https://github.com/pastas/pastastore
:spaghetti: :convenience_store: Tools for managing timeseries and Pastas models
https://github.com/pastas/pastastore
analysis arctic data-management database groundwater hydrology pandas pastas python timeseries
Last synced: 5 months ago
JSON representation
:spaghetti: :convenience_store: Tools for managing timeseries and Pastas models
- Host: GitHub
- URL: https://github.com/pastas/pastastore
- Owner: pastas
- License: mit
- Created: 2019-11-01T12:32:00.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2026-01-28T17:07:01.000Z (5 months ago)
- Last Synced: 2026-01-29T06:30:38.824Z (5 months ago)
- Topics: analysis, arctic, data-management, database, groundwater, hydrology, pandas, pastas, python, timeseries
- Language: Python
- Homepage: https://pastastore.readthedocs.io
- Size: 12.8 MB
- Stars: 19
- Watchers: 6
- Forks: 6
- Open Issues: 9
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
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# pastastore
This module stores
[Pastas](https://pastas.readthedocs.io/latest/) time series and models in a
database.
Storing time series 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.
## Installation
Install the module with `pip install pastastore`.
For development, clone the repository and install all development, testing, and
documentation dependencies with:
```sh
pip install -e .[dev]
```
For plotting background maps, the `contextily` and `pyproj` packages are
required. For a full install, including optional dependencies for plotting and
labeling data on maps, use: `pip install pastastore[optional]` Windows users
are asked to install `rasterio` themselves since it often cannot be installed
using `pip`. `rasterio` is a dependency of `contextily`.
## Usage
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).
```python
import pastastore as pst
# create connector instance
conn = pst.PasConnector(name="pastas_db", path=".")
```
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.
```python
# create PastaStore instance
pstore = pst.PastaStore(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:
```python
import pandas as pd
import pastas as ps
# 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
pstore.oseries
# 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"])
# create a model with pastas
ml = ps.Model(oseries, name="my_model")
# add model to database
pstore.add_model(ml)
# load model from database
ml2 = pstore.get_models("my_model")
# export whole database to a zip file
pstore.to_zip("my_backup.zip")
```
For more elaborate examples, refer to the
[Notebooks](https://pastastore.readthedocs.io/en/latest/examples.html#example-notebooks).