hydropandas

The hydropandas module is a Python package for reading timeseries data into DataFrames. The basic idea behind the package is to allow users to manipulate data using all of the wonderful features included in pandas, but to also allow the addition of 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 by typing pip install hydropandas.

Please note that some of the dependencies cannot be installed automatically on Windows. If you do not have these packages already you can install them manually using the following instructions:

Download these packages from this Christoph Gohlke's website:

• GDAL
• Fiona
• Shapely
• Python-snappy
• Fastparquet

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, open Anaconda Prompt, and 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

For installing in development mode, clone the repository and install by typing pip install -e . from the module root directory.

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 csv
• WISKI csv
• Artesia Fieldlogger for Android and iOS
• Pastas projects (deprecated)
• Pastastore, for managing Pastas timeseries and models
• PyStore, a fast datastore for pandas timeseries
• Arctic, a timeseries / dataframe database that sits atop MongoDB
• KNMI data
• MODFLOW groundwater models
• IMOD groundwater models

ObsCollection can be exported to:

• Artesia Fieldlogger
• Shapefile
• Pastas projects (deprecated)
• Pastastore
• Arctic
• Pystore

Example usage

Importing a single DINO csv file:

import observation as obs
fname = './tests/data/2019-Dino-test/Grondwaterstanden_Put/B33F0080001_1.csv'
gw = obs.GroundwaterObs.from_dino(fname=fname, verbose=True)

Or for a zipfile:

import observation as obs
dinozip = './tests/data/2019-Dino-test/dino.zip'
dino_gw = obs.ObsCollection.from_dino(dirname=dinozip,
                                      subdir='Grondwaterstanden_Put',
                                      suffix='1.csv',
                                      ObsClass=obs.GroundwaterObs,
                                      keep_all_obs=False,
                                      verbose=False)

Authors

• Onno Ebbens, Artesia
• Ruben Caljé, Artesia
• Davíd Brakenhoff, Artesia