def run(self, simData):
# find the seasons associated with each visit.
seasonSimData = SeasonStacker().run(simData)
seasons = seasonSimData['season']
years = seasonSimData['year']
# check how many entries in the >10 season
ind = np.where(seasons > 9)[0]
print 'Seasons to wrap ', np.unique(
seasons[ind]), 'with total entries: ', len(seasons[ind])
# should be only 1 extra seasons ..
#if len(np.unique(seasons[ind])) > 1:
# print 'ERROR: Too many seasons'
# stop
# wrap the season around: 10th == 0th
seasons[ind] = seasons[ind] % 10
# Add the new columns to simData.
simData = self._addStackers(simData)
# Generate the spiral offset vertices.
self._generatePentagonOffsets()
print 'Total visits for all fields:', len(seasons)
print ''
# for debugging; fill dithered as non-dithered
#simData['PentagonDitherPerSeasonRA'] = simData[self.raCol]
#simData['PentagonDitherPerSeasonDec'] = simData[self.decCol]
# Add to RA and dec values.
vertexID = 0
for s in np.unique(seasons):
#match = np.where((seasons == s) & (years == 5))[0] # for debugging purposes; check only the first year's data.
match = np.where(seasons == s)[0]
# print details
print 'season', s
print 'numEntries ', len(match), '; ', float(
len(match)) / len(seasons) * 100, '% of total'
matchYears = np.unique(years[match])
print 'Corresponding years: ', matchYears
for i in matchYears:
print ' Entries in year', i, ': ', len(
np.where(i == years[match])[0])
print ''
vertexID = vertexID % len(self.xOff)
simData['PentagonDitherPerSeasonRA'][match] = simData[
self.raCol][match] + self.xOff[vertexID] / np.cos(
simData[self.decCol][match])
simData['PentagonDitherPerSeasonDec'][match] = simData[
self.decCol][match] + self.yOff[vertexID]
vertexID += 1
# Wrap into expected range.
simData['PentagonDitherPerSeasonRA'], simData['PentagonDitherPerSeasonDec'] = \
wrapRADec(simData['PentagonDitherPerSeasonRA'], simData['PentagonDitherPerSeasonDec'])
return simData
def _run(self, simData):
# find the seasons associated with each visit.
seasonSimData = SeasonStacker().run(simData)
seasons = seasonSimData['season']
years = seasonSimData['year']
# check how many entries in the >10 season
ind = np.where(seasons > 9)[0]
# should be only 1 extra seasons ..
if len(np.unique(seasons[ind])) > 1:
raise ValueError(
'Too many seasons (more than 11). Check SeasonStacker.')
if self.wrapLastSeason:
# check how many entries in the >10 season
print('Seasons to wrap ', np.unique(seasons[ind]),
'with total entries: ', len(seasons[ind]))
seasons[ind] = seasons[ind] % 10
# Generate the spiral offset vertices.
self._generatePentagonOffsets()
# print details
print('Total visits for all fields:', len(seasons))
print('')
# Add to RA and dec values.
vertexID = 0
for s in np.unique(seasons):
match = np.where(seasons == s)[0]
# print details
print('season', s)
print('numEntries ', len(match), '; ',
float(len(match)) / len(seasons) * 100, '% of total')
matchYears = np.unique(years[match])
print('Corresponding years: ', matchYears)
for i in matchYears:
print(' Entries in year', i, ': ',
len(np.where(i == years[match])[0]))
print('')
vertexID = vertexID % len(self.xOff)
simData['pentagonDitherPerSeasonRa'][match] = simData[
self.raCol][match] + self.xOff[vertexID] / np.cos(
simData[self.decCol][match])
simData['pentagonDitherPerSeasonDec'][match] = simData[
self.decCol][match] + self.yOff[vertexID]
vertexID += 1
# Wrap into expected range.
simData['pentagonDitherPerSeasonRa'], simData['pentagonDitherPerSeasonDec'] = \
wrapRADec(simData['pentagonDitherPerSeasonRa'], simData['pentagonDitherPerSeasonDec'])
return simData
def run(self, simData):
# find the seasons associated with each visit.
seasonSimData = SeasonStacker().run(simData)
seasons = seasonSimData['season']
# check how many entries in the >10 season
ind = np.where(seasons > 9)[0]
print 'Seasons to wrap ', np.unique(seasons[ind])
# should be only 1 extra seasons ..
if len(np.unique(seasons[ind])) > 1:
print 'ERROR: Too many seasons'
stop
# wrap the season around: 10th == 0th
seasons[ind] = seasons[ind] % 10
# Add the new columns to simData.
simData = self._addStackers(simData)
# Generate the spiral offset vertices.
self._generateOffsets()
uniqSeasons = np.unique(seasons)
# Add to RA and dec values.
vertexID = 0
for s in uniqSeasons:
match = np.where(seasons == s)[0]
vertexID = vertexID % len(self.xOff)
simData['PentagonDiamondDitherPerSeasonRA'][match] = simData[
self.raCol][match] + self.xOff[vertexID] / np.cos(
simData[self.decCol][match])
simData['PentagonDiamondDitherPerSeasonDec'][match] = simData[
self.decCol][match] + self.yOff[vertexID]
vertexID += 1
# Wrap into expected range.
simData['PentagonDiamondDitherPerSeasonRA'], simData['PentagonDiamondDitherPerSeasonDec'] = \
wrapRADec(simData['PentagonDiamondDitherPerSeasonRA'],
simData['PentagonDiamondDitherPerSeasonDec'])
return simData
def _run(self, simData):
# find the seasons associated with each visit.
seasonSimData = SeasonStacker().run(simData)
seasons = seasonSimData['season']
# check how many entries in the >10 season
ind = np.where(seasons > 9)[0]
# should be only 1 extra seasons ..
if len(np.unique(seasons[ind])) > 1:
raise ValueError(
'Too many seasons (more than 11). Check SeasonStacker.')
if self.wrapLastSeason:
print('Seasons to wrap ', np.unique(seasons[ind]))
# wrap the season around: 10th == 0th
seasons[ind] = seasons[ind] % 10
# Generate the spiral offset vertices.
self._generateOffsets()
uniqSeasons = np.unique(seasons)
# Add to RA and dec values.
vertexID = 0
for s in uniqSeasons:
match = np.where(seasons == s)[0]
vertexID = vertexID % len(self.xOff)
simData['pentagonDiamondDitherPerSeasonRa'][match] = simData[
self.raCol][match] + self.xOff[vertexID] / np.cos(
simData[self.decCol][match])
simData['pentagonDiamondDitherPerSeasonDec'][match] = simData[
self.decCol][match] + self.yOff[vertexID]
vertexID += 1
# Wrap into expected range.
simData['pentagonDiamondDitherPerSeasonRa'], simData['pentagonDiamondDitherPerSeasonDec'] = \
wrapRADec(simData['pentagonDiamondDitherPerSeasonRa'], simData['pentagonDiamondDitherPerSeasonDec'])
return simData
def _run(self, simData):
# find the seasons associated with each visit.
seasonSimData = SeasonStacker().run(simData)
seasons = seasonSimData['season']
# check how many entries in the >10 season
ind = np.where(seasons > 9)[0]
# should be only 1 extra seasons ..
if len(np.unique(seasons[ind])) > 1:
raise ValueError(
'Too many seasons (more than 11). Check SeasonStacker.')
if self.wrapLastSeason:
print('Seasons to wrap ', np.unique(seasons[ind]))
# wrap the season around: 10th == 0th
seasons[ind] = seasons[ind] % 10
# Generate the spiral offset vertices.
self._generateOffsets()
# Now apply to observations.
for fieldid in np.unique(simData[self.fieldIdCol]):
match = np.where(simData[self.fieldIdCol] == fieldid)[0]
seasonsVisited = seasons[match]
# Apply sequential dithers, increasing with each season.
vertexIdxs = np.searchsorted(np.unique(seasonsVisited),
seasonsVisited)
vertexIdxs = vertexIdxs % len(self.xOff)
simData['pentagonDiamondDitherFieldPerSeasonRa'][match] = simData[self.raCol][match] + \
self.xOff[vertexIdxs]/np.cos(simData[self.decCol][match])
simData['pentagonDiamondDitherFieldPerSeasonDec'][match] = simData[
self.decCol][match] + self.yOff[vertexIdxs]
# Wrap into expected range.
simData['pentagonDiamondDitherFieldPerSeasonRa'], simData['pentagonDiamondDitherFieldPerSeasonDec'] = \
wrapRADec(simData['pentagonDiamondDitherFieldPerSeasonRa'],
simData['pentagonDiamondDitherFieldPerSeasonDec'])
return simData
def run(self, simData):
# find the seasons associated with each visit.
seasonSimData = SeasonStacker().run(simData)
seasons = seasonSimData['season']
# check how many entries in the >10 season
ind = np.where(seasons > 9)[0]
print 'Seasons to wrap ', np.unique(seasons[ind])
# should be only 1 extra seasons ..
if len(np.unique(seasons[ind])) > 1:
print 'ERROR: Too many seasons'
stop
# wrap the season around: 10th == 0th
seasons[ind] = seasons[ind] % 10
# Add the new columns to simData.
simData = self._addStackers(simData)
# Generate the spiral offset vertices.
self._generatePentagonOffsets()
# Now apply to observations.
for fieldid in np.unique(simData[self.fieldIdCol]):
match = np.where(simData[self.fieldIdCol] == fieldid)[0]
seasonsVisited = seasons[match]
# Apply sequential dithers, increasing with each season.
vertexIdxs = np.searchsorted(np.unique(seasonsVisited),
seasonsVisited)
vertexIdxs = vertexIdxs % len(self.xOff)
simData['PentagonDitherFieldPerSeasonRA'][match] = simData[self.raCol][match] + \
self.xOff[vertexIdxs]/np.cos(simData[self.decCol][match])
simData['PentagonDitherFieldPerSeasonDec'][match] = simData[
self.decCol][match] + self.yOff[vertexIdxs]
# Wrap into expected range.
simData['PentagonDitherFieldPerSeasonRA'], simData['PentagonDitherFieldPerSeasonDec'] = \
wrapRADec(simData['PentagonDitherFieldPerSeasonRA'], simData['PentagonDitherFieldPerSeasonDec'])
return simData