Merge pull request #83 from scipp/upgrade-scn-0.4.2

Upgrade to scippneutron 0.4.2

Author: nvaytet

conda/meta.yaml

@@ -13,7 +13,7 @@ requirements:
     - ipywidgets
     - matplotlib
     - pooch
-    - scippneutron>=0.4.1
+    - scippneutron>=0.4.2
     - scipy
     - tifffile
 
@@ -43,7 +43,7 @@ build:
   number: {{ environ.get('GIT_DESCRIBE_NUMBER', 0) }}
   noarch: python
   script:
-    - python setup.py install
+    - pip install .
 
 about:
   home: https://github.com/scipp/ess

docs/instruments/amor/amor_reduction.ipynb

@@ -290,7 +290,7 @@
     "q_edges = sc.geomspace(dim='Q', start=0.008, stop=0.08, num=201, unit='1/angstrom')\n",
     "sample_q_binned = sc.bin(sample_q, edges=[q_edges])\n",
     "sample_q_summed = sample_q_binned.sum('detector_id')\n",
-    "sc.plot(sample_q_summed[\"wavelength\", 0], norm=\"log\")"
+    "sc.plot(sample_q_summed, norm=\"log\")"
    ]
   },
   {
@@ -354,7 +354,7 @@
     "reference_q = reference_wav.transform_coords([\"Q\"], graph=graph)\n",
     "reference_q_binned = sc.bin(reference_q, edges=[q_edges])\n",
     "reference_q_summed = reference_q_binned.sum('detector_id')\n",
-    "sc.plot(reference_q_summed[\"wavelength\", 0], norm=\"log\")"
+    "sc.plot(reference_q_summed, norm=\"log\")"
    ]
   },
   {
@@ -372,7 +372,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "normalized = sample_q_summed[\"wavelength\", 0] / reference_q_summed[\"wavelength\", 0]\n",
+    "normalized = sample_q_summed / reference_q_summed\n",
     "sc.plot(normalized, norm=\"log\")"
    ]
   },

ess-developer.yml

@@ -7,7 +7,7 @@ channels:
 
 dependencies:
   # Build
-  - scippneutron>=0.4.1
+  - scippneutron>=0.4.2
   - gitpython
 
   # Run

src/ess/sans/reduction.py

@@ -30,6 +30,6 @@ def reduce_to_q(data, *, q_bins, reducer, wavelength_bands=None):
         high = wavelength_bands['wavelength', i + 1]
         band = sc.histogram(data['wavelength', low:high], q_bins)
         band = reducer(band)
-        bands = sc.concatenate(bands, band, 'wavelength') if bands is not None else band
+        bands = sc.concat([bands, band], 'wavelength') if bands is not None else band
     bands.coords['wavelength'] = wavelength_bands
     return bands

src/ess/v20/imaging/operations.py

@@ -8,9 +8,9 @@ def _shift(var, dim, forward, out_of_bounds):
     fill = var[dim, 0:1].copy()
     fill.values = np.full_like(fill.values, out_of_bounds)
     if forward:
-        return sc.concatenate(fill, var[dim, :-1], dim)
+        return sc.concat([fill, var[dim, :-1]], dim)
     else:
-        return sc.concatenate(var[dim, 1:], fill, dim)
+        return sc.concat([var[dim, 1:], fill], dim)
 
 
 def mask_from_adj_pixels(mask):
@@ -47,7 +47,7 @@ def make_flip(fill):
                         shape=[
                             8,
                         ] + mask.shape,
-                        dtype=sc.dtype.bool)
+                        dtype=bool)
         flip['neighbor', 0] = _shift(mask, "x", True, fill)
         flip['neighbor', 1] = _shift(mask, "x", False, fill)
         flip['neighbor', 2] = _shift(mask, "y", True, fill)

src/ess/wfm/stitch.py

@@ -57,8 +57,8 @@ def _stitch_dense_data(item: sc.DataArray, frames: sc.Dataset, dim: str, new_dim
 def _stitch_event_data(item: sc.DataArray, frames: sc.Dataset, dim: str, new_dim: str,
                        bins: Union[int, sc.Variable]) -> Union[sc.DataArray, dict]:
 
-    edges = sc.flatten(sc.transpose(sc.concatenate(frames["time_min"].data,
-                                                   frames["time_max"].data, 'dummy'),
+    edges = sc.flatten(sc.transpose(sc.concat(
+        [frames["time_min"].data, frames["time_max"].data], 'dummy'),
                                     dims=['frame', 'dummy']),
                        to=dim)
 
@@ -76,12 +76,12 @@ def _stitch_event_data(item: sc.DataArray, frames: sc.Dataset, dim: str, new_dim
         erase = [dim]
 
     binned.masks['frame_gaps'] = (sc.arange(dim, 2 * frames.sizes["frame"] - 1) %
-                                  2).astype(sc.dtype.bool)
+                                  2).astype(bool)
 
-    new_edges = sc.concatenate(
+    new_edges = sc.concat([
         (frames["time_min"]["frame", 0] - frames["time_correction"]["frame", 0]).data,
-        (frames["time_max"]["frame", -1] - frames["time_correction"]["frame", -1]).data,
-        new_dim)
+        (frames["time_max"]["frame", -1] - frames["time_correction"]["frame", -1]).data
+    ], new_dim)
     return sc.bin(binned, edges=[new_edges], erase=erase)
 
 

src/ess/wfm/tools.py

@@ -21,11 +21,11 @@ def to_bin_edges(x: sc.Variable, dim: str) -> sc.Variable:
     idim = x.dims.index(dim)
     if x.shape[idim] < 2:
         one = 1.0 * x.unit
-        return sc.concatenate(x[dim, 0:1] - one, x[dim, 0:1] + one, dim)
+        return sc.concat([x[dim, 0:1] - one, x[dim, 0:1] + one], dim)
     else:
         center = to_bin_centers(x, dim)
         # Note: use range of 0:1 to keep dimension dim in the slice to avoid
         # switching round dimension order in concatenate step.
         left = center[dim, 0:1] - (x[dim, 1] - x[dim, 0])
         right = center[dim, -1] + (x[dim, -1] - x[dim, -2])
-        return sc.concatenate(sc.concatenate(left, center, dim), right, dim)
+        return sc.concat([left, center, right], dim)

tests/v20/groupby2d_test.py

@@ -43,7 +43,7 @@ def test_simple_case_any_naming():
     grouped = groupby2D(ds, nx_target=5, ny_target=5, x='w', y='v', z='u')
     assert grouped['a'].shape == [2, 5, 5]
     projection = sc.array(dims=['v', 'w'], values=np.ones((5, 5))) * 4
-    expected_data = sc.concatenate(projection, projection, dim='u')
+    expected_data = sc.concat([projection, projection], dim='u')
     assert sc.all(
         sc.isclose(grouped['a'].data, expected_data, atol=1e-14 * sc.units.one)).value