Command Line Interface (CLI)¶
General structure¶
The models are objects, each with a set of properties and generic functions. Before interacting with a model, it must first be instantiated, e.g.
model=charmed
General commands¶
- qMRInfo(model) : Print the help for the ‘model’ object
- qMRusage(model) : Print the methods of ‘model’ and examples of how to interact with them
- qMRgenBatch(model) : Generate a batch example script for ‘model’ (will automatically download test data)
Model structure¶
All models have the following properties:¶
- MRIinputs : Names of input data
- voxelwise : Whether the fit is voxelwise [1] or a matrix operation [0]
- xnames : Names of output data
- Prot : structure containing the protocol parameters
- buttons :
- Options : options specific to the model (e.g. linear or non-linear fir for VFA-T1)
And functions:¶
equation:
Compute MR signal USAGE: Smodel = Model.equation(x) INPUT: x: [struct] OR [vector] containing Model output parameters
fit:
Fit experimental data USAGE: FitResults = Model.fit(data) INPUT: data: [struct] containing input data IN ORDER as in MRinuts NOTE: data are 1D. For 4D datasets use FitData(data,Model)
plotModel:
Plot model equation (and fitting) USAGE: Model.plotModel(obj, x) Model.plotModel(obj, x, data) INPUT: x: [struct] OR [vector] containing Model output parameters data: [struct] containing input data in ORDER as in MRinuts
Most models have these additional functions:¶
Sim_Sensitivity_Analysis:
Simulates sensitivity to fitted parameters: (1) vary fitting parameters from lower (lb) to upper (ub) bound in 10 steps (2) run Sim_Single_Voxel_Curve Nofruns times (3) Compute mean and std across runs USAGE: SimVaryResults = Model.Sim_Sensitivity_Analysis(OptTable, Opt); INPUT: OptTable: [struct] nominal value and range for each parameter. st: [vector] nominal values for output parameters fx: [binary vector] do not vary this parameter? lb: [vector] vary from lb... ub: [vector] up to ub Opt: [struct] Options of the simulation
Sim_Single_Voxel_Curve:
Simulates Single Voxel curves: (1) use equation to generate synthetic MRI data (2) add rician noise (3) fit and plot curve USAGE: FitResults = Model.Sim_Single_Voxel_Curve(x) FitResults = Model.Sim_Single_Voxel_Curve(x, Opt,display) INPUT: x: [struct] OR [vector] containing fit results display: [binary] 1=display, 0=nodisplay
Please type the following to see the specific usage of the model you are interested in
qMRusage(model)
Or the batch example associated with your model located here Methods available