From a0f688f4eb096124062e2fd05bc91ab183fa0d80 Mon Sep 17 00:00:00 2001
From: Raphael <raphael.sturgis@lis-lab.fr>
Date: Fri, 5 Nov 2021 08:43:32 +0100
Subject: [PATCH] minor
---
.gitignore | 3 ++-
requirements.txt | 3 ++-
skais/ais/ais_trajectory.py | 26 +++++++++++++++++++++++---
skais/learn/hmm_gmm_classifier.py | 1 -
4 files changed, 27 insertions(+), 6 deletions(-)
diff --git a/.gitignore b/.gitignore
index 397680e..b139f3e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
.idea/*
build/
skais.egg-info/
-*.coverage
\ No newline at end of file
+*.coverage
+*__pycache__*
diff --git a/requirements.txt b/requirements.txt
index 7d1581b..0634b7c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,4 +3,5 @@ setuptools~=57.0.0
numpy~=1.19.5
numba~=0.53.1
scipy~=1.5.4
-POT~=0.7.0
\ No newline at end of file
+hmmlearn~=0.2.6
+scikit-learn~=1.0.1
\ No newline at end of file
diff --git a/skais/ais/ais_trajectory.py b/skais/ais/ais_trajectory.py
index c3a339c..4185386 100644
--- a/skais/ais/ais_trajectory.py
+++ b/skais/ais/ais_trajectory.py
@@ -5,6 +5,7 @@ import numpy as np
from numba import jit
from scipy.interpolate import interp1d
+from skais.stats.angluar import angular_dispersion
from skais.utils.geography import great_circle
from skais.utils.stats import calc_std_dev
@@ -155,7 +156,7 @@ def l1_angle(dat, radius):
dat = np.concatenate([np.full(radius, dat[0]), dat, np.full(radius, dat[-1])])
for i in range(radius, dat.shape[0] - radius):
- data = dat[i - radius:i + radius+1]
+ data = dat[i - radius:i + radius + 1]
l1[i - radius] = np.linalg.norm(data, ord=1)
return l1
@@ -166,11 +167,20 @@ def l2_angle(dat, radius):
dat = np.concatenate([np.full(radius, dat[0]), dat, np.full(radius, dat[-1])])
for i in range(radius, dat.shape[0] - radius):
- data = dat[i - radius:i + radius+1]
+ data = dat[i - radius:i + radius + 1]
l2[i - radius] = np.linalg.norm(data, ord=2)
return l2
+def angle_dispersion(dat, radius):
+ l2 = np.zeros(dat.shape)
+
+ dat = np.concatenate([np.full(radius, dat[0]), dat, np.full(radius, dat[-1])])
+ for i in range(radius, dat.shape[0] - radius):
+ data = dat[i - radius:i + radius + 1]
+ l2[i - radius] = angular_dispersion(np.radians(data))
+
+
class AISTrajectory:
def __init__(self, df, interpolation_time=None):
df = df.drop_duplicates(subset=['ts_sec'])
@@ -181,7 +191,7 @@ class AISTrajectory:
discrete_columns = ['navstatus', 'label']
new_df = pd.DataFrame()
t_raw = df['ts_sec'].to_numpy()
- t_interp1d = np.arange(start=t_raw[0], stop=t_raw[-1]+1,
+ t_interp1d = np.arange(start=t_raw[0], stop=t_raw[-1] + 1,
step=interpolation_time * 60)
new_df['ts_sec'] = t_interp1d
@@ -267,6 +277,16 @@ class AISTrajectory:
stds[:radius] = np.nan
self.df[f"{field}_std"] = stds
+ def compute_all_dispersions(self, radius):
+ fields = ['cog', 'heading', 'angles_diff']
+ for field in fields:
+ if field in self.df.columns:
+ dat = self.df[field].to_numpy()
+ disp = angle_dispersion(dat, radius)
+ disp[-radius:] = np.nan
+ disp[:radius] = np.nan
+ self.df[f"{field}_disp"] = disp
+
def compute_position_features(self, radius):
dat = np.stack([self.df.longitude.to_numpy(), self.df.latitude.to_numpy()], axis=1)
std = compute_position_angle_std(dat, radius)
diff --git a/skais/learn/hmm_gmm_classifier.py b/skais/learn/hmm_gmm_classifier.py
index 34507dd..953041d 100644
--- a/skais/learn/hmm_gmm_classifier.py
+++ b/skais/learn/hmm_gmm_classifier.py
@@ -1,7 +1,6 @@
import random
from hmmlearn.hmm import GMMHMM, GaussianHMM
-from matplotlib import pyplot as plt
from numba import jit
from scipy import linalg
from sklearn.datasets import make_spd_matrix
--
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