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From: Mathieu Leplatre <leplatre@gm...>  20080813 13:34:52

Hi all, I've searched in examples and archives and could not find anything about manual control of space between bars. By default, the bars in the following script overlap. So I guess the behaviour is : specify chart width (8in) + bar width (0.8) => auto bar space And I would like to know how to do : specify chart width + bar space => auto bar width specify bar space + bar width => auto chart width (fixed margins) But I can't figure it out, especially the latter. Can matplotlib.transforms help me about the former ? Do you have documentation reference or some hints about that please ? Thanks! I am plotting a chronological bar chart like this one : #!/usr/bin/env python import matplotlib, pylab, numpy import datetime def rangedates( hourstep ): dates = [] for d in range(1,31): for h in range(0,24,hourstep): dt = datetime.datetime(2008,06,d,h) dates.append(dt) return pylab.date2num(dates) # Plot value every 12H abscissa = rangedates(12) barstep = abscissa[1]  abscissa[0] barspace = 0.5 * barstep barwidth = barstep  barspace fig = pylab.figure() ax = fig.add_subplot(111) fmt = matplotlib.dates.DateFormatter('%b %d') ax.xaxis.set_major_formatter( fmt ) fig.autofmt_xdate() pylab.bar( abscissa, numpy.random.randn( len(abscissa) ), width = barwidth) pylab.show() 
From: Mathieu Leplatre <leplatre@gm...>  20080814 19:43:07

I am still investigating and I am stuck at converting transAxes values to data. If my axes goes from 10.0 to 20.0 then transAxes 0.5 should give me 15.0. This would allow me to compute bar width and space, since I am able to convert inches to transAxes values. I tried many combinations of transAxes, transData, ..., inverse_xy_tup, xy_tup, inverted(), transform(), ... without success :( Any ideas please ?  import pylab as P import matplotlib.transforms as T x = P.arange(5) y = P.rand(5) * 4 ax = P.subplot(1,1,1) P.plot(x,y) ax.set_ylim(0.0, 4.0) trans = ax.transAxes valx = valy = 0.5 actualcoords = "%s" % trans.transform([valx, valy]) #Gives me [328, 240] instead of [2.0, 2.0] ax.text(valx, valy, actualcoords, transform=trans) P.show()  On Wed, Aug 13, 2008 at 10:34 AM, Mathieu Leplatre <leplatre@...> wrote: > Hi all, > > I've searched in examples and archives and could not find anything > about manual control of space between bars. > > By default, the bars in the following script overlap. > > So I guess the behaviour is : > specify chart width (8in) + bar width (0.8) => auto bar space > > And I would like to know how to do : > specify chart width + bar space => auto bar width > specify bar space + bar width => auto chart width (fixed margins) > > But I can't figure it out, especially the latter. Can > matplotlib.transforms help me about the former ? > > Do you have documentation reference or some hints about that please ? > Thanks! > > I am plotting a chronological bar chart like this one : > > #!/usr/bin/env python > import matplotlib, pylab, numpy > import datetime > > def rangedates( hourstep ): > dates = [] > for d in range(1,31): > for h in range(0,24,hourstep): > dt = datetime.datetime(2008,06,d,h) > dates.append(dt) > return pylab.date2num(dates) > > # Plot value every 12H > abscissa = rangedates(12) > > barstep = abscissa[1]  abscissa[0] > barspace = 0.5 * barstep > barwidth = barstep  barspace > > fig = pylab.figure() > ax = fig.add_subplot(111) > fmt = matplotlib.dates.DateFormatter('%b %d') > ax.xaxis.set_major_formatter( fmt ) > fig.autofmt_xdate() > > pylab.bar( abscissa, numpy.random.randn( len(abscissa) ), > width = barwidth) > pylab.show() > 
From: JaeJoon Lee <lee.j.joon@gm...>  20080814 20:51:11

Hi Mathieu, It seems to me that you're confused with the meaning of the transAxes. It is a transform from the Axes coordinate to the Canvas(?) coordinate. As far as I can see, what you seemed to want is a transform between Data coordinate and Axes coordinate, and that would be transScale + transLimits (from Data to Axes). So, try trans = (ax.transScale + ax.transLimits).inverted() # trans = ax.transLimits.inverted() will also work in this case. and you will get 2.0, 2.0 as you expected. The "transform" argument should be transAxes still. ax.text(valx, valy, actualcoords, transform=ax.transAxes) As far as your original question is concerned, I have little idea what you are trying to do, so I'll leave that question to others. Regards, JJ On Thu, Aug 14, 2008 at 3:43 PM, Mathieu Leplatre <leplatre@...> wrote: > I am still investigating and I am stuck at converting transAxes values to data. > > If my axes goes from 10.0 to 20.0 then transAxes 0.5 should give me 15.0. > > This would allow me to compute bar width and space, since I am able to > convert inches to transAxes values. > > I tried many combinations of transAxes, transData, ..., > inverse_xy_tup, xy_tup, inverted(), transform(), ... without success > :( > > Any ideas please ? > >  > import pylab as P > import matplotlib.transforms as T > > x = P.arange(5) > y = P.rand(5) * 4 > ax = P.subplot(1,1,1) > P.plot(x,y) > ax.set_ylim(0.0, 4.0) > > trans = ax.transAxes > valx = valy = 0.5 > actualcoords = "%s" % trans.transform([valx, valy]) > #Gives me [328, 240] instead of [2.0, 2.0] > > ax.text(valx, valy, actualcoords, transform=trans) > P.show() > >  > > > > On Wed, Aug 13, 2008 at 10:34 AM, Mathieu Leplatre <leplatre@...> wrote: >> Hi all, >> >> I've searched in examples and archives and could not find anything >> about manual control of space between bars. >> >> By default, the bars in the following script overlap. >> >> So I guess the behaviour is : >> specify chart width (8in) + bar width (0.8) => auto bar space >> >> And I would like to know how to do : >> specify chart width + bar space => auto bar width >> specify bar space + bar width => auto chart width (fixed margins) >> >> But I can't figure it out, especially the latter. Can >> matplotlib.transforms help me about the former ? >> >> Do you have documentation reference or some hints about that please ? >> Thanks! >> >> I am plotting a chronological bar chart like this one : >> >> #!/usr/bin/env python >> import matplotlib, pylab, numpy >> import datetime >> >> def rangedates( hourstep ): >> dates = [] >> for d in range(1,31): >> for h in range(0,24,hourstep): >> dt = datetime.datetime(2008,06,d,h) >> dates.append(dt) >> return pylab.date2num(dates) >> >> # Plot value every 12H >> abscissa = rangedates(12) >> >> barstep = abscissa[1]  abscissa[0] >> barspace = 0.5 * barstep >> barwidth = barstep  barspace >> >> fig = pylab.figure() >> ax = fig.add_subplot(111) >> fmt = matplotlib.dates.DateFormatter('%b %d') >> ax.xaxis.set_major_formatter( fmt ) >> fig.autofmt_xdate() >> >> pylab.bar( abscissa, numpy.random.randn( len(abscissa) ), >> width = barwidth) >> pylab.show() >> > >  > This SF.Net email is sponsored by the Moblin Your Move Developer's challenge > Build the coolest Linux based applications with Moblin SDK & win great prizes > Grand prize is a trip for two to an Open Source event anywhere in the world > http://moblincontest.org/redirect.php?banner_id=100&url=/ > _______________________________________________ > Matplotlibusers mailing list > Matplotlibusers@... > https://lists.sourceforge.net/lists/listinfo/matplotlibusers > 
From: Tony S Yu <tonyyu@MIT.EDU>  20080814 22:26:25

On Aug 14, 2008, at 4:51 PM, JaeJoon Lee wrote: > Hi Mathieu, > > It seems to me that you're confused with the meaning of the transAxes. > It is a transform from the Axes coordinate to the Canvas(?) > coordinate. > As far as I can see, what you seemed to want is a transform between > Data coordinate and Axes coordinate, and that would be transScale + > transLimits (from Data to Axes). > > So, try > > trans = (ax.transScale + ax.transLimits).inverted() > # trans = ax.transLimits.inverted() will also work in this case. > > and you will get 2.0, 2.0 as you expected. > > The "transform" argument should be transAxes still. > > ax.text(valx, valy, actualcoords, transform=ax.transAxes) > > As far as your original question is concerned, I have little idea what > you are trying to do, so I'll leave that question to others. > > Regards, > > JJ > > > On Thu, Aug 14, 2008 at 3:43 PM, Mathieu Leplatre > <leplatre@...> wrote: >> I am still investigating and I am stuck at converting transAxes >> values to data. >> >> If my axes goes from 10.0 to 20.0 then transAxes 0.5 should give me >> 15.0. >> >> This would allow me to compute bar width and space, since I am able >> to >> convert inches to transAxes values. >> >> I tried many combinations of transAxes, transData, ..., >> inverse_xy_tup, xy_tup, inverted(), transform(), ... without success >> :( >> >> Any ideas please ? Hi Mathieu, I just wanted to add a little bit to JaeJoon's example. I feel like I have to relearn the axes transformations every time I deal with them. Your email reminded me to write things down, and I thought I'd share it, in case others find it useful. Let me know if anything is wrong/ unclear. Best, Tony ============================= Axes Transformations Tutorial ============================= The new transformations infrastructure is documented in the `new docs`_ (still in progress...), which talks about transformations *in general*. This document talks about transforms that are predefined attributes of `axes`. The following explanation is partially stolen from a mailing list reply by Michael Droettboom. In the following, data space the actual `x, y` input data coordinates axes space the axes coordinates which are `([0, 0], [1, 1])` at `([xmin,ymin], [xmax, ymax])` figure space the screen pixel coordinates. .. _new docs: http://matplotlib.sourceforge.net/doc/html/devel/ transformations.html `matplotlib.axes` transforms ============================ `transScale` scales data to account for nonlinearities (nonaffine) in the axis scales, e.g. loglog and semilog scales. For example, `transScale.transform` would convert `x = [1, 10, 100, 1000]` to `[1, 2, 3, 4]` (powers of ten) if the xaxis is logarithmically spaced. `transLimits` scales the data to the currently "zoomed" in portion of the data. `transScale + transLimits` maps data space to axes space. `transAxes` maps axes space to figure space. `transData` maps data space to the figure space. `transData` is a composite of `transScale`, `transLimits`, and `transAxes`. It's the "fast lane" between the data and the screen. Transforms example ================== If you want to draw a dot in the middle of the plot, you know that >>> x = y = 0.5 in axes space. Since the default transform for `matplotlib.pyplot.plot` is `transData`, you can either either change the transform of the plot operation >>> import matplotlib.pyplot as plt >>> ax = plt.subplot(111) >>> ax.plot([x], [y], 'ro', transform=ax.transAxes) Or you can transform the midpoint to data coordinates then plot them >>> trans_data2axes = ax.transScale + ax.transLimits >>> trans_axes2data = trans_data2axes.inverted() >>> mid_point = trans_axes2data.transform([x, y]) >>> x_trans, y_trans = mid_point >>> ax.plot([x_trans], [y_trans], 'gs') It's important to note that these are two **very different** approaches. The first point (red dot) above is always referenced to axes space and will remain in the center of the plot, even if you change the axes limits (try panning in interactive mode). On the other hand, the second point (green square) is referenced to the data space and will move with the data if the axes limits are changed. 
From: Mathieu Leplatre <leplatre@gm...>  20080815 12:27:34

Thank you so much JaeJoon and Tony. You saved me so much time ! The whole transforms system was very obscure (to me !). >> trans = (ax.transScale + ax.transLimits).inverted()  barspace = 0.32 #inches barstep = abscissa[1]  abscissa[0] trans = (ax.transScale + ax.transLimits).inverted() barspacedata = trans.transform([barspace,0.0]) barwidth = barstep  barspacedata[0]  Works great ! > I just wanted to add a little bit to JaeJoon's example. I feel like I have > to relearn the axes transformations every time I deal with them. Your email > reminded me to write things down, and I thought I'd share it, in case others > find it useful. Let me know if anything is wrong/unclear. Everything is very clear now. Thanks for taking the time to detail everything, it's priceless. > It's important to note that these are two **very different** approaches. The > first point (red dot) above is always referenced to axes space and will > remain > in the center of the plot, even if you change the axes limits (try panning > in > interactive mode). > > On the other hand, the second point (green square) is referenced to the data > space and will move with the data if the axes limits are changed. > > I really liked your small example. I wish we could publish it on scipy or something. Best regards. Mat. 