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diff --git a/build_training_data.py b/build_training_data.py
new file mode 100644
index 0000000..a135ae7
--- /dev/null
+++ b/build_training_data.py
@@ -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")
diff --git a/catboost_info/catboost_training.json b/catboost_info/catboost_training.json
index 2585fcc..fd0d5d1 100644
--- a/catboost_info/catboost_training.json
+++ b/catboost_info/catboost_training.json
@@ -1,1004 +1,1004 @@
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diff --git a/catboost_info/learn/events.out.tfevents b/catboost_info/learn/events.out.tfevents
index 51634fe..9e5374b 100644
Binary files a/catboost_info/learn/events.out.tfevents and b/catboost_info/learn/events.out.tfevents differ
diff --git a/catboost_info/learn_error.tsv b/catboost_info/learn_error.tsv
index c5447a3..4590590 100644
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diff --git a/catboost_info/time_left.tsv b/catboost_info/time_left.tsv
index e182215..520d0b5 100644
--- a/catboost_info/time_left.tsv
+++ b/catboost_info/time_left.tsv
@@ -1,1001 +1,1001 @@
 iter	Passed	Remaining
-0	2	2873
-1	5	2558
-2	7	2414
-3	9	2417
-4	11	2377
-5	14	2381
-6	17	2431
-7	19	2463
-8	23	2534
-9	25	2544
-10	27	2502
-11	29	2457
-12	32	2430
-13	33	2392
-14	36	2400
-15	39	2406
-16	41	2387
-17	43	2358
-18	45	2330
-19	47	2305
-20	48	2277
-21	50	2263
-22	52	2242
-23	54	2227
-24	56	2202
-25	58	2179
-26	59	2154
-27	61	2130
-28	63	2114
-29	64	2094
-30	66	2079
-31	68	2061
-32	69	2044
-33	71	2037
-34	73	2034
-35	75	2028
-36	77	2017
-37	79	2005
-38	81	1996
-39	82	1984
-40	84	1972
-41	85	1960
-42	87	1951
-43	89	1941
-44	90	1929
-45	92	1919
-46	94	1910
-47	95	1901
-48	97	1893
-49	99	1884
-50	100	1877
-51	102	1870
-52	104	1868
-53	106	1863
-54	108	1856
-55	109	1849
-56	111	1843
-57	113	1843
-58	115	1838
-59	117	1836
-60	119	1836
-61	121	1832
-62	122	1828
-63	124	1823
-64	126	1817
-65	128	1811
-66	129	1806
-67	131	1800
-68	133	1794
-69	134	1788
-70	136	1783
-71	138	1781
-72	140	1778
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diff --git a/data.pq b/data.pq
new file mode 100644
index 0000000..53f884c
Binary files /dev/null and b/data.pq differ
diff --git a/display_tle.py b/display_tle.py
new file mode 100644
index 0000000..36fed46
--- /dev/null
+++ b/display_tle.py
@@ -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)
\ No newline at end of file
diff --git a/models/CatBoostRegressor.model b/models/CatBoostRegressor.model
index a96e2b8..ed191a8 100644
Binary files a/models/CatBoostRegressor.model and b/models/CatBoostRegressor.model differ
diff --git a/parse_tle.py b/parse_tle.py
new file mode 100644
index 0000000..ba51770
--- /dev/null
+++ b/parse_tle.py
@@ -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
+# %%
diff --git a/train_model_copy.py b/train_model_copy.py
new file mode 100644
index 0000000..8951fa0
--- /dev/null
+++ b/train_model_copy.py
@@ -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()
+# %%