Removed ref to versions in static files

GeDACCMU/run.py

@@ -63,7 +63,7 @@ def algorithm(output_path, computation_name, config_file, nb_models, seed=None,
     :type logging_level: logging level (https://docs.python.org/3.6/library/logging.html?highlight=logging#levels)
     :param inline_save: indicates if the output files should be updated with the result at each step (default: True). Set to False may enhance performance but at the risk at losing data if the computation is aborted.
     :type inline_save: bool
-    :param algo_name: name of the algorithm. As for now (0.7), the only supported algorithm is 'augkf'.
+    :param algo_name: name of the algorithm. As for now, the only supported algorithm is 'augkf'.
     :type algo_name: str
     :return: CoreStates containing all the results of the computation, the forecasts and analysis.
     :rtype: CoreState, CoreState, CoreState

README.md

@@ -13,7 +13,7 @@ From Olivier Barrois, Nicolas Gillet, Loïc Huder, Yannick Martin and Franck Tho
 
 ### Requirements
 The installation of GeDACCMU requires Python 3 (tested under 3.6) to be installed with the following packages:
-* _setuptools_ (tested under 40.4.3): to be able to run `setup.py`.
+* _setuptools_ (tested against 40.4.3): to be able to run `setup.py`.
 * _numpy_ (at least 1.7): to be able to wrap Fortran files with _f2py_.
 
 The other dependencies will be automatically installed by the next step but are listed here for the sake of completeness:
@@ -64,7 +64,7 @@ The computation is launched by running `run_algo.py`. Several parameters can be
 ##### Scientific parameters
 * `-conf`: the path to the configuration file to use (no default value: must be given)
 * `-m`: the number of realisations to consider (default: sets to 2)
-* `-algo`: the name of the algorithm to use. As for 0.7, the only supported algorithm is **augkf** (Augmented state Kalman Filter implemented in Python) which is the default value.
+* `-algo`: the name of the algorithm to use. As for now, the only supported algorithm is **augkf** (Augmented state Kalman Filter implemented in Python) which is the default value.
 * `-seed`: a number used for the generation of random quantities. Set this to get reproducible results (default: generate a new one at each execution).
 ##### Output files
 * `-path`: path where the folder results will be created (default: `~/GeDACCMU_results/`).