In [1]:

import sys
sys.path.append('/Users/ck/devel/peak-o-mat/')

In [15]:

from peak_o_mat.model import Model
from peak_o_mat.spec import Spec

In [3]:

m = Model('CB LO1 GA1 LO2')

In [4]:

m.parse()

In [5]:

print m.CB

CB: function of 'const'

In [8]:

print m.LO1

LO1: function of 'amp','fwhm','pos'

In [10]:

m.LO1.amp.value = 3.4

In [14]:

print m['LO1']['amp'].value

3.4

In [17]:

s = Spec('/Users/ck/devel/peak-o-mat/data/ruby.dat')

In [42]:

plot(s.x,s.y,'o')

Out[42]:

[<matplotlib.lines.Line2D at 0x95fb230>]

In [43]:

m = Model('CB LO1 GA LO2')
m.parse()

In [44]:

m.CB.const.value = 0.0
m.LO1.amp.value = 1.0
m.LO1.fwhm.value = 0.5
m.LO1.pos.value = 6930
m.GA.amp.value = 2.0
m.GA.fwhm.value = 0.2
m.GA.pos.value = 6934
m.LO2.amp.value = 2.0
m.LO2.fwhm.value = 0.5
m.LO2.pos.value = 6944

In [45]:

import numpy as np
x = np.linspace(6925,6950,500)

In [46]:

y = m.evaluate(x)

In [47]:

plot(s.x,s.y,'ko',alpha=0.2)
plot(x,y,'r-')

Out[47]:

[<matplotlib.lines.Line2D at 0x95fb650>]

In [48]:

from peak_o_mat.fit import Fit

In [49]:

f = Fit(s, m)
res = f.run()
print res[-1]
m.update_from_fit(res)

['reason for halting: Sum of squares convergence', 'residual variance: 0.000643948']

In [50]:

y = m.evaluate(x)

In [51]:

plot(s.x,s.y,'ko',alpha=0.2)
plot(x,y,'r-')

Out[51]:

[<matplotlib.lines.Line2D at 0x96f8570>]

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