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Sinusoid

timecave.data_generation.time_series_functions.sinusoid_ts(number_samples, max_interval_size, amplitude=1, frequency=1, phase=0)

Generate a time series of a sinusoidal signal.

This function generates a time series of a sinusoidal signal with the specified parameters.

Parameters:

Name Type Description Default
number_samples int

The number of samples in the generated time series.

 required
max_interval_size float

The maximum interval size (time duration) of the generated time series.

 required
amplitude float

The amplitude of the sinusoidal signal.

 1
frequency float

The frequency of the sinusoidal signal in cycles per unit time.

 1
phase float

The phase offset of the sinusoidal signal in radians.

 0

Returns:

Type Description
ndarray

The generated sinusoidal time series.
See also

frequency_varying_sinusoid_ts: Generate a time-varying sinusoid.

Examples:

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from timecave.data_generation.time_series_functions import sinusoid_ts
>>> ts = sinusoid_ts(1000, 10, amplitude=3, frequency=0.5);
>>> _ = plt.plot(np.arange(0, ts.shape[0]), ts);
>>> plt.show();

sinusoid

Higher frequency sinusoids can be generated as well:

>>> ts2 = sinusoid_ts(1000, 10, amplitude=3, frequency=5);
>>> _ = plt.plot(np.arange(0, ts2.shape[0]), ts2);
>>> plt.show();

sinusoid_freq

Phase shifts can be added too:

>>> ts3 = sinusoid_ts(1000, 10, amplitude=3, frequency=0.5, phase=0.3);
>>> _ = plt.plot(np.arange(0, ts2.shape[0]), ts3);
>>> plt.show();

sinusoid_shift

Source code in timecave/data_generation/time_series_functions.py 
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def sinusoid_ts(
    number_samples: int,
    max_interval_size: float or int,
    amplitude: float = 1,
    frequency: float or int = 1,
    phase: float = 0,
) -> np.ndarray:
    """
    Generate a time series of a sinusoidal signal.

    This function generates a time series of a sinusoidal signal with the specified parameters.

    Parameters
    ----------
    number_samples : int
        The number of samples in the generated time series.

    max_interval_size : float
        The maximum interval size (time duration) of the generated time series.

    amplitude : float, default=1
        The amplitude of the sinusoidal signal.

    frequency : float, default=1
        The frequency of the sinusoidal signal in cycles per unit time.

    phase : float, default=0
        The phase offset of the sinusoidal signal in radians.

    Returns
    -------
    np.ndarray
        The generated sinusoidal time series.

    See also
    --------
    [frequency_varying_sinusoid_ts](time_varying_sin.md): Generate a time-varying sinusoid.

    Examples
    --------
    >>> import numpy as np
    >>> import matplotlib.pyplot as plt
    >>> from timecave.data_generation.time_series_functions import sinusoid_ts
    >>> ts = sinusoid_ts(1000, 10, amplitude=3, frequency=0.5);
    >>> _ = plt.plot(np.arange(0, ts.shape[0]), ts);
    >>> plt.show();

    ![sinusoid](../../../images/Sinusoid.png)

    Higher frequency sinusoids can be generated as well:

    >>> ts2 = sinusoid_ts(1000, 10, amplitude=3, frequency=5);
    >>> _ = plt.plot(np.arange(0, ts2.shape[0]), ts2);
    >>> plt.show();

    ![sinusoid_freq](../../../images/Sinusoid_high_freq.png)

    Phase shifts can be added too:

    >>> ts3 = sinusoid_ts(1000, 10, amplitude=3, frequency=0.5, phase=0.3);
    >>> _ = plt.plot(np.arange(0, ts2.shape[0]), ts3);
    >>> plt.show();

    ![sinusoid_shift](../../../images/Shifted_sinusoid.png)
    """
    _check_number_samples(number_samples)
    _check_max_interval_size(max_interval_size)
    time_values = np.linspace(0, max_interval_size, number_samples)
    sinusoid = amplitude * np.sin(2 * np.pi * frequency * time_values + phase)

    return sinusoid