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%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)
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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])
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#initialize data object for speed
data = np.zeros(ny*nx)
start = 0
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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))
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print data[5,5]
We can simply use the imshow method
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plt.imshow(data,aspect=sy/sx)
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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)
hotcold.reverse()
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),aspect=sy/sx)
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plt.imshow(data,cmap=sns.blend_palette(hot,as_cmap=True),aspect=sy/sx)
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plt.imshow(data,cmap=sns.blend_palette(hotcold,as_cmap=True),aspect=sy/sx)
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plt.imshow(data,cmap=sns.blend_palette(rainbow,as_cmap=True),aspect=sy/sx)
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#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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