# Refactor of DOS-like data objects
# towards replacing ase.dft.dos and ase.dft.pdos
import warnings
from abc import ABCMeta, abstractmethod
from typing import Any, Dict, Sequence, Tuple, TypeVar, Union

import numpy as np

from ase.utils.plotting import SimplePlottingAxes

# This import is for the benefit of type-checking / mypy
if False:
    import matplotlib.axes

# For now we will be strict about Info and say it has to be str->str. Perhaps
# later we will allow other types that have reliable comparison operations.
Info = Dict[str, str]

# Still no good solution to type checking with arrays.
Floats = Union[Sequence[float], np.ndarray]


class DOSData(metaclass=ABCMeta):
    """Abstract base class for a single series of DOS-like data

    Only the 'info' is a mutable attribute; DOS data is set at init"""

    def __init__(self,
                 info: Info = None) -> None:
        if info is None:
            self.info = {}
        elif isinstance(info, dict):
            self.info = info
        else:
            raise TypeError("Info must be a dict or None")

    @abstractmethod
    def get_energies(self) -> Floats:
        """Get energy data stored in this object"""

    @abstractmethod
    def get_weights(self) -> Floats:
        """Get DOS weights stored in this object"""

    @abstractmethod
    def copy(self) -> 'DOSData':
        """Returns a copy in which info dict can be safely mutated"""

    def _sample(self,
                energies: Floats,
                width: float = 0.1,
                smearing: str = 'Gauss') -> np.ndarray:
        """Sample the DOS data at chosen points, with broadening

        Note that no correction is made here for the sampling bin width; total
        intensity will vary with sampling density.

        Args:
            energies: energy values for sampling
            width: Width of broadening kernel
            smearing: selection of broadening kernel (only "Gauss" is currently
                supported)

        Returns:
            Weights sampled from a broadened DOS at values corresponding to x
        """

        self._check_positive_width(width)
        weights_grid = np.zeros(len(energies), float)
        weights = self.get_weights()
        energies = np.asarray(energies, float)

        for i, raw_energy in enumerate(self.get_energies()):
            delta = self._delta(energies, raw_energy, width, smearing=smearing)
            weights_grid += weights[i] * delta
        return weights_grid

    def _almost_equals(self, other: Any) -> bool:
        """Compare with another DOSData for testing purposes"""
        if not isinstance(other, type(self)):
            return False
        if self.info != other.info:
            return False
        if not np.allclose(self.get_weights(), other.get_weights()):
            return False
        return np.allclose(self.get_energies(), other.get_energies())

    @staticmethod
    def _delta(x: np.ndarray,
               x0: float,
               width: float,
               smearing: str = 'Gauss') -> np.ndarray:
        """Return a delta-function centered at 'x0'.

        This function is used with numpy broadcasting; if x is a row and x0 is
        a column vector, the returned data will be a 2D array with each row
        corresponding to a different delta center.
        """
        if smearing.lower() == 'gauss':
            x1 = -0.5 * ((x - x0) / width)**2
            return np.exp(x1) / (np.sqrt(2 * np.pi) * width)
        else:
            msg = 'Requested smearing type not recognized. Got {}'.format(
                smearing)
            raise ValueError(msg)

    @staticmethod
    def _check_positive_width(width):
        if width <= 0.0:
            msg = 'Cannot add 0 or negative width smearing'
            raise ValueError(msg)

    def sample_grid(self,
                    npts: int,
                    xmin: float = None,
                    xmax: float = None,
                    padding: float = 3,
                    width: float = 0.1,
                    smearing: str = 'Gauss',
                    ) -> 'GridDOSData':
        """Sample the DOS data on an evenly-spaced energy grid

        Args:
            npts: Number of sampled points
            xmin: Minimum sampled x value; if unspecified, a default is chosen
            xmax: Maximum sampled x value; if unspecified, a default is chosen
            padding: If xmin/xmax is unspecified, default value will be padded
                by padding * width to avoid cutting off peaks.
            width: Width of broadening kernel
            smearing: selection of broadening kernel (only 'Gauss' is
                implemented)

        Returns:
            (energy values, sampled DOS)
        """

        if xmin is None:
            xmin = min(self.get_energies()) - (padding * width)
        if xmax is None:
            xmax = max(self.get_energies()) + (padding * width)
        energies_grid = np.linspace(xmin, xmax, npts)
        weights_grid = self._sample(energies_grid, width=width,
                                    smearing=smearing)

        return GridDOSData(energies_grid, weights_grid, info=self.info.copy())

    def plot(self,
             npts: int = 1000,
             xmin: float = None,
             xmax: float = None,
             width: float = 0.1,
             smearing: str = 'Gauss',
             ax: 'matplotlib.axes.Axes' = None,
             show: bool = False,
             filename: str = None,
             mplargs: dict = None) -> 'matplotlib.axes.Axes':
        """Simple 1-D plot of DOS data, resampled onto a grid

        If the special key 'label' is present in self.info, this will be set
        as the label for the plotted line (unless overruled in mplargs). The
        label is only seen if a legend is added to the plot (i.e. by calling
        ``ax.legend()``).

        Args:
            npts, xmin, xmax: output data range, as passed to self.sample_grid
            width: Width of broadening kernel for self.sample_grid()
            smearing: selection of broadening kernel for self.sample_grid()
            ax: existing Matplotlib axes object. If not provided, a new figure
                with one set of axes will be created using Pyplot
            show: show the figure on-screen
            filename: if a path is given, save the figure to this file
            mplargs: additional arguments to pass to matplotlib plot command
                (e.g. {'linewidth': 2} for a thicker line).


        Returns:
            Plotting axes. If "ax" was set, this is the same object.
        """

        if mplargs is None:
            mplargs = {}
        if 'label' not in mplargs:
            mplargs.update({'label': self.label_from_info(self.info)})

        return self.sample_grid(npts, xmin=xmin, xmax=xmax,
                                width=width,
                                smearing=smearing
                                ).plot(ax=ax, xmin=xmin, xmax=xmax,
                                       show=show, filename=filename,
                                       mplargs=mplargs)

    @staticmethod
    def label_from_info(info: Dict[str, str]):
        """Generate an automatic legend label from info dict"""
        if 'label' in info:
            return info['label']
        else:
            return '; '.join(map(lambda x: f'{x[0]}: {x[1]}',
                                 info.items()))


class GeneralDOSData(DOSData):
    """Base class for a single series of DOS-like data

    Only the 'info' is a mutable attribute; DOS data is set at init

    This is the base class for DOSData objects that accept/set seperate
    "energies" and "weights" sequences of equal length at init.

    """

    def __init__(self,
                 energies: Floats,
                 weights: Floats,
                 info: Info = None) -> None:
        super().__init__(info=info)

        n_entries = len(energies)
        if len(weights) != n_entries:
            raise ValueError("Energies and weights must be the same length")

        # Internally store the data as a np array with two rows; energy, weight
        self._data = np.empty((2, n_entries), dtype=float, order='C')
        self._data[0, :] = energies
        self._data[1, :] = weights

    def get_energies(self) -> np.ndarray:
        return self._data[0, :].copy()

    def get_weights(self) -> np.ndarray:
        return self._data[1, :].copy()

    D = TypeVar('D', bound='GeneralDOSData')

    def copy(self: D) -> D:  # noqa F821
        return type(self)(self.get_energies(), self.get_weights(),
                          info=self.info.copy())


class RawDOSData(GeneralDOSData):
    """A collection of weighted delta functions which sum to form a DOS

    This is an appropriate data container for density-of-states (DOS) or
    spectral data where the energy data values not form a known regular
    grid. The data may be plotted or resampled for further analysis using the
    sample_grid() and plot() methods. Multiple weights at the same
    energy value will *only* be combined in output data, and data stored in
    RawDOSData is never resampled. A plot_deltas() function is also provided
    which plots the raw data.

    Metadata may be stored in the info dict, in which keys and values must be
    strings. This data is used for selecting and combining multiple DOSData
    objects in a DOSCollection object.

    When RawDOSData objects are combined with the addition operator::

      big_dos = raw_dos_1 + raw_dos_2

    the energy and weights data is *concatenated* (i.e. combined without
    sorting or replacement) and the new info dictionary consists of the
    *intersection* of the inputs: only key-value pairs that were common to both
    of the input objects will be retained in the new combined object. For
    example::

      (RawDOSData([x1], [y1], info={'symbol': 'O', 'index': '1'})
       + RawDOSData([x2], [y2], info={'symbol': 'O', 'index': '2'}))

    will yield the equivalent of::

      RawDOSData([x1, x2], [y1, y2], info={'symbol': 'O'})

    """

    def __add__(self, other: 'RawDOSData') -> 'RawDOSData':
        if not isinstance(other, RawDOSData):
            raise TypeError("RawDOSData can only be combined with other "
                            "RawDOSData objects")

        # Take intersection of metadata (i.e. only common entries are retained)
        new_info = dict(set(self.info.items()) & set(other.info.items()))

        # Concatenate the energy/weight data
        new_data = np.concatenate((self._data, other._data), axis=1)

        new_object = RawDOSData([], [], info=new_info)
        new_object._data = new_data

        return new_object

    def plot_deltas(self,
                    ax: 'matplotlib.axes.Axes' = None,
                    show: bool = False,
                    filename: str = None,
                    mplargs: dict = None) -> 'matplotlib.axes.Axes':
        """Simple plot of sparse DOS data as a set of delta functions

        Items at the same x-value can overlap and will not be summed together

        Args:
            ax: existing Matplotlib axes object. If not provided, a new figure
                with one set of axes will be created using Pyplot
            show: show the figure on-screen
            filename: if a path is given, save the figure to this file
            mplargs: additional arguments to pass to matplotlib Axes.vlines
                command (e.g. {'linewidth': 2} for a thicker line).

        Returns:
            Plotting axes. If "ax" was set, this is the same object.
        """

        if mplargs is None:
            mplargs = {}

        with SimplePlottingAxes(ax=ax, show=show, filename=filename) as ax:
            ax.vlines(self.get_energies(), 0, self.get_weights(), **mplargs)

        return ax


class GridDOSData(GeneralDOSData):
    """A collection of regularly-sampled data which represents a DOS

    This is an appropriate data container for density-of-states (DOS) or
    spectral data where the intensity values form a regular grid. This
    is generally the result of sampling or integrating into discrete
    bins, rather than a collection of unique states. The data may be
    plotted or resampled for further analysis using the sample_grid()
    and plot() methods.

    Metadata may be stored in the info dict, in which keys and values must be
    strings. This data is used for selecting and combining multiple DOSData
    objects in a DOSCollection object.

    When RawDOSData objects are combined with the addition operator::

      big_dos = raw_dos_1 + raw_dos_2

    the weights data is *summed* (requiring a consistent energy grid) and the
    new info dictionary consists of the *intersection* of the inputs: only
    key-value pairs that were common to both of the input objects will be
    retained in the new combined object. For example::

      (GridDOSData([0.1, 0.2, 0.3], [y1, y2, y3],
                   info={'symbol': 'O', 'index': '1'})
       + GridDOSData([0.1, 0.2, 0.3], [y4, y5, y6],
                     info={'symbol': 'O', 'index': '2'}))

    will yield the equivalent of::

      GridDOSData([0.1, 0.2, 0.3], [y1+y4, y2+y5, y3+y6], info={'symbol': 'O'})

    """

    def __init__(self,
                 energies: Floats,
                 weights: Floats,
                 info: Info = None) -> None:
        n_entries = len(energies)
        if not np.allclose(energies,
                           np.linspace(energies[0], energies[-1], n_entries)):
            raise ValueError("Energies must be an evenly-spaced 1-D grid")

        if len(weights) != n_entries:
            raise ValueError("Energies and weights must be the same length")

        super().__init__(energies, weights, info=info)
        self.sigma_cutoff = 3

    def _check_spacing(self, width) -> float:
        current_spacing = self._data[0, 1] - self._data[0, 0]
        if width < (2 * current_spacing):
            warnings.warn(
                "The broadening width is small compared to the original "
                "sampling density. The results are unlikely to be smooth.")
        return current_spacing

    def _sample(self,
                energies: Floats,
                width: float = 0.1,
                smearing: str = 'Gauss') -> np.ndarray:
        current_spacing = self._check_spacing(width)
        return super()._sample(energies=energies,
                               width=width, smearing=smearing
                               ) * current_spacing

    def __add__(self, other: 'GridDOSData') -> 'GridDOSData':
        # This method uses direct access to the mutable energy and weights data
        # (self._data) to avoid redundant copying operations. The __init__
        # method of GridDOSData will write this to a new array, so on this
        # occasion it is safe to pass references to the mutable data.

        if not isinstance(other, GridDOSData):
            raise TypeError("GridDOSData can only be combined with other "
                            "GridDOSData objects")
        if len(self._data[0, :]) != len(other.get_energies()):
            raise ValueError("Cannot add GridDOSData objects with different-"
                             "length energy grids.")

        if not np.allclose(self._data[0, :], other.get_energies()):
            raise ValueError("Cannot add GridDOSData objects with different "
                             "energy grids.")

        # Take intersection of metadata (i.e. only common entries are retained)
        new_info = dict(set(self.info.items()) & set(other.info.items()))

        # Sum the energy/weight data
        new_weights = self._data[1, :] + other.get_weights()

        new_object = GridDOSData(self._data[0, :], new_weights,
                                 info=new_info)
        return new_object

    @staticmethod
    def _interpret_smearing_args(npts: int,
                                 width: float = None,
                                 default_npts: int = 1000,
                                 default_width: float = 0.1
                                 ) -> Tuple[int, Union[float, None]]:
        """Figure out what the user intended: resample if width provided"""
        if width is not None:
            if npts:
                return (npts, float(width))
            else:
                return (default_npts, float(width))
        else:
            if npts:
                return (npts, default_width)
            else:
                return (0, None)

    def plot(self,
             npts: int = 0,
             xmin: float = None,
             xmax: float = None,
             width: float = None,
             smearing: str = 'Gauss',
             ax: 'matplotlib.axes.Axes' = None,
             show: bool = False,
             filename: str = None,
             mplargs: dict = None) -> 'matplotlib.axes.Axes':
        """Simple 1-D plot of DOS data

        Data will be resampled onto a grid with `npts` points unless `npts` is
        set to zero, in which case:

        - no resampling takes place
        - `width` and `smearing` are ignored
        - `xmin` and `xmax` affect the axis limits of the plot, not the
          underlying data.

        If the special key 'label' is present in self.info, this will be set
        as the label for the plotted line (unless overruled in mplargs). The
        label is only seen if a legend is added to the plot (i.e. by calling
        ``ax.legend()``).

        Args:
            npts, xmin, xmax: output data range, as passed to self.sample_grid
            width: Width of broadening kernel, passed to self.sample_grid().
                If no npts was set but width is set, npts will be set to 1000.
            smearing: selection of broadening kernel for self.sample_grid()
            ax: existing Matplotlib axes object. If not provided, a new figure
                with one set of axes will be created using Pyplot
            show: show the figure on-screen
            filename: if a path is given, save the figure to this file
            mplargs: additional arguments to pass to matplotlib plot command
                (e.g. {'linewidth': 2} for a thicker line).

        Returns:
            Plotting axes. If "ax" was set, this is the same object.
        """

        npts, width = self._interpret_smearing_args(npts, width)

        if mplargs is None:
            mplargs = {}
        if 'label' not in mplargs:
            mplargs.update({'label': self.label_from_info(self.info)})

        if npts:
            assert isinstance(width, float)
            dos = self.sample_grid(npts, xmin=xmin,
                                   xmax=xmax, width=width,
                                   smearing=smearing)
        else:
            dos = self

        energies, intensity = dos.get_energies(), dos.get_weights()

        with SimplePlottingAxes(ax=ax, show=show, filename=filename) as ax:
            ax.plot(energies, intensity, **mplargs)
            ax.set_xlim(left=xmin, right=xmax)

        return ax
