Analysis

Analysis#

class vampires_dpp.analysis.MetricInfo(category, comment, unit='', scalar=False)#

Registry entry describing how a metric should be written into FITS headers.

Parameters:

  • category (str)

  • comment (str)

  • unit (str)

  • scalar (bool)

class vampires_dpp.analysis.Moffat(x0, y0, gammax, gammay, theta, alpha, amplitude, background, **kwargs)#
static evaluate(x, y, x0, y0, gammax, gammay, theta, alpha, amplitude, background)#

Evaluate the model on some input variables.

static fit_deriv(x, y, x0, y0, gammax, gammay, theta, alpha, amplitude, background)#
param_names = ('x0', 'y0', 'gammax', 'gammay', 'theta', 'alpha', 'amplitude', 'background')#

Names of the parameters that describe models of this type.

The parameters in this tuple are in the same order they should be passed in when initializing a model of a specific type. Some types of models, such as polynomial models, have a different number of parameters depending on some other property of the model, such as the degree.

When defining a custom model class the value of this attribute is automatically set by the ~astropy.modeling.Parameter attributes defined in the class body.

vampires_dpp.analysis.add_coadd_metrics_to_header(hdul, metrics)#

Write scalar coadd-time metrics into the FITS headers.

Overrides any time-averaged values written earlier by add_metrics_to_header. Expects each metric to have shape (nfields, npsfs). Unit strings in the comments are retuned to the current BUNIT so labels match the cube state after specphot calibration.

Return type:

HDUList

Parameters:

hdul (HDUList)

vampires_dpp.analysis.add_metrics_to_header(hdul, metrics)#

Write per-field metric values into the primary HDU header.

Keys follow hierarch DPP <CATEGORY> <KEY> <FIELD> [<PSF#>]. Per-PSF values are written when a metric is multi-PSF; a field-aggregate (mean over PSFs of the time-mean) is also written without the PSF suffix. Unknown metric keys are skipped with a debug log so the registry stays explicit.

Return type:

HDUList

Parameters:

hdul (HDUList)

vampires_dpp.analysis.analyze_coadded_hdul(hdul, window_centers, psfs=None, *, aper_rad=8, ann_rad=None, window_size=21, do_phot=True, do_strehl=False, do_psf_model=False, psf_model='moffat')#

Measure scalar non-centroid stats on each (field, PSF window) of a coadded HDUList.

PSF positions follow the recenter convention: each PSF sits at frame_center(field_frame) + window_offsets[field_idx, psf_idx] where window_offsets are the per-PSF offsets from the field’s mean window center. Returns a dict where each value has shape (nfields, npsfs).

Return type:

dict[str, ndarray]

Parameters:

  • hdul (HDUList)

  • aper_rad (float)

  • window_size (int)

  • do_phot (bool)

  • do_strehl (bool)

  • do_psf_model (bool)

  • psf_model (Literal['moffat'])

vampires_dpp.analysis.analyze_window(frame, frame_err, center, *, aper_rad=8, ann_rad=None, window_size=21, do_phot=True, do_strehl=False, psf=None, do_psf_model=False, psf_model='moffat')#

Run non-centroid statistics on a single 2D frame at a known center.

Returns scalar values per metric. Used for measuring coadded frames where there is no time axis and the PSF position is already known.

Return type:

dict[str, float]

Parameters:

  • aper_rad (float)

  • window_size (int)

  • do_phot (bool)

  • do_strehl (bool)

  • do_psf_model (bool)

  • psf_model (Literal['moffat'])

vampires_dpp.analysis.find_norm_peak(image, center, window_size=20, phot_rad=8, oversamp=4)#

usage: peak = find_peak(image, xc, yc, boxsize) finds the subpixel peak of an image

image: an image of a point source for which we would like to find the peak xc, yc: approximate coordinate of the point source boxsize: region in which most of the flux is contained (typically 20) oversamp: how many times to oversample the image in the FFT interpolation in order to find the peak

Return type:

float

Returns:

peak of the oversampled image

Marcos van Dam, October 2022, translated from IDL code of the same name

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