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Mastering SciPy

You're reading from   Mastering SciPy Implement state-of-the-art techniques to visualize solutions to challenging problems in scientific computing, with the use of the SciPy stack

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781783984749
Length 404 pages
Edition 1st Edition
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Authors (2):
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Francisco Javier Blanco-Silva Francisco Javier Blanco-Silva
Author Profile Icon Francisco Javier Blanco-Silva
Francisco Javier Blanco-Silva
Francisco Javier B Silva Francisco Javier B Silva
Author Profile Icon Francisco Javier B Silva
Francisco Javier B Silva
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Toc

Motivation

Consider a meteorological experiment that measures the temperature of a set of buoys located on a rectangular grid at sea. We can emulate such an experiment by indicating the longitude and latitude of the buoys on a grid of 16 × 16 locations, and random temperatures on them between say 36ºF and 46ºF:

In [1]: import numpy as np, matplotlib.pyplot as plt, \
   ...: matplotlib.cm as cm; \
   ...: from mpl_toolkits.basemap import Basemap
In [2]: map1 = Basemap(projection='ortho', lat_0=20, lon_0=-60, \
   ...:                resolution='l', area_thresh=1000.0); \
   ...: map2 = Basemap(projection='merc', lat_0=20, lon_0=-60, \
   ...:                resolution='l', area_thresh=1000.0, \
   ...:                llcrnrlat=0,  urcrnrlat=45, \
   ...:                llcrnrlon=-75, urcrnrlon=-15)
In [3]: longitudes = np.linspace(-60, -30, 16); \
   ...: latitudes = np.linspace(15, 30, 16); \
   ...: lons, lats = np.meshgrid(longitudes...
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