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英文字典中文字典相关资料:


  • Neuro Digital Signal Processing Toolbox — neurodsp 2. 3. 0 documentation
    neurodsp is a collection of approaches for applying digital signal processing, and related algorithms, to neural time series It also includes simulation tools for generating plausible simulations of neural time series
  • Tutorials — neurodsp 2. 3. 0 documentation - GitHub Pages
    Analyzing Aperiodic Signal Properties ¶ Tutorials for the aperiodic module Autocorrelation Measures Autocorrelation Measures
  • neurodsp. filt. filter_signal — neurodsp 2. 3. 0 documentation - GitHub Pages
    Apply a bandpass, bandstop, highpass, or lowpass filter to a neural signal Parameters: sig array Time series to be filtered fs float Sampling rate, in Hz pass_type {‘bandpass’, ‘bandstop’, ‘lowpass’, ‘highpass’} Which kind of filter to apply: ‘bandpass’: apply a bandpass filter ‘bandstop’: apply a bandstop (notch
  • GitHub Pages
    """ Autocorrelation Measures ===== Apply autocorrelation measures to neural signals Autocorrelation is the correlation of a signal with delayed copies of itself Autocorrelation measures can be useful to investigate properties of neural signals
  • neurodsp. plts. plot_spectra_3d — neurodsp 2. 3. 0 documentation
    neurodsp plts plot_spectra_3d¶ neurodsp plts plot_spectra_3d (freqs, powers, log_freqs = False, log_powers = True, colors = None, orientation = (20,-50), zoom = 1 0, ax = None, ** kwargs) [source] ¶ Plot a series of power spectra in a 3D plot Parameters: freqs 1d or 2d array or list of 1d array Frequency vector powers 2d array or list of 1d array Power values log_freqs bool, optional
  • neurodsp-tools. github. io
    Though here we using simulated\naperiodic time series, in analyses of neural field data, DFA is most often used to examine\namplitude time series of neural oscillations \n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Simulation settings\nn_seconds = 10\nfs = 500\n\n
  • neurodsp. rhythm. compute_lagged_coherence — neurodsp 2. 3. 0 documentation
    neurodsp rhythm compute_lagged_coherence¶ neurodsp rhythm compute_lagged_coherence (sig, fs, freqs, n_cycles = 3, return_spectrum = False) [source] ¶ Compute lagged coherence, reflecting the rhythmicity across a frequency range Parameters:
  • neurodsp. sim. combined. sim_peak_oscillation — neurodsp 2. 3. 0 documentation
    neurodsp sim combined sim_peak_oscillation¶ neurodsp sim combined sim_peak_oscillation (sig_ap, fs, freq, bw, height) [source] ¶ Simulate a signal with an aperiodic component and a specific oscillation peak
  • neurodsp. rhythm. sliding_window_matching — neurodsp 2. 3. 0 documentation
    neurodsp rhythm sliding_window_matching¶ neurodsp rhythm sliding_window_matching (sig, fs, win_len, win_spacing, max_iterations = 100, window_starts_custom = None, var_thresh = None) [source] ¶ Find recurring patterns in a time series using the sliding window matching algorithm Parameters:





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