def gfit1d(y, x=None, err = None, weights=None, par=None, parinfo=None,
maxiter=200, quiet=0):
"""
Return the gaussian fit as an object.
Parameters
----------
y: 1D Numarray array
The data to be fitted
x: 1D Numarray array
(optional) The x values of the y array. x and y must
have the same shape.
err: 1D Numarray array
(optional) 1D array with measurement errors, must be
the same shape as y
weights: 1D Numarray array
(optiional) 1D array with weights, must be the same
shape as y
par: List
(optional) Starting values for the parameters to be fitted
parinfo: Dictionary of lists
(optional) provides additional information for the
parameters. For a detailed description see nmpfit.py.
Parinfo can be used to limit parameters or keep
some of them fixed.
maxiter: number
Maximum number of iterations to perform
Default: 200
quiet: number
if set to 1, nmpfit does not print to the screen
Default: 0
Examples
--------
>>> x=N.arange(10,20, 0.1)
>>> y= 10*N.e**(-(x-15)**2/4)
>>> print gfit1d(y,x=x, maxiter=20,quiet=1).params
[ 10. 15. 1.41421356]
"""
if numerixenv.check_input(x) or numerixenv.check_input(y):
raise ValueError, "Input is a NumArray array. This version of %s requires a Numpy array\n" % __name__
y = y.astype(N.float)
if weights != None:
weights = weights.astype(N.float)
if err != None:
err = err.astype(N.float)
if x == None and len(y.shape)==1 :
x = N.arange(len(y)).astype(N.float)
if x.shape != y.shape:
print "input arrays X and Y must be of equal shape.\n"
return
fa = {'x':x, 'y':y, 'err':err, 'weights':weights}
if par != None:
p = par
else:
ysigma = y.std()
ind = N.nonzero(y > ysigma)[0]
if len(ind) != 0:
xind = int(ind.mean())
p2 = x[xind]
p1 = y[xind]
p3 = 1.0
else:
ymax = y.max()
ymin = y.min()
ymean= y.mean()
if (ymax - ymean) > (abs(ymin - ymean)):
p1 = ymax
else: p1 = ymin
ind = (N.nonzero(y == p1))[0]
p2 = x.mean()
p3 = 1.
p = [p1, p2, p3]
m=nmpfit.mpfit(_gauss_funct, p,parinfo = parinfo, functkw=fa,
maxiter=maxiter, quiet=quiet)
if (m.status <=0): print 'error message = ', m.errmsg
return m
y=nn[nMaxArg-5:nMaxArg+5]
#x=bins
#y=nn
print x.shape, y.shape
def g(p,fjac = None, x = None, y=None, err=None,weights=None):
if p[2] != 0.0:
Z = (x - p[1])
model = p[0]*n.e ** (-Z**2 / (p[2] * p[2] * 2.0))
else:
model = N.zeros(N.size(x))
status = 0
# if n.sqrt(y)!=0.0:
return [status, (y-model)]#/ (y)]
# else:
# return [status, (y-model)]
fa = {'x':x, 'y':y}
m=nmpfit.mpfit(g,p,functkw=fa)
p=m.params
print p
#figure()
print p[2] / abs(bins[1] - bins[0])
yy = p[0] * n.e**(-0.5*(bins-p[1])**2/p[2]**2)
l=plot(bins, yy, 'b--')
setp(l, 'linewidth', 3)
grid(1)
show()
#x=bins
#y=nn
print x.shape, y.shape
def g(p, fjac=None, x=None, y=None, err=None, weights=None):
if p[2] != 0.0:
Z = (x - p[1])
model = p[0] * n.e**(-Z**2 / (p[2] * p[2] * 2.0))
else:
model = N.zeros(N.size(x))
status = 0
# if n.sqrt(y)!=0.0:
return [status, (y - model)] #/ (y)]
# else:
# return [status, (y-model)]
fa = {'x': x, 'y': y}
m = nmpfit.mpfit(g, p, functkw=fa)
p = m.params
print p
#figure()
print p[2] / abs(bins[1] - bins[0])
yy = p[0] * n.e**(-0.5 * (bins - p[1])**2 / p[2]**2)
l = plot(bins, yy, 'b--')
setp(l, 'linewidth', 3)
grid(1)
show()
