/
targetpixelfile.py
1510 lines (1339 loc) · 65.9 KB
/
targetpixelfile.py
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"""Defines TargetPixelFile, KeplerTargetPixelFile, and TessTargetPixelFile."""
from __future__ import division
import datetime
import os
import warnings
import logging
from astropy.io import fits
from astropy.io.fits import Undefined
from astropy.nddata import Cutout2D
from astropy.table import Table
from astropy.wcs import WCS
from matplotlib import patches
import matplotlib.pyplot as plt
import numpy as np
from scipy.ndimage import label
from tqdm import tqdm
from astropy.coordinates import SkyCoord
from astropy.stats.funcs import median_absolute_deviation as MAD
from . import PACKAGEDIR, MPLSTYLE
from .lightcurve import KeplerLightCurve, TessLightCurve
from .prf import KeplerPRF
from .utils import KeplerQualityFlags, TessQualityFlags, \
plot_image, bkjd_to_astropy_time, btjd_to_astropy_time, \
LightkurveWarning, detect_filetype
__all__ = ['KeplerTargetPixelFile', 'TessTargetPixelFile']
log = logging.getLogger(__name__)
class TargetPixelFile(object):
"""Abstract class representing FITS files which contain time series imaging data.
You should probably not be using this abstract class directly;
see `KeplerTargetPixelFile` and `TessTargetPixelFile` instead.
"""
def __init__(self, path, quality_bitmask='default', targetid=None, **kwargs):
self.path = path
if isinstance(path, fits.HDUList):
self.hdu = path
else:
self.hdu = fits.open(self.path, **kwargs)
self.quality_bitmask = quality_bitmask
self.targetid = targetid
def __getitem__(self, key):
"""Implements indexing and slicing.
Note: the implementation below cannot be be simplified using
`copy[1].data = copy[1].data[self.quality_mask][key]`
due to the complicated behavior of AstroPy's `FITS_rec`.
"""
# Step 1: determine the indexes of the data to return.
# We start by determining the indexes of the good-quality cadences.
quality_idx = np.where(self.quality_mask)[0]
# Then we apply the index or slice to the good-quality indexes.
if isinstance(key, int):
# Ensure we always have a range; this is necessary to ensure
# that we always ge a `FITS_rec` instead of a `FITS_record` below.
if key == -1:
selected_idx = quality_idx[key:]
else:
selected_idx = quality_idx[key:key+1]
else:
selected_idx = quality_idx[key]
# Step 2: use the indexes to create a new copy of the data.
with warnings.catch_warnings():
# Ignore warnings about empty fields
warnings.simplefilter('ignore', UserWarning)
# AstroPy added `HDUList.copy()` in v3.1, but we don't want to make
# v3.1 a minimum requirement yet, so we copy in a funny way.
copy = fits.HDUList([myhdu.copy() for myhdu in self.hdu])
copy[1].data = copy[1].data[selected_idx]
return self.__class__(copy, quality_bitmask=self.quality_bitmask, targetid=self.targetid)
@property
def hdu(self):
return self._hdu
@hdu.setter
def hdu(self, value, keys=['FLUX', 'QUALITY']):
"""Verify the file format when setting the value of `self.hdu`.
Raises a ValueError if `value` does not appear to be a Target Pixel File.
"""
for key in keys:
if ~(np.any([value[1].header[ttype] == key
for ttype in value[1].header['TTYPE*']])):
raise ValueError("File {} does not have a {} column, "
"is this a target pixel file?".format(self.path, key))
else:
self._hdu = value
def get_keyword(self, keyword, hdu=0, default=None):
"""Returns a header keyword value.
If the keyword is Undefined or does not exist,
then return ``default`` instead.
"""
try:
kw = self.hdu[hdu].header[keyword]
except KeyError:
return default
if isinstance(kw, Undefined):
return default
return kw
@property
def header(self):
"""Returns the header of the primary extension."""
return self.hdu[0].header
@property
def ra(self):
"""Right Ascension of target ('RA_OBJ' header keyword)."""
return self.get_keyword('RA_OBJ')
@property
def dec(self):
"""Declination of target ('DEC_OBJ' header keyword)."""
return self.get_keyword('DEC_OBJ')
@property
def column(self):
"""CCD pixel column number ('1CRV5P' header keyword)."""
return self.get_keyword('1CRV5P', hdu=1, default=0)
@property
def row(self):
"""CCD pixel row number ('2CRV5P' header keyword)."""
return self.get_keyword('2CRV5P', hdu=1, default=0)
@property
def pos_corr1(self):
"""Returns the column position correction."""
return self.hdu[1].data['POS_CORR1'][self.quality_mask]
@property
def pos_corr2(self):
"""Returns the row position correction."""
return self.hdu[1].data['POS_CORR2'][self.quality_mask]
@property
def pipeline_mask(self):
"""Returns the optimal aperture mask used by the pipeline."""
# Both Kepler and TESS flag the pixels in the optimal aperture using
# bit number 2 in the aperture mask extension, e.g. see Section 6 of
# the TESS Data Products documentation (EXP-TESS-ARC-ICD-TM-0014.pdf).
try:
return self.hdu[2].data & 2 > 0
except TypeError: # Early versions of TESScut returned floats in HDU 2
return np.ones(self.hdu[2].data.shape, dtype=bool)
@property
def shape(self):
"""Return the cube dimension shape."""
return self.flux.shape
@property
def time(self):
"""Returns the time for all good-quality cadences."""
return self.hdu[1].data['TIME'][self.quality_mask]
@property
def cadenceno(self):
"""Return the cadence number for all good-quality cadences."""
cadenceno = self.hdu[1].data['CADENCENO'][self.quality_mask]
# The TESScut service returns an array of zeros as CADENCENO.
# If this is the case, return frame numbers from 0 instead.
if cadenceno[0] == 0:
return np.arange(0, len(cadenceno), 1, dtype=int)
return cadenceno
@property
def nan_time_mask(self):
"""Returns a boolean mask flagging cadences whose time is `nan`."""
return ~np.isfinite(self.time)
@property
def flux(self):
"""Returns the flux for all good-quality cadences."""
return self.hdu[1].data['FLUX'][self.quality_mask]
@property
def flux_err(self):
"""Returns the flux uncertainty for all good-quality cadences."""
return self.hdu[1].data['FLUX_ERR'][self.quality_mask]
@property
def flux_bkg(self):
"""Returns the background flux for all good-quality cadences."""
return self.hdu[1].data['FLUX_BKG'][self.quality_mask]
@property
def flux_bkg_err(self):
return self.hdu[1].data['FLUX_BKG_ERR'][self.quality_mask]
@property
def quality(self):
"""Returns the quality flag integer of every good cadence."""
return self.hdu[1].data['QUALITY'][self.quality_mask]
@property
def wcs(self):
"""Returns an astropy.wcs.WCS object with the World Coordinate System
solution for the target pixel file.
Returns
-------
w : astropy.wcs.WCS object
WCS solution
"""
if 'MAST' in self.hdu[0].header['ORIGIN']: # Is it a TessCut TPF?
# TPF's generated using the TESSCut service in early 2019 only appear
# to contain a valid WCS in the second extension (the aperture
# extension), so we treat such files as a special case.
return WCS(self.hdu[2])
else:
# For standard (Ames-pipeline-produced) TPF files, we use the WCS
# keywords provided in the first extension (the data table extension).
# Specifically, we use the WCS keywords for the 5th data column (FLUX).
wcs_keywords = {'1CTYP5': 'CTYPE1',
'2CTYP5': 'CTYPE2',
'1CRPX5': 'CRPIX1',
'2CRPX5': 'CRPIX2',
'1CRVL5': 'CRVAL1',
'2CRVL5': 'CRVAL2',
'1CUNI5': 'CUNIT1',
'2CUNI5': 'CUNIT2',
'1CDLT5': 'CDELT1',
'2CDLT5': 'CDELT2',
'11PC5': 'PC1_1',
'12PC5': 'PC1_2',
'21PC5': 'PC2_1',
'22PC5': 'PC2_2',
'NAXIS1': 'NAXIS1',
'NAXIS2': 'NAXIS2'}
mywcs = {}
for oldkey, newkey in wcs_keywords.items():
mywcs[newkey] = self.hdu[1].header[oldkey]
return WCS(mywcs)
@classmethod
def from_fits(cls, path_or_url, **kwargs):
"""WARNING: THIS FUNCTION IS DEPRECATED AND WILL BE REMOVED VERY SOON.
Please use `lightkurve.open()` instead.
"""
warnings.warn('`TargetPixelFile.from_fits()` is deprecated and will be '
'removed soon, please use `lightkurve.open()` instead.',
LightkurveWarning)
return cls(path_or_url, **kwargs)
def get_coordinates(self, cadence='all'):
"""Returns two 3D arrays of RA and Dec values in decimal degrees.
If cadence number is given, returns 2D arrays for that cadence. If
cadence is 'all' returns one RA, Dec value for each pixel in every cadence.
Uses the WCS solution and the POS_CORR data from TPF header.
Parameters
----------
cadence : 'all' or int
Which cadences to return the RA Dec coordinates for.
Returns
-------
ra : numpy array, same shape as tpf.flux[cadence]
Array containing RA values for every pixel, for every cadence.
dec : numpy array, same shape as tpf.flux[cadence]
Array containing Dec values for every pixel, for every cadence.
"""
w = self.wcs
X, Y = np.meshgrid(np.arange(self.shape[2]), np.arange(self.shape[1]))
pos_corr1_pix = np.copy(self.hdu[1].data['POS_CORR1'])
pos_corr2_pix = np.copy(self.hdu[1].data['POS_CORR2'])
# We zero POS_CORR* when the values are NaN or make no sense (>50px)
with warnings.catch_warnings(): # Comparing NaNs to numbers is OK here
warnings.simplefilter("ignore", RuntimeWarning)
bad = np.any([~np.isfinite(pos_corr1_pix),
~np.isfinite(pos_corr2_pix),
np.abs(pos_corr1_pix - np.nanmedian(pos_corr1_pix)) > 50,
np.abs(pos_corr2_pix - np.nanmedian(pos_corr2_pix)) > 50], axis=0)
pos_corr1_pix[bad], pos_corr2_pix[bad] = 0, 0
# Add in POSCORRs
X = (np.atleast_3d(X).transpose([2, 0, 1]) +
np.atleast_3d(pos_corr1_pix).transpose([1, 2, 0]))
Y = (np.atleast_3d(Y).transpose([2, 0, 1]) +
np.atleast_3d(pos_corr2_pix).transpose([1, 2, 0]))
# Pass through WCS
ra, dec = w.wcs_pix2world(X.ravel(), Y.ravel(), 1)
ra = ra.reshape((pos_corr1_pix.shape[0], self.shape[1], self.shape[2]))
dec = dec.reshape((pos_corr2_pix.shape[0], self.shape[1], self.shape[2]))
ra, dec = ra[self.quality_mask], dec[self.quality_mask]
if cadence is not 'all':
return ra[cadence], dec[cadence]
return ra, dec
def show_properties(self):
"""Prints a description of all non-callable attributes.
Prints in order of type (ints, strings, lists, arrays, others).
"""
attrs = {}
for attr in dir(self):
if not attr.startswith('_'):
res = getattr(self, attr)
if callable(res):
continue
if attr == 'hdu':
attrs[attr] = {'res': res, 'type': 'list'}
for idx, r in enumerate(res):
if idx == 0:
attrs[attr]['print'] = '{}'.format(r.header['EXTNAME'])
else:
attrs[attr]['print'] = '{}, {}'.format(attrs[attr]['print'],
'{}'.format(r.header['EXTNAME']))
continue
else:
attrs[attr] = {'res': res}
if isinstance(res, int):
attrs[attr]['print'] = '{}'.format(res)
attrs[attr]['type'] = 'int'
elif isinstance(res, np.ndarray):
attrs[attr]['print'] = 'array {}'.format(res.shape)
attrs[attr]['type'] = 'array'
elif isinstance(res, list):
attrs[attr]['print'] = 'list length {}'.format(len(res))
attrs[attr]['type'] = 'list'
elif isinstance(res, str):
if res == '':
attrs[attr]['print'] = '{}'.format('None')
else:
attrs[attr]['print'] = '{}'.format(res)
attrs[attr]['type'] = 'str'
elif attr == 'wcs':
attrs[attr]['print'] = 'astropy.wcs.wcs.WCS'.format(attr)
attrs[attr]['type'] = 'other'
else:
attrs[attr]['print'] = '{}'.format(type(res))
attrs[attr]['type'] = 'other'
output = Table(names=['Attribute', 'Description'], dtype=[object, object])
idx = 0
types = ['int', 'str', 'list', 'array', 'other']
for typ in types:
for attr, dic in attrs.items():
if dic['type'] == typ:
output.add_row([attr, dic['print']])
idx += 1
output.pprint(max_lines=-1, max_width=-1)
def to_lightcurve(self, method='aperture', **kwargs):
"""Performs photometry on the pixel data and returns a LightCurve object.
See the docstring of `aperture_photometry()` for valid
arguments if the method is 'aperture'. Otherwise, see the docstring
of `prf_photometry()` for valid arguments if the method is 'prf'.
Parameters
----------
method : 'aperture' or 'prf'.
Photometry method to use.
**kwargs : dict
Extra arguments to be passed to the `aperture_photometry` or the
`prf_photometry` method of this class.
Returns
-------
lc : LightCurve object
Object containing the resulting lightcurve.
"""
if method == 'aperture':
return self.extract_aperture_photometry(**kwargs)
elif method == 'prf':
return self.prf_lightcurve(**kwargs)
else:
raise ValueError("Photometry method must be 'aperture' or 'prf'.")
def _parse_aperture_mask(self, aperture_mask):
"""Parse the `aperture_mask` parameter as given by a user.
The `aperture_mask` parameter is accepted by a number of methods.
This method ensures that the parameter is always parsed in the same way.
Parameters
----------
aperture_mask : array-like, 'pipeline', 'all', 'threshold', or None
A boolean array describing the aperture such that `True` means
that the pixel will be used.
If None or 'all' are passed, all pixels will be used.
If 'pipeline' is passed, the mask suggested by the official pipeline
will be returned.
If 'threshold' is passed, all pixels brighter than 3-sigma above
the median flux will be used.
Returns
-------
aperture_mask : ndarray
2D boolean numpy array containing `True` for selected pixels.
"""
with warnings.catch_warnings():
# `aperture_mask` supports both arrays and string values; these yield
# uninteresting FutureWarnings when compared, so let's ignore that.
warnings.simplefilter(action='ignore', category=FutureWarning)
if aperture_mask is None or aperture_mask == 'all':
aperture_mask = np.ones((self.shape[1], self.shape[2]), dtype=bool)
elif aperture_mask == 'pipeline':
aperture_mask = self.pipeline_mask
elif aperture_mask == 'threshold':
aperture_mask = self.create_threshold_mask()
elif ((aperture_mask & 2) == 2).any(): # Kepler-pipeline style
aperture_mask = (aperture_mask & 2) == 2
self._last_aperture_mask = aperture_mask
return aperture_mask
def create_threshold_mask(self, threshold=3, reference_pixel='center'):
"""Returns an aperture mask creating using the thresholding method.
This method will identify the pixels in the TargetPixelFile which show
a median flux that is brighter than `threshold` times the standard
deviation above the overall median. The standard deviation is estimated
in a robust way by multiplying the Median Absolute Deviation (MAD)
with 1.4826.
If the thresholding method yields multiple contiguous regions, then
only the region closest to the (col, row) coordinate specified by
`reference_pixel` is returned. For exmaple, `reference_pixel=(0, 0)`
will pick the region closest to the bottom left corner.
By default, the region closest to the center of the mask will be
returned. If `reference_pixel=None` then all regions will be returned.
Parameters
----------
threshold : float
A value for the number of sigma by which a pixel needs to be
brighter than the median flux to be included in the aperture mask.
reference_pixel: (int, int) tuple, 'center', or None
(col, row) pixel coordinate closest to the desired region.
For example, use `reference_pixel=(0,0)` to select the region
closest to the bottom left corner of the target pixel file.
If 'center' (default) then the region closest to the center pixel
will be selected. If `None` then all regions will be selected.
Returns
-------
aperture_mask : ndarray
2D boolean numpy array containing `True` for pixels above the
threshold.
"""
if reference_pixel == 'center':
reference_pixel = (self.shape[1] / 2, self.shape[2] / 2)
# Calculate the median image
with warnings.catch_warnings():
warnings.simplefilter('ignore')
median_image = np.nanmedian(self.flux, axis=0)
vals = median_image[np.isfinite(median_image)].flatten()
# Calculate the theshold value in flux units
mad_cut = (1.4826 * MAD(vals) * threshold) + np.nanmedian(median_image)
# Create a mask containing the pixels above the threshold flux
threshold_mask = np.nan_to_num(median_image) > mad_cut
if reference_pixel is None:
# return all regions above threshold
return threshold_mask
else:
# Return only the contiguous region closest to `region`.
# First, label all the regions:
labels = label(threshold_mask)[0]
# For all pixels above threshold, compute distance to reference pixel:
label_args = np.argwhere(labels > 0)
distances = [np.hypot(crd[0], crd[1])
for crd in label_args - np.array([reference_pixel[1], reference_pixel[0]])]
# Which label corresponds to the closest pixel?
closest_arg = label_args[np.argmin(distances)]
closest_label = labels[closest_arg[0], closest_arg[1]]
return labels == closest_label
def centroids(self, **kwargs):
"""DEPRECATED: use `estimate_cdpp()` instead."""
warnings.warn('`TargetPixelFile.centroids()` is deprecated and will be '
'removed in Lightkurve v1.0.0, '
'please use `TargetPixelFile.estimate_centroids()` instead.',
LightkurveWarning)
return self.estimate_centroids(**kwargs)
def estimate_centroids(self, aperture_mask='pipeline'):
"""Returns centroid positions estimated using sample moments.
Parameters
----------
aperture_mask : array-like, 'pipeline', or 'all'
A boolean array describing the aperture such that `True` means
that the pixel will be used.
If None or 'all' are passed, all pixels will be used.
If 'pipeline' is passed, the mask suggested by the official pipeline
will be returned.
If 'threshold' is passed, all pixels brighter than 3-sigma above
the median flux will be used.
Returns
-------
col_centr, row_centr : tuple
Arrays containing centroids for column and row at each cadence
"""
aperture_mask = self._parse_aperture_mask(aperture_mask)
yy, xx = np.indices(self.shape[1:]) + 0.5
yy = self.row + yy
xx = self.column + xx
total_flux = np.nansum(self.flux[:, aperture_mask], axis=1)
with warnings.catch_warnings():
# RuntimeWarnings may occur below if total_flux contains zeros
warnings.simplefilter("ignore", RuntimeWarning)
col_centr = np.nansum(xx * aperture_mask * self.flux, axis=(1, 2)) / total_flux
row_centr = np.nansum(yy * aperture_mask * self.flux, axis=(1, 2)) / total_flux
return col_centr, row_centr
def plot(self, ax=None, frame=0, cadenceno=None, bkg=False, aperture_mask=None,
show_colorbar=True, mask_color='pink', style='lightkurve', **kwargs):
"""Plot the pixel data for a single frame (i.e. at a single time).
The time can be specified by frame index number (`frame=0` will show the
first frame) or absolute cadence number (`cadenceno`).
Parameters
----------
ax : matplotlib.axes._subplots.AxesSubplot
A matplotlib axes object to plot into. If no axes is provided,
a new one will be generated.
frame : int
Frame number. The default is 0, i.e. the first frame.
cadenceno : int, optional
Alternatively, a cadence number can be provided.
This argument has priority over frame number.
bkg : bool
If True, background will be added to the pixel values.
aperture_mask : ndarray or str
Highlight pixels selected by aperture_mask.
show_colorbar : bool
Whether or not to show the colorbar
mask_color : str
Color to show the aperture mask
style : str
Path or URL to a matplotlib style file, or name of one of
matplotlib's built-in stylesheets (e.g. 'ggplot').
Lightkurve's custom stylesheet is used by default.
kwargs : dict
Keywords arguments passed to `lightkurve.utils.plot_image`.
Returns
-------
ax : matplotlib.axes._subplots.AxesSubplot
The matplotlib axes object.
"""
if style == 'lightkurve' or style is None:
style = MPLSTYLE
if cadenceno is not None:
try:
frame = np.argwhere(cadenceno == self.cadenceno)[0][0]
except IndexError:
raise ValueError("cadenceno {} is out of bounds, "
"must be in the range {}-{}.".format(
cadenceno, self.cadenceno[0], self.cadenceno[-1]))
try:
if bkg and np.any(np.isfinite(self.flux_bkg[frame])):
pflux = self.flux[frame] + self.flux_bkg[frame]
else:
pflux = self.flux[frame]
except IndexError:
raise ValueError("frame {} is out of bounds, must be in the range "
"0-{}.".format(frame, self.shape[0]))
with plt.style.context(style):
img_title = 'Target ID: {}'.format(self.targetid)
img_extent = (self.column, self.column + self.shape[2],
self.row, self.row + self.shape[1])
ax = plot_image(pflux, ax=ax, title=img_title, extent=img_extent,
show_colorbar=show_colorbar, **kwargs)
ax.grid(False)
if aperture_mask is not None:
aperture_mask = self._parse_aperture_mask(aperture_mask)
for i in range(self.shape[1]):
for j in range(self.shape[2]):
if aperture_mask[i, j]:
ax.add_patch(patches.Rectangle((j+self.column, i+self.row),
1, 1, color=mask_color, fill=True,
alpha=.6))
return ax
def to_fits(self, output_fn=None, overwrite=False):
"""Writes the TPF to a FITS file on disk."""
if output_fn is None:
output_fn = "{}-targ.fits".format(self.targetid)
self.hdu.writeto(output_fn, overwrite=overwrite, checksum=True)
def interact(self, notebook_url='localhost:8888', max_cadences=30000,
aperture_mask='pipeline', exported_filename=None):
"""Display an interactive Jupyter Notebook widget to inspect the pixel data.
The widget will show both the lightcurve and pixel data. By default,
the lightcurve shown is obtained by calling the `to_lightcurve()` method,
unless the user supplies a custom `LightCurve` object.
This feature requires an optional dependency, bokeh (v0.12.15 or later).
This dependency can be installed using e.g. `conda install bokeh`.
At this time, this feature only works inside an active Jupyter
Notebook, and tends to be too slow when more than ~30,000 cadences
are contained in the TPF (e.g. short cadence data).
Parameters
----------
notebook_url: str
Location of the Jupyter notebook page (default: "localhost:8888")
When showing Bokeh applications, the Bokeh server must be
explicitly configured to allow connections originating from
different URLs. This parameter defaults to the standard notebook
host and port. If you are running on a different location, you
will need to supply this value for the application to display
properly. If no protocol is supplied in the URL, e.g. if it is
of the form "localhost:8888", then "http" will be used.
max_cadences : int
Raise a RuntimeError if the number of cadences shown is larger than
this value. This limit helps keep browsers from becoming unresponsive.
aperture_mask : array-like, 'pipeline', 'threshold' or 'all'
A boolean array describing the aperture such that `True` means
that the pixel will be used.
If None or 'all' are passed, all pixels will be used.
If 'pipeline' is passed, the mask suggested by the official pipeline
will be returned.
If 'threshold' is passed, all pixels brighter than 3-sigma above
the median flux will be used.
exported_filename: str
An optional filename to assign to exported fits files containing
the custom aperture mask generated by clicking on pixels in interact.
The default adds a suffix '-custom-aperture-mask.fits' to the
TargetPixelFile basename.
"""
from .interact import show_interact_widget
return show_interact_widget(self, notebook_url=notebook_url,
max_cadences=max_cadences,
aperture_mask=aperture_mask,
exported_filename=exported_filename)
def interact_sky(self, notebook_url='localhost:8888', magnitude_limit=18):
"""Display a Jupyter Notebook widget showing Gaia DR2 positions on top of the pixels.
Parameters
----------
notebook_url: str
Location of the Jupyter notebook page (default: "localhost:8888")
When showing Bokeh applications, the Bokeh server must be
explicitly configured to allow connections originating from
different URLs. This parameter defaults to the standard notebook
host and port. If you are running on a different location, you
will need to supply this value for the application to display
properly. If no protocol is supplied in the URL, e.g. if it is
of the form "localhost:8888", then "http" will be used.
magnitude_limit : float
A value to limit the results in based on Gaia Gmag. Default, 18.
"""
from .interact import show_skyview_widget
return show_skyview_widget(self, notebook_url=notebook_url,
magnitude_limit=magnitude_limit)
def to_corrector(self, method="pld"):
"""Returns a `Corrector` instance to remove systematics.
Parameters
----------
methods : string
Currently, only "pld" is supported. This will return a
`PLDCorrector` class instance.
Returns
-------
correcter : `lightkurve.Correcter`
Instance of a Corrector class, which typically provides `correct()`
and `diagnose()` methods.
"""
allowed_methods = ["pld"]
if method == "sff":
raise ValueError("The 'sff' method requires a `LightCurve` instead "
"of a `TargetPixelFile` object. Use `to_lightcurve()` "
"to obtain a `LightCurve` first.")
if method not in allowed_methods:
raise ValueError(("Unrecognized method '{0}'\n"
"allowed methods are: {1}")
.format(method, allowed_methods))
if method == "pld":
from .correctors import PLDCorrector
return PLDCorrector(self)
class KeplerTargetPixelFile(TargetPixelFile):
"""Represents pixel data products created by NASA's Kepler pipeline.
This class enables extraction of custom light curves and centroid positions.
Parameters
----------
path : str or `astropy.io.fits.HDUList`
Path to a Kepler Target Pixel (FITS) File or a `HDUList` object.
quality_bitmask : str or int
Bitmask (integer) which identifies the quality flag bitmask that should
be used to mask out bad cadences. If a string is passed, it has the
following meaning:
* "none": no cadences will be ignored (`quality_bitmask=0`).
* "default": cadences with severe quality issues will be ignored
(`quality_bitmask=1130799`).
* "hard": more conservative choice of flags to ignore
(`quality_bitmask=1664431`). This is known to remove good data.
* "hardest": removes all data that has been flagged
(`quality_bitmask=2096639`). This mask is not recommended.
See the :class:`KeplerQualityFlags` class for details on the bitmasks.
kwargs : dict
Keyword arguments passed to `astropy.io.fits.open()`.
References
----------
.. [1] Kepler: A Search for Terrestrial Planets. Kepler Archive Manual.
http://archive.stsci.edu/kepler/manuals/archive_manual.pdf
"""
def __init__(self, path, quality_bitmask='default', **kwargs):
super(KeplerTargetPixelFile, self).__init__(path,
quality_bitmask=quality_bitmask,
**kwargs)
self.quality_mask = KeplerQualityFlags.create_quality_mask(
quality_array=self.hdu[1].data['QUALITY'],
bitmask=quality_bitmask)
# check to make sure the correct filetype has been provided
filetype = detect_filetype(self.header)
if filetype == 'TessTargetPixelFile':
warnings.warn("A TESS data product is being opened using the "
"`KeplerTargetPixelFile` class. "
"Please use `TessTargetPixelFile` instead.",
LightkurveWarning)
elif filetype is None:
warnings.warn("File header not recognized as Kepler or TESS "
"observation.", LightkurveWarning)
# Use the KEPLERID keyword as the default targetid
if self.targetid is None:
try:
self.targetid = self.header['KEPLERID']
except KeyError:
pass
@staticmethod
def from_archive(target, cadence='long', quarter=None, month=None,
campaign=None, quality_bitmask='default', **kwargs):
"""WARNING: THIS FUNCTION IS DEPRECATED AND WILL BE REMOVED VERY SOON.
Use `lightkurve.search_targetpixelfile()` instead.
Parameters
----------
target : str or int
KIC/EPIC ID or object name.
cadence : str
'long' or 'short'.
quarter, campaign : int, list of ints, or 'all'
Kepler Quarter or K2 Campaign number.
month : 1, 2, 3, list or 'all'
For Kepler's prime mission, there are three short-cadence
Target Pixel Files for each quarter, each covering one month.
Hence, if cadence='short' you need to specify month=1, 2, or 3.
quality_bitmask : str or int
Bitmask (integer) which identifies the quality flag bitmask that should
be used to mask out bad cadences. If a string is passed, it has the
following meaning:
* "none": no cadences will be ignored (`quality_bitmask=0`).
* "default": cadences with severe quality issues will be ignored
(`quality_bitmask=1130799`).
* "hard": more conservative choice of flags to ignore
(`quality_bitmask=1664431`). This is known to remove good data.
* "hardest": removes all data that has been flagged
(`quality_bitmask=2096639`). This mask is not recommended.
See the :class:`KeplerQualityFlags` class for details on the bitmasks.
kwargs : dict
Keywords arguments passed to the constructor of
:class:`KeplerTargetPixelFile`.
Returns
-------
tpf : :class:`KeplerTargetPixelFile` or :class:`TargetPixelFileCollection` object.
"""
warnings.warn('`TargetPixelFile.from_archive` is deprecated and will be removed soon, '
'please use `lightkurve.search_targetpixelfile()` instead.',
LightkurveWarning)
if os.path.exists(str(target)) or str(target).startswith('http'):
log.warning('Warning: from_archive() is not intended to accept a '
'direct path, use KeplerTargetPixelFile(path) instead.')
return KeplerTargetPixelFile(target)
else:
from .search import search_targetpixelfile
sr = search_targetpixelfile(target, cadence=cadence,
quarter=quarter, month=month,
campaign=campaign)
if len(sr) == 1:
return sr.download(quality_bitmask=quality_bitmask, **kwargs)
elif len(sr) > 1:
return sr.download_all(quality_bitmask=quality_bitmask, **kwargs)
else:
raise ValueError("No target pixel files found that match the search criteria.")
def __repr__(self):
return('KeplerTargetPixelFile Object (ID: {})'.format(self.targetid))
def get_prf_model(self):
"""Returns an object of KeplerPRF initialized using the
necessary metadata in the tpf object.
Returns
-------
prf : instance of SimpleKeplerPRF
"""
return KeplerPRF(channel=self.channel, shape=self.shape[1:],
column=self.column, row=self.row)
@property
def obsmode(self):
"""'short cadence' or 'long cadence'. ('OBSMODE' header keyword)"""
return self.get_keyword('OBSMODE')
@property
def module(self):
"""Kepler CCD module number. ('MODULE' header keyword)"""
return self.get_keyword('MODULE')
@property
def output(self):
"""Kepler CCD module output number. ('OUTPUT' header keyword)"""
return self.get_keyword('OUTPUT')
@property
def channel(self):
"""Kepler CCD channel number. ('CHANNEL' header keyword)"""
return self.get_keyword('CHANNEL')
@property
def astropy_time(self):
"""Returns an AstroPy Time object for all good-quality cadences."""
return bkjd_to_astropy_time(bkjd=self.time)
@property
def quarter(self):
"""Kepler quarter number. ('QUARTER' header keyword)"""
return self.get_keyword('QUARTER')
@property
def campaign(self):
"""K2 Campaign number. ('CAMPAIGN' header keyword)"""
return self.get_keyword('CAMPAIGN')
@property
def mission(self):
"""'Kepler' or 'K2'. ('MISSION' header keyword)"""
return self.get_keyword('MISSION')
def extract_aperture_photometry(self, aperture_mask='pipeline'):
"""Returns a LightCurve obtained using aperture photometry.
Parameters
----------
aperture_mask : array-like, 'pipeline', 'threshold' or 'all'
A boolean array describing the aperture such that `True` means
that the pixel will be used.
If None or 'all' are passed, all pixels will be used.
If 'pipeline' is passed, the mask suggested by the official pipeline
will be returned.
If 'threshold' is passed, all pixels brighter than 3-sigma above
the median flux will be used.
Returns
-------
lc : KeplerLightCurve object
Array containing the summed flux within the aperture for each
cadence.
"""
aperture_mask = self._parse_aperture_mask(aperture_mask)
if aperture_mask.sum() == 0:
log.warning('Warning: aperture mask contains zero pixels.')
centroid_col, centroid_row = self.estimate_centroids(aperture_mask)
# Ignore warnings related to zero or negative errors
with warnings.catch_warnings():
warnings.simplefilter("ignore", RuntimeWarning)
flux_err = np.nansum(self.flux_err[:, aperture_mask]**2, axis=1)**0.5
keys = {'centroid_col': centroid_col,
'centroid_row': centroid_row,
'quality': self.quality,
'channel': self.channel,
'campaign': self.campaign,
'quarter': self.quarter,
'mission': self.mission,
'cadenceno': self.cadenceno,
'ra': self.ra,
'dec': self.dec,
'label': self.header['OBJECT'],
'targetid': self.targetid}
return KeplerLightCurve(time=self.time,
time_format='bkjd',
time_scale='tdb',
flux=np.nansum(self.flux[:, aperture_mask], axis=1),
flux_err=flux_err,
**keys)
def get_bkg_lightcurve(self, aperture_mask=None):
aperture_mask = self._parse_aperture_mask(aperture_mask)
# Ignore warnings related to zero or negative errors
with warnings.catch_warnings():
warnings.simplefilter("ignore", RuntimeWarning)
flux_bkg_err = np.nansum(self.flux_bkg_err[:, aperture_mask]**2, axis=1)**0.5
keys = {'quality': self.quality,
'channel': self.channel,
'campaign': self.campaign,
'quarter': self.quarter,
'mission': self.mission,
'cadenceno': self.cadenceno,
'ra': self.ra,
'dec': self.dec,
'label': self.header['OBJECT'],
'targetid': self.targetid}
return KeplerLightCurve(time=self.time,
time_format='bkjd',
time_scale='tdb',
flux=np.nansum(self.flux_bkg[:, aperture_mask], axis=1),
flux_err=flux_bkg_err,
**keys)
def get_model(self, star_priors=None, **kwargs):
"""Returns a default `TPFModel` object for PRF fitting.
The default model only includes one star and only allows its flux
and position to change. A different set of stars can be added using
the `star_priors` parameter.
Parameters
----------
**kwargs : dict
Arguments to be passed to the `TPFModel` constructor, e.g.
`star_priors`.
Returns
-------
model : TPFModel object
Model with appropriate defaults for this Target Pixel File.
"""
from .prf import TPFModel, StarPrior, BackgroundPrior
from .prf import UniformPrior, GaussianPrior
# Set up the model
if 'star_priors' not in kwargs:
centr_col, centr_row = self.estimate_centroids()
star_priors = [StarPrior(col=GaussianPrior(mean=np.nanmedian(centr_col),
var=np.nanstd(centr_col)**2),
row=GaussianPrior(mean=np.nanmedian(centr_row),
var=np.nanstd(centr_row)**2),
flux=UniformPrior(lb=0.5*np.nanmax(self.flux[0]),
ub=2*np.nansum(self.flux[0]) + 1e-10),
targetid=self.targetid)]
kwargs['star_priors'] = star_priors
if 'prfmodel' not in kwargs:
kwargs['prfmodel'] = self.get_prf_model()
if 'background_prior' not in kwargs:
if np.all(np.isnan(self.flux_bkg)): # If TargetPixelFile has no background flux data
# Use the median of the lower half of flux as an estimate for flux_bkg
clipped_flux = np.ma.masked_where(self.flux > np.percentile(self.flux, 50),
self.flux)
flux_prior = GaussianPrior(mean=np.ma.median(clipped_flux),
var=np.ma.std(clipped_flux)**2)
else:
flux_prior = GaussianPrior(mean=np.nanmedian(self.flux_bkg),
var=np.nanstd(self.flux_bkg)**2)
kwargs['background_prior'] = BackgroundPrior(flux=flux_prior)
return TPFModel(**kwargs)
def extract_prf_photometry(self, cadences=None, parallel=True, **kwargs):
"""Returns the results of PRF photometry applied to the pixel file.
Parameters
----------
cadences : list of int
Cadences to fit. If `None` (default) then all cadences will be fit.
parallel : bool
If `True`, fitting cadences will be distributed across multiple
cores using Python's `multiprocessing` module.
**kwargs : dict
Keywords to be passed to `tpf.get_model()` to create the
`TPFModel` object that will be fit.
Returns