From 240ae0e058c21a3d791a428c0a87e6b8fadbde8c Mon Sep 17 00:00:00 2001
From: Raphael <raphael.sturgis@gmail.com>
Date: Mon, 30 Jan 2023 19:06:30 +0100
Subject: [PATCH] jit compiling for basic_features_operations.py

---
 skais/process/basic_features_operations.py | 48 ++++++++++++----------
 1 file changed, 27 insertions(+), 21 deletions(-)

diff --git a/skais/process/basic_features_operations.py b/skais/process/basic_features_operations.py
index 350ae17..bd1bbca 100644
--- a/skais/process/basic_features_operations.py
+++ b/skais/process/basic_features_operations.py
@@ -1,44 +1,50 @@
 import cmath
-
+from numba import jit
 import numpy as np
 
 
+@jit(nopython=True, fastmath=True)
 def angular_average_vector(angles):
-    n = len(angles)
-    x = np.sum(np.cos(angles)) / n
-    y = np.sum(np.sin(angles)) / n
+	n = len(angles)
+	x = np.sum(np.cos(angles)) / n
+	y = np.sum(np.sin(angles)) / n
 
-    return np.array([x, y])
+	return np.array([x, y])
 
 
+@jit(nopython=True, fastmath=True)
 def angular_dispersion(angles):
-    x, y = angular_average_vector(angles)
-    return np.sqrt(x ** 2 + y ** 2)
+	x, y = angular_average_vector(angles)
+	return np.sqrt(x ** 2 + y ** 2)
 
 
+@jit(nopython=True, fastmath=True)
 def angular_mean(angles):
-    x, y = angular_average_vector(angles)
-    theta = np.arctan(y/x)
+	x, y = angular_average_vector(angles)
+	theta = np.arctan(y / x)
 
-    if y > 0 and x > 0:
-        return theta
-    elif x <= 0:
-        return np.pi + theta
-    else:
-        return 2*np.pi + theta
+	if y > 0 and x > 0:
+		return theta
+	elif x <= 0:
+		return np.pi + theta
+	else:
+		return 2 * np.pi + theta
 
 
+@jit(nopython=True, fastmath=True)
 def angular_std(angles):
-    return 1 - angular_dispersion(angles)
+	return 1 - angular_dispersion(angles)
 
 
+@jit(nopython=True, fastmath=True)
 def angular_time_derivative(angle1, angle2, ts1, ts2):
-    z1 = np.cos(angle1) + np.sin(angle1) * 1j
-    z2 = np.cos(angle2) + np.sin(angle2) * 1j
+	z1 = np.cos(angle1) + np.sin(angle1) * 1j
+	z2 = np.cos(angle2) + np.sin(angle2) * 1j
 
-    z = z2 / z1
-    return cmath.polar(z)[1] / (ts2 - ts1)
+	z = z2 / z1
+	return cmath.polar(z)[1] / (ts2 - ts1)
 
 
+@jit(nopython=True, fastmath=True)
 def time_derivative(f1, f2, ts1, ts2):
-    return (f2 - f1) / (ts2 - ts1)
+	return (f2 - f1) / (ts2 - ts1)
-- 
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