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 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 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 expose(i, tag=''):
int_t = cam.set_int_time(i)
frame_t = cam.set_frame_time(i + 100)
frame, int_header = cam.simple_cap()
if args.c != '':
int_header.append(('COMMENT', args.c, 'User-defined comment'))
fits.writeto(unsorted_img, frame, int_header)
cam.weather_to_fits(unsorted_img)
cam.file_sorting(local_img_dir, int_t, frame_t, tag=tag)
def expose(i,tag=''):
int_t = cam.set_int_time(i)
frame_t = cam.set_frame_time(i+250)
frame , int_header = cam.simple_cap()
if args.c != '':
int_header.append(('COMMENT',args.c,'User-defined comment'))
# os.remove(unsorted_img)
fits.writeto(unsorted_img,frame,int_header,overwrite=True)
cam.weather_to_fits(unsorted_img)
cam.file_sorting(img_dir,int_t,frame_t,tag=tag)
print('EXPOSE 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 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')
else:
frame_t = cam.set_frame_time(args.t)
else:
if args.v:
frame_t = cam.read_frame_time(verbose=True)
else:
frame_t = cam.read_frame_time()
#Capture image w/ or w/out defined location
if args.l:
for i in range(args.l):
if args.r:
capture_resp, _ = capture(args.p, args.r, 'f')
cam.file_sorting(args.r,
int_t,
frame_t,
'.raw',
args.p,
tag=args.g)
elif args.b:
capture_resp, _ = capture(args.p, args.b, 'b')
cam.file_sorting(args.b,
int_t,
frame_t,
routine=args.p,
tag=args.g)
elif args.f:
capture_resp, _ = capture(args.p, args.f, 'f')
cam.file_sorting(args.f,
int_t,
frame_t,
'.fits',
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)