549 lines
17 KiB
Python
549 lines
17 KiB
Python
import glob
|
|
import pathlib
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import rlxnix as rlx
|
|
from IPython import embed
|
|
from scipy.signal import welch
|
|
|
|
def all_coming_together(freq_array, power_array, points_list, categories, num_harmonics_list, colors, delta=2.5, threshold=0.5):
|
|
"""
|
|
Process a list of points, calculating integrals, checking validity, and preparing harmonics for valid points.
|
|
|
|
Parameters
|
|
----------
|
|
freq_array : np.array
|
|
Array of frequencies corresponding to the power values.
|
|
power_array : np.array
|
|
Array of power spectral density values.
|
|
points_list : list
|
|
List of harmonic frequency points to process.
|
|
categories : list
|
|
List of corresponding categories for each point.
|
|
num_harmonics_list : list
|
|
List of the number of harmonics for each point.
|
|
colors : list
|
|
List of colors corresponding to each point's category.
|
|
delta : float, optional
|
|
Radius of the range for integration around each point (default is 2.5).
|
|
threshold : float, optional
|
|
Threshold value to compare integrals with local mean (default is 0.5).
|
|
|
|
Returns
|
|
-------
|
|
valid_points : list
|
|
A continuous list of harmonics for all valid points.
|
|
color_mapping : dict
|
|
A dictionary mapping categories to corresponding colors.
|
|
category_harmonics : dict
|
|
A mapping of categories to their harmonic frequencies.
|
|
messages : list
|
|
A list of messages for each point, stating whether it was valid or not.
|
|
"""
|
|
valid_points = [] # A continuous list of harmonics for valid points
|
|
color_mapping = {}
|
|
category_harmonics = {}
|
|
messages = []
|
|
|
|
for i, point in enumerate(points_list):
|
|
category = categories[i]
|
|
num_harmonics = num_harmonics_list[i]
|
|
color = colors[i]
|
|
|
|
# Step 1: Calculate the integral for the point
|
|
integral, local_mean, _ = calculate_integral(freq_array, power_array, point, delta)
|
|
|
|
# Step 2: Check if the point is valid
|
|
valid = valid_integrals(integral, local_mean, point, threshold)
|
|
if valid:
|
|
# Step 3: Prepare harmonics if the point is valid
|
|
harmonics, color_map, category_harm = prepare_harmonic(point, category, num_harmonics, color)
|
|
valid_points.extend(harmonics) # Use extend() to append harmonics in a continuous manner
|
|
color_mapping.update(color_map)
|
|
category_harmonics.update(category_harm)
|
|
messages.append(f"The point {point} is valid.")
|
|
else:
|
|
messages.append(f"The point {point} is not valid.")
|
|
|
|
return valid_points, color_mapping, category_harmonics, messages
|
|
|
|
|
|
|
|
def AM(EODf, stimulus):
|
|
"""
|
|
Calculates the Amplitude Modulation and Nyquist frequency
|
|
|
|
Parameters
|
|
----------
|
|
EODf : float or int
|
|
The current EODf.
|
|
stimulus : float or int
|
|
The absolute frequency of the stimulus.
|
|
|
|
Returns
|
|
-------
|
|
AM : float
|
|
The amplitude modulation resulting from the stimulus.
|
|
nyquist : float
|
|
The maximum frequency possible to resolve with the EODf.
|
|
|
|
"""
|
|
nyquist = EODf * 0.5
|
|
AM = np.mod(stimulus, nyquist)
|
|
return AM, nyquist
|
|
|
|
def binary_spikes(spike_times, duration, dt):
|
|
"""
|
|
Converts the spike times to a binary representations
|
|
|
|
Parameters
|
|
----------
|
|
spike_times : np.array
|
|
The spike times.
|
|
duration : float
|
|
The trial duration:
|
|
dt : float
|
|
The temporal resolution.
|
|
|
|
Returns
|
|
-------
|
|
binary : np.array
|
|
The binary representation of the spike train.
|
|
|
|
"""
|
|
binary = np.zeros(int(np.round(duration / dt))) #create the binary array with the same length as potential
|
|
|
|
spike_indices = np.asarray(np.round(spike_times / dt), dtype = int) # get the indices
|
|
binary[spike_indices] = 1 # put the indices into binary
|
|
return binary
|
|
|
|
def calculate_integral(freq, power, point, delta = 2.5):
|
|
"""
|
|
Calculate the integral around a single specified point.
|
|
|
|
Parameters
|
|
----------
|
|
frequency : np.array
|
|
An array of frequencies corresponding to the power values.
|
|
power : np.array
|
|
An array of power spectral density values.
|
|
point : float
|
|
The harmonic frequency at which to calculate the integral.
|
|
delta : float, optional
|
|
Radius of the range for integration around the point. The default is 2.5.
|
|
|
|
Returns
|
|
-------
|
|
integral : float
|
|
The calculated integral around the point.
|
|
local_mean : float
|
|
The local mean value (adjacent integrals).
|
|
p_power : float
|
|
The local maxiumum power.
|
|
"""
|
|
indices = (freq >= point - delta) & (freq <= point + delta)
|
|
integral = np.trapz(power[indices], freq[indices])
|
|
p_power = np.max(power[indices])
|
|
|
|
left_indices = (freq >= point - 5 * delta) & (freq < point - delta)
|
|
right_indices = (freq > point + delta) & (freq <= point + 5 * delta)
|
|
|
|
l_integral = np.trapz(power[left_indices], freq[left_indices])
|
|
r_integral = np.trapz(power[right_indices], freq[right_indices])
|
|
|
|
local_mean = np.mean([l_integral, r_integral])
|
|
return integral, local_mean, p_power
|
|
|
|
def contrast_sorting(sams, con_1 = 20, con_2 = 10, con_3 = 5, stim_count = 3, stim_dur = 2):
|
|
'''
|
|
sorts the sams into three contrasts
|
|
|
|
Parameters
|
|
----------
|
|
sams : ReproRuns
|
|
The sams to be sorted.
|
|
con_1 : int, optional
|
|
the first contrast. The default is 20.
|
|
con_2 : int, optional
|
|
the second contrast. The default is 10.
|
|
con_3 : int, optional
|
|
the third contrast. The default is 5.
|
|
stim_count : int, optional
|
|
the amount of stimuli per sam in a good sam. The default is 3.
|
|
stim_dur : int, optional
|
|
The stimulus duration. The default is 2.
|
|
|
|
Returns
|
|
-------
|
|
contrast_sams : dictionary
|
|
A dictionary containing all sams sorted to the contrasts.
|
|
|
|
'''
|
|
# dictionary for the contrasts
|
|
contrast_sams = {con_1 : [],
|
|
con_2 : [],
|
|
con_3 : []}
|
|
# loop over all sams
|
|
for sam in sams:
|
|
# get the contrast
|
|
avg_dur, contrast, _, _, _, _, _ = sam_data(sam)
|
|
# check for valid trails
|
|
if np.isnan(contrast):
|
|
continue
|
|
elif sam.stimulus_count < stim_count: #aborted trials
|
|
continue
|
|
elif avg_dur < (stim_dur * 0.8):
|
|
continue
|
|
else:
|
|
contrast = int(contrast) # get integer of contrast
|
|
# sort them accordingly
|
|
if contrast == con_1:
|
|
contrast_sams[con_1].append(sam)
|
|
elif contrast == con_2:
|
|
contrast_sams[con_2].append(sam)
|
|
elif contrast == con_3:
|
|
contrast_sams[con_3].append(sam)
|
|
else:
|
|
continue
|
|
return contrast_sams
|
|
|
|
def extract_stim_data(stimulus):
|
|
'''
|
|
extracts all necessary metadata for each stimulus
|
|
|
|
Parameters
|
|
----------
|
|
stimulus : Stimulus object or rlxnix.base.repro module
|
|
The stimulus from which the data is needed.
|
|
|
|
Returns
|
|
-------
|
|
amplitude : float
|
|
The relative signal amplitude in percent.
|
|
df : float
|
|
Distance of the stimulus to the current EODf.
|
|
eodf : float
|
|
Current EODf.
|
|
stim_freq : float
|
|
The total stimulus frequency (EODF+df).
|
|
stim_dur : float
|
|
The stimulus duration.
|
|
amp_mod : float
|
|
The current amplitude modulation.
|
|
ny_freq : float
|
|
The current nyquist frequency.
|
|
|
|
'''
|
|
# extract metadata
|
|
# the stim.name adjusts the first key as it changes with every stimulus
|
|
amplitude = stimulus.metadata[stimulus.name]['Contrast'][0][0]
|
|
df = stimulus.metadata[stimulus.name]['DeltaF'][0][0]
|
|
eodf = round(stimulus.metadata[stimulus.name]['EODf'][0][0])
|
|
stim_freq = round(stimulus.metadata[stimulus.name]['Frequency'][0][0])
|
|
stim_dur = stimulus.duration
|
|
# calculates the amplitude modulation
|
|
amp_mod, ny_freq = AM(eodf, stim_freq)
|
|
return amplitude, df, eodf, stim_freq,stim_dur, amp_mod, ny_freq
|
|
|
|
def find_exceeding_points(frequency, power, points, delta, threshold):
|
|
"""
|
|
Find the points where the integral exceeds the local mean by a given threshold.
|
|
|
|
Parameters
|
|
----------
|
|
frequency : np.array
|
|
An array of frequencies corresponding to the power values.
|
|
power : np.array
|
|
An array of power spectral density values.
|
|
points : list
|
|
A list of harmonic frequencies to evaluate.
|
|
delta : float
|
|
Half-width of the range for integration around the point.
|
|
threshold : float
|
|
Threshold value to compare integrals with local mean.
|
|
|
|
Returns
|
|
-------
|
|
exceeding_points : list
|
|
A list of points where the integral exceeds the local mean by the threshold.
|
|
"""
|
|
exceeding_points = []
|
|
|
|
for point in points:
|
|
# Calculate the integral and local mean for the current point
|
|
integral, local_mean = calculate_integral(frequency, power, point, delta)
|
|
|
|
# Check if the integral exceeds the threshold
|
|
valid, message = valid_integrals(integral, local_mean, threshold, point)
|
|
|
|
if valid:
|
|
exceeding_points.append(point)
|
|
|
|
return exceeding_points
|
|
|
|
def firing_rate(binary_spikes, dt = 0.000025, box_width = 0.01):
|
|
'''
|
|
Calculates the firing rate from binary spikes
|
|
|
|
Parameters
|
|
----------
|
|
binary_spikes : np.array
|
|
The binary representation of the spike train.
|
|
dt : float, optional
|
|
Time difference between two datapoints. The default is 0.000025.
|
|
box_width : float, optional
|
|
Time window on which the rate should be computed on. The default is 0.01.
|
|
|
|
Returns
|
|
-------
|
|
rate : np.array
|
|
Array of firing rates.
|
|
|
|
'''
|
|
box = np.ones(int(box_width // dt))
|
|
box /= np.sum(box) * dt # normalisierung des box kernels to an integral of one
|
|
rate = np.convolve(binary_spikes, box, mode = 'same')
|
|
return rate
|
|
|
|
def power_spectrum(stimulus):
|
|
'''
|
|
Computes a power spectrum based from a stimulus
|
|
|
|
Parameters
|
|
----------
|
|
stimulus : Stimulus object or rlxnix.base.repro module
|
|
The stimulus for which the data is needed.
|
|
|
|
Returns
|
|
-------
|
|
freq : np.array
|
|
All the frequencies of the power spectrum.
|
|
power : np.array
|
|
Power of the frequencies calculated.
|
|
|
|
'''
|
|
spikes, duration, dt = spike_times(stimulus)
|
|
# binarizes spikes
|
|
binary = binary_spikes(spikes, duration, dt)
|
|
# computes firing rates
|
|
rate = firing_rate(binary, dt = dt)
|
|
# creates power spectrum
|
|
freq, power = welch(binary, fs = 1/dt, nperseg = 2**16, noverlap = 2**15)
|
|
return freq, power
|
|
|
|
def prepare_harmonic(frequency, category, num_harmonics, color):
|
|
"""
|
|
Prepare harmonic frequencies and assign color based on category for a single point.
|
|
|
|
Parameters
|
|
----------
|
|
frequency : float
|
|
Base frequency to generate harmonics.
|
|
category : str
|
|
Corresponding category for the base frequency.
|
|
num_harmonics : int
|
|
Number of harmonics for the base frequency.
|
|
color : str
|
|
Color corresponding to the category.
|
|
|
|
Returns
|
|
-------
|
|
harmonics : list
|
|
A list of harmonic frequencies.
|
|
color_mapping : dict
|
|
A dictionary mapping the category to its corresponding color.
|
|
category_harmonics : dict
|
|
A mapping of the category to its harmonic frequencies.
|
|
"""
|
|
harmonics = [frequency * (i + 1) for i in range(num_harmonics)]
|
|
|
|
color_mapping = {category: color}
|
|
category_harmonics = {category: harmonics}
|
|
|
|
return harmonics, color_mapping, category_harmonics
|
|
|
|
def remove_poor(files):
|
|
"""
|
|
Removes poor datasets from the set of files for analysis
|
|
|
|
Parameters
|
|
----------
|
|
files : list
|
|
list of files.
|
|
|
|
Returns
|
|
-------
|
|
good_files : list
|
|
list of files without the ones with the label poor.
|
|
|
|
"""
|
|
# create list for good files
|
|
good_files = []
|
|
# loop over files
|
|
for i in range(len(files)):
|
|
# print(files[i])
|
|
# load the file (takes some time)
|
|
data = rlx.Dataset(files[i])
|
|
# get the quality
|
|
quality = str.lower(data.metadata["Recording"]["Recording quality"][0][0])
|
|
# check the quality
|
|
if quality != "poor":
|
|
# if its good or fair add it to the good files
|
|
good_files.append(files[i])
|
|
return good_files
|
|
|
|
def sam_data(sam):
|
|
'''
|
|
Gets metadata for each SAM
|
|
|
|
Parameters
|
|
----------
|
|
sam : ReproRun object
|
|
The sam the metdata should be extracted from.
|
|
|
|
Returns
|
|
-------
|
|
avg_dur : float
|
|
Average stimulus duarion.
|
|
sam_amp : float
|
|
amplitude in percent, relative to the fish amplitude.
|
|
sam_am : float
|
|
Amplitude modulation frequency.
|
|
sam_df : float
|
|
Difference from the stimulus to the current fish eodf.
|
|
sam_eodf : float
|
|
The current EODf.
|
|
sam_nyquist : float
|
|
The Nyquist frequency of the EODf.
|
|
sam_stim : float
|
|
The stimulus frequency.
|
|
|
|
'''
|
|
# create lists for the values we want
|
|
amplitudes = []
|
|
dfs = []
|
|
eodfs = []
|
|
stim_freqs = []
|
|
amp_mods = []
|
|
ny_freqs = []
|
|
durations = []
|
|
|
|
# get the stimuli
|
|
stimuli = sam.stimuli
|
|
|
|
# loop over the stimuli
|
|
for stim in stimuli:
|
|
amplitude, df, eodf, stim_freq,stim_dur, amp_mod, ny_freq = extract_stim_data(stim)
|
|
amplitudes.append(amplitude)
|
|
dfs.append(df)
|
|
eodfs.append(eodf)
|
|
stim_freqs.append(stim_freq)
|
|
amp_mods.append(amp_mod)
|
|
ny_freqs.append(ny_freq)
|
|
durations.append(stim_dur)
|
|
|
|
# get the means
|
|
sam_amp = np.mean(amplitudes)
|
|
sam_am = np.mean(amp_mods)
|
|
sam_df = np.mean(dfs)
|
|
sam_eodf = np.mean(eodfs)
|
|
sam_nyquist = np.mean(ny_freqs)
|
|
sam_stim = np.mean(stim_freqs)
|
|
avg_dur = np.mean(durations)
|
|
return avg_dur, sam_amp, sam_am, sam_df, sam_eodf, sam_nyquist, sam_stim
|
|
|
|
def sam_spectrum(sam):
|
|
"""
|
|
Creates a power spectrum for a ReproRun of a SAM.
|
|
|
|
Parameters
|
|
----------
|
|
sam : ReproRun Object
|
|
The Reprorun the powerspectrum should be generated from.
|
|
|
|
Returns
|
|
-------
|
|
sam_frequency : np.array
|
|
The frequencies of the powerspectrum.
|
|
sam_power : np.array
|
|
The powers of the frequencies.
|
|
|
|
"""
|
|
stimuli = sam.stimuli
|
|
# lists for the power spectra
|
|
frequencies = []
|
|
powers = []
|
|
# loop over the stimuli
|
|
for stimulus in stimuli:
|
|
# get the powerspectrum for each stimuli
|
|
frequency, power = power_spectrum(stimulus)
|
|
# append the power spectrum data
|
|
frequencies.append(frequency)
|
|
powers.append(power)
|
|
#average over the stimuli
|
|
sam_frequency = np.mean(frequencies, axis = 0)
|
|
sam_power = np.mean(powers, axis = 0)
|
|
return sam_frequency, sam_power
|
|
|
|
def spike_times(stim):
|
|
"""
|
|
Reads out the spike times and other necessary parameters
|
|
|
|
Parameters
|
|
----------
|
|
stim : Stimulus object or rlxnix.base.repro module
|
|
The stimulus from which the spike times should be calculated.
|
|
|
|
Returns
|
|
-------
|
|
spike_times : np.array
|
|
The spike times of the stimulus.
|
|
stim_dur : float
|
|
The duration of the stimulus.
|
|
dt : float
|
|
Time interval between two data points.
|
|
|
|
"""
|
|
# reads out the spike times
|
|
spikes, _ = stim.trace_data('Spikes-1')
|
|
# reads out the duration
|
|
stim_dur = stim.duration
|
|
# get the stimulus interval
|
|
ti = stim.trace_info("V-1")
|
|
dt = ti.sampling_interval
|
|
return spikes, stim_dur, dt # se changed spike_times to spikes so its not the same as name of function
|
|
|
|
def valid_integrals(integral, local_mean, point, threshold = 0.1):
|
|
"""
|
|
Check if the integral exceeds the threshold compared to the local mean and
|
|
provide feedback on whether the given point is valid or not.
|
|
|
|
Parameters
|
|
----------
|
|
integral : float
|
|
The calculated integral around the point.
|
|
local_mean : float
|
|
The local mean value (adjacent integrals).
|
|
threshold : float
|
|
Threshold value to compare integrals with local mean.
|
|
point : float
|
|
The harmonic frequency point being evaluated.
|
|
|
|
Returns
|
|
-------
|
|
valid : bool
|
|
True if the integral exceeds the local mean by the threshold, otherwise False.
|
|
"""
|
|
valid = integral > (local_mean * (1 + threshold))
|
|
if valid:
|
|
print(f"The point {point} is valid, as its integral exceeds the threshold.")
|
|
else:
|
|
print(f"The point {point} is not valid, as its integral does not exceed the threshold.")
|
|
return valid
|
|
|
|
'''TODO Sarah: AM-freq plot:
|
|
meaning of am peak in spectrum? why is it there how does it change with stim intensity?
|
|
make plot with AM 1/2 EODf over stim frequency (df+eodf), get amplitude of am peak and plot
|
|
amplitude over frequency of peak'''
|
|
|
|
""" files = glob.glob("../data/2024-10-16*.nix") gets all the filepaths from the 16.10""" |