tilupy.plot
===========

.. py:module:: tilupy.plot



Functions
---------

.. autoapisummary::

   tilupy.plot.plot_shotgather
   tilupy.plot.plot_heatmaps



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


Functions
^^^^^^^^^

.. py:function:: plot_shotgather(x: numpy.ndarray, t: numpy.ndarray, data: numpy.ndarray, xlabel: str = 'X (m)', ylabel: str = 'Time (s)', **kwargs) -> matplotlib.axes._axes.Axes

   Plot shotgather image.

   Plot shotgather like image, with vertical axis as time and horizontal axis
   and spatial dimension. This is a simple call to plot_shotgather, but
   input data is transposed because in tilupy the last axis is time by
   convention.

   :param x: Spatial coordinates, size NX.
   :type x: numpy.ndarray
   :param t: Time array (assumed in seconds), size NT.
   :type t: numpy.ndarray
   :param data: NX*NT array of data to be plotted.
   :type data: numpy.ndarray
   :param xlabel: Label for x-axis, by default "X (m)".
   :type xlabel: string, optional
   :param ylabel: Label for x-axis, by default "Time (s)".
   :type ylabel: string, optional
   :param \*\*kwargs: parameters passed on to :func:`pytopomap.plot.plot_imshow`.
   :type \*\*kwargs: dict, optional

   :returns: Axes instance where data is plotted
   :rtype: matplotlib.axes._axes.Axes


.. py:function:: plot_heatmaps(df, values, index, columns, aggfunc='mean', figsize=None, ncols=3, heatmap_kws=None, notations=None, best_values=None, plot_best_value='point', text_kwargs=None) -> matplotlib.figure.Figure

   Plot one or several heatmaps from a pandas DataFrame.

   Each heatmap is created by pivoting the DataFrame with the given
   `index`, `columns`, and a variable from `values`.

   :param df: Input DataFrame containing the data.
   :type df: pandas.DataFrame
   :param values: Column names in :data:`df` to plot as separate heatmaps.
   :type values: list[str]
   :param index: Column name to use as rows of the pivot table.
   :type index: str
   :param columns: Column name to use as columns of the pivot table.
   :type columns: str
   :param aggfunc: Aggregation function applied when multiple values exist for
                   a given (index, column) pair. By default "mean".
   :type aggfunc: str or callable, optional
   :param figsize: Size of the matplotlib figure, by default None.
   :type figsize: tuple of float, optional
   :param ncols: Maximum number of heatmaps per row, by default 3.
   :type ncols: int, optional
   :param heatmap_kws: Keyword arguments passed to :data:`seaborn.heatmap`.
                       If dict of dict, keys must match the values in :data:`values`.
   :type heatmap_kws: dict or dict[dict], optional
   :param notations: Mapping from variable names to readable labels
                     (used for axis and colorbar labels).
   :type notations: dict, optional
   :param best_values: Mapping from variable names to selection criterion:
                       "min", "min_abs", or "max".
   :type best_values: dict, optional
   :param plot_best_value: How to highlight best values:
                           - "point" : mark with circles
                           - "text" : display numeric values
                           By default "point".
   :type plot_best_value: {"point", "text"}, optional
   :param text_kwargs: Keyword arguments passed to :data:`matplotlib.axes.Axes.text` when
                       annotating best values. Only used if :data:`plot_best_value="text"`.
   :type text_kwargs: dict, optional

   :returns: The matplotlib Figure containing the heatmaps.
   :rtype: matplotlib.figure.Figure