matplotlib-users Mailing List for matplotlib

On Wed, Aug 22, 2012 at 12:31 PM, Joe Borġ <ma...@jd...> wrote:
> Not sure if this is an issue with an out-of-date GCC or if something else
> is wrong. I've got 1.1.0 on no problem.
>
> $python setup.py build
> ...
> gcc -pthread -fno-strict-aliasing -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall
> -fPIC -DPY_ARRAY_UNIQUE_SYMBOL=MPL_ARRAY_API -DPYCXX_ISO_CPP_LIB=1
> -I/software/Python/272/lib/python2.7/site-packages/numpy/core/include
> -I/usr/include/freetype2 -I/usr/local/include -I/usr/include -I.
> -I/software/Python/272/include/python2.7 -c src/ft2font.cpp -o
> build/temp.linux-x86_64-2.7/src/ft2font.o
> In file included from
> /software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:7,
> from
> /software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
> from
> /software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:14,
> from src/ft2font.cpp:7:
> /software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/npy_common.h:120:2:
> error: #error npy_cdouble definition is not compatible with C99 complex
> definition ! Please contact Numpy maintainers and give detailed information
> about your compiler and platform
> /software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/npy_common.h:127:2:
> error: #error npy_cfloat definition is not compatible with C99 complex
> definition ! Please contact Numpy maintainers and give detailed information
> about your compiler and platform
> /software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/npy_common.h:134:2:
> error: #error npy_clongdouble definition is not compatible with C99 complex
> definition ! Please contact Numpy maintainers and give detailed information
> about your compiler and platform
> In file included from
> /software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:26,
> from
> /software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:14,
> from src/ft2font.cpp:7:
> /software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/__multiarray_api.h:
> In function 'int _import_array()':
> /software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/__multiarray_api.h:1226:
> error: 'NPY_ABI_VERSION' was not declared in this scope
> /software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/__multiarray_api.h:1232:
> error: 'NPY_API_VERSION' was not declared in this scope
> error: command 'gcc' failed with exit status 1
>
> $ gcc --version
> gcc (GCC) 4.4.5 20110214 (Red Hat 4.4.5-6)
> Copyright (C) 2010 Free Software Foundation, Inc.
> This is free software; see the source for copying conditions. There is NO
> warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
>
>
Joe,
This appears to be a problem with NumPy. I would suggest sending this
email to the numpy-discussion list. Be sure to include detailed
information about your compiler, OS, and your machine.
Cheers!
Ben Root

Not sure if this is an issue with an out-of-date GCC or if something else
is wrong. I've got 1.1.0 on no problem.
$python setup.py build
...
gcc -pthread -fno-strict-aliasing -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall
-fPIC -DPY_ARRAY_UNIQUE_SYMBOL=MPL_ARRAY_API -DPYCXX_ISO_CPP_LIB=1
-I/software/Python/272/lib/python2.7/site-packages/numpy/core/include
-I/usr/include/freetype2 -I/usr/local/include -I/usr/include -I.
-I/software/Python/272/include/python2.7 -c src/ft2font.cpp -o
build/temp.linux-x86_64-2.7/src/ft2font.o
In file included from
/software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:7,
 from
/software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
 from
/software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:14,
 from src/ft2font.cpp:7:
/software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/npy_common.h:120:2:
error: #error npy_cdouble definition is not compatible with C99 complex
definition ! Please contact Numpy maintainers and give detailed information
about your compiler and platform
/software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/npy_common.h:127:2:
error: #error npy_cfloat definition is not compatible with C99 complex
definition ! Please contact Numpy maintainers and give detailed information
about your compiler and platform
/software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/npy_common.h:134:2:
error: #error npy_clongdouble definition is not compatible with C99 complex
definition ! Please contact Numpy maintainers and give detailed information
about your compiler and platform
In file included from
/software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:26,
 from
/software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:14,
 from src/ft2font.cpp:7:
/software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/__multiarray_api.h:
In function 'int _import_array()':
/software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/__multiarray_api.h:1226:
error: 'NPY_ABI_VERSION' was not declared in this scope
/software/Python/272/lib/python2.7/site-packages/numpy/core/include/numpy/__multiarray_api.h:1232:
error: 'NPY_API_VERSION' was not declared in this scope
error: command 'gcc' failed with exit status 1
$ gcc --version
gcc (GCC) 4.4.5 20110214 (Red Hat 4.4.5-6)
Copyright (C) 2010 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
Regards,
Joseph David Borġ
http://www.jdborg.com

On Aug 22, 2012, at 10:04 AM, Virgil Stokes wrote:
> On 21-Aug-2012 17:52, Jeffrey Blackburne wrote:
>>
>> On Aug 21, 2012, at 10:58 AM, Virgil Stokes wrote:
>>
>>> In reference to my previous email.
>>>
>>> How can I find the outliers (samples points beyond the whiskers) 
>>> in the data
>>> used for the boxplot?
>>>
>>> Here is a code snippet that shows how it was used for the timings 
>>> data (a list
>>> of 4 sublists (y1,y2,y3,y4), each containing 400,000 real data 
>>> values),
>>> ...
>>> ...
>>> ...
>>> # Box Plots
>>> plt.subplot(2,1,2)
>>> timings = [y1,y2,y3,y4]
>>> pos = np.array(range(len(timings)))+1
>>> bp = plt.boxplot( timings, sym='k+', patch_artist=True,
>>> positions=pos, notch=1, bootstrap=5000 )
>>>
>>> plt.xlabel('Algorithm')
>>> plt.ylabel('Exection time (sec)')
>>> plt.ylim(0.9*ymin,1.1*ymax)
>>>
>>> plt.setp(bp['whiskers'], color='k', linestyle='-' )
>>> plt.setp(bp['fliers'], markersize=3.0)
>>> plt.title('Box plots (%4d trials)' %(n))
>>> plt.show()
>>> ...
>>> ...
>>> ...
>>>
>>> Again my questions:
>>> 1) How to get the value of the median?
>>
>> This is easily calculated from your data. Numpy will even do it 
>> for you: np.median(timings)
>>
>>> 2) How to find the outliers (outside the whiskers)?
>>
>> From the boxplot documentation: the whiskers extend to the most 
>> extreme data point within distance X of the bottom or top of the 
>> box, where X is 1.5 times the extent of the box. Any points more 
>> extreme than that are the outliers. The box itself of course 
>> extends from the 25th percentile to the 75th percentile of your 
>> data. Again, you can easily calculate these values from your data.
>>
>>> 3) How to find the width of the notch?
>>
>> Again, from the docs: with bootstrap=5000, it calculates the width 
>> of the notch by bootstrap resampling your data (the timings array) 
>> 5000 times and finding the 95% confidence interval of the median, 
>> and uses that as the notch width. You can redo that yourself 
>> pretty easily. Here is some bootstrap code for you to adapt:
>> http://mail.scipy.org/pipermail/scipy-user/2009-July/021704.html
>>
>> I encourage you to read the documentation! This page is very 
>> useful for reference:
>> http://matplotlib.sourceforge.net/api/pyplot_api.html
>>
>> -Jeff
>>
> Yes Jeff,
> These are very useful links; however, box plots have a parameter 
> called the "adjacent value" (from the McGill reference),
>
> "The plotted whisker extends to the adjacent value, which is the 
> most extreme data value that is not an outlier."
>
> It seems there should be one for the lower and one for the upper 
> whisker --- how can one get these two values from boxplot?
Look at bp['whiskers']
For those who got here by searching: bp is the object returned by 
plt.boxplot()
> Also, is there anyway to directly get the indices of the outliers?
Look into np.where()

On 22-Aug-2012 11:23, Virgil Stokes wrote:
> On 21-Aug-2012 17:59, Paul Hobson wrote:
>> On Tue, Aug 21, 2012 at 8:56 AM, Virgil Stokes <vs...@it...> wrote:
>>> On 21-Aug-2012 17:50, Paul Hobson wrote:
>>>> On Tue, Aug 21, 2012 at 7:58 AM, Virgil Stokes <vs...@it...> wrote:
>>>>> In reference to my previous email.
>>>>>
>>>>> How can I find the outliers (samples points beyond the whiskers) in the
>>>>> data
>>>>> used for the boxplot?
>>>>>
>>>>> Here is a code snippet that shows how it was used for the timings data (a
>>>>> list
>>>>> of 4 sublists (y1,y2,y3,y4), each containing 400,000 real data values),
>>>>> ...
>>>>> ...
>>>>> ...
>>>>> # Box Plots
>>>>> plt.subplot(2,1,2)
>>>>> timings = [y1,y2,y3,y4]
>>>>> pos = np.array(range(len(timings)))+1
>>>>> bp = plt.boxplot( timings, sym='k+', patch_artist=True,
>>>>> positions=pos, notch=1, bootstrap=5000 )
>>>>>
>>>>> plt.xlabel('Algorithm')
>>>>> plt.ylabel('Exection time (sec)')
>>>>> plt.ylim(0.9*ymin,1.1*ymax)
>>>>>
>>>>> plt.setp(bp['whiskers'], color='k', linestyle='-' )
>>>>> plt.setp(bp['fliers'], markersize=3.0)
>>>>> plt.title('Box plots (%4d trials)' %(n))
>>>>> plt.show()
>>>>> ...
>>>>> ...
>>>>> ...
>>>>>
>>>>> Again my questions:
>>>>> 1) How to get the value of the median?
>>>>> 2) How to find the outliers (outside the whiskers)?
>>>>> 3) How to find the width of the notch?
>>>> Virgil, the objects stuffed inside the `bp` dictionary should have
>>>> methods to retrieve their values. Let's see:
>>>>
>>>> In [35]: x = np.random.lognormal(mean=1.25, sigma=1.35, size=(37,3))
>>>>
>>>> In [36]: bp = plt.boxplot(x, bootstrap=5000, notch=True)
>>>>
>>>> In [37]: # Question 1
>>>> ...: print('medians')
>>>> ...: for n, median in enumerate(bp['medians']):
>>>> ...: print('%d: %f' % (n, median.get_ydata()[0]))
>>>> ...:
>>>> medians
>>>> 0: 6.339692
>>>> 1: 3.449320
>>>> 2: 4.503706
>>>>
>>>> In [38]: # Question 2
>>>> ...: print('fliers')
>>>> ...: for n in range(0, len(bp['fliers']), 2):
>>>> ...: print('%d: upper outliers = \t' % (n/2,))
>>>> ...: print(bp['fliers'][n].get_ydata())
>>>> ...: print('\n%d: lower outliers = \t' % (n/2,))
>>>> ...: print(bp['fliers'][n+1].get_ydata())
>>>> ...: print('\n')
>>>> ...:
>>> You had no outliers!
>>>
>>>> In [39]: # Question 3
>>>> ...: print('Confidence Intervals')
>>>> ...: for n, box in enumerate(bp['boxes']):
>>>> ...: print('%d: lower CI: %f' % (n, box.get_ydata()[2]))
>>>> ...: print('%d: upper CI: %f' % (n, box.get_ydata()[4]))
>>>> ...:
>>>> Confidence Intervals
>>>> 0: lower CI: 1.760701
>>>> 0: upper CI: 10.102221
>>>> 1: lower CI: 1.626386
>>>> 1: upper CI: 5.601927
>>>> 2: lower CI: 2.173173
>>>>
>>>> Hope that helps,
>>>> -paul
>>> Just what I was looking for Paul! Thanks very much.
>>>
>>> One final question --- Where can I find the documentation that answers my
>>> questions and gives more details about the equations used for the width of
>>> notch. etc.?
>>>
>>> Thanks again :-)
>> That should all be in the boxplot docstring. Do you use ipython? If
>> not, you should :)
>>
>> if so, just do `plt.boxplot?` at the ipython terminal and it'll show up.
>> -paul
> I still have a problem...
> Let me show the updated code snippet again
> ...
> ...
> ...
> # Box Plots
> iplt += 1
> plt.figure(iplt)
> timings = [ya[0],ya[1],ya[2],ya[3]]
> pos = np.array(range(len(timings)))+1
> bp = plt.boxplot( timings, sym='k+', patch_artist=True,
> positions=pos, notch=1, bootstrap=5000 )
> print ('medians')
> for nn,median in enumerate(bp['medians']):
> print('%d: %f' %(nn,median.get_ydata()[0]))
>
> print('fliers')
> for nn in range(0, len(bp['fliers']), 2):
> print('%d: upper outliers = \t' % (nn/2,))
> print(bp['fliers'][nn].get_ydata())
> print('\n%d: lower outliers = \t' % (nn/2,))
> print(bp['fliers'][nn+1].get_ydata())
> print('\n')
>
> print('Confidence Intervals')
> for nn, box in enumerate(bp['boxes']):
> print('%d: lower CI: %f' % (nn, box.get_ydata()[2]))<--- FAILS!
> print('%d: upper CI: %f' % (nn, box.get_ydata()[4]))
> ...
> ...
> ...
>
> Medians and fliers work perfectly; but, I get the following error message when
> trying to access the confidence intervals:
>
> AttributeError: 'PathPatch' object has no attribute 'get_ydata'
>
> Note, I am using boxplot with 4 sets of data and I am using matplotlib vers. 1.1.0.
>
> Any suggestions on how to fix this problem?
I found the solution,
 one must have,
patch_artist=False
in the boxplot call.
:-)

On 21-Aug-2012 17:52, Jeffrey Blackburne wrote:
>
> On Aug 21, 2012, at 10:58 AM, Virgil Stokes wrote:
>
>> In reference to my previous email.
>>
>> How can I find the outliers (samples points beyond the whiskers) in the data
>> used for the boxplot?
>>
>> Here is a code snippet that shows how it was used for the timings data (a list
>> of 4 sublists (y1,y2,y3,y4), each containing 400,000 real data values),
>> ...
>> ...
>> ...
>> # Box Plots
>> plt.subplot(2,1,2)
>> timings = [y1,y2,y3,y4]
>> pos = np.array(range(len(timings)))+1
>> bp = plt.boxplot( timings, sym='k+', patch_artist=True,
>> positions=pos, notch=1, bootstrap=5000 )
>>
>> plt.xlabel('Algorithm')
>> plt.ylabel('Exection time (sec)')
>> plt.ylim(0.9*ymin,1.1*ymax)
>>
>> plt.setp(bp['whiskers'], color='k', linestyle='-' )
>> plt.setp(bp['fliers'], markersize=3.0)
>> plt.title('Box plots (%4d trials)' %(n))
>> plt.show()
>> ...
>> ...
>> ...
>>
>> Again my questions:
>> 1) How to get the value of the median?
>
> This is easily calculated from your data. Numpy will even do it for you: 
> np.median(timings)
>
>> 2) How to find the outliers (outside the whiskers)?
>
> From the boxplot documentation: the whiskers extend to the most extreme data 
> point within distance X of the bottom or top of the box, where X is 1.5 times 
> the extent of the box. Any points more extreme than that are the outliers. The 
> box itself of course extends from the 25th percentile to the 75th percentile 
> of your data. Again, you can easily calculate these values from your data.
>
>> 3) How to find the width of the notch?
>
> Again, from the docs: with bootstrap=5000, it calculates the width of the 
> notch by bootstrap resampling your data (the timings array) 5000 times and 
> finding the 95% confidence interval of the median, and uses that as the notch 
> width. You can redo that yourself pretty easily. Here is some bootstrap code 
> for you to adapt:
> http://mail.scipy.org/pipermail/scipy-user/2009-July/021704.html
>
> I encourage you to read the documentation! This page is very useful for 
> reference:
> http://matplotlib.sourceforge.net/api/pyplot_api.html
>
> -Jeff
>
Yes Jeff,
These are very useful links; however, box plots have a parameter called the 
"adjacent value" (from the McGill reference),
"The plotted whisker extends to the adjacent value, which is the most extreme 
data value that is not an outlier."
It seems there should be one for the lower and one for the upper whisker --- how 
can one get these two values from boxplot?
Also, is there anyway to directly get the indices of the outliers?

Michael Droettboom <md...@st...>
writes:
> Can you try the GtkAgg backend instead and confirm the bug isn't there? 
> The "pure" Gtk backend doesn't see a lot of use these days and isn't 
> very well tested.
>
> Mike
>
Thanks. It solved the problem.

Can you try the GtkAgg backend instead and confirm the bug isn't there? 
The "pure" Gtk backend doesn't see a lot of use these days and isn't 
very well tested.
Mike
On 08/22/2012 08:17 AM, Petro Khoroshyy wrote:
> Damon McDougall
> <dam...@gm...> writes:
>
>> On Wed, Aug 22, 2012 at 11:28:54AM +0200, Petro wrote:
>>> Hi list.
>>> I generate some png images using matplotlib, and get very different
>>> results depending on figuresize
>>> __________________________________________________________________
>>> from pylab import figure, plot
>>> import pylab as plt
>>> import numpy as np
>>> figure()
>>> plt.subplot(2,1,1)
>>> plot(np.random.rand(10),'o')
>>> plt.subplot(2,1,2)
>>> plot(np.random.rand(10),'o')
>>> pic_name='fit_rates1.png'
>>> path_name='/home/petro/tmp/'
>>> plt.savefig(path_name + pic_name)
>>> __________________________________________________________________
>>>
>>> the code above generates the following image:
>>> https://lh3.googleusercontent.com/-107Ducz_CA0/UDShKMtejtI/AAAAAAAACls/YOeahS3tQA8/s400/fit_rates1.png
>>>
>>> now if I increase a figure size parameter:
>>> __________________________________________________________________
>>> from pylab import figure, plot
>>> import pylab as plt
>>> import numpy as np
>>> plt.ioff()
>>> from matplotlib import rcParams
>>> golden_mean = (np.sqrt(5)-1.0)/2.0 # Aesthetic ratio
>>> fig_width = 5.6 # width in inches
>>> fig_height = fig_width*golden_mean # height in inches
>>> rcParams['figure.figsize']=fig_width, fig_height*3
>>> figure()
>>> plt.subplot(2,1,1)
>>> plot(np.random.rand(10),'o')
>>> plt.subplot(2,1,2)
>>> plot(np.random.rand(10),'o')
>>> pic_name='fit_rates2.png'
>>> path_name='/home/petro/tmp/'
>>> plt.savefig(path_name + pic_name)
>>> 
>> What backend are you using?
>>
>> print plt.get_backend()
> It outputs GTK.
>
>
>
>
> ------------------------------------------------------------------------------
> Live Security Virtual Conference
> Exclusive live event will cover all the ways today's security and
> threat landscape has changed and how IT managers can respond. Discussions
> will include endpoint security, mobile security and the latest in malware
> threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users

Damon McDougall
<dam...@gm...> writes:
> On Wed, Aug 22, 2012 at 11:28:54AM +0200, Petro wrote:
>> Hi list.
>> I generate some png images using matplotlib, and get very different
>> results depending on figuresize
>> __________________________________________________________________
>> from pylab import figure, plot
>> import pylab as plt 
>> import numpy as np
>> figure()
>> plt.subplot(2,1,1)
>> plot(np.random.rand(10),'o')
>> plt.subplot(2,1,2)
>> plot(np.random.rand(10),'o')
>> pic_name='fit_rates1.png'
>> path_name='/home/petro/tmp/'
>> plt.savefig(path_name + pic_name) 
>> __________________________________________________________________
>> 
>> the code above generates the following image:
>> https://lh3.googleusercontent.com/-107Ducz_CA0/UDShKMtejtI/AAAAAAAACls/YOeahS3tQA8/s400/fit_rates1.png
>> 
>> now if I increase a figure size parameter:
>> __________________________________________________________________
>> from pylab import figure, plot
>> import pylab as plt 
>> import numpy as np
>> plt.ioff()
>> from matplotlib import rcParams 
>> golden_mean = (np.sqrt(5)-1.0)/2.0 # Aesthetic ratio
>> fig_width = 5.6 # width in inches
>> fig_height = fig_width*golden_mean # height in inches
>> rcParams['figure.figsize']=fig_width, fig_height*3
>> figure()
>> plt.subplot(2,1,1)
>> plot(np.random.rand(10),'o')
>> plt.subplot(2,1,2)
>> plot(np.random.rand(10),'o')
>> pic_name='fit_rates2.png'
>> path_name='/home/petro/tmp/'
>> plt.savefig(path_name + pic_name) 
>> 
>
> What backend are you using?
>
> print plt.get_backend()
It outputs GTK.

On Wed, Aug 22, 2012 at 11:28:54AM +0200, Petro wrote:
> Hi list.
> I generate some png images using matplotlib, and get very different
> results depending on figuresize
> __________________________________________________________________
> from pylab import figure, plot
> import pylab as plt 
> import numpy as np
> figure()
> plt.subplot(2,1,1)
> plot(np.random.rand(10),'o')
> plt.subplot(2,1,2)
> plot(np.random.rand(10),'o')
> pic_name='fit_rates1.png'
> path_name='/home/petro/tmp/'
> plt.savefig(path_name + pic_name) 
> __________________________________________________________________
> 
> the code above generates the following image:
> https://lh3.googleusercontent.com/-107Ducz_CA0/UDShKMtejtI/AAAAAAAACls/YOeahS3tQA8/s400/fit_rates1.png
> 
> now if I increase a figure size parameter:
> __________________________________________________________________
> from pylab import figure, plot
> import pylab as plt 
> import numpy as np
> plt.ioff()
> from matplotlib import rcParams 
> golden_mean = (np.sqrt(5)-1.0)/2.0 # Aesthetic ratio
> fig_width = 5.6 # width in inches
> fig_height = fig_width*golden_mean # height in inches
> rcParams['figure.figsize']=fig_width, fig_height*3
> figure()
> plt.subplot(2,1,1)
> plot(np.random.rand(10),'o')
> plt.subplot(2,1,2)
> plot(np.random.rand(10),'o')
> pic_name='fit_rates2.png'
> path_name='/home/petro/tmp/'
> plt.savefig(path_name + pic_name) 
> 
What backend are you using?
print plt.get_backend()
-- 
Damon McDougall
http://www.damon-is-a-geek.com
B2.39
Mathematics Institute
University of Warwick
Coventry
West Midlands
CV4 7AL
United Kingdom

Hi list.
I generate some png images using matplotlib, and get very different
results depending on figuresize
__________________________________________________________________
 from pylab import figure, plot
 import pylab as plt 
 import numpy as np
 figure()
 plt.subplot(2,1,1)
 plot(np.random.rand(10),'o')
 plt.subplot(2,1,2)
 plot(np.random.rand(10),'o')
 pic_name='fit_rates1.png'
 path_name='/home/petro/tmp/'
 plt.savefig(path_name + pic_name) 
__________________________________________________________________
the code above generates the following image:
https://lh3.googleusercontent.com/-107Ducz_CA0/UDShKMtejtI/AAAAAAAACls/YOeahS3tQA8/s400/fit_rates1.png
now if I increase a figure size parameter:
__________________________________________________________________
 from pylab import figure, plot
 import pylab as plt 
 import numpy as np
 plt.ioff()
 from matplotlib import rcParams 
 golden_mean = (np.sqrt(5)-1.0)/2.0 # Aesthetic ratio
 fig_width = 5.6 # width in inches
 fig_height = fig_width*golden_mean # height in inches
 rcParams['figure.figsize']=fig_width, fig_height*3
 figure()
 plt.subplot(2,1,1)
 plot(np.random.rand(10),'o')
 plt.subplot(2,1,2)
 plot(np.random.rand(10),'o')
 pic_name='fit_rates2.png'
 path_name='/home/petro/tmp/'
 plt.savefig(path_name + pic_name) 
 
__________________________________________________________________
the result looks strange like this:
https://lh5.googleusercontent.com/-4GRQxuRFvh4/UDSiRrNy59I/AAAAAAAACmA/Kho3prHFpUU/s640/fit_rates2.png
Has anyone experienced behaviour like this?
Thanks.
Petro

On 21-Aug-2012 17:59, Paul Hobson wrote:
> On Tue, Aug 21, 2012 at 8:56 AM, Virgil Stokes <vs...@it...> wrote:
>> On 21-Aug-2012 17:50, Paul Hobson wrote:
>>> On Tue, Aug 21, 2012 at 7:58 AM, Virgil Stokes <vs...@it...> wrote:
>>>> In reference to my previous email.
>>>>
>>>> How can I find the outliers (samples points beyond the whiskers) in the
>>>> data
>>>> used for the boxplot?
>>>>
>>>> Here is a code snippet that shows how it was used for the timings data (a
>>>> list
>>>> of 4 sublists (y1,y2,y3,y4), each containing 400,000 real data values),
>>>> ...
>>>> ...
>>>> ...
>>>> # Box Plots
>>>> plt.subplot(2,1,2)
>>>> timings = [y1,y2,y3,y4]
>>>> pos = np.array(range(len(timings)))+1
>>>> bp = plt.boxplot( timings, sym='k+', patch_artist=True,
>>>> positions=pos, notch=1, bootstrap=5000 )
>>>>
>>>> plt.xlabel('Algorithm')
>>>> plt.ylabel('Exection time (sec)')
>>>> plt.ylim(0.9*ymin,1.1*ymax)
>>>>
>>>> plt.setp(bp['whiskers'], color='k', linestyle='-' )
>>>> plt.setp(bp['fliers'], markersize=3.0)
>>>> plt.title('Box plots (%4d trials)' %(n))
>>>> plt.show()
>>>> ...
>>>> ...
>>>> ...
>>>>
>>>> Again my questions:
>>>> 1) How to get the value of the median?
>>>> 2) How to find the outliers (outside the whiskers)?
>>>> 3) How to find the width of the notch?
>>> Virgil, the objects stuffed inside the `bp` dictionary should have
>>> methods to retrieve their values. Let's see:
>>>
>>> In [35]: x = np.random.lognormal(mean=1.25, sigma=1.35, size=(37,3))
>>>
>>> In [36]: bp = plt.boxplot(x, bootstrap=5000, notch=True)
>>>
>>> In [37]: # Question 1
>>> ...: print('medians')
>>> ...: for n, median in enumerate(bp['medians']):
>>> ...: print('%d: %f' % (n, median.get_ydata()[0]))
>>> ...:
>>> medians
>>> 0: 6.339692
>>> 1: 3.449320
>>> 2: 4.503706
>>>
>>> In [38]: # Question 2
>>> ...: print('fliers')
>>> ...: for n in range(0, len(bp['fliers']), 2):
>>> ...: print('%d: upper outliers = \t' % (n/2,))
>>> ...: print(bp['fliers'][n].get_ydata())
>>> ...: print('\n%d: lower outliers = \t' % (n/2,))
>>> ...: print(bp['fliers'][n+1].get_ydata())
>>> ...: print('\n')
>>> ...:
>> You had no outliers!
>>
>>> In [39]: # Question 3
>>> ...: print('Confidence Intervals')
>>> ...: for n, box in enumerate(bp['boxes']):
>>> ...: print('%d: lower CI: %f' % (n, box.get_ydata()[2]))
>>> ...: print('%d: upper CI: %f' % (n, box.get_ydata()[4]))
>>> ...:
>>> Confidence Intervals
>>> 0: lower CI: 1.760701
>>> 0: upper CI: 10.102221
>>> 1: lower CI: 1.626386
>>> 1: upper CI: 5.601927
>>> 2: lower CI: 2.173173
>>>
>>> Hope that helps,
>>> -paul
>> Just what I was looking for Paul! Thanks very much.
>>
>> One final question --- Where can I find the documentation that answers my
>> questions and gives more details about the equations used for the width of
>> notch. etc.?
>>
>> Thanks again :-)
> That should all be in the boxplot docstring. Do you use ipython? If
> not, you should :)
>
> if so, just do `plt.boxplot?` at the ipython terminal and it'll show up.
> -paul
I still have a problem...
Let me show the updated code snippet again
 ...
 ...
 ...
 # Box Plots
 iplt += 1
 plt.figure(iplt)
 timings = [ya[0],ya[1],ya[2],ya[3]]
 pos = np.array(range(len(timings)))+1
 bp = plt.boxplot( timings, sym='k+', patch_artist=True,
 positions=pos, notch=1, bootstrap=5000 )
 print ('medians')
 for nn,median in enumerate(bp['medians']):
 print('%d: %f' %(nn,median.get_ydata()[0]))
 print('fliers')
 for nn in range(0, len(bp['fliers']), 2):
 print('%d: upper outliers = \t' % (nn/2,))
 print(bp['fliers'][nn].get_ydata())
 print('\n%d: lower outliers = \t' % (nn/2,))
 print(bp['fliers'][nn+1].get_ydata())
 print('\n')
 print('Confidence Intervals')
 for nn, box in enumerate(bp['boxes']):
 print('%d: lower CI: %f' % (nn, box.get_ydata()[2]))<--- FAILS!
 print('%d: upper CI: %f' % (nn, box.get_ydata()[4]))
 ...
 ...
 ...
Medians and fliers work perfectly; but, I get the following error message when 
trying to access the confidence intervals:
AttributeError: 'PathPatch' object has no attribute 'get_ydata'
Note, I am using boxplot with 4 sets of data and I am using matplotlib vers. 1.1.0.
Any suggestions on how to fix this problem?