matplotlib-users Mailing List for matplotlib

xerr is +/- relative to the data:
*xerr*/*yerr*: [ scalar | N, Nx1, or 2xN array-like ]
 If a scalar number, len(N) array-like object, or an Nx1
 array-like object, errorbars are drawn at +/-value relative
 to the data.
 If a sequence of shape 2xN, errorbars are drawn at -row1
 and +row2 relative to the data.
I think you want:
xdat=10**data_x_log
ax.errorbar(10**data_x_log,data_y,xerr=[xdat-error_x_lower,error_x_upper-xdat],ls='',marker='o')
Cheers, Jody
> On 7 Apr 2015, at 13:51 PM, Markus Haider <mar...@ui...> wrote:
> 
> I have the error from a table which is in log units, and the error is 
> given to be symmetric in log space.
> 
> Cheers,
> Markus
> 
> On 2015年04月07日 16:40, Yuxiang Wang wrote:
>> Typo - "standard deviation OR standard error of mean", not "OF".
>> 
>> Sorry.
>> 
>> Shawn
>> 
>> 
>> On Tue, Apr 7, 2015 at 10:39 AM, Yuxiang Wang <yw...@vi...> wrote:
>>> If you error bars denote standard deviation of standard error of mean,
>>> shouldn't they be non-symmetric in log scale?
>>> 
>>> Shawn
>>> 
>>> On Tue, Apr 7, 2015 at 10:11 AM, Markus Haider <mar...@ui...> wrote:
>>>> Hi,
>>>> 
>>>> I am trying to make an errorbar plot with a logarithmic x-axis. I have
>>>> symmetric errors in logspace, however if I plot them, the errors are not
>>>> symmetric anymore, as you can see in the enclosed image. Am I
>>>> misunderstanding something or is this a bug?
>>>> 
>>>> Thanks for your help,
>>>> Markus
>>>> 
>>>> Here the code I used to produce the plot:
>>>> 
>>>> import matplotlib.pyplot as plt
>>>> 
>>>> import numpy as np
>>>> 
>>>> data_x_log = np.array([13.0,15.0])
>>>> 
>>>> data_y = np.array([0.5,1])
>>>> 
>>>> error_x_log = np.array([0.5,1.])
>>>> 
>>>> error_x_lower = 10**(data_x_log-error_x_log)
>>>> 
>>>> error_x_upper = 10**(data_x_log+error_x_log)
>>>> 
>>>> fig = plt.figure()
>>>> 
>>>> ax = fig.add_subplot(111)
>>>> 
>>>> ax.errorbar(10**data_x_log,data_y,xerr=[error_x_lower,error_x_upper],ls='',marker='o')
>>>> 
>>>> ax.set_xscale('log')
>>>> 
>>>> ax.set_xlim([1E11,1E17])
>>>> 
>>>> ax.set_ylim([0,2])
>>>> 
>>>> plt.savefig('error.png')
>>>> 
>>>> 
>>>> ------------------------------------------------------------------------------
>>>> BPM Camp - Free Virtual Workshop May 6th at 10am PDT/1PM EDT
>>>> Develop your own process in accordance with the BPMN 2 standard
>>>> Learn Process modeling best practices with Bonita BPM through live exercises
>>>> http://www.bonitasoft.com/be-part-of-it/events/bpm-camp-virtual- event?utm_
>>>> source=Sourceforge_BPM_Camp_5_6_15&utm_medium=email&utm_campaign=VA_SF
>>>> _______________________________________________
>>>> Matplotlib-users mailing list
>>>> Mat...@li...
>>>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>>> 
>>> 
>>> 
>>> --
>>> Yuxiang "Shawn" Wang
>>> Gerling Research Lab
>>> University of Virginia
>>> yw...@vi...
>>> +1 (434) 284-0836
>>> https://sites.google.com/a/virginia.edu/yw5aj/
>> 
>> 
> 
> 
> ------------------------------------------------------------------------------
> BPM Camp - Free Virtual Workshop May 6th at 10am PDT/1PM EDT
> Develop your own process in accordance with the BPMN 2 standard
> Learn Process modeling best practices with Bonita BPM through live exercises
> http://www.bonitasoft.com/be-part-of-it/events/bpm-camp-virtual- event?utm_
> source=Sourceforge_BPM_Camp_5_6_15&utm_medium=email&utm_campaign=VA_SF
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
--
Jody Klymak 
http://web.uvic.ca/~jklymak/

I have the error from a table which is in log units, and the error is 
given to be symmetric in log space.
Cheers,
Markus
On 2015年04月07日 16:40, Yuxiang Wang wrote:
> Typo - "standard deviation OR standard error of mean", not "OF".
>
> Sorry.
>
> Shawn
>
>
> On Tue, Apr 7, 2015 at 10:39 AM, Yuxiang Wang <yw...@vi...> wrote:
>> If you error bars denote standard deviation of standard error of mean,
>> shouldn't they be non-symmetric in log scale?
>>
>> Shawn
>>
>> On Tue, Apr 7, 2015 at 10:11 AM, Markus Haider <mar...@ui...> wrote:
>>> Hi,
>>>
>>> I am trying to make an errorbar plot with a logarithmic x-axis. I have
>>> symmetric errors in logspace, however if I plot them, the errors are not
>>> symmetric anymore, as you can see in the enclosed image. Am I
>>> misunderstanding something or is this a bug?
>>>
>>> Thanks for your help,
>>> Markus
>>>
>>> Here the code I used to produce the plot:
>>>
>>> import matplotlib.pyplot as plt
>>>
>>> import numpy as np
>>>
>>> data_x_log = np.array([13.0,15.0])
>>>
>>> data_y = np.array([0.5,1])
>>>
>>> error_x_log = np.array([0.5,1.])
>>>
>>> error_x_lower = 10**(data_x_log-error_x_log)
>>>
>>> error_x_upper = 10**(data_x_log+error_x_log)
>>>
>>> fig = plt.figure()
>>>
>>> ax = fig.add_subplot(111)
>>>
>>> ax.errorbar(10**data_x_log,data_y,xerr=[error_x_lower,error_x_upper],ls='',marker='o')
>>>
>>> ax.set_xscale('log')
>>>
>>> ax.set_xlim([1E11,1E17])
>>>
>>> ax.set_ylim([0,2])
>>>
>>> plt.savefig('error.png')
>>>
>>>
>>> ------------------------------------------------------------------------------
>>> BPM Camp - Free Virtual Workshop May 6th at 10am PDT/1PM EDT
>>> Develop your own process in accordance with the BPMN 2 standard
>>> Learn Process modeling best practices with Bonita BPM through live exercises
>>> http://www.bonitasoft.com/be-part-of-it/events/bpm-camp-virtual- event?utm_
>>> source=Sourceforge_BPM_Camp_5_6_15&utm_medium=email&utm_campaign=VA_SF
>>> _______________________________________________
>>> Matplotlib-users mailing list
>>> Mat...@li...
>>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>>
>>
>>
>> --
>> Yuxiang "Shawn" Wang
>> Gerling Research Lab
>> University of Virginia
>> yw...@vi...
>> +1 (434) 284-0836
>> https://sites.google.com/a/virginia.edu/yw5aj/
>
>

Hello list,
I want to axes above each other. They share the x-axis. The top figure has
'aspect=1' (it is a map), the bottom figure shows a cross-section along a
horizontal line on the map, so it doesn't have 'aspect=1'. When I do this
with code, for example like this:
fig, axes = plt.subplots(nrows=2,sharex=True)
plt.setp(axes[0], aspect=1.0, adjustable='box-forced')
then the physical size of the top axes is much sorter than the physical
size of the bottom axes (although they are poperly linked, as they have the
same data limit, and when zooming in the top figure, the bottom figure
adjusts). It just looks weird, as the size of the horizontal axis of the
bottom figure should have the same physical size as the horizontal axis of
the top figure. This used to be possible (a few years ago; haven't tried it
for a while). Is there a way to do it with the current matpotlib? (1.4.3)
Thanks,
Mark

Hi Tom,
Thanks for your help.
interpolation='nearest' doesn't produce any problems. I'm currently 
using TkAgg, and I checked with a buddy of mine, using MacOSX backend. 
Neither of us see any problems using interpolation='nearest'. He is 
using an older version of MPL which doesn't let him use 
interpolation='none'.
I will ask around to see if anyone can run a few more test cases.
Steven
On 4/7/15 10:39 AM, Thomas Caswell wrote:
> This probably should be made into an issue on github as this is 
> clearly a bug.
>
> On further consideration, the fact that in my example the bad pixels 
> show up only on the edge and are not symmetric makes me think that my 
> original suggestion is wrong. Does `interpoltation='nearest'` work 
> any better?
>
> I also am not sure that the rasterzation is happening so this may be 
> an issue on the renderer end.
>
> This needs more investigation that I have time for today.
>
> Tom
>
> On Tue, Apr 7, 2015 at 11:02 AM Steven Boada <bo...@ph... 
> <mailto:bo...@ph...>> wrote:
>
> Thomas,
>
> Thanks for the smaller example. I would have come up with one, but I
> wasn't sure what was causing it to begin with.
>
> Is there anything to be done to prevent this? Just use another
> backend?
>
> Steven
>
> On 4/6/15 8:47 PM, Thomas Caswell wrote:
> > This is probaly due to issues with not all of the vector backends
> > supporting alpha gracefully.
> >
> > This can be reproduced more simply by
> >
> > x, y = np.ogrid[-5:5:.1, -5:5:.1]
> > dd = np.exp(-(x**2 + y**2))
> > dd[dd < .1] = np.nan
> >
> > fig, ax = plt.subplots()
> > ax.imshow(dd, interpolation='none', cmap='gray_r')
> > plt.savefig('test.pdf')
> >
> > @steven In the future it is best to report bugs with minimal
> > copy-paste able examples.
> >
> > On Mon, Apr 6, 2015 at 5:41 PM Steven Boada
> <bo...@ph... <mailto:bo...@ph...>
> > <mailto:bo...@ph... <mailto:bo...@ph...>>>
> wrote:
> >
> > Getting some strange artifacts when I save a figure as a PDF in
> > matplotlib. Here are some screen shots. PDF
> > <http://imgur.com/oQDXkWn> and PNG
> <http://imgur.com/bCw3Fn4>. Any
> > idea why that is happening?
> >
> > Here is (most of) the source code that makes the plot. I
> stripped
> > out the data generation, because it is long and involved, and
> > doesn't really matter. Basically what the script is supposed
> to do
> > is make a scatter plot where the density is below some
> threshold,
> > and a 2d histogram when it is above that threshold. The code
> seems
> > to work fine, but when I save the figure (using savefig in
> > Ipython) it shows up funny.
> >
> > Thanks.
> >
> > import pylab as pyl
> >
> > bins = [50,50]
> > thresh = 3
> >
> > xdat = #generate or load some data
> > ydat = #generate or load some data
> >
> > hh, locx, locy = pyl.histogram2d(xdat, ydat,
> > range=[[-1,4],[-26,-10]], bins=bins)
> > posx = pyl.digitize(xdat, locx)
> > posy = pyl.digitize(ydat, locy)
> >
> > # finds the bins which contain points. posx = 0 for points
> > outside "range"
> > ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <=
> > bins[1])
> > # values of histogram with points in the bins.
> > hhsub = hh[posx[ind] - 1, posy[ind] - 1]
> >
> > xdat1 = xdat[ind][hhsub < thresh] # low density points
> > ydat1 = ydat[ind][hhsub < thresh]
> > hh[hh < thresh] = pyl.nan # fill the areas with low
> density by
> > NaNs
> >
> > pyl.scatter(xdat1, ydat1, s=20, c='0.8')
> > pyl.imshow(pyl.log10(hh.T), cmap='gray_r',
> > extent=pyl.array([[-1,4],[-26,-10]]).flatten(),
> > interpolation='none')
> >
> > pyl.show()
> >
> > --
> >
> > Steven Boada
> >
> > Doctoral Student
> > Dept of Physics and Astronomy
> > Texas A&M University
> > bo...@ph... <mailto:bo...@ph...>
> <mailto:bo...@ph... <mailto:bo...@ph...>>
> >
> > 
> ------------------------------------------------------------------------------
> > BPM Camp - Free Virtual Workshop May 6th at 10am PDT/1PM EDT
> > Develop your own process in accordance with the BPMN 2 standard
> > Learn Process modeling best practices with Bonita BPM
> through live
> > exercises
> > http://www.bonitasoft.com/be-part-of-it/events/bpm-camp-virtual-
> > event?utm_
> > 
> source=Sourceforge_BPM_Camp_5_6_15&utm_medium=email&utm_campaign=VA_SF_______________________________________________
> > Matplotlib-users mailing list
> > Mat...@li...
> <mailto:Mat...@li...>
> > <mailto:Mat...@li...
> <mailto:Mat...@li...>>
> > https://lists.sourceforge.net/lists/listinfo/matplotlib-users
> >
>
> --
>
> Steven Boada
>
> Doctoral Student
> Dept of Physics and Astronomy
> Texas A&M University
> bo...@ph... <mailto:bo...@ph...>
>
>
> ------------------------------------------------------------------------------
> BPM Camp - Free Virtual Workshop May 6th at 10am PDT/1PM EDT
> Develop your own process in accordance with the BPMN 2 standard
> Learn Process modeling best practices with Bonita BPM through live
> exercises
> http://www.bonitasoft.com/be-part-of-it/events/bpm-camp-virtual-
> event?utm_
> source=Sourceforge_BPM_Camp_5_6_15&utm_medium=email&utm_campaign=VA_SF
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> <mailto:Mat...@li...>
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
-- 
Steven Boada
Doctoral Student
Dept of Physics and Astronomy
Texas A&M University
bo...@ph...

This probably should be made into an issue on github as this is clearly a
bug.
On further consideration, the fact that in my example the bad pixels show
up only on the edge and are not symmetric makes me think that my original
suggestion is wrong. Does `interpoltation='nearest'` work any better?
I also am not sure that the rasterzation is happening so this may be an
issue on the renderer end.
This needs more investigation that I have time for today.
Tom
On Tue, Apr 7, 2015 at 11:02 AM Steven Boada <bo...@ph...> wrote:
> Thomas,
>
> Thanks for the smaller example. I would have come up with one, but I
> wasn't sure what was causing it to begin with.
>
> Is there anything to be done to prevent this? Just use another backend?
>
> Steven
>
> On 4/6/15 8:47 PM, Thomas Caswell wrote:
> > This is probaly due to issues with not all of the vector backends
> > supporting alpha gracefully.
> >
> > This can be reproduced more simply by
> >
> > x, y = np.ogrid[-5:5:.1, -5:5:.1]
> > dd = np.exp(-(x**2 + y**2))
> > dd[dd < .1] = np.nan
> >
> > fig, ax = plt.subplots()
> > ax.imshow(dd, interpolation='none', cmap='gray_r')
> > plt.savefig('test.pdf')
> >
> > @steven In the future it is best to report bugs with minimal
> > copy-paste able examples.
> >
> > On Mon, Apr 6, 2015 at 5:41 PM Steven Boada <bo...@ph...
> > <mailto:bo...@ph...>> wrote:
> >
> > Getting some strange artifacts when I save a figure as a PDF in
> > matplotlib. Here are some screen shots. PDF
> > <http://imgur.com/oQDXkWn> and PNG <http://imgur.com/bCw3Fn4>. Any
> > idea why that is happening?
> >
> > Here is (most of) the source code that makes the plot. I stripped
> > out the data generation, because it is long and involved, and
> > doesn't really matter. Basically what the script is supposed to do
> > is make a scatter plot where the density is below some threshold,
> > and a 2d histogram when it is above that threshold. The code seems
> > to work fine, but when I save the figure (using savefig in
> > Ipython) it shows up funny.
> >
> > Thanks.
> >
> > import pylab as pyl
> >
> > bins = [50,50]
> > thresh = 3
> >
> > xdat = #generate or load some data
> > ydat = #generate or load some data
> >
> > hh, locx, locy = pyl.histogram2d(xdat, ydat,
> > range=[[-1,4],[-26,-10]], bins=bins)
> > posx = pyl.digitize(xdat, locx)
> > posy = pyl.digitize(ydat, locy)
> >
> > # finds the bins which contain points. posx = 0 for points
> > outside "range"
> > ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <=
> > bins[1])
> > # values of histogram with points in the bins.
> > hhsub = hh[posx[ind] - 1, posy[ind] - 1]
> >
> > xdat1 = xdat[ind][hhsub < thresh] # low density points
> > ydat1 = ydat[ind][hhsub < thresh]
> > hh[hh < thresh] = pyl.nan # fill the areas with low density by
> > NaNs
> >
> > pyl.scatter(xdat1, ydat1, s=20, c='0.8')
> > pyl.imshow(pyl.log10(hh.T), cmap='gray_r',
> > extent=pyl.array([[-1,4],[-26,-10]]).flatten(),
> > interpolation='none')
> >
> > pyl.show()
> >
> > --
> >
> > Steven Boada
> >
> > Doctoral Student
> > Dept of Physics and Astronomy
> > Texas A&M University
> > bo...@ph... <mailto:bo...@ph...>
> >
> > ------------------------------------------------------------
> ------------------
> > BPM Camp - Free Virtual Workshop May 6th at 10am PDT/1PM EDT
> > Develop your own process in accordance with the BPMN 2 standard
> > Learn Process modeling best practices with Bonita BPM through live
> > exercises
> > http://www.bonitasoft.com/be-part-of-it/events/bpm-camp-virtual-
> > event?utm_
> > source=Sourceforge_BPM_Camp_5_6_15&utm_medium=email&utm_
> campaign=VA_SF_______________________________________________
> > Matplotlib-users mailing list
> > Mat...@li...
> > <mailto:Mat...@li...>
> > https://lists.sourceforge.net/lists/listinfo/matplotlib-users
> >
>
> --
>
> Steven Boada
>
> Doctoral Student
> Dept of Physics and Astronomy
> Texas A&M University
> bo...@ph...
>
>
> ------------------------------------------------------------
> ------------------
> BPM Camp - Free Virtual Workshop May 6th at 10am PDT/1PM EDT
> Develop your own process in accordance with the BPMN 2 standard
> Learn Process modeling best practices with Bonita BPM through live
> exercises
> http://www.bonitasoft.com/be-part-of-it/events/bpm-camp-virtual-
> event?utm_
> source=Sourceforge_BPM_Camp_5_6_15&utm_medium=email&utm_campaign=VA_SF
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

Thomas,
Thanks for the smaller example. I would have come up with one, but I 
wasn't sure what was causing it to begin with.
Is there anything to be done to prevent this? Just use another backend?
Steven
On 4/6/15 8:47 PM, Thomas Caswell wrote:
> This is probaly due to issues with not all of the vector backends 
> supporting alpha gracefully.
>
> This can be reproduced more simply by
>
> x, y = np.ogrid[-5:5:.1, -5:5:.1]
> dd = np.exp(-(x**2 + y**2))
> dd[dd < .1] = np.nan
>
> fig, ax = plt.subplots()
> ax.imshow(dd, interpolation='none', cmap='gray_r')
> plt.savefig('test.pdf')
>
> @steven In the future it is best to report bugs with minimal 
> copy-paste able examples.
>
> On Mon, Apr 6, 2015 at 5:41 PM Steven Boada <bo...@ph... 
> <mailto:bo...@ph...>> wrote:
>
> Getting some strange artifacts when I save a figure as a PDF in
> matplotlib. Here are some screen shots. PDF
> <http://imgur.com/oQDXkWn> and PNG <http://imgur.com/bCw3Fn4>. Any
> idea why that is happening?
>
> Here is (most of) the source code that makes the plot. I stripped
> out the data generation, because it is long and involved, and
> doesn't really matter. Basically what the script is supposed to do
> is make a scatter plot where the density is below some threshold,
> and a 2d histogram when it is above that threshold. The code seems
> to work fine, but when I save the figure (using savefig in
> Ipython) it shows up funny.
>
> Thanks.
>
> import pylab as pyl
>
> bins = [50,50]
> thresh = 3
>
> xdat = #generate or load some data
> ydat = #generate or load some data
>
> hh, locx, locy = pyl.histogram2d(xdat, ydat,
> range=[[-1,4],[-26,-10]], bins=bins)
> posx = pyl.digitize(xdat, locx)
> posy = pyl.digitize(ydat, locy)
>
> # finds the bins which contain points. posx = 0 for points
> outside "range"
> ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <=
> bins[1])
> # values of histogram with points in the bins.
> hhsub = hh[posx[ind] - 1, posy[ind] - 1]
>
> xdat1 = xdat[ind][hhsub < thresh] # low density points
> ydat1 = ydat[ind][hhsub < thresh]
> hh[hh < thresh] = pyl.nan # fill the areas with low density by
> NaNs
>
> pyl.scatter(xdat1, ydat1, s=20, c='0.8')
> pyl.imshow(pyl.log10(hh.T), cmap='gray_r',
> extent=pyl.array([[-1,4],[-26,-10]]).flatten(),
> interpolation='none')
>
> pyl.show()
>
> -- 
>
> Steven Boada
>
> Doctoral Student
> Dept of Physics and Astronomy
> Texas A&M University
> bo...@ph... <mailto:bo...@ph...>
>
> ------------------------------------------------------------------------------
> BPM Camp - Free Virtual Workshop May 6th at 10am PDT/1PM EDT
> Develop your own process in accordance with the BPMN 2 standard
> Learn Process modeling best practices with Bonita BPM through live
> exercises
> http://www.bonitasoft.com/be-part-of-it/events/bpm-camp-virtual-
> event?utm_
> source=Sourceforge_BPM_Camp_5_6_15&utm_medium=email&utm_campaign=VA_SF_______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> <mailto:Mat...@li...>
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
-- 
Steven Boada
Doctoral Student
Dept of Physics and Astronomy
Texas A&M University
bo...@ph...

Typo - "standard deviation OR standard error of mean", not "OF".
Sorry.
Shawn
On Tue, Apr 7, 2015 at 10:39 AM, Yuxiang Wang <yw...@vi...> wrote:
> If you error bars denote standard deviation of standard error of mean,
> shouldn't they be non-symmetric in log scale?
>
> Shawn
>
> On Tue, Apr 7, 2015 at 10:11 AM, Markus Haider <mar...@ui...> wrote:
>> Hi,
>>
>> I am trying to make an errorbar plot with a logarithmic x-axis. I have
>> symmetric errors in logspace, however if I plot them, the errors are not
>> symmetric anymore, as you can see in the enclosed image. Am I
>> misunderstanding something or is this a bug?
>>
>> Thanks for your help,
>> Markus
>>
>> Here the code I used to produce the plot:
>>
>> import matplotlib.pyplot as plt
>>
>> import numpy as np
>>
>> data_x_log = np.array([13.0,15.0])
>>
>> data_y = np.array([0.5,1])
>>
>> error_x_log = np.array([0.5,1.])
>>
>> error_x_lower = 10**(data_x_log-error_x_log)
>>
>> error_x_upper = 10**(data_x_log+error_x_log)
>>
>> fig = plt.figure()
>>
>> ax = fig.add_subplot(111)
>>
>> ax.errorbar(10**data_x_log,data_y,xerr=[error_x_lower,error_x_upper],ls='',marker='o')
>>
>> ax.set_xscale('log')
>>
>> ax.set_xlim([1E11,1E17])
>>
>> ax.set_ylim([0,2])
>>
>> plt.savefig('error.png')
>>
>>
>> ------------------------------------------------------------------------------
>> BPM Camp - Free Virtual Workshop May 6th at 10am PDT/1PM EDT
>> Develop your own process in accordance with the BPMN 2 standard
>> Learn Process modeling best practices with Bonita BPM through live exercises
>> http://www.bonitasoft.com/be-part-of-it/events/bpm-camp-virtual- event?utm_
>> source=Sourceforge_BPM_Camp_5_6_15&utm_medium=email&utm_campaign=VA_SF
>> _______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>
>
>
> --
> Yuxiang "Shawn" Wang
> Gerling Research Lab
> University of Virginia
> yw...@vi...
> +1 (434) 284-0836
> https://sites.google.com/a/virginia.edu/yw5aj/
-- 
Yuxiang "Shawn" Wang
Gerling Research Lab
University of Virginia
yw...@vi...
+1 (434) 284-0836
https://sites.google.com/a/virginia.edu/yw5aj/

If you error bars denote standard deviation of standard error of mean,
shouldn't they be non-symmetric in log scale?
Shawn
On Tue, Apr 7, 2015 at 10:11 AM, Markus Haider <mar...@ui...> wrote:
> Hi,
>
> I am trying to make an errorbar plot with a logarithmic x-axis. I have
> symmetric errors in logspace, however if I plot them, the errors are not
> symmetric anymore, as you can see in the enclosed image. Am I
> misunderstanding something or is this a bug?
>
> Thanks for your help,
> Markus
>
> Here the code I used to produce the plot:
>
> import matplotlib.pyplot as plt
>
> import numpy as np
>
> data_x_log = np.array([13.0,15.0])
>
> data_y = np.array([0.5,1])
>
> error_x_log = np.array([0.5,1.])
>
> error_x_lower = 10**(data_x_log-error_x_log)
>
> error_x_upper = 10**(data_x_log+error_x_log)
>
> fig = plt.figure()
>
> ax = fig.add_subplot(111)
>
> ax.errorbar(10**data_x_log,data_y,xerr=[error_x_lower,error_x_upper],ls='',marker='o')
>
> ax.set_xscale('log')
>
> ax.set_xlim([1E11,1E17])
>
> ax.set_ylim([0,2])
>
> plt.savefig('error.png')
>
>
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-- 
Yuxiang "Shawn" Wang
Gerling Research Lab
University of Virginia
yw...@vi...
+1 (434) 284-0836
https://sites.google.com/a/virginia.edu/yw5aj/

Hi,
I am trying to make an errorbar plot with a logarithmic x-axis. I have 
symmetric errors in logspace, however if I plot them, the errors are not 
symmetric anymore, as you can see in the enclosed image. Am I 
misunderstanding something or is this a bug?
Thanks for your help,
Markus
Here the code I used to produce the plot:
import matplotlib.pyplot as plt
import numpy as np
data_x_log = np.array([13.0,15.0])
data_y = np.array([0.5,1])
error_x_log = np.array([0.5,1.])
error_x_lower = 10**(data_x_log-error_x_log)
error_x_upper = 10**(data_x_log+error_x_log)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.errorbar(10**data_x_log,data_y,xerr=[error_x_lower,error_x_upper],ls='',marker='o')
ax.set_xscale('log')
ax.set_xlim([1E11,1E17])
ax.set_ylim([0,2])
plt.savefig('error.png')

Dear All,
I have made a post on Stackoverflow that has not garnered any help so far,
and I'm drawing this to your attention here because it seems like this could
be a bug:
http://stackoverflow.com/questions/29469179/potential-bug-in-either-matplotlib-or-pandas
Please look at my post, and I would greatly appreciate any help! I'm do
puzzled by that behavior!
Thank you.
--
View this message in context: http://matplotlib.1069221.n5.nabble.com/Perplexing-behavior-from-combined-use-of-Matplotlib-and-pandas-tp45345.html
Sent from the matplotlib - users mailing list archive at Nabble.com.

This is probaly due to issues with not all of the vector backends
supporting alpha gracefully.
This can be reproduced more simply by
x, y = np.ogrid[-5:5:.1, -5:5:.1]
dd = np.exp(-(x**2 + y**2))
dd[dd < .1] = np.nan
fig, ax = plt.subplots()
ax.imshow(dd, interpolation='none', cmap='gray_r')
plt.savefig('test.pdf')
@steven In the future it is best to report bugs with minimal copy-paste
able examples.
On Mon, Apr 6, 2015 at 5:41 PM Steven Boada <bo...@ph...> wrote:
> Getting some strange artifacts when I save a figure as a PDF in
> matplotlib. Here are some screen shots. PDF <http://imgur.com/oQDXkWn>
> and PNG <http://imgur.com/bCw3Fn4>. Any idea why that is happening?
>
> Here is (most of) the source code that makes the plot. I stripped out the
> data generation, because it is long and involved, and doesn't really
> matter. Basically what the script is supposed to do is make a scatter plot
> where the density is below some threshold, and a 2d histogram when it is
> above that threshold. The code seems to work fine, but when I save the
> figure (using savefig in Ipython) it shows up funny.
>
> Thanks.
>
> import pylab as pyl
>
> bins = [50,50]
> thresh = 3
>
> xdat = #generate or load some data
> ydat = #generate or load some data
>
> hh, locx, locy = pyl.histogram2d(xdat, ydat, range=[[-1,4],[-26,-10]],
> bins=bins)
> posx = pyl.digitize(xdat, locx)
> posy = pyl.digitize(ydat, locy)
>
> # finds the bins which contain points. posx = 0 for points outside
> "range"
> ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])
> # values of histogram with points in the bins.
> hhsub = hh[posx[ind] - 1, posy[ind] - 1]
>
> xdat1 = xdat[ind][hhsub < thresh] # low density points
> ydat1 = ydat[ind][hhsub < thresh]
> hh[hh < thresh] = pyl.nan # fill the areas with low density by NaNs
>
> pyl.scatter(xdat1, ydat1, s=20, c='0.8')
> pyl.imshow(pyl.log10(hh.T), cmap='gray_r',
> extent=pyl.array([[-1,4],[-26,-10]]).flatten(),
> interpolation='none')
>
> pyl.show()
>
> --
>
> Steven Boada
>
> Doctoral Student
> Dept of Physics and Astronomy
> Texas A&M Uni...@ph...
>
> ------------------------------------------------------------
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> Develop your own process in accordance with the BPMN 2 standard
> Learn Process modeling best practices with Bonita BPM through live
> exercises
> http://www.bonitasoft.com/be-part-of-it/events/bpm-camp-virtual-
> event?utm_
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