Enhanced Data Generation and Visualization Functions

• Day: 2023-02-23
• Time: 06:45 to 08:20
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Data Generation, Visualization, Refactoring

Description

Session Goal

The primary aim of this session was to enhance data generation and visualization functions in Python, focusing on flexibility and code efficiency.

Key Activities

• Updated the generate_data() function to include a boolean argument use_formula_2, allowing selection between two treatment distribution formulas.
• Implemented random sampling for treatment variables using np.random.choice for equal probability distribution.
• Modified the generate_data function to introduce a use_prob_dist argument, enabling choice between binomial and choice distributions.
• Developed a function to generate synthetic data for regression problems, incorporating treatment effect options and filename tagging.
• Refactored code to reduce duplication by defining a function for similar operations with different parameters.
• Updated Python plotting functions to pass figure and axis objects as arguments, improving maintainability.
• Enhanced the scatterplot_results function with a plot_kwargs parameter for flexible plotting.
• Demonstrated the use of plot_kwargs in the scatterplot_sts() function for passing additional arguments to plotting functions.

Achievements

• Successfully updated and refactored data generation and visualization functions, improving flexibility and reducing code redundancy.

Pending Tasks

• Further testing of the updated functions in diverse scenarios to ensure robustness.

Evidence

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• event_ids: []