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explicit setting of datatypes #2

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1c2886a
changed prt, tot, pct outputs to float32, PNR to test
steelec Apr 12, 2021
0a18afe
two more cases that should be float32 matrices
steelec Apr 13, 2021
4de3f67
update print statement
steelec Feb 26, 2022
5360013
more print issues
steelec Feb 26, 2022
9f4ca7b
another print statement
steelec Feb 26, 2022
99a27c7
more print statements
steelec Feb 26, 2022
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71 changes: 36 additions & 35 deletions TractREC/TractREC.py
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Original file line number Diff line number Diff line change
Expand Up @@ -312,7 +312,7 @@ def map_values_to_label_file(values_label_lut_csv_fname, label_img_fname,

for idx,index in enumerate(indices):
if VERBOSE:
print index, values[idx]
print("{}, {}".format(index, values[idx]))
d_out[d==index] = values[idx]

niiSave(out_mapped_label_fname,d_out,a,header=h)
Expand Down Expand Up @@ -837,19 +837,19 @@ def extract_quantitative_metric(metric_files, label_files, IDs=None, label_df=No
label_file = [s for s in label_files if ID in s] # make sure our label file is in the list that was passed
if len(metric_file) > 1:
print("")
print "OH SHIT, too many metric files. This should not happen!"
print("OH SHIT, too many metric files. This should not happen!")
elif len(metric_file) == 0:
print("")
print "OH SHIT, no matching metric file for: " + ID
print("OH SHIT, no matching metric file for: " + ID)
print("This subject has not been processed")
DATA_EXISTS = False

if len(label_file) > 1:
print("")
print "OH SHIT, too many label files. This should not happen!"
print("OH SHIT, too many label files. This should not happen!")
elif len(label_file) == 0:
print("")
print "OH SHIT, no matching label file for: " + ID
print("OH SHIT, no matching label file for: " + ID)
print("This subject has not been processed")
DATA_EXISTS = False
else: #files should already be ordered
Expand All @@ -866,11 +866,11 @@ def extract_quantitative_metric(metric_files, label_files, IDs=None, label_df=No
# is in the list that was passed
if len(thresh_mask_fname) > 1:
print("")
print "OH SHIT, too many threshold mask files. This should not happen!"
print("OH SHIT, too many threshold mask files. This should not happen!")

elif len(thresh_mask_fname) == 0:
print("")
print "OH SHIT, no matching threshold mask file for: " + ID
print("OH SHIT, no matching threshold mask file for: " + ID)
DATA_EXISTS = False
else:
thresh_mask_fname = thresh_mask_files[idx]
Expand All @@ -884,10 +884,10 @@ def extract_quantitative_metric(metric_files, label_files, IDs=None, label_df=No
ROI_mask_fname = [s for s in ROI_mask_files if
ID in s] # make sure our label file is in the list that was passed
if len(ROI_mask_fname) > 1:
print "OH SHIT, too many threshold mask files. This should not happen!"
print("OH SHIT, too many threshold mask files. This should not happen!")

elif len(ROI_mask_fname) == 0:
print "OH SHIT, no matching ROI mask file for: " + ID
print("OH SHIT, no matching ROI mask file for: " + ID)
DATA_EXISTS = False
else:
ROI_mask_fname = ROI_mask_files[idx]
Expand Down Expand Up @@ -986,7 +986,7 @@ def extract_quantitative_metric(metric_files, label_files, IDs=None, label_df=No
print("##=====================================================================##")
print("Darn! There is something wrong with: " + ID)
print("##=====================================================================##")
print ""
print("")
if metric is not 'data':
return df_4d
else:
Expand Down Expand Up @@ -1250,18 +1250,18 @@ def qcheck(user='stechr', delay=5 * 60):
import subprocess

print(time.strftime("%Y_%m_%d %H:%M:%S"))
print "=== start time ===",
print("=== start time ===")
start = time.time()
print(start)
try:
while len(subprocess.check_output(['qstat', '-u', user, '|', 'grep', user], shell=True)) > 0:
print ". ",
print(". ")
# print(len(subprocess.check_output(['qstat', '-u', 'tachr'],shell=True)))
time.sleep(delay)
except:
pass

print "=== end time ===",
print("=== end time ===")
print(time.time())
print(time.strftime("%Y_%m_%d %H:%M:%S"))
duration = time.time() - start
Expand Down Expand Up @@ -1318,7 +1318,7 @@ def tract_seg3(files, out_basename='', segmentation_index=None, CLOBBER=False, B

if not (os.path.isfile(seg_idx_fname)) or CLOBBER: # if the idx file exists, don't bother doing this again
if not BY_SLICE:
data_list = [nb.load(fn).get_data()[..., np.newaxis] for fn in files] # load all of the files
data_list = [nb.load(fn).get_fdata()[..., np.newaxis] for fn in files] # load all of the files
combined = np.concatenate(data_list, axis=-1) # concatenate all of the input data

combined = np.concatenate((np.zeros_like(data_list[0]), combined),
Expand All @@ -1334,8 +1334,8 @@ def tract_seg3(files, out_basename='', segmentation_index=None, CLOBBER=False, B
axis=-1) == 0] = 0 # where there is no difference between volumes, this should be the mask, set to 0

##%% create soft segmentation to show strength of the dominant tract in each voxel
seg_part = np.zeros_like(hard_seg)
seg_temp = np.zeros_like(hard_seg)
seg_part = np.zeros_like(hard_seg,dtype=np.float32)
seg_temp = np.zeros_like(hard_seg,dtype=np.float32)
seg_total = combined.sum(axis=-1)

idx = 1
Expand All @@ -1355,7 +1355,7 @@ def tract_seg3(files, out_basename='', segmentation_index=None, CLOBBER=False, B
hard_seg_indexed[hard_seg == idx] = seg_val
idx += 1
else:
print ""
print("")
print("====== YOU DID NOT ENTER THE CORRECT NUMBER OF VALUES FOR segmentation_index ======")
return

Expand Down Expand Up @@ -1383,19 +1383,20 @@ def tract_seg3(files, out_basename='', segmentation_index=None, CLOBBER=False, B
# seg_pct2[seg_pct2==-1]=0 #remove those -1s in the regions that used to be 0

##%%save
## we are assuming that
aff = nb.load(files[0]).affine
header = nb.load(files[0]).header

new_nii = nb.Nifti1Image(hard_seg.astype('uint32'), aff, header)
new_nii.set_data_dtype('uint32')
new_nii.to_filename(seg_idx_fname)

new_nii = nb.Nifti1Image(seg_total.astype('uint32'), aff, header)
new_nii.set_data_dtype('uint32')
new_nii = nb.Nifti1Image(seg_total.astype('float32'), aff, header)
new_nii.set_data_dtype('float32')
new_nii.to_filename(seg_tot_fname)

new_nii = nb.Nifti1Image(seg_part.astype('uint32'), aff, header)
new_nii.set_data_dtype('uint32')
new_nii = nb.Nifti1Image(seg_part.astype('float32'), aff, header)
new_nii.set_data_dtype('float32')
new_nii.to_filename(seg_prt_fname)

"""
Expand Down Expand Up @@ -1427,7 +1428,7 @@ def tract_seg3(files, out_basename='', segmentation_index=None, CLOBBER=False, B
hard_seg_full = np.zeros(data_shape)
seg_part_full = np.zeros(data_shape)
seg_total_full = np.zeros(data_shape)
seg_pct_full = np.zeros_like(hard_seg_full)
seg_pct_full = np.zeros_like(hard_seg_full,dtype=np.float32)

print("Data shape (single image): " + str(data_shape))
print("Slice: "),
Expand All @@ -1436,7 +1437,7 @@ def tract_seg3(files, out_basename='', segmentation_index=None, CLOBBER=False, B
for slice_idx in np.arange(0, data_shape[-1]):
print(slice_idx),

data_list = [nb.load(fn).get_data()[:, :, slice_idx, np.newaxis] for fn in
data_list = [nb.load(fn).get_fdata()[:, :, slice_idx, np.newaxis] for fn in
files] # load all of the files
combined = np.concatenate(data_list, axis=-1) # concatenate all of the input data
combined = np.concatenate((np.zeros_like(data_list[0]), combined),
Expand All @@ -1455,8 +1456,8 @@ def tract_seg3(files, out_basename='', segmentation_index=None, CLOBBER=False, B
seg_total_full[:, :, slice_idx] = combined.sum(axis=-1)

# declare empty matrices for this loop for partial and temp for calculating the partial (num of winning seg) file
seg_part = np.zeros_like(hard_seg)
seg_temp = np.zeros_like(hard_seg)
seg_part = np.zeros_like(hard_seg,dtype=np.float32)
seg_temp = np.zeros_like(hard_seg,dtype=np.float32)

idx = 1
for seg in files:
Expand All @@ -1477,7 +1478,7 @@ def tract_seg3(files, out_basename='', segmentation_index=None, CLOBBER=False, B
hard_seg_indexed[hard_seg == idx] = seg_val
idx += 1
else:
print ""
print("")
print("====== YOU DID NOT ENTER THE CORRECT NUMBER OF VALUES FOR segmentation_index ======")
return None

Expand All @@ -1497,12 +1498,12 @@ def tract_seg3(files, out_basename='', segmentation_index=None, CLOBBER=False, B
new_nii.set_data_dtype('uint32')
new_nii.to_filename(seg_idx_fname)

new_nii = nb.Nifti1Image(seg_total_full.astype('uint32'), aff, header)
new_nii.set_data_dtype('uint32')
new_nii = nb.Nifti1Image(seg_total_full.astype('float32'), aff, header)
new_nii.set_data_dtype('float32')
new_nii.to_filename(seg_tot_fname)

new_nii = nb.Nifti1Image(seg_part_full.astype('uint32'), aff, header)
new_nii.set_data_dtype('uint32')
new_nii = nb.Nifti1Image(seg_part_full.astype('float32'), aff, header)
new_nii.set_data_dtype('float32')
new_nii.to_filename(seg_prt_fname)

"""
Expand Down Expand Up @@ -1601,15 +1602,15 @@ def dki_dke_prep_data_bvals_bvecs(data_fname, bvals_file, bvecs_file, out_dir=No
os.path.basename(data_fname).split(".nii")[0] + "_bval" + str(bval) + ".nii.gz")
vol_list = str([i for i, v in enumerate(bvals) if v == bval]).strip('[]').replace(" ", "")
cmd_input = ['fslselectvols', '-i', data_fname, '-o', out_fname, '--vols=' + vol_list]
print ""
print " ".join(cmd_input)
print("")
print(" ".join(cmd_input))
cmd_txt.append(cmd_input)
if not os.path.isfile(out_fname) or CLOBBER:
if RUN_LOCALLY:
subprocess.call(cmd_input)
if bval == 0: # we mean this value if we are working with b=0 file
cmd_input = ['fslmaths', out_fname, '-Tmean', out_fname]
print " ".join(cmd_input)
print(" ".join(cmd_input))
cmd_txt.append(cmd_input)
if RUN_LOCALLY:
subprocess.call(cmd_input) # no CLOBBER check here, since we actually want to overwrite this file
Expand All @@ -1629,8 +1630,8 @@ def dki_dke_prep_data_bvals_bvecs(data_fname, bvals_file, bvecs_file, out_dir=No
cmd_input = ['fslmerge', '-t', out_fname]
for fname in fname_list:
cmd_input = cmd_input + [fname]
print ""
print " ".join(cmd_input)
print("")
print(" ".join(cmd_input))
cmd_txt.append(cmd_input)
if not os.path.isfile(out_fname) or CLOBBER:
if RUN_LOCALLY:
Expand Down
10 changes: 5 additions & 5 deletions TractREC/preprocessing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -348,10 +348,10 @@ def run_diffusion_kurtosis_estimator_dipy(data_fnames,bvals_fnames,bvecs_fnames,
create_dir(out_dir)
#check that we are pulling the correct files
if len(fname)>1 or len(bvals)>1 or len(bvecs)>1:
print "OH SHIT, too many possible files. This should not happen!"
print("OH SHIT, too many possible files. This should not happen!")
DATA_EXISTS=False
elif len(fname)<1 or len(bvals)<1 or len(bvecs)<1:
print "OH SHIT, no matching file for this ID: " + ID
print("OH SHIT, no matching file for this ID: " + ID)
DATA_EXISTS=False
else:
fname=fname[0] #break it out of the list of one
Expand Down Expand Up @@ -434,7 +434,7 @@ def run_amico_noddi_dipy(subject_root_dir,out_root_dir,subject_dirs=None,b0_thr=
code=["#!/usr/bin/python","","import sys","sys.path.append('{0}')".format(caller_path),"sys.path.append('{0}')".format(spams_path),"import spams","import amico"]
code.append("import spams" )
code.append("")
code.append("print amico.__file__")
code.append("print(amico.__file__)")
code.append("amico.core.setup()")
code.append("ae=amico.Evaluation('{subject_root_dir}','{ID}',output_path='{output_dir}')".format(subject_root_dir=subject_root_dir,ID=ID,output_dir=out_dir))
code.append("amico.util.fsl2scheme('{bvals_fname}', '{bvecs_fname}','{scheme_fname}',{bStep})".format(bvals_fname=bvals_fname, bvecs_fname=bvecs_fname,scheme_fname=scheme_fname,bStep=bStep))
Expand Down Expand Up @@ -547,7 +547,7 @@ def run_amico_noddi_dipy_v2(subject_root_dir,dwi_fnames,brain_mask_fnames,bvals_
code=["#!/usr/bin/python","","import sys","sys.path.append('{0}')".format(caller_path),"sys.path.append('{0}')".format(spams_path),"import spams","import amico"]
code.append("import spams" )
code.append("")
code.append("print amico.__file__")
code.append("print(amico.__file__)")
code.append("amico.core.setup()")
code.append("ae=amico.Evaluation('{subject_root_dir}','{ID}',output_path='{output_dir}')".format(subject_root_dir=subject_root_dir,ID=ID,output_dir=out_dir))
code.append("amico.util.fsl2scheme('{bvals_fname}', '{bvecs_fname}','{scheme_fname}',{bStep})".format(bvals_fname=bvals_fname, bvecs_fname=bvecs_fname,scheme_fname=scheme_fname,bStep=bStep))
Expand Down Expand Up @@ -610,7 +610,7 @@ def interp_discrete_hist_peaks(input_fname, output_fname=None, value_count_cutof

for idx, mybin in enumerate(left):
#vox_vals = np.unique(d[np.logical_and(d >= left[idx], d <= right[idx])])
print mybin
print(mybin)
# should only be one value, but loop over it just in case...
# loop across vox_vals
#for vox_val in vox_vals:
Expand Down
2 changes: 1 addition & 1 deletion TractREC/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -535,7 +535,7 @@ def combine_connectome_matrices_sparse_hdf5(connectome_files_list, connectome_fi
for row in lookup_row:
#for col in lookup_col:
dset[row, lookup_col] = np.ndarray.flatten(submat[row,:])
print idx
print(idx)
idx+=1

dset[lookup_row,lookup_col] = submat
Expand Down