fixing tests

Signed-off-by: travis-recurve <[email protected]>

eemeter/common/base_settings.py

@@ -44,7 +44,7 @@ def __lower__(value: Any) -> Any:
         return __lower__(values)
 
     """Make all property values lowercase and strip whitespace before validation"""
-    @pydantic.validator('*', pre=True)
+    @pydantic.field_validator("*", mode="before")
     def lowercase_values(cls, v):
         if isinstance(v, str):
             return v.lower().strip()
@@ -53,5 +53,5 @@ def lowercase_values(cls, v):
 
 # add developer field to pydantic Field
 def CustomField(developer=False, *args, **kwargs):
-    field = pydantic.Field(developer=developer, *args, **kwargs)
+    field = pydantic.Field(json_schema_extra={"developer": developer}, *args, **kwargs)
     return field

eemeter/eemeter/models/daily/utilities/settings.py

@@ -559,6 +559,9 @@ def update_daily_settings(settings, update_dict):
     settings_dict = settings.model_dump()
     settings_dict.update(update_dict)
 
+    if isinstance(settings, DailyLegacySettings):
+        return DailyLegacySettings(**settings_dict)
+
     return DailySettings(**settings_dict)
 
 

tests/daily_model/base_models/test_c_hdd_tidd_smooth.py

@@ -48,9 +48,10 @@ def test_fit_c_hdd_tidd_smooth():
         cdd_k=None,
     )
     bnds = None
+    weights = None
     initial_fit = True
     smooth = True
-    res = fit_c_hdd_tidd(T, obs, settings, opt_options, smooth, x0, bnds, initial_fit)
+    res = fit_c_hdd_tidd(T, obs, weights, settings, opt_options, smooth, x0, bnds, initial_fit)
     assert res.success == True
 
     # Test case 2: Test with initial_fit=False
@@ -77,7 +78,7 @@ def test_fit_c_hdd_tidd_smooth():
     bnds = None
     initial_fit = False
     smooth = True
-    res = fit_c_hdd_tidd(T, obs, settings, opt_options, smooth, x0, bnds, initial_fit)
+    res = fit_c_hdd_tidd(T, obs, weights, settings, opt_options, smooth, x0, bnds, initial_fit)
     assert res.success == True
 
     # Test case 3: Test with x0=None
@@ -95,5 +96,5 @@ def test_fit_c_hdd_tidd_smooth():
     bnds = None
     initial_fit = True
     smooth = True
-    res = fit_c_hdd_tidd(T, obs, settings, opt_options, smooth, x0, bnds, initial_fit)
-    assert res.success == True
+    res = fit_c_hdd_tidd(T, obs, weights, settings, opt_options, smooth, x0, bnds, initial_fit)
+    assert res.success == True

tests/daily_model/test_fit_base_models.py

@@ -22,7 +22,7 @@
 import pytest
 from eemeter.eemeter.models.daily.parameters import ModelCoefficients
 
-from eemeter.eemeter.models.daily.utilities.config import DailySettings as Settings
+from eemeter.eemeter.models.daily.utilities.settings import DailySettings as Settings
 from eemeter.eemeter.models.daily.parameters import ModelType
 from eemeter.eemeter.models.daily.fit_base_models import (
     fit_initial_models_from_full_model,

tests/daily_model/test_objective_function.py

@@ -125,6 +125,7 @@ def test_obj_fcn_decorator():
     TSS_fcn = None
     T = np.array([1, 2, 3]).astype(float)
     obs = np.array([4, 5, 6]).astype(float)
+    weights = None
     settings = type(
         "Settings",
         (object,),
@@ -143,6 +144,7 @@ def test_obj_fcn_decorator():
         TSS_fcn,
         T,
         obs,
+        weights,
         settings,
         alpha,
         coef_id,

tests/daily_model/test_optimize.py

@@ -47,19 +47,14 @@ def obj_fcn(x):
     assert idx_opt == [0, 1, 2]
 
 
-@pytest.fixture
-def get_settings():
-    return Settings()
-
-
-@pytest.fixture
-def get_obj_fcn(get_settings):
+def get_obj_fcn(settings):
     # Test case 1: Test with minimum input values
     model_fcn_full = evaluate_hdd_tidd_cdd_smooth
     weight_fcn = _hdd_tidd_cdd_smooth_weight
     TSS_fcn = None
     T = np.array([1, 2, 3, 4, 5, 6, 7]).astype(float)
     obs = np.array([2, 4, 6, 8, 10, 12, 14]).astype(float)
+    base_weights = None
     alpha = 2.0
     coef_id = [
         "hdd_bp",
@@ -77,7 +72,8 @@ def get_obj_fcn(get_settings):
         TSS_fcn,
         T,
         obs,
-        get_settings,
+        base_weights,
+        settings,
         alpha,
         coef_id,
         initial_fit,
@@ -104,7 +100,7 @@ def get_bnds():
     )
 
 
-def test_optimizer_run(get_settings, get_obj_fcn, get_x0, get_bnds):
+def test_optimizer_run(get_x0, get_bnds):
     x0 = get_x0
     bnds = get_bnds
     coef_id = [
@@ -118,28 +114,25 @@ def test_optimizer_run(get_settings, get_obj_fcn, get_x0, get_bnds):
     ]
 
     # Test case 1: Test with empty options
-    opt_options = _get_opt_settings(get_settings)
-    optimizer = Optimizer(get_obj_fcn, x0, bnds, coef_id, get_settings, opt_options)
+    settings = Settings()
+    opt_settings = _get_opt_settings(settings)
+    obj_fcn = get_obj_fcn(settings)
+    optimizer = Optimizer(obj_fcn, x0, bnds, coef_id, settings, opt_settings)
     res = optimizer.run()
     assert np.allclose(res.x, np.array([20.13393]), rtol=1e-5, atol=1e-5)
 
     # Test case 2: Test with scipy algorithm
     settings = Settings(developer_mode=True, algorithm_choice="scipy_Nelder-Mead")
-    opt_options = _get_opt_settings(settings)
-    optimizer = Optimizer(get_obj_fcn, x0, bnds, coef_id, settings, opt_options)
+    opt_settings = _get_opt_settings(settings)
+    obj_fcn = get_obj_fcn(settings)
+    optimizer = Optimizer(obj_fcn, x0, bnds, coef_id, settings, opt_settings)
     res = optimizer.run()
     assert np.allclose(res.x, np.array([20.13393]), rtol=1e-5, atol=1e-5)
 
     # Test case 3: Test with nlopt algorithm
-    settings = Settings(developer_mode=True, algorithm_choice="nlopt_direct")
-    opt_options = _get_opt_settings(get_settings)
-    optimizer = Optimizer(get_obj_fcn, x0, bnds, coef_id, get_settings, opt_options)
-    res = optimizer.run()
-    assert np.allclose(res.x, np.array([20.13393]), rtol=1e-5, atol=1e-5)
-
-    # Test case 4: Test with multistart algorithm
-    settings = Settings(developer_mode=True, algorithm_choice="nlopt_mlsl_lds")
-    opt_options = _get_opt_settings(get_settings)
-    optimizer = Optimizer(get_obj_fcn, x0, bnds, coef_id, settings, opt_options)
+    settings = Settings(developer_mode=True, algorithm_choice="nlopt_sbplx")
+    opt_settings = _get_opt_settings(settings)
+    obj_fcn = get_obj_fcn(settings)
+    optimizer = Optimizer(obj_fcn, x0, bnds, coef_id, settings, opt_settings)
     res = optimizer.run()
-    assert np.allclose(res.x, np.array([20.13393783]), rtol=1e-5, atol=1e-5)
+    assert np.allclose(res.x, np.array([20.13393]), rtol=1e-5, atol=1e-5)