def projpvalonatlas(atlas, pars, ipsi, tt_pval, outdir):
# project onto labels
nii = nib.load('%s' % atlas)
img = nii.get_data()
# replace intensities with p-values
for p, par in enumerate(pars):
newimg = np.ones(img.shape)
for t, ttx in enumerate(tt_pval[p]):
orgipsi = ipsi[t]
idxip = (img == orgipsi)
newimg[idxip] = tt_pval[p][t]
# # if want both sides
# orgcont = contra[w]
# idxcont = (img == orgcont)
# newimg[idxcont] = tt_pval[p][w]
# save new nifti
mat = np.eye(4) * 0.025
mat[3, 3] = 1
newnii = nib.Nifti1Image(newimg, mat)
niiname = '%s/%s_paired_ttest_pval.nii.gz' % (outdir, pars[p])
nib.save(newnii, niiname)
# ort out niftis
with add_paths():
for par in pars:
call(["c3d", "%s/%s_paired_ttest_pval.nii.gz" % (outdir, par), "-orient", "ASR", "-o",
"%s/%s_paired_ttest_pval.nii.gz" % (outdir, par)])
def main(args):
starttime = datetime.now()
parser = parsefn()
pl, hemi, res = parse_inputs(parser, args)
# load annotations
print("Reading ARA annotation with %s hemispheres and %d voxel size" %
(hemi, res))
miracl_home = os.environ['MIRACL_HOME']
nii = '%s/atlases/ara/annotation/annotation_hemi_%s_%dum.nii.gz' % (
miracl_home, hemi, res)
img = nib.load(nii)
data = img.get_data()
# load structure graph
print("Reading ARA ontology structure_graph")
arastrctcsv = "%s/atlases/ara/ara_mouse_structure_graph_hemi_split.csv" % miracl_home
aragraph = pd.read_csv(arastrctcsv)
# get lbls
lbls = getalllbls(data)
# loop over intensities
parentdata = np.copy(data)
print("Computing parent labels at parent-level/generation %d" % pl)
parentdata = getlblparent(aragraph, lbls, pl, parentdata, lblsplit,
maxannotlbl)
vx = img.header.get_zooms()[0]
orgname = basename(nii).split('.')[0]
outnii = '%s_parent-level_%s.nii.gz' % (orgname, pl)
saveniiparents(parentdata, vx, outnii)
# orient
with add_paths():
call([
"c3d",
"%s" % outnii, "-orient", "ASR", "-type", "ushort", "-o",
"%s" % outnii
])
# set origin
# aratemplate = "%s/atlases/ara/template/average_template_50um.nii.gz" % miracl_home
# aranii = nib.load('%s' % aratemplate)
#
# sform = aranii.get_sform()
# qx = sform[0,3]
# qy = sform[1,3]
# qz = sform[2,3]
# call(["c3d", "%s" % outnii, "-origin", "-11.4x0x0mm", "-o", "%s" % outnii])
print(
"\n Grand-parent labels generation done in %s ... Have a good day!\n" %
(datetime.now() - starttime))
def main(args):
starttime = datetime.now()
parser = parsefn()
d, inlbls, hemi, res = parse_inputs(parser, args)
miracl_home = os.environ['MIRACL_HOME']
if inlbls == "Allen":
# load annotations
print("Reading ARA annotation with %s hemispheres and %d voxel size" % (hemi, res))
nii = '%s/atlases/ara/annotation/annotation_hemi_%s_%dum.nii.gz' % (miracl_home, hemi, res)
else:
print("Reading input labels")
nii = inlbls
img = nib.load(nii)
data = img.get_data()
# load structure graph
print("Reading ARA ontology structure_graph")
arastrctcsv = "%s/atlases/ara/ara_mouse_structure_graph_hemi_split.csv" % miracl_home
aragraph = pd.read_csv(arastrctcsv)
# get lbls
lbls = getalllbls(data)
# loop over intensities
parentdata = np.copy(data)
print("Computing parent labels at depth %d" % d)
pastparents = getpastlabelsdepth(aragraph, lbls, d, lblsplit, maxannotlbl)
for pastlbl, pastparent in pastparents.iteritems():
replacechildren(data, parentdata, pastlbl, pastparent)
vx = img.header.get_zooms()[0]
orgname = basename(nii).split('.')[0]
outnii = '%s_depth_%s.nii.gz' % (orgname, d)
saveniiparents(parentdata, vx, outnii)
if inlbls == "Allen":
# orient
with add_paths():
call(["c3d", "%s" % outnii, "-orient", "ASR", "-type", "ushort", "-o", "%s" % outnii])
call(["c3d", "%s" % outnii, "-origin", "-11.4x0x0mm", "-o", "%s" % outnii])
print ("\n Parent labels at depth done in %s ... Have a good day!\n" % (datetime.now() - starttime))
def main(args=None):
""" Main command line interface (CLI) call. Parse all arguments, perform required action.
If no commands are passed, return to GUI."""
if args is None:
args = sys.argv[1:]
# set miracl home
if 'MIRACL_HOME' not in os.environ:
cli_file = os.path.realpath(__file__)
miracl_dir = Path(cli_file).parents[0]
os.environ['MIRACL_HOME'] = '%s' % miracl_dir
parser = get_parser()
argcomplete.autocomplete(parser)
args = parser.parse_args(args)
with depends_manager.add_paths():
args.func(parser, args)
def main(args):
starttime = datetime.now()
# parse in args
parser = parsefn()
lbl, trans, projmet = parse_inputs(parser, args)
[cutoff, miracl_home, annot_csv, exclude] = initialize()
mcc = MouseConnectivityCache(
manifest_file=
'%s/connect/connectivity_exps/mouse_connectivity_manifest.json' %
miracl_home)
# Load all injection experiments from Allen api
all_experiments = mcc.get_experiments(dataframe=True)
# Filter to only wild-type strain
if trans:
print("\n Searching for experiments with the %s mouse line" % trans)
projexps = all_experiments[all_experiments['transgenic-line'] == "%s" %
trans]
else:
print(
"\n Searching for experiments with the wild-type (C57BL/6J) strain"
)
# wild-type
projexps = all_experiments[all_experiments['strain'] == "C57BL/6J"]
# no transgenic mice
projexps = projexps[projexps['transgenic-line'] == ""]
# ---------------
# Check if labels have injection exps
print(
"\n Checking if labels have injection experiments in the connectivity search"
)
inj_exp = projexps[projexps['structure-abbrev'] == lbl].id.index[0]
while inj_exp is None:
pid = annot_csv.parent_structure_id[annot_csv.id == lbl].values[0]
inj_exp = projexps[projexps['structure-id'] == pid].id.index[0]
# ---------------
# Get projection density
projd = getprojden(mcc, inj_exp)
# lbl_abrv = annot_csv[annot_csv['id'] == lbl]['acronym'].values[0]
# lbl_abrv = lbl_abrv[1:] # drop 1st char
print(
"\n Downloading projection density volume for experiment %d of lbl %s"
% (inj_exp, lbl))
outpd = '%s_exp%s_projection_density_image.nii.gz' % (lbl, inj_exp)
outtif = '%s_exp%s_projection_density_image.tif' % (lbl, inj_exp)
# outind = '%s_injection_density.nii.gz' % experiment_id
# outdm = '%s_binary_mask.nii.gz' % experiment_id
vx = 0.025
savenii(projd, vx, outpd)
savetiff(projd, outtif)
# orient
with add_paths():
call(["c3d", "%s" % outpd, "-orient", "ASR", "-o", "%s" % outpd])
# savenii(ind, vx, outind)
# savenii(dm, vx, outdm)
# ---------------
# Get connectivity graph
# query structure connectivity from Allen API
print(
"\n Quering structural connectivity of injection labels in the Allen API & sorting by %s"
% projmet)
[all_connect_ids, all_norm_proj] = query_connect(inj_exp, cutoff, exclude,
mcc, projmet)
# save csv
export_connect_abv = saveconncsv(all_connect_ids, annot_csv, lbl, inj_exp,
projmet)
# compute & save proj map
exportprojmap(all_norm_proj, export_connect_abv, lbl, inj_exp, projmet)
print(
"\n Downloading connectivity graph & projection map done in %s ... Have a good day!\n"
% (datetime.now() - starttime))
def main(args):
# parse in args
parser = parsefn()
invol, lbls, outfile, sort, hemi, label_depth = parse_inputs(parser, args)
# extract stats
print(" Extracting stats from input volume using registered labels ...\n")
# subprocess.check_call('ImageIntensityStatistics 3 %s %s > %s' % (invol, lbls, outfile), shell=True,
# stdout=subprocess.PIPE,
# stderr=subprocess.PIPE)
with add_paths():
subprocess.check_call('c3d %s %s -lstat > %s' % (invol, lbls, outfile), shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
# read fwf
out_stats = pd.read_fwf('%s' % outfile)
# read Allen ontology
miracl_home = os.environ['MIRACL_HOME']
# combined or split labels
if hemi == "combined":
annot_csv = pd.read_csv('%s/atlases/ara/ara_mouse_structure_graph_hemi_combined.csv' % miracl_home)
else:
annot_csv = pd.read_csv('%s/atlases/ara/ara_mouse_structure_graph_hemi_split.csv' % miracl_home)
# extract labels at certain depth only
if label_depth is not None:
annot_csv = annot_csv[annot_csv.depth == label_depth]
# Add label Name, Abrv, PathID
# make dic
name_dict = annot_csv.set_index('id')['name'].to_dict()
acronym_dict = annot_csv.set_index('id')['acronym'].to_dict()
pathid_dict = annot_csv.set_index('id')['structure_id_path'].to_dict()
parent_dict = annot_csv.set_index('id')['parent_structure_id'].to_dict()
# replace label info
out_stats['name'] = out_stats.LabelID.replace(name_dict)
out_stats['acronym'] = out_stats.LabelID.replace(acronym_dict)
out_stats['parent'] = out_stats.LabelID.replace(parent_dict)
out_stats['pathid'] = out_stats.LabelID.replace(pathid_dict)
# sort data-frame
out_stats = out_stats.sort_values([sort], ascending=False)
# remove background
out_stats = out_stats[out_stats['LabelID'] != 0]
# re-oder columns with info then sorted column of choice
cols = ['LabelID', 'acronym', 'name', 'parent', sort]
df_cols = out_stats.columns.values
all_cols = np.hstack([cols, df_cols])
_, idx = np.unique(all_cols, return_index=True)
columns = all_cols[np.sort(idx)]
out_stats = out_stats[columns]
# remove labels not in allen graph (prob interp errors!)
out_stats = out_stats[~out_stats.name.apply(lambda x: np.isreal(x))]
# save to csv
out_stats.to_csv('%s' % outfile, index=False)