<commit_before>__author__ = '<NAME>'

import numpy as np
import matplotlib.pyplot as plt
from scipy.linalg import solve
from scipy.misc import comb
from os import environ
import samplers
import _carmcmc as carmcmcLib


class CarmaMCMC(object):
    """
    Class for running the MCMC sampler assuming a CARMA(p,q) model.
    """

    def __init__(self, time, y, ysig, p, nsamples, q=0, nwalkers=None, nburnin=None, nthin=1):
        """
        Constructor for the CarmaMCMC class.

        :param time: The observation times.
        :param y: The measured time series.
        :param ysig: The standard deviation in the measurements errors on the time series.
        :param p: The order of the autoregressive (AR) polynomial.
        :param nsamples: The number of MCMC samples to generate.
        :param q: The order of the moving average polynomial. Default is q = 0. Note that p > q.
        :param doZcarma: If true, then use the z-transformed CAR parameterization.
        :param nwalkers: Number of parallel MCMC chains to run in the parallel tempering algorithm. Default is 1 (no
            tempering) for p = 1 and max(10, p+q) for p > 1.
        :param nburnin: Number of burnin iterations to run. The default is nsamples / 2.
        :param nthin: Thinning interval for the MCMC sampler. Default is 1.
        """
        try:
            p > q
        except ValueError:
            " Order of AR polynomial, p, must be larger than order of MA polynimial, q."

        # convert input to std::vector<double> extension class
        self._time = carmcmcLib.vecD()
        self._time.extend(time)
        self._y = carmcmcLib.vecD()
        self._y.extend(y)
        self._ysig = carmcmcLib.vecD()
        self._ysig.extend(ysig)

        if nwalkers is None:
                nwalkers = max(10, p + q)

        if nburnin is None:
            nburnin = nsamples / 2

        # save parameters
        self.time = time
        self.y = y
        self.ysig = ysig
        self.p = p
        self.nsamples = nsamples
        self.nburnin = nburnin
        self.q = q
        self.nwalkers = nwalkers
        self.nthin = nthin

    def RunMCMC(self):
        """
        Run the MCMC sampler. This is actually a wrapper that calls the C++ code that runs the MCMC sampler.

        :return: Either a CarmaSample, ZCarmaSample, or CarSample1 object, depending on the values of self.p and
                 self.doZcarma.
        """
        if self.p == 1:
            # Treat the CAR(1) case separately
            cppSample = carmcmcLib.run_mcmc_car1(self.nsamples, self.nburnin, self._time, self._y, self._ysig,
                                                 self.nthin)
            # run_mcmc_car1 returns a wrapper around the C++ CAR1 class, convert to python object
            sample = CarSample1(self.time, self.y, self.ysig, cppSample)
        else:
            cppSample = carmcmcLib.run_mcmc_carma(self.nsamples, self.nburnin, self._time, self._y, self._ysig,
                                                  self.p, self.q, self.nwalkers, False, self.nthin)
            # run_mcmc_car1 returns a wrapper around the C++ CARMA/ZCAR class, convert to a python object
            sample = CarmaSample(self.time, self.y, self.ysig, cppSample, q=self.q)

        return sample


class CarmaSample(samplers.MCMCSample):
    """
    Class for storing and analyzing the MCMC samples of a CARMA(p,q) model.
    """

    def __init__(self, time, y, ysig, sampler, q=0, filename=None):
        """
        Constructor for the CarmaSample class.

        :param filename: A string of the name of the file containing the MCMC samples generated by carpack.
        """
        self.time = time  # The time values of the time series
        self.y = y  # The measured values of the time series
        self.ysig = ysig  # The standard deviation of the measurement errors of the time series
        self.q = q  # order of moving average polynomial

        logpost = np.array(sampler.GetLogLikes())
        trace = np.array(sampler.getSamples())

        super(CarmaSample, self).__init__(filename=filename, logpost=logpost, trace=trace)<commit_msg>Started working on MLE computed in python<commit_after>__author__ = '<NAME>'

import numpy as np
import matplotlib.pyplot as plt
from scipy.linalg import solve
from scipy.misc import comb
from os import environ
import samplers
import _carmcmc as carmcmcLib


class CarmaMCMC(object):
    """
    Class for running the MCMC sampler assuming a CARMA(p,q) model.
    """

    def __init__(self, time, y, ysig, p, nsamples, q=0, nwalkers=None, nburnin=None, nthin=1):
        """
        Constructor for the CarmaMCMC class.

        :param time: The observation times.
        :param y: The measured time series.
        :param ysig: The standard deviation in the measurements errors on the time series.
        :param p: The order of the autoregressive (AR) polynomial.
        :param nsamples: The number of MCMC samples to generate.
        :param q: The order of the moving average polynomial. Default is q = 0. Note that p > q.
        :param doZcarma: If true, then use the z-transformed CAR parameterization.
        :param nwalkers: Number of parallel MCMC chains to run in the parallel tempering algorithm. Default is 1 (no
            tempering) for p = 1 and max(10, p+q) for p > 1.
        :param nburnin: Number of burnin iterations to run. The default is nsamples / 2.
        :param nthin: Thinning interval for the MCMC sampler. Default is 1.
        """
        try:
            p > q
        except ValueError:
            " Order of AR polynomial, p, must be larger than order of MA polynimial, q."

        # convert input to std::vector<double> extension class
        self._time = carmcmcLib.vecD()
        self._time.extend(time)
        self._y = carmcmcLib.vecD()
        self._y.extend(y)
        self._ysig = carmcmcLib.vecD()
        self._ysig.extend(ysig)

        if nwalkers is None:
                nwalkers = max(10, p + q)

        if nburnin is None:
            nburnin = nsamples / 2

        # save parameters
        self.time = time
        self.y = y
        self.ysig = ysig
        self.p = p
        self.nsamples = nsamples
        self.nburnin = nburnin
        self.q = q
        self.nwalkers = nwalkers
        self.nthin = nthin

    def run_mcmc(self):
        """
        Run the MCMC sampler. This is actually a wrapper that calls the C++ code that runs the MCMC sampler.

        :return: Either a CarmaSample, ZCarmaSample, or CarSample1 object, depending on the values of self.p and
                 self.doZcarma.
        """
        if self.p == 1:
            # Treat the CAR(1) case separately
            cppSample = carmcmcLib.run_mcmc_car1(self.nsamples, self.nburnin, self._time, self._y, self._ysig,
                                                 self.nthin)
            # run_mcmc_car1 returns a wrapper around the C++ CAR1 class, convert to python object
            sample = CarSample1(self.time, self.y, self.ysig, cppSample)
        else:
            cppSample = carmcmcLib.run_mcmc_carma(self.nsamples, self.nburnin, self._time, self._y, self._ysig,
                                                  self.p, self.q, self.nwalkers, False, self.nthin)
            # run_mcmc_car1 returns a wrapper around the C++ CARMA/ZCAR class, convert to a python object
            sample = CarmaSample(self.time, self.y, self.ysig, cppSample, q=self.q)

        return sample

    def get_mle(self):
        pass

    def choose_order(self, pmax, qmax=None, pqlist=None):
        pass


class CarmaSample(samplers.MCMCSample):
    """
    Class for storing and analyzing the MCMC samples of a CARMA(p,q) model.
    """

    def __init__(self, time, y, ysig, sampler, q=0, filename=None):
        """
        Constructor for the CarmaSample class.

        :param filename: A string of the name of the file containing the MCMC samples generated by carpack.
        """
        self.time = time  # The time values of the time series
        self.y = y  # The measured values of the time series
        self.ysig = ysig  # The standard deviation of the measurement errors of the time series
        self.q = q  # order of moving average polynomial

        logpost = np.array(sampler.GetLogLikes())
        trace = np.array(sampler.getSamples())

        super(CarmaSample, self).__init__(filename=filename, logpost=logpost, trace=trace)
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{'dir': 'git-commits-cleaned', 'id': '741558', 'n_tokens_mistral': 2590, 'n_tokens_neox': 2486, 'n_words': 894}