Merge pull request #104 from ImperialCollegeLondon/63-add-docstrings-…

…to-unit-tests-where-these-are-missing

Add docstrings to tests

setup.cfg

@@ -17,7 +17,7 @@ per-file-ignores =
 	# Flattening namespace of submodules
 
 max-line-length = 88
-exclude = tests/*
+# exclude = tests/*
 max-complexity = 10
 docstring-convention = google
 
@@ -51,4 +51,4 @@ ignore_errors = True
 [mypy-tests.*]
 disallow_untyped_calls = False
 disallow_untyped_defs = False
-disallow_incomplete_defs = False
+disallow_incomplete_defs = False

tests/__init__.py

@@ -0,0 +1,6 @@
+"""The tests module.
+
+This file is required to make tests a module. For some reason, `mypy` will only respect
+per-module options, such as suppressing typing checks in tests, for _modules_. Adding a
+`[mypy-tests.*]` section in setup.cfg does nothing if tests is not a module.
+"""

tests/hygro/test_hygro.py

@@ -1,3 +1,5 @@
+"""Test the functionality of the pyrealm.hygro module."""
+
 import json
 from pathlib import Path
 
@@ -7,6 +9,7 @@
 
 @pytest.fixture
 def bigleaf():
+    """A fixture to provide benchmark predictions from the bigleaf package."""
     path = Path(__file__).parent
     with open(path / "bigleaf_test_values.json") as tvals:
         return json.load(tvals)
@@ -22,7 +25,9 @@ def bigleaf():
 @pytest.mark.parametrize(
     argnames="formula", argvalues=["Allen1998", "Alduchov1996", "Sonntag1990"]
 )
-def test_calc_vp_sat(bigleaf, formula):
+def test_calc_vp_sat(bigleaf, formula) -> None:
+    """Test the calc_vp_sat function."""
+
     from pyrealm.constants import HygroConst
     from pyrealm.hygro import calc_vp_sat
 
@@ -37,6 +42,8 @@ def test_calc_vp_sat(bigleaf, formula):
     argnames="formula", argvalues=["Allen1998", "Alduchov1996", "Sonntag1990"]
 )
 def test_convert_vp_to_vpd(bigleaf, formula):
+    """Test the convert_vp_to_vpd function."""
+
     from pyrealm.constants import HygroConst
     from pyrealm.hygro import convert_vp_to_vpd
 
@@ -51,6 +58,8 @@ def test_convert_vp_to_vpd(bigleaf, formula):
     argnames="formula", argvalues=["Allen1998", "Alduchov1996", "Sonntag1990"]
 )
 def test_convert_rh_to_vpd(bigleaf, formula):
+    """Test the convert_rh_to_vpd function."""
+
     from pyrealm.constants import HygroConst
     from pyrealm.hygro import convert_rh_to_vpd
 
@@ -62,6 +71,8 @@ def test_convert_rh_to_vpd(bigleaf, formula):
 
 
 def test_convert_sh_to_vp(bigleaf):
+    """Test the convert_sh_to_vp function."""
+
     from pyrealm.hygro import convert_sh_to_vp
 
     results = convert_sh_to_vp(SH, PATM)
@@ -74,6 +85,8 @@ def test_convert_sh_to_vp(bigleaf):
     argnames="formula", argvalues=["Allen1998", "Alduchov1996", "Sonntag1990"]
 )
 def test_convert_sh_to_vpd(bigleaf, formula):
+    """Test the convert_sh_to_vpd function."""
+
     from pyrealm.constants import HygroConst
     from pyrealm.hygro import convert_sh_to_vpd
 

tests/pmodel/test_c3c4competition.py

@@ -75,8 +75,13 @@
     ],
 )
 def test_c3c4competition(pmodel_c3_args, pmodel_c4_args, expected):
+    """Test the C3/C4 competition model"""
     env = PModelEnvironment(
-        tc=np.array([20, 35]), patm=101325, co2=400, vpd=1000, theta=0.5
+        tc=np.array([20, 35]),
+        patm=np.array([101325]),
+        co2=np.array([400]),
+        vpd=np.array([1000]),
+        theta=np.array([0.5]),
     )
 
     pmodel_c3 = PModel(env, **pmodel_c3_args)
@@ -88,17 +93,21 @@ def test_c3c4competition(pmodel_c3_args, pmodel_c4_args, expected):
     pmodel_c4.estimate_productivity(fapar=fapar, ppfd=ppfd)
 
     comp = C3C4Competition(
-        pmodel_c3.gpp, pmodel_c4.gpp, treecover=0, below_t_min=False, cropland=False
+        pmodel_c3.gpp,
+        pmodel_c4.gpp,
+        treecover=np.array([0, 0]),
+        below_t_min=np.array([False, False]),
+        cropland=np.array([False, False]),
     )
 
-    d13CO2 = -8.4
-    D14CO2 = 19.2
+    d13CO2 = np.array([-8.4])
+    D14CO2 = np.array([19.2])
 
     pmodel_c3_iso = CalcCarbonIsotopes(pmodel_c3, d13CO2=d13CO2, D14CO2=D14CO2)
     pmodel_c4_iso = CalcCarbonIsotopes(pmodel_c4, d13CO2=d13CO2, D14CO2=D14CO2)
 
     comp.estimate_isotopic_discrimination(
-        d13CO2=-8.4,
+        d13CO2=np.array([-8.4]),
         Delta13C_C3_alone=pmodel_c3_iso.Delta13C,
         Delta13C_C4_alone=pmodel_c4_iso.Delta13C,
     )

tests/pmodel/test_calc_carbon_isotopes.py

@@ -71,10 +71,11 @@
         ),
     ],
 )
-def test_temporal_interpolator_init_errors(pmodelenv_args, pmodel_args, expected):
+def test_CalcCarbonIsotopes(pmodelenv_args, pmodel_args, expected):
+    """Tests the CalcCarbonIsotopes class."""
     env = PModelEnvironment(**pmodelenv_args)
     pmodel = PModel(env, **pmodel_args)
-    cci = CalcCarbonIsotopes(pmodel, d13CO2=-8.4, D14CO2=19.2)
+    cci = CalcCarbonIsotopes(pmodel, d13CO2=np.array([-8.4]), D14CO2=np.array([19.2]))
 
     for attr in expected:
         assert np.allclose(getattr(cci, attr), expected[attr], atol=0.001)

tests/pmodel/test_fast_slow_pmodel.py

@@ -1,3 +1,4 @@
+"""Tests the implementation of the FastSlowModel against the reference benchmark."""
 from importlib import resources
 
 import numpy as np
@@ -6,7 +7,7 @@
 
 @pytest.fixture(scope="module")
 def be_vie_data():
-    """Import the test data"""
+    """Import the benchmark test data."""
 
     # This feels like a hack but it isn't obvious how to reference the data files
     # included in the source distribution from the package path.
@@ -26,7 +27,7 @@ def be_vie_data():
 
 @pytest.fixture(scope="module")
 def be_vie_data_components(be_vie_data):
-    """Convert the test data into a PModelEnv and arrays"""
+    """Convert the test data into a PModelEnv and arrays."""
 
     from pyrealm.pmodel import PModelEnvironment
 
@@ -48,8 +49,11 @@ def be_vie_data_components(be_vie_data):
 
 
 def test_FSPModel_JAMES(be_vie_data_components):
-    """This tests the legacy calculations from the Mengoli et al JAMES paper, using that
-    version of the weighted average calculations without acclimating xi."""
+    """Test FastSlowPModel_JAMES.
+
+    This tests the legacy calculations from the Mengoli et al JAMES paper, using that
+    version of the weighted average calculations without acclimating xi.
+    """
 
     from pyrealm.pmodel import FastSlowScaler
     from pyrealm.pmodel.subdaily import FastSlowPModel_JAMES
@@ -95,8 +99,12 @@ def test_FSPModel_JAMES(be_vie_data_components):
 
 
 def test_FSPModel_corr(be_vie_data_components):
-    """This tests the pyrealm implementation correlates well with the legacy
-    calculations from the Mengoli et al JAMES paper without acclimating xi."""
+    """Test FastSlowPModel.
+
+    This tests that the pyrealm implementation including acclimating xi at least
+    correlates well with the legacy calculations from the Mengoli et al JAMES paper
+    without acclimating xi.
+    """
 
     from pyrealm.pmodel import FastSlowPModel, FastSlowScaler
 
@@ -127,12 +135,12 @@ def test_FSPModel_corr(be_vie_data_components):
     gpp_in_micromols = fs_pmodel.gpp[valid] / env.const.k_c_molmass
     assert np.allclose(gpp_in_micromols, expected_gpp[valid], rtol=0.2)
     r_vals = np.corrcoef(gpp_in_micromols, expected_gpp[valid])
-    assert np.alltrue(r_vals > 0.995)
+    assert np.all(r_vals > 0.995)
 
 
 @pytest.mark.parametrize("ndims", [2, 3, 4])
 def test_FSPModel_dimensionality(be_vie_data, ndims):
-    """This tests that the FastSlowPModel handles dimensions correctly.
+    """Tests that the FastSlowPModel handles dimensions correctly.
 
     This broadcasts the BE-Vie onto more dimensions and checks that the code iterates
     over those dimensions correctly. fAPAR and PPFD are then fixed across the other

tests/pmodel/test_fast_slow_scaler.py

@@ -1,4 +1,7 @@
-"""This module tests the interpolator routines for the subdaily model.
+"""This module tests the FastSlowScaler class.
+
+This class handles estimating daily reference values and then interpolating lagged
+responses back to subdaily time scales.
 """  # noqa: D205, D415
 
 from contextlib import nullcontext as does_not_raise
@@ -9,6 +12,7 @@
 
 @pytest.fixture
 def fixture_FSS():
+    """A fixture providing a FastSlowScaler object."""
     from pyrealm.pmodel import FastSlowScaler
 
     return FastSlowScaler(
@@ -111,10 +115,11 @@ def fixture_FSS():
     ],
 )
 def test_FSS_init(ctext_mngr, msg, datetimes):
+    """Test the FastSlowScaler init handling of date ranges."""
     from pyrealm.pmodel import FastSlowScaler
 
     with ctext_mngr as cman:
-        drep = FastSlowScaler(datetimes=datetimes)
+        _ = FastSlowScaler(datetimes=datetimes)
 
     if msg is not None:
         assert str(cman.value) == msg
@@ -158,6 +163,8 @@ def test_FSS_init(ctext_mngr, msg, datetimes):
     ],
 )
 def test_FSS_set_window(fixture_FSS, ctext_mngr, msg, kwargs, samp_mean, samp_max):
+    """Test the FastSlowScaler set_window method."""
+
     with ctext_mngr as cman:
         fixture_FSS.set_window(**kwargs)
 
@@ -221,6 +228,7 @@ def test_FSS_set_window(fixture_FSS, ctext_mngr, msg, kwargs, samp_mean, samp_ma
     ],
 )
 def test_FSS_set_include(fixture_FSS, ctext_mngr, msg, include, samp_mean, samp_max):
+    """Test the FastSlowScaler set_include method."""
     with ctext_mngr as cman:
         fixture_FSS.set_include(include)
 
@@ -283,6 +291,7 @@ def test_FSS_set_include(fixture_FSS, ctext_mngr, msg, include, samp_mean, samp_
     ],
 )
 def test_FSS_set_nearest(fixture_FSS, ctext_mngr, msg, time, samp_mean, samp_max):
+    """Test the FastSlowScaler set_nearest method."""
     with ctext_mngr as cman:
         fixture_FSS.set_nearest(time)
 
@@ -309,13 +318,14 @@ def test_FSS_set_nearest(fixture_FSS, ctext_mngr, msg, time, samp_mean, samp_max
     ],
 )
 def test_FSS_get_wv_errors(fixture_FSS, ctext_mngr, msg, values):
+    """Test errors arising in the FastSlowScaler get_window_value method."""
     fixture_FSS.set_window(
         window_center=np.timedelta64(12, "h"),
         half_width=np.timedelta64(2, "h"),
     )
 
     with ctext_mngr as cman:
-        res = fixture_FSS.get_window_values(values)
+        _ = fixture_FSS.get_window_values(values)
 
     assert str(cman.value) == msg
 
@@ -360,7 +370,7 @@ class Test_FSS_get_vals:
     """
 
     def test_FSS_get_vals_window(self, fixture_FSS, values, expected_means):
-        """Test a window"""
+        """Test a window."""
         fixture_FSS.set_window(
             window_center=np.timedelta64(12, "h"),
             half_width=np.timedelta64(2, "h"),
@@ -370,7 +380,7 @@ def test_FSS_get_vals_window(self, fixture_FSS, values, expected_means):
         assert np.allclose(calculated_means, expected_means)
 
     def test_FSS_get_vals_include(self, fixture_FSS, values, expected_means):
-        """Test include"""
+        """Test include."""
 
         # This duplicates the selection of the window test but using direct include
         inc = np.zeros(48, dtype=np.bool_)
@@ -381,7 +391,7 @@ def test_FSS_get_vals_include(self, fixture_FSS, values, expected_means):
         assert np.allclose(calculated_means, expected_means)
 
     def test_FSS_get_vals_nearest(self, fixture_FSS, values, expected_means):
-        """Test nearest"""
+        """Test nearest."""
 
         # This assumes the data are symmetrical about the middle hour, which is bit of a
         # reach
@@ -524,6 +534,8 @@ def test_FSS_resample_subdaily(
     exp_values,
     fill_from,
 ):
+    """Test the calculation of subdaily samples using FastSlowScaler."""
+
     # Set the included observations - the different parameterisations here and for
     # the update point should all select the same update point.
     func = getattr(fixture_FSS, method_name)
@@ -602,6 +614,8 @@ def test_FSS_resample_subdaily_linear(
     input_values,
     exp_values,
 ):
+    """Test FastSlowScaler resampling to subdaily timescale by linear interpolation."""
+
     # Set the included observations
     fixture_FSS.set_window(
         window_center=np.timedelta64(13, "h"), half_width=np.timedelta64(1, "h")

tests/pmodel/test_memory_effect.py

@@ -1,3 +1,5 @@
+"""Tests the application of the memory effect in the subdaily model."""
+
 from contextlib import nullcontext as does_not_raise
 
 import numpy as np
@@ -80,8 +82,7 @@ def test_memory_effect(inputs, alpha):
 )
 @pytest.mark.parametrize(argnames="ndim", argvalues=(1, 2, 3))
 def test_memory_effect_inputs(inputs, handle_nan, context_manager, expected, ndim):
-    """Simple testing of nan handling and expected predictions across multiple
-    dimensions."""
+    """Simple testing of nan handling and predictions across multiple dimensions."""
     from pyrealm.pmodel.subdaily import memory_effect
 
     if ndim == 2: