BaseData

BaseData represents one scientific quantity. Its required fields are a NumPy array-like signal and a Pint units value. It can additionally carry named absolute uncertainties, weights, coordinate axes, and rank_of_data.

import numpy as np

from modacor import ureg
from modacor.dataclasses.basedata import BaseData

intensity = BaseData(
    signal=np.array([100.0, 144.0]),
    units=ureg.count,
    uncertainties={"Poisson": np.array([10.0, 12.0])},
    rank_of_data=1,
)

Scalar and list inputs are converted to floating NumPy arrays. Existing NumPy arrays remain arrays without an unconditional dtype conversion. Every uncertainty and weight must be scalar or broadcast exactly to the signal shape; an operand that would enlarge the signal is rejected.

uncertainties stores one-standard-deviation values. The dictionary-like variances property exposes their squares and accepts variances on assignment. Use uncertainties in new code unless a variance-based algorithm specifically requires the view.

BaseData arithmetic applies Pint unit algebra and the documented uncertainty rules. It preserves axes, rank, and weights only when their structural metadata is compatible. Modules requiring stricter coordinate equality must validate that domain rule before arithmetic.

Useful operations include unit conversion, copying, slicing, squeezing, addition, subtraction, multiplication, division, powers, square roots, logarithms, exponentials, and trigonometric functions. Invalid units or domains fail explicitly or produce documented NaNs; uncertainty arrays are never silently discarded merely to make an operation succeed.