Pre-release

This release is a pre-release and may not be stable for production use.

PyGazpar

PyGazpar is a Python library for getting natural gas consumption from GrDF French provider.

Their natural gas meter is called Gazpar. It is wireless and transmit the gas consumption once per day.

All consumption data is available on the client account at GrDF Web Site (https://monespace.grdf.fr).

PyGazpar automatically goes through the Web Site and download the consumption data, and make it available in a Python structure.

Installation

Requirements

PyGazpar does not require Selenium and corresponding geckodriver to work.

With the new GrDF web site, it is possible to load the consumption data far easily than before.

PyGazpar uses Poetry for dependency and package management.

Create your virtual environment

$ cd /path/to/my_project_folder/

$ poetry install

PyGazpar installation

Use the package manager pip to install PyGazpar.

pip install pygazpar

You can also download the source code and install it manually.

cd /path/to/pygazpar/

$ poetry install

Usage

Command line:

Standard usage (using Json GrDF API).

$ pygazpar -u 'your login' -p 'your password' -c 'your PCE identifier' --datasource 'json'

Alternate usage (using Excel GrDF document).

$ pygazpar -u 'your login' -p 'your password' -c 'your PCE identifier' -t 'temporary directory where to store Excel file (ex: /tmp)' --datasource 'excel'

Test usage (using local static data files, do not connect to GrDF site).

$ pygazpar -u 'your login' -p 'your password' -c 'your PCE identifier' --datasource 'test'

Library:

Standard usage (using Json GrDF API).

import pygazpar

client = pygazpar.Client(pygazpar.JsonWebDataSource(
    username='your login',
    password='your password')
)

# Returns the list of your PCE identifiers attached to your account.
pce_identifiers = client.get_pce_identifiers()

# Returns the daily and monthly consumptions for the last 60 days on your PCE identifier.
data = client.load_since(pce_identifier='your PCE identifier',
                        last_n_days=60,
                        frequencies=[pygazpar.Frequency.DAILY, pygazpar.Frequency.MONTHLY])

See samples/jsonSample.py file for the full example.

Alternate usage (using Excel GrDF document).

import pygazpar

client = pygazpar.Client(pygazpar.ExcelWebDataSource(
    username='your login',
    password='your password')
)

# Returns the list of your PCE identifiers attached to your account.
pce_identifiers = client.get_pce_identifiers()

# Returns the daily and monthly consumptions for the last 60 days on your PCE identifier.
data = client.load_since(pce_identifier='your PCE identifier',
                        last_n_days=60,
                        frequencies=[pygazpar.Frequency.DAILY, pygazpar.Frequency.MONTHLY])

See samples/excelSample.py file for the full example.

Test usage (using local static data files, do not connect to GrDF site).

import pygazpar

client = pygazpar.Client(pygazpar.TestDataSource())

data = client.load_since(pce_identifier='your PCE identifier',
                        last_n_days=10,
                        frequencies=[pygazpar.Frequency.DAILY, Frequency.MONTHLY])

See samples/testSample.py file for the full example.

Output:

data =>
{
  "daily": [
    {
      "time_period": "13/10/2022",
      "start_index_m3": 15724,
      "end_index_m3": 15725,
      "volume_m3": 2,
      "energy_kwh": 17,
      "converter_factor_kwh/m3": 11.16,
      "temperature_degC": null,
      "type": "Mesur\u00e9",
      "timestamp": "2022-12-13T23:58:35.606763"
    },
    ...
    {
      "time_period": "11/12/2022",
      "start_index_m3": 16081,
      "end_index_m3": 16098,
      "volume_m3": 18,
      "energy_kwh": 201,
      "converter_factor_kwh/m3": 11.27,
      "temperature_degC": -1.47,
      "type": "Mesur\u00e9",
      "timestamp": "2022-12-13T23:58:35.606763"
    }
  ],
  "monthly": [
    {
      "time_period": "Novembre 2022",
      "start_index_m3": 15750,
      "end_index_m3": 15950,
      "volume_m3": 204,
      "energy_kwh": 2227,
      "timestamp": "2022-12-13T23:58:35.606763"
    },
    {
      "time_period": "D\u00e9cembre 2022",
      "start_index_m3": 15950,
      "end_index_m3": 16098,
      "volume_m3": 148,
      "energy_kwh": 1664,
      "timestamp": "2022-12-13T23:58:35.606763"
    }
  ]
}

Limitation

PyGazpar relies on how GrDF Web Site is built.

Any change in the Web site may break this library.

We expect in close Future that GrDF makes available an open API from which we can get safely their data.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT

Project status

PyGazpar has been initiated for integration with Home Assistant.

Corresponding Home Assistant integration custom component is available here.

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