#%% concatenate movies (it will add to the original movie)
# you have to create another movie new_mov=XMovie(...)
m.append(new_mov)
#%% motion correct run 3 times
# WHEN YOU RUN motion_correct YOUR ARE MODIFYING THE OBJECT!!!!
templates=[];
shifts=[];
max_shift=5;
num_iter=10; # numer of times motion correction is executed
template=None # here you can use your own template (best representation of the FOV)
for j in range(0,num_iter):
template_used,shift=m.motion_correct(max_shift=max_shift,template=None,show_movie=False);
templates.append(template_used)
shift=np.asarray(shift)
shifts.append(shift)
plt.plot(np.asarray(shifts).reshape((j+1)*shift.shape[0],shift.shape[1]))
#%% apply shifts to another channel, you need to reload the original movie other channel (mov_other_channel)
if False:
# here reload the original imaging channel from movie
totalShifts=np.sum(np.asarray(shifts),axis=0)[:,0:2].tolist()
mov_other_channel.applyShifstToMovie(totalShifts)
#%% if you want to apply the shifts only ones to reduce the smoothing
# you have to create another movie new_mov=XMovie(...)
m.append(new_mov)
#%% motion correct run 3 times
# WHEN YOU RUN motion_correct YOUR ARE MODIFYING THE OBJECT!!!!
templates=[];
shifts=[];
max_shift_w=5;
max_shift_h=5;
num_iter=3; # numer of times motion correction is executed
template=None # here you can use your own template (best representation of the FOV)
for j in range(0,num_iter):
template_used,shift=m.motion_correct(max_shift_w=max_shift_w,max_shift_h=max_shift_h,template=None,show_movie=False);
templates.append(template_used)
shift=np.asarray(shift)
shifts.append(shift)
plt.plot(np.asarray(shifts).reshape((j+1)*shift.shape[0],shift.shape[1]))
#%% motion correct Cristina
initTime=time.time()
m=XMovie(mat=np.load(filename_py)['mov'], frameRate=np.load(filename_py)['frameRate']);
m.makeSubMov(range(2000))
m.crop(crop_top=0,crop_bottom=1,crop_left=0,crop_right=0,crop_begin=0,crop_end=0)
m_tmp=m.copy()
_,shift=m_tmp.motion_correct(max_shift_w=60,max_shift_h=20,template=None,show_movie=False);
template=np.median(m_tmp.mov,axis=0)
