added test

examples/KWK_process.py

@@ -100,6 +100,7 @@
         # compute and plot tidal indicators
         dict_wltidalindicators = kw.calc_wltidalindicators(df_meas=df_meas_todate, min_coverage=min_coverage)
         dict_HWLWtidalindicators = kw.calc_HWLWtidalindicators(df_ext=df_ext_todate, min_coverage=min_coverage)
+        dict_HWLW_springneap = kw.calc_HWLW_springneap(df_ext=df_ext_todate, min_coverage=min_coverage)
 
         # add hat/lat
         hat, lat = kw.calc_hat_lat_frommeasurements(df_meas_todate)
@@ -108,6 +109,7 @@
 
         # merge dictionaries
         dict_wltidalindicators.update(dict_HWLWtidalindicators)
+        dict_wltidalindicators.update(dict_HWLW_springneap)
 
         # csv for yearlymonthly indicators
         for key in ['wl_mean_peryear','wl_mean_permonth']:
@@ -119,7 +121,7 @@
         fig.savefig(os.path.join(dir_indicators,f'kw{year_slotgem}-tidalindicators-{current_station}.png'))
 
 
-
+    continue
 
     #### SLOTGEMIDDELDEN
     # TODO: more data is needed for proper working of fitting for some stations (2011: BAALHK, BRESKVHVN, GATVBSLE, SCHAARVDND) >> still after linear?

kenmerkendewaarden/havengetallen.py

@@ -74,7 +74,7 @@ def calc_havengetallen(
     raise_extremes_with_aggers(df_ext)
     df_ext_10y = crop_timeseries_last_nyears(df=df_ext, nyears=10)
 
-    # check if coverage is high enough for havengetallen
+    # check if coverage is high enough
     if min_coverage is not None:
         check_min_coverage_extremes(df_ext=df_ext_10y, min_coverage=min_coverage)
 
@@ -96,8 +96,12 @@ def calc_HWLW_springneap(
     raise_extremes_with_aggers(df_ext)
 
     # TODO: moonculminations cannot be computed before 1900
-    df_ext = df_ext.loc["1901":]
+    if df_ext.index.min().year < 1901:
+        logger.warning("calc_HWLW_springneap() only supports timestamps after 1900 "
+                       "all older data will be ignored")
+        df_ext = df_ext.loc["1901":]
 
+    # check if coverage is high enough
     if min_coverage is not None:
         check_min_coverage_extremes(df_ext=df_ext, min_coverage=min_coverage)
 
@@ -127,10 +131,10 @@ def calc_HWLW_springneap(
 
     # merge in dict
     dict_hwlw_springneap = {
-        "hw_spring_peryear": hw_spring_peryear,
-        "lw_spring_peryear": lw_spring_peryear,
-        "hw_neap_peryear": hw_neap_peryear,
-        "lw_neap_peryear": lw_neap_peryear,
+        "HW_spring_peryear": hw_spring_peryear,
+        "LW_spring_peryear": lw_spring_peryear,
+        "HW_neap_peryear": hw_neap_peryear,
+        "LW_neap_peryear": lw_neap_peryear,
     }
     return dict_hwlw_springneap
 

tests/test_havengetallen.py

@@ -152,6 +152,35 @@ def test_calc_havengetallen_toolittle_data(df_ext_12_2010_2014):
     assert "coverage of some years is lower than min_coverage" in str(e.value)
 
 
+@pytest.mark.unittest
+def test_calc_HWLW_springneap(df_ext_12_2010_2014):
+    dict_spnp = kw.calc_HWLW_springneap(df_ext_12_2010_2014)
+    df_columns = ['HW_spring_peryear', 'LW_spring_peryear', 
+                  'HW_neap_peryear', 'LW_neap_peryear']
+    assert set(dict_spnp.keys()) == set(df_columns)
+
+    for key in df_columns:
+        years_act = dict_spnp[key].index.year.tolist()
+        years_exp = [2010, 2011, 2012, 2013, 2014]
+        assert years_act == years_exp
+
+    vals_act = dict_spnp['HW_spring_peryear'].values
+    vals_exp = np.array([1.33551724, 1.28111111, 1.29563636, 1.33185185, 1.37745455])
+    assert np.allclose(vals_act, vals_exp)
+
+    vals_act = dict_spnp['LW_spring_peryear'].values
+    vals_exp = np.array([-0.59724138, -0.6212963, -0.61872727, -0.60407407, -0.60145455])
+    assert np.allclose(vals_act, vals_exp)
+
+    vals_act = dict_spnp['HW_neap_peryear'].values
+    vals_exp = np.array([0.83887097, 0.95854839, 0.86225806, 0.87903226, 0.9696875])
+    assert np.allclose(vals_act, vals_exp)
+
+    vals_act = dict_spnp['LW_neap_peryear'].values
+    vals_exp = np.array([-0.62919355, -0.46758065, -0.5633871 , -0.60193548, -0.51421875])
+    assert np.allclose(vals_act, vals_exp)
+
+
 @pytest.mark.unittest
 def test_calc_HWLW_culmhr_summary_tidalcoeff(df_ext_12_2010):
     """