Plasma detection from photodiode signal

This is a quick plasma detection code from the photodiode signal. This procedure is run as soon as possible in order to provide the information on plasma existence to other routines.

(author: L. Lobko)

import numpy as np
import os
from scipy import signal as sigproc

shot_no = 44562

def update_db_current_shot(field_name, value):
    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')
    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')

os.makedirs('Results', exist_ok=True)

def save_scalar(phys_quant, value, format_str='%.3f'):
    with open("Results/"+phys_quant, 'w') as f:
        f.write(format_str % value)
    update_db_current_shot(phys_quant,value)

ds = np.DataSource('/tmp')  # temporary storage for downloaded files
data_URL = 'http://golem.fjfi.cvut.cz/shots/{shot_no}/Diagnostics/PlasmaDetection/U_LeybPhot.csv'

Load data

def load_data(shot_no):
    fname = ds.open(data_URL.format(shot_no=shot_no)).name
    data = np.loadtxt(fname, delimiter=',')
    data[:, 0] = data[:, 0] * 1e3 # from micros to ms
    return data

U_photodiode = load_data(shot_no)

Remove offset

def offset_remove(data):
    x_size, y_size = data.shape
    data_for_offset = data[0:int(x_size/100)]
    offset = np.mean(data_for_offset[:, 1])
    data[:, 1] -= offset
    return data

U_photodiode = offset_remove(U_photodiode)

Smooth signal

def smooth(y, box_pts):
    box = np.ones(box_pts) / box_pts
    y_smooth = np.convolve(y, box, mode='same')
    return y_smooth

U_photodiode[:, 1] = smooth(U_photodiode[:, 1], 100)

def find_peaks(data):
    peaks_indexes, _ = sigproc.find_peaks(data[:, 1], prominence=1e-5)
    return np.vstack((data[peaks_indexes, 0], data[peaks_indexes, 1])).T

Calculate plasma boundaries from signal derivation

def calc_plasma_boundaries(data, position):
    deriv = data.copy()
    deriv[:, 1] = np.gradient(data[:, 1])
    deriv[:, 1] = smooth(deriv[:, 1], 200)
    if position == 'start':
        index = np.where(deriv[:, 1] >= np.max(deriv[:, 1])/5)
        deriv = deriv[index]
        return deriv[0, 0]
    else:
        deriv = np.abs(deriv)
        peaks = find_peaks(deriv)
        return peaks[-1, 0]

if np.max(U_photodiode[:, 1]) < 0.01:
    print('No plasma in vacuum chamber.')
    t_plasma_start = -1.0
    t_plasma_end = -1.0
else:
    t_plasma_start = calc_plasma_boundaries(U_photodiode, 'start')
    print('Plasma starts at {:.2f} ms.'.format(t_plasma_start))
    t_plasma_end = calc_plasma_boundaries(U_photodiode, 'end')
    print('Plasma ends at {:.2f} ms.'.format(t_plasma_end))

No plasma in vacuum chamber.

b_plasma = int(t_plasma_start > 0 and t_plasma_end > 0)

if b_plasma:
    t_plasma_duration = t_plasma_end - t_plasma_start
    print('Plasma duration is {:.2f} ms.'.format(t_plasma_duration))
else:
    t_plasma_duration = -1.0  # convention instead of nan

Save data

save_scalar("b_plasma", b_plasma)
save_scalar("t_plasma_start", t_plasma_start)
save_scalar("t_plasma_end", t_plasma_end)
save_scalar("t_plasma_duration", t_plasma_duration)