type anno corrections

ci

mypy install prereq

.github/workflows/ci.yml

@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.6, 3.9]
+        python-version: [3.7, 3.9]
     steps:
     - uses: actions/checkout@v2
     - uses: actions/setup-python@v2

README.md

@@ -11,7 +11,7 @@ Batch converts NAV and OBS GPS RINEX (including Hatanaka compressed OBS) data in
 [xarray.Dataset](http://xarray.pydata.org/en/stable/api.html#dataset)
 for easy use in analysis and plotting.
 This gives remarkable speed vs. legacy iterative methods, and allows for HPC / out-of-core operations on massive amounts of GNSS data.
-GeoRinex works in Python ≥ 3.6 and has over 125 unit tests driven by Pytest.
+GeoRinex has over 125 unit tests driven by Pytest.
 
 Pure compiled language RINEX processors such as within Fortran NAPEOS give perhaps 2x faster performance than this Python program--that's pretty good for a scripted language like Python!
 However, the initial goal of this Python program was to be for one-time offline conversion of ASCII (and compressed ASCII) RINEX to HDF5/NetCDF4,

setup.cfg

@@ -55,6 +55,7 @@ lint =
   flake8-builtins
   flake8-blind-except
   mypy
+  types-python-dateutil
 plot =
   matplotlib
   seaborn

src/georinex/base.py

@@ -59,13 +59,7 @@ def load(
         if tlim[1] < tlim[0]:
             raise ValueError("stop time must be after start time")
 
-    try:
-        info = rinexinfo(rinexfn)
-    except RuntimeError:
-        logging.error(
-            f"could not read {rinexfn} header. It may not be a known type of RINEX file."
-        )
-        return None
+    info = rinexinfo(rinexfn)
 
     if info["rinextype"] == "nav":
         return rinexnav(rinexfn, outfn, use=use, tlim=tlim, overwrite=overwrite)

src/georinex/geo.py

@@ -19,7 +19,9 @@ def get_locations(files: list[Path]) -> pandas.DataFrame:
     if isinstance(files[0], io.StringIO):
         locs = pandas.DataFrame(index=["0"], columns=["lat", "lon", "interval"])
     elif isinstance(files[0], Path):
-        locs = pandas.DataFrame(index=[file.name for file in files], columns=["lat", "lon", "interval"])
+        locs = pandas.DataFrame(
+            index=[file.name for file in files], columns=["lat", "lon", "interval"]
+        )
     else:
         raise TypeError("Expecting pathlib.Path")
 

src/georinex/keplerian.py

@@ -9,7 +9,9 @@
 import numpy as np
 
 
-def keplerian2ecef(sv: xarray.DataArray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+def keplerian2ecef(
+    sv: xarray.Dataset,
+) -> tuple[xarray.DataArray, xarray.DataArray, xarray.DataArray]:
     """
     based on:
     https://ascelibrary.org/doi/pdf/10.1061/9780784411506.ap03

src/georinex/obs2.py

@@ -403,7 +403,7 @@ def obsheader2(
             hdr["Nobs"] = len(hdr["fields"])
 
         if isinstance(meas, (tuple, list, np.ndarray)):
-            ind = np.zeros(len(hdr["fields"]), dtype=bool)
+            ind: T.Any = np.zeros(len(hdr["fields"]), dtype=bool)
             for m in meas:
                 for i, field in enumerate(hdr["fields"]):
                     if field.startswith(m):

src/georinex/obs3.py

@@ -348,7 +348,7 @@ def obsheader3(f: T.TextIO, use: list[str] = None, meas: list[str] = None) -> di
 
     # perhaps this could be done more efficiently, but it's probably low impact on overall program.
     # simple set and frozenset operations do NOT preserve order, which would completely mess up reading!
-    sysind = {}
+    sysind: dict[str, T.Any] = {}
     if isinstance(meas, (tuple, list, np.ndarray)):
         for sk in fields:  # iterate over each system
             # ind = np.isin(fields[sk], meas)  # boolean vector

src/georinex/tests/test_scripts.py

@@ -13,6 +13,7 @@ def test_convenience():
 
 
 def test_time():
+    pytest.importorskip("unlzw3")
     subprocess.check_call(["georinex_time", str(R)])
 
 

src/georinex/utils.py

@@ -117,7 +117,7 @@ def rinexheader(fn: T.TextIO | Path) -> dict[str, T.Any]:
     return hdr
 
 
-def _tlim(tlim: tuple[str, str] | tuple[datetime, datetime] = None) -> tuple[datetime, datetime]:
+def _tlim(tlim: tuple[datetime, datetime] = None) -> tuple[datetime, datetime]:
     if tlim is None:
         pass
     elif len(tlim) == 2 and isinstance(tlim[0], datetime):