def test_list_nexus(self, kg_client):
cells = PatchedCell.list(kg_client, api="nexus", size=50)
assert len(cells) == 30
assert cells[0].brain_location == BrainRegion("hippocampus CA1")
assert isinstance(cells[0].collection, KGQuery)
assert cells[0].cell_type == CellType("hippocampus CA1 pyramidal cell")
assert isinstance(cells[0].experiments, KGQuery)
assert cells[0].pipette_id is None
assert cells[0].seal_resistance is None
assert cells[0].pipette_resistance is None
assert cells[0].liquid_junction_potential is None
assert cells[0].labeling_compound is None
assert cells[0].reversal_potential_cl == QuantitativeValue(-16.0, unit_text="mV")
def test_list_with_filter(self, kg_client):
cells = PatchedCell.list(kg_client, api="nexus", brain_region=BrainRegion("hippocampus CA1"), size=50)
assert len(cells) == 26
def test_all():
## Search based on brain region
cells_in_ca1 = PatchedCell.list(
client, brain_region=BrainRegion("hippocampus CA1"))
pprint([cell.brain_location for cell in cells_in_ca1])
### Download the recorded data for one of these cells
example_cell = cells_in_ca1[3]
experiment = example_cell.experiments.resolve(client)
trace = experiment.traces.resolve(client)
print(trace.time_step)
print(trace.data_unit)
download_url = trace.data_location.location
print(download_url)
data = np.genfromtxt(BytesIO(requests.get(download_url).content))
### Plot the data
times = data[:, 0]
signals = data[:, 1:]
# plot the first 100 signals
plt.figure(figsize=(18, 16), dpi=80, facecolor='w', edgecolor='k')
xlim = (times.min(), times.max())
for i in range(1, 101):
plt.subplot(10, 10, i)
plt.plot(times, data[:, i], 'grey')
plt.xlim(*xlim)
plt.axis('off')
## Search based on species
cells_from_mouse = PatchedCell.list(client,
species=Species("Mus musculus"))
for cell in cells_from_mouse[:10]:
pprint(
cell.collection.resolve(client).slice.resolve(
client).slice.resolve(client).subject.resolve(client).species)
## Search based on cell type
pyramidal_neurons = PatchedCell.list(
client, cell_type=CellType("hippocampus CA1 pyramidal cell"))
pprint([pn.cell_type for pn in pyramidal_neurons[:10]])
# An activity dataset with minimal metadata
Dataset.set_strict_mode(False)
query = {
"path":
"minds:specimen_group / minds:subjects / minds:samples / minds:methods / schema:name",
"op":
"in",
"value": [
"Electrophysiology recording", "Voltage clamp recording",
"Single electrode recording",
"functional magnetic resonance imaging"
]
}
context = {
"schema": "http://schema.org/",
"minds": "https://schema.hbp.eu/minds/"
}
activity_datasets = KGQuery(Dataset, query, context).resolve(client)
for dataset in activity_datasets:
print("* " + getattr(dataset, "name", "unknown"))
dataset = activity_datasets[-1]
#dataset.owners[0].resolve(client)
#dataset.license.resolve(client)
## An activity dataset with extended metadata
dataset = client.by_name(
Dataset,
"sIPSCs from juvenile (P21-30) C57Bl6/J male mice from CA1 pyramidal neurons receiving input from PV+ interneurons"
)
query = {"path": "nsg:partOf", "op": "eq", "value": dataset.id}
context = {
"nsg":
"https://bbp-nexus.epfl.ch/vocabs/bbp/neurosciencegraph/core/v0.1.0/"
}
traces = KGQuery(Trace, query, context).resolve(client)
print(traces)
tr0 = traces[0]
print(tr0.name)
print(tr0.channel)
print(tr0.time_step)
print(tr0.data_unit)
print(tr0.data_location)
experiment = tr0.generated_by.resolve(client)
print(experiment.name)
cell = experiment.recorded_cell.resolve(client)
print(cell.brain_location)
print(cell.cell_type)
print(cell.reversal_potential_cl)