Mpl toolkits.basemap pip install

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Mpl_toolkits.basemap pip install

In [1]: from IPython.display import YouTubeVideo YouTubeVideo('1fzQKMp_tdE') Out[1]: Pyrex wrapper to provide python interfaces to PROJ.4 ( ) functions. Performs cartographic transformations (converts from longitude, latitude to native map projection x,y coordinates and vice versa, or from one map projection coordinate system directly to another). Source: In [3]: from pyproj import Proj p = Proj(init='epsg:3857') p.srs Out[3]: '+units=m +init=epsg:3857 ' Out[4]: (-10881480.225042492, 3535725.659799159) In [5]: p(-10881480.225042492, 3535725.659799159, inverse=True) Out[5]: (-97.75, 30.24999999999999) The matplotlib basemap toolkit is a library for plotting 2D data on maps in Python. It is similar in functionality to the matlab mapping toolbox, the IDL mapping facilities, GrADS, or the Generic Mapping Tools. PyNGL and CDAT are other libraries that provide similar capabilities in Python. Basemap does not do any plotting on it's own, but provides the facilities to transform coordinates to one of 25 different map projections (using the PROJ.4 C library). Matplotlib is then used to plot contours, images, vectors, lines or points in the transformed coordinates. Shoreline, river and political boundary datasets (from Generic Mapping Tools) are provided, along with methods for plotting them. The GEOS library is used internally to clip the coastline and polticial boundary features to the desired map projection region. Basemap provides facilities for reading shapefiles. Basemap is geared toward the needs of earth scientists, particular oceanographers and meteorologists. I originally wrote Basemap to help in my research (climate and weather forecasting), since at the time CDAT was the only other tool in python for plotting data on map projections. Over the years, the capabilities of Basemap have evolved as scientists in other disciplines (such as biology, geology and geophysics) requested and contributed new features. Source: In [6]: """ Exercise: Plotting with basemap 1. Draw a world map centered on Austin, Texas (if possible) in the following projections: a) Mercator b) Robinson c) Orthographic d) Azimuthal equidistant 2. Plot the following great circle routes on a global map: a) Hawaii to Hong Kong b) Hong Kong to Moscow c) Moscow to Havana, Cuba d) Havana to Quito, Ecuador Coordinates of these locations are given below. Try to choose projection parameters that allow you to see all of the great circles at once. Plot black dots at the start and end points as well. Author: Kelsey Jordahl, Enthought Scipy 2013 geospatial tutorial """ import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.basemap import Basemap # (lon, lat) austin = (-97.75, 30.25) hawaii = (-157.8, 21.3) hongkong = (114.16, 22.28) moscow = (37.62, 55.75) havana = (-82.38, 23.13) quito = (-78.58, -0.25) land_color = 'lightgray' water_color = 'lightblue' # or choose your own colors... 1- Draw a world map centered on Austin, Texas (if possible) in the following projections: a) Mercator b) Robinson c) Orthographic d) Azimuthal equidistant 2- Plot the following great circle routes on a global map: a) Hawaii to Hong Kong b) Hong Kong to Moscow c) Moscow to Havana, Cuba d) Havana to Quito, Ecuador Coordinates of these locations are given below. Try to choose projection parameters that allow you to see all of the great circles at once. Plot black dots at the start and end points as well. In [7]: fig, ax = subplots(figsize=(12,12)) map = Basemap(projection='merc', llcrnrlat=-80, urcrnrlat=80, llcrnrlon=-180, urcrnrlon=180, resolution='l') # draw great circle route land_color = 'lightgray' water_color = 'lightblue' map.fillcontinents(color=land_color, lake_color=water_color) map.drawgreatcircle(hawaii[0],hawaii[1],hongkong[0],hongkong[1],color='b') map.drawgreatcircle(hongkong[0],hongkong[1],moscow[0],moscow[1],color='b') map.drawgreatcircle(moscow[0],moscow[1],havana[0],havana[1],color='b') map.drawgreatcircle(havana[0],havana[1],quito[0],quito[1],color='b') map.drawcoastlines() map.drawparallels(np.arange(-90.,120.,30.)) map.drawmeridians(np.arange(0.,420.,60.)) map.drawmapboundary(fill_color=water_color) ax.set_title('Mercator') map.ax = ax x, y = map(*zip(*[hawaii, hongkong, moscow, havana, quito])) map.plot(x, y, marker='o', markersize=6, markerfacecolor='black', linewidth=0) Out[7]: [] In [8]: fig, ax = subplots(figsize=(12,12)) map = Basemap(projection='robin', llcrnrlat=-80, urcrnrlat=80, llcrnrlon=-180, urcrnrlon=180, resolution='l', lon_0=austin[0]) # draw great circle route land_color = 'lightgray' water_color = 'lightblue' map.fillcontinents(color=land_color, lake_color=water_color) map.drawgreatcircle(hawaii[0],hawaii[1],hongkong[0],hongkong[1],color='b') map.drawgreatcircle(hongkong[0],hongkong[1],moscow[0],moscow[1],color='b') map.drawgreatcircle(moscow[0],moscow[1],havana[0],havana[1],color='b') map.drawgreatcircle(havana[0],havana[1],quito[0],quito[1],color='b') map.drawcoastlines() map.drawparallels(np.arange(-90.,120.,30.)) map.drawmeridians(np.arange(0.,420.,60.)) map.drawmapboundary(fill_color=water_color) ax.set_title('Robinson') map.ax = ax x, y = map(*zip(*[hawaii, hongkong, moscow, havana, quito])) map.plot(x, y, marker='o', markersize=6, markerfacecolor='black', linewidth=0) Out[8]: [] In [9]: fig, ax = subplots(figsize=(12,12)) map = Basemap(projection='ortho', lon_0=austin[0], lat_0=austin[1]) # draw great circle route land_color = 'lightgray' water_color = 'lightblue' map.fillcontinents(color=land_color, lake_color=water_color) map.drawgreatcircle(hawaii[0],hawaii[1],hongkong[0],hongkong[1],color='b') map.drawgreatcircle(hongkong[0],hongkong[1],moscow[0],moscow[1],color='b') map.drawgreatcircle(moscow[0],moscow[1],havana[0],havana[1],color='b') map.drawgreatcircle(havana[0],havana[1],quito[0],quito[1],color='b') map.drawcoastlines() map.drawparallels(np.arange(-90.,120.,30.)) map.drawmeridians(np.arange(0.,420.,60.)) map.drawmapboundary(fill_color=water_color) ax.set_title('Orthographic') map.ax = ax x, y = map(*zip(*[hawaii, hongkong, moscow, havana, quito])) map.plot(x, y, marker='o', markersize=6, markerfacecolor='black', linewidth=0) Out[9]: [] In [10]: fig, ax = subplots(figsize=(12,12)) map = Basemap(projection='aeqd', lon_0=austin[0], lat_0=austin[1]) # draw great circle route land_color = 'lightgray' water_color = 'lightblue' map.fillcontinents(color=land_color, lake_color=water_color) map.drawgreatcircle(hawaii[0],hawaii[1],hongkong[0],hongkong[1],color='b') map.drawgreatcircle(hongkong[0],hongkong[1],moscow[0],moscow[1],color='b') map.drawgreatcircle(moscow[0],moscow[1],havana[0],havana[1],color='b') map.drawgreatcircle(havana[0],havana[1],quito[0],quito[1],color='b') map.drawcoastlines() map.drawparallels(np.arange(-90.,120.,30.)) map.drawmeridians(np.arange(0.,420.,60.)) map.drawmapboundary(fill_color=water_color) ax.set_title('Azimuthal equidistant') map.ax = ax x, y = map(*zip(*[hawaii, hongkong, moscow, havana, quito])) map.plot(x, y, marker='o', markersize=6, markerfacecolor='black', linewidth=0) Out[10]: [] GDAL (Geospatial Data Abstraction Library) is a library for reading and writing raster geospatial data formats, and is released under the permissive X/MIT style free software license by the Open Source Geospatial Foundation. As a library, it presents a single abstract data model to the calling application for all supported formats. It may also be built with a variety of useful command-line utilities for data translation and processing. The related OGR library (OGR Simple Features Library[2]), which is part of the GDAL source tree, provides a similar capability for simple features vector data. Source: This Python package and extensions are a number of tools for programming and manipulating the GDAL Geospatial Data Abstraction Library. Actually, it is two libraries -- GDAL for manipulating geospatial raster data and OGR for manipulating geospatial vector data -- but we'll refer to the entire package as the GDAL library for the purposes of this document. Source: In [11]: """ Exercise: Read a geotiff file as a numpy array and display with matplotlib. 1. Download the data file from kjordahl/scipy/manhattan.tif 2. Open the file with GDAL. What projection is it in? 3. Read the image data into a numpy array and plot as a 3-color image with matplotlib. 4. Set the plot axis limits to the proper map coordinates. BONUS 5. plot the locations of the Citibike stations in the file citibike.json (or real-time from Author: Kelsey Jordahl, Enthought Scipy 2013 geospatial tutorial """ import os from osgeo import gdal import matplotlib.pyplot as plt # GDAL does not use python exceptions by default gdal.UseExceptions() In [13]: ! wget kjordahl/scipy/manhattan.tif --2013-07-13 00:59:44-- kjordahl/scipy/manhattan.tif Resolving public. (public.)... 50.17.225.20 Connecting to public. (public.)|50.17.225.20|:80... connected. HTTP request sent, awaiting response... 200 OK Length: 119987605 (114M) [image/tiff] Saving to: `manhattan.tif' 100%[======================================>] 119.987.605 2,03MB/s in 51s 2013-07-13 01:00:35 (2,26 MB/s) - `manhattan.tif' saved [119987605/119987605] 2- Open the file with GDAL. What projection is it in? In [14]: gtif = gdal.Open('manhattan.tif') gtif.GetProjectionRef() Out[14]: 'PROJCS["UTM",GEOGCS["NAD83",DATUM["North_American_Datum_1983",SPHEROID["GRS 1980",6378137,298.2572221010002,AUTHORITY["EPSG","7019"]],AUTHORITY["EPSG","6269"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4269"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-75],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["meters",1],AUTHORITY["EPSG","26918"]]' 3- Read the image data into a numpy array and plot as a 3-color image with matplotlib. 4- Set the plot axis limits to the proper map coordinates. In [15]: arr = gtif.ReadAsArray() trans = gtif.GetGeoTransform() extent = (trans[0], trans[0] + gtif.RasterXSize*trans[1], trans[3] + gtif.RasterYSize*trans[5], trans[3]) plt.imshow(arr[:3,:,:].transpose((1, 2, 0)), extent=extent) plt.show() Fiona provides a minimal, uncomplicated Python interface to the open source GIS community's most trusted geodata access library and integrates readily with other Python GIS packages such as pyproj, Rtree, and Shapely. How minimal? Fiona can read feature records as mappings from shapefiles or other GIS vector formats and write mappings as records to files using the same formats. That's all. There aren't any feature or geometry classes. Records and their geometries are just data. Source: In [16]: """ Exercise: Reading a shapefile with Fiona 1. Create a list of all the unique rock types in the data (in properties ROCKTYPE1 and ROCKTYPE2). 2. Calculate the total area of each primary rocktype (ROCKTYPE1) by summing the property AREA. 3. Plot the polygons in the data with basemap, coloring by primary rock type. BONUS: 4. Calculate the total area again, this time by using Shapely to calculate the area of each polygon. HINT: You may want to use New Jersey State Plane coordinates, EPSG:32111 Author: Kelsey Jordahl, Enthought Scipy 2013 geospatial tutorial """ import os import zipfile import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.basemap import Basemap from matplotlib.patches import Polygon from collections import defaultdict import fiona 1- Create a list of all the unique rock types in the data (in properties ROCKTYPE1 and ROCKTYPE2). In [19]: rocks = [] with fiona.open('njgeol_poly_dd.shp') as f: for rec in f: rocks.append( rec['properties']['ROCKTYPE1'] ) rocks.append( rec['properties']['ROCKTYPE2'] ) len(set(rocks)) 2- Calculate the total area of each primary rocktype (ROCKTYPE1) by summing the property AREA. In [20]: from collections import defaultdict d = defaultdict(float) with fiona.open('njgeol_poly_dd.shp') as f: for rec in f: d[rec['properties']['ROCKTYPE1']] += rec['properties']['AREA'] In [21]: fig, ax = subplots(figsize=(12,12)) ax.bar(arange(len(d.values())), d.values()) _ = ax.set_xticks(arange(len(d.keys()))) _ = ax.set_xticklabels( d.keys(), rotation='vertical' ) 3- Plot the polygons in the data with basemap, coloring by primary rock type. In [22]: fig, ax = subplots(figsize=(12,12)) west, east, south, north = -75.6, -73.5, 38.5, 41.5 m = Basemap(projection='merc', llcrnrlat=south, urcrnrlat=north, llcrnrlon=west, urcrnrlon=east, lat_ts=0, resolution='l') colormap = defaultdict(lambda: np.random.random(3)) with fiona.open('njgeol_poly_dd.shp') as f: for idx,rec in enumerate(f): coords = rec['geometry']['coordinates'][0] rocktype = rec['properties']['ROCKTYPE1'] x, y = m(*zip(*coords)) poly = Polygon(zip(x,y), facecolor=colormap[rocktype]) ax.add_patch(poly) m.drawmapboundary() m.drawcoastlines() Out[22]: Shapely is a BSD-licensed Python package for manipulation and analysis of planar geometric objects. It is based on the widely deployed GEOS (the engine of PostGIS) and JTS (from which GEOS is ported) libraries. This C dependency is traded for the ability to execute with blazing speed. Shapely is not concerned with data formats or coordinate systems, but can be readily integrated with packages that are. Source: In [24]: """ Exercise: Boroughs of New York City 1. Read the borough boundaries shapefile from data/nybb_13a/nybb.shp into a dictionary of Shapely geometries keyed by the property BoroName. 2. Calculate the area of each borough. What are the units? 3. Calculate the fraction of the area of each borough that lies more than 1 km (3281 feet) from its boundary BONUS 4. Extract the simple polygon representing the island (not the borough) of Manhattan. HINT: It will be the largest polygon in the borough. Author: Kelsey Jordahl, Enthought Scipy 2013 geospatial tutorial """ import os import numpy as np import fiona from shapely.geometry import shape def plot_polygon(ax, poly, color='red'): a = np.asarray(poly.exterior) ax.add_patch(Polygon(a, facecolor=color, alpha=0.3)) ax.plot(a[:, 0], a[:, 1], color='black') def plot_multipolygon(ax, geom, color='red'): """ Can safely call with either Polygon or Multipolygon geometry """ if geom.type == 'Polygon': plot_polygon(ax, geom, color) elif geom.type == 'MultiPolygon': for poly in geom.geoms: plot_polygon(ax, poly, color) 1- Read the borough boundaries shapefile from data/nybb_13a/nybb.shp into a dictionary of Shapely geometries keyed by the property BoroName. In [27]: nyc_geom = defaultdict() colors = ['red', 'green', 'orange', 'brown', 'purple'] fig, ax = subplots(figsize=(12,12)) with fiona.open('nybb.shp') as f: crs = f.crs units = crs['units'] print 'units', units for rec in f: color = colors.pop() print rec['geometry']['type'] boro = rec['properties']['BoroName'] nyc_geom[boro] = shape(rec['geometry']) plot_multipolygon(ax, nyc_geom[boro], color=color) labels = ax.get_xticklabels() for label in labels: label.set_rotation(90) units us-ft MultiPolygon MultiPolygon MultiPolygon MultiPolygon MultiPolygon 2- Calculate the area of each borough. What are the units? In [28]: for boro, geom in nyc_geom.iteritems(): print '%s area %10.0f square feet (%5.2f sq miles)' % (boro, geom.area, (geom.area / 27878400)) Bronx area 1186805997 square feet (42.57 sq miles) Brooklyn area 1959433451 square feet (70.29 sq miles) Staten Island area 1623855480 square feet (58.25 sq miles) Manhattan area 636441883 square feet (22.83 sq miles) Queens area 3049948268 square feet (109.40 sq miles) 3 - Calculate the fraction of the area of each borough that lies more than 1 km (3281 feet) from its boundary In [29]: figure, ax = subplots(figsize=(12,12)) for boro, geom in nyc_geom.iteritems(): interior = geom.buffer(-3281) plot_multipolygon(ax, interior, color='gray') print '%4.1f%% of %s more than 1 km from boundary' % (100 * interior.area / geom.area, boro) 46.0% of Bronx more than 1 km from boundary 50.2% of Brooklyn more than 1 km from boundary 62.7% of Staten Island more than 1 km from boundary 25.0% of Manhattan more than 1 km from boundary 56.7% of Queens more than 1 km from boundary 4- Extract the simple polygon representing the island (not the borough) of Manhattan. HINT: It will be the largest polygon in the borough. In [30]: fig, ax = subplots(figsize=(12,12)) idx = argmax([s.area for s in nyc_geom['Manhattan'].geoms]) manhattan_i = nyc_geom['Manhattan'].geoms[idx] plot_polygon(ax, manhattan_i)

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