Create material and solutions for week1 workshop

week1/README.md

 # Week 1 Workshop - Python Basics, Virtual Environments and Numpy
+
+conda create -n cosc350
+conda activate cosc350
+conda install pip
+
+pip install -e .
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week1/material/buggy_copy.py

+import numpy as np
+
+def multiply_item(original_matrix, r, c, multiplier):
+    original_matrix[r, c] *= multiplier
+    return original_matrix
+
+my_matrix = np.array([[1, 2, 3], [4, 5, 6]])
+print("Your newly created matrix:", my_matrix)
+
+result_matrix = multiply_item(my_matrix, 1, 1, 2)
+
+print("Your matrix now:", my_matrix)
+print("Resulting matrix:", result_matrix)
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week1/material/lambda_loop.py

+first_list = [8, 10, 9]
+second_list = [5, 7, 1, 2]
+
+lambdas = []
+for first_item in first_list:
+    for second_item in second_list:
+        cur_lambda = lambda: first_item + second_item
+        lambdas.append(cur_lambda)
+
+results = []
+for f in lambdas:
+    results.append(f())
+
+print("Results: ", results)
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week1/material/my_matrix.py

+import numpy as np
+
+def main():
+    # Place your code for matrix operations here
+    pass
+
+if __name__ == '__main__':
+    main()
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week1/solution/README.md

-# Week 1 Workshop - Proposed Solutions
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week1/solution/buggy_copy.py

+import numpy as np
+
+def multiply_item(original_matrix, r, c, multiplier):
+    # we need to create a copy of the original matrix
+    # so that we do not risk to modify it
+    matrix_copy = original_matrix.copy()
+    # now we can modify the item in the copy of the matrix
+    matrix_copy[r, c] *= multiplier
+    # and return it
+    return matrix_copy
+
+my_matrix = np.array([[1, 2, 3], [4, 5, 6]])
+print("Your newly created matrix:", my_matrix)
+
+result_matrix = multiply_item(my_matrix, 1, 1, 2)
+
+print("Your matrix now:", my_matrix)
+print("Resulting matrix:", result_matrix)
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week1/solution/lambda_loop.py

+first_list = [8, 10, 9]
+second_list = [5, 7, 1, 2]
+
+lambdas = []
+for first_item in first_list:
+    for second_item in second_list:
+        # we need to add two parameters to the lambda function
+        # and we can pass the desired items as the default
+        # values for this lambda function
+        cur_lambda = lambda a=first_item, b=second_item: a + b
+        lambdas.append(cur_lambda)
+
+results = []
+for f in lambdas:
+    results.append(f())
+
+print("Results: ", results)
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week1/solution/my_matrix.py

+import numpy as np
+
+# creating a function to retrieve valid inputs from the user
+def get_valid_input(prompt, validation_function, error_message):
+    # setting return value to None
+    value = None
+
+    # looping until we do not have a valid value
+    while value is None:
+        # retrieving the input from the user
+        value = input(prompt)
+
+        # checking the value fits the validation criteria
+        if not validation_function(value):
+            # invalid input
+            # we notify the user with the error message
+            print(error_message)
+            # set value back to None so we loop again
+            value = None
+    
+    # returning the valid value
+    return value
+
+def main():
+    my_matrix = np.zeros((3, 4), dtype=int)
+
+    print("Please, input the values for the matrix")
+    for r in range(0, my_matrix.shape[0]):
+        print("Row {0}:".format(r))
+        for c in range(0, my_matrix.shape[1]):
+            value = get_valid_input(
+                "Please, input item {0} for the current row:".format(c),
+                lambda v: v.isnumeric(),
+                "Invalid input. The values must be int."
+            )
+            
+            # if we are here, it means the value is valid
+            # we add it to the matrix
+            my_matrix[r, c] = int(value)
+    
+    # asking for the operation to perform
+    valid_operations = ['mean', 'sum', 'std']
+    operation = get_valid_input(
+        "Please, select the desired operation to perform (mean, sum or std):",
+        lambda v: v in valid_operations,
+        "The selected operation is invalid."
+    )
+    
+    # asking for the row / column
+    valid_choices = ['row', 'colum']
+    choice = get_valid_input(
+        "Do you want to compute the operation on a row or on a column?",
+        lambda v: v in valid_choices,
+        "Invalid input value. The input must be 'row' or 'column'"
+    )
+
+    # asking for the index
+    valid_idx_row = range(0, my_matrix.shape[0])
+    valid_idx_col = range(0, my_matrix.shape[1])
+    idx = get_valid_input(
+        "Specify the index of the {0}:".format(choice),
+        lambda v: int(v) in valid_idx_row if choice == 'row' else int(v) in valid_idx_col,
+        "Invalid input value. The input must be a valid positive index."
+    )
+    idx = int(idx)
+
+    if choice == 'row':
+        items = my_matrix[idx, :]
+    else:
+        items = my_matrix[:, idx]
+    
+    if operation == 'mean':
+        result = np.mean(items)
+    elif operation == 'sum':
+        result = np.sum(items)
+    else:
+        result = np.std(items)
+    
+    print("Your matrix:")
+    print(my_matrix)
+    print("The operation '{0}' applied on the {1} at index {2} gives the result {3}".format(operation, choice, idx, result))
+
+if __name__ == '__main__':
+    main()
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