chore: auto-cancel & blacken-docs

.github/workflows/ci.yml

@@ -9,6 +9,10 @@ on:
 env:
   FORCE_COLOR: 3
 
+concurrency:
+  group: ${ github.workflow }-${ github.head_ref }
+  cancel-in-progress: true
+
 jobs:
   pre-commit:
     runs-on: ubuntu-latest

.pre-commit-config.yaml

@@ -8,6 +8,12 @@ repos:
   hooks:
   - id: black-jupyter
 
+- repo: https://github.com/asottile/blacken-docs
+  rev: "1.13.0"
+  hooks:
+  - id: blacken-docs
+    additional_dependencies: [black==23.1.0]
+
 - repo: https://github.com/pre-commit/pre-commit-hooks
   rev: v4.4.0
   hooks:

README.md

@@ -97,10 +97,9 @@ from hist import Hist
 
 # Quick construction, no other imports needed:
 h = (
-  Hist.new
-  .Reg(10, 0 ,1, name="x", label="x-axis")
-  .Var(range(10), name="y", label="y-axis")
-  .Int64()
+    Hist.new.Reg(10, 0, 1, name="x", label="x-axis")
+    .Var(range(10), name="y", label="y-axis")
+    .Int64()
 )
 
 # Filling by names is allowed:
@@ -111,7 +110,7 @@ h.project("x")
 h[{"y": 0.5j + 3, "x": 5j}]
 
 # You can access data coordinates or rebin with a `j` suffix:
-h[.3j:, ::2j] # x from .3 to the end, y is rebinned by 2
+h[0.3j:, ::2j]  # x from .3 to the end, y is rebinned by 2
 
 # Elegant plotting functions:
 h.plot()

docs/banner_slides.md

@@ -16,12 +16,10 @@ Converted to GIF with ezgif.com, 300 ms delay time.
 import hist
 
 # Make a histogram
-h = hist.Hist(
-    hist.axes.Regular(10, 0, 1, name="x")
-)
+h = hist.Hist(hist.axes.Regular(10, 0, 1, name="x"))
 
 # Fill it with events
-h.fill(x=[.2, .3, .6, .9])
+h.fill(x=[0.2, 0.3, 0.6, 0.9])
 
 # Compute the sum
 total = h.sum()
@@ -36,7 +34,7 @@ from hist import Hist
 h = Hist.new.Reg(10, 0, 1, name="x").Double()
 
 # Fill it with events
-h.fill(x=[.2, .3, .6, .9])
+h.fill(x=[0.2, 0.3, 0.6, 0.9])
 
 # Compute the sum
 total = h.sum()
@@ -66,7 +64,7 @@ total = h.sum()
 
 ```python
 # Slice in data coordinates
-sliced_h = h[.5j:1.5j]
+sliced_h = h[0.5j:1.5j]
 
 # Sum over and rebin easily
 smaller_1d = h_2d[sum, 2j]
@@ -137,6 +135,7 @@ Supports the UHI `PlottableHistogram` protocol!
 
 ```python
 import mplhep
+
 mplhep.histplot(h)
 ```
 

docs/user-guide/axes.rst

@@ -184,20 +184,25 @@ One use for an IntCategory axis is for an IntEnum:
 
     import enum
 
+
     class MyEnum(enum.IntEnum):
         a = 1
         b = 5
 
+
     my_enum_axis = hist.axis.IntEnum(list(MyEnum), underflow=False, overflow=False)
 
 
 You can sort the Categorty axes via ``.sort()`` method:
 
 .. code-block:: python3
 
-    h = Hist(axis.IntCategory([3, 1, 2], label="Number"), axis.StrCategory(["Teacher", "Police", "Artist"], label="Profession"))
-    h.sort(0).axes[0] # IntCategory([1, 2, 3], label='Number')
-    h.sort(1, reverse=True).axes[1] # StrCategory(['Teacher', 'Police', 'Artist'], label='Profession')
+    h = Hist(
+        axis.IntCategory([3, 1, 2], label="Number"),
+        axis.StrCategory(["Teacher", "Police", "Artist"], label="Profession"),
+    )
+    # Sort Number axis increasing and Profession axis decreasing
+    h1 = h.sort("Number").sort("Profession", reverse=True)
 
 
 .. py:function:: hist.axis.StrCategory([str1, ...], name, label, metadata="", growth=False)

docs/user-guide/numpy.rst

@@ -41,13 +41,15 @@ If you try the following in an IPython session, you will get:
    import numpy as np
    import hist
 
-   norm_vals = np.concatenate([
-       np.random.normal(loc=5, scale=1, size=1_000_000),
-       np.random.normal(loc=2, scale=.2, size=200_000),
-       np.random.normal(loc=8, scale=.2, size=200_000),
-   ])
+   norm_vals = np.concatenate(
+       [
+           np.random.normal(loc=5, scale=1, size=1_000_000),
+           np.random.normal(loc=2, scale=0.2, size=200_000),
+           np.random.normal(loc=8, scale=0.2, size=200_000),
+       ]
+   )
 
-.. code-block:: python3
+.. code-block:: ipython
 
    %%timeit
    bins, edges = np.histogram(norm_vals, bins=100, range=(0, 10))
@@ -64,7 +66,7 @@ ND histograms. But if you already use NumPy histograms and you really
 don’t want to rewrite your code, hist has adaptors for the
 three histogram functions in NumPy:
 
-.. code-block:: python3
+.. code-block:: ipython
 
    %%timeit
    bins, edges = hist.numpy.histogram(norm_vals, bins=100, range=(0, 10))
@@ -95,15 +97,12 @@ So you can transition your code slowly to hist.
 
 .. code-block:: python3
 
-   data  = np.random.multivariate_normal(
-       (0, 0),
-       ((1, 0),(0, .5)),
-       10_000_000).T.copy()
+   data = np.random.multivariate_normal((0, 0), ((1, 0), (0, 0.5)), 10_000_000).T.copy()
 
 We can check the performance against NumPy again; NumPy does not do well
 with regular spaced bins in more than 1D:
 
-.. code-block:: python3
+.. code-block:: ipython
 
    %%timeit
    np.histogram2d(*data, bins=(400, 200), range=((-2, 2), (-1, 1)))
@@ -112,7 +111,7 @@ with regular spaced bins in more than 1D:
 
    1.31 s ± 17.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
 
-.. code-block:: python3
+.. code-block:: ipython
 
    %%timeit
    hist.numpy.histogram2d(*data, bins=(400, 200), range=((-2, 2), (-1, 1)))

docs/user-guide/quickstart.rst

@@ -46,8 +46,7 @@ The exact same syntax is used any number of dimensions:
 .. code-block:: python3
 
    hist3D = (
-        Hist.new
-       .Regular(10, 0, 100, circular=True, name="x")
+       Hist.new.Regular(10, 0, 100, circular=True, name="x")
        .Regular(10, 0.0, 10.0, name="y")
        .Variable([1, 2, 3, 4, 5, 5.5, 6], name="z")
        .Weight()
@@ -85,7 +84,7 @@ in the third slice entry, or remove an entire axis using ``sum``:
         hist.axis.Regular(10, 0, 1, name="y"),
         hist.axis.Regular(10, 0, 1, name="z"),
     )
-    mini = h[1:5, .2j:.9j, sum]
+    mini = h[1:5, 0.2j:0.9j, sum]
     # Will be 4 bins x 7 bins
 
 See :ref:`usage-indexing`.

docs/user-guide/storages.rst

@@ -94,11 +94,7 @@ non-integer fills for data that should represent raw, unweighed counts.
 .. code-block:: python3
 
     # Method 2
-    h = (
-        Hist.new.Reg(10, 0, 1, name="x")
-        .Int64()
-        .fill([0.5, 0.5])
-    )
+    h = Hist.new.Reg(10, 0, 1, name="x").Int64().fill([0.5, 0.5])
 
     print(h[0.5j])
 

docs/user-guide/subclassing.rst

@@ -13,9 +13,11 @@ When you subclass, you will need to add a family. Any object can be used - the m
     import hist
     import my_package
 
+
     class Histogram(hist.Hist, family=my_package):
         ...
 
+
     class Regular(hist.axis.Regular, family=my_package):
         ...