def run(self, dataslice, slicePoint=None):
filters = np.unique(dataslice['filter'])
filters = [str(f) for f in filters]
precis = np.zeros(dataslice.size, dtype='float')
for f in filters:
observations = np.where(dataslice['filter'] == f)
if np.size(observations[0]) < 2:
precis[observations] = self.badval
else:
snr = mafUtils.m52snr(self.mags[f],
dataslice[self.m5Col][observations])
precis[observations] = mafUtils.astrom_precision(
dataslice[self.seeingCol][observations], snr)
precis[observations] = np.sqrt(precis[observations]**2 +
self.atm_err**2)
good = np.where(precis != self.badval)
result = mafUtils.sigma_slope(dataslice[self.mjdCol][good],
precis[good])
result = result * 365.25 * 1e3 # Convert to mas/yr
if (self.normalize) & (good[0].size > 0):
new_dates = dataslice[self.mjdCol][good] * 0
nDates = new_dates.size
new_dates[nDates // 2:] = self.baseline * 365.25
result = (mafUtils.sigma_slope(new_dates, precis[good]) * 365.25 *
1e3) / result
# Observations that are very close together can still fail
if np.isnan(result):
result = self.badval
return result
def run(self, dataslice, slicePoint=None):
filters = np.unique(dataslice['filter'])
precis = np.zeros(dataslice.size, dtype='float')
for f in filters:
observations = np.where(dataslice['filter'] == f)
if np.size(observations[0]) < 2:
precis[observations] = self.badval
else:
snr = mafUtils.m52snr(self.mags[f],
dataslice[self.m5Col][observations])
precis[observations] = mafUtils.astrom_precision(
dataslice[self.seeingCol][observations], snr)
precis[observations] = np.sqrt(precis[observations]**2 + self.atm_err**2)
good = np.where(precis != self.badval)
result = mafUtils.sigma_slope(dataslice['expMJD'][good], precis[good])
result = result*365.25*1e3 #convert to mas/yr
if (self.normalize) & (good[0].size > 0):
new_dates=dataslice['expMJD'][good]*0
nDates = new_dates.size
new_dates[nDates/2:] = self.baseline*365.25
result = (mafUtils.sigma_slope(new_dates, precis[good])*365.25*1e3)/result
# Observations that are very close together can still fail
if np.isnan(result):
result = self.badval
return result