Merge pull request #168 from scipp/american-spelling

Change to american spelling

Author: jl-wynen

.github/workflows/release.yml

@@ -78,7 +78,7 @@ jobs:
       - run: anaconda --token ${{ secrets.ANACONDATOKEN }} upload --user scipp --label main $(ls conda-package-*/*/*.tar.bz2)
       - uses: actions/setup-python@v3
 
-      # We disable pypi upload for now, because the name `ess` is not authorised
+      # We disable pypi upload for now, because the name `ess` is not authorized
       # - uses: pypa/[email protected]
       #   with:
       #     user: __token__

docs/about/release-notes.rst

@@ -99,7 +99,7 @@ Features
 ~~~~~~~~
 
 * Added resolution function for Amor and support for Orso file format in the ``reflectometry`` module `#115 <https://github.com/scipp/ess/pull/115>`_ (*Reflectometry*).
-* Added footprint correction, super-mirror calibration, and normalisation between sample and supermirror measurements on a per-pixel & per-Q-bin level in the Amor workflow `#97 <https://github.com/scipp/ess/pull/97>`_ (*Reflectometry*).
+* Added footprint correction, super-mirror calibration, and normalization between sample and supermirror measurements on a per-pixel & per-Q-bin level in the Amor workflow `#97 <https://github.com/scipp/ess/pull/97>`_ (*Reflectometry*).
 
 Contributors
 ~~~~~~~~~~~~
@@ -153,7 +153,7 @@ Breaking changes
 
 * A major rewrite of the reflectometry code was undertaken `#61 <https://github.com/scipp/ess/pull/61>`_:
 
-  * The reflectometry and Amor Data classes were removed in favour of a formalism consisting of free-functions that accept Scipp DataArrays and Datasets.
+  * The reflectometry and Amor Data classes were removed in favor of a formalism consisting of free-functions that accept Scipp DataArrays and Datasets.
   * Moved the chopper class from the ``wfm`` submodule to free functions in its own ``choppers`` module.
   * The unit conversion (computing wavelength and Q) now use ``transform_coords``.
   * The Amor reduction notebook from ``ess-notebooks`` has been imported into the ``ess`` repository.

docs/developer/style-guide.rst

@@ -15,7 +15,7 @@ Layout
 ------
 
 This repository is ESS instrument centric,
-and we therefore strongly prefer having all code organised into ``src/ess/{instrument}`` directories.
+and we therefore strongly prefer having all code organized into ``src/ess/{instrument}`` directories.
 
 If your code is specific to the ESS facility,
 but is intended to be ubiquitous across a class of instruments,

docs/instruments/amor/amor_reduction.ipynb

@@ -53,9 +53,9 @@
     "![Figure5](https://ars.els-cdn.com/content/image/1-s2.0-S0168900216300250-gr5.jpg)\n",
     "\n",
     "The yellow area shows the incoming and reflected beam, both with the divergence $\\Delta \\theta$.\n",
-    "The inclination of the sample relative to the centre of the incoming beam (here identical to the instrument horizon) is called $\\omega$, and the respective angle of the reflected beam relative to the same axis is $\\gamma$.\n",
+    "The inclination of the sample relative to the center of the incoming beam (here identical to the instrument horizon) is called $\\omega$, and the respective angle of the reflected beam relative to the same axis is $\\gamma$.\n",
     "\n",
-    "In general the detector centre is located at $\\gamma_{\\rm D} = 2\\omega$.\n",
+    "In general the detector center is located at $\\gamma_{\\rm D} = 2\\omega$.\n",
     "These are instrument coordinates and should not be confused with the situation on the sample, where the take-off angle of an individual neutron trajectory is called $\\theta$.\n",
     "\n",
     "### The supermirror reference\n",
@@ -183,9 +183,9 @@
     "The pixel positions are wrong in the `sample.nxs` and `reference.nxs` files, and require an ad-hoc correction.\n",
     "We apply an arbitrary shift in the vertical (`y`) direction.\n",
     "We first move the pixels down by 0.955 degrees,\n",
-    "so that the centre of the beam goes through the centre of the top half of the detector blades\n",
+    "so that the center of the beam goes through the center of the top half of the detector blades\n",
     "(the bottom half of the detectors was turned off).\n",
-    "Next, we move all the pixels so that the centre of the top half of the detector pixels lies at an angle of $2 \\omega$,\n",
+    "Next, we move all the pixels so that the center of the top half of the detector pixels lies at an angle of $2 \\omega$,\n",
     "as described in the beamline diagram."
    ]
   },

docs/techniques/wfm/introduction-to-wfm.ipynb

@@ -571,7 +571,7 @@
     "where each wavelength frame $N$ contains a subsequent part of the spectrum of the source pulse.\n",
     "\n",
     "The main reason for using the WFM concept is to redefine the burst time $t_{P} = \\Delta t$ as implied by Eq. (1), in order to match the required wavelength resolution of the experiment.\n",
-    "A secondary objective, or constraint of the first, is to utilise as much of the source pulse as possible."
+    "A secondary objective, or constraint of the first, is to utilize as much of the source pulse as possible."
    ]
   },
   {

docs/techniques/wfm/reducing-wfm-data.ipynb

@@ -235,7 +235,7 @@
    "id": "d59e57c4",
    "metadata": {},
    "source": [
-    "The `wfm.plot` submodule provides a useful tool to visualise the chopper cascade as a time-distance diagram.\n",
+    "The `wfm.plot` submodule provides a useful tool to visualize the chopper cascade as a time-distance diagram.\n",
     "This is achieved by calling"
    ]
   },

src/ess/amor/load.py

@@ -15,7 +15,7 @@ def _tof_correction(data: sc.DataArray, dim: str = 'tof') -> sc.DataArray:
     """
     A correction for the presence of the chopper with respect to the "true" ToF.
     Also fold the two pulses.
-    TODO: generalise mechanism to fold any number of pulses.
+    TODO: generalize mechanism to fold any number of pulses.
 
     Parameters
     ----------

src/ess/diffraction/corrections.py

@@ -53,7 +53,7 @@ def normalize_by_monitor(data: sc.DataArray,
         mon = mon.rebin(wavelength=wavelength_edges)
     if smooth_args is not None:
         get_logger('diffraction').info(
-            "Smoothing monitor '%s' for normalisation using "
+            "Smoothing monitor '%s' for normalization using "
             "ess.diffraction.smoothing.lowpass with %s.", monitor, smooth_args)
         mon = lowpass(mon, dim='wavelength', **smooth_args)
     return data.bins / sc.lookup(func=mon, dim='wavelength')

src/ess/v20/imaging/beamline.py

@@ -13,7 +13,7 @@ def make_beamline() -> dict:
     https://doi.org/10.1016/j.nima.2016.09.034
 
     The +15.0 increments added to the angles correspond to an offset between the
-    zero angle and the chopper top-dead centre.
+    zero angle and the chopper top-dead center.
     """
 
     dim = 'frame'

src/ess/v20/imaging/operations.py

@@ -17,7 +17,7 @@ def mask_from_adj_pixels(mask):
     """
     Checks if the adjacent pixels (in 8 directions) are masked to remove
     any noisy pixels which are erroneously masked or unmasked compared to
-    it's neighbours
+    it's neighbors
 
     If all adj. pixels are then the pixel considered is set to True
     If no adj. pixels are then the pixel considered is set to False
@@ -63,13 +63,13 @@ def make_flip(fill):
 
 def mean_from_adj_pixels(data):
     """
-    Applies a mean across 8 neighboring pixels (plus centre value)
+    Applies a mean across 8 neighboring pixels (plus center value)
     for data with 'x' and 'y' dimensions (at least).
     Result will calculate mean from slices across additional dimensions.
 
     For example if there is a tof dimension in addition to x, and y,
-    for each set of neighbours the returned mean will take the mean
-    tof value in the neighbour group.
+    for each set of neighbors the returned mean will take the mean
+    tof value in the neighbor group.
     """
     fill = np.finfo(data.values.dtype).min
     has_variances = data.variances is not None
@@ -105,13 +105,13 @@ def _median(neighbors, edges_mask, dim):
 
 def median_from_adj_pixels(data):
     """
-    Applies a median across 8 neighboring pixels (plus centre value)
+    Applies a median across 8 neighboring pixels (plus center value)
     for data with 'x' and 'y' dimensions (at least).
     Result will calculate median from slices across additional dimensions.
 
     For example if there is a tof dimension in addition to x, and y,
-    for each set of neighbours the returned median will take the median
-    tof value in the neighbour group.
+    for each set of neighbors the returned median will take the median
+    tof value in the neighbor group.
     """
     fill = np.finfo(data.values.dtype).min
     has_variances = data.variances is not None

src/ess/wfm/plot.py

@@ -33,7 +33,7 @@ def time_distance_diagram(data: sc.DataArray, **kwargs) -> plt.Figure:
     ax.grid(True, color='lightgray', linestyle="dotted")
     ax.set_axisbelow(True)
 
-    # Draw a light grey rectangle from the origin to t_0 + pulse_length + t_0
+    # Draw a light gray rectangle from the origin to t_0 + pulse_length + t_0
     # The second t_0 should in fact be the end of the pulse tail, but since this
     # information is not needed for computing the frame properties, it may
     # not be present in the description of the beamline.
@@ -47,7 +47,7 @@ def time_distance_diagram(data: sc.DataArray, **kwargs) -> plt.Figure:
                   fc='lightgrey',
                   ec='k',
                   zorder=10))
-    # Draw a dark grey rectangle from t_0 to t_0 + pulse_length to represent the usable
+    # Draw a dark gray rectangle from t_0 to t_0 + pulse_length to represent the usable
     # pulse.
     ax.add_patch(
         Rectangle((data.meta["source_pulse_t_0"].value, 0),

tests/nexus/load_test.py

@@ -29,7 +29,7 @@ def nxroot(request):
     """
     with h5py.File('dummy.nxs', mode='w', driver="core", backing_store=False) as f:
         root = snx.NXroot(f)
-        # This is modelled after the basic recommended NeXus structure
+        # This is modeled after the basic recommended NeXus structure
         entry = root.create_class('entry0', snx.NXentry)
         entry['start_time'] = sc.scalar("yesterday")
         sample = entry.create_class('sample0', snx.NXsample)

tests/v20/operations_test.py

@@ -65,7 +65,7 @@ def test_mean_filter(self):
         test_value = 4
         data = np.array([bulk_value] * 9 * 9 * 4, dtype=np.float64).reshape(9, 9, 4)
         data = sc.Variable(dims=['y', 'x', 'z'], values=data)
-        data['z', 1]['x', 4]['y', 4].value = test_value  # centre at z == 1
+        data['z', 1]['x', 4]['y', 4].value = test_value  # center at z == 1
         data['z', 2]['x', 4]['y', 0].value = test_value  # edge at z == 3
         data['z', 3]['x', 0]['y', 0].value = test_value  # corner at z == 2
 
@@ -98,7 +98,7 @@ def test_median_filter(self):
         test_value = 4.0
         data = np.array([bulk_value] * 9 * 9 * 4, dtype=np.float64).reshape(9, 9, 4)
         data = sc.Variable(dims=['y', 'x', 'z'], values=data)
-        data['z', 1]['x', 4]['y', 4].value = test_value  # centre at z == 1
+        data['z', 1]['x', 4]['y', 4].value = test_value  # center at z == 1
 
         data['z', 2]['x', 3]['y', 0].value = test_value  # edge at z == 3
         data['z', 2]['x', 4]['y', 0].value = test_value  # edge at z == 3