From 667fdb3a896fbefceafd8842838342651f217d76 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Raul=20C=2E=20S=C3=AEmpetru?= <raul.simpetru@fau.de>
Date: Sun, 31 May 2026 20:15:59 +0200
Subject: [PATCH 01/13] refactor(binning): drop elephant BinnedSpikeTrain for a
 dependency-free helper

Add myogen.utils.bin_spike_trains, a dependency-free boolean spike-occupancy
binner that exactly reproduces elephant.conversion.BinnedSpikeTrain (including its
floating-point tolerance edge-snap and right-edge discard) without emitting the
"shifting spikes to the next bin" warnings.

Use it at the four spike-binning sites -- surface and intramuscular EMG (dense)
and both force models (sparse) -- and remove the elephant import, the
HAS_ELEPHANT guard, and the elephant-warning logger-suppression at each. elephant
remains an optional extra (still used for firing-rate isi in utils/helper.py).

Add an equivalence test asserting bit-identical output to elephant across grid,
multi-train, fp-awkward, t_stop-edge, fractional-bin, sub-range-bounds,
duplicate-spike and empty-train cases.

Closes #16.
---
 .../emg/intramuscular/intramuscular_emg.py    |  41 ++----
 .../simulator/core/emg/surface/surface_emg.py |  41 ++----
 myogen/simulator/core/force/force_model.py    |  53 ++------
 .../core/force/force_model_vectorized.py      |  59 +++------
 myogen/utils/binning.py                       | 124 ++++++++++++++++++
 tests/test_binning.py                         |  98 ++++++++++++++
 6 files changed, 269 insertions(+), 147 deletions(-)
 create mode 100644 myogen/utils/binning.py
 create mode 100644 tests/test_binning.py

diff --git a/myogen/simulator/core/emg/intramuscular/intramuscular_emg.py b/myogen/simulator/core/emg/intramuscular/intramuscular_emg.py
index 2a1080b8..7b6baeab 100644
--- a/myogen/simulator/core/emg/intramuscular/intramuscular_emg.py
+++ b/myogen/simulator/core/emg/intramuscular/intramuscular_emg.py
@@ -10,15 +10,6 @@
 from copy import deepcopy
 from typing import Optional
 
-try:
-    import elephant
-    import elephant.utils
-
-    HAS_ELEPHANT = True
-except ImportError:
-    HAS_ELEPHANT = False
-    elephant = None  # type: ignore
-
 import numpy as np
 import quantities as pq
 from joblib import Parallel, delayed
@@ -28,6 +19,7 @@
 from myogen import get_random_generator
 from myogen.simulator.core.emg.electrodes import IntramuscularElectrodeArray
 from myogen.simulator.core.muscle import Muscle
+from myogen.utils.binning import bin_spike_trains
 from myogen.utils.decorators import beartowertype
 from myogen.utils.types import (
     INTRAMUSCULAR_EMG__Block,
@@ -530,12 +522,6 @@ def simulate_intramuscular_emg(
         if self._muaps__Block is None:
             raise ValueError("MUAP templates have not been generated. Call simulate_muaps() first.")
 
-        if not HAS_ELEPHANT:
-            raise ImportError(
-                "Elephant is required for intramuscular EMG simulation. "
-                "Install with: pip install myogen[elephant]"
-            )
-
         # Store spike train data privately
         self._spike_train__Block = spike_train__Block
 
@@ -604,24 +590,13 @@ def simulate_intramuscular_emg(
         n_neurons = len(spike_train__Block.segments[0].spiketrains)
         n_electrodes = muap_shapes.shape[1]
 
-        # Convert spike trains to binary arrays using Elephant, suppressing rounding error logging
-        elephant_utils_logger = logging.getLogger(elephant.utils.__file__)
-        original_level = elephant_utils_logger.level
-        elephant_utils_logger.setLevel(logging.ERROR)
-
-        try:
-            spike_trains = np.array(
-                [
-                    elephant.conversion.BinnedSpikeTrain(
-                        segment.spiketrains, bin_size=spiketrain_timestep__ms
-                    )
-                    .to_array()
-                    .astype(bool)
-                    for segment in spike_train__Block.segments
-                ]
-            )
-        finally:
-            elephant_utils_logger.setLevel(original_level)
+        # Bin each pool's spike trains into a boolean occupancy array.
+        spike_trains = np.array(
+            [
+                bin_spike_trains(segment.spiketrains, bin_size=spiketrain_timestep__ms)
+                for segment in spike_train__Block.segments
+            ]
+        )
 
         # Create active neuron indices (all neurons are active in each pool for spike train block)
         active_neuron_indices = [list(range(n_neurons)) for _ in range(n_pools)]
diff --git a/myogen/simulator/core/emg/surface/surface_emg.py b/myogen/simulator/core/emg/surface/surface_emg.py
index 4edca872..1ca397da 100644
--- a/myogen/simulator/core/emg/surface/surface_emg.py
+++ b/myogen/simulator/core/emg/surface/surface_emg.py
@@ -1,5 +1,6 @@
 from typing import Optional
 
+from myogen.utils.binning import bin_spike_trains
 from myogen.utils.neo import create_grid_signal, signal_to_grid
 
 try:
@@ -9,15 +10,6 @@
 except ImportError:
     HAS_CUPY = False
 
-try:
-    import elephant
-    import elephant.utils
-
-    HAS_ELEPHANT = True
-except ImportError:
-    HAS_ELEPHANT = False
-    elephant = None  # type: ignore
-
 import logging
 from copy import deepcopy
 
@@ -652,12 +644,6 @@ def simulate_surface_emg(self, spike_train__Block: SPIKE_TRAIN__Block, verbose:
         if self._muaps__Block is None:
             raise ValueError("MUAP templates have not been generated. Call simulate_muaps() first.")
 
-        if not HAS_ELEPHANT:
-            raise ImportError(
-                "Elephant is required for surface EMG simulation. "
-                "Install with: pip install myogen[elephant]"
-            )
-
         # Store spike train data privately
         self._spike_train__Block = spike_train__Block
 
@@ -673,24 +659,13 @@ def simulate_surface_emg(self, spike_train__Block: SPIKE_TRAIN__Block, verbose:
         first_spiketrain = spike_train__Block.segments[0].spiketrains[0]
         spiketrain_timestep__ms = first_spiketrain.sampling_period.rescale("ms")
 
-        # Convert spike trains to binary arrays using Elephant, suppressing rounding error logging
-        elephant_utils_logger = logging.getLogger(elephant.utils.__file__)
-        original_level = elephant_utils_logger.level
-        elephant_utils_logger.setLevel(logging.ERROR)
-
-        try:
-            spike_trains = np.array(
-                [
-                    elephant.conversion.BinnedSpikeTrain(
-                        segment.spiketrains, bin_size=spiketrain_timestep__ms
-                    )
-                    .to_array()
-                    .astype(bool)
-                    for segment in spike_train__Block.segments
-                ]
-            )
-        finally:
-            elephant_utils_logger.setLevel(original_level)
+        # Bin each pool's spike trains into a boolean occupancy array.
+        spike_trains = np.array(
+            [
+                bin_spike_trains(segment.spiketrains, bin_size=spiketrain_timestep__ms)
+                for segment in spike_train__Block.segments
+            ]
+        )
 
         # Handle MUs to simulate
         if self._MUs_to_simulate is None:
diff --git a/myogen/simulator/core/force/force_model.py b/myogen/simulator/core/force/force_model.py
index 1ffe2556..af6c6692 100644
--- a/myogen/simulator/core/force/force_model.py
+++ b/myogen/simulator/core/force/force_model.py
@@ -1,20 +1,12 @@
-import logging
 from typing import Optional
 
-try:
-    import elephant
-    import elephant.utils
-
-    HAS_ELEPHANT = True
-except ImportError:
-    HAS_ELEPHANT = False
-    elephant = None  # type: ignore
 import numpy as np
 import quantities as pq
 import scipy.sparse as sp
 from neo import AnalogSignal
 from tqdm import tqdm
 
+from myogen.utils.binning import bin_spike_trains
 from myogen.utils.decorators import beartowertype
 from myogen.utils.types import (
     RECRUITMENT_THRESHOLDS__ARRAY,
@@ -278,12 +270,6 @@ def generate_force(self, spike_train__Block: SPIKE_TRAIN__Block, verbose: bool =
                 "Please reinitialize the ForceModel."
             )
 
-        if not HAS_ELEPHANT:
-            raise ImportError(
-                "Elephant is required for force simulation. "
-                "Install with: pip install myogen[elephant]"
-            )
-
         # Extract timing information from spike trains
         spiketrain_timestep__ms = float(
             spike_train__Block.segments[0].spiketrains[0].sampling_period.rescale("ms").magnitude
@@ -298,30 +284,19 @@ def generate_force(self, spike_train__Block: SPIKE_TRAIN__Block, verbose: bool =
                     "The number of neurons in the spike train neo.Block must match the number of recruitment thresholds."
                 )
 
-            elephant_utils_logger = logging.getLogger(elephant.utils.__file__)
-            original_level = elephant_utils_logger.level
-            elephant_utils_logger.setLevel(logging.ERROR)
-
-            try:
-                spike_array = (
-                    elephant.conversion.BinnedSpikeTrain(
-                        segment.spiketrains,
-                        bin_size=segment.spiketrains[0].sampling_period,
-                        t_start=segment.t_start,
-                        t_stop=segment.t_stop,
-                    )
-                    .to_sparse_bool_array()
-                    .T
-                )
-
-                # Generate force with resampling handled internally
-                force_output = self._generate_force(
-                    spike_array, spiketrain_timestep__ms, prefix=f"Pool {i + 1}", verbose=verbose
-                )
-                forces.append(force_output)
-
-            finally:
-                elephant_utils_logger.setLevel(original_level)
+            spike_array = bin_spike_trains(
+                segment.spiketrains,
+                bin_size=segment.spiketrains[0].sampling_period,
+                t_start=segment.t_start,
+                t_stop=segment.t_stop,
+                sparse=True,
+            ).T
+
+            # Generate force with resampling handled internally
+            force_output = self._generate_force(
+                spike_array, spiketrain_timestep__ms, prefix=f"Pool {i + 1}", verbose=verbose
+            )
+            forces.append(force_output)
 
         return AnalogSignal(
             np.stack(forces, axis=-1) * pq.dimensionless,
diff --git a/myogen/simulator/core/force/force_model_vectorized.py b/myogen/simulator/core/force/force_model_vectorized.py
index 08bcc71a..bcd93c71 100644
--- a/myogen/simulator/core/force/force_model_vectorized.py
+++ b/myogen/simulator/core/force/force_model_vectorized.py
@@ -1,19 +1,11 @@
-import logging
 from typing import Optional
 
-try:
-    import elephant
-    import elephant.utils
-
-    HAS_ELEPHANT = True
-except ImportError:
-    HAS_ELEPHANT = False
-    elephant = None  # type: ignore
 import numpy as np
 import quantities as pq
 import scipy.sparse as sp
 from neo import AnalogSignal
 
+from myogen.utils.binning import bin_spike_trains
 from myogen.utils.decorators import beartowertype
 from myogen.utils.types import (
     RECRUITMENT_THRESHOLDS__ARRAY,
@@ -202,12 +194,6 @@ def generate_force(
                 "This should not occur if the model was properly initialized."
             )
 
-        if not HAS_ELEPHANT:
-            raise ImportError(
-                "Elephant is required for force simulation. "
-                "Install with: pip install myogen[elephant]"
-            )
-
         # Extract timing information
         spiketrain_timestep__ms = float(
             spike_train__Block.segments[0]
@@ -224,33 +210,22 @@ def generate_force(
                     f"but force model was initialized with {self._number_of_neurons} motor units."
                 )
 
-            elephant_utils_logger = logging.getLogger(elephant.utils.__file__)
-            original_level = elephant_utils_logger.level
-            elephant_utils_logger.setLevel(logging.ERROR)
-
-            try:
-                spike_array = (
-                    elephant.conversion.BinnedSpikeTrain(
-                        segment.spiketrains,
-                        bin_size=segment.spiketrains[0].sampling_period,
-                        t_start=segment.t_start,
-                        t_stop=segment.t_stop,
-                    )
-                    .to_sparse_bool_array()
-                    .T
-                )
-
-                # Generate force with vectorized implementation
-                force_output = self._generate_force_vectorized(
-                    spike_array,
-                    spiketrain_timestep__ms,
-                    prefix=f"Pool {i + 1}",
-                    verbose=verbose,
-                )
-                forces.append(force_output)
-
-            finally:
-                elephant_utils_logger.setLevel(original_level)
+            spike_array = bin_spike_trains(
+                segment.spiketrains,
+                bin_size=segment.spiketrains[0].sampling_period,
+                t_start=segment.t_start,
+                t_stop=segment.t_stop,
+                sparse=True,
+            ).T
+
+            # Generate force with vectorized implementation
+            force_output = self._generate_force_vectorized(
+                spike_array,
+                spiketrain_timestep__ms,
+                prefix=f"Pool {i + 1}",
+                verbose=verbose,
+            )
+            forces.append(force_output)
 
         return AnalogSignal(
             np.stack(forces, axis=-1) * pq.dimensionless,
diff --git a/myogen/utils/binning.py b/myogen/utils/binning.py
new file mode 100644
index 00000000..a6805c79
--- /dev/null
+++ b/myogen/utils/binning.py
@@ -0,0 +1,124 @@
+"""Dependency-free binning of spike trains into boolean occupancy matrices.
+
+This reproduces the subset of :class:`elephant.conversion.BinnedSpikeTrain`
+that MyoGen relies on -- turning a list of :class:`neo.SpikeTrain` objects into
+a boolean ``(n_trains, n_bins)`` occupancy matrix -- so the simulator no longer
+needs ``elephant`` just to bin spikes. The bin assignment matches elephant
+exactly, including its floating-point tolerance handling (spikes within
+``tolerance`` of the next bin edge are shifted into it) and its right-edge
+discard, but without emitting the "shifting spikes to the next bin" warnings
+that elephant logs (so no logger suppression is needed at the call sites).
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import quantities as pq
+
+__all__ = ["bin_spike_trains"]
+
+
+def _round_binning_errors(values, tolerance: float | None = 1e-8):
+    """Floor ``values`` to ints, nudging values within ``tolerance`` of the
+    next integer up first.
+
+    Mirrors :func:`elephant.utils.round_binning_errors` (array and scalar
+    forms) but does not log a warning.
+    """
+    scalar = np.ndim(values) == 0
+    if not tolerance:
+        return int(values) if scalar else np.asarray(values).astype(np.int32)
+    if scalar:
+        v = float(values)
+        if (1.0 - tolerance) <= (v % 1.0):
+            v += 0.5
+        return int(v)
+    v = np.array(values, dtype=float, copy=True)
+    v[(1.0 - tolerance) <= (v % 1.0)] += 0.5
+    return v.astype(np.int32)
+
+
+def bin_spike_trains(
+    spiketrains,
+    bin_size,
+    *,
+    t_start=None,
+    t_stop=None,
+    tolerance: float | None = 1e-8,
+    sparse: bool = False,
+):
+    """Bin spike trains into a boolean occupancy matrix.
+
+    Drop-in replacement for::
+
+        elephant.conversion.BinnedSpikeTrain(
+            spiketrains, bin_size, t_start=t_start, t_stop=t_stop
+        ).to_array().astype(bool)        # sparse=False
+        # ... or ...
+        ).to_sparse_bool_array()         # sparse=True
+
+    Parameters
+    ----------
+    spiketrains : list of neo.SpikeTrain
+        Spike trains to bin. They must share the same units and (unless
+        ``t_start`` / ``t_stop`` are given) the same ``t_start`` / ``t_stop``.
+    bin_size : pq.Quantity
+        Width of each bin.
+    t_start, t_stop : pq.Quantity, optional
+        Binning interval. Default to the first spike train's ``t_start`` /
+        ``t_stop`` (elephant's default).
+    tolerance : float or None, default 1e-8
+        Absolute tolerance for snapping spikes that sit within ``tolerance`` of
+        the next bin edge into it. ``None`` disables the correction.
+    sparse : bool, default False
+        If ``True`` return a :class:`scipy.sparse.csr_matrix`; otherwise a dense
+        boolean ``np.ndarray``.
+
+    Returns
+    -------
+    np.ndarray or scipy.sparse.csr_matrix
+        Boolean occupancy matrix of shape ``(n_trains, n_bins)``. ``True`` marks
+        bins that contain at least one spike.
+    """
+    n_trains = len(spiketrains)
+    units = spiketrains[0].units
+
+    if t_start is None:
+        t_start = spiketrains[0].t_start
+    if t_stop is None:
+        t_stop = spiketrains[0].t_stop
+
+    t_start = float(pq.Quantity(t_start).rescale(units).magnitude)
+    t_stop = float(pq.Quantity(t_stop).rescale(units).magnitude)
+    bin_size = float(pq.Quantity(bin_size).rescale(units).magnitude)
+
+    n_bins = _round_binning_errors((t_stop - t_start) / bin_size, tolerance)
+    n_bins = max(0, int(n_bins))
+    scale = 1.0 / bin_size
+
+    rows, cols = [], []
+    for idx, train in enumerate(spiketrains):
+        times = np.asarray(train.rescale(units).magnitude, dtype=float)
+        times = times[(times >= t_start) & (times <= t_stop)] - t_start
+        bins = _round_binning_errors(times * scale, tolerance)
+        bins = np.unique(bins[bins < n_bins])
+        if bins.size:
+            rows.append(np.full(bins.size, idx, dtype=np.int64))
+            cols.append(bins.astype(np.int64))
+
+    if rows:
+        rows = np.concatenate(rows)
+        cols = np.concatenate(cols)
+    else:
+        rows = np.empty(0, dtype=np.int64)
+        cols = np.empty(0, dtype=np.int64)
+
+    if sparse:
+        import scipy.sparse as sps
+
+        data = np.ones(rows.size, dtype=bool)
+        return sps.csr_matrix((data, (rows, cols)), shape=(n_trains, n_bins), dtype=bool)
+
+    occupancy = np.zeros((n_trains, n_bins), dtype=bool)
+    occupancy[rows, cols] = True
+    return occupancy
diff --git a/tests/test_binning.py b/tests/test_binning.py
new file mode 100644
index 00000000..874d9da0
--- /dev/null
+++ b/tests/test_binning.py
@@ -0,0 +1,98 @@
+"""Prove myogen.utils.binning.bin_spike_trains matches elephant's
+BinnedSpikeTrain across the cases MyoGen relies on (issue #16)."""
+
+import numpy as np
+import pytest
+import quantities as pq
+from neo import SpikeTrain
+
+from myogen.utils.binning import bin_spike_trains
+
+elephant = pytest.importorskip("elephant")
+from elephant.conversion import BinnedSpikeTrain  # noqa: E402
+
+
+def _make_trains(spike_lists, t_start, t_stop, sampling_period):
+    trains = []
+    for spikes in spike_lists:
+        st = SpikeTrain(
+            np.asarray(spikes) * pq.ms, t_start=t_start * pq.ms, t_stop=t_stop * pq.ms
+        )
+        st.sampling_period = sampling_period * pq.ms
+        trains.append(st)
+    return trains
+
+
+# (spike_lists, t_start_ms, t_stop_ms, dt_ms)
+CASES = [
+    # Regular grid, the common MyoGen case.
+    ([[0, 1, 2, 5, 10]], 0, 11, 1.0),
+    # Multiple trains in one pool.
+    ([[5, 7], [0, 5, 10], []], 0, 11, 1.0),
+    # IEEE-754 awkward length (cf. issue #12): N=1001 bins at dt=1 ms.
+    ([list(range(0, 1001, 3))], 0, 1001, 1.0),
+    # Spike exactly on t_stop must be discarded (right edge).
+    ([[0, 5, 11]], 0, 11, 1.0),
+    # Sub-ms bins / fractional timestep.
+    ([[0.0, 0.1, 0.25, 0.9, 1.0]], 0, 2, 0.1),
+    # Non-zero t_start.
+    ([[3, 4, 7, 9]], 3, 10, 1.0),
+    # Dense, irregular spike times.
+    ([sorted(np.random.RandomState(0).uniform(0, 50, 40).tolist())], 0, 50, 0.5),
+    # Duplicate spikes in the same bin -> occupancy stays boolean (one True).
+    ([[0, 0, 1, 5, 5, 5]], 0, 11, 1.0),
+    # Every train empty.
+    ([[], []], 0, 11, 1.0),
+]
+
+
+@pytest.mark.parametrize("spike_lists,t0,t1,dt", CASES)
+def test_dense_matches_elephant_default_bounds(spike_lists, t0, t1, dt):
+    trains = _make_trains(spike_lists, t0, t1, dt)
+    expected = BinnedSpikeTrain(trains, bin_size=dt * pq.ms).to_array().astype(bool)
+    observed = bin_spike_trains(trains, bin_size=dt * pq.ms)
+    assert observed.dtype == bool
+    np.testing.assert_array_equal(observed, expected)
+
+
+@pytest.mark.parametrize("spike_lists,t0,t1,dt", CASES)
+def test_sparse_matches_elephant_explicit_bounds(spike_lists, t0, t1, dt):
+    # Mirrors the force-model usage: explicit t_start/t_stop + sparse bool.
+    trains = _make_trains(spike_lists, t0, t1, dt)
+    expected = BinnedSpikeTrain(
+        trains, bin_size=dt * pq.ms, t_start=t0 * pq.ms, t_stop=t1 * pq.ms
+    ).to_sparse_bool_array()
+    observed = bin_spike_trains(
+        trains, bin_size=dt * pq.ms, t_start=t0 * pq.ms, t_stop=t1 * pq.ms, sparse=True
+    )
+    assert observed.shape == expected.shape
+    assert (observed != expected).nnz == 0
+
+
+def test_explicit_subrange_bounds_match_elephant():
+    # Bin over a window strictly inside the train's [t_start, t_stop] (allowed
+    # by elephant), exercising the spike filtering against custom bounds.
+    st = SpikeTrain(
+        [1, 3, 5, 7, 9] * pq.ms, t_start=0 * pq.ms, t_stop=20 * pq.ms
+    )
+    st.sampling_period = 1 * pq.ms
+    expected = (
+        BinnedSpikeTrain([st], bin_size=1 * pq.ms, t_start=2 * pq.ms, t_stop=8 * pq.ms)
+        .to_array()
+        .astype(bool)
+    )
+    observed = bin_spike_trains(
+        [st], bin_size=1 * pq.ms, t_start=2 * pq.ms, t_stop=8 * pq.ms
+    )
+    np.testing.assert_array_equal(observed, expected)
+
+
+def test_tolerance_shifts_edge_spikes_like_elephant():
+    # A spike a hair below a bin edge should snap into the next bin (elephant's
+    # tolerance behaviour), not stay in the lower bin.
+    edge = 5.0 - 1e-12
+    trains = _make_trains([[edge]], 0, 11, 1.0)
+    expected = BinnedSpikeTrain(trains, bin_size=1.0 * pq.ms).to_array().astype(bool)
+    observed = bin_spike_trains(trains, bin_size=1.0 * pq.ms)
+    np.testing.assert_array_equal(observed, expected)
+    assert observed[0, 5]  # snapped up to bin 5, not 4

From 03b4edbbfa55c8b01c2315909ee6870f112b8a7d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Raul=20C=2E=20S=C3=AEmpetru?= <raul.simpetru@fau.de>
Date: Sun, 31 May 2026 21:23:23 +0200
Subject: [PATCH 02/13] refactor: replace elephant.statistics.isi with numpy in
 firing-rate stats

calculate_firing_rate_statistics now derives inter-spike intervals with
np.diff over the (sorted) spike times -- identical to elephant.statistics.isi
for sorted SpikeTrains -- removing the last elephant import from the library.
Restructure test_binning into self-contained golden tests (always run) plus
elephant-equivalence tests gated on elephant being installed.
---
 myogen/utils/helper.py |  24 ++-----
 tests/test_binning.py  | 144 +++++++++++++++++++++++++++--------------
 2 files changed, 101 insertions(+), 67 deletions(-)

diff --git a/myogen/utils/helper.py b/myogen/utils/helper.py
index 47389ba0..cbe5a165 100644
--- a/myogen/utils/helper.py
+++ b/myogen/utils/helper.py
@@ -2,14 +2,6 @@
 import pandas as pd
 import quantities as pq
 
-try:
-    from elephant.statistics import isi
-
-    HAS_ELEPHANT = True
-except ImportError:
-    HAS_ELEPHANT = False
-    isi = None  # type: ignore
-
 
 def get_gamma_shape_for_mvc(
     mvc_percent,
@@ -105,12 +97,6 @@ def calculate_firing_rate_statistics(
         If return_per_neuron=True:
             DataFrame with columns: MU_ID, mean_firing_rate_Hz, CV_ISI
     """
-    if not HAS_ELEPHANT:
-        raise ImportError(
-            "Elephant is required for firing rate statistics (ISI calculation). "
-            "Install with: pip install myogen[elephant]"
-        )
-
     # Set default min_firing_rate based on mode
     if min_firing_rate is None:
         min_firing_rate = 0.5 if not return_per_neuron else 0.0
@@ -133,11 +119,13 @@ def calculate_firing_rate_statistics(
         min_spikes = min_spikes_for_cv if return_per_neuron else 2
 
         if len(plateau_spiketrain) > min_spikes - 1:
-            # Extract ISIs for this neuron (only from plateau phase)
-            isis_values = isi(plateau_spiketrain.rescale(pq.s))
+            # Inter-spike intervals (in seconds). For a time-sorted SpikeTrain
+            # this is exactly elephant.statistics.isi: np.diff over the spike
+            # times (elephant additionally only warns on negative/unsorted
+            # intervals, which cannot occur for a sorted spike train).
+            isis_array = np.diff(plateau_spiketrain.rescale(pq.s).magnitude)
 
-            if len(isis_values) > 0:
-                isis_array = np.array(isis_values.magnitude)  # type: ignore
+            if len(isis_array) > 0:
                 neuron_fr = 1.0 / np.mean(isis_array)
 
                 if neuron_fr >= min_firing_rate:
diff --git a/tests/test_binning.py b/tests/test_binning.py
index 874d9da0..c99d21ec 100644
--- a/tests/test_binning.py
+++ b/tests/test_binning.py
@@ -1,5 +1,14 @@
-"""Prove myogen.utils.binning.bin_spike_trains matches elephant's
-BinnedSpikeTrain across the cases MyoGen relies on (issue #16)."""
+"""Tests for myogen.utils.binning.bin_spike_trains (issue #16).
+
+Two layers:
+
+* **Golden tests** assert the binner against hand-verified expected occupancy.
+  They are self-contained and require no optional dependencies, so they give
+  CI coverage even when ``elephant`` is not installed.
+* **Equivalence tests** assert bit-identical output to
+  ``elephant.conversion.BinnedSpikeTrain`` (the implementation it replaces).
+  They run only where ``elephant`` is available.
+"""
 
 import numpy as np
 import pytest
@@ -8,8 +17,12 @@
 
 from myogen.utils.binning import bin_spike_trains
 
-elephant = pytest.importorskip("elephant")
-from elephant.conversion import BinnedSpikeTrain  # noqa: E402
+try:
+    from elephant.conversion import BinnedSpikeTrain
+
+    HAS_ELEPHANT = True
+except ImportError:
+    HAS_ELEPHANT = False
 
 
 def _make_trains(spike_lists, t_start, t_stop, sampling_period):
@@ -23,41 +36,103 @@ def _make_trains(spike_lists, t_start, t_stop, sampling_period):
     return trains
 
 
-# (spike_lists, t_start_ms, t_stop_ms, dt_ms)
+def _occupancy(n_trains, n_bins, true_indices):
+    """Build a boolean (n_trains, n_bins) matrix from per-train True-bin lists."""
+    out = np.zeros((n_trains, n_bins), dtype=bool)
+    for row, idxs in enumerate(true_indices):
+        for j in idxs:
+            out[row, j] = True
+    return out
+
+
+# --------------------------------------------------------------------------- #
+# Golden tests (no elephant required)
+# --------------------------------------------------------------------------- #
+
+# (spike_lists, t_start, t_stop, dt, n_bins, expected True bins per train)
+GOLDEN = [
+    ("grid", [[0, 1, 2, 5, 10]], 0, 11, 1.0, 11, [[0, 1, 2, 5, 10]]),
+    ("multi_train", [[5, 7], [0, 5, 10], []], 0, 11, 1.0, 11, [[5, 7], [0, 5, 10], []]),
+    # Spike on t_stop (11) is discarded (bins cover [t_start, t_stop)).
+    ("tstop_discard", [[0, 5, 11]], 0, 11, 1.0, 11, [[0, 5]]),
+    # Fractional bins: 0.25/0.1 -> bin 2, 1.0/0.1 -> bin 10.
+    ("fractional", [[0, 0.1, 0.25, 0.9, 1.0]], 0, 2, 0.1, 20, [[0, 1, 2, 9, 10]]),
+    # Duplicate spikes collapse to one occupied bin.
+    ("duplicates", [[0, 0, 1, 5, 5, 5]], 0, 11, 1.0, 11, [[0, 1, 5]]),
+    # Every train empty.
+    ("empty", [[], []], 0, 11, 1.0, 11, [[], []]),
+]
+
+
+@pytest.mark.parametrize(
+    "name,spike_lists,t0,t1,dt,n_bins,true_idx",
+    GOLDEN,
+    ids=[g[0] for g in GOLDEN],
+)
+def test_golden_dense(name, spike_lists, t0, t1, dt, n_bins, true_idx):
+    trains = _make_trains(spike_lists, t0, t1, dt)
+    expected = _occupancy(len(spike_lists), n_bins, true_idx)
+    observed = bin_spike_trains(trains, bin_size=dt * pq.ms)
+    assert observed.dtype == bool
+    np.testing.assert_array_equal(observed, expected)
+
+
+def test_golden_sparse_matches_dense():
+    trains = _make_trains([[5, 7], [0, 5, 10]], 0, 11, 1.0)
+    dense = bin_spike_trains(trains, bin_size=1.0 * pq.ms)
+    sparse = bin_spike_trains(trains, bin_size=1.0 * pq.ms, sparse=True)
+    assert sparse.shape == dense.shape
+    np.testing.assert_array_equal(sparse.toarray().astype(bool), dense)
+
+
+def test_golden_tolerance_snaps_edge_spike_up():
+    # A spike a hair below bin 5's edge snaps up into bin 5, not 4.
+    trains = _make_trains([[5.0 - 1e-12]], 0, 11, 1.0)
+    observed = bin_spike_trains(trains, bin_size=1.0 * pq.ms)
+    expected = _occupancy(1, 11, [[5]])
+    np.testing.assert_array_equal(observed, expected)
+
+
+def test_golden_explicit_subrange_bounds():
+    # Bin over [2, 8) of a train spanning [0, 20]; spikes 3,5,7 land in bins
+    # 1,3,5 relative to t_start=2 (1 and 9 are outside the window).
+    st = SpikeTrain([1, 3, 5, 7, 9] * pq.ms, t_start=0 * pq.ms, t_stop=20 * pq.ms)
+    st.sampling_period = 1 * pq.ms
+    observed = bin_spike_trains(
+        [st], bin_size=1 * pq.ms, t_start=2 * pq.ms, t_stop=8 * pq.ms
+    )
+    expected = _occupancy(1, 6, [[1, 3, 5]])
+    np.testing.assert_array_equal(observed, expected)
+
+
+# --------------------------------------------------------------------------- #
+# Equivalence tests vs elephant (only where elephant is installed)
+# --------------------------------------------------------------------------- #
+
 CASES = [
-    # Regular grid, the common MyoGen case.
     ([[0, 1, 2, 5, 10]], 0, 11, 1.0),
-    # Multiple trains in one pool.
     ([[5, 7], [0, 5, 10], []], 0, 11, 1.0),
-    # IEEE-754 awkward length (cf. issue #12): N=1001 bins at dt=1 ms.
-    ([list(range(0, 1001, 3))], 0, 1001, 1.0),
-    # Spike exactly on t_stop must be discarded (right edge).
+    ([list(range(0, 1001, 3))], 0, 1001, 1.0),  # IEEE-754 awkward length (cf. #12)
     ([[0, 5, 11]], 0, 11, 1.0),
-    # Sub-ms bins / fractional timestep.
     ([[0.0, 0.1, 0.25, 0.9, 1.0]], 0, 2, 0.1),
-    # Non-zero t_start.
     ([[3, 4, 7, 9]], 3, 10, 1.0),
-    # Dense, irregular spike times.
     ([sorted(np.random.RandomState(0).uniform(0, 50, 40).tolist())], 0, 50, 0.5),
-    # Duplicate spikes in the same bin -> occupancy stays boolean (one True).
     ([[0, 0, 1, 5, 5, 5]], 0, 11, 1.0),
-    # Every train empty.
     ([[], []], 0, 11, 1.0),
 ]
 
 
+@pytest.mark.skipif(not HAS_ELEPHANT, reason="elephant not installed")
 @pytest.mark.parametrize("spike_lists,t0,t1,dt", CASES)
-def test_dense_matches_elephant_default_bounds(spike_lists, t0, t1, dt):
+def test_dense_matches_elephant(spike_lists, t0, t1, dt):
     trains = _make_trains(spike_lists, t0, t1, dt)
     expected = BinnedSpikeTrain(trains, bin_size=dt * pq.ms).to_array().astype(bool)
-    observed = bin_spike_trains(trains, bin_size=dt * pq.ms)
-    assert observed.dtype == bool
-    np.testing.assert_array_equal(observed, expected)
+    np.testing.assert_array_equal(bin_spike_trains(trains, bin_size=dt * pq.ms), expected)
 
 
+@pytest.mark.skipif(not HAS_ELEPHANT, reason="elephant not installed")
 @pytest.mark.parametrize("spike_lists,t0,t1,dt", CASES)
 def test_sparse_matches_elephant_explicit_bounds(spike_lists, t0, t1, dt):
-    # Mirrors the force-model usage: explicit t_start/t_stop + sparse bool.
     trains = _make_trains(spike_lists, t0, t1, dt)
     expected = BinnedSpikeTrain(
         trains, bin_size=dt * pq.ms, t_start=t0 * pq.ms, t_stop=t1 * pq.ms
@@ -67,32 +142,3 @@ def test_sparse_matches_elephant_explicit_bounds(spike_lists, t0, t1, dt):
     )
     assert observed.shape == expected.shape
     assert (observed != expected).nnz == 0
-
-
-def test_explicit_subrange_bounds_match_elephant():
-    # Bin over a window strictly inside the train's [t_start, t_stop] (allowed
-    # by elephant), exercising the spike filtering against custom bounds.
-    st = SpikeTrain(
-        [1, 3, 5, 7, 9] * pq.ms, t_start=0 * pq.ms, t_stop=20 * pq.ms
-    )
-    st.sampling_period = 1 * pq.ms
-    expected = (
-        BinnedSpikeTrain([st], bin_size=1 * pq.ms, t_start=2 * pq.ms, t_stop=8 * pq.ms)
-        .to_array()
-        .astype(bool)
-    )
-    observed = bin_spike_trains(
-        [st], bin_size=1 * pq.ms, t_start=2 * pq.ms, t_stop=8 * pq.ms
-    )
-    np.testing.assert_array_equal(observed, expected)
-
-
-def test_tolerance_shifts_edge_spikes_like_elephant():
-    # A spike a hair below a bin edge should snap into the next bin (elephant's
-    # tolerance behaviour), not stay in the lower bin.
-    edge = 5.0 - 1e-12
-    trains = _make_trains([[edge]], 0, 11, 1.0)
-    expected = BinnedSpikeTrain(trains, bin_size=1.0 * pq.ms).to_array().astype(bool)
-    observed = bin_spike_trains(trains, bin_size=1.0 * pq.ms)
-    np.testing.assert_array_equal(observed, expected)
-    assert observed[0, 5]  # snapped up to bin 5, not 4

From 0941362625c37d2ee672453f8700e21d6f0e8442 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Raul=20C=2E=20S=C3=AEmpetru?= <raul.simpetru@fau.de>
Date: Sun, 31 May 2026 21:23:23 +0200
Subject: [PATCH 03/13] examples: remove elephant/viziphant from gallery
 examples

Replace elephant.statistics.mean_firing_rate with len/duration, time_histogram
with a native numpy PSTH, instantaneous_rate (Gaussian KDE) with scipy
gaussian_filter1d, and BinnedSpikeTrain with myogen.utils.bin_spike_trains. The
viziphant rasterplot_rates in example 02 is replaced by a small native
matplotlib raster (raster + marginal axes).
---
 ...simulate_spike_trains_current_injection.py | 63 ++++++++++++++-----
 ..._simulate_spike_trains_descending_drive.py | 37 +++++++----
 .../12_extract_data_from_neo_blocks.py        | 12 +++-
 .../04_extract_isi_and_cv_per_ramps.py        | 10 ++-
 examples/03_papers/watanabe/06_visualize.py   | 19 +++---
 5 files changed, 99 insertions(+), 42 deletions(-)

diff --git a/examples/01_basic/02_simulate_spike_trains_current_injection.py b/examples/01_basic/02_simulate_spike_trains_current_injection.py
index e3435adc..1221b822 100644
--- a/examples/01_basic/02_simulate_spike_trains_current_injection.py
+++ b/examples/01_basic/02_simulate_spike_trains_current_injection.py
@@ -39,7 +39,6 @@
 
 from pathlib import Path
 
-import elephant
 import joblib
 import neuron
 import numpy as np
@@ -48,7 +47,7 @@
 from matplotlib import pyplot as plt
 from neo import Block, Segment, SpikeTrain
 from neuron import h
-from viziphant.rasterplot import rasterplot_rates
+from scipy.ndimage import gaussian_filter1d
 
 from myogen import get_random_generator
 from myogen.simulator.neuron.populations import AlphaMN__Pool
@@ -61,6 +60,40 @@
 
 plt.style.use("fivethirtyeight")
 
+
+def mean_firing_rate(spiketrain):
+    """Mean firing rate of a neo.SpikeTrain (replaces elephant.statistics.mean_firing_rate)."""
+    return (len(spiketrain) / (spiketrain.t_stop - spiketrain.t_start)).rescale(pq.Hz)
+
+
+def rasterplot_rates(spiketrains, filter_function=None):
+    """Native spike raster with top/right marginal axes.
+
+    Lightweight stand-in for ``viziphant.rasterplot.rasterplot_rates``: draws a
+    spike raster (one row per train), a right marginal showing each train's mean
+    firing rate, and an (initially empty) top marginal that the caller fills with
+    the smoothed population rate. Returns ``(ax, axhistx, axhisty)`` as
+    absolutely-positioned axes so manual position tweaks downstream still work.
+    """
+    if filter_function is not None:
+        spiketrains = [st for st in spiketrains if filter_function(st)]
+
+    fig = plt.figure()
+    ax = fig.add_axes((0.10, 0.10, 0.62, 0.62))
+    axhistx = fig.add_axes((0.10, 0.74, 0.62, 0.16), sharex=ax)
+    axhisty = fig.add_axes((0.74, 0.10, 0.16, 0.62), sharey=ax)
+
+    ax.eventplot(
+        [st.rescale(pq.s).magnitude for st in spiketrains],
+        colors="black",
+        linelengths=0.8,
+        linewidths=0.7,
+    )
+    rates = [float(mean_firing_rate(st).magnitude) for st in spiketrains]
+    axhisty.barh(np.arange(len(spiketrains)), rates, height=0.85, color="C0")
+    ax.set_ylim(-1, max(len(spiketrains), 1))
+    return ax, axhistx, axhisty
+
 ##############################################################################
 # Create Motor Neuron Populations (Pools)
 # ---------------------------------------
@@ -267,7 +300,7 @@
 firing_rates = [
     np.array(
         [
-            elephant.statistics.mean_firing_rate(st__s.time_slice(st__s.min(), st__s.max()))
+            mean_firing_rate(st__s.time_slice(st__s.min(), st__s.max()))
             for st__s in spike_train__segment.spiketrains
             if len(st__s) > 1  # Need at least 2 spikes to compute rate over spike range
         ]
@@ -312,19 +345,21 @@
 
 
 if len(active_spiketrains) > 0:
-    from elephant.kernels import GaussianKernel
-
-    rate = elephant.statistics.instantaneous_rate(
-        active_spiketrains,
-        sampling_period=(h.dt * pq.ms).rescale(pq.s),
-        kernel=GaussianKernel(sigma=15 * pq.ms),  # type: ignore
+    # Population firing rate over time, Gaussian-smoothed (sigma = 15 ms).
+    # Native replacement for elephant.statistics.instantaneous_rate.
+    sampling_period_s = (h.dt * pq.ms).rescale(pq.s).magnitude
+    t_start = min(st.t_start for st in active_spiketrains).rescale(pq.s).magnitude
+    t_stop = max(st.t_stop for st in active_spiketrains).rescale(pq.s).magnitude
+    n_bins = int(round((t_stop - t_start) / sampling_period_s))
+    edges = t_start + np.arange(n_bins + 1) * sampling_period_s
+    all_spikes = np.concatenate(
+        [st.rescale(pq.s).magnitude for st in active_spiketrains]
     )
+    counts, _ = np.histogram(all_spikes, bins=edges)
+    rate_hz = counts / sampling_period_s / len(active_spiketrains)
+    rate_hz = gaussian_filter1d(rate_hz, sigma=(15e-3) / sampling_period_s, mode="constant")
 
-    axhistx.plot(
-        rate.times.rescale(pq.s).magnitude,
-        rate.magnitude.mean(axis=1).flatten(),
-        linewidth=2,
-    )
+    axhistx.plot(edges[:-1] + sampling_period_s / 2, rate_hz, linewidth=2)
     axhistx.set_ylabel("FR (pps)")
     axhistx.set_xlim(ax.get_xlim())  # Match x-axis with raster plot
 
diff --git a/examples/01_basic/03_simulate_spike_trains_descending_drive.py b/examples/01_basic/03_simulate_spike_trains_descending_drive.py
index fe893998..812b204f 100644
--- a/examples/01_basic/03_simulate_spike_trains_descending_drive.py
+++ b/examples/01_basic/03_simulate_spike_trains_descending_drive.py
@@ -50,7 +50,6 @@
 import itertools
 from pathlib import Path
 
-import elephant
 import joblib
 import numpy as np
 import quantities as pq
@@ -67,6 +66,26 @@
 
 plt.style.use("fivethirtyeight")
 
+
+def mean_firing_rate(spiketrain):
+    """Mean firing rate of a neo.SpikeTrain (replaces elephant.statistics.mean_firing_rate)."""
+    return (len(spiketrain) / (spiketrain.t_stop - spiketrain.t_start)).rescale(pq.Hz)
+
+
+def population_psth(spiketrains, bin_size):
+    """Total spike counts per bin across spiketrains, plus bin left edges (s).
+
+    Native replacement for elephant.statistics.time_histogram (output="counts").
+    """
+    t_start = min(st.t_start for st in spiketrains).rescale(pq.s).magnitude
+    t_stop = max(st.t_stop for st in spiketrains).rescale(pq.s).magnitude
+    bs = (bin_size).rescale(pq.s).magnitude
+    n_bins = int((t_stop - t_start) / bs)
+    edges = t_start + np.arange(n_bins + 1) * bs
+    spikes = np.concatenate([st.rescale(pq.s).magnitude for st in spiketrains])
+    counts, _ = np.histogram(spikes, bins=edges)
+    return counts, edges[:-1]
+
 ##############################################################################
 # Create Populations
 # ------------------------
@@ -322,7 +341,7 @@
 # Calculate DD firing rates
 dd_firing_rates = np.array(
     [
-        elephant.statistics.mean_firing_rate(st__s.time_slice(st__s.min(), st__s.max()))
+        mean_firing_rate(st__s.time_slice(st__s.min(), st__s.max()))
         for st__s in dd_segment.spiketrains
         if len(st__s) > 0
     ]
@@ -331,7 +350,7 @@
 # Calculate MN firing rates
 mn_firing_rates = np.array(
     [
-        elephant.statistics.mean_firing_rate(st__s.time_slice(st__s.min(), st__s.max()))
+        mean_firing_rate(st__s.time_slice(st__s.min(), st__s.max()))
         for st__s in mn_segment.spiketrains
         if len(st__s) > 0
     ]
@@ -401,15 +420,11 @@
 # 4. Population firing rates over time (binned)
 bin_size_ms = 100
 
-dd_psth = elephant.statistics.time_histogram(dd_segment.spiketrains, bin_size_ms * pq.ms)
-dd_rates_binned = (
-    (dd_psth / (bin_size_ms * pq.ms) / descending_drive_pool.n).rescale(pq.Hz).magnitude
-)
-
-mn_psth = elephant.statistics.time_histogram(mn_segment.spiketrains, bin_size_ms * pq.ms)
-mn_rates_binned = (mn_psth / (bin_size_ms * pq.ms) / motor_neuron_pool.n).rescale(pq.Hz).magnitude
+dd_counts, bin_centers_s = population_psth(dd_segment.spiketrains, bin_size_ms * pq.ms)
+dd_rates_binned = dd_counts / (bin_size_ms / 1000.0) / descending_drive_pool.n
 
-bin_centers_s = dd_psth.times.rescale(pq.s).magnitude
+mn_counts, _ = population_psth(mn_segment.spiketrains, bin_size_ms * pq.ms)
+mn_rates_binned = mn_counts / (bin_size_ms / 1000.0) / motor_neuron_pool.n
 axes[3].plot(bin_centers_s, dd_rates_binned, "b-", linewidth=2, label="DD Population", alpha=0.8)
 axes[3].plot(bin_centers_s, mn_rates_binned, "r-", linewidth=2, label="MN Population", alpha=0.8)
 
diff --git a/examples/01_basic/12_extract_data_from_neo_blocks.py b/examples/01_basic/12_extract_data_from_neo_blocks.py
index ce508e4b..8212c0cc 100644
--- a/examples/01_basic/12_extract_data_from_neo_blocks.py
+++ b/examples/01_basic/12_extract_data_from_neo_blocks.py
@@ -22,10 +22,10 @@
 
 from pathlib import Path
 
-import elephant.statistics
 import joblib
 import matplotlib.pyplot as plt
 import numpy as np
+import quantities as pq
 import seaborn as sns
 
 from myogen.utils.neo import signal_to_grid
@@ -37,6 +37,12 @@
     SURFACE_MUAP__Block,
 )
 
+
+def _mean_firing_rate(spiketrain):
+    """Mean firing rate of a neo.SpikeTrain (replaces elephant.statistics.mean_firing_rate)."""
+    return (len(spiketrain) / (spiketrain.t_stop - spiketrain.t_start)).rescale(pq.Hz)
+
+
 plt.style.use("fivethirtyeight")
 sns.set_palette("husl")
 
@@ -101,7 +107,7 @@
         print(f"  Last spike: {spike_times__s[-1]:.4f} s")
 
         # Calculate firing rate
-        firing_rate = elephant.statistics.mean_firing_rate(spiketrain)
+        firing_rate = _mean_firing_rate(spiketrain)
         print(f"  Mean firing rate: {firing_rate:.2f}")
 
     # Calculate statistics across all neurons
@@ -110,7 +116,7 @@
 
     for st in motor_pool.spiketrains:
         if len(st) > 0:
-            rate = elephant.statistics.mean_firing_rate(st)
+            rate = _mean_firing_rate(st)
             firing_rates.append(rate.magnitude)
             active_neurons += 1
 
diff --git a/examples/02_finetune/04_extract_isi_and_cv_per_ramps.py b/examples/02_finetune/04_extract_isi_and_cv_per_ramps.py
index f04dd1c2..7807b325 100644
--- a/examples/02_finetune/04_extract_isi_and_cv_per_ramps.py
+++ b/examples/02_finetune/04_extract_isi_and_cv_per_ramps.py
@@ -49,7 +49,6 @@
 import warnings
 from pathlib import Path
 
-import elephant
 import joblib
 import numpy as np
 import quantities as pq
@@ -65,6 +64,11 @@
 from myogen.utils.helper import calculate_firing_rate_statistics
 from myogen.utils.nmodl import load_nmodl_mechanisms
 
+
+def _mean_firing_rate(spiketrain):
+    """Mean firing rate of a neo.SpikeTrain (replaces elephant.statistics.mean_firing_rate)."""
+    return (len(spiketrain) / (spiketrain.t_stop - spiketrain.t_start)).rescale(pq.Hz)
+
 warnings.filterwarnings("ignore")
 plt.style.use("fivethirtyeight")
 
@@ -460,7 +464,7 @@
 # Calculate DD firing rates
 dd_firing_rates = np.array(
     [
-        elephant.statistics.mean_firing_rate(st__s.time_slice(st__s.min(), st__s.max()))
+        _mean_firing_rate(st__s.time_slice(st__s.min(), st__s.max()))
         for st__s in dd_segment.spiketrains
         if len(st__s) > 0
     ]
@@ -469,7 +473,7 @@
 # Calculate MN firing rates
 mn_firing_rates = np.array(
     [
-        elephant.statistics.mean_firing_rate(st__s.time_slice(st__s.min(), st__s.max()))
+        _mean_firing_rate(st__s.time_slice(st__s.min(), st__s.max()))
         for st__s in mn_segment.spiketrains
         if len(st__s) > 0
     ]
diff --git a/examples/03_papers/watanabe/06_visualize.py b/examples/03_papers/watanabe/06_visualize.py
index 2d513d48..aed186c4 100644
--- a/examples/03_papers/watanabe/06_visualize.py
+++ b/examples/03_papers/watanabe/06_visualize.py
@@ -31,9 +31,9 @@
 
 **External Libraries Used**:
 
-- **Elephant** (``elephant.conversion.BinnedSpikeTrain``):
+- **MyoGen** (``myogen.utils.bin_spike_trains``):
   Converts NEO spike trains to binned format for coherence analysis.
-  Part of the Electrophysiology Analysis Toolkit, designed to work with NEO.
+  Dependency-free binning of NEO spike trains into boolean occupancy matrices.
 
 - **SciPy** (``scipy.signal.coherence``):
   Computes magnitude-squared coherence between signals using Welch's method.
@@ -75,7 +75,6 @@
 
 from pathlib import Path
 
-import elephant
 import joblib
 import matplotlib.gridspec as gridspec
 import matplotlib.pyplot as plt
@@ -87,6 +86,8 @@
 from matplotlib.patches import Rectangle
 from scipy import signal as scipy_signal
 
+from myogen.utils import bin_spike_trains
+
 plt.style.use("fivethirtyeight")
 
 ##############################################################################
@@ -190,15 +191,11 @@
     aMN_spikes_windowed = [st.time_slice(t_start * pq.s, t_stop * pq.s) for st in aMN_spikes]
 
     # Convert to binned spike trains
-    spike_trains_binned = elephant.conversion.BinnedSpikeTrain(
+    spike_trains_binned = bin_spike_trains(
         aMN_spikes_windowed,
-        n_bins=int(
-            (
-                aMN_spikes_windowed[0].sampling_rate.rescale("1/s")
-                * aMN_spikes_windowed[0].duration.rescale("s")
-            ).magnitude
-        ),
-    ).to_sparse_bool_array()
+        bin_size=(1.0 / aMN_spikes_windowed[0].sampling_rate.rescale("1/s")),
+        sparse=True,
+    )
 
     # Generate random pairs (composite spike trains)
     np.random.seed(42)

From d1f6b26b3566db02a9759807686ce91fd1f1758d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Raul=20C=2E=20S=C3=AEmpetru?= <raul.simpetru@fau.de>
Date: Sun, 31 May 2026 21:23:23 +0200
Subject: [PATCH 04/13] build,docs: drop the elephant and viziphant
 dependencies

Remove the [elephant] optional extra and the elephant/viziphant entries from
the dev and docs dependency-groups, and update the neo-block docs snippets to
compute firing rate natively. import myogen, the test suite, and the docs build
no longer require elephant. Add a CHANGELOG entry.
---
 CHANGELOG.md                     |  3 +++
 docs/neo_blocks.md               |  5 ++---
 docs/source/neo_blocks_guide.rst |  4 +---
 pyproject.toml                   | 14 --------------
 4 files changed, 6 insertions(+), 20 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0cf0da85..010dce81 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,6 +7,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Removed
+- **`elephant` (and `viziphant`) dependency dropped entirely.** Spike binning via `elephant.conversion.BinnedSpikeTrain` is replaced by a dependency-free `myogen.utils.bin_spike_trains` helper (an exact reimplementation, verified bit-identical across grid/edge/fractional/sparse cases), and `elephant.statistics.isi` in `utils/helper.py` is replaced by `numpy.diff` (identical for sorted spike trains). The example scripts now compute firing rates, PSTHs and rasters natively (the `viziphant` raster is replaced by a small matplotlib helper). The `[elephant]` optional extra and the `elephant`/`viziphant` dev/docs dependencies are removed. `import myogen`, the test suite, and the docs build no longer require `elephant`.
+
 ## [0.9.0] - 2026-04-19
 
 ### Added
diff --git a/docs/neo_blocks.md b/docs/neo_blocks.md
index bd4ee94a..5b6bbdaa 100644
--- a/docs/neo_blocks.md
+++ b/docs/neo_blocks.md
@@ -71,9 +71,8 @@ windowed = spiketrain.time_slice(t_start, t_stop)
 # Calculate RMS
 rms = np.sqrt(np.mean(signal.magnitude ** 2))
 
-# Firing rate (requires elephant)
-import elephant.statistics
-rate = elephant.statistics.mean_firing_rate(spiketrain)
+# Mean firing rate (spikes over the active duration)
+rate = (len(spiketrain) / (spiketrain.t_stop - spiketrain.t_start)).rescale("Hz")
 ```
 
 ## Iteration Examples
diff --git a/docs/source/neo_blocks_guide.rst b/docs/source/neo_blocks_guide.rst
index 86adf7de..4b56220a 100644
--- a/docs/source/neo_blocks_guide.rst
+++ b/docs/source/neo_blocks_guide.rst
@@ -93,14 +93,12 @@ The ``SPIKE_TRAIN__Block`` stores neural firing patterns from motor neuron pools
 
 .. code-block:: python
 
-   import elephant.statistics
-
    motor_pool = spike_train__Block.segments[0]
    firing_rates = []
 
    for spiketrain in motor_pool.spiketrains:
        if len(spiketrain) > 0:
-           rate = elephant.statistics.mean_firing_rate(spiketrain)
+           rate = (len(spiketrain) / (spiketrain.t_stop - spiketrain.t_start)).rescale("Hz")
            firing_rates.append(rate.magnitude)
 
    print(f"Mean firing rate: {np.mean(firing_rates):.2f} Hz")
diff --git a/pyproject.toml b/pyproject.toml
index cbfe9959..823544f2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -49,10 +49,6 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
-elephant = [
-    "elephant>=1.1.1",
-    "viziphant>=0.4.0",
-]
 nwb = [
     "pynwb>=2.8.0",
     "nwbinspector>=0.5.0",
@@ -78,11 +74,6 @@ dev = [
     "poethepoet>=0.37.0",
     "pytest>=8.0",
     "scipy-stubs>=1.16.1.0",
-    # Optional runtime extras pulled into the dev env so the full
-    # pytest suite (including elephant-dependent firing-rate tests)
-    # runs out of the box.
-    "elephant>=1.1.1",
-    "viziphant>=0.4.0",
 ]
 docs = [
     "enum-tools[sphinx]>=0.12.0",
@@ -102,11 +93,6 @@ docs = [
     # NWB support for examples
     "pynwb>=2.8.0",
     "nwbinspector>=0.5.0",
-    # Spike-train analysis + visualization needed by several examples
-    # (elephant is also declared as an optional extra; the docs build
-    # needs both for sphinx-gallery to render those examples).
-    "elephant>=1.1.1",
-    "viziphant>=0.4.0",
 ]
 
 [tool.pytest.ini_options]

From ac48dbce72f05a84bc0df583949c1315b0e479b6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Raul=20C=2E=20S=C3=AEmpetru?= <raul.simpetru@fau.de>
Date: Sun, 31 May 2026 21:31:22 +0200
Subject: [PATCH 05/13] examples: fix raster row offsets and PSTH right-edge
 binning

Address review: pass explicit lineoffsets to eventplot so each spike train
renders on its own raster row, and drop spikes on the right bin edge (t_stop)
before np.histogram so the native PSTH matches the old elephant binning.
---
 examples/01_basic/02_simulate_spike_trains_current_injection.py | 2 ++
 examples/01_basic/03_simulate_spike_trains_descending_drive.py  | 1 +
 2 files changed, 3 insertions(+)

diff --git a/examples/01_basic/02_simulate_spike_trains_current_injection.py b/examples/01_basic/02_simulate_spike_trains_current_injection.py
index 1221b822..fdbbb545 100644
--- a/examples/01_basic/02_simulate_spike_trains_current_injection.py
+++ b/examples/01_basic/02_simulate_spike_trains_current_injection.py
@@ -85,6 +85,7 @@ def rasterplot_rates(spiketrains, filter_function=None):
 
     ax.eventplot(
         [st.rescale(pq.s).magnitude for st in spiketrains],
+        lineoffsets=np.arange(len(spiketrains)),
         colors="black",
         linelengths=0.8,
         linewidths=0.7,
@@ -355,6 +356,7 @@ def rasterplot_rates(spiketrains, filter_function=None):
     all_spikes = np.concatenate(
         [st.rescale(pq.s).magnitude for st in active_spiketrains]
     )
+    all_spikes = all_spikes[(all_spikes >= edges[0]) & (all_spikes < edges[-1])]
     counts, _ = np.histogram(all_spikes, bins=edges)
     rate_hz = counts / sampling_period_s / len(active_spiketrains)
     rate_hz = gaussian_filter1d(rate_hz, sigma=(15e-3) / sampling_period_s, mode="constant")
diff --git a/examples/01_basic/03_simulate_spike_trains_descending_drive.py b/examples/01_basic/03_simulate_spike_trains_descending_drive.py
index 812b204f..c93ae017 100644
--- a/examples/01_basic/03_simulate_spike_trains_descending_drive.py
+++ b/examples/01_basic/03_simulate_spike_trains_descending_drive.py
@@ -83,6 +83,7 @@ def population_psth(spiketrains, bin_size):
     n_bins = int((t_stop - t_start) / bs)
     edges = t_start + np.arange(n_bins + 1) * bs
     spikes = np.concatenate([st.rescale(pq.s).magnitude for st in spiketrains])
+    spikes = spikes[(spikes >= edges[0]) & (spikes < edges[-1])]  # drop right-edge spikes
     counts, _ = np.histogram(spikes, bins=edges)
     return counts, edges[:-1]
 

From 2ba1da465b82eddab23bd7cfa6ea0e466e3719e4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Raul=20C=2E=20S=C3=AEmpetru?= <raul.simpetru@fau.de>
Date: Sun, 31 May 2026 21:40:27 +0200
Subject: [PATCH 06/13] build: support NumPy 2.x (drop the numpy<2.0 pin)

With elephant removed (the reason for the cap), relax the runtime numpy
requirement to >=1.26 and build the Cython extensions against numpy>=2.0.
Wheels built against NumPy 2.0 stay backward-compatible with NumPy 1.x at
runtime (verified: the compiled _gto extension imports under both NumPy 2.4 and
1.26). The scipy<1.17 build cap is unrelated (manylinux2014 wheel availability)
and is left in place.

Relates to #15.
---
 CHANGELOG.md   | 3 +++
 pyproject.toml | 4 ++--
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 010dce81..f3dec748 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,6 +7,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Changed
+- **NumPy 2.x support.** With `elephant` gone (the reason for the `numpy<2.0` pin), the runtime requirement is relaxed to `numpy>=1.26` and the build-system now compiles the Cython extensions against `numpy>=2.0`. Wheels built against NumPy 2.0 remain backward-compatible with NumPy 1.x at runtime (verified: the compiled extensions import under both NumPy 2.4 and 1.26). The `scipy<1.17` build cap is unrelated (manylinux2014 wheel availability) and is unchanged.
+
 ### Removed
 - **`elephant` (and `viziphant`) dependency dropped entirely.** Spike binning via `elephant.conversion.BinnedSpikeTrain` is replaced by a dependency-free `myogen.utils.bin_spike_trains` helper (an exact reimplementation, verified bit-identical across grid/edge/fractional/sparse cases), and `elephant.statistics.isi` in `utils/helper.py` is replaced by `numpy.diff` (identical for sorted spike trains). The example scripts now compute firing rates, PSTHs and rasters natively (the `viziphant` raster is replaced by a small matplotlib helper). The `[elephant]` optional extra and the `elephant`/`viziphant` dev/docs dependencies are removed. `import myogen`, the test suite, and the docs build no longer require `elephant`.
 
diff --git a/pyproject.toml b/pyproject.toml
index 823544f2..84e32ae1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -35,7 +35,7 @@ dependencies = [
     #"neuron-nightly>=9.0a1.dev1492",
     "neo>=0.14.0",
     "numba>=0.61.2",
-    "numpy>=1.26,<2.0",
+    "numpy>=1.26",
     "optuna>=4.5.0",
     "pandas>=2.0",
     "quantities>=0.16.2",
@@ -102,7 +102,7 @@ testpaths = ["tests"]
 setup_myogen = "python -c 'from myogen import _setup_myogen; _setup_myogen()'"
 
 [build-system]
-requires = ["setuptools>=61.0", "Cython>=3.0.0", "numpy>=1.26,<2.0", "scipy>=1.15.3,<1.17"]
+requires = ["setuptools>=61.0", "Cython>=3.0.0", "numpy>=2.0", "scipy>=1.15.3,<1.17"]
 build-backend = "setuptools.build_meta"
 
 [tool.setuptools]

From e800c58de5653d813b83fbf22244f7e20a8980a7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Raul=20C=2E=20S=C3=AEmpetru?= <raul.simpetru@fau.de>
Date: Sun, 31 May 2026 21:49:29 +0200
Subject: [PATCH 07/13] build: add Python 3.13 support

Widen requires-python to >=3.12,<3.14, add the Python 3.13 classifier, and
extend the CI test matrix and cibuildwheel build (cp312-* cp313-*) to 3.13.
NEURON 8.2.7 already ships cp313 wheels, and the Cython extensions build and
import under Python 3.13 against NumPy 2.x (verified locally on macOS arm64).
---
 .github/workflows/build-wheels.yml | 6 +++---
 .github/workflows/tests.yml        | 5 +++--
 CHANGELOG.md                       | 1 +
 pyproject.toml                     | 3 ++-
 4 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/build-wheels.yml b/.github/workflows/build-wheels.yml
index bdaf0cbc..ee42a7c7 100644
--- a/.github/workflows/build-wheels.yml
+++ b/.github/workflows/build-wheels.yml
@@ -30,8 +30,8 @@ jobs:
       - name: Build wheels
         uses: pypa/cibuildwheel@v2.21
         env:
-          # Only build for Python 3.12+
-          CIBW_BUILD: "cp312-*"
+          # Build for Python 3.12 and 3.13
+          CIBW_BUILD: "cp312-* cp313-*"
 
           # Skip 32-bit builds and musl (Alpine) on Linux
           CIBW_SKIP: "*-win32 *-manylinux_i686 *-musllinux_*"
@@ -119,7 +119,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macos-latest]  # Match build_wheels matrix
-        python-version: ["3.12"]
+        python-version: ["3.12", "3.13"]
 
     steps:
       - name: Set up Python ${{ matrix.python-version }}
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 2a92b585..cf9eb10e 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -14,13 +14,14 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest]
+        python-version: ["3.12", "3.13"]
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python 3.12
+      - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v5
         with:
-          python-version: "3.12.8"
+          python-version: ${{ matrix.python-version }}
 
       - name: System packages for NEURON / MPI (Linux)
         if: runner.os == 'Linux'
diff --git a/CHANGELOG.md b/CHANGELOG.md
index f3dec748..7fe23bd9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [Unreleased]
 
 ### Changed
+- **Python 3.13 support.** `requires-python` is widened to `>=3.12,<3.14`, a `Python :: 3.13` classifier is added, and the CI test and wheel matrices now cover 3.13 (`cp312-* cp313-*`). NEURON 8.2.7 ships cp313 wheels, and the Cython extensions build and import under Python 3.13 against NumPy 2.x (verified locally).
 - **NumPy 2.x support.** With `elephant` gone (the reason for the `numpy<2.0` pin), the runtime requirement is relaxed to `numpy>=1.26` and the build-system now compiles the Cython extensions against `numpy>=2.0`. Wheels built against NumPy 2.0 remain backward-compatible with NumPy 1.x at runtime (verified: the compiled extensions import under both NumPy 2.4 and 1.26). The `scipy<1.17` build cap is unrelated (manylinux2014 wheel availability) and is unchanged.
 
 ### Removed
diff --git a/pyproject.toml b/pyproject.toml
index 84e32ae1..68431c91 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,7 +3,7 @@ name = "MyoGen"
 version = "0.9.0"
 description = "Modular and extensible neuromuscular simulation framework for generating physiologically grounded motor-unit activity, muscle force, and EMG signals (surface and intramuscular)"
 readme = "README.md"
-requires-python = ">=3.12,<3.13"
+requires-python = ">=3.12,<3.14"
 license = "AGPL-3.0-or-later"
 authors = [
     {name = "Raul C. Sîmpetru", email = "raul.simpetru@fau.de"},
@@ -21,6 +21,7 @@ classifiers = [
     "Operating System :: Microsoft :: Windows",
     "Programming Language :: Python :: 3",
     "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
     "Programming Language :: Python :: 3 :: Only",
     "Topic :: Scientific/Engineering",
     "Topic :: Scientific/Engineering :: Bio-Informatics",

From 3fd42f934b24f29052667e527de3432930b2170a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Raul=20C=2E=20S=C3=AEmpetru?= <raul.simpetru@fau.de>
Date: Sun, 31 May 2026 22:00:35 +0200
Subject: [PATCH 08/13] ci: install only the python-matching wheel in
 test_wheels

Now that build_wheels produces both cp312 and cp313 wheels, the test_wheels
'pip install dist/*.whl' tried to install both and pip rejected the wheel for
the other ABI. Select the wheel matching the runner's Python tag instead.
---
 .github/workflows/build-wheels.yml | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/build-wheels.yml b/.github/workflows/build-wheels.yml
index ee42a7c7..c4b4f9b3 100644
--- a/.github/workflows/build-wheels.yml
+++ b/.github/workflows/build-wheels.yml
@@ -151,7 +151,10 @@ jobs:
 
       - name: Install wheel
         run: |
-          pip install dist/*.whl
+          # dist/ now holds both cp312 and cp313 wheels; install only the one
+          # matching this runner's Python so pip doesn't reject the other ABI.
+          tag="cp$(echo '${{ matrix.python-version }}' | tr -d .)"
+          pip install dist/*"${tag}-${tag}"*.whl
 
       - name: Test import
         run: |

From 3fba7fe954a1165b6ea81c15299e708d1e240d18 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Raul=20C=2E=20S=C3=AEmpetru?= <raul.simpetru@fau.de>
Date: Sun, 31 May 2026 22:13:47 +0200
Subject: [PATCH 09/13] ci: bump actions to Node 24 versions (checkout v5,
 setup-python v6, artifacts v6/v7)

Node 20 actions are deprecated (forced to Node 24 on 2026-06-16). Move
actions/checkout to v5, actions/setup-python to v6, actions/upload-artifact to
v6, and actions/download-artifact to v7 -- the minimal majors that run on
Node 24 (artifact v5 and download v6 are still Node 20).
---
 .github/workflows/build-wheels.yml | 16 ++++++++--------
 .github/workflows/sphinx.yml       |  4 ++--
 .github/workflows/tests.yml        |  4 ++--
 3 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/.github/workflows/build-wheels.yml b/.github/workflows/build-wheels.yml
index c4b4f9b3..a8d477f2 100644
--- a/.github/workflows/build-wheels.yml
+++ b/.github/workflows/build-wheels.yml
@@ -25,7 +25,7 @@ jobs:
         os: [ubuntu-latest, macos-latest]
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       - name: Build wheels
         uses: pypa/cibuildwheel@v2.21
@@ -80,7 +80,7 @@ jobs:
         run: ls -lh wheelhouse/
 
       - name: Upload wheels
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: wheels-${{ matrix.os }}
           path: wheelhouse/*.whl
@@ -90,10 +90,10 @@ jobs:
     name: Build source distribution
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: "3.12"
 
@@ -106,7 +106,7 @@ jobs:
         run: python -m build --sdist --outdir dist/
 
       - name: Upload sdist
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: sdist
           path: dist/*.tar.gz
@@ -123,12 +123,12 @@ jobs:
 
     steps:
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
 
       - name: Download wheels
-        uses: actions/download-artifact@v4
+        uses: actions/download-artifact@v7
         with:
           name: wheels-${{ matrix.os }}
           path: dist/
@@ -177,7 +177,7 @@ jobs:
 
     steps:
       - name: Download all artifacts
-        uses: actions/download-artifact@v4
+        uses: actions/download-artifact@v7
         with:
           path: dist/
           merge-multiple: true
diff --git a/.github/workflows/sphinx.yml b/.github/workflows/sphinx.yml
index c27cdc84..f58b5a9b 100644
--- a/.github/workflows/sphinx.yml
+++ b/.github/workflows/sphinx.yml
@@ -9,10 +9,10 @@ jobs:
   deploy-docs:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v5
 
     - name: Set up Python 3.12
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v6
       with:
         python-version: 3.12.8
 
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index cf9eb10e..6c8a5055 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -16,10 +16,10 @@ jobs:
         os: [ubuntu-latest, macos-latest]
         python-version: ["3.12", "3.13"]
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
 

From fc089730d9a25d539b513b9896fd2972aa76be1c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Raul=20C=2E=20S=C3=AEmpetru?= <raul.simpetru@fau.de>
Date: Sun, 31 May 2026 22:16:52 +0200
Subject: [PATCH 10/13] ci: bump third-party actions to Node 24 (peaceiris v4,
 softprops v3)

peaceiris/actions-gh-pages@v3 was node16 and softprops/action-gh-release@v1
was node16 (v2 is still node20); move to v4 and v3 respectively, both node24.
Inputs are unchanged (github_token/publish_dir for peaceiris, files for
softprops). pypa/cibuildwheel and pypa/gh-action-pypi-publish are composite
actions and unaffected by the Node 20 deprecation.
---
 .github/workflows/build-wheels.yml | 2 +-
 .github/workflows/sphinx.yml       | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/build-wheels.yml b/.github/workflows/build-wheels.yml
index a8d477f2..b3312286 100644
--- a/.github/workflows/build-wheels.yml
+++ b/.github/workflows/build-wheels.yml
@@ -184,7 +184,7 @@ jobs:
 
       - name: Upload artifacts to GitHub Release
         if: github.event_name == 'release'
-        uses: softprops/action-gh-release@v1
+        uses: softprops/action-gh-release@v3
         with:
           files: dist/*
 
diff --git a/.github/workflows/sphinx.yml b/.github/workflows/sphinx.yml
index f58b5a9b..8daf0d1e 100644
--- a/.github/workflows/sphinx.yml
+++ b/.github/workflows/sphinx.yml
@@ -36,7 +36,7 @@ jobs:
         cd ..
 
     - name: Deploy
-      uses: peaceiris/actions-gh-pages@v3
+      uses: peaceiris/actions-gh-pages@v4
       with:
         github_token: ${{ secrets.GITHUB_TOKEN }}
         publish_dir: docs/build/html

From 089ab75ad3284f0457228a2d1c3db30ead455b05 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Raul=20C=2E=20S=C3=AEmpetru?= <raul.simpetru@fau.de>
Date: Sun, 31 May 2026 22:37:31 +0200
Subject: [PATCH 11/13] build: bump Linux wheels to manylinux_2_28 and lift
 scipy<1.17

scipy >= 1.17 only ships manylinux_2_28 wheels, so the cibuildwheel Linux
base moves manylinux2014 -> manylinux_2_28 (glibc >= 2.28; drops EOL
CentOS/RHEL 7) and the build-system scipy<1.17 cap is removed. The repair step
tags wheels manylinux_2_28, and openblas-devel is dropped from the Linux
pre-build deps (only needed for the old scipy source-build fallback, which no
longer happens now that scipy installs from a wheel).
---
 .github/workflows/build-wheels.yml | 15 +++++++++------
 CHANGELOG.md                       |  2 +-
 pyproject.toml                     |  2 +-
 3 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/.github/workflows/build-wheels.yml b/.github/workflows/build-wheels.yml
index b3312286..95d2cf3a 100644
--- a/.github/workflows/build-wheels.yml
+++ b/.github/workflows/build-wheels.yml
@@ -36,10 +36,12 @@ jobs:
           # Skip 32-bit builds and musl (Alpine) on Linux
           CIBW_SKIP: "*-win32 *-manylinux_i686 *-musllinux_*"
 
-          # Install system dependencies before building (Linux)
-          # openblas-devel needed if scipy builds from source
+          # Install system dependencies before building (Linux).
+          # On manylinux_2_28 scipy installs from a prebuilt wheel, so the
+          # openblas-devel that the old source-build fallback needed (and which
+          # lives in the PowerTools repo on AlmaLinux 8) is no longer required.
           CIBW_BEFORE_ALL_LINUX: |
-            yum install -y openmpi-devel openblas-devel
+            yum install -y openmpi-devel
 
           # Install system dependencies before building (macOS)
           CIBW_BEFORE_ALL_MACOS: |
@@ -57,8 +59,9 @@ jobs:
             pip install neuron==8.2.7
             cd myogen/simulator/nmodl_files && nrnivmodl .
 
-          # Use manylinux2014 (manylinux_2_17)
-          CIBW_MANYLINUX_X86_64_IMAGE: manylinux2014
+          # manylinux_2_28 (glibc >= 2.28) so scipy >= 1.17 prebuilt wheels are
+          # available in the build container (scipy dropped manylinux2014).
+          CIBW_MANYLINUX_X86_64_IMAGE: manylinux_2_28
 
           # Custom repair to preserve NEURON mechanisms while adding proper platform tags
           # Standard tools (auditwheel/delocate) strip the architecture directories
@@ -69,7 +72,7 @@ jobs:
             wheel = Path(sys.argv[1])
             dest = Path(sys.argv[2])
             # Rename to manylinux without modifying contents
-            new_name = wheel.name.replace('linux_', 'manylinux_2_17_').replace('manylinux2014_', 'manylinux_2_17_')
+            new_name = wheel.name.replace('linux_', 'manylinux_2_28_').replace('manylinux2014_', 'manylinux_2_28_')
             shutil.copy2(wheel, dest / new_name)
             " {wheel} {dest_dir}
 
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7fe23bd9..59f0e710 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,7 +9,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Changed
 - **Python 3.13 support.** `requires-python` is widened to `>=3.12,<3.14`, a `Python :: 3.13` classifier is added, and the CI test and wheel matrices now cover 3.13 (`cp312-* cp313-*`). NEURON 8.2.7 ships cp313 wheels, and the Cython extensions build and import under Python 3.13 against NumPy 2.x (verified locally).
-- **NumPy 2.x support.** With `elephant` gone (the reason for the `numpy<2.0` pin), the runtime requirement is relaxed to `numpy>=1.26` and the build-system now compiles the Cython extensions against `numpy>=2.0`. Wheels built against NumPy 2.0 remain backward-compatible with NumPy 1.x at runtime (verified: the compiled extensions import under both NumPy 2.4 and 1.26). The `scipy<1.17` build cap is unrelated (manylinux2014 wheel availability) and is unchanged.
+- **NumPy 2.x support.** With `elephant` gone (the reason for the `numpy<2.0` pin), the runtime requirement is relaxed to `numpy>=1.26` and the build-system now compiles the Cython extensions against `numpy>=2.0`. Wheels built against NumPy 2.0 remain backward-compatible with NumPy 1.x at runtime (verified: the compiled extensions import under both NumPy 2.4 and 1.26). The Linux wheel build base is bumped from manylinux2014 to **manylinux_2_28** (glibc ≥ 2.28, which drops EOL CentOS/RHEL 7) so the `scipy<1.17` build cap can be lifted (scipy ≥ 1.17 only ships manylinux_2_28 wheels).
 
 ### Removed
 - **`elephant` (and `viziphant`) dependency dropped entirely.** Spike binning via `elephant.conversion.BinnedSpikeTrain` is replaced by a dependency-free `myogen.utils.bin_spike_trains` helper (an exact reimplementation, verified bit-identical across grid/edge/fractional/sparse cases), and `elephant.statistics.isi` in `utils/helper.py` is replaced by `numpy.diff` (identical for sorted spike trains). The example scripts now compute firing rates, PSTHs and rasters natively (the `viziphant` raster is replaced by a small matplotlib helper). The `[elephant]` optional extra and the `elephant`/`viziphant` dev/docs dependencies are removed. `import myogen`, the test suite, and the docs build no longer require `elephant`.
diff --git a/pyproject.toml b/pyproject.toml
index 68431c91..eb1e500d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -103,7 +103,7 @@ testpaths = ["tests"]
 setup_myogen = "python -c 'from myogen import _setup_myogen; _setup_myogen()'"
 
 [build-system]
-requires = ["setuptools>=61.0", "Cython>=3.0.0", "numpy>=2.0", "scipy>=1.15.3,<1.17"]
+requires = ["setuptools>=61.0", "Cython>=3.0.0", "numpy>=2.0", "scipy>=1.15.3"]
 build-backend = "setuptools.build_meta"
 
 [tool.setuptools]

From f03314a8db2edddff8af91777ee3656fb78db6dd Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Raul=20C=2E=20S=C3=AEmpetru?= <raul.simpetru@fau.de>
Date: Sun, 31 May 2026 22:40:55 +0200
Subject: [PATCH 12/13] build: refresh uv.lock (drop elephant/viziphant, numpy
 2.4, scipy 1.17, py3.13)

Regenerate the lockfile for the updated dependency set: elephant and
viziphant removed, requires-python widened to >=3.12,<3.14, and numpy/scipy
upgraded to 2.4.6 / 1.17.1 now that the <2.0 / <1.17 caps are gone.
---
 uv.lock | 1648 +++++++++++++++++++++++++++++--------------------------
 1 file changed, 879 insertions(+), 769 deletions(-)

diff --git a/uv.lock b/uv.lock
index cebe7c3e..48d45fad 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,6 +1,14 @@
 version = 1
 revision = 3
-requires-python = "==3.12.*"
+requires-python = ">=3.12, <3.14"
+resolution-markers = [
+    "python_full_version >= '3.13' and sys_platform == 'win32'",
+    "python_full_version < '3.13' and sys_platform == 'win32'",
+    "python_full_version >= '3.13' and sys_platform == 'emscripten'",
+    "python_full_version < '3.13' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
 
 [[package]]
 name = "accessible-pygments"
@@ -16,16 +24,16 @@ wheels = [
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.6.1"
+version = "2.6.2"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/33/c6/61a2d7b7572279226bb2e7f61d7a19ca7c90da0329c93fa0d560cbf288d8/aiohappyeyeballs-2.6.2.tar.gz", hash = "sha256:e202810ee718bd01fc6ef49e8ea53d023d5cb6b581076d7925aa499fa55dbe64", size = 22591, upload-time = "2026-05-20T15:12:24.631Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/fc/a7bf5b6e4e617b45f90f2d9d2a68519c249c81dd4fc2658c7a2a61c4f4b7/aiohappyeyeballs-2.6.2-py3-none-any.whl", hash = "sha256:4708045e2d7a6c6bdf8aafa8ed39649eaf926a4543b54560659129e3365953c4", size = 15062, upload-time = "2026-05-20T15:12:23.328Z" },
 ]
 
 [[package]]
 name = "aiohttp"
-version = "3.13.2"
+version = "3.13.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -36,25 +44,42 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
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From 63e07758f67aa1491de88a90c7bb4f7f9716aa30 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Raul=20C=2E=20S=C3=AEmpetru?= <raul.simpetru@fau.de>
Date: Sun, 31 May 2026 22:49:14 +0200
Subject: [PATCH 13/13] fix: use scipy.integrate.trapezoid (np.trapz removed in
 NumPy 2.0)

np.trapz was removed in NumPy 2.0, which broke fiber_simulation under the
upgraded lock (numpy 2.4). scipy.integrate.trapezoid has the same signature and
works on both NumPy 1.26 and 2.x. Verified: full suite passes on numpy 2.4.6 /
scipy 1.17.1.
---
 myogen/simulator/core/emg/fiber_simulation.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/myogen/simulator/core/emg/fiber_simulation.py b/myogen/simulator/core/emg/fiber_simulation.py
index 8479c7f4..9d64941b 100644
--- a/myogen/simulator/core/emg/fiber_simulation.py
+++ b/myogen/simulator/core/emg/fiber_simulation.py
@@ -18,6 +18,7 @@
 import math
 
 import numpy as np
+from scipy.integrate import trapezoid
 from scipy.special import jv as Jn
 
 from myogen.simulator.core.emg.electrodes import SurfaceElectrodeArray
@@ -945,7 +946,7 @@ def simulate_fiber_unified(
                 kernel_interp = interp1d(z_kernel, kernel_ch, kind='linear',
                                          bounds_error=False, fill_value=0.0)
                 kernel_vals = kernel_interp(kernel_pos)
-                phi_right[idx] = np.trapz(src_vals * kernel_vals, dx=du)
+                phi_right[idx] = trapezoid(src_vals * kernel_vals, dx=du)
 
         # ---- Leftward wave (propagates toward left tendon) ----
         # s_left(t) = -zi - v*t + z0  (flipped direction)
@@ -982,7 +983,7 @@ def simulate_fiber_unified(
                 kernel_interp = interp1d(z_kernel, kernel_ch, kind='linear',
                                          bounds_error=False, fill_value=0.0)
                 kernel_vals = kernel_interp(kernel_pos)
-                phi_left[idx] = np.trapz(src_vals * kernel_vals, dx=du)
+                phi_left[idx] = trapezoid(src_vals * kernel_vals, dx=du)
 
         # Total: rightward minus leftward
         phi[ch] = phi_right - phi_left