Given the title of your question, I will assume that the discretization size is constant. You can find this discretization size (or at least, strictly, n times that size as you may not have two adjacent samples in your data) np.diff(np.unique(data)).min() This finds the unique values in your data (np.unique), finds the differences between then … Read more
I have the same issue on a plot with a high degree of discretization. Here the starting situation: import matplotlib.pyplot as plt grid=[x for x in range(10)] graphs=[ [1,1,1,4,4,4,3,5,6,0], [1,1,1,5,5,5,3,5,6,0], [1,1,1,0,0,3,3,2,4,0], [1,2,4,4,3,2,3,2,4,0], [1,2,3,3,4,4,3,2,6,0], [1,1,3,3,0,3,3,5,4,3], ] for gg,graph in enumerate(graphs): plt.plot(grid,graph,label=”g”+str(gg)) plt.legend(loc=3,bbox_to_anchor=(1,0)) plt.show() No one can say where the green and blue lines run exactly and … Read more
If you want to remove the axis spines, but not the other information (ticks, labels, etc.), you can do that like so: fig, ax = plt.subplots(7,1, sharex=True) t = np.arange(0, 1, 0.01) for i, a in enumerate(ax): a.plot(t, np.sin((i + 1) * 2 * np.pi * t)) a.spines[“top”].set_visible(False) a.spines[“right”].set_visible(False) a.spines[“bottom”].set_visible(False) or, more easily, using seaborn: … Read more
plt.minorticks_off() Turns em off! To change the number of them/position them, you can use the subsx parameter. like this: plt.xscale(‘log’, subsx=[2, 3, 4, 5, 6, 7, 8, 9]) From the docs: subsx/subsy: Where to place the subticks between each major tick. Should be a sequence of integers. For example, in a log10 scale: [2, 3, … Read more
If you plot a line twice it won’t show up in the legend. It’s indeed better to use patheffects. Here are two simple examples: import matplotlib.pyplot as plt import numpy as np import matplotlib.patheffects as pe # setup data x = np.arange(0.0, 1.0, 0.01) y = np.sin(2*2*np.pi*t) # create line plot including an outline (stroke) … Read more
I know I’m late to the party. But, there is a simpler way if you just want the cdf for your plot and not for future calculations: plt.hist(put_data_here, normed=True, cumulative=True, label=”CDF”, histtype=”step”, alpha=0.8, color=”k”) As an example, plt.hist(dataset, bins=bins, normed=True, cumulative=True, label=”CDF DATA”, histtype=”step”, alpha=0.55, color=”purple”) # bins and (lognormal / normal) datasets are pre-defined … Read more
I solved the issue by changing ax.savefig(‘file.png’) to ax.figure.savefig(‘file.png’) I guess accessing the figure directly is one way to get to the savefig() method for the barplot. @WoodChopper also has a working solution, but it requires another import statement, and utilizing pyplot’s savefig() directly. Either solution does require setting matplotlib.use(‘Agg’) to get around the DISPLAY … Read more
It seems you are missing a package for the dvi rendering. On debian-based distributions, this should suffice to get all the needed packages: sudo apt-get install texlive-latex-extra texlive-fonts-recommended dvipng cm-super
The bound can changes automatically, if you later plot something that is not within the bound. In contrast, limits are fixed and do not change automatically. import pylab as p t = p.arange(0.0, 2.0, 0.01) s = p.sin(2*p.pi*t) ax=p.subplot(111) ax.plot(t, s, color=”r”,linewidth=1.0) ax.set_ylim(-1,1) ax.plot(t, s+1, color=”g”,linewidth=1.0, label=”Graph2″) p.show() ax=p.subplot(111) ax.plot(t, s, color=”r”,linewidth=1.0) ax.set_ybound(-1,1) ax.plot(t, s+1, … Read more