{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plasma detection from $U_{loop}$\n",
"\n",
"This is a quick plasma start and end detection from the peaks in the derivative of the $U_{loop}$ signal.\n",
"This procedure is run as soon as possible in order to provide the information on plasma existance to other routines."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy import signal # for peak detection\n",
"import pandas as pd\n",
"import requests\n",
"import subprocess #workarround for db operation\n",
"import os\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"shot_no = 39159"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def update_db_current_shot_alchemy(field_name, value):\n",
" try:\n",
" # local UNIX socket connection\n",
" engine = sqlalchemy.create_engine('postgresql://golem@/golem_database?host=/var/run/postgresql')\n",
" #engine = sqlalchemy.create_engine('postgresql://golem:rabijosille@192.168.2.116/golem_database')\n",
" except:\n",
" return\n",
" engine.execute(f\"\"\"UPDATE shots SET \"{field_name}\"={value} WHERE shot_no IN(SELECT max(shot_no) FROM shots)\"\"\") \n",
"\n",
"def update_db_current_shot(field_name, value):\n",
" os.system('export PGPASSWORD=\"rabijosille\";psql -c \"UPDATE shots SET '+field_name+'='+str(value)+'WHERE shot_no IN(SELECT max(shot_no) FROM shots)\" -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) \n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"t_CD = requests.get(\n",
" f'http://golem.fjfi.cvut.cz/shots/{shot_no}/Production/Parameters/Tcd')\n",
"t_CD = float(t_CD.content) * 1e-3 # from us to ms\n",
"t_CD"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"U_loop = pd.read_csv(f'http://golem.fjfi.cvut.cz/shots/{shot_no}/Diagnostics/BasicDiagnostics/U_Loop.csv',\n",
" names=['Time', 'U_loop'], index_col='Time', squeeze=True)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The processing is done only beyond the CD system activation time (to prevent thyristor noise from e.g. the $B_t$ system)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"U_loop.index *= 1e3 # s to ms "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"U_loop = U_loop.loc[t_CD:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dt = np.diff(U_loop.index[[0,-1]]).item() / U_loop.index.size\n",
"dt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def to_points(time, must_be_odd=False):\n",
" n = int(np.rint(time / dt))\n",
" if n % 2 == 0 and must_be_odd:\n",
" n += 1\n",
" return n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Estimate $\\frac{d U_{loop}}{dt}$ by a second order Savitzky-Golay 1. derivative filter."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Dt = 0.1 # window length in ms\n",
"dU = pd.Series(signal.savgol_filter(U_loop.loc[t_CD:], to_points(Dt, must_be_odd=True),\n",
" 2, 1, delta=dt), index=U_loop.index, name='dU_dt')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def argrelmax_time(sig, Dt, res_index, threshold_value=None, \n",
" comparator=np.greater):\n",
" idxs = signal.argrelextrema(sig.values, comparator, order=to_points(Dt))\n",
" sig_sel = sig.iloc[idxs]\n",
" if threshold_value is not None:\n",
" sig_sel = sig_sel[comparator(sig_sel, threshold_value)]\n",
" return sig_sel.index[res_index]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The plasma starts when the $U_{loop}$ decreases significantly for the first (index 0) time."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" t_plasma_start = argrelmax_time(dU, Dt/2, 0, threshold_value=-3,\n",
" comparator=np.less)\n",
"except IndexError:\n",
" t_plasma_start = -1.0 # not found, nan convention\n",
"t_plasma_start"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The plasma ends when the $U_{loop}$ increases significantly for the last (index -1) time."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" t_plasma_end = argrelmax_time(dU.loc[t_plasma_start:], Dt/2, -1,\n",
" threshold_value=10,\n",
" comparator=np.greater)\n",
"except IndexError:\n",
" t_plasma_end = -1.0 # not found, nan convention\n",
"t_plasma_end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"is_plasma = int(t_plasma_start > 0 and t_plasma_end > 0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if is_plasma:\n",
" t_plasma_duration = t_plasma_end - t_plasma_start\n",
"else:\n",
" t_plasma_duration = -1.0 # convention instead of nan"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(f'plasma: {bool(is_plasma)}, from {t_plasma_start:.1f} to {t_plasma_end:.1f} ms (lifetime {t_plasma_duration:.1f} ms)')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"save_scalar(\"is_plasma\", is_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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