Loading src/yapsut/__init__.py +1 −0 Original line number Original line Diff line number Diff line Loading @@ -18,6 +18,7 @@ from .template_subs import discoverKeys, templateFiller from .periodic_stats import periodic_stats, periodic_centroid from .periodic_stats import periodic_stats, periodic_centroid from .stats import CumulativeOfData from .stats import CumulativeOfData from .colored_noise import gaussian_colored_noise from .colored_noise import gaussian_colored_noise from .montecarlo_fitting import EnsembleFitting_Base # planck legacy is under editing # planck legacy is under editing #from .planck_legacy import cosmological_dipole #from .planck_legacy import cosmological_dipole Loading src/yapsut/montecarlo_fitting.py 0 → 100644 +187 −0 Original line number Original line Diff line number Diff line import numpy as np import pandas as pd class EnsembleFitting_Base : """This class implements Ensemble fitting with curve_fit for (a,b,c,Rdark) out of the input curve. """ @property def initial_values(self) : """return the initial values""" return self._initial_values # @initial_values.setter def initial_values(self,this) : """return the initial values""" self._initial_values=this # @property def param_names(self) : return self._param_names # @property def Model(self) : return self._Model # @Model.setter def Model(self,Model,param_names) : self._param_names=param_names self._Model=this # @property def fit_success(self) : return self._success # @property def size(self) : return self._size # @property def fit(self) : return self._fit # def copy(self) : from copy import deepcopy return copy(self) # def __len__(self) : return self._size # def __init__(self,X,Xerr,Y,Yerr) : """It takes in input X, Y and their errors""" self._X=X self._Xerr=Xerr if not Xerr is None else X*0 # self._Y=Y self._Yerr=Yerr if not Yerr is None else Y*0 # self._size=len(X) # self._fitting_clean() # def _fitting_clean(self) : self._mc=None self._initial_values=None self._Model=None self._param_names=None self._success=False self._fit=None self._fitting_kargs={} # @property def fitting_kargs(self) : """access to the fitting arguments dictionary""" return self._fitting_kargs # @property def fitting_result(self) : """returns the whole last fitting result""" return self._fitting_result # def fitting_bounds(self) : """returns fitting bounds""" return self._fitting_args['bounds'] # @property def residuals(self) : if not self.fit_success : return np.nan*self._X return self._Y-self._Model(self._X,*self._fit) # @property def chisq(self) : if not self.fit_success : return np.nan r=(self.residuals/self._Yerr)**2 return r.sum() # def mc_shot(self) : """creates a montecarlo realization of the I,P data. Assumes uniform error for I and gaussian error for P """ n=self._size X=np.random.uniform(low=-0.5,high=0.5,size=n)*self._Xerr+self._X Y=np.random.normal(size=n)*self._Yerr+self._Y return X,Y # def montecarlo(self,nmc,force_positive=True,as_pandas=True,max_nmc=-3) : """creates a montecarlo serie of parameters output: mc realization of parameters """ out=[] n=len(self) imc=nmc mcount=abs(max_nmc)*nmc if max_nmc < 0 else max_nmc if nmc == 0 : mcount = nmc while imc > 0 and mcount > 0: mcount-=1 # X,Y=self.mc_shot() fit,fit_success=self.fitting(X,Y,self._Yerr) # if fit_success : if self.valid_fit(fit) : imc+=-1 out.append(fit) self._mc=np.array(out) if as_pandas : out=pd.DataFrame(self._mc,columns=self._param_names) return out # @property def montecarlo_ensemble_averages(self) : """returns the ensemble averaged values from the montecarlo simulation""" try : return np.array([np.median(k) for k in self._mc.T]) except : return np.ones(len(self._param_names))+np.nan # def fitting(self,X,Y,sgm) : """ the fitter function, must be specialized according to the this template def fitting(self,X,Y,sgm) : from scipy.optimize import curve_fit # # resets all the variables to be generated self._success=False self._fit=None self._fitting_result=None # # perfom the fit try : self._fitting_result=curve_fit(self.Model,X,Y,sigma=sgm,p0=self._initial_values,full_output=True,**self._fitting_kargs) # # saves the fit result and the flags success self._fit=self._fitting_result[0] self._success=self._fitting_result[-1] in [1,2,3,4] except : self._success=False # # returns the result return self._fit,self._success """ from scipy.optimize import curve_fit # # resets all the variables to be generated self._success=False self._fit=None self._fitting_result=None # # perfom the fit try : self._fitting_result=curve_fit(self.Model,X,Y,sigma=sgm,p0=self._initial_values,full_output=True,**self._fitting_kargs) # # saves the fit result and the flags success self._fit=self._fitting_result[0] self._success=self._fitting_result[-1] in [1,2,3,4] except : self._success=False # # returns the result return self._fit,self._success # def valid_fit(self,fit) : """test wether the fit is valid: a specialized function for the application""" #return True or False return True Loading
src/yapsut/__init__.py +1 −0 Original line number Original line Diff line number Diff line Loading @@ -18,6 +18,7 @@ from .template_subs import discoverKeys, templateFiller from .periodic_stats import periodic_stats, periodic_centroid from .periodic_stats import periodic_stats, periodic_centroid from .stats import CumulativeOfData from .stats import CumulativeOfData from .colored_noise import gaussian_colored_noise from .colored_noise import gaussian_colored_noise from .montecarlo_fitting import EnsembleFitting_Base # planck legacy is under editing # planck legacy is under editing #from .planck_legacy import cosmological_dipole #from .planck_legacy import cosmological_dipole Loading
src/yapsut/montecarlo_fitting.py 0 → 100644 +187 −0 Original line number Original line Diff line number Diff line import numpy as np import pandas as pd class EnsembleFitting_Base : """This class implements Ensemble fitting with curve_fit for (a,b,c,Rdark) out of the input curve. """ @property def initial_values(self) : """return the initial values""" return self._initial_values # @initial_values.setter def initial_values(self,this) : """return the initial values""" self._initial_values=this # @property def param_names(self) : return self._param_names # @property def Model(self) : return self._Model # @Model.setter def Model(self,Model,param_names) : self._param_names=param_names self._Model=this # @property def fit_success(self) : return self._success # @property def size(self) : return self._size # @property def fit(self) : return self._fit # def copy(self) : from copy import deepcopy return copy(self) # def __len__(self) : return self._size # def __init__(self,X,Xerr,Y,Yerr) : """It takes in input X, Y and their errors""" self._X=X self._Xerr=Xerr if not Xerr is None else X*0 # self._Y=Y self._Yerr=Yerr if not Yerr is None else Y*0 # self._size=len(X) # self._fitting_clean() # def _fitting_clean(self) : self._mc=None self._initial_values=None self._Model=None self._param_names=None self._success=False self._fit=None self._fitting_kargs={} # @property def fitting_kargs(self) : """access to the fitting arguments dictionary""" return self._fitting_kargs # @property def fitting_result(self) : """returns the whole last fitting result""" return self._fitting_result # def fitting_bounds(self) : """returns fitting bounds""" return self._fitting_args['bounds'] # @property def residuals(self) : if not self.fit_success : return np.nan*self._X return self._Y-self._Model(self._X,*self._fit) # @property def chisq(self) : if not self.fit_success : return np.nan r=(self.residuals/self._Yerr)**2 return r.sum() # def mc_shot(self) : """creates a montecarlo realization of the I,P data. Assumes uniform error for I and gaussian error for P """ n=self._size X=np.random.uniform(low=-0.5,high=0.5,size=n)*self._Xerr+self._X Y=np.random.normal(size=n)*self._Yerr+self._Y return X,Y # def montecarlo(self,nmc,force_positive=True,as_pandas=True,max_nmc=-3) : """creates a montecarlo serie of parameters output: mc realization of parameters """ out=[] n=len(self) imc=nmc mcount=abs(max_nmc)*nmc if max_nmc < 0 else max_nmc if nmc == 0 : mcount = nmc while imc > 0 and mcount > 0: mcount-=1 # X,Y=self.mc_shot() fit,fit_success=self.fitting(X,Y,self._Yerr) # if fit_success : if self.valid_fit(fit) : imc+=-1 out.append(fit) self._mc=np.array(out) if as_pandas : out=pd.DataFrame(self._mc,columns=self._param_names) return out # @property def montecarlo_ensemble_averages(self) : """returns the ensemble averaged values from the montecarlo simulation""" try : return np.array([np.median(k) for k in self._mc.T]) except : return np.ones(len(self._param_names))+np.nan # def fitting(self,X,Y,sgm) : """ the fitter function, must be specialized according to the this template def fitting(self,X,Y,sgm) : from scipy.optimize import curve_fit # # resets all the variables to be generated self._success=False self._fit=None self._fitting_result=None # # perfom the fit try : self._fitting_result=curve_fit(self.Model,X,Y,sigma=sgm,p0=self._initial_values,full_output=True,**self._fitting_kargs) # # saves the fit result and the flags success self._fit=self._fitting_result[0] self._success=self._fitting_result[-1] in [1,2,3,4] except : self._success=False # # returns the result return self._fit,self._success """ from scipy.optimize import curve_fit # # resets all the variables to be generated self._success=False self._fit=None self._fitting_result=None # # perfom the fit try : self._fitting_result=curve_fit(self.Model,X,Y,sigma=sgm,p0=self._initial_values,full_output=True,**self._fitting_kargs) # # saves the fit result and the flags success self._fit=self._fitting_result[0] self._success=self._fitting_result[-1] in [1,2,3,4] except : self._success=False # # returns the result return self._fit,self._success # def valid_fit(self,fit) : """test wether the fit is valid: a specialized function for the application""" #return True or False return True
