Add comments to processing functions

tools/processing.py

@@ -8,6 +8,15 @@ import scipy.signal as scs
 
 #%%
 def autocorrelation (time: np.ndarray, signal: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    """Auto-correlates a signal
+
+    Args:
+        time (np.ndarray): Time axis
+        signal (np.ndarray): Signal to auto-correlate
+
+    Returns:
+        Tuple[np.ndarray, np.ndarray]: Lag and correlation axis
+    """
 
     time_step = time[1] - time[0]
     output_time = np.arange(-len(signal) + 1, len(signal)) * time_step
@@ -19,6 +28,16 @@ def autocorrelation (time: np.ndarray, signal: np.ndarray) -> Tuple[np.ndarray,
 
 #%%
 def correlation (time: np.ndarray, signal: np.ndarray, known: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    """Correlates a signal with a known one
+
+    Args:
+        time (np.ndarray): Time axis
+        signal (np.ndarray): Signal to correlate
+        known (np.ndarray): Knonw signal
+
+    Returns:
+        Tuple[np.ndarray, np.ndarray]: Lag and correlation axis
+    """
 
     time_step = time[1] - time[0]
     output_time = np.arange(-len(signal) + 1, len(signal)) * time_step
@@ -29,6 +48,15 @@ def correlation (time: np.ndarray, signal: np.ndarray, known: np.ndarray) -> Tup
 
 #%%
 def Window (data: pd.Series | np.ndarray):
+    """Applies a hanning window to the signal. The width of the window
+    is the width of the signal.
+
+    Args:
+        data (pd.Series | np.ndarray): Signal to window
+
+    Returns:
+        The windowed signal
+    """
     window = np.hanning(len(data))
     return window * data
 
@@ -50,6 +78,16 @@ def moving_window (signal: np.ndarray, iterations: int):
 
 # %%
 def FFT (data: pd.Series | np.ndarray, sampling: int, zero_pad: int = 0):
+    """FFT of a signal in the Shannon band
+
+    Args:
+        data (pd.Series | np.ndarray): The signal to analyse
+        sampling (int): The sampling rate of the signal, in ms
+        zero_pad (int, optional): The padding added to the length of the data
+
+    Returns:
+        Tuple[ndarray, ndarray]: The frequency axis and the absolute magnitude axis
+    """
 
     # Defining the constants
     Fe = 1000 / sampling
@@ -68,12 +106,29 @@ def FFT (data: pd.Series | np.ndarray, sampling: int, zero_pad: int = 0):
 
 #%%
 def fundamental (freqs: np.ndarray, fft: np.ndarray):
+    """Finds the spike with the greatest amplitude
+
+    Args:
+        freqs (np.ndarray): Frequency axis
+        fft (np.ndarray): Magnitude axis
 
+    Returns:
+        The frequency of the spike with the greatest amplitude
+    """
     return freqs[fft.argmax()]
 
 #%%
 def Filter (data: pd.Series | np.ndarray, h: np.ndarray, plot: bool | str = False):
-    
+    """Applies a filter to some signal
+
+    Args:
+        data (pd.Series | np.ndarray): Signal to filter
+        h (np.ndarray): Filter
+
+    Returns:
+        The filtered signal
+    """
+
     filtered =  np.convolve(data, h)
 
     len_diff = (len(filtered) - len(data)) // 2
@@ -111,6 +166,12 @@ def Filter (data: pd.Series | np.ndarray, h: np.ndarray, plot: bool | str = Fals
 
 #%%
 def MakeFilter (*filters: List[np.ndarray]):
+    """Convolves multiple filters to one
+
+    Returns:
+        The merged filter
+    """
+
     h = [ 1 ]
     for f in filters:
         h = np.convolve(h, f)
@@ -118,6 +179,17 @@ def MakeFilter (*filters: List[np.ndarray]):
 
 # %%
 def high_pass (fc: float, sampling_rate: int, N: int = None):
+    """Creates a high-pass filter with a cutoff frequency fc
+
+    Args:
+        fc (float): Cutoff frequency
+        sampling_rate (int): Sampling rate, in ms
+        N (int, optional): Number of samples making the filter
+
+    Returns:
+        FIR high-pass filter, computed using the window method, with a kaiser window
+    """
+   
     Fe = 1000 / sampling_rate
     N = N if N is not None else 500 // sampling_rate
     N += (1 - (N % 2)) # Make N odd
@@ -145,6 +217,17 @@ def multi_zero_filter (f: float, sampling_rate: int):
 
 #%%
 def low_pass (fc: float, sampling_rate: int, N: int = None):
+    """Creates a low-pass filter with a cutoff frequency fc
+
+    Args:
+        fc (float): Cutoff frequency
+        sampling_rate (int): Sampling rate, in ms
+        N (int, optional): Number of samples making the filter
+
+    Returns:
+        FIR low-pass filter, computed using the window method, with a kaiser window
+    """
+    
     Fe = 1000 / sampling_rate
     N = N if N is not None else 500 // sampling_rate
     N += (1 - (N % 2)) # Make N odd