https://softwaredefinedbuildings.github.io/smap/
For the fastest way to get a full sMAP stack running with TimescaleDB, see DOCKER_README.md.
For information on running the test suites, see TESTING.md.
To setup a clean environment, create a new python virtual environment with:
virtualenv venv
Before proceeding with installation, make sure you've sourced the virtual environment with source venv/bin/activate.
You must install the dependencies listed in requirements.txt before installing smap. Do this by issuing the following:
pip install -r osx_requirements.txt
After the dependencies are installed, run the installation:
python setup.py install
Full documentation is located under python/doc/en/2.0/.
- Introduction — Architecture overview: sources, archiver, and frontends
- Installation — Installing the sMAP library and dependencies
- Getting Started Tutorial — Start a driver, write a custom driver, send data to the archiver
- Archiver Tutorial — Configuring queries and downloading data
- Core API Reference —
SmapInstance,Timeseries,Collection, andReportingclasses - Internals & Configuration — Config file syntax, server settings, SSL, programmatic usage
- Driver Patterns — Periodic scraping, XML/XSLT transforms, and actuation
- Driver Index — Catalog of all bundled drivers (meters, weather, ISO markets, etc.)
- Archiver Install — Setting up the archiver stack
- Archiver Manual — Step-by-step: readingdb, PostgreSQL, powerdb2, archiver service
- Archiver Query Language — REST API and SQL-like query DSL
- Python Client —
SmapClientfor querying and plotting with numpy/matplotlib - R Client —
RSmappackage for querying and plotting in R
- CLI Tools —
smap-query,smap-tool,smap-monitize,smap-load,smap-load-csv - Metadata Tags — Standard tag names for instrument, location, and custom metadata
- Additional Resources — Papers, talks, and mailing list
