def test_fetch_adjacencies(client):
bodies = [
294792184, 329566174, 329599710, 417199910, 420274150, 424379864,
425790257, 451982486, 480927537, 481268653
]
neuron_df, roi_conn_df = fetch_adjacencies(SC(bodyId=bodies),
SC(bodyId=bodies))
# Should not include non-primary ROIs (except 'NotPrimary')
assert not ({*roi_conn_df['roi'].unique()} - {*fetch_primary_rois()} -
{'NotPrimary'})
#
# For backwards compatibility with the previous API,
# You can also pass a list of bodyIds to this function (instead of SegmentCriteria).
#
bodies = [
294792184, 329566174, 329599710, 417199910, 420274150, 424379864,
425790257, 451982486, 480927537, 481268653
]
neuron_df2, roi_conn_df2 = fetch_adjacencies(bodies, bodies)
# Should not include non-primary ROIs (except 'NotPrimary')
assert not ({*roi_conn_df2['roi'].unique()} - {*fetch_primary_rois()} -
{'NotPrimary'})
assert (neuron_df.fillna('') == neuron_df2.fillna('')).all().all()
assert (roi_conn_df == roi_conn_df2).all().all()
def test_fetch_adjacencies(client):
bodies = [
294792184, 329566174, 329599710, 417199910, 420274150, 424379864,
425790257, 451982486, 480927537, 481268653
]
neuron_df, roi_conn_df = fetch_adjacencies(NC(bodyId=bodies),
NC(bodyId=bodies))
# Should not include non-primary ROIs (except 'NotPrimary')
assert not ({*roi_conn_df['roi'].unique()} - {*fetch_primary_rois()} -
{'NotPrimary'})
#
# For backwards compatibility with the previous API,
# You can also pass a list of bodyIds to this function (instead of NeuronCriteria).
#
bodies = [
294792184, 329566174, 329599710, 417199910, 420274150, 424379864,
425790257, 451982486, 480927537, 481268653
]
neuron_df2, roi_conn_df2 = fetch_adjacencies(bodies, bodies)
# Should not include non-primary ROIs (except 'NotPrimary')
assert not ({*roi_conn_df2['roi'].unique()} - {*fetch_primary_rois()} -
{'NotPrimary'})
assert (neuron_df.fillna('') == neuron_df2.fillna('')).all().all()
assert (roi_conn_df == roi_conn_df2).all().all()
# What happens if results are empty
neuron_df, roi_conn_df = fetch_adjacencies(879442155, 5813027103)
assert len(neuron_df) == 0
assert len(roi_conn_df) == 0
assert neuron_df.columns.tolist() == ['bodyId', 'instance', 'type']
def pn_kc_connections(properties=None,
sum_across_rois=False,
checks=True,
**kwargs):
"""
Returns `neuron_df`, `connection_df` as `neuprint.fetch_adjacencies`, but
only for PN->KC connections.
See also keyword arguments added by the `@fetch_function` decorator.
Keywords not covered above are passed to `neuprint.fetch_adjacencies`.
"""
if properties is None:
# TODO TODO TODO replace hardcoded properties w/ something enumerated
# using a cypher query
properties = nc
# TODO TODO TODO the docs make this sound like it might only return weights
# for connections shared by ALL neurons matched. is this true? see also the
# two kwargs dealing with minimum weights
# TODO consider using rois=[<appropriate str for calyx>] to just match
# those. compare results to those from manually filtering output not
# specifying those rois.
neuron_df, conn_df = nu.fetch_adjacencies(
# TODO if i end up factoring some of this fn into something to be shared
# across fns that get connections from one type to another, maybe just
# test whether input strs have asterisk in them, and set regex=True if
# so? first check if * is in any of the types that exist in the db!!!
NC(type='.*PN.*', regex=True),
NC(type='.*KC.*', regex=True),
# Default is just ['type','instance']
properties=properties,
**kwargs)
# TODO test the number of things returned above is the same as when doing
# the equivalent CONTAINS query in the web interface
# (also check just .* as suffix, not also prefix)
if checks:
# There CAN be (pre ID, post ID) duplicates, because weights are
# reported per ROI, so need to sum across ROIs if we just want
# the total weight between two neurons, ignoring where the synapses are.
assert not conn_df.duplicated(
[c for c in conn_df.columns if c != 'weight']).any()
# TODO TODO technically only do this check if include_nonprimary=False
# (so check kwargs) (if it's True, would need to do another check)
assert (set(conn_df.roi.unique()) -
set(nu.fetch_primary_rois()) == {'NotPrimary'})
if sum_across_rois:
conn_df = conn_df.groupby(['bodyId_pre',
'bodyId_post']).weight.sum().reset_index()
# No need to call filter_nontraced_or_cropped, at least as of 2020-05-31,
# where it had no effect.
return neuron_df, conn_df
def test_fetch_primary_rois(client):
primary_rois = fetch_primary_rois()
assert isinstance(primary_rois, list)
