def analyse_darks(cam):
"""
Try to fit a linear model to dark frames.
"""
# Collect the data
groups = group_darks(cam, "EXPTIME", "SETTEMP")
print cam
data = []
for time in groups:
for temp in groups[time]:
for image in groups[time][temp]:
data.append([image.MEAN, time, temp])
# Do the regression
beta0, beta1, beta2 = regress(data)
print "count = %f + %ft + %fT" % (beta0, beta1, beta2)
# Calculate residuals
resids = []
key_sorted = groups.keys()
key_sorted.sort()
for time in key_sorted:
res = []
key2_sorted = groups[time].keys()
key2_sorted.sort()
for temp in key2_sorted:
r = []
for image in groups[time][temp]:
r.append(image.MEAN - (beta0 + beta1*time + beta2*temp))
res.append(avg(r))
resids.append(res)
# Display residual results
print resids
print key_sorted, key2_sorted
def analyse_darks(cam):
"""
Try to fit a linear model to dark frames.
"""
# Collect the data
groups = group_darks(cam, "EXPTIME", "SETTEMP")
print cam
data = []
for time in groups:
for temp in groups[time]:
for image in groups[time][temp]:
data.append([image.MEAN, time, temp])
# Do the regression
beta0, beta1, beta2 = regress(data)
print "count = %f + %ft + %fT" % (beta0, beta1, beta2)
# Calculate residuals
resids = []
key_sorted = groups.keys()
key_sorted.sort()
for time in key_sorted:
res = []
key2_sorted = groups[time].keys()
key2_sorted.sort()
for temp in key2_sorted:
r = []
for image in groups[time][temp]:
r.append(image.MEAN - (beta0 + beta1 * time + beta2 * temp))
res.append(avg(r))
resids.append(res)
# Display residual results
print resids
print key_sorted, key2_sorted
def plot_regression(self, ax, X, Y):
dat = N.vstack((Y, X)).transpose()
b, m = regress(dat)
print b, m
xs = N.linspace(X.min(), X.max(), 10)
ax.plot(xs, b + m*xs, linewidth=2)