Interactive 3D scatter plot (GUI)

A 3-dimensional data plotting software for demonstrational purposes. 

Note1: GITHUB 3Dplot repository

Note2: Interactive Notebook bug fix post (8 Sep 2024)

The original idea came from a clustered data plot in which a part of the full dataset should have been shown only and verified from different angles. This is not doable with a simple Matplotlib plot which creates a still image from a specific angle of view. There is no option to zoom in or to rotate. There are solutions online for interactive plotting with Python line magic (link1, link2, link3) or ipympl (link1, link2, link3) Python widgets involved. I had no success with Anaconda Jupyter notebook (7.0) or JupyterLab (4.0) as applying the widgets I could not get a properly working plot.
This triggered me to create a simple code of a graphical software in which the dataset can be visually reorientated to be able to see the points from any angle of interest. Also, a restricting the plot view to defined parts of the dataset was planned and a simple solution was created.

The software is plotting 3 dimensional (X-Y-Z) data using matplotlib 3D scatter plot. Two datasets are available on pressing the corresponding button:

1) randomized data in the range of 0-100 in all axis, which varies on each button pressing; [Test Random] button

2) iris (flower) dataset from Python scikitLearn package; [Test Iris] button

With the help of this (fixed window size) GUI 3 dimansional data can be (scatter) plotted and rearranged 

- by limiting the range along one or all axis,
- by rotating the plot with mouse gestures.

Key Features

1)  Buttons

A) The Test Random and Test Iris buttons load the corresponding dataset and plot it.

B) Apply Button
when clicked, it retrieves the values from the editable fields (see below) and applies them to the plot, updating the axis limits.

No field should remain empty!

C) Reset Button
the axis limits are reset to their optimal values (which are the minimum and maximum of the actual data points along each corresponding axis).

This ensures that all points become visible. Use this button if any plot axis range has been changed previously.

D) Clear button
all loaded data is cleared from the plot.

2) Editable Fields

Each axis (X, Y, Z) has a pair of fields for the minimum and maximum values to limit the related axis to a certain range.

These fields are editable, allowing users to input custom values and consequently exclude some parts of the full dataset from the plot.

Push "Apply" button after limits have been modified.

Rescale data (gif, download)

3) Title (Label) a text indicating the title of the current plot.

Initialization and run

On startup, the editable fields are filled in with the optimal limits (i.e., the min and max of the loaded data values).

Calculation of axis limits: The get_optimal_limits method in the Plot3DWidget class calculates the min and max values for each axis, which are initially populated in the editable fields.

Dynamic plot updates: The set_axes_limits method allows dynamic updates to the axis limits based on user input from the editable fields.

Updating the Title (Label):

Whenever plot_random_data or plot_clusters_data is called ("TEST random", "TEST iris" buttons), a (QT Event) signal is emitted that updates the text in the MainWindow label.

Prerequisites: Python modules required
- sys (python default)
- random (python default)
- PyQt5
- matplotlib, mpl_toolkits
- numpy
- sklearn

Notes: Possible functionality developments

- resizable window (fixed size at the moment, the current version is a proof of concept only)
- editable fields value check, user defined number should fall in the min-max range
- determining clusters for the Iris data using SciKitLearn
- UX design development (e.g. inactive/active buttons and editable fields)

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Created on Thu Sep 5 20:58:00 2024
Animated gif images created from mp4 https://ezgif.com/