StepScanStokes

class sofia_redux.instruments.hawc.steps.stepscanstokes.StepScanStokes

Bases: StepParent

Compute Stokes parameters for scanning polarimetry data.

This step derives Stokes I, Q, and U images with associated uncertainties and covariances from R and T array images.

Since the input data was produced by the scan map algorithm, it has already been resampled into sky coordinates. The R and T images for each HWP angle must be shifted into a common reference frame before addition and subtraction. Shift values are determined from the WCS values recorded in the extension headers, as output by sofia_redux.instruments.hawc.steps.StepScanMapPol. Shift interpolations are performed via sofia_redux.toolkit.image.adjust.shift.

Optionally, a zero-level correction may be applied to the R and T images, using a mean- or median-filter to identify the lowest negative region in the image.

Thereafter, R and T arrays are directly added and subtracted to produce Stokes parameter fluxes, as in the standard Stokes algorithm for chop-nod polarimetry data (see sofia_redux.instruments.hawc.steps.StepStokes).

Input for this step must be a single DataFits that contains 3 image planes (HDUs) for each subarray (R0 and T0), at each of 4 HWP angles. The three images are: DATA, ERROR, and EXPOSURE. Output from this step is a DataFits with the following image extensions: STOKES I, ERROR I, STOKES Q, ERROR Q, STOKES U, ERROR U, COVAR Q I, COVAR U I, COVAR Q U.

Methods Summary

correct_zero_level_auto(r_data, t_data, ...)	Correct image zero level from automatically determined regions.
correct_zero_level_region(r_data, t_data, ...)	Correct image zero level from specified circular regions.
read_radius()	Read a radius value from the parameters.
run()	Run the data reduction algorithm.
setup()	Set parameters and metadata for the pipeline step.
stokes(idx1, idx2, rmt_data, rmt_sigma, ...)	Compute stokes Q and U.
wcs_shift(header, refheader)	Calculate the WCS shift between two headers.

Methods Documentation

correct_zero_level_auto(r_data, t_data, method, radius)

Correct image zero level from automatically determined regions.

Data arrays are updated in place.

Parameters:

r_dataarray-like

R data to correct.

t_dataarray_like

T data corresponding to r_data.

method{‘mean’, ‘median’}

Filter method.

radiusint

Radius in pixels for filter kernel.

correct_zero_level_region(r_data, t_data, method, reglist, refheader, robust=5.0)

Correct image zero level from specified circular regions.

Data arrays are updated in place.

Parameters:

r_dataarray-like

R data to correct.

t_dataarray_like

T data corresponding to r_data.

method{‘mean’, ‘median’}

Statistics method.

reglistlist of list of float

List of regions as [RA, Dec, radius] in degrees, matching length of r_data and t_data lists.

refheaderastropy.Header

Reference header to use for WCS.

robustfloat

Sigma value to use for clipping statistics. Set to 0 to turn off clipping.

Raises:

ValueError

If any specified region is not on the array.

read_radius()

Read a radius value from the parameters.

The parameters are expected to be defined as a list, with one entry for each HAWC band. The correct value for the input data is selected from the list.

Returns:

radiusfloat

Radius value for the input data.

run()

Run the data reduction algorithm.

Because this step is single-in, single-out (SISO), self.datain must be a DataFits object. The output is also a DataFits object, stored in self.dataout.

The process is:

1. Check and gather all input data.

2. Shift all data to a common reference frame.

3. Compute Stokes I from R+T at all angles.

4. Compute Stokes Q and U from R-T at angles separated by 45 degrees.

5. Propagate errors and covariances.

setup()

Set parameters and metadata for the pipeline step.

Output files have PRODTYPE = ‘scanstokes’, and are named with the step abbreviation ‘STK’.

Parameters defined for this step are:

hwp_tolfloat

Tolerance for difference from expected values for HWP angles. HWP angles for Stokes parameters must differ by no more than 45 +/- hwp_tol degrees.

zero_level_method{‘mean’, ‘median’, ‘none’}

Statistic for zero-level calculation. If ‘none’, the zero-level will not be corrected. For the other options, either a mean or median statistic will be used to determine the zero-level value from the region set by the region parameters.

zero_level_regionstr

If set to ‘header’, the zero-level region will be determined from the ZERO_RA, ZERO_DEC, ZERO_RAD keywords (for RA center, Dec center, and radius, respectively). If set to ‘auto’, a mean- or median-filter will be applied to the R and T images, with the radius specified by the zero_level_radius parameter. The lowest negative local average that is negative in both R and T for all HWP angles is assumed to be the zero level. R and T values are applied separately, from the value of the average at the same pixel. Otherwise, a region may be directly provided as a list of [RA center, Dec center, radius], in degrees.

zero_level_radiuslist of float

Filter radius for zero-level calculation, in arcseconds (per band). Used only for zero_level_region = ‘auto’.

zero_level_sigmafloat

Sigma value for statistics clipping. Ignored for zero_level_region = ‘auto’.

stokes(idx1, idx2, rmt_data, rmt_sigma, r_var, t_var)

Compute stokes Q and U.

The index parameters control which Stokes parameter image is computed.

Parameters:

idx1list of int

Index for angle 1.

idx2list of int

Index for angle 2.

rmt_dataarray-like

R - T flux data array. Should have three dimensions, where the first dimension indexes the HWP angle.

rmt_sigmaarray-like.

R - T error data array. Dimensions should match rmt_data.

r_vararray-like

Variance for the R array. Dimensions should match rmt_data.

t_vararray-like

Variance for the T array. Dimensions should match rmt_data.

Returns:

stokesarray-like

The Stokes Q or U flux image.

dstokesarray-like

The error on the Stokes Q or U flux.

stokes_icovarray-like

The covariance on the Stokes Q or U image, with respect to the Stokes I image.

wcs_shift(header, refheader)

Calculate the WCS shift between two headers.

Parameters:

headerfits.Header

The header for the data to shift.

refheaderfits.Header

The reference header to shift to.

Returns:

shiftarray-like

The (x, y) shift to apply to the data associated with header (dx = shift[0], dy = shift[1]).