Update type hints and pre-commit hooks

Added future annotations import and modified type hints to use Union for compatibility. Updated versions of various pre-commit hooks and removed the docformatter hook.

Author: jonas-eschle

.pre-commit-config.yaml

@@ -19,11 +19,6 @@ repos:
       - id: check-ast
       - id: end-of-file-fixer
 
-  - repo: https://github.com/PyCQA/docformatter
-    rev: v1.7.5
-    hooks:
-      - id: docformatter
-        args: [ -r, --in-place, --wrap-descriptions, '120', --wrap-summaries, '120', -- ]
 
   - repo: https://github.com/pre-commit/pygrep-hooks
     rev: v1.10.0 # Use the ref you want to point at
@@ -35,13 +30,13 @@ repos:
 
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.17.0
+    rev: v3.19.0
     hooks:
       - id: pyupgrade
         args: [ --py38-plus ]
 
   - repo: https://github.com/asottile/setup-cfg-fmt
-    rev: v2.5.0
+    rev: v2.7.0
     hooks:
       - id: setup-cfg-fmt
 
@@ -57,7 +52,7 @@ repos:
         args: [ --py38-plus ]
 
   - repo: https://github.com/mgedmin/check-manifest
-    rev: '0.49'
+    rev: '0.50'
     hooks:
       - id: check-manifest
         stages: [ manual ]
@@ -67,10 +62,11 @@ repos:
     hooks:
       - id: upgrade-type-hints
 
-  - repo: https://github.com/janosh/format-ipy-cells
-    rev: v0.1.11
-    hooks:
-      - id: format-ipy-cells
+# TODO: requires Python 3.10+
+#  - repo: https://github.com/janosh/format-ipy-cells
+#    rev: v0.1.11
+#    hooks:
+#      - id: format-ipy-cells
 
   - repo: https://github.com/kynan/nbstripout
     rev: 0.7.1

_website/tutorials/components/77 - Custom Minimizer.ipynb

@@ -15,6 +15,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
     "from typing import Sequence\n",
     "\n",
     "import zfit\n",
@@ -139,7 +141,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
+    "\n",
     "import zfit.z.numpy as znp\n",
     "from zfit.result import FitResult\n",
     "\n",
@@ -164,8 +166,9 @@
     "        i = 1\n",
     "        while True:\n",
     "            value, gradients = evaluator.value_gradient(paramvals)\n",
-    "            result = FitResult(loss=loss, params={p: v for p, v in zip(params, paramvals)}, minimizer=self, valid=False, converged=False, edm=None, fminopt=None, approx={'gradient': gradients}, criterion=criterion,\n",
-    "                              )\n",
+    "            result = FitResult(loss=loss, params={p: v for p, v in zip(params, paramvals)}, minimizer=self, valid=False, converged=False, edm=None, fminopt=None,\n",
+    "                               approx={'gradient': gradients}, criterion=criterion,\n",
+    "                               )\n",
     "            if criterion.converged(result=result):\n",
     "                result = FitResult(loss=loss, params={p: v for p, v in zip(params, paramvals)}, minimizer=self, valid=True, converged=True, edm=None,\n",
     "                                   fminopt=None, approx={'gradient': gradients}, criterion=criterion)\n",

components/77 - Custom Minimizer.ipynb

@@ -15,6 +15,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
     "from typing import Sequence\n",
     "\n",
     "import zfit\n",
@@ -139,7 +141,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
+    "\n",
     "import zfit.z.numpy as znp\n",
     "from zfit.result import FitResult\n",
     "\n",
@@ -164,8 +166,9 @@
     "        i = 1\n",
     "        while True:\n",
     "            value, gradients = evaluator.value_gradient(paramvals)\n",
-    "            result = FitResult(loss=loss, params={p: v for p, v in zip(params, paramvals)}, minimizer=self, valid=False, converged=False, edm=None, fminopt=None, approx={'gradient': gradients}, criterion=criterion,\n",
-    "                              )\n",
+    "            result = FitResult(loss=loss, params={p: v for p, v in zip(params, paramvals)}, minimizer=self, valid=False, converged=False, edm=None, fminopt=None,\n",
+    "                               approx={'gradient': gradients}, criterion=criterion,\n",
+    "                               )\n",
     "            if criterion.converged(result=result):\n",
     "                result = FitResult(loss=loss, params={p: v for p, v in zip(params, paramvals)}, minimizer=self, valid=True, converged=True, edm=None,\n",
     "                                   fminopt=None, approx={'gradient': gradients}, criterion=criterion)\n",