A small @dataclass-like decorator for python classes. The class will store its values in a single contiguous numpy array. It can also be converted to and from plain numpy arrays.
poetry add dataclasses or pip install dataclasses
def simulate(steps: int, fs: float) -> np.ndarray:
# Define the state class.
# Imagine we had 20 variables here...
@arrayclasses.arrayclass
class State:
x: float
y: float
def step(t, xy):
# normally, this would be `x, y, ... = xy`
s = arrayclasses.from_array(State, xy)
a = 1 - np.sqrt(s.x ** 2 + s.y ** 2)
w = 2 * np.pi / (1 * fs)
# normally, this would be `return (..., ...)`
return State(
x=a * s.x - w * s.y,
y=a * s.y + w * s.x,
)
solved = integrate.solve_ivp(
fun=step,
y0=State(-1, 0),
t_span=(0, steps),
method="RK45"
)
return solved.y@arrayclasses.arrayclass(dtype=object) # You can coerce the array dtype manually
class Object:
x: int # A single value.
y: tuple[int, int] # Will yield np.ndarray windows, not tuples. Should be np.ndarray[float, ...] but requires PEP 646 to work.
a = Object(x=5, y=(2, 3))
print(len(a)) # 3
print(tuple(a)) # (5, 2, 3)You may be forced, or inclined, to use numpy arrays in some situations where classes would be more appropriate.
An example might be scipy.integrate - You are working with an array of numbers that really wants to be a class.
Packing and unpacking tuples is a common workaround. However, when you approach 10 or 20 variables, this gets quite messy fast. Now, you might prefer to use an @arrayclass to get nicer code that plays well with your IDE.