Metadata-Version: 2.1
Name: hydropandas
Version: 0.3.6
Summary: hydropandas module by Artesia
Home-page: https://github.com/ArtesiaWater/hydropandas
Author: Artesia
License: MIT
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        # hydropandas
        
        The hydropandas module is a Python package for reading timeseries data into 
        DataFrames. 
        
        The Hydropandas package allows users to manipulate data using 
        all of the wonderful features included in pandas extented with custom methods and attributes related to the timeseries. The hydropandas 
        module extends pandas.DataFrame with extra functionality and stores metadata 
        related to the type of measurements.
        
        ## Installation
        
        Install the module with pip:
        
        `pip install hydropandas`
        
        Hydropandas requires `numpy`, `scipy`, `matplotlib`, `pandas`, `geopandas`, 
        `requests` and `zeep`. 
        
        For some functionality additional packages are required:
        
        -   `pastastore`: for reading or storing data from PastaStore
        -   `bokeh`, `branca`, `folium`: for interactive maps
        -   `flopy`: for reading data from MODFLOW models
        -   `xarray`: for loading data from REGIS
        
        For installing in development mode, clone the repository and install by
        typing `pip install -e .` from the module root directory.
        
        If you have trouble installing hydropandas, refer to the 
        [Dependencies section](#dependencies) below.
        
        ## Example usage
        
        Importing a single DINO csv file:
        
        ```python
        import hydropandas as hpd
        fname = './tests/data/2019-Dino-test/Grondwaterstanden_Put/B33F0080001_1.csv'
        gw = hpd.GroundwaterObs.from_dino(fname=fname, verbose=True)
        ```
        
        Or for a zipfile:
        
        ```python
        import hydropandas as hpd
        dinozip = './tests/data/2019-Dino-test/dino.zip'
        dino_gw = hpd.ObsCollection.from_dino(dirname=dinozip,
                                              subdir='Grondwaterstanden_Put',
                                              suffix='1.csv',
                                              ObsClass=hpd.GroundwaterObs,
                                              keep_all_obs=False,
                                              verbose=False)
        ```
        
        ## The Obs class
        
        The Obs class holds the measurements and metadata for one timeseries. There are 
        currently 5 specific Obs classes for different types of measurements:
        
        -   GroundwaterObs: for groundwater measurements
        -   GroundwaterQualityObs: for groundwater quality measurements
        -   WaterlvlObs: for surface water level measurements
        -   ModelObs: for hydropandas from a MODFLOW model
        -   KnmiObs: for (daily) KNMI hydropandas
        
        Each of these Obs classes is essentially a pandas DataFrame with additional 
        methods and attributes related to the type of measurement that it holds. 
        The classes also contain specific methods to read data from specific sources.
        
        ## The ObsCollection class
        
        The ObsCollection class, as the name implies, represents a collection of Obs 
        classes, e.g. 10 timeseries of the groundwater level in a certain area. The 
        ObsCollection is also a pandas DataFrame in which each timeseries is stored 
        in a different row. Each row contains metadata (e.g. latitude and longitude 
        of the observation point) and the Obs object (DataFrame) that holds the 
        measurements. It is recommended to let an ObsCollection contain only one Obs 
        type, e.g. to create an ObsCollection for 10 GroundwaterObs, and a separate 
        ObsCollection for 5 KnmiObs.
        
        Like the Obs class, the ObsCollection class contains a bunch of methods for 
        reading data from different sources. See the next section for supported data 
        sources.
        
        ## Supported data sources
        
        Currently supported datasources that can be read:
        
        -   FEWS PI-XML
        -   [DINO](https://www.dinoloket.nl) csv
        -   WISKI csv
        -   Artesia Fieldlogger for [Android](https://play.google.com/store/apps/details?id=nl.artesia.fieldlogger&hl=en) and [iOS](https://apps.apple.com/nl/app/fieldlogger/id924565721)
        -   [Pastas](https://github.com/pastas/pastas) projects (deprecated)
        -   [Pastastore](https://github.com/pastas/pastastore), for managing Pastas timeseries and models
        -   [PyStore](https://github.com/ranaroussi/pystore), a fast datastore for pandas timeseries
        -   [Arctic](https://github.com/man-group/arctic), a timeseries / dataframe database that sits atop MongoDB
        -   [KNMI](https://www.knmi.nl/kennis-en-datacentrum/achtergrond/data-ophalen-vanuit-een-script) data
        -   MODFLOW groundwater models
        -   IMOD groundwater models
        
        ObsCollection can be exported to:
        
        -   Artesia Fieldlogger
        -   Shapefile
        -   Pastas projects (deprecated)
        -   Pastastore
        -   Arctic
        -   Pystore
        
        ## Dependencies
        
        Hydropandas (indirectly) uses some packages that cannot be installed 
        automatically with `pip` on Windows. These packages are:
        
        -   GDAL
        -   Fiona
        -   Shapely
        -   Python-snappy
        -   Fastparquet
        
        If you do not have these packages already it is recommended to first try 
        installing them with `conda install <pkg>`. Otherwise, read the instructions 
        below how to install them manually.
        
        Download the packages from [Christoph Gohlke's website](https://www.lfd.uci.edu/~gohlke/pythonlibs). 
        Use CTRL+F to find the download link on the page. Be sure to download the 
        correct version of the package. The Python version should match your Python 
        version. Also the architecture should match (i.e. 64bits vs 32bits). 
        For example:
        
        -   GDAL-3.1.4-cp38-cp38-win_amd64.whl
        
        This is the GDAL version for Python 3.8 (as can be seen from the cp38 in the 
        name), for 64-bits Python (as derived from the amd64 in the name).
        
        Once you have downloaded the correct files, navigate to the directory in which 
        you saved your downloads. Now type the following commands (the order is 
        important):
        
        1.  `pip install GDAL-3.1.4-cp38-cp38-win_amd64.whl`
        2.  `pip install Fiona-1.8.17-cp38-cp38-win_amd64.whl`
        3.  `pip install Shapely-1.7.1-cp38-cp38-win_amd64.whl`
        4.  `pip install python_snappy-0.5.4-cp38-cp38-win_amd64.whl`
        5.  `pip install fastparquet-0.4.1-cp38-cp38-win_amd64.whl`
        
        After you've done this you can install hydropandas using 
        `pip install hydropandas`.
        
        ## Authors
        
        -   Onno Ebbens, Artesia
        -   Ruben Caljé, Artesia
        -   Davíd Brakenhoff, Artesia
        
Platform: Windows
Platform: Mac OS-X
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Other Audience
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
