Reenabling equity tests

tests/conftest.py

@@ -4,6 +4,7 @@
 from dscim.menu.simple_storage import Climate, EconVars
 from dscim.menu.baseline import Baseline
 from dscim.menu.risk_aversion import RiskAversionRecipe
+from dscim.menu.equity import EquityRecipe
 from pathlib import Path
 from itertools import product
 import numpy as np
@@ -61,7 +62,7 @@ def discount_types(request):
     return request.param
 
 
-all_menu_classes = [Baseline, RiskAversionRecipe]
+all_menu_classes = [Baseline, RiskAversionRecipe, EquityRecipe]
 
 
 @pytest.fixture(params=all_menu_classes, scope="module")

tests/test_equity.py

@@ -1,123 +1,88 @@
-# import os
-# import pytest
-# import pandas as pd
-# import xarray as xr
-
-# from pathlib import Path
-# from pandas.testing import assert_frame_equal
-# from xarray.testing import assert_equal, assert_allclose
-
-# from dscim.tests import open_example_dataset, open_zipped_results
-# from dscim.menu.equity import EquityRecipe
-
-
-# @pytest.fixture(
-#     params=[
-#         "constant",
-#         # "constant_model_collapsed", # not used
-#         "naive_ramsey",
-#         "euler_ramsey",
-#         "naive_gwr",
-#         "gwr_gwr",
-#         "euler_gwr",
-#     ]
-# )
-# def discount_types(request):
-#     return request.param
-
-
-# @pytest.fixture
-# def equity(discount_types, econ, climate):
-#     datadir = os.path.join(os.path.dirname(__file__), "data")
-
-#     recipe = EquityRecipe(
-#         sector_path=[{"dummy_sector": os.path.join(datadir, "damages")}],
-#         save_path=None,
-#         econ_vars=econ,
-#         climate_vars=climate,
-#         fit_type="ols",
-#         variable=[{"dummy_sector": "damages"}],
-#         sector="dummy_sector",
-#         discounting_type=discount_types,
-#         ext_method="global_c_ratio",
-#         ce_path= os.path.join(datadir, "CEs"),
-
-#         subset_dict={
-#             "ssp": ["SSP2", "SSP3", "SSP4"],
-#             "region": [
-#                 "IND.21.317.1249",
-#                 "CAN.2.33.913",
-#                 "USA.14.608",
-#                 "EGY.11",
-#                 "SDN.4.11.50.164",
-#                 "NGA.25.510",
-#                 "SAU.7",
-#                 "RUS.16.430.430",
-#                 "SOM.2.5",
-#             ],
-#         },
-#         fair_aggregation=["ce", "median_params", "mean"],
-#         extrap_formula=None,
-#         formula="damages ~ -1 + anomaly + np.power(anomaly, 2)",
-#     )
-
-#     yield recipe
-
-# @pytest.mark.xfail
-# def test_equity_points(equity, discount_types):
-#     path = f"equity_{discount_types}_eta{equity.eta}_rho{equity.rho}_damage_function_points.csv"
-#     expected = open_zipped_results(path)
-#     actual = equity.damage_function_points
-#     assert_frame_equal(expected, actual, rtol=1e-4, atol=1e-4)
-
-# @pytest.mark.xfail
-# def test_equity_coefficients(equity, discount_types):
-#     path = f"equity_{discount_types}_eta{equity.eta}_rho{equity.rho}_damage_function_coefficients.nc4"
-#     expected = open_zipped_results(path)
-#     actual = equity.damage_function_coefficients
-#     assert_allclose(
-#         expected.transpose(*sorted(expected.dims)).sortby(list(expected.dims)),
-#         actual.transpose(*sorted(actual.dims)).sortby(list(actual.dims)),
-#     )
-
-# @pytest.mark.xfail
-# def test_equity_fit(equity, discount_types):
-#     path = f"equity_{discount_types}_eta{equity.eta}_rho{equity.rho}_damage_function_fit.nc4"
-#     expected = open_zipped_results(path)
-#     actual = equity.damage_function_fit
-#     assert_allclose(
-#         expected.transpose(*sorted(expected.dims)).sortby(list(expected.dims)),
-#         actual.transpose(*sorted(actual.dims)).sortby(list(actual.dims)),
-#     )
-
-# @pytest.mark.xfail
-# def test_equity_global_consumption(equity, discount_types):
-#     path = f"equity_{discount_types}_eta{equity.eta}_rho{equity.rho}_global_consumption.nc4"
-#     expected = open_zipped_results(path)
-#     actual = equity.global_consumption.squeeze()
-#     # Small format hack from I/O
-#     if isinstance(expected, xr.Dataset):
-#         expected = expected.to_array().squeeze().drop("variable")
-
-#     assert_allclose(
-#         expected.transpose(*sorted(expected.dims)).sortby(list(expected.dims)),
-#         actual.transpose(*sorted(actual.dims)).sortby(list(actual.dims)),
-#     )
-
-
-# @pytest.mark.xfail
-# def test_equity_scc(equity, discount_types):
-#     path = f"equity_{discount_types}_eta{equity.eta}_rho{equity.rho}_scc.nc4"
-#     expected = open_zipped_results(path)
-#     actual = equity.calculate_scc.squeeze()
-
-#     # Small format hack from I/O
-#     if isinstance(expected, xr.Dataset):
-#         expected = expected.to_array().squeeze().drop("variable")
-
-#     assert_allclose(
-#         expected.transpose(*sorted(expected.dims)).sortby(list(expected.dims)),
-#         actual.transpose(*sorted(actual.dims)).sortby(list(actual.dims)),
-#         rtol=1.5e-4,
-#         atol=1e-4,
-#     )
+import pandas
+import xarray as xr
+from pandas.testing import assert_frame_equal
+from xarray.testing import assert_allclose
+import pytest
+
+from . import open_zipped_results
+from dscim.menu.equity import EquityRecipe
+
+
+@pytest.mark.parametrize("menu_class", [EquityRecipe], indirect=True)
+def test_equity_points(menu_instance, discount_types):
+    path = f"equity_{discount_types}_eta{menu_instance.eta}_rho{menu_instance.rho}_damage_function_points.csv"
+    expected = open_zipped_results(path)
+    actual = menu_instance.damage_function_points
+    assert_frame_equal(
+        expected,
+        actual,
+        rtol=1e-4,
+        atol=1e-4,
+    )
+
+
+@pytest.mark.parametrize("menu_class", [EquityRecipe], indirect=True)
+def test_equity_coefficients(menu_instance, discount_types):
+    path = f"equity_{discount_types}_eta{menu_instance.eta}_rho{menu_instance.rho}_damage_function_coefficients.nc4"
+    expected = open_zipped_results(path)
+    actual = menu_instance.damage_function_coefficients
+    assert_allclose(
+        expected.transpose(*sorted(expected.dims)).sortby(list(expected.dims)),
+        actual.transpose(*sorted(actual.dims)).sortby(list(actual.dims)),
+    )
+
+
+@pytest.mark.parametrize("menu_class", [EquityRecipe], indirect=True)
+def test_equity_fit(menu_instance, discount_types):
+    path = f"equity_{discount_types}_eta{menu_instance.eta}_rho{menu_instance.rho}_damage_function_fit.nc4"
+    expected = open_zipped_results(path)
+    actual = menu_instance.damage_function_fit
+    assert_allclose(
+        expected.transpose(*sorted(expected.dims)).sortby(list(expected.dims)),
+        actual.transpose(*sorted(actual.dims)).sortby(list(actual.dims)),
+    )
+
+
+@pytest.mark.parametrize("menu_class", [EquityRecipe], indirect=True)
+def test_equity_global_consumption(menu_instance, discount_types):
+    path = f"equity_{discount_types}_eta{menu_instance.eta}_rho{menu_instance.rho}_global_consumption.nc4"
+    expected = open_zipped_results(path)
+    actual = menu_instance.global_consumption.squeeze()
+    # Small format hack from I/O
+    if isinstance(expected, xr.Dataset):
+        expected = expected.to_array().squeeze().drop("variable")
+
+    assert_allclose(
+        expected.transpose(*sorted(expected.dims)).sortby(list(expected.dims)),
+        actual.transpose(*sorted(actual.dims)).sortby(list(actual.dims)),
+    )
+
+
+@pytest.mark.parametrize("menu_class", [EquityRecipe], indirect=True)
+def test_equity_scc(menu_instance, discount_types):
+    path = (
+        f"equity_{discount_types}_eta{menu_instance.eta}_rho{menu_instance.rho}_scc.nc4"
+    )
+    expected = open_zipped_results(path)
+    actual = menu_instance.calculate_scc.squeeze()
+
+    # Small format hack from I/O
+    if isinstance(expected, xr.Dataset):
+        expected = expected.to_array().squeeze().drop("variable")
+
+    assert_allclose(
+        expected.transpose(*sorted(expected.dims)).sortby(list(expected.dims)),
+        actual.transpose(*sorted(actual.dims)).sortby(list(actual.dims)),
+        rtol=1e-4,
+        atol=1e-4,
+    )
+
+
+@pytest.mark.parametrize("discount_types", ["euler_ramsey"], indirect=True)
+@pytest.mark.parametrize("menu_class", [EquityRecipe], indirect=True)
+def test_global_damages_calculation(menu_instance):
+    global_damages = menu_instance.global_damages_calculation()
+    assert (
+        isinstance(global_damages, pandas.DataFrame)
+        and "region" not in global_damages.columns
+    )