examples/Revisiting Lambert's problem in Python.ipynb

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    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cycler import cycler\n",
    "\n",
    "from poliastro.iod import izzo\n",
    "\n",
    "plt.rc('text', usetex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Reproducing the original figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "source": [
    "x = np.linspace(-1, 2, num=1000)\n",
    "M_list = 0, 1, 2, 3\n",
    "ll_list = 1, 0.9, 0.7, 0, -0.7, -0.9, -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [
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       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
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       "    this.root.append(canvas_div);\n",
       "\n",
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       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
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       "    this.context = canvas[0].getContext(\"2d\");\n",
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       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
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       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
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       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
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       "\n",
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       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
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       "\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'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\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",
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       "\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"
    },
    {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5f064a1358>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.set_prop_cycle(cycler('linestyle', ['-', '--']) *\n",
    "                  (cycler('color', ['black']) * len(ll_list)))\n",
    "for M in M_list:\n",
    "    for ll in ll_list:\n",
    "        T_x0 = np.zeros_like(x)\n",
    "        for ii in range(len(x)):\n",
    "            y = izzo._compute_y(x[ii], ll)\n",
    "            T_x0[ii] = izzo._tof_equation(x[ii], y, 0.0, ll, M)\n",
    "        if M == 0 and ll == 1:\n",
    "            T_x0[x > 0] = np.nan\n",
    "        elif M > 0:\n",
    "            # Mask meaningless solutions\n",
    "            T_x0[x > 1] = np.nan\n",
    "        l, = ax.plot(x, T_x0)\n",
    "\n",
    "ax.set_ylim(0, 10)\n",
    "\n",
    "ax.set_xticks((-1, 0, 1, 2))\n",
    "ax.set_yticks((0, np.pi, 2 * np.pi, 3 * np.pi))\n",
    "ax.set_yticklabels(('$0$', '$\\pi$', '$2 \\pi$', '$3 \\pi$'))\n",
    "\n",
    "ax.vlines(1, 0, 10)\n",
    "ax.text(0.65, 4.0, \"elliptic\")\n",
    "ax.text(1.16, 4.0, \"hyperbolic\")\n",
    "\n",
    "ax.text(0.05, 1.5, \"$M = 0$\", bbox=dict(facecolor='white'))\n",
    "ax.text(0.05, 5, \"$M = 1$\", bbox=dict(facecolor='white'))\n",
    "ax.text(0.05, 8, \"$M = 2$\", bbox=dict(facecolor='white'))\n",
    "\n",
    "ax.annotate(\"$\\lambda = 1$\", xy=(-0.3, 1), xytext=(-0.75, 0.25), arrowprops=dict(arrowstyle=\"simple\", facecolor=\"black\"))\n",
    "ax.annotate(\"$\\lambda = -1$\", xy=(0.3, 2.5), xytext=(0.65, 2.75), arrowprops=dict(arrowstyle=\"simple\", facecolor=\"black\"))\n",
    "\n",
    "ax.grid()\n",
    "ax.set_xlabel(\"$x$\")\n",
    "ax.set_ylabel(\"$T$\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Locating $T_{min}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for M in M_list:\n",
    "    for ll in ll_list:\n",
    "        x_T_min, T_min = izzo._compute_T_min(ll, M, 10, 1e-8)\n",
    "        ax.plot(x_T_min, T_min, 'kx', mew=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: Try out solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.43344673453504257"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T_ref = 1\n",
    "ll_ref = 0\n",
    "\n",
    "(x_ref, _), = izzo._find_xy(ll_ref, T_ref, 0, 10, 1e-8)\n",
    "x_ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5f06437208>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.plot(x_ref, T_ref, 'o', mew=2, mec='red', mfc='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 4: Run some examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy import units as u\n",
    "\n",
    "from poliastro.bodies import Earth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Single revolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[-3.4515641,~0.91031477,~0] \\; \\mathrm{\\frac{km}{s}}$"
      ],
      "text/plain": [
       "<Quantity [-3.45156412, 0.91031477, 0.        ] km / s>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = Earth.k\n",
    "r0 = [15945.34, 0.0, 0.0] * u.km\n",
    "r = [12214.83399, 10249.46731, 0.0] * u.km\n",
    "tof = 76.0 * u.min\n",
    "\n",
    "expected_va = [2.058925, 2.915956, 0.0] * u.km / u.s\n",
    "expected_vb = [-3.451569, 0.910301, 0.0] * u.km / u.s\n",
    "\n",
    "(v0, v), = izzo.lambert(k, r0, r, tof)\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[-3.3124603,~-4.1966173,~-0.38528762] \\; \\mathrm{\\frac{km}{s}}$"
      ],
      "text/plain": [
       "<Quantity [-3.31246031,-4.19661731,-0.38528762] km / s>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = Earth.k\n",
    "r0 = [5000.0, 10000.0, 2100.0] * u.km\n",
    "r = [-14600.0, 2500.0, 7000.0] * u.km\n",
    "tof = 1.0 * u.h\n",
    "\n",
    "expected_va = [-5.9925, 1.9254, 3.2456] * u.km / u.s\n",
    "expected_vb = [-3.3125, -4.1966, -0.38529] * u.km / u.s\n",
    "\n",
    "(v0, v), = izzo.lambert(k, r0, r, tof)\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple revolutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "k = Earth.k\n",
    "r0 = [22592.145603, -1599.915239, -19783.950506] * u.km\n",
    "r = [1922.067697, 4054.157051, -8925.727465] * u.km\n",
    "tof = 10 * u.h\n",
    "\n",
    "expected_va = [2.000652697, 0.387688615, -2.666947760] * u.km / u.s\n",
    "expected_vb = [-3.79246619, -1.77707641, 6.856814395] * u.km / u.s\n",
    "\n",
    "expected_va_l = [0.50335770, 0.61869408, -1.57176904] * u.km / u.s\n",
    "expected_vb_l = [-4.18334626, -1.13262727, 6.13307091] * u.km / u.s\n",
    "\n",
    "expected_va_r = [-2.45759553, 1.16945801, 0.43161258] * u.km / u.s\n",
    "expected_vb_r = [-5.53841370, 0.01822220, 5.49641054] * u.km / u.s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[-3.7924642,~-1.7770752,~6.8568103] \\; \\mathrm{\\frac{km}{s}}$"
      ],
      "text/plain": [
       "<Quantity [-3.7924642 ,-1.77707516, 6.85681026] km / s>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(v0, v), = izzo.lambert(k, r0, r, tof, M=0)\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(_, v_l), (_, v_r) = izzo.lambert(k, r0, r, tof, M=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[-4.1833446,~-1.1326259,~6.1330668] \\; \\mathrm{\\frac{km}{s}}$"
      ],
      "text/plain": [
       "<Quantity [-4.18334456,-1.13262586, 6.13306676] km / s>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[-5.5384092,~0.018221573,~5.4964072] \\; \\mathrm{\\frac{km}{s}}$"
      ],
      "text/plain": [
       "<Quantity [-5.53840923, 0.01822157, 5.49640718] km / s>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_r"
   ]
  }
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