<jupyter_start><jupyter_text>***Introduction to Radar Using Python and MATLAB*** <NAME> - Copyright (C) 2019 Artech House Noise Figure*** For cascaded networks, the noise figure is the contribution from each stage in the receive chain as (Equation 5.2)$$F_{total} = F_1 + \sum_{i=2}^{N}\left(\frac{F_i - 1}{\prod_{j=1}^{i-1}G_j}\right)$$*** Begin by setting the library path<jupyter_code>import lib_path<jupyter_output><empty_output><jupyter_text>Set the gain (dB) and noise figure (dB) for each stage in the receiver chain<jupyter_code>gain_db = [20.0, -0.5, -6.0, -1.0, 30.0]

noise_figure_db = [3.0, 0.5, 6.0, 1.0, 5.0]<jupyter_output><empty_output><jupyter_text>Calculate the total noise figure (dB) using the `total_noise_figure` routine<jupyter_code>from Libs.receivers import noise_figure

total_noise_figure = noise_figure.total_noise_figure(gain_db, noise_figure_db)<jupyter_output><empty_output><jupyter_text>Display the total noise figure<jupyter_code>print('{:.4f}'.format(total_noise_figure))<jupyter_output>3.3507
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