Remove unused _mask_var_with_weight_threshold() function

Author: tomvothecoder

tests/test_utils.py

@@ -1,14 +1,7 @@
-import numpy as np
 import pytest
 import xarray as xr
 
-from tests.fixtures import generate_dataset
-from xcdat.utils import (
-    _validate_min_weight,
-    compare_datasets,
-    mask_var_with_weight_threshold,
-    str_to_bool,
-)
+from xcdat.utils import _validate_min_weight, compare_datasets, str_to_bool
 
 
 class TestCompareDatasets:
@@ -112,132 +105,6 @@ def test_raises_error_if_str_is_not_a_python_bool(self):
             str_to_bool("1")
 
 
-class TestMaskVarWithWeightThreshold:
-    @pytest.fixture(autouse=True)
-    def setup(self):
-        self.ds = generate_dataset(
-            decode_times=True, cf_compliant=False, has_bounds=True
-        )
-
-    def test_returns_mask_var_with_spatial_min_weight_of_100(self):
-        ds = self.ds.copy()
-        ds = ds.isel({"time": slice(0, 3), "lat": slice(0, 3), "lon": slice(0, 3)})
-        ds["ts"][0, :, 2] = np.nan
-
-        # Function arguments.
-        dv = ds["ts"].copy()
-        weights = ds.spatial.get_weights(
-            axis=["X", "Y"],
-            lat_bounds=(-5.0, 5),
-            lon_bounds=(-170, -120.1),
-            data_var="ts",
-        )
-
-        result = mask_var_with_weight_threshold(dv, weights, min_weight=1.0)
-        expected = xr.DataArray(
-            data=np.array(
-                [
-                    [
-                        [np.nan, np.nan, np.nan],
-                        [np.nan, np.nan, np.nan],
-                        [np.nan, np.nan, np.nan],
-                    ],
-                    [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
-                    [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
-                ]
-            ),
-            coords={"time": ds.time, "lat": ds.lat, "lon": ds.lon},
-            dims=["time", "lat", "lon"],
-        )
-
-        xr.testing.assert_allclose(result, expected)
-
-    def test_returns_mask_var_with_spatial_min_weight_of_0(self):
-        ds = self.ds.copy()
-        ds = ds.isel({"time": slice(0, 3), "lat": slice(0, 3), "lon": slice(0, 3)})
-
-        # Function arguments.
-        dv = ds["ts"].copy()
-        weights = ds.spatial.get_weights(
-            axis=["X", "Y"],
-            lat_bounds=(-5.0, 5),
-            lon_bounds=(-170, -120.1),
-            data_var="ts",
-        )
-
-        result = mask_var_with_weight_threshold(dv, weights, min_weight=0)
-        expected = xr.DataArray(
-            data=np.array(
-                [
-                    [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
-                    [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
-                    [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
-                ]
-            ),
-            coords={"time": ds.time, "lat": ds.lat, "lon": ds.lon},
-            dims=["time", "lat", "lon"],
-        )
-
-        xr.testing.assert_allclose(result, expected)
-
-    def test_returns_mask_var_with_temporal_min_weight_of_100(self):
-        ds = self.ds.copy()
-        ds = ds.isel({"time": slice(0, 3), "lat": slice(0, 3), "lon": slice(0, 3)})
-        ds["ts"][0, :, 2] = np.nan
-
-        # Function arguments.
-        dv = ds["ts"].copy()
-        weights = xr.DataArray(
-            name="time_wts",
-            data=np.array([1.0, 1.0, 1.0]),
-            dims="time",
-            coords={"time": ds.time},
-        )
-
-        result = mask_var_with_weight_threshold(dv, weights, min_weight=0)
-        expected = xr.DataArray(
-            data=np.array(
-                [
-                    [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [np.nan, 1.0, 1.0]],
-                    [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [np.nan, 1.0, 1.0]],
-                    [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [np.nan, 1.0, 1.0]],
-                ]
-            ),
-            coords={"lat": ds.lat, "lon": ds.lon, "time": ds.time},
-            dims=["lat", "lon", "time"],
-        )
-
-        xr.testing.assert_allclose(result, expected)
-
-    def test_returns_mask_var_with_temporal_min_weight_of_0(self):
-        ds = self.ds.copy()
-        ds = ds.isel({"time": slice(0, 3), "lat": slice(0, 3), "lon": slice(0, 3)})
-
-        # Function arguments.
-        dv = ds["ts"].copy()
-        weights = xr.DataArray(
-            name="time_wts",
-            data=np.array([1.0, 1.0, 1.0]),
-            dims="time",
-            coords={"time": ds.time},
-        )
-
-        result = mask_var_with_weight_threshold(dv, weights, min_weight=0)
-        expected = xr.DataArray(
-            data=np.array(
-                [
-                    [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
-                    [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
-                    [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
-                ]
-            ),
-            coords={"lat": ds.lat, "lon": ds.lon, "time": ds.time},
-            dims=["lat", "lon", "time"],
-        )
-
-        xr.testing.assert_allclose(result, expected)
-
-
 class TestValidateMinWeight:
     def test_pass_None_returns_0(self):
         result = _validate_min_weight(None)

xcdat/utils.py

@@ -2,7 +2,6 @@
 import json
 from typing import Dict, List, Optional, Union
 
-import numpy as np
 import xarray as xr
 from dask.array.core import Array
 
@@ -135,51 +134,6 @@ def _if_multidim_dask_array_then_load(
     return None
 
 
-def mask_var_with_weight_threshold(
-    dv: xr.DataArray, weights: xr.DataArray, min_weight: float
-) -> xr.DataArray:
-    """Mask values that do not meet the minimum weight threshold using np.nan.
-
-    This function is useful for cases where the weighting of data might be
-    skewed based on the availability of data. For example, if one season in a
-    time series has more significantly more missing data than other seasons, it
-    can result in inaccurate calculations of climatologies. Masking values that
-    do not meet the minimum weight threshold ensures more accurate calculations.
-
-    Parameters
-    ----------
-    dv : xr.DataArray
-        The weighted variable.
-    weights : xr.DataArray
-        A DataArray containing either the regional or temporal weights used for
-        weighted averaging. ``weights`` must include the same axis dimensions
-        and dimensional sizes as the data variable.
-    min_weight : float
-        Fraction of data coverage (i..e, weight) needed to return a
-        spatial average value. Value must range from 0 to 1.
-
-    Returns
-    -------
-    xr.DataArray
-        The variable with the minimum weight threshold applied.
-    """
-    masked_weights = _get_masked_weights(dv, weights)
-
-    # Sum all weights, including zero for missing values.
-    dim = weights.dims
-    weight_sum_all = weights.sum(dim=dim)
-    weight_sum_masked = masked_weights.sum(dim=dim)
-
-    # Get fraction of the available weight.
-    frac = weight_sum_masked / weight_sum_all
-
-    # Nan out values that don't meet specified weight threshold.
-    dv_new = xr.where(frac >= min_weight, dv, np.nan, keep_attrs=True)
-    dv_new.name = dv.name
-
-    return dv_new
-
-
 def _get_masked_weights(dv: xr.DataArray, weights: xr.DataArray) -> xr.DataArray:
     """Get weights with missing data (`np.nan`) receiving no weight (zero).