fig06_smrt_memls.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "# general purpose imports\n",
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import scipy\n",
    "\n",
    "# SMRT imports\n",
    "from smrt import make_snowpack, sensor_list, make_model\n",
    "from smrt.utils import dB\n",
    "\n",
    "# MEMLS bindings import\n",
    "# you can disable MEMLS by setting to False\n",
    "have_memls = True\n",
    "# note that to import this binding, you need to install oct2py.\n",
    "if have_memls:\n",
    "    from smrt.utils import memls_legacy\n",
    "\n",
    "\n",
    "# Graphics import\n",
    "import matplotlib.pyplot as plt\n",
    "color = ['#1f77b4','#ff7f0e','#2ca02c','#d62728']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## prepare inputs: snowpack with two layers\n",
    "\n",
    "density = [300]\n",
    "temperature = [265]\n",
    "thickness = [200]\n",
    "corr_length = [100e-6]\n",
    "\n",
    "snowpack = make_snowpack(thickness, \"exponential\",\n",
    "                        density=density, temperature=temperature, corr_length=corr_length)\n",
    "\n",
    "# create the sensor\n",
    "theta = np.arange(10, 65, 5)\n",
    "radiometer = sensor_list.passive(37e9, theta)\n",
    "radar = sensor_list.active(37e9, theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "        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",
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       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
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       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
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       "    var fmt_picker = $('<select/>');\n",
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       "    fmt_picker_span.append(fmt_picker);\n",
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       "    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",
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       "\n",
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       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
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       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
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       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
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       "\n",
       "\n",
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       "    var format_dropdown = fig.format_dropdown;\n",
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       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
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       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
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       "\n",
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       "    y1 = Math.floor(y1) + 0.5;\n",
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       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.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",
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       "        cursor = 'default';\n",
       "        break;\n",
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       "        cursor = 'crosshair';\n",
       "        break;\n",
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       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.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.\n",
       "mpl.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\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 overriden (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=\"550\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSD(MEMLS-SMRT IBA EXP) 1.2646838442759363 1.3764158108356146\n"
     ]
    }
   ],
   "source": [
    "# create the SMRT Model with emmodel=\"dmrt_qcacp_shortrange\" and rtsolver=\"dort\"\n",
    "m_orig = make_model(\"iba_original\", \"dort\")\n",
    "m = make_model(\"iba\", \"dort\")\n",
    "\n",
    "\n",
    "# run the model\n",
    "res_orig_p = m_orig.run(radiometer, snowpack)\n",
    "res_p = m.run(radiometer, snowpack)\n",
    "\n",
    "# plots\n",
    "f, axs =  plt.subplots(1, 1, figsize=(5.5,5))\n",
    "axs = [axs]\n",
    "\n",
    "axs[0].plot(theta, res_orig_p.TbV(), label='SMRT IBA Original V', color=color[0])\n",
    "axs[0].plot(theta, res_orig_p.TbH(), label='SMRT IBA Original H', color=color[1])\n",
    "axs[0].plot(theta, res_p.TbV(), '--', label='SMRT IBA V', color=color[0], alpha=0.5)\n",
    "axs[0].plot(theta, res_p.TbH(), '--', label='SMRT IBA H', color=color[1], alpha=0.5)\n",
    "\n",
    "\n",
    "axs[0].set_xlabel('Incidence angle (Degree)')\n",
    "axs[0].set_ylabel ('Brightness temperature (K)')\n",
    "axs[0].legend(loc='best')\n",
    "\n",
    "print(\"RMSD(MEMLS-SMRT IBA EXP)\", rmsd(res_orig_p.TbV()-res_p.TbV()), rmsd(res_orig_p.TbH()-res_p.TbH()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rmsd(x):\n",
    "    return float(np.sqrt(np.mean(np.square(x))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no coherent layers detected: 37 GHz\n",
      "no coherent layers detected: 37 GHz\n",
      "no coherent layers detected: 37 GHz\n",
      "no coherent layers detected: 37 GHz\n",
      "no coherent layers detected: 37 GHz\n",
      "no coherent layers detected: 37 GHz\n",
      "no coherent layers detected: 37 GHz\n",
      "no coherent layers detected: 37 GHz\n",
      "no coherent layers detected: 37 GHz\n",
      "no coherent layers detected: 37 GHz\n",
      "no coherent layers detected: 37 GHz\n",
      "RMSD(MEMLS-SMRT IBA EXP) 1.1873990188175292 1.618573419596577\n",
      "epsi =  1.5244\n",
      "MEMLS ks, ka= memls_emmodel(ks=0.20542420725262811, ka=0.30921544384096578)\n",
      "SMRT IBA ks, ka 0.205657797376 0.342646793202\n",
      "SMRT eps (1.52365200701+0.000545417016185j)\n",
      "SMRT IBA orig ks, ka 0.205657797376 0.308761748077\n",
      "SMRT eps orig (1.52365200701+0.000545417016185j)\n"
     ]
    }
   ],
   "source": [
    "# run MEMLS in passive mode\n",
    "if have_memls:\n",
    "    res_memls = memls_legacy.run(radiometer, snowpack)\n",
    "\n",
    "    axs[0].plot(theta, res_memls.TbV(), '*', label='MEMLS V', color=color[0])\n",
    "    axs[0].plot(theta, res_memls.TbH(), '*', label='MEMLS H', color=color[1])\n",
    "    \n",
    "    print(\"RMSD(MEMLS-SMRT IBA EXP)\", rmsd(res_memls.TbV()-res_orig_p.TbV()), rmsd(res_memls.TbH()-res_orig_p.TbH()))\n",
    "    \n",
    "    \n",
    "print(\"MEMLS ks, ka=\", memls_legacy.memls_emmodel(radiometer, snowpack.layers[0]))\n",
    "\n",
    "em = m.emmodel(radiometer, snowpack.layers[0])\n",
    "print(\"SMRT IBA ks, ka\", em.ks, em.ka)\n",
    "print(\"SMRT eps\", em.effective_permittivity())\n",
    "\n",
    "em_orig = m_orig.emmodel(radiometer, snowpack.layers[0])\n",
    "print(\"SMRT IBA orig ks, ka\", em_orig.ks, em_orig.ka)\n",
    "print(\"SMRT eps orig\", em_orig.effective_permittivity())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_memls.TbV(), res_orig_p.TbV()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
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