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Use numpy.nan to hide unfit portions of polymer tension columns.
author
W. Trevor King
<wking@drexel.edu>
Tue, 10 Aug 2010 17:10:40 +0000
(13:10 -0400)
committer
W. Trevor King
<wking@drexel.edu>
Tue, 10 Aug 2010 17:10:40 +0000
(13:10 -0400)
hooke/plugin/polymer_fit.py
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diff --git
a/hooke/plugin/polymer_fit.py
b/hooke/plugin/polymer_fit.py
index a5e892e696096ee9b8568e4f44e96e7f767a40be..43ed44ba00d5eb8cb3bff8d64d12a3143d9323e3 100644
(file)
--- a/
hooke/plugin/polymer_fit.py
+++ b/
hooke/plugin/polymer_fit.py
@@
-1032,10
+1032,9
@@
Name (without units) for storing the fit parameters in the `.info` dictionary.
L = model.L(Lp)
T = info['temperature (K)']
fit_info = queue.get(block=False)
L = model.L(Lp)
T = info['temperature (K)']
fit_info = queue.get(block=False)
- mask = numpy.zeros(z_data.shape, dtype=numpy.bool)
- mask[start:stop] = True
- return [FJC_fn(z_data, T=T, L=L, a=a) * mask,
- fit_info]
+ f_data = numpy.ones(z_data.shape, dtype=z_data.dtype) * numpy.nan
+ f_data[start:stop] = FJC_fn(z_data[start:stop], T=T, L=L, a=a)
+ return [f_data, fit_info]
def fit_FJC_PEG_model(self, params, z_data, d_data, start, stop,
outqueue=None):
def fit_FJC_PEG_model(self, params, z_data, d_data, start, stop,
outqueue=None):
@@
-1049,7
+1048,7
@@
Name (without units) for storing the fit parameters in the `.info` dictionary.
if True: # TODO: optionally free persistence length
info['Kuhn length (m)'] = (
params['FJC Kuhn length'])
if True: # TODO: optionally free persistence length
info['Kuhn length (m)'] = (
params['FJC Kuhn length'])
- model = FJC(d_data[start:stop], info=info, rescale=True)
+ model = FJC
_PEG
(d_data[start:stop], info=info, rescale=True)
queue = Queue()
params = model.fit(outqueue=queue)
if True: # TODO: if Kuhn length fixed
queue = Queue()
params = model.fit(outqueue=queue)
if True: # TODO: if Kuhn length fixed
@@
-1061,10
+1060,9
@@
Name (without units) for storing the fit parameters in the `.info` dictionary.
N = model.L(Nr)
T = info['temperature (K)']
fit_info = queue.get(block=False)
N = model.L(Nr)
T = info['temperature (K)']
fit_info = queue.get(block=False)
- mask = numpy.zeros(z_data.shape, dtype=numpy.bool)
- mask[start:stop] = True
- return [FJC_PEG_fn(z_data, **kwargs) * mask,
- fit_info]
+ f_data = numpy.ones(z_data.shape, dtype=z_data.dtype) * numpy.nan
+ f_data[start:stop] = FJC_PEG_fn(z_data[start:stop], **kwargs)
+ return [f_data, fit_info]
def fit_WLC_model(self, params, z_data, d_data, start, stop,
outqueue=None):
def fit_WLC_model(self, params, z_data, d_data, start, stop,
outqueue=None):
@@
-1089,11
+1087,9
@@
Name (without units) for storing the fit parameters in the `.info` dictionary.
L = model.L(Lp)
T = info['temperature (K)']
fit_info = queue.get(block=False)
L = model.L(Lp)
T = info['temperature (K)']
fit_info = queue.get(block=False)
- mask = numpy.zeros(z_data.shape, dtype=numpy.bool)
- mask[start:stop] = True
- return [WLC_fn(z_data, T=T, L=L, p=p) * mask,
- fit_info]
-
+ f_data = numpy.ones(z_data.shape, dtype=z_data.dtype) * numpy.nan
+ f_data[start:stop] = WLC_fn(z_data[start:stop], T=T, L=L, p=p)
+ return [f_data, fit_info]
class PolymerFitPeaksCommand (Command):
class PolymerFitPeaksCommand (Command):