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README.md
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###Jupyter Notebooks###
###OSGeo-live###
This directory host a set of **[IPython Notebook](http://ipython.org/notebook.html)** that are used in the **[OSGeo-live](http://live.osgeo.org/en/index.html)** project to demostrate the capabilities of *Open Source Geospatial Tools* installed on the *OSGeo-Live*.
To start the *Jupyter Notebook* server on the *OSGeo-Live*, use the *GEOSPATIAL* menu *start Jupyter Notebook*.
To access to the server select in the same menu *Jupyter Notebook* it will open the browser pointing to the *Jupyter Notebook* dashboard running on ```htpp://localhost:8883``` .
In this repository are stored a set of [Jupyter notebooks](http://jupyter.org/) to demostrate the use of open source geospatial tools.
The ```OSGeo-live``` directory host notebooks that can be executed on the OSGeo live project.
Other Jupyter Notebooks, running open source tools not installed on the live will be stored in the current directory.
This repository can be rendered as static html using the [NBViewer](http://nbviewer.jupyter.org/github/OSGeo/OSGeoLive-Notebooks/tree/master/) service.
From the OSGeo-Live you can upgrade to the latest version of this repository running the command ```git pull``` from the notebook directory : ```/home/user/jupyter/notebooks```.

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import fiona\n",
"\n",
"from matplotlib.patches import Polygon\n",
"from shapely.geometry import shape, box\n",
"from shapely.ops import cascaded_union\n",
"\n",
"## Fiona, IPython Notebook interaction\n",
"## Live 8.5 * darkblue-b\n",
"##\n",
"## "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Terminal Commands\n",
"----------------------\n",
"``Shell script can be executed with results stored into python variables``\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"shps = !ls /home/user/data/north_carolina/shape/*shp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Task: quickly examine the bounding areas of a directory of shapefiles\n",
"------------------------------------------------------------------\n",
"* use ``fiona.open()`` to read data files on disk\n",
"* save the filename and bounding box into a python ``dict``\n",
"\n",
".\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"td = {}\n",
"\n",
"for shp in shps:\n",
" with fiona.open( shp, 'r') as inp:\n",
" td[ inp.name ] = inp.bounds\n",
"\n",
"## Fiona inp.bounds => ( lower_lng, lower_lat, upper_lng, upper_lat)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Create shapely geometry from the coords\n",
"## shapely/geometry/geo.py\n",
"## box(minx, miny, maxx, maxy, ccw=True)\n",
"\n",
"nboxes = []\n",
"for k,v in iter(td.iteritems()):\n",
" nboxes.append( box( v[0], v[1], v[2], v[3]) )\n",
"\n",
"print 'Found BBOXs: ',len(nboxes)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## create a single cascaded UNION too\n",
"dst_poly = cascaded_union(nboxes)\n",
"dst_poly.bounds"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Draw every BBOX for all files, transparently\n",
"## use matplotlib.Polygon to draw; let autoscale calculate the area\n",
"\n",
"fig, ax = plt.subplots(figsize=(12,12))\n",
"for polygon in nboxes:\n",
" mpl_poly = Polygon(np.array(polygon.exterior), facecolor=\"y\", alpha=0.02)\n",
" ax.add_patch(mpl_poly)\n",
"\n",
"## Indicate the exterior of the study area with a heavy line\n",
"ax.add_patch( Polygon(np.array(dst_poly.exterior), fill=False, lw=4, ec=\"b\", alpha=0.9) )\n",
"\n",
"ax.relim()\n",
"ax.autoscale()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.core.display import Image\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"\n",
"import fiona\n",
"import mapnik\n",
"import shapely.geometry\n",
"\n",
"import os\n",
"\n",
"## Fiona, mapnik demo\n",
"## Live 8.5 * darkblue-b\n",
"##\n",
"## based on UoLPythonGroup/data-hack-0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"BASE_FOLDER = '/home/user/data/north_carolina/shape'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"c_shapefile = os.path.join(BASE_FOLDER, 'nc_state.shp')\n",
"f = fiona.open(c_shapefile)\n",
"shps = list(f)\n",
"print 'f: ',type(f)\n",
"print f.schema"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"type(f[2])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Use the Shapely geometry classes\n",
"## instantiate a Polygon from the Fiona collection "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import shapely.geometry\n",
"\n",
"geo = shapely.geometry.shape(f[0]['geometry'])\n",
"geo"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## plt.plot(*geo.xy) ## hmm not implemented"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Mapnik\n",
"## load the Mapnik python interfaces\n",
"## read the shapefile directly with Mapnik libs\n",
"## use the IPython Image interface as the drawing target"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def show_mapnik(m):\n",
" \"\"\"Returns an IPython Image of the rendered map.\"\"\"\n",
" im = mapnik.Image(m.width, m.height)\n",
" mapnik.render(m, im)\n",
" return Image(data=im.tostring('png32'))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import mapnik\n",
"\n",
"m = mapnik.Map(600, 600)\n",
"\n",
"layer = mapnik.Layer('contour')\n",
"layer.datasource = mapnik.Shapefile(file=c_shapefile)\n",
"\n",
"style = mapnik.Style()\n",
"rule = mapnik.Rule()\n",
"\n",
"line_symbolizer = mapnik.LineSymbolizer(mapnik.Color('green'),0.4)\n",
"rule.symbols.append(line_symbolizer)\n",
"\n",
"m.layers.append(layer)\n",
"style.rules.append(rule)\n",
"m.append_style('My Style', style)\n",
"layer.styles.append('My Style')\n",
"\n",
"m.layers.append(layer)\n",
"m.zoom_all()\n",
"show_mapnik(m)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from matplotlib.patches import Polygon\n",
"%matplotlib inline\n",
"\n",
"import fiona\n",
"from shapely.geometry import shape\n",
"\n",
"import numpy as np\n",
"import os\n",
"from collections import defaultdict, OrderedDict \n",
"\n",
"## Fiona, ogr2ogr, pyplot demo\n",
"## Live 8.5 * darkblue-b\n",
"##"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`Demo Area Analysis in IPython Notebook`\n",
"\n",
" * Use North Carolina sample data supplied on the OSGeo Live \n",
" * Extract an area of interest\n",
" * Extract a subset of a state soils record based on a bounding box (BBOX)\n",
" * Plot geometry graphically\n",
" * Buffer the geometry by a fixed amount; calculate the areas of intersection for soil types\n",
" * Produce a chart of the totals with pyplot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"infile = '/usr/local/share/data/north_carolina/shape/urbanarea.shp'\n",
"infile_soils = '/usr/local/share/data/north_carolina/shape/soils_general.shp'\n",
"\n",
"out_farmville = '/tmp/farmville_shp'\n",
"out_farmville_soils = '/tmp/farmville_soil_shp'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## -- Call the gdal/ogr tool suite to extract and subset the original statewide dataset\n",
"## use a feature of IPython Notebook to pass python variables to a command line\n",
"\n",
"!rm -rf $out_farmville\n",
"!ogr2ogr -f 'ESRI Shapefile' $out_farmville $infile -where \"NAME='Farmville'\" -dim 2 -a_srs EPSG:3358\n",
"\n",
"##-- advanced example -- raleigh is multiple polygons\n",
"#!rm -rf /tmp/raleigh1_shp\n",
"#!ogr2ogr -f 'ESRI Shapefile' /tmp/raleigh1_shp $infile -where \"NAME='Raleigh'\" -dim 2 -a_srs EPSG:3358\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## -- use the IPython notebook to get help on py module Fiona\n",
"# fiona?\n",
"# fiona.open?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## open and inspect a Shapefile using Fiona\n",
"## a POLYGON record could be very long, so dont print the record w/ geometry\n",
"with fiona.open( out_farmville ) as f:\n",
" crs = f.crs\n",
" print 'CRS:',crs\n",
" rec = f.next()\n",
" #print rec\n",
" print 'SHAPELY REC:',rec.keys()\n",
" print 'SHAPEFILE FLDS: ',rec['properties'].keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with fiona.open( out_farmville,'r') as inp:\n",
" #print dir(inp) ## whats inside this object?\n",
" #print inp.bounds\n",
" \n",
" ## take the bounding box of the area of interest\n",
" ## add 300 meters on each side for aesthetics\n",
" ## (left, bottom, right, top)\n",
" left_bnds = inp.bounds[0] - 300\n",
" bottom_bnds = inp.bounds[1] - 300\n",
" right_bnds = inp.bounds[2] + 300\n",
" top_bnds = inp.bounds[3] + 300\n",
"\n",
"## echo one variable to sanity check\n",
"#left_bnds"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## ogr2ogr ... -clipsrc [xmin ymin xmax ymax] ...\n",
"\n",
"!rm -rf $out_farmville_soils\n",
"!ogr2ogr -f 'ESRI Shapefile' $out_farmville_soils $infile_soils -clipsrc $left_bnds $bottom_bnds $right_bnds $top_bnds -dim 2 -a_srs EPSG:3358\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## ----------------------------------------\n",
"## plot_multipolygon function\n",
"## Author: Kelsey Jordahl, Enthought\n",
"## Scipy 2013 geospatial tutorial\n",
"\n",
"def plot_polygon(ax, poly, color='black'):\n",
" a = np.asarray(poly.exterior)\n",
" ax.add_patch(Polygon(a, facecolor=color, alpha=0.5))\n",
" ax.plot(a[:, 0], a[:, 1], color=color)\n",
"\n",
"def plot_multipolygon(ax, geom, color='red'):\n",
" \"\"\" Can safely call with either Polygon or Multipolygon geometry\n",
" \"\"\"\n",
" if geom.type == 'Polygon':\n",
" plot_polygon(ax, geom, color)\n",
" elif geom.type == 'MultiPolygon':\n",
" for poly in geom.geoms:\n",
" plot_polygon(ax, poly, color)\n",
"## ----------------------------------------"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"soil_colors = ['green', 'brown', '#ceFFde']\n",
"\n",
"dst_geoms = OrderedDict()\n",
"src_geoms = OrderedDict()\n",
"\n",
"cnt = 0\n",
"with fiona.open( out_farmville ) as f:\n",
" for rec in f:\n",
" src_geoms[cnt] = shape(rec['geometry'])\n",
" cnt += 1\n",
"\n",
"cnt = 0\n",
"with fiona.open( out_farmville_soils ) as f:\n",
" for rec in f:\n",
" ## check the geometry type if desired\n",
" ## intersections may result in POINT or LINESTRING\n",
" if rec['geometry']['type'] != 'Polygon':\n",
" continue\n",
" gsl = rec['properties']['GSL_NAME']\n",
" dst_geoms[gsl] = shape(rec['geometry'])\n",
" cnt += 1\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"fig, ax = plt.subplots(figsize=(11,11))\n",
"plt.title(\"Soil Class and Urban Area via N. Carolina Sample Dataset (CC-by-SA) OSGeo\")\n",
"\n",
"cnt = 0\n",
"for key in dst_geoms :\n",
" ## cnt mod (number of colors) is always a safe index into colors[]\n",
" color = soil_colors[ cnt%len(soil_colors) ]\n",
" plot_multipolygon(ax, dst_geoms[key], color=color)\n",
" cnt += 1\n",
" \n",
"cnt = 0\n",
"color = 'gray'\n",
"with fiona.open( out_farmville ) as f:\n",
" for rec in f:\n",
" plot_multipolygon(ax, src_geoms[cnt], color=color)\n",
" cnt += 1\n",
"\n",
"#ax.add_patch(Polygon( src_geoms[0].centroid, facecolor='black', alpha=0.5))\n",
"\n",
"labels = ax.get_xticklabels() \n",
"for label in labels: \n",
" label.set_rotation(90) \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## show all the variables defined so far\n",
"%whos"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## what geomtries have been created ?\n",
"print 'src_geoms len: ',len(src_geoms)\n",
"print 'dst_geoms len: ',len(dst_geoms )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## buffer the urbanarea by N meters\n",
"## save the result in the source-geometry list\n",
"\n",
"src_geom_buffered = src_geoms[0].buffer(166)\n",
"src_geoms[1] = src_geom_buffered\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig, ax = plt.subplots(figsize=(11,11))\n",
"\n",
"cnt = 0\n",
"for key in dst_geoms :\n",
" color = soil_colors[ cnt%len(soil_colors) ]\n",
" plot_multipolygon(ax, dst_geoms[key], color=color)\n",
" cnt += 1\n",
"\n",
"plot_multipolygon(ax, src_geoms[1], color='gray')\n",
"plot_multipolygon(ax, src_geoms[0], color='gray')\n",
" \n",
"labels = ax.get_xticklabels() \n",
"for label in labels: \n",
" label.set_rotation(90) \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## take the intersection of each soil area against the buffered poly of interest\n",
"## store into a third convenient list\n",
"res_geoms = OrderedDict()\n",
"for key in dst_geoms:\n",
" res_geoms[key] = src_geom_buffered.intersection( dst_geoms[key] )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig, ax = plt.subplots(figsize=(11,11))\n",
"\n",
"cnt = 0\n",
"for key in res_geoms :\n",
" color = soil_colors[ cnt%len(soil_colors) ]\n",
" plot_multipolygon(ax, res_geoms[key], color=color)\n",
" cnt += 1\n",
" \n",
"labels = ax.get_xticklabels() \n",
"for label in labels: \n",
" label.set_rotation(90) \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## define a lookup table of mock attributes of each soil type\n",
"## (substitute a real data source here)\n",
"\n",
"soils_lkup = {\n",
" 'NC038' : { 'TEXTURE_CLAY':0.53, 'TEXTURE_SILT':0.33, 'TEXTURE_SAND':0.44 },\n",
" 'NC035' : { 'TEXTURE_CLAY':0.70, 'TEXTURE_SILT':0.33, 'TEXTURE_SAND':0.44 },\n",
" 'NC034' : { 'TEXTURE_CLAY':0.23, 'TEXTURE_SILT':0.74, 'TEXTURE_SAND':0.44 }\n",
"}\n",
"\n",
"## get a rough total area for display purposes\n",
"sum_areas = 0.0\n",
"for key in res_geoms:\n",
" sum_areas += int(res_geoms[key].area)\n",
"\n",
"## record the area and percentage area in a convenient dictionary \n",
"tdd = {}\n",
"for key in res_geoms:\n",
" tdd[key] = soils_lkup[key]\n",
" tdd[key]['area'] = int(res_geoms[key].area)\n",
" tdd[key]['perc'] = int((res_geoms[key].area/sum_areas)*100)/100.0\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Now all attributes for visualization are available in a single dict\n",
"## in a larger system this could be delivered for serving graphical reports\n",
"tdd"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## What matplotlib pylot graphs are available ?\n",
"## http://matplotlib.org/api/pyplot_api.html\n",
"##\n",
"## Use IPython built-in help to discover pie chart attributes\n",
"\n",
"plt.pie?\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# The slices will be ordered and plotted counter-clockwise.\n",
"labels = [ 'NC034', 'NC035', 'NC038' ]\n",
"sizes = [ tdd['NC034']['perc']*100, tdd['NC035']['perc']*100, tdd['NC038']['perc']*100 ]\n",
"pie_colors = ['lightcoral', '#eeefee', 'yellowgreen']\n",
"# only \"explode\" the 2nd slice (i.e. 'NC035')\n",
"explode = (0, 0.1, 0)\n",
"\n",
"plt.pie(sizes, explode=explode, labels=labels, colors=pie_colors,\n",
" autopct='%1.1f%%', shadow=True, startangle=90)\n",
"# Set aspect ratio to be equal so that pie is drawn as a circle.\n",
"plt.axis('equal')\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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State,Unemployment
AL,7.1
AK,6.8
AZ,8.1
AR,7.2
CA,10.1
CO,7.7
CT,8.4
DE,7.1
FL,8.2
GA,8.8
HI,5.4
ID,6.6
IL,8.8
IN,8.4
IA,5.1
KS,5.6
KY,8.1
LA,5.9
ME,7.2
MD,6.8
MA,6.7
MI,9.1
MN,5.6
MS,9.1
MO,6.7
MT,5.8
NE,3.9
NV,10.3
NH,5.7
NJ,9.6
NM,6.8
NY,8.4
NC,9.4
ND,3.2
OH,6.9
OK,5.2
OR,8.5
PA,8
RI,10.1
SC,8.8
SD,4.4
TN,7.8
TX,6.4
UT,5.5
VT,5
VA,5.8
WA,7.8
WV,7.5
WI,6.8
WY,5.1
1 State Unemployment
2 AL 7.1
3 AK 6.8
4 AZ 8.1
5 AR 7.2
6 CA 10.1
7 CO 7.7
8 CT 8.4
9 DE 7.1
10 FL 8.2
11 GA 8.8
12 HI 5.4
13 ID 6.6
14 IL 8.8
15 IN 8.4
16 IA 5.1
17 KS 5.6
18 KY 8.1
19 LA 5.9
20 ME 7.2
21 MD 6.8
22 MA 6.7
23 MI 9.1
24 MN 5.6
25 MS 9.1
26 MO 6.7
27 MT 5.8
28 NE 3.9
29 NV 10.3
30 NH 5.7
31 NJ 9.6
32 NM 6.8
33 NY 8.4
34 NC 9.4
35 ND 3.2
36 OH 6.9
37 OK 5.2
38 OR 8.5
39 PA 8
40 RI 10.1
41 SC 8.8
42 SD 4.4
43 TN 7.8
44 TX 6.4
45 UT 5.5
46 VT 5
47 VA 5.8
48 WA 7.8
49 WV 7.5
50 WI 6.8
51 WY 5.1

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[{"WA": 7.8, "DE": 7.1, "WI": 6.8, "WV": 7.5, "HI": 5.4, "FL": 8.2, "WY": 5.1, "NH": 5.7, "NJ": 9.6, "NM": 6.8, "TX": 6.4, "LA": 5.9, "NC": 9.4, "ND": 3.2, "NE": 3.9, "TN": 7.8, "NY": 8.4, "PA": 8.0, "CA": 10.1, "NV": 10.3, "VA": 5.8, "CO": 7.7, "AK": 6.8, "AL": 7.1, "AR": 7.2, "VT": 5.0, "IL": 8.8, "GA": 8.8, "IN": 8.4, "IA": 5.1, "OK": 5.2, "AZ": 8.1, "ID": 6.6, "CT": 8.4, "ME": 7.2, "MD": 6.8, "MA": 6.7, "OH": 6.9, "UT": 5.5, "MO": 6.7, "MN": 5.6, "MI": 9.1, "RI": 10.1, "KS": 5.6, "MT": 5.8, "MS": 9.1, "SC": 8.8, "KY": 8.1, "OR": 8.5, "SD": 4.4}]

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.display import HTML\n",
"import folium\n",
"\n",
"def inline_map(map):\n",
" \"\"\"\n",
" Embeds the HTML source of the map directly into the IPython notebook.\n",
" \n",
" This method will not work if the map depends on any files (json data). Also this uses\n",
" the HTML5 srcdoc attribute, which may not be supported in all browsers.\n",
" \"\"\"\n",
" map._build_map()\n",
" return HTML('<iframe srcdoc=\"{srcdoc}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(srcdoc=map.HTML.replace('\"', '&quot;')))\n",
"\n",
"def embed_map(map, path=\"m213map.html\"):\n",
" \"\"\"\n",
" Embeds a linked iframe to the map into the IPython notebook.\n",
" \n",
" Note: this method will not capture the source of the map into the notebook.\n",
" This method should work for all maps (as long as they use relative urls).\n",
" \"\"\"\n",
" map.create_map(path=path)\n",
" return HTML('<iframe src=\"files/{path}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(path=path))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"map = folium.Map(width=500,height=500,location=[44, -73], zoom_start=4)\n",
"\n",
"map.simple_marker([40.67, -73.94], popup='Add <b>popup</b> text here.',marker_color='green',marker_icon='ok-sign',clustered_marker=True)\n",
"map.simple_marker([44.67, -73.94], popup='Add <b>popup</b> text here.',marker_color='red',marker_icon='remove-sign',clustered_marker=True)\n",
"map.simple_marker([44.67, -71.94], popup='Add <b>popup</b> text here.',clustered_marker=True)\n",
"\n",
"map.circle_marker([44, -71], popup='', fill_color='#ff0000', radius=5000, line_color='#ff0000')\n",
"\n",
"points1 = [40,-71]\n",
"points2 = [42,-73]\n",
"map.line([points1, points2])\n",
"\n",
"inline_map(map)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import folium\n",
"import pandas as pd\n",
"\n",
"#folium.initialize_notebook()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Standard OSM\n",
"map_osm = folium.Map(location=[45.5236, -122.6750])\n",
"map_osm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"stamen = folium.Map(location=[45.5236, -122.6750], tiles='Stamen Toner',\n",
" zoom_start=13)\n",
"stamen"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mt_hood = folium.Map(location=[45.372, -121.6972], zoom_start=12,\n",
" tiles='Stamen Terrain')\n",
"mt_hood.simple_marker([45.3288, -121.6625], popup='Mt. Hood Meadows')\n",
"mt_hood.simple_marker([45.3311, -121.7113], popup='Timberline Lodge')\n",
"mt_hood"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"portland = folium.Map(location=[45.5236, -122.6750], tiles='Stamen Toner',\n",
" zoom_start=13)\n",
"portland.simple_marker(location=[45.5244, -122.6699], popup='The Waterfront')\n",
"portland.circle_marker(location=[45.5215, -122.6261], radius=500,\n",
" popup='Laurelhurst Park', line_color='#3186cc',\n",
" fill_color='#3186cc')\n",
"portland"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"latlng = folium.Map(location=[46.1991, -122.1889], tiles='Stamen Terrain',\n",
" zoom_start=13)\n",
"latlng.lat_lng_popover()\n",
"latlng"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"waypoints = folium.Map(location=[46.8527, -121.7649], tiles='Stamen Terrain',\n",
" zoom_start=13)\n",
"waypoints.simple_marker(location=[46.8354, -121.7325], popup='Camp Muir')\n",
"waypoints.click_for_marker(popup='Waypoint')\n",
"waypoints"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"polygons = folium.Map(location=[45.5236, -122.6750], zoom_start=13)\n",
"polygons.polygon_marker(location=[45.5012, -122.6655], popup='Ross Island Bridge',\n",
" fill_color='#132b5e', num_sides=3, radius=10)\n",
"polygons.polygon_marker(location=[45.5132, -122.6708], popup='Hawthorne Bridge',\n",
" fill_color='#45647d', num_sides=4, radius=10)\n",
"polygons.polygon_marker(location=[45.5275, -122.6692], popup='Steel Bridge',\n",
" fill_color='#769d96', num_sides=6, radius=10)\n",
"polygons.polygon_marker(location=[45.5318, -122.6745], popup='Broadway Bridge',\n",
" fill_color='#769d96', num_sides=8, radius=10)\n",
"polygons"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"state_geo = r'us-states.json'\n",
"state_unemployment = r'US_Unemployment_Oct2012.csv'\n",
"\n",
"state_data = pd.read_csv(state_unemployment)\n",
"\n",
"#Let Folium determine the scale\n",
"states = folium.Map(location=[48, -102], zoom_start=3)\n",
"states.geo_json(geo_path=state_geo, data=state_data,\n",
" columns=['State', 'Unemployment'],\n",
" key_on='feature.id',\n",
" fill_color='YlGn', fill_opacity=0.7, line_opacity=0.2,\n",
" legend_name='Unemployment Rate (%)')\n",
"states.create_map(path='us_state_map.html')\n",
"states"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"states2 = folium.Map(location=[48, -102], zoom_start=3)\n",
"states2.geo_json(geo_path=state_geo, data=state_data,\n",
" columns=['State', 'Unemployment'],\n",
" threshold_scale=[5, 6, 7, 8, 9, 10],\n",
" key_on='feature.id',\n",
" fill_color='BuPu', fill_opacity=0.7, line_opacity=0.5,\n",
" legend_name='Unemployment Rate (%)',\n",
" reset=True)\n",
"states2.create_map(path='us_state_map_2.html')\n",
"states2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"county_data = r'us_county_data.csv'\n",
"county_geo = r'us_counties_20m_topo.json'\n",
"\n",
"#Read into Dataframe, cast to string for consistency\n",
"df = pd.read_csv(county_data, na_values=[' '])\n",
"df['FIPS_Code'] = df['FIPS_Code'].astype(str)\n",
"\n",
"\n",
"def set_id(fips):\n",
" '''Modify FIPS code to match GeoJSON property'''\n",
" if fips == '0':\n",
" return None\n",
" elif len(fips) <= 4:\n",
" return ''.join(['0500000US0', fips])\n",
" else:\n",
" return ''.join(['0500000US', fips])\n",
"\n",
"#Apply set_id, drop NaN\n",
"df['GEO_ID'] = df['FIPS_Code'].apply(set_id)\n",
"df = df.dropna()\n",
"\n",
"#Number of employed with auto scale\n",
"map_1 = folium.Map(location=[48, -102], zoom_start=3)\n",
"map_1.geo_json(geo_path=county_geo, data_out='data1.json', data=df,\n",
" columns=['GEO_ID', 'Employed_2011'],\n",
" key_on='feature.id',\n",
" fill_color='YlOrRd', fill_opacity=0.7, line_opacity=0.3,\n",
" topojson='objects.us_counties_20m')\n",
"map_1.create_map(path='map_1.html')\n",
"map_1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"map_2 = folium.Map(location=[40, -99], zoom_start=4)\n",
"map_2.geo_json(geo_path=county_geo, data_out='data2.json', data=df,\n",
" columns=['GEO_ID', 'Unemployment_rate_2011'],\n",
" key_on='feature.id',\n",
" threshold_scale=[0, 5, 7, 9, 11, 13],\n",
" fill_color='YlGnBu', line_opacity=0.3,\n",
" legend_name='Unemployment Rate 2011 (%)',\n",
" topojson='objects.us_counties_20m')\n",
"map_2.create_map(path='map_2.html')\n",
"map_2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"map_3 = folium.Map(location=[40, -99], zoom_start=4)\n",
"map_3.geo_json(geo_path=county_geo, data_out='data3.json', data=df,\n",
" columns=['GEO_ID', 'Median_Household_Income_2011'],\n",
" key_on='feature.id',\n",
" fill_color='PuRd', line_opacity=0.3,\n",
" legend_name='Median Household Income 2011 ($)',\n",
" topojson='objects.us_counties_20m')\n",
"map_3.create_map(path='map_3.html')\n",
"map_3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.display import HTML\n",
"import folium"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def inline_map(map):\n",
" \"\"\"\n",
" Embeds the HTML source of the map directly into the IPython notebook.\n",
" \n",
" This method will not work if the map depends on any files (json data). Also this uses\n",
" the HTML5 srcdoc attribute, which may not be supported in all browsers.\n",
" \"\"\"\n",
" map._build_map()\n",
" return HTML('<iframe srcdoc=\"{srcdoc}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(srcdoc=map.HTML.replace('\"', '&quot;')))\n",
"\n",
"def embed_map(map, path=\"m213map.html\"):\n",
" \"\"\"\n",
" Embeds a linked iframe to the map into the IPython notebook.\n",
" \n",
" Note: this method will not capture the source of the map into the notebook.\n",
" This method should work for all maps (as long as they use relative urls).\n",
" \"\"\"\n",
" map.create_map(path=path)\n",
" return HTML('<iframe src=\"files/{path}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(path=path))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"map = folium.Map(width=500,height=500,location=[44, -73], zoom_start=4)\n",
"\n",
"map.simple_marker([40.67, -73.94], popup='Add <b>popup</b> text here.',marker_color='green',marker_icon='ok-sign')\n",
"map.simple_marker([44.67, -73.94], popup='Add <b>popup</b> text here.',marker_color='red',marker_icon='remove-sign')\n",
"map.simple_marker([44.67, -71.94], popup='Add <b>popup</b> text here.')\n",
"inline_map(map)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import folium\n",
"from IPython.display import HTML, Javascript, display"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def inline_map(m):\n",
" m._build_map()\n",
" srcdoc = m.HTML.replace('\"', '&quot;')\n",
" embed = HTML('<iframe srcdoc=\"{srcdoc}\" '\n",
" 'style=\"width: 100%; height: 500px; '\n",
" 'border: none\"></iframe>'.format(srcdoc=srcdoc))\n",
" return embed"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#station = st_list[st_list.keys()[0]]\n",
"#map = folium.Map(location=[station[\"lat\"], station[\"lon\"]], zoom_start=4)\n",
"\n",
"map = folium.Map(location=[41, -71], zoom_start=4)\n",
"#map.line(get_coordinates(bounding_box, bounding_box_type), line_color='#FF0000', line_weight=5)\n",
"\n",
"#plot the obs station, \n",
"#for st in st_list: \n",
"map.simple_marker([41,-72], popup='something something...<br>',marker_color=\"green\",marker_icon=\"arrow-up\",icon_angle=0)\n",
"inline_map(map) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.display import HTML\n",
"import folium"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def inline_map(map):\n",
" \"\"\"\n",
" Embeds the HTML source of the map directly into the IPython notebook.\n",
" \n",
" This method will not work if the map depends on any files (json data). Also this uses\n",
" the HTML5 srcdoc attribute, which may not be supported in all browsers.\n",
" \"\"\"\n",
" map._build_map()\n",
" return HTML('<iframe srcdoc=\"{srcdoc}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(srcdoc=map.HTML.replace('\"', '&quot;')))\n",
"\n",
"def embed_map(map, path=\"map.html\"):\n",
" \"\"\"\n",
" Embeds a linked iframe to the map into the IPython notebook.\n",
" \n",
" Note: this method will not capture the source of the map into the notebook.\n",
" This method should work for all maps (as long as they use relative urls).\n",
" \"\"\"\n",
" map.create_map(path=path)\n",
" return HTML('<iframe src=\"files/{path}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(path=path))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"map = folium.Map(width=800,height=600,location=[44, -73], zoom_start=3)\n",
"\n",
"map.add_wms_layer(wms_name=\"Temperature\",\n",
" wms_url=\"http://gis.srh.noaa.gov/arcgis/services/NDFDTemps/MapServer/WMSServer\",\n",
" wms_format=\"image/png\",\n",
" wms_layers= 16\n",
" )\n",
"\n",
"map.add_tile_layer(tile_name='hfradar 1km',\n",
" tile_url='http://hfradar.ndbc.noaa.gov/tilesavg.php?s=10&e=100&x={x}&y={y}&z={z}&t=2014-8-18 14:00:00&rez=1')\n",
"map.add_tile_layer(tile_name='hfradar 2km',\n",
" tile_url='http://hfradar.ndbc.noaa.gov/tilesavg.php?s=10&e=100&x={x}&y={y}&z={z}&t=2014-8-18 14:00:00&rez=2')\n",
"map.add_tile_layer(tile_name='hfradar 6km',\n",
" tile_url='http://hfradar.ndbc.noaa.gov/tilesavg.php?s=10&e=100&x={x}&y={y}&z={z}&t=2014-8-18 14:00:00&rez=6')\n",
"\n",
"map.add_layers_to_map()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"inline_map(map) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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<!DOCTYPE html>
<head>
<meta http-equiv="content-type" content="text/html; charset=UTF-8" />
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.css" />
<script src="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js" charset="utf-8"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/queue-async/1.0.7/queue.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/topojson/1.6.9/topojson.min.js"></script>
<style>
html, body {
width: 100%;
height: 100%;
margin: 0;
padding: 0;
}
.legend {
padding: 0px 0px;
font: 10px sans-serif;
background: white;
background: rgba(255,255,255,0.8);
box-shadow: 0 0 15px rgba(0,0,0,0.2);
border-radius: 5px;
}
.key path {
display: none;
}
</style>
</head>
<body>
<div id="map" style="width: 100%; height: 100%"></div>
<script>
queue()
.defer(d3.json, 'data1.json')
.defer(d3.json, 'us_counties_20m_topo.json')
.await(makeMap)
function onEachFeature(feature, layer) {
// does this feature have a property named popupContent?
if (feature.properties && feature.properties.popupContent) {
layer.bindPopup(feature.properties.popupContent);
}
};
function makeMap(error, data_1,tjson_1) {
topo_1 = topojson.feature(tjson_1, tjson_1.objects.us_counties_20m);
function matchKey(datapoint, key_variable){
if (typeof key_variable[0][datapoint] === 'undefined') {
return null;
}
else {
return parseFloat(key_variable[0][datapoint]);
};
};
var color = d3.scale.threshold()
.domain([40.0, 10000.0, 30000.0, 70000.0, 100000.0])
.range(['#FFFFB2', '#FED976', '#FEB24C', '#FD8D3C', '#FC4E2A', '#E31A1C']);
var map = L.map('map').setView([48, -102], 3);
L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {
maxZoom: 18,
minZoom: 1,
attribution: 'Map data (c) <a href="http://openstreetmap.org">OpenStreetMap</a> contributors'
}).addTo(map);
function style_1(feature) {
return {
fillColor: color(matchKey(feature.id, data_1)),
weight: 1,
opacity: 0.3,
color: 'black',
fillOpacity: 0.7
};
}
gJson_layer_1 = L.geoJson(topo_1, {style: style_1,onEachFeature: onEachFeature}).addTo(map)
var legend = L.control({position: 'topright'});
legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
legend.addTo(map);
var x = d3.scale.linear()
.domain([0, 110000])
.range([0, 400]);
var xAxis = d3.svg.axis()
.scale(x)
.orient("top")
.tickSize(1)
.tickValues([40.0, 10000.0, 30000.0, 70000.0, 100000.0]);
var svg = d3.select(".legend.leaflet-control").append("svg")
.attr("id", 'legend')
.attr("width", 450)
.attr("height", 40);
var g = svg.append("g")
.attr("class", "key")
.attr("transform", "translate(25,16)");
g.selectAll("rect")
.data(color.range().map(function(d, i) {
return {
x0: i ? x(color.domain()[i - 1]) : x.range()[0],
x1: i < color.domain().length ? x(color.domain()[i]) : x.range()[1],
z: d
};
}))
.enter().append("rect")
.attr("height", 10)
.attr("x", function(d) { return d.x0; })
.attr("width", function(d) { return d.x1 - d.x0; })
.style("fill", function(d) { return d.z; });
g.call(xAxis).append("text")
.attr("class", "caption")
.attr("y", 21)
.text('Employed_2011');
};
</script>
</body>

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<!DOCTYPE html>
<head>
<meta http-equiv="content-type" content="text/html; charset=UTF-8" />
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.css" />
<script src="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js" charset="utf-8"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/queue-async/1.0.7/queue.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/topojson/1.6.9/topojson.min.js"></script>
<style>
html, body {
width: 100%;
height: 100%;
margin: 0;
padding: 0;
}
.legend {
padding: 0px 0px;
font: 10px sans-serif;
background: white;
background: rgba(255,255,255,0.8);
box-shadow: 0 0 15px rgba(0,0,0,0.2);
border-radius: 5px;
}
.key path {
display: none;
}
</style>
</head>
<body>
<div id="map" style="width: 100%; height: 100%"></div>
<script>
queue()
.defer(d3.json, 'data2.json')
.defer(d3.json, 'us_counties_20m_topo.json')
.await(makeMap)
function onEachFeature(feature, layer) {
// does this feature have a property named popupContent?
if (feature.properties && feature.properties.popupContent) {
layer.bindPopup(feature.properties.popupContent);
}
};
function makeMap(error, data_1,tjson_1) {
topo_1 = topojson.feature(tjson_1, tjson_1.objects.us_counties_20m);
function matchKey(datapoint, key_variable){
if (typeof key_variable[0][datapoint] === 'undefined') {
return null;
}
else {
return parseFloat(key_variable[0][datapoint]);
};
};
var color = d3.scale.threshold()
.domain([0, 5, 7, 9, 11, 13])
.range(['#FFFFCC', '#C7E9B4', '#7FCDBB', '#41B6C4', '#1D91C0', '#225EA8', '#0C2C84']);
var map = L.map('map').setView([40, -99], 4);
L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {
maxZoom: 18,
minZoom: 1,
attribution: 'Map data (c) <a href="http://openstreetmap.org">OpenStreetMap</a> contributors'
}).addTo(map);
function style_1(feature) {
return {
fillColor: color(matchKey(feature.id, data_1)),
weight: 1,
opacity: 0.3,
color: 'black',
fillOpacity: 0.6
};
}
gJson_layer_1 = L.geoJson(topo_1, {style: style_1,onEachFeature: onEachFeature}).addTo(map)
var legend = L.control({position: 'topright'});
legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
legend.addTo(map);
var x = d3.scale.linear()
.domain([0, 14])
.range([0, 400]);
var xAxis = d3.svg.axis()
.scale(x)
.orient("top")
.tickSize(1)
.tickValues([0, 5, 7, 9, 11, 13]);
var svg = d3.select(".legend.leaflet-control").append("svg")
.attr("id", 'legend')
.attr("width", 450)
.attr("height", 40);
var g = svg.append("g")
.attr("class", "key")
.attr("transform", "translate(25,16)");
g.selectAll("rect")
.data(color.range().map(function(d, i) {
return {
x0: i ? x(color.domain()[i - 1]) : x.range()[0],
x1: i < color.domain().length ? x(color.domain()[i]) : x.range()[1],
z: d
};
}))
.enter().append("rect")
.attr("height", 10)
.attr("x", function(d) { return d.x0; })
.attr("width", function(d) { return d.x1 - d.x0; })
.style("fill", function(d) { return d.z; });
g.call(xAxis).append("text")
.attr("class", "caption")
.attr("y", 21)
.text('Unemployment Rate 2011 (%)');
};
</script>
</body>

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<!DOCTYPE html>
<head>
<meta http-equiv="content-type" content="text/html; charset=UTF-8" />
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.css" />
<script src="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js" charset="utf-8"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/queue-async/1.0.7/queue.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/topojson/1.6.9/topojson.min.js"></script>
<style>
html, body {
width: 100%;
height: 100%;
margin: 0;
padding: 0;
}
.legend {
padding: 0px 0px;
font: 10px sans-serif;
background: white;
background: rgba(255,255,255,0.8);
box-shadow: 0 0 15px rgba(0,0,0,0.2);
border-radius: 5px;
}
.key path {
display: none;
}
</style>
</head>
<body>
<div id="map" style="width: 100%; height: 100%"></div>
<script>
queue()
.defer(d3.json, 'data3.json')
.defer(d3.json, 'us_counties_20m_topo.json')
.await(makeMap)
function onEachFeature(feature, layer) {
// does this feature have a property named popupContent?
if (feature.properties && feature.properties.popupContent) {
layer.bindPopup(feature.properties.popupContent);
}
};
function makeMap(error, data_1,tjson_1) {
topo_1 = topojson.feature(tjson_1, tjson_1.objects.us_counties_20m);
function matchKey(datapoint, key_variable){
if (typeof key_variable[0][datapoint] === 'undefined') {
return null;
}
else {
return parseFloat(key_variable[0][datapoint]);
};
};
var color = d3.scale.threshold()
.domain([20000.0, 40000.0, 50000.0, 50000.0, 60000.0])
.range(['#F1EEF6', '#D4B9DA', '#C994C7', '#DF65B0', '#E7298A', '#CE1256']);
var map = L.map('map').setView([40, -99], 4);
L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {
maxZoom: 18,
minZoom: 1,
attribution: 'Map data (c) <a href="http://openstreetmap.org">OpenStreetMap</a> contributors'
}).addTo(map);
function style_1(feature) {
return {
fillColor: color(matchKey(feature.id, data_1)),
weight: 1,
opacity: 0.3,
color: 'black',
fillOpacity: 0.6
};
}
gJson_layer_1 = L.geoJson(topo_1, {style: style_1,onEachFeature: onEachFeature}).addTo(map)
var legend = L.control({position: 'topright'});
legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
legend.addTo(map);
var x = d3.scale.linear()
.domain([0, 66000])
.range([0, 400]);
var xAxis = d3.svg.axis()
.scale(x)
.orient("top")
.tickSize(1)
.tickValues([20000.0, 40000.0, 50000.0, 50000.0, 60000.0]);
var svg = d3.select(".legend.leaflet-control").append("svg")
.attr("id", 'legend')
.attr("width", 450)
.attr("height", 40);
var g = svg.append("g")
.attr("class", "key")
.attr("transform", "translate(25,16)");
g.selectAll("rect")
.data(color.range().map(function(d, i) {
return {
x0: i ? x(color.domain()[i - 1]) : x.range()[0],
x1: i < color.domain().length ? x(color.domain()[i]) : x.range()[1],
z: d
};
}))
.enter().append("rect")
.attr("height", 10)
.attr("x", function(d) { return d.x0; })
.attr("width", function(d) { return d.x1 - d.x0; })
.style("fill", function(d) { return d.z; });
g.call(xAxis).append("text")
.attr("class", "caption")
.attr("y", 21)
.text('Median Household Income 2011 ($)');
};
</script>
</body>

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projects/FOLIUM/us_counties_20m_topo.json Normal file
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projects/FOLIUM/us_county_data.csv Normal file
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<!DOCTYPE html>
<head>
<meta http-equiv="content-type" content="text/html; charset=UTF-8" />
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.css" />
<script src="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js" charset="utf-8"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/queue-async/1.0.7/queue.min.js"></script>
<style>
html, body {
width: 100%;
height: 100%;
margin: 0;
padding: 0;
}
.legend {
padding: 0px 0px;
font: 10px sans-serif;
background: white;
background: rgba(255,255,255,0.8);
box-shadow: 0 0 15px rgba(0,0,0,0.2);
border-radius: 5px;
}
.key path {
display: none;
}
</style>
</head>
<body>
<div id="map" style="width: 100%; height: 100%"></div>
<script>
queue()
.defer(d3.json, 'data.json')
.defer(d3.json, 'us-states.json')
.await(makeMap)
function onEachFeature(feature, layer) {
// does this feature have a property named popupContent?
if (feature.properties && feature.properties.popupContent) {
layer.bindPopup(feature.properties.popupContent);
}
};
function makeMap(error, data_1,gjson_1) {
function matchKey(datapoint, key_variable){
if (typeof key_variable[0][datapoint] === 'undefined') {
return null;
}
else {
return parseFloat(key_variable[0][datapoint]);
};
};
var color = d3.scale.threshold()
.domain([3.0, 7.0, 8.0, 9.0, 9.0])
.range(['#FFFFCC', '#D9F0A3', '#ADDD8E', '#78C679', '#41AB5D', '#238443']);
var map = L.map('map').setView([48, -102], 3);
L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {
maxZoom: 18,
minZoom: 1,
attribution: 'Map data (c) <a href="http://openstreetmap.org">OpenStreetMap</a> contributors'
}).addTo(map);
function style_1(feature) {
return {
fillColor: color(matchKey(feature.id, data_1)),
weight: 1,
opacity: 0.2,
color: 'black',
fillOpacity: 0.7
};
}
gJson_layer_1 = L.geoJson(gjson_1, {style: style_1,onEachFeature: onEachFeature}).addTo(map)
var legend = L.control({position: 'topright'});
legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
legend.addTo(map);
var x = d3.scale.linear()
.domain([0, 9])
.range([0, 400]);
var xAxis = d3.svg.axis()
.scale(x)
.orient("top")
.tickSize(1)
.tickValues([3.0, 7.0, 8.0, 9.0, 9.0]);
var svg = d3.select(".legend.leaflet-control").append("svg")
.attr("id", 'legend')
.attr("width", 450)
.attr("height", 40);
var g = svg.append("g")
.attr("class", "key")
.attr("transform", "translate(25,16)");
g.selectAll("rect")
.data(color.range().map(function(d, i) {
return {
x0: i ? x(color.domain()[i - 1]) : x.range()[0],
x1: i < color.domain().length ? x(color.domain()[i]) : x.range()[1],
z: d
};
}))
.enter().append("rect")
.attr("height", 10)
.attr("x", function(d) { return d.x0; })
.attr("width", function(d) { return d.x1 - d.x0; })
.style("fill", function(d) { return d.z; });
g.call(xAxis).append("text")
.attr("class", "caption")
.attr("y", 21)
.text('Unemployment Rate (%)');
};
</script>
</body>

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<!DOCTYPE html>
<head>
<meta http-equiv="content-type" content="text/html; charset=UTF-8" />
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.css" />
<script src="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js" charset="utf-8"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/queue-async/1.0.7/queue.min.js"></script>
<style>
html, body {
width: 100%;
height: 100%;
margin: 0;
padding: 0;
}
.legend {
padding: 0px 0px;
font: 10px sans-serif;
background: white;
background: rgba(255,255,255,0.8);
box-shadow: 0 0 15px rgba(0,0,0,0.2);
border-radius: 5px;
}
.key path {
display: none;
}
</style>
</head>
<body>
<div id="map" style="width: 100%; height: 100%"></div>
<script>
queue()
.defer(d3.json, 'data.json')
.defer(d3.json, 'us-states.json')
.await(makeMap)
function onEachFeature(feature, layer) {
// does this feature have a property named popupContent?
if (feature.properties && feature.properties.popupContent) {
layer.bindPopup(feature.properties.popupContent);
}
};
function makeMap(error, data_1,gjson_1) {
function matchKey(datapoint, key_variable){
if (typeof key_variable[0][datapoint] === 'undefined') {
return null;
}
else {
return parseFloat(key_variable[0][datapoint]);
};
};
var color = d3.scale.threshold()
.domain([5, 6, 7, 8, 9, 10])
.range(['#EDF8FB', '#BFD3E6', '#9EBCDA', '#8C96C6', '#8C6BB1', '#88419D', '#6E016B']);
var map = L.map('map').setView([48, -102], 3);
L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {
maxZoom: 18,
minZoom: 1,
attribution: 'Map data (c) <a href="http://openstreetmap.org">OpenStreetMap</a> contributors'
}).addTo(map);
function style_1(feature) {
return {
fillColor: color(matchKey(feature.id, data_1)),
weight: 1,
opacity: 0.5,
color: 'black',
fillOpacity: 0.7
};
}
gJson_layer_1 = L.geoJson(gjson_1, {style: style_1,onEachFeature: onEachFeature}).addTo(map)
var legend = L.control({position: 'topright'});
legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
legend.addTo(map);
var x = d3.scale.linear()
.domain([0, 11])
.range([0, 400]);
var xAxis = d3.svg.axis()
.scale(x)
.orient("top")
.tickSize(1)
.tickValues([5, 6, 7, 8, 9, 10]);
var svg = d3.select(".legend.leaflet-control").append("svg")
.attr("id", 'legend')
.attr("width", 450)
.attr("height", 40);
var g = svg.append("g")
.attr("class", "key")
.attr("transform", "translate(25,16)");
g.selectAll("rect")
.data(color.range().map(function(d, i) {
return {
x0: i ? x(color.domain()[i - 1]) : x.range()[0],
x1: i < color.domain().length ? x(color.domain()[i]) : x.range()[1],
z: d
};
}))
.enter().append("rect")
.attr("height", 10)
.attr("x", function(d) { return d.x0; })
.attr("width", function(d) { return d.x1 - d.x0; })
.style("fill", function(d) { return d.z; });
g.call(xAxis).append("text")
.attr("class", "caption")
.attr("y", 21)
.text('Unemployment Rate (%)');
};
</script>
</body>

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import cartopy\n",
"import cartopy.crs as ccrs\n",
"import cartopy.feature as cfeature\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"## Natural Earth 2 cache test\n",
"## Live 8.5 * darkblue-b\n",
"##"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ax = plt.axes(projection=ccrs.PlateCarree() ) \n",
"\n",
"ax.add_feature(cfeature.LAND)\n",
"ax.add_feature(cfeature.OCEAN)\n",
"ax.add_feature(cfeature.COASTLINE)\n",
"ax.add_feature(cfeature.BORDERS, linestyle=':')\n",
"ax.add_feature(cfeature.LAKES, alpha=0.5)\n",
"ax.add_feature(cfeature.RIVERS)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"\n",
"import cartopy.crs as ccrs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Regridding vectors with quiver\n",
"------------------------------\n",
"\n",
"This example demonstrates the regridding functionality in quiver (there exists\n",
"equivalent functionality in :meth:`cartopy.mpl.geoaxes.GeoAxes.barbs`).\n",
"\n",
"Regridding can be an effective way of visualising a vector field, particularly\n",
"if the data is dense or warped.\n",
"\n",
"### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def sample_data(shape=(20, 30)):\n",
" \"\"\"\n",
" Returns ``(x, y, u, v, crs)`` of some vector data\n",
" computed mathematically. The returned CRS will be a North Polar\n",
" Stereographic projection, meaning that the vectors will be unevenly\n",
" spaced in a PlateCarree projection.\n",
"\n",
" \"\"\"\n",
" crs = ccrs.NorthPolarStereo()\n",
" scale = 1e7\n",
" x = np.linspace(-scale, scale, shape[1])\n",
" y = np.linspace(-scale, scale, shape[0])\n",
"\n",
" x2d, y2d = np.meshgrid(x, y)\n",
" u = 10 * np.cos(2 * x2d / scale + 3 * y2d / scale)\n",
" v = 20 * np.cos(6 * x2d / scale)\n",
"\n",
" return x, y, u, v, crs\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def main():\n",
" plt.figure(figsize=(8, 10))\n",
"\n",
" x, y, u, v, vector_crs = sample_data(shape=(50, 50))\n",
" ax1 = plt.subplot(2, 1, 1, projection=ccrs.PlateCarree())\n",
" ax1.coastlines()\n",
" ax1.set_extent([-45, 55, 20, 80], ccrs.PlateCarree())\n",
" ax1.quiver(x, y, u, v, transform=vector_crs)\n",
"\n",
" ax2 = plt.subplot(2, 1, 2, projection=ccrs.PlateCarree())\n",
" plt.title('The same vector field regridded')\n",
" ax2.coastlines()\n",
" ax2.set_extent([-45, 55, 20, 80], ccrs.PlateCarree())\n",
" ax2.quiver(x, y, u, v, transform=vector_crs, regrid_shape=20)\n",
"\n",
" plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"main()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"\n",
"import cartopy\n",
"import cartopy.crs as ccrs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def sample_data(shape=(20, 30)):\n",
" \"\"\"\n",
" Returns ``(x, y, u, v, crs)`` of some vector data\n",
" computed mathematically. The returned crs will be a rotated\n",
" pole CRS, meaning that the vectors will be unevenly spaced in\n",
" regular PlateCarree space.\n",
"\n",
" \"\"\"\n",
" crs = ccrs.RotatedPole(pole_longitude=177.5, pole_latitude=37.5)\n",
"\n",
" x = np.linspace(311.9, 391.1, shape[1])\n",
" y = np.linspace(-23.6, 24.8, shape[0])\n",
"\n",
" x2d, y2d = np.meshgrid(x, y)\n",
" u = 10 * (2 * np.cos(2 * np.deg2rad(x2d) + 3 * np.deg2rad(y2d + 30)) ** 2)\n",
" v = 20 * np.cos(6 * np.deg2rad(x2d))\n",
"\n",
" return x, y, u, v, crs\n",
"\n",
"\n",
"def main():\n",
" ax = plt.axes(projection=ccrs.Orthographic(-10, 45))\n",
"\n",
" ax.add_feature(cartopy.feature.OCEAN, zorder=0)\n",
" ax.add_feature(cartopy.feature.LAND, zorder=0, edgecolor='black')\n",
"\n",
" ax.set_global()\n",
" ax.gridlines()\n",
"\n",
" x, y, u, v, vector_crs = sample_data()\n",
" ax.quiver(x, y, u, v, transform=vector_crs)\n",
"\n",
" plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"main()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`cartopy.geoaxes.quiver`\n",
"\n",
" **Cartopy matplotlib.quiver PolyCollection.**\n",
"\n",
"Plot a 2-D field of arrows on a projected plane.\n",
"\n",
" Extra Kwargs:\n",
"\n",
" * transform: :class:`cartopy.crs.Projection` or matplotlib transform\n",
" The coordinate system in which the vectors are defined.\n",
"\n",
" * regrid_shape: int or 2-tuple of ints\n",
" If given, specifies that the points where the arrows are\n",
" located will be interpolated onto a regular grid in\n",
" projection space. If a single integer is given then that\n",
" will be used as the minimum grid length dimension, while the\n",
" other dimension will be scaled up according to the target\n",
" extent's aspect ratio. If a pair of ints are given they\n",
" determine the grid length in the x and y directions\n",
" respectively.\n",
"\n",
" * target_extent: 4-tuple\n",
" If given, specifies the extent in the target CRS that the\n",
" regular grid defined by *regrid_shape* will have. Defaults\n",
" to the current extent of the map projection.\n",
"\n",
" See :func:`matplotlib.pyplot.quiver` for details on arguments\n",
" and other keyword arguments.\n",
"\n",
" .. note::\n",
"\n",
" The vector components must be defined as grid eastward and\n",
" grid northward.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"`matplotlib.quiver`\n",
"\n",
" **Specialized PolyCollection for arrows.**\n",
"\n",
"Plot a 2-D field of barbs.\n",
"\n",
"Call signatures::\n",
"\n",
" barb(U, V, **kw)\n",
" barb(U, V, C, **kw)\n",
" barb(X, Y, U, V, **kw)\n",
" barb(X, Y, U, V, C, **kw)\n",
"\n",
"*Arguments:*\n",
"\n",
" *X*, *Y*:\n",
" The x and y coordinates of the barb locations\n",
" (default is head of barb; see *pivot* kwarg)\n",
"\n",
" *U*, *V*:\n",
" Give the x and y components of the barb shaft\n",
"\n",
" *C*:\n",
" An optional array used to map colors to the barbs\n",
"\n",
"All arguments may be 1-D or 2-D arrays or sequences. If *X* and *Y*\n",
"are absent, they will be generated as a uniform grid. If *U* and *V*\n",
"are 2-D arrays but *X* and *Y* are 1-D, and if ``len(X)`` and ``len(Y)``\n",
"match the column and row dimensions of *U*, then *X* and *Y* will be\n",
"expanded with :func:`numpy.meshgrid`.\n",
"\n",
" *U*, *V*, *C* may be masked arrays, but masked *X*, *Y* are not\n",
" supported at present.\n",
"\n",
"*Keyword arguments:*\n",
"\n",
" *length*:\n",
" Length of the barb in points; the other parts of the barb\n",
" are scaled against this.\n",
" Default is 9\n",
"\n",
" *pivot*: [ 'tip' | 'middle' ]\n",
" The part of the arrow that is at the grid point; the arrow rotates\n",
" about this point, hence the name *pivot*. Default is 'tip'\n",
"\n",
" *barbcolor*: [ color | color sequence ]\n",
" Specifies the color all parts of the barb except any flags. This\n",
" parameter is analagous to the *edgecolor* parameter for polygons,\n",
" which can be used instead. However this parameter will override\n",
" facecolor.\n",
"\n",
" *flagcolor*: [ color | color sequence ]\n",
" Specifies the color of any flags on the barb. This parameter is\n",
" analagous to the *facecolor* parameter for polygons, which can be\n",
" used instead. However this parameter will override facecolor. If\n",
" this is not set (and *C* has not either) then *flagcolor* will be\n",
" set to match *barbcolor* so that the barb has a uniform color. If\n",
" *C* has been set, *flagcolor* has no effect.\n",
"\n",
" *sizes*:\n",
" A dictionary of coefficients specifying the ratio of a given\n",
" feature to the length of the barb. Only those values one wishes to\n",
" override need to be included. These features include:\n",
"\n",
" - 'spacing' - space between features (flags, full/half barbs)\n",
"\n",
" - 'height' - height (distance from shaft to top) of a flag or\n",
" full barb\n",
"\n",
" - 'width' - width of a flag, twice the width of a full barb\n",
"\n",
" - 'emptybarb' - radius of the circle used for low magnitudes\n",
"\n",
" *fill_empty*:\n",
" A flag on whether the empty barbs (circles) that are drawn should\n",
" be filled with the flag color. If they are not filled, they will\n",
" be drawn such that no color is applied to the center. Default is\n",
" False\n",
"\n",
" *rounding*:\n",
" A flag to indicate whether the vector magnitude should be rounded\n",
" when allocating barb components. If True, the magnitude is\n",
" rounded to the nearest multiple of the half-barb increment. If\n",
" False, the magnitude is simply truncated to the next lowest\n",
" multiple. Default is True\n",
"\n",
" *barb_increments*:\n",
" A dictionary of increments specifying values to associate with\n",
" different parts of the barb. Only those values one wishes to\n",
" override need to be included.\n",
"\n",
" - 'half' - half barbs (Default is 5)\n",
"\n",
" - 'full' - full barbs (Default is 10)\n",
"\n",
" - 'flag' - flags (default is 50)\n",
"\n",
" *flip_barb*:\n",
" Either a single boolean flag or an array of booleans. Single\n",
" boolean indicates whether the lines and flags should point\n",
" opposite to normal for all barbs. An array (which should be the\n",
" same size as the other data arrays) indicates whether to flip for\n",
" each individual barb. Normal behavior is for the barbs and lines\n",
" to point right (comes from wind barbs having these features point\n",
" towards low pressure in the Northern Hemisphere.) Default is\n",
" False\n",
"\n",
"Barbs are traditionally used in meteorology as a way to plot the speed\n",
"and direction of wind observations, but can technically be used to\n",
"plot any two dimensional vector quantity. As opposed to arrows, which\n",
"give vector magnitude by the length of the arrow, the barbs give more\n",
"quantitative information about the vector magnitude by putting slanted\n",
"lines or a triangle for various increments in magnitude, as show\n",
"schematically below::\n",
"\n",
" : /\\ \\\\\n",
" : / \\ \\\\\n",
" : / \\ \\ \\\\\n",
" : / \\ \\ \\\\\n",
" : ------------------------------\n",
"\n",
".. note the double \\\\ at the end of each line to make the figure\n",
".. render correctly\n",
"\n",
"The largest increment is given by a triangle (or \"flag\"). After those\n",
"come full lines (barbs). The smallest increment is a half line. There\n",
"is only, of course, ever at most 1 half line. If the magnitude is\n",
"small and only needs a single half-line and no full lines or\n",
"triangles, the half-line is offset from the end of the barb so that it\n",
"can be easily distinguished from barbs with a single full line. The\n",
"magnitude for the barb shown above would nominally be 65, using the\n",
"standard increments of 50, 10, and 5.\n",
"\n",
"linewidths and edgecolors can be used to customize the barb.\n",
"Additional :class:`~matplotlib.collections.PolyCollection` keyword\n",
"arguments:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print 'matplotlib.__version__ => ' + matplotlib.__version__\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from osgeo import gdal\n",
"from osgeo import gdal_array\n",
"\n",
"import rasterio\n",
"import cartopy.crs as ccrs\n",
"\n",
"## DEM plot via cartopy, rasterio, pyplot imshow\n",
"## Live 8.5 * darkblue-b\n",
"##"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"=="
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Rasterio\n",
"## Clean and fast\n",
"## geospatial raster I/O for Python programmers who use Numpy\n",
"\n",
"with rasterio.open('sample_data/SanMateo_CA.tif') as src:\n",
" data = src.read()\n",
" data = np.transpose( data, [1,2,0])\n",
"\n",
"\n",
"## show selected data attributes\n",
"## -------------------------------------\n",
"# type(data) numpy.ma.core.MaskedArray\n",
"# data.ndim 3\n",
"# data.shape (1080, 864, 1)\n",
"# data.dtype dtype('float32')\n",
"\n",
"## NOTE: when using rasterio and matplotlib only,\n",
"## the column order for the trivial case of lat/lon/measure\n",
"## is NOT handled automatically.. \n",
"## column reordering is REQUIRED np.transpose()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Rasterio supplies a simple dictionary of important GeoTIFF metadata\n",
"src.meta"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## numpy ndarray indexing example\n",
"## show the measure values for a 2x2 area\n",
"## no-data options are available using a masked array\n",
"## http://docs.scipy.org/doc/numpy/reference/maskedarray.generic.html#what-is-a-masked-array\n",
"\n",
"data[0:2,0:2,0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## pyplot Image-Show \n",
"## http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.imshow\n",
"##\n",
"## Example of using matplotlib to directly display a GeoTIFF\n",
"##\n",
"## Cartopy supplies a library of mapping transformations\n",
"## use an idiom to calculate the correct display bounds\n",
"##\n",
"## data[:,:,0] refers to a numpy ndarray: all-x, all-y, 0th measure\n",
"##\n",
"\n",
"xmin = src.transform[0]\n",
"xmax = src.transform[0] + src.transform[1]*src.width\n",
"ymin = src.transform[3] + src.transform[5]*src.height\n",
"ymax = src.transform[3]\n",
"\n",
"## Mercator etc..\n",
"crs = ccrs.PlateCarree()\n",
"\n",
"ax = plt.axes(projection=crs)\n",
"plt.imshow( data[:,:,0], origin='upper', \n",
" extent=[xmin, xmax, ymin, ymax], \n",
" cmap=plt.get_cmap('gist_earth'),\n",
" transform=crs, interpolation='nearest')\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"=="
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Show the same content, but with a colorbar legend for the data\n",
"##\n",
"plt.figure(figsize=(15, 10))\n",
"ax = plt.subplot(111, projection=ccrs.PlateCarree())\n",
"\n",
"#elev, crs, extent = srtm_composite(12, 47, 1, 1)\n",
"plt.imshow( data[:,:,0], transform=ccrs.PlateCarree(),\n",
" cmap='gist_earth')\n",
"cb = plt.colorbar(orientation='vertical')\n",
"cb.set_label('Altitude')\n",
"plt.title(\"DEM\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%whos"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import cartopy.crs as ccrs\n",
"\n",
"import iris\n",
"import iris.coord_systems\n",
"import iris.cube\n",
"\n",
"import iris.plot as iplt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cube_path = 'sample_data/rotated_pole.nc'\n",
"cube = iris.load_cube(cube_path)\n",
"\n",
"cube.coord('grid_latitude').guess_bounds()\n",
"cube.coord('grid_longitude').guess_bounds()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print cube.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Basic Plots\n",
"iplt.contourf(cube)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iplt.pcolor(cube)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## subplots\n",
"\n",
"plt.subplot(221)\n",
"plt.title('Default')\n",
"iplt.contourf(cube)\n",
"plt.gca().coastlines()\n",
"\n",
"# Second sub-plot\n",
"plt.subplot(222, projection=ccrs.Mollweide(central_longitude=120))\n",
"plt.title('Molleweide')\n",
"iplt.contourf(cube)\n",
"plt.gca().coastlines()\n",
"\n",
"# Third sub-plot (the projection part is redundant, but a useful\n",
"# test none-the-less)\n",
"ax = plt.subplot(223, projection=iplt.default_projection(cube))\n",
"plt.title('Native')\n",
"iplt.contour(cube)\n",
"ax.coastlines()\n",
"\n",
"# Fourth sub-plot\n",
"ax = plt.subplot(2, 2, 4, projection=ccrs.PlateCarree())\n",
"plt.title('PlateCarree')\n",
"iplt.contourf(cube)\n",
"ax.coastlines()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.axes(projection=ccrs.PlateCarree(central_longitude=40))\n",
"plt.gca().coastlines()\n",
"iplt.pcolormesh(cube)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iplt.outline(cube)\n",
"plt.gca().coastlines()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iplt.points(cube)\n",
"plt.gca().coastlines()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Deriving Exner Pressure and Air Temperature\n",
"===========================================\n",
"\n",
"This example shows some processing of cubes in order to derive further related\n",
"cubes; in this case the derived cubes are Exner pressure and air temperature\n",
"which are calculated by combining air pressure, air potential temperature and\n",
"specific humidity. Finally, the two new cubes are presented side-by-side in a\n",
"plot.\n",
"\n",
"### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.ticker\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import iris\n",
"import iris.coords as coords\n",
"import iris.iterate\n",
"import iris.plot as iplt\n",
"import iris.quickplot as qplt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"def limit_colorbar_ticks(contour_object):\n",
" \"\"\"\n",
" Takes a contour object which has an associated colorbar and limits the\n",
" number of ticks on the colorbar to 4.\n",
"\n",
" \"\"\"\n",
" # Under Matplotlib v1.2.x the colorbar attribute of a contour object is\n",
" # a tuple containing the colorbar and an axes object, whereas under\n",
" # Matplotlib v1.3.x it is simply the colorbar.\n",
" try:\n",
" colorbar = contour_object.colorbar[0]\n",
" except (AttributeError, TypeError):\n",
" colorbar = contour_object.colorbar\n",
"\n",
" colorbar.locator = matplotlib.ticker.MaxNLocator(4)\n",
" colorbar.update_ticks()\n",
"\n",
"\n",
"\n",
"def main():\n",
" #fname = iris.sample_data_path('colpex.pp')\n",
" fname = 'sample_data/colpex.pp'\n",
"\n",
" # The list of phenomena of interest\n",
" phenomena = ['air_potential_temperature', 'air_pressure']\n",
"\n",
" # Define the constraint on standard name and model level\n",
" constraints = [iris.Constraint(phenom, model_level_number=1) for\n",
" phenom in phenomena]\n",
"\n",
" air_potential_temperature, air_pressure = iris.load_cubes(fname,\n",
" constraints)\n",
"\n",
" # Define a coordinate which represents 1000 hPa\n",
" p0 = coords.AuxCoord(1000, long_name='P0', units='hPa')\n",
" # Convert reference pressure 'p0' into the same units as 'air_pressure'\n",
" p0.convert_units(air_pressure.units)\n",
"\n",
" # Calculate Exner pressure\n",
" exner_pressure = (air_pressure / p0) ** (287.05 / 1005.0)\n",
" # Set the name (the unit is scalar)\n",
" exner_pressure.rename('exner_pressure')\n",
"\n",
" # Calculate air_temp\n",
" air_temperature = exner_pressure * air_potential_temperature\n",
" # Set the name (the unit is K)\n",
" air_temperature.rename('air_temperature')\n",
"\n",
" # Now create an iterator which will give us lat lon slices of\n",
" # exner pressure and air temperature in the form\n",
" # (exner_slice, air_temp_slice).\n",
" lat_lon_slice_pairs = iris.iterate.izip(exner_pressure,\n",
" air_temperature,\n",
" coords=['grid_latitude',\n",
" 'grid_longitude'])\n",
"\n",
" # For the purposes of this example, we only want to demonstrate the first\n",
" # plot.\n",
" lat_lon_slice_pairs = [next(lat_lon_slice_pairs)]\n",
"\n",
" plt.figure(figsize=(8, 4))\n",
" for exner_slice, air_temp_slice in lat_lon_slice_pairs:\n",
" plt.subplot(121)\n",
" cont = qplt.contourf(exner_slice)\n",
"\n",
" # The default colorbar has a few too many ticks on it, causing text to\n",
" # overlap. Therefore, limit the number of ticks.\n",
" limit_colorbar_ticks(cont)\n",
"\n",
" plt.subplot(122)\n",
" cont = qplt.contourf(air_temp_slice)\n",
" limit_colorbar_ticks(cont)\n",
" iplt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"main()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"\n",
"import iris\n",
"import iris.plot as iplt"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Seasonal ensemble model plots\n",
"=============================\n",
"\n",
"This example demonstrates the loading of a lagged ensemble dataset from the GloSea4 model, which is then used to\n",
"produce two types of plot:\n",
"\n",
" * The first shows the \"postage stamp\" style image with an array of 14 images, one for each ensemble member with\n",
" a shared colorbar. (The missing image in this example represents ensemble member number 6 which was a failed run)\n",
"\n",
" * The second plot shows the data limited to a region of interest, in this case a region defined for forecasting\n",
" ENSO (El Nino-Southern Oscillation), which, for the purposes of this example, has had the ensemble mean subtracted\n",
" from each ensemble member to give an anomaly surface temperature. In practice a better approach would be to take the\n",
" climatological mean, calibrated to the model, from each ensemble member.\n",
"\n",
"### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"def realization_metadata(cube, field, fname):\n",
" \"\"\"\n",
" A function which modifies the cube's metadata to add a \"realization\" (ensemble member) coordinate from the filename if one\n",
" doesn't already exist in the cube.\n",
"\n",
" \"\"\"\n",
" # add an ensemble member coordinate if one doesn't already exist\n",
" if not cube.coords('realization'):\n",
" # the ensemble member is encoded in the filename as *_???.pp where ??? is the ensemble member\n",
" realization_number = fname[-6:-3]\n",
"\n",
" import iris.coords\n",
" realization_coord = iris.coords.AuxCoord(np.int32(realization_number), 'realization')\n",
" cube.add_aux_coord(realization_coord)\n",
"\n",
"\n",
"def main():\n",
" # extract surface temperature cubes which have an ensemble member coordinate, adding appropriate lagged ensemble metadata\n",
" #dpath = iris.sample_data_path('GloSea4', 'ensemble_???.pp')\n",
" dpath = 'sample_data/GloSea4/ensemble_???.pp'\n",
" surface_temp = iris.load_cube( dpath,\n",
" iris.Constraint('surface_temperature', realization=lambda value: True),\n",
" callback=realization_metadata,\n",
" )\n",
"\n",
" # ----------------------------------------------------------------------------------------------------------------\n",
" # Plot #1: Ensemble postage stamps\n",
" # ----------------------------------------------------------------------------------------------------------------\n",
"\n",
" # for the purposes of this example, take the last time element of the cube\n",
" last_timestep = surface_temp[:, -1, :, :]\n",
"\n",
" # Make 50 evenly spaced levels which span the dataset\n",
" contour_levels = np.linspace(np.min(last_timestep.data), np.max(last_timestep.data), 50)\n",
"\n",
" # Create a wider than normal figure to support our many plots\n",
" plt.figure(figsize=(12, 6), dpi=100)\n",
"\n",
" # Also manually adjust the spacings which are used when creating subplots\n",
" plt.gcf().subplots_adjust(hspace=0.05, wspace=0.05, top=0.95, bottom=0.05, left=0.075, right=0.925)\n",
"\n",
" # iterate over all possible latitude longitude slices\n",
" for cube in last_timestep.slices(['latitude', 'longitude']):\n",
"\n",
" # get the ensemble member number from the ensemble coordinate\n",
" ens_member = cube.coord('realization').points[0]\n",
"\n",
" # plot the data in a 4x4 grid, with each plot's position in the grid being determined by ensemble member number\n",
" # the special case for the 13th ensemble member is to have the plot at the bottom right\n",
" if ens_member == 13:\n",
" plt.subplot(4, 4, 16)\n",
" else:\n",
" plt.subplot(4, 4, ens_member+1)\n",
"\n",
" cf = iplt.contourf(cube, contour_levels)\n",
"\n",
" # add coastlines\n",
" plt.gca().coastlines()\n",
"\n",
" # make an axes to put the shared colorbar in\n",
" colorbar_axes = plt.gcf().add_axes([0.35, 0.1, 0.3, 0.05])\n",
" colorbar = plt.colorbar(cf, colorbar_axes, orientation='horizontal')\n",
" colorbar.set_label('%s' % last_timestep.units)\n",
"\n",
" # limit the colorbar to 8 tick marks\n",
" import matplotlib.ticker\n",
" colorbar.locator = matplotlib.ticker.MaxNLocator(8)\n",
" colorbar.update_ticks()\n",
"\n",
" # get the time for the entire plot\n",
" time_coord = last_timestep.coord('time')\n",
" time = time_coord.units.num2date(time_coord.bounds[0, 0])\n",
"\n",
" # set a global title for the postage stamps with the date formated by \"monthname year\"\n",
" plt.suptitle('Surface temperature ensemble forecasts for %s' % time.strftime('%B %Y'))\n",
"\n",
" iplt.show()\n",
"\n",
" # ----------------------------------------------------------------------------------------------------------------\n",
" # Plot #2: ENSO plumes\n",
" # ----------------------------------------------------------------------------------------------------------------\n",
"\n",
" # Nino 3.4 lies between: 170W and 120W, 5N and 5S, so define a constraint which matches this\n",
" nino_3_4_constraint = iris.Constraint(longitude=lambda v: -170+360 <= v <= -120+360, latitude=lambda v: -5 <= v <= 5)\n",
"\n",
" nino_cube = surface_temp.extract(nino_3_4_constraint)\n",
"\n",
" # Subsetting a circular longitude coordinate always results in a circular coordinate, so set the coordinate to be non-circular\n",
" nino_cube.coord('longitude').circular = False\n",
"\n",
" # Calculate the horizontal mean for the nino region\n",
" mean = nino_cube.collapsed(['latitude', 'longitude'], iris.analysis.MEAN)\n",
"\n",
" # Calculate the ensemble mean of the horizontal mean. To do this, remove the \"forecast_period\" and\n",
" # \"forecast_reference_time\" coordinates which span both \"relalization\" and \"time\".\n",
" mean.remove_coord(\"forecast_reference_time\")\n",
" mean.remove_coord(\"forecast_period\")\n",
" ensemble_mean = mean.collapsed('realization', iris.analysis.MEAN)\n",
"\n",
" # take the ensemble mean from each ensemble member\n",
" mean -= ensemble_mean.data\n",
"\n",
" plt.figure()\n",
"\n",
" for ensemble_member in mean.slices(['time']):\n",
" # draw each ensemble member as a dashed line in black\n",
" iplt.plot(ensemble_member, '--k')\n",
"\n",
" plt.title('Mean temperature anomaly for ENSO 3.4 region')\n",
" plt.xlabel('Time')\n",
" plt.ylabel('Temperature anomaly / K')\n",
"\n",
" iplt.show()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"main()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.dates as mdates\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import iris\n",
"import iris.plot as iplt\n",
"import iris.quickplot as qplt\n",
"import iris.unit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hovmoller diagram of monthly surface temperature\n",
"================================================\n",
"\n",
"This example demonstrates the creation of a Hovmoller diagram with fine control over plot ticks and labels.\n",
"The data comes from the Met Office OSTIA project and has been pre-processed to calculate the monthly mean sea\n",
"surface temperature.\n",
"\n",
"### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"def main():\n",
" #fname = iris.sample_data_path('ostia_monthly.nc')\n",
" fname = 'sample_data/ostia_monthly.nc'\n",
"\n",
" # load a single cube of surface temperature between +/- 5 latitude\n",
" cube = iris.load_cube(fname, iris.Constraint('surface_temperature', latitude=lambda v: -5 < v < 5))\n",
"\n",
" # Take the mean over latitude\n",
" cube = cube.collapsed('latitude', iris.analysis.MEAN)\n",
"\n",
" # Now that we have our data in a nice way, lets create the plot\n",
" # contour with 20 levels\n",
" qplt.contourf(cube, 20)\n",
"\n",
" # Put a custom label on the y axis\n",
" plt.ylabel('Time / years')\n",
"\n",
" # Stop matplotlib providing clever axes range padding\n",
" plt.axis('tight')\n",
"\n",
" # As we are plotting annual variability, put years as the y ticks\n",
" plt.gca().yaxis.set_major_locator(mdates.YearLocator())\n",
"\n",
" # And format the ticks to just show the year\n",
" plt.gca().yaxis.set_major_formatter(mdates.DateFormatter('%Y'))\n",
"\n",
" iplt.show()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"main()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import cartopy.crs as ccrs\n",
"\n",
"import iris\n",
"import iris.analysis.cartography\n",
"import iris.plot as iplt\n",
"import iris.quickplot as qplt"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Tri-Polar Grid Projected Plotting\n",
"=================================\n",
"\n",
"This example demonstrates cell plots of data on the semi-structured ORCA2 model\n",
"grid.\n",
"\n",
"First, the data is projected into the PlateCarree coordinate reference system.\n",
"\n",
"Second four pcolormesh plots are created from this projected dataset,\n",
"using different projections for the output image.\n",
"\n",
"### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def main():\n",
" # Load data\n",
" #filepath = iris.sample_data_path('orca2_votemper.nc')\n",
" filepath = 'sample_data/orca2_votemper.nc'\n",
" cube = iris.load_cube(filepath)\n",
"\n",
" # Choose plot projections\n",
" projections = {}\n",
" projections['Mollweide'] = ccrs.Mollweide()\n",
" projections['PlateCarree'] = ccrs.PlateCarree()\n",
" projections['NorthPolarStereo'] = ccrs.NorthPolarStereo()\n",
" projections['Orthographic'] = ccrs.Orthographic(central_longitude=-90,\n",
" central_latitude=45)\n",
"\n",
" pcarree = projections['PlateCarree']\n",
" # Transform cube to target projection\n",
" new_cube, extent = iris.analysis.cartography.project(cube, pcarree,\n",
" nx=400, ny=200)\n",
"\n",
" # Plot data in each projection\n",
" for name in sorted(projections):\n",
" fig = plt.figure()\n",
" fig.suptitle('ORCA2 Data Projected to {}'.format(name))\n",
" # Set up axes and title\n",
" ax = plt.subplot(projection=projections[name])\n",
" # Set limits\n",
" ax.set_global()\n",
" # plot with Iris quickplot pcolormesh\n",
" qplt.pcolormesh(new_cube)\n",
" # Draw coastlines\n",
" ax.coastlines()\n",
"\n",
" iplt.show()\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"main()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
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"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import iris\n",
"import iris.plot as iplt\n",
"import iris.quickplot as qplt\n",
"from iris.util import rolling_window\n",
"from iris.analysis import Aggregator"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Calculating a custom statistic\n",
"==============================\n",
"\n",
"This example shows how to define and use a custom\n",
":class:`iris.analysis.Aggregator`, that provides a new statistical operator for\n",
"use with cube aggregation functions such as :meth:`~iris.cube.Cube.collapsed`,\n",
":meth:`~iris.cube.Cube.aggregated_by` or\n",
":meth:`~iris.cube.Cube.rolling_window`.\n",
"\n",
"In this case, we have a 240-year sequence of yearly average surface temperature\n",
"over North America, and we want to calculate in how many years these exceed a\n",
"certain temperature over a spell of 5 years or more.\n",
"\n",
"\n",
"### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"# Define a function to perform the custom statistical operation.\n",
"# Note: in order to meet the requirements of iris.analysis.Aggregator, it must\n",
"# do the calculation over an arbitrary (given) data axis.\n",
"def count_spells(data, threshold, axis, spell_length):\n",
" \"\"\"\n",
" Function to calculate the number of points in a sequence where the value\n",
" has exceeded a threshold value for at least a certain number of timepoints.\n",
"\n",
" Generalised to operate on multiple time sequences arranged on a specific\n",
" axis of a multidimensional array.\n",
"\n",
" Args:\n",
"\n",
" * data (array):\n",
" raw data to be compared with value threshold.\n",
"\n",
" * threshold (float):\n",
" threshold point for 'significant' datapoints.\n",
"\n",
" * axis (int):\n",
" number of the array dimension mapping the time sequences.\n",
" (Can also be negative, e.g. '-1' means last dimension)\n",
"\n",
" * spell_length (int):\n",
" number of consecutive times at which value > threshold to \"count\".\n",
"\n",
" \"\"\"\n",
" if axis < 0:\n",
" # just cope with negative axis numbers\n",
" axis += data.ndim\n",
" # Threshold the data to find the 'significant' points.\n",
" data_hits = data > threshold\n",
" # Make an array with data values \"windowed\" along the time axis.\n",
" hit_windows = rolling_window(data_hits, window=spell_length, axis=axis)\n",
" # Find the windows \"full of True-s\" (along the added 'window axis').\n",
" full_windows = np.all(hit_windows, axis=axis+1)\n",
" # Count points fulfilling the condition (along the time axis).\n",
" spell_point_counts = np.sum(full_windows, axis=axis, dtype=int)\n",
" return spell_point_counts\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"def main():\n",
" # Load the whole time-sequence as a single cube.\n",
" #file_path = iris.sample_data_path('E1_north_america.nc')\n",
" file_path = 'sample_data/E1_north_america.nc'\n",
" cube = iris.load_cube(file_path)\n",
"\n",
" # Make an aggregator from the user function.\n",
" SPELL_COUNT = Aggregator('spell_count',\n",
" count_spells,\n",
" units_func=lambda units: 1)\n",
"\n",
" # Define the parameters of the test.\n",
" threshold_temperature = 280.0\n",
" spell_years = 5\n",
"\n",
" # Calculate the statistic.\n",
" warm_periods = cube.collapsed('time', SPELL_COUNT,\n",
" threshold=threshold_temperature,\n",
" spell_length=spell_years)\n",
" warm_periods.rename('Number of 5-year warm spells in 240 years')\n",
"\n",
" # Plot the results.\n",
" qplt.contourf(warm_periods, cmap='RdYlBu_r')\n",
" plt.gca().coastlines()\n",
" iplt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"main()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib.colors as mcols\n",
"%matplotlib inline\n",
"\n",
"import cartopy.crs as ccrs\n",
"\n",
"import iris\n",
"import iris.coord_categorisation\n",
"import iris.plot as iplt"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Colouring anomaly data with logarithmic scaling\n",
"===============================================\n",
"\n",
"In this example, we need to plot anomaly data where the values have a\n",
"\"logarithmic\" significance -- i.e. we want to give approximately equal ranges\n",
"of colour between data values of, say, 1 and 10 as between 10 and 100.\n",
"\n",
"As the data range also contains zero, that obviously does not suit a simple\n",
"logarithmic interpretation. However, values of less than a certain absolute\n",
"magnitude may be considered \"not significant\", so we put these into a separate\n",
"\"zero band\" which is plotted in white.\n",
"\n",
"To do this, we create a custom value mapping function (normalization) using\n",
"the matplotlib Norm class `matplotlib.colours.SymLogNorm\n",
"<http://matplotlib.org/api/colors_api.html#matplotlib.colors.SymLogNorm>`_.\n",
"We use this to make a cell-filled pseudocolour plot with a colorbar.\n",
"\n",
"NOTE: By \"pseudocolour\", we mean that each data point is drawn as a \"cell\"\n",
"region on the plot, coloured according to its data value.\n",
"This is provided in Iris by the functions :meth:`iris.plot.pcolor` and\n",
":meth:`iris.plot.pcolormesh`, which call the underlying matplotlib\n",
"functions of the same names (i.e. `matplotlib.pyplot.pcolor\n",
"<http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.pcolor>`_\n",
"and `matplotlib.pyplot.pcolormesh\n",
"<http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.pcolormesh>`_).\n",
"See also: http://en.wikipedia.org/wiki/False_color#Pseudocolor.\n",
"\n",
"### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def main():\n",
" # Load a sample air temperatures sequence.\n",
" #file_path = iris.sample_data_path('E1_north_america.nc')\n",
" file_path = 'sample_data/E1_north_america.nc'\n",
" temperatures = iris.load_cube(file_path)\n",
"\n",
" # Create a year-number coordinate from the time information.\n",
" iris.coord_categorisation.add_year(temperatures, 'time')\n",
"\n",
" # Create a sample anomaly field for one chosen year, by extracting that\n",
" # year and subtracting the time mean.\n",
" sample_year = 1989\n",
" year_temperature = temperatures.extract(iris.Constraint(year=sample_year))\n",
" time_mean = temperatures.collapsed('time', iris.analysis.MEAN)\n",
" anomaly = year_temperature - time_mean\n",
"\n",
" # Construct a plot title string explaining which years are involved.\n",
" years = temperatures.coord('year').points\n",
" plot_title = 'Temperature anomaly'\n",
" plot_title += '\\n{} differences from {}-{} average.'.format(\n",
" sample_year, years[0], years[-1])\n",
"\n",
" # Define scaling levels for the logarithmic colouring.\n",
" minimum_log_level = 0.1\n",
" maximum_scale_level = 3.0\n",
"\n",
" # Use a standard colour map which varies blue-white-red.\n",
" # For suitable options, see the 'Diverging colormaps' section in:\n",
" # http://matplotlib.org/examples/color/colormaps_reference.html\n",
" anom_cmap = 'bwr'\n",
"\n",
" # Create a 'logarithmic' data normalization.\n",
" anom_norm = mcols.SymLogNorm(linthresh=minimum_log_level,\n",
" linscale=0,\n",
" vmin=-maximum_scale_level,\n",
" vmax=maximum_scale_level)\n",
" # Setting \"linthresh=minimum_log_level\" makes its non-logarithmic\n",
" # data range equal to our 'zero band'.\n",
" # Setting \"linscale=0\" maps the whole zero band to the middle colour value\n",
" # (i.e. 0.5), which is the neutral point of a \"diverging\" style colormap.\n",
"\n",
" # Create an Axes, specifying the map projection.\n",
" plt.axes(projection=ccrs.LambertConformal())\n",
"\n",
" # Make a pseudocolour plot using this colour scheme.\n",
" mesh = iplt.pcolormesh(anomaly, cmap=anom_cmap, norm=anom_norm)\n",
"\n",
" # Add a colourbar, with extensions to show handling of out-of-range values.\n",
" bar = plt.colorbar(mesh, orientation='horizontal', extend='both')\n",
"\n",
" # Set some suitable fixed \"logarithmic\" colourbar tick positions.\n",
" tick_levels = [-3, -1, -0.3, 0.0, 0.3, 1, 3]\n",
" bar.set_ticks(tick_levels)\n",
"\n",
" # Modify the tick labels so that the centre one shows \"+/-<minumum-level>\".\n",
" tick_levels[3] = r'$\\pm${:g}'.format(minimum_log_level)\n",
" bar.set_ticklabels(tick_levels)\n",
"\n",
" # Label the colourbar to show the units.\n",
" bar.set_label('[{}, log scale]'.format(anomaly.units))\n",
"\n",
" # Add coastlines and a title.\n",
" plt.gca().coastlines()\n",
" plt.title(plot_title)\n",
"\n",
" # Display the result.\n",
" iplt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"main()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import iris\n",
"import iris.quickplot as qplt"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Fitting a polynomial\n",
"====================\n",
"\n",
"This example demonstrates computing a polynomial fit to 1D data from an Iris\n",
"cube, adding the fit to the cube's metadata, and plotting both the 1D data and\n",
"the fit.\n",
"\n",
"### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"def main():\n",
" #fname = iris.sample_data_path('A1B_north_america.nc')\n",
" fname = 'sample_data/A1B_north_america.nc'\n",
" cube = iris.load_cube(fname)\n",
"\n",
" # Extract a single time series at a latitude and longitude point.\n",
" location = next(cube.slices(['time']))\n",
"\n",
" # Calculate a polynomial fit to the data at this time series.\n",
" x_points = location.coord('time').points\n",
" y_points = location.data\n",
" degree = 2\n",
"\n",
" p = np.polyfit(x_points, y_points, degree)\n",
" y_fitted = np.polyval(p, x_points)\n",
"\n",
" # Add the polynomial fit values to the time series to take\n",
" # full advantage of Iris plotting functionality.\n",
" long_name = 'degree_{}_polynomial_fit_of_{}'.format(degree, cube.name())\n",
" fit = iris.coords.AuxCoord(y_fitted, long_name=long_name,\n",
" units=location.units)\n",
" location.add_aux_coord(fit, 0)\n",
"\n",
" qplt.plot(location.coord('time'), location, label='data')\n",
" qplt.plot(location.coord('time'),\n",
" location.coord(long_name),\n",
" 'g-', label='polynomial fit')\n",
" plt.legend(loc='best')\n",
" plt.title('Trend of US air temperature over time')\n",
"\n",
" qplt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"main()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import cartopy.crs as ccrs\n",
"\n",
"import iris\n",
"import iris.plot as iplt"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Plotting in different projections\n",
"=================================\n",
"\n",
"This example shows how to overlay data and graphics in different projections,\n",
"demonstrating various features of Iris, Cartopy and matplotlib.\n",
"\n",
"We wish to overlay two datasets, defined on different rotated-pole grids.\n",
"To display both together, we make a pseudocoloured plot of the first, overlaid\n",
"with contour lines from the second.\n",
"We also add some lines and text annotations drawn in various projections.\n",
"\n",
"We plot these over a specified region, in two different map projections.\n",
"\n",
"\n",
"### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"# Define a Cartopy 'ordinary' lat-lon coordinate reference system.\n",
"crs_latlon = ccrs.PlateCarree()\n",
"\n",
"\n",
"def make_plot(projection_name, projection_crs):\n",
"\n",
" # Create a matplotlib Figure.\n",
" fig = plt.figure()\n",
"\n",
" # Add a matplotlib Axes, specifying the required display projection.\n",
" # NOTE: specifying 'projection' (a \"cartopy.crs.Projection\") makes the\n",
" # resulting Axes a \"cartopy.mpl.geoaxes.GeoAxes\", which supports plotting\n",
" # in different coordinate systems.\n",
" ax = plt.axes(projection=projection_crs)\n",
"\n",
" # Set display limits to include a set region of latitude * longitude.\n",
" # (Note: Cartopy-specific).\n",
" ax.set_extent((-80.0, 20.0, 10.0, 80.0), crs=crs_latlon)\n",
"\n",
" # Add coastlines and meridians/parallels (Cartopy-specific).\n",
" ax.coastlines(linewidth=0.75, color='navy')\n",
" ax.gridlines(crs=crs_latlon, linestyle='-')\n",
"\n",
" # Plot the first dataset as a pseudocolour filled plot.\n",
" #maindata_filepath = iris.sample_data_path('rotated_pole.nc')\n",
" maindata_filepath = 'sample_data/rotated_pole.nc'\n",
" main_data = iris.load_cube(maindata_filepath)\n",
" # NOTE: iplt.pcolormesh calls \"pyplot.pcolormesh\", passing in a coordinate\n",
" # system with the 'transform' keyword: This enables the Axes (a cartopy\n",
" # GeoAxes) to reproject the plot into the display projection.\n",
" iplt.pcolormesh(main_data, cmap='RdBu_r')\n",
"\n",
" # Overplot the other dataset (which has a different grid), as contours.\n",
" #overlay_filepath = iris.sample_data_path('space_weather.nc')\n",
" overlay_filepath = 'sample_data/space_weather.nc'\n",
" overlay_data = iris.load_cube(overlay_filepath, 'total electron content')\n",
" # NOTE: as above, \"iris.plot.contour\" calls \"pyplot.contour\" with a\n",
" # 'transform' keyword, enabling Cartopy reprojection.\n",
" iplt.contour(overlay_data, 20,\n",
" linewidths=2.0, colors='darkgreen', linestyles='-')\n",
"\n",
" # Draw a margin line, some way in from the border of the 'main' data...\n",
" # First calculate rectangle corners, 7% in from each corner of the data.\n",
" x_coord, y_coord = main_data.coord(axis='x'), main_data.coord(axis='y')\n",
" x_start, x_end = np.min(x_coord.points), np.max(x_coord.points)\n",
" y_start, y_end = np.min(y_coord.points), np.max(y_coord.points)\n",
" margin = 0.07\n",
" margin_fractions = np.array([margin, 1.0 - margin])\n",
" x_lower, x_upper = x_start + (x_end - x_start) * margin_fractions\n",
" y_lower, y_upper = y_start + (y_end - y_start) * margin_fractions\n",
" box_x_points = x_lower + (x_upper - x_lower) * np.array([0, 1, 1, 0, 0])\n",
" box_y_points = y_lower + (y_upper - y_lower) * np.array([0, 0, 1, 1, 0])\n",
" # Get the Iris coordinate sytem of the X coordinate (Y should be the same).\n",
" cs_data1 = x_coord.coord_system\n",
" # Construct an equivalent Cartopy coordinate reference system (\"crs\").\n",
" crs_data1 = cs_data1.as_cartopy_crs()\n",
" # Draw the rectangle in this crs, with matplotlib \"pyplot.plot\".\n",
" # NOTE: the 'transform' keyword specifies a non-display coordinate system\n",
" # for the plot points (as used by the \"iris.plot\" functions).\n",
" plt.plot(box_x_points, box_y_points, transform=crs_data1,\n",
" linewidth=2.0, color='white', linestyle='--')\n",
"\n",
" # Mark some particular places with a small circle and a name label...\n",
" # Define some test points with latitude and longitude coordinates.\n",
" city_data = [('London', 51.5072, 0.1275),\n",
" ('Halifax, NS', 44.67, -63.61),\n",
" ('Reykjavik', 64.1333, -21.9333)]\n",
" # Place a single marker point and a text annotation at each place.\n",
" for name, lat, lon in city_data:\n",
" plt.plot(lon, lat, marker='o', markersize=7.0, markeredgewidth=2.5,\n",
" markerfacecolor='black', markeredgecolor='white',\n",
" transform=crs_latlon)\n",
" # NOTE: the \"plt.annotate call\" does not have a \"transform=\" keyword,\n",
" # so for this one we transform the coordinates with a Cartopy call.\n",
" at_x, at_y = ax.projection.transform_point(lon, lat,\n",
" src_crs=crs_latlon)\n",
" plt.annotate(\n",
" name, xy=(at_x, at_y), xytext=(30, 20), textcoords='offset points',\n",
" color='black', backgroundcolor='white', size='large',\n",
" arrowprops=dict(arrowstyle='->', color='white', linewidth=2.5))\n",
"\n",
" # Add a title, and display.\n",
" plt.title('A pseudocolour plot on the {} projection,\\n'\n",
" 'with overlaid contours.'.format(projection_name))\n",
" iplt.show()\n",
"\n",
"\n",
"def main():\n",
" # Demonstrate with two different display projections.\n",
" make_plot('Equidistant Cylindrical', ccrs.PlateCarree())\n",
" make_plot('North Polar Stereographic', ccrs.NorthPolarStereo())\n",
" make_plot('LambertConformal', ccrs.LambertConformal())\n",
" make_plot('Robinson', ccrs.Robinson())\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"main()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ccrs.Projection?"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"* AlbersEqualArea\n",
"* AzimuthalEquidistant\n",
"* EuroPP\n",
"* Gnomonic\n",
"* GOOGLE_MERCATOR\n",
"* LambertConformal\n",
"* LambertCylindrical\n",
"* Mercator\n",
"* Miller\n",
"* Mollweide\n",
"* Orthographic\n",
"* OSGB\n",
"* Robinson\n",
"* RotatedGeodetic\n",
"* RotatedPole\n",
"* Stereographic\n",
"* TransverseMercator\n",
"* UTM\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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}
},
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"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import iris\n",
"import iris.quickplot as qplt\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"## IRIS Quickplot example -- Contour, fill, pcolormesh\n",
"## Live 8.5 * darkblue-b\n",
"##"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## demo data of Sea Surface Temperatures as netCDF\n",
"file_path = '/usr/lib/python2.7/dist-packages/cartopy/data/netcdf/'\n",
"filename = 'HadISST1_SST_update.nc'\n",
"cubes = iris.load(file_path+filename)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## IRIS format - show a text description of the 0th cube\n",
"\n",
"print 'Cubes defined: ' + str(len(cubes))\n",
"print cubes[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## store the 0th time slice of all lat,lon data points\n",
"sst0 = cubes[0][0,...]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## IRIS Quickplot contour\n",
"## http://scitools.org.uk/iris/docs/v1.6/iris/iris/quickplot.html#iris.quickplot.contour\n",
"contour = qplt.contour(sst0)\n",
"\n",
"# Add coastlines to the map created by contour.\n",
"plt.gca().coastlines()\n",
"\n",
"# Add contour labels based on the contour we have just created.\n",
"plt.clabel(contour)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Filled polygons, values legend, continental interior legend\n",
"## http://scitools.org.uk/iris/docs/v1.6/iris/iris/quickplot.html#iris.quickplot.contour\n",
"\n",
"qplt.contourf(sst0)\n",
"plt.gca().stock_img()\n",
"plt.gca().coastlines()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Pseudocolor Mesh\n",
"## http://scitools.org.uk/iris/docs/v1.6/iris/iris/quickplot.html#iris.quickplot.pcolormesh\n",
"\n",
"qplt.pcolormesh(sst0)\n",
"plt.gca().stock_img()\n",
"plt.gca().coastlines()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import iris\n",
"import iris.plot as iplt\n",
"import iris.quickplot as qplt\n",
"\n",
"## netCDF via IRIS example\n",
"## Live 8.5 * darkblue-b\n",
"##"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Convert a DEM GeoTIFF to netCDF with gdal\n",
"!rm 'sample_data/SanMateo_CA.cf'\n",
"!gdal_translate -of netcdf sample_data/SanMateo_CA.tif sample_data/SanMateo_CA.cf\n",
"\n",
"## previously extracted from a large USGS DEM via\n",
"## gdal_translate -projwin -122.44 37.7 -122.2 37.4 -of GTiff imgn38w123_1.img SanMateo_CA.tif"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Open the netCDF with IRIS\n",
"res = iris.load('sample_data/SanMateo_CA.cf')\n",
"\n",
"print ' TYPE: ', type(res)\n",
"print 'CUBES: ', res\n",
"print ' REPR: ', res[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## color visualization of elevation data using IRIS Quickplot\n",
"iplt.pcolormesh(res[0], cmap=plt.get_cmap('gist_earth'))\n",
"iplt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"contour = qplt.contour(res[0])\n",
"\n",
"## use the center point for a quick demo\n",
"lon = -122.3200000\n",
"lat = 37.5500000\n",
"\n",
"plt.scatter(lon, lat, marker=(5, 1), color='red', s=200)\n",
"plt.title(\"Welcome to San Mateo\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import numpy.ma as ma\n",
"\n",
"import iris\n",
"import iris.plot as iplt\n",
"import iris.quickplot as qplt"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Ionosphere space weather\n",
"========================\n",
"\n",
"This space weather example plots a filled contour of rotated pole point\n",
"data with a shaded relief image underlay. The plot shows aggregated\n",
"vertical electron content in the ionosphere.\n",
"\n",
"The plot exhibits an interesting outline effect due to excluding data\n",
"values below a certain threshold.\n",
"\n",
"### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"\n",
"def main():\n",
" # Load the \"total electron content\" cube.\n",
" #filename = iris.sample_data_path('space_weather.nc')\n",
" filename = 'sample_data/space_weather.nc'\n",
" cube = iris.load_cube(filename, 'total electron content')\n",
"\n",
" # Explicitly mask negative electron content.\n",
" cube.data = ma.masked_less(cube.data, 0)\n",
"\n",
" # Plot the cube using one hundred colour levels.\n",
" qplt.contourf(cube, 100)\n",
" plt.title('Total Electron Content')\n",
" plt.xlabel('longitude / degrees')\n",
" plt.ylabel('latitude / degrees')\n",
" plt.gca().stock_img()\n",
" plt.gca().coastlines()\n",
" iplt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"main()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import cartopy.crs as ccrs\n",
"\n",
"import iris\n",
"import iris.plot as iplt\n",
"import iris.quickplot as qplt"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Quickplot of a 2d cube on a map\n",
"===============================\n",
"\n",
"This example demonstrates a contour plot of global air temperature.\n",
"The plot title and the labels for the axes are automatically derived from the metadata.\n",
"(added example of to_xml -ed)\n",
"\n",
"\n",
"### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"def main():\n",
" #fname = iris.sample_data_path('air_temp.pp')\n",
" fname = 'sample_data/air_temp.pp'\n",
" temperature = iris.load_cube(fname)\n",
"\n",
" # Plot #1: contourf with axes longitude from -180 to 180\n",
" fig = plt.figure(figsize=(12, 5))\n",
" plt.subplot(121)\n",
" qplt.contourf(temperature, 15)\n",
" plt.gca().coastlines()\n",
"\n",
" # Plot #2: contourf with axes longitude from 0 to 360\n",
" proj = ccrs.PlateCarree(central_longitude=-180.0)\n",
" ax = plt.subplot(122, projection=proj)\n",
" qplt.contourf(temperature, 15)\n",
" plt.gca().coastlines()\n",
" iplt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"main()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#fname = iris.sample_data_path('air_temp.pp')\n",
"fname = 'sample_data/air_temp.pp'\n",
"t0 = iris.load_cube(fname)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"t0.xml()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"whos"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import cartopy.crs as ccrs\n",
"\n",
"import iris\n",
"import iris.plot as iplt\n",
"import iris.quickplot as qplt\n",
"import iris.analysis.cartography"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Rotated pole mapping\n",
"=====================\n",
"\n",
"This example uses several visualisation methods to achieve an array of\n",
"differing images, including:\n",
"\n",
" * Visualisation of point based data\n",
" * Contouring of point based data\n",
" * Block plot of contiguous bounded data\n",
" * Non native projection and a Natural Earth shaded relief image underlay\n",
"\n",
"\n",
"### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"def main():\n",
" #fname = iris.sample_data_path('rotated_pole.nc')\n",
" fname = 'sample_data/rotated_pole.nc'\n",
" air_pressure = iris.load_cube(fname)\n",
"\n",
" # Plot #1: Point plot showing data values & a colorbar\n",
" plt.figure()\n",
" points = qplt.points(air_pressure, c=air_pressure.data)\n",
" cb = plt.colorbar(points, orientation='horizontal')\n",
" cb.set_label(air_pressure.units)\n",
" plt.gca().coastlines()\n",
" iplt.show()\n",
"\n",
" # Plot #2: Contourf of the point based data\n",
" plt.figure()\n",
" qplt.contourf(air_pressure, 15)\n",
" plt.gca().coastlines()\n",
" iplt.show()\n",
"\n",
" # Plot #3: Contourf overlayed by coloured point data\n",
" plt.figure()\n",
" qplt.contourf(air_pressure)\n",
" iplt.points(air_pressure, c=air_pressure.data)\n",
" plt.gca().coastlines()\n",
" iplt.show()\n",
"\n",
" # For the purposes of this example, add some bounds to the latitude\n",
" # and longitude\n",
" air_pressure.coord('grid_latitude').guess_bounds()\n",
" air_pressure.coord('grid_longitude').guess_bounds()\n",
"\n",
" # Plot #4: Block plot\n",
" plt.figure()\n",
" plt.axes(projection=ccrs.PlateCarree())\n",
" iplt.pcolormesh(air_pressure)\n",
" plt.gca().stock_img()\n",
" plt.gca().coastlines()\n",
" iplt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"main()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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# Spatial Data Analysis with PySAL and GeoDaSpace
**[Sergio Rey] and [Luc Anselin]**
**November 11, 2015**
**NARSC 2015, Portland, Oregon**
## Tutorial Description
A unique feature of this tutorial is the use of Python based software tools for spatial data analysis. Python is an object oriented scripting language that is gaining rapid adoption in the scientific computing and data science communities. To facilitate its adoption within the GIScience community, Rey and Anselin have collaborated on the creation of [PySAL]: Python Library for Spatial Analysis. Since its initial release in July 2010, PySAL has been downloaded over 75,000 times and is now included in well- known open source scientific data analysis distributions, such as Anaconda. This two-part tutorial will introduce participants to the latest version of PySAL as well as to GeoDaSpace, a GUI application based on PySAL designed for spatial econometric analysis. The first component introduces the basic organization and philosophy of PySAL, with a special focus on exploratory spatial data analysis. In the second part of the tutorial the focus moves to practical spatial regression analysis using GeoDaSpace and the PySAL spreg module.
## Prerequisites
- Previous experience with Python programming is recommended
- Participants should bring their own laptops to the workshop
- Software should be installed prior to traveling to the workshop (instructions below)
### Software Requirements
**Please note that PySAL is pre-installed here -- feb2016
For the workshop we will require the following packages be installed
- PySAL 1.10.0
- SciPy
- Numpy
- iPython
- ipywidgets
- jupyter
- matplotlib
- pandas
- folium
- [GeoDaSpace][GeoDaSpace]
There are a number of ways to install PySAL and these dependencies. For the workshop, if you do not yet have the dependencies installed we suggest using one of two scientific Python distributions (below). These have the advantages of including most of the dependencies for PySAL as well as PySAL itself. Moreover, both allow for updating PySAL to the most recent release (1.10 released July 31, 2015) which is more current that what is listed in either distribution. Both of these distributions also allow for installation of our final dependency, folium.
#### PySAL via Anaconda Python Distribution
1. Install [Anaconda Python Distribution][Anaconda]
2. Open a terminal (Mac or Linux) or Powershell (Windows)
2. `pip install -U pysal`
3. `pip install -U folium`
#### Creating a Custom Conda Environment
If you already have Anaconda installed and you did not want to modify your default environemnt, you can create a *custom conda environment* for this session using the following commands:
1. `conda create -n pysal110 scipy matplotlib jupyter ipython pandas ipywidgets`
2. `source activate pysal110`
4. `pip install -U pysal`
5. `pip install -U folium`
When you are done working in this environment, you can get back to your default environment with:
`source deactivate`
#### PySAL via Enthought Canopy
Note that the Academic version of Canopy comes with PySAL version 1.7. For this workshop we will be using PySAL 1.10. Upgrading in Canopy can be done as follows:
1. Install [Canopy][Canopy]
2. Run Canopy
3. From the menu select `Tools Canopy Terminal`
4. `pip install -U pysal`
5. `pip install -U folium`
#### Testing Your Installation
Once you have installed all the dependencies, you can check to confirm everything is ready to go.
For Anaconda:
1. Open a terminal (Mac or Linux) or Powershell (Windows)
2. `ipython notebook`
3. In the browser click `New Notebook`
3. In the first cell in the notebook enter
`import pysal`
`pysal.version`
Then `<Shift-Enter>` (i.e., hit the Shift then the Enter Key)
4. In the second cell in the notebook enter
`import folium`
Then `<Shift-Enter>`
5. In the third cell enter
`!which python`
Then `<Shift-Enter>`
Your screen should look something like:
![Anaconda setup](esda/figures/anaconda.png)
For Enthought Canopy:
2. Run Canopy
3. From the menu select `Tools Canopy Terminal`
2. `ipython notebook`
3. In the browser click `New Notebook`
3. In the first cell in the notebook enter
`import pysal`
`pysal.version`
Then `<Shift-Enter>` (i.e., hit the Shift then the Enter Key)
4. In the second cell in the notebook enter
`import folium`
Then `<Shift-Enter>`
5. In the third cell enter
`!which python`
Then `<Shift-Enter>`
Your screen should look something like:
![Enthought setup](esda/figures/enthought.png)
#### Issues
If you run into any problems, double check that you have installed both the upgraded version of PySAL and folium (see above). If problems persist, please contact me <sjsrey@gmail.com>.
[PySAL]: http://pysal.org
[GeoDaSpace]: https://geodacenter.asu.edu/software/downloads/geodaspace
[Anaconda]: http://continuum.io/downloads.html
[Canopy]: https://www.enthought.com/store
[VirtualBox]: https://www.virtualbox.org/wiki/Downloads
[VirtualBox 4.3.12]: http://download.virtualbox.org/virtualbox/4.3.12/VirtualBox-4.3.12-93733-Win.exe
[Vagrant]: http://www.vagrantup.com/downloads.html
[Vagrantfile]: Vagrantfile
[Sergio Rey]: https://geoplan.asu.edu/people/sergio-j-rey
[Luc Anselin]: https://geoplan.asu.edu/people/luc-anselin

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Spatial Data Analysis with PySAL and GeoDaSpace\n",
"\n",
"\n",
"Authors: Sergio Rey and Luc Anselin\n",
"\n",
"Date: 2015-11-11 \n",
"\n",
"Location: NARSC 2015, Portland, Oregon"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"A unique feature of this tutorial is the use of Python based software tools for spatial data analysis. Python is an object oriented scripting language that is gaining rapid adoption in the scientific computing and data science communities. To facilitate its adoption within the GIScience community, Rey and Anselin have collaborated on the creation of PySAL: Python Library for Spatial Analysis. Since its initial release in July 2010, PySAL has been downloaded over 75,000 times and is now included in well- known open source scientific data analysis distributions, such as Anaconda. This two-part tutorial will introduce participants to the latest version of PySAL as well as to GeoDaSpace, a GUI application based on PySAL designed for spatial econometric analysis. The first component introduces the basic organization and philosophy of PySAL, with a special focus on exploratory spatial data analysis. In the second part of the tutorial the focus moves to practical spatial regression analysis using GeoDaSpace and the PySAL spreg module."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Outline\n",
"\n",
"This is a full-day tutorial broken into two components.\n",
"\n",
"### Part I: Exploratory Spatial Data Analysis with PySAL\n",
"\n",
"- PySAL introduction\n",
"- Notebooks and installation\n",
"- Visualization\n",
"- Spatial data processing\n",
"- Spatial weights\n",
"- GeoVisualization\n",
"- Global spatial autocorrelation\n",
"- Local spatial autocorrelation\n",
"\n",
"### Part II: Spatial Regression Analysis with GeoDaSpace\n",
"\n",
"- GeoDaSpace introduction\n",
"- Basic regression\n",
"- Spatial diagnostics\n",
"- Nonspatial endogeneity\n",
"- Spatial lag model\n",
"- Spatial error model\n",
"- Spatial heterogeneity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PySAL: Python Spatial Analysis Library\n",
"\n",
"\n",
"\n",
"## Origins and Objectives\n",
"\n",
"- Leverage Existing Tools Development\n",
"\n",
" - GeoDa/PySpace\n",
" - STARS\n",
"\n",
"- Develop Core Library\n",
"\n",
" - spatial data *analytical* functions\n",
" - enhanced specialization and modularity\n",
" - fill *void* in geospatial Python libraries\n",
" \n",
"- Flexible Delivery Mechanisms\n",
"\n",
" - interactive shell\n",
" - GUI\n",
" - Toolkits\n",
" - webservices\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Components\n",
"![PySAL](figures/pysalgraph.png)\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Delivery Mechanisms\n",
"\n",
"### Interactive Shell: iPython Notebook\n",
"![pysalipnb](figures/pysalnb.png)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"### Toolkits: ArcGIS + spreg\n",
"![arctool](figures/10_arctool.png)\n",
"### Toolkits: QGIS + pysal\n",
"![arctool](figures/qgis.png)\n",
"### Webservices: cgPySAL\n",
"\n",
"\n",
"![cgpysal](figures/cgpysal.png)\n",
"\n",
"\n",
"### GUI: Crime Analytics for Space-Time (CAST)\n",
"\n",
"![cast](figures/cast.png)\n",
"\n",
"### GUI: GeoDaSpace (PySAL-spreg)\n",
"![gsgui](figures/gsgui.png)\n",
"\n",
"![gsout](figures/gsoutput.png)\n",
"\n",
"![weightsviewer](figures/weightsviewer.png)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Team\n",
"\n",
"\n",
"\n",
"| Serge Rey | Luc Anselin |\n",
"|-----------------|------------------|\n",
"| Charles Schnidt | David Folch |\n",
"| Myunghwa Hwang | Dani Arribas |\n",
"| Phil Stephens | Julia Koschinsky |\n",
"| Pedro Amaral | Nick Malizia |\n",
"| Xing Kang | Xun Li |\n",
"| Mark McCann | Ran Wei |\n",
"| Nancy Lozano | Jing Yao |\n",
"| Jay Laura | Levi Wolf |\n",
"| Sizhe Wang | Wei Kang |\n",
"\n",
"\n",
"and, contributions from *many* others!\n",
"\n",
"\n",
"\n",
"## Releases\n",
"\n",
"### Six-Month Release Cycle\n",
" - 1.0 July 2010\n",
" - 1.1 January 2011\n",
" - 1.2 July 2011\n",
" - 1.3 January 2012\n",
" - 1.4 July 2012\n",
" - 1.5 January 2013\n",
" - 1.6 July 2013\n",
" - 1.7 January 2014\n",
" - **1.8 July 2014**\n",
" \n",
"### Scientific Python Distributions\n",
"\n",
"#### Anaconda Python Distribution\n",
"![anaconda](figures/anaconda_pysal.png)\n",
"\n",
"#### Enthought Canopy \n",
"\n",
"![anaconda](figures/enthought_pysal.png)\n",
"\n",
"\n",
"\n",
"## Acknowledgements\n",
"\n",
"![funding](figures/ack.png)\n",
"\n",
"\n",
" \n",
"## Development\n",
"\n",
"[![PySAL.org](figures/pysal_org.png)](http://pysal.org)\n",
"\n",
"## References\n",
"\n",
"1. S. J. Rey. Python Spatial Analysis Library (PySAL): An update and illustration. In C. Brunsdon and A. SIngleton, editors, Geocomputation. Sage, In Press 2014.\n",
"2. S. J. Rey and L. Anselin. PySAL: A Python library of spatial analytical methods. In M. M. Fischer and A. Getis, editors, Handbook of Applied Spatial Analysis, pages 175–193. Springer, Berlin, 2010.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PySAL"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Documentation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.display import HTML\n",
"HTML('<iframe src=http://pysal.org/?useformat=mobile width=800 height=550></iframe>')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Source Code"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Source is at http://github.com/pysal"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to IPython Notebook\n",
"\n",
"We will cover the basic operations/interface of the notebook which we will, in turn, use for the remainder of the workshop"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What is IPython Notebook (now called Jupyter)\n",
"\n",
"- web-based interface for working with Python (and other languages)\n",
"- Similar in spirit to a scientific notebook\n",
" - record experiments\n",
" - document\n",
" - share\n",
"- literate programming\n",
"- simple JSON format\n",
" - web citizen\n",
" - git friendly"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Starting the notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From a shell or terminal we can start the notebook with:\n",
"```\n",
"ipython notebook\n",
"```\n",
"\n",
"This brings up the **dashboard** which will list any notebooks encountered in the current working directory.\n",
"\n",
"You can either open an existing notebook or create a new one from the dashboard."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.display import Image\n",
"def show_image(img):\n",
" i = Image(filename = img)\n",
" return i"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"show_image('images/dashboard.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Notebook Inteface"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Menu"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"show_image('images/menu.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Help System"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"show_image('images/help.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Keyboard Shortcuts\n",
"\n",
"A list of keyboard shortcuts can be revealed by entering `h`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"show_image('images/shortcuts.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note there are two *modes* for the notebook, *Edit Mode* and *Command mode*. The latter is used to *manipulate* notebook cells, while the former is used to *edit* the content of a cell.\n",
"\n",
"- To enter *Command Mode* press `ESC`.\n",
"- To enter *Edit Mode* press `Enter` on a cell.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Navigation\n",
"\n",
"When in command mode, you can use keyboard shortcuts to move from cell to cell\n",
"\n",
"- `k` moves the cursor up one cell\n",
"- `j` moves the cursor down one cell\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cell Types"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Code Cells"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = range(10) # Shift-Enter to execute and create a new cell"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = range(10)\n",
"y = [ xi*3 for xi in x] # Ctrl-Return to execute but stay in the current cell"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comments\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Text Cells"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In command mode, `t` will set the cell to text. After that, pressing `Enter` will let you edit the text."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Markdown Cells"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In command mode, ```m``` gives us a [Markdown] cell.\n",
"\n",
"\n",
"[Markdown]: http://daringfireball.net/projects/markdown/\n",
"\n",
"We can then use Markdown syntax to do things like lists:\n",
"\n",
"- first\n",
"- second\n",
"- third\n",
" - nested one\n",
" - nested two\n",
"- fourth\n",
"\n",
"\n",
"As well as **LaTeX**.\n",
"\n",
"This is an in-line equation $\\hat{\\beta} = (X'X)^{-1}X'y$ for the ordinary least squares estimator.\n",
"\n",
"A display equation is done with\n",
"\n",
"$$ y = \\rho W y + X\\beta + \\epsilon$$\n",
"\n",
"**NOTE**: LaTeX will only render if mathjax is available via a network connection or if it has been installed locally."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Kernel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Stoping and Restarting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`00` (enter the zero key twice) will restart the kernel."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exporting and Saving"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can save in several formats via the **File** menu entry"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"show_image('images/save.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Saving\n",
"\n",
"In command mode, `s` will save the notebook, and mark a checkpoint. You can use checkpoints to rollback to a previous version if needed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Spatial Data Processing with PySAL\n",
"\n",
"This notebook will cover basic spatial data processing and using PySAL with the Notebook\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Common Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# our convention is to alias PySAL, NumPy and Pandas\n",
"import pysal as ps\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# check the versions\n",
"ps.version"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.version.short_version"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pd.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reading a CSV File"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!head data/mexico.csv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"f = ps.open(\"data/mexico.csv\")\n",
"vnames = [\"pcgdp%d\"%decade for decade in range(1940, 2010, 10)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"vnames"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Y = np.transpose(np.array([f.by_col[v] for v in vnames]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"state = f.by_col['State']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"f.close() # done with the file"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"state"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reading a Shapefile"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# us counties example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"### First the attributes from the dbf"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dbf = ps.open('data/NAT.dbf')\n",
"header = dbf.header"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"header"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Read all the numeric variables into a big array\n",
"# find the first offset we need\n",
"start_col = header.index(\"SOUTH\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"start_col"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"vars = header[8:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"vars"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nat_array = np.array([np.array(dbf.by_col(var)) for var in vars])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nat_array.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nat_array = nat_array.T"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nat_array.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dbf.close() # done with the dbf file\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Now the geometries "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"shp_file = ps.open(\"data/NAT.shp\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"shp_file.header"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"len(shp_file)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"shapes = [ shp_file.next() for i in xrange(len(shp_file)) ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"type(shapes)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"len(shapes)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"type(shapes)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"s0 = shapes[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"s0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dir(s0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"shp_file.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reading a GeoJSON File"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import json"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"f = open('data/nat.json')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fj = json.load(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fj.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"features = []\n",
"for feature in fj['features']:\n",
" features.append(feature)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"features[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

473
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@ -0,0 +1,473 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pysal as ps\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## X,Y Plots"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d = np.linspace(1,10,200)\n",
"I = np.exp(-d)\n",
"plt.plot(d,I)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d = np.linspace(1,10,200)\n",
"I = np.exp(-d)\n",
"plt.ylabel('I')\n",
"plt.xlabel('d')\n",
"plt.plot(d,I)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d = np.linspace(1,10,200)\n",
"I = np.exp(-d)\n",
"plt.ylabel(r'$I= \\exp(-d)$')\n",
"plt.xlabel('d')\n",
"plt.plot(d,I)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"type(d)\n",
"d[0:10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d = np.linspace(1,10,200)\n",
"I = d**(-2)\n",
"plt.ylabel(r'$I= 1 / d^2$')\n",
"plt.xlabel('d')\n",
"plt.plot(d,I)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d = np.linspace(1,10,200)\n",
"Iid2 = d**(-2)\n",
"plt.plot(d,Iid2)\n",
"plt.ylabel(r'$I$')\n",
"Inex = np.exp(-d)\n",
"plt.plot(d,Inex)\n",
"plt.xlabel('d')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d = np.linspace(1,10,200)\n",
"Iid2 = d**(-2)\n",
"plt.plot(d,Iid2, label = r'$I = d^{-2}$')\n",
"plt.ylabel(r'$I$')\n",
"Inex = np.exp(-d)\n",
"plt.plot(d,Inex, label = r'$I = \\exp(-d)$')\n",
"plt.xlabel('d')\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.legend?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d = np.linspace(1,10,200)\n",
"Iid2 = d**(-2)\n",
"plt.plot(d,Iid2, 'r-', label = r'$I = d^{-2}$')\n",
"plt.ylabel(r'$I$')\n",
"Inex = np.exp(-d)\n",
"plt.plot(d,Inex, 'g--',label = r'$I = \\exp(-d)$')\n",
"plt.xlabel('d')\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d = np.linspace(1,10,200)\n",
"Iid = d**(-1)\n",
"plt.plot(d,Iid, 'b-.', label = r'$I = d^{-1}$')\n",
"Iid2 = d**(-2)\n",
"plt.plot(d,Iid2, 'r-', label = r'$I = d^{-2}$')\n",
"plt.ylabel(r'$I$')\n",
"Inex = np.exp(-d)\n",
"plt.plot(d,Inex, 'g--',label = r'$I = \\exp(-d)$')\n",
"plt.xlabel('d')\n",
"plt.legend()\n",
"plt.title('Distance Decay')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scatter Plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = np.arange(100)\n",
"y = 0.5 + np.random.normal(size=(100,))\n",
"plt.scatter(x,y)\n",
"plt.xlabel('x')\n",
"plt.ylabel('y')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.scatter(x,y)\n",
"plt.vlines(x.mean(),y.min(),y.max())\n",
"plt.hlines(x.min(),x.max(),y.mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Distributions and Densities"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.mlab as mlab\n",
"\n",
"x = np.random.multivariate_normal([100,70],[[2.,1.7],[1.7,2.]], (50))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(x[:,0], x[:,1], \"o\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x0h = plt.hist(x[:,0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x1h = plt.hist(x[:,1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n,bins,bars = plt.hist(x[:,1])\n",
"\n",
"y = mlab.normpdf(bins, np.mean(x[:,1]), np.std(x[:,1]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(bins,y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(bins,y)\n",
"plt.hist(x[:,1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(bins,y)\n",
"plt.hist(x[:,1],normed=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Saving to a file"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(bins,y)\n",
"plt.hist(x[:,1],normed=1)\n",
"plt.savefig(\"hist.png\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!ls *.png"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from scipy.stats import gaussian_kde"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y = x[:,1]\n",
"density = gaussian_kde(y)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"xs = np.linspace(y.min(),y.max(),30)\n",
"plt.plot(xs, density(xs))\n",
"plt.xlabel('y')\n",
"plt.ylabel('f(y)')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"xs = np.linspace(y.min()*.8,y.max()*1.2,30)\n",
"plt.plot(xs, density(xs))\n",
"plt.xlabel('y')\n",
"plt.ylabel('f(y)')\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is a lot more to matplotlib. One can visit the [gallery](http://matplotlib.org/gallery.html) and pull examples in to get a sense of what is possible, and how to adapt examples for your own purposes.\n",
"\n",
"\n",
"For now we move on to generating choropleth maps."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Choropleth Mapping With Folium and PySAL"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"____\n",
"## Author\n",
"\n",
"Serge Rey [sjsrey@gmail.com](sjsrey@gmail)\n",
"\n",
"## Requirements\n",
"\n",
"Since we will be pulling in tiles for basemaps, we need internet connectivity for what follows to work.\n",
"____\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.display import HTML\n",
"\n",
"import pysal as ps\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"#import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import folium\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Help functions\n",
"In order to have folium maps appear in-line we will need two utility functions.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def inline_map(map):\n",
" \"\"\"\n",
" Embeds the HTML source of the map directly into the IPython notebook.\n",
" \n",
" This method will not work if the map depends on any files (json data). Also this uses\n",
" the HTML5 srcdoc attribute, which may not be supported in all browsers.\n",
" \"\"\"\n",
" map._build_map()\n",
" return HTML('<iframe srcdoc=\"{srcdoc}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(srcdoc=map.HTML.replace('\"', '&quot;')))\n",
"\n",
"def embed_map(map, path=\"map.html\"):\n",
" \"\"\"\n",
" Embeds a linked iframe to the map into the IPython notebook.\n",
" \n",
" Note: this method will not capture the source of the map into the notebook.\n",
" This method should work for all maps (as long as they use relative urls).\n",
" \"\"\"\n",
" map.create_map(path=path)\n",
" return HTML('<iframe src=\"files/{path}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(path=path))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The source for these functions is an iPython notebook by [Blake Burkhart](http://nbviewer.ipython.org/gist/bburky/7763555/folium-ipython.ipynb)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## OpenStreet Map Tile with Folium"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"map = folium.Map(location=[45.52, -122.68], zoom_start=11)\n",
"map.simple_marker([45.52, -122.68], popup='Welcome to PySAL @<b>NARSC 2015</b>!')\n",
"inline_map(map)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Base layer for south"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=4)\n",
"map_osm.geo_json(geo_path = 'data/south.json')\n",
"embed_map(map_osm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Change the zoom level"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5)\n",
"map_osm.geo_json(geo_path = 'data/south.json')\n",
"embed_map(map_osm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Binding Attribute Data to the Map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import json\n",
"\n",
"f = open(r'data/south.json')\n",
"q = json.load(f)\n",
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"q.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"features = q['features']\n",
"len(features)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"feature_0 = features[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"feature_0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"indices = []\n",
"values = []\n",
"for feature in features:\n",
" indices.append(str(feature['properties']['FIPS']))\n",
" values.append(feature['properties']['HR80'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.DataFrame({'HR80': values,\n",
" 'FIPS': indices} )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
"map_osm.geo_json(geo_path='data/south.json',\n",
" key_on='feature.properties.FIPS',\n",
" data_out='data.json', data=df,\n",
" columns=['FIPS','HR80'],\n",
" fill_color='YlGnBu', fill_opacity=0.7,\n",
" line_opacity=0.2,\n",
" legend_name='Homicide Rate HR80')\n",
"embed_map(map_osm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using PySAL Map Classification Schemes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y = np.array(df.HR80.tolist())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pysal as ps"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ps.version"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Maximum Breaks"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"max_breaks = ps.Maximum_Breaks(y, 5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bins = max_breaks.bins.tolist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
"map_osm.geo_json(geo_path='data/south.json',\n",
" key_on='feature.properties.FIPS',\n",
" data_out='data.json', data=df,\n",
" columns=['FIPS','HR80'],\n",
" fill_color='YlGnBu', fill_opacity=0.7,\n",
" line_opacity=0.2,\n",
" threshold_scale=bins[:-1],\n",
" legend_name='Homicide Rate HR80 (Maximum Breaks)')\n",
"embed_map(map_osm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Quantiles"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"q5 = ps.Quantiles(y, 5).bins.tolist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
"map_osm.geo_json(geo_path='data/south.json',\n",
" key_on='feature.properties.FIPS',\n",
" data_out='data.json', data=df,\n",
" columns=['FIPS','HR80'],\n",
" fill_color='YlGnBu', fill_opacity=0.7,\n",
" line_opacity=0.2,\n",
" threshold_scale=q5[:-1],\n",
" legend_name='Homicide Rate HR80 (Quintiles)')\n",
"embed_map(map_osm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"q6 = ps.Quantiles(y, 6).bins.tolist()\n",
"\n",
"map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
"map_osm.geo_json(geo_path='data/south.json',\n",
" key_on='feature.properties.FIPS',\n",
" data_out='data.json', data=df,\n",
" columns=['FIPS','HR80'],\n",
" fill_color='YlGnBu', fill_opacity=0.7,\n",
" line_opacity=0.2,\n",
" threshold_scale=q6[:-1],\n",
" legend_name='Homicide Rate HR80 (Quantiles k=6)')\n",
"embed_map(map_osm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fisher-Jenks"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fj= ps.Fisher_Jenks(y, 5).bins.tolist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
"map_osm.geo_json(geo_path='data/south.json',\n",
" key_on='feature.properties.FIPS',\n",
" data_out='data.json', data=df,\n",
" columns=['FIPS','HR80'],\n",
" fill_color='YlGnBu', fill_opacity=0.7,\n",
" line_opacity=0.2,\n",
" threshold_scale=fj[:-1],\n",
" legend_name='Homicide Rate HR80 (Fisher Jenks)')\n",
"embed_map(map_osm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ps.esda.mapclassify.Fisher_Jenks_Sampled?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pysal as ps\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import folium\n",
"import random as RD\n",
"\n",
"\n",
"from pysal.contrib.viz import mapping as maps\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Guide for the `mapping` module in `PySAL`\n",
"\n",
"Contributors:\n",
"\n",
"* Dani Arribas-Bel `<daniel.arribas.bel@gmail.com>`\n",
"* Serge Rey `<sjsrey@gmail.com>`\n",
"\n",
"\n",
"This document describes the main structure, components and usage of the mapping module in `PySAL`. The is organized around three main layers:\n",
"\n",
"* A lower-level layer that reads polygon, line and point shapefiles and returns a Matplotlib collection.\n",
"* A medium-level layer that performs some usual transformations on a Matplotlib object (e.g. color code polygons according to a vector of values).\n",
"* A higher-level layer intended for end-users for particularly useful cases and style preferences pre-defined (e.g. Create a choropleth)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lower-level component\n",
"\n",
"This includes basic functionality to read spatial data from a file (currently only shapefiles supported) and produce rudimentary Matplotlib objects. The main methods are:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* `map_poly_shape`: to read in polygon shapefiles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* `map_line_shape`: to read in line shapefiles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* `map_point_shape`: to read in point shapefiles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These methods all support an option to subset the observations to be plotted (very useful when missing values are present). They can also be overlaid and combined by using the `setup_ax` function. the resulting object is very basic but also very flexible so, for minds used to matplotlib this should be good news as it allows to modify pretty much any property and attribute."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"shp_link = ps.examples.get_path('columbus.shp')\n",
"shp = ps.open(shp_link)\n",
"some = [bool(RD.getrandbits(1)) for i in ps.open(shp_link)]\n",
"\n",
"fig = plt.figure()\n",
"\n",
"base = maps.map_poly_shp(shp)\n",
"base.set_facecolor('none')\n",
"base.set_linewidth(0.75)\n",
"base.set_edgecolor('0.8')\n",
"some = maps.map_poly_shp(shp, which=some)\n",
"some.set_alpha(0.5)\n",
"some.set_linewidth(0.)\n",
"cents = np.array([poly.centroid for poly in ps.open(shp_link)])\n",
"pts = plt.scatter(cents[:, 0], cents[:, 1])\n",
"pts.set_color('red')\n",
"\n",
"ax = maps.setup_ax([base, some, pts])\n",
"fig.add_axes(ax)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"some"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Medium-level component\n",
"\n",
"This layer comprises functions that perform usual transformations on matplotlib objects, such as color coding objects (points, polygons, etc.) according to a series of values. This includes the following methods:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* `base_choropleth_classless`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* `base_choropleth_unique`\n",
"\n",
"### Example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"net_link = ps.examples.get_path('eberly_net.shp')\n",
"net = ps.open(net_link)\n",
"values = np.array(ps.open(net_link.replace('.shp', '.dbf')).by_col('TNODE'))\n",
"\n",
"pts_link = ps.examples.get_path('eberly_net_pts_onnetwork.shp')\n",
"pts = ps.open(pts_link)\n",
"\n",
"fig = plt.figure()\n",
"\n",
"netm = maps.map_line_shp(net)\n",
"netc = maps.base_choropleth_unique(netm, values)\n",
"\n",
"ptsm = maps.map_point_shp(pts)\n",
"ptsm = maps.base_choropleth_classif(ptsm, values)\n",
"ptsm.set_alpha(0.5)\n",
"ptsm.set_linewidth(0.)\n",
"\n",
"ax = maps.setup_ax([netc, ptsm])\n",
"fig.add_axes(ax)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"* `base_choropleth_classif`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Higher-level component\n",
"\n",
"This currently includes the following end-user functions:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* `plot_poly_lines`: very quick shapefile plotting."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"maps.plot_poly_lines(ps.examples.get_path('columbus.shp'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* `plot_choropleth`: for quick plotting of several types of chocopleths."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"shp_link = ps.examples.get_path('columbus.shp')\n",
"values = np.array(ps.open(ps.examples.get_path('columbus.dbf')).by_col('HOVAL'))\n",
"\n",
"types = ['classless', 'unique_values', 'quantiles', 'equal_interval']\n",
"for typ in types:\n",
" maps.plot_choropleth(shp_link, values, typ, title=typ)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# To-Do list\n",
"\n",
"General concepts and specific ideas to implement over time, with enough description so they can be brought to life.\n",
"\n",
"* Support for points in medium and higher layer\n",
"* LISA cluster maps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Caution note on plotting points\n",
"\n",
"Support for points (dots) is still not quite polished. Ideally, one would like to create a `PathCollection` from scratch so it is analogue to the creation of a `PolyCollection` or `LineCollection`. However, for the time being, we are relying on the wrapper `plt.scatter`, which makes it harder to extract the collection and plug it in a different figure. For that reason, it is recommended that, for the time being, one creates the line and/or polygon map as shown in this notebook and then grabs the output axis and uses `ax.scatter` to overlay the points.\n",
"\n",
"**NOTE**: the `PathCollection` created by `plt.scatter` is detailed on line 3142 of [`_axes.py`](https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/axes/_axes.py). Maybe we can take some inspiration from there to create our own `PathCollection` for points so they live at the same level as polygons."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pysal as ps\n",
"import numpy as np\n",
"#import pandas as pd\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Regular Lattice Weights "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w = ps.lat2W()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w.n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w.histogram"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w.neighbors"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w.weights"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$s_0 = \\sum_i \\sum_j w_{i,j}$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w.s0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w.histogram"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.sum( [ c[0]*c[1] for c in w.histogram ] )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w.pct_nonzero"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$pctnonzero = s_0 / n^2$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w.s0 / w.n**2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Queen Contiguity"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wq = ps.lat2W(rook=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wq.s0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wq.histogram"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.sum( [ c[0] * c[1] for c in wq.histogram ] )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wq.pct_nonzero"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"144. / wq.n**2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bishop"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wb = ps.w_difference(wq,w, constrained = False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wb.n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wb.s0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wb[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wq[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ps.w_difference?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wb.histogram"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Spatial Lag"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$l_i = \\sum_{j} w_{i,j} y_j$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y = np.arange(w.n)\n",
"y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wy = ps.lag_spatial(w,y)\n",
"wy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y[[1,5]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y *= 10"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wy = ps.lag_spatial(w,y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y[[1,5]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y[[1,5]].sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w.transform = 'R'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"${w}_{i,j}^* = w_{i,j} / \\sum_j w_{i,j}$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wry = ps.lag_spatial(w,y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wry"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w[24]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y[[19,23]].mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Weights From External Files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## shapefiles"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ls data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wsouth = ps.rook_from_shapefile(\"data/south.shp\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wsouth.n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wsouth.pct_nonzero"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wsq = ps.queen_from_shapefile(\"data/south.shp\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wsq.pct_nonzero"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wsouth.histogram"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wsq.histogram"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wh = np.array(wsouth.histogram)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.bar(wh[:,0],wh[:,1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"whq = np.array(wsq.histogram)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.bar(whq[:,0],whq[:,1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Distance Based Weights"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x,y = np.indices((5,5))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x.shape = (25,1)\n",
"y.shape = (25,1)\n",
"data = np.hstack([x,y])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### knn weights"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wknn3 = ps.knnW_from_array(data, k = 4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wknn3[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wknn3.histogram"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Distance Bands"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wdb = ps.threshold_binaryW_from_array(data,2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wdb[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wdb.histogram"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wdb.cardinalities"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Writing to external files"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w = ps.rook_from_shapefile('data/NAT.shp')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w.n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"gal = ps.open('data/nat_copy.gal', 'w')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"gal.write(w)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"gal.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"gal = ps.open('data/nat_copy.gal', 'r')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w1 = gal.read()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w1.n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w.pct_nonzero == w1.pct_nonzero"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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projects/PYSAL/esda/06_taz_example.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.display import HTML\n",
"\n",
"import pysal as ps\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import folium\n",
"import random as RD\n",
"\n",
"from pysal.contrib.viz import mapping as maps\n",
"from matplotlib.collections import LineCollection\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"shp = ps.open(ps.examples.get_path(\"taz.shp\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dbf = ps.open(ps.examples.get_path(\"taz.dbf\"))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dbf.header"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig = plt.figure(figsize=(9,9))\n",
"base = maps.map_poly_shp(shp)\n",
"base.set_linewidth(0.75)\n",
"base.set_facecolor('none')\n",
"base.set_edgecolor('0.8')\n",
"ax = maps.setup_ax([base])\n",
"fig.add_axes(ax)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## County as unique values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cnty = np.array(dbf.by_col(\"CNTY\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"fig = plt.figure(figsize=(9,9))\n",
"base = maps.map_poly_shp(shp)\n",
"base.set_linewidth(0.75)\n",
"base.set_facecolor('none')\n",
"base.set_edgecolor('0.8')\n",
"counties = maps.base_choropleth_unique(maps.map_poly_shp(shp), cnty)\n",
"counties.set_linewidth(0)\n",
"counties.set_alpha(.5)\n",
"ax = maps.setup_ax([base, counties])\n",
"fig.add_axes(ax)\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cents = np.array([poly.centroid for poly in shp])\n",
"cents[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wrook = ps.rook_from_shapefile(ps.examples.get_path(\"taz.shp\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w = wrook\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cents.min(axis=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def w2line_graph(w, centroids):\n",
" \n",
" segments = []\n",
" for i in w.id2i:\n",
" origin = cents[i]\n",
" for j in w.neighbors[i]:\n",
" dest = cents[j]\n",
" ij = [i,j]\n",
" ij.sort()\n",
" segments.append([origin, dest])\n",
"\n",
" return segments \n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"segs = w2line_graph(wrook, cents)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## FIXME\n",
"if False:\n",
" fig = plt.figure(figsize=(9,9))\n",
" base = maps.map_poly_shp(shp)\n",
" base.set_linewidth(0.75)\n",
" base.set_facecolor('none')\n",
" base.set_edgecolor('0.8')\n",
" segs2 = LineCollection(segs)\n",
" maps._add_axes2col(segs2, shp.bbox)\n",
" segs2.set_linewidth(0.20)\n",
" ax = maps.setup_ax([base, segs])\n",
" fig.add_axes(ax)\n",
" plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Intersection weights"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wb = ps.regime_weights(np.array(dbf.by_col(\"CNTY\")))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wb.n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wint = ps.weights.Wsets.w_intersection(wb, wrook)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"segs = w2line_graph(wint, cents)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## FIXME\n",
"if False:\n",
" fig = plt.figure(figsize=(9,9))\n",
" base = maps.map_poly_shp(shp)\n",
" base.set_linewidth(0.75)\n",
" base.set_facecolor('none')\n",
" base.set_edgecolor('0.8')\n",
" segs = LineCollection(segs)\n",
" maps._add_axes2col(segs, shp.bbox)\n",
" segs.set_linewidth(0.20)\n",
" ax = maps.setup_ax([base, segs])\n",
" fig.add_axes(ax)\n",
" plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"segments = w2line_graph(wint, cents)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig = plt.figure(figsize=(9,9))\n",
"base = maps.map_poly_shp(shp)\n",
"base.set_linewidth(0.75)\n",
"base.set_facecolor('none')\n",
"base.set_edgecolor('0.8')\n",
"counties = maps.base_choropleth_unique(maps.map_poly_shp(shp), cnty)\n",
"counties.set_linewidth(0)\n",
"counties.set_alpha(.5)\n",
"segs = LineCollection(segments)\n",
"maps._add_axes2col(segs, shp.bbox)\n",
"segs.set_linewidth(0.20)\n",
"segs.set_color('0.1')\n",
"ax = maps.setup_ax([base, counties, segs])\n",
"fig.add_axes(ax)\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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projects/PYSAL/esda/07_global_spatial_autocorrelation.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pysal as ps\n",
"import numpy as np\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y = np.arange(25)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y.shape = (5,5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.matshow(y,cmap=plt.cm.gray_r)\n",
"plt.colorbar()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y = np.zeros((5,5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Perfect negative autocorrelation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y = np.zeros((25,))\n",
"ids = range(0,25,2)\n",
"y[ids] = 1\n",
"y.shape = (5,5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.matshow(y, cmap=plt.cm.gray)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y.shape = (25,)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Join Counts"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pysal as ps\n",
"w = ps.lat2W(5,5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$bb_i = \\sum_j y_i w_{i,j} y_j = y_i \\sum_j w_{i,j} y_j$ "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bbi = y * ps.lag_spatial(w,y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bbi"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$ww_i = \\sum_j (1-y_i) w_{i,j} (1-y_j) = (1-y_i) \\sum_j w_{i,j} (1-y_j)$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"yc = 1 - y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wwi = yc * ps.lag_spatial(w,yc)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wwi"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$bw_i = \\sum_{j} w_{i,j} - bb_i - ww_i$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$2 bw = \\sum_i bw_i = \\sum_i ( \\sum_j w_{i,j} - bb_i - ww_i ) = S_0 - bb - ww$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w.s0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bw = (w.s0 - bbi.sum() - wwi.sum())/2."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bw"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"jc = ps.Join_Counts(y, w)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"jc.bw"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"jc.p_sim_bb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"jc.p_sim_bw"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from scipy.stats import gaussian_kde"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"density = gaussian_kde(jc.sim_bw)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"xs = np.linspace(0,50,200)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(xs, density(xs))\n",
"plt.axvline(x=jc.bw, color='r')\n",
"plt.xlabel('bw')\n",
"plt.ylabel('f(bw)')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"density = gaussian_kde(jc.sim_bb)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(xs, density(xs))\n",
"plt.axvline(x=jc.bb, color='r')\n",
"plt.xlabel('bb')\n",
"plt.ylabel('f(bb)')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"jc.bb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w.s0/2\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random pattern"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from scipy.stats import bernoulli"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y = bernoulli.rvs(0.5, size=25)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"jc_random = ps.Join_Counts(y,w)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y.shape = (5,5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.matshow(y,cmap=plt.cm.gray_r)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"jc_random.p_sim_bb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"density = gaussian_kde(jc_random.sim_bb)\n",
"xs = np.linspace(0,30,200)\n",
"plt.plot(xs, density(xs))\n",
"plt.axvline(x=jc_random.bb, color='r')\n",
"plt.xlabel('bb')\n",
"plt.ylabel('f(bb)')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"jc_random.p_sim_bw"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"jc_random.ww"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"jc_random.bw"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"jc_random.bb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Continuous Variable"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y = np.arange(w.n)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"yc = y.copy()\n",
"yc.shape = (5,5)\n",
"plt.matshow(yc,cmap=plt.cm.gray_r)\n",
"plt.colorbar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Moran's I\n",
"\n",
"$$I = \\frac{n}{S_0} \\frac{\\sum_i \\sum_j z_i w_{i,j} z_j}{\\sum_i z_iz_i}$$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mi = ps.Moran(y,w)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mi.I"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mi.EI"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mi.p_norm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mi.p_sim"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"mi.sim"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"density = gaussian_kde(mi.sim)\n",
"xs = np.linspace(mi.sim.min(),mi.sim.max(),200)\n",
"plt.plot(xs, density(xs))\n",
"plt.axvline(x=mi.I, color='r')\n",
"plt.xlabel('I')\n",
"plt.ylabel('f(I)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Moran Scatter Plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w.transform = \"R\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wy = ps.lag_spatial(w, y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.scatter(y,wy)\n",
"plt.vlines(y.mean(),wy.min(),wy.max())\n",
"plt.hlines(wy.mean(),y.min(),y.max())\n",
"plt.xlabel('y')\n",
"plt.ylabel('wy')\n",
"plt.title('Moran Scatter Plot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getis Ord $G$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"g = ps.G(y,w)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"g.p_sim"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ps.G?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pysal as ps\n",
"import numpy as np\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from scipy.linalg import inv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def draw_map(lamb):\n",
" s = 20\n",
" n = s**2\n",
" w = ps.lat2W(s, s, rook=False)\n",
" w.transform = 'R'\n",
" e = np.random.random((n, 1))\n",
" u = inv(np.eye(n) - lamb * w.full()[0])\n",
" u = np.dot(u, e)\n",
" ul = ps.lag_spatial(w, u)\n",
" u = (u - u.mean()) / np.std(u)\n",
" ul = (ul - ul.mean()) / np.std(ul)\n",
" gu = u.reshape((s, s))\n",
" # Figure\n",
" f = plt.figure(figsize=(9, 4))\n",
" ax1 = f.add_subplot(121)\n",
" ax1.matshow(gu, cmap=plt.cm.YlGn)\n",
" ax1.set_frame_on(False)\n",
" ax1.axes.get_xaxis().set_visible(False)\n",
" ax1.axes.get_yaxis().set_visible(False)\n",
" #---\n",
" ax2 = f.add_subplot(122)\n",
" sc = ax2.scatter(u, ul, linewidth=0)\n",
" ols = ps.spreg.OLS(ul, u)\n",
" tag = \"b = %.3f\"%ols.betas[1][0]\n",
" ax2.plot(u, ols.predy, c='red', label=tag)\n",
" ax2.axvline(0, c='0.5')\n",
" ax2.axhline(0, c='0.5')\n",
" ax2.legend()\n",
" plt.xlabel('u')\n",
" plt.ylabel('Wu')\n",
" plt.suptitle(\"$\\lambda$ = %.2f\"%lamb)\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Interactive spatial autocorrelation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook illustrates the concept of spatial autocorrelation using the new interactivity in IPython. The data generating process (DGP) considered here is the following:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. $u = \\lambda Wu + \\epsilon$\n",
"\n",
"1. $u - \\lambda Wu = \\epsilon$\n",
"\n",
"1. $u (I - \\lambda W) = \\epsilon$\n",
"\n",
"1. $u = (I - \\lambda W)^{-1} \\epsilon$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Where `u` is a vector spatially autocorrelated, `W` is a spatial weights matrix as you could created with `PySAL`, and $\\epsilon$ is an i.i.d. random vector."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To implement the previous DGP, the simple method `draw_map` (actual code pasted at the bottom of the notebook, so make sure to run that cell beforehand) creates a random vector with degree of spatial autocorrelation $\\lambda$ and allocates it to a lattice geography, where every pixel is assumed to be an area with a value. Right next to it, the function also displays the Moran's scatter plot. Both map and plot depend on the $\\lambda$ parameter that controls the degree of spatial autocorrelation.\n",
"\n",
"Here's a static version of the function:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"draw_map(0.95)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we can make the $\\lambda$ value change in an interactive way:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from ipywidgets import interact\n",
"interact(draw_map, lamb=(-0.9, 0.9))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Actual plotting function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.display import HTML\n",
"\n",
"import pysal as ps\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import folium\n",
"import random as RD\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"import folium\n",
"def inline_map(map):\n",
" \"\"\"\n",
" Embeds the HTML source of the map directly into the IPython notebook.\n",
" \n",
" This method will not work if the map depends on any files (json data). Also this uses\n",
" the HTML5 srcdoc attribute, which may not be supported in all browsers.\n",
" \"\"\"\n",
" map._build_map()\n",
" return HTML('<iframe srcdoc=\"{srcdoc}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(srcdoc=map.HTML.replace('\"', '&quot;')))\n",
"\n",
"def embed_map(map, path=\"map.html\"):\n",
" \"\"\"\n",
" Embeds a linked iframe to the map into the IPython notebook.\n",
" \n",
" Note: this method will not capture the source of the map into the notebook.\n",
" This method should work for all maps (as long as they use relative urls).\n",
" \"\"\"\n",
" map.create_map(path=path)\n",
" return HTML('<iframe src=\"files/{path}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(path=path))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Base layer for south"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import folium\n",
"map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5)\n",
"map_osm.geo_json(geo_path = 'data/south.json')\n",
"embed_map(map_osm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Binding Attribute Data to the Map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import json\n",
"\n",
"f = open(r'data/south.json')\n",
"q = json.load(f)\n",
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"q.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"features = q['features']\n",
"len(features)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"indices = []\n",
"values = []\n",
"for feature in features:\n",
" indices.append(str(feature['properties']['FIPS']))\n",
" values.append(feature['properties']['HR80'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.DataFrame({'HR80': values,\n",
" 'FIPS': indices} )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y = np.array(df.HR80.tolist())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pysal as ps\n",
"w = ps.queen_from_shapefile('data/south.shp')\n",
"w.transform = 'r'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y = np.array(df['HR80'].tolist())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Local Moran\n",
"$li_i = \\frac{n-1}{\\sum_j z_j^2}z_i \\sum_j w_{i,j} z_j$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"li_hr80 = ps.Moran_Local(y,w)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"li_hr80.p_sim.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sig01 = 1.* (li_hr80.p_sim<=0.01)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sig01.sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sig05 = 1.* (li_hr80.p_sim<=0.05)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sig05.sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df['sig01'] = sig01\n",
"df['sig05'] = sig05"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
"map_osm.geo_json(geo_path='data/south.json',\n",
" key_on='feature.properties.FIPS',\n",
" data_out='data.json', data=df,\n",
" columns=['FIPS','HR80'],\n",
" fill_color='YlGnBu', fill_opacity=0.7,\n",
" line_opacity=0.2,\n",
" legend_name='Homicide Rate HR80')\n",
"embed_map(map_osm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
"map_osm.geo_json(geo_path='data/south.json',\n",
" key_on='feature.properties.FIPS',\n",
" data_out='data.json', data=df,\n",
" columns=['FIPS','sig01'],\n",
" fill_color='YlOrRd', fill_opacity=0.7,\n",
" line_opacity=0.2,\n",
" threshold_scale=[0,1],\n",
" legend_name='Homicide Rate HR80 - Significant LISA (0.01)')\n",
"embed_map(map_osm)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Note that folium only is bound to threshold scales - no discrete or ordinal scale yet, so we have to fake it with two class threshold scales."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
"map_osm.geo_json(geo_path='data/south.json',\n",
" key_on='feature.properties.FIPS',\n",
" data_out='data.json', data=df,\n",
" columns=['FIPS','sig01'],\n",
" fill_color='YlOrRd', fill_opacity=0.7,\n",
" line_opacity=0.2,\n",
" legend_name='Homicide Rate HR80 - Significant LISA (0.01)')\n",
"embed_map(map_osm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
"map_osm.geo_json(geo_path='data/south.json',\n",
" key_on='feature.properties.FIPS',\n",
" data_out='data.json', data=df,\n",
" columns=['FIPS','sig05'],\n",
" fill_color='YlOrRd', fill_opacity=0.7,\n",
" line_opacity=0.2,\n",
" legend_name='Homicide Rate HR80 - Significant LISA (0.05)')\n",
"embed_map(map_osm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"li_hr80.q"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cold_spots = 1. * (li_hr80.q==3) * (li_hr80.p_sim < 0.01)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cold_spots.sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Cold spots in pandas'y way\n",
"lisa_df = pd.DataFrame({'q': li_hr80.q, 'p_sim': li_hr80.p_sim})\n",
"cold = lisa_df[(lisa_df['q']==3) & (lisa_df['p_sim']<0.01)]\n",
"print cold.info()\n",
"print '\\n~~~~~~~~~~\\n'\n",
"# Hot spots in pandas'y way\n",
"hot = lisa_df[(lisa_df['q']==1) & (lisa_df['p_sim']<0.01)]\n",
"print hot.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hot_spots = 1. * (li_hr80.q==1) * (li_hr80.p_sim < 0.01)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hot_spots.sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df['hot_01'] = hot_spots\n",
"df['cold_01'] = cold_spots"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df.hot_01"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df.cold_01"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
"map_osm.geo_json(geo_path='data/south.json',\n",
" key_on='feature.properties.FIPS',\n",
" data_out='data.json', data=df,\n",
" columns=['FIPS','hot_01'],\n",
" fill_color='YlOrRd', fill_opacity=0.7,\n",
" line_opacity=0.2,\n",
" threshold_scale=[0,1],\n",
" legend_name='Homicide Rate HR80 - Hot Spots (0.01)')\n",
"embed_map(map_osm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
"map_osm.geo_json(geo_path='data/south.json',\n",
" key_on='feature.properties.FIPS',\n",
" data_out='data.json', data=df,\n",
" columns=['FIPS','cold_01'],\n",
" fill_color='YlGnBu', fill_opacity=0.7,\n",
" line_opacity=0.2,\n",
" threshold_scale=[0,1],\n",
" legend_name='Homicide Rate HR80 - Cold Spots (0.01)')\n",
"embed_map(map_osm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dbf = ps.open('data/south.dbf')\n",
"dbf.header"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
" df['STATE_NAME'] = dbf.by_col('STATE_NAME')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df['County'] = dbf.by_col('NAME')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hot_01 = df['hot_01']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hot = df[hot_01>0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df[['STATE_NAME', 'County']]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df.tail()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df[[0,2,4]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df[df.hot_01 > 0 ].all()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df[0:10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hot = df[df['hot_01']>0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df[df['hot_01']>0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df[df['cold_01']>0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print df[df['cold_01']>0].to_string() "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hot_01"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w = ps.queen_from_shapefile('data/south.shp')\n",
"w.transform = 'r'\n",
"wy = ps.lag_spatial(w,y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.scatter(y,wy, alpha=0.5, lw=0)\n",
"plt.vlines(y.mean(),wy.min(),wy.max())\n",
"plt.hlines(wy.mean(),y.min(),y.max())\n",
"plt.xlabel('y')\n",
"plt.ylabel('wy')\n",
"lm = ps.spreg.OLS(wy[:, None], y[:, None])\n",
"plt.plot(y, lm.predy, color='red')\n",
"plt.title('Moran Scatter Plot')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"c = 2*hot_01 + df.cold_01\n",
"plt.scatter(y,wy, c=c, s=(sig01+1)*50)\n",
"plt.vlines(y.mean(),wy.min(),wy.max())\n",
"plt.hlines(wy.mean(),y.min(),y.max())\n",
"plt.xlabel('y')\n",
"plt.ylabel('wy')\n",
"plt.title('Moran Scatter Plot')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sig01.sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df['wHR80'] = wy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print df[df['cold_01']>0].to_string() "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print df[df['hot_01']>0].to_string() "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lg = ps.G_Local(y, w)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lg.n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sig = lg.p_sim < 0.01"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sig"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lg.p_sim[sig]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lgs = ps.G_Local(y,w, star=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lgs.p_sim[lgs.p_sim < 0.01]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sum(sig)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sum(lgs.p_sim < 0.01)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

642
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conway's Game of Life with PySAL"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rules ##\n",
"\n",
" 1. A cell that is currently alive and that has two **or** three live neighbors stays alive\n",
" 2. A cell that is currently dead with **exactly** three live neighbors comes alive \n",
" 3. All other cells remain dead, or die due to loneliness (less than 2 neighbors) or overcrowding (more than 3 neighbors)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pysal as ps\n",
"import numpy as np\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from scipy.stats import bernoulli\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"k = 8 # dimension of lattice"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w = ps.lat2W(k,k,rook=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w.n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w.neighbors[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w.neighbors[45]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w.weights[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y = bernoulli.rvs(0.45,size=w.n)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wy = ps.lag_spatial(w,y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rules ##\n",
"\n",
" 1. A cell that is currently alive and that has two **or** three live neighbors stays alive\n",
" 2. A cell that is currently dead with **exactly** three live neighbors comes alive \n",
" 3. All other cells remain dead, or die due to loneliness (less than 2 neighbors) or overcrowding (more than 3 neighbors)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rule 1: find live cells and count their neighbors"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ywy = y*wy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lw23 = np.nonzero( (ywy==2) + (ywy==3) )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lw23"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rule 2: find dead cells with exactly 3 neighbors"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dw3 = (1-y) * wy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.nonzero(dw3==3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rules 1 and 2 give us the surviving cells"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"live_next = np.nonzero( (ywy==2) + (ywy==3) + (dw3==3) )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"live_next"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y[live_next]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we see that in the future, some dead cells will becoming alive. But what about live cells now that die in the next period?\n",
"\n",
"We know that they will be dead next period. Allocate an array with zeros for the next period and assign the live cells. Everyone else is dead."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y1 = np.zeros_like(y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y1[live_next] = 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y1"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"We would then iterate these steps for future generations. \n",
"\n",
"Let's place the process inside a function for reuse later"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def generation(y,w):\n",
" y1 = np.zeros_like(y)\n",
" wy = ps.lag_spatial(w,y)\n",
" ywy = y * wy\n",
" live_next = np.nonzero( ( ywy == 2 ) + ( ywy == 3 ) + ( ( 1-y ) * wy == 3 ) )\n",
" y1[live_next] = 1\n",
" return y1\n",
" "
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Try this out on some fresh data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y = bernoulli.rvs(0.45,size=w.n)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y1 = generation(y,w)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y2 = generation(y1,w)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One interesting initial pattern is the so called R-pentomino.\n",
"\n",
"We will create one and then run a simulation to see how the solutions evolve."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ngen=350\n",
"k = 50\n",
"w = ps.lat2W(k, k, rook=False)\n",
"#y = bernoulli.rvs(0.45,size=w.n)\n",
"y = np.zeros((w.n,))\n",
"#R-pentomino pattern\n",
"kd2 = k/2\n",
"top = kd2 + k * ( kd2 - 1 )\n",
"topr = top + 1\n",
"midl = top + k -1\n",
"mid = midl + 1\n",
"bot = mid + k\n",
"y[[top, topr, midl, mid, bot]] = 1\n",
"results = {}\n",
"for i in xrange(ngen):\n",
" y1 = generation(y,w)\n",
" results[i] = y1\n",
" if np.all(y == y1):\n",
" break\n",
" print i, y.sum(), y1.sum()\n",
" y = y1\n",
" "
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Create some graphs to look at both the the individual maps from every 10th generation and then plot the aggregate population size by generation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"generations = np.zeros((ngen,))\n",
"living = np.zeros_like(generations)\n",
"keys = results.keys()\n",
"keys.sort()\n",
"for i in keys:\n",
" generations[i] = i\n",
" living[i] = results[i].sum()\n",
" if not i%10:\n",
" ymat = results[i]\n",
" ymat.shape = (50,50)\n",
" plt.imshow(ymat,cmap='Greys', interpolation='nearest')\n",
" plt.title(\"Generation %d\"%i)\n",
" plt.show()\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"generations.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(generations,living)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ymat = results[ngen-1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ymat.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ymat.shape=(50,50)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.imshow(ymat, cmap='Greys', interpolation='nearest')\n",
"plt.title(\"Last Generation\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ymat = results[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ymat.shape = (50,50)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.imshow(ymat, cmap='Greys', interpolation='nearest')\n",
"plt.title('First Generation: R-pentomino')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ymat = results[ngen-2]\n",
"ymat.shape=(50,50)\n",
"plt.imshow(ymat, cmap='Greys', interpolation='nearest')\n",
"plt.title(\"Penultimate Generation\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ymat = results[ngen-1]\n",
"ymat.shape=(50,50)\n",
"plt.imshow(ymat, cmap='Greys', interpolation='nearest')\n",
"plt.title(\"Final Generation\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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State,pcgdp1940,pcgdp1950,pcgdp1960,pcgdp1970,pcgdp1980,pcgdp1990,pcgdp2000,hanson03,hanson98,esquivel99,inegi,inegi2
Aguascalientes,10384.000,6234.000,8714.000,16078.000,21022.000,20787.000,27782.000,2.000,2.000,3.000,4.000,4.000
Baja California,22361.000,20977.000,17865.000,25321.000,29283.000,26839.000,29855.000,1.000,1.000,5.000,1.000,1.000
Baja California Sur,9573.000,16013.000,16707.000,24384.000,29038.000,25842.000,26103.000,2.000,2.000,6.000,1.000,1.000
Campeche,3758.000,4929.000,5925.000,10274.000,12166.000,51123.000,36163.000,6.000,5.000,4.000,5.000,5.000
Chiapas,2934.000,4138.000,5280.000,7015.000,16200.000,8637.000,8684.000,5.000,5.000,7.000,5.000,5.000
Chihuahua,8578.000,13997.000,16265.000,19178.000,23399.000,25332.000,30735.000,1.000,1.000,5.000,1.000,2.000
Coahuila,8537.000,9673.000,12318.000,20562.000,25688.000,26084.000,28460.000,1.000,1.000,5.000,2.000,2.000
Colima,6909.000,6049.000,6036.000,12551.000,17427.000,18313.000,21358.000,3.000,3.000,6.000,4.000,4.000
Distrito Federal,17816.000,17119.000,23174.000,32386.000,42028.000,43810.000,54349.000,4.000,4.000,1.000,3.000,3.000
Durango,12132.000,8859.000,9323.000,12700.000,16726.000,17353.000,17379.000,2.000,2.000,3.000,1.000,2.000
Guanajuato,4359.000,5686.000,8209.000,11635.000,13864.000,13607.000,15585.000,3.000,3.000,3.000,4.000,4.000
Guerrero,2181.000,3629.000,4991.000,6497.000,8727.000,9084.000,11820.000,5.000,5.000,7.000,5.000,5.000
Hidalgo,4414.000,5194.000,6399.000,7767.000,12391.000,13091.000,12348.000,3.000,3.000,2.000,3.000,3.000
Jalisco,5309.000,8232.000,9953.000,16288.000,20659.000,20133.000,21610.000,3.000,3.000,6.000,4.000,4.000
Mexico,3408.000,4972.000,9053.000,17164.000,20165.000,18547.000,16322.000,4.000,4.000,1.000,3.000,3.000
Michoacan,3327.000,5272.000,5244.000,8109.000,11206.000,10980.000,11838.000,3.000,3.000,7.000,4.000,4.000
Morelos,6936.000,8962.000,10499.000,13892.000,16513.000,17701.000,18170.000,3.000,3.000,2.000,3.000,3.000
Nayarit,4836.000,7515.000,7621.000,10880.000,13354.000,12757.000,11478.000,2.000,2.000,6.000,4.000,4.000
Nuevo Leon,9073.000,11490.000,20117.000,28206.000,34856.000,34726.000,38672.000,1.000,1.000,5.000,2.000,2.000
Oaxaca,1892.000,4538.000,4140.000,5230.000,7730.000,8465.000,9010.000,5.000,5.000,7.000,5.000,5.000
Puebla,3569.000,6415.000,6542.000,9775.000,13374.000,11895.000,15685.000,3.000,3.000,2.000,3.000,5.000
Quertaro,11016.000,5560.000,7110.000,14073.000,20088.000,22441.000,26149.000,3.000,3.000,3.000,3.000,4.000
Quintana Roo,21965.000,28747.000,9677.000,17046.000,26695.000,25049.000,33442.000,6.000,5.000,4.000,5.000,5.000
San Luis Potosi,4372.000,7533.000,6440.000,9721.000,12691.000,15436.000,15866.000,2.000,2.000,3.000,4.000,4.000
Sinaloa,4840.000,6663.000,9613.000,14477.000,15312.000,15823.000,15242.000,2.000,2.000,6.000,1.000,1.000
Sonora,6399.000,10345.000,12134.000,22662.000,23181.000,24784.000,24068.000,1.000,1.000,5.000,1.000,1.000
Tabasco,2459.000,3857.000,6494.000,9367.000,42361.000,16055.000,13360.000,6.000,5.000,4.000,5.000,5.000
Tamaulipas,7508.000,8536.000,8383.000,17128.000,21937.000,19983.000,23546.000,1.000,1.000,5.000,2.000,2.000
Tlaxcala,3605.000,4178.000,4357.000,6245.000,9882.000,10339.000,11701.000,3.000,3.000,2.000,3.000,3.000
Veracruz,5203.000,10143.000,11404.000,12240.000,14252.000,13796.000,12191.000,3.000,3.000,4.000,5.000,5.000
Yucatan,7990.000,8428.000,10067.000,11665.000,15239.000,13979.000,17509.000,6.000,5.000,4.000,5.000,5.000
Zacatecas,3734.000,6435.000,5821.000,7426.000,8876.000,11656.000,11130.000,2.000,2.000,3.000,4.000,4.000
1 State pcgdp1940 pcgdp1950 pcgdp1960 pcgdp1970 pcgdp1980 pcgdp1990 pcgdp2000 hanson03 hanson98 esquivel99 inegi inegi2
2 Aguascalientes 10384.000 6234.000 8714.000 16078.000 21022.000 20787.000 27782.000 2.000 2.000 3.000 4.000 4.000
3 Baja California 22361.000 20977.000 17865.000 25321.000 29283.000 26839.000 29855.000 1.000 1.000 5.000 1.000 1.000
4 Baja California Sur 9573.000 16013.000 16707.000 24384.000 29038.000 25842.000 26103.000 2.000 2.000 6.000 1.000 1.000
5 Campeche 3758.000 4929.000 5925.000 10274.000 12166.000 51123.000 36163.000 6.000 5.000 4.000 5.000 5.000
6 Chiapas 2934.000 4138.000 5280.000 7015.000 16200.000 8637.000 8684.000 5.000 5.000 7.000 5.000 5.000
7 Chihuahua 8578.000 13997.000 16265.000 19178.000 23399.000 25332.000 30735.000 1.000 1.000 5.000 1.000 2.000
8 Coahuila 8537.000 9673.000 12318.000 20562.000 25688.000 26084.000 28460.000 1.000 1.000 5.000 2.000 2.000
9 Colima 6909.000 6049.000 6036.000 12551.000 17427.000 18313.000 21358.000 3.000 3.000 6.000 4.000 4.000
10 Distrito Federal 17816.000 17119.000 23174.000 32386.000 42028.000 43810.000 54349.000 4.000 4.000 1.000 3.000 3.000
11 Durango 12132.000 8859.000 9323.000 12700.000 16726.000 17353.000 17379.000 2.000 2.000 3.000 1.000 2.000
12 Guanajuato 4359.000 5686.000 8209.000 11635.000 13864.000 13607.000 15585.000 3.000 3.000 3.000 4.000 4.000
13 Guerrero 2181.000 3629.000 4991.000 6497.000 8727.000 9084.000 11820.000 5.000 5.000 7.000 5.000 5.000
14 Hidalgo 4414.000 5194.000 6399.000 7767.000 12391.000 13091.000 12348.000 3.000 3.000 2.000 3.000 3.000
15 Jalisco 5309.000 8232.000 9953.000 16288.000 20659.000 20133.000 21610.000 3.000 3.000 6.000 4.000 4.000
16 Mexico 3408.000 4972.000 9053.000 17164.000 20165.000 18547.000 16322.000 4.000 4.000 1.000 3.000 3.000
17 Michoacan 3327.000 5272.000 5244.000 8109.000 11206.000 10980.000 11838.000 3.000 3.000 7.000 4.000 4.000
18 Morelos 6936.000 8962.000 10499.000 13892.000 16513.000 17701.000 18170.000 3.000 3.000 2.000 3.000 3.000
19 Nayarit 4836.000 7515.000 7621.000 10880.000 13354.000 12757.000 11478.000 2.000 2.000 6.000 4.000 4.000
20 Nuevo Leon 9073.000 11490.000 20117.000 28206.000 34856.000 34726.000 38672.000 1.000 1.000 5.000 2.000 2.000
21 Oaxaca 1892.000 4538.000 4140.000 5230.000 7730.000 8465.000 9010.000 5.000 5.000 7.000 5.000 5.000
22 Puebla 3569.000 6415.000 6542.000 9775.000 13374.000 11895.000 15685.000 3.000 3.000 2.000 3.000 5.000
23 Quertaro 11016.000 5560.000 7110.000 14073.000 20088.000 22441.000 26149.000 3.000 3.000 3.000 3.000 4.000
24 Quintana Roo 21965.000 28747.000 9677.000 17046.000 26695.000 25049.000 33442.000 6.000 5.000 4.000 5.000 5.000
25 San Luis Potosi 4372.000 7533.000 6440.000 9721.000 12691.000 15436.000 15866.000 2.000 2.000 3.000 4.000 4.000
26 Sinaloa 4840.000 6663.000 9613.000 14477.000 15312.000 15823.000 15242.000 2.000 2.000 6.000 1.000 1.000
27 Sonora 6399.000 10345.000 12134.000 22662.000 23181.000 24784.000 24068.000 1.000 1.000 5.000 1.000 1.000
28 Tabasco 2459.000 3857.000 6494.000 9367.000 42361.000 16055.000 13360.000 6.000 5.000 4.000 5.000 5.000
29 Tamaulipas 7508.000 8536.000 8383.000 17128.000 21937.000 19983.000 23546.000 1.000 1.000 5.000 2.000 2.000
30 Tlaxcala 3605.000 4178.000 4357.000 6245.000 9882.000 10339.000 11701.000 3.000 3.000 2.000 3.000 3.000
31 Veracruz 5203.000 10143.000 11404.000 12240.000 14252.000 13796.000 12191.000 3.000 3.000 4.000 5.000 5.000
32 Yucatan 7990.000 8428.000 10067.000 11665.000 15239.000 13979.000 17509.000 6.000 5.000 4.000 5.000 5.000
33 Zacatecas 3734.000 6435.000 5821.000 7426.000 8876.000 11656.000 11130.000 2.000 2.000 3.000 4.000 4.000

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"Name","STATE_FIPS",1929,1930,1931,1932,1933,1934,1935,1936,1937,1938,1939,1940,1941,1942,1943,1944,1945,1946,1947,1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009
"Alabama",1,323,267,224,162,166,211,217,251,267,244,252,281,375,518,658,738,784,754,805,881,833,909,1045,1106,1161,1139,1273,1356,1421,1468,1526,1558,1587,1667,1758,1890,2030,2169,2294,2516,2748,2979,3225,3544,3960,4351,4765,5323,5817,6500,7199,7892,8712,9185,9783,10800,11583,12202,12912,13842,14899,15832,16536,17462,17991,18860,19683,20329,21129,22123,22987,23471,24467,25161,26065,27665,29097,30634,31988,32819,32274
"Arizona",4,600,520,429,321,308,362,416,462,504,478,490,505,638,917,1004,1050,1124,1117,1186,1324,1305,1367,1623,1716,1716,1696,1752,1850,1893,1885,1974,2059,2103,2167,2204,2310,2412,2587,2754,3092,3489,3843,4145,4487,4929,5329,5528,6074,6642,7586,8604,9590,10658,10945,11654,12885,13808,14463,15130,15795,16568,17211,17563,18131,18756,19774,20634,21611,22781,24133,25189,25578,26232,26469,27106,28753,30671,32552,33470,33445,32077
"Arkansas",5,310,228,215,157,157,187,207,247,256,231,249,260,342,481,559,687,740,756,741,886,813,847,957,1027,1066,1074,1176,1230,1244,1314,1412,1405,1511,1574,1656,1777,1886,2094,2219,2409,2619,2849,3096,3415,3985,4376,4655,5155,5670,6509,7088,7586,8564,8952,9476,10560,11264,11734,12184,13016,13813,14509,15255,16425,16995,17750,18546,19442,20229,21260,22244,22257,23532,23929,25074,26465,27512,29041,31070,31800,31493
"California",6,991,887,749,580,546,603,660,771,795,771,781,844,1013,1286,1549,1583,1583,1671,1693,1763,1744,1877,2080,2207,2249,2227,2379,2495,2579,2596,2740,2823,2880,3004,3102,3274,3417,3663,3878,4207,4540,4815,5034,5451,5947,6553,7091,7815,8570,9618,10846,12029,13205,13774,14491,15927,16909,17628,18625,19713,20765,21889,22024,22722,22927,23473,24496,25563,26759,28280,29910,32275,32750,32900,33801,35663,37463,40169,41943,42377,40902
"Colorado",8,634,578,471,354,353,368,444,542,532,506,516,545,649,895,1039,1065,1189,1211,1359,1454,1430,1521,1796,1880,1813,1773,1869,1960,2104,2157,2261,2340,2417,2471,2539,2638,2800,2982,3142,3381,3686,4055,4413,4791,5310,5864,6321,6895,7567,8539,9596,10809,12141,12945,13570,14751,15416,15772,16417,17285,18548,19703,20487,21447,22526,23498,24865,26231,27950,29860,31546,32949,34228,33963,34092,35543,37388,39662,41165,41719,40093
"Connecticut",9,1024,921,801,620,583,653,706,806,860,769,836,918,1145,1418,1593,1599,1565,1580,1702,1720,1663,1891,2158,2298,2393,2350,2477,2684,2810,2731,2832,2926,3042,3175,3250,3401,3583,3874,4195,4443,4847,5090,5300,5697,6241,6813,7239,7885,8712,9720,10971,12439,13865,14903,15799,17580,18763,20038,21895,24007,25797,26736,26863,28635,29602,30532,31947,33472,35596,37452,39300,40640,42279,42021,42398,45009,47022,51133,53930,54528,52736
"Delaware",10,1032,857,775,590,564,645,701,868,949,795,899,1027,1164,1291,1464,1504,1526,1565,1664,1685,1805,2075,2155,2244,2341,2306,2507,2749,2645,2651,2724,2805,2815,2933,3049,3210,3468,3610,3785,4079,4421,4608,4892,5303,5871,6347,6729,7349,7913,8658,9549,10803,11873,12727,13529,14816,16056,16781,17933,19312,20930,21636,22342,23094,23823,24530,25391,26640,27405,29571,30778,31255,32664,33463,34123,35998,37297,39358,40251,40698,40135
"Florida",12,518,470,398,319,288,348,376,450,487,460,495,522,609,787,1009,1114,1174,1169,1167,1201,1208,1304,1388,1474,1567,1562,1673,1791,1836,1887,2010,2023,2039,2113,2200,2348,2498,2685,2909,3249,3659,4006,4286,4703,5235,5616,5895,6376,7010,7921,8879,10049,11195,11789,12637,13764,14705,15423,16415,17593,19045,19855,20189,20661,21652,22340,23512,24616,25722,26930,27780,28145,28852,29499,30277,32462,34460,36934,37781,37808,36565
"Georgia",13,347,307,256,200,204,244,268,302,313,290,309,337,419,567,725,831,879,845,887,988,969,1065,1204,1280,1326,1302,1423,1499,1523,1583,1660,1698,1744,1844,1961,2082,2258,2462,2655,2891,3162,3394,3666,4038,4497,4867,5152,5694,6221,6989,7722,8474,9435,10054,10849,12185,13143,13990,14820,15876,16803,17738,18289,19333,20129,21170,22230,23586,24547,26134,27340,27940,28596,28660,29060,29995,31498,32739,33895,34127,33086
"Idaho",16,507,503,374,274,227,403,399,475,423,426,437,463,596,914,1025,1095,1135,1203,1278,1345,1277,1329,1497,1644,1550,1559,1596,1728,1783,1817,1891,1898,1975,2092,2168,2250,2554,2591,2755,2910,3269,3558,3761,4150,4709,5382,5571,6116,6510,7319,7894,8735,9405,9621,10315,11069,11647,11968,12611,13548,14803,15866,16195,17236,18258,18846,19630,20353,20830,21923,22835,24180,25124,25485,25912,27846,29003,30954,32168,32322,30987
"Illinois",17,948,807,671,486,437,505,573,650,731,648,704,751,892,1036,1258,1386,1465,1534,1639,1816,1685,1831,2026,2091,2209,2177,2270,2452,2522,2511,2642,2700,2789,2902,2981,3131,3360,3599,3785,4045,4354,4580,4874,5266,5903,6464,6986,7623,8385,9277,10243,11077,12250,12771,13289,14682,15508,16284,17289,18461,19634,20756,21320,22764,23386,24440,25643,27005,28347,29974,31145,32259,32808,33325,34205,35599,36825,39220,41238,42049,40933
"Indiana",18,607,514,438,310,294,359,421,481,547,472,518,551,724,913,1138,1198,1251,1204,1313,1456,1361,1524,1711,1778,1943,1805,1907,2008,2042,2015,2131,2209,2246,2399,2486,2617,2856,3041,3153,3401,3714,3810,4105,4455,5100,5440,5830,6516,7199,8006,8814,9449,10355,10698,11203,12445,13143,13821,14664,15616,16770,17625,18055,19269,20112,21153,21845,22775,23748,25182,26143,27011,27590,28059,29089,30126,30768,32305,33151,33978,33174
"Iowa",19,581,510,400,297,253,269,425,393,523,458,475,501,609,835,1024,1003,1092,1245,1217,1642,1356,1532,1638,1725,1657,1788,1670,1758,1939,1990,2026,2061,2180,2284,2432,2549,2833,3067,3093,3312,3652,3862,4005,4473,5398,5596,6192,6580,7283,8438,9114,9671,10968,11227,11485,12798,13395,14020,14899,15315,16562,17380,17859,18939,18929,20498,21181,22713,23798,24844,25615,26723,27315,28232,28835,31027,31656,33177,35008,36726,35983
"Kansas",20,532,467,401,266,250,287,362,387,428,383,382,425,552,851,1045,1169,1165,1136,1310,1352,1299,1463,1602,1827,1739,1791,1753,1821,1911,2098,2106,2165,2227,2305,2384,2516,2689,2894,3032,3268,3548,3816,4145,4613,5274,5717,6186,6721,7307,8082,9240,10038,11248,11989,12373,13602,14330,14904,15583,16331,17093,18182,18832,19955,20510,21352,21889,23121,24355,25687,26824,27816,28979,29067,30109,31181,32367,34934,36546,37983,37036
"Kentucky",21,393,325,291,211,205,233,265,294,341,297,305,320,395,538,701,767,803,826,865,995,938,990,1153,1237,1305,1289,1346,1437,1491,1543,1604,1633,1731,1822,1906,1972,2134,2329,2501,2718,2971,3184,3391,3715,4145,4607,4933,5462,6095,6784,7640,8231,9110,9589,9859,11062,11558,11995,12782,13570,14602,15484,16241,17320,17815,18514,19215,20155,21215,22353,23237,24294,24816,25297,25777,26891,27881,29392,30443,31302,31250
"Louisiana",22,414,355,318,241,227,265,290,330,353,348,360,364,449,592,786,876,889,833,885,1019,1074,1117,1210,1278,1343,1339,1397,1502,1616,1635,1685,1690,1741,1806,1909,2003,2139,2331,2527,2749,2901,3106,3328,3593,3994,4510,4956,5557,6135,6951,7813,8833,10037,10558,10865,11628,12121,12028,12266,13113,13997,15223,16076,16968,17717,18779,19541,20254,21209,22352,22847,23334,25116,25683,26434,27776,29785,33438,34986,35730,35151
"Maine",23,601,576,491,377,371,416,430,506,510,471,495,526,631,857,1102,1102,1079,1134,1169,1240,1183,1195,1319,1438,1449,1448,1581,1669,1720,1797,1850,1919,1903,1983,2049,2212,2383,2539,2658,2872,3140,3423,3594,3864,4319,4764,5019,5708,6142,6751,7497,8408,9231,9873,10551,11665,12533,13463,14595,15813,16886,17479,17662,18350,18810,19531,20240,21293,22305,23529,24603,25623,27068,27731,28727,30201,30721,32340,33620,34906,35268
"Maryland",24,768,712,638,512,466,523,548,618,665,633,663,710,870,1117,1287,1324,1312,1315,1355,1500,1496,1642,1815,1944,2017,1938,2047,2184,2267,2258,2324,2407,2507,2638,2722,2881,3055,3284,3516,3831,4209,4573,4894,5291,5822,6382,6878,7536,8191,9062,10035,11230,12403,13246,14228,15693,16961,17966,19216,20626,22001,23023,23571,24358,25104,26046,26896,27844,29222,30850,32465,33872,35430,36293,37309,39651,41555,43990,45827,47040,47159
"Massachusetts",25,906,836,759,613,559,609,643,714,732,672,724,779,901,1073,1262,1299,1348,1396,1435,1512,1480,1656,1817,1895,1947,1930,2071,2194,2296,2321,2430,2511,2605,2734,2799,2932,3101,3331,3583,3903,4207,4486,4748,5106,5551,6024,6439,6994,7636,8480,9472,10673,11830,12803,13859,15549,16720,17954,19504,21334,22458,23223,23749,24876,25664,26841,28051,29618,31332,33394,35551,37992,39247,39238,39869,41792,43520,46893,49361,50607,49590
"Michigan",26,790,657,540,394,347,453,530,619,685,572,625,680,829,1050,1354,1391,1325,1329,1466,1563,1527,1718,1896,1985,2202,2076,2238,2275,2309,2246,2358,2438,2427,2592,2728,2949,3215,3450,3554,3906,4145,4194,4501,4966,5552,5926,6279,7084,7957,8834,9701,10369,11125,11462,12243,13576,14734,15573,16130,17198,18276,19022,19318,20278,21390,22862,23975,24447,25570,26807,28113,29612,30196,30410,31446,31890,32516,33452,34441,35215,34280
"Minnesota",27,599,552,457,363,308,358,451,472,540,494,517,524,615,797,947,1006,1110,1190,1270,1451,1328,1437,1582,1633,1711,1724,1790,1838,1930,2016,2065,2155,2236,2331,2460,2540,2781,2997,3182,3463,3779,4053,4275,4628,5431,5838,6216,6729,7559,8471,9409,10320,11320,11992,12594,14255,15093,15881,16899,17592,18966,20011,20489,21698,22068,23467,24583,26267,27548,29503,30793,32101,32835,33553,34744,36505,37400,39367,41059,42299,40920
"Mississippi",28,286,202,175,127,131,174,177,229,224,201,205,215,307,439,533,629,629,611,670,802,705,770,851,906,940,928,1045,1051,1063,1156,1245,1237,1322,1362,1499,1557,1688,1839,2001,2197,2408,2641,2867,3208,3613,3936,4205,4757,5259,5806,6549,7076,7901,8301,8615,9463,9922,10293,10913,11695,12540,13164,13806,14711,15468,16549,17185,18044,18885,20013,20688,20993,22222,22540,23365,24501,26120,27276,28772,29591,29318
"Missouri",29,621,561,491,365,334,367,420,466,508,475,504,519,640,806,963,1068,1130,1191,1224,1376,1327,1427,1554,1659,1737,1726,1818,1905,1949,2044,2126,2156,2215,2330,2427,2533,2738,2895,3067,3370,3561,3843,4107,4443,4937,5282,5733,6306,6991,7787,8713,9390,10457,11035,11716,12960,13868,14505,15250,16086,17083,17751,18560,19542,20295,21267,22094,23099,24252,25403,26376,27445,28156,28771,29702,30847,31644,33354,34558,35775,35106
"Montana",30,592,501,382,339,298,364,476,475,512,517,533,569,713,901,1150,1183,1206,1308,1484,1642,1412,1654,1806,1823,1810,1771,1894,1929,1983,2068,2023,2075,2025,2363,2330,2366,2548,2730,2805,2955,3284,3625,3789,4355,5012,5380,5794,6200,6636,7721,8299,9143,10244,10672,11045,11705,11900,12465,12996,13362,14623,15524,16509,17114,18072,18129,18764,19383,20167,21324,22019,22569,24342,24699,25963,27517,28987,30942,32625,33293,32699
"Nebraska",31,596,521,413,307,275,259,409,396,415,405,400,442,551,822,1019,1091,1186,1186,1274,1558,1346,1560,1641,1761,1676,1753,1650,1675,1936,2025,2022,2125,2134,2292,2341,2392,2656,2890,2990,3167,3572,3796,4121,4527,5269,5465,6168,6453,6993,8120,8784,9272,10685,11228,11601,12968,13743,14215,15035,15984,16878,18088,18766,19688,20167,21168,22196,24045,24590,25861,27049,27829,29098,29499,31262,32371,33395,34753,36880,38128,37057
"Nevada",32,868,833,652,550,495,546,658,843,762,780,861,895,982,1558,1511,1483,1611,1757,1770,1758,1795,1991,2211,2397,2447,2415,2527,2488,2562,2594,2749,2890,2957,3184,3174,3209,3299,3471,3660,4114,4520,4946,5227,5557,6114,6490,7009,7719,8550,9780,10765,11780,12780,12986,13465,14435,15332,16027,16886,18180,19568,20674,21283,22694,23465,24635,25808,27142,28201,29806,31022,30529,30718,30849,32182,34757,37555,38652,40326,40332,38009
"New Hampshire",33,686,647,558,427,416,476,498,537,565,533,561,579,708,851,976,1054,1111,1150,1217,1291,1274,1348,1508,1577,1653,1703,1829,1900,2007,2004,2124,2197,2281,2392,2427,2552,2708,2963,3162,3441,3744,3896,4102,4423,4880,5279,5592,6258,6892,7786,8781,9915,11079,11906,13041,14534,15819,16974,18371,19759,20635,20713,21326,22154,22521,23820,25008,26042,27607,29679,31114,33332,33940,34335,34892,36758,37536,39997,41720,42461,41882
"New Jersey",34,918,847,736,587,523,573,625,709,747,697,749,820,957,1167,1429,1554,1582,1526,1571,1648,1624,1802,2000,2114,2232,2219,2300,2448,2551,2525,2660,2764,2830,2990,3064,3224,3414,3652,3892,4237,4525,4835,5127,5521,6043,6576,7037,7703,8462,9408,10469,11778,13057,13999,15036,16549,17652,18711,20230,22142,23595,24766,25153,26597,27101,27885,29277,30795,32372,34310,35551,36983,37959,38240,38768,40603,42142,45668,48172,49233,48123
"New Mexico",35,410,334,289,208,211,247,292,343,362,338,357,378,474,636,773,881,942,929,1013,1124,1145,1204,1346,1423,1444,1462,1543,1626,1733,1825,1895,1884,1952,2006,2054,2134,2250,2386,2490,2709,2921,3197,3431,3761,4137,4568,5045,5527,6087,6847,7619,8402,9334,9894,10367,11215,11999,12226,12686,13322,14085,14960,15744,16425,17226,17946,18852,19478,20233,21178,21853,22203,24193,24446,25128,26606,28180,29778,31320,32585,32197
"New York",36,1152,1035,881,676,626,680,722,808,838,789,825,869,996,1169,1384,1538,1644,1697,1725,1771,1729,1858,2002,2057,2144,2175,2295,2416,2522,2553,2695,2788,2866,2980,3070,3254,3422,3657,3923,4295,4603,4887,5179,5538,5980,6492,6955,7477,8153,8979,9927,11095,12364,13344,14188,15739,16734,17827,19031,20604,21966,23315,23942,25199,25589,26359,27721,29266,30480,32236,33890,34547,35371,35332,36077,38312,40592,43892,47514,48692,46844
"North Carolina",37,332,292,248,187,208,253,271,297,324,295,315,324,422,572,693,766,823,866,900,1003,969,1077,1192,1230,1274,1293,1368,1436,1424,1505,1581,1629,1689,1797,1877,2009,2143,2363,2525,2754,3051,3285,3510,3899,4365,4743,5039,5584,6058,6780,7461,8247,9184,9690,10480,11788,12649,13444,14325,15461,16539,17367,17879,19120,20042,20931,21938,22940,24188,25454,26003,27194,27650,27726,28208,29769,31209,32692,33966,34340,33564
"North Dakota",38,382,311,187,176,146,180,272,234,326,282,319,355,529,665,966,1031,1046,1088,1494,1483,1211,1360,1444,1318,1336,1364,1490,1556,1598,1847,1679,1821,1653,2320,2142,2112,2463,2507,2592,2719,3052,3214,3669,4377,6172,6120,6334,6184,6427,8136,8398,8095,10342,10990,11386,12307,12811,13126,13565,12745,14357,15880,16270,17692,17830,19033,19084,21166,20798,22767,23313,25068,26118,26770,29109,29676,31644,32856,35882,39009,38672
"Ohio",39,771,661,563,400,385,455,516,593,648,561,615,658,821,1021,1253,1312,1340,1310,1401,1539,1456,1608,1835,1916,2024,1965,2087,2182,2243,2177,2314,2391,2405,2515,2604,2754,2948,3181,3309,3616,3934,4101,4328,4691,5218,5733,6087,6753,7511,8326,9251,10103,10982,11485,12167,13449,14295,14933,15675,16739,17825,18792,19217,20242,20999,22063,22887,23613,24913,26164,27152,28400,28966,29522,30345,31240,32097,33643,34814,35521,35018
"Oklahoma",40,455,368,301,216,222,252,298,321,376,346,349,374,432,626,782,947,970,952,1028,1143,1166,1144,1290,1401,1475,1458,1516,1593,1657,1794,1857,1916,1947,1994,2055,2196,2361,2517,2702,2948,3198,3477,3711,4020,4524,4986,5475,5974,6586,7387,8485,9580,11003,11817,11725,12687,13265,13288,13464,14257,15265,16214,16721,17526,18085,18730,19394,20151,21106,22199,22953,23517,25059,25059,25719,27516,29122,31753,32781,34378,33708
"Oregon",41,668,607,505,379,358,439,458,548,556,531,571,609,818,1118,1381,1389,1357,1379,1504,1646,1604,1657,1830,1912,1916,1867,1978,2070,2055,2106,2244,2283,2349,2457,2541,2685,2852,3033,3201,3448,3677,3940,4212,4625,5135,5726,6181,6913,7556,8476,9415,10196,10862,11128,11832,12866,13547,14162,14911,16062,17222,18253,18806,19558,20404,21421,22668,23649,24845,25958,27023,28350,28866,29387,30172,31217,32108,34212,35279,35899,35210
"Pennsylvania",42,772,712,600,449,417,482,517,601,636,563,601,650,776,951,1146,1250,1280,1289,1364,1436,1405,1552,1713,1789,1894,1827,1915,2063,2174,2164,2240,2301,2334,2439,2511,2662,2830,3040,3246,3507,3815,4077,4294,4683,5168,5704,6170,6800,7493,8305,9225,10151,11184,11887,12455,13512,14445,15186,16142,17323,18725,19823,20505,21550,22211,22864,23738,24838,26092,27358,28605,29539,30085,30840,31709,33069,34131,36375,38003,39008,38827
"Rhode Island",44,874,788,711,575,559,600,645,711,731,672,720,750,934,1149,1201,1274,1278,1369,1459,1433,1378,1553,1717,1765,1855,1847,1958,1998,2024,2067,2183,2234,2329,2457,2552,2691,2870,3113,3336,3595,3865,4114,4295,4625,4972,5405,5844,6411,7004,7693,8595,9742,10815,11605,12439,13717,14685,15587,16651,18271,19657,20194,20363,21257,22137,22762,24046,25123,26631,28012,29377,29685,31378,32374,33690,35318,36461,38610,40421,41542,41283
"South Carolina",45,271,243,205,159,175,211,229,258,273,250,276,310,394,545,648,732,753,780,793,911,873,925,1115,1196,1233,1166,1229,1260,1286,1316,1392,1437,1498,1602,1669,1787,1958,2176,2340,2570,2827,3064,3274,3603,4029,4459,4720,5257,5684,6334,7044,7794,8651,9071,9775,10910,11666,12258,13056,14045,14834,16050,16409,17165,17805,18686,19473,20403,21385,22544,23545,24321,24871,25279,25875,27057,28337,29990,30958,31510,30835
"South Dakota",46,426,366,241,189,129,184,309,244,323,320,345,361,475,757,846,979,1086,1132,1270,1526,1116,1283,1497,1327,1436,1457,1342,1419,1669,1740,1564,1870,1863,2092,2020,2000,2278,2500,2577,2773,2995,3256,3538,4065,5163,5178,5667,5591,6351,7347,8158,8142,9451,9915,10195,11619,11942,12486,13217,13807,14767,16238,16961,17966,18565,19607,19848,21736,22275,23797,25045,26115,27531,27727,30072,31765,32726,33320,35998,38188,36499
"Tennessee",47,378,325,277,198,204,245,264,304,334,300,311,340,436,561,728,864,912,872,892,965,951,1028,1122,1178,1276,1274,1327,1422,1476,1512,1598,1618,1690,1771,1855,1965,2128,2332,2473,2741,2967,3189,3451,3808,4298,4696,5017,5574,6108,6895,7618,8319,9196,9695,10276,11453,12247,12995,13909,14910,15883,16821,17503,18840,19741,20696,21800,22450,23324,24576,25574,26239,27059,27647,28501,29734,30764,32314,33578,34243,33512
"Texas",48,479,412,348,266,257,294,326,372,418,404,417,438,528,719,944,1045,1058,1047,1147,1214,1300,1363,1488,1564,1598,1630,1697,1784,1856,1871,1948,1955,2021,2079,2155,2284,2433,2630,2840,3105,3373,3646,3861,4192,4683,5194,5738,6362,6979,7912,8929,9957,11391,11961,12303,13396,14196,14165,14486,15324,16323,17458,18150,19146,19825,20590,21526,22557,24242,25803,26858,27871,28519,28295,28929,30392,32448,34489,36020,36969,35674
"Utah",49,551,498,369,305,298,310,389,463,444,444,458,480,594,880,1126,1049,1121,1094,1180,1257,1262,1348,1544,1596,1604,1573,1666,1758,1862,1888,1970,2035,2091,2230,2281,2386,2494,2605,2721,2900,3103,3391,3658,3979,4326,4743,5150,5739,6328,7041,7786,8464,9290,9807,10333,11233,11846,12248,12638,13156,13977,14996,15661,16354,17031,17912,18858,19955,21156,22294,23288,23907,24899,25010,25192,26169,27905,29582,31009,31253,30107
"Vermont",50,634,576,474,365,338,383,414,471,485,457,491,516,643,775,932,953,1035,1090,1127,1192,1125,1169,1334,1380,1432,1456,1524,1655,1720,1732,1832,1923,1994,2072,2128,2266,2456,2729,2885,3128,3388,3634,3856,4176,4548,4869,5192,5753,6179,7036,7853,8702,9717,10287,10968,12048,12994,13842,14992,16197,17517,18055,18218,19293,19785,20553,21359,22295,23362,24803,25889,26901,28140,28651,29609,31240,31920,34394,36018,36940,36752
"Virginia",51,434,384,370,284,285,320,350,390,423,390,426,467,582,785,843,900,950,1002,1015,1148,1132,1257,1420,1510,1533,1554,1637,1712,1738,1784,1885,1936,2005,2121,2218,2403,2563,2746,2967,3252,3558,3795,4092,4486,4972,5484,5934,6534,7192,8040,8995,10176,11291,12075,12936,14298,15286,16237,17332,18556,19780,20538,21092,21965,22773,23709,24456,25495,26768,28343,29789,31162,32747,33235,34451,36285,38304,40644,42506,43409,43211
"Washington",53,741,658,534,402,376,443,490,569,599,582,614,658,864,1196,1469,1527,1419,1401,1504,1624,1595,1721,1874,1973,2066,2077,2116,2172,2262,2281,2380,2436,2535,2680,2735,2858,3078,3385,3566,3850,4097,4205,4381,4731,5312,5919,6533,7181,7832,8887,9965,10913,11903,12431,13124,14021,14738,15522,16300,17270,18670,20026,20901,21917,22414,23119,23878,25287,26817,28632,30392,31528,32053,32206,32934,34984,35738,38477,40782,41588,40619
"West Virginia",54,460,408,356,257,259,313,337,390,418,370,388,407,498,613,739,820,888,925,1032,1110,1023,1056,1182,1247,1276,1225,1318,1477,1594,1551,1597,1625,1671,1762,1853,1974,2126,2271,2417,2573,2798,3117,3378,3682,4026,4457,4974,5479,6053,6703,7432,8172,8866,9439,9626,10417,10936,11464,11950,12708,13529,14579,15219,16118,16724,17413,17913,18566,19388,20246,20966,21915,23333,24103,24626,25484,26374,28379,29769,31265,31843
"Wisconsin",55,673,588,469,362,333,380,461,518,551,507,513,547,672,866,1053,1109,1182,1211,1296,1434,1387,1506,1736,1799,1839,1774,1875,1990,2060,2075,2225,2258,2304,2412,2458,2616,2789,3025,3180,3441,3751,3983,4238,4595,5130,5622,6061,6670,7417,8292,9281,10161,11006,11592,12046,13182,13845,14530,15358,16201,17299,18160,18711,19872,20639,21699,22573,23554,24790,26245,27390,28232,29161,29838,30657,31703,32625,34535,35839,36594,35676
"Wyoming",56,675,585,476,374,371,411,496,551,607,561,587,602,779,944,1150,1224,1258,1362,1506,1610,1647,1719,1955,1912,1932,1858,1913,2011,2132,2172,2278,2312,2387,2502,2535,2588,2743,2906,3121,3315,3584,3919,4269,4709,5410,6172,6701,7212,8152,9384,10572,11753,12879,13251,12723,13493,14242,14004,14194,14968,16383,17996,18867,19550,20287,20957,21514,22098,23820,24927,26396,27230,29122,29828,31544,33721,36683,41548,43453,45177,42504
1 Name STATE_FIPS 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
2 Alabama 1 323 267 224 162 166 211 217 251 267 244 252 281 375 518 658 738 784 754 805 881 833 909 1045 1106 1161 1139 1273 1356 1421 1468 1526 1558 1587 1667 1758 1890 2030 2169 2294 2516 2748 2979 3225 3544 3960 4351 4765 5323 5817 6500 7199 7892 8712 9185 9783 10800 11583 12202 12912 13842 14899 15832 16536 17462 17991 18860 19683 20329 21129 22123 22987 23471 24467 25161 26065 27665 29097 30634 31988 32819 32274
3 Arizona 4 600 520 429 321 308 362 416 462 504 478 490 505 638 917 1004 1050 1124 1117 1186 1324 1305 1367 1623 1716 1716 1696 1752 1850 1893 1885 1974 2059 2103 2167 2204 2310 2412 2587 2754 3092 3489 3843 4145 4487 4929 5329 5528 6074 6642 7586 8604 9590 10658 10945 11654 12885 13808 14463 15130 15795 16568 17211 17563 18131 18756 19774 20634 21611 22781 24133 25189 25578 26232 26469 27106 28753 30671 32552 33470 33445 32077
4 Arkansas 5 310 228 215 157 157 187 207 247 256 231 249 260 342 481 559 687 740 756 741 886 813 847 957 1027 1066 1074 1176 1230 1244 1314 1412 1405 1511 1574 1656 1777 1886 2094 2219 2409 2619 2849 3096 3415 3985 4376 4655 5155 5670 6509 7088 7586 8564 8952 9476 10560 11264 11734 12184 13016 13813 14509 15255 16425 16995 17750 18546 19442 20229 21260 22244 22257 23532 23929 25074 26465 27512 29041 31070 31800 31493
5 California 6 991 887 749 580 546 603 660 771 795 771 781 844 1013 1286 1549 1583 1583 1671 1693 1763 1744 1877 2080 2207 2249 2227 2379 2495 2579 2596 2740 2823 2880 3004 3102 3274 3417 3663 3878 4207 4540 4815 5034 5451 5947 6553 7091 7815 8570 9618 10846 12029 13205 13774 14491 15927 16909 17628 18625 19713 20765 21889 22024 22722 22927 23473 24496 25563 26759 28280 29910 32275 32750 32900 33801 35663 37463 40169 41943 42377 40902
6 Colorado 8 634 578 471 354 353 368 444 542 532 506 516 545 649 895 1039 1065 1189 1211 1359 1454 1430 1521 1796 1880 1813 1773 1869 1960 2104 2157 2261 2340 2417 2471 2539 2638 2800 2982 3142 3381 3686 4055 4413 4791 5310 5864 6321 6895 7567 8539 9596 10809 12141 12945 13570 14751 15416 15772 16417 17285 18548 19703 20487 21447 22526 23498 24865 26231 27950 29860 31546 32949 34228 33963 34092 35543 37388 39662 41165 41719 40093
7 Connecticut 9 1024 921 801 620 583 653 706 806 860 769 836 918 1145 1418 1593 1599 1565 1580 1702 1720 1663 1891 2158 2298 2393 2350 2477 2684 2810 2731 2832 2926 3042 3175 3250 3401 3583 3874 4195 4443 4847 5090 5300 5697 6241 6813 7239 7885 8712 9720 10971 12439 13865 14903 15799 17580 18763 20038 21895 24007 25797 26736 26863 28635 29602 30532 31947 33472 35596 37452 39300 40640 42279 42021 42398 45009 47022 51133 53930 54528 52736
8 Delaware 10 1032 857 775 590 564 645 701 868 949 795 899 1027 1164 1291 1464 1504 1526 1565 1664 1685 1805 2075 2155 2244 2341 2306 2507 2749 2645 2651 2724 2805 2815 2933 3049 3210 3468 3610 3785 4079 4421 4608 4892 5303 5871 6347 6729 7349 7913 8658 9549 10803 11873 12727 13529 14816 16056 16781 17933 19312 20930 21636 22342 23094 23823 24530 25391 26640 27405 29571 30778 31255 32664 33463 34123 35998 37297 39358 40251 40698 40135
9 Florida 12 518 470 398 319 288 348 376 450 487 460 495 522 609 787 1009 1114 1174 1169 1167 1201 1208 1304 1388 1474 1567 1562 1673 1791 1836 1887 2010 2023 2039 2113 2200 2348 2498 2685 2909 3249 3659 4006 4286 4703 5235 5616 5895 6376 7010 7921 8879 10049 11195 11789 12637 13764 14705 15423 16415 17593 19045 19855 20189 20661 21652 22340 23512 24616 25722 26930 27780 28145 28852 29499 30277 32462 34460 36934 37781 37808 36565
10 Georgia 13 347 307 256 200 204 244 268 302 313 290 309 337 419 567 725 831 879 845 887 988 969 1065 1204 1280 1326 1302 1423 1499 1523 1583 1660 1698 1744 1844 1961 2082 2258 2462 2655 2891 3162 3394 3666 4038 4497 4867 5152 5694 6221 6989 7722 8474 9435 10054 10849 12185 13143 13990 14820 15876 16803 17738 18289 19333 20129 21170 22230 23586 24547 26134 27340 27940 28596 28660 29060 29995 31498 32739 33895 34127 33086
11 Idaho 16 507 503 374 274 227 403 399 475 423 426 437 463 596 914 1025 1095 1135 1203 1278 1345 1277 1329 1497 1644 1550 1559 1596 1728 1783 1817 1891 1898 1975 2092 2168 2250 2554 2591 2755 2910 3269 3558 3761 4150 4709 5382 5571 6116 6510 7319 7894 8735 9405 9621 10315 11069 11647 11968 12611 13548 14803 15866 16195 17236 18258 18846 19630 20353 20830 21923 22835 24180 25124 25485 25912 27846 29003 30954 32168 32322 30987
12 Illinois 17 948 807 671 486 437 505 573 650 731 648 704 751 892 1036 1258 1386 1465 1534 1639 1816 1685 1831 2026 2091 2209 2177 2270 2452 2522 2511 2642 2700 2789 2902 2981 3131 3360 3599 3785 4045 4354 4580 4874 5266 5903 6464 6986 7623 8385 9277 10243 11077 12250 12771 13289 14682 15508 16284 17289 18461 19634 20756 21320 22764 23386 24440 25643 27005 28347 29974 31145 32259 32808 33325 34205 35599 36825 39220 41238 42049 40933
13 Indiana 18 607 514 438 310 294 359 421 481 547 472 518 551 724 913 1138 1198 1251 1204 1313 1456 1361 1524 1711 1778 1943 1805 1907 2008 2042 2015 2131 2209 2246 2399 2486 2617 2856 3041 3153 3401 3714 3810 4105 4455 5100 5440 5830 6516 7199 8006 8814 9449 10355 10698 11203 12445 13143 13821 14664 15616 16770 17625 18055 19269 20112 21153 21845 22775 23748 25182 26143 27011 27590 28059 29089 30126 30768 32305 33151 33978 33174
14 Iowa 19 581 510 400 297 253 269 425 393 523 458 475 501 609 835 1024 1003 1092 1245 1217 1642 1356 1532 1638 1725 1657 1788 1670 1758 1939 1990 2026 2061 2180 2284 2432 2549 2833 3067 3093 3312 3652 3862 4005 4473 5398 5596 6192 6580 7283 8438 9114 9671 10968 11227 11485 12798 13395 14020 14899 15315 16562 17380 17859 18939 18929 20498 21181 22713 23798 24844 25615 26723 27315 28232 28835 31027 31656 33177 35008 36726 35983
15 Kansas 20 532 467 401 266 250 287 362 387 428 383 382 425 552 851 1045 1169 1165 1136 1310 1352 1299 1463 1602 1827 1739 1791 1753 1821 1911 2098 2106 2165 2227 2305 2384 2516 2689 2894 3032 3268 3548 3816 4145 4613 5274 5717 6186 6721 7307 8082 9240 10038 11248 11989 12373 13602 14330 14904 15583 16331 17093 18182 18832 19955 20510 21352 21889 23121 24355 25687 26824 27816 28979 29067 30109 31181 32367 34934 36546 37983 37036
16 Kentucky 21 393 325 291 211 205 233 265 294 341 297 305 320 395 538 701 767 803 826 865 995 938 990 1153 1237 1305 1289 1346 1437 1491 1543 1604 1633 1731 1822 1906 1972 2134 2329 2501 2718 2971 3184 3391 3715 4145 4607 4933 5462 6095 6784 7640 8231 9110 9589 9859 11062 11558 11995 12782 13570 14602 15484 16241 17320 17815 18514 19215 20155 21215 22353 23237 24294 24816 25297 25777 26891 27881 29392 30443 31302 31250
17 Louisiana 22 414 355 318 241 227 265 290 330 353 348 360 364 449 592 786 876 889 833 885 1019 1074 1117 1210 1278 1343 1339 1397 1502 1616 1635 1685 1690 1741 1806 1909 2003 2139 2331 2527 2749 2901 3106 3328 3593 3994 4510 4956 5557 6135 6951 7813 8833 10037 10558 10865 11628 12121 12028 12266 13113 13997 15223 16076 16968 17717 18779 19541 20254 21209 22352 22847 23334 25116 25683 26434 27776 29785 33438 34986 35730 35151
18 Maine 23 601 576 491 377 371 416 430 506 510 471 495 526 631 857 1102 1102 1079 1134 1169 1240 1183 1195 1319 1438 1449 1448 1581 1669 1720 1797 1850 1919 1903 1983 2049 2212 2383 2539 2658 2872 3140 3423 3594 3864 4319 4764 5019 5708 6142 6751 7497 8408 9231 9873 10551 11665 12533 13463 14595 15813 16886 17479 17662 18350 18810 19531 20240 21293 22305 23529 24603 25623 27068 27731 28727 30201 30721 32340 33620 34906 35268
19 Maryland 24 768 712 638 512 466 523 548 618 665 633 663 710 870 1117 1287 1324 1312 1315 1355 1500 1496 1642 1815 1944 2017 1938 2047 2184 2267 2258 2324 2407 2507 2638 2722 2881 3055 3284 3516 3831 4209 4573 4894 5291 5822 6382 6878 7536 8191 9062 10035 11230 12403 13246 14228 15693 16961 17966 19216 20626 22001 23023 23571 24358 25104 26046 26896 27844 29222 30850 32465 33872 35430 36293 37309 39651 41555 43990 45827 47040 47159
20 Massachusetts 25 906 836 759 613 559 609 643 714 732 672 724 779 901 1073 1262 1299 1348 1396 1435 1512 1480 1656 1817 1895 1947 1930 2071 2194 2296 2321 2430 2511 2605 2734 2799 2932 3101 3331 3583 3903 4207 4486 4748 5106 5551 6024 6439 6994 7636 8480 9472 10673 11830 12803 13859 15549 16720 17954 19504 21334 22458 23223 23749 24876 25664 26841 28051 29618 31332 33394 35551 37992 39247 39238 39869 41792 43520 46893 49361 50607 49590
21 Michigan 26 790 657 540 394 347 453 530 619 685 572 625 680 829 1050 1354 1391 1325 1329 1466 1563 1527 1718 1896 1985 2202 2076 2238 2275 2309 2246 2358 2438 2427 2592 2728 2949 3215 3450 3554 3906 4145 4194 4501 4966 5552 5926 6279 7084 7957 8834 9701 10369 11125 11462 12243 13576 14734 15573 16130 17198 18276 19022 19318 20278 21390 22862 23975 24447 25570 26807 28113 29612 30196 30410 31446 31890 32516 33452 34441 35215 34280
22 Minnesota 27 599 552 457 363 308 358 451 472 540 494 517 524 615 797 947 1006 1110 1190 1270 1451 1328 1437 1582 1633 1711 1724 1790 1838 1930 2016 2065 2155 2236 2331 2460 2540 2781 2997 3182 3463 3779 4053 4275 4628 5431 5838 6216 6729 7559 8471 9409 10320 11320 11992 12594 14255 15093 15881 16899 17592 18966 20011 20489 21698 22068 23467 24583 26267 27548 29503 30793 32101 32835 33553 34744 36505 37400 39367 41059 42299 40920
23 Mississippi 28 286 202 175 127 131 174 177 229 224 201 205 215 307 439 533 629 629 611 670 802 705 770 851 906 940 928 1045 1051 1063 1156 1245 1237 1322 1362 1499 1557 1688 1839 2001 2197 2408 2641 2867 3208 3613 3936 4205 4757 5259 5806 6549 7076 7901 8301 8615 9463 9922 10293 10913 11695 12540 13164 13806 14711 15468 16549 17185 18044 18885 20013 20688 20993 22222 22540 23365 24501 26120 27276 28772 29591 29318
24 Missouri 29 621 561 491 365 334 367 420 466 508 475 504 519 640 806 963 1068 1130 1191 1224 1376 1327 1427 1554 1659 1737 1726 1818 1905 1949 2044 2126 2156 2215 2330 2427 2533 2738 2895 3067 3370 3561 3843 4107 4443 4937 5282 5733 6306 6991 7787 8713 9390 10457 11035 11716 12960 13868 14505 15250 16086 17083 17751 18560 19542 20295 21267 22094 23099 24252 25403 26376 27445 28156 28771 29702 30847 31644 33354 34558 35775 35106
25 Montana 30 592 501 382 339 298 364 476 475 512 517 533 569 713 901 1150 1183 1206 1308 1484 1642 1412 1654 1806 1823 1810 1771 1894 1929 1983 2068 2023 2075 2025 2363 2330 2366 2548 2730 2805 2955 3284 3625 3789 4355 5012 5380 5794 6200 6636 7721 8299 9143 10244 10672 11045 11705 11900 12465 12996 13362 14623 15524 16509 17114 18072 18129 18764 19383 20167 21324 22019 22569 24342 24699 25963 27517 28987 30942 32625 33293 32699
26 Nebraska 31 596 521 413 307 275 259 409 396 415 405 400 442 551 822 1019 1091 1186 1186 1274 1558 1346 1560 1641 1761 1676 1753 1650 1675 1936 2025 2022 2125 2134 2292 2341 2392 2656 2890 2990 3167 3572 3796 4121 4527 5269 5465 6168 6453 6993 8120 8784 9272 10685 11228 11601 12968 13743 14215 15035 15984 16878 18088 18766 19688 20167 21168 22196 24045 24590 25861 27049 27829 29098 29499 31262 32371 33395 34753 36880 38128 37057
27 Nevada 32 868 833 652 550 495 546 658 843 762 780 861 895 982 1558 1511 1483 1611 1757 1770 1758 1795 1991 2211 2397 2447 2415 2527 2488 2562 2594 2749 2890 2957 3184 3174 3209 3299 3471 3660 4114 4520 4946 5227 5557 6114 6490 7009 7719 8550 9780 10765 11780 12780 12986 13465 14435 15332 16027 16886 18180 19568 20674 21283 22694 23465 24635 25808 27142 28201 29806 31022 30529 30718 30849 32182 34757 37555 38652 40326 40332 38009
28 New Hampshire 33 686 647 558 427 416 476 498 537 565 533 561 579 708 851 976 1054 1111 1150 1217 1291 1274 1348 1508 1577 1653 1703 1829 1900 2007 2004 2124 2197 2281 2392 2427 2552 2708 2963 3162 3441 3744 3896 4102 4423 4880 5279 5592 6258 6892 7786 8781 9915 11079 11906 13041 14534 15819 16974 18371 19759 20635 20713 21326 22154 22521 23820 25008 26042 27607 29679 31114 33332 33940 34335 34892 36758 37536 39997 41720 42461 41882
29 New Jersey 34 918 847 736 587 523 573 625 709 747 697 749 820 957 1167 1429 1554 1582 1526 1571 1648 1624 1802 2000 2114 2232 2219 2300 2448 2551 2525 2660 2764 2830 2990 3064 3224 3414 3652 3892 4237 4525 4835 5127 5521 6043 6576 7037 7703 8462 9408 10469 11778 13057 13999 15036 16549 17652 18711 20230 22142 23595 24766 25153 26597 27101 27885 29277 30795 32372 34310 35551 36983 37959 38240 38768 40603 42142 45668 48172 49233 48123
30 New Mexico 35 410 334 289 208 211 247 292 343 362 338 357 378 474 636 773 881 942 929 1013 1124 1145 1204 1346 1423 1444 1462 1543 1626 1733 1825 1895 1884 1952 2006 2054 2134 2250 2386 2490 2709 2921 3197 3431 3761 4137 4568 5045 5527 6087 6847 7619 8402 9334 9894 10367 11215 11999 12226 12686 13322 14085 14960 15744 16425 17226 17946 18852 19478 20233 21178 21853 22203 24193 24446 25128 26606 28180 29778 31320 32585 32197
31 New York 36 1152 1035 881 676 626 680 722 808 838 789 825 869 996 1169 1384 1538 1644 1697 1725 1771 1729 1858 2002 2057 2144 2175 2295 2416 2522 2553 2695 2788 2866 2980 3070 3254 3422 3657 3923 4295 4603 4887 5179 5538 5980 6492 6955 7477 8153 8979 9927 11095 12364 13344 14188 15739 16734 17827 19031 20604 21966 23315 23942 25199 25589 26359 27721 29266 30480 32236 33890 34547 35371 35332 36077 38312 40592 43892 47514 48692 46844
32 North Carolina 37 332 292 248 187 208 253 271 297 324 295 315 324 422 572 693 766 823 866 900 1003 969 1077 1192 1230 1274 1293 1368 1436 1424 1505 1581 1629 1689 1797 1877 2009 2143 2363 2525 2754 3051 3285 3510 3899 4365 4743 5039 5584 6058 6780 7461 8247 9184 9690 10480 11788 12649 13444 14325 15461 16539 17367 17879 19120 20042 20931 21938 22940 24188 25454 26003 27194 27650 27726 28208 29769 31209 32692 33966 34340 33564
33 North Dakota 38 382 311 187 176 146 180 272 234 326 282 319 355 529 665 966 1031 1046 1088 1494 1483 1211 1360 1444 1318 1336 1364 1490 1556 1598 1847 1679 1821 1653 2320 2142 2112 2463 2507 2592 2719 3052 3214 3669 4377 6172 6120 6334 6184 6427 8136 8398 8095 10342 10990 11386 12307 12811 13126 13565 12745 14357 15880 16270 17692 17830 19033 19084 21166 20798 22767 23313 25068 26118 26770 29109 29676 31644 32856 35882 39009 38672
34 Ohio 39 771 661 563 400 385 455 516 593 648 561 615 658 821 1021 1253 1312 1340 1310 1401 1539 1456 1608 1835 1916 2024 1965 2087 2182 2243 2177 2314 2391 2405 2515 2604 2754 2948 3181 3309 3616 3934 4101 4328 4691 5218 5733 6087 6753 7511 8326 9251 10103 10982 11485 12167 13449 14295 14933 15675 16739 17825 18792 19217 20242 20999 22063 22887 23613 24913 26164 27152 28400 28966 29522 30345 31240 32097 33643 34814 35521 35018
35 Oklahoma 40 455 368 301 216 222 252 298 321 376 346 349 374 432 626 782 947 970 952 1028 1143 1166 1144 1290 1401 1475 1458 1516 1593 1657 1794 1857 1916 1947 1994 2055 2196 2361 2517 2702 2948 3198 3477 3711 4020 4524 4986 5475 5974 6586 7387 8485 9580 11003 11817 11725 12687 13265 13288 13464 14257 15265 16214 16721 17526 18085 18730 19394 20151 21106 22199 22953 23517 25059 25059 25719 27516 29122 31753 32781 34378 33708
36 Oregon 41 668 607 505 379 358 439 458 548 556 531 571 609 818 1118 1381 1389 1357 1379 1504 1646 1604 1657 1830 1912 1916 1867 1978 2070 2055 2106 2244 2283 2349 2457 2541 2685 2852 3033 3201 3448 3677 3940 4212 4625 5135 5726 6181 6913 7556 8476 9415 10196 10862 11128 11832 12866 13547 14162 14911 16062 17222 18253 18806 19558 20404 21421 22668 23649 24845 25958 27023 28350 28866 29387 30172 31217 32108 34212 35279 35899 35210
37 Pennsylvania 42 772 712 600 449 417 482 517 601 636 563 601 650 776 951 1146 1250 1280 1289 1364 1436 1405 1552 1713 1789 1894 1827 1915 2063 2174 2164 2240 2301 2334 2439 2511 2662 2830 3040 3246 3507 3815 4077 4294 4683 5168 5704 6170 6800 7493 8305 9225 10151 11184 11887 12455 13512 14445 15186 16142 17323 18725 19823 20505 21550 22211 22864 23738 24838 26092 27358 28605 29539 30085 30840 31709 33069 34131 36375 38003 39008 38827
38 Rhode Island 44 874 788 711 575 559 600 645 711 731 672 720 750 934 1149 1201 1274 1278 1369 1459 1433 1378 1553 1717 1765 1855 1847 1958 1998 2024 2067 2183 2234 2329 2457 2552 2691 2870 3113 3336 3595 3865 4114 4295 4625 4972 5405 5844 6411 7004 7693 8595 9742 10815 11605 12439 13717 14685 15587 16651 18271 19657 20194 20363 21257 22137 22762 24046 25123 26631 28012 29377 29685 31378 32374 33690 35318 36461 38610 40421 41542 41283
39 South Carolina 45 271 243 205 159 175 211 229 258 273 250 276 310 394 545 648 732 753 780 793 911 873 925 1115 1196 1233 1166 1229 1260 1286 1316 1392 1437 1498 1602 1669 1787 1958 2176 2340 2570 2827 3064 3274 3603 4029 4459 4720 5257 5684 6334 7044 7794 8651 9071 9775 10910 11666 12258 13056 14045 14834 16050 16409 17165 17805 18686 19473 20403 21385 22544 23545 24321 24871 25279 25875 27057 28337 29990 30958 31510 30835
40 South Dakota 46 426 366 241 189 129 184 309 244 323 320 345 361 475 757 846 979 1086 1132 1270 1526 1116 1283 1497 1327 1436 1457 1342 1419 1669 1740 1564 1870 1863 2092 2020 2000 2278 2500 2577 2773 2995 3256 3538 4065 5163 5178 5667 5591 6351 7347 8158 8142 9451 9915 10195 11619 11942 12486 13217 13807 14767 16238 16961 17966 18565 19607 19848 21736 22275 23797 25045 26115 27531 27727 30072 31765 32726 33320 35998 38188 36499
41 Tennessee 47 378 325 277 198 204 245 264 304 334 300 311 340 436 561 728 864 912 872 892 965 951 1028 1122 1178 1276 1274 1327 1422 1476 1512 1598 1618 1690 1771 1855 1965 2128 2332 2473 2741 2967 3189 3451 3808 4298 4696 5017 5574 6108 6895 7618 8319 9196 9695 10276 11453 12247 12995 13909 14910 15883 16821 17503 18840 19741 20696 21800 22450 23324 24576 25574 26239 27059 27647 28501 29734 30764 32314 33578 34243 33512
42 Texas 48 479 412 348 266 257 294 326 372 418 404 417 438 528 719 944 1045 1058 1047 1147 1214 1300 1363 1488 1564 1598 1630 1697 1784 1856 1871 1948 1955 2021 2079 2155 2284 2433 2630 2840 3105 3373 3646 3861 4192 4683 5194 5738 6362 6979 7912 8929 9957 11391 11961 12303 13396 14196 14165 14486 15324 16323 17458 18150 19146 19825 20590 21526 22557 24242 25803 26858 27871 28519 28295 28929 30392 32448 34489 36020 36969 35674
43 Utah 49 551 498 369 305 298 310 389 463 444 444 458 480 594 880 1126 1049 1121 1094 1180 1257 1262 1348 1544 1596 1604 1573 1666 1758 1862 1888 1970 2035 2091 2230 2281 2386 2494 2605 2721 2900 3103 3391 3658 3979 4326 4743 5150 5739 6328 7041 7786 8464 9290 9807 10333 11233 11846 12248 12638 13156 13977 14996 15661 16354 17031 17912 18858 19955 21156 22294 23288 23907 24899 25010 25192 26169 27905 29582 31009 31253 30107
44 Vermont 50 634 576 474 365 338 383 414 471 485 457 491 516 643 775 932 953 1035 1090 1127 1192 1125 1169 1334 1380 1432 1456 1524 1655 1720 1732 1832 1923 1994 2072 2128 2266 2456 2729 2885 3128 3388 3634 3856 4176 4548 4869 5192 5753 6179 7036 7853 8702 9717 10287 10968 12048 12994 13842 14992 16197 17517 18055 18218 19293 19785 20553 21359 22295 23362 24803 25889 26901 28140 28651 29609 31240 31920 34394 36018 36940 36752
45 Virginia 51 434 384 370 284 285 320 350 390 423 390 426 467 582 785 843 900 950 1002 1015 1148 1132 1257 1420 1510 1533 1554 1637 1712 1738 1784 1885 1936 2005 2121 2218 2403 2563 2746 2967 3252 3558 3795 4092 4486 4972 5484 5934 6534 7192 8040 8995 10176 11291 12075 12936 14298 15286 16237 17332 18556 19780 20538 21092 21965 22773 23709 24456 25495 26768 28343 29789 31162 32747 33235 34451 36285 38304 40644 42506 43409 43211
46 Washington 53 741 658 534 402 376 443 490 569 599 582 614 658 864 1196 1469 1527 1419 1401 1504 1624 1595 1721 1874 1973 2066 2077 2116 2172 2262 2281 2380 2436 2535 2680 2735 2858 3078 3385 3566 3850 4097 4205 4381 4731 5312 5919 6533 7181 7832 8887 9965 10913 11903 12431 13124 14021 14738 15522 16300 17270 18670 20026 20901 21917 22414 23119 23878 25287 26817 28632 30392 31528 32053 32206 32934 34984 35738 38477 40782 41588 40619
47 West Virginia 54 460 408 356 257 259 313 337 390 418 370 388 407 498 613 739 820 888 925 1032 1110 1023 1056 1182 1247 1276 1225 1318 1477 1594 1551 1597 1625 1671 1762 1853 1974 2126 2271 2417 2573 2798 3117 3378 3682 4026 4457 4974 5479 6053 6703 7432 8172 8866 9439 9626 10417 10936 11464 11950 12708 13529 14579 15219 16118 16724 17413 17913 18566 19388 20246 20966 21915 23333 24103 24626 25484 26374 28379 29769 31265 31843
48 Wisconsin 55 673 588 469 362 333 380 461 518 551 507 513 547 672 866 1053 1109 1182 1211 1296 1434 1387 1506 1736 1799 1839 1774 1875 1990 2060 2075 2225 2258 2304 2412 2458 2616 2789 3025 3180 3441 3751 3983 4238 4595 5130 5622 6061 6670 7417 8292 9281 10161 11006 11592 12046 13182 13845 14530 15358 16201 17299 18160 18711 19872 20639 21699 22573 23554 24790 26245 27390 28232 29161 29838 30657 31703 32625 34535 35839 36594 35676
49 Wyoming 56 675 585 476 374 371 411 496 551 607 561 587 602 779 944 1150 1224 1258 1362 1506 1610 1647 1719 1955 1912 1932 1858 1913 2011 2132 2172 2278 2312 2387 2502 2535 2588 2743 2906 3121 3315 3584 3919 4269 4709 5410 6172 6701 7212 8152 9384 10572 11753 12879 13251 12723 13493 14242 14004 14194 14968 16383 17996 18867 19550 20287 20957 21514 22098 23820 24927 26396 27230 29122 29828 31544 33721 36683 41548 43453 45177 42504

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