electronic_checks.py

from util import BaseCheckList, annotate_all, invalidate_records, update_variable_qc, _compute_robust_zscore, _compute_robust_rate_zscore
import numpy as np
import pandas as pd
import itertools

electronic_checks = BaseCheckList()

def find_bb_outliers(frame, parameters, bb):
    """
    Find outliers in blackbody temperatures.
    Annotate any problems, and set quality flags for each thermistor
    """
    window_length = parameters.get('window_length', 50)

    if bb == 'HBB':
        delta_thresh = .001
    else:
        delta_thresh = .01
    bbb_outliers = np.array(list(_scan_for_outliers(frame, '{}bottomTemp'.format(bb), delta_thresh)))
    bba_outliers = np.array(list(_scan_for_outliers(frame, '{}apexTemp'.format(bb), delta_thresh)))
    bbt_outliers = np.array(list(_scan_for_outliers(frame, '{}topTemp'.format(bb), delta_thresh)))

    variable_qcs = pd.DataFrame({
        'qc_{}bottomTemp'.format(bb) : bbb_outliers * 1,
        'qc_{}topTemp'.format(bb) : bbt_outliers * 1,
        'qc_{}apexTemp'.format(bb) : bba_outliers * 1
    }, index=frame.index)
    frame = update_variable_qc(frame, variable_qcs)

    annotate_all(frame, bbb_outliers, '{} bottom temperature outlier'.format(bb))
    annotate_all(frame, bba_outliers, '{} apex temperature outlier'.format(bb))
    annotate_all(frame, bbt_outliers, '{} top temperature outlier'.format(bb))

    frame['{}_temp_outlier_check'.format(bb.lower())] = ( bbb_outliers | bba_outliers | bbt_outliers ) * 1
    frame = invalidate_records(frame, '{}_temp_outlier_check'.format(bb.lower()))
    return frame

@electronic_checks.check(
    depends=[ 'HBBapexTemp', 'HBBbottomTemp', 'HBBtopTemp' ],
    updates=['HBBbottomTemp', 'HBBapexTemp','HBBtopTemp']
)
def hbb_temp_outlier_check(frame, parameters):
    return find_bb_outliers(frame, parameters, 'HBB')

@electronic_checks.check(
    depends=[ 'ABBapexTemp', 'ABBbottomTemp', 'ABBtopTemp' ],
    updates=['ABBbottomTemp', 'ABBapexTemp','ABBtopTemp']
)
def abb_temp_outlier_check(frame, parameters):
    return find_bb_outliers(frame, parameters, 'ABB')
    
def _find_6sigma_outliers(frame, window_length, estimation_func=_compute_robust_zscore):
    # Find outliers with deviation greater than 6 sigma
    return estimation_func(frame, window_length) > 6

def _scan_for_outliers(frame, variable, delta_thresh):
    """
    Check for temperature changes exceeding the delta_thresh rate in degK / sec
    """
    last = None
    for i, row in frame.iterrows():
        # First row cannot be an outlier
        if last is None:
            yield False
            last = row
        else:
            # Compare each row to previous non-outlier row
            time_diff = (row.datetime - last.datetime).total_seconds()
            variable_diff = row[variable] - last[variable]
            if abs(variable_diff / time_diff) < delta_thresh:
                yield False
                last = row
            else:
                yield True


#### TESTS ####

#def test_hbb_temp_outlier_check():
    #frame = pd.DataFrame({
        #'HBBapexTemp':[0,1,10,1],
        #'HBBbottomTemp':[1,1,1,1],
        #'HBBtopTemp':[0,1,10,1],
        #'qc_notes':'',
        #'sceneMirrorPosition':[ord(x) for x in 'HASA']
    #})
    #assert hbb_temp_outlier_check(frame, {})['hbb_temp_outlier_check'].values.tolist() == [0,0,1,0]
#