def nonlinear_fit(d, e, p0):
"""
======== ===========================================================
Name Description
======== ===========================================================
d list of values of the (single) discretization
parameter in each experiment:
``d[i]`` provides the values of the discretization,
parameter in experiement no. i.
e list of error values; ``e = (e_1, e_2, ...)``,
``e[i]`` is the error associated with the parameters
``d[i]``
p0 starting values for the unknown parameters vector
return r, C; r is the exponent, C is the factor in front
======== ===========================================================
"""
if len(d) != len(e):
raise ValueError('d and e must have the same length')
if not isinstance(d[0], (float,int)):
raise TypeError('d must be an array of numbers, not %s' % \
str(type(d[0])))
# transform d and e to the data format required by
# the Scientific package:
data = []
for d_i, e_i in zip(d, e):
data.append(((d_i,) , e_i)) # recall (a,) conversion to tuple
leastSquaresFit = import_module('Scientific.Functions.LeastSquares',
'leastSquaresFit')
sol = leastSquaresFit(OneDiscretizationPrm.error_model, p0, data)
C = sol[0][0]
a = sol[0][1]
return a, C
def nonlinear_fit(d, e, initial_guess):
"""
============== ================================================
Argument Description
============== ================================================
d list of values of the set of discretization
parameters in each experiment:
``d = ((d_1,d_2,d_3),(d_1,d_2,d_3,),...)``;
``d[i]`` provides the values of the
discretization parameters in experiement no. i.
e list of error values; ``e = (e_1, e_2, ...)``:
``e[i]`` is the error associated with the
parameters ``d[i]``
initial_guess the starting value for the unknown
parameters vector
return list of fitted parameters
============== ================================================
"""
if len(d) != len(e):
raise ValueError('len(d) != len(e)')
# transform d and e to the data format required by
# the Scientific package:
data = []
for d_i, e_i in zip(d, e):
if isinstance(d_i, (float, int)):
data.append(((d_i,), e_i))
else: # d_i is tuple, list, array, NumArray, ...
data.append((d_i, e_i))
leastSquaresFit = import_module('Scientific.Functions.LeastSquares',
'leastSquaresFit')
sol = leastSquaresFit(ManyDiscretizationPrm.error_model,
initial_guess, data)
# return list of fitted parameters (p in error_model)
# (sol[1] is a measure of the quality of the fit)
return sol[0]
def nonlinear_fit(d, e, p0):
"""
======== ===========================================================
Name Description
======== ===========================================================
d list of values of the (single) discretization
parameter in each experiment:
``d[i]`` provides the values of the discretization,
parameter in experiement no. i.
e list of error values; ``e = (e_1, e_2, ...)``,
``e[i]`` is the error associated with the parameters
``d[i]``
p0 starting values for the unknown parameters vector
return r, C; r is the exponent, C is the factor in front
======== ===========================================================
"""
if len(d) != len(e):
raise ValueError('d and e must have the same length')
if not isinstance(d[0], (float, int)):
raise TypeError('d must be an array of numbers, not %s' % \
str(type(d[0])))
# transform d and e to the data format required by
# the Scientific package:
data = []
for d_i, e_i in zip(d, e):
data.append(((d_i, ), e_i)) # recall (a,) conversion to tuple
leastSquaresFit = import_module('Scientific.Functions.LeastSquares',
'leastSquaresFit')
sol = leastSquaresFit(OneDiscretizationPrm.error_model, p0, data)
C = sol[0][0]
a = sol[0][1]
return a, C
def nonlinear_fit(d, e, initial_guess):
"""
============== ================================================
Argument Description
============== ================================================
d list of values of the set of discretization
parameters in each experiment:
``d = ((d_1,d_2,d_3),(d_1,d_2,d_3,),...)``;
``d[i]`` provides the values of the
discretization parameters in experiement no. i.
e list of error values; ``e = (e_1, e_2, ...)``:
``e[i]`` is the error associated with the
parameters ``d[i]``
initial_guess the starting value for the unknown
parameters vector
return list of fitted parameters
============== ================================================
"""
if len(d) != len(e):
raise ValueError('len(d) != len(e)')
# transform d and e to the data format required by
# the Scientific package:
data = []
for d_i, e_i in zip(d, e):
if isinstance(d_i, (float, int)):
data.append(((d_i, ), e_i))
else: # d_i is tuple, list, array, NumArray, ...
data.append((d_i, e_i))
leastSquaresFit = import_module('Scientific.Functions.LeastSquares',
'leastSquaresFit')
sol = leastSquaresFit(ManyDiscretizationPrm.error_model, initial_guess,
data)
# return list of fitted parameters (p in error_model)
# (sol[1] is a measure of the quality of the fit)
return sol[0]
def __init__(self, data):
self.data = data # (x,y,f) data for an f(x,y) function
from scitools.misc import import_module
InterpolatingFunction = import_module(
'Scientific.Functions.Interpolation', 'InterpolatingFunction')
import Scientific
v = Scientific.__version__
target = '2.9.1'
if v < target:
raise ImportError(
'ScientificPython is in (old) version %s, need %s'
% (v, target))
self.interpolating_function = \
InterpolatingFunction(self.data[:-1], self.data[-1])
self.ndims = len(self.data[:-1]) # no of spatial dim.
def __init__(self, data):
self.data = data # (x,y,f) data for an f(x,y) function
from scitools.misc import import_module
InterpolatingFunction = import_module(
'Scientific.Functions.Interpolation', 'InterpolatingFunction')
import Scientific
v = Scientific.__version__
target = '2.9.1'
if v < target:
raise ImportError(
'ScientificPython is in (old) version %s, need %s' %
(v, target))
self.interpolating_function = \
InterpolatingFunction(self.data[:-1], self.data[-1])
self.ndims = len(self.data[:-1]) # no of spatial dim.
