from single_evaluation import single_eval
import numpy as np
import csv
x1 = -5
x2 = 5
v, a, t = single_eval('Robin_Pre2', 'pre', 0)
x1_marker = np.where(t >= x1)
x1_marker = x1_marker[0][0]
x2_marker = np.where(t >= x2)
x2_marker = x2_marker[0][0]
iteration = 'optim_in_progress'
data_choice = 'pre'
change_index = 0
h = 0.1
X = np.zeros([len(t[x1_marker:x2_marker]), 234])
for i in range(234):
V_cPlus = np.ones(234)
V_cMinus = np.ones(234)
V_cPlus[i] = V_cPlus[i] + h
V_cMinus[i] = V_cMinus[i] - h
with open('./Samples/Temp_optim.csv', 'w') as csv_file:
csv_writer = csv.writer(csv_file, delimiter=',')
#list_of_low_reactions=[4,10,11,12,13,15,17,18,19,22,52,54] # Cube 1
#list_of_low_reactions=[59,58,48,42,4,11]
list_of_low_reactions = [5, 8, 10]
n = len(list_of_low_reactions)
for i in range(2**n):
b = bin(i)[2:]
l = len(b)
b = str(0) * (n - l) + b
combos.append(b)
numpairs = len(combos)
combos_for_ST = combos.copy()
v_nominal, a_nominal, time = single_eval(values, model, [])
if model == 'pre':
dataset = 'tots_LNAME_pre'
Data = im.import_Berwick_HET_LNAME_Data(dataset, area='Whisker')
results_v = np.zeros([numpairs, 51])
QoIs = np.zeros([numpairs, 3])
sobolev_norm_R = np.zeros([numpairs, 1])
sobolev_norm_HBR = np.zeros([numpairs, 1])
sobolev_norm_Ca_i = np.zeros([numpairs, 1])
list_removed = list()
for i in range(numpairs):
print('starting ', i + 1, 'of ', numpairs)
current_removed_index = list(combos[i])
from numpy.linalg import norm
## Tim these three lines are the ones to edit
reactions_to_remove = [4, 11, 8, 52, 49,
50] # reactions to be removed as a list
xlim1 = -1 #limits /for x values in graph
xlim2 = 20
values = 'Robin_Pre2'
model = 'pre'
norm_flag = 2
v_a_flag = 'v'
v_nominal, a_nominal, t = single_eval(values, model, [])
v = []
a = []
v_temp, a_temp, t = single_eval(values, model, reactions_to_remove)
if model == 'pre':
dataset = 'tots_LNAME_pre'
Data = im.import_Berwick_HET_LNAME_Data(dataset, area='Whisker')
interpolator = interp1d(t, a_temp.HBO_N)
HBO_interp = interpolator(Data.time)
Error_HBO = HBO_interp - Data.HbOwhisk_mean
Error_HBO = sum(map(lambda x: x * x, Error_HBO))
interpolator = interp1d(t, a_temp.HBR_N)
from test_switch_functions import compare_results_v
from test_switch_functions import compare_results_a
from test_switch_functions import compare_results_sobolev
import math
import numpy as np
from numpy.linalg import norm
values = 'Robin_Pre2'
model = 'pre'
norm_flag = 2
v_a_flag = 'sobolev'
v_nominal, a_nominal, time = single_eval(values, model, [])
v = []
a = []
if v_a_flag == 'v':
for i in range(59 + 1):
v_temp, a_temp, time = single_eval(values, model, i)
v.append(v_temp)
difference = np.zeros([59 + 1, 51])
for i in range(59 + 1):
print('%i done' % i)
difference[i, :] = compare_results_v(v_nominal, v[i], norm_flag)
if v_a_flag == 'a_diff':
for i in range(59 + 1):