Re: [Matplotlib-users] boxplot -- how (more)
On 21-Aug-2012 17:59, Paul Hobson wrote:
> On Tue, Aug 21, 2012 at 8:56 AM, Virgil Stokes wrote:
>> On 21-Aug-2012 17:50, Paul Hobson wrote:
>>> On Tue, Aug 21, 2012 at 7:58 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?
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?
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[Matplotlib-users] problem with png image
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/Cls/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/CmA/Kho3prHFpUU/s640/fit_rates2.png Has anyone experienced behaviour like this? Thanks. Petro -- 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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] problem with png image
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/Cls/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 -- 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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] problem with png image
Damon McDougall 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/Cls/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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] problem with png image
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 > 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/Cls/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 > Matplotlib-users@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/matplotlib-users -- 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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] problem with png image
Michael Droettboom 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. -- 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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] boxplot -- how (more)
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?
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Re: [Matplotlib-users] boxplot -- how (more)
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 wrote:
>>> On 21-Aug-2012 17:50, Paul Hobson wrote:
On Tue, Aug 21, 2012 at 7:58 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?
> 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.
:-)
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Re: [Matplotlib-users] boxplot -- how (more)
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()
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[Matplotlib-users] GCC failure when updating from 1.1.0 to 1.1.1
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 -- 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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] GCC failure when updating from 1.1.0 to 1.1.1
On Wed, Aug 22, 2012 at 12:31 PM, Joe Borġ 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 -- 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 Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
