{ "metadata": { "name": "", "signature": "sha256:a7d490a0ad483632848c8e472492012a8b333a472de76ff8227087564dfcf4c0" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "Fourier transform deconvolution\n", "-------------------------------\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import os\n", "import numpy as np\n", "import scipy as sp\n", "import scipy.stats as stats\n", "import scipy.signal as signal\n", "import matplotlib.pyplot as plt\n", "from sympy import *\n", "import math\n", "# import mne.filter" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "raw", "metadata": {}, "source": [ "Let's do inline plotting:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "raw", "metadata": {}, "source": [ "Let's first create and plot the true pupil response to a transient input. This is called an \"Inpulse Response Function\" (IRF)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# define an Impulse Response Function:\n", "def pupil_IRF(timepoints, s=1.0/(10**26), n=10.1, tmax=0.93):\n", " \n", " \"\"\" pupil_IRF defines the IRF (response to a transient input) of the pupil.\n", " \n", " Parameters\n", " ----------\n", " t: IRF is defined with respect to 't'\n", " s: scaling factor\n", " n: sets the width\n", " tmax: sets the time to peak \n", " \n", " Returns\n", " -------\n", " y: IRF evaluated for t = [0:IRF_len]\n", " yprime: IRF first derivative evaluated for t = [0:IRF_len]\n", " \n", " \"\"\"\n", " \n", " # in sympy:\n", " t = Symbol('t')\n", " y = ( (s) * (t**n) * (math.e**((-n*t)/tmax)) )\n", " yprime = y.diff(t)\n", " \n", " # lambdify:\n", " y = lambdify(t, y, \"numpy\")\n", " yprime = lambdify(t, yprime, \"numpy\")\n", " \n", " # evaluate:\n", " y = y(timepoints)\n", " yprime = yprime(timepoints)\n", " \n", " return (y, yprime)\n", "\n", "# create the IRF:\n", "sample_rate = 10\n", "IRF_len = 3.0 # in seconds\n", "timepoints = np.linspace(0,IRF_len,IRF_len*sample_rate)\n", "\n", "IRF, IRF_prime = pupil_IRF(timepoints=timepoints)\n", "IRF = IRF / IRF.std()\n", "IRF_prime = IRF_prime / IRF_prime.std()\n", "\n", "# plot the IRF:\n", "fig = plt.figure()\n", "plt.plot(timepoints, IRF, color='r')\n", "# plt.plot(timepoints, IRF_prime, color='g')\n", "plt.legend(['IRF'])\n", "plt.title('Impulse Response Function')\n", "plt.xlabel('time (s)')\n", "plt.ylabel('a.u.')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 6 }, { "cell_type": "raw", "metadata": {}, "source": [ "Let's simulate convolved timeseries data based this IRF." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# input:\n", "nr_events = 20\n", "transient_times = np.cumsum(np.repeat(6, nr_events)) # in seconds\n", "ramp_durs = np.repeat(1.5, nr_events) # in seconds\n", "ramp_times = transient_times - ramp_durs\n", "ramp_heights = 1 / ramp_durs / 2.0\n", "duration = transient_times[-1] + 10 # in seconds\n", "input_signal_transient = np.zeros(duration*sample_rate)\n", "input_signal_ramp = np.zeros(duration*sample_rate)\n", "for i in range(len(transient_times)):\n", " t_time = int(transient_times[i]*sample_rate)\n", " r_time = int(ramp_times[i]*sample_rate)\n", " r_height = ramp_heights[i]\n", " input_signal_transient[t_time] = 1\n", " input_signal_ramp[r_time:t_time] = np.linspace(0,r_height,ramp_durs[i]*sample_rate)\n", "input_signal = input_signal_transient + input_signal_ramp\n", "\n", "# plot input\n", "timepoints = np.linspace(0,duration,duration*sample_rate)\n", "fig = plt.figure()\n", "plt.plot(timepoints, input_signal, 'r')\n", "plt.legend(['input signal'], loc=2)\n", "plt.title('input signal')\n", "plt.xlabel('time (s)')\n", "plt.ylabel('a.u.')\n", "\n", "# convolve inputs with IRF: \n", "convolved_signal = (sp.convolve(input_signal, IRF, 'full'))[:-(IRF.shape[0]-1)]\n", "\n", "# plot simulated convolved signal without noise:\n", "fig = plt.figure()\n", "plt.plot(timepoints, input_signal, 'r')\n", "plt.plot(timepoints, convolved_signal)\n", "plt.legend(['input signal', 'simulated convolved signal'], loc=2)\n", "plt.title('simulated convolved signal without noise')\n", "plt.xlabel('time (s)')\n", "plt.ylabel('a.u.')\n", "\n", "# let's add some noise:\n", "convolved_signal_noise = convolved_signal + np.random.normal(0,0.125,len(convolved_signal))\n", "\n", "# plot simulated convolved signal with noise:\n", "fig = plt.figure()\n", "plt.plot(timepoints, input_signal, 'r')\n", "plt.plot(timepoints, convolved_signal_noise)\n", "plt.legend(['input signal', 'simulated convolved signal'], loc=2)\n", "plt.title('simulated convolved signal with noise')\n", "plt.xlabel('time (s)')\n", "plt.ylabel('a.u.')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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bCwqC7fhbWrzr1B3G8RsYBBTJOOG8PMjPD7bjT0an7sjaHH+2PWfdwEA3hFMZ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\nPGu/14l7bxF5UkRWisgKETlGM/4z7bHzoYjMFZH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77rsvhx12WMZz20Nrq/KdBZ0wdS0Fe2IP06cgpZ4A07VMcgK6W7aF8UeOb8cZ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\nk1rOKRRsLdWL/v0Vnektu+uuql1tbYrq8qKiT5uiCj3lSkq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"text": [ "" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's epoch the data and compute mean response:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# times for epoching:\n", "epoch_times = [-3, 3] # in seconds\n", "\n", "# mean input:\n", "epochs = np.vstack([input_signal[i+(epoch_times[0]*sample_rate):i+(epoch_times[1]*sample_rate)] for i in transient_times*sample_rate])\n", "mean_input = np.mean(epochs, axis=0)\n", "\n", "# mean response:\n", "epochs = np.vstack([convolved_signal_noise[i+(epoch_times[0]*sample_rate):i+(epoch_times[1]*sample_rate)] for i in transient_times*sample_rate])\n", "mean_response = np.mean(epochs, axis=0)\n", "\n", "# plot mean response versus IRF:\n", "timepoints = np.linspace(epoch_times[0],epoch_times[1],(epoch_times[1]-epoch_times[0])*sample_rate)\n", "fig = plt.figure()\n", "plt.plot(timepoints, mean_input, color='r')\n", "plt.plot(timepoints, mean_response, color='b')\n", "plt.axvline(0, color='k')\n", "plt.legend(['mean response'], loc=2)\n", "plt.title('mean response')\n", "plt.xlabel('time from response (s)')\n", "plt.ylabel('a.u.')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is uninformative! Due to the sluggishness of our impulse response function, it is hard to see what exactly is driving this mean response. Something seems to be going on already before t=0, but what exactly? \n", "\n", "Let's do fft deconvolution!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given that fft(IRF) has low power at high frequencies, we should filter all the high frequency noise from our measured time series. Let's first determine a sensible cut-off frequency: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "freqs = np.fft.fftfreq(int(IRF_len*sample_rate), 1.0/sample_rate)\n", "max_freq = np.ceil(max(freqs))\n", "print max_freq\n", "fig = plt.figure()\n", "plt.plot(freqs, abs(np.fft.fft(IRF)))\n", "plt.axvline(max_freq, color='r')\n", "plt.xlabel('frequencies (hz)')\n", "plt.ylabel('power')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "5.0\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# filter the data:\n", "def butter_lowpass(lowcut, fs, order=3):\n", " nyq = 0.5 * fs\n", " low = lowcut / nyq\n", " b, a = signal.butter(order, low, btype='low')\n", " return b, a\n", "\n", "def butter_lowpass_filter(data, lowcut, fs, order=3):\n", " b, a = butter_lowpass(lowcut, fs, order=order)\n", " y = signal.filtfilt(b, a, data)\n", " return y\n", "\n", "lowcut = 2.0 * sample_rate\n", "fs = convolved_signal_noise.shape[0] / sample_rate\n", "convolved_signal_filtered = butter_lowpass_filter(convolved_signal_noise, lowcut, fs, order=3)\n", "\n", "convolved_signal_filtered[convolved_signal==0] = 0\n", "\n", "\n", "# Fp = 1.0\n", "# convolved_signal_filtered = mne.filter.low_pass_filter(convolved_signal_noise, Fs=sample_rate, Fp=Fp)\n", "\n", "# plt.plot(convolved_signal_noise)\n", "plt.figure()\n", "plt.plot(convolved_signal_noise)\n", "plt.plot(convolved_signal_filtered, 'r', lw=2)\n", "plt.xlabel('time (s)')\n", "plt.xlim(xmax=120)\n", "plt.ylabel('a.u.')\n", "# plt.legend(['true convolved signal', 'filtered signal, {} hz'.format(Fp)])\n", "\n", "# freqs = np.fft.fftfreq(convolved_signal_filtered.shape[0], 1.0/sample_rate)\n", "# plt.figure()\n", "# plt.plot(freqs, abs(np.fft.fft(convolved_signal_filtered)))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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0Y6/NkShbOD+bQZe5ZwJEVRbZ8ZRi4E8issEYEw6sNcZ8LyLbbYxJqQoV/7iC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\nqV87IMMYM80Ys84Y84ExJox6UD8ROQa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"text": [ "" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "# times for epoching:\n", "epoch_times = [-3, 3] # in seconds\n", "\n", "# mean input:\n", "epochs = np.vstack([input_signal[i+(epoch_times[0]*sample_rate):i+(epoch_times[1]*sample_rate)] for i in transient_times*sample_rate])\n", "mean_input = np.mean(epochs, axis=0)\n", "\n", "# mean response:\n", "epochs = np.vstack([convolved_signal_filtered[i+(epoch_times[0]*sample_rate):i+(epoch_times[1]*sample_rate)] for i in transient_times*sample_rate])\n", "mean_response = np.mean(epochs, axis=0)\n", "\n", "# plot mean response versus IRF:\n", "timepoints = np.linspace(epoch_times[0],epoch_times[1],(epoch_times[1]-epoch_times[0])*sample_rate)\n", "fig = plt.figure()\n", "plt.plot(timepoints, mean_input, color='r')\n", "plt.plot(timepoints, mean_response, color='b')\n", "plt.axvline(0, color='k')\n", "plt.legend(['mean response'], loc=2)\n", "plt.title('mean response')\n", "plt.xlabel('time from response (s)')\n", "plt.ylabel('a.u.')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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zN7+p+Xkr+zTOW2/BVVfBggW+I8ls9Sn7WPI3ppo1a+DQQ2HZMjcsQU0s+TdO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\nkr8xxmQhS/7GGJOF/j9OeILEy/PAcgAAAABJRU5ErkJggg==\n", "text": [ "" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "convolved_signal_fft = np.fft.fft(mean_response)\n", "IRF_fft = np.fft.fft(IRF, mean_response.shape[0])\n", "D = np.fft.ifft(convolved_signal_fft / IRF_fft)\n", "\n", "plt.figure()\n", "timepoints = np.linspace(0,mean_response.shape[0]/mean_response.shape[0],mean_response.shape[0])\n", "plt.plot(timepoints, mean_input, 'r')\n", "plt.plot(timepoints, D, 'k')\n", "plt.legend(['true input signal', 'fft deconvolved filtered signal'], loc=2)\n", "plt.title('simulated convolved signal with noise')\n", "plt.xlabel('time (s)')\n", "plt.ylabel('a.u.')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 82, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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eli9fDj8/P1W7RWnF0EiDQmnBlJWVwdPTE4mJidDV1cWIESMQHh4OAwMDVbtGaaVQ0aBQ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\nZyMhIQFlZWX4+OOPMWXKFEyfPh1tbW0AgLq6OsydOxeZmZm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"text": [ "" ] } ], "prompt_number": 82 }, { "cell_type": "code", "collapsed": false, "input": [ "convolved_signal_fft = np.fft.fft(convolved_signal)\n", "IRF_fft = np.fft.fft(IRF, convolved_signal.shape[0])\n", "D = np.fft.ifft(convolved_signal_fft / IRF_fft)\n", "\n", "plt.figure()\n", "timepoints = np.linspace(0,duration,duration*sample_rate)\n", "plt.plot(timepoints, input_signal, 'r')\n", "plt.plot(timepoints, D, 'k')\n", "plt.legend(['true input signal', 'fft deconvolved convolved signal'], loc=2)\n", "plt.title('simulated convolved signal with noise')\n", "plt.xlabel('time (s)')\n", "plt.ylabel('a.u.')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 83, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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5cychhJCjR4+SiIgIQkj9za1z586kqqpKOHbPnj3kgQceIIQQ8sADDwiOxChr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\nyNbobKwcmZfqioBer8fcuXORmpqKS5cuYe/evbh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