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main.py
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main.py
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import argparse
import numpy as np
from sympy import Symbol
from sympy.tensor.array import derive_by_array
from sympy.utilities.lambdify import lambdify
import pdb
# Proposed Algorithm - Determining the amount of Compliance for the
# Creation of Cardiovascular Grafts
# Stephannie Jimenez - Esteban Galvis
# Arguments for the client
parser = argparse.ArgumentParser(description='Compliance calculation')
parser.add_argument('--mode', default='healthy',
help='Case for changing the parameters value.')
parser.add_argument('--vol', default=5, help='Total volume of blood')
args = parser.parse_args()
if args.mode == 'healthy':
Rs = 17.5
Rp = 1.79
Kr = 2.8
Kl = 1.12
V = args.vol
elif args.mode == 'heart-failure':
Rs = 6.82
Rp = 1.36
Kr = 4.72
Kl = 9.5
V = args.vol
elif args.mode == 'hypertension':
Rs = 40.5
Rp = 3.21
Kr = 3
Kl = 1.7
V = args.vol
def main():
"""Run main."""
print('Starting program...')
# Definition of variables
Csa = Symbol('Csa')
Csv = Symbol('Csv')
Cpa = Symbol('Cpa')
Cpv = Symbol('Cpv')
# Definition of equations
Tsa = Csa/Kr + Csa*Rs
Tsv = Csv/Kr
Tpa = Cpa/Kl + Cpa*Rp
Tpv = Cpv/Kl
Tsum = Tsa+Tsv+Tpa+Tpv
# Volumes
Vsa = Tsa*V/Tsum
Vsv = Tsv*V/Tsum
Vpa = Tpa*V/Tsum
Vpv = Tpv*V/Tsum
Vsum = Vsa + Vsv + Vpa + Vpv
Vtot = lambdify((Csa, Csv, Cpa, Cpv), Vsum)
# Pressures
Psa = Tsa*V/(Csa*Tsum)
Psv = Tsv*V/(Csv*Tsum)
Ppa = Tpa*V/(Cpa*Tsum)
Ppv = Tpv*V/(Cpv*Tsum)
# Objective Function
f_obj = V/(Tsum)
f = lambdify((Csa, Csv, Cpa, Cpv), f_obj)
# Partial derivatives of objective function
partial = derive_by_array(f_obj, (Csa, Csv, Cpa, Cpv))
grad = lambdify((Csa, Csv, Cpa, Cpv), partial)
# Tolerance - num iterations
# tol = 0.001
n = 10000
# Start seed for the random function
# np.random.seed(1)
# Generate random values for init Cpa, Cpv, Csa, Csv
x = np.random.rand(4)
min_val = 5000000
min_x = x
while(n > 0):
# Check all x are positive
for val in x:
if val < 0:
val = abs(val)
# Check the volume restriction
act_v = Vtot(x[0], x[1], x[2], x[3])
if act_v != V:
y = V*np.ones(4)
diff = np.abs(np.subtract(x, y))
val, idx = min((val, idx) for (idx, val) in enumerate(diff))
x[idx] = val
# Calculate gradient given the points
g = grad(x[0], x[1], x[2], x[3])
# Calculate the value of the objective function
act = f(x[0], x[1], x[2], x[3])
if(act < min_val):
min_val = act
min_x = x
# Modify the actual value depending the gradient
val, idx = max((val, idx) for (idx, val) in enumerate(g))
x[idx] = abs(x[idx] + val)
n = n - 1
print(' Csa: {} \n Csv: {} \n Cpa: {} \n Cpv: {} \n'.format(
min_x[0], min_x[1], min_x[2], min_x[3]))
if __name__ == '__main__':
main()