Drop elephant; add NumPy 2.x and Python 3.13 support

.github/workflows/build-wheels.yml

@@ -25,21 +25,23 @@ jobs:
         os: [ubuntu-latest, macos-latest]
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
       - 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_*"
 
-          # 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}
 
@@ -80,7 +83,7 @@ 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 +93,10 @@ 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 +109,7 @@ 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
@@ -119,16 +122,16 @@ 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 }}
-        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/
@@ -151,7 +154,10 @@ 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: |
@@ -174,14 +180,14 @@ jobs:
 
     steps:
       - name: Download all artifacts
-        uses: actions/download-artifact@v4
+        uses: actions/download-artifact@v7
         with:
           path: dist/
           merge-multiple: true
 
       - 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/*
 

.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
 
@@ -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

.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
+      - uses: actions/checkout@v5
 
-      - name: Set up Python 3.12
-        uses: actions/setup-python@v5
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v6
         with:
-          python-version: "3.12.8"
+          python-version: ${{ matrix.python-version }}
 
       - name: System packages for NEURON / MPI (Linux)
         if: runner.os == 'Linux'

CHANGELOG.md

@@ -7,6 +7,13 @@ 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 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`.
+
 ## [0.9.0] - 2026-04-19
 
 ### Added

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

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")

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 +60,41 @@
 
 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],
+        lineoffsets=np.arange(len(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 +301,7 @@
 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 +346,22 @@
 
 
 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]
     )
+    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")
 
-    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
 

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 +66,27 @@
 
 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])
+    spikes = spikes[(spikes >= edges[0]) & (spikes < edges[-1])]  # drop right-edge spikes
+    counts, _ = np.histogram(spikes, bins=edges)
+    return counts, edges[:-1]
+
 ##############################################################################
 # Create Populations
 # ------------------------
@@ -322,7 +342,7 @@
 # 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 +351,7 @@
 # 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 +421,11 @@
 # 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)