fixes

code/utils/smoothing.py

@@ -0,0 +1,50 @@
+import numpy as np
+from scipy.signal import butter, filtfilt
+from statsmodels.tsa.stl._stl import STL
+
+
+def highpass_filter(data, cutoff_freq, fs=288):
+    nyquist = 0.5 * fs
+    normal_cutoff = cutoff_freq / nyquist
+    b, a = butter(N=3, Wn=normal_cutoff, btype="high", analog=False)
+    return filtfilt(b, a, data)
+
+
+def mirror_extend(series, extend_len):
+    """Mirrors the beginning and end of the time series to stabilize smoothing."""
+    # Mirror extension
+    start_extension = series[:extend_len][::-1]  # Reverse first part
+    end_extension = series[-extend_len:][::-1]  # Reverse last part
+
+    extended_series = np.concatenate([start_extension, series, end_extension])
+    return extended_series
+
+
+def get_trend(input_curve: np.ndarray | list, measurements_per_day: int = 288) -> np.ndarray:
+    extension_len = 3
+    extended_input_curve = mirror_extend(input_curve, extension_len * measurements_per_day)
+    stl = STL(extended_input_curve, period=measurements_per_day, robust=False, trend=measurements_per_day * 14 + 1)
+    trend = stl.fit().trend
+    return trend[extension_len * measurements_per_day:-extension_len * measurements_per_day]
+
+
+def get_curve_composition(input_curve: np.ndarray | list, measurements_per_day: int = 288) -> tuple:
+    """
+    Decomposes the input curve into trend, seasonal, residual and smoothed components.
+    :param input_curve: raw input curve
+    :param measurements_per_day: seasonal period, here: measurements per day -> 288
+    :return: composition of curve as tuple (trend, seasonal, residual, smoothed)
+    """
+    extension_len = 3
+    extended_input_curve = mirror_extend(input_curve, extension_len * measurements_per_day)
+    stl_results = STL(extended_input_curve, period=measurements_per_day, robust=False).fit()
+    long_term = (extended_input_curve - stl_results.seasonal)
+    wiggles = highpass_filter(long_term, 0.1)
+    smoothed = long_term - wiggles
+
+    return (
+        stl_results.trend[extension_len * measurements_per_day:-extension_len * measurements_per_day],
+        stl_results.seasonal[extension_len * measurements_per_day:-extension_len * measurements_per_day],
+        stl_results.resid[extension_len * measurements_per_day:-extension_len * measurements_per_day],
+        smoothed[extension_len * measurements_per_day:-extension_len * measurements_per_day],
+    )