diff --git a/skais/ais/ais_trajectory.py b/skais/ais/ais_trajectory.py
index 81a26b73d98b2d38a0f99468df7fe3bc97a2f831..b719d3de5350b6ba15da13c857dd69462ac711c0 100644
--- a/skais/ais/ais_trajectory.py
+++ b/skais/ais/ais_trajectory.py
@@ -4,100 +4,102 @@ from numba import jit
from scipy.interpolate import interp1d
from skais.ais.ais_points import AISPoints
-from skais.process.geography import bearing
-from skais.utils.geography import great_circle
-from skais.utils.stats import calc_std_dev
-# @jit(nopython=True)
-# def compute_trajectory(times, time_gap, size_limit):
-# n_samples = len(times)
-#
-# previous_date = times[0]
-#
-# i = 0
-# for i in range(size_limit):
-# if i >= n_samples:
-# break
-# if (times[i] - previous_date) / 60 > time_gap:
-# break
-# previous_date = times[i]
-#
-# return i
-
-# @jit(nopython=True)
-def compute_position_angle_std(dat, radius):
- angles_stds = np.empty(dat.shape[0])
- for i in range(radius, dat.shape[0] - radius):
- data = dat[i - radius:i + radius]
- n_samples = len(data)
- center = (data[:, 0].mean(), data[:, 1].mean())
-
- angles_sum = []
- for j in range(n_samples):
- p1 = (data[j][0], data[j][1])
- alpha = bearing(p1, center)
- angles_sum.append(alpha)
-
- angles_stds[i] = calc_std_dev(angles_sum)
- return angles_stds
+class NoTimeInformation(Exception):
+ pass
@jit(nopython=True)
-def compute_position_angle_mean(dat, radius):
- angles_means = np.empty(dat.shape[0])
- for i in range(radius, dat.shape[0] - radius):
- data = dat[i - radius:i + radius]
- n_samples = len(data)
- center = (data[:, 0].mean(), data[:, 1].mean())
-
- cos = sin = 0
- for j in range(n_samples):
- p1 = (data[j][0], data[j][1])
-
- alpha = bearing(p1, center)
-
- cos += np.cos(np.radians(alpha))
- sin += np.sin(np.radians(alpha))
-
- angles_means[i] = np.arctan2(sin, cos)
- return angles_means
-
-
-def compute_position_dist_mean(dat, radius):
- dist_means = np.empty(dat.shape[0])
- for i in range(radius, dat.shape[0] - radius):
- data = dat[i - radius:i + radius]
- n_samples = len(data)
- center = (data[:, 0].mean(), data[:, 1].mean())
+def compute_trajectory(times, time_gap):
+ n_samples = len(times)
- dist_sum = 0
- for j in range(n_samples - 1):
- p1 = (data[j][0], data[j][1])
+ previous_date = times[0]
- dist_sum += great_circle(p1[0], center[0], p1[1], center[1])
+ i = 0
+ while i < n_samples:
+ if (times[i] - previous_date) > time_gap:
+ break
+ previous_date = times[i]
+ i += 1
- dist_means[i] = dist_sum / (n_samples - 1)
- return dist_means
-
-
-def compute_position_dist_std(dat, radius):
- dist_means = np.empty(dat.shape[0])
- for i in range(radius, dat.shape[0] - radius):
- data = dat[i - radius:i + radius]
- n_samples = len(data)
- center = (data[:, 0].mean(), data[:, 1].mean())
-
- dist_sum = []
- for j in range(n_samples - 1):
- p1 = (data[j][0], data[j][1])
-
- dist_sum.append(great_circle(p1[0], center[0], p1[1], center[1]))
-
- dist_means[i] = np.std(dist_sum)
- return dist_means
+ return i
+# @jit(nopython=True)
+# def compute_position_angle_std(dat, radius):
+# angles_stds = np.empty(dat.shape[0])
+# for i in range(radius, dat.shape[0] - radius):
+# data = dat[i - radius:i + radius]
+# n_samples = len(data)
+# center = (data[:, 0].mean(), data[:, 1].mean())
+#
+# angles_sum = []
+# for j in range(n_samples):
+# p1 = (data[j][0], data[j][1])
+#
+# alpha = bearing(p1, center)
+# angles_sum.append(alpha)
+#
+# angles_stds[i] = calc_std_dev(angles_sum)
+# return angles_stds
+#
+#
+# @jit(nopython=True)
+# def compute_position_angle_mean(dat, radius):
+# angles_means = np.empty(dat.shape[0])
+# for i in range(radius, dat.shape[0] - radius):
+# data = dat[i - radius:i + radius]
+# n_samples = len(data)
+# center = (data[:, 0].mean(), data[:, 1].mean())
+#
+# cos = sin = 0
+# for j in range(n_samples):
+# p1 = (data[j][0], data[j][1])
+#
+# alpha = bearing(p1, center)
+#
+# cos += np.cos(np.radians(alpha))
+# sin += np.sin(np.radians(alpha))
+#
+# angles_means[i] = np.arctan2(sin, cos)
+# return angles_means
+#
+#
+# def compute_position_dist_mean(dat, radius):
+# dist_means = np.empty(dat.shape[0])
+# for i in range(radius, dat.shape[0] - radius):
+# data = dat[i - radius:i + radius]
+# n_samples = len(data)
+# center = (data[:, 0].mean(), data[:, 1].mean())
+#
+# dist_sum = 0
+# for j in range(n_samples - 1):
+# p1 = (data[j][0], data[j][1])
+#
+# dist_sum += great_circle(p1[0], center[0], p1[1], center[1])
+#
+# dist_means[i] = dist_sum / (n_samples - 1)
+# return dist_means
+#
+#
+# def compute_position_dist_std(dat, radius):
+# dist_means = np.empty(dat.shape[0])
+# for i in range(radius, dat.shape[0] - radius):
+# data = dat[i - radius:i + radius]
+# n_samples = len(data)
+# center = (data[:, 0].mean(), data[:, 1].mean())
+#
+# dist_sum = []
+# for j in range(n_samples - 1):
+# p1 = (data[j][0], data[j][1])
+#
+# dist_sum.append(great_circle(p1[0], center[0], p1[1], center[1]))
+#
+# dist_means[i] = np.std(dist_sum)
+# return dist_means
+#
+#
# def l1_angle(dat, radius):
@@ -243,7 +245,24 @@ class AISTrajectory(AISPoints):
return {"type": "LineString", "coordinates": coordinates}
- #
+ def split_trajectory(self, time_gap=600):
+ if 'ts_sec' not in self.df:
+ raise NoTimeInformation()
+
+ n_sample = len(self.df.index)
+ result = []
+ work_df = self.df.copy()
+
+ index = 0
+ while index < n_sample:
+ i = compute_trajectory(self.df['ts_sec'][index:].to_numpy(), time_gap)
+ trajectory = AISTrajectory(work_df[:i])
+ result.append(trajectory)
+ work_df = work_df[i:]
+ index += i
+
+ return result
+
# def compute_angle_l1(self, radius):
# dat = self.df['angles_diff'].to_numpy()
# l1 = l1_angle(dat, radius)
diff --git a/skais/tests/ais/test_ais_trajectory.py b/skais/tests/ais/test_ais_trajectory.py
index 404244a96b01418cc37486b69eb89e51a09ff02d..78fff0bb0b83fc724d6671a4d2ad9582d00b00c8 100644
--- a/skais/tests/ais/test_ais_trajectory.py
+++ b/skais/tests/ais/test_ais_trajectory.py
@@ -182,6 +182,37 @@ class TestAISTrajectory(unittest.TestCase):
pd.testing.assert_frame_equal(trajectory.df, expected)
+ def test_split_trajectory_simple(self):
+ trajectory = AISTrajectory(
+ pd.DataFrame(
+ {
+ "ts_sec": [i for i in range(0, 3001, 600)] + [i for i in range(4001, 7001, 600)],
+ "label": [0 for _ in range(0, 3001, 600)] + [1 for _ in range(4001, 7001, 600)]
+ }
+ )
+ )
+
+ expected = [
+ AISTrajectory(
+ pd.DataFrame(
+ {
+ "ts_sec": [i for i in range(0, 3001, 600)],
+ "label": [0 for _ in range(0, 3001, 600)]
+ }
+ )
+ ),
+ AISTrajectory(
+ pd.DataFrame(
+ {
+ "ts_sec": [i for i in range(4001, 7001, 600)],
+ "label": [1 for i in range(4001, 7001, 600)]
+ }
+ )
+ )
+ ]
+
+ for e, r in zip(expected, trajectory.split_trajectory(800)):
+ pd.testing.assert_frame_equal(e.df.reset_index(drop=True), r.df.reset_index(drop=True))
# def test_compute_angle_l1(self):
# trajectory = AISTrajectory(
# pd.DataFrame(