def left_right_noise():
test = ProcessSignal('191106')
noise_dict = noise_signal_plot(191106, type='amp_freq', left_right=True)
pl_d = noise_dict['pl_ds']
r_power = noise_dict['r_power']
l_power = noise_dict['l_power']
full_power = noise_dict['e_integs']
plasma_nums = noise_dict['plasma_nums']
fig = plt.figure(num=1, dpi=300)
ax = fig.add_subplot(111)
line_r, = ax.plot(pl_d, r_power, marker='^', linewidth=0.9, color='blue', ms=3)
line_l, = ax.plot(pl_d, l_power, marker='o', linewidth=0.9, color='red', ms=3)
line_l.set_label(r'$f < 2.725\/GHz$')
line_r.set_label(r'$f > 2.755\/GHz$')
ax.set_xlabel('Plasma density, arb.units', fontsize=12)
ax.set_ylabel(r'$K\int A^2 df $', fontsize=12)
ax.set_ylim(bottom=0)
ax.legend()
ax.set_title(r'$191106. P_{in}=0 $')
ax.grid(which='both', axis='both')
png_name = test.pics_path / 'noise_power'
fig.savefig(png_name)
test.left_right_table(plasma_nums, full_power, l_power,
r_power, pl_d, 'noise')
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)