median upsampling with NaN values.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x117153550>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "arr1 = np.arange(16).reshape((4,4)).astype(np.float32)\n",
    "arr1[0,3] = np.nan\n",
    "arr2 = np.zeros((2,2))\n",
    "\n",
    "plt.imshow(arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.5,  6. ],\n",
       "       [10.5, 12.5]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(arr2.shape[0]):\n",
    "    for j in range(arr2.shape[1]):\n",
    "        arr2[j,i] = np.nanmedian(arr1[j*2:(j+1)*2,i*2:(i+1)*2])\n",
    "        \n",
    "        \n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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