def test_null(dataframe):
data = dataframe((1, 1400, 1200), 150, 10)
table, grid, photons = find_photons(data.astype(np.uint16),
threshold=250,
box=2)
assert len(table) == 0
def test_find_photons(dataframe, gauss):
image_x = 1400
image_y = 1200
x = 17
y = 20
cen_x = 0.4
cen_y = 0.15
sigma = 0.46
bgnd = 150
box = 3
pixel_photon = 9
pixel_bgnd = 12
photon = gauss(box, cen_x, cen_y, 500, sigma)
data = dataframe((1, image_x, image_y), bgnd, 5)
data[0, y - box:y + box + 1, x - box:x + box + 1] += photon
data = data.astype(np.uint16)
int_photon = data[0, y - box:y + box + 1, x - box:x + box + 1]
photon_sorted = np.sort(int_photon.ravel())[::-1]
photon_bgnd = photon_sorted[pixel_bgnd:].mean()
photon_int = (photon_sorted[:pixel_photon] - photon_bgnd).sum()
photon_bgnd = photon_sorted[pixel_bgnd:].mean()
table, grid, photons = find_photons(data.astype(np.uint16),
None,
threshold=250,
box=box,
search_box=box,
sum_min=800,
sum_max=1400,
pixel_photon=pixel_photon,
pixel_bgnd=pixel_bgnd,
return_map=True,
return_pixels='unsorted')
assert len(table) == 1
assert photons.shape == (1, 2 * box + 1, 2 * box + 1)
assert grid.shape == (1, image_x, image_y)
# Check returned photons
assert_array_equal(photons[0], data[0, y - box:y + box + 1,
x - box:x + box + 1])
# Check photon mask
mask = np.zeros_like(grid)
mask[0, y - box:y + box + 1, x - box:x + box + 1] = 1
assert_array_equal(mask, grid)
# Check pixel values and fit
assert table['Pixel X'][0] == x
assert table['Pixel Y'][0] == y
assert pytest.approx(table['COM X'][0], 0.01) == x + cen_x
assert pytest.approx(table['COM Y'][0], 0.01) == y + cen_y
assert pytest.approx(table['Int'][0]) == photon_int
assert pytest.approx(table['Bgnd'][0]) == photon_bgnd
def test_find_photons(dataframe, gauss):
x = 17
y = 20
cen_x = 0.2
cen_y = 0.15
sigma = 0.46
bgnd = 150
box = 2
pixel_photon = 9
pixel_bgnd = 12
photon = gauss(box, cen_x, cen_y, 500, sigma)
data = dataframe((1, 1400, 1200), bgnd, 5)
data[0, y - box:y + box + 1, x - box:x + box + 1] += photon
data = data.astype(np.uint16)
int_photon = data[0, y - box:y + box + 1, x - box:x + box + 1]
photon_sorted = np.sort(int_photon.ravel())[::-1]
photon_bgnd = photon_sorted[pixel_bgnd:].mean()
photon_int = (photon_sorted[:pixel_photon] - photon_bgnd).sum()
photon_bgnd = photon_sorted[pixel_bgnd:].mean()
table, grid, photons = find_photons(data.astype(np.uint16),
threshold=250,
box=box,
sum_min=800,
sum_max=1200,
pixel_photon=pixel_photon,
pixel_bgnd=pixel_bgnd)
assert len(table) == 1
assert table['Pixel X'][0] == x
assert table['Pixel Y'][0] == y
assert pytest.approx(table['Fit X'][0], 0.01) == x + cen_x
assert pytest.approx(table['Fit Y'][0], 0.01) == y + cen_y
assert pytest.approx(table['COM X'][0], 0.01) == x + cen_x
assert pytest.approx(table['COM Y'][0], 0.01) == y + cen_y
assert pytest.approx(table['Fit Sigma'][0], 0.01) == sigma
assert pytest.approx(table['Bgnd'][0]) == photon_bgnd
assert pytest.approx(table['Int'][0]) == photon_int
def test_mask(dataframe, gauss):
image_x = 1400
image_y = 1200
x = np.array([17, 50, 89, 400, 17, 762, 90, 1100])
y = np.array([20, 100, 400, 753, 20, 1000, 41, 200])
cen_x = 0.4
cen_y = 0.15
sigma = 0.46
bgnd = 150
box = 3
pixel_photon = 9
pixel_bgnd = 12
photon = gauss(box, cen_x, cen_y, 500, sigma)
data = dataframe((2, image_x, image_y), bgnd, 5)
for _x, _y in zip(x[:4], y[:4]):
data[0, _y - box:_y + box + 1, _x - box:_x + box + 1] += photon
for _x, _y in zip(x[4:], y[4:]):
data[1, _y - box:_y + box + 1, _x - box:_x + box + 1] += photon
data = data.astype(np.uint16)
mask = np.zeros_like(data)
table, grid, photons = find_photons(data,
mask,
threshold=250,
box=box,
search_box=box,
sum_min=800,
sum_max=1400,
pixel_photon=pixel_photon,
pixel_bgnd=pixel_bgnd,
return_map=True,
return_pixels='unsorted')
assert len(table) == len(x)
for _x, _y in zip(x, y):
tx = table['Pixel X'] == _x
ty = table['Pixel Y'] == _y
assert (tx & ty).any()
table, grid, photons = find_photons(data,
mask[0],
threshold=250,
box=box,
search_box=box,
sum_min=800,
sum_max=1400,
pixel_photon=pixel_photon,
pixel_bgnd=pixel_bgnd,
return_map=True,
return_pixels='unsorted')
assert len(table) == len(x)
for _x, _y in zip(x, y):
tx = table['Pixel X'] == _x
ty = table['Pixel Y'] == _y
assert (tx & ty).any()
test_mask = np.copy(mask[0])
test_mask[y[0], x[0]] = 1
table, grid, photons = find_photons(data,
test_mask,
threshold=250,
box=box,
search_box=box,
sum_min=800,
sum_max=1400,
pixel_photon=pixel_photon,
pixel_bgnd=pixel_bgnd,
return_map=True,
return_pixels='unsorted')
assert len(table) == (len(x) - 2)
for _x, _y in zip(np.concatenate((x[1:3], x[5:])),
np.concatenate((y[1:3], y[5:]))):
tx = table['Pixel X'] == _x
ty = table['Pixel Y'] == _y
assert (tx & ty).any()
# Check returned mask has MSB set
assert_array_equal(grid[:, y[0], x[0]], np.ones(data.shape[0]) * 0x8000)
test_mask = np.copy(mask)
test_mask[0, y[0], x[0]] = 1
table, grid, photons = find_photons(data,
test_mask,
threshold=250,
box=box,
search_box=box,
sum_min=800,
sum_max=1400,
pixel_photon=pixel_photon,
pixel_bgnd=pixel_bgnd,
return_map=True,
return_pixels='unsorted')
assert len(table) == (len(x) - 1)
for _x, _y in zip(x[1:], y[1:]):
tx = table['Pixel X'] == _x
ty = table['Pixel Y'] == _y
assert (tx & ty).any()
def test_find_photons(dataframe, gauss):
image_x = 1400
image_y = 1200
x = 17
y = 20
cen_x = 0.4
cen_y = 0.15
sigma = 0.46
bgnd = 150
box = 3
pixel_photon = 9
pixel_bgnd = 12
photon = gauss(box, cen_x, cen_y, 500, sigma)
data = dataframe((1, image_x, image_y), bgnd, 1)
data[0, y - box:y + box + 1, x - box:x + box + 1] += photon
data = data.astype(np.uint16)
int_photon = data[0, y - box:y + box + 1, x - box:x + box + 1]
photon_sorted = np.sort(int_photon.ravel())[::-1]
photon_bgnd = photon_sorted[pixel_bgnd:].mean()
photon_int = (photon_sorted[:pixel_photon] - photon_bgnd).sum()
photon_bgnd = photon_sorted[pixel_bgnd:].mean()
fit_constraints = {
"pos_range": 0.5,
"pos_cent": 0,
"sigma_range": 0.2,
"sigma_cent": 0.45
}
table, grid, photons = find_photons(data.astype(np.uint16),
None,
threshold=250,
box=box,
search_box=box,
sum_min=800,
sum_max=1400,
pixel_lut=np.linspace(-2, 2, 2000),
pixel_lut_range=(-1, 1),
pixel_photon=pixel_photon,
pixel_bgnd=pixel_bgnd,
fit_constraints=fit_constraints,
return_map=True,
return_pixels='unsorted')
assert len(table) == 1
assert photons.shape == (1, 2 * box + 1, 2 * box + 1)
assert grid.shape == (1, image_x, image_y)
# Check returned photons
assert_array_equal(photons[0], data[0, y - box:y + box + 1,
x - box:x + box + 1])
# Check photon mask
mask = np.zeros_like(grid)
mask[0, y - box:y + box + 1, x - box:x + box + 1] = 1
assert_array_equal(mask, grid)
# Check pixel values and fit
print(table)
assert table['Pixel X'][0] == x
assert table['Pixel Y'][0] == y
assert pytest.approx(table['COM X'][0], 0.01) == x + cen_x
assert pytest.approx(table['COM Y'][0], 0.01) == y + cen_y
assert pytest.approx(table['COR COM X'][0], 0.01) == x + 2 * cen_x
assert pytest.approx(table['COR COM Y'][0], 0.01) == y + 2 * cen_y
assert pytest.approx(table['Int'][0]) == photon_int
assert pytest.approx(table['Bgnd'][0]) == photon_bgnd
assert pytest.approx(table['Fit X'][0], 0.05) == x + cen_x
assert pytest.approx(table['Fit Y'][0], 0.05) == y + cen_y
assert pytest.approx(table['Fit Sigma'][0], 0.1) == sigma
assert pytest.approx(table['Fit 1DX X'][0], 0.05) == x + cen_x
assert pytest.approx(table['Fit 1DY Y'][0], 0.05) == y + cen_y
assert pytest.approx(table['Fit 1DX Sigma'][0], 0.1) == sigma
assert pytest.approx(table['Fit 1DY Sigma'][0], 0.1) == sigma
