def test_map():
"""Test contrib.tmap"""
with closing(StringIO()) as our_file:
a = range(9)
b = [i + 1 for i in a]
if sys.version_info[:1] < (3,):
assert tmap(lambda x: x + 1, a, file=our_file) == map(incr, a)
else:
gen = tmap(lambda x: x + 1, a, file=our_file)
assert gen != b
assert list(gen) == b
def make_dataset(self, files, dset):
dataset = data.Dataset(
list(
tmap(lambda d: data.Example.fromdict(d, self.example_fields),
chain(*map(lambda f: dset[f], files)),
mininterval=0.5)), self.dataset_fields)
for file in files:
del dset[file]
return dataset
def run(search_dir, glob="*.nii*"):
search_dir = Path(search_dir)
assert search_dir.is_dir()
fnames = list(map(str, search_dir.glob(glob)))
log.info("found %d files", len(fnames))
fnames = nsort(fnames)
res = imscroll(list(tmap(imread, fnames)))
plt.show()
return res
def run(self, workers=None):
# the sequence of seeds give you the nb of iterations. See also
# https://numpy.org/devdocs/reference/random/parallel.html
seeds = SEED_SEQ.spawn(self.n_draws)
if workers:
output = concurrent.process_map(self._run,
seeds,
max_workers=workers)
else:
output = list(tmap(self._run, seeds))
self.output = np.array(output)
def parallel_executor(func, iterable, **kwargs):
if "chunksize" in kwargs:
# Chunk size only applies to thread/process parallel executors
del kwargs["chunksize"]
return list(tmap(func, iterable, **kwargs))
from tqdm.contrib import tenumerate, tzip, tmap
import numpy as np
for _ in tenumerate(range(int(1e6)), desc="builtin enumerate"):
pass
for _ in tenumerate(np.random.random((999, 999)), desc="numpy.ndenumerate"):
pass
for _ in tzip(np.arange(1e6), np.arange(1e6) + 1, desc="builtin zip"):
pass
mapped = tmap(lambda x: x + 1, np.arange(1e6), desc="builtin map")
assert (np.arange(1e6) + 1 == list(mapped)).all()
def run(
dir_MRI="data/ALFA_PET",
dir_PET="data/ALFA_PET",
dir_RR="data/Atlas/CL_2mm",
outfile="data/ALFA_PET/Quant_realigned.csv",
glob_PET="*_PET.nii.gz",
glob_MRI="*_MRI.nii.gz",
):
"""
Args:
dir_MRI (str or Path): MRI directory
dir_PET (str or Path): PET directory
dir_RR (str or Path): Reference regions ROIs directory
(standard Centiloid RR from GAAIN Centioid website: 2mm, nifti)
outfile (str or Path): Output quantification file
Returns:
fname (list[str])
greyCerebellum (list[float])
wholeCerebellum (list[float])
wholeCerebellumBrainStem (list[float])
pons (list[float])
"""
# PET & MR images lists
s_PET_dir = list(tmap(gunzip, Path(dir_PET).glob(glob_PET), leave=False))
s_MRI_dir = list(tmap(gunzip, Path(dir_MRI).glob(glob_MRI), leave=False))
if len(s_PET_dir) != len(s_MRI_dir):
raise IndexError("Different number of PET and MR images")
eng = get_matlab()
dir_spm = fspath(Path(eng.which("spm")).parent)
for d_PET, d_MRI in tzip(s_PET_dir, s_MRI_dir):
with tic("Step 0: Reorient PET subject"):
eng.f_acpcReorientation(d_PET, nargout=0)
with tic("Step 0: Reorient MRI subject"):
eng.f_acpcReorientation(d_MRI, nargout=0)
with tic("Step 1: CorregisterEstimate"):
eng.f_1CorregisterEstimate(d_MRI, dir_spm, nargout=0)
# Check Reg
with tic("Step 2: CorregisterEstimate"):
eng.f_2CorregisterEstimate(d_MRI, d_PET, nargout=0)
# Check Reg
with tic("Step 3: Segment"):
eng.f_3Segment(d_MRI, dir_spm, nargout=0)
with tic("Step 4: Normalise"):
d_file_norm = fspath(
Path(d_MRI).parent / ("y_" + Path(d_MRI).name))
eng.f_4Normalise(d_file_norm, d_MRI, d_PET, nargout=0)
s_PET = list(
map(
fspath,
Path(dir_PET).glob("w" + (glob_PET[:-3] if glob_PET.lower().
endswith(".gz") else glob_PET))))
res = eng.f_Quant_centiloid(s_PET, fspath(dir_RR), nargout=5)
if outfile:
with open(outfile, "w") as fd:
f = csv_writer(fd)
f.writerow(("Fname", "GreyCerebellum", "WholeCerebellum",
"WholeCerebellumBrainStem", "Pons"))
f.writerows(zip(*res))
return res
