models/dr_constant.py [54:68]:
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        self.aR = theta.aR
        self.aS = theta.aS

        # Activation constants for convenience
        nR = torch.clamp(theta.nR, 0.5, 3.0)
        nS = torch.clamp(theta.nS, 0.5, 3.0)
        lb = 1e-12
        ub = 1e0
        if version == 1:
            KR6 = torch.clamp(theta.KR6, lb, ub)
            KR12 = torch.clamp(theta.KR12, lb, ub)
            KS6 = torch.clamp(theta.KS6, lb, ub)
            KS12 = torch.clamp(theta.KS12, lb, ub)
            self.fracLuxR = (power(KR6 * c6, nR) + power(KR12 * c12, nR)) / power(1.0 + KR6 * c6 + KR12 * c12, nR)
            self.fracLasR = (power(KS6 * c6, nS) + power(KS12 * c12, nS)) / power(1.0 + KS6 * c6 + KS12 * c12, nS)
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models/relay_constant.py [73:87]:
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        self.aR = theta.aR
        self.aS = theta.aS

        # Activation constants for convenience
        nR = torch.clamp(theta.nR, 0.5, 3.0)
        nS = torch.clamp(theta.nS, 0.5, 3.0)
        lb = 1e-12
        ub = 1e0
        if version == 1:
            KR6 = torch.clamp(theta.KR6, lb, ub)
            KR12 = torch.clamp(theta.KR12, lb, ub)
            KS6 = torch.clamp(theta.KS6, lb, ub)
            KS12 = torch.clamp(theta.KS12, lb, ub)
            self.fracLuxR = (power(KR6 * c6, nR) + power(KR12 * c12, nR)) / power(1.0 + KR6 * c6 + KR12 * c12, nR)
            self.fracLasR = (power(KS6 * c6, nS) + power(KS12 * c12, nS)) / power(1.0 + KS6 * c6 + KS12 * c12, nS)
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