Skip to content

testing.md

Latest commit

 

History

History
53 lines (37 loc) · 2.28 KB

testing.md

File metadata and controls

53 lines (37 loc) · 2.28 KB

Testing Dashboard Functions

This document describes the unit tests for the dashboard module functions: select_subject, select_date_range, and select_resolution. The tests are written using Python's unittest framework.

Overview

The test_dashboard.py script contains unit tests for the following functions in the src.dashboard module:

  • select_subject
  • select_date_range
  • select_resolution

These functions are used to filter and manipulate a DataFrame for a hypothetical dashboard application.

Test Cases

Test select_subject

Functionality: This function filters a DataFrame to include only the specified subjects.

Test Case:

  • Purpose: To ensure the function correctly filters the DataFrame based on a list of subject IDs.
  • Setup:
    • Create a test DataFrame test_df with an 'Id' column.
    • Define a list of subject IDs test_subject to filter.
  • Execution: Call select_subject(test_df, test_subject).
  • Assertion: Verify that the resulting DataFrame contains only the rows with the specified subject IDs.

Test select_date_range

Functionality: This function filters a DataFrame to include only the rows within a specified date range.

Test Case:

  • Purpose: To ensure the function correctly filters the DataFrame based on a date range.
  • Setup:
    • Create a test DataFrame test_df with a 'DateTime' column.
    • Define a start date test_start_date and an end date test_end_date for the range.
  • Execution: Call select_date_range(test_df, test_start_date, test_end_date).
  • Assertion: Verify that the resulting DataFrame contains only the rows within the specified date range.

Test select_resolution

Functionality: This function aggregates the data in a DataFrame based on a specified time resolution (e.g., hourly).

Test Case:

  • Purpose: To ensure the function correctly aggregates the DataFrame based on the specified resolution.
  • Setup:
    • Create a test DataFrame test_df with 'Id', 'DateTime', and 'Steps' columns.
    • Define the resolution test_resolution to aggregate by (e.g., 'Hours').
  • Execution: Call select_resolution(test_df, test_resolution).
  • Assertion: Verify that the resulting DataFrame correctly aggregates the 'Steps' data based on the specified resolution.