CODEWITHSUNDEEP
pythonmatplotlibcolorscolormapcolor-mapping
Mac
Mac

Reputation: 1031

Personalised colourmap plot using set numbers using matplotlib

I have a data which looks like (example)

x y  d
0 0 -2
1 0  0
0 1  1 
1 1  3

And I want to turn this into a coloumap plot which looks like one of these:

enter image description here

where x and y are in the table and the color is given by 'd'. However, I want a predetermined color for each number, for example:

-2 - orange
 0 - blue
 1 - red
 3 - yellow

Not necessarily these colours but I need to address a number to a colour and the numbers are not in order or sequence, the are just a set of five or six random numbers which repeat themselves across the entire array.

Any ideas, I haven't got a code for that as I don't know where to start. I have however looked at the examples in here such as:

Matplotlib python change single color in colormap

However they only show how to define colours and not how to link those colours to an specific value.

Upvotes: 0

Views: 1953

Answers (1)

ImportanceOfBeingErnest
ImportanceOfBeingErnest

Reputation: 339052

It turns out this is harder than I thought, so maybe someone has an easier way of doing this.

Since we need to create an image of the data, we will store them in a 2D array. We can then map the data to the integers 0 .. number of different data values and assign a color to each of them. The reason is that we want the final colormap to be equally spaced. So

value -2 --> integer 0 --> color orange
value 0 --> integer 1 --> color blue and so on.

Having nicely spaced integers, we can use a ListedColormap on the image of newly created integer values.

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.colors

# define the image as a 2D array
d = np.array([[-2,0],[1,3]])

# create a sorted list of all unique values from d
ticks = np.unique(d.flatten()).tolist()
# create a new array of same shape as d
# we will later use this to store values from 0 to number of unique values
dc = np.zeros(d.shape)
#fill the array dc
for i in range(d.shape[0]):
    for j in range(d.shape[1]):
        dc[i,j] = ticks.index(d[i,j])

# now we need n (= number of unique values) different colors        
colors= ["orange", "blue", "red", "yellow"]
# and put them to a listed colormap
colormap =  matplotlib.colors.ListedColormap(colors)


plt.figure(figsize=(5,3))
#plot the newly created array, shift the colorlimits, 
# such that later the ticks are in the middle
im = plt.imshow(dc, cmap=colormap, interpolation="none", vmin=-0.5, vmax=len(colors)-0.5)
# create a colorbar with n different ticks
cbar = plt.colorbar(im, ticks=range(len(colors)) )
#set the ticklabels to the unique values from d
cbar.ax.set_yticklabels(ticks)
#set nice tickmarks on image
plt.gca().set_xticks(range(d.shape[1]))
plt.gca().set_yticks(range(d.shape[0]))

plt.show()

enter image description here

As it may not be intuitively clear how to get the array d in the shape needed for plotting with imshow, i.e. as 2D array, here are two ways of converting the input data columns:

import numpy as np

x = np.array([0,1,0,1])
y  = np.array([ 0,0,1,1])
d_original = np.array([-2,0,1,3])

#### Method 1 ####
# Intuitive method. 
# Assumption: 
#    * Indexing in x and y start at 0 
#    * every index pair occurs exactly once.
# Create an empty array of shape (n+1,m+1) 
#   where n is the maximum index in y and
#         m is the maximum index in x
d = np.zeros((y.max()+1 , x.max()+1), dtype=np.int) 
for k in range(len(d_original)) :
    d[y[k],x[k]] = d_original[k]  
print d

#### Method 2 ####
# Fast method
# Additional assumption: 
#  indizes in x and y are ordered exactly such
#  that y is sorted ascendingly first, 
#  and for each index in y, x is sorted. 
# In this case the original d array can bes simply reshaped
d2 = d_original.reshape((y.max()+1 , x.max()+1))
print d2

Upvotes: 1

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