Final Merge Request - Letzte Abgabe

Application/packages.txt

@@ -0,0 +1,110 @@
+# Name                    Version                   Build  Channel
+asttokens                 2.4.1              pyhd8ed1ab_0    conda-forge
+blas                      1.0                         mkl
+bottleneck                1.4.2           py312h4b0e54e_0
+brotli                    1.0.9                h2bbff1b_8
+brotli-bin                1.0.9                h2bbff1b_8
+bzip2                     1.0.8                h2bbff1b_6
+ca-certificates           2024.9.24            haa95532_0
+colorama                  0.4.6              pyhd8ed1ab_0    conda-forge
+comm                      0.2.2              pyhd8ed1ab_0    conda-forge
+contourpy                 1.2.0           py312h59b6b97_0
+cycler                    0.11.0             pyhd3eb1b0_0
+debugpy                   1.6.7           py312hd77b12b_0
+decorator                 5.1.1              pyhd8ed1ab_0    conda-forge
+exceptiongroup            1.2.2              pyhd8ed1ab_0    conda-forge
+executing                 2.1.0              pyhd8ed1ab_0    conda-forge
+expat                     2.6.3                h5da7b33_0
+fonttools                 4.51.0          py312h2bbff1b_0
+freetype                  2.12.1               ha860e81_0
+icc_rt                    2022.1.0             h6049295_2
+icu                       73.1                 h6c2663c_0
+importlib-metadata        8.5.0              pyha770c72_0    conda-forge
+intel-openmp              2023.1.0         h59b6b97_46320
+ipykernel                 6.29.5             pyh4bbf305_0    conda-forge
+ipython                   8.29.0             pyh7428d3b_0    conda-forge
+jedi                      0.19.2             pyhff2d567_0    conda-forge
+joblib                    1.4.2           py312haa95532_0
+jpeg                      9e                   h827c3e9_3
+jupyter_client            8.6.3              pyhd8ed1ab_0    conda-forge
+jupyter_core              5.7.2           py312haa95532_0
+kiwisolver                1.4.4           py312hd77b12b_0
+krb5                      1.20.1               h5b6d351_0
+lcms2                     2.12                 h83e58a3_0
+lerc                      3.0                  hd77b12b_0
+libbrotlicommon           1.0.9                h2bbff1b_8
+libbrotlidec              1.0.9                h2bbff1b_8
+libbrotlienc              1.0.9                h2bbff1b_8
+libclang                  14.0.6          default_hb5a9fac_1
+libclang13                14.0.6          default_h8e68704_1
+libdeflate                1.17                 h2bbff1b_1
+libffi                    3.4.4                hd77b12b_1
+libpng                    1.6.39               h8cc25b3_0
+libpq                     12.20                h70ee33d_0
+libsodium                 1.0.18               h8d14728_1    conda-forge
+libtiff                   4.5.1                hd77b12b_0
+libwebp-base              1.3.2                h3d04722_1
+lz4-c                     1.9.4                h2bbff1b_1
+matplotlib                3.9.2           py312haa95532_0
+matplotlib-base           3.9.2           py312hbdc63d0_0
+matplotlib-inline         0.1.7              pyhd8ed1ab_0    conda-forge
+mkl                       2023.1.0         h6b88ed4_46358
+mkl-service               2.4.0           py312h2bbff1b_1
+mkl_fft                   1.3.11          py312h827c3e9_0
+mkl_random                1.2.8           py312h0158946_0
+nest-asyncio              1.6.0              pyhd8ed1ab_0    conda-forge
+numexpr                   2.10.1          py312h4cd664f_0
+numpy                     1.26.4          py312hfd52020_0
+numpy-base                1.26.4          py312h4dde369_0
+openjpeg                  2.5.2                hae555c5_0
+openssl                   3.0.15               h827c3e9_0
+packaging                 24.1            py312haa95532_0
+pandas                    2.2.2           py312h0158946_0
+parso                     0.8.4              pyhd8ed1ab_0    conda-forge
+patsy                     0.5.6           py312haa95532_0
+pickleshare               0.7.5                   py_1003    conda-forge
+pillow                    10.4.0          py312h827c3e9_0
+pip                       24.2            py312haa95532_0
+platformdirs              4.3.6              pyhd8ed1ab_0    conda-forge
+ply                       3.11            py312haa95532_1
+prompt-toolkit            3.0.48             pyha770c72_0    conda-forge
+psutil                    5.9.0           py312h2bbff1b_0
+pure_eval                 0.2.3              pyhd8ed1ab_0    conda-forge
+pybind11-abi              5                    hd3eb1b0_0
+pygments                  2.18.0             pyhd8ed1ab_0    conda-forge
+pyparsing                 3.2.0           py312haa95532_0
+pyqt                      5.15.10         py312hd77b12b_0
+pyqt5-sip                 12.13.0         py312h2bbff1b_0
+python                    3.12.7               h14ffc60_0
+python-dateutil           2.9.0post0      py312haa95532_2
+python-tzdata             2023.3             pyhd3eb1b0_0
+pytz                      2024.1          py312haa95532_0
+pywin32                   305             py312h2bbff1b_0
+pyzmq                     25.1.2          py312hd77b12b_0
+qt-main                   5.15.2              h19c9488_10
+scikit-learn              1.5.1           py312h0158946_0
+scipy                     1.13.1          py312hbb039d4_0
+seaborn                   0.13.2          py312haa95532_0
+setuptools                75.1.0          py312haa95532_0
+sip                       6.7.12          py312hd77b12b_0
+six                       1.16.0             pyhd3eb1b0_1
+sqlite                    3.45.3               h2bbff1b_0
+stack_data                0.6.2              pyhd8ed1ab_0    conda-forge
+statsmodels               0.14.2          py312h4b0e54e_0    anaconda
+tbb                       2021.8.0             h59b6b97_0
+threadpoolctl             3.5.0           py312hfc267ef_0
+tk                        8.6.14               h0416ee5_0
+tornado                   6.4.1           py312h827c3e9_0
+traitlets                 5.14.3             pyhd8ed1ab_0    conda-forge
+typing_extensions         4.12.2             pyha770c72_0    conda-forge
+tzdata                    2024b                h04d1e81_0
+unicodedata2              15.1.0          py312h2bbff1b_0
+vc                        14.40                h2eaa2aa_1
+vs2015_runtime            14.40.33807          h98bb1dd_1
+wcwidth                   0.2.13             pyhd8ed1ab_0    conda-forge
+wheel                     0.44.0          py312haa95532_0
+xz                        5.4.6                h8cc25b3_1
+zeromq                    4.3.5                hd77b12b_0
+zipp                      3.21.0             pyhd8ed1ab_0    conda-forge
+zlib                      1.2.13               h8cc25b3_1
+zstd                      1.5.6                h8880b57_0

Application/run.py

@@ -1,58 +1,69 @@
 from Grid import Grid
-from Cell import Cell, SpawnCell, BorderCell, ObstacleCell, Agent, TargetCell
+from Cell import Cell, SpawnCell, ObstacleCell, Agent, TargetCell
 import matplotlib.pyplot as plt
 import numpy as np 
 from Grid import Grid, Visualization
-from tests import room_square, ChickenTest, RiMEA9, RiMEA4
-import tests
-grid = RiMEA9(1, "floodfill")
+from tests import room_square, ChickenTest, RiMEA9, RiMEA4, Experiment
+import matplotlib
+#This line is needed for the plots to render in Pychamr Sciplot-View
+matplotlib.use("Qt5Agg")
+#Abschnitt in dem Die simulations Parameter angegeben werden.
+time_input = input("Bitte geben sie die Anzahl Zeitschritte an (ein Zeitschritt ist 1 Sekunde)")
+timesteps = int(time_input)
+user_input = input("Bitte geben sie an welchen Test (RiMEA4, RiMEA9 oder Experiment) sie durchführen wollen")
+user_input = user_input.lower()
+if user_input == "rimea9":
+    door_input = input("Bitte Anzahl (1-4) Türen angeben")
+    pathfinding = input("Bitte Algorithmus wählen (dijkstra, floodfill)")
+    if pathfinding == "floodfill":
+        algo = "floodfill"
+    elif pathfinding == "dijkstra":
+        algo = "dijkstra"
+    else:
+        algo = "dijkstra" #Standardwert ist dijkstra
+    doors = int(door_input)
+    grid, door_cells, roi = RiMEA9(movement_method=algo, Doors=doors)
+elif user_input == "rimea4":
+    pathfinding = input("Bitte Algorithmus wählen (dijkstra, floodfill)")
+    if pathfinding == "floodfill":
+        algo = "floodfill"
+    elif pathfinding == "dijkstra":
+        algo = "dijkstra"
+    else:
+        algo = "dijkstra" #Standardwert ist dijkstra
+    spawn_input = input("Bitte spawnrate zwischen 0 und 1 eingeben um Personendichte zu variieren (Wird mit einer Zufallsvariable zwischen 0 und 1 verglichen um spawn zu bestimmen)")
+    grid, door_cells, roi = RiMEA4(movement_method=algo,spawn_rate=float(spawn_input))
+elif user_input == "experiment":
+    pathfinding = input("Bitte Algorithmus wählen (dijkstra, floodfill)")
+    if pathfinding == "floodfill":
+        algo = "floodfill"
+    elif pathfinding == "dijkstra":
+        algo = "dijkstra"
+    else:
+        algo = "dijkstra"  # Standardwert ist dijkstra
+    spawn_input = input("Bitte spawnrate zwischen 0 und 1 eingeben um Personendichte zu variieren (Wird mit einer Zufallsvariable zwischen 0 und 1 verglichen um spawn zu bestimmen)")
+    grid, door_cells, roi = Experiment(movement_method=algo, spawn_rate=float(spawn_input))
+####################################################
+#Visualisierung initialisieren und Variablen für Resultate anlegen
 visualization = Visualization(grid)
-
 agent_count_list = []
-average_distance = [] 
-density_list = []
-timesteps = 10
+fundamental_data = []
 
-for i in range(timesteps):
-    grid.update(target_list=grid.target_cells, timestep=i)
-    #visualization.plot_grid_state(i)
-    grid.plot_grid_state(i)
-    plt.pause(0.2)
-
-    agent_count = len(grid.agents)
-    agent_count_list.append(agent_count)
-
-    # mittlere distanz von Agenten zu Ziel
-    total_distance_to_target = 0
-    if len(grid.agents) > 0:
-        total_istance_to_target = 0
-        for agent in grid.agents:
-            target = agent.find_target(grid.target_cells)
-            total_distance_to_target += agent.euclidean_distance_to(grid.grid[target[0]][target[1]])
-
-        average_distance.append(total_distance_to_target / len(grid.agents))
-    else:
-        average_distance.append(np.nan)  
 
 
+agents_crossed = {}  # Dictionary to track agents crossing the boundary
+for i in range(timesteps):
+    grid.update(target_list=grid.target_cells, timestep=i)
+    #Print statement trent einzelne Zeitschritte von einander (für einfacheres debugging)
+    print("--------------------------------------------------------------------------------------------------")
+    #Visualisierung aktueller grid-state
+    visualization.plot_grid_state(i)
+    #Bereich für Density berechnung von aktuelem Grid state wählen
+    area = grid.select_area_by_coordinates(roi[0], roi[1], roi[2], roi[3])
+    #grid.plot_grid_state(i)
 
-plt.figure(figsize=(10,5)) 
-
-plt.subplot(1, 3, 1)
-plt.plot(agent_count_list)
-plt.title('Anzahl Agenten über Zeit')
-plt.xlabel('Zeitschritt')
-plt.ylabel('Anzahl Agenten') 
-
-plt.subplot(1, 3, 2)
-plt.plot(average_distance)
-plt.title('Mittlere Distanz Agenten zum Ziel')
-plt.xlabel('Zeitschritt')
-plt.ylabel('Distanz') 
-
-
-
-plt.tight_layout()
-plt.show()
-
-visualization.animate_grid_states(timesteps)
+    #Berechnung fürs Fundamentaldiagram (Aktuelle Agenten dichte, Fluss und durchschnittsgeschwindigkeit)
+    fd_results = grid.calculate_fundamental_diagram(door_cells, i, agents_crossed, area)
+    fundamental_data.append(fd_results)
+#Nachdem die Simulation durchgeführt wurde plotten wir noch die Ergebnisse der Fundamentaldiagram Berechnungen
+visualization.plot_fundamental_diagram(fundamental_data)