stage_rasterize.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "\n",
    "# PDG packages\n",
    "import pdgstaging\n",
    "import pdgraster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configure the stager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the .json file that configures the stager\n",
    "# another option is to include .json code directly into this function, works the same \n",
    "config = '/home/jcohen/lake_change_sample/ingmar-config.json'\n",
    "stager = pdgstaging.TileStager(config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the files within the lake_change.gpkg file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label_id</th>\n",
       "      <th>id</th>\n",
       "      <th>Area_start_ha</th>\n",
       "      <th>Area_end_ha</th>\n",
       "      <th>NetChange_ha</th>\n",
       "      <th>NetChange_perc</th>\n",
       "      <th>GrossIncrease_ha</th>\n",
       "      <th>GrossIncrease_perc</th>\n",
       "      <th>GrossDecrease_ha</th>\n",
       "      <th>GrossDecrease_perc</th>\n",
       "      <th>StableWater_ha</th>\n",
       "      <th>Perimeter_meter</th>\n",
       "      <th>Orientation_degree</th>\n",
       "      <th>Solidity_ratio</th>\n",
       "      <th>Eccentricity_ratio</th>\n",
       "      <th>ChangeRateNet_myr-1</th>\n",
       "      <th>ChangeRateGrowth_myr-1</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3.2400</td>\n",
       "      <td>2.8260</td>\n",
       "      <td>-0.4140</td>\n",
       "      <td>-12.777777</td>\n",
       "      <td>0.0504</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>0.4644</td>\n",
       "      <td>16.433121</td>\n",
       "      <td>2.7756</td>\n",
       "      <td>668.345238</td>\n",
       "      <td>14.492468</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>0.968097</td>\n",
       "      <td>-0.309720</td>\n",
       "      <td>0.037705</td>\n",
       "      <td>POLYGON ((396630.000 7782540.000, 396660.000 7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1.4850</td>\n",
       "      <td>1.4886</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.242424</td>\n",
       "      <td>0.0711</td>\n",
       "      <td>4.787879</td>\n",
       "      <td>0.0675</td>\n",
       "      <td>4.534462</td>\n",
       "      <td>1.4175</td>\n",
       "      <td>348.639610</td>\n",
       "      <td>176.485802</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.883279</td>\n",
       "      <td>0.005163</td>\n",
       "      <td>0.101968</td>\n",
       "      <td>POLYGON ((396990.000 7782510.000, 397080.000 7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>3.5748</td>\n",
       "      <td>3.3498</td>\n",
       "      <td>-0.2250</td>\n",
       "      <td>-6.294058</td>\n",
       "      <td>0.0837</td>\n",
       "      <td>2.341390</td>\n",
       "      <td>0.3087</td>\n",
       "      <td>9.215476</td>\n",
       "      <td>3.2661</td>\n",
       "      <td>885.624458</td>\n",
       "      <td>145.381869</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.878298</td>\n",
       "      <td>-0.127029</td>\n",
       "      <td>0.047255</td>\n",
       "      <td>POLYGON ((396840.000 7782120.000, 396900.000 7...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label_id  id  Area_start_ha  Area_end_ha  NetChange_ha  NetChange_perc  \\\n",
       "0         5   5         3.2400       2.8260       -0.4140      -12.777777   \n",
       "1         6   6         1.4850       1.4886        0.0036        0.242424   \n",
       "2         9   9         3.5748       3.3498       -0.2250       -6.294058   \n",
       "\n",
       "   GrossIncrease_ha  GrossIncrease_perc  GrossDecrease_ha  GrossDecrease_perc  \\\n",
       "0            0.0504            1.555556            0.4644           16.433121   \n",
       "1            0.0711            4.787879            0.0675            4.534462   \n",
       "2            0.0837            2.341390            0.3087            9.215476   \n",
       "\n",
       "   StableWater_ha  Perimeter_meter  Orientation_degree  Solidity_ratio  \\\n",
       "0          2.7756       668.345238           14.492468        0.653846   \n",
       "1          1.4175       348.639610          176.485802        0.937500   \n",
       "2          3.2661       885.624458          145.381869        0.714286   \n",
       "\n",
       "   Eccentricity_ratio  ChangeRateNet_myr-1  ChangeRateGrowth_myr-1  \\\n",
       "0            0.968097            -0.309720                0.037705   \n",
       "1            0.883279             0.005163                0.101968   \n",
       "2            0.878298            -0.127029                0.047255   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((396630.000 7782540.000, 396660.000 7...  \n",
       "1  POLYGON ((396990.000 7782510.000, 397080.000 7...  \n",
       "2  POLYGON ((396840.000 7782120.000, 396900.000 7...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_dir = '/home/pdg/data/nitze_lake_change/data_sample_2022-09-09/32607/05_Lake_Dataset_Raster_02_final/'\n",
    "file = 'lake_change.gpkg'\n",
    "lakes = gpd.read_file(base_dir + file)\n",
    "lakes.head(3)\n",
    "#lakes.shape\n",
    "# there are 31,596 lakes in this geodataframe\n",
    "# 18 variables such as \"net change in ha\" and \"net change percentage\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3000x3000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a sample of the lakes\n",
    "lakes.sample(10000).plot(figsize = (30,30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Execute the stager on all lake polygons within the lake_change.gpkg file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for staging multiple files:\n",
    "# stager.stage_all(insert-data-dir)\n",
    "\n",
    "# stage the lake change data to convert them to tiles, which then will be converted into rasters\n",
    "stager.stage(base_dir + file)\n",
    "# took 5 min 45 sec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By staging the files using the stager we configured with the .json script, we created a directory of the tiles in a deliberate hierarchial structure. Each layer of the directory is as follows:\n",
    "- **staged**: base folder for all tiles (lakes)\n",
    "- **WorldCRS84Quad**: the tile matrix set grid, which is in geographic coordinates, allowing the tiles to appear square when represented on the 3D Globe on the PDG web portal (in Cesium format)\n",
    "- **11**: style (number of zoom levels? the \"z-range\")\n",
    "- **numbered subfolders, for example 406-481**: tile matrix (x)\n",
    "- **numbered tiles, for example 228.gpkg:** tile column (y)\n",
    "\n",
    "See [here](https://github.com/PermafrostDiscoveryGateway/viz-staging/blob/main/docs/tile_path_structure.md) for a schematic of this hierarchial directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x3000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize one tile (one lake)\n",
    "tile_dir = '/home/jcohen/sample_data/staged/WorldCRS84Quad/11'\n",
    "tile_example = '/407/229.gpkg' # tile matrix (x) and tile column (y)\n",
    "tile = gpd.read_file(tile_dir + tile_example)\n",
    "tile.plot(figsize = (30,30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rasterize the tiles\n",
    "\n",
    "Convert the tiled vectors into a series of GeoTIFFs at a range of zoom levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n",
      "/home/jcohen/anaconda3/envs/pdgviz/lib/python3.10/site-packages/pdgraster/WebImage.py:110: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (255 / (max_val - min_val))\n"
     ]
    }
   ],
   "source": [
    "# define the .json file that configures the rasterizer\n",
    "rasterizer = pdgraster.RasterTiler(config)\n",
    "# rasterize all files\n",
    "rasterizer.rasterize_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the above code chunk to run, I had to make an edit to the 'palette' argument in the .json config file. I was getting an error with the \"palette\": \"blue_medb717b\" part (in 2 places) so I replaced it with just 1 color (?) and it ran. This is something to bring up with Robyn in order to better understand the configuration .json doc. I think it is probably something to do with a package I have to download in order to use the default palette in the .json document."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 ('pdgviz')",
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   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
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  "vscode": {
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