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Added airport elevation parameter #66

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Added airport elevation parameter #66

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a757562
Added airport elevation parameter
BrakingChanges Sep 24, 2025
03b138a
Merge branch 'master' into airport-elevation
BrakingChanges Mar 7, 2026
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39 changes: 33 additions & 6 deletions openap/gen.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,6 +80,8 @@ def climb(self, **kwargs) -> pd.DataFrame:
cas_const_cl: Constant CAS for climb (kt).
mach_const_cl: Constant Mach for climb.
alt_cr: Target cruise altitude (ft).
airport_elevation: Airport elevation (ft).

random: Generate trajectory with random parameters. Defaults to False.

Returns:
Expand Down Expand Up @@ -119,6 +121,11 @@ def climb(self, **kwargs) -> pd.DataFrame:
/ aero.ft,
)

airport_elevation = kwargs.get(
"airport_elevation",
0
)

vs_pre_constcas = self.rng.uniform(
self.wrap.climb_vs_pre_concas()["minimum"],
self.wrap.climb_vs_pre_concas()["maximum"],
Expand Down Expand Up @@ -147,6 +154,11 @@ def climb(self, **kwargs) -> pd.DataFrame:
alt_cr = kwargs.get(
"alt_cr", self.wrap.cruise_alt()["default"] * 1000 / aero.ft
)

airport_elevation = kwargs.get(
"airport_elevation",
0
) * aero.ft
vs_pre_constcas = self.wrap.climb_vs_pre_concas()["default"]
vs_constcas = self.wrap.climb_vs_concas()["default"]
vs_constmach = self.wrap.climb_vs_conmach()["default"]
Expand All @@ -164,7 +176,7 @@ def climb(self, **kwargs) -> pd.DataFrame:
# intitial conditions
t = 0
tcr = 0
h = 0
h = airport_elevation
s = 0
v = 0
vs = 0
Expand All @@ -181,7 +193,7 @@ def climb(self, **kwargs) -> pd.DataFrame:
v = v + a_tof * dt
vs = 0
seg = "TO"
elif h < 1500 * aero.ft:
elif h < airport_elevation + 1500 * aero.ft:
v = v + a * dt
if aero.tas2cas(v, h) >= vcas_const:
v = aero.cas2tas(vcas_const, h)
Expand Down Expand Up @@ -237,11 +249,13 @@ def climb(self, **kwargs) -> pd.DataFrame:
def descent(self, **kwargs) -> pd.DataFrame:
"""Generate descent trajectory based on WRAP model.


Keyword Args:
dt: Time step in seconds. Defaults to 1.
cas_const_de: Constant CAS for descent (kt).
mach_const_de: Constant Mach for descent.
alt_cr: Top of descent altitude (ft).
arr_airport_elevation (float): airport elevation(ft).
random: Generate trajectory with random parameters. Defaults to False.
withcr: Include a short cruise segment of 60 seconds. Defaults to True.

Expand Down Expand Up @@ -284,6 +298,11 @@ def descent(self, **kwargs) -> pd.DataFrame:
/ aero.kts,
)

arr_airport_elevation = kwargs.get(
"arr_airport_elevation",
0
) * aero.ft

vs_constmach = self.rng.uniform(
self.wrap.descent_vs_conmach()["minimum"],
self.wrap.descent_vs_conmach()["maximum"],
Expand All @@ -309,6 +328,12 @@ def descent(self, **kwargs) -> pd.DataFrame:
alt_cr = kwargs.get(
"alt_cr", self.wrap.cruise_alt()["default"] * 1000 / aero.ft
)

arr_airport_elevation = kwargs.get(
"arr_airport_elevation",
0
) * aero.ft

vs_constmach = self.wrap.descent_vs_conmach()["default"]
vs_constcas = self.wrap.descent_vs_concas()["default"]
vs_post_constcas = self.wrap.descent_vs_post_concas()["default"]
Expand Down Expand Up @@ -350,18 +375,18 @@ def descent(self, **kwargs) -> pd.DataFrame:
v = aero.cas2tas(vcas_const, h)
vs = vs_constcas
seg = "CAS"
elif h > 1000 * aero.ft:
elif h > arr_airport_elevation + 1000 * aero.ft:
v = v + a * dt
if aero.tas2cas(v, h) < v_app:
v = aero.cas2tas(v_app, h)
vs = vs_post_constcas
seg = "POST-CAS"
elif h > 0:
elif h > arr_airport_elevation:
v = v_app
vs = vs_fa
seg = "FA"
else:
h = 0
h = arr_airport_elevation
vs = 0
v = v + a_lnd * dt
seg = "LD"
Expand Down Expand Up @@ -492,7 +517,7 @@ def cruise(self, **kwargs) -> pd.DataFrame:

def complete(self, **kwargs) -> pd.DataFrame:
"""Generate a complete trajectory based on WRAP model.

Keyword Args:
dt: Time step in seconds. Defaults to 1.
cas_const_cl: Constant CAS for climb (kt).
Expand All @@ -502,6 +527,8 @@ def complete(self, **kwargs) -> pd.DataFrame:
range_cr: Cruise range (km).
alt_cr: Target cruise altitude (ft).
mach_cr: Cruise Mach number.
airport_elevation (float): Airport elevation (ft).
arr_airport_elevation(float): Arrival airport elevation(ft).
random: Generate trajectory with random parameters. Defaults to False.

Returns:
Expand Down