masked-array.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "59eaafa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install dask numpy matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "238bc8ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask\n",
    "import dask.array\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ec8b1bad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2025.3.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dask.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fbba50f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate random array\n",
    "image = dask.array.random.random((15, 15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ec6d95bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set a semi-random threshold\n",
    "threshold = 0.618"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5eeb1cb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the issue\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.imshow(image, cmap='gray')\n",
    "plt.title('Original array')\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.imshow(dask.array.ma.masked_less(image > threshold, 1).compute(),\n",
    "           cmap='viridis',\n",
    "           alpha=0.5)\n",
    "plt.title('Masked array\\nwith \".compute()\"')\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.imshow(dask.array.ma.masked_less(image > threshold, 1),\n",
    "           cmap='viridis',\n",
    "           alpha=0.5)\n",
    "plt.title('Masked array\\nwithout \".compute()\"')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d460665",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ecc58c4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tibia",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}