Processing example 1: Colormaps in 2d Data

Step 0: Setup environment



In [62]:

    
%matplotlib inline
import matplotlib
import seaborn as sns
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
%cd C:\Users\da.angulo39\Documents\ipython_notebooks\Assignment1\Example1\data

#figure size
matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)









    



C:\Users\da.angulo39\Documents\ipython_notebooks\Assignment1\Example1\data

Step 1: Load the data



In [63]:

    
vortex_file = "vorticity.asc"
in_file = open(vortex_file)
#first line contains dimension and spacing of grid
l = in_file.readline().split()
nx, ny = int(l[0]), int(l[1])
sx, sy = float(l[2]), float(l[3])



In [64]:

    
#initialize data object for speed
data = np.zeros((ny,nx))



In [65]:

    
for j in xrange(ny):
    l = in_file.readline().split()
    values = [float(x) for x in l]
    data[j,:] = values
in_file.close()



In [66]:

    
print data[5,5]









    



0.0026946405

Step 2: Display as image

We can simply use the imshow method



In [67]:

    
plt.imshow(data)









    Out[67]:





<matplotlib.image.AxesImage at 0x1770cc18>

Step 3: Use colormaps

We are going to use some of the colormaps defined in colorbrewer via seaborn

We will use 8 colors to illustrate the palettes



In [68]:

    
grayscale = sns.color_palette("Greys",8)
sns.palplot(grayscale)



In [69]:

    
hot = sns.color_palette("YlOrRd",8)
sns.palplot(hot)



In [70]:

    
hotcold = sns.color_palette("RdBu",8)
sns.palplot(hotcold)



In [71]:

    
rainbow = sns.color_palette("husl",8)
sns.palplot(rainbow)



In [72]:

    
plt.imshow(data,cmap=sns.blend_palette(grayscale,as_cmap=True))









    Out[72]:





<matplotlib.image.AxesImage at 0x1943eda0>



In [73]:

    
plt.imshow(data,cmap=sns.blend_palette(hot,as_cmap=True))









    Out[73]:





<matplotlib.image.AxesImage at 0x1981abe0>



In [74]:

    
plt.imshow(data,cmap=sns.blend_palette(hotcold,as_cmap=True))









    Out[74]:





<matplotlib.image.AxesImage at 0x19b79a58>



In [75]:

    
plt.imshow(data,cmap=sns.blend_palette(rainbow,as_cmap=True))









    Out[75]:





<matplotlib.image.AxesImage at 0x191dfb00>



In [75]: