drippy.timeseries
=================

.. py:module:: drippy.timeseries

.. autoapi-nested-parse::

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



Functions
---------

.. autoapisummary::

   drippy.timeseries.run_sequence_plot
   drippy.timeseries.spectral_plot
   drippy.timeseries.autocorrelation_plot
   drippy.timeseries.complex_demodulation_amplitude_plot
   drippy.timeseries.complex_demodulation_phase_plot


Module Contents
---------------

.. py:function:: 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]

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

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

   :returns: The figure and axes containing the plot.


.. py:function:: 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]

   Creates a Lomb-Scargle periodogram.

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

   :returns: The figure and axes containing the plot.


.. py:function:: 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]

   Creates autocorrelation plot with confidence intervals.

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

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

   :returns: The figure and axes containing the plot.


.. py:function:: 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]

   Creates instantaneous amplitude plot via Hilbert transform.

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

   :returns: The figure and axes containing the plot.


.. py:function:: 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]

   Creates instantaneous phase plot via Hilbert transform with linear fit.

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

   :returns: The figure and axes containing the plot.