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 01/20] 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 02/20] 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 03/20] 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 04/20] 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 05/20] 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 06/20] 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 07/20] 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 08/20] 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 09/20] 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 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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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 10/20] 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 11/20] 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 12/20] 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}
 }
 ```
 

From 35ecb06e4b75d079776d49b4c95f856ab0b53d01 Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Tue, 21 Jul 2026 13:54:47 +0200
Subject: [PATCH 13/20] fix(api): warn when real LFP input is interpreted as
 phase

---
 src/pygpla/api.py           | 15 ++++++++++++++-
 src/pygpla/config.py        |  5 +++--
 src/pygpla/core/coupling.py |  6 ++++--
 tests/test_api.py           | 35 +++++++++++++++++++++++++++++++++++
 4 files changed, 56 insertions(+), 5 deletions(-)
 create mode 100644 tests/test_api.py

diff --git a/src/pygpla/api.py b/src/pygpla/api.py
index f93b35c..1ce801d 100644
--- a/src/pygpla/api.py
+++ b/src/pygpla/api.py
@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import warnings
 from dataclasses import asdict, dataclass, is_dataclass
 from typing import Any, Dict, Optional, Union
 
@@ -89,7 +90,9 @@ def gpla(
     spike_trains :
         List of spike arrays shaped (units, samples) per trial.
     lfp_signal :
-        Complex analytic LFP array shaped (channels, samples, trials).
+        Complex analytic LFP array shaped (channels, samples, trials). Real-valued
+        inputs are interpreted as phase angles in radians, not as raw LFP voltage,
+        and produce a warning.
     flag_gPLVnrmlz :
         Legacy gPLV normalization flag (0 keep raw, nonzero scales by matrix size).
     nSpikeThreshold :
@@ -140,6 +143,16 @@ def gpla(
     spike_list = [np.asarray(st) for st in spike_trains]
     lfp_array = np.asarray(lfp_signal)
 
+    if np.isrealobj(lfp_array):
+        warnings.warn(
+            "gpla received a real-valued LFP array. Real inputs are interpreted "
+            "as phase angles in radians, not as raw LFP voltage. If this is raw "
+            "LFP data, band-pass filter it and compute its analytic signal "
+            "(for example, using a Hilbert transform) before calling gpla().",
+            UserWarning,
+            stacklevel=2,
+        )
+
     (
         spikeTrains_allTrLong,
         lfpPhases_allTrLong,
diff --git a/src/pygpla/config.py b/src/pygpla/config.py
index 009c8a2..3269c83 100644
--- a/src/pygpla/config.py
+++ b/src/pygpla/config.py
@@ -69,10 +69,11 @@ def validate_spike_trains(spike_trains: Sequence[np.ndarray]) -> None:
 
 def validate_lfp_signal(lfp_signal: ArrayLike) -> np.ndarray:
     """
-    Ensure LFP input can be treated as a complex analytic array.
+    Validate the dimensions of an analytic-LFP or phase-array input.
 
     Requires a 3D array shaped (channels, samples, trials); raises ValueError otherwise.
-    Returns the array as np.ndarray for downstream use.
+    Complex arrays represent analytic LFP signals. Real arrays are interpreted downstream
+    as phase angles in radians, not as raw LFP voltage. Returns the input as an ndarray.
     """
 
     arr = np.asarray(lfp_signal)
diff --git a/src/pygpla/core/coupling.py b/src/pygpla/core/coupling.py
index 60b00be..fa5f03b 100644
--- a/src/pygpla/core/coupling.py
+++ b/src/pygpla/core/coupling.py
@@ -56,8 +56,10 @@ def compute_coupling_matrix(
     spike_trains:
         Array of shape (units, samples) with binary or count spikes.
     lfp_signal:
-        Real or complex LFP array of shape (channels, samples). Real inputs are wrapped
-        to unit-magnitude complex exponentials; complex inputs preserve amplitude.
+        Real phase-angle or complex analytic-LFP array of shape (channels, samples).
+        Real inputs are interpreted as radians and wrapped to unit-magnitude complex
+        exponentials; they are not treated as raw LFP voltage. Complex inputs preserve
+        amplitude.
     normalization_method:
         One of ``{"nSpk", "nSpk-square-root", "var1_theoretical"}`` controlling spike-count
         scaling in the coupling matrix.
diff --git a/tests/test_api.py b/tests/test_api.py
new file mode 100644
index 0000000..9eea4ca
--- /dev/null
+++ b/tests/test_api.py
@@ -0,0 +1,35 @@
+"""Tests for the public GPLA API."""
+
+import warnings
+
+import numpy as np
+import pytest
+
+from pygpla.api import gpla
+
+
+def _minimal_inputs():
+    spikes = [np.array([[0, 1, 0, 0]], dtype=int)]
+    lfp_analytic = np.ones((1, 4, 1), dtype=complex)
+    return spikes, lfp_analytic
+
+
+def test_gpla_warns_that_real_lfp_is_interpreted_as_phase():
+    spikes, lfp_analytic = _minimal_inputs()
+    lfp_phase = np.angle(lfp_analytic)
+
+    with pytest.warns(UserWarning, match="phase angles in radians, not as raw LFP voltage"):
+        result = gpla(spikes, lfp_phase, plvNrmlzMethed="nSpk")
+
+    assert np.isfinite(result.gplv)
+
+
+def test_gpla_does_not_warn_for_complex_analytic_lfp():
+    spikes, lfp_analytic = _minimal_inputs()
+
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+        result = gpla(spikes, lfp_analytic, plvNrmlzMethed="nSpk")
+
+    assert not caught
+    assert np.isfinite(result.gplv)

From 7a9b27c0dcba86bdc6460cea64e384ce20986425 Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Tue, 21 Jul 2026 13:54:56 +0200
Subject: [PATCH 14/20] docs: clarify analytic LFP input requirements

---
 README.md                     | 11 +++++++++--
 docs/quickstart.md            | 16 +++++++++++-----
 docs/software_architecture.md |  7 +++++--
 docs/usage.md                 |  8 +++++++-
 4 files changed, 32 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index ccf3d74..49af894 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,8 @@ GPLA summarizes high-dimensional **spike–[Local Field Potential (LFP)](https:/
 - **Statistical testing**:
   - fast **RMT-based** heuristic (Marchenko–Pastur edge)
   - **spike-jitter** surrogate tests (interval / ISI-preserved / group-preserved / population)
-- **Preprocessing hooks**: trial concatenation, spike-count filtering, optional PCA whitening
+- **Data preparation**: trial concatenation, spike-count filtering, temporal/unit selection,
+  and optional PCA whitening of user-provided analytic LFP signals
 - **Simulations**: phase-locked and transient coupling generators
 - **Figure reproduction**: a Figure 2-style simulation and visualization script
 
@@ -106,7 +107,13 @@ print("Spike vector shape:", result.spike_vector.shape)
 Input conventions:
 
 - spike trains: list of trials, each `(n_units, n_samples)`
-- analytic LFP: `(n_channels, n_samples, n_trials)` complex array (bandpass + Hilbert in real data)
+- preferred LFP input: `(n_channels, n_samples, n_trials)` complex analytic array
+- supported alternative: a real array of phase angles in radians
+
+> `gpla()` does not treat real-valued input as raw LFP voltage. Raw LFP data must be
+> band-pass filtered and converted to an analytic signal upstream, for example with a
+> Hilbert transform. If a real array is passed, PyGPLA warns that it will be interpreted
+> as phase angles in radians.
 
 ## Reproducing Figure 2 from the original paper (Safavi et al., 2023)
 
diff --git a/docs/quickstart.md b/docs/quickstart.md
index b55a122..69fbd39 100644
--- a/docs/quickstart.md
+++ b/docs/quickstart.md
@@ -116,12 +116,14 @@ print("Selected units:", result.metadata["selected_units"])
 `gpla(spike_trains, lfp_signal, ...)` expects:
 
 - `spike_trains`: list of length `n_trials`, each element a 2D array `(n_units, n_samples)`
-- `lfp_signal`: a 3D **complex** array `(n_channels, n_samples, n_trials)`
+- `lfp_signal`: preferably a 3D **complex analytic** array
+  `(n_channels, n_samples, n_trials)`
 
-:::{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`.
+:::{important}
+`gpla()` does not interpret a real-valued array as raw LFP voltage. Real input is supported
+only as phase angles in radians and produces a warning. For raw LFP data, first bandpass
+filter around the frequency band of interest and apply a Hilbert transform to obtain the
+complex analytic signal. The Figure 2 tutorial shows a concrete example: {doc}`tutorials`.
 :::
 
 ## Interpreting the outputs (what you get back)
@@ -171,6 +173,10 @@ def bandpass_hilbert(x: np.ndarray, sf: float, band_hz: tuple[float, float]) ->
 
 You can then pass `lfp_analytic` into `{py:func}`pygpla.api.gpla``.
 
+If you already have phase angles in radians, you may pass that real-valued phase array
+directly. PyGPLA emits a warning to distinguish this supported phase representation from
+accidentally supplied raw LFP voltage.
+
 ## Adding significance testing (optional)
 
 PyGPLA supports two main significance-testing modes:
diff --git a/docs/software_architecture.md b/docs/software_architecture.md
index 28509a4..6984e11 100644
--- a/docs/software_architecture.md
+++ b/docs/software_architecture.md
@@ -110,9 +110,12 @@ This keeps the numerical path explicit and lightweight:
 The package assumes:
 
 - spikes as trial list of `(units, samples)` arrays
-- analytic LFP as `(channels, samples, trials)` complex array
+- preferably an analytic LFP as `(channels, samples, trials)` complex array
+- alternatively, phase angles in radians as a real array of the same shape
 
-These contracts are enforced in preprocessing and validation utilities.
+Raw LFP voltage must be converted to the desired frequency-specific analytic signal upstream.
+The public API warns when it receives real input so that raw voltage is not silently confused
+with the supported phase-angle representation.
 
 ### Deterministic and Stochastic Paths
 
diff --git a/docs/usage.md b/docs/usage.md
index 0271821..786b15d 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -24,7 +24,10 @@ pip install -e .[docs]
 ## Data expectations
 
 - **Spike trains**: list of arrays shaped `(units, samples)` per trial, binary or counts.
-- **LFP analytic signal**: complex array shaped `(channels, samples, trials)`; use a bandpass + Hilbert transform upstream.
+- **LFP analytic signal (preferred)**: complex array shaped `(channels, samples, trials)`;
+  use a bandpass filter and Hilbert transform upstream when starting from raw LFP voltage.
+- **LFP phase representation (supported)**: real array of the same shape containing phase
+  angles in radians. Real input triggers a warning because it is not interpreted as raw voltage.
 - **Sampling**: spike and LFP sample counts must match within each trial; supply `sampling_frequency` when using jitter surrogates.
 
 ## Minimal example
@@ -65,6 +68,9 @@ print("Spike vector shape:", result.spike_vector.shape)
 
 ## Preprocessing knobs
 
+These options prepare an analytic-signal or phase input for GPLA; they do not bandpass-filter
+raw LFP voltage or compute its Hilbert transform.
+
 - `plvNrmlzMethed`: `nSpk`, `nSpk-square-root` (default), or `var1_theoretical`.
 - `flag_whitening`: 0 (off), 1/2 for PCA whitening variants; optionally set `PreprocessingConfig.whitening.variance_proportion`.
 - `flag_lfpNrmlz`: normalize analytic LFP amplitude if set.

From 74a795174e8c3b468b8ab46c56e6de696bd9a5e8 Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Tue, 21 Jul 2026 13:55:11 +0200
Subject: [PATCH 15/20] docs(paper): correct LFP preprocessing description

---
 paper/paper.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/paper/paper.md b/paper/paper.md
index f40d72d..41200e1 100644
--- a/paper/paper.md
+++ b/paper/paper.md
@@ -37,7 +37,7 @@ url: "https://pygpla.readthedocs.io/en/latest/"
 
 ## Summary
 
-PyGPLA is a Python implementation of Generalized Phase Locking Analysis (GPLA) for multivariate analysis of coupling between spikes and local field potentials (LFPs) [@safavi2023uncovering]. For a given frequency, GPLA constructs a complex coupling matrix $\hat{C}(f) \in \mathbb{C}^{N_c \times N_u}$ between LFP channels ($N_c$) and spike units ($N_u$), then applies singular value decomposition (SVD) to reduce the dimensionality of data. The leading singular value summarizes population-level coupling strength, while the corresponding singular vectors describe dominant LFP and spike coupling modes. PyGPLA provides a complete analysis workflow including LFP preprocessing, coupling-matrix construction, SVD-based decomposition, and diverse statistical significance testing [@safavi2021univariate].
+PyGPLA is a Python implementation of Generalized Phase Locking Analysis (GPLA) for multivariate analysis of coupling between spikes and local field potentials (LFPs) [@safavi2023uncovering]. For a given frequency, GPLA constructs a complex coupling matrix $\hat{C}(f) \in \mathbb{C}^{N_c \times N_u}$ between LFP channels ($N_c$) and spike units ($N_u$), then applies singular value decomposition (SVD) to reduce the dimensionality of data. The leading singular value summarizes population-level coupling strength, while the corresponding singular vectors describe dominant LFP and spike coupling modes. PyGPLA accepts a user-provided frequency-specific analytic LFP signal or phase representation and provides data selection, optional whitening and normalization, coupling-matrix construction, SVD-based decomposition, and statistical significance testing [@safavi2021univariate].
 
 
 ## Statement of need
@@ -56,7 +56,7 @@ Existing spike–field coupling methods - including the phase-locking value (PLV
 
 PyGPLA provides a comprehensive solution for multivariate spike–field coupling analysis, including:
 
-- **LFP preprocessing:** Band-pass filtering, Hilbert transform for analytic signal extraction [@chavez2006proper], and optional reduced-rank whitening to decorrelate channels while avoiding noise amplification [@safavi2023uncovering].
+- **LFP input and data preparation:** PyGPLA accepts a user-provided complex analytic LFP signal or a real phase representation in radians. Raw LFP voltage must first be converted upstream to the desired frequency-specific analytic signal, for example through band-pass filtering followed by a Hilbert transform [@chavez2006proper]. PyGPLA then supports temporal and unit selection, optional channel-wise normalization, and optional reduced-rank whitening to decorrelate channels while avoiding noise amplification [@safavi2023uncovering].
 - **Coupling-matrix construction:** Assembly of the complex-valued coupling matrix $\widehat{\mathbf{C}}(f) \in \mathbb{C}^{N_c \times N_u}$, where each entry sums the analytic LFP evaluated at all spike times of a given unit.
 - **SVD-based decomposition:** Extraction of the generalized phase locking value or gPLV (the leading singular value) and associated LFP and spike spatial vectors, with rotational phase alignment, unwhitening of LFP vectors, and spike-vector rescaling.
 - **Statistical testing:** Significance assessment via two complementary approaches: (1) surrogate-based testing using multiple spike-jittering schemes, and (2) an analytical test based on Marchenko–Pastur Random Matrix Theory (RMT) [@anderson2010random; @safavi2023uncovering].

From 883e22113145fdd724cf78e9321bafd09560e60d Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Tue, 21 Jul 2026 14:51:35 +0200
Subject: [PATCH 16/20] Improve paper figure layout and readability

---
 paper/figures/figure2.py         |    55 +-
 paper/figures/figure2_python.eps | 20438 ++++++++++++++---------------
 paper/figures/figure2_python.pdf |   Bin 94272 -> 93901 bytes
 paper/figures/figure2_python.png |   Bin 1000587 -> 950178 bytes
 paper/figures/figure2_python.svg | 11497 ++++++++--------
 5 files changed, 15354 insertions(+), 16636 deletions(-)

diff --git a/paper/figures/figure2.py b/paper/figures/figure2.py
index 49c826f..690c7a9 100644
--- a/paper/figures/figure2.py
+++ b/paper/figures/figure2.py
@@ -31,9 +31,9 @@
 
 DPI = 600
 PANEL_LABEL_SIZE = 14
-TITLE_SIZE = 9
-AXIS_LABEL_SIZE = 8
-TICK_LABEL_SIZE = 7
+TITLE_SIZE = 9.5
+AXIS_LABEL_SIZE = 8.5
+TICK_LABEL_SIZE = 7.5
 SAVE_FORMATS = ("png", "pdf", "svg", "eps")
 
 
@@ -79,7 +79,8 @@ def _polar_spkvec(ax, spk_vec: np.ndarray, title: str = "", color="red"):
     ax.set_thetagrids(np.arange(0, 360, 45))
     ax.grid(True, color="0.82", linewidth=0.45)
     ax.tick_params(labelsize=TICK_LABEL_SIZE, pad=1)
-    ax.set_title(title, fontsize=TITLE_SIZE, pad=8)
+    if title:
+        ax.set_title(title, fontsize=TITLE_SIZE, pad=4)
 
 
 def _bandpass_and_analytic(
@@ -275,7 +276,7 @@ def main():
         lfp_examples.append(lfp_real[0, :, 0])
         spike_rasters.append(spikeTrains_raw[0])
 
-    fig = plt.figure(figsize=(7.2, 8.2))
+    fig = plt.figure(figsize=(7.2, 6.6))
 
     model_colors = ["#2E8B57", "#DAA520", "#CD853F", "#708090"]
 
@@ -317,10 +318,12 @@ def main():
         if spine in ax2.spines:
             ax2.spines[spine].set_visible(False)
 
+    model_axes = []
     for i, case in enumerate(cases):
         row = i + 2
 
         ax_model = plt.subplot(5, 3, (row - 1) * 3 + 1)
+        model_axes.append(ax_model)
         _plot_model_schematic(ax_model, i + 1)
         ax_model.set_title(f"{case['name']}", fontsize=TITLE_SIZE, fontweight="bold")
 
@@ -352,35 +355,14 @@ def main():
 
         ax_combined.set_ylabel("LFP Amplitude")
         ax_combined.set_xlabel("Time (s)")
-        ax_combined.set_title("LFP + spike trains")
+        if i == 0:
+            ax_combined.set_title("LFP + spike trains")
         for spine in ("top", "right"):
             if spine in ax_combined.spines:
                 ax_combined.spines[spine].set_visible(False)
 
         ax_polar = plt.subplot(5, 3, (row - 1) * 3 + 3, projection="polar")
-        _polar_spkvec(ax_polar, spk_vecs[i], title="Spike vector", color="red")
-
-
-    label_positions = [
-        (0.02, 0.92, "A"),
-        (0.67, 0.92, "B"),
-        (0.02, 0.75, "C"),
-        (0.02, 0.58, "D"),
-        (0.02, 0.41, "E"),
-        (0.02, 0.24, "F"),
-    ]
-
-    for x_pos, y_pos, label in label_positions:
-        fig.text(
-            x_pos,
-            y_pos,
-            label,
-            fontsize=PANEL_LABEL_SIZE,
-            fontweight="bold",
-            ha="left",
-            va="top",
-            color="black",
-        )
+        _polar_spkvec(ax_polar, spk_vecs[i], color="red")
 
     if len(cases) >= 3:
         cax = fig.add_axes([0.15, 0.02, 0.3, 0.02])
@@ -394,9 +376,22 @@ def main():
         cbar.ax.tick_params(labelsize=TICK_LABEL_SIZE, width=0.8, length=2.5)
 
     fig.subplots_adjust(
-        left=0.08, right=0.97, top=0.9, bottom=0.08, hspace=1.0, wspace=0.65
+        left=0.08, right=0.97, top=0.94, bottom=0.10, hspace=0.62, wspace=0.60
     )
 
+    for ax, label in zip([ax1, ax2, *model_axes], "ABCDEF"):
+        position = ax.get_position()
+        fig.text(
+            max(0.01, position.x0 - 0.055),
+            min(0.985, position.y1 + 0.018),
+            label,
+            fontsize=PANEL_LABEL_SIZE,
+            fontweight="bold",
+            ha="left",
+            va="top",
+            color="black",
+        )
+
     figures_dir = Path(__file__).resolve().parent
     figures_dir.mkdir(parents=True, exist_ok=True)
     basepath = figures_dir / "figure2_python"
diff --git a/paper/figures/figure2_python.eps b/paper/figures/figure2_python.eps
index 84b16ef..981c113 100644
--- a/paper/figures/figure2_python.eps
+++ b/paper/figures/figure2_python.eps
@@ -2,10 +2,10 @@
 %%LanguageLevel: 3
 %%Title: figure2_python.eps
 %%Creator: Matplotlib v3.10.5, https://matplotlib.org/
-%%CreationDate: Fri Jul 17 15:15:40 2026
+%%CreationDate: Tue Jul 21 14:32:31 2026
 %%Orientation: portrait
-%%BoundingBox: 0 0 499 560
-%%HiResBoundingBox: 0.000000 0.000000 498.240000 559.448125
+%%BoundingBox: 0 0 496 479
+%%HiResBoundingBox: 0.000000 0.000000 495.648000 478.256500
 %%EndComments
 %%BeginProlog
 /mpldict 10 dict def
@@ -37,7 +37,7 @@ mpldict begin
         /FontBBox [-1360.017 -665 4096 2060] def
         /PaintType 0 def
         /CIDMap 0 def
-        /CharStrings 52 dict dup begin
+        /CharStrings 49 dict dup begin
 /.notdef 0 def
 /.null 1 def
 /nonmarkingreturn 2 def
@@ -62,702 +62,664 @@ mpldict begin
 /L 21 def
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diff --git a/paper/figures/figure2_python.pdf b/paper/figures/figure2_python.pdf
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From 734a52b04050eab3c1705ebd1239be71e90169d8 Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Tue, 21 Jul 2026 14:51:35 +0200
Subject: [PATCH 17/20] Keep example figure within main paper text

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

diff --git a/paper/paper.md b/paper/paper.md
index 41200e1..c12f393 100644
--- a/paper/paper.md
+++ b/paper/paper.md
@@ -70,7 +70,7 @@ The package supports two normalization modes for the coupling matrix: PLV-type n
 
 We illustrate PyGPLA on synthetic transient-coupling simulations generated with the script `paper/figures/figure2.py`. As shown in \autoref{fig:gpla_results}, the figure compares four coupling models and reports their recovered gPLV values, together with representative LFP and spike-train windows and the associated spike-vector structure.
 
-![Illustrative PyGPLA simulation example produced by `paper/figures/figure2.py`. Panel A shows an LFP trace with the analysis window. Panel B shows gPLV values across four simulated coupling models (M1-M4). Panels C-F display, for each model, a schematic, LFP plus spike-train segment, and the recovered spike vector in polar coordinates.](figures/figure2_python.png){#fig:gpla_results}
+![Illustrative PyGPLA simulation. (A) LFP analysis window. (B) gPLV across models M1-M4. (C-F) Model schematics, LFP and spikes, and recovered spike vectors.](figures/figure2_python.png){#fig:gpla_results width="70%"}
 
 Code snippets and detailed instructions for reproducing these results are available in the package documentation and example scripts in the repository.
 

From 31e3f272e57a631688c064c0824b924152824559 Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Thu, 23 Jul 2026 10:59:17 +0200
Subject: [PATCH 18/20] Add required JOSS sections and refine manuscript

Add State of the field and Research impact sections, expand Software design, update author affiliations and terminology, and rebuild the JOSS PDF.
---
 paper/paper.md  |  48 +++++++++++++++++++++++++++++++++++-------------
 paper/paper.pdf | Bin 1170603 -> 1136477 bytes
 2 files changed, 35 insertions(+), 13 deletions(-)

diff --git a/paper/paper.md b/paper/paper.md
index c12f393..65d88bf 100644
--- a/paper/paper.md
+++ b/paper/paper.md
@@ -11,23 +11,31 @@ authors:
   - name: Amir Khani
     orcid: 0009-0008-8042-9590
     corresponding: true
-    affiliation: 1
+    affiliation: "1, 2"
+  - name: Christian Beste
+    affiliation: "3, 4"
   - name: Shervin Safavi
     orcid: 0000-0002-2868-530X
     corresponding: true
-    affiliation: "2, 3"
+    affiliation: "5, 6"
 affiliations:
   - name: Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands
     index: 1
     ror: "016xsfp80"
-  - name: Computational Neuroscience, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden 01307, Germany
+  - name: Department of Computer Science, Amirkabir University of Technology, Tehran, Iran
     index: 2
+  - name: Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
+    index: 3
+  - name: German Center for Child and Adolescent Health (DZKJ), partner site Leipzig/Dresden, Dresden, Germany
+    index: 4
+  - name: Computational Neuroscience, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden 01307, Germany
+    index: 5
     ror: "042aqky30"
   - name: Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Tübingen 72076, Germany
-    index: 3
+    index: 6
     ror: "026nmvv73"
 
-date: "2026-03-22"
+date: "22 March 2026"
 bibliography: paper.bib
 repository: "https://github.com/CMC-lab/PyGPLA"
 crossref: true
@@ -48,7 +56,12 @@ However, commonly used spike-LFP coupling measures are pairwise, making them sub
 
 GPLA was developed to address these challenges by providing an efficient multivariate framework together with statistical routines [@safavi2021univariate] for characterizing spike-LFP coupling at the population level [@safavi2023uncovering]. However, the original algorithm was implemented in MATLAB, limiting its accessibility. PyGPLA bridges this gap as an open-source Python implementation, aligning with the extensive use of Python in neuroscience as solidified by libraries such as MNE-Python [@GramfortEtAl2013a], Nilearn [@Nilearn], and TranCIT [@Nouri2025].
 
-Existing spike–field coupling methods - including the phase-locking value (PLV), pairwise phase consistency (PPC) [@vinck2010pairwise], and spike-field coherence - operate on individual spike–LFP channel pairs. While informative for small-scale recordings, these pairwise approaches do not directly capture the population-level structure of coupling, which is of paramount importance for recordings with modern high-density probes. GPLA addresses this gap by providing a multivariate method that summarizes all spike–field interactions simultaneously, analogous to how multivariate statitical methods summarizes covariance structure (well-known example is Principal component analysis  or PCA). To our knowledge, no other Python package implements this multivariate approach to spike–field coupling analysis.
+Existing spike–field coupling methods - including the phase-locking value (PLV), pairwise phase consistency (PPC) [@vinck2010pairwise], and spike-field coherence - operate on individual spike–LFP channel pairs. While informative for small-scale recordings, these pairwise approaches do not directly capture the population-level structure of coupling, which is of paramount importance for recordings with modern high-density probes. GPLA addresses this gap by providing a multivariate method that summarizes all spike–field interactions simultaneously, analogous to how multivariate statistical methods such as principal component analysis (PCA) summarize covariance structure. To our knowledge, no other Python package implements this multivariate approach to spike–field coupling analysis.
+
+## State of the field
+
+Common spike–field coupling measures, including the phase-locking value, pairwise phase consistency, and spike–field coherence, quantify relationships between individual spike units and LFP channels [@vinck2010pairwise; @vinck2012improved]. General-purpose Python packages such as MNE-Python provide extensive signal-processing and electrophysiology infrastructure [@GramfortEtAl2013a], but do not implement GPLA’s joint decomposition of the complete spike–field coupling matrix. The only directly comparable implementation is the MATLAB research code accompanying the original GPLA study [@safavi2023uncovering].
+PyGPLA was developed as a separate package because GPLA requires a method-specific pipeline comprising coupling-matrix construction, normalization, optional reduced-rank whitening, SVD-based extraction of population coupling modes, and analytical or surrogate-based statistical inference. These operations do not extend an existing pairwise measure or general preprocessing function in a natural way. PyGPLA’s scholarly contribution is therefore a dedicated, open-source Python implementation of the complete GPLA methodology.
 
 ## Functionality
 
@@ -64,24 +77,33 @@ PyGPLA provides a comprehensive solution for multivariate spike–field coupling
 
 ### Normalization options
 
-The package supports two normalization modes for the coupling matrix: PLV-type normalization ($1/N_m^{\mathrm{tot}}$) for phase-only analysis, and unit-variance normalization ($1/\sqrt{N_m^{\mathrm{tot}}}$) required for the theoretical RMT-based significance test.
+The package supports two normalization modes for the coupling matrix, expressed in terms of the total spike count $N_m^{\mathrm{tot}}$ for unit $m$: PLV-type normalization ($1/N_m^{\mathrm{tot}}$) for phase-only analysis, and unit-variance normalization ($1/\sqrt{N_m^{\mathrm{tot}}}$) required for the theoretical RMT-based significance test.
 
 ## Example
 
 We illustrate PyGPLA on synthetic transient-coupling simulations generated with the script `paper/figures/figure2.py`. As shown in \autoref{fig:gpla_results}, the figure compares four coupling models and reports their recovered gPLV values, together with representative LFP and spike-train windows and the associated spike-vector structure.
 
-![Illustrative PyGPLA simulation. (A) LFP analysis window. (B) gPLV across models M1-M4. (C-F) Model schematics, LFP and spikes, and recovered spike vectors.](figures/figure2_python.png){#fig:gpla_results width="70%"}
+![Illustrative PyGPLA simulation. (A) LFP analysis window. (B) gPLV across Models 1-4. (C-F) Model schematics, LFP and spikes, and recovered spike vectors.](figures/figure2_python.png){#fig:gpla_results width="70%"}
 
 Code snippets and detailed instructions for reproducing these results are available in the package documentation and example scripts in the repository.
 
-## Implementation details
+## Software design
+
+PyGPLA uses a function-oriented, layered design that separates the main stages of the analysis while providing a unified high-level workflow. The high-level `gpla()` function coordinates data preparation, GPLA decomposition, and optional statistical testing, returning the results and relevant bookkeeping in a single `GPLAResult` object. The underlying operations—coupling-matrix construction, SVD factorization, whitening, jitter generation, and simulation—remain independently accessible. This design provides a concise default workflow while allowing researchers to inspect, test, or replace individual methodological stages. Automated tests validate these independently accessible numerical components.
+
+PyGPLA accepts standard NumPy arrays rather than requiring a package-specific data container, facilitating integration with existing electrophysiology workflows. Frequency selection and conversion of raw LFP voltage to an analytic signal are intentionally left upstream because these operations require experiment-specific filtering choices. PyGPLA therefore operates on a frequency-specific complex analytic signal or phase representation and warns when real-valued input is supplied.
+
+Several numerical and interface conventions preserve continuity with the original MATLAB implementation, facilitating validation against the reference implementation and migration of existing GPLA analyses. These include its normalization alternatives, phase convention, reduced-rank whitening methods, and selected legacy parameter names. Optional PCA-based whitening reduces correlations among LFP channels, while an unwhitening operator maps the resulting coupling modes back to the original channel coordinates. Statistical inference is separated from the deterministic decomposition, allowing users to choose between a computationally inexpensive analytical RMT-based decision and more expensive spike-jitter surrogate tests. The core package depends only on NumPy; SciPy is provided as an optional dependency for documented simulation and signal-processing workflows.
+
+## Research impact statement
 
-The `pygpla` package is distributed under the BSD-2-Clause license. PyGPLA follows a layered architecture separating preprocessing (`pygpla.preprocessing`), core computation (`pygpla.core`), statistical testing (`pygpla.stats`), simulation utilities (`pygpla.simulations`), and configuration (`pygpla.config`). The high-level API (`pygpla.api.gpla`) orchestrates the full pipeline in a single call, keeping default usage compact while preserving access to lower-level components for advanced analyses. The package is built on NumPy [@harris2020array] and SciPy [@virtanen2020fundamental], and includes a `pytest` test suite with continuous integration via GitHub Actions.
+GPLA has been evaluated in published simulations, biophysical network models, and multielectrode recordings, where it revealed population-level spike–field coupling patterns related to properties of the underlying neural circuits [@safavi2023uncovering]. PyGPLA transfers this established methodology from MATLAB research code into an installable, open-source Python package. To our knowledge, it is the first Python implementation that jointly decomposes the complete spike–field coupling matrix. Its immediate research contribution is therefore to make population-level GPLA available to researchers using Python-based electrophysiology workflows. Because PyGPLA is newly released, broader external adoption and independent applications remain to be established.
 
-## Use of generative AI
+## AI usage disclosure
 
-Generative AI tools were used only to assist with drafting documentation and
-refactoring code for style. All AI-assisted output was reviewed, tested, and
+Generative artificial intelligence (AI) tools were used to assist with drafting
+documentation and portions of this paper, and with 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.
diff --git a/paper/paper.pdf b/paper/paper.pdf
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From 2825df459a9f3b71fbc9e43d3ffe7e3e03e95797 Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Thu, 23 Jul 2026 10:59:30 +0200
Subject: [PATCH 19/20] Harden JOSS paper build workflow

---
 .github/workflows/draft-pdf.yml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml
index 01d84a7..7ea4c72 100644
--- a/.github/workflows/draft-pdf.yml
+++ b/.github/workflows/draft-pdf.yml
@@ -11,6 +11,9 @@ on:
       - ".github/workflows/draft-pdf.yml"
   workflow_dispatch:
 
+permissions:
+  contents: read
+
 jobs:
   paper:
     runs-on: ubuntu-latest
@@ -33,3 +36,4 @@ jobs:
           # This is the output path where Pandoc will write the compiled
           # PDF. Note, this should be the same directory as the input paper.md
           path: paper/paper.pdf
+          if-no-files-found: error

From 7fd9c69cb662ef49dc6ddb167eb7372c60fab4d8 Mon Sep 17 00:00:00 2001
From: Amir Khani <99316850+IAmirKhani@users.noreply.github.com>
Date: Thu, 23 Jul 2026 15:00:43 +0200
Subject: [PATCH 20/20] Fix typed statistical test configuration

---
 src/pygpla/api.py    | 23 +++++++++++----
 src/pygpla/config.py | 11 ++++++--
 tests/test_api.py    | 67 +++++++++++++++++++++++++++++++++++++++++++-
 3 files changed, 91 insertions(+), 10 deletions(-)

diff --git a/src/pygpla/api.py b/src/pygpla/api.py
index 1ce801d..24052af 100644
--- a/src/pygpla/api.py
+++ b/src/pygpla/api.py
@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import warnings
-from dataclasses import asdict, dataclass, is_dataclass
+from dataclasses import dataclass
 from typing import Any, Dict, Optional, Union
 
 import numpy as np
@@ -59,8 +59,19 @@ def _stats_config_to_dict(
         return None
     if isinstance(stats_config, dict):
         return stats_config
-    if is_dataclass(stats_config):
-        return asdict(stats_config)
+    if isinstance(stats_config, StatTestConfig):
+        legacy_config = dict(stats_config.additional_params)
+        legacy_config.update(
+            {
+                "testType": stats_config.test_type,
+                "nJtr": stats_config.n_surrogates,
+                "jitterWinWidth": stats_config.jitter_window_width,
+                "spkSF": stats_config.sampling_frequency,
+                "jitterType": stats_config.jitter_type,
+                "alphaValue": stats_config.alpha,
+            }
+        )
+        return legacy_config
     raise TypeError("stats_config must be a dict, StatTestConfig, or None.")
 
 
@@ -153,6 +164,8 @@ def gpla(
             stacklevel=2,
         )
 
+    stat_dict = _stats_config_to_dict(stats_config)
+
     (
         spikeTrains_allTrLong,
         lfpPhases_allTrLong,
@@ -167,7 +180,7 @@ def gpla(
         unitSubset=unitSubset,
         temporalWindow=temporalWindow,
         flag_origDimEigVec=flag_origDimEigVec,
-        statTestInfo=stats_config,
+        statTestInfo=stat_dict,
         iSV=iSV,
         checkSameElecStuff_flag=0,
         plvNrmlzMethed=plvNrmlzMethed,
@@ -187,8 +200,6 @@ def gpla(
     else:
         sameElecCheckInfo = None
 
-    stat_dict = _stats_config_to_dict(stats_config)
-
     (
         gPLV,
         pValue,
diff --git a/src/pygpla/config.py b/src/pygpla/config.py
index 3269c83..10657ef 100644
--- a/src/pygpla/config.py
+++ b/src/pygpla/config.py
@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, field
-from typing import Dict, Optional, Sequence, Tuple, Union
+from typing import Any, Dict, Optional, Sequence, Tuple, Union
 
 import numpy as np
 
@@ -37,7 +37,12 @@ class PreprocessingConfig:
 
 @dataclass(slots=True)
 class StatTestConfig:
-    """Statistical testing controls for the GPLA pipeline."""
+    """Statistical testing controls for the GPLA pipeline.
+
+    ``additional_params`` accepts specialized legacy runner options not represented
+    by named fields, such as ``rngSeed`` or ``SVspectrumStatsType``. Named fields
+    take precedence if the same legacy key is also present in ``additional_params``.
+    """
 
     test_type: str = "RMT-based"
     n_surrogates: int = 0
@@ -45,7 +50,7 @@ class StatTestConfig:
     sampling_frequency: float = 1.0
     jitter_type: str = "interval-jittering"
     alpha: float = 0.05
-    additional_params: Dict[str, ArrayLike] = field(default_factory=dict)
+    additional_params: Dict[str, Any] = field(default_factory=dict)
 
 
 def validate_spike_trains(spike_trains: Sequence[np.ndarray]) -> None:
diff --git a/tests/test_api.py b/tests/test_api.py
index 9eea4ca..1be7bbc 100644
--- a/tests/test_api.py
+++ b/tests/test_api.py
@@ -5,7 +5,8 @@
 import numpy as np
 import pytest
 
-from pygpla.api import gpla
+from pygpla.api import _stats_config_to_dict, gpla
+from pygpla.config import StatTestConfig
 
 
 def _minimal_inputs():
@@ -33,3 +34,67 @@ def test_gpla_does_not_warn_for_complex_analytic_lfp():
 
     assert not caught
     assert np.isfinite(result.gplv)
+
+
+def test_gpla_accepts_stat_test_config_for_rmt():
+    spikes, lfp_analytic = _minimal_inputs()
+
+    result = gpla(
+        spikes,
+        lfp_analytic,
+        stats_config=StatTestConfig(test_type="RMT-based"),
+        plvNrmlzMethed="var1_theoretical",
+    )
+
+    assert np.isnan(result.p_value)
+    assert isinstance(result.stats["gPLV_stats"]["nullHypoReject"], bool)
+
+
+def test_gpla_accepts_stat_test_config_for_spike_jittering():
+    spikes, lfp_analytic = _minimal_inputs()
+    config = StatTestConfig(
+        test_type="spike-jittering",
+        n_surrogates=2,
+        jitter_window_width=0.01,
+        sampling_frequency=1000.0,
+        jitter_type="fake-jittering",
+        alpha=0.01,
+        additional_params={"rngSeed": 123, "SVspectrumStatsType": "default"},
+    )
+
+    result = gpla(
+        spikes,
+        lfp_analytic,
+        stats_config=config,
+        plvNrmlzMethed="nSpk",
+    )
+
+    assert result.p_value == pytest.approx(0.0)
+    assert result.stats["gPLV_stats"]["nullDistribution"].shape == (2,)
+
+
+def test_stat_test_config_maps_all_fields_to_legacy_keys():
+    config = StatTestConfig(
+        test_type="spike-jittering",
+        n_surrogates=25,
+        jitter_window_width=0.02,
+        sampling_frequency=500.0,
+        jitter_type="group-preserved-interval-jittering",
+        alpha=0.01,
+        additional_params={
+            "rngSeed": 42,
+            "SVspectrumStatsType": "RMT-heuristic",
+            "testType": "ignored-in-favor-of-named-field",
+        },
+    )
+
+    assert _stats_config_to_dict(config) == {
+        "testType": "spike-jittering",
+        "nJtr": 25,
+        "jitterWinWidth": 0.02,
+        "spkSF": 500.0,
+        "jitterType": "group-preserved-interval-jittering",
+        "alphaValue": 0.01,
+        "rngSeed": 42,
+        "SVspectrumStatsType": "RMT-heuristic",
+    }