The __import__ function can be a bit hard to understand. If you change i = __import__(‘matplotlib.text’) to i = __import__(‘matplotlib.text’, fromlist=[”]) then i will refer to matplotlib.text. In Python 2.7 and Python 3.1 or later, you can use importlib: import importlib i = importlib.import_module(“matplotlib.text”) Some notes If you’re trying to import something from a sub-folder … Read more
Caveat: Unless the ticklabels are already set to a string (as is usually the case in e.g. a boxplot), this will not work with any version of matplotlib newer than 1.1.0. If you’re working from the current github master, this won’t work. I’m not sure what the problem is yet… It may be an unintended … Read more
How about doing it with Pillow: from PIL import Image img = Image.open(‘image.png’).convert(‘L’) img.save(‘greyscale.png’) If an alpha (transparency) channel is present in the input image and should be preserved, use mode LA: img = Image.open(‘image.png’).convert(‘LA’) Using matplotlib and the formula Y’ = 0.2989 R + 0.5870 G + 0.1140 B you could do: import numpy … Read more
The main problem is that (on your system) matplotlib chooses an x-using backend by default. I just had the same problem on one of my servers. The solution for me was to add the following code in a place that gets read before any other pylab/matplotlib/pyplot import: import matplotlib # Force matplotlib to not use … Read more
While the question has been answered, I’d like to add some useful tips when using matplotlib.pyplot.savefig. The file format can be specified by the extension: from matplotlib import pyplot as plt plt.savefig(‘foo.png’) plt.savefig(‘foo.pdf’) Will give a rasterized or vectorized output respectively, both which could be useful. In addition, there’s often an undesirable, whitespace around the … 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 … Read more
You get it directly from the axes’ patches: for p in ax.patches: ax.annotate(str(p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005)) You’ll want to tweak the string formatting and the offsets to get things centered, maybe use the width from p.get_width(), but that should get you started. It may not work with stacked bar plots unless you … Read more
Unfortunately matplotlib 3.1.1 broke seaborn heatmaps; and in general inverted axes with fixed ticks. This is fixed in the current development version; you may hence revert to matplotlib 3.1.0 use matplotlib 3.1.2 or higher set the heatmap limits manually (ax.set_ylim(bottom, top) # set the ylim to bottom, top)
It seems none of the other answers here actually answer the question. So here is a code that uses a scatter and shows an annotation upon hovering over the scatter points. import matplotlib.pyplot as plt import numpy as np; np.random.seed(1) x = np.random.rand(15) y = np.random.rand(15) names = np.array(list(“ABCDEFGHIJKLMNO”)) c = np.random.randint(1,5,size=15) norm = plt.Normalize(1,4) … Read more
In your case, you’re actually wanting to disable the offset. Using scientific notation is a separate setting from showing things in terms of an offset value. However, ax.ticklabel_format(useOffset=False) should have worked (though you’ve listed it as one of the things that didn’t). For example: fig, ax = plt.subplots() ax.plot(range(2003,2012,1),range(200300,201200,100)) ax.ticklabel_format(useOffset=False) plt.show() If you want to … Read more