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https://github.com/rasterio/rasterio.git
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New notebook section.
This commit is contained in:
Sean Gillies
parent
210fe503ce
commit
68b6a7e76a
@ -1,7 +1,7 @@
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{
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"metadata": {
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"name": "",
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"signature": "sha256:9c5860eecfa65f7a3892acd0ea0bbd0d8bdf4776f00b9d0d406f1df74aa2402c"
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"nbformat": 3,
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"nbformat_minor": 0,
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@ -14,9 +14,13 @@
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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 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 provides a few operations that are described in the next notebooks.\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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"## Structure of a dataset\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 \u2013 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."
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]
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 1
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"prompt_number": 9
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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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"Now open the GeoTIFF file \"RGB.byte.tif\" using `rasterio.open()`."
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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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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 2
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"prompt_number": 10
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},
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{
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"cell_type": "markdown",
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 3,
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"prompt_number": 11,
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"text": [
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"'../tests/data/RGB.byte.tif'"
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]
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}
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],
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"prompt_number": 3
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"prompt_number": 11
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},
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{
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"cell_type": "code",
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 4,
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"prompt_number": 12,
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"text": [
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"'r'"
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]
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}
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],
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"prompt_number": 4
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"prompt_number": 12
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},
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{
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"cell_type": "code",
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 5,
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"prompt_number": 13,
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"text": [
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"False"
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]
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}
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],
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"prompt_number": 5
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"prompt_number": 13
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},
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{
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"cell_type": "markdown",
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 6,
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"prompt_number": 14,
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"text": [
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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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]
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}
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],
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"prompt_number": 6
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"prompt_number": 14
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},
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{
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"cell_type": "code",
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 7,
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"prompt_number": 15,
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"text": [
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"{'init': u'epsg:32618'}"
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]
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}
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],
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"prompt_number": 7
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"prompt_number": 15
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},
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{
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"cell_type": "markdown",
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 8,
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"prompt_number": 16,
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"text": [
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"True"
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]
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}
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],
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"prompt_number": 8
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"prompt_number": 16
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},
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{
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"cell_type": "markdown",
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@ -235,6 +239,146 @@
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],
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"prompt_number": 23
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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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"collapsed": false,
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"input": [
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"src.shape"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 26,
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"text": [
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"(718, 791)"
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]
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}
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],
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"prompt_number": 26
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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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"collapsed": false,
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"input": [
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"src.count"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 27,
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"text": [
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"3"
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]
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}
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],
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"prompt_number": 27
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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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"collapsed": false,
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"input": [
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"src.dtypes"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 28,
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"text": [
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"['uint8', 'uint8', 'uint8']"
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]
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}
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],
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"prompt_number": 28
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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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"collapsed": false,
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"input": [
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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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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 25,
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"text": [
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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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],
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"prompt_number": 25
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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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