shift.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import rasterio\n",
    "import matplotlib.pyplot as plt\n",
    "from rasterio.plot import show\n",
    "from rasterio.windows import Window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "xoff, yoff, xsize, ysize = 51667529, 27435585, 10, 10\n",
    "source = \"/vsicurl/https://raw.githubusercontent.com/satellogic/telluric/master/tests/data/google.xml\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 4, figsize=(20, 10))\n",
    "for i in range(4):\n",
    "    xoff_ = xoff + i\n",
    "    out_window = Window(xoff_, yoff, xsize, ysize)\n",
    "    with rasterio.open(source, OVERVIEW_LEVEL=1) as src:\n",
    "        image = src.read(window=out_window, out_shape=(src.count, ysize, xsize),)\n",
    "        show(image, ax=axs[i], title=\"xoff_={}\".format(i))"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}