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.
Further information about the design of PhilDB can be found in the paper: PhilDB: the time series database with built-in change logging. That paper explores existing time series database solutions, discusses the motivation for PhilDB, describes the architecture and philosophy of the PhilDB software, and includes an evaluation between InfluxDB, PhilDB, and SciDB.
Requires Python 3.7 or greater (mostly tested on Mac OSX and Linux). Test suite runs on Linux using Travis CI with Python 3.6, 3.7, and 3.8. Test suite runs on Windows using Appveyor with Python 3.7.
All the python dependencies are recorded in the python_requirements file.
PhilDB is pip installable.
The latest stable version can be installed from pypi with:
pip install phildb
The latest stable version can also be installed from conda with:
conda install -c amacd31 phildb
The latest development version can be installed from github with:
pip install git+https://github.com/amacd31/phildb.git@dev
The latest development version can be installed from conda with:
conda install -c amacd31/label/dev phildb
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:
Test everything is working:
Build the documentation:
View the generated documentation at doc/build/html/index.html
For additional details see the INSTALL file.
Create a new PhilDB
Open the newly created PhilDB
If using the development environment built with make, Load it along with adding PhilDB tools to your path:
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 Example.ipynb located in examples/acorn-sat 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.
Bureau of Meterology observations example.ipynb located in examples/bom_observations 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 Example.ipynb located in examples/hrs 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.
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