import numpy as np
import rlxnix as rlx
from scipy.signal import welch
from scipy import signal
import matplotlib.pyplot as plt
def all_coming_together(freq_array, power_array, points_list, categories, num_harmonics_list, colors, delta=2.5, threshold=0.5):
# Initialize dictionaries and lists
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]
# Calculate the integral for the point
integral, local_mean = calculate_integral_2(freq_array, power_array, point, delta)
# Check if the point is valid
valid = valid_integrals(integral, local_mean, point, threshold)
if valid:
# Prepare harmonics if the point is valid
harmonics, color_map, category_harm = prepare_harmonic(point, category, num_harmonics, color)
valid_points.extend(harmonics)
color_mapping[category] = color # Store color for category
category_harmonics[category] = harmonics
messages.append(f"The point {point} is valid.")
else:
messages.append(f"The point {point} is not valid.")
# Debugging print statements
print("Color Mapping:", color_mapping)
print("Category Harmonics:", category_harmonics)
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 calculate_integral_2(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])
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
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
_, ny_freq = AM(eodf, stim_freq)
amp_mod = find_AM(eodf, ny_freq, stim_freq)
return amplitude, df, eodf, stim_freq, stim_dur, amp_mod, ny_freq
def find_AM(eodf, nyquist, stimulus_frequency):
t = signal.windows.triang(eodf) * nyquist
length_t2 = int(eodf*10)
t2 = np.tile(t, length_t2)
x_values = np.arange(len(t2))
#fig, ax = plt.subplots()
#ax.plot(t2)
#ax.scatter(stimulus_frequency, t2[np.argmin(np.abs(x_values - stimulus_frequency))])
#plt.grid()
AM = t2[np.argmin(np.abs(x_values - stimulus_frequency))]
return AM
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 true_eodf(eodf_file):
'''
Calculates the Eodf of the fish when it was awake from a nix file.
Parameters
----------
eodf_file : str
path to the file with nix-file for the eodf.
Returns
-------
orig_eodf : int
The original eodf.
'''
eod_data = rlx.Dataset(eodf_file)#load eodf file
baseline = eod_data.repro_runs('baseline')[0]
eod, time = baseline.trace_data('EOD') # get time and eod
dt = baseline.trace_info('EOD').sampling_interval
eod_freq, eod_power = welch(eod, fs = 1/dt, nperseg = 2**16, noverlap = 2**15)
orig_eodf = round(eod_freq[np.argmax(eod_power)])
return orig_eodf
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.")
else:
print(f"The point {point} is not valid.")
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"""