added docstrings

code/chirpdetection.py

@@ -9,14 +9,56 @@ from scipy.ndimage import gaussian_filter1d
 from thunderfish.dataloader import DataLoader
 from thunderfish.powerspectrum import spectrogram, decibel
 
-
 from modules.filters import bandpass_filter, envelope, highpass_filter
 
 
+class LoadData:
+    """
+
+    Attributes
+    ----------
+    data : DataLoader object containing raw data
+    samplerate : sampling rate of raw data
+    time : array of time for tracked fundamental frequency
+    freq : array of fundamental frequency
+    idx : array of indices to access time array
+    ident : array of identifiers for each tracked fundamental frequency
+    ids : array of unique identifiers exluding NaNs
+    """
+
+    def __init__(self, datapath: str) -> None:
+
+        # load raw data
+        file = os.path.join(datapath, "traces-grid1.raw")
+        self.data = DataLoader(file, 60.0, 0, channel=-1)
+        self.samplerate = self.data.samplerate
+
+        # load wavetracker files
+        self.time = np.load(datapath + "times.npy", allow_pickle=True)
+        self.freq = np.load(datapath + "fund_v.npy", allow_pickle=True)
+        self.idx = np.load(datapath + "idx_v.npy", allow_pickle=True)
+        self.ident = np.load(datapath + "ident_v.npy", allow_pickle=True)
+        self.ids = np.unique(self.ident[~np.isnan(self.ident)])
+
+
 def instantaneos_frequency(
     signal: np.ndarray, samplerate: int
 ) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Compute the instantaneous frequency of a signal.
 
+    Parameters
+    ----------
+    signal : np.ndarray
+        Signal to compute the instantaneous frequency from.
+    samplerate : int
+        Samplerate of the signal.
+
+    Returns
+    -------
+    tuple[np.ndarray, np.ndarray]
+
+    """
     # calculate instantaneos frequency with zero crossings
     roll_signal = np.roll(signal, shift=1)
     time_signal = np.arange(len(signal)) / samplerate
@@ -44,7 +86,19 @@ def instantaneos_frequency(
 
 
 def plot_spectrogram(axis, signal: np.ndarray, samplerate: float) -> None:
-
+    """
+    Plot a spectrogram of a signal.
+
+    Parameters
+    ----------
+    axis : matplotlib axis
+        Axis to plot the spectrogram on.
+    signal : np.ndarray
+        Signal to plot the spectrogram from.
+    samplerate : float
+        Samplerate of the signal.
+
+    """
     # compute spectrogram
     spec_power, spec_freqs, spec_times = spectrogram(
         signal,
@@ -65,7 +119,26 @@ def plot_spectrogram(axis, signal: np.ndarray, samplerate: float) -> None:
 def double_bandpass(
     data: DataLoader, samplerate: int, freqs: np.ndarray, search_freq: float
 ) -> tuple[np.ndarray, np.ndarray]:
-
+    """
+    Apply a bandpass filter to the baseline of a signal and a second bandpass
+    filter above or below the baseline.
+
+    Parameters
+    ----------
+    data : DataLoader
+        Data to apply the filter to.
+    samplerate : int
+        Samplerate of the signal.
+    freqs : np.ndarray
+        Tracked fundamental frequencies of the signal.
+    search_freq : float
+        Frequency to search for above or below the baseline.
+
+    Returns
+    -------
+    tuple[np.ndarray, np.ndarray]
+
+    """
     # compute boundaries to filter baseline
     q25, q75 = np.percentile(freqs, [25, 75])
 
@@ -98,6 +171,9 @@ def main(datapath: str) -> None:
     ident = np.load(datapath + "ident_v.npy", allow_pickle=True)
 
     # set time window # <------------------------ Iterate through windows here
+    window_duration = 60 * data.samplerate
+    window_overlap = 0.3
+
     t0 = 3 * 60 * 60 + 6 * 60 + 43.5
     dt = 60
     start_index = t0 * data.samplerate
@@ -216,13 +292,15 @@ def main(datapath: str) -> None:
             np.arange(len(baseline)) / data.samplerate, baseline_envelope
         )
 
-        axs[5].plot(np.arange(len(baseline)) / data.samplerate, search_envelope)
+        axs[5].plot(np.arange(len(baseline)) /
+                    data.samplerate, search_envelope)
 
         axs[6].plot(baseline_freq_time, np.abs(inst_freq_filtered))
 
         # detect peaks baseline_enelope
         prominence = iqr(baseline_envelope)
-        baseline_peaks, _ = find_peaks(baseline_envelope, prominence=prominence)
+        baseline_peaks, _ = find_peaks(
+            baseline_envelope, prominence=prominence)
         axs[4].scatter(
             (np.arange(len(baseline)) / data.samplerate)[baseline_peaks],
             baseline_envelope[baseline_peaks],
@@ -245,8 +323,6 @@ def main(datapath: str) -> None:
             c="red",
         )
 
-        #
-
         axs[0].set_title("Spectrogram")
         axs[1].set_title("Fitered baseline instanenous frequency")
         axs[2].set_title("Fitered baseline")