348 lines
12 KiB
Python
348 lines
12 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from pathlib import Path
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from scipy.stats import linregress
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from numba import jit
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from thunderlab.tabledata import TableData
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from plotstyle import plot_style, lighter, darker
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data_path = Path('data')
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cell_path = data_path / 'cells'
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def load_models(file):
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""" Load model parameter from csv file.
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Parameters
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----------
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file: string
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Name of file with model parameters.
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Returns
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-------
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parameters: list of dict
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For each cell a dictionary with model parameters.
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"""
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parameters = []
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with file.open('r') as file:
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header_line = file.readline()
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header_parts = header_line.strip().split(",")
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keys = header_parts
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for line in file:
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line_parts = line.strip().split(",")
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parameter = {}
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for i in range(len(keys)):
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parameter[keys[i]] = float(line_parts[i]) if i > 0 else line_parts[i]
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parameters.append(parameter)
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return parameters
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def cell_parameters(parameters, cell_name):
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for params in parameters:
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if params['cell'] == cell_name:
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return params
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print('cell', cell_name, 'not found!')
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exit()
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return None
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@jit(nopython=True)
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def simulate(stimulus, deltat=0.00005, v_zero=0.0, a_zero=2.0,
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threshold=1.0, v_base=0.0, delta_a=0.08, tau_a=0.1,
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v_offset=-10.0, mem_tau=0.015, noise_strength=0.05,
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input_scaling=60.0, dend_tau=0.001, ref_period=0.001):
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""" Simulate a P-unit.
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Returns
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-------
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spike_times: 1-D array
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Simulated spike times in seconds.
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"""
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# initial conditions:
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v_dend = stimulus[0]
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v_mem = v_zero
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adapt = a_zero
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# prepare noise:
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noise = np.random.randn(len(stimulus))
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noise *= noise_strength / np.sqrt(deltat)
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# rectify stimulus array:
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stimulus = stimulus.copy()
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stimulus[stimulus < 0.0] = 0.0
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# integrate:
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spike_times = []
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for i in range(len(stimulus)):
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v_dend += (-v_dend + stimulus[i]) / dend_tau * deltat
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v_mem += (v_base - v_mem + v_offset + (
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v_dend * input_scaling) - adapt + noise[i]) / mem_tau * deltat
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adapt += -adapt / tau_a * deltat
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# refractory period:
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if len(spike_times) > 0 and (deltat * i) - spike_times[-1] < ref_period + deltat/2:
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v_mem = v_base
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# threshold crossing:
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if v_mem > threshold:
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v_mem = v_base
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spike_times.append(i * deltat)
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adapt += delta_a / tau_a
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return np.array(spike_times)
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def punit_spikes(parameter, alpha, beatf1, beatf2, tmax, trials):
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tini = 0.2
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model_params = dict(parameter)
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cell = model_params.pop('cell')
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eodf0 = model_params.pop('EODf')
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time = np.arange(-tini, tmax, model_params['deltat'])
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stimulus = np.sin(2*np.pi*eodf0*time)
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stimulus += alpha*np.sin(2*np.pi*(eodf0 + beatf1)*time)
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stimulus += alpha*np.sin(2*np.pi*(eodf0 + beatf2)*time)
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spikes = []
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for i in range(trials):
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model_params['v_zero'] = np.random.rand()
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model_params['a_zero'] += 0.02*parameter['a_zero']*np.random.randn()
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spiket = simulate(stimulus, **model_params)
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spikes.append(spiket[spiket > tini] - tini)
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return spikes
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def plot_am(ax, s, alpha, beatf1, beatf2, tmax):
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time = np.arange(0, tmax, 0.0001)
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am = alpha*np.sin(2*np.pi*beatf1*time)
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am += alpha*np.sin(2*np.pi*beatf2*time)
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ax.show_spines('l')
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ax.plot(1000*time, -100*am, **s.lsStim)
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ax.set_xlim(0, 1000*tmax)
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ax.set_ylim(-50, 50)
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#ax.set_xlabel('Time', 'ms')
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ax.set_ylabel('AM', r'\%')
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ax.text(1, 1.2, f'Contrast = {100*alpha:g}\\,\\%',
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transform=ax.transAxes, ha='right')
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def plot_raster(ax, s, spikes, tmax):
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spikes_ms = [1000*s[s<tmax] for s in spikes[:16]]
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ax.show_spines('')
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ax.eventplot(spikes_ms, linelengths=0.9, **s.lsRaster)
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ax.set_xlim(0, 1000*tmax)
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#ax.set_xlabel('Time', 'ms')
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#ax.set_ylabel('Trials')
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def compute_power(spikes, nfft, dt):
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psds = []
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time = np.arange(nfft + 1)*dt
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tmax = nfft*dt
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rates = []
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cvs = []
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for s in spikes:
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rates.append(len(s)/tmax)
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isis = np.diff(s)
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cvs.append(np.std(isis)/np.mean(isis))
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b, _ = np.histogram(s, time)
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fourier = np.fft.rfft(b - np.mean(b))
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psds.append(np.abs(fourier)**2)
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#psds.append(fourier)
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freqs = np.fft.rfftfreq(nfft, dt)
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#print('mean rate', np.mean(rates))
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#print('CV', np.mean(cvs))
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return freqs, np.mean(psds, 0)
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#return freqs, np.abs(np.mean(psds, 0))**2/dt
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def decibel(x):
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return 10*np.log10(x/1e8)
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def plot_psd(ax, s, spikes, nfft, dt, beatf1, beatf2):
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offs = 3
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freqs, psd = compute_power(spikes, nfft, dt)
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psd /= freqs[1]
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ax.plot(freqs, decibel(psd), **s.lsPower)
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ax.plot(beatf2, decibel(peak_ampl(freqs, psd, beatf2)) + offs,
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label=r'$f_{\rm base}$', clip_on=False, **s.psF0)
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ax.plot(beatf1, decibel(peak_ampl(freqs, psd, beatf1)) + offs,
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label=r'$\Delta f_1$', clip_on=False, **s.psF01)
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ax.plot(beatf2, decibel(peak_ampl(freqs, psd, beatf2)) + offs + 4.5,
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label=r'$\Delta f_2$', clip_on=False, **s.psF02)
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ax.plot(beatf2 - beatf1, decibel(peak_ampl(freqs, psd, beatf2 - beatf1)) + offs,
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label=r'$\Delta f_2 - \Delta f_1$', clip_on=False, **s.psF01_2)
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ax.plot(beatf1 + beatf2, decibel(peak_ampl(freqs, psd, beatf1 + beatf2)) + offs,
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label=r'$\Delta f_1 + \Delta f_2$', clip_on=False, **s.psF012)
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ax.set_xlim(0, 300)
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ax.set_ylim(-40, 0)
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ax.set_xlabel('Frequency', 'Hz')
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ax.set_ylabel('Power [dB]')
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def plot_example(axs, axr, axp, s, cell, alpha, beatf1, beatf2, nfft, trials):
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dt = 0.0001
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tmax = nfft*dt
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t1 = 0.1
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spikes = punit_spikes(cell, alpha, beatf1, beatf2, tmax, trials)
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plot_am(axs, s, alpha, beatf1, beatf2, t1)
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plot_raster(axr, s, spikes, t1)
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plot_psd(axp, s, spikes, nfft, dt, beatf1, beatf2)
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def peak_ampl(freqs, psd, f):
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df = 2
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psd_snippet = psd[(freqs > f - df) & (freqs < f + df)]
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return np.max(psd_snippet)
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def compute_peaks(name, cell, alpha_max, beatf1, beatf2, nfft, trials):
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data_file = cell_path / f'{name}-contrastpeaks.csv'
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data = TableData(data_file)
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return data
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"""
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if data_file.exists():
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data = TableData(data_file)
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return data
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dt = 0.0001
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tmax = nfft*dt
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alphas = np.linspace(0, alpha_max, 200)
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ampl_f1 = np.zeros(len(alphas))
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ampl_f2 = np.zeros(len(alphas))
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ampl_sum = np.zeros(len(alphas))
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ampl_diff = np.zeros(len(alphas))
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for k, alpha in enumerate(alphas):
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print(alpha)
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spikes = punit_spikes(cell, alpha, beatf1, beatf2, tmax, trials)
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freqs, psd = compute_power(spikes, nfft, dt)
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ampl_f1[k] = peak_ampl(freqs, psd, beatf1)
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ampl_f2[k] = peak_ampl(freqs, psd, beatf2)
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ampl_sum[k] = peak_ampl(freqs, psd, beatf1 + beatf2)
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ampl_diff[k] = peak_ampl(freqs, psd, beatf2 - beatf1)
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data = TableData()
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data.append('contrast', '%', '%.1f', 100*alphas)
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data.append('f1', 'Hz', '%g', ampl_f1)
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data.append('f2', 'Hz', '%g', ampl_f2)
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data.append('f1+f2', 'Hz', '%g', ampl_sum)
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data.append('f2-f1', 'Hz', '%g', ampl_diff)
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data.write(data_file)
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return data
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"""
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def amplitude(power):
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power -= power[0]
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power[power<0] = 0
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return np.sqrt(power)
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def amplitude_linearfit(contrast, power, max_contrast):
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power -= power[0]
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power[power<0] = 0
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ampl = np.sqrt(power)
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a = ampl[contrast <= max_contrast]
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c = contrast[contrast <= max_contrast]
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r = linregress(c, a)
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return r.intercept + r.slope*contrast
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def amplitude_squarefit(contrast, power, max_contrast):
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power -= power[0]
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power[power<0] = 0
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ampl = np.sqrt(power)
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a = np.sqrt(ampl[contrast <= max_contrast])
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c = contrast[contrast <= max_contrast]
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r = linregress(c, a)
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return (r.intercept + r.slope*contrast)**2
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def plot_peaks(ax, s, data, alphas):
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contrast = data[:, 'contrast']
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ax.plot(contrast, amplitude_linearfit(contrast, data[:, 'f1'], 4), **s.lsF01m)
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ax.plot(contrast, amplitude_linearfit(contrast, data[:, 'f2'], 2), **s.lsF02m)
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ax.plot(contrast, amplitude_squarefit(contrast, data[:, 'f1+f2'], 4), **s.lsF012m)
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ax.plot(contrast, amplitude_squarefit(contrast, data[:, 'f2-f1'], 4), **s.lsF01_2m)
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ax.plot(contrast, amplitude(data[:, 'f1']), **s.lsF01)
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ax.plot(contrast, amplitude(data[:, 'f2']), **s.lsF02)
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ax.plot(contrast, amplitude(data[:, 'f1+f2']), **s.lsF012)
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ax.plot(contrast, amplitude(data[:, 'f2-f1']), **s.lsF01_2)
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for alpha, tag in zip(alphas, ['A', 'B', 'C', 'D']):
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contrast = 100*alpha
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ax.plot(contrast, 630, 'vk', ms=4, clip_on=False)
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ax.text(contrast, 660, tag, ha='center')
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#ax.axvline(contrast, **s.lsGrid)
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#ax.text(contrast, 630, tag, ha='center')
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ax.axvline(1.5, **s.lsLine)
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ax.axvline(4, **s.lsLine)
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yoffs = 340
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ax.text(1.5/2, yoffs, 'linear\nregime',
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ha='center', va='center')
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ax.text((1.5 + 4)/2, yoffs, 'weakly\nnonlinear\nregime',
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ha='center', va='center')
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ax.text(10, yoffs, 'strongly\nnonlinear\nregime',
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ha='center', va='center')
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ax.set_xlim(0, 16.5)
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ax.set_ylim(0, 600)
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ax.set_xticks_delta(5)
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ax.set_yticks_delta(300)
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ax.set_xlabel('Contrast', r'\%')
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ax.set_ylabel('Amplitude', 'Hz')
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if __name__ == '__main__':
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parameters = load_models(data_path / 'punitmodels.csv')
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cell_name = '2013-01-08-aa-invivo-1' # 138Hz, CV=0.26: perfect!
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beatf1 = 40
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beatf2 = 138
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# cell_name = '2012-07-03-ak-invivo-1' # 128Hz, CV=0.24
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# cell_name = '2018-05-08-ae-invivo-1' # 142Hz, CV=0.48
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cell = cell_parameters(parameters, cell_name)
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s = plot_style()
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s.lwmid = 1.0
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s.lwthick = 1.6
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s.lsStim = dict(color='gray', lw=s.lwmid)
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s.lsRaster = dict(color='black', lw=s.lwthin)
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s.lsPower = dict(color='gray', lw=s.lwmid)
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s.lsF0 = dict(color='blue', lw=s.lwthick)
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s.lsF01 = dict(color='green', lw=s.lwthick)
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s.lsF02 = dict(color='purple', lw=s.lwthick)
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s.lsF012 = dict(color='orange', lw=s.lwthick)
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s.lsF01_2 = dict(color='red', lw=s.lwthick)
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s.lsF0m = dict(color=lighter('blue', 0.5), lw=s.lwthin)
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s.lsF01m = dict(color=lighter('green', 0.6), lw=s.lwthin)
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s.lsF02m = dict(color=lighter('purple', 0.5), lw=s.lwthin)
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s.lsF012m = dict(color=darker('orange', 0.9), lw=s.lwthin)
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s.lsF01_2m = dict(color=darker('red', 0.9), lw=s.lwthin)
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s.psF0 = dict(color='blue', marker='o', linestyle='none', markersize=5, mec='none', mew=0)
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s.psF01 = dict(color='green', marker='o', linestyle='none', markersize=5, mec='none', mew=0)
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s.psF02 = dict(color='purple', marker='o', linestyle='none', markersize=5, mec='none', mew=0)
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s.psF012 = dict(color='orange', marker='o', linestyle='none', markersize=5, mec='none', mew=0)
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s.psF01_2 = dict(color='red', marker='o', linestyle='none', markersize=5, mec='none', mew=0)
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nfft = 2**18
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fig, axs = plt.subplots(5, 4, cmsize=(s.plot_width, 0.8*s.plot_width),
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height_ratios=[1, 1.5, 2, 1.5, 4])
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fig.subplots_adjust(leftm=8, rightm=2, topm=2, bottomm=3.5,
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wspace=0.3, hspace=0.3)
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ax0 = fig.merge(axs[3, :])
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ax0.set_visible(False)
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axa = fig.merge(axs[4, :])
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fig.show_spines('lb')
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alphas = [0.01, 0.03, 0.05, 0.16]
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#alphas = [0.002, 0.01, 0.05, 0.1]
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for c, alpha in enumerate(alphas):
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plot_example(axs[0, c], axs[1, c], axs[2, c], s, cell,
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alpha, beatf1, beatf2, nfft, 100)
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axs[1, 0].xscalebar(1, -0.1, 30, 'ms', ha='right')
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axs[2, 0].legend(loc='center left', bbox_to_anchor=(0, -0.8),
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ncol=5, columnspacing=2)
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data = compute_peaks(cell_name, cell, 0.2, beatf1, beatf2, nfft, 1000)
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plot_peaks(axa, s, data, alphas)
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fig.common_yspines(axs[0, :])
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fig.common_yticks(axs[2, :])
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#fig.common_xlabels(axs[2, :])
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fig.tag(axs[0, :], xoffs=-2, yoffs=1.6)
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fig.tag(axa)
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fig.savefig()
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