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Timeseries database

Project description

PhilDB project

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Timeseries database project: For storing potentially changing timeseries data. For example hydrological data, like streamflow data, where the timeseries may be revised as quality control processes improve the recorded dataset over time.

PhilDB should be capable of storing data at any frequency supported by Pandas. At this time only daily data has been extensively tested with some limited sub-daily usage.

Dependencies

Requires Python 2.7 or greater (mostly tested with Python 2.7 and Python 3.4 on Mac OSX and Linux). Test suite runs on Travis CI with Python 2.7, 3.4 and 3.5.

All the python dependencies are recorded in the python_requirements file.

Installation

PhilDB is pip installable.

The latest stable version can be installed from pypi with:

pip install phildb

The latest development version can be installed from github with:

pip install git+https://github.com/amacd31/phildb.git@dev

Development environment

A number of processes for a development environment with tests and documentation generation have been automated in a Makefile.

The virtualenv package can be used to create an isolated install of required Python packages.

Create a virtual environment with dependencies installed:

make venv

Test everything is working:

make test

Build the documentation:

make docs

View the generated documentation at doc/build/html/index.html

For additional details see the INSTALL file.

Usage

Create a new PhilDB

phil-create new_tsdb

Open the newly created PhilDB

phil new_tsdb

If using the development environment built with make, Load it along with adding PhilDB tools to your path:

. load_env

Examples

See the examples directory for code on setting up test phil databases with different data sets. Each example comes with a README file outlining the steps to acquire some data and load it. The loading scripts in each example can be used as a basis for preparing a timeseries database and loading it with data.

The examples/hrs/ example also contains an example script (autocorr.py) for processing the HRS data using phildb. The script calculates auto-correlation for all the streamflow timeseries in the HRS dataset.

Presently there are three sets of example code, acorn-sat, bom_observations, and hrs.

ACORN-SAT

Located in examples/acorn-sat this example demonstrates loading minimum and maximum daily temperature records for 112 stations around Australia.

The dataset used in this example is the Australian Climate Observations Reference Network – Surface Air Temperature (ACORN-SAT) as found on the Australian Bureau of Meteorology website ACORN-SAT website.

BOM Observations

Located in examples/bom_observations this example demonstrates loading half hourly air temperature data from a 72 hour observations JSON file.

The data used in this example is a 72 hour observations JSON file from the Australian Bureau of Meteorology website (e.g. JSON file as linked on this page: Sydney Airport observations

HRS

Located in examples/hrs this example demonstrates loading daily streamflow data for 221 streamflow stations around Australia.

The dataset used in this example is the Hydrologic Reference Stations (HRS) dataset as found on the Australian Bureau of Meteorology website HRS website.

This example also includes a script to calculate the auto-correlation for all the streamflow timeseries in the HRS dataset.

Project details


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