def calculate_number(data, base, time_uncertainty=1e-6):
""" returns the measured base flux * count time for each point """
assert base.normbase == 'time', "can't calculate time-integrated flux from monitor-normalized base"
S, varS = err1d.interp(data.angular_resolution,
base.angular_resolution, base.v, base.dv**2)
F, varF = err1d.mul(data.monitor.count_time, time_uncertainty**2, S, varS)
return F, varF
def calculate_number(data, base, time_uncertainty=1e-6):
""" returns the measured base flux * count time for each point """
assert base.normbase == 'time', "can't calculate time-integrated flux from monitor-normalized base"
S, varS = err1d.interp(data.angular_resolution, base.angular_resolution,
base.v, base.dv**2)
F, varF = err1d.mul(data.monitor.count_time, time_uncertainty**2, S, varS)
return F, varF
def apply_intensity_norm(data, base):
assert data.normbase == base.normbase, "can't mix time and monitor normalized data"
if data.angular_resolution.ndim == 1:
data_x, base_x = data.angular_resolution, base.angular_resolution
else:
data_x, base_x = data.slit1.x, base.slit1.x
S, varS = err1d.interp(data_x, base_x, base.v, base.dv**2)
# Candor may have only one detector bank active, and so the other may
# have zeros in it. Ignore those channels.
I, varI = err1d.div(data.v, data.dv**2, S + (S == 0), varS)
data.v, data.dv = I, np.sqrt(varI)
def apply_smoothing(slits, dx, degree, span):
assert span > degree, "Span must be greater than degree"
s = np.hstack([[d.angular_resolution, d.slit1.x, d.slit2.x] for d in slits])
s = find_common(s.T, dx=dx).T
#import pylab;pylab.plot(np.arange(len(s[0])),s.T); pylab.show(); import sys; sys.exit()
for d in slits:
if span > 2:
v, dv = smooth(s[0], d.angular_resolution, d.v, d.dv,
degree=degree, span=span)
else:
v, var = interp(s[0], d.angular_resolution, d.v, d.dv**2)
dv = np.sqrt(var)
d.slit1.x = s[1]
d.slit2.x = s[2]
d.v, d.dv = v, dv
def saturation_correction(counts, time, saturation):
# type: (np.ndarray, np.ndarray, np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]
rate = counts / time
# TODO: assert that saturation is sorted by the first value
if saturation is None:
C, varC = counts, counts
mask = (rate >= 0.)
elif saturation.shape[0] == 3:
E, varE = err1d.interp(rate, saturation[0], saturation[1],
saturation[2]**2)
C, varC = err1d.div(counts, counts, E, varE)
mask = (rate <= saturation[0, -1])
else:
E = np.interp(rate, saturation[0], saturation[1])
C, varC = counts/E, counts/E**2
mask = (rate <= saturation[0, -1])
return C, varC, mask
def saturation_correction(counts, time, saturation):
# type: (np.ndarray, np.ndarray, np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]
rate = counts / time
# TODO: assert that saturation is sorted by the first value
if saturation is None:
C, varC = counts, counts
mask = (rate >= 0.)
elif saturation.shape[0] == 3:
E, varE = err1d.interp(rate, saturation[0], saturation[1],
saturation[2]**2)
C, varC = err1d.div(counts, counts, E, varE)
mask = (rate <= saturation[0, -1])
else:
E = np.interp(rate, saturation[0], saturation[1])
C, varC = counts / E, counts / E**2
mask = (rate <= saturation[0, -1])
return C, varC, mask
def apply_intensity_norm(data, base):
assert data.normbase == base.normbase, "can't mix time and monitor normalized data"
S, varS = err1d.interp(data.angular_resolution,
base.angular_resolution, base.v, base.dv**2)
I, varI = err1d.div(data.v, data.dv**2, S, varS)
data.v, data.dv = I, np.sqrt(varI)
def apply_intensity_norm(data, base):
assert data.normbase == base.normbase, "can't mix time and monitor normalized data"
S, varS = err1d.interp(data.angular_resolution, base.angular_resolution,
base.v, base.dv**2)
I, varI = err1d.div(data.v, data.dv**2, S, varS)
data.v, data.dv = I, np.sqrt(varI)