def _run(self, simData):
# find the seasons associated with each visit.
seasons = calcSeason(simData[self.raCol], simData[self.expMJDCol])
# 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._generateSpiralOffsets()
# Add to RA and dec values.
vertexID = 0
for s in np.unique(seasons):
match = np.where(seasons == s)[0]
vertexID = vertexID % self.numPoints
simData['spiralDitherPerSeasonRa'][match] = simData[self.raCol][match] \
+ self.xOff[vertexID]/np.cos(simData[self.decCol][match])
simData['spiralDitherPerSeasonDec'][match] = simData[
self.decCol][match] + self.yOff[vertexID]
vertexID += 1
# Wrap into expected range.
simData['spiralDitherPerSeasonRa'], simData['spiralDitherPerSeasonDec'] = \
wrapRADec(simData['spiralDitherPerSeasonRa'],
simData['spiralDitherPerSeasonDec'])
return simData
def _run(self, simData):
# What 'year' is each visit in?
year = np.floor(
(simData[self.expMJDCol] - simData[self.expMJDCol][0]) / 365.25)
# Define dither based on year.
ditherRA = np.zeros(len(simData[self.raCol]))
ditherDec = np.zeros(len(simData[self.decCol]))
# In y1, y3, y5, y6, y8 & y10 ra dither = 0.
# In y2 & y7, ra dither = +ditherOffset
# In y4 & y9, ra dither = -ditherOffset
condition = ((year == 2) | (year == 7))
ditherRA[condition] = self.ditherOffset
condition = ((year == 4) | (year == 9))
ditherRA[condition] = -1. * self.ditherOffset
# In y1, y2, y4, y6, y7 & y9, dec dither = 0
# In y3 & y8, dec dither = -ditherOffset
# In y5 & y10, dec dither = ditherOffset
condition = ((year == 3) | (year == 8))
ditherDec[condition] = -1. * self.ditherOffset
condition = ((year == 5) | (year == 10))
ditherDec[condition] = self.ditherOffset
# Calculate actual RA/Dec and wrap into appropriate range.
simData['yearlyDitherRA'], simData['yearlyDitherDec'] = wrapRADec(
simData[self.raCol] + ditherRA, simData[self.decCol] + ditherDec)
return simData
def _run(self, simData):
# Generate random numbers for dither, using defined seed value if desired.
if self.randomSeed is not None:
np.random.seed(self.randomSeed)
# analysis is simplified if deal with each field separately.
for fieldid in np.unique(simData[self.fieldIdCol]):
# Identify observations of this field.
match = np.where(simData[self.fieldIdCol] == fieldid)[0]
noffsets = len(match)
numTiles = np.ceil(
np.sqrt(noffsets) *
1.5)**2 # number of tiles must be a perfect square.
# arbitarily chosen factor of 1.5 to have more than necessary tiles inside hexagon.
self._generateRepRandomOffsets(noffsets, numTiles)
# Add to RA and dec values.
simData['repulsiveRandomDitherFieldPerVisitRa'][match] = simData[
self.raCol][match] + self.xOff / np.cos(
simData[self.decCol][match])
simData['repulsiveRandomDitherFieldPerVisitDec'][match] = simData[
self.decCol][match] + self.yOff
# Wrap back into expected range.
simData['repulsiveRandomDitherFieldPerVisitRa'], simData['repulsiveRandomDitherFieldPerVisitDec'] = \
wrapRADec(simData['repulsiveRandomDitherFieldPerVisitRa'], simData['repulsiveRandomDitherFieldPerVisitDec'])
return simData
def _run(self, simData):
# Generate random numbers for dither, using defined seed value if desired.
if self.randomSeed is not None:
np.random.seed(self.randomSeed)
for fieldid in np.unique(simData[self.fieldIdCol]):
# Identify observations of this field.
match = np.where(simData[self.fieldIdCol] == fieldid)[0]
noffsets = len(match)
numTiles = np.ceil(np.sqrt(noffsets) * 1.5)**2
self._generateRepRandomOffsets(noffsets, numTiles)
# Apply dithers, increasing each night.
vertexIdxs = np.arange(0, len(match), 1)
nights = simData[self.nightCol][match]
vertexIdxs = np.searchsorted(np.unique(nights), nights)
vertexIdxs = vertexIdxs % len(self.xOff)
simData['repulsiveRandomDitherFieldPerNightRa'][match] = simData[self.raCol][match] + \
self.xOff[vertexIdxs]/np.cos(simData[self.decCol][match])
simData['repulsiveRandomDitherFieldPerNightDec'][match] = simData[
self.decCol][match] + self.yOff[vertexIdxs]
# Wrap into expected range.
simData['repulsiveRandomDitherFieldPerNightRa'], simData['repulsiveRandomDitherFieldPerNightDec'] = \
wrapRADec(simData['repulsiveRandomDitherFieldPerNightRa'], simData['repulsiveRandomDitherFieldPerNightDec'])
return simData
def _run(self, simData):
# Generate random numbers for dither, using defined seed value if desired.
if self.randomSeed is not None:
np.random.seed(self.randomSeed)
for fieldid in np.unique(simData[self.fieldIdCol]):
# Identify observations of this field.
match = np.where(simData[self.fieldIdCol] == fieldid)[0]
noffsets= len(match)
numTiles= np.ceil(np.sqrt(noffsets)*1.5)**2
self._generateRepRandomOffsets(noffsets,numTiles)
# Apply dithers, increasing each night.
vertexIdxs = np.arange(0, len(match), 1)
nights = simData[self.nightCol][match]
vertexIdxs = np.searchsorted(np.unique(nights), nights)
vertexIdxs = vertexIdxs % len(self.xOff)
simData['repulsiveRandomDitherFieldPerNightRa'][match] = simData[self.raCol][match] + \
self.xOff[vertexIdxs]/np.cos(simData[self.decCol][match])
simData['repulsiveRandomDitherFieldPerNightDec'][match] = simData[self.decCol][match] + self.yOff[vertexIdxs]
# Wrap into expected range.
simData['repulsiveRandomDitherFieldPerNightRa'], simData['repulsiveRandomDitherFieldPerNightDec'] = \
wrapRADec(simData['repulsiveRandomDitherFieldPerNightRa'], simData['repulsiveRandomDitherFieldPerNightDec'])
return simData
def run(self, simData):
# Add new columns to simData.
simData = self._addStackers(simData)
# What 'year' is each visit in?
year = np.floor((simData[self.expMJDCol] - simData[self.expMJDCol][0]) / 365.25)
# Define dither based on year.
ditherRA = np.zeros(len(simData[self.raCol]))
ditherDec = np.zeros(len(simData[self.decCol]))
# In y1, y3, y5, y6, y8 & y10 ra dither = 0.
# In y2 & y7, ra dither = +ditherOffset
# In y4 & y9, ra dither = -ditherOffset
condition = ((year == 2) | (year == 7))
ditherRA[condition] = self.ditherOffset
condition = ((year == 4) | (year == 9))
ditherRA[condition] = -1.*self.ditherOffset
# In y1, y2, y4, y6, y7 & y9, dec dither = 0
# In y3 & y8, dec dither = -ditherOffset
# In y5 & y10, dec dither = ditherOffset
condition = ((year == 3) | (year == 8))
ditherDec[condition] = -1.*self.ditherOffset
condition = ((year == 5) | (year == 10))
ditherDec[condition] = self.ditherOffset
# Calculate actual RA/Dec and wrap into appropriate range.
simData['yearlyDitherRA'], simData['yearlyDitherDec'] = wrapRADec(simData[self.raCol] + ditherRA,
simData[self.decCol] + ditherDec)
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._generateSpiralOffsets()
# Add to RA and dec values.
vertexID= 0
for s in np.unique(seasons):
match = np.where(seasons == s)[0]
vertexID= vertexID % self.numPoints
simData['spiralDitherPerSeasonRa'][match] = simData[self.raCol][match] + self.xOff[vertexID]/np.cos(simData[self.decCol][match])
simData['spiralDitherPerSeasonDec'][match] = simData[self.decCol][match] + self.yOff[vertexID]
vertexID += 1
# Wrap into expected range.
simData['spiralDitherPerSeasonRa'], simData['spiralDitherPerSeasonDec'] = \
wrapRADec(simData['spiralDitherPerSeasonRa'], simData['spiralDitherPerSeasonDec'])
return simData
def _run(self, simData):
# find the seasons associated with each visit.
seasons = calcSeason(simData[self.raCol], simdata[self.expMJDCol])
# 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._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
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']
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):
# Generate the spiral offset vertices.
self._generateFermatSpiralOffsets()
# Now apply to observations.
for fieldid in np.unique(simData[self.fieldIdCol]):
match = np.where(simData[self.fieldIdCol] == fieldid)[0]
# Apply sequential dithers, increasing with each visit.
vertexIdxs = np.arange(0, len(match), 1)
vertexIdxs = vertexIdxs % self.numPoints
simData['fermatSpiralDitherFieldPerVisitRa'][match] = simData[self.raCol][match] + \
self.xOff[vertexIdxs]/np.cos(simData[self.decCol][match])
simData['fermatSpiralDitherFieldPerVisitDec'][match] = simData[self.decCol][match] + self.yOff[vertexIdxs]
# Wrap into expected range.
simData['fermatSpiralDitherFieldPerVisitRa'], simData['fermatSpiralDitherFieldPerVisitDec'] = \
wrapRADec(simData['fermatSpiralDitherFieldPerVisitRa'], simData['fermatSpiralDitherFieldPerVisitDec'])
return simData
def _run(self, simData):
# Generate the spiral offset vertices.
self._generateFermatSpiralOffsets()
# Now apply to observations.
for fieldid in np.unique(simData[self.fieldIdCol]):
match = np.where(simData[self.fieldIdCol] == fieldid)[0]
# Apply sequential dithers, increasing with each visit.
vertexIdxs = np.arange(0, len(match), 1)
vertexIdxs = vertexIdxs % self.numPoints
simData['fermatSpiralDitherFieldPerVisitRa'][match] = simData[self.raCol][match] + \
self.xOff[vertexIdxs]/np.cos(simData[self.decCol][match])
simData['fermatSpiralDitherFieldPerVisitDec'][match] = simData[
self.decCol][match] + self.yOff[vertexIdxs]
# Wrap into expected range.
simData['fermatSpiralDitherFieldPerVisitRa'], simData['fermatSpiralDitherFieldPerVisitDec'] = \
wrapRADec(simData['fermatSpiralDitherFieldPerVisitRa'], simData['fermatSpiralDitherFieldPerVisitDec'])
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):
# Generate the spiral offset vertices.
self._generateFermatSpiralOffsets()
vertexID= 0
nights = np.unique(simData[self.nightCol])
for n in nights:
match = np.where(simData[self.nightCol] == n)[0]
vertexID= vertexID % self.numPoints
simData['fermatSpiralDitherPerNightRa'][match] = simData[self.raCol][match] + \
self.xOff[vertexID]/np.cos(simData[self.decCol][match])
simData['fermatSpiralDitherPerNightDec'][match] = simData[self.decCol][match] + self.yOff[vertexID]
vertexID += 1
# Wrap into expected range.
simData['fermatSpiralDitherPerNightRa'], simData['fermatSpiralDitherPerNightDec'] = \
wrapRADec(simData['fermatSpiralDitherPerNightRa'], simData['fermatSpiralDitherPerNightDec'])
return simData
def _run(self, simData):
# Generate the spiral offset vertices.
self._generateFermatSpiralOffsets()
vertexID = 0
nights = np.unique(simData[self.nightCol])
for n in nights:
match = np.where(simData[self.nightCol] == n)[0]
vertexID = vertexID % self.numPoints
simData['fermatSpiralDitherPerNightRa'][match] = simData[self.raCol][match] + \
self.xOff[vertexID]/np.cos(simData[self.decCol][match])
simData['fermatSpiralDitherPerNightDec'][match] = simData[
self.decCol][match] + self.yOff[vertexID]
vertexID += 1
# Wrap into expected range.
simData['fermatSpiralDitherPerNightRa'], simData['fermatSpiralDitherPerNightDec'] = \
wrapRADec(simData['fermatSpiralDitherPerNightRa'], simData['fermatSpiralDitherPerNightDec'])
return simData
def _run(self, simData):
# Generate random numbers for dither, using defined seed value if desired.
if self.randomSeed is not None:
np.random.seed(self.randomSeed)
# Generate the random dither values, one per night.
nights = np.unique(simData[self.nightCol])
numNights= len(nights)
numTiles= np.ceil(np.sqrt(numNights)*1.5)**2
self._generateRepRandomOffsets(numNights, numTiles)
# Add to RA and dec values.
for n, x, y in zip(nights, self.xOff, self.yOff):
match = np.where(simData[self.nightCol] == n)[0]
simData['repulsiveRandomDitherPerNightRa'][match] = simData[self.raCol][match] + x/np.cos(simData[self.decCol][match])
simData['repulsiveRandomDitherPerNightDec'][match] = simData[self.decCol][match] + y
# Wrap RA/Dec into expected range.
simData['repulsiveRandomDitherPerNightRa'], simData['repulsiveRandomDitherPerNightDec'] = \
wrapRADec(simData['repulsiveRandomDitherPerNightRa'], simData['repulsiveRandomDitherPerNightDec'])
return simData
def _run(self, simData):
# Generate random numbers for dither, using defined seed value if desired.
if self.randomSeed is not None:
np.random.seed(self.randomSeed)
# analysis is simplified if deal with each field separately.
for fieldid in np.unique(simData[self.fieldIdCol]):
# Identify observations of this field.
match = np.where(simData[self.fieldIdCol] == fieldid)[0]
noffsets= len(match)
numTiles= np.ceil(np.sqrt(noffsets)*1.5)**2 # number of tiles must be a perfect square.
# arbitarily chosen factor of 1.5 to have more than necessary tiles inside hexagon.
self._generateRepRandomOffsets(noffsets, numTiles)
# Add to RA and dec values.
simData['repulsiveRandomDitherFieldPerVisitRa'][match] = simData[self.raCol][match] + self.xOff/np.cos(simData[self.decCol][match])
simData['repulsiveRandomDitherFieldPerVisitDec'][match] = simData[self.decCol][match] + self.yOff
# Wrap back into expected range.
simData['repulsiveRandomDitherFieldPerVisitRa'], simData['repulsiveRandomDitherFieldPerVisitDec'] = \
wrapRADec(simData['repulsiveRandomDitherFieldPerVisitRa'], simData['repulsiveRandomDitherFieldPerVisitDec'])
return simData
def _run(self, simData):
# Generate random numbers for dither, using defined seed value if desired.
if self.randomSeed is not None:
np.random.seed(self.randomSeed)
# Generate the random dither values, one per night.
nights = np.unique(simData[self.nightCol])
numNights = len(nights)
numTiles = np.ceil(np.sqrt(numNights) * 1.5)**2
self._generateRepRandomOffsets(numNights, numTiles)
# Add to RA and dec values.
for n, x, y in zip(nights, self.xOff, self.yOff):
match = np.where(simData[self.nightCol] == n)[0]
simData['repulsiveRandomDitherPerNightRa'][match] = simData[
self.raCol][match] + x / np.cos(simData[self.decCol][match])
simData['repulsiveRandomDitherPerNightDec'][match] = simData[
self.decCol][match] + y
# Wrap RA/Dec into expected range.
simData['repulsiveRandomDitherPerNightRa'], simData['repulsiveRandomDitherPerNightDec'] = \
wrapRADec(simData['repulsiveRandomDitherPerNightRa'], simData['repulsiveRandomDitherPerNightDec'])
return simData