fitting (#43)
adjustments to fit to see if fit is at least running, could help with #39 Co-authored-by: Dominik Demuth <dominik.demuth@physik.tu-darmstadt.de> Reviewed-on: #43
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@ -58,7 +58,7 @@ class RdBuCMap:
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elif val < self.min:
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col = QtGui.QColor.fromRgb(*RdBuCMap._rdbu[-1])
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else:
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col = QtGui.QColor.fromRgb(*(float(self.spline[i](val)) for i in range(3)))
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col = QtGui.QColor.fromRgb(*(int(self.spline[i](val)) for i in range(3)))
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return col
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@ -66,7 +66,6 @@ class NMRMainWindow(QtWidgets.QMainWindow, Ui_BaseWindow):
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self._block_window_change = False
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self.fname = None
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self.tim = QtCore.QTimer()
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self.settings = QtCore.QSettings('NMREVal', 'settings')
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self._init_gui()
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@ -887,6 +886,11 @@ class NMRMainWindow(QtWidgets.QMainWindow, Ui_BaseWindow):
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self.fit_dialog.fit_button.setEnabled(False)
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self.management.start_fit(parameter, links, fit_options)
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self.status.setText('Fit running...'.format(self.management.fitter.step))
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tim = QtCore.QTimer()
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tim.setInterval(500)
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tim.timeout.connect(lambda: self.status.setText(f'Fit running... ({self.management.fitter.step} evaluations)'))
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tim.start(500)
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@QtCore.pyqtSlot(dict, int, bool)
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def show_fit_preview(self, funcs: dict, num: int, show: bool):
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@ -910,6 +914,7 @@ class NMRMainWindow(QtWidgets.QMainWindow, Ui_BaseWindow):
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@QtCore.pyqtSlot(list)
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def show_fit_results(self, results: list):
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self.fit_dialog.fit_button.setEnabled(True)
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self.status.setText('')
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if results:
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res_dialog = QFitResult(results, self.management, parent=self)
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res_dialog.add_graphs(self.management.graphs.list())
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@ -398,7 +398,7 @@ class UpperManagement(QtCore.QObject):
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self.__fit_options = (parameter, links, fit_options)
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fitter = FitRoutine()
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self.fitter = FitRoutine()
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models = {}
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fit_limits = fit_options['limits']
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fit_mode = fit_options['fit_mode']
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@ -442,18 +442,18 @@ class UpperManagement(QtCore.QObject):
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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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fitter.add_data(d)
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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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m.set_global_parameter(**model_p['glob'])
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for links_i in links:
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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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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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with busy_cursor():
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self.fit_worker = FitWorker(fitter, fit_mode)
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self.fit_worker = FitWorker(self.fitter, fit_mode)
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self.fit_thread = QtCore.QThread()
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self.fit_worker.moveToThread(self.fit_thread)
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@ -469,7 +469,7 @@ class UpperManagement(QtCore.QObject):
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@QtCore.pyqtSlot(list, bool)
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def end_fit(self, result: list, success: bool):
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print('FIT FINISHED')
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logger.info('FIT FINISHED')
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if success:
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self.fitFinished.emit(result)
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else:
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@ -1,3 +1,4 @@
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import time
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import warnings
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from itertools import product
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@ -31,6 +32,7 @@ class FitRoutine(object):
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self.result = []
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self.linked = []
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self._abort = False
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self.step = 0
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def add_data(self, x, y=None, we=None, idx=None):
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if isinstance(x, Data):
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@ -165,7 +167,7 @@ class FitRoutine(object):
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self.find_paths(neighbor, graph, coupled_nodes, visited_nodes)
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def abort(self):
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print('ABORT ???')
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logger.info('Fit aborted by user')
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self._abort = True
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def run(self, mode='lsq'):
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@ -311,14 +313,17 @@ class FitRoutine(object):
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return r
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def _least_squares_single(self, data, p0, lb, ub, var):
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self.step = 0
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def cost(p):
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self.step += 1
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if self._abort:
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raise FitAbortException(f'Fit aborted by user')
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return self.__cost_scipy(p, data, var, data.para_keys)
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with np.errstate(all='ignore'):
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res = optimize.least_squares(cost, p0, bounds=(lb, ub), max_nfev=1000 * len(p0))
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res = optimize.least_squares(cost, p0, bounds=(lb, ub), max_nfev=500 * len(p0))
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err, corr, partial_corr = self._calc_error(res.jac, np.sum(res.fun**2), *res.jac.shape)
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self.make_results(data, res.x, var, data.para_keys, res.jac.shape,
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@ -326,12 +331,13 @@ class FitRoutine(object):
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def _least_squares_global(self, data, p0, lb, ub, var, data_pars):
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def cost(p):
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self.step += 1
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if self._abort:
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raise FitAbortException(f'Fit aborted by user')
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return self.__cost_scipy_glob(p, data, var, data_pars)
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with np.errstate(all='ignore'):
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res = optimize.least_squares(cost, p0, bounds=(lb, ub), max_nfev=1000 * len(p0))
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res = optimize.least_squares(cost, p0, bounds=(lb, ub), max_nfev=500 * len(p0))
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err, corr, partial_corr = self._calc_error(res.jac, np.sum(res.fun**2), *res.jac.shape)
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for v, var_pars_k in zip(data, data_pars):
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@ -340,25 +346,27 @@ class FitRoutine(object):
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def _nm_single(self, data, p0, lb, ub, var):
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def cost(p):
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self.step += 1
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if self._abort:
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raise FitAbortException(f'Fit aborted by user')
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return (self.__cost_scipy(p, data, var, data.para_keys)**2).sum()
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with np.errstate(all='ignore'):
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res = optimize.minimize(cost, p0, bounds=[(b1, b2) for (b1, b2) in zip(lb, ub)],
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method='Nelder-Mead', options={'maxiter': 1000 * len(p0)})
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method='Nelder-Mead', options={'maxiter': 500 * len(p0)})
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self.make_results(data, res.x, var, data.para_keys, (len(data), len(p0)))
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def _nm_global(self, data, p0, lb, ub, var, data_pars):
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def cost(p):
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self.step += 1
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if self._abort:
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raise FitAbortException(f'Fit aborted by user')
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return (self.__cost_scipy_glob(p, data, var, data_pars)**2).sum()
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with np.errstate(all='ignore'):
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res = optimize.minimize(cost, p0, bounds=[(b1, b2) for (b1, b2) in zip(lb, ub)],
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method='Nelder-Mead', options={'maxiter': 1000 * len(p0)})
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method='Nelder-Mead', options={'maxiter': 500 * len(p0)})
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for v, var_pars_k in zip(data, data_pars):
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self.make_results(v, res.x, var, var_pars_k, (sum(len(d) for d in data), len(p0)))
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@ -367,6 +375,7 @@ class FitRoutine(object):
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odr_data = odr.Data(data.x, data.y)
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def func(p, _):
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self.step += 1
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if self._abort:
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raise FitAbortException(f'Fit aborted by user')
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return self.__cost_odr(p, data, var_pars, data.para_keys)
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@ -390,6 +399,7 @@ class FitRoutine(object):
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def _odr_global(self, data, p0, var, data_pars):
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def func(p, _):
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self.step += 1
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if self._abort:
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raise FitAbortException(f'Fit aborted by user')
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return self.__cost_odr_glob(p, data, var, data_pars)
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@ -155,8 +155,8 @@ class AsciiReader:
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# more than one axis, append column number
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kwargs['name'] = filename + '_' + str(y[j-1])
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if j+num_y+1 < raw_data.shape[2]:
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kwargs['y_err'] = raw_data[i, j+num_y+1]
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if j+num_y < raw_data.shape[2]:
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kwargs['y_err'] = raw_data[i, :, j+num_y]
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imported_sets.append(cls(x=raw_data[i, :, 0], y=raw_data[i, :, j:j+single_len].T, **kwargs))
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