Miscellaneous¶
Random number generator¶
The RNG used internally for random subsets and probability filtering is automatically seeded with a random seed when
the module is being imported. To manually set the seed use the init_random
function.
-
catana.
init_random
(seed=None)¶ Sets the random seed for the io and decomposition module (used for filtering)
The catana.io and catana.decomposition module contain their own random number generator. Calling this function will initialize both of them and setting the same seed, if seed is not None.
Parameters: seed (unsigned int) – The random seed. If None, will be drawn from a random device.
Function Interpolator¶
-
class
catana.
FunctionInterpolator
(self: catana.basictypes.FunctionInterpolator, function: Callable[[float], float], interpolation_points: int, x_min: float, x_max: float) → None¶ A function sampler and interpolator
Samples the given function with
interpolation_points
number of equidistantly distributed points betweenx_min
andx_max
. When called at a given position, interpolates linearly from the two closest sample points. Note that evaluating the function for valuesx >= x_max
andx < x_min
raises a ValueError.The FunctionInterpolator mainly serves to speed up computations since we do not need to do expensive Python object calls once it is initialized.
Construct a FunctionInterpolator from a function
Parameters: - function (callable object) – the function to be sampled and interpolated
- points (interpolation) – number of points at which the function needs to be sampled
- x_min (float) – lower boundary of sample points
- x_max (float) – upper boundary of sample points
-
__call__
(*args, **kwargs)¶ Overloaded function.
- __call__(self: catana.basictypes.FunctionInterpolator, x: float) -> float
Evaluate the FunctionInterpolator at the given value x.
Warning
x must be within the boundaries [x_min, x_max) for which the FunctionInterpolator was set up.
Parameters: x (float) – evaluation point - __call__(self: catana.basictypes.FunctionInterpolator, x: numpy.ndarray[float64]) -> object
Evaluate the FunctionInterpolator at the given values x.
Warning
each element in x must be within the boundaries [x_min, x_max) for which the FunctionInterpolator was set up.
Parameters: x (numpy.ndarray[float]) – evaluation points