1. Go to the anaconda webpage and download the installer for Python 3.11 . Click on 'Free Download' in the upper right corner and provide your ITU email.
2. Install Anaconda by following the installation guide for your platform .

1. Copy the course material (Course Material tab on LearnIt under Exercises/jupyterlab/W00) for this week before proceeding with this task.

2. On macOS and Linux, open a terminal. On Windows, open the Anaconda Prompt application.

3. Navigate to your local folder containing the yml environment file.

4. Execute the command

   conda env create -f iml_env.yml

   This creates a new environment called iml and automatically installs the necessary packages used for the course, including Scikit Learn , Numpy , and Pytorch .

For Windows users:

1. Open the file Activate.bat in C:\Users\<YourUserName>\AppData\Local\Continuum\anaconda3\Scripts . If it doesn't exist in this location, try searching for the file Activate.bat in your user folder.

2. Add conda activate iml to the end of the file.

3. Test that the environment is automatically activated by opening a new anaconda prompt (find the app in the start menu).

For macOS and Linux users:

1. Open the ~/.bashrc file (or equivalent if you are using another shell).

2. Add the line conda activate iml to the end of the file.

3. Open a new terminal and check that the environment is activated (the prompt should be preceded by (iml) )

This exercise aims to introduce you to a basic workflow using Scikit Image and Python with either a text editor and terminal, or using the Jupyter Lab environment.

1. In a text editor or IDE create a new document called ex1_editor.py .

2. Enter the code snippet and save the changes

   from skimage import data
   from matplotlib import pyplot as plt
                        
   img = data.astronaut()
   plt.show(img)

See here for a full list of possible sample images in Scikit Image.

1. In a terminal (or Anaconda prompt on Windows) navigate to the directory where the script is located.

2. Activate the iml environment by conda activate iml .

3. Run the python script by typing python ex1_editor.py in the terminal.

1. Open a terminal (or Anaconda Prompt on windows) and activate the iml environment using the command conda activate iml .

2. Start Jupyter Lab by running the command jupyter lab . This should open Jupyter in a browser window.

3. Create a new notebook by clicking the Python 3 button under notebook in the center of the screen as shown to the right.

   Alternatively, you can create new files by navigating to File $\triangleright$ New $\triangleright$ Notebook and then selecting Python 3 as the kernel.

   

4. Paste the following code into the code cell:

   from skimage import data
   from matplotlib import pyplot as plt
                        
   img = data.astronaut()
   plt.imshow(img)

   Execute the code by pressing Shift + Enter.

5. Change the code so that it converts the color image to grayscale and show the result. Use the documentation for skimage.color.rgb2gray here .

6. Paste the following code into a new cell:

   import numpy as np
      
   # Create a one-dimensional array of zeros
   zeros_array = np.zeros(10)
   print("Original array:", zeros_array)
   
   # Replace the fifth element
   zeros_array[4] = 1
   print("Modified array:", zeros_array)

   Execute the code by pressing Shift + Enter.

7. Change the code so that the first and last elements of the array are modified to -1. Then, use the code below to plot your modified array:

   import matplotlib.pyplot as plt
   
   plt.figure(figsize=(10, 5))
   plt.stem(zeros_array, basefmt=" ", use_line_collection=True) 
   plt.title('Plot of the Modified Array')
   plt.xlabel('Index')
   plt.ylabel('Value')
   plt.grid(True)
   plt.show()

8. Paste the following code into a new cell:

   from scipy import stats
      
   # Through scipy.stats.describe() we obtain descriptive statistics
   description = stats.describe(zeros_array)
   print("Mean: ", description.mean)

   Execute the code by pressing Shift + Enter.

9. Write a new print statement, which outputs the variance, and the minimum and maxium values identified in the previous step. Use the documentation for scipy.stats.describe here .

10. Paste the following code into a new cell:

    import torch
       
    # Create a tensor
    tensor = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0])
    mean_value = torch.mean(tensor)
    print("Mean of elements:", mean_value)

    Execute the code by pressing Shift + Enter.

11. Write a new print statement, which outputs the sum of the previously created tensor . Use the documentation for torch.sum here .