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Tools for managing Pastas timeseries models

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This module stores Pastas timeseries and models in a database.

Storing timeseries and models in a database allows the user to manage time series and Pastas models on disk, which allows the user to pick up where they left off without having to reload everything.


Install the module with pip install pastastore.

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

For plotting background maps, the contextily and pyproj packages are required. For a full install, including an optional dependency for plotting and labeling data on maps, use: pip install pastastore[full] or pip install .[full] when on MacOS or Linux. Windows users are asked to install rasterio themselves since it often cannot be installed using pip. rasterio is a dependency of contextily. Windows users can install pastastore with the optional labeling package adjustText using pip install pastastore[adjusttext] or .[adjusttext].

Note: There are external dependencies when using the pystore or arctic connectors. To install these dependencies read (see Connector Dependencies section)! since these are not automatically installed.


The following snippets show typical usage. The first step is to define a so-called Connector object. This object contains methods to store time series or models to the database, or read objects from the database.

The following code creates a PasConnector, which uses Pastas JSON-styled ".pas-files" to save models in a folder on your computer (in this case a folder called pastas_db in the current directory).

import pastastore as pst

# create connector instance
conn = pst.PasConnector("my_db", path="./pastas_db")

The next step is to pass that connector to the PastaStore object. This object contains all kinds of useful methods to analyze and visualize time series, and build and analyze models.

# create PastaStore instance
pstore = pst.PastaStore("my_project", conn)

Now the user can add time series, models or analyze or visualize existing objects in the database. Some examples showing the functionality of the PastaStore object are shown below:

import pandas as pd

# load oseries from CSV and add to database
oseries = pd.read_csv("oseries.csv")
pstore.add_oseries(oseries, "my_oseries", metadata={"x": 100_000, "y": 400_000})

# read oseries from database
oseries = pstore.get_oseries("my_oseries")

# view oseries metadata DataFrame

# plot oseries location on map
ax = pstore.maps.oseries()
pstore.maps.add_background_map(ax)  # add a background map

# plot my_oseries time series
ax2 = pstore.plot.oseries(names=["my_oseries"])

# export whole database to a zip file

For more elaborate examples, refer to the Notebooks.

Which Connector should you pick

There are currently four Connectors included in pastastore. Each of the Connectors are briefly described below.

  • PasConnector (works out of the box, preferred choice)
  • DictConnector (works out of the box)
  • ArcticConnector (requires arctic and MongoDB, best performance)
  • PystoreConnector (requires pystore and python-snappy)


For most people the PasConnector is the best choice, as it does not require any other (external) dependencies, and uses human-readable files to store time series and pastas Models on disk.

# requires a name, and path to a folder
conn = pst.PasConnector("my_db", path="./pastas_db")


The DictConnector does not store files on disk, storing everything in memory. This is usually not what you'd want, but it can be useful as a temporary storage container. All "stored" data will be lost if you restart the kernel.

# requires a name
conn = pst.DictConnector("my_temporary_db")


Store data in MongoDB using Arctic. Only works if there is an instance of MongoDB running and the arctic python package is installed. This Connector has the best performance, both in terms of read/write speeds and data compression.

# provide a name and a connection string to a running instance of MongoDB
connstr = "mongodb://localhost:27017/"  # local instance of mongodb
conn = pst.ArcticConnector("my_db", connstr)


Store data on disk as parquet files using compression. Only works if python-snappy and pystore are installed. Does not require separate database software running somewhere, but installation of python-snappy is a little more challenging. Slightly less performant than ArcticConnector, but faster than PasConnector.

# provide a name and a path to a folder on disk
conn = pst.PystoreConnector("my_db", path="./pastas_db")

Connector Dependencies

This module has several dependencies (depending on which connector is used):

If using Dictconnector or PasConnector:

  • No additional dependencies are required.

If using ArcticConnector:

  • Arctic requires MongoDB, e.g. install the Community edition (Windows, MacOS).

  • OR, if you wish to use Docker for running MongoDB see the installation instructions here.

If using PystoreConnector:

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