def test_can_mmap_fcn_that_curries_given_fcn():
input_args_list = [[simple_fcn_two_args], [simple_fcn_two_args]]
output = multiprocessable_map(fcn_that_curries_a_given_fcn,
input_args_list, multiprocessing=False)
assert list(output) == [20, 20]
output = multiprocessable_map(fcn_that_curries_a_given_fcn,
input_args_list, multiprocessing=True)
assert list(output) == [20, 20]
def test_can_mmap_fcn_with_partial_subfcn():
input_args_list = [[1], [2], [3]]
output = multiprocessable_map(simple_fcn_with_nested_partial_fcn,
input_args_list, multiprocessing=False)
assert list(output) == [10, 20, 30]
output = multiprocessable_map(simple_fcn_with_nested_partial_fcn,
input_args_list, multiprocessing=True)
assert list(output) == [10, 20, 30]
def test_can_mmap_variable_num_args():
input_args_list = [[1], [5], [3, 5]]
output = multiprocessable_map(simple_fcn_two_args, input_args_list,
multiprocessing=False)
assert list(output) == [1, 5, 15]
output = multiprocessable_map(simple_fcn_two_args, input_args_list,
multiprocessing=True)
assert list(output) == [1, 5, 15]
def test_can_mmap_simple_helper_fcn():
input_args_list = [[1], [2], [3]]
output = multiprocessable_map(simple_fcn, input_args_list,
multiprocessing=False)
assert list(output) == [2, 4, 6]
output = multiprocessable_map(simple_fcn, input_args_list,
multiprocessing=True)
assert list(output) == [2, 4, 6]
def test_can_mmap_fcn_containing_functional_subfcn():
input_args_list = [[simple_fcn_two_args, 1],
[simple_fcn_two_args, 2]]
output = multiprocessable_map(fcn_with_nested_fucntional_fcn,
input_args_list, multiprocessing=False)
assert list(output) == [10, 20]
output = multiprocessable_map(fcn_with_nested_fucntional_fcn,
input_args_list, multiprocessing=True)
assert list(output) == [10, 20]
def test_can_mmap_fcn_with_partial_subfcn():
input_args_list = [[1], [2], [3]]
output = multiprocessable_map(simple_fcn_with_nested_partial_fcn,
input_args_list,
multiprocessing=False)
assert list(output) == [10, 20, 30]
output = multiprocessable_map(simple_fcn_with_nested_partial_fcn,
input_args_list,
multiprocessing=True)
assert list(output) == [10, 20, 30]
def test_can_mmap_variable_num_args():
input_args_list = [[1], [5], [3, 5]]
output = multiprocessable_map(simple_fcn_two_args,
input_args_list,
multiprocessing=False)
assert list(output) == [1, 5, 15]
output = multiprocessable_map(simple_fcn_two_args,
input_args_list,
multiprocessing=True)
assert list(output) == [1, 5, 15]
def test_can_mmap_simple_helper_fcn():
input_args_list = [[1], [2], [3]]
output = multiprocessable_map(simple_fcn,
input_args_list,
multiprocessing=False)
assert list(output) == [2, 4, 6]
output = multiprocessable_map(simple_fcn,
input_args_list,
multiprocessing=True)
assert list(output) == [2, 4, 6]
def test_can_mmap_fcn_containing_functional_subfcn():
input_args_list = [[simple_fcn_two_args, 1], [simple_fcn_two_args, 2]]
output = multiprocessable_map(fcn_with_nested_fucntional_fcn,
input_args_list,
multiprocessing=False)
assert list(output) == [10, 20]
output = multiprocessable_map(fcn_with_nested_fucntional_fcn,
input_args_list,
multiprocessing=True)
assert list(output) == [10, 20]
def test_can_mmap_fcn_that_curries_given_fcn():
input_args_list = [[simple_fcn_two_args], [simple_fcn_two_args]]
output = multiprocessable_map(fcn_that_curries_a_given_fcn,
input_args_list,
multiprocessing=False)
assert list(output) == [20, 20]
output = multiprocessable_map(fcn_that_curries_a_given_fcn,
input_args_list,
multiprocessing=True)
assert list(output) == [20, 20]
def scandata_list_fit(scandata_list, yfield, fitfunction,
free_params, initial_params, param_bounds,
max_fcn_evals=20000, excluded_intervals=None,
ignore_weights=False, multiprocessing=False):
"""
Takes a list (or other iterable) of ScanData and performs a fit on all
of them.
Optionally, each parameter after scandata_list (except multiprocessing)
can be replaced with a list of equal length to scandata_list. This will
fit each ScanData with the corresponding parameters from the other lists
instead of using the same parameters for each fit.
FitData resulting from fits are stored in the info dict of each ScanData
under the key 'fitdata_[yfield]'. 'None' is stored for failed fits.
These FitData (and Nones) are also returned as a list.
"""
scandata_list = list(scandata_list) # ensure an actual list
if len(scandata_list) == 0:
return []
try: # assume first all args are equal length, correspond to ScanData 1:1
assert len(fitfunction) == len(scandata_list)
assert len(yfield) == len(scandata_list)
assert len(free_params) == len(scandata_list)
assert len(initial_params) == len(scandata_list)
assert len(param_bounds) == len(scandata_list)
assert len(max_fcn_evals) == len(scandata_list)
assert len(excluded_intervals) == len(scandata_list)
assert len(ignore_weights) == len(scandata_list)
yfield = [field if field is not None else scandata.yfield
for scandata, field in zip(scandata_list, yfield)]
xyyerr_lists = \
list(zip(*[scandata.get_field_xyyerr(field)
for scandata, field in zip(scandata_list, yfield)]))
assert len(xyyerr_lists[0]) == len(scandata_list) # zip error check
input_args_list = list(zip(*xyyerr_lists,
fitfunction, free_params, initial_params,
param_bounds, max_fcn_evals,
excluded_intervals, ignore_weights))
assert len(input_args_list) == len(scandata_list) # zip error check
yfield_list = yfield
except (TypeError, AssertionError): # assume all scandata share fit params
if yfield is None:
yfield = scandata_list[0].yfield
input_args_list = [[*scandata.get_field_xyyerr(yfield),
fitfunction, free_params, initial_params,
param_bounds, max_fcn_evals,
excluded_intervals, ignore_weights]
for scandata in scandata_list]
yfield_list = [yfield] * len(scandata_list)
fitdata_list = multiprocessable_map(generic_curve_fit, input_args_list,
multiprocessing)
for scandata, fitdata, field in \
zip(scandata_list, fitdata_list, yfield_list):
setattr(scandata, 'fitdata_' + field, fitdata)
return fitdata_list
# get data
input_arglist_list = []
for relative_amp in relative_amp_list:
for b_field in b_field_list:
species2_kwargs['relative_amp'] = relative_amp
input_arglist_list.append([electric_field, # V/cm
b_field,
gen_params_dict.copy(),
pump_laser_kwargs.copy(),
probe_laser_kwargs.copy(),
species1_kwargs.copy(),
species2_kwargs.copy(),
suppress_plot])
output_list = multiprocessable_map(model_spin_channels,
input_arglist_list,
multiprocessing=True)
output_iter = iter(output_list)
yvals_vs_pos_vs_field_vs_relative_amp = []
for i in range(n_relative_amps):
yvals_vs_pos_vs_field = []
for j in range(n_b_fields):
times, yvals_vs_pos = next(output_iter)
yvals_vs_pos_vs_field.append(yvals_vs_pos)
yvals_vs_pos_vs_field_vs_relative_amp.append(yvals_vs_pos_vs_field)
# plot
plt.figure()
for amp_ind, amp in enumerate(relative_amp_list):
yvals_vs_pos_vs_field = \
yvals_vs_pos_vs_field_vs_relative_amp[amp_ind]
def scandata_list_fit(scandata_list,
yfield,
fitfunction,
free_params,
initial_params,
param_bounds,
max_fcn_evals=20000,
excluded_intervals=None,
ignore_weights=False,
multiprocessing=False):
"""
Takes a list (or other iterable) of ScanData and performs a fit on all
of them.
Optionally, each parameter after scandata_list (except multiprocessing)
can be replaced with a list of equal length to scandata_list. This will
fit each ScanData with the corresponding parameters from the other lists
instead of using the same parameters for each fit.
FitData resulting from fits are stored in the info dict of each ScanData
under the key 'fitdata_[yfield]'. 'None' is stored for failed fits.
These FitData (and Nones) are also returned as a list.
"""
scandata_list = list(scandata_list) # ensure an actual list
if len(scandata_list) == 0:
return []
try: # assume first all args are equal length, correspond to ScanData 1:1
assert len(fitfunction) == len(scandata_list)
assert len(yfield) == len(scandata_list)
assert len(free_params) == len(scandata_list)
assert len(initial_params) == len(scandata_list)
assert len(param_bounds) == len(scandata_list)
assert len(max_fcn_evals) == len(scandata_list)
assert len(excluded_intervals) == len(scandata_list)
assert len(ignore_weights) == len(scandata_list)
yfield = [
field if field is not None else scandata.yfield
for scandata, field in zip(scandata_list, yfield)
]
xyyerr_lists = \
list(zip(*[scandata.get_field_xyyerr(field)
for scandata, field in zip(scandata_list, yfield)]))
assert len(xyyerr_lists[0]) == len(scandata_list) # zip error check
input_args_list = list(
zip(*xyyerr_lists, fitfunction, free_params, initial_params,
param_bounds, max_fcn_evals, excluded_intervals,
ignore_weights))
assert len(input_args_list) == len(scandata_list) # zip error check
yfield_list = yfield
except (TypeError, AssertionError): # assume all scandata share fit params
if yfield is None:
yfield = scandata_list[0].yfield
input_args_list = [[
*scandata.get_field_xyyerr(yfield), fitfunction, free_params,
initial_params, param_bounds, max_fcn_evals, excluded_intervals,
ignore_weights
] for scandata in scandata_list]
yfield_list = [yfield] * len(scandata_list)
fitdata_list = multiprocessable_map(generic_curve_fit, input_args_list,
multiprocessing)
for scandata, fitdata, field in \
zip(scandata_list, fitdata_list, yfield_list):
setattr(scandata, 'fitdata_' + field, fitdata)
return fitdata_list
for relative_amp in relative_amp_list:
for b_field in b_field_list:
species2_kwargs['relative_amp'] = relative_amp
input_arglist_list.append([
electric_field, # V/cm
b_field,
gen_params_dict.copy(),
pump_laser_kwargs.copy(),
probe_laser_kwargs.copy(),
species1_kwargs.copy(),
species2_kwargs.copy(),
suppress_plot
])
output_list = multiprocessable_map(model_spin_channels,
input_arglist_list,
multiprocessing=True)
output_iter = iter(output_list)
yvals_vs_pos_vs_field_vs_relative_amp = []
for i in range(n_relative_amps):
yvals_vs_pos_vs_field = []
for j in range(n_b_fields):
times, yvals_vs_pos = next(output_iter)
yvals_vs_pos_vs_field.append(yvals_vs_pos)
yvals_vs_pos_vs_field_vs_relative_amp.append(yvals_vs_pos_vs_field)
# plot
plt.figure()
for amp_ind, amp in enumerate(relative_amp_list):
yvals_vs_pos_vs_field = \
yvals_vs_pos_vs_field_vs_relative_amp[amp_ind]