Merge branch 'main' of https://whale.am28.uni-tuebingen.de/git/mbergmann/gpgrewe2024
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Diana
commit
a90f35b8b9
75
code/test.py
75
code/test.py
@ -60,21 +60,81 @@ def extract_stim_data(stimulus):
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Current EODf.
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stim_freq : float
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The total stimulus frequency (EODF+df).
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amp_mod : float
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The current amplitude modulation.
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ny_freq : float
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The current nyquist frequency.
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'''
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# extract metadata
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# the stim.name adjusts the first key as it changes with every stimulus
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amplitude = stim.metadata[stim.name]['Contrast'][0][0]
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df = stim.metadata[stim.name]['DeltaF'][0][0]
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eodf = stim.metadata[stim.name]['EODf'][0][0]
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stim_freq = stim.metadata[stim.name]['Frequency'][0][0]
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return amplitude, df, eodf, stim_freq
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eodf = round(stim.metadata[stim.name]['EODf'][0][0])
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stim_freq = round(stim.metadata[stim.name]['Frequency'][0][0])
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# calculates the amplitude modulation
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amp_mod, ny_freq = AM(eodf, stim_freq)
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return amplitude, df, eodf, stim_freq, amp_mod, ny_freq
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def AM(EODf, stimulus):
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"""
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Calculates the Amplitude Modulation and Nyquist frequency
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Parameters
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----------
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EODf : float or int
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The current EODf.
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stimulus : float or int
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The absolute frequency of the stimulus.
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Returns
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-------
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AM : float
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The amplitude modulation resulting from the stimulus.
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nyquist : float
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The maximum frequency possible to resolve with the EODf.
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"""
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nyquist = EODf * 0.5
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AM = np.mod(stimulus, nyquist)
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return AM, nyquist
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def remove_poor(files):
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"""
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Removes poor datasets from the set of files for analysis
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Parameters
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----------
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files : list
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list of files.
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Returns
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-------
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good_files : list
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list of files without the ones with the label poor.
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"""
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# create list for good files
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good_files = []
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# loop over files
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for i in range(len(files)):
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# print(files[i])
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# load the file (takes some time)
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data = rlx.Dataset(files[i])
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# get the quality
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quality = str.lower(data.metadata["Recording"]["Recording quality"][0][0])
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# check the quality
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if quality != "poor":
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# if its good or fair add it to the good files
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good_files.append(files[i])
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return good_files
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#find example data
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datafolder = "../data"
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datafolder = "../../data"
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example_file = datafolder + "/" + "2024-10-16-ad-invivo-1.nix"
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example_file = datafolder + "/" + "2024-10-16-ah-invivo-1.nix"
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data_files = glob.glob("../../data/*.nix")
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#load dataset
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dataset = rlx.Dataset(example_file)
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@ -121,6 +181,10 @@ for stim in stims:
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freq, power = power_spectrum(rate, dt)
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ax2.plot(freq,power)
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ax2.set_xlim(0,1000)
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plt.close()
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if stim == stims[-1]:
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amplitude, df, eodf, stim_freq = extract_stim_data(stim)
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print(amplitude, df, eodf, stim_freq)
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# make an eventplot
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fig = plt.figure(figsize = (5, 3), layout = 'constrained')
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@ -128,4 +192,3 @@ ax = fig.add_subplot()
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ax.eventplot(spikes, linelength = 0.8)
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ax.set_xlabel('time [ms]')
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ax.set_ylabel('loop no.')
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plt.show()
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173
code/useful_functions.py
Normal file
173
code/useful_functions.py
Normal file
@ -0,0 +1,173 @@
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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 matplotlib.pyplot as plt
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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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def AM(EODf, stimulus):
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"""
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Calculates the Amplitude Modulation and Nyquist frequency
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Parameters
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----------
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EODf : float or int
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The current EODf.
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stimulus : float or int
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The absolute frequency of the stimulus.
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Returns
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-------
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AM : float
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The amplitude modulation resulting from the stimulus.
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nyquist : float
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The maximum frequency possible to resolve with the EODf.
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"""
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nyquist = EODf * 0.5
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AM = np.mod(stimulus, nyquist)
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return AM, nyquist
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def binary_spikes(spike_times, duration, dt):
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"""
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Converts the spike times to a binary representations
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Parameters
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----------
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spike_times : np.array
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The spike times.
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duration : float
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The trial duration:
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dt : float
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The temporal resolution.
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Returns
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-------
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binary : np.array
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The binary representation of the spike train.
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"""
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binary = np.zeros(int(np.round(duration / dt))) #create the binary array with the same length as potential
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spike_indices = np.asarray(np.round(spike_times / dt), dtype = int) # get the indices
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binary[spike_indices] = 1 # put the indices into binary
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return binary
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def extract_stim_data(stimulus):
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'''
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extracts all necessary metadata for each stimulus
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Parameters
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----------
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stimulus : Stimulus object or rlxnix.base.repro module
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The stimulus from which the data is needed.
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Returns
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-------
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amplitude : float
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The relative signal amplitude in percent.
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df : float
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Distance of the stimulus to the current EODf.
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eodf : float
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Current EODf.
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stim_freq : float
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The total stimulus frequency (EODF+df).
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amp_mod : float
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The current amplitude modulation.
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ny_freq : float
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The current nyquist frequency.
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'''
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# extract metadata
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# the stim.name adjusts the first key as it changes with every stimulus
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amplitude = stimulus.metadata[stimulus.name]['Contrast'][0][0]
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df = stimulus.metadata[stimulus.name]['DeltaF'][0][0]
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eodf = round(stimulus.metadata[stimulus.name]['EODf'][0][0])
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stim_freq = round(stimulus.metadata[stimulus.name]['Frequency'][0][0])
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# calculates the amplitude modulation
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amp_mod, ny_freq = AM(eodf, stim_freq)
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return amplitude, df, eodf, stim_freq, amp_mod, ny_freq
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def firing_rate(binary_spikes, dt = 0.000025, box_width = 0.01):
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'''
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Calculates the firing rate from binary spikes
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Parameters
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----------
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binary_spikes : np.array
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The binary representation of the spike train.
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dt : float, optional
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Time difference between two datapoints. The default is 0.000025.
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box_width : float, optional
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Time window on which the rate should be computed on. The default is 0.01.
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Returns
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-------
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rate : np.array
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Array of firing rates.
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'''
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box = np.ones(int(box_width // dt))
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box /= np.sum(box) * dt # normalisierung des box kernels to an integral of one
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rate = np.convolve(binary_spikes, box, mode = 'same')
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return rate
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def power_spectrum(spike_times, duration, dt):
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'''
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Computes a power spectrum based on the spike times
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Parameters
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----------
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spike_times : np.array
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The spike times.
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duration : float
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The trial duration:
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dt : float
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The temporal resolution.
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Returns
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-------
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freq : np.array
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All the frequencies of the power spectrum.
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power : np.array
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Power of the frequencies calculated.
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'''
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# binarizes spikes
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binary = binary_spikes(spike_times, duration, dt)
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# computes firing rates
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rate = firing_rate(binary, dt = dt)
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# creates power spectrum
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freq, power = welch(rate, fs = 1/dt, nperseg = 2**16, noverlap = 2**15)
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return freq, power
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def remove_poor(files):
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"""
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Removes poor datasets from the set of files for analysis
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Parameters
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----------
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files : list
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list of files.
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Returns
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-------
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good_files : list
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list of files without the ones with the label poor.
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"""
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# create list for good files
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good_files = []
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# loop over files
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for i in range(len(files)):
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# print(files[i])
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# load the file (takes some time)
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data = rlx.Dataset(files[i])
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# get the quality
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quality = str.lower(data.metadata["Recording"]["Recording quality"][0][0])
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# check the quality
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if quality != "poor":
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# if its good or fair add it to the good files
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good_files.append(files[i])
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return good_files
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