Merge remote-tracking branch 'remotes/origin/master' into notebook-debug
epifanio
commit
de81a1004f
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# An introduction to Rasterio\n",
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"\n",
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"The smallest interesting problems [1] addressed by Rasterio are reading raster data from files as [Numpy](http://www.numpy.org/) arrays and writing such arrays back to files. In between, you can use the world of scientific python software to analyze and process the data. Rasterio also provides a few operations that are described in the next notebooks in this series.\n",
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"\n",
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"This notebook demonstrates the basics of reading and writing raster data with Rasterio.\n",
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"\n",
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"## Overview of a dataset\n",
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"\n",
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"A raster dataset consists of one or more dense (as opposed to sparse) 2-D arrays of scalar values. An RGB TIFF image file is a good example of a raster dataset. It has 3 bands (or channels – we'll call them bands here) and each has a number of rows (its `height`) and columns (its `width`) and a uniform data type (unsigned 8-bit integers, 64-bit floats, etc). Geospatially referenced datasets will also possess a mapping from image to world coordinates (a `transform`) in a specific coordinate reference system (`crs`). This metadata about a dataset is readily accessible using Rasterio.\n",
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"\n",
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"The Scientific Python community often imports numpy as `np`. Do this and also import rasterio.\n",
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"\n",
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"\n",
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"------------------------------\n",
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" Rasterio's tests require several raster data files. Grab them from\n",
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"\n",
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" https://github.com/mapbox/rasterio/tree/master/tests/data\n",
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"\n",
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" and copy them to this directory.\n",
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"\n",
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" The RGB.byte.tif file is derived from USGS Landsat 7 ETM imagery. The shade.tif\n",
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" file is derived from USGS SRTM 90 data. The float.tif and float_nan.tif files\n",
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" are original works of the Rasterio authors. All test images are licensed under\n",
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" the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication:\n",
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" http://creativecommons.org/publicdomain/zero/1.0/.\n",
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"\n",
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"\n",
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"`` Copyright (c) 2013, MapBox. All rights reserved. ``"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"import rasterio"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Rasterio uses for many of its tests a small 3-band GeoTIFF file named \"RGB.byte.tif\". Open it using the function `rasterio.open()`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"src = rasterio.open('data/RGB.byte.tif')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This function returns a dataset object. It has many of the same properties as a Python file object."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'../tests/data/RGB.byte.tif'"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"src.name"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'r'"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"src.mode"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"False"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"src.closed"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Raster datasets have additional structure and a description can be had from its `meta` property or individually."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'affine': Affine(300.0379266750948, 0.0, 101985.0,\n",
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" 0.0, -300.041782729805, 2826915.0),\n",
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" 'count': 3,\n",
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" 'crs': {'init': u'epsg:32618'},\n",
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" 'driver': u'GTiff',\n",
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" 'dtype': 'uint8',\n",
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" 'height': 718,\n",
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" 'nodata': 0.0,\n",
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" 'transform': (101985.0,\n",
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" 300.0379266750948,\n",
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" 0.0,\n",
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" 2826915.0,\n",
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" 0.0,\n",
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" -300.041782729805),\n",
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" 'width': 791}"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"src.meta"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'init': u'epsg:32618'}"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"src.crs"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To close an opened dataset, use its `close()` method."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"src.close()\n",
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"src.closed"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can't read from or write to a closed dataset, but you can continue access its properties."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"u'GTiff'"
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]
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},
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"src.driver"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Dataset layout\n",
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"\n",
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"Three properties of a Rasterio dataset tell you a lot about it in Numpy terms. The `shape` of a dataset is a `height, width` tuple and is exactly the shape of Numpy arrays that would be read from it. The testing dataset has 718 rows and 791 columns."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(718, 791)"
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]
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},
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"execution_count": 26,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"src.shape"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The `count` of bands in the dataset is 3."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"3"
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]
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},
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"execution_count": 27,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"src.count"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"All three of its bands contain 8-bit unsigned integers."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['uint8', 'uint8', 'uint8']"
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]
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},
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"execution_count": 28,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"src.dtypes"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Numpy concepts are the model here. If you wanted to create a 3-D Numpy array into which the testing data file's bands would fit without any resampling, you would use the following Python code."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[[0, 0, 0, ..., 0, 0, 0],\n",
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" [0, 0, 0, ..., 0, 0, 0],\n",
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" [0, 0, 0, ..., 0, 0, 0],\n",
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" ..., \n",
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" [0, 0, 0, ..., 0, 0, 0],\n",
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" [0, 0, 0, ..., 0, 0, 0],\n",
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" [0, 0, 0, ..., 0, 0, 0]],\n",
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"\n",
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" [[0, 0, 0, ..., 0, 0, 0],\n",
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" [0, 0, 0, ..., 0, 0, 0],\n",
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" [0, 0, 0, ..., 0, 0, 0],\n",
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" ..., \n",
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" [0, 0, 0, ..., 0, 0, 0],\n",
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" [0, 0, 0, ..., 0, 0, 0],\n",
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" [0, 0, 0, ..., 0, 0, 0]],\n",
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"\n",
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" [[0, 0, 0, ..., 0, 0, 0],\n",
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" [0, 0, 0, ..., 0, 0, 0],\n",
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" [0, 0, 0, ..., 0, 0, 0],\n",
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" ..., \n",
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" [0, 0, 0, ..., 0, 0, 0],\n",
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" [0, 0, 0, ..., 0, 0, 0],\n",
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" [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8)"
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]
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},
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"execution_count": 25,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dest = np.empty((src.count,) + src.shape, dtype='uint8')\n",
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"dest"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## References"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"[1]: Mike Bostock's words from his FOSS4G keynote, 2014-09-10"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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Loading…
Reference in New Issue