488 lines
16 KiB
Python
488 lines
16 KiB
Python
import numpy as np
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from warnings import warn
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from thunderfish.eventdetection import detect_peaks, threshold_crossing_times, threshold_crossings
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from scipy.optimize import curve_fit
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import functions as fu
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from numba import jit
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def fit_clipped_line(x, y):
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popt, pcov = curve_fit(fu.clipped_line, x, y)
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return popt
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def fit_boltzmann(x, y):
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max_f0 = float(max(y))
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min_f0 = 0.1 # float(min(self.f_zeros))
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mean_int = float(np.mean(x))
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total_increase = max_f0 - min_f0
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total_change_int = max(x) - min(x)
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start_k = float((total_increase / total_change_int * 4) / max_f0)
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popt, pcov = curve_fit(fu.full_boltzmann, x, y,
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p0=(max_f0, min_f0, start_k, mean_int),
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maxfev=10000, bounds=([0, 0, -np.inf, -np.inf], [np.inf, np.inf, np.inf, np.inf]))
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return popt
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@jit(nopython=True)
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def rectify_stimulus_array(stimulus_array: np.ndarray):
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return np.array([x if x > 0 else 0 for x in stimulus_array])
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def merge_similar_intensities(intensities, spiketimes, trans_amplitudes):
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i = 0
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diffs = np.diff(sorted(intensities))
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margin = np.mean(diffs) * 0.6666
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while True:
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if i >= len(intensities):
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break
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intensities, spiketimes, trans_amplitudes = merge_intensities_similar_to_index(intensities, spiketimes, trans_amplitudes, i, margin)
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i += 1
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# Sort the lists so that intensities are increasing
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x = [list(x) for x in zip(*sorted(zip(intensities, spiketimes), key=lambda pair: pair[0]))]
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intensities = x[0]
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spiketimes = x[1]
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return intensities, spiketimes, trans_amplitudes
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def merge_intensities_similar_to_index(intensities, spiketimes, trans_amplitudes, index, margin):
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intensity = intensities[index]
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indices_to_merge = []
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for i in range(index+1, len(intensities)):
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if np.abs(intensities[i]-intensity) < margin:
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indices_to_merge.append(i)
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if len(indices_to_merge) != 0:
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indices_to_merge.reverse()
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trans_amplitude_values = [trans_amplitudes[k] for k in indices_to_merge]
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all_the_same = True
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for j in range(1, len(trans_amplitude_values)):
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if not trans_amplitude_values[0] == trans_amplitude_values[j]:
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all_the_same = False
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break
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if all_the_same:
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for idx in indices_to_merge:
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del trans_amplitudes[idx]
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else:
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raise RuntimeError("Trans_amplitudes not the same....")
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for idx in indices_to_merge:
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spiketimes[index].extend(spiketimes[idx])
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del spiketimes[idx]
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del intensities[idx]
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return intensities, spiketimes, trans_amplitudes
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def all_calculate_mean_isi_frequency_traces(spiketimes, sampling_interval, stimulus_start=0, time_in_ms=False):
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"""
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Expects spiketimes to be a 3dim list with the first dimension being the trial
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the second the count of runs of spikes and the last the individual spikes_times:
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[[[trial1-run1-spike1, trial1-run1-spike2, ...],[trial1-run2-spike1, ...]],[[trial2-run1-spike1, ...], [..]]]
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:param stimulus_start: the time point at which the actual stimulus starts
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:param spiketimes: time points of action potentials
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:param sampling_interval: the sampling interval used / will also be used for the frequency trace
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:param time_in_ms: whether the time is in ms or seconds
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:return: the mean frequency trace for each trial and its time trace
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"""
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times = []
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mean_frequencies = []
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for i in range(len(spiketimes)):
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trial_time_trace = []
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trial_freq_trace = []
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for j in range(len(spiketimes[i])):
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time, isi_freq = calculate_time_and_frequency_trace(spiketimes[i][j], sampling_interval, time_in_ms)
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if time[0] > stimulus_start:
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print("Trial not used as its frequency trace started after the stimulus start!")
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continue
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trial_freq_trace.append(isi_freq)
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trial_time_trace.append(time)
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time, mean_freq = calculate_mean_of_frequency_traces(trial_time_trace, trial_freq_trace, sampling_interval)
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times.append(time)
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mean_frequencies.append(mean_freq)
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return times, mean_frequencies
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def calculate_isi_frequency_trace(spiketimes, sampling_interval, time_in_ms=False):
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"""
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Calculates the frequency over time according to the inter spike intervals.
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:param spiketimes: sorted time points spikes were measured array_like
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:param sampling_interval: the sampling interval in which the frequency should be given back
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:param time_in_ms: whether the time is in ms or in s for BOTH the spiketimes and the sampling interval
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:return: an np.array with the isi frequency starting at the time of first spike and ending at the time of the last spike
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"""
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if len(spiketimes) <= 1:
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return []
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isis = np.diff(spiketimes)
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if sampling_interval > round(min(isis), 7):
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raise ValueError("The sampling interval is bigger than the some isis! cannot accurately compute the trace.\n"
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"Sampling interval {:.5f}, smallest isi: {:.5f}".format(sampling_interval, min(isis)))
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if time_in_ms:
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isis = isis / 1000
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sampling_interval = sampling_interval / 1000
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full_frequency = np.array([])
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for isi in isis:
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if isi < 0:
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raise ValueError("There was a negative interspike interval, the spiketimes need to be sorted")
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if isi == 0:
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warn("An ISI was zero in FiCurve:__calculate_mean_isi_frequency__()")
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print("ISI was zero:", spiketimes)
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continue
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freq = 1 / isi
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frequency_step = np.full(int(round(isi * (1 / sampling_interval))), freq)
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full_frequency = np.concatenate((full_frequency, frequency_step))
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return full_frequency
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def calculate_time_and_frequency_trace(spiketimes, sampling_interval, time_in_ms=False):
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if len(spiketimes) < 2:
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return [0], [0]
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# raise ValueError("Cannot compute a time and frequency vector with fewer than 2 spikes")
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frequency = calculate_isi_frequency_trace(spiketimes, sampling_interval, time_in_ms)
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time = np.arange(spiketimes[0], spiketimes[-1], sampling_interval)
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if len(time) != len(frequency):
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if len(time) > len(frequency):
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time = time[:len(frequency)]
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return time, frequency
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def calculate_mean_of_frequency_traces(trial_time_traces, trial_frequency_traces, sampling_interval):
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"""
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calculates the mean_trace of the given frequency traces -> mean at each time point
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for traces starting at different times
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:param trial_time_traces:
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:param trial_frequency_traces:
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:param sampling_interval:
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:return:
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"""
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ends = [t[-1] for t in trial_time_traces]
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starts = [t[0] for t in trial_time_traces]
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latest_start = max(starts)
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earliest_end = min(ends)
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shortened_time = np.arange(latest_start, earliest_end, sampling_interval)
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shortened_freqs = []
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for i in range(len(trial_frequency_traces)):
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start_idx = int(round((latest_start - trial_time_traces[i][0]) / sampling_interval))
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end_idx = int(round((earliest_end - trial_time_traces[i][0]) / sampling_interval))
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shortened_freqs.append(trial_frequency_traces[i][start_idx:end_idx])
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mean_freq = [sum(e) / len(e) for e in zip(*shortened_freqs)]
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return shortened_time, mean_freq
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def mean_freq_of_spiketimes_after_time_x(spiketimes, time_x, time_in_ms=False):
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""" Calculates the mean frequency of the portion of spiketimes that is after last_x_time """
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spiketimes = np.array(spiketimes)
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if len(spiketimes) <= 1:
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return 0
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relevant_spikes = spiketimes[spiketimes > time_x]
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if len(relevant_spikes) <= 1:
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return 0
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if time_in_ms:
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relevant_spikes = relevant_spikes / 1000
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isis = np.diff(relevant_spikes)
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isi_freqs = 1 / isis
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weights = isis / min(isis)
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mean_freq = sum(isi_freqs * weights) / sum(weights)
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return mean_freq
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# freq = calculate_isi_frequency_trace(spiketimes, sampling_interval, time_in_ms)
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# # returned frequency starts at the
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# idx = int((time_x-spiketimes[0]) / sampling_interval)
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# rest_array = freq[idx:]
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# mean_freq = np.mean(rest_array)
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# return mean_freq
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def calculate_mean_isi_freq(spiketimes, time_in_ms=False):
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if len(spiketimes) < 2:
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return 0
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isis = np.diff(spiketimes)
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if time_in_ms:
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isis = isis / 1000
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freqs = 1 / isis
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weights = isis / np.min(isis)
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return sum(freqs * weights) / sum(weights)
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# @jit(nopython=True) # only faster at around 30 000 calls
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def calculate_coefficient_of_variation(spiketimes: np.ndarray) -> float:
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# CV (stddev of ISI divided by mean ISI (np.diff(spiketimes))
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isi = np.diff(spiketimes)
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std = np.std(isi)
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mean = np.mean(isi)
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return std/mean
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# @jit(nopython=True) # maybe faster with more than ~60 000 calls
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def calculate_serial_correlation(spiketimes: np.ndarray, max_lag: int) -> np.ndarray:
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isi = np.diff(spiketimes)
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if len(spiketimes) < max_lag + 1:
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raise ValueError("Given list to short, with given max_lag")
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cor = np.zeros(max_lag)
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for lag in range(max_lag):
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lag = lag + 1
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first = isi[:-lag]
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second = isi[lag:]
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cor[lag-1] = np.corrcoef(first, second)[0][1]
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return cor
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def calculate_eod_frequency(time, eod):
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# TODO for few samples very volatile measure!
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up_indicies, down_indicies = threshold_crossings(eod, 0)
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up_times, down_times = threshold_crossing_times(time, eod, 0, up_indicies, down_indicies)
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if len(up_times) == 0:
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return 0
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durations = np.diff(up_times)
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mean_duration = np.mean(durations)
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return 1/mean_duration
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def calculate_vector_strength_from_v1_trace(times, eods, v1_traces):
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# Vectorstaerke (use EOD frequency from header (metadata)) VS > 0.8
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# dl.iload_traces(repro='BaselineActivity')
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relative_spike_times = []
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eod_durations = []
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if len(times) == 0:
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print("-----LENGTH OF TIMES = 0")
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for recording in range(len(times)):
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spiketime_idices = detect_spikes(v1_traces[recording])
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rel_spikes, eod_durs = eods_around_spikes(times[recording], eods[recording], spiketime_idices)
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relative_spike_times.extend(rel_spikes)
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eod_durations.extend(eod_durs)
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# print(__vector_strength__(np.array(rel_spikes), np.array(eod_durs)))
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relative_spike_times = np.array(relative_spike_times)
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eod_durations = np.array(eod_durations)
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return __vector_strength__(relative_spike_times, eod_durations)
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def calculate_vector_strength_from_spiketimes(time, eod, spiketimes, sampling_interval):
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spiketime_indices = np.array(np.around((np.array(spiketimes) + time[0]) / sampling_interval), dtype=int)
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rel_spikes, eod_durs = eods_around_spikes(time, eod, spiketime_indices)
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return __vector_strength__(rel_spikes, eod_durs)
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def detect_spikes(v1, split=20, threshold=3):
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total = len(v1)
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all_peaks = []
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for n in range(split):
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length = int(total / split)
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first_index = n * length
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last_index = (n + 1) * length
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std = np.std(v1[first_index:last_index])
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peaks, _ = detect_peaks(v1[first_index:last_index], std * threshold)
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peaks = peaks + first_index
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all_peaks.extend(peaks)
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all_peaks = np.array(all_peaks)
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return all_peaks
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def calculate_phases(relative_spike_times, eod_durations):
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phase_times = np.zeros(len(relative_spike_times))
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for i in range(len(relative_spike_times)):
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phase_times[i] = (relative_spike_times[i] / eod_durations[i]) * 2 * np.pi
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return phase_times
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def eods_around_spikes(time, eod, spiketime_idices):
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eod_durations = []
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relative_spike_times = []
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sign_changes = np.sign(eod[:-1]) != np.sign(eod[1:])
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eod_trace_increasing = eod[:-1] < eod[1:]
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eod_zero_crossings_indices = np.where(sign_changes & eod_trace_increasing)[0]
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for spike_idx in spiketime_idices:
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# test if it is inside two detected crossings
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if eod_zero_crossings_indices[0] > spike_idx > eod_zero_crossings_indices[-1]:
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continue
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zero_crossing_index_of_eod_end = np.argmax(eod_zero_crossings_indices > spike_idx)
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end_time_idx = eod_zero_crossings_indices[zero_crossing_index_of_eod_end]
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start_time_idx = eod_zero_crossings_indices[zero_crossing_index_of_eod_end - 1]
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eod_durations.append(time[end_time_idx] - time[start_time_idx])
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relative_spike_times.append(time[spike_idx] - time[start_time_idx])
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# try:
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# start_time, end_time = search_eod_start_and_end_times(time, eod, spike_idx)
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#
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# eod_durations.append(end_time-start_time)
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# spiketime = time[spike_idx]
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# relative_spike_times.append(spiketime - start_time)
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# except IndexError as e:
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# continue
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return np.array(relative_spike_times), np.array(eod_durations)
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def search_eod_start_and_end_times(time, eod, index):
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# TODO might break if a spike is in the cut off first or last eod!
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# search start_time:
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previous = index
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working_idx = index-1
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while True:
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if eod[working_idx] < 0 < eod[previous]:
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first_value = eod[working_idx]
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second_value = eod[previous]
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dif = second_value - first_value
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part = np.abs(first_value/dif)
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time_dif = np.abs(time[previous] - time[working_idx])
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start_time = time[working_idx] + time_dif*part
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break
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previous = working_idx
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working_idx -= 1
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# search end_time
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previous = index
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working_idx = index + 1
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while True:
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if eod[previous] < 0 < eod[working_idx]:
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first_value = eod[previous]
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second_value = eod[working_idx]
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dif = second_value - first_value
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part = np.abs(first_value / dif)
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time_dif = np.abs(time[previous] - time[working_idx])
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end_time = time[working_idx] + time_dif * part
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break
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previous = working_idx
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working_idx += 1
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return start_time, end_time
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def __vector_strength__(relative_spike_times: np.ndarray, eod_durations: np.ndarray):
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# adapted from Ramona
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n = len(relative_spike_times)
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if n == 0:
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return -1
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phase_times = (relative_spike_times / eod_durations) * 2 * np.pi
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vs = np.sqrt((1 / n * np.sum(np.cos(phase_times))) ** 2 + (1 / n * np.sum(np.sin(phase_times))) ** 2)
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return vs
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def detect_f_zero_in_frequency_trace(time, frequency, stimulus_start, sampling_interval, peak_buffer_percent=0.05, buffer=0.025):
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stimulus_start = stimulus_start - time[0] # time start is generally != 0 and != delay
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freq_before = frequency[int(buffer/sampling_interval):int((stimulus_start - buffer) / sampling_interval)]
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if len(freq_before) < 3:
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print("mäh")
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return 0
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min_before = min(freq_before)
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max_before = max(freq_before)
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mean_before = np.mean(freq_before)
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# time where the f-zero is searched in
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start_idx = int((stimulus_start-0.1*buffer) / sampling_interval)
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end_idx = int((stimulus_start + buffer) / sampling_interval)
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min_during_start_of_stim = min(frequency[start_idx:end_idx])
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max_during_start_of_stim = max(frequency[start_idx:end_idx])
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if abs(mean_before-min_during_start_of_stim) > abs(max_during_start_of_stim-mean_before):
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f_zero = min_during_start_of_stim
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else:
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f_zero = max_during_start_of_stim
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peak_buffer = (max_before - min_before) * peak_buffer_percent
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if min_before - peak_buffer <= f_zero <= max_before + peak_buffer:
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end_idx = start_idx + int((end_idx-start_idx)/2)
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f_zero = np.mean(frequency[start_idx:end_idx])
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# import matplotlib.pyplot as plt
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# plt.plot(time, frequency)
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# plt.plot(time[start_idx:end_idx], [f_zero for i in range(end_idx-start_idx)])
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# plt.show()
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return f_zero
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def detect_f_infinity_in_freq_trace(time, frequency, stimulus_start, stimulus_duration, sampling_interval, length=0.1, buffer=0.025):
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stimulus_end_time = stimulus_start + stimulus_duration - time[0]
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start_idx = int((stimulus_end_time - length - buffer) / sampling_interval)
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end_idx = int((stimulus_end_time - buffer) / sampling_interval)
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return np.mean(frequency[start_idx:end_idx])
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|
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def detect_f_baseline_in_freq_trace(time, frequency, stimulus_start, sampling_interval, buffer=0.025):
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stim_start = stimulus_start - time[0]
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if stim_start < 0.1:
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warn("FICurve:__calculate_f_baseline__(): Quite short delay at the start.")
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start_idx = int(buffer/sampling_interval)
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end_idx = int((stim_start-buffer)/sampling_interval)
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f_baseline = np.mean(frequency[start_idx:end_idx])
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return f_baseline |