'''
This module provides helper classes for managing sample parameters. This is
for internal use, if you are not modifying lenstronomy sampling to include
new parameters you can safely ignore this.
'''

__author__ = 'jhodonnell'
__all__ = ['ModelParamGroup', 'SingleParam', 'ArrayParam']

import numpy as np


class ModelParamGroup:
    '''
    This abstract class represents any lenstronomy fitting parameters used
    in the Param class.

    Subclasses should implement num_params(), set_params(), and get_params()
    to convert parameters from lenstronomy's semantic dictionary format to a
    flattened array format and back.

    This class also contains three static methods to easily aggregate groups
    of parameter classes, called `compose_num_params()`, `compose_set_params()`,
    and `compose_get_params()`.
    '''
    def num_params(self):
        '''
        Tells the number of parameters that this group samples and their names.

        :returns: 2-tuple of (num param, list of names)
        '''
        raise NotImplementedError

    def set_params(self, kwargs):
        '''
        Converts lenstronomy semantic parameters in dictionary format into a
        flattened array of parameters.

        The flattened array is for use in optimization algorithms, e.g. MCMC,
        Particle swarm, etc.

        :returns: flattened array of parameters as floats
        '''
        raise NotImplementedError

    def get_params(self, args, i):
        '''
        Converts a flattened array of parameters back into a lenstronomy dictionary,
        starting at index i.

        :param args: flattened arguments to convert to lenstronomy format
        :type args: list
        :param i: index to begin at in args
        :type i: int
        :returns: dictionary of parameters
        '''
        raise NotImplementedError

    @staticmethod
    def compose_num_params(each_group, *args, **kwargs):
        '''
        Aggregates the number of parameters for a group of parameter groups,
        calling each instance's `num_params()` method and combining the results

        :param each_group: collection of parameter groups. Should each be subclasses of ModelParamGroup.
        :type each_group: list
        :param args: Extra arguments to be passed to each call of `num_params()`
        :param kwargs: Extra keyword arguments to be passed to each call of `num_params()`

        :returns: As in each individual `num_params()`, a 2-tuple of (num params, list of param names)
        '''
        tot_param = 0
        param_names = []
        for group in each_group:
            npar, names = group.num_params(*args, **kwargs)
            tot_param += npar
            param_names += names
        return tot_param, param_names

    @staticmethod
    def compose_set_params(each_group, param_kwargs, *args, **kwargs):
        '''
        Converts lenstronomy semantic arguments in dictionary format to a
        flattened list of floats for use in optimization/fitting algorithms.
        Combines the results for a set of arbitrarily many parameter groups.

        :param each_group: collection of parameter groups. Should each be subclasses of ModelParamGroup.
        :type each_group: list
        :param param_kwargs: the kwargs to process
        :type param_kwargs: dict
        :param args: Extra arguments to be passed to each call of `set_params()`
        :param kwargs: Extra keyword arguments to be passed to each call of `set_params()`

        :returns: As in each individual `set_params()`, a list of floats
        '''
        output_args = []
        for group in each_group:
            output_args += group.set_params(param_kwargs, *args, **kwargs)
        return output_args

    @staticmethod
    def compose_get_params(each_group, flat_args, i, *args, **kwargs):
        '''
        Converts a flattened array of parameters to lenstronomy semantic
        parameters in dictionary format.
        Combines the results for a set of arbitrarily many parameter groups.

        :param each_group: collection of parameter groups. Should each be subclasses of ModelParamGroup.
        :type each_group: list
        :param flat_args: the input array of parameters
        :type flat_args: list
        :param i: the index in `flat_args` to start at
        :type i: int
        :param args: Extra arguments to be passed to each call of `set_params()`
        :param kwargs: Extra keyword arguments to be passed to each call of `set_params()`

        :returns: As in each individual `get_params()`, a 2-tuple of (dictionary of params, new index)
        '''
        output_kwargs = {}
        for group in each_group:
            kwargs_grp, i = group.get_params(flat_args, i, *args, **kwargs)
            output_kwargs = dict(output_kwargs, **kwargs_grp)
        return output_kwargs, i


class SingleParam(ModelParamGroup):
    '''
    Helper for handling parameters which are a single float.

    Subclasses should define:

    :param on: Whether this parameter is sampled
    :type on: bool
    :param param_names: List of strings, the name of each parameter
    :param _kwargs_lower: Dictionary. Lower bounds of each parameter
    :param _kwargs_upper: Dictionary. Upper bounds of each parameter
    '''
    def __init__(self, on):
        '''
        :param on: Whether this paramter should be sampled
        :type on: bool
        '''
        self._on = bool(on)

    def num_params(self, kwargs_fixed):
        '''
        Tells the number of parameters that this group samples and their names.

        :param kwargs_fixed: Dictionary of fixed arguments
        :type kwargs_fixed: dict

        :returns: 2-tuple of (num param, list of names)
        '''
        if self.on:
            npar, names = 0, []
            for name in self.param_names:
                if name not in kwargs_fixed:
                    npar += 1
                    names.append(name)
            return npar, names
        return 0, []

    def set_params(self, kwargs, kwargs_fixed):
        '''
        Converts lenstronomy semantic parameters in dictionary format into a
        flattened array of parameters.

        The flattened array is for use in optimization algorithms, e.g. MCMC,
        Particle swarm, etc.

        :param kwargs: lenstronomy parameters to flatten
        :type kwargs: dict
        :param kwargs_fixed: Dictionary of fixed arguments
        :type kwargs_fixed: dict

        :returns: flattened array of parameters as floats
        '''
        if self.on:
            output = []
            for name in self.param_names:
                if name not in kwargs_fixed:
                    output.append(kwargs[name])
            return output
        return []

    def get_params(self, args, i, kwargs_fixed, kwargs_upper=None, kwargs_lower=None):
        '''
        Converts a flattened array of parameters back into a lenstronomy dictionary,
        starting at index i.

        :param args: flattened arguments to convert to lenstronomy format
        :type args: list
        :param i: index to begin at in args
        :type i: int
        :param kwargs_fixed: Dictionary of fixed arguments
        :type kwargs_fixed: dict

        :returns: dictionary of parameters
        '''
        out = {}
        if self.on:
            for name in self.param_names:
                if name in kwargs_fixed:
                    out[name] = kwargs_fixed[name]
                else:
                    out[name] = args[i]
                    if kwargs_lower is not None:
                        out[name] = np.maximum(out[name], kwargs_lower[name])
                    if kwargs_upper is not None:
                        out[name] = np.minimum(out[name], kwargs_upper[name])
                    i += 1
        return out, i

    @property
    def kwargs_lower(self):
        if not self.on:
            return {}
        return self._kwargs_lower

    @property
    def kwargs_upper(self):
        if not self.on:
            return {}
        return self._kwargs_upper

    @property
    def on(self):
        return self._on


class ArrayParam(ModelParamGroup):
    '''
    Helper for handling parameters which are an array of values. Examples
    include mass_scaling, which is an array of scaling parameters, and wavelet
    or gaussian decompositions which have different coefficients for each mode.

    Subclasses should define:

    :param on:  Whether this parameter is sampled
    :type on: bool
    :param param_names: Dictionary mapping the name of each parameter to the number of values needed.
    :param _kwargs_lower: Dictionary. Lower bounds of each parameter
    :param _kwargs_upper: Dictionary. Upper bounds of each parameter
    '''
    def __init__(self, on):
        '''
        :param on: Whether this paramter should be sampled
        :type on: bool
        '''
        self._on = bool(on)

    def num_params(self, kwargs_fixed):
        '''
        Tells the number of parameters that this group samples and their names.

        :param kwargs_fixed: Dictionary of fixed arguments
        :type kwargs_fixed: dict

        :returns: 2-tuple of (num param, list of names)
        '''
        if not self.on:
            return 0, []

        npar = 0
        names = []
        for name, count in self.param_names.items():
            if name not in kwargs_fixed:
                npar += count
                names += [name] * count

        return npar, names

    def set_params(self, kwargs, kwargs_fixed):
        '''
        Converts lenstronomy semantic parameters in dictionary format into a
        flattened array of parameters.

        The flattened array is for use in optimization algorithms, e.g. MCMC,
        Particle swarm, etc.

        :param kwargs: lenstronomy parameters to flatten
        :type kwargs: dict
        :param kwargs_fixed: Dictionary of fixed arguments
        :type kwargs_fixed: dict

        :returns: flattened array of parameters as floats
        '''
        if not self.on:
            return []

        args = []
        for name, count in self.param_names.items():
            if name not in kwargs_fixed:
                args.extend(kwargs[name])
        return args

    def get_params(self, args, i, kwargs_fixed, kwargs_lower=None, kwargs_upper=None):
        '''
        Converts a flattened array of parameters back into a lenstronomy dictionary,
        starting at index i.

        :param args: flattened arguments to convert to lenstronomy format
        :type args: list
        :param i: index to begin at in args
        :type i: int
        :param kwargs_fixed: Dictionary of fixed arguments
        :type kwargs_fixed: dict
        :param kwargs_lower: Dictionary of lower bounds
        :type kwargs_lower: dict
        :param kwargs_upper: Dictionary of upper bounds
        :type kwargs_upper: dict
        :returns: dictionary of parameters
        '''
        if not self.on:
            return {}, i

        params = {}
        for name, count in self.param_names.items():
            if name not in kwargs_fixed:
                params[name] = args[i:i + count]

                if kwargs_lower is not None:
                    for j in range(len(params[name])):
                        if params[name][j] < kwargs_lower[name][j]:
                            params[name][j] = kwargs_lower[name][j]

                if kwargs_upper is not None:
                    for j in range(len(params[name])):
                        if params[name][j] > kwargs_upper[name][j]:
                            params[name][j] = kwargs_upper[name][j]

                i += count
            else:
                params[name] = kwargs_fixed[name]

        return params, i

    @property
    def kwargs_lower(self):
        if not self.on:
            return {}

        out = {}
        for name, count in self.param_names.items():
            out[name] = [self._kwargs_lower[name]] * count
        return out

    @property
    def kwargs_upper(self):
        if not self.on:
            return {}

        out = {}
        for name, count in self.param_names.items():
            out[name] = [self._kwargs_upper[name]] * count
        return out

    @property
    def on(self):
        return self._on
