def _sample_poisson_process(self, n=None, length=None):
"""Generate a realization of a Poisson process.
Generate a poisson process sample up to count of length if time=False,
otherwise generate a sample up to time t=length if time=True
"""
if n is not None:
check_positive_integer(n)
exponentials = self.rng.exponential(scale=1.0 / self.rate, size=n)
s = np.array([0] + list(np.cumsum(exponentials)))
return s
elif length is not None:
check_positive_number(length, "Sample length")
t = 0
times = [0]
exp_rate = 1.0 / self.rate
while t < length:
t += self.rng.exponential(scale=exp_rate)
times.append(t)
return np.array(times)
else:
raise ValueError("Must provide either argument n or length.")
def test_check_positive_number(positive_number_fixture,
parameter_name_fixture):
if positive_number_fixture <= 0:
with pytest.raises(ValueError):
check_positive_number(positive_number_fixture,
parameter_name_fixture)
else:
assert check_positive_number(positive_number_fixture,
parameter_name_fixture) is None
def _sample_geometric_brownian_motion(self, n, initial=1.0):
"""Generate a realization of geometric Brownian motion."""
check_positive_integer(n)
check_positive_number(initial, "Initial")
# Opt for repeated use
if self._n != n:
self._n = n
self._line = generate_times(self.drift - self.volatility**2 / 2.0,
n)
noise = self.volatility * self._brownian_motion.sample(n)
return initial * np.exp(self._line + noise)
def scale(self, value):
check_positive_number(value, "Scale")
self._scale = value
def t(self, value):
check_positive_number(value, "Time end")
self._t = float(value)
def volatility(self, value):
check_positive_number(value, "Volatility")
self._volatility = value
def variance(self, value):
check_positive_number(value, "Variance")
self._variance = value
def rate(self, value):
check_positive_number(value, "Rate parameter")
self._rate = value
def mean(self, value):
check_positive_number(value, "Mean parameter")
self._mean = float(value)
def alpha(self, value):
check_positive_number(value)
self._alpha = value
