Better ruff settings and adjustments, improvements to README (#871)

- Use ruff to enforce pydocstyle
- Switch to standard line length of 88, adjust files 
- Improvements to README

Author: HDembinski

README.rst

@@ -26,7 +26,7 @@
 .. image:: https://mybinder.org/badge_logo.svg
    :target: https://mybinder.org/v2/gh/scikit-hep/iminuit/develop?filepath=doc%2Ftutorial
 
-*iminuit* is a Jupyter-friendly Python interface for the *Minuit2* C++ library maintained by CERN's ROOT team.
+``iminuit`` is a Jupyter-friendly Python interface for the ``Minuit2`` C++ library maintained by CERN's ROOT team.
 
 Minuit was designed to minimise statistical cost functions, for likelihood and least-squares fits of parametric models to data. It provides the best-fit parameters and error estimates from likelihood profile analysis.
 
@@ -39,12 +39,22 @@ The iminuit package comes with additional features:
 - Builtin cost functions for statistical fits
 
   - Binned and unbinned maximum-likelihood
+  - Template fits with error propagation [H. Dembinski, A. Abldemotteleb, Eur.Phys.J.C 82 (2022) 11, 1043](https://doi.org/10.1140/epjc/s10052-022-11019-z)
   - Non-linear regression with (optionally robust) weighted least-squares
   - Gaussian penalty terms
   - Cost functions can be combined by adding them: ``total_cost = cost_1 + cost_2``
 - Support for SciPy minimisers as alternatives to Minuit's Migrad algorithm (optional)
 - Support for Numba accelerated functions (optional)
 
+Dependencies
+------------
+
+``iminuit`` is will always be a lean package which only depends on ``numpy``, but additional features are enabled if the following optional packages are installed
+- ``matplotlib``: Visualization of fitted model for builtin cost functions
+- ``ipywidgets``: Interactive fitting (also requires ``matplotlib``)
+- ``scipy``: Compute Minos intervals for arbitrary confidence levels
+- ``unicodeitplus``: Render names of model parameters in simple LaTeX as Unicode
+
 Documentation
 -------------
 
@@ -73,8 +83,9 @@ iminuit is intended to be used with a user-provided negative log-likelihood func
     m.migrad()  # run optimiser
     m.hesse()   # run covariance estimator
 
-    print(m.values)  # x: 2, y: 3, z: 4
-    print(m.errors)  # x: 1, y: 1, z: 1
+.. image:: doc/_static/minuit_jupyter.png
+   :alt: Jupyter display of Minuit
+
 
 Interactive fitting
 -------------------

doc/_static/minuit_jupyter.png

@@ -1067,10 +1067,11 @@
             ]
         },
         {
+            "attachments": {},
             "cell_type": "markdown",
             "metadata": {},
             "source": [
-                "The paramaters $a$ and $b$ are stronly anti-correlated, the numerical value of the correlation is shown in parentheses. The correlation is also highlighed by the blue color of the off-diagonal elements."
+                "The paramaters α and β are stronly anti-correlated, the numerical value of the correlation is shown in parentheses. The correlation is also highlighed by the blue color of the off-diagonal elements."
             ]
         },
         {
@@ -1562,7 +1563,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.10.9"
+            "version": "3.9.16"
         },
         "nteract": {
             "version": "0.12.3"

doc/notebooks/basic.ipynb

@@ -65,7 +65,7 @@ test = [
     "tabulate",
     "boost_histogram",
     "resample",
-    "unicodeit"
+    "unicodeitplus"
 ]
 doc = [
     "sphinx~=5.3.0",
@@ -90,7 +90,19 @@ filterwarnings = [
 ]
 
 [tool.ruff]
-line-length = 90
+select = ["E", "F", "D"]
+extend-ignore = ["D203", "D212"]
+
+[tool.ruff.pydocstyle]
+convention = "numpy"
+
+[tool.ruff.per-file-ignores]
+"test_*.py" = ["B", "D"]
+".ci/*.py" = ["D"]
+"bench/*.py" = ["D"]
+"doc/*.py" = ["D"]
+"setup.py" = ["D"]
+"cmake_ext.py" = ["D"]
 
 [tool.mypy]
 ignore_missing_imports = true
@@ -100,9 +112,6 @@ pretty = true
 files = ["src"]
 no_implicit_optional = false
 
-[tool.pydocstyle]
-convention = "numpy"
-
 [tool.cibuildwheel]
 # update skip when numpy wheels become available
 skip = ["*-musllinux_*", "cp31?-win32", "cp31?-manylinux_i686"]

pyproject.toml

@@ -1,4 +1,5 @@
-"""Jupyter-friendly Python interface for the Minuit2 library in C++.
+"""
+Jupyter-friendly Python interface for the Minuit2 library in C++.
 
 Basic usage example::
 

src/iminuit/__init__.py

@@ -114,7 +114,9 @@ def fmin(fm):
                     ff[2],
                     colspan=3,
                     style="text-align:center",
-                    title="Total number of function and (optional) gradient evaluations",
+                    title=(
+                        "Total number of function and (optional) gradient evaluations"
+                    ),
                 ),
             ),
             tr(
@@ -316,29 +318,3 @@ def matrix(arr):
         rows.append(tr(*cols))
 
     return to_str(table(tr(td(), *[th(v) for v in names]), *rows))
-
-
-# def _parse_latex(s):
-#     if s.startswith("$") and s.endswith("$"):
-#         with optional_module_for("displaying LaTeX"):
-#             from matplotlib.backends.backend_svg import RendererSVG
-#             from matplotlib.font_manager import FontProperties
-#             from io import StringIO
-
-#             fp = FontProperties(size=9)
-#             # get bounding box of rendered text
-#             r = RendererSVG(0, 0, StringIO())
-#             w, h, _ = r.get_text_width_height_descent(s, fp, ismath=True)
-
-#             with StringIO() as f:
-#                 # render LaTeX as path
-#                 r = RendererSVG(w, h, f)
-#                 r.draw_text(r.new_gc(), 0, h, s, fp, angle=0, ismath=True)
-#                 f.seek(0)
-#                 svg = f.read()
-
-#             # strip preamble
-#             svg = svg[svg.index("<svg") + 4 :]
-#             return f'<svg role="img" style="fill:#f0f0f0" {svg}'
-
-#     return s