Documentation for Aperture
Collection of aperture utils lifted from
Kepler-Apertures and adapted to work with PSFMachine.
Some this functions inputs and operate on a Machine object but we move them out of
mahine.py to keep the latter smowhow clean and short.
compute_CROWDSAP(psf_models, aperture_mask, idx=None)
Compute the ratio of target flux relative to flux from all sources within
the photometric aperture (i.e. 1 - Crowdeness). Follows definition by Kinemuchi at al. 2012.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
psf_models |
scipy.sparce.csr_matrix |
Sparse matrix with the PSF models for all targets in the scene. It has shape [n_sources, n_pixels]. |
required |
aperture_mask |
numpy.ndarray |
Array of boolean indicating the aperture for the target source. It has shape of [n_sources, n_pixels]. |
required |
idx |
int |
Source index for what the metric is computed. Value has to be betweeen 0 and psf_model first dimension size. If None, it returns the metric for all sources (first dimension of psf_model). |
None |
Returns:
| Type | Description |
|---|---|
numpy.ndarray |
Crowdeness metric |
compute_FLFRCSAP(psf_models, aperture_mask)
Compute fraction of target flux enclosed in the optimal aperture to total flux
for a given source (flux completeness). Follows definition by Kinemuchi at al. 2012.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
psf_models |
scipy.sparce.csr_matrix |
Sparse matrix with the PSF models for all targets in the scene. It has shape [n_sources, n_pixels]. |
required |
aperture_mask |
numpy.ndarray |
Array of boolean indicating the aperture for the target source. It has shape of [n_sources, n_pixels]. |
required |
Returns:
| Type | Description |
|---|---|
numpy.ndarray |
Completeness metric |
estimate_source_centroids_aperture(aperture_mask, flux, column, row)
Computes the centroid via 2D moments methods for all sources all times. It needs
aperture_mask to be computed first by runing compute_aperture_photometry.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
aperture_mask |
numpy.ndarray |
Aperture mask, shape is [n_surces, n_pixels] |
required |
flux |
numpy.ndarray |
Flux values at each pixels and times in units of electrons / sec |
required |
column |
numpy.ndarray |
Data array containing the "columns" of the detector that each pixel is on. |
required |
row |
numpy.ndarray |
Data array containing the "rows" of the detector that each pixel is on. |
required |
Returns:
| Type | Description |
|---|---|
numpy.ndarray |
Column pixel number of the moments centroid, shape is [nsources, ntimes]. |
goodness_metric_obj_fun(percentile, optim_params)
The objective function to minimize with scipy.optimize.minimize_scalar called
during optimization of the photometric aperture.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
percentile |
int |
Percentile of the normalized flux distribution that defines the isophote. |
required |
optim_params |
dictionary |
Dictionary with the variables needed to evaluate the metric: * psf_models * sdx * target_complete * target_crowd |
required |
Returns:
| Type | Description |
|---|---|
float |
Value of the objective function to be used for optiization. |
optimize_aperture(psf_model, target_complete=0.9, target_crowd=0.9, max_iter=100, percentile_bounds=[0, 100], quiet=False)
Function to optimize the aperture mask for a given source.
The optimization is done using scipy Brent's algorithm and it uses a custom
loss function goodness_metric_obj_fun that uses a Leaky ReLU term to
achive the target value for both metrics.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
psf_model |
scipy.sparce.csr_matrix |
Sparse matrix with the PSF models for all targets in the scene. It has shape [n_sources, n_pixels]. |
required |
target_complete |
float |
Value of the target completeness metric. |
0.9 |
target_crowd |
float |
Value of the target crowdeness metric. |
0.9 |
max_iter |
int |
Numer of maximum iterations to be performed by the optimizer. |
100 |
percentile_bounds |
tuple |
Tuple of minimun and maximun values for allowed percentile values during the optimization. Default is the widest range of [0, 100]. |
[0, 100] |
Returns:
| Type | Description |
|---|---|
numpy.ndarray |
An array with the percentile value to defines the "optimal" aperture for each source. |
plot_flux_metric_diagnose(psf_model, idx=0, ax=None, optimal_percentile=None)
Function to evaluate the flux metrics for a single source as a function of
the parameter that controls the aperture size. The flux metrics are computed by taking into account the PSF models of neighbor sources.
This function is meant to be used only to generate diagnostic figures.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
psf_model |
scipy.sparce.csr_matrix |
Sparse matrix with the PSF models for all targets in the scene. It has shape [n_sources, n_pixels]. |
required |
idx |
int |
Index of the source for which the metrcs will be computed. Has to be a number between 0 and psf_models.shape[0]. |
0 |
ax |
matplotlib.axes |
Axis to be used to plot the figure |
None |
Returns:
| Type | Description |
|---|---|
matplotlib.axes |
Figure axes |