From 30eed170680790efbfb6f20eef714b81c1b7f083 Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Wed, 1 Jul 2026 11:13:08 +0200
Subject: [PATCH 01/12] docs: fix PyPI wording, MyST {doc} roles, and stale
 test description

---
 docs/contact.md               |  2 +-
 docs/installation.md          | 12 +++++-------
 docs/legal.md                 |  2 +-
 docs/quickstart.md            | 10 +++++-----
 docs/software_architecture.md |  3 ++-
 5 files changed, 14 insertions(+), 15 deletions(-)

diff --git a/docs/contact.md b/docs/contact.md
index 7caf09b..de1bfa6 100644
--- a/docs/contact.md
+++ b/docs/contact.md
@@ -13,7 +13,7 @@ Please use the GitHub issue tracker for:
 - bug reports (include a minimal reproduction and your environment)
 - feature requests and discussion
 
-If you plan to contribute code or documentation, see :doc:`contributing`.
+If you plan to contribute code or documentation, see {doc}`contributing`.
 
 ## Lab
 
diff --git a/docs/installation.md b/docs/installation.md
index 7be1708..408ce16 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -33,15 +33,13 @@ conda activate pygpla
 
 ## Install PyGPLA
 
-### From PyPI (placeholder)
-
-Once published:
+### From PyPI
 
 ```bash
 pip install pygpla
 ```
 
-### From source (recommended for now)
+### From source
 
 ```bash
 git clone https://github.com/CMC-lab/PyGPLA.git
@@ -118,6 +116,6 @@ pip install -e ".[docs]"
 
 ## Next steps
 
-- Quickstart: :doc:`quickstart`
-- Tutorials (Figure 2 walkthrough): :doc:`tutorials`
-- Usage notes and configuration knobs: :doc:`usage`
+- Quickstart: {doc}`quickstart`
+- Tutorials (Figure 2 walkthrough): {doc}`tutorials`
+- Usage notes and configuration knobs: {doc}`usage`
diff --git a/docs/legal.md b/docs/legal.md
index 460e9b4..ea57ccd 100644
--- a/docs/legal.md
+++ b/docs/legal.md
@@ -9,7 +9,7 @@ This page summarizes licensing and legal notes for PyGPLA.
 PyGPLA is distributed under the **BSD 2-Clause License**.
 
 - Full text: `LICENSE`
-- Docs summary: :doc:`license`
+- Docs summary: {doc}`license`
 
 ## Third-party content
 
diff --git a/docs/quickstart.md b/docs/quickstart.md
index 7384aa5..b55a122 100644
--- a/docs/quickstart.md
+++ b/docs/quickstart.md
@@ -11,7 +11,7 @@ You will:
 - (optionally) add a significance test
 
 If you want the full Figure 2-style reproduction with transient models and multi-panel plots,
-see :doc:`tutorials`.
+see {doc}`tutorials`.
 
 ## Install
 
@@ -121,7 +121,7 @@ print("Selected units:", result.metadata["selected_units"])
 :::{note}
 For real data, you typically build `lfp_signal` by bandpass filtering around a target
 frequency and applying a Hilbert transform to get the complex analytic signal.
-The Figure 2 tutorial shows a concrete example of that preprocessing: :doc:`tutorials`.
+The Figure 2 tutorial shows a concrete example of that preprocessing: {doc}`tutorials`.
 :::
 
 ## Interpreting the outputs (what you get back)
@@ -243,6 +243,6 @@ normalization. Use `nSpikeThreshold` (or a `PreprocessingConfig`) to filter low-
 
 ## What’s next?
 
-- Full reproduction and deeper explanation: :doc:`tutorials`
-- API reference: :doc:`api`
-- Usage notes (normalization, whitening, statistics knobs): :doc:`usage`
+- Full reproduction and deeper explanation: {doc}`tutorials`
+- API reference: {doc}`api`
+- Usage notes (normalization, whitening, statistics knobs): {doc}`usage`
diff --git a/docs/software_architecture.md b/docs/software_architecture.md
index 4820492..f0caa36 100644
--- a/docs/software_architecture.md
+++ b/docs/software_architecture.md
@@ -126,7 +126,8 @@ The split helps users reason about reproducibility and runtime costs.
 - Test framework: **pytest**
 - Current tests include:
   - transient simulation sanity/integration checks
-  - placeholder baseline test
+  - unit tests for the coupling matrix, SVD factorization, whitening, spike-jitter
+    surrogates, and input validation
 - Lint tooling configuration is present via **Ruff** in `pyproject.toml`.
 
 As the package matures, parity/regression tests against MATLAB reference outputs are the next

From 8773f9bb795c14293b3d69eab9c4769d4dcc6f04 Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Wed, 1 Jul 2026 11:42:08 +0200
Subject: [PATCH 02/12] paper: finalize AI-usage disclosure wording

---
 paper/paper.md | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/paper/paper.md b/paper/paper.md
index 904ab05..a70b960 100644
--- a/paper/paper.md
+++ b/paper/paper.md
@@ -80,10 +80,6 @@ The `pygpla` package is distributed under the BSD-2-Clause license. PyGPLA follo
 
 ## Use of generative AI
 
-<!-- TODO(authors): Edit this paragraph so it accurately reflects your actual
-use of generative-AI tools. JOSS requires a transparent disclosure (or an
-explicit statement that none was used). Delete whichever option does not apply. -->
-
 Generative AI tools were used to assist with auxiliary development tasks such as
 drafting documentation, refactoring code for style, and generating boilerplate
 for tests and continuous-integration configuration. All AI-assisted output was

From 8c4c9e9c1aeebbeaa63802acc0c72cf320335d35 Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Wed, 1 Jul 2026 11:45:22 +0200
Subject: [PATCH 03/12] paper: scope AI-usage disclosure to documentation and
 style refactoring

---
 paper/paper.md | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/paper/paper.md b/paper/paper.md
index a70b960..9d80b0c 100644
--- a/paper/paper.md
+++ b/paper/paper.md
@@ -80,13 +80,11 @@ The `pygpla` package is distributed under the BSD-2-Clause license. PyGPLA follo
 
 ## Use of generative AI
 
-Generative AI tools were used to assist with auxiliary development tasks such as
-drafting documentation, refactoring code for style, and generating boilerplate
-for tests and continuous-integration configuration. All AI-assisted output was
-reviewed, tested, and validated by the authors, who take full responsibility for
-the correctness of the software and the content of this paper. The scientific
-method, algorithmic design, and numerical implementation of GPLA were carried out
-by the authors.
+Generative AI tools were used only to assist with drafting documentation and
+refactoring code for style. All AI-assisted output was reviewed, tested, and
+validated by the authors, who take full responsibility for the correctness of the
+software and the content of this paper. The scientific method, algorithmic design,
+and numerical implementation of GPLA were carried out by the authors.
 
 ## Acknowledgments
 

From bbc96810a8ac81976b08167d030bec90118af854 Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Thu, 2 Jul 2026 19:59:38 +0200
Subject: [PATCH 04/12] fix(packaging): declare matplotlib dependency and trim
 sdist bloat

- paper/figures/figure2.py imports matplotlib, but it was undeclared
  anywhere in pyproject.toml; a reviewer following the README's own
  'pip install -e .' + 'python paper/figures/figure2.py' instructions
  would hit ModuleNotFoundError. Add a 'figures' extra and update
  README/tutorials.md instructions accordingly.
- The sdist had no include/exclude config, so it bundled the entire
  repo (paper.pdf, all figure exports, dev notes, .claude/, .github/),
  ballooning it to 2.27MB. Constrain sdist/wheel targets to just the
  package, tests, README, and LICENSE (23.7KB).
- Regenerate figure exports and paper.pdf as part of verification.
---
 README.md                        |     5 +-
 docs/tutorials.md                |     7 +-
 paper/figures/figure2_python.eps | 14916 +++++++++++++++--------------
 paper/figures/figure2_python.pdf |   Bin 94294 -> 96921 bytes
 paper/figures/figure2_python.png |   Bin 1001594 -> 1001572 bytes
 paper/figures/figure2_python.svg |  1935 ++--
 paper/paper.pdf                  |   Bin 1176361 -> 1170603 bytes
 pyproject.toml                   |    14 +
 8 files changed, 8556 insertions(+), 8321 deletions(-)

diff --git a/README.md b/README.md
index 6d95802..9b88030 100644
--- a/README.md
+++ b/README.md
@@ -110,13 +110,14 @@ Input conventions:
 
 ## Reproducing Figure 2 from the original paper (Safavi et al., 2023)
 
-Run:
+Install the extras it needs (SciPy for filtering, Matplotlib for plotting), then run:
 
 ```bash
+pip install -e ".[sim,figures]"
 python paper/figures/figure2.py
 ```
 
-This generates `paper/figures/figure2_python.png`.
+This generates `paper/figures/figure2_python.png` (plus PDF/SVG/EPS exports).
 
 <!-- DOCS_INDEX_END -->
 
diff --git a/docs/tutorials.md b/docs/tutorials.md
index 54794fb..d7dc9c8 100644
--- a/docs/tutorials.md
+++ b/docs/tutorials.md
@@ -39,16 +39,15 @@ signal processing libraries.
 From the repo root:
 
 ```bash
-pip install -e ".[sim,docs]"
-pip install matplotlib
+pip install -e ".[sim,figures,docs]"
 ```
 
 - `.[sim]` provides SciPy (needed for bandpass filtering + Hilbert transform).
-- `matplotlib` is required to render and save the figure.
+- `.[figures]` provides Matplotlib, required to render and save the figure.
 
 :::{note}
 If you only want to **build the docs**, you typically only need `.[docs]`. The figure script
-is separate and can be run in an environment that has `matplotlib` and `scipy`.
+is separate and can be run in an environment that has `.[sim,figures]` installed.
 :::
 
 ### Quick reproduction (one command)
diff --git a/paper/figures/figure2_python.eps b/paper/figures/figure2_python.eps
index 9f9b2be..04acadf 100644
--- a/paper/figures/figure2_python.eps
+++ b/paper/figures/figure2_python.eps
@@ -1,11 +1,11 @@
 %!PS-Adobe-3.0 EPSF-3.0
 %%LanguageLevel: 3
 %%Title: figure2_python.eps
-%%Creator: Matplotlib v3.10.5, https://matplotlib.org/
-%%CreationDate: Tue Jun 30 23:05:07 2026
+%%Creator: Matplotlib v3.11.0, https://matplotlib.org/
+%%CreationDate: Thu Jul  2 19:57:02 2026
 %%Orientation: portrait
 %%BoundingBox: 0 0 499 560
-%%HiResBoundingBox: 0.000000 0.000000 498.240000 559.448125
+%%HiResBoundingBox: 0.000000 0.000000 498.240000 559.701055
 %%EndComments
 %%BeginProlog
 /mpldict 10 dict def
@@ -23,7 +23,7 @@ mpldict begin
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         /FontMatrix [1 0 0 1 0 0] def
-        /FontName /ArialMT def
+        /FontName /ArialMT-0 def
         /FontInfo 7 dict dup begin
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@@ -37,734 +37,1041 @@ mpldict begin
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-        /CharStrings 52 dict dup begin
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diff --git a/pyproject.toml b/pyproject.toml
index 5e1f096..1ad6893 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -42,6 +42,9 @@ tests = [
 sim = [
   "scipy>=1.9",
 ]
+figures = [
+  "matplotlib>=3.5",
+]
 
 [project.urls]
 Repository = "https://github.com/CMC-lab/PyGPLA"
@@ -53,6 +56,17 @@ path = "src/pygpla/_version.py"
 [tool.hatch.metadata]
 allow-direct-references = true
 
+[tool.hatch.build.targets.sdist]
+include = [
+  "src/pygpla",
+  "tests",
+  "README.md",
+  "LICENSE",
+]
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/pygpla"]
+
 [tool.pytest.ini_options]
 testpaths = ["tests"]
 

From 2cfc31f977fd583ae27415b94e47af88273e86ee Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Wed, 15 Jul 2026 23:41:26 +0200
Subject: [PATCH 05/12] Update acknowledgments

---
 paper/paper.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/paper/paper.md b/paper/paper.md
index 9d80b0c..bf0919a 100644
--- a/paper/paper.md
+++ b/paper/paper.md
@@ -88,6 +88,6 @@ and numerical implementation of GPLA were carried out by the authors.
 
 ## Acknowledgments
 
-We acknowledge contributions from collaborators who provided feedback during the development of PyGPLA. Shervin Safavi acknowledges the support from the Max Planck Society and an add-on fellowship from the Joachim Herz Foundation.
+We acknowledge contributions from collaborators who provided feedback during the development of pyGPLA. S.S. acknowledges support from the Max Planck Society and an add-on fellowship from the Joachim Herz Foundation. C.B. acknowledges support from the Federal Ministry of Research, Technology and Space (Bundesministerium für Forschung, Technologie und Raumfahrt; BMFTR) as part of the German Center for Child and Adolescent Health (DZKJ) under funding code 01GL2405B.
 
 ## References

From fba9e4800f84db2da52db49bc05b9de1ace877ad Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Thu, 16 Jul 2026 16:19:42 +0200
Subject: [PATCH 06/12] update the README

---
 README.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/README.md b/README.md
index 9b88030..731e8fb 100644
--- a/README.md
+++ b/README.md
@@ -155,8 +155,6 @@ If you use PyGPLA in your research, please cite the original GPLA method paper:
 }
 ```
 
-A dedicated software citation (JOSS) will be added here once the paper is published.
-
 ## License
 
 BSD 2-Clause License. See `LICENSE`.

From 5fa3134786aa50bb75e8c11864a8dc6c84881032 Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Fri, 17 Jul 2026 11:12:31 +0200
Subject: [PATCH 07/12] docs: describe the real public API in the package
 docstring

---
 src/pygpla/__init__.py | 32 +++++++++++++++++++++++++++++---
 1 file changed, 29 insertions(+), 3 deletions(-)

diff --git a/src/pygpla/__init__.py b/src/pygpla/__init__.py
index e2f5286..3b998dc 100644
--- a/src/pygpla/__init__.py
+++ b/src/pygpla/__init__.py
@@ -1,9 +1,35 @@
 """
 PyGPLA: Generalized Phase Locking Analysis tools for Python.
 
-The package is under active development; upcoming releases will expose high-level
-analysis helpers via ``pygpla.gpla`` alongside lower-level preprocessing and
-statistics modules. See the project notes in ``notes/`` for the migration plan.
+GPLA summarizes multichannel spike-LFP coupling by building a complex coupling
+matrix and extracting its dominant low-rank structure via SVD, yielding a scalar
+coupling strength (gPLV) together with an LFP vector and a spike vector.
+
+The main entry point is :func:`pygpla.api.gpla`::
+
+    from pygpla.api import gpla
+
+    result = gpla(spikes, lfp_analytic, stats_config={"testType": "RMT-based"})
+    result.gplv, result.lfp_vector, result.spike_vector
+
+Modules
+-------
+api
+    High-level :func:`~pygpla.api.gpla` entry point and its
+    :class:`~pygpla.api.GPLAResult` container.
+core
+    Coupling-matrix construction, SVD factorization, and PCA whitening.
+preprocessing
+    Trial concatenation, spike-count filtering, and related data preparation.
+simulations
+    Phase-locked, transient-coupling, and Poisson spike-train generators.
+stats
+    RMT-based and spike-jitter surrogate significance testing.
+config
+    Dataclasses describing whitening, preprocessing, and statistical-test options.
+
+The method is described in Safavi et al. (2023), *Uncovering the organization of
+neural circuits with Generalized Phase Locking Analysis*, PLOS Computational Biology.
 """
 
 from ._version import __version__

From 7603ede2bfdb413cf3c535d1bea580ef9aca993e Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Fri, 17 Jul 2026 13:56:13 +0200
Subject: [PATCH 08/12] remove unused stats.summaries module

---
 docs/api.md                   |  2 --
 src/pygpla/stats/summaries.py | 29 -----------------------------
 2 files changed, 31 deletions(-)
 delete mode 100644 src/pygpla/stats/summaries.py

diff --git a/docs/api.md b/docs/api.md
index b3eeb8b..c31de35 100644
--- a/docs/api.md
+++ b/docs/api.md
@@ -49,8 +49,6 @@ pygpla.stats.jitter.interval_jitter
 pygpla.stats.jitter.isi_preserved_jitter
 pygpla.stats.jitter.group_preserved_jitter
 pygpla.stats.jitter.population_jitter
-pygpla.stats.summaries.summarize_gplv_results
-pygpla.stats.summaries.summarize_plv_matrix
 ```
 
 ## Simulations
diff --git a/src/pygpla/stats/summaries.py b/src/pygpla/stats/summaries.py
deleted file mode 100644
index 23a156c..0000000
--- a/src/pygpla/stats/summaries.py
+++ /dev/null
@@ -1,29 +0,0 @@
-"""Helpers to summarize GPLA statistical outputs."""
-
-from __future__ import annotations
-
-from typing import Dict, Iterable
-
-import numpy as np
-
-__all__ = ["summarize_gplv_results", "summarize_plv_matrix"]
-
-
-def summarize_gplv_results(gplv_values: Iterable[float]) -> Dict[str, float]:
-    """Compute summary statistics over GPLA values."""
-
-    values = np.asarray(list(gplv_values), dtype=float)
-    if values.size == 0:
-        return {"mean": float("nan"), "std": float("nan")}
-    return {"mean": float(values.mean()), "std": float(values.std())}
-
-
-def summarize_plv_matrix(plv_matrix: np.ndarray) -> Dict[str, float]:
-    """Summaries for PLV matrices (placeholder)."""
-
-    if plv_matrix.size == 0:
-        return {"max": float("nan"), "min": float("nan")}
-    return {
-        "max": float(np.nanmax(plv_matrix)),
-        "min": float(np.nanmin(plv_matrix)),
-    }

From fffbc1f5b8e6f40ac43d31400ac40f3da10e683f Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Fri, 17 Jul 2026 15:24:07 +0200
Subject: [PATCH 09/12] fix(simulations): honor the rng argument when
 generating spikes

Note: np.random.seed() no longer influences these generators. Pass rng
instead.
---
 paper/figures/figure2_python.eps       | 14842 +++++++++++------------
 paper/figures/figure2_python.pdf       |   Bin 96921 -> 94272 bytes
 paper/figures/figure2_python.png       |   Bin 1001572 -> 1000587 bytes
 paper/figures/figure2_python.svg       |  1884 ++-
 src/pygpla/simulations/phase_locked.py |    10 +-
 src/pygpla/simulations/poisson.py      |    22 +-
 src/pygpla/simulations/transient.py    |     4 +-
 tests/test_simulation_rng.py           |   104 +
 8 files changed, 8249 insertions(+), 8617 deletions(-)
 create mode 100644 tests/test_simulation_rng.py

diff --git a/paper/figures/figure2_python.eps b/paper/figures/figure2_python.eps
index 04acadf..84b16ef 100644
--- a/paper/figures/figure2_python.eps
+++ b/paper/figures/figure2_python.eps
@@ -1,11 +1,11 @@
 %!PS-Adobe-3.0 EPSF-3.0
 %%LanguageLevel: 3
 %%Title: figure2_python.eps
-%%Creator: Matplotlib v3.11.0, https://matplotlib.org/
-%%CreationDate: Thu Jul  2 19:57:02 2026
+%%Creator: Matplotlib v3.10.5, https://matplotlib.org/
+%%CreationDate: Fri Jul 17 15:15:40 2026
 %%Orientation: portrait
 %%BoundingBox: 0 0 499 560
-%%HiResBoundingBox: 0.000000 0.000000 498.240000 559.701055
+%%HiResBoundingBox: 0.000000 0.000000 498.240000 559.448125
 %%EndComments
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 /mpldict 10 dict def
@@ -23,7 +23,7 @@ mpldict begin
         10 dict begin
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-        /FontName /ArialMT-0 def
+        /FontName /ArialMT def
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@@ -37,1041 +37,734 @@ mpldict begin
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     <g id="text_109">
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+     <text style="font-size: 8px; font-family: 'Arial', 'Helvetica', 'DejaVu Sans', sans-serif; text-anchor: middle" x="148.032" y="554.867969" transform="rotate(-0 148.032 554.867969)">Phase lag [deg]</text>
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@@ -6517,38 +6511,38 @@ z
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   </g>
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+   <text style="font-weight: 700; font-size: 14px; font-family: 'Arial', 'Helvetica', 'DejaVu Sans', sans-serif; text-anchor: start" x="2.88" y="414.372937" transform="rotate(-0 2.88 414.372937)">F</text>
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+  <clipPath id="paa27620def">
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   </clipPath>
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 z
 "/>
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 z
 "/>
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 z
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diff --git a/src/pygpla/simulations/phase_locked.py b/src/pygpla/simulations/phase_locked.py
index ebe32f4..ee08f1c 100644
--- a/src/pygpla/simulations/phase_locked.py
+++ b/src/pygpla/simulations/phase_locked.py
@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-from typing import Dict, Tuple
+from typing import Dict, Optional, Tuple
 
 import numpy as np
 
@@ -147,11 +147,15 @@ def _repeat_params(arr, n_unit, n_tr):
 def generate_phase_locked_spikes(
     spike_params: Dict,
     signal_params: Dict,
+    rng: Optional[np.random.Generator] = None,
 ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
     """
     Generate phase-locked Poisson spike trains using von Mises modulation.
 
     Mirrors the MATLAB/PyGPLA_dev implementation.
+
+    Pass ``rng`` (a ``numpy.random.Generator``) for reproducible output; when it
+    is None a fresh, unseeded generator is used.
     """
 
     a = np.asarray(spike_params.get("lockingPhase", 0.0))
@@ -180,5 +184,7 @@ def generate_phase_locked_spikes(
             phase = 2 * np.pi * F[u, tr] * t - A[u, tr]
             FRmodulators[u, :, tr] = R[u, tr] * np.exp(K[u, tr] * np.cos(phase)) / _i0(K[u, tr])
 
-    spikes = generate_inhomogeneous_poisson(FRmodulators, duration_s, sf, n_tr=n_tr, n_unit=n_unit)
+    spikes = generate_inhomogeneous_poisson(
+        FRmodulators, duration_s, sf, n_tr=n_tr, n_unit=n_unit, rng=rng
+    )
     return spikes, theoPLV, FRmodulators
diff --git a/src/pygpla/simulations/poisson.py b/src/pygpla/simulations/poisson.py
index 0115567..ce06b8e 100644
--- a/src/pygpla/simulations/poisson.py
+++ b/src/pygpla/simulations/poisson.py
@@ -16,6 +16,7 @@ def generate_homogeneous_poisson(
     n_tr: Optional[int] = None,
     n_unit: Optional[int] = None,
     method: str = "based_spikingProbality",
+    rng: Optional[np.random.Generator] = None,
 ) -> np.ndarray:
     """
     Generate homogeneous Poisson spike trains.
@@ -23,8 +24,14 @@ def generate_homogeneous_poisson(
     Parameters replicate the MATLAB/Python port: firing_rates can be scalar,
     per-unit, or per-trial.
     Returns an array of shape (units, n_bins, trials) with 0/1 spikes.
+
+    Pass ``rng`` (a ``numpy.random.Generator``) for reproducible output; when it
+    is None a fresh, unseeded generator is used.
     """
 
+    if rng is None:
+        rng = np.random.default_rng()
+
     n_bins = int(round(duration_s * sf))
 
     fr = np.asarray(firing_rates)
@@ -49,7 +56,7 @@ def generate_homogeneous_poisson(
         tmp[:, 0, :] = fr
         prob = np.repeat(tmp, n_bins, axis=1) / sf
 
-    spikes = (np.random.rand(n_unit, n_bins, n_tr) < prob).astype(int)
+    spikes = (rng.random((n_unit, n_bins, n_tr)) < prob).astype(int)
     return spikes
 
 
@@ -59,13 +66,20 @@ def generate_inhomogeneous_poisson(
     sf: float,
     n_tr: Optional[int] = None,
     n_unit: Optional[int] = None,
+    rng: Optional[np.random.Generator] = None,
 ) -> np.ndarray:
     """
     Generate inhomogeneous Poisson spike trains via thinning.
 
     `fr_modulators` has shape (units, n_bins[, trials]) containing rate profiles (Hz).
+
+    Pass ``rng`` (a ``numpy.random.Generator``) for reproducible output; when it
+    is None a fresh, unseeded generator is used.
     """
 
+    if rng is None:
+        rng = np.random.default_rng()
+
     fr = np.asarray(fr_modulators)
     if fr.ndim == 2:
         fr = fr[:, :, None]
@@ -79,12 +93,14 @@ def generate_inhomogeneous_poisson(
 
     r_max = fr.max(axis=1, keepdims=True)
     base_fr = np.squeeze(r_max, axis=1)
-    hp = generate_homogeneous_poisson(base_fr, duration_s, sf, n_tr=n_tr, n_unit=n_unit)
+    hp = generate_homogeneous_poisson(
+        base_fr, duration_s, sf, n_tr=n_tr, n_unit=n_unit, rng=rng
+    )
 
     with np.errstate(divide="ignore", invalid="ignore"):
         ratio = fr / r_max
     ratio = np.nan_to_num(ratio, nan=0.0, posinf=0.0, neginf=0.0)
-    reject = ratio > np.random.rand(*fr.shape)
+    reject = ratio > rng.random(fr.shape)
 
     spikes = (reject & (hp == 1)).astype(int)
     return spikes
diff --git a/src/pygpla/simulations/transient.py b/src/pygpla/simulations/transient.py
index 3645e4e..9786940 100644
--- a/src/pygpla/simulations/transient.py
+++ b/src/pygpla/simulations/transient.py
@@ -132,7 +132,9 @@ def simulate_transient_locked(
             num = np.exp(kappa_spk[:, tr][:, None] * np.cos(phase))
             FR[:, start:stop, tr] = avFR[:, tr][:, None] * (num / I0_k[:, tr][:, None])
 
-    spikes_3d = generate_inhomogeneous_poisson(FR, duration_s, sf, n_tr=n_tr, n_unit=n_unit)
+    spikes_3d = generate_inhomogeneous_poisson(
+        FR, duration_s, sf, n_tr=n_tr, n_unit=n_unit, rng=rng
+    )
     spikes: List[np.ndarray] = [spikes_3d[:, :, tr] for tr in range(n_tr)]
 
     if return_analytic:
diff --git a/tests/test_simulation_rng.py b/tests/test_simulation_rng.py
new file mode 100644
index 0000000..9112f0d
--- /dev/null
+++ b/tests/test_simulation_rng.py
@@ -0,0 +1,104 @@
+"""Reproducibility tests for the simulation generators (pygpla.simulations).
+
+The generators must draw exclusively from the ``rng`` they are handed, so that a
+seeded call is reproducible and a library call never disturbs the caller's own
+global NumPy random state.
+"""
+
+import numpy as np
+import pytest
+
+from pygpla.simulations import (
+    generate_homogeneous_poisson,
+    generate_inhomogeneous_poisson,
+    generate_phase_locked_spikes,
+    simulate_transient_locked,
+)
+
+SIGNAL_PARAMS = dict(nCh=3, nUnit=6, SF=400.0, nTr=2, signalLength=2.0)
+GLOBAL_PARAMS = dict(
+    oscFreq=20.0,
+    nCycl=10,
+    syncSigProportion=0.7,
+    lfpPhaseNoise_kappa=8.0,
+    whiteNoise_sigma=0.05,
+)
+
+
+def _transient(seed):
+    return simulate_transient_locked(
+        GLOBAL_PARAMS,
+        dict(avefiringRate=18.0),
+        dict(lockingStrength_kappa=10.0, lockingPhase=0.0),
+        SIGNAL_PARAMS,
+        return_analytic=True,
+        rng=np.random.default_rng(seed),
+    )
+
+
+def _phase_locked(seed):
+    spikes, _, _ = generate_phase_locked_spikes(
+        dict(lockingPhase=0.0, kappa=5.0, avefiringRate=18.0, lockingFreq=20.0),
+        SIGNAL_PARAMS,
+        rng=np.random.default_rng(seed),
+    )
+    return spikes
+
+
+def _homogeneous(seed):
+    return generate_homogeneous_poisson(
+        20.0, 1.0, 200.0, n_tr=2, n_unit=4, rng=np.random.default_rng(seed)
+    )
+
+
+def _inhomogeneous(seed):
+    fr = np.full((4, 200, 2), 20.0)
+    return generate_inhomogeneous_poisson(
+        fr, 1.0, 200.0, n_tr=2, n_unit=4, rng=np.random.default_rng(seed)
+    )
+
+
+SPIKE_GENERATORS = [_phase_locked, _homogeneous, _inhomogeneous]
+
+
+@pytest.mark.parametrize("generator", SPIKE_GENERATORS)
+def test_same_seed_reproduces_spikes(generator):
+    np.testing.assert_array_equal(generator(0), generator(0))
+
+
+@pytest.mark.parametrize("generator", SPIKE_GENERATORS)
+def test_different_seeds_give_different_spikes(generator):
+    assert not np.array_equal(generator(0), generator(1))
+
+
+def test_transient_simulation_is_reproducible_including_spikes():
+    _, lfp_a, spikes_a, _ = _transient(42)
+    _, lfp_b, spikes_b, _ = _transient(42)
+
+    np.testing.assert_array_equal(lfp_a, lfp_b)
+    assert len(spikes_a) == len(spikes_b)
+    for tr_a, tr_b in zip(spikes_a, spikes_b, strict=True):
+        np.testing.assert_array_equal(tr_a, tr_b)
+
+
+def test_transient_simulation_varies_with_seed():
+    *_, spikes_a, _ = _transient(42)
+    *_, spikes_b, _ = _transient(43)
+
+    assert not all(
+        np.array_equal(tr_a, tr_b) for tr_a, tr_b in zip(spikes_a, spikes_b, strict=True)
+    )
+
+
+@pytest.mark.parametrize("generator", SPIKE_GENERATORS)
+def test_generators_do_not_disturb_global_random_state(generator):
+    """A seeded caller must get the same draw whether or not pygpla ran."""
+
+    np.random.seed(0)
+    expected = np.random.rand()
+
+    np.random.seed(0)
+    generator(0)
+    actual = np.random.rand()
+
+    assert actual == expected

From 0f0de8d4efa03cd63ffd0da9c0018ec937b8fa7e Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Fri, 17 Jul 2026 15:41:54 +0200
Subject: [PATCH 10/12] fix(simulations): use .item() for scalar firing rates

Use float(fr.item()), which is correct for any single-element array.
---
 src/pygpla/simulations/poisson.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/pygpla/simulations/poisson.py b/src/pygpla/simulations/poisson.py
index ce06b8e..a4205d3 100644
--- a/src/pygpla/simulations/poisson.py
+++ b/src/pygpla/simulations/poisson.py
@@ -47,7 +47,7 @@ def generate_homogeneous_poisson(
         raise ValueError("Only 'based_spikingProbality' method is supported.")
 
     if fr.size == 1:
-        prob = np.full((n_unit, n_bins, n_tr), float(fr) / sf)
+        prob = np.full((n_unit, n_bins, n_tr), float(fr.item()) / sf)
     elif fr.shape[1] == 1:
         prob = np.repeat(fr, n_bins, axis=1)[:, :, None] / sf
         prob = np.repeat(prob, n_tr, axis=2)

From 3674f72463f4bb5e23754bd1e216be56e7e79af8 Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Fri, 17 Jul 2026 17:10:16 +0200
Subject: [PATCH 11/12] release: bump to 0.0.2 and single-source the version

---
 docs/conf.py                  |  2 +-
 docs/software_architecture.md | 12 ++++++------
 pyproject.toml                |  2 +-
 src/pygpla/_version.py        |  2 +-
 4 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index abbadf0..75d4a65 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -16,7 +16,7 @@
 try:
     release = version("pygpla")
 except PackageNotFoundError:
-    release = "0.0.1"
+    release = "0.0.2"
 version = release
 
 extensions = [
diff --git a/docs/software_architecture.md b/docs/software_architecture.md
index f0caa36..28509a4 100644
--- a/docs/software_architecture.md
+++ b/docs/software_architecture.md
@@ -141,11 +141,11 @@ important QA step.
 
 ## Version Management
 
-PyGPLA currently uses an explicit package version:
+PyGPLA keeps its version in a single source:
 
-- `project.version = "0.0.1"` in `pyproject.toml`
-- `__version__ = "0.0.1"` in `src/pygpla/_version.py`
+- `__version__` in `src/pygpla/_version.py`
 
-The Hatch setting `tool.hatch.version.path = "src/pygpla/_version.py"` keeps the package's
-runtime version source in one place. Version bumps are therefore currently a deliberate,
-manual release action rather than git-tag-driven automation.
+`pyproject.toml` declares `dynamic = ["version"]` and points Hatch at that file via
+`tool.hatch.version.path = "src/pygpla/_version.py"`, so the build metadata and the runtime
+`pygpla.__version__` are read from the same place. Version bumps are a deliberate, manual
+release action (edit `_version.py`) rather than git-tag-driven automation.
diff --git a/pyproject.toml b/pyproject.toml
index 1ad6893..ddbaf37 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "pygpla"
-version = "0.0.1"
+dynamic = ["version"]
 description = "Generalized Phase Locking Analysis (GPLA) toolkit in Python."
 readme = "README.md"
 requires-python = ">=3.10"
diff --git a/src/pygpla/_version.py b/src/pygpla/_version.py
index ebedfe9..5784c8e 100644
--- a/src/pygpla/_version.py
+++ b/src/pygpla/_version.py
@@ -2,4 +2,4 @@
 
 __all__ = ["__version__"]
 
-__version__ = "0.0.1"
+__version__ = "0.0.2"

From 5b7913f53bf407d886606958db816a59228989ab Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Sat, 18 Jul 2026 19:13:47 +0200
Subject: [PATCH 12/12] docs: expand citation author lists and add 2021
 framework paper

---
 README.md | 40 ++++++++++++++++++++++++++++++++++------
 1 file changed, 34 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index 731e8fb..ccf3d74 100644
--- a/README.md
+++ b/README.md
@@ -141,17 +141,45 @@ pytest
 
 ## Citing
 
-If you use PyGPLA in your research, please cite the original GPLA method paper:
+If you use PyGPLA in your research, please cite the following papers.
 
-> Safavi, S., et al. (2023). *Uncovering the organization of neural circuits with
-> Generalized Phase Locking Analysis.* PLOS Computational Biology.
+The original GPLA method paper:
+
+> Safavi, S., Panagiotaropoulos, T. I., Kapoor, V., Ramirez-Villegas, J. F.,
+> Logothetis, N. K., & Besserve, M. (2023). *Uncovering the organization of neural
+> circuits with Generalized Phase Locking Analysis.* PLOS Computational Biology,
+> 19(4), e1010983.
 
 ```bibtex
 @article{safavi2023uncovering,
   title   = {Uncovering the organization of neural circuits with Generalized Phase Locking Analysis},
-  author  = {Safavi, Shervin and others},
-  journal = {PLOS Computational Biology},
-  year    = {2023}
+  author  = {Safavi, Shervin and Panagiotaropoulos, Theofanis I. and Kapoor, Vishal and Ramirez-Villegas, Juan F. and Logothetis, Nikos K. and Besserve, Michel},
+  journal = {PLoS Computational Biology},
+  year    = {2023},
+  volume  = {19},
+  number  = {4},
+  pages   = {e1010983},
+  doi     = {10.1371/journal.pcbi.1010983}
+}
+```
+
+The mathematical framework underlying GPLA's coupling measure and its Random Matrix
+Theory–based significance test:
+
+> Safavi, S., Logothetis, N. K., & Besserve, M. (2021). *From Univariate to
+> Multivariate Coupling Between Continuous Signals and Point Processes: A
+> Mathematical Framework.* Neural Computation, 33(7), 1751–1817.
+
+```bibtex
+@article{safavi2021univariate,
+  title   = {From Univariate to Multivariate Coupling Between Continuous Signals and Point Processes: A Mathematical Framework},
+  author  = {Safavi, Shervin and Logothetis, Nikos K. and Besserve, Michel},
+  journal = {Neural Computation},
+  year    = {2021},
+  volume  = {33},
+  number  = {7},
+  pages   = {1751--1817},
+  doi     = {10.1162/neco_a_01389}
 }
 ```