@Hüfer yes, you can access all the simulation results as well as the parameters of you EM-neuronal simulations using the Python interface. For example, you can access titration data, or the transmembrane voltage profiles stored on a point or line sensor data, etc. or you can dynamically change the stimulation pulse shape parameters (phase duration, amplitude, etc.). Sim4Life provides you already with some Python tutorials. There are at least 3 tutorials provided that show how to use Python to setup a T-Neuron simulation, how to run it and how to access the simulation results. Please have a look at these tutorials to learn Sim4Life programming (if you are a Python expert, learning will take you a day!). To access these tutorials, open the 'Scripter' panel in Sim4Life, and load one of the scripts in the folder opened when selecting "Open Example Script" clicking on the open folder button.

Hi Michael, What type of simulation/solver you are running? If I understand correct, all the simulations are of the same size, but the output files are of different sizes? This should not be the case. Unless simulations have larger grids, more sensors recorded, more frequency snapshots, etc, the output file size must not change. Best, Habib

I have managed to solve this. The function below seems to work def DiscretizeAxonModel(Axon_name, Diameter, type,folder): axon_entity = model.AllEntities()[Axon_name] if type=='motor': model_properties=model.MotorMrgNeuronProperties() elif type=='sensory': model_properties=model.SensoryMrgNeuronProperties() else: model_properties=model.MotorNeuronProperties() model_properties.AxonDiameter=Diameter discretized_axon = model.CreateAxonNeuron(axon_entity,model_properties) discretized_axon.Name = Axon_name +'_neuron' folder.Add(discretized_axon)

solved with h5py read : import h5py with h5py.File(fname,'r') as hdf: FieldGroups=hdf['FieldGroups'] keygroup = list(FieldGroups.keys()) valField = FieldGroups[keygroup[0]] AllFields = valField['AllFields'] EMPotential = AllFields['EM Potential(x,y,z,f0)'] EMPotentialObject = EMPotential['_Object'] AdditionalFieldDataCollection = EMPotentialObject['AdditionalFieldDataCollection'] Snapshots = EMPotentialObject['Snapshots'] Potential = Snapshots['0']['comp0'] #(n, m, k, 2) numpy array PotentialData = Potential[()] ========== this can be plotted import pyvista as pv

@montanaro I've tried it and it worked. Thank you very much!

can you supply some code example ? thanks

@Spuky sorry, I guess the legacy code was confusing: masking_filter = s4l.analysis.core.FieldMaskingFilter() masking_filter.SetInputConnection(0, J_port) I edited the original answer to use the current API. The J_port was the output port for the J(x,y,z,f0) field.

@AntoninoMC thanks, that worked perfect!

Hmm I am not sure I understand. Is it possible to share more of your code and explain exactly what is not working? I use the lines of code you posted regularly and it works just fine.

Mesh_material = sim.AddMaterialSettings(themesh(fromstl)) Mesh_material.Name = yourname Mesh_material.ElectricProps.Conductivity = v # S/m

Nevermind, I found example code in the "tutorial_emlf_unstructured_neuron_electroceuticals.py" example file, see below: def CreateMesh(): """ Creates a Multidomain Mesh with predefined priorities between domains and specified global and local options """ sel=.05 # Suface Edge Length mel=.002 # Min Edge Length #create multi-domain mesh global_opt = xcm.GlobalUnstructuredMeshingOptions() global_opt.SurfaceEdgeLength = sel global_opt.MinEdgeLength = mel # Gets all the entities in the folder 'Nerve_Model' entities=s4l.model.AllEntities()['Nerve_Model'].Entities local_opt_dict={} for ent in entities: local_opt = xcm.LocalUnstructuredMeshingOptions() print((ent, ent.Name)) if 'Saline' in ent.Name : local_opt.Priority=0 elif 'Silicone' in ent.Name : local_opt.Priority=1 elif 'Electrode' in ent.Name : local_opt.Priority=2 elif 'Nerve' in ent.Name : local_opt.Priority=3 elif 'Interstitial' in ent.Name : local_opt.Priority=4 elif 'Connective' == ent.Name : local_opt.Priority=5 elif 'Connective_' in ent.Name : local_opt.Priority=5 elif 'Blood' in ent.Name : local_opt.Priority=6 elif 'Fascicle' in ent.Name : local_opt.Priority=7 local_opt_dict[ent]=local_opt print((local_opt.Priority)) # Creates an unstructured mesh on the domains using the defined options mesh = xcm.GenerateUnstructuredMesh(entities, global_opt, local_opt_dict) return I did have a followup, how do we optimize the surface edge length and minimum edge lengths?

really thanks this is direction of solution what i did with friend Mesh2 = s4l.model.Import(r'C:\sintetic data\bones.stl')[0] so now Mesh2 is not tuple and has its own name bones meanwhile i write all in one script without functions so i dont need realy the entities as in the example of 2 plates supplied with sim4life ...

Hi, unfortunately most algorithms (this one included) do not have the option to mute their info messages. So I don't think there is a way to suppress this kind of output in the console. As for the possible options for the Update() function, there is only one: overwrite=True/False, to determine the behavior of the exporter if the target file already exists. The Update() function is the equivalent of clicking the Refresh button in the algorithm in the GUI: it triggers the algorithm. If the API Browser is lacking, you can find more information (like the function signature) by typing e.g. help(excel_exporter.Update) in the Console.

@jasper-goethals hei im new here, for sim4life Light v7.. still cant use Tkinter so use another module PyQt5 its not best solution but its work for me

This works indeed! Thanks a lot!