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'''
Module :mod:`openquake.hmtk.seismicity.max_magnitude.kijko_sellevol` defines
the Kijko & Sellevol algorithm for maximum magnitude
'''

import warnings
import numpy as np
from math import fabs
from scipy.integrate import quadrature
from openquake.hmtk.seismicity.max_magnitude.base import (
    BaseMaximumMagnitude, MAX_MAGNITUDE_METHODS,
    _get_observed_mmax, _get_magnitude_vector_properties)


def check_config(config, data):
    '''Checks that the config file contains all required parameters

    :param dict config:
        Configuration file

    :returns:
        Configuration file with all correct parameters
    '''
    if 'tolerance' not in config.keys() or not config['tolerance']:
        config['tolerance'] = 1E-5

    if not config.get('maximum_iterations', None):
        config['maximum_iterations'] = 1000

    mmin_obs = np.min(data['magnitude'])
    if config.get('input_mmin', 0) < mmin_obs:
        config['input_mmin'] = mmin_obs

    if fabs(config['b-value']) < 1E-7:
        config['b-value'] = 1E-7

    return config


@MAX_MAGNITUDE_METHODS.add(
    "get_mmax",
    **{"input_mmin": lambda cat: np.min(cat.data['magnitude']),
       "input_mmax": lambda cat: cat.data['magnitude'][
           np.argmax(cat.data['magnitude'])],
       "input_mmax_uncertainty": lambda cat: cat.get_observed_mmax_sigma(0.2),
       "b-value": 1E-7,
       "maximum_iterations": 1000,
       "tolerance": 1E-5})
class KijkoSellevolFixedb(BaseMaximumMagnitude):
    '''
    Implements Kijko and Sellevol estimator for maximim magnitude assuming
    a fixed b-value. Coded from description in Kijko (2004):

    <NAME>. (2004), ..., Pure & Applied Geophysics,
    '''

    def get_mmax(self, catalogue, config):
        '''
        Calculates Maximum magnitude

        :param catalogue:
            Earthquake catalogue as instance of :class:
            openquake.hmtk.seismicity.catalogue.Catalogue

        :param dict config:
            Configuration file for algorithm, contains the attributes:
            * 'b-value': b-value (positive float)
            * 'input_mmin': Minimum magnitude for integral (if less than
            minimum observed magnitude, will be overwritten by
            minimum observed magnitude)
            * 'tolerance': Tolerance of stabilising of iterator
            * 'maximum_interations': Maximum number of iterations

        :returns: **mmax** Maximum magnitude and **mmax_sig** corresponding
                    uncertainty
        '''
        config = check_config(config, catalogue.data)

        obsmax, obsmaxsig = _get_observed_mmax(catalogue.data, config)

        mmin = config['input_mmin']
        beta = config['b-value'] * np.log(10.)

        neq, mmin = _get_magnitude_vector_properties(catalogue.data, config)

        mmax = np.copy(obsmax)
        d_t = np.inf
        iterator = 0
        print(mmin, mmax, neq, beta)
        while d_t > config['tolerance']:
            delta = quadrature(self._ks_intfunc, mmin, mmax,
                               args=(neq, mmax, mmin, beta))[0]
            tmmax = obsmax + delta
            d_t = np.abs(tmmax - mmax)
            mmax = np.copy(tmmax)
            iterator += 1
            if iterator > config['maximum_iterations']:
                print('Kijko-Sellevol estimator reached '
                      'maximum # of iterations')
                d_t = -np.inf
        return mmax.item(), np.sqrt(obsmaxsig ** 2. + delta ** 2.)

    def _ks_intfunc(self, mval, neq, mmax, mmin, beta):
        '''Integral function inside Kijko-Sellevol estimator
        (Eq. 6 in Kijko, 2004)

        :param float mval:
            Magnitude value
        :param float neq:
            Number of earthquakes
        :param float mmax:
            Maximum Magnitude
        :param float mmin:
            Minimum Magnitude
        :param float beta:
            Beta-value of the distribution
        :returns:
            Integrand of Kijko-Sellevol estimator
        '''
        if mmin >= mmax:
            raise ValueError('Maximum magnitude smaller than minimum magnitude'
                             ' in Kijko & Sellevol (Fixed-b) integral')

        func1 = 1. - np.exp(-beta * (mval - mmin))
        if np.fabs(beta) > 1e-3:
            func1 = (func1 / (1. - np.exp(-beta * (mmax - mmin)))) ** neq
        else:
            warnings.warn('beta is lower or equal to 0', RuntimeWarning)
        return func1
