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accuracy_test.py
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accuracy_test.py
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import math
import random
# pygame imports
# -----------------------------------------------------
import pygame
from pygame.locals import *
# pid tuning parameters
# -----------------------------------------------------
P = 0.5
I = 0.014
D = 0.01
# map model vars
# -----------------------------------------------------
b0 = 0.18661
bm = 0.07464
a1 = 0.74082
m = 3
d = 3
previous_map_change = 0
map_change = 0
# stores 10 most recent infusion rates
# -----------------------------------------------------
infusion_log = []
for i in range(0, 100):
infusion_log.append(0)
# gui variables
# -----------------------------------------------------
initial_map = 80
current_map = 80
target_map = 65
new_target_bp = target_map # temporary var for target bp
low_bound = target_map - 5 # lower bound of target bp range
high_bound = target_map + 5 # upper bound of target bp range
in_range = False
max_infusion = 140 # 140 ml/hr
#create the bp log
# -----------------------------------------------------
bp_log = [] # initial bp
for t in range(0, 380):
bp_log.append(current_map)
initial_avg = sum(bp_log)/len(bp_log)
# patient response to infusion rate
# -----------------------------------------------------
def response(infusion):
global current_map
global infusion_log
global map_change
global previous_map_change
global initial_map
global bp_log
infusion_log.append(infusion)
infusion_log.pop(0)
map_change = (b0 * infusion_log[len(infusion_log) - 1 - d]+ bm * infusion_log[len(infusion_log) - 1 - d - m] + a1 * previous_map_change)
previous_map_change = map_change
current_map = initial_map - map_change + random.randrange(-2, 2)
bp_log.append(current_map)
bp_log.pop(0)
timestep = 1
integral = 0
derivative = 0
system_stop = False
counter = 0
target_map_list = [60, 62, 65, 68, 70]
starting_map_list = [115, 120, 125, 130, 140]
repetitions = 3
total_tests = len(target_map_list) * len(starting_map_list) * repetitions
# main loop
for target in target_map_list:
for start in starting_map_list:
for repetition in range(0, repetitions):
print("\ntarget map: ", target, " starting map: ", start)
# reset things
derivative = 0
integral = 0
target_map = target
current_map = start
low_bound = target_map - 5
high_bound = target_map + 5
map_change = 0
previous_map_change = 0
initial_map = start
infusion_log = []
for i in range(0, 100):
infusion_log.append(0)
bp_log = [] # initial bp
for t in range(0, 380):
bp_log.append(current_map)
# run algorithm/simulation for 300 seconds
for t in range(0, 300):
error = current_map - target_map
integral += error * timestep
derivative = (bp_log[len(bp_log) - 1] - bp_log[len(bp_log) - 2]) / timestep
control = P * error + I * integral + D * derivative
if control < 0 or current_map < low_bound:
control = 0
if control > max_infusion:
control = max_infusion
response(control)
#avg bp in given frame
avg = sum(bp_log)/len(bp_log)
if avg >= low_bound and avg <= high_bound:
in_range = True
else:
in_range = False
if current_map >= low_bound and current_map <= high_bound:
print("final map: ", current_map)
print("test passed")
counter = counter + 1
else:
print("test failed")
print("total tests passed: ", counter, " out of " , total_tests)
print("pass rate: ", counter/total_tests)