flow.addSource(glycolaldehyde)
flow.addSink(glycolaldehyde)
flow.addConstraint(inFlow(formaldehyde) == 2)
flow.addConstraint(outFlow(glycolaldehyde) == 2)
flow.addConstraint(inFlow(glycolaldehyde) == 1)
flow.calc()
solution = list(flow.solutions)[0]
solution.print()
flow_graph = HyperGraph.from_flow_solution(solution)
ode = dgDynamicSim(flow_graph)
stochastic = dgDynamicSim(flow_graph, simulator_choice="stochastic")
parameters = {edge: 0.5 for edge in ode.abstract_edges}
initial_conditions = {
'Formaldehyde': 100,
'Glycolaldehyde': 1,
}
drain_parameters = {symbol: {
'out': {
'factor': 0.015
}
} for symbol in ode.symbols}
def add_natural_drain(symbols, natural_drain=0.0001):
count = 0
for sym in symbols:
if sym not in drain_params or 'out' not in drain_params[sym]:
drain_params[sym] = {'out': {'factor': natural_drain}}
count += 1
print("Natural out drain set to {} for {} reactions".format(
natural_drain, count))
parameter_matrix = tuple({
r: rate_dict
for r, rate_dict in zip(reactions, generate_rates(reactions))
} for _ in range(runs))
ode = dgDynamicSim(dg)
stochastic = dgDynamicSim(dg, 'stochastic')
add_natural_drain(ode.symbols)
sim_end_time = 60000
period_bounds = (600, sim_end_time / 2) # looking from 600 to 30000
dt = "{:%Y%m%d%H%M%S}".format(datetime.datetime.now())
plot_names_file = "plots_scores_{}_{}_{}_{}.txt".format(
file_prefix, plugin_name, method_name, dt)
# shot format file that should help in sample classification
measurement_output = {
'variance': [],
'fourier_amp': [],
'fourier_freq': [],
recovered_stays_recovered
)
initial_conditions = {
'S': 400,
'I': 2,
}
parameters = {
susceptible_infected: 0.001,
infected_stays_infected: 0.001,
recovered_stays_recovered: 0.001,
recovered: 0.03,
}
ode = dgDynamicSim(dg, simulator_choice="ODE")
stochastic = dgDynamicSim(dg, simulator_choice="stochastic")
# Name of the data set
name = "infected"
# figure_size in centimetres
figure_size = (40, 20)
end_t = 200
with stochastic("spim") as spim:
for i in range(3):
spim(end_t, initial_conditions, parameters,).plot(figure_size=figure_size)
with stochastic("stochkit2") as stochkit2:
stochkit2.trajectories = 3
stochkit2.simulate(end_t, initial_conditions, parameters,).plot(figure_size=figure_size)
parameters = {
foxes_hunts: 0.005,
rabbit_multiples: 0.7,
}
drain_parameters = {
'F': {
'out': {
'factor': 0.5
}
}
}
end_t = 100
ode = dgDynamicSim(dg, simulator_choice='ode')
stochastic = dgDynamicSim(dg, simulator_choice='stochastic')
# Name of the data set
name = "foxesRabbits"
figure_size = (40, 20)
scipy = ode('scipy')
matlab = ode('matlab')
# Run the simulation and give us the output and the analytic class
# which gives access to computation of the Fourier transformation etc..
# The output can be inspected and plotted like a normal output class
output, analytics = DynamicAnalysisDevice.from_simulation(scipy, end_t, initial_conditions,
parameters, drain_parameters)
