u (1a60c8b5) · Commits · Michele Maris / YAPSUT

src/yapsut/montecarlo_fitting.py

+18 −0

Original line number	Original line	Diff line number	Diff line
	import numpy as np		import numpy as np
	import pandas as pd		import pandas as pd
			from .stats import CumulativeOfData
			from .struct import struct as STRUCT

	class EnsembleFitting_Base :		class EnsembleFitting_Base :
	"""This class implements Ensemble fitting with curve_fit for (a,b,c,Rdark) out of the input curve.		"""This class implements Ensemble fitting with curve_fit for (a,b,c,Rdark) out of the input curve.
	@@ -135,6 +137,22 @@ class EnsembleFitting_Base :
	except :		except :
	return np.ones(len(self._param_names))+np.nan		return np.ones(len(self._param_names))+np.nan
	#		#
			def montecarlo_cdf(self) :
			"""returns a dictionary with the cdf for the montecarlo simulations.
			CDF are instatiations of .stats/CumulativeOfData so they are interpolating functions
			of the montecarlo distribution of each variable.
			"""
			try :
			self._mc[:,0]
			except :
			raise Exception("Error: no montecarlo simulation stored")
			#
			out=STRUCT()

			for ik,k in enumerate(self.param_names) :
			out[k]=CumulativeOfData(self._mc[:,ik])
			return out
			#
	def fitting(self,X,Y,sgm) :		def fitting(self,X,Y,sgm) :
	"""		"""
	the fitter function, must be specialized according to the this template		the fitter function, must be specialized according to the this template

src/yapsut/stats.py

+56 −5

Original line number	Original line	Diff line number	Diff line
	@@ -5,11 +5,13 @@ Simple stats
	import numpy as np		import numpy as np

	class CumulativeOfData :		class CumulativeOfData :
	"""strucuture to handle the cumulative of 1d data"""		"""strucuture to handle the cumulative of 1d data
			It uses linear interpolations
			"""
	@property		@property
	def cdf(self) :		def CDF(self) :
	"""the cdf """		"""the cdf sampled curve """
	return self._cdf		return self._eff
	@property		@property
	def x(self) :		def x(self) :
	"""the score"""		"""the score"""
	@@ -18,6 +20,10 @@ class CumulativeOfData :
	def N(self) :		def N(self) :
	"""the number of finite samples"""		"""the number of finite samples"""
	return self._N		return self._N
			@property
			def z(self) :
			"""normalized x: z(x) = (x-x.min())/(x.max()-x.min())"""
			return (self._x-self._x[0])/(self._x[-1]-self._x[0])
	def __init__(self,X) :		def __init__(self,X) :
	""":X: 1d array of scores"""		""":X: 1d array of scores"""
	idx=np.where(np.isfinite(X))		idx=np.where(np.isfinite(X))
	@@ -32,12 +38,57 @@ class CumulativeOfData :
	:x: the score		:x: the score
	"""		"""
	return np.interp(x,self._x,self._eff,left=0.,right=1.)		return np.interp(x,self._x,self._eff,left=0.,right=1.)
			#
			def sampled_pdf(self,x,method='hist') :
			"""returns the sample pdf for a list of x
			output: hh, xx

			x must be monotously increasing or an integer

			if method = 'hist' the pdf is calculated using an histogram like function (default)
			if method = 'tan' the pdf is calculated using the local tangent to the cdf
			"""
			if not method in ['hist','tan'] :
			raise Exception("Error: method must be either 'hist' or 'tan'")
			#
			if method == 'hist' :
			if np.isscalar(x) :
			n=int(x)
			_x=np.linspace(self._x[0],self._x[-1],n)
			else :
			_x=np.sort(x)
			#
			yy=self.cdf(_x)
			hh=(yy[:-1]-yy[1:])/(_x[:-1]-_x[1:])
			return hh, _x
			elif method == 'tan' :
			h=(self._x[-1]-self._x[0])*eps
			ydotF=(self.cdf(_x+h)-self.cdf(_x-h))/(2*h)
			ydotH=(self.cdf(_x+h/2)-self.cdf(_x-h/2))/(h)
			ydot=2*ydotH-ydotF
			#
			# if the lower limit is below the minimum value in the cdf
			if _x[0]-h<self._x[0] :
			ydotF=(self.cdf(_x[0]+h)-self.cdf(_x[0]))/(h)
			ydotH=((self.cdf(_x[0]+h)-self.cdf(_x[0])))/(h/2)
			ydot[0]=2*ydotH-ydotF
			#
			# if the upper limit is above the maximum value of the cdf
			if _x[-1]+h>self._x[-1] :
			ydotF=(self.cdf(_x[-1])-self.cdf(_x[-1]-h))/(h)
			ydotH=(self.cdf(_x[-1])-self.cdf(_x[-1]-h/2))/(h/2)
			ydot[-1]=2*ydotH-ydotF
			#
			return ydot, _x
			else :
			raise Exception("Error: method must be either 'hist' or 'tan'")
			#
	def percentile(self,eff) :		def percentile(self,eff) :
	"""computes the percentile of samples for which x<=percentile(eff)		"""computes the percentile of samples for which x<=percentile(eff)

	:eff: the required percentile [0,1]		:eff: the required percentile [0,1]
	if eff<0 the result is -infty		if eff<0 the result is -infty
	if eff>0 the result is +infty		if eff>1 the result is +infty
	"""		"""
	return np.interp(eff,self._eff,self._x,left=-np.infty,right=np.infty)		return np.interp(eff,self._eff,self._x,left=-np.infty,right=np.infty)