Source: https://jmrplens.github.io/phonometry/aircraft/anp-fleet/ # The ANP fleet database [Airport noise (ECAC Doc 29)](https://jmrplens.github.io/phonometry/aircraft/airport-noise/) computes an event level from a noise-power-distance table and a flight path. That page supplies both by hand, which is what you want while learning the method and what you never want afterwards: for a real study the numbers come from the **Aircraft Noise and Performance (ANP)** database that EASA and EUROCONTROL publish for the aircraft types actually flying. That database ships with phonometry. This page is the bridge between it and the Doc 29 functions: how to open it, what one aircraft record holds, and how to go from an aircraft identifier to an event level or a contour without writing a table yourself. ## Opening the database `load_anp_database()` with no argument reads the copy shipped with the package. Point it at a directory to read any other ANP CSV export instead. ```python from phonometry import load_anp_database db = load_anp_database() print(len(db.aircraft_ids)) # 155 aircraft types ac = db.aircraft("747100") print(ac.description) # Boeing 747-100 / JT9DBD print(ac.engine_type, ac.num_engines, ac.weight_class) print(ac.power_parameter, ac.mounting) # CNT (lb) wing ``` An `AnpAircraft` describes itself with the engine type and count and the ICAO wake weight class, and carries two fields you will use directly. The **power parameter** names the quantity the NPD table is indexed by: not a force in newtons but whatever the manufacturer tabulated against, corrected net thrust in pounds for most jets, so a power you pass to `level` has to be in those units. The engine **mounting** is the one field of the record the Doc 29 chain itself reads; it is derived from the ANP lateral directivity identifier, is one of `"wing"`, `"fuselage"` or `"propeller"`, and selects the engine-installation correction that [Airport noise](https://jmrplens.github.io/phonometry/aircraft/airport-noise/) applies by hand. The choice is not cosmetic: at small depression angles the wing and fuselage corrections differ by more than a decibel, a propeller takes none of that correction at all, and the shipped fleet splits 70 / 55 / 30 across the three. An unrecognised identifier falls back to `"wing"`. ## The noise-power-distance curves `npd_curves` returns the tabulated NPD surface for one operation (`"D"` for departure, `"A"` for arrival) and one metric (`"SEL"` or `"LAmax"`): a level for each combination of engine power setting and slant distance. Between the tabulated nodes the Doc 29 interpolation is logarithmic in distance and linear in power. ```python from phonometry import load_anp_database curves = load_anp_database().aircraft("A320-232").npd_curves("D", "SEL") print(curves.powers) # [10000. 14000. 19000. 23000.] lb print(curves.level(19000.0, [304.8, 1000.0, 3000.0])) ``` The distances are metres. The database tabulates them in feet, at the ten Doc 29 nodes from 200 ft to 25000 ft, and this bridge converts on read so everything downstream stays in SI. `curves.plot()` draws the whole family: The three tabulated thrust settings are close to parallel: power mostly shifts the level, while distance sets the shape.

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```python import matplotlib.pyplot as plt from phonometry import load_anp_database ac = load_anp_database().aircraft("747100") curves = ac.npd_curves("D", "SEL") fig, ax = plt.subplots(figsize=(10, 6)) curves.plot(ax=ax) ax.set_title(f"ANP NPD Curves - {ac.description} (SEL, departure)") ax.text(0.02, 0.06, f"power parameter: {ac.power_parameter}\n" "markers: tabulated NPD nodes", transform=ax.transAxes, va="bottom", fontsize=9) plt.show() ```

## The default trajectory An aircraft record also carries default trajectories. `profile` returns one as a Doc 29 flight path: an `(N, 5)` array of along-track, lateral and vertical position plus the power setting and true airspeed, with boolean masks marking which segments are the takeoff ground roll or the landing rollout. The **stage length** selects the trip-distance bin: a longer stage means more fuel, more weight and a shallower climb, so the same aircraft has one profile per bin. Only the **fixed-point** profiles are read as ready-to-use trajectories. Most ANP entries describe their departures as procedural steps instead (climb at this rate to that altitude, accelerate, retract flaps), which have to be flown through a flight-mechanics performance model before they become a path. That model is outside this bridge, so of the 155 aircraft in the shipped database 13 have a fixed-point *departure* profile and 20 a fixed-point *arrival* profile. Doc 29 Vol. 2 Appendix G3.5 defines the bins by trip length in nautical miles (1 is 0-500, 2 is 500-1 000, 3 is 1 000-1 500, 4 is 1 500-2 500, then 1 000 nmi steps), and the shipped profiles cover stage lengths 1 to 7 for departures and 1 only for arrivals. Asking for one that does not exist raises a `KeyError` naming the stage lengths that do, which is how to discover them: ```python from phonometry import load_anp_database db = load_anp_database() for identifier, operation, stage in (("A320-232", "D", 1), ("747100", "D", 9)): try: db.profile(identifier, operation, stage) except KeyError as exc: print(exc) # ... 'A320-232', operation 'D', stage length 1 (available stage lengths: []) # ... '747100', operation 'D', stage length 9 (available: [1, 2, 3, 4, 5, 6]) ``` An empty list means the type has no fixed-point profile at all and needs a substitute trajectory. NPD curves, on the other hand, are tabulated for every aircraft in the database. ```python from phonometry import load_anp_database profile = load_anp_database().aircraft("747100").profile("D", stage_length=1) print(profile.profile_id, profile.stage_length, profile.path.shape) profile.plot() ``` The first points sit at zero altitude and are the ground roll; the climb gradient after them fixes the slant distance at every receiver, so it decides the contour.

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```python import matplotlib.pyplot as plt from phonometry import load_anp_database ac = load_anp_database().aircraft("747100") profile = ac.profile("D", stage_length=1) fig, ax = plt.subplots(figsize=(10, 6)) profile.plot(ax=ax) ax.set_title(f"ANP Default Departure Profile - {ac.description}") ax.text(0.98, 0.06, f"stage length {profile.stage_length}, " f"{profile.path.shape[0]} fixed points", transform=ax.transAxes, va="bottom", ha="right", fontsize=9) plt.show() ```

## Straight to an event level or a contour With both halves in the record, the aircraft can run the Doc 29 chain itself. `event_level` places one flyover at a receiver, and `noise_contour` sweeps it over a ground grid, each wiring the NPD curves and the default profile into the functions the airport-noise page builds by hand. ```python from phonometry import load_anp_database ac = load_anp_database().aircraft("747100") flyover = ac.event_level([3000.0, 500.0, 0.0], "D") print(round(float(flyover.level), 1)) # 100.4 dB ``` The observer is `(x, y, z)` in metres in the runway frame, whose origin is not the airport boundary: `x` runs along the runway centre line with `x = 0` at start of roll for a departure and at the landing threshold for an arrival, so arrival profiles carry negative `x` on final approach and the two frames sit nearly 35 km apart. `y` is the lateral offset from the extended centre line, positive to starboard, and its sign selects the depression-angle branch of the banked-segment rule; `z` is the receiver height above local ground, left at 0 here where Doc 29 measures at 1.2 m. The metric defaults to the sound exposure level and the stage length to 1, and the optional `temperature` and `pressure` arguments feed the Doc 29 atmospheric impedance adjustment, defaulting to the 15 °C and 101.325 kPa of the standard atmosphere — a bookkeeping term worth +0.07 dB there, not a weather correction. ```python import numpy as np from phonometry import load_anp_database contour = load_anp_database().aircraft("747100").noise_contour( "D", x=np.linspace(-2000.0, 12000.0, 40), y=np.linspace(-3000.0, 3000.0, 30), ) print(contour.level.shape) # (30, 40): one SEL per grid point, indexed (y, x) contour.plot() ``` Everything these two return is the same result type the airport-noise page uses, so the plotting, the contour extraction and the per-segment breakdown all work unchanged. ## What this guide covers **Covered.** Opening the shipped EASA ANP database, or another ANP CSV export, with `load_anp_database`; what one aircraft record holds, including the power parameter its NPD table is indexed by and the engine mounting the Doc 29 chain reads; reading and interpolating the NPD surface with `npd_curves` and `level`; the default fixed-point trajectories and their stage-length bins; and driving the Doc 29 single-event level and ground-grid contour from an aircraft identifier with `event_level` and `noise_contour`. **Not covered.** The procedural-step profiles, which is how most ANP entries describe a departure. Turning those into a flight path needs the ICAO Doc 9911 flight-mechanics performance model, which this bridge does not implement, so only the 13 types with a fixed-point *departure* profile and the 20 with a fixed-point *arrival* profile come with a trajectory ready to fly. The database is read and never written: version 2.3 ships with the package and the package does not update it. ## See also Pages elsewhere on the site that this section leans on: - [Airport Noise (ECAC Doc 29)](https://jmrplens.github.io/phonometry/aircraft/airport-noise/): the method itself, built from a hand-written NPD table and flight path. - [Aircraft noise: Effective Perceived Noise Level](https://jmrplens.github.io/phonometry/aircraft/aircraft-noise/): the certification metric, which is measured rather than tabulated. - API reference: [`aircraft.anp_fleet`](https://jmrplens.github.io/phonometry/reference/api/aeroacoustics/anp-fleet/). ## References - EASA and EUROCONTROL, *Aircraft Noise and Performance (ANP) database*, version 2.3 (2020). . The provenance of the copy shipped with the package is recorded in `aircraft/data/anp/PROVENANCE.md`. - ECAC, *Report on standard method of computing noise contours around civil airports*, Volume 2: Technical guide, Doc 29 4th ed. (2016).