def remove_etaloning(bkfree, psfree, etalon):
if type(psfree) is None.__class__:
psfree = bkfree
if type(etalon) is not None.__class__:
etmn1 = np.squeeze(np.mean(np.mean(etalon.s1[:, :, :, :], 1), 0))
etmn2 = np.squeeze(np.mean(np.mean(etalon.s2[:, :, :, :], 1), 0))
[a, b, c, d] = bkfree.s1.shape
bkfrees1 = bkfree.s1
bkfrees2 = bkfree.s2
psfrees1 = psfree.s1
psfrees2 = psfree.s2
etalonfree1 = np.full((a, b, c, d), 0, dtype='float')
etalonfree2 = np.full((a, b, c, d), 0, dtype='float')
etalonSumfree1 = np.full((a, b, c, d), 0, dtype='float')
etalonSumfree2 = np.full((a, b, c, d), 0, dtype='float')
sgetmn1 = sg_filt(etmn1, window_length=7, polyorder=3)
sgetmn2 = sg_filt(etmn2, window_length=7, polyorder=3)
for ai in range(0, a):
for bi in range(0, b):
for ci in range(0, c):
etalonfree1[ai, bi, ci, :] = (np.squeeze(
bkfrees1[ai, bi, ci, :])) / sgetmn1
etalonfree2[ai, bi, ci, :] = (np.squeeze(
bkfrees2[ai, bi, ci, :])) / sgetmn2
etalonSumfree1[ai, bi, ci, :] = (np.squeeze(
psfrees1[ai, bi, ci, :])) / sgetmn1
etalonSumfree2[ai, bi, ci, :] = (np.squeeze(
psfrees2[ai, bi, ci, :])) / sgetmn2
etfree = EmptyClass()
etfree.s1 = etalonfree1
etfree.s2 = etalonfree2
etSumfree = EmptyClass()
etSumfree.s1 = etalonSumfree1
etSumfree.s2 = etalonSumfree2
return etfree, etSumfree
else:
return bkfree, psfree
def remove_stokespump(cars, stokes, pump):
if not (type(cars) is None.__class__ or type(stokes) is None.__class__
or type(pump) is None.__class__):
pumpstokes1 = np.squeeze(np.mean(np.mean(
stokes.s1[:, :, :, :], 1), 0)) + np.squeeze(
np.mean(np.mean(pump.s1[:, :, :, :], 1), 0))
pumpstokes2 = np.squeeze(np.mean(np.mean(
stokes.s2[:, :, :, :], 1), 0)) + np.squeeze(
np.mean(np.mean(pump.s2[:, :, :, :], 1), 0))
[a, b, c, d] = cars.s1.shape
psfree1 = np.full((a, b, c, d), 0, dtype='float')
psfree2 = np.full((a, b, c, d), 0, dtype='float')
pumpstokes1 = sg_filt(pumpstokes1, window_length=5, polyorder=3)
pumpstokes2 = sg_filt(pumpstokes2, window_length=5, polyorder=3)
max1 = max(pumpstokes1)
max2 = max(pumpstokes2)
for ai in range(0, a):
for bi in range(0, b):
for ci in range(0, c):
psfree1[ai, bi,
ci, :] = np.squeeze(cars.s1[ai, bi, ci, :]) - (
max(np.squeeze(cars.s1[ai, bi, ci, :])) /
max1) * pumpstokes1
psfree2[ai, bi,
ci, :] = np.squeeze(cars.s2[ai, bi, ci, :]) - (
max(np.squeeze(cars.s2[ai, bi, ci, :])) /
max2) * pumpstokes2
psfree = EmptyClass()
psfree.s1 = psfree1
psfree.s2 = psfree2
return psfree
else:
return cars
def ellipse(data_epl, pos, cm_px, peak_width=20, rel_height=0.8,
plot=False):
"""
Function that takes in the EPL image and finds the best elliptical fit
EPL diameter. Optionally plots the result against the 'optical diameter'
as well for quick diagnostic of single image.
=============
--VARIABLES--
data_epl: EPL converted image of spray from convert2EPL
function (array).
pos: Vertical pixel location for the elliptical fitting
plot (integer).
peak_width: Width in pixels to be used for the find_peaks
function. Defaulted to 20 px (integer).
rel_height: Relative height to be used for the peak_widths
function. Defaulted to 0.8 for the 'fifth_maximum'
(scalar).
cm_px: Pixel to cm conversion, dependent on experimental
setup (scalar).
plot: Check to see if plots are wanted. Defaulted to
False (boolean).
"""
peaks, _ = find_peaks(
sg_filt(data_epl[pos, :], 105, 7),
width=peak_width,
prominence=0.1
)
[rel_width, rel_max, lpos, rpos] = peak_widths(
sg_filt(
data_epl[pos, :],
105,
7
),
peaks,
rel_height=rel_height
)
rel_width = rel_width[0]
rel_max = rel_max[0]
lft = int(round(lpos[0]))
rgt = int(round(rpos[0]))
model_epl, fitted_graph, epl_graph = ideal_ellipse(
data_epl[pos, :]
[lft:rgt],
rel_width,
rel_max,
cm_px
)
if plot:
plot_ellipse(epl_graph, fitted_graph)
plt.figure()
plt.imshow(data_epl, vmin=0, vmax=1)
plt.plot([pos]*len(data_epl[pos, :]), color='r', linestyle='--')
plt.colorbar()
plt.title('EPL [cm] Mapping of Spray')
plt.figure()
plt.plot(data_epl[pos, :])
plt.title('Line Scan at ' + str(pos))
plt.xlabel('Horizontal Location [px]')
plt.ylabel('EPL [cm]')
def convert2EPL(test_path, offset, model_pckl, cm_pix,
dark_path, flat_path, cropped_view=None, plot=False):
"""
Function that takes in the designated raw test and converts to EPL.
Optionally plots the result as well.
=============
--VARIABLES--
test_path: Path to an individual raw test image (.tif)
offset: Background EPL value correction. Given as either a
tuple of slices that designate a region outside the
spray, or as a float if the offset value is known
beforehand.
model_pckl: White beam model to be used. Generated by the
specific whitebeam_*.py script (.pckl).
cm_pix: Pixel to cm conversion, dependent on experimental
setup (scalar).
dark_path: Path to the image to be used for dark current
subtraction (.tif).
flat_path: Path to the image to be used for flat field
normalization (.tif).
cropped_view: Optional integer array parameter to crop down the
image before conversion. Defaulted to None (int).
plot: Check to see if plots are wanted. Defaulted to
False (boolean).
"""
f = open(model_pckl, 'rb')
model = pickle.load(f)
f.close()
dark = np.array(Image.open(dark_path))
flatfield = np.array(Image.open(flat_path))
flatfield_darksub = flatfield - dark
beam_middle = np.zeros((flatfield_darksub.shape[1]))
for i in range(flatfield_darksub.shape[1]):
beam_middle[i] = np.argmax(
sg_filt(
flatfield_darksub[:, i],
55,
3
)
)
beam_middle_avg = int(np.mean(beam_middle).round())
angles = model[0]
angle_to_px = [
beam_middle_avg+3500*np.tan(x*10**-3)/cm_pix
for x in angles
]
data = np.array(Image.open(test_path))
data_norm = (data-dark) / flatfield_darksub
data_epl = np.empty(np.shape(data_norm), dtype=float)
if isinstance(cropped_view, None):
cropped_view = np.linspace(1, data_norm.shape[0])
for z, _ in enumerate(cropped_view):
j = np.argmin(abs(z-np.array(angle_to_px)))
data_epl[z, :] = model[1][j](data_norm[z, :])
if type(offset) == tuple:
offset_epl = data_epl[offset[0], offset[1]]
data_epl -= offset_epl
else:
data_epl -= offset
if plot:
plt.imshow(data_epl, vmin=0, vmax=1)
plt.colorbar
plt.title('EPL [cm] Mapping of Spray')
return data_epl