# Processing example 1: Colormaps in 2d Data

## Step 0: Setup environment

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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)

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

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## Step 1: Load the data

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In [12]:

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

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In [13]:

#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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In [66]:

print data[5,5]

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0.0026946405

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## Step 2: Display as image

We can simply use the imshow method

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In [24]:

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

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Out[24]:

<matplotlib.image.AxesImage at 0x16e306a0>

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## 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

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In [16]:

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

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In [17]:

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

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In [37]:

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

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In [34]:

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

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In [20]:

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

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Out[20]:

<matplotlib.image.AxesImage at 0x162b9cf8>

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In [21]:

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

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Out[21]:

<matplotlib.image.AxesImage at 0x1653acf8>

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In [38]:

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

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Out[38]:

<matplotlib.image.AxesImage at 0x17df57b8>

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In [52]:

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

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Out[52]:

<matplotlib.image.AxesImage at 0x1a6b5ef0>

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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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