def intgrals_calc_25():
#magnetron_in_vals = [8.7, 10.7, 13]
test = ProcessSignal('201008')
csv_files_list = os.listdir(test.csv_files_path)
csv_types = test.read_type_file()
csv_signals = csv_types['signal_files']
csv_signal_nums = csv_types['signal_nums']
excel_dicts = test.read_excel(csv_signal_nums)['numbers']
magnetron_nums = excel_dicts['magnetron']
reb_nums = excel_dicts['noise']
#m_classif_dict = m_signals_classification(magnetron_in_vals, magnetron_nums)
magnetron_dict = {}
reb_dict = {}
for csv_signal in csv_signals:
if csv_signal in csv_files_list:
num = csv_signal[3:6]
file = test.open_file(csv_signal, reduced=True)
t, u = file['time'], file['voltage']
pl_density = test.read_excel(csv_signal)['dicts'][num]['Ток плазмы, А']
full_integral = np.round(test.e_square(t, u) / 1e-8, 3)
if num in reb_nums:
reb_dict[num] = {'n': pl_density,
'full_integral': full_integral,
'num': num}
else:
len_t = len(t)
dt = np.abs(t[1] - t[0])
fft_u = rfft(u)
freqs_fft = rfftfreq(len_t, dt)
ind_mask = np.logical_and(2.695e9 < freqs_fft, freqs_fft < 2.725e9)
u_empty = np.zeros(len(ind_mask))
u_empty[ind_mask] = fft_u[ind_mask]
fft_filtered_u = irfft(u_empty)
#plt.plot(t, u, color='k')
#plt.plot(t, fft_filtered_u)
#plt.show()
absorbers = test.read_excel(csv_signal)['dicts'][num]['Поглотители в тракте магнетрона']
filtered_integral = np.round(test.e_square(t, fft_filtered_u) / 1e-8, 3)
pedestal_integral = full_integral - filtered_integral
magnetron_dict[num] = {'n': pl_density,
'full_integral': full_integral,
'peak_integral': filtered_integral,
'pedestal_integral': pedestal_integral,
'abs': absorbers,
'num': num}
plt.plot(pl_density, full_integral, color='blue', marker='o')
plt.plot(pl_density, filtered_integral, color='red', marker='o', linestyle='-')
return reb_dict, magnetron_dict
def noise_integrals_block(exp_num, file_nums):
proc = ProcessSignal(str(exp_num))
shot_nums, density_vals, full_ints = [], [], []
for num in file_nums:
file_name = f'str{num}.csv'
file = proc.open_file(file_name, reduced=True)
u = file['voltage']
t = file['time']
dt_2 = (t[-1] - t[0])**2
pl_density = proc.read_excel(file_name)['dicts'][num]['Ток плазмы, А']
freqs, amps = proc.fft_amplitude(t,
u)['frequency'], proc.fft_amplitude(
t, u)['amplitude']
full_int = np.round(dt_2 * proc.e_square(freqs, amps) / 2e-8, 3)
density_vals.append(pl_density)
shot_nums.append(num)
full_ints.append(full_int)
sorted_inds = np.argsort(density_vals)
density_vals = np.asarray(density_vals)[sorted_inds]
shot_nums = np.asarray(shot_nums)[sorted_inds]
full_ints = np.asarray(full_ints)[sorted_inds]
integrals_dict = {
'nums': shot_nums,
'density': density_vals,
'full_ints': full_ints
}
return integrals_dict
def magnetron_integrals_bloc(exp_num,
file_nums,
central_freq=2.714,
band_half_width=0.05):
proc = ProcessSignal(str(exp_num))
shot_nums, density_vals, full_ints, peak_ints, exp_nums = [], [], [], [], []
for num in file_nums:
file_name = f'str{num}.csv'
file = proc.open_file(file_name, reduced=True)
u = file['voltage']
t = file['time']
f_low = (central_freq - band_half_width) * 1e9
f_high = (central_freq + band_half_width) * 1e9
dt_2 = (t[-1] - t[0])**2
#pl_num = f'{(int(num) - 1):03d}'
pl_density = proc.read_excel(file_name)['dicts'][num]['Ток плазмы, А']
freqs, amps = proc.fft_amplitude(t,
u)['frequency'], proc.fft_amplitude(
t, u)['amplitude']
peak_inds = np.logical_and(freqs >= f_low, freqs <= f_high)
p_freqs, p_amps = freqs[peak_inds], amps[peak_inds]
full_int = np.round(dt_2 * proc.e_square(freqs, amps) / 2e-8, 3)
peak_int = np.round(dt_2 * proc.e_square(p_freqs, p_amps) / 2e-8, 3)
density_vals.append(pl_density)
shot_nums.append(num)
full_ints.append(full_int)
peak_ints.append(peak_int)
exp_nums.append(exp_num)
sorted_inds = np.argsort(density_vals)
density_vals = np.asarray(density_vals)[sorted_inds]
shot_nums = np.asarray(shot_nums)[sorted_inds]
full_ints, peak_ints = np.asarray(full_ints)[sorted_inds], np.asarray(
peak_ints)[sorted_inds]
integrals_dict = {
'nums': shot_nums,
'density': density_vals,
'full_ints': full_ints,
'peak_ints': peak_ints,
'exp_nums': exp_nums
}
return integrals_dict
def two_exp_integrals(exp_num, table=False, data=True):
test = ProcessSignal('{}'.format(exp_num))
csv_types = test.read_type_file()
csv_signals = csv_types['signal_files']
pl_densities = np.zeros(len(csv_signals))
integrals = np.zeros(len(csv_signals))
nums = np.zeros(len(csv_signals))
csv_path = test.csv_files_path
csv_dir_files = os.listdir(csv_path)
test.reduce_fft()
for i, csv_signal in enumerate(csv_signals):
csv_signal_num = csv_signal[3:6]
if csv_signal in csv_dir_files:
file = test.open_file(csv_signal, reduced=True)
pl_density = test.read_excel(csv_signal)['dicts'][csv_signal_num]['Ток плазмы, А']
pl_densities[i] = pl_density
nums[i] = csv_signal_num
t, u, dt = file['time'], file['voltage'], file['time_resolution']
plt.plot(t, u)
integrals[i] = np.round(test.e_square(t, u) / 1e-7, 2)
print(csv_signal_num, integrals[i])
plt.title("№ {}, e^2 = {}".format(csv_signal_num, integrals[i]))
#plt.show()
sorted_inds = np.argsort(pl_densities)
pl_densities = pl_densities[sorted_inds]
integrals = integrals[sorted_inds]
nums = nums[sorted_inds]
if table:
ex_table = excel.Workbook()
ex_table.create_sheet(title='Integral', index=0)
sheet = ex_table['Integral']
sheet['A1'] = 'Номер'
sheet['B1'] = 'Плотность плазмы, отн.ед.'
sheet['C1'] = 'Интеграл, *10-8'
table_size = integrals.size
for k in range(table_size):
cell = sheet.cell(row=k+2, column=1)
cell.value = nums[k]
cell = sheet.cell(row=k+2, column=2)
cell.value = pl_densities[k]
cell = sheet.cell(row=k+2, column=3)
cell.value = integrals[k]
path = test.excel_folder_path / 'Integral_{}.xlsx'.format(exp_num)
ex_table.save(path)
if data:
integral_dict = {'pl_ds': pl_densities,
'integral_vals': integrals,
'shot_nums': nums}
return integral_dict
nums = np.zeros(len(csv_signals))
test.reduce_fft()
for i, csv_signal in enumerate(csv_signals):
csv_signal_num = csv_signal[3:6]
file = test.open_file(csv_signal, reduced=True)
pl_density = test.read_excel(
csv_signal)['dicts'][csv_signal_num]['Ток плазмы, А']
pl_densities[i] = pl_density
time = file['time']
voltage = file['voltage']
t, u, dt = file['time'], file['voltage'], file['time_resolution']
fft = test.fft_amplitude(t, u, dt)
freqs = fft['frequency']
amps = fft['amplitude']
n_dt_2 = (t[-1] - t[0])**2
full_integral = 2 * n_dt_2 * test.e_square(freqs, amps) / 1e-8
integrals[i] = full_integral
nums[i] = csv_signal_num
ex_table = excel.Workbook()
ex_table.create_sheet(title='Integral', index=0)
sheet = ex_table['Integral']
sheet['A1'] = 'Номер'
sheet['B1'] = 'Плотность плазмы, отн.ед.'
sheet['C1'] = 'Интеграл, *10-8'
table_size = integrals.size
for k in range(table_size):
cell = sheet.cell(row=k + 2, column=1)
cell.value = nums[k]
cell = sheet.cell(row=k + 2, column=2)
cell.value = pl_densities[k]
print(reb_nums)
reb_dict = {}
ambigous_nums = []
dt_list = []
for csv_signal in csv_signals:
if csv_signal in csv_files_list:
num = csv_signal[3:6]
if num in magnetron_nums or num in reb_nums:
full_file = test.open_file(csv_signal, reduced=False)
t_full, u_full = full_file['time'], full_file['voltage']
file = test.open_file(csv_signal, reduced=True)
t, u = file['time'], file['voltage']
dt = np.abs(t[1] - t[0])
pl_density = test.read_excel(
csv_signal)['dicts'][num]['Ток плазмы, А']
full_integral = np.round(test.e_square(t, u) / 1e-8, 2)
full_file_full_integral = np.round(
test.e_square(t_full, u_full) / 1e-8, 2)
filtered_u = fft_filter(t, u)
filtered_u_full = fft_filter(t_full, u_full)
filt_integral = np.round(test.e_square(t, filtered_u) / 1e-8, 3)
filt_integral_full = np.round(
test.e_square(t_full, filtered_u_full) / 1e-8, 2)
b_filtered_u = test.bandpass_filter(t, u, 2.695e9, 2.725e9)
b_filtered_u_full = test.bandpass_filter(t_full, u_full, 2.695e9,
2.725e9)
b_filt_integral = np.round(
test.e_square(t, b_filtered_u) / 1e-8, 2)
b_filt_integral_full = np.round(
else:
t_min, t_max = 60e-9, 322e-9
cut_inds = np.logical_and(t >= t_min, t <= t_max)
t_cut = t[cut_inds]
u_cut = u[cut_inds]
print(t[0])
dt_2 = (t_cut[-1] - t_cut[0])**2
u_filt = proc.bandpass_filter(t, u, 2.68e9, 2.74e9)
'''
plt.plot(t, u)
plt.plot(t_cut, u_cut)
plt.plot(t, u_filt)
plt.title(signal)
plt.show()
'''
integral = np.round(proc.e_square(t_cut, u_cut) / 1e-8, 3)
freqs, amps = proc.fft_amplitude(t_cut,
u_cut)['frequency'], proc.fft_amplitude(
t_cut, u_cut)['amplitude']
peak_inds = np.logical_and(freqs >= 2.695e9, freqs <= 2.725e9)
p_freqs, p_amps = freqs[peak_inds], amps[peak_inds]
plt.plot(freqs, amps)
plt.title(signal)
plt.xlim(left=0e9, right=4e9)
plt.show()
full_int = np.round(dt_2 * proc.e_square(freqs, amps) / 2e-8, 3)
peak_int = np.round(dt_2 * proc.e_square(p_freqs, p_amps) / 2e-8, 3)
print('num =', signal, integral, full_int)
n = proc.read_excel(
import numpy as np
import openpyxl as excel
from ProcessClass_10 import ProcessSignal
test = ProcessSignal('191120')
file_name = 'str018.csv'
file = test.open_file(file_name, reduced=True)
t, u, dt = file['time'], file['voltage'], file['time_resolution']
fft = test.fft_amplitude(t, u, dt)
freqs = fft['frequency']
amps = fft['amplitude']
n_dt_2 = (t[-1] - t[0])**2
full_integral = 2 * n_dt_2 * test.e_square(freqs, amps) / 1e-8
print(full_integral)
df_s = np.arange(5, 105, 5) * 1e6
noise_integrals = np.zeros(len(df_s))
peak_integrals = np.zeros(len(df_s))
magnetron_freq = 2.74e9
for i, df in enumerate(df_s):
low_freq = magnetron_freq - df
high_freq = magnetron_freq + df
noise_inds_l = freqs < low_freq
noise_inds_r = freqs > high_freq
peak_inds = np.logical_and(freqs > low_freq, freqs < high_freq)
noise_freqs_l, noise_amps_l = freqs[noise_inds_l], amps[noise_inds_l]
noise_freqs_r, noise_amps_r = freqs[noise_inds_r], amps[noise_inds_r]
peak_freqs, peak_amps = freqs[peak_inds], amps[peak_inds]
magnetron_nums = excel_dicts['magnetron'][0:0:]
nums, n_s, l_ints, r_ints = [], [], [], []
l_noise_ints, r_noise_ints, noise_ns, n_nums = [], [], [], []
for signal in csv_signals:
num = signal[3:6]
file = proc.open_file(signal, reduced=True)
u = file['voltage']
t = file['time']
dt = file['time_resolution']
dt_2 = (t[-1] - t[0])**2
fft_data = proc.fft_amplitude(t, u)
freqs, amps = fft_data['frequency'][1::], fft_data['amplitude'][1::]
left_inds = freqs <= 2.71e9 - 50e6
l_freqs = freqs[left_inds]
l_amps = amps[left_inds]
l_int = np.round(dt_2 * proc.e_square(l_freqs, l_amps) / 2e-8, 3)
r_inds = freqs >= 2.71e9 + 50e6
r_freqs = freqs[r_inds]
r_amps = amps[r_inds]
r_int = np.round(dt_2 * proc.e_square(r_freqs, r_amps) / 2e-8, 3)
'''
plt.plot(freqs, amps)
plt.plot(l_freqs, l_amps)
plt.plot(r_freqs, r_amps)
plt.title(f'l_int = {l_int}, r_int = {r_int}')
plt.xlim(right=6e9)
plt.show()
'''
pl_density = proc.read_excel(signal)['dicts'][num]['Ток плазмы, А']
if num in magnetron_nums:
nums.append(num)
def all_integrals_proc(exp_num,
nums,
table=True,
central_freq=2.714,
band_half_width=0.05):
proc = ProcessSignal(str(exp_num))
csv_types = proc.read_type_file()
csv_signals = csv_types['signal_files']
csv_signal_nums = csv_types['signal_nums']
excel_dicts = proc.read_excel(csv_signal_nums)['numbers']
noise_nums = [
str(int(excel_dicts['noise'][i]) + 1)
for i in range(len(excel_dicts['noise']))
if int(excel_dicts['noise'][i]) + 1 in nums
]
magnetron_nums = [
str(int(excel_dicts['magnetron'][i]) + 1)
for i in range(len(excel_dicts['magnetron']))
if int(excel_dicts['magnetron'][i]) + 1 in nums
]
print('Noise:', noise_nums, '\n', 'Amplifier:', magnetron_nums)
nums, n_s, full_ints, peak_ints = [], [], [], []
noise_ints, noise_ns, n_nums = [], [], []
for signal in csv_signals:
num = signal[3:6]
if num in magnetron_nums:
file = proc.open_file(signal, reduced=True)
u = file['voltage']
t = file['time']
f_low = (central_freq - band_half_width) * 1e9
f_high = (central_freq + band_half_width) * 1e9
u_filt = proc.fft_filter(t, u, f_low, f_high)
dt_2 = (t[-1] - t[0])**2
pl_density = proc.read_excel(signal)['dicts'][num]['Ток плазмы, А']
integral = np.round(proc.e_square(t, u) / 1e-8, 3)
filt_int = np.round(proc.e_square(t, u_filt) / 1e-8, 3)
freqs, amps = proc.fft_amplitude(
t, u)['frequency'], proc.fft_amplitude(t, u)['amplitude']
peak_inds = np.logical_and(freqs >= f_low, freqs <= f_high)
p_freqs, p_amps = freqs[peak_inds], amps[peak_inds]
full_int = np.round(dt_2 * proc.e_square(freqs, amps) / 2e-8, 3)
peak_int = np.round(dt_2 * proc.e_square(p_freqs, p_amps) / 2e-8,
3)
n_s.append(pl_density)
nums.append(num)
full_ints.append(full_int)
peak_ints.append(peak_int)
print('peak_int = ', np.round(integral, 2), 'noise_int =',
np.round(integral - filt_int, 2), 'noise_fft =',
np.round(full_int - peak_int, 2))
if num in noise_nums:
file = proc.open_file(signal, reduced=True)
u = file['voltage']
t = file['time']
dt = file['time_resolution']
f_low = (central_freq - band_half_width) * 1e9
f_high = (central_freq + band_half_width) * 1e9
u_filt = proc.fft_filter(t, u, f_low, f_high)
dt_2 = (t[-1] - t[0])**2
pl_density = proc.read_excel(signal)['dicts'][num]['Ток плазмы, А']
freqs, amps = proc.fft_amplitude(
t, u)['frequency'], proc.fft_amplitude(t, u)['amplitude']
noise_int = np.round(dt_2 * proc.e_square(freqs, amps) / 2e-8, 3)
noise_ints.append(noise_int)
noise_ns.append(pl_density)
n_nums.append(num)
ind_sort = np.argsort(np.asarray(n_s))
ns_sort = np.asarray(n_s)[ind_sort]
nums_sort = np.asarray(nums)[ind_sort]
full_ints_sort, peak_ints_sort = np.asarray(
full_ints)[ind_sort], np.asarray(peak_ints)[ind_sort]
print('Sorted amplifier numbers:', nums_sort)
n_ind_sort = np.argsort(np.asarray(noise_ns))
noise_ns_sort, n_nums_sort, noise_ints_sort = np.asarray(
noise_ns)[n_ind_sort], np.asarray(n_nums)[n_ind_sort], np.asarray(
noise_ints)[n_ind_sort]
print('Sorted noise numbers:', n_nums_sort)
if table:
ex_table = excel.Workbook()
ex_table.create_sheet(title='Integral', index=0)
sheet = ex_table['Integral']
sheet['A1'] = 'Номер(шум)'
sheet['B1'] = 'Плотность плазмы, отн.ед.'
sheet['C1'] = 'W_f0, *10-8'
sheet['D1'] = 'W_1, *10-8'
sheet['F1'] = 'Номер'
sheet['G1'] = 'Плотность плазмы, отн.ед.'
sheet['H1'] = 'W_2, *10-8'
for z in range(full_ints_sort.size):
cell = sheet.cell(row=z + 2, column=1)
cell.value = int(nums_sort[z])
cell = sheet.cell(row=z + 2, column=2)
cell.value = ns_sort[z]
cell = sheet.cell(row=z + 2, column=3)
cell.value = peak_ints_sort[z]
cell = sheet.cell(row=z + 2, column=4)
cell.value = full_ints_sort[z] - peak_ints_sort[z]
for k in range(noise_ints_sort.size):
cell = sheet.cell(row=k + 2, column=6)
cell.value = int(n_nums_sort[k])
cell = sheet.cell(row=k + 2, column=7)
cell.value = noise_ns_sort[k]
cell = sheet.cell(row=k + 2, column=8)
cell.value = noise_ints_sort[k]
path = proc.excel_folder_path / f'Integrals_{exp_num}_2_antennas.xlsx'
ex_table.save(path)
else:
noise_dict = {
'nums': n_nums_sort,
'density_vals': noise_ns_sort,
'w_2': noise_ints_sort
}
magnetron_dict = {
'nums': nums_sort,
'density_vals': ns_sort,
'w_f0': peak_ints_sort,
'w_1': full_ints_sort - peak_ints_sort
}
return noise_dict, magnetron_dict
def left_right_power():
test = ProcessSignal('191011')
csv_types = test.read_type_file()
csv_signals = csv_types['signal_files']
csv_signal_nums = csv_types['signal_nums']
excel_dicts = test.read_excel(csv_signal_nums)['numbers']
magnetron_nums = excel_dicts['magnetron']
print('All magnetron nums:', magnetron_nums)
m_1_nums = []
m_2_nums = []
for num in magnetron_nums:
if 66 <= int(num) < 112:
m_1_nums.append(num)
elif 112 <= int(num) < 152:
m_2_nums.append(num)
m_lists = [m_1_nums, m_2_nums]
labels_list = ['№2, 3', '№2, 3, A']
for i, m_list in enumerate(m_lists):
e_left = []
e_right = []
pl_d = []
fig = plt.figure(num=1, dpi=200, figsize=[11.69, 8.27])
ax = fig.add_subplot(111)
ax.set_prop_cycle(color=['orange', 'indigo'])
for signal in csv_signals:
num = signal[3:6]
if num in m_list:
file = test.open_file(signal, reduced=True)
u = file['voltage']
t = file['time']
dt = file['time_resolution']
u_highpass = test.highpass(t, u, 2.769e9)
u_lowpass = test.lowpass(t, u, 2.706e9)
fft_pass = test.fft_signal(t, u, dt)
freqs = fft_pass['frequency']
amps = fft_pass['amplitude']
high_inds = freqs > 2.769e9
low_inds = freqs < 2.709e9
high_freqs = freqs[high_inds]
low_freqs = freqs[low_inds]
high_amps = amps[high_inds]
low_amps = amps[low_inds]
e_high = test.e_square(high_freqs, high_amps)
e_low = test.e_square(low_freqs, low_amps)
'''
e_high = test.e_square(t, u_highpass)
e_low = test.e_square(t, u_lowpass)
'''
pl_density = test.read_excel(csv_signal_nums)['dicts'][num]['Ток плазмы, А']
#plt.plot(fft_high['frequency'], fft_high['amplitude'], color='blue')
#plt.plot(high_freqs, high_amps, color='red')
#plt.plot(low_freqs, low_amps, color='k')
e_left.append(e_low)
e_right.append(e_high)
pl_d.append(pl_density)
#plt.show()
pl_d = np.array(pl_d)
inds = np.argsort(pl_d)
pl_d = pl_d[inds]
e_left = np.array(e_left)[inds]
line1, = ax.plot(pl_d, e_left, marker='o')
line1.set_label('f < 2,72 GHz')
line2, = ax.plot(pl_d, e_right, marker='o')
line2.set_label('f > 2,76 GHz')
ax.set_ylim(bottom=0)
ax.set_xlim(left=5)
ax.set_xlabel(r'$Plasma\/density, arb.units $', fontsize=16)
ax.set_ylabel(r'$\int E^2 dt $', fontsize=16)
ax.legend(loc='best', fontsize=12)
ax.grid(which='both', axis='both')
ax.set_title('191011, {}'.format(labels_list[i]), fontsize=20)
mean_png = test.pics_path / 'left_right_power{}.png'.format(i)
fig.savefig(mean_png)
plt.close(fig)
plt.show()
def integral_plots(exp, type='integrals', table=True):
fft_test = ProcessSignal(exp)
csv_types = fft_test.read_type_file()
csv_signals = csv_types['signal_files']
csv_signal_nums = csv_types['signal_nums']
print(csv_signals)
excel_dicts = fft_test.read_excel(csv_signal_nums)['numbers']
magnetron_nums = excel_dicts['magnetron']
print('All magnetron nums:', magnetron_nums)
m_1_nums = magnetron_nums
m_2_nums = []
'''
for num in magnetron_nums:
if 11 <= int(num) < 67:
m_1_nums.append(num)
elif 125 <= int(num) <= 184:
m_2_nums.append(num)
'''
m_lists = [m_1_nums, m_2_nums]
labels_list = ['№2, 3 (четверть)', '№2, 3, A']
for j, list in enumerate(m_lists):
if len(list) != 0:
fig = plt.figure(num=1, dpi=200, figsize=[11.69, 8.27])
ax = fig.add_subplot(111)
flist_len = len(list)
print('We have {} nums'.format(flist_len))
print('Magnetron nums are:', list)
pl_densities = np.zeros(flist_len)
e_noise_ints = np.zeros(flist_len)
e_peak_ints = np.zeros(flist_len)
csv_nums = np.zeros(flist_len)
magnetron_nums = np.zeros(flist_len)
e_noise_ls = []
e_noise_rs = []
power_koefs = np.zeros(flist_len)
for i, num in enumerate(list):
for csv_signal in csv_signals:
csv_num = csv_signal[3:6]
if csv_num == num:
magnetron_nums[i] = int(num)
print('I am working on {} signal'.format(csv_num))
pl_density = fft_test.read_excel(csv_signal_nums)['dicts'][num]['Ток плазмы, А']
pl_densities[i] = pl_density
csv_nums[i] = csv_num
file = fft_test.open_file(csv_signal, reduced=True)
time = file['time']
voltage = file['voltage']
dt = file['time_resolution']
n_dt = time[-1] - time[0]
n_dt_2 = n_dt ** 2
fft = fft_test.fft_amplitude(time, voltage, dt)
freqs = fft['frequency']
amps = fft['amplitude']
noise_inds_left = freqs < 2.725e9
noise_inds_right = freqs > 2.755e9
peak_inds = np.logical_and(freqs > 2.725e9, freqs < 2.755e9)
freq_noise_l = freqs[noise_inds_left]
amp_noise_l = amps[noise_inds_left]
freq_noise_r = freqs[noise_inds_right]
amp_noise_r = amps[noise_inds_right]
freq_peak = freqs[peak_inds]
amp_peak = amps[peak_inds]
amp_noise_l = 2 * n_dt_2 * fft_test.e_square(freq_noise_l, amp_noise_l)
amp_noise_r = 2 * n_dt_2 * fft_test.e_square(freq_noise_r, amp_noise_r)
amp_noise = amp_noise_l + amp_noise_r
amp_peak = 2 * n_dt_2 * fft_test.e_square(freq_peak, amp_peak)
e_noise_ls.append(amp_noise_l)
e_noise_rs.append(amp_noise_r)
e_noise_ints[i] = amp_noise
e_peak_ints[i] = amp_peak
power_koefs[i] = amp_noise_r / amp_noise_l
print('Creating plasma plot ...')
pl_plot = noise_signal_plot(int(exp), type='amp_freq', left_right=True)
pl_ds = pl_plot['pl_ds']
pl_nums = pl_plot['plasma_nums']
pl_e_ints = pl_plot['e_integs']
pl_koefs = pl_plot['koefs']
ind_sort = np.argsort(pl_densities)
pl_densities = pl_densities[ind_sort]
e_noise_ints = e_noise_ints[ind_sort]
e_peak_ints = e_peak_ints[ind_sort]
x_min = min(pl_ds[0], pl_densities[0]) - 0.5
x_max = max(pl_ds[-1], pl_densities[-1]) + 0.5
if type == 'integrals':
ax.set_prop_cycle(color=['orange', 'indigo', 'green'])
line3, = ax.plot(pl_ds, pl_e_ints, marker='D', linewidth=2)
line3.set_label('Энергия в шумовом импульсе')
line, = ax.plot(pl_densities, e_peak_ints, marker='o', linewidth=2)
line.set_label('Энергия в пике')
line2, = ax.plot(pl_densities, e_noise_ints, marker='^', linewidth=2)
line2.set_label('Энергия в шумовом пъедестале')
ax.set_ylabel(r'$K\int A^2 df $', fontsize=16)
path_png = fft_test.pics_path / 'integral_{}.png'.format(labels_list[j])
if table:
magnetron_nums = magnetron_nums[ind_sort]
fft_test.integrals_table(magnetron_nums, pl_densities,
e_peak_ints, e_noise_ints,
pl_nums, pl_ds, pl_e_ints,
labels_list[j])
if type == 'left_right':
e_noise_ls = e_noise_ls[ind_sort]
e_noise_rs = e_noise_rs[ind_sort]
ax.set_prop_cycle(color=['red', 'blue'])
line_pl_1, = ax.plot(pl_densities, e_noise_ls, marker='o')
line_pl_1.set_label(r'$f < 2.725\/GHz$')
line_pl_2, = ax.plot(pl_densities, e_noise_rs, marker='^')
line_pl_2.set_label(r'$f > 2.755\/GHz$')
ax.set_ylabel(r'$K\int A^2 df $', fontsize=16)
path_png = fft_test.pics_path / 'left_right_power{}.png'.format(labels_list[j])
if table:
magnetron_nums = magnetron_nums[ind_sort]
fft_test.left_right_table(magnetron_nums, e_noise_ints, e_noise_ls,
e_noise_rs, pl_densities, labels_list[j])
if type == 'left_right_koef':
power_koefs = power_koefs[ind_sort]
line, = ax.plot(pl_densities, power_koefs, marker='o', linewidth=2)
line.set_label(r'$ P_{in} \ne 0 $')
line1, = ax.plot(pl_ds, pl_koefs, marker='^', linewidth=2)
line1.set_label(r'$P_{in} = 0$')
ax.set_ylabel(r'$\frac{W(f>2.755\/ГГц)}{W(f<2.725\/ГГц)} $', fontsize=22)
path_png = fft_test.pics_path / 'power_koef_{}.png'.format(labels_list[j])
ax.set_ylim(bottom=0)
ax.set_xlim(left=x_min, right=x_max)
ax.legend(loc='best', fontsize=12)
ax.grid(which='both', axis='both')
ax.set_xlabel(r'$Plasma\/density, arb.units $', fontsize=16)
ax.set_title('{}, {}'.format(exp, labels_list[j]), fontsize=20)
fig.savefig(path_png)
plt.close(fig)
test = ProcessSignal('201026')
csv_types = test.read_type_file()
csv_signals = csv_types['signal_files'][1::]
csv_signal_nums = csv_types['signal_nums']
excel_dicts = test.read_excel(csv_signal_nums)['numbers']
magnetron_nums = excel_dicts['magnetron']
reb_nums = excel_dicts['noise']
for csv_signal in csv_signals:
num = csv_signal[3:6]
file = test.open_file(csv_signal, reduced=False)
t, u = file['time'], file['voltage']
cut_file_inds = np.logical_and(t > 70e-9, t < 332e-9)
t_cut, u_cut = t[cut_file_inds], u[cut_file_inds]
simple_cut_integral = np.round(test.e_square(t_cut, u_cut) / 1e-8, 2)
pl_density = test.read_excel(csv_signal)['dicts'][num]['Ток плазмы, А']
#plt.plot(t, u)
#plt.title(f'{u[0], u[-1], t[-1]-t[0]}')
#plt.show()
dt_2 = (t[-1] - t[0])**2
fft_old = fft_amplitude_old(t, u)
amps_old, freqs_old = fft_old['amplitude'], fft_old['frequency']
fft_new = fft_amplitude_new(t, u)
amps_new, freqs_new = fft_new['amplitude'], fft_new['frequency']
integral_u = np.round(test.e_square(t, u) / 1e-8, 2)
fft_signal = test.fft_signal(t, u, np.abs(t[1] - t[0]))
simple_amps_fft, simple_freqs_fft = fft_signal['amplitude'], fft_signal[
'frequency']
simple_fft_integral = np.round(
dt_2 * test.e_square(simple_freqs_fft, simple_amps_fft) / 2e-8, 2)