from .mcmcposteriorsamplergamma import fit
from scipy.stats import norm, gamma
import pandas as pd
import numpy as np
import pickle as pk

from ..shared_functions import *

class mcmcsamplergamma:
    """
    Class for the mcmc sampler of the deconvolution gaussian model
    """
    def __init__(self, K=1, Kc=1, alpha = 1, alphac = 1):
        """
        Constructor of the class


        Parameters
        -------------
            K: int, Number of components of the noise distribution
            Kc: int, Number of components of the convolved distribution
            **kwargs:
                alpha: float, parameter to determine the hyperprior of the noise weight components
                alphac: float, parameter to determine the hyperprior of the target weight components

        """
        
        self.K = K
        self.Kc = Kc
        self.alpha = alpha
        self.alphac = alphac

        self.fitted = False

        return

    def fit(self, dataNoise, dataConvolution, iterations = 1000, ignored_iterations = 1000, chains = 1, 
            priors = None,
            precission = 0.99, method = "moments", bias = None,
            initial_conditions = [], show_progress = True, seed = 0):
        """
        Fit the model to the posterior distribution


        Parameters
        -------------
            dataNoise: list/npArray, 1D array witht he data of the noise
            dataConvolution: list/npArray, 1D array witht he data of the convolution
            iterations: int, number of samples to be drawn and stored for each chain during the sampling
            ignored_iterations: int, number of samples to be drawn and ignored for each chain during the sampling
            chains: int, number of independently initialised realisations of the markov chain
            priors: array, parameter of the prior gamma distribution acording to the definition of the wikipedia
            kconst: float, parameter k of the prior gamma distribution
            initialConditions: list, 1D array with all the parameters required to initialise manually all the components of all the chains the chains
            show_progress: bool, indicate if the method should show the progress in the generation of the new data
            seed: int, value to initialise the random generator and obtain reproducible results

        Returns
        ---------------
            Nothing
        """

        self.data = dataNoise
        self.datac = dataConvolution
        self.iterations = iterations
        self.ignored_iterations = ignored_iterations
        self.chains = chains

        if bias == None:
            m = np.min([dataNoise,dataConvolution])
            if m < 0:
                self.bias = m - 0.01
            else:
                self.bias = 0
        elif bias < np.min([dataNoise,dataConvolution]):
            self.bias = bias
        else:
            self.bias = np.min([dataNoise,dataConvolution])*0.9999

        if priors==None:
            m = np.mean(dataNoise-self.bias)
            v = np.var(dataNoise-self.bias)
            self.priortheta_theta = 100*v/m
            self.priork_theta = 100*v/m
            self.priortheta_k = 1.1
            self.priork_k = 1.1
            m = np.mean(dataConvolution-self.bias)
            v = np.var(dataConvolution-self.bias)
            self.priortheta_thetac = 100*v/m
            self.priork_thetac = 100*v/m
            self.priortheta_kc = 1.1
            self.priork_kc = 1.1


        self.precission = precission
        self.method = method

        self.samples = np.array(fit(dataNoise-self.bias, dataConvolution-self.bias,
                          self.ignored_iterations, self.iterations, self.chains,
                          self.K, self.Kc, 
                          self.alpha, self.alphac, 
                          self.priortheta_k, self.priortheta_theta, self.priork_k, self.priork_theta, 
                          self.priortheta_kc, self.priortheta_thetac, self.priork_kc, self.priork_thetac, 
                          0,
                          self.precission, self.method,
                          initial_conditions, show_progress, seed))
        
        self.fitted = True

        return

    def save(self, name):
        """
        Pickle save the model.

        Parameters
        ----------------
            name: string, name in which to store the model

        Return:
            nothing
        """
        if self.fitted:
            pickling_on = open(name+".pickle","wb")
            pk.dump({"K":self.K, "Kc":self.Kc, "alpha": self.alpha, "alphac": self.alphac, "iterations": self.iterations,
                     "ignored_iterations": self.ignored_iterations,
                     "priortheta_k": self.priortheta_k, "priortheta_theta": self.priortheta_theta, "priork_k": self.priork_k, 
                     "priork_theta": self.priork_theta, "priortheta_kc": self.priortheta_kc, "priortheta_thetac": self.priortheta_thetac, 
                     "priortheta_thetac": self.priortheta_thetac, "priork_thetac": self.priork_thetac, 
                     "bias":self.bias, "chains":self.chains, "samples":self.samples}, pickling_on)
            pickling_on.close()
        else:
            print("The model has not been fitted so there is nothing to save.")
        return

    def load(self, name):
        """
        Pickle load the model.

        Parameters
        ----------------
            name: string, name from which to recover the model

        Return:
            nothing
        """
        pickle_off = open(name+".pickle","rb")
        aux = pk.load(pickle_off)
        pickle_off.close()

        self.K = aux["K"]
        self.Kc = aux ["Kc"]
        self.alpha = aux["alpha"]
        self.alphac = aux["alphac"]
        self.iterations = aux["iterations"]
        self.ignored_iterations = aux["ignored_iterations"]
        self.chains = aux["chains"]
        self.samples = aux["samples"]
        self.priortheta_k = aux["priortheta_k"]
        self.priortheta_theta = aux["priortheta_theta"]
        self.priork_k = aux["priork_k"] 
        self.priork_theta = aux["priork_theta"]
        self.priortheta_kc = aux["priortheta_kc"] 
        self.priortheta_thetac = aux["priortheta_thetac"] 
        self.priortheta_thetac = aux["priortheta_thetac"] 
        self.priork_thetac = aux["priork_thetac"]
        self.bias = aux["bias"]

        self.fitted = True

        return

    def sample_autofluorescence(self, size = 1, style = "full", pos = None):
        """
        Generate samples from the fitted posterior distribution according to the noise distribution

        Parameters
        -------------
            size: int, number of samples to be drawn

        Returns 
        -------------
            list: list, 1D array with *size* samples from the model
        """

        if style=="full":
            return  np.array(sample_autofluorescence_gamma(self.samples,self.K,self.Kc,size=size, bias=0))+self.bias
        elif style=="single":
            if pos == None:
                pos = np.random.choice(range(len(self.samples))) 
                return  np.array(sample_autofluorescence_gamma(self.samples,self.K,self.Kc,size=size,pos=pos, bias=0))+self.bias
            else:
                return  np.array(sample_autofluorescence_gamma(self.samples,self.K,self.Kc,size=size,pos=pos, bias=0))+self.bias

#        return  np.array(sample_autofluorescence_gamma(self.samples,self.K,self.Kc,size))

    def sample_deconvolution(self, size = 1, style = "full", pos = None):
        """
        Generate samples from the fitted posterior distribution according to the deconvolved distribution

        Parameters
        -------------
            size: int, number of samples to be drawn

        Returns
        -------------
            list: list, 1D array with *size* samples from the model
        """

        if style=="full":
            return  np.array(sample_deconvolution_gamma(self.samples,self.K,self.Kc,size=size, bias=0))+self.bias
        elif style=="single":
            if pos == None:
                pos = np.random.choice(range(len(self.samples))) 
                return  np.array(sample_deconvolution_gamma(self.samples,self.K,self.Kc,size=size,pos=pos, bias=0))+self.bias
            else:
                return  np.array(sample_deconvolution_gamma(self.samples,self.K,self.Kc,size=size,pos=pos, bias=0))+self.bias

#        return  np.array(sample_deconvolution_gamma(self.samples,self.K,self.Kc,size))

    def sample_convolution(self, size = 1, style = "full", pos = None):
        """
        Generate samples from the fitted posterior distribution according to the convolved distribution

        Parameters
        -------------
            size: int, number of samples to be drawn

        Returns
        -------------
            list: list, 1D array with *size* samples from the model
        """

        if style=="full":
            return  np.array(sample_convolution_gamma(self.samples,self.K,self.Kc,size=size, bias=0))+self.bias
        elif style=="single":
            if pos == None:
                pos = np.random.choice(range(len(self.samples))) 
                return  np.array(sample_convolution_gamma(self.samples,self.K,self.Kc,size=size,pos=pos, bias=0))+self.bias
            else:
                return  np.array(sample_convolution_gamma(self.samples,self.K,self.Kc,size=size,pos=pos, bias=0))+self.bias

#        return  np.array(sample_convolution_gamma(self.samples,self.K,self.Kc,size))

    def score_autofluorescence(self, x, percentiles = [5, 95], size = 100):
        """
        Evaluate the mean and percentiles of the the pdf at certain position acording to the convolved distribution

        Parameters
        -------------
            x: list/array, positions where to evaluate the distribution
            percentiles: list/array, percentiles to be evaluated
            size: int, number of samples to draw from the posterior to make the statistics, bigger numbers give more stability

        Returns
        -------------
            list: list, 2D array with the mean and all the percentile evaluations at all points in x
        """
        yT = []
        for l in range(size):
            i = np.random.choice(self.iterations)
            y = np.zeros(len(x))
            for k in range(self.K):
                thetastar = self.samples[i,self.K+k]
                kconststar = self.samples[i,2*self.K+k]
                    
                y += self.samples[i,k]*gamma.pdf(x,a=kconststar,scale=thetastar)
            yT.append(y)

        return  np.mean(yT,axis=0),np.percentile(yT,percentiles,axis=0)

    def score_deconvolution(self, x, percentiles = [5, 95], size = 100):
        """
        Evaluate the mean and percentiles of the the pdf at certain position acording to the deconvolved distribution

        Parameters
        -------------
            x: list/array, positions where to evaluate the distribution
            percentiles: list/array, percentiles to be evaluated
            size: int, number of samples to draw from the posterior to make the statistics, bigger numbers give more stability

        Returns
        -------------
            list: list, 2D array with the mean and all the percentile evaluations at all points in x
        """

        yT = []
        for l in range(size):
            i = np.random.choice(self.iterations)
            y = np.zeros(len(x))
            for j in range(self.Kc):
                thetastar = self.samples[i,3*self.K+self.Kc+j]
                kconststar = self.samples[i,3*self.K+2*self.Kc+j]
                    
                y += self.samples[i,3*self.K+j]*gamma.pdf(x,a=kconststar,scale=thetastar)
            yT.append(y)

        return  np.mean(yT,axis=0),np.percentile(yT,percentiles,axis=0)

    def score_convolution(self, x, percentiles = [5, 95], size = 100):
        """
        Evaluate the mean and percentiles of the the pdf at certain position acording to the convolved distribution

        Parameters
        -------------
            x: list/array, positions where to evaluate the distribution
            percentiles: list/array, percentiles to be evaluated
            size: int, number of samples to draw from the posterior to make the statistics, bigger numbers give more stability

        Returns
        -------------
            list: list, 2D array with the mean and all the percentile evaluations at all points in x
        """

        yT = []
        for l in range(size):
            i = np.random.choice(self.iterations)
            y = np.zeros(len(x))
            for j in range(self.Kc):
                for k in range(self.K):
                    theta1 = self.samples[i,self.K+k]
                    theta2 = self.samples[i,3*self.K+self.Kc+j]
                    k1 = self.samples[i,2*self.K+k]
                    k2 = self.samples[i,3*self.K+2*self.Kc+j]
                    mu = theta1*k1+theta2*k2
                    s = theta1*theta1*k1+theta2*theta2*k2
                    thetastar = s/mu
                    kconststar = mu*mu/s
                    
                    y += self.samples[i,k]*self.samples[i,3*self.K+j]*gamma.pdf(x,a=kconststar,scale=thetastar)
            yT.append(y)

        return  np.mean(yT,axis=0),np.percentile(yT,percentiles,axis=0)

    def sampler_statistics(self, sort="weight"):
        """
        Show statistics of correct mixing of the mcmc sampler
        
        Args:
            sort: ["weight", "none", "means"], method for sorting the samples from the different chains

        Returns
        -------------
            DataFrame: DataFrame the mean, std, percentiles, mixing ratio(rhat) and effective number of samples for each parameter of the model
        """
        
        self.sampler_statistics = pd.DataFrame(columns=["Mean","Std","5%","50%","95%","Rhat","Neff"])

        samples = self.samples.copy()

        if sort == "weight":
            argsort = np.argsort(samples[:,0:self.K],axis=1)
            samples[:,0:self.K] = np.take_along_axis(samples[:,0:self.K],argsort,axis=1)
            samples[:,self.K:2*self.K] = np.take_along_axis(samples[:,self.K:2*self.K],argsort,axis=1)
            samples[:,2*self.K:3*self.K] = np.take_along_axis(samples[:,2*self.K:3*self.K],argsort,axis=1)

            argsort = np.argsort(samples[:,3*self.K:3*self.K+self.Kc],axis=1)
            samples[:,3*self.K:3*self.K+self.Kc] = np.take_along_axis(samples[:,3*self.K:3*self.K+self.Kc],argsort,axis=1)
            samples[:,(3*self.K+self.Kc):(3*self.K+2*self.Kc)] = np.take_along_axis(samples[:,(3*self.K+self.Kc):(3*self.K+2*self.Kc)],argsort,axis=1)
            samples[:,(3*self.K+2*self.Kc):(3*self.K+3*self.Kc)] = np.take_along_axis(samples[:,(3*self.K+2*self.Kc):(3*self.K+3*self.Kc)],argsort,axis=1)

        if sort == "mean":
            argsort = np.argsort(samples[:,self.K:2*self.K],axis=1)
            samples[:,0:self.K] = np.take_along_axis(samples[:,0:self.K],argsort,axis=1)
            samples[:,self.K:2*self.K] = np.take_along_axis(samples[:,self.K:2*self.K],argsort,axis=1)
            samples[:,2*self.K:3*self.K] = np.take_along_axis(samples[:,2*self.K:3*self.K],argsort,axis=1)

            argsort = np.argsort(samples[:,3*self.K+self.Kc:3*self.K+2*self.Kc],axis=1)
            samples[:,3*self.K:3*self.K+self.Kc] = np.take_along_axis(samples[:,3*self.K:3*self.K+self.Kc],argsort,axis=1)
            samples[:,(3*self.K+self.Kc):(3*self.K+2*self.Kc)] = np.take_along_axis(samples[:,(3*self.K+self.Kc):(3*self.K+2*self.Kc)],argsort,axis=1)
            samples[:,(3*self.K+2*self.Kc):(3*self.K+3*self.Kc)] = np.take_along_axis(samples[:,(3*self.K+2*self.Kc):(3*self.K+3*self.Kc)],argsort,axis=1)

        measures = np.zeros(7)
        for i in range(3*self.K+3*self.Kc):
            measures[0] = np.mean(samples[:,i])
            measures[1] = np.std(samples[:,i])
            measures[2:5] = np.percentile(samples[:,i],[5,50,95])
            measures[5] = rstat(samples[:,i],self.chains)
            measures[6] = effnumber(samples[:,i],self.chains)

            #Name the component
            if i < self.K:
                name = "weight_K"+str(1+i)
            elif i < 2*self.K:
                name = "mean_K"+str(1+i-self.K)
            elif i < 3*self.K:
                name = "std_K"+str(1+i-2*self.K)
            elif i < 3*self.K+self.Kc:
                name = "weight_Kc"+str(1+i-3*self.K)
            elif i < 3*self.K+2*self.Kc:
                name = "mean_Kc"+str(1+i-3*self.K-self.Kc)
            else:
                name = "std_Kc"+str(1+i-3*self.K-2*self.Kc)

            self.sampler_statistics = self.sampler_statistics.append(pd.Series(measures, ["Mean","Std","5%","50%","95%","Rhat","Neff"], name=name))

        return self.sampler_statistics

from .mcmcposteriorsamplergamma import fit
from scipy.stats import norm, gamma
import pandas as pd
import numpy as np
import pickle as pk

from ..shared_functions import *

class mcmcsamplergamma:
    """
    Class for the mcmc sampler of the deconvolution gaussian model
    """
    def __init__(self, K=1, Kc=1, alpha = 1, alphac = 1):
        """
        Constructor of the class


        Parameters
        -------------
            K: int, Number of components of the noise distribution
            Kc: int, Number of components of the convolved distribution
            **kwargs:
                alpha: float, parameter to determine the hyperprior of the noise weight components
                alphac: float, parameter to determine the hyperprior of the target weight components

        """
        
        self.K = K
        self.Kc = Kc
        self.alpha = alpha
        self.alphac = alphac

        self.fitted = False

        return

    def fit(self, dataNoise, dataConvolution, iterations = 1000, ignored_iterations = 1000, chains = 1, 
            priors = None,
            precission = 0.99, method = "moments", bias = None,
            initial_conditions = [], show_progress = True, seed = 0):
        """
        Fit the model to the posterior distribution


        Parameters
        -------------
            dataNoise: list/npArray, 1D array witht he data of the noise
            dataConvolution: list/npArray, 1D array witht he data of the convolution
            iterations: int, number of samples to be drawn and stored for each chain during the sampling
            ignored_iterations: int, number of samples to be drawn and ignored for each chain during the sampling
            chains: int, number of independently initialised realisations of the markov chain
            priors: array, parameter of the prior gamma distribution acording to the definition of the wikipedia
            kconst: float, parameter k of the prior gamma distribution
            initialConditions: list, 1D array with all the parameters required to initialise manually all the components of all the chains the chains
            show_progress: bool, indicate if the method should show the progress in the generation of the new data
            seed: int, value to initialise the random generator and obtain reproducible results

        Returns
        ---------------
            Nothing
        """

        self.data = dataNoise
        self.datac = dataConvolution
        self.iterations = iterations
        self.ignored_iterations = ignored_iterations
        self.chains = chains

        if bias == None:
            m = np.min([dataNoise,dataConvolution])
            if m < 0:
                self.bias = m - 0.01
            else:
                self.bias = 0
        elif bias < np.min([dataNoise,dataConvolution]):
            self.bias = bias
        else:
            self.bias = np.min([dataNoise,dataConvolution])*0.9999

        if priors==None:
            m = np.mean(dataNoise-self.bias)
            v = np.var(dataNoise-self.bias)
            self.priortheta_theta = 100*v/m
            self.priork_theta = 100*v/m
            self.priortheta_k = 1.1
            self.priork_k = 1.1
            m = np.mean(dataConvolution-self.bias)
            v = np.var(dataConvolution-self.bias)
            self.priortheta_thetac = 100*v/m
            self.priork_thetac = 100*v/m
            self.priortheta_kc = 1.1
            self.priork_kc = 1.1


        self.precission = precission
        self.method = method

        self.samples = np.array(fit(dataNoise-self.bias, dataConvolution-self.bias,
                          self.ignored_iterations, self.iterations, self.chains,
                          self.K, self.Kc, 
                          self.alpha, self.alphac, 
                          self.priortheta_k, self.priortheta_theta, self.priork_k, self.priork_theta, 
                          self.priortheta_kc, self.priortheta_thetac, self.priork_kc, self.priork_thetac, 
                          0,
                          self.precission, self.method,
                          initial_conditions, show_progress, seed))
        
        self.fitted = True

        return

    def save(self, name):
        """
        Pickle save the model.

        Parameters
        ----------------
            name: string, name in which to store the model

        Return:
            nothing
        """
        if self.fitted:
            pickling_on = open(name+".pickle","wb")
            pk.dump({"K":self.K, "Kc":self.Kc, "alpha": self.alpha, "alphac": self.alphac, "iterations": self.iterations,
                     "ignored_iterations": self.ignored_iterations,
                     "priortheta_k": self.priortheta_k, "priortheta_theta": self.priortheta_theta, "priork_k": self.priork_k, 
                     "priork_theta": self.priork_theta, "priortheta_kc": self.priortheta_kc, "priortheta_thetac": self.priortheta_thetac, 
                     "priortheta_thetac": self.priortheta_thetac, "priork_thetac": self.priork_thetac, 
                     "bias":self.bias, "chains":self.chains, "samples":self.samples}, pickling_on)
            pickling_on.close()
        else:
            print("The model has not been fitted so there is nothing to save.")
        return

    def load(self, name):
        """
        Pickle load the model.

        Parameters
        ----------------
            name: string, name from which to recover the model

        Return:
            nothing
        """
        pickle_off = open(name+".pickle","rb")
        aux = pk.load(pickle_off)
        pickle_off.close()

        self.K = aux["K"]
        self.Kc = aux ["Kc"]
        self.alpha = aux["alpha"]
        self.alphac = aux["alphac"]
        self.iterations = aux["iterations"]
        self.ignored_iterations = aux["ignored_iterations"]
        self.chains = aux["chains"]
        self.samples = aux["samples"]
        self.priortheta_k = aux["priortheta_k"]
        self.priortheta_theta = aux["priortheta_theta"]
        self.priork_k = aux["priork_k"] 
        self.priork_theta = aux["priork_theta"]
        self.priortheta_kc = aux["priortheta_kc"] 
        self.priortheta_thetac = aux["priortheta_thetac"] 
        self.priortheta_thetac = aux["priortheta_thetac"] 
        self.priork_thetac = aux["priork_thetac"]
        self.bias = aux["bias"]

        self.fitted = True

        return

    def sample_autofluorescence(self, size = 1, style = "full", pos = None):
        """
        Generate samples from the fitted posterior distribution according to the noise distribution

        Parameters
        -------------
            size: int, number of samples to be drawn

        Returns 
        -------------
            list: list, 1D array with *size* samples from the model
        """

        if style=="full":
            return  np.array(sample_autofluorescence_gamma(self.samples,self.K,self.Kc,size=size, bias=0))+self.bias
        elif style=="single":
            if pos == None:
                pos = np.random.choice(range(len(self.samples))) 
                return  np.array(sample_autofluorescence_gamma(self.samples,self.K,self.Kc,size=size,pos=pos, bias=0))+self.bias
            else:
                return  np.array(sample_autofluorescence_gamma(self.samples,self.K,self.Kc,size=size,pos=pos, bias=0))+self.bias

#        return  np.array(sample_autofluorescence_gamma(self.samples,self.K,self.Kc,size))

    def sample_deconvolution(self, size = 1, style = "full", pos = None):
        """
        Generate samples from the fitted posterior distribution according to the deconvolved distribution

        Parameters
        -------------
            size: int, number of samples to be drawn

        Returns
        -------------
            list: list, 1D array with *size* samples from the model
        """

        if style=="full":
            return  np.array(sample_deconvolution_gamma(self.samples,self.K,self.Kc,size=size, bias=0))+self.bias
        elif style=="single":
            if pos == None:
                pos = np.random.choice(range(len(self.samples))) 
                return  np.array(sample_deconvolution_gamma(self.samples,self.K,self.Kc,size=size,pos=pos, bias=0))+self.bias
            else:
                return  np.array(sample_deconvolution_gamma(self.samples,self.K,self.Kc,size=size,pos=pos, bias=0))+self.bias

#        return  np.array(sample_deconvolution_gamma(self.samples,self.K,self.Kc,size))

    def sample_convolution(self, size = 1, style = "full", pos = None):
        """
        Generate samples from the fitted posterior distribution according to the convolved distribution

        Parameters
        -------------
            size: int, number of samples to be drawn

        Returns
        -------------
            list: list, 1D array with *size* samples from the model
        """

        if style=="full":
            return  np.array(sample_convolution_gamma(self.samples,self.K,self.Kc,size=size, bias=0))+self.bias
        elif style=="single":
            if pos == None:
                pos = np.random.choice(range(len(self.samples))) 
                return  np.array(sample_convolution_gamma(self.samples,self.K,self.Kc,size=size,pos=pos, bias=0))+self.bias
            else:
                return  np.array(sample_convolution_gamma(self.samples,self.K,self.Kc,size=size,pos=pos, bias=0))+self.bias

#        return  np.array(sample_convolution_gamma(self.samples,self.K,self.Kc,size))

    def score_autofluorescence(self, x, percentiles = [5, 95], size = 100):
        """
        Evaluate the mean and percentiles of the the pdf at certain position acording to the convolved distribution

        Parameters
        -------------
            x: list/array, positions where to evaluate the distribution
            percentiles: list/array, percentiles to be evaluated
            size: int, number of samples to draw from the posterior to make the statistics, bigger numbers give more stability

        Returns
        -------------
            list: list, 2D array with the mean and all the percentile evaluations at all points in x
        """
        yT = []
        for l in range(size):
            i = np.random.choice(self.iterations)
            y = np.zeros(len(x))
            for k in range(self.K):
                thetastar = self.samples[i,self.K+k]
                kconststar = self.samples[i,2*self.K+k]
                    
                y += self.samples[i,k]*gamma.pdf(x,a=kconststar,scale=thetastar)
            yT.append(y)

        return  np.mean(yT,axis=0),np.percentile(yT,percentiles,axis=0)

    def score_deconvolution(self, x, percentiles = [5, 95], size = 100):
        """
        Evaluate the mean and percentiles of the the pdf at certain position acording to the deconvolved distribution

        Parameters
        -------------
            x: list/array, positions where to evaluate the distribution
            percentiles: list/array, percentiles to be evaluated
            size: int, number of samples to draw from the posterior to make the statistics, bigger numbers give more stability

        Returns
        -------------
            list: list, 2D array with the mean and all the percentile evaluations at all points in x
        """

        yT = []
        for l in range(size):
            i = np.random.choice(self.iterations)
            y = np.zeros(len(x))
            for j in range(self.Kc):
                thetastar = self.samples[i,3*self.K+self.Kc+j]
                kconststar = self.samples[i,3*self.K+2*self.Kc+j]
                    
                y += self.samples[i,3*self.K+j]*gamma.pdf(x,a=kconststar,scale=thetastar)
            yT.append(y)

        return  np.mean(yT,axis=0),np.percentile(yT,percentiles,axis=0)

    def score_convolution(self, x, percentiles = [5, 95], size = 100):
        """
        Evaluate the mean and percentiles of the the pdf at certain position acording to the convolved distribution

        Parameters
        -------------
            x: list/array, positions where to evaluate the distribution
            percentiles: list/array, percentiles to be evaluated
            size: int, number of samples to draw from the posterior to make the statistics, bigger numbers give more stability

        Returns
        -------------
            list: list, 2D array with the mean and all the percentile evaluations at all points in x
        """

        yT = []
        for l in range(size):
            i = np.random.choice(self.iterations)
            y = np.zeros(len(x))
            for j in range(self.Kc):
                for k in range(self.K):
                    theta1 = self.samples[i,self.K+k]
                    theta2 = self.samples[i,3*self.K+self.Kc+j]
                    k1 = self.samples[i,2*self.K+k]
                    k2 = self.samples[i,3*self.K+2*self.Kc+j]
                    mu = theta1*k1+theta2*k2
                    s = theta1*theta1*k1+theta2*theta2*k2
                    thetastar = s/mu
                    kconststar = mu*mu/s
                    
                    y += self.samples[i,k]*self.samples[i,3*self.K+j]*gamma.pdf(x,a=kconststar,scale=thetastar)
            yT.append(y)

        return  np.mean(yT,axis=0),np.percentile(yT,percentiles,axis=0)

    def sampler_statistics(self, sort="weight"):
        """
        Show statistics of correct mixing of the mcmc sampler
        
        Args:
            sort: ["weight", "none", "means"], method for sorting the samples from the different chains

        Returns
        -------------
            DataFrame: DataFrame the mean, std, percentiles, mixing ratio(rhat) and effective number of samples for each parameter of the model
        """
        
        self.sampler_statistics = pd.DataFrame(columns=["Mean","Std","5%","50%","95%","Rhat","Neff"])

        samples = self.samples.copy()

        if sort == "weight":
            argsort = np.argsort(samples[:,0:self.K],axis=1)
            samples[:,0:self.K] = np.take_along_axis(samples[:,0:self.K],argsort,axis=1)
            samples[:,self.K:2*self.K] = np.take_along_axis(samples[:,self.K:2*self.K],argsort,axis=1)
            samples[:,2*self.K:3*self.K] = np.take_along_axis(samples[:,2*self.K:3*self.K],argsort,axis=1)

            argsort = np.argsort(samples[:,3*self.K:3*self.K+self.Kc],axis=1)
            samples[:,3*self.K:3*self.K+self.Kc] = np.take_along_axis(samples[:,3*self.K:3*self.K+self.Kc],argsort,axis=1)
            samples[:,(3*self.K+self.Kc):(3*self.K+2*self.Kc)] = np.take_along_axis(samples[:,(3*self.K+self.Kc):(3*self.K+2*self.Kc)],argsort,axis=1)
            samples[:,(3*self.K+2*self.Kc):(3*self.K+3*self.Kc)] = np.take_along_axis(samples[:,(3*self.K+2*self.Kc):(3*self.K+3*self.Kc)],argsort,axis=1)

        if sort == "mean":
            argsort = np.argsort(samples[:,self.K:2*self.K],axis=1)
            samples[:,0:self.K] = np.take_along_axis(samples[:,0:self.K],argsort,axis=1)
            samples[:,self.K:2*self.K] = np.take_along_axis(samples[:,self.K:2*self.K],argsort,axis=1)
            samples[:,2*self.K:3*self.K] = np.take_along_axis(samples[:,2*self.K:3*self.K],argsort,axis=1)

            argsort = np.argsort(samples[:,3*self.K+self.Kc:3*self.K+2*self.Kc],axis=1)
            samples[:,3*self.K:3*self.K+self.Kc] = np.take_along_axis(samples[:,3*self.K:3*self.K+self.Kc],argsort,axis=1)
            samples[:,(3*self.K+self.Kc):(3*self.K+2*self.Kc)] = np.take_along_axis(samples[:,(3*self.K+self.Kc):(3*self.K+2*self.Kc)],argsort,axis=1)
            samples[:,(3*self.K+2*self.Kc):(3*self.K+3*self.Kc)] = np.take_along_axis(samples[:,(3*self.K+2*self.Kc):(3*self.K+3*self.Kc)],argsort,axis=1)

        measures = np.zeros(7)
        for i in range(3*self.K+3*self.Kc):
            measures[0] = np.mean(samples[:,i])
            measures[1] = np.std(samples[:,i])
            measures[2:5] = np.percentile(samples[:,i],[5,50,95])
            measures[5] = rstat(samples[:,i],self.chains)
            measures[6] = effnumber(samples[:,i],self.chains)

            #Name the component
            if i < self.K:
                name = "weight_K"+str(1+i)
            elif i < 2*self.K:
                name = "mean_K"+str(1+i-self.K)
            elif i < 3*self.K:
                name = "std_K"+str(1+i-2*self.K)
            elif i < 3*self.K+self.Kc:
                name = "weight_Kc"+str(1+i-3*self.K)
            elif i < 3*self.K+2*self.Kc:
                name = "mean_Kc"+str(1+i-3*self.K-self.Kc)
            else:
                name = "std_Kc"+str(1+i-3*self.K-2*self.Kc)

            self.sampler_statistics = self.sampler_statistics.append(pd.Series(measures, ["Mean","Std","5%","50%","95%","Rhat","Neff"], name=name))

        return self.sampler_statistics

Class for the mcmc sampler of the deconvolution gaussian model Constructor of the class           Parameters         -------------             K: int, Number of components of the noise distribution             Kc: int, Number of components of the convolved distribution             **kwargs:                 alpha: float, parameter to determine the hyperprior of the noise weight components                 alphac: float, parameter to determine the hyperprior of the target weight components Fit the model to the posterior distribution           Parameters         -------------             dataNoise: list/npArray, 1D array witht he data of the noise             dataConvolution: list/npArray, 1D array witht he data of the convolution             iterations: int, number of samples to be drawn and stored for each chain during the sampling             ignored_iterations: int, number of samples to be drawn and ignored for each chain during the sampling             chains: int, number of independently initialised realisations of the markov chain             priors: array, parameter of the prior gamma distribution acording to the definition of the wikipedia             kconst: float, parameter k of the prior gamma distribution             initialConditions: list, 1D array with all the parameters required to initialise manually all the components of all the chains the chains             show_progress: bool, indicate if the method should show the progress in the generation of the new data             seed: int, value to initialise the random generator and obtain reproducible results          Returns         ---------------             Nothing Pickle save the model.          Parameters         ----------------             name: string, name in which to store the model          Return:             nothing Pickle load the model.          Parameters         ----------------             name: string, name from which to recover the model          Return:             nothing Generate samples from the fitted posterior distribution according to the noise distribution          Parameters         -------------             size: int, number of samples to be drawn          Returns          -------------             list: list, 1D array with *size* samples from the model #        return  np.array(sample_autofluorescence_gamma(self.samples,self.K,self.Kc,size)) Generate samples from the fitted posterior distribution according to the deconvolved distribution          Parameters         -------------             size: int, number of samples to be drawn          Returns         -------------             list: list, 1D array with *size* samples from the model #        return  np.array(sample_deconvolution_gamma(self.samples,self.K,self.Kc,size)) Generate samples from the fitted posterior distribution according to the convolved distribution          Parameters         -------------             size: int, number of samples to be drawn          Returns         -------------             list: list, 1D array with *size* samples from the model #        return  np.array(sample_convolution_gamma(self.samples,self.K,self.Kc,size)) Evaluate the mean and percentiles of the the pdf at certain position acording to the convolved distribution          Parameters         -------------             x: list/array, positions where to evaluate the distribution             percentiles: list/array, percentiles to be evaluated             size: int, number of samples to draw from the posterior to make the statistics, bigger numbers give more stability          Returns         -------------             list: list, 2D array with the mean and all the percentile evaluations at all points in x Evaluate the mean and percentiles of the the pdf at certain position acording to the deconvolved distribution          Parameters         -------------             x: list/array, positions where to evaluate the distribution             percentiles: list/array, percentiles to be evaluated             size: int, number of samples to draw from the posterior to make the statistics, bigger numbers give more stability          Returns         -------------             list: list, 2D array with the mean and all the percentile evaluations at all points in x Evaluate the mean and percentiles of the the pdf at certain position acording to the convolved distribution          Parameters         -------------             x: list/array, positions where to evaluate the distribution             percentiles: list/array, percentiles to be evaluated             size: int, number of samples to draw from the posterior to make the statistics, bigger numbers give more stability          Returns         -------------             list: list, 2D array with the mean and all the percentile evaluations at all points in x Show statistics of correct mixing of the mcmc sampler                  Args:             sort: ["weight", "none", "means"], method for sorting the samples from the different chains          Returns         -------------             DataFrame: DataFrame the mean, std, percentiles, mixing ratio(rhat) and effective number of samples for each parameter of the model #Name the component