test_tomo_reconstruct: fit.py comparison

comparison fit.py @ 0:98e23dff1de2 draft default tip

planemo upload for repository https://github.com/rolfverberg/galaxytools commit f8c4bdb31c20c468045ad5e6eb255a293244bc6c-dirty

author	rv43
date	Tue, 21 Mar 2023 16:22:42 +0000
parents
children

comparison

equal deleted inserted replaced

--1:000000000000
+:98e23dff1de2
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Dec  6 15:36:22 2021
+@author: rv43
+"""
+import logging
+from asteval import Interpreter, get_ast_names
+from copy import deepcopy
+from lmfit import Model, Parameters
+from lmfit.model import ModelResult
+from lmfit.models import ConstantModel, LinearModel, QuadraticModel, PolynomialModel,\
+ExponentialModel, StepModel, RectangleModel, ExpressionModel, GaussianModel,\
+LorentzianModel
+import numpy as np
+from os import cpu_count, getpid, listdir, mkdir, path
+from re import compile, sub
+from shutil import rmtree
+try:
+from sympy import diff, simplify
+except:
+pass
+try:
+from joblib import Parallel, delayed
+have_joblib = True
+except:
+have_joblib = False
+try:
+import xarray as xr
+have_xarray = True
+except:
+have_xarray = False
+try:
+from .general import illegal_value, is_int, is_dict_series, is_index, index_nearest, \
+almost_equal, quick_plot #, eval_expr
+except:
+try:
+from sys import path as syspath
+syspath.append(f'/nfs/chess/user/rv43/msnctools/msnctools')
+from general import illegal_value, is_int, is_dict_series, is_index, index_nearest, \
+almost_equal, quick_plot #, eval_expr
+except:
+from general import illegal_value, is_int, is_dict_series, is_index, index_nearest, \
+almost_equal, quick_plot #, eval_expr
+from sys import float_info
+float_min = float_info.min
+float_max = float_info.max
+# sigma = fwhm_factor*fwhm
+fwhm_factor = {
+'gaussian': f'fwhm/(2*sqrt(2*log(2)))',
+'lorentzian': f'0.5*fwhm',
+'splitlorentzian': f'0.5*fwhm', # sigma = sigma_r
+'voight': f'0.2776*fwhm', # sigma = gamma
+'pseudovoight': f'0.5*fwhm'} # fraction = 0.5
+# amplitude = height_factor*height*fwhm
+height_factor = {
+'gaussian': f'height*fwhm*0.5*sqrt(pi/log(2))',
+'lorentzian': f'height*fwhm*0.5*pi',
+'splitlorentzian': f'height*fwhm*0.5*pi', # sigma = sigma_r
+'voight': f'3.334*height*fwhm', # sigma = gamma
+'pseudovoight': f'1.268*height*fwhm'} # fraction = 0.5
+class Fit:
+"""Wrapper class for lmfit
+"""
+def __init__(self, y, x=None, models=None, normalize=True, **kwargs):
+if not isinstance(normalize, bool):
+raise ValueError(f'Invalid parameter normalize ({normalize})')
+self._mask = None
+self._model = None
+self._norm = None
+self._normalized = False
+self._parameters = Parameters()
+self._parameter_bounds = None
+self._parameter_norms = {}
+self._linear_parameters = []
+self._nonlinear_parameters = []
+self._result = None
+self._try_linear_fit = True
+self._y = None
+self._y_norm = None
+self._y_range = None
+if 'try_linear_fit' in kwargs:
+try_linear_fit = kwargs.pop('try_linear_fit')
+if not isinstance(try_linear_fit, bool):
+illegal_value(try_linear_fit, 'try_linear_fit', 'Fit.fit', raise_error=True)
+self._try_linear_fit = try_linear_fit
+if y is not None:
+if isinstance(y, (tuple, list, np.ndarray)):
+self._x = np.asarray(x)
+elif have_xarray and isinstance(y, xr.DataArray):
+if x is not None:
+logging.warning('Ignoring superfluous input x ({x}) in Fit.__init__')
+if y.ndim != 1:
+illegal_value(y.ndim, 'DataArray dimensions', 'Fit:__init__', raise_error=True)
+self._x = np.asarray(y[y.dims[0]])
+else:
+illegal_value(y, 'y', 'Fit:__init__', raise_error=True)
+self._y = y
+if self._x.ndim != 1:
+raise ValueError(f'Invalid dimension for input x ({self._x.ndim})')
+if self._x.size != self._y.size:
+raise ValueError(f'Inconsistent x and y dimensions ({self._x.size} vs '+
+f'{self._y.size})')
+if 'mask' in kwargs:
+self._mask = kwargs.pop('mask')
+if self._mask is None:
+y_min = float(self._y.min())
+self._y_range = float(self._y.max())-y_min
+if normalize and self._y_range > 0.0:
+self._norm = (y_min, self._y_range)
+else:
+self._mask = np.asarray(self._mask).astype(bool)
+if self._x.size != self._mask.size:
+raise ValueError(f'Inconsistent x and mask dimensions ({self._x.size} vs '+
+f'{self._mask.size})')
+y_masked = np.asarray(self._y)[~self._mask]
+y_min = float(y_masked.min())
+self._y_range = float(y_masked.max())-y_min
+if normalize and self._y_range > 0.0:
+if normalize and self._y_range > 0.0:
+self._norm = (y_min, self._y_range)
+if models is not None:
+if callable(models) or isinstance(models, str):
+kwargs = self.add_model(models, **kwargs)
+elif isinstance(models, (tuple, list)):
+for model in models:
+kwargs = self.add_model(model, **kwargs)
+self.fit(**kwargs)
+@classmethod
+def fit_data(cls, y, models, x=None, normalize=True, **kwargs):
+return(cls(y, x=x, models=models, normalize=normalize, **kwargs))
+@property
+def best_errors(self):
+if self._result is None:
+return(None)
+return({name:self._result.params[name].stderr for name in sorted(self._result.params)
+if name != 'tmp_normalization_offset_c'})
+@property
+def best_fit(self):
+if self._result is None:
+return(None)
+return(self._result.best_fit)
+@property
+def best_parameters(self):
+if self._result is None:
+return(None)
+parameters = {}
+for name in sorted(self._result.params):
+if name != 'tmp_normalization_offset_c':
+par = self._result.params[name]
+parameters[name] = {'value': par.value, 'error': par.stderr,
+'init_value': par.init_value, 'min': par.min, 'max': par.max,
+'vary': par.vary, 'expr': par.expr}
+return(parameters)
+@property
+def best_results(self):
+"""Convert the input data array to a data set and add the fit results.
+"""
+if self._result is None:
+return(None)
+if isinstance(self._y, xr.DataArray):
+best_results = self._y.to_dataset()
+dims = self._y.dims
+fit_name = f'{self._y.name}_fit'
+else:
+coords = {'x': (['x'], self._x)}
+dims = ('x')
+best_results = xr.Dataset(coords=coords)
+best_results['y'] = (dims, self._y)
+fit_name = 'y_fit'
+best_results[fit_name] = (dims, self.best_fit)
+if self._mask is not None:
+best_results['mask'] = self._mask
+best_results.coords['par_names'] = ('peak', [name for name in self.best_values.keys()])
+best_results['best_values'] = (['par_names'], [v for v in self.best_values.values()])
+best_results['best_errors'] = (['par_names'], [v for v in self.best_errors.values()])
+best_results.attrs['components'] = self.components
+return(best_results)
+@property
+def best_values(self):
+if self._result is None:
+return(None)
+return({name:self._result.params[name].value for name in sorted(self._result.params)
+if name != 'tmp_normalization_offset_c'})
+@property
+def chisqr(self):
+if self._result is None:
+return(None)
+return(self._result.chisqr)
+@property
+def components(self):
+components = {}
+if self._result is None:
+logging.warning('Unable to collect components in Fit.components')
+return(components)
+for component in self._result.components:
+if 'tmp_normalization_offset_c' in component.param_names:
+continue
+parameters = {}
+for name in component.param_names:
+par = self._parameters[name]
+parameters[name] = {'free': par.vary, 'value': self._result.params[name].value}
+if par.expr is not None:
+parameters[name]['expr'] = par.expr
+expr = None
+if isinstance(component, ExpressionModel):
+name = component._name
+if name[-1] == '_':
+name = name[:-1]
+expr = component.expr
+else:
+prefix = component.prefix
+if len(prefix):
+if prefix[-1] == '_':
+prefix = prefix[:-1]
+name = f'{prefix} ({component._name})'
+else:
+name = f'{component._name}'
+if expr is None:
+components[name] = {'parameters': parameters}
+else:
+components[name] = {'expr': expr, 'parameters': parameters}
+return(components)
+@property
+def covar(self):
+if self._result is None:
+return(None)
+return(self._result.covar)
+@property
+def init_parameters(self):
+if self._result is None or self._result.init_params is None:
+return(None)
+parameters = {}
+for name in sorted(self._result.init_params):
+if name != 'tmp_normalization_offset_c':
+par = self._result.init_params[name]
+parameters[name] = {'value': par.value, 'min': par.min, 'max': par.max,
+'vary': par.vary, 'expr': par.expr}
+return(parameters)
+@property
+def init_values(self):
+if self._result is None or self._result.init_params is None:
+return(None)
+return({name:self._result.init_params[name].value for name in
+sorted(self._result.init_params) if name != 'tmp_normalization_offset_c'})
+@property
+def normalization_offset(self):
+if self._result is None:
+return(None)
+if self._norm is None:
+return(0.0)
+else:
+if self._result.init_params is not None:
+normalization_offset = self._result.init_params['tmp_normalization_offset_c']
+else:
+normalization_offset = self._result.params['tmp_normalization_offset_c']
+return(normalization_offset)
+@property
+def num_func_eval(self):
+if self._result is None:
+return(None)
+return(self._result.nfev)
+@property
+def parameters(self):
+return({name:{'min': par.min, 'max': par.max, 'vary': par.vary, 'expr': par.expr}
+for name, par in self._parameters.items() if name != 'tmp_normalization_offset_c'})
+@property
+def redchi(self):
+if self._result is None:
+return(None)
+return(self._result.redchi)
+@property
+def residual(self):
+if self._result is None:
+return(None)
+return(self._result.residual)
+@property
+def success(self):
+if self._result is None:
+return(None)
+if not self._result.success:
+#            print(f'ier = {self._result.ier}')
+#            print(f'lmdif_message = {self._result.lmdif_message}')
+#            print(f'message = {self._result.message}')
+#            print(f'nfev = {self._result.nfev}')
+#            print(f'redchi = {self._result.redchi}')
+#            print(f'success = {self._result.success}')
+if self._result.ier == 0 or self._result.ier == 5:
+logging.warning(f'ier = {self._result.ier}: {self._result.message}')
+else:
+logging.warning(f'ier = {self._result.ier}: {self._result.message}')
+return(True)
+#            self.print_fit_report()
+#            self.plot()
+return(self._result.success)
+@property
+def var_names(self):
+"""Intended to be used with covar
+"""
+if self._result is None:
+return(None)
+return(getattr(self._result, 'var_names', None))
+@property
+def x(self):
+return(self._x)
+@property
+def y(self):
+return(self._y)
+def print_fit_report(self, result=None, show_correl=False):
+if result is None:
+result = self._result
+if result is not None:
+print(result.fit_report(show_correl=show_correl))
+def add_parameter(self, **parameter):
+if not isinstance(parameter, dict):
+raise ValueError(f'Invalid parameter ({parameter})')
+if parameter.get('expr') is not None:
+raise KeyError(f'Illegal "expr" key in parameter {parameter}')
+name = parameter['name']
+if not isinstance(name, str):
+raise ValueError(f'Illegal "name" value ({name}) in parameter {parameter}')
+if parameter.get('norm') is None:
+self._parameter_norms[name] = False
+else:
+norm = parameter.pop('norm')
+if self._norm is None:
+logging.warning(f'Ignoring norm in parameter {name} in '+
+f'Fit.add_parameter (normalization is turned off)')
+self._parameter_norms[name] = False
+else:
+if not isinstance(norm, bool):
+raise ValueError(f'Illegal "norm" value ({norm}) in parameter {parameter}')
+self._parameter_norms[name] = norm
+vary = parameter.get('vary')
+if vary is not None:
+if not isinstance(vary, bool):
+raise ValueError(f'Illegal "vary" value ({vary}) in parameter {parameter}')
+if not vary:
+if 'min' in parameter:
+logging.warning(f'Ignoring min in parameter {name} in '+
+f'Fit.add_parameter (vary = {vary})')
+parameter.pop('min')
+if 'max' in parameter:
+logging.warning(f'Ignoring max in parameter {name} in '+
+f'Fit.add_parameter (vary = {vary})')
+parameter.pop('max')
+if self._norm is not None and name not in self._parameter_norms:
+raise ValueError(f'Missing parameter normalization type for paremeter {name}')
+self._parameters.add(**parameter)
+def add_model(self, model, prefix=None, parameters=None, parameter_norms=None, **kwargs):
+# Create the new model
+#        print(f'at start add_model:\nself._parameters:\n{self._parameters}')
+#        print(f'at start add_model: kwargs = {kwargs}')
+#        print(f'parameters = {parameters}')
+#        print(f'parameter_norms = {parameter_norms}')
+#        if len(self._parameters.keys()):
+#            print('\nAt start adding model:')
+#            self._parameters.pretty_print()
+#            print(f'parameter_norms:\n{self._parameter_norms}')
+if prefix is not None and not isinstance(prefix, str):
+logging.warning('Ignoring illegal prefix: {model} {type(model)}')
+prefix = None
+if prefix is None:
+pprefix = ''
+else:
+pprefix = prefix
+if parameters is not None:
+if isinstance(parameters, dict):
+parameters = (parameters, )
+elif not is_dict_series(parameters):
+illegal_value(parameters, 'parameters', 'Fit.add_model', raise_error=True)
+parameters = deepcopy(parameters)
+if parameter_norms is not None:
+if isinstance(parameter_norms, dict):
+parameter_norms = (parameter_norms, )
+if not is_dict_series(parameter_norms):
+illegal_value(parameter_norms, 'parameter_norms', 'Fit.add_model', raise_error=True)
+new_parameter_norms = {}
+if callable(model):
+# Linear fit not yet implemented for callable models
+self._try_linear_fit = False
+if parameter_norms is None:
+if parameters is None:
+raise ValueError('Either "parameters" or "parameter_norms" is required in '+
+f'{model}')
+for par in parameters:
+name = par['name']
+if not isinstance(name, str):
+raise ValueError(f'Illegal "name" value ({name}) in input parameters')
+if par.get('norm') is not None:
+norm = par.pop('norm')
+if not isinstance(norm, bool):
+raise ValueError(f'Illegal "norm" value ({norm}) in input parameters')
+new_parameter_norms[f'{pprefix}{name}'] = norm
+else:
+for par in parameter_norms:
+name = par['name']
+if not isinstance(name, str):
+raise ValueError(f'Illegal "name" value ({name}) in input parameters')
+norm = par.get('norm')
+if norm is None or not isinstance(norm, bool):
+raise ValueError(f'Illegal "norm" value ({norm}) in input parameters')
+new_parameter_norms[f'{pprefix}{name}'] = norm
+if parameters is not None:
+for par in parameters:
+if par.get('expr') is not None:
+raise KeyError(f'Illegal "expr" key ({par.get("expr")}) in parameter '+
+f'{name} for a callable model {model}')
+name = par['name']
+if not isinstance(name, str):
+raise ValueError(f'Illegal "name" value ({name}) in input parameters')
+# RV FIX callable model will need partial deriv functions for any linear pars to get the linearized matrix, so for now skip linear solution option
+newmodel = Model(model, prefix=prefix)
+elif isinstance(model, str):
+if model == 'constant':      # Par: c
+newmodel = ConstantModel(prefix=prefix)
+new_parameter_norms[f'{pprefix}c'] = True
+self._linear_parameters.append(f'{pprefix}c')
+elif model == 'linear':      # Par: slope, intercept
+newmodel = LinearModel(prefix=prefix)
+new_parameter_norms[f'{pprefix}slope'] = True
+new_parameter_norms[f'{pprefix}intercept'] = True
+self._linear_parameters.append(f'{pprefix}slope')
+self._linear_parameters.append(f'{pprefix}intercept')
+elif model == 'quadratic':   # Par: a, b, c
+newmodel = QuadraticModel(prefix=prefix)
+new_parameter_norms[f'{pprefix}a'] = True
+new_parameter_norms[f'{pprefix}b'] = True
+new_parameter_norms[f'{pprefix}c'] = True
+self._linear_parameters.append(f'{pprefix}a')
+self._linear_parameters.append(f'{pprefix}b')
+self._linear_parameters.append(f'{pprefix}c')
+elif model == 'gaussian':    # Par: amplitude, center, sigma (fwhm, height)
+newmodel = GaussianModel(prefix=prefix)
+new_parameter_norms[f'{pprefix}amplitude'] = True
+new_parameter_norms[f'{pprefix}center'] = False
+new_parameter_norms[f'{pprefix}sigma'] = False
+self._linear_parameters.append(f'{pprefix}amplitude')
+self._nonlinear_parameters.append(f'{pprefix}center')
+self._nonlinear_parameters.append(f'{pprefix}sigma')
+# parameter norms for height and fwhm are needed to get correct errors
+new_parameter_norms[f'{pprefix}height'] = True
+new_parameter_norms[f'{pprefix}fwhm'] = False
+elif model == 'lorentzian':    # Par: amplitude, center, sigma (fwhm, height)
+newmodel = LorentzianModel(prefix=prefix)
+new_parameter_norms[f'{pprefix}amplitude'] = True
+new_parameter_norms[f'{pprefix}center'] = False
+new_parameter_norms[f'{pprefix}sigma'] = False
+self._linear_parameters.append(f'{pprefix}amplitude')
+self._nonlinear_parameters.append(f'{pprefix}center')
+self._nonlinear_parameters.append(f'{pprefix}sigma')
+# parameter norms for height and fwhm are needed to get correct errors
+new_parameter_norms[f'{pprefix}height'] = True
+new_parameter_norms[f'{pprefix}fwhm'] = False
+elif model == 'exponential': # Par: amplitude, decay
+newmodel = ExponentialModel(prefix=prefix)
+new_parameter_norms[f'{pprefix}amplitude'] = True
+new_parameter_norms[f'{pprefix}decay'] = False
+self._linear_parameters.append(f'{pprefix}amplitude')
+self._nonlinear_parameters.append(f'{pprefix}decay')
+elif model == 'step':        # Par: amplitude, center, sigma
+form = kwargs.get('form')
+if form is not None:
+kwargs.pop('form')
+if form is None or form not in ('linear', 'atan', 'arctan', 'erf', 'logistic'):
+raise ValueError(f'Invalid parameter form for build-in step model ({form})')
+newmodel = StepModel(prefix=prefix, form=form)
+new_parameter_norms[f'{pprefix}amplitude'] = True
+new_parameter_norms[f'{pprefix}center'] = False
+new_parameter_norms[f'{pprefix}sigma'] = False
+self._linear_parameters.append(f'{pprefix}amplitude')
+self._nonlinear_parameters.append(f'{pprefix}center')
+self._nonlinear_parameters.append(f'{pprefix}sigma')
+elif model == 'rectangle':   # Par: amplitude, center1, center2, sigma1, sigma2
+form = kwargs.get('form')
+if form is not None:
+kwargs.pop('form')
+if form is None or form not in ('linear', 'atan', 'arctan', 'erf', 'logistic'):
+raise ValueError('Invalid parameter form for build-in rectangle model '+
+f'({form})')
+newmodel = RectangleModel(prefix=prefix, form=form)
+new_parameter_norms[f'{pprefix}amplitude'] = True
+new_parameter_norms[f'{pprefix}center1'] = False
+new_parameter_norms[f'{pprefix}center2'] = False
+new_parameter_norms[f'{pprefix}sigma1'] = False
+new_parameter_norms[f'{pprefix}sigma2'] = False
+self._linear_parameters.append(f'{pprefix}amplitude')
+self._nonlinear_parameters.append(f'{pprefix}center1')
+self._nonlinear_parameters.append(f'{pprefix}center2')
+self._nonlinear_parameters.append(f'{pprefix}sigma1')
+self._nonlinear_parameters.append(f'{pprefix}sigma2')
+elif model == 'expression':  # Par: by expression
+expr = kwargs['expr']
+if not isinstance(expr, str):
+raise ValueError(f'Illegal "expr" value ({expr}) in {model}')
+kwargs.pop('expr')
+if parameter_norms is not None:
+logging.warning('Ignoring parameter_norms (normalization determined from '+
+'linearity)}')
+if parameters is not None:
+for par in parameters:
+if par.get('expr') is not None:
+raise KeyError(f'Illegal "expr" key ({par.get("expr")}) in parameter '+
+f'({par}) for an expression model')
+if par.get('norm') is not None:
+logging.warning(f'Ignoring "norm" key in parameter ({par}) '+
+'(normalization determined from linearity)}')
+par.pop('norm')
+name = par['name']
+if not isinstance(name, str):
+raise ValueError(f'Illegal "name" value ({name}) in input parameters')
+ast = Interpreter()
+expr_parameters = [name for name in get_ast_names(ast.parse(expr))
+if name != 'x' and name not in self._parameters
+and name not in ast.symtable]
+#                print(f'\nexpr_parameters: {expr_parameters}')
+#                print(f'expr = {expr}')
+if prefix is None:
+newmodel = ExpressionModel(expr=expr)
+else:
+for name in expr_parameters:
+expr = sub(rf'\b{name}\b', f'{prefix}{name}', expr)
+expr_parameters = [f'{prefix}{name}' for name in expr_parameters]
+#                    print(f'\nexpr_parameters: {expr_parameters}')
+#                    print(f'expr = {expr}')
+newmodel = ExpressionModel(expr=expr, name=name)
+#                print(f'\nnewmodel = {newmodel.__dict__}')
+#                print(f'params_names = {newmodel._param_names}')
+#                print(f'params_names = {newmodel.param_names}')
+# Remove already existing names
+for name in newmodel.param_names.copy():
+if name not in expr_parameters:
+newmodel._func_allargs.remove(name)
+newmodel._param_names.remove(name)
+#                print(f'params_names = {newmodel._param_names}')
+#                print(f'params_names = {newmodel.param_names}')
+else:
+raise ValueError(f'Unknown build-in fit model ({model})')
+else:
+illegal_value(model, 'model', 'Fit.add_model', raise_error=True)
+# Add the new model to the current one
+#        print('\nBefore adding model:')
+#        print(f'\nnewmodel = {newmodel.__dict__}')
+#        if len(self._parameters):
+#            self._parameters.pretty_print()
+if self._model is None:
+self._model = newmodel
+else:
+self._model += newmodel
+new_parameters = newmodel.make_params()
+self._parameters += new_parameters
+#        print('\nAfter adding model:')
+#        print(f'\nnewmodel = {newmodel.__dict__}')
+#        print(f'\nnew_parameters = {new_parameters}')
+#        self._parameters.pretty_print()
+# Check linearity of expression model paremeters
+if isinstance(newmodel, ExpressionModel):
+for name in newmodel.param_names:
+if not diff(newmodel.expr, name, name):
+if name not in self._linear_parameters:
+self._linear_parameters.append(name)
+new_parameter_norms[name] = True
+#                        print(f'\nADDING {name} TO LINEAR')
+else:
+if name not in self._nonlinear_parameters:
+self._nonlinear_parameters.append(name)
+new_parameter_norms[name] = False
+#                        print(f'\nADDING {name} TO NONLINEAR')
+#        print(f'new_parameter_norms:\n{new_parameter_norms}')
+# Scale the default initial model parameters
+if self._norm is not None:
+for name, norm in new_parameter_norms.copy().items():
+par = self._parameters.get(name)
+if par is None:
+new_parameter_norms.pop(name)
+continue
+if par.expr is None and norm:
+value = par.value*self._norm[1]
+_min = par.min
+_max = par.max
+if not np.isinf(_min) and abs(_min) != float_min:
+_min *= self._norm[1]
+if not np.isinf(_max) and abs(_max) != float_min:
+_max *= self._norm[1]
+par.set(value=value, min=_min, max=_max)
+#        print('\nAfter norm defaults:')
+#        self._parameters.pretty_print()
+#        print(f'parameters:\n{parameters}')
+#        print(f'all_parameters:\n{list(self.parameters)}')
+#        print(f'new_parameter_norms:\n{new_parameter_norms}')
+#        print(f'parameter_norms:\n{self._parameter_norms}')
+# Initialize the model parameters from parameters
+if prefix is None:
+prefix = ""
+if parameters is not None:
+for parameter in parameters:
+name = parameter['name']
+if not isinstance(name, str):
+raise ValueError(f'Illegal "name" value ({name}) in input parameters')
+if name not in new_parameters:
+name = prefix+name
+parameter['name'] = name
+if name not in new_parameters:
+logging.warning(f'Ignoring superfluous parameter info for {name}')
+continue
+if name in self._parameters:
+parameter.pop('name')
+if 'norm' in parameter:
+if not isinstance(parameter['norm'], bool):
+illegal_value(parameter['norm'], 'norm', 'Fit.add_model',
+raise_error=True)
+new_parameter_norms[name] = parameter['norm']
+parameter.pop('norm')
+if parameter.get('expr') is not None:
+if 'value' in parameter:
+logging.warning(f'Ignoring value in parameter {name} '+
+f'(set by expression: {parameter["expr"]})')
+parameter.pop('value')
+if 'vary' in parameter:
+logging.warning(f'Ignoring vary in parameter {name} '+
+f'(set by expression: {parameter["expr"]})')
+parameter.pop('vary')
+if 'min' in parameter:
+logging.warning(f'Ignoring min in parameter {name} '+
+f'(set by expression: {parameter["expr"]})')
+parameter.pop('min')
+if 'max' in parameter:
+logging.warning(f'Ignoring max in parameter {name} '+
+f'(set by expression: {parameter["expr"]})')
+parameter.pop('max')
+if 'vary' in parameter:
+if not isinstance(parameter['vary'], bool):
+illegal_value(parameter['vary'], 'vary', 'Fit.add_model',
+raise_error=True)
+if not parameter['vary']:
+if 'min' in parameter:
+logging.warning(f'Ignoring min in parameter {name} in '+
+f'Fit.add_model (vary = {parameter["vary"]})')
+parameter.pop('min')
+if 'max' in parameter:
+logging.warning(f'Ignoring max in parameter {name} in '+
+f'Fit.add_model (vary = {parameter["vary"]})')
+parameter.pop('max')
+self._parameters[name].set(**parameter)
+parameter['name'] = name
+else:
+illegal_value(parameter, 'parameter name', 'Fit.model', raise_error=True)
+self._parameter_norms = {**self._parameter_norms, **new_parameter_norms}
+#        print('\nAfter parameter init:')
+#        self._parameters.pretty_print()
+#        print(f'parameters:\n{parameters}')
+#        print(f'new_parameter_norms:\n{new_parameter_norms}')
+#        print(f'parameter_norms:\n{self._parameter_norms}')
+#        print(f'kwargs:\n{kwargs}')
+# Initialize the model parameters from kwargs
+for name, value in {**kwargs}.items():
+full_name = f'{pprefix}{name}'
+if full_name in new_parameter_norms and isinstance(value, (int, float)):
+kwargs.pop(name)
+if self._parameters[full_name].expr is None:
+self._parameters[full_name].set(value=value)
+else:
+logging.warning(f'Ignoring parameter {name} in Fit.fit (set by expression: '+
+f'{self._parameters[full_name].expr})')
+#        print('\nAfter kwargs init:')
+#        self._parameters.pretty_print()
+#        print(f'parameter_norms:\n{self._parameter_norms}')
+#        print(f'kwargs:\n{kwargs}')
+# Check parameter norms (also need it for expressions to renormalize the errors)
+if self._norm is not None and (callable(model) or model == 'expression'):
+missing_norm = False
+for name in new_parameters.valuesdict():
+if name not in self._parameter_norms:
+print(f'new_parameters:\n{new_parameters.valuesdict()}')
+print(f'self._parameter_norms:\n{self._parameter_norms}')
+logging.error(f'Missing parameter normalization type for {name} in {model}')
+missing_norm = True
+if missing_norm:
+raise ValueError
+#        print(f'at end add_model:\nself._parameters:\n{list(self.parameters)}')
+#        print(f'at end add_model: kwargs = {kwargs}')
+#        print(f'\nat end add_model: newmodel:\n{newmodel.__dict__}\n')
+return(kwargs)
+def fit(self, interactive=False, guess=False, **kwargs):
+# Check inputs
+if self._model is None:
+logging.error('Undefined fit model')
+return
+if not isinstance(interactive, bool):
+illegal_value(interactive, 'interactive', 'Fit.fit', raise_error=True)
+if not isinstance(guess, bool):
+illegal_value(guess, 'guess', 'Fit.fit', raise_error=True)
+if 'try_linear_fit' in kwargs:
+try_linear_fit = kwargs.pop('try_linear_fit')
+if not isinstance(try_linear_fit, bool):
+illegal_value(try_linear_fit, 'try_linear_fit', 'Fit.fit', raise_error=True)
+if not self._try_linear_fit:
+logging.warning('Ignore superfluous keyword argument "try_linear_fit" (not '+
+'yet supported for callable models)')
+else:
+self._try_linear_fit = try_linear_fit
+#        if self._result is None:
+#            if 'parameters' in kwargs:
+#                raise ValueError('Invalid parameter parameters ({kwargs["parameters"]})')
+#        else:
+if self._result is not None:
+if guess:
+logging.warning('Ignoring input parameter guess in Fit.fit during refitting')
+guess = False
+# Check for circular expressions
+# FIX TODO
+#        for name1, par1 in self._parameters.items():
+#            if par1.expr is not None:
+# Apply mask if supplied:
+if 'mask' in kwargs:
+self._mask = kwargs.pop('mask')
+if self._mask is not None:
+self._mask = np.asarray(self._mask).astype(bool)
+if self._x.size != self._mask.size:
+raise ValueError(f'Inconsistent x and mask dimensions ({self._x.size} vs '+
+f'{self._mask.size})')
+# Estimate initial parameters with build-in lmfit guess method (only for a single model)
+#        print(f'\nat start fit: kwargs = {kwargs}')
+#RV        print('\nAt start of fit:')
+#RV        self._parameters.pretty_print()
+#        print(f'parameter_norms:\n{self._parameter_norms}')
+if guess:
+if self._mask is None:
+self._parameters = self._model.guess(self._y, x=self._x)
+else:
+self._parameters = self._model.guess(np.asarray(self._y)[~self._mask],
+x=self._x[~self._mask])
+#            print('\nAfter guess:')
+#            self._parameters.pretty_print()
+# Add constant offset for a normalized model
+if self._result is None and self._norm is not None and self._norm[0]:
+self.add_model('constant', prefix='tmp_normalization_offset_', parameters={'name': 'c',
+'value': -self._norm[0], 'vary': False, 'norm': True})
+#'value': -self._norm[0]/self._norm[1], 'vary': False, 'norm': False})
+# Adjust existing parameters for refit:
+if 'parameters' in kwargs:
+parameters = kwargs.pop('parameters')
+if isinstance(parameters, dict):
+parameters = (parameters, )
+elif not is_dict_series(parameters):
+illegal_value(parameters, 'parameters', 'Fit.fit', raise_error=True)
+for par in parameters:
+name = par['name']
+if name not in self._parameters:
+raise ValueError(f'Unable to match {name} parameter {par} to an existing one')
+if self._parameters[name].expr is not None:
+raise ValueError(f'Unable to modify {name} parameter {par} (currently an '+
+'expression)')
+if par.get('expr') is not None:
+raise KeyError(f'Illegal "expr" key in {name} parameter {par}')
+self._parameters[name].set(vary=par.get('vary'))
+self._parameters[name].set(min=par.get('min'))
+self._parameters[name].set(max=par.get('max'))
+self._parameters[name].set(value=par.get('value'))
+#RV            print('\nAfter adjust:')
+#RV            self._parameters.pretty_print()
+# Apply parameter updates through keyword arguments
+#        print(f'kwargs = {kwargs}')
+#        print(f'parameter_norms = {self._parameter_norms}')
+for name in set(self._parameters) & set(kwargs):
+value = kwargs.pop(name)
+if self._parameters[name].expr is None:
+self._parameters[name].set(value=value)
+else:
+logging.warning(f'Ignoring parameter {name} in Fit.fit (set by expression: '+
+f'{self._parameters[name].expr})')
+# Check for uninitialized parameters
+for name, par in self._parameters.items():
+if par.expr is None:
+value = par.value
+if value is None or np.isinf(value) or np.isnan(value):
+if interactive:
+value = input_num(f'Enter an initial value for {name}', default=1.0)
+else:
+value = 1.0
+if self._norm is None or name not in self._parameter_norms:
+self._parameters[name].set(value=value)
+elif self._parameter_norms[name]:
+self._parameters[name].set(value=value*self._norm[1])
+# Check if model is linear
+try:
+linear_model = self._check_linearity_model()
+except:
+linear_model = False
+#        print(f'\n\n--------> linear_model = {linear_model}\n')
+if kwargs.get('check_only_linearity') is not None:
+return(linear_model)
+# Normalize the data and initial parameters
+#RV        print('\nBefore normalization:')
+#RV        self._parameters.pretty_print()
+#        print(f'parameter_norms:\n{self._parameter_norms}')
+self._normalize()
+#        print(f'norm = {self._norm}')
+#RV        print('\nAfter normalization:')
+#RV        self._parameters.pretty_print()
+#        self.print_fit_report()
+#        print(f'parameter_norms:\n{self._parameter_norms}')
+if linear_model:
+# Perform a linear fit by direct matrix solution with numpy
+try:
+if self._mask is None:
+self._fit_linear_model(self._x, self._y_norm)
+else:
+self._fit_linear_model(self._x[~self._mask],
+np.asarray(self._y_norm)[~self._mask])
+except:
+linear_model = False
+if not linear_model:
+# Perform a non-linear fit with lmfit
+# Prevent initial values from sitting at boundaries
+self._parameter_bounds = {name:{'min': par.min, 'max': par.max} for name, par in
+self._parameters.items() if par.vary}
+for par in self._parameters.values():
+if par.vary:
+par.set(value=self._reset_par_at_boundary(par, par.value))
+#            print('\nAfter checking boundaries:')
+#            self._parameters.pretty_print()
+# Perform the fit
+#            fit_kws = None
+#            if 'Dfun' in kwargs:
+#                fit_kws = {'Dfun': kwargs.pop('Dfun')}
+#            self._result = self._model.fit(self._y_norm, self._parameters, x=self._x,
+#                    fit_kws=fit_kws, **kwargs)
+if self._mask is None:
+self._result = self._model.fit(self._y_norm, self._parameters, x=self._x, **kwargs)
+else:
+self._result = self._model.fit(np.asarray(self._y_norm)[~self._mask],
+self._parameters, x=self._x[~self._mask], **kwargs)
+#RV        print('\nAfter fit:')
+#        print(f'\nself._result ({self._result}):\n\t{self._result.__dict__}')
+#RV        self._parameters.pretty_print()
+#        self.print_fit_report()
+# Set internal parameter values to fit results upon success
+if self.success:
+for name, par in self._parameters.items():
+if par.expr is None and par.vary:
+par.set(value=self._result.params[name].value)
+#            print('\nAfter update parameter values:')
+#            self._parameters.pretty_print()
+# Renormalize the data and results
+self._renormalize()
+#RV        print('\nAfter renormalization:')
+#RV        self._parameters.pretty_print()
+#        self.print_fit_report()
+def plot(self, y=None, y_title=None, result=None, skip_init=False, plot_comp_legends=False,
+plot_residual=False, plot_masked_data=True, **kwargs):
+if result is None:
+result = self._result
+if result is None:
+return
+plots = []
+legend = []
+if self._mask is None:
+mask = np.zeros(self._x.size).astype(bool)
+plot_masked_data = False
+else:
+mask = self._mask
+if y is not None:
+if not isinstance(y, (tuple, list, np.ndarray)):
+illegal_value(y, 'y', 'Fit.plot')
+if len(y) != len(self._x):
+logging.warning('Ignoring parameter y in Fit.plot (wrong dimension)')
+y = None
+if y is not None:
+if y_title is None or not isinstance(y_title, str):
+y_title = 'data'
+plots += [(self._x, y, '.')]
+legend += [y_title]
+if self._y is not None:
+plots += [(self._x, np.asarray(self._y), 'b.')]
+legend += ['data']
+if plot_masked_data:
+plots += [(self._x[mask], np.asarray(self._y)[mask], 'bx')]
+legend += ['masked data']
+if isinstance(plot_residual, bool) and plot_residual:
+plots += [(self._x[~mask], result.residual, 'k-')]
+legend += ['residual']
+plots += [(self._x[~mask], result.best_fit, 'k-')]
+legend += ['best fit']
+if not skip_init and hasattr(result, 'init_fit'):
+plots += [(self._x[~mask], result.init_fit, 'g-')]
+legend += ['init']
+components = result.eval_components(x=self._x[~mask])
+num_components = len(components)
+if 'tmp_normalization_offset_' in components:
+num_components -= 1
+if num_components > 1:
+eval_index = 0
+for modelname, y in components.items():
+if modelname == 'tmp_normalization_offset_':
+continue
+if modelname == '_eval':
+modelname = f'eval{eval_index}'
+if len(modelname) > 20:
+modelname = f'{modelname[0:16]} ...'
+if isinstance(y, (int, float)):
+y *= np.ones(self._x[~mask].size)
+plots += [(self._x[~mask], y, '--')]
+if plot_comp_legends:
+if modelname[-1] == '_':
+legend.append(modelname[:-1])
+else:
+legend.append(modelname)
+title = kwargs.get('title')
+if title is not None:
+kwargs.pop('title')
+quick_plot(tuple(plots), legend=legend, title=title, block=True, **kwargs)
+@staticmethod
+def guess_init_peak(x, y, *args, center_guess=None, use_max_for_center=True):
+""" Return a guess for the initial height, center and fwhm for a peak
+"""
+#        print(f'\n\nargs = {args}')
+#        print(f'center_guess = {center_guess}')
+#        quick_plot(x, y, vlines=center_guess, block=True)
+center_guesses = None
+x = np.asarray(x)
+y = np.asarray(y)
+if len(x) != len(y):
+logging.error(f'Invalid x and y lengths ({len(x)}, {len(y)}), skip initial guess')
+return(None, None, None)
+if isinstance(center_guess, (int, float)):
+if len(args):
+logging.warning('Ignoring additional arguments for single center_guess value')
+center_guesses = [center_guess]
+elif isinstance(center_guess, (tuple, list, np.ndarray)):
+if len(center_guess) == 1:
+logging.warning('Ignoring additional arguments for single center_guess value')
+if not isinstance(center_guess[0], (int, float)):
+raise ValueError(f'Invalid parameter center_guess ({type(center_guess[0])})')
+center_guess = center_guess[0]
+else:
+if len(args) != 1:
+raise ValueError(f'Invalid number of arguments ({len(args)})')
+n = args[0]
+if not is_index(n, 0, len(center_guess)):
+raise ValueError('Invalid argument')
+center_guesses = center_guess
+center_guess = center_guesses[n]
+elif center_guess is not None:
+raise ValueError(f'Invalid center_guess type ({type(center_guess)})')
+#        print(f'x = {x}')
+#        print(f'y = {y}')
+#        print(f'center_guess = {center_guess}')
+# Sort the inputs
+index = np.argsort(x)
+x = x[index]
+y = y[index]
+miny = y.min()
+#        print(f'miny = {miny}')
+#        print(f'x_range = {x[0]} {x[-1]} {len(x)}')
+#        print(f'y_range = {y[0]} {y[-1]} {len(y)}')
+#        quick_plot(x, y, vlines=center_guess, block=True)
+#        xx = x
+#        yy = y
+# Set range for current peak
+#        print(f'n = {n}')
+#        print(f'center_guesses = {center_guesses}')
+if center_guesses is not None:
+if len(center_guesses) > 1:
+index = np.argsort(center_guesses)
+n = list(index).index(n)
+#                print(f'n = {n}')
+#                print(f'index = {index}')
+center_guesses = np.asarray(center_guesses)[index]
+#                print(f'center_guesses = {center_guesses}')
+if n == 0:
+low = 0
+upp = index_nearest(x, (center_guesses[0]+center_guesses[1])/2)
+elif n == len(center_guesses)-1:
+low = index_nearest(x, (center_guesses[n-1]+center_guesses[n])/2)
+upp = len(x)
+else:
+low = index_nearest(x, (center_guesses[n-1]+center_guesses[n])/2)
+upp = index_nearest(x, (center_guesses[n]+center_guesses[n+1])/2)
+#            print(f'low = {low}')
+#            print(f'upp = {upp}')
+x = x[low:upp]
+y = y[low:upp]
+#            quick_plot(x, y, vlines=(x[0], center_guess, x[-1]), block=True)
+# Estimate FHHM
+maxy = y.max()
+#        print(f'x_range = {x[0]} {x[-1]} {len(x)}')
+#        print(f'y_range = {y[0]} {y[-1]} {len(y)} {miny} {maxy}')
+#        print(f'center_guess = {center_guess}')
+if center_guess is None:
+center_index = np.argmax(y)
+center = x[center_index]
+height = maxy-miny
+else:
+if use_max_for_center:
+center_index = np.argmax(y)
+center = x[center_index]
+if center_index < 0.1*len(x) or center_index > 0.9*len(x):
+center_index = index_nearest(x, center_guess)
+center = center_guess
+else:
+center_index = index_nearest(x, center_guess)
+center = center_guess
+height = y[center_index]-miny
+#        print(f'center_index = {center_index}')
+#        print(f'center = {center}')
+#        print(f'height = {height}')
+half_height = miny+0.5*height
+#        print(f'half_height = {half_height}')
+fwhm_index1 = 0
+for i in range(center_index, fwhm_index1, -1):
+if y[i] < half_height:
+fwhm_index1 = i
+break
+#        print(f'fwhm_index1 = {fwhm_index1} {x[fwhm_index1]}')
+fwhm_index2 = len(x)-1
+for i in range(center_index, fwhm_index2):
+if y[i] < half_height:
+fwhm_index2 = i
+break
+#        print(f'fwhm_index2 = {fwhm_index2} {x[fwhm_index2]}')
+#        quick_plot((x,y,'o'), vlines=(x[fwhm_index1], center, x[fwhm_index2]), block=True)
+if fwhm_index1 == 0 and fwhm_index2 < len(x)-1:
+fwhm = 2*(x[fwhm_index2]-center)
+elif fwhm_index1 > 0 and fwhm_index2 == len(x)-1:
+fwhm = 2*(center-x[fwhm_index1])
+else:
+fwhm = x[fwhm_index2]-x[fwhm_index1]
+#        print(f'fwhm_index1 = {fwhm_index1} {x[fwhm_index1]}')
+#        print(f'fwhm_index2 = {fwhm_index2} {x[fwhm_index2]}')
+#        print(f'fwhm = {fwhm}')
+# Return height, center and FWHM
+#        quick_plot((x,y,'o'), (xx,yy), vlines=(x[fwhm_index1], center, x[fwhm_index2]), block=True)
+return(height, center, fwhm)
+def _check_linearity_model(self):
+"""Identify the linearity of all model parameters and check if the model is linear or not
+"""
+if not self._try_linear_fit:
+logging.info('Skip linearity check (not yet supported for callable models)')
+return(False)
+free_parameters = [name for name, par in self._parameters.items() if par.vary]
+for component in self._model.components:
+if 'tmp_normalization_offset_c' in component.param_names:
+continue
+if isinstance(component, ExpressionModel):
+for name in free_parameters:
+if diff(component.expr, name, name):
+#                        print(f'\t\t{component.expr} is non-linear in {name}')
+self._nonlinear_parameters.append(name)
+if name in self._linear_parameters:
+self._linear_parameters.remove(name)
+else:
+model_parameters = component.param_names.copy()
+for basename, hint in component.param_hints.items():
+name = f'{component.prefix}{basename}'
+if hint.get('expr') is not None:
+model_parameters.remove(name)
+for name in model_parameters:
+expr = self._parameters[name].expr
+if expr is not None:
+for nname in free_parameters:
+if name in self._nonlinear_parameters:
+if diff(expr, nname):
+#                                    print(f'\t\t{component} is non-linear in {nname} (through {name} = "{expr}")')
+self._nonlinear_parameters.append(nname)
+if nname in self._linear_parameters:
+self._linear_parameters.remove(nname)
+else:
+assert(name in self._linear_parameters)
+#                                print(f'\n\nexpr ({type(expr)}) = {expr}\nnname ({type(nname)}) = {nname}\n\n')
+if diff(expr, nname, nname):
+#                                    print(f'\t\t{component} is non-linear in {nname} (through {name} = "{expr}")')
+self._nonlinear_parameters.append(nname)
+if nname in self._linear_parameters:
+self._linear_parameters.remove(nname)
+#        print(f'\nfree parameters:\n\t{free_parameters}')
+#        print(f'linear parameters:\n\t{self._linear_parameters}')
+#        print(f'nonlinear parameters:\n\t{self._nonlinear_parameters}\n')
+if any(True for name in self._nonlinear_parameters if self._parameters[name].vary):
+return(False)
+return(True)
+def _fit_linear_model(self, x, y):
+"""Perform a linear fit by direct matrix solution with numpy
+"""
+# Construct the matrix and the free parameter vector
+#        print(f'\nparameters:')
+#        self._parameters.pretty_print()
+#        print(f'\nparameter_norms:\n\t{self._parameter_norms}')
+#        print(f'\nlinear_parameters:\n\t{self._linear_parameters}')
+#        print(f'nonlinear_parameters:\n\t{self._nonlinear_parameters}')
+free_parameters = [name for name, par in self._parameters.items() if par.vary]
+#        print(f'free parameters:\n\t{free_parameters}\n')
+expr_parameters = {name:par.expr for name, par in self._parameters.items()
+if par.expr is not None}
+model_parameters = []
+for component in self._model.components:
+if 'tmp_normalization_offset_c' in component.param_names:
+continue
+model_parameters += component.param_names
+for basename, hint in component.param_hints.items():
+name = f'{component.prefix}{basename}'
+if hint.get('expr') is not None:
+expr_parameters.pop(name)
+model_parameters.remove(name)
+#        print(f'expr parameters:\n{expr_parameters}')
+#        print(f'model parameters:\n\t{model_parameters}\n')
+norm = 1.0
+if self._normalized:
+norm = self._norm[1]
+#        print(f'\n\nself._normalized = {self._normalized}\nnorm = {norm}\nself._norm = {self._norm}\n')
+# Add expression parameters to asteval
+ast = Interpreter()
+#        print(f'Adding to asteval sym table:')
+for name, expr in expr_parameters.items():
+#            print(f'\tadding {name} {expr}')
+ast.symtable[name] = expr
+# Add constant parameters to asteval
+# (renormalize to use correctly in evaluation of expression models)
+for name, par in self._parameters.items():
+if par.expr is None and not par.vary:
+if self._parameter_norms[name]:
+#                    print(f'\tadding {name} {par.value*norm}')
+ast.symtable[name] = par.value*norm
+else:
+#                    print(f'\tadding {name} {par.value}')
+ast.symtable[name] = par.value
+A = np.zeros((len(x), len(free_parameters)), dtype='float64')
+y_const = np.zeros(len(x), dtype='float64')
+have_expression_model = False
+for component in self._model.components:
+if isinstance(component, ConstantModel):
+name = component.param_names[0]
+#                print(f'\nConstant model: {name} {self._parameters[name]}\n')
+if name in free_parameters:
+#                    print(f'\t\t{name} is a free constant set matrix column {free_parameters.index(name)} to 1.0')
+A[:,free_parameters.index(name)] = 1.0
+else:
+if self._parameter_norms[name]:
+delta_y_const = self._parameters[name]*np.ones(len(x))
+else:
+delta_y_const = (self._parameters[name]*norm)*np.ones(len(x))
+y_const += delta_y_const
+#                    print(f'\ndelta_y_const ({type(delta_y_const)}):\n{delta_y_const}\n')
+elif isinstance(component, ExpressionModel):
+have_expression_model = True
+const_expr = component.expr
+#                print(f'\nExpression model:\nconst_expr: {const_expr}\n')
+for name in free_parameters:
+dexpr_dname = diff(component.expr, name)
+if dexpr_dname:
+const_expr = f'{const_expr}-({str(dexpr_dname)})*{name}'
+#                        print(f'\tconst_expr: {const_expr}')
+if not self._parameter_norms[name]:
+dexpr_dname = f'({dexpr_dname})/{norm}'
+#                        print(f'\t{component.expr} is linear in {name}\n\t\tadd "{str(dexpr_dname)}" to matrix as column {free_parameters.index(name)}')
+fx = [(lambda _: ast.eval(str(dexpr_dname)))(ast(f'x={v}')) for v in x]
+#                        print(f'\tfx:\n{fx}')
+if len(ast.error):
+raise ValueError(f'Unable to evaluate {dexpr_dname}')
+A[:,free_parameters.index(name)] += fx
+#                        if self._parameter_norms[name]:
+#                            print(f'\t\t{component.expr} is linear in {name} add "{str(dexpr_dname)}" to matrix as column {free_parameters.index(name)}')
+#                            A[:,free_parameters.index(name)] += fx
+#                        else:
+#                            print(f'\t\t{component.expr} is linear in {name} add "({str(dexpr_dname)})/{norm}" to matrix as column {free_parameters.index(name)}')
+#                            A[:,free_parameters.index(name)] += np.asarray(fx)/norm
+# FIX: find another solution if expr not supported by simplify
+const_expr = str(simplify(f'({const_expr})/{norm}'))
+#                print(f'\nconst_expr: {const_expr}')
+delta_y_const = [(lambda _: ast.eval(const_expr))(ast(f'x = {v}')) for v in x]
+y_const += delta_y_const
+#                print(f'\ndelta_y_const ({type(delta_y_const)}):\n{delta_y_const}\n')
+if len(ast.error):
+raise ValueError(f'Unable to evaluate {const_expr}')
+else:
+free_model_parameters = [name for name in component.param_names
+if name in free_parameters or name in expr_parameters]
+#                print(f'\nBuild-in model ({component}):\nfree_model_parameters: {free_model_parameters}\n')
+if not len(free_model_parameters):
+y_const += component.eval(params=self._parameters, x=x)
+elif isinstance(component, LinearModel):
+if f'{component.prefix}slope' in free_model_parameters:
+A[:,free_parameters.index(f'{component.prefix}slope')] = x
+else:
+y_const += self._parameters[f'{component.prefix}slope'].value*x
+if f'{component.prefix}intercept' in free_model_parameters:
+A[:,free_parameters.index(f'{component.prefix}intercept')] = 1.0
+else:
+y_const += self._parameters[f'{component.prefix}intercept'].value* \
+np.ones(len(x))
+elif isinstance(component, QuadraticModel):
+if f'{component.prefix}a' in free_model_parameters:
+A[:,free_parameters.index(f'{component.prefix}a')] = x**2
+else:
+y_const += self._parameters[f'{component.prefix}a'].value*x**2
+if f'{component.prefix}b' in free_model_parameters:
+A[:,free_parameters.index(f'{component.prefix}b')] = x
+else:
+y_const += self._parameters[f'{component.prefix}b'].value*x
+if f'{component.prefix}c' in free_model_parameters:
+A[:,free_parameters.index(f'{component.prefix}c')] = 1.0
+else:
+y_const += self._parameters[f'{component.prefix}c'].value*np.ones(len(x))
+else:
+# At this point each build-in model must be strictly proportional to each linear
+#   model parameter. Without this assumption, the model equation is needed
+#   For the current build-in lmfit models, this can only ever be the amplitude
+assert(len(free_model_parameters) == 1)
+name = f'{component.prefix}amplitude'
+assert(free_model_parameters[0] == name)
+assert(self._parameter_norms[name])
+expr = self._parameters[name].expr
+if expr is None:
+#                        print(f'\t{component} is linear in {name} add to matrix as column {free_parameters.index(name)}')
+parameters = deepcopy(self._parameters)
+parameters[name].set(value=1.0)
+index = free_parameters.index(name)
+A[:,free_parameters.index(name)] += component.eval(params=parameters, x=x)
+else:
+const_expr = expr
+#                        print(f'\tconst_expr: {const_expr}')
+parameters = deepcopy(self._parameters)
+parameters[name].set(value=1.0)
+dcomp_dname = component.eval(params=parameters, x=x)
+#                        print(f'\tdcomp_dname ({type(dcomp_dname)}):\n{dcomp_dname}')
+for nname in free_parameters:
+dexpr_dnname =  diff(expr, nname)
+if dexpr_dnname:
+assert(self._parameter_norms[name])
+#                                print(f'\t\td({expr})/d{nname} = {dexpr_dnname}')
+#                                print(f'\t\t{component} is linear in {nname} (through {name} = "{expr}", add to matrix as column {free_parameters.index(nname)})')
+fx = np.asarray(dexpr_dnname*dcomp_dname, dtype='float64')
+#                                print(f'\t\tfx ({type(fx)}): {fx}')
+#                                print(f'free_parameters.index({nname}): {free_parameters.index(nname)}')
+if self._parameter_norms[nname]:
+A[:,free_parameters.index(nname)] += fx
+else:
+A[:,free_parameters.index(nname)] += fx/norm
+const_expr = f'{const_expr}-({dexpr_dnname})*{nname}'
+#                                print(f'\t\tconst_expr: {const_expr}')
+const_expr = str(simplify(f'({const_expr})/{norm}'))
+#                        print(f'\tconst_expr: {const_expr}')
+fx = [(lambda _: ast.eval(const_expr))(ast(f'x = {v}')) for v in x]
+#                        print(f'\tfx: {fx}')
+delta_y_const = np.multiply(fx, dcomp_dname)
+y_const += delta_y_const
+#                        print(f'\ndelta_y_const ({type(delta_y_const)}):\n{delta_y_const}\n')
+#            print(A)
+#            print(y_const)
+solution, residual, rank, s = np.linalg.lstsq(A, y-y_const, rcond=None)
+#        print(f'\nsolution ({type(solution)} {solution.shape}):\n\t{solution}')
+#        print(f'\nresidual ({type(residual)} {residual.shape}):\n\t{residual}')
+#        print(f'\nrank ({type(rank)} {rank.shape}):\n\t{rank}')
+#        print(f'\ns ({type(s)} {s.shape}):\n\t{s}\n')
+# Assemble result (compensate for normalization in expression models)
+for name, value in zip(free_parameters, solution):
+self._parameters[name].set(value=value)
+if self._normalized and (have_expression_model or len(expr_parameters)):
+for name, norm in self._parameter_norms.items():
+par = self._parameters[name]
+if par.expr is None and norm:
+self._parameters[name].set(value=par.value*self._norm[1])
+#        self._parameters.pretty_print()
+#        print(f'\nself._parameter_norms:\n\t{self._parameter_norms}')
+self._result = ModelResult(self._model, deepcopy(self._parameters))
+self._result.best_fit = self._model.eval(params=self._parameters, x=x)
+if self._normalized and (have_expression_model or len(expr_parameters)):
+if 'tmp_normalization_offset_c' in self._parameters:
+offset = self._parameters['tmp_normalization_offset_c']
+else:
+offset = 0.0
+self._result.best_fit = (self._result.best_fit-offset-self._norm[0])/self._norm[1]
+if self._normalized:
+for name, norm in self._parameter_norms.items():
+par = self._parameters[name]
+if par.expr is None and norm:
+value = par.value/self._norm[1]
+self._parameters[name].set(value=value)
+self._result.params[name].set(value=value)
+#        self._parameters.pretty_print()
+self._result.residual = self._result.best_fit-y
+self._result.components = self._model.components
+self._result.init_params = None
+#        quick_plot((x, y, '.'), (x, y_const, 'g'), (x, self._result.best_fit, 'k'), (x, self._result.residual, 'r'), block=True)
+def _normalize(self):
+"""Normalize the data and initial parameters
+"""
+if self._normalized:
+return
+if self._norm is None:
+if self._y is not None and self._y_norm is None:
+self._y_norm = np.asarray(self._y)
+else:
+if self._y is not None and self._y_norm is None:
+self._y_norm = (np.asarray(self._y)-self._norm[0])/self._norm[1]
+self._y_range = 1.0
+for name, norm in self._parameter_norms.items():
+par = self._parameters[name]
+if par.expr is None and norm:
+value = par.value/self._norm[1]
+_min = par.min
+_max = par.max
+if not np.isinf(_min) and abs(_min) != float_min:
+_min /= self._norm[1]
+if not np.isinf(_max) and abs(_max) != float_min:
+_max /= self._norm[1]
+par.set(value=value, min=_min, max=_max)
+self._normalized = True
+def _renormalize(self):
+"""Renormalize the data and results
+"""
+if self._norm is None or not self._normalized:
+return
+self._normalized = False
+for name, norm in self._parameter_norms.items():
+par = self._parameters[name]
+if par.expr is None and norm:
+value = par.value*self._norm[1]
+_min = par.min
+_max = par.max
+if not np.isinf(_min) and abs(_min) != float_min:
+_min *= self._norm[1]
+if not np.isinf(_max) and abs(_max) != float_min:
+_max *= self._norm[1]
+par.set(value=value, min=_min, max=_max)
+if self._result is None:
+return
+self._result.best_fit = self._result.best_fit*self._norm[1]+self._norm[0]
+for name, par in self._result.params.items():
+if self._parameter_norms.get(name, False):
+if par.stderr is not None:
+par.stderr *= self._norm[1]
+if par.expr is None:
+_min = par.min
+_max = par.max
+value = par.value*self._norm[1]
+if par.init_value is not None:
+par.init_value *= self._norm[1]
+if not np.isinf(_min) and abs(_min) != float_min:
+_min *= self._norm[1]
+if not np.isinf(_max) and abs(_max) != float_min:
+_max *= self._norm[1]
+par.set(value=value, min=_min, max=_max)
+if hasattr(self._result, 'init_fit'):
+self._result.init_fit = self._result.init_fit*self._norm[1]+self._norm[0]
+if hasattr(self._result, 'init_values'):
+init_values = {}
+for name, value in self._result.init_values.items():
+if name not in self._parameter_norms or self._parameters[name].expr is not None:
+init_values[name] = value
+elif self._parameter_norms[name]:
+init_values[name] = value*self._norm[1]
+self._result.init_values = init_values
+for name, par in self._result.init_params.items():
+if par.expr is None and self._parameter_norms.get(name, False):
+value = par.value
+_min = par.min
+_max = par.max
+value *= self._norm[1]
+if not np.isinf(_min) and abs(_min) != float_min:
+_min *= self._norm[1]
+if not np.isinf(_max) and abs(_max) != float_min:
+_max *= self._norm[1]
+par.set(value=value, min=_min, max=_max)
+par.init_value = par.value
+# Don't renormalize chisqr, it has no useful meaning in physical units
+#self._result.chisqr *= self._norm[1]*self._norm[1]
+if self._result.covar is not None:
+for i, name in enumerate(self._result.var_names):
+if self._parameter_norms.get(name, False):
+for j in range(len(self._result.var_names)):
+if self._result.covar[i,j] is not None:
+self._result.covar[i,j] *= self._norm[1]
+if self._result.covar[j,i] is not None:
+self._result.covar[j,i] *= self._norm[1]
+# Don't renormalize redchi, it has no useful meaning in physical units
+#self._result.redchi *= self._norm[1]*self._norm[1]
+if self._result.residual is not None:
+self._result.residual *= self._norm[1]
+def _reset_par_at_boundary(self, par, value):
+assert(par.vary)
+name = par.name
+_min = self._parameter_bounds[name]['min']
+_max = self._parameter_bounds[name]['max']
+if np.isinf(_min):
+if not np.isinf(_max):
+if self._parameter_norms.get(name, False):
+upp = _max-0.1*self._y_range
+elif _max == 0.0:
+upp = _max-0.1
+else:
+upp = _max-0.1*abs(_max)
+if value >= upp:
+return(upp)
+else:
+if np.isinf(_max):
+if self._parameter_norms.get(name, False):
+low = _min+0.1*self._y_range
+elif _min == 0.0:
+low = _min+0.1
+else:
+low = _min+0.1*abs(_min)
+if value <= low:
+return(low)
+else:
+low = 0.9*_min+0.1*_max
+upp = 0.1*_min+0.9*_max
+if value <= low:
+return(low)
+elif value >= upp:
+return(upp)
+return(value)
+class FitMultipeak(Fit):
+"""Fit data with multiple peaks
+"""
+def __init__(self, y, x=None, normalize=True):
+super().__init__(y, x=x, normalize=normalize)
+self._fwhm_max = None
+self._sigma_max = None
+@classmethod
+def fit_multipeak(cls, y, centers, x=None, normalize=True, peak_models='gaussian',
+center_exprs=None, fit_type=None, background_order=None, background_exp=False,
+fwhm_max=None, plot_components=False):
+"""Make sure that centers and fwhm_max are in the correct units and consistent with expr
+for a uniform fit (fit_type == 'uniform')
+"""
+fit = cls(y, x=x, normalize=normalize)
+success = fit.fit(centers, fit_type=fit_type, peak_models=peak_models, fwhm_max=fwhm_max,
+center_exprs=center_exprs, background_order=background_order,
+background_exp=background_exp, plot_components=plot_components)
+if success:
+return(fit.best_fit, fit.residual, fit.best_values, fit.best_errors, fit.redchi, \
+fit.success)
+else:
+return(np.array([]), np.array([]), {}, {}, float_max, False)
+def fit(self, centers, fit_type=None, peak_models=None, center_exprs=None, fwhm_max=None,
+background_order=None, background_exp=False, plot_components=False,
+param_constraint=False):
+self._fwhm_max = fwhm_max
+# Create the multipeak model
+self._create_model(centers, fit_type, peak_models, center_exprs, background_order,
+background_exp, param_constraint)
+# RV: Obsolete Normalize the data and results
+#        print('\nBefore fit before normalization in FitMultipeak:')
+#        self._parameters.pretty_print()
+#        self._normalize()
+#        print('\nBefore fit after normalization in FitMultipeak:')
+#        self._parameters.pretty_print()
+# Perform the fit
+try:
+if param_constraint:
+super().fit(fit_kws={'xtol': 1.e-5, 'ftol': 1.e-5, 'gtol': 1.e-5})
+else:
+super().fit()
+except:
+return(False)
+# Check for valid fit parameter results
+fit_failure = self._check_validity()
+success = True
+if fit_failure:
+if param_constraint:
+logging.warning('  -> Should not happen with param_constraint set, fail the fit')
+success = False
+else:
+logging.info('  -> Retry fitting with constraints')
+self.fit(centers, fit_type, peak_models, center_exprs, fwhm_max=fwhm_max,
+background_order=background_order, background_exp=background_exp,
+plot_components=plot_components, param_constraint=True)
+else:
+# RV: Obsolete Renormalize the data and results
+#            print('\nAfter fit before renormalization in FitMultipeak:')
+#            self._parameters.pretty_print()
+#            self.print_fit_report()
+#            self._renormalize()
+#            print('\nAfter fit after renormalization in FitMultipeak:')
+#            self._parameters.pretty_print()
+#            self.print_fit_report()
+# Print report and plot components if requested
+if plot_components:
+self.print_fit_report()
+self.plot()
+return(success)
+def _create_model(self, centers, fit_type=None, peak_models=None, center_exprs=None,
+background_order=None, background_exp=False, param_constraint=False):
+"""Create the multipeak model
+"""
+if isinstance(centers, (int, float)):
+centers = [centers]
+num_peaks = len(centers)
+if peak_models is None:
+peak_models = num_peaks*['gaussian']
+elif isinstance(peak_models, str):
+peak_models = num_peaks*[peak_models]
+if len(peak_models) != num_peaks:
+raise ValueError(f'Inconsistent number of peaks in peak_models ({len(peak_models)} vs '+
+f'{num_peaks})')
+if num_peaks == 1:
+if fit_type is not None:
+logging.debug('Ignoring fit_type input for fitting one peak')
+fit_type = None
+if center_exprs is not None:
+logging.debug('Ignoring center_exprs input for fitting one peak')
+center_exprs = None
+else:
+if fit_type == 'uniform':
+if center_exprs is None:
+center_exprs = [f'scale_factor*{cen}' for cen in centers]
+if len(center_exprs) != num_peaks:
+raise ValueError(f'Inconsistent number of peaks in center_exprs '+
+f'({len(center_exprs)} vs {num_peaks})')
+elif fit_type == 'unconstrained' or fit_type is None:
+if center_exprs is not None:
+logging.warning('Ignoring center_exprs input for unconstrained fit')
+center_exprs = None
+else:
+raise ValueError(f'Invalid fit_type in fit_multigaussian {fit_type}')
+self._sigma_max = None
+if param_constraint:
+min_value = float_min
+if self._fwhm_max is not None:
+self._sigma_max = np.zeros(num_peaks)
+else:
+min_value = None
+# Reset the fit
+self._model = None
+self._parameters = Parameters()
+self._result = None
+# Add background model
+if background_order is not None:
+if background_order == 0:
+self.add_model('constant', prefix='background', parameters=
+{'name': 'c', 'value': float_min, 'min': min_value})
+elif background_order == 1:
+self.add_model('linear', prefix='background', slope=0.0, intercept=0.0)
+elif background_order == 2:
+self.add_model('quadratic', prefix='background', a=0.0, b=0.0, c=0.0)
+else:
+raise ValueError(f'Invalid parameter background_order ({background_order})')
+if background_exp:
+self.add_model('exponential', prefix='background', parameters=(
+{'name': 'amplitude', 'value': float_min, 'min': min_value},
+{'name': 'decay', 'value': float_min, 'min': min_value}))
+# Add peaks and guess initial fit parameters
+ast = Interpreter()
+if num_peaks == 1:
+height_init, cen_init, fwhm_init = self.guess_init_peak(self._x, self._y)
+if self._fwhm_max is not None and fwhm_init > self._fwhm_max:
+fwhm_init = self._fwhm_max
+ast(f'fwhm = {fwhm_init}')
+ast(f'height = {height_init}')
+sig_init = ast(fwhm_factor[peak_models[0]])
+amp_init = ast(height_factor[peak_models[0]])
+sig_max = None
+if self._sigma_max is not None:
+ast(f'fwhm = {self._fwhm_max}')
+sig_max = ast(fwhm_factor[peak_models[0]])
+self._sigma_max[0] = sig_max
+self.add_model(peak_models[0], parameters=(
+{'name': 'amplitude', 'value': amp_init, 'min': min_value},
+{'name': 'center', 'value': cen_init, 'min': min_value},
+{'name': 'sigma', 'value': sig_init, 'min': min_value, 'max': sig_max}))
+else:
+if fit_type == 'uniform':
+self.add_parameter(name='scale_factor', value=1.0)
+for i in range(num_peaks):
+height_init, cen_init, fwhm_init = self.guess_init_peak(self._x, self._y, i,
+center_guess=centers)
+if self._fwhm_max is not None and fwhm_init > self._fwhm_max:
+fwhm_init = self._fwhm_max
+ast(f'fwhm = {fwhm_init}')
+ast(f'height = {height_init}')
+sig_init = ast(fwhm_factor[peak_models[i]])
+amp_init = ast(height_factor[peak_models[i]])
+sig_max = None
+if self._sigma_max is not None:
+ast(f'fwhm = {self._fwhm_max}')
+sig_max = ast(fwhm_factor[peak_models[i]])
+self._sigma_max[i] = sig_max
+if fit_type == 'uniform':
+self.add_model(peak_models[i], prefix=f'peak{i+1}_', parameters=(
+{'name': 'amplitude', 'value': amp_init, 'min': min_value},
+{'name': 'center', 'expr': center_exprs[i]},
+{'name': 'sigma', 'value': sig_init, 'min': min_value,
+'max': sig_max}))
+else:
+self.add_model('gaussian', prefix=f'peak{i+1}_', parameters=(
+{'name': 'amplitude', 'value': amp_init, 'min': min_value},
+{'name': 'center', 'value': cen_init, 'min': min_value},
+{'name': 'sigma', 'value': sig_init, 'min': min_value,
+'max': sig_max}))
+def _check_validity(self):
+"""Check for valid fit parameter results
+"""
+fit_failure = False
+index = compile(r'\d+')
+for name, par in self.best_parameters.items():
+if 'background' in name:
+#                if ((name == 'backgroundc' and par['value'] <= 0.0) or
+#                        (name.endswith('amplitude') and par['value'] <= 0.0) or
+if ((name.endswith('amplitude') and par['value'] <= 0.0) or
+(name.endswith('decay') and par['value'] <= 0.0)):
+logging.info(f'Invalid fit result for {name} ({par["value"]})')
+fit_failure = True
+elif (((name.endswith('amplitude') or name.endswith('height')) and
+par['value'] <= 0.0) or
+((name.endswith('sigma') or name.endswith('fwhm')) and par['value'] <= 0.0) or
+(name.endswith('center') and par['value'] <= 0.0) or
+(name == 'scale_factor' and par['value'] <= 0.0)):
+logging.info(f'Invalid fit result for {name} ({par["value"]})')
+fit_failure = True
+if name.endswith('sigma') and self._sigma_max is not None:
+if name == 'sigma':
+sigma_max = self._sigma_max[0]
+else:
+sigma_max = self._sigma_max[int(index.search(name).group())-1]
+if par['value'] > sigma_max:
+logging.info(f'Invalid fit result for {name} ({par["value"]})')
+fit_failure = True
+elif par['value'] == sigma_max:
+logging.warning(f'Edge result on for {name} ({par["value"]})')
+if name.endswith('fwhm') and self._fwhm_max is not None:
+if par['value'] > self._fwhm_max:
+logging.info(f'Invalid fit result for {name} ({par["value"]})')
+fit_failure = True
+elif par['value'] == self._fwhm_max:
+logging.warning(f'Edge result on for {name} ({par["value"]})')
+return(fit_failure)
+class FitMap(Fit):
+"""Fit a map of data
+"""
+def __init__(self, ymap, x=None, models=None, normalize=True, transpose=None, **kwargs):
+super().__init__(None)
+self._best_errors = None
+self._best_fit = None
+self._best_parameters = None
+self._best_values = None
+self._inv_transpose = None
+self._max_nfev = None
+self._memfolder = None
+self._new_parameters = None
+self._out_of_bounds = None
+self._plot = False
+self._print_report = False
+self._redchi = None
+self._redchi_cutoff = 0.1
+self._skip_init = True
+self._success = None
+self._transpose = None
+self._try_no_bounds = True
+# At this point the fastest index should always be the signal dimension so that the slowest
+#    ndim-1 dimensions are the map dimensions
+if isinstance(ymap, (tuple, list, np.ndarray)):
+self._x = np.asarray(x)
+elif have_xarray and isinstance(ymap, xr.DataArray):
+if x is not None:
+logging.warning('Ignoring superfluous input x ({x}) in Fit.__init__')
+self._x = np.asarray(ymap[ymap.dims[-1]])
+else:
+illegal_value(ymap, 'ymap', 'FitMap:__init__', raise_error=True)
+self._ymap = ymap
+# Verify the input parameters
+if self._x.ndim != 1:
+raise ValueError(f'Invalid dimension for input x {self._x.ndim}')
+if self._ymap.ndim < 2:
+raise ValueError('Invalid number of dimension of the input dataset '+
+f'{self._ymap.ndim}')
+if self._x.size != self._ymap.shape[-1]:
+raise ValueError(f'Inconsistent x and y dimensions ({self._x.size} vs '+
+f'{self._ymap.shape[-1]})')
+if not isinstance(normalize, bool):
+logging.warning(f'Invalid value for normalize ({normalize}) in Fit.__init__: '+
+'setting normalize to True')
+normalize = True
+if isinstance(transpose, bool) and not transpose:
+transpose = None
+if transpose is not None and self._ymap.ndim < 3:
+logging.warning(f'Transpose meaningless for {self._ymap.ndim-1}D data maps: ignoring '+
+'transpose')
+if transpose is not None:
+if self._ymap.ndim == 3 and isinstance(transpose, bool) and transpose:
+self._transpose = (1, 0)
+elif not isinstance(transpose, (tuple, list)):
+logging.warning(f'Invalid data type for transpose ({transpose}, '+
+f'{type(transpose)}) in Fit.__init__: setting transpose to False')
+elif len(transpose) != self._ymap.ndim-1:
+logging.warning(f'Invalid dimension for transpose ({transpose}, must be equal to '+
+f'{self._ymap.ndim-1}) in Fit.__init__: setting transpose to False')
+elif any(i not in transpose for i in range(len(transpose))):
+logging.warning(f'Invalid index in transpose ({transpose}) '+
+f'in Fit.__init__: setting transpose to False')
+elif not all(i==transpose[i] for i in range(self._ymap.ndim-1)):
+self._transpose = transpose
+if self._transpose is not None:
+self._inv_transpose = tuple(self._transpose.index(i)
+for i in range(len(self._transpose)))
+# Flatten the map (transpose if requested)
+# Store the flattened map in self._ymap_norm, whether normalized or not
+if self._transpose is not None:
+self._ymap_norm = np.transpose(np.asarray(self._ymap), list(self._transpose)+
+[len(self._transpose)])
+else:
+self._ymap_norm = np.asarray(self._ymap)
+self._map_dim = int(self._ymap_norm.size/self._x.size)
+self._map_shape = self._ymap_norm.shape[:-1]
+self._ymap_norm = np.reshape(self._ymap_norm, (self._map_dim, self._x.size))
+# Check if a mask is provided
+if 'mask' in kwargs:
+self._mask = kwargs.pop('mask')
+if self._mask is None:
+ymap_min = float(self._ymap_norm.min())
+ymap_max = float(self._ymap_norm.max())
+else:
+self._mask = np.asarray(self._mask).astype(bool)
+if self._x.size != self._mask.size:
+raise ValueError(f'Inconsistent mask dimension ({self._x.size} vs '+
+f'{self._mask.size})')
+ymap_masked = np.asarray(self._ymap_norm)[:,~self._mask]
+ymap_min = float(ymap_masked.min())
+ymap_max = float(ymap_masked.max())
+# Normalize the data
+self._y_range = ymap_max-ymap_min
+if normalize and self._y_range > 0.0:
+self._norm = (ymap_min, self._y_range)
+self._ymap_norm = (self._ymap_norm-self._norm[0])/self._norm[1]
+else:
+self._redchi_cutoff *= self._y_range**2
+if models is not None:
+if callable(models) or isinstance(models, str):
+kwargs = self.add_model(models, **kwargs)
+elif isinstance(models, (tuple, list)):
+for model in models:
+kwargs = self.add_model(model, **kwargs)
+self.fit(**kwargs)
+@classmethod
+def fit_map(cls, ymap, models, x=None, normalize=True, **kwargs):
+return(cls(ymap, x=x, models=models, normalize=normalize, **kwargs))
+@property
+def best_errors(self):
+return(self._best_errors)
+@property
+def best_fit(self):
+return(self._best_fit)
+@property
+def best_results(self):
+"""Convert the input data array to a data set and add the fit results.
+"""
+if self.best_values is None or self.best_errors is None or self.best_fit is None:
+return(None)
+if not have_xarray:
+logging.warning('Unable to load xarray module')
+return(None)
+best_values = self.best_values
+best_errors = self.best_errors
+if isinstance(self._ymap, xr.DataArray):
+best_results = self._ymap.to_dataset()
+dims = self._ymap.dims
+fit_name = f'{self._ymap.name}_fit'
+else:
+coords = {f'dim{n}_index':([f'dim{n}_index'], range(self._ymap.shape[n]))
+for n in range(self._ymap.ndim-1)}
+coords['x'] = (['x'], self._x)
+dims = list(coords.keys())
+best_results = xr.Dataset(coords=coords)
+best_results['y'] = (dims, self._ymap)
+fit_name = 'y_fit'
+best_results[fit_name] = (dims, self.best_fit)
+if self._mask is not None:
+best_results['mask'] = self._mask
+for n in range(best_values.shape[0]):
+best_results[f'{self._best_parameters[n]}_values'] = (dims[:-1], best_values[n])
+best_results[f'{self._best_parameters[n]}_errors'] = (dims[:-1], best_errors[n])
+best_results.attrs['components'] = self.components
+return(best_results)
+@property
+def best_values(self):
+return(self._best_values)
+@property
+def chisqr(self):
+logging.warning('property chisqr not defined for fit.FitMap')
+return(None)
+@property
+def components(self):
+components = {}
+if self._result is None:
+logging.warning('Unable to collect components in FitMap.components')
+return(components)
+for component in self._result.components:
+if 'tmp_normalization_offset_c' in component.param_names:
+continue
+parameters = {}
+for name in component.param_names:
+if self._parameters[name].vary:
+parameters[name] = {'free': True}
+elif self._parameters[name].expr is not None:
+parameters[name] = {'free': False, 'expr': self._parameters[name].expr}
+else:
+parameters[name] = {'free': False, 'value': self.init_parameters[name]['value']}
+expr = None
+if isinstance(component, ExpressionModel):
+name = component._name
+if name[-1] == '_':
+name = name[:-1]
+expr = component.expr
+else:
+prefix = component.prefix
+if len(prefix):
+if prefix[-1] == '_':
+prefix = prefix[:-1]
+name = f'{prefix} ({component._name})'
+else:
+name = f'{component._name}'
+if expr is None:
+components[name] = {'parameters': parameters}
+else:
+components[name] = {'expr': expr, 'parameters': parameters}
+return(components)
+@property
+def covar(self):
+logging.warning('property covar not defined for fit.FitMap')
+return(None)
+@property
+def max_nfev(self):
+return(self._max_nfev)
+@property
+def num_func_eval(self):
+logging.warning('property num_func_eval not defined for fit.FitMap')
+return(None)
+@property
+def out_of_bounds(self):
+return(self._out_of_bounds)
+@property
+def redchi(self):
+return(self._redchi)
+@property
+def residual(self):
+if self.best_fit is None:
+return(None)
+if self._mask is None:
+return(np.asarray(self._ymap)-self.best_fit)
+else:
+ymap_flat = np.reshape(np.asarray(self._ymap), (self._map_dim, self._x.size))
+ymap_flat_masked = ymap_flat[:,~self._mask]
+ymap_masked = np.reshape(ymap_flat_masked,
+list(self._map_shape)+[ymap_flat_masked.shape[-1]])
+return(ymap_masked-self.best_fit)
+@property
+def success(self):
+return(self._success)
+@property
+def var_names(self):
+logging.warning('property var_names not defined for fit.FitMap')
+return(None)
+@property
+def y(self):
+logging.warning('property y not defined for fit.FitMap')
+return(None)
+@property
+def ymap(self):
+return(self._ymap)
+def best_parameters(self, dims=None):
+if dims is None:
+return(self._best_parameters)
+if not isinstance(dims, (list, tuple)) or len(dims) != len(self._map_shape):
+illegal_value(dims, 'dims', 'FitMap.best_parameters', raise_error=True)
+if self.best_values is None or self.best_errors is None:
+logging.warning(f'Unable to obtain best parameter values for dims = {dims} in '+
+'FitMap.best_parameters')
+return({})
+# Create current parameters
+parameters = deepcopy(self._parameters)
+for n, name in enumerate(self._best_parameters):
+if self._parameters[name].vary:
+parameters[name].set(value=self.best_values[n][dims])
+parameters[name].stderr = self.best_errors[n][dims]
+parameters_dict = {}
+for name in sorted(parameters):
+if name != 'tmp_normalization_offset_c':
+par = parameters[name]
+parameters_dict[name] = {'value': par.value, 'error': par.stderr,
+'init_value': self.init_parameters[name]['value'], 'min': par.min,
+'max': par.max, 'vary': par.vary, 'expr': par.expr}
+return(parameters_dict)
+def freemem(self):
+if self._memfolder is None:
+return
+try:
+rmtree(self._memfolder)
+self._memfolder = None
+except:
+logging.warning('Could not clean-up automatically.')
+def plot(self, dims, y_title=None, plot_residual=False, plot_comp_legends=False,
+plot_masked_data=True):
+if not isinstance(dims, (list, tuple)) or len(dims) != len(self._map_shape):
+illegal_value(dims, 'dims', 'FitMap.plot', raise_error=True)
+if self._result is None or self.best_fit is None or self.best_values is None:
+logging.warning(f'Unable to plot fit for dims = {dims} in FitMap.plot')
+return
+if y_title is None or not isinstance(y_title, str):
+y_title = 'data'
+if self._mask is None:
+mask = np.zeros(self._x.size).astype(bool)
+plot_masked_data = False
+else:
+mask = self._mask
+plots = [(self._x, np.asarray(self._ymap[dims]), 'b.')]
+legend = [y_title]
+if plot_masked_data:
+plots += [(self._x[mask], np.asarray(self._ymap)[(*dims,mask)], 'bx')]
+legend += ['masked data']
+plots += [(self._x[~mask], self.best_fit[dims], 'k-')]
+legend += ['best fit']
+if plot_residual:
+plots += [(self._x[~mask], self.residual[dims], 'k--')]
+legend += ['residual']
+# Create current parameters
+parameters = deepcopy(self._parameters)
+for name in self._best_parameters:
+if self._parameters[name].vary:
+parameters[name].set(value=
+self.best_values[self._best_parameters.index(name)][dims])
+for component in self._result.components:
+if 'tmp_normalization_offset_c' in component.param_names:
+continue
+if isinstance(component, ExpressionModel):
+prefix = component._name
+if prefix[-1] == '_':
+prefix = prefix[:-1]
+modelname = f'{prefix}: {component.expr}'
+else:
+prefix = component.prefix
+if len(prefix):
+if prefix[-1] == '_':
+prefix = prefix[:-1]
+modelname = f'{prefix} ({component._name})'
+else:
+modelname = f'{component._name}'
+if len(modelname) > 20:
+modelname = f'{modelname[0:16]} ...'
+y = component.eval(params=parameters, x=self._x[~mask])
+if isinstance(y, (int, float)):
+y *= np.ones(self._x[~mask].size)
+plots += [(self._x[~mask], y, '--')]
+if plot_comp_legends:
+legend.append(modelname)
+quick_plot(tuple(plots), legend=legend, title=str(dims), block=True)
+def fit(self, **kwargs):
+#        t0 = time()
+# Check input parameters
+if self._model is None:
+logging.error('Undefined fit model')
+if 'num_proc' in kwargs:
+num_proc = kwargs.pop('num_proc')
+if not is_int(num_proc, ge=1):
+illegal_value(num_proc, 'num_proc', 'FitMap.fit', raise_error=True)
+else:
+num_proc = cpu_count()
+if num_proc > 1 and not have_joblib:
+logging.warning(f'Missing joblib in the conda environment, running FitMap serially')
+num_proc = 1
+if num_proc > cpu_count():
+logging.warning(f'The requested number of processors ({num_proc}) exceeds the maximum '+
+f'number of processors, num_proc reduced to ({cpu_count()})')
+num_proc = cpu_count()
+if 'try_no_bounds' in kwargs:
+self._try_no_bounds = kwargs.pop('try_no_bounds')
+if not isinstance(self._try_no_bounds, bool):
+illegal_value(self._try_no_bounds, 'try_no_bounds', 'FitMap.fit', raise_error=True)
+if 'redchi_cutoff' in kwargs:
+self._redchi_cutoff = kwargs.pop('redchi_cutoff')
+if not is_num(self._redchi_cutoff, gt=0):
+illegal_value(self._redchi_cutoff, 'redchi_cutoff', 'FitMap.fit', raise_error=True)
+if 'print_report' in kwargs:
+self._print_report = kwargs.pop('print_report')
+if not isinstance(self._print_report, bool):
+illegal_value(self._print_report, 'print_report', 'FitMap.fit', raise_error=True)
+if 'plot' in kwargs:
+self._plot = kwargs.pop('plot')
+if not isinstance(self._plot, bool):
+illegal_value(self._plot, 'plot', 'FitMap.fit', raise_error=True)
+if 'skip_init' in kwargs:
+self._skip_init = kwargs.pop('skip_init')
+if not isinstance(self._skip_init, bool):
+illegal_value(self._skip_init, 'skip_init', 'FitMap.fit', raise_error=True)
+# Apply mask if supplied:
+if 'mask' in kwargs:
+self._mask = kwargs.pop('mask')
+if self._mask is not None:
+self._mask = np.asarray(self._mask).astype(bool)
+if self._x.size != self._mask.size:
+raise ValueError(f'Inconsistent x and mask dimensions ({self._x.size} vs '+
+f'{self._mask.size})')
+# Add constant offset for a normalized single component model
+if self._result is None and self._norm is not None and self._norm[0]:
+self.add_model('constant', prefix='tmp_normalization_offset_', parameters={'name': 'c',
+'value': -self._norm[0], 'vary': False, 'norm': True})
+#'value': -self._norm[0]/self._norm[1], 'vary': False, 'norm': False})
+# Adjust existing parameters for refit:
+if 'parameters' in kwargs:
+#            print('\nIn FitMap before adjusting existing parameters for refit:')
+#            self._parameters.pretty_print()
+#            if self._result is None:
+#                raise ValueError('Invalid parameter parameters ({parameters})')
+#            if self._best_values is None:
+#                raise ValueError('Valid self._best_values required for refitting in FitMap.fit')
+parameters = kwargs.pop('parameters')
+#            print(f'\nparameters:\n{parameters}')
+if isinstance(parameters, dict):
+parameters = (parameters, )
+elif not is_dict_series(parameters):
+illegal_value(parameters, 'parameters', 'Fit.fit', raise_error=True)
+for par in parameters:
+name = par['name']
+if name not in self._parameters:
+raise ValueError(f'Unable to match {name} parameter {par} to an existing one')
+if self._parameters[name].expr is not None:
+raise ValueError(f'Unable to modify {name} parameter {par} (currently an '+
+'expression)')
+value =  par.get('value')
+vary =  par.get('vary')
+if par.get('expr') is not None:
+raise KeyError(f'Illegal "expr" key in {name} parameter {par}')
+self._parameters[name].set(value=value, vary=vary, min=par.get('min'),
+max=par.get('max'))
+# Overwrite existing best values for fixed parameters when a value is specified
+#                print(f'best values befored resetting:\n{self._best_values}')
+if isinstance(value, (int, float)) and vary is False:
+for i, nname in enumerate(self._best_parameters):
+if nname == name:
+self._best_values[i] = value
+#                print(f'best values after resetting (value={value}, vary={vary}):\n{self._best_values}')
+#RV            print('\nIn FitMap after adjusting existing parameters for refit:')
+#RV            self._parameters.pretty_print()
+# Check for uninitialized parameters
+for name, par in self._parameters.items():
+if par.expr is None:
+value = par.value
+if value is None or np.isinf(value) or np.isnan(value):
+value = 1.0
+if self._norm is None or name not in self._parameter_norms:
+self._parameters[name].set(value=value)
+elif self._parameter_norms[name]:
+self._parameters[name].set(value=value*self._norm[1])
+# Create the best parameter list, consisting of all varying parameters plus the expression
+#   parameters in order to collect their errors
+if self._result is None:
+# Initial fit
+assert(self._best_parameters is None)
+self._best_parameters = [name for name, par in self._parameters.items()
+if par.vary or par.expr is not None]
+num_new_parameters = 0
+else:
+# Refit
+assert(len(self._best_parameters))
+self._new_parameters = [name for name, par in self._parameters.items()
+if name != 'tmp_normalization_offset_c' and name not in self._best_parameters and
+(par.vary or par.expr is not None)]
+num_new_parameters = len(self._new_parameters)
+num_best_parameters = len(self._best_parameters)
+# Flatten and normalize the best values of the previous fit, remove the remaining results
+#   of the previous fit
+if self._result is not None:
+#            print('\nBefore flatten and normalize:')
+#            print(f'self._best_values:\n{self._best_values}')
+self._out_of_bounds = None
+self._max_nfev = None
+self._redchi = None
+self._success = None
+self._best_fit = None
+self._best_errors = None
+assert(self._best_values is not None)
+assert(self._best_values.shape[0] == num_best_parameters)
+assert(self._best_values.shape[1:] == self._map_shape)
+if self._transpose is not None:
+self._best_values = np.transpose(self._best_values,
+[0]+[i+1 for i in self._transpose])
+self._best_values = [np.reshape(self._best_values[i], self._map_dim)
+for i in range(num_best_parameters)]
+if self._norm is not None:
+for i, name in enumerate(self._best_parameters):
+if self._parameter_norms.get(name, False):
+self._best_values[i] /= self._norm[1]
+#RV            print('\nAfter flatten and normalize:')
+#RV            print(f'self._best_values:\n{self._best_values}')
+# Normalize the initial parameters (and best values for a refit)
+#        print('\nIn FitMap before normalize:')
+#        self._parameters.pretty_print()
+#        print(f'\nparameter_norms:\n{self._parameter_norms}\n')
+self._normalize()
+#        print('\nIn FitMap after normalize:')
+#        self._parameters.pretty_print()
+#        print(f'\nparameter_norms:\n{self._parameter_norms}\n')
+# Prevent initial values from sitting at boundaries
+self._parameter_bounds = {name:{'min': par.min, 'max': par.max}
+for name, par in self._parameters.items() if par.vary}
+for name, par in self._parameters.items():
+if par.vary:
+par.set(value=self._reset_par_at_boundary(par, par.value))
+#        print('\nAfter checking boundaries:')
+#        self._parameters.pretty_print()
+# Set parameter bounds to unbound (only use bounds when fit fails)
+if self._try_no_bounds:
+for name in self._parameter_bounds.keys():
+self._parameters[name].set(min=-np.inf, max=np.inf)
+# Allocate memory to store fit results
+if self._mask is None:
+x_size = self._x.size
+else:
+x_size = self._x[~self._mask].size
+if num_proc == 1:
+self._out_of_bounds_flat = np.zeros(self._map_dim, dtype=bool)
+self._max_nfev_flat = np.zeros(self._map_dim, dtype=bool)
+self._redchi_flat = np.zeros(self._map_dim, dtype=np.float64)
+self._success_flat = np.zeros(self._map_dim, dtype=bool)
+self._best_fit_flat = np.zeros((self._map_dim, x_size),
+dtype=self._ymap_norm.dtype)
+self._best_errors_flat = [np.zeros(self._map_dim, dtype=np.float64)
+for _ in range(num_best_parameters+num_new_parameters)]
+if self._result is None:
+self._best_values_flat = [np.zeros(self._map_dim, dtype=np.float64)
+for _ in range(num_best_parameters)]
+else:
+self._best_values_flat = self._best_values
+self._best_values_flat += [np.zeros(self._map_dim, dtype=np.float64)
+for _ in range(num_new_parameters)]
+else:
+self._memfolder = './joblib_memmap'
+try:
+mkdir(self._memfolder)
+except FileExistsError:
+pass
+filename_memmap = path.join(self._memfolder, 'out_of_bounds_memmap')
+self._out_of_bounds_flat = np.memmap(filename_memmap, dtype=bool,
+shape=(self._map_dim), mode='w+')
+filename_memmap = path.join(self._memfolder, 'max_nfev_memmap')
+self._max_nfev_flat = np.memmap(filename_memmap, dtype=bool,
+shape=(self._map_dim), mode='w+')
+filename_memmap = path.join(self._memfolder, 'redchi_memmap')
+self._redchi_flat = np.memmap(filename_memmap, dtype=np.float64,
+shape=(self._map_dim), mode='w+')
+filename_memmap = path.join(self._memfolder, 'success_memmap')
+self._success_flat = np.memmap(filename_memmap, dtype=bool,
+shape=(self._map_dim), mode='w+')
+filename_memmap = path.join(self._memfolder, 'best_fit_memmap')
+self._best_fit_flat = np.memmap(filename_memmap, dtype=self._ymap_norm.dtype,
+shape=(self._map_dim, x_size), mode='w+')
+self._best_errors_flat = []
+for i in range(num_best_parameters+num_new_parameters):
+filename_memmap = path.join(self._memfolder, f'best_errors_memmap_{i}')
+self._best_errors_flat.append(np.memmap(filename_memmap, dtype=np.float64,
+shape=self._map_dim, mode='w+'))
+self._best_values_flat = []
+for i in range(num_best_parameters):
+filename_memmap = path.join(self._memfolder, f'best_values_memmap_{i}')
+self._best_values_flat.append(np.memmap(filename_memmap, dtype=np.float64,
+shape=self._map_dim, mode='w+'))
+if self._result is not None:
+self._best_values_flat[i][:] = self._best_values[i][:]
+for i in range(num_new_parameters):
+filename_memmap = path.join(self._memfolder,
+f'best_values_memmap_{i+num_best_parameters}')
+self._best_values_flat.append(np.memmap(filename_memmap, dtype=np.float64,
+shape=self._map_dim, mode='w+'))
+# Update the best parameter list
+if num_new_parameters:
+self._best_parameters += self._new_parameters
+# Perform the first fit to get model component info and initial parameters
+current_best_values = {}
+#        print(f'0 before:\n{current_best_values}')
+#        t1 = time()
+self._result = self._fit(0, current_best_values, return_result=True, **kwargs)
+#        t2 = time()
+#        print(f'0 after:\n{current_best_values}')
+#        print('\nAfter the first fit:')
+#        self._parameters.pretty_print()
+#        print(self._result.fit_report(show_correl=False))
+# Remove all irrelevant content from self._result
+for attr in ('_abort', 'aborted', 'aic', 'best_fit', 'best_values', 'bic', 'calc_covar',
+'call_kws', 'chisqr', 'ci_out', 'col_deriv', 'covar', 'data', 'errorbars',
+'flatchain', 'ier', 'init_vals', 'init_fit', 'iter_cb', 'jacfcn', 'kws',
+'last_internal_values', 'lmdif_message', 'message', 'method', 'nan_policy',
+'ndata', 'nfev', 'nfree', 'params', 'redchi', 'reduce_fcn', 'residual', 'result',
+'scale_covar', 'show_candidates', 'calc_covar', 'success', 'userargs', 'userfcn',
+'userkws', 'values', 'var_names', 'weights', 'user_options'):
+try:
+delattr(self._result, attr)
+except AttributeError:
+#                logging.warning(f'Unknown attribute {attr} in fit.FtMap._cleanup_result')
+pass
+#        t3 = time()
+if num_proc == 1:
+# Perform the remaining fits serially
+for n in range(1, self._map_dim):
+#                print(f'{n} before:\n{current_best_values}')
+self._fit(n, current_best_values, **kwargs)
+#                print(f'{n} after:\n{current_best_values}')
+else:
+# Perform the remaining fits in parallel
+num_fit = self._map_dim-1
+#            print(f'num_fit = {num_fit}')
+if num_proc > num_fit:
+logging.warning(f'The requested number of processors ({num_proc}) exceeds the '+
+f'number of fits, num_proc reduced to ({num_fit})')
+num_proc = num_fit
+num_fit_per_proc = 1
+else:
+num_fit_per_proc = round((num_fit)/num_proc)
+if num_proc*num_fit_per_proc < num_fit:
+num_fit_per_proc +=1
+#            print(f'num_fit_per_proc = {num_fit_per_proc}')
+num_fit_batch = min(num_fit_per_proc, 40)
+#            print(f'num_fit_batch = {num_fit_batch}')
+with Parallel(n_jobs=num_proc) as parallel:
+parallel(delayed(self._fit_parallel)(current_best_values, num_fit_batch,
+n_start, **kwargs) for n_start in range(1, self._map_dim, num_fit_batch))
+#        t4 = time()
+# Renormalize the initial parameters for external use
+if self._norm is not None and self._normalized:
+init_values = {}
+for name, value in self._result.init_values.items():
+if name not in self._parameter_norms or self._parameters[name].expr is not None:
+init_values[name] = value
+elif self._parameter_norms[name]:
+init_values[name] = value*self._norm[1]
+self._result.init_values = init_values
+for name, par in self._result.init_params.items():
+if par.expr is None and self._parameter_norms.get(name, False):
+_min = par.min
+_max = par.max
+value = par.value*self._norm[1]
+if not np.isinf(_min) and abs(_min) != float_min:
+_min *= self._norm[1]
+if not np.isinf(_max) and abs(_max) != float_min:
+_max *= self._norm[1]
+par.set(value=value, min=_min, max=_max)
+par.init_value = par.value
+# Remap the best results
+#        t5 = time()
+self._out_of_bounds = np.copy(np.reshape(self._out_of_bounds_flat, self._map_shape))
+self._max_nfev = np.copy(np.reshape(self._max_nfev_flat, self._map_shape))
+self._redchi = np.copy(np.reshape(self._redchi_flat, self._map_shape))
+self._success = np.copy(np.reshape(self._success_flat, self._map_shape))
+self._best_fit = np.copy(np.reshape(self._best_fit_flat,
+list(self._map_shape)+[x_size]))
+self._best_values = np.asarray([np.reshape(par, list(self._map_shape))
+for par in self._best_values_flat])
+self._best_errors = np.asarray([np.reshape(par, list(self._map_shape))
+for par in self._best_errors_flat])
+if self._inv_transpose is not None:
+self._out_of_bounds = np.transpose(self._out_of_bounds, self._inv_transpose)
+self._max_nfev = np.transpose(self._max_nfev, self._inv_transpose)
+self._redchi = np.transpose(self._redchi, self._inv_transpose)
+self._success = np.transpose(self._success, self._inv_transpose)
+self._best_fit = np.transpose(self._best_fit,
+list(self._inv_transpose)+[len(self._inv_transpose)])
+self._best_values = np.transpose(self._best_values,
+[0]+[i+1 for i in self._inv_transpose])
+self._best_errors = np.transpose(self._best_errors,
+[0]+[i+1 for i in self._inv_transpose])
+del self._out_of_bounds_flat
+del self._max_nfev_flat
+del self._redchi_flat
+del self._success_flat
+del self._best_fit_flat
+del self._best_values_flat
+del self._best_errors_flat
+#        t6 = time()
+# Restore parameter bounds and renormalize the parameters
+for name, par in self._parameter_bounds.items():
+self._parameters[name].set(min=par['min'], max=par['max'])
+self._normalized = False
+if self._norm is not None:
+for name, norm in self._parameter_norms.items():
+par = self._parameters[name]
+if par.expr is None and norm:
+value = par.value*self._norm[1]
+_min = par.min
+_max = par.max
+if not np.isinf(_min) and abs(_min) != float_min:
+_min *= self._norm[1]
+if not np.isinf(_max) and abs(_max) != float_min:
+_max *= self._norm[1]
+par.set(value=value, min=_min, max=_max)
+#        t7 = time()
+#        print(f'total run time in fit: {t7-t0:.2f} seconds')
+#        print(f'run time first fit: {t2-t1:.2f} seconds')
+#        print(f'run time remaining fits: {t4-t3:.2f} seconds')
+#        print(f'run time remapping results: {t6-t5:.2f} seconds')
+#        print('\n\nAt end fit:')
+#        self._parameters.pretty_print()
+#        print(f'self._best_values:\n{self._best_values}\n\n')
+# Free the shared memory
+self.freemem()
+def _fit_parallel(self, current_best_values, num, n_start, **kwargs):
+num = min(num, self._map_dim-n_start)
+for n in range(num):
+#            print(f'{n_start+n} before:\n{current_best_values}')
+self._fit(n_start+n, current_best_values, **kwargs)
+#            print(f'{n_start+n} after:\n{current_best_values}')
+def _fit(self, n, current_best_values, return_result=False, **kwargs):
+#RV        print(f'\n\nstart FitMap._fit {n}\n')
+#RV        print(f'current_best_values = {current_best_values}')
+#RV        print(f'self._best_parameters = {self._best_parameters}')
+#RV        print(f'self._new_parameters = {self._new_parameters}\n\n')
+#        self._parameters.pretty_print()
+# Set parameters to current best values, but prevent them from sitting at boundaries
+if self._new_parameters is None:
+# Initial fit
+for name, value in current_best_values.items():
+par = self._parameters[name]
+par.set(value=self._reset_par_at_boundary(par, value))
+else:
+# Refit
+for i, name in enumerate(self._best_parameters):
+par = self._parameters[name]
+if name in self._new_parameters:
+if name in current_best_values:
+par.set(value=self._reset_par_at_boundary(par, current_best_values[name]))
+elif par.expr is None:
+par.set(value=self._best_values[i][n])
+#RV        print(f'\nbefore fit {n}')
+#RV        self._parameters.pretty_print()
+if self._mask is None:
+result = self._model.fit(self._ymap_norm[n], self._parameters, x=self._x, **kwargs)
+else:
+result = self._model.fit(self._ymap_norm[n][~self._mask], self._parameters,
+x=self._x[~self._mask], **kwargs)
+#        print(f'\nafter fit {n}')
+#        self._parameters.pretty_print()
+#        print(result.fit_report(show_correl=False))
+out_of_bounds = False
+for name, par in self._parameter_bounds.items():
+value = result.params[name].value
+if not np.isinf(par['min']) and value < par['min']:
+out_of_bounds = True
+break
+if not np.isinf(par['max']) and value > par['max']:
+out_of_bounds = True
+break
+self._out_of_bounds_flat[n] = out_of_bounds
+if self._try_no_bounds and out_of_bounds:
+# Rerun fit with parameter bounds in place
+for name, par in self._parameter_bounds.items():
+self._parameters[name].set(min=par['min'], max=par['max'])
+# Set parameters to current best values, but prevent them from sitting at boundaries
+if self._new_parameters is None:
+# Initial fit
+for name, value in current_best_values.items():
+par = self._parameters[name]
+par.set(value=self._reset_par_at_boundary(par, value))
+else:
+# Refit
+for i, name in enumerate(self._best_parameters):
+par = self._parameters[name]
+if name in self._new_parameters:
+if name in current_best_values:
+par.set(value=self._reset_par_at_boundary(par,
+current_best_values[name]))
+elif par.expr is None:
+par.set(value=self._best_values[i][n])
+#            print('\nbefore fit')
+#            self._parameters.pretty_print()
+#            print(result.fit_report(show_correl=False))
+if self._mask is None:
+result = self._model.fit(self._ymap_norm[n], self._parameters, x=self._x, **kwargs)
+else:
+result = self._model.fit(self._ymap_norm[n][~self._mask], self._parameters,
+x=self._x[~self._mask], **kwargs)
+#            print(f'\nafter fit {n}')
+#            self._parameters.pretty_print()
+#            print(result.fit_report(show_correl=False))
+out_of_bounds = False
+for name, par in self._parameter_bounds.items():
+value = result.params[name].value
+if not np.isinf(par['min']) and value < par['min']:
+out_of_bounds = True
+break
+if not np.isinf(par['max']) and value > par['max']:
+out_of_bounds = True
+break
+#            print(f'{n} redchi < redchi_cutoff = {result.redchi < self._redchi_cutoff} success = {result.success} out_of_bounds = {out_of_bounds}')
+# Reset parameters back to unbound
+for name in self._parameter_bounds.keys():
+self._parameters[name].set(min=-np.inf, max=np.inf)
+assert(not out_of_bounds)
+if result.redchi >= self._redchi_cutoff:
+result.success = False
+if result.nfev == result.max_nfev:
+#            print(f'Maximum number of function evaluations reached for n = {n}')
+#            logging.warning(f'Maximum number of function evaluations reached for n = {n}')
+if result.redchi < self._redchi_cutoff:
+result.success = True
+self._max_nfev_flat[n] = True
+if result.success:
+assert(all(True for par in current_best_values if par in result.params.values()))
+for par in result.params.values():
+if par.vary:
+current_best_values[par.name] = par.value
+else:
+logging.warning(f'Fit for n = {n} failed: {result.lmdif_message}')
+# Renormalize the data and results
+self._renormalize(n, result)
+if self._print_report:
+print(result.fit_report(show_correl=False))
+if self._plot:
+dims = np.unravel_index(n, self._map_shape)
+if self._inv_transpose is not None:
+dims= tuple(dims[self._inv_transpose[i]] for i in range(len(dims)))
+super().plot(result=result, y=np.asarray(self._ymap[dims]), plot_comp_legends=True,
+skip_init=self._skip_init, title=str(dims))
+#RV        print(f'\n\nend FitMap._fit {n}\n')
+#RV        print(f'current_best_values = {current_best_values}')
+#        self._parameters.pretty_print()
+#        print(result.fit_report(show_correl=False))
+#RV        print(f'\nself._best_values_flat:\n{self._best_values_flat}\n\n')
+if return_result:
+return(result)
+def _renormalize(self, n, result):
+self._redchi_flat[n] = np.float64(result.redchi)
+self._success_flat[n] = result.success
+if self._norm is None or not self._normalized:
+self._best_fit_flat[n] = result.best_fit
+for i, name in enumerate(self._best_parameters):
+self._best_values_flat[i][n] = np.float64(result.params[name].value)
+self._best_errors_flat[i][n] = np.float64(result.params[name].stderr)
+else:
+pars = set(self._parameter_norms) & set(self._best_parameters)
+for name, par in result.params.items():
+if name in pars and self._parameter_norms[name]:
+if par.stderr is not None:
+par.stderr *= self._norm[1]
+if par.expr is None:
+par.value *= self._norm[1]
+if self._print_report:
+if par.init_value is not None:
+par.init_value *= self._norm[1]
+if not np.isinf(par.min) and abs(par.min) != float_min:
+par.min *= self._norm[1]
+if not np.isinf(par.max) and abs(par.max) != float_min:
+par.max *= self._norm[1]
+self._best_fit_flat[n] = result.best_fit*self._norm[1]+self._norm[0]
+for i, name in enumerate(self._best_parameters):
+self._best_values_flat[i][n] = np.float64(result.params[name].value)
+self._best_errors_flat[i][n] = np.float64(result.params[name].stderr)
+if self._plot:
+if not self._skip_init:
+result.init_fit = result.init_fit*self._norm[1]+self._norm[0]
+result.best_fit = np.copy(self._best_fit_flat[n])

Mercurial > repos > rv43 > test_tomo_reconstruct

comparison fit.py @ 0:98e23dff1de2 draft default tip