Merge branch 'master' into update-data-per-streamline

Author: effigies

.github/dependabot.yml

@@ -0,0 +1,10 @@
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "monthly"
+    groups:
+      actions-infrastructure:
+        patterns:
+          - "actions/*"

.github/workflows/test.yml

@@ -44,7 +44,7 @@ jobs:
       - uses: actions/checkout@v4
         with:
           fetch-depth: 0
-      - uses: actions/setup-python@v4
+      - uses: actions/setup-python@v5
         with:
           python-version: 3
       - run: pip install --upgrade build twine
@@ -54,12 +54,12 @@ jobs:
       - name: Build git archive
         run: mkdir archive && git archive -v -o archive/nibabel-archive.tgz HEAD
       - name: Upload sdist and wheel artifacts
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: dist
           path: dist/
       - name: Upload git archive artifact
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: archive
           path: archive/
@@ -73,17 +73,17 @@ jobs:
     steps:
       - name: Download sdist and wheel artifacts
         if: matrix.package != 'archive'
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
         with:
           name: dist
           path: dist/
       - name: Download git archive artifact
         if: matrix.package == 'archive'
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
         with:
           name: archive
           path: archive/
-      - uses: actions/setup-python@v4
+      - uses: actions/setup-python@v5
         with:
           python-version: 3
       - name: Display Python version
@@ -147,7 +147,7 @@ jobs:
           submodules: recursive
           fetch-depth: 0
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
           architecture: ${{ matrix.architecture }}
@@ -162,14 +162,15 @@ jobs:
         run: tox c
       - name: Run tox
         run: tox -v --exit-and-dump-after 1200
-      - uses: codecov/codecov-action@v3
+      - uses: codecov/codecov-action@v4
         if: ${{ always() }}
         with:
           files: cov.xml
+          token: ${{ secrets.CODECOV_TOKEN }}
       - name: Upload pytest test results
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
-          name: pytest-results-${{ matrix.os }}-${{ matrix.python-version }}
+          name: pytest-results-${{ matrix.os }}-${{ matrix.python-version }}-${{ matrix.dependencies }}-${{ matrix.architecture }}
           path: test-results.xml
         if: ${{ always() }}
 
@@ -183,7 +184,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: 3
       - name: Display Python version
@@ -204,7 +205,7 @@ jobs:
       id-token: write
     if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/')
     steps:
-      - uses: actions/download-artifact@v3
+      - uses: actions/download-artifact@v4
         with:
           name: dist
           path: dist/

.gitmodules

@@ -19,3 +19,6 @@
 [submodule "nibabel-data/nitest-dicom"]
 	path = nibabel-data/nitest-dicom
 	url = https://github.com/effigies/nitest-dicom
+[submodule "nibabel-data/dcm_qa_xa30"]
+	path = nibabel-data/dcm_qa_xa30
+	url = https://github.com/neurolabusc/dcm_qa_xa30.git

.zenodo.json

@@ -270,7 +270,7 @@
       "orcid": "0000-0003-1076-5122"
     },
     {
-      "affiliation": "Universit\u00e9 de Sherbrooke",
+      "affiliation": "Brigham and Women's Hospital, Mass General Brigham/Harvard Medical School",
       "name": "Legarreta, Jon Haitz",
       "orcid": "0000-0002-9661-1396"
     },

Changelog

@@ -25,6 +25,28 @@ Eric Larson (EL), Demian Wassermann, Stephan Gerhard and Ross Markello (RM).
 
 References like "pr/298" refer to github pull request numbers.
 
+5.2.1 (Monday 26 February 2024)
+===============================
+
+Bug-fix release in the 5.2.x series.
+
+Enhancements
+------------
+* Support "flat" ASCII-encoded GIFTI DataArrays (pr/1298) (PM, reviewed by CM)
+
+Bug fixes
+---------
+* Tolerate missing ``git`` when reporting version info (pr/1286) (CM, reviewed by
+  Yuri Victorovich)
+* Handle Siemens XA30 derived DWI DICOMs (pr/1296) (CM, reviewed by YOH and
+  Mathias Goncalves)
+
+Maintenance
+-----------
+* Add tool for generating GitHub-friendly release notes (pr/1284) (CM)
+* Accommodate pytest 8 changes (pr/1297) (CM)
+
+
 5.2.0 (Monday 11 December 2023)
 ===============================
 

nibabel-data/dcm_qa_xa30

@@ -17,7 +17,7 @@
 from .optpkg import optional_package
 
 if ty.TYPE_CHECKING:  # pragma: no cover
-    import indexed_gzip  # type: ignore[import-not-found]
+    import indexed_gzip  # type: ignore[import]
     import pyzstd
 
     HAVE_INDEXED_GZIP = True

nibabel/_compression.py

@@ -365,7 +365,7 @@ def rescale_affine(affine, shape, zooms, new_shape=None):
         A new affine transform with the specified voxel sizes
 
     """
-    shape = np.array(shape, copy=False)
+    shape = np.asarray(shape)
     new_shape = np.array(new_shape if new_shape is not None else shape)
 
     s = voxel_sizes(affine)

nibabel/affines.py

@@ -26,7 +26,7 @@
 
 # if memory_profiler is installed, we get memory usage results
 try:
-    from memory_profiler import memory_usage  # type: ignore[import-not-found]
+    from memory_profiler import memory_usage  # type: ignore[import]
 except ImportError:
     memory_usage = None
 

nibabel/benchmarks/bench_arrayproxy_slicing.py

@@ -611,7 +611,7 @@ def int_abs(arr):
     >>> int_abs(np.array([-128, 127], dtype=np.float32))
     array([128., 127.], dtype=float32)
     """
-    arr = np.array(arr, copy=False)
+    arr = np.asarray(arr)
     dt = arr.dtype
     if dt.kind == 'u':
         return arr

nibabel/casting.py

@@ -25,7 +25,7 @@ class dummy_fuse:
 
 
 try:
-    import fuse  # type: ignore[import-not-found]
+    import fuse  # type: ignore[import]
 
     uid = os.getuid()
     gid = os.getgid()

nibabel/cmdline/dicomfs.py

@@ -745,7 +745,7 @@ def agg_data(self, intent_code=None):
         >>> triangles_2 = surf_img.agg_data('triangle')
         >>> triangles_3 = surf_img.agg_data(1009)  # Numeric code for pointset
         >>> print(np.array2string(triangles))
-        [0 1 2]
+        [[0 1 2]]
         >>> np.array_equal(triangles, triangles_2)
         True
         >>> np.array_equal(triangles, triangles_3)

nibabel/gifti/gifti.py

@@ -68,17 +68,21 @@ def read_data_block(darray, fname, data, mmap):
     if mmap is True:
         mmap = 'c'
     enclabel = gifti_encoding_codes.label[darray.encoding]
-    dtype = data_type_codes.type[darray.datatype]
 
+    if enclabel not in ('ASCII', 'B64BIN', 'B64GZ', 'External'):
+        raise GiftiParseError(f'Unknown encoding {darray.encoding}')
+
+    # Encode the endianness in the dtype
+    byteorder = gifti_endian_codes.byteorder[darray.endian]
+    dtype = data_type_codes.dtype[darray.datatype].newbyteorder(byteorder)
+
+    shape = tuple(darray.dims)
+    order = array_index_order_codes.npcode[darray.ind_ord]
+
+    # GIFTI_ENCODING_ASCII
     if enclabel == 'ASCII':
-        # GIFTI_ENCODING_ASCII
-        c = StringIO(data)
-        da = np.loadtxt(c, dtype=dtype)
-        return da  # independent of the endianness
-    elif enclabel not in ('B64BIN', 'B64GZ', 'External'):
-        return 0
-
-    # GIFTI_ENCODING_EXTBIN
+        return np.loadtxt(StringIO(data), dtype=dtype, ndmin=1).reshape(shape, order=order)
+
     # We assume that the external data file is raw uncompressed binary, with
     # the data type/endianness/ordering specified by the other DataArray
     # attributes
@@ -94,53 +98,41 @@ def read_data_block(darray, fname, data, mmap):
         newarr = None
         if mmap:
             try:
-                newarr = np.memmap(
+                return np.memmap(
                     ext_fname,
                     dtype=dtype,
                     mode=mmap,
                     offset=darray.ext_offset,
-                    shape=tuple(darray.dims),
+                    shape=shape,
+                    order=order,
                 )
             # If the memmap fails, we ignore the error and load the data into
             # memory below
             except (AttributeError, TypeError, ValueError):
                 pass
         # mmap=False or np.memmap failed
         if newarr is None:
-            # We can replace this with a call to np.fromfile in numpy>=1.17,
-            # as an "offset" parameter was added in that version.
-            with open(ext_fname, 'rb') as f:
-                f.seek(darray.ext_offset)
-                nbytes = np.prod(darray.dims) * dtype().itemsize
-                buff = f.read(nbytes)
-                newarr = np.frombuffer(buff, dtype=dtype)
+            return np.fromfile(
+                ext_fname,
+                dtype=dtype,
+                count=np.prod(darray.dims),
+                offset=darray.ext_offset,
+            ).reshape(shape, order=order)
 
     # Numpy arrays created from bytes objects are read-only.
     # Neither b64decode nor decompress will return bytearrays, and there
     # are not equivalents to fobj.readinto to allow us to pass them, so
     # there is not a simple way to avoid making copies.
     # If this becomes a problem, we should write a decoding interface with
     # a tunable chunk size.
+    dec = base64.b64decode(data.encode('ascii'))
+    if enclabel == 'B64BIN':
+        buff = bytearray(dec)
     else:
-        dec = base64.b64decode(data.encode('ascii'))
-        if enclabel == 'B64BIN':
-            # GIFTI_ENCODING_B64BIN
-            buff = bytearray(dec)
-        else:
-            # GIFTI_ENCODING_B64GZ
-            buff = bytearray(zlib.decompress(dec))
-        del dec
-        newarr = np.frombuffer(buff, dtype=dtype)
-
-    sh = tuple(darray.dims)
-    if len(newarr.shape) != len(sh):
-        newarr = newarr.reshape(sh, order=array_index_order_codes.npcode[darray.ind_ord])
-
-    # check if we need to byteswap
-    required_byteorder = gifti_endian_codes.byteorder[darray.endian]
-    if required_byteorder in ('big', 'little') and required_byteorder != sys.byteorder:
-        newarr = newarr.byteswap()
-    return newarr
+        # GIFTI_ENCODING_B64GZ
+        buff = bytearray(zlib.decompress(dec))
+    del dec
+    return np.frombuffer(buff, dtype=dtype).reshape(shape, order=order)
 
 
 def _str2int(in_str):

nibabel/gifti/parse_gifti_fast.py

@@ -0,0 +1,76 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE GIFTI SYSTEM "http://www.nitrc.org/frs/download.php/115/gifti.dtd">
+<GIFTI Version="1.0"  NumberOfDataArrays="2">
+   <MetaData>
+      <MD>
+         <Name><![CDATA[Caret-Version]]></Name>
+         <Value><![CDATA[5.512]]></Value>
+      </MD>
+      <MD>
+         <Name><![CDATA[date]]></Name>
+         <Value><![CDATA[Thu Dec 27 14:27:43 2007]]></Value>
+      </MD>
+      <MD>
+         <Name><![CDATA[encoding]]></Name>
+         <Value><![CDATA[XML]]></Value>
+      </MD>
+   </MetaData>
+   <LabelTable/>
+   <DataArray Intent="NIFTI_INTENT_POINTSET"
+              DataType="NIFTI_TYPE_FLOAT32"
+              ArrayIndexingOrder="RowMajorOrder"
+              Dimensionality="2"
+              Dim0="10"
+              Dim1="3"
+              Encoding="ASCII"
+              Endian="LittleEndian"
+              ExternalFileName=""
+              ExternalFileOffset="">
+      <MetaData>
+         <MD>
+            <Name><![CDATA[AnatomicalStructurePrimary]]></Name>
+            <Value><![CDATA[CortexLeft]]></Value>
+         </MD>
+         <MD>
+            <Name><![CDATA[AnatomicalStructureSecondary]]></Name>
+            <Value><![CDATA[Pial]]></Value>
+         </MD>
+         <MD>
+            <Name><![CDATA[GeometricType]]></Name>
+            <Value><![CDATA[Anatomical]]></Value>
+         </MD>
+         <MD>
+            <Name><![CDATA[UniqueID]]></Name>
+            <Value><![CDATA[{70e032e9-4123-47ee-965d-5b29107cbd83}]]></Value>
+         </MD>
+      </MetaData>
+      <CoordinateSystemTransformMatrix>
+         <DataSpace><![CDATA[NIFTI_XFORM_TALAIRACH]]></DataSpace>
+         <TransformedSpace><![CDATA[NIFTI_XFORM_TALAIRACH]]></TransformedSpace>
+         <MatrixData>1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000</MatrixData>
+      </CoordinateSystemTransformMatrix>
+      <Data>155.17539978 135.58103943 98.30715179 140.33973694 190.0491333 73.24776459 157.3598938 196.97969055 83.65809631 171.46174622 137.43661499 78.4709549 148.54592896 97.06752777 65.96373749 123.45701599 111.46841431 66.3571167 135.30892944 202.28720093 36.38148499 178.28155518 162.59469604 37.75128937 178.11087036 115.28820038 57.17986679 142.81582642 82.82115173 31.02205276</Data>
+   </DataArray>
+   <DataArray Intent="NIFTI_INTENT_TRIANGLE"
+              DataType="NIFTI_TYPE_INT32"
+              ArrayIndexingOrder="RowMajorOrder"
+              Dimensionality="2"
+              Dim0="10"
+              Dim1="3"
+              Encoding="ASCII"
+              Endian="LittleEndian"
+              ExternalFileName=""
+              ExternalFileOffset="">
+      <MetaData>
+         <MD>
+            <Name><![CDATA[TopologicalType]]></Name>
+            <Value><![CDATA[CLOSED]]></Value>
+         </MD>
+         <MD>
+            <Name><![CDATA[UniqueID]]></Name>
+            <Value><![CDATA[{747d8015-455b-43ad-82ac-dcfb7606004a}]]></Value>
+         </MD>
+      </MetaData>
+      <Data>6402 17923 25602 14085 25602 17923 25602 14085 4483 17923 1602 14085 4483 25603 25602 25604 25602 25603 25602 25604 6402 25603 3525 25604 1123 17922 12168 25604 12168 17922 </Data>
+   </DataArray>
+</GIFTI>