imshow

I am trying to plot to fields with the same scale. The upper image values are a 10 times higher than the one bellow, but they turn out to be the same color in the imshow. How can I set both to have the same scales in colours? I added the code I am using bellow the image.. def show_field(field1,field2): fig = plt.figure() ax = fig.add_subplot(2, 1, 1) ax.imshow(field1,cmap=plt.cm.YlGn) ax.set_adjustable('box-forced') ax.autoscale(False) ax2 = fig.add_subplot(2, 1, 2) ax2.set_adjustable('box-forced') ax2.imshow(field2,cmap=plt.cm.YlGn) ax2.autoscale(False) plt.show() First you need to define the

I have a medium-sized array (e.g. 1500x3000) that I want to plot at scale since it is an image. However, the vertical and horizontal scales are very different. For simplification let say that there is one meter/row and 10/column. The plot should then produce an image which is c. 1500x30000. I use the kwarg extent for the scales and aspect = 1 to avoid deformation. Either by using the plotting windows (QT4) and imshow() or by using savefig(), I never succeeded in producing the image at scale and at full resolution. I have looked to many proposed solutions as indicated in here , here , or here

I'm trying to create a grid using a matplotlib function like imshow . From this array: [[ 1 8 13 29 17 26 10 4], [16 25 31 5 21 30 19 15]] I would like to plot the value as a color AND the text value itself (1,2, ...) on the same grid. This is what I have for the moment (I can only plot the color associated to each value): from matplotlib import pyplot import numpy as np grid = np.array([[1,8,13,29,17,26,10,4],[16,25,31,5,21,30,19,15]]) print 'Here is the array' print grid fig1, (ax1, ax2)= pyplot.subplots(2, sharex = True, sharey = False) ax1.imshow(grid, interpolation ='none', aspect = 'auto

This question already has an answer here: How to 'turn off' blurry effect of imshow() in matplotlib? 2 answers I am plotting a density of counts with imshow from matplotlib.pyplot but I'd like to have a smoother plot. Can I apply any filter on this? Try using the interpolation argument: ax.imshow(grid, interpolation=interp_method) matplotlib demo matplotlib api If you manually want to handle how strong the filter is you could do something along the lines of (scipy.ndimage has a lot of filters) from scipy.ndimage.filters import gaussian_filter arr=np.zeros((20,20)) arr[0,:]=3 arr[0,0]=20 arr[19

The following code snippet results a double image . f = imread('C:\Users\Administrator\Desktop\2.tif'); h = double(f); figure; imshow(h); whereas, this other code snippet results a uint8 image . f = imread('C:\Users\Administrator\Desktop\2.tif'); figure; imshow(f); While displaying these two figures, the displayed results of these two images using imshow are different, but what is the reason behind this difference? Images of type double are assumed to have values between 0 and 1 and uint8 images are assumed to have values between 0 and 255. Since your double data contains values between 0 and