"""Defines Corrector classes that utilize Kepler/K2/TESS Cotrending Basis Vectors.
"""
import logging
import copy
import requests
import urllib.request
import glob
import os
import warnings
from astropy.io import fits as pyfits
from astropy.table import Table
from astropy.time import Time
from astropy.timeseries import TimeSeries
from astropy.units import Quantity, Unit, UnitsWarning
from astropy.utils.decorators import deprecated
from astropy.utils.masked import Masked
from bs4 import BeautifulSoup
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import PchipInterpolator
from sklearn import linear_model
from scipy.optimize import minimize_scalar
from .designmatrix import DesignMatrix, DesignMatrixCollection
from .. import MPLSTYLE
from ..lightcurve import LightCurve
from ..utils import channel_to_module_output, validate_method, LightkurveDeprecationWarning
from ..search import search_lightcurve
from .regressioncorrector import RegressionCorrector
from ..collections import LightCurveCollection
from .metrics import overfit_metric_lombscargle, underfit_metric_neighbors, MinTargetsError
log = logging.getLogger(__name__)
__all__ = ['CBVCorrector', 'CotrendingBasisVectors', 'KeplerCotrendingBasisVectors',
'TessCotrendingBasisVectors', 'load_kepler_cbvs','load_tess_cbvs',
'download_kepler_cbvs', 'download_tess_cbvs']
#*******************************************************************************
# CBV Corrector Class
[docs]class CBVCorrector(RegressionCorrector):
"""Class for removing systematics using Cotrending Basis Vectors (CBVs)
from Kepler/K2/TESS.
On construction of this object, the relevant CBVs will be downloaded from
MAST appropriate for the lightcurve object passed to the constructor.
For TESS there are multiple CBV types. All are loaded and the user must
specify which to use in the correction.
Attributes
----------
lc : LightCurve
The light curve loaded into CBVCorrector in electrons / second
cbvs : CotrendingBasisVectors list
The retrieved CBVs, can contain multiple types of CBVs
interpolated_cbvs : bool
If true then the CBVs have been interpolated to the lightcurve
cbv_design_matrix : DesignMatrix
The retrieved CBVs ported into a DesignMatrix object
extra_design_matrix : DesignMatrix
An extra design matrix to include in the fit with the CBVs
design_matrix_collection : DesignMatrixCollection
The design matrix collection composed of cbv_design_matrix and extra_design_matrix
corrected_lc : LightCurve
The returned light curve from correct() in electrons / second
coefficients : float ndarray
The fit coefficients corresponding to the design_matrix_collection
coefficients_err : float ndarray
The error estimates for the coefficients, see regressioncorrector
model_lc : LightCurve
The model fit to the lightcurve 'lc'
diagnostic_lightcurves : dict
Model fits for each of the sub design matrices fit in model_lc
lc_neighborhood : LightCurveCollection
SPOC SAP light curves of all targets within the defined neighborhood of the
target under study for use with the under-fitting metric
lc_neighborhood_flux : list of arrays
Neighboring target flux aligned or interpolated to the target under
study cadence
cadence_mask : np.ndarray of bool
Mask, where True indicates a cadence that was used in
RegressionCorrector.correct.
Note: The saved cadence_mask is overwritten for each call to correct().
over_fitting_score : float
Over-fitting score from the most recent run of correct()
under_fitting_score : float
Under-fitting score from the most recent run of correct()
alpha : float
L2-norm regularization term used in most recent fit
Equivalent to: designmatrix prior sigma = np.median(self.lc.flux_err) / np.sqrt(alpha)
"""
[docs] def __init__(self, lc, interpolate_cbvs=False, extrapolate_cbvs=False, do_not_load_cbvs=False,
cbv_dir=None):
"""Constructor
This constructor will retrieve all relevant CBVs from MAST and then
align or interpolate them with the passed-in light curve.
Parameters
----------
lc : LightCurve
The light curve to correct
interpolate_cbvs : bool
By default, the cbvs will be 'aligned' to the lightcurve. If you
wish to interpolate the cbvs instead then set this to True.
Uses Piecewise Cubic Hermite Interpolating Polynomial (PCHIP).
extrapolate_cbvs : bool
Set to True if the CBVs also have to be extrapolated outside their time
stamp range. (If False then those cadences are filled with NaNs.)
do_not_load_cbvs : bool
If True then the CBVs will NOT be loaded from MAST.
Use this option if you wish to use the CBV corrector methods with only a
custom design matrix (via the ext_dm argument in the corrector methods)
cbv_dir : str
Path to specific directory holding TESS CBVs. If this is None, will query
MAST by default.
"""
if not isinstance(lc, LightCurve):
raise Exception('<lc> must be a LightCurve class')
assert lc.flux.unit==Unit('electron / second'), \
'cbvCorrector expects light curve to be passed in e-/s units.'
if extrapolate_cbvs and (extrapolate_cbvs != interpolate_cbvs):
raise Exception('interpolate_cbvs must be True if extrapolate_cbvs is True')
# We do not want any NaNs
lc = lc.remove_nans()
# Call the RegresssionCorrector Constructor
super(CBVCorrector, self).__init__(lc)
#***
# Retrieve all relevant CBVs from either MAST or a local directory
cbvs = []
if (not do_not_load_cbvs):
if self.lc.mission == 'Kepler':
cbvs.append(load_kepler_cbvs(cbv_dir=cbv_dir,mission=self.lc.mission, quarter=self.lc.quarter,
channel=self.lc.channel))
elif self.lc.mission == 'K2':
cbvs.append(load_kepler_cbvs(cbv_dir=cbv_dir,mission=self.lc.mission, campaign=self.lc.campaign,
channel=self.lc.channel))
elif self.lc.mission == 'TESS':
# For TESS we load multiple CBV types
# Single-Scale
cbvs.append(load_tess_cbvs(cbv_dir=cbv_dir,sector=self.lc.sector,
camera=self.lc.camera, ccd=self.lc.ccd, cbv_type='SingleScale'))
# Multi-Scale
# Although there has always been 3 bands, there could be more,
# continue to load more bands until no more are left to load
iBand = int(0)
moreData = True
while moreData:
iBand += 1
cbvObj = load_tess_cbvs(cbv_dir=cbv_dir,sector=self.lc.sector,
camera=self.lc.camera, ccd=self.lc.ccd, cbv_type='MultiScale',
band=iBand)
if (cbvObj.band == iBand):
cbvs.append(cbvObj)
else:
moreData = False
# Spike
cbvs.append(load_tess_cbvs(cbv_dir=cbv_dir,sector=self.lc.sector,
camera=self.lc.camera, ccd=self.lc.ccd, cbv_type='Spike'))
else:
raise ValueError('Unknown mission type')
for idx in np.arange(len(cbvs)):
if (not isinstance(cbvs[idx], CotrendingBasisVectors)):
raise Exception('CBVs could not be loaded. CBVCorrector must exit')
# Set the CBV time format units to the lightcurve time format units
for idx in np.arange(len(cbvs)):
# astropy.time.Time makes this easy!
cbvs[idx].time.format = lc.time.format
# Align or interpolate the CBVs with the lightcurve flux using the cadence numbers
for idx in np.arange(len(cbvs)):
if interpolate_cbvs:
cbvs[idx] = cbvs[idx].interpolate(self.lc, extrapolate=extrapolate_cbvs)
else:
cbvs[idx] = cbvs[idx].align(self.lc)
self.cbvs = cbvs
self.interpolated_cbvs = interpolate_cbvs
self.extrapolated_cbvs = extrapolate_cbvs
# Initialize all extra attributes to None
self.cbv_design_matrix = None
self.extra_design_matrix = None
self.design_matrix_collection = None
self.corrected_lc = None
self.coefficients = None
self.coefficients_err = None
self.model_lc = None
self.diagnostic_lightcurves = None
self.lc_neighborhood = None
self.lc_neighborhood_flux = None
self.cadence_mask = None
self.over_fitting_score = None
self.under_fitting_score = None
self.alpha = None
def correct_gaussian_prior(self, cbv_type=['SingleScale'],
cbv_indices=[np.arange(1,9)],
alpha=1e-20, ext_dm=None, cadence_mask=None, **kwargs):
""" Performs the correction using RegressionCorrector methods.
This method will assemble the full design matrix collection composed of
cbv_design_matrix and extra_design_matrix (ext_dm). It then uses the
alpha L2-Norm (Ridge Regression) penalty term to set the width on the
design matrix priors. Then uses the super-class
RegressionCorrector.correct to perform the correction.
The relation between the L2-Norm alpha term and the Gaussian prior sigma
is:
alpha = flux_sigma^2 / sigma^2
By default this method will use the first 8 "SingleScale" basis vectors.
Parameters
----------
cbv_type : str list
List of CBV types to use
cbv_indices : list of lists
List of CBV vectors to use in each passed cbv_type. {'ALL' => Use all}
NOTE: 1-Based indexing!
alpha : float
L2-norm regularization penatly term. Default = 1e-20
{0 => no regularization}
ext_dm : `.DesignMatrix` or `.DesignMatrixCollection`
Optionally pass an extra design matrix to also be used in the fit
cadence_mask : np.ndarray of bools (optional)
Mask, where True indicates a cadence that should be used.
**kwargs : dict
Additional keyword arguments passed to
`RegressionCorrector.correct`.
Returns
-------
`.LightCurve`
Corrected light curve, with noise removed. In units of electrons / second
Examples
--------
The following example will perform the correction using the
SingleScale and Spike basis vectors with a weak regularization alpha
term of 0.1. It also adds in an external design matrix to perfomr a
joint fit.
>>> cbv_type = ['SingleScale', 'Spike']
>>> cbv_indices = [np.arange(1,9), 'ALL']
>>> corrected_lc = cbvCorrector.correct_gaussian_prior(cbv_type=cbv_type, # doctest: +SKIP
>>> cbv_indices=cbv_indices, alpha=0.1, # doctest: +SKIP
>>> ext_dm=design_matrix ) # doctest: +SKIP
"""
# Perform all the preparatory stuff common to all correct methods
self._correct_initialization(cbv_type=cbv_type,
cbv_indices=cbv_indices, ext_dm=ext_dm)
# Add in a width to the Gaussian priors
# alpha = flux_sigma^2 / sigma^2
if (alpha == 0.0):
sigma = None
else:
sigma = np.median(self.lc.flux_err.value) / np.sqrt(np.abs(alpha))
self._set_prior_width(sigma)
# Use RegressionCorrector.correct for the actual fitting
self.correct_regressioncorrector(self.design_matrix_collection,
cadence_mask=cadence_mask, **kwargs)
self.alpha = alpha
return self.corrected_lc
def correct_elasticnet(self, cbv_type='SingleScale', cbv_indices=np.arange(1,9),
alpha=1e-20, l1_ratio=0.01, ext_dm=None, cadence_mask=None, **kwargs):
""" Performs the correction using scikit-learn's ElasticNet which
utilizes combined L1- and L2-Norm priors as a regularizer.
This method will assemble the full design matrix collection composed of
cbv_design_matrix and extra_design_matrix (ext_dm). Then uses
scikit-learn.linear_model.ElasticNet to perform the correction.
By default this method will use the first 8 "SingleScale" basis vectors.
This method will preserve the median value of the light curve flux.
Note that the alpha term in scikit-learn's ElasticNet does not have the
same scaling as when used in CBVCorrector.correct_gaussian_prior or
CBVCorrector.correct. Do not assume similar results with a
similar alpha value.
Parameters
----------
cbv_type : str list
List of CBV types to use
cbv_indices : list of lists
List of CBV vectors to use in each passed cbv_type. {'ALL' => Use all}
NOTE: 1-Based indexing!
alpha : float
L2-norm regularization pentaly term.
{0 => no regularization}
l1_ratio : float
Elastic-Net mixing parameter
l1_ratio = 0 => L2 penalty (Ridge). l1_ratio = 1 => L1 penalty (Lasso).
ext_dm : `.DesignMatrix` or `.DesignMatrixCollection`
Optionally pass an extra design matrix to also be used in the fit
cadence_mask : np.ndarray of bools (optional)
Mask, where True indicates a cadence that should be used.
**kwargs : dict
Additional keyword arguments passed to
`sklearn.linear_model.ElasticNet`.
Returns
-------
`.LightCurve`
Corrected light curve, with noise removed. In units of electrons / second
Examples
--------
The following example will perform the ElasticNet correction using the
SingleScale and Spike basis vectors with a strong regualrization alpha
term of 1.0 and an L1 ratio of 0.9 which means predominantly a Lasso
regularization but with a slight amount of Ridge Regression.
>>> cbv_type = ['SingleScale', 'Spike']
>>> cbv_indices = [np.arange(1,9), 'ALL']
>>> corrected_lc = cbvCorrector.correct_elasticnet(cbv_type=cbv_type, # doctest: +SKIP
>>> cbv_indices=cbv_indices, alpha=1.0, l1_ratio=0.9) # doctest: +SKIP
"""
# Perform all the preparatory stuff common to all correct methods
self._correct_initialization(cbv_type=cbv_type,
cbv_indices=cbv_indices, ext_dm=ext_dm)
# Default cadence mask
if cadence_mask is None:
cadence_mask = np.ones(len(self.lc.flux), bool)
# Use Scikit-learn ElasticNet
self.regressor = linear_model.ElasticNet(alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=False, **kwargs)
X = self.design_matrix_collection.values
y = self.lc.flux
# Set mask
# note: ElasticNet has no internal way to do this so we have to just
# remove the cadences from X and y
XMasked = X.copy()
yMasked = y.copy()
XMasked = XMasked[cadence_mask,:]
yMasked = yMasked[cadence_mask]
# Perform the ElasticNet fit
self.regressor.fit(XMasked, yMasked)
# Finishing work
# When creating the model do not include the constant
model_flux = np.dot(X[:,0:-1], self.regressor.coef_[0:-1])
model_flux -= np.median(model_flux)
# TODO: Propagation of uncertainties. They really do not change much.
model_err = np.zeros(len(model_flux))
self.coefficients = self.regressor.coef_
self.model_lc = LightCurve(time=self.lc.time,
flux=model_flux*self.lc.flux.unit,
flux_err=model_err*self.lc.flux_err.unit)
self.corrected_lc = self.lc.copy()
self.corrected_lc.flux = self.lc.flux - self.model_lc.flux
self.corrected_lc.flux_err = (self.lc.flux_err**2 + model_err**2)**0.5
self.diagnostic_lightcurves = self._create_diagnostic_lightcurves()
self.cadence_mask = cadence_mask
self.alpha = alpha
return self.corrected_lc
[docs] def correct(self, cbv_type=['SingleScale'],
cbv_indices=[np.arange(1,9)],
ext_dm=None, cadence_mask=None, alpha_bounds=[1e-4,1e4],
target_over_score=0.5, target_under_score=0.5, max_iter=100):
""" Optimizes the correction by adjusting the L2-Norm (Ridge Regression)
regularization penalty term, alpha, based on the introduced noise
(over-fitting) and residual correlation (under-fitting) goodness
metrics. The numercial optimization is performed using the
scipy.optimize.minimize_scalar Brent's method.
The optimizer attempts to maximize the over- and under-fitting goodness
metrics. However, once the target_over_score or target_under_score is
reached, a "Leaky ReLU" is used so that the optimization "pressure"
concentrates on the other metric until both metrics rise above their
respective target scores, instead of driving a single metric to near
1.0.
The optimization parameters used are stored in self.optimization_params
as a record of how the optimization was performed.
The optimized correction is performed using LightKurve's
RegressionCorrector methods. See correct_gaussian_prior for details.
Parameters
----------
cbv_type : str list
List of CBV types to use in correction {'ALL' => Use all}
cbv_indices : list of lists
List of CBV vectors to use in each of cbv_type passed. {'ALL' => Use all}
NOTE: 1-Based indexing!
ext_dm : `.DesignMatrix` or `.DesignMatrixCollection`
Optionally pass an extra design matrix to also be used in the fit
cadence_mask : np.ndarray of bools (optional)
Mask, where True indicates a cadence that should be used.
alpha_bounds : float list(len=2)
upper anbd lowe bounds for alpha
target_over_score : float
Target Over-fitting metric score
target_under_score : float
Target under-fitting metric score
max_iter : int
Maximum number of iterations to optimize goodness metrics
Returns
-------
`.LightCurve`
Corrected light curve, with noise removed. In units of electrons / second
Examples
--------
The following example will perform the correction using the
SingleScale and Spike basis vectors. It will use alpha bounds of
[1.0,1e3]. The target over-fitting score is 0.5 and the target
under-fitting score is 0.8.
>>> cbv_type = ['SingleScale', 'Spike']
>>> cbv_indices = [np.arange(1,9), 'ALL']
>>> cbvCorrector.correct(cbv_type=cbv_type, cbv_indices=cbv_indices, # doctest: +SKIP
>>> alpha_bounds=[1.0,1e3], # doctest: +SKIP
>>> target_over_score=0.5, target_under_score=0.8) # doctest: +SKIP
"""
# Perform all the preparatory stuff common to all correct methods
self._correct_initialization(cbv_type=cbv_type,
cbv_indices=cbv_indices, ext_dm=ext_dm)
# Create a dictionary for optimization parameters to easily pass to the
# objective function, and also to save for posterity
self.optimization_params = {'alpha_bounds': alpha_bounds,
'target_over_score': target_over_score,
'target_under_score': target_under_score,
'max_iter': max_iter,
'cadence_mask': cadence_mask,
'over_metric_nSamples': 1}
#***
# Use scipy.optimize.minimize_scalar
# Minimize the introduced metric
minimize_result = minimize_scalar(self._goodness_metric_obj_fun, method='Bounded',
bounds=alpha_bounds,
options={'maxiter':max_iter, 'disp': False})
# Re-fit with final alpha value
# (scipy.optimize.minimize_scalar does not exit with the final fit!)
self._goodness_metric_obj_fun(minimize_result.x)
# Only display over- or under-fitting scores if requested to optimize
# for each
if (self.optimization_params['target_over_score'] > 0):
self.over_fitting_score = self.over_fitting_metric(n_samples=10)
print('Optimized Over-fitting metric: {}'.format(self.over_fitting_score))
else:
self.over_fitting_score = -1.0
if (self.optimization_params['target_under_score'] > 0):
self.under_fitting_score = self.under_fitting_metric()
print('Optimized Under-fitting metric: {}'.format(self.under_fitting_score))
else:
self.under_fitting_score = -1.0
self.alpha = minimize_result.x
print('Optimized Alpha: {0:2.3e}'.format(self.alpha))
return self.corrected_lc
def correct_regressioncorrector(self, design_matrix_collection, **kwargs):
""" Pass-through method to gain access to the superclass
RegressionCorrector.correct() method.
"""
# All this does is call the superclass 'correct' method as pass the
# input arguments.
return super(CBVCorrector, self).correct(design_matrix_collection, **kwargs)
def over_fitting_metric(self,
n_samples: int = 10):
""" Computes the over-fitting metric using
metrics.overfit_metric_lombscargle
See that function for a description of the algorithm.
Parameters
----------
n_samples : int
The number of times to compute and average the metric
This can stabalize the value, defaut = 10
Returns
-------
over_fitting_metric : float
A float in the range [0,1] where 0 => Bad, 1 => Good
"""
# Check if corrected_lc is present
if (self.corrected_lc is None):
log.warning('A corrected light curve does not exist, please run '
'correct first')
return None
# Ignore masked cadences
orig_lc = self.lc.copy()
orig_lc = orig_lc[self.cadence_mask]
corrected_lc = self.corrected_lc.copy()
corrected_lc = corrected_lc[self.cadence_mask]
return overfit_metric_lombscargle (orig_lc, corrected_lc, n_samples=n_samples)
def under_fitting_metric(self,
radius: float = None,
min_targets: int = 30,
max_targets: int = 50):
""" Computes the under-fitting metric using
metrics.underfit_metric_neighbors
See that function for a description of the algorithm.
For TESS, the default radius is 5000 arcseconds.
For Kepler/K2, the default radius is 1000 arcseconds
This function will begin with the given radius in arcseconds and
finds all neighboring targets. If not enough were found (< min_targets)
the radius is increased until a minimum number are found.
The downloaded neighboring targets will be "aligned" to the
corrected_lc, meaning the cadence numbers are used to align the targets
to the corrected_lc. However, if the CBVCorrector object was
instantiated with interpolated_cbvs=True then the targets will be
interpolated to the corrected_lc cadence times.
Parameters
----------
radius : float
Search radius to find neighboring targets in arcseconds
min_targets : float
Minimum number of targets to use in correlation metric
Using too few can cause unreliable results. Default = 30
max_targets : float
Maximum number of targets to use in correlation metric
Using too many can slow down the metric due to large data
download. Default = 50
Returns
-------
under_fitting_metric : float
A float in the range [0,1] where 0 => Bad, 1 => Good
"""
# Check if corrected_lc is present
if (self.corrected_lc is None):
raise Exception('A corrected light curve does not exist, please run '
'correct first')
return None
# Set default radius if one is not provided.
if (radius is None):
if (self.lc.mission == 'TESS'):
radius = 5000
else:
radius = 1000
interpolate = self.interpolated_cbvs
extrapolate = self.extrapolated_cbvs
# Make a copy of radius because it changes locally
dynamic_search_radius = radius
# Max search radius is the diagonal distance along a CCD in arcseconds
# 1 pixel in TESS is 21.09 arcseconds
# 1 pixel in Kepler/K2 is 3.98 arcseconds
if (self.lc.mission == 'TESS'):
# 24 degrees of a TESS CCD array (2 CCD's wide) is 86,400 arcseconds
max_search_radius = np.sqrt(2) * (86400/2.0)
elif (self.lc.mission == 'Kepler' or self.lc.mission == 'K2'):
# One Kepler CCD spans 4,096 arcseconds
max_search_radius = np.sqrt(2) * 4096
else:
raise Exception('Unknown mission')
# Ignore masked cadences
corrected_lc = self.corrected_lc.copy()
corrected_lc = corrected_lc[self.cadence_mask]
# Dynamically increase radius until min_targets reached.
continue_searching = True
while (continue_searching):
try:
metric = underfit_metric_neighbors (corrected_lc,
dynamic_search_radius, min_targets, max_targets,
interpolate, extrapolate)
except MinTargetsError:
# Too few targets found, try increasing search radius
if (dynamic_search_radius > max_search_radius):
# Hit the edge of the CCD, we have to give up
raise Exception('Not enough neighboring targets were '
'found. under_fitting_metric failed')
# Too few found, increase search radius
dynamic_search_radius *= 1.5
else:
continue_searching = False
return metric
def _correct_initialization(self, cbv_type='SingleScale', cbv_indices='ALL',
ext_dm=None):
""" Performs all the preparatory work needed before applying a 'correct'
method.
This helper function is used so that multiple correct methods can be used
without the need to repeat preparatory code.
The main thing this method does is set up the design matrix, given the
requested CBVs and external design matrix.
Parameters
----------
cbv_type : str list
List of CBV types to use
Can be None if only ext_dm is used
cbv_indices : list of lists
List of CBV vectors to use in each passed cbv_type. {'ALL' => Use all}
Can be None if only ext_dm is used
ext_dm : `.DesignMatrix` or `.DesignMatrixCollection`
Optionally pass an extra design matrix to additionally be used in the fit
"""
assert not ((cbv_type is None) ^ (cbv_indices is None)), \
'Both cbv_type and cbv_indices must be None, or neither'
if (cbv_type is None and cbv_indices is None):
use_cbvs = False
else:
use_cbvs = True
# If any DesignMatrix was passed then store it
self.extra_design_matrix = ext_dm
# Check that extra design matrix is aligned with lc flux
if ext_dm is not None:
assert isinstance(ext_dm, DesignMatrix), \
'ext_dm must be a DesignMatrix'
if (ext_dm.df.shape[0] != len(self.lc.flux)):
raise ValueError(
'ext_dm must contain the same number of cadences as lc.flux')
# Create a CBV design matrix for each CBV set requested
self.cbv_design_matrix = []
if use_cbvs:
assert (not isinstance(cbv_type, str) and
not isinstance(cbv_indices[0], int)), \
'cbv_type and cbv_indices must be lists of strings'
if (self.lc.mission in ['Kepler', 'K2']):
assert len(cbv_type) == 1 , \
'cbv_type must be only Single-Scale for Kepler and K2 missions'
assert cbv_type == ['SingleScale'], \
'cbv_type must be Single-Scale for Kepler and K2 missions'
if (isinstance(cbv_type, list) and len(cbv_type) != 1):
assert (self.lc.mission == 'TESS'), \
'Multiple CBV types are only allowed for TESS'
assert (len(cbv_type) == len(cbv_indices)), \
'cbv_type and cbv_indices must be the same list length'
# Loop through all the stored CBVs and find the ones matching the
# requested cbv_type list
for idx in np.arange(len(cbv_type)):
for cbvs in self.cbvs:
# Temporarily copy the cbv_indices requested
cbv_idx_loop = cbv_indices[idx]
# If requesting 'ALL' CBVs then set to max default number
# Remember, cbv indices is 1-based!
if (isinstance(cbv_idx_loop, str) and (cbv_idx_loop == 'ALL')):
cbv_idx_loop = cbvs.cbv_indices
# Trim to nCBVs in cbvs
cbv_idx_loop = np.array([idx for idx in cbv_idx_loop if
bool(np.in1d(idx, cbvs.cbv_indices))])
if cbv_type[idx].find('MultiScale') >= 0:
# Find the correct band if this is a multi-scale CBV set
band = int(cbv_type[idx][-1])
if (cbvs.cbv_type in cbv_type[idx] and cbvs.band == band):
self.cbv_design_matrix.append(cbvs.to_designmatrix(
cbv_indices=cbv_idx_loop, name=cbv_type[idx]))
else:
if (cbvs.cbv_type in cbv_type[idx]):
self.cbv_design_matrix.append(cbvs.to_designmatrix(
cbv_indices=cbv_idx_loop, name=cbv_type[idx]))
#***
# Create the design matrix collection with CBVs, plus extra passed basis vectors
# Create the full design matrix collection from all the sub-design
# matrices (I.e 'flatten' the design matrix collection)
if self.extra_design_matrix is not None and \
self.cbv_design_matrix != []:
# Combine cbv_design_matrix and extra_design_matrix
dm_to_flatten = [[cbv_dm for cbv_dm in self.cbv_design_matrix],
[self.extra_design_matrix]]
flattened_dm_list = [item for sublist in dm_to_flatten for item in sublist]
elif self.cbv_design_matrix != []:
# Just use cbv_design_matrix
dm_to_flatten = [[cbv_dm for cbv_dm in self.cbv_design_matrix]]
flattened_dm_list = [item for sublist in dm_to_flatten for item in sublist]
else:
# Just use extra_design_matrix
flattened_dm_list = [self.extra_design_matrix]
# Add in a constant to the design matrix collection
# Note: correct_elasticnet ASSUMES the the last vector in the
# design_matrix_collection is the constant
flattened_dm_list.append(DesignMatrix(np.ones(flattened_dm_list[0].shape[0]),
columns=['Constant'], name='Constant'))
self.design_matrix_collection = DesignMatrixCollection(flattened_dm_list)
def _set_prior_width(self, sigma):
""" Sets the Gaussian prior in the design_matrix_collection widths to sigma
Parameters
----------
sigma : scalar float
all widths are set to the same value
If sigma = None then uniform sigma is set
"""
if (isinstance(sigma, list)):
raise Exception("separate widths is not yet implemented")
for dm in self.design_matrix_collection:
nCBVs = len(dm.prior_sigma)
if sigma is None:
dm.prior_sigma = np.ones(nCBVs) * np.inf
else:
dm.prior_sigma = np.ones(nCBVs) * sigma
def _goodness_metric_obj_fun(self, alpha):
""" The objective function to minimize with
scipy.optimize.minimize_scalar
First sets the alpha regularization penalty then runs
RegressionCorrector.correct and then computes the over- and
under-fitting goodness metrics to return a scalar penalty term to
minimize.
Uses the paramaters in self.optimization_params.
Parameters (in self.optimization_params)
----------
alpha : float
regularization penalty term value to set
cadence_mask : np.ndarray of bools (optional)
Mask, where True indicates a cadence that should be used.
target_over_score : float
Target Over-fitting metric score
If <=0 then ignore over-fitting metric
target_under_score : float
Target under-fitting metric score
If <=0 then ignore under-fitting metric
Returns
-------
penalty : float
Penalty term for minimizer, based on goodness metrics
"""
# Add in a width to the Gaussian priors
# alpha = flux_sigma^2 / sigma^2
sigma = np.median(self.lc.flux_err.value) / np.sqrt(np.abs(alpha))
self._set_prior_width(sigma)
# Use RegressionCorrector.correct for the actual fitting
self.correct_regressioncorrector(self.design_matrix_collection,
cadence_mask=self.optimization_params['cadence_mask'])
# Do not compute and ignore if target score < 0
if (self.optimization_params['target_over_score'] > 0):
overMetric = self.over_fitting_metric(
n_samples=self.optimization_params['over_metric_nSamples'])
else:
overMetric = 1.0
# Do not compute and ignore if target score < 0
if (self.optimization_params['target_under_score'] > 0):
underMetric = self.under_fitting_metric()
else:
underMetric = 1.0
# Once we hit the target we want to ease-back on increasing the metric
# However, we don't want to ease-back to zero pressure, that will
# unconstrain the penalty term and cause the optmizer to run wild.
# So, use a "Leaky ReLU"
# metric' = threshold + (metric - threshold) * leakFactor
leakFactor = 0.01
if (self.optimization_params['target_over_score'] > 0 and
overMetric >= self.optimization_params['target_over_score']):
overMetric = (self.optimization_params['target_over_score'] +
leakFactor *
(overMetric -
self.optimization_params['target_over_score']))
if (self.optimization_params['target_under_score'] > 0 and
underMetric >= self.optimization_params['target_under_score']):
underMetric = (self.optimization_params['target_under_score'] +
leakFactor *
(underMetric -
self.optimization_params['target_under_score']))
penalty = -(overMetric + underMetric)
return penalty
[docs] def diagnose(self):
""" Returns diagnostic plots to assess the most recent correction.
If a correction has not yet been fitted, a ``ValueError`` will be raised.
Returns
-------
`~matplotlib.axes.Axes`
The matplotlib axes object.
"""
axs = self._diagnostic_plot()
plt.title('Alpha = {0:2.3e}'.format(self.alpha))
return axs
def goodness_metric_scan_plot(self, cbv_type=['SingleScale'],
cbv_indices=[np.arange(1,9)], alpha_range_log10=[-4, 4],
ext_dm=None, cadence_mask=None):
""" Returns a diagnostic plot of the over and under goodness metrics as a
function of the L2-Norm regularization term, alpha.
alpha is scanned by default to the range 10^-4 : 10^4 in logspace
cbvCorrector.correct_gaussian_prior is used to make the correction for
each alpha. Then the over and under goodness metric are computed.
If a correction has already been performed (via one of the correct_*
methods) then the used alpha value is also plotted for reference.
Parameters
----------
cbv_type : str list
List of CBV types to use in correction {'ALL' => Use all}
cbv_indices : list of lists
List of CBV vectors to use in each of cbv_type passed. {'ALL' => Use all}
NOTE: 1-Based indexing!
alpha_range_log10 : [list of two] The start and end exponent for the logspace scan.
Default = [-4, 4]
ext_dm : `.DesignMatrix` or `.DesignMatrixCollection`
Optionally pass an extra design matrix to also be used in the fit
cadence_mask : np.ndarray of bools (optional)
Mask, where True indicates a cadence that should be used.
Returns
-------
`~matplotlib.axes.Axes`
The matplotlib axes object.
"""
alphaArray = np.logspace(alpha_range_log10[0], alpha_range_log10[1], num=100)
# We need to make a copy of self so that the scan's final fit parameters
# do not over-write any stored fit parameters
cbvCorrectorCopy = self.copy()
# Compute both metrics vs. alpha
overMetric = []
underMetric = []
for thisAlpha in alphaArray:
cbvCorrectorCopy.correct_gaussian_prior(cbv_type=cbv_type, cbv_indices=cbv_indices,
alpha=thisAlpha, ext_dm=ext_dm,
cadence_mask=cadence_mask)
overMetric.append(cbvCorrectorCopy.over_fitting_metric(n_samples=1))
underMetric.append(cbvCorrectorCopy.under_fitting_metric())
# plot both
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.semilogx(alphaArray, underMetric, 'b.', label='UnderFit')
ax.semilogx(alphaArray, overMetric, 'r.', label='OverFit')
if (isinstance(self.alpha, float)):
ax.semilogx([self.alpha, self.alpha], [0, 1.0], 'k-',
label='corrected_lc Alpha = {0:2.3e}'.format(self.alpha))
plt.title('Goodness Metrics vs. L2-Norm Penalty (alpha)')
plt.xlabel('Regularization Factor Alpha')
plt.ylabel('Goodness Metric')
ax.grid(':', alpha=0.3)
ax.legend()
return ax
def copy(self):
"""Returns a copy of this `cbvCorrector` object.
This method uses Python's `copy.deepcopy` function to ensure that all
objects stored within the cbvCorrector instance are fully copied.
Returns
-------
cbvCorrector_copy : `cbvCorrector`
A new object which is a copy of the original.
"""
return copy.deepcopy(self)
def __repr__(self):
""" This will print all attributes of the class kinda like in
self.__dict__
"""
dictionary = self.__dict__.copy()
dictionary['lc'] = '<{} targetid={} length={}>'.format(type(self.lc),
self.lc.targetid, len(self.lc))
if self.corrected_lc is not None:
dictionary['corrected_lc'] = '<{} targetid={} length={}>'.format(
type(self.corrected_lc), self.corrected_lc.targetid,
len(self.corrected_lc))
dict_string = '\n'
for key in dictionary.keys():
dict_string += '\t{} = {}\n'.format(key, dictionary[key])
return dict_string
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
# Cotrending Basis Vectors Classes and Functions
#*******************************************************************************
#*******************************************************************************
#*******************************************************************************
class CotrendingBasisVectors(TimeSeries):
"""
Defines a CotrendingBasisVectors class, which is the Superclass for
KeplerCotrendingBasisVectors and TessCotrendingBasisVectors.
Normally, one would use these latter classes instead of instantiating
CotrendingBasisVectors directly. However, for generating custom CBVs one can
use this super class.
Stores Cotrending Basis Vectors for the Kepler/K2/TESS missions.
Each CotrendingBasisVectors object contains only ONE set of CBVs.
Instantiate multiple objects to store multiple set of CBVS, for example, to
save each of the three multi-scale bands in TESS.
CotrendingBasisVectors calls the standard __init__ from
astropy.timeseries.TimeSeries
Parameters
----------
data : `~astropy.table.Table`
Data to initialize CotrendingBasisVectors. The
CBVs should be in columns called ``'CADENCENO'``, ``'GAP'``, ``'VECTOR_1'``,
``'VECTOR_2'``, ... ``'VECTOR_N'``
If 'GAP' is not given then it is filled with all False.
If 'CADENCENO' is not given then it is filled with np.arange(nCadences)
time : `~astropy.time.Time`
Time values.
**kwargs : dict
Additional keyword arguments are passed to `~astropy.table.QTable`.
Attributes
----------
cadenceno : int array-like
Cadence indices
time : flaot array-like
CBV cadence times
gap_indicators : bool array-like
True => cadence is gapped
cbv_indices : list int-like
List of CBV indices available
1-based indexing
['VECTOR_#'] : astropy.table.column.Column
CBV number #
"""
#***
def __init__(self, data=None, time=None, **kwargs):
# Add some columns if not existant
if data is not None:
if not 'GAP' in data.colnames:
data['GAP'] = np.full(data[data.colnames[0]].size, False)
if not 'CADENCENO' in data.colnames:
data['CADENCENO'] = np.arange(data[data.colnames[0]].size)
# Initialize the astropy.timeseries.TimeSeries attributes
super().__init__(data=data, time=time, **kwargs)
# Ensure all columns are Quantity objects
for col in self.columns:
if not isinstance(self[col], (Quantity, Time)):
self.replace_column(col, Quantity(self[col], dtype=self[col].dtype))
# cbv_indices are always determined by the 'VECTOR_#' columns in the
# TimeSeries
@property
def cbv_indices(self):
cbv_indices = []
for name in self.colnames:
if name.find('VECTOR_') > -1:
cbv_indices.append(int(name[7:]))
return cbv_indices
@property
def time(self):
"""The time values."""
return self['time']
@time.setter
def time(self, time):
self['time'] = time
@property
def gap_indicators(self):
return self['GAP']
@gap_indicators.setter
def gap_indicators(self, gap_indicators):
self['GAP'] = gap_indicators
@property
def cadenceno(self):
return self['CADENCENO']
@cadenceno.setter
def cadenceno(self, cadenceno):
self['CADENCENO'] = cadenceno
def to_designmatrix(self, cbv_indices='all', name='CBVs'):
"""Returns a `DesignMatrix` where the columns are the
requested CBVs.
Parameters
----------
cbv_indices : list of ints
List of CBV vectors to use. 1-based indexing!
{'all' => Use all}
name : str
A Name for the DesignMatrix
Returns
-------
design_matrix : designmatrix.DesignMatrix
"""
if isinstance(cbv_indices, str) and not cbv_indices == 'all':
raise ValueError('cbv_indices must either be list of ints or "all"')
elif not isinstance(cbv_indices, str) and 0 in cbv_indices:
raise ValueError("CBVs use 1-based indexing. Do not request CBV index '0'")
if (isinstance(cbv_indices, str) and (cbv_indices == 'all')):
cbv_indices = self.cbv_indices
cbv_names = []
cbv_matrix = np.array([])
for idx in cbv_indices:
# Check that the CBV index is available
if idx in self.cbv_indices:
# If so, append it as a column to the matrix
if len(cbv_matrix) == 0:
cbv_matrix = np.array(self['VECTOR_{}'.format(idx)])[...,None]
else:
cbv_matrix = np.hstack((cbv_matrix,
np.array(self['VECTOR_{}'.format(idx)])[...,None]))
cbv_names.append('VECTOR_{}'.format(idx))
return DesignMatrix(cbv_matrix, columns=cbv_names, name=name)
def plot(self, cbv_indices='all', ax=None, **kwargs):
"""Plots the requested CBVs evenly spaced out vertically for legibility.
Does not plot gapped cadences
Parameters
----------
cbv_indices : list of ints
The list of cotrending basis vectors to plot. For example:
[1, 2] will fit the first two basis vectors. 'all' => plot all
NOTE: 1-based indexing
ax : matplotlib.pyplot.Axes.AxesSubplot
Matplotlib axis object. If `None`, one will be generated.
kwargs : dict
Dictionary of arguments to be passed to `matplotlib.pyplot.plot`.
Returns
-------
ax : matplotlib.pyplot.Axes.AxesSubplot
Matplotlib axis object
"""
if isinstance(cbv_indices, str) and not cbv_indices == 'all':
raise ValueError('cbv_indices must either be list of ints or "all"')
elif not isinstance(cbv_indices, str) and 0 in cbv_indices:
raise ValueError("CBVs use 1-based indexing. Do not request CBV index '0'")
with plt.style.context(MPLSTYLE):
if (isinstance(cbv_indices, str) and (cbv_indices == 'all')):
cbv_indices = []
for name in self.colnames:
if name.find('VECTOR_') > -1:
cbv_indices.append(int(name[7:]))
cbv_designmatrix = self.to_designmatrix(cbv_indices)
if ax is None:
_, ax = plt.subplots(1)
# Plot gaps as NaN
# time array is a Masked array so need to fill masks with nans
timeArray = self.time.copy().value
if isinstance(timeArray, (Masked, np.ma.MaskedArray)):
if np.issubdtype(timeArray.dtype, np.int_):
timeArray = timeArray.astype(float)
timeArray = timeArray.filled(np.nan)
timeArray[np.nonzero(self.gap_indicators)[0]] = np.nan
# Get the CBV arrays that were requested
for idx, cbv_name in enumerate(cbv_designmatrix.columns):
cbvIndex = cbv_name[7:]
cbv = cbv_designmatrix[cbv_name]
# Plot gaps as NaN
cbv[np.nonzero(self.gap_indicators)[0]] = np.nan
ax.plot(timeArray, cbv-idx/10., label='{}'.format(cbvIndex), **kwargs)
ax.set_yticks([])
ax.set_xlabel('Time [{}]'.format(self['time'].format))
if hasattr(self, 'mission'):
if self.mission == 'Kepler':
ax.set_title('Kepler CBVs (Quarter.Module.Output : {}.{}.{})'
''.format(self.quarter, self.module, self.output),
fontdict={'fontsize': 10})
elif self.mission == 'K2':
ax.set_title('K2 CBVs (Campaign.Module.Output : {}.{}.{})'
''.format( self.campaign, self.module, self.output),
fontdict={'fontsize': 10})
elif self.mission == 'TESS':
if (self.cbv_type == 'MultiScale'):
ax.set_title('TESS CBVs (Sector.Camera.CCD : {}.{}.{}, CBVType.Band : {}.{})'
''.format(self.sector, self.camera, self.ccd, self.cbv_type, self.band),
fontdict={'fontsize': 9})
else:
ax.set_title('TESS CBVs (Sector.Camera.CCD : {}.{}.{}, CBVType : {})'
''.format(self.sector, self.camera, self.ccd, self.cbv_type),
fontdict={'fontsize': 10})
else:
# This is a generic CotrendingBasisVectors object
ax.set_title('CBVs', fontdict={'fontsize': 10})
ax.grid(':', alpha=0.3)
ax.legend(fontsize='small', ncol=2)
return ax
def align(self, lc):
"""Aligns the CBVs to a light curve. The lightCurve object might not
have the same cadences as the CBVs. This will trim the CBVs to be
aligned with the light curve.
This method will use the cadence number (lc.cadenceno) to
perform the synchronization. Only cadence numbers that exist in both
the CBVs and the light curve will have values in the returned CBVs. All
cadence numbers that exist in the light curve but not in the CBVs will
have NaNs returned for the CBVs on those cadences and the GAP set to
True.
Any cadences in the CBVs not in the light curve will be removed from the CBVs.
The returned cbvs object is sorted by cadenceno.
If you wish to interpolate the CBVs to arbitrary light curve cadence
times then use the interpolate method.
Parameters
----------
lc : LightCurve object
The reference light curve to align to
Returns
-------
cbvs : CotrendingBasisVectors object
Aligned to the light curve
"""
# The fraction of cadences that do not align to throw a
# warning about the CBVs being poorly aligned to the light curve
poorly_aligned_threshold = 0.5
poorly_aligned_flag = False
if not isinstance(lc, LightCurve):
raise Exception('<lc> must be a LightCurve class')
if hasattr(lc, 'cadenceno'):
# Make a deepcopy so we do not just return a modified original
cbvs = copy.deepcopy(self)
# NaN any CBV cadences that are in the light curve and not in CBVs
# This requires us to add rows to the CBV table
lc_nan_mask = np.logical_not(np.in1d(lc.cadenceno, cbvs.cadenceno))
# Determine if the CBVs are poorly aligned to the light curve
if ((np.count_nonzero(lc_nan_mask) / len(lc_nan_mask)) >
poorly_aligned_threshold):
poorly_aligned_flag = True
lc_nan_indices = np.nonzero(lc_nan_mask)[0]
# Sadly, there is no TimesSeries.add_rows (plural), so we have to
# add each row in a for-loop
if len(lc_nan_indices) > 0:
for idx in lc_nan_indices:
dict_to_add = {}
dict_to_add['time'] = lc.time[idx]
dict_to_add['CADENCENO'] = lc.cadenceno[idx]
dict_to_add['GAP'] = True
for cbvIdx in cbvs.cbv_indices:
dict_to_add['VECTOR_{}'.format(cbvIdx)] = np.nan
cbvs.add_row(dict_to_add)
# There appears to be a bug in astropy.timeseries when using ts[x:y]
# in combination with ts.remove_row() or ts.remove_rows.
# See LightKurve Issue #836.
# To get around the error for now, we will attempt to use
# ts[x:y]. If it errors out then revert to remove_rows, which is
# REALLY slow.
try:
# This method is fast but might cause errors
keep_indices = np.nonzero(np.in1d(cbvs.cadenceno, lc.cadenceno))[0]
# Determine if the CBVs are poorly aligned to the light curve
if (len(keep_indices) / len(cbvs)) < poorly_aligned_threshold:
poorly_aligned_flag = True
cbvs = cbvs[keep_indices]
except:
# This method is slow but appears to be more robust
trim_indices = np.nonzero(np.logical_not(
np.in1d(cbvs.cadenceno, lc.cadenceno)))[0]
# Determine if the CBVs are poorly aligned to the light curve
if (len(trim_indices) / len(cbvs)) > poorly_aligned_threshold:
poorly_aligned_flag = True
cbvs.remove_rows(trim_indices)
# Now sort the CBVs by cadenceno
cbvs.sort('CADENCENO')
else:
raise Exception('align requires cadence numbers for the ' + \
'light curve. NO SYNCHRONIZATION OCCURED')
# Only issue this warning once
if poorly_aligned_flag:
log.warning('The {} CBVs do not appear to be well aligned to the '
'light curve. Consider using "interpolate_cbvs=True"'.format(cbvs.cbv_type))
return cbvs
def interpolate(self, lc, extrapolate=False):
"""Interpolates the CBV to the cadence times in the given light curve
using Piecewise Cubic Hermite Interpolating Polynomial (PCHIP).
Uses scipy.interpolate.PchipInterpolator
Each CBV is interpolated independently. All gaps are set to False.
The cadence numbers are taken from the light curve.
Parameters
----------
lc : LightCurve object
The reference light curve cadence times to interpolate to
extrapolate : bool, optional
Whether to extrapolate to out-of-bounds points based on first
and last intervals, or to return NaNs.
Returns
-------
cbvs_interpolated: CotrendingBasisVectors object
interpolated to the light curve cadence times
"""
if not isinstance(lc, LightCurve):
raise Exception('<lc> must be a LightCurve class')
# If not extrapolating then check if extrapolation is necessary.
# If so, throw a warning
if extrapolate==False:
gapRemovedCBVtime = self.time.value[np.logical_not(self.gap_indicators.value)]
if (np.min(lc.time.value) < np.min(gapRemovedCBVtime) or
np.max(lc.time.value) > np.max(gapRemovedCBVtime) ):
log.warning('Extrapolation of CBVs appears to be necessary. '
'Extrapolated values will be filled with zeros. '
'Recommend setting extrapolate=True')
# Create the new cbv object with no basis vectors, yet...
cbvNewTime = lc.time.copy()
# Gaps are all false
gaps = np.full(len(lc.time), False)
dataTbl = Table([lc.cadenceno, gaps], names=('CADENCENO', 'GAP'))
# We are PCHIP interpolating each CBV independently.
# Do not include gaps when interpolating
warning_posted = False
for idx in self.cbv_indices:
fInterp = PchipInterpolator(
self.time.value[np.logical_not(self.gap_indicators.value)],
self['VECTOR_{}'.format(idx)][np.logical_not(self.gap_indicators.value)],
extrapolate=extrapolate)
dataTbl['VECTOR_{}'.format(idx)] = fInterp(lc.time.value)
# Replace NaNs with 0.0
if (np.any(np.isnan(dataTbl['VECTOR_{}'.format(idx)]))):
dataTbl['VECTOR_{}'.format(idx)][np.isnan(dataTbl['VECTOR_{}'.format(idx)])] = \
np.full(np.count_nonzero(np.isnan(dataTbl['VECTOR_{}'.format(idx)])), 0.0)
# Only post this warning once
if (not warning_posted):
log.warning('Some interpolated (or extrapolated) CBV values have been set to zero')
warning_posted = True
dataTbl.meta = self.meta.copy()
# We need to return a new CotrendingBasisVectors class. Make sure we
# instantiate the correct type.
if isinstance(self, KeplerCotrendingBasisVectors):
return KeplerCotrendingBasisVectors(data=dataTbl, time=cbvNewTime)
elif isinstance(self, TessCotrendingBasisVectors):
return TessCotrendingBasisVectors(data=dataTbl, time=cbvNewTime)
else:
return CotrendingBasisVectors(data=dataTbl, time=cbvNewTime)
class KeplerCotrendingBasisVectors(CotrendingBasisVectors):
"""Sub-class for Kepler/K2 cotrending basis vectors
See CotrendingBasisVectors for class details
Attributes
----------
CotrendingBasisVectors attributes
astropy.timeseries.TimeSeries attributes
mission : [str] ('Kepler', 'K2')
cbv_type : [str] always 'SingleScale'
quarter : [int] Kepler Quarter
campaign : [int] K2 Campaign
module : [int] Kepler instrument CCD module
output : [int] Kepler instrument CCD output
"""
#***
validMissionOptions = ('Kepler', 'K2')
validCBVTypes = ('SingleScale')
#***
def __init__(self, data=None, time=None, **kwargs):
"""Initiates a KeplerCotrendingBasisVectors object.
Normally one would use KeplerCotrendingBasisVectors.from_hdu to
automatically set up the object. However, for certain functionality
one must instantiate the object directly.
"""
# Initialize attributes common to all CotrendingBasisVector classes
super(KeplerCotrendingBasisVectors, self).__init__(data=data,
time=time, **kwargs)
@classmethod
def from_hdu(self, hdu=None, module=None, output=None,
**kwargs):
"""Class method to instantiate a KeplerCotrendingBasisVectors object
from a CBV FITS HDU.
Kepler/K2 CBVs are all in the same FITS file for each quarter/campaign,
so, when instantiating the CBV object we must specify which module and
output we desire. Only Single-Scale CBVs are stored for Kepler.
Parameters
----------
hdu : astropy.io.fits.hdu.hdulist.HDUList
A pyfits opened FITS file containing the CBVs
module : int
Kepler CCD module 2 - 84
output : int
Kepler CCD output 1 - 4
**kwargs : Optional arguments
Passed to the TimeSeries superclass
"""
assert module > 1 and module < 85, 'Invalid module number'
assert output > 0 and output < 5, 'Invalid output number'
# Get the mission: Kepler or K2
# Sadly, the HDU does not explicitly say if this is Kepler or K2 CBVs.
if 'QUARTER' in hdu['PRIMARY'].header:
mission = 'Kepler'
elif 'CAMPAIGN' in hdu['PRIMARY'].header:
mission = 'K2'
else:
raise Exception('This does not appear to be a Kepler or K2 FITS HDU')
extName = 'MODOUT_{0}_{1}'.format(module, output)
try:
# Read the columns and meta data
with warnings.catch_warnings():
# By default, AstroPy emits noisy warnings about units commonly used
# in archived TESS data products (e.g., "e-/s" and "pixels").
# We ignore them here because they don't affect Lightkurve's features.
# Inconsistencies between TESS data products and the FITS standard
# out to be addressed at the archive level. (See issue #1216.)
warnings.simplefilter("ignore", category=UnitsWarning)
dataTbl = Table.read(hdu[extName], format="fits")
dataTbl.meta.update(hdu[0].header)
dataTbl.meta.update(hdu[extName].header)
# TimeSeries-based objects require a dedicated time column
# Replace NaNs with default time '2000-01-01', otherwise,
# astropy.time.Time complains
nanHere = np.nonzero(np.isnan(dataTbl['TIME_MJD'].data))[0]
timeData = dataTbl['TIME_MJD'].data
timeData[nanHere] = Time(['2000-01-01'], scale='utc').mjd
cbvTime = Time(timeData, format='mjd', scale='utc')
dataTbl.remove_column('TIME_MJD')
# Gaps are labelled as 'GAPFLAG' so rename!
dataTbl['GAP'] = dataTbl['GAPFLAG']
dataTbl.remove_column('GAPFLAG')
dataTbl.meta['MISSION'] = mission
dataTbl.meta['CBV_TYPE'] = 'SingleScale'
except:
dataTbl = None
cbvTime = None
# Here we instantiate the actual object
return self(data=dataTbl, time=cbvTime, **kwargs)
@property
def mission(self):
return self.meta.get('MISSION', None)
@mission.setter
def mission(self, mission):
self.meta['MISSION'] = mission
@property
def cbv_type(self):
return self.meta.get('CBV_TYPE', None)
@cbv_type.setter
def cbv_type(self, cbv_type):
self.meta['CBV_TYPE'] = cbv_type
@property
def quarter(self):
return self.meta.get('QUARTER', None)
@quarter.setter
def quarter(self, quarter):
if (self.mission == 'Kepler'):
self.meta['QUARTER'] = quarter
else:
pass
@property
def campaign(self):
return self.meta.get('CAMPAIGN', None)
@campaign.setter
def campaign(self, campaign):
if (self.mission == 'K2'):
self.meta['CAMPAIGN'] = campaign
else:
pass
@property
def module(self):
return self.meta.get('MODULE', None)
@module.setter
def module(self, module):
self.meta['MODULE'] = module
@property
def output(self):
return self.meta.get('OUTPUT', None)
@output.setter
def output(self, output):
self.meta['OUTPUT'] = output
def __repr__(self):
if self.mission == 'Kepler':
repr_string = 'Kepler CBVs, Quarter.Module.Output : {}.{}.{}, nCBVs : {}'\
''.format(self.quarter, self.module, self.output, len(self.cbv_indices))
elif self.mission == 'K2':
repr_string = 'K2 CBVs, Campaign.Module.Output : {}.{}.{}, nCBVs : {}'\
''.format( self.campaign, self.module, self.output, len(self.cbv_indices))
return repr_string
class TessCotrendingBasisVectors(CotrendingBasisVectors):
""" Sub-class for TESS cotrending basis vectors
See CotrendingBasisVectors for class details
Attributes
----------
CotrendingBasisVectors attributes
astropy.timeseries.TimeSeries attributes
mission : [str] ('TESS')
cbv_type : [str ('SingleScale', 'MultiScale', 'Spike')
sector : [int] TESS Sector
camera : [int] TESS Camera Index
ccd : [int] TESS CCD Index
band : [int] MultiScale band number (invalid for other CBV types)
"""
validMissionOptions = ('TESS')
validCBVTypes = ('SingleScale', 'MultiScale', 'Spike')
def __init__(self, data=None, time=None, **kwargs):
"""Initiates a TessCotrendingBasisVectors object.
Normally one would use TessCotrendingBasisVectors.from_hdu to
automatically set up the object. However, for certain functionaility
one must instantiate the object directly.
"""
# Initialize attributes common to all CotrendingBasisVector classes
super(TessCotrendingBasisVectors, self).__init__(data=data,
time=time, **kwargs)
@classmethod
def from_hdu(self, hdu=None, cbv_type=None, band=None, **kwargs):
"""Class method to instantiate a TessCotrendingBasisVectors object
from a CBV FITS HDU.
TESS CBVs are in separate FITS files for each camera.CCD, so camera.CCD
is already specified in the HDU, here we need to specify
which CBV type and band is desired.
If the requested CBV type does not exist in the HDU then None is
returned
Parameters
----------
hdu : astropy.io.fits.hdu.hdulist.HDUList
A pyfits opened FITS file containing the CBVs
cbv_type : str
'SingleScale', 'MultiScale' or 'Spike'
band : int
Band number for 'MultiScale' CBVs
Ignored for 'SingleScale' or 'Spike'
**kwargs : Optional arguments
Passed to the TimeSeries superclass
"""
mission = hdu['PRIMARY'].header['TELESCOP']
assert mission == 'TESS', 'This does not appear to be a TESS FITS HDU'
# Check if a valid cbv_type and band was passed
if not cbv_type in self.validCBVTypes:
raise ValueError('Invalid cbv_type')
if band is not None and band < 1:
raise ValueError('Invalid band')
# Get the requested cbv_type
# Curiosly, camera and CCD are not in the primary header!
camera = hdu[1].header['CAMERA']
ccd = hdu[1].header['CCD']
switcher = {
'SingleScale': 'CBV.single-scale.{}.{}'.format(camera, ccd),
'MultiScale': 'CBV.multiscale-band-{}.{}.{}'.format(band,
camera, ccd),
'Spike': 'CBV.spike.{}.{}'.format(camera, ccd),
'unknown': 'error'
}
extName = switcher.get(cbv_type, switcher['unknown'])
if (extName == 'error'):
raise Exception('Invalide cbv_type')
try:
# Read the columns and meta data
with warnings.catch_warnings():
# By default, AstroPy emits noisy warnings about units commonly used
# in archived TESS data products (e.g., "e-/s" and "pixels").
# We ignore them here because they don't affect Lightkurve's features.
# Inconsistencies between TESS data products and the FITS standard
# out to be addressed at the archive level. (See issue #1216.)
warnings.simplefilter("ignore", category=UnitsWarning)
dataTbl = Table.read(hdu[extName], format="fits")
dataTbl.meta.update(hdu[0].header)
dataTbl.meta.update(hdu[extName].header)
# TimeSeries-based objects require a dedicated time column
# Replace NaNs with default time '2000-01-01', otherwise,
# astropy.time.Time complains
nanHere = np.nonzero(np.isnan(dataTbl['TIME'].data))[0]
timeData = dataTbl['TIME'].data
timeData[nanHere] = Time(['2000-01-01'], scale='tdb').mjd
cbvTime = Time(timeData, format='btjd', scale='tdb')
dataTbl.remove_column('TIME')
dataTbl.meta['MISSION'] = 'TESS'
dataTbl.meta['CBV_TYPE'] = cbv_type
dataTbl.meta['BAND'] = band
except:
dataTbl = None
cbvTime = None
# Here we instantiate the actual object
return self(data=dataTbl, time=cbvTime, **kwargs)
@property
def mission(self):
return self.meta.get('MISSION', None)
@mission.setter
def mission(self, mission):
self.meta['MISSION'] = mission
@property
def cbv_type(self):
return self.meta.get('CBV_TYPE', None)
@cbv_type.setter
def cbv_type(self, cbv_type):
self.meta['CBV_TYPE'] = cbv_type
@property
def band(self):
return self.meta.get('BAND', None)
@band.setter
def band(self, band):
self.meta['BAND'] = band
@property
def sector(self):
return self.meta.get('SECTOR', None)
@sector.setter
def sector(self, sector):
self.meta['SECTOR'] = sector
@property
def camera(self):
return self.meta.get('CAMERA', None)
@camera.setter
def camera(self, camera):
self.meta['CAMERA'] = camera
@property
def ccd(self):
return self.meta.get('CCD', None)
@ccd.setter
def ccd(self, ccd):
self.meta['CCD'] = ccd
def __repr__(self):
if (self.cbv_type == 'MultiScale'):
repr_string = 'TESS CBVs, Sector.Camera.CCD : {}.{}.{}, CBVType.Band: {}.{}, nCBVs : {}' \
''.format(self.sector, self.camera, self.ccd, self.cbv_type,
self.band, len(self.cbv_indices))
else:
repr_string = 'TESS CBVs, Sector.Camera.CCD : {}.{}.{}, CBVType : {}, nCBVS : {}'\
''.format(self.sector, self.camera, self.ccd, self.cbv_type, len(self.cbv_indices))
return repr_string
#*******************************************************************************
# Functions
@deprecated("2.1", alternative="load_kepler_cbvs", warning_type=LightkurveDeprecationWarning)
def download_kepler_cbvs(*args, **kwargs):
return load_kepler_cbvs(*args, **kwargs)
[docs]def load_kepler_cbvs(cbv_dir=None,mission=None, quarter=None, campaign=None,
channel=None, module=None, output=None):
"""Loads Kepler or K2 cotrending basis vectors, either from a local directory cbv_dir
or searches the public data archive at MAST <https://archive.stsci.edu>.
This function fetches the Cotrending Basis Vectors FITS HDU for the desired
mission, quarter/campaign and channel or module/output, etc...
and then extracts the requested basis vectors and returns a
KeplerCotrendingBasisVectors object
For Kepler/K2, the FITS files contain all channels in a single file per
quarter/campaign.
For Kepler this extracts the DR25 CBVs.
Parameters
----------
cbv_dir : str
Path to specific directory holding Kepler CBVs. If None, queries MAST.
mission : str, list of str
'Kepler' or 'K2'
quarter or campaign : int
Kepler Quarter or K2 Campaign.
channel or (module and output) : int
Kepler/K2 requested channel or module and output.
Must provide either channel, or module and output,
but not both.
Returns
-------
result : :class:`KeplerCotrendingBasisVectors` object
Examples
--------
This example will read in the CBVs for Kepler quarter 8,
and then extract the first 8 CBVs for module.output 16.4
>>> cbvs = load_kepler_cbvs(mission='Kepler', quarter=8, module=16, output=4) # doctest: +SKIP
"""
#***
# Validate inputs
# Make sure only the appropriate arguments are passed
if (mission == 'Kepler'):
assert isinstance(quarter, int), 'quarter must be passed for Kepler mission'
assert campaign is None, 'campaign must not be passed for Kepler mission'
elif (mission == 'K2'):
assert isinstance(campaign, int), 'campaign must be passed for K2 mission'
assert quarter is None, 'quarter must not be passed for K2 mission'
else:
raise ValueError('Unknown mission type')
# CBV FITS files use module/output, not channel
# So if channel is passed, convert to module/output
if (isinstance(channel, int)):
assert module is None, 'module must NOT be passed if channel is passed'
assert output is None, 'output must NOT be passed if channel is passed'
module, output = channel_to_module_output(channel)
channel = None
else:
assert module is not None, 'module must be passed'
assert output is not None, 'output must be passed'
if cbv_dir:
cbvBaseUrl = ""
elif (mission == 'Kepler'):
cbvBaseUrl = "http://archive.stsci.edu/missions/kepler/cbv/"
elif (mission == 'K2'):
cbvBaseUrl = "http://archive.stsci.edu/missions/k2/cbv/"
try:
kepler_cbv_fname = None
if cbv_dir:
cbv_files = glob.glob(os.path.join(cbv_dir,'*.fits'))
else:
soup = BeautifulSoup(requests.get(cbvBaseUrl).text, 'html.parser')
cbv_files = [fn['href'] for fn in soup.find_all('a') if fn['href'].endswith('fits')]
if mission == 'Kepler':
quarter = 'q{:02}'.format(quarter)
for cbv_file in cbv_files:
if quarter + '-d25' in cbv_file:
break
elif mission == 'K2':
campaign = 'c{:02}'.format(campaign)
for cbv_file in cbv_files:
if campaign in cbv_file:
break
kepler_cbv_fname = cbvBaseUrl + cbv_file
hdu = pyfits.open(kepler_cbv_fname)
return KeplerCotrendingBasisVectors.from_hdu(hdu=hdu, module=module, output=output)
except Exception as e:
raise Exception('CBVS were not found') from e
@deprecated("2.1", alternative="load_tess_cbvs", warning_type=LightkurveDeprecationWarning)
def download_tess_cbvs(*args, **kwargs):
return load_tess_cbvs(*args, **kwargs)
[docs]def load_tess_cbvs(cbv_dir=None,sector=None, camera=None,
ccd=None, cbv_type='SingleScale', band=None):
"""Loads TESS cotrending basis vectors, either from a directory of
CBV files already saved locally if cbv_dir is passed, or else
will retrieve the relevant files programmatically from MAST.
This function fetches the Cotrending Basis Vectors FITS HDU for the desired
cotrending basis vectors.
For TESS, each CCD CBVs are stored in a separate FITS files.
For now, this function will only load 2-minute cadence CBVs. Once other
cadence CBVs become available this function will be updated to support
their downloads.
Parameters
----------
cbv_dir : str
Path to specific directory holding TESS CBVs. If None, queries MAST.
sector : int, list of ints
TESS Sector number.
camera and ccd : int
TESS camera and CCD
cbv_type : str
'SingleScale' or 'MultiScale' or 'Spike'
band : int
Multi-scale band number
Returns
-------
result : :class:`TessCotrendingBasisVectors` object
Examples
--------
This example will load presaved CBVs from directory '.' for TESS Sector 10 Camera.CCD 2.4
Multi-Scale band 2
>>> cbvs = load_tess_cbvs('.',sector=10, camera=2, ccd=4, # doctest: +SKIP
>>> cbv_type='MultiScale', band=2) # doctest: +SKIP
"""
# The easiest way to obtain a link to the CBV file for a TESS Sector and
# camera.CCD is
#
# 1. Download the bulk download curl script (with a predictable url) for the
# desired sector and search it for the camera.CCD needed
# 2. Download the CBV FITS file based on the link in the curl script
#
# The bulk download curl links have urls such as:
#
# https://archive.stsci.edu/missions/tess/download_scripts/sector/tesscurl_sector_17_cbv.sh
#
# Then the individual CBV files found in the curl file have urls such as:
#
# https://archive.stsci.edu/missions/tess/ffi/s0017/2019/279/1-1/tess2019279210107-s0017-1-1-0161-s_cbv.fits
#***
# Validate inputs
# Make sure only the appropriate arguments are passed
assert isinstance(sector, int), 'sector must be passed for TESS mission'
assert isinstance(camera, int), 'camera must be passed'
assert isinstance(ccd, int), 'CCD must be passed'
if cbv_type == 'MultiScale':
assert isinstance(band, int), 'band must be passed for multi-scale CBVs'
else:
assert band is None, 'band must NOT be passed for single-scale or spike CBVs'
# This is the string to search for in the curl script file
# Pad the sector number with a first '0' if less than 10
# TODO: figure out a way to pad an integer number with forward zeros
# without needing a conditional
sector = int(sector)
try:
SearchString = 's%04d-%s-%s-' % (sector, str(camera),str(ccd))
except:
raise Exception('Error parsing sector string when getting TESS CBV FITS files')
try:
if cbv_dir is not None:
# Read in the relevant curl script file and find the line for the CBV
# data we are looking for
data = glob.glob(os.path.join(cbv_dir,'*.fits'))
fname = None
for line in data:
strLine = str(line)
if SearchString in strLine:
fname = strLine
break
if (fname is None):
raise Exception('CBV FITS file not found')
# Extract url from strLine
hdu = pyfits.open(fname)
else:
curlBaseUrl = 'https://archive.stsci.edu/missions/tess/download_scripts/sector/tesscurl_sector_'
curlEndUrl = '_cbv.sh'
curlUrl = curlBaseUrl + str(sector) + curlEndUrl
# This is the string to search for in the curl script file
# Read in the relevant curl script file and find the line for the CBV
# data we are looking for
data = urllib.request.urlopen(curlUrl)
foundIndex = None
for line in data:
strLine = str(line)
if SearchString in strLine:
foundIndex = strLine.index(SearchString)
break
if (foundIndex is None):
raise Exception('CBV FITS file not found')
# Extract url from strLine
htmlStartIndex = strLine.find('https:')
htmlEndIndex = strLine.rfind('fits')
# Add 4 for length of 'fits' string
tess_cbv_url = strLine[htmlStartIndex:htmlEndIndex+4]
hdu = pyfits.open(tess_cbv_url)
# Check that this is a TESS CBV FITS file
mission = hdu['Primary'].header['TELESCOP']
validate_method(mission, ['tess'])
return TessCotrendingBasisVectors.from_hdu(hdu=hdu, cbv_type=cbv_type, band=band)
except:
raise Exception('CBVS were not found')