"""
Created on 2018-04-09
@author Carl Schaffer
@mail   carl.schaffer@leibniz-kis.de
"""

import datetime  # date handling
import os  # path and file manipulations
import sys  # error handling
from glob import glob
from os.path import basename, exists, join, dirname
from warnings import warn

import astropy.io.fits as fitsio  # fits reading and writing
import astropy.units as u
import matplotlib.pyplot as plt
import numpy as np
from astropy.coordinates import SkyCoord
from astropy.wcs import WCS

from ..generic.fits import FitsFile
from ..util import calc_mu, calc_theta
from ..util.util import date_from_fn, gris_obs_mode


class GrisFitsFile(FitsFile):
    """Class for handling fits files for GRIS db
    When the constructor is called, the filename is stored and basic
    metadata parameters such as date and runnumber are extracted from
    the filename. Once the class function GrisFitsFile.parse() is
    called, metadata from the fits file is available as a dictionary
    stored at GrisFitsFile.properties. Any errors ocurring during
    opening and parsing of the file are stored as strings in
    GrisFitsFile.errors

    Args:
      filename: filename of target fits file

    Returns:

    """

    def __init__(self, filename):
        """Constructor"""
        super().__init__(filename)
        assert exists(filename)
        self.properties = {}
        self.errors = []

        self.filename = filename
        self.name = basename(filename)
        self.path = self.filename

        self.dirname = dirname(self.path)
        self.parse_filename()
        self.is_parsed = False

    def parse_filename(self):
        """Extract metadata from filename"""
        # Extract time from Filename
        name = os.path.splitext(basename(self.filename))[0].split("_")
        year = int(name[1][:4])
        month = int(name[1][4:6])
        day = int(name[1][6:])

        # Get time
        # hh = int(name[2][:2])
        # mm = int(name[2][2:4])
        # outstring = int(name[2][4:6])

        # self.date = datetime.datetime(year,month,day,hh,mm,outstring)
        self.date = datetime.datetime(year, month, day, 0, 0, 0)

        # Extract Run-, Map-, and Stepnumber, as well as mode string
        self.runnumber = int(name[4])
        self.mapnumber = int(name[5])
        self.slitposnumber = name[6]
        self.modestring = name[3]

    @property
    def verbose_runname(self):
        """
        Get verbose run name such as 24apr14.002

        Returns:
            outstring: verbose run name
        """
        outstring = self.date.strftime("%d%b%y").lower()
        outstring += f".{int(self.runnumber):03d}"
        return outstring

    @property
    def _coords_from_simple_header(self):
        """coordinates for slit, the Keywords 'SLITPOSX' and 'SLITPOSY' are assumed to be the center of the slit
         while the keyword 'SLITORIE' describes the rotation of the slit w.r.t. the helioprojective axes. The
         algorithm further assumes that each pixel represents a square area with side length of 'STEPSIZE'.
         These assumptions are used to calculate the coordinates for the center of each pixel within the data array.

        Returns:
            (X,Y):ndarray(float) x and y coordinates for each pixel of the slit


        WARNING! The code below is not checked for correctness! We do not know what direction SLITORIE references
        off and coordinates provided by GREGOR are very error-prone.
        """

        h = self.header
        slit_length_pixels = h["NAXIS2"]
        center_pixel = slit_length_pixels // 2

        x_slitcenter = h["SLITPOSX"] if h["SLITPOSX"] != "" else 0
        y_slitcenter = h["SLITPOSY"] if h["SLITPOSY"] != "" else 0
        angle = h["SLITORIE"] if h["SLITORIE"] != "" else 0

        delta_pix = [i - center_pixel for i in range(slit_length_pixels)]
        delta_pix = np.array(delta_pix)
        arcsec_per_pixel_x = h["STEPSIZE"]  # assuming square pixels
        arcsec_per_pixel_y = h["STEPSIZE"] if h["STEPSIZE"] else 0.135  # assuming square pixels

        X = x_slitcenter - np.sin(angle) * delta_pix * arcsec_per_pixel_x
        Y = y_slitcenter - np.cos(angle) * delta_pix * arcsec_per_pixel_y

        message = "Using FITS file coordinates, they are probably wrong by at least 100 arcsec!" \
                  " Also the slit angle is definitely off!"
        warn(message)

        coord_array = np.array([X, Y]) * u.arcsec
        # reorder axes for consistency with other coordinate retrieval methods
        coord_array = np.swapaxes(coord_array, 0, 1)

        return coord_array

    @property
    def coords(self):
        try:
            coords = self._coords_from_wcs
        except AssertionError:
            coords = self._coords_from_simple_header
        return coords

    @property
    def old_header(self):
        """Check if the header of this file has already been processed"""
        old_header = True
        if any([k in self.header for k in ['SOLARNET']]):
            old_header = False
        return old_header

    @property
    def mu(self):
        """
        Calculate mu from coordinates
        Returns: mu

        """
        coords = self.coords.value
        X, Y = coords[:, 0], coords[:, 1]
        x = np.mean(X)
        y = np.mean(Y)
        mu = calc_mu(x, y)
        return mu

    @property
    def theta(self):
        """
        Calculate theta from coordinates
        Returns: theta

        """
        coords = self.coords.value
        X, Y = coords[:, 0], coords[:, 1]
        x = np.mean(X)
        y = np.mean(Y)
        theta = calc_theta(x, y)
        return theta

    @property
    def slit_orientation(self):
        """Get slit_orientation, defaults to 0. Not sure what this angle means...

        Returns:
            slit_orientation: float
        """

        slit_orientation = self._get_value(["SLITORIE"])
        slit_orientation = slit_orientation if slit_orientation != "" else 0
        return slit_orientation

    @property
    def number_of_maps(self):
        """

        Returns:
            nmaps: int number of maps in associated observation
        """
        return self._get_value(["NMAPS", "SERIES"])

    @property
    def map_index(self):
        """

        Returns:
            i_map: int map index within observation
        """
        return self._get_value(["IMAP", "ISERIE"])

    @property
    def n_steps(self):
        """
        Determine the number of slit scanning steps in the associated map.

        Returns:
            nsteps: number of steps in the associated map

        """
        # get initial guess from header
        nsteps = self._get_value(["STEPS", "NSTEPS"])

        # check if other files matching the same run are present. If so, check the running step number
        # for the same map to determine the actual number of steps. It is assumed, that all files belonging to the
        # same map are stored in the same directory
        pattern = (
            f"gris_*_*_*_{self.runnumber:03d}_{self.mapnumber:03d}_*.fits"
        )
        matching_files = glob(join(self.dirname, pattern))
        max_steps = int(
            max(
                [
                    basename(m).split(".")[0].split("_")[-1]
                    for m in matching_files
                ]
            )
        )

        if max_steps < nsteps:
            nsteps = max_steps

        return nsteps

    @property
    def obs_time(self):
        """

        Returns:
            obs_time: observation time given in filename
        """
        return date_from_fn(self.filename)

    @property
    def step_index(self):
        """

        Returns:
            step_index: index of scanning step within current map
        """
        return self._get_value(["ISTEP"])

    @property
    def step_angle(self):
        """

        Returns:
            step_angle: scanning angle
        """

        return self._get_value(["STEPANGL"])

    @property
    def wavelength_step_size(self):
        """Get step length along wavelength axis. We use the values from the continuum correction fitting"""
        return self._kw_mean(["FF1WLDSP", "FF2WLDSP"])

    @property
    def wavelength_region(self):
        """Get wavelength region covered by file. Returns tuple of (upper, lower)"""
        step_size = self.wavelength_step_size
        n_steps_wl = self.data.shape[-1]

        # average for lower border:
        lower = self._kw_mean(["FF1WLOFF", "FF2WLOFF"])
        # add wl steps to calculate upper border
        upper = lower + step_size * n_steps_wl

        return lower, upper

    @property
    def derotator_in(self):
        """
        Determine whether the derotator is inserted

        Returns:
            bool: derotator in

        """
        try:
            rotval = self._get_value(["ROTCODE"])
        except KeyError:
            # if no derotator keywords are scontained, assume it doesn't exist yet
            return False

        if rotval == 0:
            return True
        else:
            return False

    @property
    def states(self):
        """
        Determine number of polarimetric states

        Returns:
            n_states int: number of states
        """

        # Number of polarimetric states is stored in the last axis. Determine
        # number of axes from NAXIS keyword and query value for last axis.
        naxes = self.query("NAXIS")
        keyword = f"NAXIS{naxes}"
        states = self.query(keyword)

        return states

    @property
    def obs_mode(self):
        """Get observation mode"""
        self.parse()
        # extract obs mode keywords
        states = self.states
        n_maps = self.number_of_maps
        n_steps = self.n_steps

        mode = gris_obs_mode(states, n_maps, n_steps)
        return mode

    def parse(self):
        """extract data and metadata from file"""
        if self.is_parsed:
            return

        def is_valid(value):
            valid = True
            if isinstance(value, fitsio.card.Undefined):
                valid = False
            elif value == "-NAN":
                valid = False

            return valid

        # Read file
        try:
            header = self.header

            # Add header to self.properties, cast strange data types where
            # necessary, fill invalid values with empty strings

            for key in header.keys():
                value = header[key]
                # check validity:
                value = value if is_valid(value) else ""
                if key in ["COMMENT", "HISTORY"]:
                    self.properties[key] = str(value)
                else:
                    self.properties[key] = value

            self.is_parsed = True
        except:
            exception = sys.exc_info()[0]
            self.errors.append("File %s" % self.filename + str(exception))

    def make_bson(self):
        """construct BSON document for mongodb storage"""
        return self.properties

    @property
    def _coords_from_wcs(self):
        """
        Retrieve spatial coordinates for each pixel in for slices projected into the spatial dimensions.
        Returns a data cube shaped s the spatial dimensions
        Returns: arcsec, cube of (NAXIS1, NAXIS2, 2) Values are Helioprojective X and Y in arcseconds
        """

        assert not self.old_header, f"Can't get coordinates from {self.path} ! Run header conversion first!"

        wcs = WCS(self.header)
        pixel_shape = wcs.pixel_shape
        n_y = pixel_shape[1]
        slit_list = np.zeros((n_y, self["NAXIS"]))
        slit_list[:, 1] = np.arange(n_y)
        slit_list[:, 0] = 0
        converted = np.array(wcs.pixel_to_world_values(slit_list))
        degrees = u.degree * converted[:, :2]
        arcsec = degrees.to(u.arcsec)

        return arcsec

    def plot_slit(self):
        m = self.full_disk_map

        # retrieve different data sources
        coordinates = self.coords

        X, Y = coordinates[:, 0], coordinates[:, 1]

        # determine fov limits for plotting

        plotting_padding = 300 * u.arcsec
        x_min = min(X) - plotting_padding
        x_max = max(X) + plotting_padding
        y_min = min(Y) - plotting_padding
        y_max = max(Y) + plotting_padding

        # Create submap of FOV and plot
        submap = m.submap(SkyCoord(x_min, y_min, frame=m.coordinate_frame),
                          SkyCoord(x_max, y_max, frame=m.coordinate_frame))
        fig = plt.figure()  # noqa F841
        ax = plt.subplot(projection=submap)
        im = submap.plot()  # noqa:F841

        # create SkyCoord objects and overplot
        slit_wcs = SkyCoord(X, Y, frame=submap.coordinate_frame)
        ax.plot_coord(slit_wcs, 'g-', label="WCS Coordinates")

        # Create Legend and annotations
        title = "Gris Slit {0}_{1:03d}_{2:03d}_{3:03d}"
        title = title.format(self.obs_time.strftime('%Y%m%d'), self.runnumber, self.mapnumber, self.step_index)
        plt.title(title)

        annotation = f"Spectrum taken at \n{self.obs_time.strftime('%Y-%m-%d %H:%M:%S')}"
        annotation += f"\nHMI Reference taken at \n{submap.date.strftime('%Y-%m-%d %H:%M:%S')}"
        plt.text(0.05, 0.8, annotation, horizontalalignment='left', transform=ax.transAxes)
        plt.tight_layout(pad=3.5)
        _ = plt.legend()

        return fig, ax

    def __str__(self):
        outstring = "GrisFitsFile %s from %s:\n\tRun: %s\n\tMap: %s\n\tSlitposition: %s\n"
        outstring = outstring % (
            os.path.basename(self.filename),
            self.date,
            self.runnumber,
            self.map_index,
            self.slitposnumber,
        )
        return outstring


if __name__ == '__main__':
    gf = GrisFitsFile(sys.argv[1])
    gf.parse()
    print(gf)