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run_SA_unsafe.py
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run_SA_unsafe.py
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from src.model import configSimulation, simulationLoopUnsafe
import jax
import sys
import time
import os
from functools import partial
from jax import block_until_ready, jit
import numpy as np
from SALib import ProblemSpec
os.chdir(os.path.dirname(__file__))
jax.config.update("jax_enable_x64", True)
config_filename = ""
if len(sys.argv) == 1:
# base cases
#modelname = "single-artery"
#modelname = "tapering"
#modelname = "conjunction"
#modelname = "bifurcation"
#modelname = "aspirator"
# openBF-hub
modelname = "test/adan56/adan56.yml"
# vascularmodels.com
#modelname = "0007_H_AO_H"
#modelname = "0029_H_ABAO_H"
#modelname = "0053_H_CERE_H"
input_filename = "test/" + modelname + "/" + modelname + ".yml"
else:
config_filename = "test/" + sys.argv[1] + "/" + sys.argv[1] + ".yml"
verbose = True
(N, B, J,
sim_dat, sim_dat_aux,
sim_dat_const, sim_dat_const_aux,
timepoints, conv_tol, Ccfl, edges, input_data, rho,
masks, strides, edges,
vessel_names, cardiac_T) = configSimulation(config_filename, verbose)
if verbose:
starting_time = time.time_ns()
sim_loop_jit = partial(jit, static_argnums=(0, 1, 12))(simulationLoopUnsafe)
sim_dat_base, t_t_base, P_t_base = block_until_ready(sim_loop_jit(N, B,
sim_dat, sim_dat_aux,
sim_dat_const, sim_dat_const_aux,
Ccfl, input_data, rho,
masks, strides, edges,
upper=120000))
starts = strides[:,0]
ends = strides[:,1]
def sim_loop_jit_wrapper(rho, Ccfl, Ls, R0s, Es, R1s, R2s, Ccs):
for i in range(N):
sim_dat_const[0,starts[i]-B:ends[i]+B] = np.pi*R0s[i]*R0s[i]*np.ones(ends[i]-starts[i]+2*B)
sim_dat_const[3,starts[i]-B:ends[i]+B] = Es[i]*np.ones(ends[i]-starts[i]+2*B)
sim_dat_const[7,starts[i]-B:ends[i]+B] = R1s[i]*np.ones(ends[i]-starts[i]+2*B)
sim_dat_const[8,starts[i]-B:ends[i]+B] = R2s[i]*np.ones(ends[i]-starts[i]+2*B)
sim_dat_const[9,starts[i]-B:ends[i]+B] = Ccs[i]*np.ones(ends[i]-starts[i]+2*B)
sim_dat_const[10,starts[i]-B:ends[i]+B] = Ls[i]*np.ones(ends[i]-starts[i]+2*B)
sim_dat_new, t_t_new, P_t_new = sim_loop_jit(N, B,
sim_dat, sim_dat_aux,
sim_dat_const, sim_dat_const_aux,
Ccfl, input_data, rho,
masks, strides, edges,
upper=120000)
return np.linalg.norm(P_t_base-P_t_new)
grid = np.arange(0.1,2,0.5)
print(np.array([0, 6.8123e7, 6.8123e7])[np.newaxis,:]*grid[:,np.newaxis])
sensitivity_dict = {
'rho': 1060.0*grid,
'Ccfl': 4.e-3*grid,
'Ls': np.array([8.6e-2, 8.5e-2, 8.5e-2])[np.newaxis,:]*grid[:,np.newaxis],
'R0s': np.array([0.758242250e-2, 0.5492e-2, 0.5492e-2])[np.newaxis,:]*grid[:,np.newaxis],
'Es': np.array([500.0e3,700.0e3,700.0e3,])[np.newaxis,:]*grid[:,np.newaxis],
'R1s': np.array([0, 6.8123e7, 6.8123e7])[np.newaxis,:]*grid[:,np.newaxis],
'R2s': np.array([0, 3.1013e9, 3.1013e9])[np.newaxis,:]*grid[:,np.newaxis],
'Ccs': np.array([0, 3.6664e-10, 3.6664e-10])[np.newaxis,:]*grid[:,np.newaxis],
}
#sa = SensitivityAnalyzer(sensitivity_dict, sim_loop_jit_wrapper)
#
#sa.df
#print(sa.df)
print(P_t_base)
def quick_wrap(N, B,
sim_dat, sim_dat_aux, sim_dat_const, sim_dat_const_aux,
Ccfl, edges, input_data,
rho, strides,
masks, upper=120000):
_, _, P_t_new = sim_loop_jit(N, B,
sim_dat, sim_dat_aux,
sim_dat_const, sim_dat_const_aux,
Ccfl, input_data, rho,
masks, strides, edges,
upper=120000)
return np.linalg.norm(P_t_base-P_t_new)
def wrapped_linear(X: np.ndarray, l = 4, func=sim_loop_jit) -> np.ndarray:
import numpy as np
M, D = X.shape
results = np.empty(M)
for i in range(M):
#R01, R02, R03, E1, E2, E3, R11, R12, R21, R22, Cc1, Cc2 = X[i, :] #, L1, L2, L3 = X[i, :]
#E1, E2, E3, R11, R12, R21, R22, Cc1, Cc2 = X[i, :] #, L1, L2, L3 = X[i, :]
#R01, R02, R03, R11, R12, R21, R22, Cc1, Cc2 = X[i, :] #, L1, L2, L3 = X[i, :]
(A01, A02, A03,
beta1, beta2, beta3,
gamma1, gamma2, gamma3,
viscT1, viscT2, viscT3,
R11, R12, R21, R22, Cc1, Cc2,
L1, L2, L3, rho, Ccfl) = X[i,:]
sim_dat_const[0,starts[0]-B:ends[0]+B] = A01*np.ones(ends[0]-starts[0]+2*B)
sim_dat_const[0,starts[1]-B:ends[1]+B] = A02*np.ones(ends[1]-starts[1]+2*B)
sim_dat_const[0,starts[2]-B:ends[2]+B] = A03*np.ones(ends[2]-starts[2]+2*B)
sim_dat_const[1,starts[0]-B:ends[0]+B] = beta1*np.ones(ends[0]-starts[0]+2*B)
sim_dat_const[1,starts[1]-B:ends[1]+B] = beta2*np.ones(ends[1]-starts[1]+2*B)
sim_dat_const[1,starts[2]-B:ends[2]+B] = beta3*np.ones(ends[2]-starts[2]+2*B)
sim_dat_const[2,starts[0]-B:ends[0]+B] = gamma1*np.ones(ends[0]-starts[0]+2*B)
sim_dat_const[2,starts[1]-B:ends[1]+B] = gamma2*np.ones(ends[1]-starts[1]+2*B)
sim_dat_const[2,starts[2]-B:ends[2]+B] = gamma3*np.ones(ends[2]-starts[2]+2*B)
sim_dat_const[5,starts[0]-B:ends[0]+B] = viscT1*np.ones(ends[0]-starts[0]+2*B)
sim_dat_const[5,starts[1]-B:ends[1]+B] = viscT2*np.ones(ends[1]-starts[1]+2*B)
sim_dat_const[5,starts[2]-B:ends[2]+B] = viscT3*np.ones(ends[2]-starts[2]+2*B)
sim_dat_const[7,starts[1]-B:ends[1]+B] = R11*np.ones(ends[1]-starts[1]+2*B)
sim_dat_const[7,starts[2]-B:ends[2]+B] = R12*np.ones(ends[2]-starts[2]+2*B)
sim_dat_const[8,starts[1]-B:ends[1]+B] = R21*np.ones(ends[1]-starts[1]+2*B)
sim_dat_const[8,starts[2]-B:ends[2]+B] = R22*np.ones(ends[2]-starts[2]+2*B)
sim_dat_const[9,starts[1]-B:ends[1]+B] = Cc1*np.ones(ends[1]-starts[1]+2*B)
sim_dat_const[9,starts[2]-B:ends[2]+B] = Cc2*np.ones(ends[2]-starts[2]+2*B)
sim_dat_const[10,starts[0]-B:ends[0]+B] = L1*np.ones(ends[0]-starts[0]+2*B)
sim_dat_const[10,starts[1]-B:ends[1]+B] = L2*np.ones(ends[1]-starts[1]+2*B)
sim_dat_const[10,starts[2]-B:ends[2]+B] = L3*np.ones(ends[2]-starts[2]+2*B)
sim_dat_new, _, _ = func(N, B,
sim_dat, sim_dat_aux,
sim_dat_const, sim_dat_const_aux,
Ccfl, input_data, rho,
masks, strides, edges,
upper=120000)
#print(np.linalg.norm(sim_dat_base[l,:]-sim_dat_new[l,:])/np.linalg.norm(sim_dat_base[l,:]))
results[i] = np.linalg.norm(sim_dat_base[l,:]-sim_dat_new[l,:])/np.linalg.norm(sim_dat_base[l,:])
return results
print(sim_dat_const[:,starts[0]])
print(sim_dat_const[:,starts[1]])
print(sim_dat_const[:,starts[2]])
sp = ProblemSpec({
#'names': ['R01', 'R02', 'R03', 'E1', 'E2', 'E3', 'R11', 'R12', 'R21', 'R22', 'Cc1', 'Cc2'], #, 'L1', 'L2', 'L3'],
'names': ['A01', 'A02', 'A03',
'beta1', 'beta2', 'beta3',
'gamma1', 'gamma2', 'gamma3',
'viscT1', 'viscT2', 'viscT3',
'R11', 'R12', 'R21', 'R22', 'Cc1', 'Cc2',
'L1', 'L2', 'L3', 'rho', 'Ccfl'],
#'names': ['R01', 'R02', 'R03', 'R11', 'R12', 'R21', 'R22', 'Cc1', 'Cc2'], #, 'L1', 'L2', 'L3'],
'bounds': [
[1.80619998e-04*0.9,1.80619998e-04*1.1],
[9.47569187e-05*0.9,9.47569187e-05*1.1],
[9.47569187e-05*0.9,9.47569187e-05*1.1],
[8.51668358e+04*0.9,8.51668358e+04*1.1],
[1.32543517e+05*0.9,1.32543517e+05*1.1],
[1.32543517e+05*0.9,1.32543517e+05*1.1],
[1.99278514e+03*0.9,1.99278514e+03*1.1],
[4.28179535e+03*0.9,4.28179535e+03*1.1],
[4.28179535e+03*0.9,4.28179535e+03*1.1],
[2.60811466e-04*0.9,2.60811466e-04*1.1],
[2.60811466e-04*0.9,2.60811466e-04*1.1],
[2.60811466e-04*0.9,2.60811466e-04*1.1],
#[0.758242250e-2*0.9, 0.758242250e-2*1.1],
#[0.5492e-2*0.9, 0.5492e-2*1.1],
#[0.5492e-2*0.9, 0.5492e-2*1.1],
#[500.0e3*0.9999, 500.0e3*1],
#[700.0e3*0.9999, 700.0e3*1],
#[700.0e3*0.9999, 700.0e3*1],
[6.8123e7*0.9, 6.8123e7*1.1],
[6.8123e7*0.9, 6.8123e7*1.1],
[3.1013e9*0.9, 3.1013e9*1.1],
[3.1013e9*0.9, 3.1013e9*1.1],
[3.6664e-10*0.9, 3.6664e-10*1.1],
[3.6664e-10*0.9, 3.6664e-10*1.1],
[1e-3*0.9, 1e-3*1.1],
[1e-3*0.9, 1e-3*1.1],
[1e-3*0.9, 1e-3*1.1],
[rho*0.9, rho*1.1],
[Ccfl*0.9, Ccfl*1.1],
],
})
if __name__ == "__main__":
(sp.sample_sobol(2**8).evaluate(wrapped_linear).analyze_sobol()
)
sp.to_df()
print(sp.to_df())