gapfinder should work now, need data for testing
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weygoldt
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@ -15,7 +15,6 @@ from modules.plotstyle import PlotStyle
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from modules.logger import makeLogger
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from modules.datahandling import (
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flatten,
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norm,
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purge_duplicates,
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group_timestamps,
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instantaneous_frequency,
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@ -351,9 +350,16 @@ def extract_frequency_bands(
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def window_median_all_track_ids(
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data: LoadData, window_start_seconds: float, window_duration_seconds: float
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) -> tuple[float, list[int]]:
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) -> tuple[list[tuple[float, float, float]], list[int]]:
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"""
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Calculate the median frequency of all fish in a given time window.
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Calculate the median and quantiles of the frequency of all fish in a
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given time window.
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Iterate over all track ids and calculate the 25, 50 and 75 percentile
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in a given time window to pass this data to 'find_searchband' function,
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which then determines whether other fish in the current window fall
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within the searchband of the current fish and then determine the
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gaps that are outside of the percentile ranges.
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Parameters
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----------
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@ -366,14 +372,16 @@ def window_median_all_track_ids(
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Returns
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-------
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tuple[float, list[int]]
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tuple[list[tuple[float, float, float]], list[int]]
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"""
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median_freq = []
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frequency_percentiles = []
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track_ids = []
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for _, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
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# the window index combines the track id and the time window
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window_idx = np.arange(len(data.idx))[
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(data.ident == track_id)
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& (data.time[data.idx] >= window_start_seconds)
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@ -384,20 +392,21 @@ def window_median_all_track_ids(
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]
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if len(data.freq[window_idx]) > 0:
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median_freq.append(np.median(data.freq[window_idx]))
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frequency_percentiles.append(
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np.percentile(data.freq[window_idx], [25, 50, 75]))
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track_ids.append(track_id)
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# convert to numpy array
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median_freq = np.asarray(median_freq)
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frequency_percentiles = np.asarray(frequency_percentiles)
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track_ids = np.asarray(track_ids)
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return median_freq, track_ids
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return frequency_percentiles, track_ids
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def find_searchband(
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freq_temp: np.ndarray,
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median_ids: np.ndarray,
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median_freq: np.ndarray,
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current_frequency: np.ndarray,
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percentiles_ids: np.ndarray,
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frequency_percentiles: np.ndarray,
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config: ConfLoader,
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data: LoadData,
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) -> float:
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@ -407,13 +416,13 @@ def find_searchband(
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Parameters
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----------
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freq_temp : np.ndarray
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current_frequency : np.ndarray
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Current EOD frequency array / the current fish of interest.
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median_ids : np.ndarray
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percentiles_ids : np.ndarray
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Array of track IDs of the medians of all other fish in the current
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window.
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median_freq : np.ndarray
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Array of median frequencies of all other fish in the current window.
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frequency_percentiles : np.ndarray
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Array of percentiles frequencies of all other fish in the current window.
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config : ConfLoader
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Configuration file.
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data : LoadData
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@ -424,19 +433,27 @@ def find_searchband(
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float
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"""
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# frequency where second filter filters
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# frequency window where second filter filters is potentially allowed
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# to filter. This is the search window, in which we want to find
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# a gap in the other fish's EODs.
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search_window = np.arange(
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np.median(freq_temp) + config.search_df_lower,
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np.median(freq_temp) + config.search_df_upper,
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np.median(current_frequency) + config.search_df_lower,
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np.median(current_frequency) + config.search_df_upper,
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config.search_res,
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)
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# search window in boolean
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search_window_bool = np.ones(len(search_window), dtype=bool)
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search_window_bool = np.ones_like(len(search_window), dtype=bool)
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# make seperate arrays from the qartiles
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q25 = np.asarray([i[0] for i in frequency_percentiles])
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q75 = np.asarray([i[2] for i in frequency_percentiles])
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# get tracks that fall into search window
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check_track_ids = median_ids[
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(median_freq > search_window[0]) & (median_freq < search_window[-1])
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check_track_ids = percentiles_ids[
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(q25 > search_window[0]) & (
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q75 < search_window[-1])
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]
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# iterate through theses tracks
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@ -444,25 +461,26 @@ def find_searchband(
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for j, check_track_id in enumerate(check_track_ids):
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q1, q2 = np.percentile(
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data.freq[data.ident == check_track_id], [25, 75]
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)
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print(q1, q2)
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q25_temp = q25[percentiles_ids == check_track_id]
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q75_temp = q75[percentiles_ids == check_track_id]
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print(q25_temp, q75_temp)
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search_window_bool[
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(search_window > q1) & (search_window < q2)
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(search_window > q25_temp) & (search_window < q75_temp)
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] = False
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# find gaps in search window
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search_window_indices = np.arange(len(search_window))
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# get search window gaps
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# taking the diff of a boolean array gives non zero values where the
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# array changes from true to false or vice versa
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search_window_gaps = np.diff(search_window_bool, append=np.nan)
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nonzeros = search_window_gaps[np.nonzero(search_window_gaps)[0]]
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nonzeros = nonzeros[~np.isnan(nonzeros)]
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embed()
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# if the first value is -1, the array starst with true, so a gap
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if nonzeros[0] == -1:
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stops = search_window_indices[search_window_gaps == -1]
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@ -494,14 +512,16 @@ def find_searchband(
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search_windows_lens = [len(x) for x in search_windows]
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longest_search_window = search_windows[np.argmax(search_windows_lens)]
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# the center of the search frequency band is then the center of
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# the longest gap
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search_freq = (
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longest_search_window[-1] - longest_search_window[0]
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) / 2
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else:
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search_freq = config.default_search_freq
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return search_freq
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return search_freq
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return config.default_search_freq
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def main(datapath: str, plot: str) -> None:
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@ -637,10 +657,10 @@ def main(datapath: str, plot: str) -> None:
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search_frequency = find_searchband(
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config=config,
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freq_temp=current_frequencies,
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median_ids=median_ids,
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current_frequency=current_frequencies,
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percentiles_ids=median_ids,
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data=data,
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median_freq=median_freq,
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frequency_percentiles=median_freq,
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)
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# add all chirps that are detected on mulitple electrodes for one
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@ -1001,4 +1021,4 @@ if __name__ == "__main__":
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datapath = "../data/2022-06-02-10_00/"
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# datapath = "/home/weygoldt/Data/uni/efishdata/2016-colombia/fishgrid/2016-04-09-22_25/"
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# datapath = "/home/weygoldt/Data/uni/chirpdetection/GP2023_chirp_detection/data/mount_data/2020-03-13-10_00/"
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main(datapath, plot="save")
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main(datapath, plot="show")
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