docs: fix spelling and common typos

.github/workflows/examples.yml

@@ -20,7 +20,7 @@ jobs:
     - uses: actions/setup-python@v1
       with:
         python-version: '3.8'
-    - name: Pre-install Numpy
+    - name: Pre-install NumPy
       run: python -m pip install -r dev-requirements.txt nbconvert ipykernel
     - name: Install kernel
       run: python -m ipykernel install --user --name boost-hist

.pre-commit-config.yaml

@@ -82,13 +82,20 @@ repos:
   - id: check-manifest
     stages: [manual]
 
+- repo: https://github.com/codespell-project/codespell
+  rev: v2.1.0
+  hooks:
+  - id: codespell
+    args: ["-L", "hist,nd,circularly,ba"]
+    exclude: ^(notebooks/xarray.ipynb|notebooks/BoostHistogramHandsOn.ipynb)$
+
 - repo: local
   hooks:
-  - id: disallow-pybind
-    name: Disallow PyBind (dual caps)
+  - id: disallow-caps
+    name: Disallow improper capitalization
     language: pygrep
-    entry: PyBind
-    exclude: .pre-commit.*.yaml
+    entry: PyBind|Numpy|Cmake|CCache|Github|PyTest
+    exclude: .pre-commit-config.yaml
 
 - repo: local
   hooks:

README.md

@@ -48,7 +48,7 @@ hist.fill(
     [0.1, 0.4, 0.9],
 )
 
-# Numpy array view into histogram counts, no overflow bins
+# NumPy array view into histogram counts, no overflow bins
 values = hist.values()
 
 # Make a new histogram with just the second axis, summing over the first, and
@@ -142,7 +142,7 @@ histograms can be plotted via any compatible library, such as [mplhep][].
         * `v = h[{0:b}]`: All actions can be represented by `axis:item` dictionary instead of by position (mostly useful for slicing)
     * Slicing to get histogram or set array of values
         * `h2 = h[a:b]`: Access a slice of a histogram, cut portions go to flow bins if present
-        * `h2 = h[:, ...]`: Using `:` and `...` supported just like Numpy
+        * `h2 = h[:, ...]`: Using `:` and `...` supported just like NumPy
         * `h2 = h[::sum]`: Third item in slice is the "action"
         * `h[...] = array`: Set the bin contents, either include or omit flow bins
     * Special accessors

docs/CHANGELOG.md

@@ -15,7 +15,7 @@
 #### Developer changes
 * Test on Python 3.10 beta releases [#600][]
 * Provide a CMakeLists for quick standalone Boost.Histogram C++ experiments [#591][]
-* Adding logging with PyTest failure output [#575][]
+* Adding logging with pytest failure output [#575][]
 
 [#575]: https://github.com/scikit-hep/boost-histogram/pull/575
 [#576]: https://github.com/scikit-hep/boost-histogram/pull/576
@@ -130,7 +130,7 @@ Python 2, and mostly equivalent in API to 1.0.
 * Supports converting user histogram objects that provide a
   `_to_boost_histogram_` method. [#483][]
 * A `view=True` parameter must now be passed to get a View instead of a standard
-  NumPy values arrray from `to_numpy()`. [#498][]
+  NumPy values array from `to_numpy()`. [#498][]
 
 #### Bug fixes
 
@@ -484,7 +484,7 @@ library. Using Boost 1.72 release.
 * Properties on accumulator views now resolve correctly [#273][]
 * Division of a histogram by a number is supported again [#278][]
 * Setting a histogram with length one slice fixed [#279][]
-* Numpy functions now work with Numpy ints in `bins=` [#282][]
+* NumPy functions now work with NumPy ints in `bins=` [#282][]
 * In-place addition avoids a copy [#284][]
 
 [#273]: https://github.com/scikit-hep/boost-histogram/pull/273
@@ -524,7 +524,7 @@ transition existing 0.5.x code to the new API.
 * `h.axes` now has the functions from axis as well. [#183][]
 * `bh.project` has become `bh.sum` [#185][]
 * `.reduce(...)` and the reducers in `bh.algorithm` have been removed in favor of dictionary based UHI slicing [#259][]
-* `bh.numpy` module interface updates, `histogram=bh.Histogram` replaces cryptic `bh=True`, and `density=True` is now supported in Numpy mode [#256][]
+* `bh.numpy` module interface updates, `histogram=bh.Histogram` replaces cryptic `bh=True`, and `density=True` is now supported in NumPy mode [#256][]
 * Added `hist.copy()` [#218][] and `hist.shape` [#264][]
 * Signatures are much nicer in Python 3 [#188][]
 * Reprs are better, various properties like `__module__` are now set correctly [#200][]

docs/index.rst

@@ -43,7 +43,7 @@ virtual environment, etc. See :ref:`usage-installation` for more details. An exa
    hist.fill([.3, .5, .2],
              [.1, .4, .9])
 
-   # Numpy array view into histogram counts, no overflow bins
+   # NumPy array view into histogram counts, no overflow bins
    counts = hist.view()
 
 See :ref:`usage-quickstart` for more.

docs/usage/accumulators.rst

@@ -156,7 +156,7 @@ Views
 -----
 
 Most of the accumulators (except Sum) support a View. This is what is returned from
-a histogram when ``.view()`` is requested. This is a structured Numpy ndarray, with a few small
-additions to make them easier to work with. Like a Numpy recarray, you can access the fields with
+a histogram when ``.view()`` is requested. This is a structured NumPy ndarray, with a few small
+additions to make them easier to work with. Like a NumPy recarray, you can access the fields with
 attributes; you can even access (but not set) computed attributes like ``.variance``. A view will
 also return an accumulator instance if you select a single item.

docs/usage/analyses.rst

@@ -36,7 +36,7 @@ True ``is_valid`` selection, you can use a ``sum``:
 
 .. code:: python3
 
-   h1 = hist[:, True, :: bh.sum]
+   h1 = hist[:, True, sum]
 
 You can expand this example to any number of dimensions, boolean flags,
 and categories.

docs/usage/installation.rst

@@ -79,7 +79,7 @@ are dictated by Boost.Histogram’s C++ requirements: gcc >= 5.5, clang >=
 3.8, msvc >= 14.1. You should have a version of pip less than 2-3 years
 old (10+).
 
-Numpy is downloaded during the build (enables multithreaded builds).
+NumPy is downloaded during the build (enables multithreaded builds).
 Boost is not required or needed (this only depends on included
 header-only dependencies).This library is under active development; you
 can install directly from GitHub if you would like.

docs/usage/quickstart.rst

@@ -64,10 +64,10 @@ should give arrays, but single values work as well:
 Slicing and rebinning
 ---------------------
 
-You can slice into a histogram using bin coordinates
-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:
+You can slice into a histogram using bin coordinates or data coordinates using
+``bh.loc(v)``. You can also rebin with ``bh.rebin(n)`` or remove an entire axis
+using ``sum`` (technically as the third slice argument, though it is allowed by
+itself as well):
 
 .. code:: python3
 
@@ -76,15 +76,15 @@ using ``bh.sum`` as the third slice argument:
         bh.axis.Regular(10, 0, 1),
         bh.axis.Regular(10, 0, 1),
     )
-    mini = hist[1:5, bh.loc(0.2) : bh.loc(0.9), :: bh.sum]
+    mini = hist[1:5, bh.loc(0.2) : bh.loc(0.9), sum]
     # Will be 4 bins x 7 bins
 
 See :ref:`usage-indexing`.
 
 Accessing the contents
 ----------------------
 
-You can use ``hist.values()`` to get a Numpy array from any histogram. You can
+You can use ``hist.values()`` to get a NumPy array from any histogram. You can
 get the variances with ``hist.variances()``, though if you fill an unweighted
 storage with weights, this will return None, as you no longer can compute the
 variances correctly (please use a weighted storage if you need to). You can

docs/usage/transforms.rst

@@ -38,7 +38,7 @@ transformed axis, and this will be 15-90 times slower than a compiled method, li
        10, 1, 4, transform=bh.axis.transform.Function(ftype(math.log), ftype(math.exp))
    )
 
-   # Pure Python: Numpy (90x slower)
+   # Pure Python: NumPy (90x slower)
    bh.axis.Regular(
        10, 1, 4, transform=bh.axis.transform.Function(ftype(np.log), ftype(np.exp))
    )
@@ -152,7 +152,7 @@ That's it.
 Using Numba
 ^^^^^^^^^^^
 
-The same procedure works for numba decorators. Numpy only supports functions, not builtins like ``math.log``,
+The same procedure works for numba decorators. NumPy only supports functions, not builtins like ``math.log``,
 so if you want to pass those, you'll need to wrap them in a lambda function or add a bit of logic to the convert
 function. Here are your options:
 

examples/simple_2d.py

@@ -10,12 +10,12 @@
     bh.axis.Regular(20, -3, 3, metadata="x"), bh.axis.Regular(20, -3, 3, metadata="y")
 )
 
-# Generate some Numpy arrays with data to fill into histogram,
+# Generate some NumPy arrays with data to fill into histogram,
 # in this case normal distributed random numbers in x and y
 x = np.random.randn(1_000)
 y = 0.5 * np.random.randn(1_000)
 
-# Fill histogram with Numpy arrays, this is very fast
+# Fill histogram with NumPy arrays, this is very fast
 h.fill(x, y)
 
 # Get numpy.histogram compatible representation of the histogram

examples/simple_numpy.py

@@ -10,15 +10,15 @@
     bh.axis.Regular(10, -3, 3, metadata="x"), bh.axis.Regular(10, -3, 3, metadata="y")
 )
 
-# Generate some Numpy arrays with data to fill into histogram,
+# Generate some NumPy arrays with data to fill into histogram,
 # in this case normal distributed random numbers in x and y
 x_data = np.random.randn(1000)
 y_data = 0.5 * np.random.randn(1000)
 
 # Fill histogram with numpy arrays, this is very fast
 h.fill(x_data, y_data)
 
-# Get representations of the bin edges as Numpy arrays
+# Get representations of the bin edges as NumPy arrays
 x = h.axes[0].edges
 y = h.axes[1].edges
 

include/bh_python/transform.hpp

@@ -25,7 +25,7 @@ struct func_transform {
     py::object _forward_converted; // Held for reference counting if conversion makes a
                                    // new object (ctypes does not bump the refcount)
     py::object _inverse_converted;
-    py::object _convert_ob; // Called before computing tranform if not None
+    py::object _convert_ob; // Called before computing transform if not None
     py::str _name;          // Optional name (uses repr from objects otherwise)
 
     /// Convert an object into a std::function. Can handle ctypes
@@ -84,7 +84,7 @@ struct func_transform {
                                        src);
             }
 
-            // Note that each error is slighly different just to help with debugging
+            // Note that each error is slightly different just to help with debugging
             throw py::type_error("Only ctypes double(double) and C++ functions allowed "
                                  "(must be stateless)");
         }

notebooks/BoostHistogramHandsOn.ipynb

@@ -231,7 +231,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2: Drop-in replacement for Numpy\n",
+    "## 2: Drop-in replacement for NumPy\n",
     "\n",
     "To start using this yourself, you don't even need to change your code. Let's try the numpy adapters."
    ]
@@ -330,7 +330,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we can move over to boost-histogram one step at a time! Just to be complete, we can also go back to a Numpy tuple from a Histogram object:"
+    "Now we can move over to boost-histogram one step at a time! Just to be complete, we can also go back to a NumPy tuple from a Histogram object:"
    ]
   },
   {
@@ -948,7 +948,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's try to make a density histogram like Numpy's."
+    "Let's try to make a density histogram like NumPy's."
    ]
   },
   {

notebooks/PerformanceComparison.ipynb

@@ -81,7 +81,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Traditional 1D Numpy Histogram\n",
+    "#### Traditional 1D NumPy Histogram\n",
     "\n",
     "This is reasonably optimized; it should provide good perforance."
    ]
@@ -232,7 +232,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Traditional 2D Numpy histogram\n",
+    "#### Traditional 2D NumPy histogram\n",
     "\n",
     "Not as well optimized for regular filling."
    ]

notebooks/ThreadedFills.ipynb

@@ -136,7 +136,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This makes four seperate histograms, then fills them and adds at the end."
+    "This makes four separate histograms, then fills them and adds at the end."
    ]
   },
   {

notebooks/xarray.ipynb

@@ -235,7 +235,7 @@
     "### More features\n",
     "\n",
     "Let's add a few more features to our function defined above.\n",
-    "* Let's allow bins to be a list of axes or even a completly prepared histogram; this will allow us to take advantage of boost-histogram features later.\n",
+    "* Let's allow bins to be a list of axes or even a completely prepared histogram; this will allow us to take advantage of boost-histogram features later.\n",
     "* Let's add a weights keyword so we can do weighted histograms as well."
    ]
   },

scripts/performance_report.py

@@ -72,7 +72,7 @@ def print_timer(setup, statement, name, storage, fill, flow, base=None, n=10):
 base = print_timer(
     setup_1d,
     "h, _ = np.histogram(vals, bins=bins, range=ranges)",
-    name="Numpy",
+    name="NumPy",
     storage="uint64",
     fill="",
     flow="no",
@@ -163,7 +163,7 @@ def print_timer(setup, statement, name, storage, fill, flow, base=None, n=10):
 base = print_timer(
     setup_2d,
     "H, *ledges = np.histogram2d(*vals, bins=bins, range=ranges)",
-    name="Numpy",
+    name="NumPy",
     storage="uint64",
     fill="",
     flow="no",

src/boost_histogram/_internal/hist.py

@@ -648,7 +648,7 @@ def to_numpy(
         self, flow: bool = False, *, dd: bool = False, view: bool = False
     ) -> Union[Tuple[np.ndarray, ...], Tuple[np.ndarray, Tuple[np.ndarray, ...]]]:
         """
-        Convert to a Numpy style tuple of return arrays. Edges are converted to
+        Convert to a NumPy style tuple of return arrays. Edges are converted to
         match NumPy standards, with upper edge inclusive, unlike
         boost-histogram, where upper edge is exclusive.
 

src/boost_histogram/_internal/view.py

@@ -24,7 +24,7 @@ def __getitem__(self, ind: StrIndex) -> np.ndarray:
             return sliced  # type: ignore
 
     def __repr__(self) -> str:
-        # Numpy starts the ndarray class name with "array", so we replace it
+        # NumPy starts the ndarray class name with "array", so we replace it
         # with our class name
         return f"{self.__class__.__name__}(\n      " + repr(self.view(np.ndarray))[6:]
 

src/boost_histogram/numpy.py

@@ -38,7 +38,7 @@ def histogramdd(
 
     if normed is not None:
         raise KeyError(
-            "normed=True is not recommended for use in Numpy, and is not supported in boost-histogram; use density=True instead"
+            "normed=True is not recommended for use in NumPy, and is not supported in boost-histogram; use density=True instead"
         )
     if density and histogram is not None:
         raise KeyError(

src/register_accumulators.cpp

@@ -59,7 +59,7 @@ decltype(auto) make_buffer() {
 
 void register_accumulators(py::module& accumulators) {
     // Naming convention:
-    // If a value is publically available in Boost.Histogram accumulators
+    // If a value is publicly available in Boost.Histogram accumulators
     // as a method, it has the same name in the numpy record array.
     // If it is not available except through a computation, it has
     // the same name as the private property without the trailing _.

src/register_axis.cpp

@@ -55,7 +55,7 @@ void register_axes(py::module& mod) {
              "bins"_a,
              "start"_a,
              "stop"_a,
-             "tranform"_a)
+             "transform"_a)
         .def_property_readonly("transform", [](const axis::regular_trans& self) {
             return self.transform();
         });

tests/test_axis.py

@@ -88,7 +88,7 @@ def test_metadata(axis, args, opt, kwargs):
 
 
 # The point of this ABC is to force all the tests listed here to be implemented
-# for each axis type. PyTest instantiates these test classes for us, so missing
+# for each axis type. Pytest instantiates these test classes for us, so missing
 # one really does fail the test.
 class Axis(abc.ABC):
     @abc.abstractmethod

tests/test_histogram.py

@@ -733,7 +733,7 @@ def test_pickle_bool():
     assert_array_equal(a.view(), b.view())
 
 
-# Numpy tests
+# NumPy tests
 
 
 def test_numpy_conversion_0():