2--The Python world of science and data.ipynb

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    "# The Python world of science and data\n",
    "\n",
    "The [Jupyter notebook](http://nbviewer.jupyter.org/github/barbagroup/jupyter-tutorial/blob/master/1--The%20Notebook.ipynb) is a nice way to write and share some content online, but you'll get superpowers from Python libraries for science and data!\n",
    "\n",
    "We already used code cells to do simple calculations via the arithmetic operators in Python:\n",
    "```python\n",
    "    +   -   *   /   **   %   //\n",
    "```\n",
    "\n",
    "\n",
    "\n",
    "In addition to arithmetics, you can do comparisons with operators that return Boolean values (`True` or `False`). These are:\n",
    "```python\n",
    "    ==   !=   <   >   <=   >=\n",
    "```\n",
    "\n",
    "On top of those, you have assignment operators, bitwise operators, logical operators, membership operators, and identity operators. You can find online several \"cheat sheets\" for Python operators. For example: [\"Operators and expressions\"](http://pymbook.readthedocs.io/en/latest/operatorsexpressions.html) in the online book _\"Python for You and Me.\"_\n",
    "\n",
    "Go ahead and experiment with various Python operators until you're satisfied. You can open a new, empty notebook and experiment in code cells, taking notes of the things that you find interesting in markdown cells. Or, if you have _this_ notebook open in the Jupyter Notebook App, you can add a new cell by clicking the plus button: <button class='fa fa-plus icon-plus btn btn-xs btn-default'></button>, and work right here. Remember to type `[shift] + [enter]` to exectue any cell.\n",
    "\n",
    "Next, let's learn about the Python world of science and data."
   ]
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   "cell_type": "markdown",
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    "## Two libraries that made Python useful for science: NumPy and Matplotlib\n",
    "\n",
    "Python is a general-purpose language: you can use it to create websites, to write programs that crawl the web, to support you in scientific research or data analysis, etc. Because it can be used in so many fields, the core language is supported by many libraries (not everyone needs to have every functionality). In science, two libraries made Python really useful: [**NumPy**](http://www.numpy.org) and [**Matplotlib**](http://matplotlib.org).\n",
    "\n",
    "**NumPy** gives you access to numerical mathematics on arrays (like vectors and matrices). **Matplotlib** gives you a catalog of plotting functions for visualizing data."
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The command `import` followed by the name of a library will extend your Python session with all of the functions in that library. After executing the code cell above, we have all of **NumPy** available to us. If you have this notebook open in the Jupyter Notebook App, make sure to execute the cell above by clicking on it and typing ``[shift] + [enter]``.\n",
    "\n",
    "Now, to use one of **NumPy**'s functions, we prepend `numpy.` (with the dot) to the function name. For example:"
   ]
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      "text/plain": [
       "array([ 0.        ,  0.55555556,  1.11111111,  1.66666667,  2.22222222,\n",
       "        2.77777778,  3.33333333,  3.88888889,  4.44444444,  5.        ])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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   "source": [
    "numpy.linspace(0, 5, 10)"
   ]
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   "metadata": {},
   "source": [
    "The **NumPy** function [`linspace()`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html)  creates an array with equally spaced numbers between a start and end. It's a very useful function! In the code cell above, we create an array of 10 numbers from 0 to 5. Go ahead and try it with different argument values.\n",
    "\n",
    "To be able to do something with this array later, we normally want to give it a name. Like,"
   ]
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  {
   "cell_type": "code",
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   "metadata": {
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   "source": [
    "xarray = numpy.linspace(0, 5, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can use **NumPy** to do computations with the array. Like take its square:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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      "text/plain": [
       "array([  0.        ,   0.30864198,   1.2345679 ,   2.77777778,\n",
       "         4.9382716 ,   7.71604938,  11.11111111,  15.12345679,\n",
       "        19.75308642,  25.        ])"
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     "execution_count": 4,
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   "source": [
    "xarray ** 2"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In **NumPy**, the square of an array of numbers takes the square of each element. You will likely want to give your result a name, too. So let's do this again, and also take the cube, and the square root of the array at the same time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "source": [
    "yarray = xarray ** 2\n",
    "zarray = xarray ** 3\n",
    "warray = numpy.sqrt(xarray)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You notice that **NumPy** knows how to take the power of an array, and it has a built-in function for the square-root. Now, you may want to draw a plot of these results with the original array on the x-axis. For that we need the module `pyplot` from **Matplotlib**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The command `%matplotlib notebook` is there to get our plots inside the notebook (instead of a pop-up window, which is the default behavior of `pyplot`). Let's try a line plot now! We use the `pyplot.plot()` function, specifying the line color (`'k'` for black) and line style (`'-'`, `'--'` and `':'` for continuous, dashed and dotted line), and giving each line a label. Note that the values for `color`, `linestyle` and `label` are given in quotes."
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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",
       "            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;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.plot(xarray,yarray,color='k',linestyle='-', label='square')\n",
    "pyplot.plot(xarray,zarray,color='k',linestyle='--', label='cube')\n",
    "pyplot.plot(xarray,warray,color='k',linestyle=':', label='square root')\n",
    "pyplot.legend(loc='best')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's very nice! By now, you are probably imagining all the great stuff you can do with Jupyter notebooks, Python and its scientific libraries **NumPy** and **Matplotlib**. Explore all the beautiful plots you can make by browsing the [Matplotlib gallery](http://matplotlib.org/gallery.html).\n",
    "\n",
    "Today, the world of Pyhon for science and data includes several other amazing libraries. For data analysis and modeling, you have [**pandas**](http://pandas.pydata.org). For symbolic mathematics, [**SymPy**](http://www.sympy.org/) gives you a Python-powered computer algebra system. You can draw beautiful 3D visualizations thanks to the [**Mayavi**](http://code.enthought.com/projects/mayavi/) project. And there are many more!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The world of Python, so far\n",
    "\n",
    "You learned about:\n",
    "\n",
    "* Python operators\n",
    "* the `import` statement\n",
    "* the **NumPy** library for array mathematics\n",
    "* the **Matplotlib** library for plotting\n",
    "* calling library functions by prepending the library name with a dot, e.g., `numpy.linspace()`\n",
    "* making a line plot\n",
    "* there's a world of Python libraries for science and data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optional next step: explore published notebooks\n",
    "\n",
    "There are many ways you can go from here. To whet your appetite, you could browse for a bit on the [Gallery of Interesting IPython Notebooks](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks). Find a topic that interests you and see if there is a notebook that someone has shared; study the code examples and see if you can replicate some of it on your own fresh notebook.\n",
    "\n",
    "If you would like to follow a step-by-step tutorial that teaches a foundation in computational fluid dynamics, let me introduce you to our very own [**\"CFD Python. 12 steps to Navier-Stokes\"**](https://github.com/barbagroup/CFDPython) — you can follow this 12-step program to build a solution to the Navier-Stokes equations for 2D cavity flow and 2D channel flow, using the finite-difference method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next\n",
    "\n",
    "* Get things done in [Jupyter like a pro](http://nbviewer.jupyter.org/github/barbagroup/jupyter-tutorial/blob/master/3--Jupyter%20like%20a%20pro.ipynb)!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<p style=\"font-size:smaller\">(c) 2016 Lorena A. Barba. Free to use under Creative Commons Attribution <a href=\"https://creativecommons.org/licenses/by/4.0/\">CC-BY 4.0 License</a>. This notebook was written for the tutorial <a href=\"https://github.com/barbagroup/jupyter-tutorial/blob/master/World-of-Jupyter.md\">\"The world of Jupyter\"</a> at the Huazhong University of Science and Technology (HUST), Wuhan, China.\n",
    "</p>"
   ]
  }
 ],
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