def track(params):
# Read in the image data
image_data = images.ImageData()
image_data.read(params.name + ".tif", params)
if params.ALEX==True:
imageL=np.zeros((image_data.num_frames//2,image_data.frame_size[1],image_data.frame_size[0]//2))
imageR=np.zeros((image_data.num_frames//2,image_data.frame_size[1],image_data.frame_size[0]//2))
if params.start_channel=='L':
for i in range(0,image_data.num_frames-1,2):
imageL[i//2,:,:] = image_data.pixel_data[i,:,:image_data.frame_size[0]//2]
imageR[i//2,:,:] = image_data.pixel_data[i+1,:,image_data.frame_size[0]//2:]
else:
for i in range(0,image_data.num_frames-1,2):
imageR[i//2,:,:] = image_data.pixel_data[i,:,:image_data.frame_size[0]//2]
imageL[i//2,:,:] = image_data.pixel_data[i+1,:,image_data.frame_size[0]//2:]
image_data.num_frames = image_data.num_frames//2
#LHS
image_data.pixel_data = imageL
image_data.frame_size = [image_data.frame_size[0]//2,image_data.frame_size[1]]
all_spots = []
for frame in range(image_data.num_frames):
all_spots.append(track_frame(image_data[frame], frame, params))
trajs = trajectories.build_trajectories(all_spots, params)
trajectories.write_trajectories(trajs, params.name + "_Lchannel_trajectories.tsv")
#RHS
image_data.pixel_data = imageR
all_spots = []
for frame in range(image_data.num_frames):
all_spots.append(track_frame(image_data[frame], frame, params))
trajs = trajectories.build_trajectories(all_spots, params)
trajectories.write_trajectories(trajs, params.name + "_Rchannel_trajectories.tsv")
# For each frame, detect spots
else:
all_spots = []
if params.num_procs == 0:
for frame in range(image_data.num_frames):
all_spots.append(track_frame(image_data[frame], frame, params))
else:
res = [None] * image_data.num_frames
with mp.Pool(params.num_procs) as pool:
for frame in range(image_data.num_frames):
res[frame] = pool.apply_async(track_frame, (image_data[frame], frame, params))
for frame in range(image_data.num_frames):
all_spots.append(res[frame].get())
# Link the spot trajectories across the frames
trajs = trajectories.build_trajectories(all_spots, params)
trajectories.write_trajectories(trajs, params.name + "_trajectories.tsv")
def get_copy_number(params, calculated_isingle, channel=None):
image_data = images.ImageData()
image_data.read(params)
frame = image_data.pixel_data[spots.laser_on_frame, :, :]
copy_nums = []
f = open(params.name + "_copy_numbers.tsv")
f.write("Cell\tCopy number\n")
bg = np.mean(frame[image_data.mask == 0])
for i in range(1, np.amax(image_data.mask_data) + 1):
copy_nums.append(
np.sum(frame[image_data.mask_data == i]) / calculated_isingle)
f.write(str(i) + "\t" + str(copy_nums[i - 1]) + "\n")
f.close()
def update_slider(render_selection, vis_frame, active_file):
if active_file:
params = Parameters()
params.name = active_file.replace('.tif','')
image_data = images.ImageData()
image_data.read(active_file, params)
fig = px.imshow(
image_data.pixel_data[vis_frame-1,:,:],
color_continuous_scale='gray',
zmin = 0,
zmax = np.max(image_data.pixel_data[:,:,:]),
)
if (render_selection == "render-all-trajectories" or
render_selection == "render-current-trajectories"):
trajs = read_trajectories(params.name + "_trajectories.tsv")
colors = px.colors.qualitative.Plotly
if trajs:
for traj in trajs:
if (render_selection == "render-current-trajectories" and
(vis_frame < traj.start_frame or vis_frame > traj.end_frame)):
continue
Xs = []
Ys = []
color = colors[traj.id % len(colors)]
for frame in range(traj.start_frame, traj.end_frame):
Xs.append(traj.path[frame - traj.start_frame][0])
Ys.append(traj.path[frame - traj.start_frame][1])
fig.add_trace(go.Scatter(x=Xs, y=Ys, marker=dict(color=color, size=5)))
fig.update_layout(showlegend=False)
label = f"Frame {vis_frame} of {image_data.num_frames}"
num_frames = image_data.num_frames
else:
logo_path = f"{Path(__file__).parent}/assets/pystachio_logo.tif"
logo_tif = tifffile.imread(logo_path)
fig = px.imshow(
logo_tif[:,:,0],
color_continuous_scale='gray',
zmin = 0,
zmax = np.max(logo_tif[:,:,:]),
)
label = f"Upload a file to get started..."
num_frames = 1
return fig, label, num_frames
def colocalize(params, Ltrajs, Rtrajs):
image_data = images.ImageData()
image_data.read(params.name + '.tif', params)
imageL = np.zeros((image_data.num_frames // 2, image_data.frame_size[1],
image_data.frame_size[0] // 2))
imageR = np.zeros((image_data.num_frames // 2, image_data.frame_size[1],
image_data.frame_size[0] // 2))
if params.start_channel == 'L':
for i in range(0, image_data.num_frames - 1, 2):
imageL[i // 2, :, :] = image_data.pixel_data[
i, :, :image_data.frame_size[0] // 2]
imageR[i //
2, :, :] = image_data.pixel_data[i + 1, :,
image_data.frame_size[0] //
2:]
else:
for i in range(0, image_data.num_frames - 1, 2):
imageR[i // 2, :, :] = image_data.pixel_data[
i, :, :image_data.frame_size[0] // 2]
imageL[i //
2, :, :] = image_data.pixel_data[i + 1, :,
image_data.frame_size[0] //
2:]
Llinks = []
Rlinks = []
nlinks = []
for i in range(params.num_frames):
s1 = []
s2 = []
for traj in Ltrajs:
if traj.start_frame <= i and traj.end_frame >= i:
s1.append(
[traj.path[i - traj.start_frame], traj.width, traj.id])
for traj in Rtrajs:
if traj.start_frame <= i and traj.end_frame >= i:
s2.append(
[traj.path[i - traj.start_frame], traj.width, traj.id])
id1, id2 = linker(params, s1, s2)
for j in range(len(id1)):
found = False
if id1[i] in Llinks:
indices = np.where(Llinks == id1)
for index in indices:
if Rlinks[index] == id2[j]:
nlinks[index] += 1
found = True
if found == False:
Llinks.append(id1[j])
Rlinks.append(id2[j])
nlinks.append(1)
outfile = params.name + "_colocalized_trajectories.tsv"
f = open(outfile, 'w')
f.write("Left_traj\tRight_traj\n_frames")
for i in range(len(Llinks)):
if nlinks[i] >= params.colocalize_n_frames:
f.write("$i\t%i\t%i" % (Llinks[i], Rlinks[i], nlinks[i]))
colors = ['r', 'b', 'g', 'm', 'y', 'c']
c = 0
z, y, x = imageL.shape
disp_frame = np.zeros((y, 2 * x))
disp_frame[:, :x] = imageL[1, :, :]
disp_frame[:, x:] = imageR[1, :, :]
plt.imshow(disp_frame, cmap="Greys_r")
plt.plot([x, x], [0, x], 'w--')
plt.xticks([])
plt.yticks([])
plt.ylim([0, x])
if display_figures:
plt.show()
plt.imshow(disp_frame, cmap="Greys_r")
for traj in Ltrajs:
if traj.id in id1:
plt.scatter(traj.path[0][0],
traj.path[0][1],
10,
marker='x',
color=colors[c])
for t2 in Rtrajs:
index = np.where(id1 == traj.id)[0][0]
if t2.id == id2[index]:
plt.scatter(t2.path[0][0] + x,
t2.path[0][1],
10,
marker='x',
color=colors[c])
c += 1
plt.yticks([])
plt.xticks([])
plt.plot([x, x], [0, x], 'w--')
plt.ylim([0, x])
plt.savefig("colocalized_spots.png", dpi=300)
if params.display_figures:
plt.show()
plt.close()
def render(params):
image_data = images.ImageData()
image_data.read(params.name+".tif", params)
trajs = trajectories.read_trajectories(params.name + "_trajectories.tsv")
true_trajs = trajectories.read_trajectories(params.name + "_simulated.tsv")
maxval = image_data.max_intensity()
figure = plt.figure()
plt_frames = []
px_size = params.pixel_size
fr_size = image_data.frame_size
colors = list(mcolors.TABLEAU_COLORS.keys())
if trajs:
if params.verbose:
print(f"Displaying {len(trajs)} trajectories")
for traj in trajs:
x = []
y = []
for frame in range(traj.start_frame, traj.end_frame):
x.append(traj.path[frame - traj.start_frame][0])
y.append(traj.path[frame - traj.start_frame][1])
x = np.array(x)
y = np.array(y)
x_scaled = (x+0.5) * px_size
y_scaled = (fr_size[1]-y-0.5) * px_size
plt.plot(x_scaled,y_scaled,"o-",c=colors[traj.id % len(colors)])
if true_trajs:
if params.verbose:
print(f"Displaying {len(true_trajs)} true trajectories")
for traj in true_trajs:
x = []
y = []
for frame in range(traj.start_frame, traj.end_frame):
x.append(traj.path[frame - traj.start_frame][0])
y.append(traj.path[frame - traj.start_frame][1])
x = np.array(x)
y = np.array(y)
x_scaled = (x+0.5) * px_size
y_scaled = (fr_size[1]-y-0.5) * px_size
plt.plot(x_scaled,y_scaled,"+--",c="tab:orange")
plts = []
for frame in range(image_data.num_frames):
plt_frame = plt.imshow(
image_data.pixel_data[frame,:,:],
vmin=0,
animated=True,
vmax=maxval,
cmap=plt.get_cmap("gray"),
extent=[0, fr_size[0]*px_size, 0, fr_size[1]*px_size]
)
plts.append([plt_frame])
ani = animation.ArtistAnimation(figure, plts, interval=50,)
plt.title(f"Simulated spot data, frame {image_data.num_frames}")
plt.xlabel("μm")
plt.ylabel("μm")
plt.colorbar()
plt.show()