{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A Discrimination Experiment: Competing MCs\n", "\n", "\n", "A nice feature of MCs is the ability to superpose them without much interference. Indeed, summing two gratings generates intereference patterns (Moiré or plaids) which are interfering with the sensory processes that we study. On the contrary, MotionClouds are defined as uncoherent textures and interefrences are just happening \"by chance\"." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting ../files/experiment_competing.py\n" ] } ], "source": [ "%%writefile ../files/experiment_competing.py\n", "#!/usr/bin/env python\n", "\"\"\"\n", "\n", "Superposition of MotionClouds to generate competing motions.\n", "\n", "(c) Laurent Perrinet - INT/CNRS\n", "\n", "\"\"\"\n", "import numpy\n", "import MotionClouds as mc\n", "fx, fy, ft = mc.get_grids(mc.N_X, mc.N_Y, mc.N_frame)\n", "\n", "name = 'competing'\n", "z = (.5*mc.envelope_gabor(fx, fy, ft, sf_0=0.2, V_X=-1.5)\n", " + .1*mc.envelope_gabor(fx, fy, ft, sf_0=0.4, V_X=.5)#, theta=numpy.pi/2.)\n", " )\n", "mc.figures(z, name)\n", "mc.in_show_video(name)\n", "\n", "name = 'two_bands'\n", "# and now selecting blobs:\n", "# one band\n", "one = mc.envelope_gabor(fx, fy, ft, B_theta=10.)\n", "# a second band\n", "two = mc.envelope_gabor(fx, fy, ft, sf_0=.45, B_theta=10.)\n", "mc.figures(one + two, name)\n", "mc.in_show_video(name)\n", "\n", "# explore parameters\n", "for sf_0 in [0.0, 0.1 , 0.2, 0.3, 0.8, 0.9]:\n", " name_ = name + '-sf_0' + str(sf_0).replace('.', '_')\n", " one = mc.envelope_gabor(fx, fy, ft, B_theta=10.)\n", " two = mc.envelope_gabor(fx, fy, ft, sf_0=sf_0, B_theta=10.)\n", " mc.figures(one + two, name_)\n", " mc.in_show_video(name_)\n", "\n", "name = 'counterphase_grating'\n", "right = mc.envelope_speed(fx, fy, ft, V_X=.5 )\n", "left = mc.envelope_speed(fx, fy, ft, V_X=-.5 )\n", "grating = mc.envelope_gabor(fx, fy, ft)\n", "z = grating * (left + right ) # thanks to the addititivity of MCs\n", "mc.figures(z, name)\n", "mc.in_show_video(name)\n", "\n", "name = 'plaid'\n", "diag1 = mc.envelope_gabor(fx, fy, ft, theta=numpy.pi/4.)\n", "diag2 = mc.envelope_gabor(fx, fy, ft, theta=-numpy.pi/4.)\n", "z = (diag1 + diag2)\n", "mc.figures(z, name)\n", "mc.in_show_video(name)\n", "\n", "# explore parameters\n", "for V_X in [0., 0.5, 1.]:\n", " name_ = name + '-V_X' + str(V_X).replace('.', '_')\n", " diag = mc.envelope_gabor(fx, fy, ft, V_X=V_X)\n", " z = (diag + diag2)\n", " mc.figures(z, name_)\n", " mc.in_show_video(name_)\n", "\n", "for V_Y in [0., 0.5, 1.]:\n", " name_ = name + '-V_Y' + str(V_Y).replace('.', '_')\n", " diag = mc.envelope_gabor(fx, fy, ft, V_Y=V_Y)\n", " z = (diag + diag2)\n", " mc.figures(z, name_)\n", " mc.in_show_video(name_)\n", "\n", "for div in [1, 2, 3, 5, 8, 13 ]:\n", " name_ = name + '-theta_pi-over-' + str(div).replace('.', '_')\n", " z = (mc.envelope_gabor(fx, fy, ft, theta=numpy.pi/div) + mc.envelope_gabor(fx, fy, ft, theta=-numpy.pi/div))\n", " mc.figures(z, name_)\n", " mc.in_show_video(name)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/lolo/pool/blog/motionclouds.invibe.net/files\n" ] }, { "data": { "text/html": [ "\n", "
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We show below a simplistic script to show a MC and then exit:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting ../files/psychopy_simple.py\n" ] } ], "source": [ "%%writefile ../files/psychopy_simple.py\n", "#!/usr/bin/env python\n", "\"\"\"\n", "\n", "A basic presentation in psychopy\n", "\n", "(c) Laurent Perrinet - INT/CNRS\n", "\n", "\n", "\"\"\"\n", "# width and height of your screen\n", "w, h = 1920, 1200\n", "w, h = 2560, 1440\n", "\n", "# width and height of the stimulus\n", "w_stim, h_stim = 1024, 1024\n", "\n", "loops = 1\n", "\n", "import MotionClouds as mc\n", "fx, fy, ft = mc.get_grids(mc.N_X, mc.N_Y, mc.N_frame)\n", "env = (mc.envelope_gabor(fx, fy, ft, V_X=1.) + mc.envelope_gabor(fx, fy, ft, V_X=-1.))\n", "z = 2*mc.rectif(mc.random_cloud(env), contrast=.5) -1.\n", "\n", "#from pyglet.gl import gl_info\n", "from psychopy import visual, core, event, logging\n", "logging.console.setLevel(logging.DEBUG)\n", "\n", "win = visual.Window([w, h], fullscr=True)\n", "stim = visual.GratingStim(win, \n", " size=(w_stim, h_stim), units='pix',\n", " interpolate=True,\n", " mask='gauss',\n", " autoLog=False)#this stim changes too much for autologging to be useful\n", "\n", "for i_frame in range(mc.N_frame * loops):\n", " #creating a new stimulus every time\n", " stim.setTex(z[:, :, i_frame % mc.N_frame])\n", " stim.draw()\n", " win.flip()\n", "\n", "win.close()\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/laurentperrinet/pool/blog/motionclouds.invibe.net/files\n", "2016-11-21 09:47:00.825 Python[21683:1704885] ApplePersistenceIgnoreState: Existing state will not be touched. New state will be written to /var/folders/3p/m0g52j9j69z3gj8ktpgg1dm00000gn/T/org.python.python.savedState\n", "2.1400 \tWARNING \tMonitor specification not found. Creating a temporary one...\n", "2.1416 \tWARNING \tUser requested fullscreen with size [2560 1440], but screen is actually [1440, 900]. Using actual size\n", "4.3594 \tEXP \tCreated window1 = Window(allowGUI=True, allowStencil=False, autoLog=True, bitsMode=UNKNOWN, blendMode='avg', color=array([ 0., 0., 0.]), colorSpace='rgb', fullscr=UNKNOWN, gamma=None, lms=UNKNOWN, monitor=, name='window1', pos=[0, 0], screen=0, size=array([1440, 900]), stereo=False, units=u'norm', useFBO=False, useRetina=False, viewOri=0.0, viewPos=None, viewScale=None, waitBlanking=True, winType='pyglet')\n", "8.7605 \tEXP \twindow1: mouseVisible = True\n", "/Users/laurentperrinet/pool/blog/motionclouds.invibe.net/posts\n" ] } ], "source": [ "%cd ../files\n", "!python2 psychopy_simple.py\n", "%cd ../posts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We may now test what motion is detected when superposing 2 MCs:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting ../files/psychopy_competing.py\n" ] } ], "source": [ "%%writefile ../files/psychopy_competing.py\n", "#!/usr/bin/env python\n", "\"\"\"\n", "\n", "Using psychopy to perform an experiment on competing clouds\n", "\n", "(c) Laurent Perrinet - INT/CNRS\n", "\n", " See http://www.motionclouds.invibe.net/posts/instructions-for-psychophysicists.html for a small tutorial\n", "\n", "\"\"\"\n", "# width and height of your screen\n", "w, h = 1920, 1200\n", "w, h = 2560, 1440 # iMac 27''\n", "\n", "# width and height of the stimulus\n", "w_stim, h_stim = 1024, 1024\n", "\n", "print('launching experiment')\n", "from psychopy import visual, core, event, logging, misc\n", "logging.console.setLevel(logging.DEBUG)\n", "\n", "import os, numpy\n", "import MotionClouds as mc\n", "import time, datetime\n", "experiment = 'competing_v2_'\n", "print('launching experiment')\n", "logging.console.setLevel(logging.DEBUG)\n", "\n", "#if no file use some defaults\n", "info = {}\n", "info['observer'] = 'anonymous'\n", "info['screen_width'] = w\n", "info['screen_height'] = h\n", "info['nTrials'] = 5\n", "info['N_X'] = mc.N_X # size of image\n", "info['N_Y'] = mc.N_Y # size of image\n", "info['N_frame_total'] = 64 # a full period. in time frames\n", "info['N_frame'] = 64 # length of the presented period. in time frames\n", "info['timeStr'] = datetime.datetime.now().date().isoformat() + time.strftime(\"_%H%M\", time.localtime())\n", "fileName = 'data/' + experiment + info['observer'] + '_' + info['timeStr'] + '.pickle'\n", "\n", "print('generating data')\n", "fx, fy, ft = mc.get_grids(info['N_X'], info['N_Y'], info['N_frame_total'])\n", "up = 2*mc.rectif(mc.random_cloud(mc.envelope_gabor(fx, fy, ft, V_X=+.5))) - 1\n", "down = 2*mc.rectif(mc.random_cloud(mc.envelope_gabor(fx, fy, ft, V_X=-.5))) - 1\n", "\n", "print('go! ')\n", "win = visual.Window([info['screen_width'], info['screen_height']], fullscr=True)\n", "\n", "stim = visual.GratingStim(win,\n", " size=(info['screen_height'], info['screen_height']), units='pix',\n", " interpolate=True,\n", " mask = 'gauss',\n", " autoLog=False)#this stim changes too much for autologging to be useful\n", "\n", "wait_for_response = visual.TextStim(win,\n", " text = u\"?\", units='norm', height=0.15, color='DarkSlateBlue',\n", " pos=[0., -0.], alignHoriz='center', alignVert='center' )\n", "wait_for_next = visual.TextStim(win,\n", " text = u\"+\", units='norm', height=0.15, color='BlanchedAlmond',\n", " pos=[0., -0.], alignHoriz='center', alignVert='center' )\n", "\n", "def getResponse():\n", " event.clearEvents()#clear the event buffer to start with\n", " resp = None#initially\n", " while 1:#forever until we return a keypress\n", " for key in event.getKeys():\n", " #quit\n", " if key in ['escape', 'q']:\n", " win.close()\n", " core.quit()\n", " return None\n", " #valid response - check to see if correct\n", " elif key in ['down', 'up']:\n", " if key in ['down'] :return -1\n", " else: return 1\n", " else:\n", " print( \"hit DOWN or UP (or Esc) (You hit %s)\" %key)\n", "\n", "clock = core.Clock()\n", "FPS = 50.\n", "def presentStimulus(C_A, C_B):\n", " \"\"\"Present stimulus\n", " \"\"\"\n", " phase_up = numpy.floor(numpy.random.rand() *(info['N_frame_total']-info['N_frame']))\n", " phase_down = numpy.floor(numpy.random.rand() *(info['N_frame_total']-info['N_frame']))\n", " clock.reset()\n", " for i_frame in range(info['N_frame']): # length of the stimulus\n", " stim.setTex(C_A * up[:, :, i_frame+phase_up]+C_B * down[:, :, i_frame+phase_down])\n", " stim.draw()\n", "# while clock.getTime() < i_frame/FPS:\n", "# print clock.getTime(), i_frame/FPS\n", "# print('waiting')\n", " win.flip()\n", "\n", "results = numpy.zeros((2, info['nTrials']))\n", "for i_trial in range(info['nTrials']):\n", " wait_for_next.draw()\n", " win.flip()\n", " core.wait(0.5)\n", " C_A = numpy.random.rand() # a random number between 0 and 1\n", " presentStimulus(C_A, 1. - C_A)\n", " wait_for_response.draw()\n", " win.flip()\n", " ans = getResponse()\n", " results[0, i_trial] = ans\n", " results[1, i_trial] = C_A\n", "\n", "win.update()\n", "core.wait(0.5)\n", "\n", "win.close()\n", "\n", "#save data\n", "fileName = 'data/' + experiment + info['observer'] + '_' + info['timeStr'] + '.npy'\n", "numpy.save(fileName, results)\n", "misc.toFile(fileName.replace('npy', 'pickle'), info)\n", "\n", "\n", "core.quit() # quit" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/laurentperrinet/pool/blog/motionclouds.invibe.net/files\n", "launching experiment\n", "2016-11-21 09:47:25.929 Python[21721:1705469] ApplePersistenceIgnoreState: Existing state will not be touched. New state will be written to /var/folders/3p/m0g52j9j69z3gj8ktpgg1dm00000gn/T/org.python.python.savedState\n", "launching experiment\n", "generating data\n", "go! \n", "psychopy_competing.py:89: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", " stim.setTex(C_A * up[:, :, i_frame+phase_up]+C_B * down[:, :, i_frame+phase_down])\n", "44.2654 \tWARNING \tMonitor specification not found. Creating a temporary one...\n", "44.2670 \tWARNING \tUser requested fullscreen with size [2560 1440], but screen is actually [1440, 900]. Using actual size\n", "62.4625 \tEXP \tCreated window1 = Window(allowGUI=True, allowStencil=False, autoLog=True, bitsMode=UNKNOWN, blendMode='avg', color=array([ 0., 0., 0.]), colorSpace='rgb', fullscr=UNKNOWN, gamma=None, lms=UNKNOWN, monitor=, name='window1', pos=[0, 0], screen=0, size=array([1440, 900]), stereo=False, units=u'norm', useFBO=False, useRetina=False, viewOri=0.0, viewPos=None, viewScale=None, waitBlanking=True, winType='pyglet')\n", "62.8206 \tEXP \tCreated unnamed TextStim = TextStim(alignHoriz='center', alignVert='center', antialias=True, autoLog=True, bold=False, color='DarkSlateBlue', colorSpace='named', contrast=1.0, depth=0, flipHoriz=False, flipVert=False, font='', fontFiles=[], height=0.15, italic=False, name='unnamed TextStim', opacity=1.0, ori=0.0, pos=array([ 0., -0.]), rgb=array([ 72., 61., 139.]), text=u'?', units='norm', win=Window(...), wrapWidth=1)\n", "62.9088 \tEXP \tCreated unnamed TextStim = TextStim(alignHoriz='center', alignVert='center', antialias=True, autoLog=True, bold=False, color='BlanchedAlmond', colorSpace='named', contrast=1.0, depth=0, flipHoriz=False, flipVert=False, font='', fontFiles=[], height=0.15, italic=False, name='unnamed TextStim', opacity=1.0, ori=0.0, pos=array([ 0., -0.]), rgb=array([ 255., 235., 205.]), text=u'+', units='norm', win=Window(...), wrapWidth=1)\n", "404.5721 \tDATA \tKeypress: escape\n", "404.5723 \tEXP \twindow1: mouseVisible = True\n", "/Users/laurentperrinet/pool/blog/motionclouds.invibe.net/posts\n" ] } ], "source": [ "%cd ../files\n", "!python2 psychopy_competing.py\n", "%cd ../posts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysis of the Discrimination Experiment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the ``psychopy_competition.py`` script, we implemented an experiment to test whether one could discriminate the dominating motion in a sum of two motion clouds in opposite directions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Herein, we analyse the data that was collected over different sessions and try to draw some conclusions." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "np.set_printoptions(precision=3, suppress=True)\n", "import pylab\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysis, version 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a first version of the experiment, we only stored the results in a numpy file:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "../files/data/competing_v1_bruno_Dec_14_1210.npy\r\n", "../files/data/competing_v1_lup_Dec_12_1003.npy\r\n", "../files/data/competing_v1_lup_Dec_12_1013.npy\r\n", "../files/data/competing_v1_lup_Dec_14_1201.npy\r\n", "../files/data/competing_v1_meduz_Dec_14_1204.npy\r\n" ] } ], "source": [ "!ls ../files/data/competing_v1_*npy" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "experiment ../files/data/competing_v1_bruno_Dec_14_1210.npy , # trials= 50\n", "experiment ../files/data/competing_v1_lup_Dec_12_1003.npy , # trials= 50\n", "experiment ../files/data/competing_v1_lup_Dec_12_1013.npy , # trials= 50\n", "experiment ../files/data/competing_v1_lup_Dec_14_1201.npy , # trials= 10\n", "experiment ../files/data/competing_v1_meduz_Dec_14_1204.npy , # trials= 50\n" ] }, { "data": { "image/png": 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YpJIkaWCm2iTZ2dS+FXqPzNwVEdvry9q5FNgC/Ax4GvAp4InAiycoTkmSNECV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\neBPw78Bc4NyIuCszHfPJE/W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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import glob\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "for fn in glob.glob('../files/data/competing_v1_*npy'):\n", " results = np.load(fn)\n", " print ('experiment ', fn, ', # trials=', results.shape[1])\n", " ax.scatter(results[1, :], results[0, :])\n", "_ = ax.axis([0., 1., -1.1, 1.1])\n", "_ = ax.set_xlabel('contrast')\n", "_ = ax.set_ylabel('perceived direction')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ooCoqio60NFLGjeNMQ0Ov5YYisXvQuNAYSElh65kzLJw/H2sMF6uqemLq1U7J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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "alpha = .3\n", "fig = plt.figure(figsize=(12,5))\n", "ax1 = fig.add_subplot(121)\n", "ax2 = fig.add_subplot(122)\n", "data = []\n", "for fn in glob.glob('../files/data/competing_v1_*npy'):\n", " results = np.load(fn)\n", " data_ = np.empty(results.shape)\n", " # lower stronger, detected lower = CORRECT is 1\n", " ax1.scatter(results[1, results[1, :]<.5], \n", " 1.*(results[0, results[1, :]<.5]==-1), \n", " c='r', alpha=alpha)\n", " # copying data: contrast (from .5 to 1), correct \n", " data_[0, results[1, :]<.5] = 1. - results[1, results[1, :]<.5]\n", " data_[1, results[1, :]<.5] = 1.*(results[0, results[1, :]<.5]==-1)\n", " # upper stronger, detected lower = INCORRECT is 1\n", " ax1.scatter(results[1, results[1, :]>.5], \n", " 1.*(results[0, results[1, :]>.5]==-1), \n", " c='b', alpha=alpha)\n", " # lower stronger, detected upper = INCORRECT is 1\n", " ax2.scatter(results[1, results[1, :]<.5], \n", " 1.*(results[0, results[1, :]<.5]==1), \n", " c='b', alpha=alpha, marker='x')\n", " # upper stronger, detected upper = CORRECT is 1\n", " ax2.scatter(results[1, results[1, :]>.5], \n", " 1.*(results[0, results[1, :]>.5]==1), \n", " c='r', alpha=alpha, marker='x')\n", " # copying data: contrast (from .5 to 1), correct \n", " data_[0, results[1, :]>=.5] = results[1, results[1, :]>=.5]\n", " data_[1, results[1, :]>=.5] = 1.*(results[0, results[1, :]>=.5]==1)\n", " data.append(data_)\n", "\n", "for ax in [ax1, ax2]:\n", " ax.axis([0., 1., -.1, 1.1])\n", " _ = ax.set_xlabel('contrast')\n", "_ = ax1.set_ylabel('detected lower')\n", "_ = ax1.legend(['lower', 'upper'], loc='right')\n", "_ = ax2.set_ylabel('detected upper')\n", "_ = ax2.legend(['lower', 'upper'], loc='right')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(note the subplots are equivalent up to a flip)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One could not fit psychometric curves for this 2AFC. See for instance https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3110332/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TODO: look at http://nbviewer.ipython.org/github/arokem/teach_optimization/blob/master/optimization.ipynb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "let's explore another way:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pylab\n", "from scipy.optimize import curve_fit\n", " \n", "def sigmoid(c, c0, k):\n", " y = 1 / (1 + np.exp(-k*(c-c0)))\n", " return y\n", " " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "../files/data/competing_v2_anonymous_2015-07-01.npy\r\n", "../files/data/competing_v2_anonymous_2015-07-01.pickle\r\n", "../files/data/competing_v2_anonymous_2015-07-01_1344.npy\r\n", "../files/data/competing_v2_anonymous_2015-07-01_1344.pickle\r\n", "../files/data/competing_v2_jean_Sep_03_1536.npy\r\n", "../files/data/competing_v2_jean_Sep_03_1536.pickle\r\n", "../files/data/competing_v2_laurent_Sep_17_1522.npy\r\n", "../files/data/competing_v2_laurent_Sep_17_1522.pickle\r\n" ] } ], "source": [ "!ls ../files/data/competing_v2_*" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.291 6.067]\n", "[ 0.345 13.492]\n", "[ 0.287 39.2 ]\n", "[ 0.637 160.796]\n", "[ 0.362 13.633]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.5/site-packages/matplotlib/axes/_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", " warnings.warn(\"No labelled objects found. \"\n" ] }, { "data": { "image/png": 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Buoqfi0EfpR0rf+nNeu1zhLfScZ3QHrw6AYoLdIYqxyh7VzXlR0Ggq9tNr1GwmTK7QgMq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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for fn in glob.glob('../files/data/competing_v1_*npy'):\n", " results = np.load(fn)\n", " cdata, ydata = results[1, :], .5*results[0, : ]+.5\n", " pylab.plot(cdata, ydata, 'o')\n", "\n", " popt, pcov = curve_fit(sigmoid, cdata, ydata)\n", " print (popt)\n", " c = np.linspace(0, 1, 50)\n", " y = sigmoid(c, *popt)\n", " pylab.plot(c, y)\n", " \n", "pylab.ylim(-.05, 1.05)\n", "pylab.legend(loc='best')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysis, version 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the second version, we also stored some information about the experiment" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'nTrials': 5, 'N_frame_total': 64, 'timeStr': '2015-07-01', 'N_Y': 256, 'N_X': 256, 'N_frame': 64, 'screen_width': 2560, 'screen_height': 1440, 'observer': 'anonymous'}\n", "{'nTrials': 5, 'N_frame_total': 64, 'timeStr': '2015-07-01_1344', 'N_Y': 256, 'N_X': 256, 'N_frame': 64, 'screen_width': 2560, 'screen_height': 1440, 'observer': 'anonymous'}\n", "{'nTrials': 50, 'N_frame_total': 128, 'timeStr': 'Sep_03_1536', 'N_Y': 128, 'N_X': 128, 'N_frame': 128, 'screen_width': 2560, 'screen_height': 1440, 'observer': 'jean'}\n", "{'nTrials': 50, 'N_frame_total': 128, 'timeStr': 'Sep_17_1522', 'N_Y': 128, 'N_X': 128, 'N_frame': 128, 'screen_width': 2560, 'screen_height': 1440, 'observer': 'laurent'}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.5/site-packages/matplotlib/axes/_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", " warnings.warn(\"No labelled objects found. \"\n" ] }, { "data": { "image/png": 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ZTJhQjMMhk16IKKOUirgHMA5QxcXFqruceuqp6pZbblFz585VSUlJKj09Xf3x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\nEoTcXGO56y644ALI93Crjffee89tUujAwed8zlKWdq2b5ZjFs6XPMss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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pickle\n", "#from psychopy import misc\n", "mean, slope = [], []\n", "for fn in glob.glob('../files/data/competing_v2_*npy'):\n", " results = np.load(fn)\n", " #data = misc.fromFile(fn.replace('npy', 'pickle'))\n", " with open(fn.replace('npy', 'pickle'), 'rb') as f: data = pickle.load(f)\n", " print (data)\n", " cdata, ydata = results[1, :], .5*results[0, : ]+.5\n", " pylab.plot(cdata, ydata, 'o')\n", "\n", " popt, pcov = curve_fit(sigmoid, cdata, ydata)\n", " mean.append(popt[0])\n", " slope.append(popt[1])\n", " c = np.linspace(0, 1, 50)\n", " y = sigmoid(c, *popt)\n", " pylab.plot(c, y)\n", "\n", "pylab.text(0.05, 0.8, 'mean : %0.2f , slope : %0.2f ' %(np.mean(mean), np.mean(slope)))\n", "pylab.ylim(-.05, 1.05)\n", "pylab.legend(loc='best')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 0 }