def td_hamiltonian(N, E, J, gamma, gammabar, timesteps, dt):
import numpy as np
from hamiltonian import get_hamiltonian
from densitymatrix import get_densitymatrix
from population import get_population
from liouville import get_liouvilleoperator
# Setup Hamiltonian and Initial conditions
H = get_hamiltonian(N, E, J)
rho = get_densitymatrix(N)
# Init population
pop = np.zeros((timesteps, N))
# Run in time
for t in range(timesteps):
# Calculate population first and normalize
pop[t, :] = get_population(rho)
norm = np.sum(pop[t, :])
rho = rho / norm
# Calculate rho_dot and update rho
rho_dot = get_liouvilleoperator(H, rho, gamma, gammabar)
rho = rho + rho_dot * dt
return (pop)
import numpy as np
import scipy.integrate
import matplotlib.pyplot as plt
import matplotlib.widgets # Cursor
import matplotlib.dates
import matplotlib.ticker
import shared
import world_data
import population
COUNTRY = 'Germany' # e.g. 'all' South Korea' 'France' 'Republic of Korea' 'Italy' 'Germany' 'US' 'Spain'
PROVINCE = 'all' # 'all' 'Hubei' # for provinces other than Hubei the population value needs to be set manually
EXCLUDECOUNTRIES = ['China'] if COUNTRY == 'all' else [] # massive measures early on
population = population.get_population(COUNTRY, PROVINCE, EXCLUDECOUNTRIES)
# --- parameters ---
if COUNTRY == 'Germany':
intensiveUnits = 28000 / 1.5 / 2 # ICU units available # Germany: 28000 # note that infections might show up in local clusters making things worse
else:
intensiveUnits = population * 1.0 / 10000 # rough guess for industrialized country
#intensiveUnits = ... # your country
logPlot = 1
E0 = 1 # exposed at initial time step
daysTotal = 365 # total days to model
dataOffset = 'auto' # position of real world data relative to model in whole days. 'auto' will choose optimal offset based on matching of deaths curves
cases = int(XCDR_data[-1, 1]) # last row, third column
deaths = int(XCDR_data[-1, 2]) # last row, third column
if deaths < 10:
continue
recovered = int(XCDR_data[-1, 3]) # last row, third column
date = XCDR_data[-1, 0]
countryDeaths.append((country, cases, deaths, recovered, date))
except Exception as e:
print("fail: ", country, sys.exc_info()[0], e)
countryDeathsPC = []
for ccdrd in countryDeaths:
country, cases, deaths, recovered, date = ccdrd
try:
pop = population.get_population(country)
countryDeathsPC.append(
(country, deaths * 1.0e6 / pop, deaths, pop, date))
#countryDeathrate.append((country, 100.0 * deaths / cases, deaths, pop))
except KeyError:
print("fail: ", country)
print()
countryDeathsPC = sorted(countryDeathsPC,
key=lambda x: x[1]) # sort by second subitem
countryDeathsPC.reverse() # in place
dCountryDeathsPCXY = {}
for country, trash, trash, trash, trash in countryDeathsPC[0:20]:
province = 'all'
country2 = country
import numpy as np
import scipy.integrate
import matplotlib.pyplot as plt
import matplotlib.widgets # Cursor
import world_data
import population
COUNTRY = 'Italy' #South Korea' # e.g. 'France' 'Republic of Korea' 'Italy' 'Germany'
PROVINCE = 'all' #'all' # 'Hubei'
#populations = {'Germany' : 81E6, 'France' : 67E6, 'Italy' : 61E6, 'Iran' : 81E6,
#'China' : 1438E6, 'Hubei' : 59E6, 'Republic of Korea' : 51E6}
if PROVINCE == 'all':
population = population.get_population(COUNTRY)
else:
population = populations[PROVINCE] # to be implemented
intensiveUnits = 14000 / 2 # ICU units available # Germany: 28000
logPlot = True
E0 = 1 # exposed at initial time step
days = 165 # total days to model
dataOffset = 25 # how many days will the real world country data be delayed in the model
days0 = dataOffset + 35 # days before lockdown measures
