def register_command(input, output, overwrite, url, method, size):
input = abspath(input)
output = input + '_registered' if output is None else abspath(output)
if isdir(output) and not overwrite:
error('directory already exists and overwrite is false')
return
elif isdir(output) and overwrite:
rmtree(output)
mkdir(output)
engine = setup_spark(url)
status('reading data from %s' % input)
if len(glob(join(input, '*.tif'))) > 0:
data = fromtif(input, engine=engine)
ext = 'tif'
elif len(glob(join(input, '*.tiff'))) > 0:
data = fromtif(input, ext='tiff', engine=engine)
ext = 'tif'
elif len(glob(join(input, '*.bin'))) > 0:
data = frombinary(input, engine=engine)
ext = 'bin'
else:
error('no tif or binary files found in %s' % input)
return
status('registering')
if method == 'normal':
newdata, shifts = register(data)
elif method == 'blocks':
newdata = register_blocks(data, size=(size, size))
elif method == 'piecewise':
newdata = register_blocks_piecewise(data, size=(size, size))
else:
error('registration method %s not recognized' % method)
if ext == 'tif':
newdata.totif(output, overwrite=overwrite)
elif ext == 'bin':
newdata.tobinary(output, overwrite=overwrite)
else:
error('extenstion %s not recognized' % ext)
metafiles = glob(join(input, 'meta*.json'))
if len(metafiles) > 0:
status('copying metadata')
for f in metafiles:
copy(f, output)
#shifts = DataFrame(shifts)
#shifts.to_csv(join(output, 'shifts.csv'))
success('registration complete')
def preprocess_command(input, output, bidi, amount, detrend, order, url, overwrite):
input = abspath(input)
output = input + '_preprocessed' if output is None else abspath(output)
if isdir(output) and not overwrite:
error('directory already exists and overwrite is false')
return
elif isdir(output) and overwrite:
rmtree(output)
mkdir(output)
engine = setup_spark(url)
status('reading data from %s' % input)
if len(glob(join(input, '*.tif'))) > 0:
data = fromtif(input, engine=engine)
ext = 'tif'
elif len(glob(join(input, '*.tiff'))) > 0:
data = fromtif(input, ext='tiff', engine=engine)
ext = 'tif'
elif len(glob(join(input, '*.bin'))) > 0:
data = frombinary(input, engine=engine)
ext = 'bin'
else:
error('no tif or binary files found in %s' % input)
return
if bidi:
status('start bidi correction')
if len(data.shape) > 4:
error('Data length %d currently not supported' % len(data.shape))
else:
newdata, amount = correct(data, amount=amount)
status('shifted %s pixels' % amount)
else:
newdata = data
if detrend:
status('start detrending')
newdata = newdata.map_as_series(lambda x: detrend_func(x, order))
if ext == 'tif':
newdata.totif(output, overwrite=overwrite)
elif ext == 'bin':
newdata.tobinary(output, overwrite=overwrite)
else:
error('extenstion %s not recognized' % ext)
metafiles = glob(join(input, 'meta*.json'))
if len(metafiles) > 0:
status('copying metadata')
for f in metafiles:
copy(f, output)
success('preprocessing complete')
def register_command(input, output, overwrite, url, method, size):
input = abspath(input)
output = input + '_registered' if output is None else abspath(output)
if isdir(output) and not overwrite:
error('directory already exists and overwrite is false')
return
elif isdir(output) and overwrite:
rmtree(output)
mkdir(output)
engine = setup_spark(url)
status('reading data from %s' % input)
if len(glob(join(input, '*.tif'))) > 0:
data = fromtif(input, engine=engine)
ext = 'tif'
elif len(glob(join(input, '*.tiff'))) > 0:
data = fromtif(input, ext='tiff', engine=engine)
ext = 'tif'
elif len(glob(join(input, '*.bin'))) > 0:
data = frombinary(input, engine=engine)
ext = 'bin'
else:
error('no tif or binary files found in %s' % input)
return
status('registering')
if method == 'normal':
newdata, shifts = register(data)
elif method == 'blocks':
newdata = register_blocks(data, size=(size, size))
elif method == 'piecewise':
newdata = register_blocks_piecewise(data, size=(size, size))
else:
error('registration method %s not recognized' % method)
if ext == 'tif':
newdata.totif(output, overwrite=overwrite)
elif ext == 'bin':
newdata.tobinary(output, overwrite=overwrite)
else:
error('extenstion %s not recognized' % ext)
metafiles = glob(join(input, 'meta*.json'))
if len(metafiles) > 0:
status('copying metadata')
for f in metafiles:
copy(f, output)
#shifts = DataFrame(shifts)
#shifts.to_csv(join(output, 'shifts.csv'))
success('registration complete')
def extract_command(input, output, diameter, method, url, overwrite):
input = abspath(input)
output = input + '_extracted' if output is None else abspath(output)
if isfile(join(output, 'regions-' + method + '.json')) and not overwrite:
error('file already exists and overwrite is false')
return
elif not isdir(output):
mkdir(output)
engine = setup_spark(url)
status('reading data from %s' % input)
if len(glob(join(input, '*.tif'))) > 0:
data = fromtif(input, engine=engine)
ext = 'tif'
elif len(glob(join(input, '*.tiff'))) > 0:
data = fromtif(input, ext='tiff', engine=engine)
ext = 'tif'
elif len(glob(join(input, '*.bin'))) > 0:
data = frombinary(input, engine=engine)
ext = 'bin'
else:
error('no tif or binary files found in %s' % input)
return
status('extracting')
if method == 'CC':
algorithm = CC(diameter=diameter, clip_limit=0.04, theshold=0.2, sigma_blur=1, boundary=(1,1))
unmerged = algorithm.fit(data)
model = unmerged.merge(0.1)
model = filter_shape(model, min_diameter = 0.7*diameter, max_diameter = 1.3*diameter, min_eccentricity = 0.2)
elif method == 'NMF':
algorithm = NMF(k=10, percentile=99, max_iter=50, overlap=0.1)
unmerged = algorithm.fit(data, chunk_size=(50,50), padding=(25,25))
model = unmerged.merge(overlap=0.20, max_iter=3, k_nearest=10)
else:
error('extraction method %s not recognized' % method)
model.save(join(output, 'regions-' + method + '.json'))
success('extraction complete')
def trace_command(input, output, regions, url, overwrite):
input = abspath(input)
output = input + '_traces' if output is None else abspath(output)
if isdir(output) and not overwrite:
error('directory already exists and overwrite is false')
return
elif isdir(output) and overwrite:
rmtree(output)
mkdir(output)
model = load(regions)
engine = setup_spark(url)
status('reading data from %s' % input)
if len(glob(join(input, '*.tif'))) > 0:
data = fromtif(input, engine=engine)
ext = 'tif'
elif len(glob(join(input, '*.tiff'))) > 0:
data = fromtif(input, ext='tiff', engine=engine)
ext = 'tif'
elif len(glob(join(input, '*.bin'))) > 0:
data = frombinary(input, engine=engine)
ext = 'bin'
else:
error('no tif or binary files found in %s' % input)
return
status('calculating traces')
traces = dff(data, model)
traces.tobinary(output, overwrite=overwrite)
metafiles = glob(join(input, 'meta*.json'))
if len(metafiles) > 0:
status('copying metadata')
for f in metafiles:
copy(f, output)
success('traces complete')
def trace_command(input, output, regions, url, overwrite):
input = abspath(input)
output = input + '_traces' if output is None else abspath(output)
if isdir(output) and not overwrite:
error('directory already exists and overwrite is false')
return
elif isdir(output) and overwrite:
rmtree(output)
mkdir(output)
model = load(regions)
engine = setup_spark(url)
status('reading data from %s' % input)
if len(glob(join(input, '*.tif'))) > 0:
data = fromtif(input, engine=engine)
ext = 'tif'
elif len(glob(join(input, '*.tiff'))) > 0:
data = fromtif(input, ext='tiff', engine=engine)
ext = 'tif'
elif len(glob(join(input, '*.bin'))) > 0:
data = frombinary(input, engine=engine)
ext = 'bin'
else:
error('no tif or binary files found in %s' % input)
return
status('calculating traces')
traces = dff(data, model)
traces.tobinary(output, overwrite=overwrite)
metafiles = glob(join(input, 'meta*.json'))
if len(metafiles) > 0:
status('copying metadata')
for f in metafiles:
copy(f, output)
success('traces complete')
def test_register_shape():
data = fromtif('test/resources/output')
registered, shifts = register(data)
assert registered.shape == (23, 464, 576)
assert shifts.shape == (23, 2)
def test_downsample_shape():
data = fromtif('test/resources/output')
result = downsample(data, ds=2, dt=2)
assert result.shape == (12, 232, 288)
def test_reference_shape():
data = fromtif('test/resources/output')
ref = reference(data)
assert ref.shape == (464, 576)
def test_convert_ground_truth():
data, meta = load('test/resources/input')
newdata, newmeta = convert(data, meta)
truth = fromtif('test/resources/output')
assert newdata.shape == truth.shape
assert allclose(newdata.clip(0, inf), truth)
def load(path, nplanes=None, engine=None):
data = fromtif(path, nplanes=nplanes, engine=engine)
return data
def load(path, nplanes=None, engine=None):
data = fromtif(path, nplanes=nplanes, engine=engine)
return data
def summarize_command(input, output, localcorr, mean, movie, ds, dt, size, url, overwrite):
input = abspath(input)
output = input + '_summary' if output is None else abspath(output)
engine = setup_spark(url)
status('reading data from %s' % input)
if len(glob(join(input, '*.tif'))) > 0:
data = fromtif(input, engine=engine)
ext = 'tif'
elif len(glob(join(input, '*.tiff'))) > 0:
data = fromtif(input, ext='tiff', engine=engine)
ext = 'tif'
elif len(glob(join(input, '*.bin'))) > 0:
data = frombinary(input, engine=engine)
ext = 'bin'
else:
error('no tif or binary files found in %s' % input)
return
if not isdir(output):
mkdir(output)
if mean:
if not isfile(join(output, 'mean.tif')) or overwrite:
status('summarizing-mean')
m = data.mean().toarray()
imsave(join(output, 'mean.tif'), m.clip(0, inf).astype('uint16'), plugin='tifffile', photometric='minisblack')
else:
warn('mean.tif already exists and overwrite is false')
if localcorr:
if type(size) == tuple:
name = 'localcorr-ds%s.tif' % ''.join(map(str,size))
else:
name = 'localcorr-ds%s.tif' % size
if not isfile(join(output, name)) or overwrite:
status('summarizing-localcorr')
if len(data.shape) == 4:
size = (1, size, size)
lc = data.localcorr(size)
imsave(join(output, name), lc.astype('float32'), plugin='tifffile', photometric='minisblack')
else:
warn('%s already exists and overwrite is false' % name)
if movie:
if type(ds) == tuple:
dsString = '-ds'.join(map(str,ds))
elif ds == None:
dsString = ''
else:
dsString = '-ds' + str(ds)
if dt == None:
dtString = ''
else:
dtString = '-dt' + str(dt)
if len(data.shape) == 4:
pString = '-p%s' % 0
else:
pString = ''
name = 'movie%s%s%s.mp4' % (dsString, dtString, pString)
if not isfile(join(output, name)) or overwrite:
status('summarizing-movie')
metafiles = glob(join(input, '*.json'))
if len(metafiles) == 0:
warn('no json metadata found in %s' % input)
meta = None
else:
with open(metafiles[0]) as fid:
meta = json.load(fid)
rate = meta['volumeRate']
ppum = meta['rois'][0]['npixels'][1]/meta['rois'][0]['size'][1]/1000
scale = 100*round(meta['rois'][0]['size'][1]*1.8)
if dt is not None and meta is not None:
rate = rate/dt
if ds is not None and meta is not None:
ppum = ppum/ds
if len(data.shape) == 4:
ds = (1, ds, ds)
result = downsample(data, ds=ds, dt=dt).toarray()
if dt == None:
dt = 0
writer = animation.FFMpegWriter(fps=15, metadata=dict(artist='Me'), bitrate=40000)
def animate(mv, name):
clim = 5*percentile(mv, 90)
img = mv[mv.shape[0]/2,:,:]
nframes = mv.shape[0]-dt
fig = plt.figure(figsize=[12, 12.0*img.shape[0]/img.shape[1]])
fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=None, hspace=None)
ax = plt.gca()
im = image(img, clim=(0, clim), ax=ax)
if meta is not None:
time = ax.text(.97*img.shape[1], .04*img.shape[0], '%.1f s' % 0, color='white', fontsize=22, ha='right', fontdict={'family': 'monospace'});
ax.plot([.04*img.shape[1], .04*img.shape[1]+scale*ppum], [.94*img.shape[0], .94*img.shape[0]], 'w', lw=2);
sclae = ax.text(.04*img.shape[1]+scale*ppum/2, .97*img.shape[0], '%d um' % scale, color='white', fontsize=22, ha='center', fontdict={'family': 'monospace'});
plt.xlim([0, img.shape[1]]);
plt.ylim([img.shape[0], 0]);
def update(f):
im.set_array(mv[dt/2+f])
if meta is not None:
time.set_text('%.1f s' % ((dt/2+f)/rate))
ani = animation.FuncAnimation(fig, update, nframes, blit=False)
ani.save(join(output, name), writer=writer)
if len(result.shape) == 4:
for plane in range(result.shape(1)):
pString = '-p%s' % plane
name = 'movie%s%s%s.mp4' % (dsString, dtString, pString)
animate(result[:,plane,:,:], name)
else:
animate(result, name)
else:
warn('%s already exists and overwrite is false' % name)
metafiles = glob(join(input, 'meta*.json'))
if len(metafiles) > 0:
status('copying metadata')
for f in metafiles:
copy(f, output)
success('summary complete')
