Merge pull request #332 from jorenham/black-style

black style

.github/workflows/ci.yml

@@ -23,10 +23,6 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - uses: scientific-python/repo-review@v0.11.2
-        with:
-          plugins: sp-repo-review
-
       - name: install poetry
         run: |
           pipx install poetry
@@ -44,17 +40,30 @@ jobs:
       - name: poetry install
         run: poetry install --without debug,docs --all-extras
 
+      - name: repo-review
+        run: poetry run repo-review .
+
+      - name: markdownlint
+        uses: DavidAnson/markdownlint-cli2-action@v17
+        with:
+          config: ".markdownlint.yaml"
+          globs: "*.md"
+
+      - name: blacken-docs
+        run: git ls-files -z -- '*.md' | xargs -0 poetry run blacken-docs
+
       - name: codespell
         run: poetry run codespell .
 
       - name: ruff check
-        run: poetry run ruff check --output-format=github
+        run: |
+          poetry run ruff check --output-format=github
+          poetry run ruff format --check
 
       - name: basedpyright
-        run: poetry run basedpyright
-
-      - name: basedpyright --verifytypes
-        run: poetry run basedpyright --ignoreexternal --verifytypes optype
+        run: |
+          poetry run basedpyright
+          poetry run basedpyright --ignoreexternal --verifytypes lmo
 
   test:
     timeout-minutes: 20

.markdownlint.yaml

@@ -2,6 +2,8 @@ default: true
 no-hard-tabs: true
 MD007:
   indent: 4
+MD013:
+  line_length: 88
 MD033:
   allowed_elements:
     - "br"

.pre-commit-config.yaml

@@ -6,6 +6,7 @@ ci:
     - poetry-check
     - codespell
     - ruff
+    - ruff-format
     - basedpyright
     - basedpyright-verifytypes
 
@@ -50,16 +51,22 @@ repos:
       - id: rst-inline-touching-normal
 
   - repo: https://github.com/rbubley/mirrors-prettier
-    rev: "v3.3.3"
+    rev: v3.3.3
     hooks:
       - id: prettier
         types_or: [yaml, html, css, scss, javascript, json]
 
   - repo: https://github.com/igorshubovych/markdownlint-cli
-    rev: v0.41.0
+    rev: v0.42.0
     hooks:
       - id: markdownlint
 
+  - repo: https://github.com/adamchainz/blacken-docs
+    rev: 1.19.0
+    hooks:
+      - id: blacken-docs
+        additional_dependencies: [black==24.*]
+
   - repo: https://github.com/python-poetry/poetry
     rev: 1.8.4
     hooks:
@@ -75,6 +82,7 @@ repos:
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]
+      - id: ruff-format
 
   - repo: local
     hooks:

.vscode/settings.json

@@ -1,17 +1,15 @@
 {
     "[python]": {
         "editor.defaultFormatter": "charliermarsh.ruff",
-        "editor.rulers": [79, 130]
+        "editor.formatOnPaste": true,
+        "editor.formatOnSave": true,
+        "editor.formatOnSaveMode": "modificationsIfAvailable",
+        "editor.formatOnType": true
     },
     "[bibtex]": {
         "editor.rulers": []
     },
-    "[markdown]": {
-        "editor.rulers": [80]
-    },
-    "[markdown_latex_combined]": {
-        "editor.rulers": [80]
-    },
+    "editor.rulers": [88],
     "markdown.extension.completion.root": "docs",
     "PandocCiter.DefaultBib": "docs/refs.bib",
     "PandocCiter.DefaultBibs": ["docs"],

README.md

@@ -2,23 +2,14 @@
 
 # Lmo - Trimmed L-moments and L-comoments
 
-![Lmo - License][IMG-BSD]
-[![Lmo - PyPI][IMG-PYPI]](https://pypi.org/project/Lmo/)
-[![Lmo - Versions][IMG-VER]](https://github.com/jorenham/Lmo)
-[![Lmo - SPEC 0][IMG-SPEC0]](https://scientific-python.org/specs/spec-0000/)
-![Lmo - CI][IMG-CI]
-[![Lmo - Pre-commit][IMG-PC]](https://github.com/pre-commit/pre-commit)
-[![Lmo - Ruff][IMG-RUFF]](https://github.com/astral-sh/ruff)
-[![Lmo - BassedPyright][IMG-BPR]](https://detachhead.github.io/basedpyright)
-
-[IMG-CI]: https://img.shields.io/github/actions/workflow/status/jorenham/Lmo/ci.yml?branch=master
-[IMG-BSD]: https://img.shields.io/github/license/jorenham/Lmo
-[IMG-PYPI]: https://img.shields.io/pypi/v/Lmo
-[IMG-VER]: https://img.shields.io/pypi/pyversions/Lmo
-[IMG-pC]: https://img.shields.io/badge/pre--commit-enabled-orange?logo=pre-commit
-[IMG-RUFF]: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json
-[IMG-BPR]: https://img.shields.io/badge/basedpyright-checked-42b983
-[IMG-SPEC0]: https://img.shields.io/badge/SPEC-0-green?labelColor=%23004811&color=%235CA038
+[![Lmo - PyPI][IMG-PYPI]][PYPI]
+[![Lmo - Python versions][IMG-PY]][REPO]
+[![Lmo - SPEC 0][IMG-SPEC0]][SPEC0]
+[![Lmo - BSD License][IMG-BSD]][BSD]
+[![Lmo - CI][IMG-CI]][CI]
+[![Lmo - pre-commit][IMG-PC]][PC]
+[![Lmo - ruff][IMG-RUFF]][RUFF]
+[![Lmo - bassedpyright][IMG-BPR]][BPR]
 
 Unlike the legacy
 [product-moments](https://wikipedia.org/wiki/Moment_(mathematics)), the
@@ -40,7 +31,7 @@ So Lmo is as fast as sorting your samples (in terms of time-complexity).
 - Goodness-of-fit test, using L-moment or L-moment ratio's.
 - Exact (co)variance structure of the sample- and population L-moments.
 - Theoretical & empirical influence functions of L-moments & L-ratio's.
-- Complete [docs](https://jorenham.github.io/Lmo/), including detailed API
+- Complete [docs][DOCS], including detailed API
 reference with usage examples and with mathematical $\TeX$ definitions.
 - Clean Pythonic syntax for ease of use.
 - Vectorized functions for very fast fitting.
@@ -52,33 +43,54 @@ reference with usage examples and with mathematical $\TeX$ definitions.
 Even if your data is pathological like
 [Cauchy](https://wikipedia.org/wiki/Cauchy_distribution), and the L-moments
 are not defined, the trimmed L-moments (TL-moments) can be used instead.
-Let's calculate the TL-location and TL-scale of a small amount of samples:
+
+Let's calculate the first two TL-moments (the TL-location and the TL-scale) of a small
+amount of samples drawn from the standard Cauchy distribution:
 
 ```pycon
 >>> import numpy as np
 >>> import lmo
 >>> rng = np.random.default_rng(1980)
->>> x = rng.standard_cauchy(96)  # pickle me, Lmo
->>> lmo.l_moment(x, [1, 2], trim=(1, 1)).
+>>> data = rng.standard_cauchy(96)
+>>> lmo.l_moment(data, [1, 2], trim=1)
 array([-0.17937038,  0.68287665])
 ```
 
-Now compare with the theoretical standard Cauchy TL-moments:
+Compared with the theoretical standard Cauchy TL-moments, that pretty close!
 
 ```pycon
 >>> from scipy.stats import cauchy
->>> cauchy.l_moment([1, 2], trim=(1, 1))
+>>> cauchy.l_moment([1, 2], trim=1)
 array([0.        , 0.69782723])
 ```
 
+Now let's try this again using the first two conventional moments, i.e. the mean
+and the standard deviation:
+
+```pycon
+>>> from scipy.stats import moment
+>>> np.r_[data.mean(), data.std()]
+array([-1.7113441 , 19.57350731])
+```
+
+So even though the `data` was drawn from the *standard* Cauchy distribution, we can
+immediately see that this look standard at all.
+
+The reason is that the Cauchy distribution doesn't have a mean or standard
+deviation:
+
+```pycon
+>>> np.r_[cauchy.mean(), cauchy.std()]
+array([nan, nan])
+```
+
 ---
 
-See the [documentation](https://jorenham.github.io/Lmo/) for more examples and
-the API reference.
+See the [documentation][DOCS] for more examples and the API reference.
 
 ## Installation
 
-Lmo is available on [PyPI](https://pypi.org/project/Lmo/), and can be installed
+Lmo is available on [PyPI][PYPI], and can be installed
 with:
 
 ```shell
@@ -116,23 +128,36 @@ Additionally, Lmo supports the following optional packages:
 
 See [SPEC 0][SPEC0] for more information.
 
+## Foundational Literature
+
+- [*J.R.M. Hosking* (1990) – L-moments: Analysis and Estimation of Distributions using Linear Combinations of Order Statistics](https://doi.org/10.1111/j.2517-6161.1990.tb01775.x)
+- [*E.A.H. Elamir & A.H. Seheult* (2003) – Trimmed L-moments](https://doi.org/10.1016/S0167-9473(02)00250-5)
+- [*J.R.M. Hosking* (2007) – Some theory and practical uses of trimmed L-moments](https://doi.org/10.1016/j.jspi.2006.12.002)
+- [*R. Serfling & P. Xiao* (2007) – A contribution to multivariate L-moments: L-comoment matrices](https://doi.org/10.1016/j.jmva.2007.01.008)
+
+[IMG-PYPI]: https://img.shields.io/pypi/v/Lmo
+[IMG-PY]: https://img.shields.io/pypi/pyversions/Lmo
+[IMG-SPEC0]: https://img.shields.io/badge/SPEC-0-green?labelColor=%23004811&color=%235CA038
+[IMG-BSD]: https://img.shields.io/github/license/jorenham/Lmo
+[IMG-CI]: https://img.shields.io/github/actions/workflow/status/jorenham/Lmo/ci.yml?branch=master
+[IMG-PC]: https://img.shields.io/badge/pre--commit-enabled-orange?logo=pre-commit
+[IMG-RUFF]: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json
+[IMG-BPR]: https://img.shields.io/badge/basedpyright-checked-42b983
+
+[PYPI]: https://pypi.org/project/Lmo/
+[REPO]: https://github.com/jorenham/Lmo
+[DOCS]: https://jorenham.github.io/Lmo/
+[BSD]: https://github.com/jorenham/Lmo/blob/master/LICENSE
+[CI]: https://github.com/jorenham/Lmo/actions/workflows/ci.yml?query=branch%3Amaster
+
+[PC]: https://github.com/pre-commit/pre-commit
+[RUFF]: https://github.com/astral-sh/ruff
+[BPR]: https://github.com/detachhead/basedpyright/
 [PY]: https://github.com/python/cpython
 [NP]: https://github.com/numpy/numpy
 [SP]: https://github.com/scipy/scipy
 [PD]: https://github.com/pandas-dev/pandas
 [SPT]: https://github.com/jorenham/scipy-stubs
 [SPEC0]: https://scientific-python.org/specs/spec-0000/
 
-## Foundational Literature
-
-- [*J.R.M. Hosking* (1990) – L-moments: Analysis and Estimation of
-  Distributions using Linear Combinations of Order Statistics
-  ](https://doi.org/10.1111/j.2517-6161.1990.tb01775.x)
-- [*E.A.H. Elamir & A.H. Seheult* (2003) – Trimmed L-moments
-  ](https://doi.org/10.1016/S0167-9473(02)00250-5)
-- [*J.R.M. Hosking* (2007) – Some theory and practical uses of trimmed
-  L-moments](https://doi.org/10.1016/j.jspi.2006.12.002)
-- [*R. Serfling & P. Xiao* (2007) – A contribution to multivariate
-  L-moments: L-comoment matrices](https://doi.org/10.1016/j.jmva.2007.01.008)
-
 <!--overview-end-->

docs/examples/lmm.ipynb

@@ -23,9 +23,9 @@
    "source": [
     "np.set_printoptions(precision=5, suppress=True)\n",
     "plt.style.use([\n",
-    "    'dark_background',\n",
-    "    'seaborn-v0_8-notebook',\n",
-    "    '../styles/gallery.mplstyle',\n",
+    "    \"dark_background\",\n",
+    "    \"seaborn-v0_8-notebook\",\n",
+    "    \"../styles/gallery.mplstyle\",\n",
     "])"
    ]
   },
@@ -45,7 +45,7 @@
     "data = [\n",
     "    123, 2250, 543, 178, 67, 5100, 248, 1500, 342, 329,\n",
     "    543, 980, 1020, 4502, 3406, 856, 297,\n",
-    "]"
+    "]  # fmt: skip"
    ]
   },
   {
@@ -77,8 +77,8 @@
    ],
    "source": [
     "l_stats_data = lmo.l_stats(data)\n",
-    "print('L-moment statistics [L-loc, L-scale, L-skew, L-kurt]:\\n')\n",
-    "print('Data:', l_stats_data)"
+    "print(\"L-moment statistics [L-loc, L-scale, L-skew, L-kurt]:\\n\")\n",
+    "print(\"Data:\", l_stats_data)"
    ]
   },
   {
@@ -141,12 +141,12 @@
     "\n",
     "# MM (method of moments) fails, as the variance is non-finite\n",
     "# params_mm = GEV.fit(data, method='MM')\n",
-    "params_mle = GEV.fit(data, method='MLE')\n",
+    "params_mle = GEV.fit(data, method=\"MLE\")\n",
     "params_lmm = GEV.l_fit(data)\n",
     "\n",
-    "print('Fitted GEV parameters [shape (c), loc, scale]:\\n')\n",
-    "print('MLE:', np.round(params_mle, 5))\n",
-    "print('L-MM:', np.round(params_lmm, 5))"
+    "print(\"Fitted GEV parameters [shape (c), loc, scale]:\\n\")\n",
+    "print(\"MLE:\", np.round(params_mle, 5))\n",
+    "print(\"L-MM:\", np.round(params_lmm, 5))"
    ]
   },
   {
@@ -170,10 +170,10 @@
     "X_mle = GEV(*params_mle)\n",
     "X_lmm = GEV(*params_lmm)\n",
     "\n",
-    "print('Fitted GEV L-moment statistics (loc, scale, skew, kurtosis):\\n')\n",
-    "print('Data:', l_stats_data)\n",
-    "print('MLE: ', X_mle.l_stats())\n",
-    "print('L-MM:', X_lmm.l_stats())"
+    "print(\"Fitted GEV L-moment statistics (loc, scale, skew, kurtosis):\\n\")\n",
+    "print(\"Data:\", l_stats_data)\n",
+    "print(\"MLE: \", X_mle.l_stats())\n",
+    "print(\"L-MM:\", X_lmm.l_stats())"
    ]
   },
   {
@@ -208,8 +208,8 @@
     "fig, axs = plt.subplots(1, 2, sharey=True, figsize=(10, 4.5))\n",
     "probplot(data, dist=X_mle, plot=axs[0], rvalue=True)\n",
     "probplot(data, dist=X_lmm, plot=axs[1], rvalue=True)\n",
-    "axs[0].set_title('MLE')\n",
-    "axs[1].set_title('L-MM')\n",
+    "axs[0].set_title(\"MLE\")\n",
+    "axs[1].set_title(\"L-MM\")\n",
     "plt.tight_layout()"
    ]
   }