def get_data(self, parameters: dict, x0: float, y0: float, min_t: float, max_t: float, subdivisions: int) -> list:
x, y = self.get_prediction(parameters, x0, y0)
data = []
for i in range(subdivisions):
t = lerp(min_t, max_t, i/(subdivisions-1))
data.append((t, x(t), y(t)))
return data
def y(t):
# check whether the value of t is not too large or too small
if t < 0 or t > self.max_t:
raise ModelException("t={} out of bounds".format(t))
# find the indices of the nearest points in time for which the amount was computed
index = math.floor(t / self.dt)
next_index = math.ceil(t / self.dt)
# interpolate
if index == next_index:
return y_values[index]
else:
return lerp(y_values[index], y_values[index + 1],
t / self.dt - index)
def y(t):
# check whether the value of t is not too large or too small
if t < 0 or t > self.max_t:
raise ModelException("t={} out of bounds".format(t))
# find the indices of the nearest points in time for which the amount was computed
index = math.floor(t * steps_per_time)
next_index = math.ceil(t * steps_per_time)
# interpolate
if index == next_index:
return result[index, 1]
else:
return lerp(result[index, 1], result[index + 1, 1],
t * steps_per_time - index)