From c9a4f4a7c383bf2a0e11c2b57e0dcc649c321aa8 Mon Sep 17 00:00:00 2001
From: Pierre LOTTE <pierrelotte.dev@gmail.com>
Date: Wed, 25 Sep 2024 16:33:12 +0200
Subject: [PATCH] Frequency anomalies added
---
config_maker/anomalies/frequency.py | 22 +++++++++++++
generator/anomaly/frequency.py | 50 +++++++++++++++++++++++++++++
2 files changed, 72 insertions(+)
create mode 100644 config_maker/anomalies/frequency.py
create mode 100644 generator/anomaly/frequency.py
diff --git a/config_maker/anomalies/frequency.py b/config_maker/anomalies/frequency.py
new file mode 100644
index 0000000..8df01c6
--- /dev/null
+++ b/config_maker/anomalies/frequency.py
@@ -0,0 +1,22 @@
+"""
+This module provides a frequency anomaly generator class
+"""
+import numpy as np
+
+from .base import BaseAnomaly
+
+
+class FrequencyAnomaly(BaseAnomaly):
+ """
+ This class generates and handles its anomaly type
+ """
+ def generate(self):
+ """
+ This function creates and returns the anomaly configuration.
+ """
+ return {
+ "kind": "FREQUENCY",
+ "length": int(self.length),
+ "position": np.random.choice(["start", "middle", "end"]),
+ "frequency": np.random.uniform(8, 18)
+ }
diff --git a/generator/anomaly/frequency.py b/generator/anomaly/frequency.py
new file mode 100644
index 0000000..b2e9069
--- /dev/null
+++ b/generator/anomaly/frequency.py
@@ -0,0 +1,50 @@
+"""
+This module defines the behavior of a frequency anomaly generator.
+"""
+from typing import Tuple
+
+import numpy as np
+
+from .base import BaseAnomaly
+
+
+class FrequencyAnomaly(BaseAnomaly):
+ """
+ This class defines the behavior that every anomaly generator should have.
+ """
+ OSCILLATING_FUNCTIONS = {
+ "sin": np.sin,
+ "cos": np.cos,
+ "versine": lambda x: 1 - np.cos(x),
+ "vercosine": lambda x: 1 + np.cos(x),
+ "coversine": lambda x: 1 - np.sin(x),
+ "covercosine": lambda x: 1 + np.sin(x),
+ "haversine": lambda x: (1 - np.cos(x)) / 2,
+ "havercosine": lambda x: (1 + np.cos(x)) / 2,
+ "hacoversine": lambda x: (1 - np.sin(x)) / 2,
+ "hacovercosine": lambda x: (1 + np.sin(x)) / 2,
+ }
+
+ def inject(self) -> Tuple[np.array, np.array]:
+ start, end = self.find_anomaly_index()
+ dt = .001
+
+ res = np.zeros(len(self.data[self.idx]))
+
+ self.data[self.idx] = np.zeros(len(self.data[self.idx]))
+ self.data[self.idx, start:end] = 2 * np.pi * self.params["frequency"] * dt
+
+ for term in self.dimension:
+ func = self.OSCILLATING_FUNCTIONS[term["function"]]
+
+ # Generate testing data
+ signal = self.data[self.idx]
+ signal += 2 * np.pi * term["frequency"] * dt
+ signal = func(np.cumsum(signal))
+ signal *= term["amplitude"]
+ res += signal
+
+ self.data[self.idx] = res
+
+
+ return self.data, self.labels
--
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