GP_code nach code verschoben
This commit is contained in:
parent
b0adb74ae3
commit
e8d4ca90fe
@ -1,192 +1,192 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Thu Oct 17 09:23:10 2024
|
||||
|
||||
@author: diana
|
||||
"""
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import glob
|
||||
import os
|
||||
import rlxnix as rlx
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import scipy.signal as sig
|
||||
from scipy.integrate import quad
|
||||
|
||||
|
||||
### FUNCTIONS ###
|
||||
def binary_spikes(spike_times, duration, dt):
|
||||
""" Converts the spike times to a binary representation.
|
||||
Zeros when there is no spike, One when there is.
|
||||
|
||||
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 times.
|
||||
|
||||
"""
|
||||
binary = np.zeros(int(np.round(duration / dt))) #Vektor, der genauso lang ist wie die stim time
|
||||
spike_indices = np.asarray(np.round(spike_times / dt), dtype=int)
|
||||
binary[spike_indices] = 1
|
||||
return binary
|
||||
|
||||
|
||||
def firing_rate(binary_spikes, box_width, dt=0.000025):
|
||||
"""Calculate the firing rate from binary spike data.
|
||||
|
||||
This function computes the firing rate using a boxcar (moving average)
|
||||
filter of a specified width.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
binary_spikes : np.array
|
||||
A binary array representing spike occurrences.
|
||||
box_width : float
|
||||
The width of the box filter in seconds.
|
||||
dt : float, optional
|
||||
The temporal resolution (time step) in seconds. Default is 0.000025 seconds.
|
||||
|
||||
Returns
|
||||
-------
|
||||
rate : np.array
|
||||
An array representing the firing rate at each time step.
|
||||
"""
|
||||
box = np.ones(int(box_width // dt))
|
||||
box /= np.sum(box) * dt #Normalization of box kernel to an integral of 1
|
||||
rate = np.convolve(binary_spikes, box, mode="same")
|
||||
return rate
|
||||
|
||||
|
||||
def powerspectrum(rate, dt):
|
||||
"""Compute the power spectrum of a given firing rate.
|
||||
|
||||
This function calculates the power spectrum using the Welch method.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
rate : np.array
|
||||
An array of firing rates.
|
||||
dt : float
|
||||
The temporal resolution (time step) in seconds.
|
||||
|
||||
Returns
|
||||
-------
|
||||
frequency : np.array
|
||||
An array of frequencies corresponding to the power values.
|
||||
power : np.array
|
||||
An array of power spectral density values.
|
||||
"""
|
||||
frequency, power = sig.welch(rate, fs=1/dt, nperseg=2**15, noverlap=2**14)
|
||||
return frequency, power
|
||||
|
||||
|
||||
def prepare_harmonics(frequencies, categories, num_harmonics, colors):
|
||||
points_categories = {}
|
||||
for idx, (freq, category) in enumerate(zip(frequencies, categories)):
|
||||
points_categories[category] = [freq * (i + 1) for i in range(num_harmonics[idx])]
|
||||
|
||||
points = [p for harmonics in points_categories.values() for p in harmonics]
|
||||
color_mapping = {category: colors[idx] for idx, category in enumerate(categories)}
|
||||
|
||||
return points, color_mapping, points_categories
|
||||
|
||||
|
||||
def plot_power_spectrum_with_integrals(frequency, power, points, delta, color_mapping, points_categories):
|
||||
"""Create a figure of the power spectrum with integrals highlighted around specified points.
|
||||
|
||||
This function creates a plot of the power spectrum and shades areas around
|
||||
specified harmonic points to indicate the calculated integrals.
|
||||
|
||||
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 highlight.
|
||||
delta : float
|
||||
Half-width of the range for integration around each point.
|
||||
color_mapping : dict
|
||||
A mapping of point categories to colors.
|
||||
points_categories : dict
|
||||
A mapping of categories to lists of points.
|
||||
|
||||
Returns
|
||||
-------
|
||||
fig : matplotlib.figure.Figure
|
||||
The created figure object.
|
||||
"""
|
||||
fig, ax = plt.subplots()
|
||||
ax.plot(frequency, power)
|
||||
integrals = []
|
||||
|
||||
for point in points:
|
||||
indices = (frequency >= point - delta) & (frequency <= point + delta)
|
||||
integral = np.trapz(power[indices], frequency[indices])
|
||||
integrals.append(integral)
|
||||
|
||||
# Get color based on point category
|
||||
color = next((c for cat, c in color_mapping.items() if point in points_categories[cat]), 'gray')
|
||||
ax.axvspan(point - delta, point + delta, color=color, alpha=0.3, label=f'{point:.2f} Hz')
|
||||
print(f"Integral around {point:.2f} Hz: {integral:.5e}")
|
||||
|
||||
ax.set_xlim([0, 1200])
|
||||
ax.set_xlabel('Frequency (Hz)')
|
||||
ax.set_ylabel('Power')
|
||||
ax.set_title('Power Spectrum with marked Integrals')
|
||||
ax.legend()
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
|
||||
|
||||
### Data retrieval ###
|
||||
datafolder = "../data" # Geht in der Hierarchie einen Ordern nach oben (..) und dann in den Ordner 'data'
|
||||
example_file = os.path.join("..", "data", "2024-10-16-ad-invivo-1.nix")
|
||||
dataset = rlx.Dataset(example_file)
|
||||
sams = dataset.repro_runs("SAM")
|
||||
sam = sams[2]
|
||||
|
||||
## Daten für Funktionen
|
||||
df = sam.metadata["RePro-Info"]["settings"]["deltaf"][0][0]
|
||||
stim = sam.stimuli[1]
|
||||
potential, time = stim.trace_data("V-1")
|
||||
spikes, _ = stim.trace_data("Spikes-1")
|
||||
duration = stim.duration
|
||||
dt = stim.trace_info("V-1").sampling_interval
|
||||
|
||||
|
||||
### Anwendung Functionen ###
|
||||
b = binary_spikes(spikes, duration, dt)
|
||||
rate = firing_rate(b, box_width=0.05, dt=dt)
|
||||
frequency, power = powerspectrum(b, dt)
|
||||
|
||||
## Important stuff
|
||||
eodf = stim.metadata[stim.name]["EODf"][0][0]
|
||||
stimulus_frequency = eodf + df
|
||||
AM = 50 # Hz
|
||||
#print(f"EODf: {eodf}, Stimulus Frequency: {stimulus_frequency}, AM: {AM}")
|
||||
|
||||
frequencies = [AM, eodf, stimulus_frequency]
|
||||
categories = ["AM", "EODf", "Stimulus frequency"]
|
||||
num_harmonics = [4, 2, 2]
|
||||
colors = ["green", "orange", "red"]
|
||||
delta = 2.5
|
||||
|
||||
|
||||
### Peaks im Powerspektrum finden ###
|
||||
points, color_mapping, points_categories = prepare_harmonics(frequencies, categories, num_harmonics, colors)
|
||||
fig = plot_power_spectrum_with_integrals(frequency, power, points, delta, color_mapping, points_categories)
|
||||
plt.show()
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Thu Oct 17 09:23:10 2024
|
||||
|
||||
@author: diana
|
||||
"""
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import glob
|
||||
import os
|
||||
import rlxnix as rlx
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import scipy.signal as sig
|
||||
from scipy.integrate import quad
|
||||
|
||||
|
||||
### FUNCTIONS ###
|
||||
def binary_spikes(spike_times, duration, dt):
|
||||
""" Converts the spike times to a binary representation.
|
||||
Zeros when there is no spike, One when there is.
|
||||
|
||||
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 times.
|
||||
|
||||
"""
|
||||
binary = np.zeros(int(np.round(duration / dt))) #Vektor, der genauso lang ist wie die stim time
|
||||
spike_indices = np.asarray(np.round(spike_times / dt), dtype=int)
|
||||
binary[spike_indices] = 1
|
||||
return binary
|
||||
|
||||
|
||||
def firing_rate(binary_spikes, box_width, dt=0.000025):
|
||||
"""Calculate the firing rate from binary spike data.
|
||||
|
||||
This function computes the firing rate using a boxcar (moving average)
|
||||
filter of a specified width.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
binary_spikes : np.array
|
||||
A binary array representing spike occurrences.
|
||||
box_width : float
|
||||
The width of the box filter in seconds.
|
||||
dt : float, optional
|
||||
The temporal resolution (time step) in seconds. Default is 0.000025 seconds.
|
||||
|
||||
Returns
|
||||
-------
|
||||
rate : np.array
|
||||
An array representing the firing rate at each time step.
|
||||
"""
|
||||
box = np.ones(int(box_width // dt))
|
||||
box /= np.sum(box) * dt #Normalization of box kernel to an integral of 1
|
||||
rate = np.convolve(binary_spikes, box, mode="same")
|
||||
return rate
|
||||
|
||||
|
||||
def powerspectrum(rate, dt):
|
||||
"""Compute the power spectrum of a given firing rate.
|
||||
|
||||
This function calculates the power spectrum using the Welch method.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
rate : np.array
|
||||
An array of firing rates.
|
||||
dt : float
|
||||
The temporal resolution (time step) in seconds.
|
||||
|
||||
Returns
|
||||
-------
|
||||
frequency : np.array
|
||||
An array of frequencies corresponding to the power values.
|
||||
power : np.array
|
||||
An array of power spectral density values.
|
||||
"""
|
||||
frequency, power = sig.welch(rate, fs=1/dt, nperseg=2**15, noverlap=2**14)
|
||||
return frequency, power
|
||||
|
||||
|
||||
def prepare_harmonics(frequencies, categories, num_harmonics, colors):
|
||||
points_categories = {}
|
||||
for idx, (freq, category) in enumerate(zip(frequencies, categories)):
|
||||
points_categories[category] = [freq * (i + 1) for i in range(num_harmonics[idx])]
|
||||
|
||||
points = [p for harmonics in points_categories.values() for p in harmonics]
|
||||
color_mapping = {category: colors[idx] for idx, category in enumerate(categories)}
|
||||
|
||||
return points, color_mapping, points_categories
|
||||
|
||||
|
||||
def plot_power_spectrum_with_integrals(frequency, power, points, delta, color_mapping, points_categories):
|
||||
"""Create a figure of the power spectrum with integrals highlighted around specified points.
|
||||
|
||||
This function creates a plot of the power spectrum and shades areas around
|
||||
specified harmonic points to indicate the calculated integrals.
|
||||
|
||||
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 highlight.
|
||||
delta : float
|
||||
Half-width of the range for integration around each point.
|
||||
color_mapping : dict
|
||||
A mapping of point categories to colors.
|
||||
points_categories : dict
|
||||
A mapping of categories to lists of points.
|
||||
|
||||
Returns
|
||||
-------
|
||||
fig : matplotlib.figure.Figure
|
||||
The created figure object.
|
||||
"""
|
||||
fig, ax = plt.subplots()
|
||||
ax.plot(frequency, power)
|
||||
integrals = []
|
||||
|
||||
for point in points:
|
||||
indices = (frequency >= point - delta) & (frequency <= point + delta)
|
||||
integral = np.trapz(power[indices], frequency[indices])
|
||||
integrals.append(integral)
|
||||
|
||||
# Get color based on point category
|
||||
color = next((c for cat, c in color_mapping.items() if point in points_categories[cat]), 'gray')
|
||||
ax.axvspan(point - delta, point + delta, color=color, alpha=0.3, label=f'{point:.2f} Hz')
|
||||
print(f"Integral around {point:.2f} Hz: {integral:.5e}")
|
||||
|
||||
ax.set_xlim([0, 1200])
|
||||
ax.set_xlabel('Frequency (Hz)')
|
||||
ax.set_ylabel('Power')
|
||||
ax.set_title('Power Spectrum with marked Integrals')
|
||||
ax.legend()
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
|
||||
|
||||
### Data retrieval ###
|
||||
datafolder = "../data" # Geht in der Hierarchie einen Ordern nach oben (..) und dann in den Ordner 'data'
|
||||
example_file = os.path.join("..", "data", "2024-10-16-ad-invivo-1.nix")
|
||||
dataset = rlx.Dataset(example_file)
|
||||
sams = dataset.repro_runs("SAM")
|
||||
sam = sams[2]
|
||||
|
||||
## Daten für Funktionen
|
||||
df = sam.metadata["RePro-Info"]["settings"]["deltaf"][0][0]
|
||||
stim = sam.stimuli[1]
|
||||
potential, time = stim.trace_data("V-1")
|
||||
spikes, _ = stim.trace_data("Spikes-1")
|
||||
duration = stim.duration
|
||||
dt = stim.trace_info("V-1").sampling_interval
|
||||
|
||||
|
||||
### Anwendung Functionen ###
|
||||
b = binary_spikes(spikes, duration, dt)
|
||||
rate = firing_rate(b, box_width=0.05, dt=dt)
|
||||
frequency, power = powerspectrum(b, dt)
|
||||
|
||||
## Important stuff
|
||||
eodf = stim.metadata[stim.name]["EODf"][0][0]
|
||||
stimulus_frequency = eodf + df
|
||||
AM = 50 # Hz
|
||||
#print(f"EODf: {eodf}, Stimulus Frequency: {stimulus_frequency}, AM: {AM}")
|
||||
|
||||
frequencies = [AM, eodf, stimulus_frequency]
|
||||
categories = ["AM", "EODf", "Stimulus frequency"]
|
||||
num_harmonics = [4, 2, 2]
|
||||
colors = ["green", "orange", "red"]
|
||||
delta = 2.5
|
||||
|
||||
|
||||
### Peaks im Powerspektrum finden ###
|
||||
points, color_mapping, points_categories = prepare_harmonics(frequencies, categories, num_harmonics, colors)
|
||||
fig = plot_power_spectrum_with_integrals(frequency, power, points, delta, color_mapping, points_categories)
|
||||
plt.show()
|
||||
Loading…
Reference in New Issue
Block a user