From 266181fca48133088dfcda79804dc29ce0415e99 Mon Sep 17 00:00:00 2001
From: ashwinmaran <amaran@wisc.edu>
Date: Tue, 14 Nov 2023 19:43:25 -0600
Subject: [PATCH] add lab-p11 and p11
---
lab-p11/expected_plots.json | 1 +
lab-p11/images/README.md | 3 +
lab-p11/images/color-example.jpg | Bin 0 -> 9948 bytes
lab-p11/images/multiple-example.jpg | Bin 0 -> 10476 bytes
lab-p11/images/q1.jpg | Bin 0 -> 10084 bytes
lab-p11/images/q10.jpg | Bin 0 -> 24614 bytes
lab-p11/images/q11.jpg | Bin 0 -> 23807 bytes
lab-p11/images/q12.jpg | Bin 0 -> 50356 bytes
lab-p11/images/q13.jpg | Bin 0 -> 56096 bytes
lab-p11/images/q14.jpg | Bin 0 -> 43489 bytes
lab-p11/images/q15.jpg | Bin 0 -> 40438 bytes
lab-p11/images/q2.jpg | Bin 0 -> 10085 bytes
lab-p11/images/q3.jpg | Bin 0 -> 10324 bytes
lab-p11/images/q4.jpg | Bin 0 -> 10995 bytes
lab-p11/images/q7.jpg | Bin 0 -> 16838 bytes
lab-p11/images/q8.jpg | Bin 0 -> 24582 bytes
lab-p11/images/q9.jpg | Bin 0 -> 17166 bytes
lab-p11/images/size-example.jpg | Bin 0 -> 10339 bytes
lab-p11/lab-p11.ipynb | 2997 ++
lab-p11/planets_small.json | 65018 ++++++++++++++++++++++++++
lab-p11/public_tests.py | 821 +
lab-p11/sample_data.zip | Bin 0 -> 1714 bytes
p11/README.md | 129 +
p11/broken_data.zip | Bin 0 -> 11293 bytes
p11/data.zip | Bin 0 -> 228004 bytes
p11/expected_plots.json | 1 +
p11/images/README.md | 3 +
p11/images/add_group_member.png | Bin 0 -> 160694 bytes
p11/images/gradescope.png | Bin 0 -> 61912 bytes
p11/images/q4.png | Bin 0 -> 17292 bytes
p11/images/q5.png | Bin 0 -> 22867 bytes
p11/images/q7.png | Bin 0 -> 18438 bytes
p11/images/q8.png | Bin 0 -> 26364 bytes
p11/images/q9.png | Bin 0 -> 25696 bytes
p11/images/summary.png | Bin 0 -> 20543 bytes
p11/p11.ipynb | 3860 ++
p11/public_tests.py | 923 +
p11/rubric.md | 150 +
38 files changed, 73906 insertions(+)
create mode 100644 lab-p11/expected_plots.json
create mode 100644 lab-p11/images/README.md
create mode 100644 lab-p11/images/color-example.jpg
create mode 100644 lab-p11/images/multiple-example.jpg
create mode 100644 lab-p11/images/q1.jpg
create mode 100644 lab-p11/images/q10.jpg
create mode 100644 lab-p11/images/q11.jpg
create mode 100644 lab-p11/images/q12.jpg
create mode 100644 lab-p11/images/q13.jpg
create mode 100644 lab-p11/images/q14.jpg
create mode 100644 lab-p11/images/q15.jpg
create mode 100644 lab-p11/images/q2.jpg
create mode 100644 lab-p11/images/q3.jpg
create mode 100644 lab-p11/images/q4.jpg
create mode 100644 lab-p11/images/q7.jpg
create mode 100644 lab-p11/images/q8.jpg
create mode 100644 lab-p11/images/q9.jpg
create mode 100644 lab-p11/images/size-example.jpg
create mode 100644 lab-p11/lab-p11.ipynb
create mode 100644 lab-p11/planets_small.json
create mode 100644 lab-p11/public_tests.py
create mode 100644 lab-p11/sample_data.zip
create mode 100644 p11/README.md
create mode 100644 p11/broken_data.zip
create mode 100644 p11/data.zip
create mode 100644 p11/expected_plots.json
create mode 100644 p11/images/README.md
create mode 100644 p11/images/add_group_member.png
create mode 100644 p11/images/gradescope.png
create mode 100644 p11/images/q4.png
create mode 100644 p11/images/q5.png
create mode 100644 p11/images/q7.png
create mode 100644 p11/images/q8.png
create mode 100644 p11/images/q9.png
create mode 100644 p11/images/summary.png
create mode 100644 p11/p11.ipynb
create mode 100644 p11/public_tests.py
create mode 100644 p11/rubric.md
diff --git a/lab-p11/expected_plots.json b/lab-p11/expected_plots.json
new file mode 100644
index 0000000..ba88c88
--- /dev/null
+++ b/lab-p11/expected_plots.json
@@ -0,0 +1 @@
+{"q1": {"x": [1, 2, 3, 4], "y": [1, 2, 3, 4]}, "q2": {"x": [1, 2, 3, 4], "y": [4, 1, 2, 3]}, "q3": {"x": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "y": [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]}, "q4": {"x": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "y": [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]}, "q7": {"orbital_periods": [326.03, 516.21997, 185.84, 798.5, 578.38, 993.3, 30.3506, 335.10001, 269.3, 479.1, 11688.0, 4.230785, 934.3, 116.688, 691.9, 93.4, 952.7, 181.4, 7520.0, 481.9, 268.94, 153.22, 137.48, 578.2, 41.6855, 307.88, 392.6, 733.0, 2590.0, 265.59, 1198.48, 723.2, 3.48777, 714.29999, 655.6, 2.2907, 10037.5, 8040000.0, 402000000.0, 1.5089557, 13.2406, 2.99433, 2.828042, 4.03519, 1.51214, 5.35227, 3.7681, 1.9000693, 3.89713, 1.7429935, 2.72474, 9.75598, 3.6313, 4.86069, 4.20474, 3.57532, 5.20851, 2.85057, 4.2568, 9.06005, 4.62941, 3.22748, 5.616531, 9.20205, 4.0378962, 8.886593, 6.21229, 95.272656, 6.71837, 4098.1, 246.36, 5.2072, 6.6732, 10220.0, 6.7987, 2.0478, 4.1698, 21.1701963, 19.49221, 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\ No newline at end of file
diff --git a/lab-p11/images/README.md b/lab-p11/images/README.md
new file mode 100644
index 0000000..6d2e03f
--- /dev/null
+++ b/lab-p11/images/README.md
@@ -0,0 +1,3 @@
+# Images
+
+Images from lab-p11 are stored here.
diff --git a/lab-p11/images/color-example.jpg b/lab-p11/images/color-example.jpg
new file mode 100644
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diff --git a/lab-p11/lab-p11.ipynb b/lab-p11/lab-p11.ipynb
new file mode 100644
index 0000000..83e9b16
--- /dev/null
+++ b/lab-p11/lab-p11.ipynb
@@ -0,0 +1,2997 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "93940fc2",
+ "metadata": {
+ "cell_type": "code",
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "# import and initialize otter\n",
+ "import otter\n",
+ "grader = otter.Notebook(\"lab-p11.ipynb\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "33a13627",
+ "metadata": {
+ "editable": false,
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:53.233836Z",
+ "iopub.status.busy": "2023-11-15T01:37:53.232836Z",
+ "iopub.status.idle": "2023-11-15T01:37:53.494131Z",
+ "shell.execute_reply": "2023-11-15T01:37:53.493115Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import public_tests"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "86a952bb",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "# Lab-P11: Scatter Plots and Recursion\n",
+ "\n",
+ "In this lab, you will practice how to...\n",
+ "* create scatter plots,\n",
+ "* definine recursive functions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e08f1bc5",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "<h2 style=\"color:red\">Warning (Note on Academic Misconduct):</h2>\n",
+ "\n",
+ "**IMPORTANT**: **P10 and P11 are two parts of the same data analysis.** You **cannot** switch project partners between these two projects. That is if you partnered up with someone for P10, you have to sustain that partnership until end of P11. **You must acknowledge to the Lab TA to receive lab attendance credit.**\n",
+ "Be careful not to work with more than one partner. If you work with a partner on Lab-P11, you are **not** allowed to finish your project with a different partner.\n",
+ "You may either continue to work with the same partner, or work on P11 alone. Now may be a good time to review [our course policies](https://cs220.cs.wisc.edu/f23/syllabus.html).\n",
+ "\n",
+ "Under any circumstances, **no more than two students are allowed to work together on a project** as mentioned in the course policies. If your code is flagged by our code similarity detection tools, **both partners will be responsible** for sharing/copying the code, even if the code is shared/copied by one of the partners with/from other non-partner student(s). Note that each case of plagiarism will be reported to the Dean of Students with a zero grade on the project. **If you think that someone cannot be your project partner then don’t make that student your lab partner.**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b1fb32f",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "## Setup:\n",
+ "\n",
+ "Before proceeding much further, download `public_tests.py`, `expected_plots.json`, and `planets_small.json` and store them in the same directory as `lab-p11.ipynb`. You should also download `sample_data.zip` and extract the contents of the directory `sample_data` into the same directory which contains the files `lab-p11.ipynb`, `public_tests.py`, `planets_small.json` and `expected_plots.json`.\n",
+ "\n",
+ "You need to make sure that the project files are stored in the following structure:\n",
+ "\n",
+ "```\n",
+ "+-- lab-p11.ipynb\n",
+ "+-- planets_small.json\n",
+ "+-- public_tests.py\n",
+ "+-- expected_plots.json\n",
+ "+-- sample_data\n",
+ "| +-- .DS_Store\n",
+ "| +-- file_1.json\n",
+ "| +-- sample_1\n",
+ "| | +-- .ipynb_checkpoints\n",
+ "| | +-- file_2.json\n",
+ "| | +-- file_3.json\n",
+ "| +-- sample_2\n",
+ "| | +-- file_4.json\n",
+ "| | +-- sample_3\n",
+ "| | | +-- .DS_Store\n",
+ "| | | +-- file_5.json\n",
+ "```\n",
+ "\n",
+ "Make sure that **all** files are stored in this **exact** file structure."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "10b22d09",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "## Introduction:\n",
+ "\n",
+ "In P10, you parsed the data in the `data` directory and created some useful data structures. In P11, you will be creating scatter plots, and using recursion to gather even more data.\n",
+ "\n",
+ "In Lab-P11, you will practice creating scatter plots on a smaller dataset, and also practice writing some recursive functions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8beeed9f",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "## Questions and Functions:\n",
+ "\n",
+ "Let us start by importing all the modules we will need for this project."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4a6e5d9a",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:53.499137Z",
+ "iopub.status.busy": "2023-11-15T01:37:53.499137Z",
+ "iopub.status.idle": "2023-11-15T01:37:54.443847Z",
+ "shell.execute_reply": "2023-11-15T01:37:54.442833Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+ "# please place all your import statements in this cell if you need to import any more modules for this lab\n",
+ "\n",
+ "# we have imported these modules for you\n",
+ "import os\n",
+ "import json\n",
+ "from collections import namedtuple\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b1a0982c",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Loading in data:\n",
+ "\n",
+ "Before proceeding further, we will need to load in some data. Recall that in P10, we created a **list** of `Planet` objects called `planets_list`. In P11, you will be analyzing this **list** and using the data in there to create scatter plots. If you have not finished P10, do not worry about it. For Lab-P11, you will instead be working with a sample dataset. The data we will be analyzing in Lab-P11 is stored in `planets_small.json`.\n",
+ "\n",
+ "**Warning:** Note that the data in `planets_small.json` is **not** stored in the same format as the files in the `data` directory that you parsed in P10. This file has been created solely for Lab-P11, so it is easier to load in the data without having to create the functions that you need to create in P10 to read the actual dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7a3ce7f3",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:54.449845Z",
+ "iopub.status.busy": "2023-11-15T01:37:54.448844Z",
+ "iopub.status.idle": "2023-11-15T01:37:54.455862Z",
+ "shell.execute_reply": "2023-11-15T01:37:54.454850Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# we have defined the namedtuple 'Planet' for you\n",
+ "\n",
+ "planets_attributes = ['planet_name',\n",
+ " 'host_name', \n",
+ " 'discovery_method',\n",
+ " 'discovery_year',\n",
+ " 'controversial_flag',\n",
+ " 'orbital_period',\n",
+ " 'planet_radius',\n",
+ " 'planet_mass',\n",
+ " 'semi_major_radius',\n",
+ " 'eccentricity',\n",
+ " 'equilibrium_temperature',\n",
+ " 'insolation_flux']\n",
+ "\n",
+ "Planet = namedtuple(\"Planet\", planets_attributes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "73724c3e",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:54.459861Z",
+ "iopub.status.busy": "2023-11-15T01:37:54.458861Z",
+ "iopub.status.idle": "2023-11-15T01:37:54.531253Z",
+ "shell.execute_reply": "2023-11-15T01:37:54.530245Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# we have parsed the data in 'planets_small.json' and created a list of Planet objects for you\n",
+ "\n",
+ "def read_json(path):\n",
+ " with open(path, encoding=\"utf-8\") as f:\n",
+ " return json.load(f)\n",
+ "\n",
+ "# add the planets in 'planets_small.json' as Planet objects to 'small_planets_list'\n",
+ "small_planets_list = []\n",
+ "for planet in read_json('planets_small.json'):\n",
+ " small_planets_list.append(Planet(**planet))\n",
+ "\n",
+ "# display the first three Planet objects in 'small_planets_list'\n",
+ "small_planets_list[:3]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ff724f62",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "## Segment 1: Scatter Plots\n",
+ "\n",
+ "We will first learn to create simple scatter plots."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "502016aa",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Function 1: `plot_scatter(x_data, y_data, x_label, y_label, c, s)`\n",
+ "\n",
+ "You have been provided with a function `plot_scatter` which can create scatter plots. You do not need to worry about how it works just yet. The function will be explained to you over the course of the lab."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "37fc3c80",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:54.536251Z",
+ "iopub.status.busy": "2023-11-15T01:37:54.536251Z",
+ "iopub.status.idle": "2023-11-15T01:37:54.542318Z",
+ "shell.execute_reply": "2023-11-15T01:37:54.541282Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "def plot_scatter(x_data, y_data, x_label='x axis', y_label='y axis', c=None, s=7):\n",
+ " plt.scatter(x_data, y_data, c=c, s=s)\n",
+ " plt.xlabel(x_label)\n",
+ " plt.ylabel(y_label)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "97c6ed04",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Task 1.1: Simple plots with `scatter_plot`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c537a82d",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 1:** Create a **scatter plot** with the points *(1, 1)*, *(2, 2)*, *(3, 3)*, and *(4, 4)*.\n",
+ "\n",
+ "You **must** first compute **two lists** which contain the *x*-coordinates and *y*-coordinates of the above points respectively. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "47e6815c",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:54.546553Z",
+ "iopub.status.busy": "2023-11-15T01:37:54.546553Z",
+ "iopub.status.idle": "2023-11-15T01:37:54.552797Z",
+ "shell.execute_reply": "2023-11-15T01:37:54.551756Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# first compute and store the lists 'x' and 'y', but do NOT display them\n",
+ "# do NOT create the plot just yet\n",
+ "\n",
+ "# we have done this one for you\n",
+ "x = [1, 2, 3, 4]\n",
+ "y = [1, 2, 3, 4]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fb3fc395",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q1\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1483cef2",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Now, **plot** `x` and `y` as **scatter plot**. Your plot should look like this:"
+ ]
+ },
+ {
+ "attachments": {
+ "q1.jpg": {
+ "image/jpeg": 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puWrfo0ePqrGx0buNHz/+Wk4FAAAYJuDflsrJydGBAwe0efNm/ehHP9JXX32lwsJCTZ8+XU888YSWL1/u93Pt3bvXZ/+ll15SXFycDh48qFtvvbXbvpfuuQMAAPB1AV+5aW9v1+HDh/WjH/1IkjRkyBCVlpZq+/btWrNmzTcqxu12S5L3njrdmTp1qhISEpSVlaV9+/Z12a61tVUej8dnAwAA5urR+9ycOnVKf/EXf3FNfS3L0pw5c3T69Gn97ne/67Ld0aNHVVlZqfT0dLW2tmrLli1at26dXC5Xp1d7Hn/8cT3xxBNXHOc+NwAA9B+B3OcmaG7iV1BQoF27dumdd95RUlJSQH1nz54th8OhHTt2XPFYa2urWltbvfsej0fJycmEGwAA+pEe/+HMr+vo6NCaNWv0n//5n6qvr1dbW5vP43/+858DfUotWrRIO3bsUGVlZcDBRpKmT5+urVu3dvpYeHi4wsPDA35OAADQPwW85uaJJ57QM888o7/7u7+T2+1WYWGh/uZv/kYhISF6/PHHA3ouy7K0cOFCvf7663r77beVkpISaDmSpJqaGiUkJFxTXwAAYJaAr9z8x3/8h1544QXdeeedeuKJJ3T33Xdr3LhxSktL0/79+7V48WK/n6ugoEC//vWv9V//9V+KiopSU1OTpAv30hkyZIgkqaioSCdOnNDmzZslSWvXrtWYMWM0adIktbW1aevWrXI6nXI6nYGeCgAAMFDA4aapqUk33XSTJCkyMtL7Dae77rpLq1evDui5SktLJUnf//73fY6/9NJLuu+++yRJjY2Nqq+v9z7W1tamZcuW6cSJExoyZIgmTZqkXbt2KTc3N9BTAQAABgo43CQlJamxsVGjR49WamqqysvLdfPNN+vAgQMBr23xZy3zyy+/7LO/fPnygO6lAwAABpaA19z89V//tf77v/9bkrRkyRKtXr1a48eP17333qsHHnigxwsEAAAIxDf+Kvgf/vAH/f73v1dqaqr+6q/+qqfq6jWBfJUMAAAEh179Kvjlvvvd7+q73/3uN30aAACAHnFNP5wJAAAQrAg3AADAKIQbAABgFMINAAAwSsDh5r777lNlZWVv1AIAAPCNBRxuzpw5o+zsbI0fP15PPvmkTpw40Rt1AQAAXJOAw43T6dSJEye0cOFC/eY3v9GYMWOUk5Oj7du369y5c71RIwAAgN+uac3NyJEjtWTJEtXU1Ojdd99Vamqq8vLylJiYqKVLl+rjjz/u6ToBAAD88o0WFDc2Nqq8vFzl5eUKDQ1Vbm6uPvjgA02cOFFr1qzpqRoBAAD8FnC4OXfunJxOp+666y5dd911+s1vfqOlS5eqsbFRr7zyisrLy7Vlyxb9/Oc/7416AQAAuhXwzy8kJCTo/Pnzuvvuu/Xuu+9qypQpV7SZNWuWhg0b1gPlAQAABCbgcLNmzRr97d/+rSIiIrpsM3z4cNXV1X2jwgAAAK5FwOEmLy+vN+oAAADoEdyhGAAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUWwNN8XFxZo2bZqioqIUFxenuXPn6ujRo1ftV1FRofT0dEVERGjs2LFat25dH1QLAAD6A1vDTUVFhQoKCrR//369+eabam9vV3Z2tlpaWrrsU1dXp9zcXM2YMUM1NTVauXKlFi9eLKfT2YeVA32r47xldwkA0G84LMsKmr+aJ0+eVFxcnCoqKnTrrbd22mbFihXasWOHjhw54j2Wn5+v999/X1VVVVd9DY/Ho5iYGLndbkVHR/dY7UBv+OTkWS3YclDHms8qNS5S6/PSNS420u6yAKDPBfL+HVRrbtxutyRpxIgRXbapqqpSdna2z7FZs2apurpa586du6J9a2urPB6Pzwb0Fwu2HFTdyQtXMutOtmjBloM2VwQAwS9owo1lWSosLNQtt9yiG2+8sct2TU1Nio+P9zkWHx+v9vZ2nTp16or2xcXFiomJ8W7Jyck9XjvQGzrOWzrWfFYdFy+udlgX9/mICgC6FTThZuHChTp8+LBeffXVq7Z1OBw++5c+Wbv8uCQVFRXJ7XZ7t4aGhp4pGOhloSEOpcZFKvTivA51XNwPuXKeAwD+v6AIN4sWLdKOHTu0b98+JSUlddt21KhRampq8jnW3NyssLAwjRw58or24eHhio6O9tmA/mJ9XrpSYodKklJih2p9XrrNFQFA8Auz88Uty9KiRYtUVlYml8ullJSUq/bJzMzUzp07fY6Vl5crIyNDgwYN6q1SAVuMi43UW4W3qeO8xRUbAPCTrVduCgoKtHXrVv36179WVFSUmpqa1NTUpK+++srbpqioSPfee693Pz8/X8ePH1dhYaGOHDmiTZs2aePGjVq2bJkdpwD0CYINAPjP1nBTWloqt9ut73//+0pISPBur732mrdNY2Oj6uvrvfspKSnavXu3XC6XpkyZol/84hcqKSnRvHnz7DgFAAAQZILqPjd9gfvcAADQ//Tb+9wAAAB8U4QbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjGJruKmsrNTs2bOVmJgoh8OhN954o9v2LpdLDofjiu2jjz7qm4IBAEDQC7PzxVtaWjR58mTdf//9mjdvnt/9jh49qujoaO9+bGxsb5QHAAD6IVvDTU5OjnJycgLuFxcXp2HDhvV8QQAAoN/rl2tupk6dqoSEBGVlZWnfvn3dtm1tbZXH4/HZAACAufpVuElISNCGDRvkdDr1+uuva8KECcrKylJlZWWXfYqLixUTE+PdkpOT+7BiAADQ1xyWZVl2FyFJDodDZWVlmjt3bkD9Zs+eLYfDoR07dnT6eGtrq1pbW737Ho9HycnJcrvdPut2AABA8PJ4PIqJifHr/btfXbnpzPTp0/Xxxx93+Xh4eLiio6N9NgAAYK5+H25qamqUkJBgdxkAACBI2PptqbNnz+rYsWPe/bq6Oh06dEgjRozQ6NGjVVRUpBMnTmjz5s2SpLVr12rMmDGaNGmS2tratHXrVjmdTjmdTrtOAQAABBlbw011dbVuv/12735hYaEkaf78+Xr55ZfV2Nio+vp67+NtbW1atmyZTpw4oSFDhmjSpEnatWuXcnNz+7x2AAAQnIJmQXFfCWRBEgAACA4DakExAADA1xFuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMIqt4aayslKzZ89WYmKiHA6H3njjjav2qaioUHp6uiIiIjR27FitW7eu9wsFAAD9hq3hpqWlRZMnT9Zzzz3nV/u6ujrl5uZqxowZqqmp0cqVK7V48WI5nc5ertR/Hectu0sAAGBAC7PzxXNycpSTk+N3+3Xr1mn06NFau3atJOmGG25QdXW1nn76ac2bN6+XqvTPJyfPasGWgzrWfFapcZFan5eucbGRttYEAMBA1K/W3FRVVSk7O9vn2KxZs1RdXa1z58512qe1tVUej8dn6w0LthxU3ckWSVLdyRYt2HKwV14HAAB0r1+Fm6amJsXHx/sci4+PV3t7u06dOtVpn+LiYsXExHi35OTkHq+r47ylY81n1WFd+Eiqw7q4z0dUAAD0uX4VbiTJ4XD47FsXA8Xlxy8pKiqS2+32bg0NDT1eU2iIQ6lxkQq9WEOo4+J+SOc1AQCA3tOvws2oUaPU1NTkc6y5uVlhYWEaOXJkp33Cw8MVHR3ts/WG9XnpSokdKklKiR2q9XnpvfI6AACge7YuKA5UZmamdu7c6XOsvLxcGRkZGjRokE1VXTAuNlJvFd6mjvMWV2wAALCRrVduzp49q0OHDunQoUOSLnzV+9ChQ6qvr5d04SOle++919s+Pz9fx48fV2FhoY4cOaJNmzZp48aNWrZsmR3ld4pgAwCAvWy9clNdXa3bb7/du19YWChJmj9/vl5++WU1NjZ6g44kpaSkaPfu3Vq6dKmef/55JSYmqqSkxPavgQMAgODhsC6tyB0gPB6PYmJi5Ha7e239DQAA6FmBvH/3qwXFAAAAV0O4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACM0q9+OLMnXLohs8fjsbkSAADgr0vv2/78sMKACzdnzpyRJCUnJ9tcCQAACNSZM2cUExPTbZsB99tS58+f12effaaoqCg5HD37C94ej0fJyclqaGjgd6uugrHyH2PlP8YqMIyX/xgr//XWWFmWpTNnzigxMVEhId2vqhlwV25CQkKUlJTUq68RHR3N5PcTY+U/xsp/jFVgGC//MVb+642xutoVm0tYUAwAAIxCuAEAAEYh3PSg8PBw/exnP1N4eLjdpQQ9xsp/jJX/GKvAMF7+Y6z8FwxjNeAWFAMAALNx5QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbvxUWVmp2bNnKzExUQ6HQ2+88cZV+1RUVCg9PV0REREaO3as1q1b1/uFBolAx8vlcsnhcFyxffTRR31TsE2Ki4s1bdo0RUVFKS4uTnPnztXRo0ev2m8gzq1rGauBOq8kqbS0VGlpad4bqWVmZmrPnj3d9hmI80oKfKwG8rz6uuLiYjkcDj3yyCPdtrNjXhFu/NTS0qLJkyfrueee86t9XV2dcnNzNWPGDNXU1GjlypVavHixnE5nL1caHAIdr0uOHj2qxsZG7zZ+/PheqjA4VFRUqKCgQPv379ebb76p9vZ2ZWdnq6Wlpcs+A3VuXctYXTLQ5pUkJSUl6amnnlJ1dbWqq6v1gx/8QHPmzNEHH3zQafuBOq+kwMfqkoE4ry45cOCANmzYoLS0tG7b2TavLARMklVWVtZtm+XLl1vXX3+9z7EFCxZY06dP78XKgpM/47Vv3z5LknX69Ok+qSlYNTc3W5KsioqKLtswty7wZ6yYV76GDx9uvfjii50+xrzy1d1YDfR5debMGWv8+PHWm2++ad12223WkiVLumxr17ziyk0vqaqqUnZ2ts+xWbNmqbq6WufOnbOpquA3depUJSQkKCsrS/v27bO7nD7ndrslSSNGjOiyDXPrAn/G6pKBPq86Ojq0bds2tbS0KDMzs9M2zKsL/BmrSwbqvCooKNCdd96pmTNnXrWtXfNqwP1wZl9pampSfHy8z7H4+Hi1t7fr1KlTSkhIsKmy4JSQkKANGzYoPT1dra2t2rJli7KysuRyuXTrrbfaXV6fsCxLhYWFuuWWW3TjjTd22Y655f9YDfR5VVtbq8zMTP3f//2fIiMjVVZWpokTJ3badqDPq0DGaiDPq23btum9997TgQMH/Gpv17wi3PQih8Phs29dvBn05cchTZgwQRMmTPDuZ2ZmqqGhQU8//bTxfywuWbhwoQ4fPqx33nnnqm0H+tzyd6wG+ryaMGGCDh06pC+++EJOp1Pz589XRUVFl2/aA3leBTJWA3VeNTQ0aMmSJSovL1dERITf/eyYV3ws1UtGjRqlpqYmn2PNzc0KCwvTyJEjbaqqf5k+fbo+/vhju8voE4sWLdKOHTu0b98+JSUlddt2oM+tQMaqMwNpXg0ePFipqanKyMhQcXGxJk+erGeffbbTtgN9XgUyVp0ZCPPq4MGDam5uVnp6usLCwhQWFqaKigqVlJQoLCxMHR0dV/Sxa15x5aaXZGZmaufOnT7HysvLlZGRoUGDBtlUVf9SU1Nj/KVwy7K0aNEilZWVyeVyKSUl5ap9Burcupax6sxAmFddsSxLra2tnT42UOdVV7obq84MhHmVlZWl2tpan2P333+/rr/+eq1YsUKhoaFX9LFtXvXqcmWDnDlzxqqpqbFqamosSdYzzzxj1dTUWMePH7csy7Iee+wxKy8vz9v+008/tb71rW9ZS5cutT788ENr48aN1qBBg6zt27fbdQp9KtDxWrNmjVVWVmb98Y9/tP73f//XeuyxxyxJltPptOsU+sTDDz9sxcTEWC6Xy2psbPRuX375pbcNc+uCaxmrgTqvLMuyioqKrMrKSquurs46fPiwtXLlSiskJMQqLy+3LIt59XWBjtVAnleXu/zbUsEyrwg3frr01b/Lt/nz51uWZVnz58+3brvtNp8+LpfLmjp1qjV48GBrzJgxVmlpad8XbpNAx+tf//VfrXHjxlkRERHW8OHDrVtuucXatWuXPcX3oc7GSJL10ksvedswty64lrEaqPPKsizrgQcesK677jpr8ODBVmxsrJWVleV9s7Ys5tXXBTpWA3leXe7ycBMs88phWRdX9gAAABiABcUAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAGJJfLJYfDoS+++MLuUgD0MO5QDGBAamtr05///GfFx8fL4XDYXQ6AHkS4AQAARuFjKQC2OnnypEaNGqUnn3zSe+wPf/iDBg8erPLy8i77rVixQt/5znf0rW99S2PHjtXq1at17tw5SZJlWZo5c6buuOMOXfr/2xdffKHRo0dr1apVkq78WOr48eOaPXu2hg8frqFDh2rSpEnavXt3L501gN4UZncBAAa22NhYbdq0SXPnzlV2drauv/56/eM//qP+6Z/+SdnZ2V32i4qK0ssvv6zExETV1tbqoYceUlRUlJYvXy6Hw6FXXnlFN910k0pKSrRkyRLl5+crPj5ejz/+eKfPV1BQoLa2NlVWVmro0KH68MMPFRkZ2UtnDaA3EW4A2C43N1cPPfSQ7rnnHk2bNk0RERF66qmnuu3zz//8z95/jxkzRo8++qhee+01LV++XJL07W9/W+vXr1deXp4+//xz7dy5UzU1NRo0aFCnz1dfX6958+bppptukiSNHTu2h84OQF9jzQ2AoPDVV1/pxhtvVENDg6qrq5WWltZt++3bt2vt2rU6duyYzp49q/b2dkVHR6u5udmn3Y9//GO9+uqrKi0tVX5+vve4y+XS7bffrtOnT2vYsGF68cUX9fDDD+sv//IvNXPmTM2bN++qNQAITqy5ARAUPv30U3322Wc6f/68jh8/3m3b/fv36x/+4R+Uk5Oj3/72t6qpqdGqVavU1tbm0+7LL7/UwYMHFRoaqo8//rjb5/zJT36iTz/9VHl5eaqtrVVGRoZ+9atffePzAtD3CDcAbNfW1qZ77rlHf//3f69f/vKXevDBB/X555932f73v/+9rrvuOq1atUoZGRkaP358p4Ho0UcfVUhIiPbs2aOSkhK9/fbb3daRnJys/Px8vf7663r00Uf1wgsvfONzA9D3WHMDwHarVq2S2+1WSUmJIiMjtWfPHj344IP67W9/22n71NRU1dfXa9u2bZo2bZp27dqlsrIynza7du3Spk2bVFVVpZtvvlmPPfaY5s+fr8OHD2v48OFXPOcjjzyinJwcfec739Hp06f19ttv64YbbuiV8wXQu7hyA8BWLpdLa9eu1ZYtWxQdHa2QkBBt2bJF77zzjkpLSzvtM2fOHC1dulQLFy7UlClT9D//8z9avXq19/GTJ0/qwQcf1OOPP66bb75ZkvSzn/1MiYmJPutuvq6jo0MFBQW64YYbdMcdd2jChAn693//954/YQC9jgXFAADAKFy5AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBR/h8OGIbesyDXNAAAAABJRU5ErkJggg=="
+ }
+ },
+ "cell_type": "markdown",
+ "id": "f2d53d22",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "<div><img src=\"attachment:q1.jpg\" style=\"height: 300px;\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6949919b",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:54.570381Z",
+ "iopub.status.busy": "2023-11-15T01:37:54.570381Z",
+ "iopub.status.idle": "2023-11-15T01:37:54.868605Z",
+ "shell.execute_reply": "2023-11-15T01:37:54.867595Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'x' and 'y'\n",
+ "\n",
+ "plot_scatter(..., ...)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7b8c8355",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "As you can see, in order to create the **scatter plot**, we had to create **two** lists `x` and `y`. The **list** `x` contains the data in the **x axis** and the **list** `y` contains the data in the **y axis**. For example, the element at index *0* of `x` is *1*, and the element at index *0* of `y` is *1*. Therefore, a point is added to the **scatter plot** at the position *(1, 1)*. Similarly, the other points are also added to the **scatter plot** using the `x` and `y` lists."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0e4fd201",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 2:** Create a **scatter plot** with the points *(1, 4)*, *(2, 1)*, *(3, 2)*, and *(4, 3)*.\n",
+ "\n",
+ "You **must** create **two lists** and store the **x** and **y** values in these lists. In order to pass `grader.check` for scatter plots, you **must** define **both** **lists** properly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "56e4093f",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:54.872625Z",
+ "iopub.status.busy": "2023-11-15T01:37:54.872625Z",
+ "iopub.status.idle": "2023-11-15T01:37:54.878448Z",
+ "shell.execute_reply": "2023-11-15T01:37:54.877439Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# first compute and store the lists 'x' and 'y', but do NOT display them\n",
+ "# do NOT create the plot just yet\n",
+ "\n",
+ "# replace the ... with your code\n",
+ "\n",
+ "x = [1, 2, 3, 4]\n",
+ "y = ..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0aec3aa",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q2\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8e8e143d",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Now, **plot** `x` and `y` as **scatter plot**. Your plot should look like this:"
+ ]
+ },
+ {
+ "attachments": {
+ "q2.jpg": {
+ "image/jpeg": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "fae9982e",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "<div><img src=\"attachment:q2.jpg\" style=\"height: 300px;\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9acf1d64",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:54.892643Z",
+ "iopub.status.busy": "2023-11-15T01:37:54.892643Z",
+ "iopub.status.idle": "2023-11-15T01:37:55.141177Z",
+ "shell.execute_reply": "2023-11-15T01:37:55.140169Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'x' and 'y'\n",
+ "\n",
+ "plot_scatter(..., ...)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9fc8dd0e",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 3:** Create a **scatter plot** having the numbers *(1, 2, 3, ... 10)* on the **x axis** and their **squares** on the **y axis**.\n",
+ "\n",
+ "You **must** create **two lists** and store the **x** and **y** values in these lists. In order to pass `grader.check` for scatter plots, you **must** define **both** **lists** properly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bb3ca11c",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:55.146177Z",
+ "iopub.status.busy": "2023-11-15T01:37:55.145183Z",
+ "iopub.status.idle": "2023-11-15T01:37:55.151468Z",
+ "shell.execute_reply": "2023-11-15T01:37:55.150456Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# first compute and store the lists 'x' and 'y', but do NOT display them\n",
+ "# do NOT create the plot just yet\n",
+ "\n",
+ "# replace the ... with your code\n",
+ "\n",
+ "x = [i for i in range(1, 11)]\n",
+ "y = [... for j in x]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "582c19ef",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q3\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5f01f63a",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Now, **plot** `x` and `y` as **scatter plot**. Your plot should look like this:"
+ ]
+ },
+ {
+ "attachments": {
+ "q3.jpg": {
+ "image/jpeg": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "e69f5f08",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "<div><img src=\"attachment:q3.jpg\" style=\"height: 300px;\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c5623a98",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:55.166397Z",
+ "iopub.status.busy": "2023-11-15T01:37:55.165404Z",
+ "iopub.status.idle": "2023-11-15T01:37:55.382534Z",
+ "shell.execute_reply": "2023-11-15T01:37:55.381521Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'x' and 'y'\n",
+ "\n",
+ "plot_scatter(..., ...)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c1730c5b",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Note that the labels on the **x axis** and the **y axis** are not very descriptive. We can add in our own labels using the `plot_scatter` function by passing two optional arguments to the parameters `x_label` and `y_label` respectively."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "430df302",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 4:** Create a **scatter plot** having the numbers *(1, 2, 3, .... 10)* on the **x axis** and their **squares** on the **y axis**. Moreover, add the label `\"Numbers\"` on the **x axis** and the `\"Squares\"` on the **y axis**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3ffa1887",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:55.387551Z",
+ "iopub.status.busy": "2023-11-15T01:37:55.386531Z",
+ "iopub.status.idle": "2023-11-15T01:37:55.392149Z",
+ "shell.execute_reply": "2023-11-15T01:37:55.391139Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# since you have already computed the lists 'x' and 'y' you do not have to redefine them\n",
+ "# just run the next cell to confirm that the lists are still correct\n",
+ "# do NOT create the plot just yet"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "27eba0b2",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q4\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8cfe6d01",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Now, **plot** `x` and `y` as **scatter plot** with the **x-axis** labelled *Numbers*, and the **y-axis** labelled *Squares*. Your plot should look like this:"
+ ]
+ },
+ {
+ "attachments": {
+ "q4.jpg": {
+ "image/jpeg": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "27012280",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "<div><img src=\"attachment:q4.jpg\" style=\"height: 300px;\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd31a5ee",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:55.408978Z",
+ "iopub.status.busy": "2023-11-15T01:37:55.407959Z",
+ "iopub.status.idle": "2023-11-15T01:37:55.653739Z",
+ "shell.execute_reply": "2023-11-15T01:37:55.652727Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# we have already added in the label for the x axis\n",
+ "# complete the rest of the code and add the label for the y axis\n",
+ "\n",
+ "plot_scatter(..., ..., x_label=\"Numbers\", y_label=...)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aaff939b",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Task 1.2: Plotting planets with `scatter_plot`\n",
+ "\n",
+ "We will now create more interesting plots using the data in the variable `small_planets_list`. However, let us first get acquainted with the dataset."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dab9092a",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 5:** What is the **number** of `Planet` objects in `small_planets_list` that were discovered in the time period *2000* to *2010* (both, inclusive)?\n",
+ "\n",
+ "**Hint:** You can find the year of discovery of each `Planet` using the `discovery_year` attribute of the `Planet` objects."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "972df04c",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:55.658738Z",
+ "iopub.status.busy": "2023-11-15T01:37:55.657738Z",
+ "iopub.status.idle": "2023-11-15T01:37:55.667293Z",
+ "shell.execute_reply": "2023-11-15T01:37:55.666283Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'planet_count_2000_2010', then display it\n",
+ "\n",
+ "# TODO: initialize the variable 'planet_count_2000_2010'\n",
+ "# TODO: loop through the planets in 'small_planets_list'\n",
+ " # TODO: update 'planet_count_2000_2010' if the 'discovery_year'\n",
+ " # of the planet is between 2000 and 2010 \n",
+ " \n",
+ "# TODO: display 'planet_count_2000_2010'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "80abdfbb",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q5\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1207c30f",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 6:** What is the **percentage** of planets (in the range *0* to *100*) that have `insolation_flux` **greater** than *1000*?\n",
+ "\n",
+ "You **must** ignore the `Planet` objects with **missing** `insolation_flux` data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f951cfea",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:55.681355Z",
+ "iopub.status.busy": "2023-11-15T01:37:55.680358Z",
+ "iopub.status.idle": "2023-11-15T01:37:55.694781Z",
+ "shell.execute_reply": "2023-11-15T01:37:55.693766Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'flux_1000_percentage', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "783b9d56",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q6\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "01860db9",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 7:** Create a **scatter plot** representing the `orbital_period` (on the **x-axis**) against the `semi_major_radius` (on the **y-axis**) of each `Planet` object in `small_planets_list`.\n",
+ "\n",
+ "You **must** ignore any `Planet` objects for which **either** the `orbital_period` or the `semi_major_radius` data is **missing**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7d166fe2",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:55.711192Z",
+ "iopub.status.busy": "2023-11-15T01:37:55.711192Z",
+ "iopub.status.idle": "2023-11-15T01:37:55.721672Z",
+ "shell.execute_reply": "2023-11-15T01:37:55.720660Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# we have computed the lists 'orbital_period' and 'semi_major_radii' for you\n",
+ "\n",
+ "orbital_periods = []\n",
+ "semi_major_radii = []\n",
+ "for planet in small_planets_list:\n",
+ " orbital_period = planet.orbital_period\n",
+ " semi_major_radius = planet.semi_major_radius\n",
+ " if orbital_period == None or semi_major_radius == None:\n",
+ " continue\n",
+ " orbital_periods.append(orbital_period)\n",
+ " semi_major_radii.append(semi_major_radius)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ddd2ac0d",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q7\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2ba9f7aa",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Now, we will **plot** `orbital_periods` and `semi_major_radii` as **scatter plot** with the **x-axis** labelled *Orbital Period*, and the **y-axis** labelled *Semi Major Radius*."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8970ad42",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:56.117354Z",
+ "iopub.status.busy": "2023-11-15T01:37:56.117354Z",
+ "iopub.status.idle": "2023-11-15T01:37:56.495640Z",
+ "shell.execute_reply": "2023-11-15T01:37:56.494626Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# we have done this for you\n",
+ "\n",
+ "plot_scatter(orbital_periods, semi_major_radii, 'Orbital Period','Semi Major Radius')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "74c36a3c",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Note that almost all the points in the plot are clustered near the **y axis**. This is because there are a few outliers with **extremely high** `orbital_period`. To get a better idea of what the data looks like, it would be useful to create a **scatter plot** without the **outliers**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8491507c",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 8:** Create the same **scatter plot** as above, but **exclude** planets with `orbital_period` **more than** *1000*.\n",
+ "\n",
+ "You **must** ignore any `Planet` objects for which **either** the `orbital_period` or the `semi_major_radius` data is **missing**. You **must** also ignore any `Planet` object with `orbital_period` **greater than** *1000*."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7bc93ba8",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:56.499659Z",
+ "iopub.status.busy": "2023-11-15T01:37:56.499659Z",
+ "iopub.status.idle": "2023-11-15T01:37:56.509501Z",
+ "shell.execute_reply": "2023-11-15T01:37:56.508491Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# first compute and store the lists 'orbital_periods_no_outliers' and 'semi_major_radii_no_outliers', \n",
+ "# but do NOT display them\n",
+ "# do NOT create the plot just yet\n",
+ "\n",
+ "# replace the ... with your own code\n",
+ "\n",
+ "orbital_periods_no_outliers = []\n",
+ "semi_major_radii_no_outliers = []\n",
+ "# TODO: loop through each planet in small_planets_list\n",
+ " # TODO: skip the planets with missing 'orbital_period' or 'semi_major_radius' data\n",
+ " # TODO: skip the planets with 'orbital_period' greater than 1000\n",
+ " # TODO: add 'orbital_period' and 'semi_major_radius' data to the appropriate lists"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6cad14c6",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q8\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21c00a49",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Now, **plot** `orbital_periods_no_outliers` and `semi_major_radii_no_outliers` as **scatter plot** with the **x-axis** labelled *Orbital Period*, and the **y-axis** labelled *Semi Major Radius*. Your plot should look like this:"
+ ]
+ },
+ {
+ "attachments": {
+ "q8.jpg": {
+ "image/jpeg": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "4f8232d4",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "<div><img src=\"attachment:q8.jpg\" style=\"height: 300px;\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fcb720d3",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:56.835801Z",
+ "iopub.status.busy": "2023-11-15T01:37:56.835801Z",
+ "iopub.status.idle": "2023-11-15T01:37:57.118147Z",
+ "shell.execute_reply": "2023-11-15T01:37:57.118147Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'orbital_periods_no_outliers' and 'semi_major_radii_no_outliers'\n",
+ "\n",
+ "plot_scatter(..., ..., ..., ...)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3bcc7781",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 9:** Create a **scatter plot** representing the `planet_mass` (on the **x-axis**) against the `planet_radius` (on the **y-axis**) of each `Planet` object in `small_planets_list` whose `discovery_method` is *Transit*.\n",
+ "\n",
+ "You **must** ignore any `Planet` objects for which **either** the `planet_mass`, `planet_radius` or `discovery_method` data is **missing**. You **must** also ignore any `Planet` objects whose `discovery_method` is **not** *Transit*."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3ffe471e",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:57.123177Z",
+ "iopub.status.busy": "2023-11-15T01:37:57.123177Z",
+ "iopub.status.idle": "2023-11-15T01:37:57.132196Z",
+ "shell.execute_reply": "2023-11-15T01:37:57.131183Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# first compute and store the lists 'transit_planet_masses', and 'transit_planet_radii'\n",
+ "# do NOT create the plot just yet\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1bc82674",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q9\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7780b50d",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Now, **plot** `transit_planet_masses` and `transit_planet_radii` as **scatter plot** with the **x-axis** labelled *Planet Mass*, and the **y-axis** labelled *Planet Radius*. Your plot should look like this:"
+ ]
+ },
+ {
+ "attachments": {
+ "q9.jpg": {
+ "image/jpeg": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "0ce14599",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "<div><img src=\"attachment:q9.jpg\" style=\"height: 300px;\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "36bf1d4c",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:57.420402Z",
+ "iopub.status.busy": "2023-11-15T01:37:57.419402Z",
+ "iopub.status.idle": "2023-11-15T01:37:57.691255Z",
+ "shell.execute_reply": "2023-11-15T01:37:57.691255Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'transit_planet_masses' and 'transit_planet_radii'\n",
+ "\n",
+ "plot_scatter(..., ..., ..., ...)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "77d5cdc5",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 10:** Create the same **scatter plot** as above, but **exclude** planets with `planet_mass` **more than** *250*.\n",
+ "\n",
+ "You **must** ignore any `Planet` objects for which **either** the `planet_mass`, `planet_radius` or `discovery_method` data is **missing**. You **must** also ignore any `Planet` objects whose `discovery_method` is **not** *Transit* or have a `planet_mass` greater than *250*."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b535ec08",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:57.698274Z",
+ "iopub.status.busy": "2023-11-15T01:37:57.697268Z",
+ "iopub.status.idle": "2023-11-15T01:37:57.709152Z",
+ "shell.execute_reply": "2023-11-15T01:37:57.708139Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# first compute and store the lists 'transit_planet_masses_no_outliers', and 'transit_planet_radii_no_outliers'\n",
+ "# do NOT create the plot just yet\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "16d62fce",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Now, **plot** `transit_planet_masses` and `transit_planet_radii` as **scatter plot** with the **x-axis** labelled *Planet Mass*, and the **y-axis** labelled *Planet Radius*. Your plot should look like this:"
+ ]
+ },
+ {
+ "attachments": {
+ "q10.jpg": {
+ "image/jpeg": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "84bc93ac",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "<div><img src=\"attachment:q10.jpg\" style=\"height: 300px;\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "903474ca",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:57.716152Z",
+ "iopub.status.busy": "2023-11-15T01:37:57.715164Z",
+ "iopub.status.idle": "2023-11-15T01:37:58.002142Z",
+ "shell.execute_reply": "2023-11-15T01:37:58.001112Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'transit_planet_masses' and 'transit_planet_radii'\n",
+ "\n",
+ "plot_scatter(..., ..., ..., ...)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d9041fb2",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Task 1.3: Changing the color and the size of points with `plot_scatter`\n",
+ "\n",
+ "So far, we have not used two of the **optional arguments** of the `plot_scatter` function: `c` and `s`. The parameter `c` can be used to change the **color** to the points on the **scatter plot**, while `s` can be used to change the **sizes** of the points on the **scatter plot**. These arguments can help in visualizing the data better, as in the examples below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c5efee52",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:58.007126Z",
+ "iopub.status.busy": "2023-11-15T01:37:58.007126Z",
+ "iopub.status.idle": "2023-11-15T01:37:58.243299Z",
+ "shell.execute_reply": "2023-11-15T01:37:58.242276Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# run this cell\n",
+ "\n",
+ "x = [10, 20, 30, 40]\n",
+ "y = [10, 20, 30, 40]\n",
+ "c = [10, 20, 30, 40]\n",
+ "\n",
+ "plot_scatter(x, y, c=c)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3a3bd03e",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "#### Understanding the `c` argument:\n",
+ "\n",
+ "In the example above, the argument passed to the `c` parameter was a **list** of numbers. The `plot_scatter` function used the values in this **list** to color each point in the plot. For example, the element at index *0* of `x` is *10*, the element at index *0* of `y` is *10*, and the element at index *0* of `c` is *10*. Therefore, the point at the position *(10, 10)* is colored using the number *10*. Similarly, the point at position *(20, 20)* is colored using the number *20*.\n",
+ "\n",
+ "A **high** color value means that the color will be **lighter** while a **low** color value means that the color will be **darker**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dafb40d4",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "#### Understanding the `s` argument:\n",
+ "\n",
+ "In comparison to the `c` argument, the `s` argument is relatively simpler to understand. Run the cell below, and try to figure out what happened."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d96127b8",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:58.248292Z",
+ "iopub.status.busy": "2023-11-15T01:37:58.248292Z",
+ "iopub.status.idle": "2023-11-15T01:37:58.481643Z",
+ "shell.execute_reply": "2023-11-15T01:37:58.480632Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# run this cell\n",
+ "\n",
+ "x = [10, 20, 30, 40]\n",
+ "y = [10, 20, 30, 40]\n",
+ "s = [10, 20, 30, 40]\n",
+ "\n",
+ "plot_scatter(x, y, s=s)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3b6b7e19",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 11**: Create a **scatter plot** representing the `planet_mass` (on the **x-axis**) against the `planet_radius` (on the **y-axis**) of each `Planet` object in `small_planets_list` whose `discovery_method` is *Radial Velocity*. Additionally, **color** the points on the **scatter plot** depending on the `discovery_year`.\n",
+ "\n",
+ "You **must** ignore all `Planet` objects with **missing** `planet_mass`, `planet_radius`, or `discovery_year` data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c9833e12",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:58.486646Z",
+ "iopub.status.busy": "2023-11-15T01:37:58.486646Z",
+ "iopub.status.idle": "2023-11-15T01:37:58.496980Z",
+ "shell.execute_reply": "2023-11-15T01:37:58.495974Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# we have computed the lists 'orbital_period' and 'semi_major_radii' for you\n",
+ "\n",
+ "rad_vel_planet_masses = []\n",
+ "rad_vel_planet_radii = []\n",
+ "rad_vel_discovery_years = []\n",
+ "for planet in small_planets_list:\n",
+ " mass = planet.planet_mass\n",
+ " radius = planet.planet_radius\n",
+ " year = planet.discovery_year\n",
+ " if mass == None or radius == None or year == None:\n",
+ " continue\n",
+ " if planet.discovery_method == 'Radial Velocity':\n",
+ " rad_vel_planet_masses.append(mass)\n",
+ " rad_vel_planet_radii.append(radius)\n",
+ " rad_vel_discovery_years.append(year)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "77c48f9d",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q11\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a3e8f337",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Now, we will **plot** `rad_vel_planet_masses` and `rad_vel_planet_radii` as **scatter plot** with the **x-axis** labelled *Planet Mass*, and the **y-axis** labelled *Planet Radius*, and **colored** using `rad_vel_discovery_years`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8e27b128",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:58.592631Z",
+ "iopub.status.busy": "2023-11-15T01:37:58.591610Z",
+ "iopub.status.idle": "2023-11-15T01:37:58.919950Z",
+ "shell.execute_reply": "2023-11-15T01:37:58.918939Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# we have done this for you\n",
+ "\n",
+ "plot_scatter(rad_vel_planet_masses, rad_vel_planet_radii, 'Planet Mass','Planet Radius', c=rad_vel_discovery_years)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "134c0125",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Food for thought:** What do you think this plot signifies? Recall that a **lighter** color implies a **larger** value in the list `rad_vel_discovery_years`. Where are the lighter colors present in this plot? Can you guess why the colors are spread out the way they are?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0d7c2abd",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 12:** Excluding planets with `orbital_period` **more than** *1000*, create a **scatter plot** representing the `orbital_period` (on the **x-axis**) against the `semi_major_radius` (on the **y-axis**) of each `Planet` object in `small_planets_list`. Additionally, **color** the points on the **scatter plot** depending on the `discovery_year`.\n",
+ "\n",
+ "You **must** ignore any `Planet` objects for which **either** the `orbital_period`, `semi_major_radius` or `discovery_year` data is **missing**. You **must** also ignore any `Planet` objects whose `orbital_period` is greater than *1000*. You **must** use the `discovery_year` to **color** the points in the **scatter plot**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4d948b1c",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:58.924949Z",
+ "iopub.status.busy": "2023-11-15T01:37:58.924949Z",
+ "iopub.status.idle": "2023-11-15T01:37:58.938401Z",
+ "shell.execute_reply": "2023-11-15T01:37:58.937395Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# first compute and store the lists 'orbital_periods', 'semi_major_radii', and 'discovery_years'\n",
+ "# do NOT create the plot just yet\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "53c0c78a",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q12\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cf152303",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Now, **plot** `orbital_periods` and `semi_major_radii` as **scatter plot** with the **x-axis** labelled *Orbital Period*, and the **y-axis** labelled *Semi Major Radius*, and **colored** using `discovery_years`. Your plot should look like this:"
+ ]
+ },
+ {
+ "attachments": {
+ "q12.jpg": {
+ "image/jpeg": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "084438d8",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "<div><img src=\"attachment:q12.jpg\" style=\"height: 300px;\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "876fadba",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:59.298928Z",
+ "iopub.status.busy": "2023-11-15T01:37:59.298928Z",
+ "iopub.status.idle": "2023-11-15T01:37:59.722734Z",
+ "shell.execute_reply": "2023-11-15T01:37:59.721721Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'orbital_periods' and 'semi_major_radii' with 'discovery_years' as colors\n",
+ "\n",
+ "plot_scatter(..., ..., x_label=..., y_label=..., c=...)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "42a91b8a",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 13:** Create the same plot as above, but additionally change the **size** of the points depending on the `planet_mass`.\n",
+ "\n",
+ "You **must** ignore any `Planet` objects for which **either** the `orbital_period`, `semi_major_radius`, `discovery_year` or `planet_mass` data is **missing**. You **must** also ignore any `Planet` objects whose `orbital_period` is greater than *1000*. You **must** use the `discovery_year` to **color** the points in the **scatter plot** and you **must** use the `planet_mass` to change the **size** of the points."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "60f7b2aa",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:37:59.727733Z",
+ "iopub.status.busy": "2023-11-15T01:37:59.726734Z",
+ "iopub.status.idle": "2023-11-15T01:37:59.740667Z",
+ "shell.execute_reply": "2023-11-15T01:37:59.739656Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# first compute and store the lists 'orbital_periods', 'semi_major_radii', 'discovery_years', and 'planet_masses'\n",
+ "# do NOT create the plot just yet\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "31740a65",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q13\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "09043827",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Now, **plot** `orbital_periods` and `semi_major_radii` as **scatter plot** with the **x-axis** labelled *Orbital Period*, and the **y-axis** labelled *Semi Major Radius*, **colored** using `discovery_years`, and **sized** using `planet_masses`. Your plot should look like this:"
+ ]
+ },
+ {
+ "attachments": {
+ "q13.jpg": {
+ "image/jpeg": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "f5fe61a2",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "<div><img src=\"attachment:q13.jpg\" style=\"height: 300px;\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9ebf14f0",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:00.146885Z",
+ "iopub.status.busy": "2023-11-15T01:38:00.145902Z",
+ "iopub.status.idle": "2023-11-15T01:38:00.644173Z",
+ "shell.execute_reply": "2023-11-15T01:38:00.643163Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'orbital_periods' and 'semi_major_radii' with 'discovery_years' as colors and 'planet_masses' as sizes\n",
+ "\n",
+ "plot_scatter(..., ..., x_label=..., y_label=..., c=..., s=...)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2145b250",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Task 1.4: Plotting multiple scatter plots with `plot_scatter_multiple`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "79ab9547",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Function 2: `plot_scatter_multiple(x_data_dict, y_data_dict, x_label, y_label)`\n",
+ "\n",
+ "So far, we have used `plot_scatter` to create **scatter plots** using two **lists**, and we have seen how to use the `c` argument to color the **scatter plots** based on another **list** containing *numeric* data. It would be good if we could also **color** the points using *non-numeric* data. For example, we might want to create a **scatter plot** of the `planet_mass` against the `planet_radius`, and **color** the points differently depending on the `discovery_method`. However, since the `discovery_method` is **non-numeric**, the `plot_scatter` function will not be of help (you can try!)\n",
+ "\n",
+ "You have been provided with another function `plot_scatter_multiple` which can create **multiple** scatter plots on the same plot."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5afbd70a",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:00.649328Z",
+ "iopub.status.busy": "2023-11-15T01:38:00.648329Z",
+ "iopub.status.idle": "2023-11-15T01:38:00.654130Z",
+ "shell.execute_reply": "2023-11-15T01:38:00.654130Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def plot_scatter_multiple(x_data_dict, y_data_dict, x_label='x axis', y_label='y axis'):\n",
+ " legend_values = list(x_data_dict.keys())\n",
+ " for key in x_data_dict:\n",
+ " plt.scatter(x_data_dict[key], y_data_dict[key], s=7)\n",
+ " plt.xlabel(x_label)\n",
+ " plt.ylabel(y_label)\n",
+ " plt.legend(legend_values)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6a2273f8",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Here is a quick example to see how this function works:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eee29241",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:00.659144Z",
+ "iopub.status.busy": "2023-11-15T01:38:00.658139Z",
+ "iopub.status.idle": "2023-11-15T01:38:01.005505Z",
+ "shell.execute_reply": "2023-11-15T01:38:01.004495Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# run this cell\n",
+ "\n",
+ "x_dict = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}\n",
+ "y_dict = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}\n",
+ "plot_scatter_multiple(x_dict, y_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5df64ca4",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "As you can see, in order to create the **scatter plot**, we had to create **two** dictionaries `x_dict` and `y_dict`. Both **dictionaries** contain the same **keys** (which correspond to the **different colors** on the plot). Corresponding to each **key**, the value in the **dictionary** `x_dict` contains the data in the **x axis** and the value in the **dictionary** `y_dict` contains the data in the **y axis**. For example, the element at index *0* of `x_dict['A']` is *1*, and the element at index *0* of `y_dict['A']` is *1*. Therefore, a point is added to the **scatter plot** at the position *(1, 1)* in the **color** corresponding to the **key** `A`. On the other hand, the element at index *0* of `x_dict['B']` is *5*, and the element at index *0* of `y_dict['B']` is *5*. Therefore, a point is added to the **scatter plot** at the position *(5, 5)* in the **color** corresponding to the **key** `B`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4553d72c",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 14:** Excluding planets with `planet_mass` **more than** *250*, create a **scatter plot** representing the `planet_mass` (on the **x-axis**) against the `planet_radius` (on the **y-axis**) of each `Planet` object of **each** `discovery_method`.\n",
+ "\n",
+ "You **must** ignore any `Planet` objects for which **either** the `planet_mass`, `planet_radius` or `discovery_method` data is **missing**. You **must** also ignore any `Planet` objects whose `planet_mass` is more than *250*. You **must** color the points depending on the `discovery_method`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "216f387f",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:01.010507Z",
+ "iopub.status.busy": "2023-11-15T01:38:01.009515Z",
+ "iopub.status.idle": "2023-11-15T01:38:01.022530Z",
+ "shell.execute_reply": "2023-11-15T01:38:01.021515Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# we have computed the dictionaries 'planet_masses' and 'planet_radii' for you\n",
+ "\n",
+ "planet_masses = {}\n",
+ "planet_radii = {}\n",
+ "for planet in small_planets_list:\n",
+ " method = planet.discovery_method\n",
+ " mass = planet.planet_mass\n",
+ " radius = planet.planet_radius\n",
+ " if method == None or mass == None or radius == None:\n",
+ " continue\n",
+ " if mass > 250:\n",
+ " continue\n",
+ " if method not in planet_masses:\n",
+ " planet_masses[method] = []\n",
+ " planet_radii[method] = []\n",
+ " planet_masses[method].append(mass)\n",
+ " planet_radii[method].append(radius)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9f5c593d",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q14\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "87d9fb6a",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Now, **plot** `planet_masses` and `planet_radii` as **scatter plot** with the **x-axis** labelled *Planet Mass*, and the **y-axis** labelled *Planet Radius*. Your plot should look like this:"
+ ]
+ },
+ {
+ "attachments": {
+ "q14.jpg": {
+ "image/jpeg": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "4e1f0552",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "<div><img src=\"attachment:q14.jpg\" style=\"height: 300px;\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c14e0a51",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:01.036323Z",
+ "iopub.status.busy": "2023-11-15T01:38:01.035330Z",
+ "iopub.status.idle": "2023-11-15T01:38:01.776549Z",
+ "shell.execute_reply": "2023-11-15T01:38:01.775533Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# we have done this for you\n",
+ "\n",
+ "plot_scatter_multiple(planet_masses, planet_radii, 'Planet Mass','Planet Radius')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "61081765",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 15:** Excluding planets with `planet_mass` **more than** *250*, create a **scatter plot** representing the `planet_mass` (on the **x-axis**) against the `planet_radius` (on the **y-axis**) of each `Planet` object discovered in **each decade**.\n",
+ "\n",
+ "You **must** ignore any `Planet` objects for which **either** the `planet_mass`, `planet_radius` or `discovery_year` data is **missing**. You **must** also ignore any `Planet` object which has `planet_mass` more than *250*. You **must** color the points depending on the **decade** of the `discovery_year`.\n",
+ "\n",
+ "**Hint:** You can use the `year_to_decade` function defined in the next cell to determine the **decade** that a given year belongs to."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0e2cbddc",
+ "metadata": {
+ "deletable": false,
+ "editable": false,
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:01.780551Z",
+ "iopub.status.busy": "2023-11-15T01:38:01.780551Z",
+ "iopub.status.idle": "2023-11-15T01:38:01.787754Z",
+ "shell.execute_reply": "2023-11-15T01:38:01.786744Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# we have defined this function for you\n",
+ "\n",
+ "def year_to_decade(year):\n",
+ " if year % 10 == 0:\n",
+ " decade = str(year - 9) + ' to ' + str(year)\n",
+ " else:\n",
+ " decade_start = year - (year % 10) + 1\n",
+ " decade = str(decade_start) + ' to ' + str(decade_start + 9)\n",
+ " return decade"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d5cf32d4",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:01.791755Z",
+ "iopub.status.busy": "2023-11-15T01:38:01.791755Z",
+ "iopub.status.idle": "2023-11-15T01:38:01.808085Z",
+ "shell.execute_reply": "2023-11-15T01:38:01.807074Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# first compute and store the dictionaries 'planet_masses', and 'planet_radii'\n",
+ "# do NOT create the plot just yet\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b9cd1ca2",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q15\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f3bacdfb",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Now, **plot** `planet_masses` and `planet_radii` as **scatter plot** with the **x-axis** labelled *Planet Mass*, and the **y-axis** labelled *Planet Radius*. Your plot should look like this:"
+ ]
+ },
+ {
+ "attachments": {
+ "q15.jpg": {
+ "image/jpeg": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "0cb66d9e",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "<div><img src=\"attachment:q15.jpg\" style=\"height: 300px;\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d35e5845",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:01.826462Z",
+ "iopub.status.busy": "2023-11-15T01:38:01.825466Z",
+ "iopub.status.idle": "2023-11-15T01:38:02.318200Z",
+ "shell.execute_reply": "2023-11-15T01:38:02.317190Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'planet_masses' and 'planet_radii'\n",
+ "\n",
+ "plot_scatter_multiple(..., ..., x_label = ..., y_label = ...)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ac014ddc",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "## Segment 2: Recursion\n",
+ "\n",
+ "In addition to **scatter plots**, you will also have to write some **recursive** functions in P11. You can now practice creating some **recursive** functions here."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "370039a1",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Task 2.1: Flattening a Nested List\n",
+ "\n",
+ "A **nested list** is a **list** which contains **lists** as its elements, and these **lists** can themselves contain other **lists** (and so on). **Flattening** a **nested list** involves extracting all the elements in a **nested list**. For example, if a **nested list** is as follows:\n",
+ "\n",
+ "```python\n",
+ "[[1, 2], 3, [4, [5, 6], [[7], 8, [9, 10]]]]\n",
+ "```\n",
+ "\n",
+ "then the **flattened** version of this list would be:\n",
+ "\n",
+ "```python\n",
+ "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3a077fe2",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Function 3: `flatten(some_list)`\n",
+ "\n",
+ "We are now going to write a **recursive** function to **flatten** a **nested list** and print out all the elements in list.\n",
+ "\n",
+ "Let's first consider a **simple case**: when there are no **nested lists** inside the input **list** (i.e., there could be **lists** inside the input **list** but these **lists** have no additional **lists** inside them)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9122e785",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:02.323213Z",
+ "iopub.status.busy": "2023-11-15T01:38:02.322219Z",
+ "iopub.status.idle": "2023-11-15T01:38:02.328861Z",
+ "shell.execute_reply": "2023-11-15T01:38:02.328861Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# we have started this function for you\n",
+ "# you will have to come back to modify this function after a few questions \n",
+ "\n",
+ "def flatten(some_list):\n",
+ " # initialize an empty list into a variable called 'flattened_list' \n",
+ " flattened_list = []\n",
+ " # iterate over every item inside some_list\n",
+ " for item in some_list:\n",
+ " # if item is a list, extend new list by item\n",
+ " if type(item) == list:\n",
+ " flattened_list.extend(item)\n",
+ " # otherwise, append item to the new list\n",
+ " else:\n",
+ " flattened_list.append(item)\n",
+ " return flattened_list"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "737dbef6",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 16:** What is the output of `flatten(['rabbit', ['cat'], ['lion', 'chimpanzee']])`?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ca71f37a",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:02.333891Z",
+ "iopub.status.busy": "2023-11-15T01:38:02.333891Z",
+ "iopub.status.idle": "2023-11-15T01:38:02.340234Z",
+ "shell.execute_reply": "2023-11-15T01:38:02.340234Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# we have done this one for you\n",
+ "\n",
+ "animals = flatten(['rabbit', ['cat'], ['lion', 'chimpanzee']])\n",
+ "\n",
+ "animals"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e00406c9",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q16\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7ba2b8a8",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "The function `flatten` works when the input is a **list** which does not have any **nested lists** inside it. While this is a good start, we are not finished yet, as this next example shows:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "040a0ace",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:02.354040Z",
+ "iopub.status.busy": "2023-11-15T01:38:02.353021Z",
+ "iopub.status.idle": "2023-11-15T01:38:02.359564Z",
+ "shell.execute_reply": "2023-11-15T01:38:02.358556Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# run this cell\n",
+ "\n",
+ "nested_animals = [\"turtle\", [\"skunk\", [\"raccoon\"], \"crocodile\"]]\n",
+ "\n",
+ "print(\"flatten does this: \", flatten(nested_animals))\n",
+ "print(\"flatten should do this: \", ['turtle', 'skunk', 'raccoon', 'crocodile'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4288c4f0",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Go back and **update** the function so that it works even if the **lists** in the input `some_list` have additional **nested lists** inside them.\n",
+ "\n",
+ "Here is some hints for how your function **must** look:\n",
+ "\n",
+ "```python\n",
+ "def flatten(some_list):\n",
+ " # TODO: initialize an empty list into a variable called 'flattened_list'\n",
+ " # TODO: iterate over every 'item' inside 'some_list'\n",
+ " # TODO: check if type of 'item' is list\n",
+ " # TODO: call 'flatten' function on 'item'\n",
+ " # TODO: add the elements of the returned list to 'flattened_list'\n",
+ " # TODO: if item is not a list, just add to 'flattened_list'\n",
+ " # TODO: return 'flattened_list'\n",
+ "```\n",
+ "\n",
+ "**Hint:** Now would be a good time for you to review `append` vs `extend` list methods. Run the following cell to see what these list methods do:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d525c667",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:02.364585Z",
+ "iopub.status.busy": "2023-11-15T01:38:02.364585Z",
+ "iopub.status.idle": "2023-11-15T01:38:02.371009Z",
+ "shell.execute_reply": "2023-11-15T01:38:02.369980Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# run this cell\n",
+ "\n",
+ "list1 = [1, 2, 3, 4]\n",
+ "list1.append([4, 5, 6])\n",
+ "print(\"'append' does this:\", list1)\n",
+ "\n",
+ "list2 = [1, 2, 3, 4]\n",
+ "list2.extend([4, 5, 6])\n",
+ "print(\"'extend' does this:\", list2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f4c98fb5",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Warning:** `flatten` is quite a complicated function, so do **not** try to implement it in one sitting. Practice incremental coding here. First, try to **print** the outcome the recursive call to `flatten(item)` before you figure out how to add it to your list `flattened_list`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "01003ead",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 17:** What is the output of the `flatten` function when run against the below **nested list** `nested_names`?\n",
+ "\n",
+ "```python\n",
+ "nested_names = [\"Adam\", [\"Bob\", [\"Chet\"]], \"Bea\", [\"Treasure\", \"Andy\", [\"Ann\"]]]\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "89a09123",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:02.375010Z",
+ "iopub.status.busy": "2023-11-15T01:38:02.375010Z",
+ "iopub.status.idle": "2023-11-15T01:38:02.382770Z",
+ "shell.execute_reply": "2023-11-15T01:38:02.381741Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'flattened_names', then display it\n",
+ "\n",
+ "nested_names = [\"Adam\", [\"Bob\", [\"Chet\"]], \"Bea\", [\"Treasure\", \"Andy\", [\"Ann\"]]]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5dd9fdcc",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q17\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cc1291c9",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 18:** What is the output of the `flatten` function when run against the below **nested list** `more_nested_names`?\n",
+ "\n",
+ "```python\n",
+ "more_nested_names = [\"Adam\", [\"Bob\", [\"Chet\", \"Cat\"], \"Barb\", \"Bert\"], \"Alex\", [\"Bea\", [\"Gold\", \\\n",
+ " [\"Andy\"]], \"Ann\"]]\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fd040f6f",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:02.399916Z",
+ "iopub.status.busy": "2023-11-15T01:38:02.397935Z",
+ "iopub.status.idle": "2023-11-15T01:38:02.407228Z",
+ "shell.execute_reply": "2023-11-15T01:38:02.406215Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'more_flattened_names', then display it\n",
+ "\n",
+ "more_nested_names = [\"Adam\", [\"Bob\", [\"Chet\", \"Cat\"], \"Barb\", \"Bert\"], \"Alex\", [\"Bea\", [\"Gold\", \\\n",
+ " [\"Andy\"]], \"Ann\"]]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "55bed0f1",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q18\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ab28e1c8",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Task 2.2: Explore a directory recursively\n",
+ "\n",
+ "In Task 2.1, we practiced how to **recursively** collect elements in a **nested list**. In Task 2.2, we are going to **recursively** collect all the **paths** of files in a **given directory**. After collecting them, your function `get_all_paths_in` will return a **sorted list of paths** to files inside this directory. We will practice our function on the `sample_data` directory inside the `sample_data.zip`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4537563c",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 19:** What are the **files** and **sub-directories** that are inside the `sample_data` directory?\n",
+ "\n",
+ "Your output **must** be a **list** containing the **names** of the **files** and **sub-directories**. You **must** ignore all files and sub-directories whose names begin `\".\"`. You **must** **explicitly** sort the **list** in **descending** (i.e., reverse alphabetical) **order**.\n",
+ "\n",
+ "**Hint:** Use `os.listdir` to get the **files** and **sub-directories** in a **directory**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6a68bb8b",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:02.422920Z",
+ "iopub.status.busy": "2023-11-15T01:38:02.421917Z",
+ "iopub.status.idle": "2023-11-15T01:38:02.431016Z",
+ "shell.execute_reply": "2023-11-15T01:38:02.430006Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# we have done this one for you\n",
+ "\n",
+ "directory = \"sample_data\"\n",
+ "sample_data_contents = sorted([content for content in os.listdir(directory) if not content.startswith(\".\")], reverse=True)\n",
+ "\n",
+ "sample_data_contents"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "df406924",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q19\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7881d122",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 20:** How **many** **files** (i.e., **not** **sub-directories**) are in the `sample_data` directory?\n",
+ "\n",
+ "Your output **must** be an **int**. You **must** ignore all files whose names begin `\".\"`.\n",
+ "\n",
+ "**Hints:**\n",
+ "\n",
+ "1. Use the `os.path.isfile` function to recognize **files** from **directories**.\n",
+ "2. You **must** pass the **relative path** of the **file** to determine if it is a file. For example, if you want to determine if the file `file_1.json` inside the directory `sample_data` is a **file**, you must use\n",
+ "\n",
+ "```python\n",
+ "os.path.isfile(os.path.join(\"sample_data\", \"file_1.json\"))\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7cb47a4f",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:02.444565Z",
+ "iopub.status.busy": "2023-11-15T01:38:02.444565Z",
+ "iopub.status.idle": "2023-11-15T01:38:02.452297Z",
+ "shell.execute_reply": "2023-11-15T01:38:02.452297Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'num_sample_data_files', then display it\n",
+ "\n",
+ "directory = \"sample_data\"\n",
+ "# TODO: find the number of files in 'directory'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4c424cab",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q20\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "53a9917b",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 21:** What are the **relative paths** of the **sub-directories** (i.e., **not files**) that are inside the `sample_data` directory?\n",
+ "\n",
+ "Your output **must** be a **list** containing the **relative paths** of the **sub-directories**. You **must** ignore all sub-directories whose names begin `\".\"`. You **must** **explicitly** sort the **list** in **descending** (i.e., reverse alphabetical) **order**.\n",
+ "\n",
+ "**Hint:** Use the `os.path.isdir` function to recognize **files** from **directories**.\n",
+ "\n",
+ "**Warning:** Use `os.path.join` to get the **relative paths** of the **sub-directories**. Remember you **must not** hardcode `'\\\\'` or `'/'` in your paths."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1f171980",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:02.466858Z",
+ "iopub.status.busy": "2023-11-15T01:38:02.466858Z",
+ "iopub.status.idle": "2023-11-15T01:38:02.475614Z",
+ "shell.execute_reply": "2023-11-15T01:38:02.474603Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'sample_data_directories', then display it\n",
+ "\n",
+ "directory = \"sample_data\"\n",
+ "# TODO: list the sub-directories in 'directory'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2ddf8b55",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q21\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b2cfe27",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "We are now ready to define our recursive function."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6cff9352",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Function 4: `get_all_paths_in(directory)`\n",
+ "\n",
+ "You **must** write this function that takes in the **relative path** of a `directory` as its input, and returns a **list** of **relative paths** of all the **files** inside `directory` and its subdirectories.\n",
+ "\n",
+ "You **must** **ignore** all files that start with `\".\"`, and your output **must** be **explicitly** sorted in **reverse alphabetical** order.\n",
+ "\n",
+ "In other words, if a directory `small_data` looks like this:\n",
+ "```\n",
+ "+-- sample_data\n",
+ "| +-- .DS_Store\n",
+ "| +-- file_1.json\n",
+ "| +-- sample_1\n",
+ "| | +-- .ipynb_checkpoints\n",
+ "| | +-- file_2.json\n",
+ "| | +-- file_3.json\n",
+ "| +-- sample_2\n",
+ "| | +-- file_4.json\n",
+ "| | +-- sample_3\n",
+ "| | | +-- .DS_Store\n",
+ "| | | +-- file_5.json\n",
+ "```\n",
+ "\n",
+ "then the output of the function call `get_all_paths_in(\"sample_data\")` (depending on whether the code is run on a Windows computer or a Mac) **must** either be the **list**\n",
+ "```python\n",
+ "['sample_data\\\\sample_2\\\\sample_3\\\\file_5.json',\n",
+ " 'sample_data\\\\sample_2\\\\file_4.json',\n",
+ " 'sample_data\\\\sample_1\\\\file_3.json',\n",
+ " 'sample_data\\\\sample_1\\\\file_2.json',\n",
+ " 'sample_data\\\\file_1.json']\n",
+ "```\n",
+ "\n",
+ "or the **list**\n",
+ "```python\n",
+ "['sample_data/sample_2/sample_3/file_5.json',\n",
+ " 'sample_data/sample_2/file_4.json',\n",
+ " 'sample_data/sample_1/file_3.json',\n",
+ " 'sample_data/sample_1/file_2.json',\n",
+ " 'sample_data/file_1.json']\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b82200bf",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:02.488958Z",
+ "iopub.status.busy": "2023-11-15T01:38:02.488958Z",
+ "iopub.status.idle": "2023-11-15T01:38:02.496849Z",
+ "shell.execute_reply": "2023-11-15T01:38:02.495823Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define the 'get_all_paths_in' function here\n",
+ "\n",
+ "def get_all_paths_in(directory):\n",
+ " pass # replace with your code\n",
+ " # TODO: initialize an empty list\n",
+ " # TODO: loop through all files and directories in 'directory' that do not start with \".\"\n",
+ " # TODO: check if it is a directory\n",
+ " # TODO: call 'get_all_paths_in' and add returned paths to your list\n",
+ " # TODO: if it is a file, directly add to your list\n",
+ " # TODO: sort the list of paths and then return the list"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e8f19ebd",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 22:** How **many** files are there in total, in the `sample_2` directory of the `sample_data` directory?\n",
+ "\n",
+ "You **must** use the `get_all_paths_in` function to answer this question."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ceb68dfc",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:02.500868Z",
+ "iopub.status.busy": "2023-11-15T01:38:02.500868Z",
+ "iopub.status.idle": "2023-11-15T01:38:02.507403Z",
+ "shell.execute_reply": "2023-11-15T01:38:02.507403Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# we have done this one for you\n",
+ "\n",
+ "num_files_sample_2 = len(get_all_paths_in(os.path.join(\"sample_data\", \"sample_2\")))\n",
+ "\n",
+ "num_files_sample_2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b90c0175",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q22\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "48e19950",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 23:** What are the **relative paths** of all the **files** that are inside the `sample_data` directory?\n",
+ "\n",
+ "Your output **must** be a **list** containing the **relative paths** of **files**. You **must** use the `get_all_paths_in` function to answer this question."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8bf83cb3",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:38:02.521927Z",
+ "iopub.status.busy": "2023-11-15T01:38:02.520920Z",
+ "iopub.status.idle": "2023-11-15T01:38:02.529520Z",
+ "shell.execute_reply": "2023-11-15T01:38:02.529520Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'sample_data_all_files', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a4d1e24f",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q23\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4e68ea65",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "## Great work! You are now ready to start P11!"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.11.4"
+ },
+ "otter": {
+ "OK_FORMAT": true,
+ "tests": {
+ "q1": {
+ "name": "q1",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q1', {'x': x, 'y': y})\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q11": {
+ "name": "q11",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q11', {'rad_vel_planet_masses': rad_vel_planet_masses, 'rad_vel_planet_radii': rad_vel_planet_radii, 'rad_vel_discovery_years': rad_vel_discovery_years})\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q12": {
+ "name": "q12",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q12', {'orbital_periods': orbital_periods, 'semi_major_radii': semi_major_radii, 'discovery_years': discovery_years})\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q13": {
+ "name": "q13",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q13', {'orbital_periods': orbital_periods, 'semi_major_radii': semi_major_radii, 'discovery_years': discovery_years, 'planet_masses': planet_masses})\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q14": {
+ "name": "q14",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q14', {'planet_masses': planet_masses, 'planet_radii': planet_radii})\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q15": {
+ "name": "q15",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q15', {'planet_masses': planet_masses, 'planet_radii': planet_radii})\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q16": {
+ "name": "q16",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q16', animals)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q17": {
+ "name": "q17",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q17', flattened_names)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q18": {
+ "name": "q18",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q18', more_flattened_names)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q19": {
+ "name": "q19",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q19', sample_data_contents)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q2": {
+ "name": "q2",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q2', {'x': x, 'y': y})\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q20": {
+ "name": "q20",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q20', num_sample_data_files)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q21": {
+ "name": "q21",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q21', sample_data_directories)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q22": {
+ "name": "q22",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q22', num_files_sample_2)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q23": {
+ "name": "q23",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q23', sample_data_all_files)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q3": {
+ "name": "q3",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q3', {'x': x, 'y': y})\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q4": {
+ "name": "q4",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q4', {'x': x, 'y': y})\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q5": {
+ "name": "q5",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q5', planet_count_2000_2010)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q6": {
+ "name": "q6",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q6', flux_1000_percentage)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q7": {
+ "name": "q7",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q7', {'orbital_periods': orbital_periods, 'semi_major_radii': semi_major_radii})\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q8": {
+ "name": "q8",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q8', {'orbital_periods_no_outliers': orbital_periods_no_outliers, 'semi_major_radii_no_outliers': semi_major_radii_no_outliers})\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q9": {
+ "name": "q9",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q9', {'transit_planet_masses': transit_planet_masses, 'transit_planet_radii': transit_planet_radii})\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ }
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lab-p11/planets_small.json b/lab-p11/planets_small.json
new file mode 100644
index 0000000..f01964a
--- /dev/null
+++ b/lab-p11/planets_small.json
@@ -0,0 +1,65018 @@
+[
+ {
+ "planet_name": "11 Com b",
+ "host_name": "11 Com",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2007,
+ "controversial_flag": false,
+ "orbital_period": 326.03,
+ "planet_radius": 12.1,
+ "planet_mass": 6165.6,
+ "semi_major_radius": 1.29,
+ "eccentricity": 0.231,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "11 UMi b",
+ "host_name": "11 UMi",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2009,
+ "controversial_flag": false,
+ "orbital_period": 516.21997,
+ "planet_radius": 12.3,
+ "planet_mass": 4684.8142,
+ "semi_major_radius": 1.53,
+ "eccentricity": 0.08,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "14 And b",
+ "host_name": "14 And",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2008,
+ "controversial_flag": false,
+ "orbital_period": 185.84,
+ "planet_radius": 12.9,
+ "planet_mass": 1525.5,
+ "semi_major_radius": 0.83,
+ "eccentricity": 0.0,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "16 Cyg B b",
+ "host_name": "16 Cyg B",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 1996,
+ "controversial_flag": false,
+ "orbital_period": 798.5,
+ "planet_radius": 13.5,
+ "planet_mass": 565.7374,
+ "semi_major_radius": 1.66,
+ "eccentricity": 0.68,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "17 Sco b",
+ "host_name": "17 Sco",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2020,
+ "controversial_flag": false,
+ "orbital_period": 578.38,
+ "planet_radius": 12.9,
+ "planet_mass": 1373.01872,
+ "semi_major_radius": 1.45,
+ "eccentricity": 0.06,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "18 Del b",
+ "host_name": "18 Del",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2008,
+ "controversial_flag": false,
+ "orbital_period": 993.3,
+ "planet_radius": 12.5,
+ "planet_mass": 3273.5,
+ "semi_major_radius": 2.6,
+ "eccentricity": 0.08,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "1RXS J160929.1-210524 b",
+ "host_name": "1RXS J160929.1-210524",
+ "discovery_method": "Imaging",
+ "discovery_year": 2008,
+ "controversial_flag": false,
+ "orbital_period": null,
+ "planet_radius": 18.647,
+ "planet_mass": 3000.0,
+ "semi_major_radius": 330.0,
+ "eccentricity": null,
+ "equilibrium_temperature": 1700.0,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "24 Boo b",
+ "host_name": "24 Boo",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2018,
+ "controversial_flag": false,
+ "orbital_period": 30.3506,
+ "planet_radius": 13.9,
+ "planet_mass": 289.2253,
+ "semi_major_radius": 0.19,
+ "eccentricity": 0.042,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "2M0437 b",
+ "host_name": "2MASS J04372171+2651014",
+ "discovery_method": "Imaging",
+ "discovery_year": 2021,
+ "controversial_flag": false,
+ "orbital_period": null,
+ "planet_radius": 13.0,
+ "planet_mass": 1271.31363,
+ "semi_major_radius": 118.0,
+ "eccentricity": null,
+ "equilibrium_temperature": 1450.0,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "2MASS J01033563-5515561 AB b",
+ "host_name": "2MASS J01033563-5515561 A",
+ "discovery_method": "Imaging",
+ "discovery_year": 2013,
+ "controversial_flag": false,
+ "orbital_period": null,
+ "planet_radius": 12.3,
+ "planet_mass": 4131.79,
+ "semi_major_radius": 84.0,
+ "eccentricity": null,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "2MASS J01225093-2439505 b",
+ "host_name": "2MASS J01225093-2439505",
+ "discovery_method": "Imaging",
+ "discovery_year": 2013,
+ "controversial_flag": false,
+ "orbital_period": null,
+ "planet_radius": 11.209,
+ "planet_mass": 7786.5,
+ "semi_major_radius": 52.0,
+ "eccentricity": null,
+ "equilibrium_temperature": 1600.0,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "2MASS J02192210-3925225 b",
+ "host_name": "2MASS J02192210-3925225",
+ "discovery_method": "Imaging",
+ "discovery_year": 2015,
+ "controversial_flag": false,
+ "orbital_period": null,
+ "planet_radius": 16.141,
+ "planet_mass": 4417.837,
+ "semi_major_radius": 156.0,
+ "eccentricity": null,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "2MASS J04414489+2301513 b",
+ "host_name": "2MASS J04414489+2301513",
+ "discovery_method": "Imaging",
+ "discovery_year": 2010,
+ "controversial_flag": false,
+ "orbital_period": null,
+ "planet_radius": 12.6,
+ "planet_mass": 2383.6,
+ "semi_major_radius": 15.0,
+ "eccentricity": null,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "2MASS J12073346-3932539 b",
+ "host_name": "2MASS J12073346-3932539",
+ "discovery_method": "Imaging",
+ "discovery_year": 2004,
+ "controversial_flag": false,
+ "orbital_period": null,
+ "planet_radius": 12.9,
+ "planet_mass": 1589.15,
+ "semi_major_radius": 55.0,
+ "eccentricity": null,
+ "equilibrium_temperature": 1250.0,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "2MASS J22362452+4751425 b",
+ "host_name": "2MASS J22362452+4751425",
+ "discovery_method": "Imaging",
+ "discovery_year": 2016,
+ "controversial_flag": false,
+ "orbital_period": null,
+ "planet_radius": 12.3,
+ "planet_mass": 3972.875,
+ "semi_major_radius": 230.0,
+ "eccentricity": null,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "30 Ari B b",
+ "host_name": "30 Ari B",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2009,
+ "controversial_flag": false,
+ "orbital_period": 335.10001,
+ "planet_radius": 12.3,
+ "planet_mass": 4392.4106,
+ "semi_major_radius": 0.99,
+ "eccentricity": 0.29,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "4 UMa b",
+ "host_name": "4 UMa",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2006,
+ "controversial_flag": false,
+ "orbital_period": 269.3,
+ "planet_radius": 12.7,
+ "planet_mass": 2256.5,
+ "semi_major_radius": 0.87,
+ "eccentricity": 0.432,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "42 Dra b",
+ "host_name": "42 Dra",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2008,
+ "controversial_flag": false,
+ "orbital_period": 479.1,
+ "planet_radius": 13.0,
+ "planet_mass": 1233.13,
+ "semi_major_radius": 1.19,
+ "eccentricity": 0.38,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "51 Eri b",
+ "host_name": "51 Eri",
+ "discovery_method": "Imaging",
+ "discovery_year": 2015,
+ "controversial_flag": false,
+ "orbital_period": 11688.0,
+ "planet_radius": 13.4,
+ "planet_mass": 635.66,
+ "semi_major_radius": 13.2,
+ "eccentricity": 0.45,
+ "equilibrium_temperature": 700.0,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "51 Peg b",
+ "host_name": "51 Peg",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 1995,
+ "controversial_flag": false,
+ "orbital_period": 4.230785,
+ "planet_radius": 14.3,
+ "planet_mass": 146.2018,
+ "semi_major_radius": 0.0527,
+ "eccentricity": 0.013,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "6 Lyn b",
+ "host_name": "6 Lyn",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2008,
+ "controversial_flag": false,
+ "orbital_period": 934.3,
+ "planet_radius": 13.4,
+ "planet_mass": 638.8383,
+ "semi_major_radius": 2.11,
+ "eccentricity": 0.073,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "70 Vir b",
+ "host_name": "70 Vir",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 1996,
+ "controversial_flag": false,
+ "orbital_period": 116.688,
+ "planet_radius": 12.6,
+ "planet_mass": 2380.45,
+ "semi_major_radius": 0.481,
+ "eccentricity": 0.3988,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "75 Cet b",
+ "host_name": "75 Cet",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2012,
+ "controversial_flag": false,
+ "orbital_period": 691.9,
+ "planet_radius": 13.1,
+ "planet_mass": 953.49,
+ "semi_major_radius": 2.1,
+ "eccentricity": 0.117,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "8 UMi b",
+ "host_name": "8 UMi",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2015,
+ "controversial_flag": false,
+ "orbital_period": 93.4,
+ "planet_radius": 13.6,
+ "planet_mass": 416.3573,
+ "semi_major_radius": 0.49,
+ "eccentricity": 0.06,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "81 Cet b",
+ "host_name": "81 Cet",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2008,
+ "controversial_flag": false,
+ "orbital_period": 952.7,
+ "planet_radius": 12.8,
+ "planet_mass": 1684.4,
+ "semi_major_radius": 2.5,
+ "eccentricity": 0.206,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "91 Aqr b",
+ "host_name": "91 Aqr",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2013,
+ "controversial_flag": false,
+ "orbital_period": 181.4,
+ "planet_radius": 13.1,
+ "planet_mass": 1017.0,
+ "semi_major_radius": 0.7,
+ "eccentricity": 0.027,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "AB Aur b",
+ "host_name": "AB Aur",
+ "discovery_method": "Imaging",
+ "discovery_year": 2022,
+ "controversial_flag": false,
+ "orbital_period": null,
+ "planet_radius": 12.5,
+ "planet_mass": 2860.45566,
+ "semi_major_radius": 93.9,
+ "eccentricity": 0.4,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "AB Pic b",
+ "host_name": "AB Pic",
+ "discovery_method": "Imaging",
+ "discovery_year": 2005,
+ "controversial_flag": false,
+ "orbital_period": null,
+ "planet_radius": 12.3,
+ "planet_mass": 4290.5,
+ "semi_major_radius": 260.0,
+ "eccentricity": null,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "AF Lep b",
+ "host_name": "AF Lep",
+ "discovery_method": "Imaging",
+ "discovery_year": 2023,
+ "controversial_flag": false,
+ "orbital_period": 7520.0,
+ "planet_radius": 12.8,
+ "planet_mass": 1664.46737,
+ "semi_major_radius": 7.99,
+ "eccentricity": 0.47,
+ "equilibrium_temperature": 1400.0,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "BD+03 2562 b",
+ "host_name": "BD+03 2562",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2017,
+ "controversial_flag": false,
+ "orbital_period": 481.9,
+ "planet_radius": 12.7,
+ "planet_mass": 2034.112,
+ "semi_major_radius": 1.3,
+ "eccentricity": 0.2,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "BD+14 4559 b",
+ "host_name": "BD+14 4559",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2009,
+ "controversial_flag": false,
+ "orbital_period": 268.94,
+ "planet_radius": 13.8,
+ "planet_mass": 330.5432,
+ "semi_major_radius": 0.78,
+ "eccentricity": 0.29,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "BD+15 2375 b",
+ "host_name": "BD+15 2375",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2016,
+ "controversial_flag": false,
+ "orbital_period": 153.22,
+ "planet_radius": 13.8,
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+ "orbital_period": 24.1396,
+ "planet_radius": 10.27,
+ "planet_mass": 74.9,
+ "semi_major_radius": 0.1688,
+ "eccentricity": 0.29,
+ "equilibrium_temperature": 677.0,
+ "insolation_flux": 35.0
+ },
+ {
+ "planet_name": "V1298 Tau c",
+ "host_name": "V1298 Tau",
+ "discovery_method": "Transit",
+ "discovery_year": 2019,
+ "controversial_flag": false,
+ "orbital_period": 8.24958,
+ "planet_radius": 5.59,
+ "planet_mass": 26.7,
+ "semi_major_radius": 0.0825,
+ "eccentricity": 0.43,
+ "equilibrium_temperature": 968.0,
+ "insolation_flux": 146.0
+ },
+ {
+ "planet_name": "V1298 Tau d",
+ "host_name": "V1298 Tau",
+ "discovery_method": "Transit",
+ "discovery_year": 2019,
+ "controversial_flag": false,
+ "orbital_period": 12.4032,
+ "planet_radius": 6.41,
+ "planet_mass": 33.6,
+ "semi_major_radius": 0.1083,
+ "eccentricity": 0.21,
+ "equilibrium_temperature": 845.0,
+ "insolation_flux": 85.0
+ },
+ {
+ "planet_name": "V1298 Tau e",
+ "host_name": "V1298 Tau",
+ "discovery_method": "Transit",
+ "discovery_year": 2019,
+ "controversial_flag": false,
+ "orbital_period": 60.0,
+ "planet_radius": 8.74,
+ "planet_mass": 57.0,
+ "semi_major_radius": 0.308,
+ "eccentricity": 0.57,
+ "equilibrium_temperature": 492.0,
+ "insolation_flux": 10.0
+ },
+ {
+ "planet_name": "WASP-47 b",
+ "host_name": "WASP-47",
+ "discovery_method": "Transit",
+ "discovery_year": 2012,
+ "controversial_flag": false,
+ "orbital_period": 4.1591492,
+ "planet_radius": 12.64,
+ "planet_mass": 363.6,
+ "semi_major_radius": 0.052,
+ "eccentricity": 0.0028,
+ "equilibrium_temperature": 1275.0,
+ "insolation_flux": 534.0
+ },
+ {
+ "planet_name": "WASP-47 c",
+ "host_name": "WASP-47",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2015,
+ "controversial_flag": false,
+ "orbital_period": 588.8,
+ "planet_radius": 13.5,
+ "planet_mass": 500.0,
+ "semi_major_radius": 1.393,
+ "eccentricity": 0.295,
+ "equilibrium_temperature": 247.0,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "WASP-47 d",
+ "host_name": "WASP-47",
+ "discovery_method": "Transit",
+ "discovery_year": 2015,
+ "controversial_flag": false,
+ "orbital_period": 9.03055,
+ "planet_radius": 3.567,
+ "planet_mass": 14.2,
+ "semi_major_radius": 0.085,
+ "eccentricity": 0.01,
+ "equilibrium_temperature": null,
+ "insolation_flux": 190.0
+ },
+ {
+ "planet_name": "WASP-47 e",
+ "host_name": "WASP-47",
+ "discovery_method": "Transit",
+ "discovery_year": 2015,
+ "controversial_flag": false,
+ "orbital_period": 0.7895933,
+ "planet_radius": 1.808,
+ "planet_mass": 6.77,
+ "semi_major_radius": 0.01673,
+ "eccentricity": 0.03,
+ "equilibrium_temperature": null,
+ "insolation_flux": 4895.0
+ },
+ {
+ "planet_name": "tau Cet e",
+ "host_name": "tau Cet",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2017,
+ "controversial_flag": false,
+ "orbital_period": 162.87,
+ "planet_radius": 1.81,
+ "planet_mass": 3.93,
+ "semi_major_radius": 0.538,
+ "eccentricity": 0.18,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "tau Cet f",
+ "host_name": "tau Cet",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2017,
+ "controversial_flag": false,
+ "orbital_period": 636.13,
+ "planet_radius": 1.81,
+ "planet_mass": 3.93,
+ "semi_major_radius": 1.334,
+ "eccentricity": 0.16,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "tau Cet g",
+ "host_name": "tau Cet",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2017,
+ "controversial_flag": false,
+ "orbital_period": 20.0,
+ "planet_radius": 1.18,
+ "planet_mass": 1.75,
+ "semi_major_radius": 0.133,
+ "eccentricity": 0.06,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ },
+ {
+ "planet_name": "tau Cet h",
+ "host_name": "tau Cet",
+ "discovery_method": "Radial Velocity",
+ "discovery_year": 2017,
+ "controversial_flag": false,
+ "orbital_period": 49.41,
+ "planet_radius": 1.19,
+ "planet_mass": 1.83,
+ "semi_major_radius": 0.243,
+ "eccentricity": 0.23,
+ "equilibrium_temperature": null,
+ "insolation_flux": null
+ }
+]
\ No newline at end of file
diff --git a/lab-p11/public_tests.py b/lab-p11/public_tests.py
new file mode 100644
index 0000000..8da3154
--- /dev/null
+++ b/lab-p11/public_tests.py
@@ -0,0 +1,821 @@
+#!/usr/bin/python
+# +
+import os, json, math, copy
+from collections import namedtuple
+from bs4 import BeautifulSoup
+
+HIDDEN_FILE = os.path.join("hidden", "hidden_tests.py")
+if os.path.exists(HIDDEN_FILE):
+ import hidden.hidden_tests as hidn
+# -
+
+MAX_FILE_SIZE = 750 # units - KB
+REL_TOL = 6e-04 # relative tolerance for floats
+ABS_TOL = 15e-03 # absolute tolerance for floats
+TOTAL_SCORE = 100 # total score for the project
+
+DF_FILE = 'expected_dfs.html'
+PLOT_FILE = 'expected_plots.json'
+
+PASS = "All test cases passed!"
+
+TEXT_FORMAT = "TEXT_FORMAT" # question type when expected answer is a type, str, int, float, or bool
+TEXT_FORMAT_UNORDERED_LIST = "TEXT_FORMAT_UNORDERED_LIST" # question type when the expected answer is a list or a set where the order does *not* matter
+TEXT_FORMAT_ORDERED_LIST = "TEXT_FORMAT_ORDERED_LIST" # question type when the expected answer is a list or tuple where the order does matter
+TEXT_FORMAT_DICT = "TEXT_FORMAT_DICT" # question type when the expected answer is a dictionary
+TEXT_FORMAT_SPECIAL_ORDERED_LIST = "TEXT_FORMAT_SPECIAL_ORDERED_LIST" # question type when the expected answer is a list where order does matter, but with possible ties. Elements are ordered according to values in special_ordered_json (with ties allowed)
+TEXT_FORMAT_NAMEDTUPLE = "TEXT_FORMAT_NAMEDTUPLE" # question type when expected answer is a namedtuple
+PNG_FORMAT_SCATTER = "PNG_FORMAT_SCATTER" # question type when the expected answer is a scatter plot
+HTML_FORMAT = "HTML_FORMAT" # question type when the expected answer is a DataFrame
+FILE_JSON_FORMAT = "FILE_JSON_FORMAT" # question type when the expected answer is a JSON file
+SLASHES = "SLASHES" # question SUFFIX when expected answer contains paths with slashes
+
+def get_expected_format():
+ """get_expected_format() returns a dict mapping each question to the format
+ of the expected answer."""
+ expected_format = {'q1': 'PNG_FORMAT_SCATTER',
+ 'q2': 'PNG_FORMAT_SCATTER',
+ 'q3': 'PNG_FORMAT_SCATTER',
+ 'q4': 'PNG_FORMAT_SCATTER',
+ 'q5': 'TEXT_FORMAT',
+ 'q6': 'TEXT_FORMAT',
+ 'q7': 'PNG_FORMAT_SCATTER',
+ 'q8': 'PNG_FORMAT_SCATTER',
+ 'q9': 'PNG_FORMAT_SCATTER',
+ 'q10': 'PNG_FORMAT_SCATTER',
+ 'q11': 'PNG_FORMAT_SCATTER',
+ 'q12': 'PNG_FORMAT_SCATTER',
+ 'q13': 'PNG_FORMAT_SCATTER',
+ 'q14': 'PNG_FORMAT_SCATTER',
+ 'q15': 'PNG_FORMAT_SCATTER',
+ 'q16': 'TEXT_FORMAT_ORDERED_LIST',
+ 'q17': 'TEXT_FORMAT_ORDERED_LIST',
+ 'q18': 'TEXT_FORMAT_ORDERED_LIST',
+ 'q19': 'TEXT_FORMAT_ORDERED_LIST',
+ 'q20': 'TEXT_FORMAT',
+ 'q21': 'TEXT_FORMAT_ORDERED_LIST',
+ 'q22': 'TEXT_FORMAT',
+ 'q23': 'TEXT_FORMAT_ORDERED_LIST'}
+ return expected_format
+
+
+def get_expected_json():
+ """get_expected_json() returns a dict mapping each question to the expected
+ answer (if the format permits it)."""
+ expected_json = {'q5': 480,
+ 'q6': 9.482758620689655,
+ 'q16': ['rabbit', 'cat', 'lion', 'chimpanzee'],
+ 'q17': ['Adam', 'Bob', 'Chet', 'Bea', 'Treasure', 'Andy', 'Ann'],
+ 'q18': ['Adam',
+ 'Bob',
+ 'Chet',
+ 'Cat',
+ 'Barb',
+ 'Bert',
+ 'Alex',
+ 'Bea',
+ 'Gold',
+ 'Andy',
+ 'Ann'],
+ 'q19': ['sample_2', 'sample_1', 'file_1.json'],
+ 'q20': 1,
+ 'q21': ['sample_data\\sample_2', 'sample_data\\sample_1'],
+ 'q22': 2,
+ 'q23': ['sample_data\\sample_2\\sample_3\\file_5.json',
+ 'sample_data\\sample_2\\file_4.json',
+ 'sample_data\\sample_1\\file_3.json',
+ 'sample_data\\sample_1\\file_2.json',
+ 'sample_data\\file_1.json']}
+ return expected_json
+
+
+def get_special_json():
+ """get_special_json() returns a dict mapping each question to the expected
+ answer stored in a special format as a list of tuples. Each tuple contains
+ the element expected in the list, and its corresponding value. Any two
+ elements with the same value can appear in any order in the actual list,
+ but if two elements have different values, then they must appear in the
+ same order as in the expected list of tuples."""
+ special_json = {}
+ return special_json
+
+
+def compare(expected, actual, q_format=TEXT_FORMAT):
+ """compare(expected, actual) is used to compare when the format of
+ the expected answer is known for certain."""
+ try:
+ if q_format == TEXT_FORMAT:
+ return simple_compare(expected, actual)
+ elif q_format == TEXT_FORMAT_UNORDERED_LIST:
+ return list_compare_unordered(expected, actual)
+ elif q_format == TEXT_FORMAT_ORDERED_LIST:
+ return list_compare_ordered(expected, actual)
+ elif q_format == TEXT_FORMAT_DICT:
+ return dict_compare(expected, actual)
+ elif q_format == TEXT_FORMAT_SPECIAL_ORDERED_LIST:
+ return list_compare_special(expected, actual)
+ elif q_format == TEXT_FORMAT_NAMEDTUPLE:
+ return namedtuple_compare(expected, actual)
+ elif q_format == PNG_FORMAT_SCATTER:
+ return compare_flip_dicts(expected, actual)
+ elif q_format == HTML_FORMAT:
+ return compare_cell_html(expected, actual)
+ elif q_format == FILE_JSON_FORMAT:
+ return compare_json(expected, actual)
+ else:
+ if expected != actual:
+ return "expected %s but found %s " % (repr(expected), repr(actual))
+ except:
+ if expected != actual:
+ return "expected %s" % (repr(expected))
+ return PASS
+
+
+def print_message(expected, actual, complete_msg=True):
+ """print_message(expected, actual) displays a simple error message."""
+ msg = "expected %s" % (repr(expected))
+ if complete_msg:
+ msg = msg + " but found %s" % (repr(actual))
+ return msg
+
+
+def simple_compare(expected, actual, complete_msg=True):
+ """simple_compare(expected, actual) is used to compare when the expected answer
+ is a type/Nones/str/int/float/bool. When the expected answer is a float,
+ the actual answer is allowed to be within the tolerance limit. Otherwise,
+ the values must match exactly, or a very simple error message is displayed."""
+ msg = PASS
+ if 'numpy' in repr(type((actual))):
+ actual = actual.item()
+ if isinstance(expected, type):
+ if expected != actual:
+ if isinstance(actual, type):
+ msg = "expected %s but found %s" % (expected.__name__, actual.__name__)
+ else:
+ msg = "expected %s but found %s" % (expected.__name__, repr(actual))
+ elif not isinstance(actual, type(expected)):
+ if not (isinstance(expected, (float, int)) and isinstance(actual, (float, int))):
+ if not is_namedtuple(expected):
+ msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+ elif isinstance(expected, float):
+ if not math.isclose(actual, expected, rel_tol=REL_TOL, abs_tol=ABS_TOL):
+ msg = print_message(expected, actual, complete_msg)
+ elif isinstance(expected, (list, tuple)) or is_namedtuple(expected):
+ new_msg = print_message(expected, actual, complete_msg)
+ if len(expected) != len(actual):
+ return new_msg
+ for i in range(len(expected)):
+ val = simple_compare(expected[i], actual[i])
+ if val != PASS:
+ return new_msg
+ elif isinstance(expected, dict):
+ new_msg = print_message(expected, actual, complete_msg)
+ if len(expected) != len(actual):
+ return new_msg
+ val = simple_compare(sorted(list(expected.keys())), sorted(list(actual.keys())))
+ if val != PASS:
+ return new_msg
+ for key in expected:
+ val = simple_compare(expected[key], actual[key])
+ if val != PASS:
+ return new_msg
+ else:
+ if expected != actual:
+ msg = print_message(expected, actual, complete_msg)
+ return msg
+
+
+def intelligent_compare(expected, actual, obj=None):
+ """intelligent_compare(expected, actual) is used to compare when the
+ data type of the expected answer is not known for certain, and default
+ assumptions need to be made."""
+ if obj == None:
+ obj = type(expected).__name__
+ if is_namedtuple(expected):
+ msg = namedtuple_compare(expected, actual)
+ elif isinstance(expected, (list, tuple)):
+ msg = list_compare_ordered(expected, actual, obj)
+ elif isinstance(expected, set):
+ msg = list_compare_unordered(expected, actual, obj)
+ elif isinstance(expected, (dict)):
+ msg = dict_compare(expected, actual)
+ else:
+ msg = simple_compare(expected, actual)
+ msg = msg.replace("CompDict", "dict").replace("CompSet", "set").replace("NewNone", "None")
+ return msg
+
+
+def is_namedtuple(obj, init_check=True):
+ """is_namedtuple(obj) returns True if `obj` is a namedtuple object
+ defined in the test file."""
+ bases = type(obj).__bases__
+ if len(bases) != 1 or bases[0] != tuple:
+ return False
+ fields = getattr(type(obj), '_fields', None)
+ if not isinstance(fields, tuple):
+ return False
+ if init_check and not type(obj).__name__ in [nt.__name__ for nt in _expected_namedtuples]:
+ return False
+ return True
+
+
+def list_compare_ordered(expected, actual, obj=None):
+ """list_compare_ordered(expected, actual) is used to compare when the
+ expected answer is a list/tuple, where the order of the elements matters."""
+ msg = PASS
+ if not isinstance(actual, type(expected)):
+ msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+ return msg
+ if obj == None:
+ obj = type(expected).__name__
+ for i in range(len(expected)):
+ if i >= len(actual):
+ msg = "at index %d of the %s, expected missing %s" % (i, obj, repr(expected[i]))
+ break
+ val = intelligent_compare(expected[i], actual[i], "sub" + obj)
+ if val != PASS:
+ msg = "at index %d of the %s, " % (i, obj) + val
+ break
+ if len(actual) > len(expected) and msg == PASS:
+ msg = "at index %d of the %s, found unexpected %s" % (len(expected), obj, repr(actual[len(expected)]))
+ if len(expected) != len(actual):
+ msg = msg + " (found %d entries in %s, but expected %d)" % (len(actual), obj, len(expected))
+
+ if len(expected) > 0:
+ try:
+ if msg != PASS and list_compare_unordered(expected, actual, obj) == PASS:
+ msg = msg + " (%s may not be ordered as required)" % (obj)
+ except:
+ pass
+ return msg
+
+
+def list_compare_helper(larger, smaller):
+ """list_compare_helper(larger, smaller) is a helper function which takes in
+ two lists of possibly unequal sizes and finds the item that is not present
+ in the smaller list, if there is such an element."""
+ msg = PASS
+ j = 0
+ for i in range(len(larger)):
+ if i == len(smaller):
+ msg = "expected %s" % (repr(larger[i]))
+ break
+ found = False
+ while not found:
+ if j == len(smaller):
+ val = simple_compare(larger[i], smaller[j - 1], complete_msg=False)
+ break
+ val = simple_compare(larger[i], smaller[j], complete_msg=False)
+ j += 1
+ if val == PASS:
+ found = True
+ break
+ if not found:
+ msg = val
+ break
+ return msg
+
+class NewNone():
+ """alternate class in place of None, which allows for comparison with
+ all other data types."""
+ def __str__(self):
+ return 'None'
+ def __repr__(self):
+ return 'None'
+ def __lt__(self, other):
+ return True
+ def __le__(self, other):
+ return True
+ def __gt__(self, other):
+ return False
+ def __ge__(self, other):
+ return other == None
+ def __eq__(self, other):
+ return other == None
+ def __ne__(self, other):
+ return other != None
+
+class CompDict(dict):
+ """subclass of dict, which allows for comparison with other dicts."""
+ def __init__(self, vals):
+ super(self.__class__, self).__init__(vals)
+ if type(vals) == CompDict:
+ self.val = vals.val
+ elif isinstance(vals, dict):
+ self.val = self.get_equiv(vals)
+ else:
+ raise TypeError("'%s' object cannot be type casted to CompDict class" % type(vals).__name__)
+
+ def get_equiv(self, vals):
+ val = []
+ for key in sorted(list(vals.keys())):
+ val.append((key, vals[key]))
+ return val
+
+ def __str__(self):
+ return str(dict(self.val))
+ def __repr__(self):
+ return repr(dict(self.val))
+ def __lt__(self, other):
+ return self.val < CompDict(other).val
+ def __le__(self, other):
+ return self.val <= CompDict(other).val
+ def __gt__(self, other):
+ return self.val > CompDict(other).val
+ def __ge__(self, other):
+ return self.val >= CompDict(other).val
+ def __eq__(self, other):
+ return self.val == CompDict(other).val
+ def __ne__(self, other):
+ return self.val != CompDict(other).val
+
+class CompSet(set):
+ """subclass of set, which allows for comparison with other sets."""
+ def __init__(self, vals):
+ super(self.__class__, self).__init__(vals)
+ if type(vals) == CompSet:
+ self.val = vals.val
+ elif isinstance(vals, set):
+ self.val = self.get_equiv(vals)
+ else:
+ raise TypeError("'%s' object cannot be type casted to CompSet class" % type(vals).__name__)
+
+ def get_equiv(self, vals):
+ return sorted(list(vals))
+
+ def __str__(self):
+ return str(set(self.val))
+ def __repr__(self):
+ return repr(set(self.val))
+ def __getitem__(self, index):
+ return self.val[index]
+ def __lt__(self, other):
+ return self.val < CompSet(other).val
+ def __le__(self, other):
+ return self.val <= CompSet(other).val
+ def __gt__(self, other):
+ return self.val > CompSet(other).val
+ def __ge__(self, other):
+ return self.val >= CompSet(other).val
+ def __eq__(self, other):
+ return self.val == CompSet(other).val
+ def __ne__(self, other):
+ return self.val != CompSet(other).val
+
+def make_sortable(item):
+ """make_sortable(item) replaces all Nones in `item` with an alternate
+ class that allows for comparison with str/int/float/bool/list/set/tuple/dict.
+ It also replaces all dicts (and sets) with a subclass that allows for
+ comparison with other dicts (and sets)."""
+ if item == None:
+ return NewNone()
+ elif isinstance(item, (type, str, int, float, bool)):
+ return item
+ elif isinstance(item, (list, set, tuple)):
+ new_item = []
+ for subitem in item:
+ new_item.append(make_sortable(subitem))
+ if is_namedtuple(item):
+ return type(item)(*new_item)
+ elif isinstance(item, set):
+ return CompSet(new_item)
+ else:
+ return type(item)(new_item)
+ elif isinstance(item, dict):
+ new_item = {}
+ for key in item:
+ new_item[key] = make_sortable(item[key])
+ return CompDict(new_item)
+ return item
+
+def list_compare_unordered(expected, actual, obj=None):
+ """list_compare_unordered(expected, actual) is used to compare when the
+ expected answer is a list/set where the order of the elements does not matter."""
+ msg = PASS
+ if not isinstance(actual, type(expected)):
+ msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+ return msg
+ if obj == None:
+ obj = type(expected).__name__
+
+ try:
+ sort_expected = sorted(make_sortable(expected))
+ sort_actual = sorted(make_sortable(actual))
+ except:
+ return "unexpected datatype found in %s; expected entries of type %s" % (obj, obj, type(expected[0]).__name__)
+
+ if len(actual) == 0 and len(expected) > 0:
+ msg = "in the %s, missing " % (obj) + sort_expected[0]
+ elif len(actual) > 0 and len(expected) > 0:
+ val = intelligent_compare(sort_expected[0], sort_actual[0])
+ if val.startswith("expected to find type"):
+ msg = "in the %s, " % (obj) + simple_compare(sort_expected[0], sort_actual[0])
+ else:
+ if len(expected) > len(actual):
+ msg = "in the %s, missing " % (obj) + list_compare_helper(sort_expected, sort_actual)
+ elif len(expected) < len(actual):
+ msg = "in the %s, found un" % (obj) + list_compare_helper(sort_actual, sort_expected)
+ if len(expected) != len(actual):
+ msg = msg + " (found %d entries in %s, but expected %d)" % (len(actual), obj, len(expected))
+ return msg
+ else:
+ val = list_compare_helper(sort_expected, sort_actual)
+ if val != PASS:
+ msg = "in the %s, missing " % (obj) + val + ", but found un" + list_compare_helper(sort_actual,
+ sort_expected)
+ return msg
+
+
+def namedtuple_compare(expected, actual):
+ """namedtuple_compare(expected, actual) is used to compare when the
+ expected answer is a namedtuple defined in the test file."""
+ msg = PASS
+ if not is_namedtuple(actual, False):
+ msg = "expected namedtuple but found %s" % (type(actual).__name__)
+ return msg
+ if type(expected).__name__ != type(actual).__name__:
+ return "expected namedtuple %s but found namedtuple %s" % (type(expected).__name__, type(actual).__name__)
+ expected_fields = expected._fields
+ actual_fields = actual._fields
+ msg = list_compare_ordered(list(expected_fields), list(actual_fields), "namedtuple attributes")
+ if msg != PASS:
+ return msg
+ for field in expected_fields:
+ val = intelligent_compare(getattr(expected, field), getattr(actual, field))
+ if val != PASS:
+ msg = "at attribute %s of namedtuple %s, " % (field, type(expected).__name__) + val
+ return msg
+ return msg
+
+
+def clean_slashes(item):
+ """clean_slashes()"""
+ if isinstance(item, str):
+ return item.replace("\\", "/").replace("/", os.path.sep)
+ elif item == None or isinstance(item, (type, int, float, bool)):
+ return item
+ elif isinstance(item, (list, tuple, set)) or is_namedtuple(item):
+ new_item = []
+ for subitem in item:
+ new_item.append(clean_slashes(subitem))
+ if is_namedtuple(item):
+ return type(item)(*new_item)
+ else:
+ return type(item)(new_item)
+ elif isinstance(item, dict):
+ new_item = {}
+ for key in item:
+ new_item[clean_slashes(key)] = clean_slashes(item[key])
+ return item
+
+
+def list_compare_special_initialize(special_expected):
+ """list_compare_special_initialize(special_expected) takes in the special
+ ordering stored as a sorted list of items, and returns a list of lists
+ where the ordering among the inner lists does not matter."""
+ latest_val = None
+ clean_special = []
+ for row in special_expected:
+ if latest_val == None or row[1] != latest_val:
+ clean_special.append([])
+ latest_val = row[1]
+ clean_special[-1].append(row[0])
+ return clean_special
+
+
+def list_compare_special(special_expected, actual):
+ """list_compare_special(special_expected, actual) is used to compare when the
+ expected answer is a list with special ordering defined in `special_expected`."""
+ msg = PASS
+ expected_list = []
+ special_order = list_compare_special_initialize(special_expected)
+ for expected_item in special_order:
+ expected_list.extend(expected_item)
+ val = list_compare_unordered(expected_list, actual)
+ if val != PASS:
+ return val
+ i = 0
+ for expected_item in special_order:
+ j = len(expected_item)
+ actual_item = actual[i: i + j]
+ val = list_compare_unordered(expected_item, actual_item)
+ if val != PASS:
+ if j == 1:
+ msg = "at index %d " % (i) + val
+ else:
+ msg = "between indices %d and %d " % (i, i + j - 1) + val
+ msg = msg + " (list may not be ordered as required)"
+ break
+ i += j
+ return msg
+
+
+def dict_compare(expected, actual, obj=None):
+ """dict_compare(expected, actual) is used to compare when the expected answer
+ is a dict."""
+ msg = PASS
+ if not isinstance(actual, type(expected)):
+ msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+ return msg
+ if obj == None:
+ obj = type(expected).__name__
+
+ expected_keys = list(expected.keys())
+ actual_keys = list(actual.keys())
+ val = list_compare_unordered(expected_keys, actual_keys, obj)
+
+ if val != PASS:
+ msg = "bad keys in %s: " % (obj) + val
+ if msg == PASS:
+ for key in expected:
+ new_obj = None
+ if isinstance(expected[key], (list, tuple, set)):
+ new_obj = 'value'
+ elif isinstance(expected[key], dict):
+ new_obj = 'sub' + obj
+ val = intelligent_compare(expected[key], actual[key], new_obj)
+ if val != PASS:
+ msg = "incorrect value for key %s in %s: " % (repr(key), obj) + val
+ return msg
+
+
+def is_flippable(item):
+ """is_flippable(item) determines if the given dict of lists has lists of the
+ same length and is therefore flippable."""
+ item_lens = set(([str(len(item[key])) for key in item]))
+ if len(item_lens) == 1:
+ return PASS
+ else:
+ return "found lists of lengths %s" % (", ".join(list(item_lens)))
+
+def flip_dict_of_lists(item):
+ """flip_dict_of_lists(item) flips a dict of lists into a list of dicts if the
+ lists are of same length."""
+ new_item = []
+ length = len(list(item.values())[0])
+ for i in range(length):
+ new_dict = {}
+ for key in item:
+ new_dict[key] = item[key][i]
+ new_item.append(new_dict)
+ return new_item
+
+def compare_flip_dicts(expected, actual, obj="lists"):
+ """compare_flip_dicts(expected, actual) flips a dict of lists (or dicts) into
+ a list of dicts (or dict of dicts) and then compares the list ignoring order."""
+ msg = PASS
+ example_item = list(expected.values())[0]
+ if isinstance(example_item, (list, tuple)):
+ val = is_flippable(actual)
+ if val != PASS:
+ msg = "expected to find lists of length %d, but " % (len(example_item)) + val
+ return msg
+ msg = list_compare_unordered(flip_dict_of_lists(expected), flip_dict_of_lists(actual), "lists")
+ elif isinstance(example_item, dict):
+ expected_keys = list(example_item.keys())
+ for key in actual:
+ val = list_compare_unordered(expected_keys, list(actual[key].keys()), "dictionary %s" % key)
+ if val != PASS:
+ return val
+ for cat_key in expected_keys:
+ expected_category = {}
+ actual_category = {}
+ for key in expected:
+ expected_category[key] = expected[key][cat_key]
+ actual_category[key] = actual[key][cat_key]
+ val = list_compare_unordered(flip_dict_of_lists(expected), flip_dict_of_lists(actual), "category " + repr(cat_key))
+ if val != PASS:
+ return val
+ return msg
+
+
+def get_expected_tables():
+ """get_expected_tables() reads the html file with the expected DataFrames
+ and returns a dict mapping each question to a html table."""
+ if not os.path.exists(DF_FILE):
+ return None
+
+ expected_tables = {}
+ f = open(DF_FILE, encoding='utf-8')
+ soup = BeautifulSoup(f.read(), 'html.parser')
+ f.close()
+
+ tables = soup.find_all('table')
+ for table in tables:
+ expected_tables[table.get("data-question")] = table
+
+ return expected_tables
+
+def parse_df_html_table(table):
+ """parse_df_html_table(table) takes in a table as a html string and returns
+ a dict mapping each row and column index to the value at that position."""
+ rows = []
+ for tr in table.find_all('tr'):
+ rows.append([])
+ for cell in tr.find_all(['td', 'th']):
+ rows[-1].append(cell.get_text().strip("\n "))
+
+ cells = {}
+ for r in range(1, len(rows)):
+ for c in range(1, len(rows[0])):
+ rname = rows[r][0]
+ cname = rows[0][c]
+ cells[(rname,cname)] = rows[r][c]
+ return cells
+
+
+def get_expected_namedtuples():
+ """get_expected_namedtuples() defines the required namedtuple objects
+ globally. It also returns a tuple of the classes."""
+ expected_namedtuples = []
+
+ global Planet
+ planets_attributes = ['planet_name', 'host_name', 'discovery_method', 'discovery_year', 'controversial_flag', 'orbital_period', 'planet_radius', 'planet_mass', 'semi_major_radius', 'eccentricity', 'equilibrium_temperature', 'insolation_flux']
+ Planet = namedtuple('Planet', planets_attributes)
+ expected_namedtuples.append(Planet)
+ return tuple(expected_namedtuples)
+
+_expected_namedtuples = get_expected_namedtuples()
+
+
+def compare_cell_html(expected, actual):
+ """compare_cell_html(expected, actual) is used to compare when the
+ expected answer is a DataFrame stored in the `expected_dfs` html file."""
+ expected_cells = parse_df_html_table(expected)
+ try:
+ actual_cells = parse_df_html_table(BeautifulSoup(actual, 'html.parser').find('table'))
+ except Exception as e:
+ return "expected to find type DataFrame but found type %s instead" % type(actual).__name__
+
+ expected_cols = list(set(["column %s" % (loc[1]) for loc in expected_cells]))
+ actual_cols = list(set(["column %s" % (loc[1]) for loc in actual_cells]))
+ msg = list_compare_unordered(expected_cols, actual_cols, "DataFrame")
+ if msg != PASS:
+ return msg
+
+ expected_rows = list(set(["row index %s" % (loc[0]) for loc in expected_cells]))
+ actual_rows = list(set(["row index %s" % (loc[0]) for loc in actual_cells]))
+ msg = list_compare_unordered(expected_rows, actual_rows, "DataFrame")
+ if msg != PASS:
+ return msg
+
+ for location, expected in expected_cells.items():
+ location_name = "column {} at index {}".format(location[1], location[0])
+ actual = actual_cells.get(location, None)
+ if actual == None:
+ return "in %s, expected to find %s" % (location_name, repr(expected))
+ try:
+ actual_ans = float(actual)
+ expected_ans = float(expected)
+ if math.isnan(actual_ans) and math.isnan(expected_ans):
+ continue
+ except Exception as e:
+ actual_ans, expected_ans = actual, expected
+ msg = simple_compare(expected_ans, actual_ans)
+ if msg != PASS:
+ return "in %s, " % location_name + msg
+ return PASS
+
+
+def get_expected_plots():
+ """get_expected_plots() reads the json file with the expected plot data
+ and returns a dict mapping each question to a dictionary with the plots data."""
+ if not os.path.exists(PLOT_FILE):
+ return None
+
+ f = open(PLOT_FILE, encoding='utf-8')
+ expected_plots = json.load(f)
+ f.close()
+ return expected_plots
+
+
+def compare_file_json(expected, actual):
+ """compare_file_json(expected, actual) is used to compare when the
+ expected answer is a JSON file."""
+ msg = PASS
+ if not os.path.isfile(expected):
+ return "file %s not found; make sure it is downloaded and stored in the correct directory" % (expected)
+ elif not os.path.isfile(actual):
+ return "file %s not found; make sure that you have created the file with the correct name" % (actual)
+ try:
+ e = open(expected, encoding='utf-8')
+ expected_data = json.load(e)
+ e.close()
+ except json.JSONDecodeError:
+ return "file %s is broken and cannot be parsed; please delete and redownload the file correctly" % (expected)
+ try:
+ a = open(actual, encoding='utf-8')
+ actual_data = json.load(a)
+ a.close()
+ except json.JSONDecodeError:
+ return "file %s is broken and cannot be parsed" % (actual)
+ if type(expected_data) == list:
+ msg = list_compare_ordered(expected_data, actual_data, 'file ' + actual)
+ elif type(expected_data) == dict:
+ msg = dict_compare(expected_data, actual_data)
+ return msg
+
+
+_expected_json = get_expected_json()
+_special_json = get_special_json()
+_expected_plots = get_expected_plots()
+_expected_tables = get_expected_tables()
+_expected_format = get_expected_format()
+
+def check(qnum, actual):
+ """check(qnum, actual) is used to check if the answer in the notebook is
+ the correct answer, and provide useful feedback if the answer is incorrect."""
+ msg = PASS
+ error_msg = "<b style='color: red;'>ERROR:</b> "
+ q_format = _expected_format[qnum]
+
+ if q_format == TEXT_FORMAT_SPECIAL_ORDERED_LIST:
+ expected = _special_json[qnum]
+ elif q_format == PNG_FORMAT_SCATTER:
+ if _expected_plots == None:
+ msg = error_msg + "file %s not parsed; make sure it is downloaded and stored in the correct directory" % (PLOT_FILE)
+ else:
+ expected = _expected_plots[qnum]
+ elif q_format == HTML_FORMAT:
+ if _expected_tables == None:
+ msg = error_msg + "file %s not parsed; make sure it is downloaded and stored in the correct directory" % (DF_FILE)
+ else:
+ expected = _expected_tables[qnum]
+ else:
+ expected = _expected_json[qnum]
+
+ if SLASHES in q_format:
+ q_format = q_format.replace(SLASHES, "").strip("_ ")
+ expected = clean_slashes(expected)
+ actual = clean_slashes(actual)
+
+ if msg != PASS:
+ print(msg)
+ else:
+ msg = compare(expected, actual, q_format)
+ if msg != PASS:
+ msg = error_msg + msg
+ print(msg)
+
+
+def check_file_size(path):
+ """check_file_size(path) throws an error if the file is too big to display
+ on Gradescope."""
+ size = os.path.getsize(path)
+ assert size < MAX_FILE_SIZE * 10**3, "Your file is too big to be displayed by Gradescope; please delete unnecessary output cells so your file size is < %s KB" % MAX_FILE_SIZE
+
+
+def reset_hidden_tests():
+ """reset_hidden_tests() resets all hidden tests on the Gradescope autograder where the hidden test file exists"""
+ if not os.path.exists(HIDDEN_FILE):
+ return
+ hidn.reset_hidden_tests()
+
+def rubric_check(rubric_point, ignore_past_errors=True):
+ """rubric_check(rubric_point) uses the hidden test file on the Gradescope autograder to grade the `rubric_point`"""
+ if not os.path.exists(HIDDEN_FILE):
+ print(PASS)
+ return
+ error_msg_1 = "ERROR: "
+ error_msg_2 = "TEST DETAILS: "
+ try:
+ msg = hidn.rubric_check(rubric_point, ignore_past_errors)
+ except:
+ msg = "hidden tests crashed before execution"
+ if msg != PASS:
+ hidn.make_deductions(rubric_point)
+ if msg == "public tests failed":
+ comment = "The public tests have failed, so you will not receive any points for this question."
+ comment += "\nPlease confirm that the public tests pass locally before submitting."
+ elif msg == "answer is hardcoded":
+ comment = "In the datasets for testing hardcoding, all numbers are replaced with random values."
+ comment += "\nIf the answer is the same as in the original dataset for all these datasets"
+ comment += "\ndespite this, that implies that the answer in the notebook is hardcoded."
+ comment += "\nYou will not receive any points for this question."
+ else:
+ comment = hidn.get_comment(rubric_point)
+ msg = error_msg_1 + msg
+ if comment != "":
+ msg = msg + "\n" + error_msg_2 + comment
+ print(msg)
+
+def get_summary():
+ """get_summary() returns the summary of the notebook using the hidden test file on the Gradescope autograder"""
+ if not os.path.exists(HIDDEN_FILE):
+ print("Total Score: %d/%d" % (TOTAL_SCORE, TOTAL_SCORE))
+ return
+ score = min(TOTAL_SCORE, hidn.get_score(TOTAL_SCORE))
+ display_msg = "Total Score: %d/%d" % (score, TOTAL_SCORE)
+ if score != TOTAL_SCORE:
+ display_msg += "\n" + hidn.get_deduction_string()
+ print(display_msg)
+
+def get_score_digit(digit):
+ """get_score_digit(digit) returns the `digit` of the score using the hidden test file on the Gradescope autograder"""
+ if not os.path.exists(HIDDEN_FILE):
+ score = TOTAL_SCORE
+ else:
+ score = hidn.get_score(TOTAL_SCORE)
+ digits = bin(score)[2:]
+ digits = "0"*(7 - len(digits)) + digits
+ return int(digits[6 - digit])
diff --git a/lab-p11/sample_data.zip b/lab-p11/sample_data.zip
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diff --git a/p11/README.md b/p11/README.md
new file mode 100644
index 0000000..23274b5
--- /dev/null
+++ b/p11/README.md
@@ -0,0 +1,129 @@
+# Project 11 (P11): Analyzing Stars and Planets
+
+
+## Corrections and clarifications:
+
+* None yet.
+
+**Find any issues?** Report to us:
+
+- Shutong Wu <shutong.wu@wisc.edu>
+- Abhinav Harish <aharish2@wisc.edu>
+- Aboli Pai <apai7@wisc.edu>
+- Cathy Cao <ccao35@wisc.edu>
+- Muhammad Musa <mmusa2@wisc.edu>
+- Samuel Guo <sguo258@wisc.edu>
+
+
+## Instructions:
+
+This project will focus on **data analysis**, **creating scatter plots**, and **recursion**. In this project, you will continue from where you left off in P10. You will analyze the data structures you created in P10 to answer some interesting questions about our dataset.
+
+To start, download [`p11.ipynb`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/p11/p11.ipynb), [`public_tests.py`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/p11/public_tests.py), [`expected_plots.json`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/p11/expected_plots.json), and [`broken_data.zip`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/p11/broken_data.zip).
+
+If it takes too long to load the file `expected_plots.json` on GitLab, you can directly download the file from this link: [`expected_plots.json`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/raw/main/p11/expected_plots.json). You will need to **Right Click**, and click on the **Save as...** button to save the file through this method.
+
+After downloading `broken_data.zip`, make sure to extract it (using [Mac directions](http://osxdaily.com/2017/11/05/how-open-zip-file-mac/) or [Windows directions](https://support.microsoft.com/en-us/help/4028088/windows-zip-and-unzip-files)). After extracting, you should see a folder called `broken_data`. You may delete `broken_data.zip` after extracting the files inside it.
+
+You may also download [`data.zip`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/p11/data.zip) and extract it. Alternatively, you could also copy/paste the `data` directory that you worked with in P10 into your P11 directory.
+
+**Important**: You **must** make sure that your file structure looks like this:
+
+```
++-- p11.ipynb
++-- public_tests.py
++-- expected_plots.json
++-- data
+| +-- .DS_Store
+| +-- .ipynb_checkpoints
+| +-- mapping_1.json
+| +-- mapping_2.json
+| +-- mapping_3.json
+| +-- mapping_4.json
+| +-- mapping_5.json
+| +-- planets_1.csv
+| +-- planets_2.csv
+| +-- planets_3.csv
+| +-- planets_4.csv
+| +-- planets_5.csv
+| +-- stars_1.csv
+| +-- stars_2.csv
+| +-- stars_3.csv
+| +-- stars_4.csv
+| +-- stars_5.csv
++-- broken_data
+| +-- .DS_Store
+| +-- hd
+| | +-- 10000
+| | | +-- .ipynb_checkpoints
+| | | +-- .DS_Store
+| | | +-- 10000s.json
+| | +-- others.json
+| +-- k2
+| | +-- .ipynb_checkpoints
+| | +-- .DS_Store
+| | +-- k2s.json
+| +-- kepler
+| | +-- 10
+| | | +-- .DS_Store
+| | | +-- 30
+| | | | +-- .ipynb_checkpoints
+| | | | +-- .DS_Store
+| | | | +-- 30s.json
+| | | +-- other
+| | | | +-- others.json
+| | | +-- 20s.json
+| | | +-- 80s.json
+| | +-- 100
+| | | +-- 100
+| | | | +-- other
+| | | | | +-- .ipynb_checkpoints
+| | | | | +-- .DS_Store
+| | | | | +-- others.json
+| | | | +-- 100s.json
+| | | +-- 200
+| | | | +-- 220
+| | | | | +-- 220s.json
+| | | | +-- 290
+| | | | | +-- 290s.json
+| | | | +-- other
+| | | | | +-- .ipynb_checkpoints
+| | | | | +-- .DS_Store
+| | | | | +-- others.json
+| | | +-- others.json
+| | +-- other
+| | | +-- .ipynb_checkpoints
+| | | +-- others.json
+| +-- other
+| | +-- .ipynb_checkpoints
+| | +-- others.json
+| +-- toi
+| | +-- .ipynb_checkpoints
+| | +-- tois.json
+```
+
+Otherwise, your code may pass on **your computer**, but **fail** on the testing computer.
+
+You will work on `p11.ipynb` and hand it in. You should follow the provided directions for each question. Questions have **specific** directions on what **to do** and what **not to do**.
+
+------------------------------
+
+## IMPORTANT Submission instructions:
+- Review the [Grading Rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/p11/rubric.md), to ensure that you don't lose points during code review.
+- Login to [Gradescope](https://www.gradescope.com/) and upload the zip file into the P11 assignment.
+- If you completed the project with a **partner**, make sure to **add their name** by clicking "Add Group Member"
+in Gradescope when uploading the P11 zip file.
+
+ <img src="images/add_group_member.png" width="400">
+
+ **Warning:** You will have to add your partner on Gradescope even if you have filled out this information in your `p11.ipynb` notebook.
+
+- It is **your responsibility** to make sure that your project clears auto-grader tests on the Gradescope test system. Otter test results should be available within forty minutes after your submission (usually within ten minutes). **Ignore** the `-/100.00` that is displayed to the right. You should be able to see both PASS / FAIL results for the 20 test cases, which is accessible via Gradescope Dashboard (as in the image below):
+
+ <img src="images/gradescope.png" width="400">
+
+- You can view your **final score** at the **end of the page**. If you pass all tests, then you will receive **full points** for the project. Otherwise, you can see your final score in the **summary** section of the test results (as in the image below):
+
+ <img src="images/summary.png" width="400">
+
+ If you want more details on why you lost points on a particular test, you can scroll up to find more details about the test.
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diff --git a/p11/expected_plots.json b/p11/expected_plots.json
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index 0000000..bf51c81
--- /dev/null
+++ b/p11/expected_plots.json
@@ -0,0 +1 @@
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diff --git a/p11/images/README.md b/p11/images/README.md
new file mode 100644
index 0000000..86d526a
--- /dev/null
+++ b/p11/images/README.md
@@ -0,0 +1,3 @@
+# Images
+
+Images from p11 are stored here.
diff --git a/p11/images/add_group_member.png b/p11/images/add_group_member.png
new file mode 100644
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diff --git a/p11/p11.ipynb b/p11/p11.ipynb
new file mode 100644
index 0000000..43f524d
--- /dev/null
+++ b/p11/p11.ipynb
@@ -0,0 +1,3860 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0d4e51a4",
+ "metadata": {
+ "cell_type": "code",
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "# import and initialize otter\n",
+ "import otter\n",
+ "grader = otter.Notebook(\"p11.ipynb\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "73b10e60",
+ "metadata": {
+ "editable": false,
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:14.125503Z",
+ "iopub.status.busy": "2023-11-15T01:29:14.125503Z",
+ "iopub.status.idle": "2023-11-15T01:29:17.250329Z",
+ "shell.execute_reply": "2023-11-15T01:29:17.249320Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import public_tests"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "db59b584",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:17.254332Z",
+ "iopub.status.busy": "2023-11-15T01:29:17.254332Z",
+ "iopub.status.idle": "2023-11-15T01:29:17.259617Z",
+ "shell.execute_reply": "2023-11-15T01:29:17.258611Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# PLEASE FILL IN THE DETAILS\n",
+ "# Enter none if you don't have a project partner\n",
+ "# You will have to add your partner as a group member on Gradescope even after you fill this\n",
+ "\n",
+ "# project: p11\n",
+ "# submitter: NETID1\n",
+ "# partner: NETID2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6840bc51",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "# Project 11: Analyzing Stars and Planets"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "42b6b6a4",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "# Learning Objectives:\n",
+ "\n",
+ "In this project, you will demonstrate how to:\n",
+ " \n",
+ "* analyze the data from P10,\n",
+ "* make scatter plots using `matplotlib`,\n",
+ "* remove outliers to make the plots more useful,\n",
+ "* use recursion to gather new data.\n",
+ "\n",
+ "**Please go through [Lab-P11](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/lab-p11) before working on this project.** The lab introduces some important techniques related to this project."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c0792bd3",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "<h2 style=\"color:red\">Warning (Note on Academic Misconduct):</h2>\n",
+ "\n",
+ "**IMPORTANT**: **P10 and P11 are two parts of the same data analysis.** You **cannot** switch project partners between these two projects. That is if you partnered up with someone for P10, you have to sustain that partnership until the end of P11. Now may be a good time to review [our course policies](https://cs220.cs.wisc.edu/f23/syllabus.html).\n",
+ "\n",
+ "Under any circumstances, **no more than two students are allowed to work together on a project** as mentioned in the course policies. If your code is flagged by our code similarity detection tools, **both partners will be responsible** for sharing/copying the code, even if the code is shared/copied by one of the partners with/from other non-partner student(s). Note that each case of plagiarism will be reported to the Dean of Students with a zero grade on the project. **If you think that someone cannot be your project partner then don’t make that student your lab partner.**\n",
+ "\n",
+ "**<font color = \"red\">Project partners must submit only one copy of their project on Gradescope, but they must include the names of both partners.</font>**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9352e285",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "## Testing your code:\n",
+ "\n",
+ "Along with this notebook, you must have downloaded the files `public_tests.py` and `expected_plots.json`. If you are curious about how we test your code, you can explore this file, and specifically the output of the function `get_expected_json`, to understand the expected answers to the questions.\n",
+ "\n",
+ "**Important:** `public_tests.py` **cannot** verify your answers when the output is an image. Your **plots** will be **checked** by the Gradescope autograder, so you must **manually** confirm that your plots look correct by comparing with the images provided in the notebook."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ae904646",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "## Setup:\n",
+ "\n",
+ "In P11, you will be analyzing the same dataset that you parsed in P10. You can either copy/paste the `data` directory to your P11 directory, or download the `data.zip` file provided with this project, and extract it. In addition to the `data` directory, you will also need to download additional datasets for P11. You must download `broken_data.zip` and extract it. You must extract the contents of the directory `broken_data` into the same directory which contains the `data` directory, `p11.ipynb`, `public_tests.py`, and `expected_plots.json`.\n",
+ "\n",
+ "You need to make sure that the project files are stored in the following structure:\n",
+ "\n",
+ "```\n",
+ "+-- p11.ipynb\n",
+ "+-- public_tests.py\n",
+ "+-- expected_plots.json\n",
+ "+-- data\n",
+ "| +-- .DS_Store\n",
+ "| +-- .ipynb_checkpoints\n",
+ "| +-- mapping_1.json\n",
+ "| +-- mapping_2.json\n",
+ "| +-- mapping_3.json\n",
+ "| +-- mapping_4.json\n",
+ "| +-- mapping_5.json\n",
+ "| +-- planets_1.csv\n",
+ "| +-- planets_2.csv\n",
+ "| +-- planets_3.csv\n",
+ "| +-- planets_4.csv\n",
+ "| +-- planets_5.csv\n",
+ "| +-- stars_1.csv\n",
+ "| +-- stars_2.csv\n",
+ "| +-- stars_3.csv\n",
+ "| +-- stars_4.csv\n",
+ "| +-- stars_5.csv\n",
+ "+-- broken_data\n",
+ "| +-- .DS_Store\n",
+ "| +-- hd\n",
+ "| | +-- 10000\n",
+ "| | | +-- .ipynb_checkpoints\n",
+ "| | | +-- .DS_Store\n",
+ "| | | +-- 10000s.json\n",
+ "| | +-- others.json\n",
+ "| +-- k2\n",
+ "| | +-- .ipynb_checkpoints\n",
+ "| | +-- .DS_Store\n",
+ "| | +-- k2s.json\n",
+ "| +-- kepler\n",
+ "| | +-- 10\n",
+ "| | | +-- .DS_Store\n",
+ "| | | +-- 30\n",
+ "| | | | +-- .ipynb_checkpoints\n",
+ "| | | | +-- .DS_Store\n",
+ "| | | | +-- 30s.json\n",
+ "| | | +-- other\n",
+ "| | | | +-- others.json\n",
+ "| | | +-- 20s.json\n",
+ "| | | +-- 80s.json\n",
+ "| | +-- 100\n",
+ "| | | +-- 100\n",
+ "| | | | +-- other\n",
+ "| | | | | +-- .ipynb_checkpoints\n",
+ "| | | | | +-- .DS_Store\n",
+ "| | | | | +-- others.json\n",
+ "| | | | +-- 100s.json\n",
+ "| | | +-- 200\n",
+ "| | | | +-- 220\n",
+ "| | | | | +-- 220s.json\n",
+ "| | | | +-- 290\n",
+ "| | | | | +-- 290s.json\n",
+ "| | | | +-- other\n",
+ "| | | | | +-- .ipynb_checkpoints\n",
+ "| | | | | +-- .DS_Store\n",
+ "| | | | | +-- others.json\n",
+ "| | | +-- others.json\n",
+ "| | +-- other\n",
+ "| | | +-- .ipynb_checkpoints\n",
+ "| | | +-- others.json\n",
+ "| +-- other\n",
+ "| | +-- .ipynb_checkpoints\n",
+ "| | +-- others.json\n",
+ "| +-- toi\n",
+ "| | +-- .ipynb_checkpoints\n",
+ "| | +-- tois.json\n",
+ "```\n",
+ "\n",
+ "Make sure that **all** files are stored in this **exact** file structure. Otherwise, then there is a possibility that your code will **fail on Gradescope** even after passing local tests."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d4dc2fd9",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "## Project Description:\n",
+ "\n",
+ "You have already parsed the data in the `data` directory in P10. You will now dive deeper by analyzing this data and arrive at some exciting conclusions about various planets and stars outside our Solar System. You will also use recursion to retrieve data from the broken JSON file in the `data` directory, and ask some interesting questions about the data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1b7ab261",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "## Project Requirements:\n",
+ "\n",
+ "You **may not** hardcode indices in your code, unless the question explicitly says so. If you open your `.csv` files with Excel, manually count through the rows and use this number to loop through the dataset, this is also considered as hardcoding. If any instances of hardcoding are found during code review, the Gradescope autograder will **deduct** points from your public score.\n",
+ "\n",
+ "**Store** your final answer for each question in the **variable specified for each question**. This step is important because Otter grades your work by comparing the value of this variable against the correct answer.\n",
+ "\n",
+ "For some of the questions, we'll ask you to write (then use) a function to compute the answer. If you compute the answer **without** creating the function we ask you to write, the Gradescope autograder will **deduct** points from your public test score, even if the way you did it produced the correct answer.\n",
+ "\n",
+ "#### Required Functions:\n",
+ "- `star_cell`\n",
+ "- `get_stars`\n",
+ "- `planet_cell`\n",
+ "- `get_planets`\n",
+ "- `plot_scatter`\n",
+ "- `plot_scatter_multiple`\n",
+ "- `get_all_paths_in`\n",
+ "- `get_surface_gravity`\n",
+ "- `get_distances_to_star`\n",
+ "- `get_liquid_water_distances`\n",
+ "- `get_surface_temperatures`\n",
+ "\n",
+ "In this project, you will also be required to define certain **data structures**. If you do not create these data structures exactly as specified, the Gradescope autograder will **deduct** points from your public test score, even if the way you did it produced the correct answer.\n",
+ "\n",
+ "#### Required Data Structures:\n",
+ "- `Star` (**namedtuple**)\n",
+ "- `stars_dict` (**dictionary** mapping **strings** to `Star` objects)\n",
+ "- `Planet` (**namedtuple**)\n",
+ "- `planets_list` (**list** of `Planet` objects)\n",
+ "- `star_classes` (**dictionary**)\n",
+ "- `all_planets_list` (**list** of `Planet` objects)\n",
+ "\n",
+ "In addition, you are also **required** to follow the requirements below:\n",
+ "\n",
+ "* You **must** never use the output of the `os.listdir` function directly. You **must** always first remove all files and directories that start with `\".\"`, and **explicitly sort** the list before doing anything with it.\n",
+ "* You are **not** allowed to use **modules** like `pandas` to answer the questions in this project.\n",
+ "* You **must** properly **label** the axes of all your **plots**.\n",
+ "* Do **not** define multiple functions with the same name or define multiple versions of one function with different names. Just keep the best version.\n",
+ "* Do **not** leave in irrelevant output or test code that we didn't ask for.\n",
+ "\n",
+ "Otherwise, the Gradescope autograder will **deduct** points from your public score.\n",
+ "\n",
+ "For more details on what will cause you to lose points during code review and specific requirements, please take a look at the [Grading rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/blob/main/p11/rubric.md)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "deb1cc46",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "## Questions and Functions:\n",
+ "\n",
+ "Let us start by importing all the modules we will need for this project."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3c7058fc",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:17.265639Z",
+ "iopub.status.busy": "2023-11-15T01:29:17.264624Z",
+ "iopub.status.idle": "2023-11-15T01:29:17.939313Z",
+ "shell.execute_reply": "2023-11-15T01:29:17.938304Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+ "# please place all your import statements in this cell if you need to import any more modules for this project\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f803a52b",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Loading in the Stars and Planets:\n",
+ "\n",
+ "Before we can analyze the data in the `data` directory, you must first copy/paste all the functions and data strucutres you created in P10 to parse the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d8f07acf",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:17.944332Z",
+ "iopub.status.busy": "2023-11-15T01:29:17.943313Z",
+ "iopub.status.idle": "2023-11-15T01:29:17.951509Z",
+ "shell.execute_reply": "2023-11-15T01:29:17.950496Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the namedtuple 'Star' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d055f32e",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:17.955526Z",
+ "iopub.status.busy": "2023-11-15T01:29:17.955526Z",
+ "iopub.status.idle": "2023-11-15T01:29:17.961302Z",
+ "shell.execute_reply": "2023-11-15T01:29:17.960291Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the function 'process_csv' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3e9faed4",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:17.965320Z",
+ "iopub.status.busy": "2023-11-15T01:29:17.965320Z",
+ "iopub.status.idle": "2023-11-15T01:29:17.984198Z",
+ "shell.execute_reply": "2023-11-15T01:29:17.983183Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the function 'star_cell' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "84ff604d",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:17.988213Z",
+ "iopub.status.busy": "2023-11-15T01:29:17.988213Z",
+ "iopub.status.idle": "2023-11-15T01:29:17.996929Z",
+ "shell.execute_reply": "2023-11-15T01:29:17.995916Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the function 'get_stars' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4edbb641",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:18.000867Z",
+ "iopub.status.busy": "2023-11-15T01:29:18.000867Z",
+ "iopub.status.idle": "2023-11-15T01:29:18.192401Z",
+ "shell.execute_reply": "2023-11-15T01:29:18.191391Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the dictionary 'stars_dict' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a29bb092",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:18.197404Z",
+ "iopub.status.busy": "2023-11-15T01:29:18.196412Z",
+ "iopub.status.idle": "2023-11-15T01:29:18.205324Z",
+ "shell.execute_reply": "2023-11-15T01:29:18.204313Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the namedtuple 'Planet' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc05e373",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:18.210324Z",
+ "iopub.status.busy": "2023-11-15T01:29:18.209325Z",
+ "iopub.status.idle": "2023-11-15T01:29:18.216864Z",
+ "shell.execute_reply": "2023-11-15T01:29:18.215850Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the function 'read_json' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9d2d74f3",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:18.221870Z",
+ "iopub.status.busy": "2023-11-15T01:29:18.220862Z",
+ "iopub.status.idle": "2023-11-15T01:29:18.245578Z",
+ "shell.execute_reply": "2023-11-15T01:29:18.244569Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the function 'planet_cell' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9a3837f3",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:18.251580Z",
+ "iopub.status.busy": "2023-11-15T01:29:18.251580Z",
+ "iopub.status.idle": "2023-11-15T01:29:18.262762Z",
+ "shell.execute_reply": "2023-11-15T01:29:18.261751Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the function 'get_planets' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "48873401",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:18.268762Z",
+ "iopub.status.busy": "2023-11-15T01:29:18.267763Z",
+ "iopub.status.idle": "2023-11-15T01:29:18.416600Z",
+ "shell.execute_reply": "2023-11-15T01:29:18.415591Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the list 'planets_list' here\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "15549220",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "You used two functions `plot_scatter` and `plot_scatter_multiple` in Lab-P11 to create your **scatter plots**. These functions are again provided for you here to use in P11."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82659dd6",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:18.421621Z",
+ "iopub.status.busy": "2023-11-15T01:29:18.420600Z",
+ "iopub.status.idle": "2023-11-15T01:29:18.427262Z",
+ "shell.execute_reply": "2023-11-15T01:29:18.426251Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# remember to import matplotlib.pyplot as plt at the top of the notebook to make this functions work\n",
+ "\n",
+ "def plot_scatter(x_data, y_data, x_label='x axis', y_label='y axis', c=None, s=7):\n",
+ " plt.scatter(x_data, y_data, c=c, s=s)\n",
+ " plt.xlabel(x_label)\n",
+ " plt.ylabel(y_label)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9e6ac2ea",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:18.432275Z",
+ "iopub.status.busy": "2023-11-15T01:29:18.431264Z",
+ "iopub.status.idle": "2023-11-15T01:29:18.438610Z",
+ "shell.execute_reply": "2023-11-15T01:29:18.437599Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# remember to import matplotlib.pyplot as plt at the top of the notebook to make this functions work\n",
+ "\n",
+ "def plot_scatter_multiple(x_data_dict, y_data_dict, x_label='x axis', y_label='y axis'):\n",
+ " legend_values = list(x_data_dict.keys())\n",
+ " for key in x_data_dict:\n",
+ " plt.scatter(x_data_dict[key], y_data_dict[key], s=7)\n",
+ " plt.xlabel(x_label)\n",
+ " plt.ylabel(y_label)\n",
+ " plt.legend(legend_values)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "369a9948",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Verifying Laws of Nature:\n",
+ "\n",
+ "We will now use our dataset to verify some well-known laws of nature. Unless specified otherwise, you **must** use `stars_dict` and `planets_list` to access all data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "38cd2cae",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "#### Kepler's Third Law:\n",
+ "\n",
+ "We will first verify [Kepler's Third Law](https://en.wikipedia.org/wiki/Kepler%27s_laws_of_planetary_motion#Third_law). This law states that the **square** of the `orbital_period` of each planet in a solar system is directly proportional to the **cube** of the `semi_major_radius` of its orbit around its host star.\n",
+ "\n",
+ "Since this law relates only to planets that orbit the same host star, we will verify this law using the several planets orbiting around a star named *GJ 9827*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ff516734",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 1:** Compute the **ratio** of the **square** of the `orbital_period` to the **cube** of the `semi_major_radius` of each planet orbiting the star *GJ 9827*.\n",
+ "\n",
+ "Your output **must** be a **list** of **floats**. You may **assume** that the planets orbiting this star do not have any missing `orbital_period` or `semi_major_radius` data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3c5fdc49",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:18.442612Z",
+ "iopub.status.busy": "2023-11-15T01:29:18.442612Z",
+ "iopub.status.idle": "2023-11-15T01:29:18.452697Z",
+ "shell.execute_reply": "2023-11-15T01:29:18.451684Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'ratios_gj9827', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1f6d1457",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q1\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "af99d856",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "The **ratios** of the three stars in this system appear to be very close to each other. It will be useful if we could quantify exactly how close these ratios are to each other. One way to do that would to be compute the [coefficient of variance](https://en.wikipedia.org/wiki/Coefficient_of_variation), which is defined as the **standard deviation** divided by the **mean** of a sequence of numbers. A low value would imply that the numbers are very **close** to each other."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bab2002",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 2:** Compute the **coefficient of variance** of the **list** `ratios_gj9827`.\n",
+ "\n",
+ "**Hint:** You can compute the **standard deviation** and the **mean** of a **list** of numbers using the `statistics.stdev` and `statistics.mean` functions inside the `statistics` module. To do this, you must first **import** the `statistics` module. You can read the documentation for the `statistics.stdev` and `statistics.mean` functions by creating a new cell and using `help(statistics.mean)` and `help(statistics.mean)`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ccc96a1d",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:18.506669Z",
+ "iopub.status.busy": "2023-11-15T01:29:18.506669Z",
+ "iopub.status.idle": "2023-11-15T01:29:18.515069Z",
+ "shell.execute_reply": "2023-11-15T01:29:18.514057Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'coeff_gj9827', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "655f8b11",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q2\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dccf3b68",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "As we can see, the **coefficient of variance** is indeed very low. This lends credibility to Kepler's Third Law. However, there is yet more we can do with this data. After we adjust for the units used in our dataset, we find that Kepler's Third Law predicts the following:\n",
+ "\n",
+ "$$\\texttt{stellar mass} = \\frac{133408}{\\texttt{ratio}}$$\n",
+ "\n",
+ "where $\\texttt{ratio}$ is the **mean** of the ratios of the **square** of the `orbital_period` to the **cube** of the `semi_major_radius` computed above, and $\\texttt{stellar mass}$ is the mass of the planets' host star.\n",
+ "\n",
+ "We can therefore check how close this **predicted** `stellar_mass` is to the **actual** `stellar_mass` of the star."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b3439173",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 3:** Compute the percentage change of the **predicted** `stellar_mass` from the **actual** `stellar_mass` of the star *GJ 9827*.\n",
+ "\n",
+ "You **must** compute the **predicted** `stellar_mass` as the number *133408* divided by the **mean** of the ratios of the three planets computed in Question 1. You **must** find the **actual** `stellar_mass` by accessing the correct attribute of the `Star` object of *GJ 9827*. The percentage change can be computed as:\n",
+ "\n",
+ "$$\\texttt{percent change} = \\frac{\\texttt{predicted stellar mass} - \\texttt{actual stellar mass}}{\\texttt{actual stellar mass}} \\times 100$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41e7253b",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:18.560414Z",
+ "iopub.status.busy": "2023-11-15T01:29:18.560414Z",
+ "iopub.status.idle": "2023-11-15T01:29:18.569612Z",
+ "shell.execute_reply": "2023-11-15T01:29:18.568597Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'percentage_change', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1073076e",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q3\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "898e4a0a",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "#### Stefan-Boltzmann Law:\n",
+ "\n",
+ "We will now verify the [Stefan-Boltzmann Law](https://en.wikipedia.org/wiki/Stefan%E2%80%93Boltzmann_law). This law states that the `insolation_flux` of a *black body* is directly proportional to the **fourth** power of the `equilibrium_temperature`. In our dataset, we have the `insolation_flux` and `equilibrium_temperature` data of the `Planet` objects. So, we can verify how well this law is obeyed by the planets in our dataset."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fd8dd606",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 4:** Create a **scatter plot** representing the `insolation_flux` (on the **x-axis**) against the **fourth power** of the `equilibrium_temperature` (on the **y-axis**) of each `Planet` object in `planets_list`.\n",
+ "\n",
+ "You **must** ignore all `Planet` objects with **missing** `insolation_flux`, or `equilibrium_temperature` data.\n",
+ "\n",
+ "You **must** first compute two **lists** containing the **insolation_flux**, and the fourth power of the **equilibrium_temperature** of each `Planet` object (which has all the data available). Then, you **must** use `plot_scatter` to plot the **insolation_flux** against the fourth power of the **equilibrium_temperature**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e12fe649",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:18.615949Z",
+ "iopub.status.busy": "2023-11-15T01:29:18.614948Z",
+ "iopub.status.idle": "2023-11-15T01:29:18.624981Z",
+ "shell.execute_reply": "2023-11-15T01:29:18.623973Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# first compute and store the lists 'flux_list', and 'temp_4th_power_list'\n",
+ "# do NOT display or plot just yet\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1ad78e3e",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q4\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4ca063f6",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Now, **plot** `flux_list` and `temp_4th_power_list` as **scatter plot** with the **x-axis** labelled *Insolation Flux*, and the **y-axis** labelled *(Equilibrium Temperature)\\*\\*4*.\n",
+ "\n",
+ "**Important Warning:** `public_tests.py` can check that the **lists** are correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible in the required cell, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+ "\n",
+ "Your plot should look like this:"
+ ]
+ },
+ {
+ "attachments": {
+ "q4.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "bcd19e35",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "<div><img src=\"attachment:q4.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3b8c7d5a",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Hint:** If the `grader.export` cell fails to run because the file size is too large, you can delete the plot above to reduce the size of your notebook. Make sure your plot matches the plot above, before you do so."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1ad0c854",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:18.988940Z",
+ "iopub.status.busy": "2023-11-15T01:29:18.987938Z",
+ "iopub.status.idle": "2023-11-15T01:29:19.333121Z",
+ "shell.execute_reply": "2023-11-15T01:29:19.332111Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# create a scatter plot using 'flux_list' and 'temp_4th_power_list'\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1f20df9e",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Food for thought:** Why does this graph look so strange with all the points bunched up near the bottom-left corner?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3b99c423",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:19.337121Z",
+ "iopub.status.busy": "2023-11-15T01:29:19.337121Z",
+ "iopub.status.idle": "2023-11-15T01:29:19.341721Z",
+ "shell.execute_reply": "2023-11-15T01:29:19.340710Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Food for thought is an entirely OPTIONAL exercise\n",
+ "# you may leave your thoughts here as a comment if you wish to\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d6e8d8f1",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 5:** Create the same **scatter plot** as above, but **exclude** planets with `insolation_flux` **more than** *7000*.\n",
+ "\n",
+ "You **must** ignore all `Planet` objects with **missing** `insolation_flux`, or `equilibrium_temperature` data. You **must** also exclude all `Planet` objects with `insolation_flux` more than *7000*.\n",
+ "\n",
+ "You **must** first compute two **lists** containing the **insolation_flux**, and the fourth power of the **equilibrium_temperature** of each `Planet` object (which has all the data available). Then, you **must** use `plot_scatter` to plot the **insolation_flux** against the fourth power of the **equilibrium_temperature**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f9f6277c",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:19.345721Z",
+ "iopub.status.busy": "2023-11-15T01:29:19.345721Z",
+ "iopub.status.idle": "2023-11-15T01:29:19.356138Z",
+ "shell.execute_reply": "2023-11-15T01:29:19.355133Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# first compute and store the lists 'flux_list_no_outliers', and 'temp_4th_power_list_no_outliers'\n",
+ "# do NOT display or plot just yet\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cbdd1ab0",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q5\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e0328d2c",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Now, **plot** `flux_list_no_outliers` and `temp_4th_power_list_no_outliers` as **scatter plot** with the **x-axis** labelled *Insolation Flux*, and the **y-axis** labelled *(Equilibrium Temperature)\\*\\*4*.\n",
+ "\n",
+ "**Important Warning:** `public_tests.py` can check that the **lists** are correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible in the required cell, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+ "\n",
+ "Your plot should look like this:"
+ ]
+ },
+ {
+ "attachments": {
+ "q5.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "0d9ce3f0",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "<div><img src=\"attachment:q5.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7b933e1c",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Hint:** If the `grader.export` cell fails to run because the file size is too large, you can delete the plot above to reduce the size of your notebook. Make sure your plot matches the plot above, before you do so."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0551009d",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:19.731208Z",
+ "iopub.status.busy": "2023-11-15T01:29:19.731208Z",
+ "iopub.status.idle": "2023-11-15T01:29:20.051737Z",
+ "shell.execute_reply": "2023-11-15T01:29:20.050725Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Create a scatter plot using 'flux_list_no_outliers' and 'temp_4th_power_list_no_outliers'\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5e3f3961",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Food for thought:** Does the relationship between **insolation flux** and the **fourth power of the equilibrium temperature** appear to be **linear** as predicted by the Stefan-Boltzmann Law? Can you explain why the graph isn't perfectly linear?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0e64cd0d",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:20.056750Z",
+ "iopub.status.busy": "2023-11-15T01:29:20.055742Z",
+ "iopub.status.idle": "2023-11-15T01:29:20.061809Z",
+ "shell.execute_reply": "2023-11-15T01:29:20.060797Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Food for thought is an entirely OPTIONAL exercise\n",
+ "# you may leave your thoughts here as a comment if you wish to\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c5dce3b0",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Stellar Evolution:\n",
+ "\n",
+ "[Stellar Evolution](https://en.wikipedia.org/wiki/Stellar_evolution) is a description of the way that stars change with time. The primary factor determining how a star evolves is its `stellar_mass`. Depending on the `stellar_mass` of each `Star`, astronomers can predict how the `Star` will end up. A `Star` whose `stellar_mass` is $\\geq 0.3$ and $< 8$ times the mass of the Sun will become a [Red Giant](https://en.wikipedia.org/wiki/Red_giant), while a `Star` whose `stellar_mass` is $\\geq 8$ and $< 10.5$ times the mass of the Sun will become a [White Dwarf](https://en.wikipedia.org/wiki/White_dwarf). A `Star` that is even bigger will end up as a [Neutron Star](https://en.wikipedia.org/wiki/Neutron_star)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0a91ea12",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Data Structure 1: `star_classes`\n",
+ "\n",
+ "You **must** now classify the `Star` objects in `stars_dict` using their `stellar_mass`. You **must** create a **dictionary** `star_classes` with the **keys**: `Red Giant`, `White Dwarf`, and `Neutron Star`. The **value** of each **key** must be a **list** of **strings** containing the **names** of the `Star` objects.\n",
+ "\n",
+ "You **must** **ignore** `Star` objects for which we do not have `stellar_mass` data or have `stellar_mass` **less** than *0.3* Solar masses.\n",
+ "\n",
+ "**Hint:** Recall that the `stellar_mass` data already uses units of *Solar masses*. So, a `stellar_mass` of *1* means that the `Star` object has the same mass as the Sun, and a `stellar_mass` of 2 means the `Star` object has twice the mass of the Sun, and so on."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4c4b8a16",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:20.066820Z",
+ "iopub.status.busy": "2023-11-15T01:29:20.066820Z",
+ "iopub.status.idle": "2023-11-15T01:29:20.078396Z",
+ "shell.execute_reply": "2023-11-15T01:29:20.077370Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define the variable 'star_classes' here\n",
+ "# but do NOT display\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8a0a952",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"star_classes\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "639d8b0c",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "You can **verify** that you have defined `star_classes` correctly by checking that there are *3970* Red Giants, *3* White Dwarfs, and *1* Neutron Star in `star_classes`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "caffcf0b",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 6:** What is the **average** `stellar_luminosity` of each class of `Star` objects in `star_classes`?\n",
+ "\n",
+ "Your output **must** be a **dictionary** mapping the class of the star to the **average** `stellar_luminosity` value of all `Star` objects of that class. You **must** ignore the `Star` objects with **missing** `stellar_luminosity` data.\n",
+ "\n",
+ "The expected output of this question is:\n",
+ "\n",
+ "```python\n",
+ "{'Red Giant': -0.025334640522875783,\n",
+ " 'White Dwarf': 2.787333333333333,\n",
+ " 'Neutron Star': 2.86}\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e00b7011",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:20.113804Z",
+ "iopub.status.busy": "2023-11-15T01:29:20.112806Z",
+ "iopub.status.idle": "2023-11-15T01:29:20.124825Z",
+ "shell.execute_reply": "2023-11-15T01:29:20.123815Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'star_classes_avg_lum', then display it\n",
+ "\n",
+ "# TODO: initialize 'star_classes_avg_lum'\n",
+ "# TODO: loop through each 'star_class' in 'star_classes'\n",
+ " # TODO: loop through each 'star' in the 'star_class'\n",
+ " # TODO: skip 'star' if 'stellar_luminosity' data is missing\n",
+ " # TODO: for the remaining stars, compute the mean of the 'stellar_luminosity'\n",
+ " # TODO: add the mean luminosity to 'star_classes_avg_lum'\n",
+ " \n",
+ "# TODO: display 'star_classes_avg_lum'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f5fc4994",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q6\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4011b0c7",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Food for thought:** Recall that the `stellar_luminosity` values of the `Star` objects are represented in units of the logarithm of the Sun's luminosity. What does this difference in `stellar_luminosity` signify?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d13cce68",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:20.168229Z",
+ "iopub.status.busy": "2023-11-15T01:29:20.167248Z",
+ "iopub.status.idle": "2023-11-15T01:29:20.172479Z",
+ "shell.execute_reply": "2023-11-15T01:29:20.171468Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Food for thought is an entirely OPTIONAL exercise\n",
+ "# you may leave your thoughts here as a comment if you wish to\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fcd8e759",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Just as the different classes of `Star` objects have different **average luminosities**, they also have different **average densities**. This difference will be easier to visualize as a **scatter plot**.\n",
+ "\n",
+ "However, before you can do that, there is a minor hurdle you need to overcome - we do **not** have the *stellar density* data available for the `Star` objects in our dataset. However, we do have `stellar_mass` and `stellar_radius` data, which allows us to **compute** the *stellar density*. Since the `stellar_mass` and `stellar_radius` data is stored in units of the Sun's mass and radius respectively, we can compute the *stellar density* (i.e., density of the `Star` in units of the Sun's density) as follows:\n",
+ "\n",
+ "$$\\texttt{stellar density} = \\frac{\\texttt{stellar mass}}{(\\texttt{stellar radius})^{3}}.$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8ad9b993",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 7:** Create a **scatter plot** representing the *stellar density* (on the **x-axis**) against the `stellar_luminosity` (on the **y-axis**) of each `Star` object of **each class** in `star_classes`.\n",
+ "\n",
+ "You **must** ignore all `Star` objects with **missing** `stellar_mass`, `stellar_radius`, or `stellar_luminosity` data.\n",
+ "\n",
+ "You **must** first compute two **dictionaries**. The **keys** of both dictionaries must be the different **star classes**, and the corresponding values must be the **list** of **densities** and **list** of **luminosities** of `Star` objects of that **star class**. Then, you **must** use `plot_scatter_multiple` to plot the **density** against the **luminosity** of each **star class**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e62245f0",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:20.177478Z",
+ "iopub.status.busy": "2023-11-15T01:29:20.176486Z",
+ "iopub.status.idle": "2023-11-15T01:29:20.190332Z",
+ "shell.execute_reply": "2023-11-15T01:29:20.189319Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# first compute and store the dictionaries 'density_dict', and 'lum_dict'\n",
+ "# do NOT plot just yet\n",
+ "\n",
+ "# TODO: initialize the two dictionaries 'density_dict', and 'lum_dict'\n",
+ "# TODO: loop through each 'star_class' in 'star_classes'\n",
+ " # TODO: add the 'star_class' to 'density_dict' and 'lum_dict'\n",
+ " # TODO: loop through each 'star' in the 'star_class'\n",
+ " # TODO: skip 'star' if mass, radius, or luminosity data is missing\n",
+ " # TODO: otherwise add the luminosity to the correct key of 'lum_dict'\n",
+ " # TODO: compute the density and add to the correct key of 'density_dict'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5297e0b1",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q7\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "88e3fac3",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Now, **plot** `density_dict` and `lum_dict` as **scatter plot** with the **x-axis** labelled *Density*, and the **y-axis** labelled *Luminosity*.\n",
+ "\n",
+ "**Important Warning:** `public_tests.py` can check that the **lists** are correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible in the required cell, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+ "\n",
+ "Your plot should look like this:"
+ ]
+ },
+ {
+ "attachments": {
+ "q7.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "4b63b55c",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "<div><img src=\"attachment:q7.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "65921f9e",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Hint:** If the `grader.export` cell fails to run because the file size is too large, you can delete the plot above to reduce the size of your notebook. Make sure your plot matches the plot above, before you do so."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bb3bdb77",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:20.252163Z",
+ "iopub.status.busy": "2023-11-15T01:29:20.252163Z",
+ "iopub.status.idle": "2023-11-15T01:29:20.756306Z",
+ "shell.execute_reply": "2023-11-15T01:29:20.755295Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'density_dict' and 'lum_dict'\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "264c70ea",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Food for thought:** As you can see, there are **two** extreme outliers with a very high density. If you are interested, you can try to find out the names of these stars, and why they have such extremely high densities (and low luminosities). What (**incorrect**) assumption did we make when we classified the `Star` objects in `star_classes`? Can you suggest a more **accurate** way of classifying the stars now, based on the data we have observed?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "382a8483",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:20.761304Z",
+ "iopub.status.busy": "2023-11-15T01:29:20.760303Z",
+ "iopub.status.idle": "2023-11-15T01:29:20.765577Z",
+ "shell.execute_reply": "2023-11-15T01:29:20.764569Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Food for thought is an entirely OPTIONAL exercise\n",
+ "# you may leave your thoughts here as a comment if you wish to\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "acf3fd31",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "As you can see, almost all the `Star` objects have low *stellar density*, and the presence of a few extreme outliers is obscuring our view of the other `Star` objects. In fact, it turns out that there are only *26* `Star` objects in the dataset with a *stellar density* **greater than** *25*. We could get a much clearer view of the relationship between *stellar density* and `stellar_luminosity` if we did **not** plot these outliers."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a59cf942",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 8:** Create the same **scatter plot** as above, but exclude stars with *stellar density* **more than** *25*.\n",
+ "\n",
+ "You **must** ignore all `Star` objects with **missing** `stellar_mass`, `stellar_radius`, or `stellar_luminosity` data. You **must** also ignore all `Star` objects with *stellar density* more than *25*.\n",
+ "\n",
+ "You **must** first compute two **dictionaries**. The **keys** of both dictionaries must be the different **star classes**, and the corresponding values must be the **list** of **densities** and **list** of **luminosities** of `Star` objects of that **star class**. Then, you **must** use `plot_scatter_multiple` to plot the **density** against the **luminosity** of each **star class**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e05f4d55",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:20.771576Z",
+ "iopub.status.busy": "2023-11-15T01:29:20.771576Z",
+ "iopub.status.idle": "2023-11-15T01:29:20.787616Z",
+ "shell.execute_reply": "2023-11-15T01:29:20.786604Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# first compute and store the dictionaries 'density_dict_no_outliers', and 'lum_dict_no_outliers'\n",
+ "# do NOT plot just yet\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2498b21f",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q8\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e97dc5e0",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Now, **plot** `density_dict_no_outliers` and `lum_dict_no_outliers` as **scatter plot** with the **x-axis** labelled *Density*, and the **y-axis** labelled *Luminosity*.\n",
+ "\n",
+ "**Important Warning:** `public_tests.py` can check that the **lists** are correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible in the required cell, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+ "\n",
+ "Your plot should look like this:"
+ ]
+ },
+ {
+ "attachments": {
+ "q8.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "0d33a3ef",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "<div><img src=\"attachment:q8.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9fcb18ef",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Hint:** If the `grader.export` cell fails to run because the file size is too large, you can delete the plot above to reduce the size of your notebook. Make sure your plot matches the plot above, before you do so."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ca05890a",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:20.859828Z",
+ "iopub.status.busy": "2023-11-15T01:29:20.858825Z",
+ "iopub.status.idle": "2023-11-15T01:29:21.342078Z",
+ "shell.execute_reply": "2023-11-15T01:29:21.341065Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'density_dict_no_outliers' and 'lum_dict_no_outliers'\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9ed81454",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Food for thought:** Can you guess the relationship between **density** and **luminosity**? Can you spot the `Star` objects in this graph which will end up as White Dwarfs and Neutron Stars? Do they appear to follow the same relationship as the Red Giants? How do they compare to the outliers you found in Question 7?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f52cd2c2",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:21.346075Z",
+ "iopub.status.busy": "2023-11-15T01:29:21.346075Z",
+ "iopub.status.idle": "2023-11-15T01:29:21.351143Z",
+ "shell.execute_reply": "2023-11-15T01:29:21.350122Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Food for thought is an entirely OPTIONAL exercise\n",
+ "# you may leave your thoughts here as a comment if you wish to\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ee254b68",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Hertzsprung–Russell Diagram:\n",
+ "\n",
+ "The [Hertzsprung–Russell diagram](https://en.wikipedia.org/wiki/Hertzsprung%E2%80%93Russell_diagram) is a scatter plot of stars showing the relationship between the stars' `stellar_luminosity` versus their `stellar_effective_temperature`. The diagram is exceedingly useful for understanding the stellar evolution of stars. We will now use the data we have available to plot this diagram ourselves, so we can better understand stellar evolution.\n",
+ "\n",
+ "We want to plot the `stellar_effective_temperature` against the `stellar_luminosity`, but more importantly, we will use the **color** and **size** parameters to represent the `stellar_age` and `stellar_mass` of the `Star` objects as well. This will allow us to see the effects of `stellar_age` and `stellar_mass` on `stellar_effective_temperature` and `stellar_luminosity`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "14422302",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 9:** Create a **scatter plot** representing the `stellar_effective_temperature` (on the **x-axis**) against the `stellar_luminosity` (on the **y-axis**) of each `Star` object in `stars_dict`. Moreover, represent the `stellar_age` of each `Star` object using the **color** and represent the `stellar_mass` of each `Star` object using the **size** of the star.\n",
+ "\n",
+ "You **must** first compute four **lists** containing the `stellar_effective_temperature`, `stellar_luminosity`, `stellar_age` and the `stellar_mass` of each `Star` object (which has **all** the data available). You **must** ignore any `Star` object which has any of these four attributes **missing**. Then, you **must** use `plot_scatter` to plot the `stellar_effective_temperature` against the `stellar_luminosity` with the `stellar_age` as the **color** and the `stellar_mass` as the **size** of the points."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2e3308a4",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:21.356136Z",
+ "iopub.status.busy": "2023-11-15T01:29:21.355155Z",
+ "iopub.status.idle": "2023-11-15T01:29:21.368686Z",
+ "shell.execute_reply": "2023-11-15T01:29:21.367680Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# first compute and store the lists 'temp_list', 'lum_list', 'age_list', and 'mass_list'\n",
+ "# do NOT plot just yet\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "74c4c4a1",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q9\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11398b3f",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "Now, **plot** `temp_list` and `lum_list` as **scatter plot** with the **x-axis** labelled *Effective Temperature*, and the **y-axis** labelled *Luminosity*, **colored** using `age_list`, and **sized** using `mass_list`. Your plot should look like this:"
+ ]
+ },
+ {
+ "attachments": {
+ "q9.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "9563e9a4",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "<div><img src=\"attachment:q9.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6ae58612",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Hint:** If the `grader.export` cell fails to run because the file size is too large, you can delete the plot above to reduce the size of your notebook. Make sure your plot matches the plot above, before you do so."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c961e343",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:21.715379Z",
+ "iopub.status.busy": "2023-11-15T01:29:21.714363Z",
+ "iopub.status.idle": "2023-11-15T01:29:22.188648Z",
+ "shell.execute_reply": "2023-11-15T01:29:22.185648Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'temp_list' and 'lum_list' with 'age_list' as colors and 'mass_list' as sizes\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "63145a7c",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Food for thought:** Can you tell if there is any relationship between the **temperature**, **luminosity**, **age**, and **mass** of the stars? You might want to remove the outliers with the extremely high `stellar_effective_temperature` to get a better view of the diagram. What effect does the **age** seem to have on the **temperature**? Recall that a **lighter** color implies that the value is higher, while a **darker** color implies that the value is lower. What effect does the **mass** have?\n",
+ "\n",
+ "**Food for thought:** Notice that there are **two distinct** *clusters* of points in this diagram. If you are interested, look up more information on the Hertzsprung–Russell Diagram to understand what these clusters are. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "93eb0c3e",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:22.198650Z",
+ "iopub.status.busy": "2023-11-15T01:29:22.197649Z",
+ "iopub.status.idle": "2023-11-15T01:29:22.205793Z",
+ "shell.execute_reply": "2023-11-15T01:29:22.204781Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Food for thought is an entirely OPTIONAL exercise\n",
+ "# you may leave your thoughts here as a comment if you wish to\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d89ec375",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Recursion:\n",
+ "\n",
+ "You are not done exploring the dataset, and you have more questions left to answer. However, something more important has happened! We have managed to find the data from the corrupted json file (`mapping_5.json`)!\n",
+ "\n",
+ "If you will recall, when we were parsing the files in P10, we found that `mapping_5.json` was **broken**, and we couldn't read it. Therefore, we had no choice but to leave all the planets in `planets_5.csv` out of our analysis. Luckily for you now, the data has shown up intact in the directory `broken_data`. Unfortunately, the data is now no longer stored in a single file, but has been **split up** into **multiple files** and stored in **different subdirectories**.\n",
+ "\n",
+ "You will now create a function to help parse all the data stored within this directory."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e17445cb",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Function 1: `get_all_paths_in(directory)`\n",
+ "\n",
+ "You **must** write this function that takes in the **relative path** of a `directory` as its input, and returns a **list** of **relative paths** of all the **files** inside `directory` and its subdirectories.\n",
+ "\n",
+ "In other words, if a directory `sample_data` looks like this:\n",
+ "```\n",
+ "+-- sample_data\n",
+ "| +-- .DS_Store\n",
+ "| +-- file_1.json\n",
+ "| +-- sample_1\n",
+ "| | +-- .ipynb_checkpoints\n",
+ "| | +-- file_2.json\n",
+ "| | +-- file_3.json\n",
+ "| +-- sample_2\n",
+ "| | +-- file_4.json\n",
+ "| | +-- sample_3\n",
+ "| | | +-- .DS_Store\n",
+ "| | | +-- file_5.json\n",
+ "```\n",
+ "\n",
+ "then the output of the function call `get_all_paths_in(\"sample_data\")` **must** be a **list** containing the **relative paths** of the files `file_5.json`, `files_4.json`, `file_3.json`, `file_2.json`, and `file_1.json`.\n",
+ "\n",
+ "You **must** **ignore** all files that start with `\".\"`, and your output **must** be **explicitly** sorted in **reverse alphabetical** order.\n",
+ "\n",
+ "**Important Warning:** You **must** write a **recursive** function here. You are **only allowed** to use the functions from the `os` module, which have been covered in lecture. Here is a list of these functions (you will only need a few of these functions to define `get_all_paths_in`):\n",
+ "* `os.mkdir`\n",
+ "* `os.path.join`\n",
+ "* `os.listdir`\n",
+ "* `os.path.exists`\n",
+ "* `os.path.isfile`\n",
+ "* `os.path.isdir`\n",
+ "\n",
+ "You may copy/paste this function from Lab-P11 if you finished writing it there."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8517fd60",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:22.215796Z",
+ "iopub.status.busy": "2023-11-15T01:29:22.214796Z",
+ "iopub.status.idle": "2023-11-15T01:29:22.229237Z",
+ "shell.execute_reply": "2023-11-15T01:29:22.227224Z"
+ },
+ "lines_to_next_cell": 0,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define the function 'get_all_paths_in' here\n",
+ "\n",
+ "def get_all_paths_in(directory):\n",
+ " '''get_all_paths_in(directory) recursively \n",
+ " explores that directory for any files, \n",
+ " and returns a list of paths to \n",
+ " files inside that directory'''\n",
+ " pass # replace with your code"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea279634",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"get_all_paths_in\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d97da08c",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 10:** What are the **paths** of the files in the `other` directory of the `broken_data` directory?\n",
+ "\n",
+ "Your output **must** be a **list** of **relative paths**. You **must** answer this question by calling the `get_paths_in` function.\n",
+ "\n",
+ "**Warning:** Remember that you **must** only use `os.path.join` to create paths."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea5c9c38",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:22.320492Z",
+ "iopub.status.busy": "2023-11-15T01:29:22.320492Z",
+ "iopub.status.idle": "2023-11-15T01:29:22.330760Z",
+ "shell.execute_reply": "2023-11-15T01:29:22.329747Z"
+ },
+ "lines_to_next_cell": 0,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'broken_data_others', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8522e746",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q10\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d0178bbe",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 11:** What are the **paths** of the files in the `100` directory of the `kepler` directory of the `broken_data` directory?\n",
+ "\n",
+ "Your output **must** be a **list** of **relative paths**. You **must** answer this question by calling the `get_all_paths_in` function.\n",
+ "\n",
+ "**Hint:** You can pass multiple **strings** as arguments to `os.path.join` to join them together at the same time. For example, to get the path of the required directory here, you could say\n",
+ "```python\n",
+ "os.path.join(\"broken_data\", \"kepler\", \"100\")\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "428924ec",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:22.378535Z",
+ "iopub.status.busy": "2023-11-15T01:29:22.377536Z",
+ "iopub.status.idle": "2023-11-15T01:29:22.388005Z",
+ "shell.execute_reply": "2023-11-15T01:29:22.388005Z"
+ },
+ "lines_to_next_cell": 0,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'broken_data_keplers_100s', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bd561cc6",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q11\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "93d6609a",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 12:** What are the **paths** of the files in the `other` directory of the `10` directory of the `kepler` directory of the `broken_data` directory?\n",
+ "\n",
+ "Your output **must** be a **list** of **relative paths**. You **must** answer this question by calling the `get_all_paths_in` function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "48766e8d",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:22.436159Z",
+ "iopub.status.busy": "2023-11-15T01:29:22.436159Z",
+ "iopub.status.idle": "2023-11-15T01:29:22.450273Z",
+ "shell.execute_reply": "2023-11-15T01:29:22.448256Z"
+ },
+ "lines_to_next_cell": 0,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'broken_data_kepler_10_others', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7c95e492",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q12\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8ab5d4cd",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 13:** What are the **paths** of the files in the `broken_data` directory?\n",
+ "\n",
+ "Your output **must** be a **list** of **relative paths**. You **must** answer this question by calling the `get_paths_in` function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "55620533",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:22.511177Z",
+ "iopub.status.busy": "2023-11-15T01:29:22.510178Z",
+ "iopub.status.idle": "2023-11-15T01:29:22.527417Z",
+ "shell.execute_reply": "2023-11-15T01:29:22.527417Z"
+ },
+ "lines_to_next_cell": 0,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'broken_data', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2b46866b",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q13\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dec94018",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Data Structure 2: `all_planets_list`\n",
+ "\n",
+ "We want to read the data that is stored inside the directory `broken_data`. We already have a function `get_planets` (from P10) which can read a CSV file and a JSON file and combine them to create a **list** of `Planet` objects. So, we can repeatedly call `get_planets` on the CSV file `planets_5.csv` and each of the JSON files inside `broken_data` to get a **list** of `Planet` objects of **all** the planets in `planets_5.csv`.\n",
+ "\n",
+ "You **must** **create** the **list** `all_planets_list` by adding in all `Planet` objects from `planets_list`, and then also adding in the `Planet` objects in `planets_5.csv` and the directory `broken_data`.\n",
+ "\n",
+ "**Hint:** You should loop through every file in the list `broken_data`, and use `get_planets` on `planets_5.csv` (inside the `data` directory), and this file (from the loop) to create a list of `Planet` objects, and then **extend** `all_planets_list` by the list of new `Planet` objects.\n",
+ "\n",
+ "**Warning:** Do **not** update the value of the **list** `planets_list` when you do this. Otherwise, your answers to some of the previous questions will become incorrect. Instead, make sure that the new `Planet` objects are only added to `all_planets_list` and **not** to `planets_list`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "543a9fc0",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:22.560012Z",
+ "iopub.status.busy": "2023-11-15T01:29:22.559013Z",
+ "iopub.status.idle": "2023-11-15T01:29:22.655574Z",
+ "shell.execute_reply": "2023-11-15T01:29:22.654564Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# create the variable 'all_planets_list' here,\n",
+ "# but do NOT display the variable at the end\n",
+ "\n",
+ "# TODO: initialize 'all_planets_list'\n",
+ "# TODO: add the planets in 'planets_list' to 'all_planets_list'\n",
+ "# TODO: loop through all paths in 'broken_data'\n",
+ " # TODO: use 'get_planets' to get the planets in this file and add them to 'all_planets_list'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c55e646f",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"all_planets_list\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2ffb7227",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "You can verify that you have not made any mistakes by confirming that `all_planets_list` now has *5526* `Planet` objects in it."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "62cc8fec",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Exploring habitability of exoplanets:\n",
+ "\n",
+ "Now that we have gathered the data on all the `Planet` objects, we are ready to have some fun with this dataset. Over the course of the rest of this project, we will try to find out if there are any planets in our dataset which could potentially support human habitation. Naturally, using the available data, we cannot say with any certainty that any particular planet is habitable, but we *can* say with some confidence when a planet is **not** habitable (notwithstanding major technological gains). That is exactly what we will do now."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "af0f11f3",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "#### Surface Gravitational Force:\n",
+ "\n",
+ "It seems reasonable to expect that for humans to be able to survive on a planet, the gravitational force of the planet on its surface is not too different from that of the Earth.\n",
+ "\n",
+ "We note that the `planet_mass` and `planet_radius` attributes of the `Planet` objects stores these values in units of the mass of the Earth, and the radius of the Earth respectively. So, the **ratio** of the gravitational force experienced on the surface of a given planet to the force experienced on the surface of the Earth can be computed as:\n",
+ "\n",
+ "$$\\frac{g_{\\texttt{planet}}}{g_{\\texttt{earth}}} = \\frac{\\texttt{planet mass}}{\\texttt{planet radius}^{2}}$$\n",
+ "\n",
+ "So, a **ratio** greater than 1 would imply that a person on the planet's surface would experience a greater force due to gravity than on Earth, while a value lower than 1 would imply that a person on the planet's surface would experience a lesser force due to gravity than on Earth."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8668b7af",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Function 2: `get_surface_gravity(planet)`\n",
+ "\n",
+ "You **must** define this function which takes in a `Planet` object as its input, and then **returns** the **ratio** of the gravitational force experienced on the surface of a given planet to the force experienced on the surface of the Earth. If either the `planet_mass` or `planet_radius` data is **missing**, then your function **must** return `None`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "842264ba",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:22.706225Z",
+ "iopub.status.busy": "2023-11-15T01:29:22.706225Z",
+ "iopub.status.idle": "2023-11-15T01:29:22.710617Z",
+ "shell.execute_reply": "2023-11-15T01:29:22.710617Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define the function 'get_surface_gravity' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "92a40936",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"get_surface_gravity\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "33821dab",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 14:** What is the **ratio** of gravitational force experienced on the surface of the planet *GJ 674 b* to the gravitational force experienced on the surface of the Earth?\n",
+ "\n",
+ "You will have to first loop through `all_planets_list` to identify the correct `Planet` object. Remember to `break` out of your loop after you identify the correct `Planet` object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2bee6aae",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:22.737247Z",
+ "iopub.status.busy": "2023-11-15T01:29:22.737247Z",
+ "iopub.status.idle": "2023-11-15T01:29:22.745567Z",
+ "shell.execute_reply": "2023-11-15T01:29:22.744553Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'gj_674_b_gravity', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9813b514",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q14\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6df16c13",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "#### Distance to the star:\n",
+ "\n",
+ "Planets follow **elliptical** orbits around their host star. The `eccentricity` of a planet's orbit is a number that measures *how* elliptical the orbit is. An eccentricity of *0* would imply that the orbit is in fact perfectly circular, while an eccentricity close to *1* would imply that the orbit is very skewed and elliptical. As you may expect, if a planet has a highly eccentric orbit, its distance to its host star would vary wildly, leading to a highly variable climate. To determine if a planet could support human habitation, it is therefore important to know the closest and shortest distances between the planet and its host star.\n",
+ "\n",
+ "We can compute these quantities using the attributes `eccentricity` and `semi_major_radius` of each `Planet` object. These distances can be computed as:\n",
+ "\n",
+ "$$\\texttt{shortest distance} = \\texttt{semi major radius} \\times (1 - \\texttt{abs}(\\texttt{eccentricity}))$$\n",
+ "\n",
+ "$$\\texttt{longest distance} = \\texttt{semi major radius} \\times (1 + \\texttt{abs}(\\texttt{eccentricity}))$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c21fa7d8",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Function 3: `get_distances_to_star(planet)`\n",
+ "\n",
+ "You **must** define this function which takes in a `Planet` object as its input, and then **returns** a **list** of two **floats**. The first float should be the **shortest distance** of the `Planet` object to its host star, and the second float should be the **longest distance** to its host star. If either the `eccentricity` or `semi_major_radius` data of the `Planet` is missing, then the function **must** return `None`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c35f568e",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:22.789056Z",
+ "iopub.status.busy": "2023-11-15T01:29:22.789056Z",
+ "iopub.status.idle": "2023-11-15T01:29:22.795966Z",
+ "shell.execute_reply": "2023-11-15T01:29:22.794953Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define the function 'get_distances_to_star' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9c46526a",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"get_distances_to_star\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9d6d7a06",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 15:** Find the **shortest** and **longest** distances for the planet *b Cen AB b* to its host star.\n",
+ "\n",
+ "Your output **must** be a **list** of two **floats** representing the **shortest** and **longest** distances to its host star. Remember to `break` out of your loop after you identify the correct `Planet` object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "866d7351",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:22.818560Z",
+ "iopub.status.busy": "2023-11-15T01:29:22.818560Z",
+ "iopub.status.idle": "2023-11-15T01:29:22.827084Z",
+ "shell.execute_reply": "2023-11-15T01:29:22.826075Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'distances_to_star_b_cen_ab_b', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "87703ee8",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q15\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ddf486a5",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "#### Presence of Liquid Water :\n",
+ "\n",
+ "It is safe to say that planets which cannot sustain liquid are inhabitable. While we do not have any data on whether the `Planet` objects in our dataset have naturally occurring water, we are able to determine whether the planet can *support* liquid water based on its distance to its host star, and the luminosity of this star. \n",
+ "\n",
+ "Astronomers have [computed](https://pubmed.ncbi.nlm.nih.gov/11536936/) that for Earth-like planets, there is a certain range of distances that a planet can have to its host star, which depends on the `luminosity` of the star, within which, water on the planet's surface can stay in liquid form. These distances are as follows:\n",
+ "\n",
+ "$$\\texttt{liquid water shortest dist} = \\sqrt{\\frac{\\texttt{absolute luminosity}}{1.15}}$$\n",
+ "\n",
+ "$$\\texttt{liquid water longest dist} = \\sqrt{\\frac{\\texttt{absolute luminosity}}{0.53}}$$\n",
+ "\n",
+ "In our dataset, the `stellar_luminosity` is stored in units of the logarithm of the absolute luminosity. So, the distances can be computed from our dataset as follows:\n",
+ "\n",
+ "$$\\texttt{liquid water shortest dist} = \\sqrt{\\frac{10^{\\texttt{stellar luminosity}}}{1.15}}$$\n",
+ "\n",
+ "$$\\texttt{liquid water longest dist} = \\sqrt{\\frac{10^{\\texttt{stellar luminosity}}}{0.53}}$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7e9bdd17",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Function 4: `get_liquid_water_distances(planet)`\n",
+ "\n",
+ "You **must** define this function which takes in a `Planet` object as its input, and then **returns** a **list** of two **floats**. The first float should be the **shortest distance** the `Planet` object can be to its host star while being able to support liquid water, and the second float should be the **longest distance** it can be to its host star while being able to support liquid water. If the `stellar_luminosity` data of the host `Star` object is missing, then the function **must** return `None`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "87eedb8c",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:22.874218Z",
+ "iopub.status.busy": "2023-11-15T01:29:22.873219Z",
+ "iopub.status.idle": "2023-11-15T01:29:22.880868Z",
+ "shell.execute_reply": "2023-11-15T01:29:22.879858Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define the function 'get_liquid_water_distances' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eb780cd3",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"get_liquid_water_distances\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69aa89a8",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 16:** Find the **shortest** and **longest** distances for the planet *Kepler-197 e* from its host star, at which it can support liquid water.\n",
+ "\n",
+ "Your output **must** be a **list** of two **floats** representing the **shortest** and **longest** distances that the planet can be from its host star and still support liquid water. Remember to `break` out of your loop after you identify the correct `Planet` object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ff90461",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:22.912979Z",
+ "iopub.status.busy": "2023-11-15T01:29:22.911980Z",
+ "iopub.status.idle": "2023-11-15T01:29:22.921042Z",
+ "shell.execute_reply": "2023-11-15T01:29:22.920030Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'liquid_water_distances_kepler_197_e', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "43fbc4bb",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q16\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bca7698d",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 17:** **List** the names (`planet_name`) of all the `Planet` objects which can support liquid water when they are at **both** their **shortest** and **longest** distances to their host star.\n",
+ "\n",
+ "Your output **must** be a **list**. You **must** ignore `Planet` objects with missing `eccentricity`, or `semi_major_radius` data and planets whose host `Star` has missing `stellar_luminosity` data.\n",
+ "\n",
+ "**Hint:** You can find the actual shortest and longest distances of the planet with the `get_distances_to_star` function, and the shortest and longest distances at which liquid water can be supported with the `get_liquid_water_distances` function. You must consider `Planet` objects for which the actual distances to their host star lie **within** the distances at which liquid water can be supported."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "571c3099",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:22.967102Z",
+ "iopub.status.busy": "2023-11-15T01:29:22.966102Z",
+ "iopub.status.idle": "2023-11-15T01:29:22.994935Z",
+ "shell.execute_reply": "2023-11-15T01:29:22.993927Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'planets_with_liquid_water', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3b457b20",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q17\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "74dfc7dd",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "#### Surface temperature:\n",
+ "\n",
+ "The temperature on the surface of the planet is another important criteria for deciding whether a planet is habitable. The `equilibrium_temperature` of a `Planet` is the temperature that the planet would be, if it were a [black body](https://en.wikipedia.org/wiki/Black_body) (i.e., if it were able to absorb all the radiation it receives from its host star). However, most planets are not perfect black bodies and reflect some of the radiation that they receive from their host star. Astronomers use the quantity [albedo](https://en.wikipedia.org/wiki/Albedo) to measure how much radiation is reflected by the planet. An albedo of *0* implies that the planet is a perfect black body which absorbs all its radiation, while an albedo of *1* implies that the planet is perfectly reflective, and does not retain any radiation. In the real world, most planets have an albedo value between *0* and *0.5*.\n",
+ "\n",
+ "Using the albedo of a planet, we can compute the temperature on the surface of a planet as follows\n",
+ "\n",
+ "$$ \\texttt{surface temperature} = \\left(1- \\texttt{albedo}\\right) ^{1/4} \\times \\texttt{equilibrium temperature}$$\n",
+ "\n",
+ "Unfortunately, we do **not** have the albedo values of the `Planet` objects in our dataset. So, we will instead make some educated guesses and find the **maximum** and **minimum** surface temperatures, assuming that the albedo is within the range of *0* to *0.5* (which is known to be the case for most planets)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "30812ed7",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "### Function 5: `get_surface_temperatures(planet)`\n",
+ "\n",
+ "You **must** define this function which takes in a `Planet` object as its input, and then **returns** a **list** of two **floats**. The first float should be the **minimum surface temperature** of the `Planet` object (which can be computed by assuming a **albedo** value of *0.5*), and the second float should be the **maximum surface temperature** (which can be computed by assuming a **albedo** value of *0.0*). If the `equilibrium_temperature` data of the `Planet` is missing, then the function **must** return `None`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a6abc3d1",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:23.048013Z",
+ "iopub.status.busy": "2023-11-15T01:29:23.048013Z",
+ "iopub.status.idle": "2023-11-15T01:29:23.053203Z",
+ "shell.execute_reply": "2023-11-15T01:29:23.053203Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define the function 'get_surface_temperatures' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2ecd557",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"get_surface_temperatures\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3971dd33",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 18:** Find the **minimum** and **maximum** surface temperatures for the planet *HD 20794 d*.\n",
+ "\n",
+ "Your output **must** be a **list** of two **floats** representing the **minimum** and **maximum** surface temperatures. Remember to `break` out of your loop after you identify the correct `Planet` object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "add522b3",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:23.077256Z",
+ "iopub.status.busy": "2023-11-15T01:29:23.077256Z",
+ "iopub.status.idle": "2023-11-15T01:29:23.085981Z",
+ "shell.execute_reply": "2023-11-15T01:29:23.084966Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'surface_temp_hd_20794_d', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "43e3bce4",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q18\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "faa892cd",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 19:** **List** the names (`planet_name`) of all the `Planet` objects whose **minimum surface temperature** is **greater** than *263* (Kelvin) and **maximum surface temperature** is **less** than *323* (Kelvin).\n",
+ "\n",
+ "Your output **must** be a **list**. You **must** ignore `Planet` objects with missing `equilibrium_temperature` data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4a9c9ce3",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:23.138882Z",
+ "iopub.status.busy": "2023-11-15T01:29:23.138882Z",
+ "iopub.status.idle": "2023-11-15T01:29:23.150591Z",
+ "shell.execute_reply": "2023-11-15T01:29:23.149583Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'pleasant_planets', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6638a57c",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q19\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a54f6551",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "#### Putting it all together:\n",
+ "\n",
+ "We are finally ready to combine all our various criteria of habitability to make a list of planets which satisfy all the criteria above, and could potentially be habitable. Unsurprisingly, if we are too strict with our expectations, no planets in the dataset will meet them. So, allowing for some technological improvements in the future, we will make more modest requests of the planets in our dataset."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3c7c34d0",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 20:** List the `planet_name` of all the `Planet` objects which satisfy the criteria below:\n",
+ "\n",
+ "1. The gravitational force experienced on the surface of the `Planet` must be **greater** than *0.75* and **less** than *1.25* times that of the Earth.\n",
+ "2. The planet must always **lie within** the range at which it is able to support liquid water.\n",
+ "3. The **minimum** surface temperature must be **greater** than *200* and the **maximum** surface temperature must be **less** than *350*.\n",
+ "\n",
+ "Your output **must** be a **list** of **strings**. You **must** ignore any `Planet` objects for which you cannot determine if any of these criteria are met."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ec5b1d1b",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:23.200819Z",
+ "iopub.status.busy": "2023-11-15T01:29:23.200819Z",
+ "iopub.status.idle": "2023-11-15T01:29:23.231240Z",
+ "shell.execute_reply": "2023-11-15T01:29:23.230207Z"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'habitable_planets', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e644a763",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q20\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9c304bc9",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Food for thought:** If you are interested, you can play around these values more, and introduce more stringent requirements to try and find the single **most** habitable planet. Notice how so many of the habitable planets we found start with the string *Kepler*. Do you know why?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e27f6114",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-11-15T01:29:23.288336Z",
+ "iopub.status.busy": "2023-11-15T01:29:23.288336Z",
+ "iopub.status.idle": "2023-11-15T01:29:23.293012Z",
+ "shell.execute_reply": "2023-11-15T01:29:23.291979Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Food for thought is an entirely OPTIONAL exercise\n",
+ "# you may leave your thoughts here as a comment if you wish to\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e573dd76",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"general_deductions\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9648a334",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"summary\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dd1b15c0",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "## Submission\n",
+ "It is recommended that at this stage, you Restart and Run all Cells in your notebook.\n",
+ "That will automatically save your work and generate a zip file for you to submit.\n",
+ "\n",
+ "**SUBMISSION INSTRUCTIONS**:\n",
+ "1. **Upload** the zipfile to Gradescope.\n",
+ "2. If you completed the project with a **partner**, make sure to **add their name** by clicking \"Add Group Member\"\n",
+ "in Gradescope when uploading the zip file.\n",
+ "3. Check **Gradescope** results as soon as the auto-grader execution gets completed.\n",
+ "4. Your **final score** for this project is the score that you see on **Gradescope**.\n",
+ "5. You are **allowed** to resubmit on Gradescope as many times as you want to.\n",
+ "6. **Contact** a TA/PM if you lose any points on Gradescope for any **unclear reasons**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0fe20ac9",
+ "metadata": {
+ "cell_type": "code",
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "# running this cell will create a new save checkpoint for your notebook\n",
+ "from IPython.display import display, Javascript\n",
+ "display(Javascript('IPython.notebook.save_checkpoint();'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "31ca3fde",
+ "metadata": {
+ "cell_type": "code",
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "!jupytext --to py p11.ipynb"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4acd40b3",
+ "metadata": {
+ "cell_type": "code",
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "public_tests.check_file_size(\"p11.ipynb\")\n",
+ "grader.export(pdf=False, run_tests=False, files=[\"p11.py\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "02e78338",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ " "
+ ]
+ }
+ ],
+ "metadata": {
+ "jupytext": {
+ "cell_metadata_filter": "-all",
+ "encoding": "# coding: utf-8",
+ "executable": "/usr/bin/env python",
+ "notebook_metadata_filter": "-all"
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.11.4"
+ },
+ "otter": {
+ "OK_FORMAT": true,
+ "tests": {
+ "all_planets_list": {
+ "name": "all_planets_list",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('all_planets_list: data structure is defined incorrectly')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'data structure is defined incorrectly (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('all_planets_list: `get_planets` function is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`get_planets` function is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('all_planets_list: `broken_data` data structure is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`broken_data` data structure is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('all_planets_list: paths are hardcoded using slashes')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'paths are hardcoded using slashes (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('all_planets_list: `planets_list` data structure is modified or redefined')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`planets_list` data structure is modified or redefined (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "general_deductions": {
+ "name": "general_deductions",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('general_deductions: Outputs not visible/did not save the notebook file prior to running the cell containing \"export\". We cannot see your output if you do not save before generating the zip file.')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'Outputs not visible/did not save the notebook file prior to running the cell containing \"export\". We cannot see your output if you do not save before generating the zip file. (-3)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('general_deductions: Used concepts/modules such as csv.DictReader, os.walk, and pandas not covered in class yet. Note that built-in functions that you have been introduced to can be used.')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'Used concepts/modules such as csv.DictReader, os.walk, and pandas not covered in class yet. Note that built-in functions that you have been introduced to can be used. (-3)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('general_deductions: Large outputs such as stars_dict or planets_list are displayed in the notebook.')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'Large outputs such as stars_dict or planets_list are displayed in the notebook. (-3)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('general_deductions: Import statements are not mentioned in the required cell at the top of the notebook.')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'Import statements are not mentioned in the required cell at the top of the notebook. (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "get_all_paths_in": {
+ "name": "get_all_paths_in",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('get_all_paths_in: function does not remove all files and directories that start with `.`')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'function does not remove all files and directories that start with `.` (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('get_all_paths_in: function does not sort file names explicitly')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'function does not sort file names explicitly (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('get_all_paths_in: function logic is incorrect')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'function logic is incorrect (-2)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('get_all_paths_in: hardcoding the name of directory inside the function')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'hardcoding the name of directory inside the function (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('get_all_paths_in: paths are hardcoded using slashes')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'paths are hardcoded using slashes (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> public_tests.rubric_check('get_all_paths_in: public tests')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "get_distances_to_star": {
+ "name": "get_distances_to_star",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('get_distances_to_star: function did not return `None` for missing data')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'function did not return `None` for missing data (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('get_distances_to_star: function logic is incorrect')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'function logic is incorrect (-2)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "get_liquid_water_distances": {
+ "name": "get_liquid_water_distances",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('get_liquid_water_distances: function did not return `None` for missing data')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'function did not return `None` for missing data (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('get_liquid_water_distances: function logic is incorrect')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'function logic is incorrect (-2)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "get_surface_gravity": {
+ "name": "get_surface_gravity",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('get_surface_gravity: function did not return `None` for missing data')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'function did not return `None` for missing data (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('get_surface_gravity: function logic is incorrect')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'function logic is incorrect (-2)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "get_surface_temperatures": {
+ "name": "get_surface_temperatures",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('get_surface_temperatures: function did not return `None` for missing data')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'function did not return `None` for missing data (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('get_surface_temperatures: function logic is incorrect')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'function logic is incorrect (-2)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q1": {
+ "name": "q1",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q1', ratios_gj9827)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q1: incorrect logic is used to answer')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q1: `planets_list` data structure is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`planets_list` data structure is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> public_tests.rubric_check('q1: public tests')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q10": {
+ "name": "q10",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q10', broken_data_others)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q10: `get_all_paths_in` function is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`get_all_paths_in` function is not used to answer (-2)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q10: paths are hardcoded using slashes')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'paths are hardcoded using slashes (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> public_tests.rubric_check('q10: public tests')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q11": {
+ "name": "q11",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q11', broken_data_keplers_100s)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q11: `get_all_paths_in` function is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`get_all_paths_in` function is not used to answer (-2)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q11: paths are hardcoded using slashes')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'paths are hardcoded using slashes (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> public_tests.rubric_check('q11: public tests')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q12": {
+ "name": "q12",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q12', broken_data_kepler_10_others)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q12: `get_all_paths_in` function is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`get_all_paths_in` function is not used to answer (-2)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q12: paths are hardcoded using slashes')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'paths are hardcoded using slashes (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> public_tests.rubric_check('q12: public tests')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q13": {
+ "name": "q13",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q13', broken_data)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q13: `get_all_paths_in` function is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`get_all_paths_in` function is not used to answer (-2)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> public_tests.rubric_check('q13: public tests')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q14": {
+ "name": "q14",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q14', gj_674_b_gravity)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q14: `get_surface_gravity` function is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`get_surface_gravity` function is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q14: `all_planets_list` data structure is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`all_planets_list` data structure is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q14: did not exit loop and instead iterated further after finding the answer')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not exit loop and instead iterated further after finding the answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q15": {
+ "name": "q15",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q15', distances_to_star_b_cen_ab_b)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q15: `get_distances_to_star` function is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`get_distances_to_star` function is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q15: `all_planets_list` data structure is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`all_planets_list` data structure is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q15: did not exit loop and instead iterated further after finding the answer')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not exit loop and instead iterated further after finding the answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q16": {
+ "name": "q16",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q16', liquid_water_distances_kepler_197_e)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q16: `get_liquid_water_distances` function is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`get_liquid_water_distances` function is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q16: `all_planets_list` data structure is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`all_planets_list` data structure is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q16: did not exit loop and instead iterated further after finding the answer')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not exit loop and instead iterated further after finding the answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q17": {
+ "name": "q17",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q17', planets_with_liquid_water)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q17: incorrect logic is used to answer')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q17: `get_liquid_water_distances` function is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`get_liquid_water_distances` function is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q17: `all_planets_list` data structure is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`all_planets_list` data structure is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> public_tests.rubric_check('q17: public tests')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q18": {
+ "name": "q18",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q18', surface_temp_hd_20794_d)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q18: `get_surface_temperatures` function is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`get_surface_temperatures` function is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q18: `all_planets_list` data structure is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`all_planets_list` data structure is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q18: did not exit loop and instead iterated further after finding the answer')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not exit loop and instead iterated further after finding the answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q19": {
+ "name": "q19",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q19', pleasant_planets)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q19: incorrect comparison operators are used')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect comparison operators are used (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q19: `get_surface_temperatures` function is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`get_surface_temperatures` function is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q19: `all_planets_list` data structure is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`all_planets_list` data structure is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> public_tests.rubric_check('q19: public tests')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q2": {
+ "name": "q2",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q2', coeff_gj9827)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q2: incorrect logic is used to answer')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q2: recomputed variable defined in Question 1')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'recomputed variable defined in Question 1 (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> public_tests.rubric_check('q2: public tests')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q20": {
+ "name": "q20",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q20', habitable_planets)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q20: incorrect logic is used to answer')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-2)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q20: `get_surface_gravity`, `get_distances_to_star`, `get_liquid_water_distances`, and `get_surface_temperatures` functions are not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`get_surface_gravity`, `get_distances_to_star`, `get_liquid_water_distances`, and `get_surface_temperatures` functions are not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q20: `all_planets_list` data structure is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`all_planets_list` data structure is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> public_tests.rubric_check('q20: public tests')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q3": {
+ "name": "q3",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q3', percentage_change)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q3: incorrect logic is used to answer')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q3: recomputed variable defined in Question 1')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'recomputed variable defined in Question 1 (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q3: `stars_dict` data structure is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`stars_dict` data structure is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q4": {
+ "name": "q4",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q4', {'flux_list': flux_list, 'temp_4th_power_list': temp_4th_power_list})\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q4: incorrect logic is used to answer')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q4: `planets_list` data structure is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`planets_list` data structure is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q4: plot is incorrect')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is incorrect (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q4: plot is not properly labeled')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is not properly labeled (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> public_tests.rubric_check('q4: public tests')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q5": {
+ "name": "q5",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q5', {'flux_list_no_outliers': flux_list_no_outliers, 'temp_4th_power_list_no_outliers': temp_4th_power_list_no_outliers})\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q5: incorrect comparison operator is used')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect comparison operator is used (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q5: `planets_list` data structure is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`planets_list` data structure is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q5: plot is incorrect')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is incorrect (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q5: plot is not properly labeled')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is not properly labeled (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> public_tests.rubric_check('q5: public tests')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q6": {
+ "name": "q6",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q6', star_classes_avg_lum)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q6: did not ignore the `Star` objects with missing `stellar_luminosity` data')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not ignore the `Star` objects with missing `stellar_luminosity` data (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q6: incorrect logic is used to answer')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q6: `star_classes` data structure is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`star_classes` data structure is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q7": {
+ "name": "q7",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q7', {'density_dict': density_dict, 'lum_dict': lum_dict})\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q7: incorrect logic is used to answer')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q7: `star_classes` data structure is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`star_classes` data structure is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q7: plot is incorrect')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is incorrect (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q7: plot is not properly labeled')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is not properly labeled (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> public_tests.rubric_check('q7: public tests')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q8": {
+ "name": "q8",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q8', {'density_dict_no_outliers': density_dict_no_outliers, 'lum_dict_no_outliers': lum_dict_no_outliers})\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q8: incorrect comparison operator is used')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect comparison operator is used (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q8: `star_classes` data structure is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`star_classes` data structure is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q8: plot is incorrect')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is incorrect (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q8: plot is not properly labeled')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is not properly labeled (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> public_tests.rubric_check('q8: public tests')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q9": {
+ "name": "q9",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.check('q9', {'temp_list': temp_list, 'lum_list': lum_list, 'age_list': age_list, 'mass_list': mass_list})\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q9: incorrect logic is used to answer')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q9: `stars_dict` data structure is not used to answer', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`stars_dict` data structure is not used to answer (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q9: plot is incorrect')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is incorrect (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('q9: plot is not properly labeled')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is not properly labeled (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> public_tests.rubric_check('q9: public tests')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "star_classes": {
+ "name": "star_classes",
+ "points": 0,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('star_classes: data structure is defined incorrectly')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'data structure is defined incorrectly (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('star_classes: incorrect comparison operators are used')\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect comparison operators are used (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ },
+ {
+ "code": ">>> \n>>> public_tests.rubric_check('star_classes: `stars_dict` data structure is not used', False)\nAll test cases passed!\n",
+ "hidden": false,
+ "locked": false,
+ "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`stars_dict` data structure is not used (-1)'. The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "summary": {
+ "name": "summary",
+ "points": 127,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> public_tests.get_summary()\nTotal Score: 100/100\n",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ }
+ }
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/p11/public_tests.py b/p11/public_tests.py
new file mode 100644
index 0000000..2cf8408
--- /dev/null
+++ b/p11/public_tests.py
@@ -0,0 +1,923 @@
+#!/usr/bin/python
+# +
+import os, json, math, copy
+from collections import namedtuple
+from bs4 import BeautifulSoup
+
+HIDDEN_FILE = os.path.join("hidden", "hidden_tests.py")
+if os.path.exists(HIDDEN_FILE):
+ import hidden.hidden_tests as hidn
+# -
+
+MAX_FILE_SIZE = 750 # units - KB
+REL_TOL = 6e-04 # relative tolerance for floats
+ABS_TOL = 15e-03 # absolute tolerance for floats
+TOTAL_SCORE = 100 # total score for the project
+
+DF_FILE = 'expected_dfs.html'
+PLOT_FILE = 'expected_plots.json'
+
+PASS = "All test cases passed!"
+
+TEXT_FORMAT = "TEXT_FORMAT" # question type when expected answer is a type, str, int, float, or bool
+TEXT_FORMAT_UNORDERED_LIST = "TEXT_FORMAT_UNORDERED_LIST" # question type when the expected answer is a list or a set where the order does *not* matter
+TEXT_FORMAT_ORDERED_LIST = "TEXT_FORMAT_ORDERED_LIST" # question type when the expected answer is a list or tuple where the order does matter
+TEXT_FORMAT_DICT = "TEXT_FORMAT_DICT" # question type when the expected answer is a dictionary
+TEXT_FORMAT_SPECIAL_ORDERED_LIST = "TEXT_FORMAT_SPECIAL_ORDERED_LIST" # question type when the expected answer is a list where order does matter, but with possible ties. Elements are ordered according to values in special_ordered_json (with ties allowed)
+TEXT_FORMAT_NAMEDTUPLE = "TEXT_FORMAT_NAMEDTUPLE" # question type when expected answer is a namedtuple
+PNG_FORMAT_SCATTER = "PNG_FORMAT_SCATTER" # question type when the expected answer is a scatter plot
+HTML_FORMAT = "HTML_FORMAT" # question type when the expected answer is a DataFrame
+FILE_JSON_FORMAT = "FILE_JSON_FORMAT" # question type when the expected answer is a JSON file
+SLASHES = "SLASHES" # question SUFFIX when expected answer contains paths with slashes
+
+def get_expected_format():
+ """get_expected_format() returns a dict mapping each question to the format
+ of the expected answer."""
+ expected_format = {'q1': 'TEXT_FORMAT_UNORDERED_LIST',
+ 'q2': 'TEXT_FORMAT',
+ 'q3': 'TEXT_FORMAT',
+ 'q4': 'PNG_FORMAT_SCATTER',
+ 'q5': 'PNG_FORMAT_SCATTER',
+ 'q6': 'TEXT_FORMAT_DICT',
+ 'q7': 'PNG_FORMAT_SCATTER',
+ 'q8': 'PNG_FORMAT_SCATTER',
+ 'q9': 'PNG_FORMAT_SCATTER',
+ 'q10': 'TEXT_FORMAT_ORDERED_LIST_SLASHES',
+ 'q11': 'TEXT_FORMAT_ORDERED_LIST_SLASHES',
+ 'q12': 'TEXT_FORMAT_ORDERED_LIST_SLASHES',
+ 'q13': 'TEXT_FORMAT_ORDERED_LIST_SLASHES',
+ 'q14': 'TEXT_FORMAT',
+ 'q15': 'TEXT_FORMAT_ORDERED_LIST',
+ 'q16': 'TEXT_FORMAT_ORDERED_LIST',
+ 'q17': 'TEXT_FORMAT_UNORDERED_LIST',
+ 'q18': 'TEXT_FORMAT_ORDERED_LIST',
+ 'q19': 'TEXT_FORMAT_UNORDERED_LIST',
+ 'q20': 'TEXT_FORMAT_UNORDERED_LIST'}
+ return expected_format
+
+
+def get_expected_json():
+ """get_expected_json() returns a dict mapping each question to the expected
+ answer (if the format permits it)."""
+ expected_json = {'q1': [219971.31454007205, 220096.7011785142, 220167.8343525934],
+ 'q2': 0.000452112209723501,
+ 'q3': -0.6256752095402645,
+ 'q6': {'Red Giant': -0.025334640522875783,
+ 'White Dwarf': 2.787333333333333,
+ 'Neutron Star': 2.86},
+ 'q10': ['broken_data\\other\\others.json'],
+ 'q11': ['broken_data\\kepler\\100\\others.json',
+ 'broken_data\\kepler\\100\\200\\other\\others.json',
+ 'broken_data\\kepler\\100\\200\\290\\290s.json',
+ 'broken_data\\kepler\\100\\200\\220\\220s.json',
+ 'broken_data\\kepler\\100\\100\\other\\others.json',
+ 'broken_data\\kepler\\100\\100\\100s.json'],
+ 'q12': ['broken_data\\kepler\\10\\other\\others.json'],
+ 'q13': ['broken_data\\toi\\tois.json',
+ 'broken_data\\other\\others.json',
+ 'broken_data\\kepler\\other\\others.json',
+ 'broken_data\\kepler\\10\\other\\others.json',
+ 'broken_data\\kepler\\10\\80s.json',
+ 'broken_data\\kepler\\10\\30\\30s.json',
+ 'broken_data\\kepler\\10\\20s.json',
+ 'broken_data\\kepler\\100\\others.json',
+ 'broken_data\\kepler\\100\\200\\other\\others.json',
+ 'broken_data\\kepler\\100\\200\\290\\290s.json',
+ 'broken_data\\kepler\\100\\200\\220\\220s.json',
+ 'broken_data\\kepler\\100\\100\\other\\others.json',
+ 'broken_data\\kepler\\100\\100\\100s.json',
+ 'broken_data\\k2\\k2s.json',
+ 'broken_data\\hd\\others.json',
+ 'broken_data\\hd\\10000\\10000s.json',
+ 'broken_data\\gj\\gjs.json'],
+ 'q14': 1.0000991982973964,
+ 'q15': [333.59999999999997, 778.4],
+ 'q16': [1.204068181137186, 1.7736260760497164],
+ 'q17': ['GJ 422 b',
+ 'HD 10697 b',
+ 'HD 114729 b',
+ 'HD 145934 b',
+ 'HD 147379 b',
+ 'HD 17674 b',
+ 'HD 188015 b',
+ 'HD 564 b',
+ 'Kepler-1341 b',
+ 'Kepler-1410 b',
+ 'Kepler-1540 b',
+ 'Kepler-1544 b',
+ 'Kepler-1554 b',
+ 'Kepler-1593 b',
+ 'Kepler-1628 b',
+ 'Kepler-1647 b',
+ 'Kepler-1652 b',
+ 'Kepler-1653 b',
+ 'Kepler-1661 b',
+ 'Kepler-1690 b',
+ 'Kepler-1868 b',
+ 'Kepler-1981 b',
+ 'Kepler-442 b',
+ 'Kepler-443 b',
+ 'Kepler-452 b',
+ 'Kepler-453 b',
+ 'Kepler-705 b',
+ 'Proxima Cen b',
+ 'HD 156668 c',
+ 'HD 159868 b',
+ 'HD 28185 b',
+ 'HD 45364 c',
+ 'Kepler-1086 c',
+ 'Kepler-1143 c',
+ 'Kepler-155 c',
+ 'Kepler-283 c',
+ 'Kepler-553 c',
+ 'Kepler-712 c',
+ 'TOI-199 c',
+ "Teegarden's Star b",
+ 'GJ 180 c',
+ 'GJ 433 d',
+ 'Kepler-174 d',
+ 'Kepler-454 c',
+ 'Kepler-47 c',
+ 'TOI-700 d',
+ 'TRAPPIST-1 d',
+ 'TRAPPIST-1 e',
+ 'Kepler-68 d',
+ 'HD 160691 b',
+ 'HD 191939 g',
+ 'HD 34445 f',
+ 'GJ 667 C c',
+ 'GJ 667 C f'],
+ 'q18': [326.2678091184412, 388.0],
+ 'q19': ['K2-323 b',
+ 'K2-9 b',
+ 'Kepler-1503 b',
+ 'Kepler-1512 b',
+ 'Kepler-505 b',
+ 'Kepler-577 b',
+ 'TOI-4562 b',
+ 'EPIC 212737443 c',
+ 'GJ 3929 c',
+ 'KOI-1783.02',
+ 'Kepler-1085 b',
+ 'Kepler-436 b',
+ 'Kepler-610 c',
+ 'Kepler-281 d',
+ 'Kepler-351 d',
+ 'TOI-712 d',
+ 'Kepler-169 f',
+ 'Kepler-186 e'],
+ 'q20': ['Kepler-1341 b',
+ 'Kepler-1410 b',
+ 'Kepler-1540 b',
+ 'Kepler-1544 b',
+ 'Kepler-1554 b',
+ 'Kepler-1593 b',
+ 'Kepler-1628 b',
+ 'Kepler-1652 b',
+ 'Kepler-1653 b',
+ 'Kepler-1661 b',
+ 'Kepler-1690 b',
+ 'Kepler-452 b',
+ 'Kepler-705 b',
+ 'Kepler-155 c',
+ 'Kepler-283 c',
+ 'TOI-700 d',
+ 'TRAPPIST-1 e',
+ 'HD 191939 g']}
+ return expected_json
+
+
+def get_special_json():
+ """get_special_json() returns a dict mapping each question to the expected
+ answer stored in a special format as a list of tuples. Each tuple contains
+ the element expected in the list, and its corresponding value. Any two
+ elements with the same value can appear in any order in the actual list,
+ but if two elements have different values, then they must appear in the
+ same order as in the expected list of tuples."""
+ special_json = {}
+ return special_json
+
+
+def compare(expected, actual, q_format=TEXT_FORMAT):
+ """compare(expected, actual) is used to compare when the format of
+ the expected answer is known for certain."""
+ try:
+ if q_format == TEXT_FORMAT:
+ return simple_compare(expected, actual)
+ elif q_format == TEXT_FORMAT_UNORDERED_LIST:
+ return list_compare_unordered(expected, actual)
+ elif q_format == TEXT_FORMAT_ORDERED_LIST:
+ return list_compare_ordered(expected, actual)
+ elif q_format == TEXT_FORMAT_DICT:
+ return dict_compare(expected, actual)
+ elif q_format == TEXT_FORMAT_SPECIAL_ORDERED_LIST:
+ return list_compare_special(expected, actual)
+ elif q_format == TEXT_FORMAT_NAMEDTUPLE:
+ return namedtuple_compare(expected, actual)
+ elif q_format == PNG_FORMAT_SCATTER:
+ return compare_flip_dicts(expected, actual)
+ elif q_format == HTML_FORMAT:
+ return compare_cell_html(expected, actual)
+ elif q_format == FILE_JSON_FORMAT:
+ return compare_json(expected, actual)
+ else:
+ if expected != actual:
+ return "expected %s but found %s " % (repr(expected), repr(actual))
+ except:
+ if expected != actual:
+ return "expected %s" % (repr(expected))
+ return PASS
+
+
+def print_message(expected, actual, complete_msg=True):
+ """print_message(expected, actual) displays a simple error message."""
+ msg = "expected %s" % (repr(expected))
+ if complete_msg:
+ msg = msg + " but found %s" % (repr(actual))
+ return msg
+
+
+def simple_compare(expected, actual, complete_msg=True):
+ """simple_compare(expected, actual) is used to compare when the expected answer
+ is a type/Nones/str/int/float/bool. When the expected answer is a float,
+ the actual answer is allowed to be within the tolerance limit. Otherwise,
+ the values must match exactly, or a very simple error message is displayed."""
+ msg = PASS
+ if 'numpy' in repr(type((actual))):
+ actual = actual.item()
+ if isinstance(expected, type):
+ if expected != actual:
+ if isinstance(actual, type):
+ msg = "expected %s but found %s" % (expected.__name__, actual.__name__)
+ else:
+ msg = "expected %s but found %s" % (expected.__name__, repr(actual))
+ elif not isinstance(actual, type(expected)):
+ if not (isinstance(expected, (float, int)) and isinstance(actual, (float, int))):
+ if not is_namedtuple(expected):
+ msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+ elif isinstance(expected, float):
+ if not math.isclose(actual, expected, rel_tol=REL_TOL, abs_tol=ABS_TOL):
+ msg = print_message(expected, actual, complete_msg)
+ elif isinstance(expected, (list, tuple)) or is_namedtuple(expected):
+ new_msg = print_message(expected, actual, complete_msg)
+ if len(expected) != len(actual):
+ return new_msg
+ for i in range(len(expected)):
+ val = simple_compare(expected[i], actual[i])
+ if val != PASS:
+ return new_msg
+ elif isinstance(expected, dict):
+ new_msg = print_message(expected, actual, complete_msg)
+ if len(expected) != len(actual):
+ return new_msg
+ val = simple_compare(sorted(list(expected.keys())), sorted(list(actual.keys())))
+ if val != PASS:
+ return new_msg
+ for key in expected:
+ val = simple_compare(expected[key], actual[key])
+ if val != PASS:
+ return new_msg
+ else:
+ if expected != actual:
+ msg = print_message(expected, actual, complete_msg)
+ return msg
+
+
+def intelligent_compare(expected, actual, obj=None):
+ """intelligent_compare(expected, actual) is used to compare when the
+ data type of the expected answer is not known for certain, and default
+ assumptions need to be made."""
+ if obj == None:
+ obj = type(expected).__name__
+ if is_namedtuple(expected):
+ msg = namedtuple_compare(expected, actual)
+ elif isinstance(expected, (list, tuple)):
+ msg = list_compare_ordered(expected, actual, obj)
+ elif isinstance(expected, set):
+ msg = list_compare_unordered(expected, actual, obj)
+ elif isinstance(expected, (dict)):
+ msg = dict_compare(expected, actual)
+ else:
+ msg = simple_compare(expected, actual)
+ msg = msg.replace("CompDict", "dict").replace("CompSet", "set").replace("NewNone", "None")
+ return msg
+
+
+def is_namedtuple(obj, init_check=True):
+ """is_namedtuple(obj) returns True if `obj` is a namedtuple object
+ defined in the test file."""
+ bases = type(obj).__bases__
+ if len(bases) != 1 or bases[0] != tuple:
+ return False
+ fields = getattr(type(obj), '_fields', None)
+ if not isinstance(fields, tuple):
+ return False
+ if init_check and not type(obj).__name__ in [nt.__name__ for nt in _expected_namedtuples]:
+ return False
+ return True
+
+
+def list_compare_ordered(expected, actual, obj=None):
+ """list_compare_ordered(expected, actual) is used to compare when the
+ expected answer is a list/tuple, where the order of the elements matters."""
+ msg = PASS
+ if not isinstance(actual, type(expected)):
+ msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+ return msg
+ if obj == None:
+ obj = type(expected).__name__
+ for i in range(len(expected)):
+ if i >= len(actual):
+ msg = "at index %d of the %s, expected missing %s" % (i, obj, repr(expected[i]))
+ break
+ val = intelligent_compare(expected[i], actual[i], "sub" + obj)
+ if val != PASS:
+ msg = "at index %d of the %s, " % (i, obj) + val
+ break
+ if len(actual) > len(expected) and msg == PASS:
+ msg = "at index %d of the %s, found unexpected %s" % (len(expected), obj, repr(actual[len(expected)]))
+ if len(expected) != len(actual):
+ msg = msg + " (found %d entries in %s, but expected %d)" % (len(actual), obj, len(expected))
+
+ if len(expected) > 0:
+ try:
+ if msg != PASS and list_compare_unordered(expected, actual, obj) == PASS:
+ msg = msg + " (%s may not be ordered as required)" % (obj)
+ except:
+ pass
+ return msg
+
+
+def list_compare_helper(larger, smaller):
+ """list_compare_helper(larger, smaller) is a helper function which takes in
+ two lists of possibly unequal sizes and finds the item that is not present
+ in the smaller list, if there is such an element."""
+ msg = PASS
+ j = 0
+ for i in range(len(larger)):
+ if i == len(smaller):
+ msg = "expected %s" % (repr(larger[i]))
+ break
+ found = False
+ while not found:
+ if j == len(smaller):
+ val = simple_compare(larger[i], smaller[j - 1], complete_msg=False)
+ break
+ val = simple_compare(larger[i], smaller[j], complete_msg=False)
+ j += 1
+ if val == PASS:
+ found = True
+ break
+ if not found:
+ msg = val
+ break
+ return msg
+
+class NewNone():
+ """alternate class in place of None, which allows for comparison with
+ all other data types."""
+ def __str__(self):
+ return 'None'
+ def __repr__(self):
+ return 'None'
+ def __lt__(self, other):
+ return True
+ def __le__(self, other):
+ return True
+ def __gt__(self, other):
+ return False
+ def __ge__(self, other):
+ return other == None
+ def __eq__(self, other):
+ return other == None
+ def __ne__(self, other):
+ return other != None
+
+class CompDict(dict):
+ """subclass of dict, which allows for comparison with other dicts."""
+ def __init__(self, vals):
+ super(self.__class__, self).__init__(vals)
+ if type(vals) == CompDict:
+ self.val = vals.val
+ elif isinstance(vals, dict):
+ self.val = self.get_equiv(vals)
+ else:
+ raise TypeError("'%s' object cannot be type casted to CompDict class" % type(vals).__name__)
+
+ def get_equiv(self, vals):
+ val = []
+ for key in sorted(list(vals.keys())):
+ val.append((key, vals[key]))
+ return val
+
+ def __str__(self):
+ return str(dict(self.val))
+ def __repr__(self):
+ return repr(dict(self.val))
+ def __lt__(self, other):
+ return self.val < CompDict(other).val
+ def __le__(self, other):
+ return self.val <= CompDict(other).val
+ def __gt__(self, other):
+ return self.val > CompDict(other).val
+ def __ge__(self, other):
+ return self.val >= CompDict(other).val
+ def __eq__(self, other):
+ return self.val == CompDict(other).val
+ def __ne__(self, other):
+ return self.val != CompDict(other).val
+
+class CompSet(set):
+ """subclass of set, which allows for comparison with other sets."""
+ def __init__(self, vals):
+ super(self.__class__, self).__init__(vals)
+ if type(vals) == CompSet:
+ self.val = vals.val
+ elif isinstance(vals, set):
+ self.val = self.get_equiv(vals)
+ else:
+ raise TypeError("'%s' object cannot be type casted to CompSet class" % type(vals).__name__)
+
+ def get_equiv(self, vals):
+ return sorted(list(vals))
+
+ def __str__(self):
+ return str(set(self.val))
+ def __repr__(self):
+ return repr(set(self.val))
+ def __getitem__(self, index):
+ return self.val[index]
+ def __lt__(self, other):
+ return self.val < CompSet(other).val
+ def __le__(self, other):
+ return self.val <= CompSet(other).val
+ def __gt__(self, other):
+ return self.val > CompSet(other).val
+ def __ge__(self, other):
+ return self.val >= CompSet(other).val
+ def __eq__(self, other):
+ return self.val == CompSet(other).val
+ def __ne__(self, other):
+ return self.val != CompSet(other).val
+
+def make_sortable(item):
+ """make_sortable(item) replaces all Nones in `item` with an alternate
+ class that allows for comparison with str/int/float/bool/list/set/tuple/dict.
+ It also replaces all dicts (and sets) with a subclass that allows for
+ comparison with other dicts (and sets)."""
+ if item == None:
+ return NewNone()
+ elif isinstance(item, (type, str, int, float, bool)):
+ return item
+ elif isinstance(item, (list, set, tuple)):
+ new_item = []
+ for subitem in item:
+ new_item.append(make_sortable(subitem))
+ if is_namedtuple(item):
+ return type(item)(*new_item)
+ elif isinstance(item, set):
+ return CompSet(new_item)
+ else:
+ return type(item)(new_item)
+ elif isinstance(item, dict):
+ new_item = {}
+ for key in item:
+ new_item[key] = make_sortable(item[key])
+ return CompDict(new_item)
+ return item
+
+def list_compare_unordered(expected, actual, obj=None):
+ """list_compare_unordered(expected, actual) is used to compare when the
+ expected answer is a list/set where the order of the elements does not matter."""
+ msg = PASS
+ if not isinstance(actual, type(expected)):
+ msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+ return msg
+ if obj == None:
+ obj = type(expected).__name__
+
+ try:
+ sort_expected = sorted(make_sortable(expected))
+ sort_actual = sorted(make_sortable(actual))
+ except:
+ return "unexpected datatype found in %s; expected entries of type %s" % (obj, obj, type(expected[0]).__name__)
+
+ if len(actual) == 0 and len(expected) > 0:
+ msg = "in the %s, missing " % (obj) + sort_expected[0]
+ elif len(actual) > 0 and len(expected) > 0:
+ val = intelligent_compare(sort_expected[0], sort_actual[0])
+ if val.startswith("expected to find type"):
+ msg = "in the %s, " % (obj) + simple_compare(sort_expected[0], sort_actual[0])
+ else:
+ if len(expected) > len(actual):
+ msg = "in the %s, missing " % (obj) + list_compare_helper(sort_expected, sort_actual)
+ elif len(expected) < len(actual):
+ msg = "in the %s, found un" % (obj) + list_compare_helper(sort_actual, sort_expected)
+ if len(expected) != len(actual):
+ msg = msg + " (found %d entries in %s, but expected %d)" % (len(actual), obj, len(expected))
+ return msg
+ else:
+ val = list_compare_helper(sort_expected, sort_actual)
+ if val != PASS:
+ msg = "in the %s, missing " % (obj) + val + ", but found un" + list_compare_helper(sort_actual,
+ sort_expected)
+ return msg
+
+
+def namedtuple_compare(expected, actual):
+ """namedtuple_compare(expected, actual) is used to compare when the
+ expected answer is a namedtuple defined in the test file."""
+ msg = PASS
+ if not is_namedtuple(actual, False):
+ msg = "expected namedtuple but found %s" % (type(actual).__name__)
+ return msg
+ if type(expected).__name__ != type(actual).__name__:
+ return "expected namedtuple %s but found namedtuple %s" % (type(expected).__name__, type(actual).__name__)
+ expected_fields = expected._fields
+ actual_fields = actual._fields
+ msg = list_compare_ordered(list(expected_fields), list(actual_fields), "namedtuple attributes")
+ if msg != PASS:
+ return msg
+ for field in expected_fields:
+ val = intelligent_compare(getattr(expected, field), getattr(actual, field))
+ if val != PASS:
+ msg = "at attribute %s of namedtuple %s, " % (field, type(expected).__name__) + val
+ return msg
+ return msg
+
+
+def clean_slashes(item):
+ """clean_slashes()"""
+ if isinstance(item, str):
+ return item.replace("\\", "/").replace("/", os.path.sep)
+ elif item == None or isinstance(item, (type, int, float, bool)):
+ return item
+ elif isinstance(item, (list, tuple, set)) or is_namedtuple(item):
+ new_item = []
+ for subitem in item:
+ new_item.append(clean_slashes(subitem))
+ if is_namedtuple(item):
+ return type(item)(*new_item)
+ else:
+ return type(item)(new_item)
+ elif isinstance(item, dict):
+ new_item = {}
+ for key in item:
+ new_item[clean_slashes(key)] = clean_slashes(item[key])
+ return item
+
+
+def list_compare_special_initialize(special_expected):
+ """list_compare_special_initialize(special_expected) takes in the special
+ ordering stored as a sorted list of items, and returns a list of lists
+ where the ordering among the inner lists does not matter."""
+ latest_val = None
+ clean_special = []
+ for row in special_expected:
+ if latest_val == None or row[1] != latest_val:
+ clean_special.append([])
+ latest_val = row[1]
+ clean_special[-1].append(row[0])
+ return clean_special
+
+
+def list_compare_special(special_expected, actual):
+ """list_compare_special(special_expected, actual) is used to compare when the
+ expected answer is a list with special ordering defined in `special_expected`."""
+ msg = PASS
+ expected_list = []
+ special_order = list_compare_special_initialize(special_expected)
+ for expected_item in special_order:
+ expected_list.extend(expected_item)
+ val = list_compare_unordered(expected_list, actual)
+ if val != PASS:
+ return val
+ i = 0
+ for expected_item in special_order:
+ j = len(expected_item)
+ actual_item = actual[i: i + j]
+ val = list_compare_unordered(expected_item, actual_item)
+ if val != PASS:
+ if j == 1:
+ msg = "at index %d " % (i) + val
+ else:
+ msg = "between indices %d and %d " % (i, i + j - 1) + val
+ msg = msg + " (list may not be ordered as required)"
+ break
+ i += j
+ return msg
+
+
+def dict_compare(expected, actual, obj=None):
+ """dict_compare(expected, actual) is used to compare when the expected answer
+ is a dict."""
+ msg = PASS
+ if not isinstance(actual, type(expected)):
+ msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+ return msg
+ if obj == None:
+ obj = type(expected).__name__
+
+ expected_keys = list(expected.keys())
+ actual_keys = list(actual.keys())
+ val = list_compare_unordered(expected_keys, actual_keys, obj)
+
+ if val != PASS:
+ msg = "bad keys in %s: " % (obj) + val
+ if msg == PASS:
+ for key in expected:
+ new_obj = None
+ if isinstance(expected[key], (list, tuple, set)):
+ new_obj = 'value'
+ elif isinstance(expected[key], dict):
+ new_obj = 'sub' + obj
+ val = intelligent_compare(expected[key], actual[key], new_obj)
+ if val != PASS:
+ msg = "incorrect value for key %s in %s: " % (repr(key), obj) + val
+ return msg
+
+
+def is_flippable(item):
+ """is_flippable(item) determines if the given dict of lists has lists of the
+ same length and is therefore flippable."""
+ item_lens = set(([str(len(item[key])) for key in item]))
+ if len(item_lens) == 1:
+ return PASS
+ else:
+ return "found lists of lengths %s" % (", ".join(list(item_lens)))
+
+def flip_dict_of_lists(item):
+ """flip_dict_of_lists(item) flips a dict of lists into a list of dicts if the
+ lists are of same length."""
+ new_item = []
+ length = len(list(item.values())[0])
+ for i in range(length):
+ new_dict = {}
+ for key in item:
+ new_dict[key] = item[key][i]
+ new_item.append(new_dict)
+ return new_item
+
+def compare_flip_dicts(expected, actual, obj="lists"):
+ """compare_flip_dicts(expected, actual) flips a dict of lists (or dicts) into
+ a list of dicts (or dict of dicts) and then compares the list ignoring order."""
+ msg = PASS
+ example_item = list(expected.values())[0]
+ if isinstance(example_item, (list, tuple)):
+ val = is_flippable(actual)
+ if val != PASS:
+ msg = "expected to find lists of length %d, but " % (len(example_item)) + val
+ return msg
+ msg = list_compare_unordered(flip_dict_of_lists(expected), flip_dict_of_lists(actual), "lists")
+ elif isinstance(example_item, dict):
+ expected_keys = list(example_item.keys())
+ for key in actual:
+ val = list_compare_unordered(expected_keys, list(actual[key].keys()), "dictionary %s" % key)
+ if val != PASS:
+ return val
+ for cat_key in expected_keys:
+ expected_category = {}
+ actual_category = {}
+ for key in expected:
+ expected_category[key] = expected[key][cat_key]
+ actual_category[key] = actual[key][cat_key]
+ val = list_compare_unordered(flip_dict_of_lists(expected), flip_dict_of_lists(actual), "category " + repr(cat_key))
+ if val != PASS:
+ return val
+ return msg
+
+
+def get_expected_tables():
+ """get_expected_tables() reads the html file with the expected DataFrames
+ and returns a dict mapping each question to a html table."""
+ if not os.path.exists(DF_FILE):
+ return None
+
+ expected_tables = {}
+ f = open(DF_FILE, encoding='utf-8')
+ soup = BeautifulSoup(f.read(), 'html.parser')
+ f.close()
+
+ tables = soup.find_all('table')
+ for table in tables:
+ expected_tables[table.get("data-question")] = table
+
+ return expected_tables
+
+def parse_df_html_table(table):
+ """parse_df_html_table(table) takes in a table as a html string and returns
+ a dict mapping each row and column index to the value at that position."""
+ rows = []
+ for tr in table.find_all('tr'):
+ rows.append([])
+ for cell in tr.find_all(['td', 'th']):
+ rows[-1].append(cell.get_text().strip("\n "))
+
+ cells = {}
+ for r in range(1, len(rows)):
+ for c in range(1, len(rows[0])):
+ rname = rows[r][0]
+ cname = rows[0][c]
+ cells[(rname,cname)] = rows[r][c]
+ return cells
+
+
+def get_expected_namedtuples():
+ """get_expected_namedtuples() defines the required namedtuple objects
+ globally. It also returns a tuple of the classes."""
+ expected_namedtuples = []
+
+ global Star
+ star_attributes = ['spectral_type', 'stellar_effective_temperature', 'stellar_radius', 'stellar_mass', 'stellar_luminosity', 'stellar_surface_gravity', 'stellar_age']
+ Star = namedtuple('Star', star_attributes)
+ expected_namedtuples.append(Star)
+ global Planet
+ planets_attributes = ['planet_name', 'host_name', 'discovery_method', 'discovery_year', 'controversial_flag', 'orbital_period', 'planet_radius', 'planet_mass', 'semi_major_radius', 'eccentricity', 'equilibrium_temperature', 'insolation_flux']
+ Planet = namedtuple('Planet', planets_attributes)
+ expected_namedtuples.append(Planet)
+ return tuple(expected_namedtuples)
+
+_expected_namedtuples = get_expected_namedtuples()
+
+
+def compare_cell_html(expected, actual):
+ """compare_cell_html(expected, actual) is used to compare when the
+ expected answer is a DataFrame stored in the `expected_dfs` html file."""
+ expected_cells = parse_df_html_table(expected)
+ try:
+ actual_cells = parse_df_html_table(BeautifulSoup(actual, 'html.parser').find('table'))
+ except Exception as e:
+ return "expected to find type DataFrame but found type %s instead" % type(actual).__name__
+
+ expected_cols = list(set(["column %s" % (loc[1]) for loc in expected_cells]))
+ actual_cols = list(set(["column %s" % (loc[1]) for loc in actual_cells]))
+ msg = list_compare_unordered(expected_cols, actual_cols, "DataFrame")
+ if msg != PASS:
+ return msg
+
+ expected_rows = list(set(["row index %s" % (loc[0]) for loc in expected_cells]))
+ actual_rows = list(set(["row index %s" % (loc[0]) for loc in actual_cells]))
+ msg = list_compare_unordered(expected_rows, actual_rows, "DataFrame")
+ if msg != PASS:
+ return msg
+
+ for location, expected in expected_cells.items():
+ location_name = "column {} at index {}".format(location[1], location[0])
+ actual = actual_cells.get(location, None)
+ if actual == None:
+ return "in %s, expected to find %s" % (location_name, repr(expected))
+ try:
+ actual_ans = float(actual)
+ expected_ans = float(expected)
+ if math.isnan(actual_ans) and math.isnan(expected_ans):
+ continue
+ except Exception as e:
+ actual_ans, expected_ans = actual, expected
+ msg = simple_compare(expected_ans, actual_ans)
+ if msg != PASS:
+ return "in %s, " % location_name + msg
+ return PASS
+
+
+def get_expected_plots():
+ """get_expected_plots() reads the json file with the expected plot data
+ and returns a dict mapping each question to a dictionary with the plots data."""
+ if not os.path.exists(PLOT_FILE):
+ return None
+
+ f = open(PLOT_FILE, encoding='utf-8')
+ expected_plots = json.load(f)
+ f.close()
+ return expected_plots
+
+
+def compare_file_json(expected, actual):
+ """compare_file_json(expected, actual) is used to compare when the
+ expected answer is a JSON file."""
+ msg = PASS
+ if not os.path.isfile(expected):
+ return "file %s not found; make sure it is downloaded and stored in the correct directory" % (expected)
+ elif not os.path.isfile(actual):
+ return "file %s not found; make sure that you have created the file with the correct name" % (actual)
+ try:
+ e = open(expected, encoding='utf-8')
+ expected_data = json.load(e)
+ e.close()
+ except json.JSONDecodeError:
+ return "file %s is broken and cannot be parsed; please delete and redownload the file correctly" % (expected)
+ try:
+ a = open(actual, encoding='utf-8')
+ actual_data = json.load(a)
+ a.close()
+ except json.JSONDecodeError:
+ return "file %s is broken and cannot be parsed" % (actual)
+ if type(expected_data) == list:
+ msg = list_compare_ordered(expected_data, actual_data, 'file ' + actual)
+ elif type(expected_data) == dict:
+ msg = dict_compare(expected_data, actual_data)
+ return msg
+
+
+_expected_json = get_expected_json()
+_special_json = get_special_json()
+_expected_plots = get_expected_plots()
+_expected_tables = get_expected_tables()
+_expected_format = get_expected_format()
+
+def check(qnum, actual):
+ """check(qnum, actual) is used to check if the answer in the notebook is
+ the correct answer, and provide useful feedback if the answer is incorrect."""
+ msg = PASS
+ error_msg = "<b style='color: red;'>ERROR:</b> "
+ q_format = _expected_format[qnum]
+
+ if q_format == TEXT_FORMAT_SPECIAL_ORDERED_LIST:
+ expected = _special_json[qnum]
+ elif q_format == PNG_FORMAT_SCATTER:
+ if _expected_plots == None:
+ msg = error_msg + "file %s not parsed; make sure it is downloaded and stored in the correct directory" % (PLOT_FILE)
+ else:
+ expected = _expected_plots[qnum]
+ elif q_format == HTML_FORMAT:
+ if _expected_tables == None:
+ msg = error_msg + "file %s not parsed; make sure it is downloaded and stored in the correct directory" % (DF_FILE)
+ else:
+ expected = _expected_tables[qnum]
+ else:
+ expected = _expected_json[qnum]
+
+ if SLASHES in q_format:
+ q_format = q_format.replace(SLASHES, "").strip("_ ")
+ expected = clean_slashes(expected)
+ actual = clean_slashes(actual)
+
+ if msg != PASS:
+ print(msg)
+ else:
+ msg = compare(expected, actual, q_format)
+ if msg != PASS:
+ msg = error_msg + msg
+ print(msg)
+
+
+def check_file_size(path):
+ """check_file_size(path) throws an error if the file is too big to display
+ on Gradescope."""
+ size = os.path.getsize(path)
+ assert size < MAX_FILE_SIZE * 10**3, "Your file is too big to be displayed by Gradescope; please delete unnecessary output cells so your file size is < %s KB" % MAX_FILE_SIZE
+
+
+def reset_hidden_tests():
+ """reset_hidden_tests() resets all hidden tests on the Gradescope autograder where the hidden test file exists"""
+ if not os.path.exists(HIDDEN_FILE):
+ return
+ hidn.reset_hidden_tests()
+
+def rubric_check(rubric_point, ignore_past_errors=True):
+ """rubric_check(rubric_point) uses the hidden test file on the Gradescope autograder to grade the `rubric_point`"""
+ if not os.path.exists(HIDDEN_FILE):
+ print(PASS)
+ return
+ error_msg_1 = "ERROR: "
+ error_msg_2 = "TEST DETAILS: "
+ try:
+ msg = hidn.rubric_check(rubric_point, ignore_past_errors)
+ except:
+ msg = "hidden tests crashed before execution"
+ if msg != PASS:
+ hidn.make_deductions(rubric_point)
+ if msg == "public tests failed":
+ comment = "The public tests have failed, so you will not receive any points for this question."
+ comment += "\nPlease confirm that the public tests pass locally before submitting."
+ elif msg == "answer is hardcoded":
+ comment = "In the datasets for testing hardcoding, all numbers are replaced with random values."
+ comment += "\nIf the answer is the same as in the original dataset for all these datasets"
+ comment += "\ndespite this, that implies that the answer in the notebook is hardcoded."
+ comment += "\nYou will not receive any points for this question."
+ else:
+ comment = hidn.get_comment(rubric_point)
+ msg = error_msg_1 + msg
+ if comment != "":
+ msg = msg + "\n" + error_msg_2 + comment
+ print(msg)
+
+def get_summary():
+ """get_summary() returns the summary of the notebook using the hidden test file on the Gradescope autograder"""
+ if not os.path.exists(HIDDEN_FILE):
+ print("Total Score: %d/%d" % (TOTAL_SCORE, TOTAL_SCORE))
+ return
+ score = min(TOTAL_SCORE, hidn.get_score(TOTAL_SCORE))
+ display_msg = "Total Score: %d/%d" % (score, TOTAL_SCORE)
+ if score != TOTAL_SCORE:
+ display_msg += "\n" + hidn.get_deduction_string()
+ print(display_msg)
+
+def get_score_digit(digit):
+ """get_score_digit(digit) returns the `digit` of the score using the hidden test file on the Gradescope autograder"""
+ if not os.path.exists(HIDDEN_FILE):
+ score = TOTAL_SCORE
+ else:
+ score = hidn.get_score(TOTAL_SCORE)
+ digits = bin(score)[2:]
+ digits = "0"*(7 - len(digits)) + digits
+ return int(digits[6 - digit])
diff --git a/p11/rubric.md b/p11/rubric.md
new file mode 100644
index 0000000..d8a664c
--- /dev/null
+++ b/p11/rubric.md
@@ -0,0 +1,150 @@
+# Project 10 (P10) grading rubric
+
+## Code reviews
+
+- The Gradescope autograder will make deductions based on the rubric provided below.
+- To ensure that you don't lose any points, you must review the rubric and make sure that you have followed the instructions provided in the project correctly.
+
+## Rubric
+
+### General guidelines:
+
+- Outputs not visible/did not save the notebook file prior to running the cell containing "export". We cannot see your output if you do not save before generating the zip file. (-3)
+- Used concepts/modules such as `csv.DictReader`, `os.walk`, and `pandas` not covered in class yet. Note that built-in functions that you have been introduced to can be used. (-3)
+- Large outputs such as `stars_dict` or `planets_list` are displayed in the notebook. (-3)
+- Import statements are not mentioned in the required cell at the top of the notebook. (-1)
+
+### Question specific guidelines:
+
+- q1 (3)
+ - incorrect logic is used to answer (-1)
+ - `planets_list` data structure is not used to answer (-1)
+
+- q2 (3)
+ - incorrect logic is used to answer (-1)
+ - recomputed variable defined in Question 1 (-1)
+
+- q3 (3)
+ - incorrect logic is used to answer (-1)
+ - recomputed variable defined in Question 1 (-1)
+ - `stars_dict` data structure is not used to answer (-1)
+
+- q4 (5)
+ - incorrect logic is used to answer (-1)
+ - `planets_list` data structure is not used to answer (-1)
+ - plot is incorrect (-1)
+ - plot is not properly labeled (-1)
+
+- q5 (5)
+ - incorrect comparison operator is used (-1)
+ - `planets_list` data structure is not used to answer (-1)
+ - plot is incorrect (-1)
+ - plot is not properly labeled (-1)
+
+- `star_classes` (3)
+ - data structure is defined incorrectly (-1)
+ - incorrect comparison operators are used (-1)
+ - `stars_dict` data structure is not used (-1)
+
+- q6 (3)
+ - did not ignore the `Star` objects with missing `stellar_luminosity` data (-1)
+ - incorrect logic is used to answer (-1)
+ - `star_classes` data structure is not used to answer (-1)
+
+- q7 (5)
+ - incorrect logic is used to answer (-1)
+ - `star_classes` data structure is not used to answer (-1)
+ - plot is incorrect (-1)
+ - plot is not properly labeled (-1)
+
+- q8 (5)
+ - incorrect comparison operator is used (-1)
+ - `star_classes` data structure is not used to answer (-1)
+ - plot is incorrect (-1)
+ - plot is not properly labeled (-1)
+
+- q9 (5)
+ - incorrect logic is used to answer (-1)
+ - `stars_dict` data structure is not used to answer (-1)
+ - plot is incorrect (-1)
+ - plot is not properly labeled (-1)
+
+- `get_all_paths_in` (6)
+ - function does not remove all files and directories that start with `.` (-1)
+ - function does not sort file names explicitly (-1)
+ - function logic is incorrect (-2)
+ - hardcoding the name of directory inside the function (-1)
+ - paths are hardcoded using slashes (-1)
+
+- q10 (3)
+ - `get_all_paths_in` function is not used to answer (-2)
+ - paths are hardcoded using slashes (-1)
+
+- q11 (3)
+ - `get_all_paths_in` function is not used to answer (-2)
+ - paths are hardcoded using slashes (-1)
+
+- q12 (3)
+ - `get_all_paths_in` function is not used to answer (-2)
+ - paths are hardcoded using slashes (-1)
+
+- q13 (3)
+ - `get_all_paths_in` function is not used to answer (-2)
+
+- `all_planets_list` (5)
+ - data structure is defined incorrectly (-1)
+ - `get_planets` function is not used to answer (-1)
+ - `broken_data` data structure is not used to answer (-1)
+ - paths are hardcoded using slashes (-1)
+ - `planets_list` data structure is modified or redefined (-1)
+
+- `get_surface_gravity` (3)
+ - function did not return `None` for missing data (-1)
+ - function logic is incorrect (-2)
+
+- q14 (3)
+ - `get_surface_gravity` function is not used to answer (-1)
+ - `all_planets_list` data structure is not used to answer (-1)
+ - did not exit loop and instead iterated further after finding the answer (-1)
+
+- `get_distances_to_star` (3)
+ - function did not return `None` for missing data (-1)
+ - function logic is incorrect (-2)
+
+- q15 (3)
+ - `get_distances_to_star` function is not used to answer (-1)
+ - `all_planets_list` data structure is not used to answer (-1)
+ - did not exit loop and instead iterated further after finding the answer (-1)
+
+- `get_liquid_water_distances` (3)
+ - function did not return `None` for missing data (-1)
+ - function logic is incorrect (-2)
+
+- q16 (3)
+ - `get_liquid_water_distances` function is not used to answer (-1)
+ - `all_planets_list` data structure is not used to answer (-1)
+ - did not exit loop and instead iterated further after finding the answer (-1)
+
+- q17 (4)
+ - incorrect logic is used to answer (-1)
+ - `get_liquid_water_distances` function is not used to answer (-1)
+ - `all_planets_list` data structure is not used to answer (-1)
+
+- `get_surface_temperatures` (3)
+ - function did not return `None` for missing data (-1)
+ - function logic is incorrect (-2)
+
+- q18 (3)
+ - `get_surface_temperatures` function is not used to answer (-1)
+ - `all_planets_list` data structure is not used to answer (-1)
+ - did not exit loop and instead iterated further after finding the answer (-1)
+
+- q19 (4)
+ - incorrect comparison operators are used (-1)
+ - `get_surface_temperatures` function is not used to answer (-1)
+ - `all_planets_list` data structure is not used to answer (-1)
+
+- q20 (5)
+ - incorrect logic is used to answer (-2)
+ - `get_surface_gravity`, `get_distances_to_star`, `get_liquid_water_distances`, and `get_surface_temperatures` functions are not used to answer (-1)
+ - `all_planets_list` data structure is not used to answer (-1)
--
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