Lab 1 - Convolution - Problems.ipynb

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    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
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   "cell_type": "markdown",
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   "source": [
    "# 1 A Discrete Convolution Program (5 pts)"
   ]
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    "Write a discrete convolution function `myConv` that convolves two arrays {$f_i, i = 0, \\dots , N_f-1$}\n",
    "and {$w_j, j = 0, \\dots , N_w-1$} to obtain an output time series {$g_n$}. For simplicity, assume a fixed sampling interval $\\Delta = 1$, and further, that $f$ and $w$ are $0$ outside of their sampled regions. \n",
    "\n",
    " 1. How long is {$g_n$}? In other words, how many non-zero points can it have? Justify your answer. \n",
    " 2. Please copy and paste your function `g = myConv(f, w)` to the PDF report.\n",
    " 3. Provide a test to convince yourself (and me) that your function agrees with `numpy.convolve`. For example, generate two random timeseries $f$, $w$ with $N_f=50$, $N_w=100$, drawing each element from $U[0,1]$, and plot the difference between your function's output and numpy's. Include the code for your test in the PDF report.\n",
    " 4. Compare the speed of your `myConv` function to the NumPy function. Provide a plot of the comparison, and include your python code in the PDF report. Is your function faster or slower than the NumPy function? Can you suggest why that is the case?\n",
    "\n",
    "_Hint_: For the speed test part, make up your own $f_i$ and $w_j$ time series, and for simplicity, study the cases\n",
    "of $N_f = N_w = 10, 100, 1000, 10000$. To accurately time each computation of the convolution function, import the\n",
    "time module and place calls to `time.time` around your code:\n",
    "```\n",
    "import time\n",
    "t1 = time.time()\n",
    "g = myConv(f, w)\n",
    "t2 = time.time()\n",
    "print(t2-t1)\n",
    "```\n",
    "Alternatively, use the `timeit` module:\n",
    "```\n",
    "import timeit\n",
    "print(timeit.timeit('g = myConv(f, w)', number=10000))\n",
    "```"
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    "# EXPLANATIONS FOR THE FIRST PROBLEM\n",
    "\n",
    "1. This is explained in the \"PHY408_lecture3_slides_2020.pdf\" document, on slide number 6.\n",
    "\n",
    "Suppose:\n",
    "\n",
    "$ f_i ≠ 0, 0 ≤ i ≤ N_f − 1$ has $N_f$ points, and $ w_i ≠ 0, 0 ≤ k ≤ N_w − 1$ has $N_w$ points.\n",
    "        \n",
    "Then for a given index $n$, as it is a convolution $i$ becomes $n-k$, so:\n",
    "\n",
    "from before: $0 ≤ n − k ≤ N_f − 1 ⇒ n − N_f + 1 ≤ k ≤ n$\n",
    "    \n",
    "also, from before: $0 ≤ k ≤ Nw − 1$\n",
    "    \n",
    "\n",
    "The intersection of both of these inequalities implies:\n",
    "\n",
    "$$max(0, n − N_f + 1) ≤ k ≤ min(n, N_w − 1)$$\n",
    "    \n",
    "\n",
    "However, if\n",
    "\n",
    "$$max(0, n − N_f + 1) > min(n, N_w − 1)$$\n",
    "\n",
    "i.e., $n < 0 $ or $ n > N_f + N_w − 2$ ; then $g_n = 0$\n",
    "\n",
    "\n",
    "If there are no non-zero terms in the summation:\n",
    "\n",
    "$$g_n = 0$$\n",
    "\n",
    "\n",
    "Therefore, in order to guarantee $g_n ≠ 0$,\n",
    "\n",
    "$$0 ≤ n ≤ N_f + N_w − 2$$\n",
    "    \n",
    "\n",
    "And so, the convolved time series is of length:\n",
    "\n",
    "$$Ng = Nf + Nw − 1$$\n",
    "\n",
    "2. It can be found on the code cell, next below.\n",
    "3. It can be found on the code cell, next below.\n",
    "4. The code can be found on the code cell, next below.\n",
    "\n",
    "The function myConv is slower than the NumPy function. This might be because each time a NumPy method is called additional memory is used. The numpy.convolve method calls only once to the NumPy library, and also doesn't use any extra unoptimized python code. However, the difference becomes significant when the arrays contain 10 000 points. That could mean that intermediate arrays with similar array sizes take up memory space with each call, reducing the cache's size."
   ]
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       "            /* 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",
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       "\n",
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       "            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",
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       "            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 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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\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=\"943\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "myConv [0.         0.         0.03124809 0.65623093]\n",
      "numpy.convolve [0.         0.         0.         0.06249928]\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time #for speed test\n",
    "\n",
    "def myConv(f,w): #takes two arrays f and w, and returns their convolution\n",
    "    delta = 1 #sampling interval\n",
    "    fa = np.asarray(f) #converts f and w into numpy arrays\n",
    "    wa = np.asarray(w)\n",
    "    nf = len(fa) #length of farray, its Nf points\n",
    "    nw = len(wa) #length of warray, its Nw points\n",
    "    ng = nf + nw - 1 #length of convolved time series\n",
    "    ga = np.zeros(ng) #initialize array to be returned\n",
    "    \n",
    "    mn = max(nf,nw) #saves length for the larger array\n",
    "    fa = np.pad(fa,(0,mn-nf), mode='constant') #fills with zeros to be the same size as warray\n",
    "    aw = np.pad(wa[::-1],(mn,), mode='constant') #flips array and fills with zeroes before and after the array\n",
    "    \n",
    "    #loop for n = 0,1,2,...,Nf+Nw-2\n",
    "    for n in range(1, ng+1): #begins with 1 to be able to slice array\n",
    "        ga[n-1] = np.sum((aw[mn+nw-n:-n])*fa) #fills the array to be returned with the sum of the product of farray and arrayw displaced\n",
    "    return ga*delta\n",
    "\n",
    "#test against numpy.convolve, using U[0,1] to generate points\n",
    "f = np.random.uniform(0,1,50) #Nf = 50\n",
    "w = np.random.uniform(0,1,100) #Nw = 100\n",
    "g = myConv(f,w)\n",
    "npg = np.convolve(f,w)\n",
    "\n",
    "#plot config\n",
    "grid = plt.GridSpec(2, 3, wspace=0.5, hspace=0.7)\n",
    "plt.subplot(grid[0, 1]) #plot f\n",
    "plt.plot(np.arange(len(f)),f)\n",
    "plt.xlabel('i')\n",
    "plt.ylabel('$f_i$')\n",
    "plt.title('Array f ($N_f$ = '+str(len(f))+')')\n",
    "plt.subplot(grid[0, 2]) #plot w\n",
    "plt.plot(np.arange(len(w)),w)\n",
    "plt.xlabel('j')\n",
    "plt.ylabel('$w_j$')\n",
    "plt.title('Array w ($N_w$ = '+str(len(w))+')')\n",
    "plt.subplot(grid[0, 0]) #plot myConv's output\n",
    "plt.plot(np.arange(len(g)),g)\n",
    "plt.xlabel('n')\n",
    "plt.ylabel('f * w')\n",
    "plt.title('myConv')\n",
    "plt.subplot(grid[1, 0]) #plot numpy.convolve's output\n",
    "plt.plot(np.arange(len(npg)),npg)\n",
    "plt.xlabel('n')\n",
    "plt.ylabel('f * w')\n",
    "plt.title('numpy.convolve')\n",
    "plt.subplot(grid[1, 1]) #plot difference between outputs\n",
    "plt.plot(np.arange(len(g)),g-npg)\n",
    "plt.xlabel('n')\n",
    "plt.ylabel('${[f * w]}_{myConv} - {[f * w]}_{numpy.convolve}$')\n",
    "plt.title('Difference',loc='right')\n",
    "\n",
    "#speed test\n",
    "sz= 4 #largest number of points, 10**sz\n",
    "gt = np.zeros(sz) #initialize arrays to be plotted\n",
    "npgt = np.zeros(sz)\n",
    "for n in np.logspace(1,sz,sz): #loop for different amounts of points Nf = Nw; x10 each time\n",
    "    f = np.random.uniform(0,1,int(n))\n",
    "    w = np.random.uniform(0,1,int(n))\n",
    "    t1 = time.time()\n",
    "    g = myConv(f,w)\n",
    "    t2 = time.time()\n",
    "    gt[int(np.log10(n/10))] = t2 - t1 #array with times from myConv\n",
    "    t1 = time.time()\n",
    "    npg = np.convolve(f,w)\n",
    "    t2 = time.time()\n",
    "    npgt[int(np.log10(n/10))] = t2 - t1 #array with times from numpy.convolve\n",
    "\n",
    "#speed test plot config\n",
    "plt.subplot(grid[1, 2])\n",
    "plt.semilogx(np.logspace(1,sz,sz),gt,label=\"myConv\")\n",
    "plt.semilogx(np.logspace(1,sz,sz),npgt,label=\"numpy.convolve\")\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel('$N_f = N_w$')\n",
    "plt.ylabel('time')\n",
    "plt.title('Speed test')\n",
    "plt.suptitle('1. A Discrete Convolution Program',fontweight='bold')\n",
    "\n",
    "#print test results by console\n",
    "print(\"myConv\",gt)\n",
    "print(\"numpy.convolve\",npgt)\n",
    "\n",
    "#show plots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Simple Physical System: RL Circuit Response (8 pts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider a simple physical system consisting of a resistor (with resistance `R`) and an inductor (with\n",
    "inductance `L`) in series. We apply an input voltage $a(t)$ across the pair in series, and measure the output\n",
    "voltage $b(t)$ across the inductor alone. For this linear system,\n",
    "\n",
    " 1. Show analytically that its step response (i.e., the $b(t)$ we obtain when the input voltage $a(t) = H(t)$,\n",
    "    the Heaviside function) is given by\n",
    "\n",
    "    $$ S(t) = e^{-Rt/L} H(t), $$\n",
    "\n",
    "    and its impulse response (i.e., the output voltage $b(t)$ when $a(t) = \\delta(t)$) is given by\n",
    "\n",
    "    $$ R(t) = \\delta(t) - \\frac{R}{L} e^{-Rt/L} H(t). $$\n",
    "\n",
    "    _Hint_: Construct and solve the ODE relating the voltages under consideration. Consider the two $b(t)$ choices to derive $S(t)$ and $R(t)$. Formulas $\\frac{d}{dt} H(t) = \\delta(t)$ and $\\delta(t) f(t) = \\delta(t) f(0)$ may help.\n",
    " 2. Discretize the impulse response $R(t)$ function, realizing that $H(t)$ should be discretized as\n",
    "\n",
    "    $$H = [0.5, 1, 1, \\dots],$$\n",
    "\n",
    "    and $\\delta(t)$ should be discretized as\n",
    "\n",
    "    $$D = [1/dt, 0, 0, \\dots].$$\n",
    "\n",
    "    Take advantage of your `myConv` function, or the NumPy built-in function `convolve`, and write\n",
    "    your own Python function `V_out = RLresponse(R,L,V_in,dt)` to take an input series $V_{in}$ sampled\n",
    "    at $\\Delta = dt$, and calculate the output series $V_{out}$ sampled by the same $dt$. Please paste your\n",
    "    Python function here. (Hint: here $\\Delta$ may not be 1, so remember to build the multiplication of\n",
    "    $\\Delta$ into your convolution function.)\n",
    " 3. Using $R = 850 \\Omega$, $L = 2 H$, and sampling period $dt = 0.20 \\text{ ms}$, test your RL-response\n",
    "    function with {$H_n$} series (discretized $H(t)$) as input, and plot the output time series (as circles) on\n",
    "    top of the theoretical curve $S(t)$ given by part 1 (as a solid line). Repeat this for {$D_n$} (discretized\n",
    "    $\\delta(t)$) and $R(t)$. Make the time range of the plots 0 to at least 20 ms. Please list your Python\n",
    "    code here."
   ]
  },
  {
   "attachments": {
    "Figure_1.png": {
     "image/png": 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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXPLANATIONS FOR THE SECOND PROBLEM\n",
    "\n",
    "![Figure_1.png](attachment:Figure_1.png)\n",
    "\n",
    "1.\n",
    "- Using Kirchhoff's voltage law, we can say:\n",
    "\n",
    "$$a(t) = v_R (t) + v_L (t)$$\n",
    "\n",
    "- According to Ohm's law: $v_R (t) = R \\cdot i(t)$\n",
    "\n",
    "- According to the definition of inductance: $v_L (t) = L \\cdot \\frac{d}{dt} i(t)$\n",
    "\n",
    "Then,\n",
    "\n",
    "$$a(t) = v_R (t) + v_L (t) = R \\cdot i(t) + L \\cdot \\frac{d}{dt} i(t)$$\n",
    "\n",
    "Thus,\n",
    "\n",
    "$$\\frac{a(t)}{L} = \\frac{R}{L} \\cdot i(t) + \\frac{d}{dt} i(t)$$\n",
    "\n",
    "This is a linear non-homogeneous ordinary differential equation.\n",
    "\\\n",
    "\\\n",
    "Also, for our case: $b(t) = v_L (t)$\n",
    "\\\n",
    "\\\n",
    "\\\n",
    "\\\n",
    "To solve these ODEs it is helpful to remember that their solution consist on two components: an homogeneous solution and a particular solution:\n",
    "\n",
    "$$ i(t) = i_h (t) + i_p (t) $$\n",
    "\n",
    "The homogeneous solution comes from rewriting the original ODE as a linear homogeneous ODE:\n",
    "\n",
    "$$ 0 = \\frac{R}{L} \\cdot i_h(t) + \\frac{d}{dt} i_h(t) $$\n",
    "\n",
    "This has a solution of the form $ i_h(t) = C_1 \\cdot e^{-\\frac{R}{L} t} $\n",
    "\n",
    "Where $C_1$ depends on the initial conditions.\n",
    "\\\n",
    "\\\n",
    "\\\n",
    "\\\n",
    "Now, the particular solution will depend on $a(t)$. For our particular case, it will be either the Heaviside function, or the Dirac delta function depending on the type of respose we want from the system. The former gives us the $step$ response $S(t)$, while the latter gives us the $impulse$ response $R(t)$.\n",
    "\n",
    "When we say:\n",
    "\n",
    "$$ a(t) = H(t) $$\n",
    "\n",
    "We're essentialy saying $ a(t) = 1 $ for all positive values of $t$\n",
    "\n",
    "Then, our ODE becomes:\n",
    "\n",
    "$$\\frac{1}{L} = \\frac{R}{L} \\cdot i(t) + \\frac{d}{dt} i(t)$$\n",
    "\n",
    "We need to find the particular solution, so $ i(t) = i_h (t) + i_p (t) $\n",
    "\n",
    "$$\\frac{1}{L} = \\frac{R}{L} \\cdot \\left[i_h (t) + i_p (t)\\right] + \\frac{d}{dt} \\left[i_h (t) + i_p (t)\\right]$$\n",
    "\n",
    "Plugging in our homogeneous solution:\n",
    "\n",
    "$$\\frac{1}{L} = \\left(\\frac{R}{L}\\right) \\cdot \\left[C_1 e^{-\\frac{R}{L} t} + i_p (t) \\right] + \\frac{d}{dt} \\left[C_1 e^{-\\frac{R}{L} t} + i_p (t) \\right]$$\n",
    "\n",
    "$$\\frac{1}{L} = \\left(\\frac{R}{L} \\cdot C_1 e^{-\\frac{R}{L} t}\\right) + \\left[\\left(\\frac{R}{L}\\right) i_p (t) \\right] + \\left[C_1 \\left(-\\frac{R}{L}\\right) e^{-\\frac{R}{L} t}\\right] + \\left[\\frac{d}{dt}i_p (t) \\right]$$\n",
    "\n",
    "$$\\frac{1}{L} = \\left(\\frac{R}{L}\\right) i_p (t) + \\frac{d}{dt}i_p (t) $$\n",
    "\n",
    "Here we can see that $ i_p (t) = \\frac{1}{R} $ satisfies this equation:\n",
    "\n",
    "$$\\frac{1}{L} = \\left(\\frac{R}{L}\\right) \\left(\\frac{1}{R}\\right) + \\frac{d}{dt}\\left(\\frac{1}{R}\\right) ⇒ \\frac{1}{L} = \\frac{1}{L} + 0 $$\n",
    "\n",
    "So, by adding together both $ i_h (t) $ and $ i_p (t) $ we reach the solution:\n",
    "\n",
    "$$ i(t) = \\frac{1}{R} + C_1 \\cdot e^{-\\frac{R}{L} t}$$\n",
    "\n",
    "If we say $i(0) = 0 $ A :\n",
    "\n",
    "$$ 0 = \\frac{1}{R} + C_1 \\cdot e^{-\\frac{R}{L} (0)} ⇒ 0 = \\frac{1}{R} + C_1 \\cdot (1) ⇒ C_1 = - \\frac{1}{R}$$\n",
    "\n",
    "Then,\n",
    "\n",
    "$$ i(t) = \\frac{1}{R} - \\frac{1}{R} \\cdot e^{-\\frac{R}{L} t} $$\n",
    "\n",
    "And so, for $ b(t) = v_L (t) = L \\cdot \\frac{d}{dt} i(t) $\n",
    "\n",
    "$$ b(t) = L \\cdot \\frac{d}{dt} \\left[\\frac{1}{R} - \\frac{1}{R} \\cdot e^{-\\frac{R}{L} t}\\right] $$\n",
    "\n",
    "$$ b(t) = L \\cdot [(0) - \\left(\\frac{1}{R}\\right) \\cdot \\left(-\\frac{R}{L}\\right) \\cdot e^{-\\frac{R}{L} t}] $$\n",
    "\n",
    "$$ b(t) = e^{-\\frac{R}{L} t} $$\n",
    "\n",
    "Or, $ b(t) = e^{-\\frac{R}{L} t} H(t) $ for all positive values of $t$\n",
    "\n",
    "We can verify that for negative values of $t$, the solution is the same. In this case $ a(t) = 0 $; and so the homogeneous solution becomes the only solution:\n",
    "\n",
    "$$ i(t) = C \\cdot e^{-\\frac{R}{L} t} $$\n",
    "\n",
    "If we say $i(0) = 0 $ A :\n",
    "\n",
    "$$ 0 = C \\cdot e^{-\\frac{R}{L} (0)} ⇒ 0 = C \\cdot (1) ⇒ C = 0 $$\n",
    "\n",
    "Then, $ i(t) = 0 $ for all negative values of $t$\n",
    "\n",
    "And, in turn, $ b(t) = 0 $ for all negative values of $t$\n",
    "\n",
    "Or,\n",
    "\n",
    "$ b(t) = e^{-\\frac{R}{L} t} H(t) $ for all negative values of $t$ as well as all the positives.\n",
    "\n",
    "----------------------------------------------------------------------------------------------------------------------------\n",
    "For the $impulse$ response, thankfully we have the same homogeneous solution:\n",
    "\n",
    "$ i_h(t) = C_1 \\cdot e^{-\\frac{R}{L} t} $\n",
    "\n",
    "We need the particular solution for $a(t) = \\delta(t)$\n",
    "\n",
    "$$\\frac{\\delta(t)}{L} = \\frac{R}{L} \\cdot \\left[i_h (t) + i_p (t)\\right] + \\frac{d}{dt} \\left[i_h (t) + i_p (t)\\right]$$\n",
    "\n",
    "Once again, plugging in our homogeneous solution:\n",
    "\n",
    "$$\\frac{\\delta(t)}{L} = \\left(\\frac{R}{L}\\right) \\cdot \\left[C_1 e^{-\\frac{R}{L} t} + i_p (t) \\right] + \\frac{d}{dt} \\left[C_1 e^{-\\frac{R}{L} t} + i_p (t) \\right]$$\n",
    "\n",
    "$$\\frac{\\delta(t)}{L} = \\left(\\frac{R}{L}\\right) i_p (t) + \\frac{d}{dt}i_p (t) $$\n",
    "\n",
    "This one is a nit more difficult, but using the hints we can find that a solution of the form $ i_p(t) = \\frac{H(t)}{L} \\cdot e^{-\\frac{R}{L} t} $ satisfies the equation. Let's see:\n",
    "\n",
    "$$\\frac{\\delta(t)}{L} = \\left(\\frac{R}{L}\\right) \\left(\\frac{H(t)}{L} \\cdot e^{-\\frac{R}{L} t}\\right) + \\frac{d}{dt}\\left(\\frac{H(t)}{L} \\cdot e^{-\\frac{R}{L} t}\\right) $$\n",
    "\n",
    "$$\\frac{\\delta(t)}{L} = \\left(\\frac{R}{L}\\right) \\left(\\frac{H(t)}{L} \\cdot e^{-\\frac{R}{L} t}\\right) + \\frac{d}{dt}\\left(\\frac{H(t)}{L}\\right) \\cdot e^{-\\frac{R}{L} t} + \\frac{H(t)}{L} \\cdot \\frac{d}{dt}\\left(e^{-\\frac{R}{L} t}\\right)$$\n",
    "\n",
    "Remembering that $\\frac{d}{dt} H(t) = \\delta(t)$\n",
    "\n",
    "$$\\frac{\\delta(t)}{L} = \\left(\\frac{R}{L}\\right) \\left(\\frac{H(t)}{L} \\cdot e^{-\\frac{R}{L} t}\\right) + \\left(\\frac{\\delta(t)}{L}\\right) \\cdot e^{-\\frac{R}{L} t} - \\frac{R}{L} \\cdot \\left(\\frac{H(t)}{L} \\cdot e^{-\\frac{R}{L} t}\\right)$$\n",
    "\n",
    "$$\\frac{\\delta(t)}{L} = \\left(\\frac{\\delta(t)}{L}\\right) \\cdot e^{-\\frac{R}{L} t}$$\n",
    "\n",
    "Also, that  $\\delta(t) f(t) = \\delta(t) f(0)$\n",
    "\n",
    "$$\\left(\\frac{\\delta(t)}{L}\\right) \\cdot e^{-\\frac{R}{L} t} = \\left(\\frac{\\delta(t)}{L}\\right) \\cdot e^{-\\frac{R}{L} (0)}$$\n",
    "\n",
    "So,\n",
    "\n",
    "$$\\frac{\\delta(t)}{L} = \\left(\\frac{\\delta(t)}{L}\\right) \\cdot e^{-\\frac{R}{L} (0)}$$\n",
    "\n",
    "$$\\frac{\\delta(t)}{L} = \\left(\\frac{\\delta(t)}{L}\\right) \\cdot (1)$$\n",
    "\n",
    "$$\\frac{\\delta(t)}{L} = \\frac{\\delta(t)}{L}$$\n",
    "\n",
    "And so, we can say\n",
    "\n",
    "$$ i(t) = C_1 \\cdot e^{-\\frac{R}{L} t} + \\frac{H(t)}{L} \\cdot e^{-\\frac{R}{L} t} $$\n",
    "\n",
    "If we say $i(0) = 0 $ A :\n",
    "\n",
    "$$ 0 = C_1 \\cdot e^{-\\frac{R}{L} (0)} + \\frac{H(0)}{L} \\cdot e^{-\\frac{R}{L} (0)} $$\n",
    "\n",
    "$$ 0 = C_1 \\cdot (1) + (0) \\cdot e^{-\\frac{R}{L} (0)} $$\n",
    "\n",
    "$$ C_1 = 0 $$\n",
    "\n",
    "Therefore,\n",
    "\n",
    "$$ i(t) = \\frac{H(t)}{L} \\cdot e^{-\\frac{R}{L} t} $$\n",
    "\n",
    "And for $ b(t) = v_L (t) = L \\cdot \\frac{d}{dt} i(t) $\n",
    "\n",
    "$$ b(t) = L \\cdot \\frac{d}{dt} \\left[\\frac{H(t)}{L} \\cdot e^{-\\frac{R}{L} t}\\right] $$\n",
    "\n",
    "$$ b(t) = L \\cdot \\frac{d}{dt} \\left[\\frac{H(t)}{L}\\right] e^{-\\frac{R}{L} t} + L \\cdot \\left(\\frac{H(t)}{L}\\right) \\cdot \\frac{d}{dt} \\left(e^{-\\frac{R}{L} t}\\right)$$\n",
    "\n",
    "$$ b(t) = L \\cdot \\left[\\frac{\\delta(t)}{L}\\right] e^{-\\frac{R}{L} t} + L \\cdot \\left(-\\frac{R}{L}\\right) \\cdot \\frac{H(t)}{L} \\left(e^{-\\frac{R}{L} t}\\right)$$\n",
    "\n",
    "$$ b(t) = \\delta(t) e^{-\\frac{R}{L} t} + \\left(-\\frac{R \\cdot H(t)}{L}\\right) \\left(e^{-\\frac{R}{L} t}\\right)$$\n",
    "\n",
    "Once again, $\\delta(t) f(t) = \\delta(t) f(0)$\n",
    "\n",
    "$$ \\delta(t) e^{-\\frac{R}{L} t} = \\delta(t) e^{-\\frac{R}{L} (0)} $$\n",
    "\n",
    "$$ \\delta(t) e^{-\\frac{R}{L} t} = \\delta(t) (1) $$\n",
    "\n",
    "$$ \\delta(t) e^{-\\frac{R}{L} t} = \\delta(t) $$\n",
    "\n",
    "Finally,\n",
    "\n",
    "$$ b(t) = \\delta(t) - \\frac{R}{L} e^{-\\frac{R}{L} t} \\cdot H(t) $$\n",
    "\n",
    "----------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "2. It can be found on the code cell, next below.\n",
    "3. It can be found on the code cell, next below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\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",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.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 backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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",
       "\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) {\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",
       "\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",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\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 / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\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",
       "    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",
       "\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 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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\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=\"997\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def myConv(f,w,d): #takes two arrays f and w, and returns their convolution for a given sampling interval d\n",
    "    delta = d #sampling interval\n",
    "    fa = np.asarray(f) #converts f and w into numpy arrays\n",
    "    wa = np.asarray(w)\n",
    "    nf = len(fa) #length of farray, its Nf points\n",
    "    nw = len(wa) #length of warray, its Nw points\n",
    "    ng = nf + nw - 1 #length of convolved time series\n",
    "    ga = np.zeros(ng) #initialize array to be returned\n",
    "    \n",
    "    mn = max(nf,nw) #saves length for the larger array\n",
    "    fa = np.pad(fa,(0,mn-nf), mode='constant') #fills with zeros to be the same size as warray\n",
    "    aw = np.pad(wa[::-1],(mn,), mode='constant') #flips array and fills with zeroes before and after the array\n",
    "    \n",
    "    #loop for n = 0,1,2,...,Nf+Nw-2\n",
    "    for n in range(1, ng+1): #begins with 1 to be able to alice array\n",
    "        ga[n-1] = np.sum((aw[mn+nw-n:-n])*fa) #fills the array to be returned with the sum of the product of farray and arrayw displaced\n",
    "    return ga*delta\n",
    "\n",
    "def RLresponse(R,L,V_in,dt):\n",
    "    H = np.ones(len(V_in)) #discrete H\n",
    "    H[0] = 0.5\n",
    "    D = np.zeros(len(V_in)) #discrete D\n",
    "    D[0] = 1/dt\n",
    "    t = np.arange(0,dt*len(V_in),dt) #discrete time array, starting at 0\n",
    "    resp = D - (R/L)*np.exp((-R/L)*t)*H #discrete system\n",
    "    return myConv(resp,V_in,dt)[0:len(V_in)] #discrete response array sliced to be the same size as V_in\n",
    "\n",
    "#Defining values for R,L,start,stop and dt from the original statement\n",
    "R = 850\n",
    "L = 2\n",
    "start = 0\n",
    "stop = 20/1000\n",
    "dt = 0.2/1000\n",
    "tm = np.arange(start,stop,dt) #discrete time array\n",
    "H = np.ones(len(tm)) #discrete H\n",
    "H[0] = 0.5\n",
    "D = np.zeros(len(tm)) #discrete D\n",
    "D[0] = 1/dt\n",
    "V_out_S = RLresponse(R,L,H,dt) #b(t) as a step response\n",
    "V_out_R = RLresponse(R,L,D,dt) #b(t) as an impulse response\n",
    "\n",
    "contn = 1000 #continuous plot points\n",
    "conttm = np.linspace(start,stop,contn)\n",
    "contH = np.ones(contn) #continuous H\n",
    "contH[0] = 0.5\n",
    "contD = np.zeros(contn) #continuous D\n",
    "contD[0] = 1/dt\n",
    "S = np.exp((-R/L)*conttm) * contH #continuous S\n",
    "R = contD - (R/L)*np.exp((-R/L)*conttm)*contH #continuous R\n",
    "\n",
    "#plot config\n",
    "plt.subplot(121) #plot S\n",
    "plt.plot(conttm, S)\n",
    "plt.plot(tm,V_out_S,'.r')\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('$b(t)$')\n",
    "plt.title('Step response')\n",
    "plt.subplot(122) #plot R\n",
    "plt.plot(conttm, R)\n",
    "plt.plot(tm,V_out_R,'.r')\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('$b(t)$')\n",
    "plt.title('Impulse response')\n",
    "plt.suptitle('2. Simple Physical System: RL Circuit Response',fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 Convolution of Synthetic Seismograms (5 pts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numerical simulations of seismic wave propagation can now be routinely done for [global and regional\n",
    "earthquakes](http://global.shakemovie.princeton.edu/). For a recent southern Pakistan earthquake (Jan 18,\n",
    "2011, 20:23:33 UTC), raw vertical synthetic seismogram (i.e., displacement field simulated at a seismic\n",
    "station) for station `RAYN` (Ar Rayn, Saudi Arabia) is provided (`RAYN.II.LHZ.sem`). A common practice in\n",
    "seismology is to convolve synthetic seismograms with a Gaussian function\n",
    "\n",
    "$$ g(t) = \\frac{1}{\\sqrt{\\pi}t_H} e^{-(t/t_H)^2} $$\n",
    "\n",
    "to reflect either the time duration of the event or the accuracy of the numerical simulation.\n",
    "\n",
    " 1. Provide two plots. Plot 1: the raw synthetic seismogram for station `RAYN` between 0 and 800 seconds. Plot 2: Gaussian\n",
    "    functions with half duration $t_H = 10 \\text{ sec}$ and $t_H = 20 \\text{ sec}$ (include a legend). For the gaussians, use the same timestep $dt$ as the seismogram data.\n",
    " 2. Use numpy's convolve function to convolve the raw timeseries with a Gaussian function (both $t_H = 10$ and $t_H = 20$ cases). Plot the raw data and the two convolved time series between 0 and 800 seconds on the same graph (include a legend) and comment on your results.\n",
    "\n",
    "__Hints__\n",
    "\n",
    "* The raw synthetics `RAYN.II.LHZ.sem` is given as a text file with two columns: time in seconds and\n",
    "displacement in meters.\n",
    "* Gaussian functions quickly decay to zero beyond $[-3t_H, 3t_H ]$, therefore it is sufficient to sample $g(t)$\n",
    "within this interval.\n",
    "* Use `mode='same'` when calling numpy convolve to truncate the convolution to the max of the supplied arrays (i.e. length of the raw timeseries in our case). This is convenient, since we want to compare the convolution output to the original timeseries. Alternatively, use the default mode (`'full'`) and truncate the output manually. \n",
    "* As a check for part 2, ensure that your convolved timeseries is aligned with (or \"overlaps\") the raw data timeseries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXPLANATIONS FOR THE THIRD PROBLEM\n",
    "\n",
    "1. They can be found on the code cell, next below.\n",
    "2. It can be found on the code cell, next below. It can be noted that the convolved plots better show the overall behavior of the seismographic data. This makes sense given that the gaussian functions are closer to zero farther from the origin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\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",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.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 backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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",
       "\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) {\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",
       "\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",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\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 / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\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",
       "    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",
       "\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 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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\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=\"1409\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def gaussianf(t,th): #define gaussian function using the formula from the original statement\n",
    "    return ( 1 / ( np.sqrt(np.pi) * th ) )*np.exp(-(t/th)**2)\n",
    "\n",
    "f1 = open(\"RAYN.II.LHZ.sem\",\"r\") #open raw seismogram\n",
    "t = np.zeros(len(f1.readlines())) #initialize time array\n",
    "d = np.zeros(len(t)) #initialize displacement array\n",
    "aux = np.zeros(2) #auxiliary array to split time and displacement from the same reading\n",
    "start = 0 #initialize index for when t > 0s\n",
    "flag1 = 0\n",
    "stop = 0 #initialize index for when t > 800s\n",
    "flag2 = 0\n",
    "f1.seek(0,0) #back to the start of the file\n",
    "for i in range(len(t)): #read lines from the file, and put each column to the corresponding array\n",
    "    aux = f1.readline().split()\n",
    "    t[i] = aux[0]\n",
    "    d[i] = aux[1]\n",
    "    if t[i] > 0 and flag1 == 0:\n",
    "        start = i\n",
    "        flag1 = 1\n",
    "    if t[i] > 800 and flag2 == 0:\n",
    "        stop = i-1\n",
    "        flag2 = 1\n",
    "f1.close()\n",
    "\n",
    "dt = t[1]-t[0] #sampling interval\n",
    "tg = np.arange(-3*20,3*20,dt) #time array for gaussian functions. It takes the max tH\n",
    "g1 = gaussianf(tg,10) #gaussian function for tH = 10s\n",
    "g2 = gaussianf(tg,20) #gaussian function for tH = 20s\n",
    "\n",
    "c1 = np.convolve(d,g1,mode='same') #convolution with g1\n",
    "c2 = np.convolve(d,g2,mode='same') #convolution with g2\n",
    "\n",
    "#plot config\n",
    "plt.subplot(131) #plot 1\n",
    "plt.plot(t[start:stop],d[start:stop])\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('displacement')\n",
    "plt.title('Raw synthetic seismogram')\n",
    "plt.subplot(132) #plot 2\n",
    "plt.plot(tg,g1,color='orange',label='tH = 10s')\n",
    "plt.plot(tg,g2,color='green',label='tH = 20s')\n",
    "plt.legend()\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('g(t)')\n",
    "plt.title('Gaussian functions')\n",
    "plt.subplot(133) #plot 3\n",
    "plt.plot(t[start:stop],d[start:stop])\n",
    "plt.plot(t[start:stop],c1[start:stop],color='orange',label='tH = 10s')\n",
    "plt.plot(t[start:stop],c2[start:stop],color='green',label='tH = 20s')\n",
    "plt.legend()\n",
    "plt.xlabel('time')\n",
    "plt.title('Convolutions')\n",
    "plt.suptitle('3. Convolution of Synthetic Seismograms',fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
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