clean v2.0.0

optim_esm_tools/__init__.py

@@ -5,13 +5,11 @@
 from . import config
 from . import analyze
 from . import region_finding
-from . import synda_files
 from . import _test_utils
 from . import plotting
 
 # Forward some of the essential tools to this main
 from .analyze.cmip_handler import read_ds
 from .analyze.io import load_glob
-from .plotting.map_maker import MapMaker
 from .utils import print_versions
 from .config import get_logger

optim_esm_tools/analyze/clustering.py

@@ -280,12 +280,9 @@ def _build_cluster_with_kw(
     clusters, sub_masks = build_clusters(**cluster_kw, keep_masks=True)
 
     if global_mask is None:
-        global_mask = np.ones(lat.shape, dtype=np.bool_)
+        raise ValueError('global_mask is required')
     clusters = [np.rad2deg(cluster) for cluster in clusters]
-    if show_tqdm is not None:
-        get_logger().warning(
-            'Calling "_build_cluster_with_kw" with show_tqdm is deprecated',
-        )
+
     if lat.shape != lon.shape:
         raise ValueError(
             f'Got inconsistent input {lat.shape} != {lon.shape}',
@@ -327,33 +324,16 @@ def infer_max_step_size(
     """
     if off_by_factor is None:
         off_by_factor = float(config['analyze']['clustering_fudge_factor'])
-    if len(lat.shape) == 1:
-        return off_by_factor * np.max(calculate_distance_map(lat, lon))  # type: ignore
-
-    get_logger().info(
-        '(Irregular) grid, max_step_size based on first points above equator',
-    )
-    # We have to get two points from the potentially irregular grid and guess the distance from
-    # that. This is not as reliable as calculating this for a regular grid.
-
-    equator_idx = np.argmin(np.abs(np.mean(lat, axis=1)) - 90)
-    lon_0 = lon[0]
-    coords = [
-        [[lat[equator_idx][0], lon_0[0]], [lat[equator_idx][0], lon_0[1]]],
-        [[lat[equator_idx][0], lon_0[0]], [lat[equator_idx + 1][0], lon_0[0]]],
-        [[lat[equator_idx][0], lon_0[0]], [lat[equator_idx + 1][0], lon_0[1]]],
-    ]
-    # assert False, coords
-    # Return the distance between grid cells * off_by_factor
-    return off_by_factor * max(_distance(c) for c in coords)
+    assert len(lat.shape) == 1
+    # Simple 1D array
+    return off_by_factor * np.max(calculate_distance_map(lat, lon))
 
 
 def calculate_distance_map(lat: np.ndarray, lon: np.ndarray) -> np.ndarray:
     """For each point in a spanned lat lon grid, calculate the distance to the
     neighboring points."""
-    if isinstance(lat, xr.DataArray):
-        lat = lat.values
-        lon = lon.values
+    if isinstance(lat, xr.DataArray):  # pragma: no cover
+        raise ValueError('Numpy array required')
     return _calculate_distance_map(lat, lon)
 
 

optim_esm_tools/analyze/combine_variables.py

@@ -1,4 +1,3 @@
-import os
 import string
 import typing as ty
 from collections import defaultdict
@@ -11,7 +10,6 @@
 from matplotlib.legend_handler import HandlerTuple
 
 import optim_esm_tools as oet
-from optim_esm_tools.analyze.time_statistics import default_thresholds
 
 
 class VariableMerger:

optim_esm_tools/analyze/find_matches.py

@@ -291,7 +291,7 @@ def associate_parent(
     data_set = _read_dataset(data_set=data_set, required_file=required_file, path=path)
     base = base_from_path(path, look_back_extra=look_back_extra)
     search = _get_search_kw(data_set)
-    search = _check_search_kw(search)
+    search = _check_search_kw(search=search, data_set=data_set, log=log, path=path)
 
     if all(v == '*' for v in search.values()) or search['source_id'] == '*':
         raise ValueError(f'Unclear search for {path} - attributes are missing')