def test_initialize_several_variables(self):
"""Ensure aggregation file is created correctly according to the variable config."""
config = Config.from_dict(
{
"dimensions": [{"name": "x", "size": None}, {"name": "y", "size": 10}],
"variables": [
{
"name": "foo",
"dimensions": ["x", "y"],
"datatype": "float32",
"attributes": {"units": "seconds"},
},
{
"name": "foo_x",
"dimensions": ["x"],
"datatype": "float64",
"attributes": {"units": "floops", "created_by": "the flooper"},
},
],
"global attributes": [],
}
)
initialize_aggregation_file(config, self.filename)
with nc.Dataset(self.filename) as nc_check:
self.assertEqual(len(nc_check.variables), 2)
self.assertEqual(nc_check.variables["foo"].dimensions, ("x", "y"))
self.assertEqual(nc_check.variables["foo"].datatype, np.dtype(np.float32))
self.assertEqual(nc_check.variables["foo"].units, "seconds")
self.assertEqual(nc_check.variables["foo_x"].dimensions, ("x",))
self.assertEqual(nc_check.variables["foo_x"].datatype, np.dtype(np.float64))
self.assertEqual(nc_check.variables["foo_x"].units, "floops")
self.assertEqual(
nc_check.variables["foo_x"].getncattr("created_by"), "the flooper"
)
def setUp(self):
# tmp file to aggregate to
_, self.nc_out_filename = tempfile.mkstemp()
pwd = os.path.dirname(__file__)
self.files = sorted(glob.glob(os.path.join(pwd, "data", "*.nc")))
with open(os.path.join(pwd, "new_dim_config.json")) as config_in:
self.config = Config.from_dict(json.load(config_in))
def test_initialize_basic(self):
"""Ensure aggregation file is created with proper dimensions according to the config."""
config = Config.from_dict(
{
"dimensions": [{"name": "x", "size": None}, {"name": "y", "size": 10}],
"variables": [
{"name": "x", "dimensions": ["x", "y"], "datatype": "int8"}
],
"global attributes": [],
}
)
initialize_aggregation_file(config, self.filename)
with nc.Dataset(self.filename) as nc_check:
self.assertEqual(len(nc_check.dimensions), 2)
self.assertEqual(nc_check.dimensions["y"].size, 10)
self.assertFalse(nc_check.dimensions["y"].isunlimited())
self.assertTrue(nc_check.dimensions["x"].isunlimited())
def test_initialize_with_list_attribute(self):
"""Ensure aggregation file is created with proper dimensions according to the config."""
config = Config.from_dict(
{
"dimensions": [{"name": "x", "size": None}, {"name": "y", "size": 10}],
"variables": [
{
"name": "x",
"dimensions": ["x", "y"],
"datatype": "int8",
"attributes": {"valid_range": [0, 10]},
}
],
"global attributes": [],
}
)
initialize_aggregation_file(config, self.filename)
with nc.Dataset(self.filename) as nc_check:
self.assertEqual(len(nc_check.dimensions), 2)
self.assertEqual(nc_check.variables["x"].valid_range[0], 0)
self.assertEqual(nc_check.variables["x"].valid_range[1], 10)
def setUpClass(cls):
super(TestEvaluateAggregationList, cls).setUpClass()
pwd = os.path.dirname(__file__)
cls.start_time = datetime(2017, 6, 8, 16, 45)
cls.end_time = datetime(2017, 6, 8, 16, 50)
cls.files = glob.glob(os.path.join(pwd, "data", "*.nc"))
with open(os.path.join(
pwd, "seis-l1b-sgps-east.json")) as product_config_file:
cls.config = Config.from_dict(json.load(product_config_file))
cls.config.dims["report_number"].update({
"index_by": "L1a_SciData_TimeStamp",
"min": cls.
start_time, # for convenience, will convert according to index_by units if this is datetime
"max": cls.end_time,
"expected_cadence": {
"report_number": 1,
"sensor_unit": 0
},
})
_, cls.filename = tempfile.mkstemp()
agg_list = generate_aggregation_list(cls.config, cls.files)
evaluate_aggregation_list(cls.config, agg_list, cls.filename)
cls.output = nc.Dataset(cls.filename, "r")