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Add more timing information #622

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merged 3 commits into from
Jan 4, 2021
Merged

Add more timing information #622

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bdbfae1
add more timing
36000 Dec 28, 2020
6fc5bef
pep8
36000 Dec 28, 2020
4bd0828
Use underscores to be safe
36000 Jan 4, 2021
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37 changes: 28 additions & 9 deletions AFQ/api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -520,6 +520,7 @@ def __init__(self,
# Initialize dict to store relevant timing information
timing_dict = {
"Tractography": 0,
"Registration_pre_align": 0,
"Registration": 0,
"Segmentation": 0,
"Cleaning": 0,
Expand Down Expand Up @@ -764,6 +765,7 @@ def _dti(self, row):
brain_mask_file = self._brain_mask(row)
mask = nib.load(brain_mask_file).get_fdata()

start_time = time()
if self.robust_tensor_fitting:
bvals, bvecs = read_bvals_bvecs(
row['bval_file'], row['bvec_file'])
Expand All @@ -783,6 +785,7 @@ def _dti(self, row):
OutlierRejection=False,
ModelURL=f"{DIPY_GH}reconst/dti.py")
afd.write_json(meta_fname, meta)
row['timing']['DTI'] = time() - start_time
return dti_params_file

def _dki_fit(self, row):
Expand All @@ -798,6 +801,7 @@ def _dki(self, row):
data, gtab, _ = self._get_data_gtab(row)
brain_mask_file = self._brain_mask(row)
mask = nib.load(brain_mask_file).get_fdata()
start_time = time()
dkf = dki_fit(gtab, data, mask=mask)
nib.save(nib.Nifti1Image(dkf.model_params, row['dwi_affine']),
dki_params_file)
Expand All @@ -808,6 +812,7 @@ def _dki(self, row):
OutlierRejection=False,
ModelURL=f"{DIPY_GH}reconst/dki.py")
afd.write_json(meta_fname, meta)
row['timing']['DKI'] = time() - start_time
return dki_params_file

def _csd(self, row, response=None, sh_order=None, lambda_=1, tau=0.1,
Expand All @@ -825,6 +830,7 @@ def _csd(self, row, response=None, sh_order=None, lambda_=1, tau=0.1,
data, gtab, _ = self._get_data_gtab(row)
brain_mask_file = self._brain_mask(row)
mask = nib.load(brain_mask_file).get_fdata()
start_time = time()
csdf = csd_fit(gtab, data, mask=mask,
response=response, sh_order=sh_order,
lambda_=lambda_, tau=tau, msmt=msmt)
Expand All @@ -841,6 +847,7 @@ def _csd(self, row, response=None, sh_order=None, lambda_=1, tau=0.1,
lambda_=lambda_,
tau=tau)
afd.write_json(meta_fname, meta)
row['timing']['CSD'] = time() - start_time
return csd_params_file

def _anisotropic_power_map(self, row):
Expand Down Expand Up @@ -1032,6 +1039,7 @@ def _reg_prealign(self, row):
row, '_prealign_from-DWI_to-MNI_xfm.npy')
if not op.exists(prealign_file):
reg_subject_img, _ = self._reg_img(self.reg_subject, True, row)
start_time = time()
_, aff = reg.affine_registration(
reg_subject_img.get_fdata(),
self.reg_template_img.get_fdata(),
Expand All @@ -1042,6 +1050,8 @@ def _reg_prealign(self, row):
row, '_prealign_from-DWI_to-MNI_xfm.json')
meta = dict(type="rigid")
afd.write_json(meta_fname, meta)
row['timing']['Registration_pre_align'] =\
row['timing']['Registration_pre_align'] + time() - start_time
return prealign_file

def _export_registered_b0(self, row):
Expand Down Expand Up @@ -1703,13 +1713,17 @@ def _plot_tract_profiles(self, row):

return fnames

def _export_timing(self, row):
df = pd.DataFrame.from_dict(
row["timing"],
'index',
columns=['Time (s)'])
def _export_timing(self, row, all_sub_sess=None):
timing_fname = self._get_fname(row, "_desc-timing.csv", True, True)
df.to_csv(timing_fname, index=True, index_label='step')
if not op.exists(timing_fname):
if all_sub_sess is not None:
row["timing"]["all_sub_sess"] = all_sub_sess
df = pd.DataFrame.from_dict(
row["timing"],
'index',
columns=['Time (s)'])

df.to_csv(timing_fname, index=True, index_label='step')

def _get_affine(self, fname):
return nib.load(fname).affine
Expand Down Expand Up @@ -2049,11 +2063,15 @@ def combine_profiles(self):
self.tract_profiles,
op.join(self.afq_path, 'tract_profiles.csv'))

def export_timing(self):
self.data_frame.apply(self._export_timing, axis=1)
def export_timing(self, all_sub_sess=None):
self.data_frame.apply(
self._export_timing,
axis=1,
all_sub_sess=all_sub_sess)

def export_all(self):
""" Exports all the possible outputs"""
start_time = time()
self.export_registered_b0()
self.get_template_xform()
self.export_bundles()
Expand All @@ -2065,7 +2083,8 @@ def export_all(self):
self.export_rois()
if len(self.tract_profiles) > 1:
self.combine_profiles()
self.export_timing()
all_sub_sess = time() - start_time
self.export_timing(all_sub_sess=all_sub_sess)

def upload_to_s3(self, s3fs, remote_path):
""" Upload entire AFQ derivatives folder to S3"""
Expand Down