Add a minimum distance in generate_unit_locations.

src/spikeinterface/core/generate.py

@@ -1336,15 +1336,49 @@ def generate_channel_locations(num_channels, num_columns, contact_spacing_um):
     return channel_locations
 
 
-def generate_unit_locations(num_units, channel_locations, margin_um=20.0, minimum_z=5.0, maximum_z=40.0, seed=None):
+def generate_unit_locations(num_units, channel_locations, margin_um=20.0, minimum_z=5.0, maximum_z=40.0, 
+                            minimum_distance=20., max_iteration=100, distance_strict=False,  seed=None):
     rng = np.random.default_rng(seed=seed)
     units_locations = np.zeros((num_units, 3), dtype="float32")
-    for dim in (0, 1):
-        lim0 = np.min(channel_locations[:, dim]) - margin_um
-        lim1 = np.max(channel_locations[:, dim]) + margin_um
-        units_locations[:, dim] = rng.uniform(lim0, lim1, size=num_units)
+
+    minimum_x, maximum_x = np.min(channel_locations[:, 0]) - margin_um, np.max(channel_locations[:, 0]) + margin_um
+    minimum_y, maximum_y = np.min(channel_locations[:, 1]) - margin_um, np.max(channel_locations[:, 1]) + margin_um
+
+    units_locations[:, 0] = rng.uniform(minimum_x, maximum_x, size=num_units)
+    units_locations[:, 1] = rng.uniform(minimum_y, maximum_y, size=num_units)
     units_locations[:, 2] = rng.uniform(minimum_z, maximum_z, size=num_units)
 
+    if minimum_distance is not None:
+        solution_found = False
+        renew_inds = None
+        for i in range(max_iteration):
+            distances = np.linalg.norm(units_locations[:, np.newaxis] - units_locations[np.newaxis, :], axis=2)
+            inds0, inds1 = np.nonzero(distances < minimum_distance)
+            mask = inds0 != inds1
+            inds0 = inds0[mask]
+            inds1 = inds1[mask]
+
+            if inds0.size > 0:
+                if renew_inds is None:
+                    renew_inds = np.unique(inds0)
+                else:
+                    # random only bad ones in the previous set
+                    renew_inds = renew_inds[np.isin(renew_inds, np.unique(inds0))]
+
+                units_locations[:, 0][renew_inds] = rng.uniform(minimum_x, maximum_x, size=renew_inds.size)
+                units_locations[:, 1][renew_inds] = rng.uniform(minimum_y, maximum_y, size=renew_inds.size)
+                units_locations[:, 2][renew_inds] = rng.uniform(minimum_z, maximum_z, size=renew_inds.size)
+            else:
+                solution_found = True
+                break
+
+    if not solution_found:
+        if distance_strict:
+            raise ValueError(f"generate_unit_locations(): no solution for {minimum_distance=} and {max_iteration=} "
+                             "You can use distance_strict=False or reduce minimum distance")
+        else:
+            warnings.warn(f"generate_unit_locations(): no solution for {minimum_distance=} and {max_iteration=}")
+
     return units_locations
 
 
@@ -1369,7 +1403,7 @@ def generate_ground_truth_recording(
     upsample_vector=None,
     generate_sorting_kwargs=dict(firing_rates=15, refractory_period_ms=4.0),
     noise_kwargs=dict(noise_level=5.0, strategy="on_the_fly"),
-    generate_unit_locations_kwargs=dict(margin_um=10.0, minimum_z=5.0, maximum_z=50.0),
+    generate_unit_locations_kwargs=dict(margin_um=10.0, minimum_z=5.0, maximum_z=50.0, minimum_distance=20),
     generate_templates_kwargs=dict(),
     dtype="float32",
     seed=None,

src/spikeinterface/core/tests/test_generate.py

@@ -4,6 +4,8 @@
 import numpy as np
 
 from spikeinterface.core import load_extractor, extract_waveforms
+
+from probeinterface import generate_multi_columns_probe
 from spikeinterface.core.generate import (
     generate_recording,
     generate_sorting,
@@ -289,6 +291,35 @@ def test_generate_single_fake_waveform():
     # plt.show()
 
 
+def test_generate_unit_locations():
+
+    seed = 0
+
+    probe = generate_multi_columns_probe(num_columns=2, num_contact_per_column=20, xpitch=20, ypitch=20)
+    channel_locations = probe.contact_positions
+
+    num_units = 100
+    minimum_distance = 20.
+    unit_locations = generate_unit_locations(num_units, channel_locations,
+                                             margin_um=20.0, minimum_z=5.0, maximum_z=40.0, 
+                                             minimum_distance=minimum_distance, max_iteration=500,
+                                             distance_strict=False, seed=seed)
+    distances = np.linalg.norm(unit_locations[:, np.newaxis] - unit_locations[np.newaxis, :], axis=2)
+    dist_flat = np.triu(distances, k=1).flatten()
+    dist_flat = dist_flat[dist_flat>0]
+    assert np.all(dist_flat > minimum_distance)
+
+
+    # import matplotlib.pyplot as plt
+    # fig, ax = plt.subplots()
+    # ax.hist(dist_flat, bins = np.arange(0, 400, 10))
+    # fig, ax = plt.subplots()
+    # from probeinterface.plotting import plot_probe
+    # plot_probe(probe, ax=ax)
+    # ax.scatter(unit_locations[:, 0], unit_locations[:,1], marker='*', s=20)
+    # plt.show()
+
+
 def test_generate_templates():
     seed = 0
 
@@ -297,7 +328,7 @@ def test_generate_templates():
     num_units = 10
     margin_um = 15.0
     channel_locations = generate_channel_locations(num_chans, num_columns, 20.0)
-    unit_locations = generate_unit_locations(num_units, channel_locations, margin_um, seed)
+    unit_locations = generate_unit_locations(num_units, channel_locations, margin_um=margin_um, seed=seed)
 
     sampling_frequency = 30000.0
     ms_before = 1.0
@@ -436,7 +467,8 @@ def test_generate_ground_truth_recording():
     # test_noise_generator_consistency_after_dump(strategy, None)
     # test_generate_recording()
     # test_generate_single_fake_waveform()
+    test_generate_unit_locations()
     # test_generate_templates()
     # test_inject_templates()
     # test_generate_ground_truth_recording()
-    test_generate_sorting_with_spikes_on_borders()
+    # test_generate_sorting_with_spikes_on_borders()