vincentsarago/data_boundless_resampling.ipynb

## data_boundless_resampling.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "resampling doesn't give the same result when `boundless=True`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "import rasterio\n",
    "from rasterio.plot import reshape_as_image\n",
    "from rasterio.enums import Resampling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1121fc940>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ea27438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "src = rasterio.open('s3://remotepixel/data/image/LC82330572016015LGN00.tif')\n",
    "expected_data = src.read(indexes=1, window=((0,100), (0,100)), resampling=Resampling.bilinear)\n",
    "imshow(expected_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Are equals? :  False\n",
      "data diff\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11210b630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reading the data withing the image bounds with, boundless=True\n",
    "data = src.read(1, window=((0,100), (0,100)), boundless=True, resampling=Resampling.bilinear)\n",
    "print('Are equals? : ', numpy.all(data == expected_data))\n",
    "imshow(data - expected_data)\n",
    "print('data diff')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Are equals? :  True\n",
      "data diff\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAACz1JREFUeJzt2n+o3fV9x/Hna7n5MS2tiRshTWRmKC1S6CyXqjjGMC1zrlT/kGIpIwwh/3Sr/QGtbn/tvwml1j9GIehKGNLapTJFSotL7R/9JzNWWTXRmumqCdE4pnUISxP63h/n67iT6D3ee8655/J+PuBy7/d7viffNx/yvOd7vvekqpDUy2+t9QCSZs/wpYYMX2rI8KWGDF9qyPClhgxfamhV4Se5PsmzSY4nuX1SQ0marqz0AzxJNgC/AD4JnAAeAz5bVUcnN56kaVhYxXM/DhyvqucBknwXuBF4x/A3ZXNt4cJVnFLSu/kf3uTXdSbLHbea8HcCLy3ZPgFc9faDkuwD9gFs4QKuyp5VnFLSuzlch8Y6buo396pqf1UtVtXiRjZP+3SSxrCa8E8ClyzZ3jXskzTnVhP+Y8DlSXYn2QTcAjw0mbEkTdOK3+NX1bkkfwn8CNgA/ENVPT2xySRNzWpu7lFVPwB+MKFZJM2In9yTGjJ8qSHDlxoyfKkhw5caMnypIcOXGjJ8qSHDlxoyfKkhw5caMnypIcOXGjJ8qSHDlxoyfKkhw5caMnypIcOXGjJ8qSHDlxoyfKkhw5caMnypIcOXGjJ8qSHDlxoyfKkhw5caMnypIcOXGjJ8qSHDlxoyfKmhZcNPckmSR5McTfJ0ktuG/duSPJLkueH71umPK2kSxnnFPwd8paquAK4GPp/kCuB24FBVXQ4cGrYlrQPLhl9Vp6rqZ8PP/w0cA3YCNwIHhsMOADdNa0hJk/We3uMnuRS4EjgMbK+qU8NDLwPbJzqZpKkZO/wk7wO+D3yxqt5Y+lhVFVDv8Lx9SY4kOXKWM6saVtJkjBV+ko2Mor+vqh4Ydr+SZMfw+A7g9PmeW1X7q2qxqhY3snkSM0tapXHu6ge4FzhWVd9Y8tBDwN7h573Ag5MfT9I0LIxxzLXAnwM/T/LksO+vgb8DvpfkVuCXwGemM6KkSVs2/Kr6KZB3eHjPZMeRNAt+ck9qyPClhgxfasjwpYYMX2rI8KWGDF9qyPClhgxfasjwpYYMX2rI8KWGDF9qyPClhgxfasjwpYYMX2rI8KWGDF9qyPClhgxfasjwpYYMX2rI8KWGDF9qyPClhgxfasjwpYYMX2rI8KWGDF9qyPClhgxfasjwpYYMX2po7PCTbEjyRJKHh+3dSQ4nOZ7k/iSbpjempEl6L6/4twHHlmzfCdxVVZcBrwG3TnIwSdMzVvhJdgF/BtwzbAe4Djg4HHIAuGkaA0qavHFf8b8JfBX4zbB9MfB6VZ0btk8AO8/3xCT7khxJcuQsZ1Y1rKTJWDb8JJ8CTlfV4ys5QVXtr6rFqlrcyOaV/BOSJmxhjGOuBT6d5AZgC/B+4G7goiQLw6v+LuDk9MaUNEnLvuJX1R1VtauqLgVuAX5cVZ8DHgVuHg7bCzw4tSklTdRq/o7/NeDLSY4zes9/72RGkjRt41zq/5+q+gnwk+Hn54GPT34kSdPmJ/ekhgxfasjwpYYMX2rI8KWGDF9qyPClhgxfasjwpYYMX2rI8KWGDF9qyPClhgxfasjwpYYMX2rI8KWGDF9qyPClhgxfasjwpYYMX2rI8KWGDF9qyPClhgxfasjwpYYMX2rI8KWGDF9qyPClhgxfasjwpYYMX2porPCTXJTkYJJnkhxLck2SbUkeSfLc8H3rtIeVNBnjvuLfDfywqj4MfBQ4BtwOHKqqy4FDw7akdWDZ8JN8APgj4F6Aqvp1Vb0O3AgcGA47ANw0rSElTdY4r/i7gVeBbyd5Isk9SS4EtlfVqeGYl4Ht0xpS0mSNE/4C8DHgW1V1JfAmb7usr6oC6nxPTrIvyZEkR85yZrXzSpqAccI/AZyoqsPD9kFGvwheSbIDYPh++nxPrqr9VbVYVYsb2TyJmSWt0rLhV9XLwEtJPjTs2gMcBR4C9g779gIPTmVCSRO3MOZxfwXcl2QT8DzwF4x+aXwvya3AL4HPTGdESZM2VvhV9SSweJ6H9kx2HEmz4Cf3pIYMX2rI8KWGDF9qyPClhgxfasjwpYYMX2rI8KWGDF9qyPClhgxfasjwpYYMX2rI8KWGDF9qyPClhgxfasjwpYYMX2rI8KWGDF9qyPClhgxfasjwpYYMX2rI8KWGDF9qyPClhgxfasjwpYYMX2rI8KWGDF9qyPClhsYKP8mXkjyd5Kkk30myJcnuJIeTHE9yf5JN0x5W0mQsG36SncAXgMWq+giwAbgFuBO4q6ouA14Dbp3moJImZ9xL/QXgt5MsABcAp4DrgIPD4weAmyY/nqRpWDb8qjoJfB14kVHwvwIeB16vqnPDYSeAned7fpJ9SY4kOXKWM5OZWtKqjHOpvxW4EdgNfBC4ELh+3BNU1f6qWqyqxY1sXvGgkiZnnEv9TwAvVNWrVXUWeAC4FrhouPQH2AWcnNKMkiZsnPBfBK5OckGSAHuAo8CjwM3DMXuBB6czoqRJG+c9/mFGN/F+Bvx8eM5+4GvAl5McBy4G7p3inJImKFU1s5O9P9vqquyZ2fmkbg7XId6o/8pyx/nJPakhw5caMnypIcOXGjJ8qSHDlxoyfKkhw5caMnypIcOXGjJ8qSHDlxoyfKkhw5caMnypIcOXGjJ8qSHDlxoyfKkhw5caMnypIcOXGjJ8qSHDlxoyfKkhw5caMnypIcOXGjJ8qSHDlxoyfKkhw5caMnypIcOXGjJ8qSHDlxoyfKmhVNXsTpa8CrwJ/OfMTro6v8P6mRXW17zraVZYP/P+XlX97nIHzTR8gCRHqmpxpiddofU0K6yvedfTrLD+5l2Ol/pSQ4YvNbQW4e9fg3Ou1HqaFdbXvOtpVlh/876rmb/Hl7T2vNSXGppZ+EmuT/JskuNJbp/VeceV5JIkjyY5muTpJLcN+7cleSTJc8P3rWs961uSbEjyRJKHh+3dSQ4Pa3x/kk1rPeNbklyU5GCSZ5IcS3LNvK5tki8N/weeSvKdJFvmeW1XYibhJ9kA/D3wp8AVwGeTXDGLc78H54CvVNUVwNXA54cZbwcOVdXlwKFhe17cBhxbsn0ncFdVXQa8Bty6JlOd393AD6vqw8BHGc09d2ubZCfwBWCxqj4CbABuYb7X9r2rqql/AdcAP1qyfQdwxyzOvYqZHwQ+CTwL7Bj27QCeXevZhll2MYrlOuBhIIw+YLJwvjVf41k/ALzAcE9pyf65W1tgJ/ASsA1YGNb2T+Z1bVf6NatL/bcW8y0nhn1zKcmlwJXAYWB7VZ0aHnoZ2L5GY73dN4GvAr8Zti8GXq+qc8P2PK3xbuBV4NvDW5N7klzIHK5tVZ0Evg68CJwCfgU8zvyu7Yp4c+9tkrwP+D7wxap6Y+ljNfp1v+Z/BknyKeB0VT2+1rOMaQH4GPCtqrqS0ce2/99l/Ryt7VbgRka/rD4IXAhcv6ZDTcGswj8JXLJke9ewb64k2cgo+vuq6oFh9ytJdgyP7wBOr9V8S1wLfDrJfwDfZXS5fzdwUZKF4Zh5WuMTwImqOjxsH2T0i2Ae1/YTwAtV9WpVnQUeYLTe87q2KzKr8B8DLh/ujG5idLPkoRmdeyxJAtwLHKuqbyx56CFg7/DzXkbv/ddUVd1RVbuq6lJGa/njqvoc8Chw83DYXMwKUFUvAy8l+dCwaw9wlDlcW0aX+FcnuWD4P/HWrHO5tis2w5smNwC/AP4d+Ju1vrlxnvn+kNGl5r8BTw5fNzB673wIeA74F2DbWs/6trn/GHh4+Pn3gX8FjgP/BGxe6/mWzPkHwJFhff8Z2Dqvawv8LfAM8BTwj8DmeV7blXz5yT2pIW/uSQ0ZvtSQ4UsNGb7UkOFLDRm+1JDhSw0ZvtTQ/wIBrqR32W9TRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11220e898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reading the data with boundless=True, resampling=Resampling.nearest\n",
    "data = src.read(1, window=((0,100), (0,100)), boundless=True, resampling=Resampling.nearest)\n",
    "print()\n",
    "print('Are equals? : ', numpy.all(data == expected_data))\n",
    "imshow(data - expected_data)\n",
    "print('data diff')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}