def write_2_antennas_osc_csv(exp_num, file_nums):
print(f'Experiment {exp_num}')
test = ProcessSignal(str(exp_num))
nums_mtrx = np.reshape(np.asarray(file_nums), (int(len(file_nums) / 2), 2))
central_freq = 2.714E9
for j in range(nums_mtrx.shape[0]):
antennas_data_dict = {}
for i in range(nums_mtrx.shape[1]):
file_num = f'{nums_mtrx[j, i]}'
file_name = f'str{file_num}.csv'
data = test.open_file(file_name, reduced=False)
filt_freq_min, filt_freq_max = central_freq - 15e6, central_freq + 15e6
if int(nums_mtrx[j, i]) % 2 == 0:
t, u = data['time'], data['voltage'] - np.mean(data['voltage'])
u_filt = test.fft_filter(t, u, filt_freq_min, filt_freq_max)
antennas_data_dict['t_main'], antennas_data_dict[
'u_main'] = t, u
antennas_data_dict['u_filt_main'] = u_filt
plt.plot(t, u)
else:
#t, u = data['time'], (data['voltage'] - np.mean(data['voltage'])) / 3.16
t, u = data['time'], (data['voltage'] -
np.mean(data['voltage']))
u_filt = test.fft_filter(t, u, filt_freq_min, filt_freq_max)
antennas_data_dict['t'], antennas_data_dict['u'] = t, u
antennas_data_dict['u_filt'] = u_filt
plt.plot(t + 4.347E-9, u)
plt.show()
print(f'Writing {nums_mtrx[j, i]} csv file...')
print(antennas_data_dict.keys())
dif_file_path = test.exp_file_path / '2_antennas_CSV'
dif_file_path.mkdir(parents=True, exist_ok=True)
file = open(str(dif_file_path /
f'{nums_mtrx[j, 0]}_{nums_mtrx[j, 1]}.csv'),
'w',
newline='')
with file:
writer = csv.writer(file)
writer.writerow([
't_main(s)', 'V_main(V)', 'V_main_filt(V)', 't(s)', 'V(V)',
'V_filt(V)'
])
for i in range(
1,
max(len(antennas_data_dict['t_main']),
len(antennas_data_dict['t']))):
writer.writerow([
antennas_data_dict['t_main'][i],
antennas_data_dict['u_main'][i],
antennas_data_dict['u_filt_main'][i],
antennas_data_dict['t'][i], antennas_data_dict['u'][i],
antennas_data_dict['u_filt'][i]
])
nums, n_s, mean_freqs = [], [], []
noise_freqs, noise_ns, n_nums = [], [], []
for signal in csv_signals:proc = ProcessSignal('201110')
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 = excel_dicts['noise']
magnetron_nums = excel_dicts['magnetron'][:18:]
num = signal[3:6]
if num in magnetron_nums:
file = proc.open_file(signal, reduced=True)
u = file['voltage']
t = file['time']
u_filt = proc.fft_filter(t, u, 2.695e9, 2.725e9, filt_type='bandstop')
dt = file['time_resolution']
dt_2 = (t[-1] - t[0]) ** 2
pl_density = proc.read_excel(signal)['dicts'][num]['Ток плазмы, А']
fft_data = proc.fft_amplitude(t, u_filt)
freqs, amps = fft_data['frequency'], fft_data['amplitude']
mean_freq = proc.mean_frequency(freqs, amps)['mean_freq']
nums.append(num)
n_s.append(pl_density)
mean_freqs.append(mean_freq)
if num in noise_nums:
file = proc.open_file(signal, reduced=True)
u = file['voltage']
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 two_antennas_ampl_relate(exp_num, file_nums, pts):
print(f'Experiment {exp_num}')
test = ProcessSignal(str(exp_num))
nums_mtrx = np.reshape(np.asarray(file_nums), (int(len(file_nums) / 2), 2))
print(nums_mtrx)
central_freq = 2.714E9
compare_pts = [pts[i] * 1E-9 for i in range(len(pts))]
for pt in compare_pts:
for j in range(nums_mtrx.shape[0]):
for i in range(nums_mtrx.shape[1]):
print(nums_mtrx[j, i], 'pt=', np.round(pt / 1e-9, 1))
file_num = f'{nums_mtrx[j, i]}'
file_name = f'str{file_num}.csv'
data = test.open_file(file_name, reduced=False)
filt_freq_min, filt_freq_max = central_freq - 15e6, central_freq + 15e6
if int(nums_mtrx[j, i]) % 2 == 0:
t, u = data['time'], data['voltage'] - np.mean(
data['voltage'])
plt.plot(t, u)
plt.show()
plt.close()
u_filt = test.fft_filter(t, u, filt_freq_min,
filt_freq_max)
#pl_density = test.read_excel(file_name)['dicts'][file_num]['Ток плазмы, А']
ind_mask = np.logical_and(t >= pt, t <= pt + 1E-9)
t, u = t[ind_mask], u[ind_mask]
peak_dict = find_zeros(t, u)
time_max, volt_max, time_min, volt_min = peak_dict[
'time_max'], peak_dict['volt_max'], peak_dict[
'time_min'], peak_dict['volt_min']
delta_main = volt_max - volt_min
print('volt_max =', volt_max, 'time_max', time_max)
u_filt = u_filt[ind_mask]
filt_dict = find_zeros(t, u_filt)
time_max_filt, volt_max_filt = filt_dict[
'time_max'], filt_dict['volt_max']
time_min_filt, volt_min_filt = filt_dict[
'time_min'], filt_dict['volt_min']
delta_main_filt = volt_max_filt - volt_min_filt
plt.plot(t, u)
plt.plot(time_min, volt_min, marker='o')
plt.plot(time_max, volt_max, marker='o')
else:
#t, u = data['time'] + 4.347E-9, (data['voltage'] - np.mean(data['voltage']))
t, u = data['time'], (data['voltage'] -
np.mean(data['voltage']))
plt.plot(t, u)
plt.show()
plt.close()
u_filt = test.fft_filter(t, u, filt_freq_min,
filt_freq_max)
ind_mask = np.logical_and(t >= pt, t <= pt + 1E-9)
t, u = t[ind_mask], u[ind_mask]
peak_dict = find_zeros(t, u)
time_max, volt_max, time_min, volt_min = peak_dict[
'time_max'], peak_dict['volt_max'], peak_dict[
'time_min'], peak_dict['volt_min']
delta_sub = volt_max - volt_min
print('volt_max =', volt_max, 'time_max', time_max)
u_filt = u_filt[ind_mask]
filt_dict = find_zeros(t, u_filt)
time_max_filt, volt_max_filt = filt_dict[
'time_max'], filt_dict['volt_max']
time_min_filt, volt_min_filt = filt_dict[
'time_min'], filt_dict['volt_min']
delta_sub_filt = volt_max_filt - volt_min_filt
#plt.plot(t, u_filt)
#plt.plot(time_min_filt, volt_min_filt, marker='o')
#plt.plot(time_max_filt, volt_max_filt, marker='o')
u_relat_filt = delta_main_filt / delta_sub_filt
u_relat = delta_main / delta_sub
print('Отн_без_фильтра:', np.round(u_relat, 3))
print('Oтн_фильтр:', np.round(u_relat_filt, 3))
def two_antennas_max_table(exp_num, file_nums):
print(f'Experiment {exp_num}')
test = ProcessSignal(str(exp_num))
nums_mtrx = np.reshape(np.asarray(file_nums), (int(len(file_nums) / 2), 2))
print(nums_mtrx)
central_freq = 2.714E9
pts = [100, 200, 300, 400]
compare_pts = [pts[i] * 1E-9 for i in range(len(pts))]
col_nums = [i for i in range(3, 3 + len(compare_pts))]
print(col_nums)
ex_table = excel.Workbook()
ex_table.create_sheet(title='Integral', index=0)
sheet = ex_table['Integral']
sheet['A1'] = exp_num
sheet['A2'] = 'No (центр/бок)'
sheet['B2'] = 'n, отн.ед.'
sheet['C1'] = 'Без фильтра'
sheet[f'{get_column_letter(3 + len(compare_pts) +1)}1'] = 'Фильтрованный'
for n in range(len(col_nums)):
letter = get_column_letter(col_nums[n])
sheet[
f'{letter}2'] = f't_{n + 1} = {np.round(compare_pts[n] / 1e-9, 0)}'
filt_letter = get_column_letter(col_nums[n] + len(col_nums) + 1)
sheet[
f'{filt_letter}2'] = f't_{n + 1} = {np.round(compare_pts[n] / 1e-9, 0)} c'
for k, pt in enumerate(compare_pts):
for j in range(nums_mtrx.shape[0]):
antennas_data_dict = {}
for i in range(nums_mtrx.shape[1]):
print(nums_mtrx[j, i], 'pt=', pt)
file_num = f'{nums_mtrx[j, i]}'
file_name = f'str{file_num}.csv'
data = test.open_file(file_name, reduced=False)
filt_freq_min, filt_freq_max = central_freq - 15e6, central_freq + 15e6
if int(nums_mtrx[j, i]) % 2 == 0:
t, u = data['time'], data['voltage'] - np.mean(
data['voltage'])
u_filt = test.fft_filter(t, u, filt_freq_min,
filt_freq_max)
pl_density = test.read_excel(
file_name)['dicts'][file_num]['Ток плазмы, А']
ind_mask = np.logical_and(t >= pt, t <= pt + 1E-9)
t, u = t[ind_mask], u[ind_mask]
peak_dict = find_zeros(t, u)
time_max, volt_max, time_min, volt_min = peak_dict[
'time_max'], peak_dict['volt_max'], peak_dict[
'time_min'], peak_dict['volt_min']
delta_main = volt_max - volt_min
u_filt = u_filt[ind_mask]
filt_dict = find_zeros(t, u_filt)
time_max_filt, volt_max_filt = filt_dict[
'time_max'], filt_dict['volt_max']
time_min_filt, volt_min_filt = filt_dict[
'time_min'], filt_dict['volt_min']
delta_main_filt = volt_max_filt - volt_min_filt
cell = sheet.cell(row=j + 3, column=2)
cell.value = f'{pl_density}'
plt.plot(t, u)
plt.plot(time_min, volt_min, marker='o')
plt.plot(time_max, volt_max, marker='o')
else:
t_shift = 4.347E-9
t_shift = 0
t, u = data['time'] + t_shift, (data['voltage'] -
np.mean(data['voltage']))
u_filt = test.fft_filter(t, u, filt_freq_min,
filt_freq_max)
ind_mask = np.logical_and(t >= pt, t <= pt + 1E-9)
t, u = t[ind_mask], u[ind_mask]
peak_dict = find_zeros(t, u)
time_max, volt_max, time_min, volt_min = peak_dict[
'time_max'], peak_dict['volt_max'], peak_dict[
'time_min'], peak_dict['volt_min']
delta_sub = volt_max - volt_min
u_filt = u_filt[ind_mask]
filt_dict = find_zeros(t, u_filt)
time_max_filt, volt_max_filt = filt_dict[
'time_max'], filt_dict['volt_max']
time_min_filt, volt_min_filt = filt_dict[
'time_min'], filt_dict['volt_min']
delta_sub_filt = volt_max_filt - volt_min_filt
plt.plot(t, u_filt)
plt.plot(time_min_filt, volt_min_filt, marker='o')
plt.plot(time_max_filt, volt_max_filt, marker='o')
u_relat_filt = delta_main_filt / delta_sub_filt
u_relat = delta_main / delta_sub
column = col_nums[k]
column_filt = column + len(compare_pts) + 1
cell = sheet.cell(row=j + 3, column=column)
cell.value = f'{np.round(u_relat, 3)}'
cell = sheet.cell(row=j + 3, column=column_filt)
cell.value = f'{np.round(u_relat_filt, 3)}'
print(u_relat_filt)
plt.title(nums_mtrx[j, i])
#plt.show()
cell = sheet.cell(row=j + 3, column=1)
cell.value = f'{int(nums_mtrx[j, i]) -1 } / {nums_mtrx[j, i]}'
path = test.excel_folder_path / f'Ampl_{exp_num}_2_antennas_123_129.xlsx'
ex_table.save(path)
ind_mask = np.logical_and(low_freq < freqs_fft, freqs_fft < high_freq)
bandpass_filter = np.zeros(len(fft_u))
bandpass_filter[ind_mask] = 1
b_filt_u = irfft(fft_u * bandpass_filter)
return b_filt_u
'''
file_data = open_file(file)
t, u = file_data['t'], np.asarray(file_data['u'])
fft_u = fft_filter(t, u)
origin_data = open_file(origin_filt_file, filtered=True)
t_filt_or, u_filt_or = origin_data['t_filt'], origin_data['u_filt']
'''
#plt.plot(t, u)
#plt.plot(t, fft_u)
#plt.plot(t_filt_or, u_filt_or)
#plt.show()
proc = ProcessSignal('201111')
file = 'str019.csv'
file_data = proc.open_file(file)
t, u = file_data['time'], file_data['voltage']
fft_filt_u = proc.fft_filter(t, u)
bandpass_u = proc.bandpass_filter(t, u, 2.695e9, 2.725e9)
new_file_path = path / 'new_file_19_1.csv'
new_file = open(str(new_file_path), 'w', newline='')
with new_file:
writer = csv.writer(new_file)
for i in range(0, len(t)):
writer.writerow([0, 0, 0, t[i], u[i], fft_filt_u[i], bandpass_u[i]])