Metadata-Version: 2.1
Name: tidegravity
Version: 0.4.0b1
Summary: Tide gravitational correction based on I.M. Longman's Formulas for Computing the Tidal Accelerations Due to the Moon and the Sun
Home-page: https://github.com/bradyzp/LongmanTide/
Author: Zachery P. Brady, John R. Leeman
Author-email: bradyzp@dynamicgravitysystems.com
License: UNKNOWN
Download-URL: https://github.com/bradyzp/LongmanTide
Description: Tide Gravity
        ============
        
        tidegravity is a Python library which implements Ivor Longman's scheme for computing the tidal accelerations due to the
        moon and sun, as published by I.M. Longman in the Journal of Geophysical Research, Vol 64, no. 12, 1959
        This can be useful for correcting gravimetric survey data, as the gravitational forces due to the tidal effects of the
        Sun and Moon can be on the order of several hundred microGals, depending on the surveyors location and the relative
        positions of the Sun and Moon to each other, and the surveyor.
        
        Requirements
        ------------
        
        - numpy
        - pandas
        
        The numpy and pandas libraries are required for processing tide corrections, and importing trajectory data for correction
        
        The matplotlib library is an optional requirement and is currently only used in the examples to plot a visual
        representation of the data.
        
        API
        ---
        
        .. role:: py(code)
            :language: python
        
        The following API functions are provided (subject to change in future releases):
        
        * :py:`solve_longman_tide(lat, lon, alt, time)`
        
          Solve for total gravity correction due to Sun/Moon from numpy array inputs
        * :py:`solve_longman_tide_scalar(lat, lon, alt, time)`
        
          Wrapper around solve_longman_tide, accepts single scalar values for lat/lon/alt and a single DateTime object
        * :py:`solve_point_corr(lat, lon, alt, t0, n=3600, increment='S')`
        
          Return tidal correction over a time span defined by t0 with n points at given increment for static (scalar)
          position parameters
        * :py:`solve_tide_df(df, lat='lat', lon='lon', alt='alt')`
        
          Wrapper accepting a pandas DataFrame (df) object as the input, df should have a DatetimeIndex, and lat/lon/alt
          columns. Alternate column names can be provided via parameters, which will then be used to extract components from
          the input DataFrame.
        
        
        References
        ----------
        
        * I.M. Longman "Forumlas for Computing the Tidal Accelerations Due to the Moon
          and the Sun" Journal of Geophysical Research, vol. 64, no. 12, 1959,
          pp. 2351-2355
        * P\. Schureman "Manual of harmonic analysis and prediction of tides" U.S. Coast and Geodetic Survey, 1958
        
        
        Acknowledgements
        ----------------
        
        .. _LongmanTide: https://github.com/jrleeman/LongmanTide
        
        This library is based on the work of John Leeman's LongmanTide Python implementation.
        LongmanTide_
        
        
        Examples
        --------
        
        There are several example scripts in the examples directory illustrating how to use the longmantide solving functions.
        
        Here is a simple demonstration of calculating a correction series for a static latitude/longitude/altitude over a
        specified time period, with intervals of 1 second.
        
        .. code-block:: python
        
            from datetime import datetime
            from tidegravity import solve_point_corr
        
            # Example static data for Denver, January 1, 2018
            lat = 39.7392
            lon = -104.9903
            # Note: West should be entered as a negative longitude value
            alt = 1609.3
            t0 = datetime(2018, 1, 1, 12, 0, 0)
        
            # Calculate corrections for one day (60*60*24 points), with 1 second resolution
            result_df = solve_point_corr(lat, lon, alt, t0, n=60*60*24, increment='S')
        
            # Result is a pandas DataFrame, with a DatetimeIndex, and correction
            # values in the 'total_corr' column i.e.
            corrections = result_df['total_corr'].values
        
            # Plot the corrections using matplotlib
            from matplotlib import pyplot as plt
        
            plt.plot(corrections)
            plt.ylabel('Tidal Correction [mGals]')
            plt.show()
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: Microsoft
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: GIS
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.5.*
Provides-Extra: MPL
