utils.py

"""This module provides various helper functions."""
from __future__ import division, print_function
import logging
import sys
import os
import warnings

from astropy.visualization import (PercentileInterval, ImageNormalize,
                                   SqrtStretch, LinearStretch)
from astropy.time import Time
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import numpy as np
from functools import wraps

log = logging.getLogger(__name__)

__all__ = ['LightkurveWarning',
           'KeplerQualityFlags', 'TessQualityFlags',
           'bkjd_to_astropy_time', 'btjd_to_astropy_time']


class QualityFlags(object):
    """Abstract class"""

    STRINGS = {}
    OPTIONS = {}

    @classmethod
    def decode(cls, quality):
        """Converts a Kepler QUALITY value into a list of human-readable strings.

        This function takes the QUALITY bitstring that can be found for each
        cadence in Kepler/K2's pixel and light curve files and converts into
        a list of human-readable strings explaining the flags raised (if any).

        Parameters
        ----------
        quality : int
            Value from the 'QUALITY' column of a Kepler/K2 pixel or lightcurve file.

        Returns
        -------
        flags : list of str
            List of human-readable strings giving a short description of the
            quality flags raised.  Returns an empty list if no flags raised.
        """
        result = []
        for flag in cls.STRINGS.keys():
            if quality & flag > 0:
                result.append(cls.STRINGS[flag])
        return result

    @classmethod
    def create_quality_mask(cls, quality_array, bitmask=None):
        """Returns a boolean array which flags good cadences given a bitmask.

        This method is used by the constructors of :class:`KeplerTargetPixelFile`
        and :class:`KeplerLightCurveFile` to initialize their `quality_mask`
        class attribute which is used to ignore bad-quality data.

        Parameters
        ----------
        quality_array : array of int
            'QUALITY' column of a Kepler target pixel or lightcurve file.
        bitmask : int or str
            Bitmask (int) or one of 'none', 'default', 'hard', or 'hardest'.

        Returns
        -------
        boolean_mask : array of bool
            Boolean array in which `True` means the data is of good quality.
        """
        # Return an array filled with `True` by default (i.e. ignore nothing)
        if bitmask is None:
            return np.ones(len(quality_array), dtype=bool)
        # A few pre-defined bitmasks can be specified as strings
        if isinstance(bitmask, str):
            try:
                bitmask = cls.OPTIONS[bitmask]
            except KeyError:
                valid_options = tuple(cls.OPTIONS.keys())
                raise ValueError("quality_bitmask='{}' is not supported, "
                                 "expected one of {}"
                                 "".format(bitmask, valid_options))
        # The bitmask is applied using the bitwise AND operator
        quality_mask = (quality_array & bitmask) == 0
        log.info("{} cadences will be ignored (bitmask={})"
                 "".format((~quality_mask).sum(), bitmask))
        return quality_mask


class KeplerQualityFlags(QualityFlags):
    """
    This class encodes the meaning of the various Kepler QUALITY bitmask flags,
    as documented in the Kepler Archive Manual (Ref. [1], Table 2.3).

    References
    ----------
    .. [1] Kepler: A Search for Terrestrial Planets. Kepler Archive Manual.
        http://archive.stsci.edu/kepler/manuals/archive_manual.pdf
    """
    AttitudeTweak = 1
    SafeMode = 2
    CoarsePoint = 4
    EarthPoint = 8
    ZeroCrossing = 16
    Desat = 32
    Argabrightening = 64
    ApertureCosmic = 128
    ManualExclude = 256
    # Bit 2**10 = 512 is unused by Kepler
    SensitivityDropout = 1024
    ImpulsiveOutlier = 2048
    ArgabrighteningOnCCD = 4096
    CollateralCosmic = 8192
    DetectorAnomaly = 16384
    NoFinePoint = 32768
    NoData = 65536
    RollingBandInAperture = 131072
    RollingBandInMask = 262144
    PossibleThrusterFiring = 524288
    ThrusterFiring = 1048576

    #: DEFAULT bitmask identifies all cadences which are definitely useless.
    DEFAULT_BITMASK = (AttitudeTweak | SafeMode | CoarsePoint | EarthPoint |
                       Desat | ManualExclude | DetectorAnomaly | NoData | ThrusterFiring)
    #: HARD bitmask is conservative and may identify cadences which are useful.
    HARD_BITMASK = (DEFAULT_BITMASK | SensitivityDropout | ApertureCosmic |
                    CollateralCosmic | PossibleThrusterFiring)
    #: HARDEST bitmask identifies cadences with any flag set. Its use is not recommended.
    HARDEST_BITMASK = 2096639

    #: Dictionary which provides friendly names for the various bitmasks.
    OPTIONS = {'none': 0,
               'default': DEFAULT_BITMASK,
               'hard': HARD_BITMASK,
               'hardest': HARDEST_BITMASK}

    #: Pretty string descriptions for each flag
    STRINGS = {
        1: "Attitude tweak",
        2: "Safe mode",
        4: "Coarse point",
        8: "Earth point",
        16: "Zero crossing",
        32: "Desaturation event",
        64: "Argabrightening",
        128: "Cosmic ray in optimal aperture",
        256: "Manual exclude",
        1024: "Sudden sensitivity dropout",
        2048: "Impulsive outlier",
        4096: "Argabrightening on CCD",
        8192: "Cosmic ray in collateral data",
        16384: "Detector anomaly",
        32768: "No fine point",
        65536: "No data",
        131072: "Rolling band in optimal aperture",
        262144: "Rolling band in full mask",
        524288: "Possible thruster firing",
        1048576: "Thruster firing"
    }


class TessQualityFlags(QualityFlags):
    """
    This class encodes the meaning of the various TESS QUALITY bitmask flags,
    as documented in the TESS Data Products Description Document (Ref. [1], Table 26).

    References
    ----------
    .. [1] TESS Science Data Products Description Document (EXP-TESS-ARC-ICD-0014)
        https://archive.stsci.edu/missions/tess/doc/EXP-TESS-ARC-ICD-TM-0014.pdf
    """
    AttitudeTweak = 1
    SafeMode = 2
    CoarsePoint = 4
    EarthPoint = 8
    Argabrightening = 16
    Desat = 32
    ApertureCosmic = 64
    ManualExclude = 128
    Discontinuity = 256
    ImpulsiveOutlier = 512
    CollateralCosmic = 1024
    Straylight = 2048

    #: DEFAULT bitmask identifies all cadences which are definitely useless.
    DEFAULT_BITMASK = (AttitudeTweak | SafeMode | CoarsePoint | EarthPoint |
                       Desat | ManualExclude)
    #: HARD bitmask is conservative and may identify cadences which are useful.
    HARD_BITMASK = (DEFAULT_BITMASK | ApertureCosmic |
                    CollateralCosmic | Straylight)
    #: HARDEST bitmask identifies cadences with any flag set. Its use is not recommended.
    HARDEST_BITMASK = 4095

    #: Dictionary which provides friendly names for the various bitmasks.
    OPTIONS = {'none': 0,
               'default': DEFAULT_BITMASK,
               'hard': HARD_BITMASK,
               'hardest': HARDEST_BITMASK}

    #: Pretty string descriptions for each flag
    STRINGS = {
        1: "Attitude tweak",
        2: "Safe mode",
        4: "Coarse point",
        8: "Earth point",
        16: "Argabrightening",
        32: "Desaturation event",
        64: "Cosmic ray in optimal aperture",
        128: "Manual exclude",
        256: "Discontinuity corrected",
        512: "Impulsive outlier",
        1024: "Cosmic ray in collateral data",
        2048: "Straylight"
    }

def channel_to_module_output(channel):
    """Returns a (module, output) pair given a CCD channel number.

    Parameters
    ----------
    channel : int
        Channel number

    Returns
    -------
    module, output : tuple of ints
        Module and Output number
    """
    if channel < 1 or channel > 88:
        raise ValueError("Channel number must be in the range 1-88.")
    lookup = _get_channel_lookup_array()
    lookup[:, 0] = 0
    modout = np.where(lookup == channel)
    return (modout[0][0], modout[1][0])


def module_output_to_channel(module, output):
    """Returns the CCD channel number for a given module and output pair.

    Parameters
    ----------
    module : int
        Module number
    output : int
        Output number

    Returns
    -------
    channel : int
        Channel number
    """
    if module < 1 or module > 26:
        raise ValueError("Module number must be in range 1-26.")
    if output < 1 or output > 4:
        raise ValueError("Output number must be 1, 2, 3, or 4.")
    return _get_channel_lookup_array()[module, output]


def _get_channel_lookup_array():
    """Returns a lookup table which maps (module, output) onto channel."""
    # In the array below, channel == array[module][output]
    # Note: modules 1, 5, 21, 25 are the FGS guide star CCDs.
    return np.array([
        [0,     0,    0,    0,    0],
        [1,    85,    0,    0,    0],
        [2,     1,    2,    3,    4],
        [3,     5,    6,    7,    8],
        [4,     9,   10,   11,   12],
        [5,    86,    0,    0,    0],
        [6,    13,   14,   15,   16],
        [7,    17,   18,   19,   20],
        [8,    21,   22,   23,   24],
        [9,    25,   26,   27,   28],
        [10,   29,   30,   31,   32],
        [11,   33,   34,   35,   36],
        [12,   37,   38,   39,   40],
        [13,   41,   42,   43,   44],
        [14,   45,   46,   47,   48],
        [15,   49,   50,   51,   52],
        [16,   53,   54,   55,   56],
        [17,   57,   58,   59,   60],
        [18,   61,   62,   63,   64],
        [19,   65,   66,   67,   68],
        [20,   69,   70,   71,   72],
        [21,   87,    0,    0,    0],
        [22,   73,   74,   75,   76],
        [23,   77,   78,   79,   80],
        [24,   81,   82,   83,   84],
        [25,   88,    0,    0,    0],
    ])


def running_mean(data, window_size):
    """Returns the moving average of an array `data`.

    Parameters
    ----------
    data : array of numbers
        The running mean will be computed on this data.
    window_size : int
        Window length used to compute the running mean.
    """
    cumsum = np.cumsum(np.insert(data, 0, 0))
    return (cumsum[window_size:] - cumsum[:-window_size]) / float(window_size)


def bkjd_to_astropy_time(bkjd, bjdref=2454833.):
    """Converts BKJD time values to an `astropy.time.Time` object.

    Kepler Barycentric Julian Day (BKJD) is a Julian day minus 2454833.0
    (UTC=January 1, 2009 12:00:00) and corrected to the arrival times
    at the barycenter of the Solar System.
    BKJD is the format in which times are recorded in the Kepler data products.
    The time is in the Barycentric Dynamical Time frame (TDB), which is a
    time system that is not affected by leap seconds.
    See Section 2.3.2 in the Kepler Archive Manual for details.

    Parameters
    ----------
    bkjd : array of floats
        Barycentric Kepler Julian Day.
    bjdref : float
        BJD reference date, for Kepler this is 2454833.

    Returns
    -------
    time : astropy.time.Time object
        Resulting time object
    """
    jd = bkjd + bjdref
    # Some data products have missing time values;
    # we need to set these to zero or `Time` cannot be instantiated.
    jd[~np.isfinite(jd)] = 0
    return Time(jd, format='jd', scale='tdb')


def btjd_to_astropy_time(btjd, bjdref=2457000.):
    """Converts BTJD time values to an `astropy.time.Time` object.

    TESS Barycentric Julian Day (BTJD) is a Julian day minus 2457000.0
    and corrected to the arrival times at the barycenter of the Solar System.
    BTJD is the format in which times are recorded in the TESS data products.
    The time is in the Barycentric Dynamical Time frame (TDB), which is a
    time system that is not affected by leap seconds.

    Parameters
    ----------
    btjd : array of floats
        Barycentric Kepler Julian Day
    bjdref : float
        BJD reference date.

    Returns
    -------
    time : astropy.time.Time object
        Resulting time object
    """
    jd = btjd + bjdref
    jd[~np.isfinite(jd)] = 0
    return Time(jd, format='jd', scale='tdb')


def plot_image(image, ax=None, scale='linear', origin='lower',
               xlabel='Pixel Column Number', ylabel='Pixel Row Number',
               clabel='Flux ($e^{-}s^{-1}$)', title=None, show_colorbar=True,
               **kwargs):
    """Utility function to plot a 2D image

    Parameters
    ----------
    image : 2d array
        Image data.
    ax : matplotlib.axes._subplots.AxesSubplot
        A matplotlib axes object to plot into. If no axes is provided,
        a new one will be generated.
    scale : str
        Scale used to stretch the colormap.
        Options: 'linear', 'sqrt', or 'log'.
    origin : str
        The origin of the coordinate system.
    xlabel : str
        Label for the x-axis.
    ylabel : str
        Label for the y-axis.
    clabel : str
        Label for the color bar.
    title : str or None
        Title for the plot.
    show_colorbar : bool
        Whether or not to show the colorbar
    kwargs : dict
        Keyword arguments to be passed to `matplotlib.pyplot.imshow`.

    Returns
    -------
    ax : matplotlib.axes._subplots.AxesSubplot
        The matplotlib axes object.
    """
    if ax is None:
        _, ax = plt.subplots()
    with warnings.catch_warnings():
        warnings.simplefilter("ignore", RuntimeWarning)  # ignore image NaN values
        mask = np.nan_to_num(image) > 0
        if mask.any() > 0:
            vmin, vmax = PercentileInterval(95.).get_limits(image[mask])
        else:
            vmin, vmax = 0, 0

    norm = None
    if scale is not None:
        if scale == 'linear':
            norm = ImageNormalize(vmin=vmin, vmax=vmax, stretch=LinearStretch(), clip=False)
        elif scale == 'sqrt':
            norm = ImageNormalize(vmin=vmin, vmax=vmax, stretch=SqrtStretch(), clip=False)
        elif scale == 'log':
            # To use log scale we need to guarantee that vmin > 0, so that
            # we avoid division by zero and/or negative values.
            norm = LogNorm(vmin=max(vmin, sys.float_info.epsilon), vmax=vmax,
                           clip=True)
        else:
            raise ValueError("scale {} is not available.".format(scale))
    cax = ax.imshow(image, origin=origin, norm=norm, **kwargs)
    ax.set_xlabel(xlabel)
    ax.set_ylabel(ylabel)
    ax.set_title(title)
    if show_colorbar:
        cbar = plt.colorbar(cax, ax=ax, norm=norm, label=clabel)
        cbar.ax.yaxis.set_tick_params(tick1On=False, tick2On=False)
        cbar.ax.minorticks_off()
    return ax


class LightkurveWarning(Warning):
    """Class for all Lightkurve warnings."""
    pass


def suppress_stdout(f, *args, **kwargs):
    """A simple decorator to suppress function print outputs."""
    @wraps(f)
    def wrapper(*args, **kwargs):
        # redirect output to `null`
        with open(os.devnull, 'w') as devnull:
            old_out = sys.stdout
            sys.stdout = devnull
            try:
                return f(*args, **kwargs)
            # restore to default
            finally:
                sys.stdout = old_out
    return wrapper


def detect_filetype(header):
    """Returns Kepler and TESS file types given their primary header.

    This function will detect the file type by looking at both the TELESCOP and
    CREATOR keywords in the first extension of the FITS header. If the file is
    recognized as a Kepler or TESS data product, one of the following strings
    will be returned:

        * `'KeplerTargetPixelFile'`
        * `'TessTargetPixelFile'`
        * `'KeplerLightCurveFile'`
        * `'TessLightCurveFile'`

    If the file is not recognized as a Kepler or TESS data product, then
    `None` will be returned.

    Parameters
    ----------
    header : astropy.io.fits.Header object
        The primary header of a FITS file.

    Returns
    -------
    filetype : str or None
        A string describing the detected filetype. If the filetype is not
        recognized, `None` will be returned.
    """
    try:
        # use `telescop` keyword to determine mission
        # and `creator` to determine tpf or lc
        if 'TELESCOP' in header.keys():
            telescop = header['telescop'].lower()
        else:
            # Some old custom TESS data did not define the `TELESCOP` card
            telescop = header['mission'].lower()
        creator = header['creator'].lower()
        origin = header['origin'].lower()
        if telescop == 'kepler':
            # Kepler TPFs will contain "TargetPixelExporterPipelineModule"
            if 'targetpixel' in creator:
                return 'KeplerTargetPixelFile'
            # Kepler LCFs will contain "FluxExporter2PipelineModule"
            elif ('fluxexporter' in creator or 'lightcurve' in creator
                or 'lightcurve' in creator):
                return 'KeplerLightCurveFile'
        elif telescop == 'tess':
            # TESS TPFs will contain "TargetPixelExporterPipelineModule"
            if 'targetpixel' in creator:
                return 'TessTargetPixelFile'
            # TESS LCFs will contain "LightCurveExporterPipelineModule"
            elif 'lightcurve' in creator:
                return 'TessLightCurveFile'
            # Early versions of TESScut did not set a good CREATOR keyword
            elif 'stsci' in origin:
                return 'TessTargetPixelFile'
    # If the TELESCOP or CREATOR keywords don't exist we expect a KeyError;
    # if one of them is Undefined we expect `.lower()` to yield an AttributeError.
    except (KeyError, AttributeError):
        return None