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smutch/compare_to_old.ipynb

Last active March 28, 2016 23:04
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compare_to_old.ipynb
{
"cells": [
{
"metadata": {
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},
"cell_type": "code",
"source": "from IPython.display import HTML\n\nHTML('''<script>\ncode_show=true; \nfunction code_toggle() {\n if (code_show){\n $('div.input').hide();\n } else {\n $('div.input').show();\n }\n code_show = !code_show\n} \n$( document ).ready(code_toggle);\n</script>\n<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')",
"execution_count": 1,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": "<script>\ncode_show=true; \nfunction code_toggle() {\n if (code_show){\n $('div.input').hide();\n } else {\n $('div.input').show();\n }\n code_show = !code_show\n} \n$( document ).ready(code_toggle);\n</script>\n<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>",
"text/plain": "<IPython.core.display.HTML object>"
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"execution_count": 1
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{
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"cell_type": "markdown",
"source": "**This would need to be thoroughly debugged** (I hacked Meraxes quickly to achieve this), but here is a test of tracking the redshift of reionisation on a per halo basis (as opposed to per cell). I have also tried to use the time averaged UVB background intensity instead of the value at the time the halo was first ionised. As you can see, there are differences, but I reckon on the grand scheme of things they are minor..."
},
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"metadata": {
"collapsed": false,
"trusted": true
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"cell_type": "code",
"source": "%matplotlib nbagg\n%load_ext autoreload\n%autoreload 1\n\nimport sys\nsys.path.append(\"/home/smutch/projects/meraxes/paper_plots/\")",
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": "/home/smutch/pyenv/lib/python2.7/site-packages/matplotlib/__init__.py:866: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n warnings.warn(self.msg_depr % (key, alt_key))\n",
"name": "stderr"
}
]
},
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"metadata": {
"collapsed": false,
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"source": "import matplotlib.pyplot as plt\nimport numpy as np\nfrom IPython.display import clear_output, display_html\nfrom dragons import meraxes, plotutils, nbody, munge\nfrom astropy.utils.console import ProgressBar\nimport pandas as pd\nimport seaborn as sns\nfrom collections import OrderedDict\nfrom astropy import log, cosmology\nimport tools\nfrom matplotlib.gridspec import GridSpec\nimport smf_z5\n\nsns.set(font_scale=1.3, rc={'lines.linewidth':3})\npd.set_option('mode.use_inf_as_null', True)\n\nclear_output()",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
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"cell_type": "code",
"source": "colors = sns.color_palette('deep')\nmodels = OrderedDict()\nmodels['fiducial'] = {\n 'fname': '/home/smutch/data_agama/meraxes/paper_runs/128/fiducial/output/meraxes.hdf5',\n 'plotkw': {\n 'label': 'fiducial',\n 'dashes': [],\n 'color': colors[0],\n },\n}\nmodels['new'] = {\n 'fname': 'output/meraxes.hdf5',\n 'plotkw': {\n 'label': r'$z_{\\rm reion}$ follows galaxy and use $\\left< J_{\\rm UVB} \\right>$',\n 'dashes': [8, 3],\n 'color': colors[1],\n },\n}",
"execution_count": 4,
"outputs": []
},
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"cell_type": "code",
"source": "log.setLevel('WARNING')\nfig, ax = plt.subplots(1, 1)\nweighting = \"volume\"\nxH = {}\nz_HI_50pc = {}\nfor model, aes in models.iteritems():\n fname = aes['fname']\n if model == \"fiducial\":\n # read in the model run parameters and set up the cosmology\n run_params = meraxes.read_input_params(fname)\n cosmo = cosmology.FlatLambdaCDM(H0=run_params['Hubble_h']*100.,\n Om0=run_params['OmegaM'],\n Ob0=run_params['OmegaM']\n * run_params['BaryonFrac'])\n meraxes.set_little_h(run_params[\"Hubble_h\"])\n run_params = meraxes.read_input_params(fname)\n\n outputs = meraxes.io.read_snaplist(fname)\n xH[model] = np.ones([outputs[0].max()+1, 3])\n xH[model][:, 1] = 0\n sel = np.s_[outputs[0].min():]\n xH[model][sel, 0] = outputs[1]\n xH[model][sel, 2] = outputs[2] / 1000.\n\n if weighting == \"volume\":\n xH[model][sel, 1] = meraxes.read_global_xH(fname, outputs[0], quiet=True)\n\n elif weighting == \"mass\":\n for ii, snap in tqdm(enumerate(outputs[0]), total=xH[model].shape[0]):\n try:\n xh_grid = meraxes.read_grid(fname, snap, \"xH\", quiet=True)\n density = meraxes.read_grid(fname, snap, \"deltax\", quiet=True) + 1.0\n density /= density.sum()\n xH[model][sel, 1][ii] = np.average(xh_grid, weights=density)\n except UnboundLocalError:\n pass\n\n # calculate the redshift of xH=0.5\n temp = xH[model][~np.isnan(xH[model][:, 1])]\n z_HI_50pc[model] = np.interp(0.5, temp[:, 1][::-1], temp[:, 0][::-1])\n print \"model: %10s :: z_{x_HI=0.5} = %.2f\" % (model, z_HI_50pc[model])\n\n\nfor model, aes in models.iteritems():\n kwargs = aes['plotkw']\n l, = ax.plot(xH[model][:, 0], xH[model][:, 1], **kwargs)\n\nax.legend(loc=3)\n\nax.set_ylim([1e-2, 1])\nax.set_xlim([15, 5])\nax.set_xlabel(\"redshift\")\nax.set_ylabel(r\"$\\left< x_{\\rm HI}\\right>$\")\n\nplt.setp(ax.get_yticklines(), visible=False)\nplt.setp(ax.get_xticklines(), visible=False)\n\ntools.pdf_plot(\"plots/xH_fraction.pdf\")\nplt.show()",
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function() {\n if (typeof(WebSocket) !== 'undefined') {\n return WebSocket;\n } else if (typeof(MozWebSocket) !== 'undefined') {\n return MozWebSocket;\n } else {\n alert('Your browser does not have WebSocket support.' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.');\n };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = (this.ws.binaryType != undefined);\n\n if (!this.supports_binary) {\n var warnings = document.getElementById(\"mpl-warnings\");\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent = (\n \"This browser does not support binary websocket messages. \" +\n \"Performance may be slow.\");\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = $('<div/>');\n this._root_extra_style(this.root)\n this.root.attr('style', 'display: inline-block');\n\n $(parent_element).append(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n fig.send_message(\"send_image_mode\", {});\n fig.send_message(\"refresh\", {});\n }\n\n this.imageObj.onload = function() {\n if (fig.image_mode == 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function() {\n this.ws.close();\n }\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n var titlebar = $(\n '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n 'ui-helper-clearfix\"/>');\n var titletext = $(\n '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n 'text-align: center; padding: 3px;\"/>');\n titlebar.append(titletext)\n this.root.append(titlebar);\n this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n var fig = this;\n\n var canvas_div = $('<div/>');\n\n canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n function canvas_keyboard_event(event) {\n return fig.key_event(event, event['data']);\n }\n\n canvas_div.keydown('key_press', canvas_keyboard_event);\n canvas_div.keyup('key_release', canvas_keyboard_event);\n this.canvas_div = canvas_div\n this._canvas_extra_style(canvas_div)\n this.root.append(canvas_div);\n\n var canvas = $('<canvas/>');\n canvas.addClass('mpl-canvas');\n canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n this.canvas = canvas[0];\n this.context = canvas[0].getContext(\"2d\");\n\n var rubberband = $('<canvas/>');\n rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n var pass_mouse_events = true;\n\n canvas_div.resizable({\n start: function(event, ui) {\n pass_mouse_events = false;\n },\n resize: function(event, ui) {\n fig.request_resize(ui.size.width, ui.size.height);\n },\n stop: function(event, ui) {\n pass_mouse_events = true;\n fig.request_resize(ui.size.width, ui.size.height);\n },\n });\n\n function mouse_event_fn(event) {\n if (pass_mouse_events)\n return fig.mouse_event(event, event['data']);\n }\n\n rubberband.mousedown('button_press', mouse_event_fn);\n rubberband.mouseup('button_release', mouse_event_fn);\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband.mousemove('motion_notify', mouse_event_fn);\n\n rubberband.mouseenter('figure_enter', mouse_event_fn);\n rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n canvas_div.on(\"wheel\", function (event) {\n event = event.originalEvent;\n event['data'] = 'scroll'\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n mouse_event_fn(event);\n });\n\n canvas_div.append(canvas);\n canvas_div.append(rubberband);\n\n this.rubberband = rubberband;\n this.rubberband_canvas = rubberband[0];\n this.rubberband_context = rubberband[0].getContext(\"2d\");\n this.rubberband_context.strokeStyle = \"#000000\";\n\n this._resize_canvas = function(width, height) {\n // Keep the size of the canvas, canvas container, and rubber band\n // canvas in synch.\n canvas_div.css('width', width)\n canvas_div.css('height', height)\n\n canvas.attr('width', width);\n canvas.attr('height', height);\n\n rubberband.attr('width', width);\n rubberband.attr('height', height);\n }\n\n // Set the figure to an initial 600x600px, this will subsequently be updated\n // upon first draw.\n this._resize_canvas(600, 600);\n\n // Disable right mouse context menu.\n $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n return false;\n });\n\n function set_focus () {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n}\n\nmpl.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) {\n // put a spacer in here.\n continue;\n }\n var button = $('<button/>');\n button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n 'ui-button-icon-only');\n button.attr('role', 'button');\n button.attr('aria-disabled', 'false');\n button.click(method_name, toolbar_event);\n button.mouseover(tooltip, toolbar_mouse_event);\n\n var icon_img = $('<span/>');\n icon_img.addClass('ui-button-icon-primary ui-icon');\n icon_img.addClass(image);\n icon_img.addClass('ui-corner-all');\n\n var tooltip_span = $('<span/>');\n tooltip_span.addClass('ui-button-text');\n tooltip_span.html(tooltip);\n\n button.append(icon_img);\n button.append(tooltip_span);\n\n nav_element.append(button);\n }\n\n var fmt_picker_span = $('<span/>');\n\n var fmt_picker = $('<select/>');\n fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n fmt_picker_span.append(fmt_picker);\n nav_element.append(fmt_picker_span);\n this.format_dropdown = fmt_picker[0];\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = $(\n '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n fmt_picker.append(option)\n }\n\n // Add hover states to the ui-buttons\n $( \".ui-button\" ).hover(\n function() { $(this).addClass(\"ui-state-hover\");},\n function() { $(this).removeClass(\"ui-state-hover\");}\n );\n\n var status_bar = $('<span class=\"mpl-message\"/>');\n nav_element.append(status_bar);\n this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n var size = msg['size'];\n if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n fig._resize_canvas(size[0], size[1]);\n fig.send_message(\"refresh\", {});\n };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n var x0 = msg['x0'];\n var y0 = fig.canvas.height - msg['y0'];\n var x1 = msg['x1'];\n var y1 = fig.canvas.height - msg['y1'];\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0, 0, fig.canvas.width, fig.canvas.height);\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n var cursor = msg['cursor'];\n switch(cursor)\n {\n case 0:\n cursor = 'pointer';\n break;\n case 1:\n cursor = 'default';\n break;\n case 2:\n cursor = 'crosshair';\n break;\n case 3:\n cursor = 'move';\n break;\n }\n fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n // Called whenever the canvas gets updated.\n this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n evt.data.type = \"image/png\";\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src);\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n evt.data);\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n 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\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n }\n }\n };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.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 */\nfunction 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\nmpl.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;\n var y = canvas_pos.y;\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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n // Handle any extra behaviour associated with a key event\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.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 overriden (by mpl) onmessage function.\n ws.onmessage(msg['content']['data'])\n });\n return ws;\n}\n\nmpl.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\nmpl.figure.prototype.handle_close = function(fig, msg) {\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 + '\">');\n fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n fig.send_message('closing', msg);\n // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function(el){\n var fig = this\n el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n });\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_save = function(fig, msg) {\n fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (IPython.notebook.kernel != null) {\n IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n",
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\">"
},
"metadata": {}
},
{
"output_type": "stream",
"text": "model: fiducial :: z_{x_HI=0.5} = 7.91\nmodel: new :: z_{x_HI=0.5} = 7.76\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/html": "<a href=\"#\" onclick=\"window.open('http://localhost:8881/files/plots/xH_fraction.pdf', 'NewWin');\">PDF version</a>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "meraxes.set_little_h(models[\"fiducial\"][\"fname\"])\nredshifts = [5, 7.8]\nsnapshots = [meraxes.check_for_redshift(models[\"fiducial\"][\"fname\"], z)[0] for z in redshifts]\n\nfor model in models.itervalues():\n model['params'] = meraxes.read_input_params(model[\"fname\"])",
"execution_count": 6,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "nbins = 50\nsmf = {}\nwith ProgressBar(len(snapshots)*len(models), ipython_widget=True) as pbar:\n for snapshot, redshift in zip(snapshots, redshifts):\n smf[str(redshift)] = {}\n for name, model in models.iteritems():\n fname = model['fname']\n gals = meraxes.read_gals(fname, snapshot,\n props=(\"StellarMass\", \"MaxLen\", \"GhostFlag\"),\n pandas=True)\n\n gals.StellarMass = np.log10(1e10 * gals.StellarMass).dropna()\n\n # generate mass function\n mf = munge.mass_function(gals.StellarMass,\n model[\"params\"][\"Volume\"], nbins,\n range=(6, 12),\n poisson_uncert=True)\n\n smf[str(redshift)][name] = mf\n pbar.update()",
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": "\n",
"name": "stdout"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "figsize = np.array(plt.rcParams[\"figure.figsize\"])\nfigsize[0] *= 2\nfig = plt.figure(figsize=figsize)\ngs = GridSpec(2, 2, top=0.95, bottom=0.15, left=0.05, hspace=0.015, wspace=0.02,\n width_ratios=None, height_ratios=[2.5, 1.0])\n\nfor ii, redshift in enumerate(redshifts):\n ax = fig.add_subplot(gs[ii])\n ax_diff = fig.add_subplot(gs[ii+2], sharex=ax)\n\n for name, model in models.iteritems():\n kwargs = model['plotkw']\n mf = smf[str(redshift)][name]\n \n ax.semilogy(mf[:,0], mf[:,1], nonposy='mask', **kwargs)\n \n if name not in (\"no_sn_fb\", \"no_reion_no_sn_fb\"):\n ax_diff.plot(mf[:,0], mf[:,1]/smf[str(redshift)][\"fiducial\"][:,1], **kwargs)\n\n\n ax.text(0.05, 0.05, \"z=%.1f\" % redshift, horizontalalignment=\"left\",\n verticalalignment=\"bottom\", transform=ax.transAxes)\n\n if redshift == 5:\n smf_z5.plot_obs(ax, logy=False)\n handles, _ = ax.get_legend_handles_labels()\n handles = handles[-3:]\n leg = ax.legend(loc=1, handles=handles, markerfirst=False)\n \n if ii==1:\n leg = ax.legend(loc=1, markerfirst=False)\n\n if ii==1:\n for a in (ax, ax_diff):\n a.yaxis.tick_right()\n a.yaxis.set_ticks_position(\"both\")\n a.yaxis.set_label_position(\"right\")\n\n ax.set_xlim(6, 11)\n ax.set_ylim(1e-5, 5)\n ax.set_ylabel(r\"$\\phi [\\rm Mpc^{-3} dex^{-1}]$\")\n\n plt.setp(ax.get_xticklabels(), visible=False)\n ax.get_yticklabels()[1].set_visible(False)\n\n ax_diff.set_ylim(0.7, 1.6)\n ax_diff.yaxis.set_major_locator(plt.FixedLocator([0.8, 0.6, 1.0, 1.6, 1.4, 1.2]))\n ax_diff.yaxis.set_major_formatter(plt.FormatStrFormatter('%.1f'))\n ax_diff.set_xlabel(r\"$\\log_{10}(M_*/M_\\odot)$\")\n ax_diff.set_ylabel(r'$^{\\phi}/_{\\phi_{\\rm fiducial}}$')\n \n if ii==1:\n ax_diff.get_xticklabels()[0].set_visible(False)\n\ntools.pdf_plot(\"plots/smf_multi_model.pdf\")",
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
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' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.');\n };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = (this.ws.binaryType != undefined);\n\n if (!this.supports_binary) {\n var warnings = document.getElementById(\"mpl-warnings\");\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent = (\n \"This browser does not support binary websocket messages. \" +\n \"Performance may be slow.\");\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = $('<div/>');\n this._root_extra_style(this.root)\n this.root.attr('style', 'display: inline-block');\n\n $(parent_element).append(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n fig.send_message(\"send_image_mode\", {});\n fig.send_message(\"refresh\", {});\n }\n\n this.imageObj.onload = function() {\n if (fig.image_mode == 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function() {\n this.ws.close();\n }\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n var titlebar = $(\n '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n 'ui-helper-clearfix\"/>');\n var titletext = $(\n '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n 'text-align: center; padding: 3px;\"/>');\n titlebar.append(titletext)\n this.root.append(titlebar);\n this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n var fig = this;\n\n var canvas_div = $('<div/>');\n\n canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n function canvas_keyboard_event(event) {\n return fig.key_event(event, event['data']);\n }\n\n canvas_div.keydown('key_press', canvas_keyboard_event);\n canvas_div.keyup('key_release', canvas_keyboard_event);\n this.canvas_div = canvas_div\n this._canvas_extra_style(canvas_div)\n this.root.append(canvas_div);\n\n var canvas = $('<canvas/>');\n canvas.addClass('mpl-canvas');\n canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n this.canvas = canvas[0];\n this.context = canvas[0].getContext(\"2d\");\n\n var rubberband = $('<canvas/>');\n rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n var pass_mouse_events = true;\n\n canvas_div.resizable({\n start: function(event, ui) {\n pass_mouse_events = false;\n },\n resize: function(event, ui) {\n fig.request_resize(ui.size.width, ui.size.height);\n },\n stop: function(event, ui) {\n pass_mouse_events = true;\n fig.request_resize(ui.size.width, ui.size.height);\n },\n });\n\n function mouse_event_fn(event) {\n if (pass_mouse_events)\n return fig.mouse_event(event, event['data']);\n }\n\n rubberband.mousedown('button_press', mouse_event_fn);\n rubberband.mouseup('button_release', mouse_event_fn);\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband.mousemove('motion_notify', mouse_event_fn);\n\n rubberband.mouseenter('figure_enter', mouse_event_fn);\n rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n canvas_div.on(\"wheel\", function (event) {\n event = event.originalEvent;\n event['data'] = 'scroll'\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n mouse_event_fn(event);\n });\n\n canvas_div.append(canvas);\n canvas_div.append(rubberband);\n\n this.rubberband = rubberband;\n this.rubberband_canvas = rubberband[0];\n this.rubberband_context = rubberband[0].getContext(\"2d\");\n this.rubberband_context.strokeStyle = \"#000000\";\n\n this._resize_canvas = function(width, height) {\n // Keep the size of the canvas, canvas container, and rubber band\n // canvas in synch.\n canvas_div.css('width', width)\n canvas_div.css('height', height)\n\n canvas.attr('width', width);\n canvas.attr('height', height);\n\n rubberband.attr('width', width);\n rubberband.attr('height', height);\n }\n\n // Set the figure to an initial 600x600px, this will subsequently be updated\n // upon first draw.\n this._resize_canvas(600, 600);\n\n // Disable right mouse context menu.\n $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n return false;\n });\n\n function set_focus () {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n}\n\nmpl.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) {\n // put a spacer in here.\n continue;\n }\n var button = $('<button/>');\n button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n 'ui-button-icon-only');\n button.attr('role', 'button');\n button.attr('aria-disabled', 'false');\n button.click(method_name, toolbar_event);\n button.mouseover(tooltip, toolbar_mouse_event);\n\n var icon_img = $('<span/>');\n icon_img.addClass('ui-button-icon-primary ui-icon');\n icon_img.addClass(image);\n icon_img.addClass('ui-corner-all');\n\n var tooltip_span = $('<span/>');\n tooltip_span.addClass('ui-button-text');\n tooltip_span.html(tooltip);\n\n button.append(icon_img);\n button.append(tooltip_span);\n\n nav_element.append(button);\n }\n\n var fmt_picker_span = $('<span/>');\n\n var fmt_picker = $('<select/>');\n fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n fmt_picker_span.append(fmt_picker);\n nav_element.append(fmt_picker_span);\n this.format_dropdown = fmt_picker[0];\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = $(\n '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n fmt_picker.append(option)\n }\n\n // Add hover states to the ui-buttons\n $( \".ui-button\" ).hover(\n function() { $(this).addClass(\"ui-state-hover\");},\n function() { $(this).removeClass(\"ui-state-hover\");}\n );\n\n var status_bar = $('<span class=\"mpl-message\"/>');\n nav_element.append(status_bar);\n this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n var size = msg['size'];\n if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n fig._resize_canvas(size[0], size[1]);\n fig.send_message(\"refresh\", {});\n };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n var x0 = msg['x0'];\n var y0 = fig.canvas.height - msg['y0'];\n var x1 = msg['x1'];\n var y1 = fig.canvas.height - msg['y1'];\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0, 0, fig.canvas.width, fig.canvas.height);\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n var cursor = msg['cursor'];\n switch(cursor)\n {\n case 0:\n cursor = 'pointer';\n break;\n case 1:\n cursor = 'default';\n break;\n case 2:\n cursor = 'crosshair';\n break;\n case 3:\n cursor = 'move';\n break;\n }\n fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n // Called whenever the canvas gets updated.\n this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n evt.data.type = \"image/png\";\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src);\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n evt.data);\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n 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\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n }\n }\n };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.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 */\nfunction 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\nmpl.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;\n var y = canvas_pos.y;\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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n // Handle any extra behaviour associated with a key event\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.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 overriden (by mpl) onmessage function.\n ws.onmessage(msg['content']['data'])\n });\n return ws;\n}\n\nmpl.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\nmpl.figure.prototype.handle_close = function(fig, msg) {\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 + '\">');\n fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n fig.send_message('closing', msg);\n // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function(el){\n var fig = this\n el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n });\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_save = function(fig, msg) {\n fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (IPython.notebook.kernel != null) {\n IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n",
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\">"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/html": "<a href=\"#\" onclick=\"window.open('http://localhost:8881/files/plots/smf_multi_model.pdf', 'NewWin');\">PDF version</a>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
}
],
"metadata": {
"kernelspec": {
"name": "python2",
"display_name": "Python 2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"pygments_lexer": "ipython2",
"version": "2.7.2",
"file_extension": ".py",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
},
"gist_id": "09e4c147a005659c05eb"
},
"nbformat": 4,
"nbformat_minor": 0
}
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