See more/MATLAB_Wrapper_Examples.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Running [MATLAB in an IPython Notebook](https://github.com/arokem/python-matlab-bridge)\n",
      "=====================================\n",
      "\n",
      "With [Interactive MATLAB Graphing in Plotly](https://plot.ly/api/MATLAB)\n",
      "------------------------------------------------------------------------\n",
      "NOW WITH THE EASY TO USE MATLAB WRAPPER!\n",
      "\n",
      "Check out more examples interactive Plotly graphs in IPython notebooks here: [nbviewer.ipython.org/github/plotly/IPython-plotly/tree/master/](nbviewer.ipython.org/github/plotly/IPython-plotly/tree/master/)\n",
      "\n",
      "Questions? <andrey@plot.ly>, [@plotlygraphs](http://twitter.com/plotlygraphs)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The Plotly MATLAB API allows you to leverage all the power of Plotly graphs conveniently from your scientific computing environment. You can work in MATLAB, share your graphs and data, and then edit it together online. Then it's all backed up, collaborative, and can be edited with other APIs and the GUI. No installation required; it's free, embeddable, and keeps data and graphs together.\n",
      "\n",
      "The API provides a set of functions that structure your data and send it to Plotly. All editing can be done through the API or from the online GUI. In addition, the PLOTLY WRAPPER function can take away all the hassle of learning a new graphing syntax. Just build your figures in MATLAB as usual and send them to Plotly with one call to the wrapper.\n",
      "\n",
      "This notebook goes through some usage scenarios to get MATLAB users started with the tool. Some steps are specific to making everything work in an IPython notebook, but you can always skip through those and get to the good stuff. Let's get started!\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, we need to start a MATLAB server:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pymatbridge as pymat\n",
      "ip = get_ipython()\n",
      "pymat.load_ipython_extension(ip, \n",
      "    matlab='C:\\Users\\Andrey\\matlab_shortcut.lnk')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Starting MATLAB on http://localhost:60199\n",
        " visit http://localhost:60199/exit.m to shut down same\n",
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "MATLAB started and connected!\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note to Windows users: Python doesn't like file paths with spaces! Above, a shortcut to matlab.exe was created and placed it in a path that did not contain spaces. It's a bit of a hack, there are better ways to do it I'm sure."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we are ready to run MATLAB! Let's set up the Plotly environment next. This needs to be done only once per session. The next few lines import the Plotly MATLAB API and sign in to your account. For these examples we are using the MATLAB-demos account. Find out more about generating your API key [here](https://plot.ly/api/MATLAB/getting-started/authentication).\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%matlab\n",
      "api_path = 'C:\\Users\\Andrey\\Dropbox\\ANDREY\\PLOTLY\\MATLAB-api\\plotly';\n",
      "addpath(genpath(api_path))\n",
      "signin('MATLAB-demos', '3qhoowzv5n');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We are now signed in and can convert and publish the Matlab figures in Plotly! The Plotly MATLAB API helps generate `data` and `layout` data structures that contain all the information needed to make a plot. The `plotly` command sends these to the server and the result is displayed via the `response` vairable. In it, a URL points to the generated plot online. In this notebook, we focus of the Plotly wrapper. The `fig2plotly` command at the end of the code will convert the current figure into a format that Plotly understands and publish it."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's make a simple plot in Matlab and publish it to Plotly:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%matlab\n",
      "\n",
      "clear all\n",
      "close all\n",
      "\n",
      "% some ransom points\n",
      "my_random_data = rand(20,4);\n",
      "\n",
      "% a 2D scatter plot with size and color inputs\n",
      "figure\n",
      "scatter(my_random_data(:,1), my_random_data(:,2), 100*my_random_data(:,3), my_random_data(:,4), 'filled')\n",
      "\n",
      "% Work that Plotly magic!\n",
      "response = fig2plotly();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "High five! You successfuly sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~MATLAB-demos/0 or inside your plot.ly account where it is named 'untitled'"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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LNwQAAJhhQlhBP+GW59zgkToQAAB4kyA8zn2m9xxy93umzv+c2up+pywEAAA2c8roZVhR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\n"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The plot has been sent to the [MATLAB-demos account](https://plot.ly/~andreyDim) and the command returns the unique url [https://plot.ly/~MATLAB-demos/0](https://plot.ly/~MATLAB-demos/0) where anyone can view the rendered graph. By default, the plot is named \"untitled\". In the following examples, we see how the command allows the user to give a custom name, otherwise it will try to extract it from the figure title.\n",
      "\n",
      "Every plotly graph URL can be [embedded in HTML as wrapped in an IFrame](https://plot.ly/api/MATLAB/docs/iframes). Here's a quick utility function that will embed a plotly graph in IPython notebooks."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "\n",
      "def show_plot(url, width=700, height=500):\n",
      "    s = '<iframe height=\"%s\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"%s\" width=\"%s\"></iframe>' %\\\n",
      "    (height+50, \"/\".join(map(str,[url, width, height])), width+50)\n",
      "    return HTML(s)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To see the resulting plot, we only need to input the fowwoling command:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "show_plot('https://plot.ly/~MATLAB-demos/0')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe height=\"550\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~MATLAB-demos/0/700/500\" width=\"750\"></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<IPython.core.display.HTML at 0x5f12c88>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "EXAMPLE 1: The Convex Hull Property. The original example data can be found at the [Matlab Plot Gallery](http://www.mathworks.com/discovery/gallery.html)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%matlab\n",
      "% This is an example of how to add text to a plot in MATLAB.\n",
      "% \n",
      "% Read about the <http://www.mathworks.com/help/matlab/ref/text.html |text|> function in the MATLAB documentation.\n",
      "%\n",
      "% Go to <http://www.mathworks.com/discovery/gallery.html MATLAB Plot Gallery>\n",
      "%\n",
      "% Copyright 2012 The MathWorks, Inc.\n",
      "\n",
      "clear all\n",
      "close all\n",
      "\n",
      "% Load the points for creating a spline curve\n",
      "load 'C:\\Users\\Andrey\\Dropbox\\ANDREY\\PLOTLY\\Matlab_Examples\\Add_Text_to_Plot_1\\splineData'\n",
      "\n",
      "\n",
      "% Plot the points for the spline curve\n",
      "figure;\n",
      "plot(points(:,1),points(:,2),':ok');\n",
      "hold on;\n",
      "\n",
      "% Plot the spline curve\n",
      "plot(x, y, 'LineWidth', 2);\n",
      "axis([.5 7 -.8 1.8]);\n",
      "\n",
      "% Add a title and axis labels\n",
      "title('The Convex-Hull Property');\n",
      "xlabel('x');\n",
      "ylabel('y');\n",
      "\n",
      "% Label points 3, 4, & 5 of the spline curve\n",
      "xt = points(3,1) - 0.05; yt = points(3,2) - 0.1;\n",
      "text(xt, yt, 'Point 3', 'FontSize',12);\n",
      "\n",
      "xt = points(4,1) - 0.05; yt = points(4,2) + 0.1;\n",
      "text(xt, yt, 'Point 4', 'FontSize',12);\n",
      "\n",
      "xt = points(5,1) + 0.15; yt = points(5,2) - 0.05;\n",
      "text(6.1,.5, 'Point 5', 'FontSize',12);\n",
      "\n",
      "% Work that Plotly magic!\n",
      "response = fig2plotly();\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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b3WfCHgD6JggBonPpIACEJQjhEfvpaJ0aBLJZPWnamg5lCUI4ZXq6MlymG2oQ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\n1POdiVgXXvgAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This time, the plot was named \"The Convex Hull Property\"."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "show_plot('https://plot.ly/~MATLAB-demos/1')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe height=\"550\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~MATLAB-demos/1/700/500\" width=\"750\"></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<IPython.core.display.HTML at 0x6372780>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Sweet! Let's keep going..."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will reuse the same code and add some more inputs to the `fig2plotly` command. We can specify explicitly the figure object that will be published (it doesn't need to be the current one!) using the 'name' property. In addition, we add 'open' to turn off the automatic opening of a browser window with the response URL, and we 'strip' the plot of all MATLAB styling and revert to Plotly defaults. This allows users to use Plotly's styling capabilities from the API of at the online UI."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%matlab\n",
      "% This is an example of how to add text to a plot in MATLAB.\n",
      "% \n",
      "% Read about the <http://www.mathworks.com/help/matlab/ref/text.html |text|> function in the MATLAB documentation.\n",
      "%\n",
      "% Go to <http://www.mathworks.com/discovery/gallery.html MATLAB Plot Gallery>\n",
      "%\n",
      "% Copyright 2012 The MathWorks, Inc.\n",
      "\n",
      "clear all\n",
      "close all\n",
      "\n",
      "% Load the points for creating a spline curve\n",
      "load 'C:\\Users\\Andrey\\Dropbox\\ANDREY\\PLOTLY\\Matlab_Examples\\Add_Text_to_Plot_1\\splineData'\n",
      "\n",
      "\n",
      "% Plot the points for the spline curve\n",
      "figure;\n",
      "plot(points(:,1),points(:,2),':ok');\n",
      "hold on;\n",
      "\n",
      "% Plot the spline curve\n",
      "plot(x, y, 'LineWidth', 2);\n",
      "axis([.5 7 -.8 1.8]);\n",
      "\n",
      "% Add a title and axis labels\n",
      "title('The Convex-Hull Property');\n",
      "xlabel('x');\n",
      "ylabel('y');\n",
      "\n",
      "% Label points 3, 4, & 5 of the spline curve\n",
      "xt = points(3,1) - 0.05; yt = points(3,2) - 0.1;\n",
      "text(xt, yt, 'Point 3', 'FontSize',12);\n",
      "\n",
      "xt = points(4,1) - 0.05; yt = points(4,2) + 0.1;\n",
      "text(xt, yt, 'Point 4', 'FontSize',12);\n",
      "\n",
      "xt = points(5,1) + 0.15; yt = points(5,2) - 0.05;\n",
      "text(6.1,.5, 'Point 5', 'FontSize',12);\n",
      "\n",
      "% Work that Plotly magic!\n",
      "f = get(gcf); % some figure, for now we use the current.\n",
      "response = fig2plotly(f, 'strip', true, 'open', false, 'name', 'Stripped plot');\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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b3WfCHgD6JggBonPpIACEJQjhEfvpaJ0aBLJZPWnamg5lCUI4ZXq6MlymG2oQ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\n1POdiVgXXvgAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "show_plot('https://plot.ly/~MATLAB-demos/2')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe height=\"550\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~MATLAB-demos/2/700/500\" width=\"750\"></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<IPython.core.display.HTML at 0x63727b8>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "EXAMPLE 2: Multiple Plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%matlab\n",
      "\n",
      "% Create data\n",
      "t = linspace(0,2*pi);\n",
      "t(1) = eps;\n",
      "y = sin(t);\n",
      "\n",
      "% Place axes at (0.1,0.1) with width and height of 0.8\n",
      "figure;\n",
      "handaxes1 = axes('position', [0.1 0.1 0.8 0.8]);\n",
      "\n",
      "% Main plot\n",
      "plot(t, y);\n",
      "xlabel('t'); ylabel('sin(t)');\n",
      "set(handaxes1, 'box', 'off');\n",
      "\n",
      "% Adjust XY label font\n",
      "handxlabel1 = get(gca, 'xlabel');\n",
      "set(handxlabel1, 'fontsize', 16, 'fontweight', 'bold');\n",
      "handylabel1 = get(gca, 'ylabel');\n",
      "set(handylabel1, 'fontsize', 16, 'fontweight', 'bold');\n",
      "\n",
      "% Place second set of axes on same plot\n",
      "handaxes2 = axes('position', [0.6 0.6 0.2 0.2]);\n",
      "fill(t, y.^2, 'g');\n",
      "set(handaxes2, 'box', 'off');\n",
      "xlabel('t'); ylabel('(sin(t))^2');\n",
      "\n",
      "% Adjust XY label font\n",
      "set(get(handaxes2, 'xlabel'), 'fontname', 'times')\n",
      "set(get(handaxes2, 'ylabel'), 'fontname', 'times')\n",
      "\n",
      "% Add another set of axes\n",
      "handaxes3 = axes('position', [0.25 0.25 0.2 0.2]);\n",
      "plot(t, y.^3);\n",
      "set(handaxes3, 'box','off');\n",
      "xlabel('t'); ylabel('(sin(t))^3');\n",
      "\n",
      "% Work that Plotly magic!\n",
      "\n",
      "% The curent figure can be found with the `gcf` command. Any figure can be stores as the double of struct form.\n",
      "% The plotly command accepts these forms as well as custom plot names\n",
      "\n",
      "response = fig2plotly(gcf, 'name', 'Multiple_Plots');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RCVREIOQ7+gp4kpKQYw4c6+9JgOf/FQCisagMANTq49Z/ALBNQ8gXNBXwPCUhGzau5VPf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\nzwxAlQAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "show_plot('https://plot.ly/~MATLAB-demos/3')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe height=\"550\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~MATLAB-demos/3/700/500\" width=\"750\"></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<IPython.core.display.HTML at 0x63721d0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looking sharp! Currently, Plotly doesn't support the Tex formatting. That's why we don't see the exponents shown properly. However, we do support Latex and, in the next example, we see it at work. We use the intermediate command `convertFigure` to output the two Plotly data structures. This allows further editing with the API before sending it out. Features only available in Plotly (like the different hover modes) can be set at this point."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "EXAMPLE 3: Gain vs Frequency"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%matlab\n",
      "\n",
      "close all\n",
      "\n",
      "% Create a set of values for the damping factor\n",
      "zeta = [0.01 .02 0.05 0.1 .2 .5 1 ];\n",
      "\n",
      "% Define a color for each damping factor\n",
      "colors = ['r' 'g' 'b' 'c' 'm' 'y' 'k'];\n",
      "\n",
      "% Create a range of frequency values equally spaced logarithmically\n",
      "w = logspace(-1, 1, 1000);\n",
      "\n",
      "% Plot the gain vs. frequency for each of the seven damping factors\n",
      "figure;\n",
      "for i = 1:7\n",
      "    a = w.^2 - 1;\n",
      "    b = 2*w*zeta(i);\n",
      "    gain = sqrt(1./(a.^2 + b.^2));\n",
      "    loglog(w, gain, 'color', colors(i), 'linewidth', 2);\n",
      "    hold on;\n",
      "end\n",
      "\n",
      "% Set the axis limits\n",
      "axis([0.1 10 0.01 100]);\n",
      "\n",
      "% Add a title and axis labels\n",
      "title('Gain vs Frequency');\n",
      "xlabel('Frequency');\n",
      "ylabel('Gain');\n",
      "\n",
      "% Turn the grid on\n",
      "grid on;\n",
      "\n",
      "% ----------------------------------------\n",
      "% Let's convert the figure to plotly structures, and set stripping to false\n",
      "[data, layout] = convertFigure(get(gcf), false);\n",
      "\n",
      "% But, before we publish, let's modify and add some features:\n",
      "% Naming the traces\n",
      "for i=1:numel(data)\n",
      "    data{i}.name =  ['$\\\\zeta = ' num2str(zeta(i)) '$']; %LATEX FORMATTING\n",
      "    data{i}.showlegend = true;\n",
      "end\n",
      "% Adding a nice the legend\n",
      "legendstyle = struct( ...\n",
      "    'x' , 0.15, ...\n",
      "    'y' , 0.9, ...\n",
      "    'bgcolor' , '#E2E2E2', ...\n",
      "    'bordercolor' , '#FFFFFF', ...\n",
      "    'borderwidth' , 2, ...\n",
      "    'traceorder' , 'normal' ...\n",
      "    );\n",
      "layout.legend = legendstyle;\n",
      "layout.showlegend = true;\n",
      "\n",
      "% Setting the hover mode\n",
      "layout.hovermode = 'closest';\n",
      "\n",
      "% Giving the plot a custom name\n",
      "plot_name = 'My_improved_plot';\n",
      "\n",
      "% Sending to Plotly\n",
      "response = plotly(data, struct('layout', layout, ...\n",
      "    'filename',plot_name, ...\n",
      "\t'fileopt', 'overwrite'));\n",
      "\n",
      "display(response.url)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "https://plot.ly/~MATLAB-demos/4\r\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4U9YOtgrdZl/fT6DFPNOtAC5j6via/1VyxYoyLbuneP68KgrXdyaEGQIbCQUO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\nAAAoSkEIAABQ1H9iuNz9/CXWGwAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And the Plotly version:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "show_plot('https://plot.ly/~MATLAB-demos/4')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe height=\"550\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~MATLAB-demos/4/700/500\" width=\"750\"></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<IPython.core.display.HTML at 0x6372d68>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "EXAMPLE 4: Just throw everything in there!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%matlab\n",
      "\n",
      "close all\n",
      "\n",
      "% Load data\n",
      "load 'C:\\Users\\Andrey\\Dropbox\\ANDREY\\PLOTLY\\Matlab_Examples\\Publication_Quality_Graphics\\data'\n",
      "\n",
      "% Create basic plot\n",
      "figure;\n",
      "hold on;\n",
      "hFit   = line(xfit  , yfit   );\n",
      "hE     = errorbar(xdata_m, ydata_m, ydata_s);\n",
      "hData  = line(xVdata, yVdata );\n",
      "hModel = line(xmodel, ymodel );\n",
      "hCI(1) = line(xmodel, ymodelL);\n",
      "hCI(2) = line(xmodel, ymodelU);\n",
      "\n",
      "% Adjust line properties (functional)\n",
      "set(hFit                          , ...\n",
      "  'Color'           , [0 0 .5]    );\n",
      "set(hE                            , ...\n",
      "  'LineStyle'       , 'none'      , ...\n",
      "  'Marker'          , '.'         , ...\n",
      "  'Color'           , [.3 .3 .3]  );\n",
      "set(hData                         , ...\n",
      "  'LineStyle'       , 'none'      , ...\n",
      "  'Marker'          , '.'         );\n",
      "set(hModel                        , ...\n",
      "  'LineStyle'       , '--'        , ...\n",
      "  'Color'           , 'r'         );\n",
      "set(hCI(1)                        , ...\n",
      "  'LineStyle'       , '-.'        , ...\n",
      "  'Color'           , [0 .5 0]    );\n",
      "set(hCI(2)                        , ...\n",
      "  'LineStyle'       , '-.'        , ...\n",
      "  'Color'           , [0 .5 0]    );\n",
      "\n",
      "% Adjust line properties (aesthetics)\n",
      "set(hFit                          , ...\n",
      "  'LineWidth'       , 2           );\n",
      "set(hE                            , ...\n",
      "  'LineWidth'       , 1           , ...\n",
      "  'Marker'          , 'o'         , ...\n",
      "  'MarkerSize'      , 6           , ...\n",
      "  'MarkerEdgeColor' , [.2 .2 .2]  , ...\n",
      "  'MarkerFaceColor' , [.7 .7 .7]  );\n",
      "set(hData                         , ...\n",
      "  'Marker'          , 'o'         , ...\n",
      "  'MarkerSize'      , 5           , ...\n",
      "  'MarkerEdgeColor' , 'none'      , ...\n",
      "  'MarkerFaceColor' , [.75 .75 1] );\n",
      "set(hModel                        , ...\n",
      "  'LineWidth'       , 1.5         );\n",
      "set(hCI(1)                        , ...\n",
      "  'LineWidth'       , 1.5         );\n",
      "set(hCI(2)                        , ...\n",
      "  'LineWidth'       , 1.5         );\n",
      "\n",
      "% Adjust error bar width\n",
      "hE_c                   = get(hE     , 'Children'    );\n",
      "errorbarXData          = get(hE_c(2), 'XData'       );\n",
      "errorbarXData(4:9:end) = errorbarXData(1:9:end) - 0.2;\n",
      "errorbarXData(7:9:end) = errorbarXData(1:9:end) - 0.2;\n",
      "errorbarXData(5:9:end) = errorbarXData(1:9:end) + 0.2;\n",
      "errorbarXData(8:9:end) = errorbarXData(1:9:end) + 0.2;\n",
      "set(hE_c(2), 'XData', errorbarXData);\n",
      "\n",
      "% Add labels\n",
      "hTitle  = title ('My Complex Plot');\n",
      "hXLabel = xlabel('Length (m)'                     );\n",
      "hYLabel = ylabel('Mass (kg)'                      );\n",
      "\n",
      "% Add text\n",
      "hText   = text(10, 800, ...\n",
      "  sprintf('{\\\\itC = %0.1g \\\\pm %0.1g (CI)}', c, cint(2)-c));\n",
      "\n",
      "% Add legend\n",
      "hLegend = legend([hE, hFit, hData, hModel, hCI(1)], ...\n",
      "  'Data ({\\it\\mu} \\pm {\\it\\sigma})' , ...\n",
      "  'Fit (C{\\itx}^3)'      , ...\n",
      "  'Validation Data'       , ...\n",
      "  'Model (C{\\itx}^3)'    , ...\n",
      "  '95% CI'                , ...\n",
      "  'location', 'NorthWest' );\n",
      "\n",
      "% Adjust font\n",
      "set( gca                             , 'FontName'   , 'Helvetica' );\n",
      "set([hTitle, hXLabel, hYLabel, hText], 'FontName'   , 'AvantGarde');\n",
      "set([hLegend, gca]                   , 'FontSize'   , 8           );\n",
      "set([hXLabel, hYLabel, hText]        , 'FontSize'   , 10          );\n",
      "set( hTitle                          , 'FontSize'   , 12          , ...\n",
      "                                       'FontWeight' , 'bold'      );\n",
      "\n",
      "% Adjust axes properties\n",
      "set(gca, ...\n",
      "  'Box'         , 'off'     , ...\n",
      "  'TickDir'     , 'out'     , ...\n",
      "  'TickLength'  , [.02 .02] , ...\n",
      "  'XMinorTick'  , 'on'      , ...\n",
      "  'YMinorTick'  , 'on'      , ...\n",
      "  'YGrid'       , 'on'      , ...\n",
      "  'XColor'      , [.3 .3 .3], ...\n",
      "  'YColor'      , [.3 .3 .3], ...\n",
      "  'YTick'       , 0:500:2500, ...\n",
      "  'LineWidth'   , 1         );\n",
      "\n",
      "\n",
      "% Work that Plotly magic!\n",
      "\n",
      "% The curent figure can be found with the `gcf` command. Any figure can be stores as the double of struct form.\n",
      "% The plotly command accepts these forms as well as custom plot names\n",
      "\n",
      "some_figure_struct = get(gcf);\n",
      "response = fig2plotly(some_figure_struct, 'name', 'Complex_Plot', 'open', false);\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mtAE9LX/Bzc39fq+q6vF4TD7aBk+ZP+mDVUbn6m7N/XOrjy58+fgVcvpZKPnXFQDFepp8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\nX+gmHAAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "show_plot('https://plot.ly/~MATLAB-demos/6')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe height=\"550\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~MATLAB-demos/6/700/500\" width=\"750\"></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<IPython.core.display.HTML at 0x63729b0>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}