Address mypy issues

Author: tomvothecoder

e3sm_diags/derivations/formulas.py

@@ -75,7 +75,7 @@ def qsat(temp: xr.DataArray, surfp: xr.DataArray) -> xr.DataArray:
 
     # Calculation of saturation water vapour pressure (sat_wvp) from Teten's
     # formula/
-    sat_wvp: xr.DataArray = a1 * np.exp(a3 * (temp - T0) / (temp - a4))  # type: ignore
+    sat_wvp: xr.DataArray = a1 * np.exp(a3 * (temp - T0) / (temp - a4))
 
     # Calculation of saturation specific humidity at 2m qsat  (equal to huss)
     # with units g/kg.

e3sm_diags/driver/tc_analysis_driver.py

@@ -86,8 +86,8 @@ def run_diag(parameter: TCAnalysisParameter) -> TCAnalysisParameter:
     ref_data = collections.OrderedDict()
 
     test_data["metrics"] = generate_tc_metrics_from_te_stitch_file(test_te_file)
-    test_data["cyclone_density"] = test_cyclones_hist  # type: ignore
-    test_data["aew_density"] = test_aew_hist  # type: ignore
+    test_data["cyclone_density"] = test_cyclones_hist
+    test_data["aew_density"] = test_aew_hist
     test_num_years = int(test_end_yr) - int(test_start_yr) + 1
     test_data["aew_num_years"] = test_num_years  # type: ignore
     test_data["cyclone_num_years"] = test_num_years  # type: ignore
@@ -119,8 +119,8 @@ def run_diag(parameter: TCAnalysisParameter) -> TCAnalysisParameter:
         # Note the refactor included subset that was missed in original implementation
         ref_aew_hist = xr.open_dataset(ref_aew_file).sel()["density"]
         ref_data["metrics"] = generate_tc_metrics_from_te_stitch_file(ref_te_file)
-        ref_data["cyclone_density"] = ref_cyclones_hist  # type: ignore
-        ref_data["aew_density"] = ref_aew_hist  # type: ignore
+        ref_data["cyclone_density"] = ref_cyclones_hist
+        ref_data["aew_density"] = ref_aew_hist
         ref_num_years = int(ref_end_yr) - int(ref_start_yr) + 1
         ref_data["aew_num_years"] = ref_num_years  # type: ignore
         ref_data["cyclone_num_years"] = ref_num_years  # type: ignore
@@ -140,9 +140,9 @@ def run_diag(parameter: TCAnalysisParameter) -> TCAnalysisParameter:
         ref_aew_hist = xr.open_dataset(ref_aew_file).sel(
             lat=slice(0, 35), lon=slice(180, 360)
         )["density"]
-        ref_data["cyclone_density"] = ref_cyclones_hist  # type: ignore
+        ref_data["cyclone_density"] = ref_cyclones_hist
         ref_data["cyclone_num_years"] = 40  # type: ignore
-        ref_data["aew_density"] = ref_aew_hist  # type: ignore
+        ref_data["aew_density"] = ref_aew_hist
         # Question, should the num_years = 5?
         ref_data["aew_num_years"] = 1  # type: ignore
         parameter.ref_name = "Observation"

e3sm_diags/driver/utils/dataset_xr.py

@@ -918,7 +918,7 @@ def _get_dataset_with_derivation_func(
         func_args = [ds[var].copy() for var in src_var_keys]
 
         if func in FUNC_NEEDS_TARGET_VAR:
-            func_args = [target_var_key] + func_args  # type: ignore # pragma: nocover
+            func_args = [target_var_key] + func_args  # pragma: nocover
 
         # If the target variable key contains a wildcard, there are can be
         # N number of function arguments. For the cases, we need to pass the

e3sm_diags/driver/utils/zwf_functions.py

@@ -336,7 +336,7 @@ def resolveWavesHayashi(varfft: xr.DataArray, nDayWin: int, spd: int) -> xr.Data
     varspacetime = np.full(nshape, np.nan, dtype=type(varfft))
     # first two are the negative wavenumbers (westward), second two are the positive wavenumbers (eastward)
     logging.debug(
-        f"[Hayashi] Assign values into array. Notable numbers: mlon//2={mlon//2}, N//2={N//2}"
+        f"[Hayashi] Assign values into array. Notable numbers: mlon//2={mlon // 2}, N//2={N // 2}"
     )
     varspacetime[..., 0 : mlon // 2, 0 : N // 2] = varfft[
         ..., mlon // 2 : 0 : -1, N // 2 :
@@ -354,8 +354,8 @@ def resolveWavesHayashi(varfft: xr.DataArray, nDayWin: int, spd: int) -> xr.Data
     #  Create the real power spectrum pee = sqrt(real^2+imag^2)^2
     logging.debug("calculate power")
     pee = (
-        np.abs(varspacetime)
-    ) ** 2  # JJB: abs(a+bi) = sqrt(a**2 + b**2), which is what is done in NCL's resolveWavesHayashi
+        (np.abs(varspacetime)) ** 2
+    )  # JJB: abs(a+bi) = sqrt(a**2 + b**2), which is what is done in NCL's resolveWavesHayashi
     logging.debug("put into DataArray")
     # add meta data for use upon return
     wave = np.arange(-mlon // 2, (mlon // 2) + 1, 1, dtype=int)
@@ -374,9 +374,9 @@ def resolveWavesHayashi(varfft: xr.DataArray, nDayWin: int, spd: int) -> xr.Data
         if (c != "wavenumber") and (c != "frequency"):
             ocoords[c] = varfft[c]
         elif c == "wavenumber":
-            ocoords["wavenumber"] = wave  # type: ignore
+            ocoords["wavenumber"] = wave
         elif c == "frequency":
-            ocoords["frequency"] = freq  # type: ignore
+            ocoords["frequency"] = freq
     pee = xr.DataArray(pee, dims=odims, coords=ocoords)
     return pee
 
@@ -616,9 +616,7 @@ def spacetime_power(
     return z_final
 
 
-def genDispersionCurves(
-    nWaveType=6, nPlanetaryWave=50, rlat=0, Ahe=[50, 25, 12]
-):  # noqa: C901
+def genDispersionCurves(nWaveType=6, nPlanetaryWave=50, rlat=0, Ahe=[50, 25, 12]):  # noqa: C901
     """
     Function to derive the shallow water dispersion curves. Closely follows NCL version.
 

e3sm_diags/plot/taylor_diagram.py

@@ -49,9 +49,9 @@ def __init__(self, refstd, fig=None, rect=111, label="_"):
         tf2 = GF.DictFormatter(dict(gl2_ticks))
 
         # Standard deviation axis extent
-        self.smin = min(gl2_num)  # type: ignore
+        self.smin = min(gl2_num)
         # self.smax = 1.5*self.refstd
-        self.smax = max(gl2_num)  # type: ignore
+        self.smax = max(gl2_num)
 
         ghelper = FA.GridHelperCurveLinear(
             tr,

e3sm_diags/plot/tropical_subseasonal_plot.py

@@ -339,10 +339,10 @@ def _wave_frequency_plot(  # noqa: C901
         print("west_power: %12.5f" % west_power)
         print("ew_ratio: %12.5f\n" % ew_ratio)
 
-        z = np.log10(z)  # type: ignore
+        z = np.log10(z)
 
     if "spec_background" in var_name and subplot_num < 2:
-        z = np.log10(z)  # type: ignore
+        z = np.log10(z)
 
     z.attrs["long_name"] = PlotDesc[var_name]["long_name_desc"]
     z.attrs["method"] = (