CircuitModel View reference
The model view reference describes the different objects related to circuit modeling.
For a tutorial on defining compact models and circuits, please check the circuit modeling tutorial.
Instances of CircuitModelView
 class ipkiss3.all.ModelInstance
Instances to other models.
 Parameters:
 reference: CompactModelView, required
 name: ( String that contains only alphanumeric characters from the ASCII set or contains _$<>. ASCII set is extended on PY3. ), optional, *None allowed*
Unique name of the instance within the scope of the View/Cell
 owner: ( _View ), optional, *None allowed*
link to the owner of the instance.In most cases this is automatically added by the _generate_instances method
Defining and testing models and model view
The CircuitModelView calculates a CompactModel with a circuit solver 

Base class to define a compact model, which approximates the behavior of a device using an Smatrix or a set of differential equations. 

A hierarchical model is a model of a circuit, that describes the circuit in terms of model instances, nets (to connect the instances), and terms. 
Component that can be placed in a circuit (attached to an existing port) to 'measure' the light. 

Component that can be placed in a circuit (attached to an existing port) to send a signal into the chip. 
A 3D matrix. 

Numerical Smatrix model consisting of a univariate Bspline representations over wavelength. 

Test a compact model by running a wavelength sweep. 

Map the terms of a model to different names 
Spectrum analysis
Highlevel interface:
SpectrumAnalyzer
and Spectrum
give a convenient interface to analyze a group of spectra for different channels of a device or an individual spectrum, respectively.
Tool to analyze the transmission spectra of an Smatrix. 

Object representing a transmission spectrum. 
Lowlevel functions
If SpectrumAnalyzer
or Spectrum
don’t satisfy your needs you can also use the lowerlevel analysis functions:

Extract information about the peaks in the given Sparameter spectra 

Return the power for each sample in the given spectrum: abs(y)**2 
Return the power in dB for each sample in the given spectrum: 20 * log10(abs(y)) 


Find peaks in data varying in one parameter. 

Find peaks in data varying in one parameter by means of a spline fit. 

Find peaks in data varying in one parameter by means of a wavelet convolution 