Remove inadvertant rebase diffs

Author: tomvothecoder

auxiliary_tools/template_cdat_regression_test.ipynb

@@ -8,10 +8,7 @@
 import scipy.fftpack
 import xarray as xr
 import xcdat as xc
-<<<<<<< HEAD
 from scipy.signal import detrend
-=======
->>>>>>> c7ef34e9 (CDAT Migration Phase 2: Refactor `qbo` set (#826))
 
 from e3sm_diags.driver.utils.dataset_xr import Dataset
 from e3sm_diags.driver.utils.io import _get_output_dir, _write_to_netcdf
@@ -29,12 +26,9 @@
 # The region will always be 5S5N
 REGION = "5S5N"
 
-<<<<<<< HEAD
 # Target power spectral vertical level for the wavelet diagnostic.
 POW_SPEC_LEV = 20.0
 
-=======
->>>>>>> c7ef34e9 (CDAT Migration Phase 2: Refactor `qbo` set (#826))
 
 class MetricsDict(TypedDict):
     qbo: xr.DataArray
@@ -43,11 +37,8 @@ class MetricsDict(TypedDict):
     period_new: np.ndarray
     psd_x_new: np.ndarray
     amplitude_new: np.ndarray
-<<<<<<< HEAD
     wave_period: np.ndarray
     wavelet: np.ndarray
-=======
->>>>>>> c7ef34e9 (CDAT Migration Phase 2: Refactor `qbo` set (#826))
     name: str
 
 
@@ -105,7 +96,6 @@ def run_diag(parameter: QboParameter) -> QboParameter:
             x_ref, ref_dict["period_new"]
         )
 
-<<<<<<< HEAD
         # Diagnostic 4: calculate the Wavelet
         test_dict["wave_period"], test_dict["wavelet"] = _calculate_wavelet(
             test_dict["qbo"]
@@ -114,8 +104,6 @@ def run_diag(parameter: QboParameter) -> QboParameter:
             ref_dict["qbo"]
         )
 
-=======
->>>>>>> c7ef34e9 (CDAT Migration Phase 2: Refactor `qbo` set (#826))
         parameter.var_id = var_key
         parameter.output_file = "qbo_diags"
         parameter.main_title = (
@@ -135,15 +123,7 @@ def run_diag(parameter: QboParameter) -> QboParameter:
 
         # Write the metrics to .json files.
         test_dict["name"] = test_ds._get_test_name()
-<<<<<<< HEAD
         ref_dict["name"] = ref_ds._get_ref_name()
-=======
-
-        try:
-            ref_dict["name"] = ref_ds._get_ref_name()
-        except AttributeError:
-            ref_dict["name"] = parameter.ref_name
->>>>>>> c7ef34e9 (CDAT Migration Phase 2: Refactor `qbo` set (#826))
 
         _save_metrics_to_json(parameter, test_dict, "test")  # type: ignore
         _save_metrics_to_json(parameter, ref_dict, "ref")  # type: ignore
@@ -172,11 +152,7 @@ def _save_metrics_to_json(
             metrics_dict[key] = metrics_dict[key].tolist()  # type: ignore
 
     with open(abs_path, "w") as outfile:
-<<<<<<< HEAD
         json.dump(metrics_dict, outfile, default=str)
-=======
-        json.dump(metrics_dict, outfile)
->>>>>>> c7ef34e9 (CDAT Migration Phase 2: Refactor `qbo` set (#826))
 
     logger.info("Metrics saved in: {}".format(abs_path))
 
@@ -379,7 +355,6 @@ def deseason(xraw):
             # i.e., get the difference between this month's value and it's "usual" value
             x_deseasoned[month_index] = xraw[month_index] - xclim[month]
     return x_deseasoned
-<<<<<<< HEAD
 
 
 def _calculate_wavelet(var: xr.DataArray) -> Tuple[np.ndarray, np.ndarray]:
@@ -444,5 +419,3 @@ def _get_psd_from_wavelet(data: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
     psd = np.mean(np.square(np.abs(cwtmatr)), axis=1)
 
     return (period, psd)
-=======
->>>>>>> c7ef34e9 (CDAT Migration Phase 2: Refactor `qbo` set (#826))

e3sm_diags/driver/qbo_driver.py

@@ -316,15 +316,6 @@ def _get_ref_name(self, default_name: str | None = None) -> str:
 
             return self.parameter.ref_name
 
-<<<<<<< HEAD
-=======
-        raise AttributeError(
-            "Either `parameter.short_ref_name`, `parameter.reference_name`, or "
-            "`parameter.ref_name` must be set to get the name and years attribute for "
-            "reference datasets."
-        )
-
->>>>>>> c7ef34e9 (CDAT Migration Phase 2: Refactor `qbo` set (#826))
     def _get_global_attr_from_climo_dataset(
         self, attr: str, season: ClimoFreq
     ) -> str | None:
@@ -444,7 +435,6 @@ def _get_climo_dataset(self, season: str) -> xr.Dataset:
             )
 
         ds = squeeze_time_dim(ds)
-<<<<<<< HEAD
         ds = self._subset_vars_and_load(ds, self.var)
 
         return ds
@@ -475,9 +465,6 @@ def _add_cf_attrs_to_z_axes(self, ds: xr.Dataset) -> xr.Dataset:
 
             if axis_attr is None:
                 ds[dim].attrs["axis"] = "Z"
-=======
-        ds = self._subset_vars_and_load(ds)
->>>>>>> c7ef34e9 (CDAT Migration Phase 2: Refactor `qbo` set (#826))
 
         return ds
 
@@ -763,7 +750,7 @@ def _get_dataset_with_derived_climo_var(self, ds: xr.Dataset) -> xr.Dataset:
             return ds
 
         raise IOError(
-            f"Neither does {target_var}, nor the variables in {list(target_var_map.keys())} exist in the dataset file."
+            f"The dataset file has no matching source variables for {target_var}"
         )
 
     def _get_matching_climo_src_vars(
@@ -815,45 +802,6 @@ def _get_matching_climo_src_vars(
 
         return None
 
-    def _subset_vars_and_load(self, ds: xr.Dataset) -> xr.Dataset:
-        """Subset for variables needed for processing and load into memory.
-
-        Subsetting the dataset reduces its memory footprint. Loading is
-        necessary because there seems to be an issue with `open_mfdataset()`
-        and using the multiprocessing scheduler defined in e3sm_diags,
-        resulting in timeouts and resource locking. To avoid this, we load the
-        multi-file dataset into memory before performing downstream operations.
-
-        Source: https://github.com/pydata/xarray/issues/3781
-
-        Parameters
-        ----------
-        ds : xr.Dataset
-            The dataset.
-
-        Returns
-        -------
-        xr.Dataset
-            The dataset subsetted and loaded into memory.
-        """
-        # slat and slon are lat lon pair for staggered FV grid included in
-        # remapped files.
-        if "slat" in ds.dims:
-            ds = ds.drop_dims(["slat", "slon"])
-
-        all_vars_keys = list(ds.data_vars.keys())
-        hybrid_var_keys = set(list(sum(HYBRID_SIGMA_KEYS.values(), ())))
-        keep_vars = [
-            var
-            for var in all_vars_keys
-            if "bnd" in var or "bounds" in var or var in hybrid_var_keys
-        ]
-        ds = ds[[self.var] + keep_vars]
-
-        ds.load(scheduler="sync")
-
-        return ds
-
     # --------------------------------------------------------------------------
     # Time series related methods
     # --------------------------------------------------------------------------

e3sm_diags/driver/utils/dataset_xr.py

@@ -46,10 +46,6 @@
     from e3sm_diags.driver.utils.dataset_xr import Dataset
 
 
-if TYPE_CHECKING:
-    from e3sm_diags.driver.utils.dataset_xr import Dataset
-
-
 class CoreParameter:
     def __init__(self):
         # File I/O