From 2e90dbfe4bfb38e4fe38d8e7d619b56905fda62a Mon Sep 17 00:00:00 2001 From: Hannah Heesen Date: Wed, 18 Mar 2026 12:34:38 +0700 Subject: [PATCH 1/8] Extra pumpingtest examples --- .../confined1_oude_korendijk.ipynb | 166 +++++- .../04pumpingtests/confined2_grindley.ipynb | 20 +- .../04pumpingtests/confined3_sioux.ipynb | 12 + .../04pumpingtests/confined4_nevada.ipynb | 526 ++++++++++++++++++ .../04pumpingtests/figs/Dawsonville.png | Bin 0 -> 24690 bytes docs/transient/04pumpingtests/figs/Moench.png | Bin 0 -> 31379 bytes .../04pumpingtests/figs/Vennebulten.png | Bin 0 -> 20566 bytes docs/transient/04pumpingtests/index.rst | 18 +- .../04pumpingtests/leaky1_dalem.ipynb | 14 +- .../04pumpingtests/leaky2_hardixveld.ipynb | 12 + .../04pumpingtests/leaky3_texashill.ipynb | 12 + .../04pumpingtests/slug1_pratt_county.ipynb | 129 ++++- .../04pumpingtests/slug2_multiwell.ipynb | 178 +++++- .../04pumpingtests/slug3_falling_head.ipynb | 240 +++++++- .../04pumpingtests/slug4_dawsonville.ipynb | 409 ++++++++++++++ .../04pumpingtests/unconfined2_moench.ipynb | 420 ++++++++++++++ 16 files changed, 2089 insertions(+), 67 deletions(-) create mode 100644 docs/transient/04pumpingtests/confined4_nevada.ipynb create mode 100644 docs/transient/04pumpingtests/figs/Dawsonville.png create mode 100644 docs/transient/04pumpingtests/figs/Moench.png create mode 100644 docs/transient/04pumpingtests/figs/Vennebulten.png create mode 100644 docs/transient/04pumpingtests/slug4_dawsonville.ipynb create mode 100644 docs/transient/04pumpingtests/unconfined2_moench.ipynb diff --git a/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb b/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb index 555473c..e0b771b 100644 --- a/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb +++ b/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -105,9 +105,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "# timflow model\n", "ml = tft.ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)\n", @@ -125,9 +134,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".................................\n", + "Fit succeeded.\n" + ] + } + ], "source": [ "# unknown parameters: kaq, Saq\n", "cal = tft.Calibrate(ml)\n", @@ -140,9 +158,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
optimal
kaq_0_066.089354
Saq_0_00.000025
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 66.089354\n", + "Saq_0_0 0.000025" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.050 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.3f} m\")" @@ -150,9 +222,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "hm1 = ml.head(ro1, 0, to1)\n", "hm2 = ml.head(ro2, 0, to2)\n", @@ -187,9 +270,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]RMSE [m]
timflow66.092.54e-050.050
MLU63.513.58e-050.833
K&dR55.711.70e-041.293
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", @@ -241,6 +369,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/confined2_grindley.ipynb b/docs/transient/04pumpingtests/confined2_grindley.ipynb index b1a6713..805dec5 100644 --- a/docs/transient/04pumpingtests/confined2_grindley.ipynb +++ b/docs/transient/04pumpingtests/confined2_grindley.ipynb @@ -302,10 +302,16 @@ "tags": [] }, "source": [ - "* Carlson, F. and Randall, J. (2012), MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems, Ground Water 50(4):504–510\n", "* Duffield, G.M. (2007), AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", "* Walton, W.C. (1962), Selected analytical methods for well and aquifer evaluation, Illinois, department of Registration & Education.bulletin 49." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -313,6 +319,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/confined3_sioux.ipynb b/docs/transient/04pumpingtests/confined3_sioux.ipynb index 6a5530c..d14cd69 100644 --- a/docs/transient/04pumpingtests/confined3_sioux.ipynb +++ b/docs/transient/04pumpingtests/confined3_sioux.ipynb @@ -299,6 +299,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/confined4_nevada.ipynb b/docs/transient/04pumpingtests/confined4_nevada.ipynb new file mode 100644 index 0000000..0e8310b --- /dev/null +++ b/docs/transient/04pumpingtests/confined4_nevada.ipynb @@ -0,0 +1,526 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Confined Aquifer Test - Nevada" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import timflow.transient as tft\n", + "\n", + "plt.rcParams[\"figure.figsize\"] = (5, 3) # default figure size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction and Conceptual Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example is taken from Kruseman and de Ridder (1990). It is based on a pumping test conducted in a fractured tertiary volcanic aquifer near Yucca Mountains, Nevada, US. \n", + "\n", + "The borehole extends to 1219 m depth and penetrates a 400 m thick sequence of fractured volcanic rocks with water entry points. At every entry point, the head is more or less the same, so they have good hydraulic connection. The water table is about 470 m below land surface, which indicates confined conditions. Drawdown data was monitored at the well, named ***UE-25b#1*** and at an observation well, named ***UE-25a#1***, 110 m away. Three pumping tests were conducted at the site and will be the object of our investigation. \n", + "\n", + "Although conventional Darcy flow does not apply to flow in discrete fractures, the fracture network at this site is sufficiently pervasive to justify the application of Darcy’s law at the aquifer scale. Groundwater flow to the well occurs through fractures, while the matrix exchanges water with the fracture network. For implementation in `timflow`, the fractured aquifer system is approximated using a ModelMaq configuration. This is achieved by representing the matrix as an upper aquifer layer, separated by a 1 m thick aquitard from a lower aquifer layer representing the fracture network." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "scrolled": true, + "tags": [ + "hide-input" + ] + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# time and drawdown of pumped well UE-25b#1\n", + "data1 = np.loadtxt(\"data/double-porosity-pumpingwell.txt\", skiprows=1)\n", + "t1 = data1[:, 0] # days\n", + "h1 = data1[:, 1] # m\n", + "\n", + "# time and drawdown of observation well UE-25a#1\n", + "data2 = np.loadtxt(\"data/double-porosity-110m.txt\", skiprows=1)\n", + "t2 = data2[:, 0]\n", + "h2 = data2[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Paramaters and model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# known parameters\n", + "H = 400 # aquifer thickness in m\n", + "Q = 3093.12 # constant pumping rate in m^3/d\n", + "r = 110 # distance from pumping well to observation well in m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the `timflow` model, we will adopt the parameters for the first layer from the results obtained from Kruseman and de Ridder (1970). " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# parameters calculated by Kruseman and de Ridder (1970)\n", + "km = 0.1 / H # hydraulic conductivity of matrix\n", + "Sm = 3.75e-4 # specific storage of matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml = tft.ModelMaq(\n", + " kaq=[km, 1],\n", + " z=[0, -400, -401, -801],\n", + " c=5,\n", + " Saq=[Sm, 1e-3],\n", + " Sll=0,\n", + " topboundary=\"conf\",\n", + " tmin=1e-5,\n", + " tmax=3,\n", + ")\n", + "w = tft.Well(ml, xw=0, yw=0, rw=0.11, rc=0, tsandQ=[0, Q], layers=1)\n", + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimate aquifer parameters\n", + "The aquifer parameters are estimated using head observations at both wells." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".........................................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 198\n", + " # data points = 138\n", + " # variables = 4\n", + " chi-square = 5.38947309\n", + " reduced chi-square = 0.04021995\n", + " Akaike info crit = -439.507237\n", + " Bayesian info crit = -427.798222\n", + "[[Variables]]\n", + " kaq_1_1: 0.87713604 +/- 0.00687861 (0.78%) (init = 10)\n", + " Saq_1_1: 5.0658e-06 +/- 4.9878e-07 (9.85%) (init = 0.0001)\n", + " c_1_1: 12.9124741 +/- 1.56932257 (12.15%) (init = 10)\n", + " rc: 0.10567818 +/- 0.00318208 (3.01%) (init = 0)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq_1_1, c_1_1) = +0.8555\n", + " C(kaq_1_1, Saq_1_1) = -0.7271\n", + " C(Saq_1_1, c_1_1) = -0.5393\n", + " C(Saq_1_1, rc) = -0.4022\n" + ] + } + ], + "source": [ + "cal = tft.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq\", initial=10, layers=1)\n", + "cal.set_parameter(name=\"Saq\", initial=1e-4, pmin=0, layers=1)\n", + "cal.set_parameter(name=\"c\", initial=10, layers=1)\n", + "cal.set_parameter_by_reference(name=\"rc\", parameter=w.rc, initial=0)\n", + "\n", + "cal.seriesinwell(name=\"UE-25b#1\", element=w, t=t1, h=h1)\n", + "cal.series(name=\"UE-25a#1\", x=r, y=0, t=t2, h=h2, layer=1)\n", + "cal.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
layersoptimalstdperc_stdpminpmaxinitialinhomsparray
kaq_1_11.00.8771366.878610e-030.784212-infinf10.0000None[[0.8771360363656927]]
Saq_1_11.00.0000054.987801e-079.8459950.0inf0.0001None[[5.0658166983463815e-06]]
c_1_11.012.9124741.569323e+0012.153539-infinf10.0000None[[12.912474111111717]]
rcNaN0.1056783.182079e-033.011103-infinf0.0000NaN[[0.1056781836698406]]
\n", + "
" + ], + "text/plain": [ + " layers optimal std perc_std pmin pmax initial \\\n", + "kaq_1_1 1.0 0.877136 6.878610e-03 0.784212 -inf inf 10.0000 \n", + "Saq_1_1 1.0 0.000005 4.987801e-07 9.845995 0.0 inf 0.0001 \n", + "c_1_1 1.0 12.912474 1.569323e+00 12.153539 -inf inf 10.0000 \n", + "rc NaN 0.105678 3.182079e-03 3.011103 -inf inf 0.0000 \n", + "\n", + " inhoms parray \n", + "kaq_1_1 None [[0.8771360363656927]] \n", + "Saq_1_1 None [[5.0658166983463815e-06]] \n", + "c_1_1 None [[12.912474111111717]] \n", + "rc NaN [[0.1056781836698406]] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.19762123575324378\n" + ] + } + ], + "source": [ + "display(cal.parameters)\n", + "print(\"RMSE:\", cal.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1 = w.headinside(t1)\n", + "hm2 = ml.head(r, 0, t2)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, -h1, \".\", label=\"obs UE-25b#1\")\n", + "plt.semilogx(t1, -hm1[-1], label=\"timflow UE-25b#1\")\n", + "plt.semilogx(t2, -h2, \".\", label=\"obs UE-25a#1\")\n", + "plt.semilogx(t2, -hm2[-1], label=\"timflow UE-25a#1\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.title(\"Model Results\")\n", + "plt.legend()\n", + "plt.grid();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparison of results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The performance of `timflow` was evaluated by comparison to the results obtained from the software AQTESOLV (Duffield, 2007), MLU (Hemker & Post, 2014), and Kruseman and de Ridder (1990), here abbreviated to K&dR.\n", + "\n", + "The problem is solved in AQTESOLV using a double-porosity analytical solution proposed by Moench (1984). The MLU (Hemker & Post, 2014) solution used a similar approach to our `timflow` model by simulating the matrix as a very-low transmissivity aquifer on top of the fractured aquifer and separated by a zero-storage aquitard. However, the calibrated MLU model only fits the observations of well UE-25b#1. When the observations of well UE-25a#1 are included, the fit is not as good as initially thought. Kruseman et al. (1990) solved the problem using a graphical method, where the transmissivity was calculated as one aquifer and the storativity was separated between matrix and fractures. \n", + "\n", + "Overall, `timflow` showed similar results to MLU but with a better fit since both observation wells are taken into account with the `timflow` model. While the AQTESOLV obtained the best fit using Moench's analytical solution." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 km [m/d]Sm [1/m]kf [m/d]Sf [1/m]c [d]rc [m]RMSE [m]
timflow0.003.75e-040.885.07e-0612.910.110.198
AQTESOLV0.155.48e-040.941.95e-03-0.11-
MLU0.003.75e-040.848.05e-065.20--
K&dR0.833.75e-040.834.00e-06---
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\n", + " \"km [m/d]\",\n", + " \"Sm [1/m]\",\n", + " \"kf [m/d]\",\n", + " \"Sf [1/m]\",\n", + " \"c [d]\",\n", + " \"rc [m]\",\n", + " \"RMSE [m]\",\n", + " ],\n", + " index=[\"timflow\", \"AQTESOLV\", \"MLU\", \"K&dR\"],\n", + ")\n", + "\n", + "t.loc[\"timflow\"] = np.concatenate(\n", + " [np.array([0.00025, 3.750e-04]), cal.parameters[\"optimal\"].values, [cal.rmse()]]\n", + ")\n", + "t.loc[\"AQTESOLV\"] = [0.1513, 5.4800e-4, 0.9366, 1.9508e-3, \"-\", 0.11, \"-\"]\n", + "t.loc[\"MLU\"] = [0.00025, 3.750e-04, 0.8351125, 8.05e-6, 5.2035, \"-\", \"-\"]\n", + "t.loc[\"K&dR\"] = [0.8325, 3.750e-4, 0.8325, 4.000e-6, \"-\", \"-\", \"-\"]\n", + "\n", + "t_formatted = t.style.format(\n", + " {\n", + " \"km [m/d]\": \"{:.2f}\",\n", + " \"Sm [1/m]\": \"{:.2e}\",\n", + " \"kf [m/d]\": \"{:.2f}\",\n", + " \"Sf [1/m]\": \"{:.2e}\",\n", + " \"c [d]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.2f}\",\n", + " \"rc [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.2f}\",\n", + " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",\n", + " }\n", + ")\n", + "t_formatted" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reference\n", + "\n", + "* Duffield, G.M. (2007), AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Hemker, K. en Post V. (2014) MLU for Windows: well flow modeling in multilayer aquifer systems; MLU User's guide. https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmicrofem.com%2Fdownload%2Fmlu-user.pdf&data=05%7C02%7CMark.Bakker%40tudelft.nl%7Cad7f16364d2d4fd55dbf08de73832eaa%7C096e524d692940308cd38ab42de0887b%7C0%7C0%7C639075204580287861%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=OBoe8seXZUfoat89Dfr4g6lF%2Bn1FdtXqtp%2F18BMXCn0%3D&reserved=0\n", + "* Kruseman, G.P. and De Ridder, N.A. (1990), Analysis and evaluation of pumping test data, 2nd edn. ILRI Publ. 47, ILRI, Wageningen, The Netherlands\n", + "* Moench, A.F. (1984), Double-Porosity Models for a Fissured Groundwater Reservoir With Fracture Skin, Water Resour. Res., 20( 7), 831– 846, doi:10.1029/WR020i007p00831." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/transient/04pumpingtests/figs/Dawsonville.png b/docs/transient/04pumpingtests/figs/Dawsonville.png new file mode 100644 index 0000000000000000000000000000000000000000..b5464b26cfd331dfb43793d9661a5c975b9af4e8 GIT binary patch literal 24690 zcmeFZbySsow=TTIphQqY5d=Xx1OX{27a$!f-AYOeB3+N9NOwz@fV4DKw#Z_AuKE}AuN1b-paz@g{eLQA?_O=VXN?TkjT5E znyXqq1n+HFr-{)MG;y42@!-1*_b~A!>w*xJoFWRAB9DX9G1%ySx}^vQ7f`$dQNvZ|{t86U(VL53q*XB8E+?rFOw4}s z@%J;-K|gnS4RPKS@zb)X2uZZ}2rU}T>Bl?MX#BnF)fHE6?;kSs2)_G_I86*ISEam% zkdJydyRkQ@L43W5n_oGOx=gMqs+2>fomH}(QuQx3rBLe)CUvKsXnkCsO*Ric;uh=n zU;1lPBeawLJlAU81?v!BpT*cKXRD#nee+7fOX$1BQ~|M*EGL34WaRFQ^;11lX5A1v zw!fGs?GZo4o3z)uSyWm(@LO=dwYRF>y+)PcLrkOb)XP5<-*2L$LuS_RTPy1oF8@2f z?)``+RwbRVLKt)$sS?X*UOjM*LUHZ0kM2UJMzR$&Iq?gcMV$2?H{YmOzd4%uK-qp?s6|^*pn?f#R2s(zV zw=o^{7&}*;vA>I*-*5BlijImu?D*@%S^1OnNh1-JYk;np2-~M?kK31Gj?~aNZX-m6 z&Myd&%$>)tyHHwp^%Da3=Xtb;bUqhe>fHPJ7eUZDk=xe^+)Ccx2>UBnhc@$)&HJky zZ#XS)ymHt5$-#|&*S$sO!pONNtXC4;=-04Xh44H)8}SIAc)z+W+(=sJsS-pob^fC8 zRe91d3~FJdI2xhQ;`=9=q}g5zNd|N3b9Aq6Rv>VM%icdWx^_V5fPRM+GySvL$E&6n zi*)3;F#4{L1d}q>Z}lDSx6QSdb^X0ZPPi@@f&qyXbolQu^E||n49}R8 zLOLIDOMQ~Edq{O{+eh}RWwP5RFBJ(s5k5Im>Kf|D;s#Wl^zKv?W)#b`@sy*K)we^N z9_is)@u^CWA- z>si;dn#6r`zdlw=Q%HHGFz?k}%%(W_h)01(*uKchZ{y;$7y;8apI^cgkKfWA(izg} zDtKhe=|3&+wdsAdpt`_~Bg~$DvGd7`v|XoWm&da<2^zz zqF#8IOc^hAap$bRx?&d4Lb?(c!s9(V((iG^!R?uPTldmm-M2Zmp|04jcvHdd@ZI6D zgQvr?!|oR50n;(n@vVdEgQu5xFHu~&cq#W%8P2*FQ$0bwrq{7ozW+P_=kGXg*bwux zDY0K+ZRaw-Ut@yge95J1(5++DMxfiwCc{2z<}i4fvxrOTUd}xdHt)7utrur1+q{`O ziDZe8ewX}6q_6daM%SC_2dH&3zhTB^u4GD7C06;SZdSBVc3@;?5pO@l&o!-Cjr=Kock1Ka@4VLxUHg}9!c#hM%7P|(Wa-Nc$Q#&`}Hsh8h)KC}h z?))(I{l$lU4{`r$`%CmQKZT{ZPUB8c92^3`IOa^EtIPw(IOJb4k?5w_{p&;+6{IMVGZKCxzClqYrXC9 z!T)O0pgWl|8T%{a`T^5Oxh3Ccfh6vU1IkHJd%wF({VH)adwbWuQO8k>UF-6_=gaK7 z%99j#seP;4Ev-Z<q1|xatkrT?`>ffe z{$BlKsm`mk+2#Yq&g`@t2j#{5yK4M3-!0nQOFT+mn7Gmv-(_NPD!*%>DHp8N6_Z@B z?XsxuNBCCs8yDv%)68L?A-W&jpI)q_XUmXF*->AY%9Uc29JiP;uh|Q4Qx4a59ttVe z|Ja&j+M(2{G||5}JW_UxPWf&J3;8fvVYM~7u2@q*dTjuEI8XpZj1XX%Q)s5ET<0*RN7bOFRlORQOZzMR@*ht*I8|Is6A-El;ZW)Im(&m zkc{&Y*N$u3K}ESlN>=fFy~~8lyt~hN=eB^hHs%;+{ankORGST*FP~e+?Oc0CznkV* zWRyjcp?Onzlexz3EpOL9^mYvV7*OnNxTk4`UXqfEzo0S3(-+=Ny&SMeCUDcPxU3;9 zW74r@b(g${U&&fy+HMf@i*GW1qU=m!oItqE!upGYBljw)!yq9iWdf-j!UBU7|bA>p8%jwtK z!|)#E>imRCBFp9O#-reg#m55{W8$AEs+%hJ=add|XUnY3J4RHkuRE7bC3IK6J&f5U zap5>F&|J99FR;MRyQcH`%>&($0$M@+(=C^(Ig0&f-#pqyHt!6JHPATIvIuJJ*&Rvr zhel_76J$KqIBcC7w5_!}Vq+zIaQ{SO>!>F`Ry(uy$7%P9{_3kATgS@8`372%RgYb* ze=;wcomNKK;K-FKwP(st9Z-@f55a9SG7hUDEd+V&Sg=Dufg>Q36X~i zc5zFiu9l?R&U>e2YT4K53X~fz-_#||4y6r7BYY_<8`Df6{L>?zLPFHquu?Eis6bBd z##5ivMmyGX@8pg)SQP~65-Q?XIjjuKZX{fm#MD{C1$I9-0|v#qWMO+?Rw#9NW` zxP}hnf@9>hwZo&8J);XQOU8+Tf+5?7J6gMA)g`On4h=OqV+moPexvz)6{v{0PpJDm z0-@oaijF{hLX*HnAS^G2kRcF1v8ebFh&n=X2beBn&=#Dke0%;J0+IK>dx3nR9$UHd z;ZA#ddqqo3Y;0v^WkW-QiROKTke1D$X=%R=g7i+b^v>=sIk%-yPq94RmSgR%W8%>i zttXyE&Hjx0dGu?(9s`S`Wft0EVq&=_SO`|i`EzgYKX_32dU;5i1|Q2a!D&MxutAsm z+2PJIe0dG&Q9qH8kf7Fk6%jO$zdBN0gnEpQgTn!~_TCrQ;{jJsWYXu)pL6mmnw{Ej z*^a!N?@m);;^g76hYt@{KBXLNUqDO=OX0HC>aopl?z2U|OMgM`wB}y!FP2|k&J!$- zLGk_j_x@aUAg#LQM;KiL1IpZ!SCM?%y-zoO2A<3XcF({1#)yuf zyCQ+h8aOyO=i4S>X6;WJ7+F?O9;A)sJqmAr%%Td0*AJO4@vbk}aEU>5kB7*Z__xaX4 zWPvSj(xf7Z_9LjJA~Gj;$6afWY|s#40jcPO3Wn?{DJj_Haf&%gg!VIU2^ont9wOW% zVYizt_BW{cpcs3E@y$BV59p7J{-w!N?H+=i>F;1Fhn#oqCXQgtr zrdjC44?uhlq&uGJhXxo%$cZ-c9u~b4@yeYe+q| z=vxn76L4EQI5-#?iDO*F@+>tSe3Eu>4)GQSLZEMHDL=z6EG&Hf{CS>^=!Art*#t)q zyd?R#CfdXg#>WW6W|q1hV^-ajUS9r^Jn?r1i|hI2+S%(Gy_dT)EQ!Q4-y$$7U!_LySiR(Of^|@GnLH^eSB8GvphVj z*@BLccP@s9Y4mlcr1s3eLB|z1Tq^PvL0&PgIb5k!S5s3-#6y_=m5M$++ny>G9Up(( z;Cj@Mm6atzc@WC$U% zKK-IWwSjX1wkJpSGCf#vYek92OA+ekS!&J*1gowzZnF+3d@Pqm`|iTu7qxYDzuBPc z{2|Pai>SL|H2=_k%48`i8Xcj74=ZSD#ktejdLHo+Q_Vr1bLKl)j6DLe^H2)6x#`a( z0&oe>Jc*;x3@j2BGd~@B-|u@VsRR@2(~vl}Cko1ahdU6$9b8~?f;I~TV2bOMep_GG zAmablIMWTL%fv1;>-D6|l+@frxT(Wg)HyjhjJg~NLI@$7kFvN-YIcQB#-=bqa^}Xs z*#B>v?*F(O|L2{<|LrH}JWTE~a&&amJUdu##Kp(w+W1@DL3uhN&_QrgI3%1SuBXR? zrkvrD_c=I5tXi16jEegeTaxa^w)IzBv5cyy;HCKbB{9zG+c`w9q@m&L*RNmK*484J z)h~Mni&NdW-2c(s02GuM_EZSt9xH3-@bKccgIoe%77IB!xpu8f^>1I&Fqx&e-FmiH z)KVEemA0!y$O~xa>ZKO@bMgO}uHN?s`@!Ij-9m2hSW$&GVNjUY>>F(6xYTN6T=jp{ zGykf@B!V2}$ms@aoDZiyZ%nte<&U`>E+c)UX{?ui6d3nsv!YzQd?Cv1bar(;{#k50 z?p(&uM~`)OU0W67`UjN)Bw5vVzj}DSYyoPYg1hs8&LGkFJSfC@)LM%B*~oS zrL?ov!e=7eCf6Itxgw=yWQZj%XMmBMak`U4^ptLYiit1moo+og6+se)DBS~(!t#lU zXZsy{KYm2*D<*c(dVnK6AY^olI9mkA)oSG)4vMeC7PV$}^zpI{lP@Vmw6(QyjFsGY zaM@^Md3rkHsI9H7J=C_6TIB*t9%$S~-5c4*7k0;^{uu>?Blp9^D6dj@q`$~g z$fhA|$;eD3wQj1?o){S+O(HU(b1p0llJ`n&5V$T*hrIe$z62-S=E|Typ|41*Cr@Ry zvEK?a0-@8DB<$@Zplxew%W;zs3EK+-2=>-j1Ql2;4n-4)`?Su__Vy|S+b{n7c^h@( zv*|jZEx&g6po9tQC(7?cN~V38_&Bggh%`bumutQvxs2iJnmZd7fuF$~PJcEl8&z^@ z^2MhWIN8&s_}ZteJvuffn<+{xd6$;^{{50V`2+6K3L(@uJqm^C!ZgQLa#O?t$6gPc z9jLR^;tKon$%KEMe5smT^8(4-EFUsz42uD0`_U6LcZ}@mn*YTfskd1?GiZt6OKK)- zKAKX&4&THquD^H(z`QFSqWkxX^wW`ebvE<4Ce}SvLYgr z{9Z8e`$1v1kV#8X)uNdp!qGkYCP`N5qpv^xo>#U#odVjZIt015E=$M`=g3^D z!_U5!v?bS$L=qR-%+3}`v}%lq9AlU9P~ zQuDvA{Bw7+OeTg&EaCbNn*q&?fQ|%y*r~nAK=_cu5}Xk&g_54Lne=`UPKw*m7v_om zNy>{zke|k(P9*(%mZHK*OYfNZlXYAiw)>P9vD^0NL#U-NroH5mKAzqoOZLAvU2#}G z6T_NJPnOd+4_)kCw+z%tQq*T6&>j2J;-h+Db6Ltp$zksr@I5w z&7P8Hm{--IBX00QX$T=kxMUhwP#O;(5y}YRwpP^9Sc|5WZ#z-a9CMd7T++tY)?(yS zSI`#sR%s09oK{$H`KM2i|9$POG5OQcjd|%1~X?&lx`>ZzitxTS`*22&-S8l~=&RTHpO}g}@-wszYMF6z?{A@GFI$#wa93~6j=?ea$ffqv zi0{mZGxBsPRp+Av$@m?H&B}-+J@5{Y)_-(#a@f35 zrnZ7JiAbBS$-IQJ_c_jX?df4{e0)4UK7Qd^PvXfI=yZQqS1^kLTlCSU;OQnlt?bd! zte`8HVJCrcd^tXFMJK;lPJVqxCl8zL?dzKtJeY~Kh&TsNJot()he0uQibav0p1xHg z3BKp`^4mu#1KO^~?eP!Vu1Cpc?5>O+9QoRY?zHrozYYfoWZrpCsSX-@`CcY?*p_Sfoh#U4GX zIUHS0?|EUJxjbCvSUmQ0&A10_V ziD`SAa+3Ic>y^q@Dk>_1v<|d>8JneQZ8Pz-Dkx0mh*OB1Gy_K zHEuCu5^;Bc_RrC%clj0o*DOz=ojE;~hu#K>FRmbdXR zq-A9V*1zIIz!5C&G{c@azqt6oej}A4tV((Q74MD9uWfC$;Tdv*$FHcay)ZK3w)|Na zbKgYEWnZ5};PTzeo*V33CsDCKTd4>6a_u=u5ptP@-fV(A{m5x2v}H?ok3zXjOYRS@ zvU-`~RFv3vf_*0ZljOxQCN}rCUiLQgB7Ibt(s9#%H9Ws33<{9yDuVAT!VrGYtXxCc zvduW&h#eOl-Nn(yDwkdN+-u*!BC8Bspm6RMVZZlKg-uTSi1%H2kVq^V1W71;{9!dfR3JY-fy(8)t)%tfeYMjdXxSbD*Yn=4-^o)!e z7q|1YYqhjj*6oyX)k@ZyIqu)L=Su+bbfSG_W24U&-9Y9jt^K3Bn-Y~RFD)&7$qDWu zv8W1e(z}F?4k>cY5o~UYy$i?9_^2qAT9;#5fptv9xpg}-`>Ct;M|;z!Rp*EZg|8{- z(LAsiGY(aC^~e^XI}R-ZH%;4#a?^(%A^yBc@{M?s0({yh8 z)b$vLV*F@^3&%#DggeTFx&_CKwlJG(%r0f$s{e!P9V;B_b}oI*zJ5!05Tq9Elfnu- z!t9(JDWYZrvij;~9(~Qyxl98J!iCuJqx1tC@w6wU4mUIi|LN>bAL@bE%O+uXW!d`9LCVXGfO$Tu&dztQ20khW##bfrsgNhiM5IP8GM&@Kkdfz z<@Lhb@sP*UBrQ0<6~Ulkiqt@&r*=|3c(FD#;`wHZ_pR^>$Is_5T36r+XBm{ zPKB!=6T&-#g(pgG{TQ{&MrC1NlfGV{i!3Ufv_?aPB4$}$p4PG4B?XAm0% z(c}q}yhFt1C{+z!qfvh0C{Fm9m6nbwh;V8H>jUXg#?3Kx1{Y=J$Twt{$~=+Ooz)lX zYNnhr<>@#qGWwRw#_!@PjLU>GYgQvE5{g$>KCcjj^|%eZQ+VA*@=*Ru4fxHq17Z;zR}ERtp#ufoA%Gv z)^|6F%tF0Cn&8(N=g*T8NwyAu=bn;qIVmyolcK;g_DA$&9{;ix^+J(!=8WNUr?@vvejc=V?_Rqy!3db1d)A zCf1&w98Rl)tIIh3)B#5K3_jWi)NM2%AV80e*jMBP$#${#{*`1N}2 zo~SULOb-|?T-Uw&;NW0p^-=&~z-0n7Cy0m9NO(otWq;bAgzKcG_NWCy$TIzw2xV>O z`(%NJY|+=X>?Qy)ia7LX(yTk#PdruixQQ^`@o;SubHx=> zl!I`G>yOvG1^K{}j4UN*WEjr2$M4wR7$L=)4ub%C@WO28xit?43kc*`9sT4LG4#hg z7y8vL?lZC9;Ytab%^|ua+EZLmP~cQlM&^37esbbcudXK?d^5@kgw`xpzHsmZ zuXYayp^WDH1%3A3hd5uAu4mqi_Q0#JulG`$*ACZP3|vbcS4 zd;+kmaa+v&1*G1Cpdnji?Yzgt8-UPE_gFu%q5`|Six)SZ9w!v=Y(DL~EuH!8mLx{O zntTX9m>N#Z68p^=^3%PBhJafwdTauTs}tVjL`Xm&DFA%i*>R{IL#-F!yhxhRU<+bW z(xth%8#itQwNU`7*Cj@7W^C**>Cg4`G|iY0%QJ#Wwa{@Fw7I9he`mkL)dc{-1o#Yj z6ePfVhp^c)GBOaCNZqmPkQ>9>oI5sx2q}8d$6b*(Gz{ z>dMKlQ5z$+clY8SD>EOhuvzR^VJfwnmLxWYUvzbKarw98DT6<)eua zdH(A=iNz60YRC8d$LE%o6x7vDK%pF#3K{}Pc_@c1uK9kI;Tqk7q?(hH^8~hb>+l$G zD=mP;@P4hHo~%x*gGdItY@J1MX^~nXxevASciqv+J^`eum!hCr^UzU1IRL}+E$cty z;7)uANCW?EzGl4c+03P1U0MRE7?uZ;8YLan-rcl}V8_HAmcWid;I+{S_Y=AIqYzm8u_)Bwh?%G}BClpg{fp>7^V@Yv znQXR>(ClZZTYgEBfBnkhoXS-9{1yt)Scmm&Ucqv6jNZI$T^P-N+^YJ#9wobubeo~N z%nI$aomZ}lXMQ7)%zctVYa66Oiu&y)R(RbKGcuf1GVucwRT#y5LC2q5)J$0aF)=&< zfv}|u!i(;a6T@|*RVYU`(RzblLFLqnkfWqjEKQG-y@4#a`3*$lxqBfH-8z9sL;9fh zH#bl=QXiT_U|e`!pdxYAz`y`2gPic&*Y18KV>}*?Ae+os7@h_n0LM0ULm+u1wO3Cob$<2E z%`^C<>MmcH!^+?A31rf$v6!AnuXkeIw#w6d4<)Fs#iZll;^qhxzEv#6)7|a=A4up2 zKbKq2LS5nc-CaAr@%?t&Qu9#;4+yf5?ii3g?^#nUzijpV|nEk z1PB{mA#zuW%Fe)n?BpcUhdWM~6kjGRYpem#K|xClv28;+X<`Jea;Wl;a}tlBW}zNt z(g@x^E#hy?r1>Q((~X;k)T6q(x)th`--^J>KXY>Arp$Rlh z6eN*7lN3jJ%@>1$L#Aavn{+EBu}2FQ@lVP-YXd27NAsiu7y?pG)fi6pz~MHwo%M9psruX$$buG)UY?E2xcyU=Yz z`{mGt1{U0pfGBaUgHcz!L)e&7$MmY4)S8Zt382J?F)m4T_LH;9Q#p zSH^3#$udJmD{b=u@Wgm|ur*&(BT)as#>S@a12f|NyVpxUKv+R^tZNt|8BviGwn$Jd z8ss&dNy6co&t$3iKN`J49SUTz> z#G*a^el8|yN|isypcU>lD8i*i;dF-^aX9R)O$d8mAyhGyXYMiGfs_@HNSpTi0~${y zWv;)CAXA}=wZ|%++uGia7jRa?D1aaw#!Vu79>Fckant!^e-_)259tE|ixW#07ZG=c zmAHe>V<{;q6O$Z1rDF|%xU)sf1qS;1sITjD#of8SegVNPI`Pi%_3PJqdQ{_dHPfuP}#6+gN1Tt57q4WsJeKI#*icW%;AeUeaO+EKDSE9?FX6t*%>Pk* zCEncp=VNL}UEu_z4?xxNJl5YWZz2$ikEC!VzlYf%-0-`)&-+EeU@eKxTwfj_*-X#t zAHx(_)6uDQcZd+gmX9m{<^t6D{wc?xu18H2|6V#e(LI>RPo^_Jr2IbZ@83bZGSkx1 zAl@d+ak+ax(F6o-MSKYMkn00$93{DhS2ZqA~=c9k~l!S<8#4>6&t5AuK8X)tdf36Q)N)3*~oX zwTOkfC)AEROR!}4NDfX;^WoCA--`j~O!cc-^#2z8oAlY`#Ds6o}79 zKW2kkBqGiSCC9t6TwH6JAs|O^0>xLTOt;7J=yKyA+!#{QV7*Y|^fdxmJlh8B#WVH+ zYE23z=wJpndfxn*+do$8Y>n%DLAn3zLj}yxjj{6|`w~K?(%aqLJv77+s3;g2({Uf- zbX%(Bg@q6jr5o+OB9w}Gtcbd4l*WL%2z(1hrw)e_iA?JlvgvGRDKD`>(L?#YBR0`J z{adh8urod=2{cUlTLK@magtsHv61+DTf2VCuY2pCGbvz7p`oEjjQh+|Hab}${Ej=Y zOqdimNl41fh7HaZ;0~N)Wx~v3EQUd0n6W4|Gc%+2%-Okm|4eY*Tj_{VTOun05*Ff^ z5ldpHAORsHoPhVMJ59>4J?>uIC4^8olq^He#wk*~RY!TY^qw|nym(<|7T%DZDAhK5^VY5K2O+O*Y?^QwFVBMG zWl+9>_-Ta_TaekrUyW}V$hpzw%^NaOve(3HUM2+QPw2TD3zYQ}!H0H6_+SD9!yYWbn870I6!K?rFo}%T9&lrlv*<14aE{H*CKFhV^|jlx~~O z#!0F6x88i=J!8h8*5piYh`w*V|1-D0SL^R}{r-=&M|s7cSpVD$us|in#l@wi&ANRZ zX*1UT0RhdoBq}Q^ro0HftSN)V!x=9lEO}63J2aFn_j2l?1T`oUeo>kHA4PI+{mswO z>6)7}n!sErj5eJ7?hikKc(73n-is&x{m!@Io;3&45Zidk;C=#fks+61|vKavA zo&c-I|Iq_p-oHNo?ClLpe}VK#N=gEv12zSK+rXJooqUV+vL1)43ee_~JW=9SN9B|* zBMXbwpFee2Z$QT9^C$%casoUUm$3HP>_+3e5U?f;hJZFgb@jJD&t|>p59liT8(;!` zzS+;50a7JG`7CpdvZu2%s7VjvUokOnCW(xmSWYuum!mx>#6h*vEC-z+kbd!_xB*{; zp~X3#>Vt(Gqz}{+HMMU52=H0Q-Q=T`y{IN;)392{^i zHbwV*vDz(KTq7H z889OI?e|ZJSL{L6pn%C1&9oL_#4$8DNZ%1@G6lDX>q-QI92C2?3n!@SbMft07T_Yj z+Ojg`h)j$bQQzdQ%!*_$FE1TdqZ0xf!j0uKyt}!@aO^=gEq0i=01rAYnF2M*UB(&@ zNb-t2N6%H-|KGrdd4|PyZcJ>rm5)zWo z=_gD?vC+}N*6rKR3!((@}DJCz>Y&*1BBt}PLUO>y{BfXOGgx>k0Lizq%a8iI` zUO)pQ3pFj-dLkr1Ui(Q78w^!it3Q6IaK=JyX~I%Ps|N4O>h5*>j%l;4eWwjhnrpss zWsgtGweppFiUBOK7gpfMNsN_D>&<1=$Uh*ckMDr|Lj5v%M$J`c{%LRH7*0MuQonDM z!9G|wd_{VBU>Xa=Ylp{Pt8{o61opdb7k|;BQ-_|)qU!#!Z7zENbHEQT91;g0FgQAH zQ{xIAePjYOXrEk_QmzM`Vm^yvU)Mlf0E$-=*QK5i2iC|RznXXb2q^CMEi$s5eW#f! zhVfO68d7}&0~$c{$Q2Zq!GueuDg1yD1N6vNq{2kwYI0wO{X!UK#KapIT(jfl#5_Rw zUwG+>&+vbvzw%&g}WTjjmzY(1`E7&XIWSK_1Yr|KKZJx5QO zLrH|6B)dqqEt4;x194)ZrEfL5yieqqa^{1|g!V({oHsQZ$M0h{&)bN4mDyEm3CAmz zpP;L28wyiKwHZ9kQjn04Yj3^dxSx&lP=Pmu{d&{j7X{<)JJXdpUR*KSA#%a{rBf}p z6yJWxrX3D#4R*(RCJoxR$}HvxXv^2>G`sDZb$^S-_AtTyP{GHZa{Sw2sLgidBdgyB zFsSe`ZLqjV%45Y7DC;PZRqg%q^Rg`|D^t(g0TxnJkw$Ldz`%f^3gar&+c*;i!3Ky^ zZ5=aI_9P~Gc%(F&WY{H2tZJJ61?#d>NrfA6&!}eB_;#SZ=lv!9VJ(Ggm2=%fAib4 zhXEwyHZ;cCo={uN*bp+8nFbBv(astg8Umcu_SaWTVupBGv6L=wDsizqjW##iCPPgr z<7Hbk)`m>-?~l~qH|fvp8FM{#t|w7c!&D%D+W#zg$Wq~?YhTxc zZ+_SP1XYiXpw7cEi<$j+_Go_-`-4hfqX!M?l^hyxG`&Yp#u68RHfp;%24-J}6;_x< z0N9i>RbuG``{s^qS?G6KWZ5jQp!{WDUrA7PnL@sx@myr>(R9^p)$W+%#!v7w3^bya zK~FPe+Z<)74W8m&zPz=w+%s(Yz-Ous` za^-c`4FHs{pZLzds(^&Ltc>fL@8v63h#CnMOhz88Ru!#v0!*vs1ptx)cy$Tp+_Z*t6cd7i`fd(DYF%Grr06PO$it zhOit)ux!TkE&2Ln_Gsye?el&Gx(xw#d@Gy#GoylvIIAVg26`*Nsd?jpw>HO0PGz8s}tXN%UJjf1V7bBBw7< zX=BW`(1g;T^q_cAr@Cy>9AV

uWJiequohVz?t?jJs#lV=O69sOOr(`CHG@r z{A@oJ%D!5I58LFW7`2_TexX0&895@c8L*D%*+=t2XhJxX+$fb z7QrpTvmeS+OP^zSBBA2(D5!<1`DdZXSTy{-eZWGvYkZs>`n&G|tP32Nv&mL8G%x)eY7hdrS$1K{+mBLgwhf6L=b2@l1wp)`?I?iji z_Fh_liyA1b;nY_3Z-J`W+%t7fLauFkB!$6nEa0ddnb_sZb}$Yj zTT9>0mf+Dr`=KT;dVYZAKG-nq80k;nAroeDwwRA8$8D1ujVF}xR zbFp&@H-Rg>z9w8~Tc zFrLzTlow}lZ}lrNmZt()ddH>KtzF4@`eARZaW4LnhKzwl#qePD#AdSxIJ4l8%UM1+ z`5nA>k_dd4eu@#T)ks5K!l*h?8MErxYL8nqXbMPW(iSM7i>N{`s{Tfz-<84z^GLjDda<*WWXb>$A4zhURSQL?jQ=N)jM5~) z(*6aK@pn1~7{`C17ypG`{1Y>j`^@ zBX$meE){Re=C5})P81~k8})k@f7T_1LUhhhKWtsVvVTLIe`7s=qe8lWBS(J_*MA^M zNO&wn`D*Iw(*+B&Pd3ZJ4@?K7LXK`#k5vynMXAk%mk^&wQJ|ZdSziYC#{1N3fM5F# zs{erjzX}Y(j1F!W5at5zvWM%aPBPm@! z>k5-%piB^m59O`?|}nEVcfql zTdUPq*5?BmUYMG~3D=Pt(2LO;&HmeCq455*aiU=0-+BXm3cpGJDyf{L;WFGLT;^di z+}Wx5`En6JLjTKv3}IRPyJHNH0sD@)goG=X-T-Y7N;Gf)0}|Nyb^Fj$qoeb_x>_?~ zd}L%~bTnR?rbT)8l>E=GxH$s;0A+8$wZu;f)&z%!fs;{U5?Ma7x?2lH9kRd%c>%z( z`uf5lP@0B{-xYYu=Zgw_=-&Z-b{m1hg>0gL^U6p$rQ8@?nEv9k&Kkg0RhAQ9pdn2U z$$sx%!Hb@BsNl?Z$Yu1fO|I`dx$b$p0*?krfk48z?E&pgB%G!yHBR=>Iz!Cj9r3${ z@skr^7&hmo-N4tp@ml3p`m8scHkxR+nRx-B$quKx<~v7Xap}?}h>?%siF53+fR91S zX23>_qPFteZ|D){|Mh1JlP>iO078v-B92nZMKfU6^4hDOf5R%lrf=gze5B() z-s;)cYK|VX<0G>!3Mw@2V;JOC&_dzE|G#AoAv5Qb%|w=MW;paeApX4;xRGRv+M?2O zg8c|c=`bmP{bnC=hy=KdGP+VF0acCh1%f3u#gU8ax^M_Cxd4;L9waB4V=-q9H42?j zo%6uE_-O40#ALMBeC6pB#zY-1EN<1Im3);|UAuthc5L(dwXC$XPxomTbPsA38OU&x zuFTKVpA6q79#I0YfEg94PPp@-X@LQC5YZ4Xt#Tk6=wh8)x6b zuUDeu6Jwjd728wCZ#-T0iIOdk-l+{GeEZ^5GGzA$=ll4a*{~`@;1Tq2L~lU{X`HHf z@US>gcI{ml!b5muo=q?BFg*KG{jcuw1Qy3C)qUpNuG|{ThbC`J*h!2p{9`9C2I$()~u0AuM7SKtTEdou8ZolXAW>YHc)!bHm z8vj9F`cS#s++MCGHA9HQL?Wf+r`jkmMOLBQ{fAkYH$K*7&Gc6J~w?K!xe`a>)P|g;JhI zbdw&0F}-gMVfX9uZJ3C#J&_yMo_H>`g4cYbSeyV?f`%_G&zR0sZ6odPkNpPc7MC{n z;TnbFa7N{m&*Vq$N8E`WhPmdKfX!A7$kSJSfkc+^p_d1#4e}B;x;W!|RT6Sk*MeJ? zuqdXURC%$Rox}~7J52G1Lz@z<$?9yn)jcyUiHswK2bAeg&C_L8QlCA$;oPmOa;!gC zsP8NCrHUa(BU9_{3ky4m#9h!)k}<}N29hc1>r(_PKp6lQd49Os$^Bq)4ck$hgB(_- z+a!R7C}l`O9i^SOrHa;Y)XMxZhs7vWxqmR;FZJ<*eqgjwd8m|IO+ziy6Hg$qBW}0x zHcBa|`_#9__hKz-vhKFw$;`N(adH}-7l>0On|n@p4IlB8p?0r1kMCdY-h&Z|@~0BbW0wYEw#gg>b*0fY@7IbvCl z15M}YCtN?`)Ehs1Srg7Prhb~Iz_5*K9EHA%Vy3FefCqXJDaMqG=^Y+=*J!Q>$)r62 z-DXIH))f>9lX){C7RWd-zX&IKJd7KLH}Y%|2*Oa*djVFo?%DJt(tC8eiZPV1WjHWc zZ#Yylq;$Jp&t^MF&jPGte06|BCQ)tP!t)uSW~|`s>pU`hIs0(t`KF=6@+=MMSmkbd z!M*^W`LNg*p%0?PsahohQ+Z{k1PBkJ4n!UJT4clNu?$7BWB1N#}b~#V#{wXPYUE!Jzan%dryIkZhhUGfUYlebd>Gdw3C?UCGaO&KTr;= z%Ft+zJF^=%o)7IrDw6jkxJENx?>{+HP)pib83$liP4EoV>e(sZlL#yi$u1D9&WZAm z-%h+sqM#QLviSv@il0ZDjU#!kgQdjW0k{HyN?%G12_dr?#?g+r*^?sY0H(bvyw#qo zu=KR74C#uqtjc9uksM`MMuj4b#KfopE7Rw-*uLoT7^nw zz?C~b+yw+4=;?qJZC1%S{lfny*0}q!c3ZtxpBoQZ1+?ak+c_q#3?Xf&%ekG}!p|mM zzmKLLNd#v8(AE6zHjR^pHdG!Go~h;eWk}FKG*tZAVJ-J-mv%)V#U^+m0Cc3(Ikk(C zJ1abCWz74OTou>}BXK>NB%gIE*{gu^GCFXap>@F7*%>~#H%zM9jlPYPriqP-k#vqk zJ@l(~ec{)ugolPBiVPuJKFxgn(>N-aoDgDUEBk)j2~;hoDty<)aP=edUW@sd7N#_R!9;xroSs zu5bEN1dq6slpkMEwP2SKhqi6s*48VIo4HzTuOze8m6ergLV??d-C@_;FbW;9n+Z&j zm++8F!(}YvJblY ziz_NQjizU0Osk~9OBI-otOIX7put7K>-KWy#>OE{gI~Xj*xHuudqB7jQTI0*e<3$A z30yZ(#&}sdX=w}!GRLKRil1IiKC;IPY^wJ-b zZ(GohNwHK@dCaY@vamh1|LKl?R!3mH(aW^}!Ko2M;DmvM0X-@U`2fOyf?b{EQWOfBOMi@C3O&CGrGPj+i9_KcxL@atEPj#}6~Kbjnpv==cWUX(U6Ea^g$3gzywnsjnR84CLSZ(p~< zJ?x2n-B|M?q8;8xQTN%v(6G-X`EYlYW#G{qf`}R*2Fq;dIJZt3Uln{cC%?^jzU~Co zpnb-v`M>)rmwz$(|GAgNGzV>&4i#sa-y14^(euUP3Ig%bx5AXOvJ($lyt3ZnFdxkT z2p#(=S;HM_`QQCXrq@r752qixhvc0d_HsFjnWAw`=MX<-^`#v2#y&!@1Jo1@3P{#R z2L@=h3nN^AP!J#xDjX=@AVMR_6uN1@8bo!Wj>p&*AJpi$AM?fwSWPwR2J-LJoyUa& zDx{Q97J!V^SLCnHI%y_z1dx0E{QL$Q_e|GquL0H3R3Z?(=fMFyhS@^}sF*9GmD5uF z8-YV!Z$&8kSI!$#M@z$Zw$N%4+7!0+9o5%?>YPOBdo9)o)0~n5lkx^7aLWBj_v%H*~;Tq@4lj2vr{A_H=$xVUnTnwV$ z!Nx_>mX(!3wHGY+7xH-73?L_w+`KuZ{{Pi>=KoNyeHb4{Nl85Aw2-wJ%h-2`EZLed zW#1$Fa_q?nqi{r)aI7VICS;;vq7nvqS%&OMI1Goe6m3#QjOX+1^}L?{;PHc>X86v` z?fzb$>%QKXuI}#j=_Efj!Gm1PqOUK#l46nfE&rG;0ATA~GY6!fx5? zHlnYwm!^kuxJTuw_$TNEw#BAw0ajDzJ-f&7inTjI1PrUm3mho7l3{h96BA29QXi_n zvh3@uSO}hc&4Pi>PU~=LPXs9ZZ)Y(O=N^huheV5>y~%B3i8=?&$wPs~Qc!GwDVBC^s)S4wrbm$Y2O7^-Dr4ntm}<)P_2ZZW*(IBz!wT<3Fl#+cxyeQ zykRgG`l9gq6NUEB$8cD>70EhXD734Fv$ij>CqOTNGMR+-g8xx5{%+XPz zDO_NQs;+L(reB;SGo@DxIXk7@C5fONjaXr1CD?|i%6GTErM2dwX1TosSF(B0a5hLS zS^%N1eb>r{4lsc66T1_(R#V6-=mY>AgfK9GB3Y)~!+_spnDiSNEqrZHHU0;Z*S`ue zU}dYHiRt*0mzul?<`(RRcd&Pp_@R<$W_?hf5}%`va|Wz5{aMtSleX^}AZH^Z;jbGC zBm9f=>0WQ2xIPZ~1QkDSt?_YC_@E}}3ME0Nrroo%W!qsH-Sgz41?0zu{gcEdAsMvx zb_3Q^vK^7YVixcgJOj56askB8?fU>cTUlP_=1K$vDfjo8T93pBVbX#b!S8C@p<_)a z(#+DX;93xp-C(Rk^Ti_I$*&v5GGGIbS}nBDgWD)rQ3oA;q;j?4tXH>v{>!gKd1Ma- zzjG;%TO74w!t1 zv<$xy#!REmAF8)e+Z$WZ~GuYKd8%Kiwsn`AQWq7;7dsPGsK!Ywu)64`f7Bs4C* z&$%5^3hv`q`9%};JcniHE7XEk%Ds?m0=dy8K21E6J(|qFB{arhDDrULNZ7g<*E9?h z+Q7@Ifa4z%EXp>Lp-{AKO?Jcbw}gJ>>vAEF0&+Z40gi%bS$rueGxN|+xD4IPD)>++ zEDS*jfEZ7O52eTB8iC-;{W=R-B6qc#@1EPKMLZx8s?5z4S+ixq<9_cv+z&5v+F&nJj=Ir|{CurQJuf7?x9zlRrCaLArgL*t{Qp1yRz*mZT~0a-`qs)V~P1K56*KFDbf`$Yx> z$TFHhr~mlG5X~?#dN2eRx{+AH;U4Dssiye>xw@uCUL@qi%12bzAiBc(CJIyP^R$!I3wzl2XQ-3vkUtQP>5 z0J?N?`!zS@D8B*n6b)<#jYi|s+bna*;zw!iop#n3dGkgf!-P5O?RYq=8?sh`Zr4q| zWAniOm5wJ#PncS3gD_WQFI<};REDcp-9ZZQn7q-jhF9HY- zXc+*j_+bIclo9(wa9iOO2|wlbQ0*TE~X7xa_&9HO86$q%1B=)LNUSzcb& zf?c!N4MM5+35zFKIf8$0AubhbCfFgm7NzdbP?9jFG&MPk^l%kIG%%#9EmFsI+f)d; z4X`5r8=O?Mt&JY`lOC*Lze_-6NUR)(B_6-zQeLZ+)lI6H&B)5yC9X{?Th3zz+P<(d z-ODR)$!(ck9KKOC@(^`fqtR5nh1v4c zy1Hy{ICscz4~mGeX6uM#O|3=6U+rPBSlcTP)yr|ur(B(#`?CHb4KJ^);o@f|Cv%t` z8!nJ5bS1HBn9S zt#CDcfdb}EeuL&cCkOr1ig!l{IyQVF|2=>-(jpe1{GJq<7_#XMF__b?jQ>on4 zli5_en1$6?>rgt#^TA46tvNJ?PD>gU z67(u`S&^%Jbl$`84^!IfBD+qNR@1AhX0__`Sl-!Hii7oqrPgk0Fh@e^?VF@d+=|hT zD!eJ?hnwEz3vkHmIi`^7KhspisIX!n2J6M--cNRh#YB@*MHY5c^{{pNoFBZ?0h literal 0 HcmV?d00001 diff --git a/docs/transient/04pumpingtests/figs/Moench.png b/docs/transient/04pumpingtests/figs/Moench.png new file mode 100644 index 0000000000000000000000000000000000000000..a892c7e3c2f63e5aadfcf376cc3939de14acb765 GIT binary patch literal 31379 zcmd43by!sG+BZBFn1CQCf*>j(sYpm8ARvvTLn;!|(mj|+tCTc|w19|oj0i{$NDLh! z-8sZi-#O^L_r0HI@8kL2Ki=nf=Qw1LVXbwowXSuZzq;^MQIa`z{QPkQ0&(h&tkhiu z;?O1nanSwfLHG$j%XeXTJ8XJO@fHG+8+>B>{t*P?`1MDUk}7v3B`>Pj*_b@CG)5p~ zePhF&)zx~ad|Jx+%T?Zz`Gx+lFuQwD_Efp-8&1~i$H?TqzD8W)lTx>pQhk$hl$+(J zcar3r+)I8h4yfkN9Hy4)cx-!yCj8FBEb5^$*9A=U5dFHBMSOY7fa!u~X9?o<_Om13 z6ts_dpG?#~GdxlAD$w85kodsKMZ_UX#82D2JPE1|>4T96J-P!dBPlk{mY0xR+}vVq z7YnFF?8S!`-@9}jq4F+ZVtJ!Sm-=k|O%bga#zN)#cLh9(&DZ3bFWq~}tr=p!%&z0H z7HLc>u)_UZn7Yxv`IqtHXg~8vpa6MAz#BvAvlB-*in%M8jGj5%c`5$gW;B=DLx~T; z^0uFI++OXWC5O>l7Vf7UBd&;_vh@awZCu(-EfkHUHO);(PVy_Muc(=H)m{dMQ1}nG zv?xya*EC%t;Saq0>w0%2^*!2QJA_Hg_PsN%R@9#_33cBTxOvTr!>WR%1Esee{dV0c zT>m+0o?422qiRjdEAkZLQ;KnZ58lV>5hX%z=737+jt|C=3*q;0 zgliv6W;ieIMTcNvJ$><*`$M)L3!W#w%N*Lo__s#Bi`{B@y6dS`q_A5@bp;h?qMlS!RP7KigCh*Oe<394q~ zcuF_o%h!&jR3bl}wmgz&sC@Hi(DSpE^^4p$={cVkehsrd} zdy}0OeD8nlG}Sk0i8n4+j-6)6d`>c4&04m>bfq1Iz0L$KA=? z$=#0#1$jQZ{L8_%;HH7Du+l5GYK@;rTV`7=TZ$s;GV(#Pg#gl;mBq#-;|*yKQm><8 zLGcVM6amMwpUX}>VtYB1?HMzof z^~n_mD~37d*h>SK$}fi0OFtyF6TYV~C}GVM&oIP{R!v~lWe$Wm(t1g>t%hjs#TH6>PZgjQ!m@TaBK8P3#bc7y5`yWFCQ6` zIm2G_>X+oOsvpZ1iz$nd`tuBBW3}QA#}4V~d($^hN!~~~^5gEK)zg`*=sZkOqf_vPQ);6P6FlmXU)UPjgLM!pe2@Vx>C< z({FTmcC>dCraPw(wTnM`_vpFBsQGN`&CDeS604v_+WDBb0zMP{ozHnOJePU2%;wu^ zf5ddQbuPCzv}LpjCgIehv*!|HGs-2mp09l%(Wq6FNyw9&_|R?M5q^S&e_tb(eLAiZdsiT0E^yt!oSWjGWu!%;_JX zD=WHopFK-@R+osm-`4u|dTVay5|RJ+<3?5{vCa}gt^!$3g5#^5J(c%nJ-Ro~o=uUD zlJ{>+a{a!tv2uPQuz0%IPIpEx+4^LmRAIUGU1vS#Aopimhe$n1<77f=pQomqW=z~_ zG1c$Xzm!9g(`9M8CbxFgrn;}CwA%fL&o7EGwK@ACtrq^CLacK`%b?ARp{uAbsMmXZ zdo%}-2l~Coz0<{OzlJ^Wy!9dB$Sv$G(x;fGw<|>$Y-J7W)?SycoSHZ>;r!z15APpy ziR<42s={A*(gz(QJ&|(^b?!ceDp@PqyTq{mY50+xOXZguFD*z#O5J(ymPDCE@jg1q zpw^`}v{u$A>w(sHeZTfs6xEKkMk7fh(Z5p9;@SI)ZG|gE6K@XVFOA%G_2*>o)Q+jx z*dVWAjA4`^Z}q+I%i+5qkQhVMyxQiSoG+hRWI4|{PZ39KLu~VQp@C~c`ARWU7_*3+ zlj2~UaOAJJyKk1&zf!56h)cz%b|zOSolxwHU%M*8R4#HT;?29q>awR(j%{)0-&o=H z$!p5<%lpt66EUaD$9SBa*I2U0|8r>#PftsSceT`KeeS4{aJ7&XCVs(n**LqnYc__( zJh7QRS4S0*&+{Kypjf_e zvU7P9bDHQWztL=sdY_B)n$hv6^t)Zd&sSub*7vHHrTb>lqxqB!MS6J`#cMXf|mMch2b>RYME8Zk=Pe zfN?|Pn~9QM`gy+d6xgEUljdJTVelozcaqY-OjUUedrdugb;uJFgu!q`aTsUWW`1y7 zHvCrEIOKwAANX#WX_HzQL3hySiVqzsdSeb(b<4*+_*2jqPty%OE8_g54;0h7g96yF zHpaQ289LGPE?)|3lT%0B8yD8;+eI|(b;n$Kj(ziePZ6&)9v>qb<~ThyHQH`eF7LJw z<11pH#Z_jrX!)aOWx}H+c4~cA%ykgAoqMABY>@s#z1fmc+jRS{UI`<_Tl9iN7l?#> zXnjh!g!JaAuVkoaK-JI0KXH68^TetMz zvp?%uI2zYh?za`SPUFS1ldCs25WR_Bf+mwz;oS3i0mhu5P#IL>r``yZ74WjTaoc zS9i3ib4>Ij%2ZMIKRITs_DbJ5Q`{(C$X#F)d*?N|d2V%SJ~co&-s{l%^Odbxs{{(+?Wsu@8<{v9)9FKi9k4BLm;LN5eUI|2*g?2m=aY2@H}QKd(R$$V2UFA zJD?ctJO+qm-P z8g)vC1G|s{f2`BA?3Xj)oySQ6Y6_0Mk)sNK%Axi-WPl;O|4N?FKyUXoN%bq-itK6S z9FnH5)T_7|^TOW0oTraXys^9cIK188OTYfJm#!E+YC!iw`FwF#&`#%4@kCU3L#F}tA0DrJ$W1{(!5NWRUOw=U zJ5nKKZOy5$?}D!se4pe_3}ez$Xl!f@oIeyL%M>Euwwl&5K_THH9M@-EYSo^iDDkRr z&~4_$S$)|nUGu8=rBh2Um z-BE_>;VPf8?}O#;jeG^jET>XEYq71_yv!~(_>GT;^F^zhniTk=!}@dzt+6ZNT%J?% zc1Gsrei}F7Kv8f%t7mf1*#)H~C97p)_?aB7KGT-CEYEn@`qP6BJef_|(#!s9(HOxY zyBbQZv^j&_<%s+R3iwzv07;LsYggo-vUJ62Xl0obF3%^s4m`(*GkQxesdSNfGq?wc zmv~-crw#RbzkQ1&smK?2x%u35o{wn`_xJbD>^cQobsWz3Vsph$rREfmM@4CEYw5ct z_p4+sh2pk`_Vlf62Fr?H#=>~Tvs7jZouW#)@AMo`dH*d*_UzNo4+nt3KjySBA*3zIlt8ro9n8%Jbd*{G_ z-uuT_$f?hrBa>KNUzfS`CPQ$42en0Br+6e2QDb3Rob2rC<(#Ql^9wXIA$_DGt13(( zS39{w>Rrn98J-VVqvnezGK6ReWA{d)O~SQ$<_d@O7mMKZ$KX3J{OVMDs`8L+)hUhj zp*bg?s{0I5m);cZ?QWe2-%3|+7_&3ZyH`3O%FAnM?4*p1em7PxBAII?t-gd{u;1C!62sj|G7_u|A%&RST1g{M2u zdA)L0v7zef>I%=T4zc~`f_=paMua?K?}C)Zn9hds^6>D8h*wIviOw}KF`;w$ z`Q#z@)#q?ujf{-?qv5ju7)`@j7;<#r?_MDnaGHHbJF@N~Tpqv?bvL~O$mB(`b98jp4ykaE*ZxQ}Q74j6>Ab_g3KB}T_y1angMl}+-ZP(%Le&oNMbg9DH70XUc>DS;4F~t?|G^JS;Q6@r zv<0=h5;#wgl3q7{DJ$!r6y*^LslW1Ed-z4houlyM1p5E0XZH;GojEWlh!1Z;7=xt1 z<6lyetUBNSe$7s1FrXC87|i9Ar}&bRl9KHm^sPixKU1qpoxXkM_m?ah4f>R^;6{}* zM%?X|bD;WEY7YSyu^PVv|NV9)A@RA;n4CM8up z$PKk_@9gYsZN2s9X#2-KRUH*QmOBn034uI4^6#;O7q#F*Yc_VLOEAg2o9+FpOMWcB zzf99>(Eq_3xg^T2AFE&jU6u$t5u$M*PF32SN?g349BGD zXO1on(3s&=zg+s4Tl3%NI@|dAR&8yqe4(#&5}Z8KKX*hJk3^P1Zcq@3f6d(zMI9X- z85!cg-9#x~G7IbQ+Nfwaj@j*i|AxcFBO)WIBO1r-iq8+6MtJLY;EgBCWJ)#H4Q2u| z?&s`ZT{#(J?nqx>-{$7#()kh#YwPSgZu>VCJFSYl1!a(BRcJ5@!fVI` zP^|ezR*FL_zW!mqb)hi6qa(Crwg#UPc%0~&h=@p!CEr8>fWIxu(5@fDwsl$;NMx>^ zYkJ6i1pPEpf$7{ybDF(HAF=JlDpGk=*r`PhIl}4Y%c-8q3$Qb{ODyX34pmCs+S(f1 zdnJA`!SiaQLhqc@=3REG5TVr6RIE}l>i+#oILJ1O+8kwYtr2wOT?fGnBtB!McF3TB zfTIkvPVU^19c%DQTa+T@JJXntbO)mFf1CIjIHq-*p8bhlh>JH4ygwKq%pI;zV0Kz z;K;fBS-qDZ`((SKJ}oAbgX<0Zdj6Jiq77fPz38Dd514*t3{Araesq+SlsqQ`d>OIl zG5q4JiK*#;bz#ke(-J37ovKfqPfx!(SL(IoZ*Y{sZal`p**WvGj-dUx!tUuT&CGu7 z7*eLvCvSnwLI47JY@~?(4mleAJmm1J;{`ucR&| z#vaDy=Xi2NV}b$$>)Aef;4%%cC@H{Y37y104i;GTG4aQ_O{d44?-;fnEsO~10=m!| z6XminSl){gj8y`fQrK5$JrI?LrYh%@jFWYDFU|OtrD@^p>}<8gcjHEg0uz>A`b&q~ zW)6C{q#urHXENO!E41RS6W@3ck;VY$pEWm~nbSPv+GkaGjA8V!vnz2%|AJGg#&wS8 zlhe~BE=#|*x69Z|Z$v6wA%A?6*52M8H(5gVe&bMvIhOxkExn*aU8v0M@}=)Ib|V0- zC5}d8!dIRgNBnr7hHG5h+Hvche3Y_cD;O+$Mm39$@wp2iWOK@}syU~|KIOcgk1Z2@ zpN;*yW_AE?YkVx%O)_Z==a31a-JCTQ({VzSzU-Rb-dsth`EWP=539=oDy60@qA~Te zEAmKVHLKh4_1wzB7$&t`ugLI_$OR$OlEwX>v9bY_94`{q~# zj$afe#Bow$gc&)^G{c8f{>CmY8?nxVs#Sv{BO}0u?6z`i>Ey4FJC&CA9QRZ$t*B3S ze_Q{sd89s6fyw^o*8@vSZ)GH2S@M<2S&$t-OalY+KJaLvmXcm;+x&pLqw@5|M$v|g zNQ{YEW<`Bp=nFFEAm*#aQLxou**)t_8Mjzr*U#D)lXek5Bmi!8L?fk^G5kXv z^UBG0nU!MFu&P0HB2js!W@fV1X>FPf#X@{xa$yw)np+teaWH$_ikCWdY#!0njx2?p zsSR6GGz%Y$@azN9RrZJ>8e=_FAwuq(7H%gcM(*41zEE!0Qbj7p&QC%ws=N5?2@FmR zAugZLne%RFA!5XeQ7TC_cHNBPcE|aE&)V$ESC!P%)PU+syi#DgasB$RQ|aC>u3d=& z*R>wg8<7F5IvNLdcDEKLR;SyN!14a6ShSKtG2c zXgYVK;k6=*F3!zqxLMqhO~6h@--^m{M~0HdL$$R*?-cZ>?`kgAT@J46;kL7iudlC% zGJU6F6{;(0MrY_Q%JwzQriEDpMd%T?+=^HM`|lGgN1jz~`{{bs`PPoWSiR7DGGh2v zf36A^g9eklLx%`mgp<&Gt-3!ALmLx~UmzCScw+g}sdVf|#kOHW$_@-HR>(OcySb_9 z!`-WM21-||Jp9QC-4`pIp_+zc0|=$MmNRW-Wp&}g1p>s%O*4&imA0PkD{OhlP1rH0 z88;m!f4n?JGrF6ep1!q!-UBHiknB=Oec9r-6Lx!VF3apLbM4)|4TK6;-S6vrOv5u< zr*NJW1*Y3v-?Peu?XR!pl65&FdMrCdPp`$f&%dF8iAcj|+5PE$l`jRezSpL4@wg{n z^uVY4l7g|jedwLOm^J4C$B6oeZa|5#5AhZ`<(d^y7!!9lH^2cHlMvDb4bz<}`d!=m z_iuUKjC}X*wQ?4@M4h?l1X5(Fnt~C(2Ap2>+(}Ljj`TZg=K1u+p}PalLpuWtURu() zwY8U3_ru~bCKKc14~OYUd=`pENanO`ot!kowxBo0#>R3+SU_Qj&~u6u?DSk8h}biw zXgxfnwGT8EX|UG<^^c|<=@^xGod1Lt+K!#WkV)Q=gHId;%gf4Wj~*Ts!;*(g$00e2 zXN@Z3AKBG6ieJ3O&Hc+ka11mT6Cl)f$>hghd>K&jya6*RPvFe)u`l%0)Ni<;)76Pu z965MEfX=3{uWI;H{}gN_X6&BtWfruJ6K9*#xk(#72aR{7g(p(MO_YPLN&AM=*zu124ky5IfXr#57Sz7bJMk#u$G|#jp!?f>yz9+stes|s( zt+O~Bz^Drpy*8MBVPU}_SszQB?^W}@b#i;#V~S5+UcS{V^ba|Bm2p3`Z%$udKQ2DL zP@A1?K|@QctH%Eh1sL#+3|%^BgLyuZohr5co@&lV%^5D}_bpy)dCAdJ!j;mAf9DVd z|G?}~*UU{!n8M_;%yY6U@a9;{7plCo8kK1ew36b=1>IK7_@edhgsw@(?&^odmbZ`Q zi_R>D))GX9kGv#4K%APIo3jTQ0GfhwnWaM4F>D?hE|)y(w6(jl<>TX{_4e{iE}%Iv zX1d_@)zzbbu?R|_T+krdKKl3g_T&(81Bq771&l2(%sQR*`R?!gBzq$SFutS@32(_+ z%==-j%LMsp@Fl9-Opm~-66Bbb}c8-ZFVaEnpvk(!fdS-4OdE)caTJCYroEUDZzlX>S-HU8jTM{ z9=zK^2`$EaNd`38t;;nJt~)}7@m^!xl!lVQ7oA^z0SX55I|U|V8dqouu22gnuqtQU zE%^YBDl043!WK(&PNnAI_5*em(k6Mu)%O}NXEdhaRa%Q#rdc@vb^E87ZG9{+JA&?C zCdaL>M`OlSSkCy@0Qwcg*xuBg{V*LcofiPj@ADq1%>GM$Vsg9XBeNXe<=;#)?WIh5 z*uxBasMIsF*l*t?1Yw_KoI2%$(-cpqHI(@6{KSgIChT9Znj14_h*Xm2dsX_sR!TK?H81S*zpbdO zJRuW_F|nG8G$?DZV00cwv8!rbMr(1)xgUVxZQ=3iQ|!*;x`iq%4?#usdA`k-5_ngJ zeJzYt`&DMDak{;#syP~CLPJgMTl0WG7)a@WIV6)N&V_kMJzE>O1;Zb*+cfZhnb66V z)4f-P_kn7q9_RAwnBHyA)5^k2Qn!87c<=2V%{-rkp;nHKi{sQ}dRaC6fNQJ+FIdXp zJYFWRF}&%SZKiAR)~Nee2)!Q2R_#*`N{*RzZ;{Fo$JbEBqu5V`YM1qMciT%@(-M8EF29U%4ET*rslrO0rezfbY)X?C9*|* zeXzx2{Dxi4ogKMt>fw}VYjTO3u}Xz~Spz7L9;w_LQxv0F1>JhA>sk=Y=_L&uh5dI2 zRFC7Qwa~2mc1#hO#J2;3gD0BQ_6kv3g)w&&?Ra{v3vC8Ucui5aV7z<@FR9ZdXScSl?y6fkAW7#_ zr1$fY#+1?aViv4nf}r8|cMdMa$R&fV(ZPK9j_5UQ5wDve^-`Bk`&3=$=hsp-eRu&i z`6F>V`k6)tjZK;AB0H;~%CUUGvkcWgeSiBO*K1go=ua?Ck8b1|GQu!^;aqJ(ij}^YOy2 zZ9pc1544Ewl{ul?r`R=h8gcdntEJZ>M+DcQ78W$q7PwkBB@ z_L=8f_ZKZKH*%e&N1X(C0C1qZyj(yPa4Ar}wM~y)_x=e_&^ zSjVJ6jkmpN*L=H0O9ho9hdR;{E0ns2_&VNj~=U&KC(D;e(}yJ3r7mg85z z-TG{`8viqXrzbwlD*SSo+^Lf%k227qwjVp*1x)Mq9miHaYreY!6bhSALH0f?Yi_O2 z=Bz{$IN6+>bV5$EGX{HZULAzi3zM%49=!hAkI56r`fvC`^o~`o3_-s-L`>4LsN*nD z;wYdREV8D3=}n!$SrLQEZT<8uGXi+DQm@SUsiCFE$PYJ6bN2AFN<*~(e3zXny(io+ zx7imM6E+b{CN;DU-f2*-Rp{Z!Kh#`iyK|GKB}28*pQ}q=FLN-4o8&lPFi!_qIt$5_ zA7nhhU}ET^FyqjY=TebKeZJTJ_z9I$$4#b(a;PHMH##!L#*Bk&ljztA%091ftc7K) z>iUhbTZEfpip^>&o|@~mJ|&e4A=PYmb;`^b9Kl1swJvOFX<;V!9UB`nstvjz>d`%H z%TpBv3VVXNpwF6)SxSYI{*Pb3-Zwj!g9+4&iW9I;3HQXmE0OG)SP{WbCYhs=X{I3L zq0#7SKYoF{xV!1f!@B?Qx^J!ue ziFg|4I!?_{U03(2+4yP4kiz%cTF^fV#Fkvja&mHh(rZlTWaoPc)6t(hXJ%s)9~&Fi zm0Mc6RV%jp_|hBOV%^X!0HWC!gq%D;BVivH_cz#n-#oncRIZ$PFk0Gzq4VV?@tNo#x)=#0fbo&DU?bgrIn#nInx^ zqA?BUTL@St`xFfyt)RnX%~^vErN&~1sk_(-w%Y_}EUf-v$0a3fY$K2!JCiPO*@}w= zN(a0ae^$mk%UB4B3AU=Ls+vAs4D1vx;;=0+e2`7!;^^{vgoTB52Of2559~fopqq`^ z^SZ^fOjZ7!ZSx@YyE0 zFCZu=y!k54d$E7w3#I@(RHLn@Vew?|Gcz-%^(l2Z^*ZN|1XKFg9N#@~t1~@g@kax- zJuNl$^=QoJOSE)!;pMEYfiS^|>0HFaK0trZ_^4%H8&dy4xbmq$FUe%_EUECKcaJ6K zMjK2*Ikt0@$6t&xQ@Boin8mKHei1gFyW&2FE!tjgl#!NRJ|W=BmV!^qv+65|NuN7{ zmaeh(QTZ}-5-yd#4(Lzqb|-pC1jmV9BSmtJpcc)Cv&UG92v;zC**NM+Iu93&pX~ zCZ{RV;(8@yXhAEJmhes3K0r8F+=rB|V@(*sr?`_5l2+7BQ-iQ&{dGKY3SGnEpUcB{m;pJ9QJWyu87X*i;c+~Jc` z0p_9jtg3cau%t$U#p2KggSw>>1RpEmMaNowy12g}!5rJ=>TuUZzhk8{J_!Ul;kh6d zzaK5Ab1nPyR)J~kd#c#Z6yD(&fpr%MVum+CujS|EO-?li7HRIB@@3mc=u-v3^+Bq} ze#c=sih+D?=A_)cTCBSGrWJmU$WTc|qDxErEcs3_93O1l&NSGOvxcaH2ySZMO>@DO z@;mSC?npr2eN+F?PTy?@*YyH)hT>y3*p|o^wU2>uCW54?E3f_l0D=aTZ>{e6Zkq3z za7evn&u7MH42U9t9X-?Y@}Sfa9egr8JdAfpxzyAJ8h|)khCvqB^LLSv#WUHZ09~D4 zC>FO{7e;uh-vR@K;Kfp69X)pj2M29$#wajBJujnoe(>NIm?of@gI0>0C0qeR7o1mk zc;8%5%>#mVYr-9=?=t)%H#fJZ=N_0)6LEyo{jS+1Ak5(>KrqM6ZsX{>_z~zKF;P+d z5=EZ#Sl3{_zj;(IgU!#F@Ax@7IE2PN?)Vp=uz#+ns%hhv-~A7E-pgk9FBXn_`ORtP z*Z%2``K94yKolU{8AFASRcdrIi|}m%<@IdBMgQ+HrGH}}{pB!bJOQo&h8NN)?V)#V_+NHFhfStd^ttEDQEUjR(2i(CvFH!?yt5 z3C1>~Y*@~aOS~h%>x)Bx06>w!bB#C%%F}jDIfPpgfrSEw;MRx;EeYV8si{YRwAX?I z0!Go-W0e~D_@Wgn>kWI_6iC!Qy|Cm0dd~6GPGaxy1iyutHV;s6PLa9_$Hh3;O8~kS6I0na z*pP!GxU4pjZ{EBSzxWfvN({#fi2rdA{~AAR|A`YPrinAxYC&)c7qbWZRsLZ(=PyW& zK=JzWnmoC9TR!Iui4WEb$CmPQ$VbCODZxXFDkHFecSwPUf# zUZ2cuiUi!(pvqH9=svEYJTlZhFp#yAX?;sGGhce(FoLJMe0?ypAozk%N`Zmf6l1Zf zCDZ!i;wLBWU>*Sh>fS9|*Ze?f<64Gggae<&RHG7Mo?uy385=D8 zYu=k|Y=d*Iu{Go}a;o@^{Y3VftzAh_Cx9AY&&%$RQ>$2sU8tLrkKi1Wp=@u9h)pX= z?5stu6M}x$x~LtTUDXVZlarImvcIoSC)FCPwtD*AMdEzVkwE^2z}?+l1tzsmJK&l{ z<^feYb7BbWQ9q%hXIdxkNO2@Eo%PGqGi4l~;o{;V%!j?RDl0fbyA?2%OZH+6I)O0b z`~B%z-N(0f{7y@BTNY@u7km><`}%-C;DCmaF13+kIJVixC0oot}rd%jnQOXqyb8>c$zxnuqnHkFjGnn1+@7|@|$yxqwWMI3~)LF+? zHbGTZWKsi76g)ZQw%GXi7W#8K@7vF0NWdKGZ_l*%l=?{Ep>+36Zoaubve+lNBUT@Lk z5ut^$kQEA#p$bp^9Otp}1{uQay{&GPG+#6l)YH=g&Os2o0t5BRTpj~ropXu^`VGV3 zK{j_(T3WiY3(9tZ7CaO_r1TKG1&iHzs+k{~e<{N+P5L?qs?74J-#?8VakPu@V0-&z zLRt}^7Mm{w5aH0))=G1cpie&D3f)HLY!@NC1&7$1=Z~8OQ1uqPgYKe#s zSn|O%CvVY){0Df7!?po|fmx$P;>SSsHwMWb#%z1@;~J1NzE99e#ybWEJa@NUam2%x zeD150t>km`?z1rWq!WWRZfB8h$GZsAoh0%vpLQyhOnlwXxiujSvkweSayIVZz`%4y zw+;%Yl?yj*4ZXZ}iznhTJ@*i&%sqc94}ug$o!DE#?(KYNAH1)dbM;w-|gBA|e6`-I-$$PrpS-O<*u#`qQV<&g6T!LjIZA6q~2Q;;>M@9=@ZNp0Qtldgh#l zocgSy`K-t#%77>i?d`6Q*=qy=mSblPsgDtMwS!5y9KT&gpPD@}Q9#%l z)Mywgfn5IbV|z}^*A*uJE!prp!yLq7d?<1mIl7M*gOZJea>Zb^t@Gt%Zum>j9?`=AVuLi@-1e?I` z9DytSo4_dn`%g?v$QRy2BK@S3{@M#+e^~L~MGrD*4m3MM;(z;$S~Npd+b31Ha~SYC zptJul#y^MrFEjj~PA-Ay{U0!lza=mUaR0xI=XYx0ey+A$;iG?dNimpZZF@p;|G!g^ ze~c5d7+ur57O`MnQvTzLo>9dGXOI#Ud5Gx#k7sQzp0!|CtndPII(X-l^6US1Ur z(f`8i_eXw8eE|vc6O`()1&C_{fN73RSNf0n%)1MYF1nmu08GHRwqJu{#cP$-vLDkn zyX}~TQ%euUn1H0HsMC3Nf4Pdcrg|50Xa)yLHdM)y3%N}D@!}tT(^@l)xBi6q<1cp} z-{+A?)tz~a=W;#s@6b%+Sk$|BW?>p2`XIciG*XdbVRGQeoM8CeGuz(}k@qdYU|lni z{KHL&W4FD~aeQu3Q^GOLm-kZ~e|lVrDp*4T!tk8z?Jn%ap?gz$EO(cJQT?NNZhx4s z`T{TijmRdpoHD!F?(0?qLrozY>%ZAvCeQvg?@%na@MPR?2>g)>`;RTOF<1$5zAdC} z)Wri9MI17d09-#>9|AqedROerK?LjFg^6)iFoR$@M}FTNAv8p22hupHV1kq5>beOj z|K&{&NUOlYgxCFfWo7K^Cr_R+^86biPf>%U7FZs?9~tNZeBn`H$=5eIk3g7z zyq{k|_U%Egf}$cAehk%_ITgKM<3jUYSKvP{PMjTdUl3uaE^(Y0rDJ&BpVV6?0^Rqm z`=}C@2oZ((`O^@ELM6E$Kxk9)rr{KCgCzfX0rVYEMUTEg^;zdp)6#}AZjFwP5|*~a zRaV|AmT8{Sa>wKG9qg#$)QjjLt_WsUhVy~bWdqT`vXS@IuaJXijOk>%8YcG$mDZV| z1!s=h9--gcs1?f=21vV4iMjRFlX&cO4N6v;g&=>r{4fmtctl!2X%9>uO4?>Ecc|!G z>vgdiiUbhw+4<_1{Ct9ZF~Uf%l&x}5#KZ&cU^#YL+-1bcA-bAhl}gjUo;8<dMxiZVi+LH?l3G|F zRJ8uRe#pGpxbkj#=zujKy}iZ7Mf1_d_^S8CTDK1&)Q3NZ$#jf5w?M_#r2CxV(79?i z(vhx~subJMb1dOe^4;{U`O+N7&Ss#|@ie9f5E^FNv~dyoZuORYCK$YFidCuA0G>W<@R_cr7Y#>jTZG12m* zD~-22=R`_z3pWf60RVxq$a(0;zK;(z=`RZ4CP`=I|pDj5%^DY;Up$k2wf} zp4>}+`~@_)x;ohlcokm9Fe1?ueb(Fwct!+-9QEh+qk^g;m{*s~G16eSzr&aW*FuhY zOso*%mZ)!|JU~z-Ua7ELS)Uy&*V$EuglVq{Xy#{**GD)l4DN*Gcra4=!*jyt6CHn+E;oL6z)M@L6z=SJld60gwsOmuIVt37~QEv<-twb}41{xxs+ zm`l+hDa6IbYBXZ^)(X)gcB8W3B2X3pbr&OD+2Z5j2_53k#P$LwvJNlDPiK~jr34j0 z4Fb%u38ans{CU`IUf-N%@KxG#=jt|}WyFwt0k`WJF=+!Dmi4WHtrA`~2nm19_~0{k zld^QJ`=JR)pa?K^jXsRGj|+f_zM#p|g#Ng>h42mo)qX!~nkPo(KNc|4xaS}sguPhq zWTsnD{&SaHwbwcQvF9TQ9M2c_SWXk`u~Vs`x<97bWOz2KhKucHKHA+V#~VEbw`EFW zH(5ttpN^XP>SH{@yHf+Q{Hv3M^weBdORnZI5E(JzqM?Ey!NNp-MS%XzR?xvrJv=-J zQQX+VemnUKg08Sot}bxFbHUyGzR8@SM;4xm{^%$BiRPG{>6~<5DG2D*i3%@%JtXn! zX>eUR)FRRgg6dPP2N0N?B{+t+mu9AhQrFU^2Rte6#M9l~?+V7cyStZ{KbPEhe*Cy~ z1~&-7H{5JiMzMXz2ODtE2k({<9Q4}o#+v11SUIM1^fb)o`VVBk^5Sc{C*~6&=r5i;# zTpm5TdOk3Gj!so7D>luNZ;NYhnaiA04h->g=WK7%LIDv_g)jfrWAB)a@^jNDkjE!= z91EZ9gGD#?BC2A0n=NAnB@u;EIY2}dMN}DiCio8#5Q=&xfdp3QQ)2Ou5r}O@qGvDL zt)jTdZ+GfW4mo_ZFw!C?p3Gwk1{mJiA1AUy>6GY_A&0L zE+4-9M%K4tXL~}diz4~>D%`TE1uVdBjY}ENQ+7=DA*#7=+ROCXQ(vg+AY!N*9AN6c*R1H z_zNejllXFOuHEzn8T2ZkhRZJpfPfUQK!viqY}%%DBTZwd!>R#t)(~YySN{6-Yo$dD z8ea)O^08*wE5LsIjK_oVhY-l~aQ-17A<_IcR1&a6Z+-}E;IZ7PG+CKUqGy#cG4Rya%k|FSGLn}-k`T`BnhB#v zKpTQUs$0Q4oGh%5=omOi@3oOADCf18Ij?ht{7t+#Fg34YQx_L)j6qaX)Ll}h1z43a zH3b~ESB6Y@rRj#9k%xzF>?>hBEH}MUXyA#{nDYfuT|bHWG_TN{27n&B-SU1a~gMpYvZa> zp%rZc0j3oTc)-@K4{zT1nK`M`eCX2`iuLxXFM=`QP)bmI#cpxgB`=*y2TbznPrO6j zKY+*r_3@-el9t1b`T7uMd#CA-xZk-g9M5IHG+r85TPi{qjC>+<^f9cb#o=&cJOF1i zq~;+52@y2aVI4t~jCho19(Gb1y*f$PUvDymT$NU7G2xc+=X3~45#+;`il8IAen zZLF=($qL5GM9?I$1`FWzK61Ax=JqY7kUK{i{Be+UX}r1xv^Fm0Gnc4BWEEgKg!sTUgMGr4 z(s+L0NqD0Dw!U?mkNL6R?C2Rx3_D20FjTiIi+K)zJHk-?J&=apWAWDJ+o-0?eRU#s zcgW`A1f94Ir?@PNL0cGXoZ#2Zfa(~1ZmhU*+ zgWs#XsK$SeKp@fXQdx0mKU4&(c4On=s#0oix^v$F3*eg)_6)p_Anv~(~u!g@~J zc>wdz@KBxXR8Y3IDkR;?N=gk39{H)zZc1)>qK6jNw+IiwUvAl@zZTIM{=U3$r3tGO zjeZU4mj7|kULXz53f$V8N{z3u9ezsoVB~usspv+6#~sCSRkjt7Nex8rYn7%pLzzV& z(r}J%+E=-Bi!R!<=>6N-l$lEsJhVVhPtWDqZ^o0c;$-ueHwc4@x{;xJuB+>|<|Yll zwJvN=_A4n^l+ag3<&XHRbOB*Ferj=Ia&q#MHhVJZz`o6?THy27WrGXIYXvsqxC!;g zVz61gscMQU^nYKb&Tk&SIW^$`L6`*?xKbtj!LU4}vRcbEly9FsoVm9K%v*gJqp~Q- zWGmUWe;XW-JLZ5j3y&uXj~RT|hoK^-8WhpPZS|L3@?=UpAPYnNhPE{oa|SlLIRXV`4Yo zx0>nbL{|;7Jnjen53fEObh*QN)D5|eU#V*ue;nO3D!;aLN9EUPyyRGxrr}mty|vJt zD{00w2EC@<&S0*1zGP-u3^57~;(5*E}x-FLOURaIwZ zcyHchA;vK?Gmj{$|3UGjHO!FA*Uk@^2UQ(>ad*P=}oaH>qz-QgR0h6jBqT?Z5 zK6eX&bWu3vyHEGuhYh_66JBxe+lv#+YyBW-*NaFTEtbi@T29by(rCtKX7bO^EN=X^ z2WtaiHL1-`AH7u0~A-x0|6{S>8N8x!9z3CnMEPtu?pnZWATYc`cKZq-ofof8MT-#rKFTtg{birXE17 zjuaB9*ayta)cmen9Qz6$Xl{=^_5KjaGuxPG{!APX?&L~Dk;qvXzBVIlKo~xudbQYjod4??=SF0e`PMQwkIvxc=*u+araxJ0a#gOd8- z!KI10Bl~}Zps%Or>d?IH&8L-yOd;v30p1VZRjIvnn#ag>4j=QWdMd`9{du3}tG#~$ zv5Fo`gN<)zVM(VG8swwsXtoOwz67i7f^+|4NPVoYtxYdz?Eis+Zurr4@M-q+!0Dy( zl^UhhV|d;^)ek{a2nLJ7($4tpQD$gnc6)Vob(PPE^B2H3P!`?%U=B7TtY09k3EXoX zZ;GT~R=PrdjFRT)Vfd+^VU-|^Ef5{A}8frI@%Ry>j&K_W8YM^vj@M!01H=U ztCL$lc9)Bb%dJakCE3CHDs48Jh9U1!Y;5G%`WtV6>6AD~MgM$RrPYjRJXBjt_wF++ zTL0s=f);;_W#lB0a>1t@V7mALFrcg~n3klW6hmP&S^CkCtaKj^!U*mQ7Kwe0UALuF zbi?3e<;myU6S#kriy<5-045I7B!e;X-*p51ll`eMxf)97OTO@0P8)X_J4;4=DPLIOE#l z#7!DH^=k|L=*D+*)JgHnf9dM%R8&?L3+!351GEDAU$?dyKc(W%SdG1CeLsirNVBrB zK^R(P#6+NY^RV(|Ed0Wm{ZmQV(P4K2uJVhZ6G>|c<9eOTTvHP7RfVNaVMTNQ8}J9Je;6>lv7{q zdJnWLf^B>ZIy&Z^r8apE<~s_@761`Etxi)F`{MNG2}lYPa`y{E;aL|fnDAZdvlBCy z1yPhGWn~36weX>D2p;NJ7Ckj0>p|CB;w`e{@apnLw98ONKIQ1Z=%_*9Lq+$2yRl?0 zK-y{aZ0MXR-J5XHI9z@Vu~Urx&YW{agUNK()xC-6Dw~Jqr^li;E)Yl-wP;uWfTQxd zMp{knp{}diL4-EKVIFd6NjfXjlUphErp3L7l7RDMCZB9S|T-Q~Cr#>b~){(aEg7!cvOJqP^*RwgB}F+mX?XU!h_ z>$trTC0IRBZ$JI3DAtI25@nR^sZ;tvfS0VE6kgE_`(pq>JSmzEnRzDApVdjPmX`M> z1GVqtoM#0rWgylkb}`+SH+G?nbkk7S;JKzSR|DS~rj&z>`yD;sQ~ERQP9Ya>WVXi2 zsJVnS>}fJnAGKdM{rIm@y{jou%B&OJ7FM-3YQ>4(<<`UCu}9N73M5z|AZr8ZXmjkPxLbf9{Q=$I%HI(ir@d>xsTfzS|VsGL!@M7WtEkw zUrk|Gzwze#h)W<&sZ^d7Xie90Uaxg*Y*d|@jt(RTO?L+dLSFK{r~FjvKEtd3r+aDd zTr?%5W7jFKy3y1NH{w%rLmWpxJUo~dv5rZ6UG9T0_Ag~TF2Z#OIBva!pcG|Kv|v<8 zi`N0StID}eqNaEA`(cw&V}0%waM^C`Pg(=SXh+OIYJs&EUhhhr~XCnpH*TsVd>%JK;LP;wq}pz zz=fG&u`^nBfdcpUhtn!HznfqwMMiv7(yWLw4HZSZ@t5_O&6{pN9TWq@cjhx8W zZ>Go>_)8z1?om^cJ?cL+&~>4tYkur%)>z)keb(U(e=t-WJ`#~j{4K9t>YJK!twUzu zAp+d!+)7HI^W)Q4*;#Ps1ug@6 z!F*sq+t#x3ne@LT?(hT=b^+4O&z~_t!rz?UCM_k^C@{lCZ6(V(jU?aWF>NfUwpvjT zv0GYNCd{cBBVHk(&a2SWJaEw?iT4jdGNzCmjx|hnN<+o|8-kBKTv`ff-QUabiHeFk z&c+o)dObExgL8e|%d5Y|n|BMAeZHKXlk+s7E;=Trcc&neO6t+wJ6Zgpej4p4bmoLu zzZbP4=5^RSCbve29~NLzzaRGIv$*1ZCJS?Pb*X;OOH0o!>cdzww+=Y>%=ez`THM}< zn;WA@cru6`xNMf;hsj|KuYFZf@W}NXzJDnCNprsHRCQQrXr=Bq&_Ic04LSAI=6&O) z5+szDunUDTjQB|!KwiNbF6JPVmMU}KAa=-vZ9m4edhPzSNLd12IO^JWmx37|XQh8CY44N-|_b3^KIY*T5S0LkHW3bweYejQuL^e{$ z&@T~oBhJ2zXLdL{(olhrZP7O6Es4FAlJ2SBE*U&M`i3rIQR3-Q^I~ag81CY+>)O0S;5$(7{Va? z0Zb%0>!oQ8nds|gEDt1x8K*PS?ofn`mQDTvJ*d~D7@XM}EAsb;-VRt98Xp!&wFs7V z{DSGA1aI)?kDcWGND$IYF}IAnDFmc`ViD@y*3`gnpZ;h)qbx5kRQ#*t&xjx4pZMsi>&3iRKi*4VZe{Ye{nBQdws}RnIz`HGxux*OG8R#R$514@ z<@9JL;Xm_JfBYfw@YgN8ykxJxBv078T_twH!T)%#{TyN`!lU{}(U+Y-85MAdrR(Bn zmy$pG`KNL3XFVyw{rJ%%N@)k&y1`Mt|G-6(Z1vJ=sV=wD$GuQ=05$J;3uJp9Sq1N( zReJWrORO6)^htVc+-mi*g4&Z}4qVb}X{ zGT)cZVz~8YUP882w~vmsqO#p0 zCflW3NmlJddPHGSk624aQboCk?+};< zldru)1}!IN5*3GKhcMS%5a|r;-G;)M85tForNza*-rn9ghhUtlg{QZyETz1L#tRpx z#<~MB!vGV$_U4Sx1R_MR5wz}Bng||w^Z9(1A)F)jhR2E3)b3b4I|l{b`^X6>3siUR zxOQ=AwqNt)BJ_qEN#K7mh+S$Xdl4%=m00QW47P0w{PM%Lwzk%rN*{5o`6aI}Z_tyM!nSf< ziu`wBm*E>npDeF#f z5qA7Em-gM&Eynh5=`D)7%POg(E?S*G^JIOQ8x!T`%v^5oG#EWYrdskF8r8XB1%Xou)Ax{AD?uQx7^*xQiP39 zod-S6#fJ#e4xi1l-Q_%)JhXf)-B-A;H1D#+$u}B`Y(XRUo7<8kba@>RxFM_kM^coN z(U415F)h8kM_?X*r7n3S{Na=}pW>VRQ6xvZujS^4DW|6)UGD$$4kX)qW(XUlGQf7n z?2D_3j;-vU7*Dkj|Lap^xRt8Jr8`7}|Eckm|g>xr@5X2A;}&YAcgxBKV-M2+b2}@ z9XR2%_TPVcys_30H6z2j#LQ@D_0`w(J;g7rvq0Q*Gs8@YNSKQXj*gDi4la^;fB82m z>h^QBrHnhO+W1Pp@1HObMxyK(F-R%Ez*PEh98QrD-@>{0}t4)vs?;F$Nv4PgZqLDE5gJa6Ihsox< z4z$0xC*{+?Sl->Nn76jCGN|@&XUa1xiRP%V!BVES6=z8?}1T zWv=0I?o6VPa&@<3rGItzh8Qju+0yy!G`NbE zQiHE&T=Z`-o^ZAkb7nN#bt%|gp>1&R&qjoiG{<=Ni*2KlR)=ubefHEG&$hK{R5CpNptuOJvwU?3$X zVa|?qn}VHx<;=_sda-N7h$IpgF7z&x+wQ_Lt!cg8Cf4?Lxfzyqm-6-;rwcYs{7Jm7 z*js9D^qWXz%2`PbxW;Y?+5<4q`1h&GQH(bsA?r00m{iU?BkJ~&5`4hITtsSNk`9+S zICMFv3FYNst2@+gBUWfS3Q$zi`Ph*JbDB3e1_|y?w`~mSNCMNV=Cjk@XPkStSbyd?;*R-d_*x}(tgl0uk3_+^ z8e6{#U=4t>gGeDR+b{jrhBgz))S`T84K|INWw3D2^+;;&ubx08b4QWK@%#K1zHWsb zjg6dvM?;Az{1d{3NH-CZRVK5alUaMM*QXaY zW{Se(;U5eC@{_c*#&M##{vk>b^NwQgb#diZb)t=q+v3a^%ZFqD8={7Bbn z#nNwW-EvEHX(Db}?@=L#Qzr- zdymWmR(&&_^336p+r(E*S+yG}U&m7V#MYwG8>d0#V@Ay%R=VKqT;1n}86r0~7nQ`VJPG?S#8@S~nRP*_lq zl1XE}@utjMBUp6t9+Ha4*MGDnR3bS#N@MT7y?db}u`juER=2#Y482a9<@g@bO>EmI zC7pT2Rkwd)7RHGLKczVPpShnMN9GF=B5*ACkMQXfZ)ilsH)GeZ_rMom)^JWiAb?~I zq*$(Xvwmi!G#w;zqWKHC+t_{QUdLCs(VY8pb0rI9B$p>A-3QyBrFdjz@8M17t;*ed z;jED}SWJhoKq?2O74i z?oH`_%LLagTMF@v0LgKm5_|(mJPJ*lT6k5izGoYbY??sb7=3ZY#YXRb zm=eNJ*6f#(vWa<6(|>$ACA(I7}ew5)8 z(qQdx-{z9WY$(~4Gk{}_tI01YDDb9U7d45mBui#QnHuf5R@E2&Yl(efa%2_z&_>Dv z^Y80^>pOlYHvrjICMLS&#ZYyjD>cu&#-3zO($GGv6jw93r`)hSP4ld=yZGi9pwAwK z>IT)3ViNbvci*WB?{?5IF)ZOtt}Z^75y;m6?FpeiLZqYQfdfS7_-wxnBHxbgMr>wP zsJ=NrC+yy!_-t+aVJNeXssL_=p#?u}3Q|P{i|!K)U_`WaZWa*{fy^ST`~s1@;25sg zeSLwNm2U;DOBp-01>R;1O(JQt?sAFNFT^p)KY5ou3*uDx^jR z8|)djjq}fg7~AE+fM;>H|MY3u#jwDI#QpsQpJ!PoG2`aqLf!f@-cUFe9w$w6V`HeG z1f2`pM=}R}WM!WO462M{XYm466>tGIGa!AFy}eIkU{jyR9mVa$kaSc?dCx6XOEdV9dGlo!|){zN@pJoR?N$*qb$5R4~|Kl?ei62Xvn*_P&7Xpy3$7)cQf zciYv-I(m+XO0Q?5hEqnt{>Qm|DNGXg7Zi{-nAK%^7_U1rGO8XpA~CwT&xtb8WrtiE zcj=Umll&vOoq!}wO*!`{?>$Oqe-cZcwJyG^uYK0fK%~e$ppZ>kN;0k{I6Urm4cRhV zYf@{ZOQN)K$mw0qj9pB3R z*P+C5-#8L#a}cn@>Jn{TKst=euDwlJkoC=}N>*bn2=GCe$4xyvGCC?Xx_KF(#o8S{ zK0fLjKE}wtC@d^|`O;JhC#cx{OMhIT=rtL5JO_hf>Fg8JwB^%E1%9*H>|Y2wmYe>7 z^3r!UAt95trLW*6g=H`*U+ zoU_|9`|IMj&u_|ZA?7kny&qOI_y9>ak-Zb4G>@gyn0Bnaw7d(6+fvc`8(-dQtFEvL ze>!^PB3AY6s}WDOb?d}eaWF;GLE4?3A0Lj$e)9r%>t~@XWDh^acaFxW)kYM9u`iO0 z7Q=nC-A=RZ=`pjFWTElPA?JoEw`b@=c5=!N#|i#U(Gr~<{cH;WM##l5bf^LnkERufe(>KBvEgCpe={pUNnu4r+VOJAwC1O#6!6Qf+5!n z6^Dj~nr)YG{py#iMRydqzNvIF_xrBa(^hMTCad&FCVYz%h{Anm`5pXX^zaMiz@-IZ zEd;tqBbpT1wgOziD{{KR;q!6g3C%}_-t~Nk90c#x1Dw+j09TyvsBFW!Y;^-=cE_U( z5$EEQYgDQ#!i&@y&?Vw!@u;9g{*0sur2^z}ra28sZbW_ZepHFDMAT(7GpWe=L3iBF z8;7Dayxl_8;{2zKdPd!2t8E+wgJ|HrjOG6{8HJ~dk-=cwDs%qJ+qK?>d47&cOEW!4 z2%@@wyr`h0pfJqvp_-Lc)_iJqWo0uKxL*PO7ZgxC94Do&J!MF7f{NsIeJlJ4%?o+X zhcx|I3bs8%)4RfQ5ZWhw>;&>S-g|7; zFjj~otURs5%CX(cT4!1;Eys+sMza2q93!WGeWAo;$(a91CBSE-ByWfi$i>(76RvMV z#2!>_<0AZvN|9B4y&L;MvnT6b%MQn z=5wQHSV^oz&OnpEu89JattXgtI6s0EUt3vu+b{k4v#sE!*-pgBXy<(mRow|xjZH*! z64J9sHXChd@tv7p;j$}!i{8u2IP=zqs^cK{kUGM z(jR)5;nfkb1HBnlsRoo0olZHYJxxV*a?{AP%qj?Bz4|pJ)n)Ca*jSw3GctSjVoX`} zQ%?k%`3eEI1V4)yx9QQ-vTAWDHpDohsSAI8AFC<((rVVNcONe%NqWU=-Bml=x*@lU zpz_n}fjQn$%j*~_>v5G#xjM&;OmWcDu3nF;`Y_)0dKPDhwLL8^Hd)UntDvwo_}!f} z1WjYgG4mafoHh6O15#rJ5cf$Mv(mMK^e8$hemh$!<(IfekC=lK}EG&$avCAJr0ulavI0buRaG8N6j_OYn%Q^sB}rp&?ioy zqO*}cfr2vxYoocEG%$DN95;!4N%;uPt%KfmChGxR;rhnl8G8*9Orgh_mNB}Hto1*oRMOX52 zqK{=p47Pl&s5s_9i$e2!y4bTK5)n6uE&JfkCdMeSb8rwuI;GfeKe)f~@6X}_&;q2# z^y~Irv1)x^AAG%uL+CXBR&*(artw-AF}(Ff;3-ANJI$Q}0=jy>t8kbUpT({NSd2`cs0dq+y1Vm z4iLaR4{Q3-G;2t2q`C!jF;(wV4wH82>ge*PHby{WUMV1_D(St?>g9`qj-js;-mOL^ zKIQz9a?kDoO&rN2yf?X(#o-CJ|&dxG?)GINrGrMgn~!=`dtcgI`w$4RHQJmjd9 zYVV%_l86NQmxaYO{X)JIVu^lA)BqiE=RG|;1$mt@3D|GdX_%=iI~4o()awjqq{-lA~O`z zI3C56gk-J6Mf1osbsq(L*!P54~6MfC{$lC zz#>@@M9h&R|awMbZ}|LvAl>D&YcdN;^fkyQ8WkrKI+j zi?z`L181v6uCedr3WiDdIOB0u#%{L9pnB+26$F9)8LDPIL4+|=q|JKRRcr{YfKnW_e#`Q? z{Q2>|D!T7c@y(_tCh~af7KI%Hl@DLlxybB5g^?##4d)&0B(8L@$T-P&FRNpeeirMa z&WJdU!3t@8586V&K$FQEXA4C*nQ($bi58#6J% zAdpx{jJAO10f=%k1A7WD{taJ^va(A@Pp?}ca^&iP=nSgZyU9GSCzDz9mo<}g5OZ)v z;dX@R8kLC(|0@d%ba5@4(+OweGT($MF*&GZbf0gWBV%i1w|gJzun@n98=z71{Nj@8 z&d(FGXdyJ!&VwA8=?OzfeN`VfZWNL}!dz*3L)a@I14uonP6;D}61Sdq_#yh+d@DJAeL3;_`w8THrfjVO0X{LdCEH zK-+MQok#-m=7cTvbxBSw3GfTz_a;f1g8E~G8Kt3`(yL~Xq#PT$ZGp1SMA;A&Rn%gJ zCyzEj%4s|W>wg5U+$4WH!Dvm_uqWvy2^sv9v+?6+dl~%65d>3tX`@@5=fu})@Sw8R zzSh%_S!_M+Wo4Dvr)@lv@>33T&kr^e(9&~74;Pxo-WMY#+g4o~R`U8Fqac|f&-5&z z`9?{fo2-Y&7#@~UJXUHl72wPCJPG-0G#FOKv*er)hg?1=Cnq>|=S+;{qyE1+?A-ePMm`-6Lu&T)mE_vhT0*>@b|Ie{ u8LKRK7gwGmbFl7jB6r2a`~MV-UF1vdhE literal 0 HcmV?d00001 diff --git a/docs/transient/04pumpingtests/figs/Vennebulten.png b/docs/transient/04pumpingtests/figs/Vennebulten.png new file mode 100644 index 0000000000000000000000000000000000000000..aa23334b2afea048598723640a0f39b6f13e42c9 GIT binary patch literal 20566 zcmch91z45ay6#j&!2qO@5~N!|LQ3gI8l*)^x}-x%Noi>X>5^`c?(Xi636t(SCc4&M zYwvx|x#!+^5E{HyATNEftaYE90Y>E4T0P^Lb?gQ zvP;&r0{*yV@KWj}1d<9~n1I0JS61fwMkaa?i0G&INIM1jF>Ifn zYWC_^p=f^Ly~c)eH$@**i-s`JJVizm|M?k0$|k5_At)P?io{Gc>z*tSl27XAbwf6P z;})Laprr*Sex%sj9K4w-2WVI94Dpexabk7PlmXObxB~L|+yk**QU%%l{x=oOxs@j0 zKz{@6J2&p{KoCqIvlfMg{Mg6BH=}Plj|P}TV;tjDSD-vTIinfi3#fx!C5D%)l0JpJ z`Vz1LJ04TV!)fE-QH~=om1+A@!Yb9xB;HM``jJ^F%o;|g=6o2fhswFf?8%MSVck8i zx3f4&xe&;SUK0?ajfb;>bX?9{L!s+oBj)|0*?ch{&smxcLKQm6uxu^=)`VU+l#2Ny z{elBzR55`Pmk1!e^VzD3VYy_`KP1N zc&Y?*RuKK3b5%?SQ@jFFu2Bw74klB2(;BKlH;wby(4+T}nx1akc!I>o4Ts9E(GMU$ zQuT_*PJK*`upk;6H(pg<_;hJALi`G9Y*Ab+i7K2m zB-oKE`B*ZtSgjSq7crXO`^9l$%rR=i<@d4j$WP%w_1Z-J3DzuWzzGh6uetSbu@P zih%L+R>{wYWf0U^gqts^d~aE5Kb`%E`T2(6V;oHP;;*>jA9J)R({I~;eaPy;W`S$- zL3fsw;|{}z4((f$H{_U565OeG?smRF^Ym&(!;6DH);y4OD*njjWx%WxnC zdE(Ty?J#o?Gkh%l8DW9ojJEA5(-1ZLP~|S)hqQ<8Xb+z!`7=Glt{3JHu_r@*NRss# z{rgR$7vrCaz7v=GiH4-Ls$t$15_~!fOuDNzGTuem+dF;rF1)>c<dA{MuP^RHtp?n?CKp0JEOWH@dYK4P+p|6aAFa zDEm4^A=yS@&AY#dS#eC5Q-M>!q0q`7hPWhxN!R2%FEA(TM|DPJK&7kTnJJ?uUp{C% zD7>z^&hbEiB^9w(&M2kG+|Z+l8q3cwUc5*=K|HYC5{sJf8-5?c>e@c@sm`hGDVHL> zqKsnAI>iv%5N&4O&~my%`tFeU;P{a6;B*Gdpv&OEU}=V3#>~JAqc29D#*6PZ`#7?8 zZBR^uItaGoLOFd_CWk$rb+JBSRW{rnAn1)7?jME?wD)KBKTkeTh|S&l7N1!yaOQdV z9YwK4O5|&yz{>Y-UGKXMVIke=Z}*?3+B~zpuqCfJsPL#@acp*!b@X!Fay;5cKBc=L zyC6KRK9#@y{5HvL#M?QyOCRid(=}i=Xn0?E=LH4?z6oH%wZ-FMR$@V6>Si~4R%6W1 zX34Iq->+@eg{j-lEX6Wq>Ns|vt&m;fY1UJGW}hy?PQ>NPE+6_{Y-wzM|J(lflIR0r z(G4bgL9hGiJ?QVzSJEY_;;A&LnHH{>o*J5($D2(Vy0mz|!7!9J9MIj%%88~QDQP|& zSBI(3lxMBH?{P6RFl#BD(6t*#ct`xEwytL6(!(R!qxD;tPJiFer+xXuySM$D@9LWB z$J_C9IdJB@f4;mwJXWW=={$OZgOe%|BjMkX?9jY-y!U7&uzbDTN_|5k#q55mU}?3P zoSlYUkdwz50;&sYf=F14XIh3q=E}<+1I=Mg(ma&B5fg($@k46!#)+QFMyFn%d5k6b zE$bQO9`>=)A-ndTX>+d`YPTY{&sS_$_%~kPm~>xu&v?=DGvb5G%kN(iUk<%Q{n+(U zsE&ukLR7o;@N?zfgOz(Lc3vNQ-Fvsb9n}XkM0&Xp2O*>0%R_d<)xnTO>#KFI&~Ez} ziI_)PP1->U6^s@XOL9!cO2$ZvP1b6$ZwYS^)y;XM+^p$0;ET~{+oHRWybwE|hI2|c zS#H5y$NPX)oOltU8m8f_EqgiiMCD3O8co|Z)QrC zqw+=`!)u}+Kw=@Y)= zRHGbaM%$^GQp6JWI=`9pD0M2$4R1_LmJ(7a zGxRVLPY@MUTi?+YX$y+tirTDtrK2oc>iryTvQ z)bWu@hswOo-O&N1G)3jtM`n52&@RW?)9%~J-hM7$TsY5&*o4^+-MUUI%Egj1iq;xj z=Umr5_#(J;1$A}N$I$ELSY&;-g=yE0Rx+R;`kp=rnnD3+_Fc5NV-sp=9LKJhnd<8xD(3YvQB5Qa|fl9J8ynKWApb z;(B(ezJETD7ps+CJ9^c>tp~Lk-M>)A%hT74s*-iHo~7R~y{i0Thrv<~&4<$ReQ{kq z!QJ28P79DpbVWGw+&kMeyQC}tB?gK6oPCas%i~^kPO5ByOBX2u>(F25fgl=U>~q0Y!5iWHhJKLwC?e)aO4c8Y-#6CYLNNmHF) z&4b>e8xV-0d+GxSLVN(DJdy6H8q);nX`Obqxs2sd5kl* zHy~fDKZb;aIFCTs4ULS93=MsSlWlEnXHJJp%__IpNE&^JUCT_ym`NI&1MzFGE}&5n z5lfYl5QvS?!0kJ%;Kk1F#GNprq&?IlH(_OLTQS+#EZhL5Dzc z`ikmuz3$;`&(zK=>=u=jP;9oHo}Qk&y0Jbwu;>2vD!rh9`AFy{)2MZI2@<~r`AN^w4<6-BzDeWdJWlOr_VtYod1+>*;C!Bw#jr6d zun~62MzLtiJ&9L#Zbx?BIvig(IXI4C9gH0lYm$t|&FnQK=7Pz6)lNsxRgfVNj2luD zg1bFe=RNFGHent)6Me}dT@$dI#BEbn9rVjv4L+z`a$x%~?nq5E&TDTB4-Du|U)JN) ze734OIA7acXffq|4!->WTxRiDwv;umW_j;~z<~n-p?Dlq@~LBYm_7yU)Fd|=1hOqK zkec$*a(#XM5p8?Oa86|!p#doT-&0!%%-UUxh)`WSxqs*Zv}Oj;(ug z6T%eIS0u%8IPq)%Lx!8gyr`(?J_-s2@p&`8c`JS}&!I~D5(bECf|wHI#=7@XnR$6f zXBYgwhODQZEInj+V&H5!n)EfuSSi-m=yn%S~BaqfCG zAj3yE67~QE<#JVo@12?1p*?qA?WLoXlvHFp1oCqVEMVBnfk;yi508#0#;BHlc+6E) zJRhq#I5=>**w8}tX+S>X1o_Jw!*)S6S|kR7uJ*WzQPcHAZ=<=Qq9TG;d3a=GC5r|E znf#C{#n;YeDkmb+!d`nKwfDS2Lqmf=8v*i@S3kI3=h?GoIXO92N*QSLAtFR#4$r^_ ze@2(Wk7IW_+;llRG@UGyMS6JG3!6b*bmlh1T`G=fD0n_iB&VXXa!E+onRmvzho@yw z76M@w9!Mn)jA0`Y1iwmxv6?I|o0gJ%62zZ##{-D_Zj#YJu%=WjD+h=D{%RkHNMb(M z^U-`=>y_@QQgf{u|C^8(l&q$wefjW0!}jzcs6hXZYR`XA;mRmx5Va?j@-*Y2phCRJ z#R>=rSgZue2>JeV4LsrhepyV7Tk43y87*->fjRS`LLjqb47pixA3xp{u{!3v2XT-6 z5fG8IS)?vq>;irfVH^=4EB@z7>wo7?|G(D!|A`wLXE$|mae46I!9bb}BNbJ8X6Czh z@7nEzz_q&vDvgYdjCRj1;^X7Ly)u0JcCmaV!3owznU$4w=gyryjYpu}B?}h~O;0nx z(;J6D-FIU3=qv}{T6Z8->f|yJ)Kc}7ii+_~RvVsvHc)cA%w6S?b{jEd!XqN0Cyd{{ z108}2({VZ40H#a`uGWy-W_JS*{vK2|wE1Z#Xy93&2P!Y_T`xA1(Vd?Ns0vC-%h}(- z-f!@25S_Yq6M>s3lXGw=cc|2U`<5&?QCUqM~B5+iQ9G zC+1bKc3Mt)+K`eGZ8bHfcpZiyxgTUnzNCKX*}~K+=&}sH{RKu`R*Sw>`Py|K$r|{- zy^?MR3D^3ZUM3jSj@olr$D2)zudu}ow500lDej#}-RmelTyt?`#r7vB{=8E}4Q`^Q z$Yfk{Z@H_(4lEs{{Hq-tMKHZAD>TA)A^bdvblU^$MO#bbo2Ly-a>97tPnnozhp7-H zNd2gieB0^7>q334UMldvW-|pF5XRnV^xbli?iNWSPR))N!4R1z8Y%Am)VP92{!OQ6 zf#I?j7e>{btbu<#OlJ}%-6tv>LGtfQn|-ONsSohkeDe*0WBf4|cgLPOVL~9IX-Au~ z)|W5qJwAi5feC}+*DVg3NGlC}Y)MSaqu`b&o}$Ny1t6L_@Zl)K>NPe<$zj8>B4l{8 ze@Y@qh53kpAmFTTt^q7DQSi!#J4CGlfJ%g@el;E_c{fDk@M9~dblUGMM4fIfzlDgF zmbT;1?<*-O9oFAtL?UrLZXkYL3Z1HCzs4e-7?@Tv8(v1L9MNDQNNEb;YtoJ=`?{Ox zS#ePjomz>>T7L@oe@1rpWWH_-s_TwNx%EoT@w~qSUuW|sC`a0f(}f~ZpeJl?ZSf-| z_9(x4wX;7UV?I%0I#c7~038Ll7-uV0bMHqMSQ_w3NghTK=g*|u?z8iydOqM{-v7sH(S{dn#0$}ZMYa%LE#KgoT+<+`+o#?dYAo0VCuO%gHb5j%J z;~(p^VLrV_+oww1Q{`NCE89lX8Z+(Xl`(dxw>?ZvO+m7$7wQLEY^?PK1qA^pIPG#Y zMU4aLN>-uM#}y7r=DlETU>t3FdS6#t`$c>l>(bIQJ3o&+EXGQ-H}>`%@jCBnHuF_j zm%hx+=^$zys?bGB@8+v0)J07(1%wlSTjQ>@*|}H0n{JbSq7(7iDW=5R+xv+~5WphK zm7g)+$UD1@*8~w8aF=AGezp zig=+RA|ieqOYQ`HYHD-3`fRZ+Y%RF<;&390PBpaJ@hj3uH~7{I`GxgSW?a-MjWGCp z@$(7;>AbC9xVZR38HNG)e@B$t)u8-U_&~u|ND$cOuLQL-jOL#FB-Rp^yO`xmgBjCR zir(k4)kd_q0`l_mH4+Gr$7kf!-%MKPlYS#iQybI z46{UbbIl-HF`tnQdRkfu-qasIde6_OKZS<&&r>&<{iVtf&{3OJne=6gjC*_Mp6>>` zRXLv=Dd*lNudV#Zh$*|^C+;iZ`u1MbIl#64=lhWu57E2%ki7WyQE~28qSY3UQkX9vaoMb7rkYw4G{j7M)153ZvgL8Pnne*k#BpDlamh(4T&{_nM8$7Nme$5yPYYZ zzP=s?vr@ip81NL;mqm#rYh%o6y9%04pg`23N@~W6Koc2{!@Qq*xj~8Te{h6Poeb*q zhYvSO|Mn>bRn=(bmi5C4^PZQAf=lEyB>KVn_kM*YJg6YO$p3DxEZOXL)Ol@-ElmOM z7X7rpx&>yTNTJtQG6*n$U_qR%dOk5lF$7iMPHFc4X%(0ig-^no>O(~Um$N;4rl_op zg9(2rC@MZhE==;|hqY-p64 z#p61We$>IexB$VYosH&@`V1|do|^jL>_UvlEIYmI6OyTET|n|Y$Dg|zeO(0^d<&Rm zf;GdI2{G9@IHMNzf+=x!c6Oxp@&J50__A+vtPl*rH0ExnYiZ>< z_rOJ-ni-C_x3`0(VH=i#R_%4GV*^b8`2qNHzfA{y>Eh+fV$Frlj6Z?EyYP?X50^et z9kH8w>mZRtf|=S%lM-x6fjYAj>nAXJT5wEGPbYXtxT^f>>nkN=$+~N+t4KZ#8^bvr zmM!ptMi<3yD#N|&*|Kl~k~9v=54Oz2vds?(888gGSCfpqu-vv9JLayBsCzI}W0(H|N3$6kqxdZf}LfEg{A8`jEq z-oUr0A8#+ugnQqM9}ai&FI}JLDY&`4Hb;^A*Y;?{q{QoK(HJx7U#I50nM&nl%OV-6$U4#h+b##VfnkQzR1YP5Z1*4U9i~}k@(l>VkG(RyiwfX=;-L=WSnz=4z7_jnOJ#1 zMvd&5%T{CYW#_fsB!PVfB&9>7{kBmeuF5U9&Yo`pYHz# z^8k26jVoDw%Y+Vl12kvhgQK*AKg|2L1-`w4Jfb{$afeIq9zKAYP67*1>G)Fe`>GKY)OIUkv z1evS>A4jDDG$BVyjoo~@s@P~Fq1tKD{vh$Otl)0Bh=OL)7oHgUPNF8ygL;2P&mK=N zv?D%?tLzDVasun));WM(oBVNP;-4RY3YX?6jZEU_=dYBjGI!A3X;cpR%4U7I`s^eJ z!ToH~vW0MtWex^|fzs@W;3$1S;Jop5tbfYBA+V{b>05X2PVY{%#TO8b=t)Khvp-Zz zyB=*oVv7uVh)5a-GvvGI_f~p>g_Bph5K-urn52Sv9mxSwMgKk%gL$C&O7AmgFoh(xedne0Xu`AKbVjP!Xp)n|>_Y>9$ zadAH{taL_OZ{(C-F0|stQsS=#mj&tNyk|56m=Nx;|)5}^{_PXM^*tcbK?`t@u9mO|@p!7tutmHb_ z36ZX7xm~!S-oJmG=`0aR7cIw4o-=|132yx@!S)iWSr#WjjDrTHS@B$+a#WwE1qa>XET>dmxfk4-#8_C z4LJyER*a82au(#F^b3dSn@c2+HScs znq^uU9fBz`aNBTm^Nzv9#Do``?Y)gf%V#~TJqW10J9}5Vdvo9BdUqH6ap+5epqxW{ z<5_tTQBjO;_T>(A>&uz3N1Ic+TOYntH_0z|$8%K}g#}w}n3e{ajn|wnG4jA#pMZgA zf3>EIrRxdpIe?@b))v&>4|PQSYfgh)a}El z<>|A|6mi7_dt^6hDFUOt(Z&L42__EK48X?1>56$~e0hFCz#J?9p1IS+J-f3osc$vW zMmnVRWJJ>?2L!-)zSt$FwJ^IW-e;FjJuCM#I>~ZY6lB1fUEbL)jbu-;Z`bNN%K|?Q zD4=EjVHCb?%j zK|7r)?Vy+aQ8h*T6Ela72Ew|1p!ZfCt{Ve1qw8Wzg3fsbtG(Rl?fpC%wUmDjh;uic zPvIPrf~^cWBOJO9cn^{@ookZjlR)a@vFNL`9A~1dCzVtu3m`o;9;Vw97zil1$~Vk; zH(q2U=1q?5aMn7(@4H7FAB?ZB>20U2{ZqYXA7mD+Ia{@`)8oA+RC2?*4SH2KJ*_#G zk4)n2;|)StG2JhHQjl6j&Mb*EWDneO!gloK4$NnU)8+Xn_P*pEchxz(`0PaQfZY_C zS(>f$=;66?-hX4WiR*VE05GjafjAENBI+%Lm<)KU_UddT0fd z0sNTeg&nS9L@9gcwqnGyC|(pEwD)4E9$wQkrgbHr4GijKXm*;<#Dc$Q35EHGo?}>~ zL|q+d`S^GvNZhoX*uWmbBBA z+sO0-P0I-{J+{@=pJaRmm#o|L?|79?&8Iw{Wj%T1zdczlPh>(%!!W;3r7q)YtQ3%s za|Mg4CBVg{qG2d-H5!`SC4PO`+oi~^))LFdOi3c80^rr8K*`I?cW$92vv$N+3@wU! zbdUTilFN4`_p;UV>*IW9gD4TnEKm2>$oLLUoIz$Z2s8k?OCRGsY*EbN$u<{JJ+pE~ zR+jNcc2kNNwimka#fy%hzCtIgIUS42ay{CGq7xa(w*ZPsePG7Enq`4v=OzQ&sXv|) z`=q@`Ro~2vxvOTh{8#ktgBlvGV@VEY3TM65yDVaeQutqsf%5U;!w0ar;cnjT#@~@i zx<$GTYI7x>=k95JW)D4AbO9Aq&AfW(89m`=#$~J@OAeg^J}9={&%Mz6aTrUiC28lL zJ(3Eel({q+b(1#XJF8Q5crrIhO}i1L+O_ei^`5a>9u_GhmYJ&-=_7w4POI-}X$JO& zqbyU_BUP0)wI82I#fAaKM@&o%0NT)}`4aFsc2|ogR0Qurc<#H{(vp^y9zd5ykI1h? z<5`VD`^?FLnbndK5)#Oi`5qBHlEx#?I$aEt2@QV8A9uCt+@9>Xi~*w7KGi7+CmHlj z9;YJ})K_gInPus#$GZz+H8rUVS1$pe10YB&X+GE8bRQp#?oMe(geN^9LDeg4jIx^~ zLPv{?!urfHr5Vl}hWL<3KyIvciV<%Z<6l8_>-NU942hk0>Oki_o{l~WBI0?M+ln42 z+*SA>(XgO0;*iMyl(|1J1<(G~-x3jVYxD=&B4iYTm2K zJ!wTfTw%~I{f<-}6|Y%?*CHBK_I;1A0)-egt8m$yO+JBa)pDL|Dw8Nah+7X~_xAS5 zmR&BDgb(36d?~u)J#yi}Tt% z-T^;XWo2b;E#HY&+vz1SnfL|(X5#2rc8Hs$!Bs8?>q9<~d__)2+n^&bx~xeo&y%Nu zqH?u*E~0hPt}LfyyESO+c2qfoOu~DM3P5y33^EahhJ7=*E>Ehh>*`!-8B?MBay>m zNEwy&a#?v#j9POHLV|_+i7a+$ng4+UXtaDgvk1d^a%5NPas-7UiUuX>$$2v&JAOp5 z?B+q0_b5I()w~Zr_q}iHy|^L^(8tVq*9@57MTiv6z-_SRx!No|V4uGFc>!aT-IM%) zHlN@Gjb~CG6%h{c+K1w_1iMXvQ*HWyChvEw1JnFXt!A zt@(Z7Yfu~;Z99?8*!`t%x{rj5UMm8crFHQRCbJ%!W#(#lB4A!~l6ry$57fmwDd%an z`tA;xQlUBD&nt(|{m00nKH#{WvhRn_Q_q}q+W{KIjJ}US8rv(&K){dx1s(hE-ps&sPp6@pa=qy7{J?VV?ES09D$x)H64vQ zdF9Sc5}FF`zUHLwIJmZQdFt7zjd18dIC4v{-mqz5sH6_QGN!xb+SXQ=3F*fwFd#VZ z_laEfOk9cNV$byT&F!7s+(Tf|?>s)aIxa`6XPMG1kzZyef!1ER*2j;>8MJ>S1C9EGTF>WgwIo?sE<(1KGZ7cgPEX;siM4^WG?udD z_oJXM`e75W zV{$~vAX5O{gT@b1AiMBY7nCI+VTY)+8rVkSMObT%W1R!q^AxQZqTvkq(V)D{Gj{e4 zw^V1jV7|*^z-$1*_h^Cmsw_L(1XO8bkffz%Q-Gj~qiM@17GX*7{aHH6#Nd~=fc*2N zF8z%-axs0gn$z(tSY^(Y%Y|ETtfF=|8hcuqxY)2@w@LZigYtKWt3iZZ-3dHgN{PHc zbpr#f8X&ITl^6n)P;Rr<@BIMZ6Ak6i)SV$Izo&)+-HwNC6!TLY`v{TLN`a#L**>Vh zgcBF2lYA9wF2c#GPCRtO7-`@E(CoLn6IXOB4 zUAyb1`WM5_d(%P1yfDylsBmzBT8jPkyA@ z87);+qbW@wWGO3`x?NoYb3p>9HHE)os=?XemV91(#rvt(8`vbCwh~mBcfI(B>6e)v zOIb$s;1WHGq{KO9`B?*SqBU|nXanFc(mVnW0XlETA3J*@#enR%lj9-OBn0LoF{>N! zNftU;&N7G>!)~5%;uBakuPbZW2TEp3g|2?CX7##>HbZ%)kwivbo;hIi_$+qz_!NyOu>*C!efa;JsX4gs&NdpyM1ZS z3+?P^D_D*5XUI7VG(IHiyv%giblK5H_ZO;y!%(+_2xWj4;R7-p=fPV}S(*J>X)W;h z04xf2pThhoVZB(BlKGK8#Yu>R(TujSMRNdN4AJ4Gv#zdgjj=`NJ;tiX!9{@w-(62S zw1~LvUm@*+v1Zy<`J6EmIGqd`xw#oMI+6FD%DEIZWMBKtZEWon z6%+!n`iF-pJmFAc2FlLfUYPXZ#m|bAq@*`ZeptiwK+TF}GYv&vp4C>{0fNBN(vldB zjA}&TFJ>c11WSf``|_&dYJu-k07+Is-3EANFmbR}s%er4g5`dECxaK6?dN^uGm-qh z*8^BzGI7G$4%|_+rEI%D+6NzYHUvk$*NfGN&$!G~n0LopXPckpCy7f)m{)F>w8&#q8kOGTfLZ7}5r6%jtS5uN@5^qEi{tF`9)_SMq`1qF+`cPXW4=&7an zSYVcd6v5MSa&m8`3<-4=U(3DbCg08T+f`apfOCjK;R~{`#CJKb8}*sn?-qf zJu7={6Ng&Z*UIOn^$T(05};FJlNA7qQ2+~1LXWGR*J_6cTmn3DfvE~t$jeab1gs?8uWCF*t!Ab5Im*$)F&iWSOf9pE4ZI% ze%bHv;J^!Le=qa_ZZ|M6092dA)8rXZJS?PJ*~Y;opxNELu`!-(c`u-y-bzJ1ywCjp z{W$CIG-=nO|KF5psj}3Z)KvY2=0JYmv;@cYii>E2gopellVyF~wsszMUpLP~eVg8R z>wFK$%*iQcGp*D9Q38JrR|ugLnuRpXCjqM(?s02KW}M%(xu9KG0ni^g2*wR&2lYCk6iXZX&`wK)k@0%j#QZj#_E%>tdR=@hxCW>FVmT`hqr}#&@v>`T?8S zB*$r-Q8VBMB`x|?YyfozMl*h2FpLA7NvX<01ZjTLO=Q~;QUV0}j~_pR3K%QlZ#Mcv z2b2-9+*Qz_N;E3pR|7u>=*$2M3Ud&EFthuii{SpZ@q14V6*5(>Lt1}&urZRS#W(E) z_RT=|W2Q&h;+?O>No_)6m&lNXCGXU(2yi#6x&6wj3hHkYh;sH7mn%sjj)13k&)&J7 zC`oP3%h2li3{bQT5wyebYX(x8u>_JNNZG@<=Fb8)mMr)FNRG> zi1ZTQ#*}xX_hsHtvn@$9=eby=AYVlo4+~win0h56Ki7ezj5K1bUHNONDi+my# zKT7}GM*YE)z)d)SW_8b|ict?=OoT9L{-%;K)LF5o%B|D5N0k)R8-ejGTIVqRHrO*m zK#CPwHQlu)y-A1$0;9&3mMek*30*Y^wTZSkRJnga+;20C6ojY(*aGLB#YBL!U8tvnI zg;7LAxwW|}{==`KFB=p^W}c@~Ue9!&L{Lb`%RzT`@+a{nlo)6YmpeXupeiXSC~$+R zv^>Kvoj`;fsL+&%9vM;YflY6$jwEtH_Cb%-!hp~SUO8%uOdHsobPPau{_r7~Afx>s z;vgN31bH-jC(2Kz0KW4XzE*nB--e%ZX8EPZ34yC#6Eu*=2+IcclU|iKi$A=g+CS^4 zEj61%a3f_h2)6nT1iu#u&S_E_>M#Td1>GK#+E=#6=4z$qmoO+G`Ja#U`o#7YQ3G`a zct^=MhZfglHzI*^%5`0U*TNCFh^|GXrjNTQ%V= zU9RQura$kdlSqhQm^_{7ziQpX$H&b|=efGJ zrdsXz%((f93!wQ8Sxx=MYh2C-jne=`1@#F@X9^u;%D2z9{+#z;OdlK`wg*WeJ>_Pd zoS1L|UQ0Nvyk% zzedpe-r~^k2mOIxy3t?SY(!0a(tOXOVo=h=wNmaXK3uX_h!lS`ve_4>D5TJxgg!4C z8&;)huBxgEdkO+6aKk*gwdG+nx1)rZ*eBa$+WzQXG>ekTeBeE0z*i@O%>DiGAujOdeST`(-0Rp=`ZsEHGo9N^1E!h9W^zOAQf`)|Q zB!nKm)t^7y$1a&en3*bD^noW31fzGWbjB67*k&2nqxtmJUHCL}R~3-Nq2v-U4MYXO zfmAB&03abMfn3ZDz@gCHl7-J8RKMI%|JI%K?B}&l2@raqC0hNM#3sx$mwb+*5ViY)hmqHJoKQ*3nawg<>l@B$zIV(U+U zjdLwZiUcYAMn4K{a1Pd-(0=3Rhf?sjbp@gDYcL2GOy2;-42%k2WGL zq-w+=%%t4;#EJ|(-|VRmTrHpD`nv!XO)@{b*{7|xjwL&g{nvu?N24b&vJU;j9Awe| z9cWPdEKi6<-ojStfn#PkaQLZG3V8|ul7xS&SATq59ri>GbYBSMM^Dw_9g%Uk1qWa} zc-1!nDobpKiYkr}#Qi?J_8>0Ym=eswn9gt5SYW>^MzFt1GCcB$Xs5Z<8t{%G^R>kv~NxMVz+UAu=V*kNGc{EVj` z{LUR-fB@8`RnBIjBn3u-A3(-~6f|wbm)rNvJ1gJ#Ne=q;uX}A0yakGOU3Yi)qVAih zH^Q|c%rt07#}}@9iT@ws_%A-RzgmWtH)Y}eF`%!0LR$Z2TY+cpue_6ndxC)SN?)nM zG+jqBR@!5xmRNKl0~2I^Vm-X@DLE zIIR$*1=oKHVce@W{<<+oK>u4RC4;<9Hj-B$Lx0F`pSMRbW_1Ev5+!>x^#%puv)wOc z0@4h+!QVvDe;bo6$uuqzLQRR!-hpk5s1e7?Bn3tCmtz#!@_UzgWda*EZ z3GsoR0B3@bw&c?A@NgCO*DWd$k>S`f)bIbq&IG}*YoIsZ#eSq=-SrHdRFMf~EHb(t zkbapIqRv}?k1q?M#E-PEhZcb604{m}K#?pRF7S3{bM!}Q;4w-zk0)P4u+!h(Vi4eP zKl@*O4)CA@*$am=zpro&tCT>7$uZ$>Zf;8QQoN_u-v76LiW>Z73_h+3s;%X3 z8DI2oIOeyH(CJA9 zV0ij`V~dg$oHGH(j|7utt$z_ATz1>eTg4;#JqhtUn*U&-?B?pdz${D1^`1_%${u*U zfnT>naW3PRTeiQ_Xk&X@v&1AC?F}#h!&z^%`3PmrkQthLJ`F(ObOX&0==by6LxY1w zdL5A|5>YYq<3Oim(5NWVX#iR;sSEzKvo2yfO@!TKboS`1Va`HAqT|Dl3RBiXQBB_O?$ie z#vynM>TkycJw3hUk>mU)N>4P`TE1;fJv}`jz3}@2e(1Rh*}Z12e;Qr`4T1qeg{#eg zy^%Q{0K8xaV})v>ww6|6&k6=V+4(6T(ZaZqMnK<@m)9-i{$;C}{BgJnDCE#{8z7#Y z9YKR9j@PpQe*{j{Btx0ar@0*#y4YtrvyCRI9ISx{uj*n7nkULZfrb=N6COiWn;coU z5TqEe5Nx)x?ci*6$voiL1U?$- zKoW-8Ost2(VKW*aG$vO4i^L9B@W}r3P4$_7XAx+u8%wS$8Kg~0RgIAUR|AW>y`znV z#ZDV#Vs=&*zVREQL+~r|#|7nq6b3rhnt?EHQ0rnJ++3TDpQD=%iiUq0CYRiQaIK=6 z8eYu1gglNmHdo>2n`sU&Kj+^Lxc0k34vj3@f&nc-5r_5)FoYmX^0$Zj`>kewegF&W zDN@Bv6kKJm1`Y+SvU~^9%6YjC-#c z6dqdJU^r)yCqp*3{}JRX$PjQ=kN#v4p}Ybd(Ex^lWVd%_tUhB=yY+9gdBm?N7W02l zEc?I$3MAwm=+z}uUK%GJoHH|NMIqo|I!&|E1(JKHt}Uox4&N>|`@#v;1OysD@^$iN z|8Z=?vtbV8iS-vw+rf~bdBp1n#b5;{9u&A%4(Q^E!9mO;`35F%$=O|du*3Rh*%=vQ zWtIzoFi3D*r`|X;NN^BF%C}r-rqi%vNBA{>2F8~BGHrH9*prnP?Br@x&X-&hF2czm zWkEWC(`xAa=lf}VQw4e*;CQ4PEG+SDu|8PLPD5u$8z2uC=t^t@eh@itY#6u$Wv=#R z3MSMlY}V{*&om5z^Yh_V{zuOP<6uykEXf||=FlpFH6I(JZUM!Oqd)(D0tpyR7Ex3E z_R0M>Ano6vK%h)8P0%k3F#GkflrJ2t2|VkLaL^BdlMPMRUNFA_&~xA?S34Mf^B2CP z;!(f*=D!%XC`i*mGycDVnC?}p>)RE&hlFFI*ZeP=6=mu#N;j>d;x&@x(mT32+~gVX zLz(I6xU_<~w-`Ok07Q-TI*|`kpUvKqiRN5x->oarR#qNKyckO)&*0Wm)E@-t)!xz3 zf@1_xu~RIq>LAa$&f6h_vO8ak)>xWy^o4& zaDFQ%ICjkVSp_U08C{%ZJ&?z|%4Kou^74{v8@JG0g0lTx``h5EIq$J#U>T6%IT!@= zUH#k-w0h_f-|?&mh$zEkj+d}?j8-`m79(74A1ROCzXi^Yssl9< z_0qM&;0m{I5(FnWkhw~^ws!)~BZf}qS65f(=1QaFW@Qy>A-B*0GkjrYR#um^02EpT z&QLO{l}IAx1I>gBS_RHqwbSN-V*wZl!ikZA%!LQTN38gvgh2wOj2 zJdt!zP*z@n_qQLm3*c9F8URhFg3K-|P2NjDWW2e5e4K`OY0Mz8OS!(awKdQ6fELec zzVTC4xK5h;(TtlLFud^lLS3Lvc-%!EBu`4T?wskRf1XwFz3b$2SEez+dTxnh02u;%Xo1M3mF1DJk@fm6tp+sQa~uHxnLDrNTt6%%>~He zfXfhvv3tQSVccZkbbHTPt{8oLu_jg~-jeMepCf=hVlVfmIgYID;t zC_Lndu<^36_4t{>+y&j(`Olu??Gs1!G<1SV3=v`B#ekn&+hq-|;omI>N(_1u067?V zAiw8t)+SIeRn&$kAR<>CxcFspkvlxxym3($_JGxTxzmQ|>D-qx$~U}qtk%trchW$S ztqJFDzpq-TF9q|FAkuRo?00^e)6ne5uqKk?{4CCqCrm_Dt4F;iV?L>QytY4BeC0qA zFJjr;KR-#h7G+H#!9ry+h%J}uj93Y158^9(G+EHBzMeN#&@8fAvoMi19|js4@iEr7 zXYov@)B&_K4h?Nz$B&tNLST)FAt5tP9NS8lzSfx=5QXh^qAgvB1Qo3=%V-48|W zct4=8SG!P>`1ul_N*zwusE0BgGE zNy*G@ycr&?;0^7M9wM#NZdZj~t!{^fiDNR>4gOsUpCqR~c3vE5auv}CXegB?Sq`mbF#3cOIDvKI&tPDX#@!1`r2$sKk;P?u5K5oddJ_yJh3HRy0Cufbt^4B(|H8~ zL9~Pay8!$Oqg)F1q$rmfW(FeG-vg?kj}H@tn)ANhg&MRi-*IiW>ls;i9x=Q5umCO* zrH-9v#&Ll&$Mk~>yJT^x+7Sz>rh*^XliprEcw2a%3o(6XqcQvl{tR(fWX{r**V~LM{EsnxR52tzGr2uf?y^EtUUJ8CB#tTg>mHNx# zRj@jn>65!v56REWAIIvAQor!hqD-xFWB5yMfRz{7-1;xVHSYMP_r2H>kO}tO39M&7 zs#t$r_D*eOyy|}=3zm!{C8rHmIG#~9Unby!h`E~vgi9@bWCoLQAh|vtK9~^hP#%D< z``0+a>YLldRFKzj78msdYN!h;qKQ@Uw|DMX}a(g+LsQmdva_bMS2Y z&bHjhQA`;$%C!K^Ow9qtD|NyNQO($rhlta@a?`byrq;|EcWb4;|7}gd=Tf=fYfw;x zF-H%Ew0-A08|(2z1Lq>~p?GdU`#C&3%+VF);3Yr-|H6R{1+mIfsS&>HY#^21`E9Gr ze;)%JUR~Wmsr4pUSQKIU=Z`XXTq8|fXAPW>oOyEPk-McSs-ZP!jmJI5SrbKp9iY#E z1M2o%;67u1-K)O&<2xNcw{rN%D7hB4^Nq-3KHy)0 zNM+H4rb@jx0Ta!f@(jobaF#BVkuOvFx?a3JH-FsH@k1598e~|!@X#U?45@wsm1(S_ zHUal7q@TqxWUHs{?)+d6cJujx^p6vV*xjkUgEDDy4&G?i3#j3{$)dLMB+`g{lzNRa z3mqV#FT;;gES6pN!7hcvn-6Zc3&S&GapcoKrSspHW^s3Fb&#v>FO>qUHv=f4Yc?i8 z7grtwQ76G+50E8i*b#e@JWR#=U-vNfa2M1a2j{1X_IwRNRaKXWPp%yN&@2MMR5Ubn zPz`lqgpwSNF$#d<#jh|MLphIf4*lu*>oDero~23vuOQ$5a;WTo|A_qcA%K7DmH!3V h0Fe8iJrRF?r5(5+u9oZw)Bp%XOh{TVmtXti{{n};D{BA% literal 0 HcmV?d00001 diff --git a/docs/transient/04pumpingtests/index.rst b/docs/transient/04pumpingtests/index.rst index 1726a26..2e06971 100644 --- a/docs/transient/04pumpingtests/index.rst +++ b/docs/transient/04pumpingtests/index.rst @@ -13,6 +13,7 @@ problems such as pumping tests and slug tests. More to come soon. confined1_oude_korendijk confined2_grindley confined3_sioux + confined4_nevada .. toctree:: :maxdepth: 1 @@ -29,15 +30,23 @@ problems such as pumping tests and slug tests. More to come soon. :caption: Slug tests slug1_pratt_county - slug2_falling_head + slug2_multiwelll + slug3_falling_head + slug4_dawsonville +.. toctree:: + :maxdepth: 1 + :hidden: + :caption: unconfined aquifers + unconfined1_moench Confined Pumping Tests ---------------------- 1. :doc:`Oude Korendijk ` - One layer confined pumping test with two observation wells 2. :doc:`Grindley ` - One layer confined pumping test with data from both observation well and pumping well. 3. :doc:`Sioux ` - One layer confined pumping test with three observation wells. +4. :doc:`Nevada ` - Multiple layer confined pumping test with two observation wells. Leaky Pumping Tests (Semi-confined) ----------------------------------- @@ -51,3 +60,10 @@ Slug Tests 1. :doc:`Pratt County ` - Slug test with data from the partially penetrating test well. 2. :doc:`Multi well ` - Slug test with data from both fully penetrating test well and observation well. +3. :doc:`Falling head ` - Slug test in an unconfined aquifer with a partially penetrating well. +4. :doc: `Dawsonville ` - Slug test in a confined aquifer with a fully penetrating well. + +Unconfined Pumping Tests +---------- + +1. :doc:`Moench ` - Unconfined pumping test with a partially penetrating well and observation wells at multiple depths. diff --git a/docs/transient/04pumpingtests/leaky1_dalem.ipynb b/docs/transient/04pumpingtests/leaky1_dalem.ipynb index d37aab9..7e2a1a3 100644 --- a/docs/transient/04pumpingtests/leaky1_dalem.ipynb +++ b/docs/transient/04pumpingtests/leaky1_dalem.ipynb @@ -314,7 +314,7 @@ " cal2.parameters[\"optimal\"].values, cal2.rmse(weighted=False)\n", ")\n", "t.loc[\"AQTESOLV\"] = [41.5575, 4.4625e-05, 327.920, 0.03818]\n", - "t.loc[\"MLU\"] = [48.108, 4.324e-05, 539, 0.042426]\n", + "t.loc[\"MLU\"] = [45.297, 4.865e-05, 328, 0.042426]\n", "t.loc[\"Hantush\"] = [45.332, 4.762e-5, 331.141, 0.005917]\n", "\n", "t_formatted = t.style.format(\n", @@ -349,6 +349,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb b/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb index 08d9c43..3e51f70 100644 --- a/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb +++ b/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb @@ -307,6 +307,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/leaky3_texashill.ipynb b/docs/transient/04pumpingtests/leaky3_texashill.ipynb index a4abfec..351f2fd 100644 --- a/docs/transient/04pumpingtests/leaky3_texashill.ipynb +++ b/docs/transient/04pumpingtests/leaky3_texashill.ipynb @@ -301,6 +301,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug1_pratt_county.ipynb b/docs/transient/04pumpingtests/slug1_pratt_county.ipynb index 070ad14..493601f 100644 --- a/docs/transient/04pumpingtests/slug1_pratt_county.ipynb +++ b/docs/transient/04pumpingtests/slug1_pratt_county.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -96,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -148,9 +148,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "volume of slug: 0.00863 m^3\n" + ] + } + ], "source": [ "Q = np.pi * rc**2 * H0 # instantaneous discharge\n", "print(f\"volume of slug: {Q:.5f} m^3\")" @@ -172,9 +180,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "ml = tft.Model3D(kaq=10, z=[0, zt, zb, -b], Saq=1e-4, kzoverkh=1, tmin=1e-6, tmax=0.01)\n", "w = tft.Well(ml, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=1, wbstype=\"slug\")\n", @@ -197,9 +214,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".......................\n", + "Fit succeeded.\n" + ] + } + ], "source": [ "cal = tft.Calibrate(ml)\n", "cal.set_parameter(name=\"kaq\", layers=[0, 1, 2], initial=10)\n", @@ -210,9 +236,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "

" + ], + "text/plain": [ + " optimal\n", + "kaq_0_2 6.048673\n", + "Saq_0_2 0.000213" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.00286 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.5f} m\")" @@ -220,9 +300,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "tm = np.logspace(np.log10(to[0]), np.log10(to[-1]), 100)\n", "hm = ml.head(0, 0, tm, layers=1)\n", @@ -311,6 +402,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug2_multiwell.ipynb b/docs/transient/04pumpingtests/slug2_multiwell.ipynb index 52773ae..50f2705 100644 --- a/docs/transient/04pumpingtests/slug2_multiwell.ipynb +++ b/docs/transient/04pumpingtests/slug2_multiwell.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -84,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -140,9 +140,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Slug: 0.02286 m^3\n" + ] + } + ], "source": [ "Q = np.pi * rc1**2 * H0\n", "print(f\"Slug: {Q:.5f} m^3\")" @@ -150,9 +158,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "ml = tft.ModelMaq(kaq=10, z=[0, -b], Saq=1e-4, tmin=1e-5, tmax=0.01)\n", "w = tft.Well(ml, xw=0, yw=0, rw=rw1, rc=rc1, tsandQ=[(0, -Q)], layers=0, wbstype=\"slug\")\n", @@ -175,9 +192,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...................................\n", + "Fit succeeded.\n" + ] + } + ], "source": [ "# unknown parameters: kaq, Saq\n", "cal = tft.Calibrate(ml)\n", @@ -190,9 +216,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
optimal
kaq_0_01.166107
Saq_0_00.000009
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 1.166107\n", + "Saq_0_0 0.000009" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.01024 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.5f} m\")" @@ -200,9 +280,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "hm1 = w.headinside(t1)\n", "hm2 = ml.head(r, 0, t2, layers=0)\n", @@ -234,9 +325,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]RMSE [m]
timflow1.179.38e-060.010
AQTESOLV1.179.37e-06-
MLU1.318.20e-06-
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", @@ -288,6 +424,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug3_falling_head.ipynb b/docs/transient/04pumpingtests/slug3_falling_head.ipynb index afb000f..413af3e 100644 --- a/docs/transient/04pumpingtests/slug3_falling_head.ipynb +++ b/docs/transient/04pumpingtests/slug3_falling_head.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -78,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -102,9 +102,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "slug: 0.00366 m^3\n" + ] + } + ], "source": [ "Q = np.pi * rc**2 * H0\n", "print(f\"slug: {Q:.5f} m^3\")" @@ -119,9 +127,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "18" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# z = np.array([zt, zt - 0.1, zst, zsb, zb])\n", "z = np.hstack(\n", @@ -138,18 +157,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , -0.01 , -0.043314, -0.076628, -0.109942, -0.143256,\n", + " -1.194816, -2.246376, -3.297936, -4.349496, -4.969256, -5.589016,\n", + " -6.208776, -6.828536, -7.448296, -8.068056, -8.687816, -9.307576,\n", + " -9.927336])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "z" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 5\n", + "solution complete\n" + ] + } + ], "source": [ "ml = tft.Model3D(\n", " kaq=10,\n", @@ -175,9 +217,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "r = np.linspace(-10, 10, 100)\n", "h = ml.headalongline(r, 0, 0.01).squeeze()\n", @@ -188,9 +251,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAb8AAAEWCAYAAAD2AJlUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA2uElEQVR4nO3de1iUdf4//ufMwMwwCAM4wgCiiFpIClOQhNnmbiSYtbpbLVafVfn2tV3L3frRZlEGdmVf1FyXrUw2+2rqdnDbbft+rtalXIo+HVAL81Ae0oI4OcNBYTjOwMz9+wNmcBKUwRnuOTwf13Vfys09N6+ZK3n2vt8niSAIAoiIiPyIVOwCiIiIxhrDj4iI/A7Dj4iI/A7Dj4iI/A7Dj4iI/A7Dj4iI/A7Dj4iI/A7Dj4iI/A7Dj4iI/A7Dj4iI/M6owm/Lli2Ij4+HUqlEeno6Dh48OOy177zzDtLS0hAWFobg4GDodDrs3r3b4Zrly5dDIpE4HNnZ2aMpjYiI6LICnH3Bnj17kJeXh5KSEqSnp6O4uBhZWVk4deoUIiMjL7o+IiICTz31FBITEyGXy/Hee+8hNzcXkZGRyMrKsl+XnZ2NHTt22L9WKBQjrslqtaKhoQEhISGQSCTOviUiIvIRgiCgvb0dMTExkEov0b4TnDR79mzhoYcesn9tsViEmJgYoaioaMT3uPbaa4U1a9bYv162bJmwaNEiZ0uxq62tFQDw4MGDBw8eAgChtrb2krnhVMvPbDajsrIS+fn59nNSqRSZmZmoqKi47OsFQcCHH36IU6dOYcOGDQ7fKy8vR2RkJMLDw/Gzn/0M69atw/jx44e8j8lkgslkcrgvANTW1iI0NNSZt0RERD7EaDQiLi4OISEhl7zOqfBrbm6GxWJBVFSUw/moqCicPHly2Ne1tbUhNjYWJpMJMpkML7/8Mm699Vb797Ozs/HLX/4SU6ZMwXfffYcnn3wSCxYsQEVFBWQy2UX3KyoqwjPPPHPR+dDQUIYfERFdtgvM6T6/0QgJCcHhw4fR0dGBsrIy5OXlISEhAfPmzQMALFmyxH7trFmzkJycjKlTp6K8vBy33HLLRffLz89HXl6e/Wtb0hMREY2EU+Gn0Wggk8lgMBgczhsMBmi12mFfJ5VKMW3aNACATqfDiRMnUFRUZA+/H0tISIBGo8GZM2eGDD+FQuHUgBgiIqILOTXVQS6XIzU1FWVlZfZzVqsVZWVlyMjIGPF9rFarQ5/dj9XV1aGlpQXR0dHOlEdERDQiTj/2zMvLw7Jly5CWlobZs2ejuLgYnZ2dyM3NBQAsXboUsbGxKCoqAtDfP5eWloapU6fCZDJh79692L17N7Zu3QoA6OjowDPPPIM777wTWq0W3333HVavXo1p06Y5TIUgIiJyFafDLycnB01NTSgoKIBer4dOp0Npaal9EExNTY3D3IrOzk48+OCDqKurQ1BQEBITE/HXv/4VOTk5AACZTIajR49i586daG1tRUxMDObPn49nn32WjzaJiMgtJIJtnoAXMxqNUKvVaGtr42hPIiI/NtI8GJPRnkR0aec6zTjeYBS7DPIAKoUMKRPDIJNytSp3YvgRiUwQBPzi5c/wQ0uX2KWQh1h7RxKW3zhF7DJ8GsOPSGQtnWZ78CVqL70qBfk2Y3cvGtp68MnpZoafmzH8iERW1dwJAIgNC0LpIz8RuRoS06Ga8/jly5/jcG0rBEHgQv1uxP38iERmC78pmmCRKyGxJUWHIlAmQUunGXXnu8Uux6cx/IhEZgu/eI1K5EpIbMpAGWZE949QPFLXKm4xPo7hRySyanvLb5zIlZAnSJkYBgA4Utsqah2+juFHJLLBx55s+RGQEhcGADhS2yZuIT6O4UckIqtVQHULW340SBenBgAcq29Dn8UqcjW+i+FHJCJDew96eq2QSSWYGB4kdjnkARI04xCiCEB3rwXfGjrELsdnMfyIRFTV1N/qmxShQqCM/xwJkEolSB5o/XHQi/vwXxuRiKoGHnnGj2d/Hw3ioBf3Y/gRiajaPs2Bc/xokG3Qy2GGn9sw/IhEZBvpmcDwowvoBsLvW0M7usx94hbjoxh+RCKqYsuPhhAVqoQ2VAmrAByr45QHd2D4EYmkz2JFzbn+Ba25tBn9WAoHvbgVw49IJA2tPei1CJAHSBGj5jQHcqSLCwfAye7uwvAjEoltpOfkCBWk3LiUfsTW8uOgF/dg+BGJpKqpfwIzH3nSUGbFqiGRAPWt3WhqN4ldjs9h+BGJpLqF/X00vBBlIKZN6F/yjvP9XG9U4bdlyxbEx8dDqVQiPT0dBw8eHPbad955B2lpaQgLC0NwcDB0Oh12797tcI0gCCgoKEB0dDSCgoKQmZmJ06dPj6Y0Iq/xPffxo8uwL3LNQS8u53T47dmzB3l5eSgsLMShQ4eQkpKCrKwsNDY2Dnl9REQEnnrqKVRUVODo0aPIzc1Fbm4u3n//ffs1GzduxAsvvICSkhIcOHAAwcHByMrKQk9Pz+jfGZGH4wR3uhwdJ7u7jdPht3nzZqxYsQK5ublISkpCSUkJVCoVtm/fPuT18+bNwy9+8QvMmDEDU6dOxcMPP4zk5GR8+umnAPpbfcXFxVizZg0WLVqE5ORk7Nq1Cw0NDXj33Xev6M0ReSpznxV15/nYky5NZ9/eqBWCIIhbjI9xKvzMZjMqKyuRmZk5eAOpFJmZmaioqLjs6wVBQFlZGU6dOoWf/OQnAICqqiro9XqHe6rVaqSnpw97T5PJBKPR6HAQeZOac12wCoBKLkNkiELscshDXa0NgTxACmNPn72PmFzDqfBrbm6GxWJBVFSUw/moqCjo9fphX9fW1oZx48ZBLpdj4cKFePHFF3HrrbcCgP11ztyzqKgIarXafsTFxTnzNohEZ3/kOT4YEgmnOdDQAmVSzIwJBQAcrj0vcjW+ZUxGe4aEhODw4cP44osv8NxzzyEvLw/l5eWjvl9+fj7a2trsR21treuKJRoD9t3bJ/CRJ10ad3Z3jwBnLtZoNJDJZDAYDA7nDQYDtFrtsK+TSqWYNm0aAECn0+HEiRMoKirCvHnz7K8zGAyIjo52uKdOpxvyfgqFAgoFHxWR97JNcJ8ynuFHl8ZBL+7hVMtPLpcjNTUVZWVl9nNWqxVlZWXIyMgY8X2sVitMpv5Jm1OmTIFWq3W4p9FoxIEDB5y6J5E3sW1iy5GedDm28DveYIS5zypuMT7EqZYfAOTl5WHZsmVIS0vD7NmzUVxcjM7OTuTm5gIAli5ditjYWBQVFQHo759LS0vD1KlTYTKZsHfvXuzevRtbt24FAEgkEjzyyCNYt24dpk+fjilTpuDpp59GTEwMFi9e7Lp3SuRBqls4x49GZlKECmGqQLR29eKk3ojkgY1u6co4HX45OTloampCQUEB9Ho9dDodSktL7QNWampqIJUONig7Ozvx4IMPoq6uDkFBQUhMTMRf//pX5OTk2K9ZvXo1Ojs78cADD6C1tRVz585FaWkplEqlC94ikWfpNltwtq1/DivDjy5HIpEgZWIYPv62CUdqWxl+LiIRfGDyiNFohFqtRltbG0JDQ8Uuh+iSTpw1YsGfP4E6KBCHC27laE+6rM37vsULZafxy+tisflXOrHL8WgjzQOu7Uk0xi5c2YXBRyOhs+3tx0EvLsPwIxpjtjU9E/jIk0YoZeBR53dNnTD29IpbjI9g+BGNsQsnuBONxPhxCsRF9G94fKyO8/1cgeFHNMZsIz3jNSqRKyFvYmv9cb6fazD8iMZYlf2x5ziRKyFvwsnursXwIxpDxp5eNHeYAbDlR85JuSD8fGCQvugYfkRjyNbfpxmnQIgyUORqyJvMjFFDJpWgqd0EvZF7nV4phh/RGLIvaM1WHzkpSC7D1VEhADjlwRUYfkRjqLq5f082jvSk0Rh89MkRn1eK4Uc0hqqaOwBwKyMaHdtkd+7td+UYfkRjqGpgN25uZUSjYWv5Hatrg8XKQS9XguFHNEYEQUBVE1t+NHrTI0OgksvQabbgu4H/lmh0GH5EY+R8Vy+MPX0AgMkRDD9ynkwqwaxY26PPVnGL8XIMP6IxYhvpGa1WIkguE7ka8la2ye4c8XllGH5EY2RwmgNbfTR6KVzpxSUYfkRj5MKtjIhGyxZ+J/Xt6Om1iFuMF2P4EY2RKm5lRC4Qo1ZCM04Bi1XANw2c7zdaDD+iMVLFrYzIBSQSyQWLXDP8RovhRzQGBEG4YCsjhh9dGe7sfuUYfkRjoLHdhC6zBVIJMCmC63rSlbH1+x2paxW1Dm/G8CMaA7ZHnhPDVZAH8J8dXZnk2DAAwA8tXTjXaRa3GC81qn+FW7ZsQXx8PJRKJdLT03Hw4MFhr922bRtuuukmhIeHIzw8HJmZmRddv3z5ckgkEocjOzt7NKUReSROcyBXUqsC7QOn2PobHafDb8+ePcjLy0NhYSEOHTqElJQUZGVlobGxccjry8vLcc899+Cjjz5CRUUF4uLiMH/+fNTX1ztcl52djbNnz9qPN998c3TviMgDVTP8yMU42f3KOB1+mzdvxooVK5Cbm4ukpCSUlJRApVJh+/btQ17/+uuv48EHH4ROp0NiYiJeffVVWK1WlJWVOVynUCig1WrtR3h4+LA1mEwmGI1Gh4PIk33P8CMXS2H4XRGnws9sNqOyshKZmZmDN5BKkZmZiYqKihHdo6urC729vYiIiHA4X15ejsjISFx99dVYuXIlWlpahr1HUVER1Gq1/YiLi3PmbRCNOU5wJ1cbHPTSBkHgDg/Ocir8mpubYbFYEBUV5XA+KioKer1+RPd4/PHHERMT4xCg2dnZ2LVrF8rKyrBhwwZ8/PHHWLBgASyWoVcvyM/PR1tbm/2ora115m0QjSmLVcAP57iVEbnWjOgQBMokONdpRu25brHL8ToBY/nD1q9fj7feegvl5eVQKpX280uWLLH/fdasWUhOTsbUqVNRXl6OW2655aL7KBQKKBSKMamZ6Eo1tHbD3GdFoEyC2PAgscshH6EIkCEpOhRH6tpwuK4Vk8ZzCo0znGr5aTQayGQyGAwGh/MGgwFarfaSr920aRPWr1+PDz74AMnJyZe8NiEhARqNBmfOnHGmPCKPZJvcPilCBZlUInI15EvY7zd6ToWfXC5Hamqqw2AV2+CVjIyMYV+3ceNGPPvssygtLUVaWtplf05dXR1aWloQHR3tTHlEHmlwmsM4kSshX8MRn6Pn9GjPvLw8bNu2DTt37sSJEyewcuVKdHZ2Ijc3FwCwdOlS5Ofn26/fsGEDnn76aWzfvh3x8fHQ6/XQ6/Xo6OjfhbijowOPPfYY9u/fj+rqapSVlWHRokWYNm0asrKyXPQ2icQzGH58LEWuZWv5fd3Qhl6LVdxivIzTfX45OTloampCQUEB9Ho9dDodSktL7YNgampqIJUOZurWrVthNptx1113OdynsLAQa9euhUwmw9GjR7Fz5060trYiJiYG8+fPx7PPPst+PfIJHOlJ7jJlfDBClAFo7+nDKX07Zg7s8k6XN6oBL6tWrcKqVauG/F55ebnD19XV1Ze8V1BQEN5///3RlEHkFbi6C7mLVCpBysQwfHqmGUfqWhl+TuAig0Ru1GuxovZ8/zB0hh+5Qwp3eBgVhh+RG9We64LFKiAoUIaoEOXlX0DkJF1c/2pYR7i3n1MYfkRuZJvmMHm8ClJOcyA3SJnY3/L7trEdHaY+kavxHgw/Ijeqah5Y2YWPPMlNIkOViFErIQjA1/Vs/Y0Uw4/Ijaqa+6f0MPzInWxTHg6z32/EGH5EblQ90PLjNAdyJ6704jyGH5Eb2aY5JDD8yI240ovzGH5EbtLTa0FDW/80B7b8yJ1mxaohlQANbT1oNPaIXY5XYPgRuUnNuS4IAhCiDMD4YLnY5ZAPC1YEYHpkCID+/f3o8hh+RG7yfdPgyi4SCac5kHvZJrsfrj0vciXegeFH5Ca2OX7x3MCWxsDgoBe2/EaC4UfkJlVNXNOTxk7KxDAAwJG6VlitgrjFeAGGH5GbVLUw/GjsXK0NgTJQivaePvt/ezQ8hh+Rm3A3BxpLgTIpZsZwkeuRYvgRuUGHqQ9N7SYAnOZAY4crvYwcw4/IDWwb2EYEy6EOChS5GvIXXOll5Bh+RG7AR54kBt3AoJfjZ40w9VnELcbDMfyI3MDW8uM0BxpLcRFBiAiWo9ci4MTZdrHL8WgMPyI3sK/pOYHhR2NHIpHY9/fjo89LY/gRuUEVJ7iTSDjoZWRGFX5btmxBfHw8lEol0tPTcfDgwWGv3bZtG2666SaEh4cjPDwcmZmZF10vCAIKCgoQHR2NoKAgZGZm4vTp06Mpjcgj2B97alQiV0L+hoNeRsbp8NuzZw/y8vJQWFiIQ4cOISUlBVlZWWhsbBzy+vLyctxzzz346KOPUFFRgbi4OMyfPx/19fX2azZu3IgXXngBJSUlOHDgAIKDg5GVlYWeHq5OTt6ntcuM8129ANjyo7FnW+nl++ZOtA38d0gXczr8Nm/ejBUrViA3NxdJSUkoKSmBSqXC9u3bh7z+9ddfx4MPPgidTofExES8+uqrsFqtKCsrA9Df6isuLsaaNWuwaNEiJCcnY9euXWhoaMC77757RW+OSAy2/r6oUAWCFQEiV0P+JiJYjkkR/U8cjta3iluMB3Mq/MxmMyorK5GZmTl4A6kUmZmZqKioGNE9urq60Nvbi4iICABAVVUV9Hq9wz3VajXS09OHvafJZILRaHQ4iDwFpzmQ2Li57eU5FX7Nzc2wWCyIiopyOB8VFQW9Xj+iezz++OOIiYmxh53tdc7cs6ioCGq12n7ExcU58zaI3Kqa4UciGxz0wh0ehjOmoz3Xr1+Pt956C//85z+hVCpHfZ/8/Hy0tbXZj9raWhdWSXRlqlq6ALC/j8Sjs+/t1wpB4A4PQ3Eq/DQaDWQyGQwGg8N5g8EArVZ7yddu2rQJ69evxwcffIDk5GT7edvrnLmnQqFAaGiow0HkKaqaOwCw5UfiuSZGDZlUguYOExraOHBwKE6Fn1wuR2pqqn2wCgD74JWMjIxhX7dx40Y8++yzKC0tRVpamsP3pkyZAq1W63BPo9GIAwcOXPKeRJ5IEARUN/e3/Bh+JBZloAyJ2hAA7PcbjtOPPfPy8rBt2zbs3LkTJ06cwMqVK9HZ2Ync3FwAwNKlS5Gfn2+/fsOGDXj66aexfft2xMfHQ6/XQ6/Xo6Oj//+OJRIJHnnkEaxbtw7//d//jWPHjmHp0qWIiYnB4sWLXfMuicZIU4cJHaY+SCTApPGc40fi4aCXS3N6HHZOTg6amppQUFAAvV4PnU6H0tJS+4CVmpoaSKWDmbp161aYzWbcddddDvcpLCzE2rVrAQCrV69GZ2cnHnjgAbS2tmLu3LkoLS29on5BIjHYWn2xYUFQBMhErob8WUpcGF4/UMOVXoYhEXygN9RoNEKtVqOtrY39fySqv31Ri9X/OIqbpmuw+/50scshP/atoR3z//Q/UMllOLY2CzKpROySxsRI84BrexK50Pec5kAeYuqEcQiWy9BltuB0I3d4+DGGH5ELcSsj8hQyqQSzuMPDsBh+RC5kX92FWxmRB9DFhQPgZPehMPyIXMRqFVA9sJXRFLb8yAPYJruz5Xcxhh+Ri+iNPTD1WREglWBieJDY5RDZlzk7ZWhHt9kibjEehuFH5CK2R56TIlQIkPGfFolPG6pEZIgCFquArxv46PNC/BdK5CJV9g1s+ciTPINEIuHmtsNg+BG5CLcyIk+ks+/w0CpqHZ6G4UfkItVs+ZEHsi9zVtcqah2ehuFH5CK2ll8Cw488iG2uX+25brR0mESuxnMw/IhcoM9iRc25gX38GH7kQUKVgZg6MO+Urb9BDD8iF6hv7UafVYAiQIroUC7ITp6FO7tfjOFH5ALfX7CsmdRPFhAm78HtjS7G8CNygcHBLtzDjzzPhYNefGAjH5dg+BG5wOA0h3EiV0J0sURtKOQyKVq7eu190/6O4UfkAoPhx5YfeR55gBRJMf1723G+Xz+GH5EL2Ba05lZG5Kk42d0Rw4/oCpn6LKg/3w2AWxmR50rhDg8OGH5EV6j2XBesAhAsl2HCOIXY5RANyba339cNRvRarCJXIz6GH9EV+r5pcANbiYTTHMgzxY9XIVQZAHOfFaf07WKXI7pRhd+WLVsQHx8PpVKJ9PR0HDx4cNhrv/nmG9x5552Ij4+HRCJBcXHxRdesXbsWEonE4UhMTBxNaURjjv195A0u3OHhKz76dD789uzZg7y8PBQWFuLQoUNISUlBVlYWGhsbh7y+q6sLCQkJWL9+PbRa7bD3veaaa3D27Fn78emnnzpbGpEoqpr7h45zTU/ydJzsPsjp8Nu8eTNWrFiB3NxcJCUloaSkBCqVCtu3bx/y+uuvvx7PP/88lixZAoVi+P6QgIAAaLVa+6HRaJwtjUgUVc0dALimJ3m+lIlhABh+gJPhZzabUVlZiczMzMEbSKXIzMxERUXFFRVy+vRpxMTEICEhAffddx9qamqGvdZkMsFoNDocRGKpbuaC1uQdbI89zzR1oL2nV9xiROZU+DU3N8NisSAqKsrhfFRUFPR6/aiLSE9Px2uvvYbS0lJs3boVVVVVuOmmm9DePnSnbFFREdRqtf2Ii4sb9c8muhJd5j7ojT0A+NiTPN+EEAViw4IgCMCxev9e5NojRnsuWLAAd999N5KTk5GVlYW9e/eitbUVf/vb34a8Pj8/H21tbfajtrZ2jCsm6mdr9YWpAhGmkotcDdHlDfb7+Xf4BThzsUajgUwmg8FgcDhvMBguOZjFWWFhYbjqqqtw5syZIb+vUCgu2X9INFZsIz2nsNVHXiIlTo1/HTuLw7XnxS5FVE61/ORyOVJTU1FWVmY/Z7VaUVZWhoyMDJcV1dHRge+++w7R0dEuuyeRO9jX9OQ0B/ISg4Ne2PJzSl5eHpYtW4a0tDTMnj0bxcXF6OzsRG5uLgBg6dKliI2NRVFREYD+QTLHjx+3/72+vh6HDx/GuHHjMG3aNADAH/7wB9xxxx2YPHkyGhoaUFhYCJlMhnvuucdV75PILarsWxkx/Mg7zIxVQyoB9MYe6Nt6oFX75+bLTodfTk4OmpqaUFBQAL1eD51Oh9LSUvsgmJqaGkilgw3KhoYGXHvttfavN23ahE2bNuHmm29GeXk5AKCurg733HMPWlpaMGHCBMydOxf79+/HhAkTrvDtEbnX4G4ODD/yDsGKAFwVFYKT+nYcqWuFVu26Litv4nT4AcCqVauwatWqIb9nCzSb+Pj4y26e+NZbb42mDCLRVTP8yAvp4sL6w6+2FVnX+Gf4ecRoTyJv1Nbdi5ZOMwA+9iTvksLtjRh+RKNla/VNCFFgnGJUD1GIRGEb9HK0rg1W66WfzPkqhh/RKNmnOXCkJ3mZq6LGQRkoRYepD98PLM/nbxh+RKNk38qIjzzJywTIpJgV27+57WE/nfLA8CMaJftWRgw/8kL+vsMDw49olDjNgbyZvw96YfgRjYIgCAw/8mq2QS8nzhrR02sRtxgRMPyIRuFcpxntPX2QSIDJ41Vil0PktInhQRgfLEefVcDxs/63LRzDj2gUbK2+GHUQlIEykashcp5EIrE/+vTHfj+GH9EoDK7pyVYfeS9/HvTC8CMaBfb3kS/w50EvDD+iUbBPc+AEd/JiKRP75/pVt3ShtcsscjVji+FHNApVAzu4J0xg+JH3ClPJET8wYOtInX9Ndmf4ETlJEAT7up5s+ZG389dBLww/IicZjCZ091ogk0oQF8EBL+Td/HXQC8OPyEm2hYDjwoMQKOM/IfJu9pZfXetl9171JfyXS+Sk6oH+Pq7pSb4gKToUAVIJmjvMqDvfLXY5Y4bhR+Qk+1ZGDD/yAcpAGWZEhwLob/35C4YfkZO4lRH5mpS4/ikP/tTvx/AjchLn+JGvsS1yfcSP9vZj+BE5wWIVUNPS3+fHlh/5imsnhQEAjtW3oc9iFbeYMTKq8NuyZQvi4+OhVCqRnp6OgwcPDnvtN998gzvvvBPx8fGQSCQoLi6+4nsSiaWhtRtmixVymRQxYUFil0PkEgmacRinCEB3rwXfGjrELmdMOB1+e/bsQV5eHgoLC3Ho0CGkpKQgKysLjY2NQ17f1dWFhIQErF+/Hlqt1iX3JBKLbU3PyeNVkEklIldD5BpSqQTJA0ud+cugF6fDb/PmzVixYgVyc3ORlJSEkpISqFQqbN++fcjrr7/+ejz//PNYsmQJFAqFS+5pMplgNBodDqKxMLibAx95km/xt5VenAo/s9mMyspKZGZmDt5AKkVmZiYqKipGVcBo7llUVAS1Wm0/4uLiRvWziZzF3RzIV9kGvfjLDg9OhV9zczMsFguioqIczkdFRUGv14+qgNHcMz8/H21tbfajtrZ2VD+byFkMP/JVtkEv3xra0WXuE7eYMeCVoz0VCgVCQ0MdDqKxwGkO5KuiQpXQhiphFYBjfrDDg1Php9FoIJPJYDAYHM4bDIZhB7OIcU8idzD3We3LP3ErI/JF9snufjDoxanwk8vlSE1NRVlZmf2c1WpFWVkZMjIyRlWAO+5J5A6157tgsQpQyWWIDBl68BaRNxsc9OL7Lb8AZ1+Ql5eHZcuWIS0tDbNnz0ZxcTE6OzuRm5sLAFi6dCliY2NRVFQEoH9Ay/Hjx+1/r6+vx+HDhzFu3DhMmzZtRPck8gQX7uEnkXCaA/kenR8NenE6/HJyctDU1ISCggLo9XrodDqUlpbaB6zU1NRAKh1sUDY0NODaa6+1f71p0yZs2rQJN998M8rLy0d0TyJPwMEu5OtmTVRDIgHqW7vR1G7CBB9+wiERfGADJ6PRCLVajba2Ng5+Ibd54h9H8dYXtXjop1PxWFai2OUQucWtmz/G6cYO/N9labhlhvc1QEaaB1452pNorB2ra8M7h+oBAMkDj4aIfJGt38/XH30y/Iguw9jTi4feOASzxYr5SVGYn+R9/zdMNFIMPyKCIAh4/O9HUXOuCxPDg/D8XSkc7EI+TWff3qgVPtArNiyGH9El7Kr4Af/+Wo9AmQQv3Xsd1KpAsUsicqurtSGQB0hh7OlD9cD2Xb6I4Uc0jKN1rVj3r/5pOvkLZkA38DiIyJfJA6SYGdM/UMSXF7lm+BENoa27v5+v1yIg65oo5N4YL3ZJRGPGH/r9GH5EPyIIAlb//Qhqz3UjLiIIG9nPR35Gx/Aj8j+vfV6N978xIFAmwZZ7r4M6iP185F9s2xsdbzDC3GcVtxg3YfgRXeBIbSv+z94TAICnbpvBOX3klyaPVyFMFQizxYqTet/cLJzhRzSgrWuwn2/BTC2WzYkXuyQiUUgkEnvrz1cHvTD8iNDfz/fY34+g7nw3JkWosOGuZPbzkV+zDXr5iuFH5Lt2fFaND44bIJdJseXe6xCqZD8f+TedbW8/hh+Rbzpc24qif/f38625fQZmTVSLXBGR+Gz93d81dcLY0ytuMW7A8CO/1tbVi4de7+/nu22WFr++YbLYJRF5BM04BSaGBwHoX9jd1zD8yG8JgoA//P0I6lu7MXm8CuvvZD8f0YV8ebI7w4/81v/9tAr72M9HNKxrGX5EvuWrmvNY/++TAICnb5+BmbHs5yP6sQtbfr62wwPDj/xOa5cZq974Cn1WAQtnReO/2M9HNKRrYkIhk0rQ1G6C3tgjdjkuxfAjvyIIAv7w9oX9fLPYz0c0DJU8AFdFhQDwvSkPDD/yK69+UoX/nGiEPKC/ny+E/XxEl2Sb73e41rdGfI4q/LZs2YL4+HgolUqkp6fj4MGDl7z+7bffRmJiIpRKJWbNmoW9e/c6fH/58uWQSCQOR3Z29mhKIxpW5Q/nsaG0v5+v4PYk9vMRjYBthwe/b/nt2bMHeXl5KCwsxKFDh5CSkoKsrCw0NjYOef3nn3+Oe+65B/fffz+++uorLF68GIsXL8bXX3/tcF12djbOnj1rP958883RvSOiIbR2mfG7Nw6hzyrg9uRo3Jc+SeySiLyCbdDL0bpWWKy+M+jF6fDbvHkzVqxYgdzcXCQlJaGkpAQqlQrbt28f8vo///nPyM7OxmOPPYYZM2bg2WefxXXXXYeXXnrJ4TqFQgGtVms/wsPDR/eOiH7EahXw6N+OoKGtB1M0wSj6Jfv5iEZqemQIVHIZOs0WfNfUIXY5LuNU+JnNZlRWViIzM3PwBlIpMjMzUVFRMeRrKioqHK4HgKysrIuuLy8vR2RkJK6++mqsXLkSLS0tw9ZhMplgNBodDqLhvPrp9yg72d/P99K917Kfj8gJMqnE3kXgS/P9nAq/5uZmWCwWREVFOZyPioqCXq8f8jV6vf6y12dnZ2PXrl0oKyvDhg0b8PHHH2PBggWwWCxD3rOoqAhqtdp+xMXFOfM2yI9U/nAOG0pPAQAK70jCNTHs5yNyli/2+wWIXQAALFmyxP73WbNmITk5GVOnTkV5eTluueWWi67Pz89HXl6e/Wuj0cgApIuc7zTjd298BYtVwB0pMbh3Nvv5iEbDHn51raLW4UpOtfw0Gg1kMhkMBoPDeYPBAK1WO+RrtFqtU9cDQEJCAjQaDc6cOTPk9xUKBUJDQx0OogtZrQIefZv9fESuYBv0cvJsO04b2sUtxkWcCj+5XI7U1FSUlZXZz1mtVpSVlSEjI2PI12RkZDhcDwD79u0b9noAqKurQ0tLC6Kjo50pj8julU++x4cnG6EYmM83TuERDzmIvFKMWomMhPHoswpYvuMLNLZ7/2ovTo/2zMvLw7Zt27Bz506cOHECK1euRGdnJ3JzcwEAS5cuRX5+vv36hx9+GKWlpfjjH/+IkydPYu3atfjyyy+xatUqAEBHRwcee+wx7N+/H9XV1SgrK8OiRYswbdo0ZGVluehtkj/5svocnn+/v59v7c+vQVIMnwwQXQmJRIIt912HKZpg1Ld243/v/BJd5j6xy7oiTodfTk4ONm3ahIKCAuh0Ohw+fBilpaX2QS01NTU4e/as/fo5c+bgjTfewCuvvIKUlBT8/e9/x7vvvouZM2cCAGQyGY4ePYqf//znuOqqq3D//fcjNTUVn3zyCRQKhYveJvmL75o68Ls3+/v5FulisOR69gUTuUJEsBw7ll+PcFUgjta14fdvHvbqeX8SwQeW6jYajVCr1Whra2P/n59q7jDhz/85jTcO1sBiFZCgCcZ//24uH3cSuVjlD+dwz7YDMPdZsXxOPArvSPKo/vSR5gHX9iSv1m22YMtHZzDv+XLs3v8DLFYBmTMisfN/zWbwEblB6uQI/OlXOgDAa59XY/tn1aLWM1r87UBeyWIV8M6hOvzxg2/tW63MilXjydtmIGPqeJGrI/JtC5OjUXc+EUX/Pol1/zqO2LAgZM8cfgS/J2L4kdf55HQT/s/ekzhxtn9ln9iwIKzOvhp3JMdAKvWcxy9EvuyBnySg5lwXXj9Qg0f2fIU3Q2/AtZO8Z1lKhh95jZN6I4r2nsTH3zYBAEKUAVj102lYNiceykCZyNUR+ReJRIJnfn4N6lu7UX6qCf9755f454M3YtJ4ldiljQgHvJDHMxh78McPTuHvlXWwCkCgTIL/umEyfv+z6QgPlotdHpFf6zD14VclFTh+1oiECcF4Z+UchKnE+3c50jxg+JHH6jD14ZWPv8O2T6rQ3du/zutts7RYnZWIeE2wyNURkY3B2IPFWz7D2bYepE+JwK77Z0MRIM7TGIYfea0+ixV7vqzFn/adRnOHCQCQOjkcT942A6mTvadPgcifnNQbcdfWCnSY+rBYF4M/5ehEmQIx0jxgnx95DEEQ8OHJRhT9+yTONPbvGxY/XoXHsxORPVPrUXOJiMhRojYUW//rOuTu+ALvHm7ApAgV8uZfLXZZw2L4kUc4VteG5/Yex/7vzwEAwlWB+P0t03Ff+mTIAzgdlcgb3DR9Ap77xUw8/o9jeOHDM5gYocKv0jxzlSWGH4mq7nwXNr1/Cu8ebgAAyAOkyL0xHg/OmwZ1EDedJfI2OddPQu25brz00Rk8+c4xxKiDMHe6RuyyLsLwI1G0dffi5fIz2PFZNcx9VgDAL66NxaPzr8LEcO8YKk1EQ3t0/lWoPd+F/3e4ASv/Wom3V2YgUetZ4zEYfjQmrFYBx88a8emZZnx2phkHq87BNBB6NyRE4KnbkjBrIndZJ/IFEokEG+9KxtnWHhysPof/teML/POhGxEVqhS7NDuO9iS3qW/txqenm/DJ6WZ8/l0LznWaHb4/PXIcnliQiJ8lRnIwC5EPau0y45dbP8f3TZ24JiYUf/tNBoLdvOYupzrQmGvr7sX+71vw6elmfHqmGVXNnQ7fD5bLcEPCeNw4TYObpmswLXIcQ4/Ix9W0dOEXL3+Glk4zfpYYiVd+nYoAmfsGsTH8yO3MfVYcrm3tb92dacaR2lZcuL2XTCpBykQ15k6fgLnTNNDFhXHkJpEfOlRzHve8sh+mPiv+64ZJeHbRTLf9jy/n+ZHLCYKA040d9pbd/u9b0GW2OFyToAnG3OkazJ2mwQ1TxyNUyRGbRP7uuknhKM7R4cE3DuGv+2swOSIYK36SIGpNDD+6pEZjDz4902wfqGIwmhy+Pz5YjjnTNLhpmgY3TtcgNixIpEqJyJMtmBWNp26bgXX/OoHn9p5AbHgQbpsVLVo9DD8/JwgCmtpNqDnX5XDUDvz547BTBEgxe0oEbpquwY3TNJihDeU2QkQ0IvfPnYLac13YWfED/r89hxEVqhRtyUKGnx/oNltQd37ocKs514WeXuuwr5VIgJkxasyd3t+6u25yOLcPIqJRkUgkKLijfxuk/5xoxIpdX+KdlXNEWaieA158gNUqoKljoPXWcnG4NbabLvl6qQSICQvCpAgVJkWoEDfw56QIFeI1wVxphYhcqsvch5y/7Mex+jZMHq/COyvnYPw4hUvu7dbRnlu2bMHzzz8PvV6PlJQUvPjii5g9e/aw17/99tt4+umnUV1djenTp2PDhg247bbb7N8XBAGFhYXYtm0bWltbceONN2Lr1q2YPn36iOrxhfDrs1jR3tOHtu5eGHt6YezuG/iz/+u27h+fG7i2uxet3b32VVKGE6IIwKTxg6F24d9jwoIQ6Mahx0REP9bUbsIvt36G2nPdSIkLw5sr0qGSX/nDSLeF3549e7B06VKUlJQgPT0dxcXFePvtt3Hq1ClERkZedP3nn3+On/zkJygqKsLtt9+ON954Axs2bMChQ4cwc+ZMAMCGDRtQVFSEnTt3YsqUKXj66adx7NgxHD9+HErl5VcEcHX4CYIAi1VAr0WAuc8Ks6X/6O2zotdihWngT3OfFb0WYYhzF14n2M91mPrsYWbsvjDoetH5o1GTzpJJJYgJUw7ZepsUoYI6KJBz6ojIo3zX1IG7tn6O8129LpsD6LbwS09Px/XXX4+XXnoJAGC1WhEXF4ff/e53eOKJJy66PicnB52dnXjvvffs52644QbodDqUlJRAEATExMTg0UcfxR/+8AcAQFtbG6KiovDaa69hyZIlLnuzl5P67D50mPpgtlgh1sPgYLkMoUGBCFUGQh0UiNCgAIQqAwfOBfT/OfB92/fUQYHQqpVsvRGR16n84Tzu3dY/B/BXaROx4c7kK/ofdbfM8zObzaisrER+fr79nFQqRWZmJioqKoZ8TUVFBfLy8hzOZWVl4d133wUAVFVVQa/XIzMz0/59tVqN9PR0VFRUDBl+JpMJJtNgP5bRaHTmbQyrp9diX2/yx+QyKeQBUgTKJAgc+PvgOcfvKRzODV4brJDZg0w9RICFKAPcuvIBEZGnSZ0cjpfuvQ6/2f0l/vZlHSaGq/D7W0bW5XUlnAq/5uZmWCwWREVFOZyPiorCyZMnh3yNXq8f8nq9Xm//vu3ccNf8WFFREZ555hlnSh+Rf/3+JsikkovCK1Am4SNDIiI3uTUpCusWz8Lmfacw7+oJY/IzvXKqQ35+vkNr0mg0Ii7uyjdMFGO4LRERAfemT8LCWdFQq8ZmdLlTz9g0Gg1kMhkMBoPDeYPBAK1WO+RrtFrtJa+3/enMPRUKBUJDQx0OIiLybmMVfICT4SeXy5GamoqysjL7OavVirKyMmRkZAz5moyMDIfrAWDfvn3266dMmQKtVutwjdFoxIEDB4a9JxER0ZVw+rFnXl4eli1bhrS0NMyePRvFxcXo7OxEbm4uAGDp0qWIjY1FUVERAODhhx/GzTffjD/+8Y9YuHAh3nrrLXz55Zd45ZVXAPTP+H/kkUewbt06TJ8+3T7VISYmBosXL3bdOyUiIhrgdPjl5OSgqakJBQUF0Ov10Ol0KC0ttQ9YqampgVQ62KCcM2cO3njjDaxZswZPPvkkpk+fjnfffdc+xw8AVq9ejc7OTjzwwANobW3F3LlzUVpaOqI5fkRERM7i8mZEROQzRpoHnFRGRER+h+FHRER+xyvn+f2Y7cmtq1Z6ISIi72TLgcv16PlE+LW3twOASya6ExGR92tvb4darR72+z4x4MVqtaKhoQEhISGQSCT2FV9qa2s5AGYY/IxGhp/T5fEzujx+RiPjis9JEAS0t7cjJibGYebBj/lEy08qlWLixIkXnefqL5fHz2hk+DldHj+jy+NnNDJX+jldqsVnwwEvRETkdxh+RETkd3wy/BQKBQoLC6FQKMQuxWPxMxoZfk6Xx8/o8vgZjcxYfk4+MeCFiIjIGT7Z8iMiIroUhh8REfkdhh8REfkdhh8REfkdhh8REfkdnwu/5557DnPmzIFKpUJYWNiQ19TU1GDhwoVQqVSIjIzEY489hr6+vrEt1MN8++23WLRoETQaDUJDQzF37lx89NFHYpflcf71r38hPT0dQUFBCA8Px+LFi8UuySOZTCbodDpIJBIcPnxY7HI8SnV1Ne6//35MmTIFQUFBmDp1KgoLC2E2m8UuTVRbtmxBfHw8lEol0tPTcfDgQbf+PJ8LP7PZjLvvvhsrV64c8vsWiwULFy6E2WzG559/jp07d+K1115DQUHBGFfqWW6//Xb09fXhww8/RGVlJVJSUnD77bdDr9eLXZrH+Mc//oFf//rXyM3NxZEjR/DZZ5/h3nvvFbssj7R69WrExMSIXYZHOnnyJKxWK/7yl7/gm2++wZ/+9CeUlJTgySefFLs00ezZswd5eXkoLCzEoUOHkJKSgqysLDQ2Nrrvhwo+aseOHYJarb7o/N69ewWpVCro9Xr7ua1btwqhoaGCyWQawwo9R1NTkwBA+J//+R/7OaPRKAAQ9u3bJ2JlnqO3t1eIjY0VXn31VbFL8Xh79+4VEhMThW+++UYAIHz11Vdil+TxNm7cKEyZMkXsMkQze/Zs4aGHHrJ/bbFYhJiYGKGoqMhtP9PnWn6XU1FRgVmzZiEqKsp+LisrC0ajEd98842IlYln/PjxuPrqq7Fr1y50dnair68Pf/nLXxAZGYnU1FSxy/MIhw4dQn19PaRSKa699lpER0djwYIF+Prrr8UuzaMYDAasWLECu3fvhkqlErscr9HW1oaIiAixyxCF2WxGZWUlMjMz7eekUikyMzNRUVHhtp/rd+Gn1+sdgg+A/Wt/fcQnkUjwn//8B1999RVCQkKgVCqxefNmlJaWIjw8XOzyPML3338PAFi7di3WrFmD9957D+Hh4Zg3bx7OnTsncnWeQRAELF++HL/97W+RlpYmdjle48yZM3jxxRfxm9/8RuxSRNHc3AyLxTLk72V3/k72ivB74oknIJFILnmcPHlS7DI9zkg/N0EQ8NBDDyEyMhKffPIJDh48iMWLF+OOO+7A2bNnxX4bbjXSz8hqtQIAnnrqKdx5551ITU3Fjh07IJFI8Pbbb4v8LtxrpJ/Riy++iPb2duTn54tdsihG83uqvr4e2dnZuPvuu7FixQqRKvdPXrGf36OPPorly5df8pqEhIQR3Uur1V40ishgMNi/50tG+rl9+OGHeO+993D+/Hn7Hlovv/wy9u3bh507d+KJJ54Yg2rFMdLPyPY/AUlJSfbzCoUCCQkJqKmpcWeJonPmv6OKioqLFiVOS0vDfffdh507d7qxSvE5+3uqoaEBP/3pTzFnzhy88sorbq7Oc2k0GshkMvvvYRuDweDW38leEX4TJkzAhAkTXHKvjIwMPPfcc2hsbERkZCQAYN++fQgNDXX4xeYLRvq5dXV1AcBFux5LpVJ7i8dXjfQzSk1NhUKhwKlTpzB37lwAQG9vL6qrqzF58mR3lymqkX5GL7zwAtatW2f/uqGhAVlZWdizZw/S09PdWaJHcOb3VH19PX7605/anyBcasdxXyeXy5GamoqysjL71CGr1YqysjKsWrXKbT/XK8LPGTU1NTh37hxqampgsVjsc4ymTZuGcePGYf78+UhKSsKvf/1rbNy4EXq9HmvWrMFDDz3kt9uNZGRkIDw8HMuWLUNBQQGCgoKwbds2VFVVYeHChWKX5xFCQ0Px29/+FoWFhYiLi8PkyZPx/PPPAwDuvvtukavzDJMmTXL4ety4cQCAqVOnYuLEiWKU5JHq6+sxb948TJ48GZs2bUJTU5P9e7729Gmk8vLysGzZMqSlpWH27NkoLi5GZ2cncnNz3fdD3TaOVCTLli0TAFx0fPTRR/ZrqqurhQULFghBQUGCRqMRHn30UaG3t1e8oj3AF198IcyfP1+IiIgQQkJChBtuuEHYu3ev2GV5FLPZLDz66KNCZGSkEBISImRmZgpff/212GV5rKqqKk51GMKOHTuG/B3lg7+OnfLiiy8KkyZNEuRyuTB79mxh//79bv153M+PiIj8jv8+aCYiIr/F8CMiIr/D8CMiIr/D8CMiIr/D8CMiIr/D8CMiIr/D8CMiIr/D8CMiIr/D8CMiIr/D8CMiIr/D8CMiIr/z/wNUufAJSa9izgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "t = np.logspace(-5, -2, 100)\n", "h = ml.head(0, 0, t)\n", @@ -200,9 +284,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.plot(z)" ] @@ -216,9 +321,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".....................................\n", + "Fit succeeded.\n" + ] + }, + { + "data": { + "text/plain": [ + "kaq_1_17 0.439172\n", + "Saq_1_17 0.000461\n", + "Name: optimal, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.006011434252180949\n" + ] + } + ], "source": [ "cal = tft.Calibrate(ml)\n", "cal.set_parameter(name=\"kaq\", layers=list(range(1, nlay)), initial=10, pmin=0)\n", @@ -231,9 +363,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "tm = np.logspace(np.log10(to[0]), np.log10(to[-1]), 100)\n", "hm = w.headinside(tm)\n", @@ -256,9 +399,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]RMSE [m]
timflow0.444.61e-040.006
AQTESOLV0.425.70e-04-
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", @@ -308,6 +490,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug4_dawsonville.ipynb b/docs/transient/04pumpingtests/slug4_dawsonville.ipynb new file mode 100644 index 0000000..667d2fe --- /dev/null +++ b/docs/transient/04pumpingtests/slug4_dawsonville.ipynb @@ -0,0 +1,409 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Slug test for confined aquifer - Dawsonville" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "import timflow.transient as tft\n", + "\n", + "plt.rcParams[\"figure.figsize\"] = [5, 3]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Introduction and Conceptual Model\n", + "\n", + "This Slug Test, was reported by Cooper Jr et al. (1967), and it was performed in Dawsonville, Georgia, USA. \n", + "\n", + "A fully penetrated well (Ln-2) is screened in a confined aquifer, located between depths 24 and 122 (98 m thick). The volume of the slug is 10.16 litres. Head change has been recorded at the slug well. Both the well and the casing radii of the slug well is 0.076 m." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "data = np.loadtxt(\"data/dawsonville_slug.txt\")\n", + "to = data[:, 0]\n", + "ho = data[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Parameters and model" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# known parameters\n", + "b = 98 # aquifer thickness in m\n", + "zt = -24 # top of aquifer in m\n", + "zb = zt - b # bottom of aquifer in m\n", + "rw = 0.076 # well radius of Ln-2 Well in m\n", + "rc = 0.076 # casing radius of Ln-2 Well in m\n", + "Q = 10.16 / 1000 # slug volume in m^3 (10.16 l = 0.01016 m^3)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml = tft.ModelMaq(kaq=10, z=[zt, zb], Saq=1e-4, tmin=1e-6, tmax=1e-3, topboundary=\"conf\")\n", + "w = tft.Well(ml, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=0, wbstype=\"slug\")\n", + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimate aquifer parameters\n", + "\n", + "We calibrate hydraulic conductivity and specific storage, as in the KGS model (Hyder et al. 1994)." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".....................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 34\n", + " # data points = 22\n", + " # variables = 2\n", + " chi-square = 4.2779e-04\n", + " reduced chi-square = 2.1389e-05\n", + " Akaike info crit = -234.654459\n", + " Bayesian info crit = -232.472374\n", + "[[Variables]]\n", + " kaq0_0_0: 0.42101398 +/- 0.01839446 (4.37%) (init = 10)\n", + " Saq0_0_0: 1.6975e-05 +/- 5.2892e-06 (31.16%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_0_0, Saq0_0_0) = -0.9853\n" + ] + } + ], + "source": [ + "# unknown parameters: kay, Saq\n", + "cal = tft.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq0\", initial=10, pmin=0, layers=0)\n", + "cal.set_parameter(name=\"Saq0\", initial=1e-4, layers=0)\n", + "cal.seriesinwell(name=\"obs\", element=w, t=to, h=ho)\n", + "cal.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
layersoptimalstdperc_stdpminpmaxinitialinhomsparray
kaq0_0_000.4210140.0183944.3690860.0inf10.0000None[[0.42101397957109743]]
Saq0_0_000.0000170.00000531.159403-infinf0.0001None[[1.697465569017154e-05]]
\n", + "
" + ], + "text/plain": [ + " layers optimal std perc_std pmin pmax initial inhoms \\\n", + "kaq0_0_0 0 0.421014 0.018394 4.369086 0.0 inf 10.0000 None \n", + "Saq0_0_0 0 0.000017 0.000005 31.159403 -inf inf 0.0001 None \n", + "\n", + " parray \n", + "kaq0_0_0 [[0.42101397957109743]] \n", + "Saq0_0_0 [[1.697465569017154e-05]] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse: 0.004409650627757543\n" + ] + } + ], + "source": [ + "display(cal.parameters)\n", + "print(\"rmse:\", cal.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tm = np.logspace(np.log10(to[0]), np.log10(to[-1]), 100)\n", + "hm = ml.head(0, 0, tm)\n", + "plt.semilogx(to, ho, \".\", label=\"obs\")\n", + "plt.semilogx(tm, hm[0], label=\"timflow\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.title(\"Model Results - Single-layer model\")\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparison of results\n", + "\n", + "We now compare the values in `timflow` and add the results of the modelling done in MLU (Hemker & Post, 2014). Results are similar between both models. The RMSE of MLU is slightly better than the one from `timflow`." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]RMSE [m]
timflow0.421.70e-050.004
MLU0.411.94e-050.004
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", + " index=[\"timflow\", \"MLU\"],\n", + ")\n", + "\n", + "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", + "t.loc[\"MLU\"] = [0.4133, 1.9388e-05, 0.004264]\n", + "\n", + "t_formatted = t.style.format(\n", + " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"RMSE [m]\": \"{:.3f}\"}\n", + ")\n", + "t_formatted" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\n", + "* Cooper Jr, H.H., Bredehoeft, J.D. and Papadopulos, I.S. (1967) Response of a finite diameter well to an instantaneous charge of water, Water Resources Research 3, 263–269\n", + "* Duffield, G.M. (2007), AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Hemker, K. en Post V. (2014) MLU for Windows: well flow modeling in multilayer aquifer systems; MLU User's guide. https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmicrofem.com%2Fdownload%2Fmlu-user.pdf&data=05%7C02%7CMark.Bakker%40tudelft.nl%7Cad7f16364d2d4fd55dbf08de73832eaa%7C096e524d692940308cd38ab42de0887b%7C0%7C0%7C639075204580287861%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=OBoe8seXZUfoat89Dfr4g6lF%2Bn1FdtXqtp%2F18BMXCn0%3D&reserved=0\n", + "* Hyder, Z., Butler Jr, J.J., McElwee, C.D. and Liu, W. (1994) Slug tests in partially penetrating wells, Water Resources Research 30, 2945–2957." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/transient/04pumpingtests/unconfined2_moench.ipynb b/docs/transient/04pumpingtests/unconfined2_moench.ipynb new file mode 100644 index 0000000..803ce19 --- /dev/null +++ b/docs/transient/04pumpingtests/unconfined2_moench.ipynb @@ -0,0 +1,420 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2.Test for an anisotropic water-table aquifer - Moench Example" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import timflow.transient as tft\n", + "\n", + "plt.rcParams[\"figure.figsize\"] = (5, 3) # default figure size" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Introduction and Conceptual Model\n", + "\n", + "This test is based on a synthetic example reported by Barlow and Moench (1999), utilizing an analytical solution developed by Moench and Allen (1997) for the transient flow of partially-penetrating wells in unconfined aquifers. \n", + "\n", + "The aquifer is partially saturated with water (10 m water table). A pumping well is screened from 5 to 10 m depth. The well and the well-casing radius is 0.1 m. \n", + "\n", + "Drawdown is recorded at the pumping well and four piezometers located at two different distances and two different depths. Two piezometers, PS1 and PS2, are located at one-meter depth below the water table and 3.16 and 31.6 m distance, respectively. Another two (PD1 and PD2) piezometers are at 7.5 m depth below the water table and the same distances, directly below the previous piezometers. The figure below shows the location of the well and the piezometers\n", + "\n", + "Pumping starts at time t = 0 at a constant rate of 172.8 m3/d. Drawdown is recorded until t = 3 days." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data\n", + "\n", + "The dataset for each well consists of a column with the time data in seconds and drawdown in meters. We are loading it and converting it to days and meters." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "data0 = np.loadtxt(\"data/moench_pumped.txt\", skiprows=1)\n", + "t0 = data0[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h0 = -data0[:, 1] # converting drawdown to heads\n", + "data1 = np.loadtxt(\"data/moench_ps1.txt\", skiprows=1)\n", + "t1 = data1[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h1 = -data1[:, 1]\n", + "data2 = np.loadtxt(\"data/moench_pd1.txt\", skiprows=1)\n", + "t2 = data2[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h2 = -data2[:, 1]\n", + "data3 = np.loadtxt(\"data/moench_ps2.txt\", skiprows=1)\n", + "t3 = data3[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h3 = -data3[:, 1]\n", + "data4 = np.loadtxt(\"data/moench_pd2.txt\", skiprows=1)\n", + "t4 = data4[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h4 = -data4[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameters and model" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "b = 10 # aquifer thickness, m\n", + "Q = 172.8 # constant discharge rate, m^3/d\n", + "rw = 0.1 # well radius, m\n", + "rc = 0.1 # casing radius, m\n", + "r1 = 3.16 # distance of closer wells, m\n", + "r2 = 31.6 # distance of wells more far away, m" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml = tft.Model3D(\n", + " kaq=1,\n", + " z=[0, -0.1, -2.1, -5.1, -10.1],\n", + " Saq=[0.1, 1e-4, 1e-4, 1e-4],\n", + " kzoverkh=1,\n", + " tmin=1e-5,\n", + " tmax=3,\n", + " phreatictop=True,\n", + ")\n", + "w = tft.Well(ml, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=3)\n", + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimate aquifer parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "............................................................\n", + "Fit succeeded.\n" + ] + } + ], + "source": [ + "cal = tft.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq\", initial=1, layers=[0, 1, 2, 3])\n", + "cal.set_parameter(name=\"Saq\", initial=0.2, layers=[0])\n", + "cal.set_parameter(name=\"Saq\", initial=1e-4, pmin=0, layers=[1, 2, 3])\n", + "cal.set_parameter(name=\"kzoverkh\", initial=0.1, pmin=0, layers=[0, 1, 2, 3])\n", + " \n", + "cal.seriesinwell(name=\"pumped\", element=w, t=t0, h=h0)\n", + " \n", + "cal.series(name=\"PS1\", x=r1, y=0, t=t1, h=h1, layer=1)\n", + "cal.series(name=\"PD1\", x=r1, y=0, t=t2, h=h2, layer=3)\n", + "cal.series(name=\"PS2\", x=r2, y=0, t=t3, h=h3, layer=1)\n", + "cal.series(name=\"PD2\", x=r2, y=0, t=t4, h=h4, layer=3)\n", + " \n", + "cal.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
optimal
kaq_0_39.067651
Saq_0_00.172880
Saq_1_30.000039
kzoverkh_0_30.535051
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_3 9.067651\n", + "Saq_0_0 0.172880\n", + "Saq_1_3 0.000039\n", + "kzoverkh_0_3 0.535051" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.01038 m\n" + ] + } + ], + "source": [ + "display(cal.parameters.loc[:, [\"optimal\"]])\n", + "print(f\"RMSE: {cal.rmse():.5f} m\")" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm0 = ml.head(0, 0, t0, layers=3)[0]\n", + "hm1 = ml.head(r1, 0, t1, layers=1)[0]\n", + "hm2 = ml.head(r1, 0, t2, layers=3)[0]\n", + "hm3 = ml.head(r2, 0, t3, layers=1)[0]\n", + "hm4 = ml.head(r2, 0, t4, layers=3)[0]\n", + "\n", + "plt.semilogx(t0, -h0, \".\", label=\"pumped\")\n", + "plt.semilogx(t0, -hm0, label=\"ttim pumped\")\n", + "plt.semilogx(t1, -h1, \".\", label=\"PS1\")\n", + "plt.semilogx(t1, -hm1, label=\"ttim PS1\")\n", + "plt.semilogx(t2, -h2, \".\", label=\"PD1\")\n", + "plt.semilogx(t2, -hm2, label=\"ttim PD1\")\n", + "plt.semilogx(t3, -h3, \".\", label=\"PS2\")\n", + "plt.semilogx(t3, -hm3, label=\"ttim PS2\")\n", + "plt.semilogx(t4, -h4, \".\", label=\"PD2\")\n", + "plt.semilogx(t4, -hm4, label=\"ttim PD2\")\n", + "\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"drawdown (m)\")\n", + "plt.title(\"Model Results\")\n", + "plt.legend(bbox_to_anchor=(1.05, 1))\n", + "plt.grid();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparison of results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The performance of `timflow` is compared with the stimulated results using Moench's parameters (Barlow and Moench, 1999). The RMSE decreased with the performance of `timflow`. " + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Sy [-]Ss [1/m]kz/khRMSE [m]
timflow9.070.23.87e-050.50.0104
Moench8.640.22.00e-050.50.0613
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Sy [-]\", \"Ss [1/m]\", \"kz/kh\", \"RMSE [m]\"],\n", + " index= [\"timflow\", \"Moench\"],\n", + ")\n", + "\n", + "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", + "t.loc[\"Moench\"] = [8.64, 0.2, 2e-5, 0.5, 0.061318]\n", + "\n", + "t_formatted = t.style.format(\n", + " {\"k [m/d]\": \"{:.2f}\", \n", + " \"Sy [-]\": \"{:.1f}\",\n", + " \"Ss [1/m]\": \"{:.2e}\", \n", + " \"kz/kh\": \"{:.1f}\", \n", + " \"RMSE [m]\": \"{:.4f}\"}\n", + ")\n", + "t_formatted" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Barlow, P.M., Moench, A.F., 1999. WTAQ, a computer program for calculating drawdowns and estimating hydraulic properties for confined and water-table aquifers. 99-4225, US Dept. of the Interior, US Geological Survey\n", + "* Moench, Allen, F., 1997. Flow to a well of finite diameter in a homogeneous, anisotropic water table aquifer. Water Resources Research 34, 2431–2432." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From e16f500f40ad4e8671a2863c847ea20bf223e813 Mon Sep 17 00:00:00 2001 From: Hannah Heesen Date: Wed, 18 Mar 2026 12:47:51 +0700 Subject: [PATCH 2/8] fix lint --- docs/transient/04pumpingtests/slug4_dawsonville.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/transient/04pumpingtests/slug4_dawsonville.ipynb b/docs/transient/04pumpingtests/slug4_dawsonville.ipynb index 667d2fe..1fbf643 100644 --- a/docs/transient/04pumpingtests/slug4_dawsonville.ipynb +++ b/docs/transient/04pumpingtests/slug4_dawsonville.ipynb @@ -26,8 +26,8 @@ }, "outputs": [], "source": [ - "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "import pandas as pd\n", "\n", "import timflow.transient as tft\n", From d705ba7591b711b647bff10f27a7cc7de3aee650 Mon Sep 17 00:00:00 2001 From: Hannah Heesen Date: Wed, 18 Mar 2026 12:50:54 +0700 Subject: [PATCH 3/8] lint fix --- .../04pumpingtests/slug4_dawsonville.ipynb | 6 +++--- .../04pumpingtests/unconfined2_moench.ipynb | 20 ++++++++++--------- 2 files changed, 14 insertions(+), 12 deletions(-) diff --git a/docs/transient/04pumpingtests/slug4_dawsonville.ipynb b/docs/transient/04pumpingtests/slug4_dawsonville.ipynb index 1fbf643..a75e590 100644 --- a/docs/transient/04pumpingtests/slug4_dawsonville.ipynb +++ b/docs/transient/04pumpingtests/slug4_dawsonville.ipynb @@ -104,11 +104,11 @@ "source": [ "# known parameters\n", "b = 98 # aquifer thickness in m\n", - "zt = -24 # top of aquifer in m\n", - "zb = zt - b # bottom of aquifer in m\n", + "zt = -24 # top of aquifer in m\n", + "zb = zt - b # bottom of aquifer in m\n", "rw = 0.076 # well radius of Ln-2 Well in m\n", "rc = 0.076 # casing radius of Ln-2 Well in m\n", - "Q = 10.16 / 1000 # slug volume in m^3 (10.16 l = 0.01016 m^3)" + "Q = 10.16 / 1000 # slug volume in m^3 (10.16 l = 0.01016 m^3)" ] }, { diff --git a/docs/transient/04pumpingtests/unconfined2_moench.ipynb b/docs/transient/04pumpingtests/unconfined2_moench.ipynb index 803ce19..8b748b6 100644 --- a/docs/transient/04pumpingtests/unconfined2_moench.ipynb +++ b/docs/transient/04pumpingtests/unconfined2_moench.ipynb @@ -169,14 +169,14 @@ "cal.set_parameter(name=\"Saq\", initial=0.2, layers=[0])\n", "cal.set_parameter(name=\"Saq\", initial=1e-4, pmin=0, layers=[1, 2, 3])\n", "cal.set_parameter(name=\"kzoverkh\", initial=0.1, pmin=0, layers=[0, 1, 2, 3])\n", - " \n", + "\n", "cal.seriesinwell(name=\"pumped\", element=w, t=t0, h=h0)\n", - " \n", + "\n", "cal.series(name=\"PS1\", x=r1, y=0, t=t1, h=h1, layer=1)\n", "cal.series(name=\"PD1\", x=r1, y=0, t=t2, h=h2, layer=3)\n", "cal.series(name=\"PS2\", x=r2, y=0, t=t3, h=h3, layer=1)\n", "cal.series(name=\"PD2\", x=r2, y=0, t=t4, h=h4, layer=3)\n", - " \n", + "\n", "cal.fit()" ] }, @@ -362,18 +362,20 @@ "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Sy [-]\", \"Ss [1/m]\", \"kz/kh\", \"RMSE [m]\"],\n", - " index= [\"timflow\", \"Moench\"],\n", + " index=[\"timflow\", \"Moench\"],\n", ")\n", "\n", "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", "t.loc[\"Moench\"] = [8.64, 0.2, 2e-5, 0.5, 0.061318]\n", "\n", "t_formatted = t.style.format(\n", - " {\"k [m/d]\": \"{:.2f}\", \n", - " \"Sy [-]\": \"{:.1f}\",\n", - " \"Ss [1/m]\": \"{:.2e}\", \n", - " \"kz/kh\": \"{:.1f}\", \n", - " \"RMSE [m]\": \"{:.4f}\"}\n", + " {\n", + " \"k [m/d]\": \"{:.2f}\",\n", + " \"Sy [-]\": \"{:.1f}\",\n", + " \"Ss [1/m]\": \"{:.2e}\",\n", + " \"kz/kh\": \"{:.1f}\",\n", + " \"RMSE [m]\": \"{:.4f}\",\n", + " }\n", ")\n", "t_formatted" ] From 38e09653f034e5330c513eb76cdcb5a22a19eefa Mon Sep 17 00:00:00 2001 From: Hannah Heesen Date: Thu, 19 Mar 2026 20:42:25 +0700 Subject: [PATCH 4/8] Adjustments --- .../confined1_oude_korendijk.ipynb | 54 ++-- .../04pumpingtests/confined2_grindley.ipynb | 166 ++++++++++- .../04pumpingtests/confined3_sioux.ipynb | 166 ++++++++++- .../04pumpingtests/confined4_nevada.ipynb | 271 ++++++++---------- docs/transient/04pumpingtests/index.rst | 2 +- .../04pumpingtests/leaky1_dalem.ipynb | 112 ++++++-- .../04pumpingtests/leaky2_hardixveld.ipynb | 24 +- .../04pumpingtests/leaky3_texashill.ipynb | 174 ++++++++++- .../04pumpingtests/slug1_pratt_county.ipynb | 79 ++++- .../04pumpingtests/slug2_multiwell.ipynb | 88 +++--- .../04pumpingtests/slug3_falling_head.ipynb | 150 +++++++--- .../04pumpingtests/slug4_dawsonville.ipynb | 106 +++---- .../04pumpingtests/unconfined2_moench.ipynb | 84 +++--- 13 files changed, 1055 insertions(+), 421 deletions(-) diff --git a/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb b/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb index e0b771b..26aa6af 100644 --- a/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb +++ b/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb @@ -158,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -223,7 +223,13 @@ { "cell_type": "code", "execution_count": 14, - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { "data": { @@ -271,46 +277,54 @@ { "cell_type": "code", "execution_count": 17, - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, "outputs": [ { "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 k [m/d]Ss [1/m]RMSE [m]k [m/d]Ss [1/m]RMSE [m]
timflow66.092.54e-050.050timflow66.092.54e-050.050
MLU63.513.58e-050.833MLU63.513.58e-050.833
K&dR55.711.70e-041.293K&dR55.711.70e-041.293
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 17, diff --git a/docs/transient/04pumpingtests/confined2_grindley.ipynb b/docs/transient/04pumpingtests/confined2_grindley.ipynb index 805dec5..8572384 100644 --- a/docs/transient/04pumpingtests/confined2_grindley.ipynb +++ b/docs/transient/04pumpingtests/confined2_grindley.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -93,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -123,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -153,9 +153,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "ml = tft.ModelMaq(kaq=10, z=[0, -H], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\")\n", "w = tft.Well(ml, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], rc=rw, layers=0)\n", @@ -190,9 +199,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".............................\n", + "Fit succeeded.\n" + ] + } + ], "source": [ "# unknown parameters: kaq, Saq\n", "cal = tft.Calibrate(ml) # create Calibrate object\n", @@ -207,9 +225,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
optimal
kaq_0_038.087051
Saq_0_00.000001
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 38.087051\n", + "Saq_0_0 0.000001" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.259 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.3f} m\")" @@ -217,9 +289,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "hm1 = ml.head(r, 0, to1)\n", "hm2 = w.headinside(to2)\n", @@ -264,9 +353,56 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 20, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]RMSE [m]
timflow38.091.19e-060.259
AQTESOLV22.953.72e-06-
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"], index=[\"timflow\", \"AQTESOLV\"]\n", diff --git a/docs/transient/04pumpingtests/confined3_sioux.ipynb b/docs/transient/04pumpingtests/confined3_sioux.ipynb index d14cd69..c35f31c 100644 --- a/docs/transient/04pumpingtests/confined3_sioux.ipynb +++ b/docs/transient/04pumpingtests/confined3_sioux.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -149,9 +149,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "# timflow model\n", "ml = tft.ModelMaq(kaq=10, z=[0, -b], Saq=0.001, tmin=0.001, tmax=10, topboundary=\"conf\")\n", @@ -174,9 +183,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...............................................\n", + "Fit succeeded.\n" + ] + } + ], "source": [ "# unknown parameters: k, Saq\n", "cal = tft.Calibrate(ml)\n", @@ -190,9 +208,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
optimal
kaq_0_0282.795232
Saq_0_00.004209
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 282.795232\n", + "Saq_0_0 0.004209" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.004 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.3f} m\")" @@ -200,9 +272,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 15, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "hm1 = ml.head(r1, 0, t1)\n", "hm2 = ml.head(r2, 0, t2)\n", @@ -238,9 +327,56 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]RMSE [m]
timflow282.804.21e-030.004
AQTESOLV282.664.04e-03-
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"], index=[\"timflow\", \"AQTESOLV\"]\n", diff --git a/docs/transient/04pumpingtests/confined4_nevada.ipynb b/docs/transient/04pumpingtests/confined4_nevada.ipynb index 0e8310b..a9089f7 100644 --- a/docs/transient/04pumpingtests/confined4_nevada.ipynb +++ b/docs/transient/04pumpingtests/confined4_nevada.ipynb @@ -17,7 +17,13 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -69,7 +75,13 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [ "# time and drawdown of pumped well UE-25b#1\n", @@ -92,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -111,8 +123,14 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": {}, + "execution_count": 12, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [ "# parameters calculated by Kruseman and de Ridder (1970)\n", @@ -122,8 +140,14 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": {}, + "execution_count": 13, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -159,34 +183,21 @@ }, { "cell_type": "code", - "execution_count": 42, - "metadata": {}, + "execution_count": 47, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ".........................................................................................................................................................................................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 198\n", - " # data points = 138\n", - " # variables = 4\n", - " chi-square = 5.38947309\n", - " reduced chi-square = 0.04021995\n", - " Akaike info crit = -439.507237\n", - " Bayesian info crit = -427.798222\n", - "[[Variables]]\n", - " kaq_1_1: 0.87713604 +/- 0.00687861 (0.78%) (init = 10)\n", - " Saq_1_1: 5.0658e-06 +/- 4.9878e-07 (9.85%) (init = 0.0001)\n", - " c_1_1: 12.9124741 +/- 1.56932257 (12.15%) (init = 10)\n", - " rc: 0.10567818 +/- 0.00318208 (3.01%) (init = 0)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq_1_1, c_1_1) = +0.8555\n", - " C(kaq_1_1, Saq_1_1) = -0.7271\n", - " C(Saq_1_1, c_1_1) = -0.5393\n", - " C(Saq_1_1, rc) = -0.4022\n" + "Fit succeeded.\n" ] } ], @@ -199,13 +210,19 @@ "\n", "cal.seriesinwell(name=\"UE-25b#1\", element=w, t=t1, h=h1)\n", "cal.series(name=\"UE-25a#1\", x=r, y=0, t=t2, h=h2, layer=1)\n", - "cal.fit(report=True)" + "cal.fit(report=False)" ] }, { "cell_type": "code", - "execution_count": 43, - "metadata": {}, + "execution_count": 41, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { "data": { @@ -228,82 +245,36 @@ " \n", " \n", " \n", - " layers\n", " optimal\n", - " std\n", - " perc_std\n", - " pmin\n", - " pmax\n", - " initial\n", - " inhoms\n", - " parray\n", " \n", " \n", " \n", " \n", " kaq_1_1\n", - " 1.0\n", " 0.877136\n", - " 6.878610e-03\n", - " 0.784212\n", - " -inf\n", - " inf\n", - " 10.0000\n", - " None\n", - " [[0.8771360363656927]]\n", " \n", " \n", " Saq_1_1\n", - " 1.0\n", " 0.000005\n", - " 4.987801e-07\n", - " 9.845995\n", - " 0.0\n", - " inf\n", - " 0.0001\n", - " None\n", - " [[5.0658166983463815e-06]]\n", " \n", " \n", " c_1_1\n", - " 1.0\n", " 12.912474\n", - " 1.569323e+00\n", - " 12.153539\n", - " -inf\n", - " inf\n", - " 10.0000\n", - " None\n", - " [[12.912474111111717]]\n", " \n", " \n", " rc\n", - " NaN\n", " 0.105678\n", - " 3.182079e-03\n", - " 3.011103\n", - " -inf\n", - " inf\n", - " 0.0000\n", - " NaN\n", - " [[0.1056781836698406]]\n", " \n", " \n", "\n", "" ], "text/plain": [ - " layers optimal std perc_std pmin pmax initial \\\n", - "kaq_1_1 1.0 0.877136 6.878610e-03 0.784212 -inf inf 10.0000 \n", - "Saq_1_1 1.0 0.000005 4.987801e-07 9.845995 0.0 inf 0.0001 \n", - "c_1_1 1.0 12.912474 1.569323e+00 12.153539 -inf inf 10.0000 \n", - "rc NaN 0.105678 3.182079e-03 3.011103 -inf inf 0.0000 \n", - "\n", - " inhoms parray \n", - "kaq_1_1 None [[0.8771360363656927]] \n", - "Saq_1_1 None [[5.0658166983463815e-06]] \n", - "c_1_1 None [[12.912474111111717]] \n", - "rc NaN [[0.1056781836698406]] " + " optimal\n", + "kaq_1_1 0.877136\n", + "Saq_1_1 0.000005\n", + "c_1_1 12.912474\n", + "rc 0.105678" ] }, "metadata": {}, @@ -313,19 +284,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "RMSE: 0.19762123575324378\n" + "RMSE: 0.198 m\n" ] } ], "source": [ - "display(cal.parameters)\n", - "print(\"RMSE:\", cal.rmse())" + "display(cal.parameters.loc[:, [\"optimal\"]])\n", + "print(f\"RMSE: {cal.rmse():.3f} m\")" ] }, { "cell_type": "code", - "execution_count": 44, - "metadata": {}, + "execution_count": 17, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { "data": { @@ -373,76 +350,84 @@ }, { "cell_type": "code", - "execution_count": 53, - "metadata": {}, + "execution_count": 20, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, "outputs": [ { "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 km [m/d]Sm [1/m]kf [m/d]Sf [1/m]c [d]rc [m]RMSE [m]km [m/d]Sm [1/m]kf [m/d]Sf [1/m]c [d]rc [m]RMSE [m]
timflow0.003.75e-040.885.07e-0612.910.110.198timflow0.003.75e-040.885.07e-0612.910.110.198
AQTESOLV0.155.48e-040.941.95e-03-0.11-AQTESOLV0.155.48e-040.941.95e-03-0.11-
MLU0.003.75e-040.848.05e-065.20--MLU0.003.75e-040.848.05e-065.20--
K&dR0.833.75e-040.834.00e-06---K&dR0.833.75e-040.834.00e-06---
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 53, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -469,14 +454,13 @@ "t.loc[\"K&dR\"] = [0.8325, 3.750e-4, 0.8325, 4.000e-6, \"-\", \"-\", \"-\"]\n", "\n", "t_formatted = t.style.format(\n", - " {\n", - " \"km [m/d]\": \"{:.2f}\",\n", - " \"Sm [1/m]\": \"{:.2e}\",\n", - " \"kf [m/d]\": \"{:.2f}\",\n", - " \"Sf [1/m]\": \"{:.2e}\",\n", - " \"c [d]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.2f}\",\n", - " \"rc [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.2f}\",\n", - " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",\n", + " {\"km [m/d]\": \"{:.2f}\",\n", + " \"Sm [1/m]\": \"{:.2e}\",\n", + " \"kf [m/d]\": \"{:.2f}\",\n", + " \"Sf [1/m]\": \"{:.2e}\",\n", + " \"c [d]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.2f}\",\n", + " \"rc [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.2f}\",\n", + " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",\n", " }\n", ")\n", "t_formatted" @@ -493,13 +477,6 @@ "* Kruseman, G.P. and De Ridder, N.A. (1990), Analysis and evaluation of pumping test data, 2nd edn. ILRI Publ. 47, ILRI, Wageningen, The Netherlands\n", "* Moench, A.F. (1984), Double-Porosity Models for a Fissured Groundwater Reservoir With Fracture Skin, Water Resour. Res., 20( 7), 831– 846, doi:10.1029/WR020i007p00831." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/transient/04pumpingtests/index.rst b/docs/transient/04pumpingtests/index.rst index 2e06971..cadd924 100644 --- a/docs/transient/04pumpingtests/index.rst +++ b/docs/transient/04pumpingtests/index.rst @@ -61,7 +61,7 @@ Slug Tests 1. :doc:`Pratt County ` - Slug test with data from the partially penetrating test well. 2. :doc:`Multi well ` - Slug test with data from both fully penetrating test well and observation well. 3. :doc:`Falling head ` - Slug test in an unconfined aquifer with a partially penetrating well. -4. :doc: `Dawsonville ` - Slug test in a confined aquifer with a fully penetrating well. +4. :doc:`Dawsonville ` - Slug test in a confined aquifer with a fully penetrating well. Unconfined Pumping Tests ---------- diff --git a/docs/transient/04pumpingtests/leaky1_dalem.ipynb b/docs/transient/04pumpingtests/leaky1_dalem.ipynb index 7e2a1a3..ba4c300 100644 --- a/docs/transient/04pumpingtests/leaky1_dalem.ipynb +++ b/docs/transient/04pumpingtests/leaky1_dalem.ipynb @@ -29,7 +29,13 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -86,7 +92,13 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [ "# data of observation well 30 m away from pumping well\n", @@ -126,7 +138,13 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [ "# known parameters\n", @@ -158,7 +176,13 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [ "# unkonwn parameters: kaq, Saq, c\n", @@ -186,7 +210,13 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [ "cal = tft.Calibrate(ml)\n", @@ -204,7 +234,13 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", @@ -214,7 +250,13 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [ "hm_1 = ml.head(r1, 0, t1)\n", @@ -257,7 +299,13 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [ "cal2 = tft.Calibrate(ml)\n", @@ -269,29 +317,47 @@ "cal2.series(name=\"obs2\", x=60, y=0, t=t2, h=h2, layer=0, weights=1 / np.max(np.abs(h2)))\n", "cal2.series(name=\"obs3\", x=90, y=0, t=t3, h=h3, layer=0, weights=1 / np.max(np.abs(h3)))\n", "cal2.series(name=\"obs4\", x=120, y=0, t=t4, h=h4, layer=0, weights=1 / np.max(np.abs(h4)))\n", - "cal2.fit(report=True)" + "cal2.fit(report=False)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [ - "display(cal2.parameters[[\"layers\", \"optimal\"]])\n", - "print(f\"RMSE: {cal2.rmse(weighted=False):.5f} m\")" + "display(cal2.parameters[[\"optimal\"]])\n", + "print(f\"RMSE: {cal2.rmse(weighted=False):.3f} m\")" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "### Comparison of results" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "The performance of `timflow` is compared with other models and with parameter values reported in Kruseman and de Ridder (1970). The published values were obtained by graphical matching to a Hantush type curve (Hantush, 1955). In addition to the `timflow` results and the literature values, results from AQTESOLV (Duffield, 2007) and MLU (Hemker & Post, 2014) are included for comparison. Both AQTESOLV and MLU were applied to minimize the sum of the squared log-transformed drawdowns. AQTESOLV only fitted the model to the observation well at r = 90 m, whereas the other models were calibrated using data from multiple observation wells.\n", "\n", @@ -301,7 +367,15 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, "outputs": [], "source": [ "t = pd.DataFrame(\n", @@ -325,7 +399,13 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "## References\n", "\n", diff --git a/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb b/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb index 3e51f70..2de7e8e 100644 --- a/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb +++ b/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb @@ -164,7 +164,7 @@ "outputs": [], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", - "print(f\"RMSE: {cal.rmse():.5f} m\")" + "print(f\"RMSE: {cal.rmse():.3f} m\")" ] }, { @@ -208,14 +208,20 @@ "metadata": {}, "outputs": [], "source": [ - "display(cal1.parameters[\"optimal\"])\n", - "print(f\"RMSE: {cal1.rmse():.5f} m\")" + "display(cal1.parameters[[\"optimal\"]])\n", + "print(f\"RMSE: {cal1.rmse(weighted=False):.3f} m\")" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [ "tm = np.logspace(np.log10(to[0]), np.log10(to[-1]), 100)\n", @@ -266,7 +272,15 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, "outputs": [], "source": [ "t = pd.DataFrame(\n", diff --git a/docs/transient/04pumpingtests/leaky3_texashill.ipynb b/docs/transient/04pumpingtests/leaky3_texashill.ipynb index 351f2fd..4d1fffa 100644 --- a/docs/transient/04pumpingtests/leaky3_texashill.ipynb +++ b/docs/transient/04pumpingtests/leaky3_texashill.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -146,9 +146,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "ml = tft.ModelMaq(\n", " kaq=10,\n", @@ -180,9 +189,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...................................................\n", + "Fit succeeded.\n" + ] + } + ], "source": [ "# unknown parameters: kaq, Saq, c\n", "cal = tft.Calibrate(ml)\n", @@ -197,9 +215,74 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
optimal
kaq_0_0224.627650
Saq_0_00.000213
c_0_043.880381
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 224.627650\n", + "Saq_0_0 0.000213\n", + "c_0_0 43.880381" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.060 m\n" + ] + } + ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", "print(f\"RMSE: {cal.rmse():.3f} m\")" @@ -207,9 +290,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# plot the fitted curves\n", "hm1_1 = ml.head(r1, 0, t1)\n", @@ -255,9 +349,59 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 18, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]c [d]RMSE [m]
timflow224.632.13e-0443.880.060
AQTESOLV224.722.12e-0444.00-
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\", \"RMSE [m]\"],\n", diff --git a/docs/transient/04pumpingtests/slug1_pratt_county.ipynb b/docs/transient/04pumpingtests/slug1_pratt_county.ipynb index 493601f..85862eb 100644 --- a/docs/transient/04pumpingtests/slug1_pratt_county.ipynb +++ b/docs/transient/04pumpingtests/slug1_pratt_county.ipynb @@ -96,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -148,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -180,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -214,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -236,8 +236,14 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, + "execution_count": 17, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { "data": { @@ -289,18 +295,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "RMSE: 0.00286 m\n" + "RMSE: 0.003 m\n" ] } ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", - "print(f\"RMSE: {cal.rmse():.5f} m\")" + "print(f\"RMSE: {cal.rmse():.3f} m\")" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -355,9 +361,56 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 21, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]RMSE [m]
timflow6.052.13e-040.0029
AQTESOLV4.033.83e-040.0030
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", diff --git a/docs/transient/04pumpingtests/slug2_multiwell.ipynb b/docs/transient/04pumpingtests/slug2_multiwell.ipynb index 50f2705..cd0a723 100644 --- a/docs/transient/04pumpingtests/slug2_multiwell.ipynb +++ b/docs/transient/04pumpingtests/slug2_multiwell.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -84,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -158,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -192,14 +192,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "...................................\n", + "...........................................\n", "Fit succeeded.\n" ] } @@ -216,8 +216,14 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, + "execution_count": 31, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [ { "data": { @@ -246,7 +252,7 @@ " \n", " \n", " kaq_0_0\n", - " 1.166107\n", + " 1.166109\n", " \n", " \n", " Saq_0_0\n", @@ -258,7 +264,7 @@ ], "text/plain": [ " optimal\n", - "kaq_0_0 1.166107\n", + "kaq_0_0 1.166109\n", "Saq_0_0 0.000009" ] }, @@ -269,23 +275,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "RMSE: 0.01024 m\n" + "RMSE: 0.010 m\n" ] } ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", - "print(f\"RMSE: {cal.rmse():.5f} m\")" + "print(f\"RMSE: {cal.rmse():.3f} m\")" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdcAAAFBCAYAAAAlu+WfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAB+9UlEQVR4nO3dd3hTZRvA4d9Jmu5NJ1Batuwie4Oyt6BWQJYIiiAgsmWqLBUo4kBQAREEhboQEUQKH4pMB3u2lNEWCnSvNDnfH7WB0AINpHTw3Fy5aE7OeJKcnqfve96hqKqqIoQQQgir0RR2AEIIIURJI8lVCCGEsDJJrkIIIYSVSXIVQgghrEySqxBCCGFlklyFEEIIK5PkKoQQQliZJFchhBDCyiS5CiGEEFYmyVU8EEVRmDlzpsXbRUZGoigKK1eutHpMD1tQUBCDBg0q7DAs9rC+g5kzZ6IoitX2t3LlShRFITIy0mr7fFQ8yHceHh6OoiiEh4dbPa6SSJJrCZBzsVEUhd27d+d6XVVVAgICUBSFrl27FkKE9y/nFzrnodVq8fHx4emnn+b48eOFHV6ejh07xsyZMwv14v/jjz/SqlUrfHx8cHR0pEKFCjz77LNs2bKl0GIS4lFiU9gBCOuxt7dn7dq1NG/e3Gz5zp07uXjxInZ2doUU2YMbNWoUDRo0QK/X8++//7J06VLCw8M5cuQIfn5+hR2emWPHjjFr1ixat25NUFDQQz/+e++9x/jx42nVqhWTJ0/G0dGRM2fO8Ouvv7Ju3To6duwIQGBgIGlpaeh0uoceoxAlnSTXEqRz58588803vP/++9jY3Pxq165dS7169YiLiyvE6B5MixYtePrpp03Pq1atyvDhw/niiy+YMGFCIUZWtGRlZfHWW2/Rrl07tm7dmuv1K1eumH5WFAV7e/uHGd4jISUlBScnp8IOQxQyqRYuQfr06cO1a9fYtm2baVlmZiYbNmygb9++eW6TkpLC66+/TkBAAHZ2dlStWpX33nuP2ydLysjI4LXXXsPb2xsXFxe6d+/OxYsX89znpUuXeOGFF/D19cXOzo4aNWrw+eefW++Nkp1sAc6ePXtfx16yZAk1atTA0dERDw8P6tevz9q1a02vDxo0KM9S573uH65cuZJnnnkGgDZt2piqs3PuUx04cIAOHTrg5eWFg4MD5cuX54UXXrD07d9RXFwciYmJNGvWLM/XfXx8TD/ndf9t0KBBODs7c+nSJXr27ImzszPe3t6MGzcOg8Fgtq9r167Rv39/XF1dcXd3Z+DAgfzzzz/5vqf35ZdfUq9ePRwcHPD09OS5557jwoUL9/W+v//+e7p06ULp0qWxs7OjYsWKvPXWW2Yxz5gxA51Ox9WrV3NtP2zYMNzd3UlPTzct+/nnn2nRogVOTk64uLjQpUsXjh49arZdzud19uxZOnfujIuLC/369btjnDnnz6lTp3j++edxc3PD29ubadOmoaoqFy5coEePHri6uuLn58eCBQty7ePKlSsMGTIEX19f7O3tqVOnDqtWrcq1Xnx8PIMGDcLNzc30/cTHx+cZ14kTJ3j66afx9PTE3t6e+vXr88MPP9zxfYh7k+RaggQFBdGkSRO++uor07Kff/6ZhIQEnnvuuVzrq6pK9+7dWbRoER07dmThwoVUrVqV8ePHM3bsWLN1X3zxRUJDQ2nfvj3z5s1Dp9PRpUuXXPuMjY2lcePG/Prrr4wcOZLFixdTqVIlhgwZQmhoqNXea879TA8PD4uPvXz5ckaNGkX16tUJDQ1l1qxZBAcHs3fv3geOq2XLlowaNQqAKVOmsHr1alavXk21atW4cuUK7du3JzIykkmTJrFkyRL69evHn3/++cDHzeHj44ODgwM//vgj169fv699GAwGOnToQKlSpXjvvfdo1aoVCxYsYNmyZaZ1jEYj3bp146uvvmLgwIHMnj2b6OhoBg4cmK9jzJ49mwEDBlC5cmUWLlzImDFj2L59Oy1btrxjAriblStX4uzszNixY1m8eDH16tVj+vTpTJo0ybRO//79ycrKYv369Wbb5vwB2rt3b1NJfvXq1XTp0gVnZ2fmz5/PtGnTOHbsGM2bN891Lz0rK4sOHTrg4+PDe++9R+/eve8Zb0hICEajkXnz5tGoUSPefvttQkNDadeuHWXKlGH+/PlUqlSJcePGsWvXLtN2aWlptG7dmtWrV9OvXz/effdd3NzcGDRoEIsXLzatp6oqPXr0YPXq1Tz//PO8/fbbXLx4Mc/v5+jRozRu3Jjjx48zadIkFixYgJOTEz179uTbb7/N1+cv8qCKYm/FihUqoO7fv1/94IMPVBcXFzU1NVVVVVV95pln1DZt2qiqqqqBgYFqly5dTNt99913KqC+/fbbZvt7+umnVUVR1DNnzqiqqqp///23CqivvPKK2Xp9+/ZVAXXGjBmmZUOGDFH9/f3VuLg4s3Wfe+451c3NzRRXRESECqgrVqy463vbsWOHCqiff/65evXqVfXy5cvqli1b1EqVKqmKoqj79u2z+Ng9evRQa9SocdfjDhw4UA0MDMy1fMaMGertvzaBgYHqwIEDTc+/+eYbFVB37Nhhtt63335r+p4K0vTp01VAdXJyUjt16qTOnj1bPXjwYK718voOBg4cqALqm2++abZu3bp11Xr16pmeb9y4UQXU0NBQ0zKDwaA+8cQTufZ5+2cWGRmparVadfbs2WbHOHz4sGpjY5Nr+e1yzveIiAjTspzv9lYvvfSS6ujoqKanp5uWNWnSRG3UqJHZemFhYWbfV1JSkuru7q4OHTrUbL2YmBjVzc3NbHnO5zVp0qS7xpwj57MYNmyYaVlWVpZatmxZVVEUdd68eablN27cUB0cHMzOrdDQUBVQv/zyS9OyzMxMtUmTJqqzs7OamJioqurN3+133nnH7DgtWrTI9f08+eSTaq1atcw+J6PRqDZt2lStXLmyaVnO7+Lt57XIm5RcS5hnn32WtLQ0Nm3aRFJSEps2bbpjlfDmzZvRarWmklaO119/HVVV+fnnn03rAbnWGzNmjNlzVVXZuHEj3bp1Q1VV4uLiTI8OHTqQkJDAoUOH7ut9vfDCC3h7e1O6dGk6duxIQkICq1evpkGDBhYf293dnYsXL7J///77iuV+ubu7A7Bp0yb0en2BHWfWrFmsXbuWunXr8ssvv/DGG29Qr149Hn/88Xy3sH755ZfNnrdo0YJz586Znm/ZsgWdTsfQoUNNyzQaDSNGjLjnvsPCwjAajTz77LNm35Ofnx+VK1dmx44d+XynNzk4OJh+TkpKIi4ujhYtWpCamsqJEydMrw0YMIC9e/ea3U5Ys2YNAQEBtGrVCoBt27YRHx9Pnz59zOLTarU0atQoz/iGDx9uUbwvvvii6WetVkv9+vVRVZUhQ4aYlru7u1O1alWzz33z5s34+fnRp08f0zKdTseoUaNITk5m586dpvVsbGzM4tJqtbz66qtmcVy/fp3ffvuNZ5991vS5xcXFce3aNTp06MDp06e5dOmSRe9NZJMGTSWMt7c3bdu2Ze3ataSmpmIwGMwaAt3q/PnzlC5dGhcXF7Pl1apVM72e879Go6FixYpm61WtWtXs+dWrV4mPj2fZsmVmVYi3urVBjSWmT59OixYtSE5O5ttvv2XdunVoNDf/NrTk2BMnTuTXX3+lYcOGVKpUifbt29O3b9873qe0llatWtG7d29mzZrFokWLaN26NT179qRv3753bcmdkJBAWlqa6bmtrS2enp53PVafPn3o06cPiYmJ7N27l5UrV7J27Vq6devGkSNH7tqQyd7eHm9vb7NlHh4e3Lhxw/T8/Pnz+Pv74+joaLZepUqV7hoXwOnTp1FVlcqVK+f5+v20Xj569ChTp07lt99+IzEx0ey1hIQE088hISGMGTOGNWvWMH36dBISEti0aROvvfaa6V766dOnAXjiiSfyPJarq6vZcxsbG8qWLWtRvOXKlTN77ubmhr29PV5eXrmWX7t2zfT8/PnzVK5c2ezch7x/Z/39/XF2djZb7/bf2TNnzqCqKtOmTWPatGl5xnrlyhXKlCljwbsTIMm1ROrbty9Dhw4lJiaGTp06mUpMBc1oNALw/PPP3/HeW+3ate9r37Vq1aJt27YA9OzZk9TUVIYOHUrz5s0JCAiw6NjVqlXj5MmTbNq0iS1btrBx40Y++ugjpk+fzqxZswDu2Gjp9kY9llAUhQ0bNvDnn3/y448/8ssvv/DCCy+wYMEC/vzzz1wXwhyjR482a7DSqlWrfHfkd3V1pV27drRr1w6dTseqVavYu3evqZSWF61Wa9H7spTRaERRFH7++ec8j3Wnz+FO4uPjadWqFa6urrz55ptUrFgRe3t7Dh06xMSJE03nBmT/kdC1a1dTct2wYQMZGRk8//zzZvFB9n3XvLp53doSH8DOzi5XsruXvN73nT539bbGhdaU817HjRtHhw4d8lwnP38widwkuZZATz31FC+99BJ//vlnrsYbtwoMDOTXX38lKSnJrPSaU40WGBho+t9oNHL27Fmzv3xPnjxptr+clsQGg8GUCAvKvHnz+Pbbb5k9ezZLly61+NhOTk6EhIQQEhJCZmYmvXr1Yvbs2UyePBl7e3s8PDzybFiTUzK4m3uNRtS4cWMaN27M7NmzWbt2Lf369WPdunVmVYW3mjBhgtnF/9ZGXJaoX78+q1atIjo6+r62v1VgYCA7duwgNTXVrPR65syZe25bsWJFVFWlfPnyVKlS5YFjCQ8P59q1a4SFhdGyZUvT8oiIiDzXHzBgAD169GD//v2sWbOGunXrUqNGDbP4ILtxWEGfx5YKDAzk33//xWg0miX0vH5nt2/fTnJystkfK7f/zlaoUAHIri0oau+1uJN7riWQs7MzH3/8MTNnzqRbt253XK9z584YDAY++OADs+WLFi1CURQ6deoEYPr//fffN1vv9ta/Wq2W3r17s3HjRo4cOZLreHl1gbhfFStWpHfv3qxcuZKYmBiLjn1rNRtkV7NWr14dVVVN90IrVqxIQkIC//77r2m96OjofLWezOnjeHtyvnHjRq5SSHBwMJDd1elOqlevTtu2bU2PevXq3XHd1NRU9uzZk+drOffQb68avB8dOnRAr9ezfPly0zKj0ciHH354z2179eqFVqtl1qxZuT4PVVVzfT/3klPiu3VfmZmZfPTRR3mu36lTJ7y8vJg/fz47d+40+8MFst+bq6src+bMyfPeuDXPY0t17tyZmJgYsz+as7KyWLJkCc7OzqYaic6dO5OVlcXHH39sWs9gMLBkyRKz/fn4+NC6dWs++eSTPP/oKsz3WtxJybWEyk+XiG7dutGmTRveeOMNIiMjqVOnDlu3buX7779nzJgxpr/gg4OD6dOnDx999BEJCQk0bdqU7du351lKmTdvHjt27KBRo0YMHTqU6tWrc/36dQ4dOsSvv/56391D8jJ+/Hi+/vprQkNDmTdvXr6P3b59e/z8/GjWrBm+vr4cP36cDz74gC5duphK8M899xwTJ07kqaeeYtSoUaSmpvLxxx9TpUqVezbKCg4ORqvVMn/+fBISErCzs+OJJ55g7dq1fPTRRzz11FNUrFiRpKQkli9fjqurK507d7bKZ5KamkrTpk1p3LgxHTt2JCAggPj4eL777jv+97//0bNnT+rWrfvAx+nZsycNGzbk9ddf58yZMzz22GP88MMPps/4bqX3ihUr8vbbbzN58mQiIyPp2bMnLi4uRERE8O233zJs2DDGjRuX71iaNm2Kh4cHAwcOZNSoUSiKwurVq+9YnarT6Xjuuef44IMP0Gq1Zo2DILsq/eOPP6Z///48/vjjPPfcc3h7exMVFcVPP/1Es2bNcv1B+rAMGzaMTz75hEGDBnHw4EGCgoLYsGEDv//+O6Ghoabzt1u3bjRr1oxJkyYRGRlJ9erVCQsLM7v/nOPDDz+kefPm1KpVi6FDh1KhQgViY2PZs2cPFy9e5J9//nnYb7NkePgNlIW13doV525u74qjqtndDl577TW1dOnSqk6nUytXrqy+++67qtFoNFsvLS1NHTVqlFqqVCnVyclJ7datm3rhwoVcXXFUVVVjY2PVESNGqAEBAapOp1P9/PzUJ598Ul22bJlpHUu74nzzzTd5vt66dWvV1dVVjY+Pz/exP/nkE7Vly5ZqqVKlVDs7O7VixYrq+PHj1YSEBLN9b926Va1Zs6Zqa2urVq1aVf3yyy/z1RVHVVV1+fLlaoUKFVStVmvqvnDo0CG1T58+arly5VQ7OzvVx8dH7dq1q3rgwIG7fgaW0Ov16vLly9WePXuqgYGBqp2dnero6KjWrVtXfffdd9WMjAzTunfqiuPk5JRrv3m976tXr6p9+/ZVXVxcVDc3N3XQoEHq77//rgLqunXr7rqtqmZ352nevLnq5OSkOjk5qY899pg6YsQI9eTJk3d9j3l1xfn999/Vxo0bqw4ODmrp0qXVCRMmqL/88ssdu47s27dPBdT27dvf8Tg7duxQO3TooLq5uan29vZqxYoV1UGDBpl9X3f6vO4k57O4evWq2fI77adVq1a5uo3FxsaqgwcPVr28vFRbW1u1Vq1aef4eXbt2Te3fv7/q6uqqurm5qf3791f/+uuvPH/vzp49qw4YMED18/NTdTqdWqZMGbVr167qhg0bzD6PO32eIjdFVQvwbrkQ4pHy3Xff8dRTT7F79+4Cb339IP755x+Cg4P54osv6N+/f2GHI0oguecqhLgvt3YPgpv39FxdXXn88ccLKar8Wb58Oc7OzvTq1auwQxEllNxzFULcl1dffZW0tDSaNGlCRkYGYWFh/PHHH8yZM8dsUIei5Mcff+TYsWMsW7aMkSNHygD7osBItbAQ4r6sXbuWBQsWcObMGdLT06lUqRLDhw9n5MiRhR3aHQUFBREbG0uHDh1YvXp1rgFUhLAWSa5CCCGElck9VyGEEMLKJLkKIYQQVlaoDZp27drFu+++y8GDB02j3/Ts2fOu24SHhzN27FiOHj1KQEAAU6dOZdCgQfk+ptFo5PLly7i4uNxzmDohhBAll6qqJCUlUbp0aYvHh76XQk2uKSkp1KlThxdeeCFfTeIjIiLo0qULL7/8MmvWrGH79u28+OKL+Pv733HQ6dtdvnyZgICABw1dCCFECXHhwgWLZza6lyLToElRlHuWXCdOnMhPP/1kNnbsc889R3x8PFu2bMnXcRISEnB3d+fChQu5po4SJYder2fr1q20b9/+vqYwEyWHnAsix+3nQmJiommIUDc3N6seq1j1c92zZ0+umRs6dOiQa9LuW2VkZJgNip6UlARkT65cVPviiQdnY2ODo6MjDg4OckF9xMm5IHLcfi7kTMxQELcIi1VyjYmJwdfX12yZr68viYmJpKWl5Zks586da5qj81Zbt27NNdGzKHm2bdtW2CGIIkLOBZEj51xITU0tsGMUq+R6PyZPnszYsWNNz3OqAdq3by/VwiWYXq9n27ZtpknCxaNLzgWR4/ZzITExscCOVaySq5+fH7GxsWbLYmNjcXV1vWMVr52dHXZ2drmW63Q6+UV7BMj3LHLIuSBy5JwLBXk+FKvk2qRJEzZv3my2bNu2bTRp0qSQIhJCFFVGo5HMzEz0ej02Njakp6djMBgKOyzxEOl0OrRabaEcu1CTa3JystmE2xEREfz99994enpSrlw5Jk+ezKVLl/jiiy8AePnll/nggw+YMGECL7zwAr/99htff/01P/30U2G9BSFEEZSZmUlERARGoxFVVfHz8+PChQvSt/0R5O7ujp+f30P/7gs1uR44cIA2bdqYnufcGx04cCArV64kOjqaqKgo0+vly5fnp59+4rXXXmPx4sWULVuWTz/9NN99XIUQJZ+qqkRHR6PVak192pOTk3F2drb6QAGi6FJVldTUVK5cuQKAv7//Qz1+oSbX1q1bc7dutitXrsxzm7/++qsAo8oHfTrs+QDcAsA9ANzKgos/aO+//j46IY2IuBTKeznh7yZdhIS4X1lZWaSmplK6dGkcHR1N1cP29vaSXB8xOW1xrly5go+Pz0M9drG651pkJF6C394yX6ZoshOsW9nspOtW1vxn9wCwz7uT8vr9UUwOO4xRBY0Cc3vVIqRBuYfwRoQoeXLuq9ra2hZyJKIoyOlyqdfrH+r9V0mu90PRkFr9WfTXonBKj8Em+TIYMrOTbuIluLA37+3sXG9JutmJ94atLxu/j8FP9SIWDwyqlilhR2hZxVtKsEI8ALm/KqDwzgNJrvdh/Vktk//qebOk+VQNQqo5QMJFSIj67/+cx4Xs/1OvQUYiXDmW/fiPB/D1f39g61UtUaoP59TSGH/ZAZVqQanKUKoSOHmBXCyEEKJYkORqoeiENFMVLoBRhSnfHqNl1Tb4l60HZevlvWFmCiRcupls/3tkXD9PTNRp/LmGrWKgohJNRaLh2EE4dsv29m7Zidbrv2RbqlL2z54VQWdf4O9bCFG4wsPDadOmDTdu3MDd3d0q+wwKCmLMmDF3HUJW3B9JrhaKiEsxJdYcBlUlMi717tW4tk7gXSX7cQs74M/9UbwR9i/e6nUqaaIZUxfqOV2Da2fg2mmIvwDpCXDpQPbDjEKWa1mSnCugK10L58C64FszO/lq5esVQhSs8PBwFi1axL59+0hMTKRy5cqMHz+efv36FXZohUquvhYq7+WERsEswWoVhSCv+x+nOKRBOVpW8SYyLpUgL8fcSVqfBtfPQdzp7GQbd+Zm4k1PwCbxAh6JF+DyTsjJvVo78HkMfGuBX83shOtbAxw9pWWyEMJq/vjjD2rXrs3EiRPx9fVl06ZNDBgwADc3N7p27VrY4RUaSa4W8ndzYG6vWkwJO4JBVdEqCnN61XzgJOXv5nDnfegcshOjbw2zxdHxqXSf/x1BRFNFc5Fqynmqa6IItruERp8K0f9kP26Rau/L8ZTSHFaD+FytSNdO3ejZPPiBYheipHqYf4hmZGQwfvx41q1bR2JiIvXr12fRokU0aNDAbL3ff/+dyZMnc+rUKYKDg/n000+pWbMmAOfPn2fkyJHs3r2bzMxMgoKCePfdd+ncuXO+YlAUheXLl/PTTz/xyy+/UKZMGRYsWED37t3vuM2UKVPMno8ePZqtW7cSFhYmyVVY5p4lzYck4loqV1U3ruLGfsNjpuVfDWhIE49EiD0CMUcg9ijEHob4KBzTY3lCG8sT/NdX+Nf3yNpXDpuA+lC2PpSpD/61QecgJVzxSHvYXeQmTJjAxo0bWbVqFYGBgbzzzjt06NCBM2fO4OnpaVpv/PjxLF68GD8/P6ZMmUK3bt04deoUOp2OESNGkJmZya5du3BycuLYsWM4OztbFMesWbN45513ePfdd1myZAn9+vXj/PnzZjHcS0JCAtWqVbPouCWNJNf7dNeS5kNyxypqb2dw84ZSFaF6D9Nre49H8N4XYVTXnKe25hx1lLNU0lzGJjEKjkbB0bDsFTU2XHeuzG83SrPfUJUD6mO82quN9L0Vj4w8Gy4WYBe5lJQUPv74Y1auXEmnTp0AWL58Odu2beOzzz5j/PjxpnVnzJhBu3btAFi1ahVly5bl22+/5dlnnyUqKorevXtTq1YtACpUqGBxLIMGDaJPnz4AzJkzh/fff599+/bRsWPHfG3/9ddfs3//fj755BOLj12SSHItxiytoi5X2o+DPJZdyv1v/HJ3JZXtfVwpFf8vXDoEFw9AyhU8E4/TT3ucftrtAFzc5EXqudY4VmoJgc2gVEWiE9OlZCtKpPtuuHifzp49i16vp1mzZqZlOp2Ohg0bcvz4cbN1b52oxNPTk6pVq5rWGTVqFMOHD2fr1q20bduW3r17U7t2bYtiuXV9JycnXF1dTUMI1qhRg/PnzwPQokULfv75Z7Ntd+zYweDBg1m+fDk1apjfxnrUSHIt5iypos4rGU/u1YhStcsB/43PrKocOHyYz9d9Q13NGRpoTlJTiaCsEgfHN2Q/gDS7UhxKrcSfxmr8odZiWM92hDQMfAjvWIiCVxANFx+GF198kQ4dOvDTTz+xdetW5s6dy4IFC3j11VfzvY/bp2FTFAWj0QjA5s2b0ev1ALmm+dy5cyfdunVj0aJFDBgw4AHfSfEnybUEsKSK+p7JWFEoE1iZLWpjNmc1BsCRdOprTvNhi3RcYvahXjyAQ8Y1umiv0UWbPRrVpZ+8SI1qj+NjbaF8a6KzHKVUK4qtgmq4eCcVK1bE1taW33//ncDA7D9S9Xo9+/fvz9UH9c8//6RcuexbNDdu3ODUqVNm9zcDAgJ4+eWXefnll5k8eTLLly+3KLneTU5stwsPD6dr167Mnz+fYcOGWeVYxZ0k10fQvZLx7ReWDMWBLk/1xeW/e65/nrrEwhXraKQ5TlPNUeppTlFGiYMja+HIWlQU4oxB/G2sRagxmKd7PMWzjSy/9yNEYXqYDRednJwYPnw448ePN025+c4775CamsqQIUPM1n3zzTcpVaoUvr6+vPHGG3h5edGzZ08AxowZQ6dOnahSpQo3btxgx44dBd6waMeOHXTt2pXRo0fTu3dvYmJigOyxnS1pBFXSSHIVebrbhSXI19N07/YDw1M4kE4T7UnebxSP3fmd6K6doJYmglqaCF7hB25sXkhaRAccanSGSk8SnWkvpVpRLDzMhovz5s3DaDTSv39/kpKSqF+/Pr/88gseHh651hs9ejSnT58mODiYH3/80TRJgcFgYMSIEVy8eBFXV1c6duzIokWLCjTuVatWkZqayty5c5k7d65peatWrQgPDy/QYxdlinq3Od9KoMTERNzc3EhISMDV1bWwwym21u+PylVlFtKgHH+cjWP08i001xyhlfYfWmv+wV1JMW1nVLTsN1Rmm6EeW40NGNHryQJphazX69m8eTOdO3fOdQ9JlGzp6elERERQvnx57O3tMRqNJCYm4urqKlPOPYJuPR+0Wq3ZdaEg84GUXMV9uVPJtryXE9cUD741tuBbYwu0GKinOcPnTa9hF7EN3bWTNNKcoJHmBFNZw5Efg0i68RwudXsRbVtOSrRCiBJB/owT983fzYEmFUuZJcKc+7XanBl8FBt6P/U0zl1ns7/zZppnhDJDP5A/DNUxqAo1NZG4/DEPPmxI8oJ67F8xnv7zV7N+fxSQ3d/wj7NxRCekFcZbFEKI+yIlV2F1dyvVXsaHVYYOrDJ0wJNE2msPMaX8Kewv/I/KmkuM1oQxmjCO/vgBf57owbhjlbioeskk8kKIYkWSqygQeTUEub0VcoLiRt2er3LE05GXlu+greYg3bR7aKE5TA3NeTj7PrvtYJ+xKhsNLZkTlkbLKt4AUn0shCjSJLmKhyqvUm10QhopiqPpPq0HiXTW7qerZg+NNMdpqDlJQ81JZqkrObuqDe/E1OV/xlqgaJjbqxYtq3hLshVCFCmSXMVDd3up9vYSbaLiRrn2I+i35Ul81Gv01P5Ob212tXGN61tZZbuVS2opvjG04v2NcUxWvB7a4OpCCJEfklxFkZBXidbdUceUsCMsNXRnubE7U4PT0BxeR3ftHsoo1xhjE8Yo7bfsNNZmneEJfjU+zpSwIzzm50JiaibxGYX9roQQjypJrqLIuL1Ee3vCBWj2twNzsvrRQXOA57S/0VR7jDbaf2ij/YfLqidrs57kxY/iuaq6oaBFV+4ifRuXL6y3JIR4RElyFUXa7Qk3p/r4B2NTNhmbUi4rhhDtDp7R7qS0cp1xum8YpYaxxdiQz7M6MfV7hRpl3EnJNMg9WSHEQyP9XEWxEtKgHLsnteGroY35ffITDO/VjvcMfWmasYTXMl/hoLEytoqB7to9fGc3nQ266SxfuoABy3+n2bzfWL8/SvrOimIjPDwcRVGIj49/oP2kpqbSu3dvXF1dTfsLCgoiNDTUKnHmR+vWrXNNQlCSSclVFDu3lmZvrTp2tG3FUx81pxqRDNJuobv2Dx7XnOFxzRIu2ZTi86xOvLUxjVTFQRpAiSKndevWBAcHmyW8pk2bEh0djZub2wPte9WqVfzvf//jjz/+wMvL64H3V1j++ecf5s2bx+7du4mLiyMoKIiXX36Z0aNHF3ZouVicXLOysjh69Khp5gM/Pz+qV68u47eKQnNrss2uNlYYn/Uy72SF0M9mO89rf6WMco1pui8ZZRPGakM7VmZ1JE51Y/LGwzjZ2VAv0EOqjEWRY2tri5+f3wPv5+zZs1SrVo2aNWtaIarCc/DgQXx8fPjyyy8JCAjgjz/+YNiwYWi1WkaOHFnY4ZnJd7Ww0Whk6tSpeHt7U7duXTp16kSnTp2oW7cuPj4+TJs2zTShrhCFJafa+MsX6tO/pgvvG56mWcb7TNK/yFmjP25KKiNtvme33Shm2KzCl2uMXPuXqcpYiMIwaNAgdu7cyeLFi1EUBUVRiIyMzFUtvHLlStzd3dm0aRNVq1bF0dGRp59+mtTUVFatWkVQUBAeHh6MGjUKg8EAZJeIFyxYwK5du1AUhdatW+cZQ1RUFD169MDZ2RlXV1eeffZZYmNjAUhISECr1XLgwAEgOx94enrSuHFj0/Y5CS+/goKCmDNnDi+88AIuLi6UK1eOZcuW3XWbF154gcWLF9OqVSsqVKjA888/z+DBgwkLC8v3cR+WfCfXSZMmsWzZMubNm8e5c+dISUkhJSWFc+fOMX/+fJYtW8bkyZMLMlYh8sXfzYFG5T0JdIG3e1QnS7FjneEJ2me+y7DM1/jLWAl7Rc9gm1/YaTeGuTbL8VGvMSXsiNyHLYlUFfSpkJny8B/5nHRs8eLFNGnShKFDhxIdHU10dPQdE1Vqairvv/8+69atY8uWLYSHh/PUU0+xefNmNm/ezOrVq/nkk0/YsGEDAGFhYQwdOpQmTZoQHR2dZyIyGo306NGD69evs3PnTrZt28a5c+cICQkBwM3NjeDgYNMUcocPH0ZRFP766y+Sk5MB2LlzJ61atbLoq1mwYAH169fnr7/+4pVXXmH48OGcPHnSon0kJCQUyXlj810t/MUXX7B69Wo6dOhgtjwoKIhhw4YRGBjIgAEDmD9/vtWDFOJ+PVOvLG2q+Zm68+w6VYenwxrQSDnCSO13NNUeo4/NDp7S7maloQMXL1XF361iYYctrEmfivuHBTth+B1NuQy2Tvdczc3NDVtbWxwdHe9ZDazX6/n444+pWDH7PH366adZvXo1sbGxODs7U716ddq0acOOHTsICQnB09MTR0fHu1Yxb9++ncOHDxMREWFK6l988QU1atRg//79NGjQgNatWxMeHs64ceMIDw+nXbt2nDhxgt27d9OxY0fCw8OZMGGCRR9P586deeWVVwCYOHEiixYtYseOHVStWjVf2//xxx+sX7+en376yaLjPgz5LrkmJSVRunTpO77u7+9PSkrKHV8XorDcOntPdrXxE/QN6c/zWVPpnTGDvcbHsFf0vGyziXrft4Hdi0AvJVhRNDk6OpoSK4Cvry9BQUE4OzubLbty5Uq+93n8+HECAgLMSsvVq1fH3d2d48ePA9mTn+/evRuDwcDOnTtp3bq1KeFevnyZM2fO3LHK+U5q165t+llRFPz8/Exxd+rUCWdnZ5ydnalRo0aubY8cOUKPHj2YMWMG7du3t+i4D0O+S66tW7dm3LhxrFmzBi8vL7PX4uLimDhxosUfrBCFwd/Nga51HEjJzGJKmEJI5jSe1P7DAs9vcU86Db/OhL3LoPUkCO4HWmlUX6zpHIkfcRxXF5eHP1m6ztH6u7yt8aiiKHkus3YbmJYtW5KUlMShQ4fYtWsXc+bMwc/Pj3nz5lGnTh1Kly5N5cqVLdrn3eL+9NNPSUtLy3O9Y8eO8eSTTzJs2DCmTp36AO+q4OT7qrF06VI6d+6Mv78/tWrVwtfXF4DY2FgOHz5M9erV2bRpU4EFKoS1mY8A9STuLpPg8Dfw29uQcAF+HAV7PoAnp8NjXSFnjlpRvChKdpKzdYKHnVwtYGtra2qE9LBVq1aNCxcucOHCBVPp9dixY8THx1O9enUA3N3dqV27Nh988AE6nY7HHnsMHx8fQkJC2LRpk8X3W++lTJkyeS4/evQoTzzxBAMHDmT27NlWPaY15ftMCwgI4J9//uGHH36gW7dulCtXjnLlytGtWzd+/PFH/vrrL4taiuX48MMPCQoKwt7enkaNGrFv3767rh8aGkrVqlVxcHAgICCA1157jfT0dIuPKwTcNuG7Rgt1noORB6DDHHDwhLhTsP55+KwdRP5u2k4GohDWFhQUxN69e4mMjCQuLu6h9r5o27YttWrVol+/fhw6dIh9+/YxYMAAWrVqRf369U3rtW7dmjVr1pgSqaenJ9WqVWP9+vVWT655OXLkCG3atKF9+/aMHTuWmJgYYmJiuHr1aoEf21IW1XdpNBpTFxxrWL9+PWPHjmXp0qU0atSI0NBQOnTowMmTJ/Hx8cm1/tq1a5k0aRKff/45TZs25dSpUwwaNAhFUVi4cKFVYhICnT00GQF1n4c/lsCeD+HifljZGSq3Z4vfMF75NVMGohBWNW7cOAYOHEj16tVJS0sjIiLioR1bURS+//57Xn31VVq2bIlGo6Fjx44sWbLEbL1WrVoRGhpqdguwdevW/PPPPw/ltuCGDRu4evUqX375JV9++aVpeWBgIJGRkQV+fEsoqpq/tuL//vtvvnZ46w3qe2nUqBENGjTggw8+ALKbgwcEBPDqq68yadKkXOuPHDmS48ePs337dtOy119/nb1797J79+58HTMxMRE3NzcSEhJwdXXNd6yieNHr9WzevJnOnTs/+AAnSTGw8x04tAqMWRhVhW+NzViU9QwXVW+0isLuSW1kEIoiIj09nYiICMqXL4+9vT1Go5HExERcXV0f/j1XUehuPR+0Wq3ZdaEg80G+S67BwcEoikJOLlb+u/90a25WFCXf9wwyMzM5ePCgWd9YjUZD27Zt2bNnT57bNG3alC+//JJ9+/bRsGFDzp07x+bNm+nfv/8dj5ORkUFGxs25xxITE4Hsi69er89XrKL4yflurfId25eCDvOhwTDif5qJd9TP9NbupqvmT5YZuvJBVk/Oxibi5SgNn4oCvV6PqqoYjUaMRqPpGpWzTDxacs4BvV5v+v6ten24g3xfDW6tolBVlZo1a7J582YCAwPv68BxcXEYDAZTw6gcvr6+nDhxIs9t+vbtS1xcHM2bN0dVVbKysnj55ZeZMmXKHY8zd+5cZs2alWv51q1bcXS0fks+UbRs27bNqvuLd+7DxoymTLBZR3PtUV61+Y5umj2c2j2QzceL99ByJYWNjQ1+fn4kJyeTmZlpWp6UlFSIUYnCkpmZSVpaGrt27SIrKwu4eV1ITU0tsOPmO7nenkQVRaFs2bL3nVzvR3h4OHPmzOGjjz6iUaNGnDlzhtGjR/PWW28xbdq0PLeZPHkyY8eONT1PTEwkICCA9u3bS7VwCabX69m2bRvt2rWz+rjXunIXGfB9BdoZDjBTt4ogTSxBUe9gdH0GQ9s3wcnbqscTlklPT+fChQs4Oztjb2+PqqokJSXh4uJiqnETj4709HQcHBxo2bIlWq3W7LqQU5NZEAqtHsvLywutVmsauzJHbGzsHUcRmTZtGv379+fFF18EoFatWqSkpDBs2DDeeOONPO+n2NnZYWdnl2u5TqeTyQYeAQXxPfdtXP6/UZ+aoLi8AgcXwN5P0Bz5Bs2ZbdD+LQh+vkh3+yjJDAYDiqKg0WjQaDSmqsCcZeLRotFoTH2BtVotcPO6UJA5oNDONFtbW+rVq2fWOMloNLJ9+3aaNGmS5zapqam5fjlyPqx8tssSwipyuvD4+fhAp/kwdDv41YL0ePjhVVjZBa7cvL0hXXeEeLQ8UMn1QatYxo4dy8CBA6lfvz4NGzYkNDSUlJQUBg8eDMCAAQMoU6YMc+fOBaBbt24sXLiQunXrmqqFp02bRrdu3UxJVohCUaYeDA2HvUthx2yI+gOWNofmY/jGMYSJ35+SrjtCPELynVzr1q1rlkzT0tLo1q0btra2ZusdOnQo3wcPCQnh6tWrTJ8+nZiYGIKDg9myZYupkVNUVJRZSXXq1KkoisLUqVO5dOkS3t7edOvWrUiP0iEeIVobaDoSqneHzePh1BbY9S4NjF/QRHmB39VaGFWYEnaEllW8peuOECVYvpNrjx49zJJrjx49rBLAyJEj7zjJbc70RjlsbGyYMWMGM2bMsMqxhSgQ7uWgzzo4/iMZP44jKC2WNbZz2WhowSz9ABJVJyLjUiW5ClGC5Tu5TpgwQbquCJFfigLVu3PDqzG/LBlBf802emv/RyPNccbrhxPk9URhRyiEKED5btDk5eVF165dWbZsGTExMQUZkxAlhp+PD/bdF/CsfibnjT6UVeJYa/s2/vvmQVbmvXcgxH/Cw8NRFIX4+Hir7TMoKIjQ0FCr7U/clO/kevz4cTp06MDXX39NUFAQjRo1Yvbs2Rw+fLgg4xOi2AtpUI4lE18ipu+vpNbsi4IKv4fCp0+atSgWojg6efIkbdq0wdfXF3t7eypUqMDUqVMf+RHw8p1cAwMDefXVV/n111+JjY1lzJgxHD58mBYtWlChQgXGjBnDb7/9VmhTJglRlPm7OdDosUAcn/4YQr7MnnEn5l9Y1ip77tj/upJJlx1R3Oh0OgYMGMDWrVs5efIkoaGhLF++/JFvG3Nf/Vzd3Nzo06cP69at4+rVqyxduhSDwcDgwYPx9vZmzZo11o5TiJKjWjd4ZQ9UfBKy0uHn8bDmab7/30GazfuNvsv30mzeb6zfH1XYkT7yYlJi2Be9j5iUgr8VlpGRwahRo/Dx8cHe3p7mzZuzf//+XOv9/vvv1K5dG3t7exo3bsyRI0dMr50/f55u3brh4eGBk5MTNWrUYPPmzfmOQVEUPv30U5566ikcHR2pXLkyP/zww123qVChAoMHD6ZOnToEBgbSvXt3+vXrx//+97/8v/kS6IEHkdDpdLRv354lS5Zw/vx5tm/fTpUqVawRmxAll4sfPL8ROr0LNvZw5lda/Nqddkr2xTSny46UYAtP2OkwOmzswJCtQ+iwsQNhp8MK9HgTJkxg48aNrFq1ikOHDlGpUiU6dOjA9evXzdYbP348CxYsYP/+/abuiDlVsCNGjCAjI4Ndu3Zx+PBh5s+fj7Ozs0VxzJo1i2effZZ///2Xzp07069fv1wx3M2ZM2fYsmXLQ5nftSi7r0Ek4uPj2bdvH1euXDGbZUJRlLvOUCOEuIWiQKNhUL4lyV8NxvPGMT6xXcTqrLa8ldWfTFUnXXYKSUxKDLP2zMKoZl/fjKqRWXtm0bR0U/yc8h6e9UGkpKTw8ccfs3LlStN82cuXL2fbtm189tlnjB8/3rTujBkzaNeuHQCrVq2ibNmyfPvttzz77LNERUXRu3dvatWqBWSXKi01aNAg+vTpA8CcOXN4//332bdvHx07drzrdk2bNuXQoUNkZGQwbNgw3nzzTYuPXZJYnFx//PFH+vXrR3JyMq6urmZ9XyW5CnEffB4jqf8Wvlw4gmHaTfS3+ZVamnO8qh9DkJd0fysMUYlRpsSaw6gauZB0oUCS69mzZ9Hr9TRr1sy0TKfT0bBhQ44fP2627q3Dw3p6elK1alXTOqNGjWL48OFs3bqVtm3b0rt3b4vm2AbzObmdnJxwdXXlypUrANSoUYPz588D0KJFC37++WfTuuvXrycpKYl//vmH8ePH89577zFhwgSLjl2SWFwt/Prrr/PCCy+QnJxMfHw8N27cMD0sqToQQtzk7+mGR485DNFP4IbqTLDmHL86T8P/yu7CDu2RVM61HBrF/PKoUTQEuAQUUkT58+KLL3Lu3Dn69+/P4cOHqV+/PkuWLLFoH7cPZq8oiqmGcvPmzfz999/8/ffffPrpp2brBQQEUL16dfr06cO8efOYOXPmI93A1eLkeunSJUaNGiUDSghhZSENyjFn4muc6/Uzmb51sNMnwJpn4LfZYHx0L1KFwc/JjxlNZpgSrEbRMKPJjAIptQJUrFgRW1tbfv/9d9MyvV7P/v37qV69utm6f/75p+nnGzducOrUKapVq2ZaFhAQwMsvv0xYWBivv/46y5cvt1qcgYGBVKpUiUqVKlGmTJk7rmc0Gs0mJ38UWVwt3KFDBw4cOHBfdflCiLvzd3PAv05tqLENtkyGA5/Brnfg4n7o/RnRWY5ExKVQ3stJ7sUWsF6Ve9G0dFMuJF0gwCWgwBIrZFe/Dh8+nPHjx+Pp6Um5cuV45513SE1NZciQIWbrvvnmm5QqVQpfX1/eeOMNvLy86NmzJwBjxoyhU6dOVKlShRs3brBjxw6zxFsQ1qxZg06no1atWtjZ2XHgwAEmT55MSEjIIz2tZ76S661Nsbt06cL48eM5duwYtWrVyvXhde/e3boRCvEosrGDrgshoBH8OBrO7SB1SRNGJL7CIWNlmV3nIfFz8ivQpHqrefPmYTQa6d+/P0lJSdSvX59ffvkFDw+PXOuNHj2a06dPExwczI8//miaQMVgMDBixAguXryIq6srHTt2ZNGiRQUat42NDfPnz+fUqVOoqkpgYCAjR47ktddeK9DjFnWKmo+JUPM7wbCiKEW+jj0xMRE3NzcSEhJwdXUt7HBEAdHr9WzevJnOnTsX/7+eY4+Rte55bG6cJVPV8mbWAL40tEOrKOye1EZKsLdJT08nIiKC8uXLY29vj9FoJDExEVdXV5ks/RF06/mg1WrNrgsFmQ/ydaYZjcZ8PYp6YhWiWPKtzsH2G9lsaIitYuBt3QresvkcRdUTGZda2NEJIfIgf8YJUQyUK+3HyKzRzNc/h1FV6G/zK6ts36G8c0ZhhyaEyIMkVyGKAX83B+b2qs0yYw9e0r9GimpHM80R/NZ3gaunABmXWIii5L5GaBJCPHwhDcrRsoo3kXGNSFW74vTjQLh+Dj5tS3idd3jhfy4YVaSxkxBFgJRchShG/N0caFKxFN6V6sHQHRDQGDISaLH3ZQZotgCqjEssRBEgyVWI4srZGwb+wJWKvdEqKjN1XzDb5nO0GDCoqjR2EqIQSXIVojizscPQ7QNmZ/XDqCr0s9nOMt1CnJUMGZdYiEJ0X8m1TZs2DBo0yGzZwIEDeeKJJ6wRkxDCAv7ujlTqMYnhWWNJU215UvsXu3zew1+bVNihCfHIuq/kGhQUROnSpc2WlSlThsDAQKsEJYSwTEiDcsycMJ4znb7CaO+JZ8JR+KwdxJ2RVsRCFIL7Sq4rVqxgzpw5ZsvmzJnDihUrrBKUEMJy/m4O1GrcFs2L28AjCG5EkvHJk4ycv5S+y/fSbN5vrN8fVdhhCguEh4ejKArx8fEPtJ/U1FR69+5tmiY0Pj6eoKAgQkNDrRJnfrRu3ZoxY8Y8tOMVNrnnKkRJ41UJhvxKpm8wdvp41uhm005zQFoRF3F5JZ+mTZsSHR2Nm5vbA+171apV/O9//+OPP/6wyv4Ky7Vr1+jYsSOlS5fGzs6OgIAARo4cSWJiYmGHlku++rm+//77+d7hqFGj7jsYIYSVOHtzqM1qktcMoK32L5bqFvFG1hDWGZ4gMi5VxiMuJmxtbfHze/CJA86ePUu1atWoWbOmFaIqPBqNhh49evD222/j7e3NmTNnGDFiBNevX2ft2rWFHZ45NR+CgoLMHk5OTqqiKKqHh4fq4eGhKoqiOjk5qeXLl8/P7gpVQkKCCqgJCQmFHYooQJmZmep3332nZmZmFnYoheZyfKpaadL36po3eqjqDFdVneGqznvjJfVyfGphh1ag0tLS1GPHjqlpaWmqqqqqwWBQb9y4oRoMhkKO7M4GDhyoAmaPiIgIdceOHSqg3rhxQ1VVVV2xYoXq5uam/vjjj2qVKlVUBwcHtXfv3mpKSoq6cuVKNTAwUHV3d1dfffVVNSsrS1VVVW3VqpXZflu1aqWqqqoGBgaqixYtMsVw/vx5tXv37qqTk5Pq4uKiPvPMM2pMTIyqqqoaHx+vajQadf/+/aqqZn+mHh4eaqNGjUzbr169Wi1btuwd32OrVq3U0aNHm54HBgaqs2fPVgcPHqw6OzurAQEB6ieffGLxZ7d48eK7HvfW8+H260JB5oN8VQtHRESYHrNnzyY4OJjjx49z/fp1rl+/zvHjx3n88cd56623CuYvACGExfzdHHi7VzDTDEP5KCt7KsiJNl/hv28eqOoj09BJVVWMaWkYU1Mf+kO996RjACxevJgmTZowdOhQoqOjiY6OJiAgIM91U1NTef/991m3bh1btmwhPDycp556is2bN7N582ZWr17NJ598woYNGwAICwtj6NChNGnShOjoaMLCwnLt02g00qNHD65fv87OnTvZtm0b586dIyQkBAA3NzeCg4MJDw8H4PDhwyiKwl9//UVycjIAO3fupFWrVhZ9NwsWLKB+/fr89ddfvPLKKwwfPpyTJ0/me/vLly8TFhZm8XEfBouHP5w2bRobNmygatWqpmVVq1Zl0aJFPP300/Tr18+qAQoh7t/NIRMbkxhRG9fdb8PvoZy9cJEOp3uSpWpK/HCJaloasW2eILYQjl310EEUx3v3N3Zzc8PW1hZHR8d7VgPr9Xo+/vhjKlasCMDTTz/N6tWriY2NxdnZmerVq9OmTRt27NhBSEgInp6eODo63rWKefv27Rw+fJiIiAhTUv/iiy+oUaMG+/fvp0GDBrRu3Zrw8HDGjRtHeHg47dq148SJE+zevZuOHTsSHh7OhAkTLPp8OnfuzCuvvALAxIkTWbRoETt27DDLL3np06cP33//PWlpaXTr1o1PP/3UouM+DBY3aIqOjiYrKyvXcoPBQGxsYZy+Qoi7yRky0bXteOi2GBWFilEbCLX5AB1Z0tCpmHF0dDQlVgBfX1+CgoJwdnY2W3blypV87/P48eMEBASYlZarV6+Ou7s7x48fB6BVq1bs3r0bg8HAzp07ad26tSnhXr58mTNnztC6dWuL3kvt2rVNPyuKgp+fnynuTp064ezsjLOzMzVq1DDbbtGiRRw6dIjvv/+es2fPMnbsWIuO+zBYXHJ98skneemll/j00095/PHHATh48CDDhw+nbdu2Vg9QCGFF9QZxKkFD+Z1j6Kr9E2fSeEn/GhmqbYlt6KQ4OOC74zdcXVwe+mTpioP1P0+dTmd+DEXJc5nRaLTqcVu2bElSUhKHDh1i165dzJkzBz8/P+bNm0edOnUoXbo0lStXtmifd4v7008/JS0tLc/1/Pz88PPz47HHHsPT05MWLVowbdo0/P39H+AdWpfFyfXzzz9n4MCB1K9f3/SGs7Ky6NChQ5EsmgshzLnWe4ahv17kY10orbX/sIJ3GJY1vsQOl6goChoHBzSOjg89uVrC1tYWg8FQKMeuVq0aFy5c4MKFC6bS67Fjx4iPj6d69eoAuLu7U7t2bT744AN0Oh2PPfYYPj4+hISEsGnTJqvf9yxTpky+1stJxhkZRWtuY4uTq7e3N5s3b+bUqVOcOHECgMcee4wqVapYPTghhPX5uznQ+annGfStPZ/p3qGp9hg7vJbgbdeW6ASIiEuhvJdTiSzFFmVBQUHs3buXyMhInJ2d8fT0fGjHbtu2LbVq1aJfv36EhoaSlZXFK6+8QqtWrahfv75pvdatW7NkyRKefvppADw9PalWrRrr16/nww8/LPA4N2/eTGxsLA0aNMDZ2ZmjR48yfvx4mjVrRlBQUIEf3xL3/WdclSpV6N69O927d3+gxPrhhx8SFBSEvb09jRo1Yt++fXddPz4+nhEjRuDv74+dnR1VqlRh8+bN9318IR5FIQ3KsXjicCI7rcFo54r3jUNcW9qFjvN+lNGcCsm4cePQarVUr14db29voqIe3uevKArff/89Hh4etGzZkrZt21KhQgXWr19vtl6rVq0wGAxm91Zbt26da1lBcXBwYPny5TRv3pxq1arx2muv0b17dzZt2lTgx7aUoua3rfgtLl68yA8//EBUVBSZmZlmry1cuDDf+1m/fj0DBgxg6dKlNGrUiNDQUL755htOnjyJj49PrvUzMzNp1qwZPj4+TJkyhTJlynD+/Hnc3d2pU6dOvo6ZmJiIm5sbCQkJuLq65jtWUbzo9Xo2b95M586dc92vEbe5/BfGL55Ck36Dw8Yg+mdOJh4XtIrC7kltil0JNj09nYiICMqXL4+9vT1Go5HExERcXV2LdLWwKBi3ng9ardbsulCQ+cDiauHt27fTvXt3KlSowIkTJ6hZsyaRkZGoqmpq4JRfCxcuZOjQoQwePBiApUuX8tNPP/H5558zadKkXOt//vnnXL9+nT/++MN0wSxqVQFCFDul6/Jv2y8p+2Mfamki+cr2bfplvsF11bXENnISoqBZnFwnT57MuHHjmDVrFi4uLmzcuBEfHx/69etHx44d872fzMxMDh48yOTJk03LNBoNbdu2Zc+ePXlu88MPP9CkSRNGjBjB999/j7e3N3379mXixIlotdo8t8nIyDC70Z0zBqVer0ev1+c7XlG85Hy38h3nj2dQHfrop/GlbjbVNBdYYzub5/VvUMbNtth9hnq9PnvgCKMRo9FoGsghZ5l4tOScA3q93vT9P4zrg8XJ9fjx43z11VfZG9vYkJaWhrOzM2+++SY9evRg+PDh+dpPXFwcBoMBX19fs+W+vr6mhlK3O3fuHL/99hv9+vVj8+bNnDlzhldeeQW9Xs+MGTPy3Gbu3LnMmjUr1/KtW7fimI/O3aJ427ZtW2GHUGzUC/Kjb8QbrLXNTrCbnGbz904Df9m4FHZoFrGxscHPz4/k5GSz21ZJSTK/7aMoMzOTtLQ0du3aZRqjIee6kJqaWmDHtTi5Ojk5mU5Yf39/zp49a+rgGxcXZ93obmM0GvHx8WHZsmVotVrq1avHpUuXePfdd++YXCdPnmzWwTgxMZGAgADat28v91xLML1ez7Zt22jXrp3cc82nzkB0QjoXI2vh9dsA/FMv4Bf7MZe6fUVEqh2BpRzxd7Mv7DDvKT09nQsXLuDs7Iy9vT2qqpKUlISLiwuKohR2eOIhS09Px8HBgZYtW6LVas2uCwU5m47FybVx48bs3r2batWq0blzZ15//XUOHz5MWFgYjRs3zvd+vLy80Gq1uUZ1io2NveMQXf7+/uh0OrMq4GrVqhETE0NmZia2tra5trGzs8POzi7Xcp1OJxfdR4B8z5Yp56WjnFcTCPwJVnZBuXKEhOVdeTVzComKS7EYJtFgMKAoSnb/Vo3GVBWY81w8WnLOhVtzR851oSCvDRafaQsXLqRRo0YAzJo1iyeffJL169cTFBTEZ599lu/92NraUq9ePbZv325aZjQa2b59O02aNMlzm2bNmnHmzBmz+yanTp3C398/z8QqhLhP3lW42nsDV1U3amjO86XtXJzV5GIxTGLOBfT2ngzi0ZRT9fuw/8i2uORaoUIF089OTk4sXbr0vg8+duxY02hPDRs2JDQ0lJSUFFPr4QEDBlCmTBnmzp0LwPDhw/nggw8YPXo0r776KqdPn2bOnDkyh6wQBeC0WobpmW/wle3b1NREssr2HZ7PnFzkWxDb2Njg6OjI1atXTRfUzMxM0tPTpeT6CFFVldTUVK5cuYK7uztarfahNmizOLlC9kAOGzZs4OzZs4wfPx5PT08OHTqEr69vvoesAggJCeHq1atMnz6dmJgYgoOD2bJli6mRU1RUlNkvQ0BAAL/88guvvfYatWvXpkyZMowePZqJEyfez9sQQtxFeS8nzlGW5zOnsM72bepqzrDC9l0clCb8cTauyI7ipCgK/v7+REREcP78eVRVJS0tDQcHB7nn+ghyd3e3yoTzlrJ4EIl///2Xtm3b4ubmRmRkJCdPnqRChQpMnTqVqKgovvjii4KK1SpkEIlHgwwiYR3r90cxJewI1TjLWts5uCqp7DbWZEjmOPSKbZG+B2s0GsnMzESv17Nr1y5atmwp58Ij5vY2OrdfF4rUIBJjx45l0KBBvPPOO7i43Gyi37lzZ/r27WvV4IQQhevmfLCNiL1RDe2PfWmuOcJHusW8rH+NKWFHaFnFu0iWYDUaDfb29mi1WrKysrC3t5fkKh4ai29A7N+/n5deeinX8jJlyhATE2OVoIQQRUfOfLBXPYJ5IXMC6aqOJ7V/sUj3IapqIDKu4PoKClFcWZxc7ezs8uwbdOrUKby9va0SlBCi6Cnv5cR+qjFMP5ZMVUtX7V7m6j7DUafwx9m4It+KWIiHyeLk2r17d958803TsFGKohAVFcXEiRPp3bu31QMUQhQN/m4OzO1Vi9/VYEbpX8WgKoRod3Bg2Sv0Xf6nzKQjxC0sTq4LFiwgOTkZHx8f0tLSaNWqFZUqVcLFxYXZs2cXRIxCiCIipEE5dk9qw8Aho7jU8h0Ahtj8zCjttxhVikU/WCEeBosbNLm5ubFt2zZ2797Nv//+S3JyMo8//jht27YtiPiEEEWMv5sD/m4O/EEvPv/tMDN1XzBWt4FEHFlp6Fjk+8EK8TDcVz9XgObNm9O8eXNrxiKEKEbKeznxhbEjLvpUXtdtYKbuCxJwIcjricIOTYhCl+/k+v777+drPRktSYhHQ8492Clh4JmVxGCbX1hg+wnxEU34w6VJkR1kQoiHId/JddGiRWbPL1y4gL+/PzY2N3ehKIokVyEeIaZ+sFcbknZgIg4nNuLw7QssyJzMX1Qt0oNMCFGQ8p1cIyIizJ67uLiwc+dOs7GGhRCPnpx7sNGeofx59CxttH/zue27PJs5nSlhSpEdZEKIgiSjWAshrCLiRibD9aM5YKyCm5LKKtv5+KlXZJAJ8UiS5CqEsIryXk5kKnYMyRzHKWMZ/JQbfGE7HxdjvAwyIR45klyFEFaR08ApWXFhQOYkLqulqKi5jH71swxZvlMGmRCPlHzfc719yENFUUhOTs61XGaaEeLRdXOg/1QSU6vhsKEndTVn+Ei3mKH614v0QP9CWFO+S67u7u54eHiYHsnJydStW9f0POd1IcSjLWeg/+uO5RmSOY401ZY22n+YbfM5BtUo92DFIyHfJdcdO3YUZBxCiBKmvJcTf1OFV/Wv8oluISE24cTiybWUx4lOSJPSqyjR8p1cW7VqVZBxCCFKmJuDTChMzXqBubrPGGUTxuT1HowyPil9YEWJdt/DHwohxL3k3IM9GBnM+9/cYJRNGG/bfE6s3kP6wIoSTVoLCyEKlL+bA57OtizM6s36rNZoFZUPdEuowRl++jdauuiIEkmSqxCiwJX3ckKjKLyR9QLhhjo4Khl8bvsuX2wOly46okSS5CqEKHA5919VRccI/SiOGIPwUhJZqZuPm5oo88CKEkeSqxDiociZaP21Lo8zOHM8F1UvKmhiWG67EBs1Q7roiBIlXw2aevXqle8dhoWF3XcwQoiSzd/NgS61/Zmz2YOBmRMJs51Bfc0pQnUfcS25gXTRESVGvkqubm5upoerqyvbt2/nwIEDptcPHjzI9u3bcXNzK7BAhRAlQ04VcSRlGZb5Opmqlk7afVz+ZoLcfxUlRr5KritWrDD9PHHiRJ599lmWLl2KVqsFwGAw8Morr8jQh0KIfLm1i86Er68TqvuQYTY/cVH1ki46okSw+J7r559/zrhx40yJFUCr1TJ27Fg+//xzqwYnhCi5crrofGdoxjv6ZwGYYfMFrZUD0kVHFHsWJ9esrCxOnDiRa/mJEycwGo1WCUoI8WjI7qIDHxl6sO6/PrBLdB/w3eafpIpYFGsWJ9fBgwczZMgQFi5cyO7du9m9ezcLFizgxRdfZPDgwQURoxCihMq5/6pVNEzNeoFdhlr/9YF9D3/1qnTREcWWxcMfvvfee/j5+bFgwQKio6MB8Pf3Z/z48bz++utWD1AIUbLl3H/96d9oXvlpNN8os6imucBntu/xTOYMfvo3mi61/eUerChWLC65ajQaJkyYwKVLl4iPjyc+Pp5Lly4xYcIEs/uwQgiRXzlddFIVR17InECs6s5jmgt8qFvMvJ8OSxWxKHbuaxCJrKwsfv31V7766isURQHg8uXLJCcnWzU4IcSjI6eK+IrixZDMcaSqdrTUHuYtmxUYVVWqiEWxYnG18Pnz5+nYsSNRUVFkZGTQrl07XFxcmD9/PhkZGSxdurQg4hRCPAJuVhE/xqs/32CZbiF9bHYQqfrxiaEbByNv0LWOVA+Los/ikuvo0aOpX78+N27cwMHh5kn+1FNPsX379vsK4sMPPyQoKAh7e3saNWrEvn378rXdunXrUBSFnj173tdxhRBFT04V8Q61Hm9mDQBgsu4rOmn2MmrdX1I9LIoFi5Pr//73P6ZOnYqtra3Z8qCgIC5dumRxAOvXr2fs2LHMmDGDQ4cOUadOHTp06MCVK1fuul1kZCTjxo2jRYsWFh9TCFG05VQRrzZ0YEVWBwAW6T6iDqelelgUCxYnV6PRiMFgyLX84sWLuLi4WBzAwoULGTp0KIMHD6Z69eosXboUR0fHuw5IYTAY6NevH7NmzaJChQoWH1MIUfSFNCjH+33r8lZWf3411MVe0bPMdgH+xMogE6LIszi5tm/fntDQUNNzRVFITk5mxowZdO7c2aJ9ZWZmcvDgQdq2bXszII2Gtm3bsmfPnjtu9+abb+Lj48OQIUMsDV8IUYzUC/QARcMo/ascMQbhrSSyQvcu7/90QFoQiyLN4gZNCxYsoEOHDlSvXp309HT69u3L6dOn8fLy4quvvrJoX3FxcRgMBnx9fc2W+/r65jkKFMDu3bv57LPP+Pvvv/N1jIyMDDIyMkzPExMTAdDr9ej1eoviFcVHzncr33Hx5uVow9s9qjP1+2MMyRzHd3bTqay5xEe6UAbpJzI57DBNynvg72Z/x33IuSBy3H4uFOQ5YXFyLVu2LP/88w/r16/nn3/+ITk5mSFDhtCvXz+zBk4FISkpif79+7N8+XK8vLzytc3cuXOZNWtWruVbt27F0dHR2iGKImbbtm2FHYJ4QE7AjLrw1zU3hkSN4xvbWTTXHuVt9XMmZQ3l8+93UNdLved+5FwQOXLOhdTUgptDWFFV9d5nZQHJzMzE0dGRDRs2mLX4HThwIPHx8Xz//fdm6//999/UrVvXbLCKnPGMNRoNJ0+epGLFimbb5FVyDQgIIC4uTmbxKcH0ej3btm2jXbt26HS6wg5HWEF0QjqtF+yitXKI5boFaBWVefrnWGbszts9qvNMvbJ5bifngshx+7mQmJiIl5cXCQkJVs8HFpdctVotLVu2ZOPGjXh6epqWx8bGUrp06TwbO92Jra0t9erVY/v27abkajQa2b59OyNHjsy1/mOPPcbhw4fNlk2dOpWkpCQWL15MQEBArm3s7Oyws7PLtVyn08kv2iNAvueSo5yXjrm9ajF5I7yZNYBZulVM0q0jKtOHad8rtKnmd9chEuVcEDlyzoWCPB8sTq6qqpKRkUH9+vX58ccfqVGjhtlrlho7diwDBw6kfv36NGzYkNDQUFJSUkyTAAwYMIAyZcowd+5c7O3tqVmzptn27u7uALmWCyFKnpAG5XCys2HkWghSYhhs8wsLdR/TJ7MUkXGNZPxhUWRY3FpYURQ2btxIt27daNKkiVnVbc5QiJYICQnhvffeY/r06QQHB/P333+zZcsWUyOnqKgo0wQBQghRL9ADjYJZF51PbRdQUXe1sEMTwuS+Sq5arZbFixdTo0YNQkJCmDp1Ki+++OJ9BzFy5Mg8q4EBwsPD77rtypUr7/u4QojiJ2eAiSlhRxilf5WvlTepqYmEH/rDkK3g4FHYIQpheXK91bBhw6hcuTLPPPMMu3btslZMQghxVzljEEfGpeJtXxfWdYG4U/D1AOi3kegUAxFxKZT3csLL8YEuc0LcF4vPusDAQLPWum3atOHPP/+kW7duVg1MCCHuxt/N4b97rKWg39fweUeI2MW5lUNpe/YZjKqCRoG3e1THqbCDFY8ci++5RkREUKpUKbNllSpV4q+//uLcuXNWC0wIIfLNrxY8vQJV0VDh4ne8rMluC2JUYer3x4jPuMf2QljZfc3nmhd7e3sCAwOttTshhLBMlfZENJgBwATd13TT/AFkJ9ir6ZY3thTiQeQruXp6ehIXFweAh4cHnp6ed3wIIURhcWj2Ep9ldQLgPd0n1FdOoAC2mkIbK0c8ovJ1z3XRokWmGW9uHbRfCCGKEn83B1y6z2Xrj1dprz3ActuFPJU5i0VH/PCqdJG+jcsXdojiEZGv5Dpw4MA8fxZCiKLm2Ybl+dd7Nf+s6EIdzTlW6t6hV+Ys3vjuGDXKuFMnQLrqiIKXr2rhxMTEfD+EEKKwJRttGZI5ngtGb4I0sSy3XYAtmfT86A+Zpk48FPlKru7u7nh4eNz1kbOOEEIUtvJeTlxX3Bikn0CC6kg9zWkW6T4C1ciUsCMy0boocPmqFt6xY0dBxyGEEFaTM4rT5I0wLPN1vrCdS2ftPqaoa5md9TwHI2/QtY6MQywKTr6Sa6tWrQo6DiGEsKqQBuV4zM+Fnh/BeP1LvG/7IUNtNnNJ9WLUOkjJzCKkQbnCDlOUUPc9LlhqaipRUVFkZmaaLa9du/YDByWEENZQJ8CD2T2qM+U7lTL6a0zUrWO6zWqi9aWYvBEe83ORBk6iQFicXK9evcrgwYP5+eef83zdkvlchRCioD1Tryynjh7m49PdKKtcpZ/NdhbrPqBv5hv0/Ajm9aolJVhhdRaP0DRmzBji4+PZu3cvDg4ObNmyhVWrVlG5cmV++OGHgohRCCEeSHkXFY2iMD1r0C3T1L1HENFM3nhYGjgJq7M4uf72228sXLiQ+vXro9FoCAwM5Pnnn+edd95h7ty5BRGjEEI8EHe77AH8VbS8qn+Vv40V8FSSWaWbhycJrNgdWdghihLG4uSakpKCj48PkD0U4tWr2RMU16pVi0OHDlk3OiGEsJJn6pXl2xFNScOeIZnjiTT6Uk5zlRW281n7v6P8c+FGYYcoShCLk2vVqlU5efIkAHXq1OGTTz7h0qVLLF26FH9/f6sHKIQQ1lInwINhLcpzDTcG6icSp7pSSxPJR7pQnvlolwwwIazG4uQ6evRooqOjAZgxYwY///wz5cqV4/3332fOnDlWD1AIIaxpcPPyaBQ4r/oxJHMcqaodLbWHeddmKW+E/Sv3X4VVWNxa+Pnnnzf9XK9ePc6fP8+JEycoV64cXl5eVg1OCCGs7eYAE4f5R63EcP0YPtW9Rw/tH1xXXYi82vi/SdiFuH8PPJ+ro6Mjjz/+uCRWIUSxEdKgHN+OaIqiwE5jHV7XvwzAYJtfqHn2k0KOTpQEFpdcVVVlw4YN7NixgytXrmA0Gs1eDwsLs1pwQghRUOoEeDCvVy2mhB3hB2MzSumTmaFbhcued8DTFxq8WNghimLM4uQ6ZswYPvnkE9q0aYOvry+KohREXEIIUeBCGpSjZRVvIuNSCfJ6Ag76wq534Kdx4OAJNXsVdoiimLI4ua5evZqwsDA6d+5cEPEIIcRD5e/mcPMea5spkBoHBz6HsGHg4A4VnyjU+ETxZPE9Vzc3NypUqFAQsQghROFSFOj8HtR4Cox6WPc8cSd/54+zcdKKWFjE4uQ6c+ZMZs2aRVqanGhCiBJIo4WnPoEKrUGfgmbts0z7NIxm836TfrAi3yxOrs8++yw3btzAx8eHWrVq8fjjj5s9hBCi2LOxI6bTZ/zz3zCJq23nUpqrMtG6yDeL77kOHDiQgwcP8vzzz0uDJiFEiXUuEUZkTuAb2zeppLnMGt1sQjKnERmXKv1gxT1ZnFx/+uknfvnlF5o3b14Q8QghRJFQ3suJBMWVfplTWG/7FkGaWNbazsHJoRVQqrDDE0WcxdXCAQEBuLq6FkQsQghRZOSM5BSnlKJv5htcVL2ooInG99tnISWusMMTRZzFyXXBggVMmDCByMjIAghHCCGKjpAG5dg9qQ0LhnbF9oVN4FIarp6A1T0h9XphhyeKsPsaWzg1NZWKFSvi6OiITqcze/36dTnhhBAlx81+sKVg4A+wojPEHIYvexPTcx3nkrSU93KS+7DCjMXJNTQ0tADCEEKIYsCrcnaCXdkFLh/i4gddeTFzEumKPXN71SKkQbnCjlAUERYlV71ez86dO5k2bRrly5cvqJiEEKLo8qnG1afWY/tld+prTvG57bu8kDmeKWFHaFnFW0qwArDwnqtOp2Pjxo1WD+LDDz8kKCgIe3t7GjVqxL59++647vLly2nRogUeHh54eHjQtm3bu64vhBDWdlpTnv6Zk0lUHWisOc4q23k4qClExqUWdmiiiLC4QVPPnj357rvvrBbA+vXrGTt2LDNmzODQoUPUqVOHDh06cOXKlTzXDw8Pp0+fPuzYsYM9e/YQEBBA+/btuXTpktViEkKIuynv5cQRKtI/czIJqiMNNKf40nYu5Z0zCzs0UURYfM+1cuXKvPnmm/z+++/Uq1cPJycns9dHjRpl0f4WLlzI0KFDGTx4MABLly7lp59+4vPPP2fSpEm51l+zZo3Z808//ZSNGzeyfft2BgwYYOG7EUIIy+V005kSptA3cyqrbecQrDkL3z4D/b8HJ+kH+6izOLl+9tlnuLu7c/DgQQ4ePGj2mqIoFiXXzMxMDh48yOTJk03LNBoNbdu2Zc+ePfnaR2pqKnq9Hk9Pzzxfz8jIICMjw/Q8MTERyL5/rNfr8x2rKF5yvlv5jkVBnQu9gv1pUt6DqOv1yFAbov7QByXmMOrKzlzq9hURaU4ElnLE383eqscV9+/2c6Egrw8WJ9eIiAirHTwuLg6DwYCvr6/Zcl9fX06cOJGvfUycOJHSpUvTtm3bPF+fO3cus2bNyrV869atODo6Wh60KFa2bdtW2CGIIqIgz4VrgHO512l6Zj4OV0+Q8WlnxmW+wRU8CKlgpImvWmDHFpbLORdSUwvuHrnFyfVWqpp9whTW+MLz5s1j3bp1hIeHY2+f91+HkydPZuzYsabniYmJpvu0MtJUyaXX69m2bRvt2rXL1RdbPFoe5rlwNao517/oSUVNNF/bvsnz+sl8HeHLK71aSgm2CLj9XMipySwI95Vcv/jiC959911Onz4NQJUqVRg/fjz9+/e3aD9eXl5otVpiY2PNlsfGxuLn53fXbd977z3mzZvHr7/+Su3ate+4np2dHXZ2drmW63Q6ueg+AuR7FjkexrkQiT/jM6axxnYOQZpYwmxnMihzIpcSGlPOy6VAjy3yL+dcKMjzweLWwgsXLmT48OF07tyZr7/+mq+//pqOHTvy8ssvs2jRIov2ZWtrS7169di+fbtpmdFoZPv27TRp0uSO273zzju89dZbbNmyhfr161v6FoQQokCU93IiWvHmmczpHDMG4q0ksM72LaqkHirs0MRDZnHJdcmSJXz88cdmLXO7d+9OjRo1mDlzJq+99ppF+xs7diwDBw6kfv36NGzYkNDQUFJSUkythwcMGECZMmWYO3cuAPPnz2f69OmsXbuWoKAgYmJiAHB2dsbZ2dnStyOEEFZzsxXxEUIyp7HcdiGNNcfgu76gfEJ0QCci4lJkuMRHgMXJNTo6mqZNm+Za3rRpU6Kjoy0OICQkhKtXrzJ9+nRiYmIIDg5my5YtpkZOUVFRaDQ3C9gff/wxmZmZPP3002b7mTFjBjNnzrT4+EIIYU0hDcrRsoo3kXGpBLq3g+2j4Nj3qBte4BP9AFYaOqBRkOESSziLk2ulSpX4+uuvmTJlitny9evXU7ly5fsKYuTIkYwcOTLP18LDw82ey2w8Qoii7uZg/8DTK0j5/nWc/lnBTN0qfJQbvJv1rAyXWMJZnFxnzZpFSEgIu3btolmzZgD8/vvvbN++na+//trqAQohRLGm0fJPrTf4/UAK43Vf84rND5RXohmrH05kXKok1xLK4uTau3dv9u7dy6JFi0zDIFarVo19+/ZRt25da8cnhBDFXnlvZ5439uRyZinm6ZbTSbufACUOb/u6gIzmVBLdV1ecevXq8eWXX1o7FiGEKJFuHS4xKtOHZbaLqKmJgK86EddtJadsKksjpxLmgQaREEIIkT83Gzo1wqDrAj8OhKvHcVrbnTX6l/lZbSyNnEqQfPdz1Wg0aLXauz5sbCRXCyHEnfi7OdCkYil8ylUl5pkf2GEIxkHJ5EPb95mkXcO0sH+ITkgr7DCFFeQ7G3777bd3fG3Pnj28//77GI1GqwQlhBAl3bkkDUP045igruNlm00Ms/mJOpqzXLpQGQiU/rDFXL6Ta48ePXItO3nyJJMmTeLHH3+kX79+vPnmm1YNTgghSqryXk6gaJiX1Ze/jJV4T/cJjTQnSPmhCy8kv8Je42PSH7YYs3j4Q4DLly8zdOhQatWqRVZWFn///TerVq0iMDDQ2vEJIUSJlNPISaso/GJsyFP6t7nuVBGnzDjW6N7mJe2PqKqRKWFHpKq4GLLoJmlCQgJz5sxhyZIlBAcHs337dlq0aFFQsQkhRIl262hOQV6OnIpuR+yal+mh/YPJuq9oofmX1/XDORh5A09nqSYuTvKdXN955x3mz5+Pn58fX331VZ7VxEIIISxjNpoTPvTNGsEfxhrMsPmC5tqj/KyZxKSvh7HVUF+qiYuRfCfXSZMm4eDgQKVKlVi1ahWrVq3Kc72wsDCrBSeEEI+S7Kri2kwJ07A/syrv6z6gpiaSZbqFrFXaMCernwybWEzkO7kOGDCg0CZFF0IUHTEpMUQlRlHOtRx+Tn4WPQfu62cHGwfSstLuuMzP6e7zPxcnt1YVRyW24/eNU3jJ5if62uygpfYwk/UvcjAyWKqJi7h8J9eVK1cWYBhCiKLg1kTo6+iLMSWVmMuniL58Cl+jMyeiDvLTv1/jkK7ioFeo4ViBS1fOYqtXOZKlEGDnw/XEWGyyVC6rCqV07iSmxQMqV289jqJg1IBBo3JOq5BlA5k2cMgGMnQKGTqVNDuFNFtIsYcUu+z/kx00JDtAgqOKQZv9x75G0TCjyQx6Ve6V63042DiQlJHExayL7I/dTwWPCsUiEedUFUcnODLS0I8dxrq8Y/MJ5TRXWW07j/Ub/mSy/nlSFEepJi6iZNQHIUq420uSqtGI4fp1Ys4e5sr543imaHBMSOfs2QNEnDuEW7JKZipcS7dBo88CwAlIBsoCL5nt/TTVTT+rQAxlzZ7fwCvPqNQ7/Hzr89uXA9zsS59sDwmOcMNFIe6HaZyrdxj3MhU4aIzgk8vfcNVF5YYzqJr/aty2g4LCwBoDaR/YnkvJlwAI9gkusgn31mETO2bOZ4LNegbZ/EKINpzWmr95S9+fKWHwmJ8LKZkGKckWIYqqqnmdwSVWYmIibm5uJCQk4OrqWtjhiAKi1+vZvHkznTt3RqfTFXY4D0VOEg1wCcA7w5bMqCj+OPAtv+8LwzveiHciVMn0xO5aEmpmZr73m2kDSQ63liAVUu0gzRYybCFdp5Bhm71epg3otZClBYMGjBowKtmPHBr15kNrAJ0BbAxgqwfbLLDTg0Omin0mOGaAQwY4Zag4pYNLWvZDk8+rVpYG4lzhirvCFXeIdVeI8YAYj+z/M2yzAxtUYxD9qvUrskk2OiGNyLhUrqVksOqrr5ivW0YFTQwA/zPUZKZhEGeNpaXB0z3cfl0oyHwgJVchipFbS6HeBkcyIyLIiIjg8MEtnP13F743VGziIf6/3FkGeNZsD9dQyU52153hugvccFaId1a44Qw3nCHeCRIdFRIdIdHxZgIqKhRVxSkN3FLBPUXFIwlKJSu84NeT5MtRnD2zH89k8EwCGyP4xYNffN6l4TgXuFxK4VKpz3jHewXtWr9A+zZD0Lq5mda5veRfGG5WE6cxisfolDmPl7SbGGHzPS20R/hZM5HPDJ35KKsHkzcexsnOhnqBHlKKLUSSXIUoQnJV4aoqhmvXyDhzhn2/b+DI/p8pfc2I4TrcSLm5ndd/jxxGwOjtzgmHBFOJLc4N4lwVejUfxryIZaZ7ljkUFNRbko/y379bq2IVFBRFwaga0SgaulboyqZzm/L1PHtfoKJa/POtx0dRSHZUSXaES16K6Z5rhcq9iEmJof/GDtnHM6p4JoNPPPjEq/jEq/jdAL8bKv43wDkdvJLAK0mldiSAAbYs5xTLsfHxwa5SJaK8FTbo/yTSGy75aJjccmae93YfVuK9WU18hPcNvfjB2IwZNitpo/2H4TY/8ox2J6FZvRm91oCqaKUUW4ikWliUSMWxWvi7v9fy1aa5lLlqpFwcNM8IxPVSPIYbN+64jeLthb6sN79xgkulsqs6Yz0UrrrB3CcXMGHXBIzqzeSoUTR82elLnv/5+VzLxzw+htBDoabEOKPJDABm7Zlltqxp6aZcSLpAgEuAqTVwfp8D9/WzvdaedEP6HZfdmtjCToeZYr4b51QV/+tQ5rpKmTiVgKsQEKfinZj3+kYFLntC+fpP4FH7cQ64XOOta1+S4Kjm2aiqIOVUEzvaanjqo99poxxiis1aKmqiAThtLMOCrGfYZqzP4r71pBT7n4dZLSzJVZRIRS253t4KV3/pEunHj5Nx4iTpJ0+QeuwoxssxeW+sKBhL+3DA6QoXvOBSKYXLpRSiPeHD7isIcAmgw3+ltRwaRcMvvX/hj8t/5EqOvSr3MktAty6/PTHmxH77sqIuJ2Z7rT3JGckc2nuI1IBUvjzx5V2TrkbRsKVDGO7RSZw4sJXtO1cScBXKXVFxT817m2suEOGrcM5fwwu938K/XnPiHA0PrUS7fn8UU8KOoKh6+mq3M8ZmI55KMgBHjEG8l/UMu9Rg5vaqTcsq3o/0hACSXAuQJNdHQ1FJrmpmJpt3LGPr1qUExhgJugJVrtmiTc3Ic/04F4jyVrjgDRe8FV7oPpO6DbpyxZhwxwTq5+R3x2QJd06OxTFp3o9bz4VrmddMSfdyymX2Xt7LxjMb7/i5mT5zVcU9BSpcUZjpNYiko/9y9d99+F3Pe4D2685wzk/hXGkNjZ94nic7vISNh0eBVSNHJ6RxMPIGo9b9hbOawhCbzQzR/oyzkg7Av8byfJTVg21qfQyq5pFt+CTJtQBJcn00FHRyzesiaUxPJ+PUKdKPHSP96LHs/0+dAr0+9w5sbLCrXBn7qlWxr/YYKYE+hJycSJL9zV/HW5MncNcEmhPTo5AsLXWvc+Fun9vdSvgdNnZAl2EgKBYqxKhUjIEWyaVRIy/mmXAzfT046JnAaX84V1rDcz3e4Klaz1k14eaUYg2qigeJvGSziYHarTgo2S3cThvLsMzQhR8MTdFjy/t96z5SVcaSXAuQJNdHQ0Em17DTYczbOZNysUYqxMBTxjr4Xkgm4+xZMBhyrZ9sn11tGOkLkT4K530VZj63nAYBTXLt927JEySB3o8HPRfu9Jnn9X2VdS7LK5teICgWKkarpkfpPG6bGxVIC/Bmr8c1zvyXcAd2m06vas88UMK9tRRrVMGTRAbZbGGQdiuuSnbddpzqyhrDk6zJastVPBjaojyDm5cv8UlWkmsBkuT6aMjvBTU/FzFjSgrpx4+TfvQoN/45yLm92yh9Le/qQK2nJ/bVq2Nfowb21auTVN6bznsGYuTOJdLb45HkaV0F+YdWXo238qq+fzd4BivDplExOjvpVopWKZWUe3+ZNpBRoTS7XWP+S7hahnWeTtOyzS1OtreWYjWAE6k8p/2NgTZbKavEAZClavjVWI+1hif4Q63F+I7VqVXWrcTek5V+rkIUoJyEevTa0VytY3v4t79ZrXv0KOnHjpEZEQG3/A2aMwJRzn21CD/o2mk0tZr1wMbX12wMbldghjIzVwnnThdIPyc/SarFyO3fl5+THzOazMj1fdcu3ZSjFWw4XP5m0vVMggrRRipFq1S8DBVjVJzTwfbUZTqZ1jKSsnIa//NTOOsP5/y1dOs8mq7Nh9xzrPfbp7PbdeoqU8Kc+DyjE+01Bxhss4WGmpN01O6no3Y/l1VPwra14A1DS87jz8SOj5XoRFvQpOQqSpyYlBjO3TjHmf1n6NO1D9cyr5n+6r+19axjukpQrEqFmOx7ZhViVEpfz3ufNn5+2NeoQVblcsy8vpqzvioJzjfHtr1TSfTWmKREWjgKo3FbXt/37dXIt3Z9guzBMfyvKzcTbrRK+djsUatup7o6Y6gSiHPNOpQKbkBSeR8uuegp5xZ41/Pr9irjyspF+mq300v7P9yUm82hDxuD+MHQlM2GRlzGm6EtytOltn+xH2JRqoULkCTX4u9us67cmjwVFLqW78pPkT/hkmygfAyUj81OqOVjVPzi896/jb8/9jWq41CjRnb1bo0a2JQqZXo9P/dGRdFRVFqOQ+6ke6+EqzWolI2DStHZf/xViFYJvJI98tTtUuzgvK+CR63Hca/1OD7BjdAGleNCWnSu6uT1+6OYvPGwaXgQOzJ5UnOIp7W7aKn5Fxvl5gEOG4P4xdCA7cbHOa6WQ6MoxbZUK8m1AElyLV5uT6S3X4xuHwFIUVV8bmQn0KArKkExEHQle6SevFxxgwg/Jbt611/D/Be/oXTZx/IVl5REi4eilFzzcreEm9coVTZZKuWuZte2lP+vxqXc1ewxmm+XpYGLXhDlo6Fqg/ZUfLwNMX52lC1fm6vJGXy2Zz+bDukx6G8O9+hJIp21e+mq/ZMGygm0ys1jX1JLsctQm13G2vxurEESzmalWidbbZEu3UpyLUCSXAvfnRoR3SuR3v5XvUN69kWl3FWVwCvZCbXcVbDPo+eLEYj2hEhf5b9kmt2CN8XhztOWiZKhqCfXvNyacG+vjVHzmC0op4QbFKuaamcCr4BT3t2pSbbP7kd90Qsuein4Vm2DXemuhB66hmp7DWOmF2qWG6VIoK32EG01B2muOWLq0gNgVBWOqoH8aazOPuNjHDBW4QbZ11QFimRVsiTXAiTJtXDdqUr1bolUl6VS+hqUuwoBV43ZI+ZcvfMwdZk2EOUFkX4KkT4KEb4KUT7ZA9DfPjbumMfHUNOrppRAS7DimFxvd+uIU7cPXXlHqop3QnbNTbkr2b8z5a5kj6t8p1mFUm2zh3iMLqUQZVuFSw4BXHL24rKbE3rViybGS7TQHKal5l8qay7l2v6c0Y+/1Mr8bazIYWMFjqvlyMAWBejTMICmlbwI8HAotIQrybUASXK1nrt1Y8nrtTt1U8gZ69Y23UCZa1DmmkrZa1AmLnvMV7/4O18M4lyzRzQ67wPnfRSifDTElFLIUlSze653GxtXlGwlIbne6m63RvIj54/VgKsqZa9ll3jLxqn43gDtXbJBgiNEO7ty2T6QaCd3kt2ycHFMw9PtKo1tL9FEuZxrmyxVw1m1NMfUQE4aAzihBnBGLcsltRSgybNKuSCrliW5FiBJrvl3t+R5t0Y9d3ptX/Q+hv38Aj7x2TOTlL4Opa+rNMoMIOt81B3vi8LNaiyP6nXYrB7mvDdc9NHQpkY3s1lYcpJnxI0ITu8/bWotLMn00VXSkivk3b/2QtIFjsQdMdX43GuGodtpDdmzBpW5lj2hQelrKv43VPyuc8dxlXMYFLjuZEeqgyNZTgYcHDNwcE7G1TGVdCewc8gizRHKGbLwMxhIVe2IVP04p/oTofpxXvXlvNGXC6o3V/DAiCZXaTfqeiqKopj9bOnoUpJcC5Ak1/wNnHCvsWrvNM6tqqqEfNEBzwQj3gkqvvHZ82l2sKmD8dJlsmJi7zrRdbwTXCylcNlLoXLdJ1iTGk6Ul0qis4YZTWfmObh8Xo2LSuIFVdyfR+1cuNMsRPm5d3snDhnZJVu/G9m/077xKt7x4JOg4pWYd2Oq2xmBRCfQ2BtxtM9CY29E76BiY28g00HF1s5AugNodSoXHdxIwpXLWjf0md6k6H2JUT25orpzRXUnDjeysEEB5vXO/xjJj9wgEh9++CHvvvsuMTEx1KlThyVLltCwYcM7rv/NN98wbdo0IiMjqVy5MvPnz6dz584PMeKixZKh0vI7xN6tU3YZVSOz9syiiX8TvPX2XDz2O7XOZuGeDF6J4JWo4p1g4NrqPiix1/gkM3eLoiz+ArJHNUrXQYwHRHtqqFynJbUf78jv2kjevvQZSfY3p+/qXLkXj+eROPPquC8lUiGy5fX7AdCrci+zWyIAa46v4YtjX9yxZXKONDuFSL/sdgy3U1TV7FpQ6r//PZPAMyn7f4/k7Cpn9xQgRQPYAmD33z5yyp5O//1fCUjXJVPWIZlkh0u42mRhb2vEaKvipDOQYA9ptjZ8Wuo5JodByyreRaLB1K0KPbmuX7+esWPHsnTpUho1akRoaCgdOnTg5MmT+Pj45Fr/jz/+oE+fPsydO5euXbuydu1aevbsyaFDh6hZs+ZDi9vas1vc7/4s6XOZV9J8e/dMGts+hme6DYYbNzDcuE505N88dSgL11QVj2RwT87uynLj3fbE67NwAt7I+wjZ+wXineGqW/Yk3Vc8FAa0n4h3pZrYBgRw1SGLrOSL1L0lYXYGHk8JuWciFULcv9t/n16v/zr9qvXLVdK9tXr5XlRF4YYL3HCB02XyHjVKMaq4poFHErinZF9X3FLALSV7tiHXVHBLVXFJBZe07H689vrsR3bDRfNU5fHfI33A92gVHZFxjYtcci30auFGjRrRoEEDPvjgAwCMRiMBAQG8+uqrTJo0Kdf6ISEhpKSksGnTJtOyxo0bExwczNKlS+95PGtUA1h7EAFL96eqKmpmJjE3LtAnrBdavRF7PThkgFOmwtvBk3HR22BMTsGYnIwhOQljcgpxcVEcjzqIQ0b2MGuuqXduqn83Wnd3UtztOKG5wnVniHPT0LxeTxoHd0NXpjSbkvYw68DsQh1k4VGrChR3JufC/bm1hfLW81vzVcK1ClXFMQNTonVJy75eOaeBUzo4p6s4pWf//Hl7DVddNazt+AO1/YLuuetHplo4MzOTgwcPMnnyZNMyjUZD27Zt2bNnT57b7Nmzh7Fjx5ot69ChA999911BhmoSkxLDe7/NpN1RAxojaFQjB/+czuPVI3G2cUQ1GMBgRDXe8n+WAdVoBIPBfLnBSFpGMtfP/8Zoo4rGCLosA8Yvp3LKbQ02WSpqRgZqRgbG//5XMzJQM2/2Ncvrz4mU9W+RksdyHVA7rzelKGjd3dF6eKD19MDGw4MoTTw7kw5x3QniXTT0ajKEto8/g9bbG41tdpWO/x0GUuhFAE0DW0ojIiGKsVtLubW8a+VZwr098VqFopBqD6n2EJu94O6ro5LBVSDIOse3kkJNrnFxcRgMBnx9fc2W+/r6cuLEiTy3iYmJyXP9mJiYPNfPyMggI+Nm8SwxMbtzpF6vR5/XPJv3cO7GOVxSjAzZan4ipf22nDSL95atWR7LDBwjH20EMAKZuuy+nal22fdGKpWphZ2rB1pnFzROTijOzmidndE4O3Eo5SRfXfqeZDuVZEcNQ5u/TtfaIShardl+fYFyqbGmXyZfR9//4gLDf59bKdtSlPpvWMDbP8u7vfYw5ByzMI4tihY5F6zj1t9pwPTzY+6PEVI5+5aOvY096Vnpuf7fG7OXFUdXYMRKCfgWGkWDv4N/vr7f28+FgjwnCv2ea0GbO3cus2bNyrV869atODo6Wry/BGMCaXYKex5TMCrZczKqGqhmVwtbjR2qRgMaBVW55X+txvy5RsleT9GQpmTyW+YODAoYNdlJUm+j0MW1Jw46N1SdDapOh9HGBtXGBtVGl73MxgajTscB/SG+T//B1Ny+h0MP9Hb17/wGXBvQxrcK1wzXKKUthTbajZ+jf7nre77CFYs/p6Ji27ZthR2CKCLkXCg85SnP666vc81wDVvFlkw1857/n806y66MXXetflZQ6G7fnYPhBy2KJ+dcSE29Rx+jB1CoydXLywutVktsbKzZ8tjYWPz87jAll5+fRetPnjzZrBo5MTGRgIAA2rdvf9917A5nHXjb5W3TPcWpDafSpGLP+9oXQOLZ73h7n/n+OuVzf53pxkupL+cqYT7q9Ho927Zto127dnKf7REn50LxFftf7Zm9jT3RydEA+Dv7m36u7V3bomve7edCTk1mQSjU5Gpra0u9evXYvn07PXv2BLIbNG3fvp2RI0fmuU2TJk3Yvn07Y8aMMS3btm0bTZo0yXN9Ozs77Ozsci3X6XT3/Yv2zGPP0CKghdXuKT7o/sq6laWsW9l7r/gIepDvWZQsci4UP7de2+pS17T81p/vR865UJDnQ6FXC48dO5aBAwdSv359GjZsSGhoKCkpKQwePBiAAQMGUKZMGebOnQvA6NGjadWqFQsWLKBLly6sW7eOAwcOsGzZsocat7W7iEiXEyGEKDkKPbmGhIRw9epVpk+fTkxMDMHBwWzZssXUaCkqKgqNRmNav2nTpqxdu5apU6cyZcoUKleuzHffffdQ+7gKIYQQd1PoyRVg5MiRd6wGDg8Pz7XsmWee4ZlnningqIQQQoj7o7n3KkIIIYSwhCRXIYQQwsokuQohhBBWViTuuT5MOUMpF2T/JlH49Ho9qampJCYmSveLR5ycCyLH7edCTh4oiCH2H7nkmpSUBEBAQEAhRyKEEKIoSEpKws3Nzar7LPRZcR42o9HI5cuXcXFxQVHuPiC0tTVo0ID9+/cXyX3fz/b53SY/691rnbu9ntdrOSNxXbhwweqzXVjDo3ou5GddOReK1r4t3UdxOhdUVSUpKYnSpUubdfm0hkeu5KrRaChbtnBGM9JqtQX2y/2g+76f7fO7TX7Wu9c6d3v9bq+5uroWyQvqo3ou5GddOReK1r4t3UdxOxesXWLNIQ2aHqIRI0YU2X3fz/b53SY/691rnbu9XpCfa0F5VM+F/Kwr50LR2rel+5BzIdsjVy0sHg0FOQmyKF7kXBA5Hua5ICVXUSLZ2dkxY8aMPCdtEI8WORdEjod5LkjJVQghhLAyKbkKIYQQVibJVQghhLAySa5CCCGElUlyFUIIIaxMkqsQQghhZZJchQBSU1MJDAxk3LhxhR2KKCTx8fHUr1+f4OBgatasyfLlyws7JFFILly4QOvWralevTq1a9fmm2++sXgf0hVHCOCNN97gzJkzBAQE8N577xV2OKIQGAwGMjIycHR0JCUlhZo1a3LgwAFKlSpV2KGJhyw6OprY2FiCg4OJiYmhXr16nDp1Cicnp3zvQ0qu4pF3+vRpTpw4QadOnQo7FFGItFotjo6OAGRkZKCqaoFMRSaKPn9/f4KDgwHw8/PDy8uL69evW7QPSa6iSNu1axfdunWjdOnSKIrCd999l2udDz/8kKCgIOzt7WnUqBH79u2z6Bjjxo1j7ty5VopYFJSHcS7Ex8dTp04dypYty/jx4/Hy8rJS9MKaHsa5kOPgwYMYDAaLpymV5CqKtJSUFOrUqcOHH36Y5+vr169n7NixzJgxg0OHDlGnTh06dOjAlStXTOvk3EO7/XH58mW+//57qlSpQpUqVR7WWxL3qaDPBQB3d3f++ecfIiIiWLt2LbGxsQ/lvQnLPIxzAeD69esMGDCAZcuWWR6kKkQxAajffvut2bKGDRuqI0aMMD03GAxq6dKl1blz5+Zrn5MmTVLLli2rBgYGqqVKlVJdXV3VWbNmWTNsUQAK4ly43fDhw9VvvvnmQcIUD0FBnQvp6elqixYt1C+++OK+4pKSqyi2MjMzOXjwIG3btjUt02g0tG3blj179uRrH3PnzuXChQtERkby3nvvMXToUKZPn15QIYsCYo1zITY2lqSkJAASEhLYtWsXVatWLZB4RcGxxrmgqiqDBg3iiSeeoH///vcVhyRXUWzFxcVhMBjw9fU1W+7r60tMTEwhRSUKgzXOhfPnz9OiRQvq1KlDixYtePXVV6lVq1ZBhCsKkDXOhd9//53169fz3XffERwcTHBwMIcPH7YoDhuL1haiBBs0aFBhhyAKUcOGDfn7778LOwxRBDRv3hyj0fhA+5CSqyi2vLy80Gq1uRqdxMbG4ufnV0hRicIg54LIUVTOBUmuotiytbWlXr16bN++3bTMaDSyfft2mjRpUoiRiYdNzgWRo6icC1ItLIq05ORkzpw5Y3oeERHB33//jaenJ+XKlWPs2LEMHDiQ+vXr07BhQ0JDQ0lJSWHw4MGFGLUoCHIuiBzF4ly4rzbGQjwkO3bsUIFcj4EDB5rWWbJkiVquXDnV1tZWbdiwofrnn38WXsCiwMi5IHIUh3NBxhYWQgghrEzuuQohhBBWJslVCCGEsDJJrkIIIYSVSXIVQgghrEySqxBCCGFlklyFEEIIK5PkKoQQQliZJFchhBDCyiS5ClHEhYeHoygK8fHxD/3YiqKgKAru7u53XW/mzJkEBwebPc/ZNjQ0tEBjFKIokuQqRBHSunVrxowZY7asadOmREdH4+bmVigxrVixglOnTlm0zbhx44iOjqZs2bIFFJUQRZsM3C9EEWdra1uo06a5u7vj4+Nj0TbOzs44Ozuj1WoLKCohijYpuQpRRAwaNIidO3eyePFiU5VqZGRkrmrhlStX4u7uzqZNm6hatSqOjo48/fTTpKamsmrVKoKCgvDw8GDUqFEYDAbT/jMyMhg3bhxlypTBycmJRo0aER4efl+xzps3D19fX1xcXBgyZAjp6elW+ASEKDkkuQpRRCxevJgmTZowdOhQoqOjiY6OJiAgIM91U1NTef/991m3bh1btmwhPDycp556is2bN7N582ZWr17NJ598woYNG0zbjBw5kj179rBu3Tr+/fdfnnnmGTp27Mjp06ctivPrr79m5syZzJkzhwMHDuDv789HH330QO9diJJGqoWFKCLc3NywtbXF0dHxntXAer2ejz/+mIoVKwLw9NNPs3r1amJjY3F2dqZ69eq0adOGHTt2EBISQlRUFCtWrCAqKorSpUsD2fdFt2zZwooVK5gzZ06+4wwNDWXIkCEMGTIEgLfffptff/1VSq9C3EJKrkIUQ46OjqbECuDr60tQUBDOzs5my65cuQLA4cOHMRgMVKlSxXQ/1NnZmZ07d3L27FmLjn38+HEaNWpktqxJkyYP8G6EKHmk5CpEMaTT6cyeK4qS5zKj0QhAcnIyWq2WgwcP5mpkdGtCFkJYhyRXIYoQW1tbs0ZI1lK3bl0MBgNXrlyhRYsWD7SvatWqsXfvXgYMGGBa9ueffz5oiEKUKJJchShCgoKC2Lt3L5GRkTg7O+Pp6WmV/VapUoV+/foxYMAAFixYQN26dbl69Srbt2+ndu3adOnSJd/7Gj16NIMGDaJ+/fo0a9aMNWvWcPToUSpUqGCVWIUoCeSeqxBFyLhx49BqtVSvXh1vb2+ioqKstu8VK1YwYMAAXn/9dapWrUrPnj3Zv38/5cqVs2g/ISEhTJs2jQkTJlCvXj3Onz/P8OHDrRanECWBoqqqWthBCCGKJkVR+Pbbb+nZs+d9bR8UFMSYMWNyjTolREknJVchxF316dPH4mEM58yZg7Ozs1VL3kIUJ1JyFULc0ZkzZwDQarWUL18+39tdv36d69evA+Dt7V1o4yILUVgkuQohhBBWJtXCQgghhJVJchVCCCGsTJKrEEIIYWWSXIUQQggrk+QqhBBCWJkkVyGEEMLKJLkKIYQQVibJVQghhLAySa5CCCGElf0fC1CaxqMCOCUAAAAASUVORK5CYII=", + "image/png": "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", "text/plain": [ "
" ] @@ -325,50 +331,58 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": {}, + "execution_count": 18, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, "outputs": [ { "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 k [m/d]Ss [1/m]RMSE [m]k [m/d]Ss [1/m]RMSE [m]
timflow1.179.38e-060.010timflow1.179.38e-060.010
AQTESOLV1.179.37e-06-AQTESOLV1.179.37e-06-
MLU1.318.20e-06-MLU1.318.20e-06-
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -421,9 +435,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -435,7 +449,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.3" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug3_falling_head.ipynb b/docs/transient/04pumpingtests/slug3_falling_head.ipynb index 413af3e..bf80dc3 100644 --- a/docs/transient/04pumpingtests/slug3_falling_head.ipynb +++ b/docs/transient/04pumpingtests/slug3_falling_head.ipynb @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -78,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -136,7 +136,7 @@ "18" ] }, - "execution_count": 17, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -157,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -169,7 +169,7 @@ " -9.927336])" ] }, - "execution_count": 19, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -180,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -217,16 +217,16 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 22, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, @@ -251,16 +251,16 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 25, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, @@ -284,16 +284,16 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 27, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, @@ -321,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -331,13 +331,68 @@ ".....................................\n", "Fit succeeded.\n" ] + } + ], + "source": [ + "cal = tft.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq\", layers=list(range(1, nlay)), initial=10, pmin=0)\n", + "cal.set_parameter(name=\"Saq\", layers=list(range(1, nlay)), initial=1e-4, pmin=0)\n", + "cal.seriesinwell(name=\"obs\", element=w, t=to, h=ho)\n", + "cal.fit(report=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" }, + "tags": [] + }, + "outputs": [ { "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
optimal
kaq_1_170.439172
Saq_1_170.000461
\n", + "
" + ], "text/plain": [ - "kaq_1_17 0.439172\n", - "Saq_1_17 0.000461\n", - "Name: optimal, dtype: float64" + " optimal\n", + "kaq_1_17 0.439172\n", + "Saq_1_17 0.000461" ] }, "metadata": {}, @@ -347,23 +402,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "RMSE: 0.006011434252180949\n" + "RMSE: 0.006 m\n" ] } ], "source": [ - "cal = tft.Calibrate(ml)\n", - "cal.set_parameter(name=\"kaq\", layers=list(range(1, nlay)), initial=10, pmin=0)\n", - "cal.set_parameter(name=\"Saq\", layers=list(range(1, nlay)), initial=1e-4, pmin=0)\n", - "cal.seriesinwell(name=\"obs\", element=w, t=to, h=ho)\n", - "cal.fit(report=False)\n", - "display(cal.parameters[\"optimal\"])\n", - "print(\"RMSE:\", cal.rmse())" + "display(cal.parameters.loc[:, [\"optimal\"]])\n", + "print(f\"RMSE: {cal.rmse():.3f} m\")" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -399,44 +449,52 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": {}, + "execution_count": 23, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, "outputs": [ { "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 k [m/d]Ss [1/m]RMSE [m]k [m/d]Ss [1/m]RMSE [m]
timflow0.444.61e-040.006timflow0.444.61e-040.006
AQTESOLV0.425.70e-04-AQTESOLV0.425.70e-04-
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 35, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/transient/04pumpingtests/slug4_dawsonville.ipynb b/docs/transient/04pumpingtests/slug4_dawsonville.ipynb index a75e590..531d22e 100644 --- a/docs/transient/04pumpingtests/slug4_dawsonville.ipynb +++ b/docs/transient/04pumpingtests/slug4_dawsonville.ipynb @@ -2,7 +2,13 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "# 4. Slug test for confined aquifer - Dawsonville" ] @@ -16,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 3, "metadata": { "editable": true, "slideshow": { @@ -74,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -113,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -142,29 +148,15 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - ".....................................\n", - "Fit succeeded.\n", - "[[Fit Statistics]]\n", - " # fitting method = leastsq\n", - " # function evals = 34\n", - " # data points = 22\n", - " # variables = 2\n", - " chi-square = 4.2779e-04\n", - " reduced chi-square = 2.1389e-05\n", - " Akaike info crit = -234.654459\n", - " Bayesian info crit = -232.472374\n", - "[[Variables]]\n", - " kaq0_0_0: 0.42101398 +/- 0.01839446 (4.37%) (init = 10)\n", - " Saq0_0_0: 1.6975e-05 +/- 5.2892e-06 (31.16%) (init = 0.0001)\n", - "[[Correlations]] (unreported correlations are < 0.100)\n", - " C(kaq0_0_0, Saq0_0_0) = -0.9853\n" + "..............................\n", + "Fit succeeded.\n" ] } ], @@ -174,12 +166,12 @@ "cal.set_parameter(name=\"kaq0\", initial=10, pmin=0, layers=0)\n", "cal.set_parameter(name=\"Saq0\", initial=1e-4, layers=0)\n", "cal.seriesinwell(name=\"obs\", element=w, t=to, h=ho)\n", - "cal.fit(report=True)" + "cal.fit(report=False)" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -218,26 +210,26 @@ " \n", " kaq0_0_0\n", " 0\n", - " 0.421014\n", - " 0.018394\n", - " 4.369086\n", + " 0.420912\n", + " 0.018404\n", + " 4.372498\n", " 0.0\n", " inf\n", " 10.0000\n", " None\n", - " [[0.42101397957109743]]\n", + " [[0.4209121893500565]]\n", " \n", " \n", " Saq0_0_0\n", " 0\n", " 0.000017\n", " 0.000005\n", - " 31.159403\n", + " 31.179338\n", " -inf\n", " inf\n", " 0.0001\n", " None\n", - " [[1.697465569017154e-05]]\n", + " [[1.700498848737957e-05]]\n", " \n", " \n", "\n", @@ -245,12 +237,12 @@ ], "text/plain": [ " layers optimal std perc_std pmin pmax initial inhoms \\\n", - "kaq0_0_0 0 0.421014 0.018394 4.369086 0.0 inf 10.0000 None \n", - "Saq0_0_0 0 0.000017 0.000005 31.159403 -inf inf 0.0001 None \n", + "kaq0_0_0 0 0.420912 0.018404 4.372498 0.0 inf 10.0000 None \n", + "Saq0_0_0 0 0.000017 0.000005 31.179338 -inf inf 0.0001 None \n", "\n", " parray \n", - "kaq0_0_0 [[0.42101397957109743]] \n", - "Saq0_0_0 [[1.697465569017154e-05]] " + "kaq0_0_0 [[0.4209121893500565]] \n", + "Saq0_0_0 [[1.700498848737957e-05]] " ] }, "metadata": {}, @@ -260,7 +252,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "rmse: 0.004409650627757543\n" + "rmse: 0.004409555968801705\n" ] } ], @@ -271,12 +263,12 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdsAAAFBCAYAAAA/hwURAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABXuUlEQVR4nO3deVxU1fvA8c9l2EEWZREVQckF3DBwT9FSyTVcErdcMiuXrEj7aotLmZi55VKW5Zaaprn8MnOX3Jfczb3cRRQXQFAYmPv7Y2QSAWWQYVie9+t1X8PcOffeZ+YwPJx7zzlXUVVVRQghhBAmY2HuAIQQQoiiTpKtEEIIYWKSbIUQQggTk2QrhBBCmJgkWyGEEMLEJNkKIYQQJibJVgghhDAxSbZCCCGEiUmyFUIIIUxMkq3INUVRGD16tNHbXbhwAUVRmDdvXp7HlN98fX3p06ePucMwWn7VwejRo1EUJU/32bRpU5o2bZqn+ywunqU++vTpg6+vb94GVIxIsi3k5s2bh6IoKIrCjh07Mr2uqire3t4oikLbtm3NEGHuRUVFGd6boihoNBo8PDzo3LkzJ0+eNHd4WTpx4gSjR4/mwoULZovht99+IyQkBA8PD+zt7alYsSJdunRh3bp1ZotJiOLO0twBiLxha2vL4sWLeeGFFzKs//PPP7ly5Qo2NjZmiuzZDRkyhDp16qDVajl69CizZs0iKiqK48ePU7p0aXOHl8GJEycYM2YMTZs2NUsrYOLEiQwbNoyQkBBGjBiBvb09586dY9OmTSxZsoSXX34ZAB8fH+7fv4+VlVW+xyhEcSTJtoho3bo1y5YtY9q0aVha/letixcvJigoiNjYWDNG92waN25M586dDc+rVKnCgAEDWLBgAR9++KEZIytYUlNT+fzzz2nRogUbNmzI9PqNGzcMPyuKgq2tbX6GVyzodDpSUlLksxWZyGnkIqJbt27cunWLjRs3GtalpKSwfPlyunfvnuU2iYmJfPDBB3h7e2NjY0OVKlWYOHEij98IKjk5mffffx93d3dKlChB+/btuXLlSpb7vHr1Kq+//jqenp7Y2NhQrVo15syZk3dvFH3yBfjnn39ydezp06dTrVo17O3tcXV1JTg4mMWLFxtez+7a1NOud82bN49XX30VgGbNmhlOf0dFRQHw119/ERoaipubG3Z2dlSoUIHXX3/d2LefrdjYWOLj42nUqFGWr3t4eBh+zuqabZ8+fXB0dOTq1auEhYXh6OiIu7s7Q4cOJS0tLcO+bt26xWuvvYaTkxMuLi707t2bI0eO5Pg68MKFCwkKCsLOzo6SJUvStWtXLl++nKv3nZKSwsiRIwkKCsLZ2RkHBwcaN27M1q1bDWVUVcXX15dXXnkl0/YPHjzA2dmZt956y7AuOTmZUaNG8dxzz2FjY4O3tzcffvghycnJGbZVFIXBgwezaNEiqlWrho2NzRNP1/v6+tK2bVuioqIIDg7Gzs6OGjVqGH5HVqxYQY0aNbC1tSUoKIhDhw5l2seWLVto3LgxDg4OuLi48Morr2R5WWXHjh3UqVMHW1tb/Pz8+O6777KNKy/rQ2RNWrZFhK+vLw0aNODnn3+mVatWAPzxxx/ExcXRtWtXpk2blqG8qqq0b9+erVu30q9fPwIDA1m/fj3Dhg3j6tWrTJkyxVD2jTfeYOHChXTv3p2GDRuyZcsW2rRpkymGmJgY6tevb/gD5O7uzh9//EG/fv2Ij4/nvffey5P3mn491NXV1ehjz549myFDhtC5c2feffddHjx4wNGjR9m7d2+2/5TkVJMmTRgyZAjTpk3jo48+wt/fHwB/f39u3LhBy5YtcXd3Z/jw4bi4uHDhwgVWrFjxTMd8lIeHB3Z2dvz222+88847lCxZ0uh9pKWlERoaSr169Zg4cSKbNm1i0qRJ+Pn5MWDAAEDfemvXrh379u1jwIABVK1aldWrV9O7d+8cHeOLL77g008/pUuXLrzxxhvcvHmT6dOn06RJEw4dOoSLi4tRMcfHx/PDDz/QrVs3+vfvT0JCAj/++COhoaHs27ePwMBAFEWhZ8+eTJgwgdu3b2f4bH777Tfi4+Pp2bOn4f21b9+eHTt28Oabb+Lv78+xY8eYMmUKZ86cYdWqVRmOv2XLFn755RcGDx6Mm5vbUy8fnDt3ju7du/PWW2/Rs2dPJk6cSLt27Zg1axYfffQRAwcOBCAyMpIuXbpw+vRpLCz07aJNmzbRqlUrKlasyOjRo7l//z7Tp0+nUaNGHDx40HDsY8eOGX7fRo8eTWpqKqNGjcLT09Pk9SGyoYpCbe7cuSqg7t+/X50xY4ZaokQJNSkpSVVVVX311VfVZs2aqaqqqj4+PmqbNm0M261atUoF1LFjx2bYX+fOnVVFUdRz586pqqqqhw8fVgF14MCBGcp1795dBdRRo0YZ1vXr10/18vJSY2NjM5Tt2rWr6uzsbIjr/PnzKqDOnTv3ie9t69atKqDOmTNHvXnzpnrt2jV13bp16nPPPacqiqLu27fP6GO/8sorarVq1Z543N69e6s+Pj6Z1o8aNUp9/Cvj4+Oj9u7d2/B82bJlKqBu3bo1Q7mVK1ca6smURo4cqQKqg4OD2qpVK/WLL75QDxw4kKlcVnXQu3dvFVA/++yzDGVr166tBgUFGZ7/+uuvKqBOnTrVsC4tLU198cUXM+3z8c/swoULqkajUb/44osMxzh27JhqaWmZaX1WQkJC1JCQEMPz1NRUNTk5OUOZO3fuqJ6enurrr79uWHf69GkVUL/99tsMZdu3b6/6+vqqOp1OVVVV/emnn1QLCwt1+/btGcrNmjVLBdSdO3ca1gGqhYWF+vfffz81blXV/74A6q5duwzr1q9frwKqnZ2devHiRcP67777LtPvUmBgoOrh4aHeunXLsO7IkSOqhYWF2qtXL8O6sLAw1dbWNsP+Tpw4oWo0mlzXR3bfC5Ezchq5COnSpQv3799nzZo1JCQksGbNmmxba2vXrkWj0TBkyJAM6z/44ANUVeWPP/4wlAMylXu8laqqKr/++ivt2rVDVVViY2MNS2hoKHFxcRw8eDBX7+v111/H3d2dMmXK8PLLLxMXF8dPP/1EnTp1jD62i4sLV65cYf/+/bmKJbfSWwdr1qxBq9Wa7Dhjxoxh8eLF1K5dm/Xr1/Pxxx8TFBTE888/n+Me3G+//XaG540bN+bff/81PF+3bh1WVlb079/fsM7CwoJBgwY9dd8rVqxAp9PRpUuXDPVUunRpKlWqlOHUb05pNBqsra0Bfav09u3bpKamEhwcnOF3rnLlytSrV49FixYZ1t2+fZs//viDHj16GC4RLFu2DH9/f6pWrZohxhdffBEgU4whISEEBATkON6AgAAaNGhgeF6vXj0AXnzxRcqXL59pffpnHx0dzeHDh+nTp0+GlnnNmjVp0aKF4bualpbG+vXrCQsLy7A/f39/QkNDM8RiivoQWZPTyEWIu7s7zZs3Z/HixSQlJZGWlpahY9GjLl68SJkyZShRokSG9emnPi9evGh4tLCwwM/PL0O5KlWqZHh+8+ZN7t69y/fff8/333+f5TEf7aBjjJEjR9K4cWPu3bvHypUrWbJkieG0mrHH/t///semTZuoW7cuzz33HC1btqR79+7ZXufMKyEhIXTq1IkxY8YwZcoUmjZtSlhYGN27d39iT/G4uDju379veG5tbf3U08PdunWjW7duxMfHs3fvXubNm8fixYtp164dx48ff2LnHVtbW9zd3TOsc3V15c6dO4bnFy9exMvLC3t7+wzlnnvuuSfGBXD27FlUVaVSpUpZvp7eO/revXvcu3fPsF6j0WSK61Hz589n0qRJnDp1KsM/MxUqVMhQrlevXgwePJiLFy/i4+PDsmXL0Gq1vPbaaxliPHnyZLbHe/z3+PFjPM2jCRDA2dkZAG9v7yzXp3/26d/Jx797oP/erl+/nsTERBISErh//36Wn3GVKlUMSRlyXh/i2UmyLWK6d+9O//79uX79Oq1atcq36y06nQ6Anj17ZnvtrmbNmrnad40aNWjevDkAYWFhJCUl0b9/f1544QW8vb2NOra/vz+nT59mzZo1rFu3jl9//ZVvvvmGkSNHMmbMGIBsO0E93knIGIqisHz5cvbs2cNvv/3G+vXref3115k0aRJ79uzB0dExy+3effdd5s+fb3geEhJi6EzzNE5OTrRo0YIWLVpgZWXF/Pnz2bt3LyEhIdluo9FojHpfxtLpdCiKwh9//JHlsdI/h4kTJxrqA/RDlbIbu7xw4UL69OlDWFgYw4YNw8PDA41GQ2RkZKZOdF27duX9999n0aJFfPTRRyxcuJDg4OAMCUyn01GjRg0mT56c5fEeT4p2dnY5eu/psvuMs1uvPtZhMS/ltD7Es5NkW8R06NCBt956iz179rB06dJsy/n4+LBp0yYSEhIytG5PnTpleD39UafT8c8//2T4g3T69OkM+0vvqZyWlmZIjKYyfvx4Vq5cyRdffMGsWbOMPraDgwPh4eGEh4eTkpJCx44d+eKLLxgxYgS2tra4urpy9+7dTNultyye5Gmz89SvX5/69evzxRdfsHjxYnr06MGSJUt44403siz/4YcfGjruQMZOYcYIDg5m/vz5REdH52r7R/n4+LB161aSkpIytG7PnTv31G39/PxQVZUKFSpQuXLlbMv16tUrw5jxJyW05cuXU7FiRVasWJHh8x81alSmsiVLlqRNmzYsWrSIHj16sHPnTqZOnZopxiNHjvDSSy/l+exXzyL9O/n4dw/031s3NzccHBywtbXFzs6Os2fPZir3+LY5rQ/x7OSabRHj6OjIt99+y+jRo2nXrl225Vq3bk1aWhozZszIsH7KlCkoimLo0Zz++Hhv5sf/QGk0Gjp16sSvv/7K8ePHMx3v5s2buXk7WfLz86NTp07MmzeP69evG3XsW7duZXjN2tqagIAAVFU1nH708/MjLi6Oo0ePGspFR0ezcuXKp8bm4OAAkClZ37lzJ1MLJTAwECDTcJJHBQQE0Lx5c8MSFBSUbdmkpCR2796d5Wvp1+CzOgVprNDQULRaLbNnzzas0+l0zJw586nbduzYEY1Gw5gxYzJ9HqqqGuqnYsWKGd73k07zp7fIHt3f3r17s/0sXnvtNU6cOMGwYcPQaDR07do1w+tdunTh6tWrGd5fuvv375OYmPjU92kKXl5eBAYGMn/+/Ay/X8ePH2fDhg20bt0a0H8eoaGhrFq1ikuXLhnKnTx5kvXr12fYZ07rQzw7adkWQTkZgtGuXTuaNWvGxx9/zIULF6hVqxYbNmxg9erVvPfee4ZrtIGBgXTr1o1vvvmGuLg4GjZsyObNm7NsxYwfP56tW7dSr149+vfvT0BAALdv3+bgwYNs2rSJ27dv59l7HDZsGL/88gtTp05l/PjxOT52y5YtKV26NI0aNcLT05OTJ08yY8YM2rRpY2jhd+3alf/973906NCBIUOGkJSUxLfffkvlypWf2skrMDAQjUbDl19+SVxcHDY2Nrz44ossXryYb775hg4dOuDn50dCQgKzZ8/GycnJ8EfyWSUlJdGwYUPq16/Pyy+/jLe3N3fv3mXVqlVs376dsLAwateu/czHCQsLo27dunzwwQecO3eOqlWr8n//93+Gz/hJrUE/Pz/Gjh3LiBEjuHDhAmFhYZQoUYLz58+zcuVK3nzzTYYOHWpUPG3btmXFihV06NCBNm3acP78eWbNmkVAQECG677p2rRpQ6lSpVi2bBmtWrXKMP4Y9Mn4l19+4e2332br1q00atSItLQ0Tp06xS+//ML69esJDg42Ksa88tVXX9GqVSsaNGhAv379DEN/nJ2dM8xTPmbMGNatW0fjxo0ZOHAgqamphvHlj/4TaYr6ENnI9/7PIk89OvTnSR4f+qOqqpqQkKC+//77apkyZVQrKyu1UqVK6ldffWUYApHu/v376pAhQ9RSpUqpDg4Oart27dTLly9nGvqjqqoaExOjDho0SPX29latrKzU0qVLqy+99JL6/fffG8oYO/Rn2bJlWb7etGlT1cnJSb17926Oj/3dd9+pTZo0UUuVKqXa2Niofn5+6rBhw9S4uLgM+96wYYNavXp11draWq1SpYq6cOHCHA39UVVVnT17tlqxYkXDMIutW7eqBw8eVLt166aWL19etbGxUT08PNS2bduqf/311xM/A2NotVp19uzZalhYmOrj46Pa2Nio9vb2au3atdWvvvoqw/CY7Ib+ODg4ZNpvVu/75s2bavfu3dUSJUqozs7Oap8+fdSdO3eqgLpkyZInbquq+uFDL7zwgurg4KA6ODioVatWVQcNGqSePn36qe/z8aE/Op1OHTdunOE9165dW12zZs0Th6oMHDhQBdTFixdn+XpKSor65ZdfqtWqVVNtbGxUV1dXNSgoSB0zZkyG3xVAHTRo0FNjTpfV9zC7/aTX0VdffZVh/aZNm9RGjRqpdnZ2qpOTk9quXTv1xIkTmfb5559/qkFBQaq1tbVasWJFddasWc9UHzL059koqmrCq+9CiGJj1apVdOjQgR07dpi8d/ezev/99/nxxx+5fv16pl7VQpiCXLMVQhjt0eFIoO+pPX36dJycnHj++efNFFXOPHjwgIULF9KpUydJtCLfyDVbIYTR3nnnHe7fv0+DBg1ITk5mxYoV7Nq1i3Hjxhk9FCa/3Lhxg02bNrF8+XJu3brFu+++a+6QRDEiyVYIYbQXX3yRSZMmsWbNGh48eMBzzz3H9OnTGTx4sLlDy9aJEyfo0aMHHh4eTJs2zdAbXIj8INdshRBCCBOTa7ZCCCGEiUmyFUIIIUys2F2z1el0XLt2jRIlShSoqdiEEELkL1VVSUhIoEyZMhlubmIKxS7ZXrt2LdNE4kIIIYqvy5cvU65cOZMeo9gl2/Qp+S5fvoyTk1O25bRaLRs2bKBly5Zym6liROq9eJJ6L360Wi2rVq3ijTfeyHSrUVModsk2/dSxk5PTU5Otvb09Tk5O8uUrRqTeiyep9+Invc7h6XfrygvSQUoIIYQwMUm2QgghhIlJshVCCCFMrNhdsxVCCFNLS0tDq9WaO4xiz8rKCo1GY+4wAEm2QgiRZ1RV5fr169y9e9fcoYiHXFxcKF26tNnnVZBkm0vRcfc5H5tIBTcHvJwL5l1OhBD5Kz3Renh4YG9vb/Y/8MWZqqokJSVx48YNALy8vMwajyTbXFi6/xIjVhxDp4KFApEdaxBep7y5wxJCmFFaWpoh0ZYqVcrc4Qgw3O7xxo0beHh4mPWUsnSQMlJ03H1DogXQqfDRiuNEx91/8oZCiCIt/Rqt3JC+YEmvD3NfQ5dka6TzsYmGRJsuTVW5EJtknoCEEAWKnDouWApKfUiyNVIFNwcsHqs7jaLg6yb/zQohhMiaXLM1kpezHVNbuVN7c3cSVVuSsKWMRylK/7EIrB0eWRz1i036Ywn9YvjZSf9oaW3utySEENmKioqiWbNm3LlzBxcXF3OHU2hJss2F9lVLwJabkN7CjT0LsbncmcZGn3RtnfQJ2NYJbJ3Bxln/+Ohi5/Lw0RVsXfSPVrZ586aEEEKYjCTb3HDxgX6bIOUeaJMgJRGSEzL+nJL4cLn38PnDx+R7kByvLwuQlgxJyZCUy2xtaadPunauYF/ykceSYF8q4+JQCuzd9C3vAnIdQwghigNJtrlhbQ/edZ5tH7q0h8k3Xv/4IF7/84N4SI6DB+lL/MPHu3D/7iOPcaCmQep9SLgPCddyfmxLW3BwBwe3h4/u4OgBDh76R0fPh4uHviUtiVmIfJefY/mTk5MZNmwYS5YsIT4+nuDgYKZMmUKdOv/9ndu5cycjRozgzJkzBAYG8sMPP1C9enUALl68yODBg9mxYwcpKSn4+vry1Vdf0bp1a5PGXZhIsjUXC43+tLCdS+62V1V9kr5/5+FyG5Ju639Ouv3w+a3/lsRb+tZz6gP9EndZvzyNpa0+8ZYo/XDx0i9OZf5bSpSR09lC5KH8Hsv/4Ycf8uuvvzJ//nx8fHyYMGECoaGhnDt3zlBm2LBhfP3115QuXZqPPvqIdu3acebMGaysrBg0aBApKSls27YNBwcHTpw4gaOjo8niLYwk2RZWivLw+q4TuPrkbBtV1Z/aTrz5MAHfhHs39I/pP9+7Afdi9I/JcfrEfPeifnkS+1LgVBacvcG5LDiX0//sUl6/OLhLC1mIHMhuLH+Tyu4maeEmJiby7bffMm/ePFq1agXA7Nmz2bhxIz/++KOhdTtq1ChatGgBwPz58ylXrhwrV66kS5cuXLp0iU6dOlGjRg0AKlasmOdxFnaSbIsTRdH3jrZxhJIVnl5eex8SruuTb8L1h8s1/WP8tf+W1Pv/taCvH816X5a2DxOvj/6fA1ffh0sF/aON/BcsBDx5LL8pku0///yDVqulUaNGhnVWVlbUrVuXkydPGpJtgwYNDK+XLFmSKlWqcPLkSQCGDBnCgAED2LBhA82bN6dTp07UrFkzz2MtzCTZiuxZ2emT8pMSs6rqT13HX4W4qxB/BeKuwN3L+se4yw8T8gOIPaNfsuLoCSUrQkk/KJW+PKd/LqeoRTGSPpb/0YRb0Mfyv/HGG4SGhvL777+zYcMGIiMjmTRpEu+88465QyswJNmKZ6Mo+t7P9iWhdI2sy6Sm6JPwnYtw9xLcufDIcl6frO/F6JdLux8/ALh4g1tl/eJeBdyr6n+2L2na9yaEGXg52xHZsQYfrThOmqqiURTGdaxusk5Sfn5+WFtbs3PnTnx89JektFot+/fv57333jOU27NnD+XL668b37lzhzNnzuDv72943dvbm7fffpu3336bESNGMHv2bEm2j5BkK0zP0vphqzWb6zj378Dt83D7X7j1D9z+B26d0y8P4vQJ+u4lOLcp43aOnuDhDx4BDx+r6R+tC24LQIicCK9TniaV3bkQm4Svm71JeyM7ODgwYMAAhg0bRsmSJSlfvjwTJkwgKSmJfv36ceTIEQA+++wzSpUqhaenJx9//DFubm6EhYUB8N5779GqVSsqV67MnTt32Lp1a4ZELCTZioLAzhXKukLZ5zOuV1VIjNWfer51Fm6egdjTcPO0/vR0emv436hHNlL0p6A9q0HpmvrFq6a+J7UQhYiXs12+3b5z/Pjx6HQ6XnvtNRISEggODmb9+vW4urpmKPPuu+9y9uxZAgMD+e2337C21s+Al5aWxqBBg7hy5QpOTk68/PLLTJkyJV9iLywk2YqCS1HA0V2/+DbK+Fpygj7p3jj5cPkbYk5A4o3/WsUnVv9X3tETvGqBVyCUCYSyQZKAhXjI1taWadOmMW3atEyvNW3aFFXVX0Bu27ZtlttPnz7dpPEVBZJsReFkUwLKBeuXR927ATHH4fpxfc/o68f0LeN7MXB2g35JV6KMvjVdLhjK1YEytUGRuaqFEHlPkq0oWhw9wPFF8Hvxv3UpSfoEfO0wRB+Ga4fg5in9MKZT1+DUGn05RYOlRwA10jxR/n4AFRrpxwsLIcQzkmQrij5re/Cuq1/SJd+D6CNw9QBc2a9fEqJRYo5RkWOw6mFnLGdv8Gn4cGmkH44kk3MIIYwkyVYUTzaO+uvAj14LjrtK6oVdXNzxCxU0MVjEHNd3xDq6VL8AOJYG3xegQmOo0CT7HtZCCPEISbZCpHMuixoQxvEL1pRv3RoLXbK+xXtxl365sh/uXYfjy/UL6GfEqtj0v0XG/gohsiDJVojs2DiCXzP9AqB9AFf2wfntcH4bXP1LP2f0wfn6RbHQ93L2ewmea67vfGWhMe97EEIUCJJshcgpK1v9qeMKTYCP9cOPLu7Sj/P9Z4u+01X69d8/x+tvzlCppX557iX97QqFEMWShbkDAJg5cya+vr7Y2tpSr1499u3bl23ZefPmoShKhsXWVubOFWZgUwIqh8LLkTBoL7z/N7SfDgGvgI2T/sYMR36G5X1hgh8sCIN9s/VzSAshihWzt2yXLl1KREQEs2bNol69ekydOpXQ0FBOnz6Nh4dHlts4OTlx+vRpw3NFeoeKgsC5HDzfS7+kafXzPJ9Zr19unYV/t+qXtUP1p5v920NAe+lkJUQxYPaW7eTJk+nfvz99+/YlICCAWbNmYW9vz5w5c7LdRlEUSpcubVg8PT3zMWIhckBjpT/dHPoFvPMXDP4Lmo8B73qAoh9ytGkUTKsNs16A7ZP1N2YQogCIiopCURTu3r37TPtJSkqiU6dOODk5Gfbn6+vL1KlT8yTOwsSsLduUlBQOHDjAiBEjDOssLCxo3rw5u3c/fveX/9y7dw8fHx90Oh3PP/8848aNo1q1almWTU5OJjk52fA8Pj4e0N/VQqvVZnuM9NeeVEYUPSard2dfqDdIvyRcx+LMWpRTa1Au7kS5fkw/09XmMei8aqNW74wuoIN+gg6RL/Ki3rVaLaqqotPp0Ol0eRVavnjxxRepVauWYT7j+vXrc/XqVUqUKPFM72Xu3Lls376dHTt24ObmRokSJQAMn1N+0Ol0qKqKVqtFo/mvw2J+/203a7KNjY0lLS0tU8vU09OTU6dOZblNlSpVmDNnDjVr1iQuLo6JEyfSsGFD/v77b8qVyzzbT2RkJGPGjMm0fsOGDdjbP/3uMBs3bszhuxFFienrvTS4voF1iXC87v5F2Tt7cbt3EovoQxB9CIuNn3CzRDUul2xEtHMwaRobE8cj4Nnq3dLSktKlS3Pv3j1SUlLyMCrTS01NJSUlxdAYAbC3tychIeGZ9nvq1CkqVapkuDVfQkICOp2OBw8eZDiWKaWkpHD//n22bdtGampqvhwzK2a/ZmusBg0a0KBBA8Pzhg0b4u/vz3fffcfnn3+eqfyIESOIiIgwPI+Pj8fb25uWLVvi5OSU7XG0Wi0bN26kRYsWWFlZ5e2bEAWWeeo9HIDUezewOLka5fhyLK4dwCPhOB4Jx1GtF6L6h6GrGY7q3UBmsDKBvKj3Bw8ecPnyZRwdHQtVp82+ffuyc+dOdu7cyaxZswD48ccf6devH7du3cLFxYV58+YRERHBggULGDZsGJcvX6ZVq1bMnz+fZcuWMWbMGOLi4ujZsyeTJ09Go9Hw4osv8ueffwLg6upKSEgIW7ZswcLCAltbW8Pf30uXLjFkyBDDa6GhoUybNg1PT0/i4uJwc3Nj9+7dBAcHo9Pp8PDwoHLlyuzatQuAhQsX8vHHH3Px4sUs39+DBw+ws7OjSZMmGepFq9WyevXqLLcxBbMmWzc3NzQaDTExMRnWx8TEULp0zu7IYmVlRe3atTl37lyWr9vY2GBjk7lVYGVllaMvVU7LiaLFLPXuWhYaDtQvt/6BY8vgyM8ody6gHFmExZFFUNIPnn8NanWHEtJXIa89S72npaWhKAoWFhZYWDzsDqOqoE3KwwiNYGWfo3/Mpk2bxtmzZ6levTqfffYZAH///TeA4b1YWFiQlJTEjBkzWLJkCQkJCXTs2JFOnTrh4uLC2rVr+ffff+nUqRMvvPAC4eHhrFixguHDh3P8+HFWrFiBtbW14XNJ/5x0Oh0dOnTA0dGRP//8k9TUVAYNGkS3bt2IiorC1dWVwMBAtm3bRt26dTl27BiKonDo0CGSkpJwdHRk+/bthISE/PeZP8bCwgJFUcz+t9ysydba2pqgoCA2b95suAmxTqdj8+bNDB48OEf7SEtL49ixY7Ru3dqEkQqRz0r5QdPhEPI/fa/mw4vh75Vw+x/YNBo2fw5VWkHw61CxGWTzh0aYmTYJxpUxz7E/ugbWDk8t5uzsjLW1Nfb29oZGTlaX8bRaLd9++y1+fn4AdO7cmZ9++omYmBgcHR0JCAigWbNmbN26lfDwcEqWLIm9vT3W1tbZNp42b97MsWPHOH/+PN7e3gAsWLCAatWqsX//furUqUPTpk2Jiopi6NChREVF0aJFC06dOsWOHTt4+eWXiYqK4sMPP8ztp5RvzP4NjYiIYPbs2cyfP5+TJ08yYMAAEhMT6du3LwC9evXK0IHqs88+Y8OGDfz7778cPHiQnj17cvHiRd544w1zvQUhTEdR9DdBeGUGfHAaXpkJ5eqCmqa/W9HCjjD9edj5NSTdNne0ogizt7c3JFrQ963x9fXF0dExw7obN27keJ8nT57E29vbkGgBAgICcHFx4eTJkwCEhISwY8cO0tLS+PPPP2natKkhAV+7do1z587RtGnTZ3+DJmb2a7bh4eHcvHmTkSNHcv36dQIDA1m3bp2h09SlS5cynB64c+cO/fv35/r167i6uhIUFMSuXbsICAgw11sQIn/YOELtnvrlxkn4a65+0ow752HjSNg6Dmp2gbpvQenq5o5WgP5U7kfXzHfsvNzdY6dg00/NPr4ur3sZN2nShISEBA4ePMi2bdsYN24cpUuXZvz48dSqVYsyZcpQqVKlPD2mKZg92QIMHjw429PGUVFRGZ5PmTLF0D1diGLLwx9aT4Dmo+DYctg/Wz986OAC/eLbGBoMgkqhcorZnBQlR6dyzc3a2pq0tLR8P66/vz+XL1/m8uXLhtbtiRMnuHv3rqEB5eLiQs2aNZkxYwZWVlZUrVoVDw8PwsPDWbNmDSEhIfked27It1CIwszaAYJ6w1vboe86qNYBFA1c2A4/d4WZdeGvOaC9b+5IRQHm6+vL3r17uXDhArGxsfk2BrZ58+bUqFGDHj16cPDgQfbt20evXr0ICQkhODjYUK5p06YsWrTIkFhLliyJv78/S5culWQrhMhHigI+DeDVefDeUWj0Ltg466eJXPM+TK0B2yfB/bvmjlQUQEOHDkWj0RAQEIC7uzuXLl3Kl+MqisLq1atxdXWlSZMmNG/enIoVK7J06dIM5UJCQkhLS8twbbZp06aZ1hVkiqqqqrmDyE/x8fE4OzsTFxf31HG2a9eupXXr1jL0pxgpUvWenAAHf4I930DcZf06Gyd9D+YGg8HR3bzxFSB5Ue8PHjzg/PnzVKhQoVCNsy3qsqsXrVbL8uXL6d69+1PzQV6Qlq0QRZVNCWgwEIYcgg7fgbs/JMfDzqn6lu66jyDhurmjFKJYkGQrRFGnsYJaXWHALuj6s/6OQ6n3Yc9MmFoT/vgfJMQ8fT9CiFyTZCtEcWFhAVVbwxuboeev+jsQpSXD3lnwdS3Y8Ckk3jJ3lEIUSZJshShuFAWeaw6vr4fXVkG5OvqW7q5p8HVNiBqvv94rhMgzkmyFKK4UBfyaQb+N0H0ZlK4JKfcgKlJ/n92930Nq4bp7TUFQzPqcFngFpT4k2QpR3CkKVG4Jb23TDx0qWRESb8Ifw0idXodTWxcRfddMk+kXIum9mJOS5LMqSNLrw9yjCwrEDFJCiAJAUfSTYlRtCwcXcH/TF9jFXaDqnwPZt3UaxxuPokULueFHdjQaDS4uLoa5ge3t7VHkdohmo6oqSUlJ3LhxAxcXlww3jjcHSbZCiIw0VkRX7k6LFS701/zGm5rfqWtxCnZ2I+lOF+xbfQ4lcnYLzOIm/e42xkzGL0zLxcUlx7dsNSVJtkKITM7HJnJPtWVK6qv8nPoiw6yW0kmzA/sTv8C5PyBkGNQbAJbW5g61QFEUBS8vLzw8PNBqteYOp9izsrIye4s2nSRbIUQmFdwcsFBAp8J1SvGBdiCL0kJZUn4F1tcP6e8ydHABtJkEFZuaO9wCR6PRFJg/8qJgkA5SQohMvJztiOxYA83Da44aRSG8QxjWb26BV74BB3e4dQ4WvALL+8lMVEI8hbRshRBZCq9TniaV3bkQm4Svmz1eznb6F2r3gKptYOsXsP8HOL4czm6Al0ZCcD+5pZ8QWZBvhRAiW17OdjTwK/Vfok1n5wKtv4L+W6DM8/o5l9cOhTmh+hvbCyEykGQrhMi9MrXhjU3QeiJYl4Ar+2BWY9jyBaQmmzs6IQoMSbZCiGdjoYG6/WHQXqjSGnRa2DYBvguBqwfNHZ0QBYIkWyFE3nAuC10Xw6vz9R2obp6EH5rDpjHSyhXFniRbIUTeURSoFgYD90L1TqCmwY7J+lZu9BFzRyeE2UiyFULkPYdS0HkOhC/8r5U7+yXYPgl0aeaOToh8J8lWCGE6/u1g4B79fMs6LWz+DOa2gjsXzB2ZEPlKkq0QwrQc3PQt3LBv9T2WL+/V91g+uszckQmRbyTZCiFMT1EgsDsM2Ane9fTjcle8ASveggfx5o5OCJPL0QxSzz//vFE7VRSF//u//6Ns2bK5CkoIUUS5+kCftbDtK/3woKNL9C3dV+dBmUBzRyeEyeQo2R4+fJgPPvgAR0fHp5ZVVZXx48eTnCxd/YUQWdBYQrMR+hsYrOgPd87Djy0gdBzUeUPfChaiiMnx3MjDhg3Dw8MjR2UnTZqU64CEEMWETwN4axusHgSn1+qne7ywHdpPB1tnc0cnRJ7K0TXb8+fP4+7unuOdnjhxAh8fn1wHJYQoJuxL6ifCCB0HFlZwYjV83wxi/jZ3ZELkqRwlWx8fHxQjTu14e3vLvRyFEDmjKNBgELy+HpzKwe1/9GNyj/5i7siEyDO5usXegwcPOHr0KDdu3ECn02V4rX379nkSmBCimCkXpD+t/Gs/+Her/nru5X36Vq+ltbmjE+KZGJ1s161bR69evYiNjc30mqIopKXJ7DBCiFxyKAU9f4Wo8freyvtn608pd1kAjjm/lCVEQWP0ONt33nmHV199lejoaHQ6XYYlt4l25syZ+Pr6YmtrS7169di3b1+OtluyZAmKohAWFpar4wohCiALDbz4MXRbop8E49Iu+D4Erh0yd2RC5JrRyTYmJoaIiAg8PT3zJIClS5cSERHBqFGjOHjwILVq1SI0NJQbN248cbsLFy4wdOhQGjdunCdxCCEKmCqt9DenL/UcxF+FH0PlOq4otIxOtp07dyYqKirPApg8eTL9+/enb9++BAQEMGvWLOzt7ZkzZ06226SlpdGjRw/GjBlDxYoV8ywWIUQB415Zn3ArvwxpyfrruJs/h8f6ighR0Bl9zXbGjBm8+uqrbN++nRo1amBlZZXh9SFDhuR4XykpKRw4cIARI0YY1llYWNC8eXN2796d7XafffYZHh4e9OvXj+3btxv7FoQQhYmtM3T9GTaPgZ1TYftEiD0NHb4DawdzRydEjhidbH/++Wc2bNiAra0tUVFRGYYEKYpiVLKNjY0lLS0t0ylpT09PTp06leU2O3bs4Mcff+Tw4cM5OkZycnKG2azi4/XzsGq1WrRabbbbpb/2pDKi6JF6L8CafoJSshKate+jnPwN9fYFUsMXQwmvZ9611Hvxk991bXSy/fjjjxkzZgzDhw/HwiJ/72OQkJDAa6+9xuzZs3Fzc8vRNpGRkYwZMybT+g0bNmBvb//U7Tdu3Gh0nKLwk3ovqEpg5f0hTS99jX3MMVK/DWG33wck2Hnnyd6l3oWpGJ1sU1JSCA8Pz5NE6+bmhkajISYmJsP6mJgYSpcunan8P//8w4ULF2jXrp1hXfo4X0tLS06fPo2fn1+GbUaMGEFERITheXx8PN7e3rRs2RInJ6dsY9NqtWzcuJEWLVpkOlUuii6p94Jt2YErfLLHkrJ8xjyrCfhpo2n2byRpHeeg+r2Y6/1KvRc/Wq2W1atX59vxjE62vXv3ZunSpXz00UfPfHBra2uCgoLYvHmzYfiOTqdj8+bNDB48OFP5qlWrcuzYsQzrPvnkExISEvj666/x9s78362NjQ02NjaZ1ltZWeXoS5XTcqJokXoveKLj7vPJ6hPoVLiMJx1TxvCd9RTqp5zEcmk3aDsFgno/0zGk3oWpGJ1s09LSmDBhAuvXr6dmzZqZfjEnT55s1P4iIiLo3bs3wcHB1K1bl6lTp5KYmEjfvn0B6NWrF2XLliUyMhJbW1uqV6+eYXsXFxeATOuFEEXL+dhEdOp/z+NwpFfKcHb6r8T9/Cr4bQgkXIeQD+XOQaLAMTrZHjt2jNq1awNw/PjxDK8ZM39yuvDwcG7evMnIkSO5fv06gYGBrFu3ztBp6tKlS/l+bVgIUfBUcHPAQiFDwk1TrEl95Vs44AfbJ0HUOEiIhjaT9JNjCFFAGJ1st27dmudBDB48OMvTxsBTx/TOmzcvz+MRQhQ8Xs52RHaswUcrjpOmqmgUhXEdq+PlYg8vjdT3Sl47DA7MhXs3oPOPYGVn7rCFAHJ5IwIhhDCH8DrlaVLZnQuxSfi62ePl/EgyrdsfHD3h1zfg9O+wsBN0+1nujSsKhBydn+3YsaNhfGpO9OjR46nTLQohRG54OdvRwK9UxkSbLqA9vLYSbJzg4k6Y1xbu3cz/IIV4TI6S7erVq7l58ybx8fFPXeLi4vjtt9+4d++eqWMXQojMfBtBnzXg4A7Xj8KcULhz0dxRiWIuR6eRVVWlcuXKpo5FCCHyhlct/c3oF4Tpb0Y/txX0+j9we87ckYliKkfJNjedosqWLWv0NkIIkWdK+UG/hwk39vTDhLsKPKuZOzJRDOUo2YaEhJg6DiGEyHtOZaDvWvgpDK4fg3ltoOcKKPu8uSMTxYwMYBVCFG0ObtD7NygbDPfvwPz2cGmvuaMSxYwkWyFE0Wfnqj+F7PMCpCTAwo5wcZe5oxLFiCRbIUTxYFMCeiyDik0h5Z5+HO55uR+2yB+SbIUQxYe1PXRbAn4vgTYJFr0K/0aZOypRDEiyFUIUL1Z20HUxVGoJqfdhcTjKhW3mjkoUcUYn25iYGF577TXKlCmDpaUlGo0mwyKEEAWelS2EL4TKL0PqAzRLe1Aq4ZS5oxJFmNFzI/fp04dLly7x6aef4uXllas7/QghhNlZ2kCXBbCkB8q5jdT/dxJcagB+TcwdmSiCjE62O3bsYPv27QQGBpogHCGEyEeWNhC+EN3PXbH8dyvq0q7w2irwrmvuyEQRY/RpZG9vb1RVfXpBIYQoDKxsSeu8gBslqqGkJJK6oAM3z+wxd1SiiDE62U6dOpXhw4dz4cIFE4QjhBBmYGXHdIf32auriqX2HpaLOrFuy2ZzRyWKEKNPI4eHh5OUlISfnx/29vZYWVlleP327dt5FpwQQuSH6LgHLLxgxwqGsdA6ktoW5wj6sy83KvyOR4Ua5g5PFAFGJ9spU6ZIpyghRJFy8VYSKgqJ2NE75UN+tv6CahYXSV7WGfpvAFcfc4coCrlc9UYWQoiixKeUPQoqKgrxOPJaygiWWn9OpaSr+psY9F0HJTzNHaYoxIy+ZturVy/mzp3LP//8Y4p4hBAi33k52xJeUYfFw5N2cYozJ1vMB5fycPtf/VzK9++YN0hRqBndsrW2tiYyMpJ+/fpRtmxZQkJCaNq0KSEhIVSqVMkUMQohhMk18FQZ2LEJV+NS8HWzx8vZDgJW6e+DG3McFnXR38zA2sHcoYpCyOiW7Q8//MCZM2e4fPkyEyZMwNHRkUmTJlG1alXKlStnihiFECJfeDnb0sCvlD7Rgv4G9K+tBFsXuLIPlr4GqSlmjVEUTrmeG9nV1ZVSpUrh6uqKi4sLlpaWuLu752VsQghhfp7VoMdysLKHfzbDqgGg05k7KlHIGJ1sP/roIxo2bEipUqUYPnw4Dx48YPjw4Vy/fp1Dhw6ZIkYhhDAv7zrQ5SewsITjy2H9CJDJfYQRjL5mO378eNzd3Rk1ahQdO3akcuXKpohLCCEKlkrNIexbWNEf9s4CBzdoMszcUYlCwuiW7aFDh/j444/Zt28fjRo1omzZsnTv3p3vv/+eM2fOmCJGIYQoGGp2gZfH63/eMhYO/mTeeEShYXSyrVWrFkOGDGHFihXcvHmTtWvXYm1tzaBBg/D39zdFjEIIUXDUHwAvvK//+bd34cwG88YjCgWjTyOrqsqhQ4eIiooiKiqKHTt2EB8fT82aNQkJCTFFjEIIUbC8NAoSrsORn2FZb+izBsoGmTsqUYAZnWxLlizJvXv3qFWrFiEhIfTv35/GjRvj4uJigvCEEKIAUhRoPx3uxcA/W/RjcPtt0A8VEiILRifbhQsX0rhxY5ycnEwRjxBCFA4aK/3N5+e1gegjsKgz9Nuo7zglxGOMvmbbpk0bQ6K9cuUKV65cyfOghBCiULApAd2XGaZ1TFnYhT2nrxAdd9/ckYkCxuhkq9Pp+Oyzz3B2dsbHxwcfHx9cXFz4/PPP0clAbyFEcVPCE3osJ8WyBNbRB7izsA+Nx29i6f5L5o5MFCBGJ9uPP/6YGTNmMH78eA4dOsShQ4cYN24c06dP59NPP81VEDNnzsTX1xdbW1vq1avHvn37si27YsUKgoODcXFxwcHBgcDAQH76SbrfCyHMJ9q6PK8lvUeyakkrzX6Gaxbx0Yrj0sIVBkZfs50/fz4//PAD7du3N6yrWbMmZcuWZeDAgXzxxRdG7W/p0qVEREQwa9Ys6tWrx9SpUwkNDeX06dN4eHhkKl+yZEk+/vhjqlatirW1NWvWrKFv3754eHgQGhpq7NsRQohndj42kb06f4Zp32aa9QzesPyDS6oHF2Lr/TfPsijWjG7Z3r59m6pVq2ZaX7VqVW7fvm10AJMnT6Z///707duXgIAAZs2ahb29PXPmzMmyfNOmTenQoQP+/v74+fnx7rvvUrNmTXbs2GH0sYUQIi9UcHPAQoH/0zVkgjYcgFGWC6gSv9PMkYmCwuiWba1atZgxYwbTpk3LsH7GjBnUqlXLqH2lpKRw4MABRowYYVhnYWFB8+bN2b1791O3V1WVLVu2cPr0ab788sssyyQnJ5OcnGx4Hh8fD4BWq0Wr1Wa77/TXnlRGFD1S78XTs9a7m70lY18J4JPVJ/gmrT2+FjF00UTh+sfbaN29wbN6HkYr8kJ+f8eNTrYTJkygTZs2bNq0iQYNGgCwe/duLl++zNq1a43aV2xsLGlpaXh6emZY7+npyalTp7LdLi4ujrJly5KcnIxGo+Gbb76hRYsWWZaNjIxkzJgxmdZv2LABe3v7p8a4cePGp5YRRY/Ue/H0LPXuAIyqDTcfKCTb9OLm5Rjc750kdX5HtlUZxQMr17wLVBQ6RifbkJAQzpw5w8yZMw0JsWPHjgwcOJAyZcrkeYBZKVGiBIcPH+bevXts3ryZiIgIKlasSNOmTTOVHTFiBBEREYbn8fHxeHt707JlyyeOFdZqtWzcuJEWLVpgZWVlirchCiCp9+LJJPV+vwnq/FbY3TpLy9gfSX3tN7nxfAGi1WpZvXp1vh3P6GQLUKZMGaM7QmXFzc0NjUZDTExMhvUxMTGULl062+0sLCx47rnnAAgMDOTkyZNERkZmmWxtbGywsbHJtN7KyipHX6qclhNFi9R78ZSn9W7lDj2WwQ8voVw/itWawfDqArDI9W3ERSGWo2R79OjRHO+wZs2aOS5rbW1NUFAQmzdvJiwsDNCP4928eTODBw/O8X50Ol2G67JCCFEglKwA4YtgQXs4+RtsHQsvjTR3VMIMcpRsAwMDURQFVVVRFMWwXn148+RH16WlpRkVQEREBL179yY4OJi6desydepUEhMT6du3LwC9evWibNmyREZGAvprsMHBwfj5+ZGcnMzatWv56aef+Pbbb406rhBC5AufBtBuGqx6G7ZPArfKUKuruaMS+SxHyfb8+fOGnw8dOsTQoUMZNmxYhg5SkyZNYsKECUYHEB4ezs2bNxk5ciTXr18nMDCQdevWGTpNXbp0CYtHTrskJiYycOBArly5gp2dHVWrVmXhwoWEh4cbfWwhhMgXgd0g9gzsmAz/9w64+kL5+kTH3ed8bCIV3BxkPG4Rl6Nk6+PjY/j51VdfZdq0abRu3dqwrmbNmnh7e/Ppp58aTgcbY/DgwdmeNo6KisrwfOzYsYwdO9boYwghhFm9+CncOqs/nbykB7/VW8S7626hU8FCgciONQivU97cUQoTMfpK/bFjx6hQoUKm9RUqVODEiRN5EpQQQhQ5FhbQ4TsoXQOSYnluc39s1QcA6FRkescizuhk6+/vT2RkJCkpKYZ1KSkpREZG4u/vn6fBCSFEkWLtAN2WkGLrhr/FJaZYfYOC/gYuaarKhdgkMwcoTMXooT+zZs2iXbt2lCtXztDz+OjRoyiKwm+//ZbnAQohRJHiXI74sPmU+DmMUM1ffKAuY2JqOBpFwdft6RPtiMLJ6GRbt25d/v33XxYtWmSY1CI8PJzu3bvj4CADtoUQ4mncqr7AnsAx1D/yEYMtV3NWLU/DsDelk1QRlqtJLRwcHHjzzTfzOhYhhCg26ncYxD2rqzj+NZOpdj+glO0ESAeposroa7bly5enV69e/Pjjj/z777+miEkIIYoFx9afQ6WWKKn3YUkPSIh5+kaiUDI62Y4bNw5bW1u+/PJLnnvuOby9venZsyezZ8/m7NmzpohRCCGKJgsNdPpBP9FF/FX45TVIldnwiiKjk23Pnj35/vvvOXPmDFevXuWrr74CYODAgVne51YIIcQT2DpD15/1j5f3wu8fwMPZ+UTRkatrtklJSezYsYOoqCi2bt3KoUOHqF69epY3AhBCCPEUbs9B57mwqDMc+gm8akHd/uaOSuQho5Ntw4YNOXToEP7+/jRt2pThw4fTpEkTXF3lXo1CCJFrz70EzcfAxk9h3XDw8AffF8wdlcgjRp9GPnXqFA4ODlStWpWqVavi7+8viVYIIfJCw3egRhfQpcIvveDuJQCi4+6z659YmWGqEDM62d66dYstW7ZQv3591q9fT6NGjShbtizdu3dn9uzZpohRCCGKB0WB9tP0p5GTbsGS7izfc5pG47fQffZeGo3fwtL9l8wdpcgFo5OtoijUrFmTIUOGsHz5cv744w9atGjBsmXLePvtt00RoxBCFB9Wdvp74Nq7wfVjWP/+HrqHHaZkDuXCy+hke/DgQSZPnkz79u0pVaoUDRo04OjRo7zzzjusWLHCFDEKIUTx4uINXRagUyxpr9nFG5q1hpdkDuXCKVfTNdauXZuQkBD69+9PkyZNcHZ2NkVsQghRfPk2IqHpGJy3fswIy8WcUsuzQ1dD5lAupIxOtrdv38bJyckUsQghhHiEc5NB/Ht2PxWvrGK61XQ6pIxlQMeXZA7lQsjo08iSaIUQIp8oChV7f0dK6dq4KvfYWPZ7wmuVMndUIheMTrZpaWlMnDiRunXrUrp0aUqWLJlhEUIIkYesbLHutggcPLCKPQH/947MMFUIGZ1sx4wZw+TJkwkPDycuLo6IiAg6duyIhYUFo0ePNkGIQghRzDmXhS7zwcISjv8Ku2eYOyJhJKOT7aJFi5g9ezYffPABlpaWdOvWjR9++IGRI0eyZ88eU8QohBDCpyG8PF7/88aR8M9W88YjjGJ0sr1+/To1atQAwNHRkbi4OADatm3L77//nrfRCSGE+E+dNyCwJ6g6WN4X7lwwd0Qih4xOtuXKlSM6OhoAPz8/NmzYAMD+/fuxsbHJ2+iEEEL8R1GgzSQo8zzcvwNLe0KKjLktDIxOth06dGDz5s0AvPPOO3z66adUqlSJXr168frrr+d5gEIIIR5hZQvhPxlmmOK3d4m+myRzJxdwRo+zHT9+vOHn8PBwfHx82LVrF5UqVaJdu3Z5GpwQQogsOJfTd5ia3x6O/cLsQzbMSW2FhQKRHWsQXqe8uSMUjzGqZavVann99dc5f/68YV39+vWJiIiQRCuEEPnJ9wXimowG4CPNIupbnJC5kwswo5KtlZUVv/76q6liEUIIYYS/vbuxIu0FLBUdM6ym4cUtmTu5gDL6mm1YWBirVq0yQShCCCGMUcHdkU9S+/G3zgc3JZ5Z1lOwU7Qyd3IBZPQ120qVKvHZZ5+xc+dOgoKCcHBwyPD6kCFD8iw4IYQQ2fNytmNUx2AGrPiA1dYfUcviX373W4mX0yvmDk08xuhk++OPP+Li4sKBAwc4cOBAhtcURZFkK4QQ+Si8TnmaVO5C9DEXXDb3oeKVVfDXj/oxuaLAMDrZPto5SgghhPl5Odvh9cIroIzWzy71x3DwrA7l65s7NPGQ0ddshRBCFFANh0C1DqDTwi+9ID6a6Lj7Mga3AMhRyzYiIiLHO5w8ebLRQcycOZOvvvqK69evU6tWLaZPn07dunWzLDt79mwWLFjA8ePHAQgKCmLcuHHZlhdCiGJDUeCVmXDzNNw4QeyccJrFRPBAtZIxuGaWo2R76NChDM8PHjxIamoqVapUAeDMmTNoNBqCgoKMDmDp0qVEREQwa9Ys6tWrx9SpUwkNDeX06dN4eHhkKh8VFUW3bt1o2LAhtra2fPnll7Rs2ZK///6bsmXLGn18IYQoUqwdIHwhuu+b4Xb3CJ9qFvBxaj/DGNwmld3l5vNmkKPTyFu3bjUs7dq1IyQkhCtXrnDw4EEOHjzI5cuXadasGW3atDE6gMmTJ9O/f3/69u1LQEAAs2bNwt7enjlz5mRZftGiRQwcOJDAwECqVq3KDz/8gE6nM0whKYQQxV4pP041moxOVehhuZlwjf4OQTIG13yM7iA1adIkNmzYgKurq2Gdq6srY8eOpWXLlnzwwQc53ldKSgoHDhxgxIgRhnUWFhY0b96c3bt352gfSUlJaLXabG9cn5ycTHJysuF5fHw8oJ8NS6vVZrvf9NeeVEYUPVLvxVNRrHfHgFAmb3iVoZa/8JnlXM7oynGESpR1ti5S7zO38vszMDrZxsfHc/PmzUzrb968SUJCglH7io2NJS0tDU9PzwzrPT09OXXqVI728b///Y8yZcrQvHnzLF+PjIxkzJgxmdZv2LABe/unD/zeuHFjjuIQRYvUe/FU1Oo91rstf1w5TyvNfmZZT+GH0p9zaOcWDj19U5HHjE62HTp0oG/fvkyaNMnQKWnv3r0MGzaMjh075nmATzJ+/HiWLFlCVFQUtra2WZYZMWJEhg5e8fHxeHt707JlS5ycnLLdt1arZePGjbRo0QIrK6s8j10UTFLvxVNRrffWwPWbdUlc2hbPuHOMsFxAWstVYCm3Q9VqtaxevTrfjmd0sp01axZDhw6le/fuhma4paUl/fr146uvvjJqX25ubmg0GmJiYjKsj4mJoXTp0k/cduLEiYwfP55NmzZRs2bNbMvZ2NhkeZ9dKyurHH2pclpOFC1S78VTUax37zJe0OsX+L4ZFlf3Y7HpY2j3tbnDKnaMHmdrb2/PN998w61btzh06BCHDh3i9u3bfPPNN5mmbnwaa2trgoKCMnRuSu/s1KBBg2y3mzBhAp9//jnr1q0jODjY2LcghBDFSyk/6PwjoMCBefCXvgOqjMHNP0a3bNM5ODg8sUWZUxEREfTu3Zvg4GDq1q3L1KlTSUxMpG/fvgD06tWLsmXLEhkZCcCXX37JyJEjWbx4Mb6+vly/fh0AR0dHHB0dnzkeIYQokiq1gBc/gS2fw9oP2XyrFP2jLNGpyBjcfGD2GaTCw8OZOHEiI0eOJDAwkMOHD7Nu3TpDp6lLly4RHR1tKP/tt9+SkpJC586d8fLyMiwTJ04011sQQojCofEHEBAGOi01d72Dh3oLQO6Dmw9y3bLNS4MHD2bw4MFZvhYVFZXh+YULF0wfkBBCFEWKAmHfkBh9Cvc7p/jOegpdUkaSjLVhDK5MeGEaZm/ZCiGEyEfWDiR2mM9t1ZFaFv8yzuoHQEWjKHIfXBOSZCuEEMWMR/mqHKk/lVTVgk6aHbxpuZZxHatLq9aEJNkKIUQx1KzVqyQ2+wyAEVY/E+5yxswRFW2SbIUQophyDhkMtXuiqDpY/jrEnjV3SEWWJFshhCiuFAXaTAbvepAcBz93g/t3ZfytCRSI3shCCCHMxNIGwhfC903h1lmi53SnyZW30aoaGX+bh6RlK4QQxZ2jB3RdjGpph9fNnfxPsxiQ8bd5SZKtEEIIKBPImYYTAHjD8g+6yD1w85QkWyGEEAA4Bb3K1NROAIy1nENd5aSMv80jkmyFEEIA4OVsh1f7UfyeVh9rJY1Z1lP4OtRZxt/mAUm2QgghDMLr+vD8u4u5V7I6JZV7tD3+Hty/a+6wCj1JtkIIITLwciuFY5/lUKIMxJ6BZX0gTWvusAo1SbZCCCEyc/KC7kvAyh7+3Uri6gh2nbspPZNzSZKtEEKIrHnVgk4/oqLgcHQBm+eOptH4LSzdf8nckRU6kmyFEEJkK9qrGeNSuwPwseUiWij7ZextLkiyFUIIka3zsYnMTm3NT6nNsVBUplrNpDpnZeytkSTZCiGEyFYFNwcsFIXRqb3ZkhaInZLCj9YT8bO6KXMoG0GSrRBCiGx5OdsR2bEGKJa8o32Hv3U+uCnx2C7tSuvx/0f32XvlOm4OSLIVQgjxROF1yrNjeDN+6N8Mt7dWkeZYBqfE83xvNREbUmQO5RyQZCuEEOKpvJztaOBXCs+yFTnW9AfiVXvqWJxhqtVMLNDJHMpPIclWCCGEUTwrPc9b2giSVUtaafYz0nIBGgXsrS3kGm425H62QgghjOLlbEdYh3CGropnutU0+lhuwK1MBTp8o78tn9wHNzNp2QohhDBaeJ3yfPThR5wP/gSAtje+o5NFFCD3wc2KJFshhBC54uVsR4W2w7hS7S0AxlvOpqXFfkDug/s4SbZCCCGeiabFaH5Ja4pGUZluNYN6ch/cTCTZCiGEeCZeLvbQdgob0oKxUbT8YD2Rb1+ykPvgPkKSrRBCiGfWpV5Fary3nDjPepRQ7tPywAC4ccrcYRUYkmyFEELkCa9Srjj3XQ5lnof7t2HBK3D7X3OHVSBIshVCCJF3bJ2g56/gEQD3rvPgx7bEXPnH3FGZnSRbIYQQecu+JKtrzOS8rjS2iVdJ+r41q7cfKNY3LpBJLYQQQuSp6Lj7vL82mtLqR/xi8xkVLK6j29iDV9Z+yg3VpVhOemH2lu3MmTPx9fXF1taWevXqsW/fvmzL/v3333Tq1AlfX18URWHq1Kn5F6gQQogcOR+biE6Fa7jRNeUTrqql8LOIZpHVF7gRVywnvTBrsl26dCkRERGMGjWKgwcPUqtWLUJDQ7lx40aW5ZOSkqhYsSLjx4+ndOnS+RytEEKInNDfA1f/8xXVg24pn3BNLUkli6sssv6CUsQVu0kvzJpsJ0+eTP/+/enbty8BAQHMmjULe3t75syZk2X5OnXq8NVXX9G1a1dsbGzyOVohhBA5kX4PXI2iz7hXVE+6p3zCddWVKhZXWGI9ltLK3WI16YXZrtmmpKRw4MABRowYYVhnYWFB8+bN2b17t7nCEkIIkQfC65SnSWV3LsQm4etmz7YzN+mxEn6yGksli6tsdB1PCUKAcuYONV+YLdnGxsaSlpaGp6dnhvWenp6cOpV3A6GTk5NJTk42PI+PjwdAq9Wi1Wqz3S79tSeVEUWP1HvxJPVuGm72lriVdwKgY6AXDSp04eqlWnhs7UOJhEuoc1qR2nMluPjke2z5XddFvjdyZGQkY8aMybR+w4YN2Ns//RTGxo0bTRGWKOCk3osnqff8caf8+9Q/+yVOcZdI+a45eyp9yD3bsuYOy6TMlmzd3NzQaDTExMRkWB8TE5OnnZ9GjBhBRESE4Xl8fDze3t60bNkSJyenbLfTarVs3LiRFi1aYGVllWfxiIJN6r14knrPX8sOXKHlQRt+shpHpdSrNDr3JXfDfuKsZRV8Stnj5Wxr8hi0Wi2rV682+XHSmS3ZWltbExQUxObNmwkLCwNAp9OxefNmBg8enGfHsbGxybIzlZWVVY6+VDktJ4oWqffiSerd9KLj7vPJ6hPoVFe6pHzKXOsJBGr/pcQvnflW+wF71OpFcgyuWXsjR0REMHv2bObPn8/JkycZMGAAiYmJ9O3bF4BevXpl6ECVkpLC4cOHOXz4MCkpKVy9epXDhw9z7tw5c70FIYQQRkgfgwtwByd6pHzMjrRqOCjJzLWawMvKniI5Btes12zDw8O5efMmI0eO5Pr16wQGBrJu3TpDp6lLly5hYfHf/wPXrl2jdu3ahucTJ05k4sSJhISEEBUVld/hCyGEMFL6GNz0hJuIHa9rP2QqM2mt2cc31tMYq73F70eq0KZWmSJzmz6zzyA1ePBgLl68SHJyMnv37qVevXqG16Kiopg3b57hua+vL6qqZlok0QohROHw+BhcC0CLFYO1Q5if2gKAT6wWodkwgsbjN7F0/yUzRpt3inxvZCGEEAVLVmNwP1pxnFGpfbisevCJ1SL6Wq6njHKLoSsG0aSye6Fv4UqyFUIIke+8nO0MCTQ9+f5+NJqxvytEq6WYbPUNoZq/8FZGE7XPk6Z1ny/UCdfsp5GFEEIIL2c72tT0wkKB33X16ZbyCTdVJwIsLtJ8R1fe+XJWoT6lLMlWCCFEgfDo9dyDamVeSR7LCZ0P7ko8i6zGcmjVNI5cvmPuMHNFkq0QQogCI7xOeXYMb8Ynbfy5hhudU0bxR1odbJRUxlvN5uT3fVm256y5wzSaJFshhBAFyqOnlJOwZaD2Xb7SdkGnKnTVbKXq2le5cem0ucM0iiRbIYQQBU76KWULQMWCmWlh9NIO57bqSA2L8zgtaMGt45vMHWaOSbIVQghRIIXXKc/KQQ15OCSXHboatE0ex2FdRVK1yYQv/rfQdJqSZCuEEKLAquXtyvhHJsG4hhtdUkbRLeUTzunKFpqpHSXZCiGEKNAe7TQFkIIVx9SKAKSpKhdik8wZXo5IshVCCFHgPdpp6lEaRcHX7en3Jjc3SbZCCCEKhcfnVdYoCuM6Vi8UM0vJdI1CCCEKjcfnVS4MiRYk2QohhChkHp1XubCQ08hCCCGEiUmyFUIIIUxMkq0QQghhYpJshRBCCBMrdh2kVFUFID4+/onltFotSUlJxMfHY2VllR+hiQJA6r14knovftLrHP7LC6ZU7JJtQkICAN7e3maORAghREGQkJCAs7OzSY+hqPmR0gsQnU7HtWvXKFGiBMrDgdF16tRh//79GcrFx8fj7e3N5cuXcXJyMkeo2coq3oKwX2O3z2n5p5XL7etS73mz34JY78a+VlDr3VR1/qz7zs22Ba3e0+v8xIkTVKlSBQsL015VLXYtWwsLC8qVK5dhnUajyfYL5uTkVKC+fPDkeM25X2O3z2n5p5XL7etS73mz34JY77l9raDVu6nq/Fn3nZttC2q9ly1b1uSJFqSDFACDBg0ydwhGMVW8z7pfY7fPafmnlcvt61LvebPfgljvuX2toDFlrM+y79xsW9zrvdidRs6p+Ph4nJ2diYuLK1D/6QrTknovnqTei5/8rnNp2WbDxsaGUaNGYWNjY+5QRD6Sei+epN6Ln/yuc2nZCiGEECYmLVshhBDCxCTZCiGEECYmyVYIIYQwMUm2QgghhIlJshVCCCFMTJJtHjh//jzNmjUjICCAGjVqkJiYaO6QRD7w9fWlZs2aBAYG0qxZM3OHI/JRUlISPj4+DB061NyhiHxw9+5dgoODCQwMpHr16syePdvofRS76RpNoU+fPowdO5bGjRtz+/ZtGatXjOzatQtHR0dzhyHy2RdffEH9+vXNHYbIJyVKlGDbtm3Y29uTmJhI9erV6dixI6VKlcrxPqRl+4z+/vtvrKysaNy4MQAlS5bE0lL+hxGiqDp79iynTp2iVatW5g5F5BONRoO9vT0AycnJqKpq9G35inyy3bZtG+3ataNMmTIoisKqVasylZk5cya+vr7Y2tpSr1499u3bl+P9nz17FkdHR9q1a8fzzz/PuHHj8jB6kVumrncARVEICQmhTp06LFq0KI8iF88iP+p96NChREZG5lHEIi/kR73fvXuXWrVqUa5cOYYNG4abm5tR2xf5JlhiYiK1atXi9ddfp2PHjpleX7p0KREREcyaNYt69eoxdepUQkNDOX36NB4eHgAEBgaSmpqaadsNGzaQmprK9u3bOXz4MB4eHrz88svUqVOHFi1amPy9ieyZut7LlCnDjh07KFu2LNHR0TRv3pwaNWpQs2ZNk783kT1T1/v+/fupXLkylStXZteuXSZ/PyJn8uP77uLiwpEjR4iJiaFjx4507twZT0/PnAepFiOAunLlygzr6tatqw4aNMjwPC0tTS1TpowaGRmZo33u2rVLbdmypeH5hAkT1AkTJuRJvCJvmKLeHzd06FB17ty5zxClyGumqPfhw4er5cqVU318fNRSpUqpTk5O6pgxY/IybPGM8uP7PmDAAHXZsmVGbVPkTyM/SUpKCgcOHKB58+aGdRYWFjRv3pzdu3fnaB916tThxo0b3LlzB51Ox7Zt2/D39zdVyCIP5EW9JyYmkpCQAMC9e/fYsmUL1apVM0m8Im/kRb1HRkZy+fJlLly4wMSJE+nfvz8jR440VcgiD+RFvcfExBi+73FxcWzbto0qVaoYFUeRP438JLGxsaSlpWU6FeDp6cmpU6dytA9LS0vGjRtHkyZNUFWVli1b0rZtW1OEK/JIXtR7TEwMHTp0ACAtLY3+/ftTp06dPI9V5J28qHdR+ORFvV+8eJE333zT0DHqnXfeoUaNGkbFUayTbV5p1aqV9EwsZipWrMiRI0fMHYYwoz59+pg7BJFP6taty+HDh59pH8X6NLKbmxsajYaYmJgM62NiYihdurSZohKmJvVePEm9F08Fpd6LdbK1trYmKCiIzZs3G9bpdDo2b95MgwYNzBiZMCWp9+JJ6r14Kij1XuRPI9+7d49z584Znp8/f57Dhw9TsmRJypcvT0REBL179yY4OJi6desydepUEhMT6du3rxmjFs9K6r14knovngpFveeq33MhsnXrVhXItPTu3dtQZvr06Wr58uVVa2trtW7duuqePXvMF7DIE1LvxZPUe/FUGOpdUVUj55wSQgghhFGK9TVbIYQQIj9IshVCCCFMTJKtEEIIYWKSbIUQQggTk2QrhBBCmJgkWyGEEMLEJNkKIYQQJibJVgghhDAxSbZCFHBRUVEoisLdu3fz/diKoqAoCi4uLk8sN3r0aAIDAzM8T9926tSpJo1RiMJAkq0QBUjTpk157733Mqxr2LAh0dHRODs7myWmuXPncubMGaO2GTp0KNHR0ZQrV85EUQlRuBT5GxEIUdhZW1ub9RZwLi4ueHh4GLWNo6Mjjo6OaDQaE0UlROEiLVshCog+ffrw559/8vXXXxtOwV64cCHTaeR58+bh4uLCmjVrqFKlCvb29nTu3JmkpCTmz5+Pr68vrq6uDBkyhLS0NMP+k5OTGTp0KGXLlsXBwYF69eoRFRWVq1jHjx+Pp6cnJUqUoF+/fjx48CAPPgEhii5JtkIUEF9//TUNGjSgf//+REdHEx0djbe3d5Zlk5KSmDZtGkuWLGHdunVERUXRoUMH1q5dy9q1a/npp5/47rvvWL58uWGbwYMHs3v3bpYsWcLRo0d59dVXefnllzl79qxRcf7yyy+MHj2acePG8ddff+Hl5cU333zzTO9diKJOTiMLUUA4OztjbW2Nvb39U08ba7Vavv32W/z8/ADo3LkzP/30EzExMTg6OhIQEECzZs3YunUr4eHhXLp0iblz53Lp0iXKlCkD6K+rrlu3jrlz5zJu3Lgcxzl16lT69etHv379ABg7diybNm2S1q0QTyAtWyEKIXt7e0OiBfD09MTX1xdHR8cM627cuAHAsWPHSEtLo3LlyobrqY6Ojvz555/8888/Rh375MmT1KtXL8O6Bg0aPMO7EaLok5atEIWQlZVVhueKomS5TqfTAXDv3j00Gg0HDhzI1Gnp0QQthDANSbZCFCDW1tYZOjXlldq1a5OWlsaNGzdo3LjxM+3L39+fvXv30qtXL8O6PXv2PGuIQhRpkmyFKEB8fX3Zu3cvFy5cwNHRkZIlS+bJfitXrkyPHj3o1asXkyZNonbt2ty8eZPNmzdTs2ZN2rRpk+N9vfvuu/Tp04fg4GAaNWrEokWL+Pvvv6lYsWKexCpEUSTXbIUoQIYOHYpGoyEgIAB3d3cuXbqUZ/ueO3cuvXr14oMPPqBKlSqEhYWxf/9+ypcvb9R+wsPD+fTTT/nwww8JCgri4sWLDBgwIM/iFKIoUlRVVc0dhBCiYFIUhZUrVxIWFpar7X19fXnvvfcyzYolRHEjLVshxBN169bN6GkXx40bh6OjY562zIUozKRlK4TI1rlz5wDQaDRUqFAhx9vdvn2b27dvA+Du7m62eZ2FKCgk2QohhBAmJqeRhRBCCBOTZCuEEEKYmCRbIYQQwsQk2QohhBAmJslWCCGEMDFJtkIIIYSJSbIVQgghTEySrRBCCGFikmyFEEIIE/t/twAVaC039VMAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -308,44 +300,52 @@ }, { "cell_type": "code", - "execution_count": 52, - "metadata": {}, + "execution_count": 16, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, "outputs": [ { "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 k [m/d]Ss [1/m]RMSE [m]k [m/d]Ss [1/m]RMSE [m]
timflow0.421.70e-050.004timflow0.421.70e-050.004
MLU0.411.94e-050.004MLU0.411.94e-050.004
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 52, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/transient/04pumpingtests/unconfined2_moench.ipynb b/docs/transient/04pumpingtests/unconfined2_moench.ipynb index 8b748b6..af355f0 100644 --- a/docs/transient/04pumpingtests/unconfined2_moench.ipynb +++ b/docs/transient/04pumpingtests/unconfined2_moench.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": { "editable": true, "slideshow": { @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -151,14 +151,14 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "............................................................\n", + "................................................................\n", "Fit succeeded.\n" ] } @@ -177,12 +177,12 @@ "cal.series(name=\"PS2\", x=r2, y=0, t=t3, h=h3, layer=1)\n", "cal.series(name=\"PD2\", x=r2, y=0, t=t4, h=h4, layer=3)\n", "\n", - "cal.fit()" + "cal.fit(report=False)" ] }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -212,11 +212,11 @@ " \n", " \n", " kaq_0_3\n", - " 9.067651\n", + " 9.067649\n", " \n", " \n", " Saq_0_0\n", - " 0.172880\n", + " 0.172882\n", " \n", " \n", " Saq_1_3\n", @@ -232,8 +232,8 @@ ], "text/plain": [ " optimal\n", - "kaq_0_3 9.067651\n", - "Saq_0_0 0.172880\n", + "kaq_0_3 9.067649\n", + "Saq_0_0 0.172882\n", "Saq_1_3 0.000039\n", "kzoverkh_0_3 0.535051" ] @@ -245,23 +245,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "RMSE: 0.01038 m\n" + "RMSE: 0.010 m\n" ] } ], "source": [ "display(cal.parameters.loc[:, [\"optimal\"]])\n", - "print(f\"RMSE: {cal.rmse():.5f} m\")" + "print(f\"RMSE: {cal.rmse():.3f} m\")" ] }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -311,50 +311,58 @@ }, { "cell_type": "code", - "execution_count": 168, - "metadata": {}, + "execution_count": 17, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, "outputs": [ { "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 k [m/d]Sy [-]Ss [1/m]kz/khRMSE [m]k [m/d]Sy [-]Ss [1/m]kz/khRMSE [m]
timflow9.070.23.87e-050.50.0104timflow9.070.23.87e-050.50.0104
Moench8.640.22.00e-050.50.0613Moench8.640.22.00e-050.50.0613
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 168, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } From ebbfd1b01e5cc381591d87bd48d4eeeba08c93d4 Mon Sep 17 00:00:00 2001 From: Hannah Heesen Date: Thu, 19 Mar 2026 20:45:56 +0700 Subject: [PATCH 5/8] lint fix --- .../04pumpingtests/confined4_nevada.ipynb | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/docs/transient/04pumpingtests/confined4_nevada.ipynb b/docs/transient/04pumpingtests/confined4_nevada.ipynb index a9089f7..0d0bb94 100644 --- a/docs/transient/04pumpingtests/confined4_nevada.ipynb +++ b/docs/transient/04pumpingtests/confined4_nevada.ipynb @@ -454,13 +454,14 @@ "t.loc[\"K&dR\"] = [0.8325, 3.750e-4, 0.8325, 4.000e-6, \"-\", \"-\", \"-\"]\n", "\n", "t_formatted = t.style.format(\n", - " {\"km [m/d]\": \"{:.2f}\",\n", - " \"Sm [1/m]\": \"{:.2e}\",\n", - " \"kf [m/d]\": \"{:.2f}\",\n", - " \"Sf [1/m]\": \"{:.2e}\",\n", - " \"c [d]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.2f}\",\n", - " \"rc [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.2f}\",\n", - " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",\n", + " {\n", + " \"km [m/d]\": \"{:.2f}\",\n", + " \"Sm [1/m]\": \"{:.2e}\",\n", + " \"kf [m/d]\": \"{:.2f}\",\n", + " \"Sf [1/m]\": \"{:.2e}\",\n", + " \"c [d]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.2f}\",\n", + " \"rc [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.2f}\",\n", + " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\",\n", " }\n", ")\n", "t_formatted" From 495056d45ad3730b90e90d56a3088294f0fb411e Mon Sep 17 00:00:00 2001 From: Hannah Heesen Date: Thu, 19 Mar 2026 21:07:02 +0700 Subject: [PATCH 6/8] docs build fix --- docs/transient/04pumpingtests/index.rst | 7 ++++--- docs/transient/04pumpingtests/slug2_multiwell.ipynb | 6 +++--- .../{unconfined2_moench.ipynb => unconfined1_moench.ipynb} | 0 3 files changed, 7 insertions(+), 6 deletions(-) rename docs/transient/04pumpingtests/{unconfined2_moench.ipynb => unconfined1_moench.ipynb} (100%) diff --git a/docs/transient/04pumpingtests/index.rst b/docs/transient/04pumpingtests/index.rst index cadd924..0cf5a07 100644 --- a/docs/transient/04pumpingtests/index.rst +++ b/docs/transient/04pumpingtests/index.rst @@ -30,7 +30,7 @@ problems such as pumping tests and slug tests. More to come soon. :caption: Slug tests slug1_pratt_county - slug2_multiwelll + slug2_multiwell slug3_falling_head slug4_dawsonville @@ -40,6 +40,7 @@ problems such as pumping tests and slug tests. More to come soon. :caption: unconfined aquifers unconfined1_moench + Confined Pumping Tests ---------------------- @@ -64,6 +65,6 @@ Slug Tests 4. :doc:`Dawsonville ` - Slug test in a confined aquifer with a fully penetrating well. Unconfined Pumping Tests ----------- +------------------------ -1. :doc:`Moench ` - Unconfined pumping test with a partially penetrating well and observation wells at multiple depths. +1. :doc:`Moench ` - Unconfined pumping test with a partially penetrating well and observation wells at multiple depths. diff --git a/docs/transient/04pumpingtests/slug2_multiwell.ipynb b/docs/transient/04pumpingtests/slug2_multiwell.ipynb index cd0a723..f59d806 100644 --- a/docs/transient/04pumpingtests/slug2_multiwell.ipynb +++ b/docs/transient/04pumpingtests/slug2_multiwell.ipynb @@ -435,9 +435,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:base] *", + "display_name": "Python 3", "language": "python", - "name": "conda-base-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -449,7 +449,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.13.3" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/unconfined2_moench.ipynb b/docs/transient/04pumpingtests/unconfined1_moench.ipynb similarity index 100% rename from docs/transient/04pumpingtests/unconfined2_moench.ipynb rename to docs/transient/04pumpingtests/unconfined1_moench.ipynb From 22819e408b5f93d68dbf8fcd41c53db21a92bb00 Mon Sep 17 00:00:00 2001 From: Hannah Heesen Date: Fri, 20 Mar 2026 09:25:26 +0700 Subject: [PATCH 7/8] update titel --- docs/transient/04pumpingtests/unconfined1_moench.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/transient/04pumpingtests/unconfined1_moench.ipynb b/docs/transient/04pumpingtests/unconfined1_moench.ipynb index af355f0..33ee62b 100644 --- a/docs/transient/04pumpingtests/unconfined1_moench.ipynb +++ b/docs/transient/04pumpingtests/unconfined1_moench.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 2.Test for an anisotropic water-table aquifer - Moench Example" + "# 1.Test for an anisotropic water-table aquifer - Moench Example" ] }, { From 64f9ad5b505170243728109251f33935fc9b07cd Mon Sep 17 00:00:00 2001 From: Hannah Heesen Date: Fri, 20 Mar 2026 09:34:04 +0700 Subject: [PATCH 8/8] title change --- docs/transient/04pumpingtests/unconfined1_moench.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/transient/04pumpingtests/unconfined1_moench.ipynb b/docs/transient/04pumpingtests/unconfined1_moench.ipynb index 33ee62b..7c19421 100644 --- a/docs/transient/04pumpingtests/unconfined1_moench.ipynb +++ b/docs/transient/04pumpingtests/unconfined1_moench.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Import required libraries" + "### Import packages" ] }, {