Available Classes¶

Currently there are two classes available.

Spectrum
DifferentialSpectrum

Spectrum¶

A class to contain a spectrum. The operators have been overloaded so that you can easily manipulate spectra.

class spectrum_overload.spectrum.Spectrum(*, xaxis: Union[numpy.ndarray, List[Union[int, float]], None] = None, flux: Union[numpy.ndarray, List[Union[int, float]], None] = None, calibrated: bool = True, header: Union[astropy.io.fits.header.Header, Dict[str, Any], None] = None, interp_method: str = 'spline')[source]¶

Bases: object

Spectrum class to represent and manipulate astronomical spectra.

xaxis¶: np.ndarray – The wavelength or pixel position values.

flux¶: np.ndarray, array-like, list – The extracted flux (measured intensity of light).

calibrated¶: bool – Flag to indicate calibration state. (Default = True.)

header¶: astropy.Header, dict-like – Header information of observation.

add_noise(snr: Union[float, int]) → None[source]¶: Add noise level of snr to the flux of the spectrum.

add_noise_sigma(sigma)[source]¶: Add Gaussian noise with given sigma.

calibrate_with(wl_map: Union[numpy.ndarray, List[int]]) → None[source]¶

Calibrate with a wavelength mapping polynomial.

Parameters:	wl_map – Polynomial coefficients of the form expected by np.poylval(). [p0, p1, p2 …]
Returns:	Replaces xaxis of self. Also self.calibrated is set to True.
Return type:	None

Notes

The parameters can be generated by np.polyfit(x, y, order)

continuum(method: str = 'scalar', degree: Optional[int] = None, **kwargs) → spectrum_overload.spectrum.Spectrum[source]¶

Fit the continuum of the spectrum.

Fit a function of method to the median of the highest ntop points of nbins bins of the spectrum.``

Parameters:	method (str ("scalar")) – The function type, valid functions are “scalar”, “linear”, “quadratic”, “cubic”, “poly”, and “exponential”. Default “scalar”. degree (int, None) – Degree of polynomial when method=”poly”. Default = None. nbins (int) – Number of bins to separate the spectrum into. ntop (int) – Number of highest points in bin to take median of.
Returns:	s – Spectrum of the continuum.
Return type:	Spectrum

copy() → spectrum_overload.spectrum.Spectrum[source]¶: Copy the spectrum.

crosscorr_rv(spectrum: spectrum_overload.spectrum.Spectrum, rvmin: float, rvmax: float, drv: float, **params) → Tuple[numpy.ndarray, numpy.ndarray][source]¶

Perform pyasl.crosscorrRV with another spectrum.

Parameters:

spectrum (Spectrum) – Spectrum object to cross correlate with.
rvmin (float) – Minimum radial velocity for which to calculate the cross-correlation function [km/s].
rvmax (float) – Maximum radial velocity for which to calculate the cross-correlation function [km/s].
drv (float) – The width of the radial-velocity steps to be applied in the calculation of the cross-correlation function [km/s].
params (dict) – Cross-correlation parameters.

Returns:

dRV (array) – The RV axis of the cross-correlation function. The radial velocity refer to a shift of the template, i.e., positive values indicate that the template has been red-shifted and negative numbers indicate a blue-shift of the template. The numbers are given in km/s.
CC (array) – The cross-correlation function.

Notes

The PyAstronomy function pyasl.crosscorrRV() is used

http://www.hs.uni-hamburg.de/DE/Ins/Per/Czesla/PyA/PyA/pyaslDoc/aslDoc/crosscorr.html

doppler_shift(rv: float) → None[source]¶

Doppler shift wavelength by a given Radial Velocity.

Apply Doppler shift to the wavelength values of the spectrum using the radial velocity value provided and the relation RV/c = \(\Delta\lambda/\lambda\).

Parameters:	rv (float) – Radial Velocity to Doppler shift by in km/s.

Warning

Small RV :: A lower limit of RV shift of 0.1 mm/s is set to prevent RV shifts much smaller than wavelength accuracy.
Uncalibrated xaxis :: When the xaxis is uncalibrated there is no wavelength to Doppler shift. A message is printed and no shift is done.

Notes

The Doppler shift is calculated using the relation

\[RV / c = \Delta\lambda / \lambda\]

Where RV is the radial velocity (in km/s), \(\lambda_0\) is the rest wavelength and \(\Delta\lambda\) is the wavelength shift, \((\lambda_{shift} - \lambda_0)\)

flux: Getter for the flux attribute.

instrument_broaden(R, **pya_kwargs)[source]¶

Broaden spectrum by instrumental resolution R.

Uses the PyAstronomy instrBroadGaussFast function.

Parameters:	R (int) – Instrumental Resolution pya_kwargs (dict) – kwarg parameters for pyasl.instrBroadGaussFast()
Returns:	s – Broadened spectrum array.
Return type:	ndarray

interp_method¶: Getter for the interp_method attribute.

interpolate1d_to(reference: Union[numpy.ndarray, str, Spectrum, List[int], List[float]], kind: str = 'linear', bounds_error: bool = False, fill_value: Union[str, numpy.ndarray] = nan) → None[source]¶

Interpolate wavelength solution to the reference wavelength.

This uses the scipy’s interp1d interpolation. The optional parameters are passed to scipy’s interp1d. This interpolates self to the given reference xaxis values. It overwrites the xaxis and flux of self with the new values.

Parameters:

reference (Spectrum or numpy.ndarray) – The reference xaxis values to interpolate to.
kind ((str or int, optional)) – Specifies the kind of interpolation as a string (‘linear’, ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic, ‘cubic’ where ‘slinear’, ‘quadratic’ and ‘cubic’ refer to a spline interpolation of first, second or third order) or as an integer specifying the order of the spline interpolator to use. Default is ‘linear’.
bounds_error (bool, optional) – If True, a ValueError is raised any time interpolation is attempted on a value outside of the range of x (where extrapolation is necessary). If False, out of bounds values are assigned fill_value. By default, an error is raised unless fill_value=”extrapolate”.
fill_value (array-like or “extrapolate”, optional (default = NaN)) – If a ndarray (or float), this value will be used to fill in for requested points outside of the data range. If not provided, then the default is NaN. The array-like must broadcast properly to the dimensions of the non-interpolation axes. If a two-element tuple, then the first element is used as a fill value for x_new < x[0] and the second element is used for x_new > x[-1]. Anything that is not a 2-element tuple (e.g., list or ndarray, regardless of shape) is taken to be a single array-like argument meant to be used for both bounds as below, above = fill_value, fill_value. If “extrapolate”, then points outside the data range will be extrapolated. (“nearest” and “linear” kinds only.)

Raises:

TypeError: – Cannot interpolate with the given object of type <type>.

References

scipy.interolate.interp1d https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html#scipy.interpolate.interp1d

length_check() → None[source]¶

Check length of xaxis and flux are equal.

If everything is ok then there is no response/output.

Raises:	ValueError: – The length of xaxis and flux must be the same.

normalize(method: str = 'scalar', degree: Optional[int] = None, **kwargs) → spectrum_overload.spectrum.Spectrum[source]¶

Normalize spectrum by dividing by the continuum.

Valid methods scalar, linear, quadratic, cubic, poly, exponential. poly method uses the degree value provided

Parameters:	method (str ("scalar")) – The function type, valid functions are “scalar”, “linear”, “quadratic”, “cubic”, “poly”, and “exponential”. Default “scalar”. degree (int, None) – Degree of polynomial when method=”poly”. Default = None. kwargs – Extra parameters ntop and nbin for the `continuum` method.
Returns:	s – Normalized Spectrum.
Return type:	Spectrum

plot(axis=None, **kwargs) → None[source]¶: Plot spectrum with matplotlib.

remove_nans() → spectrum_overload.spectrum.Spectrum[source]¶: Returns new spectrum. Uses slicing with isnan mask.

shape()[source]¶: Return flux shape.

spline_interpolate_to(reference: Union[numpy.ndarray, str, Spectrum, List[int], List[float]], w: None = None, bbox: Any = None, k: int = 3, ext: int = 0, check_finite: bool = True, bounds_error: bool = False) → None[source]¶

Interpolate wavelength solution using scipy’s: InterpolatedUnivariateSpline.

The optional parameters are for scipy’s InterpolatedUnivariateSpline function.

Documentation copied from Sicpy:

One-dimensional interpolating spline for a given set of data points.

Fits a spline y = spl(x) of degree k to the provided x, y data. Spline function passes through all provided points. Equivalent to UnivariateSpline with s=0.

Parameters:

x ((N,) array_like) – Input dimension of data points this much be must be in ascending order.
y ((N,) array_like) – Input dimension of data points
w ((N,) array_like, optional) – Weights for spline fitting. Must be positive. If None (default), weights are all equal.
bbox ((2,) array_like, optional) – 2-sequence specifying the boundary of the approximation interval. If None (default), bbox=[x[0], x[-1]].
k (int, optional) – Degree of the smoothing spline. Must be 1 <= k <= 5.
ext (int or str, optional) –
Controls the extrapolation mode for elements not in the interval defined by the knot sequence.

if ext=0 or ‘extrapolate’, return the extrapolated value. if ext=1 or ‘zeros’, return 0 if ext=2 or ‘raise’, raise a ValueError if ext=3 of ‘const’, return the boundary value.

Default value is 0.
check_finite (bool, optional) – Whether to check that the input arrays contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination or non-sensical results) if the inputs do contain infinities or NaNs. Default is True.
Raises –
------- –
TypeError – Cannot interpolate with the given object of type
ValueError – A value in reference is outside the interpolation range.”

Differential Spectrum¶

Compute the difference between two Spectrum objects.

This is in an introductory state and need more work.

Would be useful add an s-profile function from Ferluga et al. 1997. The subtraction of two gaussian lines with a RV offset.