def test_user_contributions(self):
self.assertIsInstance(pymaid.get_user_contributions(
config_test.test_skids, remote_instance=self.rm), pd.DataFrame)
def find_autoseg_fragments(x,
autoseg_instance,
min_node_overlap=3,
min_nodes=1,
verbose=True,
raise_none_found=True):
"""Find autoseg tracings constituting a given neuron.
This function works by querying the segmentation IDs along the neurites of
your query neuron and then fetching the corresponding skeletons in
``autoseg_instance``.
Parameters
----------
x : navis.Neuron/List
Neuron to collect fragments for.
autoseg_instance : pymaid.CatmaidInstance
Catmaid instance which contains autoseg fragments.
min_node_overlap : int, optional
Minimal overlap between `x` and a segmentation
[in nodes].
min_nodes : int, optional
Minimum node count for returned neurons.
verbose : bool, optional
If True, will be print summary of who has contributed
to the autoseg fragments by merging or tracing nodes.
raise_none_found : bool, optional
If True and none of the requested segments were found,
will raise ValueError
Return
------
pymaid.CatmaidNeuronList
CatmaidNeurons of the overlapping fragments.
Examples
--------
Setup:
>>> import pymaid
>>> import fafbseg
>>> manual = pymaid.CatmaidInstance('MANUAL_SERVER_URL', 'HTTP_USER', 'HTTP_PW', 'API_TOKEN')
>>> auto = pymaid.CatmaidInstance('AUTO_SERVER_URL', 'HTTP_USER', 'HTTP_PW', 'API_TOKEN')
>>> # Call one of the fafbseg.use_... to set a source for segmentation IDs
>>> fafbseg.use_google_storage("https://storage.googleapis.com/fafb-ffn1-20190805/segmentation")
Find autoseg fragments overlapping with a manually traced neuron:
>>> x = pymaid.get_neuron(16, remote_instance=manual)
>>> frags_of_x = fafbseg.find_autoseg_fragments(x, remote_instance=auto)
See Also
--------
fafbseg.find_fragments
Generalization of this function that can find neurons
independent of whether they have a reference to the
segmentation ID in e.g. their name or annotations. Use
this to find manual tracings overlapping with a given
neuron e.g. from autoseg.
"""
if not isinstance(x, navis.TreeNeuron):
raise TypeError(
'Expected navis.TreeNeuron or pymaid.CatmaidNeuron, got "{}"'.
format(type(x)))
if not isinstance(autoseg_instance, pymaid.CatmaidInstance):
raise TypeError('Expected pymaid.CatmaidInstance, got "{}"'.format(
type(autoseg_instance)))
# First collect segments constituting this neuron
overlap_matrix = neuron_to_segments(x)
# If none found, return empty neuronlist
if overlap_matrix.empty:
return pymaid.CatmaidNeuronList([])
# Filter
seg_ids = overlap_matrix.loc[
overlap_matrix[x.skeleton_id] >= min_node_overlap].index.tolist()
# Now try finding the corresponding skeletons
nl = segments_to_neuron(seg_ids,
autoseg_instance=autoseg_instance,
verbose=verbose,
raise_none_found=raise_none_found)
nl.sort_values('n_nodes')
# Give contribution summary
if verbose:
# Get annotation details
an_details = pymaid.get_annotation_details(
nl, remote_instance=autoseg_instance)
# Extract merge annotations
merge_an = an_details[an_details.annotation.str.contains('Merged:')]
# Group by user and count
merge_count = merge_an.groupby('user')[['annotation']].count()
# Get manual tracing contributions
contr = pymaid.get_user_contributions(nl,
remote_instance=autoseg_instance)
contr.set_index('user', inplace=True)
# Merge both
summary = pd.merge(merge_count,
contr[['nodes', 'nodes_reviewed']],
left_index=True,
right_index=True).fillna(0)
summary.columns = ['merges', 'nodes_traced', 'nodes_reviewed']
if not summary.empty:
print('Tracer have worked on these neurons:')
print(summary)
else:
print('Nobody has worked on these neurons')
return nl[nl.n_nodes >= min_nodes]