updated docs

README.md

@@ -14,7 +14,7 @@ The supported languages are: C (veryfastFVA.c) with wrappers in  MATLAB (VFFVA.m
 
 Please refer to the [documentation](https://vffva.readthedocs.io/en/latest/) and the [UserGuide](UserGuide.md) for veryfastFVA (VFFVA) usage.
 
-Add the project folder to your MATLAB path and save it.
+In MATLAB, add the project folder to your MATLAB path and save it, then use `VFFVA()`. In Python, `import VFFA` to use `VFFVA()`.
 
 For the comparison with fastFVA (FFVA), you can install FFVA [here](http://wwwen.uni.lu/lcsb/research/mol_systems_physiology/fastfva).
 

UserGuide.md

@@ -50,17 +50,21 @@ You can use the provided MATLAB script `convertProblem.m` to convert MATLAB LP p
 
 7. Run VFFVA like the following:
 
-	`mpirun -np a --bind-to none -x OMP_NUM_THREADS=b ./veryfastFVA ./models/c.mps d`
+	`mpirun -np a --bind-to none -x OMP_NUM_THREADS=b ./veryfastFVA ./models/c.mps d e f`
 
 	a: Number of non-memory sharing cores
 
 	b: Number of memory-sharing threads
 
 	c: model name, a selection of models is provided in model folder
 
-	d: optional if not specified scaling (SCAIND) parameter is set to default
+	d: (optional) if not specified scaling (SCAIND) parameter is set to default
 
 	if set to -1, scaling is deactivated
+
+        e: (optional) percentage of objective function to consider
+
+        f: (optional) .csv file containing the indices of the reactions to optimize.
 
 	with openMPI 1.10.2 you might get error messages, launch the application as following:
 
@@ -147,6 +151,5 @@ Regarding VFFVA, please set environment variables `OMP_PROC_BIND=FALSE` and expo
 	- `EmatrixCoupledAnalysisVFFVA_LA.sh`: runs VFFVA on E_Matrix_coupled model and saves loading and analysis time.
 
 #### Important note
-VFFVA is run on a suboptimal objective equal to 90% the optimal objective of the original problem, because with large models, numerical infeasibilities can occur with optimisation percentage
-equal to 100%. You can change the optPerc varibale to the desired value. In the future, this variable
-will be passed in the VFFVA call.
+VFFVA is run by default on a suboptimal objective equal to 90% the optimal objective of the original problem, because with large models, numerical infeasibilities can occur with optimisation percentage
+equal to 100%. You can change the optPerc variable to the desired value.

docs/guide/index.md

@@ -21,10 +21,11 @@ Replace the following variables with your own parameters:
 + OPTPERC: Optimization percentage of the objective value (0-100). The default is 90, where VFFVa will be computed with the objective value set to 90% of the optimal
 objective.
 
-
 + SCAIND: (optional) corresponds to the scaling CPLEX parameter SCAIND and can take the values 0 (equilibration scaling: default), 1(aggressive scaling), -1 (no scaling).
 scaling is usually desactivated with tightly constrained metabolic model such as coupled models to avoid numerical instabilities and large solution times.
 
++ ex: .csv file containing indices of reactions to optimize.
+
 Example: `mpirun -np 2 --bind-to none -x OMP_NUM_THREADS=4 veryfastFVA ecoli_core.mps`
 
 `VFFVA` will perform 2n Linear Programs (LP), where n is the number of reactions in a metabolic model, corresponding to
@@ -77,12 +78,8 @@ Then VFFVA.py can be imported into a Python 3 script  using the following functi
 
 ```
 
-    VFFVA performs Very Fast Flux Variability Analysis (VFFVA). VFFVA is a parallel implementation of FVA that
-    allows dynamically assigning reactions to each worker depending on their computational load
-    Guebila, Marouen Ben. "Dynamic load balancing enables large-scale flux variability analysis." bioRxiv (2018): 440701.
-
     USAGE:
-    minFlux,maxFlux=VFFVA(nCores, nThreads, model, scaling, memAff, schedule, nChunk, optPerc)
+    minFlux,maxFlux=VFFVA(nCores, nThreads, model, scaling, memAff, schedule, nChunk, optPerc, ex)
 
     :param nCores:   Number of non-shared memory cores/machines.
     :param nThreads: Number of shared memory threads in each core/machine.

lib/VFFVA.py

@@ -9,7 +9,7 @@ def VFFVA(nCores, nThreads, model, scaling=0, memAff='none', schedule='dynamic',
     Guebila, Marouen Ben. "Dynamic load balancing enables large-scale flux variability analysis." bioRxiv (2018): 440701.
 
     USAGE:
-    minFlux,maxFlux=VFFVA(nCores, nThreads, model, scaling, memAff, schedule, nChunk, optPerc)
+    minFlux,maxFlux=VFFVA(nCores, nThreads, model, scaling, memAff, schedule, nChunk, optPerc, ex)
 
     :param nCores:   Number of non-shared memory cores/machines.
     :param nThreads: Number of shared memory threads in each core/machine.