# add a colorbar to the bottom of the imageĬax = div.append_axes('bottom', size='5%', pad=0.4)Ĭbar = plt. Im = ax.imshow(z, interpolation='nearest', cmap=cmap, clim=(0,5000)) ![]() # plot data, apply colormap, set limit such that our interpretation is correct # some data to plot: distance to point at (50,50) Lower = np.linspace(1, upper, lower.shape)Ĭmap = (cmap, name='m圜olorMap', N=cmap.shape) # range linearly between white (1,1,1) and the first color of the upper colormap ![]() # - modify the first three columns (RGB): # - initialize all entries to 1 to make sure that the alpha channel (4th column) is 1 # - 1/5 : custom colormap, ranging from white to the first color of the colormap single- or multi-color colormaps etc.įrom mpl_toolkits.axes_grid1 import make_axes_locatable For example, you can choose different types of interpolation: linear, exponential. See Choosing Colormaps in Matplotlib for an in-depth discussion about colormaps, including colorblind-friendliness, and Creating Colormaps in Matplotlib for a guide to. A reversed version of each of these colormaps is available by appending r to the name, as shown in Reversed colormaps. The colormaps and their sizes depend on your problem. Reference for colormaps included with Matplotlib. The choices depend fully on what you want to show. ![]() For any distance lower than some critical value, the colors will linearly go from white to the first color of the previously mentioned map.For any distance higher than some critical value, the colors will be taken from a standard colormap.To do this one has to create an RGBA-matrix: a matrix with on each row the amount (between 0 and 1) of Red, Green, Blue, and Alpha (transparency 0 means that the pixel does not have any coverage information and is transparent).Īs an example the distance to some point is plotted in two dimensions. The answer to get the result smooth lies in constructing your own colormap. #speed = np.ma.masked_where(speed <</a> 0.4, speed)Ĭs = map.contourf(x,y,speed,levels, cmap='jet')Ĭbar = plt.colorbar(cs, orientation='horizontal', cmap='jet', spacing='proportional',ticks=ticks)Ĭbar.set_label('850 mb Vector Wind Anomalies (m/s)') Result with set_bad (problem: no smooth transition to white):Ĭode so far: from netCDF4 import Dataset as NetCDFFile Result with continuous colormap (problem: no white):
I tried masking those values and using set_bad but I ended up with a real blocky appearance, losing the nice smooth contours seen in the original image. I'm close but can't quite figure out how to modify a matplotlib colormap to make values <0.4 go to white. Here is how you might do that with your situation aLetter = max(aLetter(:)) - aLetter % invert image: now white = close to zeroĪLetter = aLetter - min(aLetter(:)) % make the smallest value zeroĪLetter(aLetter 90% of your image is "white", you could determine the correct threshold by sorting the pixels and finding the threshold dynamically - as I mentioned in my comment in the code "play with it" until you find something that works in your situation.I'm trying to produce a similar version of this image using Python:
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