#!/usr/bin/env python3 

import numpy as np
import math

class Evidence:
    def __init__(self):
        self.rawlun = None
        self.samlun = None
        self.devolun = None

    def updateEvidence(self, X, Z, Zmsq, Zerr, prior=None, context=None, oldsamples=0):
        """
        Get posterior weights and update evidence on the fly.
        """
        # Grow the tree (this would be some other function, handling it as a placeholder)
        self.growTree(X, prior, context)

        # Calculate the total samples and sampleratio
        inttotsamples = oldsamples + X['weights'].size
        totsamples = float(inttotsamples)
        sampleratio = oldsamples / totsamples

        # Calculate multiplicity
        X['multiplicities'] = X['weights'] * np.exp(-X['values']) / totsamples

        # Update evidence
        Z = Z * sampleratio + np.sum(X['multiplicities'])

        # Update the mean square of the weights and the standard deviation of the evidence
        Zmsq = Zmsq * sampleratio + np.sum(X['multiplicities']**2 * totsamples)
        Zerr = np.sqrt((Zmsq - Z**2) / totsamples)

        # Update the number of old samples
        return Z, Zmsq, Zerr, inttotsamples

    def polishEvidence(self, Z, Zmsq, Zerr, prior, context, Nsamples, run_params, update, path):
        """
        Recalculate evidence and all posterior weights at the end of a run.
        """
        dosam = (run_params['D_derived'] != 0 or run_params['discrete'].size != 0)

        Z = 0.0
        Zmsq = 0.0
        for i in range(Nsamples):
            # Read the vector and likelihood from raw/sam files (you'd handle actual I/O here)
            multiplicity, lnlike, civ, gen, vector = self.read_data_from_files(i, path, dosam)

            # Use the tree to get a new weight for the point
            multiplicity = self.getWeight(vector, prior, context) * np.exp(-lnlike) / Nsamples

            # Save the new multiplicity of the point (if updating)
            if update:
                self.write_data_to_files(i, path, multiplicity, lnlike, civ, gen, vector, dosam)

            # Add the contribution of the point with the new multiplicity to the evidence
            Z += multiplicity

            # Add the contribution to the error
            Zmsq += multiplicity**2

        Zmsq = Zmsq * Nsamples
        Zerr = np.sqrt((Zmsq - Z**2) / Nsamples)

        return Z, Zmsq, Zerr

    def growTree(self, X, prior, context):
        """
        Placeholder for the actual tree growing mechanism.
        """
        pass

    def getWeight(self, vector, prior, context):
        """
        Placeholder for getting weight, typically from a prior function and tree.
        """
        return 1.0  # This should be replaced by actual logic

    def read_data_from_files(self, i, path, dosam):
        """
        Simulated function for reading data from files (to be replaced with actual I/O).
        """
        # Placeholder example
        multiplicity = 1.0
        lnlike = -0.5
        civ = 1
        gen = 1
        vector = np.random.rand(5)
        return multiplicity, lnlike, civ, gen, vector

    def write_data_to_files(self, i, path, multiplicity, lnlike, civ, gen, vector, dosam):
        """
        Simulated function for writing data to files (to be replaced with actual I/O).
        """
        pass

# Example of population structure (similar to X)
X = {
    'weights': np.random.rand(10),
    'values': np.random.rand(10),
    'multiplicities': np.zeros(10)
}

# Example run_params (similar to codeparams in Fortran)
run_params = {
    'D_derived': 2,
    'discrete': np.array([0, 1])
}

# Initialize and use Evidence
evidence_module = Evidence()
Z, Zmsq, Zerr, oldsamples = evidence_module.updateEvidence(X, Z=0, Zmsq=0, Zerr=0, oldsamples=0)
Z, Zmsq, Zerr = evidence_module.polishEvidence(Z, Zmsq, Zerr, None, None, 100, run_params, update=True, path='output')
