/
utils.py
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/
utils.py
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"""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',
'channel_to_module_output', 'module_output_to_channel',
'running_mean']
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())
elif scale == 'sqrt':
norm = ImageNormalize(vmin=vmin, vmax=vmax, stretch=SqrtStretch())
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
telescop = header['telescop'].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:
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