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\section{Overview}  This document presents a parametric model to help design an Interferometric Array. It describes the design parameters in section 2 \ref{variables}  and the system performance objectives in section 3, \ref{objectives},  and the relationship between them. Section 4 provides the A  python code used to generate data in the format required for visual analysis of array design options performance. performance is presented in \ref{array-performance-data-generation---python-implementation}.  The python code is consistent with parameters and objectives selection in sections 2 and 3, and the mathematical relationships between them. \section{Variables}  This section aims to include all relevant design parameters that might influence the selected performance objectives in section 4.  \subsection{Antenna Aspects} 
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\text{Correlator cost} = 2N^2 + 112N +1360  \end{equation}  \subsubsection{Cost of Re-configuration Systems Construction}  \section{Mathematical Formulation}  Thus if we are using vector $x = {\text{antenna diameter}, \text{antenna efficiency}, \}$, the antenna diameter, as the optimization variable the problem we would like to solve is:  \begin{equation*}  \begin{aligned}  & \underset{x}{\text{minimize}}  & & f(x) = {\frac{2k_B}{\eta_s \eta_a}}{\frac{T_{sys}}{\pi x^2 \sqrt{2 \Delta \nu \tau_{acc}}}}\\  & \text{subject to}  & & X_{ij} = M_{ij}, \; (i,j) \in \Omega, \\  &&& X \succeq 0.  \end{aligned}  \end{equation*}  \section{Array Performance Data Generation - Python Implementation}  \section{Visualization Tool Notes}  \section{Conversation notes} 
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