notebooks/plot_results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fact.io import read_h5py\n",
    "from ctapipe.coordinates import CameraFrame\n",
    "from astropy.coordinates import AltAz, SkyCoord, EarthLocation, SkyOffsetFrame\n",
    "from astropy.coordinates.erfa_astrom import ErfaAstromInterpolator, erfa_astrom\n",
    "from astropy.time import Time\n",
    "import astropy.units as u\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ScienceState erfa_astrom: <astropy.coordinates.erfa_astrom.ErfaAstromInterpolator object at 0x7fc7e89c2710>>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# faster coordinate trafos\n",
    "erfa_astrom.set(ErfaAstromInterpolator(10 * u.min))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_theta_off(source_coord: SkyCoord, reco_coord: SkyCoord, pointing_coord: SkyCoord, n_off=5):\n",
    "    fov_frame = SkyOffsetFrame(origin=pointing_coord)\n",
    "    source_fov = source_coord.transform_to(fov_frame)\n",
    "    reco_fov = reco_coord.transform_to(fov_frame)\n",
    "    \n",
    "    r = source_coord.separation(pointing_coord)\n",
    "    phi0 = np.arctan2(source_fov.lat, source_fov.lon).to_value(u.rad)\n",
    "    \n",
    "    theta_offs = []\n",
    "    for off in range(1, n_off + 1):\n",
    "        \n",
    "        off_pos = SkyCoord(\n",
    "            lon=r * np.sin(phi0 + 2 * np.pi * off / (n_off + 1)),\n",
    "            lat=r * np.cos(phi0 + 2 * np.pi * off / (n_off + 1)),\n",
    "            frame=fov_frame,\n",
    "        )\n",
    "        \n",
    "        theta_offs.append(off_pos.separation(reco_fov))\n",
    "        \n",
    "    return reco_coord.separation(source_coord), np.concatenate(theta_offs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "location = EarthLocation.of_site('Roque de los Muchachos')\n",
    "\n",
    "sources = {\n",
    "    'Crab Nebula': {\n",
    "        'runs': [2767, 2768, 2769, 2770,  2771],\n",
    "    },\n",
    "    'Mrk 421': {\n",
    "        'runs': [2113, 2114, 2115, 2116, 2117, 2130, 2131, 2132, 2133]\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading data for Crab Nebula\n",
      "Ontime: 1.34 h, Duration: 1.41 h\n",
      "Total     #Events: 5330744\n",
      "Predicted #Events: 5330740\n",
      "Crab Nebula done\n",
      "\n",
      "Reading data for Mrk 421\n",
      "Ontime: 2.22 h, Duration: 2.23 h\n",
      "Total     #Events: 7274690\n",
      "Predicted #Events: 7274341\n",
      "Mrk 421 done\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# read data, apply transforms, calc run duration\n",
    "for name, source in sources.items():\n",
    "    print('Reading data for', name)\n",
    "    \n",
    "    events = []\n",
    "    durations = []\n",
    "    for run in source['runs']:\n",
    "        events.append(read_h5py(\n",
    "            f'/scratch/mnoethe/lst_mono/build_150/dl2_v0.6.1_v05_LST-1.Run{run:05d}.h5',\n",
    "            key='events',\n",
    "        ))\n",
    "        durations.append(events[-1]['dragon_time'].max() - events[-1]['dragon_time'].min())\n",
    "    \n",
    "    \n",
    "    df = pd.concat(events)\n",
    "    df.set_index(['obs_id', 'event_id'], inplace=True)\n",
    "    \n",
    "    \n",
    "    # calc ontime / duration\n",
    "    diff = df['dragon_time'].diff()\n",
    "    mean_diff = diff[diff < 1].mean()\n",
    "    source['ontime'] = (mean_diff * len(df) * u.s).to(u.hour)    \n",
    "    source['duration'] = (sum(durations) * u.s).to(u.hour)\n",
    "    print(f'Ontime: {source[\"ontime\"]:.2f}, Duration: {source[\"duration\"]:.2f}')\n",
    "    \n",
    "    print(f'Total     #Events: {len(df)}')\n",
    "    df = df.dropna(axis=0, subset=['source_x_prediction', 'source_y_prediction', 'gammaness'])\n",
    "    print(f'Predicted #Events: {len(df)}')\n",
    "    \n",
    "    obstime = Time(df.dragon_time, format='unix')\n",
    "    altaz = AltAz(obstime=obstime, location=location)\n",
    "    \n",
    "    pointing = SkyCoord(\n",
    "        alt=u.Quantity(df.alt_tel.values, u.rad, copy=False),\n",
    "        az=u.Quantity(df.az_tel.values, u.rad, copy=False),\n",
    "        frame=altaz,\n",
    "    )\n",
    "    \n",
    "    prediction_cam = SkyCoord(\n",
    "        x=u.Quantity(df.source_x_prediction.values, u.m, copy=False),\n",
    "        y=u.Quantity(df.source_y_prediction.values, u.m, copy=False),\n",
    "        frame=CameraFrame(telescope_pointing=pointing, location=location, obstime=obstime, focal_length=28 * u.m)\n",
    "    )\n",
    "    \n",
    "    prediction_icrs = prediction_cam.transform_to('icrs')\n",
    "    pointing_icrs = pointing.transform_to('icrs')\n",
    "    \n",
    "    \n",
    "    source['coord'] = SkyCoord.from_name(name)\n",
    "    source['events'] = df\n",
    "    source['pointing_coord'] = pointing_icrs\n",
    "    source['predicted_coord'] = prediction_icrs\n",
    "    \n",
    "    \n",
    "    # add as columns to df for convenience\n",
    "    df['source_ra_prediction'] = prediction_icrs.ra.deg\n",
    "    df['source_dec_prediction'] = prediction_icrs.dec.deg\n",
    "    df['pointing_ra'] = pointing_icrs.ra.deg\n",
    "    df['pointing_dec'] = pointing_icrs.dec.deg\n",
    "    \n",
    "    print(name, 'done\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
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       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
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       "        button.appendChild(icon_img);\n",
       "\n",
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       "};\n",
       "\n",
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       "    // 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",
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       "    properties['type'] = type;\n",
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       "    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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"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\";/* global mpl */\n",
       "\n",
       "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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gammaness_cut = 0.5\n",
    "\n",
    "\n",
    "fig, axs =  plt.subplots(1, len(sources), constrained_layout=True, figsize=(9, 4.5))\n",
    "axs = np.array([axs]).ravel()\n",
    "\n",
    "\n",
    "for ax, (name, source) in zip(axs, sources.items()):\n",
    "    \n",
    "    \n",
    "    ra = source['coord'].ra.deg\n",
    "    dec = source['coord'].dec.deg\n",
    "    df = source['events'].query(f'gammaness >= {gammaness_cut}')\n",
    "    \n",
    "    _, _, _, hist = ax.hist2d(\n",
    "        df.source_ra_prediction, df.source_dec_prediction,\n",
    "        bins=50, \n",
    "        range=[[ra - 2, ra + 2], [dec - 2, dec + 2]],\n",
    "        cmap='inferno'\n",
    "    )\n",
    "    ax.set_title(name)\n",
    "    ax.set_aspect(1)\n",
    "\n",
    "    circ = plt.Circle(xy=(ra, dec), radius=np.sqrt(0.04), fill=False, edgecolor='gray', lw=2)\n",
    "    ax.add_patch(circ)\n",
    "    \n",
    "    ax.legend((circ,), (name, ))\n",
    "    fig.colorbar(hist, ax=ax)\n",
    "    \n",
    "ax.set_xlabel('ra / deg')\n",
    "ax.set_ylabel('dec / deg')\n",
    "fig.suptitle(f'gammaness ≥ {gammaness_cut}')\n",
    "\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from pyirf.statistics import li_ma_significance\n",
    "\n",
    "theta_cut = 0.15 * u.deg\n",
    "gammaness_cut = 0.85\n",
    "\n",
    "n_off_positions = 5\n",
    "source = sources['Mrk 421']\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(9, 4.5), constrained_layout=True)\n",
    "\n",
    "for ax, (name, source) in zip(axs, sources.items()):\n",
    "\n",
    "    mask = source['events']['gammaness'] >= gammaness_cut\n",
    "    \n",
    "    theta, theta_off = calc_theta_off(\n",
    "        source_coord=source['coord'],\n",
    "        reco_coord=source['predicted_coord'][mask],\n",
    "        pointing_coord=source['pointing_coord'][mask],\n",
    "        n_off=5,\n",
    "    )\n",
    "\n",
    "\n",
    "    n_on = np.count_nonzero(theta <= theta_cut)\n",
    "    n_off = np.count_nonzero(theta_off <= theta_cut)\n",
    "    alpha = 1 / n_off_positions\n",
    "    n_excess = n_on - alpha * n_off\n",
    "    n_excess_unc = np.sqrt(n_on + alpha**2 * n_off)\n",
    "    sig = li_ma_significance(n_on, n_off, alpha)\n",
    "\n",
    "    # make sure theta cut is on bin edge\n",
    "    bins = np.arange(0, 0.3, theta_cut.to_value(u.deg)**2 / 3)\n",
    "    ax.hist(\n",
    "        theta_off.deg**2,\n",
    "        bins=bins,\n",
    "        color='lightgray',\n",
    "        weights=np.full(len(theta_off), 1/n_off_positions)\n",
    "\n",
    "    )\n",
    "\n",
    "    ax.hist(\n",
    "        theta.deg**2,\n",
    "        bins=bins,\n",
    "        histtype='step'\n",
    "    )\n",
    "\n",
    "    ax.axvline(theta_cut.to_value(u.deg)**2, color='k')\n",
    "    ax.margins(0, None)\n",
    "    \n",
    "    txt = rf'''$N_\\mathrm{{On}} = {n_on}, N_\\mathrm{{Off}} = {n_off}, \\alpha = {1 / n_off_positions}$\n",
    "    $N_\\mathrm{{excess}} = {n_excess:.1f} \\pm {n_excess_unc:.1f}$\n",
    "    $S_\\mathrm{{Li  Ma}} = {sig:.1f} \\sigma$ in $T = {source['duration'].to_value(u.hour):.2f}\\,\\mathrm{{h}}$\n",
    "    '''\n",
    "\n",
    "    ax.text(0.5, 0.95, txt, transform=ax.transAxes, va='top', ha='center')\n",
    "    ax.set_title(name)\n",
    "    ax.set_xlabel('θ² / deg²')"
   ]
  }
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