def test_failures():
with pytest.raises(rtd.RTDInputError):
rtd.Ncstr(tau=-1, n=1, dt=DT, time_end=TIME_END)
with pytest.raises(rtd.RTDInputError):
rtd.Ncstr(tau=1, n=-1, dt=DT, time_end=TIME_END)
with pytest.raises(rtd.RTDInputError):
rtd.Ncstr(tau=1, n=1, dt=-1, time_end=TIME_END)
with pytest.raises(rtd.RTDInputError):
rtd.Ncstr(tau=1, n=1, dt=DT, time_end=-1)
def test_fail_nonmatching_dt():
a = rtdpy.Ncstr(n=1, tau=10, dt=DT, time_end=TIME_END)
inputtime = a.time * 10
inputsignal = np.zeros(inputtime.size)
inputsignal[inputtime > 10] = 1
with pytest.raises(rtdpy.RTDInputError):
a.output(inputtime, inputsignal)
def test_ncstr(n, tau):
a = rtd.Ncstr(n=n, tau=tau, dt=DT, time_end=TIME_END)
assert (np.isclose(a.integral(), 1, rtol=rtol, atol=atol))
assert (np.isclose(a.mrt(), analytical_mrt(tau), rtol=rtol, atol=atol))
assert (np.isclose(a.sigma(),
analytical_sigma(n, tau),
rtol=rtol,
atol=atol))
def test_repr():
from rtdpy import Ncstr # noqa needed for eval of repr
a1 = rtd.Ncstr(n=2, tau=1, dt=DT, time_end=TIME_END)
a = rtd.Elist([a1, a1])
b = eval("rtd." + repr(a))
assert np.isclose(a.integral(), b.integral(), rtol=rtol, atol=atol)
assert np.isclose(a.mrt(), b.mrt(), rtol=rtol, atol=atol)
assert np.isclose(a.sigma(), b.sigma(), rtol=rtol, atol=atol)
def test_iteration():
a = rtd.Ncstr(n=1, tau=1, dt=DT, time_end=TIME_END)
b = rtd.Pfr(tau=1, dt=DT, time_end=TIME_END)
c = rtd.Elist([a, b])
elist = [c, b, a]
d = rtd.Elist(elist)
for i, j in zip(elist, d):
assert i == j
def test_repr():
ncstr_model = rtd.Ncstr(n=1, tau=10, dt=DT, time_end=TIME_END)
def fun(t):
return np.interp(t, ncstr_model.time, ncstr_model.exitage)
a = rtd.Arbitrary(fun=fun, dt=DT, time_end=TIME_END)
# object repr includes fun object and cannot be directly regenerated
assert (repr(a))
def test_frequency_response(tau, n):
def analytical_response(tau, n, omegas):
return 1 / np.sqrt(1 + (omegas * tau / n)**2)**n
a = rtdpy.Ncstr(tau=tau, n=n, dt=.001, time_end=100)
omegas = np.logspace(-2, 2, 100)
mag = a.frequencyresponse(omegas)
assert np.allclose(mag, analytical_response(tau, n, omegas),
rtol=rtol, atol=atol)
def test_arbitrary():
ncstr_model = rtd.Ncstr(n=1, tau=10, dt=DT, time_end=TIME_END)
def fun(t):
return np.interp(t, ncstr_model.time, ncstr_model.exitage)
arbitrary_model = rtd.Arbitrary(fun, dt=DT, time_end=TIME_END)
assert (np.isclose(arbitrary_model.integral(), 1, rtol=rtol, atol=atol))
assert (np.isclose(arbitrary_model.mrt(), 10, rtol=rtol, atol=atol))
assert (np.isclose(arbitrary_model.sigma(), 100, rtol=rtol, atol=atol))
def test_listoflist():
n = 1
tau_cstr = 10
tau_pfr = 1
a = rtd.Ncstr(n=n, tau=tau_cstr, dt=DT, time_end=TIME_END)
b = rtd.Pfr(tau=tau_pfr, dt=DT, time_end=TIME_END)
c = rtd.Elist([a, b])
d = rtd.Elist([c, c])
assert len(d.time) == len(d.exitage)
assert (np.isclose(d.integral(), 1, rtol=rtol, atol=atol))
assert (np.isclose(d.mrt(), 22, rtol=rtol, atol=atol))
assert (np.isclose(d.sigma(), 200, rtol=rtol, atol=atol))
def test_lotsoflist_1(n):
time_end = (n + 1) * 10 + np.sqrt((n + 1) * 10**2) * 4
dt = DT
E = []
for i in range(n):
E.append(rtd.Ncstr(n=1, tau=10, dt=dt, time_end=time_end))
f = rtd.Elist(E)
assert f.time.size == f.exitage.size
assert np.isclose(f.integral(), 1, rtol=rtol * 10, atol=atol)
assert np.isclose(f.mrt(), (i + 1) * 10, rtol=rtol * 10, atol=atol)
assert np.isclose(f.sigma(), (i + 1) * 10**2, rtol=rtol * 10, atol=atol)
def test_funnelplot():
a = rtdpy.Ncstr(tau=1, n=1, dt=.001, time_end=10)
x, y, response = a.funnelplot(times=np.array([1]), disturbances=np.array([1]))
x2, y2 = np.meshgrid([1], [1])
assert x == x2
assert y == y2
inputtime = a.time
inputsignal = np.zeros(inputtime.size)
inputsignal[inputtime < 1] = 1
outputsignal = a.output(inputtime, inputsignal)
assert response[0, 0] == np.amax(outputsignal)
def test_step_output():
# test that a CSTR convolved with a step input signal
# is the same as a CSTR+PFR stepresponse
a = rtdpy.Ncstr(n=1, tau=10, dt=DT, time_end=TIME_END)
b = rtdpy.Pfr(tau=10, dt=DT, time_end=TIME_END)
c = rtdpy.Elist([a, b])
inputtime = a.time
inputsignal = np.zeros(inputtime.size)
inputsignal[inputtime > 10] = 1
outputsignal = a.output(inputtime, inputsignal)[:inputtime.size]
assert np.allclose(outputsignal, c.stepresponse, rtol=rtol, atol=atol)
def test1(tau_cstr, tau_pfr):
n = 1
a = rtd.Ncstr(n=n, tau=tau_cstr, dt=DT, time_end=TIME_END)
b = rtd.Pfr(tau=tau_pfr, dt=DT, time_end=TIME_END)
c = rtd.Elist([a, b])
assert len(c.time) == len(c.exitage)
assert (np.isclose(c.integral(), 1, rtol=rtol, atol=atol))
assert (np.isclose(c.mrt(),
analytical_mrt(tau_cstr=tau_cstr, tau_pfr=tau_pfr),
rtol=rtol,
atol=atol))
assert (np.isclose(c.sigma(),
analytical_sigma(n=n, tau_cstr=tau_cstr),
rtol=rtol,
atol=atol))
import matplotlib.pyplot as plt
import rtdpy
a = rtdpy.Ncstr(tau=1, n=1, dt=.01, time_end=15)
b = rtdpy.Pfr(tau=10, dt=.01, time_end=15)
c = rtdpy.Elist([a, b])
plt.plot(a.time, a.exitage, label="CSTR")
plt.plot(b.time, b.exitage, label="PFR")
plt.ylim(0, 1.1)
plt.title('Original RTD models')
plt.xlabel('Time')
plt.ylabel('Exit Age Function')
plt.legend()
plt.figure()
plt.plot(c.time, c.exitage)
plt.xlabel('Time')
plt.ylabel('Exit Age Function')
plt.title('Combination of models')
plt.show()
def test_repr():
a = rtd.Ncstr(n=2, tau=1, dt=DT, time_end=TIME_END)
b = eval("rtd." + repr(a))
assert np.isclose(a.integral(), b.integral(), rtol=rtol, atol=atol)
assert np.isclose(a.mrt(), b.mrt(), rtol=rtol, atol=atol)
assert np.isclose(a.sigma(), b.sigma(), rtol=rtol, atol=atol)
def test_negative_tau_error(tau):
with pytest.raises(rtd.RTDInputError):
rtd.Ncstr(n=1, tau=tau, dt=1, time_end=1)
def test_negative_n_error(n):
with pytest.raises(rtd.RTDInputError):
rtd.Ncstr(n=n, tau=1, dt=1, time_end=1)
def test_negative_dt_error(dt):
with pytest.raises(rtd.RTDInputError):
rtd.Ncstr(n=1, tau=1, dt=dt, time_end=1)
def test_negative_time_end_error(time_end):
with pytest.raises(rtd.RTDInputError):
rtd.Ncstr(n=1, tau=1, dt=1, time_end=time_end)
import matplotlib.pyplot as plt
import rtdpy
for n in [1, 2, 10]:
a = rtdpy.Ncstr(tau=1, n=n, dt=.01, time_end=3)
plt.plot(a.time, a.exitage, label=f"n={n}")
plt.xlabel('Time')
plt.ylabel('Exit Age Function')
plt.legend()
plt.show()
def test_exitage_norm():
a = rtdpy.Ncstr(n=1, tau=1, dt=DT, time_end=TIME_END)
assert np.allclose(a.exitage_norm, a.exitage, rtol=rtol, atol=atol)
def test_exitage_norm_clipped():
a = rtdpy.Ncstr(n=1, tau=1, dt=DT, time_end=2)
assert not np.isclose(a.integral(), 1, rtol=rtol, atol=atol)
assert np.isclose(np.trapz(a.exitage_norm, a.time), 1,
rtol=rtol, atol=atol)
def test_fail_w_nonRTD():
a = rtd.Ncstr(n=1, tau=1, dt=DT, time_end=TIME_END)
with pytest.raises(rtd.RTDInputError):
rtd.Elist([a, 'NCstr'])
def test_fail_w_time_end():
a = rtd.Ncstr(n=1, tau=1, dt=DT, time_end=TIME_END)
b = rtd.Ncstr(n=1, tau=1, dt=DT, time_end=TIME_END * 2)
with pytest.raises(rtd.RTDInputError):
rtd.Elist([a, b])
