matplotlib-users Mailing List for matplotlib

I have been working on a similar problem related to finance.  What I 
have done is call the "R" statistical software from Python and then use 
matplotlib for graphing within Python

I use Python2.4,  the "R" statistical package, and a Python package 
called rpy which interfaces to "R" from Python

LINKs:
    http://rpy.sourceforge.net/
    http://www.r-project.org/

My tendency is to submit the data to "R" which does the statistical 
calculations, return the results to Python, and then use Matplotlib to 
plot.  Keep in mind that "R" also has good plotting capabilities and you 
might just go with that solution. 


           
        ###########################################


Eric Firing wrote:

>You might get a good answer here (although I don't have it), but be 
>aware that your question relates to math, not plotting, so it is not 
>really a matplotlib question.  You need nonlinear least-squares.  Look 
>in scipy, and try the amazing Google.
>
>Eric
>
>Adrian Price-Whelan wrote:
>  
>
>>Hey guys -
>>
>>I'm working on a Histogram of pixel values from an astronomical image  
>>that looks like a Gaussian curve and then polynomial decay.  I'm  
>>trying to figure out a way to fit a Gaussian regression to the  
>>histogram, but can't find any documentation on this. thanks!
>>
>>-adrian
>>    
>>
>
>-------------------------------------------------------------------------
>This SF.net email is sponsored by the 2008 JavaOne(SM) Conference 
>Don't miss this year's exciting event. There's still time to save $100. 
>Use priority code J8TL2D2. 
>http://ad.doubleclick.net/clk;198757673;13503038;p?http://java.sun.com/javaone
>_______________________________________________
>Matplotlib-users mailing list
>Mat...@li...
>https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>  
>

Hi Adrian, if you need low level access there is a python wrapper of the 
MINUIT and MINUIT2 fitting libraries : 
http://code.google.com/p/pyminuit/ and http://code.google.com/p/pyminuit2/
It is targeted primarily toward High Energy Physics people, most of whom 
are familiar with the MINUIT library, but it would solve your problem as 
well. The doc is still lacking in the project, as I do not have much 
time, so feel free to ask me questions about it or even to send me some 
files so that I can try out to script the fit on my side (I work in the 
field of astro-particle physics and astrophysics, so I might even be 
interested in the context of your problem!) .

best,
Johann

Adrian Price-Whelan wrote:
> Hey guys -
>
> I'm working on a Histogram of pixel values from an astronomical image  
> that looks like a Gaussian curve and then polynomial decay.  I'm  
> trying to figure out a way to fit a Gaussian regression to the  
> histogram, but can't find any documentation on this. thanks!
>
> -adrian
>
> -------------------------------------------------------------------------
> This SF.net email is sponsored by the 2008 JavaOne(SM) Conference 
> Don't miss this year's exciting event. There's still time to save $100. 
> Use priority code J8TL2D2. 
> http://ad.doubleclick.net/clk;198757673;13503038;p?http://java.sun.com/javaone
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>   

You might get a good answer here (although I don't have it), but be 
aware that your question relates to math, not plotting, so it is not 
really a matplotlib question.  You need nonlinear least-squares.  Look 
in scipy, and try the amazing Google.

Eric

Adrian Price-Whelan wrote:
> Hey guys -
> 
> I'm working on a Histogram of pixel values from an astronomical image  
> that looks like a Gaussian curve and then polynomial decay.  I'm  
> trying to figure out a way to fit a Gaussian regression to the  
> histogram, but can't find any documentation on this. thanks!
> 
> -adrian

Hey guys -

I'm working on a Histogram of pixel values from an astronomical image  
that looks like a Gaussian curve and then polynomial decay.  I'm  
trying to figure out a way to fit a Gaussian regression to the  
histogram, but can't find any documentation on this. thanks!

-adrian

James Boyle wrote:
> I cannot get the contourf extended color map ranges to show up in the plot.
> the extend option of contourf states:
> 
> extend = 'neither', 'both', 'min', 'max'
>       Unless this is 'neither' (default), contour levels are
>       automatically added to one or both ends of the range so that
>       all data are included.  These added ranges are then
>       mapped to the special colormap values which default to
>       the ends of the colormap range, but can be set via
>       Colormap.set_under() and Colormap.set_over() methods
> 
> The code at the of this message produces a plot with color bar 
> extensions that are the end colors of the bone colormap and not red and 
> green. The colorMap._rgba_over value is red and the colorMap._rgba_under 
> value is green, but this is not reflected in the plot.
> Any idea what I am doing wrong?
> I am using matplotlib 0.91.1 
> 
> --Jim

Jim,

I agree, this is confusing.  I'm not quite sure what to do about it, 
other than possibly add or modify an example. The problem is that by 
default, the norm is autoscaling so that your entire data range is 
within the vmin-to-vmax range, so your contour plot and your colorbar 
are simply not seeing any out-of-range values. The immediate solution is 
to use something like the clim function (or the set_clim method of the 
mappable) to define the range that is to be mapped to the normal colors; 
anything out of *this* range will then be mapped to the under or over 
value.  See below for a modification to your script that will map the 
first and last contour layers to your under and over values.  This is 
just an example; it may not be exactly what you want.

> 
> figure()
> colorMap = cm.bone
> colorMap.set_over('r')
> colorMap.set_under('g')
> CS = contourf(X, Y, Z, 10,cmap=colorMap,origin=origin, extend = 'both')

CS.set_clim(CS.cvalues[1], CS.cvalues[-2])

> cbar = colorbar(CS)
> savefig('contourf_demo1')

Eric