assorted cleanup ...

Author: ChrisBarker-NOAA

.github/workflows/conda_test.yml

@@ -1,10 +1,15 @@
 # generated test files
-*noncompliant_sgrid.nc
-*sgrid_deltares.nc
-*sgrid_deltares_no_opt_attr.nc
-*sgrid_roms.nc
-*sgrid_wrf.nc
-*sgrid_wrf_2.nc
+# NOTE: if we need these, they should be in a
+#       defined place, and ignored in a tests/.gitignore file
+# *noncompliant_sgrid.nc
+# *sgrid_deltares.nc
+# *sgrid_deltares_no_opt_attr.nc
+# *sgrid_roms.nc
+# *sgrid_wrf.nc
+# *sgrid_wrf_2.nc
+
+# version file (auto-generated)
+gridded/_version.py
 
 # OS-X stuff
 .DS_Store

.gitignore

@@ -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
 =========

docs/source/api_reference.rst

@@ -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/gridded.py

@@ -1,4 +1,3 @@
 temp_data/
 test_data/cdl/*.cdl.nc
 test_io/output
-example_data/*.nc

gridded/tests/.gitignore

@@ -14,6 +14,7 @@
 
 HERE = Path(__file__).parent
 EXAMPLE_DATA = HERE / "example_data"
+TEMP_DATA = HERE / "temp_data"
 TEST_DATA = HERE / "test_data"
 
 TEST_CDL_FILES = list((TEST_DATA / "cdl").glob("*.cdl"))
@@ -23,7 +24,7 @@
 data_file_cache = pooch.create(
     # Use a local cache folder for the operating system
     # path=pooch.os_cache("plumbus"),
-    path=EXAMPLE_DATA,
+    path=TEMP_DATA,
     # The remote data is on the pygnome server
     base_url="https://gnome.orr.noaa.gov/py_gnome_testdata/gridded_test_files/",
     # version=version,
@@ -38,133 +39,6 @@
     },
 )
 
-
-# def get_temp_test_file(filename):
-#     """
-#     returns the path to a temporary test file.
-
-#     If it exists, it will return it directly.
-
-#     If not, it will attempt to download it.
-
-#     If it can't download, it will return None
-#     """
-#     print("getting temp test file")
-#     filepath = os.path.join(os.path.dirname(__file__),
-#                             'temp_data',
-#                             filename)
-#     if os.path.isfile(filepath):
-#         print("already there")
-#         return filepath
-#     else:
-#         # attempt to download it
-#         print("trying to download")
-#         try:
-#             get_datafile(filepath)
-#         except urllib_request.HTTPError:
-#             print("got an error trying to download {}:".format(filepath))
-#             return None
-#         return None
-
-
-# @pytest.fixture
-# def two_triangles():
-#     """
-#     Returns a simple triangular grid: 4 nodes, two triangles, five edges.
-
-#     """
-#     nodes = [(0.1, 0.1),
-#              (2.1, 0.1),
-#              (1.1, 2.1),
-#              (3.1, 2.1)]
-
-#     faces = [(0, 1, 2),
-#              (1, 3, 2), ]
-
-#     edges = [(0, 1),
-#              (1, 3),
-#              (3, 2),
-#              (2, 0),
-#              (1, 2)]
-
-#     return ugrid.UGrid(nodes, faces, edges)
-
-
-# @pytest.fixture
-# def twenty_one_triangles():
-#     """
-#     Returns a basic triangular grid:  21 triangles, a hole, and a tail.
-
-#     """
-#     nodes = [(5, 1),
-#              (10, 1),
-#              (3, 3),
-#              (7, 3),
-#              (9, 4),
-#              (12, 4),
-#              (5, 5),
-#              (3, 7),
-#              (5, 7),
-#              (7, 7),
-#              (9, 7),
-#              (11, 7),
-#              (5, 9),
-#              (8, 9),
-#              (11, 9),
-#              (9, 11),
-#              (11, 11),
-#              (7, 13),
-#              (9, 13),
-#              (7, 15), ]
-
-#     faces = [(0, 1, 3),
-#              (0, 6, 2),
-#              (0, 3, 6),
-#              (1, 4, 3),
-#              (1, 5, 4),
-#              (2, 6, 7),
-#              (6, 8, 7),
-#              (7, 8, 12),
-#              (6, 9, 8),
-#              (8, 9, 12),
-#              (9, 13, 12),
-#              (4, 5, 11),
-#              (4, 11, 10),
-#              (9, 10, 13),
-#              (10, 11, 14),
-#              (10, 14, 13),
-#              (13, 14, 15),
-#              (14, 16, 15),
-#              (15, 16, 18),
-#              (15, 18, 17),
-#              (17, 18, 19), ]
-
-#     # We may want to use this later to define just the outer boundary.
-#     boundaries = [(0, 1),
-#                   (1, 5),
-#                   (5, 11),
-#                   (11, 14),
-#                   (14, 16),
-#                   (16, 18),
-#                   (18, 19),
-#                   (19, 17),
-#                   (17, 15),
-#                   (15, 13),
-#                   (13, 12),
-#                   (12, 7),
-#                   (7, 2),
-#                   (2, 0),
-#                   (3, 4),
-#                   (4, 10),
-#                   (10, 9),
-#                   (9, 6),
-#                   (6, 3), ]
-
-#     grid = ugrid.UGrid(nodes, faces, boundaries=boundaries)
-#     grid.build_edges()
-#     return grid
-
-
 @contextlib.contextmanager
 def chdir(dirname=None):
     curdir = os.getcwd()