Source code for sofia_redux.instruments.hawc.steps.stepdmdcut

# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Chop filtering pipeline step."""

from astropy import log
import numpy as np

from sofia_redux.instruments.hawc.stepparent import StepParent

__all__ = ['StepDmdCut']




class StepDmdCut(StepParent):
    """
    Filter bad chops from demodulated data.

    This step removes chop cycles from the demodulated data arrays,
    according to flags set by the demodulation algorithm.

    The expected input is a demodulated file with a 'Chop Mask'
    column in the data table. Input is generated by the
    `sofia_redux.instruments.hawc.steps.StepDemodulate` pipeline step.
    The output is a table with the same columns as the input, but some rows
    removed.

    The flag bits for the Chop Mask column are:

    - bit 0: incomplete chop
    - bit 1: check on azelstate
    - bit 2: HWP moving
    - bit 3: check on nodstate
    - bit 4: spare
    - bit 5: spare
    - bit 6: LOS rewind
    - bit 7: tracking errors too high
    - bit 8: CentroidExpMsec below threshhold
    - bit 9: extra samples before and after tracking tolerance violation

    """


    def setup(self):
        """
        Set parameters and metadata for the pipeline step.

        Output files have PRODTYPE = 'dmdcut', and are named with
        the step abbreviation 'DMC'.

        Parameters defined for this step are:

        mask_bits : int
            Bits on which to filter the data. Set to 0b1111111111
            (1023) to filter on all flags. Set to 0b001000000 (64)
            to filter on LOS rewind only. Set to 0 to skip all flags.
        min_samples : int
            Chops containing fewer than min_samples in the Samples
            column will be removed from the output.
        """
        # Name of the pipeline reduction step
        self.name = 'dmdcut'
        self.description = 'Filter Bad Chops'

        # Shortcut for pipeline reduction step and identifier for
        # saved file names.
        self.procname = 'dmc'

        # Clear Parameter list
        self.paramlist = []

        # Append parameters
        self.paramlist.append(['mask_bits', 0b1111111111,
                               'Bits on which to filter'])
        self.paramlist.append(['min_samples', 1,
                               'Minimum samples value'])




    def run(self):
        """
        Run the data reduction algorithm.

        This step is run as a single-in single-out (SISO) step:
        self.datain should be a DataFits object, and output will also
        be a single DataFits, stored in self.dataout.

        The process is:

        1. Read the desired filter flags from the input parameters
           and compare with the Chop Mask column in the input.
        2. Remove flagged samples.
        """
        # Get variables
        mask_bits = self.getarg('mask_bits')
        min_samples = self.getarg('min_samples')

        # Copy datain to dataout
        self.dataout = self.datain.copy()

        # Apply mask
        if 'Chop Mask' in self.datain.table.names:
            chopmask = self.datain.table['Chop Mask']
        else:
            log.info('Chop mask column not found, using zeros')
            chopmask = np.zeros(len(self.datain.table), dtype=np.int32)
        nsamples = self.datain.table['Samples']

        keep_index = np.where(((chopmask & mask_bits) == 0)
                              & (nsamples >= min_samples))

        self.dataout.table = self.dataout.table[keep_index]

        log.info('Retained %d out of %d chops' %
                 (len(self.dataout.table), len(self.datain.table)))