Plot Map Customize Colorscale

#!/usr/bin/python

import numpy as np

import scipy

from scipy.interpolate import griddata

import matplotlib.pyplot as plt

import matplotlib.mlab as ml

import scipy.ndimage as ndimage

from matplotlib import mlab, cm

def extrapolate_nans(x, y, v):

if np.ma.is_masked(v):

nans = v.mask

else:

nans = np.isnan(v)

notnans = np.logical_not(nans)

v[nans] = scipy.interpolate.griddata((x[notnans], y[notnans]), v[notnans],

(x[nans], y[nans]), method='nearest').ravel()

return v

data = np.genfromtxt('input2d.txt')

x1 = data[:,0]

y1 = data[:,1]

z1 = data[:,2]

numcols, numrows = 500, 500

xi = np.linspace(min(x1), max(x1), numcols)

yi = np.linspace(min(y1), max(y1), numrows)

xi, yi = np.meshgrid(xi, yi)

x, y, z = x1, y1, z1

zi = ml.griddata(x, y, z, xi, yi, interp='nn')

extrapolate_nans(xi,yi,zi)

'''

cmaps = [('Perceptually Uniform Sequential',

['viridis', 'inferno', 'plasma', 'magma']),

('Sequential', ['Blues', 'BuGn', 'BuPu',

'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd',

'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu',

'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']),

('Sequential (2)', ['afmhot', 'autumn', 'bone', 'cool',

'copper', 'gist_heat', 'gray', 'hot',

'pink', 'spring', 'summer', 'winter']),

('Diverging', ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr',

'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral',

'seismic']),

('Qualitative', ['Accent', 'Dark2', 'Paired', 'Pastel1',

'Pastel2', 'Set1', 'Set2', 'Set3']),

('Miscellaneous', ['gist_earth', 'terrain', 'ocean', 'gist_stern',

'brg', 'CMRmap', 'cubehelix',

'gnuplot', 'gnuplot2', 'gist_ncar',

'nipy_spectral', 'jet', 'rainbow',

'gist_rainbow', 'hsv', 'flag', 'prism'])]

'''

levels = np.arange(-1200, -200, 25) # Boost the upper limit to avoid truncation errors.

norm = cm.colors.Normalize(vmax=abs(z1).max(), vmin=-abs(z1).max())

cmap = cm.hsv

####plot

zis = ndimage.gaussian_filter(zi, sigma=25.0, order=0)

fig=plt.figure()

ax=fig.add_subplot(1,2,1)

plt.contour(xi, yi, zi, 20, linewidths = 0.5, colors = 'k')

plt.contourf(xi, yi, zi, levels, cmap=cm.get_cmap(cmap, len(levels) - 1),norm=norm, )

ax=fig.add_subplot(1,2,2)

plt.contour(xi, yi, zis, 20, linewidths = 0.5, colors = 'k')

plt.contourf(xi, yi, zis, levels, cmap=cm.get_cmap(cmap, len(levels) - 1),norm=norm, )

plt.colorbar()

plt.show()