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 read_noise_estimate(n):
'''
Capture n pairs of bias frames (520REFCLKS)
Produce difference image from each pair and store
read noise estimate from sigma/sqrt(2) of difference
Output histogram of final pair with RN estimate as average
of all pairs
'''
cam.set_int_time(0.033)
cam.set_frame_time(100)
cam.printProgressBar(0,
2 * n,
prefix='Progress:',
suffix='Complete',
length=50)
y = 0
RNs = []
for j in range(n):
bias_1, _ = cam.simple_cap()
y += 1
cam.printProgressBar(y, 2 * n)
bias_2, _ = cam.simple_cap()
y += 1
cam.printProgressBar(y, 2 * n)
bias_1 = np.asarray(bias_1, dtype=np.int32)
bias_2 = np.asarray(bias_2, dtype=np.int32)
#save max mean dif, max absolute dif
bias_dif = bias_2 - bias_1
dif_clipped = bias_dif.flatten()
RNs.append(np.std(dif_clipped) / np.sqrt(2))
dev = np.std(dif_clipped)
RNs = np.array(RNs)
RN = round(np.median(RNs), 3)
uncert = round(3 * np.std(RNs), 2)
sample_hist, _, _ = stats.sigmaclip(dif_clipped, 5, 5)
N, bins, _ = plt.hist(sample_hist,bins = 265,facecolor='blue', alpha=0.75,\
label = 'Bias Difference Image')
def fit_function(x, B, sigma):
return (B * np.exp(-1.0 * (x**2) / (2 * sigma**2)))
popt, _ = optimize.curve_fit(fit_function, xdata=bins[0:-1]+0.5, \
ydata=N, p0=[0, dev])
xspace = np.linspace(bins[0], bins[-1], 100000)
fit_dev = round(popt[1], 3)
delta_sig = round(abs(fit_dev - dev), 2)
plt.plot(xspace+0.5, fit_function(xspace, *popt), color='darkorange', \
linewidth=2.5, label='Gaussian fit, $\Delta\sigma$:{}'.format(delta_sig))
plt.ylabel('No. of Pixels')
plt.xlabel('ADUs')
plt.title(
'Read Noise Estimate:${}\pm{}$ ADUs ($n={}$, FPA:$-40^\circ$C)'.format(
RN, uncert, n))
plt.legend(loc='best')
plt.show()
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 gain_estimate(n):
bias = cam.get_master_bias(-40)
int_times = np.round(np.linspace(500, 5000, n), 0)
medians, sigmasqrs = [], []
for j in int_times:
cam.set_int_time(j)
cam.set_frame_time(j + 20)
#Take pair of images
first, _ = cam.simple_cap()
second, _ = cam.simple_cap()
first = np.asarray(first, dtype=np.int32)
second = np.asarray(second, dtype=np.int32)
first = first - bias
second = second - bias
diff_img = first - second #Difference of bias-subtracted frames
clipped_diff_img = cam.roi_clip(diff_img)
clipped_img = cam.roi_clip(first)
sigmasqrs.append(np.var(clipped_diff_img)) #variance from difference
medians.append(np.median(clipped_img)) #intensity from single frame
sigmasqrs = np.array(sigmasqrs) / 2 #Adjust for difference theorem
medians = np.array(medians)
#Linear fit
slope, intercept, r_value, _, _ = stats.linregress(medians, sigmasqrs)
print(slope, intercept, r_value)
#Figure out parameters
gain = round((1 / slope), 2)
#read_noise = int(np.sqrt((gain**2)*intercept))
results_path = testing_dir +'gain_testing/' + \
'study_2_results.txt'
np.savetxt(results_path, (medians, sigmasqrs, int_times))
fit = slope * medians + intercept
slope = round(slope, 2)
intercept = int(intercept)
rsqr = round((r_value**2), 4)
plt.scatter(medians, sigmasqrs, c='red', label='Data')
plt.plot(medians,fit,'g--',label = 'Linear Fit: (m = {0}, b = {1}, $r^2$ = {2})'\
.format(slope,intercept,rsqr))
plt.ylabel('$\sigma^2$ (ADUs)')
plt.xlabel('Intensity (ADUs)')
plt.grid(True)
plt.legend(loc='best')
plt.title(
'Pairwise Variance vs Intensity Gain Study, g = {0}'.format(gain))
plt.show()
print('PROGRAM HAS COMPLETED')
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 read_ramp(n):
int_times = np.round(np.linspace(0.033, 0.5, n), 3)
RNs = []
bias_level = []
cam.printProgressBar(0, n)
y = 0
for j in int_times:
int_t = cam.set_int_time(j)
if int_t < (j + 1):
cam.set_frame_time(20.33)
bias_1, _ = cam.simple_cap()
bias_2, _ = cam.simple_cap()
bias_1 = np.asarray(bias_1, dtype=np.int32)
bias_2 = np.asarray(bias_2, dtype=np.int32)
bias_dif = bias_2 - bias_1
dif_clipped = bias_dif.flatten()
RNs.append(np.std(dif_clipped) / np.sqrt(2))
bias_level.append(np.median(bias_1))
else:
RNs.append(RNs[-1])
bias_level.append(bias_level[-1])
y += 1
cam.printProgressBar(y, n)
RNs = 3.22 * np.array(RNs)
bias_level = 3.22 * np.array(bias_level)
int_times *= 1E3
fig, ax1 = plt.subplots()
color = 'tab:red'
ax1.set_xlabel('Integration Time ($\mu$s)')
ax1.set_ylabel('Median $e^-$/pixel', color=color)
ax1.scatter(int_times, bias_level, color=color)
ax1.tick_params(axis='y', labelcolor=color)
ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis
color = 'tab:blue'
ax2.set_ylabel('$\sigma$',
color=color) # we already handled the x-label with ax1
ax2.scatter(int_times, RNs, color=color)
ax2.tick_params(axis='y', labelcolor=color)
fig.tight_layout() # otherwise the right y-label is slightly clipped
plt.grid(True)
plt.title('Read-noise/Bias as function of Integration Time')
plt.show()
def frame_int_comp():
dit = 400
cam.set_int_time(dit)
deltas = np.linspace(-50, 50, 100)
amps = []
for i in deltas:
cam.set_frame_time(dit + i)
frame, _ = cam.simple_cap()
amps.append(np.median(frame))
plt.scatter(deltas, amps)
plt.xlabel('Frame Time - Integration Time')
plt.ylabel('Median Pixel Intensity (ADUs)')
plt.grid(True)
plt.show()
def bias_temp(n, loops):
bias = cam.get_master_bias(-40) #Reference
median_residuals = []
times = []
#Stamp starting time here
int_t = cam.set_int_time(0.033)
frame_t = cam.set_frame_time(20.033)
for i in range(loops):
img = np.zeros(shape=(naxis1, naxis2)) #Initiate array for coadding
for i in range(n):
frame, _ = cam.simple_cap()
img += frame
img = img / n
#Stamp time of this frame here
#Append time to list of times
residual_img = img - bias
median_residuals.append(
np.median(residual_img)) #Append median of difference
fig, ax1 = plt.subplots()
colour = 'tab:blue'
ax1 = sns.pointplot(x=temps, y=means, color=colour)
ax1.set_ylabel('Median Residual', color=colour)
ax1.tick_params(axis='y', labelcolor=colour)
ax1.set_xlabel('Time (s)')
plt.title(
r'$\mathrm{Thermal\ emission\ of\ shutter\ study\(bias\ frames)}$')
plt.show()
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 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 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 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_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='Write Frame Time', description='Change Camera Frame Time')
parser.add_argument('f', type=float, help='Frame time in milliseconds')
parser.add_argument('-v', action='store_true', help='Verbose mode')
parser.add_argument('-r', action='store_true', help='Enter data as rate')
args = parser.parse_args()
if args.v:
if args.r:
cam.set_frame_time(args.f,verbose=True,rate=True)
else:
cam.set_frame_time(args.f,verbose=True)
else:
if args.r:
cam.set_frame_time(args.f,rate=True)
else:
cam.set_frame_time(args.f)
#Option to set integration time w/ or w/out verbose
if args.i:
if args.v:
int_t = cam.set_int_time(args.i, verbose=True)
else:
int_t = cam.set_int_time(args.i)
else:
if args.v:
int_t = cam.read_int_time(verbose=True)
else:
int_t = cam.read_int_time()
#Option to set frame time w/ or w/out verbose
if args.t:
if args.v:
frame_t = cam.set_frame_time(args.t, verbose=True)
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,
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)