ocefpaf/GIS_example-2.ipynb

## GIS_example-2.ipynb
{
 "cells": [
  {
   "metadata": {
    "collapsed": false,
    "trusted": true,
    "editable": true,
    "deletable": true
   },
   "cell_type": "code",
   "source": "import fiona\n\nshp = fiona.open('zonas_farrapos.shp')",
   "execution_count": 1,
   "outputs": []
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true,
    "editable": true,
    "deletable": true
   },
   "cell_type": "code",
   "source": "res = 72\n\n\nwidth = (shp.bounds[2] - shp.bounds[0]) // res\nheight = (shp.bounds[3] - shp.bounds[1]) // res\n\nout_shape = int(height), int(width)\nout_shape",
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(714, 429)"
     },
     "metadata": {},
     "execution_count": 2
    }
   ]
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true,
    "editable": true,
    "deletable": true
   },
   "cell_type": "code",
   "source": "from rasterio.transform import from_origin\n\ntransform = from_origin(shp.bounds[0] - res / 2,\n                        shp.bounds[3] + res / 2, res, res)\ntransform",
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Affine(72.0, 0.0, 391451.64563056955,\n       0.0, -72.0, 6391938.555971239)"
     },
     "metadata": {},
     "execution_count": 3
    }
   ]
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true,
    "editable": true,
    "deletable": true
   },
   "cell_type": "code",
   "source": "shapes = [(geometry['geometry'], k) for k, geometry in shp.items()]",
   "execution_count": 4,
   "outputs": []
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true,
    "editable": true,
    "deletable": true
   },
   "cell_type": "code",
   "source": "#colors = (255, 0, 50, 0)\n#shapes = [(geometry['geometry'], color) for geometry, color in zip(list(shp.values()), colors)]",
   "execution_count": 5,
   "outputs": []
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true,
    "editable": true,
    "deletable": true
   },
   "cell_type": "code",
   "source": "import numpy as np\nimport rasterio\nfrom rasterio.features import rasterize\n\ndtype = rasterio.float64\nfill = np.nan\n\nraster = rasterize(\n    shapes,\n    out_shape=out_shape,\n    fill=fill,\n    transform=transform,\n    dtype=dtype\n)",
   "execution_count": 6,
   "outputs": []
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true,
    "editable": true,
    "deletable": true
   },
   "cell_type": "code",
   "source": "%matplotlib inline\nimport matplotlib.pyplot as plt\n\nfig, ax = plt.subplots(figsize=(9, 9))\nax.imshow(raster)\nax.axis('off');",
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<matplotlib.figure.Figure at 0x7f42a4817320>",
      "image/png": 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oxTnRE5byMVwLqutFuYdzoqRybVJ15xKRmESPmEL7cDyl1oTUJJrHkl/5YxKlO8dT6g/l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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "editable": true,
    "deletable": true
   },
   "cell_type": "markdown",
   "source": "Salvar o geotiff."
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true,
    "editable": true,
    "deletable": true
   },
   "cell_type": "code",
   "source": "with rasterio.open(\n    'test.tif', 'w',\n    crs=shp.crs,\n    driver='GTiff',\n    dtype=dtype,\n    count=3,\n    width=width,\n    height=height,\n    nodata=fill,\n    transform=transform\n) as dst:\n    dst.write(raster, indexes=1)",
   "execution_count": 8,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Round-trip"
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "with rasterio.open('test.tif') as src:\n    r, g, b = src.read()\n    print(src.width, src.height)\n    print(src.crs)\n",
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "stream",
     "text": "429 714\nCRS({'init': 'epsg:32721'})\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "total = np.zeros(r.shape)\nfor band in r, g, b:\n    total += band\ntotal /= 3",
   "execution_count": 10,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Aparentemente a figura \"preta\" é pq não tenho um visualizador corrento.\nA `tif` parece OK mesmo salvando em 3 bandas."
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "plt.imshow(total)",
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x7f429fc60390>"
     },
     "metadata": {},
     "execution_count": 11
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<matplotlib.figure.Figure at 0x7f429fd37d68>",
      "image/png": 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     },
     "metadata": {}
    }
   ]
  }
 ],
 "metadata": {
  "gist_id": "53e5be14d58c1b946952a7293f2005cf",
  "kernelspec": {
   "name": "conda-env-IOOS3-py",
   "display_name": "Python [conda env:IOOS3]",
   "language": "python"
  },
  "language_info": {
   "name": "python",
   "version": "3.6.0",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"metadata": {
	"collapsed": false,
	"trusted": true,
	"editable": true,
	"deletable": true
	},
	"cell_type": "code",
	"source": "import fiona\n\nshp = fiona.open('zonas_farrapos.shp')",
	"execution_count": 1,
	"outputs": []
	},
	{
	"metadata": {
	"collapsed": false,
	"trusted": true,
	"editable": true,
	"deletable": true
	},
	"cell_type": "code",
	"source": "res = 72\n\n\nwidth = (shp.bounds[2] - shp.bounds[0]) // res\nheight = (shp.bounds[3] - shp.bounds[1]) // res\n\nout_shape = int(height), int(width)\nout_shape",
	"execution_count": 2,
	"outputs": [
	{
	"output_type": "execute_result",
	"data": {
	"text/plain": "(714, 429)"
	},
	"metadata": {},
	"execution_count": 2
	}
	]
	},
	{
	"metadata": {
	"collapsed": false,
	"trusted": true,
	"editable": true,
	"deletable": true
	},
	"cell_type": "code",
	"source": "from rasterio.transform import from_origin\n\ntransform = from_origin(shp.bounds[0] - res / 2,\n shp.bounds[3] + res / 2, res, res)\ntransform",
	"execution_count": 3,
	"outputs": [
	{
	"output_type": "execute_result",
	"data": {
	"text/plain": "Affine(72.0, 0.0, 391451.64563056955,\n 0.0, -72.0, 6391938.555971239)"
	},
	"metadata": {},
	"execution_count": 3
	}
	]
	},
	{
	"metadata": {
	"collapsed": false,
	"trusted": true,
	"editable": true,
	"deletable": true
	},
	"cell_type": "code",
	"source": "shapes = [(geometry['geometry'], k) for k, geometry in shp.items()]",
	"execution_count": 4,
	"outputs": []
	},
	{
	"metadata": {
	"collapsed": false,
	"trusted": true,
	"editable": true,
	"deletable": true
	},
	"cell_type": "code",
	"source": "#colors = (255, 0, 50, 0)\n#shapes = [(geometry['geometry'], color) for geometry, color in zip(list(shp.values()), colors)]",
	"execution_count": 5,
	"outputs": []
	},
	{
	"metadata": {
	"collapsed": false,
	"trusted": true,
	"editable": true,
	"deletable": true
	},
	"cell_type": "code",
	"source": "import numpy as np\nimport rasterio\nfrom rasterio.features import rasterize\n\ndtype = rasterio.float64\nfill = np.nan\n\nraster = rasterize(\n shapes,\n out_shape=out_shape,\n fill=fill,\n transform=transform,\n dtype=dtype\n)",
	"execution_count": 6,
	"outputs": []
	},
	{
	"metadata": {
	"collapsed": false,
	"trusted": true,
	"editable": true,
	"deletable": true
	},
	"cell_type": "code",
	"source": "%matplotlib inline\nimport matplotlib.pyplot as plt\n\nfig, ax = plt.subplots(figsize=(9, 9))\nax.imshow(raster)\nax.axis('off');",
	"execution_count": 7,
	"outputs": [
	{
	"output_type": "display_data",
	"data": {
	"text/plain": "<matplotlib.figure.Figure at 0x7f42a4817320>",
	"image/png": 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oxTnRE5byMVwLqutFuYdzoqRybVJ15xKRmESPmEL7cDyl1oTUJJrHkl/5YxKlO8dT6g/l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\n"
	},
	"metadata": {}
	}
	]
	},
	{
	"metadata": {
	"editable": true,
	"deletable": true
	},
	"cell_type": "markdown",
	"source": "Salvar o geotiff."
	},
	{
	"metadata": {
	"collapsed": false,
	"trusted": true,
	"editable": true,
	"deletable": true
	},
	"cell_type": "code",
	"source": "with rasterio.open(\n 'test.tif', 'w',\n crs=shp.crs,\n driver='GTiff',\n dtype=dtype,\n count=3,\n width=width,\n height=height,\n nodata=fill,\n transform=transform\n) as dst:\n dst.write(raster, indexes=1)",
	"execution_count": 8,
	"outputs": []
	},
	{
	"metadata": {},
	"cell_type": "markdown",
	"source": "Round-trip"
	},
	{
	"metadata": {
	"trusted": true,
	"collapsed": false
	},
	"cell_type": "code",
	"source": "with rasterio.open('test.tif') as src:\n r, g, b = src.read()\n print(src.width, src.height)\n print(src.crs)\n",
	"execution_count": 9,
	"outputs": [
	{
	"output_type": "stream",
	"text": "429 714\nCRS({'init': 'epsg:32721'})\n",
	"name": "stdout"
	}
	]
	},
	{
	"metadata": {
	"trusted": true,
	"collapsed": false
	},
	"cell_type": "code",
	"source": "total = np.zeros(r.shape)\nfor band in r, g, b:\n total += band\ntotal /= 3",
	"execution_count": 10,
	"outputs": []
	},
	{
	"metadata": {},
	"cell_type": "markdown",
	"source": "Aparentemente a figura \"preta\" é pq não tenho um visualizador corrento.\nA `tif` parece OK mesmo salvando em 3 bandas."
	},
	{
	"metadata": {
	"trusted": true,
	"collapsed": false
	},
	"cell_type": "code",
	"source": "plt.imshow(total)",
	"execution_count": 11,
	"outputs": [
	{
	"output_type": "execute_result",
	"data": {
	"text/plain": "<matplotlib.image.AxesImage at 0x7f429fc60390>"
	},
	"metadata": {},
	"execution_count": 11
	},
	{
	"output_type": "display_data",
	"data": {
	"text/plain": "<matplotlib.figure.Figure at 0x7f429fd37d68>",
	"image/png": 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	},
	"metadata": {}
	}
	]
	}
	],
	"metadata": {
	"gist_id": "53e5be14d58c1b946952a7293f2005cf",
	"kernelspec": {
	"name": "conda-env-IOOS3-py",
	"display_name": "Python [conda env:IOOS3]",
	"language": "python"
	},
	"language_info": {
	"name": "python",
	"version": "3.6.0",
	"mimetype": "text/x-python",
	"codemirror_mode": {
	"name": "ipython",
	"version": 3
	},
	"pygments_lexer": "ipython3",
	"nbconvert_exporter": "python",
	"file_extension": ".py"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}