Merge branch 'main' into propagate-relative-to

Author: chrishalcrow

src/spikeinterface/core/generate.py

@@ -2156,6 +2156,21 @@ def generate_channel_locations(num_channels, num_columns, contact_spacing_um):
     return channel_locations
 
 
+def _generate_multimodal(rng, size, num_modes, lim0, lim1):
+    bins = np.linspace(lim0, lim1, 10000)
+    bin_step = bins[1] - bins[0]
+    prob = np.zeros(bins.size)
+    mode_step = (lim1 - lim0) / (num_modes + 1)
+    for i in range(num_modes):
+        center = mode_step * (i + 1)
+        sigma = mode_step / 5.0
+        prob += np.exp(-((bins - center) ** 2) / (2 * sigma**2))
+    prob /= np.sum(prob)
+    choices = np.random.choice(np.arange(bins.size), size, p=prob)
+    values = bins[choices] + rng.uniform(low=-bin_step / 2, high=bin_step / 2, size=size)
+    return values
+
+
 def generate_unit_locations(
     num_units,
     channel_locations,
@@ -2165,6 +2180,8 @@ def generate_unit_locations(
     minimum_distance=20.0,
     max_iteration=100,
     distance_strict=False,
+    distribution="uniform",
+    num_modes=2,
     seed=None,
 ):
     """
@@ -2205,6 +2222,14 @@ def generate_unit_locations(
         If True, the function will raise an exception if a solution meeting the distance
         constraint cannot be found within the maximum number of iterations. If False, a warning
         will be issued.
+    distribution : "uniform" | "multimodal", default: "uniform"
+        How units are spread.
+        "uniform" is units everywhere
+        "multimodal" mimic the distribution of units 'by layer'  on the 'y' axis (dim=1)
+        Important note, when using multimodal in conjonction of minimum_distance not None, there is not garanty
+        of a true multimodal because units that do not respect the distance of move again and are most chance to be in between layers.
+    num_modes : int, default 2
+        In case of distribution="multimodal", this is the number of modes (layers)
     seed : int or None, optional
         Random seed for reproducibility. If None, the seed is not set
 
@@ -2221,7 +2246,12 @@ def generate_unit_locations(
     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)
+    if distribution == "uniform":
+        units_locations[:, 1] = rng.uniform(minimum_y, maximum_y, size=num_units)
+    elif distribution == "multimodal":
+        units_locations[:, 1] = _generate_multimodal(rng, num_units, num_modes, minimum_y, maximum_y)
+    else:
+        raise ValueError("generate_unit_locations has wrong distribution must be 'uniform' or ")
     units_locations[:, 2] = rng.uniform(minimum_z, maximum_z, size=num_units)
 
     if minimum_distance is not None:
@@ -2242,20 +2272,27 @@ def generate_unit_locations(
                     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)
+                if distribution == "uniform":
+                    units_locations[:, 1][renew_inds] = rng.uniform(minimum_y, maximum_y, size=renew_inds.size)
+
+                elif distribution == "multimodal":
+                    units_locations[:, 1][renew_inds] = _generate_multimodal(
+                        rng, renew_inds.size, num_modes, minimum_y, maximum_y
+                    )
                 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=}")
+        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
 

src/spikeinterface/core/tests/test_generate.py

@@ -654,7 +654,8 @@ def test_synthesize_random_firings_length():
     # test_generate_recording()
     # test_generate_single_fake_waveform()
     # test_transformsorting()
-    test_generate_templates()
+    test_generate_unit_locations()
+    # test_generate_templates()
     # test_inject_templates()
     # test_generate_ground_truth_recording()
     # test_generate_sorting_with_spikes_on_borders()

src/spikeinterface/core/tests/test_job_tools.py

@@ -301,7 +301,7 @@ def test_get_best_job_kwargs():
 #         num_units=50,
 #         duration=120.0,
 #         sampling_frequency=30000.0,
-#         probe_name="Neuropixel-128",
+#         probe_name="Neuropixels-128",
 
 #     )
 #     # print(rec)

src/spikeinterface/generation/drifting_generator.py

@@ -25,7 +25,7 @@
 
 # this should be moved in probeinterface but later
 _toy_probes = {
-    "Neuropixel-384": dict(
+    "Neuropixels1-384": dict(
         num_columns=4,
         num_contact_per_column=[96] * 4,
         xpitch=16,
@@ -34,7 +34,15 @@
         contact_shapes="square",
         contact_shape_params={"width": 12},
     ),
-    "Neuropixel-128": dict(
+    "Neuropixels2-384": dict(
+        num_columns=2,
+        num_contact_per_column=[192] * 2,
+        xpitch=32,
+        ypitch=15,
+        contact_shapes="square",
+        contact_shape_params={"width": 12},
+    ),
+    "Neuropixels1-128": dict(
         num_columns=4,
         num_contact_per_column=[32] * 4,
         xpitch=16,
@@ -69,6 +77,8 @@ def make_one_displacement_vector(
 ):
     """
     Generates a toy displacement vector with ziagzag or bumps patterns.
+    This displacement vector has no amplitde, this generate only the shape
+    in the range [-0.5, 0.5]
 
     Parameters
     ----------
@@ -141,8 +151,19 @@ def make_one_displacement_vector(
             else:
                 displacement_vector[ind0:ind1] = -0.5
 
+    elif drift_mode == "random_walk":
+        rg = np.random.RandomState(seed=seed)
+        steps = rg.random_integers(low=0, high=1, size=num_samples)
+        steps = steps.astype("float64")
+        # 0 -> -1 and 1 -> 1
+        steps = steps * 2 - 1
+        steps[:start_drift_index] = 0
+        steps[end_drift_index:] = 0
+        displacement_vector = np.cumsum(steps, dtype="float64")
+        displacement_vector /= np.max(np.abs(displacement_vector)) * 2
+
     else:
-        raise ValueError("drift_mode must be 'zigzag' or 'bump'")
+        raise ValueError("drift_mode must be 'zigzag' or 'bump' or 'random_walk'")
 
     return displacement_vector * amplitude_factor
 
@@ -151,8 +172,8 @@ def generate_displacement_vector(
     duration,
     unit_locations,
     displacement_sampling_frequency=5.0,
-    drift_start_um=[0, 20.0],
-    drift_stop_um=[0, -20.0],
+    drift_start_um=[0, 30.0],
+    drift_stop_um=[0, -30.0],
     drift_step_um=1,
     motion_list=[
         dict(
@@ -199,6 +220,8 @@ def generate_displacement_vector(
 
     Returns
     -------
+    unit_displacements : numpy.ndarray
+        The final per unit, displacement vector with shape (num_times, num_units, 2)
     displacement_vectors : numpy.ndarray
         The drift vector is a numpy array with shape (num_times, 2, num_motions)
         num_motions is generally 1, but can be > 1 in case of combining several drift vectors
@@ -234,7 +257,9 @@ def generate_displacement_vector(
             **motion_kwargs,
             seed=seed,
         )
+
         one_displacement = one_displacement[:, np.newaxis] * (drift_stop_um - drift_start_um) + mid
+
         displacement_vectors.append(one_displacement[:, :, np.newaxis])
 
         if non_rigid_gradient is None:
@@ -253,14 +278,36 @@ def generate_displacement_vector(
 
     displacement_vectors = np.concatenate(displacement_vectors, axis=2)
 
-    return displacement_vectors, displacement_unit_factor, displacement_sampling_frequency, displacements_steps
+    # unit_displacements is the sum of all discplacements (times, units, direction_x_y)
+    unit_displacements = np.zeros((displacement_vectors.shape[0], num_units, 2))
+    for direction in (0, 1):
+        # x and y
+        for i in range(displacement_vectors.shape[2]):
+            m = displacement_vectors[:, direction, i][:, np.newaxis] * displacement_unit_factor[:, i][np.newaxis, :]
+            unit_displacements[:, :, direction] += m
+
+        lim0 = min(drift_start_um[direction], drift_stop_um[direction])
+        lim1 = max(drift_start_um[direction], drift_stop_um[direction])
+        if np.min(unit_displacements[:, :, direction]) < lim0 or np.max(unit_displacements[:, :, direction]) > lim1:
+            raise ValueError(
+                "unit_displacements is too big when combining several motion (with motion_list)."
+                "Please consider a smaller 'amplitude_factor' for each motion"
+            )
+
+    return (
+        unit_displacements,
+        displacement_vectors,
+        displacement_unit_factor,
+        displacement_sampling_frequency,
+        displacements_steps,
+    )
 
 
 def generate_drifting_recording(
     num_units=250,
     duration=600.0,
     sampling_frequency=30000.0,
-    probe_name="Neuropixel-128",
+    probe_name="Neuropixels1-128",
     generate_probe_kwargs=None,
     generate_unit_locations_kwargs=dict(
         margin_um=20.0,
@@ -269,6 +316,9 @@ def generate_drifting_recording(
         minimum_distance=18.0,
         max_iteration=100,
         distance_strict=False,
+        distribution="uniform",
+        # distribution="multimodal",
+        # num_modes=2,
     ),
     generate_displacement_vector_kwargs=dict(
         displacement_sampling_frequency=5.0,
@@ -311,7 +361,7 @@ def generate_drifting_recording(
         The duration in seconds.
     sampling_frequency : float, dfault: 30000.
         The sampling frequency.
-    probe_name : str, default: "Neuropixel-128"
+    probe_name : str, default: "Neuropixels1-128"
         The probe type if generate_probe_kwargs is None.
     generate_probe_kwargs : None or dict
         A dict to generate the probe, this supersede probe_name when not None.
@@ -371,17 +421,13 @@ def generate_drifting_recording(
         **generate_unit_locations_kwargs,
     )
 
-    displacement_vectors, displacement_unit_factor, displacement_sampling_frequency, displacements_steps = (
-        generate_displacement_vector(duration, unit_locations[:, :2], seed=seed, **generate_displacement_vector_kwargs)
-    )
-
-    # unit_displacements is the sum of all discplacements (times, units, direction_x_y)
-    unit_displacements = np.zeros((displacement_vectors.shape[0], num_units, 2))
-    for direction in (0, 1):
-        # x and y
-        for i in range(displacement_vectors.shape[2]):
-            m = displacement_vectors[:, direction, i][:, np.newaxis] * displacement_unit_factor[:, i][np.newaxis, :]
-            unit_displacements[:, :, direction] += m
+    (
+        unit_displacements,
+        displacement_vectors,
+        displacement_unit_factor,
+        displacement_sampling_frequency,
+        displacements_steps,
+    ) = generate_displacement_vector(duration, unit_locations[:, :2], seed=seed, **generate_displacement_vector_kwargs)
 
     # unit_params need to be fixed before the displacement steps
     generate_templates_kwargs = generate_templates_kwargs.copy()

src/spikeinterface/generation/tests/test_drifing_generator.py

@@ -17,6 +17,10 @@ def test_make_one_displacement_vector():
         drift_mode="bump", duration=700.0, period_s=300, bump_interval_s=(30, 90.0), t_start_drift=100.0, seed=2205
     )
 
+    displacement_vector = make_one_displacement_vector(
+        drift_mode="random_walk", duration=700.0, t_start_drift=100.0, seed=2205
+    )
+
     # import matplotlib.pyplot as plt
     # fig, ax = plt.subplots()
     # ax.plot(displacement_vector)
@@ -29,39 +33,48 @@ def test_generate_displacement_vector():
     unit_locations[:, 1] = np.linspace(-50, 50, 10)
 
     # one motion Y only
-    displacement_vectors, displacement_unit_factor, displacement_sampling_frequency, displacements_steps = (
-        generate_displacement_vector(duration, unit_locations)
-    )
+    (
+        unit_displacements,
+        displacement_vectors,
+        displacement_unit_factor,
+        displacement_sampling_frequency,
+        displacements_steps,
+    ) = generate_displacement_vector(duration, unit_locations)
+    assert unit_locations.shape[0] == unit_displacements.shape[1]
     assert unit_locations.shape[0] == displacement_unit_factor.shape[0]
     assert displacement_vectors.shape[2] == displacement_unit_factor.shape[1]
     assert displacement_vectors.shape[2] == 1
 
     # two motion X and Y
-    displacement_vectors, displacement_unit_factor, displacement_sampling_frequency, displacements_steps = (
-        generate_displacement_vector(
-            duration,
-            unit_locations,
-            drift_start_um=[-5, 20.0],
-            drift_stop_um=[5, -20.0],
-            motion_list=[
-                dict(
-                    drift_mode="zigzag",
-                    amplitude_factor=1.0,
-                    non_rigid_gradient=0.4,
-                    t_start_drift=60.0,
-                    t_end_drift=None,
-                    period_s=200,
-                ),
-                dict(
-                    drift_mode="bump",
-                    amplitude_factor=0.3,
-                    non_rigid_gradient=0.4,
-                    t_start_drift=60.0,
-                    t_end_drift=None,
-                    bump_interval_s=(30, 90.0),
-                ),
-            ],
-        )
+    (
+        unit_displacements,
+        displacement_vectors,
+        displacement_unit_factor,
+        displacement_sampling_frequency,
+        displacements_steps,
+    ) = generate_displacement_vector(
+        duration,
+        unit_locations,
+        drift_start_um=[-5, 20.0],
+        drift_stop_um=[5, -20.0],
+        motion_list=[
+            dict(
+                drift_mode="zigzag",
+                amplitude_factor=0.7,
+                non_rigid_gradient=0.4,
+                t_start_drift=60.0,
+                t_end_drift=None,
+                period_s=200,
+            ),
+            dict(
+                drift_mode="bump",
+                amplitude_factor=0.3,
+                non_rigid_gradient=0.4,
+                t_start_drift=60.0,
+                t_end_drift=None,
+                bump_interval_s=(30, 90.0),
+            ),
+        ],
     )
     assert unit_locations.shape[0] == displacement_unit_factor.shape[0]
     assert displacement_vectors.shape[2] == displacement_unit_factor.shape[1]
@@ -91,6 +104,6 @@ def test_generate_drifting_recording():
 
 if __name__ == "__main__":
     # test_make_one_displacement_vector()
-    # test_generate_displacement_vector()
+    test_generate_displacement_vector()
     # test_generate_noise()
-    test_generate_drifting_recording()
+    # test_generate_drifting_recording()