diff --git a/sum23/labs/lab11/README.md b/sum23/labs/lab11/README.md
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+# Lab 11: Scatter Plots and Recursion
+
+In this lab, you'll get practice with creating scatter plots and defining recursive functions, in preparation for P11.
+
+-----------------------------
+## Corrections/Clarifications
+
+
+**Find any issues?** Let Jane know during lab or create a post on Piazza
+
+------------------------------
+## Learning Objectives
+
+In this lab, you will practice...
+* creating scatter plots,
+* defining recursive functions.
+
+------------------------------
+
+## Note on Academic Misconduct
+
+You may do these lab exercises with only your project partner; you are not allowed to start
+working on Lab 11 with one person, then do the project with a different partner. Now may be a
+good time to review [our course policies](https://canvas.wisc.edu/courses/355767/pages/syllabus?module_item_id=6048035).
+
+**Important:** P10 and P11 are two parts of the same data analysis.
+You **cannot** switch project partners between these two projects.
+If you partnered up with someone for P10, you have to sustain that partnership until the end of P11.
+
+------------------------------
+
+## Segment 1: Setup
+
+Create a `lab11` directory and download the following files into the `lab11` directory.
+
+* `sample_data.zip`
+* `planets_small.json`
+* `practice.ipynb`
+* `practice_test.py`
+* `practice_plots.json`
+
+After downloading `sample_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 `sample_data`. You may delete `sample_data.zip` after extracting the files inside it.
+
+**Important**: You **must** make sure that the `sample_data` directory is in the same directory as `practice.ipynb`. Your file structure **must** look like this:
+
+```
++-- practice.ipynb
++-- planets_small.json
++-- practice_test.py
++-- practice_plots.json
++-- sample_data
+| +-- .DS_Store
+| +-- file_1.json
+| +-- sample_1
+| | +-- .ipynb_checkpoints
+| | +-- file_2.json
+| | +-- file_3.json
+| +-- sample_2
+| | +-- file_4.json
+| | +-- sample_3
+| | | +-- .DS_Store
+| | | +-- file_5.json
+```
+
+
+## Segment 2:
+For the remaining segments, detailed instructions are provided in `practice.ipynb`. From the terminal, open a `jupyter notebook` session, open your `practice.ipynb`, and follow the instructions in `practice.ipynb`.
+
+## Project 11
+
+You can now get started with [P11](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/sum23/projects/p11). **You may copy/paste any code created here in project P11**. Remember to work on P11 with only your partner from this point on. Have fun!
diff --git a/sum23/labs/lab11/images/README.md b/sum23/labs/lab11/images/README.md
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+# Images
+
+Images from lab11 are stored here.
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diff --git a/sum23/labs/lab11/planets_small.json b/sum23/labs/lab11/planets_small.json
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--- /dev/null
+++ b/sum23/labs/lab11/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,
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+ "orbital_period": 18.772866,
+ "planet_radius": 0.755,
+ "planet_mass": 0.326,
+ "semi_major_radius": 0.06189,
+ "eccentricity": 0.00567,
+ "equilibrium_temperature": 172.0,
+ "insolation_flux": 0.14
+ },
+ {
+ "planet_name": "V1298 Tau b",
+ "host_name": "V1298 Tau",
+ "discovery_method": "Transit",
+ "discovery_year": 2019,
+ "controversial_flag": false,
+ "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/sum23/labs/lab11/practice.ipynb b/sum23/labs/lab11/practice.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3d7ab4d6ca14264af7796bf30867a12947fbd270
--- /dev/null
+++ b/sum23/labs/lab11/practice.ipynb
@@ -0,0 +1,2431 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "704cb078",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "# Initialize Otter\n",
+ "import otter\n",
+ "grader = otter.Notebook(\"practice.ipynb\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0f868c7d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import practice_test"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa8de0e",
+ "metadata": {},
+ "source": [
+ "# Lab 11: Scatter Plots and Recursion\n",
+ "\n",
+ "In this lab, you will practice...\n",
+ "* creating scatter plots,\n",
+ "* defining recursive functions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3be8c03d",
+ "metadata": {},
+ "source": [
+ "## Note on Academic Misconduct:\n",
+ "\n",
+ "**Important:** P10 and P11 are two parts of the same data analysis.\n",
+ "You **cannot** switch project partners between these two projects.\n",
+ "If you partnered up with someone for P10, you have to sustain that partnership until the end of P11.\n",
+ "\n",
+ "You may do these lab exercises with only your project partner; you are not allowed to start\n",
+ "working on Lab 11 with one person, then do the project with a different partner. Now may be a\n",
+ "good time to review [our course policies](https://canvas.wisc.edu/courses/355767/pages/syllabus?module_item_id=6048035)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0aad4e22",
+ "metadata": {},
+ "source": [
+ "## Setup:\n",
+ "\n",
+ "Before proceeding much further, download `practice_test.py`, `practice_plots.json`, and `planets_small.json` and store them in the same directory as `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 `practice.ipynb`, `practice_test.py`, `planets_small.json` and `practice_plots.json`.\n",
+ "\n",
+ "You need to make sure that the project files are stored in the following structure:\n",
+ "\n",
+ "```\n",
+ "+-- practice.ipynb\n",
+ "+-- practice_test.py\n",
+ "+-- practice_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": "b170a3b6",
+ "metadata": {},
+ "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 11, you will practice creating scatter plots on a smaller dataset, and also practice writing some recursive functions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "67fa1127",
+ "metadata": {},
+ "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": "7295eeb6",
+ "metadata": {
+ "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": "fb7b57e0",
+ "metadata": {},
+ "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 11, you will instead be working with a sample dataset. The data we will be analyzing in Lab 11 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 11, 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": "97e0f178",
+ "metadata": {
+ "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": "70f96f6f",
+ "metadata": {
+ "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": "6a2cf5e8",
+ "metadata": {},
+ "source": [
+ "## Segment 1: Scatter Plots\n",
+ "\n",
+ "We will first learn to create simple scatter plots."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "26d5460f",
+ "metadata": {},
+ "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": "d2befcaa",
+ "metadata": {
+ "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": "dc0f1bf8",
+ "metadata": {},
+ "source": [
+ "### Task 1.1: Simple plots with `scatter_plot`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f55b37fd",
+ "metadata": {},
+ "source": [
+ "**Question 1.1:** 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": "db6ff7b8",
+ "metadata": {
+ "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": "60926a75",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q1-1\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d06c1499",
+ "metadata": {},
+ "source": [
+ "Now, **plot** `x` and `y` as **scatter plot**. Your plot should look like this:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c3220bc0",
+ "metadata": {},
+ "source": [
+ "<div><img src=\"attachment:q1-1.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "52c1fcad",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'x' and 'y'\n",
+ "\n",
+ "plot_scatter(..., ...)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ccb7f100",
+ "metadata": {},
+ "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": "a5f89b6e",
+ "metadata": {},
+ "source": [
+ "**Question 1.2:** 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": "05741c6f",
+ "metadata": {
+ "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": "96a32319",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q1-2\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1d7721a2",
+ "metadata": {},
+ "source": [
+ "Now, **plot** `x` and `y` as **scatter plot**. Your plot should look like this:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3c1ed597",
+ "metadata": {},
+ "source": [
+ "<div><img src=\"attachment:q1-2.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "671a35a8",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'x' and 'y'\n",
+ "\n",
+ "plot_scatter(..., ...)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f8e39afb",
+ "metadata": {},
+ "source": [
+ "**Question 2.1:** 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": "14ed1634",
+ "metadata": {
+ "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": "b9d12f76",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q2-1\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c1f2d324",
+ "metadata": {},
+ "source": [
+ "Now, **plot** `x` and `y` as **scatter plot**. Your plot should look like this:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c55390df",
+ "metadata": {},
+ "source": [
+ "<div><img src=\"attachment:q2-1.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9e4e482",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'x' and 'y'\n",
+ "\n",
+ "plot_scatter(..., ...)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "39c397aa",
+ "metadata": {},
+ "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": "459b46a6",
+ "metadata": {},
+ "source": [
+ "**Question 2.2:** 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": "ab839270",
+ "metadata": {
+ "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": "8a801a18",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q2-2\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b29ddb24",
+ "metadata": {},
+ "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:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "30dff6f6",
+ "metadata": {},
+ "source": [
+ "<div><img src=\"attachment:q2-2.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "40100a24",
+ "metadata": {
+ "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": "29474902",
+ "metadata": {},
+ "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": "adf76b5e",
+ "metadata": {},
+ "source": [
+ "**Question 3:** 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": "ad4e9e80",
+ "metadata": {
+ "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": "b24c7664",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q3\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f9576672",
+ "metadata": {},
+ "source": [
+ "**Question 4:** 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": "c48e8b92",
+ "metadata": {
+ "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": "0c81ba51",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q4\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1d0c1529",
+ "metadata": {},
+ "source": [
+ "**Question 5:** 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": "69886e57",
+ "metadata": {
+ "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": "9390fd09",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q5\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "06ca4e28",
+ "metadata": {},
+ "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": "7bc2d98a",
+ "metadata": {
+ "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": "6110189d",
+ "metadata": {},
+ "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": "d5b955d9",
+ "metadata": {},
+ "source": [
+ "**Question 6:** 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": "fad68df6",
+ "metadata": {
+ "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": "bdfc506d",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q6\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bbf4f119",
+ "metadata": {},
+ "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:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3c822f67",
+ "metadata": {},
+ "source": [
+ "<div><img src=\"attachment:q6.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1a65ecba",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'orbital_periods_no_outliers' and 'semi_major_radii_no_outliers'\n",
+ "\n",
+ "plot_scatter(..., ..., ..., ...)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "64b39f3b",
+ "metadata": {},
+ "source": [
+ "**Question 7.1:** 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": "9eb8fb2b",
+ "metadata": {
+ "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": "1c885159",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q7-1\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6d106deb",
+ "metadata": {},
+ "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:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ca63159a",
+ "metadata": {},
+ "source": [
+ "<div><img src=\"attachment:q7-1.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "270c0006",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'transit_planet_masses' and 'transit_planet_radii'\n",
+ "\n",
+ "plot_scatter(..., ..., ..., ...)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f36ca470",
+ "metadata": {},
+ "source": [
+ "**Question 7.2:** 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": "6a90d091",
+ "metadata": {
+ "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": "code",
+ "execution_count": null,
+ "id": "9a40d838",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q7-2\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ef1841c6",
+ "metadata": {},
+ "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:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f6f75122",
+ "metadata": {},
+ "source": [
+ "<div><img src=\"attachment:q7-2.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9974ab4a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'transit_planet_masses' and 'transit_planet_radii'\n",
+ "\n",
+ "plot_scatter(..., ..., ..., ...)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d0dcb803",
+ "metadata": {},
+ "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": "d4a7c26c",
+ "metadata": {
+ "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": "fd1f7bdd",
+ "metadata": {},
+ "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": "bf32e396",
+ "metadata": {},
+ "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": "6feff567",
+ "metadata": {
+ "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": "5c49cbbe",
+ "metadata": {},
+ "source": [
+ "**Question 8**: 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": "77dc3128",
+ "metadata": {
+ "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": "2e125c31",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q8\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9d4c7255",
+ "metadata": {},
+ "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": "cbfdc4af",
+ "metadata": {
+ "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": "ed5590b2",
+ "metadata": {},
+ "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": "1440265f",
+ "metadata": {},
+ "source": [
+ "**Question 9:** 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": "f525b180",
+ "metadata": {
+ "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": "3677d2a2",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q9\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "46ca09ad",
+ "metadata": {},
+ "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:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5937cf27",
+ "metadata": {},
+ "source": [
+ "<div><img src=\"attachment:q9.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5edafc50",
+ "metadata": {
+ "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": "fbb806d3",
+ "metadata": {},
+ "source": [
+ "**Question 10:** 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": "b879185d",
+ "metadata": {
+ "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": "36630fae",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q10\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5b24a9bc",
+ "metadata": {},
+ "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:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fbb79824",
+ "metadata": {},
+ "source": [
+ "<div><img src=\"attachment:q10.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dcd958f2",
+ "metadata": {
+ "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": "bb834775",
+ "metadata": {},
+ "source": [
+ "### Task 1.4: Plotting multiple scatter plots with `plot_scatter_multiple`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7c683076",
+ "metadata": {},
+ "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": "bb21edcb",
+ "metadata": {},
+ "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": "92725b6e",
+ "metadata": {},
+ "source": [
+ "Here is a quick example to see how this function works:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b80c4229",
+ "metadata": {
+ "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": "4e1d5299",
+ "metadata": {},
+ "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": "6372f0ab",
+ "metadata": {},
+ "source": [
+ "**Question 11:** 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": "6e8e89bc",
+ "metadata": {
+ "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": "faba42eb",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q11\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "50c34171",
+ "metadata": {},
+ "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:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d5f51ddb",
+ "metadata": {},
+ "source": [
+ "<div><img src=\"attachment:q11.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cd6b88d4",
+ "metadata": {
+ "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": "ac8f63d4",
+ "metadata": {},
+ "source": [
+ "**Question 12:** 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": "42c45a1c",
+ "metadata": {},
+ "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": "bb6394a8",
+ "metadata": {
+ "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": "f7e39384",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q12\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c8a460a7",
+ "metadata": {},
+ "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:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c7d3c080",
+ "metadata": {},
+ "source": [
+ "<div><img src=\"attachment:q12.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d5f2c82a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'planet_masses' and 'planet_radii'\n",
+ "\n",
+ "plot_scatter_multiple(..., ..., x_label = ..., y_label = ...)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "230003c6",
+ "metadata": {},
+ "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": "6ad5ae57",
+ "metadata": {},
+ "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": "c4ae0c54",
+ "metadata": {},
+ "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": "1e20a407",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# we have done this for you\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": "51d97d8c",
+ "metadata": {},
+ "source": [
+ "**Question 13:** What is the output of `flatten(['rabbit', ['cat'], ['lion', 'chimpanzee']])`?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6fa91e65",
+ "metadata": {
+ "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": "1f30d545",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q13\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d050594c",
+ "metadata": {},
+ "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": "eb7db485",
+ "metadata": {},
+ "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": "3ce98728",
+ "metadata": {},
+ "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": "8953f892",
+ "metadata": {},
+ "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": "eeb2a65c",
+ "metadata": {},
+ "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": "6f15c611",
+ "metadata": {},
+ "source": [
+ "**Question 14:** 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": "fd0e4c57",
+ "metadata": {
+ "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": "f2831123",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q14\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cf53db95",
+ "metadata": {},
+ "source": [
+ "**Question 15:** 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": "b42bd97a",
+ "metadata": {
+ "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": "089379fb",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q15\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f6740cd",
+ "metadata": {},
+ "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": "53843029",
+ "metadata": {},
+ "source": [
+ "**Question 16:** 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 **ascending order**.\n",
+ "\n",
+ "**Hint:** Use `os.listdir` to get the **files** and **sub-directories** in a **directory**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "01d13bf9",
+ "metadata": {
+ "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(\".\")])\n",
+ "\n",
+ "sample_data_contents"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "313ae0d4",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q16\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "529a34fa",
+ "metadata": {},
+ "source": [
+ "**Question 17:** 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": "d71ef362",
+ "metadata": {
+ "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": "9d35a1ed",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q17\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1107130d",
+ "metadata": {},
+ "source": [
+ "**Question 18:** 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 **ascending 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": "d4322562",
+ "metadata": {
+ "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": "6c03595c",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q18\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9b0c86d8",
+ "metadata": {},
+ "source": [
+ "We are now ready to define our recursive function."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4311d7b1",
+ "metadata": {},
+ "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 **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\\\\file_1.json',\n",
+ " 'sample_data\\\\sample_1\\\\file_2.json',\n",
+ " 'sample_data\\\\sample_1\\\\file_3.json',\n",
+ " 'sample_data\\\\sample_2\\\\file_4.json',\n",
+ " 'sample_data\\\\sample_2\\\\sample_3\\\\file_5.json']\n",
+ "```\n",
+ "\n",
+ "or the **list**\n",
+ "```python\n",
+ "['sample_data/file_1.json',\n",
+ " 'sample_data/sample_1/file_2.json',\n",
+ " 'sample_data/sample_1/file_3.json',\n",
+ " 'sample_data/sample_2/file_4.json',\n",
+ " 'sample_data/sample_2/sample_3/file_5.json']\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4f8581d3",
+ "metadata": {
+ "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": "48353dce",
+ "metadata": {},
+ "source": [
+ "**Question 19:** 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": "dbd253f6",
+ "metadata": {
+ "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": "43964ee0",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q19\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "56ae8420",
+ "metadata": {},
+ "source": [
+ "**Question 20:** 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": "5ccc45c3",
+ "metadata": {
+ "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": "4b38dcb9",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q20\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d45438a0",
+ "metadata": {},
+ "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": "3.9.13"
+ },
+ "otter": {
+ "OK_FORMAT": true,
+ "tests": {
+ "q1-1": {
+ "name": "q1-1",
+ "points": 2,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q1-1\", {'x': x, 'y': y})\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q1-2": {
+ "name": "q1-2",
+ "points": 3,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q1-2\", {'x': x, 'y': y})\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q10": {
+ "name": "q10",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q10\", {'orbital_periods': orbital_periods, 'semi_major_radii': semi_major_radii, 'discovery_years': discovery_years, 'planet_masses': planet_masses})\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q11": {
+ "name": "q11",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q11\", {'planet_masses': planet_masses, 'planet_radii': planet_radii})\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q12": {
+ "name": "q12",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q12\", {'planet_masses': planet_masses, 'planet_radii': planet_radii})\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q13": {
+ "name": "q13",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q13\", animals)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q14": {
+ "name": "q14",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q14\", flattened_names)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q15": {
+ "name": "q15",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q15\", more_flattened_names)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q16": {
+ "name": "q16",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q16\", sample_data_contents)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q17": {
+ "name": "q17",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q17\", num_sample_data_files)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q18": {
+ "name": "q18",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q18\", sample_data_directories)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q19": {
+ "name": "q19",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q19\", num_files_sample_2)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q2-1": {
+ "name": "q2-1",
+ "points": 3,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q2-1\", {'x': x, 'y': y})\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q2-2": {
+ "name": "q2-2",
+ "points": 2,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q2-2\", {'x': x, 'y': y})\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q20": {
+ "name": "q20",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q20\", sample_data_all_files)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q3": {
+ "name": "q3",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q3\", planet_count_2000_2010)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q4": {
+ "name": "q4",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q4\", flux_1000_percentage)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q5": {
+ "name": "q5",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q5\", {'orbital_periods': orbital_periods, 'semi_major_radii': semi_major_radii})\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q6": {
+ "name": "q6",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q6\", {'orbital_periods_no_outliers': orbital_periods_no_outliers, 'semi_major_radii_no_outliers': semi_major_radii_no_outliers})\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q7-1": {
+ "name": "q7-1",
+ "points": 3,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q7-1\", {'transit_planet_masses': transit_planet_masses, 'transit_planet_radii': transit_planet_radii})\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q7-2": {
+ "name": "q7-2",
+ "points": 2,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q7-2\", {'transit_planet_masses_no_outliers': transit_planet_masses_no_outliers, 'transit_planet_radii_no_outliers': transit_planet_radii_no_outliers})\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q8": {
+ "name": "q8",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q8\", {'rad_vel_planet_masses': rad_vel_planet_masses, 'rad_vel_planet_radii': rad_vel_planet_radii, 'rad_vel_discovery_years': rad_vel_discovery_years})\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q9": {
+ "name": "q9",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> practice_test.check(\"q9\", {'orbital_periods': orbital_periods, 'semi_major_radii': semi_major_radii, 'discovery_years': discovery_years})\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ }
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/sum23/labs/lab11/practice_plots.json b/sum23/labs/lab11/practice_plots.json
new file mode 100644
index 0000000000000000000000000000000000000000..afb3e0a062cec86d54729c7b5a494b2dbfbd7597
--- /dev/null
+++ b/sum23/labs/lab11/practice_plots.json
@@ -0,0 +1,21 @@
+{"1-1": {"x": [1, 2, 3, 4], "y": [1, 2, 3, 4]},
+ "1-2": {"x": [1, 2, 3, 4], "y": [4, 1, 2, 3]},
+ "2-1": {"x": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "y": [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]},
+ "2-2": {"x": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "y": [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]},
+ "5": {"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, 6.180235, 3650.0, 5.746007, 389.7, 1.58040433, 0.5182416, 3.6511, 5.2354, 2288.0, 69.971, 14.238, 15.819, 133.71, 11.949, 2.853136, 10.6666864, 4100.0, 14.207, 24.422, 3.3366496, 2.64561, 0.321962, 3.0232, 7.02679, 6.905, 20.129, 13.352, 2.64388312, 3448.0, 9.567, 1.467119, 140.43, 8.7076, 14.628, 598.3, 4.6938, 17281.66513958, 24.16, 19.466, 2.37756, 3657.0, 15.76491, 284.39, 771.36, 73.94, 18.27, 19240.75467009, 1.744579, 3.052524, 3.6915224, 3.822, 13.8508, 15.53, 4.46529976, 3.2130598, 4.62767, 10.8635, 2.77596, 5.508023, 4.008778, 5.6335158, 2.875317, 4.12448, 3.21222, 1.21288, 3.35524, 3.65281514, 4.23452, 3.039577, 3.257215, 5.72319, 2.8997, 2.8106, 5.00542, 2.1500082, 3.47447, 5.452654, 3.646706, 1.32734683, 2.797436, 4.640382, 3.54387, 3.056536, 4.45724, 2.69405, 4.641878, 3.3326414, 3.128992, 4.463129, 2.69155, 2.78849, 3.1220109, 4.2180278, 2.7535953, 1.9616241, 3.799847, 3.5852467, 2.79083, 2.4653, 4.0138379, 4.1419771, 3.85298, 4.7947813, 1.90231289, 2.6453235, 3.377728, 4.007232, 2.6054552, 2.972086, 4.8101025, 2.29840551, 4.7869491, 2.20474, 2.74432452, 3.07634, 3.92281072, 3.446459, 3.312846, 3.619188, 3.142702, 3.0440499, 2.7667641, 1.74748753, 2.686502, 16.254611, 0.83784, 1.354133, 4.7228124, 2.1605156, 1.3484978, 4.2986432, 3.3023881, 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72.46524, 127.132, 15.8915, 184.33, 137.62039, 31.783, 79.4575, 168.4499, 155.7367, 66.7443, 216.1244, 31.46517, 17.98, 117.59, 227.88411, 8.3, 139.8452, 22.2481, 114.4188, 24.472, 212.31044, 140.16303, 187.5197, 3.3, 23.4, 67.3, 14.1, 5.5, 20.0, 197.046, 174.799, 247.9074, 114.4188, 171.6282, 247.9074, 95.345, 212.9461, 139.8452, 73.1009, 151.916, 177.9848, 117.5971, 179.89178, 12.7132, 104.0, 96.7, 26.69772, 184.3414, 66.741, 114.41, 198.0, 25.0, 6.44, 4.15, 249.05159, 47.6725, 19.069, 6.3566, 104.3, 18.94937, 176.03781, 60.16492, 174.4878, 22.2481, 111.42, 127.76702, 143.0235, 222.472, 231.0, 86.0, 171.621, 5.1, 18.2, 22.9, 4.07661, 8.4, 15.8, 5.5, 1.7, 115.84733, 15.17685, 6.3566, 203.0, 171.0, 196.85951, 214.53525, 207.22516, 221.20968, 10.2, 11.8, 18.1, 6.3566, 187.5197, 43.4, 29.9, 3.33, 54.4738, 7.99, 44.812, 6.83, 14.6, 13.1, 11.75, 25.1, 23.9, 21.4, 64.4, 10.17, 116.0, 4.0, 6.6, 9.5, 1.9, 2.9, 7.3, 8.0, 2.0, 25.0]}, "planet_radii": {"1991 to 2000": [14.3, 14.1, 10.9, 14.2, 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2.867, 2.88, 2.837, 2.813, 2.509, 3.173, 2.955, 3.894, 4.485, 2.933, 4.099, 3.636, 3.089, 1.194, 3.877, 5.395, 2.825, 2.431, 3.323, 1.273, 2.178, 1.599, 3.768, 1.929, 8.935, 4.265, 2.713, 1.613, 6.004, 1.824, 4.541, 2.227, 2.44, 5.917, 1.472, 2.947, 2.571, 0.904, 2.29, 1.391, 2.76, 1.147, 4.555, 2.495, 2.279, 3.013, 2.233, 4.465, 2.841, 4.055, 2.686, 3.505, 3.182, 3.648, 2.612, 2.132, 3.017, 3.033, 3.606, 2.381, 2.229, 1.319, 2.799, 2.704, 3.163, 2.319, 2.379, 2.831, 3.145, 3.121, 2.52, 3.79, 1.794, 1.663, 2.75, 3.058, 2.309, 4.128, 0.997, 2.709, 1.596, 1.545, 2.356, 2.704, 3.025, 1.513, 3.918, 3.848, 2.325, 1.413, 2.37, 2.749, 1.968, 2.273, 1.714, 3.904, 3.119, 2.592, 2.648, 2.086, 2.381, 1.422, 1.475, 1.856, 2.116, 2.479, 3.33, 1.97, 1.227, 1.3, 1.459, 3.534, 2.553, 2.067, 1.66, 1.949, 2.778, 1.489, 2.354, 2.709, 3.036, 2.438, 1.764, 2.745, 1.409, 2.387, 0.729, 3.9, 1.886, 1.519, 2.587, 1.216, 1.734, 1.961, 2.422, 1.583, 1.139, 2.207, 2.203, 2.263, 0.754, 1.577, 2.082, 1.16, 3.15, 1.12, 2.101, 2.422, 2.384, 1.626, 2.211, 3.227, 0.98, 0.624, 2.92, 2.065, 2.117, 2.981, 1.487, 3.251, 1.814, 1.511, 2.582, 2.925, 1.326, 2.944, 0.869, 1.521, 1.837, 1.706, 1.201, 2.119, 3.099, 2.814, 0.934, 1.551, 1.623, 1.474, 1.176, 2.286, 1.39, 1.446, 2.776, 1.092, 1.184, 1.011, 1.357, 1.519, 2.943, 3.218, 2.618, 1.89, 2.298, 2.624, 2.954, 2.895, 2.291, 1.96, 2.654, 3.002, 2.315, 3.406, 1.992, 3.073, 1.53, 2.653, 1.515, 1.18, 0.913, 2.503, 3.166, 3.023, 1.828, 1.165, 1.547, 2.65, 2.117, 1.549, 2.76, 1.289, 2.488, 1.01, 1.033, 2.22, 1.527, 2.321, 1.239, 1.286, 1.802, 1.167, 1.089, 0.944, 2.051, 1.143, 1.307, 1.619, 1.215, 0.744, 1.781, 1.354, 1.105, 0.73, 1.5, 1.682, 2.0, 1.242, 3.37, 4.977, 12.33, 14.572, 13.899, 13.563, 14.202, 13.608, 11.467, 14.1, 14.0, 3.92, 2.15, 2.41, 4.18, 4.73, 12.3, 10.9, 2.41, 2.83, 14.0, 14.2, 1.39, 1.38, 6.79, 1.83, 16.701, 1.791, 2.415, 9.572, 3.65, 11.456, 9.1, 13.798, 9.427, 1.36, 3.96, 1.17, 1.672, 1.79, 14.1, 1.7, 1.41, 1.9, 3.09, 5.34, 3.14, 16.141, 1.258, 6.956, 11.657, 1.31, 11.299, 2.61, 1.3, 2.04, 2.19, 11.4, 3.51, 3.03, 11.153, 2.194, 1.62, 1.74, 8.608, 2.94, 1.68, 10.368, 1.8, 1.25, 2.75, 10.929, 7.163, 14.46, 12.072, 19.728, 2.77, 2.65, 17.262, 15.278, 12.845, 16.432, 11.4, 8.295, 11.321, 12.0, 6.0, 14.28, 12.274, 13.047, 13.305, 16.813, 2.82, 10.536, 5.351, 5.22, 9.303, 5.82, 2.03, 3.76, 5.25, 1.27, 12.5, 13.877, 1.08, 14.2, 3.24, 1.2, 2.094, 1.2, 2.3, 1.39, 1.76, 1.03, 1.1, 1.98, 1.09, 2.26, 2.63, 8.4, 5.4, 4.7, 2.888, 2.59, 2.05, 3.23, 6.0, 3.2, 1.31, 3.35, 3.13, 3.93, 6.81, 3.41, 5.83, 2.65, 4.31, 6.17, 3.23, 2.85, 2.54, 2.62, 9.35, 6.89, 1.24, 1.533, 2.45, 4.49, 2.99, 2.9, 2.79, 2.39, 1.7, 1.35, 3.228, 3.76, 4.8, 2.74, 3.12, 1.79, 1.54, 1.593, 2.213, 2.184, 2.04, 2.597, 3.395, 1.579, 3.124, 2.274, 5.098, 1.283, 1.36, 0.76, 1.233, 1.219, 1.929, 0.997, 9.86, 10.6, 1.19, 1.21, 4.3, 14.1, 1.416, 1.747, 3.293, 3.361, 1.68, 1.027, 1.844, 2.155, 1.652, 1.942, 14.0, 1.901, 0.977, 2.05, 1.671, 2.322, 0.87, 1.155, 5.221, 1.219, 1.681, 4.21, 1.227, 1.117, 2.76, 2.296, 2.781, 3.858, 0.696, 0.982, 1.32, 1.367, 1.147, 2.265, 3.1, 2.587, 2.651, 4.14, 5.59, 5.24, 11.0, 1.397, 3.46, 1.28, 2.064, 2.06, 11.063, 5.44, 13.4, 8.676, 10.133, 1.72, 2.74, 2.266, 2.415, 1.704, 2.587, 2.765, 1.85, 4.31, 1.4, 2.6, 10.8, 4.73, 4.33, 12.1, 11.8, 14.3, 2.199, 4.23, 3.25, 9.32, 1.83, 2.39, 1.83, 1.88, 2.56, 2.89, 2.93, 1.69, 3.61, 1.37, 1.332, 1.361, 1.948, 1.075, 2.0, 1.689, 2.58, 1.488, 0.792, 0.645, 9.658, 2.628, 3.332, 3.279, 2.661, 2.706, 5.937, 0.806, 1.067, 1.501, 2.985, 2.775, 2.795, 2.653, 2.41, 2.76, 3.12, 1.39, 2.12, 2.52, 2.518, 3.497, 3.232, 1.28, 1.49, 3.29, 2.049, 2.701, 2.474, 3.72, 2.017, 3.23, 13.0, 3.74, 0.75, 0.818, 1.137, 3.09, 3.013, 1.076, 1.191, 1.392, 1.345, 2.222, 1.13, 0.797, 2.039, 1.56, 1.9, 2.879, 4.627, 2.639, 3.88, 2.53, 3.01, 2.51, 3.51, 3.78, 1.152, 1.669, 2.572, 2.207, 2.287, 2.87, 1.166, 2.09, 6.35, 3.99, 0.953], "2001 to 2010": [13.34, 15.558, 6.39, 2.742, 2.83, 4.17, 12.5, 3.33, 14.0, 14.785, 10.749, 11.153, 12.689, 14.572, 12.722, 7.062, 10.536, 14.28, 15.614, 10.6, 8.47, 14.2, 4.14, 5.83, 8.18, 10.9, 11.1, 8.59, 2.4, 13.6, 8.295, 14.0, 5.0, 14.3, 11.1, 14.1, 14.1, 5.48, 14.0, 14.2, 14.2, 14.1, 9.32, 14.0, 14.3, 2.83, 14.1, 14.3, 12.5, 10.1, 14.0, 4.12, 14.1, 14.3, 6.2, 12.31, 14.2, 14.2, 9.6, 14.0, 6.16, 4.43, 13.4, 14.0, 2.12, 14.3, 5.02, 13.2, 4.002, 14.617, 18.181, 15.872, 1.63, 5.18, 9.64, 3.84, 2.21, 4.72, 14.011, 11.422, 18.046, 13.675, 15.805, 20.961, 11.99, 13.787, 11.994, 8.631, 17.363, 11.209, 11.545, 11.131, 3.61, 12.5, 11.9, 4.36, 11.769, 9.6, 13.2, 14.1, 5.38, 2.42, 1.87, 13.9, 7.87, 4.59, 2.4, 12.5, 4.57, 14.2, 9.03, 14.2, 5.02, 13.0, 14.1, 14.3, 14.0, 14.0, 11.1, 14.2, 2.11, 4.46, 5.11, 1.681, 2.83, 4.11, 2.21, 1.17, 13.3, 4.01, 2.4, 14.1, 14.2, 14.1, 14.0, 14.1, 14.0, 3.17, 3.46, 4.45, 2.4, 14.1, 8.29, 8.08, 1.64, 8.51, 1.875, 7.59, 2.51, 3.92, 3.68, 3.45, 5.39, 5.24, 4.91, 9.4, 3.17, 13.3, 1.83, 2.46, 3.04, 1.8, 2.87, 3.12, 4.19, 2.49, 3.33]}}}
diff --git a/sum23/labs/lab11/practice_test.py b/sum23/labs/lab11/practice_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9f37882c74b2ed1ef8472af557d86d2d6f3eec0
--- /dev/null
+++ b/sum23/labs/lab11/practice_test.py
@@ -0,0 +1,387 @@
+#!/usr/bin/python
+import os, json, math
+from collections import namedtuple
+
+MAX_FILE_SIZE = 450 # units - KB
+REL_TOL = 6e-04 # relative tolerance for floats
+ABS_TOL = 15e-03 # absolute tolerance for floats
+
+PASS = "PASS"
+
+TEXT_FORMAT = "text" # question type when expected answer is a str, int, float, or bool
+TEXT_FORMAT_NAMEDTUPLE = "text namedtuple" # question type when expected answer is a namedtuple
+TEXT_FORMAT_UNORDERED_LIST = "text list_unordered" # question type when the expected answer is a list where the order does *not* matter
+TEXT_FORMAT_ORDERED_LIST = "text list_ordered" # question type when the expected answer is a list where the order does matter
+TEXT_FORMAT_SPECIAL_ORDERED_LIST = "text list_special_ordered" # 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_DICT = "text dict" # question type when the expected answer is a dictionary
+PNG_FORMAT_SCATTER = "png scatter" # question type when the expected answer is a scatter plot
+
+
+def return_expected_json():
+ f = open("practice_plots.json", encoding='utf-8')
+ plots_data = json.load(f)
+ f.close()
+ expected_json = {"1-1": (PNG_FORMAT_SCATTER, plots_data["1-1"]),
+ "1-2": (PNG_FORMAT_SCATTER, plots_data["1-2"]),
+ "2-1": (PNG_FORMAT_SCATTER, plots_data["2-1"]),
+ "2-2": (PNG_FORMAT_SCATTER, plots_data["2-2"]),
+ "3": (TEXT_FORMAT, 480),
+ "4": (TEXT_FORMAT, 9.482758620689655),
+ "5": (PNG_FORMAT_SCATTER, plots_data["5"]),
+ "6": (PNG_FORMAT_SCATTER, plots_data["6"]),
+ "7-1": (PNG_FORMAT_SCATTER, plots_data["7-1"]),
+ "7-2": (PNG_FORMAT_SCATTER, plots_data["7-2"]),
+ "8": (PNG_FORMAT_SCATTER, plots_data["8"]),
+ "9": (PNG_FORMAT_SCATTER, plots_data["9"]),
+ "10": (PNG_FORMAT_SCATTER, plots_data["10"]),
+ "11": (PNG_FORMAT_SCATTER, plots_data["11"]),
+ "12": (PNG_FORMAT_SCATTER, plots_data["12"]),
+ "13": (TEXT_FORMAT_ORDERED_LIST, ['rabbit', 'cat', 'lion', 'chimpanzee']),
+ "14": (TEXT_FORMAT_ORDERED_LIST, ['Adam', 'Bob', 'Chet', 'Bea', 'Treasure', 'Andy', 'Ann']),
+ "15": (TEXT_FORMAT_ORDERED_LIST, ['Adam',
+ 'Bob',
+ 'Chet',
+ 'Cat',
+ 'Barb',
+ 'Bert',
+ 'Alex',
+ 'Bea',
+ 'Gold',
+ 'Andy',
+ 'Ann']),
+ "16": (TEXT_FORMAT_ORDERED_LIST, ['file_1.json', 'sample_1', 'sample_2']),
+ "17": (TEXT_FORMAT, 1),
+ "18": (TEXT_FORMAT_ORDERED_LIST, [os.path.join("sample_data", "sample_1"),
+ os.path.join("sample_data", "sample_2")]),
+ "19": (TEXT_FORMAT, 2),
+ "20": (TEXT_FORMAT_ORDERED_LIST, [os.path.join("sample_data", "file_1.json"),
+ os.path.join("sample_data", "sample_1", "file_2.json"),
+ os.path.join("sample_data", "sample_1", "file_3.json"),
+ os.path.join("sample_data", "sample_2", "file_4.json"),
+ os.path.join("sample_data", "sample_2", "sample_3", "file_5.json")])}
+ return expected_json
+
+def check_cell(qnum, actual):
+ expected_json = return_expected_json()
+ format, expected = expected_json[qnum[1:]]
+ try:
+ if format == TEXT_FORMAT:
+ return simple_compare(expected, actual)
+ elif format == TEXT_FORMAT_UNORDERED_LIST:
+ return list_compare_unordered(expected, actual)
+ elif format == TEXT_FORMAT_ORDERED_LIST:
+ return list_compare_ordered(expected, actual)
+ elif format == TEXT_FORMAT_DICT:
+ return dict_compare(expected, actual)
+ elif format == TEXT_FORMAT_NAMEDTUPLE:
+ return namedtuple_compare(expected, actual)
+ elif format == PNG_FORMAT_SCATTER:
+ return check_png_scatter(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 simple_compare(expected, actual, complete_msg=True):
+ msg = PASS
+ if type(expected) == type:
+ if expected != actual:
+ if type(actual) == type:
+ msg = "expected %s but found %s" % (expected.__name__, actual.__name__)
+ else:
+ msg = "expected %s but found %s" % (expected.__name__, repr(actual))
+ elif type(expected) != type(actual) and not (type(expected) in [float, int] and type(actual) in [float, int]):
+ msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+ elif type(expected) == float:
+ if not math.isclose(actual, expected, rel_tol=REL_TOL, abs_tol=ABS_TOL):
+ msg = "expected %s" % (repr(expected))
+ if complete_msg:
+ msg = msg + " but found %s" % (repr(actual))
+ else:
+ if expected != actual:
+ msg = "expected %s" % (repr(expected))
+ if complete_msg:
+ msg = msg + " but found %s" % (repr(actual))
+ return msg
+
+namedtuples = ['Star', 'Planet']
+star_attributes = ['spectral_type',
+ 'stellar_effective_temperature',
+ 'stellar_radius',
+ 'stellar_mass',
+ 'stellar_luminosity',
+ 'stellar_surface_gravity',
+ 'stellar_age']
+# Create a namedtuple type, Star
+Star = namedtuple("Star", star_attributes)
+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']
+# Create a namedtuple type, Planet
+Planet = namedtuple("Planet", planets_attributes)
+
+def namedtuple_compare(expected, actual):
+ msg = PASS
+ try:
+ actual_fields = actual._fields
+ except AttributeError:
+ msg = "expected namedtuple but found %s" % (type(actual).__name__)
+ return msg
+ if type(expected).__name__ != type(actual).__name__:
+ msg = "expected namedtuple %s but found namedtuple %s" % (type(expected).__name__, type(actual).__name__)
+ return msg
+ expected_fields = expected._fields
+ msg = list_compare_ordered(list(expected_fields), list(actual_fields), "namedtuple attributes")
+ if msg != PASS:
+ return msg
+ for field in expected_fields:
+ val = simple_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 list_compare_ordered(expected, actual, obj="list"):
+ msg = PASS
+ if type(expected) != type(actual):
+ msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+ return msg
+ for i in range(len(expected)):
+ if i >= len(actual):
+ msg = "expected missing %s in %s" % (repr(expected[i]), obj)
+ break
+ if type(expected[i]) in [int, float, bool, str]:
+ val = simple_compare(expected[i], actual[i])
+ elif type(expected[i]) in [list, tuple]:
+ val = list_compare_ordered(expected[i], actual[i], "sub" + obj)
+ elif type(expected[i]) in [dict]:
+ val = dict_compare(expected[i], actual[i])
+ elif type(expected[i]).__name__ in namedtuples:
+ val = namedtuple_compare(expected[i], actual[i])
+ if val != PASS:
+ msg = "at index %d of the %s, " % (i, obj) + val
+ break
+ if len(actual) > len(expected) and msg == PASS:
+ msg = "found unexpected %s in %s" % (repr(actual[len(expected)]), obj)
+ if len(expected) != len(actual):
+ msg = msg + " (found %d entries in %s, but expected %d)" % (len(actual), obj, len(expected))
+
+ if len(expected) > 0 and type(expected[0]) in [int, float, bool, str]:
+ if msg != PASS and list_compare_unordered(expected, actual, obj) == PASS:
+ try:
+ msg = msg + " (%s may not be ordered as required)" % (obj)
+ except:
+ pass
+ return msg
+
+
+def list_compare_helper(larger, smaller):
+ 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):
+ if type(larger[i]) in [list, tuple]:
+ val = list_compare_ordered(larger[i], smaller[j - 1])
+ else:
+ val = simple_compare(larger[i], smaller[j - 1], False)
+ break
+ if type(larger[i]) in [list, tuple]:
+ val = list_compare_ordered(larger[i], smaller[j])
+ else:
+ val = simple_compare(larger[i], smaller[j], False)
+ j += 1
+ if val == PASS:
+ found = True
+ break
+ if not found:
+ msg = val
+ break
+ return msg
+
+
+def list_compare_unordered(expected, actual, obj="list"):
+ msg = PASS
+ if type(expected) != type(actual):
+ msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+ return msg
+ try:
+ sort_expected = sorted(expected)
+ sort_actual = sorted(actual)
+ except:
+ msg = "unexpected datatype found in %s; expected entries of type %s" % (obj, obj, type(expected[0]).__name__)
+ return msg
+
+ if len(actual) == 0 and len(expected) > 0:
+ msg = "in the %s, missing" % (obj) + expected[0]
+ elif len(actual) > 0 and len(expected) > 0:
+ val = simple_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 list_compare_special_init(expected, special_order):
+ real_expected = []
+ for i in range(len(expected)):
+ if real_expected == [] or special_order[i-1] != special_order[i]:
+ real_expected.append([])
+ real_expected[-1].append(expected[i])
+ return real_expected
+
+
+def list_compare_special(expected, actual, special_order):
+ expected = list_compare_special_init(expected, special_order)
+ msg = PASS
+ expected_list = []
+ for expected_item in expected:
+ expected_list.extend(expected_item)
+ val = list_compare_unordered(expected_list, actual)
+ if val != PASS:
+ msg = val
+ else:
+ i = 0
+ for expected_item in expected:
+ 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="dict"):
+ msg = PASS
+ if type(expected) != type(actual):
+ msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+ return msg
+ try:
+ expected_keys = sorted(list(expected.keys()))
+ actual_keys = sorted(list(actual.keys()))
+ except:
+ msg = "unexpected datatype found in keys of dict; expect a dict with keys of type %s" % (
+ type(expected_keys[0]).__name__)
+ return msg
+ val = list_compare_unordered(expected_keys, actual_keys, "dict")
+ if val != PASS:
+ msg = "bad keys in %s: " % (obj) + val
+ if msg == PASS:
+ for key in expected:
+ if expected[key] == None or type(expected[key]) in [int, float, bool, str]:
+ val = simple_compare(expected[key], actual[key])
+ elif type(expected[key]) in [list]:
+ val = list_compare_ordered(expected[key], actual[key], "value")
+ elif type(expected[key]) in [dict]:
+ val = dict_compare(expected[key], actual[key], "sub" + obj)
+ elif type(expected[key]).__name__ in namedtuples:
+ val = namedtuple_compare(expected[key], actual[key])
+ if val != PASS:
+ msg = "incorrect val for key %s in %s: " % (repr(key), obj) + val
+ return msg
+
+def helper_png_scatter(expected, actual):
+ msg = PASS
+ expected_length = len(list(expected.values())[0])
+ for key in actual:
+ if len(actual[key]) != expected_length:
+ return "list '%s' has length %d but is expected to have length %d" % (key, len(actual[key]), expected_length)
+ expected_plot_points = []
+ actual_plot_points = []
+ for i in range(expected_length):
+ expected_point = []
+ actual_point = []
+ for key in expected:
+ expected_point.append(expected[key][i])
+ actual_point.append(actual[key][i])
+ expected_plot_points.append(tuple(expected_point))
+ actual_plot_points.append(tuple(actual_point))
+ sorted_expected_points = sorted(expected_plot_points)
+ sorted_actual_points = sorted(actual_plot_points)
+ val = list_compare_ordered(sorted_expected_points, sorted_actual_points, "plot")
+ if val != PASS:
+ val = list_compare_ordered(expected_plot_points, actual_plot_points, "plot")
+ bad_idx = int(val.split("index")[1].split()[0])
+ msg = "in the plot, at index %d, expected point %s but found %s" % (bad_idx, expected_plot_points[bad_idx], actual_plot_points[bad_idx])
+ return msg
+
+
+
+def check_png_scatter(expected, actual):
+ msg = PASS
+ if type(list(expected.values())[0]) == list:
+ msg = helper_png_scatter(expected, actual)
+ elif type(list(expected.values())[0]) == dict:
+ expected_keys = list(list(expected.values())[0].keys())
+ for key in actual:
+ val = list_compare_unordered(expected_keys, list(actual[key].keys()), "dictionary %s" % key)
+ if val != PASS:
+ return val
+ expected_flipped = {}
+ for key_1 in list(expected.values())[0]:
+ expected_flipped[key_1] = {}
+ for key_2 in expected:
+ expected_flipped[key_1][key_2] = expected[key_2][key_1]
+ actual_flipped = {}
+ for key_1 in list(actual.values())[0]:
+ actual_flipped[key_1] = {}
+ for key_2 in actual:
+ actual_flipped[key_1][key_2] = actual[key_2][key_1]
+ for key in expected_flipped:
+ val = helper_png_scatter(expected_flipped[key], actual_flipped[key])
+ if val != PASS:
+ if "length" in val:
+ msg = val.replace("list", "category '%s' of dictionary" % (key))
+ else:
+ msg = val.replace("in the plot,", "in the plot, for the category '%s'," % (key))
+ return msg
+ return msg
+
+
+def check(qnum, actual):
+ msg = check_cell(qnum, actual)
+ if msg == PASS:
+ return True
+ print("<b style='color: red;'>ERROR:</b> " + msg)
+
+
+def check_file_size(path):
+ size = os.path.getsize(path)
+ assert size < MAX_FILE_SIZE * 10**3, "Your file is too big to be processed by Gradescope; please delete unnecessary output cells so your file size is < %s KB" % MAX_FILE_SIZE
diff --git a/sum23/labs/lab11/sample_data.zip b/sum23/labs/lab11/sample_data.zip
new file mode 100644
index 0000000000000000000000000000000000000000..84c802ae6da8f5211fd3130757bf255300d373cb
Binary files /dev/null and b/sum23/labs/lab11/sample_data.zip differ
diff --git a/sum23/projects/p11/README.md b/sum23/projects/p11/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..6956c8cc604c29474e2c3eb776ae7da7feac931e
--- /dev/null
+++ b/sum23/projects/p11/README.md
@@ -0,0 +1,106 @@
+# Project 11 (P11): Analyzing Stars and Planets
+
+
+## Corrections and clarifications:
+
+* None yet.
+
+**Find any issues?** Create a post on Piazza
+
+
+## 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-lecture-material/-/blob/main/sum23/projects/p11/p11.ipynb), [`p11_test.py`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/blob/main/sum23/projects/p11/p11_test.py), [`p11_plots.json`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/blob/main/sum23/projects/p11/p11_plots.json), and [`broken_data.zip`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/blob/main/sum23/projects/p11/broken_data.zip).
+
+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-lecture-material/-/blob/main/sum23/projects/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
++-- p11_test.py
++-- p11_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
+| +-- .ipynb_checkpoints
+| +-- hds
+| | +-- .ipynb_checkpoints
+| | +-- hd_1000s
+| | | +-- hd_10000s.json
+| | +-- others.json
+| +-- k2s.json
+| +-- keplers
+| | +-- kepler_100s
+| | | +-- kepler_100s
+| | | | +-- kepler_100s
+| | | | | +-- kepler_100s.json
+| | | | +-- others.json
+| | | +-- kepler_200s
+| | | | +-- .ipynb_checkpoints
+| | | | +-- kepler_220s.json
+| | | | +-- kepler_290s.json
+| | | | +-- others
+| | | | | +-- others.json
+| | | +-- others.json
+| | +-- kepler_10s
+| | | +-- kepler_80s
+| | | | +-- kepler_80s.json
+| | | +-- others
+| | | | +-- kepler_20s.json
+| | | | +-- kepler_30s.json
+| | | | +-- others.json
+| | +-- others
+| | | +-- .DS_Store
+| | | +-- others.json
+| +-- others
+| | +-- .DS_Store
+| | +-- gjs.json
+| | +-- others.json
+| | +-- tois
+| | | +-- 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-lecture-material/-/blob/main/sum23/projects/p11/rubric.md), to ensure that you don't lose points during code review.
+- You must **save your notebook file** before you run the cell containing **export**.
+- 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 in a few minutes after your submission. You should be able to see both PASS / FAIL results for the 20 test cases and your total score, which is accessible via Gradescope Dashboard (as in the image below):
+
+ <img src="images/gradescope.png" width="400">
+- **Important:** After you submit, you **need to verify** that your code is visible on Gradescope. If you displayed the output of a large variable (such as `planets_list`) anywhere in your notebook, **we will not be able to view or grade your submission**. Make sure you don't have any large outputs in any of your cells, and verify after submission that your code can be viewed.
+- If you feel you have been incorrectly graded on a particular question by the Gradescope autograder, please file a regrade request.
diff --git a/sum23/projects/p11/broken_data.zip b/sum23/projects/p11/broken_data.zip
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diff --git a/sum23/projects/p11/data.zip b/sum23/projects/p11/data.zip
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diff --git a/sum23/projects/p11/images/README.md b/sum23/projects/p11/images/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..86d526a42c63541cdd24649df3ee17c428769bd7
--- /dev/null
+++ b/sum23/projects/p11/images/README.md
@@ -0,0 +1,3 @@
+# Images
+
+Images from p11 are stored here.
diff --git a/sum23/projects/p11/images/add_group_member.png b/sum23/projects/p11/images/add_group_member.png
new file mode 100644
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diff --git a/sum23/projects/p11/images/q9.png b/sum23/projects/p11/images/q9.png
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diff --git a/sum23/projects/p11/p11.ipynb b/sum23/projects/p11/p11.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..20c267dbf97b0498f251e37fcd95c63919c2882a
--- /dev/null
+++ b/sum23/projects/p11/p11.ipynb
@@ -0,0 +1,2610 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8baedc1b",
+ "metadata": {
+ "cell_type": "code",
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "import otter\n",
+ "# nb_name should be the name of your notebook without the .ipynb extension\n",
+ "nb_name = \"p11\"\n",
+ "py_filename = nb_name + \".py\"\n",
+ "grader = otter.Notebook(nb_name + \".ipynb\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ca5651f9",
+ "metadata": {
+ "deletable": false
+ },
+ "outputs": [],
+ "source": [
+ "import p11_test"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "85a61689",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "# Project 11: Analyzing Stars and Planets"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b1f98864",
+ "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 11](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/blob/main/sum23/labs/lab11) before working on this project.** The lab introduces some important techniques related to this project."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "42327026",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "## Note on Academic Misconduct:\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. Now may be a good time to review [our course policies](https://canvas.wisc.edu/courses/355767/pages/syllabus?module_item_id=6048035)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e99e0fbd",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "## Testing your code:\n",
+ "\n",
+ "Along with this notebook, you must have downloaded the files `p11_test.py` and `p11_plots.json`. If you are curious about how we test your code, you can explore this file, and specifically the value of the variable `expected_json`, to understand the expected answers to the questions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7a6f29e6",
+ "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`, `p11_test.py`, and `p11_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",
+ "+-- p11_test.py\n",
+ "+-- p11_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",
+ "| +-- .ipynb_checkpoints\n",
+ "| +-- hds\n",
+ "| | +-- .ipynb_checkpoints\n",
+ "| | +-- hd_1000s\n",
+ "| | | +-- hd_10000s.json\n",
+ "| | +-- others.json\n",
+ "| +-- k2s.json\n",
+ "| +-- keplers\n",
+ "| | +-- kepler_100s\n",
+ "| | | +-- kepler_100s\n",
+ "| | | | +-- kepler_100s\n",
+ "| | | | | +-- kepler_100s.json\n",
+ "| | | | +-- others.json\n",
+ "| | | +-- kepler_200s\n",
+ "| | | | +-- .ipynb_checkpoints\n",
+ "| | | | +-- kepler_220s.json\n",
+ "| | | | +-- kepler_290s.json\n",
+ "| | | | +-- others\n",
+ "| | | | | +-- others.json\n",
+ "| | | +-- others.json\n",
+ "| | +-- kepler_10s\n",
+ "| | | +-- kepler_80s\n",
+ "| | | | +-- kepler_80s.json\n",
+ "| | | +-- others\n",
+ "| | | | +-- kepler_20s.json\n",
+ "| | | | +-- kepler_30s.json\n",
+ "| | | | +-- others.json\n",
+ "| | +-- others\n",
+ "| | | +-- .DS_Store\n",
+ "| | | +-- others.json\n",
+ "| +-- others\n",
+ "| | +-- .DS_Store\n",
+ "| | +-- gjs.json\n",
+ "| | +-- others.json\n",
+ "| | +-- tois\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": "b50996a1",
+ "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": "b3120b0d",
+ "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. We'll **manually deduct** points from your autograder score on Gradescope during code review.\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",
+ "- `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 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",
+ "We will **manually deduct** points if you do **not** follow these guidelines.\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-lecture-material/-/blob/main/sum23/projects/p11/rubric.md)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "143a6b70",
+ "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": "61064814",
+ "metadata": {
+ "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": "32fcf278",
+ "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": "b15f5099",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the namedtuple 'Star' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4d0adafc",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the function 'process_csv' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d53b79eb",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the function 'star_cell' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e8fac4bc",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the function 'get_stars' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "edde0e8a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the dictionary 'stars_dict' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "18fb402f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the namedtuple 'Planet' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b1a8ab9d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the function 'read_json' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2343f67d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the function 'planet_cell' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ed0479a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the function 'get_planets' here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2c92ab2f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# copy/paste the definition of the list 'planets_list' here\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1f50a565",
+ "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": "90f71915",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# remember to import matplotlib.pyplot as plt at the top of the notebook to make these 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)\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": "bbb64165",
+ "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": "164feb5e",
+ "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": "8ca51ad8",
+ "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": "74127e03",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'ratios_gj9827', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cdf1647e",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q1\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "14a77337",
+ "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": "5825a2ac",
+ "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": "8037ef9c",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'coeff_gj9827', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2dc0239e",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q2\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ccb43aa1",
+ "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": "91313c5d",
+ "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": "8e9b6be0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'percentage_change', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "46a07845",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q3\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3f12f8eb",
+ "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": "1c67360b",
+ "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": "e7e5cec2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# first compute and store the lists 'flux_list', and 'temp_4th_power_list'\n",
+ "# do NOT plot just yet\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "48661904",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q4\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4ab3241f",
+ "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:** `p11_test.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, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+ "\n",
+ "Your plot should look like this:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "eb0d8e8f",
+ "metadata": {},
+ "source": [
+ "<div><img src=\"attachment:q4.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dbef98b5",
+ "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": "76e625da",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# create a scatter plot using 'flux_list' and 'temp_4th_power_list'\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "add6af97",
+ "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": "260ffc11",
+ "metadata": {},
+ "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": "bcf7f71f",
+ "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": "3582b566",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# first compute and store the lists 'flux_list_no_outliers', and 'temp_4th_power_list_no_outliers'\n",
+ "# do NOT plot just yet\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c24eeea1",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q5\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "835bc93d",
+ "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:** `p11_test.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, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+ "\n",
+ "Your plot should look like this:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1ef1c6ff",
+ "metadata": {},
+ "source": [
+ "<div><img src=\"attachment:q5.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8dde350",
+ "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": "1192a5bb",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Create a scatter plot using 'flux_list_no_outliers' and 'temp_4th_power_list_no_outliers'\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c42e74dc",
+ "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": "ce600e7a",
+ "metadata": {},
+ "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": "0c71b714",
+ "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": "d07a5329",
+ "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": "e7c9d8eb",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define the variable 'star_classes' here\n",
+ "# but do NOT display\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6defaad6",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "You can **verify** that you have defined `star_classes` correctly by checking that there are *3843* Red Giants, *3* White Dwarfs, and *1* Neutron Star in `star_classes`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "77c685cf",
+ "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': -0.01889128094725505,\n",
+ " 'White Dwarf': 2.787333333333333,\n",
+ " 'Neutron Star': 2.86}\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3d519a30",
+ "metadata": {
+ "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": "8f2ff485",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q6\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "79301a0f",
+ "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": "ab56e581",
+ "metadata": {},
+ "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": "02339fe5",
+ "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": "78b78ab5",
+ "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": "77d6cfdf",
+ "metadata": {
+ "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": "2e5b9557",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q7\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bffc9238",
+ "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:** `p11_test.py` can check that the **dictionaries** 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, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+ "\n",
+ "Your plot should look like this:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20f5a5e6",
+ "metadata": {},
+ "source": [
+ "<div><img src=\"attachment:q7.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b62d02d8",
+ "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": "83e151a2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'density_dict' and 'lum_dict'\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "92eedc9b",
+ "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?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a3620a11",
+ "metadata": {},
+ "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": "0953cc13",
+ "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 *27* `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": "4eae2b3d",
+ "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": "be407b6b",
+ "metadata": {
+ "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": "a107dd3a",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q8\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "86b7feaa",
+ "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:** `p11_test.py` can check that the **dictionaries** 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, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+ "\n",
+ "Your plot should look like this:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0cfa5a58",
+ "metadata": {},
+ "source": [
+ "<div><img src=\"attachment:q8.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1654a915",
+ "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": "cae6d923",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'density_dict_no_outliers' and 'lum_dict_no_outliers'\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5971f12e",
+ "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": "612c5af3",
+ "metadata": {},
+ "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": "6ab6ffc0",
+ "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": "81821656",
+ "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": "754474ba",
+ "metadata": {
+ "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": "75da5527",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q9\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "10b57c2c",
+ "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:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9cb3e453",
+ "metadata": {},
+ "source": [
+ "<div><img src=\"attachment:q9.png\" width=\"400\"/></div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ce5436",
+ "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": "083516bc",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# plot 'temp_list' and 'lum_list' with 'age_list' as colors and 'mass_list' as sizes\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6bfe07c5",
+ "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": "4049df31",
+ "metadata": {},
+ "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": "fead22b9",
+ "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": "f1ae6161",
+ "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_1.json`, `files_2.json`, `file_3.json`, `file_4.json`, and `file_5.json`.\n",
+ "\n",
+ "You **must** **ignore** all files that start with `\".\"`, and your output **must** be **explicitly** sorted in **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": "a130d131",
+ "metadata": {
+ "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": "markdown",
+ "id": "8f41ef03",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 10:** What are the **paths** of the files in the `others` 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": "01f3f63c",
+ "metadata": {
+ "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": "f2160349",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q10\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "07654de9",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 11:** What are the **paths** of the files in the `kepler_100s` directory of the `keplers` 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\", \"keplers\", \"kepler_100s\")\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ad4dd966",
+ "metadata": {
+ "lines_to_next_cell": 0,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'broken_data_keplers_kepler_100s', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d8355b47",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q11\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aa27e529",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "**Question 12:** What are the **paths** of the files in the `others` directory of the `kepler_10s` directory of the `keplers` 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": "357f1c40",
+ "metadata": {
+ "lines_to_next_cell": 0,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'broken_data_keplers_kepler_10s_others', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b51022f4",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q12\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "09720497",
+ "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": "91ff6f82",
+ "metadata": {
+ "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": "4ebea45e",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q13\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11b1c93e",
+ "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": "a435d9f5",
+ "metadata": {
+ "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": "markdown",
+ "id": "911bfcfb",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "You can verify that you have not made any mistakes by confirming that `all_planets_list` now has *5296* `Planet` objects in it."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f3d141d6",
+ "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": "e0889e18",
+ "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": "7dbadb7d",
+ "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": "3cc2ca9d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define the function 'get_surface_gravity' here\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1dd10e21",
+ "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": "31a2acd6",
+ "metadata": {
+ "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": "ac1615c6",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q14\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4ba91170",
+ "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": "b1e09a85",
+ "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": "9e4a9f53",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define the function 'get_distances_to_star' here\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7d3f0217",
+ "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": "e0fb3e1b",
+ "metadata": {
+ "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": "f192021a",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q15\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7bfdd98c",
+ "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": "c2d5e010",
+ "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": "a08953c3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define the function 'get_liquid_water_distances' here\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7d13e19f",
+ "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": "1737f595",
+ "metadata": {
+ "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": "f03ef2c5",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q16\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ab3a05c1",
+ "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": "98679d66",
+ "metadata": {
+ "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": "cd1288c4",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q17\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4e9ed643",
+ "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": "179e1e21",
+ "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": "a6aea194",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define the function 'get_surface_temperatures' here\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "edd6799a",
+ "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": "7f9441f0",
+ "metadata": {
+ "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": "06946336",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q18\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9f7a7a91",
+ "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": "324d99ec",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'pleasant_planets', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4693a0f8",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q19\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "51ca008e",
+ "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": "3e66e8ae",
+ "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": "1cf640af",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# compute and store the answer in the variable 'habitable_planets', then display it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8dd8c119",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "grader.check(\"q20\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "45ae4920",
+ "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."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cf34faa6",
+ "metadata": {},
+ "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": "2da7b928",
+ "metadata": {
+ "deletable": false,
+ "editable": false
+ },
+ "source": [
+ "## Submission\n",
+ "Make sure you have run all cells in your notebook in order before running the following cells, so that all images/graphs appear in the output. The following cells will generate a zip file for you to submit.\n",
+ "\n",
+ "**SUBMISSION INSTRUCTIONS**:\n",
+ "1. **Upload** the zipfile to Gradescope.\n",
+ "2. Check **Gradescope otter** results as soon as the auto-grader execution gets completed. Don't worry about the score showing up as -/100.0. You only need to check that the test cases passed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c761ae0",
+ "metadata": {
+ "cell_type": "code",
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "from IPython.display import display, Javascript\n",
+ "display(Javascript('IPython.notebook.save_checkpoint();'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2b197211",
+ "metadata": {
+ "cell_type": "code",
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "!jupytext --to py p11.ipynb"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e5d29bc0",
+ "metadata": {
+ "cell_type": "code",
+ "deletable": false,
+ "editable": false
+ },
+ "outputs": [],
+ "source": [
+ "p11_test.check_file_size(\"p11.ipynb\")\n",
+ "grader.export(pdf=False, run_tests=True, files=[py_filename])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3e400d2c",
+ "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": "3.9.13"
+ },
+ "otter": {
+ "OK_FORMAT": true,
+ "tests": {
+ "q1": {
+ "name": "q1",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q1\", ratios_gj9827)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q10": {
+ "name": "q10",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q10\", broken_data_others)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q11": {
+ "name": "q11",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q11\", broken_data_keplers_kepler_100s)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q12": {
+ "name": "q12",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q12\", broken_data_keplers_kepler_10s_others)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q13": {
+ "name": "q13",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q13\", broken_data)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q14": {
+ "name": "q14",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q14\", gj_674_b_gravity)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q15": {
+ "name": "q15",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q15\", distances_to_star_b_cen_ab_b)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q16": {
+ "name": "q16",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q16\", liquid_water_distances_kepler_197_e)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q17": {
+ "name": "q17",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q17\", planets_with_liquid_water)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q18": {
+ "name": "q18",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q18\", surface_temp_hd_20794_d)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q19": {
+ "name": "q19",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q19\", pleasant_planets)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q2": {
+ "name": "q2",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q2\", coeff_gj9827)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q20": {
+ "name": "q20",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q20\", habitable_planets)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q3": {
+ "name": "q3",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q3\", percentage_change)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q4": {
+ "name": "q4",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q4\", {'flux_list': flux_list, 'temp_4th_power_list': temp_4th_power_list})\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q5": {
+ "name": "q5",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q5\", {'flux_list_no_outliers': flux_list_no_outliers, 'temp_4th_power_list_no_outliers': temp_4th_power_list_no_outliers})\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q6": {
+ "name": "q6",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q6\", star_classes_avg_lum)\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q7": {
+ "name": "q7",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q7\", {'density_dict': density_dict, 'lum_dict': lum_dict})\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
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+ ]
+ },
+ "q8": {
+ "name": "q8",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q8\", {'density_dict_no_outliers': density_dict_no_outliers, 'lum_dict_no_outliers': lum_dict_no_outliers})\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
+ ]
+ },
+ "q9": {
+ "name": "q9",
+ "points": 5,
+ "suites": [
+ {
+ "cases": [
+ {
+ "code": ">>> p11_test.check(\"q9\", {'temp_list': temp_list, 'lum_list': lum_list, 'age_list': age_list, 'mass_list': mass_list})\nTrue",
+ "hidden": false,
+ "locked": false
+ }
+ ],
+ "scored": true,
+ "setup": "",
+ "teardown": "",
+ "type": "doctest"
+ }
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+ "vscode": {
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+ "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+ }
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diff --git a/sum23/projects/p11/p11_plots.json b/sum23/projects/p11/p11_plots.json
new file mode 100644
index 0000000000000000000000000000000000000000..b01dcd92e733f617f2d825426bcfb74f2af9f9df
--- /dev/null
+++ b/sum23/projects/p11/p11_plots.json
@@ -0,0 +1,12 @@
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2.28, 1.17, 1.49, 1.05, 1.24, 1.21, 1.34, 0.88, 2.07, 1.3, 1.04, 1.08, 0.78, 1.23, 1.12, 1.22, 1.54, 2.03, 0.97, 1.09, 0.9, 1.2, 1.01, 1.46, 0.78, 1.43, 0.67, 0.86, 0.99, 0.77, 1.62, 1.16, 0.72, 0.96, 1.05, 1.08, 0.93, 1.76, 0.93, 0.86, 0.95, 0.58, 0.94, 0.85, 0.83, 0.88, 0.92, 0.96, 0.89, 0.87, 1.08, 1.11, 0.71, 0.89, 1.06, 0.72, 0.55, 1.19, 1.82, 1.11, 1.28, 1.0, 0.93, 1.33, 0.91, 1.49, 0.98, 1.28, 1.11, 0.76, 2.78, 2.52, 0.98, 1.14, 1.46, 0.9, 1.87, 1.43, 0.58, 1.11, 0.84, 0.94, 0.92, 1.55, 1.19, 1.33, 1.67, 1.24, 1.46, 1.06, 1.18, 0.81, 1.21, 0.52, 0.03, 1.2, 0.82, 0.69, 0.88, 0.97, 0.58, 1.1, 1.04, 1.47, 1.41, 1.1, 1.13, 1.7, 1.4, 1.7, 0.82, 0.75, 1.4, 1.23, 2.6, 1.8, 1.5, 9.1, 1.61, 2.13, 1.19, 1.32, 2.3, 2.2, 0.91, 1.54, 1.34, 0.5, 0.7, 0.74, 0.65, 1.14, 0.91, 0.93, 0.18, 0.73, 0.51, 0.42, 0.12, 0.31, 0.39, 0.65, 0.49, 0.49, 0.81, 1.32, 0.92, 0.94, 1.04, 1.3, 1.09, 1.11, 0.95, 1.05, 1.08, 1.0, 0.96, 1.41, 0.58, 0.85, 1.33, 0.91, 1.02, 0.83, 0.95, 1.09, 1.1, 1.04, 0.77, 1.29, 0.99, 1.02, 0.93, 1.38, 1.21, 1.15, 0.9, 1.72, 0.92, 1.03, 0.94, 0.79, 1.12, 1.19, 1.33, 1.12, 0.98, 1.17, 0.79, 1.47, 0.85, 1.06, 1.04, 0.83, 0.98, 0.8, 1.39, 1.91, 1.32, 0.94, 1.07, 0.87, 0.82, 0.98, 1.14, 1.04, 1.1, 0.44, 0.99, 0.98, 1.78, 0.94, 0.98, 0.99, 1.03, 0.96, 1.74, 2.19, 1.31, 1.13, 1.27, 1.5, 0.99, 0.93, 0.87, 1.01, 1.36, 1.17, 1.09, 0.93, 1.01, 0.73, 1.1, 1.26, 4.3, 1.08, 1.0, 1.02, 1.15, 1.11, 1.54, 1.72, 0.78, 0.9, 0.84, 0.71, 0.33, 0.71, 0.68, 0.87, 1.07, 1.19, 0.81, 1.03, 1.13, 1.17, 1.15, 1.5, 1.02, 1.08, 0.93, 1.02, 0.93, 1.17, 0.99, 1.09, 0.87, 1.08, 0.96, 1.09, 0.94, 0.98, 1.25, 1.05, 0.7, 1.04, 0.87, 1.13, 1.07, 1.17, 0.78, 1.0, 0.75, 0.81, 1.07, 1.26, 1.21, 1.13, 1.13, 0.81, 0.92, 0.65, 0.86, 1.01, 0.86, 0.54, 0.79, 1.09, 1.02, 0.98, 0.81, 0.9, 1.02, 0.94, 1.06, 1.25, 0.55, 1.2, 1.0, 0.91, 1.19, 0.87, 1.05, 1.13, 1.15, 1.0, 1.06, 1.15, 1.32, 1.1, 1.02, 1.14, 1.0, 0.87, 1.2, 0.83, 0.83, 0.64, 0.84, 0.66, 0.63, 0.82, 0.86, 0.95, 0.88, 1.12, 0.88, 0.79, 1.03, 1.23, 0.61, 1.11, 0.94, 1.09, 1.14, 0.79, 1.12, 0.92, 0.84, 0.95, 0.65, 0.85, 1.16, 0.94, 0.81, 1.11, 0.98, 0.79, 0.86, 0.75, 1.14, 0.84, 1.12, 0.96, 0.98, 1.18, 0.68, 1.03, 0.59, 0.93, 0.77, 0.9, 1.17, 1.07, 1.11, 1.11, 0.59, 0.92, 0.84, 0.58, 0.97, 0.85, 1.11, 0.6, 0.7, 0.76, 0.75, 0.67, 0.81, 0.83, 0.79, 0.9, 0.61, 0.82, 0.93, 0.92, 0.88, 0.99, 0.8, 1.46, 0.95, 1.13, 0.92, 1.17, 0.94, 1.08, 1.12, 1.08, 0.75, 0.96, 0.6, 0.92, 0.82, 0.97, 0.76, 0.8, 0.9, 0.94, 0.95, 0.98, 1.07, 1.02, 0.57, 0.91, 0.92, 0.67, 1.08, 1.23, 1.08, 1.02, 0.98, 0.59, 0.86, 1.02, 1.33, 1.03, 0.92, 1.09, 1.15, 0.6, 0.79, 0.6, 1.02, 1.05, 1.65, 1.08, 0.93, 0.61, 0.95, 1.05, 1.27, 1.02, 0.55, 0.99, 1.17, 0.94, 1.03, 0.95, 1.17, 1.02, 1.19, 1.02, 1.06, 0.68, 0.97, 0.5, 0.98, 0.93, 0.93, 0.92, 1.06, 1.07, 0.78, 1.15, 1.08, 0.88, 0.67, 0.97, 0.82, 0.82, 0.64, 0.81, 0.86, 1.03, 1.07, 1.21, 0.62, 0.85, 0.84, 1.08, 0.93, 0.91, 1.07, 1.0, 0.89, 0.67, 1.0, 0.9, 0.96, 1.39, 1.01, 1.17, 1.32, 0.75, 1.02, 1.03, 1.59, 1.04, 0.89, 1.24, 1.03, 0.96, 1.1, 1.09, 1.07, 1.05, 0.88, 0.92, 0.96, 0.83, 0.83, 1.04, 0.93, 0.9, 0.91, 0.81, 1.1, 1.09, 1.04, 1.0, 1.07, 1.02, 0.81, 0.84, 0.83, 1.03, 1.05, 0.49, 1.15, 0.82, 0.89, 1.19, 0.94, 1.03, 1.01, 1.03, 1.1, 1.19, 0.98, 1.29, 0.89, 0.91, 1.15, 0.81, 0.95, 0.84, 0.92, 0.94, 1.05, 0.19, 0.35, 0.14, 0.12, 0.76, 0.94, 1.13, 0.94, 0.75, 0.68, 0.45, 0.85, 0.89, 1.21, 0.82, 0.85, 1.1, 0.85, 0.54, 0.68, 0.79, 0.73, 1.15, 1.0, 0.09, 0.68, 1.12, 0.78, 0.95, 1.22, 0.93, 1.04, 0.98, 1.8, 1.47, 1.54, 2.7, 0.89, 1.06, 0.94, 0.92, 1.05, 0.12, 0.4, 0.31, 0.61, 1.12, 1.05, 0.87, 1.21, 0.78, 1.35, 1.03, 0.81, 1.26, 1.52, 0.85, 1.07, 0.67, 0.86, 0.83, 1.04, 0.89, 1.01, 0.73, 0.74, 0.88, 0.79, 0.6, 0.99, 0.76, 1.08, 0.81, 0.92, 0.94, 0.56, 0.84, 0.78, 1.18, 1.27, 1.18, 0.96, 1.03, 0.51, 0.99, 1.07, 0.86, 0.92, 1.02, 1.02, 0.97, 1.09, 0.88, 0.91, 0.6, 0.94, 0.97, 0.97, 0.94, 0.84, 0.94, 1.09, 0.81, 0.97, 1.0, 1.05, 1.04, 1.37, 1.01, 1.17, 0.88, 0.91, 0.95, 0.8, 1.1, 0.96, 0.84, 0.51, 1.17, 0.83, 0.85, 0.93, 0.9, 0.85, 0.54, 0.93, 0.9, 0.86, 1.16, 1.1, 1.1, 0.98, 1.03, 1.08, 0.89, 0.66, 0.99, 0.98, 1.21, 0.91, 0.96, 0.92, 0.85, 0.59, 0.65, 0.8, 1.07, 1.09, 0.93, 1.0, 0.81, 0.8, 0.65, 0.79, 1.14, 1.1, 1.15, 1.04, 1.2, 1.05, 1.14, 0.72, 0.9, 1.17, 1.25, 0.13, 1.07, 0.18, 0.22, 1.19, 0.92, 0.87, 1.04, 0.58, 1.01, 0.98, 0.52, 1.29, 1.04, 1.04, 1.01, 1.06, 1.06, 0.94, 0.65, 0.58, 1.07, 1.07, 0.86, 0.68, 0.58, 0.82, 0.95, 0.73, 0.14, 1.3, 0.91, 1.21, 0.33, 0.32, 1.06, 0.87, 1.13, 0.92, 0.81, 0.81, 0.79, 0.85, 1.07, 0.77, 1.17, 1.51, 0.94, 0.83, 0.84, 1.28, 0.8, 1.0, 1.24, 0.96, 1.03, 1.03, 0.54, 0.63, 0.97, 0.98, 0.94, 0.78, 0.84, 1.0, 0.87, 0.54, 0.86, 0.95, 1.16, 0.94, 0.65, 0.82, 1.12, 0.82, 0.58, 1.06, 0.98, 0.86, 0.95, 0.97, 0.62, 0.98, 0.97, 0.96, 0.85, 0.5, 0.95, 0.8, 0.82, 0.81, 0.58, 1.29, 1.14, 1.0, 1.26, 0.8, 1.15, 0.87, 0.76, 0.88, 0.55, 0.63, 0.69, 1.25, 1.16, 1.17, 0.83, 0.91, 1.06, 0.92, 0.27, 1.02, 0.65, 0.74, 0.81, 0.42, 0.09, 1.1, 1.04, 0.78]}}
diff --git a/sum23/projects/p11/p11_test.py b/sum23/projects/p11/p11_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3a4266cad14b182cfb46ec9a235274049d3eaff
--- /dev/null
+++ b/sum23/projects/p11/p11_test.py
@@ -0,0 +1,479 @@
+#!/usr/bin/python
+import os, json, math
+from collections import namedtuple
+
+MAX_FILE_SIZE = 500 # units - KB
+REL_TOL = 6e-04 # relative tolerance for floats
+ABS_TOL = 15e-03 # absolute tolerance for floats
+
+PASS = "PASS"
+
+TEXT_FORMAT = "text" # question type when expected answer is a str, int, float, or bool
+TEXT_FORMAT_NAMEDTUPLE = "text namedtuple" # question type when expected answer is a namedtuple
+TEXT_FORMAT_UNORDERED_LIST = "text list_unordered" # question type when the expected answer is a list where the order does *not* matter
+TEXT_FORMAT_ORDERED_LIST = "text list_ordered" # question type when the expected answer is a list where the order does matter
+TEXT_FORMAT_SPECIAL_ORDERED_LIST = "text list_special_ordered" # 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_DICT = "text dict" # question type when the expected answer is a dictionary
+PNG_FORMAT_SCATTER = "png scatter" # question type when the expected answer is a scatter plot
+
+
+def return_expected_json():
+ f = open("p11_plots.json", encoding='utf-8')
+ plots_data = json.load(f)
+ f.close()
+ expected_json = {"1": (TEXT_FORMAT_ORDERED_LIST, [219971.31454007205, 220096.664979353, 220049.70437075992]),
+ "2": (TEXT_FORMAT, 0.00028780552063892534),
+ "3": (TEXT_FORMAT, -0.607886435301983),
+ "4": (PNG_FORMAT_SCATTER, plots_data["4"]),
+ "5": (PNG_FORMAT_SCATTER, plots_data["5"]),
+ "6": (TEXT_FORMAT_DICT, {'Red Giant': -0.01889128094725505,
+ 'White Dwarf': 2.787333333333333,
+ 'Neutron Star': 2.86}),
+ "7": (PNG_FORMAT_SCATTER,plots_data["7"]),
+ "8": (PNG_FORMAT_SCATTER, plots_data["8"]),
+ "9": (PNG_FORMAT_SCATTER, plots_data["9"]),
+ "10": (TEXT_FORMAT_ORDERED_LIST, [os.path.join("broken_data", "others", "gjs.json"),
+ os.path.join("broken_data", "others", "others.json"),
+ os.path.join("broken_data", "others", "tois", "tois.json")]),
+ "11": (TEXT_FORMAT_ORDERED_LIST, [os.path.join("broken_data", "keplers", "kepler_100s", "kepler_100s", "kepler_100s", "kepler_100s.json"),
+ os.path.join("broken_data", "keplers", "kepler_100s", "kepler_100s", "others.json"),
+ os.path.join("broken_data", "keplers", "kepler_100s", "kepler_200s", "kepler_220s.json"),
+ os.path.join("broken_data", "keplers", "kepler_100s", "kepler_200s", "kepler_290s.json"),
+ os.path.join("broken_data", "keplers", "kepler_100s", "kepler_200s", "others", "others.json"),
+ os.path.join("broken_data", "keplers", "kepler_100s", "others.json")]),
+ "12": (TEXT_FORMAT_ORDERED_LIST, [os.path.join("broken_data", "keplers", "kepler_10s", "others", "kepler_20s.json"),
+ os.path.join("broken_data", "keplers", "kepler_10s", "others", "kepler_30s.json"),
+ os.path.join("broken_data", "keplers", "kepler_10s", "others", "others.json")]),
+ "13": (TEXT_FORMAT_ORDERED_LIST, [os.path.join("broken_data", "hds", "hd_10000s", "hd_10000s.json"),
+ os.path.join("broken_data", "hds", "others.json"),
+ os.path.join("broken_data", "k2s.json"),
+ os.path.join("broken_data", "keplers", "kepler_100s", "kepler_100s", "kepler_100s", "kepler_100s.json"),
+ os.path.join("broken_data", "keplers", "kepler_100s", "kepler_100s", "others.json"),
+ os.path.join("broken_data", "keplers", "kepler_100s", "kepler_200s", "kepler_220s.json"),
+ os.path.join("broken_data", "keplers", "kepler_100s", "kepler_200s", "kepler_290s.json"),
+ os.path.join("broken_data", "keplers", "kepler_100s", "kepler_200s", "others", "others.json"),
+ os.path.join("broken_data", "keplers", "kepler_100s", "others.json"),
+ os.path.join("broken_data", "keplers", "kepler_10s", "kepler_80s", "kepler_80s.json"),
+ os.path.join("broken_data", "keplers", "kepler_10s", "others", "kepler_20s.json"),
+ os.path.join("broken_data", "keplers", "kepler_10s", "others", "kepler_30s.json"),
+ os.path.join("broken_data", "keplers", "kepler_10s", "others", "others.json"),
+ os.path.join("broken_data", "keplers", "others", "others.json"),
+ os.path.join("broken_data", "others", "gjs.json"),
+ os.path.join("broken_data", "others", "others.json"),
+ os.path.join("broken_data", "others", "tois", "tois.json")]),
+ "14": (TEXT_FORMAT, 1.0000991982973964),
+ "15": (TEXT_FORMAT_ORDERED_LIST, [333.59999999999997, 778.4]),
+ "16": (TEXT_FORMAT_ORDERED_LIST, [1.204068181137186, 1.7736260760497164]),
+ "17": (TEXT_FORMAT_UNORDERED_LIST, ['GJ 422 b',
+ 'HD 10697 b',
+ 'HD 111998 b',
+ 'HD 114729 b',
+ 'HD 145934 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-22 b',
+ 'Kepler-442 b',
+ 'Kepler-443 b',
+ 'Kepler-452 b',
+ 'Kepler-453 b',
+ 'Kepler-705 b',
+ 'Proxima Cen b',
+ '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',
+ "Teegarden's Star b",
+ 'Kepler-174 d',
+ 'Kepler-47 c',
+ 'Kepler-68 d',
+ 'HD 34445 f',
+ 'HD 160691 b',
+ 'HD 191939 g',
+ 'GJ 667 C c',
+ 'GJ 667 C f',
+ 'TRAPPIST-1 d',
+ 'TRAPPIST-1 e',
+ 'TOI-700 d']),
+ "18": (TEXT_FORMAT_ORDERED_LIST, [326.2678091184412, 388.0]),
+ "19": (TEXT_FORMAT_UNORDERED_LIST, ['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']),
+ "20": (TEXT_FORMAT_UNORDERED_LIST, ['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',
+ 'HD 191939 g',
+ 'TRAPPIST-1 e',
+ 'TOI-700 d'])}
+ return expected_json
+
+def check_cell(qnum, actual):
+ expected_json = return_expected_json()
+ format, expected = expected_json[qnum[1:]]
+ try:
+ if format == TEXT_FORMAT:
+ return simple_compare(expected, actual)
+ elif format == TEXT_FORMAT_UNORDERED_LIST:
+ return list_compare_unordered(expected, actual)
+ elif format == TEXT_FORMAT_ORDERED_LIST:
+ return list_compare_ordered(expected, actual)
+ elif format == TEXT_FORMAT_DICT:
+ return dict_compare(expected, actual)
+ elif format == TEXT_FORMAT_NAMEDTUPLE:
+ return namedtuple_compare(expected, actual)
+ elif format == PNG_FORMAT_SCATTER:
+ return check_png_scatter(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 simple_compare(expected, actual, complete_msg=True):
+ msg = PASS
+ if type(expected) == type:
+ if expected != actual:
+ if type(actual) == type:
+ msg = "expected %s but found %s" % (expected.__name__, actual.__name__)
+ else:
+ msg = "expected %s but found %s" % (expected.__name__, repr(actual))
+ elif type(expected) != type(actual) and not (type(expected) in [float, int] and type(actual) in [float, int]):
+ msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+ elif type(expected) == float:
+ if not math.isclose(actual, expected, rel_tol=REL_TOL, abs_tol=ABS_TOL):
+ msg = "expected %s" % (repr(expected))
+ if complete_msg:
+ msg = msg + " but found %s" % (repr(actual))
+ else:
+ if expected != actual:
+ msg = "expected %s" % (repr(expected))
+ if complete_msg:
+ msg = msg + " but found %s" % (repr(actual))
+ return msg
+
+namedtuples = ['Star', 'Planet']
+star_attributes = ['spectral_type',
+ 'stellar_effective_temperature',
+ 'stellar_radius',
+ 'stellar_mass',
+ 'stellar_luminosity',
+ 'stellar_surface_gravity',
+ 'stellar_age']
+# Create a namedtuple type, Star
+Star = namedtuple("Star", star_attributes)
+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']
+# Create a namedtuple type, Planet
+Planet = namedtuple("Planet", planets_attributes)
+
+def namedtuple_compare(expected, actual):
+ msg = PASS
+ try:
+ actual_fields = actual._fields
+ except AttributeError:
+ msg = "expected namedtuple but found %s" % (type(actual).__name__)
+ return msg
+ if type(expected).__name__ != type(actual).__name__:
+ msg = "expected namedtuple %s but found namedtuple %s" % (type(expected).__name__, type(actual).__name__)
+ return msg
+ expected_fields = expected._fields
+ msg = list_compare_ordered(list(expected_fields), list(actual_fields), "namedtuple attributes")
+ if msg != PASS:
+ return msg
+ for field in expected_fields:
+ val = simple_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 list_compare_ordered(expected, actual, obj="list"):
+ msg = PASS
+ if type(expected) != type(actual):
+ msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+ return msg
+ for i in range(len(expected)):
+ if i >= len(actual):
+ msg = "expected missing %s in %s" % (repr(expected[i]), obj)
+ break
+ if type(expected[i]) in [int, float, bool, str]:
+ val = simple_compare(expected[i], actual[i])
+ elif type(expected[i]) in [list, tuple]:
+ val = list_compare_ordered(expected[i], actual[i], "sub" + obj)
+ elif type(expected[i]) in [dict]:
+ val = dict_compare(expected[i], actual[i])
+ elif type(expected[i]).__name__ in namedtuples:
+ val = namedtuple_compare(expected[i], actual[i])
+ if val != PASS:
+ msg = "at index %d of the %s, " % (i, obj) + val
+ break
+ if len(actual) > len(expected) and msg == PASS:
+ msg = "found unexpected %s in %s" % (repr(actual[len(expected)]), obj)
+ if len(expected) != len(actual):
+ msg = msg + " (found %d entries in %s, but expected %d)" % (len(actual), obj, len(expected))
+
+ if len(expected) > 0 and type(expected[0]) in [int, float, bool, str]:
+ if msg != PASS and list_compare_unordered(expected, actual, obj) == PASS:
+ try:
+ msg = msg + " (%s may not be ordered as required)" % (obj)
+ except:
+ pass
+ return msg
+
+
+def list_compare_helper(larger, smaller):
+ 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):
+ if type(larger[i]) in [list, tuple]:
+ val = list_compare_ordered(larger[i], smaller[j - 1])
+ else:
+ val = simple_compare(larger[i], smaller[j - 1], False)
+ break
+ if type(larger[i]) in [list, tuple]:
+ val = list_compare_ordered(larger[i], smaller[j])
+ else:
+ val = simple_compare(larger[i], smaller[j], False)
+ j += 1
+ if val == PASS:
+ found = True
+ break
+ if not found:
+ msg = val
+ break
+ return msg
+
+
+def list_compare_unordered(expected, actual, obj="list"):
+ msg = PASS
+ if type(expected) != type(actual):
+ msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+ return msg
+ try:
+ sort_expected = sorted(expected)
+ sort_actual = sorted(actual)
+ except:
+ msg = "unexpected datatype found in %s; expected entries of type %s" % (obj, obj, type(expected[0]).__name__)
+ return msg
+
+ if len(actual) == 0 and len(expected) > 0:
+ msg = "in the %s, missing" % (obj) + expected[0]
+ elif len(actual) > 0 and len(expected) > 0:
+ val = simple_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 list_compare_special_init(expected, special_order):
+ real_expected = []
+ for i in range(len(expected)):
+ if real_expected == [] or special_order[i-1] != special_order[i]:
+ real_expected.append([])
+ real_expected[-1].append(expected[i])
+ return real_expected
+
+
+def list_compare_special(expected, actual, special_order):
+ expected = list_compare_special_init(expected, special_order)
+ msg = PASS
+ expected_list = []
+ for expected_item in expected:
+ expected_list.extend(expected_item)
+ val = list_compare_unordered(expected_list, actual)
+ if val != PASS:
+ msg = val
+ else:
+ i = 0
+ for expected_item in expected:
+ 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="dict"):
+ msg = PASS
+ if type(expected) != type(actual):
+ msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+ return msg
+ try:
+ expected_keys = sorted(list(expected.keys()))
+ actual_keys = sorted(list(actual.keys()))
+ except:
+ msg = "unexpected datatype found in keys of dict; expect a dict with keys of type %s" % (
+ type(expected_keys[0]).__name__)
+ return msg
+ val = list_compare_unordered(expected_keys, actual_keys, "dict")
+ if val != PASS:
+ msg = "bad keys in %s: " % (obj) + val
+ if msg == PASS:
+ for key in expected:
+ if expected[key] == None or type(expected[key]) in [int, float, bool, str]:
+ val = simple_compare(expected[key], actual[key])
+ elif type(expected[key]) in [list]:
+ val = list_compare_ordered(expected[key], actual[key], "value")
+ elif type(expected[key]) in [dict]:
+ val = dict_compare(expected[key], actual[key], "sub" + obj)
+ elif type(expected[key]).__name__ in namedtuples:
+ val = namedtuple_compare(expected[key], actual[key])
+ if val != PASS:
+ msg = "incorrect val for key %s in %s: " % (repr(key), obj) + val
+ return msg
+
+def helper_png_scatter(expected, actual):
+ msg = PASS
+ expected_length = len(list(expected.values())[0])
+ for key in actual:
+ if len(actual[key]) != expected_length:
+ return "list '%s' has length %d but is expected to have length %d" % (key, len(actual[key]), expected_length)
+ expected_plot_points = []
+ actual_plot_points = []
+ for i in range(expected_length):
+ expected_point = []
+ actual_point = []
+ for key in expected:
+ expected_point.append(expected[key][i])
+ actual_point.append(actual[key][i])
+ expected_plot_points.append(tuple(expected_point))
+ actual_plot_points.append(tuple(actual_point))
+ sorted_expected_points = sorted(expected_plot_points)
+ sorted_actual_points = sorted(actual_plot_points)
+ val = list_compare_ordered(sorted_expected_points, sorted_actual_points, "plot")
+ if val != PASS:
+ val = list_compare_ordered(expected_plot_points, actual_plot_points, "plot")
+ bad_idx = int(val.split("index")[1].split()[0])
+ msg = "in the plot, at index %d, expected point %s but found %s" % (bad_idx, expected_plot_points[bad_idx], actual_plot_points[bad_idx])
+ return msg
+
+
+
+def check_png_scatter(expected, actual):
+ msg = PASS
+ if type(list(expected.values())[0]) == list:
+ msg = helper_png_scatter(expected, actual)
+ elif type(list(expected.values())[0]) == dict:
+ expected_keys = list(list(expected.values())[0].keys())
+ for key in actual:
+ val = list_compare_unordered(expected_keys, list(actual[key].keys()), "dictionary %s" % key)
+ if val != PASS:
+ return val
+ expected_flipped = {}
+ for key_1 in list(expected.values())[0]:
+ expected_flipped[key_1] = {}
+ for key_2 in expected:
+ expected_flipped[key_1][key_2] = expected[key_2][key_1]
+ actual_flipped = {}
+ for key_1 in list(actual.values())[0]:
+ actual_flipped[key_1] = {}
+ for key_2 in actual:
+ actual_flipped[key_1][key_2] = actual[key_2][key_1]
+ for key in expected_flipped:
+ val = helper_png_scatter(expected_flipped[key], actual_flipped[key])
+ if val != PASS:
+ if "length" in val:
+ msg = val.replace("list", "category '%s' of dictionary" % (key))
+ else:
+ msg = val.replace("in the plot,", "in the plot, for the category '%s'," % (key))
+ return msg
+ return msg
+
+
+
+def check(qnum, actual):
+ msg = check_cell(qnum, actual)
+ if msg == PASS:
+ return True
+ print("<b style='color: red;'>ERROR:</b> " + msg)
+
+
+def check_file_size(path):
+ size = os.path.getsize(path)
+ assert size < MAX_FILE_SIZE * 10**3, "Your file is too big to be processed by Gradescope; please delete unnecessary output cells so your file size is < %s KB" % MAX_FILE_SIZE
diff --git a/sum23/projects/p12/p12.ipynb b/sum23/projects/p12/p12.ipynb
index 04565f654920d4bcd2aaeeb1ea0091e81db3132d..28d443e9cf348df43f39d09e948472c99e5911b4 100644
--- a/sum23/projects/p12/p12.ipynb
+++ b/sum23/projects/p12/p12.ipynb
@@ -957,7 +957,7 @@
"editable": false
},
"source": [
- "**Question 15:** In the `Year` *2020-2021*, **list** the institutions which do **not** feature in the **top** *50* in the world based on `World Ranking`, but have a `Alumni Employment Rank` **less than or equal** to *25*.\n",
+ "**Question 15:** In the `Year` *2020-2021*, **list** the institutions which do **not** feature in the **top** *50* in the world based on `World Rank`, but have a `Alumni Employment Rank` **less than or equal** to *25*.\n",
"\n",
"Your output **must** be a **list** of institutions. The **order** does **not** matter. You **must** use the `year_2020_ranking_df` DataFrame that you created in Question 11 to answer this question."
]