def main(args):
logfile = args['logfile']
directory = args[
'directory'] # directory will be a full path to a func/fictrac folder
width = 120
printlog = getattr(brainsss.Printlog(logfile=logfile), 'print_to_log')
fictrac_raw = load_fictrac(directory)
#fly = os.path.split(os.path.split(directory)[0])[1]
#expt = os.path.split(directory)[1]
full_id = ', '.join(directory.split('/')[-3:-1])
resolution = 10 #desired resolution in ms
expt_len = 1000 * 30 * 60
fps = 50 #of fictrac camera
behaviors = ['dRotLabY', 'dRotLabZ']
fictrac = {}
for behavior in behaviors:
if behavior == 'dRotLabY': short = 'Y'
elif behavior == 'dRotLabZ': short = 'Z'
fictrac[short] = smooth_and_interp_fictrac(fictrac_raw, fps,
resolution, expt_len,
behavior)
xnew = np.arange(0, expt_len, resolution)
make_2d_hist(fictrac, directory, full_id, save=True, fixed_crop=True)
make_velocity_trace(fictrac, directory, full_id, xnew, save=True)
def main(args):
logfile = args['logfile']
directory = args[
'directory'] # directory will be a full path to either an anat/imaging folder or a func/imaging folder
files = args['files']
width = 120
printlog = getattr(brainsss.Printlog(logfile=logfile), 'print_to_log')
#files = ['functional_channel_1', 'functional_channel_2', 'anatomy_channel_1', 'anatomy_channel_2']
for file in files:
try:
### make mean ###
brain = np.asarray(nib.load(os.path.join(directory, file +
'.nii')).get_data(),
dtype='uint16')
meanbrain = np.mean(brain, axis=-1)
### Save ###
save_file = os.path.join(directory, file + '_mean.nii')
aff = np.eye(4)
img = nib.Nifti1Image(meanbrain, aff)
img.to_filename(save_file)
fly_func_str = ('|').join(directory.split('/')[-3:-1])
fly_print = directory.split('/')[-3]
func_print = directory.split('/')[-2]
#printlog(f"COMPLETE | {fly_func_str} | {file} | {brain.shape} --> {meanbrain.shape}")
printlog(
F"meanbrn | COMPLETED | {fly_print} | {func_print} | {file} | {brain.shape} ===> {meanbrain.shape}"
)
print(brain.shape[-1]) ### IMPORTANT: for communication to main
except FileNotFoundError:
printlog(F"Not found (skipping){file:.>{width-20}}")
def main(args):
logfile = args['logfile']
directory = args['directory']
width = 120
printlog = getattr(brainsss.Printlog(logfile=logfile), 'print_to_log')
#################
### Load Data ###
#################
files = ['functional_channel_1', 'functional_channel_2']
data_mean = {}
for file in files:
full_file = os.path.join(directory, file + '.nii')
if os.path.exists(full_file):
brain = np.asarray(nib.load(full_file).get_data(), dtype='uint16')
data_mean[file] = np.mean(brain, axis=(0, 1, 2))
else:
printlog(F"Not found (skipping){file:.>{width-20}}")
##############################
### Output Bleaching Curve ###
##############################
plt.rcParams.update({'font.size': 24})
fig = plt.figure(figsize=(10, 10))
signal_loss = {}
for file in data_mean:
xs = np.arange(len(data_mean[file]))
color = 'k'
if file[-1] == '1': color = 'red'
if file[-1] == '2': color = 'green'
plt.plot(data_mean[file], color=color, label=file)
linear_fit = np.polyfit(xs, data_mean[file], 1)
plt.plot(np.poly1d(linear_fit)(xs),
color='k',
linewidth=3,
linestyle='--')
signal_loss[file] = linear_fit[0] * len(
data_mean[file]) / linear_fit[1] * -100
plt.xlabel('Frame Num')
plt.ylabel('Avg signal')
loss_string = ''
for file in data_mean:
loss_string = loss_string + file + ' lost' + F'{int(signal_loss[file])}' + '%\n'
plt.title(loss_string, ha='center', va='bottom')
# plt.text(0.5,0.9,
# loss_string,
# horizontalalignment='center',
# verticalalignment='center',
# transform=plt.gca().transAxes)
save_file = os.path.join(directory, 'bleaching.png')
plt.savefig(save_file, dpi=300, bbox_inches='tight')
def main(args):
logfile = args['logfile']
directory = args[
'directory'] # directory will be a full path to either an anat folder or a func folder
start = int(args['start'])
stop = int(args['stop'])
printlog = getattr(brainsss.Printlog(logfile=logfile), 'print_to_log')
moco_dir = os.path.join(directory, 'moco')
try:
master_path = os.path.join(directory, 'functional_channel_1.nii')
moving_path = os.path.join(directory, 'functional_channel_2.nii')
master_path_mean = os.path.join(directory,
'functional_channel_1_mean.nii')
# For the sake of memory, load only the part of the brain we will need.
master_brain = load_partial_brain(master_path, start, stop)
moving_brain = load_partial_brain(moving_path, start, stop)
mean_brain = ants.from_numpy(
np.asarray(nib.load(master_path_mean).get_data(), dtype='float32'))
except:
printlog('fuctional data not found; trying anatomical')
master_path = os.path.join(directory, 'anatomy_channel_1.nii')
moving_path = os.path.join(directory, 'anatomy_channel_2.nii')
master_path_mean = os.path.join(directory,
'anatomy_channel_1_mean.nii')
# For the sake of memory, load only the part of the brain we will need.
master_brain = load_partial_brain(master_path, start, stop)
moving_brain = load_partial_brain(moving_path, start, stop)
mean_brain = ants.from_numpy(
np.asarray(nib.load(master_path_mean).get_data(), dtype='float32'))
brainsss.motion_correction(master_brain,
moving_brain,
moco_dir,
printlog,
mean_brain,
suffix='_' + str(start))
def main(args):
logfile = args['logfile']
directory = args['directory'] # full fly func path
smooth = args['smooth']
colors = args['colors']
printlog = getattr(brainsss.Printlog(logfile=logfile), 'print_to_log')
#['red', 'green']
printlog('colors are: {}'.format(colors))
for color in colors:
brain_file = os.path.join(directory, 'moco',
'stitched_brain_{}.nii'.format(color))
if os.path.exists(brain_file):
t0 = time.time()
to_print = '/'.join(brain_file.split('/')[-4:])
printlog(f'Z-scoring{to_print:.>{120-9}}')
brain = np.asarray(nib.load(brain_file).get_data(), dtype='uint16')
# printlog("brain load duration: ({})".format(time.time()-t0))
# t0 = time.time()
printlog('smooth is {}'.format(smooth))
if smooth:
printlog('smoothing')
smoothed = gaussian_filter1d(brain,
sigma=200,
axis=-1,
truncate=1)
printlog("brain smoothed duration: ({})".format(time.time() -
t0))
t0 = time.time()
brain = brain - smoothed
printlog("brain subtracted duration: ({})".format(time.time() -
t0))
t0 = time.time()
zbrain_file = os.path.join(
directory, 'brain_zscored_{}_smooth.nii'.format(color))
else:
printlog('not smoothing')
zbrain_file = os.path.join(
directory, 'brain_zscored_{}.nii'.format(color))
# Z-score brain
brain_mean = np.mean(brain, axis=3)
# printlog("brain mean duration: ({})".format(time.time()-t0))
# t0 = time.time()
brain_std = np.std(brain, axis=3)
# printlog("brain std duration: ({})".format(time.time()-t0))
# t0 = time.time()
brain = (brain - brain_mean[:, :, :, None]) / brain_std[:, :, :,
None]
# printlog("brain zscored duration: ({})".format(time.time()-t0))
# t0 = time.time()
# Save brain
#printlog('Saving {}'.format(zbrain_file))
aff = np.eye(4)
img = nib.Nifti1Image(brain, aff)
img.to_filename(zbrain_file)
#########################
### Setup preferences ###
#########################
width = 120 # width of print log
flies = ['fly_001'] # set to None, or a list of fly dirs in dataset_path
nodes = 2 # 1 or 2
nice = True # true to lower priority of jobs. ie, other users jobs go first
#####################
### Setup logging ###
#####################
logfile = './logs/' + time.strftime("%Y%m%d-%H%M%S") + '.txt'
printlog = getattr(brainsss.Printlog(logfile=logfile), 'print_to_log')
sys.stderr = brainsss.Logger_stderr_sherlock(logfile)
###################
### Setup paths ###
###################
#CHANGE THESE PATHS
scripts_path = "/home/users/brezovec/projects/brainsss/scripts"
com_path = "/home/users/brezovec/projects/brainsss/scripts/com"
#change this path to your oak directory, something like /oak/stanford/groups/trc/data/Brezovec/data
dataset_path = "/home/users/brezovec/projects/brainsss/demo_data"
###################
### Print Title ###
def main(args):
logfile = args['logfile']
directory = args['directory'] # moco full path
dirtype = 'func'
printlog = getattr(brainsss.Printlog(logfile=logfile), 'print_to_log')
printlog('\nStitcher started for {}'.format(directory))
######################
### Get file names ###
######################
colors = ['red', 'green']
files = {}
for color in colors:
files[color] = []
for file in os.listdir(directory):
if '.nii' in file and color in file:
files[color].append(os.path.join(directory, file))
brainsss.sort_nicely(files[color])
#####################
### Stitch brains ###
#####################
for color in colors:
if len(files[color]) > 0:
brains = []
for brain_file in files[color]:
brain = np.asarray(nib.load(brain_file).get_data(), dtype=np.uint16)
# Handle edgecase of single volume brain
if len(np.shape(brain)) == 3:
brain = brain[:,:,:,np.newaxis]
#print('shape of partial brain: {}'.format(np.shape(brain)))
brains.append(brain)
#print('brains len: {}'.format(len(brains)))
stitched_brain = np.concatenate(brains, axis=-1)
printlog('Stitched brain shape: {}'.format(np.shape(stitched_brain)))
save_file = os.path.join(directory, 'stitched_brain_{}.nii'.format(color))
printlog(f'Saving {save_file}')
aff = np.eye(4)
img = nib.Nifti1Image(stitched_brain, aff)
img.to_filename(save_file)
stitched_brain = None
# delete partial brains
[os.remove(file) for file in files[color]]
##########################
### Stitch moco params ###
##########################
motcorr_param_files = []
for item in os.listdir(directory):
if '.npy' in item:
file = os.path.join(directory, item)
motcorr_param_files.append(file)
brainsss.sort_nicely(motcorr_param_files)
motcorr_params = []
for file in motcorr_param_files:
motcorr_params.append(np.load(file))
if len(motcorr_params) > 0:
stitched_params = np.concatenate(motcorr_params, axis=0)
save_file = os.path.join(directory, 'motcorr_params_stitched')
np.save(save_file, stitched_params)
[os.remove(file) for file in motcorr_param_files]
xml_dir = os.path.join(os.path.split(directory)[0], 'imaging')
#save_motion_figure(stitched_params, xml_dir, directory, dirtype)
else:
printlog('Empty motcorr params - skipping saving moco figure.')