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Python(x,y) - Python for Scientists

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Python(x,y) - Python for Scientists

Mark B.
Most excellent.
This was highly needed, I think.

Any chance you will also add a Mac version.
That would be even better,

Mark
 

Message: 10
Date: Thu, 10 Apr 2008 13:36:43 +0200 (CEST)
From: "Python(x,y) - Python for Scientists" <[hidden email]>
Subject: [SciPy-user] Python(x,y) - Python for Scientists
To: [hidden email]
Message-ID: <[hidden email]>
Content-Type: text/plain;charset=iso-8859-1

Dear all,

The scientists among you may be interested in Python(x,y), a new
scientific-oriented Python distribution. This Python/Eclipse distribution
is freely available as a one-click Windows installer (a release for
GNU/Linux with similar features will follow soon):
http://www.pythonxy.com

Please do not hesitate to forward this announcement...
(I am very sorry if you have already received this e-mail through
"python-list" mailing list)

Thanks a lots,
PR

--
P. Raybaut
Python(x,y)
http://www.pythonxy.com



------------------------------

Message: 11
Date: Thu, 10 Apr 2008 14:09:12 +0200
From: massimo sandal <[hidden email]>
Subject: Re: [SciPy-user] linear (polynomial) fit with error bars
To: SciPy Users List <[hidden email]>
Message-ID: <[hidden email]>
Content-Type: text/plain; charset="iso-8859-15"

Hi,

Fabrice Silva ha scritto:
> Using the diag input argument of leastsq :
>
>         from scipy import optimize
>         def errfunc(a,X,Y):
>             return Y-(a[0]*X+a[1])
>         #b may be the vector containing the error bars sizes.
>         weigths = 1./b
>         a, success = optimize.leastsq(errfunc, [0,0],args=(X,Y), diag=weigths)
>
> You here give more importance to points having small error bars.

Thanks for your advice. I am trying however to use your code, but I am
stuck upon an error.
Here is the script, that reads a very raw data file (see below):

#!/usr/bin/env python
from scipy import optimize

def errfunc(a,X,Y):
       return Y-(a[0]*X+a[1])
#b may be the vector containing the error bars sizes.

f=open("data.txt", "r")
datf=f.readlines()


numofdatapoints=len(datf)/3
xval=[0.0]*numofdatapoints
yval=[0.0]*numofdatapoints
err=[0.0]*numofdatapoints

# print len(datf)
# print datf

for i in range(numofdatapoints):
   xval[i]=float(datf[i])
   yval[i]=float(datf[i+numofdatapoints])
   err[i]=float(datf[i+2*numofdatapoints])

weigths=[0.0]*numofdatapoints
for i in range(numofdatapoints):
       weigths[i] = 1./err[i]

w=[0.0]*numofdatapoints
success=[0.0]*numofdatapoints

w, success = optimize.leastsq(errfunc, [0,0], args=(xval,yval),
diag=weigths)

print valA
print success

-------
data.txt:
118.877580092022
110.450590941286
108.684062758621
109.314800167624
103.090778781767
98.5714370869397
29.1
31.42
33.74
36.06
38.38
40.7
2.76010170015786
3.52143474842509
2.45059986418858
3.21254530326032
2.11363073382134
2.14664809861522

-------
and the script dies with the following error:

massimo@calliope:~/Python/linfit$ python linfit.py
Traceback (most recent call last):
   File "linfit.py", line 31, in <module>
     w, success = optimize.leastsq(errfunc, [0,0], args=(xval,yval),
diag=weigths)
   File "/usr/lib/python2.5/site-packages/scipy/optimize/minpack.py",
line 262, in leastsq
     m = check_func(func,x0,args,n)[0]
   File "/usr/lib/python2.5/site-packages/scipy/optimize/minpack.py",
line 12, in check_func
     res = atleast_1d(apply(thefunc,args))
   File "linfit.py", line 4, in errfunc
     return Y-(a[0]*X+a[1])
ValueError: shape mismatch: objects cannot be broadcast to a single shape

which baffles me. What should I look for to understand what I am doing
wrong?

m.
--
Massimo Sandal
University of Bologna
Department of Biochemistry "G.Moruzzi"

snail mail:
Via Irnerio 48, 40126 Bologna, Italy

email:
[hidden email]

tel: +39-051-2094388
fax: +39-051-2094387

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Re: Python(x,y) - Python for Scientists

emsellem (Bugzilla)
Hi,

very useful! When do you expect to have a Linux version (I am working in
openSuse 10.3...:-))

Eric
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