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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 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 firing_rate(binary_spikes, dt = 0.000025, box_width = 0.01):
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(rate, dt):
freq, power = welch(rate, fs = 1/dt, nperseg = 2**16, noverlap = 2**15)
return freq, power
#find example data
datafolder = "../data"
example_file = datafolder + "/" + "2024-10-16-ad-invivo-1.nix"
#load dataset
dataset = rlx.Dataset(example_file)
# find all sams
sams = dataset.repro_runs('SAM')
sam = sams[2] # our example sam
potential,time = sam.trace_data("V-1") #membrane potential
spike_times, _ = sam.trace_data('Spikes-1') #spike times
df = sam.metadata['RePro-Info']['settings']['deltaf'][0][0] #find df in metadata
amp = sam.metadata['RePro-Info']['settings']['contrast'][0][0] * 100 #find amplitude in metadata
#figure for a quick plot
fig = plt.figure(figsize = (5, 2.5))
ax = fig.add_subplot()
ax.plot(time[time < 0.1], potential[time < 0.1]) # plot the membrane potential in 0.1s
ax.scatter(spike_times[spike_times < 0.1],
np.ones_like(spike_times[spike_times < 0.1]) * np.max(potential)) #plot teh spike times on top
plt.show()
plt.close()
# get all the stimuli
stims = sam.stimuli
# empty list for the spike times
spikes = []
#spikes2 = np.array(range(len(stims)))
# loop over the stimuli
for stim in stims:
# get the spike times
spike, _ = stim.trace_data('Spikes-1')
# append the first 100ms to spikes
spikes.append(spike[spike < 0.1])
# get stimulus duration
duration = stim.duration
ti = stim.trace_info("V-1")
dt = ti.sampling_interval # get the stimulus interval
bin_spikes = binary_spikes(spike, duration, dt) #binarize the spike_times
print(len(bin_spikes))
pot,tim= stim.trace_data("V-1") #membrane potential
rate = firing_rate(bin_spikes, dt = dt)
print(np.mean(rate))
fig, [ax1, ax2] = plt.subplots(1, 2,layout = 'constrained')
ax1.plot(tim,rate)
ax1.set_ylim(0,600)
ax1.set_xlim(0, 0.04)
freq, power = power_spectrum(rate, dt)
ax2.plot(freq,power)
ax2.set_xlim(0,1000)
# make an eventplot
fig = plt.figure(figsize = (5, 3), layout = 'constrained')
ax = fig.add_subplot()
ax.eventplot(spikes, linelength = 0.8)
ax.set_xlabel('time [ms]')
ax.set_ylabel('loop no.')
=======
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 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 firing_rate(binary_spikes, dt = 0.000025, box_width = 0.01):
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(rate, dt):
freq, power = welch(rate, fs = 1/dt, nperseg = 2**16, noverlap = 2**15)
return freq, power
#find example data
datafolder = "../data"
example_file = datafolder + "/" + "2024-10-16-ad-invivo-1.nix"
#load dataset
dataset = rlx.Dataset(example_file)
# find all sams
sams = dataset.repro_runs('SAM')
sam = sams[2] # our example sam
potential,time = sam.trace_data("V-1") #membrane potential
spike_times, _ = sam.trace_data('Spikes-1') #spike times
df = sam.metadata['RePro-Info']['settings']['deltaf'][0][0] #find df in metadata
amp = sam.metadata['RePro-Info']['settings']['contrast'][0][0] * 100 #find amplitude in metadata
#figure for a quick plot
fig = plt.figure(figsize = (5, 2.5))
ax = fig.add_subplot()
ax.plot(time[time < 0.1], potential[time < 0.1]) # plot the membrane potential in 0.1s
ax.scatter(spike_times[spike_times < 0.1],
np.ones_like(spike_times[spike_times < 0.1]) * np.max(potential)) #plot teh spike times on top
plt.show()
plt.close()
# get all the stimuli
stims = sam.stimuli
# empty list for the spike times
spikes = []
#spikes2 = np.array(range(len(stims)))
# loop over the stimuli
for stim in stims:
# get the spike times
spike, _ = stim.trace_data('Spikes-1')
# append the first 100ms to spikes
spikes.append(spike[spike < 0.1])
# get stimulus duration
duration = stim.duration
ti = stim.trace_info("V-1")
dt = ti.sampling_interval # get the stimulus interval
bin_spikes = binary_spikes(spike, duration, dt) #binarize the spike_times
print(len(bin_spikes))
pot,tim= stim.trace_data("V-1") #membrane potential
rate = firing_rate(bin_spikes, dt = dt)
print(np.mean(rate))
fig, [ax1, ax2] = plt.subplots(1, 2,layout = 'constrained')
ax1.plot(tim,rate)
ax1.set_ylim(0,600)
ax1.set_xlim(0, 0.04)
freq, power = power_spectrum(rate, dt)
ax2.plot(freq,power)
ax2.set_xlim(0,1000)
# make an eventplot
fig = plt.figure(figsize = (5, 3), layout = 'constrained')
ax = fig.add_subplot()
ax.eventplot(spikes, linelength = 0.8)
ax.set_xlabel('time [ms]')
ax.set_ylabel('loop no.')
>>>>>>> 7e02490a89d17a96689c3f0c0ad4919df2e09b93
plt.show()