bursty_dynamics.visual

This module contains functions for plotting the results.

bursty_dynamics.visual.event_counts(train_info_df, x_limit=30, hue=None, **kwargs)

Plots the count of non-duplicate timestamped events per train from the given DataFrame.

Parameters

train_info_dfDataFrame: A DataFrame containing a column named 'unique_event_counts' which holds the count of unique events (no duplicates at same time) for different entries.
x_limitint, optional: The upper limit for the x-axis. Default is 30.
huestr, optional: Column name for hue segmentation. Default is None.
kwargs: Additional keyword arguments passed to sns.countplot.

Here is an example plot of the function:

bursty_dynamics.visual.gridplot(df, bins=25, lower_limit=0, text_scaling=6, figsize=(9, 7), **kwargs)

Create a grid plot of Memory Coefficient (MC) vs Burstiness Parameter (BP) with a colour bar.

dfDataFrame: Input DataFrame containing columns 'MC' and 'BP'.
binsint, optional: Number of bins for the histogram. Default is 25.
lower_limitint, optional: Minimum value for the color scale. Values below this will be colored grey. Default is 0.
text_scalingfloat, optional: Scaling factor for the text in the plot. Default is 6.
figsizetuple, optional: Size of the figure. Default is (9, 7).

Here is an example plot of the function:

bursty_dynamics.visual.histogram(df, hist=True, set_axis=False, hue=None, **kwargs)

Plot histograms for Burstiness Parameter (BP) and Memory Coefficient (MC).

dfDataFrame: Input DataFrame containing columns 'BP' and 'MC'.
histbool or str, optional: If True, plot separate histograms for 'BP' and 'MC'. If 'Both', plot overlapping histograms for 'BP' and 'MC'. If 'BP' or 'MC', plot histogram for the specified column. Default is True.
set_axisbool, optional: Whether to set axis limits to [-1, 1]. Default is False.
huestr, optional: Column name for hue segmentation. Default is None.
kwargs: Additional keyword arguments passed to sns.histplot.

matplotlib.figure.Figure or None: Figure object containing the generated plots if successful, otherwise None if the 'hist' parameter is invalid.

If 'hist' is True, it will create separate histograms for 'BP' and 'MC' in a vertical layout.
If 'hist' is 'Both', it will create overlapping histograms for 'BP' and 'MC' on the same plot.
If 'hist' is 'BP' or 'MC', it will create a histogram for the specified column.
The hue parameter allows for segmented histograms based on the specified column.
When hist is set to 'Both', the hue parameter is ignored.
The set_axis parameter can be used to standardize the x-axis range to [-1, 1].
Additional styling and plotting options can be passed through **kwargs.

Here is an example plot of the function:

bursty_dynamics.visual.scatterplot(df, hue=None, set_axis=False, **kwargs)

Create a scatter plot with marginal histograms showing the relationship between 'MC' and 'BP'.

dfDataFrame: Input DataFrame containing columns 'MC' and 'BP' which are plotted on the x and y axes, respectively.
huestr, optional: Column name in the DataFrame used for color encoding of the scatter plot. If None, the plot is created without color encoding.
set_axisbool, optional: Whether to set the axis limits to [-1, 1]. Default is False. If True, the x and y axes will be constrained to the range [-1, 1].
kwargs: Additional keyword arguments passed to sns.jointplot.

matplotlib.figure.Figure: The figure object containing the scatter plot and marginal histograms.

Here is an example plot of the function:

bursty_dynamics.visual.train_duration(train_info_df, x_limit=5, hue=None, **kwargs)

Plots a distribution of train durations in years from the given DataFrame.

train_info_dfDataFrame: A DataFrame containing a column named 'train_duration_yrs' which holds the duration of training in years for different entries.
x_limitint, optional: The upper limit for the x-axis. Default is 5.
huestr, optional: Column name for hue segmentation. Default is None.
kwargs: Additional keyword arguments passed to sns.histplot.

Here is an example plot of the function: