matplotlib-devel Mailing List for matplotlib

Hi Fernando,
It would be really great if galry could be integrated in the
notebook indeed. Is the code of this demo available somewhere, so that I
can get an idea about how this integration works?
In theory, galry should be compatible with WebGL because one of the main
components of galry is a shader code generator that can produce OpenGL
ES-compatible GLSL code. Apart from that, I suppose you have some way of
making Javascript and Python communicate? The interaction system of Galry,
which is based on QT but with an abstraction layer, could then be plugged
to Javascript somehow... Anyway, if I could take a look to the code of this
demo, I should be able to evaluate how complicated this integration would
be.
Cyrille
2012年11月16日 Fernando Perez <fpe...@gm...>
> Hi Cyrille,
>
> On Thu, Nov 15, 2012 at 11:24 AM, Cyrille Rossant
> <cyr...@gm...> wrote:
> > I am developing a high-performance interactive visualization package in
> > Python based on PyOpenGL (http://rossant.github.com/galry/). It is
> primarily
> > meant to be used as a framework for developing complex interactive GUIs
> (in
> > QT) that deal with very large amounts of data (tens of millions of
> points).
> > But it may also be used, like matplotlib, as a high-level interactive
> > library to plot and visualize data.
>
> quick question: how easy/feasible is WebGL integration? I ask b/c
> we're starting to get the necessary machinery for easy WebGL
> visualization in the ipython notebook, see e.g.:
>
> http://www.flickr.com/photos/47156828@N06/8183294725
>
> so bringing galry to the notebook with minimal code duplication would
> be great. I just mention it now in case it helps you make design
> decisions as you go along.
>
> Cheers,
>
> f
>

Hi Cyrille,
On Thu, Nov 15, 2012 at 11:24 AM, Cyrille Rossant
<cyr...@gm...> wrote:
> I am developing a high-performance interactive visualization package in
> Python based on PyOpenGL (http://rossant.github.com/galry/). It is primarily
> meant to be used as a framework for developing complex interactive GUIs (in
> QT) that deal with very large amounts of data (tens of millions of points).
> But it may also be used, like matplotlib, as a high-level interactive
> library to plot and visualize data.
quick question: how easy/feasible is WebGL integration? I ask b/c
we're starting to get the necessary machinery for easy WebGL
visualization in the ipython notebook, see e.g.:
http://www.flickr.com/photos/47156828@N06/8183294725
so bringing galry to the notebook with minimal code duplication would
be great. I just mention it now in case it helps you make design
decisions as you go along.
Cheers,
f

On Fri, Nov 16, 2012 at 7:58 AM, Skipper Seabold <jss...@gm...>wrote:
> On Fri, Nov 16, 2012 at 10:19 AM, Yaroslav Halchenko <sf...@on...>
> wrote:
> > I just found some code (http://www.onerussian.com/tmp/plots.py and
> > pasted below for review/feedback) laying around which I wrote around
> > matplotlib for plotting primarily pair-wise stats results. Here is a
> > demonstration:
> > http://nbviewer.ipython.org/url/www.onerussian.com/tmp/run_plots.ipynb
> >
> > I wonder if there is a need/place for it in matplotlib and what changes
> would
> > you advise. Sorry for the lack of documentation -- I guess I have not
> finished
> > it at that point (scipy dependency can easily be dropped, used only for
> > standard error function iirc):
> >
>
> Looks nice. We'd certainly be interesting in including it in
> statsmodels/graphics if there isn't sufficient interest here and/or
> you'd like to keep the scipy dependency. ;)
>
> Skipper
I was going to suggest either the same thing or adding it to pandas. I
think statsmodels if the better fit, though. I also noticed scipy is only
used for scipy.stats.sem -- so it might be easy enough to loose the scipy
dependency. Just a thought.
-paul

On Fri, Nov 16, 2012 at 10:19 AM, Yaroslav Halchenko <sf...@on...> wrote:
> I just found some code (http://www.onerussian.com/tmp/plots.py and
> pasted below for review/feedback) laying around which I wrote around
> matplotlib for plotting primarily pair-wise stats results. Here is a
> demonstration:
> http://nbviewer.ipython.org/url/www.onerussian.com/tmp/run_plots.ipynb
>
> I wonder if there is a need/place for it in matplotlib and what changes would
> you advise. Sorry for the lack of documentation -- I guess I have not finished
> it at that point (scipy dependency can easily be dropped, used only for
> standard error function iirc):
>
Looks nice. We'd certainly be interesting in including it in
statsmodels/graphics if there isn't sufficient interest here and/or
you'd like to keep the scipy dependency. ;)
Skipper
> #!/usr/bin/python
> #emacs: -*- mode: python-mode; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
> #ex: set sts=4 ts=4 sw=4 noet:
> #------------------------- =+- Python script -+= -------------------------
> """
> @file paired-plots.py
> @date Fri Jan 13 11:48:00 2012
> @brief
>
>
> Yaroslav Halchenko Dartmouth
> web: http://www.onerussian.com College
> e-mail: yo...@on... ICQ#: 60653192
>
> DESCRIPTION (NOTES):
>
> COPYRIGHT: Yaroslav Halchenko 2012
>
> LICENSE: MIT
>
> Permission is hereby granted, free of charge, to any person obtaining a copy
> of this software and associated documentation files (the "Software"), to deal
> in the Software without restriction, including without limitation the rights
> to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
> copies of the Software, and to permit persons to whom the Software is
> furnished to do so, subject to the following conditions:
>
> The above copyright notice and this permission notice shall be included in
> all copies or substantial portions of the Software.
>
> THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
> IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
> FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
> AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
> LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
> OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
> THE SOFTWARE.
> """
> #-----------------\____________________________________/------------------
>
> __author__ = 'Yaroslav Halchenko'
> __revision__ = '$Revision: $'
> __date__ = '$Date: $'
> __copyright__ = 'Copyright (c) 2012 Yaroslav Halchenko'
> __license__ = 'MIT'
>
>
> import numpy as np
> import pylab as pl
> import scipy.stats as ss
>
> def plot_boxplot_enhanced(
> v,
> contrast_labels=None,
> condition_labels=None,
> ccolors=['y', 'b'],
> rand_offsets=None,
> grid=True,
> xticks_rotation=0,
> **bp_kwargs):
>
> width = bp_kwargs.get('width', 0.5)
> pl.boxplot(v, **bp_kwargs)
>
> if v.ndim < 2: v = v[:, None]
> ncol = v.shape[1]
>
> eff = np.mean(v, axis=0) # effect sizes
> sem = ss.sem(v, axis=0)
>
> if rand_offsets is None:
> rand_offsets = np.random.randn(len(v)) * 0.02
>
> pl.plot((np.arange(ncol) + 1)[:, None] + rand_offsets,
> v.T, '.', color='k', markerfacecolor='k')
> for i in range(ncol):
> lw = 2
> pl.plot([1 - width/2. + i, 1+i],
> [0, 0],
> '--', color=ccolors[0], linewidth=lw) # first condition
> pl.plot([1+i, 1 + width/2. +i],
> [eff[i]]*2,
> '--', color=ccolors[1], linewidth=lw)
>
> # place ste
> pl.errorbar(i+1 + 1.1*width/2.,
> eff[i],
> sem[i],
> elinewidth=2, linewidth=0,
> color='r', ecolor='r')
>
> if contrast_labels and not i: # only for the first one
> pl.text(1 - 1.1*width/2 + i, 0.1, contrast_labels[0],
> verticalalignment='bottom',
> horizontalalignment='right')
> pl.text(1 + 1.2*width/2 + i, eff[i], contrast_labels[1],
> verticalalignment='bottom', horizontalalignment='left')
> ax = pl.gca()
> if condition_labels:
> ax.set_xticklabels(condition_labels, rotation=xticks_rotation)
> else:
> # hide it
> ax.axes.xaxis.set_visible(False)
>
> if grid:
> ax.grid()
> return ax
>
>
> def plot_paired_stats(
> v0, v1, contrast_labels,
> condition_labels=None,
> style=['barplot_effect',
> 'boxplot_raw',
> 'boxplot_effect'],
> ccolors=['y', 'g'],
> xticks_rotation=0,
> grid=False,
> fig=None,
> bottom_adjust=None,
> bp_kwargs={}):
>
> if isinstance(style, str):
> style = [style]
>
> nplots = len(style) # how many subplots will be needed
>
> # assure having 2nd dimension
> if v0.ndim < 2: v0 = v0[:, None]
> if v1.ndim < 2: v1 = v1[:, None]
> assert(v0.shape == v1.shape)
>
> ncol = v0.shape[1]
> v10 = (v1 - v0) # differences
> mv0 = np.mean(v0, axis=0) # means
> mv1 = np.mean(v1, axis=0)
>
> eff = np.mean(v10, axis=0) # effect sizes
> sem = ss.sem(v10, axis=0)
>
> # so that data points have are distinguishable
> rand_offsets = np.random.randn(len(v10)) * 0.02
>
> # interleaved combination for some plots
> v_ = np.hstack((v0, v1))
> v = np.zeros(v_.shape, dtype=v_.dtype)
> v[:, np.hstack((np.arange(0, ncol*2, 2),
> np.arange(1, ncol*2, 2)))] = v_
>
> #print v.shape
> #print np.mean(v0, axis=0), np.mean(v1, axis=0)
> #print np.min(v10, axis=0), np.max(v10, axis=0), \
> # np.mean(v10, axis=0), ss.sem(v10, axis=0)
> #pl.boxplot(v10 + np.mean(v1), notch=1, widths=0.05)
>
> #print v0.shape, v1.shape, np.hstack([v0, v1]).shape
>
> if fig is None:
> fig = pl.figure()
>
> bwidth = 0.5
> plot = 1
>
> if condition_labels:
> xlabels = [ '%s:%s' % (cond, contr)
> for cond in condition_labels
> for contr in contrast_labels ]
> else:
> xlabels = contrast_labels
>
> bp_kwargs_ = {
> #'bootstrap': 0,
> 'notch' : 1
> }
> bp_kwargs_.update(bp_kwargs)
>
> def plot_grid(ax):
> if grid:
> ax.grid()
>
> if 'barplot_effect' in style:
> if len(style) > 1:
> pl.subplot(1, nplots, plot)
> plot += 1
> # The simplest one
> pl.bar(np.arange(1, ncol*2+1) - bwidth/2,
> np.mean(v, axis=0),
> color=ccolors*ncol,
> edgecolor=ccolors*ncol,
> alpha=0.8,
> width=bwidth)
> #pl.minorticks_off()
> pl.tick_params('x', direction='out', length=6, width=1,
> top=False)
> ax = pl.gca()
> pl.xlim(0.5, ncol*2+0.5)
> ax.set_xticks(np.arange(1, ncol*2+1))
> ax.set_xticklabels(xlabels, rotation=xticks_rotation)
> # place ste for effect size into the 2nd column
> pl.errorbar(np.arange(ncol)*2+2,
> mv1,
> sem, elinewidth=2, linewidth=0,
> color='g', ecolor='r')
>
> plot_grid(ax)
>
> if 'boxplot_raw' in style:
> if len(style) > 1:
> pl.subplot(1, nplots, plot)
> plot += 1
>
> # Figure 1 -- "raw" data
> # plot "connections" between boxplots
> for i in range(ncol):
> pargs = (np.array([i*2+1, i*2+2])[:, None] + rand_offsets,
> np.array([v0[:,i], v1[:,i]]))
> pl.plot(*(pargs+('-',)), color='k', alpha=0.5, linewidth=0.25)
> pl.plot(*(pargs+('.',)), color='k', alpha=0.9)
> # boxplot of "raw" data
> bp1 = pl.boxplot(v, widths=bwidth, **bp_kwargs_)
> for i in range(ncol):
> for c in xrange(2):
> b = bp1['boxes'][2*i+c]
> b.set_color(ccolors[c])
> b.set_linewidth(2)
>
> ax = pl.gca()
> ax.set_xticklabels(xlabels, rotation=xticks_rotation)
> plot_grid(ax)
>
> if 'boxplot_effect' in style:
> if len(style) > 1:
> pl.subplot(1, nplots, plot)
> plot += 1
> plot_boxplot_enhanced(v10,
> contrast_labels=contrast_labels,
> condition_labels=condition_labels,
> widths=bwidth,
> rand_offsets=rand_offsets, # reuse them
> grid=grid,
> **bp_kwargs_)
>
> if bottom_adjust:
> fig.subplots_adjust(bottom=bottom_adjust)
> pl.draw_if_interactive()
> return fig
>
> if __name__ == '__main__':
>
> if True:
> v = np.random.normal(size=(50,8)) * 20 + 120
> if False:
> v[:, 1] += 40
> v[:, 3] -= 30
> v[:, 5] += 60
> v[:, 6] -= 60
> else:
> v -= np.arange(v.shape[1])*10
> v /= 10
>
> v0 = v[:, ::2]
> v1 = v[:, 1::2]
> d = v1 - v0
> print np.mean(d, axis=0)
> styles = ['barplot_effect',
> 'boxplot_raw',
> 'boxplot_effect'
> ]
> styles = styles + [styles]
> pl.close('all')
>
> if False:
> f = plot_boxplot_enhanced((v1-v0)[:,0],
> grid=True, xticks_rotation=30, notch=1)
>
> for s in styles:
> fig = pl.figure(figsize=(12,6))
> f = plot_paired_stats(v0, v1, ['cont1', 'cont2'],
> style=s, fig=fig,
> condition_labels=['exp1', 'exp2', 'exp3', 'exp4'],
> grid=True, xticks_rotation=30)
> pl.show()
>
>
> --
> Yaroslav O. Halchenko
> Postdoctoral Fellow, Department of Psychological and Brain Sciences
> Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
> Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419
> WWW: http://www.linkedin.com/in/yarik
>
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I just found some code (http://www.onerussian.com/tmp/plots.py and
pasted below for review/feedback) laying around which I wrote around
matplotlib for plotting primarily pair-wise stats results. Here is a
demonstration:
http://nbviewer.ipython.org/url/www.onerussian.com/tmp/run_plots.ipynb
I wonder if there is a need/place for it in matplotlib and what changes would
you advise. Sorry for the lack of documentation -- I guess I have not finished
it at that point (scipy dependency can easily be dropped, used only for
standard error function iirc):
#!/usr/bin/python
#emacs: -*- mode: python-mode; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*- 
#ex: set sts=4 ts=4 sw=4 noet:
#------------------------- =+- Python script -+= -------------------------
"""
 @file paired-plots.py
 @date Fri Jan 13 11:48:00 2012
 @brief
 Yaroslav Halchenko Dartmouth
 web: http://www.onerussian.com College
 e-mail: yo...@on... ICQ#: 60653192
 DESCRIPTION (NOTES):
 COPYRIGHT: Yaroslav Halchenko 2012
 LICENSE: MIT
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
 in the Software without restriction, including without limitation the rights
 to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 copies of the Software, and to permit persons to whom the Software is
 furnished to do so, subject to the following conditions:
 The above copyright notice and this permission notice shall be included in
 all copies or substantial portions of the Software.
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
 THE SOFTWARE.
"""
#-----------------\____________________________________/------------------
__author__ = 'Yaroslav Halchenko'
__revision__ = '$Revision: $'
__date__ = '$Date: $'
__copyright__ = 'Copyright (c) 2012 Yaroslav Halchenko'
__license__ = 'MIT'
import numpy as np
import pylab as pl
import scipy.stats as ss
def plot_boxplot_enhanced(
 v,
 contrast_labels=None,
 condition_labels=None,
 ccolors=['y', 'b'],
 rand_offsets=None,
 grid=True,
 xticks_rotation=0,
 **bp_kwargs):
 width = bp_kwargs.get('width', 0.5)
 pl.boxplot(v, **bp_kwargs)
 if v.ndim < 2: v = v[:, None]
 ncol = v.shape[1]
 eff = np.mean(v, axis=0) # effect sizes
 sem = ss.sem(v, axis=0)
 if rand_offsets is None:
 rand_offsets = np.random.randn(len(v)) * 0.02
 pl.plot((np.arange(ncol) + 1)[:, None] + rand_offsets,
 v.T, '.', color='k', markerfacecolor='k')
 for i in range(ncol):
 lw = 2
 pl.plot([1 - width/2. + i, 1+i],
 [0, 0],
 '--', color=ccolors[0], linewidth=lw) # first condition
 pl.plot([1+i, 1 + width/2. +i],
 [eff[i]]*2,
 '--', color=ccolors[1], linewidth=lw)
 # place ste
 pl.errorbar(i+1 + 1.1*width/2.,
 eff[i],
 sem[i],
 elinewidth=2, linewidth=0,
 color='r', ecolor='r')
 if contrast_labels and not i: # only for the first one
 pl.text(1 - 1.1*width/2 + i, 0.1, contrast_labels[0],
 verticalalignment='bottom',
 horizontalalignment='right')
 pl.text(1 + 1.2*width/2 + i, eff[i], contrast_labels[1],
 verticalalignment='bottom', horizontalalignment='left')
 ax = pl.gca()
 if condition_labels:
 ax.set_xticklabels(condition_labels, rotation=xticks_rotation)
 else:
 # hide it
 ax.axes.xaxis.set_visible(False)
 if grid:
 ax.grid()
 return ax
def plot_paired_stats(
 v0, v1, contrast_labels,
 condition_labels=None,
 style=['barplot_effect',
 'boxplot_raw',
 'boxplot_effect'],
 ccolors=['y', 'g'],
 xticks_rotation=0,
 grid=False,
 fig=None,
 bottom_adjust=None,
 bp_kwargs={}):
 if isinstance(style, str):
 style = [style]
 nplots = len(style) # how many subplots will be needed
 # assure having 2nd dimension
 if v0.ndim < 2: v0 = v0[:, None]
 if v1.ndim < 2: v1 = v1[:, None]
 assert(v0.shape == v1.shape)
 ncol = v0.shape[1]
 v10 = (v1 - v0) # differences
 mv0 = np.mean(v0, axis=0) # means
 mv1 = np.mean(v1, axis=0)
 eff = np.mean(v10, axis=0) # effect sizes
 sem = ss.sem(v10, axis=0)
 # so that data points have are distinguishable
 rand_offsets = np.random.randn(len(v10)) * 0.02
 # interleaved combination for some plots
 v_ = np.hstack((v0, v1))
 v = np.zeros(v_.shape, dtype=v_.dtype)
 v[:, np.hstack((np.arange(0, ncol*2, 2),
 np.arange(1, ncol*2, 2)))] = v_
 #print v.shape
 #print np.mean(v0, axis=0), np.mean(v1, axis=0)
 #print np.min(v10, axis=0), np.max(v10, axis=0), \
 # np.mean(v10, axis=0), ss.sem(v10, axis=0)
 #pl.boxplot(v10 + np.mean(v1), notch=1, widths=0.05)
 #print v0.shape, v1.shape, np.hstack([v0, v1]).shape
 if fig is None:
 fig = pl.figure()
 bwidth = 0.5
 plot = 1
 if condition_labels:
 xlabels = [ '%s:%s' % (cond, contr)
 for cond in condition_labels
 for contr in contrast_labels ]
 else:
 xlabels = contrast_labels
 bp_kwargs_ = {
 #'bootstrap': 0,
 'notch' : 1
 }
 bp_kwargs_.update(bp_kwargs)
 def plot_grid(ax):
 if grid:
 ax.grid()
 if 'barplot_effect' in style:
 if len(style) > 1:
 pl.subplot(1, nplots, plot)
 plot += 1
 # The simplest one
 pl.bar(np.arange(1, ncol*2+1) - bwidth/2,
 np.mean(v, axis=0),
 color=ccolors*ncol,
 edgecolor=ccolors*ncol,
 alpha=0.8,
 width=bwidth)
 #pl.minorticks_off()
 pl.tick_params('x', direction='out', length=6, width=1,
 top=False)
 ax = pl.gca()
 pl.xlim(0.5, ncol*2+0.5)
 ax.set_xticks(np.arange(1, ncol*2+1))
 ax.set_xticklabels(xlabels, rotation=xticks_rotation)
 # place ste for effect size into the 2nd column
 pl.errorbar(np.arange(ncol)*2+2,
 mv1,
 sem, elinewidth=2, linewidth=0,
 color='g', ecolor='r')
 plot_grid(ax)
 if 'boxplot_raw' in style:
 if len(style) > 1:
 pl.subplot(1, nplots, plot)
 plot += 1
 # Figure 1 -- "raw" data
 # plot "connections" between boxplots
 for i in range(ncol):
 pargs = (np.array([i*2+1, i*2+2])[:, None] + rand_offsets,
 np.array([v0[:,i], v1[:,i]]))
 pl.plot(*(pargs+('-',)), color='k', alpha=0.5, linewidth=0.25)
 pl.plot(*(pargs+('.',)), color='k', alpha=0.9)
 # boxplot of "raw" data
 bp1 = pl.boxplot(v, widths=bwidth, **bp_kwargs_)
 for i in range(ncol):
 for c in xrange(2):
 b = bp1['boxes'][2*i+c]
 b.set_color(ccolors[c])
 b.set_linewidth(2)
 ax = pl.gca()
 ax.set_xticklabels(xlabels, rotation=xticks_rotation)
 plot_grid(ax)
 if 'boxplot_effect' in style:
 if len(style) > 1:
 pl.subplot(1, nplots, plot)
 plot += 1
 plot_boxplot_enhanced(v10,
 contrast_labels=contrast_labels,
 condition_labels=condition_labels,
 widths=bwidth,
 rand_offsets=rand_offsets, # reuse them
 grid=grid,
 **bp_kwargs_)
 if bottom_adjust:
 fig.subplots_adjust(bottom=bottom_adjust)
 pl.draw_if_interactive()
 return fig
if __name__ == '__main__':
 if True:
 v = np.random.normal(size=(50,8)) * 20 + 120
 if False:
 v[:, 1] += 40
 v[:, 3] -= 30
 v[:, 5] += 60
 v[:, 6] -= 60
 else:
 v -= np.arange(v.shape[1])*10
 v /= 10
 v0 = v[:, ::2]
 v1 = v[:, 1::2]
 d = v1 - v0
 print np.mean(d, axis=0)
 styles = ['barplot_effect',
 'boxplot_raw',
 'boxplot_effect'
 ]
 styles = styles + [styles]
 pl.close('all')
 if False:
 f = plot_boxplot_enhanced((v1-v0)[:,0],
 grid=True, xticks_rotation=30, notch=1)
 for s in styles:
 fig = pl.figure(figsize=(12,6))
 f = plot_paired_stats(v0, v1, ['cont1', 'cont2'],
 style=s, fig=fig,
 condition_labels=['exp1', 'exp2', 'exp3', 'exp4'],
 grid=True, xticks_rotation=30)
 pl.show()
-- 
Yaroslav O. Halchenko
Postdoctoral Fellow, Department of Psychological and Brain Sciences
Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419
WWW: http://www.linkedin.com/in/yarik 

On Fri, Nov 16, 2012 at 7:44 AM, Mike Kaufman <mc...@gm...> wrote:
> On 11/15/12 2:54 PM, Paul Ivanov wrote:
> > On Wed, Nov 14, 2012 at 7:37 PM, Mike Kaufman <mc...@gm...
>
> >
> > I just found that *direction* accepts 'inout' as well, which
> > does indeed place the tick on both sides of the spine. So the
> > documentation should be updated to reflect this.
> >
> >
> > Thanks for the report, Mike, here's a PR for the patch:
> > https://github.com/matplotlib/matplotlib/pull/1503
> >
> > If it were me, I'd allow 'both' to work as well.
> >
> >
> > I'm amenable to that, just not sure if that counts as a new feature and
> > should go into master, or a bugfix and go into v1.2.x.
> >
> > I can add this functionality to #1503 if that makes sense to go to v1.2.x
>
> Thanks Paul,
>
> One is a doc fix, the other requires changing code (inside axis.py?). So
> best practice probably argues that 'both', if acceptable, should really
> only go into master.
>
> That said, I think the 'both' change only requires a change to a pair of
> conditionals...
>
> M
>
>
I agree with the fact that implementing "both" to mean the same as "inout"
should go into master only.
Ben Root

On 11/15/12 2:54 PM, Paul Ivanov wrote:
> On Wed, Nov 14, 2012 at 7:37 PM, Mike Kaufman <mc...@gm...
>
> I just found that *direction* accepts 'inout' as well, which
> does indeed place the tick on both sides of the spine. So the
> documentation should be updated to reflect this.
>
>
> Thanks for the report, Mike, here's a PR for the patch:
> https://github.com/matplotlib/matplotlib/pull/1503
>
> If it were me, I'd allow 'both' to work as well.
>
>
> I'm amenable to that, just not sure if that counts as a new feature and
> should go into master, or a bugfix and go into v1.2.x.
>
> I can add this functionality to #1503 if that makes sense to go to v1.2.x
Thanks Paul,
One is a doc fix, the other requires changing code (inside axis.py?). So 
best practice probably argues that 'both', if acceptable, should really 
only go into master.
That said, I think the 'both' change only requires a change to a pair of 
conditionals...
M

On 16 November 2012 05:14, Damon McDougall <dam...@gm...>wrote:
> >> I have a C++ TriFinder class
> >> that I could modify to work within matplotlib, and it is O(log N) so
> should
> >> be faster than your version for typical use cases.
> >
> > What algorithm does this use? Is the code open source and/or availabel
> > for other projects?
>
> I'm pretty sure there is an O(log n) algorithm in the Numerical
> Recipes book. It requires you to construct the triangulation in a
> specific way (this allows one to set up a tree data structure of
> triangles nicely). There may be others that I am not aware of though.
>
I think this is the standard method used for point-in-triangulation tests
for delaunay triangulations, as the search structure is created as the
triangulation is built. We need to support non-delaunay triangulations
that are specified by the user, which requires a different approach.
Ian

On 15 November 2012 21:25, Chris Barker <chr...@no...> wrote:
> On Wed, Nov 14, 2012 at 1:50 AM, Ian Thomas <ian...@gm...> wrote:
>
> > I think the code used to determine which triangle contains a certain
> point
> > should be factored out into its own TriFinder class,
>
> +1 -- this is a generally useful feature. In fact, it would be nice if
> a lot of this were in a pacakge that deals with triangular meshes,
> apart from MPL altogether (a scikit maybe?)
>
I hadn't considered any wider interest in it. From a selfish matplotlib
point of view, even if it was in say a scikit, we wouldn't want to add an
external dependency to the scikit so we would still need a copy of the
source code within matplotlib anyway, just to do our interpolation for
plotting purposes. The situation would be just like the delaunay code
which exists in a scikit but we include it in the mpl source code to avoid
the external dependency.
Once it is in mpl it is available for other projects to use if they wish,
subject to the BSD-style license. There would be a question of who is
responsible for maintaining it as I don't particularly want to spread
myself thinly on other open source projects when there are a lot of
additions to mpl that I would like to do. There would be the danger for
the other project of the reverse situation of our delaunay example, where
we have code that needs improvement but it is essentially unmaintained,
causing problems for users and embarrassment for developers.
> I have a C++ TriFinder class
> > that I could modify to work within matplotlib, and it is O(log N) so
> should
> > be faster than your version for typical use cases.
>
> What algorithm does this use? Is the code open source and/or availabel
> for other projects?
>
> I'm working on a package for working with unstructured grids in
> general, and also have a use for "what triangle is this point in" code
> for other purposes -- and I havne't found a fast, robust code for this
> yet.
>
It is code I have written recently based on the trapezoidal map algorithm
from the book 'Computational Geometry: Algorithms and Applications' by M.
de Berg et al. It currently exists only on my main linux box, but it will
be open source within mpl for others to use as mentioned above (subject to
acceptance by the mpl developers of course). It is an excellent book that
I wholeheartedly recommend to anyone with a passing interest in
computational geometry. The algorithm itself looks painful initially (as
do many of the algorithms in the book) but it reduces to a surprisingly
small amount of code.
For well-formed triangulations (i.e. no duplicate points, no zero area
triangles and no overlapping triangles) the algorithm is 'sufficiently'
robust. It is not completely robust in the sense of Shewchuk's robust
predicates, but is designed quite cleverly (or luckily) to avoid many of
the pitfalls that occur in naive point-in-triangle tests. For example, a
naive implementation of a point-in-triangle test for a point on or very
near the common edge of two adjacent triangles might return true for both
triangles (which is usually fine), or false for both (which is
catastrophic). The trapezoidal map avoids these problems by reducing the
test to a single point-line test which is therefore guaranteed to return
just one of the two triangles. It is possible to think of a situation that
causes the algorithm to fail at a triangle which has such a small area that
the slopes of two of the sides are within the machine precision of each
other, but it is also possible to use the triangle connectivity to check
for and resolve this problem. I haven't done so yet and need to consider
the tradeoff of effort required vs potential gain.
Someone who required guaranteed robustness could use Shewchuk's point-line
test with the algorithm. I would not do this with mpl however, as the
correctness of the point-line test depends a lot on computer architecture
and compiler flags. This would get out of date quickly and would need keen
monitoring by someone with knowledge of and interest in the area, plus
access to a lot of different computer architectures and OSes. I fail on
all of those counts!
> >> particularly as only a few days ago I committed to writing a triangular
> grid
> >> interpolator for quad grids
>
> what is a triangular interpolator for quad grids? sounds useful, too.
That was poor English from me. I meant interpolating from a triangular
grid *to* a quad grid, typically to make use of the wide range of quad grid
plotting functions like contour, pcolor, etc.
Ian

On Thu, Nov 15, 2012 at 3:25 PM, Chris Barker <chr...@no...> wrote:
> On Wed, Nov 14, 2012 at 1:50 AM, Ian Thomas <ian...@gm...> wrote:
>
>> I think the code used to determine which triangle contains a certain point
>> should be factored out into its own TriFinder class,
>
> +1 -- this is a generally useful feature. In fact, it would be nice if
> a lot of this were in a pacakge that deals with triangular meshes,
> apart from MPL altogether (a scikit maybe?)
>
>> I have a C++ TriFinder class
>> that I could modify to work within matplotlib, and it is O(log N) so should
>> be faster than your version for typical use cases.
>
> What algorithm does this use? Is the code open source and/or availabel
> for other projects?
I'm pretty sure there is an O(log n) algorithm in the Numerical
Recipes book. It requires you to construct the triangulation in a
specific way (this allows one to set up a tree data structure of
triangles nicely). There may be others that I am not aware of though.
>
> I'm working on a package for working with unstructured grids in
> general, and also have a use for "what triangle is this point in" code
> for other purposes -- and I havne't found a fast, robust code for this
> yet.
>
>>> particularly as only a few days ago I committed to writing a triangular grid
>>> interpolator for quad grids
>
> what is a triangular interpolator for quad grids? sounds useful, too.
>
> -Chris
>
> --
>
> Christopher Barker, Ph.D.
> Oceanographer
>
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