Smart Citizen Data
=======
[](https://zenodo.org/badge/latestdoi/97752018)
[](https://mybinder.org/v2/gh/fablabbcn/smartcitizen-data-framework/master?filepath=%2Fexamples%2Fnotebooks)
[](https://badge.fury.io/py/scdata)
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](https://plotly.com/) or [uplot](https://leeoniya.github.io/uPlot)
- Generate analysis reports in html or pdf and upload them to [Zenodo](http://zenodo.org)
A full documentation of the framework is detailed in [the Smart Citizen Docs](https://docs.smartcitizen.me/Data/Data%20Analysis/).
## Installation
You can check it out in the [](https://mybinder.org/v2/gh/fablabbcn/smartcitizen-data-framework/master?filepath=%2Fexamples%2Fnotebooks) before installing if you want. Works with `Python 3.*` (tested until `python 3.9.5`).
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
```
Install `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](http://zenodo.org), you will need to fill set an environment variable called `ZENODO_TOKEN` in your environment. You can get more instructions [here](https://docs.smartcitizen.me/Guides/data/Upload%20data%20to%20zenodo/) and with [this example](https://github.com/fablabbcn/smartcitizen-data/blob/master/examples/notebooks/06_upload_to_zenodo.ipynb).
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 `~/.cache/scdata`:
```
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
```
Also, `.env` files will be picked from `~/.cache/scdata`.
### Using with jupyter lab (optional)
It can also be used with `jupyter lab` or `jupyter`. For this [install juypterlab](https://github.com/jupyterlab/jupyterlab).
## Contribute
Issues and PR more than welcome!
## Funding
This work has received funding from the European Union's Horizon 2020 research and innovation program under the grant agreement [No. 689954](https://cordis.europa.eu/project/rcn/202639_en.html)
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"description": "Smart Citizen Data\n=======\n\n[](https://zenodo.org/badge/latestdoi/97752018)\n[](https://mybinder.org/v2/gh/fablabbcn/smartcitizen-data-framework/master?filepath=%2Fexamples%2Fnotebooks)\n[](https://badge.fury.io/py/scdata)\n\nWelcome to **SmartCitizen Data**. This is a data analysis framework for working with sensor data in different ways:\n\n- Interacting with several sensors APIs\n- Clean data, export and calculate metrics\n- Model sensor data and calibrate sensors\n- Generate data visualisations - matplotlib, [plotly](https://plotly.com/) or [uplot](https://leeoniya.github.io/uPlot)\n- Generate analysis reports in html or pdf and upload them to [Zenodo](http://zenodo.org)\n\nA full documentation of the framework is detailed in [the Smart Citizen Docs](https://docs.smartcitizen.me/Data/Data%20Analysis/). \n\n## Installation\n\nYou can check it out in the [](https://mybinder.org/v2/gh/fablabbcn/smartcitizen-data-framework/master?filepath=%2Fexamples%2Fnotebooks) before installing if you want. Works with `Python 3.*` (tested until `python 3.9.5`).\n\nYou can just run:\n\n```\npip install scdata\n```\n\n### Work on the source code\n\nSimply clone the repository with:\n\n```\ngit clone https://github.com/fablabbcn/smartcitizen-data.git\ncd smartcitizen-data\n```\n\nInstall `scdata` package with requirements:\n\n```\npython setup.py install\n```\n\nOr if you want to edit:\n\n```\ncd scdata\npip install --editable .\n```\n\n### Tokens and config\n\nIf you want to upload data to [Zenodo](http://zenodo.org), you will need to fill set an environment variable called `ZENODO_TOKEN` in your environment. You can get more instructions [here](https://docs.smartcitizen.me/Guides/data/Upload%20data%20to%20zenodo/) and with [this example](https://github.com/fablabbcn/smartcitizen-data/blob/master/examples/notebooks/06_upload_to_zenodo.ipynb).\n\nA 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 `~/.cache/scdata`:\n\n```\ndata:\n cached_data_margin: 2\n load_cached_api: true\n reload_metadata: true\n store_cached_api: true\npaths:\n config: /Users/username/.config/scdata\n data: /Users/username/.cache/scdata\n export: /Users/username/.cache/scdata/export\n interim: /Users/username/.cache/scdata/interim\n inventory: ''\n models: /Users/username/.cache/scdata/models\n processed: /Users/username/.cache/scdata/processed\n raw: /Users/username/.cache/scdata/raw\n reports: /Users/username/.cache/scdata/reports\n uploads: /Users/username/.cache/scdata/uploads\nzenodo_real_base_url: https://zenodo.org\nzenodo_sandbox_base_url: http://sandbox.zenodo.org\n```\n\nAlso, `.env` files will be picked from `~/.cache/scdata`.\n\n### Using with jupyter lab (optional)\n\nIt can also be used with `jupyter lab` or `jupyter`. For this [install juypterlab](https://github.com/jupyterlab/jupyterlab).\n\n## Contribute\n\nIssues and PR more than welcome!\n\n## Funding\n\nThis work has received funding from the European Union's Horizon 2020 research and innovation program under the grant agreement [No. 689954](https://cordis.europa.eu/project/rcn/202639_en.html)\n\n\n",
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