As a workaround, you could set the direction of the z-label manually by: ax.zaxis.set_rotate_label(False) # disable automatic rotation ax.set_zlabel(‘label text’, rotation=90) Please note that the direction of your z-label also depends on your viewpoint, e.g: import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D fg = plt.figure(1); fg.clf() axx = [fg.add_subplot(4,1,1+i, projection=’3d’) for i in range(4)] … Read more
Matplotlib isn’t optimized for this. You’d be a bit better off with simpler options if you just want to display an image at one-pixel-to-one-pixel. (Have a look at Tkinter, for example.) That having been said: import matplotlib.pyplot as plt import numpy as np # DPI, here, has _nothing_ to do with your screen’s DPI. dpi … Read more
Heh. That’s because there is no AxesSubplot class.. until one is needed, when one is built from SubplotBase. This is done by some magic in axes.py: def subplot_class_factory(axes_class=None): # This makes a new class that inherits from SubplotBase and the # given axes_class (which is assumed to be a subclass of Axes). # This is … Read more
Interestingly, I found quite nice and thorough solutions in ROS community. http://wiki.ros.org/docker/Tutorials/GUI For my problem, my final choice is the second way in the tutorial: docker run –rm -it \ –user=$(id -u) \ –env=”DISPLAY” \ –workdir=/app \ –volume=”$PWD”:/app \ –volume=”/etc/group:/etc/group:ro” \ –volume=”/etc/passwd:/etc/passwd:ro” \ –volume=”/etc/shadow:/etc/shadow:ro” \ –volume=”/etc/sudoers.d:/etc/sudoers.d:ro” \ –volume=”/tmp/.X11-unix:/tmp/.X11-unix:rw” \ deepaul python test.python
Here is my aim at a more complete answer including choosing the color map and a logarithmic normalization of the color axis. import matplotlib.pyplot as plt import matplotlib.cm as cm from matplotlib.colors import LogNorm import numpy as np x, y, z = np.loadtxt(‘data.txt’, unpack=True) N = int(len(z)**.5) z = z.reshape(N, N) plt.imshow(z+10, extent=(np.amin(x), np.amax(x), np.amin(y), … Read more
If you create a figure, you can get the available supported file format with the canvas object : import matplotlib.pyplot as plt fig = plt.figure() print fig.canvas.get_supported_filetypes() >>> { ‘svgz’: ‘Scalable Vector Graphics’, ‘ps’: ‘Postscript’, ’emf’: ‘Enhanced Metafile’, ‘rgba’: ‘Raw RGBA bitmap’, ‘raw’: ‘Raw RGBA bitmap’, ‘pdf’: ‘Portable Document Format’, ‘svg’: ‘Scalable Vector Graphics’, ‘eps’: … Read more
I preferred the answer of tacaswell in the comments, but had to search for it. Clean up your plots after you are done with them: plt.close(fig) or plt.close(‘all’)
It works for me (with pandas 0.16.2) if I set the x-axis limits using pd.Timestamp values. Example: import pandas as pd # Create a random time series with values over 100 days # starting from 1st March. N = 100 dates = pd.date_range(start=”2015-03-01″, periods=N, freq=’D’) ts = pd.DataFrame({‘date’: dates, ‘values’: np.random.randn(N)}).set_index(‘date’) # Create the plot … Read more
By using masked arrays you can avoid plotting selected regions of a curve. To remove the singularity at x=2: import matplotlib.numerix.ma as M # for older versions, prior to .98 #import numpy.ma as M # for newer versions of matplotlib from pylab import * figure() xx = np.arange(-0.5,5.5,0.01) vals = 1/(xx-2) vals = M.array(vals) mvals … Read more
Note that in recent versions of matplotlib you can achieve this using class matplotlib.legend_handler.HandlerTuple as illustrated in this answer and also in this guide: import matplotlib.pyplot as plt from matplotlib.legend_handler import HandlerTuple fig, ax1 = plt.subplots(1, 1) # First plot: two legend keys for a single entry p2, = ax1.plot([3, 4], [2, 3], ‘o’, mfc=”white”, … Read more