def test_results_current_variation(self):
dt, p, l, da, r = _init(height=self._height,
width=self._width,
bit_depth=self._bit_depth,
L=self._L,
steps=self._steps,
dark_current_ref=self._dark_current_ref,
exposure_fixed=self._exposure_fixed,
radiance_min=self._radiance_min,
exposure_max=self._exposure_max,
exposure_min=self._exposure_min,
temperature=self._temperature,
temperature_ref=self._temperature_ref,
K=self._K,
dsnu=self._dsnu,
prnu=self._prnu)
self.dataset = dt
self.parser = p
self.loader = l
self.data = da
self.results = r
data = self.data.data
# Test that s_ydark is not a fit because only 1 texp
self.assertAlmostEqual(self.results.sigma_y_dark,
np.sqrt(data['temporal']['s2_ydark'][0]),
delta=0.1)
del_obj(self.dataset, self.parser, self.loader, self.data,
self.results)
def test_results_without_pixel_area(self):
dt, p, l, da, r = _init(pixel_area=None,
height=self._height,
width=self._width,
bit_depth=self._bit_depth,
L=self._L,
steps=self._steps,
dark_current_ref=self._dark_current_ref,
exposure_max=self._exposure_max,
exposure_min=self._exposure_min,
temperature=self._temperature,
temperature_ref=self._temperature_ref,
K=self._K,
dsnu=self._dsnu,
prnu=self._prnu)
self.dataset = dt
self.parser = p
self.loader = l
self.data = da
self.results = r
# Test relevant properties are None
self.assertIs(self.results.u_p_min_area, None)
self.assertIs(self.results.u_e_min_area, None)
self.assertIs(self.results.u_p_sat_area, None)
self.assertIs(self.results.u_e_sat_area, None)
del_obj(self.dataset, self.parser, self.loader, self.data,
self.results)
def test_results_without_pixel_area(self):
dt, p, l, da, r = _init(pixel_area=None,
height=self._height,
width=self._width,
bit_depth=self._bit_depth,
L=self._L,
steps=self._steps,
dark_current_ref=self._dark_current_ref,
exposure_max=self._exposure_max,
exposure_min=self._exposure_min,
temperature=self._temperature,
temperature_ref=self._temperature_ref,
K=self._K,
dsnu=self._dsnu,
prnu=self._prnu)
self.dataset = dt
self.parser = p
self.loader = l
self.data = da
self.results = r
# Test relevant properties are None
self.assertIs(self.results.u_p_min_area, None)
self.assertIs(self.results.u_e_min_area, None)
self.assertIs(self.results.u_p_sat_area, None)
self.assertIs(self.results.u_e_sat_area, None)
del_obj(self.dataset, self.parser, self.loader, self.data,
self.results)
def test_results_current_variation(self):
dt, p, l, da, r = _init(height=self._height,
width=self._width,
bit_depth=self._bit_depth,
L=self._L,
steps=self._steps,
dark_current_ref=self._dark_current_ref,
exposure_fixed=self._exposure_fixed,
radiance_min=self._radiance_min,
exposure_max=self._exposure_max,
exposure_min=self._exposure_min,
temperature=self._temperature,
temperature_ref=self._temperature_ref,
K=self._K,
dsnu=self._dsnu,
prnu=self._prnu)
self.dataset = dt
self.parser = p
self.loader = l
self.data = da
self.results = r
data = self.data.data
# Test that s_ydark is not a fit because only 1 texp
self.assertAlmostEqual(self.results.sigma_y_dark,
np.sqrt(data['temporal']['s2_ydark'][0]),
delta=0.1)
del_obj(self.dataset, self.parser, self.loader, self.data,
self.results)
def test_data(self):
"""Test that data1288 retrieves information."""
ds, p, l, d = self._init()
self.dataset = ds
self.parser = p
self.loader = l
self.data = d
# test number of pixels
self.assertEqual(self.data.pixels, self._height * self._width)
# test data attribute
#####################
data = self.data.data
# Test spatial
self.assertEqual(data['spatial']['L'], self._L)
self.assertEqual(data['spatial']['L_dark'], self._L) # same L for dark
# spatial exposure time
texp = list(self.dataset.points['spatial'].keys())[0]
self.assertEqual(data['spatial']['texp'], texp)
# spatial photons
radiance = self.dataset.points['spatial'][texp][0]
photons = np.round(
np.sum(self.dataset.cam.get_photons(radiance), axis=2).mean(), 3)
self.assertEqual(data['spatial']['u_p'], photons)
# spatial data are images
for typ in ('sum_dark', 'sum'):
self.assertTrue(typ in data['spatial'].keys())
self.assertEqual(data['spatial'][typ].shape,
(self._height, self._width))
# test temporal
# all temporal data are arrays of length steps
for typ in ('s2_y', 's2_ydark', 'texp', 'u_p', 'u_y', 'u_ydark'):
self.assertTrue(typ in data['temporal'].keys())
self.assertEqual(len(data['temporal'][typ]), self._steps)
# test exposure times and photons have well be retrieved
times = list(self.dataset.points['temporal'].keys())
for i, (exp, photons) in enumerate(
zip(data['temporal']['texp'], data['temporal']['u_p'])):
time = times[i]
radiance = self.dataset.points['temporal'][time][0]
photon = np.round(
np.sum(self.dataset.cam.get_photons(radiance, time),
axis=2).mean(), 3)
self.assertEqual(exp, times[i])
self.assertEqual(photons, photon)
# delete objects
del_obj(self.dataset, self.parser, self.loader, self.data)
def test_data(self):
"""Test that data1288 retrieves information."""
ds, p, l, d = self._init()
self.dataset = ds
self.parser = p
self.loader = l
self.data = d
# test number of pixels
self.assertEqual(self.data.pixels, self._height * self._width)
# test data attribute
#####################
data = self.data.data
# Test spatial
self.assertEqual(data['spatial']['L'], self._L)
self.assertEqual(data['spatial']['L_dark'], self._L) # same L for dark
# spatial exposure time
texp = list(self.dataset.points['spatial'].keys())[0]
self.assertEqual(data['spatial']['texp'], texp)
# spatial photons
radiance = self.dataset.points['spatial'][texp][0]
photons = np.round(np.sum(self.dataset.cam.get_photons(radiance),
axis=2).mean(), 3)
self.assertEqual(data['spatial']['u_p'], photons)
# spatial data are images
for typ in ('sum_dark', 'sum'):
self.assertTrue(typ in data['spatial'].keys())
self.assertEqual(data['spatial'][typ].shape, (self._height,
self._width))
# test temporal
# all temporal data are arrays of length steps
for typ in ('s2_y', 's2_ydark', 'texp', 'u_p', 'u_y', 'u_ydark'):
self.assertTrue(typ in data['temporal'].keys())
self.assertEqual(len(data['temporal'][typ]), self._steps)
# test exposure times and photons have well be retrieved
times = list(self.dataset.points['temporal'].keys())
for i, (exp, photons) in enumerate(zip(data['temporal']['texp'],
data['temporal']['u_p'])):
time = times[i]
radiance = self.dataset.points['temporal'][time][0]
photon = np.round(np.sum(self.dataset.cam.get_photons(
radiance, time), axis=2).mean(), 3)
self.assertEqual(exp, times[i])
self.assertEqual(photons, photon)
# delete objects
del_obj(self.dataset, self.parser, self.loader, self.data)
def test_1exposure(self):
"""Test that when there is only one exposure time, the temporal data
dictionary has same length than the number of photons."""
self._radiance_min = 0.1
self._exposure_max = 1000000
ds, p, l, d = self._init()
self.dataset = ds
self.parser = p
self.loader = l
self.data = d
temporal = self.data.data['temporal']
l = len(temporal['u_p'])
# test that all temporal data arrays have same length
self.assertEqual(len(temporal['texp']), l)
self.assertEqual(len(temporal['u_ydark']), l)
self.assertEqual(len(temporal['s2_ydark']), l)
del_obj(self.dataset, self.parser, self.loader, self.data)
def test_1exposure(self):
"""Test that when there is only one exposure time, the temporal data
dictionary has same length than the number of photons."""
self._radiance_min = 0.1
self._exposure_max = 1000000
ds, p, l, d = self._init()
self.dataset = ds
self.parser = p
self.loader = l
self.data = d
temporal = self.data.data['temporal']
l = len(temporal['u_p'])
# test that all temporal data arrays have same length
self.assertEqual(len(temporal['texp']), l)
self.assertEqual(len(temporal['u_ydark']), l)
self.assertEqual(len(temporal['s2_ydark']), l)
del_obj(self.dataset, self.parser, self.loader, self.data)
def test_results_exposure_variation(self):
dt, p, l, da, r = _init(height=self._height,
width=self._width,
bit_depth=self._bit_depth,
L=self._L,
qe=self._qe,
steps=self._steps,
radiance_min=self._radiance_min,
exposure_max=self._exposure_max,
exposure_min=self._exposure_min,
K=self._K,
dark_current_ref=self._dark_current_ref,
temperature=self._temperature,
temperature_ref=self._temperature_ref,
dsnu=self._dsnu,
prnu=self._prnu)
self.dataset = dt
self.parser = p
self.loader = l
self.data = da
self.results = r
###########################
# Test results properties #
###########################
# test that quantification noise is really 1/12
self.assertEqual(self.results.s2q, 1.0 / 12.0)
# test that indexes are integers and in good range
for attr in ('index_start', 'index_u_ysat', 'index_sensitivity_max',
'index_sensitivity_min', 'index_linearity_min',
'index_linearity_max'):
value = getattr(self.results, attr)
self.assertTrue(type(value) is int or type(value) is np.int64,
msg="%s is not an integer but should be!" % attr)
self.assertTrue(value < len(self.data.data['temporal']['u_y']))
self.assertTrue(value >= 0)
self.assertTrue(attr in self.results.results.keys(),
msg="%s does not appear in the results!" % attr)
# Test that EMVA values are float and positive
for a in ('s2q', 'R', 'K', 'QE', 'sigma_y_dark', 'sigma_d', 'u_p_min',
'u_p_min_area', 'u_e_min', 'u_e_min_area', 'u_p_sat',
'u_p_sat_area', 'u_e_sat', 'SNR_max', 'DR', 'LE_min',
'LE_max', 'u_I_var', 'u_I_mean', 'sigma_2_y_stack',
'sigma_2_y_stack_dark', 's_2_y_measured', 's_2_y',
's_2_y_dark', 'DSNU1288', 'PRNU1288'):
value = getattr(self.results, a)
self.assertTrue(isinstance(value, float),
msg="%s is not a float but should be!" % a)
if not a == 's2q':
self.assertTrue(a in self.results.results,
msg="%s does not appear in the results!" % a)
# except for linearity errors and dark currents,
# everything always should be positive
if (a not in ('LE_min', 'LE_max') and value is not np.nan):
self.assertGreaterEqual(value,
0.0,
msg="%s is negative but"
" should be positive!" % a)
###############################################################
# The following deltas are purely guesstimates and are prone to
# errors in the future if they are not really significant
###############################################################
# Test quantum efficiency is retrieved with a +/- 5% incertainty
self.assertAlmostEqual(self._qe * 100,
self.results.QE,
delta=5.0,
msg="The difference between the expected QE and"
"the retrieved one is greater than 5%!")
# Test that overall system gain
# is retrieved with a +/- 0.01 incertainty
self.assertAlmostEqual(self.dataset.cam.K,
self.results.K,
delta=0.1,
msg="The difference between expected"
"system gain"
"and the retrieved one"
"is greater than 0.01!")
self.assertEqual(self.results.inverse_K(), 1 / self.results.K)
# Test that responsivity is coherent with QE and system gain
self.assertAlmostEqual(self.results.R,
self.results.QE * self.results.K / 100,
delta=0.001) # division errors compensation
# Test that dark current is actually retrieved from both methods
self.assertAlmostEqual(self._dark_current_ref,
self.results.u_I_mean,
delta=5.0,
msg="Dark current is not well retrieved from"
" mean dark signal.")
self.assertAlmostEqual(self._dark_current_ref,
self.results.u_I_var,
delta=10.0,
msg="Dark current is not well retrieved from"
" dark signal variance.")
# Test that u_e_sat_area = u_e_sat / area
self.assertAlmostEqual(self.results.u_e_sat_area,
self.results.u_e_sat /
self.dataset.cam.pixel_area,
delta=0.01)
# Test that SNR_max is sqrt of u_e_sat
self.assertAlmostEqual(self.results.SNR_max,
np.sqrt(self.results.u_e_sat),
delta=0.01)
# Test that SNR_max_db is 20log_10(SNR_max)
self.assertAlmostEqual(self.results.SNR_max_dB(),
20 * np.log10(self.results.SNR_max),
delta=0.01)
# Test that SNR_max_bit is log_2(SNR_max)
self.assertAlmostEqual(self.results.SNR_max_bit(),
np.log2(self.results.SNR_max),
delta=0.01)
# Test that SNR_max inverse is 100 / SNR_max
self.assertAlmostEqual(self.results.inverse_SNR_max(),
100 / self.results.SNR_max,
delta=0.01)
# Test that DR is u_p_sat / u_p_min
self.assertAlmostEqual(self.results.DR,
self.results.u_p_sat / self.results.u_p_min,
delta=0.01)
# Test that DR_dB is 20log_10(DR)
self.assertAlmostEqual(self.results.DR_dB(),
20 * np.log10(self.results.DR),
delta=0.01)
# Test that DR_bit is log_2(DR)
self.assertAlmostEqual(self.results.DR_bit(),
np.log2(self.results.DR),
delta=0.01)
# Test that DSNU is sqrt(s2_ydark) / gain
self.assertAlmostEqual(self.results.DSNU1288,
np.sqrt(self.results.s_2_y_dark) /
self.results.K,
delta=0.01)
# Test that DSNU in DN is DSNU * K
self.assertAlmostEqual(self.results.DSNU1288_DN(),
self.results.DSNU1288 * self.results.K,
delta=0.01)
# Test that PRNU is the same as defined in EMVA1288 standard
self.assertAlmostEqual(
self.results.PRNU1288,
np.sqrt(self.results.s_2_y - self.results.s_2_y_dark) * 100 /
(self.data.data['spatial']['avg_mean'] -
self.data.data['spatial']['avg_mean_dark']))
# Test that histograms contains relevant keys and are numpy arrays
hists = ('histogram_PRNU', 'histogram_PRNU_accumulated',
'histogram_DSNU', 'histogram_DSNU_accumulated')
keys = ('bins', 'model', 'values')
for hist in hists:
h = getattr(self.results, hist)
for key in keys:
self.assertTrue(key in h.keys())
self.assertTrue(isinstance(h[key], np.ndarray))
# delete objects
del_obj(self.dataset, self.parser, self.loader, self.data,
self.results)
def test_results_exposure_variation(self):
dt, p, l, da, r = _init(height=self._height,
width=self._width,
bit_depth=self._bit_depth,
L=self._L,
qe=self._qe,
steps=self._steps,
radiance_min=self._radiance_min,
exposure_max=self._exposure_max,
exposure_min=self._exposure_min,
K=self._K,
dark_current_ref=self._dark_current_ref,
temperature=self._temperature,
temperature_ref=self._temperature_ref,
dsnu=self._dsnu,
prnu=self._prnu)
self.dataset = dt
self.parser = p
self.loader = l
self.data = da
self.results = r
###########################
# Test results properties #
###########################
# test that quantification noise is really 1/12
self.assertEqual(self.results.s2q, 1.0 / 12.0)
# test that indexes are integers and in good range
for attr in ('index_start', 'index_u_ysat', 'index_sensitivity_max',
'index_sensitivity_min', 'index_linearity_min',
'index_linearity_max'):
value = getattr(self.results, attr)
self.assertTrue(type(value) is int or type(value) is np.int64,
msg="%s is not an integer but should be!" % attr)
self.assertTrue(value < len(self.data.data['temporal']['u_y']))
self.assertTrue(value >= 0)
self.assertTrue(attr in self.results.results.keys(),
msg="%s does not appear in the results!" % attr)
# Test that EMVA values are float and positive
for a in ('s2q', 'R', 'K', 'QE', 'sigma_y_dark', 'sigma_d', 'u_p_min',
'u_p_min_area', 'u_e_min', 'u_e_min_area', 'u_p_sat',
'u_p_sat_area', 'u_e_sat', 'SNR_max', 'DR', 'LE_min',
'LE_max', 'u_I_var', 'u_I_mean', 'sigma_2_y_stack',
'sigma_2_y_stack_dark', 's_2_y_measured', 's_2_y',
's_2_y_dark', 'DSNU1288', 'PRNU1288'):
value = getattr(self.results, a)
self.assertTrue(isinstance(value, float),
msg="%s is not a float but should be!" % a)
if not a == 's2q':
self.assertTrue(a in self.results.results,
msg="%s does not appear in the results!" % a)
# except for linearity errors and dark currents,
# everything always should be positive
if (a not in ('LE_min', 'LE_max') and
value is not np.nan):
self.assertGreaterEqual(value, 0.0,
msg="%s is negative but"
" should be positive!" % a)
###############################################################
# The following deltas are purely guesstimates and are prone to
# errors in the future if they are not really significant
###############################################################
# Test quantum efficiency is retrieved with a +/- 5% incertainty
self.assertAlmostEqual(self._qe * 100, self.results.QE, delta=5.0,
msg="The difference between the expected QE and"
"the retrieved one is greater than 5%!")
# Test that overall system gain
# is retrieved with a +/- 0.01 incertainty
self.assertAlmostEqual(self.dataset.cam.K, self.results.K, delta=0.1,
msg="The difference between expected"
"system gain"
"and the retrieved one"
"is greater than 0.01!")
self.assertEqual(self.results.inverse_K(), 1 / self.results.K)
# Test that responsivity is coherent with QE and system gain
self.assertAlmostEqual(self.results.R,
self.results.QE * self.results.K / 100,
delta=0.001) # division errors compensation
# Test that dark current is actually retrieved from both methods
self.assertAlmostEqual(self._dark_current_ref, self.results.u_I_mean,
delta=5.0,
msg="Dark current is not well retrieved from"
" mean dark signal.")
self.assertAlmostEqual(self._dark_current_ref, self.results.u_I_var,
delta=10.0,
msg="Dark current is not well retrieved from"
" dark signal variance.")
# Test that u_e_sat_area = u_e_sat / area
self.assertAlmostEqual(self.results.u_e_sat_area,
self.results.u_e_sat /
self.dataset.cam.pixel_area,
delta=0.01)
# Test that SNR_max is sqrt of u_e_sat
self.assertAlmostEqual(self.results.SNR_max,
np.sqrt(self.results.u_e_sat),
delta=0.01)
# Test that SNR_max_db is 20log_10(SNR_max)
self.assertAlmostEqual(self.results.SNR_max_dB(),
20 * np.log10(self.results.SNR_max),
delta=0.01)
# Test that SNR_max_bit is log_2(SNR_max)
self.assertAlmostEqual(self.results.SNR_max_bit(),
np.log2(self.results.SNR_max),
delta=0.01)
# Test that SNR_max inverse is 100 / SNR_max
self.assertAlmostEqual(self.results.inverse_SNR_max(),
100 / self.results.SNR_max,
delta=0.01)
# Test that DR is u_p_sat / u_p_min
self.assertAlmostEqual(self.results.DR,
self.results.u_p_sat / self.results.u_p_min,
delta=0.01)
# Test that DR_dB is 20log_10(DR)
self.assertAlmostEqual(self.results.DR_dB(),
20 * np.log10(self.results.DR),
delta=0.01)
# Test that DR_bit is log_2(DR)
self.assertAlmostEqual(self.results.DR_bit(),
np.log2(self.results.DR),
delta=0.01)
# Test that DSNU is sqrt(s2_ydark) / gain
self.assertAlmostEqual(self.results.DSNU1288,
np.sqrt(self.results.s_2_y_dark) /
self.results.K,
delta=0.01)
# Test that DSNU in DN is DSNU * K
self.assertAlmostEqual(self.results.DSNU1288_DN(),
self.results.DSNU1288 * self.results.K,
delta=0.01)
# Test that PRNU is the same as defined in EMVA1288 standard
self.assertAlmostEqual(self.results.PRNU1288,
np.sqrt(self.results.s_2_y -
self.results.s_2_y_dark) * 100 /
(np.mean(self.data.data['spatial']['avg']) -
np.mean(self.data.data['spatial']['avg_'
'dark'])))
# Test that histograms contains relevant keys and are numpy arrays
hists = ('histogram_PRNU', 'histogram_PRNU_accumulated',
'histogram_DSNU', 'histogram_DSNU_accumulated')
keys = ('bins', 'model', 'values')
for hist in hists:
h = getattr(self.results, hist)
for key in keys:
self.assertTrue(key in h.keys())
self.assertTrue(isinstance(h[key], np.ndarray))
# delete objects
del_obj(self.dataset, self.parser, self.loader, self.data,
self.results)
