Multiple Gas Sources.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6f3aaf71-8220-4591-86ad-fcd742065e12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ~~~~~~~~~~~~~~~~\n",
    "# PyTorch Model\n",
    "\n",
    "import torch\n",
    "import yaml\n",
    "from models.decoder import architectures\n",
    "\n",
    "with open(\"models/decoder/decoder_params.yaml\") as file:\n",
    "    params = yaml.safe_load(file)\n",
    "\n",
    "decoder = architectures.LightningDecoderNet(params[\"inner_dims\"], params[\"seq_len\"], params[\"learning_rate\"])\n",
    "decoder.load_state_dict(torch.load(\"models/decoder/decoder.pth\"))\n",
    "decoder.eval();\n",
    "\n",
    "#model_denoise = architectures.LightningNet(params[\"inner_dims\"], params[\"seq_len\"], params[\"learning_rate\"])\n",
    "#model_denoise.load_state_dict(torch.load(\"models/decoder/decoder_denoise.pth\"))\n",
    "#model_denoise.eval();\n",
    "\n",
    "# ~~~~~~~~~~~~~~~~\n",
    "# Kernel DM+V\n",
    "\n",
    "from models.kernel_dmv.my_kernel_dmv import KernelDMV\n",
    "kdm = KernelDMV()\n",
    "\n",
    "# ~~~~~~~~~~~~~~~~\n",
    "# GMRF\n",
    "\n",
    "from models.gmrf.my_gmrf import myGMRF\n",
    "gmrf = myGMRF()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e84bfc72-ee61-435e-8609-19877c429794",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils import data\n",
    "from data.gdm_dataset import GasDataSet\n",
    "\n",
    "dataset = GasDataSet(\"data/30x25_multipleSources/test.pt\")\n",
    "loader = data.DataLoader(dataset, batch_size=1, shuffle=True, drop_last=True)\n",
    "data_iter = iter(loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a2bb8be6-c860-40dd-a4e7-e6c25b1d410a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a 4x4 subplot grid\n",
    "fig, axs = plt.subplots(4, 4, figsize=(10, 10))#, sharex=True, sharey=True)\n",
    "\n",
    "# Set the horizontal and vertical spacing between the subplots\n",
    "fig.subplots_adjust(hspace=0.1, wspace=0.1)\n",
    "\n",
    "# Plot something in each subplot\n",
    "for i in range(4):\n",
    "    for j in range(4):\n",
    "        axs[i, j].imshow(np.random.rand(10, 10))\n",
    "        axs[i, j].xaxis.set_visible(False)\n",
    "        axs[i, j].yaxis.set_visible(False)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2357d710-e0f6-4b2b-b402-3a20785004f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from utils import plot_gdm, draw_random_samples\n",
    "\n",
    "def plot_dict(input_dict, figsize, cbar_base_col=0, dpi=None):\n",
    "    \"\"\" Plots the input of a dict. The dict should contain GDM data.\n",
    "    \n",
    "    Input:\n",
    "    input_dict: ...\n",
    "    figsize: ... (automatically in the future?)\n",
    "    cbar_base_col: Which col should be used as baseline vmin and vmax of plt.imshow.\n",
    "    \"\"\"\n",
    "    \n",
    "    rows = len(next(iter(input_dict.values())))\n",
    "    cols = len(input_dict)\n",
    "\n",
    "    # Create subplot grid\n",
    "    fig, axes = plt.subplots(rows, len(input_dict), figsize=figsize, dpi=dpi)#, sharex=True, sharey=True)  \n",
    "    \n",
    "    # Set the horizontal and vertical spacing between the subplots\n",
    "    fig.subplots_adjust(hspace=0.05, wspace=0.02)\n",
    "    \n",
    "    # Loop over rows (amount of samples)\n",
    "    for row in range(rows):\n",
    "        vmin=None\n",
    "        vmax=None\n",
    "\n",
    "        vmin = input_dict[list(input_dict.keys())[cbar_base_col]][row].min()\n",
    "        vmax = input_dict[list(input_dict.keys())[cbar_base_col]][row].max()   \n",
    "        \n",
    "        # Loop over cols (amount of models)\n",
    "        for col_key, col in zip(input_dict, range(cols)):\n",
    "            \"\"\"try:\n",
    "                # Try for multiple rows\n",
    "                plot_gdm(input_dict[col][row].squeeze(), axes[row][i], vmin=vmin, vmax=vmax);\n",
    "                if row == 0:\n",
    "                    axes[row][i].set_title(col)\n",
    "        \n",
    "            except:\n",
    "                # otherwise only single row\n",
    "                plot_gdm(input_dict[col][row].squeeze(), axes[i], vmin=vmin, vmax=vmax);\n",
    "                if row == 0:\n",
    "                    axes[i].set_title(col)\n",
    "            \"\"\"\n",
    "            axes[row,col].imshow(input_dict[col_key][row].squeeze().detach().numpy(), vmin=vmin, vmax=vmax)\n",
    "            axes[row,col].axis(\"off\")\n",
    "            if row == 0:\n",
    "                    axes[row][col].set_title(col_key)\n",
    "\n",
    "    return fig, axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a0aa2a86-4880-4624-bd16-6d864d017387",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x375 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#from utils import plot_dict, draw_random_samples\n",
    "\n",
    "X_list, y_list = draw_random_samples(3, dataset)\n",
    "    \n",
    "data = {\n",
    "        \"Input\": X_list,\n",
    "        \"Truth\": y_list,\n",
    "        \"Decoder\": [decoder(x.squeeze(1)) for x in X_list],\n",
    "        \"Kernel DM+V\": [kdm.calculate(x.squeeze()) for x in X_list],\n",
    "        }\n",
    "\n",
    "\n",
    "fig, axes = plot_dict(data, [3,2.5], 1, dpi=150);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b67c3d7-c8c8-4195-a622-4cc46408563e",
   "metadata": {},
   "source": [
    "### Paper visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "646c8896-340f-4c7c-8547-aa010d466e14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1250 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib as mpl\n",
    "\n",
    "#mpl.use(\"pgf\")\n",
    "#%matplotlib inline \n",
    "mpl.rcParams.update({\n",
    "    \"pgf.texsystem\": \"pdflatex\",\n",
    "    'font.family': 'serif',\n",
    "    'font.size' : '5',\n",
    "    #'text.usetex': True,\n",
    "    'pgf.rcfonts': False,\n",
    "})\n",
    "\n",
    "\n",
    "fig, axes = plot_dict(data, [3,2.5], 1, dpi=500);\n",
    "plt.savefig(\"pgf/multisource/multisource.pgf\", bbox_inches='tight', facecolor=\"w\", pad_inches=0, dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6c6e5a4-404e-4d43-a539-cfe0ea002003",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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