Skip to content

Latest commit

 

History

History
33 lines (23 loc) · 2.36 KB

README.md

File metadata and controls

33 lines (23 loc) · 2.36 KB
Raw

Project Overview

This Python project, developed using Python 3, PyQt5, h5py, numpy, scipy, and pyqtgraph, implements a GUI application that displays a table and a graph. The project demonstrates handling data stored in a 2D numpy array and offers functionality for editing, saving, and visualizing data interactively.

Key Features

1. Window Structure

  • The window contains a QTableView displaying numeric data and a graph below the table.
  • All data is stored in a 2D numpy array.

2. Table Functionality

  • Editable Column: One column allows editing values through a dropdown list, limiting selection to integers between 1 and 5.
  • Calculated Column: Another column automatically recalculates its values based on the data from another column in the same row (signal-based mechanism).
  • Accumulated Values: One column shows accumulated values derived from another column (also signal-based).
  • Conditional Formatting: Cells in one column are highlighted in red or green depending on whether the values are negative or positive.

3. Graph Functionality

  • Dynamic Plotting: When selecting two columns, a plot is generated to show the relationship between the second column (y-axis) and the first column (x-axis) using pyqtgraph.

4. Data Management

  • Save and Load Data: Buttons allow saving the numpy array to a text file or HDF5 file, and loading the data from either format.
  • Array Management: Users can adjust the size of the numpy array and fill it with random values, excluding special columns that are auto-calculated.

5. Extended Features (Optional)

  • HDF5 Dataset Interaction: An alternative version of the application is available, where data is stored and manipulated directly from an HDF5 dataset without intermediate numpy array caching.

6. Code Quality and Optimization

  • Vectorized Calculations: All operations on the numpy array are vectorized, avoiding loops for tasks such as sum calculations.
  • Thorough Code Documentation: The code is heavily commented, explaining the purpose and function of each section in detail (comments describe the "what" and "why", not the "how").

This project demonstrates the use of Python with PyQt5 and associated libraries for creating a user-friendly, interactive data manipulation interface, with efficient handling and visualization of numerical data.