Flight#

Data structure for working with flight trajectories.

[1]:

import pandas as pd
import numpy as np

from pycontrails import Flight
from pycontrails.datalib.ecmwf import ERA5

Create Flight#

From Numpy Arrays#

[2]:

# waypoints
longitude = np.linspace(0, 50, 100)
latitude = np.linspace(0, 10, 100)
altitude = np.linspace(11000, 11500, 100)
time = pd.date_range("2022-03-01 00:00:00", "2022-03-01 02:00:00", periods=100)

fl = Flight(longitude=longitude, latitude=latitude, altitude=altitude, time=time, flight_id="id")
fl

[2]:

Flight [4 keys x 100 length, 2 attributes]

Attributes
time	[2022-03-01 00:00:00, 2022-03-01 02:00:00]
longitude	[0.0, 50.0]
latitude	[0.0, 10.0]
altitude	[11000.0, 11500.0]
flight_id	id
crs	EPSG:4326

	longitude	latitude	time	altitude
0	0.000000	0.00000	2022-03-01 00:00:00.000000000	11000.000000
1	0.505051	0.10101	2022-03-01 00:01:12.727272727	11005.050505
2	1.010101	0.20202	2022-03-01 00:02:25.454545454	11010.101010
3	1.515152	0.30303	2022-03-01 00:03:38.181818181	11015.151515
4	2.020202	0.40404	2022-03-01 00:04:50.909090909	11020.202020
...	...	...	...	...
95	47.979798	9.59596	2022-03-01 01:55:09.090909090	11479.797980
96	48.484848	9.69697	2022-03-01 01:56:21.818181818	11484.848485
97	48.989899	9.79798	2022-03-01 01:57:34.545454545	11489.898990
98	49.494949	9.89899	2022-03-01 01:58:47.272727272	11494.949495
99	50.000000	10.00000	2022-03-01 02:00:00.000000000	11500.000000

100 rows × 4 columns

From Pandas DataFrame#

[3]:

# Example flight
df = pd.DataFrame()
df["longitude"] = np.linspace(0, 50, 100)
df["latitude"] = np.linspace(0, 10, 100)
df["altitude"] = 11000
df["time"] = pd.date_range("2022-03-01 00:00:00", "2022-03-01 02:00:00", periods=100)
fl = Flight(data=df, flight_id="ABC")
fl

[3]:

Flight [4 keys x 100 length, 2 attributes]

Attributes
time	[2022-03-01 00:00:00, 2022-03-01 02:00:00]
longitude	[0.0, 50.0]
latitude	[0.0, 10.0]
altitude	[11000.0, 11000.0]
flight_id	ABC
crs	EPSG:4326

	longitude	latitude	altitude	time
0	0.000000	0.00000	11000.0	2022-03-01 00:00:00.000000000
1	0.505051	0.10101	11000.0	2022-03-01 00:01:12.727272727
2	1.010101	0.20202	11000.0	2022-03-01 00:02:25.454545454
3	1.515152	0.30303	11000.0	2022-03-01 00:03:38.181818181
4	2.020202	0.40404	11000.0	2022-03-01 00:04:50.909090909
...	...	...	...	...
95	47.979798	9.59596	11000.0	2022-03-01 01:55:09.090909090
96	48.484848	9.69697	11000.0	2022-03-01 01:56:21.818181818
97	48.989899	9.79798	11000.0	2022-03-01 01:57:34.545454545
98	49.494949	9.89899	11000.0	2022-03-01 01:58:47.272727272
99	50.000000	10.00000	11000.0	2022-03-01 02:00:00.000000000

100 rows × 4 columns

Create Flight without Waypoints#

[4]:

# Example flight
attrs = dict(flight_id="1234", equip="A532")
fl = Flight.create_empty(attrs=attrs)
fl

[4]:

Flight [4 keys x 0 length, 3 attributes]

Attributes
flight_id	1234
equip	A532
crs	EPSG:4326

	altitude	latitude	time	longitude

Create from CSV file#

[5]:

# load flight
df = pd.read_csv("flight.csv")
fl = Flight(data=df, flight_id="csv")
fl

[5]:

Flight [4 keys x 175 length, 2 attributes]

Attributes
time	[2022-03-01 00:50:00, 2022-03-01 03:47:00]
longitude	[-97.02592499402108, -77.03594970703125]
latitude	[32.93064880371094, 38.854248046875]
altitude	[190.5, 11582.4]
flight_id	csv
crs	EPSG:4326

	longitude	latitude	altitude	time
0	-77.035950	38.829315	236.22	2022-03-01 00:50:00
1	-77.038223	38.772675	708.66	2022-03-01 00:51:00
2	-77.114231	38.744568	9471.66	2022-03-01 00:52:00
3	-77.201965	38.739888	2019.30	2022-03-01 00:53:00
4	-77.286191	38.745117	3032.76	2022-03-01 00:54:00
...	...	...	...	...
170	-97.025925	32.931379	190.50	2022-03-01 03:43:00
171	-97.025922	32.930649	190.50	2022-03-01 03:44:00
172	-97.025922	32.930649	190.50	2022-03-01 03:45:00
173	-97.025922	32.930649	190.50	2022-03-01 03:46:00
174	-97.025922	32.930649	190.50	2022-03-01 03:47:00

175 rows × 4 columns

Using the Flight class#

The flight.data attribute is a dictionary with np.ndarray values

[6]:

# waypoints
longitude = np.linspace(0, 50, 10)
latitude = np.linspace(0, 10, 10)
altitude = np.linspace(11000, 11500, 10)
time = pd.date_range("2022-03-01 00:00:00", "2022-03-01 02:00:00", periods=10)
attrs = {"flight_id": "ABC123"}
fl = Flight(longitude=longitude, latitude=latitude, altitude=altitude, time=time, attrs=attrs)

[7]:

fl.data.keys()

[7]:

dict_keys(['longitude', 'latitude', 'time', 'altitude'])

Flight attributes are stored in a dictionary on the attrs attribute. The crs attribute is always added by default, if not specified.

[8]:

fl.attrs

[8]:

{'flight_id': 'ABC123', 'crs': 'EPSG:4326'}

Data can be set / get from the Flight like a dictionary

[9]:

# get
lat = fl["latitude"]

# set
lat[5] = 20
fl["latitude"] = lat

# get updated
fl["latitude"][5]

[9]:

20.0

The Flight class contains the following convenience properties

[10]:

# Pressure altitude, in hPa
fl.level

[10]:

array([226.3170091 , 224.34300442, 222.3862176 , 220.44649846,
       218.52369813, 216.61766904, 214.7282649 , 212.85534072,
       210.99875274, 209.15835847])

[11]:

# Altitude, in ft
fl.altitude_ft

[11]:

array([36089.23884514, 36271.5077282 , 36453.77661126, 36636.04549431,
       36818.31437737, 37000.58326043, 37182.85214348, 37365.12102654,
       37547.38990959, 37729.65879265])

[12]:

# Values that are constant along the flight path

# set constant value along flight waypoints
fl["constant"] = np.full(shape=fl.shape, fill_value=100)

fl.constants

[12]:

{'constant': 100, 'flight_id': 'ABC123', 'crs': 'EPSG:4326'}

[13]:

# Flight distance, in meters
fl.length

[13]:

7818835.115366629

[14]:

# Time start/end
print(fl.time_start)
print(fl.time_end)

2022-03-01 00:00:00
2022-03-01 02:00:00

[15]:

# Flight duration, as a pandas Timedelta
fl.duration

[15]:

Timedelta('0 days 02:00:00')

[16]:

# Max time gap between waypoints, as a pandas Timedelta
fl.max_time_gap

[16]:

Timedelta('0 days 00:13:20')

[17]:

# Max distance gap between waypoints, in meters
fl.max_distance_gap

[17]:

1831403.3492360476

Intersect with Met data#

[18]:

# waypoints
longitude = np.linspace(0, 50, 50)
latitude = np.linspace(0, 10, 50)
altitude = np.linspace(11000, 11500, 50)
time = pd.date_range("2022-03-01 00:00:00", "2022-03-01 02:00:00", periods=50)
fl = Flight(longitude=longitude, latitude=latitude, altitude=altitude, time=time, flight_id="ABC")

[19]:

# domain
time = ("2022-03-01 00:00:00", "2022-03-01 03:00:00")
variables = ["t", "q", "u", "v", "w", "ciwc", "z", "cc"]
pressure_levels = [300, 250, 200]

# get met data
era5 = ERA5(time=time, variables=variables, pressure_levels=pressure_levels)
met = era5.open_metdataset()

[20]:

# interpolate to nearest grid member
fl.intersect_met(met["air_temperature"], method="nearest")

[20]:

array([231.6297 , 231.5037 , 231.73001, 231.08833, 218.8252 , 218.8393 ,
       219.00676, 218.07823, 218.66353, 219.61859, 219.48346, 219.66501,
       219.8988 , 219.86067, 219.95186, 220.16989, 220.15166, 220.06793,
       219.71309, 219.5191 , 219.5647 , 219.54646, 219.39972, 219.1908 ,
       219.0192 , 219.02084, 219.36572, 219.42708, 219.39308, 219.49672,
       219.63931, 219.63351, 219.80347, 219.72304, 218.88406, 218.68758,
       219.5904 , 219.66667, 219.75786, 219.57713, 219.53734, 219.87642,
       219.74045, 219.47931, 219.345  , 219.28697, 219.14354, 219.04074,
       218.90147, 218.93463], dtype=float32)

[21]:

# linear interpolation
fl.intersect_met(met["air_temperature"], method="linear")

[21]:

array([225.77794546, 225.3907536 , 225.21886695, 224.88386925,
       225.13017366, 224.7533235 , 224.69847888, 224.51217578,
       224.6564141 , 225.06851445, 225.2778995 , 225.23736206,
       225.27412295, 225.21636444, 225.02274627, 225.16204077,
       225.0248815 , 224.83874578, 224.5083198 , 224.36187816,
       224.27049915, 224.02780713, 223.71685602, 223.55300043,
       223.37676381, 223.35237865, 223.48899599, 223.45066473,
       223.46031406, 223.51036424, 223.47455769, 223.33977131,
       223.33706289, 223.19572504, 222.68132145, 222.43277286,
       222.99376949, 222.90991434, 222.94348612, 222.70607751,
       222.66989441, 222.84316583, 222.59155016, 222.31973459,
       222.06893404, 221.91065999, 221.70156579, 221.52470254,
       221.33912935, 221.26541793])

Get Lengths#

[22]:

# total flight length in meters
fl.length

[22]:

5642421.5973290345

[23]:

# intersect flight with air temperature
fl["temp"] = fl.intersect_met(met["air_temperature"], method="nearest")

# get the length of the flight where ambient temperature is > 226 K
fl.length_met("temp", threshold=226)

[23]:

462850.7958842697

Plot and Resample#

[24]:

fl.dataframe.plot.scatter(x="longitude", y="latitude", figsize=(12, 8))
fl.max_distance_gap

[24]:

115715.16936791067

[25]:

# resample with 10 minute waypoints
fl = fl.resample_and_fill("10T")

fl.dataframe.plot.scatter(x="longitude", y="latitude", figsize=(12, 8))
fl.max_distance_gap

[25]:

520697.0929684143

[26]:

# resample with 10 second waypoints
fl = fl.resample_and_fill("10S")

fl.dataframe.plot.scatter(x="longitude", y="latitude", figsize=(12, 8))
fl.max_distance_gap

[26]:

8678.45194022453