plot works

code/chirpdetection.py

@@ -1,8 +1,7 @@
-from itertools import combinations, compress
+from itertools import compress
 from dataclasses import dataclass
 
 import numpy as np
-from tqdm import tqdm
 from IPython import embed
 import matplotlib.pyplot as plt
 from scipy.signal import find_peaks
@@ -12,17 +11,19 @@ from thunderfish.powerspectrum import spectrogram, decibel
 from sklearn.preprocessing import normalize
 
 from modules.filters import bandpass_filter, envelope, highpass_filter
-from modules.filehandling import ConfLoader, LoadData
+from modules.filehandling import ConfLoader, LoadData, make_outputdir
 from modules.datahandling import flatten, purge_duplicates, group_timestamps
 from modules.plotstyle import PlotStyle
 from modules.logger import makeLogger
 
 logger = makeLogger(__name__)
+
 ps = PlotStyle()
 
 
 @dataclass
 class PlotBuffer:
+    config: ConfLoader
     t0: float
     dt: float
     track_id: float
@@ -42,20 +43,20 @@ class PlotBuffer:
     frequency_filtered: np.ndarray
     frequency_peaks: np.ndarray
 
-    def plot_buffer(self, chirps) -> None:
+    def plot_buffer(self, chirps: np.ndarray, plot: str) -> None:
 
         logger.debug("Starting plotting")
 
         # make data for plotting
 
-        # get index of track data in this time window
+        # # get index of track data in this time window
-        window_idx = np.arange(len(self.data.idx))[
+        # window_idx = np.arange(len(self.data.idx))[
-            (self.data.ident == self.track_id) & (self.data.time[self.data.idx] >= self.t0) & (
+        #     (self.data.ident == self.track_id) & (self.data.time[self.data.idx] >= self.t0) & (
-                self.data.time[self.data.idx] <= (self.t0 + self.dt))
+        #         self.data.time[self.data.idx] <= (self.t0 + self.dt))
-        ]
+        # ]
 
         # get tracked frequencies and their times
-        freq_temp = self.data.freq[window_idx]
+        # freq_temp = self.data.freq[window_idx]
         # time_temp = self.data.times[window_idx]
 
         # get indices on raw data
@@ -113,7 +114,8 @@ class PlotBuffer:
             self.frequency_filtered[self.frequency_peaks],
             c=ps.red,
         )
-        axs[0].set_ylim(np.max(self.frequency)-200, top=np.max(self.frequency)+200)
+        axs[0].set_ylim(np.max(self.frequency)-200,
+                        top=np.max(self.frequency)+200)
         axs[6].set_xlabel("Time [s]")
         axs[0].set_title("Spectrogram")
         axs[1].set_title("Fitered baseline")
@@ -123,7 +125,16 @@ class PlotBuffer:
         axs[5].set_title("Search envelope")
         axs[6].set_title(
             "Filtered absolute instantaneous frequency")
+
+        if plot == 'show':
             plt.show()
+        elif plot == 'save':
+            make_outputdir(self.config.outputdir)
+            out = make_outputdir(self.config.outputdir +
+                                 self.data.datapath.split('/')[-2] + '/')
+
+            plt.savefig(f"{out}{self.track_id}_{self.t0}.pdf")
+            plt.close()
 
 
 def instantaneos_frequency(
@@ -248,6 +259,45 @@ def double_bandpass(
     return (filtered_baseline, filtered_search_freq)
 
 
+def freqmedian_allfish(data: LoadData, t0: float, dt: float) -> tuple[float, list[int]]:
+    """
+    Calculate the median frequency of all fish in a given time window.
+
+    Parameters
+    ----------
+    data : LoadData
+        Data to calculate the median frequency from.
+    t0 : float
+        Start time of the window.
+    dt : float
+        Duration of the window.
+
+    Returns
+    -------
+    tuple[float, list[int]]
+
+    """
+
+    median_freq = []
+    track_ids = []
+
+    for _, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
+        window_idx = np.arange(len(data.idx))[
+            (data.ident == track_id) & (data.time[data.idx] >= t0) & (
+                data.time[data.idx] <= (t0 + dt))
+        ]
+
+        if len(data.freq[window_idx]) > 0:
+            median_freq.append(np.median(data.freq[window_idx]))
+            track_ids.append(track_id)
+
+    # convert to numpy array
+    median_freq = np.asarray(median_freq)
+    track_ids = np.asarray(track_ids)
+
+    return median_freq, track_ids
+
+
 def main(datapath: str, plot: str) -> None:
 
     assert plot in ["save", "show", "false"]
@@ -293,18 +343,13 @@ def main(datapath: str, plot: str) -> None:
         dtype=int
     )
 
-    # # ask how many windows should be calulated
-    # nwindows = int(
-    #     input("How many windows should be calculated (integer number)? "))
-
     # ititialize lists to store data
     chirps = []
     fish_ids = []
 
-    for st, start_index in tqdm(enumerate(window_starts)):
+    for st, start_index in enumerate(window_starts):
-        #print(f"Processing window {st/data.raw_rate} of {len(window_starts/data.raw_rate)}")
 
-        logger.debug(f"Processing window {st} of {len(window_starts)}")
+        logger.info(f"Processing window {st} of {len(window_starts)}")
 
         # make t0 and dt
         t0 = start_index / data.raw_rate
@@ -314,25 +359,12 @@ def main(datapath: str, plot: str) -> None:
         stop_index = start_index + window_duration
 
         # calucate median of fish frequencies in window
-        median_freq = []
+        median_freq, median_ids = freqmedian_allfish(data, t0, dt)
-        track_ids = []
-        for _, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
-            window_idx = np.arange(len(data.idx))[
-                (data.ident == track_id) & (data.time[data.idx] >= t0) & (
-                    data.time[data.idx] <= (t0 + dt))
-            ]
-            median_freq.append(np.median(data.freq[window_idx]))
-            track_ids.append(track_id)
-
-        # convert to numpy array
-        median_freq = np.asarray(median_freq)
-        track_ids = np.asarray(track_ids)
 
         # iterate through all fish
         for tr, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
 
-            logger.debug(f"Processing track {tr} of {len(track_ids)}")
+            logger.debug(f"Processing track {tr} of {len(data.ids)}")
-
 
             # get index of track data in this time window
             window_idx = np.arange(len(data.idx))[
@@ -350,10 +382,18 @@ def main(datapath: str, plot: str) -> None:
             expected_duration = ((t0 + dt) - t0) * track_samplerate
 
             # check if tracked data available in this window
-            if len(freq_temp) < expected_duration * 0.9:
+            if len(freq_temp) < expected_duration * 0.5:
+                logger.warning(
+                    f"Track {track_id} has no data in window {st}, skipping.")
+                continue
+
+            # check if there are powers available in this window
+            nanchecker = np.unique(np.isnan(powers_temp))
+            if (len(nanchecker) == 1) and nanchecker[0] == True:
+                logger.warning(
+                    f"No powers available for track {track_id} window {st}, skipping.")
                 continue
 
-            # get best electrode
             best_electrodes = np.argsort(np.nanmean(
                 powers_temp, axis=0))[-config.number_electrodes:]
 
@@ -366,7 +406,7 @@ def main(datapath: str, plot: str) -> None:
             search_window_bool = np.ones(len(search_window), dtype=bool)
 
             # get tracks that fall into search window
-            check_track_ids = track_ids[(median_freq > search_window[0]) & (
+            check_track_ids = median_ids[(median_freq > search_window[0]) & (
                 median_freq < search_window[-1])]
 
             # iterate through theses tracks
@@ -429,10 +469,8 @@ def main(datapath: str, plot: str) -> None:
             else:
                 search_freq = config.default_search_freq
 
-            #print(f"Search frequency: {search_freq}")
             # ----------- chrips on the two best electrodes-----------
             chirps_electrodes = []
-            electrodes_of_chirps = []
 
             # iterate through electrodes
             for el, electrode in enumerate(best_electrodes):
@@ -560,77 +598,6 @@ def main(datapath: str, plot: str) -> None:
                     prominence=prominence
                 )
 
-                # # PLOT --------------------------------------------------------
-
-                # # plot spectrogram
-                # plot_spectrogram(
-                #     axs[0, el], data_oi[:, electrode], data.raw_rate, t0)
-
-                # # plot baseline instantaneos frequency
-
-                # axs[1, el].plot(baseline_freq_time, baseline_freq -
-                #                 np.median(baseline_freq))
-
-                # # plot waveform of filtered signal
-                # axs[2, el].plot(time_oi, baseline, c=ps.green)
-
-                # # plot broad filtered baseline
-                # axs[2, el].plot(
-                #     time_oi,
-                #     broad_baseline,
-                # )
-
-                # # plot narrow filtered baseline envelope
-                # axs[2, el].plot(
-                #     time_oi,
-                #     baseline_envelope_unfiltered,
-                #     c=ps.red
-                # )
-
-                # # plot waveform of filtered search signal
-                # axs[3, el].plot(time_oi, search)
-
-                # # plot envelope of search signal
-                # axs[3, el].plot(
-                #     time_oi,
-                #     search_envelope,
-                #     c=ps.red
-                # )
-
-                # # plot filtered and rectified envelope
-                # axs[4, el].plot(time_oi, baseline_envelope)
-                # axs[4, el].scatter(
-                #     (time_oi)[baseline_peaks],
-                #     baseline_envelope[baseline_peaks],
-                #     c=ps.red,
-                # )
-
-                # # plot envelope of search signal
-                # axs[5, el].plot(time_oi, search_envelope)
-                # axs[5, el].scatter(
-                #     (time_oi)[search_peaks],
-                #     search_envelope[search_peaks],
-                #     c=ps.red,
-                # )
-
-                # # plot filtered instantaneous frequency
-                # axs[6, el].plot(baseline_freq_time, np.abs(inst_freq_filtered))
-                # axs[6, el].scatter(
-                #     baseline_freq_time[inst_freq_peaks],
-                #     np.abs(inst_freq_filtered)[inst_freq_peaks],
-                #     c=ps.red,
-                # )
-
-                # axs[6, el].set_xlabel("Time [s]")
-                # axs[0, el].set_title("Spectrogram")
-                # axs[1, el].set_title("Fitered baseline instanenous frequency")
-                # axs[2, el].set_title("Fitered baseline")
-                # axs[3, el].set_title("Fitered above")
-                # axs[4, el].set_title("Filtered envelope of baseline envelope")
-                # axs[5, el].set_title("Search envelope")
-                # axs[6, el].set_title(
-                #     "Filtered absolute instantaneous frequency")
-
                 # DETECT CHIRPS IN SEARCH WINDOW -------------------------------
 
                 baseline_ts = time_oi[baseline_peaks]
@@ -641,69 +608,14 @@ def main(datapath: str, plot: str) -> None:
                 if len(baseline_ts) == 0 or len(search_ts) == 0 or len(freq_ts) == 0:
                     continue
 
-                # current_chirps = group_timestamps_v2(
+                current_chirps = group_timestamps(
-                #    [list(baseline_ts), list(search_ts), list(freq_ts)], 3)
+                    [list(baseline_ts), list(search_ts), list(freq_ts)], 3, config.chirp_window_threshold)
-
-                # get index for each feature
-                baseline_idx = np.zeros_like(baseline_ts)
-                search_idx = np.ones_like(search_ts)
-                freq_idx = np.ones_like(freq_ts) * 2
-
-                timestamps_features = np.hstack(
-                    [baseline_idx, search_idx, freq_idx])
-                timestamps = np.hstack([baseline_ts, search_ts, freq_ts])
-
-                # sort timestamps
-                timestamps_idx = np.arange(len(timestamps))
-                timestamps_features = timestamps_features[np.argsort(
-                    timestamps)]
-                timestamps = timestamps[np.argsort(timestamps)]
-
-                # # get chirps
-                # diff = np.empty(timestamps.shape)
-                # diff[0] = np.inf  # always retain the 1st element
-                # diff[1:] = np.diff(timestamps)
-                # mask = diff < config.chirp_window_threshold
-                # shared_peak_indices = timestamp_idx[mask]
-
-                current_chirps = []
-                bool_timestamps = np.ones_like(timestamps, dtype=bool)
-                for bo, tt in enumerate(timestamps):
-                    if bool_timestamps[bo] is False:
-                        continue
-                    cm = timestamps_idx[(timestamps >= tt) & (
-                        timestamps <= tt + config.chirp_window_threshold)]
-                    if set([0, 1, 2]).issubset(timestamps_features[cm]):
-                        current_chirps.append(np.mean(timestamps[cm]))
-                        electrodes_of_chirps.append(el)
-                    bool_timestamps[cm] = False
-
                 # for checking if there are chirps on multiple electrodes
                 if len(current_chirps) == 0:
                     continue
 
                 chirps_electrodes.append(current_chirps)
 
-                # for ct in current_chirps:
-                #     axs[0, el].axvline(ct, color='r', lw=1)
-
-                # axs[0, el].scatter(
-                #     baseline_freq_time[inst_freq_peaks],
-                #     np.ones_like(baseline_freq_time[inst_freq_peaks]) * 600,
-                #     c=ps.red,
-                # )
-                # axs[0, el].scatter(
-                #     (time_oi)[search_peaks],
-                #     np.ones_like((time_oi)[search_peaks]) * 600,
-                #     c=ps.red,
-                # )
-
-                # axs[0, el].scatter(
-                #     (time_oi)[baseline_peaks],
-                #     np.ones_like((time_oi)[baseline_peaks]) * 600,
-                #     c=ps.red,
-                # )
-
                 if (el == config.number_electrodes - 1) &  \
                         (len(current_chirps) > 0) & \
                         (plot in ["show", "save"]):
@@ -712,6 +624,7 @@ def main(datapath: str, plot: str) -> None:
 
                     # save data to Buffer
                     buffer = PlotBuffer(
+                        config=config,
                         t0=t0,
                         dt=dt,
                         electrode=electrode,
@@ -735,70 +648,19 @@ def main(datapath: str, plot: str) -> None:
             logger.debug(
                 f"Processed all electrodes for fish {track_id} for this window, sorting chirps ...")
 
-            # continue if no chirps for current fish
-
-            # make one array
-            # chirps_electrodes = np.concatenate(chirps_electrodes)
-
-            # make shure they are numpy arrays
-            # electrodes_of_chirps = np.asarray(electrodes_of_chirps)
-
-            # # sort them
-            # sort_chirps_electrodes = chirps_electrodes[np.argsort(
-            #     chirps_electrodes)]
-            # sort_electrodes = electrodes_of_chirps[np.argsort(
-            #     chirps_electrodes)]
-            # bool_vector = np.ones(len(sort_chirps_electrodes), dtype=bool)
-
-            # # make index vector
-            # index_vector = np.arange(len(sort_chirps_electrodes))
-
-            # # make it more than only two electrodes for the search after chirps
-            # combinations_best_elctrodes = list(
-            #     combinations(range(3), 2))
-
             if len(chirps_electrodes) == 0:
                 continue
 
             the_real_chirps = group_timestamps(chirps_electrodes, 2, 0.05)
 
-            # for chirp_index, seoc in enumerate(sort_chirps_electrodes):
-            #     if bool_vector[chirp_index] is False:
-            #         continue
-            #     cm = index_vector[(sort_chirps_electrodes >= seoc) & (
-            #         sort_chirps_electrodes <= seoc + config.chirp_window_threshold)]
-
-            #     chirps_unique = []
-            #     for combination in combinations_best_elctrodes:
-            #         if set(combination).issubset(sort_electrodes[cm]):
-            #             chirps_unique.append(
-            #                 np.mean(sort_chirps_electrodes[cm]))
-
-            #     the_real_chirps.append(np.mean(chirps_unique))
-
-            #     """
-            #     if set([0,1]).issubset(sort_electrodes[cm]):
-            #         the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
-            #     elif set([1,0]).issubset(sort_electrodes[cm]):
-            #         the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
-            #     elif set([0,2]).issubset(sort_electrodes[cm]):
-            #         the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
-            #     elif set([1,2]).issubset(sort_electrodes[cm]):
-            #         the_real_chirps.append(np.mean(sort_chirps_electrodes[cm]))
-            #     """
-            #     bool_vector[cm] = False
-
             chirps.append(the_real_chirps)
             fish_ids.append(track_id)
 
-#             for ct in the_real_chirps:
-#                 axs[0, el].axvline(ct, color='b', lw=1)
-
             logger.debug('Found %d chirps, starting plotting ... ' %
                          len(the_real_chirps))
             if len(the_real_chirps) > 0:
                 try:
-                    buffer.plot_buffer(the_real_chirps)
+                    buffer.plot_buffer(the_real_chirps, plot)
                 except NameError:
                     pass
             else:
@@ -807,14 +669,6 @@ def main(datapath: str, plot: str) -> None:
                 except NameError:
                     pass
 
-    # fig, ax = plt.subplots()
-    # t0 = (3 * 60 * 60 + 6 * 60 + 43.5)
-    # data_oi = data.raw[window_starts[0]:window_starts[-1] + int(dt*data.raw_rate), 10]
-    # plot_spectrogram(ax, data_oi, data.raw_rate, t0)
-    # chirps_concat = np.concatenate(chirps)
-    # for ch in chirps_concat:
-    #     ax. axvline(ch, color='b', lw=1)
-
     chirps_new = []
     chirps_ids = []
     for tr in np.unique(fish_ids):
@@ -837,4 +691,4 @@ def main(datapath: str, plot: str) -> None:
 
 if __name__ == "__main__":
     datapath = "../data/2022-06-02-10_00/"
-    main(datapath, plot="show")
+    main(datapath, plot="save")

code/chirpdetector_conf.yml

@@ -1,3 +1,6 @@
+dataroot: "../data/"
+outputdir: "../output/"
+
 # Duration and overlap of the analysis window in seconds
 window: 5
 overlap: 1
@@ -40,7 +43,6 @@ search_freq_percentiles:
   - 95
 default_search_freq: 50
 
-
 chirp_window_threshold: 0.05
 
 

code/modules/datahandling.py

@@ -1,5 +1,5 @@
 import numpy as np
-from typing import List, Union, Any
+from typing import List, Any
 
 
 def purge_duplicates(

code/modules/filehandling.py

@@ -36,6 +36,7 @@ class LoadData:
     def __init__(self, datapath: str) -> None:
 
         # load raw data
+        self.datapath = datapath
         self.file = os.path.join(datapath, "traces-grid1.raw")
         self.raw = DataLoader(self.file, 60.0, 0, channel=-1)
         self.raw_rate = self.raw.samplerate
@@ -53,3 +54,23 @@ class LoadData:
 
     def __str__(self) -> str:
         return f"LoadData({self.file})"
+
+
+def make_outputdir(path: str) -> str:
+    """
+    Creates a new directory where the path leads if it does not already exist.
+
+    Parameters
+    ----------
+    path : string
+        path to the new output directory
+
+    Returns
+    -------
+    string
+        path of the newly created output directory
+    """
+
+    if os.path.isdir(path) == False:
+        os.mkdir(path)
+    return path

code/modules/logger.py

@@ -23,7 +23,7 @@ def makeLogger(name: str):
     logger = logging.getLogger(name)
     logger.addHandler(file_handler)
     logger.addHandler(console_handler)
-    logger.setLevel(logging.DEBUG)
+    logger.setLevel(logging.INFO)
 
     return logger