IPKM/nmreval
14
2
Fork 1
Code Issues 39 Pull Requests Actions Packages Projects Releases Wiki Activity

seems mostly to be working

Browse Source
This commit is contained in:
Dominik Demuth 2023-08-25 18:46:36 +02:00
parent 5d43ccb05d
commit 0b8f4932b2
8 changed files with 123 additions and 73 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
src/gui_qt/fit/fit_parameter.py
View File

@ -227,7 +227,7 @@ class QFitParameterWidget(QtWidgets.QWidget, Ui_FormFit):
kw_p = {}
p = []
if global_p is None:
global_p = {'p': [], 'idx': [], 'var': [], 'ub': [], 'lb': []}
global_p = {'value': [], 'idx': [], 'var': [], 'ub': [], 'lb': []}
for i, (p_i, g) in enumerate(zip(parameter, self.global_parameter)):
if isinstance(g, FitModelWidget):
@ -235,7 +235,7 @@ class QFitParameterWidget(QtWidgets.QWidget, Ui_FormFit):
p.append(globs[i])
if is_global[i]:
if i not in global_p['idx']:
global_p['p'].append(globs[i])
global_p['value'].append(globs[i])
global_p['idx'].append(i)
global_p['var'].append(is_fixed[i])
global_p['ub'].append(ub[i])

11
src/gui_qt/fit/fitwindow.py
View File

@ -221,7 +221,7 @@ class QFitDialog(QtWidgets.QWidget, Ui_FitDialog):
parameter: dict = None, add_idx: bool = False, cnt: int = 0) -> tuple[dict, int]:
if parameter is None:
parameter = {'parameter': {}, 'lb': (), 'ub': (), 'var': [],
'glob': {'idx': [], 'p': [], 'var': [], 'lb': [], 'ub': []},
'glob': {'idx': [], 'value': [], 'var': [], 'lb': [], 'ub': []},
'links': [], 'color': []}
for i, f in enumerate(model):
@ -269,7 +269,7 @@ class QFitDialog(QtWidgets.QWidget, Ui_FitDialog):
if f['children']:
# recurse for children
child_parameter, cnt = self._prepare(f['children'], parameter=parameter, add_idx=add_idx, cnt=cnt)
_, cnt = self._prepare(f['children'], parameter=parameter, add_idx=add_idx, cnt=cnt)
return parameter, cnt
@ -288,6 +288,13 @@ class QFitDialog(QtWidgets.QWidget, Ui_FitDialog):
if k in data:
parameter, _ = self._prepare(mod, function_use=data[k], add_idx=isinstance(func, MultiModel))
# convert positions of global parameter to corresponding names
global_parameter: dict = parameter['glob']
positions = global_parameter.pop('idx')
global_parameter['key'] = [pname for i, pname in enumerate(func.params) if i in positions]
# print(global_parameter)
if parameter is None:
return

4
src/gui_qt/main/management.py
View File

@ -467,7 +467,9 @@ class UpperManagement(QtCore.QObject):
model_globs = model_p['glob']
if model_globs:
m.set_global_parameter(**model_p['glob'])
for parameter_args in zip(*model_globs.values()):
m.set_global_parameter(**{k: v for k, v in zip(model_globs.keys(), parameter_args)})
# m.set_global_parameter(**model_p['glob'])
for links_i in links:
self.fitter.set_link_parameter((models[links_i[0]], links_i[1]),

43
src/nmreval/fit/data.py
View File

@ -68,12 +68,19 @@ class Data(object):
def get_model(self):
return self.model
def set_parameter(self, parameter, var=None, ub=None, lb=None,
default_bounds=False, fun_kwargs=None):
def set_parameter(self,
values: list[float],
*,
var: list[bool] = None,
ub: list[float] = None,
lb: list[float] = None,
default_bounds: bool = False,
fun_kwargs: dict = None
):
"""
Creates parameter for this data.
If no Model is available, it falls back to the model
:param parameter: list of parameters
:param values: list of parameters
:param var: list of boolean or boolean; False fixes parameter at given list index.
Single value is broadcast to all parameter
:param ub: list of upper boundaries or float; Single value is broadcast to all parameter.
@ -87,23 +94,37 @@ class Data(object):
model = self.model
if model is None:
# Data has no unique
if self.minimizer is None:
model = None
else:
if self.minimizer is not None:
model = self.minimizer.fit_model
self.fun_kwargs.update(model.fun_kwargs)
if model is None:
raise ValueError('No model found, please set model before parameters')
if default_bounds:
if lb is None:
if len(values) != len(model.params):
raise ValueError('Number of given parameter does not match number of model parameters')
if var is None:
var = [True] * len(values)
if lb is None:
if default_bounds:
lb = model.lb
if ub is None:
else:
lb = [None] * len(values)
if ub is None:
if default_bounds:
ub = model.ub
else:
ub = [None] * len(values)
self.para_keys = self.parameter.add_parameter(parameter, var=var, lb=lb, ub=ub, names=model.params)
arg_names = ['name', 'value', 'var', 'lb', 'ub']
for parameter_arg in zip(model.params, values, var, lb, ub):
self.parameter.add(**{arg_name: arg_value for arg_name, arg_value in zip(arg_names, parameter_arg)})
self.para_keys = list(self.parameter.keys())
self.fun_kwargs.update(model.fun_kwargs)
if fun_kwargs is not None:
self.fun_kwargs.update(fun_kwargs)

75
src/nmreval/fit/minimizer.py
View File

@ -1,3 +1,5 @@
from __future__ import annotations
import warnings
from itertools import product
@ -26,7 +28,7 @@ def _cost_scipy_glob(p: list[float], data: list[Data], varpars: list[str], used_
# replace values
for keys, values in zip(varpars, p):
for data_i in data:
if keys in data_i.parameter:
if keys in data_i.parameter.keys():
data_i.parameter[keys].scaled_value = values
data_i.parameter[keys].namespace[keys] = data_i.parameter[keys].value
r = []
@ -53,7 +55,6 @@ class FitRoutine(object):
self.data = []
self.fit_model = None
self._no_own_model = []
self.parameter = Parameters()
self.result = []
self.linked = []
self._abort = False
@ -107,28 +108,25 @@ class FitRoutine(object):
return self.fit_model
def set_link_parameter(self, parameter: tuple, replacement: tuple):
def set_link_parameter(self, dismissed_param: tuple[Model | Data, str], replacement: tuple[Model, str]):
if isinstance(replacement[0], Model):
if replacement[1] not in replacement[0].global_parameter:
raise KeyError(f'Parameter at pos {replacement[1]} of '
f'model {str(replacement[0])} is not global')
if replacement[1] not in replacement[0].parameter:
raise KeyError(f'Parameter {replacement[1]} of '
f'model {replacement[0]} is not global')
if isinstance(parameter[0], Model):
warnings.warn(f'Replaced parameter at pos {parameter[1]} in {str(parameter[0])} '
if isinstance(dismissed_param[0], Model):
warnings.warn(f'Replaced parameter {dismissed_param[1]} in {dismissed_param[0]} '
f'becomes global with linkage.')
self.linked.append((*parameter, *replacement))
self.linked.append((*dismissed_param, *replacement))
def prepare_links(self):
self._no_own_model = []
self.parameter = Parameters()
_found_models = {}
linked_sender = {}
for v in self.data:
linked_sender[v] = set()
for k, p_i in v.parameter.items():
self.parameter.add_parameter(k, p_i.copy())
# set temporary model
if v.model is None:
@ -138,35 +136,29 @@ class FitRoutine(object):
# register model
if v.model not in _found_models:
_found_models[v.model] = []
for k, p in v.model.parameter.items():
self.parameter.add_parameter(k, p)
# m_param = v.model.parameter.copy()
# self.parameter.update(m_param)
#
_found_models[v.model].append(v)
if v.model not in linked_sender:
linked_sender[v.model] = set()
linked_parameter = {}
# for par, par_parm, repl, repl_par in self.linked:
# if isinstance(par, Data):
# if isinstance(repl, Data):
# linked_parameter[par.para_keys[par_parm]] = repl.para_keys[repl_par]
# else:
# linked_parameter[par.para_keys[par_parm]] = repl.parameter[repl_par]
#
# else:
# if isinstance(repl, Data):
# par.global_parameter[par_parm] = repl.para_keys[repl_par]
# else:
# par.global_parameter[par_parm] = repl.global_parameter[repl_par]
#
# linked_sender[repl].add(par)
# linked_sender[par].add(repl)
for dismiss_model, dismiss_param, replace_model, replace_param in self.linked:
linked_sender[replace_model].add(dismiss_model)
linked_sender[replace_model].add(replace_model)
replace_key = replace_model.parameter.get_key(replace_param)
dismiss_key = dismiss_model.parameter.get_key(dismiss_param)
if isinstance(replace_model, Data):
linked_parameter[dismiss_key] = replace_key
else:
# print('dismiss model', dismiss_model.parameter)
# print('replace model', replace_model.parameter)
dismiss_model.parameter.replace_parameter(dismiss_key, replace_key, replace_model.parameter[replace_key])
# print('after replacement', dismiss_model.parameter)
for mm, m_data in _found_models.items():
# print('has global', mm.parameter)
if mm.parameter:
for dd in m_data:
linked_sender[mm].add(dd)
@ -174,14 +166,12 @@ class FitRoutine(object):
coupled_data = []
visited_data = []
# print('linked', linked_sender)
for s in linked_sender.keys():
if s in visited_data:
continue
sub_graph = []
self.find_paths(s, linked_sender, sub_graph, visited_data)
if sub_graph:
# print('sub', sub_graph)
coupled_data.append(sub_graph)
return coupled_data, linked_parameter
@ -203,12 +193,8 @@ class FitRoutine(object):
def run(self, mode='lsq'):
self._abort = False
self.parameter = Parameters()
fit_groups, linked_parameter = self.prepare_links()
# print(fit_groups, self.linked)
for data_groups in fit_groups:
if len(data_groups) == 1 and not self.linked:
data = data_groups[0]
@ -226,6 +212,7 @@ class FitRoutine(object):
self._odr_single(data, p0_k, var_pars_k)
else:
# print('INSIDE RUN')
data_pars, p0, lb, ub, var_pars = self._prep_global(data_groups, linked_parameter)
if mode == 'lsq':
@ -262,18 +249,21 @@ class FitRoutine(object):
return pp, lb, ub, var_pars
def _prep_global(self, data_group, linked):
# print('PREP GLOBAL')
# print(data_group, linked)
p0 = []
lb = []
ub = []
var = []
data_pars = []
# print(data_group)
# loopy-loop over data that belong to one fit (linked or global)
for data in data_group:
# is parameter replaced by global parameter?
# print('SET GLOBAL')
for k, v in data.model.parameter.items():
# print(k, v)
data.replace_parameter(k, v)
actual_pars = []
@ -312,7 +302,6 @@ class FitRoutine(object):
self._no_own_model = []
def __cost_odr(self, p: list[float], data: Data, varpars: list[str], used_pars: list[str]):
for keys, values in zip(varpars, p):
self.parameter[keys].scaled_value = values
@ -361,6 +350,7 @@ class FitRoutine(object):
with np.errstate(all='ignore'):
res = optimize.least_squares(cost, p0, bounds=(lb, ub), max_nfev=500 * len(p0))
err, corr, partial_corr = self._calc_error(res.jac, np.sum(res.fun**2), *res.jac.shape)
for v, var_pars_k in zip(data, data_pars):
self.make_results(v, res.x, var, var_pars_k, res.jac.shape,
@ -457,7 +447,6 @@ class FitRoutine(object):
if err is None:
err = [0] * len(p)
print(p, var_pars, used_pars)
# update parameter values
for keys, p_value, err_value in zip(var_pars, p, err):
if keys in data.parameter:

12
src/nmreval/fit/model.py
View File

@ -79,7 +79,14 @@ class Model(object):
self.fun_kwargs = {k: v.default for k, v in inspect.signature(model.func).parameters.items()
if v.default is not inspect.Parameter.empty}
def set_global_parameter(self, key, value, var=None, lb=None, ub=None, default_bounds=False):
def set_global_parameter(self,
key: str,
value: float | str,
var: bool = None,
lb: float = None,
ub: float = None,
default_bounds: bool = False
):
idx = [self.params.index(key)]
if default_bounds:
if lb is None:
@ -87,7 +94,8 @@ class Model(object):
if ub is None:
ub = [self.lb[i] for i in idx]
self.parameter.add(key, value, var=var, lb=lb, ub=ub)
p = self.parameter.add(key, value, var=var, lb=lb, ub=ub)
p.is_global = True
@staticmethod
def _prep(param_len, val):

45
src/nmreval/fit/parameter.py
View File

@ -8,6 +8,7 @@ import numpy as np
class Parameters(dict):
parameter_counter = count()
# is one global namespace a good idea?
namespace: dict = {}
def __init__(self):
@ -24,7 +25,14 @@ class Parameters(dict):
return super().__getitem__(item)
def __setitem__(self, key, value):
super().__setitem__(key, value)
self.add_parameter(key, value)
def __contains__(self, item):
for v in self.values():
if item == v.name:
return True
return False
def add(self,
name: str,
@ -34,7 +42,6 @@ class Parameters(dict):
lb: float = -np.inf, ub: float = np.inf) -> Parameter:
par = Parameter(name=name, value=value, var=var, lb=lb, ub=ub)
key = f'p{next(Parameters.parameter_counter)}'
self.add_parameter(key, par)
@ -43,11 +50,8 @@ class Parameters(dict):
def add_parameter(self, key: str, parameter: Parameter):
self._mapping[parameter.name] = key
self[key] = parameter
super().__setitem__(key, parameter)
self._mapping[parameter.name] = key
self[key] = parameter
parameter.eval_allowed = False
self.namespace[key] = parameter.value
parameter.namespace = self.namespace
@ -63,13 +67,17 @@ class Parameters(dict):
p._expr = expression
def replace_parameter(self, key_out: str, key_in: str, parameter: Parameter):
# print('replace par', key_out, key_in, parameter)
# print('name', parameter.name)
self.add_parameter(key_in, parameter)
for k, v in self._mapping.items():
if v == key_out:
self._mapping[k] = key_in
break
self.add_parameter(key_in, parameter)
del self.namespace[key_out]
if key_out in self.namespace:
del self.namespace[key_out]
for p in self.values():
try:
@ -80,6 +88,13 @@ class Parameters(dict):
expression = re.sub(re.escape(n), k, expression)
p._expr = expression
def get_key(self, name: str) -> str | None:
for k, v in self.items():
if name == v.name:
return k
return
def get_state(self):
return {k: v.get_state() for k, v in self.items()}
@ -102,6 +117,7 @@ class Parameter:
self.eval_allowed: bool = True
self._expr: None | str = None
self._expr_disp: None | str = None
self.is_global = False
if isinstance(value, str):
self._expr_disp = value
@ -126,15 +142,19 @@ class Parameter:
start = StringIO()
if self.name:
if self.function:
start.write(f"{self.name} ({self.function}): ")
start.write(f"{self.name} ({self.function})")
else:
start.write(self.name)
start.write(": ")
if self.is_global:
start.write("*")
start.write(": ")
if self.var:
start.write(f"{self.value:.4g} +/- {self.error:.4g}, init={self.init_val}")
else:
start.write(f"{self.value:}")
start.write(f"{self.value:.4g}")
if self._expr is None:
start.write(" (fixed)")
else:
@ -204,6 +224,9 @@ class Parameter:
para_copy = Parameter(name=self.name, value=val, var=self.var, lb=self.lb, ub=self.ub)
para_copy._expr = self._expr
para_copy.namespace = self.namespace
para_copy.is_global = self.is_global
para_copy.error = self.error
para_copy.function = self.function
return para_copy

2
src/nmreval/models/diffusion.py
View File

@ -125,7 +125,7 @@ class Peschier:
q = nucleus*g*tp
r1s, r1f = 1 / t1s, 1 / t1f
kf, ks = pf*k, (1-pf)*k
kf, ks = (1-pf)*k, pf*k
a_plus = 0.5 * (d*q*q + kf + ks + r1f + r1s + np.sqrt((d*q*q + kf + r1f - ks - r1s)**2 + 4*kf*ks))
a_minu = 0.5 * (d*q*q + kf + ks + r1f + r1s - np.sqrt((d*q*q + kf + r1f - ks - r1s)**2 + 4*kf*ks))