for j in range(maxi, mini):
+        y1 = data[j]
+        x2 = j + 1
+        y2 = data[x2]
+        if check_cross(y1, y2):
+            cross = root_finding(j, x2, y1, y2)  # change to function later
+            cross_count += 1
+    return -1 if cross_count > 1 else cross
+
+
+# AVERAGE METHOD FOR EDGE CENTER FINDING
+def edge_converge_base(data):
+    maxi, mini = max_min(data)
+    width_thres = 70
+    if mini - maxi > width_thres:
+        return -1
+    else:
+        return (maxi + mini) / 2
+
+
+# PSEUDO EDGE (closest noise gradient change) CENTER FINDING
+def edge_converge_extreme(data):
+    maxi, mini = max_min(data)
+    width_thres = 70
+    if mini - maxi > width_thres:
+        return -1
+    emax = -1
+    emin = -1
+    maxflag = True
+    minflag = True
+    cmax = maxi
+    cmin = mini
+    while cmax > 0 and cmin < len(data) - 1 and (maxflag or minflag):
+        max2 = cmax - 1
+        maxy1, maxy2 = data[cmax], data[max2]
+        min2 = cmin + 1
+        miny1, miny2 = data[cmin], data[min2]
+        if check_cross(maxy1, maxy2) and maxflag:
+            emax = root_finding(cmax, max2, maxy1, maxy2)
+            maxflag = False
+        if check_cross(miny1, miny2) and minflag:
+            emin = root_finding(cmin, min2, miny1, miny2)
+            minflag = False
+        if maxflag:
+            cmax -= 1
+        if minflag:
+            cmin += 1
+    return -1 if (emax == -1 or emin == -1) else (emax + emin) / 2
+
+
+def extract_extrema(data):
+    len_data = len(data)
+    mean = sum(data) / len_data
+    std_dev = 0
+    for x in data:
+        std_dev += (x - mean) ** 2
+    std_dev = sqrt(std_dev / len_data)
+    signifmax = []
+    signifmin = []
+    coeff = 3
+    for i, x in enumerate(data):
+        diff = (x - mean) / std_dev
+        if diff >= coeff:
+            signifmax.append(i)
+        elif diff <= -coeff:
+            signifmin.append(i)
+    return signifmax, signifmin
+
+
+# CENTROID METHOD FOR EDGE CENTER FINDING
+def edge_centroid(data, img_data, padding=20):
+    # With Gaussian Blur might achieve the best performance
+    try:
+        start, end = edge_preprocess(data, padding)
+    except AssertionError:
+        return -1
+    isums = 0
+    total = 0
+    for i in range(start, end):
+        isums += img_data[i] * i
+        total += img_data[i]
+    return isums / total if total else -1
+
+
+def centroid_seg(data, start, end):
+    """ Given a segment (start, end) of the data,
+    find the centroid. """
+    isums = 0
+    total = 0
+    start = 0 if start < 0 else start
+    end = len(data) if end > len(data) else end
+    for i in range(start, end):
+        isums += data[i] * i
+        total += data[i]
+    return isums / total if total else -1
+
+
+# POLYNOMIAL FITTING
+def poly_fitting(data, img_data, padding=20):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING AND THE EXTRACT_ARRAY
+    try:
+        start, end = edge_preprocess(data, padding)
+    except AssertionError:
+        return -1
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        y = img_data.extract_array()
+    else:
+        #y = np.array(img_data[start:end])
+        idata = np.zeros(end - start)
+        for i in range(start, end):
+            idata[i - start] = img_data[i]
+    degrees, params, cost = improvedCost(x, y, x, y, 1, 7)
+    ind = np.argmin(cost)
+    degree = degrees[ind]
+    param = params[ind]
+    degree_register(degree)
+    curve_x, curve_y = poly_curve(param, x)
+    center_id = np.argmax(curve_y)
+    return curve_x[center_id]
+
+
+def poly_fitting_params(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING AND THE EXTRACT_ARRAY
+    maxi, mini = max_min(data)
+    width_thres = 90
+    value_thres = 20
+    if data[maxi] < value_thres or mini - maxi > width_thres:
+        print(data[maxi], mini, maxi)
+        raise AssertionError("Bad column or row")
+    start = maxi - padding
+    end = mini + padding + 1
+    start, end = smart_interval(start, end, data)
+    if start > end:
+        try:
+            print(maxi, mini, img_data._ax, start, end)
+        except:
+            print("else", maxi, mini, start, end)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        y = img_data.extract_array()
+    else:
+        idata = np.zeros(end - start)
+        for i in range(start, end):
+            idata[i - start] = img_data[i]
+    degrees, params, cost = improvedCost(x, y, x, y, 1, 7)
+    ind = np.argmin(cost)
+    degree = degrees[ind]
+    param = params[ind]
+    degree_register(degree)
+    return param, x
+
+
+DEGREES = {}
+
+
+def degree_register(elem):
+    if elem in DEGREES:
+        DEGREES[elem] += 1
+    else:
+        DEGREES[elem] = 1
+
+
+def gaussian_center(data, img_data, maxi, mini, padding=20):
+    #padding = (mini - maxi) // 2
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.zeros(end - start)
+        for i in range(start, end):
+            idata[i - start] = img_data[i]
+    try:
+        param = gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)])
+    except RuntimeError:
+        return -1
+    return param[1]
+
+
+# GAUSSIAN FITTING
+def gaussian_fitting(data, img_data, padding=20):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING, ALONG WITH THE WIDTH THRESHOLD
+    try:
+        maxi, mini = edge_max_min(data)
+        print((mini - maxi) // 2)
+    except AssertionError:
+        return -1
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.zeros(end-start)
+        for i in range(start, end):
+            idata[i-start] = img_data[i]
+    try:
+        param = gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)])
+    except RuntimeError:
+        return -1
+    return param[1]
+
+
+def gaussian_fitting_params(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING, ALONG WITH THE WIDTH THRESHOLD
+    maxi, mini = max_min(data)
+    width_thres = 90
+    value_thres = 20  # TODO: CONSOLIDATE THIS VALUE
+    if data[maxi] < value_thres or mini - maxi > width_thres:
+        print(data[maxi], mini, maxi)
+        raise AssertionError("Bad column or row")
+    start = maxi - padding
+    end = mini + padding
+    start, end = smart_interval(start, end, data)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.zeros(end - start)
+        for i in range(start, end):
+            idata[i-start] = img_data[i]
+    return gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)]), x
+
+
+"""======================================
+======== IMAGE PROCESSING UTIL ==========
+========================================= """
+
+
+def image_diff(img1, img2):
+    i1 = img1.astype(np.int16)
+    i2 = img2.astype(np.int16)
+    res = i1 - i2
+    dst = np.zeros_like(res, dtype=np.int8)
+    return cv2.normalize(res, dst=dst, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=8)
+
+
+def gauss_bg_deduce(x, img_data):
+    # TODO: OPTIMIZE PERFORMANCE
+    idata = img_data.extract_array()
+    p0 = [1, len(img_data) / 2, std_dev(idata)]
+    a, b, c_s = gauss_reg(x, idata, p0=p0)
+    rem_gauss = gauss_hat(x, a, b, c_s)
+    new_y = idata - rem_gauss
+    return x, rem_gauss, new_y
+
+
+def significance_test(data, val):
+    """Outputs whether the maximum or minimum value is a significant value."""
+    return val in extract_extrema(data)[0] or val in extract_extrema(data)[1]
+
+
+def edge_detect_expr(edges, original):
+    """
+    TODO: 1. SIMPLE Approach: pairwise mask over original image, then compute total
+          2. Traverse the image to mark the image.
+    """
+    se = 0
+    row_sum = 0
+    col_sum = 0
+    tot_sum = 0
+    for i in range(edges.shape[0]):
+        for j in range(edges.shape[1]):
+            if edges[i][j] > 0:
+                row_sum += i * original[i][j]
+                col_sum += j * original[i][j]
+                tot_sum += original[i][j]
+                se += 1
+    print(se)
+    if tot_sum != 0:
+        print(row_sum / tot_sum)
+        print(col_sum / tot_sum)
+
+
+def hori(img, i, j, d):
+    dest = j + d
+    if dest < 0 or dest >= img.shape[1]:
+        return img.item(i, j)
+    return img.item(i, dest)
+
+
+def verti(img, i, j, d):
+    dest = i + d
+    if dest < 0 or dest >= img.shape[0]:
+        return img.item(i, j)
+    return img.item(dest, j)
+
+
+def test_blur_then_nr(iname):
+    imgs = iname
+    numIMG = 5
+    imgr = None
+    for i in range(1, numIMG + 1):
+        target = cv2.imread(imgs.format(i), 0)
+        target = cv2.GaussianBlur(target, (9, 9), 0)
+        if i == 1:
+            imgr = np.uint16(target)
+        else:
+            imgr = imgr + target
+    return imgr / numIMG
+
+
+def get_center_val(img):
+    return img.item(img.shape[0] // 2, img.shape[1] // 2)
+
+
+def test_noise_reduce(iname, numIMG=5):
+    imgs = iname
+    imgr = None
+    original = None
+    for i in range(1, numIMG + 1):
+        target = cv2.imread(imgs.format(i), 0)
+        if target is None:
+            raise AttributeError("File {0} not found".format(iname))
+        if i == 1:
+            imgr = np.uint16(target)
+            original = target
+        else:
+            imgr = imgr + target
+    return imgr / numIMG, original
+
+
+def img_add(dest, src):
+    """Destructive!"""
+    row, col = dest.shape
+    for i in range(row):
+        for j in range(col):
+            dest[i][j] += src[i][j]
+
+
+"""======================================
+========== VISUALIZATION UTIL ===========
+========================================= """
+
+
+def visualize_centers(img, centers_v, centers_h, ax):
+    xsv = [c[1] for c in centers_v]
+    ysv = [c[0] for c in centers_v]
+    xsh = [c[1] for c in centers_h]
+    ysh = [c[0] for c in centers_h]
+    ax.imshow(img, cmap='gray')
+    ax.scatter(xsv, ysv, facecolor='blue', linewidths=1)
+    ax.scatter(xsh, ysh, facecolor='red', linewidths=1)
+
+
+FM = FastDataMatrix2D
+
+
+def folder_to_imgs(img_name_scheme, num_sample):
+    """This function takes img files and return cv imgs"""
+    return [cv2.imread(img_name_scheme.format(i)) for i in range(1, num_sample + 1)]
+
+
+def center_detect_hough(img):
+    centers_v, centers_h = gather_all(img)
+    centpointsv = np.zeros_like(img, dtype=np.uint8)
+    centpointsh = np.zeros_like(img, dtype=np.uint8)
+    #print(centers_v+centers_h)
+    for r, c in centers_v:
+        try:
+            centpointsv[int(r)][int(c)] = 255
+        except:
+            continue
+    for r, c in centers_h:
+        try:
+            centpointsh[int(r)][int(c)] = 255
+        except:
+            continue
+    linesv = cv2.HoughLines(centpointsv, 0.5, 0.005, 1)
+    linesh = cv2.HoughLines(centpointsh, 0.5, 0.005, 1)
+    all_lines = []
+    try:
+        for rho, theta in linesv[0]:
+            #print(rho, theta)
+            all_lines.append(HoughLine(theta, rho))
+    except:
+        print(linesv)
+    for rho, theta in linesh[0]:
+        all_lines.append(HoughLine(theta, rho))
+    ct = HoughLine.intersect(all_lines[0], all_lines[-1])
+    return ct
+
+
+# The image taken is flipped horizontally, result x should be img.shape[1] - x
+# The image sometimes has two peaks, try experimenting with different gaussian kernels
+def center_detect_test(img_name, sample_int=30, gk=9, ks=-1):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair
+    hs: HORIZONTAL SLICE!  vs: VERTICAL SLICE!"""
+    # TODO: CHANGE DEBIAS AND CHANGE VISUALIZATION PART TO HAVE:
+    # TODO: 1. CENTER_VISUALIZATION WITH LINE DRAWN
+    # TODO: 2. ERROR BEFORE AND AFTER
+    imgr = cv2.imread(img_name, 0)
+    centers_v, centers_h = gather_all(imgr, sample_int, gk, ks)
+    centers_v, centers_h = np.array(centers_v), np.array(centers_h)
+    centers_vx, centers_vy = centers_v[:, 1], centers_v[:, 0]
+    centers_hx, centers_hy = centers_h[:, 1], centers_h[:, 0]
+    loss_regs = []
+    loss_types = ['huber', 'soft_l1', 'arctan', 'linear', 'cauchy']
+    for loss in loss_types:
+        print(loss)
+        line_v, line_h = HoughLine(x=centers_vx, data=centers_vy), HoughLine(x=centers_hx, data=centers_hy, loss=loss)
+        vp1_av, vp2_av = line_v.point_gen()
+        hp1_av, hp2_av = line_h.point_gen()
+        zv_err_av, zv_err_ap = line_v.debias()
+        zh_err_av, zh_err_ap = line_h.debias()
+        loss_regs.append(np.mean(zh_err_ap) ** 2 + np.mean(zv_err_ap) ** 2)
+        print(loss_regs[-1])
+        xv_av, xv_ap = np.arange(len(zv_err_av)), np.arange(len(zv_err_ap))
+        xh_av, xh_ap = np.arange(len(zh_err_av)), np.arange(len(zh_err_ap))
+        hp1_ap, hp2_ap = line_h.point_gen()
+        vp1_ap, vp2_ap = line_v.point_gen()
+        cenhs = list(zip(line_h.data, line_h.x))
+        cenvs = list(zip(line_v.data, line_v.x))
+        cv2.line(imgr, hp1_ap, hp2_ap, (0, 0, 255), 1)
+        cv2.line(imgr, vp1_ap, vp2_ap, (0, 0, 255), 1)
+
+        fig1 = plt.figure(figsize=(20, 10))
+        ax1 = fig1.add_subplot(121)
+        visualize_centers(imgr, centers_v, centers_h, ax1)
+        ax1.set_title('Before')
+        ax2 = fig1.add_subplot(122)
+        visualize_centers(imgr, cenvs, cenhs, ax2)
+        ax2.set_title('After')
+        fig2 = plt.figure(figsize=(20, 10))
+        ax3 = fig2.add_subplot(211)
+        ax3.plot(xh_av, zh_err_av, 'mo-', xh_ap, zh_err_ap, 'bo-')
+        ax3.set_title('HOR Loss Avant et Apres')
+        ax3.legend(['Avant', 'Apres'])
+        ax4 = fig2.add_subplot(212)
+        ax4.plot(xv_av, zv_err_av, 'mo-', xv_ap, zv_err_ap, 'bo-')
+        ax4.set_title('VER Loss Avant et Apres')
+        ax4.legend(['Avant', 'Apres'])
+        plt.show()
+    plt.figure()
+    plt.plot(range(5), loss_regs, 'b-')
+    plt.xticks(range(5), loss_types)
+    plt.show()
+    return center_detect_hough(imgr)
+
+
+def center_detect(folder_path, img_name, sample_int=30, gk=9, ks=-1, l='soft_l1', visual=False):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair
+    hs: HORIZONTAL SLICE!  vs: VERTICAL SLICE!"""
+    # TODO: CHANGE DEBIAS AND CHANGE VISUALIZATION PART TO HAVE:
+    # TODO: 1. CENTER_VISUALIZATION WITH LINE DRAWN
+    # TODO: 2. ERROR BEFORE AND AFTER
+    imgr = cv2.imread(folder_path+img_name, 0)
+    centers_v, centers_h = gather_all(imgr, sample_int, gk, ks)
+    centers_v, centers_h = np.array(centers_v), np.array(centers_h)
+    centers_vx, centers_vy = centers_v[:, 1], centers_v[:, 0]
+    centers_hx, centers_hy = centers_h[:, 1], centers_h[:, 0]
+
+    line_v, line_h = HoughLine(x=centers_vx, data=centers_vy), HoughLine(x=centers_hx, data=centers_hy, loss=l)
+    if visual:
+        namespace = 'robust_reg/{0}{1}_{2}'.format(folder_path[3:], l, img_name)
+        vp1_av, vp2_av = line_v.point_gen()
+        hp1_av, hp2_av = line_h.point_gen()
+    zv_err_av, zv_err_ap = line_v.debias()
+    zh_err_av, zh_err_ap = line_h.debias()
+
+    if visual:
+        xv_av, xv_ap = np.arange(len(zv_err_av)), np.arange(len(zv_err_ap))
+        xh_av, xh_ap = np.arange(len(zh_err_av)), np.arange(len(zh_err_ap))
+        hp1_ap, hp2_ap = line_h.point_gen()
+        vp1_ap, vp2_ap = line_v.point_gen()
+        cenhs = list(zip(line_h.data, line_h.x))
+        cenvs = list(zip(line_v.data, line_v.x))
+        cv2.line(imgr, hp1_ap, hp2_ap, (0, 0, 255), 1)
+        cv2.line(imgr, vp1_ap, vp2_ap, (0, 0, 255), 1)
+
+        fig1 = plt.figure(figsize=(20, 10))
+        ax1 = fig1.add_subplot(121)
+        visualize_centers(imgr, centers_v, centers_h, ax1)
+        ax1.set_title('Before')
+        ax2 = fig1.add_subplot(122)
+        visualize_centers(imgr, cenvs, cenhs, ax2)
+        ax2.set_title('After')
+        fig2 = plt.figure(figsize=(20, 10))
+        ax3 = fig2.add_subplot(211)
+        ax3.plot(xh_av, zh_err_av, 'mo-', xh_ap, zh_err_ap, 'bo-')
+        ax3.set_title('HOR Loss Before and After')
+        ax3.legend(['Before', 'After'])
+        ax4 = fig2.add_subplot(212)
+        ax4.plot(xv_av, zv_err_av, 'mo-', xv_ap, zv_err_ap, 'bo-')
+        ax4.set_title('VER Loss Before and After')
+        ax4.legend(['Before', 'After'])
+        fig1.savefig(namespace[:-4]+'_lines'+namespace[-4:])
+        fig2.savefig(namespace[:-4] + '_errs' + namespace[-4:])
+    return HoughLine.intersect(line_h, line_v)
+
+
+def center_detect_invar(imgr, sample_int=30, gk=9, ks=-1, l='soft_l1', visual=False,
+                        folder_path="../", img_name='.png'):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair
+    hs: HORIZONTAL SLICE!  vs: VERTICAL SLICE!"""
+    centers_v, centers_h = gather_all(imgr, sample_int, gk, ks)
+    centers_v, centers_h = np.array(centers_v), np.array(centers_h)
+    centers_vx, centers_vy = centers_v[:, 1], centers_v[:, 0]
+    centers_hx, centers_hy = centers_h[:, 1], centers_h[:, 0]
+
+    line_v, line_h = HoughLine(x=centers_vx, data=centers_vy), HoughLine(x=centers_hx, data=centers_hy, loss=l)
+    if visual:
+        namespace = 'robust_reg/{0}{1}_{2}'.format(folder_path[3:], l, img_name)
+        vp1_av, vp2_av = line_v.point_gen()
+        hp1_av, hp2_av = line_h.point_gen()
+    zv_err_av, zv_err_ap = line_v.debias()
+    zh_err_av, zh_err_ap = line_h.debias()
+
+    if visual:
+        xv_av, xv_ap = np.arange(len(zv_err_av)), np.arange(len(zv_err_ap))
+        xh_av, xh_ap = np.arange(len(zh_err_av)), np.arange(len(zh_err_ap))
+        hp1_ap, hp2_ap = line_h.point_gen()
+        vp1_ap, vp2_ap = line_v.point_gen()
+        cenhs = list(zip(line_h.data, line_h.x))
+        cenvs = list(zip(line_v.data, line_v.x))
+        cv2.line(imgr, hp1_ap, hp2_ap, (0, 0, 255), 1)
+        cv2.line(imgr, vp1_ap, vp2_ap, (0, 0, 255), 1)
+
+        fig1 = plt.figure(figsize=(20, 10))
+        ax1 = fig1.add_subplot(121)
+        visualize_centers(imgr, centers_v, centers_h, ax1)
+        ax1.set_title('Before')
+        ax2 = fig1.add_subplot(122)
+        visualize_centers(imgr, cenvs, cenhs, ax2)
+        ax2.set_title('After')
+        fig2 = plt.figure(figsize=(20, 10))
+        ax3 = fig2.add_subplot(211)
+        ax3.plot(xh_av, zh_err_av, 'mo-', xh_ap, zh_err_ap, 'bo-')
+        ax3.set_title('HOR Loss Before and After')
+        ax3.legend(['Before', 'After'])
+        ax4 = fig2.add_subplot(212)
+        ax4.plot(xv_av, zv_err_av, 'mo-', xv_ap, zv_err_ap, 'bo-')
+        ax4.set_title('VER Loss Before and After')
+        ax4.legend(['Before', 'After'])
+        fig1.savefig(namespace[:-4]+'_lines'+namespace[-4:])
+        fig2.savefig(namespace[:-4] + '_errs' + namespace[-4:])
+    return HoughLine.intersect(line_h, line_v)
+
+
+def center_detect_old(img, sample_int=50, gk=9, ks=-1, m=0, p=20,
+                       b=1, c=0):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair
+    hs: HORIZONTAL SLICE!  vs: VERTICAL SLICE!"""
+    imgr = img
+    dimr = imgr.shape[0]
+    dimc = imgr.shape[1]
+    # Image Processing
+    gksize = (gk, gk)
+    sigmaX = 0
+    img = cv2.GaussianBlur(imgr, gksize, sigmaX)
+    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=ks)
+    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=ks)
+    # ------------------------------------------------------------
+    # Parameter Setting
+    METHODS = {0: gaussian_fitting, 1: poly_fitting, 2: edge_centroid,
+               3: zero_crossing, 4: edge_converge_base, 5: edge_converge_extreme}
+    edge_method = METHODS[m]
+    nr = sample_int
+    r_thresh = dimr / (sample_int * 3.0)
+    nc = sample_int
+    c_thresh = dimc / (sample_int * 3.0)
+    # ------------------------------------------------------------
+    # Gathering Data
+    hs = []
+    vs = []
+    while nr < dimr:
+        data_x = FM(sobelx, FM.HOR, nr)
+        if m < 3:
+            ec_x = edge_method(data_x, FM(img, FM.HOR, nr), p)
+        else:
+            ec_x = edge_method(data_x)
+        nr += sample_int
+        if ec_x == -1:
+            continue
+        else:
+            hs.append((nr - sample_int, ec_x))
+
+    while nc < dimc:
+        data_y = FM(sobely, FM.VER, nc)
+        if m < 3:
+            ec_y = edge_method(data_y, FM(img, FM.VER, nc), p)
+        else:
+            ec_y = edge_method(data_y)
+        nc += sample_int
+        if ec_y == -1:
+            continue
+        else:
+            vs.append((nc - sample_int, ec_y))
+    len_hs = len(hs)
+    len_vs = len(vs)
+    # ------------------------------------------------------------
+    # --------------- PRE-CHECK DATA VALUES ----------------------
+    hxs = np.zeros(len_hs)
+    hys = np.zeros(len_hs)
+    for i in range(len_hs):
+        hxs[i] = hs[i][1]
+        hys[i] = hs[i][0]
+    vxs = np.zeros(len_vs)
+    vys = np.zeros(len_vs)
+    for i in range(len_vs):
+        vxs[i] = vs[i][0]
+        vys[i] = vs[i][1]
+    x_valid = False
+    y_valid = False
+    # OUTLIER DETECTION   TODO: OPTIMIZE, THIS IS NAIVE
+    if len_hs >= r_thresh:
+        x_valid = True
+        hys, hxs = reg_pre_debias(hys, hxs)
+        line_a = HoughLine(x=hxs, data=hys)
+    if len_vs >= c_thresh:
+        y_valid = True
+        vxs, vys = reg_pre_debias(vxs, vys)
+        line_b = HoughLine(x=vxs, data=vys)
+    # ------------------------------------------------------------
+    # ----- Following Modules Handles Hough Line Drawing ---------
+    # ------------------------------------------------------------
+    # DATA RECORDING AND PROCESSING
+    if c == 1:
+        center_x = sum(hxs) / len_hs if x_valid else -1
+        center_y = sum(vys) / len_vs if y_valid else -1
+    else:
+        if x_valid and y_valid:
+            center_x, center_y = HoughLine.intersect(line_a, line_b)
+        else:
+            center_x = sum(hxs) / len_hs if x_valid else -1
+            center_y = sum(vys) / len_vs if y_valid else -1
+    # ---------------------------------------------------------
+    return center_x, center_y
+
+
+def expr2(series):
+    folder = '../calib5/'
+    ns = 'img_{0}.png'
+    ins = folder + 'img_{0}.png'
+    repertoire = []
+    i = 0
+    while i < len(series):
+        try:
+            ambi, laser = cv2.imread(ins.format(series[i]), 0), cv2.imread(ins.format(series[i+1]), 0)
+            res = image_diff(laser, ambi)
+        except:
+            print(ins.format(series[i]))
+        repertoire.append(center_detect_invar(res, visual=True, folder_path=folder, img_name=ns.format(series[i+1])))
+        i+= 2
+    for i, ct in enumerate(repertoire):
+        print((series[2*i], series[2*i+1]), ct)
+    CALIB_VAL = 118.6
+    print(repertoire)
+    i = 0
+    while i < len(repertoire):
+        dx, dy = (np.array(repertoire[i+1]) - np.array(repertoire[i])) / CALIB_VAL
+        print((series[2*i], series[2*i+1], series[2*i+2], series[2*i+3]), dx, dy)
+        i+=2
+
+
+def convergence_test_final(folder, ns):
+    offset = '../'
+    convergence = {}
+    variations = []
+    startNP = 59
+    startP = 80
+    endP = 193
+    ls = range(5)
+    fwrite = open('meas/convergence_rob.csv', 'w')
+    cwriter = csv.writer(fwrite)
+    cwriter.writerow(['Image Number', 'Center X', 'Center Y', 'StdDev Horizontal', 'Std Dev Vertical'])
+    loss_types = ['huber', 'soft_l1', 'arctan', 'linear', 'cauchy']
+    for l in loss_types:
+        cwriter.writerow([l])
+        # Consistency Cycled
+        pv = 0
+        pvs = 0
+        rcount = 0
+        cvx = np.zeros(4)
+        cvy = np.zeros(4)
+        # print(g)
+        for i in range(startNP, startP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                #x, y = center_detect(fpath + imgfile, 5, m=m) TODO: RESOLVE THE CHANGE
+                x, y = center_detect(fpath, imgfile.format(1), l=l)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y)])
+                # CONVERGENCE
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+        for i in range(startP, endP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                #x, y = center_detect(fpath + imgfile, 5, m=m) TODO: RESOLVE THE CHANGE
+                x, y = center_detect(fpath, imgfile.format(1), l=l)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y)])
+                # CONVERGENCE
+                # Record x, y, check rcount, refresh CONSISTENCY
+                cvx[rcount] = x
+                cvy[rcount] = y
+                rcount += 1
+                if rcount == 4:
+                    pv += np.var(cvx) + np.var(cvy)
+                    pvs += 1
+                    rcount = 0
+                    cvx = np.zeros(4)
+                    cvy = np.zeros(4)
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+                # print(str(i), val)
+        msepv = sqrt(pv / pvs)
+        cvg = {'PicConsistency': msepv}
+        convergence[l] = cvg
+        variations.append(msepv)
+    print(convergence)
+    fwrite.close()
+    plt.figure(figsize=(20, 10))
+    plt.style.use(['seaborn-dark', 'seaborn-paper'])
+    plt.subplot(111)
+    plt.plot(ls, variations, color='#FA5B3D', marker='o', markeredgewidth=1, linestyle='-', linewidth=2)
+    plt.xticks(ls, loss_types)
+    plt.ylabel('deviations(+/-px)')
+    plt.xlabel('loss functions')
+    plt.title('Convergence By Loss Function Rho')
+    plt.show()
+
+if __name__ == '__main__':
+    #print(center_detect('../calib4/img_59_{0}.png', 5))
+    #convergence_test_final('calib4/', 'img_{0}')
+    repertoire = expr2([10, 11, 15, 14, 18, 19, 22, 23])
+    """skewed = '../skewed/'
+    bright = '../bright/'
+    imgn = 'img_{0}_1.png'
+    #center_detect('../calib4/', 'img_113_1.png', visual=True)
+    for i in range(90, 96):
+        ns = imgn.format(i)
+        print(skewed + ns)
+        print(center_detect(skewed, ns, visual=True))
+
+    ns = imgn.format(81)
+    print(bright + ns)
+    try:
+        print(center_detect(bright, ns, visual=True))
+    except:
+        print('NON IDEE')
+
+    for i in range(193, 195):
+        ns = imgn.format(i)
+        print(bright + ns)
+        try:
+            print(center_detect(bright, ns, visual=True))
+        except:
+            print('NON IDEE')"""
+
+
+
+
+
diff --git a/img_rec_module/sig_proc_test.py b/img_rec_module/sig_proc_test.py
new file mode 100644
index 0000000..b73fd88
--- /dev/null
+++ b/img_rec_module/sig_proc_test.py
@@ -0,0 +1,2830 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.optimize import curve_fit
+from mpl_toolkits.mplot3d import Axes3D
+#from img_rec_module import img_rec
+from random import randint
+import os
+import csv
+from datetime import datetime
+from math import atan, sqrt, tan, radians, acos, asin, degrees, sin, cos
+
+
+""" ===================================
+========== ANALYTIC GEOMETRY ==========
+======================================= """
+
+
+class Line:
+
+    inf = float('inf')
+
+    def __init__(self, a=inf, b=inf, p1=None, p2=None, data=None):
+        if p1 and p2:
+            self.point(p1, p2)
+        elif data:
+            self.reg(data)
+        else:
+            self.a = a
+            self.b = b
+
+    def reg(self, data):
+        D = np.array([[d[0], 1] for d in data])
+        y = np.array([d[1] for d in data])
+        self.a, self.b = leastSquares(D, y)
+
+    def point(self, p1, p2):
+        self.a = 1
+
+    @staticmethod
+    def intersect(l1, l2):
+        x = (l2.b - l1.b) / (l1.a - l2.a)
+        y = l1.a * x + l1.b
+        return x, y
+
+
+class HoughLine:
+
+    def __init__(self, rho_rad=0, theta=0, x=None, data=None):
+        if data is not None and x is not None:
+            self.reg(x, data)
+        else:
+            self._r = degrees(rho_rad)
+            self._t = theta
+            self._s = sin(rho_rad)
+            self._c = cos(rho_rad)
+
+    def reg(self, x, data):
+        """D = hough_data_matrix(x, data)
+        print(D)
+        y = np.zeros(len(data))
+        cosine, sine, self._r = leastSquares(D, y)
+        self._t = angle_interp(sine, cosine)
+        self._s = sine
+        self._c = cosine"""
+        x1, x2 = x[0], x[-1]
+        y1, y2 = data[0], data[-1]
+        theta0 = theta_pred(x1, y1, x2, y2)
+        #print(degrees(theta0))
+        p0 = [theta0, x1 * cos(theta0) + y1 * sin(theta0)]
+        pm, vm = curve_fit(hough_line, x, data, p0=p0)
+        #data_pred = hough_line(x, *pm)
+        #print('PRED', data_pred)
+        #res = data_pred - data
+        #print("RES", res)
+        #stderr = np.linalg.norm(res, 2)
+        #print("ERR", stderr)
+        #rankerr = res / stderr
+        #print("Z", rankerr)
+        """if pm[1] < 0:
+            pm[1] = -pm[1]
+            pm[0] -= HC
+            #pm[0] = 2 * np.pi"""
+        angle = normalize_angle(pm[0])
+        self._t = angle
+        self._r = pm[1]
+        self._s = sin(angle)
+        self._c = cos(angle)
+
+    def extract_points(self, x_input):
+        x1 = int(x_input[0])
+        x2 = int(x_input[-1])
+        y1 = int(self.fit_x(x1))
+        y2 = int(self.fit_x(x2))
+        return (x1, y1), (x2, y2)
+
+    def fit_x(self, x):
+        #print(self._r, self._t)
+        return (self._r - x * self._c) / self._s
+
+    def __str__(self):
+        return 'hough line with cos:{0}, sin:{1}, rho:{2}, theta:{3}'.format(self._c, self._s,
+                                                                             self._r, degrees(self._t))
+
+    @staticmethod
+    def intersect(l1, l2):
+        x = (l2._r / l2._s - l1._r / l1._s) / (l2._c / l2._s - l1._c / l1._s)
+        y = l1.fit_x(x)
+        return x, y
+
+
+# QC: QUARTER_CYCLE, HC: HALF_CYCLE, TQC: THIRD_QUARTER_CYCLE, FC: FULL_CYCLE
+QC = np.pi / 2
+HC = np.pi
+TQC = 3 * np.pi / 2
+FC = 2 * np.pi
+
+
+def angle_interp(s, c):
+    if c == 1.0:
+        return 0
+    elif s == 1.0:
+        return np.pi / 2
+    elif c == -1.0:
+        return np.pi
+    elif s == -1.0:
+        return np.pi * 3 / 2
+    elif c < 0 < s:
+        return acos(c)
+    elif s < 0 and c < 0:
+        return 2 * np.pi - acos(c)
+    elif s < 0 < c:
+        return asin(s) + 2 * np.pi
+    else:
+        return asin(s)
+
+
+def normalize_angle(angle):
+    res = angle - (angle // FC) * FC
+    return res
+    """
+    if QC < res < HC:
+        return res + HC
+    elif HC <= res <= TQC:
+        return res - HC
+    else:
+        return res"""
+
+
+def theta_pred(x1, y1, x2, y2):
+    dx = x2 - x1
+    dy = y2 - y1
+    angle = sin_angle_from_points(dx, dy)
+    if dx * dy <= 0:
+        return np.pi / 2 - angle
+    else:
+        root = root_finding(x1, x2, y1, y2)
+        if root < 0:
+            return np.pi / 2 + angle
+        else:
+            return angle + 3 * np.pi / 2
+
+
+def sin_angle_from_points(dx, dy):
+    return asin(abs(dy) / sqrt(dx ** 2 + dy ** 2))
+
+
+def poly_curve(params, x_input):
+    # params contains the coefficients that multiply the polynomial terms, in degree of lowest degree to highest degree
+    degree = len(params) - 1
+    x_range = [x_input[1], x_input[-1]]
+    x = np.linspace(x_range[0], x_range[1], 1000)
+    y = x * 0
+
+    for k in range(0, degree + 1):
+        coeff = params[k]
+        y = y + list(map(lambda z: coeff * z ** k, x))
+    return x, y
+
+
+def gauss_2d(x, y, a, b1, c_s1, b2, c_s2):
+    return a * np.exp(- ((x - b1) ** 2  / (2 * c_s1) + (y - b2) ** 2) / (2 * c_s2))
+
+
+def gauss_hat(x, a, b, c_s):
+    return a * np.exp(- (x - b) ** 2 / (2 * c_s))
+
+
+def gaussian_curve(x_input, a,  b, c_s):
+    x_range = [x_input[1], x_input[-1]]
+    x = np.linspace(x_range[0], x_range[1], 1000)
+    y = a * np.exp(- (x - b) ** 2 / (2 * c_s))
+    return x, y
+
+
+def hough_line(x, theta, rho):
+    return (rho - x * cos(theta)) / sin(theta)
+
+
+""" ===================================
+========== MATRIX-ARRAY UTIL ==========
+======================================= """
+
+
+class FastDataMatrix2D:
+    # TODO: HANDLE THE CASE WHEN IT WRAPS 1D
+
+    HOR = 1
+    VER = 0
+
+    def __init__(self, data, ax, index):
+        self._data = data
+        self._ax = ax
+        assert 0 <= index < self.irange(), "start: {0}, end: {1}, ax: {2}, index: {3}".format(self.start, self.end,
+                                                                                              self._ax, index)
+        self._index = index
+        self.itr = 0
+        self.initialize()
+
+    def segmentize(self, start, end):
+        assert 0 <= start < end <= self._data.shape[self._ax], "start: {0}, end: {1}, ax: {2}, index: {3}".format(start, end, self._ax, self._index)
+        self.start = start
+        self.end = end
+
+    def initialize(self):
+        self.segmentize(0, self._data.shape[self._ax])
+
+    def irange(self):
+        # index range of the fast matrix array
+        return self._data.shape[1 - self._ax]
+
+    def set_axis(self, ax):
+        """USE copy when trying to switch axis and index"""
+        self._ax = ax
+        if self._index >= self.irange():
+            raise IndexError("Old index {0} is too large for the new axis".format(self._index))
+        self.initialize()
+
+    def set_index(self, index):
+        assert 0 <= index < self.irange(), "start: {0}, end: {1}, ax: {2}, index: {3}".format(self.start, self.end, self._ax, index)
+        self._index = index
+
+    def extract_array(self):
+        """Optimize later for better performance"""
+        arr = self._data[self._index, self.start:self.end] if self._ax == FastDataMatrix2D.HOR \
+            else self._data[self.start:self.end, self._index]
+        return np.array(arr)
+
+    def copy(self, ax=None, index=None):
+        if ax is not None and index is not None:
+            return FastDataMatrix2D(self._data, ax, index)
+        else:
+            return FastDataMatrix2D(self._data, self._ax, self._index)
+
+    def __iter__(self):
+        raise RuntimeError("You need the ITER method!")
+
+    def __next__(self):
+        raise RuntimeError("You need the NEXT method!")
+
+    def __getitem__(self, item):
+        return self._data[self._index, item + self.start] if self._ax == FastDataMatrix2D.HOR \
+            else self._data[item + self.start, self._index]
+
+    def __setitem__(self, key, value):
+        if self._ax == FastDataMatrix2D.HOR:
+            self._data[self._index, key + self.start] = value
+        else:
+            self._data[key + self.start, self._index] = value
+
+    def __len__(self):
+        return self.end - self.start
+
+
+""" ===================================
+=========== REGRESSION UTILS ==========
+======================================= """
+
+
+# Generic Regression Tools 128-133
+def leastSquares(D, y):
+    return np.linalg.lstsq(D, y, rcond=None)[0]
+
+
+def MSE(y, y_hat, N):
+    return np.linalg.norm(y - y_hat) ** 2 / N
+
+
+def std_dev(data):
+    std_dev = 0
+    len_data = len(data)
+    mean = sum(data) / len_data
+    for i in range(len_data):
+        std_dev += (data[i] - mean) ** 2
+    return sqrt(std_dev / len_data)
+
+
+def reg_pre_debias(ind, data):
+    sd = np.std(data)
+    miu = np.mean(data)
+    debiased_data = []
+    debiased_ind = []
+    for i in range(len(data)):
+        dev = abs((data[i] - miu) / sd)
+        if dev < 2:
+            debiased_data.append(data[i])
+            debiased_ind.append(ind[i])
+    return debiased_ind, debiased_data
+
+
+# Polynomial Regression Tools 137-171
+def poly_data_matrix(input_data, degree):
+    # degree is the degree of the polynomial you plan to fit the data with
+    Data = np.zeros((len(input_data), degree + 1))
+
+    for k in range(0, degree + 1):
+        Data[:, k] = (list(map(lambda x: x ** k, input_data)))
+
+    return Data
+
+
+def poly_error(params, D_a, y_a):
+    '''degree=len(params)-1
+    y=x_a*0
+
+    for k in range(0,degree+1):
+        coeff=params[k]
+        y=y+list(map(lambda z:coeff*z**k,x_a))'''
+    y = np.dot(D_a, params)
+    return np.linalg.norm(y - y_a) ** 2
+
+
+def improvedCost(x, y, x_test, y_test, start, end):
+    """Given a set of x and y points training points,
+    this function calculates polynomial approximations of varying
+    degrees from start to end. Then it returns the cost, with
+    the polynomial tested on test points of each degree in an array"""
+    c = []
+    degrees = range(start, end)
+    ps = []
+    for degree in degrees:
+        # YOUR CODE HERE
+        D = poly_data_matrix(x, degree)
+        p = leastSquares(D, y)
+        ps.append(p)
+        D_t = poly_data_matrix(x_test, degree)
+        y_hat = np.dot(D_t, p)
+        c.append(MSE(y_test, y_hat, len(x_test)))
+    return np.array(degrees), ps,  np.array(c)
+
+
+# Gaussian Regression Tools 184-210
+def gauss_data_matrix(data):
+    return np.array([[1, x ** 2, x, 1] for x in data])
+
+
+def gauss_reg(x, y, p0):
+    """Given a set of x and y training points, this function
+    calculates the gaussian approximation function"""
+    """skip_x = np.array([x[i] for i in range(len(y)) if y[i] != 0])
+    y_prime = np.array([y[i] for i in range(len(y)) if y[i] != 0])
+    logy = np.log(y_prime)
+
+    sol = leastSquares(gauss_data_matrix(skip_x), logy)
+    alp, bet, gam, lam = sol
+    a = np.e ** alp
+    c_s = - 1 / (2 * bet)
+    b = gam * c_s
+    maxi, mini = max_min(y)
+    pred = gauss_hat(maxi, a, b, c_s)
+    real = y[maxi]
+    a = real / pred // 1000000000 * 10"""
+    #timeb = datetime.now()
+    param, vm = curve_fit(gauss_hat, x, y, p0=p0)
+    #timea = datetime.now()
+    #print(timea - timeb)
+    return param
+
+
+def gauss_mat(shape, a, b1, c_s1, b2, c_s2):
+    mat = np.zeros(shape)
+    for r in range(mat.shape[0]):
+        for c in range(mat.shape[1]):
+            mat[r, c] = gauss_2d(c, r, a, b1, c_s1, b2, c_s2)
+
+    return mat
+
+
+# Hough Transform Regression Tools
+def hough_data_matrix(x, y):
+    dim = len(x)
+    mat = np.full((dim, 3), -1.0)
+    for i in range(dim):
+        mat[i][0] = x[i]
+        mat[i][1] = y[i]
+    return mat
+
+
+""" =======================================
+========= EDGE DETECTION UTILS ============
+=========================================== """
+# TODO: CHANGED!!!
+
+
+# Generic Helper
+def max_min(data):
+    # Returns the maximum and minimum for the data
+    maxi = data[0]
+    max_ind = 0
+    mini = data[0]
+    min_ind = 0
+    for i in range(1, len(data)):
+        target = data[i]
+        if target > maxi:
+            maxi = target
+            max_ind = i
+        if target < mini:
+            mini = target
+            min_ind = i
+    return max_ind, min_ind
+
+
+def edge_max_min(data):
+    # Returns a *safe* edge maxi, mini for the data
+    # TODO: OPTIMIZE THE WIDTH AND VALUE THRESHOLD
+    # TODO: MADE CHANGES, BUT THE WIDTH CHECK NEEDS TO BE OPTIMIZED
+    width_thres = 90
+    value_thres = 20
+    maxi = data[0]
+    max_ind = 0
+    mini = data[0]
+    min_ind = 0
+    for i in range(1, len(data)):
+        target = data[i]
+        if target > maxi:
+            maxi = target
+            max_ind = i
+        if target < mini:
+            mini = target
+            min_ind = i
+    maxi, mini = max_ind, min_ind
+    assert data[maxi] >= value_thres and mini - maxi <= width_thres
+    return maxi, mini
+
+
+def min_max(data, max, min):
+    """ Converts the data to min max space. """
+    g_diff = max - min
+    return [(d - min) / g_diff for d in data]
+
+
+def zscore_calc(data):
+    # Takes in a numpy
+    data_len = len(data)
+    if type(data) != np.ndarray:
+        sd = std_dev(data)
+        mn = sum(data) / data_len
+    else:
+        sd = np.std(data)
+        mn = np.mean(data)
+    res = np.empty(data_len, dtype=type(data[0]))
+    for i in range(data_len):
+        res[i] = (data[i] - mn) / sd
+    return res
+
+
+def root_finding(x1, x2, y1, y2):
+    """Given two points on a line, finds its zero crossing root."""
+    return - y1 * (x2 - x1) / (y2 - y1) + x1
+
+
+def check_cross(a, b):
+    # Checks whether two points are of opposite signs
+    return a * b < 0
+
+
+def round_up(num):
+    down = int(num)
+    if num - down > 0:
+        return num + 1
+    else:
+        return num
+
+
+def max_seg(data, start, end):
+    # returns the maxima index from start to end in data
+    cursor = start + 1
+    maxi_ind = start
+    maxi = data[start]
+    while cursor < end:
+        target = data[cursor]
+        if target > maxi:
+            maxi_ind = cursor
+            maxi = target
+        cursor += 1
+    return maxi_ind
+
+
+def smart_interval(start, end, data):
+    start = 0 if start < 0 else start
+    end = len(data) if end > len(data) else end
+    return start, end
+
+
+def edge_preprocess(data, padding):
+    maxi, mini = edge_max_min(data)
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    return start, end, maxi, mini
+
+
+def edge_preprocess_new(data, padding):
+    beam_found = False
+    while not beam_found:
+        maxi, mini = edge_max_min(data)
+        seg_max_ind = max_seg(data, maxi, mini)
+    start, end = smart_interval(maxi - padding, mini + padding + 1, data)
+    return start, end, maxi, mini
+
+
+# ZERO CROSSING (PEAK FINDING)
+def zero_crossing(data):
+    # Yields the center with zero-crossing method
+    maxi, mini = max_min(data)
+    cross = -1
+    cross_count = 0
+    for j in range(maxi, mini):
+        y1 = data[j]
+        x2 = j + 1
+        y2 = data[x2]
+        if check_cross(y1, y2):
+            cross = root_finding(j, x2, y1, y2)  # change to function later
+            cross_count += 1
+    return -1 if cross_count > 1 else cross
+
+
+# AVERAGE METHOD FOR EDGE CENTER FINDING
+def edge_converge_base(data):
+    maxi, mini = max_min(data)
+    width_thres = 70
+    if mini - maxi > width_thres:
+        return -1
+    else:
+        return (maxi + mini) / 2
+
+
+# PSEUDO EDGE (closest noise gradient change) CENTER FINDING
+def edge_converge_extreme(data):
+    maxi, mini = max_min(data)
+    width_thres = 70
+    if mini - maxi > width_thres:
+        return -1
+    emax = -1
+    emin = -1
+    maxflag = True
+    minflag = True
+    cmax = maxi
+    cmin = mini
+    while cmax > 0 and cmin < len(data) - 1 and (maxflag or minflag):
+        max2 = cmax - 1
+        maxy1, maxy2 = data[cmax], data[max2]
+        min2 = cmin + 1
+        miny1, miny2 = data[cmin], data[min2]
+        if check_cross(maxy1, maxy2) and maxflag:
+            emax = root_finding(cmax, max2, maxy1, maxy2)
+            maxflag = False
+        if check_cross(miny1, miny2) and minflag:
+            emin = root_finding(cmin, min2, miny1, miny2)
+            minflag = False
+        if maxflag:
+            cmax -= 1
+        if minflag:
+            cmin += 1
+    return -1 if (emax == -1 or emin == -1) else (emax + emin) / 2
+
+
+def extract_extrema(data):
+    len_data = len(data)
+    mean = sum(data) / len_data
+    std_dev = 0
+    for x in data:
+        std_dev += (x - mean) ** 2
+    std_dev = sqrt(std_dev / len_data)
+    signifmax = []
+    signifmin = []
+    coeff = 3
+    for i, x in enumerate(data):
+        diff = (x - mean) / std_dev
+        if diff >= coeff:
+            signifmax.append(i)
+        elif diff <= -coeff:
+            signifmin.append(i)
+    return signifmax, signifmin
+
+
+# CENTROID METHOD FOR EDGE CENTER FINDING
+def edge_centroid(data, img_data, padding=10):
+    # With Gaussian Blur might achieve the best performance
+    try:
+        start, end, maxi, mini = edge_preprocess(data, padding)
+    except AssertionError:
+        return -1
+    isums = 0
+    total = 0
+    for i in range(start, end):
+        isums += img_data[i] * i
+        total += img_data[i]
+    return isums / total if total else -1
+
+
+def centroid_seg(data, start, end):
+    """ Given a segment (start, end) of the data,
+    find the centroid. """
+    isums = 0
+    total = 0
+    start = 0 if start < 0 else start
+    end = len(data) if end > len(data) else end
+    for i in range(start, end):
+        isums += data[i] * i
+        total += data[i]
+    return isums / total if total else -1
+
+
+# POLYNOMIAL FITTING
+def poly_fitting(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING AND THE EXTRACT_ARRAY
+    try:
+        start, end, maxi, mini = edge_preprocess(data, padding)
+    except AssertionError:
+        return -1
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        y = img_data.extract_array()
+    else:
+        y = np.array(img_data[start:end])
+    degrees, params, cost = improvedCost(x, y, x, y, 1, 7)
+    ind = np.argmin(cost)
+    degree = degrees[ind]
+    param = params[ind]
+    degree_register(degree)
+    curve_x, curve_y = poly_curve(param, x)
+    center_id = np.argmax(curve_y)
+    return curve_x[center_id]
+
+
+def poly_fitting_params(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING AND THE EXTRACT_ARRAY
+    maxi, mini = max_min(data)
+    width_thres = 90
+    value_thres = 20
+    if data[maxi] < value_thres or mini - maxi > width_thres:
+        print(data[maxi], mini, maxi)
+        raise AssertionError("Bad column or row")
+    start = maxi - padding
+    end = mini + padding + 1
+    start, end = smart_interval(start, end, data)
+    if start > end:
+        try:
+            print(maxi, mini, img_data._ax, start, end)
+        except:
+            print("else", maxi, mini, start, end)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        y = img_data.extract_array()
+    else:
+        y = np.array(img_data[start:end])
+    degrees, params, cost = improvedCost(x, y, x, y, 1, 7)
+    ind = np.argmin(cost)
+    degree = degrees[ind]
+    param = params[ind]
+    degree_register(degree)
+    return param, x
+
+
+DEGREES = {}
+
+
+def degree_register(elem):
+    if elem in DEGREES:
+        DEGREES[elem] += 1
+    else:
+        DEGREES[elem] = 1
+
+
+# GAUSSIAN FITTING
+def gaussian_fitting(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING, ALONG WITH THE WIDTH THRESHOLD
+    try:
+        start, end, maxi, mini = edge_preprocess(data, padding)
+    except AssertionError:
+        return -1
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.array(img_data[start:end])
+    try:
+        param = gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)])
+    except RuntimeError:
+        return -1
+    return param[1]
+
+
+def gaussian_fitting_params(data, img_data, padding=10):
+    # TODO: OPTIMIZE THE AWKWARD TYPE CHECKING, ALONG WITH THE WIDTH THRESHOLD
+    maxi, mini = max_min(data)
+    width_thres = 90
+    value_thres = 20  # TODO: CONSOLIDATE THIS VALUE
+    if data[maxi] < value_thres or mini - maxi > width_thres:
+        print(data[maxi], mini, maxi)
+        raise AssertionError("Bad column or row")
+    start = maxi - padding
+    end = mini + padding
+    start, end = smart_interval(start, end, data)
+    x = np.array(range(start, end))
+    if type(img_data) == FastDataMatrix2D:
+        img_data.segmentize(start, end)
+        idata = img_data.extract_array()
+    else:
+        idata = np.array(img_data[start:end])
+    return gauss_reg(x, idata, p0=[10, (maxi + mini) / 2, std_dev(idata)]), x
+"""========================================SIG_PROC HAS ABOVE======================================================"""
+
+
+"""======================================
+======== IMAGE PROCESSING UTIL ==========
+========================================= """
+
+
+def gradient_calc(img, ks=-1):
+    # TODO: This Function IS ADDED, MOVE TO SIGPROC
+    if len(img.shape) == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=ks)
+    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=ks)
+    dimr, dimc = img.shape
+    gradient = np.empty((dimr, dimc), dtype=np.float64)
+    for i in range(dimr):
+        for j in range(dimc):
+            gradient[i, j] = sqrt(sobelx.item(i, j) ** 2 + sobely.item(i, j) ** 2)
+    return sobelx, sobely, gradient
+
+
+def gauss_bg_deduce(x, img_data):
+    # TODO: OPTIMIZE PERFORMANCE
+    idata = img_data.extract_array()
+    p0 = [1, len(img_data) / 2, std_dev(idata)]
+    try:
+        a, b, c_s = gauss_reg(x, idata, p0=p0)
+    except RuntimeError:
+        return x, np.zeros(len(img_data)), idata
+    rem_gauss = gauss_hat(x, a, b, c_s)
+    new_y = idata - rem_gauss
+    return x, rem_gauss, new_y
+
+
+def fourier_trans(img):
+    f = np.fft.fft2(img)
+    fshift = np.fft.fftshift(f)
+    magnitude_spectrum = 20 * np.log(np.abs(fshift))
+    return f, fshift, magnitude_spectrum
+
+
+def data_masked(data, ratio_lr, ratio_lc, ratio_ur, ratio_uc):
+    row, col = data.shape
+    #pad_q = (1 - ratio) / 2
+    #pad_r = int(pad_q * row)
+    #pad_c = int(pad_q * col)
+    lr = int(ratio_lr * row)
+    ur = row - int(ratio_ur * row)
+    lc = int(ratio_lc * col)
+    uc = col - int(ratio_uc * col)
+    mask = np.zeros_like(data)
+    mask[lr:ur] = 1
+    mask[lc:uc] = 1
+    return data * mask
+
+
+def sobel_process(imgr, gks, sig):
+    gksize = (gks, gks)
+    sigmaX = sig
+    blur = cv2.GaussianBlur(imgr, gksize, sigmaX)
+    # cv2.imshow("blurred", blur)                        # REMOVES TO SHOW BLURRED IMAGE
+    if len(imgr.shape) == 3:
+        img = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
+    else:
+        img = blur
+    # GAUSSIAN PROCESSING                                 # UNCOMMENT TO SHOW GAUSSIAN PROCESSING
+    """x = np.array(range(dimc))
+    #y = np.array([img_rec.rel_lumin(img, 0, c) for c in x])
+    y = np.array([img.item(dimr // 2, c) for c in x])
+    a1, b1, c_s1 = gauss_reg(x, y)
+
+    x2 = np.array(range(dimr))
+    y2 = np.array([img.item(r, dimc // 2) for r in x2])
+    a2, b2, c_s2 = gauss_reg(x2, y2)
+    rem_gauss = gauss_mat(img.shape, (a1+a2) / 2, b1, c_s1, b2, c_s2)
+    img = img - rem_gauss
+    cv2.imshow("denoise", img)
+    y_hat = gauss_hat(x, a1, b1, c_s1)
+    plt.figure(figsize=(16, 8))
+    plt.plot(x, y, 'b-', x, y_hat, 'r-')
+    plt.show()
+    plt.close()
+    plt.figure(figsize=(16,8))
+    plt.plot(x, y-y_hat, 'b-')
+    plt.show()"""
+
+    # IMAGE PROCESSING
+    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=-1)
+    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=-1)
+    return img, sobelx, sobely
+
+
+def test_canny_detect():
+    imgn = "img_89_{0}.png"
+    IMGDIR = "../calib4/"
+    NAME = IMGDIR + imgn
+
+    while True:
+        lowp = input("Type the Lower Bound or c to cancel:    ")
+        if lowp == 'c':
+            break
+        while True:
+            try:
+                low = int(lowp)
+                break
+            except ValueError:
+                lowp = input("Bad input Try again! ")
+        highp = input("Type the Upper Bound or c to cancel:   ")
+        if highp == 'c':
+            break
+        while True:
+            try:
+                high = int(highp)
+                break
+            except ValueError:
+                highp = input("Bad input Try again! ")
+
+        denoised, original = test_noise_reduce(NAME)
+        print(get_center_val(denoised))
+        NL_denoised = cv2.fastNlMeansDenoising(original)
+        bdenoise = test_blur_then_nr(NAME)
+
+        dblur, sobelx, sobely = sobel_process(denoised, 9, 0)
+        nldblur = sobel_process(NL_denoised, 9, 0)[0]
+        pblur, sobelx, sobely = sobel_process(original, 9, 0)
+
+        de_edges = canny_detect(denoised, low, high)
+        db_edges = canny_detect(dblur, low, high)
+        nld_edges = canny_detect(nldblur, low, high)
+        blur_denoise = canny_detect(bdenoise, low, high)
+        blur_edges = canny_detect(pblur, low, high)
+        #edge_detect_expr(db_edges, original)
+        """compare_images((imgr, 'Original'), (edges, 'Canny Edge'),
+                       (sobelx, 'Sobel X'), (sobely, 'Sobel Y'))"""
+        compare_images((original, 'Original'), (NL_denoised, "NLMEANS"), (de_edges, 'DENOISE Edges'),
+                       (blur_edges, 'Plain Blur Edges'), (denoised, 'Denoised'), (nld_edges, 'NIDE-BLUR Edges'),
+                       (db_edges, 'DE-BLUR Edges'), (blur_denoise, "BDENOISE Edges"), color_map='gray')
+
+
+def canny_detect(src, low=4, high=10):
+    return cv2.Canny(np.uint8(src), 4, 10, L2gradient=True)
+
+
+def extract_verge(edges, i, j, max_blank, dir, axis):
+    """
+    :param edges: img MAT resulting from canny edge procedure
+    :param i: starting row i
+    :param j: starting col j
+    :param max_blank: maximum number of blanks to rule out the existence of a verge outside of the current verge (in which case would be a fake verge)
+    :param dir: Directions of movement, 1 for right or down, -1 for left or up
+    :param axis:
+    :return: (r, c) index of the verge on the given 'axis' in the given 'dir' from the starting point (i, j)
+    """
+
+
+def significance_test(data, val):
+    """Outputs whether the maximum or minimum value is a significant value."""
+    return val in extract_extrema(data)[0] or val in extract_extrema(data)[1]
+
+
+def fourier_expr():
+    while True:
+        root = input("type the image directory you wanna test in:    ")
+        number = input("type the picture number u wanna test or c to cancel:    ")
+        ratio_lrp = input("type in a lower row masking ratio: ")
+        ratio_urp = input("type in a upper row masking ratio: ")
+        ratio_lcp = input("type in a lower column masking ratio: ")
+        ratio_ucp = input("type in a upper column masking ratio: ")
+
+        if number == 'c':
+            break
+        try:
+            imgn = "img_%d_{0}" % int(number)
+            ratio_lr = float(ratio_lrp)
+            ratio_ur = float(ratio_urp)
+            ratio_lc = float(ratio_lcp)
+            ratio_uc = float(ratio_ucp)
+        except ValueError as e:
+            print("Bad name or ratio format! ")
+            continue
+        print(imgn)
+        IMGDIR = "../{0}/".format(root)
+        ROOTMEAS = "meas/"
+        SAVEDIR = ROOTMEAS + IMGDIR[3:] + imgn + "/"
+        num_img = 3 if root == 'testpic' else 1
+        try:
+            imgr, ori = test_noise_reduce(IMGDIR + imgn + ".png", numIMG=num_img)
+        except AttributeError as e:
+            print(e.args)
+            continue
+        dimc = imgr.shape[1]
+        dimr = imgr.shape[0]
+        sig = 0
+        gk = 9
+
+        # ----------------- Preliminary IMG_PROCESSING ---------------------
+        # INITIALIZATION
+        gksize = (gk, gk)
+        sigmaX = sig
+        blur = cv2.GaussianBlur(imgr, gksize, sigmaX)
+        # cv2.imshow("blurred", blur)                        # REMOVES TO SHOW BLURRED IMAGE
+        if len(blur.shape) == 3:
+            img = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
+        else:
+            img = blur
+        img = imgr
+
+        # IMAGE PROCESSING
+        sobelx, sobely, gradient = gradient_calc(img, ks=-1)
+        f_r, fshift_r, magnitude_r = fourier_trans(img)
+        f_r_mask = data_masked(f_r, ratio_lr, ratio_lc, ratio_ur, ratio_uc)
+        inv_r_mask = np.abs(np.fft.ifft2(f_r_mask))
+        f_b, fshift_b, magnitude_b = fourier_trans(blur)
+        f_b_mask = data_masked(f_b, ratio_lr, ratio_lc, ratio_ur, ratio_uc)
+        inv_b_mask = np.abs(np.fft.ifft2(f_b_mask))
+        sobelx_r_fm, sobely_r_fm, gradient_r_fm = gradient_calc(inv_r_mask, ks=-1)
+        sobelx_b_fm, sobely_b_fm, gradient_b_fm = gradient_calc(inv_b_mask, ks=-1)
+        f = np.fft.fft2(img)
+        compare_images((sobelx, 'Sobel X'), (sobely, 'Sobel Y'), (gradient, 'Raw Gradient'),
+                       (20 * np.log(np.abs(f_r)), 'Fourier Original'), (20 * np.log(np.abs(fshift_r)), 'FShift Original'),
+                       (magnitude_r, 'Mag Original'), (inv_r_mask, 'FourierR Inverse'), (img, 'original'),
+                       (sobelx_r_fm, 'SobelX Original Fourier'), (sobely_r_fm, 'SobelY Original Fourier'),
+                       (gradient_r_fm, 'Fourier Original Gradient'),
+                       (sobelx_b_fm, 'SobelX Blur Fourier'), (sobely_b_fm, 'SobelY Blur Fourier'),
+                       (gradient_b_fm, 'Fourier Blur Gradient'),
+                       color_map='gray', suptitle=IMGDIR+imgn[:-4])
+
+
+
+def edge_detect_expr(edges, original):
+    """
+    TODO: 1. SIMPLE Approach: pairwise mask over original image, then compute total
+          2. Traverse the image to mark the image.
+    """
+    se = 0
+    row_sum = 0
+    col_sum = 0
+    tot_sum = 0
+    for i in range(edges.shape[0]):
+        for j in range(edges.shape[1]):
+            if edges[i][j] > 0:
+                row_sum += i * original[i][j]
+                col_sum += j * original[i][j]
+                tot_sum += original[i][j]
+                se += 1
+    print(se)
+    if tot_sum != 0:
+        print(row_sum / tot_sum)
+        print(col_sum / tot_sum)
+
+
+def hori(img, i, j, d):
+    dest = j + d
+    if dest < 0 or dest >= img.shape[1]:
+        return img.item(i, j)
+    return img.item(i, dest)
+
+
+def verti(img, i, j, d):
+    dest = i + d
+    if dest < 0 or dest >= img.shape[0]:
+        return img.item(i, j)
+    return img.item(dest, j)
+
+
+def test_blur_then_nr(iname):
+    imgs = iname
+    numIMG = 5
+    imgr = None
+    for i in range(1, numIMG + 1):
+        target = cv2.imread(imgs.format(i), 0)
+        target = cv2.GaussianBlur(target, (9, 9), 0)
+        if i == 1:
+            imgr = np.uint16(target)
+        else:
+            imgr = imgr + target
+    return imgr / numIMG
+
+
+def get_center_val(img):
+    return img.item(img.shape[0] // 2, img.shape[1] // 2)
+
+
+def test_noise_reduce(iname, numIMG=5):
+    imgs = iname
+    imgr = None
+    original = None
+    for i in range(1, numIMG+1):
+        target = cv2.imread(imgs.format(i), 0)
+        if target is None:
+            raise AttributeError("File {0} not found".format(iname))
+        if i == 1:
+            imgr = np.uint16(target)
+            original = target
+        else:
+            imgr = imgr + target
+    return imgr / numIMG, original
+
+
+def img_add(dest, src):
+    """Destructive!"""
+    row, col = dest.shape
+    for i in range(row):
+        for j in range(col):
+            dest[i][j] += src[i][j]
+
+
+""" ===================================
+    ============ PLOTTING =============
+    ============= HELPER ==============
+    =================================== """
+
+
+def quick_plot(data, xs=None):
+    plt.figure()
+    if xs:
+        plt.plot(xs, data, 'bo-')
+    else:
+        plt.plot(data, 'bo-')
+    plt.show()
+    plt.close()
+
+
+def compare_images(*args, ilist=None, color_map=None, suptitle=None):
+    # Takes in a sequence of IMG(Gray) and TITLE pairs and plot them side by side
+    if ilist:
+        args = ilist
+    graph_amount = len(args)
+    row = int(sqrt(graph_amount))
+    col = round_up(float(graph_amount) / row)
+    plt.figure(figsize=(16, 8))
+    if suptitle:
+        plt.suptitle(suptitle, fontsize=14)
+    for i, pair in enumerate(args):
+        plt.subplot(row, col, i+1)
+        if color_map:
+            plt.imshow(pair[0], cmap=color_map)
+        else:
+            plt.imshow(pair[0])
+        plt.xticks([]), plt.yticks([])
+        plt.title(pair[1])
+    plt.show()
+    plt.close()
+
+
+def compare_data_plots(*args, ilist=None, suptitle=None, symbol='b-'):
+    # Takes in a sequence of tuples with data, xs, and title pairs
+    if ilist:
+        args = ilist
+    graph_amount = len(args)
+    row = int(sqrt(graph_amount))
+    col = round_up(float(graph_amount) / row)
+    plt.figure(figsize=(16, 8))
+    if suptitle:
+        plt.suptitle(suptitle, fontsize=14)
+    for i, pair in enumerate(args):
+        plt.subplot(row, col, i + 1)
+        if len(pair) == 2:
+            plt.plot(pair[0], symbol)
+            plt.title(pair[1])
+        else:
+            plt.plot(pair[1], pair[0], symbol)
+            plt.title(pair[2])
+    plt.show()
+
+
+def line_graph_contrast(img, xs, ys):
+    line = HoughLine(x=xs, data=ys)
+    #print(xs)
+    x0 = line._c * line._r
+    y0 = line._s * line._r
+
+    print('x0:{0}, y0:{1}, cos:{2}, sin:{3}, rho:{4}, theta:{5}'.format(x0, y0, line._c, line._s, line._r, degrees(line._t)))
+    x1 = int(x0 + 1000 * (-line._s))
+    y1 = int(y0 + 1000 * (line._c))
+    x2 = int(x0 - 1000 * (-line._s))
+    y2 = int(y0 - 1000 * (line._c))
+    p1, p2 = (x1, y1), (x2, y2)
+    cv2.line(img, p1, p2, (255, 0, 0), 1)
+    return p1, p2, line
+
+
+def plot_img(img, cmap='coolwarm'):
+    img = cv2.convertScaleAbs(img)
+    r, c = img.shape
+    rs = np.arange(0, r)
+    cs = np.arange(0, c)
+    xs, ys = np.meshgrid(cs, rs)
+
+    fig = plt.figure()
+    ax = fig.gca(projection='3d')
+    surf = ax.plot_surface(xs, ys, img, cmap=cmap, linewidth=0, antialiased=False)
+    fig.colorbar(surf, shrink=0.5, aspect=5)
+    plt.show()
+    plt.close()
+
+
+# 1. Try Noise Reduction:
+# ---- 1.1 Reduction By Averaging, without FastNIMEANS  ~~ GOOD
+# ---- 1.2 Reduction By FastNIMEANS                     ~~ INFERIOR
+# ---- 1.3 Reduction By Combination                     ~~ INFERIOR
+# ---- 1 NOTE: USE PLOTTING OF ORIGINAL GRAPH TO SEE RESULT
+# 2. Try Di-Axial Scharr Kernel
+# ---- 2.1 Add To Name Scheme, record graphs and compare them
+# ---- 2 NOTE: GRAPH TO SAVE: 4-CONTRAST, TRANSITION PLOTS
+# 3. Optimizing Data Processing:
+# ---- 3.1 Marking point as edge maxima and minima according to STD Dev values
+# ---- 3.2 Taking Sequential Samples
+# ---- 3.3 Explore Thresholding Techniques
+
+
+def register(img):
+    lval = []
+    for i in range(img.shape[0]):
+        for j in range(img.shape[1]):
+            if img.item(i, j) > 250:
+                lval.append((i, j, img.item(i, j)))
+    print(lval)
+
+
+""" TEST GAUSSIAN BLUR'S influence on noise distribution:
+    Method:
+        1. Preliminary
+            1. Generate a random noise matrix with n * n, change shape to a vector
+            2. Gaussian blur applied, change shape to a vector
+            3. Compare noise level
+        2. Secondary
+            1. Generate a Gaussian / Cosine Matrix and add random noise matrix
+            2. Gaussian blur applied
+            3. Compare a randomly selected row of each one
+"""
+
+IMGDIRALL = {'bright/': (5, [193, 194]), 'calib/': (5, [1, 2, 3] + list(range(28, 34))),
+               'calib2/': (5, range(32, 46)), 'calib3/': (5, range(16, 60)), 'calib4/': (5, range(59, 193)),
+               'testpic/': (3, range(1, 26)), 'new_test/': (5, range(1, 6)), 'skewed/': (5, range(90, 96))}
+
+def test_old():
+    # SETTINGS
+    imgn = "img_193_{0}"
+
+    IMGDIR = "../bright/"
+    ROOTMEAS = "meas/"
+    SAVEDIR = ROOTMEAS + IMGDIR[3:] + imgn + "/"
+    #imgr = cv2.imread(IMGDIR + imgn + ".png")
+    imgr, ori = test_noise_reduce(IMGDIR + imgn + ".png", 5)
+    dimc = imgr.shape[1]
+    dimr = imgr.shape[0]
+    r_int = dimr // 2
+    c_int = 450 #dimc // 2
+    start = 7
+    end = 10
+    sig = 0
+    ins = range(start, end, 2)
+    name_scheme = imgn + ("_({0}, {1})").format(r_int, c_int)
+    NAME_HEADER = SAVEDIR + name_scheme
+    if not os.path.exists(SAVEDIR):
+        os.mkdir(SAVEDIR)
+    fwrite = open(NAME_HEADER + ".txt", "w")
+    plot_save = NAME_HEADER + ".png"
+
+    # INITIALIZATION
+    cenvalsx = []
+    cenvalsy = []
+    edgvalsx = []
+    edgvalsy = []
+    ebvalsx = []
+    ebvalsy = []
+    ecvalsx = []
+    ecvalsy = []
+    ecbvalsx = []
+    ecbvalsy = []
+    #cv2.imshow("denoised", np.uint8(imgr))                                 # REMOVES TO SHOW IMAGE
+    x = np.array(range(dimc))
+    x2 = np.array(range(dimr))
+
+    for i in ins:
+        # INITIALIZATION
+        gksize = (i, i)
+        sigmaX = sig
+        blur = cv2.GaussianBlur(imgr,gksize,sigmaX)
+        #cv2.imshow("blurred", blur)                        # REMOVES TO SHOW BLURRED IMAGE
+        if len(blur.shape) == 3:
+            img = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
+        else:
+            img = blur
+
+        # GAUSSIAN PROCESSING                                 # UNCOMMENT TO SHOW GAUSSIAN PROCESSING
+        """x = np.array(range(dimc))
+        #y = np.array([img_rec.rel_lumin(img, 0, c) for c in x])
+        y = np.array([img.item(dimr // 2, c) for c in x])
+        a1, b1, c_s1 = gauss_reg(x, y)
+        x2 = np.array(range(dimr))
+        y2 = np.array([img.item(r, dimc // 2) for r in x2])
+        a2, b2, c_s2 = gauss_reg(x2, y2)
+        rem_gauss = gauss_mat(img.shape, (a1+a2) / 2, b1, c_s1, b2, c_s2)
+        img = img - rem_gauss
+        cv2.imshow("denoise", img)
+        y_hat = gauss_hat(x, a1, b1, c_s1)
+        plt.figure(figsize=(16, 8))
+        plt.plot(x, y, 'b-', x, y_hat, 'r-')
+        plt.show()
+        plt.close()
+        plt.figure(figsize=(16,8))
+        plt.plot(x, y-y_hat, 'b-')
+        plt.show()"""
+
+        # IMAGE PROCESSING
+        sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=-1)
+        sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=-1)
+        """kernel = np.array([[-1, -1, -1],
+                           [-1, 8, -1],
+                           [-1, -1, -1]])
+        kerneled = cv2.filter2D(img, -1, kernel)
+        cv2.imshow("kerneled", kerneled)"""                 # KERNEL EXPERIMENT
+        #laplacian = cv2.Laplacian(img,cv2.CV_64F)          # LAPLACIAN FILTERING
+        """plt.subplot(2,2,1),plt.imshow(cv2.cvtColor(imgr, cv2.COLOR_BGR2GRAY),cmap = 'gray')
+        plt.title('Original'), plt.xticks([]), plt.yticks([])
+        plt.subplot(2,2,2),plt.imshow(img,cmap = 'gray')
+        plt.title('Gaussian Filter'), plt.xticks([]), plt.yticks([])
+        plt.subplot(2,2,3),plt.imshow(sobelx,cmap = 'gray')
+        plt.title('Scharr X'), plt.xticks([]), plt.yticks([])
+        plt.subplot(2,2,4),plt.imshow(sobely,cmap = 'gray')
+        plt.title('Scharr Y'), plt.xticks([]), plt.yticks([])"""
+        #plt.savefig(NAME_HEADER + "filters.png")
+        #plt.savefig(NAME_HEADER + "2D_filters.png")
+        #plt.close()                                         # REMOVES TO SHOW CONTRAST OF FILTERS
+
+
+        # DETECTION INIT
+        gk_setting = "ksize:{0}, sigmax:{1}".format(gksize, sigmaX)
+        y_s_x = np.array([sobelx.item(r_int, c) for c in x])
+        y_s_y = np.array([sobely.item(r, c_int) for r in x2])
+        imgx = [imgr.item(r_int, c) for c in x]
+        imgy = [imgr.item(r, c_int) for r in x2]
+        blurimgx = [img.item(r_int, c) for c in x]
+        blurimgy = [img.item(r, c_int) for r in x2]
+        print(gk_setting)
+        print(extract_extrema(y_s_x))
+        print(extract_extrema(y_s_y))
+        fwrite.write(gk_setting)
+        fwrite.write('\n')
+
+        # EDGE DETECTION
+        zx = zero_crossing(y_s_x)
+        cenvalsx.append(zx)
+        ex = edge_converge_extreme(y_s_x)
+        edgvalsx.append(ex)
+        ebx = edge_converge_base(y_s_x)
+        ebvalsx.append(ebx)
+        ecx = edge_centroid(y_s_x, imgx)
+        ecvalsx.append(ecx)
+        ecbx = edge_centroid(y_s_x, blurimgx)
+        ecbvalsx.append(ecbx)
+
+        zy = zero_crossing(y_s_y)
+        cenvalsy.append(zy)
+        ey = edge_converge_extreme(y_s_y)
+        edgvalsy.append(ey)
+        eby = edge_converge_base(y_s_y)
+        ebvalsy.append(eby)
+        ecy = edge_centroid(y_s_y, imgy)
+        ecvalsy.append(ecy)
+        ecby = edge_centroid(y_s_y, blurimgy)
+        ecbvalsy.append(ecby)
+
+        # DATA RECORDING
+        fwrite.write("x: zero_crossing: {0}, edge_converge: {1}; ".format(zx, ex))
+        fwrite.write('\n')
+        fwrite.write("y: zero_crossing: {0}, edge_converge: {1}\n".format(zy, ey))
+
+        # GK_PLOT
+        """if i == 9:
+            fig = plt.figure(figsize=(16, 8))
+            fig.canvas.set_window_title(gk_setting)
+            plt.subplot(211)
+            plt.plot(y_s_x, 'b-')
+            plt.ylabel("sobel_x")
+            plt.subplot(212)
+            plt.plot(y_s_y, 'b-')
+            plt.ylabel("sobel_y")
+            #plt.savefig(NAME_HEADER + "_" + gk_setting + ".png")"""
+
+        #if i >= 13:
+        fig = plt.figure(figsize=(16, 8))
+        fig.canvas.set_window_title(gk_setting)
+        plt.subplot(211)
+        plt.plot(y_s_x, 'b-')
+        plt.ylabel("sobel_x")
+        plt.subplot(212)
+        plt.plot(y_s_y, 'b-')
+        plt.ylabel("sobel_y")
+        plt.show()
+        # plt.savefig(NAME_HEADER + "_" + gk_setting + ".png") # UNCOMMENT WHEN SAVING PLOTS
+
+        # USING MIN-MAX
+        fig = plt.figure(figsize=(16, 8))
+        fig.canvas.set_window_title(gk_setting)
+        plt.subplot(221)
+        plt.plot(y_s_x, 'b-')
+        plt.ylabel("sobel_x")
+        plt.subplot(222)
+        plt.plot(min_max(y_s_x, max(y_s_x), min(y_s_x)), 'b-')
+        plt.subplot(223)
+        plt.plot(y_s_y, 'b-')
+        plt.ylabel("sobel_y")
+        plt.xlabel("Plain")
+        plt.subplot(224)
+        plt.plot(min_max(y_s_y, max(y_s_y), min(y_s_y)), 'b-')
+        plt.xlabel("MINMAX")
+        plt.show()
+
+
+    fig = plt.figure(figsize=(16, 8))
+    plt.subplot(2, 1, 1)
+    #plt.plot(ins, cenvalsx, 'b-', ins, edgvalsx, 'r-', ins, ebvalsx, 'g-', ins, ecvalsx, 'c-', ins, ecbvalsx, 'm-')
+    #plt.legend(['zero crossing', 'edge_converging_extreme', 'edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'], loc="lower left")
+    #plt.plot(ins, cenvalsx, 'b-', ins, ebvalsx, 'g-', ins, ecvalsx, 'c-', ins, ecbvalsx, 'm-')
+    #plt.legend(['zero crossing', 'edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'],loc="lower left")
+    plt.plot(ins, ebvalsx, 'g-', ins, ecvalsx, 'c-', ins, ecbvalsx, 'm-')
+    plt.legend(['edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'],loc="lower left")
+    plt.ylabel("x_meas")
+    plt.subplot(2, 1, 2)
+    #plt.plot(ins, cenvalsy, 'b-', ins, ebvalsy, 'g-', ins, ecvalsy, 'c-', ins, ecbvalsy, 'm-')
+    #plt.legend(['zero crossing', 'edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'], loc="lower left")
+    plt.plot(ins, ebvalsy, 'g-', ins, ecvalsy, 'c-', ins, ecbvalsy, 'm-')
+    plt.legend(['edge_converging_base', 'edge_centroid', 'edge_centroid_blurred'], loc="lower left")
+    plt.ylabel("y_meas")
+    plt.xlabel("kernel size")
+    plt.show()
+    #fig.savefig(plot_save)
+    #plt.savefig(plot_save)
+
+
+def test_interactive():
+    # TODO: TEST OUT FACET MODEL AND CONDITIONAL CONVOLUTIONAL KERNEL
+    # SETTINGS
+    while True:
+        number = input("type the picture number u wanna test or c to cancel:    ")
+        if number == 'c':
+            break
+        imgn = "img_%d_{0}" % int(number)
+        print(imgn)
+        IMGDIR = "../calib4/"
+        #IMGDIR = "../skewed/"
+        ROOTMEAS = "meas/"
+        SAVEDIR =ROOTMEAS + IMGDIR[3:] + imgn + "/"
+        #SAVEDIR = ROOTMEAS + imgn + "/"
+        # imgr = cv2.imread(IMGDIR + imgn + ".png")
+        num_img = 3 if ROOTMEAS == 'testpic' else 5
+        imgr, ori = test_noise_reduce(IMGDIR + imgn + ".png", numIMG=num_img)
+        dimc = imgr.shape[1]
+        dimr = imgr.shape[0]
+        sig = 0
+        gk = 9
+
+        # ----------------- Preliminary IMG_PROCESSING ---------------------
+        # INITIALIZATION
+        gksize = (gk, gk)
+        sigmaX = sig
+        blur = cv2.GaussianBlur(imgr, gksize, sigmaX)
+        # cv2.imshow("blurred", blur)                        # REMOVES TO SHOW BLURRED IMAGE
+        if len(blur.shape) == 3:
+            img = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
+        else:
+            img = blur
+
+        # IMAGE PROCESSING
+        sobelx, sobely, gradient = gradient_calc(img, ks=-1)
+        f_r, fshift_r, magnitude_r = fourier_trans(imgr)
+        f_r_mask = data_masked(f_r, 0, 0, 0.9, 0.9)
+        inv_r_mask = np.abs(np.fft.ifft2(f_r_mask))
+        sobelx_r_fm, sobely_r_fm, gradient_r_fm = gradient_calc(inv_r_mask, ks=-1)
+        compare_images((sobelx, 'Sobel X'), (sobely, 'Sobel Y'), (gradient, 'Raw Gradient'), (magnitude_r, 'Mag Original'),
+                       (sobelx_r_fm, 'SobelX Fourier', (sobely_r_fm, 'SobelY Fourier'), (gradient_r_fm, 'Gradient Fourier')),
+                       color_map='gray', suptitle=IMGDIR+imgn[:-4])
+        #img, sobelx, sobely, gradient = inv_r_mask, sobelx_r_fm, sobely_r_fm, gradient_r_fm
+        plot_img(img)
+        plot_img(gradient)
+
+        # --------------------- INTERACTIVE SESSION -----------------------
+        while True:
+            r_prompt = input("Type in the index for horizontal slice or c to cancel: (range: [0, {0}])   ".format(dimr - 1)) #dimr // 2
+            if r_prompt == 'c':
+                break
+            c_prompt = input("Type in the index for vertical slice: (range: [0, {0}])   ".format(dimc - 1)) #dimc // 2
+            while True:
+                try:
+                    r_int = int(r_prompt)
+                    assert 0 <= r_int < dimr
+                    break
+                except ValueError:
+                    r_prompt = input("Bad Input, try again: (range: [0, {0}])    ".format(dimr - 1))  # dimr // 2
+                except AssertionError:
+                    r_prompt = input("Out of Bound, try again: (range: [0, {0}])    ".format(dimr - 1))
+            while True:
+                try:
+                    c_int = int(c_prompt)
+                    assert 0 <= c_int < dimc
+                    break
+                except ValueError:
+                    c_prompt = input("Bad Input, try again: (range: [0, {0}])    ".format(dimc - 1))  # dimc // 2
+                except AssertionError:
+                    c_prompt = input("Out of Bound, try again: (range: [0, {0}])    ".format(dimc - 1))  # dimc // 2
+
+            # ---------------- DATA RECORDING -----------------
+            name_scheme = imgn + ("_({0}, {1})").format(r_int, c_int)
+            NAME_HEADER = SAVEDIR + name_scheme
+            if not os.path.exists(SAVEDIR):
+                os.mkdir(SAVEDIR)
+            fwrite = open(NAME_HEADER + ".txt", "w")
+            plot_save = NAME_HEADER + ".png"
+
+            # DETECTION INIT
+            xh = np.array(range(dimc))
+            xv = np.array(range(dimr))
+            zeroh = np.zeros(dimc)
+            zerov = np.zeros(dimr)
+            y_s_x = FM(sobelx, FM.HOR, r_int)
+            y_s_y = FM(sobely, FM.VER, c_int)
+            ysx_t = y_s_x.extract_array()
+            ysy_t = y_s_y.extract_array()
+            zscorex = zscore_calc(ysx_t)
+            zscorey = zscore_calc(ysy_t)
+            imgx = FM(imgr, FM.HOR, r_int)
+            imgy = FM(imgr, FM.VER, c_int)
+            blurimgx = FM(img, FM.HOR, r_int)
+            blurimgy = FM(img, FM.VER, c_int)
+            gx, bgx, deducedx = gauss_bg_deduce(xh, imgx)
+            gy, bgy, deducedy = gauss_bg_deduce(xv, imgy)
+            minmax_xsobel = min_max(ysx_t, max(ysx_t), min(ysx_t))
+            minmax_ysobel = min_max(ysy_t, max(ysy_t), min(ysy_t))
+            sdevx = np.std(minmax_xsobel)
+            sdevy = np.std(minmax_ysobel)
+            DEV_MESSAGE = "SobelX StdDev: {0}, SobelY StdDev: {1}\n".format(sdevx, sdevy)
+            print(DEV_MESSAGE)
+
+            # EDGE DETECTION
+            zx = zero_crossing(y_s_x)
+            ex = edge_converge_extreme(y_s_x)
+            ebx = edge_converge_base(y_s_x)
+            ecbx = edge_centroid(y_s_x, blurimgx)
+            ecx = edge_centroid(y_s_x, imgx)
+            try:
+                #paramhp, xp1 = poly_fitting_params(y_s_x, deducedx)
+                #paramhg, xg1 = gaussian_fitting_params(y_s_x, deducedx)
+                paramhp, xp1 = poly_fitting_params(y_s_x, imgx)
+                paramhg, xg1 = gaussian_fitting_params(y_s_x, imgx)
+                xp_h_plot, yp_h_plot = poly_curve(paramhp, xp1)
+                xg_h_plot, yg_h_plot = gaussian_curve(xg1, paramhg[0], paramhg[1], paramhg[2])
+            except (AssertionError, RuntimeError):
+                print("No image data for horizontal slice!")
+                xp_h_plot, yp_h_plot = xh, zeroh
+                xg_h_plot, yg_h_plot = xh, zeroh
+
+            px, gx = xp_h_plot[np.argmax(yp_h_plot)], xg_h_plot[np.argmax(yg_h_plot)]
+            print("edge: {0}, poly:{1}, gaussian:{2}".format(ecx, px, gx))
+
+            zy = zero_crossing(y_s_y)
+            ey = edge_converge_extreme(y_s_y)
+            eby = edge_converge_base(y_s_y)
+            ecby = edge_centroid(y_s_y, blurimgy)
+            ecy = edge_centroid(y_s_y, imgy)
+            try:
+                paramvp, xp2 = poly_fitting_params(y_s_y, imgy)
+                paramvg, xg2 = gaussian_fitting_params(y_s_y, imgy)
+                xp_v_plot, yp_v_plot = poly_curve(paramvp, xp2)
+                xg_v_plot, yg_v_plot = gaussian_curve(xg2, paramvg[0], paramvg[1], paramvg[2])
+            except (AssertionError, RuntimeError):
+                print("No image data for vertical slice!")
+                xp_v_plot, yp_v_plot = xv, zerov
+                xg_v_plot, yg_v_plot = xv, zerov
+            py, gy = xp_v_plot[np.argmax(yp_v_plot)], xg_v_plot[np.argmax(yg_v_plot)]
+            print("edge: {0} poly:{1}, gaussian:{2}".format(ecy, py, gy))
+
+
+            # DATA RECORDING
+            fwrite.write("x: edge_centroid: {0}, poly: {1}, gaussian: {2}; ".format(ecx, px, gx))
+            fwrite.write('\n')
+            fwrite.write("y: edge_centroid: {0}, poly: {1}, gaussian: {2}\n".format(ecy, py, gy))
+            fwrite.write(DEV_MESSAGE)
+
+            # GK_PLOT
+            """if i == 9:
+                fig = plt.figure(figsize=(16, 8))
+                fig.canvas.set_window_title(gk_setting)
+                plt.subplot(211)
+                plt.plot(y_s_x, 'b-')
+                plt.ylabel("sobel_x")
+                plt.subplot(212)
+                plt.plot(y_s_y, 'b-')
+                plt.ylabel("sobel_y")
+                #plt.savefig(NAME_HEADER + "_" + gk_setting + ".png")"""
+
+            imgx.initialize()
+            imgy.initialize()
+            imgxarr = imgx.extract_array()
+            imgyarr = imgy.extract_array()
+            indic_up_x = np.full(len(imgxarr), 1)
+            indic_down_x = np.full(len(imgxarr), -1)
+            indic_up_y = np.full(len(imgyarr), 1)
+            indic_down_y = np.full(len(imgyarr), -1)
+            # if i >= 13:
+            fig = plt.figure(figsize=(16, 8))
+            plt.subplot(611)
+            #plt.plot(min_max(ysx_t, max(ysx_t), min(ysx_t)), 'b-')
+            plt.plot(xh, ysx_t, 'b-')
+            plt.ylabel("sobel_x")
+            plt.subplot(612)
+            plt.plot(xh, zscore_calc(imgxarr), 'b-', xh, zscorex, 'c-', xh, indic_up_x, 'r:', xh, indic_down_x, 'r:')
+            plt.ylabel("zscoresX")
+            plt.legend(['zscoreIMGX', 'zscoreSobelX'], loc="upper right")
+            plt.subplot(613)
+            plt.plot(xh, imgxarr, 'b-', xh, bgx, 'm-', xh, deducedx, 'c-', xp_h_plot, yp_h_plot, 'g-',
+                     xg_h_plot, yg_h_plot, 'r-')
+            plt.ylabel("Horizontal Slice, ECX: {0}".format(ecx))
+            plt.legend(['Raw image data', 'gaussian background', 'remnant', 'polynomial', 'gaussian'], loc="upper right")
+            plt.subplot(614)
+            #plt.plot(min_max(ysy_t, max(ysy_t), min(ysy_t)), 'b-')
+            plt.plot(xv, ysy_t, 'b-')
+            plt.ylabel("sobel_y")
+            plt.subplot(615)
+            plt.plot(xv, zscore_calc(imgyarr), 'b-', xv, zscorey, 'c-', xv, indic_up_y, 'r:', xv, indic_down_y, 'r:')
+            plt.ylabel("zscoresY")
+            plt.legend(['zscoreIMGY', 'zscoreSobelY'], loc="upper right")
+            plt.subplot(616)
+            plt.plot(xv, imgyarr, 'b-', xv, bgy, 'm-', xv, deducedy, 'c-', xp_v_plot, yp_v_plot, 'g-',
+                     xg_v_plot, yg_v_plot, 'r-')
+            plt.ylabel("Vertical Slice, ECY: {0}".format(ecy))
+            plt.legend(['Raw image data', 'gaussian background', 'remnant', 'polynomial', 'gaussian'], loc="upper right")
+            plt.show()
+            fig.savefig(NAME_HEADER + "_r{0}_c{1}.png".format(r_int, c_int)) # UNCOMMENT WHEN SAVING PLOTS
+
+
+def test_record():
+    # TODO: TEST OUT FACET MODEL AND CONDITIONAL CONVOLUTIONAL KERNEL
+    # SETTINGS
+    img_ns = "img_%d_{0}"
+    IMGDIRS = IMGDIRALL
+    ROOTMEAS = "meas/"
+    OFFSET = '../'
+    rslice = range(10, 480, 50)
+    cslice = range(20, 640, 50)
+    errors = open(ROOTMEAS + 'errors.txt', 'w')
+    for IMGDIR, spec in IMGDIRS.items():
+        IMGDIR = OFFSET + IMGDIR
+        numPic, img_range = spec
+        for img_i in img_range:
+            imgn = img_ns % img_i
+            print(imgn)
+            # IMGDIR = "../calib4/"
+            SAVEDIR = ROOTMEAS + IMGDIR[3:] + imgn + "/"
+            # SAVEDIR = ROOTMEAS + imgn + "/"
+            # imgr = cv2.imread(IMGDIR + imgn + ".png")
+            try:
+                imgr, ori = test_noise_reduce(IMGDIR + imgn + ".png", numIMG=numPic)
+            except AttributeError as e:
+                print(e.args)
+                continue
+            try:
+                dimc = imgr.shape[1]
+                dimr = imgr.shape[0]
+                sig = 0
+                gk = 9
+
+                # ----------------- Preliminary IMG_PROCESSING ---------------------
+                # INITIALIZATION
+                gksize = (gk, gk)
+                sigmaX = sig
+                blur = cv2.GaussianBlur(imgr, gksize, sigmaX)
+                # cv2.imshow("blurred", blur)                        # REMOVES TO SHOW BLURRED IMAGE
+                if len(blur.shape) == 3:
+                    img = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
+                else:
+                    img = blur
+
+                # IMAGE PROCESSING
+                sobelx, sobely, gradient = gradient_calc(img, ks=-1)
+                #compare_images((sobelx, 'Sobel X'), (sobely, 'Sobel Y'), (gradient, 'Raw Gradient'), color_map='gray')
+                #plot_img(img)
+                #plot_img(gradient)
+
+                # --------------------- INTERACTIVE SESSION -----------------------
+                for r_int, c_int in zip(rslice, cslice):
+                    # ---------------- DATA RECORDING -----------------
+                    name_scheme = imgn + ("_({0}, {1})").format(r_int, c_int)
+                    NAME_HEADER = SAVEDIR + name_scheme
+                    if not os.path.exists(SAVEDIR):
+                        os.mkdir(SAVEDIR)
+                    fwrite = open(NAME_HEADER + ".txt", "w")
+                    plot_save = NAME_HEADER + ".png"
+
+                    # DETECTION INIT
+                    xh = np.array(range(dimc))
+                    xv = np.array(range(dimr))
+                    zeroh = np.zeros(dimc)
+                    zerov = np.zeros(dimr)
+                    y_s_x = FM(sobelx, FM.HOR, r_int)
+                    y_s_y = FM(sobely, FM.VER, c_int)
+                    ysx_t = y_s_x.extract_array()
+                    ysy_t = y_s_y.extract_array()
+                    zscorex = zscore_calc(ysx_t)
+                    zscorey = zscore_calc(ysy_t)
+                    imgx = FM(imgr, FM.HOR, r_int)
+                    imgy = FM(imgr, FM.VER, c_int)
+                    blurimgx = FM(img, FM.HOR, r_int)
+                    blurimgy = FM(img, FM.VER, c_int)
+                    gx, bgx, deducedx = gauss_bg_deduce(xh, imgx)
+                    gy, bgy, deducedy = gauss_bg_deduce(xv, imgy)
+                    minmax_xsobel = min_max(ysx_t, max(ysx_t), min(ysx_t))
+                    minmax_ysobel = min_max(ysy_t, max(ysy_t), min(ysy_t))
+                    sdevx = np.std(minmax_xsobel)
+                    sdevy = np.std(minmax_ysobel)
+                    DEV_MESSAGE = "SobelX StdDev: {0}, SobelY StdDev: {1}\n".format(sdevx, sdevy)
+                    print(DEV_MESSAGE)
+
+                    # EDGE DETECTION
+                    zx = zero_crossing(y_s_x)
+                    ex = edge_converge_extreme(y_s_x)
+                    ebx = edge_converge_base(y_s_x)
+                    ecbx = edge_centroid(y_s_x, blurimgx)
+                    ecx = edge_centroid(y_s_x, imgx)
+                    try:
+                        # paramhp, xp1 = poly_fitting_params(y_s_x, deducedx)
+                        # paramhg, xg1 = gaussian_fitting_params(y_s_x, deducedx)
+                        paramhp, xp1 = poly_fitting_params(y_s_x, imgx)
+                        paramhg, xg1 = gaussian_fitting_params(y_s_x, imgx)
+                        xp_h_plot, yp_h_plot = poly_curve(paramhp, xp1)
+                        xg_h_plot, yg_h_plot = gaussian_curve(xg1, paramhg[0], paramhg[1], paramhg[2])
+                    except (AssertionError, RuntimeError):
+                        print("No image data for horizontal slice!")
+                        xp_h_plot, yp_h_plot = xh, zeroh
+                        xg_h_plot, yg_h_plot = xh, zeroh
+
+                    px, gx = xp_h_plot[np.argmax(yp_h_plot)], xg_h_plot[np.argmax(yg_h_plot)]
+                    print("edge: {0}, poly:{1}, gaussian:{2}".format(ecx, px, gx))
+
+                    zy = zero_crossing(y_s_y)
+                    ey = edge_converge_extreme(y_s_y)
+                    eby = edge_converge_base(y_s_y)
+                    ecby = edge_centroid(y_s_y, blurimgy)
+                    ecy = edge_centroid(y_s_y, imgy)
+                    try:
+                        paramvp, xp2 = poly_fitting_params(y_s_y, imgy)
+                        paramvg, xg2 = gaussian_fitting_params(y_s_y, imgy)
+                        xp_v_plot, yp_v_plot = poly_curve(paramvp, xp2)
+                        xg_v_plot, yg_v_plot = gaussian_curve(xg2, paramvg[0], paramvg[1], paramvg[2])
+                    except (AssertionError, RuntimeError):
+                        print("No image data for vertical slice!")
+                        xp_v_plot, yp_v_plot = xv, zerov
+                        xg_v_plot, yg_v_plot = xv, zerov
+                    py, gy = xp_v_plot[np.argmax(yp_v_plot)], xg_v_plot[np.argmax(yg_v_plot)]
+                    print("edge: {0} poly:{1}, gaussian:{2}".format(ecy, py, gy))
+
+                    # DATA RECORDING
+                    fwrite.write("x: edge_centroid: {0}, poly: {1}, gaussian: {2}; ".format(ecx, px, gx))
+                    fwrite.write('\n')
+                    fwrite.write("y: edge_centroid: {0}, poly: {1}, gaussian: {2}\n".format(ecy, py, gy))
+                    fwrite.write(DEV_MESSAGE)
+
+                    # GK_PLOT
+                    """if i == 9:
+                        fig = plt.figure(figsize=(16, 8))
+                        fig.canvas.set_window_title(gk_setting)
+                        plt.subplot(211)
+                        plt.plot(y_s_x, 'b-')
+                        plt.ylabel("sobel_x")
+                        plt.subplot(212)
+                        plt.plot(y_s_y, 'b-')
+                        plt.ylabel("sobel_y")
+                        #plt.savefig(NAME_HEADER + "_" + gk_setting + ".png")"""
+
+                    imgx.initialize()
+                    imgy.initialize()
+                    imgxarr = imgx.extract_array()
+                    imgyarr = imgy.extract_array()
+                    indic_up_x = np.full(len(imgxarr), 1)
+                    indic_down_x = np.full(len(imgxarr), -1)
+                    indic_up_y = np.full(len(imgyarr), 1)
+                    indic_down_y = np.full(len(imgyarr), -1)
+                    # if i >= 13:
+                    """
+                    fig = plt.figure(figsize=(16, 8))
+                    plt.subplot(611)
+                    # plt.plot(min_max(ysx_t, max(ysx_t), min(ysx_t)), 'b-')
+                    plt.plot(xh, ysx_t, 'b-')
+                    plt.ylabel("sobel_x")
+                    plt.subplot(612)
+                    plt.plot(xh, zscore_calc(imgxarr), 'b-', xh, zscorex, 'c-', xh, indic_up_x, 'r:', xh, indic_down_x, 'r:')
+                    plt.ylabel("zscoresX")
+                    plt.legend(['zscoreIMGX', 'zscoreSobelX'], loc="upper right")
+                    plt.subplot(613)
+                    plt.plot(xh, imgxarr, 'b-', xh, bgx, 'm-', xh, deducedx, 'c-', xp_h_plot, yp_h_plot, 'g-',
+                             xg_h_plot, yg_h_plot, 'r-')
+                    plt.ylabel("Horizontal Slice, ECX: {0}".format(ecx))
+                    plt.legend(['Raw image data', 'gaussian background', 'remnant', 'polynomial', 'gaussian'],
+                               loc="upper right")
+                    plt.subplot(614)
+                    # plt.plot(min_max(ysy_t, max(ysy_t), min(ysy_t)), 'b-')
+                    plt.plot(xv, ysy_t, 'b-')
+                    plt.ylabel("sobel_y")
+                    plt.subplot(615)
+                    plt.plot(xv, zscore_calc(imgyarr), 'b-', xv, zscorey, 'c-', xv, indic_up_y, 'r:', xv, indic_down_y, 'r:')
+                    plt.ylabel("zscoresY")
+                    plt.legend(['zscoreIMGY', 'zscoreSobelY'], loc="upper right")
+                    plt.subplot(616)
+                    plt.plot(xv, imgyarr, 'b-', xv, bgy, 'm-', xv, deducedy, 'c-', xp_v_plot, yp_v_plot, 'g-',
+                             xg_v_plot, yg_v_plot, 'r-')
+                    plt.ylabel("Vertical Slice, ECY: {0}".format(ecy))
+                    plt.legend(['Raw image data', 'gaussian background', 'remnant', 'polynomial', 'gaussian'],
+                               loc="upper right")
+                    #plt.show()
+                    fig.savefig(NAME_HEADER + "_r{0}_c{1}.png".format(r_int, c_int))  # UNCOMMENT WHEN SAVING PLOTS
+                    plt.close()
+                    fwrite.close()"""
+            except TypeError as e:
+                message = 'ERROR in {0}: '.format(IMGDIR + imgn) + str(e.args) + '\n'
+                print(message)
+                fwrite.write(message)
+                fwrite.close()
+                errors.write(message)
+                continue
+    errors.close()
+
+
+def test(folder, imgn):
+    # TODO: TEST OUT FACET MODEL AND CONDITIONAL CONVOLUTIONAL KERNEL
+    # SETTINGS
+    ROOTMEAS = "meas/"
+    SAVEDIR = ROOTMEAS + folder[3:] + imgn[:-4] + "/"
+    # imgr = cv2.imread(IMGDIR + imgn + ".png")
+
+    imgr, ori = test_noise_reduce(folder + imgn, numIMG=5)
+    dimc = imgr.shape[1]
+    dimr = imgr.shape[0]
+    while True:
+        r_prompt = input("Type in the index for horizontal slice or c to cancel: (range: [0, {0}])   ".format(dimr - 1))  # dimr // 2
+        c_prompt = input("Type in the index for vertical slice: (range: [0, {0}])   ".format(dimc - 1))  # dimc // 2
+        if r_prompt == 'c':
+            break
+        while True:
+            try:
+                r_int = int(r_prompt)
+                assert 0 <= r_int < dimr
+                break
+            except ValueError:
+                r_prompt = input("Bad Input, try again: (range: [0, {0}])    ".format(dimr - 1))  # dimr // 2
+            except AssertionError:
+                r_prompt = input("Out of Bound, try again: (range: [0, {0}])    ".format(dimr - 1))
+        while True:
+            try:
+                c_int = int(c_prompt)
+                assert 0 <= c_int < dimc
+                break
+            except ValueError:
+                c_prompt = input("Bad Input, try again: (range: [0, {0}])    ".format(dimc - 1))  # dimc // 2
+            except AssertionError:
+                c_prompt = input("Out of Bound, try again: (range: [0, {0}])    ".format(dimc - 1))  # dimc // 2
+        # start = 7
+        # end = 10
+        sig = 0
+        gk = 9
+        name_scheme = imgn + ("_({0}, {1})").format(r_int, c_int)
+        NAME_HEADER = SAVEDIR + name_scheme
+        if not os.path.exists(SAVEDIR):
+            os.mkdir(SAVEDIR)
+        fwrite = open(NAME_HEADER + ".txt", "w")
+        plot_save = NAME_HEADER + ".png"
+
+        # INITIALIZATION
+        # cv2.imshow("denoised", np.uint8(imgr))                                 # REMOVES TO SHOW IMAGE
+        # x = np.array(range(dimc))
+        # x2 = np.array(range(dimr))
+
+        # INITIALIZATION
+        gksize = (gk, gk)
+        sigmaX = sig
+        blur = cv2.GaussianBlur(imgr, gksize, sigmaX)
+        # cv2.imshow("blurred", blur)                        # REMOVES TO SHOW BLURRED IMAGE
+        if len(blur.shape) == 3:
+            img = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
+        else:
+            img = blur
+
+        # GAUSSIAN PROCESSING                                 # UNCOMMENT TO SHOW GAUSSIAN PROCESSING
+        """x = np.array(range(dimc))
+        #y = np.array([img_rec.rel_lumin(img, 0, c) for c in x])
+        y = np.array([img.item(dimr // 2, c) for c in x])
+        a1, b1, c_s1 = gauss_reg(x, y)
+
+        x2 = np.array(range(dimr))
+        y2 = np.array([img.item(r, dimc // 2) for r in x2])
+        a2, b2, c_s2 = gauss_reg(x2, y2)
+        rem_gauss = gauss_mat(img.shape, (a1+a2) / 2, b1, c_s1, b2, c_s2)
+        img = img - rem_gauss
+        cv2.imshow("denoise", img)
+        y_hat = gauss_hat(x, a1, b1, c_s1)
+        plt.figure(figsize=(16, 8))
+        plt.plot(x, y, 'b-', x, y_hat, 'r-')
+        plt.show()
+        plt.close()
+        plt.figure(figsize=(16,8))
+        plt.plot(x, y-y_hat, 'b-')
+        plt.show()"""
+
+        # IMAGE PROCESSING
+        sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=-1)
+        sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=-1)
+
+
+        # DETECTION INIT
+        xh = np.array(range(dimc))
+        xv = np.array(range(dimr))
+        zeroh = np.zeros(dimc)
+        zerov = np.zeros(dimr)
+        y_s_x = FM(sobelx, FM.HOR, r_int)
+        y_s_y = FM(sobely, FM.VER, c_int)
+        imgx = FM(imgr, FM.HOR, r_int)
+        imgy = FM(imgr, FM.VER, c_int)
+        blurimgx = FM(img, FM.HOR, r_int)
+        blurimgy = FM(img, FM.VER, c_int)
+        gx, bgx, deducedx = gauss_bg_deduce(xh, imgx)
+        gy, bgy, deducedy = gauss_bg_deduce(xv, imgy)
+        # print(extract_extrema(y_s_x))
+        # print(extract_extrema(y_s_y))
+        # EDGE DETECTION
+        ecx = edge_centroid(y_s_x, imgx)
+        try:
+            # paramhp, xp1 = poly_fitting_params(y_s_x, deducedx)
+            # paramhg, xg1 = gaussian_fitting_params(y_s_x, deducedx)
+            paramhp, xp1 = poly_fitting_params(y_s_x, imgx)
+            paramhg, xg1 = gaussian_fitting_params(y_s_x, imgx)
+            xp_h_plot, yp_h_plot = poly_curve(paramhp, xp1)
+            xg_h_plot, yg_h_plot = gaussian_curve(xg1, paramhg[0], paramhg[1], paramhg[2])
+        except AssertionError:
+            print("No image data for horizontal slice!")
+            xp_h_plot, yp_h_plot = xh, zeroh
+            xg_h_plot, yg_h_plot = xh, zeroh
+        except RuntimeError:
+            print("No image data for horizontal slice!")
+            xp_h_plot, yp_h_plot = xh, zeroh
+            xg_h_plot, yg_h_plot = xh, zeroh
+
+        px, gx = xp_h_plot[np.argmax(yp_h_plot)], xg_h_plot[np.argmax(yg_h_plot)]
+        print("edge: {0}, poly:{1}, gaussian:{2}".format(ecx, px, gx))
+
+        ecy = edge_centroid(y_s_y, imgy)
+        try:
+            paramvp, xp2 = poly_fitting_params(y_s_y, imgy)
+            paramvg, xg2 = gaussian_fitting_params(y_s_y, imgy)
+            xp_v_plot, yp_v_plot = poly_curve(paramvp, xp2)
+            xg_v_plot, yg_v_plot = gaussian_curve(xg2, paramvg[0], paramvg[1], paramvg[2])
+        except AssertionError:
+            print("No image data for vertical slice!")
+            xp_v_plot, yp_v_plot = xv, zerov
+            xg_v_plot, yg_v_plot = xv, zerov
+        except RuntimeError:
+            print("No image data for vertical slice!")
+            xp_v_plot, yp_v_plot = xv, zerov
+            xg_v_plot, yg_v_plot = xv, zerov
+        py, gy = xp_v_plot[np.argmax(yp_v_plot)], xg_v_plot[np.argmax(yg_v_plot)]
+        print("edge: {0} poly:{1}, gaussian:{2}".format(ecy, py, gy))
+
+        # DATA RECORDING
+        fwrite.write("x: edge_centroid: {0}, poly: {1}, gaussian: {2}; ".format(ecx, px, gx))
+        fwrite.write('\n')
+        fwrite.write("y: edge_centroid: {0}, poly: {1}, gaussian: {2}\n".format(ecy, py, gy))
+
+        # if i >= 13:
+        fig = plt.figure(figsize=(16, 8))
+        plt.subplot(411)
+        plt.plot(y_s_x.extract_array(), 'b-')
+        plt.ylabel("sobel_x")
+        plt.subplot(412)
+        imgx.initialize()
+        plt.plot(xh, imgx.extract_array(), 'b-', xh, bgx, 'm-', xh, deducedx, 'c-', xp_h_plot, yp_h_plot, 'g-',
+                 xg_h_plot, yg_h_plot, 'r-')
+        plt.ylabel("Horizontal Slice, ECX: {0}".format(ecx))
+        plt.legend(['Raw image data', 'gaussian background', 'remnant', 'polynomial', 'gaussian'], loc="upper right")
+        plt.subplot(413)
+        plt.plot(y_s_y.extract_array(), 'b-')
+        plt.ylabel("sobel_y")
+        plt.subplot(414)
+        imgy.initialize()
+        plt.plot(xv, imgy.extract_array(), 'b-', xv, bgy, 'm-', xv, deducedy, 'c-', xp_v_plot, yp_v_plot, 'g-',
+                 xg_v_plot, yg_v_plot, 'r-')
+        plt.ylabel("Vertical Slice, ECY: {0}".format(ecy))
+        plt.legend(['Raw image data', 'gaussian background', 'remnant', 'polynomial', 'gaussian'], loc="upper right")
+        plt.show()
+        # plt.savefig(NAME_HEADER + "_" + gk_setting + ".png") # UNCOMMENT WHEN SAVING PLOTS
+
+
+FM = FastDataMatrix2D
+
+
+def folder_to_imgs(img_name_scheme, num_sample):
+    """This function takes img files and return cv imgs"""
+    return [cv2.imread(img_name_scheme.format(i)) for i in range(1, num_sample+1)]
+
+
+def center_detect_old(img_name_scheme, num_sample, sample_int=50):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair"""
+    imgs = folder_to_imgs(img_name_scheme, num_sample)
+    num_imgs = len(imgs)
+    dimr = imgs[0].shape[0]
+    dimc = imgs[0].shape[1]
+    xnum_imgs = num_imgs
+    ynum_imgs = num_imgs
+    xsum = 0
+    ysum = 0
+    zw = 0
+    ew = 1
+    gk = 9
+    count = 1
+    for imgr in imgs:
+        if count == num_sample:
+            imgshow = imgr
+        count += 1
+        #print(imgr.shape)
+        # Image Processing
+        gksize = (gk, gk)
+        sigmaX = 0
+        img = cv2.GaussianBlur(imgr, gksize, sigmaX)
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=-1)
+        sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=-1)
+        # Gathering Data
+        nr = sample_int
+        r_thresh = dimr * 2.0 / (sample_int * 3)
+        xs = []
+        xdummies = []
+        ys = []
+        ydummies = []
+        while nr < dimr:
+            data_x = sobelx[nr, :]
+            zc_x = zero_crossing(data_x)
+            print("row:", nr)
+            ed_x = edge_centroid(data_x, FM(img, FM.HOR, nr))
+            print("At {0}: {1}".format(nr, ed_x))
+            nr += sample_int
+            xdummies.append(zc_x * zw + ed_x * ew)
+            if zc_x == -1:
+                continue
+            else:
+                xs.append(zc_x * zw + ed_x * ew)
+        nc = sample_int
+        c_thresh = dimc * 2.0 / (sample_int * 3)
+        while nc < dimc:
+            data_y = sobely[:, nc]
+            zc_y = zero_crossing(data_y)
+            ed_y = edge_centroid(data_y, FM(img, FM.VER, nc))
+            nc += sample_int
+            ydummies.append(zc_y * zw + ed_y * ew)
+            if zc_y == -1:
+                continue
+            else:
+                ys.append(zc_y * zw + ed_y * ew)
+        #print("x")
+        #print(xdummies)
+        #print(xs)
+        #print("y")
+        #print(ydummies)
+        #print(ys)
+        len_xs = len(xs)
+        len_ys = len(ys)
+        print("img {0}: {1}".format(count - 1, xs))
+        if len_xs < r_thresh:
+            xnum_imgs -= 1
+        else:
+            xsum += sum(xs) / len(xs)
+        if len_ys < c_thresh:
+            ynum_imgs -= 1
+        else:
+            ysum += sum(ys) / len(ys)
+
+    plt.show()
+
+    center_x = -1 if xnum_imgs == 0 else xsum / xnum_imgs
+    center_y = -1 if ynum_imgs == 0 else ysum / ynum_imgs
+    return center_x, center_y
+
+# The image taken is flipped horizontally, result x should be img.shape[1] - x
+# The image sometimes has two peaks, try experimenting with different gaussian kernels
+
+
+def center_detect_test(folder_path, img_name_scheme, num_sample, sample_int=50, debug=False, gk=9, ks=-1, m=0, p=20, b=1, c=0, hough=True):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair
+    hs: HORIZONTAL SLICE!  vs: VERTICAL SLICE!"""
+    imgr = test_noise_reduce(folder_path + img_name_scheme, num_sample)[0]
+    dimr = imgr.shape[0]
+    dimc = imgr.shape[1]
+    # Image Processing
+    gksize = (gk, gk)
+    sigmaX = 0
+    img = cv2.GaussianBlur(imgr, gksize, sigmaX)
+    #sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=ks)
+    #sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=ks)
+    sobelx, sobely, gradient = gradient_calc(img, ks)
+    # ------------------------------------------------------------
+    # Parameter Setting
+    METHODS = {0: gaussian_fitting, 1: poly_fitting, 2: edge_centroid,
+               3: zero_crossing, 4: edge_converge_base, 5: edge_converge_extreme}
+    edge_method = METHODS[m]
+    nr = sample_int
+    r_thresh = dimr / (sample_int * 3.0)
+    nc = sample_int
+    c_thresh = dimc / (sample_int * 3.0)
+    # ------------------------------------------------------------
+    # Gathering Data
+    hs = []
+    vs = []
+    while nr < dimr:
+        data_x = FM(sobelx, FM.HOR, nr)
+        if m < 3:
+            ec_x = edge_method(data_x, FM(img, FM.HOR, nr), p)
+        else:
+            ec_x = edge_method(data_x)
+        nr += sample_int
+        if ec_x == -1:
+            continue
+        else:
+            hs.append((nr - sample_int, ec_x))
+
+    while nc < dimc:
+        data_y = FM(sobely, FM.VER, nc)
+        if m < 3:
+            ec_y = edge_method(data_y, FM(img, FM.VER, nc), p)
+        else:
+            ec_y = edge_method(data_y)
+        nc += sample_int
+        if ec_y == -1:
+            continue
+        else:
+            vs.append((nc - sample_int, ec_y))
+    len_hs = len(hs)
+    len_vs = len(vs)
+    # --------------- PRE-CHECK DATA VALUES ----------------------
+    hxs = np.zeros(len_hs)
+    hys = np.zeros(len_hs)
+    for i in range(len_hs):
+        hxs[i] = hs[i][1]
+        hys[i] = hs[i][0]
+    vxs = np.zeros(len_vs)
+    vys = np.zeros(len_vs)
+    for i in range(len_vs):
+        vxs[i] = vs[i][0]
+        vys[i] = vs[i][1]
+    x_valid = False
+    y_valid = False
+    stdh = -1
+    stdv = -1
+    #valuesH = [d[1] for d in hs]
+    #valuesV = [d[1] for d in vs]
+    if len_hs >= r_thresh:
+        x_valid = True
+        stdh = std_dev(hxs)
+    if len_vs >= c_thresh:
+        y_valid = True
+        stdv = std_dev(vys)
+    #hough_img = img_name_scheme.format(1)
+
+    # OUTLIER DETECTION TODO: OPTIMIZE, THIS IS NAIVE
+    if x_valid:  # NAIVE
+        hys, hxs = reg_pre_debias(hys, hxs)
+    if y_valid:
+        vxs, vys = reg_pre_debias(vxs, vys)
+    # ---------------------------------------------------------
+    # ----- Following Modules Handles Hough Line Drawing ---------
+    if hough:
+        hough_img = folder_path + img_name_scheme.format(1)
+        print(hough_img)
+        img_h = cv2.imread(hough_img)
+        if len_hs:
+            hp1, hp2, line_a = line_graph_contrast(img_h, hxs, hys)
+            print('Drawn H')
+        if len_vs:
+            vp1, vp2, line_b = line_graph_contrast(img_h, vxs, vys)
+            print('Drawn V')
+        namespace = 'houghs/hough_{0}{1}_{2}'.format(folder_path[3:], m, img_name_scheme.format(1))
+        if len_hs:
+            print(hp1, hp2)
+        if len_vs:
+            print(vp1, vp2)
+        print(namespace)
+        cv2.imwrite(namespace, img_h)
+    else:
+        #print("MEAS", hys)
+        line_a = HoughLine(x=hxs, data=hys)
+        #print("MEAS", vys)
+        line_b = HoughLine(x=vxs, data=vys)
+    # ------------------------------------------------------------
+    # DEBUG MODULE
+    if debug:
+        print((dimr, dimc))
+        print("imgX(BEFORE):", hs)
+        print("imgX:", list(zip(hys, hxs)))
+        print("imgY(BEFORE):", vs)
+        print("imgY:", list(zip(vxs, vys)))
+        compare_images((sobelx, 'Sobel X'), (sobely, 'Sobel Y'), color_map='gray')
+        test(folder_path, img_name_scheme)
+    # --------------------------------------------------------
+    # DATA RECORDING AND PROCESSING
+    if len_hs >= r_thresh:
+        x_valid = True
+        stdh = std_dev(hxs)
+    if len_vs >= c_thresh:
+        y_valid = True
+        stdv = std_dev(vys)
+    if c == 1:
+        center_x = sum(hxs) / len_hs if x_valid else -1
+        center_y = sum(vys) / len_vs if y_valid else -1
+    else:
+        if x_valid and y_valid:
+            center_x, center_y = HoughLine.intersect(line_a, line_b)
+        else:
+            center_x = sum(hxs) / len_hs if x_valid else -1
+            center_y = sum(vys) / len_vs if y_valid else -1
+    # ---------------------------------------------------------
+    return center_x, center_y, stdh, stdv
+
+
+def center_detect(img_name_scheme, num_sample, sample_int=50, debug=False, gk=9, ks=-1, m=1, p=10, b=1):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair
+    hs: HORIZONTAL SLICE!  vs: VERTICAL SLICE!"""
+    imgr = test_noise_reduce(img_name_scheme, num_sample)[0]
+    dimr = imgr.shape[0]
+    dimc = imgr.shape[1]
+    zw = 0
+    ew = 1
+    # Image Processing
+    gksize = (gk, gk)
+    sigmaX = 0
+    img = cv2.GaussianBlur(imgr, gksize, sigmaX)
+    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=ks)
+    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=ks)
+    # Gathering Data
+    nr = sample_int
+    r_thresh = dimr / (sample_int * 3.0)
+    hs = []
+    vs = []
+    while nr < dimr:
+        data_x = FM(sobelx, FM.HOR, nr)
+        zc_x = zero_crossing(data_x)
+        ec_x = edge_centroid(data_x, FM(img, FM.HOR, nr))
+        nr += sample_int
+        if ec_x == -1:
+            continue
+        else:
+            hs.append((nr - sample_int, zc_x * zw + ec_x * ew))
+    nc = sample_int
+    c_thresh = dimc / (sample_int * 3.0)
+    while nc < dimc:
+        data_y = FM(sobely, FM.VER, nc)
+        zc_y = zero_crossing(data_y)
+        ec_y = edge_centroid(data_y, FM(img, FM.VER, nc))
+        nc += sample_int
+        if ec_y == -1:
+            continue
+        else:
+            vs.append((nc - sample_int, zc_y * zw + ec_y * ew))
+    len_hs = len(hs)
+    len_vs = len(vs)
+    if debug:
+        print((dimr, dimc))
+        print("imgX:", hs)
+        print("imgY:", vs)
+        compare_images((sobelx, 'Sobel X'), (sobely, 'Sobel Y'), color_map='gray')
+        while True:
+            ax = input("Input the axis you wanna debug, h for horizontal, v for vertical, or c to cancel:  ")
+            if ax == 'c':
+                break
+            inds = input("Input the index you wanna debug:  ")
+            while True:
+                try:
+                    index = int(inds)
+                    axis = FM.HOR if ax == 'h' else FM.VER
+                    assert 0 <= index < imgr.shape[1 - axis]
+                    break
+                except ValueError:
+                    inds = input("Invalid input, do it again:  ")
+                except AssertionError:
+                    inds = input("Index Out of Bound, do it again: {0}  ".format(imgr.shape))
+            sobelop = sobelx if axis else sobely
+            quick_plot(FM(sobelop, axis, index).extract_array())
+
+    # ----- Following Modules Handles Hough Line Drawing -----
+    hxs = np.zeros(len_hs)
+    hys = np.zeros(len_hs)
+    for i in range(len_hs):
+        hxs[i] = hs[i][1]
+        hys[i] = hs[i][0]
+    vxs = np.zeros(len_vs)
+    vys = np.zeros(len_vs)
+    for i in range(len_vs):
+        vxs[i] = vs[i][0]
+        vys[i] = vs[i][1]
+
+    hough_img = img_name_scheme.format(1)
+    img_h = cv2.imread(hough_img)
+    if len_hs:
+        hp1, hp2, lina = line_graph_contrast(img_h, hxs, hys)
+        print('Drawn H')
+    if len_vs:
+        vp1, vp2, linb = line_graph_contrast(img_h, vxs, vys)
+        print('Drawn V')
+    namespace = 'houghs/hough_{0}'.format(hough_img[3:])
+    if len_hs:
+        print(hp1, hp2)
+    if len_vs:
+        print(vp1, vp2)
+    print(namespace)
+    cv2.imwrite(namespace, img_h)
+    # --------------------------------------------------------
+    x_invalid = False
+    y_invalid = False
+    if len_hs < r_thresh:
+        x_invalid = True
+    if len_vs < c_thresh:
+        y_invalid = True
+
+    center_x = -1 if x_invalid else sum([d[1] for d in hs]) / len_hs
+    center_y = -1 if y_invalid else sum([d[1] for d in vs]) / len_vs
+    return center_x, center_y
+
+
+def center_detect(img_name_scheme, num_sample, sample_int=50, gk=9, ks=-1, m=0, p=20,
+                       b=1, c=0):
+    """This function takes in a list of images and output x, y [pixel] coordinates of the center of the cross hair
+    hs: HORIZONTAL SLICE!  vs: VERTICAL SLICE!"""
+    imgr = test_noise_reduce(img_name_scheme, num_sample)[0]
+    dimr = imgr.shape[0]
+    dimc = imgr.shape[1]
+    # Image Processing
+    gksize = (gk, gk)
+    sigmaX = 0
+    img = cv2.GaussianBlur(imgr, gksize, sigmaX)
+    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=ks)
+    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=ks)
+    # ------------------------------------------------------------
+    # Parameter Setting
+    METHODS = {0: gaussian_fitting, 1: poly_fitting, 2: edge_centroid,
+               3: zero_crossing, 4: edge_converge_base, 5: edge_converge_extreme}
+    edge_method = METHODS[m]
+    nr = sample_int
+    r_thresh = dimr / (sample_int * 3.0)
+    nc = sample_int
+    c_thresh = dimc / (sample_int * 3.0)
+    # ------------------------------------------------------------
+    # Gathering Data
+    hs = []
+    vs = []
+    while nr < dimr:
+        data_x = FM(sobelx, FM.HOR, nr)
+        if m < 3:
+            ec_x = edge_method(data_x, FM(img, FM.HOR, nr), p)
+        else:
+            ec_x = edge_method(data_x)
+        nr += sample_int
+        if ec_x == -1:
+            continue
+        else:
+            hs.append((nr - sample_int, ec_x))
+
+    while nc < dimc:
+        data_y = FM(sobely, FM.VER, nc)
+        if m < 3:
+            ec_y = edge_method(data_y, FM(img, FM.VER, nc), p)
+        else:
+            ec_y = edge_method(data_y)
+        nc += sample_int
+        if ec_y == -1:
+            continue
+        else:
+            vs.append((nc - sample_int, ec_y))
+    len_hs = len(hs)
+    len_vs = len(vs)
+    # ------------------------------------------------------------
+    # --------------- PRE-CHECK DATA VALUES ----------------------
+    hxs = np.zeros(len_hs)
+    hys = np.zeros(len_hs)
+    for i in range(len_hs):
+        hxs[i] = hs[i][1]
+        hys[i] = hs[i][0]
+    vxs = np.zeros(len_vs)
+    vys = np.zeros(len_vs)
+    for i in range(len_vs):
+        vxs[i] = vs[i][0]
+        vys[i] = vs[i][1]
+    x_valid = False
+    y_valid = False
+    # OUTLIER DETECTION   TODO: OPTIMIZE, THIS IS NAIVE
+    if len_hs >= r_thresh:
+        x_valid = True
+        hys, hxs = reg_pre_debias(hys, hxs)
+        line_a = HoughLine(x=hxs, data=hys)
+    if len_vs >= c_thresh:
+        y_valid = True
+        vxs, vys = reg_pre_debias(vxs, vys)
+        line_b = HoughLine(x=vxs, data=vys)
+    # ------------------------------------------------------------
+    # ----- Following Modules Handles Hough Line Drawing ---------
+    # ------------------------------------------------------------
+    # DATA RECORDING AND PROCESSING
+    if c == 1:
+        center_x = sum(hxs) / len_hs if x_valid else -1
+        center_y = sum(vys) / len_vs if y_valid else -1
+    else:
+        if x_valid and y_valid:
+            center_x, center_y = HoughLine.intersect(line_a, line_b)
+        else:
+            center_x = sum(hxs) / len_hs if x_valid else -1
+            center_y = sum(vys) / len_vs if y_valid else -1
+    # ---------------------------------------------------------
+    return center_x, center_y
+
+
+
+def random_test():
+    data = [27, 21, 22, 21, 21, 18, 41, 69, 83, 62, 38, 16, 21, 20, 18, 17]
+    print(len(data))
+    ids = 0
+    gs = 0
+    for i, d in enumerate(data):
+        if d == 83:
+            print(i)
+        ids += i * d
+        gs += d
+    print(ids / gs)
+
+
+TEST_PIC_TRUTH = [(385, 130),
+                  (385, 130),
+                  (385, 130),
+                  (-2, -2),
+                  (176, 52),
+                  (174, -1),
+                  (158, -1),
+                  (159, -1),
+                  (242, 212),
+                  (278, 215),
+                  (325, 218),
+                  (143, 215),
+                  (53, 219),
+                  (-1, 17),
+                  (138, 19),
+                  (355, 14),
+                  (288, -1),
+                  (369, 180),
+                  (357, 265),
+                  (225, 224),
+                  (-1, 195),
+                  (-1, 59),
+                  (-1, -1),
+                  (-1, 160),
+                  (445, 166)]
+
+
+def read_truth(filename):
+    with open(filename) as truth:
+        TRUTH = {}
+        active = None
+        for line in truth:
+            meas = line.replace(',', ' ').split()
+            if len(meas) == 0:
+                pass
+            elif len(meas) == 1:
+                active = {}
+                TRUTH[meas[0]] = active
+
+            else:
+                tp = (int(meas[1]), int(meas[2]))
+                active[meas[0]] = tp
+    return TRUTH
+
+
+def error_calc(meas, truth):
+    E = 0
+    T = 0
+    F = 0
+    terms = 0
+    #ls = ['img_5', 'img_6', 'img_7', 'img_12', 'img_13', 'img_14', 'img_15', 'img_16', 'img_17', 'img_18', 'img_20', 'img_21', 'img_22', 'img_25']
+    #ms = []
+    #es = []
+    for m, vals in meas.items():
+        """if m in ls:
+            continue"""
+        mx, my = vals
+        tx, ty = truth[m]
+        if tx == -2 or ty == -2:
+            pass
+        elif (mx == -1 and tx != -1) or (my == -1 and ty != -1):
+            F += 1
+            #ms.append(m)
+        elif (mx != -1 and tx == -1) or (my != -1 and ty == -1):
+            T += 1
+        else:
+            dx = mx - tx
+            dy = my - ty
+            E += (dx) ** 2 + (dy) ** 2
+            #es.append((dx, dy))
+            terms += 2
+    # print(ms)
+    # print(es)
+    error = sqrt(E / terms) if terms else float('inf')
+    return {'E': error, 'T': T, 'F': F}
+
+
+TRUTH_FILE = "meas/truth.txt"
+TRUTH = read_truth(TRUTH_FILE)
+
+
+""" =================================
+============= Parameter =============
+=========== Optimization ============
+===================================== """
+
+
+# TODO: OPTIMIZE ON THE THE GAUSSIAN KERNEL PARAMETER. WHAT HAPPENED THAT MAKES THE SMALL KERNEL BETTER???
+def gaussian_kernel_expr(folder, ns):
+    offset = '../'
+    truth_doc = TRUTH[folder]
+    error = {}
+    gs = range(1, 20, 2)
+    Es = []
+    Ts = []
+    Fs = []
+    for g in gs:
+        meas = {}
+        #print(g)
+        for i in range(1, 26):
+            img_name = ns.format(i)
+            imgfile = offset + folder + "/%s_{0}.png" % img_name
+            # if i in [11, 13, 21, 23, 25]:
+            if i == 0:
+                val = center_detect(imgfile, 3, debug=True, gk=g)
+            else:
+                val = center_detect(imgfile, 3, gk=g)
+            #print(str(i), val)
+            meas[img_name] = val
+            # print(str(i), val)
+
+        ed = error_calc(meas, truth_doc)
+        Es.append(ed['E'])
+        Ts.append(ed['T'])
+        Fs.append(ed['F'])
+        error[g] = ed
+    print(error)
+    compare_data_plots((Es, gs, 'Error'), (Ts, gs, 'False Positive'), (Fs, gs, 'False Negative'), suptitle="Gaussian Blur Test")
+
+
+def sobel_kernel_expr(folder, ns):
+    offset = '../'
+    truth_doc = TRUTH[folder]
+    error = {}
+    #ks = [-1]
+    #ks.extend(list(range(3, 8, 2)))
+    ks = range(-1, 8, 2)
+    Es = []
+    Ts = []
+    Fs = []
+    for k in ks:
+        # print(k)
+        meas = {}
+        for i in range(1, 26):
+            img_name = ns.format(i)
+            imgfile = offset + folder + "/%s_{0}.png" % img_name
+            # if i in [11, 13, 21, 23, 25]:
+            if i == 0:
+                val = center_detect(imgfile, 3, debug=True, ks=k)
+            else:
+                val = center_detect(imgfile, 3, ks=k)
+            #print(str(i), val)
+            meas[img_name] = val
+            # print(str(i), val)
+
+        ed = error_calc(meas, truth_doc)
+        Es.append(ed['E'])
+        Ts.append(ed['T'])
+        Fs.append(ed['F'])
+        error[k] = ed
+    print(error)
+    compare_data_plots((Es, ks, 'Error'), (Ts, ks, 'False Positive'), (Fs, ks, 'False Negative'), suptitle="Sobel Kernel Test")
+
+
+def convergence_test(folder, ns):
+    offset = '../'
+    convergence = {}
+    line_stdh = []
+    line_stdv = []
+    variations = []
+    startNP = 59
+    startP = 80
+    endP = 193
+    ms = range(3)
+    fwrite = open('meas/convergence_v1.csv', 'w')
+    cwriter = csv.writer(fwrite)
+    cwriter.writerow(['Image Number', 'Center X', 'Center Y', 'StdDev Horizontal', 'Std Dev Vertical'])
+    for m in ms:
+        cwriter.writerow([str(m)])
+        # Convergence
+        lrh = 0
+        lrhs = 0
+        lrv = 0
+        lrvs = 0
+        # Consistency Cycled
+        pv = 0
+        pvs = 0
+        rcount = 0
+        cvx = np.zeros(4)
+        cvy = np.zeros(4)
+        #print(g)
+        for i in range(startNP, startP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                x, y, stdh, stdv = center_detect_test(fpath, imgfile, 5, debug=False, m=m)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y), str(stdh), str(stdv)])
+                # CONVERGENCE
+                if x != -1:
+                    lrh += stdh ** 2
+                    lrhs += 1
+                if y != -1:
+                    lrv += stdv ** 2
+                    lrvs += 1
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+        for i in range(startP, endP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                x, y, stdh, stdv = center_detect_test(fpath, imgfile, 5, debug=False, m=m)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y), str(stdh), str(stdv)])
+                # CONVERGENCE
+                if x != -1:
+                    lrh += stdh ** 2
+                    lrhs += 1
+                if y != -1:
+                    lrv += stdv ** 2
+                    lrvs += 1
+                # Record x, y, check rcount, refresh CONSISTENCY
+                cvx[rcount] = x
+                cvy[rcount] = y
+                rcount += 1
+                if rcount == 4:
+                    pv += np.var(cvx) + np.var(cvy)
+                    pvs += 1
+                    rcount = 0
+                    cvx = np.zeros(4)
+                    cvy = np.zeros(4)
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+            # print(str(i), val)
+        mselrh = sqrt(lrh / lrhs)
+        mselrv = sqrt(lrv / lrvs)
+        msepv = sqrt(pv / pvs)
+        cvg = {'LineConvergenceH': mselrh if lrhs else float('inf'),
+               'LineConvergenceV': mselrv if lrvs else float('inf'),
+               'PicConsistency': msepv}
+        convergence[m] = cvg
+        line_stdh.append(mselrh)
+        line_stdv.append(mselrv)
+        variations.append(msepv)
+    print(convergence)
+    compare_data_plots((line_stdh, ms, 'Line Convergence Hor'), (line_stdv, ms, 'Line Convergence Ver'),
+                       (variations, ms, 'Reading Consistency'), suptitle="Convergence/Consistency Test")
+    fwrite.close()
+
+
+def convergence_test_final(folder, ns):
+    offset = '../'
+    convergence = {}
+    variations = []
+    startNP = 59
+    startP = 80
+    endP = 193
+    ms = range(3)
+    fwrite = open('meas/convergence_v1.csv', 'w')
+    cwriter = csv.writer(fwrite)
+    cwriter.writerow(['Image Number', 'Center X', 'Center Y', 'StdDev Horizontal', 'Std Dev Vertical'])
+    for m in ms:
+        cwriter.writerow([str(m)])
+        # Consistency Cycled
+        pv = 0
+        pvs = 0
+        rcount = 0
+        cvx = np.zeros(4)
+        cvy = np.zeros(4)
+        # print(g)
+        for i in range(startNP, startP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                x, y = center_detect(fpath + imgfile, 5, m=m)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y)])
+                # CONVERGENCE
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+        for i in range(startP, endP):
+            img_name = ns.format(i)
+            fpath = offset + folder
+            imgfile = "%s_{0}.png" % img_name
+            # FOR NULL ROW OR COLUMN, DO NOT COUNT THE STDDEV
+            try:
+                x, y = center_detect(fpath + imgfile, 5, m=m)
+                # PUT IN CSV
+                cwriter.writerow([str(i), str(x), str(y)])
+                # CONVERGENCE
+                # Record x, y, check rcount, refresh CONSISTENCY
+                cvx[rcount] = x
+                cvy[rcount] = y
+                rcount += 1
+                if rcount == 4:
+                    pv += np.var(cvx) + np.var(cvy)
+                    pvs += 1
+                    rcount = 0
+                    cvx = np.zeros(4)
+                    cvy = np.zeros(4)
+            except AttributeError:
+                print('No {0}'.format(fpath + imgfile))
+                pass
+                # print(str(i), val)
+        msepv = sqrt(pv / pvs)
+        cvg = {'PicConsistency': msepv}
+        convergence[m] = cvg
+        variations.append(msepv)
+    print(convergence)
+    fwrite.close()
+""""""
+
+
+def parameter_convert(method, padding, blur):
+    return method + 2 * padding + blur * 100
+
+
+def main():
+    # TODO: HANDLE THE MAXI MINI LEFT AND RIGHT PROBLEM (2 PEAKS)
+    folder = "../calib4/"
+    img_name = "img_59_{0}.jpg"
+    ns = "img_%d_{0}.png"
+    # print("Center Detection yields: ")
+    # gaussian_kernel_expr('testpic', 'img_{0}')
+    #sobel_kernel_expr('testpic', 'img_{0}')
+    convergence_test('calib4/', 'img_{0}')
+    """for i in range(59, 193):
+        #if i in [29]:
+        try:
+            if i in [133, 134, 157, 158, 159, 160]:
+                print(i)
+                val = center_detect_test(folder, ns % i, 5, debug=True, hough=False)
+                print(str(i), val)
+            #val = center_detect_test(folder, ns % i, 5, hough=False)
+            #print(str(i), val)
+        except AttributeError:
+            print('No image {0}!'.format(i))
+        print('------------------------------------------------------')"""
+    # convergence_test_final('calib4/', 'img_{0}')
+
+
+def case_calc():
+    hori_lower = 53.50 - 0.13
+    hori_upper = 53.50 + 0.13
+    verti_lower = 41.41 - 0.11
+    verti_upper = 41.41 + 0.11
+    height = 3
+    width = 4
+    print('hori_lower: {0}'.format(distance(width / 2, hori_lower / 2)))
+    print('hori_upper: {0}'.format(distance(width / 2, hori_upper / 2)))
+    print('verti_lower: {0}'.format(distance(height / 2, verti_lower / 2)))
+    print('verti_upper: {0}'.format(distance(height / 2, verti_upper / 2)))
+
+
+def distance(h, angle):
+    return 2.54 * (h / tan(radians(angle)))
+
+
+def deviation():
+    serr = 1.017
+    p_to_i = 1 / 166.1861
+    fluc = serr * p_to_i
+    dev = 2 * fluc
+    print(fluc, dev)
+    mm_per_inch = 25.4
+    mfluc, mdev = mm_per_inch * fluc, mm_per_inch * dev
+    print(mfluc, mdev)
+
+    #print(center_detect(folder + img_name, 10, debug=True))
+
+
+
+    #print("Center Detection yields: ")
+    #print(center_detect("../new_test/img_3_{0}.png", 5))
+    #2.5 + 13  = 15.5 / 2 = 7.75
+
+    """plt.figure()
+    plt.plot([1,2 ,3])
+    plt.show()"""
+
+    #print(atan(56 / 200) * 180 / 3.1415926535)
+    #print(atan(134 / 537) * 180 / 3.1415926535)
+
+    #test()
+    #test_canny_detect()
+    #random_test()
+
+
+if __name__ == '__main__':
+    #main()
+    #print(DEGREES)
+    #test_old()
+    #test('../bright/', 'img_193_{0}.png')
+    #deviation()
+    #test_interactive()
+    fourier_expr()
+    #test_record()
+    #test_canny_detect()
+
+
+
+# INSIGHT:
+"""Might be able to solve the problem by auto-thresholding and converting to binary.
+Alternative:
+    1. Mark the maximum and minimum that are certain multitudes of standard deviation above
+
+===========================================
+========= THE FOLLOWING FUNCTIONS =========
+============== ARE DEPRECATED =============
+==========================================-
+
+def OMP(imDims, sparsity, measurements, A):
+    display = None
+    numPixels = 0
+    r = measurements.copy()
+    indices = []
+
+    # Threshold to check error. If error is below this value, stop.
+    THRESHOLD = 0.1
+
+    # For iterating to recover all signal
+    i = 0
+
+    while i < sparsity and np.linalg.norm(r) > THRESHOLD:
+        # Calculate the correlations
+        print('%d - ' % i, end="", flush=True)
+        corrs = A.T.dot(r)
+
+        # Choose highest-correlated pixel location and add to collection
+        # COMPLETE THE LINE BELOW
+        best_index = np.argmax(np.abs(corrs))
+        indices.append(best_index)
+
+        # Build the matrix made up of selected indices so far
+        # COMPLETE THE LINE BELOW
+        Atrunc = A[:, indices]
+
+        # Find orthogonal projection of measurements to subspace
+        # spanned by recovered codewords
+        b = measurements
+        # COMPLETE THE LINE BELOW
+        xhat = np.linalg.lstsq(Atrunc, b)[0]
+
+        # Find component orthogonal to subspace to use for next measurement
+        # COMPLETE THE LINE BELOW
+        r = b - Atrunc.dot(xhat)
+
+        # This is for viewing the recovery process
+        if i % 10 == 0 or i == sparsity - 1 or np.linalg.norm(r) <= THRESHOLD:
+            recovered_signal = np.zeros(numPixels)
+            for j, x in zip(indices, xhat):
+                recovered_signal[j] = x
+            Ihat = recovered_signal.reshape(imDims)
+            plt.title('estimated image')
+            plt.imshow(Ihat, cmap=plt.cm.gray, interpolation='nearest')
+            display.clear_output(wait=True)
+            display.display(plt.gcf())
+
+        i = i + 1
+
+    display.clear_output(wait=True)
+
+    # Fill in the recovered signal
+    recovered_signal = np.zeros(numPixels)
+    for i, x in zip(indices, xhat):
+        recovered_signal[i] = x
+
+    return recovered_signal
+
+if ori == 'h':
+    x = np.array(range(img.shape[1]))
+    y = np.array([img_rec.rel_lumin(img, p, c) for c in x])
+else:
+    x = np.array(range(img.shape[0]))
+    y = np.array([img_rec.rel_lumin(img, r, p) for r in x])
+"""
+
+CALK = 166.1861
diff --git a/img_rec_module/~$log.docx b/img_rec_module/~$log.docx
new file mode 100644
index 0000000..0430bb4
Binary files /dev/null and b/img_rec_module/~$log.docx differ
diff --git a/ip_connected.txt b/ip_connected.txt
new file mode 100644
index 0000000..90edc97
--- /dev/null
+++ b/ip_connected.txt
@@ -0,0 +1,10 @@
+
+192.168.0.0
+192.168.0.1
+192.168.0.2
+192.168.0.3
+192.168.0.1
+192.168.0.13
+10.142.172.115
+10.142.172.116
+10.142.172.118
diff --git a/ip_test.sh b/ip_test.sh
new file mode 100755
index 0000000..c90402d
--- /dev/null
+++ b/ip_test.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+touch ip_connected.txt  
+for ip in 192.168.3.{0..15}
+  do   
+    s=`ping -c 1 $ip | grep 'icmp'`
+    check=`expr "$s" : '64 bytes'`
+    if [ "$check" -ge 5 ]
+    then 
+      echo $ip
+      echo $ip >> ip_connected.txt
+    fi
+  done
+
+
+
diff --git a/socket_module/rpi_socket.py b/socket_module/rpi_socket.py
new file mode 100644
index 0000000..985ab52
--- /dev/null
+++ b/socket_module/rpi_socket.py
@@ -0,0 +1,77 @@
+import socket
+
+
+class SteadySocket:
+
+    MAXMSGLEN = 128
+    EOFChar = b'!'
+
+    def __init__(self, sockt=None):
+        if sockt is None:
+            self.sock = socket.socket()
+        else:
+            self.sock = sockt
+
+    def connect(self, host, port):
+        self.sock.connect((host, port))
+
+    def send(self, data, flags=0):
+        if data.find(self.EOFChar == -1):
+            data += self.EOFChar
+        data_sent = 0
+        tot_len = len(data)
+        while data_sent < tot_len:
+            try:
+                sent = self.sock.send(data[data_sent:])
+                assert sent != 0
+                data_sent += sent
+            except:
+                raise RuntimeError("Connection Lost!")
+
+
+    def recv(self):
+        data_recv = []
+        bytes_recv = 0
+        count = 0
+        while bytes_recv < self.MAXMSGLEN:
+            msg = self.sock.recv(self.MAXMSGLEN - bytes_recv)
+            end = msg.find(self.EOFChar)
+            if end != -1:
+                data_recv.append(msg[:end])
+                break
+            data_recv.append(msg)
+            bytes_recv += len(msg)
+        print(count)
+        return b''.join(data_recv)
+
+    def close(self):
+        self.sock.close()
+
+
+def main():
+    IP = '192.168.3.4'
+    port = 8000
+
+    rpisock = SteadySocket()
+    connected = False
+    while True:
+        if not connected:
+            try:
+                rpisock.connect(IP, port)
+                connected = True
+                msg = input("type a message\n")
+                rpisock.send(bytes(msg, 'utf-8'))
+                data = rpisock.recv()
+                print(data)
+            except:
+                pass
+        else:
+            print("already connected")
+            msg = input("type a message\n")
+            rpisock.send(bytes(msg, 'utf-8'))
+            data = rpisock.recv()
+            print(data)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/socket_module/server_socket.py b/socket_module/server_socket.py
new file mode 100644
index 0000000..49fddb7
--- /dev/null
+++ b/socket_module/server_socket.py
@@ -0,0 +1,85 @@
+import socket
+
+
+class SteadySocket:
+
+    MAXMSGLEN = 128
+    EOFChar = b'!'
+
+    def __init__(self, sockt=None):
+        if sockt is None:
+            self.sock = socket.socket()
+        else:
+            self.sock = sockt
+
+    def connect(self, host, port):
+        self.sock.connect((host, port))
+
+    def send(self, data, flags=0):
+        if data.find(self.EOFChar == -1):
+            data += self.EOFChar
+        data_sent = 0
+        tot_len = len(data)
+        while data_sent < tot_len:
+            try:
+                sent = self.sock.send(data[data_sent:])
+                assert sent != 0
+                data_sent += sent
+            except:
+                raise RuntimeError("Connection Lost!")
+
+
+    def recv(self):
+        data_recv = []
+        bytes_recv = 0
+        count = 0
+        while bytes_recv < self.MAXMSGLEN:
+            msg = self.sock.recv(self.MAXMSGLEN - bytes_recv)
+            end = msg.find(self.EOFChar)
+            if end != -1:
+                data_recv.append(msg[:end])
+                break
+            data_recv.append(msg)
+            bytes_recv += len(msg)
+        print(count)
+        return b''.join(data_recv)
+
+    def close(self):
+        self.sock.close()
+
+
+def main():
+    ssock = socket.socket()
+    IP = "192.168.3.4"
+    port = 8000
+    ssock.bind((IP, port))
+    print("Now connected to " + IP + ":" + str(port))
+    ssock.listen(1)
+
+    conn = None
+    addr = None
+    sconn = None
+    while True:
+        if conn is None:
+            conn, addr = ssock.accept()
+            sconn = SteadySocket(conn)
+            print(addr)
+        else:
+            data = sconn.recv()
+            if data:
+                print(data)
+            if data == b"hi":
+                sconn.send(b"how are you!")
+            elif data == b'c':
+                sconn.close()
+                break
+            else:
+                sconn.send(b"It's good huh?!")
+            inp = input("value?\n")
+            if inp == 'e':
+                sconn.close()
+                break
+
+
+if __name__ == "__main__":
+    main()
diff --git a/test.sh b/test.sh
new file mode 100755
index 0000000..2af3756
--- /dev/null
+++ b/test.sh
@@ -0,0 +1,11 @@
+#!/bin/bash  
+for ip in 192.168.0.{0..255}
+  do   
+    k=`ping -c 1 $ip | grep 'icmp'`
+    if [ "$k" > 5 ]
+    then echo $ip
+    fi
+  done
+
+
+
diff --git a/testpic/gray.txt b/testpic/gray.txt
new file mode 100644
index 0000000..484b6f1
--- /dev/null
+++ b/testpic/gray.txt
@@ -0,0 +1,150 @@
+../testpic/img_1_1.png
+(205.21480189651163, 300.9431873419749)
+../testpic/img_1_2.png
+(205.63638921004812, 300.7155445882073)
+../testpic/img_1_3.png
+(205.96730122081806, 300.9461855154958)
+../testpic/img_2_1.png
+(206.294705409402, 300.3672244728102)
+../testpic/img_2_2.png
+(206.89687869797643, 300.62957126862716)
+../testpic/img_2_3.png
+(206.03173965420586, 300.5376752697736)
+../testpic/img_3_1.png
+(200.84548583250404, 305.31250145945256)
+../testpic/img_3_2.png
+(200.8508617308734, 305.61891026952617)
+../testpic/img_3_3.png
+(201.11703696912699, 305.1685157754102)
+../testpic/img_4_1.png
+(208.61251671569937, 288.58145034959693)
+../testpic/img_4_2.png
+(206.7195131495757, 288.50535061792084)
+../testpic/img_4_3.png
+(203.9452244851535, 290.2052810653526)
+../testpic/img_5_1.png
+(205.40664153558757, 287.7623893599146)
+../testpic/img_5_2.png
+(204.9789788601677, 287.8715606914188)
+../testpic/img_5_3.png
+(211.7910055674488, 286.3685028850654)
+../testpic/img_6_1.png
+(217.0322629237196, 285.0367800566558)
+../testpic/img_6_2.png
+(214.88535502987406, 284.8828179469582)
+../testpic/img_6_3.png
+(215.76065051862108, 284.9932621778891)
+../testpic/img_7_1.png
+(217.37677626205667, 288.239680625496)
+../testpic/img_7_2.png
+(217.09738081018097, 288.7231164372432)
+../testpic/img_7_3.png
+(217.83758218152386, 288.4375218250161)
+../testpic/img_8_1.png
+(219.0315518772011, 286.608607067481)
+../testpic/img_8_2.png
+(219.2035209718933, 286.97894202924783)
+../testpic/img_8_3.png
+(218.95224028991848, 286.90592653956014)
+../testpic/img_9_1.png
+(217.64093013710516, 294.7222458158094)
+../testpic/img_9_2.png
+(217.54430830417542, 294.7696235004352)
+../testpic/img_9_3.png
+(217.5159267972094, 294.98671052542886)
+../testpic/img_10_1.png
+(219.01834291880346, 294.1889201606354)
+../testpic/img_10_2.png
+(220.11165575567446, 292.63926584110834)
+../testpic/img_10_3.png
+(222.8811302926324, 296.1182964266989)
+../testpic/img_11_1.png
+(219.69710306595923, 298.06577009042303)
+../testpic/img_11_2.png
+(219.77079226305807, 297.6972685864174)
+../testpic/img_11_3.png
+(219.72552517656615, 297.73284519911294)
+../testpic/img_12_1.png
+(218.0796259620671, 291.8496429144712)
+../testpic/img_12_2.png
+(218.11775264893598, 292.46013540240716)
+../testpic/img_12_3.png
+(218.6122214239511, 292.80915989964365)
+../testpic/img_13_1.png
+(218.4344154245471, 294.0818247758554)
+../testpic/img_13_2.png
+(218.17258930976334, 294.3701968572963)
+../testpic/img_13_3.png
+(217.4153885964598, 294.2661432761171)
+../testpic/img_14_1.png
+(208.30469342400863, 287.3509032893072)
+../testpic/img_14_2.png
+(206.8162120965767, 287.5793092707929)
+../testpic/img_14_3.png
+(207.9252711084856, 287.83037136055475)
+../testpic/img_15_1.png
+(206.81576078703893, 291.4096632718139)
+../testpic/img_15_2.png
+(207.79208272428122, 292.72957625568705)
+../testpic/img_15_3.png
+(207.54221099284027, 292.68348653085314)
+../testpic/img_16_1.png
+(210.11284670279136, 300.0715380971377)
+../testpic/img_16_2.png
+(209.52751389228695, 300.1969423451505)
+../testpic/img_16_3.png
+(209.40369008536234, 301.07097191446053)
+../testpic/img_17_1.png
+(218.93320872864754, 295.37952155647326)
+../testpic/img_17_2.png
+(218.9696659360369, 294.07045075136824)
+../testpic/img_17_3.png
+(219.12438095323773, 296.2608060055451)
+../testpic/img_18_1.png
+(213.41879050983465, 304.63372960145017)
+../testpic/img_18_2.png
+(213.13174989642516, 305.4446278908936)
+../testpic/img_18_3.png
+(217.90089511643342, 300.40415858460165)
+../testpic/img_19_1.png
+(226.7269214785641, 305.75699324219954)
+../testpic/img_19_2.png
+(226.34747351012416, 306.3059651266557)
+../testpic/img_19_3.png
+(226.28901165084417, 306.0126329735141)
+../testpic/img_20_1.png
+(219.65087039784123, 297.8033969960556)
+../testpic/img_20_2.png
+(219.2187889577143, 289.4087735896516)
+../testpic/img_20_3.png
+(219.9922950570292, 299.85077919874084)
+../testpic/img_21_1.png
+(219.63674348907952, 299.79682720488404)
+../testpic/img_21_2.png
+(219.2064998236914, 300.2619177584171)
+../testpic/img_21_3.png
+(218.8902855903587, 299.87321197209826)
+../testpic/img_22_1.png
+(205.54559205763047, 296.3571238704225)
+../testpic/img_22_2.png
+(209.48089510272197, 295.58371082468915)
+../testpic/img_22_3.png
+(222.26898057258506, 292.40915820234034)
+../testpic/img_23_1.png
+(217.34067539050167, 296.13485132000216)
+../testpic/img_23_2.png
+(217.7811059263327, 296.2544105728407)
+../testpic/img_23_3.png
+(217.51753530982677, 300.09854738205064)
+../testpic/img_24_1.png
+(218.07393217622632, 300.2997811875231)
+../testpic/img_24_2.png
+(211.2929535239784, 302.1473413252606)
+../testpic/img_24_3.png
+(208.50949807153992, 301.43124519988316)
+../testpic/img_25_1.png
+(210.19723323812133, 306.7113598893541)
+../testpic/img_25_2.png
+(207.70165150262, 307.3175263979337)
+../testpic/img_25_3.png
+(208.36875361369817, 303.0375261028656)
\ No newline at end of file
diff --git a/testpic/gray_large.txt b/testpic/gray_large.txt
new file mode 100644
index 0000000..798fcc5
--- /dev/null
+++ b/testpic/gray_large.txt
@@ -0,0 +1,150 @@
+../testpic/img_1_1.png
+(2060.00857421875, 3020.9592122395834)
+../testpic/img_1_2.png
+(2055.92142578125, 3006.5083984375)
+../testpic/img_1_3.png
+(2036.7973665364584, 2976.037431640625)
+../testpic/img_2_1.png
+(1994.5098600260417, 2904.026982421875)
+../testpic/img_2_2.png
+(1965.238154296875, 2855.570888671875)
+../testpic/img_2_3.png
+(1968.0356998697916, 2870.765810546875)
+../testpic/img_3_1.png
+(2631.8355729166665, 4000.7486295572917)
+../testpic/img_3_2.png
+(2572.3222233072916, 3914.09978515625)
+../testpic/img_3_3.png
+(2542.20052734375, 3857.4532194010417)
+../testpic/img_4_1.png
+(1955.0836393229167, 2704.5398860677083)
+../testpic/img_4_2.png
+(1977.513046875, 2759.889892578125)
+../testpic/img_4_3.png
+(1972.871962890625, 2807.31193359375)
+../testpic/img_5_1.png
+(1836.8890104166667, 2573.371370442708)
+../testpic/img_5_2.png
+(1804.554326171875, 2534.3080208333336)
+../testpic/img_5_3.png
+(1748.3726692708333, 2364.02326171875)
+../testpic/img_6_1.png
+(1833.804638671875, 2408.40583984375)
+../testpic/img_6_2.png
+(1794.8656022135417, 2379.531031901042)
+../testpic/img_6_3.png
+(1825.3344010416668, 2411.0420703125)
+../testpic/img_7_1.png
+(1956.28767578125, 2594.02013671875)
+../testpic/img_7_2.png
+(1957.131474609375, 2602.8370149739585)
+../testpic/img_7_3.png
+(1929.12623046875, 2554.3452311197916)
+../testpic/img_8_1.png
+(1921.8942057291667, 2514.8496484375)
+../testpic/img_8_2.png
+(1852.7399837239584, 2425.5876822916666)
+../testpic/img_8_3.png
+(1805.0545279947917, 2365.268522135417)
+../testpic/img_9_1.png
+(2637.453131510417, 3571.55296875)
+../testpic/img_9_2.png
+(2643.413323567708, 3581.7896419270833)
+../testpic/img_9_3.png
+(2626.104762369792, 3561.4219921875)
+../testpic/img_10_1.png
+(2563.569778645833, 3443.4276822916668)
+../testpic/img_10_2.png
+(2463.009303385417, 3274.5800390625)
+../testpic/img_10_3.png
+(2770.323935546875, 3680.632825520833)
+../testpic/img_11_1.png
+(2631.7345768229166, 3570.506767578125)
+../testpic/img_11_2.png
+(2536.99267578125, 3436.5612565104166)
+../testpic/img_11_3.png
+(2515.003251953125, 3407.8838736979164)
+../testpic/img_12_1.png
+(2538.5078971354164, 3397.2115462239585)
+../testpic/img_12_2.png
+(2556.02197265625, 3427.2062825520834)
+../testpic/img_12_3.png
+(2584.2490852864585, 3461.3426399739583)
+../testpic/img_13_1.png
+(2576.20923828125, 3468.3926171875)
+../testpic/img_13_2.png
+(2568.1598307291665, 3465.099430338542)
+../testpic/img_13_3.png
+(2507.983466796875, 3394.4911946614584)
+../testpic/img_14_1.png
+(1680.2696028645832, 2317.8881868489584)
+../testpic/img_14_2.png
+(1668.6466536458333, 2320.2641959635416)
+../testpic/img_14_3.png
+(1652.1645930989582, 2287.086826171875)
+../testpic/img_15_1.png
+(1884.2619075520834, 2654.9820279947917)
+../testpic/img_15_2.png
+(1908.3619466145833, 2688.427666015625)
+../testpic/img_15_3.png
+(1897.6742317708333, 2676.16841796875)
+../testpic/img_16_1.png
+(1975.1264192708334, 2820.76623046875)
+../testpic/img_16_2.png
+(2018.3365266927083, 2891.7369498697917)
+../testpic/img_16_3.png
+(1993.0766471354166, 2865.553720703125)
+../testpic/img_17_1.png
+(2075.0250065104165, 2799.5747981770833)
+../testpic/img_17_2.png
+(2049.05498046875, 2751.82646484375)
+../testpic/img_17_3.png
+(2061.5160416666668, 2787.213369140625)
+../testpic/img_18_1.png
+(2536.3010026041666, 3620.313056640625)
+../testpic/img_18_2.png
+(2582.2423958333334, 3700.678421223958)
+../testpic/img_18_3.png
+(2264.6719498697917, 3122.138948567708)
+../testpic/img_19_1.png
+(2962.3082682291665, 3994.8783463541668)
+../testpic/img_19_2.png
+(2908.7927083333334, 3936.3397526041667)
+../testpic/img_19_3.png
+(2914.5825813802085, 3941.415817057292)
+../testpic/img_20_1.png
+(2733.4142252604165, 3705.9723014322917)
+../testpic/img_20_2.png
+(2198.65669921875, 2902.627744140625)
+../testpic/img_20_3.png
+(2607.9814453125, 3554.693896484375)
+../testpic/img_21_1.png
+(2701.4876171875, 3687.44046875)
+../testpic/img_21_2.png
+(2739.979921875, 3753.1351790364583)
+../testpic/img_21_3.png
+(2667.824397786458, 3654.8404557291665)
+../testpic/img_22_1.png
+(1895.4133854166666, 2732.820751953125)
+../testpic/img_22_2.png
+(1825.6778255208333, 2576.085166015625)
+../testpic/img_22_3.png
+(1777.4883658854167, 2338.400416666667)
+../testpic/img_23_1.png
+(2012.2138346354166, 2741.7170930989582)
+../testpic/img_23_2.png
+(2002.2059016927083, 2723.66294921875)
+../testpic/img_23_3.png
+(2379.87974609375, 3283.4063411458333)
+../testpic/img_24_1.png
+(2104.8507291666665, 2898.495052083333)
+../testpic/img_24_2.png
+(2229.5265169270833, 3188.2062239583333)
+../testpic/img_24_3.png
+(2258.8827604166668, 3265.548330078125)
+../testpic/img_25_1.png
+(2449.8590169270833, 3574.7358756510416)
+../testpic/img_25_2.png
+(2467.9384733072916, 3651.5874641927085)
+../testpic/img_25_3.png
+(2274.6644173177083, 3308.1192154947917)
\ No newline at end of file
diff --git a/testpic/rel_lumin.txt b/testpic/rel_lumin.txt
new file mode 100644
index 0000000..2151f3f
--- /dev/null
+++ b/testpic/rel_lumin.txt
@@ -0,0 +1,150 @@
+../testpic/img_1_1.png
+(206.04350300185553, 299.0540341998243)
+../testpic/img_1_2.png
+(206.4661876998688, 298.87869157975604)
+../testpic/img_1_3.png
+(206.539726576666, 299.3445028302692)
+../testpic/img_2_1.png
+(206.62574823965744, 298.364015333678)
+../testpic/img_2_2.png
+(207.15543076436487, 298.4790493172048)
+../testpic/img_2_3.png
+(206.42267550723432, 298.5091752433472)
+../testpic/img_3_1.png
+(201.97471732113223, 303.4166202623412)
+../testpic/img_3_2.png
+(201.8755908196089, 303.45606722221186)
+../testpic/img_3_3.png
+(202.04564115266442, 303.3166549610932)
+../testpic/img_4_1.png
+(208.76926943629095, 287.51138587988)
+../testpic/img_4_2.png
+(207.0018867758149, 287.656914170687)
+../testpic/img_4_3.png
+(204.44109724453457, 289.1925625342303)
+../testpic/img_5_1.png
+(205.77664478329535, 286.68530176937907)
+../testpic/img_5_2.png
+(205.6780512374802, 287.1001660889027)
+../testpic/img_5_3.png
+(211.82925866772135, 285.40585819828635)
+../testpic/img_6_1.png
+(216.1515787432525, 284.84917334617035)
+../testpic/img_6_2.png
+(214.52290017997893, 284.11390800672206)
+../testpic/img_6_3.png
+(215.3919833543449, 284.6765849105273)
+../testpic/img_7_1.png
+(217.22741430360932, 287.87844718888476)
+../testpic/img_7_2.png
+(217.00325638001345, 288.10503306376864)
+../testpic/img_7_3.png
+(217.29538500272267, 288.30482564333306)
+../testpic/img_8_1.png
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+../testpic/img_8_2.png
+(218.3788797701322, 286.60001461430176)
+../testpic/img_8_3.png
+(218.2059111736144, 286.1172453033881)
+../testpic/img_9_1.png
+(217.08171004297984, 293.66398575147895)
+../testpic/img_9_2.png
+(216.98183698735136, 293.8811851742873)
+../testpic/img_9_3.png
+(216.9994269999031, 293.90584411028874)
+../testpic/img_10_1.png
+(218.4178268983069, 292.73218415360174)
+../testpic/img_10_2.png
+(219.4433578980413, 291.3678911350465)
+../testpic/img_10_3.png
+(222.06517427244975, 294.84198971641104)
+../testpic/img_11_1.png
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+../testpic/img_11_2.png
+(219.15309034371694, 295.94924921149686)
+../testpic/img_11_3.png
+(218.92205064090555, 296.1111383515795)
+../testpic/img_12_1.png
+(217.31650922649175, 290.8263281533628)
+../testpic/img_12_2.png
+(217.43124337282256, 291.65437011556145)
+../testpic/img_12_3.png
+(217.94999730338108, 291.89445361132294)
+../testpic/img_13_1.png
+(217.7314953458256, 293.0030826815401)
+../testpic/img_13_2.png
+(217.66352922869572, 293.251933930113)
+../testpic/img_13_3.png
+(216.92528878379466, 293.0805113563251)
+../testpic/img_14_1.png
+(207.7580659152881, 286.60700596561094)
+../testpic/img_14_2.png
+(206.88115334127554, 286.2262269101382)
+../testpic/img_14_3.png
+(207.57769680927507, 287.02800557197895)
+../testpic/img_15_1.png
+(207.06218511814652, 290.6672864462294)
+../testpic/img_15_2.png
+(207.98098233293044, 291.99506585433636)
+../testpic/img_15_3.png
+(207.77505722866593, 292.006797659142)
+../testpic/img_16_1.png
+(210.40074913599636, 298.3601622520285)
+../testpic/img_16_2.png
+(209.78375083010266, 298.7840695237666)
+../testpic/img_16_3.png
+(209.641959443913, 299.44390843956813)
+../testpic/img_17_1.png
+(218.26568835261116, 294.7008282336055)
+../testpic/img_17_2.png
+(218.37299204011276, 293.384018922485)
+../testpic/img_17_3.png
+(218.66148315527113, 295.3027772591837)
+../testpic/img_18_1.png
+(213.38377251339844, 302.96352678722457)
+../testpic/img_18_2.png
+(213.07871090196872, 303.819533098452)
+../testpic/img_18_3.png
+(217.33723440473466, 299.1504826785637)
+../testpic/img_19_1.png
+(225.64353383755207, 304.42557061445035)
+../testpic/img_19_2.png
+(225.41128117140823, 304.8032542035086)
+../testpic/img_19_3.png
+(225.24899337338888, 304.69323068961785)
+../testpic/img_20_1.png
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+../testpic/img_20_2.png
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diff --git a/testpic/rel_lumin_large.txt b/testpic/rel_lumin_large.txt
new file mode 100644
index 0000000..30d34fa
--- /dev/null
+++ b/testpic/rel_lumin_large.txt
@@ -0,0 +1,150 @@
+../testpic/img_1_1.png
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diff --git a/timer.c b/timer.c
new file mode 100644
index 0000000..e6b123a
--- /dev/null
+++ b/timer.c
@@ -0,0 +1,101 @@
+// Global Variables
+int timer = 0;
+int on = 0;
+int minInterval = 2;
+
+
+void setup() {
+  // Setup pins for pin activated interrupts: p8, p7
+  pinMode(3,INPUT);//digitalWrite(8,HIGH);
+  pinMode(2,INPUT);//digitalWrite(7,HIGH);
+  
+  // Setup pin for timer activated interrupts: p13
+  pinMode(13,OUTPUT);//`******digitalWrite(13,LOW);
+  pinMode(12,OUTPUT); // Open the relay to shut of RPi
+
+  
+  // Setup the timer interrupt configuration for 1 Hz
+  /*
+  cli();
+  TCCR1A = 0;// set entire TCCR1A register to 0
+  TCCR1B = 0;// same for TCCR1B
+  TCNT1  = 0;//initialize counter value to 0
+  // set compare match register for 1hz increments
+  OCR1A = 15624;// = (16*10^6) / (1*1024) - 1 (must be <65536)
+  // Set CS12 and CS10 bits for 1024 prescaler
+  TCCR1B |= (1 << CS12) | (1 << CS10);  
+  // enable timer compare interrupt
+  TIMSK1 |= (1 << OCIE1A);
+  // Normally would turn on, but this is left for the p8 interrupt to do:
+  // turn on CTC mode
+  // TCCR1B |= (1 << WGM12);
+  //
+  sei();
+  */
+
+  // Setup p3 and p2 interrupt code
+  attachInterrupt(digitalPinToInterrupt(2),ISR_p2,FALLING);
+  attachInterrupt(digitalPinToInterrupt(3),ISR_p3,RISING);
+
+  // Turn on the RPi pin to start the flow of energy to the RPi so it 
+  // can be configured to run the system and eventually activate p8 and
+  // p9 interrupt ISR's which will begin the total system operation.
+  digitalWrite(13,HIGH); // this flips a relay energizing the RPi
+  digitalWrite(12,LOW);
+
+  Serial.begin(9600);
+  Serial.println("Running...");
+}
+
+// timer1 interrupt service routine
+ISR(TIMER1_COMPA_vect){
+  // turn on the RPi, as the time interval has elapsed
+  if(timer == 60*minInterval){
+  //if(timer == 10){
+    digitalWrite(12,LOW);
+    digitalWrite(13,HIGH);
+    timer = 1;
+    Serial.println(timer);
+  } else {
+    timer += 1;
+    Serial.println(timer);
+  }
+}
+
+// pin2 interrupt service routine
+void ISR_p2(){
+  // turn off RPi, as the signal pin has lifted saying it is shutdown
+  if (timer > 5){
+       digitalWrite(13,LOW);
+       digitalWrite(12,HIGH);
+       Serial.println("pin 2 lifted");
+  }
+}
+
+// pin3 interrup service routine
+void ISR_p3(){
+  // start the timer1, as the signal pin has been lifted saying the clocks are synchronized
+  cli();
+  TCCR1A = 0;// set entire TCCR1A register to 0
+  TCCR1B = 0;// same for TCCR1B
+  TCNT1  = 0;//initialize counter value to 0
+  // set compare match register for 1hz increments
+  OCR1A = 15624;// = (16*10^6) / (1*1024) - 1 (must be <65536)
+  // Set CS12 and CS10 bits for 1024 prescaler
+  TCCR1B |= (1 << CS12) | (1 << CS10);  
+  // enable timer compare interrupt
+  TIMSK1 |= (1 << OCIE1A);
+  TCCR1B |= (1 << WGM12); 
+  sei();
+  Serial.println("pin 3 lifted");
+  detachInterrupt(3);
+}
+
+
+
+void loop() {
+  // put your main code here, to run repeatedly:
+  //delay(1000);
+  //Serial.println("yup, again");
+
+}