There is a (somewhat) related question on StackOverflow: Showing an image with pylab.imshow() Here the problem was that an array of shape (nx,ny,1) is still considered a 3D array, and must be squeezed or sliced into a 2D array. More generally, the reason for the Exception TypeError: Invalid dimensions for image data is shown here: matplotlib.pyplot.imshow() needs a … Read more
This can happen when there are inf or nan values in the data. Use this to remove nan values:
If you are using matplotlib and want to show the image in your interactive notebook, try the following:
I am making a scatter plot in matplotlib and need to change the background of the actual plot to black. I know how to change the face color of the plot using: My issue is that this changes the color of the space around the plot. How to I change the actual background color of … Read more
You will have to import Axes3D to enable the 3d plotting in matplotlib. The official tutorials on 3d plotting can be found here. So the correct imports and code would look like
Print X_train shape. What do you see? I’d bet X_train is 2d (matrix with a single column), while y_train 1d (vector). In turn you get different sizes. I think using X_train[:,0] for plotting (which is from where the error originates) should solve the problem
If you already have the figure object use: But if you use the .subplots() command (as in the examples you’re showing) to create a new figure you can also use:
do you have any idea how to make 200 evenly spaced out bins, and have your program store the data in the appropriate bins? You can, for example, use NumPy’s arange for a fixed bin size (or Python’s standard range object), and NumPy’s linspace for evenly spaced bins. Here are 2 simple examples from my matplotlib gallery Fixed bin size … Read more
You essentially have two options: Do exactly what you’re currently doing, but call graph1.clear() and graph2.clear() before replotting the data. This is the slowest, but most simplest and most robust option. Instead of replotting, you can just update the data of the plot objects. You’ll need to make some changes in your code, but this should be much, much … Read more
The following code generates a spectrogram using either scipy.signal.spectrogram or matplotlib.pyplot.specgram. The color contrast of the specgram function is, however, rather low. Is there a way to increase it? Using matplotlib.pyplot.specgram gives the following result: Using scipy.signal.spectrogram gives the following plot Both functions seem to use the ‘jet’ colormap. I would also be generally interested in the difference between the two functions. Although they … Read more