hatyan 2.5.64

hatyan is a tidal analysis and prediction tool of Rijkswaterstaat

hatyan

hatyan is a Python program for tidal analysis and prediction, based on the FORTRAN version. Copyright (C) 2019-2021 Rijkswaterstaat. Maintained by Deltares, contact: Jelmer Veenstra ([e-mail]). Source code available at: https://github.com/Deltares/hatyan

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with this program. If not, see https://www.gnu.org/licenses/.

Installation

Install hatyan OPTION 1: Install from pip/github in an existing or new environment:

• optional: download Anaconda 64 bit Python 3.7 (or higher) from https://www.anaconda.com/distribution/#download-section
• open command window (or anaconda prompt)
• optional: conda create --name hatyan_env -c conda-forge python=3.7 git spyder -y (or higher python version)
• optional: conda activate hatyan_env
• python -m pip install hatyan (this installs hatyan and all required packages from PyPI, add a version like ==2.3.0 if you require a specific version. Optionally add --upgrade)
• alternatively: python -m pip install git+https://github.com/Deltares/hatyan (this installs hatyan and all required packages from github, add a tag like @v2.3.0, @main or @development if you require a specific release/branch. Optionally add --upgrade)

Install hatyan OPTION 2: get and install RPM on CentOS/RHEL

• get the latest rpm file (see developer information for building procedure)
• install hatyan on CentOS: rpm -i hatyan_python-2.2.30-1.x86_64.rpm
• upgrade hatyan on CentOS: rpm -U hatyan_python-2.2.31-1.x86_64.rpm
• installing the RPM results in a hatyan command in linux, this activates a Python virtual environment and sets necessary Qt environment variables. It creates a folder with a python environment hatyan_env, doc en tests (/opt/hatyan_python/hatyan_env/) and a file that provides the hatyan command (/usr/bin/hatyan)
• check version: hatyan --version
• test installation: hatyan /opt/hatyan_python/tests/examples/predictie_2019_19Ycomp4Ydia_VLISSGN_interactive.py (or use the hatyan --test shortcut)
• this should result in several interactive figures popping up, described in chapter 5 (Quick start guide) of the hatyan user manual (gebruikershandleiding).
• if you see the message "RuntimeError: Invalid DISPLAY variable", restart the MobaXterm connection and try again.
• the followning warning can be ignored: "QXcbConnection: XCB error: 145 (Unknown), sequence: 171, resource id: 0, major code: 139 (Unknown), minor code: 20". To avoid it, disable the extension RANDR in Mobaxterm settings (Settings > Configuration > X11)

Getting started

• HTML-documentation is available on Github (replace 'main' in the url with any tagname to view older versions)
• background information is available in the docs folder.
• copy the code below to your own script to get started (or run it on Colab).
• for more examples, check the examples folder.

import datetime as dt
import pandas as pd
from netCDF4 import Dataset, num2date
import hatyan
hatyan.close('all')

#defining a list of the components to be analysed (can also be 'half_year' and others, 'year' contains 94 components and the mean H0)
const_list = hatyan.get_const_list_hatyan('year')

#reading and editing time series, results in a pandas DataFrame a 'values' column (water level in meters) and a pd.DatetimeIndex as index (timestamps as datetime.datetime)
file_data_meas = 'http://uhslc.soest.hawaii.edu:80/opendap/rqds/global/hourly/h825a.nc' #Cuxhaven dataset from UHSLC database #os.path.join(r'n:\\Deltabox\\Bulletin\\veenstra\\VLISSGN_waterlevel_20101201_20140101.noos')
times_ext = [dt.datetime(2017,1,1),dt.datetime(2018,12,31)]
timestep_min = 10
ts_data = Dataset(file_data_meas)
ts_data_values = ts_data['sea_level'][0,-18000:]/1000-5 #correct from mm to meters and for 5m offset
ts_data_times = num2date(ts_data['time'][-18000:],units=ts_data['time'].units, only_use_cftime_datetimes=False)
ts_meas = pd.DataFrame({'values':ts_data_values},index=ts_data_times)
#ts_meas = hatyan.resample_timeseries(ts_meas, timestep_min=60) #resampling only works well when timesteps are rounded to seconds
ts_meas = hatyan.crop_timeseries(ts=ts_meas, times_ext=times_ext)

#tidal analysis and plotting of results. commented: saving figure  
comp_frommeas, comp_allyears = hatyan.get_components_from_ts(ts=ts_meas, const_list=const_list, nodalfactors=True, return_allyears=True, fu_alltimes=True, analysis_peryear=True)
fig,(ax1,ax2) = hatyan.plot_components(comp=comp_frommeas, comp_allyears=comp_allyears)
#fig.savefig('components.png')

#tidal prediction and plotting of results. commented: saving figure and writing to netCDF
ts_prediction = hatyan.prediction(comp=comp_frommeas, nodalfactors=True, fu_alltimes=True, times_ext=times_ext, timestep_min=timestep_min)
fig, (ax1,ax2) = hatyan.plot_timeseries(ts=ts_prediction, ts_validation=ts_meas)
ax1.legend(['prediction','measurement','difference','mean of prediction'])
ax2.set_ylim(-0.5,0.5)
#fig.savefig('prediction.png')

#calculation of HWLW and plotting of results. commented: saving figure
ts_ext_meas = hatyan.calc_HWLW(ts=ts_meas)
ts_ext_prediction = hatyan.calc_HWLW(ts=ts_prediction)
fig, (ax1,ax2) = hatyan.plot_timeseries(ts=ts_prediction, ts_validation=ts_meas, ts_ext=ts_ext_prediction, ts_ext_validation=ts_ext_meas)
ax1.set_xlim([dt.datetime(2018,6,1),dt.datetime(2018,7,1)])
ax2.set_ylim(-1,1)
#fig.savefig('prediction_HWLW.png')

fig, ax = hatyan.plot_HWLW_validatestats(ts_ext=ts_ext_prediction, ts_ext_validation=ts_ext_meas)
#fig.savefig('prediction_HWLW_validatestats.png')
#hatyan.write_tsnetcdf(ts=ts_prediction, ts_ext=ts_ext_prediction, station='Cuxhaven', vertref='MSL', filename='prediction.nc')