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authorBryan Newbold <bnewbold@charm.mit.edu>2008-03-02 21:33:33 -0500
committerBryan Newbold <bnewbold@charm.mit.edu>2008-03-02 21:33:33 -0500
commitb8af43314e05a2345d84cc248782e57f11674323 (patch)
treec90bcf6f7cc9a9631b0176c08f9238d688bced74
parentba31c3cba52fdbfb6aa27456769badf45064caee (diff)
downloadscipy-jlab-b8af43314e05a2345d84cc248782e57f11674323.zip
scipy-jlab-b8af43314e05a2345d84cc248782e57f11674323.tar.gz
-rw-r--r--importdata.py29
-rw-r--r--stats.py90
2 files changed, 80 insertions, 39 deletions
diff --git a/importdata.py b/importdata.py
index a72f71e..befb171 100644
--- a/importdata.py
+++ b/importdata.py
@@ -4,14 +4,17 @@
import shutil, os, scipy, pylab;
-def dothing(arg,thisdir,files):
+def dothing(arg,thisdir,files,prefix="d"):
global all_data
for fname in files:
if os.path.isfile(thisdir + '/' + fname):
if (fname.find('.') == -1):
try:
dat = pylab.load(thisdir + '/' + fname)
- all_data[fname.split('.')[0]] = dat
+ if(fname.split('.')[0][0].isalpha()):
+ all_data[fname.split('.')[0]] = dat
+ else:
+ all_data[prefix + fname.split('.')[0]] = dat
print "Loaded (no type): %s/%s" % (thisdir,fname);
except:
print "Tried, but didn't load (no type): %s/%s" % (thisdir,fname);
@@ -19,7 +22,10 @@ def dothing(arg,thisdir,files):
try:
dat = pylab.load(thisdir + '/' + fname)
#print all_data
- all_data[fname.split('.')[0]] = dat
+ if(fname.split('.')[0][0].isalpha()):
+ all_data[fname.split('.')[0]] = dat
+ else:
+ all_data[prefix + fname.split('.')[0]] = dat
print "Loaded .TKA: %s/%s" % (thisdir,fname);
#print "all_data has %d" % len(all_data)
except:
@@ -28,8 +34,14 @@ def dothing(arg,thisdir,files):
try:
dat = pylab.load(thisdir + '/' + fname,skiprows=1)
#print all_data
- all_data[fname.split('.')[0]] = dat
- print "Loaded .txt: %s/%s" % (thisdir,fname);
+ if(fname.split('.')[0][0].isalpha()):
+ all_data[fname.split('.')[0]] = dat
+ print "Loaded .txt: %s/%s as %s" % (thisdir,fname,
+ fname.split('.')[0]);
+ else:
+ all_data[prefix + fname.split('.')[0]] = dat
+ print "Loaded .txt: %s/%s as %s" % (thisdir,fname,
+ prefix + fname.split('.')[0]);
#print "all_data has %d" % len(all_data)
except:
print "Tried, but didn't load .txt: %s/%s" % (thisdir,fname);
@@ -42,7 +54,10 @@ def dothing(arg,thisdir,files):
# print len(dat)
# print dat
dat = scipy.array(dat);
- all_data[fname.split('.')[0]] = dat
+ if(fname.split('.')[0][0].isalpha()):
+ all_data[fname.split('.')[0]] = dat
+ else:
+ all_data[prefix + fname.split('.')[0]] = dat
print "Loaded .Spe: %s/%s" % (thisdir,fname);
# except:
print "Tried, but didn't load .Spe: %s/%s" % (thisdir,fname);
@@ -51,7 +66,7 @@ def dothing(arg,thisdir,files):
else:
print "Wasn't a file: %s/%s" % (thisdir,fname);
-def import_data(outfile='data.pydat',root='.',saveit=True,recurse=False):
+def import_data(outfile='data.pydat',root='.',saveit=True,recurse=False,prefix="d"):
"""Imports all that yummah files..."""
global all_data
if saveit:
diff --git a/stats.py b/stats.py
index 9acf3f4..eea94a2 100644
--- a/stats.py
+++ b/stats.py
@@ -50,7 +50,7 @@ def rchisqr_poisson(tp,ffunc,tx,ty,floor=1.):
terr = floored_root(tx,min=floor);
return rchisqr(tp,ffunc,tx,ty,terr);
-def fittodata(tx,ty,p0,ffunc,err=None,floor=1.,ret_all=False):
+def fittodata(tx,ty,p0,ffunc,err=None,floor=1.,ret_all=False,param_names=None):
"""does a fit and returns a dict of shit.
ret_all=True gives more shit."""
if err == None:
@@ -101,50 +101,70 @@ def fittodata(tx,ty,p0,ffunc,err=None,floor=1.,ret_all=False):
"err":err,
"x":tx,
"y":ty,
- "success":success};
+ "success":success,
+ "param_names":param_names};
else:
return {"params":p1,
"param_error":p1_err,
- "rchisqr":rcs}
+ "rchisqr":rcs,
+ "param_names":param_names};
def fittodata_gaussian(tx,ty,p0,err=None,floor=1.,ret_all=False):
"""runs fittodata for a gaussian (first parameter is mean,
- second is stddev, third is amplitude)"""
+ second is stddev)"""
#gauss_func = lambda a,b: a[2]/(a[1] * sqrt(2*pi)) * exp(-.5 * ((b - a[0])/a[1])**2)
#thing = std(ty);
bigsum = scipy.integrate.simps(ty,x=tx)
gauss_func = lambda a,b: bigsum/(a[1] * scipy.sqrt(2*pi)) * scipy.exp(-.5 * ((b - a[0])/a[1])**2)
- return fittodata(tx,ty,p0,gauss_func,err=err,floor=floor,ret_all=ret_all);
+ return fittodata(tx,ty,p0,gauss_func,err=err,floor=floor,ret_all=ret_all,
+ param_names=["Mean","StdDev"]);
+
+def fittodata_gaussian_amp(tx,ty,p0,err=None,floor=1.,ret_all=False):
+ """runs fittodata for a gaussian with amplitude parameter (first parameter
+ is mean, second is stddev, third is amplitude)"""
+ bigsum = scipy.integrate.simps(ty,x=tx)
+ gauss_func = lambda a,b: a[2] * bigsum/(a[1] * scipy.sqrt(2*pi)) * scipy.exp(-.5 * ((b - a[0])/a[1])**2)
+ return fittodata(tx,ty,p0,gauss_func,err=err,floor=floor,ret_all=ret_all,
+ param_names=["Mean","StdDev","Amplitude"]);
+
+def fittodata_gaussian_plus(tx,ty,p0,err=None,floor=1.,ret_all=False):
+ """runs fittodata for a gaussian with extra parameters (first parameter
+ is mean, second is stddev, third is amplitude, fourth is constant offset,
+ fifth is linear trend)"""
+ bigsum = scipy.integrate.simps(ty,x=tx)
+ gauss_func = lambda a,b: a[2] * bigsum/(a[1] * scipy.sqrt(2*pi)) * scipy.exp(-.5 * ((b - a[0])/a[1])**2) + a[3] + a[4]*b
+ return fittodata(tx,ty,p0,gauss_func,err=err,floor=floor,ret_all=ret_all,
+ param_names=["Mean","StdDev","Amplitude","VertOffset","Slope"]);
def fittodata_linear(tx,ty,p0,err=None,floor=1.,ret_all=False):
"""runs fittodata for a linear curve (first param is slope,
second is vertical offset)"""
lin_func = lambda a,b: a[0] * b + a[1]
- return fittodata(tx,ty,p0,lin_func,err=err,floor=floor,ret_all=ret_all);
+ return fittodata(tx,ty,p0,lin_func,err=err,floor=floor,ret_all=ret_all,
+ param_names=["VertOffset (m)","Slope (b)"]);
+
+def fittodata_quadratic(tx,ty,p0,err=None,floor=1.,ret_all=False):
+ """runs fittodata for a quadratic curve (first param is a,
+ second is b, third is c: ax^2+bx+c)"""
+ quad_func= lambda a,b: a[0] * b*b + a[1]*b + a[2]
+ return fittodata(tx,ty,p0,quad_func,err=err,floor=floor,ret_all=ret_all,
+ param_names=["QuadaticTerm (a)","VertOffset (b)","Slope (c)"]);
def fittodata_poisson(tx,ty,p0,err=None,floor=.5,ret_all=False):
- """runs fittodata for a poisson (first param is the mean"""
+ """runs fittodata for a poisson (first param is the mean)"""
bigsum = scipy.integrate.simps(ty,x=tx)
if type(p0) == type(0.1):
p0 = [p0,]
poissfunc = lambda a,b: bigsum * scipy.power(a[0],b) / scipy.factorial(b) * scipy.exp(-1 * a[0])
- #def poissfunc(a,b):
- # print "--------"
- # print a
- # print b
- # print bigsum
- # print scipy.power(a[0],b)
- # print scipy.exp(a[0])
- # print bigsum * scipy.power(a[0],b) / scipy.factorial(b) * scipy.exp(-1 * a[0])
- # return bigsum * scipy.power(a[0],b) / scipy.factorial(b) * scipy.exp(-1 * a[0])
- return fittodata(tx,ty,p0,poissfunc,err=err,floor=floor,ret_all=ret_all);
+ return fittodata(tx,ty,p0,poissfunc,err=err,floor=floor,ret_all=ret_all,
+ param_names=["Mean"]);
def fittodata_exp(tx,ty,p0,err=None,floor=1.,ret_all=False):
"""runs fittodata for an exponential (first param is x0,
second param is tau, third param is vertical offset)"""
- #exp_func = lambda a,b: a[0] * scipy.exp(- b/a[1]) + a[2]
exp_func = lambda a,b: a[0] * e ** (- b/a[1]) + a[2]
- return fittodata(tx,ty,p0,exp_func,err=err,floor=floor,ret_all=ret_all);
+ return fittodata(tx,ty,p0,exp_func,err=err,floor=floor,ret_all=ret_all,
+ param_names=["x0","tau","VertOffset"]);
def fittodata_lorentz(tx,ty,p0,err=None,floor=1.,ret_all=False):
"""runs fittodata for a lorentzian (first param is gamma,
@@ -153,7 +173,19 @@ def fittodata_lorentz(tx,ty,p0,err=None,floor=1.,ret_all=False):
if type(p0) == type(0.1):
p0 = [p0,]
lorentz_func = lambda a,b: bigsum * a[0]/(2.*pi*((a[1]-b)**2+(a[0]/2.)**2. ))
- return fittodata(tx,ty,p0,lorentz_func,err=err,floor=floor,ret_all=ret_all);
+ return fittodata(tx,ty,p0,lorentz_func,err=err,floor=floor,ret_all=ret_all,
+ param_names=["Gamma","Mean"]);
+
+def fittodata_lorentz_plus(tx,ty,p0,err=None,floor=1.,ret_all=False):
+ """runs fittodata for a lorentzian with extra parameters (first param
+ is gamma, second param is mean, third is constant offset, fourth is
+ linear trend, fifth is amplitute)"""
+ bigsum = scipy.integrate.simps(ty,x=tx)
+ if type(p0) == type(0.1):
+ p0 = [p0,]
+ lorentz_func = lambda a,b,: a[4]*bigsum * a[0]/(2.*pi*((a[1]-b)**2+(a[0]/2.)**2. )) + a[2] + a[3]*b
+ return fittodata(tx,ty,p0,lorentz_func,err=err,floor=floor,ret_all=ret_all,
+ param_names=["Gamma","Mean","VertOffset","Slope","Amplitude"]);
def plotfit(indict,newfig=True):
if not indict.has_key('x'):
@@ -164,6 +196,7 @@ def plotfit(indict,newfig=True):
y = indict['y']
err = indict['err']
ffunc = indict['ffunc']
+ param_names = indict['param_names']
if newfig: pylab.figure();
pylab.errorbar(x,y,fmt='b.',yerr=err)
d=pylab.plot(x,y,'k.')
@@ -179,19 +212,12 @@ def plotfit(indict,newfig=True):
p1_err = indict['param_error']
ax = pylab.gca()
- extra_txt = 'reduced chi squared: %g'\
- % (indict['rchisqr'])
+ extra_txt = 'reduced chi^2: %g'% (indict['rchisqr'])
for i in range(scipy.size(p1)):
- extra_txt += '\np[%i]: %g +/- %g' \
- % (i,p1[i],p1_err[i]);
-
-# if(pylab.size(p1) == 2):
-# extra_txt = 'reduced chi squared: %g\np[0]: %g\np[1]: %g' \
-# % (indict['rchisqr'],p1[0],p1[1])
-# else:
-# extra_txt= 'reduced chi squared:%g\np[0]:%g, +/- %g\n p[1]:%g\np[2]:%g' \
-# % (indict['rchisqr'],p1[0],p1[1],p1[2])
-
+ if len(param_names)>i:
+ extra_txt += '\n%s: %.3g +/- %.2g' % (param_names[i],p1[i],p1_err[i]);
+ else:
+ extra_txt += '\np[%i]: %.3g +/- %.2g' % (i,p1[i],p1_err[i]);
t = pylab.text(.77, .75, extra_txt,
horizontalalignment='center',
verticalalignment='center',