Assert nFrames key; remove bincount2D

brainbox/processing.py

@@ -1,17 +1,16 @@
-'''
-Processes data from one form into another, e.g. taking spike times and binning them into
-non-overlapping bins and convolving spike times with a gaussian kernel.
-'''
+"""Process data from one form into another.
+
+For example, taking spike times and binning them into non-overlapping bins and convolving spike
+times with a gaussian kernel.
+"""
 
 import numpy as np
 import pandas as pd
 from scipy import interpolate, sparse
 from brainbox import core
-from iblutil.numerical import bincount2D as _bincount2D
+from iblutil.numerical import bincount2D
 from iblutil.util import Bunch
 import logging
-import warnings
-import traceback
 
 _logger = logging.getLogger(__name__)
 
@@ -118,35 +117,6 @@ def sync(dt, times=None, values=None, timeseries=None, offsets=None, interp='zer
     return syncd
 
 
-def bincount2D(x, y, xbin=0, ybin=0, xlim=None, ylim=None, weights=None):
-    """
-    Computes a 2D histogram by aggregating values in a 2D array.
-
-    :param x: values to bin along the 2nd dimension (c-contiguous)
-    :param y: values to bin along the 1st dimension
-    :param xbin:
-        scalar: bin size along 2nd dimension
-        0: aggregate according to unique values
-        array: aggregate according to exact values (count reduce operation)
-    :param ybin:
-        scalar: bin size along 1st dimension
-        0: aggregate according to unique values
-        array: aggregate according to exact values (count reduce operation)
-    :param xlim: (optional) 2 values (array or list) that restrict range along 2nd dimension
-    :param ylim: (optional) 2 values (array or list) that restrict range along 1st dimension
-    :param weights: (optional) defaults to None, weights to apply to each value for aggregation
-    :return: 3 numpy arrays MAP [ny,nx] image, xscale [nx], yscale [ny]
-    """
-    for line in traceback.format_stack():
-        print(line.strip())
-    warning_text = """Future warning: bincount2D() is now a part of iblutil.
-                    brainbox.processing.bincount2D will be removed in future versions.
-                    Please replace imports with iblutil.numerical.bincount2D."""
-    _logger.warning(warning_text)
-    warnings.warn(warning_text, FutureWarning)
-    return _bincount2D(x, y, xbin, ybin, xlim, ylim, weights)
-
-
 def compute_cluster_average(spike_clusters, spike_var):
     """
     Quickish way to compute the average of some quantity across spikes in each cluster given
@@ -197,7 +167,7 @@ def bin_spikes(spikes, binsize, interval_indices=False):
 
 
 def get_units_bunch(spks_b, *args):
-    '''
+    """
     Returns a bunch, where the bunch keys are keys from `spks` with labels of spike information
     (e.g. unit IDs, times, features, etc.), and the values for each key are arrays with values for
     each unit: these arrays are ordered and can be indexed by unit id.
@@ -223,18 +193,18 @@ def get_units_bunch(spks_b, *args):
     --------
     1) Create a units bunch given a spikes bunch, and get the amps for unit #4 from the units
     bunch.
-        >>> import brainbox as bb
-        >>> import alf.io as aio
+        >>> from brainbox import processing
+        >>> import one.alf.io as alfio
         >>> import ibllib.ephys.spikes as e_spks
         (*Note, if there is no 'alf' directory, make 'alf' directory from 'ks2' output directory):
         >>> e_spks.ks2_to_alf(path_to_ks_out, path_to_alf_out)
-        >>> spks_b = aio.load_object(path_to_alf_out, 'spikes')
-        >>> units_b = bb.processing.get_units_bunch(spks_b)
+        >>> spks_b = alfio.load_object(path_to_alf_out, 'spikes')
+        >>> units_b = processing.get_units_bunch(spks_b)
         # Get amplitudes for unit 4.
         >>> amps = units_b['amps']['4']
 
     TODO add computation time estimate?
-    '''
+    """
 
     # Initialize `units`
     units_b = Bunch()
@@ -261,7 +231,7 @@ def get_units_bunch(spks_b, *args):
 
 
 def filter_units(units_b, t, **kwargs):
-    '''
+    """
     Filters units according to some parameters. **kwargs are the keyword parameters used to filter
     the units.
 
@@ -299,24 +269,24 @@ def filter_units(units_b, t, **kwargs):
     Examples
     --------
     1) Filter units according to the default parameters.
-        >>> import brainbox as bb
-        >>> import alf.io as aio
+        >>> from brainbox import processing
+        >>> import one.alf.io as alfio
         >>> import ibllib.ephys.spikes as e_spks
         (*Note, if there is no 'alf' directory, make 'alf' directory from 'ks2' output directory):
         >>> e_spks.ks2_to_alf(path_to_ks_out, path_to_alf_out)
         # Get a spikes bunch, units bunch, and filter the units.
-        >>> spks_b = aio.load_object(path_to_alf_out, 'spikes')
-        >>> units_b = bb.processing.get_units_bunch(spks_b, ['times', 'amps', 'clusters'])
+        >>> spks_b = alfio.load_object(path_to_alf_out, 'spikes')
+        >>> units_b = processing.get_units_bunch(spks_b, ['times', 'amps', 'clusters'])
         >>> T = spks_b['times'][-1] - spks_b['times'][0]
-        >>> filtered_units = bb.processing.filter_units(units_b, T)
+        >>> filtered_units = processing.filter_units(units_b, T)
 
     2) Filter units with no minimum amplitude, a minimum firing rate of 1 Hz, and a max false
     positive rate of 0.2, given a refractory period of 2 ms.
-        >>> filtered_units  = bb.processing.filter_units(units_b, T, min_amp=0, min_fr=1)
+        >>> filtered_units  = processing.filter_units(units_b, T, min_amp=0, min_fr=1)
 
     TODO: `units_b` input arg could eventually be replaced by `clstrs_b` if the required metrics
           are in `clstrs_b['metrics']`
-    '''
+    """
 
     # Set params
     params = {'min_amp': 50e-6, 'min_fr': 0.5, 'max_fpr': 0.2, 'rp': 0.002}  # defaults

brainbox/tests/test_processing.py

@@ -1,7 +1,6 @@
 from brainbox import processing, core
 import unittest
 import numpy as np
-import datetime
 
 
 class TestProcessing(unittest.TestCase):
@@ -63,15 +62,6 @@ def test_sync(self):
         self.assertTrue(times2.min() >= resamp2.times.min())
         self.assertTrue(times2.max() <= resamp2.times.max())
 
-    def test_bincount2D_deprecation(self):
-        # Timer to remove bincount2D (now in iblutil)
-        # Once this test fails:
-        # - Remove the bincount2D method in processing.py
-        # - Remove the import from iblutil at the top of that file
-        # - Delete this test
-        if datetime.datetime.now() > datetime.datetime(2024, 6, 30):
-            raise NotImplementedError
-
     def test_compute_cluster_averag(self):
         # Create fake data for 3 clusters
         clust1 = np.ones(40)
@@ -103,11 +93,6 @@ def test_compute_cluster_averag(self):
         self.assertEqual(avg_val[2], 0.75)
         self.assertTrue(np.all(count == (40, 40, 50)))
 
-    def test_deprecations(self):
-        """Ensure removal of bincount2D function."""
-        from datetime import datetime
-        self.assertTrue(datetime.today() < datetime(2024, 8, 1), 'remove brainbox.processing.bincount2D')
-
 
 if __name__ == '__main__':
     np.random.seed(0)

ibllib/io/extractors/mesoscope.py

@@ -51,6 +51,7 @@ def patch_imaging_meta(meta: dict) -> dict:
         for fov in meta.get('FOV', []):
             if 'roiUuid' in fov:
                 fov['roiUUID'] = fov.pop('roiUuid')
+    assert 'nFrames' in meta, '"nFrames" key missing from meta data; rawImagingData.meta.json likely an old version'
     return meta
 
 

ibllib/tests/test_mesoscope.py

@@ -68,6 +68,7 @@ def test_meta(self):
         }
 
         meta = {
+            'nFrames': 2000,
             'scanImageParams': {'hStackManager': {'zs': 320},
                                 'hRoiManager': {'scanVolumeRate': 6.8}},
             'FOV': [{'topLeftDeg': [-1, 1.3], 'topRightDeg': [3, 1.3], 'bottomLeftDeg': [-1, 5.2],
@@ -256,10 +257,10 @@ class TestImagingMeta(unittest.TestCase):
     """Test raw imaging metadata versioning."""
     def test_patch_imaging_meta(self):
         """Test for ibllib.io.extractors.mesoscope.patch_imaging_meta function."""
-        meta = {'version': '0.1.0', 'FOV': [{'roiUuid': None}, {'roiUUID': None}]}
+        meta = {'version': '0.1.0', 'nFrames': 2000, 'FOV': [{'roiUuid': None}, {'roiUUID': None}]}
         new_meta = mesoscope.patch_imaging_meta(meta)
         self.assertEqual(set(chain(*map(dict.keys, new_meta['FOV']))), {'roiUUID'})
-        meta = {'FOV': [
+        meta = {'nFrames': 2000, 'FOV': [
             dict.fromkeys(['topLeftDeg', 'topRightDeg', 'bottomLeftDeg', 'bottomRightDeg']),
             dict.fromkeys(['topLeftMM', 'topRightMM', 'bottomLeftMM', 'bottomRightMM'])
         ]}