Source code for pyrfu.pyrf.ts_scalar

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# Built-in imports
from typing import Mapping, Optional, Union

# 3rd party imports
import numpy as np
import xarray as xr
from numpy.typing import NDArray
from xarray.core.dataarray import DataArray

__author__ = "Louis Richard"
__email__ = "louisr@irfu.se"
__copyright__ = "Copyright 2020-2024"
__license__ = "MIT"
__version__ = "2.4.13"
__status__ = "Prototype"


def ts_scalar(
    time: NDArray[np.datetime64],
    data: NDArray[Union[np.float32, np.float64]],
    attrs: Optional[Mapping[str, object]] = None,
) -> DataArray:
    r"""Create a time series containing a 0th order tensor

    Parameters
    ----------
    time : numpy.ndarray
        Array of times.
    data : numpy.ndarray
        Data corresponding to the time list.
    attrs : dict, Optional
        Attributes of the data list.

    Returns
    -------
    DataArray
        0th order tensor time series.

    """
    # Check input type
    if not isinstance(time, np.ndarray):
        raise TypeError("time must be a numpy.ndarray")

    if not isinstance(data, np.ndarray):
        raise TypeError("data must be a numpy.ndarray")

    # Check input shape must be (n, )
    if data.ndim != 1:
        raise ValueError("Input must be a scalar")

    if len(time) != len(data):
        raise ValueError("Time and data must have the same length")

    if attrs is None:
        attrs = {"TENSOR_ORDER": 0}
    elif isinstance(attrs, dict):
        attrs["TENSOR_ORDER"] = 0
    else:
        raise TypeError("attrs must be a dict")

    return xr.DataArray(data, coords=[time[:]], dims="time", attrs=attrs)