Fixing African easterly wave density plots in TC analysis (#851)

* read in aew data without subsetting

* exclude 6hourly data cross year bounds

* use year range from stitch file name instead of data in file

* fixtests

* capture input data errors

e3sm_diags/driver/tc_analysis_driver.py

@@ -81,9 +81,7 @@ def run_diag(parameter: TCAnalysisParameter) -> TCAnalysisParameter:
         test_data_path,
         "aew_hist_{}_{}_{}.nc".format(test_name, test_start_yr, test_end_yr),
     )
-    test_aew_hist = cdms2.open(test_aew_file)(
-        "density", lat=(0, 35, "ccb"), lon=(-180, 0, "ccb"), squeeze=1
-    )
+    test_aew_hist = cdms2.open(test_aew_file)("density", squeeze=1)
 
     test_data = collections.OrderedDict()
     ref_data = collections.OrderedDict()
@@ -134,9 +132,8 @@ def run_diag(parameter: TCAnalysisParameter) -> TCAnalysisParameter:
             "density", lat=(-60, 60, "ccb"), squeeze=1
         )
         ref_aew_file = os.path.join(reference_data_path, "aew_hist_ERA5_2010_2014.nc")
-        ref_aew_hist = cdms2.open(ref_aew_file)(
-            "density", lat=(0, 35, "ccb"), lon=(180, 360, "ccb"), squeeze=1
-        )
+        ref_aew_hist = cdms2.open(ref_aew_file)("density", squeeze=1)
+
         ref_data["cyclone_density"] = ref_cyclones_hist
         ref_data["cyclone_num_years"] = 40  # type: ignore
         ref_data["aew_density"] = ref_aew_hist
@@ -163,13 +160,40 @@ def generate_tc_metrics_from_te_stitch_file(te_stitch_file: str) -> Dict[str, An
     """
     logger.info("\nGenerating TC Metrics from TE Stitch Files")
     logger.info("============================================")
+    if not os.path.exists(te_stitch_file):
+        raise FileNotFoundError(f"The file {te_stitch_file} does not exist.")
+
     with open(te_stitch_file) as f:
-        lines = f.readlines()
+        lines_orig = f.readlines()
+
+    if not lines_orig:
+        raise ValueError(f"The file {te_stitch_file} is empty.")
+
+    line_ind = []
+    data_start_year = int(te_stitch_file.split(".")[-2].split("_")[-2])
+    data_end_year = int(te_stitch_file.split(".")[-2].split("_")[-1])
+    for i in range(0, np.size(lines_orig)):
+        if lines_orig[i][0] == "s":
+            year = int(lines_orig[i].split("\t")[2])
+
+            if year <= data_end_year:
+                line_ind.append(i)
+
+    # Remove excessive time points cross year bounds from 6 hourly data
+    end_ind = line_ind[-1]
+    lines = lines_orig[0:end_ind]
 
     # Calculate number of storms and max length
     num_storms, max_len = _calc_num_storms_and_max_len(lines)
     # Parse variables from TE stitch file
     te_stitch_vars = _get_vars_from_te_stitch(lines, max_len, num_storms)
+    # Add year info
+    te_stitch_vars["year_start"] = data_start_year
+    te_stitch_vars["year_end"] = data_end_year
+    te_stitch_vars["num_years"] = data_end_year - data_start_year + 1
+    logger.info(
+        f"TE Start Year: {te_stitch_vars['year_start']}, TE End Year: {te_stitch_vars['year_end']}, Total Years: {te_stitch_vars['num_years']}"
+    )
 
     # Use E3SM land-sea mask
     mask_path = os.path.join(e3sm_diags.INSTALL_PATH, "acme_ne30_ocean_land_mask.nc")
@@ -246,15 +270,11 @@ def _get_vars_from_te_stitch(
     vars_dict = {k: np.empty((max_len, num_storms)) * np.nan for k in keys}
 
     index = 0
-    year_start = int(lines[0].split("\t")[2])
-    year_end = year_start
 
     for line in lines:
         line_split = line.split("\t")
         if line[0] == "s":
             index = index + 1
-            year = int(line_split[2])
-            year_end = max(year, year_start)
             k = 0
         else:
             k = k + 1
@@ -265,13 +285,6 @@ def _get_vars_from_te_stitch(
             vars_dict["yearmc"][k - 1, index - 1] = float(line_split[6])
             vars_dict["monthmc"][k - 1, index - 1] = float(line_split[7])
 
-    vars_dict["year_start"] = year_start  # type: ignore
-    vars_dict["year_end"] = year_end  # type: ignore
-    vars_dict["num_years"] = year_end - year_start + 1  # type: ignore
-    logger.info(
-        f"TE Start Year: {vars_dict['year_start']}, TE End Year: {vars_dict['year_end']}, Total Years: {vars_dict['num_years']}"
-    )
-
     return vars_dict
 
 

tests/e3sm_diags/drivers/test_tc_analysis_driver.py

@@ -59,9 +59,6 @@ def test_correct_output(self):
             "vsmc": np.array([[1.94, np.nan]]),
             "yearmc": np.array([[1, np.nan]]),
             "monthmc": np.array([[1, np.nan]]),
-            "year_start": 90,
-            "year_end": 90,
-            "num_years": 1,
         }
         result = _get_vars_from_te_stitch(lines, max_len, num_storms)
 
@@ -70,9 +67,6 @@ def test_correct_output(self):
         np.array_equal(result["vsmc"], expected["vsmc"])
         np.array_equal(result["yearmc"], expected["yearmc"])
         np.array_equal(result["monthmc"], expected["monthmc"])
-        self.assertEqual(result["year_start"], expected["year_start"])
-        self.assertEqual(result["year_end"], expected["year_end"])
-        self.assertEqual(result["num_years"], expected["num_years"])
 
 
 class TestDeriveMetricsPerBasin(TestCase):