Source code for ltsm.prompt_reader.stat_prompt.tsfel.utils.signal_processing

import numpy as np
import pandas as pd
from scipy.interpolate import interp1d




def signal_window_splitter(signal, window_size, overlap=0):
    """Splits the signal into windows
    Parameters
    ----------
    signal : nd-array or pandas DataFrame
        input signal
    window_size : int
        number of points of window size
    overlap : float
        percentage of overlap, value between 0 and 1 (exclusive)
        Default: 0
    Returns
    -------
    list
        list of signal windows
    """
    if not isinstance(window_size, int):
        raise SystemExit('window_size must be an integer.')
    step = int(round(window_size)) if overlap == 0 else int(round(window_size * (1 - overlap)))
    if step == 0:
        raise SystemExit('Invalid overlap. '
                         'Choose a lower overlap value.')
    if len(signal) % window_size == 0 and overlap == 0:
        return [signal[i:i + window_size] for i in range(0, len(signal), step)]
    else:
        return [signal[i:i + window_size] for i in range(0, len(signal) - window_size + 1, step)]





def merge_time_series(data, fs_resample, time_unit):
    """Time series data interpolation

    Parameters
    ----------
    data : dict
        data to interpolate
    fs_resample :
        resample sampling frequency
    time_unit :
        time unit in seconds

    Returns
    -------
    DataFrame
        Interpolated data

    """

    # time interval for interpolation
    sensors_time = np.array([[dn.iloc[0, 0], dn.iloc[-1, 0]] for k, dn in data.items()])
    t0 = np.max(sensors_time[:, 0])
    tn = np.min(sensors_time[:, 1])
    x_new = np.linspace(t0, tn, int((tn - t0) / ((1 / fs_resample) * time_unit)))

    # interpolation
    data_new = np.copy(x_new.reshape(len(x_new), 1))
    header_values = ['time']
    for k, dn in data.items():
        header_values += [k + str(i) for i in range(1, np.shape(dn)[1])]
        data_new = np.hstack((data_new, np.array([interp1d(dn.iloc[:, 0], dn.iloc[:, ax])(x_new) for ax in range(1, np.shape(dn)[1])]).T))

    return pd.DataFrame(data=data_new[:, 1:], columns=header_values[1:])





def correlated_features(features, threshold=0.95):
    """Compute pairwise correlation of features using pearson method

    Parameters
    ----------
    features : DataFrame
        features
    threshold :
        correlation value for removing highly correlated features
    Returns
    -------
    DataFrame
        correlated features names

    """
    corr_matrix = features.corr().abs()
    # Select upper triangle of correlation matrix
    upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))
    # Find index and column name of features with correlation greater than 0.95
    to_drop = [column for column in upper.columns if any(upper[column] > threshold)]

    return to_drop