def oscillograms():
test = ProcessSignal('191106')
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']
for signal in csv_signals:
file = test.open_file(signal, reduced=True)
u = file['voltage']
t = file['time']
dt = file['time_resolution']
u_filt = test.bandpass_filter(t, u, 2.725e9, 2.755e9)
num = signal[3:6]
if int(num) < 66:
absorbers = '№2, 3'
elif 66 <= int(num) < 125 and num in magnetron_nums:
absorbers = 0
elif 125 <= int(num) <= 184 and num in magnetron_nums:
absorbers = '№2, 3, A'
else:
absorbers = 0
pl_d_nums = test.read_excel(csv_signal_nums)['dicts'].keys()
if num in pl_d_nums:
pl_density = test.read_excel(csv_signal_nums)['dicts'][num]['Ток плазмы, А']
test.oscill_picture(num, t, u, u_filt, pl_density, absorbers, save=True)
def amp_max():
test = ProcessSignal('191001')
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']
m_1_nums = []
m_2_nums = []
m_3_nums = []
for num in magnetron_nums:
if 35 <= int(num) < 79:
m_1_nums.append(num)
elif 79 <= int(num) < 111:
m_2_nums.append(num)
elif 127 <= int(num) <= 150:
m_3_nums.append(num)
m_lists = [m_1_nums, m_2_nums, m_3_nums]
labels_list = ['№2, 3', '№2, 3, A, Б, В', '№2, 3, A', 'Шум']
fig = plt.figure(num=1, dpi=200, figsize=[11.69, 8.27])
ax = fig.add_subplot(111)
ax.set_prop_cycle(color=['blue', 'green', 'deepskyblue'])
for i, list in enumerate(m_lists):
u_maxs = []
pl_ds = []
for csv_signal in csv_signals:
csv_num = csv_signal[3:6]
for num in list:
if csv_num == num:
file = test.open_file(csv_signal)
u = file['voltage']
t = file['time']
red_dict = test.proc_part_130_290(t, u)
red_u = red_dict['part_u']
red_t = red_dict['part_t']
u_filt = test.bandpass_filter(red_t, red_u, 2.709e9, 2.769e9)
u_max = np.max(np.array(u_filt))
pl_d = test.read_excel(csv_signal_nums)['dicts'][num]['Ток плазмы, А']
u_maxs.append(u_max)
pl_ds.append(pl_d)
sorted_inds = np.argsort(np.array(pl_ds))
pl_ds = np.array(pl_ds)
pl_ds = pl_ds[sorted_inds]
u_maxs = np.array(u_maxs)
u_maxs = u_maxs[sorted_inds]
line, = plt.plot(pl_ds, u_maxs, marker='o', linewidth=2)
line.set_label('{}'.format(labels_list[i]))
ax.set_ylim(bottom=0)
ax.set_xlim(left=4, right=9)
ax.set_xlabel(r'$Plasma\/density, arb.units $', fontsize=14)
ax.set_ylabel(r'$Max\/amplitude$', fontsize=14)
ax.grid(which='both', axis='both')
ax.legend(loc='best', fontsize=16)
ax.set_title('Max amplitude', fontsize=20)
png_name = test.pics_path / 'max_amp.png'
plt.savefig(png_name)
plt.show()
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(
test.e_square(t_full, b_filtered_u_full) / 1e-8, 2)
'''
fft = test.fft_amplitude(t, u, dt)
freqs = fft['frequency']
amps = fft['amplitude']
n_dt_2 = (t[-1] - t[0]) ** 2
amp_full_integral = np.round(2 * n_dt_2 * test.e_square(freqs, amps) / 1e-8, 2)
#peak_fft = test.fft_amplitude(t, filtered_u, dt)
peak_inds = np.logical_and(freqs > 2.695e9, freqs < 2.725e9)
freq_peak = freqs[peak_inds]
nums = []
for signal in signal_list[1]:
signal_file = f'str{signal:03d}.csv'
file = proc.open_file(signal_file, reduced=False)
t, u = file['time'], file['voltage']
if signal < 95:
t_min, t_max = 960e-9, 1222e-9
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)
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]])