def main(args):
logfile = args['logfile']
directory = args['directory'] # full fly func path
behavior = args['behavior']
printlog = getattr(flow.Printlog(logfile=logfile), 'print_to_log')
#brain_path = os.path.join(directory, 'brain_zscored_green.nii')
brain_path = os.path.join(directory,
'brain_zscored_green_high_pass_masked.nii')
brain = np.asarray(nib.load(brain_path).get_data(), dtype='float32')
timestamps = bbb.load_timestamps(os.path.join(directory, 'imaging'))
fictrac_raw = bbb.load_fictrac(os.path.join(directory, 'fictrac'))
resolution = 10 #desired resolution in ms
expt_len = 1000 * 30 * 60
fps = 50 #of fictrac camera
fictrac_interp = interp_fictrac(fictrac_raw, fps, resolution, expt_len,
timestamps, behavior)
xnew = np.arange(0, expt_len, resolution)
printlog("Performing Correlation on {}; behavior: {}".format(
brain_path, behavior))
corr_brain = np.zeros((256, 128, 49))
for z in range(49):
for i in range(256):
for j in range(128):
corr_brain[i, j,
z] = scipy.stats.pearsonr(fictrac_interp,
brain[i, j, z, :])[0]
corr_directory = os.path.join(directory, 'corr')
if not os.path.exists(corr_directory):
os.mkdir(corr_directory)
save_file = os.path.join(corr_directory,
'20201020_corr_{}.nii'.format(behavior))
nib.Nifti1Image(corr_brain, np.eye(4)).to_filename(save_file)
printlog("Saved {}".format(save_file))
def main(directory):
### Load PCA
save_file = os.path.join(directory, 'pca', 'scores_(spatial).npy')
pca_spatial = np.load(save_file)
save_file = os.path.join(directory, 'pca', 'loadings_(temporal).npy')
pca_loadings = np.load(save_file)
print('pca_loadings_shape: {}'.format(pca_loadings.shape))
### Load timestamps
timestamps = bbb.load_timestamps(os.path.join(directory, 'imaging'))
### Load fictrac
fictrac_raw = bbb.load_fictrac(os.path.join(directory, 'fictrac'))
fictrac = bbb.smooth_and_interp_fictrac(fictrac_raw,
fps=50,
resolution=10,
expt_len=1000 * 30 * 60,
behavior='dRotLabY',
timestamps=timestamps)
### Fit model
num_pcs = 100
Y_glm = fictrac
X_glm = pca_loadings[:, :num_pcs]
model = LassoCV().fit(X_glm, Y_glm)
score = model.score(X_glm, Y_glm)
brain_map = np.tensordot(model.coef_,
pca_spatial[:num_pcs, :, :, :],
axes=1)
pca_glm_directory = os.path.join(directory, 'pca_glm')
if not os.path.exists(pca_glm_directory):
os.mkdir(pca_glm_directory)
save_file = os.path.join(pca_glm_directory, 'forward.nii')
bbb.save_brain(save_file, brain_map)
def load_timestamps(self):
self.timestamps = bbb.load_timestamps(
os.path.join(self.dir, 'imaging'))
def main(args):
logfile = args['logfile']
directory = args['directory'] # full fly func path
pca_subfolder = args['pca_subfolder']
glm_date = args['glm_date']
printlog = getattr(flow.Printlog(logfile=logfile), 'print_to_log')
### Load PCA ###
pca_directory = os.path.join(directory, 'pca', pca_subfolder)
printlog("performing glm on {}".format(pca_directory))
file = os.path.join(pca_directory, 'scores_(spatial).npy')
pca_spatial = np.load(file)
file = os.path.join(pca_directory, 'loadings_(temporal).npy')
pca_loadings = np.load(file)
### Load Fictrac and Timstamps###
timestamps = bbb.load_timestamps(os.path.join(directory, 'imaging'))
fictrac_raw = bbb.load_fictrac(os.path.join(directory, 'fictrac'))
### Prepare Fictrac ###
resolution = 100 #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] = bbb.smooth_and_interp_fictrac(fictrac_raw,
fps,
resolution,
expt_len,
behavior,
timestamps=timestamps,
smoothing=51)
fictrac[short] = fictrac[short] / np.std(fictrac[short])
xnew = np.arange(0, expt_len, resolution)
### Fit GLM ###
Y_glm = {}
Y_glm['Y'] = fictrac['Y'].copy()
Y_glm['Z'] = np.abs(fictrac['Z'].copy())
models = {}
num_pcs = 1000
behaviors = ['Y', 'Z']
for behavior in behaviors:
t0 = time.time()
models[behavior] = {'num_pcs': num_pcs, 'model': LassoCV()}
X_glm = pca_loadings[:, :num_pcs]
models[behavior]['model'].fit(X_glm, Y_glm[behavior])
models[behavior]['score'] = models[behavior]['model'].score(
X_glm, Y_glm[behavior])
### Construct Spatial Map ###
coef = models[behavior]['model'].coef_
spatial_map = np.tensordot(coef, pca_spatial[:1000, :, :, :], axes=1)
### Save map ###
glm_directory = os.path.join(directory, 'glm')
if not os.path.exists(glm_directory):
os.mkdir(glm_directory)
save_file = os.path.join(glm_directory,
'{}_{}.nii'.format(glm_date, behavior))
nib.Nifti1Image(spatial_map, np.eye(4)).to_filename(save_file)
### Save scores ###
score_file = os.path.join(glm_directory,
'{}_score_{}.txt'.format(glm_date, behavior))
with open(score_file, "a") as f:
f.write("{}:{}".format(behavior, models[behavior]['score']))
