Spectral classes and functions

Spectral classes and functions#

Classes and functions for managing and processing spectra

Core Functions#

Core functions for spectral data.

dysh.spectra.core.append_doppler_to_spectral_region_qtable(qtable, refspec)[source]#

Set the doppler_convention and doppler_rest attributes to the columns of qtable.

Parameters:

qtableQTable: Table with SpectralAxis as columns.
refspecSpectrum: The reference spectrum whose spectral axis will be used to set the attributes.

dysh.spectra.core.available_smooth_methods()[source]#

The list of smooth methods that dysh understands. These can be passed to various smooth routines via their method keywords.

Returns:

methods: list: The method names allowable to smooth

dysh.spectra.core.average(data, axis=0, weights=None)[source]#

Average a group of spectra or scans.

Parameters:

datandarray: The spectral data, typically with shape (nspect,nchan).
axisint: The axis over which to average the data. Default axis=0 will return the average spectrum if shape is (nspect,nchan)
weightsndarray: The weights to use in averaging. These might typically be system temperature based. The weights array must be the length of the axis over which the average is taken. Default: None will use equal weights

Returns:

averagendarray: The average along the input axis

dysh.spectra.core.baseline(spectrum, order, exclude=None, exclude_region_upper_bounds=True, **kwargs)[source]#

Fit a baseline to spectrum. The code uses Fitter and polynomial to compute the baseline. See the documentation for those modules for details.

Parameters:

spectrumSpectrum

The input spectrum.

orderint

The order of the polynomial series, a.k.a. baseline order.

excludelist of 2-tuples of int or Quantity, or SpectralRegion

List of region(s) to exclude from the fit. The tuple(s) represent a range in the form [lower,upper], inclusive. Examples:

One channel-based region:

>>> [11,51]

Two channel-based regions:

>>> [(11,51),(99,123)]

One Quantity region:

>>> [110.198*u.GHz,110.204*u.GHz].

One compound SpectralRegion:

>>> SpectralRegion([(110.198*u.GHz,110.204*u.GHz),(110.196*u.GHz,110.197*u.GHz)]).

Default: no exclude region

modelstr

One of ‘polynomial’ or ‘chebyshev’, Default: ‘polynomial’

fitterFitter

The fitter to use. Default: LinearLSQFitter (with calc_uncertaintes=True). Be careful when choosing a different fitter to be sure it is optimized for this problem.

exclude_region_upper_boundsbool

Makes the upper bound of any excision region(s) inclusive. Allows excising channel 0 for lower-sideband data, and the last channel for upper-sideband data.

clip_excludebool

Whether to clip the exclude or include regions when they extend outside the spectrum.spectral_axis.

Returns:

modelQuantityModel: Best fit model. See fit_continuum.

dysh.spectra.core.clip_spectral_region_subregions(spectral_region, spectrum)[source]#: Clip the values of the spectral_region.subregions if they extend outside the spectrum.spectral_axis.

dysh.spectra.core.cog_flux(c, flat_tol=0.1)[source]#

Find the flux from the curve of growth.

Parameters:

carray: Curve of growth values.
flat_tolfloat: Tolerance to define the flat portion of the curve of growth. The flat portion is that which is zero within flat_tol times the rms of the slope.

Returns:

fluxfloat: The median of the curve of growth over its flat portion.
flux_stdfloat: The standard deviation of the curve of growth over the flat portion.
slopearray: The median value of the slope for the curve of growth before it becomes flat.

dysh.spectra.core.cog_slope(c, flat_tol=0.1)[source]#

Find slope for curve of growth analysis.

Parameters:

carray: Curve of growth values.
flat_tolfloat: Tolerance to define the flat portion of the curve of growth. The flat portion is that which is zero within flat_tol times the rms of the slope.

Returns:

slopearray: Slope of c.
slope_rmsfloat: Standard deviation of the slope.
flat_idx0int: Index where the slope is consistent with zero.

dysh.spectra.core.curve_of_growth(x, y, vc=None, width_frac=None, bchan=None, echan=None, flat_tol=0.1, fw=1) → dict[source]#

Curve of growth analysis based on Yu et al. (2020) [1].

Parameters:

xarray: Velocity values.
yarray: Flux values.
vcfloat: Central velocity of the line in the same units as x. If not provided, it will be estimated from the moment 1 of the x and y values.
width_fraclist: List of fractions of the total flux at which to compute the line width. If 0.25 and 0.85 are not included, they will be added to estimate the concentration as defined in [1].
bchanNone or int: Beginning channel where there is signal. If not provided it will be estimated using fw times the width of the line at the largest width_frac.
echanNone or int: End channel where there is signal. If not provided it will be estimated using fw times the width of the line at the largest width_frac.
flat_tolfloat: Tolerance used to define the flat portion of the curve of growth. The curve of growth will be considered flat when it’s slope is within flat_tol times the standard deviation of the slope from zero.
fwfloat: When estimating the line-free range, use fw times the largest width.

Returns:

resultsdict: Dictionary with the flux (\(F\)), width (\(V\)), flux asymmetry (\(A_F\)), slope asymmetry (\(A_C\)), concentration (\(C_V\)), rms, central velocity (“vel”), redshifted flux (\(F_r\)), blueshifted flux (\(F_b\)),`bchan` and echan, and errors on the flux (“flux_std”), width (“width_std”), central velocity (“vel_std”), redshifted flux (“flux_r_std”), and blueshifted flux (“flux_b_std”). The rms is the standard deviation in the range outside of (bchan,echan).

[1] (1,2)

N. Yu, L. Ho & J. Wang, “On the Determination of Rotation Velocity and Dynamical Mass of Galaxies Based on Integrated H I Spectra”.

dysh.spectra.core.data_fshift(y, fshift, method='fft', pad=False, window=True)[source]#

Shift y by fshift channels, where abs(fshift)<1.

Parameters:

yarray: Data to be shifted.
fshiftfloat: Amount to shift the data by. abs(fshift) must be less than 1.
method“fft” | “interpolate”: Method to use for shifting. “fft” uses a phase shift. “interpolate” uses scipy.ndimage.shift.
padbool: Pad the data during the phase shift. Only used if method="fft".
windowbool: Apply a Welch window during phase shift. Only used if method="fft".

dysh.spectra.core.data_ishift(y, ishift, axis=-1, remove_wrap=True, fill_value=nan)[source]#

Shift y by ishift channels, where ishift is a natural number.

Parameters:

yarray: Data to be shifted.
ishiftint: Amount to shift data by.
axisint: Axis along which to apply the shift.
remove_wrapbool: Replace channels that wrap around with fill_value.
fill_valuefloat: Value used to replace the data in channels that wrap around after the shift.

Returns:

new_yarray: Shifted y.

dysh.spectra.core.data_shift(y, s, axis=-1, remove_wrap=True, fill_value=nan, method='fft', pad=False, window=True)[source]#

Shift y by s channels.

Parameters:

yarray: Data to be shifted.
sfloat: Amount to shift the data by.
axisint: Axis along which to apply the shift.
remove_wrapbool: Replace channels that wrap around with fill_value.
fill_valuefloat: Value used to replace the data in channels that wrap around after the shift.
method{“fft”, “interpolate”}: Method to use for shifting. “fft” uses a phase shift. “interpolate” uses scipy.ndimage.shift.
padbool: Pad the data during the phase shift. Only used if method="fft".
windowbool: Apply a Welch window during phase shift. Only used if method="fft".

dysh.spectra.core.decimate(data, n, meta=None)[source]#

Decimate a data array by n pixels.

Parameters:

datandarray or Quantity: The data to decimate.
nint: Decimation factor of the spectrum by returning every n-th channel.
metadict: Metadata dictionary with CDELT1, CRVAL1, NAXIS1, and CRPIX1 which will be recalculated.

Returns:

tuplendarray or Quantity and dict: A tuple of the decimated data and updated metadata (or None if no meta given).

dysh.spectra.core.exclude_to_region_list(exclude, spectrum, clip_exclude=True)[source]#

Convert an exclusion region, exclude, to a list of SpectralRegion. This is used for baseline fitting.

Parameters:

excludelist of 2-tuples of int or Quantity, or SpectralRegion

List of region(s) to exclude. The tuple(s) represent a range in the form [lower,upper], inclusive. Examples:

One channel-based region:

>>> [11,51]

Two channel-based regions:

>>> [(11,51),(99,123)]

One Quantity region:

>>> [110.198*u.GHz,110.204*u.GHz].

One compound SpectralRegion:

>>> SpectralRegion([(110.198*u.GHz,110.204*u.GHz),(110.196*u.GHz,110.197*u.GHz)]).

spectrumSpectrum

The reference spectrum whose spectral axis will be used when converting between exclude and axis units (e.g. channels to GHz).

clip_excludebool

Whether to clip the edges of the exclude regions when they are outside spectrum.spectral_axis.

Returns:

region_listlist of SpectralRegion: Regions defined in exclude as a list of SpectralRegion.

dysh.spectra.core.exclude_to_spectral_region(exclude, refspec)[source]#

Convert exclude to a SpectralRegion.

Parameters:

excludelist of 2-tuples of int or Quantity, or SpectralRegion

List of region(s) to exclude. The tuple(s) represent a range in the form [lower,upper], inclusive. Examples:

One channel-based region:

>>> [11,51]

Two channel-based regions:

>>> [(11,51),(99,123)]

One Quantity region:

>>> [110.198*u.GHz,110.204*u.GHz].

One compound SpectralRegion:

>>> SpectralRegion([(110.198*u.GHz,110.204*u.GHz),(110.196*u.GHz,110.197*u.GHz)]).

refspec: `~spectra.spectrum.Spectrum`

The reference spectrum whose spectral axis will be used when converting between exclude and axis units (e.g. channels to GHz).

Returns:

srSpectralRegion: A SpectralRegion corresponding to exclude.

dysh.spectra.core.fft_pad(y)[source]#

Pad signal y to the next power of 2 using its edge values.

Parameters:

y1D ndarray: Signal to be padded.

Returns:

padded1D ndarray: Padded signal.
nskipint: Number of samples added to each end of the padded signal.

dysh.spectra.core.fft_shift(y, shift, pad=True, window=True, nan_treatment='fill', fill_value=0, keep_nan=True)[source]#

Shift a signal y by shift amount using a phase shift. This requires taking the inverse FFT of the signal, shifting its phase, and then taking the FFT to shift the signal.

Parameters:

y1D ndarray: Signal to be shifted. Only 1D supported.
shiftfloat: Amount to shift the signal by in channels.
padbool: Pad the signal to prevent aliasing.
windowbool: Apply a Welch window to prevent ringing.
nan_treatment‘fill’: ‘fill’ replaces NaN values with fill_value before the FFT.
fill_valuefloat: Value used to replace NaN values. Used if nan_treatment=='fill'.
keep_nanbool: If True the output will keep the NaN values. If False NaN values will be filled.

Returns:

new_y1D ndarray: Phase shifted version of the original signal y.

dysh.spectra.core.find_blanks(data)[source]#

Finds the indices of blanked integrations

Parameters:

datandarray: The spectral data, typically with shape (nspect,nchan).

Returns:

blanksndarray: Array with indices of blanked integrations.

dysh.spectra.core.find_non_blanks(data)[source]#

Finds the indices of integrations that are not blanked.

Parameters:

datandarray: The spectral data, typically with shape (nspect,nchan).

Returns:

blanksndarray: Array with indices of non-blanked integrations.

dysh.spectra.core.find_nonblank_ints(cycle1, cycle2=None, cycle3=None, cycle4=None)[source]#

Find the indices of integrations that are not blanked.

Parameters:

cycle1ndarray: Data for cycle 1. For example, signal with the noise diode off.
cycle2ndarray: Data for cycle 2. For example, reference with the noise diode off. Default is None.
cycle3ndarray: Data for cycle 3. For example, signal with the noise diode on. Default is None.
cycle4ndarray: Data for cycle 4. For example, reference with the noise diode on. Default is None.

Returns:

goodrowsarray: Indices of the non-blanked rows.

dysh.spectra.core.get_spectral_equivalency(restfreq, velocity_convention)[source]#

dysh.spectra.core.include_to_exclude_spectral_region(include, refspec)[source]#

Convert an inclusion region to an exclude region.

Parameters:

includelist of 2-tuples of int or Quantity, or SpectralRegion

List of region(s) to exclude from the fit. The tuple(s) represent a range in the form [lower,upper], inclusive. Examples:

One channel-based region:

>>> [11,51]

Two channel-based regions:

>>> [(11,51),(99,123)]

One Quantity region:

>>> [110.198*u.GHz,110.204*u.GHz].

One compound SpectralRegion:

>>> SpectralRegion([(110.198*u.GHz,110.204*u.GHz),(110.196*u.GHz,110.197*u.GHz)]).

refspec: `~spectra.spectrum.Spectrum`

The reference spectrum whose spectral axis will be used when converting between include and axis units (e.g. channels to GHz).

Returns:

exclude_regionSpectralRegion: include as a region to be excluded.

dysh.spectra.core.integration_isnan(data)[source]#

Helper function to calculate a boolean array that indicates whether a collection of integrations is blanked.

Parameters:

datandarray: The spectral data, typically with shape (nspect,nchan).

Returns:

blanksndarray: Array with length nspect with value True where an integration is blanked.

dysh.spectra.core.invert_spectral_region(sr, refspec)[source]#

Invert an spectral region. The spectral region is sorted and the ranges has been merged previously.

Parameters:

srSpectralRegion: Spectral region to invert.
refspecSpectrum: Spectrum to define the boundaries for the inversion.

Returns:

SpectralRegion: Inverted spectral region.

dysh.spectra.core.make_channel_slice(channel: list | None)[source]#

Create a slice object from a [first,last] channel list. If channel is None, then slice(0,None) is returned.

Parameters:

channellist|None: A length 2 list containing the first and last channel numbers

Returns:

slice: a slice object representing [first:last]

dysh.spectra.core.mask_fshift(mask, fshift)[source]#

Shift mask by fshift channels. This should only be used when abs(fshift)<1. It expands the mask using binary dilation to account for the spread of the masked values when they are shifted by a fractional number of channels.

Parameters:

maskarray_like: Array with masked values. Either ones and zeros or True and False.
fshiftfloat: Amount to shift by.

Returns:

new_maskarray_like: mask shifted by fshift channels.

Raises:

ValueError: If abs(fshift) is greather than 1.

dysh.spectra.core.mean_data(data, fedge=0.1, nedge=None, median=False)[source]#

Special mean of data to exclude the edges like mean_tsys(), with an option to use the median instead of the mean.

Parameters:

datandarray: The spectral data.
fedgefloat, optional: Fraction of edge channels to exclude at each end, a number between 0 and 1. If nedge is used, this parameter is not used. Default: 0.1, meaning the central 80% bandwidth is used.
nedgeint, optional: Number of edge channels to exclude. nedge cannot be 0. Default: None, meaning use fedge.
medianboolean, optional: Use the median instead of the mean. The default is False.

Returns:

meandatafloat

dysh.spectra.core.mean_tsys(calon, caloff, tcal, mode=0, fedge=0.1, nedge=None)[source]#

Get the system temperature from the neighboring calon and caloff, which reflect the state of the noise diode. We define an extra way to set the edge size, nedge, if you prefer to use number of edge channels instead of the inverse fraction. This implementation recreates GBTIDL’s dcmeantsys.

Parameters:

calonndarray-like: ON calibration
caloffndarray-like: OFF calibration
tcalndarray-like: calibration temperature
modeint: mode=0 Do the mean before the division mode=1 Do the mean after the division
fedgefloat: Fraction of edge channels to exclude at each end, a number between 0 and 1. Default: 0.1, meaning the central 80% bandwidth is used
nedgeint: Number of edge channels to exclude. Default: None, meaning use fedge

Returns:

meanTsysndarray-like: The mean system temperature

dysh.spectra.core.region_to_axis_indices(region, refspec)[source]#

Parameters:

regionSpectralRegion
refspec: `~spectra.spectrum.Spectrum`: The reference spectrum whose spectral axis will be used when converting between exclude and axis units (e.g., channels to GHz).

Returns:

indices2-tuple of int: The array indices in refspec corresponding to region.bounds

dysh.spectra.core.smooth(data, kernel='hanning', width=1, ndecimate=0, meta=None, mask=None, boundary='extend', nan_treatment='fill', fill_value=nan, preserve_nan=True)[source]#

Smooth or Convolve spectrum, optionally decimating it. A number of methods from astropy.convolution can be selected with the method= keyword.

Default smoothing is hanning.

Parameters:

datandarray

Input data array to smooth. Note smoothing array does not need a WCS since it is channel based.

kernel{“hanning”, “boxcar”, “gaussian”}, optional

Smoothing kernel. Valid are: ‘hanning’, ‘boxcar’ and ‘gaussian’. Minimum match applies. The default is ‘hanning’.

widthint or float, optional

Effective width of the convolving kernel. Should ideally be an odd number. For ‘hanning’ this should be 1, with a 0.25,0.5,0.25 kernel. For ‘boxcar’ an even value triggers an odd one with half the signal at the edges, and will thus not reproduce GBTIDL. For ‘gaussian’ this is the FWHM of the final beam in channels (float). We normally assume the input beam has FWHM=1, pending resolution on cases where CDELT1 is not the same as FREQRES. The default is 1.

ndecimateint, optional

Decimation factor of the spectrum by returning every ndecimate-th channel.

meta: dict

metadata dictionary with CDELT1, CRVAL1, CRPIX1, NAXIS1, and FREQRES which will be recalculated if necessary

maskNone or ndarray, optional

A “mask” array. Shape must match array, and anything that is masked (i.e., not 0/False) will be set to NaN for the convolution. If None, no masking will be performed unless array is a masked array. If mask is not None and array is a masked array, a pixel is masked if it is masked in either mask or array.mask.

boundarystr, optional

A flag indicating how to handle boundaries:

None
Set the result values to zero where the kernel extends beyond the edge of the array.
‘fill’
Set values outside the array boundary to fill_value (default).
‘wrap’
Periodic boundary that wrap to the other side of array.
‘extend’
Set values outside the array to the nearest array value.

fill_valuefloat, optional

The value to use outside the array when using boundary='fill'. Default value is NaN.

nan_treatment{‘interpolate’, ‘fill’}, optional

The method used to handle NaNs in the input array:

'interpolate': NaN values are replaced with interpolated values using the kernel as an interpolation function. Note that if the kernel has a sum equal to zero, NaN interpolation is not possible and will raise an exception.
'fill': NaN values are replaced by fill_value prior to convolution.

preserve_nanbool, optional

After performing convolution, should pixels that were originally NaN again become NaN?

Returns:

sndarray: The new convolved spectrum.

Raises:

Exception: If no valid smoothing method is given.

dysh.spectra.core.sort_spectral_region_subregions(spectral_region)[source]#

dysh.spectra.core.spectral_region_to_list(spectral_region)[source]#

Turn spectral_region into a list of SpectralRegion. Each subregion in spectral_region will be a list element.

Parameters:

spectral_regionSpectralRegion: SpectralRegion to convert into a list of SpectralRegion.

Returns:

region_listlist of SpectralRegion: Subregions of spectral_region in a list of SpectralRegion.

dysh.spectra.core.spectral_region_to_list_of_tuples(spectral_region)[source]#

Convert spectral_region into a list of tuples compatible with baseline include or exclude arguments.

Parameters:

spectral_regionSpectralRegion: Region to convert to a list of tuples.

dysh.spectra.core.spectral_region_to_unit(spectral_region, refspec, unit=None, append_doppler=True)[source]#

Change the unit of spectral_region to unit using the equivalencies of refspec. If no unit is provided, it will change to the units of refspec._spectral_axis.

Parameters:

spectral_regionSpectralRegion: SpectralRegion whose units will be converted.
refspecSpectrum: The reference spectrum whose spectral axis will be used when converting to unit (e.g. channels to GHz).
unitstr or Quantity: The target units for spectral_region.
append_dopplerbool: Add the doppler_convention and doppler_rest attributes to the columns of the QTable used to convert the spectral_region units.

Returns:

spectral_regionSpectralRegion: SpectralRegion with units of unit.

dysh.spectra.core.sq_weighted_avg(a, axis=0, weights=None)[source]#

Compute the mean square weighted average of an array (2nd moment).

\(v = \sqrt{\frac{\sum_i{w_i~a_i^{2}}}{\sum_i{w_i}}}\)

Parameters:

andarray: The data to average.
axisint: The axis over which to average the data. Default: 0
weightsndarray or None: The weights to use in averaging. The weights array must be the length of the axis over which the average is taken. Default: None will use equal weights.

Returns:

averagendarray: The square root of the squared average of a along the input axis.

dysh.spectra.core.tsys_weight(exposure, delta_freq, tsys)[source]#

Compute the system temperature based weight(s) using the expression:: \(w = t_{exp} \times \delta_\nu / T_{sys}^2,\)

If exposure, delta_freq, or tsys parameters are given as Quantity, they will be converted to seconds, Hz, K, respectively for the calculation. (Not that this really matters since weights are relative to each other)

>>> import astropy.units as u
>>> [3*u.s, 4*u.s]   ### WRONG

Rather, if using Quantity, they have to be Quantity objects, e.g.,

>>> [3, 4]*u.s ### RIGHT!

Parameters:

exposurendarray, float, or Quantity: The exposure time, typically given in seconds.
delta_freqndarray, float, or Quantity: The channel width in frequency units.
tsysndarray, float, or Quantity: The system temperature, typically in K.

Returns:

weightndarray: The weights array

Spectrum#

The Spectrum class to contain and manipulate spectra.

class dysh.spectra.spectrum.Spectrum(*args, **kwargs)[source]#

Bases: Spectrum, HistoricalBase

This class contains a spectrum and its attributes. It is built on Spectrum with added attributes like baseline model. Note that Spectrum can contain multiple spectra but we probably will not use that because the restriction that it can have only one spectral axis conflicts with slight Doppler shifts. See Spectrum for the instantiation arguments.

Attributes:

array_axis_physical_types: Returns the WCS physical types that vary along each array axis.
baseline_model: Returns the computed baseline model or None if it has not yet been computed.
bin_edges
combined_wcs: The WCS transform for the NDCube, including the coordinates specified in .extra_coords.
comments: Get the comment strings.
data: ndarray - like
dimensions
doppler_convention: String representation of the velocity (Doppler) convention
energy: The energy of the spectral axis as a Quantity in units of eV.
equivalencies: Get the spectral axis equivalencies that can be used in converting the axis
exclude_regions: The baseline exclusion region(s) of this spectrum
extra_coords: Coordinates not described by NDCubeABC.wcs which vary along one or more axes.
flux: Converts the stored data and unit and mask into a Quantity object.
frequency: The spectral_axis as a Quantity in units of GHz
global_coords: Coordinate metadata which applies to the whole cube.
history: Get the history strings.
mask: any type : Mask for the dataset, if any.
meta
nchan: The number of channels in the Spectrum
observer: Returns ——- observer : BaseCoordinateFrame or derivative The coordinate frame of the observer if present.
obstime
photon_flux: The flux density of photons as a Quantity, in units of
plotter
psf: Image representation of the PSF for the dataset.
quantity: Unitful representation of the NDCube data.
radial_velocity: The radial velocity(s) of the objects represented by this spectrum.
redshift: The redshift(s) of the objects represented by this spectrum.
rest_value: Rest frequency used in velocity conversions.
shape
spectral_axis: Returns the SpectralCoord object.
spectral_axis_direction
spectral_axis_index
spectral_wcs: Returns the spectral axes of the WCS
subtracted: Has a baseline model been subtracted?”
target: The target object of this spectrum.
tscale: The descriptive brightness unit of the data.
tscale_fac: The factor by which the data have been scale from antenna temperature to corrected antenna temperature or flux density.
uncertainty: any type : Uncertainty in the dataset, if any.
unit: Unit : Unit for the dataset, if any.
velocity: Converts the spectral axis array to the given velocity space unit given the rest value.
velocity_convention: Returns the velocity convention
velocity_frame: String representation of the velocity frame
wavelength: The spectral_axis as a Quantity in units of Angstroms
wcs: any type : A world coordinate system (WCS) for the dataset, if any.
weights: The channel weights of this spectrum

Methods

`add`(operand[, operand2])	Performs addition by evaluating `self` + `operand`.
`add_comment`(comment[, add_time])	Add one or more comments to the class metadata.
`add_history`(history[, add_time])	Add one or more history entries to the class metadata
`align_to`(other[, units, frame, remove_wrap, ...])	Align the `Spectrum` with respect to `other`.
`average`(spectra[, weights, align])	Average this `Spectrum` with `spectra`.
`axis_velocity`([unit])	Get the spectral axis in velocity units.
`axis_world_coords`(*axes[, pixel_corners, wcs])	Returns objects representing the world coordinates of pixel centers for a desired axes.
`axis_world_coords_values`(*axes[, ...])	Returns the world coordinate values of all pixels for desired axes.
`baseline`(degree[, exclude, include, color])	Compute and optionally remove a baseline.
`bshow`()	Show the baseline model
`check_stats`(rms[, rtol])	Check statistics of a spectrum compared to pre-set value(s) to a relative tolerance.
`cog`([vc, width_frac, bchan, echan, ...])	Curve of growth (CoG) analysis based on Yu et al. (2020) [1].
`collapse`(method[, axis])	Collapse the flux array given a method.
`crop`(*points[, wcs, keepdims])	Crop using real world coordinates.
`crop_by_values`(*points[, units, wcs, keepdims])	Crop using real world coordinates.
`decimate`(n)	Decimate the `Spectrum` by n pixels.
`divide`(operand[, operand2])	Performs division by evaluating `self` / `operand`.
`explode_along_axis`(axis)	Separates slices of NDCubes along a given axis into an NDCubeSequence of (N-1)DCubes.
`fake_spectrum`([nchan, seed, normal, use_wcs])	Create a fake spectrum with Gaussian noise, useful for simple testing.
`find_shift`(other[, units, frame])	Find the shift required to align this `Spectrum` with `other`.
`get_selected_regions`([unit])	Get selected regions from plot.
`get_velocity_in_frame`(toframe)	Compute the radial velocity of the `Spectrum.target` in a new velocity frame.
`make_spectrum`(data, meta[, use_wcs, ...])	Factory method to create a `Spectrum` object from a data and header.
`merge_commentary`(other)	Merge the history and comments from another HistoricalBase instance.
`meta_as_table`()	Return `Spectrum.meta` as an `Table`.
`multiply`(operand[, operand2])	Performs multiplication by evaluating `self` * `operand`.
`new_flux_unit`(unit[, equivalencies, ...])
`normalness`()	Compute the p-value to check if the noise in a spectrum is Gaussian using the Anderson-Darling statistic.
`plot`(**kwargs)	Plot the spectrum.
`query_lines`([chemical_name, ...])	Query locally or remotely for lines and return a table object.
`radiometer`([roll])	Check the radiometer equation, and return the dimensionless ratio of the measured vs.
`rebin`(bin_shape[, operation, ...])	Downsample array by combining contiguous pixels into bins.
`recomb`(line[, cat, columns])	Search for recombination lines of H, He, and C in the frequency range of this Spectrum.
`recomball`([cat, columns])	Fetch all recombination lines of H, He, C in the frequency range of this Spectrum from the catalog.
`reproject_to`(target_wcs[, algorithm, ...])	Reprojects the instance to the coordinates described by another WCS object.
`roll`([rollmax])	Rolling data to check for channel correllations and channel-to-channel correllations.
`savefig`(file, **kwargs)	Save the plot
`set_convention`(doppler_convention)	Set the velocity convention of this `Spectrum`.
`set_frame`(toframe)	Set the sky coordinate and doppler tracking reference frame of this Spectrum.
`set_radial_velocity_to`(radial_velocity)	This sets the radial velocity of the spectrum to be `radial_velocity` without changing the values of the `spectral_axis`.
`set_redshift_to`(redshift)	This sets the redshift of the spectrum to be `redshift` without changing the values of the `spectral_axis`.
`shift`(s[, remove_wrap, fill_value, method])	Shift the `Spectrum` by `s` channels in place.
`shift_spectrum_to`(*[, redshift, radial_velocity])	This shifts in-place the values of the `spectral_axis`, given either a redshift or radial velocity.
`smooth`([kernel, width, decimate, meta, ...])	Smooth or Convolve the `Spectrum`, optionally decimating it.
`snr`([peak, flux, rms])	Signal-to-noise (S/N) ratio, measured either in channel or total flux mode.
`squeeze`([axis])	Removes all axes with a length of 1.
`sratio`([mean])	Signal ratio: (pSum+nSum)/(pSum-nSum) Here pSum and nSum are the sum of positive and negative values respectively in the spectrum.
`stats`([roll, qac])	Compute some statistics of this `Spectrum`.
`subtract`(operand[, operand2])	Performs subtraction by evaluating `self` - `operand`.
`to`(new_unit, **kwargs)	Convert instance to another unit.
`undo_baseline`()	Undo the most recently computed baseline.
`velocity_axis_to`([unit, toframe, ...])	Convert the spectral axis to `unit` in `toframe` using `doppler_convention` if converting from frequency/wavelength to velocity.
`with_flux_unit`(unit[, equivalencies, ...])	Returns a new spectrum with a different flux unit.
`with_frame`(toframe)	Return a copy of this `Spectrum` with a new coordinate reference frame.
`with_spectral_axis_and_flux_units`(...[, ...])	Perform `with_spectral_axis_unit()` and `with_flux_unit()` together.
`with_spectral_axis_last`()	Convenience method to return a new copy of the Spectrum with the spectral axis last.
`with_spectral_axis_unit`(unit[, ...])	Returns a new spectrum with a different spectral axis unit.
`with_spectral_unit`(unit[, ...])
`with_velocity_convention`(doppler_convention)	Returns a copy of this `Spectrum` with the input velocity convention.

max
mean
median
min
read
sum
write

align_to(other, units=None, frame=None, remove_wrap=True, fill_value=nan, method='fft')[source]#

Align the Spectrum with respect to other.

Parameters:

otherSpectrum: Target Spectrum to align to.
units{None, astropy.units.quantity.Quantity}: Find the shift to align the two Spectra in these units. If None, the Spectra will be aligned using the units of other.
frame{None, str}: Find the shift in this reference frame. If None will use the frame of other.
remove_wrapbool: If True allow spectrum to wrap around the edges. If False fill channels that wrap with fill_value.
fill_valuefloat: If wrap=False fill channels that wrapped with this value.
method“fft”: Method used to perform the fractional channel shift. “fft” uses a phase shift.

average(spectra, weights: str | ndarray | None = 'tsys', align=False)[source]#

Average this Spectrum with spectra. The resulting average will have an exposure equal to the sum of the exposures, and coordinates and system temperature equal to the weighted average of the coordinates and system temperatures.

Parameters:

spectralist of Spectrum

Spectra to be averaged. They must have the same number of channels. No checks are done to ensure they are aligned.

weights: None, str or ~numpy.ndarray

If None, the channel weights will be equal and set to unity.

If ‘tsys’ the channel weights will be calculated as:

\(w = t_{exp} \times \delta\nu/T_{sys}^2\)

If ‘spectral’, the weights in each Spectrum will be used.

If an array, it must have shape (len(spectra)+1,) or (len(spectra)+1,nchan) where nchan is the number of channels in the spectra. The first element ofthe weights array will be applied to the current spectrum.

alignbool

If True align the spectra to itself. This uses Spectrum.align_to.

Returns:

averageSpectrum: Averaged spectra.

axis_velocity(unit=Unit('km / s'))[source]#

Get the spectral axis in velocity units. Note: This is not the same as Spectrum.velocity, which includes the source radial velocity.

Parameters:

unitQuantity or str that can be converted to Quantity: The unit to which the axis is to be converted
Returns
——-
velocityQuantity: The converted spectral axis velocity

baseline(degree, exclude=None, include=None, color='k', **kwargs)[source]#

Compute and optionally remove a baseline. The code uses Fitter and polynomial to compute the baseline. See the documentation for those modules for details. This method will set the baseline_model attribute to the fitted model function which can be evaluated over a domain.

Note that include and exclude are mutually exclusive. If both are present, only include will be used.

Parameters:

degreeint

The degree of the polynomial series, a.k.a. baseline order

excludelist of 2-tuples of int or Quantity, or SpectralRegion

List of region(s) to exclude from the fit. The tuple(s) represent a range in the form [lower,upper], inclusive.

Examples:

One channel-based region: [11,51]

Two channel-based regions: [(11,51),(99,123)].

One Quantity region: [110.198*u.GHz,110.204*u.GHz].

One compound SpectralRegion: SpectralRegion([(110.198*u.GHz,110.204*u.GHz),(110.196*u.GHz,110.197*u.GHz)]).

Default: no exclude region

includelist of 2-tuples of int or Quantity, or SpectralRegion

List of region(s) to include in the fit. The tuple(s) represent a range in the form [lower,upper], inclusive. See exclude for examples.

colorstr

The color to plot the baseline model, if remove=False. Can be any type accepted by matplotlib.

model{‘chebyshev’, ‘polynomial’, ‘legendre’, ‘hermite’}

Model to describe the baseline. Default: ‘chebyshev’

fitterFitter

The fitter to use. Default: LinearLSQFitter (with calc_uncertaintes=True). Be careful when choosing a different fitter to be sure it is optimized for this problem.

removebool

If True, the baseline is removed from the spectrum. If False, the baseline will be computed and overlaid on the spectrum. Default: False

property baseline_model#: Returns the computed baseline model or None if it has not yet been computed.

bshow()[source]#: Show the baseline model

check_stats(rms, rtol=1e-05)[source]#

Check statistics of a spectrum compared to pre-set value(s) to a relative tolerance. Currently only the RMS is compared.

Parameters:

rmsfloat or Quantity: Value of the expected RMS value. If a float is given the comparison is only done on the value of the Quantity.
rtolfloat: Relative tolerance with which the value is compared. See also np.isclose(). Default: 1e-05

cog(vc=None, width_frac=None, bchan=None, echan=None, flat_tol=0.1, fw=1, xunit='km/s', vframe=None, doppler_convention=None) → dict[source]#

Curve of growth (CoG) analysis based on Yu et al. (2020) [1].

Parameters:

vcfloat: Central velocity of the line. If not provided, it will be estimated from the moment 1 of the Spectrum.
width_fraclist: List of fractions of the total flux at which to compute the line width. If 0.25 and 0.85 are not included, they will be added to estimate the concentration as defined in [1].
bchanint: Beginning channel where there is signal. If not provided it will be estimated using fw times the width of the line at the largest width_frac.
echanint: End channel where there is signal. If not provided it will be estimated using fw times the width of the line at the largest width_frac.
flat_tolfloat: Tolerance used to define the flat portion of the curve of growth. The curve of growth will be considered flat when it’s slope is within flat_tol times the standard deviation of the slope from zero.
fwfloat: When estimating the line-free range, use fw times the largest width.
xunitstr or Quantity: Units for the x axis when computing the CoG.
vframeNone or str: Velocity frame to use. The results will be provided in this velocity frame. If None, the velocity frame of the Spectrum will be used. The velocity frame of the Spectrum won’t be changed.
doppler_conventionNone or {‘radio’, ‘optical’, ‘relativistic’}: The Doppler velocity covention to use when converting to velocity units. If a string, one of ‘optical’, ‘radio’, or ‘relativistic’. If None, it will use the Spectrum.doppler_convention.

Returns:

resultsdict: Dictionary with the flux (\(F\)), width (\(V\)), flux asymmetry (\(A_F\)), slope asymmetry (\(A_C\)), concentration (\(C_V\)), rms, central velocity (“vel”), redshifted flux (\(F_r\)), blueshifted flux (\(F_b\)),`bchan` and echan, and errors on the flux (“flux_std”), width (“width_std”), central velocity (“vel_std”), redshifted flux (“flux_r_std”), and blueshifted flux (“flux_b_std”). The rms is the standard deviation in the range outside of (bchan,echan).

[1] (1,2,3)

N. Yu, L. Ho & J. Wang, “On the Determination of Rotation Velocity and Dynamical Mass of Galaxies Based on Integrated H I Spectra”.

decimate(n)[source]#

Decimate the Spectrum by n pixels.

Parameters:

nint: Decimation factor of the spectrum by returning every n-th channel.

Returns:

sSpectrum: The decimated Spectrum.

property doppler_convention: str#: String representation of the velocity (Doppler) convention

property equivalencies#: Get the spectral axis equivalencies that can be used in converting the axis between km/s and frequency or wavelength

property exclude_regions#: The baseline exclusion region(s) of this spectrum

classmethod fake_spectrum(nchan=1024, seed=None, normal=True, use_wcs=True, **kwargs)[source]#

Create a fake spectrum with Gaussian noise, useful for simple testing. A default header is created, which may be modified with kwargs.

Parameters:

nchanint, optional: Number of channels. The default is 1024.
seed{None, int, array_like[ints], numpy.random.SeedSequence, numpy.random.BitGenerator, numpy.random.Generator}, optional: A seed to initialize the BitGenerator. If None, then fresh, unpredictable entropy will be pulled from the OS. If an int or array_like[ints] is passed, then all values must be non-negative and will be passed to SeedSequence to derive the initial BitGenerator state. One may also pass in a SeedSequence instance. Additionally, when passed a BitGenerator, it will be wrapped by Generator. If passed a Generator, it will be returned unaltered. The default is None.
normalbool, optional: If set, the noise is distributed normal with a mean 0.1 and dispersion 0.1. If False, the noise is uniformly distributed between 0 and 1. The default is True.
use_wcsbool, optional: If set, create a WCS object from the metadata. This is computationally expensive, so setting it to False can speed things up when no WCS is needed. The default is True.
**kwargs: dict or key=value: Metadata to put in the header. If the key exists already in the default header, it will be replaced. Otherwise the key and value will be added to the header. Keys are case insensitive.

Returns:

spectrumSpectrum: The spectrum object.

find_shift(other, units=None, frame=None)[source]#

Find the shift required to align this Spectrum with other.

Parameters:

otherSpectrum: Target Spectrum to align to.
units{None, astropy.units.quantity.Quantity}: Find the shift to align the two Spectra in these units. If None, the Spectra will be aligned using the units of other.
frame{None, str}: Find the shift in this reference frame. If None will use the frame of other.

Returns:

shiftfloat: Number of channels that this Spectrum must be shifted to be aligned with other.

property flux#

Converts the stored data and unit and mask into a Quantity object.

Returns:

Quantity: Spectral data as a quantity. Masked values are filled with NaN.

get_selected_regions(unit=None)[source]#: Get selected regions from plot.

get_velocity_in_frame(toframe: str) → Quantity[source]#

Compute the radial velocity of the Spectrum.target in a new velocity frame. See get_velocity_in_frame().

Parameters:

toframestr: The coordinate frame to convert to, e.g. ‘hcrs’, ‘icrs’.

Returns:

radial_velocityQuantity: The radial velocity of the source in toframe

classmethod make_spectrum(data, meta, use_wcs=True, observer_location=None, observer=None)[source]#

Factory method to create a Spectrum object from a data and header. The the data are masked, the Spectrum mask will be set to the data mask.

Parameters:

datandarray: The data array. See Spectrum
metadict: The metadata, typically derived from an SDFITS header. Required items in meta are ‘CTYPE[123]’,’CRVAL[123]’, ‘CUNIT[123]’, ‘VELOCITY’, ‘EQUINOX’, ‘RADESYS’
use_wcsbool: If True, create a WCS object from meta Default: True
observer_locationEarthLocation or str: Location of the observatory. See Observatory. This will be transformed to ITRS using the time of observation DATE-OBS or MJD-OBS in meta. If this parameter is given the special str value ‘from_meta’, then an observer_location will be created from SITELONG, SITELAT, and SITEELEV in the meta dictionary.
observerBaseCoordinateFrame: Coordinate frame for the observer. Will be ignored if DATE-OBS or MJD-OBS are present in meta and observer_location is not None.

Returns:

spectrumSpectrum: The spectrum object

meta_as_table()[source]#: Return Spectrum.meta as an Table.

property nchan: int#: The number of channels in the Spectrum

normalness()[source]#: Compute the p-value to check if the noise in a spectrum is Gaussian using the Anderson-Darling statistic. If p>0.05, the spectrum can be considered Gaussian. See also “D’Agostino, R. B., & Stephens, M. A. (Eds.). (1986) Goodness-of-fit techniques” , table 4.9

property observer#

Returns:

observerBaseCoordinateFrame or derivative
The coordinate frame of the observer if present.

property obstime#

plot(**kwargs)[source]#

Plot the spectrum.

Parameters:

show_headerbool: Show informational header.
selectbool: Allow selecting regions via click and drag.
oshowlist or Spectrum: Spectra to overlay in the plot.
oshow_kwargsdict: Dictionary with parameters for SpectrumPlot.oshow. These include color, linestyle, label, and alpha.

Other Parameters:

xaxis_unitstr or Unit: The units to use on the x-axis, e.g. “km/s” to plot velocity.
yaxis_unitstr or Unit: The units to use on the y-axis.
xminfloat: Minimum x-axis value, in xaxis_unit.
xmaxfloat: Maximum x-axis value, in yaxis_unit.
yminfloat: Minimum y-axis value, in xaxis_unit.
ymaxfloat: Maximum y-axis value, in yaxis_unit.
xlabelstr: x-axis label.
ylabelstr: y-axis label.
labelstr: Label for legend.
alphafloat: Alpha value for the plot. Between 0 and 1.
gridbool: Show a plot grid or not.
figsizetuple: Figure size (see matplotlib).
linewidthfloat: Line width, default: 2.0.
drawstylestr: Line style, default ‘default’.
colorstr: Line color, c also works.
titlestr: Plot title.
vel_framestr: The velocity frame (see VELDEF FITS Keyword).
doppler_convention: str: The velocity convention (see VELDEF FITS Keyword).

property plotter#

query_lines(chemical_name: str | None = None, intensity_lower_limit: float | None = None, cat: str = 'gbtlines', columns: str | list | None = None) → Table[source]#

Query locally or remotely for lines and return a table object. The query returns lines with rest frequencies in the range of this Spectrum’s spectral_axis. The redshift value in the attribute Spectrum.redshift will be applied.

Note: If the search parameters result in no matches, a zero-length Table will be returned.

Parameters:

chemical_namestr, optional

Name of the chemical to search for. Treated as a regular expression. An empty set will match any species. Examples:

'H2CO' - 13 species have H2CO somewhere in their formula.

'Formaldehyde' - There are 8 isotopologues of Formaldehyde: (e.g., H213CO).

'formaldehyde' - Thioformaldehyde,Cyanoformaldehyde.

'formaldehyde',chem_re_flags=re.I - Formaldehyde,thioformaldehyde,: and Cyanoformaldehyde.
' H2CO ' - Just 1 species, H2CO. The spaces prevent including: others.

intensity_lower_limitfloat, optional

Lower limit on the intensity in the logarithmic CDMS/JPL scale. This corresponds to the ‘intintensity’ column in the returned table.

catstr or Path

The catalog to use. One of: [‘splatalogue’, ‘gbtlines’, ‘gbtrecomb’] (minimum string match) or a valid Path to a local astropy-compatible table. The local table must have all the columns listed in the columns parameter.

'gbtlines' is a local catalog of spectral lines between 300 MHz and 120 GHz with CDMS/JP log(intensity) > -9.

'gbtrecomb' is a local catalog of H, He, and C recombination lnes between 300 MHz and 120 GHz.

columns: str or list or None

The query result columns to include in the returned table. Any of [‘species_id’, ‘name’, ‘chemical_name’, ‘resolved_QNs’, ‘linelist’, ‘LovasASTIntensity’, ‘lower_state_energy’, ‘upper_state_energy’, ‘sijmu2’, ‘sij’, ‘aij’, ‘intintensity’, ‘Lovas_NRAO’, ‘rest_frequency’, ‘obs_frequency’, ‘lower_state_energy_K’, ‘upper_state_energy_K’, ‘upperStateDegen’, ‘moleculeTag’, ‘qnCode’, ‘labref_Lovas_NIST’, ‘rel_int_HFS_Lovas’, ‘unres_quantum_numbers’, ‘lineid’, ‘transition_in_space’, ‘transition_in_G358’, ‘obsref_Lovas_NIST’, ‘source_Lovas_NIST’, ‘telescope_Lovas_NIST’, ‘searchErrorMessage’]. The default is None which means all columns.

Returns:

Table: An astropy table containing the results of the search

radiometer(roll=0)[source]#

Check the radiometer equation, and return the dimensionless ratio of the measured vs. expected noise. Generally this number is 1.0 or higher, unless for example channels were hanning correlated, measured noise will be lower.

User is responsible for selecting the channels, via e.g. indexing:

r1 = sp0[1000:2000].radiometer()

Note that the current getsigref function may not set the exposure time correctly, and thus come up with the wrong result from the radiometer function. See issue #800 GreenBankObservatory/dysh#800

Parameters:

rollint: Subtract the data of channel i+roll from channel i before computing the rms. This helps reduce artifacts due to bad baselines or channel-to-channel correlations. A value of 1 or 2 is recommended. See also the roll function where a series of roll values can be checked. The default is 0 (no roll).

Returns:

ratioreal: The ratio of measured to expected RMS.

recomb(line, cat: str = 'gbtrecomb', columns: str | list | None = None) → Table[source]#

Search for recombination lines of H, He, and C in the frequency range of this Spectrum. The redshift value in the attribute Spectrum.redshift will be applied.

Parameters:

linestr

A string describing the line or series to search for, e.g. “Hydrogen”, “Halpha”, “Hebeta”, “C”, “carbon”.

catstr or Path

The catalog to use. One of: [‘splatalogue’, ‘gbtlines’, ‘gbtrecomb’] (minimum string match) or a valid Path to a local astropy-compatible table. The local table must have all the columns listed in the columns parameter.

'gbtlines' is a local catalog of spectral lines between 300 MHz and 120 GHz with CDMS/JP log(intensity) > -9.

'gbtrecomb' is a local catalog of H, He, and C recombination lnes between 300 MHz and 120 GHz.

columns: str or list or None

The query result columns to include in the returned table. Any of [‘species_id’, ‘name’, ‘chemical_name’, ‘resolved_QNs’, ‘linelist’, ‘LovasASTIntensity’, ‘lower_state_energy’, ‘upper_state_energy’, ‘sijmu2’, ‘sij’, ‘aij’, ‘intintensity’, ‘Lovas_NRAO’, ‘rest_frequency’, ‘obs_frequency’, ‘lower_state_energy_K’, ‘upper_state_energy_K’, ‘upperStateDegen’, ‘moleculeTag’, ‘qnCode’, ‘labref_Lovas_NIST’, ‘rel_int_HFS_Lovas’, ‘unres_quantum_numbers’, ‘lineid’, ‘transition_in_space’, ‘transition_in_G358’, ‘obsref_Lovas_NIST’, ‘source_Lovas_NIST’, ‘telescope_Lovas_NIST’, ‘searchErrorMessage’]. The default is None which means all columns.

Returns:

Table: An astropy table containing the results of the search

recomball(cat: str = 'gbtrecomb', columns: str | list | None = None) → Table[source]#

Fetch all recombination lines of H, He, C in the frequency range of this Spectrum from the catalog. The redshift value in the attribute Spectrum.redshift will be applied.

Parameters:

catstr or Path

The catalog to use. One of: [‘splatalogue’, ‘gbtlines’, ‘gbtrecomb’] (minimum string match) or a valid Path to a local astropy-compatible table. The local table must have all the columns listed in the columns parameter.

'gbtlines' is a local catalog of spectral lines between 300 MHz and 120 GHz with CDMS/JP log(intensity) > -9.

'gbtrecomb' is a local catalog of H, He, and C recombination lnes between 300 MHz and 120 GHz.

columns: str or list or None

The query result columns to include in the returned table. Any of [‘species_id’, ‘name’, ‘chemical_name’, ‘resolved_QNs’, ‘linelist’, ‘LovasASTIntensity’, ‘lower_state_energy’, ‘upper_state_energy’, ‘sijmu2’, ‘sij’, ‘aij’, ‘intintensity’, ‘Lovas_NRAO’, ‘rest_frequency’, ‘obs_frequency’, ‘lower_state_energy_K’, ‘upper_state_energy_K’, ‘upperStateDegen’, ‘moleculeTag’, ‘qnCode’, ‘labref_Lovas_NIST’, ‘rel_int_HFS_Lovas’, ‘unres_quantum_numbers’, ‘lineid’, ‘transition_in_space’, ‘transition_in_G358’, ‘obsref_Lovas_NIST’, ‘source_Lovas_NIST’, ‘telescope_Lovas_NIST’, ‘searchErrorMessage’]. The default is None which means all columns.

Returns:

Table: An astropy table containing the results of the search

property rest_value: Quantity#

Rest frequency used in velocity conversions.

Returns:

~astropy.units.quantity.quantity.Quantity: The rest frequency as a Quantity object

roll(rollmax=1)[source]#

Rolling data to check for channel correllations and channel-to-channel correllations. For all roll’s from 1 to rollmax the RMS in the rolled data is compared to the raw RMS. For well behaved (and baseline subtracted) data the ratio of the raw RMS to the rolled RMS should approach 1.0. Note: rolling the data is shifting the data by “roll” channels.

Parameters:

rollmaxint: Roll the data by values 1 through rollmax. The default is 1.

Returns:

ratiolist: The ratios of raw RMS by the rolled RMS. The list has a length of rollmax.

savefig(file, **kwargs)[source]#: Save the plot

set_convention(doppler_convention)[source]#

Set the velocity convention of this Spectrum. The spectral axis of this Spectrum will be replaced with a new spectral axis with the input velocity convention. The header ‘VELDEF’ value will be changed accordingly.

To make a copy of this Spectrum with a new velocity convention instead, use Spectrum.with_velocity_convention.

Parameters:

doppler_conventionstr: The velocity convention, one of ‘radio’, ‘optical’, ‘relativistic’

set_frame(toframe)[source]#

Set the sky coordinate and doppler tracking reference frame of this Spectrum. The header ‘CTYPE1’ will be changed accordingly.

To make a copy of this Spectrum with new coordinate reference frame instead, use with_frame.

Parameters:

toframestr or BaseCoordinateFrame or SkyCoord: The coordinate reference frame identifying string, as used by astropy, e.g. ‘hcrs’, ‘icrs’, an actual coordinate system instance, or a sky coordinate instance.

shift(s, remove_wrap=True, fill_value=nan, method='fft')[source]#

Shift the Spectrum by s channels in place.

Parameters:

sfloat: Number of channels to shift the Spectrum by.
remove_wrapbool: If False keep channels that wrap around the edges. If True fill channels that wrap with fill_value.
fill_valuefloat: If remove_wrap=True fill channels that wrapped with this value.
method“fft”: Method used to perform the fractional channel shift. “fft” uses a phase shift.

smooth(kernel='hanning', width=1, decimate=0, meta=None, mask=None, boundary='extend', nan_treatment='fill', fill_value=nan, preserve_nan=True)[source]#

Smooth or Convolve the Spectrum, optionally decimating it.

Default smoothing is hanning.

Parameters:

kernel{“hanning”, “boxcar”, “gaussian”}

Smoothing method. Valid are: ‘hanning’, ‘boxcar’ and ‘gaussian’. Minimum match applies. The default is ‘hanning’.

widthint

Effective width of the convolving kernel. For ‘hanning’ this should be 1, with a 0.25,0.5,0.25 kernel. For ‘boxcar’ an even value triggers an odd one with half the signal at the edges, and will thus not reproduce GBTIDL. For ‘gaussian’ this is the FWHM of the final frequency response. That is, the smoothed Spectrum will have an effective frequency resolution equal to CDELT1*`width`. The default is 1.

decimateint

Decimation factor of the spectrum by returning every decimate channel. -1: no decimation 0: use the width parameter >1: user supplied decimation (use with caution)

maskNone or numpy.ndarray

A “mask” array. Shape must match array, and anything that is masked (i.e., not 0/False) will be set to NaN for the convolution. If None, no masking will be performed unless array is a masked array. If mask is not None and array is a masked array, a pixel is masked if it is masked in either mask or array.mask.

boundarystr

A flag indicating how to handle boundaries:

None
Set the result values to zero where the kernel extends beyond the edge of the array.
‘fill’
Set values outside the array boundary to fill_value (default).
‘wrap’
Periodic boundary that wrap to the other side of array.
‘extend’
Set values outside the array to the nearest array value.

fill_valuefloat

The value to use outside the array when using boundary='fill'. Default value is NaN.

nan_treatment{‘interpolate’, ‘fill’}

The method used to handle NaNs in the input array:

'interpolate': NaN values are replaced with interpolated values using the kernel as an interpolation function. Note that if the kernel has a sum equal to zero, NaN interpolation is not possible and will raise an exception.
'fill': NaN values are replaced by fill_value prior to convolution.

preserve_nanbool

After performing convolution, should pixels that were originally NaN again become NaN?

Returns:

sSpectrum: The new, possibly decimated, convolved Spectrum.

Raises:

Exception: If no valid smoothing kernel is given.
ValueError: If width is less than one. If width is less than the spectral resolution (in channels). If decimate is not an integer.

snr(peak=True, flux=False, rms=None)[source]#

Signal-to-noise (S/N) ratio, measured either in channel or total flux mode. Make sure the spectrum has been baseline substracted, or the snr is meaningless. See also sratio(), the signal ratio.

Parameters:

peakbool

If True, the largest positive deviation from the mean is compared to the rms. If False, the largest negative deviation from the mean is compared to the rms. For normal noise the returned snr value depends on the number of channels via the error function.

fluxbool

If True, the integrated flux over the spectrum is compared to the expected flux given pure noise. If False, channel based snr is computed, also controlled by the value of the peak in the spectrum.

Scan#

The classes that define various types of Scan and their calibration methods.

class dysh.spectra.scan.FSScan(gbtfits, scan, sigrows, calrows, fdnum, ifnum, plnum, bintable, calibrate=True, fold=True, shift_method='fft', use_sig=True, smoothref=1, apply_flags=False, tscale='ta', ap_eff=None, surface_error=None, zenith_opacity=None, observer_location=<EarthLocation (882593.9465029, -4924896.36541728, 3943748.74743984) m>, tsys=None, tcal=None, nocal: bool = False, channel: list | None = None, vane: ~dysh.spectra.vane.VaneSpectrum | None = None)[source]#

Bases: ScanBase

GBT specific version of Frequency Switch Scan

Parameters:

gbtfitsGBTFITSLoad

input GBTFITSLoad object

scanint

Scan number that contains integrations with a series of sig/ref and calon/caloff states.

sigrows :dict

Dictionary containing with keys ‘ON’ and ‘OFF’ containing list of rows in sdfits corresponding to sig=T (ON) and sig=F (OFF) integrations.

calrowsdict

Dictionary containing with keys ‘ON’ and ‘OFF’ containing list of rows in sdfits corresponding to cal=T (ON) and cal=F (OFF) integrations.

fdnum: int

The feed number

ifnumint

The IF number

plnumint

The polarization number

bintableint

The index for BINTABLE in sdfits containing the scans.

calibratebool

Whether or not to calibrate the data. If true, data will be calibrated as TSYS*(ON-OFF)/OFF. Default: True

foldbool

Whether or not to fold the spectrum. Default: True

shift_methodstr

Method to use when shifting the spectra for folding. One of ‘fft’ or ‘interpolate’. ‘fft’ uses a phase shift in the time domain. ‘interpolate’ interpolates the signal. Default: ‘fft’

use_sigbool

Whether to use the sig as the sig, or the ref as the sig. Default: True

smoothref: int

The number of channels in the reference to boxcar smooth prior to calibration.

apply_flagsboolean, optional. If True, apply flags before calibration.

tscalestr, optional

The brightness scale unit for the output scan, must be one of (case-insensitive)

‘Ta’ : Antenna Temperature
‘Ta*’ : Antenna temperature corrected to above the atmosphere
‘Flux’ : flux density in Jansky

If ‘ta*’ or ‘flux’ the zenith opacity must also be given. Default:’ta’

ap_efffloat or None

Aperture efficiency to be used when scaling data to brightness temperature of flux. The provided aperture efficiency must be a number between 0 and 1. If None, dysh will calculate it as described in aperture_efficiency(). Only one of ap_eff or surface_error can be provided.

surface_error: Quantity or None

Surface rms error, in units of length (typically microns), to be used in the Ruze formula when calculating the aperture efficiency. If None, dysh will use the known GBT surface error model. Only one of ap_eff or surface_error can be provided.

zenith_opacity: float, optional

The zenith opacity to use in calculating the scale factors for the integrations. Default:None

observer_locationEarthLocation

Location of the observatory. See Observatory. This will be transformed to ITRS using the time of observation DATE-OBS or MJD-OBS in the SDFITS header. The default is the location of the GBT.

tsysfloat or ndarray

If given, this is the system temperature in Kelvin. It overrides the values calculated using the noise diodes. If not given, and signal and reference are scan numbers, the system temperature will be calculated from the reference scan and the noise diode. If not given, and the reference is a Spectrum, the reference system temperature as given in the metadata header will be used. The default is to use the noise diode or the metadata, as appropriate.

channel: list or None

An inclusive list of [firstchan, lastchan] to use in the calibration. The channel list is zero-based. If provided, only data channels in the inclusive range [firstchan,lastchan] will be used. If a reference spectrum has been given, it will also be trimmed to [firstchan,lastchan]. If channels have already been selected through select_channel(), a ValueError will be raised. If vane is provided, tsys will be ignored.

vaneVaneSpectrum or None

Vane calibration spectrum. This will be used to derive the system temperature. If provided, tsys will be ignored.

Attributes:

ap_eff: The aperture efficiencies for the integrations in this Scan
baseline_model: Returns the subtracted baseline model or None if it has not yet been computed.
calibrated: Returns the calibrated integrations in the Scan as a numpy array.
comments: Get the comment strings.
delta_freq: The array of channel frequency width.
duration: The array of duration times.
exposure: The array of exposure (integration) times.
fdnum: The feed number.
folded: Has the FSscan been folded?
history: Get the history strings.
ifnum: The intermediate frequency (IF) number.
is_calibrated: Have the data been calibrated?
is_scaled: Is this Scan scaled to something other than antenna temperature \(T_A\).
meta: The metadata of this Scan.
nchan: The number of channels in this scan.
nint: The number of integrations in this scan.
nrows: The number of rows in this scan.
plnum: The polarization number.
scan: The scan number
subtracted: Has a baseline model been subtracted?
surface_error: The dish surface errors for the integrations in this Scan
tscale: The descriptive brightness unit of the data.
tscale_fac: The factor(s) by which the data have been scale from antenna temperature to corrected antenna temperature or flux density.
tsys: The system temperature array.
tsys_weight: The system temperature weighting array computed from current \(T_{sys}\), \(t_{int}\), and \(\delta\nu\).
tunit: The brightness unit (temperature or flux density) of this Scan’s data
weights: The weights for each integration after an averaging operation.
zenith_opacity: The zenith opacity of this Scan, used to calculated aperture efficiency.

Methods

`add_comment`(comment[, add_time])	Add one or more comments to the class metadata.
`add_history`(history[, add_time])	Add one or more history entries to the class metadata
`calibrate`(**kwargs)	Frequency switch calibration.
`get_vane_tcal`()	Get the tcal value for the vane.
`getspec`(i[, use_wcs])	Return the i-th calibrated Spectrum from this Scan.
`merge_commentary`(other)	Merge the history and comments from another HistoricalBase instance.
`plot`(**kwargs)	Plot the data as a waterfall.
`scale`(tscale[, zenith_opacity])	Scale the data to the given brightness scale (temperature or flux density) using the zenith opacity.
`smooth`([kernel, width, decimate])	Smooth or convolve the underlying calibrated data array, optionally decimating the data.
`subtract_baseline`(model[, tol, force])	Subtract a (previously computed) baseline model from every integration in this Scan.
`timeaverage`([weights, use_wcs])	Compute the time-averaged spectrum.
`undo_baseline`()	Undo the applied (subtracted) baseline.
`write`(fileobj[, flags, output_verify, ...])	Write an SDFITS format file (FITS binary table HDU) of the calibrated data in this ScanBase

calibrate(**kwargs)[source]#

Frequency switch calibration.

Parameters:

foldbool: Fold the spectrum or not. Required keyword.

property folded#

Has the FSscan been folded?

Returns:

boolean: True if the signal and reference integrations have been folded. False if not.

class dysh.spectra.scan.NodScan(gbtfits, scan, beam1, scanrows, calrows, fdnum, ifnum, plnum, bintable, calibrate=True, smoothref=1, apply_flags=False, tsys=None, tcal=None, nocal=False, tscale='ta', ap_eff=None, surface_error=None, zenith_opacity=None, observer_location=<EarthLocation (882593.9465029, -4924896.36541728, 3943748.74743984) m>, channel=None, vane: ~dysh.spectra.vane.VaneSpectrum | None = None)[source]#

Bases: ScanBase

GBT specific version of Nodding Scan. A nod scan object has one IF, two feeds, and one polarization.

Parameters:

gbtfitsGBTFITSLoad

input GBTFITSLoad object

scandict

dictionary with keys ‘ON’ and ‘OFF’ containing unique list of ON (signal) and OFF (reference) scan numbers NOTE: there should be one ON and one OFF, a pair. There should be at least two beams (the nodding beams) which will be resp. on source in each scan.

beam1: bool

Is this scan BEAM1 or BEAM2? BEAM1 is defined as being on source in the first scan of the pair, BEAM2 in the second of the pair

scanrowsdict

dictionary with keys ‘ON’ and ‘OFF’ containing the list of rows in sdfits corresponding to ON (signal) and OFF (reference) integrations

calrowsdict

dictionary containing with keys ‘ON’ and ‘OFF’ containing list of rows in sdfits corresponding to cal=T (ON) and cal=F (OFF) integrations.

fdnum: int

The feed number

ifnumint

The IF number

plnumint

The polarization number

bintableint

The index for BINTABLE in sdfits containing the scans

calibrate: bool

Whether or not to calibrate the data. If true, data will be calibrated as TSYS*(ON-OFF)/OFF. Default: True

smoothref: int

The number of channels in the reference to boxcar smooth prior to calibration (if applicable)

apply_flagsboolean

If True, apply flags before calibration.

tsysfloat

User provided value for the system temperature. If vane is provided, tsys will be ignored.

nocalbool

True if the noise diode was not fired. False if it was fired.

tscalestr, optional

The brightness scale unit for the output scan, must be one of (case-insensitive)

‘Ta’ : Antenna Temperature
‘Ta*’ : Antenna temperature corrected to above the atmosphere
‘Flux’ : flux density in Jansky

If ‘ta*’ or ‘flux’ the zenith opacity must also be given. Default:’ta’

ap_efffloat or None

Aperture efficiency to be used when scaling data to brightness temperature of flux. The provided aperture efficiency must be a number between 0 and 1. If None, dysh will calculate it as described in aperture_efficiency(). Only one of ap_eff or surface_error can be provided.

surface_error: Quantity or None

Surface rms error, in units of length (typically microns), to be used in the Ruze formula when calculating the aperture efficiency. If None, dysh will use the known GBT surface error model. Only one of ap_eff or surface_error can be provided.

zenith_opacity: float, optional

The zenith opacity to use in calculating the scale factors for the integrations. Default:None

observer_locationEarthLocation

Location of the observatory. See Observatory. This will be transformed to ITRS using the time of observation DATE-OBS or MJD-OBS in the SDFITS header. The default is the location of the GBT.

channel: list or None

An inclusive list of [firstchan, lastchan] to use in the calibration. The channel list is zero-based. If provided, only data channels in the inclusive range [firstchan,lastchan] will be used. If a reference spectrum has been given, it will also be trimmed to [firstchan,lastchan]. If channels have already been selected through select_channel(), a ValueError will be raised.

vaneVaneSpectrum or None

Vane calibration spectrum. This will be used to derive the system temperature. If provided, tsys will be ignored.

Attributes:

ap_eff: The aperture efficiencies for the integrations in this Scan
baseline_model: Returns the subtracted baseline model or None if it has not yet been computed.
calibrated: Returns the calibrated integrations in the Scan as a numpy array.
comments: Get the comment strings.
delta_freq: The array of channel frequency width.
duration: The array of duration times.
exposure: The array of exposure (integration) times.
fdnum: The feed number.
history: Get the history strings.
ifnum: The intermediate frequency (IF) number.
is_calibrated: Have the data been calibrated?
is_scaled: Is this Scan scaled to something other than antenna temperature \(T_A\).
meta: The metadata of this Scan.
nchan: The number of channels in this scan.
nint: The number of integrations in this scan.
nrows: The number of rows in this scan.
plnum: The polarization number.
scan: The scan number
subtracted: Has a baseline model been subtracted?
surface_error: The dish surface errors for the integrations in this Scan
tscale: The descriptive brightness unit of the data.
tscale_fac: The factor(s) by which the data have been scale from antenna temperature to corrected antenna temperature or flux density.
tsys: The system temperature array.
tsys_weight: The system temperature weighting array computed from current \(T_{sys}\), \(t_{int}\), and \(\delta\nu\).
tunit: The brightness unit (temperature or flux density) of this Scan’s data
weights: The weights for each integration after an averaging operation.
zenith_opacity: The zenith opacity of this Scan, used to calculated aperture efficiency.

Methods

`add_comment`(comment[, add_time])	Add one or more comments to the class metadata.
`add_history`(history[, add_time])	Add one or more history entries to the class metadata
`calibrate`(**kwargs)	NodScan calibration
`get_vane_tcal`()	Get the tcal value for the vane.
`getspec`(i[, use_wcs])	Return the i-th calibrated Spectrum from this Scan.
`merge_commentary`(other)	Merge the history and comments from another HistoricalBase instance.
`plot`(**kwargs)	Plot the data as a waterfall.
`scale`(tscale[, zenith_opacity])	Scale the data to the given brightness scale (temperature or flux density) using the zenith opacity.
`smooth`([kernel, width, decimate])	Smooth or convolve the underlying calibrated data array, optionally decimating the data.
`subtract_baseline`(model[, tol, force])	Subtract a (previously computed) baseline model from every integration in this Scan.
`timeaverage`([weights, use_wcs])	Compute the time-averaged spectrum.
`undo_baseline`()	Undo the applied (subtracted) baseline.
`write`(fileobj[, flags, output_verify, ...])	Write an SDFITS format file (FITS binary table HDU) of the calibrated data in this ScanBase

calibrate(**kwargs)[source]#: NodScan calibration

class dysh.spectra.scan.PSScan(gbtfits, scan, scanrows, calrows, fdnum, ifnum, plnum, bintable, calibrate=True, smoothref=1, apply_flags=False, observer_location=<EarthLocation (882593.9465029, -4924896.36541728, 3943748.74743984) m>, tscale='ta', zenith_opacity=0.0, refspec=None, tsys=None, ap_eff=None, surface_error=None, tcal=None, nocal=False, channel=None, vane: ~dysh.spectra.vane.VaneSpectrum | None = None)[source]#

Bases: ScanBase

GBT specific version of Position Switch Scan. A position switch scan object has one IF, one feed, and one polarization

Parameters:

gbtfitsGBTFITSLoad

Input GBTFITSLoad object.

scandict

dictionary with keys ‘ON’ and ‘OFF’ containing unique list of ON (signal) and OFF (reference) scan numbers NOTE: there should be one ON and one OFF, a pair.

scanrowsdict

dictionary with keys ‘ON’ and ‘OFF’ containing the list of rows in sdfits corresponding to ON (signal) and OFF (reference) integrations.

calrowsdict

dictionary containing with keys ‘ON’ and ‘OFF’ containing list of rows in sdfits corresponding to cal=T (ON) and cal=F (OFF) integrations.

fdnum: int

The feed number.

ifnumint

The intermediate frequency (IF) number.

plnumint

The polarization number.

bintableint

The index for BINTABLE in sdfits containing the scans.

calibrate: bool

Whether or not to calibrate the data. If true, data will be calibrated as TSYS*(ON-OFF)/OFF. Default: True

smoothref: int

If >1 smooth the reference with a boxcar kernel with a width of smooth_ref channels. The default is to not smooth the reference.

apply_flagsboolean, optional

If True, apply flags before calibration.

observer_locationEarthLocation

Location of the observatory. See Observatory. This will be transformed to ITRS using the time of observation DATE-OBS or MJD-OBS in the SDFITS header. The default is the location of the GBT.

tscalestr, optional

The brightess unit scale for the output scan, must be one of (case-insensitive)

‘Ta’ : Antenna Temperature
‘Ta*’ : Antenna temperature corrected to above the atmosphere
‘Flux’ : flux density in Jansky

If ‘ta*’ or ‘flux’ the zenith opacity must also be given. Default: ‘ta’

zenith_opacity: float, optional

The zenith opacity to use in calculating the scale factors for the integrations. Default: None

refspecint or Spectrum, optional

If given, the Spectrum will be used as the reference rather than using scan data.

tsysfloat or ndarray

If given, this is the system temperature in Kelvin. It overrides the values calculated using the noise diodes. If not given, and signal and reference are scan numbers, the system temperature will be calculated from the reference scan and the noise diode. If not given, and the reference is a Spectrum, the reference system temperature as given in the metadata header will be used. The default is to use the noise diode or the metadata, as appropriate. If vane is provided, tsys will be ignored.

ap_efffloat or None

Aperture efficiency to be used when scaling data to brightness temperature of flux. The provided aperture efficiency must be a number between 0 and 1. If None, dysh will calculate it as described in aperture_efficiency(). Only one of ap_eff or surface_error can be provided.

surface_error: Quantity or None

Surface rms error, in units of length (typically microns), to be used in the Ruze formula when calculating the aperture efficiency. If None, dysh will use the known GBT surface error model. Only one of ap_eff or surface_error can be provided.

channel: list or None

An inclusive list of [firstchan, lastchan] to use in the calibration. The channel list is zero-based. If provided, only data channels in the inclusive range [firstchan,lastchan] will be used. If a reference spectrum has been given, it will also be trimmed to [firstchan,lastchan]. If channels have already been selected through select_channel(), a ValueError will be raised.

vaneVaneSpectrum or None

Vane calibration spectrum. This will be used to derive the system temperature. If provided, tsys will be ignored.

Attributes:

ap_eff: The aperture efficiencies for the integrations in this Scan
baseline_model: Returns the subtracted baseline model or None if it has not yet been computed.
calibrated: Returns the calibrated integrations in the Scan as a numpy array.
comments: Get the comment strings.
delta_freq: The array of channel frequency width.
duration: The array of duration times.
exposure: The array of exposure (integration) times.
fdnum: The feed number.
history: Get the history strings.
ifnum: The intermediate frequency (IF) number.
is_calibrated: Have the data been calibrated?
is_scaled: Is this Scan scaled to something other than antenna temperature \(T_A\).
meta: The metadata of this Scan.
nchan: The number of channels in this scan.
nint: The number of integrations in this scan.
nrows: The number of rows in this scan.
plnum: The polarization number.
refscan: The scan number associated with the reference.
refspec: The reference Spectrum if one was given at construction.
scan: The scan number
sigscan: The scan number associated with the signal
sigspec: The signal Spectrum if one was given at construction.
subtracted: Has a baseline model been subtracted?
surface_error: The dish surface errors for the integrations in this Scan
tscale: The descriptive brightness unit of the data.
tscale_fac: The factor(s) by which the data have been scale from antenna temperature to corrected antenna temperature or flux density.
tsys: The system temperature array.
tsys_weight: The system temperature weighting array computed from current \(T_{sys}\), \(t_{int}\), and \(\delta\nu\).
tunit: The brightness unit (temperature or flux density) of this Scan’s data
weights: The weights for each integration after an averaging operation.
zenith_opacity: The zenith opacity of this Scan, used to calculated aperture efficiency.

Methods

`add_comment`(comment[, add_time])	Add one or more comments to the class metadata.
`add_history`(history[, add_time])	Add one or more history entries to the class metadata
`calibrate`(**kwargs)	Position switch calibration, following equations 1 and 2 in the GBTIDL calibration manual
`get_vane_tcal`()	Get the tcal value for the vane.
`getspec`(i[, use_wcs])	Return the i-th calibrated Spectrum from this Scan.
`merge_commentary`(other)	Merge the history and comments from another HistoricalBase instance.
`plot`(**kwargs)	Plot the data as a waterfall.
`scale`(tscale[, zenith_opacity])	Scale the data to the given brightness scale (temperature or flux density) using the zenith opacity.
`smooth`([kernel, width, decimate])	Smooth or convolve the underlying calibrated data array, optionally decimating the data.
`subtract_baseline`(model[, tol, force])	Subtract a (previously computed) baseline model from every integration in this Scan.
`timeaverage`([weights, use_wcs])	Compute the time-averaged spectrum.
`undo_baseline`()	Undo the applied (subtracted) baseline.
`write`(fileobj[, flags, output_verify, ...])	Write an SDFITS format file (FITS binary table HDU) of the calibrated data in this ScanBase

calibrate(**kwargs)[source]#: Position switch calibration, following equations 1 and 2 in the GBTIDL calibration manual

property refscan: int | None#

The scan number associated with the reference.

Returns:

int or None;: Integer scan number or None if the reference was a Spectrum object

property refspec: Spectrum | None#

The reference Spectrum if one was given at construction.

Returns:

Spectrum or None;: Spectrum object if given as reference or None.

property sigscan: int#: The scan number associated with the signal

property sigspec: Spectrum | None#

The signal Spectrum if one was given at construction.

Returns:

Spectrum or None;: Spectrum object if given as signal or None.

class dysh.spectra.scan.ScanBase(sdfits, smoothref, apply_flags, observer_location, fdnum=-1, ifnum=-1, plnum=-1, tsys=None, tcal=None, ap_eff=None, surface_error=None, zenith_opacity=None, channel=None)[source]#

Bases: HistoricalBase, SpectralAverageMixin

This class describes the common interface to all Scan classes. A Scan represents one scan number, one IF, one feed, and one polarization. Derived classes must implement calibrate().

Attributes:

ap_eff: The aperture efficiencies for the integrations in this Scan
baseline_model: Returns the subtracted baseline model or None if it has not yet been computed.
calibrated: Returns the calibrated integrations in the Scan as a numpy array.
comments: Get the comment strings.
delta_freq: The array of channel frequency width.
duration: The array of duration times.
exposure: The array of exposure (integration) times.
fdnum: The feed number.
history: Get the history strings.
ifnum: The intermediate frequency (IF) number.
is_calibrated: Have the data been calibrated?
is_scaled: Is this Scan scaled to something other than antenna temperature \(T_A\).
meta: The metadata of this Scan.
nchan: The number of channels in this scan.
nint: The number of integrations in this scan.
nrows: The number of rows in this scan.
plnum: The polarization number.
scan: The scan number
subtracted: Has a baseline model been subtracted?
surface_error: The dish surface errors for the integrations in this Scan
tscale: The descriptive brightness unit of the data.
tscale_fac: The factor(s) by which the data have been scale from antenna temperature to corrected antenna temperature or flux density.
tsys: The system temperature array.
tsys_weight: The system temperature weighting array computed from current \(T_{sys}\), \(t_{int}\), and \(\delta\nu\).
tunit: The brightness unit (temperature or flux density) of this Scan’s data
weights: The weights for each integration after an averaging operation.
zenith_opacity: The zenith opacity of this Scan, used to calculated aperture efficiency.

Methods

`add_comment`(comment[, add_time])	Add one or more comments to the class metadata.
`add_history`(history[, add_time])	Add one or more history entries to the class metadata
`calibrate`(**kwargs)	Calibrate the Scan data
`get_vane_tcal`()	Get the tcal value for the vane.
`getspec`(i[, use_wcs])	Return the i-th calibrated Spectrum from this Scan.
`merge_commentary`(other)	Merge the history and comments from another HistoricalBase instance.
`plot`(**kwargs)	Plot the data as a waterfall.
`scale`(tscale[, zenith_opacity])	Scale the data to the given brightness scale (temperature or flux density) using the zenith opacity.
`smooth`([kernel, width, decimate])	Smooth or convolve the underlying calibrated data array, optionally decimating the data.
`subtract_baseline`(model[, tol, force])	Subtract a (previously computed) baseline model from every integration in this Scan.
`timeaverage`([weights, use_wcs])	Compute the time-averaged spectrum.
`undo_baseline`()	Undo the applied (subtracted) baseline.
`write`(fileobj[, flags, output_verify, ...])	Write an SDFITS format file (FITS binary table HDU) of the calibrated data in this ScanBase

property ap_eff: ndarray#

The aperture efficiencies for the integrations in this Scan

Returns:

ap_effndarray: The aperture efficiencies, an array of floats between 0 and 1, one value per integration.

property baseline_model#: Returns the subtracted baseline model or None if it has not yet been computed.

abstract calibrate(**kwargs)[source]#: Calibrate the Scan data

property calibrated: ndarray#: Returns the calibrated integrations in the Scan as a numpy array.

property fdnum: int#

The feed number.

Returns:

int: The index of the feed.

get_vane_tcal()[source]#: Get the tcal value for the vane.

getspec(i: int, use_wcs: bool = True) → Spectrum[source]#

Return the i-th calibrated Spectrum from this Scan.

Parameters:

iint: The index into the calibrated array
use_wcsbool: Create a WCS object for the resulting Spectrum. Creating a WCS object adds computation time to the creation of a Spectrum object. There may be cases where the WCS is not needed, so setting this boolean to False will save computation.

Returns:

spectrumSpectrum

property ifnum: int#

The intermediate frequency (IF) number.

Returns:

int: The index of the IF.

property is_calibrated: bool#

Have the data been calibrated?

Returns:

bool: True if the data have been calibrated, False if not.

property is_scaled: bool#

Is this Scan scaled to something other than antenna temperature \(T_A\).

Returns:

is_scaledbool: True if scale is e.g \(T_A^*\) or Flux (\(S_\nu\)).

property meta: dict#

The metadata of this Scan. The metadata is a list of dictionaries, the length of which is equal to the number of calibrated integrations in the Scan.

Returns:

dict: Dictionary containing the metadata of this Scan

property nchan: int#

The number of channels in this scan.

Returns:

int: The number of channels in this scan.

property nint: int#

The number of integrations in this scan.

Returns:

int: The number of integrations in this scan.

property nrows: int#

The number of rows in this scan.

Returns:

int: The number of rows in this scan.

property plnum: int#

The polarization number.

Returns:

int: The polarization number.

scale(tscale: str, zenith_opacity: float | None = None)[source]#

Scale the data to the given brightness scale (temperature or flux density) using the zenith opacity. If data are already scaled, they will be unscaled first.

Parameters:

tscalestr

Strings representing valid options for scaling spectral data, specifically

‘Ta’ : Antenna Temperature in K
‘Ta*’ : Antenna temperature corrected to above the atmosphere in K
‘Flux’: flux density in Jansky

This parameter is case-insensitive.

zenith_opacityfloat, optional

The zenith opacity. Required if tscale is ‘Ta*’ or ‘Flux’.

Returns:

None

Raises:

TypeError: If scaling to the tscale unit is not applicable to the scan type, e.g., a total power scan.
ValueError: If tscale is unrecognized or zenith_opacity is negative.

property scan: int#

The scan number

Returns:

int: The scan number of the integrations in the Scan object

subtract_baseline(model, tol: int = 1, force: bool = False)[source]#

Subtract a (previously computed) baseline model from every integration in this Scan.

Parameters:

modelModel: The baseline model to subtract. This is typically a QuantityModel derived by removing a baseline from a similar spectrum.
tolint, optional: The maximum number of channels on either end of the spectrum to extrapolate the baseline model, if the spectral domain of the baseline model is smaller than the spectral axis of the Scan. For instance, if tol=1, then one channel on the low frequency and one channel on the high frequency end are allowed to be extrapolated. The default is 1.
forcebool, optional: Force subtraction of the input baseline model, even if another baseline model has been previously subtracted. Note: The previous baseline model will not be undone (added back in) before subtraction of the input baseline model. The default is False.

Returns:

None

Raises:

ValueError: If the data are not yet calibrated or the tolerance tol is exceeded.

property subtracted: bool#

Has a baseline model been subtracted?

Returns:

True if a baseline model has been subtracted, False otherwise

property surface_error: Quantity#

The dish surface errors for the integrations in this Scan

Returns:

surface_errorQuantity: The surface_errors with dimension length, one value per integration.

timeaverage(weights='tsys', use_wcs=True)[source]#

Compute the time-averaged spectrum. For a Scan this will average all the integrations in the Scan, according to the given weights. For a ScanBlock, it will average all the integrations in all Scans contained in the ScanBlock.

Parameters:

weights: None, str or `~numpy.ndarray`

If None, the channel weights will be equal and set to unity.

If ‘tsys’ the channel weights will be calculated as:

\(w = t_{exp} \times \delta\nu/T_{sys}^2\)

If an array, it must have shape (Nint,) or (Nint,nchan) where Nint is the number of integrations in the Scan or ScanBlock and nchan is the number of channels in the Scan.

use_wcsbool

Create a WCS object for the resulting Spectrum. Creating a WCS object adds computation time to the creation of a Spectrum object. There may be cases where the WCS is not needed, so setting this boolean to False will save computation.

Returns:

spectrumSpectrum: The time-averaged spectrum. The weights array of the Spectrum will have shape (nchan,) and will be set to the sum of the input weights.

Note

Data that are masked will have values set to zero. This is a feature of numpy.ma.average. Data mask fill value is NaN (np.nan)

property tscale: str#

The descriptive brightness unit of the data. One of

‘Raw’ : raw value, e.g., count
‘Ta’ : Antenna Temperature in K
‘Ta*’ : Antenna temperature corrected to above the atmosphere in K
‘Flux’: flux density in Jansky

Returns:

tscalestr: Brightness unit string.

property tscale_fac: ndarray#

The factor(s) by which the data have been scale from antenna temperature to corrected antenna temperature or flux density.

Returns:

tscale_facndarray: An array of floats, one per integration in the scan.

property tunit: Unit#

The brightness unit (temperature or flux density) of this Scan’s data

Returns:

tunitUnit: The brightness unit

undo_baseline()[source]#: Undo the applied (subtracted) baseline. The subtracted baseline will be added back to the data. The baseline_model attribute is set to None.

write(fileobj, flags=True, output_verify='exception', overwrite=False, checksum=False)[source]#

Write an SDFITS format file (FITS binary table HDU) of the calibrated data in this ScanBase

Parameters:

fileobjstr, file-like or pathlib.Path: File to write to. If a file object, must be opened in a writeable mode.
flags: bool, optional: If True, write the applied flags to a FLAGS column in the binary table.
output_verifystr: Output verification option. Must be one of "fix", "silentfix", "ignore", "warn", or "exception". May also be any combination of "fix" or "silentfix" with "+ignore", +warn, or +exception" (e.g. ``"fix+warn"). See https://docs.astropy.org/en/latest/io/fits/api/verification.html for more info
overwritebool, optional: If True, overwrite the output file if it exists. Raises an OSError if False and the output file exists. Default is False.
checksumbool: When True adds both DATASUM and CHECKSUM cards to the headers of all HDU’s written to the file.

Returns:

None.

property zenith_opacity: float | None#

The zenith opacity of this Scan, used to calculated aperture efficiency.

Returns:

zenith_opacity: float or None: A non-negative float. If None, no zenith opacity was set for this scan (e.g. TPScan); the data cannot be scaled.

class dysh.spectra.scan.ScanBlock(*args)[source]#

Bases: UserList, HistoricalBase, SpectralAverageMixin

Attributes:

comments: Get the comment strings.
delta_freq: Returns
duration: Returns
exposure: Returns
history: Get the history strings.
nchan: The number of channels in the first Scan in this ScanBlock.
nint: The total number of integrations in this Scanblock
tscale: The descriptive brightness unit of the data.
tscale_fac: The factor(s) by which the data have been scaled from antenna temperature to corrected antenna temperature or flux density.
tsys: The system temperatures for all scans in this ScanBlock
tsys_weight: The system temperature weighting array computed from current \(T_{sys}\), \(t_{int}\), and \(\delta\nu\).
tunit: The brightness unit (temperature or flux density) of this ScanBlocks’s data
weights: The weights associated with the Scans and integrations in this ScanBlock

Methods

`add_comment`(comment[, add_time])	Add one or more comments to the class metadata.
`add_history`(history[, add_time])	Add one or more history entries to the class metadata
`append`(item)	S.append(value) -- append value to the end of the sequence
`calibrate`(**kwargs)	Calibrate all scans in this ScanBlock
`clear`()
`count`(value)
`extend`(other)	S.extend(iterable) -- extend sequence by appending elements from the iterable
`index`(value, [start, [stop]])	Raises ValueError if the value is not present.
`insert`(i, item)	S.insert(index, value) -- insert value before index
`merge_commentary`(other)	Merge the history and comments from another HistoricalBase instance.
`plot`(**kwargs)	Plot the data as a waterfall.
`pop`([index])	Raise IndexError if list is empty or index is out of range.
`remove`(item)	S.remove(value) -- remove first occurrence of value.
`reverse`()	S.reverse() -- reverse IN PLACE
`scale`(tscale, zenith_opacity)	Scale all the data in this `ScanBlock` to the given brightness scale and zenith opacity.
`smooth`([kernel, width, decimate])	Smooth all scans in this ScanBlock.
`subtract_baseline`(model[, tol, force])	Subtract a (previously computed) baseline model from every integration of every Scan in this ScanBlock.
`timeaverage`([weights, use_wcs])	Compute the time-averaged spectrum.
`undo_baseline`()	For all Scans in this ScanBlock, undo the applied (subtracted) baseline.
`write`(fileobj[, flags, output_verify, ...])	Write an SDFITS format file (FITS binary table HDU) of the calibrated data in this ScanBlock

copy
sort

calibrate(**kwargs)[source]#: Calibrate all scans in this ScanBlock

property delta_freq#

Returns:

delta_freqndarray: The channel frequency spacings for all scans in this ScanBlock

property duration#

Returns:

durationndarray: The duration times for all scans in this ScanBlock

property exposure#

Returns:

exposurendarray: The exposure times for all scans in this ScanBlock

property nchan#

The number of channels in the first Scan in this ScanBlock. (Assumes number of channels in each enclosed Scan are the same).

Returns:

int: The number of channels in this ScanBlock.

property nint#

The total number of integrations in this Scanblock

Returns:

int: The number of integerationsin this ScanBlock.

scale(tscale, zenith_opacity)[source]#

Scale all the data in this ScanBlock to the given brightness scale and zenith opacity. If data are already scaled, they will be unscaled first.

Parameters:

tscalestr

Strings representing valid options for scaling spectral data, specifically

‘Ta’ : Antenna Temperature
‘Ta*’ : Antenna temperature corrected to above the atmosphere
‘Flux’ : flux density in Jansky

This parameter is case-insensitive.

zenith_opacityfloat

The zenith opacity

Returns:

None

Raises:

TypeError: If scaling to temperature is not applicable to the scan type, e.g., a total power scan.
ValueError: if tscale is unrecognized or zenith_opacity is negative.

smooth(kernel='hanning', width=1, decimate=0)[source]#

Smooth all scans in this ScanBlock. Smooth or convolve the calibrated data arrays in the contained scans, optionally decimating the data. A number of kernels from astropy.convolution can be selected with the kernel keyword.

Default smoothing kernel is hanning.

Note: Any previously computed/removed baseline will remain unchanged.

Parameters:

kernel{‘hanning’, ‘boxcar’, ‘gaussian’}, optional: Smoothing kernel. Minimum match applies. The default is ‘hanning’.
widthint, optional: Width of the convolving kernel. Should ideally be an odd number. For ‘hanning’ this should be 1, with a 0.25,0.5,0.25 kernel. For ‘boxcar’ an even value triggers an odd one with half the signal at the edges. For ‘gaussian’ this is the FWHM of the final spectral resolution. The default is 1.
decimateint, optional: Decimation factor of the spectrum by returning every decimate channel. -1: no decimation 0: use the width parameter >1: user supplied decimation (use with caution) The default is 0, meaning decimation is by width.

Returns:

None

subtract_baseline(model, tol: int = 1, force: bool = False)[source]#

Subtract a (previously computed) baseline model from every integration of every Scan in this ScanBlock.

Parameters:

modelModel: The baseline model to subtract. This is typically a QuantityModel derived by removing a baseline from a similar spectrum.
tolint, optional: The maximum number of channels on either end of the spectrum to extrapolate the baseline model, if the spectral domain of the baseline model is smaller than the spectral axis of the Scan. For instance, if tol=1, then one channel on the low frequency and one channel on the high frequency end are allowed to be extrapolated. The default is 1.
forcebool, optional: Force subtraction of the input baseline model, even if another baseline model has been previously subtracted. Note: The previous baseline model will not be undone (added back in) before subtraction of the input baseline model. The default is False.

Returns:

None

Raises:

ValueError: If the data are not yet calibrated or the tolerance tol is exceeded.

timeaverage(weights='tsys', use_wcs: bool = True)[source]#

Compute the time-averaged spectrum. For a Scan this will average all the integrations in the Scan, according to the given weights. For a ScanBlock, it will average all the integrations in all Scans contained in the ScanBlock.

Parameters:

weights: None, str or `~numpy.ndarray`

If None, the channel weights will be equal and set to unity.

If ‘tsys’ the channel weights will be calculated as:

\(w = t_{exp} \times \delta\nu/T_{sys}^2\)

If an array, it must have shape (Nint,) or (Nint,nchan) where Nint is the number of integrations in the Scan or ScanBlock and nchan is the number of channels in the Scan.

use_wcsbool

Create a WCS object for the resulting Spectrum. Creating a WCS object adds computation time to the creation of a Spectrum object. There may be cases where the WCS is not needed, so setting this boolean to False will save computation.

Returns:

spectrumSpectrum: The time-averaged spectrum. The weights array of the Spectrum will have shape (nchan,) and will be set to the sum of the input weights.

Note

Data that are masked will have values set to zero. This is a feature of numpy.ma.average. Data mask fill value is NaN (np.nan)

property tscale#

The descriptive brightness unit of the data. One of

‘Raw’ : raw value, e.g., count
‘Ta’ : Antenna Temperature
‘Ta*’ : Antenna temperature corrected to above the atmosphere
‘Flux’ : flux density in Jansky

Returns:

tscalestr: brightness unit string

property tscale_fac#

The factor(s) by which the data have been scaled from antenna temperature to corrected antenna temperature or flux density.

Returns:

tscale_facndarray: An array of floats, one per integration in the ScanBlock.

property tsys#

The system temperatures for all scans in this ScanBlock

Returns:

tsysndarray: The system temperatures for all scans in this ScanBlock

property tunit#

The brightness unit (temperature or flux density) of this ScanBlocks’s data

Returns:

tunitUnit: The brightness unit

undo_baseline()[source]#: For all Scans in this ScanBlock, undo the applied (subtracted) baseline. The subtracted baseline will be added back to the data. The Scan’s baseline_model` attribute is set to None.

property weights: list#

The weights associated with the Scans and integrations in this ScanBlock

Returns:

list: A list containing the weight arrays for each scan. Because Scans can have different numbers of integrations, this is a list instead of a numpy array.

write(fileobj, flags=True, output_verify='exception', overwrite=False, checksum=False)[source]#

Write an SDFITS format file (FITS binary table HDU) of the calibrated data in this ScanBlock

Parameters:

fileobjstr, file-like or pathlib.Path: File to write to. If a file object, must be opened in a writeable mode.
flags: bool, optional: If True, write the applied flags to a FLAGS column in the binary table.
output_verifystr: Output verification option. Must be one of "fix", "silentfix", "ignore", "warn", or "exception". May also be any combination of "fix" or "silentfix" with "+ignore", +warn, or +exception" (e.g. ``"fix+warn"). See https://docs.astropy.org/en/latest/io/fits/api/verification.html for more info
overwritebool, optional: If True, overwrite the output file if it exists. Raises an OSError if False and the output file exists. Default is False.
checksumbool: When True adds both DATASUM and CHECKSUM cards to the headers of all HDU’s written to the file.

Returns:

None.

class dysh.spectra.scan.SpectralAverageMixin[source]#

Bases: object

Attributes:

delta_freq: The array of channel frequency width.
duration: The array of duration times.
exposure: The array of exposure (integration) times.
tsys: The system temperature array.
tsys_weight: The system temperature weighting array computed from current \(T_{sys}\), \(t_{int}\), and \(\delta\nu\).
weights: The weights for each integration after an averaging operation.

Methods

`plot`(**kwargs)	Plot the data as a waterfall.
`smooth`([kernel, width, decimate])	Smooth or convolve the underlying calibrated data array, optionally decimating the data.
`timeaverage`([weights, use_wcs])	Compute the time-averaged spectrum.

property delta_freq: ndarray#

The array of channel frequency width. How the channel width is calculated varies for different derived classes. See _calc_delta_freq().

Returns:

delta_freq: ndarray: The channel frequency width in units of the CDELT1 keyword in the SDFITS header.

property duration: ndarray#

The array of duration times. How the duration is calculated varies for different derived classes. See _calc_exposure(). Duration includes the blanking time, exposure does not, so duration>=exposure.

Returns:

durationndarray: The duration time in units of the DURATION keyword in the SDFITS header.

property exposure: ndarray#

The array of exposure (integration) times. How the exposure is calculated varies for different derived classes. See _calc_exposure().

Returns:

exposurendarray: The exposure time in units of the EXPOSURE keyword in the SDFITS header.

plot(**kwargs)[source]#: Plot the data as a waterfall.

smooth(kernel='hanning', width=1, decimate=0)[source]#

Smooth or convolve the underlying calibrated data array, optionally decimating the data.

A number of kernels from astropy.convolution can be selected with the kernel keyword.

Note: Any previously computed/removed baseline will remain unchanged.

Parameters:

kernel{‘hanning’, ‘boxcar’, ‘gaussian’}, optional: Smoothing kernel. Minimum match applies. The default is ‘hanning’.
widthint, optional: Width of the convolving kernel. Should ideally be an odd number. For ‘hanning’ this should be 1, with a 0.25,0.5,0.25 kernel. For ‘boxcar’ an even value triggers an odd one with half the signal at the edges. For ‘gaussian’ this is the FWHM of the desired spectral resolution. The default is 1.
decimateint, optional: Decimation factor of the spectrum by returning every decimate channel. -1: no decimation 0: use the width parameter >1: user supplied decimation (use with caution) The default is 0, meaning decimation is by width.

Returns:

None

Raises:

ValueError: If no valid smoothing kernel is given or if width is less than 1.

timeaverage(weights: str | ndarray = 'tsys', use_wcs=True) → Spectrum[source]#

Compute the time-averaged spectrum. For a Scan this will average all the integrations in the Scan, according to the given weights. For a ScanBlock, it will average all the integrations in all Scans contained in the ScanBlock.

Parameters:

weights: None, str or `~numpy.ndarray`

If None, the channel weights will be equal and set to unity.

If ‘tsys’ the channel weights will be calculated as:

\(w = t_{exp} \times \delta\nu/T_{sys}^2\)

If an array, it must have shape (Nint,) or (Nint,nchan) where Nint is the number of integrations in the Scan or ScanBlock and nchan is the number of channels in the Scan.

use_wcsbool

Create a WCS object for the resulting Spectrum. Creating a WCS object adds computation time to the creation of a Spectrum object. There may be cases where the WCS is not needed, so setting this boolean to False will save computation.

Returns:

spectrumSpectrum: The time-averaged spectrum. The weights array of the Spectrum will have shape (nchan,) and will be set to the sum of the input weights.

Note

Data that are masked will have values set to zero. This is a feature of numpy.ma.average. Data mask fill value is NaN (np.nan)

property tsys#

The system temperature array.

Returns:

tsysndarray: System temperature values in K.

property tsys_weight: ndarray#: The system temperature weighting array computed from current \(T_{sys}\), \(t_{int}\), and \(\delta\nu\). See tsys_weight()

property weights: ndarray#: The weights for each integration after an averaging operation. If Scan.timeaverage() has not been called, the weights will be unity. The weights array can have shape (nint,) or (nint,nchan) depending on how timeaverage() was called.

class dysh.spectra.scan.SubBeamNodScan(sigtp, reftp, fdnum, ifnum, plnum, calibrate=True, smoothref=1, apply_flags=False, tscale='ta', ap_eff=None, surface_error=None, zenith_opacity=None, tcal=None, observer_location=<EarthLocation (882593.9465029, -4924896.36541728, 3943748.74743984) m>, weights='tsys', nocal=False, vane: ~dysh.spectra.vane.VaneSpectrum | None = None, **kwargs)[source]#

Bases: ScanBase

Parameters:

sigtp: list of `~dysh.spectra.scan.TPScan`

Signal total power scans

reftp: list of `~dysh.spectra.scan.TPScan`

Reference total power scans

fdnum: int

The feed number

ifnumint

The IF number

plnumint

The polarization number

calibrate: bool

Whether or not to calibrate the data.

smoothref: int

the number of channels in the reference to boxcar smooth prior to calibration

apply_flagsboolean, optional. If True, apply flags before calibration.

tscalestr, optional

The brightness scale unit for the output scan, must be one of (case-insensitive)

‘Ta’ : Antenna Temperature
‘Ta*’ : Antenna temperature corrected to above the atmosphere
‘Flux’ : flux density in Jansky

If ‘ta*’ or ‘flux’ the zenith opacity must also be given. Default:’ta’

ap_efffloat or None

Aperture efficiency o be used when scaling data to brightness temperature of flux. The provided aperture efficiency must be a number between 0 and 1. If None, dysh will calculate it as described in aperture_efficiency(). Only one of ap_eff or surface_error can be provided.

surface_error: Quantity or None

Surface rms error, in units of length (typically microns), to be used in the Ruze formula when calculating the aperture efficiency. If None, dysh will use the known GBT surface error model. Only one of ap_eff or surface_error can be provided.

zenith_opacity: float, optional

The zenith opacity to use in calculating the scale factors for the integrations. Default:None

observer_locationEarthLocation

Location of the observatory. See Observatory. This will be transformed to ITRS using the time of observation DATE-OBS or MJD-OBS in the SDFITS header. The default is the location of the GBT.

weights: str

Weighting scheme to use when averaging the signal and reference scans ‘tsys’ or None. If ‘tsys’ the weight will be calculated as:

\(w = t_{exp} \times \delta\nu/T_{sys}^2\)

Default: ‘tsys’

vaneVaneSpectrum or None

Vane calibration spectrum. This will be used to derive the system temperature.

Attributes:

ap_eff: The aperture efficiencies for the integrations in this Scan
baseline_model: Returns the subtracted baseline model or None if it has not yet been computed.
calibrated: Returns the calibrated integrations in the Scan as a numpy array.
comments: Get the comment strings.
delta_freq: The array of channel frequency width.
duration: The array of duration times.
exposure: The array of exposure (integration) times.
fdnum: The feed number.
history: Get the history strings.
ifnum: The intermediate frequency (IF) number.
is_calibrated: Have the data been calibrated?
is_scaled: Is this Scan scaled to something other than antenna temperature \(T_A\).
meta: The metadata of this Scan.
nchan: The number of channels in this scan.
nint: The number of integrations in this scan.
nrows: The number of rows in this scan.
plnum: The polarization number.
scan: The scan number
subtracted: Has a baseline model been subtracted?
surface_error: The dish surface errors for the integrations in this Scan
tscale: The descriptive brightness unit of the data.
tscale_fac: The factor(s) by which the data have been scale from antenna temperature to corrected antenna temperature or flux density.
tsys: The system temperature array.
tsys_weight: The system temperature weighting array computed from current \(T_{sys}\), \(t_{int}\), and \(\delta\nu\).
tunit: The brightness unit (temperature or flux density) of this Scan’s data
weights: The weights for each integration after an averaging operation.
zenith_opacity: The zenith opacity of this Scan, used to calculated aperture efficiency.

Methods

`add_comment`(comment[, add_time])	Add one or more comments to the class metadata.
`add_history`(history[, add_time])	Add one or more history entries to the class metadata
`calibrate`(**kwargs)	Calibrate the SubBeamNodScan data
`get_vane_tcal`()	Get the tcal value for the vane.
`getspec`(i[, use_wcs])	Return the i-th calibrated Spectrum from this Scan.
`merge_commentary`(other)	Merge the history and comments from another HistoricalBase instance.
`plot`(**kwargs)	Plot the data as a waterfall.
`scale`(tscale[, zenith_opacity])	Scale the data to the given brightness scale (temperature or flux density) using the zenith opacity.
`smooth`([kernel, width, decimate])	Smooth or convolve the underlying calibrated data array, optionally decimating the data.
`subtract_baseline`(model[, tol, force])	Subtract a (previously computed) baseline model from every integration in this Scan.
`timeaverage`([weights, use_wcs])	Compute the time-averaged spectrum.
`undo_baseline`()	Undo the applied (subtracted) baseline.
`write`(fileobj[, flags, output_verify, ...])	Write an SDFITS format file (FITS binary table HDU) of the calibrated data in this ScanBase

calibrate(**kwargs)[source]#: Calibrate the SubBeamNodScan data

class dysh.spectra.scan.TPScan(gbtfits, scan, sigstate, calstate, scanrows, calrows, fdnum, ifnum, plnum, bintable, calibrate=True, smoothref=1, apply_flags=False, tsys=None, tcal=None, observer_location=<EarthLocation (882593.9465029, -4924896.36541728, 3943748.74743984) m>, tscale='Raw', channel=None)[source]#

Bases: ScanBase

GBT specific version of Total Power Scan

Parameters:

gbtfitsGBTFITSLoad

input GBTFITSLoad object

scan: int

scan number

sigstatebool

Select the signal state used to form the data. True means select sig=’T’, False to select sig=’F’. None means select both. See table below for explanation.

calstatebool

Select the calibration state used to form the data. True means select cal=’T’, False to select cal=’F’. None means select both. See table below for explanation.

scanrowslist-like

the list of rows in sdfits corresponding to sigstate integrations

calrowsdict

dictionary containing with keys ‘ON’ and ‘OFF’ containing list of rows in sdfits corresponding to cal=T (ON) and cal=F (OFF) integrations for scan

fdnum: int

The feed number

ifnumint

The IF number

plnumint

The polarization number

bintableint

the index for BINTABLE in sdfits containing the scans

calibrate: bool

whether or not to calibrate the data. If True, the data will be (calon + caloff)*0.5, otherwise it will be SDFITS row data. Default:True

smoothref: int

the number of channels in the reference to boxcar smooth prior to calibration

apply_flagsboolean, optional. If True, apply flags before calibration.

observer_locationEarthLocation

Location of the observatory. See Observatory. This will be transformed to ITRS using the time of observation DATE-OBS or MJD-OBS in the SDFITS header. The default is the location of the GBT.

tscalestr, optional

The brightess unit scale for the output scan, must be one of (case-insensitive)

‘Raw’ : raw value, e.g., count
‘Ta’ : Antenna Temperature
‘Ta*’ : Antenna temperature corrected to above the atmosphere
‘Flux’ : flux density in Jansky

Default: ‘Raw’

channel: list or None

An inclusive list of [firstchan, lastchan] to use in the calibration. The channel list is zero-based. If provided, only data channels in the inclusive range [firstchan,lastchan] will be used. If a reference spectrum has been given, it will also be trimmed to [firstchan,lastchan]. If channels have already been selected through select_channel(), a ValueError will be raised.

Notes

—–

How the total power and system temperature are calculated, depending on signal and reference state parameters:

CAL	SIG	RESULT	TSYS
None	None	data = 0.5* (REFCALON + REFCALOFF), regardless of sig state	use all CAL states, all SIG states
None	True	data = 0.5* (REFCALON + REFCALOFF), where sig = ‘T’	use all CAL states, SIG=’T’
None	False	data = 0.5* (REFCALON + REFCALOFF), where sig = ‘F’	use all CAL states, SIG=’F’
True	None	data = REFCALON, regardless of sig state	use all CAL states, all SIG states
False	None	data = REFCALOFF, regardless of sig state	use all CAL states, all SIG states
True	True	data = REFCALON, where sig=’T’	use all CAL states, SIG=’T’
True	False	data = REFCALON, where sig=’F’	use all CAL states, SIG=’F’
False	True	data = REFCALOFF where sig=’T’	use all CAL states, SIG=’T’
False	False	data = REFCALOFF, where sig=’F’	use all CAL states, SIG=’F’

where `REFCALON` = integrations with `cal=T` and `REFCALOFF` = integrations with `cal=F`.

Attributes:

ap_eff: The aperture efficiencies for the integrations in this Scan
baseline_model: Returns the subtracted baseline model or None if it has not yet been computed.
calibrated: Returns the calibrated integrations in the Scan as a numpy array.
calstate: The requested calibration state
comments: Get the comment strings.
delta_freq: The array of channel frequency width.
duration: The array of duration times.
exposure: The array of exposure (integration) times.
fdnum: The feed number.
history: Get the history strings.
ifnum: The intermediate frequency (IF) number.
is_calibrated: Have the data been calibrated?
is_scaled: Is this Scan scaled to something other than antenna temperature \(T_A\).
meta: The metadata of this Scan.
nchan: The number of channels in this scan.
nint: The number of integrations in this scan.
nrows: The number of rows in this scan.
plnum: The polarization number.
scan: The scan number
sigstate: The requested signal state
subtracted: Has a baseline model been subtracted?
surface_error: The dish surface errors for the integrations in this Scan
tscale: The descriptive brightness unit of the data.
tscale_fac: The factor(s) by which the data have been scale from antenna temperature to corrected antenna temperature or flux density.
tsys: The system temperature array.
tsys_weight: The system temperature weighting array computed from current \(T_{sys}\), \(t_{int}\), and \(\delta\nu\).
tunit: The brightness unit (temperature or flux density) of this Scan’s data
weights: The weights for each integration after an averaging operation.
zenith_opacity: The zenith opacity of this Scan, used to calculated aperture efficiency.

Methods

`add_comment`(comment[, add_time])	Add one or more comments to the class metadata.
`add_history`(history[, add_time])	Add one or more history entries to the class metadata
`calibrate`(**kwargs)	Calibrate the total power data according to the CAL/SIG table above
`get_vane_tcal`()	Get the tcal value for the vane.
`getspec`(i[, use_wcs])	Return the i-th calibrated Spectrum from this Scan.
`merge_commentary`(other)	Merge the history and comments from another HistoricalBase instance.
`plot`(**kwargs)	Plot the data as a waterfall.
`scale`(tscale[, zenith_opacity])	Scale the data to the given brightness scale (temperature or flux density) using the zenith opacity.
`smooth`([kernel, width, decimate])	Smooth or convolve the underlying calibrated data array, optionally decimating the data.
`subtract_baseline`(model[, tol, force])	Subtract a (previously computed) baseline model from every integration in this Scan.
`timeaverage`([weights, use_wcs])	Compute the time-averaged spectrum.
`total_power`(i)	Return the i-th total power spectrum in this Scan.
`undo_baseline`()	Undo the applied (subtracted) baseline.
`write`(fileobj[, flags, output_verify, ...])	Write an SDFITS format file (FITS binary table HDU) of the calibrated data in this ScanBase

calibrate(**kwargs)[source]#: Calibrate the total power data according to the CAL/SIG table above

property calstate#

The requested calibration state

Returns:

bool: True if calibration state is on (‘T’ in the SDFITS header), False otherwise (‘F’)

property sigstate#

The requested signal state

Returns:

bool: True if signal state is on (‘T’ in the SDFITS header), False otherwise (‘F’)

total_power(i)[source]#

Return the i-th total power spectrum in this Scan. This is a synonym for calibrated()

Parameters:

iint: The index into the data array

Returns:

spectrumSpectrum

TCal#

TCal class

class dysh.spectra.tcal.TCal(name, snu, *args, **kwargs)[source]#

Bases: Spectrum

Noise diode temperature. This behaves as a Spectrum with the addition of the name and snu attributes, which hold the name of the source used to derive the noise diode temperature and its flux density.

Attributes:

array_axis_physical_types: Returns the WCS physical types that vary along each array axis.
baseline_model: Returns the computed baseline model or None if it has not yet been computed.
bin_edges
combined_wcs: The WCS transform for the NDCube, including the coordinates specified in .extra_coords.
comments: Get the comment strings.
data: ndarray - like
dimensions
doppler_convention: String representation of the velocity (Doppler) convention
energy: The energy of the spectral axis as a Quantity in units of eV.
equivalencies: Get the spectral axis equivalencies that can be used in converting the axis
exclude_regions: The baseline exclusion region(s) of this spectrum
extra_coords: Coordinates not described by NDCubeABC.wcs which vary along one or more axes.
flux: Converts the stored data and unit and mask into a Quantity object.
frequency: The spectral_axis as a Quantity in units of GHz
global_coords: Coordinate metadata which applies to the whole cube.
history: Get the history strings.
mask: any type : Mask for the dataset, if any.
meta
name: Calibration source name.
nchan: The number of channels in the Spectrum
observer: Returns ——- observer : BaseCoordinateFrame or derivative The coordinate frame of the observer if present.
obstime
photon_flux: The flux density of photons as a Quantity, in units of
plotter
psf: Image representation of the PSF for the dataset.
quantity: Unitful representation of the NDCube data.
radial_velocity: The radial velocity(s) of the objects represented by this spectrum.
redshift: The redshift(s) of the objects represented by this spectrum.
rest_value: Rest frequency used in velocity conversions.
shape
snu: Calibration source flux density.
spectral_axis: Returns the SpectralCoord object.
spectral_axis_direction
spectral_axis_index
spectral_wcs: Returns the spectral axes of the WCS
subtracted: Has a baseline model been subtracted?”
target: The target object of this spectrum.
tscale: The descriptive brightness unit of the data.
tscale_fac: The factor by which the data have been scale from antenna temperature to corrected antenna temperature or flux density.
uncertainty: any type : Uncertainty in the dataset, if any.
unit: Unit : Unit for the dataset, if any.
velocity: Converts the spectral axis array to the given velocity space unit given the rest value.
velocity_convention: Returns the velocity convention
velocity_frame: String representation of the velocity frame
wavelength: The spectral_axis as a Quantity in units of Angstroms
wcs: any type : A world coordinate system (WCS) for the dataset, if any.
weights: The channel weights of this spectrum

Methods

`add`(operand[, operand2])	Performs addition by evaluating `self` + `operand`.
`add_comment`(comment[, add_time])	Add one or more comments to the class metadata.
`add_history`(history[, add_time])	Add one or more history entries to the class metadata
`align_to`(other[, units, frame, remove_wrap, ...])	Align the `Spectrum` with respect to `other`.
`average`(spectra[, weights, align])	Average this `Spectrum` with `spectra`.
`axis_velocity`([unit])	Get the spectral axis in velocity units.
`axis_world_coords`(*axes[, pixel_corners, wcs])	Returns objects representing the world coordinates of pixel centers for a desired axes.
`axis_world_coords_values`(*axes[, ...])	Returns the world coordinate values of all pixels for desired axes.
`baseline`(degree[, exclude, include, color])	Compute and optionally remove a baseline.
`bshow`()	Show the baseline model
`check_stats`(rms[, rtol])	Check statistics of a spectrum compared to pre-set value(s) to a relative tolerance.
`cog`([vc, width_frac, bchan, echan, ...])	Curve of growth (CoG) analysis based on Yu et al. (2020) [R475e759f9d5f-1].
`collapse`(method[, axis])	Collapse the flux array given a method.
`crop`(*points[, wcs, keepdims])	Crop using real world coordinates.
`crop_by_values`(*points[, units, wcs, keepdims])	Crop using real world coordinates.
`decimate`(n)	Decimate the `Spectrum` by n pixels.
`divide`(operand[, operand2])	Performs division by evaluating `self` / `operand`.
`explode_along_axis`(axis)	Separates slices of NDCubes along a given axis into an NDCubeSequence of (N-1)DCubes.
`fake_spectrum`([nchan, seed, normal, use_wcs])	Create a fake spectrum with Gaussian noise, useful for simple testing.
`find_shift`(other[, units, frame])	Find the shift required to align this `Spectrum` with `other`.
`from_spectrum`(spectrum, name, snu[, data])	Returns a `TCal` object given a `Spectrum` object.
`get_selected_regions`([unit])	Get selected regions from plot.
`get_tcal`([frac, method, dtype])	Reduce the temperature of the noise diode to a single value.
`get_velocity_in_frame`(toframe)	Compute the radial velocity of the `Spectrum.target` in a new velocity frame.
`make_spectrum`(data, meta[, use_wcs, ...])	Factory method to create a `Spectrum` object from a data and header.
`merge_commentary`(other)	Merge the history and comments from another HistoricalBase instance.
`meta_as_table`()	Return `Spectrum.meta` as an `Table`.
`multiply`(operand[, operand2])	Performs multiplication by evaluating `self` * `operand`.
`new_flux_unit`(unit[, equivalencies, ...])
`normalness`()	Compute the p-value to check if the noise in a spectrum is Gaussian using the Anderson-Darling statistic.
`plot`(**kwargs)	Plot the spectrum.
`query_lines`([chemical_name, ...])	Query locally or remotely for lines and return a table object.
`radiometer`([roll])	Check the radiometer equation, and return the dimensionless ratio of the measured vs.
`rebin`(bin_shape[, operation, ...])	Downsample array by combining contiguous pixels into bins.
`recomb`(line[, cat, columns])	Search for recombination lines of H, He, and C in the frequency range of this Spectrum.
`recomball`([cat, columns])	Fetch all recombination lines of H, He, C in the frequency range of this Spectrum from the catalog.
`reproject_to`(target_wcs[, algorithm, ...])	Reprojects the instance to the coordinates described by another WCS object.
`roll`([rollmax])	Rolling data to check for channel correllations and channel-to-channel correllations.
`savefig`(file, **kwargs)	Save the plot
`set_convention`(doppler_convention)	Set the velocity convention of this `Spectrum`.
`set_frame`(toframe)	Set the sky coordinate and doppler tracking reference frame of this Spectrum.
`set_radial_velocity_to`(radial_velocity)	This sets the radial velocity of the spectrum to be `radial_velocity` without changing the values of the `spectral_axis`.
`set_redshift_to`(redshift)	This sets the redshift of the spectrum to be `redshift` without changing the values of the `spectral_axis`.
`shift`(s[, remove_wrap, fill_value, method])	Shift the `Spectrum` by `s` channels in place.
`shift_spectrum_to`(*[, redshift, radial_velocity])	This shifts in-place the values of the `spectral_axis`, given either a redshift or radial velocity.
`smooth`(args, *kwargs)	Smooth or convolve `TCal`, optionally decimating it.
`snr`([peak, flux, rms])	Signal-to-noise (S/N) ratio, measured either in channel or total flux mode.
`squeeze`([axis])	Removes all axes with a length of 1.
`sratio`([mean])	Signal ratio: (pSum+nSum)/(pSum-nSum) Here pSum and nSum are the sum of positive and negative values respectively in the spectrum.
`stats`([roll, qac])	Compute some statistics of this `Spectrum`.
`subtract`(operand[, operand2])	Performs subtraction by evaluating `self` - `operand`.
`to`(new_unit, **kwargs)	Convert instance to another unit.
`undo_baseline`()	Undo the most recently computed baseline.
`velocity_axis_to`([unit, toframe, ...])	Convert the spectral axis to `unit` in `toframe` using `doppler_convention` if converting from frequency/wavelength to velocity.
`with_flux_unit`(unit[, equivalencies, ...])	Returns a new spectrum with a different flux unit.
`with_frame`(toframe)	Return a copy of this `Spectrum` with a new coordinate reference frame.
`with_spectral_axis_and_flux_units`(...[, ...])	Perform `with_spectral_axis_unit()` and `with_flux_unit()` together.
`with_spectral_axis_last`()	Convenience method to return a new copy of the Spectrum with the spectral axis last.
`with_spectral_axis_unit`(unit[, ...])	Returns a new spectrum with a different spectral axis unit.
`with_spectral_unit`(unit[, ...])
`with_velocity_convention`(doppler_convention)	Returns a copy of this `Spectrum` with the input velocity convention.

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classmethod from_spectrum(spectrum, name, snu, data=None)[source]#

Returns a TCal object given a Spectrum object.

Parameters:

spectrumSpectrum: Spectrum object. Its attributes will be copied into the TCal object, except for the data and flux attributes which can be defined by the data parameter.
namestr: Name of the calibrator used to derive the noise diode temperature.
snundarray: The flux values of the calibrator source.
dataNone or ndarray: Noise diode temperature values. If None, then it will use the flux attribute of spectrum. If set, the values will define the flux attribute of the TCal object.

Returns:

TCal: The TCal object.

get_tcal(frac=0.1, method=<function nanmean>, dtype=<class 'numpy.float32'>)[source]#

Reduce the temperature of the noise diode to a single value.

Parameters:

fracfloat: The fraction of the channels to discard on both ends of the spectrum. Default: 0.1
methodcallable: The function used to reduce the values. Default : numpy.nanmean
dtypestr or dtype: Typecode or data-type to which the output value, tcal is cast.

Returns:

tcalfloat: The noise diode temperature reduced using method over the channel range defined by frac.

property name#: Calibration source name.

property nchan#: The number of channels in the Spectrum

smooth(*args, **kwargs)[source]#

Smooth or convolve TCal, optionally decimating it.

Parameters:

kernel{“hanning”, “boxcar”, “gaussian”}

Smoothing method. Valid are: ‘hanning’, ‘boxcar’ and ‘gaussian’. Minimum match applies. The default is ‘hanning’.

widthint

Effective width of the convolving kernel. For ‘hanning’ this should be 1, with a 0.25,0.5,0.25 kernel. For ‘boxcar’ an even value triggers an odd one with half the signal at the edges, and will thus not reproduce GBTIDL. For ‘gaussian’ this is the FWHM of the final frequency response. That is, the smoothed Spectrum will have an effective frequency resolution equal to CDELT1*`width`. The default is 1.

decimateint

Decimation factor of the spectrum by returning every decimate channel. -1: no decimation 0: use the width parameter >1: user supplied decimation (use with caution)

maskNone or numpy.ndarray

A “mask” array. Shape must match array, and anything that is masked (i.e., not 0/False) will be set to NaN for the convolution. If None, no masking will be performed unless array is a masked array. If mask is not None and array is a masked array, a pixel is masked if it is masked in either mask or array.mask.

boundarystr

A flag indicating how to handle boundaries:

None
Set the result values to zero where the kernel extends beyond the edge of the array.
‘fill’
Set values outside the array boundary to fill_value (default).
‘wrap’
Periodic boundary that wrap to the other side of array.
‘extend’
Set values outside the array to the nearest array value.

fill_valuefloat

The value to use outside the array when using boundary='fill'. Default value is NaN.

nan_treatment{‘interpolate’, ‘fill’}

The method used to handle NaNs in the input array:

'interpolate': NaN values are replaced with interpolated values using the kernel as an interpolation function. Note that if the kernel has a sum equal to zero, NaN interpolation is not possible and will raise an exception.
'fill': NaN values are replaced by fill_value prior to convolution.

preserve_nanbool

After performing convolution, should pixels that were originally NaN again become NaN?

Returns:

TCal: Smoothed noise diode temperature.

property snu#: Calibration source flux density.

VaneSpectrum#

VaneSpectrum

class dysh.spectra.vane.VaneSpectrum(vane, scan, fdnum, ifnum, plnum, tcal=None, twarm=None, zenith_opacity=None, tatm=None, tbkg=None, *args, **kwargs)[source]#

Bases: Spectrum

Vane calibration spectrum.

Parameters:

vanendarray: Vane power values. The values will define the data attribute of the VaneSpectrum object.
scanint: Scan number.
fdnumint: The feed number.
ifnumint: The intermediate frequency (IF) number.
plnumint: The polarization number.
tcalNone or float: The calibration temperature in K. If None, it will be estimated using get_tcal().
twarmNone or float: The vane temperature in K. If None, it will be inferred from the TWARM column.
zenith_opacityfloat or None: The zenith opacity in nepers. If None, it will be retrieved from the GBO weather forecast scripts (only available at GBO). If None and not at GBO, tcal will equal twarm (accurate to approximately 10%).
tatmfloat or None: The atmospheric temperature towards the zenith in K. If None, it will be retrieved from the GBO weather forecast scripts (only available at GBO). If None and not at GBO, tcal will equal twarm (accurate to approximately 10%).
tbkgfloat or None: The background temperature in K. Default is the CMB temperature at 3 mm (2.725 K).

Attributes:

array_axis_physical_types: Returns the WCS physical types that vary along each array axis.
baseline_model: Returns the computed baseline model or None if it has not yet been computed.
bin_edges
combined_wcs: The WCS transform for the NDCube, including the coordinates specified in .extra_coords.
comments: Get the comment strings.
data: ndarray - like
dimensions
doppler_convention: String representation of the velocity (Doppler) convention
energy: The energy of the spectral axis as a Quantity in units of eV.
equivalencies: Get the spectral axis equivalencies that can be used in converting the axis
exclude_regions: The baseline exclusion region(s) of this spectrum
extra_coords: Coordinates not described by NDCubeABC.wcs which vary along one or more axes.
fdnum: The feed number.
flux: Converts the stored data and unit and mask into a Quantity object.
frequency: The spectral_axis as a Quantity in units of GHz
global_coords: Coordinate metadata which applies to the whole cube.
history: Get the history strings.
ifnum: The intermediate frequency (IF) number.
mask: any type : Mask for the dataset, if any.
meta
nchan: The number of channels in the Spectrum
observer: Returns ——- observer : BaseCoordinateFrame or derivative The coordinate frame of the observer if present.
obstime
photon_flux: The flux density of photons as a Quantity, in units of
plnum: The polarization number.
plotter
psf: Image representation of the PSF for the dataset.
quantity: Unitful representation of the NDCube data.
radial_velocity: The radial velocity(s) of the objects represented by this spectrum.
redshift: The redshift(s) of the objects represented by this spectrum.
rest_value: Rest frequency used in velocity conversions.
scan: The scan number.
shape
spectral_axis: Returns the SpectralCoord object.
spectral_axis_direction
spectral_axis_index
spectral_wcs: Returns the spectral axes of the WCS
subtracted: Has a baseline model been subtracted?”
target: The target object of this spectrum.
tscale: The descriptive brightness unit of the data.
tscale_fac: The factor by which the data have been scale from antenna temperature to corrected antenna temperature or flux density.
twarm: Vane temperature in K.
uncertainty: any type : Uncertainty in the dataset, if any.
unit: Unit : Unit for the dataset, if any.
velocity: Converts the spectral axis array to the given velocity space unit given the rest value.
velocity_convention: Returns the velocity convention
velocity_frame: String representation of the velocity frame
wavelength: The spectral_axis as a Quantity in units of Angstroms
wcs: any type : A world coordinate system (WCS) for the dataset, if any.
weights: The channel weights of this spectrum

Methods

`add`(operand[, operand2])	Performs addition by evaluating `self` + `operand`.
`add_comment`(comment[, add_time])	Add one or more comments to the class metadata.
`add_history`(history[, add_time])	Add one or more history entries to the class metadata
`align_to`(other[, units, frame, remove_wrap, ...])	Align the `Spectrum` with respect to `other`.
`average`(spectra[, weights, align])	Average this `Spectrum` with `spectra`.
`axis_velocity`([unit])	Get the spectral axis in velocity units.
`axis_world_coords`(*axes[, pixel_corners, wcs])	Returns objects representing the world coordinates of pixel centers for a desired axes.
`axis_world_coords_values`(*axes[, ...])	Returns the world coordinate values of all pixels for desired axes.
`baseline`(degree[, exclude, include, color])	Compute and optionally remove a baseline.
`bshow`()	Show the baseline model
`check_stats`(rms[, rtol])	Check statistics of a spectrum compared to pre-set value(s) to a relative tolerance.
`cog`([vc, width_frac, bchan, echan, ...])	Curve of growth (CoG) analysis based on Yu et al. (2020) [Re28fcf3e3187-1].
`collapse`(method[, axis])	Collapse the flux array given a method.
`crop`(*points[, wcs, keepdims])	Crop using real world coordinates.
`crop_by_values`(*points[, units, wcs, keepdims])	Crop using real world coordinates.
`decimate`(n)	Decimate the `Spectrum` by n pixels.
`divide`(operand[, operand2])	Performs division by evaluating `self` / `operand`.
`explode_along_axis`(axis)	Separates slices of NDCubes along a given axis into an NDCubeSequence of (N-1)DCubes.
`fake_spectrum`([nchan, seed, normal, use_wcs])	Create a fake spectrum with Gaussian noise, useful for simple testing.
`find_shift`(other[, units, frame])	Find the shift required to align this `Spectrum` with `other`.
`from_spectrum`(spectrum, scan, fdnum, ifnum, ...)	Returns a `VaneSpectrum` object given a `Spectrum` object.
`get_selected_regions`([unit])	Get selected regions from plot.
`get_tcal`(ref[, mjd, freq, elev, ...])	Calibration temperature.
`get_tsys`(ref[, tcal])	Compute the system temperature.
`get_velocity_in_frame`(toframe)	Compute the radial velocity of the `Spectrum.target` in a new velocity frame.
`make_spectrum`(data, meta[, use_wcs, ...])	Factory method to create a `Spectrum` object from a data and header.
`merge_commentary`(other)	Merge the history and comments from another HistoricalBase instance.
`meta_as_table`()	Return `Spectrum.meta` as an `Table`.
`multiply`(operand[, operand2])	Performs multiplication by evaluating `self` * `operand`.
`new_flux_unit`(unit[, equivalencies, ...])
`normalness`()	Compute the p-value to check if the noise in a spectrum is Gaussian using the Anderson-Darling statistic.
`plot`(**kwargs)	Plot the spectrum.
`query_lines`([chemical_name, ...])	Query locally or remotely for lines and return a table object.
`radiometer`([roll])	Check the radiometer equation, and return the dimensionless ratio of the measured vs.
`rebin`(bin_shape[, operation, ...])	Downsample array by combining contiguous pixels into bins.
`recomb`(line[, cat, columns])	Search for recombination lines of H, He, and C in the frequency range of this Spectrum.
`recomball`([cat, columns])	Fetch all recombination lines of H, He, C in the frequency range of this Spectrum from the catalog.
`reproject_to`(target_wcs[, algorithm, ...])	Reprojects the instance to the coordinates described by another WCS object.
`roll`([rollmax])	Rolling data to check for channel correllations and channel-to-channel correllations.
`savefig`(file, **kwargs)	Save the plot
`set_convention`(doppler_convention)	Set the velocity convention of this `Spectrum`.
`set_frame`(toframe)	Set the sky coordinate and doppler tracking reference frame of this Spectrum.
`set_radial_velocity_to`(radial_velocity)	This sets the radial velocity of the spectrum to be `radial_velocity` without changing the values of the `spectral_axis`.
`set_redshift_to`(redshift)	This sets the redshift of the spectrum to be `redshift` without changing the values of the `spectral_axis`.
`shift`(s[, remove_wrap, fill_value, method])	Shift the `Spectrum` by `s` channels in place.
`shift_spectrum_to`(*[, redshift, radial_velocity])	This shifts in-place the values of the `spectral_axis`, given either a redshift or radial velocity.
`smooth`([kernel, width, decimate, meta, ...])	Smooth or Convolve the `Spectrum`, optionally decimating it.
`snr`([peak, flux, rms])	Signal-to-noise (S/N) ratio, measured either in channel or total flux mode.
`squeeze`([axis])	Removes all axes with a length of 1.
`sratio`([mean])	Signal ratio: (pSum+nSum)/(pSum-nSum) Here pSum and nSum are the sum of positive and negative values respectively in the spectrum.
`stats`([roll, qac])	Compute some statistics of this `Spectrum`.
`subtract`(operand[, operand2])	Performs subtraction by evaluating `self` - `operand`.
`to`(new_unit, **kwargs)	Convert instance to another unit.
`undo_baseline`()	Undo the most recently computed baseline.
`velocity_axis_to`([unit, toframe, ...])	Convert the spectral axis to `unit` in `toframe` using `doppler_convention` if converting from frequency/wavelength to velocity.
`with_flux_unit`(unit[, equivalencies, ...])	Returns a new spectrum with a different flux unit.
`with_frame`(toframe)	Return a copy of this `Spectrum` with a new coordinate reference frame.
`with_spectral_axis_and_flux_units`(...[, ...])	Perform `with_spectral_axis_unit()` and `with_flux_unit()` together.
`with_spectral_axis_last`()	Convenience method to return a new copy of the Spectrum with the spectral axis last.
`with_spectral_axis_unit`(unit[, ...])	Returns a new spectrum with a different spectral axis unit.
`with_spectral_unit`(unit[, ...])
`with_velocity_convention`(doppler_convention)	Returns a copy of this `Spectrum` with the input velocity convention.

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property fdnum#: The feed number.

classmethod from_spectrum(spectrum, scan, fdnum, ifnum, plnum, vane=None, tcal=None, twarm=None, zenith_opacity=None, tatm=None, tbkg=None)[source]#

Returns a VaneSpectrum object given a Spectrum object.

Parameters:

spectrumSpectrum: Spectrum object. Its attributes will be copied into the VaneSpectrum object, except for the data and flux attributes which can be defined by the vane parameter.
scanint: Scan number.
fdnumint: The feed number.
ifnumint: The intermediate frequency (IF) number.
plnumint: The polarization number.
vaneNone or ndarray: Vane power values. If None, then it will use the flux attribute of spectrum. If set, the values will define the data attribute of the VaneSpectrum object.
tcalNone or float: The calibration temperature in K. If None, it will be estimated using get_tcal().
twarmNone or float: The vane temperature in K. If None, it will be inferred from the TWARM column.
zenith_opacityfloat or None: The zenith opacity in nepers. If None, it will be retrieved from the GBO weather forecast scripts (only available at GBO). If None and not at GBO, tcal will equal twarm (accurate to approximately 10%).
tatmfloat or None: The atmospheric temperature towards the zenith in K. If None, it will be retrieved from the GBO weather forecast scripts (only available at GBO). If None and not at GBO, tcal will equal twarm (accurate to approximately 10%).
tbkgfloat or None: The background temperature in K. Default is the CMB temperature at 3 mm (2.725 K).

Returns:

VaneSpectrum: A VaneSpectrum object.

Calibration temperature.

Parameters:

refSpectrum: Reference spectrum used to derive \(T_{\mathrm{cal}}\).
mjdfloat or None: Modified Julian date. If None, will use DATE-OBS in ref.meta.
freqQuantity or None: Frequency at which to compute the calibration temperature. If None, will use OBSFREQ in ref.meta.
elevQuantity or None: Elevation at which to evaluate the airmass. If None, will use ELEVATIO in ref.meta.
twarmNone or float: The vane temperature in K. If None, it will be inferred from the TWARM column.
zenith_opacityfloat or None: The zenith opacity in nepers. If None, it will be retrieved from the GBO weather forecast scripts (only available at GBO). If None and not at GBO, tcal will equal twarm (accurate to approximately 10%).
tatmfloat or None: The atmospheric temperature towards the zenith in K. If None, it will be retrieved from the GBO weather forecast scripts (only available at GBO). If None and not at GBO, tcal will equal twarm (accurate to approximately 10%).
tbkgfloat or None: The background temperature in K. Default is the CMB temperature at 3 mm (2.725 K).

Returns:

tcalfloat: Calibration temperature for the vane in K.

get_tsys(ref, tcal=None)[source]#

Compute the system temperature.

Parameters:

refSpectrum: The reference spectrum.
tcalNone or float: The calibration temperature in K. If None, it will be estimated using get_tcal().

Returns:

tsysfloat: The system temperature in K.

property ifnum#: The intermediate frequency (IF) number.

property plnum#: The polarization number.

property scan#: The scan number.

property twarm#: Vane temperature in K.

Spectral classes and functions

Contents

Spectral classes and functions#

Core Functions#

Spectrum#

Scan#

TCal#

VaneSpectrum#