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)
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])
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#initialize data object for speed
data = np.zeros((ny,nx))
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for j in xrange(ny):
l = in_file.readline().split()
values = [float(x) for x in l]
data[j,:] = values
in_file.close()
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print data[5,5]
We can simply use the imshow method
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plt.imshow(data)
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We are going to use some of the colormaps defined in colorbrewer via seaborn
We will use 8 colors to illustrate the palettes
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grayscale = sns.color_palette("Greys",8)
sns.palplot(grayscale)
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hot = sns.color_palette("YlOrRd",8)
sns.palplot(hot)
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hotcold = sns.color_palette("RdBu",8)
sns.palplot(hotcold)
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rainbow = sns.color_palette("husl",8)
sns.palplot(rainbow)
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plt.imshow(data,cmap=sns.blend_palette(grayscale,as_cmap=True))
Out[72]:
In [73]:
plt.imshow(data,cmap=sns.blend_palette(hot,as_cmap=True))
Out[73]:
In [74]:
plt.imshow(data,cmap=sns.blend_palette(hotcold,as_cmap=True))
Out[74]:
In [75]:
plt.imshow(data,cmap=sns.blend_palette(rainbow,as_cmap=True))
Out[75]:
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