updated to be consistent suggested changes

mikeofski recommended not rounding returns in the financial.py module, tests needed to be updated for expected to match out
pvlib · eccoope · Mar 6, 2023 · Mar 6, 2023 · Mar 6, 2023 · Mar 7, 2023
commit 9f1e09034c97a141008f4bb4bd322b5c1ce979f1
diff --git a/pvlib/tests/test_financial.py b/pvlib/tests/test_financial.py
@@ -3,74 +3,81 @@
 from pvlib import financial
 from numpy.testing import assert_allclose
 
+
 def test_lcoe_series():
     n, cf = 20, 0.5
     production = pd.Series(data=[cf*8670 for j in range(n)])
     capex, loan_frac, my_crf, debt_tenor = 1000, 0.5, 0.05, n
-    cap_cost = pd.Series(data=[capex*loan_frac*my_crf for i in range(debt_tenor)] + [0 for j in range(debt_tenor, n)])
+    cap_cost = pd.Series(data=[capex*loan_frac*my_crf for i in
+        range(debt_tenor)] + [0 for j in range(debt_tenor, n)])
     base_om = 25
     fixed_om = pd.Series(data=[base_om for j in range(n)])
 
-    expected = 1.15
-    out = financial.lcoe(production=production, cap_cost=cap_cost, fixed_om=fixed_om)
+    expected = 1.1534025374855825
+    out = financial.lcoe(production=production, cap_cost=cap_cost,
+        fixed_om=fixed_om)
 
     assert_allclose(expected, out)
 
+
 def test_lcoe_arrays():
     n, cf = 20, 0.5
     production = np.full(n, cf*8670)
     capex, loan_frac, my_crf, debt_tenor = 1000, 0.5, 0.05, n
-    cap_cost = np.array([capex*loan_frac*my_crf for i in range(debt_tenor)] + [0 for j in range(debt_tenor, n)])
+    cap_cost = np.array([capex*loan_frac*my_crf for i in range(debt_tenor)]
+        + [0 for j in range(debt_tenor, n)])
     base_om = 25
     fixed_om = np.full(n, base_om)
-    expected = 1.15
-
-    out = financial.lcoe(production=production, cap_cost=cap_cost, fixed_om=fixed_om)
+    expected = 1.1534025374855825
+    out = financial.lcoe(production=production, cap_cost=cap_cost,
+        fixed_om=fixed_om)
     assert_allclose(expected, out)
 
+
 def test_lcoe_nans():
     n, cf = 20, 0.5
     production = np.full(n, cf*8670)
     capex, loan_frac, my_crf, debt_tenor = 1000, 0.5, 0.05, n
-    cap_cost = np.array([capex*loan_frac*my_crf for i in range(debt_tenor)] + [0 for j in range(debt_tenor, n)])
+    cap_cost = np.array([capex*loan_frac*my_crf for i in range(debt_tenor)]
+        + [0 for j in range(debt_tenor, n)])
     base_om = 25.
     fixed_om = np.full(n, base_om)
     cap_cost[1] = np.nan
     fixed_om[2] = np.nan
     production[3] = np.nan
-    expected = 1.09
-
-    out = financial.lcoe(production=production, cap_cost=cap_cost, fixed_om=fixed_om)
+    expected = 1.092697140775815
+    out = financial.lcoe(production=production, cap_cost=cap_cost,
+        fixed_om=fixed_om)
     assert_allclose(expected, out)
 
+
 def test_crf():
     rate, n = 0.05, 20
-    expected = 0.08024259
-
+    expected = 0.08024258719069129
     out = financial.crf(rate, n)
     assert_allclose(expected, out)
 
+
 def test_nominal_to_real():
     nominal, rate = 0.04, 0.025
-    expected = 0.01463415
-
+    expected = 0.014634146341463428
     out = financial.nominal_to_real(nominal, rate)
     assert_allclose(expected, out)
 
+
 def test_real_to_nominal():
     real, rate = 0.04, 0.025
     expected = 0.066
-
     out = financial.real_to_nominal(real, rate)
     assert_allclose(expected, out)
 
+
 def test_wacc():
     loan_frac = 0.5
     rroi = 0.12
     rint = 0.04
     inflation_rate = 0.025
     tax_rate = 0.28
-    expected = 0.07098537
-
+    expected = 0.07098536585365856
     out = financial.wacc(loan_frac, rroi, rint, inflation_rate, tax_rate)
-    assert_allclose(expected, out)
+    assert_allclose(expected, out)