def get_body_ids_from_roi(roi,
server,
token,
hemibrain_neuron=True,
pre_threshold=0,
post_threshold=0,
total_threshold=0):
"""
Args:
roi (str): Neuropil abreviation from Neuprint
server (str): Neuprint server URL
token (str): Neuprint auth token
hemibrain_neuron (bool): Specifies whether to search
Hemibrain-Neuron of Hemibrain-Segment.
pre_threshold (int): Requires bodies meet threshold of presynaptic sites
in the ROI
post_threshold (int): Requires bodies meet threshold of postsynaptic sites
in the ROI
total_threshold (int): Requires bodies meet threshold of total synaptic sites
in the ROI
Returns:
Dataframe containing body IDs that innervate the ROI
"""
query_template = (
'MATCH (n:`{HEMIBRAIN}`)\n'
'WHERE n.{ROI}\n'
'RETURN n.bodyId AS ID, n.name AS NAME, n.roiInfo AS ROIINFO'
)
# Start Neuprint python client
client = Client(server, token)
client.fetch_version()
if hemibrain_neuron:
query = query_template.format(HEMIBRAIN='hemibrain-Neuron', ROI=roi)
else:
query = query_template.format(HEMIBRAIN='hemibrain-Segment', ROI=roi)
results = client.fetch_custom(query)
results['ROIINFO'] = results['ROIINFO'].apply(ast.literal_eval)
results['PRE'] = results['ROIINFO'].apply(lambda x: int(x[roi]['pre']))
results['POST'] = results['ROIINFO'].apply(lambda x: int(x[roi]['post']))
results = results[results['PRE'] + results['POST'] >= total_threshold]
results = results[results['PRE'] >= pre_threshold]
results = results[results['POST'] >= post_threshold]
return results[['ID', 'NAME', 'PRE', 'POST']].reset_index()
def test_members():
set_default_client(None)
assert default_client() is None
c = Client(NEUPRINT_SERVER, DATASET)
assert c.server == f'https://{NEUPRINT_SERVER}'
assert c.dataset == DATASET
assert default_client() is c
df = c.fetch_custom("MATCH (m:Meta) RETURN m.primaryRois as rois")
assert isinstance(df, pd.DataFrame)
assert df.columns == ['rois']
assert len(df) == 1
assert isinstance(df['rois'].iloc[0], list)
assert isinstance(c.fetch_available(), list)
assert isinstance(c.fetch_help(), str)
assert c.fetch_server_info() is True
assert isinstance(c.fetch_version(), str)
assert isinstance(c.fetch_database(), dict)
assert isinstance(c.fetch_datasets(), dict)
assert isinstance(c.fetch_db_version(), str)
assert isinstance(c.fetch_profile(), dict)
assert isinstance(c.fetch_token(), str)
assert isinstance(c.fetch_daily_type(), tuple)
assert isinstance(c.fetch_roi_completeness(), pd.DataFrame)
assert isinstance(c.fetch_roi_connectivity(), pd.DataFrame)
assert isinstance(c.fetch_roi_mesh('AB(R)'), bytes)
assert isinstance(c.fetch_skeleton(EXAMPLE_BODY), pd.DataFrame)
import os
import h5py
import neuprint
from neuprint import Client, queries, SegmentCriteria
from extract_labels_from_volume import extract_labels_from_volume
import numpy as np
c = Client(
'neuprint.janelia.org',
dataset='hemibrain:v1.1',
token=
'eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJlbWFpbCI6ImFuZHJldy5saW5Ac2hlZmZpZWxkLmFjLnVrIiwibGV2ZWwiOiJub2F1dGgiLCJpbWFnZS11cmwiOiJodHRwczovL2xoMy5nb29nbGV1c2VyY29udGVudC5jb20vYS0vQUF1RTdtQmp2LXlGaUlDWjdFQ3AwUHhzYmNVSHNDclppTU96eWtHbnFSemk_c3o9NTA_c3o9NTAiLCJleHAiOjE3NjE0MzE3Mzh9.kfurqTReeqmGsFHQdkKKGhTcaw3JR-FnpxCpMpS7uOA'
)
print(c.fetch_version())
print("Loading volume")
with h5py.File('hemibrain-v1.1-primary-roi-segmentation.h5', 'r') as f:
roi_vol_scale_5 = f['volume-256nm'][:]
KCs = SegmentCriteria(type="^KC.*", regex=True)
KCneurons, KCroicounts = neuprint.queries.fetch_neurons(KCs)
KCids = KCneurons['bodyId']
KCidsarray = np.asarray(KCids)
np.savetxt("KCids_v1.1.csv", KCidsarray, delimiter=",", fmt="%u")
for i in range(0, len(KCids)):
print(f"Fetching skeleton for KC {i}")
KC = SegmentCriteria(bodyId=KCids[i])
KCskel = c.fetch_skeleton(KCids[i], format='pandas', heal=True)
print(f"Extracting ROI for each skeleton point for KC {i}")
try:
c = Client('neuprint.janelia.org', dataset='hemibrain:v1.2', token=Token)
with open(token_file_name, "wt")as ff:
ff.writelines(Token)
check_token=1
except:
pass
else:
with open(token_file_name,"rt")as ff:
for line in ff:
if line.find("\n")!=-1:
line=line[:-1]
Token=line
break
c = Client('neuprint.janelia.org', dataset='hemibrain:v1.2', token=Token)
c.fetch_version()
def lookup(line):
if re.search('[a-zA-Z]', line) != None:
condition = "type="
if line.find("*") != -1:
condition=condition+"~"
condition=condition+"\""
if line[0].find("*")!=-1:
condition=condition+"."
if line[-1].find("*")!=-1:
condition=condition+line[:-1]+".*\""
else:
condition=condition+line+"\""
from neuprint import Client
# Please put the API token into the environment variable NEUPRINT_APPLICATION_CREDENTIALS
client = Client('neuprint.janelia.org', dataset='hemibrain:v1.1')
client.fetch_version()
output_dedge_csv = os.path.join(output_dir, "preprocessed_directed_edges.csv")
# check that output doesn't exist, or that overwrite option is set
if not args.force and any([os.path.isfile(f) for f in [output_node_csv, output_uedge_csv, output_dedge_csv]]):
logging.warning("Found existing output file(s):")
for f in [output_node_csv, output_uedge_csv, output_dedge_csv]:
if os.path.isfile(f):
print(f)
logging.warning("Force overwriting with -f, or specifiy output with --output_dir")
exit(1)
# Connect to Janelia
auth_token_file = open(args.auth_file, 'r')
auth_token = next(auth_token_file).strip()
np_client = Client('neuprint.janelia.org', dataset='hemibrain:v' + args.hemibrain_version, token=auth_token)
logging.info('Connected to neuprint; client version %s', np_client.fetch_version())
logging.debug("path = %s", path)
logging.debug("cluster_dir = %s", cluster_dir)
logging.info("Attempting to read neuron csv with pandas: %s", neuron_csv)
local_df = pd.read_csv(neuron_csv)
# Read cluster info and prepare node df
for filename in os.listdir(cluster_dir):
logging.info("Reading file %s", os.path.join(cluster_dir, filename))
if "unweighted" in filename:
cluster_name = "unweighted"
elif "lr" in filename:
cluster_name = "lr"