Explore Close Approaches of Near-Earth Objects

In this project, you'll use Python - and the skills we've developed throughout this course - to search for and explore close approaches of near-Earth objects (NEOs), using data from NASA/JPL's Center for Near Earth Object Studies.

Overview

At a high-level, you'll create Python code that implements a command-line tool to inspect and query a dataset of NEOs and their close approaches to Earth.

Concretely, you'll have to read data from both a CSV file and a JSON file, convert that data into structured Python objects, perform filtering operations on the data, limit the size of the result set, and write the results to a file in a structured format, such as CSV or JSON.

When complete, you'll be able to inspect the properties of the near-Earth objects in the data set and query the data set of close approaches to Earth using any combination of the following filters:

• Occurs on a given date.
• Occurs on or after a given start date.
• Occurs on or before a given end date.
• Approaches Earth at a distance of at least (or at most) X astronomical units.
• Approaches Earth at a relative velocity of at least (or at most) Y kilometers per second.
• Has a diameter that is at least as large as (or at least as small as) Z kilometers.
• Is marked by NASA as potentially hazardous (or not).

Learning Objectives

By completing this project, you'll have demonstrated an ability to:

• Represent structured data in Python.
• Extract data from structured files into Python.
• Transform the data within Python according to some desired behavior.
• Save the results in a structured way to a file.

Along the way, you'll have to be able to:

• Write Python functions to transform data and perform algorithms.
• Design Python classes to encapsulate useful data types.
• Provide interface abstractions for complex implementations.

It's normal to encounter bugs along the way, so in all likelihood, you'll also gain practice with valuable debugging skills, whether interpreting stack traces, chasing down system errors, handling and raising appropriate errors, walking through code with pdb, checking preconditions with assert, or simply displaying internal state with print.

Understanding the Near-Earth Object Close Approach Datasets

This project contains two important data sets, and our first step will be to explore and understand the data containing within these structured files.

One dataset (neos.csv) contains information about semantic, physical, orbital, and model parameters for certain small bodies (asteroids and comets, mostly) in our solar system. The other dataset (cad.json) contains information about NEO close approaches - moments in time when the orbit of an astronomical body brings it close to Earth. NASA helpfully provides a glossary to define any unfamiliar terms you might encounter.

Importantly, these datasets come directly from NASA - we haven't dressed them up for you at all.

Small-Bodies Dataset

NASA's Jet Propulsion Laboratory (JPL) provides a web interface to their database of "small bodies" - mostly asteroids and comets - in the solar system. A subset of these small bodies are near-Earth objects (NEOs): "comets and asteroids that have been nudged by the gravitational attraction of nearby planets into orbits that allow them to enter the Earth's neighborhood." 1

From this dataset, you can answer questions such as "what is the diameter of the Halley's Comet?" or "is the near-Earth object named 'Eros' potentially hazardous?".

NASA's web service lets you download their data on near-Earth objects in a CSV format. For this project, the data set we've provided (neos.csv) comes directly from a query in which we limited the "Object Group" to NEOs and in which we selected every output field. That's a lot of columns (75, to be exact)!

Let's take an initial look at the first three rows of neos.csv:

id,spkid,full_name,pdes,name,prefix,neo,pha,H,G,M1,M2,K1,K2,PC,diameter,extent,albedo,rot_per,GM,BV,UB,IR,spec_B,spec_T,H_sigma,diameter_sigma,orbit_id,epoch,epoch_mjd,epoch_cal,equinox,e,a,q,i,om,w,ma,ad,n,tp,tp_cal,per,per_y,moid,moid_ld,moid_jup,t_jup,sigma_e,sigma_a,sigma_q,sigma_i,sigma_om,sigma_w,sigma_ma,sigma_ad,sigma_n,sigma_tp,sigma_per,class,producer,data_arc,first_obs,last_obs,n_obs_used,n_del_obs_used,n_dop_obs_used,condition_code,rms,two_body,A1,A2,A3,DT
a0000433,2000433,"   433 Eros (A898 PA)",433,Eros,,Y,N,10.4,0.46,,,,,,16.84,34.4x11.2x11.2,0.25,5.270,4.463e-04,0.921,0.531,,S,S,,0.06,"JPL 658",2459000.5,59000,20200531.0000000,J2000,.2229512647434284,1.458045729081037,1.132972589728666,10.83054121829922,304.2993259000444,178.8822959227224,271.0717325705167,1.783118868433408,.5598186418120109,2459159.351922368362,20201105.8519224,643.0654021001488,1.76061711731731,.148623,57.83961291,3.2865,4.582,9.6497E-9,2.1374E-10,1.4063E-8,1.1645E-6,3.8525E-6,4.088E-6,1.4389E-6,2.6139E-10,1.231E-10,2.5792E-6,1.414E-7,AMO,Giorgini,46330,1893-10-29,2020-09-03,8767,4,2,0,.28397,,,,,
a0000719,2000719,"   719 Albert (A911 TB)",719,Albert,,Y,N,15.5,,,,,,,,,,5.801,,,,,S,,,,"JPL 214",2459000.5,59000,20200531.0000000,J2000,.5465584653041263,2.63860206439375,1.196451769530403,11.56748478123323,183.8669499802364,156.17633771,140.2734217745985,4.080752359257098,.2299551959241748,2458390.496728663387,20180928.9967287,1565.522355575327,4.28616661348481,.203482,79.18908994,1.41794,3.140,2.1784E-8,2.5313E-9,5.8116E-8,2.9108E-6,1.6575E-5,1.6827E-5,2.5213E-6,3.9148E-9,3.309E-10,1.0306E-5,2.2528E-6,AMO,"Otto Matic",39593,1911-10-04,2020-02-27,1874,,,0,.39148,,,,,

Before we're able to write Python code to process this data, we'll need to understand what this data represents.

In this CSV file, the first row is a header, containing names for each of the columns. Each subsequent row represents a single NEO. There are too many columns to understand fully (although we encourage you to learn more by searching NASA's website!), so we'll focus on just a few of them:

pdes - the primary designation of the NEO. This is a unique identifier in the database, and its "name" to computer systems.
name - the International Astronomical Union (IAU) name of the NEO. This is its "name" to humans.
pha - whether NASA has marked the NEO as a "Potentially Hazardous Asteroid," roughly meaning that it's large and can come quite close to Earth.
diameter - the NEO's diameter (from an equivalent sphere) in kilometers.

So, the first NEO described in the CSV file has a primary designation of 433 and an IAU name "Eros". It is ('Y') an NEO, but it is not ('N') potentially hazardous. It has a diameter of 16.84km.

Every NEO has a primary designation, but there exist NEOs without names (in fact, having an IAU name is relatively rare!). Some IAU names are reused for several NEOs. For some NEOs, the data doesn't include information about a diameter, because NASA does not have enough observations to make a reasonably-accurate estimate.

If you'd like to explore individual NEOs in more detail (and perhaps interpret a few of the rest of the columns), NASA also provides a web interface to search for a single small body as well as an API.

Close Approach Dataset

NASA's Center for Near-Earth Object Studies (CNEOS) also provides data about close approaches of NEOs to Earth. A close approach occurs when an NEO's orbit path brings it near Earth - although, "near" in astronomical terms can be quite far in human-scale units, such as kilometers. Instead of kilometers, astronomical distances within the solar system are often measured with the astronomical unit (au) - the mean distance between the Earth and the sun - although sometimes you'll see distances measured with the lunar distance (ld) - the mean distance between the Earth and the moon - or even plain old kilometers.

From this dataset, you can answer questions such as "On which date(s) does Halley's Comet pass near to Earth?" or "How fast does Eros pass by Earth, on average?"

The data is JSON-formatted, and we've downloaded it from NASA's public API. A description of the API, as well as details about the query parameters and the scheme of the returned data, can be found here. Concretely, we asked NASA for this data by querying the API at https://ssd-api.jpl.nasa.gov/cad.api?date-min=1900-01-01&date-max=2100-01-01&dist-max=1. In other words, our data set contains all currently known close approaches that have happened or will happen in the 20th and 21st centuries! Additionally, NASA provides the data is chronological order.

Let's take an initial look at the data in cad.json.

{
  "signature":{
    "source":"NASA/JPL SBDB Close Approach Data API",
    "version":"1.1"
  },
  "count":"406785",
  "fields":["des", "orbit_id", "jd", "cd", "dist", "dist_min", "dist_max", "v_rel", "v_inf", "t_sigma_f", "h"],
  "data":[
    [
       "170903",
       "105",
       "2415020.507669610",
       "1900-Jan-01 00:11",
       "0.0921795123769547",
       "0.0912006569517418",
       "0.0931589328621254",
       "16.7523040362574",
       "16.7505784933163",
       "01:00",
       "18.1"
    ],
    [
       "2005 OE3",
       "52",
       "2415020.606013490",
       "1900-Jan-01 02:33",
       "0.414975519685102",
       "0.414968315685577",
       "0.414982724454678",
       "17.918395877175",
       "17.9180375373357",
       "< 00:01",
       "20.3"
    ],
    ...
  ]
}

It certainly looks different from the CSV data!

The top-level JSON payload is a dictionary with keys "signature", "count", "fields", and "data". The "signature" field shows where this data came from - in this case, from the API provided by NASA/JPL. The "count" field tells us how many entries to expect in the "data" section. The "fields" key maps to a list of strings describing how we should interpret the entries in the "data" section. Lastly, the "data" section itself maps to a list of lists - each element is a list of data for a single close approach, corresponding (by order) with the "fields" key.

What do each of the fields mean? NASA's API documentation provides the answer:

• des - primary designation of the asteroid or comet (e.g., 443, 2000 SG344)
• orbit_id - orbit ID
• jd - time of close-approach (JD Ephemeris Time)
• cd - time of close-approach (formatted calendar date/time, in UTC)
• dist - nominal approach distance (au)
• dist_min - minimum (3-sigma) approach distance (au)
• dist_max - maximum (3-sigma) approach distance (au)
• v_rel - velocity relative to the approach body at close approach (km/s)
• v_inf - velocity relative to a massless body (km/s)
• t_sigma_f - 3-sigma uncertainty in the time of close-approach (formatted in days, hours, and minutes; days are not included if zero; example "13:02" is 13 hours 2 minutes; example "2_09:08" is 2 days 9 hours 8 minutes)
• h - absolute magnitude H (mag)

With this in mind, we can interpret that the first close approach contained in the dataset is:

• an asteroid or comet with primary designation "170903"
• an orbit ID of 105
• a close approach time of 2415020.507669610 (in JD Ephemeris time) or 1900-Jan-01 00:11 (in a normal format)
• an approach distance of 0.0921795123769547 astronomical units (with 3-sigma bounds of (0.0912006569517418au, 0.0931589328621254au))
• an approach velocity of 16.7523040362574 km/s (relative to Earth) or 16.7505784933163 km/s (relative to a massless body)
• 3-sigma uncertainty in the time of close approach of 1 hour
• an absolute magnitude of 18.1

The second close approach contained in the dataset is:

• an asteroid or comet with primary designation "2005 OE3"
• an orbit ID of 52
• a close approach time of 2415020.606013490 (in JD Ephemeris time) or 1900-Jan-01 02:33 (in a normal format)
• an approach distance of 0.414975519685102 astronomical units (with 3-sigma bounds of (0.414968315685577au, 0.414982724454678au))
• an approach velocity of 17.918395877175 km/s (relative to Earth) or 17.9180375373357 km/s (relative to a massless body)
• 3-sigma uncertainty in the time of close approach of less than 1 minute.
• an absolute magnitude of 20.3

As before, this data set contains more information than we need. For this project, we'll make use of the des, cd, dist, and v_rel measurements - although the other attributes can be useful if you wish to extend the project! Fortunately, each entry has well-formatted data for each of these attributes.

Visual Exploration

If you're someone who prefers to explore data sets by poking around a web site, NASA has a tutorial video on how to effectively navigate the CNEOS website, and an interactive close approach data table that you can investigate.

Also, it's important to realize that NASA is discovering new NEOs, and potential forecasting new close approaches, every week, so their web-based UI might contain updated information that isn't represented in the data files included with this project.

Project Interface

Now that we understand the data with which we'll be working, let's dive into what our program will actually do

This project is driven by the main.py script. That means that you'll run python3 main.py ... ... ... at the command line to invoke the program that will call your code.

At a command line, you can run python3 main.py --help for an explanation of how to invoke the script.

usage: main.py [-h] [--neofile NEOFILE] [--cadfile CADFILE] {inspect,query,interactive} ...

Explore past and future close approaches of near-Earth objects.

positional arguments:
  {inspect,query,interactive}

optional arguments:
  -h, --help            show this help message and exit
  --neofile NEOFILE     Path to CSV file of near-Earth objects.
  --cadfile CADFILE     Path to JSON file of close approach data.

There are three subcommands: inspect, query, and interactive. Let's take a look at the interfaces of each of these subcommands.

inspect

The inspect subcommand inspects a single NEO, printing its details in a human-readable format. The NEO is specified with exactly one of the --pdes option (the primary designation) and the --name option (the IAU name). The --verbose flag additionally prints out, in a human-readable form, all known close approaches to Earth made by this NEO. Each of these options has an abbreviated version. To remind yourself of the full interface, you can run python3 main.py inspect --help:

$ python3 main.py inspect --help
usage: main.py inspect [-h] [-v] (-p PDES | -n NAME)

Inspect an NEO by primary designation or by name.

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         Additionally, print all known close approaches of this NEO.
  -p PDES, --pdes PDES  The primary designation of the NEO to inspect (e.g. '433').
  -n NAME, --name NAME  The IAU name of the NEO to inspect (e.g. 'Halley').

Here are a few examples of the inspect subcommand in action:

# Inspect the NEO with a primary designation of 433 (that's Eros!)
$ python3 main.py inspect --pdes 433
NEO 433 (Eros) has a diameter of 16.840 km and is not potentially hazardous.

# Inspect the NEO with an IAU name of "Halley" (that's Halley's Comet!)
$ python3 main.py inspect --name Halley
NEO 1P (Halley) has a diameter of 11.000 km and is not potentially hazardous.

# Attempt to inspect an NEO that doesn't exist.
$ python3 main.py inspect --name fake-comet
No matching NEOs exist in the database.

# Verbosely list information about Ganymed and each of its known close approaches.
# For the record, Ganymed is HUGE - it's the largest known NEO.
$ python3 main.py inspect --verbose --name Ganymed
NEO 1036 (Ganymed) has a diameter of 37.675 km and is not potentially hazardous.
- On 1911-10-15 19:16, '1036 (Ganymed)' approaches Earth at a distance of 0.38 au and a velocity of 17.09 km/s.
- On 1924-10-17 00:51, '1036 (Ganymed)' approaches Earth at a distance of 0.50 au and a velocity of 19.36 km/s.
- On 1998-10-14 05:12, '1036 (Ganymed)' approaches Earth at a distance of 0.46 au and a velocity of 13.64 km/s.
- On 2011-10-13 00:04, '1036 (Ganymed)' approaches Earth at a distance of 0.36 au and a velocity of 14.30 km/s.
- On 2024-10-13 01:56, '1036 (Ganymed)' approaches Earth at a distance of 0.37 au and a velocity of 16.33 km/s.
- On 2037-10-15 18:31, '1036 (Ganymed)' approaches Earth at a distance of 0.47 au and a velocity of 18.68 km/s.

For an NEO to be found with the inspect subcommand, the given primary designation or IAU name must match the data exactly, so if an NEO is mysteriously missing, double-check the spelling and capitalization.

$ python3 main.py query --help
usage: main.py query [-h] [-d DATE] [-s START_DATE] [-e END_DATE] [--min-distance DISTANCE_MIN] [--max-distance DISTANCE_MAX]
                     [--min-velocity VELOCITY_MIN] [--max-velocity VELOCITY_MAX] [--min-diameter DIAMETER_MIN]
                     [--max-diameter DIAMETER_MAX] [--hazardous] [--not-hazardous] [-l LIMIT] [-o OUTFILE]

Query for close approaches that match a collection of filters.

optional arguments:
  -h, --help            show this help message and exit
  -l LIMIT, --limit LIMIT
                        The maximum number of matches to return. Defaults to 10 if no --outfile is given.
  -o OUTFILE, --outfile OUTFILE
                        File in which to save structured results. If omitted, results are printed to standard output.

Filters:
  Filter close approaches by their attributes or the attributes of their NEOs.

  -d DATE, --date DATE  Only return close approaches on the given date, in YYYY-MM-DD format (e.g. 2020-12-31).
  -s START_DATE, --start-date START_DATE
                        Only return close approaches on or after the given date, in YYYY-MM-DD format (e.g. 2020-12-31).
  -e END_DATE, --end-date END_DATE
                        Only return close approaches on or before the given date, in YYYY-MM-DD format (e.g. 2020-12-31).
  --min-distance DISTANCE_MIN
                        In astronomical units. Only return close approaches that pass as far or farther away from Earth as the given
                        distance.
  --max-distance DISTANCE_MAX
                        In astronomical units. Only return close approaches that pass as near or nearer to Earth as the given
                        distance.
  --min-velocity VELOCITY_MIN
                        In kilometers per second. Only return close approaches whose relative velocity to Earth at approach is as fast
                        or faster than the given velocity.
  --max-velocity VELOCITY_MAX
                        In kilometers per second. Only return close approaches whose relative velocity to Earth at approach is as slow
                        or slower than the given velocity.
  --min-diameter DIAMETER_MIN
                        In kilometers. Only return close approaches of NEOs with diameters as large or larger than the given size.
  --max-diameter DIAMETER_MAX
                        In kilometers. Only return close approaches of NEOs with diameters as small or smaller than the given size.
  --hazardous           If specified, only return close approaches of NEOs that are potentially hazardous.
  --not-hazardous       If specified, only return close approaches of NEOs that are not potentially hazardous.

Here are a few examples of the query subcommand in action:

# Show (the first) two close approaches in the data set.
$ python3 main.py query --limit 2
On 1900-01-01 00:11, '170903' approaches Earth at a distance of 0.09 au and a velocity of 16.75 km/s.
On 1900-01-01 02:33, '2005 OE3' approaches Earth at a distance of 0.41 au and a velocity of 17.92 km/s.

# Show (the first) three close approaches on July 29th, 1969.
$ python3 main.py query --date 1969-07-29 --limit 3
On 1969-07-29 01:47, '408982' approaches Earth at a distance of 0.36 au and a velocity of 24.24 km/s.
On 1969-07-29 13:33, '2010 MA' approaches Earth at a distance of 0.21 au and a velocity of 8.80 km/s.
On 1969-07-29 19:56, '464798' approaches Earth at a distance of 0.10 au and a velocity of 8.02 km/s.

# Show (the first) three close approaches in 2050.
$ python3 main.py query --start-date 2050-01-01 --limit 3
On 2050-01-01 04:18, '2019 AY9' approaches Earth at a distance of 0.31 au and a velocity of 8.31 km/s.
On 2050-01-01 06:00, '162361' approaches Earth at a distance of 0.19 au and a velocity of 9.08 km/s.
On 2050-01-01 09:55, '2009 LW2' approaches Earth at a distance of 0.04 au and a velocity of 19.02 km/s.

# Show (the first) four close approaches in March 2020 that passed at least 0.4au of Earth.
$ python3 main.py query --start-date 2020-03-01 --end-date 2020-03-31 --min-distance 0.4 --limit 4
On 2020-03-01 00:28, '152561' approaches Earth at a distance of 0.42 au and a velocity of 11.23 km/s.
On 2020-03-01 09:28, '462550' approaches Earth at a distance of 0.47 au and a velocity of 17.19 km/s.
On 2020-03-02 21:41, '2020 QF2' approaches Earth at a distance of 0.45 au and a velocity of 8.79 km/s.
On 2020-03-03 00:49, '2019 TU' approaches Earth at a distance of 0.49 au and a velocity of 5.92 km/s.

# Show (the first) three close approaches that passed at most 0.0025au from Earth with a relative speed of at most 5 km/s.
# That's slightly less than the average distance between the Earth and the moon.
$ python3 main.py query --max-distance 0.0025 --max-velocity 5 --limit 3
On 1949-01-01 02:53, '2003 YS70' approaches Earth at a distance of 0.00 au and a velocity of 3.64 km/s.
On 1954-03-13 00:00, '2013 RZ53' approaches Earth at a distance of 0.00 au and a velocity of 3.04 km/s.
On 1979-09-02 00:16, '2014 WX202' approaches Earth at a distance of 0.00 au and a velocity of 1.79 km/s.

# Show (the first) three close approaches in the 2000s of NEOs with a known diameter of least 6 kilometers that passed Earth at a relative velocity of at least 15 km/s.
$ python3 main.py query --start-date 2000-01-01 --min-velocity 15 --min-diameter 6 --limit 3
On 2000-05-21 10:08, '7092 (Cadmus)' approaches Earth at a distance of 0.34 au and a velocity of 28.46 km/s.
On 2004-05-25 03:54, '7092 (Cadmus)' approaches Earth at a distance of 0.41 au and a velocity of 30.52 km/s.
On 2006-06-10 20:04, '1866 (Sisyphus)' approaches Earth at a distance of 0.49 au and a velocity of 26.81 km/s.

# Show (the first) two close approaches in January 2030 of NEOs that are at most 50m in diameter and are marked not potentially hazardous.
$ python3 main.py query --start-date 2030-01-01 --end-date 2030-01-31 --max-diameter 0.05 --not-hazardous --limit 2
On 2030-01-07 20:59, '2010 GH7' approaches Earth at a distance of 0.46 au and a velocity of 18.84 km/s.
On 2030-01-13 07:29, '2010 AE30' approaches Earth at a distance of 0.06 au and a velocity of 14.00 km/s.

# Show (the first) three close approaches in 2021 of potentially hazardous NEOs at least 100m in diameter that pass within 0.1au of Earth at a relative velocity of at least 15 kilometers per second.
$ python3 main.py query --start-date 2021-01-01 --max-distance 0.1 --min-velocity 15 --min-diameter 0.1 --hazardous --limit 3
On 2021-01-21 22:56, '363024' approaches Earth at a distance of 0.07 au and a velocity of 15.31 km/s.
On 2021-02-01 22:26, '2016 CL136' approaches Earth at a distance of 0.04 au and a velocity of 18.06 km/s.
On 2021-08-21 15:10, '2016 AJ193' approaches Earth at a distance of 0.02 au and a velocity of 26.17 km/s.

# Save, to a CSV file,  all close approaches.
$ python3 main.py query --outfile results.csv

# Save, to a JSON file, all close approaches in the 2020s of NEOs at least 1km in diameter that pass between 0.01 au and 0.1 au away from Earth.
$ python3 main.py query --start-date 2020-01-01 --end-date 2029-12-31 --min-diameter 1 --min-distance 0.01 --max-distance 0.1 --outfile results.json

interactive

There's a third useful subcommand named interactive. This subcommand first loads the database and then starts a command loop so that you can repeatedly run inspect and query subcommands on the database without having to wait to reload the data each time you want to run a new command, which saves an extraordinary amount of time. This can be extremely helpful, as it lets you speed up your development cycle and even show off the project more easily to friends.

Here's what an example session might look like:

$ python3 main.py interactive
Explore close approaches of near-Earth objects. Type `help` or `?` to list commands and `exit` to exit.

(neo) inspect --pdes 433
NEO 433 (Eros) has a diameter of 16.840 km and is not potentially hazardous.
(neo) help i
Shorthand for `inspect`.
(neo) i --name Halley
NEO 1P (Halley) has a diameter of 11.000 km and is not potentially hazardous.
(neo) query --date 2020-12-31 --limit 2
On 2020-12-31 05:48, '2010 PQ10' approaches Earth at a distance of 0.45 au and a velocity of 21.69 km/s.
On 2020-12-31 16:00, '2015 YA' approaches Earth at a distance of 0.17 au and a velocity of 5.65 km/s.
(neo) q --date 2021-3-14 --min-velocity 10
On 2021-03-14 06:17, '2019 DS1' approaches Earth at a distance of 0.39 au and a velocity of 20.17 km/s.
On 2021-03-14 20:19, '483656' approaches Earth at a distance of 0.06 au and a velocity of 12.09 km/s.
...

The prompt is (neo) . At the prompt, you can enter either an inspect or a query subcommand, with the exact same options and behavior as you would on the command line. You can use the special command quit, exit, or CTRL+D to exit this session and return to the command line. The command help or ? shows a help menu, and help <command> (e.g. help query) shows a help menu specific to that command. In this environment only, you can also use the short forms i and q for inspect and query (e.g. (neo) i --verbose --name Ganymed)).

Importantly, the interactive session doesn't automatically update when you update your code. This means that, if you make a meaningful change to your Python files, you should exit and restart the session. If the interactive session detects that any Python files have changed since it began, it will warn you before it runs each new command. The interactive subcommand takes an optional argument --aggressive - if specified, the interactive session will instead preemptively exit whenever it notices any changes to any Python files.

All in all, the interactive subcommand has the following options:

$ python3 main.py interactive --help
usage: main.py interactive [-h] [-a]

Start an interactive command session to repeatedly run `interact` and `query` commands.

optional arguments:
  -h, --help        show this help message and exit
  -a, --aggressive  If specified, kill the session whenever a project file is modified.

Let's take a closer look at the purpose of each of these files and folders:

• main.py: The main Python script that wraps the command-line tool, orchestrates the data pipeline by invoking the functions and classes that you'll write. You will not need to modify this file.
• models.py: In this file, you'll define Python objects to represent a NearEarthObject and a CloseApproach. These objects will have a few attributes, a human-readable string representatino, and perhaps a property or a method here or there.
• extract.py: In this file, you'll write functions to read information from data files, creating NearEarthObjects and CloseApproaches from the data.
• database.py: In this file, you'll define an NEODatabase class to encapsulate the entire data set (connecting NEOs and close approaches) and write methods to get NEOs by primary designation and by name, as well as to query the dataset with a collection of user-specified filters to generate an iterable stream of matching results.
• filters.py: In this file, you'll create a plethora of filters to be used in conjuction with the NEODatabase to query for a stream of matching close approaches. You'll also write a utility function to limit the number of results produced from a stream.
• write.py: Finally, in this file, you'll implement functions to write a stream of results (the CloseApproach objects generated by the NEODatabase) to a file either in JSON format or in CSV format.
• helpers.py: A simple module that provides a few helpful utility functions to convert to and from datetime objects.

The data files are located in the data/ folder.

Additionally, the starter code includes unit tests that will help you check your progress as you advance through this project. The unit tests all live in the tests/ folder. When the project is fully implemented, all of the unit tests should pass. To run all of the tests, you can use python3 -m unittest --verbose at the command line, although many tests will currently fail since the project isn't yet finished.