Mailman 3 March 2017 - Matplotlib-users

Arbitrary artist data on SVG elements
by Joshua Klein April 3, 2017

April 3, 2017

Hello, I often embed figures as SVG graphics in web pages. As part of this process, I usually do the following 1. Set gids on artists and link that gid to a set of data describing that part of a graphic in an external dictionary. This includes things like setting the element’s class, extra contextual information, information that would be good to show in a tooltip, ids of related elements, and so forth. 2. Serialize the figure into a file-like object, use an element tree implementation’s XMLID to get an element id map and Element objects 3. Iterate over my data dictionary from (1) and set keys in the mapped Element’s attrib dictionary, using the id map from (2) 4. Use the element tree implementation’s tostring function to serialize the updated Element objects back into a string and then send the string out as a response to a web request. 5. After receiving the SVG string from the server on the client, add the SVG to the page’s DOM and then hang event handlers on it (or pre-specify delegated handlers) that use the added attributes to configure interactive behavior. I looked at the Artist type and saw no good place to store “arbitrary data”. Before I start working on this I wanted to know if anyone else had a better solution. I would also like to know if the devs would be opposed to a PR that adds an extra dictionary/attribute to every Artist instance created. Another alternative solution would be to find a way to push my dictionary mapping gids to extra attributes into the SVGRenderer and have it pass them as **extras to XMLWriter.element when it processes individual artists. Here’s a generic example of what I do currently: def plot_with_extras_for_svg(*data, **kwargs): # Do the plotting, generating the id-linked data in `id_mapper` ax, id_mapper = plot_my_data(*data, **kwargs) xlim = ax.get_xlim() ylim = ax.get_ylim() # compute the total space used in both dimensions when dealing with # negative axis bounds x_size = sum(map(abs, xlim)) y_size = sum(map(abs, ylim)) # Map the used axis space to the drawable region dimensions aspect_ratio = x_size / y_size canvas_x = 8. canvas_y = canvas_x / aspect_ratio # Configure the artist to draw within the new drawable region bounds fig = ax.get_figure() fig.tight_layout(pad=0.2) fig.patch.set_visible(False) fig.set_figwidth(canvas_x) fig.set_figheight(canvas_y) ax.patch.set_visible(False) # Perform the first serialization buff = StringIO() fig.savefig(buff, format='svg') # Parse XML buffer from `buff` and configure tag attributes root, ids = ET.XMLID(buff.getvalue()) root.attrib['class'] = 'plot-class-svg' for id, attributes in id_mapper.items(): element = ids[id] element.attrib.update({("data-" + k): str(v) for k, v in attributes.items()}) element.attrib['class'] = id.rsplit('-')[0] # More drawable space shenanigans min_x, min_y, max_x, max_y = map(int, root.attrib["viewBox"].split(" ")) min_x += 100 max_x += 200 view_box = ' '.join(map(str, (min_x, min_y, max_x, max_y))) root.attrib["viewBox"] = view_box width = float(root.attrib["width"][:-2]) * 1.75 root.attrib["width"] = "100%" height = width / (aspect_ratio) root.attrib["height"] = "%dpt" % (height * 1.2) root.attrib["preserveAspectRatio"] = "xMinYMin meet" # Second serialization svg = ET.tostring(root) plt.close(fig) return svg Thank you

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bug for savefig to eps in the latest version of matplotlib
by Xu, Bin April 3, 2017

April 3, 2017

Dear all, I recently update my matplotlib to the latest version, and found pyplot.savefig to eps file didn't work well like previous version. Test script: ================================== #!/usr/bin/python -tt from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import numpy as np # normal distribution center at x=0 and y=5 x = np.random.randn(100000) y = np.random.randn(100000) + 5 plt.hist2d(x, y, bins=40, norm=LogNorm()) plt.colorbar() #plt.show() plt.savefig('test.eps') #plt.savefig('test.png') ================================== The result of plt.show() is just like the result of test.png, while test.eps has a weired large file size (more than 20 MB in other case). In the meantime, the facecolor for 'NaN' value in test.eps is black, different from test.png and previous version. I consider it is a bug here. How to reach the desired style (regular size, and white color for 'NaN' value in image)? Thanks a lot. -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Xu,Bin

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How is GUI startup implemented?
by Jean CRUYPENYNCK April 1, 2017

April 1, 2017

Hi all! I’m currently writing some Python/C++ library using Boost.Python on macOS. When I import it, I get the “famous rocket icon“ bouncing in the macOS Dock and I can’t understand why. I know that this icon appears on macOS when Python detects that a program will interact with the GUI. It’s something that happens with matplotlib as well, when running from matplotlib import pyplot with the default backend. I’m wondering how this behavior is implemented, i.e. where in the source code does matplotlib call the GUI process when I execute the from matplotlib import pyplot statement? I believe that could help me solving my problem with Boost.Python. Thank you in advance for your answer, Jean.

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plotting netcdf data on a map in matplotlib
by Jason Snyder March 30, 2017

March 30, 2017

I am trying to plot data from a netcdf file onto a map through matplotlib. I initially tried to use basemap but then someone suggested that I use cartopy based on the website: http://scitools.org.uk/cartopy. Either way I am able to generate the map fine but am having difficulty plotting the data successfully. For instance using basemap I wind up getting a weird plot of data that does not make sense. Also how to I go about plotting the netcdf data around the dateline? Attached is the ncdump output (gridoutput.txt) for the netcdf file in terms of the grid and also the script that I am using to try to plot the data (netcdfplot.py). Can anyone give me some suggestions on how to go about fixing the errors that I have in the script? -Jason

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segmentation fault problems with basemap
by Jason Snyder March 30, 2017

March 30, 2017

Whenever I run a python with basemap I get a segmentation fault error. For example when running: from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt # setup Lambert Conformal basemap. # set resolution=None to skip processing of boundary datasets. m = Basemap(width=12000000,height=9000000,projection='lcc', resolution=None,lat_1=45.,lat_2=55,lat_0=50,lon_0=-107.) # draw a land-sea mask for a map background. # lakes=True means plot inland lakes with ocean color. m.drawlsmask(land_color='coral',ocean_color='aqua',lakes=True) plt.show() I get a segmentation fault core dump error. I have done some reading online about this and they say that it could be because I accidently installed two versions of geos ( http://trac.osgeo.org/geos/), which include geos.3.3.6 and geos.3.3.3 which was already available in the basemap package. Could this be the problem that is creating the segmentation fault core dump? If so how do I uninstall the geos libraries that I do not need. There is not a lot of specific information on how to uninstall geos libraries and I really need a specific guide on how to do this and get the above script working properly. -- Jason Snyder PhD

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basemap projection issues
by Jason Snyder March 30, 2017

March 30, 2017

I am trying to draw a map across the Atlantic Ocean from the eastern seaboard to Europe. I also want to draw a map across the Pacific Ocean including both sides of the dataline using the basemap utility. Unfortunately there is very limited information on how to do this. I am not sure what project would be best. For instance I want to draw a map with a latitude from 20 to 50 degrees and a longitude from 40 E to 120 W. Using the basemap utility how do I go about doing this? -- Jason Snyder PhD

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Making a solid legend
by Florian Lindner March 23, 2017

March 23, 2017

Hello, I try to make the legend non-translucent, having a solid color and laying above the plot itself. That's what I try: import matplotlib.pyplot as plt fig, ax1 = plt.subplots() ax1.plot([0, 1], [0, 2], label = "Plot 1") leg = ax1.legend(loc = 1) leg.get_frame().set_facecolor("r") leg.set_alpha(0) plt.grid() plt.show() (you may have to pan the plot) Taken from: http://matplotlib.org/examples/api/legend_demo.html http://matplotlib.org/1.3.0/examples/pylab_examples/legend_translucent.html The set_alpha seems to have no effect at all. facecolor works, but it is always translucent. Thanks, Florian

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axes properties
by Jean-Philippe GRIVET March 22, 2017

March 22, 2017

Thank you so much Vincent (and Amit) for your detailed answers. I have two questions remaining. 1. All the code examples that I have looked at (including the "getting started" example on page 1798 of the user manual for Matplotlib 2.0) uses the "add_subplot" metod. Why should a 3d projection be included in a subplot and not in a plot ? 2. In all the examples, the figure seems to be viewed down the 1,1,1 direction towards the origin. How can the view point be changed from within the program ? Thanks again, Jean-Philippe

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(no subject)
by Gbadamosi Farouk March 21, 2017

March 21, 2017

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axes properties
by Jean-Philippe GRIVET March 15, 2017

March 15, 2017

Hello, I have two questions related to axes in a 3-D plot. 1. I often find that ticks are too close together so that labels overlap. How can I change the number of ticks or the step length ? 2. The default axes lay-out has x increasing from left to right and y increasing from front to back. My finction decrease quickly with increasing y, so that the corresponding parts of the surface are hidden. Therefore, I would like to have y values increasing from back to front. I understand that yaxis_invert can do it but how is it used ? Thank you for your help Jean-Philippe

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