prydin

Configure a Private Registry on a VCF Operations Collector/Cloud Proxy for the vCommunity Management Pack for Disconnected Sites

Adapted from an original by Sean Cummins

The VCF Operations Coommunuty Management pack adds lots of useful new metrics to VCF Operations. This Management Pack was created using the VCF Operations SDK, which relies on Docker. Typically, the Collectors would contact a public registry to pull the images, but this doesn't work in air-gapped high-security sites. This document discusses a workaround.

Preparing the images

prydin

import numpy as np
import scipy.signal as signal
from matplotlib import pyplot as plt

def gen_sine(fs, f, periods):
    n = int(fs*(periods/f))
    t = np.linspace(start=0, stop=periods/f, num=n)
    return t, np.sin(t*f*2*np.pi)

def upsample(t, x, ratio):

prydin

Version 4
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WIRE -1936 -1360 -2064 -1360
WIRE -1520 -1360 -1936 -1360
WIRE -1200 -1360 -1520 -1360
WIRE -976 -1360 -1200 -1360
WIRE -480 -1360 -976 -1360
WIRE -400 -1360 -480 -1360
WIRE 64 -1360 -400 -1360
WIRE 432 -1360 64 -1360

prydin

import numpy as np

noise_db = -80  # Noise added by each amplifier
gain_db = 60    # Closed loop gain

cycles = 2
resolution = 1000
length = np.pi * 2 * cycles
signal = np.sin(np.arange(0, length, length / resolution))
raw_noise = 10**(noise_db / 20)

prydin

`timescale 1ns / 1ps
//////////////////////////////////////////////////////////////////////////////////
// Company: 
// Engineer: 
// 
// Create Date: 12/17/2024 08:29:18 AM
// Design Name: 
// Module Name: main
// Project Name: 
// Target Devices: 

prydin

Version 4
SHEET 1 5264 1236
WIRE 272 -80 272 -112
WIRE 1600 -80 1600 -112
WIRE -496 -64 -496 -144
WIRE 208 -32 128 -32
WIRE 1536 -32 1456 -32
WIRE 128 16 128 -32
WIRE 272 32 272 16
WIRE 1600 32 1600 16

prydin

Version 4
SHEET 1 880 680
WIRE -448 -80 -560 -80
WIRE -416 -80 -448 -80
WIRE -320 -80 -336 -80
WIRE 96 -80 -320 -80
WIRE -560 -48 -560 -80
WIRE -320 -32 -320 -80
WIRE -576 48 -576 32
WIRE -560 80 -560 32

prydin

import math

import numpy as np
import scipy as sci
from numpy.linalg.linalg import solve
from scipy.signal import correlate, fftconvolve
import matplotlib.pyplot as plt
import cmath

def parabolic(f, x):

prydin

`timescale 1ns / 1ps
//////////////////////////////////////////////////////////////////////////////////
// Company: 
// Engineer: 
// 
// Create Date: 03/09/2024 02:08:14 PM
// Design Name: 
// Module Name: dac
// Project Name: 
// Target Devices: 

prydin

# Generate test signal
t = linspace(0,120*pi, 1000)
baseband = cos(t/40)
carrier = cos(t)
modulated = carrier .* baseband

# Downshift and split into I and Q
fq_offset = 1.05 # Carrier and LO are off by 5%
i = cos(t*fq_offset) .* modulated
q = sin(t*fq_offset) .* modulated