Documentation for TPFMachine
Subclass of Machine that specifically works with TPFs
__init__(self, tpfs, time, flux, flux_err, ra, dec, sources, column, row, limit_radius=24.0, time_mask=None, n_r_knots=10, n_phi_knots=15, time_nknots=10, time_resolution=200, time_radius=8, rmin=1, rmax=16, cut_r=6, pix2obs=None, pos_corr1=None, pos_corr2=None, focus_mask=None, tpf_meta=None, cartesian_knot_spacing='sqrt', bkg_subtracted=True, sparse_dist_lim=40, sources_flux_column='phot_g_mean_flux')
special
Parameters are similar to Machine. Additional arguments are:
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
tpfs |
lightkurve.TargetPixelFileCollection |
Collection of TPFs from the |
required |
pix2obs |
np.ndarray |
Array of values indicating the TPF of origin for each pixel. Has shape [npixels]. |
None |
pos_corr1 |
np.ndarray |
Array with position corrector vectors for axis 1 from the TPF |
None |
pos_corr2 |
np.ndarray |
Array with position corrector vectors for axis 2 from the TPF |
None |
focus_mask |
np.ndarray boolean |
Array of boolean indicating masked cadences due to focus change. This is useful to mask out cadences when building shape or time model. |
None |
tpf_meta |
dictionary |
Dictionary containing metadata information related to the data. |
None |
cartesian_knot_spacing |
string |
Type of spacing "linear" or "sqrt" between knots when creating a cartesian design matrix. |
'sqrt' |
bkg_subtracted |
bool |
Data is background subtracted or not. |
True |
fit_lightcurves(self, plot=False, fit_bkg=False, fit_va=True, iter_negative=True, load_shape_model=False, shape_model_file=None, sap=True)
Fit the sources inside the TPFs passed to TPFMachine.
This function creates the lcs attribuite that contains a collection of light
curves in the form of lightkurve.LightCurveCollection. Each entry in the
collection is a lightkurve.KeplerLightCurve object with the different type
of photometry (SAP, PSF, and PSF velocity-aberration corrected). Also each
lightkurve.KeplerLightCurve object includes its asociated metadata.
The photometry can also be accessed independently from the following attribuites
that fit_lightcurves create:
* ws and werrs have the uncorrected PSF flux and flux errors.
* ws_va and werrs_va have the PSF flux and flux errors corrected by
velocity aberration.
* sap_flux and sap_flux_err have the flux and flux errors computed
using aperture mask.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
plot |
bool |
Whether or not to show some diagnostic plots. These can be helpful for a user to see if the PRF and time dependent models are being calculated correctly. |
False |
fit_bkg |
bool |
Fit and remove background. Useful when providing flux values without background substraction. |
False |
fit_va |
bool |
Whether or not to fit Velocity Aberration (which implicitly will try to fit other kinds of time variability). This will try to fit the "long term" trends in the dataset. If True, this will take slightly longer to fit. If you are interested in short term phenomena, like transits, you may find you do not need this to be set to True. If you have the time, it is recommended to run it. |
True |
iter_negative |
bool |
When fitting light curves, it isn't possible to force the flux to be positive. As such, when we find there are light curves that deviate into negative flux values, we can clip these targets out of the analysis and rerun the model. If iter_negative is True, PSFmachine will run up to 3 times, clipping out any negative targets each round. |
True |
load_shape_model |
bool |
Load PRF shape model from disk or not. Default models were computed from FFI of the same channel and quarter. |
False |
shape_model_file |
string |
Path to PRF model file to be passed to |
None |
sap |
boolean |
Compute or not Simple Aperture Photometry. See
|
True |
from_TPFs(tpfs, magnitude_limit=18, dr=3, sources=None, time_mask=None, apply_focus_mask=True, renormalize_tpf_bkg=False, query_ra=None, query_dec=None, query_rad=None, **kwargs)
staticmethod
Convert TPF input into Machine object:
- Parse TPFs to extract time, flux, clux erros, and bookkeeping of
the TPF-pixel correspondance
- Convert pixel-based coordinates (row, column) into WCS (ra, dec) for all pixels
- Clean pixels with no values, bad cadences, and remove duplicated pixels due to overlapping TPFs
- Query Gaia DR2 data base to find all sources present in the TPF images
- Clean unresolve sources (within 6`` = 1.5 pixels) and sources off silicon with a 1 pixel tolerance (distance from source gaia pocition and TPF edge)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
tpfs |
lightkurve TargetPixelFileCollection |
Collection of Target Pixel files |
required |
magnitude_limit |
float |
Limiting magnitude to query Gaia catalog. |
18 |
dr |
int |
Gaia data release to be use, default is 3, options are DR2 and EDR3 |
3 |
sources |
pandas.DataFrame |
Catalog with sources to be extracted by PSFMachine |
None |
time_mask |
boolean array |
Mask to be applied to discard cadences if needed. |
None |
apply_focus_mask |
boolean |
Mask or not cadances near observation gaps to remove focus change. |
True |
renormalize_tpf_bkg |
boolean |
Add or not background to the flux array. |
False |
query_ra |
numpy.array |
Array of RA to query Gaia catalog. Default is |
None |
query_dec |
numpy.array |
Array of Dec to query Gaia catalog. Default is |
None |
query_rad |
numpy.array |
Array of radius to query Gaia catalog. Default is |
None |
**kwargs |
None |
Keyword arguments to be passed to |
{} |
Returns:
| Type | Description |
|---|---|
Machine object |
A Machine class object built from TPFs. |
get_source_centroids(self, method='poscor')
Compute centroids for sources in pixel coordinates.
It implements three different methods to calculate centroids:
* "aperture": computes centroids from moments Machine.estimate_source_centroids_aperture(). This needs the aperture
masks to becomputed in advance with Machine.compute_aperture_photometry.
Note that for sources with partial data (i.e. near TPF edges) the this
method is illdefined.
* "poscor": uses Gaia RA and Dec coordinates converted to pixel
space using the TPFs WCS solution and the apply each TPF 'pos_corr'
correction
* "scene": uses Gaia coordinates again, but correction is computed from
the TPF scene jitter using 'Machine._get_centroids()'.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
method |
string |
What type of corrected centroid will be computed.
If "aperture", it creates attributes Note: "poscor" and "scene" show consistent results below 10th of a pixel. |
'poscor' |
lcs_in_tpf(self, tpf_number)
Returns the light curves from a given TPF as a lightkurve.LightCurveCollection.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
tpf_number |
int |
Index of the TPF |
required |
load_shape_model(self, input=None, plot=False)
Loads a PRF shape model from a FITs file.
Not implemented: By default this function will load PRF shapes computed from FFI data (Kepler, K2, or TESS).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
input |
string |
Name of the file containing the shape parameters and weights. The file
has to be FITS format.
If None, then previously computed shape model from Kepler's FFI will be
download from https://zenodo.org/record/5504503/ and used as default.
The file download from Zenodo happens only the first time that shape models
of a given mission (e.g. Kepler, K2) are asked. Then, shape models for all
channels and quarters will be locally available for future use.
The file is stored in |
None |
plot |
boolean |
Plot or not the mean model. |
False |
plot_background_model(self, frame_index=100)
Diagnostic plot of background model
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
frame_index |
int |
The frame index to be shown. |
100 |
plot_tpf(self, tdx, sap=True)
Make a diagnostic plot of a given TPF in the stack
If you passed a stack of TPFs, this function will show an image of that TPF, and all the light curves inside it, alongside a diagnostic of which source the light curve belongs to.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
tdx |
int |
Index of the TPF to plot |
required |
sap |
boolean |
Overplot the pixel mask used for aperture photometry. |
True |
remove_background_model(self, plot=False, data_augment=None, zero_centered=True)
Function to fit and remove the background signal of the TPF stack using
kbackground package. This is a Kepler/K2 specific tool.
Fits a smooth polynomial to the TPF background pixels (inverse of
machine.source_mask) that can also be augmented with the mission background
pixels (https://archive.stsci.edu/missions-and-data/kepler/kepler-bulk-downloads).
This helps removing the known 'rolling band' effect seen in some Kepler CCD channels.
The function changes the attribute self.flux removing the background (just once)
and creates the following new attributes:
- self.bkg_row has the bacground pixel row number (can be augmented)
- self.bkg_column has the bacground pixel column number (can be augmented)
- self.bkg_flux has the bacground pixel flux value (can be augmented)
- self.bkg_estimator has the background estimator from kbackground
- self.bkg_pixel_mask is the background pixel mask
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
plot |
boolean |
Show diagnostic plot |
False |
data_augment |
dictionary |
Dictionary with the pixel row, column and flux value to augment the background pixels. This argument is useful to include Kepler's background pixels from the mission. The dictionary has to have the following keys; "column", "row", and "flux". |
None |
zero_centered |
boolean |
If True the pixel background level will be zero centered. If False pixels will be positive. |
True |
save_shape_model(self, output=None)
Saves the weights of a PRF fit to disk as a FITS file.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
output |
str, None |
Output file name. If None, one will be generated. |
None |