def test_fetch_skeleton():
c = Client(NEUPRINT_SERVER, DATASET)
orig_df = c.fetch_skeleton(5813027016, False)
healed_df = c.fetch_skeleton(5813027016, True)
assert len(orig_df) == len(healed_df)
assert (healed_df['link'] == -1).sum() == 1
assert healed_df['link'].iloc[0] == -1
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)
neuronIDs = np.arange(len(bodyIds))
np.random.shuffle(neuronIDs)
selectedIDs = neuronIDs[0:params['max_neurons']]
bodyIds = bodyIds.iloc[selectedIDs]
# Get skeletons
skeletonFile = '%s_skeletons.pkl' % (analysedRegion)
randomColor = lambda: '#' + ''.join(
['0123456789ABCDEF'[np.random.randint(16)] for i in range(6)])
print('Getting skeletons...')
if not (os.path.isfile(skeletonFile)):
skeletons = []
# max_number = 100
for i, bodyId in tqdm(enumerate(bodyIds)):
try:
s = c.fetch_skeleton(bodyId, format='pandas')
s['bodyId'] = bodyId
s['color'] = randomColor()
skeletons.append(s)
except:
print('; Skeleton %s cannot be downloaded' % (i))
savePickle(skeletonFile, skeletons)
else:
skeletons = loadPickle(skeletonFile)
# Load MB neurons for comparison with PN neurons
if analysedRegion == 'PN':
getRandGrey = lambda: '#' + 'ABCDEF'[np.random.randint(low=0, high=6)] * 6
mb_skeletons = loadPickle('MB_skeletons.pkl')
neuronIDs = np.arange(len(mb_skeletons))
np.random.shuffle(neuronIDs)
'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}")
extract_labels_from_volume(KCskel,
roi_vol_scale_5,
vol_scale=5,
label_names=c.primary_rois)
KCskel.rename(columns={'label_name': 'roi'}, inplace=True)
KCskel.to_csv(path_or_buf="KCskel" + str(i) + ".csv")
KCinputs = neuprint.queries.fetch_synapse_connections(target_criteria=KC)
print(f"Found {len(KCinputs)} inputs to KC {i} bodyId {KCids[i]}")
KCinputs.to_csv(path_or_buf="KCinputs" + str(i) + ".csv")
KCoutputs = neuprint.queries.fetch_synapse_connections(source_criteria=KC)
print(f"Found {len(KCoutputs)} outputs from KC {i} bodyId {KCids[i]}")
KCoutputs.to_csv(path_or_buf="KCoutputs" + str(i) + ".csv")
import os
import neuprint
from neuprint import Client, queries, SegmentCriteria
c = Client(
'neuprint.janelia.org',
dataset='hemibrain:v1.1',
token=
'eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJlbWFpbCI6ImFuZHJldy5saW5Ac2hlZmZpZWxkLmFjLnVrIiwibGV2ZWwiOiJub2F1dGgiLCJpbWFnZS11cmwiOiJodHRwczovL2xoMy5nb29nbGV1c2VyY29udGVudC5jb20vYS0vQUF1RTdtQmp2LXlGaUlDWjdFQ3AwUHhzYmNVSHNDclppTU96eWtHbnFSemk_c3o9NTA_c3o9NTAiLCJleHAiOjE3NjE0MzE3Mzh9.kfurqTReeqmGsFHQdkKKGhTcaw3JR-FnpxCpMpS7uOA'
)
print(c.fetch_version())
KCs = SegmentCriteria(type="^KC.*", regex=True)
APL = SegmentCriteria(bodyId=425790257)
print("Searching for KC to APL synapses")
KCtoAPL = neuprint.queries.fetch_synapse_connections(source_criteria=KCs,
target_criteria=APL)
print(f"Found {len(KCtoAPL)} KC to APL synapses")
print("Saving KC to APL synapses to csv")
KCtoAPL.to_csv(path_or_buf="KCtoAPLv1.1.csv")
print("Searching for APL to KC synapses")
APLtoKC = neuprint.queries.fetch_synapse_connections(source_criteria=APL,
target_criteria=KCs)
print("Saving APL to KC synapses to csv")
APLtoKC.to_csv(path_or_buf="APLtoKCv1.1.csv")
print(f"Found {len(APLtoKC)} APL to KC synapses")
APLskel = c.fetch_skeleton(425790257, format='pandas', heal=True)
APLskel.to_csv(path_or_buf="APLskelv1.1.csv")
def set_picture_variables(b_clear, b_neuropil, b_neuron, b_xyz, b_search,
b_synapse, neuropil_names, neuron_names, xyz_names,
select_color, syn_roi, syn_query_neuron, upstream_id,
up_w, downstream_id, down_w, history_data,
upload_neuron_list, upload_xyz_list,
syn_neuron_source_brain, syn_neuron_version,
syn_neuron_label, syn_xyz_source_brain,
syn_xyz_version, syn_xyz_label, syn_random,
upload_synapse_list):
print("1st", select_color)
####deal_with_clear
if b_clear:
if b_clear > history_data['NeuronSyn']['syn_button_clear']:
history_data['NeuronSyn']['syn_button_clear'] = b_clear
## synapse
if os.path.isfile(eflyplot_environment.brain_space.tmp_path +
"search_button_click_record.pickle"):
os.remove(eflyplot_environment.brain_space.tmp_path +
"search_button_click_record.pickle")
## neuron
if os.path.isfile(eflyplot_environment.brain_space.tmp_path +
"syn_neuron.pickle"):
os.remove(eflyplot_environment.brain_space.tmp_path +
"syn_neuron.pickle")
## xyz
if os.path.isfile(eflyplot_environment.brain_space.tmp_path +
"syz_xyz.pickle"):
os.remove(eflyplot_environment.brain_space.tmp_path +
"syn_xyz.pickle")
## figure_all
if os.path.isfile(eflyplot_environment.brain_space.tmp_path +
"syz_3d.pickle"):
os.remove(eflyplot_environment.brain_space.tmp_path +
"syn_3d.pickle")
return eFlyPlot_layout.initialize_eFlyPlot_history(), dict(rgb={
'r': 255,
'g': 255,
'b': 255,
'a': 1
}), [], [], []
####deal_with_xyz
if b_xyz:
print("xyz", xyz_names)
if b_xyz > history_data['NeuronSyn']['syn_button_add_xyz']:
history_data['NeuronSyn']['syn_button_add_xyz'] = b_xyz
if xyz_names:
xyz_names = xyz_names.split(" ")
else:
xyz_names = []
if upload_xyz_list:
xyz_names = xyz_names + upload_xyz_list
final_xyz_names = []
for xyz_name in xyz_names:
if xyz_name.find(".txt") == -1:
final_xyz_names.append(f"{xyz_name}.txt")
else:
final_xyz_names.append(f"{xyz_name}")
print(f"setting {final_xyz_names}")
if syn_random == 'u':
color = set_color(select_color)
for xyz_name in final_xyz_names:
if not os.path.isfile(
eflyplot_environment.brain_space.xyz_path + xyz_name):
print(f"<Warning> cannot open {xyz_name}")
continue
if syn_random == 's':
select_color = set_color(select_color)
elif syn_random == 'u':
select_color = color
elif syn_random == 'i':
select_color = dict(
rgb={
'r': rd.randint(0, 255),
'g': rd.randint(0, 255),
'b': rd.randint(0, 255),
'a': 1
})
history_data['NeuronSyn']['xyz_name'][xyz_name] = select_color
return history_data, dict(rgb={
'r': 255,
'g': 255,
'b': 255,
'a': 1
}), [], [], []
####deal_with_neuron
if b_neuron:
if b_neuron > history_data['NeuronSyn']['syn_button_add_neuron']:
print('neuron', neuron_names)
history_data['NeuronSyn']['syn_button_add_neuron'] = b_neuron
if neuron_names:
neuron_names = neuron_names.split(" ")
else:
neuron_names = []
final_neuron_names = []
if neuron_names:
for neuron_name in neuron_names:
if neuron_name.find(".swc") == -1:
final_neuron_names.append(
f"{neuron_name}_{syn_neuron_source_brain}_{syn_neuron_version}_{syn_neuron_label}.swc"
)
else:
final_neuron_names.append(
f"{neuron_name}_{syn_neuron_source_brain}_{syn_neuron_version}_{syn_neuron_label}"
)
if upload_neuron_list:
final_neuron_names = final_neuron_names + upload_neuron_list
print(f"setting {neuron_names}")
if syn_random == "Uniform random color":
select_color = dict(
rgb={
'r': rd.randint(0, 255),
'g': rd.randint(0, 255),
'b': rd.randint(0, 255),
'a': 1
})
color = set_color(select_color)
elif syn_random == "Selected color":
color = set_color(select_color)
for neuron_name in final_neuron_names:
print("acquiring neuron..........")
print(
f'{eflyplot_environment.brain_space.skeleton_path}{neuron_name}'
)
if not os.path.isfile(
f'{eflyplot_environment.brain_space.skeleton_path}{neuron_name}'
):
try:
c = Client('neuprint.janelia.org',
dataset=f'hemibrain:{syn_neuron_version}',
token=eflyplot_environment.brain_space.
neuprint_tool.Token)
c.fetch_skeleton(
copy.deepcopy(neuron_name).split("_")[0],
export_path=
f"{eflyplot_environment.brain_space.skeleton_path}{neuron_name}",
format="swc")
except:
continue
if syn_random == "Individual random color":
color = set_color(
dict(
rgb={
'r': rd.randint(0, 255),
'g': rd.randint(0, 255),
'b': rd.randint(0, 255),
'a': 1
}))
history_data['NeuronSyn']['neuron_name'][neuron_name] = color
print(history_data['NeuronSyn']['neuron_name'][neuron_name])
return history_data, dict(rgb={
'r': 255,
'g': 255,
'b': 255,
'a': 1
}), [], [], []
####deal_with_neuropil
if b_neuropil is not None:
if b_neuropil > history_data['NeuronSyn']['syn_button_add_neuropil']:
history_data['NeuronSyn']['syn_button_add_neuropil'] = b_neuropil
'''
若有在history，就不更動，避免改到過去設定好的顏色
但若是history有，現在沒有
'''
if syn_random == "Uniform random color":
select_color = dict(
rgb={
'r': rd.randint(0, 255),
'g': rd.randint(0, 255),
'b': rd.randint(0, 255),
'a': 1
})
color = set_color(select_color)
elif syn_random == "Selected color":
color = set_color(select_color)
for neuropil in neuropil_names:
if neuropil not in history_data['NeuronSyn']['neuropil']:
if syn_random == "Individual random color":
color = set_color(
dict(
rgb={
'r': rd.randint(0, 255),
'g': rd.randint(0, 255),
'b': rd.randint(0, 255),
'a': 1
}))
history_data['NeuronSyn']['neuropil'][neuropil] = color
# elif neuropil in history_data['neuropil'] andhistory_data['neuropil'].index(neuropil)
for neuropil in history_data['NeuronSyn']['neuropil']:
if neuropil not in neuropil_names:
del (history_data['NeuronSyn']['neuropil'][neuropil])
return history_data, dict(rgb={
'r': 255,
'g': 255,
'b': 255,
'a': 1
}), [], [], []
####deal with search
if b_search:
if b_search > history_data['NeuronSyn']['syn_button_submit']:
history_data['NeuronSyn']['syn_button_submit'] = b_search
if not upstream_id:
upstream_id = "noinfo"
if not up_w:
up_w = 0
if not downstream_id:
downstream_id = "noinfo"
if not down_w:
down_w = 0
if not syn_query_neuron:
syn_query_neuron = "noinfo"
if not syn_roi:
syn_roi = "noinfo"
connection = f"{upstream_id} {up_w} {downstream_id} {down_w} {syn_query_neuron} {syn_roi}"
color = set_color(select_color)
history_data['NeuronSyn']['syn_search_query'][connection] = color
return history_data, dict(rgb={
'r': 255,
'g': 255,
'b': 255,
'a': 1
}), [], [], []
if b_synapse:
if b_synapse > history_data['NeuronSyn']['syn_button_add_synapse']:
history_data['NeuronSyn']['syn_button_add_synapse'] = b_synapse
if upload_synapse_list:
final_synapses = upload_synapse_list
if syn_random == "Uniform random color":
select_color = dict(
rgb={
'r': rd.randint(0, 255),
'g': rd.randint(0, 255),
'b': rd.randint(0, 255),
'a': 1
})
color = set_color(select_color)
elif syn_random == "Selected color":
color = set_color(select_color)
for synapse_file in final_synapses:
if syn_random == "Individual random color":
color = set_color(
dict(
rgb={
'r': rd.randint(0, 255),
'g': rd.randint(0, 255),
'b': rd.randint(0, 255),
'a': 1
}))
history_data['NeuronSyn']['upload-synapse_data'][
synapse_file] = color
return history_data, dict(rgb={
'r': 255,
'g': 255,
'b': 255,
'a': 1
}), [], [], []
return history_data, dict(rgb={
'r': 255,
'g': 255,
'b': 255,
'a': 1
}), [], [], []