def ramp(n, tag=''):
int_times = np.round(np.linspace(50, 5000, n), 0)
for j in int_times:
cam.set_int_time(j)
cam.set_frame_time(j + 20)
cam.img_cap(routine, img_dir, 'f')
cam.file_sorting(img_dir, j, j + 20, tag=tag)
print('PROGRAM COMPLETE')
def pair_ramp(n, tag=''):
int_times = np.round(np.linspace(400, 700, n), 3)
for j in int_times:
cam.set_int_time(j)
cam.set_frame_time(j + 250)
#Take pair of images
cam.img_cap(routine, img_dir, 'f')
cam.file_sorting(img_dir, j, j + 250, tag=tag)
print('PROGRAM COMPLETE')
def dark_current(n, T, tag='', amb_temp=''):
int_times = np.round(np.linspace(5, 500, n), 0)
cam.printProgressBar(0, sum(int_times))
y = 0
for j in int_times:
cam.set_int_time(j)
cam.set_frame_time(j + 20)
cap, _ = cam.img_cap(routine, img_dir, 'f')
hdu_img = fits.open(unsorted_img)
data = hdu_img[0].data
dark_header = fits.getheader(unsorted_img)
dark_header.append(('FPATEMP', T, 'Temperature of detector'))
dark_header.append(('TEMPAMB', amb_temp, 'Ambient Temperature'))
hdu_img.close()
os.remove(unsorted_img) #Delete image after data retrieval
fits.writeto(unsorted_img, data, dark_header)
cam.file_sorting(img_dir, j, j + 20, tag=tag)
y += j
cam.printProgressBar(y, sum(int_times))
print('PROGRAM HAS COMPLETED')
def full_well(n, int_t, tag=''):
dit = cam.set_int_time(int_t)
cam.set_frame_time(int_t + 20)
cam.printProgressBar(0, n)
for j in range(n):
cap, _ = cam.img_cap(routine, img_dir, 'f')
cam.file_sorting(img_dir, dit, dit + 20, tag=tag)
cam.printProgressBar(j, n)
def persist_routine(dit, offset, end_t, tag):
sorting_dir = persist_dir + '/' + tag
os.mkdir(sorting_dir)
img_name = sorting_dir + '/img_' + str(dit) + '_'
#Run sld_on.exe
subprocess.call(["C:\EDT\pdv\sld_on.exe"])
print("SOAK BEGIN")
cam.set_int_time(dit)
cam.set_frame_time(dit + offset)
fr = cam.read_frame_time()
it = cam.read_int_time()
print(fr, it)
#Take throwaway image to open up cam
cam.img_cap(routine, img_dir, 'f')
os.remove(unsorted_img)
#Run soak.exe
subprocess.call(["C:\EDT\pdv\soak.exe"])
t0 = time.time() #Start Timer (time since soak)
t = 0
print("SOAK END")
while t < end_t:
cam.img_cap(routine, img_dir, 'f')
t1 = time.time()
t = t1 - t0
t_s = round(t, 2)
print("Image taken: {}".format(t_s))
file_name = img_name + str(t_s) + '_.fits'
os.rename(unsorted_img, file_name)
print("PROGRAM COMPLETE")
def master_bias(n, tag, T):
'''
Enter docstring here
'''
cam.set_int_time(0.033)
cam.set_frame_time(100.033)
cam.printProgressBar(0,
n,
prefix='Progress:',
suffix='Complete',
length=50)
stack = np.zeros((naxis1, naxis2), dtype=np.uint16)
for j in range(n):
cap, _ = cam.img_cap(routine, img_dir, 'f')
hdu_img = fits.open(unsorted_img)
fits_img = hdu_img[0]
data = fits_img.data
hdu_img.close() #Close image so it can be sorted
stack = np.dstack((stack, data))
cam.printProgressBar(j,n, prefix = 'Progress:', \
suffix = 'Complete', length = 50)
if j == n - 1: #On final frame grab header
bias_header = fits.getheader(unsorted_img)
os.remove(unsorted_img) #Delete image after data retrieval
bias_header.append(('NDIT', n, 'Number of integrations'))
bias_header.append(('TYPE', 'MASTER_BIAS', '0s exposure frame'))
bias_header.append(('FPATEMP', T, 'Temperature of detector'))
#Median Stack
stack = stack[:, :, 1:] #Slice off base layer
master_bias = np.median(stack, axis=2)
master_bias = master_bias.astype(np.uint16)
#Write master frame to fits
master_path = read_path + 'master_bias_' \
+ tag + '.fits'
fits.writeto(master_path, master_bias, bias_header)
print('PROGRAM HAS COMPLETED')
def master_dark(i, n, T, tag=''):
'''
DIT and NDIT are inputs
Function can also take tag for sorting individual frames onto local drive
T is the FPA temperature used to record temperature of FPA for this dark
which is written to file name and FITS header
Program also outputs a .npy binary file containing 3D datacube of central (100,100)
window for studying temporal variance over stack
'''
cam.set_int_time(i)
cam.set_frame_time(i + 20)
bias = cam.get_master_bias(T)
cam.printProgressBar(0,
n,
prefix='Progress:',
suffix='Complete',
length=50)
stack = np.zeros((naxis1, naxis2), dtype=np.uint16)
window = np.zeros((100, 100), dtype=np.uint16)
for j in range(n):
_, _ = cam.img_cap(routine, img_dir, 'f')
hdu_img = fits.open(unsorted_img)
data = hdu_img[0].data
hdu_img.close() #Close image so it can be sorted
data = data - bias
stack = np.dstack((stack, data))
data_window = cam.window(data, 100)
window = np.dstack((window, data_window))
cam.printProgressBar(j,n, prefix = 'Progress:', \
suffix = 'Complete', length = 50)
if j == n - 1: #On final frame grab header
dark_header = fits.getheader(unsorted_img)
#Save single frame to local drive
cam.file_sorting(local_img_dir, i, i + 20, tag=tag)
#Median stack
stack = stack[:, :, 1:] #Slice off base layer
master_dark = np.median(stack, axis=2)
#Prepare window for temporal analysis
window = window[:, :, 1:] #Slice off base layer
temp_var = np.median(np.var(stack, axis=2))
temp_path = master_darks + 'dark_cube' \
+ str(i/1000) + '_' +str(T) +'C.npy'
np.save(temp_path, window)
dark_header.append(('NDIT', n, 'Number of integrations'))
dark_header.append(('TYPE', 'MASTER_DARK', 'Median stack of dark frames'))
dark_header.append(('FPATEMP', T, 'Temperature of detector'))
dark_header.append(
('TEMPVAR', temp_var,
'Median temporal variance of central (100,100) window'))
#Output master frame to fits
master_path = master_darks + 'master_dark_' \
+ str(i/1000) + '_' +str(T) +'C.fits'
fits.writeto(master_path, master_dark, dark_header)
print('PROGRAM HAS COMPLETED')
import scicam as cam
import argparse
parser = argparse.ArgumentParser(prog='capture Image', description='Captures image using specified routine')
parser.add_argument('-i', type=float, help='Integration Time')
parser.add_argument('-g', type=str, help='Naming Tag',default = '')
parser.add_argument('-l', type=int, help='Number of integrations (NDIT)')
args = parser.parse_args()
img_dir = '//merger.anu.edu.au/mbirch/images'
if args.i:
int_t = cam.set_int_time(args.i)
frame_t = cam.set_frame_time((args.i+20))
else:
int_t = cam.read_int_time()
frame_t = cam.read_frame_time()
if args.l:
for i in range(args.l):
cam.img_cap('capture',img_dir)
cam.file_sorting(img_dir,int_t,frame_t,tag=args.g)
else:
cam.img_cap('capture',img_dir)
cam.file_sorting(img_dir,int_t,frame_t,tag=args.g)