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\section{Overview}\label{sec:intro}  This document presents a parametric model to help design an Interferometric Array. It describes the design parameters in \S~\ref{sec:var} and the system performance objectives in \S~\ref{sec:obj}, and the relationship between them.   A python code used to generate data in the format required for visual analysis of array design options performance is presented in \S~\ref{sec:python}. The python code is consistent with parameters and objectives selection in sections 2 and 3, and the mathematical relationships between them.  \section{Variables}\label{sec:var}  This section aims to include all relevant design parameters that might influence the selected performance objectives in section 4. \S~\ref{sec:obj}.  \subsection{Antenna Aspects}  \subsubsection{Collecting Area}  \subsection{Pad Aspects} 
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\subsubsection{Position}  \subsubsection{Efficiency}  \section{Objectives}\label{sec:obj}  This section aims to include array performance objectives that might be influenced by design choices. variables in \S~\ref{sec:var}.  \subsection{Minimize Brightness Sensitivity Limit}  An overall measure of performance is the System Equivalent Flux Density, $SEFD$, defined as the flux density of a source that would deliver the same amount of power (see \cite{sensitivity2}):   \begin{equation}\label{eq:system_equivalent_flux_density} 
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\text{Correlator cost} = 2N^2 + 112N +1360  \end{equation}  \subsubsection{Cost of Re-configuration Systems Construction}  \section{Array Performance Data Generation \section{Data for visual analytics  - Python Implementation}\label{sec:python} This section presents a python code that produces data in the right format for performing visual analytics, consistent with variables in \S~\ref{sec:var} and objectives in \S~\ref{sec:obj}.  \section{Visualization Tool Notes}  \section{Conversation notes}  \subsection{Engineering cost vs. Calibration cost} 
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