def run_parameter_scan(parallel=False):
"""Perform a parameter scan"""
# [2] value scan
scan_changeset = ChangeSet.scan_changeset('n',
values=np.linspace(start=2,
stop=10,
num=8))
tcsims = ensemble(sim=TimecourseSim([
Timecourse(start=0, end=100, steps=100, changes={}),
Timecourse(start=0, end=60, steps=100, changes={'[X]': 10}),
Timecourse(start=0, end=60, steps=100, changes={'X': 10}),
]),
changeset=scan_changeset)
print(tcsims[0])
if parallel:
simulator = SimulatorParallel(path=MODEL_REPRESSILATOR)
results = simulator.timecourses(tcsims)
assert isinstance(results, Result)
else:
simulator = SimulatorSerial(path=MODEL_REPRESSILATOR)
results = simulator.timecourses(tcsims)
assert isinstance(results, Result)
return results
def stepped_clamp(simulator, r):
changes_init = pkpd.init_concentrations_changes(r, 'som', 0E-6) # [0 nmol/L]
tcsims = ensemble(
TimecourseSim([
Timecourse(start=0, end=60, steps=120, changes=changes_init),
Timecourse(start=0, end=60, steps=120, changes={'Ri_som': 10.0E-6}), # [mg/min],
Timecourse(start=0, end=60, steps=120, changes={'Ri_som': 20.0E-6}), # [mg/min],
Timecourse(start=0, end=60, steps=120, changes={'Ri_som': 40.0E-6}), # [mg/min],
Timecourse(start=0, end=60, steps=120, changes={'Ri_som': 80.0E-6}), # [mg/min],
]), ChangeSet.parameter_sensitivity_changeset(r, 0.1)
)
return simulator.timecourses(tcsims)
def run_sensitivity():
""" Parameter sensitivity simulations.
:return:
"""
simulator = Simulator(MODEL_REPRESSILATOR)
# parameter sensitivity
# FIXME: make work with parallel
r = load_model(MODEL_REPRESSILATOR)
changeset = ChangeSet.parameter_sensitivity_changeset(r)
tc_sim = TimecourseSim([
Timecourse(start=0, end=100, steps=100),
Timecourse(start=0,
end=200,
steps=100,
model_changes={"boundary_condition": {
"X": True
}}),
Timecourse(start=0,
end=100,
steps=100,
model_changes={"boundary_condition": {
"X": False
}}),
])
tc_sims = ensemble(tc_sim, changeset=changeset)
result = simulator.timecourses(tc_sims)
# create figure
fig, (ax1) = plt.subplots(nrows=1, ncols=1, figsize=(5, 5))
fig.subplots_adjust(wspace=0.3, hspace=0.3)
add_line(ax=ax1, data=result, xid='time', yid="X", label="X")
add_line(ax=ax1,
data=result,
xid='time',
yid="Y",
label="Y",
color="darkblue")
add_line(ax=ax1,
data=result,
xid='time',
yid="Z",
label="Z",
color="darkorange")
ax1.legend()
plt.show()
def po_bolus(simulator, r):
""" Oral bolus dose
oral dose (single dose, at given start with given dose).
Examples are the oral glucose tolerance test (OGTT) or the application of a drug orally,
e.g., codeine, midazolam, caffeine or paracetamol.
:return:
"""
# set initial concentration of somatostatin in all blood compartments
changes_init = pkpd.init_concentrations_changes(r, 'som', 0E-6) # [0 nmol/L]
# oral bolus dose
# FIXME: dosing changesets
changes_po_bolus = {
'PODOSE_som': 10.0E-6, # [mg]
}
# simulate
tcsims = ensemble(
TimecourseSim([
Timecourse(start=0, end=1200, steps=600,
changes={
**changes_init,
**changes_po_bolus}
)
]),
changeset=ChangeSet.parameter_sensitivity_changeset(r, 0.1)
)
return simulator.timecourses(tcsims)
def test_plotting():
r = load_model(MODEL_REPRESSILATOR)
simulator = Simulator(MODEL_REPRESSILATOR)
changeset = ChangeSet.parameter_sensitivity_changeset(r, sensitivity=0.5)
tcsims = ensemble(
TimecourseSim([
Timecourse(start=0, end=400, steps=400),
]), changeset)
result = simulator.timecourses(tcsims)
# create figure
fig, (ax1) = plt.subplots(nrows=1, ncols=1, figsize=(5, 5))
fig.subplots_adjust(wspace=0.3, hspace=0.3)
add_line(ax=ax1, data=result, xid='time', yid="X", label="X")
add_line(ax=ax1,
data=result,
xid='time',
yid="Y",
label="Y",
color="darkblue")
ax1.legend()
plt.show()
def mix(simulator, r):
"""
[5] combination experiments
- somatostatin infusion + c-peptide bolus
- hyperinsulinemic, euglycemic clamp
"""
changes_init = pkpd.init_concentrations_changes(r, 'som', 0E-6) # [0 nmol/L]
tcsims = ensemble(
TimecourseSim([
Timecourse(start=0, end=60, steps=120, changes=changes_init),
Timecourse(start=0, end=60, steps=120, changes={'IVDOSE_som': 10.0E-6}),
Timecourse(start=0, end=60, steps=240, changes={'Ri_som': 10.0E-6}), # [mg/min],
Timecourse(start=0, end=60, steps=120, changes={'Ri_som': 10.0E-6, 'PODOSE_som': 10.0E-4}),
Timecourse(start=0, end=120, steps=240, changes={'Ri_som': 0.0}), # [mg/min],
]), ChangeSet.parameter_sensitivity_changeset(r, 0.1))
return simulator.timecourses(tcsims)
def iv_infusion(simulator, r):
""" [3] constant infusion (for given period, tstart, tend)
- somatostatin infusion
- insulin infusion
- glucose infusion
- glucagon infusion
:return:
"""
changes_init = pkpd.init_concentrations_changes(r, 'som', 0E-6) # [0 nmol/L]
tcsims = ensemble(
TimecourseSim([
Timecourse(start=0, end=60, steps=120, changes=changes_init),
Timecourse(start=0, end=120, steps=240, changes={'Ri_som': 10.0E-6}), # [mg/min],
Timecourse(start=0, end=120, steps=240, changes={'Ri_som': 0.0}), # [mg/min],
]), ChangeSet.parameter_sensitivity_changeset(r, 0.1)
)
return simulator.timecourses(tcsims)
def clamp(simulator, r):
"""
clamping of substance (e.g. glucose, tstart, tend)
- glucose clamping (euglycemic clamp)
- insulin clamping (insulin clamp)
:return:
"""
changes_init = pkpd.init_concentrations_changes(r, 'som', 0E-6) # [0 nmol/L]
# FIXME: some bug in the concentrations and assignments
tcsims = ensemble(
TimecourseSim([
Timecourse(start=0, end=60, steps=120, changes={**changes_init, **{'PODOSE_som': 1E-9}}),
Timecourse(start=0, end=120, steps=240, model_changes={'boundary_condition': {"Ave_som": True}}), # clamp venous som
Timecourse(start=0, end=120, steps=240, model_changes={'boundary_condition': {"Ave_som": False}}), # release venous som,
]), ChangeSet.parameter_sensitivity_changeset(r, 0.1)
)
return simulator.timecourses(tcsims)
def test_timecourse_simulation():
simulator = Simulator(MODEL_REPRESSILATOR)
s = simulator.timecourse(Timecourse(start=0, end=100, steps=100))
assert s is not None
s = simulator.timecourse(
Timecourse(start=0, end=100, steps=100, changes={"PX": 10.0}))
assert s is not None
assert isinstance(s, pd.DataFrame)
assert "time" in s
assert len(s.time) == 101
assert s.PX[0] == 10.0
s = simulator.timecourse(
TimecourseSim(timecourses=[
Timecourse(start=0, end=100, steps=100, changes={"[X]": 10.0})
]))
assert s is not None
def run_clamp():
def plot_result(result: Result, title: str = None) -> None:
# create figure
fig, (ax1) = plt.subplots(nrows=1, ncols=1, figsize=(5, 5))
fig.subplots_adjust(wspace=0.3, hspace=0.3)
plotting.add_line(ax=ax1, data=result, xid='time', yid="X", label="X")
plotting.add_line(ax=ax1,
data=result,
xid='time',
yid="Y",
label="Y",
color="darkblue")
if title:
ax1.set_title(title)
ax1.legend()
plt.show()
# reference simulation
simulator = Simulator(MODEL_REPRESSILATOR)
tcsim = TimecourseSim([
Timecourse(start=0, end=400, steps=400, changes={"X": 10}),
# clamp simulation
Timecourse(start=0,
end=200,
steps=200,
model_changes={'boundary_condition': {
'X': True
}}),
# free simulation
Timecourse(start=0,
end=400,
steps=400,
model_changes={'boundary_condition': {
'X': False
}}),
])
result = simulator.timecourses(tcsim)
assert isinstance(result, Result)
plot_result(result, "clamp experiment (400-600)")
def example_multiple_actors():
"""Multiple independent simulator actors.
Actors should not be created manually, use the Simulator
classes for simulations.
"""
# create ten Simulators.
simulators = [SimulatorActor.remote(MODEL_REPRESSILATOR) for _ in range(16)]
# define timecourse
tcsim = TimecourseSim([
Timecourse(start=0, end=100, steps=100),
Timecourse(start=0, end=100, steps=100, changes={"X": 10, "Y": 20}),
])
# run simulation on simulators
tc_ids = [s.timecourse.remote(tcsim) for s in simulators]
# collect results
results = ray.get(tc_ids)
return results
def test_timecourse_combined():
simulator = Simulator(MODEL_REPRESSILATOR)
s = simulator.timecourse(simulation=TimecourseSim([
Timecourse(start=0, end=100, steps=100),
Timecourse(start=0,
end=50,
steps=100,
model_changes={"boundary_condition": {
"X": True
}}),
Timecourse(start=0,
end=100,
steps=100,
model_changes={"boundary_condition": {
"X": False
}}),
]))
assert isinstance(s, pd.DataFrame)
assert "time" in s
assert s.time.values[-1] == 250.0
def example_single_actor():
""" Creates a single stateful simulator actor and executes timecourse.
Normally multiple actors are created which execute the simulation
load together. Actors should not be created manually, use the Simulator
classes for simulations.
:return:
"""
# Create single actor process
sa = SimulatorActor.remote(MODEL_REPRESSILATOR)
# run simulation
tcsim = TimecourseSim([
Timecourse(start=0, end=100, steps=100),
Timecourse(start=0, end=100, steps=100, changes={"X": 10, "Y": 20}),
])
tc_id = sa.timecourse.remote(tcsim)
print("-" * 80)
print(ray.get(tc_id))
print("-" * 80)
def test_timecourse_ensemble():
changeset = [
{
"[X]": 10.0
},
{
"[X]": 15.0
},
{
"[X]": 20.0
},
{
"[X]": 25.0
},
]
simulator = Simulator(MODEL_REPRESSILATOR)
tcsims = ensemble(TimecourseSim([
Timecourse(start=0, end=400, steps=400),
]),
changeset=changeset)
result = simulator.timecourses(tcsims)
assert isinstance(result, Result)
def iv_bolus(simulator, r):
""" [2] bolus injection (at given time tstart, with given dose and given injection time, e.g. 5-10 min)
- c-peptide bolus
- ivGTT (glucose bolus)
- insulin injection
"""
changes_init = pkpd.init_concentrations_changes(r, 'som', 0E-6) # [0 nmol/L]
changes_iv_bolus = {
'IVDOSE_som': 10E-6, # [mg]
}
p_changeset = ChangeSet.parameter_sensitivity_changeset(r, 0.1)
tcsims = ensemble(
TimecourseSim([
Timecourse(start=0, end=1200, steps=600,
changes={
**changes_init,
**changes_iv_bolus}
)
]), p_changeset)
return simulator.timecourses(tcsims)
def test_simulation_parallel():
tcsims = []
tc_sim = TimecourseSim([
Timecourse(
start=0,
end=100,
steps=100,
changes={
'IVDOSE_som': 0.0, # [mg]
'PODOSE_som': 0.0, # [mg]
'Ri_som': 10.0E-6, # [mg/min]
})
])
# collect multiple simulation definitions (see also the ensemble functions)
nsim = 100
for _ in range(nsim):
tcsims.append(tc_sim)
simulator = Simulator(path=MODEL_GLCWB, actor_count=15)
results = simulator.timecourses(simulations=tcsims)
def run_timecourse_examples():
""" Run various timecourses. """
simulator = Simulator(MODEL_REPRESSILATOR)
# 1. simple timecourse simulation
print("*** simple timecourse ***")
tc_sim = TimecourseSim(Timecourse(start=0, end=100, steps=100))
s1 = simulator.timecourses(tc_sim)
print(tc_sim)
# 2. timecourse with parameter changes
print("*** parameter change ***")
tc_sim = TimecourseSim(
Timecourse(start=0, end=100, steps=100, changes={
"X": 10,
"Y": 200
}))
s2 = simulator.timecourses(tc_sim)
print(tc_sim)
# 3. combined timecourses
print("*** combined timecourse ***")
tc_sim = TimecourseSim([
Timecourse(start=0, end=100, steps=100),
Timecourse(start=0, end=100, steps=100, changes={
"X": 10,
"Y": 20
}),
])
s3 = simulator.timecourses(tc_sim)
print(tc_sim)
# 4. combined timecourses with model_change
print("*** model change ***")
tc_sim = TimecourseSim([
Timecourse(start=0, end=100, steps=100),
Timecourse(start=0,
end=50,
steps=100,
model_changes={"boundary_condition": {
"X": True
}}),
Timecourse(start=0,
end=100,
steps=100,
model_changes={"boundary_condition": {
"X": False
}}),
])
s4 = simulator.timecourses(tc_sim)
print(tc_sim)
# create figure
fig, (ax1, ax2, ax3, ax4) = plt.subplots(nrows=1, ncols=4, figsize=(20, 5))
fig.subplots_adjust(wspace=0.3, hspace=0.3)
ax1.set_title("simple timecourse")
ax2.set_title("parameter change")
ax3.set_title("combined timecourse")
ax4.set_title("model change")
for s, ax in [(s1, ax1), (s2, ax2), (s3, ax3), (s4, ax4)]:
df = s.frames[0]
ax.plot(df.time, df.X, label="X")
ax.plot(df.time, df.Y, label="Y")
ax.plot(df.time, df.Z, label="Z")
for ax in (ax1, ax2, ax3, ax4):
ax.legend()
ax.set_xlabel("time")
ax.set_ylabel("concentration")
plt.show()
def example_parallel_timecourse(nsim=40, actor_count=15):
"""Execute multiple simulations with model in parallel.
:param nsim: number of simulations
:return:
"""
tcsims = []
tc_sim = TimecourseSim([
Timecourse(start=0, end=100, steps=100,
changes={
'IVDOSE_som': 0.0, # [mg]
'PODOSE_som': 0.0, # [mg]
'Ri_som': 10.0E-6, # [mg/min]
})
])
# collect all simulation definitions (see also the ensemble functions)
for _ in range(nsim):
tcsims.append(tc_sim)
def message(info, time):
print(f"{info:<10}: {time:4.3f}")
# load model once for caching (fair comparison)
r = roadrunner.RoadRunner(MODEL_GLCWB)
print("-" * 80)
print(f"Run '{nsim}' simulations")
print("-" * 80)
simulator_defs = [
{
"key": "parallel",
'simulator': SimulatorParallel,
'kwargs': {'actor_count': actor_count}
},
{
"key": "serial",
'simulator': SimulatorSerial,
'kwargs': {}
},
]
sim_info = []
for sim_def in simulator_defs:
key = sim_def['key']
print("***", key, "***")
Simulator = sim_def['simulator']
kwargs = sim_def['kwargs']
# run simulation (with model reading)
start_time = time.time()
# create a simulator with 16 parallel actors
simulator = Simulator(path=MODEL_GLCWB, **kwargs)
load_time = time.time()-start_time
message(f"load", load_time)
start_time = time.time()
results = simulator.timecourses(simulations=tcsims)
sim_time = time.time()-start_time
total_time = load_time + sim_time
message("simulate", sim_time)
message("total", total_time)
assert(len(results) == len(tcsims))
time.sleep(3)
# run parallel simulation (without model reading)
start_time = time.time()
results = simulator.timecourses(simulations=tcsims)
repeat_time = time.time()-start_time
message(f"repeat", repeat_time)
assert (len(results) == len(tcsims))
actor_count = kwargs.get('actor_count', 1)
times = {
"load": load_time,
"simulate": sim_time,
"total": total_time,
"repeat": repeat_time,
}
sim_info.extend([{
"key": key,
"nsim": nsim,
"actor_count": actor_count,
"time_type": k,
"time": v,
} for (k, v) in times.items()])
print("-" * 80)
return sim_info