Hi Jody,
Thank you very much for your help. You are right, this is what I wanted :-)
Cheers,
Markus
On 2015-04-07 23:33, Jody Klymak wrote:
> 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 <markus.hai...@uibk.ac.at> 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 2015-04-07 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...@virginia.edu> 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 <markus.hai...@uibk.ac.at>
>>>> 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...@virginia.edu
>>>> +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_
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> --
> Jody Klymak
> http://web.uvic.ca/~jklymak/
>
>
>
>
>
>
> ------------------------------------------------------------------------------
> 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
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> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
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Learn Process modeling best practices with Bonita BPM through live exercises
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