draw csv file data as a heatmap using numpy and matplotlib

I was able to load my csv file into a numpy array:

data = np.genfromtxt('csv_file', dtype=None, delimiter=',')

Now I would like to generate a heatmap. I have 19 categories from 11 samples, along these lines:

 COG          station1    station2    station3    station4      
COG0001     0.019393497 0.183122497 0.089911227 0.283250444 0.074110521
COG0002     0.044632051 0.019118032 0.034625785 0.069892277 0.034073709
COG0003     0.033066112 0           0           0           0
COG0004     0.1150864开发者_StackOverflow72 0.098805295 0.148167492 0.040019101 0.043982814
COG0005     0.064613057 0.03924007  0.105262559 0.076839235 0.031070155 
COG0006     0.079920475 0.188586049 0.123607421 0.27101229  0.274806929 
COG0007     0.051727492 0.066311584 0.080655401 0.027024185 0.059156417     
COG0008     0.126254841 0.108478559 0.139106704 0.056430812 0.099823028

I wanted to use matplotlib colormesh, but I'm at loss. all the examples I could find used random number arrays. any help and insights would be greatly appreciated.

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What i can decrypt from your question is that you have an 11 x 19 array and the numbers comprising this array appear to be real numbers in the range 0 <= x <= 1 (obviously neither assumption is critical to the answer).

Below is the code to create a heatmap of your array such that the smallest values are lighter and the larger values are darker shades of grey (eg, '0' is white, and '1' is black).

So first, create an array identical in shape and value range to yours:

import numpy as NP
M = NP.random.rand(209).reshape(11, 19)
M.shape
# returns: (11, 19)
# if the array returned from your call to 'genfromtxt' 
# is not 11 x 19, 
# then you need to reshape it so that it is, 
# use, e.g., 'data.reshape(11, 19)'

from matplotlib import pyplot as PLT
from matplotlib import cm as CM


fig = PLT.figure()
ax1 = fig.add_subplot(111)

gray_r refers to a particular matplotlib color map--ie, creates a look-up table that maps each of the cell values in your 2D array to a cell color/hue (put another way: color maps just maps a palette to data;

the r just refers to reverse; i pefer this mapping because it seems more intuitive to me--ie, white is mapped to 0 and larger values are mapped to darker shades of gray;

the available colormaps are in the module cm; dir(matplotlib.cm) to get a list of the installed colormaps (there are dozens); the Matplotlib Site has an excellent visual display of them (as a set of matplotlib plots of course).

# select the color map by calling get_cmap and passing in a registered colormap 
# and an integer value for _lut_ which is just the number of different colors desired
cmap = CM.get_cmap('gray_r', 10)

# map the colors/shades to your data
ax1.imshow(M, interpolation="nearest", cmap=cmap)

# plot it
PLT.show()