#%% process files sequntially in case of failure
if 0:
fnames1 = []
for f in fnames:
if os.path.isfile(f[:-3] + 'hdf5'):
1
else:
print((1))
fnames1.append(f)
#%% motion correct
t1 = time()
file_res = cb.motion_correct_parallel(fnames1,
fr=30,
template=None,
margins_out=0,
max_shift_w=45,
max_shift_h=45,
dview=None,
apply_smooth=True)
t2 = time() - t1
print(t2)
#%% LOGIN TO MASTER NODE
# TYPE salloc -n n_nodes --exclusive
# source activate environment_name
#%%#%%
slurm_script = '/mnt/xfs1/home/agiovann/SOFTWARE/Constrained_NMF/SLURM/slurmStart.sh'
cse.utilities.start_server(slurm_script=slurm_script)
# n_processes = 27#np.maximum(psutil.cpu_count() - 2,1) # roughly number of cores on your machine minus 1
pdir, profile = os.environ['IPPPDIR'], os.environ['IPPPROFILE']
client_ = Client(ipython_dir=pdir, profile=profile)
#fnames.sort()
fnames = ['file_name.tif'
] # you ahev to eliminate the first element, it cannot be loaded!
print fnames
fnames = fnames
#%%
m = cb.load_movie_chain(fnames, fr=30)
#%%
m.play(backend='opencv', gain=3., fr=50)
#%%
apply_smooth = False # set to true if SNR too low. Slower but might work better. In my hand ot worked out just fine!
m_mc = cb.motion_correct_parallel([m],
fr=30,
template=None,
margins_out=0,
max_shift_w=25,
max_shift_h=25,
remove_blanks=True,
apply_smooth=apply_smooth,
dview=None,
save_hdf5=False)
m_mc = m_mc[0]
#%%
m_mc.resize(1, 1, .2).play(backend='opencv', gain=5., fr=10)
#%%
m_mc.save('all_mov.hdf5')
#%% in what follows there is some redundancy only due to the fact that this is meant to run oin parallel for many datasets... it can be done much faster.
#idx_x=slice(12,500,None)
#idx_y=slice(12,500,None)
#idx_xy=(idx_x,idx_y)
final_frate = 30
downsample_factor = 1 # use .2 or .1 if file is large and you want a quick answer
pl.imshow(template,cmap='gray',vmin=lq,vmax=hq)
pl.pause(.1)
counter+=1
pl.title(img.split('/')[-2])
#%% process files sequntially in case of failure
if 0:
fnames1=[]
for f in fnames:
if os.path.isfile(f[:-3]+'hdf5'):
1
else:
print((1))
fnames1.append(f)
#%% motion correct
t1 = time()
file_res=cb.motion_correct_parallel(fnames1,fr=30,template=None,margins_out=0,max_shift_w=45, max_shift_h=45,dview=None,apply_smooth=True)
t2=time()-t1
print(t2)
#%% LOGIN TO MASTER NODE
# TYPE salloc -n n_nodes --exclusive
# source activate environment_name
#%%#%%
backend='local'
if backend == 'slurm':
slurm_script='/mnt/xfs1/home/agiovann/SOFTWARE/Constrained_NMF/SLURM/slurmStart.sh'
cse.utilities.start_server(slurm_script=slurm_script)
#n_processes = 27#np.maximum(psutil.cpu_count() - 2,1) # roughly number of cores on your machine minus 1
pdir, profile = os.environ['IPPPDIR'], os.environ['IPPPROFILE']
client_ = Client(ipython_dir=pdir, profile=profile)
else:
cse.utilities.stop_server()
cse.utilities.start_server(n_processes)
#%%
#low_SNR=False
#if low_SNR:
# N=1000
# mn1=m.copy().bilateral_blur_2D(diameter=5,sigmaColor=10000,sigmaSpace=0)
#
# mn1,shifts,xcorrs, template=mn1.motion_correct()
# mn2=mn1.apply_shifts(shifts)
# #mn1=cb.movie(np.transpose(np.array(Y_n),[2,0,1]),fr=30)
# mn=cb.concatenate([mn1,mn2],axis=1)
# mn.play(gain=5.,magnification=4,backend='opencv',fr=30)
#%%
t1 = time()
file_res=cb.motion_correct_parallel(fnames[:-3],fr=30,template=None,margins_out=0,max_shift_w=45, max_shift_h=45,backend='ipyparallel',apply_smooth=True)
t2=time()-t1
print t2
#%%
all_movs=[]
for f in file_res:
with np.load(f+'npz') as fl:
pl.subplot(1,2,1)
pl.imshow(fl['template'],cmap=pl.cm.gray)
pl.subplot(1,2,2)
pl.plot(fl['shifts'])
all_movs.append(fl['template'][np.newaxis,:,:])
pl.pause(2)
pl.cla()
#%%
all_movs=cb.movie(np.concatenate(all_movs,axis=0),fr=10)
n_processes = 112#np.maximum(psutil.cpu_count() - 2,1) # roughly number of cores on your machine minus 1
#%%
#low_SNR=False
#if low_SNR:
# N=1000
# mn1=m.copy().bilateral_blur_2D(diameter=5,sigmaColor=10000,sigmaSpace=0)
#
# mn1,shifts,xcorrs, template=mn1.motion_correct()
# mn2=mn1.apply_shifts(shifts)
# #mn1=cb.movie(np.transpose(np.array(Y_n),[2,0,1]),fr=30)
# mn=cb.concatenate([mn1,mn2],axis=1)
# mn.play(gain=5.,magnification=4,backend='opencv',fr=30)
#%%
t1 = time()
file_res=cb.motion_correct_parallel(fnames,fr=30,template=None,margins_out=0,max_shift_w=45, max_shift_h=45,backend='SLURM',apply_smooth=True)
t2=time()-t1
print t2
#%%
all_movs=[]
for f in glob.glob(base_folder+'*.hdf5'):
print f
with np.load(f[:-4]+'npz') as fl:
# pl.subplot(1,2,1)
# pl.imshow(fl['template'],cmap=pl.cm.gray)
# pl.subplot(1,2,2)
# pl.plot(fl['shifts'])
all_movs.append(fl['template'][np.newaxis,:,:])
# pl.pause(2)
# pl.cla()
#%%
idx_start=[i for i in xrange(len(fnames[0])) if fnames[0][i] != fnames[- 1][i]][0]
base_name=fnames[0][:idx_start]
#%%
#low_SNR=False
#if low_SNR:
# N=1000
# mn1=m.copy().bilateral_blur_2D(diameter=5,sigmaColor=10000,sigmaSpace=0)
#
# mn1,shifts,xcorrs, template=mn1.motion_correct()
# mn2=mn1.apply_shifts(shifts)
# #mn1=cb.movie(np.transpose(np.array(Y_n),[2,0,1]),fr=30)
# mn=cb.concatenate([mn1,mn2],axis=1)
# mn.play(gain=5.,magnification=4,backend='opencv',fr=30)
#%%
t1 = time()
file_res=cb.motion_correct_parallel(fnames,fr=30,template=None,margins_out=0,max_shift_w=45, max_shift_h=45,dview=dview,apply_smooth=True)
t2=time()-t1
print t2
#%%
all_movs=[]
for f in fls:
idx=f.find('.')
with np.load(f[:idx+1]+'npz') as fl:
print f
# pl.subplot(1,2,1)
# pl.imshow(fl['template'],cmap=pl.cm.gray)
# pl.subplot(1,2,2)
all_movs.append(fl['template'][np.newaxis,:,:])
# pl.plot(fl['shifts'])
# pl.pause(.001)
# load movie
# for loading only a portion of the movie or only some channels
# you can use the option: subindices=range(0,1500,10)
m = cb.load(filename, fr=frameRate, subindices=slice(1,None,2))
print m.shape
m.save(filename_red)
m = cb.load(filename, fr=frameRate, subindices=slice(0,None,2))
m.save(filename_green)
return((filename_green,filename_red))
#%%
file_names=glob.glob('*.tif')
file_names.sort()
print file_names
#%%
res=map(separate_channels,file_names)
#%%
greens=[a for a,b in res]
#%%
file_res=cb.motion_correct_parallel(greens,fr=30,template=None,margins_out=0,max_shift_w=45, max_shift_h=45,dview=None,apply_smooth=True,save_hdf5=True)
#%% run a second time since it gives better results
file_res=cb.motion_correct_parallel(greens,fr=30,template=None,margins_out=0,max_shift_w=25, max_shift_h=25,dview=None,apply_smooth=True,save_hdf5=True)
#%%
m=cb.load(file_res[2]+'hdf5',fr=30)
m.resize(1,1,.2).play(backend='opencv',gain=3.,fr=100)
#%%
for f in file_res:
m=cb.load(f+'hdf5',fr=30)
m.save(f[:-1]+'_mc.tif')
if is_interactive:
pl.imshow(template,cmap=pl.cm.gray,vmax=100)
np.savez(base_name+'-template_total.npz',template_each=template_each, all_movs_each=np.array(all_movs_each),movie_names=movie_names)
#%
if is_interactive:
for idx,mov in enumerate(all_movs_each):
mov.play(backend='opencv',gain=50.,fr=100)
# mov.save(str(idx)+'sam_example.tif')
#%
#%
file_res=[]
for template,fn in zip(template_each,movie_names):
print fn
file_res.append(cb.motion_correct_parallel(fn,30*downsample_factor,dview= dview,template=template,margins_out=0,max_shift_w=35, max_shift_h=35,remove_blanks=False))
#%
if is_interactive:
for f1 in file_res:
for f in f1:
with np.load(f+'npz') as fl:
pl.subplot(1,2,1)
pl.cla()
pl.imshow(fl['template'],cmap=pl.cm.gray)
pl.subplot(1,2,2)
pl.plot(fl['shifts'])
pl.pause(0.001)
pl.cla()
#%
print 'Using '+ str(len(c)) + ' processes'
dview=c[:len(c)]
#%% FOR LOADING ALL TIFF FILES IN A FILE AND SAVING THEM ON A SINGLE MEMORY MAPPABLE FILE
#fnames=[]
#fnames=glob.glob('*.tif')
#fnames.sort()
fnames=['file_name.tif'] # you ahev to eliminate the first element, it cannot be loaded!
print fnames
fnames=fnames
#%%
m=cb.load_movie_chain(fnames,fr=30)
#%%
m.play(backend='opencv',gain=3.,fr=50)
#%%
apply_smooth=False # set to true if SNR too low. Slower but might work better. In my hand ot worked out just fine!
m_mc=cb.motion_correct_parallel([m], fr=30, template=None, margins_out=0, max_shift_w=25, max_shift_h=25, remove_blanks=True, apply_smooth=apply_smooth, dview=None, save_hdf5=False)
m_mc=m_mc[0]
#%%
m_mc.resize(1,1,.2).play(backend='opencv',gain=5.,fr=10)
#%%
m_mc.save('all_mov.hdf5')
#%% in what follows there is some redundancy only due to the fact that this is meant to run oin parallel for many datasets... it can be done much faster.
#idx_x=slice(12,500,None)
#idx_y=slice(12,500,None)
#idx_xy=(idx_x,idx_y)
final_frate=30
downsample_factor=1 # use .2 or .1 if file is large and you want a quick answer
final_frate=final_frate*downsample_factor
idx_xy=None
base_name='Yr'
name_new=cse.utilities.save_memmap_each(['all_mov.hdf5'], dview=None,base_name=base_name, resize_fact=(1, 1, downsample_factor), remove_init=0,idx_xy=idx_xy )
