chore: do not pin mkdocs version (#247)

* chore: pre-commit autoupdate

updates:
- [github.com/psf/black: 21.12b0 → 22.3.0](psf/black@21.12b0...22.3.0)
- [github.com/asottile/blacken-docs: v1.12.0 → v1.12.1](adamchainz/blacken-docs@v1.12.0...v1.12.1)
- [github.com/nbQA-dev/nbQA: 1.2.2 → 1.3.1](nbQA-dev/nbQA@1.2.2...1.3.1)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* build: do not pin mkdocs version

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
 - repo: https://github.com/psf/black
-  rev: 21.12b0
+  rev: 22.3.0
   hooks:
     - id: black
       language_version: python3
@@ -33,11 +33,11 @@ repos:
   hooks:
     - id: nbstripout
 -   repo: https://github.com/asottile/blacken-docs
-    rev: v1.12.0
+    rev: v1.12.1
     hooks:
     - id: blacken-docs
 - repo: https://github.com/nbQA-dev/nbQA
-  rev: 1.2.2
+  rev: 1.3.1
   hooks:
     - id: nbqa-black
       args: [--nbqa-mutate]

docs/user-guide/detection/binseg.md

@@ -42,9 +42,9 @@ In the situation in which the number of change points is unknown, one can specif
 the `pen` parameter or a threshold on the residual norm using `epsilon`.
 
 ```python
-my_bkps = algo.predict(pen=np.log(n) * dim * sigma ** 2)
+my_bkps = algo.predict(pen=np.log(n) * dim * sigma**2)
 # or
-my_bkps = algo.predict(epsilon=3 * n * sigma ** 2)
+my_bkps = algo.predict(epsilon=3 * n * sigma**2)
 ```
 
 For faster predictions, one can modify the `jump` parameter during initialization.

docs/user-guide/detection/bottomup.md

@@ -51,9 +51,9 @@ In the situation in which the number of change points is unknown, one can specif
 the `pen` parameter or a threshold on the residual norm using `epsilon`.
 
 ```python
-my_bkps = algo.predict(pen=np.log(n) * dim * sigma ** 2)
+my_bkps = algo.predict(pen=np.log(n) * dim * sigma**2)
 # or
-my_bkps = algo.predict(epsilon=3 * n * sigma ** 2)
+my_bkps = algo.predict(epsilon=3 * n * sigma**2)
 ```
 
 For faster predictions, one can modify the `jump` parameter during initialization.

docs/user-guide/detection/window.md

@@ -65,9 +65,9 @@ In the situation in which the number of change points is unknown, one can specif
 the `pen` parameter or a threshold on the residual norm using `epsilon`.
 
 ```python
-my_bkps = algo.predict(pen=np.log(n) * dim * sigma ** 2)
+my_bkps = algo.predict(pen=np.log(n) * dim * sigma**2)
 # or
-my_bkps = algo.predict(epsilon=3 * n * sigma ** 2)
+my_bkps = algo.predict(epsilon=3 * n * sigma**2)
 ```
 
 For faster predictions, one can modify the `jump` parameter during initialization.

setup.cfg

@@ -53,7 +53,7 @@ docs =
     jupyter
     librosa
     matplotlib
-    mkdocs==1.1.2
+    mkdocs
     mkdocs-macros-plugin
     mkdocs-material
     mkdocs-section-index

tests/test_detection.py

@@ -347,13 +347,13 @@ def test_model_small_signal(signal_bkps_5D_n10, algo, model):
     with pytest.raises(BadSegmentationParameters):
         algo(model=model, min_size=5, jump=2).fit_predict(signal, n_bkps=2)
     assert (
-        len(algo(model=model, min_size=5, jump=2).fit_predict(signal, pen=10 ** 6)) > 0
+        len(algo(model=model, min_size=5, jump=2).fit_predict(signal, pen=10**6)) > 0
     )
     assert (
         len(algo(model=model, min_size=5, jump=2).fit_predict(signal, epsilon=10)) > 0
     )
     assert (
-        len(algo(model=model, min_size=9, jump=2).fit_predict(signal, pen=10 ** 6)) > 0
+        len(algo(model=model, min_size=9, jump=2).fit_predict(signal, pen=10**6)) > 0
     )