reverted formatting

Deltares-research · Oct 8, 2024 · 017d4d7 · 017d4d7
1 parent b974eed
commit 017d4d7
Showing 1 changed file with 32 additions and 192 deletions.
diff --git a/tests/test_tidalindicators.py b/tests/test_tidalindicators.py
@@ -90,70 +90,21 @@ def test_calc_wltidalindicators(df_meas_2010_2014):
     wl_mean_peryear_expected = np.array(
         [0.07960731, 0.08612119, 0.0853051, 0.07010864, 0.10051922]
     )
-    wl_mean_permonth_expected = np.array(
-        [
-            -0.00227151,
-            0.089313,
-            0.04443996,
-            -0.03440509,
-            -0.00206317,
-            0.04431481,
-            0.03877688,
-            0.18267697,
-            0.13494907,
-            0.18367832,
-            0.15928009,
-            0.11707661,
-            0.1087836,
-            0.02535962,
-            -0.09558468,
-            -0.0255162,
-            -0.00076165,
-            0.05667361,
-            0.11056228,
-            0.13890681,
-            0.1495,
-            0.11866711,
-            0.07253009,
-            0.36550851,
-            0.22046819,
-            -0.10208094,
-            -0.07221102,
-            0.02279167,
-            0.02424507,
-            0.05409954,
-            0.09238127,
-            0.08972894,
-            0.15472222,
-            0.16913082,
-            0.19712963,
-            0.1639897,
-            0.05744176,
-            -0.0134375,
-            -0.10685036,
-            -0.00822222,
-            0.05911066,
-            0.019875,
-            0.02540995,
-            0.07570565,
-            0.12776389,
-            0.17321909,
-            0.23108102,
-            0.19502688,
-            0.06281138,
-            0.08588046,
-            -0.00553763,
-            0.03490278,
-            0.03113575,
-            0.03134954,
-            0.10553763,
-            0.16540771,
-            0.12535648,
-            0.20802195,
-            0.10014352,
-            0.25624552,
-        ]
-    )
+    # fmt: off
+    wl_mean_permonth_expected = np.array([
+        -0.00227151,  0.089313  ,  0.04443996, -0.03440509, -0.00206317,
+         0.04431481,  0.03877688,  0.18267697,  0.13494907,  0.18367832,
+         0.15928009,  0.11707661,  0.1087836 ,  0.02535962, -0.09558468,
+        -0.0255162 , -0.00076165,  0.05667361,  0.11056228,  0.13890681,
+         0.1495    ,  0.11866711,  0.07253009,  0.36550851,  0.22046819,
+        -0.10208094, -0.07221102,  0.02279167,  0.02424507,  0.05409954,
+         0.09238127,  0.08972894,  0.15472222,  0.16913082,  0.19712963,
+         0.1639897 ,  0.05744176, -0.0134375 , -0.10685036, -0.00822222,
+         0.05911066,  0.019875  ,  0.02540995,  0.07570565,  0.12776389,
+         0.17321909,  0.23108102,  0.19502688,  0.06281138,  0.08588046,
+        -0.00553763,  0.03490278,  0.03113575,  0.03134954,  0.10553763,
+         0.16540771,  0.12535648,  0.20802195,  0.10014352,  0.25624552])
+    # fmt: on
     assert np.allclose(wl_stats["wl_mean_peryear"].values, wl_mean_peryear_expected)
     assert np.allclose(wl_stats["wl_mean_permonth"].values, wl_mean_permonth_expected)
 
@@ -285,134 +236,23 @@ def test_calc_wltidalindicators_ext(df_ext_12_2010_2014):
     assert np.allclose(ext_stats["HW_mean_peryear"].values, hw_mean_peryear_expected)
     assert np.allclose(ext_stats["LW_mean_peryear"].values, lw_mean_peryear_expected)
 
-    hw_monthmax_permonth_expected = np.array(
-        [
-            1.94,
-            1.89,
-            1.86,
-            1.55,
-            1.74,
-            1.58,
-            1.54,
-            2.07,
-            2.11,
-            2.06,
-            1.9,
-            1.75,
-            1.69,
-            1.82,
-            1.49,
-            1.39,
-            1.4,
-            1.71,
-            1.72,
-            1.66,
-            1.69,
-            1.59,
-            2.03,
-            2.47,
-            2.31,
-            1.63,
-            1.64,
-            1.61,
-            1.44,
-            1.51,
-            1.52,
-            1.87,
-            1.71,
-            1.72,
-            1.86,
-            2.07,
-            1.87,
-            1.83,
-            1.53,
-            1.51,
-            1.62,
-            1.53,
-            1.52,
-            1.41,
-            2.08,
-            1.98,
-            2.07,
-            3.03,
-            1.76,
-            1.82,
-            1.61,
-            1.73,
-            1.48,
-            1.48,
-            1.62,
-            1.71,
-            1.58,
-            2.77,
-            1.6,
-            1.92,
-        ]
-    )
-    lw_monthmin_permonth_expected = np.array(
-        [
-            -1.33,
-            -1.05,
-            -1.05,
-            -1.06,
-            -1.12,
-            -1.11,
-            -1.07,
-            -0.92,
-            -0.96,
-            -0.99,
-            -1.01,
-            -1.08,
-            -1.16,
-            -1.17,
-            -1.21,
-            -0.98,
-            -1.1,
-            -0.98,
-            -0.97,
-            -0.94,
-            -1.04,
-            -1.22,
-            -0.94,
-            -1.21,
-            -1.22,
-            -1.32,
-            -1.22,
-            -1.04,
-            -1.18,
-            -0.95,
-            -1.05,
-            -1.0,
-            -0.9,
-            -0.81,
-            -1.03,
-            -1.21,
-            -1.11,
-            -1.65,
-            -1.37,
-            -1.11,
-            -1.11,
-            -1.05,
-            -0.98,
-            -1.07,
-            -0.88,
-            -1.05,
-            -1.15,
-            -1.07,
-            -1.32,
-            -1.31,
-            -1.21,
-            -1.08,
-            -1.0,
-            -1.03,
-            -1.07,
-            -0.83,
-            -0.98,
-            -0.97,
-            -0.99,
-            -1.3,
-        ]
-    )
+    # fmt: off
+    hw_monthmax_permonth_expected = np.array([
+        1.94, 1.89, 1.86, 1.55, 1.74, 1.58, 1.54, 2.07, 2.11, 2.06, 1.9 ,
+        1.75, 1.69, 1.82, 1.49, 1.39, 1.4 , 1.71, 1.72, 1.66, 1.69, 1.59,
+        2.03, 2.47, 2.31, 1.63, 1.64, 1.61, 1.44, 1.51, 1.52, 1.87, 1.71,
+        1.72, 1.86, 2.07, 1.87, 1.83, 1.53, 1.51, 1.62, 1.53, 1.52, 1.41,
+        2.08, 1.98, 2.07, 3.03, 1.76, 1.82, 1.61, 1.73, 1.48, 1.48, 1.62,
+        1.71, 1.58, 2.77, 1.6 , 1.92])
+    lw_monthmin_permonth_expected = np.array([
+        -1.33, -1.05, -1.05, -1.06, -1.12, -1.11, -1.07, -0.92, -0.96,
+        -0.99, -1.01, -1.08, -1.16, -1.17, -1.21, -0.98, -1.1 , -0.98,
+        -0.97, -0.94, -1.04, -1.22, -0.94, -1.21, -1.22, -1.32, -1.22,
+        -1.04, -1.18, -0.95, -1.05, -1.  , -0.9 , -0.81, -1.03, -1.21,
+        -1.11, -1.65, -1.37, -1.11, -1.11, -1.05, -0.98, -1.07, -0.88,
+        -1.05, -1.15, -1.07, -1.32, -1.31, -1.21, -1.08, -1.  , -1.03,
+        -1.07, -0.83, -0.98, -0.97, -0.99, -1.3 ])
+    # fmt: on
     assert np.allclose(
         ext_stats["HW_monthmax_permonth"].values, hw_monthmax_permonth_expected
     )