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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plasma detection from photodiode signal\n",
    "\n",
    "This is a quick plasma detection code from the photodiode signal.\n",
    "This procedure is run as soon as possible in order to provide the information on plasma existence to other routines.\n",
    "\n",
    "(author: L. Lobko)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "from scipy import signal as sigproc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shot_no = 44892"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_db_current_shot(field_name, value):\n",
    "    os.system('export PGPASSWORD=`cat /golem/production/psql_password`;psql -c \"UPDATE operation.discharges SET '+field_name+'='+str(value)+'WHERE shot_no IN(SELECT max(shot_no) FROM operation.discharges)\" -q -U golem golem_database')\n",
    "    os.system('export PGPASSWORD=`cat /golem/production/psql_password`;psql -c \"UPDATE diagnostics.basicdiagnostics SET '+field_name+'='+str(value)+'WHERE shot_no IN(SELECT max(shot_no) FROM diagnostics.basicdiagnostics)\" -q -U golem golem_database')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs('Results', exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_scalar(phys_quant, value, format_str='%.3f'):\n",
    "    with open(\"Results/\"+phys_quant, 'w') as f:\n",
    "        f.write(format_str % value)\n",
    "    update_db_current_shot(phys_quant,value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = np.DataSource('/tmp')  # temporary storage for downloaded files\n",
    "data_URL = 'http://golem.fjfi.cvut.cz/shots/{shot_no}/Diagnostics/PlasmaDetection/U_LeybPhot.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(shot_no):\n",
    "    fname = ds.open(data_URL.format(shot_no=shot_no)).name\n",
    "    data = np.loadtxt(fname, delimiter=',')\n",
    "    data[:, 0] = data[:, 0] * 1e3 # from micros to ms\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "U_photodiode = load_data(shot_no)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove offset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def offset_remove(data):\n",
    "    x_size, y_size = data.shape\n",
    "    data_for_offset = data[0:int(x_size/100)]\n",
    "    offset = np.mean(data_for_offset[:, 1])\n",
    "    data[:, 1] -= offset\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "U_photodiode = offset_remove(U_photodiode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Smooth signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def smooth(y, box_pts):\n",
    "    box = np.ones(box_pts) / box_pts\n",
    "    y_smooth = np.convolve(y, box, mode='same')\n",
    "    return y_smooth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "U_photodiode[:, 1] = smooth(U_photodiode[:, 1], 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "def find_peaks(data):\n",
    "    peaks_indexes, _ = sigproc.find_peaks(data[:, 1], prominence=1e-5)\n",
    "    return np.vstack((data[peaks_indexes, 0], data[peaks_indexes, 1])).T"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate plasma boundaries from signal derivation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_plasma_boundaries(data, position):\n",
    "    deriv = data.copy()\n",
    "    deriv[:, 1] = np.gradient(data[:, 1])\n",
    "    deriv[:, 1] = smooth(deriv[:, 1], 200)\n",
    "    if position == 'start':\n",
    "        index = np.where(deriv[:, 1] >= np.max(deriv[:, 1])/5)\n",
    "        deriv = deriv[index]\n",
    "        return deriv[0, 0]\n",
    "    else:\n",
    "        deriv = np.abs(deriv)\n",
    "        peaks = find_peaks(deriv)\n",
    "        return peaks[-1, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if np.max(U_photodiode[:, 1]) < 0.01:\n",
    "    print('No plasma in vacuum chamber.')\n",
    "    t_plasma_start = -1.0\n",
    "    t_plasma_end = -1.0\n",
    "else:\n",
    "    t_plasma_start = calc_plasma_boundaries(U_photodiode, 'start')\n",
    "    print('Plasma starts at {:.2f} ms.'.format(t_plasma_start))\n",
    "    t_plasma_end = calc_plasma_boundaries(U_photodiode, 'end')\n",
    "    print('Plasma ends at {:.2f} ms.'.format(t_plasma_end))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b_plasma = int(t_plasma_start > 0 and t_plasma_end > 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if b_plasma:\n",
    "    t_plasma_duration = t_plasma_end - t_plasma_start\n",
    "    print('Plasma duration is {:.2f} ms.'.format(t_plasma_duration))\n",
    "else:\n",
    "    t_plasma_duration = -1.0  # convention instead of nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_scalar(\"b_plasma\", b_plasma)\n",
    "save_scalar(\"t_plasma_start\", t_plasma_start)\n",
    "save_scalar(\"t_plasma_end\", t_plasma_end)\n",
    "save_scalar(\"t_plasma_duration\", t_plasma_duration)"
   ]
  }
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