drippy.timeseries

Plotting functions for time series models (y = f(t) + e).

Functions

`run_sequence_plot`(→ tuple[matplotlib.figure.Figure, ...)	Creates a run sequence plot of y vs index or continuous index t.
`spectral_plot`(→ tuple[matplotlib.figure.Figure, ...)	Creates a Lomb-Scargle periodogram.
`autocorrelation_plot`(→ tuple[matplotlib.figure.Figure, ...)	Creates autocorrelation plot with confidence intervals.
`complex_demodulation_amplitude_plot`(...)	Creates instantaneous amplitude plot via Hilbert transform.
`complex_demodulation_phase_plot`(...)	Creates instantaneous phase plot via Hilbert transform with linear fit.

Module Contents

drippy.timeseries.run_sequence_plot(data: drippy.data.EDAData, fig: matplotlib.figure.Figure | None = None, ax: matplotlib.axes.Axes | None = None) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Creates a run sequence plot of y vs index or continuous index t.

Parameters:

data – EDAData container. Uses t as x-axis if present, else index.
fig – Matplotlib figure. If None, creates new figure.
ax – Matplotlib axes. If None, creates new axes.

Returns:

The figure and axes containing the plot.

drippy.timeseries.spectral_plot(data: drippy.data.EDAData, fig: matplotlib.figure.Figure | None = None, ax: matplotlib.axes.Axes | None = None, alarm_levels: bool = True) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Creates a Lomb-Scargle periodogram.

Parameters:

data – EDAData container. Requires t.
fig – Matplotlib figure. If None, creates new figure.
ax – Matplotlib axes. If None, creates new axes.
alarm_levels – Whether to show false alarm probability levels.

Returns:

The figure and axes containing the plot.

drippy.timeseries.autocorrelation_plot(data: drippy.data.EDAData, fig: matplotlib.figure.Figure | None = None, ax: matplotlib.axes.Axes | None = None) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Creates autocorrelation plot with confidence intervals.

Includes 99%, 95%, and 80% confidence intervals.

Parameters:

data – EDAData container. Requires y.
fig – Matplotlib figure. If None, creates new figure.
ax – Matplotlib axes. If None, creates new axes.

Returns:

The figure and axes containing the plot.

drippy.timeseries.complex_demodulation_amplitude_plot(data: drippy.data.EDAData, fig: matplotlib.figure.Figure | None = None, ax: matplotlib.axes.Axes | None = None) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Creates instantaneous amplitude plot via Hilbert transform.

Parameters:

data – EDAData container. Requires t.
fig – Matplotlib figure. If None, creates new figure.
ax – Matplotlib axes. If None, creates new axes.

Returns:

The figure and axes containing the plot.

drippy.timeseries.complex_demodulation_phase_plot(data: drippy.data.EDAData, fig: matplotlib.figure.Figure | None = None, ax: matplotlib.axes.Axes | None = None) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Creates instantaneous phase plot via Hilbert transform with linear fit.

Parameters:

data – EDAData container. Requires t.
fig – Matplotlib figure. If None, creates new figure.
ax – Matplotlib axes. If None, creates new axes.

Returns:

The figure and axes containing the plot.