214 fix acceptance tests

README.md

@@ -9,11 +9,11 @@
 
 # hatyan
 
-A Python package for harmonic tidal analysis and prediction, based on the FORTRAN version and developed for Rijkswaterstaat. Hatyan contains the methods to derive water level extremes (high and low waters) and several other water level indicators (Kenmerkende Waarden). Furthermore, hatyan provides easier access to Rijkswaterstaat data via their data distribution layer (DataDistributieLaag, DDL).
+A Python package for harmonic tidal analysis and prediction, based on the FORTRAN version and developed for Rijkswaterstaat. Hatyan also contains methods to derive water level extremes (high and low waters) and it is a dependency to derive the [Kenmerkende Waarden](https://github.com/Deltares-research/kenmerkendewaarden).
 
 ## Information
 
 - install with ``pip install hatyan -U``
-- [online documentation](https://deltares.github.io/hatyan) with contributing guide, tutorials/examples, API reference and a convenient search box.
+- [online documentation](https://deltares.github.io/hatyan) with contributing guide, tutorials/examples and an API reference.
 - [docs folder](https://github.com/Deltares/hatyan/tree/main/docs) with background information
 - Bug or feature request? Create a [GitHub issue](https://github.com/Deltares/hatyan/issues)

tests/examples/WMCN_tidescourse.py

@@ -18,7 +18,7 @@
 
 
 # prediction M2 / S2, spring/neap cycle
-if 0:
+if 1:
     dir_testdata = 'C:\\DATA\\hatyan_data_acceptancetests'
 
     stat_list = ['HOEKVHLD']#,'DENHDR','IJMDBTHVN'] #'K13APFM'
@@ -313,7 +313,7 @@
 
 
 
-if 1:
+if 0:
     import xarray as xr
     data_nc_TS = xr.open_dataset(r'p:\1204257-dcsmzuno\1980-2017\DCSM-FM\A03_FES2014_GTSM_H1H2\DFM_OUTPUT_DCSM-FM_0_5nm\DCSM-FM_0_5nm_0000_his.nc')
     data_nc_tide = xr.open_dataset(r'p:\1204257-dcsmzuno\1980-2017\DCSM-FM\A03_FES2014_GTSM_H1H2_astro\DFM_OUTPUT_DCSM-FM_0_5nm\DCSM-FM_0_5nm_0000_his.nc')

tests/examples/astro2022_2cm_diff_rounding_error.py

@@ -8,6 +8,7 @@
 import os
 import pandas as pd
 import datetime as dt
+import ddlpy
 import hatyan
 hatyan.close('all')
 
@@ -16,7 +17,9 @@
 
 dir_meas = r'p:\11208031-010-kenmerkende-waarden-k\work\measurements_wl_18700101_20220101_dataTKdia'
 
-for pred_year in [2019, 2020, 2021, 2022, 2023]:
+# year_list = [2019, 2020, 2021, 2022, 2023]
+year_list = [2019, 2023]
+for pred_year in year_list:
 
     for current_station in ['HOEKVHLD']:#,'DORDT']:
 
@@ -32,15 +35,15 @@
         file_astro = os.path.join(r'c:\Users\veenstra\Downloads',f'astro_{current_station}_{pred_year}.pkl')
         if not os.path.exists(file_astro):
             print('retrieving DDL catalog')
-            catalog_dict = hatyan.get_DDL_catalog(catalog_extrainfo=['WaardeBepalingsmethoden','MeetApparaten','Typeringen'])
-            print('...done')
+            locations = ddlpy.locations()
+            bool_station = locations.index.isin([current_station])
+            bool_grootheid = locations["Grootheid.Code"].isin(["WATHTBRKD"])
+            bool_groepering = locations["Groepering.Code"].isin(["NVT"])
+            selected = locations.loc[bool_station & bool_grootheid & bool_groepering]
 
-            cat_locatielijst = catalog_dict['LocatieLijst']
-            station_dict = cat_locatielijst[cat_locatielijst['Code']==current_station].iloc[0]
+            measurements = ddlpy.measurements(selected.iloc[0], start_date=tstart_pred, end_date=tstop_pred)
 
-            ts_astro, metadata, stationdata = hatyan.get_DDL_data(station_dict=station_dict,tstart_dt=tstart_pred,tstop_dt=tstop_pred,tzone='UTC+01:00',
-                                                                  meta_dict={'Grootheid.Code':'WATHTBRKD','Groepering.Code':'NVT'},allow_multipleresultsfor=['WaardeBepalingsmethode'])
-            ts_astro['values'] = ts_astro['values']/100 #convert from cm to m
+            ts_astro = hatyan.ddlpy_to_hatyan(measurements)
             ts_astro.index = ts_astro.index.tz_localize(None)
             ts_astro.to_pickle(file_astro)