Metadata-Version: 2.1
Name: HelioSat
Version: 0.4.10
Summary: UNKNOWN
Home-page: https://github.com/ajefweiss/HelioSat
Author: Andreas J. Weiss
Author-email: andreas.weiss@oeaw.ac.at
License: UNKNOWN
Description: HelioSat
        ========
        
        A simple and small python package for handling and processing heliospheric satellite data. The current primary features are automatic data downloading & crude processing for DSCOVR, MES, PSP, STA, STB, VEX and WIND (plus BEPI and SOLO once data products are publicly available). Furthermore all related and required SPICE kernels are downloaded automatically.
        
        Installation
        ------------
        
        Install the latest version manually using `git`:
        
            git clone https://github.com/ajefweiss/HelioSat
            cd HelioSat
            pip install .
        
        or from PyPi with `pip install HelioSat`.
        
        Basic Usage
        -----------
        
        Import the `heliosat` module and create a satellite instance:
        
            import heliosat
        
            wind_sat = heliosat.WIND()
        
        This will automatically download and load any required SPICE kernels (using `spiceypy`). Note that
        kernel or data files will be stored in `~/.heliosat` by default. As this may use up alot of disk
        space you can alternatively change the default path by setting the environment variable `HELIOSAT_DATAPATH`.
        
        Querying raw data in a certain time window (any tz-unaware datetime objects are assumed to be UTC) can then be done using:
        
            import datetime
        
            t_start = datetime.datetime(2010, 1, 1)
            t_end = datetime.datetime(2010, 1, 3)
        
            t_raw, data_raw = wind_sat.get_data_raw(t_start, t_end, "mfi_h0")
        
        Alternatively processed data at specific times in a specific reference frame can be queried using:
        
            # observer datetimes for an entire week
            obs = [t_start + datetime.timedelta(minutes=12 * i) for i in range(0, 7 * 24 * 5)]
        
            # smoothing using a gaussian kernel and a smoothing scale of 5 minutes, the data is also cached
            t_sm, data_sm = heliosat.get_data(obs, "mfi_h0", frame="J2000", smoothing="kernel", cache=True,
                                              return_datetimes=True, remove_nans=True)
        
        If particular data columns are not being read out but are present within the data files, they can be added by setting the `extra_columns` parameter.
Keywords: astrophysics,solar physics,space weather
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Description-Content-Type: text/markdown
