def calc_oper(mdl):
endresults_nom, resgraph, mdlhist =propagate.nominal(mdl)
opercost = endresults_nom['classification']['expected cost']
g_soc = 20 - mdlhist['functions']['StoreEE']['soc'][-1]
g_faults = any(endresults_nom['faults'])
g_max_height = sum([i for i in mdlhist['flows']['DOFs']['elev']-122 if i>0])
return opercost, g_soc, g_faults, g_max_height
def calc_oper(mdl):
endresults_nom, resgraph, mdlhist =propagate.nominal(mdl)
opercost = endresults_nom['classification']['expected cost']
g_soc = 20 - mdlhist['functions']['StoreEE']['soc'][-1]
g_faults = any(endresults_nom['faults'])
g_max_height = sum([i for i in mdlhist['flows']['DOFs']['elev']-122 if i>0])
landtime = find_landtime(mdlhist)
mdl.params['landtime']=landtime
mdl.phases['forward'][1] = landtime
mdl.phases['taxis'][0] = landtime
return opercost, g_soc, g_faults, g_max_height
def test_model_2():
x0 = [0.1 , 2 , 5 , 10 , 0.05 , 10 ]
result0=list(SimplePandemicModel(x0))
sus_simp = result0[1]
inf_simp = result0[2]
rec_simp = result0[3]
fmdmdl = PandemicModel(params={'x0':x0})
endresults, resgraph, mdlhist_nom = nominal(fmdmdl)
sus_mdl = mdlhist_nom['functions']['City']['Susceptible']
inf_mdl = mdlhist_nom['functions']['City']['Infected']
rec_mdl = mdlhist_nom['functions']['City']['Recovered']
sus_err = np.average(abs((sus_simp-sus_mdl)/(sus_mdl+0.1)))
inf_err = np.average(abs((inf_simp-inf_mdl)/(inf_mdl+0.1)))
rec_err = np.average(abs((rec_simp-rec_mdl)/(rec_mdl+0.1)))
assert sus_err < 0.06
assert inf_err < 0.06
assert rec_err < 0.06
# -*- coding: utf-8 -*-
"""
Created on Sun Jul 5 14:58:54 2020
@author: danie
"""
import sys
sys.path.append('../')
import fmdtools.faultsim.propagate as propagate
import fmdtools.resultdisp as rd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from drone_mdl import *
import time
from drone_opt import *
mdl = x_to_mdl([0, 2, 100, 0, 0])
params = mdl.params
endresults, resgraph, mdlhist = propagate.nominal(mdl)
rd.plot.mdlhistvals(mdlhist, fxnflowvals={'StoreEE': 'soc'})
#plot_nomtraj(mdlhist, params)
#plot_xy(mdlhist, endresults)
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 11 10:23:55 2020
@author: Daniel Hulse
"""
from drone_opt import *
import fmdtools.faultsim.propagate as propagate
import fmdtools.resultdisp.graph as graph
mdl_med = x_to_mdl([1,1,80,1,1])
calc_oper(mdl_med)
endresults_med, resgraph, mdlhist_med =propagate.nominal(mdl_med)
plot_xy(mdlhist_med, endresults_med, title='Flight plan at 80 m')
app_med = SampleApproach(mdl_med, faults='single-component', phases={'forward'})
faulttime_med = app_med.times
landtime_med = min([i for i,a in enumerate(mdlhist_med['functions']['Planpath']['mode']) if a=='taxi'])
rcost_med = calc_res(mdl_med)
rcost_med2 = x_to_rcost([1,1],[80],[1,1])
mdl_low = x_to_mdl([1,1,50,1,1])
calc_oper(mdl_low)
endresults_low, resgraph, mdlhist_low =propagate.nominal(mdl_low)
plot_xy(mdlhist_low, endresults_low, title='Flight plan at 50 m')
app_low = SampleApproach(mdl_low, faults='single-component', phases={'forward'})
faulttime_low = app_low.times
landtime_low = min([i for i,a in enumerate(mdlhist_low['functions']['Planpath']['mode']) if a=='taxi'])
rcost_low = calc_res(mdl_low)
rcost_low2 = x_to_rcost([1,1],[50],[1,1])
import fmdtools.faultsim.propagate as propagate
import fmdtools.resultdisp as rd
from ex_pump import * #required to import entire module
import time
mdl = Pump()
##NOMINAL PLOTS
#Before seeing how faults propogate, it's useful to see how the model performs
#in the nominal state to check to see that the model has been defined correctly.
# Some things worth checking:
# -are all functions on the graph?
# -are the functions connected with the correct flows?
# -do any faults occur in the nominal state?
# -do all the flow states proceed as desired over time?
endresults, resgraph, mdlhist = propagate.nominal(mdl, track=True)
#plot graph
rd.graph.show(resgraph)
#plot the flows over time
rd.plot.mdlhist(mdlhist, 'Nominal')
#we can also look at the value of states over time with a table
nominal_state_table = rd.tabulate.hist(mdlhist)
print(nominal_state_table)
#this table is a pandas dataframe we can export with:
# nominal_state_table.to_csv("filename.csv")
#plots can be made on the bipartite version of the graph
endresults_bip, resgraph_bip, mdlhist2 = propagate.nominal(mdl,
gtype='bipartite')
rd.graph.show(resgraph_bip, gtype='bipartite', scale=2)
def objective(x):
dm1 = PandemicModel(x)
endresults, resgraph, mdlhist_nom = propagate.nominal(dm1)
print('hi')
return endresults['classification']['total cost']
import fmdtools.faultsim.propagate as propagate
import fmdtools.resultdisp as rd
import csv
import time
import numpy as np
from fmd_model import PandemicModel
from scipy.optimize import differential_evolution
#from disease_model import *
#x0 = np.array([0.11 , 1 , 5 , 9.27 , 0.05 , 10 ])
x0 = [0.1, 2, 5, 10, 0.05, 10]
dm1 = PandemicModel(params={'x0': x0})
rd.graph.show(dm1.graph)
endresults, resgraph, mdlhist_nom = propagate.nominal(dm1)
#rd.plot.mdlhist(mdlhist_nom, fxnflows=['Campus'])
#normal_state_table = rd.tabulate.hist(mdlhist_nom)
#normal_state_table.to_csv('normal_state_table.csv')
rd.plot.mdlhistvals(
mdlhist_nom,
fxnflowvals={'City': ['Susceptible', 'Infected', 'Recovered']})
def objective(x):
dm1 = PandemicModel(x)
endresults, resgraph, mdlhist_nom = propagate.nominal(dm1)
print('hi')
