cifparse 0.9.3

A python parser for the FAA CIFP

cifparse

A python parser for the FAA CIFP.

Versions

Version	Description	Release Date
0.9.3	Updated procedure handling (breaking changes) and database support.	2024-11-15
0.9.2	Minor fixes.	2024-07-13
0.9.0	Initial public release.	2024-07-13

A changelog is available in the CHANGELOG.md with additional detail and guidance.

Installation

Install using pip:

pip install cifparse

Usage

Usage is relatively straightforward. Setting the path to the file can be somewhat finnicky, as it will only accept relative paths. To keep things simple, place the CIFP file in your project directory. Otherwise, if you want to go up several folders into a download folder, it might end up looking like ../../../../Downloads/FAACIFP18.

Given the amount of data, parsing can take a moment. If dumping the data to a file, that can also add time. Dumping every airport to JSON can take around 15 seconds, and the resulting file is about 330MB.

Examples

Start by importing cifparse, setting the path to the CIFP file, and then parsing the data.

import cifparse

# Initialize the parser:
from cifparse import CIFP

# Set the path to where you have the CIFP file:
c = CIFP("FAACIFP18")

# Parse the data in the file:
c.parse()
# ...or to save time, parse only a specific subset:
c.parse_airports()
c.parse_heliports()
c.parse_ndbs()
c.parse_airways()
c.parse_vhf_dmes()
c.parse_waypoints()
c.parse_controlled()
c.parse_restrictive()

Working with Entire Segments

After parsing the data, the results will be in the CIFP object, accessible via getters that return lists of the objects.

all_airports = c.get_airports()
all_heliports = c.get_heliports()
all_airways = c.get_airways()
all_ndbs = c.get_ndbs()
all_vordmes = c.get_vhf_dmes()
all_waypoints = c.get_waypoints()
all_controlled = c.get_controlled()
all_restrictive = c.get_restrictive()

Working with Specific Items

airport = c.find_airport("KIAD")
heliport = c.find_heliport("DC03")
airway = c.find_airway("J146")
ndb = c.find_ndb("GTN")
vor = c.find_vhf_dme("AML")
fix = c.find_waypoint("RAVNN")
dc_class_b = c.find_controlled("KDCA")
roa_class_c = c.find_controlled("KROA")
cho_class_d = c.find_controlled("KCHO")
moa = c.find_restrictive("DEMO 1 MOA")

# Because the Alert, MOA, Restricted, and Warning airspace can occasionally be named oddly,
# there is an additional helper function that finds all matches of a particular substring:
all_5314 = c.find_restrictive_match("5314")
# Returns: [R-5314A, R-5314B, R-5314C, R-5314D, R-5314E, R-5314F, R-5314H, R-5314J]

Exporting Data

Dictionaries

Each object has its own to_dict() method. This is useful when you need to dump the data to json:

c = CIFP("FAACIFP18")
airport = c.find_airport("KIAD")
with open("output.json", "w") as json_file:
    json.dump(airport.to_dict(), json_file, indent=2)

Database

Each object has its own to_db() method. This is useful when you would like the data to persist, or query it using standard database methods:

import sqlite3

connection = sqlite.connect("FAACIFP18.db")
cursor = connection.cursor()

c = CIFP("FAACIFP18")
c.initialize_database(cursor)
c.parse()
c.to_db(cursor)

connection.commit()
connection.close()

NOTE: The resulting tables are somewhat less-optimally normalized than they could be. This is mostly to allow flexibility in querying. For example, the airway_points table can be queried directly, or it can be queried via airways with a join to airway_points. There is limited additional information, but it could also help to get higher level overviews of the underlying data. Airspace follows a similar principle.

Example File

An example file is provided in the Examples directory. It demonstrates parsing all of the CIFP data, finding an airport within the data, and then looping through the SID and STAR data to create a geoJSON file for each.

CIFP Objects

A breakdown of the different objects can be found in the Docs directory.