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Clusterpy usage: 0 Loading and exploring shapes (http://nbviewer.ipython.org/gist/sergiobuj/9159111)
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0_loading_shapes_with_clusterpy.ipynb
{
"metadata": {
"name": "0_loading_shapes_with_clusterpy"
},
"nbformat": 2,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"source": [
"# Clusterpy usage",
"",
"## Loading a shape (.shp, .dbf, .shx) and exploring it using Clusterpy.",
"",
"Before using the clustering algorithms included in Clusterpy we need to load/import a shapefile.",
"",
"The data_examples directory in Clusterpy contains some shapefiles that we can use to start exploring the different components in the library.",
"",
"",
"Let's start by importing clusterpy:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import clusterpy",
"# To use this same version use git to checkout this commit: 434b415f",
"# $ git checkout 434b415f",
"# Current version is v0.9.9"
],
"language": "python",
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"ClusterPy: Library of spatially constrained clustering algorithms"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"source": [
"The basic data structure for Clusterpy is the 'Layer' object.",
"",
"This object will hold the information of our shapefile and will hold a reference to future modification of the shape that result from running the clustering algorithms.",
"",
"To create a layer object we can import an ESRI shape file or generate a regular lattice of polygons."
]
},
{
"cell_type": "markdown",
"source": [
"### Import an ESRI file (the most common case):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Use the importArcData function",
"ca_layer = clusterpy.importArcData('clusterpy/data_examples/CA_Polygons')"
],
"language": "python",
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Loading clusterpy/data_examples/CA_Polygons.dbf",
"Loading clusterpy/data_examples/CA_Polygons.shp",
"Done"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
""
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# You can use the CPhelp function to quickly get the function's __doc__ like this:",
"clusterpy.CPhelp('importArcData')"
],
"language": "python",
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Creates a new Layer from a shapefile (<file>.shp)",
" ",
" :param filename: filename without extension ",
" :type filename: string",
" :rtype: Layer (CP project)",
"",
" **Description**",
"",
" `ESRI <http://www.esri.com/>`_ shapefile is a binary file used to",
" save and transport maps. During the last times it has become",
" the most used format for the spatial scientists around the world.",
"",
" On clusterPy's \"data_examples\" folder you can find some shapefiles. To",
" load a shapefile in clusterPy just follow the example bellow.",
"",
" **Example** ::",
"",
" import clusterpy",
" china = clusterpy.importArcData(\"clusterpy/data_examples/china\")",
"",
" "
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"source": [
"### Generate a regular lattice of polygons:",
"Another way to create a Layer file is by generating a regular lattice with the createGrid function."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clusterpy.CPhelp('createGrid')"
],
"language": "python",
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Creates a new Layer with a regular lattice",
" ",
" :param nRows: number of rows",
" :type nRows: integer",
" :param nCols: number of columns",
" :type nCols: integer",
" :type lowerLeft: tuple or none, lower-left corner coordinates; default is (0,0) ",
" :type upperRight: tuple or none, upper-right corner coordinates; default is (100,100)",
" :rtype: Layer new lattice ",
"",
" **Description**",
"",
" Regular lattices are widely used in both theoretical and empirical",
" applications in Regional Science. The example below shows how easy ",
" the creation of this kind of maps is using clusterPy.",
" ",
" **Examples**",
"",
" Create a grid of ten by ten points.::",
"",
" import clusterpy",
" points = clusterpy.createGrid(10,10)",
" ",
"",
" Create a grid of ten by ten points on the bounding box (0,0,100,100).::",
"",
" import clusterpy",
" points = clusterpy.createGrid(10, 10, lowerLeft=(0, 0), upperRight=(100, 100))",
" "
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# From the examples found in the help function we can create a simple 10x10 grid.",
"# let's create a smaller grid for simplicity",
"grid_layer = clusterpy.createGrid(5, 5)"
],
"language": "python",
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Creating grid",
"Done"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"source": [
"Now that we have our basic Layer data structure loaded with a shape, we can start working on it and exploring its contents.",
"",
"### Exploring a shapefile",
"",
"After loading a Shapefile we can easily get the contiguity matrix and also the information found in the .dbf (in case we loaded an ESRI file).",
"",
"**Contiguity matrices**: We can work with both _Wqueen_ and _Wrook_ to specify the neighbouring areas in our shapefile. In _Wqueen_, two areas are considered neighbours if they touch each other even by a point. In _Wrook_, two areas are neighbours if they share a border."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"\"\"",
"In Wqueen, the area 'A' has all neighbors named 'Q'",
"",
"+=====+=====+=====+",
"| Q | Q | Q |",
"+=====+=====+=====+",
"| Q | A | Q |",
"+=====+=====+=====+",
"| Q | Q | Q |",
"+=====+=====+=====+",
"",
"In Wrook, the area 'A' has all neighbors named 'R'",
"+=====+=====+=====+",
"| | R | |",
"+=====+=====+=====+",
"| R | A | R |",
"+=====+=====+=====+",
"| | R | |",
"+=====+=====+=====+",
"\"\"\"",
"None"
],
"language": "python",
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Contiguity matrices of our layers",
"print \"ca_layer Wqueen: \", ca_layer.Wqueen",
"print \"\\nca_layer Wrook: \", ca_layer.Wrook",
"print \"\\ngrid_layer Wqueen: \", grid_layer.Wqueen",
"print \"\\ngrid_layer Wrook: \", grid_layer.Wrook"
],
"language": "python",
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"ca_layer Wqueen: {0: [6, 38, 40, 42], 1: [2, 4, 8, 25, 54], 2: [1, 4, 8, 33, 38], 3: [5, 10, 31, 50, 51, 57], 4: [1, 2, 38, 49, 54], 5: [3, 10, 16, 50, 56], 6: [0, 33, 38, 47], 7: [11, 46], 8: [1, 2, 30, 33], 9: [13, 15, 19, 23, 25, 26, 34, 53], 10: [3, 5, 16, 22, 51], 11: [7, 22, 46, 52], 12: [32, 36], 13: [9, 14, 25, 35, 53], 14: [13, 15, 18, 35, 39, 41, 53, 55], 15: [14, 9, 26, 39, 53], 16: [10, 5, 22, 27, 48, 56], 17: [24, 31, 44, 45], 18: [14, 29, 35, 55], 19: [9, 21, 23, 25, 54], 20: [48], 21: [19, 23, 49, 54], 22: [11, 10, 16, 48, 51, 52], 23: [19, 9, 21, 34, 42, 49], 24: [17, 44, 46], 25: [13, 9, 19, 1, 54], 26: [9, 15, 34, 39, 43], 27: [16, 47, 48, 56], 28: [30, 45, 57], 29: [18, 32, 35, 36], 30: [8, 28, 33, 50, 57], 31: [17, 3, 44, 45, 51, 57], 32: [12, 29, 35, 36], 33: [30, 8, 2, 6, 38, 47, 50, 56], 34: [23, 9, 26, 42, 43], 35: [13, 32, 29, 18, 14], 36: [32, 12, 29], 37: [40], 38: [6, 33, 2, 4, 0, 49], 39: [26, 15, 14, 41], 40: [37, 0, 42, 43], 41: [39, 14, 55], 42: [0, 23, 34, 40, 43, 49], 43: [42, 34, 26, 40], 44: [24, 17, 31, 46, 51, 52], 45: [31, 17, 28, 57], 46: [24, 44, 11, 7, 52], 47: [33, 6, 27, 48, 56], 48: [20, 22, 16, 27, 47], 49: [21, 23, 42, 38, 4, 54], 50: [3, 30, 33, 5, 56, 57], 51: [44, 31, 3, 10, 22, 52], 52: [46, 44, 51, 22, 11], 53: [13, 14, 15, 9], 54: [1, 25, 19, 21, 49, 4], 55: [14, 18, 41], 56: [5, 50, 33, 47, 27, 16], 57: [45, 28, 30, 50, 3, 31]}",
"",
"ca_layer Wrook: {0: [6, 38, 40, 42], 1: [2, 4, 8, 25, 54], 2: [1, 4, 8, 33, 38], 3: [5, 10, 31, 50, 51, 57], 4: [1, 2, 38, 49, 54], 5: [3, 10, 16, 50, 56], 6: [0, 33, 38, 47], 7: [11, 46], 8: [1, 2, 30, 33], 9: [13, 15, 19, 23, 25, 26, 34, 53], 10: [3, 5, 16, 22, 51], 11: [7, 22, 46, 52], 12: [32, 36], 13: [9, 14, 25, 35, 53], 14: [13, 15, 18, 35, 39, 41, 53, 55], 15: [14, 9, 26, 39, 53], 16: [10, 5, 22, 27, 48, 56], 17: [24, 31, 44, 45], 18: [14, 29, 35, 55], 19: [9, 21, 23, 25, 54], 20: [48], 21: [19, 23, 54], 22: [11, 10, 16, 48, 51, 52], 23: [19, 9, 21, 34, 42, 49], 24: [17, 46], 25: [13, 9, 19, 1, 54], 26: [9, 15, 34, 39, 43], 27: [16, 47, 48, 56], 28: [30, 45, 57], 29: [18, 32, 35, 36], 30: [8, 28, 33, 50, 57], 31: [17, 3, 44, 45, 51, 57], 32: [12, 29, 35, 36], 33: [30, 8, 2, 6, 38, 47, 50, 56], 34: [23, 9, 26, 42, 43], 35: [13, 32, 29, 18, 14], 36: [32, 12, 29], 37: [40], 38: [6, 33, 2, 4, 0, 49], 39: [26, 15, 14, 41], 40: [37, 0, 42, 43], 41: [39, 14, 55], 42: [0, 23, 34, 40, 43, 49], 43: [42, 34, 26, 40], 44: [17, 31, 46, 51, 52], 45: [31, 17, 28, 57], 46: [24, 44, 11, 7, 52], 47: [33, 6, 27, 56], 48: [20, 22, 16, 27], 49: [23, 42, 38, 4, 54], 50: [3, 30, 33, 5, 56, 57], 51: [44, 31, 3, 10, 22, 52], 52: [46, 44, 51, 22, 11], 53: [13, 14, 15, 9], 54: [1, 25, 19, 21, 49, 4], 55: [14, 18, 41], 56: [5, 50, 33, 47, 27, 16], 57: [45, 28, 30, 50, 3, 31]}",
"",
"grid_layer Wqueen: {0: [1, 5, 6], 1: [0, 2, 5, 6, 7], 2: [1, 3, 6, 7, 8], 3: [2, 4, 7, 8, 9], 4: [3, 8, 9], 5: [0, 1, 6, 10, 11], 6: [0, 1, 5, 2, 7, 10, 11, 12], 7: [1, 2, 6, 3, 8, 11, 12, 13], 8: [2, 3, 7, 4, 9, 12, 13, 14], 9: [3, 4, 8, 13, 14], 10: [5, 6, 11, 15, 16], 11: [5, 6, 10, 7, 12, 15, 16, 17], 12: [6, 7, 11, 8, 13, 16, 17, 18], 13: [7, 8, 12, 9, 14, 17, 18, 19], 14: [8, 9, 13, 18, 19], 15: [10, 11, 16, 20, 21], 16: [10, 11, 15, 12, 17, 20, 21, 22], 17: [11, 12, 16, 13, 18, 21, 22, 23], 18: [12, 13, 17, 14, 19, 22, 23, 24], 19: [13, 14, 18, 23, 24], 20: [15, 16, 21], 21: [15, 16, 20, 17, 22], 22: [16, 17, 21, 18, 23], 23: [17, 18, 22, 19, 24], 24: [18, 19, 23]}",
"",
"grid_layer Wrook: {0: [1, 5], 1: [0, 2, 6], 2: [1, 3, 7], 3: [2, 4, 8], 4: [3, 9], 5: [0, 6, 10], 6: [1, 5, 7, 11], 7: [2, 6, 8, 12], 8: [3, 7, 9, 13], 9: [4, 8, 14], 10: [5, 11, 15], 11: [6, 10, 12, 16], 12: [7, 11, 13, 17], 13: [8, 12, 14, 18], 14: [9, 13, 19], 15: [10, 16, 20], 16: [11, 15, 17, 21], 17: [12, 16, 18, 22], 18: [13, 17, 19, 23], 19: [14, 18, 24], 20: [15, 21], 21: [16, 20, 22], 22: [17, 21, 23], 23: [18, 22, 24], 24: [19, 23]}"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"source": [
"Both the _Wqueen_ and _Wrook_ are properties of the layer object. They are a Python dictionary where the key is the area ID and the value is a Python list containing the IDs of the neighbouring areas."
]
},
{
"cell_type": "markdown",
"source": [
"**Layer's fieldNames**",
"The fieldNames property is a list with the identifiers of the data loaded with the shape file."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ca_layer.fieldNames"
],
"language": "python",
"outputs": [
{
"output_type": "pyout",
"prompt_number": 8,
"text": [
"['ID',",
" 'NAME',",
" 'STATE_FIP',",
" 'STATE',",
" 'MYID',",
" 'AREA',",
" 'PERIMETER',",
" 'PCR1969',",
" 'PCR1970',",
" 'PCR1971',",
" 'PCR1972',",
" 'PCR1973',",
" 'PCR1974',",
" 'PCR1975',",
" 'PCR1976',",
" 'PCR1977',",
" 'PCR1978',",
" 'PCR1979',",
" 'PCR1980',",
" 'PCR1981',",
" 'PCR1982',",
" 'PCR1983',",
" 'PCR1984',",
" 'PCR1985',",
" 'PCR1986',",
" 'PCR1987',",
" 'PCR1988',",
" 'PCR1989',",
" 'PCR1990',",
" 'PCR1991',",
" 'PCR1992',",
" 'PCR1993',",
" 'PCR1994',",
" 'PCR1995',",
" 'PCR1996',",
" 'PCR1997',",
" 'PCR1998',",
" 'PCR1999',",
" 'PCR2000',",
" 'PCR2001',",
" 'PCR2002',",
" 'POP1969',",
" 'POP1970',",
" 'POP1971',",
" 'POP1972',",
" 'POP1973',",
" 'POP1974',",
" 'POP1975',",
" 'POP1976',",
" 'POP1977',",
" 'POP1978',",
" 'POP1979',",
" 'POP1980',",
" 'POP1981',",
" 'POP1982',",
" 'POP1983',",
" 'POP1984',",
" 'POP1985',",
" 'POP1986',",
" 'POP1987',",
" 'POP1988',",
" 'POP1989',",
" 'POP1990',",
" 'POP1991',",
" 'POP1992',",
" 'POP1993',",
" 'POP1994',",
" 'POP1995',",
" 'POP1996',",
" 'POP1997',",
" 'POP1998',",
" 'POP1999',",
" 'POP2000',",
" 'POP2001',",
" 'POP2002']"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"source": [
"Having the data identifiers we can extract the data using the getVars method that returns a Python dictionary where the key is the area ID and the value is an array of values."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Extract one column",
"print ca_layer.getVars('NAME')",
"",
"# Or extract multiple columns",
"print ca_layer.getVars(['NAME','POP2002'])"
],
"language": "python",
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Getting variables",
"Variables successfully extracted",
"{0: ['Alameda'], 1: ['Alpine'], 2: ['Amador'], 3: ['Butte'], 4: ['Calaveras'], 5: ['Colusa'], 6: ['Contra Costa'], 7: ['Del Norte'], 8: ['El Dorado'], 9: ['Fresno'], 10: ['Glenn'], 11: ['Humboldt'], 12: ['Imperial'], 13: ['Inyo'], 14: ['Kern'], 15: ['Kings'], 16: ['Lake'], 17: ['Lassen'], 18: ['Los Angeles'], 19: ['Madera'], 20: ['Marin'], 21: ['Mariposa'], 22: ['Mendocino'], 23: ['Merced'], 24: ['Modoc'], 25: ['Mono'], 26: ['Monterey'], 27: ['Napa'], 28: ['Nevada'], 29: ['Orange'], 30: ['Placer'], 31: ['Plumas'], 32: ['Riverside'], 33: ['Sacramento'], 34: ['San Benito'], 35: ['San Bernardino'], 36: ['San Diego'], 37: ['San Francisco'], 38: ['San Joaquin'], 39: ['San Luis Obispo'], 40: ['San Mateo'], 41: ['Santa Barbara'], 42: ['Santa Clara'], 43: ['Santa Cruz'], 44: ['Shasta'], 45: ['Sierra'], 46: ['Siskiyou'], 47: ['Solano'], 48: ['Sonoma'], 49: ['Stanislaus'], 50: ['Sutter'], 51: ['Tehama'], 52: ['Trinity'], 53: ['Tulare'], 54: ['Tuolumne'], 55: ['Ventura'], 56: ['Yolo'], 57: ['Yuba']}",
"Getting variables",
"Variables successfully extracted",
"{0: ['Alameda', 1465923.0], 1: ['Alpine', 1219.0], 2: ['Amador', 36742.0], 3: ['Butte', 208779.0], 4: ['Calaveras', 43113.0], 5: ['Colusa', 19363.0], 6: ['Contra Costa', 989340.0], 7: ['Del Norte', 27481.0], 8: ['El Dorado', 165513.0], 9: ['Fresno', 831946.0], 10: ['Glenn', 26797.0], 11: ['Humboldt', 127348.0], 12: ['Imperial', 145843.0], 13: ['Inyo', 18273.0], 14: ['Kern', 692474.0], 15: ['Kings', 134802.0], 16: ['Lake', 62223.0], 17: ['Lassen', 33576.0], 18: ['Los Angeles', 9768236.0], 19: ['Madera', 128824.0], 20: ['Marin', 246824.0], 21: ['Mariposa', 17318.0], 22: ['Mendocino', 87516.0], 23: ['Merced', 224976.0], 24: ['Modoc', 9306.0], 25: ['Mono', 13043.0], 26: ['Monterey', 411140.0], 27: ['Napa', 129894.0], 28: ['Nevada', 95093.0], 29: ['Orange', 2926160.0], 30: ['Placer', 278515.0], 31: ['Plumas', 21003.0], 32: ['Riverside', 1695369.0], 33: ['Sacramento', 1301627.0], 34: ['San Benito', 55757.0], 35: ['San Bernardino', 1806450.0], 36: ['San Diego', 2904687.0], 37: ['San Francisco', 761983.0], 38: ['San Joaquin', 613153.0], 39: ['San Luis Obispo', 252064.0], 40: ['San Mateo', 700341.0], 41: ['Santa Barbara', 401757.0], 42: ['Santa Clara', 1677426.0], 43: ['Santa Cruz', 253295.0], 44: ['Shasta', 171784.0], 45: ['Sierra', 3489.0], 46: ['Siskiyou', 44231.0], 47: ['Solano', 409510.0], 48: ['Sonoma', 465862.0], 49: ['Stanislaus', 481014.0], 50: ['Sutter', 82273.0], 51: ['Tehama', 57454.0], 52: ['Trinity', 13254.0], 53: ['Tulare', 381039.0], 54: ['Tuolumne', 56008.0], 55: ['Ventura', 781159.0], 56: ['Yolo', 180011.0], 57: ['Yuba', 62386.0]}"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"source": [
"From there you can use this dictionaries as you normally would using Python.",
"",
"**Geometry** Last thing we will explore this time is the geometry of the shape we just loaded.",
"To do this we can use the following methods: _getBbox_, _getCentroids_ and _getGeometricAreas_."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print \"Bounding box of the layer:\\n\", ca_layer.getBbox()",
"print \"\\nCoordinates of each polygon's centroid:\\n\", ca_layer.getCentroids()",
"print \"\\nGeometric area of each polygon:\\n\", ca_layer.getGeometricAreas()"
],
"language": "python",
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Bounding box of the layer:",
"(-13849215.0216, 3810651.8715000004, -12705407.2537, 5133802.977899998)",
"",
"Coordinates of each polygon's centroid:",
"Processing geometric areas",
"Done",
"{0: [-13568659.919586366, 4503413.063612468], 1: [-13338372.215979917, 4637531.6768424865], 2: [-13430882.045796787, 4616096.512727589], 3: [-13536520.309179187, 4790502.499745239], 4: [-13419984.710444089, 4582013.482539735], 5: [-13607377.60687068, 4720192.822275058], 6: [-13573009.426717965, 4541750.500248381], 7: [-13792155.732439086, 5094215.939646447], 8: [-13416714.916405657, 4663289.480117444], 9: [-13319197.243995998, 4380231.914094166], 10: [-13624642.23431504, 4780557.530761684], 11: [-13789745.01937965, 4940740.548065312], 12: [-12842419.767405918, 3877368.457248818], 13: [-13070398.220065115, 4346359.124208251], 14: [-13216999.188649021, 4186062.4704793678], 15: [-13337783.804150525, 4285929.164384436], 16: [-13664853.450876404, 4709153.371158138], 17: [-13424562.10058535, 4936793.228410435], 18: [-13160714.601288402, 4048240.6935433187], 19: [-13331807.234915387, 4443863.697048869], 20: [-13661436.093311014, 4563414.568985493], 21: [-13347796.208296021, 4494482.175947623], 22: [-13735889.12302683, 4758165.55509784], 23: [-13438225.181055542, 4440263.2055810755], 24: [-13439052.651975656, 5071553.099204304], 25: [-13234556.374408357, 4544807.466138684], 26: [-13496355.326297682, 4305487.455694987], 27: [-13617772.683528531, 4624707.534311376], 28: [-13443815.780664323, 4737914.395268852], 29: [-13109123.387110595, 3965293.3584607164], 30: [-13438152.9326567, 4703903.310220157], 31: [-13451723.687715685, 4839259.111399066], 32: [-12912367.250883367, 3970782.0348184193], 33: [-13507935.889340824, 4616562.908849987], 34: [-13478055.376873886, 4358891.890145417], 35: [-12933022.016070718, 4118558.098666614], 36: [-12994910.64984747, 3876686.1000765674], 37: [-13630999.501299726, 4518811.469500262], 38: [-13499919.919868827, 4544013.408017299], 39: [-13403416.238293068, 4192003.5300912694], 40: [-13617402.38900018, 4472389.9554926595], 41: [-13360251.215654947, 4095464.879514745], 42: [-13547194.159427388, 4445769.069474665], 43: [-13581197.11435818, 4421214.244132896], 44: [-13585433.711149536, 4949833.469471982], 45: [-13415787.879356084, 4777934.4592928], 46: [-13641136.58655161, 5071983.217357874], 47: [-13573367.305089545, 4591299.189713263], 48: [-13679783.383839797, 4627817.291038316], 49: [-13469357.95243801, 4491395.628752196], 50: [-13547026.266442705, 4699770.334356141], 51: [-13606986.489451626, 4856779.688482221], 52: [-13704749.605066169, 4933513.55758898], 53: [-13224817.616867151, 4305885.060021143], 54: [-13353290.198194833, 4557087.129823579], 55: [-13256343.395780759, 4066212.6464521573], 56: [-13570056.24491133, 4650245.633251788], 57: [-13508731.568547059, 4733319.941590071]}"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"",
"",
"Geometric area of each polygon:",
"Processing geometric areas",
"Done",
"{0: 3108321800.5820312, 1: 3141322375.7226562, 2: 2547049328.8671875, 3: 7310071012.136719, 4: 4338156715.0234375, 5: 4969871133.613281, 6: 3149200450.6289062, 7: 4705894364.058594, 8: 7599260770.140625, 9: 24223594696.015625, 10: 5773798622.015625, 11: 16226764037.457031, 12: 16483687478.710938, 13: 40911603668.70703, 14: 31697765299.308594, 15: 5503534454.2421875, 16: 5703143196.484375, 17: 21192838314.5, 18: 15538836545.734375, 19: 8773033085.925781, 20: 2273285525.9296875, 21: 6017271483.527344, 22: 15218627771.582031, 23: 8027682224.042969, 24: 19404443747.214844, 25: 13009352723.707031, 26: 13149450775.453125, 27: 3324242362.1484375, 28: 4203259051.8867188, 29: 2986245536.53125, 30: 6439967903.96875, 31: 11505610290.414062, 32: 27301274272.67578, 33: 4192057172.7734375, 34: 5577376003.628906, 35: 77145020084.69531, 36: 15661603577.3125, 37: 202862566.37890625, 38: 5923598317.199219, 39: 12917199330.90625, 40: 1889379664.0625, 41: 10501274949.964844, 42: 5315647723.28125, 43: 1811574275.0820312, 44: 17319422674.6875, 45: 4182072183.6835938, 46: 29305219389.515625, 47: 3722485525.2265625, 48: 6711028502.027344, 49: 6227170623.6796875, 50: 2604709852.703125, 51: 13085397427.78125, 52: 14390904869.09375, 53: 19211393642.875, 54: 9468555258.746094, 55: 7061990877.597656, 56: 4335799114.730469, 57: 2773976792.0351562}"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"source": [
"This is what the geometric information looks like for a grid."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print \"Bounding box of the layer:\\n\", grid_layer.getBbox()",
"print \"\\nCoordinates of each polygon's centroid:\\n\", grid_layer.getCentroids()",
"print \"\\nGeometric area of each polygon:\\n\", grid_layer.getGeometricAreas()"
],
"language": "python",
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Bounding box of the layer:",
"(0, 0.0, 100.0, 100)",
"",
"Coordinates of each polygon's centroid:",
"Processing geometric areas",
"Done",
"{0: [10.0, 90.0], 1: [30.0, 90.0], 2: [50.0, 90.0], 3: [70.0, 90.0], 4: [90.0, 90.0], 5: [10.0, 70.0], 6: [30.0, 70.0], 7: [50.0, 70.0], 8: [70.0, 70.0], 9: [90.0, 70.0], 10: [10.0, 50.0], 11: [30.0, 50.0], 12: [50.0, 50.0], 13: [70.0, 50.0], 14: [90.0, 50.0], 15: [10.0, 30.0], 16: [30.0, 30.0], 17: [50.0, 30.0], 18: [70.0, 30.0], 19: [90.0, 30.0], 20: [10.0, 10.0], 21: [30.0, 10.0], 22: [50.0, 10.0], 23: [70.0, 10.0], 24: [90.0, 10.0]}",
"",
"Geometric area of each polygon:",
"Processing geometric areas",
"Done",
"{0: 400.0, 1: 400.0, 2: 400.0, 3: 400.0, 4: 400.0, 5: 400.0, 6: 400.0, 7: 400.0, 8: 400.0, 9: 400.0, 10: 400.0, 11: 400.0, 12: 400.0, 13: 400.0, 14: 400.0, 15: 400.0, 16: 400.0, 17: 400.0, 18: 400.0, 19: 400.0, 20: 400.0, 21: 400.0, 22: 400.0, 23: 400.0, 24: 400.0}"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"source": [
"We will leave it there. Here we are just covering the basics but if you are interested check out the full documentation for this version [here](http://www.rise-group.org/risem/clusterpy/clusterpy0_9_9/documentation.html)."
]
}
]
}
]
}
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