Mailman 3 November 2015 - Matplotlib-devel

matplotlib meeting minutes 2015-11-30
by Michael Droettboom Nov. 30, 2015

Nov. 30, 2015

In attendance: Michael Droettboom, Thomas Caswell, Eric Firing, Jens Nielsen The 2.0 style work has opened up a can of worms. When the colors are changed to better defaults, the places where those defaults are not used become more obvious. In fixing those places, it became more obvious that there are shortcomings in how property cyclers are exposed and implemented. Issue #5584 illustrates some of the pain points, and #5577 is a As an action plan: - Expose more about the current property cycler on a given Axes object, so it can be introspected and used as the basis for another cycler. - Cyclers on an Axes should always have colors, even if that color is just black, so that plot types that only care about color can always use it to access a list of colors. - We should provide a mechanism to specify a color that is the nth color in the current property cycle (in the global rcParam). This will go a long way to keeping things that were easy before easy. Cheers, Mike -- Michael Droettboom Continuum Analytics

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Meeting minutes 2015-11-16
by Michael Droettboom Nov. 16, 2015

Nov. 16, 2015

Weekly matplotlib meeting 2015-11-16 In attendance: Michael Droettboom, Thomas Caswell, Eric Firing, Jens Nielsen We spent the call going over the effects of the proposed style changes for 2.0. (mdboom/style-change branch) - Size of grid lines may be too thin. Need to experiment there. - Tick lines should be shorter. They interfere badly with minus signs, particularly when on the right hand side y-axis. They can run into other subplots when close together. - Various things were broken by turning of edgecolor for all patches (including unfilled ones) - We should use Vega category 10 colors for the color cycle, rather than ColorBrewer Dark2. The former is slightly better for color-blindness, though nothing is really great in that regard. - The errorbar arrows don't look right when the errorbar line width is increased. We could use arrowheads that are centered around the base rather than the tip to get around this. - The number of ticks should be relative to the length of an axis, not fixed as it is now [#5488] - It would be really nice to easily specify a color that comes from the color cycle in the currently active style. We proposed a string syntax of `[n]` where `n` is the index in the color cycle. This could be used as `plot(x, y, '[1]o-')` for example. [#5489] - The new default mathtext that uses DejaVu is nice as it's consistent with all other text, but has some ways in which it is not as legible as Computer Modern. In particular, the integral sign is way too small. Placement and size of sub/superscript isn't great, but could probably be tweaked fairly easily. [#1097] - We need Numpy datetime dtype support. [#1097] - It was noted that the fact that we do image interpolation in RGB space [#5490] - We decided that we probably don't need to address [#1099] about scatter marker size scaling for 2.0. However, as we think about generalizing the color map norms we have now to apply to other dimensions, that would be a good solution to that problem. - The clippedline.py example is now handled internally and should be removed. [#5491] Cheers, Mike

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Use of contourf changed?
by Viktoria Schubert Nov. 13, 2015

Nov. 13, 2015

Hello together, Since the 1.5 version of matplotlib together with python 3.5, I get strange results from using the contourf function. What I'm actually doing is plotting a lot of points in a 2D-space. The density equals the likelihood of the data, so what I want is to plot the 1Sigma, 2Sigma and 3Sigma areas of the data. For this purpose, I was using the following line of code: cs = plt.contourf(hist2D, extent=extent, levels=[L1,L2,L3,Lmin], linestyles=['-','-','-','-'], colors=['blue','blue','blue'], alpha=0.25) The problem now is, that with matplotlib 1.4, the resulting image shows the most dense area in white instead of darker blue, which was the previous behavior. I did not change my code, I can get the old result simply by moving back to 1.4. I link to the two pictures I created, so maybe one of you can explain me, what changed with the version so that the plot is getting messed up. V1.4: http://i.imgur.com/HH7jKBE.png V1.5: http://i.imgur.com/0LZ9Tso.png Thank you very much, Viktoria

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protected branches on GH
by Thomas Caswell Nov. 9, 2015

Nov. 9, 2015

Hey all, GH now lets you protect branches which prevents people from force-pushing or deleting those branches; https://github.com/matplotlib/matplotlib/settings/branches. I have protected our 'production' branches. There is also an option to require status checks to pass before merging to a given branch, but that would break our current cherry-pick workflow and mean that we could not use our judgement to over-rule travis.ci ;) Tom

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Non-orthogonal axis plots
by Nag Shankha Nov. 6, 2015

Nov. 6, 2015

Hello, I am required to do some contour or surface plots where the x and y axes are non-orthogonal. It might appear trivial because a non-orthogonal contour section is a part of the orthogonal coordinate system. But the problem is, for contour and surface plots with orthogonal coordinates we need a rectangular mesh. However if the mesh of datapoints are not rectangle we cannot use the contourf type functions. So a routine is required to be able to draw contours and surfaces for non-rectangular mesh (or non-orthogonal coordinate system). Moreover the problem with not having a roitine for ternary plots (posted several times) is a special case of the aforesaid issue. Thus I request the developers of matplotlib to develop a routine to address this problem. Let me know your opinion at your earliest convenience. Sincerely, Shankha Shankha Nag, Doctoral Assistant EPFL STI IGM LAMMM ME A1 398 Station 9 CH-1015 Lausanne

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Include plot example: a 3D Barplot with a line
by Elias Estatistics Nov. 3, 2015

Nov. 3, 2015

Dear developers, I wrote a code (using different examples), that is plotting together: a 3d barplot with a smooth line that follows the "points" of bars. I could not find something similar, therefore, i am sending you this example. You may edit some sections if you find something wrong. Sorry for my english - not my primary language. #The following plot presents a 3d barplot together with a smooth line that follows the "points" of bars. #needed modules import csv import pandas as pandas import numpy as np from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt from matplotlib import cm from scipy.interpolate import spline #here, we are importing a simple datafile, csv type. data are comma seperated #Also, we are importing only 1st, 2nd and 4th columns. We can call these data now by thie new name, 'io'. #io = pandas.read_csv('test.csv', sep=",",usecols=(1,2,3,4)) #not available #io = io[np.isfinite(io['alice'])] #not available #We define a new name for the column that is named 'alice' inside 'io' dataset. result=([9,6,14,7,16,4,14,19,16,7,13,10,15,16,19,27,7,6]) #we need to make these data 'array type' result = np.array(result) #we now define the dimensions of our figure/plot, as well its dpi fig=plt.figure(figsize=(14, 6), dpi=150) #This line defines our first plot, afterwards, the '112' will define our second plot. ax1=fig.add_subplot(111, projection='3d') #we define here labels xlabels =np.array([9,6,14,7,16,4,14,19,16,7,13,10,15,16,19,27,7,6]) xpos = np.arange(xlabels.shape[0]) ylabels = np.array(['series A']) ypos = np.arange(ylabels.shape[0]) xposM, yposM = np.meshgrid(xpos, ypos, copy=False) zpos=result zpos = zpos.ravel() #this defines the dimensions of the actual boxes, you can play with these values. dx=0.7 dy=0.7 dz=zpos #here, we define ticks, they are placed in the 'middle' of each bar ax1.w_xaxis.set_ticks(xpos + dx/2.) ax1.w_xaxis.set_ticklabels(xlabels) ax1.w_yaxis.set_ticks(ypos + dy/2.) ax1.w_yaxis.set_ticklabels(ylabels) #here we define the colors of the bars, rainbow style, you can play with these numbers values = np.linspace(0.2, 1., xposM.ravel().shape[0]) colors = cm.rainbow(values) #figure subtitle fig.suptitle('test title', fontsize=20) #here, we define in the x axis the size of its ticks, its numbers ax1.tick_params(axis='x', which='major', pad=15,labelsize=6) #Here, we define the limits of y axis, #NOTE that this defines WHERE bars will be placed IN relation to the rest figure, their offset point plt.ylim((-2,5)) #this says if the grid will be printed plt.grid(True) #this defines the placement of the 3d plot in its placeholders, in the surrounding white space plt.subplots_adjust(left=0.0, right=1.0, bottom=0.0, top=1.0) #this is the actual command to define the plot, all 6 parameters that we previously defined, are placed here. #colors is an extra parameter ax1.bar3d(xposM.ravel(), yposM.ravel(), dz*0, dx, dy, dz, color=colors) #here we define that we will place a second plot ax1=fig.add_subplot(112, projection='3d') #here we say to produce numbers from 0 according to how many data exist in our 'result' x=np.arange(0, len(result)) #here, i apply a math trick, in order to get the line a little higher, out of the bars x=x+0.4 y=result #here, i try to center the linein relation to the top of bars. y=y+5 #here, i try to produce more points in order to make the line to look nicer. x_smooth=np.linspace(x.min(),x.max(), 100) y_smooth=spline(x,y, x_smooth) #here, i try to produce a 'z' array of so many zeros as the length of 'x_smooth' z=np.linspace(0, 0, len(x_smooth)) #here, we define the parameters of the second plot. #note that 'ax1' symbol is duplicated, in order to plot the line in the same plot with the barplot. ax1.plot(x_smooth,z,y_smooth) #here, finally, we will check our results, visually! plt.show() ……………………………………………………………………………………………………….. For any question, request, comment, or statistical analysis you can communicate directly with me by using: ………………………………………………………………………………………………………… *estatisticseu(a)gmail.com* ………………………………………………………………………………………………………… Statistical Articles: eXplained Statistics <http://estatistics.eu/articles/> EstatisticsEU Facebook page to find some other statistical goodies! <https://www.facebook.com/estatisticseu/>

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