[SciPy-User] matplotlib: print to postscript file and some other questions

[Johannes Radinger]

Hello ,

sofar I know how to safe a plot into a *.eps file and it works good,
but there is one issue with filled areas between two functions.

When I try to use: 
plt.fill_between(x, pdf_min, pdf_max, color='0.85')

and I try to open it on my mac I fail. So far as I know
is the mac converting the eps internally to pdf to be
displayed, but it seems it can't be converted. 

If I try to set the output to *.pdf it works perfectly and I can
open the file. Something in the combination of fill_between
and eps is causing the error. I tried also color="red" but with
the same problems.

Is there anything I've to set because I need the output as 
a working eps.

/johannes


Am 07.05.2011 um 12:19 schrieb Nils Wagner:

> On Sat, 7 May 2011 15:37:16 +0530
>  Rajeev Singh <rajs2010 at gmail.com> wrote:
>> On Fri, May 6, 2011 at 3:16 PM, Johannes Radinger 
>> <JRadinger at gmx.at> wrote:
>> 
>>> Hello
>>> 
>>> I don't know if there is a special matplotlib-user list 
>>> but anyway:
>>> 
>>> I managed to draw a very simple plot of a probability 
>>> density function
>>> (actually two superimposed pdfs). Now I'd like to draw 
>>> vertical lines fat
>>> the points of the scale-parameters (only under the curve 
>>> of the function)
>>> and label them. How can I do that?
>>> 
>>> And what is the command to print the result into a 
>>> postscript-file?
>>> 
>>> Here is what I've got so far:
>>> 
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from scipy import stats
>>> 
>>> p=0.3
>>> m=0
>>> 
>>> x = np.arange(-100, 100, 0.2)
>>> 
>>> def pdf(x,s1,s2):
>>>   return p * stats.norm.pdf(x, loc=m, scale=s1) + (1-p) 
>>> *
>>> stats.norm.pdf(x, loc=m, scale=s2)
>>> 
>>> #plt.axis([-100, 100, 0, 0.03])#probably not necessary
>>> plt.plot(x, pdf(x,12,100), color='red')
>>> plt.title('Pdf of dispersal kernel')
>>> plt.text(60, 0.0025, r'$\mu=100,\ \sigma=15$')
>>> plt.ylabel('probabilty of occurance')
>>> plt.xlabel('distance from starting point')
>>> plt.grid(True)
>>> 
>>> plt.show()
>>> 
>>> thanks
>>> /johannes
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>> 
>> Hi,
>> 
>> You can try the following hack -
>> 
>> import matplotlib.pyplot as plt
>> import numpy as np
>> from scipy import stats
>> 
>> p=0.3
>> m=0
>> 
>> x = np.arange(-100, 100, 0.2)
>> 
>> def pdf(x,s1,s2):
>>  return p * stats.norm.pdf(x, loc=m, scale=s1) + (1-p) 
>> * stats.norm.pdf(x,
>> loc=m, scale=s2)
>> 
>> #plt.axis([-100, 100, 0, 0.03])#probably not necessary
>> s1, s2 = 12, 100
>> plt.plot(x, pdf(x,s1, s2), color='red')
>> plt.plot(np.array([s1,s1]), 
>> np.array([plt.ylim()[0],pdf(s1,s1,s2)]), '--k')
>> plt.plot(np.array([s2,s2]), 
>> np.array([plt.ylim()[0],pdf(s2,s1,s2)]), '--k')
>> plt.title('Pdf of dispersal kernel')
>> plt.text(60, 0.0025, r'$\mu=100,\ \sigma=15$')
>> plt.ylabel('probabilty of occurance')
>> plt.xlabel('distance from starting point')
>> #plt.grid(True)
>> 
>> #plt.show()
>> plt.savefig('filename.eps')  # this will save the figure 
>> in an eps file in
>> current directory
>> 
>> I am not sure if this is what you want!
>> 
>> Rajeev
> 
> See
> 
> http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.savefig
> http://matplotlib.sourceforge.net/users/customizing.html
> 
> for details.
> 
> HTH
> 
>                     Nils
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