cleaned up some lint.

Author: ChrisBarker-NOAA

docs/source/api_reference.rst

@@ -65,17 +65,17 @@ The user can now plot the time series for a given location and times, without ha
 Duck Typing
 -----------
 
-The Grid objects (Grid, Time, Depth) are for the most part "duck typed", rather than strict subclassing. Though there are base classes that provide shared functionality.
+The Grid objects (Grid, Time, Depth) are for the most part "duck typed", rather than strict subclassing, though there are base classes that provide shared functionality.
 
-We are trying to be clear about the "public" vs "private" API by using leading underscores for methods and attributes not intended for external use.
+We are trying to be clear about the "public" vs "private" API by using leading underscores for methods and attributes not intended for external use, but it's not as consitent as it should be.
 
 
 Lazy loading / data arrays
 --------------------------
 
 Many of the datasets users need to work with can be quite large. As a result it is impractical to load entire datasets into memory at once. ``gridded`` for the most part shifts the burden of handling lazy loading to external libraries, and does this by keeping data stored in a "numpy array-like" objects. Users can use pure numpy arrays, or any object that "acts" like a numpy array. This should allow ``gridded`` to work with netcdf variables, hdf5 arrays, dask arrays, etc.
 
-In practice, there is no clear definition of "array-like", so ``gridded`` has defined its own definition, based on features we know we need. But it is assumed that nd indexing behaves that same as numpy arrays -- as there is no way to easily confirm that. This is a goal, but in fact, only numpy arrays and ``netCDF4 Variables`` have been implimented and tested. In the future, we may use ``xarray`` as a single abstration layer, rather than rolling our own.
+In practice, there is no clear definition of "array-like", so ``gridded`` has defined its own definition, based on features we know we need. But it is assumed that nd indexing behaves that same as numpy arrays -- as there is no way to easily confirm that. This is a goal, but in fact, only numpy arrays and ``netCDF4 Variables`` have been implemented and tested. In the future, we may use ``xarray`` as a single abstraction layer, rather than rolling our own.
 
 To support that, we support converting and testing for array-like with:
 
@@ -85,7 +85,7 @@ and
 
 ``gridded.utils.isarraylike()``
 
-Those utilities will be updated as new needs arrise.
+Those utilities will be updated as new needs arise.
 
 Reference
 =========

gridded/__init__.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 
 from ._version import __version__
+
 # __version__ = "0.7.5"
 
 VALID_SGRID_LOCATIONS = (None, "center", "edge1", "edge2", "node")

gridded/depth.py

@@ -517,7 +517,7 @@ def from_netCDF(
                     grid=grid,
                     name="bathymetry",
                 )
-            else:    
+            else:
                 h = (bathy_var[0:-1,:] + bathy_var[1:,:]) / 2
                 psi_h = (h[:,0:-1] + h[:,1:]) /2
                 bathymetry = Bathymetry(
@@ -774,7 +774,7 @@ def _apply_boundary_conditions(
         if bottom_boundary_condition == "mask":
             indices.mask[below_bottom_mask] = True
             alphas.mask[below_bottom_mask] = True
-            
+
         indices.mask = np.logical_or(indices.mask, exclusion_mask)
         alphas.mask = np.logical_or(alphas.mask, exclusion_mask)
         return indices, alphas, oob_mask

gridded/gridded.py

@@ -77,6 +77,8 @@ def __init__(self, ncfile=None, grid=None, variables=None, grid_topology=None, a
         else:  # Create from grid and variables -- this is what should usually happen.
             self.filename = None
             self.grid = grid
+            self.time = None
+            self.depth = None
             self.variables = {} if variables is None else variables
             self.attributes = {} if attributes is None else attributes
 
@@ -93,7 +95,9 @@ def _init_from_netCDF(self, filename=None, grid_file=None, variable_files=None,
 
         self.nc_dataset = get_dataset(filename)
         self.filename = self.nc_dataset.filepath()
-        self.grid = Grid.from_netCDF(filename=self.filename, dataset=self.nc_dataset, grid_topology=grid_topology)
+        self.grid = Grid.from_netCDF(filename=self.filename,
+                                     dataset=self.nc_dataset,
+                                     grid_topology=grid_topology)
         # fixme: this should load the depth and time, and then the variables.
         self.variables = self._variables_from_netCDF(self.nc_dataset)
         self.attributes = get_dataset_attrs(self.nc_dataset)
@@ -102,7 +106,7 @@ def _init_from_netCDF(self, filename=None, grid_file=None, variable_files=None,
     def from_netCDF(cls, filename=None, grid_file=None, variable_files=None, grid_topology=None):
         """
         NOTE: only loading from a single file is currently implemented.
-              you can create a DATaset by hand, by loading the grid and
+              you can create a Dataset by hand, by loading the grid and
               variables separately, and then adding them
 
         load a gridded.Dataset from a netCDF file

gridded/tests/test_depth.py

@@ -307,7 +307,7 @@ def test_interpolation_alphas(self, get_roms_depth):
         )
         assert np.all(idx == expected_idx)
         assert np.all(np.isclose(alphas, expected_alpha))
-        
+
         points = np.array(
             [[20, 20, 9.9], [20, 20, 10.0], [20, 20, 10.1], [-1, -1, 5], [20, 20, -0.1], [20, 20, 0], [20, 20, 0.1]]
         )

gridded/tests/test_variable.py

@@ -5,8 +5,6 @@
 but good to have a few explicitly for the Variable object
 """
 
-import os
-
 import netCDF4
 import numpy as np
 
@@ -67,7 +65,7 @@ def test_Variable_api_at_function_edge_cases():
     p1 = (1.5, 35.5)
     p2 = (1.5, 35.5, 1)  # expected: r2 == r1
     p3 = [(1.5, 35.5), (35.5, 1.5)]  # expected: [[1],[masked]]
-    p4 = [(1.5, 35.5, 1), (35.5, 1.5, 1)]  # expected: [[1],[masked]]
+#    p4 = [(1.5, 35.5, 1), (35.5, 1.5, 1)]  # expected: [[1],[masked]]
 
     t = var.time.min_time
 
@@ -145,7 +143,7 @@ def test_VectorVariable_api_at_function_edge_cases():
     p1 = (1.5, 35.5)
     p2 = (1.5, 35.5, 1)  # expected: r2 == r1
     p3 = [(1.5, 35.5), (35.5, 1.5)]  # expected: [[1],[masked]]
-    p4 = [(1.5, 35.5, 1), (35.5, 1.5, 1)]  # expected: [[1],[masked]]
+    # p4 = [(1.5, 35.5, 1), (35.5, 1.5, 1)]  # expected: [[1],[masked]]
 
     t = var.time.min_time
 

gridded/tests/utilities.py

@@ -6,7 +6,6 @@
 """
 
 import contextlib
-import glob
 import os
 from pathlib import Path
 

gridded/utilities.py

@@ -4,12 +4,8 @@
 assorted utility functions needed by gridded
 """
 
-try:
-    from collections.abc import Collection, Iterable, Mapping
-except ImportError:  # py2
-    from collections.abc import Iterable
-
 import os
+from collections.abc import Iterable, Mapping
 
 import netCDF4 as nc4
 import numpy as np
@@ -74,7 +70,7 @@ def gen_celltree_mask_from_center_mask(center_mask, sl):
 #
 def regrid_variable(grid, o_var, location="node"):
     from gridded.depth import DepthBase, L_Depth, S_Depth
-    from gridded.grids import Grid_S, Grid_U
+    from gridded.grids import Grid_U
     from gridded.variable import Variable
 
     """
@@ -139,7 +135,8 @@ def regrid_variable(grid, o_var, location="node"):
     if o_var.time is not None:
         for t_idx, t in enumerate(o_var.time.data):
             if n_depth is not None and issubclass(n_depth.__class__, S_Depth):
-                transect = o_var.depth.get_depth_profile(o_var.grid.nodes.reshape(-1, 2), t, data_shape=(len(n_depth),)).T
+                transect = o_var.depth.get_depth_profile(o_var.grid.nodes.reshape(-1, 2),
+                                                         t, data_shape=(len(n_depth),)).T
                 for lev_idx, lev_data in enumerate(transect):
                     lev = Variable(
                         name=f"level{lev_idx}", data=lev_data.reshape(o_var.grid.node_lon.shape), grid=o_var.grid

gridded/variable.py

@@ -1,24 +1,21 @@
 import collections
 import hashlib
 import logging
-import os
 from functools import wraps
 from textwrap import dedent
 
 import netCDF4 as nc4
 import numpy as np
 
 from gridded import VALID_LOCATIONS
-from gridded.depth import Depth, DepthBase
+from gridded.depth import Depth
 from gridded.grids import Grid, Grid_R, Grid_S, Grid_U
 from gridded.time import Time
 from gridded.utilities import (
-    _align_results_to_spatial_data,
     _reorganize_spatial_data,
     asarraylike,
     get_dataset,
     parse_filename_dataset_args,
-    search_dataset_for_variables_by_varname,
 )
 
 log = logging.getLogger(__name__)
@@ -392,14 +389,13 @@ def data(self, d):
         if self.time is not None and len(d) != len(self.time):
             raise ValueError("Data/time interval mismatch")
         ## fixme: we should check Depth, too.
-        # if self.grid is not None and self.grid.infer_location(d) is None:
-        #     raise ValueError("Data/grid shape mismatch. Data shape is {0}, Grid shape is {1}".format(d.shape, self.grid.node_lon.shape))
         if self.grid is not None:  # if there is not a grid, we can't check this
             if self.location is None:  # not set, let's try to figure it out
                 self.location = self.grid.infer_location(d)
             if self.location is None:
                 raise ValueError(
-                    f"Data/grid shape mismatch: Data shape is {d.shape}, Grid shape is {self.grid.node_lon.shape}"
+                    f"Data/grid shape mismatch: Data shape is {d.shape}, "
+                    f"Grid shape is {self.grid.node_lon.shape}"
                 )
         self._data = d
 
@@ -655,7 +651,6 @@ def _xy_interp(self, points, time, extrapolate, slices=(), **kwargs):
         :type slices: tuple of integers or slice objects
         """
         _hash = kwargs["_hash"] if "_hash" in kwargs else None
-        units = kwargs["units"] if "units" in kwargs else None
 
         value = self.grid.interpolate_var_to_points(
             points[:, 0:2],
@@ -1342,8 +1337,8 @@ def _mod(n):
                 if _mod("grid"):
                     gt = kws.get("grid_topology", None)
                     kws["grid"] = Grid.from_netCDF(kws["filename"], dataset=dg, grid_topology=gt)
-                if kws.get("varnames", None) is None:
-                    varnames = cls._gen_varnames(kws["data_file"], dataset=ds)
+                # if kws.get("varnames", None) is None:
+                #     varnames = cls._gen_varnames(kws["data_file"], dataset=ds)
                 #                 if _mod('time'):
                 #                     time = Time.from_netCDF(filename=kws['data_file'],
                 #                                             dataset=ds,

pixi.toml

@@ -69,6 +69,6 @@ build_docs = {cmd = "cd docs && make html", default-environment = "docs", depend
 
 test = { cmd = "pytest --pyargs gridded", depends-on = ["install"] }
 
-lint = { cmd = "ruff check src/libgoods", default-environment = "dev"}
+lint = { cmd = "ruff check gridded", default-environment = "dev"}