Examples

This page provides some short examples and example applications in Jupyter Notebooks. The following snippets show typical usage.

The general idea is to first define the connector object. This object manages the communication between the user and the data store. The the next step is to pass that connector to the PastaStore to access all of the useful methods for building time series models.

In-memory

The following snippet shows how to use PastaStore with in-memory storage of time series and models. This is the simplest implementation because everything is stored in-memory (in dictionaries):

import pastastore as pst

# define dict connector
conn = pst.DictConnect("my_db")

# create project for managing Pastas data and models
store = pst.PastaStore(conn)

Using Pastas

The following snippet shows how to use PastaStore with storage of time series and models on disk as pas-files. This is the simplest implementation that writes data to disk as no external dependencies are required:

import pastastore as pst

# define pas connector
path = "./data/pastas_db"
conn = pst.PasConnector("my_db", path)

# create project for managing Pastas data and models
store = pst.PastaStore(conn)

Using ArcticDB

The following snippet shows how to create an ArcticDBConnector and initialize a PastaStore object:

import pastastore as pst

# define arctic connector
uri = "lmdb://./my_path_here/"
conn = pst.ArcticDBConnector("my_db", uri)

# create project for managing Pastas data and models
store = pst.PastaStore(conn)

Using Arctic

The following snippet shows how to create an ArcticConnector and initialize a PastaStore object. Please note that a MongoDB instance must be running for this to work:

import pastastore as pst

# define arctic connector
connstr = "mongodb://localhost:27017/"
conn = pst.ArcticConnector("my_db", connstr)

# create project for managing Pastas data and models
store = pst.PastaStore(conn)

Using Pystore

To use the PystoreConnector the steps are identical, the only difference is that the user has to provide a path to a location on disk instead of a connection string to a database:

# define pystore connector
path = "./data/pystore"
conn = pst.PystoreConnector("my_db", path)

# create project for managing Pastas data and models
store = pst.PastasProject(conn)

The PastaStore object

The PastaStore object provides useful methods e.g. for creating models and determining which time series are closest to one another. The database read/write/delete methods can be accessed directly from the PastaStore object:

# create a new time series
series = pd.Series(index=pd.date_range("2019", "2020", freq="D"), data=1.0)

# add an observation time series
store.add_oseries(series, "my_oseries", metadata={"x": 100, "y": 200})

# retrieve the oseries
oseries = store.get_oseries("my_oseries")

To create a Pastas time series model use store.create_model(). Note that this does not automatically add the model to the database. To store the model, it has to be explicitly added to the database:

# create a time series model
ml = store.create_model("my_oseries", add_recharge=False)

# add to the database
store.add_model(ml)

# retrieve model from database
ml = store.get_models("my_oseries")

Example Notebooks

The links below link to Jupyter Notebooks with explanation and examples of the usage of the pastastore module: