Analysis of sensors and time series data
Smart Citizen Data
Welcome to SmartCitizen Data. This is a data analysis framework for working with sensor data in different ways:
- Interacting with several sensors APIs
- Clean data, export and calculate metrics
- Model sensor data and calibrate sensors
- Generate data visualisations - matplotlib, plotly or uplot
- Generate analysis reports in html or pdf and upload them to Zenodo
A full documentation of the framework is detailed in the Smart Citizen Docs.
You can check it out in the before installing if you want. Works with
Python 3.* (tested until
You can just run:
pip install scdata
Work on the source code
Simply clone the repository with:
git clone https://github.com/fablabbcn/smartcitizen-data.git cd smartcitizen-data
scdata package with requirements:
python setup.py install
Or if you want to edit:
cd scdata pip install --editable .
Tokens and config
If you want to upload data to Zenodo, you will need to fill set an environment variable called
ZENODO_TOKEN in your environment. You can get more instructions here and with this example.
A configuration file is available at
~/.config/scdata/config.yaml, which contains a set of configurable variables to allow or not the local storage of relevant data in the data folder, normally in
data: cached_data_margin: 2 load_cached_api: true reload_metadata: true store_cached_api: true paths: config: /Users/username/.config/scdata data: /Users/username/.cache/scdata export: /Users/username/.cache/scdata/export interim: /Users/username/.cache/scdata/interim inventory: '' models: /Users/username/.cache/scdata/models processed: /Users/username/.cache/scdata/processed raw: /Users/username/.cache/scdata/raw reports: /Users/username/.cache/scdata/reports uploads: /Users/username/.cache/scdata/uploads zenodo_real_base_url: https://zenodo.org zenodo_sandbox_base_url: http://sandbox.zenodo.org
.env files will be picked from
Using with jupyter lab (optional)
It can also be used with
jupyter lab or
jupyter. For this install juypterlab.
Issues and PR more than welcome!
This work has received funding from the European Union's Horizon 2020 research and innovation program under the grant agreement No. 689954
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