Created
October 26, 2021 13:57
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Calculate grade for the electromagnetic theory course (TET) at KTH
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "id": "6d546814-14c4-472d-bb65-ce60c4af6b06", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "8b2bf4fa-fd05-46d6-ae12-d78a0a525e41", | |
| "metadata": {}, | |
| "source": [ | |
| "Main function:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "id": "c01f8c6a-02c4-480b-a420-7c3176d576a2", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def tet_grade(exam_points: np.ndarray, quiz_points: np.ndarray):\n", | |
| " \"\"\"Calculate the grade based on points from exams and points from quizzes. \n", | |
| " Returns the total number of points and the grade.\"\"\"\n", | |
| " quiz_bonus = 7 * np.sum(quiz_points) / np.sum((8, 8, 6, 5, 5, 10))\n", | |
| " total_points = np.ceil(5 * (np.mean(exam_points) - 1 + np.prod(exam_points+1)**(1/len(exam_points))) + quiz_bonus).astype(int)\n", | |
| " grade_limits = np.array([48, 50, 60, 70, 80, 90])\n", | |
| " grades = ('F', 'Fx', *'EDCBA')\n", | |
| "\n", | |
| " for upper_limit, grade in zip(grade_limits, grades):\n", | |
| " if total_points < upper_limit:\n", | |
| " return total_points, grade\n", | |
| " \n", | |
| " return total_points, grades[-1]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "baba6e9d-ad5b-41d3-b888-d0082562dfb6", | |
| "metadata": {}, | |
| "source": [ | |
| "Example use case:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 23, | |
| "id": "7aec0760-ad36-4cf8-9ab3-a666f9db13c4", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "(90, 'A')" | |
| ] | |
| }, | |
| "execution_count": 23, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "exam_points = np.array([10,9, 8,9, 8,6], dtype=int)\n", | |
| "quiz_points = np.array([8, 8, 6, 5, 5, 10], dtype=int)\n", | |
| "tet_grade(exam_points, quiz_points)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "a03694a0-9834-4b42-b4f0-7d02dbe5f3fc", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python [conda env:uni]", | |
| "language": "python", | |
| "name": "conda-env-uni-py" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.8.3" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 5 | |
| } |
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