Maboss

When computing the node probability trajectories, we would only return a node probability when it was non-zero. Now we uses the defined outputs of the model, ensuring all outputs would be present even if their probability is zero.

I also added a direct maboss.loadBNet(file) function, to load a bnet file as a maboss model directly (without explicitely callling biolqm).

Finally, when selecting output variables, there was no error checking that each variable was indeed a defined node. Now the library will return a failed assert if it is the case.

There fix removes biolqm dependency, by loading the model of the ensemble using the bnet parser in minibn.
This also allows to have the formula in the proper, simplified form

- Mostly ensemble modelling code (documentation to come)
- Bugfix for simulation palettes

UpPMaBoSS
- Parallel building of stepwise state probability distribution
- Option to filter states, via a list of nodes to include/exclude
- Building of stepwise node probability distribution (with filtering option)
- Method to create of update an external variable
- Now automatically using the number of threads defined in the cfg file to build stepwise probability distributions
- Improved tests


pyMaBoSS
- Model conversion to biolqm and minibn (maboss.to_biolqm(model), maboss.to_minibn(model))
- Support for remote files
- Workdir and Overwrite option now an argument of run() method
- Improved computation of asymptotic probability distribution
- Checking if temporary folder exists before deleting it
- Added a python version of the script MBSS_FormatTable.
- Added support for stationary distributions.
- Added command argument to load, to inform the MaBoSS executable to use for model verification.
- Fix documentation (methods who had disappeared because of classes reorg)
- Tests (probtraj, statdist)

When loading a model, MaBoSS will be called to verify if it's valid.
For models with more than 64 nodes, specific MaBoSS version needs to be called.
This fix now uses the proper executable when verifying the model validity.