use Parameter when collecting fit values
This commit is contained in:
Dominik Demuth
parent
03d172bade
commit
bd1a227e4c
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@ -208,11 +208,10 @@ class QFitParameterWidget(QtWidgets.QWidget, Ui_FormFit):
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if sid not in self.data_values:
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self.data_values[sid] = [None] * len(self.data_parameter)
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def get_parameter(self, use_func=None) -> tuple[dict[str, list[Parameter]], list[Optional[Parameter]]]:
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def get_parameter(self, use_func=None) -> tuple[dict, list]:
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bds = []
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is_global = []
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is_fixed = []
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param_general = []
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for g in self.global_parameter:
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@ -262,16 +261,16 @@ class QFitParameterWidget(QtWidgets.QWidget, Ui_FormFit):
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else:
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kw_p[g.argname] = p_i
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global_parameter = []
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for param, global_flag in zip(param_general, is_global):
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if global_flag:
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global_parameter.append(param)
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else:
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global_parameter.append(None)
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data_parameter[sid] = (p, kw_p)
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global_parameter = []
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for param, global_flag in zip(param_general, is_global):
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if global_flag:
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param.is_global = True
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global_parameter.append(param)
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else:
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global_parameter.append(None)
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return data_parameter, global_parameter
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def set_parameter(self, set_id: str | None, parameter: list[float]) -> int:
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@ -9,6 +9,7 @@ import numpy as np
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from pyqtgraph import mkPen
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from nmreval.fit._meta import MultiModel, ModelFactory
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from nmreval.fit.model import Model
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from nmreval.fit.result import FitResult
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from .fit_forms import FitTableWidget
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@ -219,16 +220,16 @@ class QFitDialog(QtWidgets.QWidget, Ui_FitDialog):
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def _prepare(self, model: list, function_use: list = None,
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parameter: dict = None, add_idx: bool = False, cnt: int = 0) -> tuple[dict, int]:
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if parameter is None:
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parameter = {
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'parameter': {},
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'glob': [],
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'data_parameter': {},
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'global_parameter': [],
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'links': [],
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'color': [],
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}
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for i, f in enumerate(model):
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print(i, f)
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if not f['active']:
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continue
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@ -239,33 +240,22 @@ class QFitDialog(QtWidgets.QWidget, Ui_FitDialog):
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QtWidgets.QMessageBox.Ok)
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return {}, -1
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print(p)
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print(glob)
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p_len = len(p)
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parameter['color'].append(f['color'])
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print(parameter)
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parameter['global_parameter'].extend(glob)
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cnt = f['cnt']
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for p_k, v_k in p.items():
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if add_idx:
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kw_k = {f'{k}_{cnt}': v for k, v in v_k[1].items()}
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else:
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kw_k = v_k[1]
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if p_k in parameter['parameter']:
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params, kw = parameter['parameter'][p_k]
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if p_k in parameter['data_parameter']:
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params, kw = parameter['data_parameter'][p_k]
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params += v_k[0]
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kw.update(kw_k)
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else:
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parameter['parameter'][p_k] = (v_k[0], kw_k)
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for g_k, g_v in glob.items():
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if g_k != 'idx':
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parameter['glob'][g_k] += g_v
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else:
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parameter['glob']['idx'] += [idx_i + p_len for idx_i in g_v]
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parameter['data_parameter'][p_k] = (v_k[0], kw_k)
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if add_idx:
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cnt += 1
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@ -283,37 +273,43 @@ class QFitDialog(QtWidgets.QWidget, Ui_FitDialog):
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data = self.data_table.collect_data(default=self.default_combobox.currentData())
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func_dict = {}
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for k, mod in self.models.items():
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func, order, param_len = ModelFactory.create_from_list(mod)
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for model_name, model_parameter in self.models.items():
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func, order, param_len = ModelFactory.create_from_list(model_parameter)
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if func is None:
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continue
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if k in data:
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parameter, _ = self._prepare(mod, function_use=data[k], add_idx=isinstance(func, MultiModel))
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func = Model(func)
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# convert positions of global parameter to corresponding names
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global_parameter: dict = parameter['glob']
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positions = global_parameter.pop('idx')
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global_parameter['key'] = [pname for i, pname in enumerate(func.params) if i in positions]
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# print(global_parameter)
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if model_name in data:
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parameter, _ = self._prepare(model_parameter, function_use=data[model_name], add_idx=isinstance(func, MultiModel))
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if parameter is None:
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return
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for (data_parameter, _) in parameter['data_parameter'].values():
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for pname, param in zip(func.params, data_parameter):
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param.name = pname
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if self._complex[model_name] is not None:
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for p_k, p_v in parameter['data_parameter'].items():
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p_v[1].update({'complex_mode': self._complex[model_name]})
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parameter['data_parameter'][p_k] = p_v[0], p_v[1]
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for pname, param_value in zip(func.params, parameter['global_parameter']):
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if param_value is not None:
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param_value.name = pname
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func.set_global_parameter(param_value)
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parameter['func'] = func
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parameter['order'] = order
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parameter['len'] = param_len
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parameter['complex'] = self._complex[k]
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if self._complex[k] is not None:
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for p_k, p_v in parameter['parameter'].items():
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p_v[1].update({'complex_mode': self._complex[k]})
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parameter['parameter'][p_k] = p_v[0], p_v[1]
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parameter['complex'] = self._complex[model_name]
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func_dict[k] = parameter
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func_dict[model_name] = parameter
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replaceable = []
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for k, v in func_dict.items():
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for model_name, v in func_dict.items():
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for i, link_i in enumerate(v['links']):
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if link_i is None:
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continue
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@ -344,7 +340,7 @@ class QFitDialog(QtWidgets.QWidget, Ui_FitDialog):
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QtWidgets.QMessageBox.Ok)
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return
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replaceable.append((k, i, rep_model, repl_idx))
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replaceable.append((model_name, i, rep_model, repl_idx))
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replace_value = None
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for p_k in f['parameter'].values():
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@ -441,21 +441,22 @@ class UpperManagement(QtCore.QObject):
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# all-encompassing error catch
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try:
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for model_id, model_p in parameter.items():
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m = Model(model_p['func'])
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m = model_p['func']
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models[model_id] = m
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m_complex = model_p['complex']
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print(model_p)
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# sets are not in active order but in order they first appeared in fit dialog
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# iterate over order of set id in active order and access parameter inside loop
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# instead of directly looping
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list_ids = list(model_p['parameter'].keys())
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list_ids = list(model_p['data_parameter'].keys())
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set_order = [self.active_id.index(i) for i in list_ids]
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for pos in set_order:
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set_id = list_ids[pos]
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data_i = self.data[set_id]
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set_params = model_p['parameter'][set_id]
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set_params = model_p['data_parameter'][set_id]
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if we_option.lower() == 'deltay':
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we = data_i.y_err**2
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@ -485,18 +486,13 @@ class UpperManagement(QtCore.QObject):
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d = fit_d.Data(_x[inside], _y[inside], we=we[inside], idx=set_id)
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d.set_model(m)
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d.set_parameter(set_params[0], var=model_p['var'],
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lb=model_p['lb'], ub=model_p['ub'],
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fun_kwargs=set_params[1])
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d.set_parameter(set_params[0], fun_kwargs=set_params[1])
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# d.set_parameter(set_params[0], var=model_p['var'],
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# lb=model_p['lb'], ub=model_p['ub'],
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# fun_kwargs=set_params[1])
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self.fitter.add_data(d)
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model_globs = model_p['glob']
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if model_globs:
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for parameter_args in zip(*model_globs.values()):
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m.set_global_parameter(**{k: v for k, v in zip(model_globs.keys(), parameter_args)})
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# m.set_global_parameter(**model_p['glob'])
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for links_i in links:
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self.fitter.set_link_parameter((models[links_i[0]], links_i[1]),
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(models[links_i[2]], links_i[3]))
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@ -1,3 +1,5 @@
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from __future__ import annotations
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import numpy as np
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from .model import Model
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@ -69,7 +71,7 @@ class Data(object):
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return self.model
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def set_parameter(self,
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values: list[float],
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values: list[float | Parameter],
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*,
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var: list[bool] = None,
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ub: list[float] = None,
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@ -103,24 +105,33 @@ class Data(object):
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if len(values) != len(model.params):
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raise ValueError('Number of given parameter does not match number of model parameters')
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if var is None:
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var = [True] * len(values)
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is_parameter = [isinstance(v, Parameter) for v in values]
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if all(is_parameter):
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for p_i in values:
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key = f"p{next(Parameters.parameter_counter)}"
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self.parameter.add_parameter(key, p_i)
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elif any(is_parameter):
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raise ValueError('list of parameter are not all float of Parameter')
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if lb is None:
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if default_bounds:
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lb = model.lb
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else:
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lb = [None] * len(values)
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else:
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if var is None:
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var = [True] * len(values)
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if ub is None:
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if default_bounds:
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ub = model.ub
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else:
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ub = [None] * len(values)
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if lb is None:
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if default_bounds:
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lb = model.lb
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else:
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lb = [None] * len(values)
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arg_names = ['name', 'value', 'var', 'lb', 'ub']
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for parameter_arg in zip(model.params, values, var, lb, ub):
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self.parameter.add(**{arg_name: arg_value for arg_name, arg_value in zip(arg_names, parameter_arg)})
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if ub is None:
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if default_bounds:
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ub = model.ub
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else:
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ub = [None] * len(values)
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arg_names = ['name', 'value', 'var', 'lb', 'ub']
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for parameter_arg in zip(model.params, values, var, lb, ub):
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self.parameter.add(**{arg_name: arg_value for arg_name, arg_value in zip(arg_names, parameter_arg)})
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self.para_keys = list(self.parameter.keys())
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@ -80,22 +80,30 @@ class Model(object):
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if v.default is not inspect.Parameter.empty}
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def set_global_parameter(self,
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key: str,
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value: float | str,
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key: str | Parameter,
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value: float | str = None,
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*,
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var: bool = None,
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lb: float = None,
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ub: float = None,
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default_bounds: bool = False
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default_bounds: bool = False,
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) -> Parameter:
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idx = [self.params.index(key)]
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if default_bounds:
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if lb is None:
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lb = [self.lb[i] for i in idx]
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if ub is None:
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ub = [self.lb[i] for i in idx]
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p = self.parameter.add(key, value, var=var, lb=lb, ub=ub)
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p.is_global = True
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if isinstance(key, Parameter):
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p = key
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key = f'p{next(Parameters.parameter_counter)}'
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self.parameter.add_parameter(key, p)
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else:
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idx = [self.params.index(key)]
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if default_bounds:
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if lb is None:
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lb = [self.lb[i] for i in idx]
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if ub is None:
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ub = [self.lb[i] for i in idx]
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p = self.parameter.add(key, value, var=var, lb=lb, ub=ub)
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p.is_global = True
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return p
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