[useful_functions.py] added true_eodf function
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import glob
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import pathlib
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import numpy as np
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					import numpy as np
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import matplotlib.pyplot as plt
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import rlxnix as rlx
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					import rlxnix as rlx
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from IPython import embed
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from scipy.signal import welch
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					from scipy.signal import welch
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def all_coming_together(freq_array, power_array, points_list, categories, num_harmonics_list, colors, delta=2.5, threshold=0.5):
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					def all_coming_together(freq_array, power_array, points_list, categories, num_harmonics_list, colors, delta=2.5, threshold=0.5):
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@ -513,6 +509,29 @@ def spike_times(stim):
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    dt = ti.sampling_interval  
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					    dt = ti.sampling_interval  
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    return spikes, stim_dur, dt # se changed spike_times to spikes so its not the same as name of function
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					    return spikes, stim_dur, dt # se changed spike_times to spikes so its not the same as name of function
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					def true_eodf(eodf_file):
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					    '''
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					    Calculates the Eodf of the fish when it was awake from a nix file.
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					    Parameters
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					    ----------
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					    eodf_file : str
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					        path to the file with nix-file for the eodf.
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					    Returns
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					    -------
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					    orig_eodf : int
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					        The original eodf.
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					    '''
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					    eod_data = rlx.Dataset(eodf_file)#load eodf file
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					    baseline = eod_data.repro_runs('baseline')[0]
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					    eod, time = baseline.trace_data('EOD') # get time and eod
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					    dt = baseline.trace_info('EOD').sampling_interval
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					    eod_freq, eod_power = welch(eod, fs = 1/dt, nperseg = 2**16, noverlap = 2**15)
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					    orig_eodf = round(eod_freq[np.argmax(eod_power)])
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					    return orig_eodf
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def valid_integrals(integral, local_mean, point, threshold = 0.1):
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					def valid_integrals(integral, local_mean, point, threshold = 0.1):
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    """
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					    """
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    Check if the integral exceeds the threshold compared to the local mean and 
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					    Check if the integral exceeds the threshold compared to the local mean and 
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