-# Copyright (C) 2010 W. Trevor King <wking@drexel.edu>
+# Copyright (C) 2010-2011 W. Trevor King <wking@drexel.edu>
#
# This file is part of Hooke.
#
"""
from numpy import arange, ndarray
+from scipy import __version__ as _scipy_version
from scipy.optimize import leastsq
-import scipy.optimize
+
+_strings = _scipy_version.split('.')
+# Don't convert third string to an integer in case of (for example) '0.7.2rc3'
+_SCIPY_VERSION = (int(_strings[0]), int(_strings[1]), _strings[2])
+del _strings
class PoorFit (ValueError):
7.000
>>> print '%.3f' % offset
-32.890
+
+ Test single-parameter models:
+
+ >>> class SingleParameterModel (LinearModel):
+ ... '''Simple linear model.
+ ... '''
+ ... def model(self, params):
+ ... return super(SingleParameterModel, self).model([params[0], 0.])
+ ... def guess_initial_params(self, outqueue=None):
+ ... return super(SingleParameterModel, self
+ ... ).guess_initial_params(outqueue)[:1]
+ ... def guess_scale(self, params, outqueue=None):
+ ... return super(SingleParameterModel, self
+ ... ).guess_scale([params[0], 0.], outqueue)[:1]
+ >>> data = 20*numpy.sin(arange(1000)) + 7.*arange(1000)
+ >>> m = SingleParameterModel(data)
+ >>> slope, = m.fit(outqueue=outqueue)
+ >>> print '%.3f' % slope
+ 7.000
"""
def __init__(self, *args, **kwargs):
self.set_data(*args, **kwargs)
params,cov,info,mesg,ier = leastsq(
func=self.residual, x0=active_params, full_output=True,
diag=scale, **kwargs)
+ if len(initial_params) == 1 and _SCIPY_VERSION < (0, 8, '0'):
+ # params is a float for scipy < 0.8.0. Convert to list.
+ params = [params]
if self._rescale == True:
active_params = params
- if len(initial_params) == 1: # params is a float
- params = params * self._param_scale_factors[0]
- else:
- params = [p*s for p,s in zip(params,
- self._param_scale_factors)]
+ params = [p*s for p,s in zip(params,
+ self._param_scale_factors)]
else:
active_params = params
if outqueue != None: