{ "cells": [ { "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" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "shot_no = 42597" ] }, { "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, "outputs": [], "source": [ "ds = np.DataSource('/tmp') # temporary storage for downloaded files\n", "data_URL = 'http://golem.fjfi.cvut.cz/shots/{shot_no}/Diagnostics/BasicDiagnostics/DetectPlasma/U_LeybPhot.csv'" ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "source": [ "Load data" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "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" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "U_photodiode = load_data(shot_no)" ] }, { "cell_type": "markdown", "source": [ "Remove offset" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "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" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "U_photodiode = offset_remove(U_photodiode)" ] }, { "cell_type": "markdown", "source": [ "Smooth signal" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "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" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "U_photodiode[:, 1] = smooth(U_photodiode[:, 1], 100)" ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "source": [ "Calculate plasma boundaries from signal derivation" ], "metadata": { "collapsed": false } }, { "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", " index = np.where(deriv[:, 1] == np.min(deriv[:, 1]))\n", " deriv = deriv[index]\n", " return deriv[0, 0]" ] }, { "cell_type": "code", "execution_count": null, "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))" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "b_plasma = int(t_plasma_start > 0 and t_plasma_end > 0)" ], "metadata": { "collapsed": false } }, { "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", "source": [ "Save data" ], "metadata": { "collapsed": false } }, { "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)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 4 }