def peak_find(ampl_resamp, dx):
peak_figs = []
peaks = []
peak_type = []
for j in range(0, ampl_resamp.shape[1] - 1):
amp_base_res = signal.convolve(ampl_resamp[:, j],
ampl_resamp[:, j],
mode="full")
amp_base_res = signal.resample(amp_base_res / np.amax(amp_base_res),
int(np.ceil(len(amp_base_res) / 2)))
for k in range(j + 1, ampl_resamp.shape[1]):
amp_overlay_res = signal.convolve(ampl_resamp[:, k],
ampl_resamp[:, k],
mode="full")
amp_overlay_res = signal.resample(
amp_overlay_res / np.amax(amp_overlay_res),
int(np.ceil(len(amp_overlay_res) / 2)),
)
# amp_overlay_res = signal.savgol_filter(ampl_resamp[:, k], 1501, 1)
peak1, _ = find_peaks(amp_base_res, prominence=0.5)
peak2, _ = find_peaks(amp_overlay_res, prominence=0.5)
if (
abs(peak2 - peak1) < 2500
): # if the two peaks are separated the two fields are not adjacent.
amp_peak = ampl_resamp[:, j] + ampl_resamp[:, k]
x = np.linspace(
0,
0 + (len(amp_peak) * dx / 10),
len(amp_peak),
endpoint=False) # definition of the distance axis
peak_pos, _ = find_peaks(
signal.savgol_filter(
amp_peak[min(peak1[0], peak2[0]
):max(peak1[0], peak2[0])],
201,
3,
),
prominence=0.010,
)
pos_prominence = signal.peak_prominences(
signal.savgol_filter(
amp_peak[min(peak1[0], peak2[0]
):max(peak1[0], peak2[0])],
201,
3,
),
peak_pos,
)
# print('#peaks pos det=', len(peak_pos), peak_pos)
# print('#pos peaks prominence=', pos_prominence[0])
peak_neg, _ = find_peaks(
signal.savgol_filter(
-amp_peak[min(peak1[0], peak2[0]
):max(peak1[0], peak2[0])],
201,
3,
),
prominence=0.010,
)
neg_prominence = signal.peak_prominences(
signal.savgol_filter(
-amp_peak[min(peak1[0], peak2[0]
):max(peak1[0], peak2[0])],
201,
3,
),
peak_neg,
)
# print('#peaks neg det=',len(peak_neg),peak_neg)
# print('#neg peaks prominence=', neg_prominence[0])
# we now need to select the peak with the largest prominence positve or negative
# we add all the peaks and prominences toghether
peaks_all = np.concatenate((peak_pos, peak_neg), axis=None)
prom_all = np.concatenate(
(pos_prominence[0], neg_prominence[0]), axis=None)
# print('all peaks',peaks_all,prom_all)
if peaks_all.size != 0:
peak = peaks_all[np.argmax(prom_all)]
if peak in peak_pos:
peak_type.append(1)
peaks.append(min(peak1[0], peak2[0]) + peak)
# print('pos peak')
elif peak in peak_neg:
peak_type.append(0)
peaks.append(min(peak1[0], peak2[0]) + peak)
# print('neg peak')
fig = plt.figure(figsize=(10, 6))
plt.plot(x, amp_peak, label="Total amplitude profile")
plt.plot(
x[min(peak1[0], peak2[0]) + peak],
amp_peak[min(peak1[0], peak2[0]) + peak],
"x",
label="Peaks detected",
)
plt.ylabel("amplitude [a.u.]")
plt.xlabel("distance [mm]")
plt.legend()
fig.suptitle("Junctions", fontsize=16)
peak_figs.append(fig)
elif peaks_all.size == 0:
peaks.append(0)
peak_type.append(0)
print("no peak has been found")
fig = plt.figure(figsize=(10, 6))
plt.plot(x, amp_peak, label="Total amplitude profile")
# plt.plot(x[min(peak1[0], peak2[0]) + peak], amp_peak[min(peak1[0], peak2[0]) + peak], "x",
# label='Peaks detected')
plt.ylabel("amplitude [a.u.]")
plt.xlabel("distance [mm]")
plt.legend()
fig.suptitle("Junctions", fontsize=16)
peak_figs.append(fig)
# else:
# print(j, k, 'the data is not contiguous finding another curve in dataset')
# print('peaks_here=',peaks)
return peaks, peak_type, peak_figs
def absolute_scans_from_mephysto(mephysto_file, absolute_dose,
depth_of_absolute_dose_mm):
distance, relative_dose, scan_curvetype, scan_depth = api.load_mephysto(
mephysto_file)
depth_testing = scan_depth[~np.isnan(scan_depth)]
depth_testing = np.unique(depth_testing)
if depth_of_absolute_dose_mm == "dmax":
choose_mephysto = scan_curvetype == "PDD"
mephysto_pdd_depth = distance[choose_mephysto][0]
mephysto_dose = relative_dose[choose_mephysto][0]
depth_of_absolute_dose_mm = mephysto_pdd_depth[np.argmax(
mephysto_dose)]
mephysto_pdd_depth, mephysto_pdd_dose = mephysto_absolute_depth_dose(
absolute_dose,
depth_of_absolute_dose_mm,
distance,
relative_dose,
scan_curvetype,
)
scans = {
"depth_dose": {
"displacement": mephysto_pdd_depth,
"dose": mephysto_pdd_dose
},
"profiles": {},
}
for depth_test in depth_testing:
(
mephysto_distance_inplane,
mephysto_normalised_dose_inplane,
) = mephysto_absolute_profiles(
"INPLANE_PROFILE",
depth_test,
distance,
relative_dose,
scan_curvetype,
scan_depth,
mephysto_pdd_depth,
mephysto_pdd_dose,
)
(
mephysto_distance_crossplane,
mephysto_normalised_dose_crossplane,
) = mephysto_absolute_profiles(
"CROSSPLANE_PROFILE",
depth_test,
distance,
relative_dose,
scan_curvetype,
scan_depth,
mephysto_pdd_depth,
mephysto_pdd_dose,
)
scans["profiles"][depth_test] = {
"inplane": {
"displacement": mephysto_distance_inplane,
"dose": mephysto_normalised_dose_inplane,
},
"crossplane": {
"displacement": mephysto_distance_crossplane,
"dose": mephysto_normalised_dose_crossplane,
},
}
return scans
def merge_view_filtrot(volume, dx, dy):
volume_resort = np.copy(
volume
) # this will hold the resorted volume 0 to 3 clockwise
junctions_comb = []
peaks_figs_comb = []
# we need to create 4 matches
# 0,1,2,3 will be tagged top left, top right, bottom right, bottom left
for i in range(0, int(np.shape(volume)[2])):
diag_stack = [
0,
0,
0,
0,
] # we will sum along one direction whichever is biggest will tag the file
for j in range(0, int(min([np.shape(volume)[0], np.shape(volume)[1]]) / 2)):
# print('j=',j,int(np.shape(volume)[0] / 2)+j, int(np.shape(volume)[1] / 2)+j)
diag_stack[0] = (
diag_stack[0]
+ volume[
int(np.shape(volume)[0] / 2) - j,
int(np.shape(volume)[1] / 2) - j,
i,
]
)
diag_stack[1] = (
diag_stack[1]
+ volume[
int(np.shape(volume)[0] / 2) - j,
int(np.shape(volume)[1] / 2) + j,
i,
]
)
diag_stack[2] = (
diag_stack[2]
+ volume[
int(np.shape(volume)[0] / 2) + j,
int(np.shape(volume)[1] / 2) + j,
i,
]
)
diag_stack[3] = (
diag_stack[3]
+ volume[
int(np.shape(volume)[0] / 2) + j,
int(np.shape(volume)[1] / 2) - j,
i,
]
)
volume_resort[:, :, np.argmax(diag_stack)] = volume[:, :, i]
# creating merged volumes
merge_vol = np.zeros((volume_resort.shape[0], volume_resort.shape[1]))
# creating vector for processing (1 horizontal & 1 vertical)
amplitude_horz = np.zeros(
(volume_resort.shape[1], volume_resort.shape[2])
) # 1 if it is vertical 0 if the bars are horizontal
amplitude_vert = np.zeros((volume_resort.shape[0], volume_resort.shape[2]))
# y = np.linspace(0, 0 + (volume_resort.shape[0] * dy), volume_resort.shape[0],
# endpoint=False) # definition of the distance axis
# x = np.linspace(0, 0 + (volume_resort.shape[1] * dy), volume_resort.shape[1],
# endpoint=False) # definition of the distance axis
ampl_resamp_y1 = np.zeros(
((volume_resort.shape[0]) * 10, int(volume_resort.shape[2] / 2))
)
ampl_resamp_y2 = np.zeros(
((volume_resort.shape[0]) * 10, int(volume_resort.shape[2] / 2))
)
ampl_resamp_x1 = np.zeros(
((volume_resort.shape[1]) * 10, int(volume_resort.shape[2] / 2))
)
ampl_resamp_x2 = np.zeros(
((volume_resort.shape[1]) * 10, int(volume_resort.shape[2] / 2))
)
amplitude_horz[:, 0] = volume_resort[
int(volume_resort.shape[0] / 3.25), :, 0
] # for profile 1
amplitude_horz[:, 1] = volume_resort[
int(volume_resort.shape[0] / 3.25), :, 1
] # for profile 1
amplitude_horz[:, 3] = volume_resort[
int(volume_resort.shape[0]) - int(volume_resort.shape[0] / 3.25), :, 2
] # the numbers here are reversed because we are going to slide the second graph (the overlay) to minimize the error #for profile 2
amplitude_horz[:, 2] = volume_resort[
int(volume_resort.shape[0]) - int(volume_resort.shape[0] / 3.25), :, 3
]
amplitude_vert[:, 0] = volume_resort[
:, int(volume_resort.shape[1]) - int(volume_resort.shape[1] / 2.8), 1
] # the numbers here are reversed because we are going to slide the second graph (the overlay) to minimize the error #for profile 3
amplitude_vert[:, 1] = volume_resort[
:, int(volume_resort.shape[1]) - int(volume_resort.shape[1] / 2.8), 2
]
amplitude_vert[:, 3] = volume_resort[
:, int(volume_resort.shape[1] / 2.8), 3
] # for profile 4
amplitude_vert[:, 2] = volume_resort[:, int(volume_resort.shape[1] / 2.8), 0]
plt.figure()
for item in tqdm(range(0, int(volume.shape[2] / 2))):
merge_vol = merge_vol + volume[:, :, item]
data_samp = amplitude_vert[:, item]
ampl_resamp_y1[:, item] = signal.resample(
data_samp, int(np.shape(amplitude_vert)[0]) * 10
)
data_samp = amplitude_horz[:, item]
ampl_resamp_x1[:, item] = signal.resample(
data_samp, int(np.shape(amplitude_horz)[0]) * 10
)
for item in tqdm(range(int(volume.shape[2] / 2), volume.shape[2])):
merge_vol = merge_vol + volume[:, :, item]
data_samp = amplitude_vert[:, item]
ampl_resamp_y2[:, item - int(volume.shape[2] / 2)] = signal.resample(
data_samp, int(np.shape(amplitude_vert)[0]) * 10
)
data_samp = amplitude_horz[:, item]
ampl_resamp_x2[:, item - int(volume.shape[2] / 2)] = signal.resample(
data_samp, int(np.shape(amplitude_horz)[0]) * 10
)
fig, ax = plt.subplots(ncols=1, nrows=1, squeeze=True, figsize=(6, 8))
extent = (0, 0 + (volume.shape[1] * dx), 0, 0 + (volume.shape[0] * dy))
ax.imshow(merge_vol, extent=extent, aspect="auto")
ax.set_aspect("equal", "box")
ax.set_xlabel("x distance [mm]")
ax.set_ylabel("y distance [mm]")
fig.suptitle("Merged volume", fontsize=16)
ax.hlines(dy * int(volume_resort.shape[0] / 3.25), 0, dx * volume_resort.shape[1])
ax.text(
dx * int(volume_resort.shape[1] / 2.25),
dy * int(volume_resort.shape[0] / 3),
"Profile 2",
)
ax.hlines(
dy * int(volume_resort.shape[0]) - dy * int(volume_resort.shape[0] / 3.25),
0,
dx * volume_resort.shape[1],
)
ax.text(
dx * int(volume_resort.shape[1] / 2.25),
dy * int(volume_resort.shape[0]) - dy * int(volume_resort.shape[0] / 3.5),
"Profile 1",
)
ax.vlines(dx * int(volume_resort.shape[1] / 2.8), 0, dy * volume_resort.shape[0])
ax.text(
dx * int(volume_resort.shape[1] / 3.1),
dy * int(volume_resort.shape[0] / 1.8),
"Profile 4",
rotation=90,
)
ax.vlines(
dx * int(volume_resort.shape[1]) - dx * int(volume_resort.shape[1] / 2.8),
0,
dy * volume_resort.shape[0],
)
ax.text(
dx * int(volume_resort.shape[1]) - dx * int(volume_resort.shape[1] / 2.9),
dy * int(volume_resort.shape[0] / 1.8),
"Profile 3",
rotation=90,
)
# plt.show()
peaks, peak_type, peak_figs = pffr.peak_find_fieldrot(
ampl_resamp_x1, dx, "Profile 1"
)
junction_figs = minFR.minimize_junction_fieldrot(
ampl_resamp_x1, peaks, peak_type, dx / 10, "Profile 1"
)
peaks_figs_comb.append(peak_figs)
junctions_comb.append(junction_figs)
peaks, peak_type, peak_figs = pffr.peak_find_fieldrot(
ampl_resamp_x2, dx, "Profile 2"
)
junction_figs = minFR.minimize_junction_fieldrot(
ampl_resamp_x2, peaks, peak_type, dx / 10, "Profile 2"
)
peaks_figs_comb.append(peak_figs)
junctions_comb.append(junction_figs)
peaks, peak_type, peak_figs = pffr.peak_find_fieldrot(
ampl_resamp_y1, dy, "Profile 3"
)
junction_figs = minFR.minimize_junction_fieldrot(
ampl_resamp_y1, peaks, peak_type, dy / 10, "Profile 3"
)
peaks_figs_comb.append(peak_figs)
junctions_comb.append(junction_figs)
peaks, peak_type, peak_figs = pffr.peak_find_fieldrot(
ampl_resamp_y2, dy, "Profile 4"
)
junction_figs = minFR.minimize_junction_fieldrot(
ampl_resamp_y2, peaks, peak_type, dy / 10, "Profile 4"
)
peaks_figs_comb.append(peak_figs)
junctions_comb.append(junction_figs)
return fig, peaks_figs_comb, junctions_comb
