Whatever reason it is you are getting 2 ticks only by default, you can fix it (customise it) by changing the ticker locator using a date locator. import matplotlib.pyplot as plt import matplotlib.dates as mdates x = [ datetime.datetime(1900,1,1,0,1,2), datetime.datetime(1900,1,1,0,1,3), … ] # ( more than 1000 elements ) y = [ 34, 33, 23, … Read more
If you want to learn more about matplotlib animations you should really follow this tutorial. It explains in great length how to create animated plots. Note: Creating animated plots require ffmpeg or mencoder to be installed. Here is a version of his first example changed to work with your scatterplot. # First import everthing you … Read more
I think I found the answer. I was having the same problem. In PyCharm Professional 2017.3, go to File | Settings | Tools | Python Scientific. There is an option there to “Show plots in toolwindow”. Uncheck this option. Then it should work ok. It has something to do with PyCharm trying to help you … Read more
When you create your legend add the handlelength keyword (doc) which sets the length of the ‘—‘ (which is called the handle).
in words: A Gaussian filter which ignores NaNs in a given array U can be easily obtained by applying a standard Gaussian filter to two auxiliary arrays V and W and by taking the ratio of the two to get the result Z. Here, V is copy of the original U with NaNs replaced by … Read more
ax.clear() clears the axes. That is, it removes all settings and data from the axes such that you are left with an axes, just as it had been just created. ax.axis(“off”) turns the axes off, such that all axes spines and ticklabels are hidden. ax.set_visible(False) turns the complete axes invisible, including the data that is … Read more
I guess you are completely on the right track. The problem is just to find the good parameters for the padding in order to have the ticklabels inside the axes. The padding is measured in points, so it makes sense to use much larger numbers than 0.5. Also the padding is different for x and … Read more
As you can see from seaborn.lineplot documentation, the function accepts matplotlib.axes.Axes.plot() arguments, which means you can pass the same arguments you can to matplotlib function in this documentation. If you want to simply adjust the width of your lineplots I find this the easiest: pass an argument linewidth = your_desired_line_width_in_float , for example, linewidth = … Read more
To get the marker to show beyond the axes you can turn the clipping off. This can be done using the keyword argument in the plot command clip_on=False. For example: import matplotlib.pyplot as plt plt.plot(range(5), range(5), ‘ro’, markersize=20, clip_on=False, zorder=100) plt.show()
As you already guessed there is no need to create a figure. You basically need three steps. Normalize your data, apply the colormap, save the image. matplotlib provides all the necessary functionality: import numpy as np import matplotlib.pyplot as plt # some data (512×512) import scipy.misc data = scipy.misc.lena() # a colormap and a normalization … Read more