Caglar Cakan

Testing: test_models with dynamic loading

1

Right now, we have a list of all models to test and have a function for each of them. This is kind of ugly. Therefore, I think we should have...

```python from neurolib.utils.parameterSpace import ParameterSpace shape = 4 pars = [np.random.rand(shape, shape) for i in range(10)] parameters = ParameterSpace({"p": pars}) parameters.getRandom() ``` Returns ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call...

```python from neurolib.utils.parameterSpace import ParameterSpace shape = 2 n_pars = 2 pars = [np.random.rand(shape, shape) for i in range(n_pars)] parameters = ParameterSpace({"p": pars}) ``` Returns ``` --------------------------------------------------------------------------- ValueError Traceback (most...

Need to do a proper optimization of parameters so the fit value is good. Right now it's... suboptimal. File: example-3-meg-functional-connectivity.ipynb 

TODO: `Exploration.useRandomICs` not implemented yet

autochunk: option to save all simulated data to disk

2

WIP, maybe netcdf?

...

Todo: enum list of models

2

There should be a way to list all available models. Similar to how TVB handles it would be nice: http://docs.thevirtualbrain.com/_modules/tvb/simulator/models.html

`tqdm` offers a very nice wrapper around for loops for easy progress bars. This could be implemented easily in `Model.integrate_chunkwise()`.