In the solution below I showcase a bigger example (with also bar plot) that may help people understand better what should be done for other cases. After the code I explain some details and answer the bonus question. import matplotlib matplotlib.use(‘Qt5Agg’) #use Qt5 as backend, comment this line for default backend from matplotlib import pyplot … Read more
Try: import matplotlib.pyplot as plt plt.rc(‘text’, usetex=True) plt.title(r’\fontsize{30pt}{3em}\selectfont{}{Mean WRFv3.5 LHF\r}{\fontsize{18pt}{3em}\selectfont{}(September 16 – October 30, 2012)}’) plt.show() That \r might want to be a \n on your system. I used Joel’s answer to address your question.
Pandas subplots=True will arange the axes in a single column. import numpy as np import matplotlib.pyplot as plt import pandas as pd df = pd.DataFrame(np.random.rand(7,20)) df.plot(subplots=True) plt.tight_layout() plt.show() Here, tight_layout isn’t applied, because the figure is too small to arange the axes nicely. One can use a bigger figure (figsize=(…)) though. In order to have … Read more
You can use plt.tick_params(axis=”y”, which=”minor”) to set the minor ticks on and format them with the matplotlib.ticker FormatStrFormatter. For example, import numpy as np import matplotlib.pyplot as plt from matplotlib.ticker import FormatStrFormatter x = np.linspace(0,4,1000) y = np.exp(x) plt.plot(x, y) ax = plt.gca() ax.set_yscale(‘log’) plt.tick_params(axis=”y”, which=”minor”) ax.yaxis.set_minor_formatter(FormatStrFormatter(“%.1f”)) plt.show()
You can use plt.tick_params(axis=”y”, which=”minor”) to set the minor ticks on and format them with the matplotlib.ticker FormatStrFormatter. For example, import numpy as np import matplotlib.pyplot as plt from matplotlib.ticker import FormatStrFormatter x = np.linspace(0,4,1000) y = np.exp(x) plt.plot(x, y) ax = plt.gca() ax.set_yscale(‘log’) plt.tick_params(axis=”y”, which=”minor”) ax.yaxis.set_minor_formatter(FormatStrFormatter(“%.1f”)) plt.show()
For a future myself, here is what I ended up with: def generate_video(img): for i in xrange(len(img)): plt.imshow(img[i], cmap=cm.Greys_r) plt.savefig(folder + “/file%02d.png” % i) os.chdir(“your_folder”) subprocess.call([ ‘ffmpeg’, ‘-framerate’, ‘8’, ‘-i’, ‘file%02d.png’, ‘-r’, ’30’, ‘-pix_fmt’, ‘yuv420p’, ‘video_name.mp4’ ]) for file_name in glob.glob(“*.png”): os.remove(file_name)
If you use a list containing the column name(s) instead of a string, data.set_index will work The following should show the dates on x-axis: #! /usr/bin/env python import pandas as pd import matplotlib.pyplot as plt data = pd.read_fwf(‘myfile.log’,header=None,names=[‘time’,’amount’],widths=[27,5]) data.time = pd.to_datetime(data[‘time’], format=”%Y-%m-%d %H:%M:%S.%f”) data.set_index([‘time’],inplace=True) data.plot() #OR plt.plot(data.index, data.amount)
To get transformations from the Axes instance ax, you can use axis_to_data = ax.transAxes + ax.transData.inverted() points_data = axis_to_data.transform(points_axis) data_to_axis = axis_to_data.inverted() numpy.testing.assert_allclose(points_axis, data_to_axis.transform(points_data))
This behavior is documented in matplotlib FAQ here. The same page suggests to install Mayavi which works OK with 3D plots. Its interface is quite similar to matplotlib. Its main problem is that it’s still tricky to install it on python 3. (got much easier now) Here’s a demo “matplotlib vs mayavi” comparison: # generate … Read more
What worked for me was adding this (as per: How to set the line width of error bar caps, in matplotlib): (_, caps, _) = plt.errorbar(x,y, yerr=err, capsize=20, elinewidth=3) for cap in caps: cap.set_color(‘red’) cap.set_markeredgewidth(10)