def main():
results_dir = "../results/hist"
lines = open("hist_results.txt", "r").readlines()
#print len(lines)
for filename, line in zip((darkfiles("sbc") + darkfiles("sky")),
lines)[1:2]:
x0, theta, k = map(float, line.split(","))
print x0, theta, k
base, _ = os.path.splitext(filename)
outname = base + ".hist.png"
_, outname = os.path.split(outname)
outname = os.path.join(results_dir, outname)
print filename, outname
data = N.asarray(getdata(filename))
#print "min =", min(data.flat)
#subplot(211)
#imshow(scipy.stats.zs(data) < 5, cmap=cm.gray)
#subplot(212)
data = data.flatten()
data = data[abs(scipy.stats.zs(data)) < 6]
x0 = min(data.flat)
mu = data.astype(float).mean()
sd = data.astype(float).std()
p = (mu - x0) / data.size
#x0 = mu - 2*(mu - x0)
print(x0, data.max())
ns = N.arange(x0, data.max())
#print "mu =", mu, mu - x0, std(data.flatten())**2
#print "a"
header = dict(getheader(filename).items())
#print "b", len(data.flat), data.max() - data.min(), histogram(data.flat)
hist(data.flat, data.max() - data.min(), normed=1)
#print "c"
# plot(ns, [float(P(n - x0, mu - x0)) for n in ns], linewidth=2, c='r')
# plot(ns, [float(Nm(n, mu, sd)) for n in ns], linewidth=2, c='y')
# plot(ns, [float(G(n, 6.0, x0, sd*0.7)) for n in ns], linewidth=2, c='g')
# plot(ns, [float(G(n, k, 0, theta)) for n in ns], linewidth=2, c='g')
#gdat, gdat0, gdat1 = ests(data, (x0,theta,k))
[gdat] = ests(data, (x0, theta, k))
print N.max(gdat), N.argmax(gdat)
plot(gdat, linewidth=2, c='y')
#plot(ns, gdat0, linewidth=2, c='r')
#plot(ns, gdat1, linewidth=2, c='y')
title("Time: %1.3fs, Temp: %1.1fC (%1.0fC) (%1.0f += %1.1f)" %
(header['EXPTIME'], header['CCD-TEMP'], header['SET-TEMP'],
mean(data.flatten().astype(float)),
std(data.flatten().astype(float))))
#savefig(outname)
show()
clf()
def main():
results_dir = "../results/hist"
lines = open("hist_results.txt", "r").readlines()
#print len(lines)
for filename, line in zip((darkfiles("sbc") + darkfiles("sky")), lines)[1:2]:
x0, theta, k = map(float, line.split(","))
print x0, theta, k
base, _ = os.path.splitext(filename)
outname = base + ".hist.png"
_, outname = os.path.split(outname)
outname = os.path.join(results_dir, outname)
print filename, outname
data = N.asarray(getdata(filename))
#print "min =", min(data.flat)
#subplot(211)
#imshow(scipy.stats.zs(data) < 5, cmap=cm.gray)
#subplot(212)
data = data.flatten()
data = data[abs(scipy.stats.zs(data)) < 6]
x0 = min(data.flat)
mu = data.astype(float).mean()
sd = data.astype(float).std()
p = (mu - x0)/data.size
#x0 = mu - 2*(mu - x0)
print (x0, data.max())
ns = N.arange(x0, data.max())
#print "mu =", mu, mu - x0, std(data.flatten())**2
#print "a"
header = dict(getheader(filename).items())
#print "b", len(data.flat), data.max() - data.min(), histogram(data.flat)
hist(data.flat, data.max() - data.min(), normed=1)
#print "c"
# plot(ns, [float(P(n - x0, mu - x0)) for n in ns], linewidth=2, c='r')
# plot(ns, [float(Nm(n, mu, sd)) for n in ns], linewidth=2, c='y')
# plot(ns, [float(G(n, 6.0, x0, sd*0.7)) for n in ns], linewidth=2, c='g')
# plot(ns, [float(G(n, k, 0, theta)) for n in ns], linewidth=2, c='g')
#gdat, gdat0, gdat1 = ests(data, (x0,theta,k))
[gdat] = ests(data, (x0,theta,k))
print N.max(gdat), N.argmax(gdat)
plot(gdat, linewidth=2, c='y')
#plot(ns, gdat0, linewidth=2, c='r')
#plot(ns, gdat1, linewidth=2, c='y')
title("Time: %1.3fs, Temp: %1.1fC (%1.0fC) (%1.0f += %1.1f)" %
(header['EXPTIME'], header['CCD-TEMP'], header['SET-TEMP'],
mean(data.flatten().astype(float)), std(data.flatten().astype(float)) ))
#savefig(outname)
show()
clf()
def group_darks(cam, key1, key2):
"""
Group the dark frames into a nested dictionary of Images,
keyed by key1, then key2.
"""
filenames = darkfiles(cam)
images = valid_images(filenames)
groups = groupby(images, key1)
for key in groups:
groups[key] = groupby(groups[key], key2)
return groups
def group_darks(cam, key1, key2):
"""
Group the dark frames into a nested dictionary of Images,
keyed by key1, then key2.
"""
filenames = darkfiles(cam)
images = valid_images(filenames)
groups = groupby(images, key1)
for key in groups:
groups[key] = groupby(groups[key], key2)
return groups
def image_sort_example():
"""
Sort a bunch of images based on different keys
"""
files = darkfiles("sbc")
images = valid_images(files)
image = Image(files[0])
print image.MIN
print image.BSCALE
sortby(images, "MAX")
print [image.MAX for image in images]
def image_sort_example():
"""
Sort a bunch of images based on different keys
"""
files = darkfiles("sbc")
images = valid_images(files)
image = Image(files[0])
print image.MIN
print image.BSCALE
sortby(images, "MAX")
print[image.MAX for image in images]
sortby(images, "MEAN")
print[image.MEAN for image in images]