Mailman 3 July 2016 - Matplotlib-users

mpl governance
by Thomas Caswell Aug. 30, 2016

Aug. 30, 2016

Folks, During the sprints at SciPy Mike Droettboom and I started to adapt the Jupyter governance documents for matplotlib. We are doing this in the open, and a pull request of the changes is at: https://github.com/matplotlib/governance/pull/1 To be clear, this is a work in progress and is not final or in force. We propose the mechanism for ratifying the these documents is to, after input from the community, have the current "matplotlib Executive Committee" (the signers of the FSA with NUMFocus) vote. Tom ps A very similar email was sent to the matplotlib-devel list on July 17

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Optimally displaying 7 X-Y plots in one window
by Jon Roadley-Battin Aug. 1, 2016

Aug. 1, 2016

Good evening, I manage a small application where I work that captures data from some bespoke hardware (via USB) and if so which, plot 7 channels. At the moment I just plot the 7 waveforms, with a bit of decorating and for the most part this is fine. There are some relatively common occurrences of data which makes viewing within the application ... tricky Say for insta

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Re: [Matplotlib-users] [Matplotlib-announce] mpl governance
by Thomas Caswell July 31, 2016

July 31, 2016

I am not sure what you mean. On Sun, Jul 31, 2016, 18:35 Dipankar “Dipu” Ganguly <dipugee(a)gmail.com> wrote: > How will this impact on the image processing aspects? > > Dipu > > > Dipankar Ganguly > Consultant: Strategy/Technology/Commercialization > Bothell, WA > Cell: 408-203-8814 > email: dipugee(a)gmail.com > http://www.linkedin.com/in/dipugee > > > > > > > > > > On Jul 31, 2016, at 3:32 PM, Thomas Caswell <tcaswell(a)gmail.com> wrote: > > Folks, > > During the sprints at SciPy Mike Droettboom and I started to adapt the > Jupyter governance documents for matplotlib. > > We are doing this in the open, and a pull request of the changes is at: > > https://github.com/matplotlib/governance/pull/1 > > To be clear, this is a work in progress and is not final or in force. > > We propose the mechanism for ratifying the these documents is to, after > input from the community, have the current "matplotlib Executive Committee" > (the signers of the FSA with NUMFocus) vote. > > Tom > > ps A very similar email was sent to the matplotlib-devel list on July 17 > > > ------------------------------------------------------------------------------ > _______________________________________________ > Matplotlib-announce mailing list > Matplotlib-announce(a)lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/matplotlib-announce > > >

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Data representation improvements
by Jon Roadley-Battin July 31, 2016

July 31, 2016

Good evening. For some time now I have managed an application which acquires data from bespoke hardware & optionally plots upto seven channels. Matplotlib is embedded within a QT4 application & the "plotting" interface is almost fully for matplotlib. I have managed to free up some spare time from my main role & I would like to vastly improve the representation of the data to the users in light of some in-use observation. The data is being acquired from a motor-drive & so there is some sinus data ( the motor's currents... 50Hz, 100A peak), data like speed (mostly DC around 3000rpm with some ripple...) and then some digital flag (16bit data...) The fixed point data are (luckily) all relatively low in magnitude ... and so plotting a 3000-value and a 100pk value is still visible, be it small amplitude BUT the moment I plot one of the digital flag registers, these dwarf the fixed-point data. To help with the I have provided means to "hide" channels but I was wondering if there is any better way? I have considered 7 stacked x-y subplots but this will eat into vertical re-estate very quickly. I have played with secondary y-axis but this equally ends up taking a lot of horizontal re-estate. I have considered plotting the digital registers akin to a digital vector so they are small, heightwise, plots as this will alleviate part of the concerns. Any advice with regards to a usable presentation Example code for multiple y-axis import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1 import host_subplot import mpl_toolkits.axisartist as AA import numpy as np t = np.arange(0,1,0.00001) data = [5000*np.sin(t*2*np.pi*10), 10*np.sin(t*2*np.pi*20), 20*np.sin(t*2*np.pi*30), np.sin(t*2*np.pi*40)+5000, np.sin(t*2*np.pi*50)-5000, np.sin(t*2*np.pi*60), np.sin(t*2*np.pi*70), ] fig = plt.figure() host = host_subplot(111, axes_class=AA.Axes) axis_list = [None]*7 for i in range(len(axis_list)): axis_list[i] = host.twinx() new_axis = axis_list[i].get_grid_helper().new_fixed_axis axis_list[i].axis['right'] = new_axis(loc='right', axes=axis_list[i], offset=(60*i,0)) axis_list[i].axis['right'].toggle(all=True) axis_list[i].plot(t,data[i]) plt.show() for i in data: plt.plot(t,i) plt.show() Yours, JonRB

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post
by Noll July 31, 2016

July 31, 2016

To anyone interested in developing democratic software, consider reading: https://amodicumofdemocracy.wordpress.com/2016/07/29/population-based-democ… and post any comments, questions, or concerns. -- Noll Roberts nollroberts(a)gmail.com

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Call for Abstracts: AMS 2017 Python Symposium
by Hannah July 29, 2016

July 29, 2016

The Core Science Keynote for the AMS2017 Python Symposium will be given by matplotlib contributor Ryan May (@dopplershift). The Seventh Symposium on Advances in Modeling and Analysis Using Python, sponsored by the American Meteorological Society, and organized by the AMS Board on Environmental Information Processing Technologies, will be held 22–26 January 2017, as part of the 97th AMS Annual Meeting in Seattle, Washington. Preliminary programs, registration, hotel, and general information will be posted on the AMS Web site <http://www.ametsoc.org/meet/annual/> in late-September 2016. Abstract submissions for the Python Symposium should be submitted via the AMS web site by 3 August 2016. The theme for the 2017 AMS Annual Meeting is “Observations Lead the Way”. For the full description of the theme, please visit the AMS 2017 Annual Meeting Web page <http://annual.ametsoc.org/2017/>. Papers for this conference are solicited on - Modeling - Time series analysis - Air quality - Satellite data processing - In-situ data analysis - GIS - Python as a software integration platform - Visualization - Gridding - Model intercomparison and ensembles - Data intensive computation and big data - Teaching and learning Python - Connecting Python with other languages The $95 abstract fee includes the submission of your abstract, the posting of your extended abstract, and the uploading and recording of your presentation which will be archived on the AMS Web site. Please submit your abstract electronically via the AMS web site <https://annual.ametsoc.org/2017/index.cfm/programs/conferences-and-symposia…> by 3 August 2016. An abstract fee of $95 (payable by credit card or purchase order) is charged at the time of submission (refundable only if abstract is not accepted). Authors of accepted presentations will be notified via e-mail by late-September 2016. All extended abstracts are to be submitted electronically and will be available on-line via the Web. Instructions for formatting extended abstracts will be posted on the AMS Web site. Authors have the option to submit manuscripts (up to 10 MB) electronically by 27 February 2017. All abstracts, extended abstracts and presentations will be available on the AMS Web site at no cost. For additional information please contact the program chairperson(s), Johnny Lin (email:johnny@johnny-lin.com)

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New "data cursors for matplotlib" package: https://github.com/anntzer/mplcursors
by Antony Lee July 28, 2016

July 28, 2016

Hi all, I have written a new "data cursors for matplotlib" package, inspired from @joferkington's mpldatacursor but rewritten from scratch, with a much simpler API. Please have a look at https://github.com/anntzer/mplcursors . I hope you'll like it. Antony

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getting extra colors in figure
by Smit, Christine E. (GSFC-610.2)[TELOPHASE CORP] July 26, 2016

July 26, 2016

Hi! I’m trying to plot data using imshow and ending up with more colors than I specify in the colormap. This is causing problems with a downstream user who is machine reading images to determine approximate data values based on colors. I think the issue has something to do with rounding because many colors in the image are only slightly off from the colors I specified. My plots have exactly one pixel per data value, so each pixel¹s color should be exactly a color in the colormap. Here is an example that replicates the issue: ---------------------------------- import numpy as np; import matplotlib.pylab as plt import matplotlib.colors as colors from PIL import Image import random ####### # Create some simple data ####### shape = (8,12) npixels = shape[0]*shape[1] data_min = 0.5 data_max = 11.5 data = np.reshape(np.linspace(data_min,data_max,npixels),shape) # mask out a quarter of the pixels mask = np.reshape([False]*npixels,shape) half_width = int(shape[0]/2) half_height = int(shape[1]/2) mask[0:half_width,0:half_height] = True; ma = np.ma.MaskedArray(data = data,mask = mask) ####### # Create a color palette with three colors, one 'over' color, and one 'under' color ####### # define some colors in hex hex_colors = { 'red':'#ff0000', 'orange':"#ffa500", 'yellow': "#ffff00", 'green': "#00ff00", 'blue' : "#0000ff" } # Create the colormap cmap = colors.ListedColormap([hex_colors['orange'],hex_colors['yellow'],hex_colors ['green']]) cmap.set_under(color=hex_colors['red']) cmap.set_over(color=hex_colors['blue']) # Calculate boundaries between colors so we get some data in every color thresholds = np.linspace(data_min+1,data_max-1,cmap.N+1) norm = colors.BoundaryNorm(thresholds, cmap.N) ####### # Make a plot. The aim here is to have one data point per pixel in the output file with no # decorators. ####### # Step 1: turn off the frame fig = plt.figure(frameon=False) # Step 2: set the size of the image in inches to the number of data points. Note: the size is # specified width, height. fig.set_size_inches(shape[1], shape[0]) # Step 3: create Axes ax = plt.Axes(fig, [0., 0., 1., 1.]) # Step 4: turn off drawing axes ax.set_axis_off() # Step 5: add the axes to the figure fig.add_axes(ax) # Step 6: plot ax.imshow(ma, # Don't interpolate. There's no need to interpolate because we have # exactly as many data points as pixels. interpolation='none', # Use our 3-color (+1 above, +1 below) colormap cmap=cmap, # Use thresholds that will spread the data over all colors norm=norm, # Match image aspect ratio to axes aspect='auto', # Put ma[0,0] on the lower left origin='lower', # Lower left and upper right corners are at the edge of the image extent=(0,1,0,1) ) # Step 7: Save to file. fig.savefig('out.png', # Set dots per square inch (dpi) to 1. Since there is 1 data point # per inch, I should get an image with 1 data point for each pixel. dpi=1, # Set transparent to True so the masked data is transparent. transparent=True ) ####### # Open the image and see what colors are inside. I expect to see 6 colors: my 5 listed colors # and 1 transparent color for the masked data. ####### # Load the image im = Image.open("out.png") # Confirm that the image is the right size. Note: once again, the image size is width, height # while the data array is arranged height, width. print "Image size(should be %d x %d): %d x %d\n" %( shape[1], shape[0], im.size[0], im.size[1]) # Throw all the pixels, which are (red,green,blue,alpha) tuples, into a list pix = im.load() all_pix = [] width = im.size[0] for i in range(im.size[0]): for j in range(im.size[1]): all_pix.append(pix[i,j]) # Get a unique list of pixels unique = list(set(all_pix)) unique.sort() print "Unique pixels values in file:\n" for u in unique: print "%.2x %.2x %.2x (alpha - %.2x)" % (u[0],u[1],u[2], u[3]) print "\nNumber of unique colors (should be 6): %d" % len(unique) ---------------------------------- The output I get from this script is: ---------------------------------- Image size(should be 12 x 8): 12 x 8 Unique pixels values in file: 00 00 ff (alpha - ff) 00 fe 01 (alpha - ff) 00 ff 00 (alpha - ff) 01 00 00 (alpha - 01) 01 01 00 (alpha - 01) 02 02 00 (alpha - 02) fd ff 00 (alpha - ff) fe ff 00 (alpha - ff) ff 01 00 (alpha - ff) ff a5 00 (alpha - ff) ff a6 00 (alpha - ff) ff ff 00 (alpha - ff) ff ff ff (alpha - 00) Number of unique colors (should be 6): 13 ---------------------------------- Note the weird colors that weren¹t in my colorbar. E.g. - 00 fe 01 (alpha - ff) --> close to 00 ff 00 (green) 01 00 00 (alpha - 00) --> close to transparent ff a6 00 (alpha - ff) --> close to ff a5 00 (orange) I am running Python 2.7.11 :: Anaconda 2.4.0 (64-bit) and matplotlib version 1.4.3. I tried matplotlib 1.5 and it had the same issue. Thanks. Christine

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Image full screen
by Piter_ July 17, 2016

July 17, 2016

Hi list. I wander if it is possible to show a full screen image using matplotlib? Only image without axis axis, window frame and so on.... ------ from numpy import random import matplotlib.pyplot as plt import matplotlib as mpl mpl.rcParams['toolbar'] = 'None' plt.ion() fig = figure() data = random.random((600,800)) img = plt.imshow(data, interpolation='nearest') img.set_cmap('hot') plt.axis('off') plt.tight_layout() plt.subplots_adjust(left=0, right=1, top=1, bottom=0) figManager = plt.get_current_fig_manager() figManager.window.showMaximized() -------- This part kind of works. There are only two problems. 1 I have to hit f button (full screen) to remove window title, 2 There still a gray part on the bottom, where mouse XY coordinates are usually shown. Any recommendations how to get rid of those? Thank you Petro

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Spikes in small wedges of a pie chart
by Torsten Bronger July 15, 2016

July 15, 2016

Hallöchen! http://wilson.bronger.org/software.svg exhibits ugly spikes in the small wedges of this pie chart. After importing the SVG into Inkscape, the problem is even worse but can be fixed by changing the style of line vertices to "rounded". It may be a bug in the SVG-displaying software, however, maybe there is a workaround? I use Python3 + matplotlib 1.5.1. The code is: from matplotlib import pyplot import matplotlib figure = pyplot.figure() axes = figure.gca() axes.pie([23, 22, 2, 42, 1, 34, 17, 21, 15, 290, 897], labels=["Fedora", "CKAN", "dLibra" + 8 * " ", "DSpace", "eSciDoc", "DataVerse", "EPrints", "MySQL", "Nesstar", "sonstige", "unbekannt"], explode=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0.15], wedgeprops={"clip_on": True, "capstyle": "round"}, shadow=True, startangle=150) axes.set_aspect("equal") figure.savefig("software.svg", bbox_inches="tight", transparent=True) Any help or workaround is appreciated. Thank you! Tschö, Torsten. -- Torsten Bronger Jabber ID: torsten.bronger(a)jabber.rwth-aachen.de

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