doc/source/examples/05MappingTemplate.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Mapping with the MappingPulseTemplate\n",
    "\n",
    "We will now have a look on how to remap parameters, channel ids and measurements. The definition of measurements is illustrated in [Definition of Measurements](08Measurements.ipynb). The `MappingPulseTemplate` class allows us to take any already existing `PulseTemplate` and specify a mapping for its parameters, channel ids and measurements. \n",
    "\n",
    "This can be useful for simply renaming things, e.g., to avoid name collisions of parameters or change the name of a channel a pulse should be executed on, but can also be employed to derive the value of certain parameters from other parameters.\n",
    "\n",
    "## Mapping Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2*pi/omega\n",
      "{'omega', 'a'}\n"
     ]
    }
   ],
   "source": [
    "from qupulse.pulses import MappingPT, FunctionPT, SequencePT, AtomicMultiChannelPT\n",
    "\n",
    "sine = FunctionPT('a*sin(omega*t)', 't_duration')\n",
    "\n",
    "my_parameter_mapping = dict(t_duration='2*pi/omega', omega='omega', a='a')\n",
    "\n",
    "single_period_sine = MappingPT(sine, parameter_mapping=my_parameter_mapping)\n",
    "\n",
    "print(single_period_sine.duration)\n",
    "print(single_period_sine.parameter_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that we had to give mappings for all parameters, not only for the ones which changed. If we omit some of the encapsulated pulse tempaltes parameters an `MissingMappingException` is raised. This is done to enforce active thinking.\n",
    "\n",
    "You can, however, allow partial parameter mappings by passing `allow_partial_paramter_mappings=True` to the constructor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we expect an exception here:\n",
      "MissingMappingException : The template <qupulse.pulses.function_pulse_template.FunctionPulseTemplate object at 0x00000000051873C8> needs a mapping function for parameter(s) {'omega', 'a'}\n",
      "\n",
      "no exception with allow_partial_parameter_mapping=True\n",
      "2*pi/omega\n",
      "{'omega', 'a'}\n"
     ]
    }
   ],
   "source": [
    "partial_parameter_mapping = dict(t_duration='2*pi/omega')\n",
    "print('we expect an exception here:')\n",
    "try:\n",
    "    single_period_sine = MappingPT(sine, parameter_mapping=partial_parameter_mapping)\n",
    "except Exception as exception:\n",
    "    print(type(exception).__name__, ':',  exception)\n",
    "print('')\n",
    "\n",
    "print('no exception with allow_partial_parameter_mapping=True')\n",
    "single_period_sine = MappingPT(sine, parameter_mapping=partial_parameter_mapping, allow_partial_parameter_mapping=True)\n",
    "print(single_period_sine.duration)\n",
    "print(single_period_sine.parameter_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mapping of Channel Ids and Measurement Names\n",
    "\n",
    "Sometimes it is necessary to rename channels or measurements. Here we see a case where we want to play a sine and a cosine in parallel by using the `AtomicMultiChannelPulseTemplate` (for a more in depth explanation of multi-channel pulse template, see [Multi-Channel Pulses](07MultiChannelTemplates.ipynb)). Of course, this doesn't work as both pulses are by default defined on the 'default' channel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ChannelMappingException : Channel <default> is defined in subtemplate 1 and subtemplate 2\n"
     ]
    }
   ],
   "source": [
    "sine_measurements = [('M', 't_duration/2', 't_duration')]\n",
    "sine = FunctionPT('a*sin(omega*t)', 't_duration', measurements=sine_measurements)\n",
    "\n",
    "cos_measurements = [('M', 0, 't_duration/2')]\n",
    "cos = FunctionPT('a*cos(omega*t)', 't_duration', measurements=cos_measurements)\n",
    "\n",
    "try:\n",
    "    both = AtomicMultiChannelPT(sine, cos)\n",
    "except Exception as exception:\n",
    "    print(type(exception).__name__, ':', exception)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The solution is to use the `MappingPT` and rename the channels as we see in the next cell. Additionally, we want to distinguish between the measurements, so we rename them, too. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "remapped_cos channels: {'cos_channel'}\n",
      "remapped_cos measurements: {'M_cos'}\n",
      "\n",
      "remapped_sine channels: {'sin_channel'}\n",
      "remapped_sine measurements: {'M_sin'}\n",
      "\n",
      "{'sin_channel', 'cos_channel'}\n",
      "{'M_cos', 'M_sin'}\n"
     ]
    }
   ],
   "source": [
    "cos_channel_mapping = dict(default='cos_channel')\n",
    "cos_measurement_mapping = dict(M='M_cos')\n",
    "remapped_cos = MappingPT(cos, channel_mapping=cos_channel_mapping, measurement_mapping=cos_measurement_mapping)\n",
    "print('remapped_cos channels:', remapped_cos.defined_channels)\n",
    "print('remapped_cos measurements:', remapped_cos.measurement_names)\n",
    "print()\n",
    "\n",
    "sine_channel_mapping = dict(default='sin_channel')\n",
    "sine_measurement_mapping = dict(M='M_sin')\n",
    "remapped_sine = MappingPT(sine, measurement_mapping=sine_measurement_mapping, channel_mapping=sine_channel_mapping)\n",
    "print('remapped_sine channels:', remapped_sine.defined_channels)\n",
    "print('remapped_sine measurements:', remapped_sine.measurement_names)\n",
    "print()\n",
    "\n",
    "both = AtomicMultiChannelPT(remapped_sine, remapped_cos)\n",
    "print(both.defined_channels)\n",
    "print(both.measurement_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also plot it to see if it looks like expected with some dummy values for our parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "\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\", \"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 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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "from qupulse.pulses.plotting import plot\n",
    "\n",
    "parameter_values = dict(omega=1.0, a=1.0, t_duration=2*3.1415)\n",
    "\n",
    "_ = plot(both, parameters=parameter_values, sample_rate=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automatically created mapping templates\n",
    "\n",
    "Besides the explicit usage of the template it is also used implicitly in some cases. All implicit uses make use of the static member function `MappingPulseTemplate.from_tuple`. This 'constructor' automatically decides which mapping belongs to which entity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "channels: {'default'}\n",
      "measurements {'M_sin'}\n",
      "parameters {'t_duration', 'omega', 'a'}\n",
      "\n",
      "channels: {'default'}\n",
      "measurements {'M_sin'}\n",
      "parameters {'omega', 'a'}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "auto_mapped = MappingPT.from_tuple((sine, sine_measurement_mapping))\n",
    "print('channels:', auto_mapped.defined_channels)\n",
    "print('measurements', auto_mapped.measurement_names)\n",
    "print('parameters', auto_mapped.parameter_names)\n",
    "print()\n",
    "\n",
    "auto_mapped = MappingPT.from_tuple((sine, sine_measurement_mapping, partial_parameter_mapping))\n",
    "print('channels:', auto_mapped.defined_channels)\n",
    "print('measurements', auto_mapped.measurement_names)\n",
    "print('parameters', auto_mapped.parameter_names)\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In many cases, you do not need to create the MappingPT yourself. Most PulseParameters accept a mapping tuple like the ones used in the last cell. We could create our combined pulse also by using this implicit conversion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sin_channel', 'cos_channel'}\n",
      "{'M_cos', 'M_sin'}\n"
     ]
    }
   ],
   "source": [
    "both_implicit = AtomicMultiChannelPT((sine, sine_channel_mapping, sine_measurement_mapping), \n",
    "                                     (cos, cos_measurement_mapping, cos_channel_mapping))\n",
    "print(both_implicit.defined_channels)\n",
    "print(both_implicit.measurement_names)"
   ]
  }
 ],
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