petitRADTRANS.retrieval.preparing
Useful functions for data reduction.
Module Contents
Functions
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SYSREM iteration for all orders at once. Starting with a. |
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SYSREM iteration for all orders at once. Starting with c. |
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Remove telluric lines with a polynomial function. |
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Remove the telluric lines using the weighted arithmetic mean over time. |
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Remove variable throughput with a polynomial function. |
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Correct for the variable throughput using the weighted arithmetic mean over wavelength. |
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Removes the telluric lines and variable throughput of some data. |
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SYSREM preparing pipeline. |
- petitRADTRANS.retrieval.preparing.__init_pipeline(spectrum, uncertainties)
- petitRADTRANS.retrieval.preparing.__init_pipeline_outputs(spectrum, reduction_matrix, uncertainties)
- petitRADTRANS.retrieval.preparing.__sysrem_iteration_orders_a(spectrum_uncertainties_squared, uncertainties_squared_inverted, a)
SYSREM iteration for all orders at once. Starting with a. For the first iteration, a should be 1. The inputs are chosen in order to maximize speed.
- Args:
spectrum_uncertainties_squared: spectral data to correct over the uncertainties ** 2 (…, exposure, wavelength) uncertainties_squared_inverted: invers of the squared uncertainties on the data (…, exposure, wavelength) a: 2-D matrix (…, exposures, wavelengths) containing the a-priori “airmass”
- Returns:
The lower-rank estimation of the spectrum (systematics), and the estimated “extinction coefficients”
- petitRADTRANS.retrieval.preparing.__sysrem_iteration_orders_c(spectrum_uncertainties_squared, uncertainties_squared_inverted, c)
SYSREM iteration for all orders at once. Starting with c. For the first iteration, c should be 1. The inputs are chosen in order to maximize speed.
- Args:
spectrum_uncertainties_squared: spectral data to correct over the uncertainties ** 2 (…, exposure, wavelength) uncertainties_squared_inverted: invers of the squared uncertainties on the data (…, exposure, wavelength) c: 2-D matrix (…, exposures, wavelengths) containing the a-priori “extinction coefficients”
- Returns:
The lower-rank estimation of the spectrum (systematics), and the estimated “extinction coefficients”
- petitRADTRANS.retrieval.preparing.bias_pipeline_metric(prepared_true_model, prepared_mock_observations, mock_observations_preparation_matrix=None, mock_noise=None)
- petitRADTRANS.retrieval.preparing.remove_noisy_wavelength_channels(spectrum, reduction_matrix, mean_subtract=False)
- petitRADTRANS.retrieval.preparing.remove_telluric_lines_fit(spectrum, reduction_matrix, airmass, uncertainties=None, mask_threshold=1e-16, polynomial_fit_degree=2, correct_uncertainties=True, uncertainties_as_weights=True)
Remove telluric lines with a polynomial function. The telluric transmittance can be written as:
T = exp(-airmass * optical_depth),
- hence the log of the transmittance can be written as a first order polynomial:
log(T) ~ b * airmass + a.
Using a 1st order polynomial might be not enough, as the atmospheric composition can change slowly over time. Using a second order polynomial, as in:
log(T) ~ c * airmass ** 2 + b * airmass + a,
might be safer.
- Args:
spectrum: spectral data to correct reduction_matrix: matrix storing all the operations made to reduce the data airmass: airmass of the data uncertainties: uncertainties on the data mask_threshold: mask wavelengths where the Earth atmospheric transmittance estimate is below this value polynomial_fit_degree: degree of the polynomial fit of the Earth atmospheric transmittance correct_uncertainties: uncertainties_as_weights:
- Returns:
Corrected spectral data, reduction matrix and uncertainties after correction
- petitRADTRANS.retrieval.preparing.remove_telluric_lines_mean(spectrum, reduction_matrix, uncertainties=None, mask_threshold=1e-16, uncertainties_as_weights=True)
Remove the telluric lines using the weighted arithmetic mean over time.
- Args:
spectrum: spectral data to correct reduction_matrix: matrix storing all the operations made to reduce the data uncertainties: uncertainties on the data mask_threshold: mask wavelengths where the Earth atmospheric transmittance estimate is below this value uncertainties_as_weights:
- Returns:
Corrected spectral data, reduction matrix and uncertainties after correction
- petitRADTRANS.retrieval.preparing.remove_throughput_fit(spectrum, reduction_matrix, wavelengths, uncertainties=None, mask_threshold=1e-16, polynomial_fit_degree=2, correct_uncertainties=True, uncertainties_as_weights=True)
Remove variable throughput with a polynomial function.
- Args:
spectrum: spectral data to correct reduction_matrix: matrix storing all the operations made to reduce the data wavelengths: wavelengths of the data uncertainties: uncertainties on the data mask_threshold: mask wavelengths where the Earth atmospheric transmittance estimate is below this value polynomial_fit_degree: degree of the polynomial fit of the Earth atmospheric transmittance correct_uncertainties: uncertainties_as_weights:
- Returns:
Corrected spectral data, reduction matrix and uncertainties after correction
- petitRADTRANS.retrieval.preparing.remove_throughput_mean(spectrum, reduction_matrix=None, uncertainties=None, uncertainties_as_weights=True)
Correct for the variable throughput using the weighted arithmetic mean over wavelength.
- Args:
spectrum: spectral data to correct reduction_matrix: matrix storing all the operations made to reduce the data uncertainties: uncertainties on the data
- Returns:
Corrected spectral data, reduction matrix and uncertainties after correction
- petitRADTRANS.retrieval.preparing.trim_spectrum(spectrum, uncertainties=None, wavelengths=None, airmass=None, threshold_low=0.8, threshold_high=1.2, threshold_outlier=None, polynomial_fit_degree=2, relative_to_continnum=True, uncertainties_as_weights=True)
- petitRADTRANS.retrieval.preparing.polyfit(spectrum, uncertainties=None, wavelengths=None, airmass=None, tellurics_mask_threshold=0.1, polynomial_fit_degree=1, apply_throughput_removal=True, apply_telluric_lines_removal=True, correct_uncertainties=True, uncertainties_as_weights=False, full=False, **kwargs)
Removes the telluric lines and variable throughput of some data. If airmass is None, the Earth atmospheric transmittance is assumed to be time-independent, so telluric transmittance will be fitted using the weighted arithmetic mean. Otherwise, telluric transmittance are fitted with a polynomial.
- Args:
spectrum: spectral data to correct uncertainties: uncertainties on the data wavelengths: wavelengths of the data airmass: airmass of the data tellurics_mask_threshold: mask wavelengths where the atmospheric transmittance estimate is below this value polynomial_fit_degree: degree of the polynomial fit of the Earth atmospheric transmittance apply_throughput_removal: if True, apply the throughput removal correction apply_telluric_lines_removal: if True, apply the telluric lines removal correction correct_uncertainties: full: if True, return the reduced matrix and reduced uncertainties in addition to the reduced spectrum
- Returns:
Reduced spectral data (and reduction matrix and uncertainties after reduction if full is True)
- petitRADTRANS.retrieval.preparing.sysrem(spectrum, uncertainties, wavelengths, n_passes=1, n_iterations_max=10, convergence_criterion=0.001, tellurics_mask_threshold=0.8, polynomial_fit_degree=1, apply_throughput_removal=True, apply_telluric_lines_removal=True, correct_uncertainties=True, uncertainties_as_weights=False, subtract=True, remove_mean=True, full=False, verbose=False, **kwargs)
SYSREM preparing pipeline. SYSREM tries to find the coefficients a and c such as:
S**2 = sum_ij ((spectrum_ij - a_j * c_i) / uncertainties)**2
is minimized. Several iterations can be performed. This assumes that the spectrum is deformed by a combination of linear effects. The coefficients a and c can be seen as estimates for any strong (linear) systematic effect in the data, they are not necessarily related to the airmass and extinction coefficients.
Source: Tamuz et al. 2005 (doi:10.1111/j.1365-2966.2004.08585.x). Thanks to Alejandro Sanchez-Lopez (26-09-2017) for sharing his version of the algorithm.
- Args:
spectrum: spectral data to correct uncertainties: uncertainties on the data wavelengths: wavelengths of the data n_passes: number of SYSREM passes n_iterations_max: maximum number of SYSREM iterations convergence_criterion: SYSREM convergence criterion tellurics_mask_threshold: mask wavelengths where the atmospheric transmittance estimate is below this value polynomial_fit_degree: degree of the polynomial fit of the instrumental deformations apply_throughput_removal: if True, divide the spectrum by its mean over wavelengths apply_telluric_lines_removal: if True, apply the telluric lines removal correction correct_uncertainties: subtract: if True, subtract the fitted systematics to the spectrum instead of dividing them remove_mean: full: if True, return the reduced matrix and reduced uncertainties in addition to the reduced spectrum verbose: if True, print the convergence status at each iteration
- Returns:
Reduced spectral data (and reduction matrix and uncertainties after reduction if full is True)