How to extract peaks from digitizer traces.ipynb

       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        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>"
      ]
     },
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     "output_type": "display_data"
    },
    {
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width=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'pulseStart': 22491,\n",
       " 'pulseStop': 22525,\n",
       " 'baseStart': 22480,\n",
       " 'baseStop': 22485,\n",
       " 'period': 96,\n",
       " 'npulses': 400}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params['period'] = 96\n",
    "params['npulses'] = 400\n",
    "params['pulseStart'] = 22491\n",
    "params['pulseStop'] = 22525\n",
    "params['baseStart'] = 22480\n",
    "params['baseStop'] = 22485\n",
    "\n",
    "params = tb.check_peak_params(run, 'FastADC0raw',\n",
    "                              bunchPattern='sase3', params=params, )\n",
    "params"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff86780b",
   "metadata": {},
   "source": [
    "### Reload data with raw trace and extract peaks using the new integration parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4225c153",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "\n",
       ".xr-attrs dt {\n",
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:            (pulse_slot: 2700, sa3_pId: 400, trainId: 1817)\n",
       "Coordinates:\n",
       "  * trainId            (trainId) uint64 1471028273 1471028274 ... 1471028272\n",
       "  * sa3_pId            (sa3_pId) int64 1056 1060 1064 1068 ... 2644 2648 2652\n",
       "Dimensions without coordinates: pulse_slot\n",
       "Data variables:\n",
       "    bunchPatternTable  (trainId, pulse_slot) uint32 2146089 0 ... 16777216\n",
       "    chem_Y             (trainId) float32 224.2802 224.28229 ... 224.2802\n",
       "    FastADC0peaks      (trainId, sa3_pId) float64 239.0 9.5 ... -69.0 101.5\n",
       "Attributes:\n",
       "    runFolder:  /gpfs/exfel/exp/SCS/202202/p002953/raw/r0001</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7043deee-5aef-45da-854f-b40f215a1e5d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7043deee-5aef-45da-854f-b40f215a1e5d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>pulse_slot</span>: 2700</li><li><span class='xr-has-index'>sa3_pId</span>: 400</li><li><span class='xr-has-index'>trainId</span>: 1817</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6946dfbb-b9b1-4c34-a41c-152823ce8335' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6946dfbb-b9b1-4c34-a41c-152823ce8335' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>trainId</span></div><div class='xr-var-dims'>(trainId)</div><div class='xr-var-dtype'>uint64</div><div class='xr-var-preview xr-preview'>1471028273 ... 1471028272</div><input id='attrs-089b892b-26da-440a-8129-9aa107dd06d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-089b892b-26da-440a-8129-9aa107dd06d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97c15a34-3e19-45f5-873c-004b27af5348' class='xr-var-data-in' type='checkbox'><label for='data-97c15a34-3e19-45f5-873c-004b27af5348' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1471028273, 1471028274, 1471028275, ..., 1471028270, 1471028271,\n",
       "       1471028272], dtype=uint64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sa3_pId</span></div><div class='xr-var-dims'>(sa3_pId)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1056 1060 1064 ... 2644 2648 2652</div><input id='attrs-d7cf2147-06e5-4273-97a9-d611559007e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7cf2147-06e5-4273-97a9-d611559007e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e28a03eb-8ca5-4583-8253-d249b35237ef' class='xr-var-data-in' type='checkbox'><label for='data-e28a03eb-8ca5-4583-8253-d249b35237ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1056, 1060, 1064, ..., 2644, 2648, 2652])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2d9e7a87-b051-4aaf-8a17-8cd129970e59' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2d9e7a87-b051-4aaf-8a17-8cd129970e59' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>bunchPatternTable</span></div><div class='xr-var-dims'>(trainId, pulse_slot)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>2146089 0 ... 16777216 16777216</div><input id='attrs-dcd2e909-e64a-4705-af8b-df1850052314' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dcd2e909-e64a-4705-af8b-df1850052314' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51a128df-9baf-46ac-9aad-85ee9e968f28' class='xr-var-data-in' type='checkbox'><label for='data-51a128df-9baf-46ac-9aad-85ee9e968f28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 2146089,        0,  2097193, ..., 16777216, 16777216, 16777216],\n",
       "       [ 2146089,        0,  2097193, ..., 16777216, 16777216, 16777216],\n",
       "       [ 2146089,        0,  2097193, ..., 16777216, 16777216, 16777216],\n",
       "       ...,\n",
       "       [ 2146089,        0,  2097193, ..., 16777216, 16777216, 16777216],\n",
       "       [ 2146089,        0,  2097193, ..., 16777216, 16777216, 16777216],\n",
       "       [ 2211625,        0,  2097193, ..., 16777216, 16777216, 16777216]],\n",
       "      dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chem_Y</span></div><div class='xr-var-dims'>(trainId)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>224.2802 224.28229 ... 224.2802</div><input id='attrs-d90379bd-f8fd-4c16-8a59-17f380df81bf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d90379bd-f8fd-4c16-8a59-17f380df81bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d46a78b-af53-4734-8116-257139ab8aff' class='xr-var-data-in' type='checkbox'><label for='data-0d46a78b-af53-4734-8116-257139ab8aff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([224.2802 , 224.28229, 224.28229, ..., 224.2802 , 224.2802 ,\n",
       "       224.2802 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FastADC0peaks</span></div><div class='xr-var-dims'>(trainId, sa3_pId)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>239.0 9.5 -169.0 ... -69.0 101.5</div><input id='attrs-c5b16cb2-b990-4a3d-8908-b9873de9d990' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5b16cb2-b990-4a3d-8908-b9873de9d990' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e948a55-3e65-463a-b5a1-b42fabcc297d' class='xr-var-data-in' type='checkbox'><label for='data-1e948a55-3e65-463a-b5a1-b42fabcc297d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 239. ,    9.5, -169. , ...,   53.5,   51.5,  113. ],\n",
       "       [-127.5,   47. ,  -22. , ...,  297.5,  115. ,  322. ],\n",
       "       [ 209.5, -281. , -221.5, ...,   33.5,  -19.5,   31.5],\n",
       "       ...,\n",
       "       [ 186. ,  117. ,  -55.5, ...,   -3. ,  -91. ,  -61. ],\n",
       "       [-284.5, -129. ,  -23.5, ...,   16. , -126. ,  120.5],\n",
       "       [-100.5, -339. ,   11. , ..., -126. ,  -69. ,  101.5]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-928505e0-a653-4fc4-94ba-fe76bdf2fe37' class='xr-section-summary-in' type='checkbox'  checked><label for='section-928505e0-a653-4fc4-94ba-fe76bdf2fe37' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>runFolder :</span></dt><dd>/gpfs/exfel/exp/SCS/202202/p002953/raw/r0001</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:            (pulse_slot: 2700, sa3_pId: 400, trainId: 1817)\n",
       "Coordinates:\n",
       "  * trainId            (trainId) uint64 1471028273 1471028274 ... 1471028272\n",
       "  * sa3_pId            (sa3_pId) int64 1056 1060 1064 1068 ... 2644 2648 2652\n",
       "Dimensions without coordinates: pulse_slot\n",
       "Data variables:\n",
       "    bunchPatternTable  (trainId, pulse_slot) uint32 2146089 0 ... 16777216\n",
       "    chem_Y             (trainId) float32 224.2802 224.28229 ... 224.2802\n",
       "    FastADC0peaks      (trainId, sa3_pId) float64 239.0 9.5 ... -69.0 101.5\n",
       "Attributes:\n",
       "    runFolder:  /gpfs/exfel/exp/SCS/202202/p002953/raw/r0001"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run, ds = tb.load(proposal, runNB, fields, extract_fadc=False)\n",
    "ds = tb.get_digitizer_peaks(run, 'FastADC0raw', merge_with=ds,\n",
    "                            integParams=params, bunchPattern='sase3')\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d0d64a0",
   "metadata": {},
   "source": [
    "Note that the `mnemonics` parameter in `get_digitizer_peaks` can take a list of mnemonics, so this peak extraction can be simultaneoulsy applied to various channels of a same digitizer (`mnemonics=['FastADC2_5raw', 'FastADC2_9raw']` for instance)."
   ]
  }
 ],
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