# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Define a container class to store parameters for the geometry of an
ellipse.
"""

import math

import numpy as np
from astropy import log

__all__ = ['EllipseGeometry']


IN_MASK = [
    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
]

OUT_MASK = [
    [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
    [1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1],
    [1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1],
    [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1],
    [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
    [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
    [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
    [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
    [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
    [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
    [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
    [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
    [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1],
    [1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1],
    [1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1],
    [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
]


def _area(sma, eps, phi, r):
    """
    Compute elliptical sector area.
    """
    aux = r * math.cos(phi) / sma
    signal = aux / abs(aux)
    if abs(aux) >= 1.0:
        aux = signal
    return abs(sma**2 * (1.0 - eps) / 2.0 * math.acos(aux))


class EllipseGeometry:
    r"""
    Container class to store parameters for the geometry of an ellipse.

    Parameters that describe the relationship of a given ellipse with
    other associated ellipses are also encapsulated in this container.
    These associated ellipses may include, e.g., the two (inner and
    outer) bounding ellipses that are used to build sectors along the
    elliptical path. These sectors are used as areas for integrating
    pixel values, when the area integration mode (mean or median) is
    used.

    This class also keeps track of where in the ellipse we are when
    performing an 'extract' operation. This is mostly relevant when
    using an area integration mode (as opposed to a pixel integration
    mode)

    Parameters
    ----------
    x0, y0 : float
        The center pixel coordinate of the ellipse.
    sma : float
        The semimajor axis of the ellipse in pixels.
    eps : float
        The ellipticity of the ellipse. The ellipticity is defined as

        .. math::

            \epsilon =  1  -  \frac{b}{a}

        where a and b are the lengths of the semimajor and semiminor
        axes, respectively.
    pa : float
        The position angle (in radians) of the semimajor axis in
        relation to the positive x axis of the image array (rotating
        towards the positive y axis). Position angles are defined in the
        range :math:`0 < PA <= \pi`. Avoid using as starting position
        angle of 0., since the fit algorithm may not work properly.
        When the ellipses are such that position angles are near either
        extreme of the range, noise can make the solution jump back and
        forth between successive isophotes, by amounts close to 180
        degrees.
    astep : float, optional
        The step value for growing/shrinking the semimajor axis. It can
        be expressed either in pixels (when ``linear_growth=True``) or
        as a relative value (when ``linear_growth=False``). The default
        is 0.1.
    linear_growth : bool, optional
        The semimajor axis growing/shrinking mode. The default is
        `False`.
    fix_center : bool, optional
        Keep center of ellipse fixed during fit? The default is False.
    fix_pa : bool, optional
        Keep position angle of semi-major axis of ellipse fixed during fit?
        The default is False.
    fix_eps : bool, optional
        Keep ellipticity of ellipse fixed during fit? The default is False.
    """

    def __init__(self, x0, y0, sma, eps, pa, astep=0.1, linear_growth=False,
                 fix_center=False, fix_pa=False, fix_eps=False):
        self.x0 = x0
        self.y0 = y0
        self.sma = sma
        self.eps = eps
        self.pa = pa

        self.astep = astep
        self.linear_growth = linear_growth

        # Fixed parameters are flagged in here. Note that the
        # ordering must follow the same ordering used in the
        # fitter._CORRECTORS list.
        self.fix = np.array([fix_center, fix_center, fix_pa, fix_eps])

        # limits for sector angular width
        self._phi_min = 0.05
        self._phi_max = 0.2

        # variables used in the calculation of the sector angular width
        sma1, sma2 = self.bounding_ellipses()
        inner_sma = min((sma2 - sma1), 3.0)
        self._area_factor = (sma2 - sma1) * inner_sma

        # sma can eventually be zero!
        if self.sma > 0.0:
            self.sector_angular_width = max(min((inner_sma / self.sma),
                                                self._phi_max), self._phi_min)
            self.initial_polar_angle = self.sector_angular_width / 2.0
            self.initial_polar_radius = self.radius(self.initial_polar_angle)

    def find_center(self, image, threshold=0.1, verbose=True):
        """
        Find the center of a galaxy.

        If the algorithm is successful the (x, y) coordinates in this
        `~photutils.isophote.EllipseGeometry` (i.e., the ``x0`` and
        ``y0`` attributes) instance will be modified.

        The isophote fit algorithm requires an initial guess for the
        galaxy center (x, y) coordinates and these coordinates must be
        close to the actual galaxy center for the isophote fit to work.
        This method provides can provide an initial guess for the galaxy
        center coordinates. See the **Notes** section below for more
        details.

        Parameters
        ----------
        image : 2D `~numpy.ndarray`
            The image array. Masked arrays are not recognized here.
            This assumes that centering should always be done on valid
            pixels.

        threshold : float, optional
            The centerer threshold. To turn off the centerer, set this
            to a large value (i.e., >> 1). The default is 0.1.

        verbose : bool, optional
            Whether to print object centering information. The default
            is `True`.

        Notes
        -----
        The centerer function scans a 10x10 window centered on the (x,
        y) coordinates in the `~photutils.isophote.EllipseGeometry`
        instance passed to the constructor of the
        `~photutils.isophote.Ellipse` class. If any of the
        `~photutils.isophote.EllipseGeometry` (x, y) coordinates
        are `None`, the center of the input image frame is
        used. If the center acquisition is successful, the
        `~photutils.isophote.EllipseGeometry` instance is modified in
        place to reflect the solution of the object centerer algorithm.

        In some cases the object centerer algorithm may fail even though
        there is enough signal-to-noise to start a fit (e.g., objects
        with very high ellipticity). In those cases the sensitivity
        of the algorithm can be decreased by decreasing the value of
        the object centerer threshold parameter. The centerer works by
        looking where a quantity akin to a signal-to-noise ratio is
        maximized within the 10x10 window. The centerer can thus be shut
        off entirely by setting the threshold to a large value (i.e.,
        >> 1; meaning no location inside the search window will achieve
        that signal-to-noise ratio).
        """
        self._centerer_mask_half_size = len(IN_MASK) / 2
        self.centerer_threshold = threshold

        # number of pixels in each mask
        sz = len(IN_MASK)
        self._centerer_ones_in = np.ma.masked_array(np.ones(shape=(sz, sz)),
                                                    mask=IN_MASK)
        self._centerer_ones_out = np.ma.masked_array(np.ones(shape=(sz, sz)),
                                                     mask=OUT_MASK)
        self._centerer_in_mask_npix = np.sum(self._centerer_ones_in)
        self._centerer_out_mask_npix = np.sum(self._centerer_ones_out)

        # Check if center coordinates point to somewhere inside the frame.
        # If not, set then to frame center.
        shape = image.shape
        _x0 = self.x0
        _y0 = self.y0
        if (_x0 is None or _x0 < 0 or _x0 >= shape[1] or _y0 is None
                or _y0 < 0 or _y0 >= shape[0]):
            _x0 = shape[1] / 2
            _y0 = shape[0] / 2

        max_fom = 0.0
        max_i = 0
        max_j = 0

        # scan all positions inside window
        window_half_size = 5
        for i in range(int(_x0 - window_half_size),
                       int(_x0 + window_half_size) + 1):
            for j in range(int(_y0 - window_half_size),
                           int(_y0 + window_half_size) + 1):

                # ensure that it stays inside image frame
                i1 = int(max(0, i - self._centerer_mask_half_size))
                j1 = int(max(0, j - self._centerer_mask_half_size))
                i2 = int(min(shape[1] - 1, i + self._centerer_mask_half_size))
                j2 = int(min(shape[0] - 1, j + self._centerer_mask_half_size))

                window = image[j1:j2, i1:i2]

                # averages in inner and outer regions.
                inner = np.ma.masked_array(window, mask=IN_MASK)
                outer = np.ma.masked_array(window, mask=OUT_MASK)
                inner_avg = np.sum(inner) / self._centerer_in_mask_npix
                outer_avg = np.sum(outer) / self._centerer_out_mask_npix

                # standard deviation and figure of merit
                inner_std = np.std(inner)
                outer_std = np.std(outer)
                stddev = np.sqrt(inner_std**2 + outer_std**2)

                fom = (inner_avg - outer_avg) / stddev

                if fom > max_fom:
                    max_fom = fom
                    max_i = i
                    max_j = j

        # figure of merit > threshold: update geometry with new coordinates.
        if max_fom > threshold:
            self.x0 = float(max_i)
            self.y0 = float(max_j)

            if verbose:
                log.info(f'Found center at x0 = {self.x0:5.1f}, '
                         f'y0 = {self.y0:5.1f}')
        elif verbose:
            log.info('Result is below the threshold -- keeping the '
                     'original coordinates.')

    def radius(self, angle):
        """
        Calculate the polar radius for a given polar angle.

        Parameters
        ----------
        angle : float
            The polar angle (radians).

        Returns
        -------
        radius : float
            The polar radius (pixels).
        """
        return (self.sma * (1.0 - self.eps)
                / np.sqrt(((1.0 - self.eps) * np.cos(angle))**2
                          + (np.sin(angle))**2))

    def initialize_sector_geometry(self, phi):
        """
        Initialize geometry attributes associated with an elliptical
        sector at the given polar angle ``phi``.

        This function computes:

        * the four vertices that define the elliptical sector on the
          pixel array.
        * the sector area (saved in the ``sector_area`` attribute)
        * the sector angular width (saved in ``sector_angular_width``
          attribute)

        Parameters
        ----------
        phi : float
            The polar angle (radians) where the sector is located.

        Returns
        -------
        x, y : 1D `~numpy.ndarray`
            The x and y coordinates of each vertex as 1D arrays.
        """
        # These polar radii bound the region between the inner
        # and outer ellipses that define the sector.
        sma1, sma2 = self.bounding_ellipses()
        eps_ = 1.0 - self.eps

        # polar vector at one side of the elliptical sector
        self._phi1 = phi - self.sector_angular_width / 2.0
        r1 = (sma1 * eps_ / math.sqrt((eps_ * math.cos(self._phi1))**2
                                      + (math.sin(self._phi1))**2))
        r2 = (sma2 * eps_ / math.sqrt((eps_ * math.cos(self._phi1))**2
                                      + (math.sin(self._phi1))**2))

        # polar vector at the other side of the elliptical sector
        self._phi2 = phi + self.sector_angular_width / 2.0
        r3 = (sma2 * eps_ / math.sqrt((eps_ * math.cos(self._phi2))**2
                                      + (math.sin(self._phi2))**2))

        r4 = (sma1 * eps_ / math.sqrt((eps_ * math.cos(self._phi2))**2
                                      + (math.sin(self._phi2))**2))

        # sector area
        sa1 = _area(sma1, self.eps, self._phi1, r1)
        sa2 = _area(sma2, self.eps, self._phi1, r2)
        sa3 = _area(sma2, self.eps, self._phi2, r3)
        sa4 = _area(sma1, self.eps, self._phi2, r4)
        self.sector_area = abs((sa3 - sa2) - (sa4 - sa1))

        # angular width of sector. It is calculated such that the sectors
        # come out with roughly constant area along the ellipse.
        self.sector_angular_width = max(min((self._area_factor / (r3 - r4)
                                             / r4), self._phi_max),
                                        self._phi_min)

        # compute the 4 vertices that define the elliptical sector.
        vertex_x = np.zeros(shape=4, dtype=float)
        vertex_y = np.zeros(shape=4, dtype=float)

        # vertices are labelled in counterclockwise sequence
        vertex_x[0:2] = np.array([r1, r2]) * math.cos(self._phi1 + self.pa)
        vertex_x[2:4] = np.array([r4, r3]) * math.cos(self._phi2 + self.pa)
        vertex_y[0:2] = np.array([r1, r2]) * math.sin(self._phi1 + self.pa)
        vertex_y[2:4] = np.array([r4, r3]) * math.sin(self._phi2 + self.pa)
        vertex_x += self.x0
        vertex_y += self.y0

        return vertex_x, vertex_y

    def bounding_ellipses(self):
        """
        Compute the semimajor axis of the two ellipses that bound the
        annulus where integrations take place.

        Returns
        -------
        sma1, sma2 : float
            The smaller and larger values of semimajor axis length that
            define the annulus bounding ellipses.
        """
        if self.linear_growth:
            a1 = self.sma - self.astep / 2.0
            a2 = self.sma + self.astep / 2.0
        else:
            a1 = self.sma * (1.0 - self.astep / 2.0)
            a2 = self.sma * (1.0 + self.astep / 2.0)

        return a1, a2

    def polar_angle_sector_limits(self):
        """
        Return the two polar angles that bound the sector.

        The two bounding polar angles become available only after
        calling the
        :meth:`~photutils.isophote.EllipseGeometry.initialize_sector_geometry`
        method.

        Returns
        -------
        phi1, phi2 : float
            The smaller and larger values of polar angle that bound the
            current sector.
        """
        return self._phi1, self._phi2

    def to_polar(self, x, y):
        r"""
        Return the radius and polar angle in the ellipse coordinate
        system given (x, y) pixel image coordinates.

        This function takes care of the different definitions for
        position angle (PA) and polar angle (phi):

        .. math::

            -\pi < PA < \pi

            0 < phi < 2 \pi

        Note that radius can be anything. The solution is not tied to
        the semimajor axis length, but to the center position and tilt
        angle.

        Parameters
        ----------
        x, y : float
            The (x, y) image coordinates.

        Returns
        -------
        radius, angle : float
            The ellipse radius and polar angle.
        """
        # We split in between a scalar version and a
        # vectorized version. This is necessary for
        # now so we don't pay a heavy speed penalty
        # that is incurred when using vectorized code.

        # The split in two separate functions helps in
        # the profiling analysis: most of the time is
        # spent in the scalar function.
        if isinstance(x, (int, float)):
            return self._to_polar_scalar(x, y)

        return self._to_polar_vectorized(x, y)

    def _to_polar_scalar(self, x, y):
        x1 = x - self.x0
        y1 = y - self.y0

        radius = x1**2 + y1**2
        if radius > 0.0:
            radius = math.sqrt(radius)
            angle = math.asin(abs(y1) / radius)
        else:
            radius = 0.0
            angle = 1.0

        if x1 >= 0.0 and y1 < 0.0:
            angle = 2 * np.pi - angle
        elif x1 < 0.0 and y1 >= 0.0:
            angle = np.pi - angle
        elif x1 < 0.0 and y1 < 0.0:
            angle = np.pi + angle

        pa1 = self.pa
        if self.pa < 0.0:
            pa1 = self.pa + 2 * np.pi
        angle = angle - pa1
        if angle < 0.0:
            angle = angle + 2 * np.pi

        return radius, angle

    def _to_polar_vectorized(self, x, y):
        x1 = np.atleast_2d(x) - self.x0
        y1 = np.atleast_2d(y) - self.y0

        radius = x1**2 + y1**2
        angle = np.ones(radius.shape)

        imask = (radius > 0.0)
        radius[imask] = np.sqrt(radius[imask])
        angle[imask] = np.arcsin(np.abs(y1[imask]) / radius[imask])
        radius[~imask] = 0.0
        angle[~imask] = 1.0

        idx = (x1 >= 0.0) & (y1 < 0.0)
        angle[idx] = 2 * np.pi - angle[idx]
        idx = (x1 < 0.0) & (y1 >= 0.0)
        angle[idx] = np.pi - angle[idx]
        idx = (x1 < 0.0) & (y1 < 0.0)
        angle[idx] = np.pi + angle[idx]

        pa1 = self.pa
        if self.pa < 0.0:
            pa1 = self.pa + 2 * np.pi
        angle = angle - pa1
        angle[angle < 0] += 2 * np.pi

        return radius, angle

    def update_sma(self, step):
        """
        Calculate an updated value for the semimajor axis, given the
        current value and the step value.

        The step value must be managed by the caller to support both
        modes: grow outwards and shrink inwards.

        Parameters
        ----------
        step : float
            The step value.

        Returns
        -------
        sma : float
            The new semimajor axis length.
        """
        if self.linear_growth:
            sma = self.sma + step
        else:
            sma = self.sma * (1.0 + step)
        return sma

    def reset_sma(self, step):
        """
        Change the direction of semimajor axis growth, from outwards to
        inwards.

        Parameters
        ----------
        step : float
            The current step value.

        Returns
        -------
        sma, new_step : float
            The new semimajor axis length and the new step value to
            initiate the shrinking of the semimajor axis length. This is
            the step value that should be used when calling the
            :meth:`~photutils.isophote.EllipseGeometry.update_sma`
            method.
        """
        if self.linear_growth:
            sma = self.sma - step
            step = -step
        else:
            aux = 1.0 / (1.0 + step)
            sma = self.sma * aux
            step = aux - 1.0

        return sma, step
