Migrate to uv and update pyproject.toml

pyproject.toml

@@ -6,12 +6,11 @@ build-backend = "setuptools.build_meta"
 name = "mtmhdf"
 description = "imutum's packages for HDF"
 readme = "README.md"
-license = { file = "LICENSE" }
+license = "MIT"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Programming Language :: Python :: 3.11",
     "Intended Audience :: Developers",
-    "License :: OSI Approved :: MIT License",
     "Operating System :: MacOS",
     "Operating System :: POSIX :: Linux",
     "Operating System :: Microsoft :: Windows",

tests/test_grid2dreader.py

@@ -2,8 +2,9 @@
 
 if __name__ == "__main__":
 
-    grid2dreader = Grid2DReader("tests\data\MOD021KM_L.1000.2015001004500.H27V04.000000.nc", do_grid_surround=False)
+    grid2dreader = Grid2DReader("tests\data\MOD021KM_L.1000.2015001004500.H27V04.000000.nc", do_grid_surround=True)
     print(grid2dreader.keys())
     dp = grid2dreader.read("/GeometricCorrection/DataSet_1000_5")
     print(data:=dp[-1:1202, -1:1202])
+    print(data:=dp[:])
     print(data.shape)

tests/test_slice.py

@@ -27,3 +27,4 @@ def __getitem__(self, item) -> np.ma.MaskedArray | np.ndarray:
     grid_size = 1200
     test = TestSlice(grid_size)
     test[-5:1202, -5:1202]
+    test[:]

tests/test_writer.py

@@ -0,0 +1,18 @@
+from netCDF4 import Dataset
+import numpy as np
+from mtmhdf.reader import HDF5Reader
+from mtmhdf._hdf5 import HDF5
+path = "test_writer.nc"
+
+x = np.random.rand(100, 100)
+x = x.astype(np.float32)
+x[1, :] = np.nan
+
+print(x)
+with Dataset(path, "w", format="NETCDF4") as fp:
+    HDF5.write(fp, x, "x", ("t", "p"), "uint16", scale_factor=0.0001)
+
+y = HDF5Reader(path).read("x")[:]
+print(y)
+print(y.data)
+print(y.fill_value)