Fix highlighting of cells

Author: henryiii

docs/conf.py

@@ -73,6 +73,8 @@
 
 nbsphinx_execute = "never"  # Can change to auto
 
+highlight_language = "python3"
+
 nbsphinx_execute_arguments = [
     "--InlineBackend.figure_formats={'png2x'}",
     "--InlineBackend.rc={'figure.dpi': 96}",

docs/index.rst

@@ -31,7 +31,7 @@ Conda via `conda-forge <https://github.com/conda-forge/boost-histogram-feedstock
 All the normal best-practices for Python apply; you should be in a
 virtual environment, etc. See :ref:`usage-installation` for more details. An example of usage:
 
-.. code:: python
+.. code:: python3
 
    import boost_histogram as bh
 

docs/usage/analyses.rst

@@ -24,7 +24,7 @@ In a traditional analysis, you might bin over ``value`` where
 each run number. With boost-histogram, you can make a single histogram,
 and use an axis for each:
 
-.. code:: python
+.. code:: python3
 
    value_ax = bh.axis.Regular(100, -5, 5)
    bool_ax = bh.axis.Integer(0, 2, underflow=False, overflow=False)
@@ -34,7 +34,7 @@ Now, you can use these axes to create a single histogram that you can
 fill. If you want to get a histogram of all run numbers and just the
 True ``is_valid`` selection, you can use a ``sum``:
 
-.. code:: python
+.. code:: python3
 
    h1 = hist[:, True, ::bh.sum]
 

docs/usage/axes.rst

@@ -85,7 +85,7 @@ Integer axis
 
 One common use for an integer axis could be a true/false axis:
 
-.. code:: python
+.. code:: python3
 
    bool_axis = bh.axis.Integer(0, 2, underflow=False, overflow=False)
 
@@ -104,7 +104,7 @@ Category axis
 
 One use for an IntCategory axis is for an IntEnum (Python 3):
 
-.. code:: python
+.. code:: python3
 
     import enum
 

docs/usage/histogram.rst

@@ -62,7 +62,7 @@ Saving a Histogram
 
 You can save a histogram using pickle:
 
-.. code:: python
+.. code:: python3
 
     import pickle
 

docs/usage/indexing.rst

@@ -19,7 +19,7 @@ implements UHI.
 Access:
 ^^^^^^^
 
-.. code:: python
+.. code:: python3
 
    v = h[b]          # Returns bin contents, indexed by bin number
    v = h[loc(b)]     # Returns the bin containing the value
@@ -29,7 +29,7 @@ Access:
 Slicing:
 ^^^^^^^^
 
-.. code:: python
+.. code:: python3
 
    h == h[:]             # Slice over everything
    h2 = h[a:b]           # Slice of histogram (includes flow bins)
@@ -46,7 +46,7 @@ Slicing:
 Setting
 ^^^^^^^
 
-.. code:: python
+.. code:: python3
 
    # Single values
    h[b] = v         # Returns bin contents, indexed by bin number
@@ -75,7 +75,7 @@ to run on an axis or a few axes out of many. For this use case, you can pass a
 dictionary to the index, and that has the syntax ``{axis:action}``. The actions
 are slices, and follow the rules listed above. This looks like:
 
-.. code:: python
+.. code:: python3
 
     h[{0: slice(None, None, bh.rebin(2))}] # rebin axis 0 by two
     h[{1: slice(0, bh.loc(3.5))}]          # slice axis 1 from 0 to the data coordinate 3.5
@@ -85,7 +85,7 @@ are slices, and follow the rules listed above. This looks like:
 If you don't like manually building slices, you can use the `Slicer()` utility
 to recover the original slicing syntax inside the dict:
 
-.. code:: python
+.. code:: python3
 
     s = bh.tag.Slicer()
 
@@ -98,7 +98,7 @@ to recover the original slicing syntax inside the dict:
 Invalid syntax:
 ^^^^^^^^^^^^^^^
 
-.. code:: python
+.. code:: python3
 
    h[1.0] # Floats are not allowed, just like numpy
    h[::2] # Skipping is not (currently) supported
@@ -107,7 +107,7 @@ Invalid syntax:
 Rejected proposals or proposals for future consideration, maybe ``hist``-only:
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. code:: python
+.. code:: python3
 
    h2 = h[1.0j:2.5j + 1] # Adding a j suffix to a number could be used in place of `loc(x)`
    h2 = h[1.0] # Floats in place of `loc(x)`: too easy to make a mistake
@@ -118,7 +118,7 @@ Examples
 
 For a histogram, the slice should be thought of like this:
 
-.. code:: python
+.. code:: python3
 
    histogram[start:stop:action]
 
@@ -145,7 +145,7 @@ axis 3 should be summed over. You have a 4D histogram.
 
 Solution:
 
-.. code:: python
+.. code:: python3
 
    ans = h[:20, bh.loc(-.5):bh.loc(1.5), ::bh.rebin(2), ::bh.sum]
 
@@ -157,7 +157,7 @@ histogram.
 
 Solution:
 
-.. code:: python
+.. code:: python3
 
    h[bh.loc(4.0):] = 0
 
@@ -166,7 +166,7 @@ length as the range you give, or the same length as the range +
 under/overflows if the range is open ended (no limit given). For
 example:
 
-.. code:: python
+.. code:: python3
 
    h = bh.Histogram(bh.axis.Regular(10, 0, 1))
    h[:] = np.ones(10) # underflow/overflow still 0
@@ -184,7 +184,7 @@ leaving all other axes alone. You have an ND histogram, with N >= 2.
 
 Solution:
 
-.. code:: python
+.. code:: python3
 
    ans = h[:, :bh.loc(2.4):bh.sum, ...]
 
@@ -193,7 +193,7 @@ this case, was large or programmatically defined. In these cases, you
 can pass a dictionary of ``{axis:slice}`` into the indexing operation. A
 shortcut to quickly generate slices is provided, as well:
 
-.. code:: python
+.. code:: python3
 
    ans = h[{1: slice(None,bh.loc(2.4),bh.sum)}]
 
@@ -208,7 +208,7 @@ You want the underflow bin of a 1D histogram.
 
 Solution:
 
-.. code:: python
+.. code:: python3
 
    val = h1[bh.underflow]
 
@@ -245,7 +245,7 @@ without the operators.
 
 Basic implementation (WIP):
 
-.. code:: python
+.. code:: python3
 
    class loc:
        "When used in the start or stop of a Histogram's slice, x is taken to be the position in data coordinates."

docs/usage/numpy.rst

@@ -36,7 +36,7 @@ tuple, and ``threads=N`` to select a number of threads to fill with.
 
 If you try the following in an IPython session, you will get:
 
-.. code:: python
+.. code:: python3
 
    import numpy as np
    import boost_histogram as bh
@@ -62,7 +62,7 @@ ND histograms. But if you already use Numpy histograms and you really
 don’t want to rewrite your code, boost-histogram has adaptors for the
 three histogram functions in Numpy:
 
-.. code:: python
+.. code:: python3
 
    %%timeit
    bins, edges = bh.numpy.histogram(norm_vals, bins=100, range=(0, 10))
@@ -75,13 +75,13 @@ This is only a hair slower than using the raw boost-histogram API,
 and is still a nice performance boost over Numpy. You can even use the
 Numpy syntax if you want a boost-histogram object later:
 
-.. code:: python
+.. code:: python3
 
    hist = bh.numpy.histogram(norm_vals, bins=100, range=(0, 10), histogram=bh.Histogram)
 
 You can later get a Numpy style output tuple from a histogram object:
 
-.. code:: python
+.. code:: python3
 
    bins, edges = hist.to_numpy()
 
@@ -91,7 +91,7 @@ So you can transition your code slowly to boost-histogram.
 2D Histogram example
 ^^^^^^^^^^^^^^^^^^^^
 
-.. code:: python
+.. code:: python3
 
    data  = np.random.multivariate_normal(
        (0, 0),
@@ -101,16 +101,16 @@ So you can transition your code slowly to boost-histogram.
 We can check the performance against Numpy again; Numpy does not do well
 with regular spaced bins in more than 1D:
 
-.. code:: python
+.. code:: python3
 
    %%timeit
    np.histogram2d(*data, bins=(400, 200), range=((-2, 2), (-1, 1)))
 
-.. code::
+.. code:: text
 
    1.31 s ± 17.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
 
-.. code:: python
+.. code:: python3
 
    %%timeit
    bh.numpy.histogram2d(*data, bins=(400, 200), range=((-2, 2), (-1, 1)))

docs/usage/quickstart.rst

@@ -5,7 +5,7 @@ Quickstart
 
 All of the examples will assume the following import:
 
-.. code:: python
+.. code:: python3
 
    import boost_histogram as bh
 
@@ -22,20 +22,20 @@ Making a histogram
 
 You can make a histogram like this:
 
-.. code:: python
+.. code:: python3
 
    hist = bh.Histogram(bh.axis.Regular(bins=10, start=0, stop=1))
 
 If you’d like to type less, you can leave out the keywords:
 
-.. code:: python
+.. code:: python3
 
    hist = bh.Histogram(bh.axis.Regular(10, 0, 1))
 
 
 The exact same syntax is used for 1D, 2D, and ND histograms:
 
-.. code:: python
+.. code:: python3
 
    hist3D = bh.Histogram(
        bh.axis.Regular(10, 0, 100, circular=True),
@@ -54,7 +54,7 @@ Filling a histogram
 Once you have a histogram, you can fill it using ``.fill``. Ideally, you
 should give arrays, but single values work as well:
 
-.. code:: python
+.. code:: python3
 
    hist = bh.Histogram(bh.axis.Regular(10, 0.0, 1.0))
    hist.fill(0.9)
@@ -69,7 +69,7 @@ or data coordinates using ``bh.loc(v)``. You can also
 rebin with ``bh.rebin(n)`` or remove an entire axis
 using ``bh.sum`` as the third slice argument:
 
-.. code:: python
+.. code:: python3
 
     hist = bh.Histogram(
         bh.axis.Regular(10, 0, 1),
@@ -90,7 +90,7 @@ Most methods like ``.view()`` offer an optional keyword
 argument that you can pass, ``flow=True``, to enable the under and
 overflow bins (disabled by default).
 
-.. code:: python
+.. code:: python3
 
     np_array = hist.view()
 
@@ -101,7 +101,7 @@ Setting the contents
 You can set the contents directly as you would a Numpy array;
 you can set either values or arrays at a time:
 
-.. code:: python
+.. code:: python3
 
     hist[2] = 3.5
     hist[bh.underflow] = 0 # set the underflow bin
@@ -117,7 +117,7 @@ The axes are directly available in the histogram, and you can access
 a variety of properties, such as the ``edges`` or the ``centers``. All
 properties and methods are also available directly on the ``axes`` tuple:
 
-.. code:: python
+.. code:: python3
 
    ax0 = hist.axes[0]
    X, Y = hist.axes.centers
@@ -130,7 +130,7 @@ Saving Histograms
 
 You can save histograms using pickle:
 
-.. code:: python
+.. code:: python3
 
     import pickle
 

docs/usage/storage.rst

@@ -21,7 +21,7 @@ up to 53 bits of information (9 quadrillion) counts per cell, and supports
 weighted fills. It can also be scaled by a floating point values without making
 a copy.
 
-.. code:: python
+.. code:: python3
 
     h = bh.Histogram(bh.axis.Regular(10, 0, 1))    # Double() is the default
     h.fill([.2, .3], weight=[.5, 2])             # Weights are optional
@@ -47,7 +47,7 @@ A true integer storage is provided, as well; this storage has the ``np.uint64``
 datatype.  This eventually should provide type safety by not accepting
 non-integer fills for data that should represent raw, unweighed counts.
 
-.. code:: python
+.. code:: python3
 
     h = bh.Histogram(bh.axis.Regular(10, 0, 1), storage=bh.storage.Int64())
     h.fill([.2, .3], weight=[1, 2])               # Integer weights supported