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adaptive/learner/triangulation.py

@@ -16,9 +16,9 @@
     ones,
     square,
     subtract,
+    zeros,
 )
 from numpy import sum as np_sum
-from numpy import zeros
 from numpy.linalg import det as ndet
 from numpy.linalg import matrix_rank, norm, slogdet, solve
 

docs/logo.py

@@ -3,8 +3,8 @@
 
 import holoviews
 import matplotlib.pyplot as plt
-import numpy as np
 import matplotlib.tri as mtri
+import numpy as np
 from PIL import Image, ImageDraw
 
 sys.path.insert(0, os.path.abspath(".."))  # to get adaptive on the path

docs/source/algorithms_and_examples.md

@@ -46,11 +46,13 @@ Click on the *Play* {fa}`play` button or move the sliders.
 :tags: [hide-cell]
 
 import itertools
-import adaptive
-from adaptive.learner.learner1D import uniform_loss, default_loss
+
 import holoviews as hv
 import numpy as np
 
+import adaptive
+from adaptive.learner.learner1D import default_loss, uniform_loss
+
 adaptive.notebook_extension()
 hv.output(holomap="scrubber")
 ```

docs/source/tutorial/tutorial.AverageLearner1D.md

@@ -1,14 +1,15 @@
 ---
-kernelspec:
-  name: python3
-  display_name: python3
 jupytext:
   text_representation:
     extension: .md
     format_name: myst
-    format_version: '0.13'
-    jupytext_version: 1.13.8
+    format_version: 0.13
+    jupytext_version: 1.14.5
+kernelspec:
+  display_name: python3
+  name: python3
 ---
+
 # Tutorial {class}`~adaptive.AverageLearner1D`
 
 ```{note}
@@ -23,9 +24,10 @@ import adaptive
 
 adaptive.notebook_extension()
 
+from functools import partial
+
 import holoviews as hv
 import numpy as np
-from functools import partial
 ```
 
 ## General use

docs/source/tutorial/tutorial.BalancingLearner.md

@@ -24,10 +24,11 @@ import adaptive
 
 adaptive.notebook_extension()
 
+import random
+from functools import partial
+
 import holoviews as hv
 import numpy as np
-from functools import partial
-import random
 ```
 
 The balancing learner is a “meta-learner” that takes a list of learners.

docs/source/tutorial/tutorial.Learner1D.md

@@ -25,9 +25,10 @@ import adaptive
 
 adaptive.notebook_extension()
 
-import numpy as np
-from functools import partial
 import random
+from functools import partial
+
+import numpy as np
 ```
 
 ## scalar output: `f:ℝ → ℝ`
@@ -41,8 +42,8 @@ offset = random.uniform(-0.5, 0.5)
 
 
 def f(x, offset=offset, wait=True):
-    from time import sleep
     from random import random
+    from time import sleep
 
     a = 0.01
     if wait:
@@ -155,8 +156,8 @@ To do this, you need to tell the learner to look at the curvature by specifying
 ```{code-cell} ipython3
 from adaptive.learner.learner1D import (
     curvature_loss_function,
-    uniform_loss,
     default_loss,
+    uniform_loss,
 )
 
 curvature_loss = curvature_loss_function()

docs/source/tutorial/tutorial.Learner2D.md

@@ -20,11 +20,12 @@ Download the notebook in order to see the real behaviour. [^download]
 ```{code-cell} ipython3
 :tags: [hide-cell]
 
-import adaptive
+from functools import partial
+
 import holoviews as hv
 import numpy as np
 
-from functools import partial
+import adaptive
 
 adaptive.notebook_extension()
 ```
@@ -33,9 +34,10 @@ Besides 1D functions, we can also learn 2D functions: $f: ℝ^2 → ℝ$.
 
 ```{code-cell} ipython3
 def ring(xy, wait=True):
-    import numpy as np
-    from time import sleep
     from random import random
+    from time import sleep
+
+    import numpy as np
 
     if wait:
         sleep(random() / 10)

docs/source/tutorial/tutorial.custom_loss.md

@@ -25,8 +25,8 @@ import adaptive
 adaptive.notebook_extension()
 
 # Import modules that are used in multiple cells
-import numpy as np
 import holoviews as hv
+import numpy as np
 ```
 
 {class}`~adaptive.Learner1D` and {class}`~adaptive.Learner2D` both work on the principle of subdividing their domain into subdomains, and assigning a property to each subdomain, which we call the *loss*.
@@ -137,7 +137,7 @@ def resolution_loss_function(min_distance=0, max_distance=1):
     because the total area is normalized to 1."""
 
     def resolution_loss(ip):
-        from adaptive.learner.learner2D import default_loss, areas
+        from adaptive.learner.learner2D import areas, default_loss
 
         loss = default_loss(ip)