freq H -> h

examples/mosa/lib.py

@@ -253,7 +253,7 @@ def classify_one(g: gpd.GeoDataFrame, *, pre: str = "", include_stats: bool = Fa
     # Assuming instantaneous times, need 5 h for the 4 continuous h criteria
     # but for accumulated (during previous time step), 4 is fine(?) (according to Andy)
     n = 4
-    meets_crit_duration = duration >= pd.Timedelta(f"{n}H")
+    meets_crit_duration = duration >= pd.Timedelta(f"{n}h")
     # TODO: ^ not really one of the 4 criteria (though needed for 1 and 2)
 
     # Compute rainfall volume

examples/mosa/run-mosa-step1.py

@@ -41,7 +41,7 @@ def proc(fn, files):
 
 if do_bench:
     # WRF 1--7 Nov 2018
-    times = pd.date_range("2018-11-01", "2018-11-07 23:00", freq="H")
+    times = pd.date_range("2018-11-01", "2018-11-07 23:00", freq="h")
     files = [BASE_DIR / "WY2019" / "WRF" / f"tb_rainrate_{t:%Y-%m-%d_%H}:00.nc" for t in times]
 
     print(files[0])
@@ -77,7 +77,7 @@ def proc(fn, files):
     fn = partial(preproc_gpm_file, out_dir=out_dir)
     for wy in wys:
         # The WRF file sets start in June of previous year
-        ts = pd.date_range(f"{wy - 1}/06/01", f"{wy}/06/01", freq="H")[:-1]
+        ts = pd.date_range(f"{wy - 1}/06/01", f"{wy}/06/01", freq="h")[:-1]
         rfns = ts.strftime(r"%Y") + "/merg_" + ts.strftime(r"%Y%m%d%H") + "_4km-pixel.nc"
         files = [BASE_DIR / "GPM" / rfn for rfn in rfns]
         print(files[0])

examples/mosa/run-mosa-step2.py

@@ -25,7 +25,7 @@
     import pandas as pd
 
     # WRF 1--7 Nov 2018
-    times = pd.date_range("2018-11-01", "2018-11-07 23:00", freq="H")
+    times = pd.date_range("2018-11-01", "2018-11-07 23:00", freq="h")
     files = [IN_DIR / f"tb_rainrate_{t:%Y-%m-%d_%H}:00_ce.parquet" for t in times]
 
     print(files[0])

examples/mosa/run-mosa-step3.py

@@ -131,7 +131,7 @@ def run_gdf(fp: Path, *, bench: bool = False) -> None:
         for vn in vns_non_time:
             ds[vn] = ds0[vn]
 
-    times_should_be = pd.date_range(f"{wy - 1}/06/01", f"{wy}/06/01", freq="H")[:-1]
+    times_should_be = pd.date_range(f"{wy - 1}/06/01", f"{wy}/06/01", freq="h")[:-1]
     nt_missing = 0
     for t_ in times_should_be:
         if t_ not in ds.time.values:

tams/data.py

@@ -150,7 +150,7 @@ def load_example_ir() -> xarray.DataArray:
     ds.lat.attrs.update(long_name="Latitude")
 
     # Times are 2006-Sep-01 00 -- 10, every 2 hours
-    ds["time"] = pd.date_range("2006-Sep-01", freq="2H", periods=6)
+    ds["time"] = pd.date_range("2006-Sep-01", freq="2h", periods=6)
 
     return ds.ch9
 
@@ -282,7 +282,7 @@ def load_example_mpas_ug() -> xarray.Dataset:
     ds["tb"] = ds.tb.where(ds.tb > 0)
 
     # Set time (time variable in there just has elapsed hours as int)
-    ds["time"] = pd.date_range("2006-Sep-08 12", freq="1H", periods=ds.sizes["time"])
+    ds["time"] = pd.date_range("2006-Sep-08 12", freq="1h", periods=ds.sizes["time"])
 
     # Diff accumulated precip to get mm/h
     ds["precip"] = ds.precip.diff("time", label="lower")