moved overlaps() calculations to stats.py

MouseLand · Jul 2, 2020 · 943832c · 943832c
1 parent 54bc5e2
commit 943832c
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Showing 5 changed files with 26 additions and 23 deletions.
diff --git a/.dvc/.gitignore b/.dvc/.gitignore
@@ -42,3 +42,6 @@
 /tmp
 /tmp
 /tmp
+/tmp
+/tmp
+/tmp
diff --git a/.dvc/tmp/gdrive-user-credentials.json b/.dvc/tmp/gdrive-user-credentials.json
@@ -1 +1 @@
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+{"access_token": "ya29.a0AfH6SMA4on6-UlQWL1FDRFX1fhQoNnAQ5v6fnXVb0assyzvK3KabjC3LDymkf7KCo8EFFyL5OIZhQnRYgW0ULFaBtKL_Cw6nOEzabtw0-nUJT7iTN36oANBD1LVNXcArLbQM7teTmjbBS7-KiZRkWOgcPMndbcwZwLlg", "client_id": "710796635688-iivsgbgsb6uv1fap6635dhvuei09o66c.apps.googleusercontent.com", "client_secret": "a1Fz59uTpVNeG_VGuSKDLJXv", "refresh_token": "1//0fpJK6wboLxL7CgYIARAAGA8SNwF-L9IrKmhg8LLnJuWRCiN_YIE88ImD9Y0yV6iCIw220vP3gD4nISe-K5aq8l_r0m1qcUW1D_w", "token_expiry": "2020-07-02T16:37:51Z", "token_uri": "https://oauth2.googleapis.com/token", "user_agent": null, "revoke_uri": "https://oauth2.googleapis.com/revoke", "id_token": null, "id_token_jwt": null, "token_response": {"access_token": "ya29.a0AfH6SMA4on6-UlQWL1FDRFX1fhQoNnAQ5v6fnXVb0assyzvK3KabjC3LDymkf7KCo8EFFyL5OIZhQnRYgW0ULFaBtKL_Cw6nOEzabtw0-nUJT7iTN36oANBD1LVNXcArLbQM7teTmjbBS7-KiZRkWOgcPMndbcwZwLlg", "expires_in": 3599, "scope": "https://www.googleapis.com/auth/drive.appdata https://www.googleapis.com/auth/drive", "token_type": "Bearer"}, "scopes": ["https://www.googleapis.com/auth/drive.appdata", "https://www.googleapis.com/auth/drive"], "token_info_uri": "https://oauth2.googleapis.com/tokeninfo", "invalid": false, "_class": "OAuth2Credentials", "_module": "oauth2client.client"}
diff --git a/suite2p/detection/detect.py b/suite2p/detection/detect.py
@@ -2,8 +2,8 @@
 import numpy as np
 from pathlib import Path
 from . import sourcery, sparsedetect, chan2detect
-from .stats import ROI
-from .masks import filter_overlappers, create_cell_masks, create_neuropil_masks, create_cell_pix
+from .stats import ROI, filter_overlappers
+from .masks import create_cell_masks, create_neuropil_masks, create_cell_pix
 
 
 def main_detect(ops):

diff --git a/suite2p/detection/masks.py b/suite2p/detection/masks.py
@@ -4,24 +4,6 @@
 
 from suite2p.detection.sparsedetect import extendROI
 
-def count_overlaps(Ly: int, Lx: int, ypixs, xpixs) -> np.ndarray:
-    overlap = np.zeros((Ly, Lx))
-    for xpix, ypix in zip(xpixs, ypixs):
-        overlap[ypix, xpix] += 1
-    return overlap
-
-
-def filter_overlappers(ypixs, xpixs, max_overlap: float, Ly: int, Lx: int) -> List[int]:
-    """returns ROI indices are remain after removing those that overlap more than fraction max_overlap with other ROIs"""
-    overlaps = count_overlaps(Ly=Ly, Lx=Lx, ypixs=ypixs, xpixs=xpixs)
-    ix = []
-    for i, (ypix, xpix) in reversed(list(enumerate(zip(ypixs, xpixs)))):  # todo: is there an ordering effect here that affects which rois will be removed and which will stay?
-        if np.mean(overlaps[ypix, xpix] > 1) > max_overlap:  # note: fancy indexing returns a copy
-            overlaps[ypix, xpix] -= 1
-        else:
-            ix.append(i)
-    return ix[::-1]
-
 
 def create_cell_pix(stats, Ly, Lx, allow_overlap=False) -> np.ndarray:
     """Returns Ly x Lx array of whether it contains a cell (1) or not (0)."""

diff --git a/suite2p/detection/stats.py b/suite2p/detection/stats.py
@@ -1,14 +1,13 @@
 from __future__ import annotations
 
-from typing import Tuple, Optional, NamedTuple, Sequence
+from typing import Tuple, Optional, NamedTuple, Sequence, List
 from dataclasses import dataclass, field
 from warnings import warn
 
 import numpy as np
 from numpy.linalg import norm
 from cached_property import cached_property
 
-from .masks import count_overlaps
 from .utils import norm_by_average
 
 
@@ -178,3 +177,22 @@ def fitMVGaus(y, x, lam, thres=2.5, npts: int = 100) -> EllipseData:
     radii = np.sort(radii)[::-1]
 
     return EllipseData(mu=mu, cov=cov, radii=radii, ellipse=ellipse)
+
+
+def count_overlaps(Ly: int, Lx: int, ypixs, xpixs) -> np.ndarray:
+    overlap = np.zeros((Ly, Lx))
+    for xpix, ypix in zip(xpixs, ypixs):
+        overlap[ypix, xpix] += 1
+    return overlap
+
+
+def filter_overlappers(ypixs, xpixs, max_overlap: float, Ly: int, Lx: int) -> List[int]:
+    """returns ROI indices are remain after removing those that overlap more than fraction max_overlap with other ROIs"""
+    overlaps = count_overlaps(Ly=Ly, Lx=Lx, ypixs=ypixs, xpixs=xpixs)
+    ix = []
+    for i, (ypix, xpix) in reversed(list(enumerate(zip(ypixs, xpixs)))):  # todo: is there an ordering effect here that affects which rois will be removed and which will stay?
+        if np.mean(overlaps[ypix, xpix] > 1) > max_overlap:  # note: fancy indexing returns a copy
+            overlaps[ypix, xpix] -= 1
+        else:
+            ix.append(i)
+    return ix[::-1]