Standardize test conventions and modernize docstring/lint tooling

.github/workflows/alltests.yml

@@ -312,6 +312,15 @@ jobs:
         run: |
           make check_colab_notebooks
           make check_colab_notebooks_smoke
+      - name: Check test-suite conventions (Linux)
+        if: runner.os == 'Linux'
+        shell: bash -l {0}
+        run: |
+          set -e
+          make check_test_style STRICT=--strict
+          python -m pip install -q "pydoclint>=0.5.0"
+          make check_docstring   # informational (exits 0 without STRICT)
+          make check_baseline    # real gate: docstring/pydoclint/annotation issues must not increase
       # -----------------------------------------------------------
       # Install minimal LaTeX required by Jupyter notebooks (OS-specific)
       # -----------------------------------------------------------

CONTRIBUTING.md

@@ -163,6 +163,10 @@ make tests
 
 Please see the targets in the makefile for more granular control over tests.
 
+### Test file layout
+
+Unit tests live flat in `test/`, named `test_<area>_<topic>.py` where `<area>` is a short code for the `qmcpy` subpackage under test (`tm` true_measure, `dd` discrete_distribution, `sc` stopping_criterion, `ig` integrand, ...) or a cross-cutting bucket (`ee`, `sr`). So `pytest test/ -k test_tm_` runs every true-measure test. A test that spans two areas goes under the component actually under test, with the other named in `<topic>` (e.g. `test_sc_cubbayes_kernels.py`); use `ee` only when neither side is the clear subject, and never coin a new code — `STRICT=--strict` rejects anything outside the table. New files should also be written as a `unittest.TestCase` subclass rather than bare `def test_*` functions. `make check_test_style` lists any file that breaks either convention (informational; also runs inside `make format`; `STRICT=--strict` makes it fail). The full area table is in [`test/README.md`](test/README.md#test-file-organization).
+
 ## Documentation
 
 ### Ensure `pyreverse` Is On Your PATH

demos/acceptance_rejection.ipynb

@@ -494,7 +494,7 @@
     "$$D^*_N(\\psi) = \\sup_{t \\in [0,1]} \\left| \\frac{\\#\\{i : x_i \\leq t\\}}{N} - \\frac{1}{C}\\int_0^t \\psi(x)\\,dx \\right|$$\n",
     "\n",
     "Theorem 1 of Zhu & Dick (2014) predicts:\n",
-    "- **Sobol driver (DAR):** $D^*_N = O(N^{-1/s})$ with $s=2$ for $d=1$, i.e. $O(N^{-1/2})$ in the worst case — but empirically faster due to the $\\log N$ factors.\n",
+    "- **Sobol driver (DAR):** $D^*_N = O(N^{-1/s})$ with $s=2$ for $d=1$, i.e., $O(N^{-1/2})$ in the worst case — but empirically faster due to the $\\log N$ factors.\n",
     "- **Random driver (standard A-R):** $D^*_N = O(N^{-1/2})$ always.\n",
     "\n",
     "The plot below measures this empirically for $\\psi(x) = 2x$ on $[0,1]$, where the CDF is $F(t) = t^2$ and $C = 1$."

demos/lebesgue_integration.ipynb

@@ -3,7 +3,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "# QMCPy for Lebesgue Integration\nThis notebook will give examples of how to use QMCPy for integration problems that are not defined in terms of a standard measure. i.e. Uniform or Gaussian. "
+   "source": "# QMCPy for Lebesgue Integration\nThis notebook will give examples of how to use QMCPy for integration problems that are not defined in terms of a standard measure. i.e., Uniform or Gaussian. "
   },
   {
    "cell_type": "markdown",