API Documentation

Subpackages

• pykoala.corrections package
  • Submodules
  • pykoala.corrections.atmospheric_corrections module
  • pykoala.corrections.correction module
  • pykoala.corrections.flux_calibration module
  • pykoala.corrections.sky module
  • pykoala.corrections.throughput module
  • Module contents
• pykoala.exceptions package
  • Submodules
  • pykoala.exceptions.exceptions module
  • Module contents
• pykoala.instruments package
  • Submodules
  • pykoala.instruments.hector_ifu module
  • pykoala.instruments.koala_ifu module
  • pykoala.instruments.weave module
  • Module contents
• pykoala.plotting package
  • Submodules
  • pykoala.plotting.qc_plot module
  • Module contents
• pykoala.register package
  • Submodules
  • pykoala.register.registration module
  • pykoala.register.registration_ext_source module
  • Module contents

Submodules

pykoala.ancillary module

This module contains a collection of ancilliary functions which are either partially implemented or not fully implemented. Currently this acts as a placeholder for functions that were in the original non-modular version of PyKOALA which have not been included in the modular version

pykoala.ancillary.centre_of_mass(w, x, y)

Compute the centre of mass of a given image. :param w: (n,) weights computing the centre of mass. :type w: np.ndarray(float) :param x: (n,) Coordinates corresponding to the x-axis (columns). :type x: np.ndarray(float) :param y: (n,) Coordinates corresponding to the y-axis (rows). :type y: np.ndarray(float)

Returns

• x_com (float)

• y_com (float)

pykoala.ancillary.cumulative_1d_moffat(r2, l_star, alpha2, beta)

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)

Combined model of cumulative_1d_moffat and cumulative_1d_sky.

pykoala.ancillary.cumulative_1d_sky(r2, sky_brightness)

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.detect_edge(rss)

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.fit_moffat(r2_growth_curve, f_growth_curve, f_guess, r2_half_light, r_max, plot=False)

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.

r2_growth_curveTYPE

DESCRIPTION.

F_growth_curveTYPE

DESCRIPTION.

F_guessTYPE

DESCRIPTION.

r2_half_lightTYPE

DESCRIPTION.

r_maxTYPE

DESCRIPTION.

plotBoolean, optional

If True generates and shows the plots. The default is False.

Returns

fit – DESCRIPTION.

Return type

TYPE

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

#TODO… :param new_wavelength: :param wavelength: :param spectra: :param interp_args:

pykoala.ancillary.gaussian_2d(xy, amplitude, x0, y0, sigma_x, sigma_y, offset)

pykoala.ancillary.growth_curve_1d(f, x, y)

TODO

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

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.in_rectangle(pos, rectangle_pos)

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

pykoala.ancillary.interpolate_image_nonfinite(image)

Use scipy.interpolate.NearestNDInterpolator to replace NaN values.

Parameters

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

• Returnrs –

• -------- –

• interpolated_image (-) –

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]))

pykoala.ancillary.med_abs_dev(x, axis=0)

pykoala.ancillary.pixel_in_circle(pixel_pos, pixel_size, circle_pos, circle_radius)

Compute the area of a pixel within a circle.

Parameters

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

• pixel_size (-) – Size of the pixel.

• circle_pos (-) – Position of the circle centre

• circle_raidus (-) – 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.running_mean(x, n_window)

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.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)

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.std_from_mad(x, axis=0, k=1.4826)

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

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 (-) – Target WCS to update.

• ra_dec_val (-) – New CRVAL of RA and DEC.

• ra_dec_offset (-) – 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.vect_norm(a, b)

Compute the norm of two vectors.

pykoala.ancillary.vprint(*arg, **kwargs)

Prints the arguments only if verbose=True.

pykoala.cubing module

pykoala.data_container module

pykoala.rss module

Module contents