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2 49138 69.9990 333.8103 0004071 295.8561 280.8354 15.69789295 3664
|
||||
0 STARLINK-3058
|
||||
1 49138U 21082J 21311.91668981 .00017918 00000-0 15132-3 0 9995
|
||||
2 49138 69.9993 332.8732 0003875 290.8466 9.0950 15.69783812 3666
|
||||
0 STARLINK-3058
|
||||
1 49138U 21082J 21312.25002315 -.00021992 00000-0 -18270-3 0 9991
|
||||
2 49138 69.9998 331.9347 0003840 293.4783 89.6495 15.69776424 8750
|
||||
0 STARLINK-3058
|
||||
1 49138U 21082J 21312.58334491 .00001964 00000-0 18208-4 0 9997
|
||||
2 49138 69.9995 330.9987 0003851 297.6027 168.5950 15.69778871 3672
|
||||
0 STARLINK-3058
|
||||
1 49138U 21082J 21312.91667824 -.00001326 00000-0 -93245-5 0 9992
|
||||
2 49138 69.9997 330.0602 0003762 290.6090 258.7592 15.69753946 3662
|
||||
0 STARLINK-3058
|
||||
1 49138U 21082J 21313.58335648 -.00529013 00000-0 -47342-2 0 9994
|
||||
2 49138 69.9996 328.1816 0003990 290.1429 65.4144 15.69645488 8965
|
||||
0 STARLINK-3058
|
||||
1 49138U 21082J 21313.75001157 -.00665704 00000-0 -60908-2 0 9999
|
||||
2 49138 70.0000 327.7125 0003825 292.5224 284.4114 15.69504253 3675
|
||||
@ -0,0 +1,74 @@
|
||||
from os import replace
|
||||
from astropy.time.core import Time, TimeDelta
|
||||
import pandas as pd
|
||||
import random
|
||||
from tqdm import tqdm
|
||||
from poliastro.bodies import Earth
|
||||
from poliastro.twobody import Orbit
|
||||
import astropy.units as U
|
||||
from poliastro.twobody.propagation import cowell
|
||||
from joblib import Parallel, delayed
|
||||
|
||||
|
||||
def rv2list(rv):
|
||||
r, v = rv
|
||||
r = r.to(U.m).to_value()
|
||||
v = v.to(U.m / U.s).to_value()
|
||||
return [*r, *v]
|
||||
|
||||
|
||||
def build_training_data(df: pd.DataFrame):
|
||||
rnd_starts = random.choices(df.index, k=50)
|
||||
result = []
|
||||
for idx in tqdm(rnd_starts, position=0):
|
||||
src = df.iloc[idx].to_numpy()
|
||||
targets = df.iloc[idx:idx + 24 * 60].sample(n=100,
|
||||
replace=True).to_numpy()
|
||||
|
||||
def calc_diff(dst):
|
||||
s = Orbit.from_vectors(Earth, src[2:5] * U.m, src[5:8] * U.m / U.s,
|
||||
Time(src[1]))
|
||||
o = Orbit.from_vectors(Earth, dst[2:5] * U.m, dst[5:8] * U.m / U.s,
|
||||
Time(dst[1]))
|
||||
dt = dst[1] - src[1]
|
||||
op = s.propagate(o.epoch, method=cowell)
|
||||
rv = rv2list(op.rv())
|
||||
rv_real = rv2list(o.rv())
|
||||
rv_diff = [x - y for x, y in zip(rv_real, rv)]
|
||||
return dt.seconds, rv, rv_diff
|
||||
|
||||
calc_diff(targets[0])
|
||||
diffs = Parallel(n_jobs=4)(
|
||||
delayed(calc_diff)(x)
|
||||
for x in tqdm(targets, position=1, leave=False))
|
||||
result += [[*src, dt, *pred, *err] for dt, pred, err in diffs]
|
||||
return pd.DataFrame(result,
|
||||
columns=[
|
||||
"id",
|
||||
"start_epoch",
|
||||
"start_r_x",
|
||||
"start_r_y",
|
||||
"start_r_z",
|
||||
"start_v_x",
|
||||
"start_v_y",
|
||||
"start_v_z",
|
||||
"elapsed_seconds",
|
||||
"pred_r_x",
|
||||
"pred_r_y",
|
||||
"pred_r_z",
|
||||
"pred_v_x",
|
||||
"pred_v_y",
|
||||
"pred_v_z",
|
||||
"err_r_x",
|
||||
"err_r_y",
|
||||
"err_r_z",
|
||||
"err_v_x",
|
||||
"err_v_y",
|
||||
"err_v_z",
|
||||
])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
df = pd.read_parquet("1.pq")
|
||||
td = build_training_data(df)
|
||||
td.to_parquet("data.pq")
|
||||
File diff suppressed because it is too large
Load Diff
Binary file not shown.
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,11 @@
|
||||
# %%
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
df = pd.read_parquet("1.pq")
|
||||
|
||||
df2 = df.iloc[:140]
|
||||
ax = plt.axes(projection="3d")
|
||||
ax.scatter(df2["r_x"], df2["r_y"], df2["r_z"])
|
||||
plt.show()
|
||||
plt.pause(0.1)
|
||||
Binary file not shown.
@ -0,0 +1,107 @@
|
||||
# %%
|
||||
from datetime import datetime, timedelta
|
||||
from os import stat
|
||||
from attr import dataclass
|
||||
from erfa.core import pas
|
||||
from joblib.parallel import Parallel, delayed
|
||||
from numpy import double
|
||||
from tletools import TLE
|
||||
from astropy import units as u
|
||||
from spacetrack import SpaceTrackClient
|
||||
|
||||
|
||||
def parse_tle(tle_lines: list[str]):
|
||||
assert (len(tle_lines) == 3)
|
||||
tle = TLE.from_lines(*tle_lines)
|
||||
orbit = tle.to_orbit()
|
||||
r, v = orbit.rv()
|
||||
r_m = r.to(u.m).to_value()
|
||||
v_ms = v.to(u.m / u.s).to_value()
|
||||
return r_m, v_ms
|
||||
|
||||
|
||||
def to_TLE(tle_lines: list[str]):
|
||||
assert (len(tle_lines) == 3)
|
||||
return TLE.from_lines(*tle_lines)
|
||||
|
||||
|
||||
def parse_tle_file(fd):
|
||||
lines = fd.readlines()
|
||||
res: list[TLE] = []
|
||||
for i in range(0, len(lines), 3):
|
||||
res.append(to_TLE(lines[i:i + 3]))
|
||||
return res
|
||||
|
||||
|
||||
def get_tle(client: SpaceTrackClient, orbit_ids, days):
|
||||
return client.tle(**{
|
||||
'epoch': f'>now-{days}',
|
||||
'norad_cat_id': orbit_ids,
|
||||
'format': '3le'
|
||||
})
|
||||
|
||||
|
||||
client = SpaceTrackClient("geulocrutusse-7937@yopmail.com", "ETionGeOmEnDiUMp")
|
||||
# print(get_tle(client, [49138], 30))
|
||||
# %%
|
||||
|
||||
with open("1.tle") as f:
|
||||
tles = parse_tle_file(f)
|
||||
|
||||
# %%
|
||||
from poliastro.twobody.propagation import cowell
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
@dataclass
|
||||
class orbit_status:
|
||||
id: int
|
||||
epoch: datetime
|
||||
r_x: float
|
||||
r_y: float
|
||||
r_z: float
|
||||
v_x: float
|
||||
v_y: float
|
||||
v_z: float
|
||||
|
||||
|
||||
stats = []
|
||||
for i in tqdm(range(1, len(tles)), position=0):
|
||||
p1 = tles[i - 1]
|
||||
p2 = tles[i]
|
||||
t1 = p1.epoch.to_datetime()
|
||||
t2 = p2.epoch.to_datetime()
|
||||
o1 = p1.to_orbit()
|
||||
o2 = p2.to_orbit()
|
||||
dt: timedelta = t2 - t1
|
||||
if dt.seconds <= 60:
|
||||
continue
|
||||
|
||||
for sec in tqdm(range(0, dt.seconds, 60), position=1, leave=False):
|
||||
ratio = sec / dt.seconds
|
||||
x1 = o1.propagate(sec * u.s, method=cowell)
|
||||
x2 = o2.propagate(-(dt.seconds - sec) * u.s, method=cowell)
|
||||
r1, v1 = x1.rv()
|
||||
r1_m = r1.to(u.m).to_value()
|
||||
v1_ms = v1.to(u.m / u.s).to_value()
|
||||
r2, v2 = x2.rv()
|
||||
r2_m = r2.to(u.m).to_value()
|
||||
v2_ms = v2.to(u.m / u.s).to_value()
|
||||
r_m = [x * (1 - ratio) + y * ratio for x, y in zip(r1_m, r2_m)]
|
||||
v_ms = [x * (1 - ratio) + y * ratio for x, y in zip(v1_ms, v2_ms)]
|
||||
stats.append(
|
||||
orbit_status(
|
||||
p1.norad,
|
||||
t1 + timedelta(seconds=sec),
|
||||
*r_m,
|
||||
*v_ms,
|
||||
).__dict__)
|
||||
|
||||
import pandas as pd
|
||||
|
||||
df = pd.DataFrame(stats)
|
||||
df.to_parquet("1.pq")
|
||||
|
||||
# %%
|
||||
df
|
||||
# %%
|
||||
@ -0,0 +1,78 @@
|
||||
# %%
|
||||
import pandas as pd
|
||||
from itertools import product
|
||||
|
||||
|
||||
def get_state_vect_cols(prefix=''):
|
||||
if prefix:
|
||||
prefix += '_'
|
||||
vectors = ['r', 'v']
|
||||
components = ['x', 'y', 'z']
|
||||
col_names = [f'{prefix}{v}_{c}' for v, c in product(vectors, components)]
|
||||
return col_names
|
||||
|
||||
|
||||
# %%
|
||||
df = pd.read_parquet("data.pq")
|
||||
|
||||
# %%
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
feature_cols = ['elapsed_seconds'
|
||||
] + get_state_vect_cols('pred') + get_state_vect_cols('start')
|
||||
# The target values are the errors between the physical model predictions
|
||||
# and the ground truth observations
|
||||
target_cols = get_state_vect_cols('err')
|
||||
print(target_cols)
|
||||
# Create feature and target matrices
|
||||
X = df[feature_cols]
|
||||
y = df[target_cols]
|
||||
data_keys = ['X_train', 'X_test', 'y_train', 'y_test']
|
||||
data_vals = train_test_split(X, y, test_size=0.2)
|
||||
train_test_data = dict(zip(data_keys, data_vals))
|
||||
# %%
|
||||
from sklearn.utils.validation import check_X_y
|
||||
import joblib
|
||||
|
||||
from catboost import CatBoostRegressor
|
||||
|
||||
|
||||
def train_model():
|
||||
X, ys = train_test_data['X_train'], train_test_data['y_train']
|
||||
check_X_y(X, ys, multi_output=True)
|
||||
models = {}
|
||||
|
||||
for target_col in ys.columns:
|
||||
y1 = ys[target_col]
|
||||
print(X.shape, y1.shape)
|
||||
reg = CatBoostRegressor()
|
||||
reg.fit(X, y1)
|
||||
models[target_col] = reg
|
||||
print(target_col)
|
||||
joblib.dump(models, f"models/{CatBoostRegressor.__name__}.model")
|
||||
|
||||
|
||||
# train_model()
|
||||
# %%
|
||||
from sklearn import metrics
|
||||
|
||||
|
||||
def eval_model():
|
||||
models = joblib.load(f"models/{CatBoostRegressor.__name__}.model")
|
||||
X, ys = train_test_data['X_test'], train_test_data['y_test']
|
||||
evals = []
|
||||
for target_col, reg in models.items():
|
||||
y_hat = reg.predict(X) # fake
|
||||
y = ys[target_col] # real
|
||||
print(y_hat[:10])
|
||||
print(y[:10])
|
||||
print("-----")
|
||||
rmse = metrics.mean_squared_error(y, y_hat, squared=False)
|
||||
r2 = metrics.r2_score(y, y_hat)
|
||||
eval_dict = {'Error': target_col, 'RMSE': rmse, 'R^2': r2}
|
||||
evals.append(eval_dict)
|
||||
print(pd.DataFrame(evals))
|
||||
|
||||
|
||||
eval_model()
|
||||
# %%
|
||||
Loading…
Reference in new issue