vincentsarago/dataset_mask_boundless.ipynb

## dataset_mask_boundless.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This code runs with code push in https://github.com/mapbox/rasterio/pull/1366"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "1.0b1\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import rasterio\n",
    "from rasterio.plot import reshape_as_image\n",
    "print(rasterio.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = 'https://s3-us-west-2.amazonaws.com/remotepixel-us-west-2/data/image/LC08_L1TP_017030_20180228_20180308_01_T1_RGB432_cogeo.tif'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'driver': 'GTiff', 'dtype': 'uint8', 'nodata': None, 'width': 7641, 'height': 7761, 'count': 3, 'crs': CRS({'init': 'epsg:32617'}), 'transform': Affine(30.0, 0.0, 582585.0,\n",
      "       0.0, -30.0, 4899915.0)}\n"
     ]
    }
   ],
   "source": [
    "src = rasterio.open(dataset)\n",
    "print(src.meta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "([<MaskFlags.per_dataset: 2>, <MaskFlags.alpha: 4>], [<MaskFlags.per_dataset: 2>, <MaskFlags.alpha: 4>], [<MaskFlags.per_dataset: 2>, <MaskFlags.alpha: 4>], [<MaskFlags.all_valid: 1>])\n"
     ]
    }
   ],
   "source": [
    "vrt = rasterio.vrt.WarpedVRT(src, crs='EPSG:3857')\n",
    "\n",
    "# Check the alpha band from the internal mask is set \n",
    "print(vrt.mask_flag_enums)\n",
    "\n",
    "# Read overview rgb\n",
    "img = vrt.read(out_shape=(3,100,100), indexes=(1,2,3))\n",
    "\n",
    "# Read overview mask\n",
    "mask = vrt.dataset_mask(out_shape=(1,100,100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1132276a0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1130f17b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imgrgb = reshape_as_image(img)\n",
    "imshow(imgrgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x112cb9860>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112bbf080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read upper left part of the mask \n",
    "mask_window = vrt.dataset_mask(window=((0, 5000), (0, 5000)), out_shape=(1,100,100))\n",
    "\n",
    "# Read the same part but with boundless=True\n",
    "mask_window_boundless = vrt.dataset_mask(window=((0, 5000), (0, 5000)), out_shape=(1,100,100), boundless=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1130be860>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11300bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(mask_window)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x113174358>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1130ca1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(mask_window_boundless)"
   ]
  },
  {
   "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
}