GrisFitsFile.py

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

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

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, Angle
from astropy.wcs import WCS

from kis_tools.generic.fits import FitsFile
from kis_tools.gris.coord_correction_ml import get_coords_ml
from kis_tools.gris.util import gregor_parallactic_angle, telescope_to_hpc
from kis_tools.util.calculations import estimate_telescope_drift
from kis_tools.util.util import date_from_fn, gris_obs_mode

logger = logging.getLogger(__name__)


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 occurring 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 telescope_centering(self):
        """
        Determine state of telescope centering at the time of observation, queries GDBS
        for centering data

        Returns:
            outstring: x_offset, y_offset, x_uncertainty, y_uncertainty all in arcseconds
        """
        # Query centering from GDBS
        try:
            from kis_tools.gdbs.query_gdbs import query_gdbs
            gdbs_usable = True
            x_telcenter, x_valid, x_age = query_gdbs('xsuncenter', self.obs_time)
            y_telcenter, y_valid, y_age = query_gdbs('ysuncenter', self.obs_time)
            # Determine uncertainties for centering values
            centering_age = max(x_age, y_age)
            drift = estimate_telescope_drift(centering_age.seconds / 3600)
            x_std, y_std = drift, drift

            # if the centering is older than 24 hours, discard
            if centering_age > datetime.timedelta(hours=24):
                gdbs_usable = False
        except IOError:
            gdbs_usable = False
        except ValueError:
            gdbs_usable = False

        if not gdbs_usable:
            # Determined from offset between uncentered and cross_correlated data
            # STD-DEV of distances in X and Y again see gris coord study for details
            uncertainty_no_center_x = 117.1
            uncertainty_no_center_y = 182.4
            x_telcenter, y_telcenter, x_std, y_std = 0, 0, uncertainty_no_center_x, uncertainty_no_center_y

        return float(x_telcenter), float(y_telcenter), x_std, y_std

    @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"] if self.old_header else h['NAXIS1']
        center_pixel = slit_length_pixels // 2

        # Slit center in telescope coordinates
        x_slitcenter = h["SLITPOSX"] if h["SLITPOSX"] != "" else 0
        y_slitcenter = h["SLITPOSY"] if h["SLITPOSY"] != "" else 0

        # Transform to HPC (shift by telescope centering, rotate by p0 angle)
        delta_x, delta_y, std_delta_x, std_delta_y = self.telescope_centering

        # If uncertainties are too large, use ML model to correct the coordinates
        from kis_tools.gris.coord_correction_ml import coord_model_stdx, coord_model_stdy
        if std_delta_x > coord_model_stdx or std_delta_y > coord_model_stdy:
            x_hpc, y_hpc, std_delta_x, std_delta_y = get_coords_ml(self)
        else:
            x_hpc, y_hpc = telescope_to_hpc(x_slitcenter, y_slitcenter, self.p0_angle, delta_x, delta_y)

        # Setup grid of pixels
        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

        angle = self.slit_orientation

        # generate coordinates for full grid of slit pixels
        X = x_hpc - np.sin(angle) * delta_pix * arcsec_per_pixel_x
        Y = y_hpc - np.cos(angle) * delta_pix * arcsec_per_pixel_y

        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)

        # track coordinate uncertainty within class
        self.coord_uncertainty = (std_delta_x, std_delta_y)
        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 zenit(self):
        """
        Get Zenit angle (90° - elevation)
        Returns: zenit float in degrees

        """
        zenit = 90 - self._get_value(['ELEV_ANG', 'ELEVATIO'])
        return zenit

    @property
    def slit_orientation(self):
        """Get slitorie:
         clockwise angle from the western section of the solar equator

        Attempts to correct for derotator and image rotation, usually correct within a couple 10s of degrees.
        Returns:
            slitorie: float (degrees)
        """
        p0_angle = self.p0_angle
        azimut = self._get_value(['AZIMUT'])
        zenit = self.zenit
        slitorie = self.SLITORIE
        const_term = -130  # determined by trial and error to match results from cross correlation with HMI
        alpha = -1 * gregor_parallactic_angle(self.obs_time) + azimut - zenit - p0_angle + const_term
        if self.derotator_in:
            rotangle = self.ROTANGLE
            alpha = -2 * rotangle + (const_term / 2)
        angle = slitorie - alpha

        return angle

    @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 p0_angle(self):
        """

        Returns:
            p0_angle: angle between telescope and solar north
        """
        return self._get_value(["P0ANGLE", 'SOLAR_P0'])

    @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] if self.old_header else self.NAXIS3

        # 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 = Angle(u.degree * converted[:, :2]).wrap_at(180 * u.degree)
        arcsec = degrees.to(u.arcsec)

        # Track coord uncertainty within instance
        self.coord_uncertainty = (self.CSYER1, self.CSYER2)

        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)