#### Overview of this book

Mastering SciPy
Credits
www.PacktPub.com
Preface
Free Chapter
Numerical Linear Algebra
Interpolation and Approximation
Differentiation and Integration
Nonlinear Equations and Optimization
Initial Value Problems for Ordinary Differential Equations
Computational Geometry
Descriptive Statistics
Inference and Data Analysis
Mathematical Imaging
Index

## 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, latitudes); \
...: temperatures = 10. * np.random...```