Your goal is to write the following 10 functions inside canvas_analyzer. py: main: Consumes a string representing the user token (e. g., 'hermione') and calls all the other functions as shown in the diagram.
print_user_info: Consumes a User dictionary and prints out the user's name, title, primary email, and bio. It does not return anything. Note: this function consumes a dictionary, not a string; it does NOT call the canvas_requests. get_user function, it consumes the result of calling the function.
filter_available_courses: Consumes a list of Course dictionaries and returns a list of Course dictionaries where the workflow_state key's value is 'available' (as opposed to 'completed' or something else).
print_courses: Consumes a list of Course dictionaries and prints out the ID and name of each course on separate lines.
get_course_ids: Consumes a list of Course dictionaries and returns a list of integers representing course IDs.
choose_course: Consumes a list of integers representing course IDs and prompts the user to enter a valid ID, and then returns an integer representing the user's chosen course ID. If the user does not enter a valid ID, the function repeatedly loops until they type in a valid ID. You will need to use the input function to get the user's choice.
summarize_points: Consumes a list of Submission dictionaries and prints out three summary statistics about the submissions where there is a score (i. e. the submissions score is not None):
Points possible so far: The sum of the assignments' points_possible multiplied by the assignment's group_weight.
Points obtained: The sum of the submissions' score multiplied by the assignment's group_weight.
Current grade: the Points obtained divided by the Points possible so far, multiplied by 100 and rounded. Note that you can use the built-in round function.
summarize_groups: Consumes a list of Submission dictionaries and prints out the group name and unweighted grade for each group. The unweighted grade is the total score for the group's submissions divided by the total points_possible for the group's submissions, multiplied by 100 and rounded. Like the summarize_points function, you should ignore the submission without a score (i. e. the submission's score is None). You are recommended to apply the Dictionary Summing Pattern to implement this function. This function is a little difficult, so you might want to complete the next function first.
plot_scores: Consumes a list of Submission dictionaries and plots each submissions' grade as a histogram. The grade is calculated as the submission's scoremultiplied by 100 and divided by the assignment's points_possible. You should only plot the submissions that have been graded (score is not None) and the assignment is worth more than 0 points (points_possible is not truthy). Title your graph as "Distribution of Grades", label the X-axis as "Grades", and label the Y-axis as "Number of Assignments".
plot_grade_trends: Consumes a list of Submission dictionaries and plots the grade trend of the submissions as a line plot. The grade trend contains three lines (ordered by the assignments' due_at date) that show you the range of grades you could get in the course:
Highest: The running sum of graded submission scores followed by the running sum of points still possible from ungraded assignments.
Lowest: The running sum of graded submission scores followed by the running sum if you scored 0 on all ungraded assignments.
Maximum: The running sum of the points possible on all assignments in the course.
Do in Python please, I need help and would appreciate any help at all