Validation fixes

dodola/cli.py

@@ -327,24 +327,24 @@ def correct_wetday_frequency(x, out, process):
 @click.argument("x", required=True)
 @click.option("--variable", "-v", required=True)
 @click.option(
-    "--data_type",
-    "-data",
+    "--data-type",
+    "-d",
     required=True,
     type=click.Choice(["cmip6", "bias_corrected", "downscaled"], case_sensitive=False),
     help="Which data type to validate",
 )
 @click.option(
-    "--time_period",
-    "-time",
+    "--time-period",
+    "-t",
     required=True,
     type=click.Choice(["historical", "future"], case_sensitive=False),
     help="Which time period to validate",
 )
 def validate_dataset(x, variable, data_type, time_period):
     """Validate a dataset"""
-    services.validate_dataset(
+    services.validate(
         str(x),
-        variable=str(variable),
+        var=str(variable),
         data_type=str(data_type),
         time_period=str(time_period),
     )

dodola/core.py

@@ -485,19 +485,17 @@ def validate_dataset(ds, var, data_type, time_period="future"):
     """
     Validate a Dataset. Valid for CMIP6, bias corrected and downscaled.
 
+    Raises AssertionError when validation fails.
+
     Parameters
     ----------
     ds : xr.Dataset
-    variable : {"tasmax", "tasmin", "dtr", "pr"}
+    var : {"tasmax", "tasmin", "dtr", "pr"}
         Variable in Dataset to validate.
     data_type : {"cmip6", "bias_corrected", "downscaled"}
         Type of data output to validate.
     time_period : {"historical", "future"}
         Time period of data that will be validated.
-
-    Returns
-    -------
-    xr.Dataset
     """
 
     # validation for all variables

dodola/services.py

@@ -457,7 +457,7 @@ def validate(x, var, data_type, time_period):
     """
 
     ds = storage.read(x)
-    validate_dataset(ds)
+    validate_dataset(ds, var, data_type, time_period)
 
 
 @log_service

dodola/tests/test_cli.py

@@ -17,6 +17,7 @@
         "correct-wetday-frequency",
         "train-aiqpd",
         "apply-aiqpd",
+        "validate-dataset",
     ],
     ids=(
         "--help",
@@ -29,6 +30,7 @@
         "correct-wetday-frequency --help",
         "train-aiqpd --help",
         "apply-aiqpd --help",
+        "validate-dataset --help",
     ),
 )
 def test_cli_helpflags(subcmd):

dodola/tests/test_services.py

@@ -56,7 +56,17 @@ def _modeloutputfactory(
 
     np.random.seed(0)
     time = xr.cftime_range(start=start_time, end=end_time, calendar="noleap")
-    data = 15 + 8 * np.random.randn(len(time))
+    # make sure that test data range is reasonable for the variable being tested
+    if variable_name == "tasmax" or variable_name == "tasmin":
+        low_val = 160
+        high_val = 340
+    elif variable_name == "dtr":
+        low_val = 1
+        high_val = 40
+    elif variable_name == "pr":
+        low_val = 0.01
+        high_val = 1900
+    data = np.random.randint(low_val, high_val, len(time)).astype(np.float64)
 
     out = xr.Dataset(
         {variable_name: (["time", "lon", "lat"], data[:, np.newaxis, np.newaxis])},
@@ -862,23 +872,13 @@ def test_downscale(domain_file, method, var):
     np.testing.assert_almost_equal(downscaled_ds.values, downscaled_test.values)
 
 
-@pytest.mark.parametrize(
-    "variable",
-    [
-        pytest.param("tasmax"),
-        pytest.param("tasmin"),
-        pytest.param("dtr"),
-        pytest.param("pr"),
-    ],
-    "data_type",
-    [pytest.param("cmip6"), pytest.param("bias_corrected"), pytest.param("downscaled")],
-    "time_period",
-    [pytest.param("historical"), pytest.param("future")],
-)
-def validate_dataset(variable, data_type, time_period):
-    """Tests that validate dataset passes for fake output data"""
+@pytest.mark.parametrize("variable", ["tasmax", "tasmin", "dtr", "pr"])
+@pytest.mark.parametrize("data_type", ["cmip6", "bias_corrected", "downscaled"])
+@pytest.mark.parametrize("time_period", ["historical", "future"])
+def test_validation(variable, data_type, time_period):
+    """Tests that validate passes for fake output data"""
     # Setup input data
-    if data_type == "bias_corrected" or "downscaled":
+    if data_type == "bias_corrected" or data_type == "downscaled":
         if time_period == "historical":
             start_time = "1950-01-01"
             end_time = "2014-12-31"
@@ -902,6 +902,4 @@ def validate_dataset(variable, data_type, time_period):
     in_url = "memory://test_validate/an/input/path.zarr"
     repository.write(in_url, ds)
 
-    # read in test data and validate
-    ds_test = repository.read(in_url)
-    validate(ds_test, variable, data_type, time_period)
+    validate(in_url, variable, data_type, time_period)