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Commit 2137c807 authored by Raphael Sturgis's avatar Raphael Sturgis
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restructure AISPoints

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skais/ais/ais_points.py
+ 55
− 68
View file @ 2137c807
import pickle
from datetime import datetime
import pandas as pd
import numpy as np
from numba import jit
import pandas as pd
from scipy.stats import stats
from skais.ais.ais_trajectory import AISTrajectory
# TODO: remove
def compute_trajectories(df, time_gap, min_size=50, size_limit=500, interpolation_time=None):
n_sample = len(df.index)
result = []
work_df = df.copy()
index = 0
while index < n_sample:
i = compute_trajectory(df['ts_sec'][index:].to_numpy(), time_gap, size_limit)
trajectory = AISTrajectory(work_df[:i], interpolation_time=interpolation_time)
if len(trajectory.df.index) > min_size:
result.append(trajectory)
work_df = work_df[i:]
index += i
return result
# TODO: remove
@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 or ((times[i] - previous_date) / 60 > time_gap):
return i
previous_date = times[i]
return i + 1
# def compute_trajectories(df, time_gap, min_size=50, size_limit=500, interpolation_time=None):
# n_sample = len(df.index)
# result = []
# work_df = df.copy()
#
# index = 0
# while index < n_sample:
# i = compute_trajectory(df['ts_sec'][index:].to_numpy(), time_gap, size_limit)
# trajectory = AISTrajectory(work_df[:i], interpolation_time=interpolation_time)
# if len(trajectory.df.index) > min_size:
# result.append(trajectory)
# work_df = work_df[i:]
# index += i
#
# return result
#
#
# @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 or ((times[i] - previous_date) / 60 > time_gap):
# return i
# previous_date = times[i]
#
# return i + 1
class AISPoints:
......@@ -50,6 +45,19 @@ class AISPoints:
self.df = df
def describe(self):
description = {
"nb vessels": len(self.df.mmsi.unique()),
"nb points": len(self.df.index),
"average speed": self.df['sog'].mean(),
"average diff": self.df['diff'].mean()
}
for n in np.sort(self.df['label'].unique()):
description[f"labeled {n}"] = len(self.df[self.df['label'] == n].index)
return description
# cleaning functions
def remove_outliers(self, features, rank=4):
if rank <= 0:
......@@ -96,43 +104,22 @@ class AISPoints:
return normalization_type, normalization_dict
# New features
# TODO: rename
def compute_diff_heading_cog(self):
self.df["diff"] = self.df.apply(lambda x: 180 - abs(abs(x['heading'] - x['cog']) - 180),
def compute_drift(self):
self.df["drift"] = self.df.apply(lambda x: 180 - abs(abs(x['heading'] - x['cog']) - 180),
axis=1)
# Trajectories
"""
Separates AISPoints into individual trajectories
"""
# TODO: redo
def get_trajectories(self, time_gap=30, min_size=50, interpolation_time=None):
if 'ts' in self.df:
self.df['ts'] = pd.to_datetime(self.df['ts'], infer_datetime_format=True)
self.df['ts_sec'] = self.df['ts'].apply(lambda x: datetime.timestamp(x))
dat = self.df
else:
raise ValueError
def get_trajectories(self):
trajectories = []
for mmsi in dat.mmsi.unique():
trajectories += compute_trajectories(dat[dat['mmsi'] == mmsi], time_gap, min_size=min_size,
interpolation_time=interpolation_time)
for mmsi in self.df.mmsi.unique():
trajectories.append(AISTrajectory(self.df[self.df['mmsi'] == mmsi].reset_index(drop=True)))
return trajectories
def describe(self):
stats = {"nb vessels": len(self.df.mmsi.unique()),
"nb points": len(self.df.index),
"average speed": self.df['sog'].mean(),
"average diff": self.df['diff'].mean()
}
for n in np.sort(self.df['label'].unique()):
stats[f"labeled {n}"] = len(self.df[self.df['label'] == n].index)
return stats
# Static methods
@staticmethod
def fuse(*args):
......
skais/ais/ais_trajectory.py
+ 3
− 0
View file @ 2137c807
......@@ -245,6 +245,9 @@ class AISTrajectory:
# self.df = df.dropna()
self.df = df
def __eq__(self, other):
return self.df.equals(other.df)
def compute_angle_l1(self, radius):
dat = self.df['angles_diff'].to_numpy()
l1 = l1_angle(dat, radius)
......
skais/tests/ais/test_ais_points.py
+ 285
− 235
View file @ 2137c807
......@@ -3,12 +3,14 @@ import unittest
import pandas as pd
import numpy as np
from skais.ais.ais_points import AISPoints, compute_trajectory, compute_trajectories
from skais.ais.ais_points import AISPoints
from skais.ais.ais_trajectory import AISTrajectory
class TestAISPositions(unittest.TestCase):
def setUp(self) -> None:
self.ais_points = AISPoints(pd.DataFrame(
def test_describe(self):
ais_points = AISPoints(pd.DataFrame(
{
"sog": [2, 3, 7, 15, 14, 12, 18, 25, 21, 12, 11, 16, 19, 2, 5, 15, 12, 7, 8, 9, 1],
"diff": [35, 45, 59, 12, 1, 2, 54, 5, 47, 86, 119, 68, 75, 54, 55, 12, 32, 62, 159, 157, 132],
......@@ -17,24 +19,197 @@ class TestAISPositions(unittest.TestCase):
}
))
self.ais_trajectories = AISPoints(
self.assertDictEqual(ais_points.describe(),
{
'nb vessels': 1,
'nb points': 21,
'labeled 0': 13,
'labeled 1': 8,
'average speed': 234 / 21,
'average diff': 1271 / 21
})
def test_remove_outliers_simple(self):
ais_points = AISPoints(pd.DataFrame(
{
"cog": [i for i in range(0, 359, 10)] + [1000] + [666],
"heading": [0.0 for i in range(0, 359, 10)] + [0] + [0]}
)
)
expected = pd.DataFrame(
{
"cog": [i for i in range(0, 359, 10)] + [666],
"heading": [0.0 for i in range(0, 359, 10)] + [0]
}
)
ais_points.remove_outliers(["cog", "heading"])
pd.testing.assert_frame_equal(expected.reset_index(drop=True), ais_points.df.reset_index(drop=True))
def test_remove_outliers_rank(self):
ais_points = AISPoints(pd.DataFrame(
{
"cog": [i for i in range(0, 359, 10)] + [1000] + [666],
"heading": [0.0 for i in range(0, 359, 10)] + [0] + [0]}
)
)
expected = pd.DataFrame(
{
"cog": [i for i in range(0, 359, 10)],
"heading": [0.0 for i in range(0, 359, 10)]
}
)
ais_points.remove_outliers(["cog", "heading"], rank=2)
pd.testing.assert_frame_equal(expected.reset_index(drop=True), ais_points.df.reset_index(drop=True))
def test_remove_outliers_not_all_features(self):
ais_points = AISPoints(pd.DataFrame(
{
"cog": [i / 350.0 for i in range(0, 359, 10)] + [500] + [0],
"heading": [0.0 for i in range(0, 359, 10)] + [0] + [10000]}
)
)
expected = pd.DataFrame(
{
"cog": [i / 350.0 for i in range(0, 359, 10)] + [0],
"heading": [0.0 for i in range(0, 359, 10)] + [10000]
}
)
ais_points.remove_outliers(["cog"])
pd.testing.assert_frame_equal(expected.reset_index(drop=True), ais_points.df.reset_index(drop=True))
def test_remove_outliers_exception(self):
ais_points = AISPoints(
pd.DataFrame(
{
"cog": [i / 350.0 for i in range(0, 359, 10)] + [500] + [0],
"heading": [0.0 for i in range(0, 359, 10)] + [0] + [10000]
}
)
)
with self.assertRaises(ValueError):
ais_points.remove_outliers(["cog"], rank=0)
def test_clean_angles(self):
ais_points = AISPoints(pd.DataFrame(
{
"cog": [i for i in range(0, 359, 10)] + [489, 456, -12] + [180, 180, 180],
"heading": [180 for i in range(0, 359, 10)] + [489, 180, 180] + [999, 666, -333],
}
)
)
expected = pd.DataFrame(
{
"cog": [i for i in range(0, 359, 10)],
"heading": [180 for i in range(0, 359, 10)]
}
)
ais_points.clean_angles()
result = ais_points.df
pd.testing.assert_frame_equal(expected.reset_index(drop=True), result.reset_index(drop=True))
def test_normalize_min_max(self):
ais_points = AISPoints(pd.DataFrame(
{
"cog": [i for i in range(0, 359, 10)],
"heading": [180 for i in range(0, 359, 10)]
}
)
)
ais_points.normalize(['cog', 'heading'])
result = ais_points.df
expected = pd.DataFrame(
{
"cog": [i / 350.0 for i in range(0, 359, 10)],
"heading": [0.0 for i in range(0, 359, 10)]
}
)
pd.testing.assert_frame_equal(expected.reset_index(drop=True), result.reset_index(drop=True))
def test_normalize_standardization(self):
ais_points = AISPoints(pd.DataFrame(
{
"cog": [i for i in range(0, 359, 10)],
"heading": [180 for i in range(0, 359, 10)]
}
)
)
ais_points.normalize(['cog', 'heading'], normalization_type="standardization")
result = ais_points.df
expected = pd.DataFrame(
{
"cog": [-1.68458833, -1.58832614, -1.49206395, -1.39580176, -1.29953957,
-1.20327738, -1.10701519, -1.010753, -0.91449081, -0.81822862,
-0.72196643, -0.62570424, -0.52944205, -0.43317986, -0.33691767,
-0.24065548, -0.14439329, -0.0481311, 0.0481311, 0.14439329,
0.24065548, 0.33691767, 0.43317986, 0.52944205, 0.62570424,
0.72196643, 0.81822862, 0.91449081, 1.010753, 1.10701519,
1.20327738, 1.29953957, 1.39580176, 1.49206395, 1.58832614,
1.68458833],
"heading": [0.0 for i in range(0, 359, 10)]
}
)
pd.testing.assert_frame_equal(expected.reset_index(drop=True), result.reset_index(drop=True),
check_exact=False, rtol=0.05)
def test_compute_drift(self):
ais_points = AISPoints(pd.DataFrame(
{
"cog": [i for i in range(0, 359, 10)],
"heading": [180 for i in range(0, 359, 10)]
}
)
)
ais_points.compute_drift()
diff = ais_points.df['drift'].to_numpy()
np.testing.assert_array_equal(diff, np.array([180, 170, 160, 150, 140, 130, 120, 110, 100, 90, 80, 70, 60, 50,
40, 30, 20, 10, 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110,
120, 130, 140, 150, 160, 170]))
def test_get_trajectories(self):
ais_points = AISPoints(
pd.DataFrame(
{
"mmsi": [123456789 for _ in range(10)] + [987654321 for _ in range(10)],
"ts_sec": [i for i in range(20)]
}
)
)
expected = [
AISTrajectory(
pd.DataFrame(
{
"mmsi": [123456789 for _ in range(10)],
"ts_sec": [i for i in range(10)]
}
)
),
AISTrajectory(
pd.DataFrame(
{
'lat': [0, 0, 1, 1, 2, 2, 3, 3, 4, 4] * 2,
'long': [0, 0, 0, 0, 0, 1, 2, 3, 4, 5] * 2,
'sog': [0, 0, 10, 10, 10, 20, 30, 40, 20, 10] * 2,
'diff': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] * 2,
'label': [1, 1, 0, 0, 0, 0, 0, 0, 0, 0] * 2,
'ts': ['2020-03-10 22:10:00', '2020-03-10 22:14:00', '2020-03-10 22:18:00', '2020-03-10 22:22:00',
'2020-03-10 22:30:00', '2020-03-10 22:32:00', '2020-03-10 22:35:00', '2020-03-10 22:40:00',
'2020-03-10 22:45:00', '2020-03-10 22:50:00'] +
['2020-03-10 22:10:00', '2020-03-10 22:14:00', '2020-03-10 22:18:00', '2020-03-10 22:20:00',
'2020-03-10 23:30:00', '2020-03-10 23:32:00', '2020-03-10 23:35:00', '2020-03-10 23:40:00',
'2020-03-10 23:45:00', '2020-03-10 23:50:00'],
'mmsi': [100 for i in range(10)] + [101 for i in range(10)]
"mmsi": [987654321 for _ in range(10)],
"ts_sec": [10+ i for i in range(10)]
}
)
)
]
for expected_trajectory, result_trajectory in zip(expected, ais_points.get_trajectories()):
pd.testing.assert_frame_equal(expected_trajectory.df, result_trajectory.df)
# def test_histogram_no_label_simple(self):
# result = np.histogramdd(self.ais_points.df[["sog", "diff"]].to_numpy(), 3, [[0, 30], [0, 180]])[0]
......@@ -88,22 +263,6 @@ class TestAISPositions(unittest.TestCase):
#
# pd.testing.assert_frame_equal(ais_points.df, self.ais_points.df)
def test_compute_diff_heading_cog(self):
ais_points = AISPoints(pd.DataFrame(
{
"cog": [i for i in range(0, 359, 10)],
"heading": [180 for i in range(0, 359, 10)]
}
)
)
ais_points.compute_diff_heading_cog()
diff = ais_points.df['diff'].to_numpy()
np.testing.assert_array_equal(diff, np.array([180, 170, 160, 150, 140, 130, 120, 110, 100, 90, 80, 70, 60, 50,
40, 30, 20, 10, 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110,
120, 130, 140, 150, 160, 170]))
# def test_histogram_x(self):
# ground_truth = np.array([[5, 1, 3],
......@@ -114,149 +273,119 @@ class TestAISPositions(unittest.TestCase):
# self.ais_points.histogram(features=["sog", "diff"], bins=3,
# ranges=[[0, 30], [0, 180]]))
def test_describe(self):
self.assertDictEqual(self.ais_points.describe(),
{
'nb vessels': 1,
'nb points': 21,
'labeled 0': 13,
'labeled 1': 8,
'average speed': 234 / 21,
'average diff': 1271 / 21
})
def test_fuse_single(self):
pd.testing.assert_frame_equal(AISPoints.fuse(self.ais_points).df, self.ais_points.df)
def test_fuse_simple_list(self):
pd.testing.assert_frame_equal(AISPoints.fuse([self.ais_points]).df, self.ais_points.df)
def test_fuse_multiple(self):
value = pd.DataFrame(
ais_points = AISPoints(pd.DataFrame(
{
"sog": [2, 3, 7, 15, 14, 12, 18, 25, 21, 12, 11, 16, 19, 2, 5, 15, 12, 7, 8, 9, 1, 2, 3, 7, 15, 14, 12,
18, 25, 21, 12, 11, 16, 19, 2, 5, 15, 12, 7, 8, 9, 1],
"diff": [35, 45, 59, 12, 1, 2, 54, 5, 47, 86, 119, 68, 75, 54, 55, 12, 32, 62, 159, 157, 132, 35, 45,
59, 12, 1, 2, 54, 5, 47, 86, 119, 68, 75, 54, 55, 12, 32, 62, 159, 157, 132],
"label": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
"mmsi": [0 for i in range(42)]
"sog": [2, 3, 7, 15, 14, 12, 18, 25, 21, 12, 11, 16, 19, 2, 5, 15, 12, 7, 8, 9, 1],
"diff": [35, 45, 59, 12, 1, 2, 54, 5, 47, 86, 119, 68, 75, 54, 55, 12, 32, 62, 159, 157, 132],
"label": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
"mmsi": [0 for i in range(21)]
}
)
pd.testing.assert_frame_equal(AISPoints.fuse(self.ais_points, self.ais_points).df.reset_index(drop=True),
value.reset_index(drop=True))
def test_compute_trajectory_simple(self):
times = np.arange(0, 100, 4) * 60
result = compute_trajectory.py_func(times, 5, 1000)
expected = 25
self.assertEqual(result, expected)
def test_compute_trajectory_cut(self):
times = np.concatenate([np.arange(0, 100, 4) * 60, np.arange(120, 200, 4) * 60])
result = compute_trajectory.py_func(times, 5, 1000)
expected = 25
self.assertEqual(result, expected)
def test_compute_trajectory_limit(self):
times = np.concatenate([np.arange(0, 100, 4) * 60, np.arange(120, 200, 4) * 60])
result = compute_trajectory.py_func(times, 5, 10)
expected = 10
self.assertEqual(result, expected)
def test_compute_trajectories_simple_split(self):
df = pd.DataFrame({'ts_sec': np.concatenate([np.arange(0, 100, 4) * 60, np.arange(120, 200, 4) * 60])})
result = compute_trajectories(df, 5, min_size=0)
expected = [
pd.DataFrame({'ts_sec': np.arange(0, 100, 4) * 60}),
pd.DataFrame({'ts_sec': np.arange(120, 200, 4) * 60})
]
self.assertEqual(len(expected), len(result))
for r, e in zip(result, expected):
pd.testing.assert_frame_equal(e.reset_index(drop=True), r.df.reset_index(drop=True))
def test_compute_trajectories_split_limit(self):
a = np.arange(0, 100, 4)
b = np.arange(120, 200, 4)
df = pd.DataFrame({'ts_sec': np.concatenate([np.arange(0, 100, 4) * 60, np.arange(120, 200, 4) * 60])})
result = compute_trajectories(df, 5, min_size=0, size_limit=10)
expected = [
pd.DataFrame({'ts_sec': a[:10] * 60}),
pd.DataFrame({'ts_sec': a[10:20] * 60}),
pd.DataFrame({'ts_sec': a[20:] * 60}),
pd.DataFrame({'ts_sec': b[:10] * 60}),
pd.DataFrame({'ts_sec': b[10:] * 60})
]
self.assertEqual(len(expected), len(result))
for r, e in zip(result, expected):
pd.testing.assert_frame_equal(e.reset_index(drop=True), r.df.reset_index(drop=True))
def test_compute_trajectories_split_min_size(self):
a = np.arange(0, 100, 4)
b = np.arange(120, 200, 4)
print(len(b))
df = pd.DataFrame({'ts_sec': np.concatenate([np.arange(0, 100, 4) * 60, np.arange(120, 200, 4) * 60])})
result = compute_trajectories(df, 5, min_size=23)
expected = [
pd.DataFrame({'ts_sec': a * 60})
]
))
self.assertEqual(len(expected), len(result))
for r, e in zip(result, expected):
pd.testing.assert_frame_equal(e.reset_index(drop=True), r.df.reset_index(drop=True))
pd.testing.assert_frame_equal(AISPoints.fuse(ais_points).df, ais_points.df)
def test_normalize_min_max(self):
def test_fuse_simple_list(self):
ais_points = AISPoints(pd.DataFrame(
{
"cog": [i for i in range(0, 359, 10)],
"heading": [180 for i in range(0, 359, 10)]
}
)
)
ais_points.normalize(['cog', 'heading'])
result = ais_points.df
expected = pd.DataFrame(
{
"cog": [i / 350.0 for i in range(0, 359, 10)],
"heading": [0.0 for i in range(0, 359, 10)]
"sog": [2, 3, 7, 15, 14, 12, 18, 25, 21, 12, 11, 16, 19, 2, 5, 15, 12, 7, 8, 9, 1],
"diff": [35, 45, 59, 12, 1, 2, 54, 5, 47, 86, 119, 68, 75, 54, 55, 12, 32, 62, 159, 157, 132],
"label": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
"mmsi": [0 for i in range(21)]
}
)
))
pd.testing.assert_frame_equal(expected.reset_index(drop=True), result.reset_index(drop=True))
pd.testing.assert_frame_equal(AISPoints.fuse([ais_points]).df, ais_points.df)
def test_normalize_standardization(self):
def test_fuse_multiple(self):
ais_points = AISPoints(pd.DataFrame(
{
"cog": [i for i in range(0, 359, 10)],
"heading": [180 for i in range(0, 359, 10)]
"sog": [2, 3, 7, 15, 14, 12, 18, 25, 21, 12, 11, 16, 19, 2, 5, 15, 12, 7, 8, 9, 1],
"diff": [35, 45, 59, 12, 1, 2, 54, 5, 47, 86, 119, 68, 75, 54, 55, 12, 32, 62, 159, 157, 132],
"label": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
"mmsi": [0 for i in range(21)]
}
)
)
))
ais_points.normalize(['cog', 'heading'], normalization_type="standardization")
result = ais_points.df
expected = pd.DataFrame(
value = pd.DataFrame(
{
"cog": [-1.68458833, -1.58832614, -1.49206395, -1.39580176, -1.29953957,
-1.20327738, -1.10701519, -1.010753, -0.91449081, -0.81822862,
-0.72196643, -0.62570424, -0.52944205, -0.43317986, -0.33691767,
-0.24065548, -0.14439329, -0.0481311, 0.0481311, 0.14439329,
0.24065548, 0.33691767, 0.43317986, 0.52944205, 0.62570424,
0.72196643, 0.81822862, 0.91449081, 1.010753, 1.10701519,
1.20327738, 1.29953957, 1.39580176, 1.49206395, 1.58832614,
1.68458833],
"heading": [0.0 for i in range(0, 359, 10)]
"sog": [2, 3, 7, 15, 14, 12, 18, 25, 21, 12, 11, 16, 19, 2, 5, 15, 12, 7, 8, 9, 1, 2, 3, 7, 15, 14, 12,
18, 25, 21, 12, 11, 16, 19, 2, 5, 15, 12, 7, 8, 9, 1],
"diff": [35, 45, 59, 12, 1, 2, 54, 5, 47, 86, 119, 68, 75, 54, 55, 12, 32, 62, 159, 157, 132, 35, 45,
59, 12, 1, 2, 54, 5, 47, 86, 119, 68, 75, 54, 55, 12, 32, 62, 159, 157, 132],
"label": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
"mmsi": [0 for i in range(42)]
}
)
pd.testing.assert_frame_equal(AISPoints.fuse(ais_points, ais_points).df.reset_index(drop=True),
value.reset_index(drop=True))
#
# def test_compute_trajectory_simple(self):
# times = np.arange(0, 100, 4) * 60
# result = compute_trajectory.py_func(times, 5, 1000)
# expected = 25
#
# self.assertEqual(result, expected)
#
# def test_compute_trajectory_cut(self):
# times = np.concatenate([np.arange(0, 100, 4) * 60, np.arange(120, 200, 4) * 60])
# result = compute_trajectory.py_func(times, 5, 1000)
# expected = 25
#
# self.assertEqual(result, expected)
#
# def test_compute_trajectory_limit(self):
# times = np.concatenate([np.arange(0, 100, 4) * 60, np.arange(120, 200, 4) * 60])
# result = compute_trajectory.py_func(times, 5, 10)
# expected = 10
#
# self.assertEqual(result, expected)
#
# def test_compute_trajectories_simple_split(self):
# df = pd.DataFrame({'ts_sec': np.concatenate([np.arange(0, 100, 4) * 60, np.arange(120, 200, 4) * 60])})
# result = compute_trajectories(df, 5, min_size=0)
# expected = [
# pd.DataFrame({'ts_sec': np.arange(0, 100, 4) * 60}),
# pd.DataFrame({'ts_sec': np.arange(120, 200, 4) * 60})
# ]
#
# self.assertEqual(len(expected), len(result))
# for r, e in zip(result, expected):
# pd.testing.assert_frame_equal(e.reset_index(drop=True), r.df.reset_index(drop=True))
#
# def test_compute_trajectories_split_limit(self):
# a = np.arange(0, 100, 4)
# b = np.arange(120, 200, 4)
# df = pd.DataFrame({'ts_sec': np.concatenate([np.arange(0, 100, 4) * 60, np.arange(120, 200, 4) * 60])})
# result = compute_trajectories(df, 5, min_size=0, size_limit=10)
# expected = [
# pd.DataFrame({'ts_sec': a[:10] * 60}),
# pd.DataFrame({'ts_sec': a[10:20] * 60}),
# pd.DataFrame({'ts_sec': a[20:] * 60}),
# pd.DataFrame({'ts_sec': b[:10] * 60}),
# pd.DataFrame({'ts_sec': b[10:] * 60})
# ]
#
# self.assertEqual(len(expected), len(result))
# for r, e in zip(result, expected):
# pd.testing.assert_frame_equal(e.reset_index(drop=True), r.df.reset_index(drop=True))
#
# def test_compute_trajectories_split_min_size(self):
# a = np.arange(0, 100, 4)
# b = np.arange(120, 200, 4)
# print(len(b))
# df = pd.DataFrame({'ts_sec': np.concatenate([np.arange(0, 100, 4) * 60, np.arange(120, 200, 4) * 60])})
# result = compute_trajectories(df, 5, min_size=23)
# expected = [
# pd.DataFrame({'ts_sec': a * 60})
# ]
#
# self.assertEqual(len(expected), len(result))
# for r, e in zip(result, expected):
# pd.testing.assert_frame_equal(e.reset_index(drop=True), r.df.reset_index(drop=True))
pd.testing.assert_frame_equal(expected.reset_index(drop=True), result.reset_index(drop=True),
check_exact=False, rtol=0.05)
# def test_disjointed_histogram_label_none(self):
# ais_points = AISPoints(pd.DataFrame(
......@@ -329,83 +458,4 @@ class TestAISPositions(unittest.TestCase):
# for r, e in zip(result, expected):
# np.testing.assert_array_equal(e, r[0])
def test_clean_angles(self):
ais_points = AISPoints(pd.DataFrame(
{
"cog": [i for i in range(0, 359, 10)] + [489, 456, -12] + [180, 180, 180],
"heading": [180 for i in range(0, 359, 10)] + [489, 180, 180] + [999, 666, -333],
}
)
)
expected = pd.DataFrame(
{
"cog": [i for i in range(0, 359, 10)],
"heading": [180 for i in range(0, 359, 10)]
}
)
ais_points.clean_angles()
result = ais_points.df
pd.testing.assert_frame_equal(expected.reset_index(drop=True), result.reset_index(drop=True))
def test_remove_outliers_simple(self):
ais_points = AISPoints(pd.DataFrame(
{
"cog": [i for i in range(0, 359, 10)] + [1000] + [666],
"heading": [0.0 for i in range(0, 359, 10)] + [0] + [0]}
)
)
expected = pd.DataFrame(
{
"cog": [i for i in range(0, 359, 10)] + [666],
"heading": [0.0 for i in range(0, 359, 10)] + [0]
}
)
ais_points.remove_outliers(["cog", "heading"])
pd.testing.assert_frame_equal(expected.reset_index(drop=True), ais_points.df.reset_index(drop=True))
def test_remove_outliers_rank(self):
ais_points = AISPoints(pd.DataFrame(
{
"cog": [i for i in range(0, 359, 10)] + [1000] + [666],
"heading": [0.0 for i in range(0, 359, 10)] + [0] + [0]}
)
)
expected = pd.DataFrame(
{
"cog": [i for i in range(0, 359, 10)],
"heading": [0.0 for i in range(0, 359, 10)]
}
)
ais_points.remove_outliers(["cog", "heading"], rank=2)
pd.testing.assert_frame_equal(expected.reset_index(drop=True), ais_points.df.reset_index(drop=True))
def test_remove_outliers_not_all_features(self):
ais_points = AISPoints(pd.DataFrame(
{
"cog": [i / 350.0 for i in range(0, 359, 10)] + [500] + [0],
"heading": [0.0 for i in range(0, 359, 10)] + [0] + [10000]}
)
)
expected = pd.DataFrame(
{
"cog": [i / 350.0 for i in range(0, 359, 10)] + [0],
"heading": [0.0 for i in range(0, 359, 10)] + [10000]
}
)
ais_points.remove_outliers(["cog"])
pd.testing.assert_frame_equal(expected.reset_index(drop=True), ais_points.df.reset_index(drop=True))
def test_remove_outliers_exception(self):
ais_points = AISPoints(
pd.DataFrame(
{
"cog": [i / 350.0 for i in range(0, 359, 10)] + [500] + [0],
"heading": [0.0 for i in range(0, 359, 10)] + [0] + [10000]
}
)
)
with self.assertRaises(ValueError):
ais_points.remove_outliers(["cog"], rank=0)
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