include free-threaded 3.14 wheels

.github/workflows/cibuildwheel.yml

@@ -79,7 +79,7 @@ jobs:
       # These needs to rotate every new Python release.
       run: |
         set -x
-        echo "CIBW_BUILD=cp310-* cp311-* cp314-*" >> $GITHUB_ENV
+        echo "CIBW_BUILD=cp310-* cp311-* cp314-* cp314t-*" >> $GITHUB_ENV
         set +x
 
       if: ${{ github.event_name }} == "pull_request"
@@ -170,7 +170,7 @@ jobs:
         uses: pypa/cibuildwheel@v3.3.0
         env:
           CIBW_ARCHS: ARM64
-          CIBW_SKIP: "cp310-* cp314t-*"
+          CIBW_SKIP: "cp310-*"
 
       - uses: actions/upload-artifact@v6
         with:

Changelog

@@ -1,9 +1,13 @@
- version 1.7.4 (not yet released)
+ version 1.7.4 (tag v1.7.4rel)
  ================================
  * Make sure automatic conversion of character arrays <--> string arrays works for Unicode strings (issue #1440).
    (previously only worked correctly for encoding="ascii").
  * Add netcdf plugins (blosc, zstd, bzip2) in wheels.  Blosc plugin doesn't work in Windows wheels.
    Macos wheels now use conda provided libs. (PR #1450)
+ * Add windows/arm (PR #1453) and free-threaded python wheels (issue #1454). Windows wheels now use netcdf-c 4.9.3.
+   WARNING:  netcdf-c is not thread-safe and netcdf4-python does have internal locking so expect segfaults if you
+   use netcdf4-python on multiple threads with free-threaded python.  Users must exercise care to only call netcdf from
+   a single thread.
 
  version 1.7.3 (tag v1.7.3rel)
  =============================

README.md

@@ -10,6 +10,12 @@
 ## News
 For details on the latest updates, see the [Changelog](https://github.com/Unidata/netcdf4-python/blob/master/Changelog).
 
+1/5/2026:  Version [1.7.4](https://pypi.python.org/pypi/netCDF4/1.7.4) released.  Compression plugins now included in wheels, windows/arm and
+free-threaded python wheels provided.   Automatic conversion of character arrays <--> string arrays works for Unicode (not just ascii) strings.
+WARNING:  netcdf-c is not thread-safe and netcdf4-python does have internal locking so expect segfaults if you
+use netcdf4-python on multiple threads with free-threaded python.  Users must exercise care to only call netcdf from
+a single thread.
+
 10/13/2025: Version [1.7.3](https://pypi.python.org/pypi/netCDF4/1.7.3) released. Minor updates/bugfixes and python 3.14 wheels, see Changelog for details.
 
 10/22/2024: Version [1.7.2](https://pypi.python.org/pypi/netCDF4/1.7.2) released. Minor updates/bugfixes and python 3.13 wheels, see Changelog for details.

pyproject.toml

@@ -85,6 +85,7 @@ pythonpath = ["test"]
 filterwarnings = [
     "error",
     "ignore::UserWarning",
+    "ignore::RuntimeWarning",
 ]
 
 [tool.mypy]
@@ -110,7 +111,6 @@ build-verbosity = 1
 build-frontend = "build"
 skip = [
   "*-musllinux*",
-  "cp314t-*",
 ]
 test-extras = "tests"
 test-sources = [

src/netCDF4/_netCDF4.pyx

@@ -1050,6 +1050,10 @@ are collective.  There are a couple of important limitations of parallel IO:
    to write to it.
  - You cannot use variable-length (VLEN) data types.
 
+***Import warning regarding threads:***  The underlying netcdf-c library is not thread-safe, so netcdf4-python cannot perform parallel
+IO in a multi-threaded environment.  Users should expect segfaults if a netcdf file is opened on multiple threads - care should
+be taken to restrict netcdf4-python usage to a single thread, even when using free-threaded python.
+
 ## Dealing with strings
 
 The most flexible way to store arrays of strings is with the

test/test_masked2.py

@@ -64,8 +64,7 @@ def setUp(self):
         v = f.createVariable('v',np.float32,'x',zlib=True,least_significant_digit=1)
         # assign masked array to that variable with one missing value.
         data =\
-        ma.array([1.5678,99.99,3.75145,4.127654],mask=np.array([False,True,False,False],np.bool_))
-        data.mask[1]=True
+        ma.MaskedArray([1.5678,99.99,3.75145,4.127654],mask=np.array([False,True,False,False],np.bool_))
         v[:] = data
         f.close()
 

test/test_masked3.py

@@ -19,7 +19,7 @@ def setUp(self):
 
         self.fillval = default_fillvals["i2"]
         self.v    = np.array([self.fillval, 5, 4, -9999], dtype = "i2")
-        self.v_ma = ma.array([self.fillval, 5, 4, -9999], dtype = "i2", mask = [True, False, False, True])
+        self.v_ma = ma.MaskedArray([self.fillval, 5, 4, -9999], dtype = "i2", mask = [True, False, False, True])
 
         self.scale_factor = 10.
         self.add_offset = 5.

test/test_masked4.py

@@ -20,7 +20,7 @@ def setUp(self):
         self.valid_max = 32765
         self.valid_range = [self.valid_min,self.valid_max]
         self.v    = np.array([self.valid_min-1, 5, 4, self.valid_max+1], dtype = "i2")
-        self.v_ma = ma.array([self.valid_min-1, 5, 4, self.valid_max+1], dtype = "i2", mask = [True, False, False, True])
+        self.v_ma = ma.MaskedArray([self.valid_min-1, 5, 4, self.valid_max+1], dtype = "i2", mask = [True, False, False, True])
 
         self.scale_factor = 10.
         self.add_offset = 5.

test/test_masked5.py

@@ -17,7 +17,7 @@ def setUp(self):
 
         self.missing_values = [-999,999,0]
         self.v    = np.array([-999,0,1,2,3,999], dtype = "i2")
-        self.v_ma = ma.array([-1,0,1,2,3,4], dtype = "i2", \
+        self.v_ma = ma.MaskedArray([-1,0,1,2,3,4], dtype = "i2", \
                     mask = [True, True, False, False, False, True])
 
         f = Dataset(self.testfile, 'w')

test/test_masked6.py

@@ -18,7 +18,7 @@ def setUp(self):
         self.testfile = tempfile.NamedTemporaryFile(suffix='.nc', delete=False).name
 
         self.v = np.array([4, 3, 2, 1], dtype="i2")
-        self.w = np.ma.array([-1, -2, -3, -4], mask=[False, True, False, False], dtype="i2")
+        self.w = np.ma.MaskedArray([-1, -2, -3, -4], mask=[False, True, False, False], dtype="i2")
 
         f = Dataset(self.testfile, 'w')
         _ = f.createDimension('x', None)

test/test_scaled.py

@@ -22,7 +22,7 @@ def setUp(self):
         self.missing_value = -9999
 
         self.v    = np.array([0, 5, 4, self.missing_value], dtype = "i2")
-        self.v_ma = ma.array([0, 5, 4, self.missing_value], dtype = "i2",
+        self.v_ma = ma.MaskedArray([0, 5, 4, self.missing_value], dtype = "i2",
                              mask = [True, False, False, True], fill_value = self.fillval)
 
         self.scale_factor = 10.

test/test_types.py

@@ -22,7 +22,7 @@
 zlib=False; complevel=0; shuffle=False; least_significant_digit=None
 datatypes = ['f8','f4','i1','i2','i4','i8','u1','u2','u4','u8','S1']
 FillValue = 1.0
-issue273_data = np.ma.array(['z']*10,dtype='S1',\
+issue273_data = np.ma.MaskedArray(['z']*10,dtype='S1',\
 mask=[False,False,False,False,False,True,False,False,False,False])
 
 class PrimitiveTypesTestCase(unittest.TestCase):