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 a count of unique 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' side by side.
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: