Coverage for python/lsst/sims/maf/metrics/phaseGapMetric.py : 23%

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""" Measure the maximum gap in phase coverage for observations of periodic variables. """ metricName='Phase Gap', **kwargs): """ Construct an instance of a PhaseGapMetric class
:param col: Name of the column to use for the observation times, commonly 'observationStartMJD' :param nPeriods: Number of periods to test :param periodMin: Minimum period to test (days) :param periodMax: Maximimum period to test (days) :param nVistisMin: minimum number of visits necessary before looking for the phase gap """ self.periodMin = periodMin self.periodMax = periodMax self.nPeriods = nPeriods self.nVisitsMin = nVisitsMin super(PhaseGapMetric, self).__init__(col, metricName=metricName, units='Fraction, 0-1', **kwargs)
""" Run the PhaseGapMetric. :param dataSlice: Data for this slice. :param slicePoint: Metadata for the slice (Optional as not used here). :return: a dictionary of the periods used here and the corresponding largest gaps. """ if len(dataSlice) < self.nVisitsMin: return self.badval # Create 'nPeriods' evenly spaced periods within range of min to max. step = (self.periodMax-self.periodMin)/self.nPeriods if step == 0: periods = np.array([self.periodMin]) else: periods = np.arange(self.nPeriods) periods = periods/np.max(periods)*(self.periodMax-self.periodMin)+self.periodMin maxGap = np.zeros(self.nPeriods, float)
for i, period in enumerate(periods): # For each period, calculate the phases. phases = (dataSlice[self.colname] % period)/period phases = np.sort(phases) # Find the largest gap in coverage. gaps = np.diff(phases) start_to_end = np.array([1.0 - phases[-1] + phases[0]], float) gaps = np.concatenate([gaps, start_to_end]) maxGap[i] = np.max(gaps)
return {'periods':periods, 'maxGaps':maxGap}
""" At each slicepoint, return the mean gap value. """ return np.mean(metricVal['maxGaps'])
""" At each slicepoint, return the median gap value. """ return np.median(metricVal['maxGaps'])
""" At each slicepoint, return the period with the largest phase gap. """ worstP = metricVal['periods'][np.where(metricVal['maxGaps'] == metricVal['maxGaps'].max())] return worstP
""" At each slicepoint, return the largest phase gap value. """ return np.max(metricVal['maxGaps'])
# To fit a periodic source well, you need to cover the full phase, and fit the amplitude. metricName='PhaseCoverageMetric', starMag=20, **kwargs): self.mjdCol = mjdCol self.m5Col = m5Col self.period = period self.starMag = starMag super(PeriodicQualityMetric, self).__init__([mjdCol, m5Col], metricName=metricName, units='Fraction, 0-1', **kwargs)
"""1 is perfectly balanced phase coverage, 0 is no effective coverage. """ angles = dataSlice[self.mjdCol] % self.period angles = angles/self.period * 2.*np.pi x = np.cos(angles) y = np.sin(angles)
snr = m52snr(self.starMag, dataSlice[self.m5Col]) x_ave = np.average(x, weights=snr) y_ave = np.average(y, weights=snr)
vector_off = np.sqrt(x_ave**2+y_ave**2) return 1.-vector_off
"""Fractional SNR on the amplitude, testing for a variety of possible phases """ phases = np.arange(0, np.pi, np.pi/8.) snr = m52snr(self.starMag, dataSlice[self.m5Col]) amp_snrs = np.sin(dataSlice[self.mjdCol]/self.period*2*np.pi + phases[:, np.newaxis])*snr amp_snr = np.min(np.sqrt(np.sum(amp_snrs**2, axis=1)))
max_snr = np.sqrt(np.sum(snr**2)) return amp_snr/max_snr
amplitude_fraction = self._calc_amp(dataSlice) phase_fraction = self._calc_phase(dataSlice) return amplitude_fraction * phase_fraction |