Add visualisation

- add .ipynb to display capacity (link with solutiion and data file to do)

Add visualisation
a4e9f18a · awenjb · b758be1b · a4e9f18a · a4e9f18a
Commit a4e9f18a authored 1 month ago by awenjb
--- a/python/visualisation_capa.ipynb
+++ b/python/visualisation_capa.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: folium in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (0.19.4)\n",
+      "Requirement already satisfied: pandas in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (2.2.3)\n",
+      "Requirement already satisfied: OSMnx in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (2.0.1)\n",
+      "Requirement already satisfied: networkx in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (3.4.2)\n",
+      "Requirement already satisfied: scikit-learn in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (1.6.1)\n",
+      "Requirement already satisfied: matplotlib in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (3.10.1)\n",
+      "Requirement already satisfied: numpy in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (2.2.3)\n",
+      "Requirement already satisfied: branca>=0.6.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from folium) (0.8.1)\n",
+      "Requirement already satisfied: jinja2>=2.9 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from folium) (3.1.5)\n",
+      "Requirement already satisfied: requests in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from folium) (2.32.3)\n",
+      "Requirement already satisfied: xyzservices in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from folium) (2025.1.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from pandas) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from pandas) (2025.1)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from pandas) (2025.1)\n",
+      "Requirement already satisfied: geopandas>=1.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from OSMnx) (1.0.1)\n",
+      "Requirement already satisfied: shapely>=2.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from OSMnx) (2.0.7)\n",
+      "Requirement already satisfied: scipy>=1.6.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from scikit-learn) (1.15.2)\n",
+      "Requirement already satisfied: joblib>=1.2.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from scikit-learn) (1.4.2)\n",
+      "Requirement already satisfied: threadpoolctl>=3.1.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from scikit-learn) (3.5.0)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from matplotlib) (1.3.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from matplotlib) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from matplotlib) (4.56.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from matplotlib) (1.4.8)\n",
+      "Requirement already satisfied: packaging>=20.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from matplotlib) (24.2)\n",
+      "Requirement already satisfied: pillow>=8 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from matplotlib) (11.1.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from matplotlib) (3.2.1)\n",
+      "Requirement already satisfied: pyogrio>=0.7.2 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from geopandas>=1.0->OSMnx) (0.10.0)\n",
+      "Requirement already satisfied: pyproj>=3.3.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from geopandas>=1.0->OSMnx) (3.7.1)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from jinja2>=2.9->folium) (3.0.2)\n",
+      "Requirement already satisfied: six>=1.5 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
+      "Requirement already satisfied: charset_normalizer<4,>=2 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from requests->folium) (3.4.1)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from requests->folium) (3.10)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from requests->folium) (2.3.0)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from requests->folium) (2025.1.31)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install folium pandas OSMnx networkx scikit-learn matplotlib numpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Capacity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_101649/181514259.py:22: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n",
+      "  colors = [cm.get_cmap('YlOrRd')(norm(value)) for value in seq]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1200 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.colors as mcolors\n",
+    "\n",
+    "def plot_number_columns(*sequences):\n",
+    "    num_sequences = len(sequences)\n",
+    "    fig, axes = plt.subplots(num_sequences, 1, figsize=(10, num_sequences * 4), sharex=True)\n",
+    "    \n",
+    "    if num_sequences == 1:\n",
+    "        axes = [axes]\n",
+    "    \n",
+    "    for i, (ax, seq) in enumerate(zip(axes, sequences)):\n",
+    "\n",
+    "        max_value = max(seq) # TODO prendre la capacity value de l'instance\n",
+    "        \n",
+    "        normalized_seq = [value / max_value * 100 for value in seq]  # Normalisation en %\n",
+    "        x_positions = np.arange(len(seq))\n",
+    "        \n",
+    "        # Couleur\n",
+    "        norm = mcolors.Normalize(vmin=min(seq), vmax=max(seq))\n",
+    "        colors = [cm.get_cmap('YlOrRd')(norm(value)) for value in seq]\n",
+    "        \n",
+    "        ax.bar(x_positions, normalized_seq, color=colors, edgecolor='black', width=1.0)  # Largeur des colonnes = 1.0 pour les coller\n",
+    "        ax.set_ylabel('Capacité utilisée (%)')\n",
+    "        ax.set_title(f'Route {i+1}')\n",
+    "        ax.set_ylim(0, 110)\n",
+    "        ax.set_xticks(x_positions)  # TODO Ecrire les position (locationID) pour chaque graphique\n",
+    "    \n",
+    "    axes[-1].set_xlabel('Position')\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "# test\n",
+    "plot_number_columns([3, 1, 4, 1, 5], [2, 7, 1, 8, 2, 8], [0, 0.5,1,2,3,5,8,5,3,2,1, 0.5, 0])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
+%% Cell type:code id: tags:
+
+``` python
+%pip install folium pandas OSMnx networkx scikit-learn matplotlib numpy
+```
+
+%% Output
+
+    Requirement already satisfied: folium in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (0.19.4)
+    Requirement already satisfied: pandas in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (2.2.3)
+    Requirement already satisfied: OSMnx in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (2.0.1)
+    Requirement already satisfied: networkx in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (3.4.2)
+    Requirement already satisfied: scikit-learn in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (1.6.1)
+    Requirement already satisfied: matplotlib in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (3.10.1)
+    Requirement already satisfied: numpy in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (2.2.3)
+    Requirement already satisfied: branca>=0.6.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from folium) (0.8.1)
+    Requirement already satisfied: jinja2>=2.9 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from folium) (3.1.5)
+    Requirement already satisfied: requests in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from folium) (2.32.3)
+    Requirement already satisfied: xyzservices in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from folium) (2025.1.0)
+    Requirement already satisfied: python-dateutil>=2.8.2 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from pandas) (2.9.0.post0)
+    Requirement already satisfied: pytz>=2020.1 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from pandas) (2025.1)
+    Requirement already satisfied: tzdata>=2022.7 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from pandas) (2025.1)
+    Requirement already satisfied: geopandas>=1.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from OSMnx) (1.0.1)
+    Requirement already satisfied: shapely>=2.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from OSMnx) (2.0.7)
+    Requirement already satisfied: scipy>=1.6.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from scikit-learn) (1.15.2)
+    Requirement already satisfied: joblib>=1.2.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from scikit-learn) (1.4.2)
+    Requirement already satisfied: threadpoolctl>=3.1.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from scikit-learn) (3.5.0)
+    Requirement already satisfied: contourpy>=1.0.1 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from matplotlib) (1.3.1)
+    Requirement already satisfied: cycler>=0.10 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from matplotlib) (0.12.1)
+    Requirement already satisfied: fonttools>=4.22.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from matplotlib) (4.56.0)
+    Requirement already satisfied: kiwisolver>=1.3.1 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from matplotlib) (1.4.8)
+    Requirement already satisfied: packaging>=20.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from matplotlib) (24.2)
+    Requirement already satisfied: pillow>=8 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from matplotlib) (11.1.0)
+    Requirement already satisfied: pyparsing>=2.3.1 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from matplotlib) (3.2.1)
+    Requirement already satisfied: pyogrio>=0.7.2 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from geopandas>=1.0->OSMnx) (0.10.0)
+    Requirement already satisfied: pyproj>=3.3.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from geopandas>=1.0->OSMnx) (3.7.1)
+    Requirement already satisfied: MarkupSafe>=2.0 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from jinja2>=2.9->folium) (3.0.2)
+    Requirement already satisfied: six>=1.5 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)
+    Requirement already satisfied: charset_normalizer<4,>=2 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from requests->folium) (3.4.1)
+    Requirement already satisfied: idna<4,>=2.5 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from requests->folium) (3.10)
+    Requirement already satisfied: urllib3<3,>=1.21.1 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from requests->folium) (2.3.0)
+    Requirement already satisfied: certifi>=2017.4.17 in /home/a24jacqb/.julia/conda/3/x86_64/lib/python3.12/site-packages (from requests->folium) (2025.1.31)
+    Note: you may need to restart the kernel to use updated packages.
+
+%% Cell type:markdown id: tags:
+
+# Capacity
+
+%% Cell type:code id: tags:
+
+``` python
+import matplotlib.pyplot as plt
+import numpy as np
+import matplotlib.cm as cm
+import matplotlib.colors as mcolors
+
+def plot_number_columns(*sequences):
+    num_sequences = len(sequences)
+    fig, axes = plt.subplots(num_sequences, 1, figsize=(10, num_sequences * 4), sharex=True)
+
+    if num_sequences == 1:
+        axes = [axes]
+
+    for i, (ax, seq) in enumerate(zip(axes, sequences)):
+
+        max_value = max(seq) # TODO prendre la capacity value de l'instance
+
+        normalized_seq = [value / max_value * 100 for value in seq]  # Normalisation en %
+        x_positions = np.arange(len(seq))
+
+        # Couleur
+        norm = mcolors.Normalize(vmin=min(seq), vmax=max(seq))
+        colors = [cm.get_cmap('YlOrRd')(norm(value)) for value in seq]
+
+        ax.bar(x_positions, normalized_seq, color=colors, edgecolor='black', width=1.0)  # Largeur des colonnes = 1.0 pour les coller
+        ax.set_ylabel('Capacité utilisée (%)')
+        ax.set_title(f'Route {i+1}')
+        ax.set_ylim(0, 110)
+        ax.set_xticks(x_positions)  # TODO Ecrire les position (locationID) pour chaque graphique
+
+    axes[-1].set_xlabel('Position')
+    plt.tight_layout()
+    plt.show()
+
+# test
+plot_number_columns([3, 1, 4, 1, 5], [2, 7, 1, 8, 2, 8], [0, 0.5,1,2,3,5,8,5,3,2,1, 0.5, 0])
+```
+
+%% Output
+
+    /tmp/ipykernel_101649/181514259.py:22: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.
+      colors = [cm.get_cmap('YlOrRd')(norm(value)) for value in seq]
+
+
--- a/python/visualisation.ipynb
+++ b/python/visualisation.ipynb