pycontrails.Flight¶
- class pycontrails.Flight(data=None, *, longitude=None, latitude=None, altitude=None, altitude_ft=None, level=None, time=None, attrs=None, copy=True, fuel=None, drop_duplicated_times=False, **attrs_kwargs)¶
Bases:
GeoVectorDataset
A single flight trajectory.
Expect latitude-longitude coordinates in WGS 84. Expect altitude in [\(m\)]. Expect pressure level (level) in [\(hPa\)].
- Parameters:
data (
dict[str
,np.ndarray] | pd.DataFrame | VectorDataDict | VectorDataset | None
) – Flight trajectory waypoints as data dictionary orpandas.DataFrame
. Must include columnstime
,latitude
,longitude
,altitude
orlevel
. Keyword arguments fortime
,latitude
,longitude
,altitude
orlevel
will overridedata
inputs. Expectsaltitude
in meters andtime
as a DatetimeLike (or array that can processed withpd.to_datetime()
). Additional waypoint-specific data can be included as additional keys/columns.longitude (
npt.ArrayLike
, optional) – Flight trajectory waypoint longitude. Defaults to None.latitude (
npt.ArrayLike
, optional) – Flight trajectory waypoint latitude. Defaults to None.altitude (
npt.ArrayLike
, optional) – Flight trajectory waypoint altitude, [\(m\)]. Defaults to None.altitude_ft (
npt.ArrayLike
, optional) – Flight trajectory waypoint altitude, [\(ft\)].level (
npt.ArrayLike
, optional) – Flight trajectory waypoint pressure level, [\(hPa\)]. Defaults to None.time (
npt.ArrayLike
, optional) – Flight trajectory waypoint time. Defaults to None.attrs (
dict[str
,Any]
, optional) – Additional flight properties as a dictionary. While different models may utilize Flight attributes differently, pycontrails applies the following conventions:flight_id
: An internal flight identifier. Used internally forFleet
interoperability.aircraft_type
: Aircraft type ICAO, e.g."A320"
.wingspan
: Aircraft wingspan, [\(m\)].n_engine
: Number of aircraft engines.engine_uid
: Aircraft engine unique identifier. Used for emissions calculations with the ICAO Aircraft Emissions Databank (EDB).max_mach_number
: Maximum Mach number at cruise altitude. Used by some aircraft performance models to clip true airspeed.
Numeric quantities that are constant over the entire flight trajectory should be included as attributes.
copy (
bool
, optional) – Copy data on Flight creation. Defaults to True.fuel (
Fuel
, optional) – Fuel used in flight trajectory. Defaults toJetA
.drop_duplicated_times (
bool
, optional) – Drop duplicate times in flight trajectory. Defaults to False.**attrs_kwargs (
Any
) – Additional flight properties passed as keyword arguments.
- Raises:
KeyError – Raises if
data
input does not contain at leasttime
,latitude
,longitude
, (altitude
orlevel
).
Notes
The Traffic library has many helpful flight processing utilities.
See
traffic.core.Flight
for more information.Examples
>>> import numpy as np >>> import pandas as pd >>> from pycontrails import Flight
>>> # Create `Flight` from a DataFrame. >>> df = pd.DataFrame({ ... "longitude": np.linspace(20, 30, 500), ... "latitude": np.linspace(40, 10, 500), ... "altitude": 10500, ... "time": pd.date_range('2021-01-01T10', '2021-01-01T15', periods=500), ... }) >>> fl = Flight(data=df, flight_id=123) # specify a flight_id by keyword >>> fl Flight [4 keys x 500 length, 1 attributes] Keys: longitude, latitude, altitude, time Attributes: time [2021-01-01 10:00:00, 2021-01-01 15:00:00] longitude [20.0, 30.0] latitude [10.0, 40.0] altitude [10500.0, 10500.0] flight_id 123
>>> # Create `Flight` from keywords >>> fl = Flight( ... longitude=np.linspace(20, 30, 200), ... latitude=np.linspace(40, 30, 200), ... altitude=11000 * np.ones(200), ... time=pd.date_range('2021-01-01T12', '2021-01-01T14', periods=200), ... ) >>> fl Flight [4 keys x 200 length, 0 attributes] Keys: longitude, latitude, time, altitude Attributes: time [2021-01-01 12:00:00, 2021-01-01 14:00:00] longitude [20.0, 30.0] latitude [30.0, 40.0] altitude [11000.0, 11000.0]
>>> # Access the underlying data as DataFrame >>> fl.dataframe.head() longitude latitude time altitude 0 20.000000 40.000000 2021-01-01 12:00:00.000000000 11000.0 1 20.050251 39.949749 2021-01-01 12:00:36.180904522 11000.0 2 20.100503 39.899497 2021-01-01 12:01:12.361809045 11000.0 3 20.150754 39.849246 2021-01-01 12:01:48.542713567 11000.0 4 20.201005 39.798995 2021-01-01 12:02:24.723618090 11000.0
- __init__(data=None, *, longitude=None, latitude=None, altitude=None, altitude_ft=None, level=None, time=None, attrs=None, copy=True, fuel=None, drop_duplicated_times=False, **attrs_kwargs)¶
Methods
T_isa
()Calculate the ICAO standard atmosphere temperature at each point.
__init__
([data, longitude, latitude, ...])broadcast_attrs
(keys[, overwrite, raise_error])Attach values from
keys
inattrs
ontodata
.broadcast_numeric_attrs
([ignore_keys, overwrite])Attach numeric values in
attrs
ontodata
.clean_and_resample
([freq, fill_method, ...])Resample and (possibly) filter a flight trajectory.
coords_intersect_met
(met)Return boolean mask of data inside the bounding box defined by
met
.copy
(**kwargs)Return a copy of this VectorDatasetType class.
create_empty
([keys, attrs])Create instance with variables defined by keys and size 0.
distance_to_coords
(distance)Convert distance along flight path to geodesic coordinates.
downselect_met
(met, *[, longitude_buffer, ...])Downselect
met
to encompass a spatiotemporal region of the data.ensure_vars
(vars[, raise_error])Ensure variables exist in column of
data
orattrs
.filter
(mask[, copy])Filter
data
according to a boolean arraymask
.filter_altitude
([kernel_size, cruise_threshold])Filter noisy altitude on a single flight.
Keep first row of group of waypoints with identical coordinates.
from_dict
(obj[, copy])Create instance from dict representation containing data and attrs.
generate_splits
(n_splits[, copy])Split instance into
n_split
sub-vectors.get
(key[, default_value])Get values from
data
withdefault_value
ifkey
not indata
.get_data_or_attr
(key[, default])Get value from
data
orattrs
.intersect_met
(mda, *[, longitude, latitude, ...])Intersect waypoints with MetDataArray.
length_met
(key[, threshold])Calculate total horizontal distance where column
key
exceedsthreshold
.plot
(**kwargs)Plot flight trajectory longitude-latitude values.
plot_profile
(**kwargs)Plot flight trajectory time-altitude values.
proportion_met
(key[, threshold])Calculate proportion of flight with certain meteorological constraint.
resample_and_fill
([freq, fill_method, ...])Resample and fill flight trajectory with geodesics and linear interpolation.
Calculate sine and cosine for the angle between each segment and the longitudinal axis.
Calculate (forward) azimuth at each waypoint.
segment_duration
([dtype])Compute time elapsed between waypoints in seconds.
segment_groundspeed
([smooth, window_length, ...])Return groundspeed across segments.
Compute Haversine (great circle) distance between flight waypoints.
Compute spherical distance between flight waypoints.
segment_mach_number
(true_airspeed, ...)Calculate the mach number of each segment.
segment_phase
([threshold_rocd, ...])Identify the phase of flight (climb, cruise, descent) for each segment.
segment_rocd
([air_temperature])Calculate the rate of climb and descent (ROCD).
segment_true_airspeed
([u_wind, v_wind, ...])Calculate the true airspeed [\(m/s\)] from the ground speed and horizontal winds.
select
(keys[, copy])Return new class instance only containing specified keys.
setdefault
(key[, default])Shortcut to
VectorDataDict.setdefault()
.sort
(by)Sort data by key(s).
sum
(vectors[, infer_attrs, fill_value])Sum a list of
VectorDataset
instances.to_dataframe
([copy])Create
pd.DataFrame
in which each key-value pair indata
is a column.to_dict
()Create dictionary with
data
andattrs
.Return trajectory as geojson FeatureCollection containing single LineString.
to_geojson_multilinestring
([key, ...])Return trajectory as GeoJSON FeatureCollection of MultiLineStrings.
to_geojson_points
()Return dataset as GeoJSON FeatureCollection of Points.
to_lon_lat_grid
(agg, *[, spatial_bbox, ...])Convert vectors to a longitude-latitude grid.
Convert Flight instance to
traffic.core.Flight
instance.transform_crs
(crs)Transform trajectory data from one coordinate reference system (CRS) to another.
update
([other])Update values in
data
dict without warning if overwriting.Attributes
Fuel used in flight trajectory
air_pressure
Get
air_pressure
values for points.altitude
Get altitude.
altitude_ft
Get altitude in feet.
attrs
Generic dataset attributes
constants
Return a dictionary of constant attributes and data values.
coords
Get geospatial coordinates for compatibility with MetDataArray.
data
Vector data with labels as keys and
numpy.ndarray
as valuesdataframe
Shorthand property to access
to_dataframe()
withcopy=False
.Determine flight duration.
hash
Generate a unique hash for this class instance.
Return flight length based on WGS84 geodesic.
level
Get pressure
level
values for points.Return maximum distance gap between waypoints along flight trajectory.
Return maximum time gap between waypoints along flight trajectory.
required_keys
Required keys for creating GeoVectorDataset
shape
Shape of each array in
data
.size
Length of each array in
data
.Last waypoint time.
First waypoint time.
vertical_keys
At least one of these vertical-coordinate keys must also be included
- clean_and_resample(freq='1min', fill_method='geodesic', geodesic_threshold=100000.0, nominal_rocd=12.7, kernel_size=17, cruise_threshold=120.0, force_filter=False, drop=True, keep_original_index=False, climb_descend_at_end=False)¶
Resample and (possibly) filter a flight trajectory.
Waypoints are resampled according to the frequency
freq
. If the original flight data has a short sampling period, filter_altitude will also be called to clean the data. Large gaps in trajectories may be interpolated as step climbs through _altitude_interpolation.- Parameters:
freq (
str
, optional) – Resampling frequency, by default “1min”fill_method (
{"geodesic", "linear"}
, optional) – Choose between"geodesic"
and"linear"
, by default"geodesic"
. In geodesic mode, large gaps between waypoints are filled with geodesic interpolation and small gaps are filled with linear interpolation. In linear mode, all gaps are filled with linear interpolation.geodesic_threshold (
float
, optional) – Threshold for geodesic interpolation, [\(m\)]. If the distance between consecutive waypoints is under this threshold, values are interpolated linearly.nominal_rocd (
float
, optional) – Nominal rate of climb / descent for aircraft type. Defaults toconstants.nominal_rocd
.kernel_size (
int
, optional) – Passed directly toscipy.signal.medfilt()
, by default 11. Passed also toscipy.signal.medfilt()
cruise_theshold (
float
, optional) – Minimal length of time, in seconds, for a flight to be in cruise to apply median filterforce_filter (
bool
, optional) – If set to true, meth:filter_altitude will always be called. otherwise, it will only be called if the flight has a median sample period under 10 secondsdrop (
bool
, optional) – Drop any columns that are not resampled and filled. Defaults toTrue
, dropping all keys outside of “time”, “latitude”, “longitude” and “altitude”. If set to False, the extra keys will be kept but filled withnan
orNone
values, depending on the data type.keep_original_index (
bool
, optional) – Keep the original index of theFlight
in addition to the new resampled index. Defaults toFalse
. .. versionadded:: 0.45.2climb_or_descend_at_end (
bool
) – If true, the climb or descent will be placed at the end of each segment rather than the start. Default is false (climb or descent immediately).
- Returns:
Flight
– Filled Flight
- copy(**kwargs)¶
Return a copy of this VectorDatasetType class.
- Parameters:
**kwargs (
Any
) – Additional keyword arguments passed into the constructor of the returned class.- Returns:
VectorDatasetType
– Copy of class
- distance_to_coords(distance)¶
Convert distance along flight path to geodesic coordinates.
Will return a tuple containing (lat, lon, index), where index indicates which flight segment contains the returned coordinate.
- Parameters:
distance (
ArrayOrFloat
) – Distance along flight path, [\(m\)]- Returns:
(ArrayOrFloat
,ArrayOrFloat
,int | npt.NDArray[int])
– latitude, longitude, and segment index cooresponding to distance.
- property duration¶
Determine flight duration.
- Returns:
pd.Timedelta
– Difference between terminal and initial time
- filter(mask, copy=True, **kwargs)¶
Filter
data
according to a boolean arraymask
.Entries corresponding to
mask == True
are kept.- Parameters:
mask (
npt.NDArray[np.bool_]
) – Boolean array with compatible shape.copy (
bool
, optional) – Copy data on filter. Defaults to True. See numpy best practices for insight into whether copy is appropriate.**kwargs (
Any
) – Additional keyword arguments passed into the constructor of the returned class.
- Returns:
VectorDatasetType
– Containing filtered data- Raises:
TypeError – If
mask
is not a boolean array.
- filter_altitude(kernel_size=17, cruise_threshold=120.0)¶
Filter noisy altitude on a single flight.
Currently runs altitude through a median filter using
scipy.signal.medfilt()
withkernel_size
, then a Savitzky-Golay filter to filter noise. The median filter is only applied during cruise segments that are longer thancruise_threshold
.- Parameters:
kernel_size (
int
, optional) – Passed directly toscipy.signal.medfilt()
, by default 11. Passed also toscipy.signal.medfilt()
cruise_theshold (
float
, optional) – Minimal length of time, in seconds, for a flight to be in cruise to apply median filter
- Returns:
Flight
– Filtered Flight
Notes
Algorithm is derived from
traffic.core.flight.Flight.filter()
.The traffic algorithm also computes thresholds on sliding windows and replaces unacceptable values with NaNs.
Errors may raised if the
kernel_size
is too large.See also
traffic.core.flight.Flight.filter()
,scipy.signal.medfilt()
- filter_by_first()¶
Keep first row of group of waypoints with identical coordinates.
Chaining this method with resample_and_fill often gives a cleaner trajectory when using noisy flight waypoints.
- Returns:
Flight
– Filtered Flight instance
Examples
>>> from datetime import datetime >>> import pandas as pd
>>> df = pd.DataFrame() >>> df['longitude'] = [0, 0, 50] >>> df['latitude'] = 0 >>> df['altitude'] = 0 >>> df['time'] = [datetime(2020, 1, 1, h) for h in range(3)]
>>> fl = Flight(df)
>>> fl.filter_by_first().dataframe longitude latitude altitude time 0 0.0 0.0 0.0 2020-01-01 00:00:00 1 50.0 0.0 0.0 2020-01-01 02:00:00
- fuel¶
Fuel used in flight trajectory
- property length¶
Return flight length based on WGS84 geodesic.
- Returns:
float
– Total flight length, [\(m\)]
Examples
>>> import numpy as np >>> fl = Flight( ... longitude=np.linspace(20, 30, 200), ... latitude=np.linspace(40, 30, 200), ... altitude=11000 * np.ones(200), ... time=pd.date_range('2021-01-01T12', '2021-01-01T14', periods=200), ... ) >>> fl.length np.float64(1436924.67...)
- length_met(key, threshold=1.0)¶
Calculate total horizontal distance where column
key
exceedsthreshold
.- Parameters:
- Returns:
float
– Total distance, [\(m\)]- Raises:
KeyError –
data
does not contain columnkey
Examples
>>> from datetime import datetime >>> import pandas as pd >>> import numpy as np >>> from pycontrails.datalib.ecmwf import ERA5 >>> from pycontrails import Flight
>>> # Get met data >>> times = (datetime(2022, 3, 1, 0), datetime(2022, 3, 1, 3)) >>> variables = ["air_temperature", "specific_humidity"] >>> levels = [300, 250, 200] >>> era5 = ERA5(time=times, variables=variables, pressure_levels=levels) >>> met = era5.open_metdataset()
>>> # Build flight >>> df = pd.DataFrame() >>> df["time"] = pd.date_range("2022-03-01T00", "2022-03-01T03", periods=11) >>> df["longitude"] = np.linspace(-20, 20, 11) >>> df["latitude"] = np.linspace(-20, 20, 11) >>> df["altitude"] = np.linspace(9500, 10000, 11) >>> fl = Flight(df).resample_and_fill("10s")
>>> # Intersect and attach >>> fl["air_temperature"] = fl.intersect_met(met["air_temperature"]) >>> fl["air_temperature"] array([235.94657007, 235.55745645, 235.56709768, ..., 234.59917962, 234.60387402, 234.60845312])
>>> # Length (in meters) of waypoints whose temperature exceeds 236K >>> fl.length_met("air_temperature", threshold=236) np.float64(3589705.998...)
>>> # Proportion (with respect to distance) of waypoints whose temperature exceeds 236K >>> fl.proportion_met("air_temperature", threshold=236) np.float64(0.576...)
- property max_distance_gap¶
Return maximum distance gap between waypoints along flight trajectory.
Distance is calculated based on WGS84 geodesic.
- Returns:
float
– Maximum distance between waypoints, [\(m\)]
Examples
>>> import numpy as np >>> fl = Flight( ... longitude=np.linspace(20, 30, 200), ... latitude=np.linspace(40, 30, 200), ... altitude=11000 * np.ones(200), ... time=pd.date_range('2021-01-01T12', '2021-01-01T14', periods=200), ... ) >>> fl.max_distance_gap np.float64(7391.27...)
- property max_time_gap¶
Return maximum time gap between waypoints along flight trajectory.
- Returns:
pd.Timedelta
– Gap size
Examples
>>> import numpy as np >>> fl = Flight( ... longitude=np.linspace(20, 30, 200), ... latitude=np.linspace(40, 30, 200), ... altitude=11000 * np.ones(200), ... time=pd.date_range('2021-01-01T12', '2021-01-01T14', periods=200), ... ) >>> fl.max_time_gap Timedelta('0 days 00:00:36.180...')
- plot(**kwargs)¶
Plot flight trajectory longitude-latitude values.
- Parameters:
**kwargs (
Any
) – Additional plot properties to passed to pd.DataFrame.plot- Returns:
matplotlib.axes.Axes
– Plot
- plot_profile(**kwargs)¶
Plot flight trajectory time-altitude values.
- Parameters:
**kwargs (
Any
) – Additional plot properties to passed to pd.DataFrame.plot- Returns:
matplotlib.axes.Axes
– Plot
- proportion_met(key, threshold=1.0)¶
Calculate proportion of flight with certain meteorological constraint.
- resample_and_fill(freq='1min', fill_method='geodesic', geodesic_threshold=100000.0, nominal_rocd=12.7, drop=True, keep_original_index=False, climb_descend_at_end=False)¶
Resample and fill flight trajectory with geodesics and linear interpolation.
Waypoints are resampled according to the frequency
freq
. Values fordata
columnslongitude
,latitude
, andaltitude
are interpolated.Resampled waypoints will include all multiples of
freq
between the flight start and end time. For example, when resampling to a frequency of 1 minute, a flight that starts at 2020/1/1 00:00:59 and ends at 2020/1/1 00:01:01 will return a single waypoint at 2020/1/1 00:01:00, whereas a flight that starts at 2020/1/1 00:01:01 and ends at 2020/1/1 00:01:59 will return an empty flight.- Parameters:
freq (
str
, optional) – Resampling frequency, by default “1min”fill_method (
{"geodesic", "linear"}
, optional) – Choose between"geodesic"
and"linear"
, by default"geodesic"
. In geodesic mode, large gaps between waypoints are filled with geodesic interpolation and small gaps are filled with linear interpolation. In linear mode, all gaps are filled with linear interpolation.geodesic_threshold (
float
, optional) – Threshold for geodesic interpolation, [\(m\)]. If the distance between consecutive waypoints is under this threshold, values are interpolated linearly.nominal_rocd (
float | None
, optional) – Nominal rate of climb / descent for aircraft type. Defaults toconstants.nominal_rocd
.drop (
bool
, optional) – Drop any columns that are not resampled and filled. Defaults toTrue
, dropping all keys outside of “time”, “latitude”, “longitude” and “altitude”. If set to False, the extra keys will be kept but filled withnan
orNone
values, depending on the data type.keep_original_index (
bool
, optional) – Keep the original index of theFlight
in addition to the new resampled index. Defaults toFalse
. .. versionadded:: 0.45.2climb_or_descend_at_end (
bool
) – If true, the climb or descent will be placed at the end of each segment rather than the start. Default is false (climb or descent immediately).
- Returns:
Flight
– Filled Flight- Raises:
ValueError – Unknown
fill_method
Examples
>>> from datetime import datetime >>> import pandas as pd
>>> df = pd.DataFrame() >>> df['longitude'] = [0, 0, 50] >>> df['latitude'] = 0 >>> df['altitude'] = 0 >>> df['time'] = [datetime(2020, 1, 1, h) for h in range(3)]
>>> fl = Flight(df) >>> fl.dataframe longitude latitude altitude time 0 0.0 0.0 0.0 2020-01-01 00:00:00 1 0.0 0.0 0.0 2020-01-01 01:00:00 2 50.0 0.0 0.0 2020-01-01 02:00:00
>>> fl.resample_and_fill('10min').dataframe # resample with 10 minute frequency longitude latitude altitude time 0 0.000000 0.0 0.0 2020-01-01 00:00:00 1 0.000000 0.0 0.0 2020-01-01 00:10:00 2 0.000000 0.0 0.0 2020-01-01 00:20:00 3 0.000000 0.0 0.0 2020-01-01 00:30:00 4 0.000000 0.0 0.0 2020-01-01 00:40:00 5 0.000000 0.0 0.0 2020-01-01 00:50:00 6 0.000000 0.0 0.0 2020-01-01 01:00:00 7 8.333333 0.0 0.0 2020-01-01 01:10:00 8 16.666667 0.0 0.0 2020-01-01 01:20:00 9 25.000000 0.0 0.0 2020-01-01 01:30:00 10 33.333333 0.0 0.0 2020-01-01 01:40:00 11 41.666667 0.0 0.0 2020-01-01 01:50:00 12 50.000000 0.0 0.0 2020-01-01 02:00:00
- segment_angle()¶
Calculate sine and cosine for the angle between each segment and the longitudinal axis.
This is different from the usual navigational angle between two points known as bearing.
Bearing in 3D spherical coordinates is referred to as azimuth.
(lon_2, lat_2) X /| / | / | / | / | / | / | (lon_1, lat_1) X -------> longitude (x-axis)
- Returns:
npt.NDArray[np.float64]
,npt.NDArray[np.float64]
– Returnssin(a), cos(a)
, wherea
is the angle between the segment and the longitudinal axis. The final values are of both arrays arenp.nan
.
See also
geo.segment_angle()
,units.heading_to_longitudinal_angle()
,segment_azimuth()
,geo.forward_azimuth()
Examples
>>> from pycontrails import Flight >>> fl = Flight( ... longitude=np.array([1, 2, 3, 5, 8]), ... latitude=np.arange(5), ... altitude=np.full(shape=(5,), fill_value=11000), ... time=pd.date_range('2021-01-01T12', '2021-01-01T14', periods=5), ... ) >>> sin, cos = fl.segment_angle() >>> sin array([0.70716063, 0.70737598, 0.44819424, 0.31820671, nan])
>>> cos array([0.70705293, 0.70683748, 0.8939362 , 0.94802136, nan])
- segment_azimuth()¶
Calculate (forward) azimuth at each waypoint.
Method calls pyproj.Geod.inv, which is slow. See geo.forward_azimuth for an outline of a faster implementation.
Changed in version 0.33.7: The dtype of the output now matches the dtype of
self["longitude"]
.- Returns:
npt.NDArray[np.float64]
– Array of azimuths.
See also
segment_angle()
,geo.forward_azimuth()
- segment_duration(dtype=<class 'numpy.float32'>)¶
Compute time elapsed between waypoints in seconds.
np.nan
appended so the length of the output is the same as number of waypoints.- Parameters:
dtype (
np.dtype
) – Numpy dtype for time difference. Defaults tonp.float64
- Returns:
npt.NDArray[np.float64]
– Time difference between waypoints, [\(s\)]. Returns an array with dtype specified by``dtype``
- segment_groundspeed(smooth=False, window_length=7, polyorder=1)¶
Return groundspeed across segments.
Calculate by dividing the horizontal segment length by the difference in waypoint times.
- Parameters:
smooth (
bool
, optional) – Smooth airspeed with Savitzky-Golay filter. Defaults to False.window_length (
int
, optional) – Passed directly toscipy.signal.savgol_filter()
, by default 7.polyorder (
int
, optional) – Passed directly toscipy.signal.savgol_filter()
, by default 1.
- Returns:
npt.NDArray[np.float64]
– Groundspeed of the segment, [\(m s^{-1}\)]
- segment_haversine()¶
Compute Haversine (great circle) distance between flight waypoints.
Helper function used in
resample_and_fill()
. np.nan appended so the length of the output is the same as number of waypoints.To account for vertical displacements when computing segment lengths, use
segment_length()
.- Returns:
npt.NDArray[np.float64]
– Array of great circle distances in [\(m\)] between waypoints
Examples
>>> from pycontrails import Flight >>> fl = Flight( ... longitude=np.array([1, 2, 3, 5, 8]), ... latitude=np.arange(5), ... altitude=np.full(shape=(5,), fill_value=11000), ... time=pd.date_range('2021-01-01T12', '2021-01-01T14', periods=5), ... ) >>> fl.segment_haversine() array([157255.03346286, 157231.08336815, 248456.48781503, 351047.44358851, nan])
See also
- segment_length()¶
Compute spherical distance between flight waypoints.
Helper function used in
length()
andlength_met()
. np.nan appended so the length of the output is the same as number of waypoints.- Returns:
npt.NDArray[np.float64]
– Array of distances in [\(m\)] between waypoints
Examples
>>> from pycontrails import Flight >>> fl = Flight( ... longitude=np.array([1, 2, 3, 5, 8]), ... latitude=np.arange(5), ... altitude=np.full(shape=(5,), fill_value=11000), ... time=pd.date_range('2021-01-01T12', '2021-01-01T14', periods=5), ... ) >>> fl.segment_length() array([157255.03346286, 157231.08336815, 248456.48781503, 351047.44358851, nan])
See also
- segment_mach_number(true_airspeed, air_temperature)¶
Calculate the mach number of each segment.
- Parameters:
true_airspeed (
npt.NDArray[np.float64]
) – True airspeed of the segment, [\(m \ s^{-1}\)]. Seesegment_true_airspeed()
.air_temperature (
npt.NDArray[np.float64]
) – Average air temperature of each segment, [\(K\)]
- Returns:
npt.NDArray[np.float64]
– Mach number of each segment
- segment_phase(threshold_rocd=250.0, min_cruise_altitude_ft=20000.0, air_temperature=None)¶
Identify the phase of flight (climb, cruise, descent) for each segment.
- Parameters:
threshold_rocd (
float
, optional) – ROCD threshold to identify climb and descent, [\(ft min^{-1}\)]. Currently set to 250 ft/min.min_cruise_altitude_ft (
float
, optional) – Minimum altitude for cruise, [\(ft\)] This is specific for each aircraft type, and can be approximated as 50% of the altitude ceiling. Defaults to 20000 ft.air_temperature (
None | npt.NDArray[np.float64]
) – Air temperature of each flight waypoint, [\(K\)]
- Returns:
npt.NDArray[np.uint8]
– Array of values enumerating the flight phase. Seeflight.FlightPhase
for enumeration.
See also
- segment_rocd(air_temperature=None)¶
Calculate the rate of climb and descent (ROCD).
- Parameters:
air_temperature (
None | npt.NDArray[np.float64]
) – Air temperature of each flight waypoint, [\(K\)]- Returns:
npt.NDArray[np.float64]
– Rate of climb and descent over segment, [\(ft min^{-1}\)]
See also
- segment_true_airspeed(u_wind=0.0, v_wind=0.0, smooth=True, window_length=7, polyorder=1)¶
Calculate the true airspeed [\(m/s\)] from the ground speed and horizontal winds.
The calculated ground speed will first be smoothed with a Savitzky-Golay filter if enabled.
- Parameters:
u_wind (
npt.NDArray[np.float64] | float
) – U wind speed, [\(m \ s^{-1}\)]. Defaults to 0 for all waypoints.v_wind (
npt.NDArray[np.float64] | float
) – V wind speed, [\(m \ s^{-1}\)]. Defaults to 0 for all waypoints.smooth (
bool
, optional) – Smooth airspeed with Savitzky-Golay filter. Defaults to True.window_length (
int
, optional) – Passed directly toscipy.signal.savgol_filter()
, by default 7.polyorder (
int
, optional) – Passed directly toscipy.signal.savgol_filter()
, by default 1.
- Returns:
npt.NDArray[np.float64]
– True wind speed of each segment, [\(m \ s^{-1}\)]
- sort(by)¶
Sort data by key(s).
This method always creates a copy of the data by calling
pandas.DataFrame.sort_values()
.- Parameters:
by (
str | list[str]
) – Key or list of keys to sort by.- Returns:
VectorDatasetType
– Instance with sorted data.
- property time_end¶
Last waypoint time.
- Returns:
pandas.Timestamp
– Last waypoint time
- property time_start¶
First waypoint time.
- Returns:
pandas.Timestamp
– First waypoint time
- to_geojson_linestring()¶
Return trajectory as geojson FeatureCollection containing single LineString.
- Returns:
dict[str
,Any]
– Python representation of geojson FeatureCollection
- to_geojson_multilinestring(key=None, split_antimeridian=True)¶
Return trajectory as GeoJSON FeatureCollection of MultiLineStrings.
If key is provided, Flight
data
is grouped according to values ofkey
. Each group gives rise to a Feature containing a MultiLineString geometry. Each MultiLineString can optionally be split over the antimeridian.- Parameters:
- Returns:
dict[str
,Any]
– Python representation of GeoJSON FeatureCollection of MultiLinestring Features- Raises:
KeyError –
key
is provided butdata
does not contain columnkey
- to_traffic()¶
Convert Flight instance to
traffic.core.Flight
instance.See https://traffic-viz.github.io/traffic.core.flight.html#traffic.core.Flight
- Returns:
traffic.core.Flight
– traffic.core.Flight instance- Raises:
ModuleNotFoundError – traffic package not installed