rio-tiler-hotosm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "{'driver': 'GTiff', 'dtype': 'uint8', 'nodata': None, 'width': 41337, 'height': 41361, 'count': 3, 'crs': CRS({'init': 'epsg:32737'}), 'transform': Affine(0.0725800022482872, 0.0, 531722.25,\n",
      "       0.0, -0.0725800022482872, 9365190.0)}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vincentsarago/Trav/Virtualenv/py4/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['tile']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "import rasterio\n",
    "from rasterio.plot import reshape_as_image\n",
    "\n",
    "from rio_tiler import main\n",
    "\n",
    "import mercantile\n",
    "\n",
    "img = 'https://oin-hotosm.s3.amazonaws.com/5ac626e091b5310010e0d482/0/5ac626e091b5310010e0d483.tif'\n",
    "\n",
    "with rasterio.open(img) as src:\n",
    "    print(src.meta)\n",
    "    \n",
    "zoom = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta = main.bounds(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'url': 'https://oin-hotosm.s3.amazonaws.com/5ac626e091b5310010e0d482/0/5ac626e091b5310010e0d483.tif', 'bounds': [39.28650720617372, -5.770217592244193, 39.31361922118217, -5.743046418788738]}\n"
     ]
    }
   ],
   "source": [
    "print(meta)\n",
    "bounds = meta[\"bounds\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "tiles = list(mercantile.tiles(*bounds + [[zoom]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "tile = tiles[0]\n",
    "x, y, z = tile\n",
    "\n",
    "# Read data \n",
    "data, mask = main.tile(img, x, y, z) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 256, 256)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Transform data to PIL compatible format\n",
    "data = reshape_as_image(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x111558128>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1181e1da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1115ea208>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQYAAAD8CAYAAACVSwr3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAADHBJREFUeJzt3F/o3fV9x/HnaxpTZi2a2YUshmlLdmEvZsMPFSrFIWs1N7E3ohc1FCG9UGihu0jbi3rZjbUFYRNSKo2j00lbMRduq4aC7EJrFBv/zZpaxYRo1lmsrJCqfe/i9409zfv38/fL73fO75zf9nzA4XzP53zP77zzJTw5/1NVSNKoP5r2AJJmj2GQ1BgGSY1hkNQYBkmNYZDUTCwMSa5N8kKSI0n2Tup+JI1fJvE5hiRnAT8D/ho4CjwO3FRVz439ziSN3aQeMVwOHKmql6rqt8C9wK4J3ZekMTt7Qn93K/DqyOWjwBWL7XxONtYHOHdCo0gCeItf/bKqPrycfScVhiUl2QPsAfgAf8wVuWZao0j/Lzxc339luftO6qnEMWDbyOWLhrX3VNW+qpqrqrkNbJzQGJJWYlJheBzYnuSSJOcANwIHJnRfksZsIk8lquqdJLcB/w6cBdxVVc9O4r4kjd/EXmOoqgeBByf19yVNjp98lNQYBkmNYZDUGAZJjWGQ1BgGSY1hkNQYBkmNYZDUGAZJjWGQ1BgGSY1hkNQYBkmNYZDUGAZJjWGQ1BgGSY1hkNQYBkmNYZDUGAZJjWGQ1BgGSY1hkNQYBkmNYZDUGAZJjWGQ1BgGSY1hkNQYBkmNYZDUGAZJzdmruXGSl4G3gHeBd6pqLskm4F+Ai4GXgRuq6lerG1PSWhrHI4a/qqrLqmpuuLwXOFhV24GDw2VJ68gknkrsAvYP2/uB6ydwH5ImaLVhKOBHSZ5IsmdY21xVx4ft14DNC90wyZ4kh5IcepuTqxxD0jit6jUG4KqqOpbkT4GHkvzn6JVVVUlqoRtW1T5gH8CHsmnBfSRNx6oeMVTVseH8BHA/cDnwepItAMP5idUOKWltrTgMSc5Nct6pbeBTwDPAAWD3sNtu4IHVDilpba3mqcRm4P4kp/7OP1fVvyV5HLgvyS3AK8ANqx9T0lpacRiq6iXgLxdY/2/gmtUMJWm6/OSjpMYwSGoMg6TGMEhqDIOkxjBIagyDpMYwSGoMg6TGMEhqDIOkxjBIagyDpMYwSGoMg6TGMEhqDIOkxjBIagyDpMYwSGoMg6TGMEhqDIOkxjBIagyDpMYwSGoMg6TGMEhqDIOkxjBIagyDpMYwSGoMg6RmyTAkuSvJiSTPjKxtSvJQkheH8wuG9SS5I8mRJIeT7Jjk8JImYzmPGL4LXHva2l7gYFVtBw4OlwGuA7YPpz3AneMZU9JaWjIMVfUI8MZpy7uA/cP2fuD6kfW7a96jwPlJtoxrWElrY6WvMWyuquPD9mvA5mF7K/DqyH5HhzVJ68iqX3ysqgLqTG+XZE+SQ0kOvc3J1Y4haYxWGobXTz1FGM5PDOvHgG0j+100rDVVta+q5qpqbgMbVziGpElYaRgOALuH7d3AAyPrNw/vTlwJvDnylEPSOnH2UjskuQe4GrgwyVHga8DXgfuS3AK8Atww7P4gsBM4AvwG+NwEZpY0YUuGoapuWuSqaxbYt4BbVzuUpOnyk4+SGsMgqTEMkhrDIKkxDJIawyCpMQySGsMgqTEMkhrDIKkxDJIawyCpMQySGsMgqTEMkhrDIKkxDJIawyCpMQySGsMgqTEMkhrDIKkxDJIawyCpMQySGsMgqTEMkhrDIKkxDJIawyCpMQySGsMgqTEMkhrDIKlZMgxJ7kpyIskzI2u3JzmW5KnhtHPkui8nOZLkhSSfntTgkiZnOY8Yvgtcu8D6t6rqsuH0IECSS4EbgY8Nt/nHJGeNa1hJa2PJMFTVI8Aby/x7u4B7q+pkVf0COAJcvor5JE3Bal5juC3J4eGpxgXD2lbg1ZF9jg5rTZI9SQ4lOfQ2J1cxhqRxW2kY7gQ+ClwGHAe+caZ/oKr2VdVcVc1tYOMKx5A0CSsKQ1W9XlXvVtXvgG/z+6cLx4BtI7teNKxJWkdWFIYkW0YufgY49Y7FAeDGJBuTXAJsB36yuhElrbWzl9ohyT3A1cCFSY4CXwOuTnIZUMDLwOcBqurZJPcBzwHvALdW1buTGV3SpKSqpj0DH8qmuiLXTHsM6f+0h+v7T1TV3HL29ZOPkhrDIKkxDJIawyCpMQySGsMgqTEMkhrDIKkxDJIawyCpMQySGsMgqTEMkhrDIKkxDJIawyCpMQySGsMgqTEMkhrDIKkxDJIawyCpMQySGsMgqTEMkhrDIKkxDJIawyCpMQySGsMgqTEMkhrDIKkxDJKaJcOQZFuSHyd5LsmzSb4wrG9K8lCSF4fzC4b1JLkjyZEkh5PsmPQ/QtJ4LecRwzvAl6rqUuBK4NYklwJ7gYNVtR04OFwGuA7YPpz2AHeOfWpJE7VkGKrqeFU9OWy/BTwPbAV2AfuH3fYD1w/bu4C7a96jwPlJtox9ckkTc0avMSS5GPg48BiwuaqOD1e9BmwetrcCr47c7OiwJmmdWHYYknwQ+AHwxar69eh1VVVAnckdJ9mT5FCSQ29z8kxuKmnClhWGJBuYj8L3quqHw/Lrp54iDOcnhvVjwLaRm180rP2BqtpXVXNVNbeBjSudX9IELOddiQDfAZ6vqm+OXHUA2D1s7wYeGFm/eXh34krgzZGnHJLWgbOXsc8ngM8CTyd5alj7CvB14L4ktwCvADcM1z0I7ASOAL8BPjfWiSVN3JJhqKr/ALLI1dcssH8Bt65yLklT5CcfJTWGQVJjGCQ1hkFSYxgkNYZBUmMYJDWGQVJjGCQ1hkFSYxgkNYZBUmMYJDWGQVJjGCQ1hkFSYxgkNYZBUmMYJDWGQVJjGCQ1hkFSYxgkNYZBUmMYJDWGQVJjGCQ1hkFSYxgkNYZBUmMYJDWGQVJjGCQ1hkFSs2QYkmxL8uMkzyV5NskXhvXbkxxL8tRw2jlymy8nOZLkhSSfnuQ/QNL4nb2Mfd4BvlRVTyY5D3giyUPDdd+qqr8f3TnJpcCNwMeAPwMeTvIXVfXuOAeXNDlLPmKoquNV9eSw/RbwPLD1fW6yC7i3qk5W1S+AI8Dl4xhW0to4o9cYklwMfBx4bFi6LcnhJHcluWBY2wq8OnKzoywQkiR7khxKcuhtTp7x4JImZ9lhSPJB4AfAF6vq18CdwEeBy4DjwDfO5I6ral9VzVXV3AY2nslNJU3YssKQZAPzUfheVf0QoKper6p3q+p3wLf5/dOFY8C2kZtfNKxJWieW865EgO8Az1fVN0fWt4zs9hngmWH7AHBjko1JLgG2Az8Z38iSJm0570p8Avgs8HSSp4a1rwA3JbkMKOBl4PMAVfVskvuA55h/R+NW35GQ1pdU1bRnIMl/Af8D/HLasyzDhayPOWH9zOqc47fQrH9eVR9ezo1nIgwASQ5V1dy051jKepkT1s+szjl+q53Vj0RLagyDpGaWwrBv2gMs03qZE9bPrM45fquadWZeY5A0O2bpEYOkGTH1MCS5dvh69pEke6c9z+mSvJzk6eGr5YeGtU1JHkry4nB+wVJ/ZwJz3ZXkRJJnRtYWnCvz7hiO8eEkO2Zg1pn72v77/MTATB3XNfkphKqa2gk4C/g58BHgHOCnwKXTnGmBGV8GLjxt7e+AvcP2XuBvpzDXJ4EdwDNLzQXsBP4VCHAl8NgMzHo78DcL7Hvp8P9gI3DJ8P/jrDWacwuwY9g+D/jZMM9MHdf3mXNsx3TajxguB45U1UtV9VvgXua/tj3rdgH7h+39wPVrPUBVPQK8cdryYnPtAu6ueY8C55/2kfaJWmTWxUzta/u1+E8MzNRxfZ85F3PGx3TaYVjWV7SnrIAfJXkiyZ5hbXNVHR+2XwM2T2e0ZrG5ZvU4r/hr+5N22k8MzOxxHedPIYyadhjWg6uqagdwHXBrkk+OXlnzj9Vm7q2dWZ1rxKq+tj9JC/zEwHtm6biO+6cQRk07DDP/Fe2qOjacnwDuZ/4h2OunHjIO5yemN+EfWGyumTvONaNf21/oJwaYweM66Z9CmHYYHge2J7kkyTnM/1bkgSnP9J4k5w6/c0mSc4FPMf/18gPA7mG33cAD05mwWWyuA8DNw6voVwJvjjw0nopZ/Nr+Yj8xwIwd18XmHOsxXYtXUZd4hXUn86+q/hz46rTnOW22jzD/au5PgWdPzQf8CXAQeBF4GNg0hdnuYf7h4tvMP2e8ZbG5mH/V/B+GY/w0MDcDs/7TMMvh4T/ulpH9vzrM+gJw3RrOeRXzTxMOA08Np52zdlzfZ86xHVM/+SipmfZTCUkzyDBIagyDpMYwSGoMg6TGMEhqDIOkxjBIav4XoTFm+s1FrB0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111514748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mask is not found due to a bug in rasterio (should be fixed today or early next week)\n",
    "imshow(mask)"
   ]
  },
  {
   "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
}