LifeTwin Graph Representation Module

Single Prediction Example Graph

Spread Prediction Example Graph

Module Contents

A class to handle prediction and visualization of data changes.

class graph.AbstractPrediction

Bases: object

A class to handle prediction and visualization of data changes.

plot_change(ax=None)

Plot the change in the data.

Args:: ax (matplotlib.axes.Axes, optional): The axes to plot on. If not provided, a new figure will be created.
Returns:: None

show_change(): Show the change in the data.

class graph.Prediction(current_age: int, prediction_years: int, labels: list, predictions: list, model: BioModel, return_category: ReturnCategory)

Bases: AbstractPrediction

A class to handle predictions and visualize the data changes using line plots for continuous data and tables for categorical data.

current_age: int

labels: list

model: BioModel

plot_change(ax=None)

Plot the change in the data using line plots for continuous data and table for categorical data.

Args:: self (Prediction): An instance of the Prediction class. ax (matplotlib.axes.Axes, optional): The axes to plot on. If not provided, a new figure will be created.
Returns:: matplotlib.axes.Axes: The axes containing the plotted data.

prediction_years: int

predictions: list

return_category: ReturnCategory

show_change()

Show the change in the data using the plot_change method and display the plot.

Args:: self (Prediction): An instance of the Prediction class.
Returns:: None

class graph.SinglePrediction(labels: list, predictions: list, model: BioModel, return_category: ReturnCategory)

Bases: AbstractPrediction

A class to handle single predictions and visualize the data changes using bar plots or tables.

labels: list

model: BioModel

plot_change(ax=None)

Plot the change in the data using bar plots.

Args:: self (Prediction): An instance of the Prediction class. ax (matplotlib.axes.Axes, optional): The axes to plot on. If not provided, a new figure will be created.
Returns:: matplotlib.axes.Axes: The axes containing the plotted data.

predictions: list

return_category: ReturnCategory

class graph.SpreadPrediction(prediction_years: int, tables: dict[str, DataFrame], model: BioModel, return_category: ReturnCategory)

Bases: AbstractPrediction

A class to handle spread predictions and visualize the data changes using line plots for continuous data.

model: BioModel

plot_change(ax=None)

Plot the change in the data using line plots for continuous data.

Args:: self (SpreadPrediction): An instance of the SpreadPrediction class. ax (matplotlib.axes.Axes, optional): The axes to plot on. If not provided, a new figure will be created.
Raises:: ValueError: If attempting to plot categorical predictions for a population.
Returns:: matplotlib.axes.Axes: The axes containing the plotted data.

prediction_years: int

return_category: ReturnCategory

show_change()

Show the change in the data using the plot_change method and display the plot.

Args:: self (SpreadPrediction): An instance of the SpreadPrediction class.
Returns:: None

tables: dict[str, DataFrame]

graph.calc_global_95CI(dfs: list[DataFrame])

Calculate the 95% confidence interval for the global mean based on the provided list of pandas DataFrames.

Args:: dfs (list[pd.DataFrame]): A list of pandas DataFrames containing the data.
Returns:: tuple: A tuple containing the minimum and maximum values of the 95% confidence interval for the global mean.

graph.integrate_horizontals(self, ax, min_interval, max_interval)

Integrate horizontal lines representing intervals into the plot.

Args:: self: The instance of the class. ax (matplotlib.axes.Axes): The axes to integrate the horizontal lines into. min_interval (float): The minimum value of the interval. max_interval (float): The maximum value of the interval.
Returns:: None

graph.set_minimum_value(ax, minimum, meaning, model)

Set the minimum value for the y-axis on the plot based on the meaning and model intervals.

Args:: ax (matplotlib.axes.Axes): The axes to set the minimum value for. minimum (float): The minimum value. meaning (str): The meaning of the data. model: The model used for the prediction.
Returns:: float: The adjusted minimum value for the y-axis.