Estimate Sigma.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimate sigma (noise) from the data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download data, 'pupil_subjects.csv'\n",
    "#### https://doi.org/10.6084/m9.figshare.6945824"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import Python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "load all data, create data frame with variables of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sdata = pd.read_csv('https://ndownloader.figshare.com/files/12739286')\n",
    "d = pd.DataFrame()\n",
    "d['response'] = sdata['response'] # 1 up, 0 down\n",
    "d['coh_dir'] = sdata['coherence'] / np.sign(sdata['stimulus'] - 0.5) # adds direction 1,-1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predictor 1 is 'coherence'<br>\n",
    "The dependent variable is the binary response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(d['coh_dir']) # coherence from neg to pos (coh_dir)\n",
    "y = np.array(d['response']) # up responses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit a binomial Probit function to the up responses.\n",
    "Probit is related to the normal distributions we are generating for the noise added to the DV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13\n"
     ]
    }
   ],
   "source": [
    "sm_probit_Link = sm.genmod.families.links.probit\n",
    "glm_binom = sm.GLM(y, sm.add_constant(x), family=sm.families.Binomial(link=sm_probit_Link))\n",
    "glm_result = glm_binom.fit() # USE PROBIT!\n",
    "\n",
    "d['probit'] = np.array(glm_result.fittedvalues) # get the parameter estimates for plotting curve\n",
    "\n",
    "sigma = np.round(1/glm_result.params[1],2) # first is constant, second is predictor 1\n",
    "print(sigma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot psychometric curve   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "            fig.waiting = false;\n",
       "            return;\n",
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       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
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       "        if (callback) {\n",
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       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# grouped for plotting\n",
    "grouped = d.groupby(['coh_dir'])['response','probit'].mean()       \n",
    "grouped = grouped.reset_index()\n",
    "\n",
    "xg = np.array(grouped['coh_dir'])\n",
    "yg = np.array(grouped['response'])\n",
    "zg = np.array(grouped['probit'])\n",
    "\n",
    "ax.scatter(xg,yg,s=5, label='Data')\n",
    "ax.plot(xg,zg, label='Probit fit', color='red')\n",
    "ax.legend(loc='upper left');\n",
    "ax.set_ylabel('Proportion up responses')\n",
    "ax.set_xlabel('Motion Coherence')\n",
    "ax.set_title('Psychometric Curve (N=15)')\n",
    "\n",
    "sns.despine(offset=10, trim=True)\n",
    "plt.tight_layout()\n",
    "# fig.savefig('psychometric_curve.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}