growth_diagnostic.py File Reference

Go to the source code of this file.

Namespaces
namespace	growth_diagnostic

Functions
	growth_diagnostic.n_to_d_nm (n)

Variables
	growth_diagnostic.MODULE_ROOT = Path(__file__).resolve().parent.parent.parent
	growth_diagnostic.REPO_ROOT = MODULE_ROOT.parent
float	growth_diagnostic.Omega = 1.18e-29
float	growth_diagnostic.b_111 = 2.482e-10
dict	growth_diagnostic.SOLVER_CONFIG
	growth_diagnostic.I_SIM
	growth_diagnostic.V_SIM
int	growth_diagnostic.i_mobile = 10
dict	growth_diagnostic.OVERRIDES
	growth_diagnostic.saved = sys.stdout, sys.stderr
	growth_diagnostic.stdout
	growth_diagnostic.stderr
	growth_diagnostic.sim
	growth_diagnostic.inp = sim.input_data
bool	growth_diagnostic.placed = False
	growth_diagnostic.results
	growth_diagnostic.d = inp.derived
	growth_diagnostic.G = d['G']
	growth_diagnostic.dose = results['dose']
	growth_diagnostic.idx_01 = np.argmin(np.abs(dose - 0.1))
	growth_diagnostic.actual_dose = dose[idx_01]
	growth_diagnostic.t_01 = results['t'][idx_01]
	growth_diagnostic.y = results['y'][:, idx_01]
	growth_diagnostic.c_i = np.maximum(y[0:I_SIM], 0.0)
	growth_diagnostic.c_v = np.maximum(y[I_SIM:2*I_SIM], 0.0)
	growth_diagnostic.ci1 = c_i[0]
	growth_diagnostic.cv1 = c_v[0]
float	growth_diagnostic.s2m = 1.0 / d['Omega']
	growth_diagnostic.rr = sim.reaction_rates
	growth_diagnostic.re = sim.rate_equations
	growth_diagnostic.cn = c_i[n-1]
	growth_diagnostic.K_g = rr.K_SIA_grow[n-1]
	growth_diagnostic.K_s = rr.K_SIA_shrink[n-1]
	growth_diagnostic.G_em = rr.G_SIA[n-1]
	growth_diagnostic.k2 = rr.k2_SIA[n-1]
	growth_diagnostic.v_net = K_g * ci1 - K_s * cv1
	growth_diagnostic.growth_flux = K_g * ci1 * cn
	growth_diagnostic.shrink_flux = K_s * cv1 * cn
int	growth_diagnostic.ratio = growth_flux / max(shrink_flux, 1e-300) if shrink_flux > 0 else float('inf')
	growth_diagnostic.Pr_1 = re.Pr_SIA[0]
	growth_diagnostic.sink_loss = rr.k2_SIA[0] * ci1
	growth_diagnostic.growth_loss = ci1 * np.dot(rr.K_SIA_grow, c_i)
	growth_diagnostic.recom_loss = rr.K_iv * ci1 * cv1
	growth_diagnostic.K_3D = rr.K_3D_cav_pref
	growth_diagnostic.m13 = np.arange(1, V_SIM+1)**(1./3.)
	growth_diagnostic.cav_loss = ci1 * K_3D * np.dot(m13, c_v)
	growth_diagnostic.total_loss = sink_loss + growth_loss + recom_loss + cav_loss
	growth_diagnostic.v_growth = np.array([rr.K_SIA_grow[n-1]*ci1 - rr.K_SIA_shrink[n-1]*cv1 for n in range(1, I_SIM+1)])
	growth_diagnostic.sorted_idx = np.argsort(c_i)[::-1]
int	growth_diagnostic.n = sorted_idx[rank] + 1