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Analysis

estimate_linear_background(x, data, points_cut=0.1)

Estimates a linear background hihi hoho

Source code in sqil_core/utils/analysis.py 
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def estimate_linear_background(x: np.ndarray, data: np.ndarray, points_cut=0.1) -> list:
    """Estimates a linear background hihi hoho"""
    is1D = len(data.shape) == 1
    points = data.shape[0] if is1D else data.shape[1]
    cut = int(points * points_cut)

    # Consider just the cut points
    x_data = x[0:cut] if is1D else x[0:cut, :]
    X = np.vstack([np.ones_like(x_data), x_data]).T
    y_data = data[0:cut] if is1D else data[0:cut, :]

    # Linear fit
    coefficients, residuals, _, _ = np.linalg.lstsq(
        X, y_data if is1D else y_data.T, rcond=None
    )

    return coefficients

remove_linear_background(x, data, points_cut=0.1)

Removes a linear background from the input data (e.g. the phase background of a spectroscopy).

Parameters

data : np.ndarray Input data. Can be a 1D vector or a 2D matrix.

Returns

np.ndarray The input data with the linear background removed. The shape of the returned array matches the input data.

Source code in sqil_core/utils/analysis.py 
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def remove_linear_background(
    x: np.ndarray, data: np.ndarray, points_cut=0.1
) -> np.ndarray:
    """Removes a linear background from the input data (e.g. the phase background
    of a spectroscopy).


    Parameters
    ----------
    data : np.ndarray
        Input data. Can be a 1D vector or a 2D matrix.

    Returns
    -------
    np.ndarray
        The input data with the linear background removed. The shape of the
        returned array matches the input `data`.
    """
    coefficients = estimate_linear_background(x, data, points_cut)

    # Remove background over the whole array
    X = np.vstack([np.ones_like(x), x]).T
    return data - (X @ coefficients).T

remove_offset(data, avg=3)

Removes the initial offset from a data matrix or vector by subtracting the average of the first avg points. After applying this function, the first point of each column of the data will be shifted to (about) 0.

Parameters

data : np.ndarray Input data, either a 1D vector or a 2D matrix avg : int, optional The number of initial points to average when calculating the offset, by default 3

Returns

np.ndarray The input data with the offset removed

Source code in sqil_core/utils/analysis.py 
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def remove_offset(data: np.ndarray, avg: int = 3) -> np.ndarray:
    """Removes the initial offset from a data matrix or vector by subtracting
    the average of the first `avg` points. After applying this function,
    the first point of each column of the data will be shifted to (about) 0.

    Parameters
    ----------
    data : np.ndarray
        Input data, either a 1D vector or a 2D matrix
    avg : int, optional
        The number of initial points to average when calculating
        the offset, by default 3

    Returns
    -------
    np.ndarray
       The input data with the offset removed
    """
    is1D = len(data.shape) == 1
    if is1D:
        return data - np.mean(data[0:avg])
    return data - np.mean(data[:, 0:avg], axis=1).reshape(data.shape[0], 1)