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Interactive Data Visualization Coursework

Python plotly pandas numpy matplotlib GitHub last commit License: MIT

This is the archive for my assignments to the Master Course Interactive Data Visualization (SS 2020) by Prof. Dr. Gitta Domik-Kienegger at Paderborn University, Germany.

Note: The assignment sheets include proprietary material owned by Sabrina Piasecki, Paderborn University, Germany. Dataset is not provided in the reposiory.

Table of Contents

  1. Assignment 1
  2. Assignment 2
  3. Assignment 3
  4. Assignment 4
  5. How To Run
  6. License

Assignment 1

Assignment_1 Description.

Task: Effective visual identification of quickest and slowest movements of water particles in a channel due to wind forces.

Output:

Output for Assignment 1

Feedback:

  1. Points: 5/10.
  2. Use of color is incorrect, false information is conveyed.
  3. Flow direction is not clear cause of areas with too small arrows and areas with too much overlapping

Assignment 2

Assignment_2 Description.

Note: The plot will be generated one after the other.

Task: (a) Draw a profile line through line 256 of this 2D data set.

Output:

Output for profile line

Task: (b) Calculate the mean and the variance value of this 2D data set.

Output: Mean = 949.99 and Variance = 134932.48

Task: (c) Display a histogram of this 2D data set.

Output:

Output for histogram

Task: (d) Rescale values to range between 0 and 255 using a linear transformation.

Output:

Output for linear transformation

Task: (e) Rescale values to range between 0 and 255 using a different (e.g. non-linear) transformation.

Output:

Output for non-linear transformation

Task: (f) Use an 11x11 boxcar smoothing filter on the 2D data set.

Output:

Output for boxcar smoothing filter

Task: (g) Use an 11x11 median filter on the 2D data set.

Output:

Output for median filter

Feedback:

  1. Points: 8.75/10.
  2. Axis title of profile line incorrect.
  3. Histogram is incorrect.

Assignment 3

Assignment_3 Description.

Note: The plot will be generated one after the other.

Task: (a) Calculate the max value, the min value, the mean value and the variance value of this 2D data set.

Output: Maximum value = 17092.306826, Minimum value = 1.71122371, Mean value = 7.341425385031528, and Variance value = 15761.151027464939.

Task: (b) Draw a profile line through the line with the maximum value of this 2D data set.

Output:

Output for ProfileLine

Task: (c) Display a histogram of this 2D data set.

Output:

Output for histogram

Task: (d) Rescale values to range between 0 and 255 using your own transformation and display on your screen. Add a legend showing the new maximum and minimum value.

Output:

Output for transformation

Task: (e) Carry out a Histogram equalization on each of the four bands and display on your screen.

Output:

Image 1 Image 2
Image 3 Image 4

Task: (f) Combine the histo-equalized data set to an RGB-image (b4=r, b3=g, b1=b).

Output:

Output for RGB_image

Feedback:

  1. Points: 12.50/15.
  2. Log scale not used in profile line.
  3. Histogram incorrect.
  4. Legend missing in subtask (e).

Assignment 4

Assignment_4 Description.

Task: (a) Visualize given data with a scatterplot matrix.

Output:

Output for scatterplot plot

Task: (b) Visualize given data with parallel coordinates.

Output:

Output for parallel coordinates

Feedback:

  1. Points: 4.5/5.
  2. Legend not labelled thus reducing effectiveness for parallel coordinates.

How to Run

This assignment uses Conda for managing environments and dependencies. The environment.yml file contains the list of all dependencies needed to run the all the assignments.

Follow these steps to run the project:

  1. Install Conda: If you haven't installed Conda yet, you can download it from here. Follow the instructions for your specific operating system.

  2. Create a Conda environment: Navigate to the project directory in your terminal and run the following command to create a new Conda environment based on the environment.yml file:

    conda env create -f environment.yml

    This will create a new Conda environment with the name specified in the environment.yml file.

  3. Activate the Conda environment: Use the following command to activate the newly created environment:

    conda activate idv-coursework

  4. Run the Assignment: Now that the environment is set up and activated, you can run the indiviual assignment. If it's a Python script, use the following command:

    Assignment_1>python FlowData.py
    Assignment_2>python Angiographic.py
    Assignment_3>python Equalization.py
    Assignment_4>python Weierstrass-Prize.py

Remember to deactivate the Conda environment once you're done by using the conda deactivate command.

License

This project is licensed under the MIT License - see the LICENSE file for details