Metadata-Version: 2.1
Name: hydropandas
Version: 0.4.2
Summary: hydropandas module by Artesia
Home-page: https://github.com/ArtesiaWater/hydropandas
Author: Artesia
License: MIT
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
License-File: LICENSE

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# hydropandas

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 "observations" from a MODFLOW model
-   KnmiObs: for (daily) KNMI observations

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
-   [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

An ObsCollection can be exported to:

-   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


