Python Program Help

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "7b47b6d141b4a60d79c2a115b8a69043", "grade": false, "grade_id": "cell-568582423fb764a7", "locked": true, "schema_version": 3, "solution": false, "task": false }, "tags": [] }, "source": [ "# INSTRUCTIONS:\n", "Before you turn this Lab in, make sure everything runs as expected. First, **restart the kernel** (in the menubar, select Kernel$\\rightarrow$Restart) and then **run all cells** (in the menubar, select Cell$\\rightarrow$Run All).\n", "\n", "**TIP**: Open another jupyter notebook to try out your code and experiment with your answers. You can then copy your answer into the Lab notebook for your final submission. This will reduce the chance that your submitted Lab will not be correctly graded.\n", "\n", "Make sure you fill in any place that says `YOUR CODE HERE` or \"YOUR ANSWER HERE\", and follow the instructions carefully.\n", "Also enter your name in the markdown cell below:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NAME = \"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "540ca9dffefd8b492e77bf0e09916deb", "grade": false, "grade_id": "cell-f57caa2f70d07ebe", "locked": true, "schema_version": 3, "solution": false, "task": false }, "tags": [] }, "source": [ "# CS1411-Introduction to Programming with Python\n", "## Homework 2.3" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "f3a77fecdaccf3c581e8b0a23fd0c285", "grade": false, "grade_id": "cell-14a0d989e994af06", "locked": true, "schema_version": 3, "solution": false, "task": false }, "tags": [] }, "source": [ "**SUBMISSION GUIDELINES**\n", "\n", "1. First design, develop and test your code in a Jupyter notebook or other development environment\n", " - You can expirement and try different things in that notebook/environment\n", "2. Then copy your final code and markdown cells into the Jupyter Notebook file (.ipynb) provided for the assignment and submit to Canvas. Make sure indentation is correct and runs properly prior to submission.\n", " - **Your submission file should be named the same as the download file**\n", " - I must be able to open and run your notebook in order to grade it\n", "3. Note that the Jupyter notebook provided for final submission may contain testing code to help check that your output and the expected match. \n", " - Follow the instructions in the notebook for copying your code and running the testing code\n", " - The instructor may run additional tests to check that your code runs correctly\n", "4. If asked, also provide any supporting files or images requested in the assignment\n", "\n", "**GRADING CRITERIA:**\n", "1. Good documentation/comments and program readability using both markdown cells and code comments\n", "2. Algorithm/pseudo-code is explained in a markdown cell and is efficiently written\n", "3. Program runs correctly for test cases with no syntax errors or logical errors\n", "\n", "***The instructor should be able to reproduce your work from your notebook.***" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "fc58f2ca7abfd481b41943408b40cf14", "grade": false, "grade_id": "cell-e4a142001aed85cb", "locked": true, "schema_version": 3, "solution": false, "task": false }, "tags": [] }, "outputs": [], "source": [ "# Run this to check that you have the correct version of Notebook\n", "import IPython\n", "assert IPython.version_info[0] >= 3, \"Your version of IPython is too old, please update it.\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "d30ee5f28af6a015b460df4bd263cb85", "grade": false, "grade_id": "cell-2286d84471166601", "locked": true, "schema_version": 3, "solution": false, "task": false }, "tags": [] }, "outputs": [], "source": [ "\"\"\" Run this setup cell - It is used to keep track of the points per question and set up test environment\"\"\"\n", "num_questions = 1\n", "points = {1.1:[0,5.0], 1.2:[0,25.0]\n", " }\n", "print(f\"***** TOTAL POINTS = {sum([value[1] for value in points.values()])}****\")\n", "\n", "# For instructor use in setting up test environment\n", "class input_vars:\n", " input_list = []\n", "L = input_vars()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "e85987097e95404b085800c08059f448", "grade": true, "grade_id": "instructor_test_setup", "locked": true, "points": 0, "schema_version": 3, "solution": false, "task": false }, "tags": [] }, "outputs": [], "source": [ "\"\"\" This cell has the instructors test setup and is hidden\"\"\"" ] }, { "attachments": { "image-4.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "6d136f506a05b819e65ca29a11aa762b", "grade": false, "grade_id": "cell-93773bc3d51e006a", "locked": true, "schema_version": 3, "solution": false, "task": false }, "tags": [] }, "source": [ "### Points: 30\n", "Q1. Write the turtle graphics statements to draw the figure shown below as closely as possible. \n", "\n", "- Q1.1 (5 points) Provide your algorithm for drawing this figure in the markdown cell below.\n", "- Q1.2 (25 points) Provide the turtle graphics statements for drawing the figure in the code cell below.\n", "```\n", "- Corners for the square are at the following co-ordinates \n", "- (90,90), (-90,90), (-90, -90), (90, -90)\n", "- For the dashed lines set short-dash to 20 pixels, long-dash to 40 pixels, and blank to 20 pixels\n", "- Set pen size to 5 for corner dots\n", "- Color the corner dots as shown\n", "```\n", "\n", "Use \"named constants\", in CAPS, to make your code more readable. Make sure your indentation is correct.\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Q1.1 [Don't Delete!]\n", "### My Algorithm Steps\n", "\n", "1. ...\n", "2. ...\n", "3. ...\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "74e12fb59f6eedc0a2f43b8514c817d2", "grade": true, "grade_id": "Q1_1_test", "locked": true, "points": 5, "schema_version": 3, "solution": false, "task": false }, "tags": [] }, "outputs": [], "source": [ "\"\"\" This cell has the instructors test and is hidden\"\"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Q1.2 - Provide code to draw the figure" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "6c79893bf9627d3e7f6a8e5e2a531b89", "grade": true, "grade_id": "Q_1_2_answer", "locked": false, "points": 25, "schema_version": 3, "solution": true, "task": false }, "tags": [] }, "outputs": [], "source": [ "# DO NOT DELETE THIS BLOCK\n", "#--------------------------------------------------------------\n", "# include the following to get the turtle to show in the foreground\n", "import turtle\n", "str_err = \"\"\n", "try:\n", " root = turtle.getscreen()._root\n", " root.attributes(\"-topmost\", True)\n", " # draw the screen if not already drawn, ignore error if any\n", " # this works to eliminate error when running again\n", " turtle.setup(600,600)\n", "except:\n", " pass\n", "#--------------------------------------------------------------\n", "\n", "try:\n", " # YOUR CODE HERE\n", " raise NotImplementedError()\n", " \n", "# DO NOT DELETE THIS BLOCK\n", "#--------------------------------------------------------------\n", "except Exception as err:\n", " print(\"ERROR: \",err)\n", " pass\n", "\n", "# the try except here suppresses the error message if you run \n", "# program again\n", "try:\n", " # Manually graded\n", " Q_num = 1.2\n", " points[Q_num][0] = float(turtle.textinput(\"POINTS\",f\"Manually Graded: Q{str(Q_num)} max points = {points[Q_num][1]}. Enter points >\"))\n", " turtle.exitonclick()\n", "except:\n", " pass\n", "#--------------------------------------------------------------" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "e32835eaeb39a86b6838e061aad01a98", "grade": true, "grade_id": "Q_1_2_test", "locked": true, "points": 0, "schema_version": 3, "solution": false, "task": false }, "tags": [] }, "outputs": [], "source": [ "\"\"\" This cell has the instructors test and is hidden\"\"\"\n", "# Manually graded\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "dad002dc0405a8cc0ff4882b9b6ef03b", "grade": true, "grade_id": "summary_points", "locked": true, "points": 0, "schema_version": 3, "solution": false, "task": false }, "tags": [] }, "outputs": [], "source": [ "\"\"\" This cell has the instructors test and is hidden\"\"\"" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "oldHeight": 122.85, "position": { "height": "174.85px", "left": "888.08px", "right": "20px", "top": "116px", "width": "225.72px" }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "varInspector_section_display": "block", "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }