Processing example 1: Colormaps in 2d Data

Step 0: Setup environment



In [1]:

    
%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 [12]:

    
vortex_file = "temperature.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])
l = in_file.readline().split()
sx, sy = float(l[0]), float(l[1])



In [13]:

    
#initialize data object for speed
data = np.zeros(ny*nx)
start = 0



In [14]:

    
for l in iter(in_file.readline,""):
    values = [float(x) for x in l.split()]
    data[start:start+len(values)] = values
    start += len(values)
in_file.close()
data = data.reshape((ny,nx))



In [66]:

    
print data[5,5]









    



0.0026946405

Step 2: Display as image

We can simply use the imshow method



In [24]:

    
plt.imshow(data,aspect=sy/sx)









    Out[24]:





<matplotlib.image.AxesImage at 0x16e306a0>

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 [16]:

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



In [17]:

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



In [37]:

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



In [34]:

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



In [20]:

    
plt.imshow(data,cmap=sns.blend_palette(grayscale,as_cmap=True),aspect=sy/sx)









    Out[20]:





<matplotlib.image.AxesImage at 0x162b9cf8>



In [21]:

    
plt.imshow(data,cmap=sns.blend_palette(hot,as_cmap=True),aspect=sy/sx)









    Out[21]:





<matplotlib.image.AxesImage at 0x1653acf8>



In [38]:

    
plt.imshow(data,cmap=sns.blend_palette(hotcold,as_cmap=True),aspect=sy/sx)









    Out[38]:





<matplotlib.image.AxesImage at 0x17df57b8>



In [52]:

    
plt.imshow(data,cmap=sns.blend_palette(rainbow,as_cmap=True),aspect=sy/sx)









    Out[52]:





<matplotlib.image.AxesImage at 0x1a6b5ef0>



In [51]:

    
#lets add contour lines
plt.figure()
plt.hold(True)
im = plt.imshow(data,cmap=sns.blend_palette(hotcold,as_cmap=True))
cs = plt.contour(data)
plt.clabel(cs)
CBI = plt.colorbar(im, orientation='vertical', shrink=0.8)



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