Smurf: System for Modelling with Uncertainty Reduction, and Forecasting
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Article 12 - NEW VERSIONS OF THE AGREEMENT
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Article 13 - GOVERNING LAW AND JURISDICTION
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13.2 Failing an amicable solution within two (2) months as from their occurrence, and unless emergency proceedings are necessary, the disagreements or disputes shall be referred to the Paris Courts having jurisdiction, by the more diligent Party.
Version 1.0 dated 2006-09-05.
Description: ### Smurf: System for Modelling with Uncertainty Reduction, and Forecasting
#### 1) Introduction Smurf is an open source modular system developed in Python for running and cycling data assimilation systems. It is organised around three super classes for the numerical model management, the assimilation schemes and the observation instruments. Any new item can be easily plugged in by defining a child class that will override as many methods as necessary. Non intrusive, Smurf can be used in any domains for numerical models written in any languages.
Smurf is provided under the CeCILL-B license.
#### 2) Installation Smurf runs on python 3.
##### 2.1) Environment The easiest way for installing Smurf, is to create first a python 3 environment:
Choose a path for your environment (replace path_env by your own path)
<pre> python -m venv path_env/pyenv </pre>
Source the environment (this must be done for any new session)
<pre> source path_env/pyenv/bin/activate </pre>
##### 2.2) Installing Smurf
Depending on the purpose, there are three different ways for installing Smurf
##### Demonstration only: The sources are not available
<pre> pip install Smurf-CERFACS </pre>
##### Self purpose development: Developments and modifications can be done on the local repository but cannot be pushed on the remote repository. A path must be chosen where to install Smurf (replace mypath by your own path)
<pre> cd mypath git clone https://gitlab.com/cerfacs/Smurf.git cd Smurf python setup.py develop </pre>
##### Smurf development: To participate to the Smurf development. This option requires to be registred on the Cerfacs Nitrox. A path must be chosen where to install Smurf (replace mypath by your own path)
<pre> cd mypath git clone https://nitrox.cerfacs.fr/globc/Smurf.git cd Smurf python setup.py develop </pre>
#### 3) Version 1.0.1 ##### 3.1) Assimilation scheme
- Ensemble Kalman filter (stochastic)
##### 3.2) Models ###### 3.2.1) Available
- Barbatruc: toy model, https://nitrox.cerfacs.fr/open-source/barbatruc
- Mascaret: Hydraulic 1D, http://www.opentelemac.org/
###### 3.2.2) Available on demand
- Opm: Simulation of porous media processes: https://opm-project.org/
- Pixie: Crowd dynamics and evacuation, https://research.csiro.au/pixie
##### 3.3) Instruments ###### 3.3.1) Available
- Barbametre (Barbatruc): tracer values
- Gauge (Mascaret): limnimetric station
###### 3.3.2) Available on demand
- Chronos (Pixie): chronometre
- Clicker (Pixie): people count
- WellInstrument (Opm): well measurements
###### 3.3.3) On-going work
- Swot (Mascaret): Swot observations * SwotPixelCloud (Mascaret): pixel cloud observations from Swot * SwotRiverNode (Mascaret): river node averaged observations from Swot * SwotRiverReach (Mascaret): river reach averaged observations from Swot
##### 3.4) Contributors ###### Authors
- Isabelle Mirouze, CECI, CNRS UMR 5318
- Sophie Ricci, CECI, CERFACS / CNRS UMR 5318
- Barbatruc: * Antoine Dauptain, CERFACS
- Mascaret * Nicole Goutal, LNHE-EDF / LHSV * Vanessya Laborie, CEREMA / LHSV * Anne-Laure Tiberi, CEREMA
- Opm * Camille Besombes, CERFACS * Corentin Lapeyre, CERFACS * Rabeb Selmi, TOTAL
- Pixie: * Matt Bolger, CSIRO Data61 * Vincent Lemiale, CSIRO Data61
- General purposes: * Antoine Dauptain, CERFACS * Corentin Lapeyre, CERFACS
#### 4) Getting started Testing cases are available in Smurf/testing_case. For a first go with Smurf, the toy model Barbatruc can easily be tried (see Smurf/testing_case/Barbatruc/readme.md). If you installed Smurf using pip, the testing cases for Mascaret and Barbatruc can be downloaded from [here](https://gitlab.com/cerfacs/Smurf/tree/master/testing_case).
#### 5) New features ##### 5.1) New experiment To help configuring a new experiment, templates of configuration files are available in Smurf/templates
- template_conf_expriment.yml: to configure the experiment
- template_conf_assim.yml: to configure the assimilation part
- template_parameter.yml: to define the parameters of the model
##### 5.1) New model To plug in a new model:
Create the new model class (replace newmodel by the name of your model) <pre> cp Smurf/templates/template_new_model.py Smurf/Smurf/models/newmodel.py </pre>
Define the required method following the instructions of the model template
##### 5.2) New observation instrument To plug in a new instrument:
Create the new instrument class (replace newinstrument by the name of your instrument) <pre> cp Smurf/templates/template_new_instrument.py Smurf/Smurf/observations/newinstrument.py </pre>
Define the required method following the instructions of the instrument template
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