def interpSpec(self, interpPoints, maxAM=2.5,
limits=[np.radians(-5.), np.radians(-20.)]):
"""
interpPoints should have airmass, azRelSun, and sunAlt.
"""
npts = np.size(self.solarWave)
result = np.zeros((np.size(interpPoints), npts), dtype=float)
good = np.where((interpPoints['sunAlt'] >= np.min(limits)) &
(interpPoints['sunAlt'] <= np.max(limits)) &
(interpPoints['airmass'] <= maxAM) &
(interpPoints['airmass'] >= 1.))[0]
# Compute the expected flux in each of the filters that we have fits for
fluxes = []
for filterName in self.filterNames:
fluxes.append(twilightFunc(interpPoints[good], *self.fitResults[filterName]))
fluxes = np.array(fluxes)
# ratio of model flux to raw solar flux:
yvals = fluxes.T/(10.**(-0.4*(self.solarMag-np.log10(3631.))))
# Find wavelengths bluer than cutoff
blueRegion = np.where(self.solarWave < np.min(self.effWave))
for i, yval in enumerate(yvals):
interpF = interp1d(self.effWave, yval, bounds_error=False, fill_value=yval[-1])
ratio = interpF(self.solarWave)
interpBlue = InterpolatedUnivariateSpline(self.effWave, yval, k=1)
ratio[blueRegion] = interpBlue(self.solarWave[blueRegion])
result[good[i]] = self.solarFlux*ratio
return {'spec': result, 'wave': self.solarWave}
def interpMag(self, interpPoints, maxAM=2.5,
limits=[np.radians(-11.), np.radians(-20.)]):
npts = np.size(self.lsstEffWave)
result = np.zeros((np.size(interpPoints), npts), dtype=float)
good = np.where((interpPoints['sunAlt'] >= np.min(limits)) &
(interpPoints['sunAlt'] <= np.max(limits)) &
(interpPoints['airmass'] <= maxAM) &
(interpPoints['airmass'] >= 1.))[0]
for i, filterName in enumerate(self.lsstFilterNames):
result[good, i] = twilightFunc(interpPoints[good], *self.lsstEquations[i, :].tolist())
return {'spec': result, 'wave': self.lsstEffWave}
def interpMag(self,
interpPoints,
maxAM=2.5,
limits=[np.radians(-11.), np.radians(-20.)]):
npts = np.size(self.lsstEffWave)
result = np.zeros((np.size(interpPoints), npts), dtype=float)
good = np.where((interpPoints['sunAlt'] >= np.min(limits))
& (interpPoints['sunAlt'] <= np.max(limits))
& (interpPoints['airmass'] <= maxAM)
& (interpPoints['airmass'] >= 1.))[0]
for i, filterName in enumerate(self.lsstFilterNames):
result[good, i] = twilightFunc(interpPoints[good],
*self.lsstEquations[i, :].tolist())
#mask = np.where(result == 0.)
#result = 10.**(-0.4*(result-np.log10(3631.)))
#result[mask] = 0.
return {'spec': result, 'wave': self.lsstEffWave}
def interpMag(self,
interpPoints,
maxAM=2.5,
limits=[np.radians(-11.), np.radians(-20.)],
filterNames=['u', 'g', 'r', 'i', 'z', 'y']):
npts = len(filterNames)
result = np.zeros((np.size(interpPoints), npts), dtype=float)
good = np.where((interpPoints['sunAlt'] >= np.min(limits))
& (interpPoints['sunAlt'] <= np.max(limits))
& (interpPoints['airmass'] <= maxAM)
& (interpPoints['airmass'] >= 1.))[0]
for i, filterName in enumerate(filterNames):
result[good, i] = twilightFunc(
interpPoints[good], *self.lsstEquations[
self.filterNameDict[filterName], :].tolist())
return {'spec': result, 'wave': self.lsstEffWave}
def interpMag(self, interpPoints, maxAM=2.5,
limits=[np.radians(-5.), np.radians(-20.)],
filterNames=['u', 'g', 'r', 'i', 'z', 'y']):
"""
Originally fit the twilight with a cutoff of sun altitude of -11 degrees. I think it can be safely
extrapolated farther, but be warned you may be entering a regime where it breaks down.
"""
npts = len(filterNames)
result = np.zeros((np.size(interpPoints), npts), dtype=float)
good = np.where((interpPoints['sunAlt'] >= np.min(limits)) &
(interpPoints['sunAlt'] <= np.max(limits)) &
(interpPoints['airmass'] <= maxAM) &
(interpPoints['airmass'] >= 1.))[0]
for i, filterName in enumerate(filterNames):
result[good, i] = twilightFunc(interpPoints[good],
*self.lsstEquations[self.filterNameDict[filterName], :].tolist())
return {'spec': result, 'wave': self.lsstEffWave}
def interpSpec(self,
interpPoints,
maxAM=2.5,
limits=[np.radians(-11.),
np.radians(-20.)]):
"""
interpPoints should have airmass, azRelSun, and sunAlt.
"""
npts = np.size(self.solarWave)
result = np.zeros((np.size(interpPoints), npts), dtype=float)
good = np.where((interpPoints['sunAlt'] >= np.min(limits))
& (interpPoints['sunAlt'] <= np.max(limits))
& (interpPoints['airmass'] <= maxAM)
& (interpPoints['airmass'] >= 1.))[0]
# Compute the expected flux in each of the filters that we have fits for
fluxes = []
for filterName in self.filterNames:
fluxes.append(
twilightFunc(interpPoints[good], *self.fitResults[filterName]))
fluxes = np.array(fluxes)
# ratio of model flux to raw solar flux:
yvals = fluxes.T / (10.**(-0.4 * (self.solarMag - np.log10(3631.))))
# Find wavelengths bluer than cutoff
blueRegion = np.where(self.solarWave < np.min(self.effWave))
for i, yval in enumerate(yvals):
interpF = interp1d(self.effWave,
yval,
bounds_error=False,
fill_value=yval[-1])
ratio = interpF(self.solarWave)
interpBlue = InterpolatedUnivariateSpline(self.effWave, yval, k=1)
ratio[blueRegion] = interpBlue(self.solarWave[blueRegion])
result[good[i]] = self.solarFlux * ratio
return {'spec': result, 'wave': self.solarWave}
def interpMag(self,
interpPoints,
maxAM=2.5,
limits=[np.radians(-5.), np.radians(-20.)],
filterNames=['u', 'g', 'r', 'i', 'z', 'y']):
"""
Originally fit the twilight with a cutoff of sun altitude of -11 degrees. I think it can be safely
extrapolated farther, but be warned you may be entering a regime where it breaks down.
"""
npts = len(filterNames)
result = np.zeros((np.size(interpPoints), npts), dtype=float)
good = np.where((interpPoints['sunAlt'] >= np.min(limits))
& (interpPoints['sunAlt'] <= np.max(limits))
& (interpPoints['airmass'] <= maxAM)
& (interpPoints['airmass'] >= 1.))[0]
for i, filterName in enumerate(filterNames):
result[good, i] = twilightFunc(
interpPoints[good], *self.lsstEquations[
self.filterNameDict[filterName], :].tolist())
return {'spec': result, 'wave': self.lsstEffWave}
