API Documentation

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pykoala.ancillary module

Utility functions.

Many of them are partially implemented. They were in the old, non-modular version of PyKOALA and have not been included in the current modular scheme.

pykoala.ancillary.detect_edge(rss)[source]

Detect the edges of a RSS. Returns the minimum and maximum wavelength that determine the maximum interval with valid (i.e. no masked) data in all the spaxels.

Parameters

rss (RSS object.)

Returns

  • min_w (float) – The lowest value (in units of the RSS wavelength) with valid data in all spaxels.

  • min_index (int) – Index of min_w in the RSS wavelength variable.

  • max_w (float) – The higher value (in units of the RSS wavelength) with valid data in all spaxels.

  • max_index (int) – Index of max_w in the RSS wavelength variable.

pykoala.ancillary.update_wcs_coords(wcs, ra_dec_val=None, ra_dec_offset=None)[source]

Update the celestial reference values of a WCS.

Update the celestial coordinates of a WCS using new central values of RA and DEC or relative offsets expressed in degree.

Parameters

  • - wcs (asteropy.wcs.WCS) – Target WCS to update.

  • - ra_dec_val (list or tupla, default=None) – New CRVAL of RA and DEC.

  • - ra_dec_offset (list or tupla, default=None) – Relative offset that will be applyied to CRVAL of RA and DEC axis. If ra_dec_val is privided, this will be ignored.

Returns

- correct_wcs – A copy of the original WCS with the reference values updated.

Return type

astropy.wcs.WCS

pykoala.ancillary.med_abs_dev(x, axis=0)[source]
pykoala.ancillary.std_from_mad(x, axis=0, k=1.4826)[source]
pykoala.ancillary.running_mean(x, n_window)[source]

This function calculates the running mean of an array.

Parameters

  • x ((n,) np.ndarray) – This is the given array.

  • n_window (Int) – Number of neighbours for computing the running mean.

Returns

running_mean_x – Running mean array.

Return type

(n - n_window,) np.ndarray

pykoala.ancillary.flux_conserving_interpolation(new_wavelength, wavelength, spectra, **interp_args)[source]

Flux-conserving linear interpolation.

Linear interpolation of a spectrum \(I_\lambda(\labmda)\) as a function of wavelength \(\lambda\), ensuring that the integrated flux math:: F(lambda_a, lambda_b) = int_{lambda_a}^{lambda_b} I_lambda(labmda) dlambda$

is conserved for any \((\lambda_a, \lambda_b)\).

np.nan values become zero.

Parameters

  • new_wavelength (ndarray) – New values of the x coordinate (wavelength) \(\lambda_{new}\).

  • wavelength (ndarray) – Old values of the x coordinate (wavelength) \(\lambda\).

  • spectra (ndarray) – Old values of the y coordinate (spectrum) \(I_\lambda(\labmda)\).

  • **interp_args (dict, optional) – Additional parameters to be passed to np.interp

Returns

Interpolated spectra \(I_\lambda(\labmda_{new})\).

Return type

ndarray

Notes

The function computes the cumulative flux with np.nancumsum, calls np.interp, and differentiates back.

pykoala.ancillary.centre_of_mass(w, x, y)[source]

Compute the centre of mass of a given image. :Parameters: * w (np.ndarray(float)) – (n,) weights computing the centre of mass.

  • x (np.ndarray(float)) – (n,) Coordinates corresponding to the x-axis (columns).

  • y (np.ndarray(float)) – (n,) Coordinates corresponding to the y-axis (rows).

Returns

  • x_com (float)

  • y_com (float)

pykoala.ancillary.growth_curve_1d(f, x, y)[source]

TODO

pykoala.ancillary.growth_curve_2d(image, x0=None, y0=None)[source]

Compute the curve of growth of an array f with respect to a given point (x0, y0).

Parameters

  • image (np.ndarray) – 2D array

  • x0 (float) – Origin of coordinates in pixels along the x-axis (columns).

  • y0 (float) – Origin of coordinates in pixels along the y-axis (rows).

Returns

  • r2 (np.ndarray) – Vector containing the square radius with respect to (x0, y0).

  • growth_curve (np.ndarray) – Curve of growth centered at (x0, y0).

pykoala.ancillary.interpolate_image_nonfinite(image)[source]

Use scipy.interpolate.NearestNDInterpolator to replace NaN values.

Parameters

  • - image ((np.ndarray)) – 2D array to be interpolated

  • Returnrs

  • ——–

  • - interpolated_image ((np.ndarray))

pykoala.ancillary.vac_to_air(vac_wl)[source]

Convert wavelength in vacuum to air using Morton (1991, ApJS, 77, 119).

Parameters

- vac_wl (np.ndarray) – Vector of vacuum wavelengths in Angstrom.

Returns

- air_wl – Vector of air wavelengths in Angstrom

Return type

np.ndarray

pykoala.ancillary.smooth_spectrum(wlm, s, wave_min=0, wave_max=0, step=50, exclude_wlm=[[0, 0]], order=7, weight_fit_median=0.5, plot=False, verbose=False, fig_size=12)[source]

THIS IS NOT EXACTLY THE SAME THING THAT applying signal.medfilter()

This needs to be checked, updated, and combine (if needed) with task fit_smooth_spectrum. The task gets the median value in steps of “step”, gets an interpolated spectrum, and fits a 7-order polynomy.

It returns fit_median + fit_median_interpolated (each multiplied by their weights).

Tasks that use this: get_telluric_correction

pykoala.ancillary.vect_norm(a, b)[source]

Compute the norm of two vectors.

pykoala.ancillary.in_rectangle(pos, rectangle_pos)[source]

Check if a point lies in the perimeter of a rectangle.

pykoala.ancillary.pixel_in_circle(pixel_pos, pixel_size, circle_pos, circle_radius)[source]

Compute the area of a pixel within a circle.

Parameters

  • - pixel_pos (tuple) – Position of the lower left corner of the pixel

  • - pixel_size (float) – Size of the pixel.

  • - circle_pos (tuple) – Position of the circle centre

  • - circle_raidus (float) – Radius of the circle.

Returns

  • - area_pixel – Area of the pixel contained within the circle

  • - area_fraction – Fration of the circle area that overlaps with the pixel.

pykoala.ancillary.cumulative_1d_sky(r2, sky_brightness)[source]

1D cumulative sky brightness. F_sky = 4*pi*r2 * B_sky

Parameters

  • r2 (np.array(float)) – Square radius from origin.

  • sky_brightness (float) – Sky surface brightness.

Returns

cumulative_sky_brightness – Cumulative sky brightness.

Return type

np.array(float)

pykoala.ancillary.cumulative_1d_moffat(r2, l_star, alpha2, beta)[source]

Cumulative Moffat ligth profile.

Parameters

  • r2 (np.array(float)) – Square radius with respect to the profile centre.

  • l_star (float) – Total luminosity integrating from 0 to inf.

  • alpha2 (float) – Characteristic square radius.

  • beta (float) – Power-low slope

Returns

cum_moffat_prof – Cumulative Moffat profile

Return type

np.array(float)

pykoala.ancillary.cumulative_1d_moffat_sky(r2, l_star, alpha2, beta, sky_brightness)[source]

Combined model of cumulative_1d_moffat and cumulative_1d_sky.

pykoala.ancillary.fit_moffat(r2_growth_curve, f_growth_curve, f_guess, r2_half_light, r_max, plot=False)[source]

Fits a Moffat profile to a flux growth curve as a function of radius squared, cutting at to r_max (in units of the half-light radius), provided an initial guess of the total flux and half-light radius squared.

# TODO :Parameters: * r2_growth_curve (TYPE) – DESCRIPTION.

  • F_growth_curve (TYPE) – DESCRIPTION.

  • F_guess (TYPE) – DESCRIPTION.

  • r2_half_light (TYPE) – DESCRIPTION.

  • r_max (TYPE) – DESCRIPTION.

  • plot (Boolean, optional) – If True generates and shows the plots. The default is False.

Returns

fit – DESCRIPTION.

Return type

TYPE

pykoala.ancillary.gaussian_2d(xy, amplitude, x0, y0, sigma_x, sigma_y, offset)[source]
pykoala.ancillary.mask_lines(wave_array, width=30, lines=dict_values([3970.1, 4101.7, 4340.4, 4861.3, 6562.79, 3934.777, 3969.588, 5176.7, 5895.6, 8500.36, 8544.44, 8664.52]))[source]

pykoala.cubing module

pykoala.data_container module

pykoala.rss module

Module contents

pykoala.log_into_file(filename, logger_name='pykoala', level='INFO')[source]
pykoala.vprint(msg, *args, **kwargs)[source]

Convenience function for using with the pykoala generic logger.

class pykoala.VerboseMixin[source]

Bases: object

property logger
property verbose

Abstract property for the verbosity of the correction.

vprint(msg, level='info')[source]

Logs a message at the specified level.

Parameters

  • msg (str) – The message to be logged.

  • level (str, optional) – The level at which to log the message (default is ‘info’).

log_into_file(filename, level='INFO')[source]