Hi,
I've a problem with some errorbars not drawn correctly in (double)
logarithmic plots. See this PDF for an example:
http://dl.wolfgang-draxinger.net/C6_77MeV_raddamage.pdf
The vertical errorbar for the datapoint at x=1e3 are not drawn. Similar
also happens for some horizontal errorbars. Using the very same drawing
commands, except switching to a logarithmic scaling the errorbars draw
just fine.
So what's going on there?
Wolfgang Draxinger
Which version of matplotlib are you using? Also, are you setting the log scale before (preferred) or after (won’t work) the call to hist()?
Ben Root
On Thursday, March 8, 2012, Wolfgang Draxinger <wdraxinger.maillist@…4015…> wrote:
Hi,
I’ve a problem with some errorbars not drawn correctly in (double)
logarithmic plots. See this PDF for an example:
http://dl.wolfgang-draxinger.net/C6_77MeV_raddamage.pdf
The vertical errorbar for the datapoint at x=1e3 are not drawn. Similar
also happens for some horizontal errorbars. Using the very same drawing
commands, except switching to a logarithmic scaling the errorbars draw
just fine.So what’s going on there?
Wolfgang Draxinger
Grrr, not hist(), but errorbar().
Ben Root
On Thursday, March 8, 2012, Benjamin Root <ben.root@…1304…> wrote:
On Thursday, March 8, 2012, Wolfgang Draxinger <wdraxinger.maillist@…4015…> wrote:
Hi,
I’ve a problem with some errorbars not drawn correctly in (double)
logarithmic plots. See this PDF for an example:
The vertical errorbar for the datapoint at x=1e3 are not drawn. Similar
also happens for some horizontal errorbars. Using the very same drawing
commands, except switching to a logarithmic scaling the errorbars draw
just fine.
So what’s going on there?
Wolfgang Draxinger
Which version of matplotlib are you using? Also, are you setting the log scale before (preferred) or after (won’t work) the call to hist()?
Ben Root
Version is matplotlib-1.1.0, installed through standard Gentoo ebuild.
And the scale parameters are set before all the drawing calls.
Wolfgang
On Thu, 8 Mar 2012 19:47:05 -0600 Benjamin Root <ben.root@...1304...> wrote:
Which version of matplotlib are you using? Also, are you setting the
log scale before (preferred) or after (won't work) the call to hist()?
Wolfgang,
Can I have the data you used to produce these errorbars so I can test this bug?
Ben Root
On Fri, Mar 9, 2012 at 3:29 AM, Wolfgang Draxinger <wdraxinger.maillist@…4015…> wrote:
On Thu, 8 Mar 2012 19:47:05 -0600 > > Benjamin Root <ben.root@…3286…> wrote:
Which version of matplotlib are you using? Also, are you setting the
log scale before (preferred) or after (won’t work) the call to hist()?
Version is matplotlib-1.1.0, installed through standard Gentoo ebuild.
And the scale parameters are set before all the drawing calls.
Wolfgang
Here's the data
# Fluence.... -sigma Signal... -sigma area
1127 48.32 9.114 10.31 0.1318
1.127e+04 482.9 35.96 16.15 0.4994
1.127e+05 4829 231.2 101.1 2.568
1.127e+06 4.829e+04 4631 1689 12.22
And here's the ploting tool source code (also used for generating the
linked PDF).
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
# vim: filetype python
import sys, os, argparse
import math, numpy, scipy, scipy.optimize
import matplotlib, matplotlib.cm
import matplotlib.pyplot as pyplot
import pylab
def expmodel(p, x):
return p[0] + numpy.exp(p[1]*x)*p[2]
def experror(p, x, y):
return y - expmodel(p, x)
def linmodel(p, x):
return p[0] + p[1]*x
def linerror(p, x, y):
return y - linmodel(p, x)
if __name__ == '__main__':
optparse = argparse.ArgumentParser(description='plot raddark dat files with errorbars and linear or exponential model regression plots', prog=sys.argv[0])
optparse.add_argument('--xlabel', type=str, default='Particle Count')
optparse.add_argument('--ylabel', type=str, default='Signal')
optparse.add_argument('--title', type=str, default='')
optparse.add_argument('--outlier', '-O', action='append', type=str)
optfitgrp = optparse.add_mutually_exclusive_group()
optfitgrp.add_argument('--exp', '-e', action='store_true')
optfitgrp.add_argument('--lin', '-l', action='store_true')
optparse.add_argument('--log', action='store_true')
optparse.add_argument('files', type=str, nargs='+')
options = optparse.parse_args(sys.argv[1:])
data = [ numpy.loadtxt(filename) for filename in options.files ]
if options.outlier:
outlier = [ numpy.loadtxt(filename) for filename in options.outlier ]
ax = pyplot.subplot(1,1,1)
if options.log:
ax.loglog()
ax.set_title(options.title)
ax.set_xlabel(options.xlabel)
ax.set_ylabel(options.ylabel)
ax.grid(True, 'both')
for f,d in zip(options.files, data):
ax.errorbar(d[..., 0], d[..., 2], d[..., 3], d[..., 1], fmt='o', label=f)
if options.outlier:
for f,d in zip(options.outlier, outlier):
ax.errorbar(d[..., 0], d[..., 2], d[..., 3], d[..., 1], fmt='+', label=f)
if options.exp or options.lin:
data_xs = numpy.concatenate( [ d[..., 0] for d in data ] )
data_ys = numpy.concatenate( [ d[..., 2] for d in data ] )
if options.outlier:
x_max = numpy.nanmax( numpy.concatenate((data_xs, numpy.concatenate([ o[..., 0] for o in outlier ]))) )
x_min = numpy.nanmin( numpy.concatenate((data_xs, numpy.concatenate([ o[..., 0] for o in outlier ]))) )
else:
x_max = numpy.nanmax(data_xs)
x_min = numpy.nanmin(data_xs)
x_ptp = x_max - x_min
xs = numpy.arange(x_min - 0.05*x_ptp, x_max + 0.05*x_ptp, x_ptp/10000.)
if options.exp:
p = scipy.optimize.leastsq(experror, [numpy.nanmin(data_ys), 1e-6/x_ptp, 1./numpy.ptp(data_ys)], args=(data_xs, data_ys))
ys = expmodel(p[0], xs)
if options.lin:
p = scipy.optimize.leastsq(linerror, [numpy.nanmin(data_ys), 1./x_ptp, 1./numpy.ptp(data_ys)], args=(data_xs, data_ys))
ys = linmodel(p[0], xs)
ax.plot(xs, ys, label="fit")
ax.legend(loc='upper left')
pyplot.show()
On Fri, 9 Mar 2012 11:19:15 -0600 Benjamin Root <ben.root@...1304...> wrote:
Can I have the data you used to produce these errorbars so I can test
this bug?
Ah, finally figured it out. The issue is that your y-value for that error bar is 9.114, but you want to plot error bars that are +/-10.31. That line gets thrown out by matplotlib because you can’t plot at negative values for log scale. There is a trick that might work. The set_yscale method has a kwarg “nonposy” which could be set to “clip”. You could also try setting to the “symlog” scale which might let you get away with a negative value.
I hope that helps!
Ben Root
On Fri, Mar 9, 2012 at 1:14 PM, Wolfgang Draxinger <wdraxinger.maillist@…4015…> wrote:
On Fri, 9 Mar 2012 11:19:15 -0600 > > Benjamin Root <ben.root@…3286…> wrote:
Can I have the data you used to produce these errorbars so I can test
this bug?
Here’s the data
Fluence… -sigma Signal… -sigma area
1127 48.32 9.114 10.31 0.13181.127e+04 482.9 35.96 16.15 0.4994
1.127e+05 4829 231.2 101.1 2.568
1.127e+06 4.829e+04 4631 1689 12.22
Ah, finally figured it out. The issue is that your y-value for that
error bar is 9.114, but you want to plot error bars that are
+/-10.31. That line gets thrown out by matplotlib because you can't
plot at negative values for log scale.
Yes, I came to the same conclusion. I think matplotlib should print
some warning or raise some exception if confronted with data like that,
it can't handle.
There is a trick that might
work. The set_yscale method has a kwarg "nonposy" which could be set
to "clip". You could also try setting to the "symlog" scale which
might let you get away with a negative value.
I'll try that.
Thanks
Wolfgang
On Mon, 12 Mar 2012 15:51:15 -0500 Benjamin Root <ben.root@...1304...> wrote:
I never got the trick with set_yscale="clip" to work for my plots (MPL
1.1.0).
So I'm passing my error values to this little function in order to correct
the yerr_neg:
def filt_neg_err(y, yerr, set_ymin=1e-6):
ymin = y - yerr
filt = ymin < 0
yerr_pos = yerr.copy()
yerr_neg = yerr.copy()
yerr_neg[filt] = y[filt] - set_ymin
return np.array([yerr_neg, yerr_pos])
Cheers,
Arne
Benjamin Root-2 wrote:
On Fri, Mar 9, 2012 at 1:14 PM, Wolfgang Draxinger < > wdraxinger.maillist@...4015...> wrote:
On Fri, 9 Mar 2012 11:19:15 -0600 >> Benjamin Root <ben.root@...1304...> wrote:
> Can I have the data you used to produce these errorbars so I can test
> this bug?Here's the data
# Fluence.... -sigma Signal... -sigma area
1127 48.32 9.114 10.31 0.1318
1.127e+04 482.9 35.96 16.15 0.4994
1.127e+05 4829 231.2 101.1 2.568
1.127e+06 4.829e+04 4631 1689 12.22Ah, finally figured it out. The issue is that your y-value for that error
bar is 9.114, but you want to plot error bars that are +/-10.31. That
line
gets thrown out by matplotlib because you can't plot at negative values
for
log scale. There is a trick that might work. The set_yscale method has a
kwarg "nonposy" which could be set to "clip". You could also try setting
to the "symlog" scale which might let you get away with a negative value.I hope that helps!
Ben Root------------------------------------------------------------------------------
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