def imagegallery(event):
global file
values = e.get().split(" ")
x0 = int(values[0])
y0 = int(values[1])
x1 = int(values[2])
y1 = int(values[3])
x = 0
y = 0
indexx = x1
for x in range(x1 - x0 + 1):
indexy = y1
for y in range(y1 - y0 + 1):
s = PixelatedSTEM(
hs.signals.Signal2D(file.inav[indexy, indexx]))
st = s.template_match_disk(disk_r=6,
lazy_result=True,
show_progressbar=False)
peak_array = st.find_peaks(lazy_result=False,
show_progressbar=False)
s.add_peak_array_as_markers(peak_array)
#plt.plot(s)
s.plot()
ax = s._plot.signal_plot.ax
ax.set_xlabel("pixel(" + str(indexx) + "_" + str(indexy) +
")")
#plt.title("pixel(" + str(indexx) + "_" + str(indexy) + ")")
#plt.show()
indexy -= 1
y += 1
indexx -= 1
x += 1
plt.show()
"""
def mousecoords(event):
global methodOfAnalysis
s = PixelatedSTEM(file.inav[0, 0])
length = len(array(s))
pointxy = (-1, -1)
while pointxy is (-1, -1):
pointxy = (int(event.x * length / 400), int(event.y * length / 400)
) # get the mouse position from event
l['text'] = l['text'] + str(pointxy[0]) + " " + str(pointxy[1]) + "\n"
r.update()
c2.unbind('<Button-1>')
c2.bind('<Button-1>', lambda event: mousecoords1(event, pointxy))
def multiprocessing_func(values):
global distances
s = PixelatedSTEM(file.inav[values[0], values[1]])
imarray = array(s)
s = s.rotate_diffraction(0, show_progressbar=False)
############################################################################################################################
st = s.template_match_disk(disk_r=5,
lazy_result=False,
show_progressbar=False)
peak_array = st.find_peaks(lazy_result=False, show_progressbar=False)
peak_array_com = s.peak_position_refinement_com(peak_array,
lazy_result=False,
show_progressbar=False)
s_rem = s.subtract_diffraction_background(lazy_result=False,
show_progressbar=False)
peak_array_rem_com = s_rem.peak_position_refinement_com(
peak_array_com, lazy_result=False, show_progressbar=False)
############################################################################################################################
center = findCenter(imarray, peak_array_rem_com)
# finds the specific spot and adding that distance to the array
posDistance = 0
closestPoint = center
for (x, y) in ndenumerate(peak_array_rem_com):
minimum = 999999
for (a, b) in y:
dis = distance(values[2], values[3], b, a)
if dis < minimum:
minimum = dis
closestPoint = (b, a)
posDistance = distance(closestPoint[0], closestPoint[1], center[0],
center[1])
distances[values[1]][values[0]] = round(posDistance, 2)
def Mousecoords(event):
global methodOfAnalysis
s = PixelatedSTEM(file.inav[100, 100])
length = len(array(s))
pointxy = (int(event.x * length / 400), int(event.y * length / 400)
) # get the mouse position from event
l['text'] = l['text'] + str(pointxy[0]) + " " + str(
pointxy[1]) + "\n"
l['text'] = l['text'] + "Starting analysis...\n"
r.update()
analysis(pointxy, values, methodOfAnalysis)
remove("temp.png")
c2.unbind('<Button-1>')
r.destroy()
label1['text'] = label1['text'] + "Analysis complete.\n"
def multiprocessing_func(values):
global singleValues, distances
s = PixelatedSTEM(file.inav[values[0], values[1]])
imarray = array(s)
sigma_est = mean(estimate_sigma(imarray, ))
# patch_size for 580 - 1, patch_size for 144 = 3
#nlm = denoise_nl_means(imarray, h=1.15 * sigma_est, fast_mode=True, patch_size=3, patch_distance=6, )
gaussian = gaussian_filter(imarray, 1.15 * sigma_est)
#wien = wiener(imarray, 5, 3)
original = gaussian # Change this to whatever filter you want to use
s = PixelatedSTEM(original)
############################################################################################################################
st = s.template_match_ring(r_inner=1,
r_outer=6,
lazy_result=True,
show_progressbar=False)
peak_array = st.find_peaks(lazy_result=False, show_progressbar=False)
peak_array_com = s.peak_position_refinement_com(peak_array,
lazy_result=False,
show_progressbar=False)
s_rem = s.subtract_diffraction_background(lazy_result=False,
show_progressbar=False)
peak_array_rem_com = s_rem.peak_position_refinement_com(
peak_array_com, lazy_result=False, show_progressbar=False)
############################################################################################################################
center = findCenter(imarray, peak_array_rem_com)
# finds the specific spot and adding that distance to the array
posDistance = 0
closestPoint = center
idx = 0
for (x, y) in ndenumerate(peak_array_rem_com):
minimum = 999999
for (a, b) in y:
if (2 < b < 142 and 2 < a < 142):
di = distance(center[0], center[1], b, a)
distances[values[1]][values[0]][idx] = round(di, 3)
idx += 1
dis = distance(values[2], values[3], b, a)
if dis < minimum:
minimum = dis
closestPoint = (b, a)
posDistance = distance(closestPoint[0], closestPoint[1], center[0],
center[1])
singleValues[values[1]][values[0]] = round(posDistance, 2)
def mousecoords1(event, pointxy):
global methodOfAnalysis
pointxy1 = None
s = PixelatedSTEM(file.inav[0, 0])
length = len(array(s))
while pointxy1 is None:
pointxy1 = (int(event.x * length / 400),
int(event.y * length / 400)
) # get the mouse position from event
print("pointxy1", pointxy1, "\n", "pointxy", pointxy)
l['text'] = l['text'] + str(pointxy1[0]) + " " + str(
pointxy1[1]) + "\n"
l['text'] = l['text'] + "Starting analysis...\n"
analysis(pointxy, pointxy1, values, methodOfAnalysis)
r.update()
remove("temp.png")
c2.unbind('<Button-1>')
r.destroy()
label1['text'] = label1['text'] + "Analysis complete.\n"
def imagegallery(event):
global file
values = e.get().split(" ")
x0 = int(values[0])
y0 = int(values[1])
x1 = int(values[2])
y1 = int(values[3])
x = 0
y = 0
indexx = x1
for x in range(x1 - x0 + 1):
indexy = y1
for y in range(y1 - y0 + 1):
s = PixelatedSTEM(
hs.signals.Signal2D(file.inav[indexx, indexy]))
st = s.template_match_ring(r_inner=1,
r_outer=6,
lazy_result=True,
show_progressbar=False)
peak_array = st.find_peaks(method='dog',
min_sigma=0.8,
max_sigma=15,
sigma_ratio=1.9,
threshold=0.42,
overlap=0.5,
lazy_result=False,
show_progressbar=True)
s.add_peak_array_as_markers(peak_array)
#plt.plot(s)
s.plot()
ax = s._plot.signal_plot.ax
ax.set_xlabel("pixel(" + str(indexx) + "_" + str(indexy) +
")")
#plt.title("pixel(" + str(indexx) + "_" + str(indexy) + ")")
#plt.show()
indexy -= 1
y += 1
indexx -= 1
x += 1
plt.show()
"""
def intensity(values):
s = PixelatedSTEM(file.inav[values[0], values[1]])
imarray = array(s)
singleValues[values[1]][values[0]] = imarray[values[3]][values[2]]
def startAnalysis(values=None):
global file, currFunc
methodOfAnalysis = ""
def assignMethod(method):
global methodOfAnalysis
methodOfAnalysis = method
def mousecoords(event):
global methodOfAnalysis
s = PixelatedSTEM(file.inav[0, 0])
length = len(array(s))
pointxy = (-1, -1)
while pointxy is (-1, -1):
pointxy = (int(event.x * length / 400), int(event.y * length / 400)
) # get the mouse position from event
l['text'] = l['text'] + str(pointxy[0]) + " " + str(pointxy[1]) + "\n"
r.update()
c2.unbind('<Button-1>')
c2.bind('<Button-1>', lambda event: mousecoords1(event, pointxy))
def mousecoords1(event, pointxy):
global methodOfAnalysis
pointxy1 = None
s = PixelatedSTEM(file.inav[0, 0])
length = len(array(s))
while pointxy1 is None:
pointxy1 = (int(event.x * length / 400),
int(event.y * length / 400)
) # get the mouse position from event
print("pointxy1", pointxy1, "\n", "pointxy", pointxy)
l['text'] = l['text'] + str(pointxy1[0]) + " " + str(
pointxy1[1]) + "\n"
l['text'] = l['text'] + "Starting analysis...\n"
analysis(pointxy, pointxy1, values, methodOfAnalysis)
r.update()
remove("temp.png")
c2.unbind('<Button-1>')
r.destroy()
label1['text'] = label1['text'] + "Analysis complete.\n"
s = PixelatedSTEM(file.inav[0, 0])
s.save("temp.png")
img = Image.open("temp.png")
img = img.resize((400, 400), Image.ANTIALIAS)
img.save('temp.png')
r = tk.Toplevel(root)
c = tk.Canvas(r, height=720, width=1080)
c.pack()
f = tk.Frame(r, bg='#333333')
f.place(relwidth=1, relheight=1)
l = tk.Message(f,
bg='#999999',
font=('Calibri', 15),
anchor='nw',
justify='left',
highlightthickness=0,
bd=0,
width=1000)
l.place(relx=0.05, rely=0.7, relwidth=0.9, relheight=0.2)
b1 = tk.Button(f,
text='Intensity Mapping',
bg='#620000',
font=('Calibri', 15),
highlightthickness=0,
bd=0,
activebackground='#800000',
activeforeground='#ffffff',
command=lambda: assignMethod("intensity"),
pady=0.02,
fg='#ffffff')
b1.place(relx=0.2, rely=0.6, relwidth=0.2, relheight=0.05)
b2 = tk.Button(f,
text='Strain Mapping',
bg='#620000',
font=('Calibri', 15),
highlightthickness=0,
bd=0,
activebackground='#800000',
activeforeground='#ffffff',
command=lambda: assignMethod("strain"),
pady=0.02,
fg='#ffffff')
b2.place(relx=0.6, rely=0.6, relwidth=0.2, relheight=0.05)
c2 = tk.Canvas(r, width=400, height=400)
c2.place(relx=0.3)
img = ImageTk.PhotoImage(Image.open("temp.png"))
c2.create_image(0, 0, anchor='nw', image=img)
c2.bind('<Button-1>', mousecoords)
l['text'] = l[
'text'] + "Please click on the method of analysis and then the point you would like to analyze from the diffraction pattern above.\n"
r.mainloop()
if path.exists("temp.png"):
remove("temp.png")
def multiprocessing_func(values):
global singleValues, distances
s = PixelatedSTEM(file.inav[values[0], values[1]])
original = array(s)
############################################################################################################################
# # FILTERS
# sigma_est = mean(estimate_sigma(original, ))
# # patch_size for 580 - 1, patch_size for 144 = 3
# nlm = denoise_nl_means(original, h=1.15*sigma_est, fast_mode=True, patch_size=3, patch_distance=6, )
# gaussian = gaussian_filter(original, 1.15*sigma_est)
# wien = wiener(original, 5, 3)
# s = PixelatedSTEM(gaussian) # Change this to whatever filter you want to use
############################################################################################################################
# # PIXSTEM
# s = s.rotate_diffraction(0,show_progressbar=False)
# st = s.template_match_disk(disk_r=5, lazy_result=False, show_progressbar=False)
# peak_array = st.find_peaks(lazy_result=False, show_progressbar=False)
# peak_array_com = s.peak_position_refinement_com(peak_array, lazy_result=False, show_progressbar=False)
# s_rem = s.subtract_diffraction_background(lazy_result=False, show_progressbar=False)
# peak_array_rem_com = s_rem.peak_position_refinement_com(peak_array_com, lazy_result=False, show_progressbar=False)
############################################################################################################################
# MY METHOD
# defines template and templates matches
# spot for 580 - [265:320, 265:320]
# spot for 144 - [65:80, 65:80]
template = original[65:80, 65:80]
result = match_template(original, template, pad_input=True)
# only takes points greater than the threshold r-value
tempList = []
for i in range(len(result)):
for j in range(len(result[i])):
if (result[i][j] > 0.87): # change correlation value
tempList.append((i, j))
# removes duplicate spots that are too close to each other
i = 0
l = []
while i < len(tempList):
j = 0
temp = []
point = tempList[i]
while j < len(tempList):
if distance(point[0], point[1], tempList[j][0],
tempList[j][1]) < 15: # change minimum center distance
temp.append(tempList[j])
tempList.pop(j)
else:
j = j + 1
max = 0
pnt = temp[0]
for j in range(len(temp)):
if (result[pnt[0]][pnt[1]] < result[temp[j][0]][temp[j][1]]):
max = result[temp[j][0]][temp[j][1]]
pnt = temp[j]
l.append(pnt)
peak_array_rem_com = [[], l]
############################################################################################################################
closestPoint = None
closestPoint1 = None
length = len(original)
for (x, y) in ndenumerate(peak_array_rem_com):
minimum = 999999
minimum1 = 999999
for (a, b) in y:
dis = distance(values[2], values[3], b, a)
dis1 = distance(values[4], values[5], b, a)
if dis < minimum and dis < length / 10:
minimum = dis
closestPoint = (b, a)
if dis1 < minimum1 and dis1 < length / 10:
minimum1 = dis1
closestPoint1 = (b, a)
print(closestPoint, closestPoint1, values[4], values[5])
posDistance = distance(closestPoint[0], closestPoint[1], closestPoint1[0],
closestPoint1[1])
singleValues[values[1]][values[0]] = round(posDistance, 2)