newp.filterW(3)
dark_mean = np.zeros_like(test_ms)
dark_var = np.zeros_like(test_ms)
for ind, integ_ms in enumerate(test_ms):
sens.set_aper(integ_ms)
frame1 = sens.get_spect()[roi_ind1:roi_ind2]
frame2 = sens.get_spect()[roi_ind1:roi_ind2]
var = np.var([frame1, frame2], 0, ddof=1)
var_av = np.mean(var)
dark_var[ind] = var_av
mean = np.mean([frame1, frame2])
dark_mean[ind] = mean
dark_curr = keys.get_current()
print("Dark current: ", dark_curr, " Amps")
"""
Light values and noise:
filter 1
10-70% of saturation exposure
2 images at each level
variance, mean, stdev
photons:
PD current - dark current
current -> light power
light power -> photons at lambda
"""
newp.filterW(1)
""" Initialize """
keys.set_range(
max_amps) # experiment with different ranges for best performance.
keys.set_output_voltage(
bias_voltage) # Bit of an arbitrary choice. Shouldn't matter much.
keys.auto_aper(True)
time.sleep(1)
curr_array = np.array([])
temp_array = np.array([])
while time.time() - t < time_s:
""" Read sensor """
#print("Reading Sony Sensor...")
curr = keys.get_current()
temp = keys.get_temperature()
curr_array = np.append(curr_array, curr)
temp_array = np.append(temp_array, temp)
fig = plt.figure(figsize=(12, 9))
plt.plot(curr_array)
fig = plt.figure(figsize=(12, 9))
plt.plot(temp_array)
print("Avg current: ", np.mean(curr_array), " Amps")
print("Temperature: ", np.mean(temp_array), " Celcius")
print("Predicted dark current: ", diode.dark(np.mean(temp_array)), " Amps")
if save_file:
curr_av = np.array([])
average_points = 2
aper_time = 0.05
# keys.set_aper(aper_time)
for lam in test_lam:
_ = newp.set_lambda(lam)
TLS_lam = np.append(TLS_lam, newp.get_lambda())
# curr_points = np.array([])
# for ind in range(average_points):
# curr = keys.get_current()
# curr_points = np.append(curr_points, curr)
#
# curr_av = np.append(curr_av, np.mean(curr_points))
curr_av = np.append(curr_av, keys.get_current())
newp.set_lambda(550) #reset it to something we can see.
plt.figure(figsize=(9, 7))
plt.plot(TLS_lam, curr_av)
plt.ylabel('current')
plt.xlabel('wavelength (nm)')
pd_cal = diodeCal().func
watts = np.divide(curr_av, pd_cal(TLS_lam))
plt.figure(figsize=(9, 7))
plt.plot(TLS_lam, watts)
plt.ylabel('power (W)')
plt.xlabel('wavelength (nm)')