Digital Drosophila Archive
54 lines
2.7 KiB
Python
54 lines
2.7 KiB
Python
"""Pure CPU contact accounting and immutable diagnostic weight operations."""
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import numpy as np
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import pandas as pd
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def contact_scope(apl_pn, pn_kc, rois):
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"""Only exact, observed PN+postsynaptic-ROI pairs; no side/name inference."""
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required = {'source_neuron_idx', 'target_neuron_idx', 'primary_post'}
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if not required.issubset(apl_pn) or not required.issubset(pn_kc):
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raise ValueError('Actual schema lacks required identifiers/primary_post')
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permitted = apl_pn.loc[apl_pn.primary_post.isin(rois),
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['target_neuron_idx', 'primary_post']].drop_duplicates()
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permitted = permitted.rename(columns={'target_neuron_idx': 'source_neuron_idx'})
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keys = pd.MultiIndex.from_frame(permitted.astype({'primary_post': 'string'}))
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observed = pd.MultiIndex.from_frame(pn_kc[['source_neuron_idx', 'primary_post']]
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.astype({'primary_post': 'string'}))
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keep = observed.isin(keys)
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pairs = ['source_neuron_idx', 'target_neuron_idx']
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total = pn_kc.groupby(pairs, observed=True).size().rename('raw_partner_count')
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ca = pn_kc.loc[keep].groupby(pairs, observed=True).size().rename('blocked_contact_count')
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frame = total.to_frame().join(ca).fillna(0).reset_index()
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for key in pairs + ['raw_partner_count', 'blocked_contact_count']:
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frame[key] = frame[key].astype(np.int64)
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frame['retained_contact_count'] = frame.raw_partner_count - frame.blocked_contact_count
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assert (frame.retained_contact_count >= 0).all()
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return frame, permitted, keep
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def edge_ids_for_pairs(frame, ptr, dst):
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out = []
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for s, t in frame[['source_neuron_idx', 'target_neuron_idx']].itertuples(index=False, name=None):
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ids = np.arange(ptr[s], ptr[s + 1], dtype=np.int64)
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found = ids[dst[ids] == t]
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if len(found) != 1:
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raise ValueError(f'Expected exactly one existing CSR edge for {s}->{t}')
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out.append(found[0])
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return np.asarray(out, np.int64)
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def blocked_weights(base, raw, contacts):
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base = np.asarray(base)
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raw = np.asarray(raw)
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contacts = np.asarray(contacts)
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if base.dtype != np.float32 or raw.dtype != np.int64 or contacts.dtype != np.int64:
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raise TypeError('Expected float32 effective weight and int64 anatomical counts')
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if base.shape != raw.shape or raw.shape != contacts.shape:
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raise ValueError('Shape mismatch')
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if not np.isfinite(base).all() or np.any(raw <= 0) or np.any(contacts < 0) or np.any(contacts > raw):
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raise ValueError('Invalid contact partition or weights')
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out = base.copy()
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m = contacts > 0 # No-op copies original bits; no rounding at strength zero.
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out[m] = (base[m].astype(np.float64) * (raw[m] - contacts[m]) / raw[m]).astype(np.float32)
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return out
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