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Commit 33081a75 authored by Vadim Gubaidulin's avatar Vadim Gubaidulin
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Class to analyze the data.

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with 179 additions and 137 deletions
growth_damp_utils.py
+ 179
− 137
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......@@ -13,30 +13,35 @@ Q_X = 18.16
Q_Y = 10.22
OMEGA_REV = 2*pi*c/CIRCUMFERENCE
r_p = e**2/(m_p*c**2)
def read_bunch_data(bunchnumber, filename, folder):
f = hp.File(folder+filename+'.hdf5')
BPM_data = f['raw'][:]
class GrowthDampDataAnalyzer():
def __init__(self, folder):
self.folder = folder
self.filelist = []
def read_bunch_data(self, filename, bunchnumber, N = 0):
with hp.File(self.folder + filename + '.hdf5') as f:
BPM_data = f[f'raw_{N:}'][:]
emitYC02before = np.array(f['emitZ_C02_before'])
emitYC02after = np.array(f['emitZ_C02_after'])
emitXC02before = np.array(f['emitX_C02_before'])
emitXC02after = np.array(f['emitX_C02_after'])
emitYC16before = np.array(f['emitZ_C16_before'])
emitYC16after = np.array(f['emitZ_C16_after'])
t0 = 1e-9*c/CIRCUMFERENCE*np.array(f['window_delay'])
t1 = 1e-6*c/CIRCUMFERENCE*np.array(f['window_width'])
Qxi = np.array(f['tuneX_before'])
Qxf = np.array(f['tuneX_after'])
Qyi = np.array(f['tuneZ_before'])
Qyf = np.array(f['tuneZ_after'])
# print('Horizontal tune before {:.3f} and after {:.3f} the measurement'.format(Qxi, Qxf))
# print('Vertical tune before {:.3f} and after {:.3f} the measurement'.format(Qyi, Qyf))
filling_pattern_before = np.array(f['filling_pattern_before'])
filling_pattern_after = np.array(f['filling_pattern_after'])
f.close()
return BPM_data[bunchnumber,:], t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after
def plot_bunch_offset_data(ax, bunchnumber, filename, folder):
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(bunchnumber, filename, folder)
def plot_bunch_offset_data(self, ax, bunchnumber, filename, N=0):
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(bunchnumber, filename, N)
ax.plot(mean_y_bunch-np.mean(mean_y_bunch))
ax.axvline(t0, linestyle='dashed')
ax.axvline(t1, linestyle='dashed')
......@@ -45,8 +50,8 @@ def plot_bunch_offset_data(ax, bunchnumber, filename, folder):
ax.set_ylabel('Bunch c.\,m. offset (arb. units)')
ax.title.set_text('Bunch \# {:} c.\,m. offset'.format(int(bunchnumber)))
return None
def plot_bunch_spectrum_data(ax, bunchnumber, filename, folder):
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(bunchnumber, filename, folder)
def plot_bunch_spectrum_data(self, ax, bunchnumber, filename, N=0):
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(bunchnumber, filename, N)
mean_y_bunch-=np.mean(mean_y_bunch)
fftfreq = rfftfreq(mean_y_bunch.shape[0])
absfft = np.abs(rfft(mean_y_bunch))
......@@ -57,13 +62,13 @@ def plot_bunch_spectrum_data(ax, bunchnumber, filename, folder):
ax.set_ylabel('Spectrum power (arb. units)')
ax.title.set_text('Bunch \# {:} spectrum'.format(int(bunchnumber)))
return None
def plot_bunch_risetime_fit(ax, bunchnumber, filename, folder, smoothing_window_size = 10, show_fitted_signal=False):
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(bunchnumber, filename, folder)
def plot_bunch_risetime_fit(ax, bunchnumber, filename, folder, smoothing_window_size = 10, show_fitted_signal=False, N=0):
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(bunchnumber, filename, folder, N)
mean_y_bunch -= np.mean(mean_y_bunch)
signal = np.abs(hilbert(mean_y_bunch))
if show_fitted_signal:
ax.plot(signal)
min_level = np.min((5.*np.mean(signal[:200]), 5.*np.mean(signal[-200:])))
min_level = np.min((10.*np.mean(signal[:200]), 10.*np.mean(signal[-200:])))
risetime = fit_risetime(signal,
min_level=min_level,
smoothing_window_size=smoothing_window_size,
......@@ -74,15 +79,15 @@ def plot_bunch_risetime_fit(ax, bunchnumber, filename, folder, smoothing_window_
ax.title.set_text('Bunch \# {:} c.\,m. offset'.format(int(bunchnumber)))
return risetime
def plot_bunch_damping_time_fit(ax, bunchnumber, filename, folder, smoothing_window_size = 2, show_fitted_signal=False):
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(bunchnumber, filename, folder)
def plot_bunch_damping_time_fit(ax, bunchnumber, filename, folder, smoothing_window_size = 20, show_fitted_signal=False, N=0):
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(bunchnumber, filename, folder, N)
mean_y_bunch -= np.mean(mean_y_bunch)
mean_y_bunch = np.flip(mean_y_bunch)
ax.plot(mean_y_bunch)
signal = np.abs(hilbert(mean_y_bunch))
if show_fitted_signal:
ax.plot(signal)
min_level = np.min((5*np.mean(signal[:200]), 5*np.mean(signal[-200:])))
min_level = np.min((15*np.mean(signal[:200]), 15*np.mean(signal[-200:])))
damping_time = fit_risetime(signal,
min_level=min_level,
smoothing_window_size=smoothing_window_size,
......@@ -92,13 +97,13 @@ def plot_bunch_damping_time_fit(ax, bunchnumber, filename, folder, smoothing_win
ax.set_ylabel('Bunch c.\,m. offset (arb. units)')
ax.title.set_text('Bunch \# {:} c.\,m. offset'.format(int(bunchnumber)))
return damping_time
def plot_beam_offset_data(ax, filename, folder, smoothing_window_size = 10):
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(0, filename, folder)
def plot_beam_offset_data(ax, filename, folder, smoothing_window_size = 10, N=0):
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(0, filename, folder, N)
size = mean_y_bunch.shape[0]
mean_y_beam = np.empty(shape=(size*H_RF), dtype=np.float64)
turns = np.linspace(0, size, H_RF*size)
for bunchnumber in range(0, H_RF):
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(bunchnumber, filename, folder)
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(bunchnumber, filename, folder, N)
mean_y_bunch -= np.mean(mean_y_bunch)
mean_y_beam[bunchnumber::H_RF] = mean_y_bunch
ax.plot(turns, mean_y_beam)
......@@ -108,31 +113,69 @@ def plot_beam_offset_data(ax, filename, folder, smoothing_window_size = 10):
ax.set_xlabel('Time (turns)')
ax.set_ylabel('Beam c.\,m. offset (arb. units)')
ax.text(mean_y_beam.shape[0]/10/H_RF, np.min(mean_y_beam)/2, '$\\varepsilon_i={:.1e}$,\n $\\varepsilon_f={:.1e}$'.format(emitYC16before, emitYC16after), fontsize=12)
signal = np.abs(hilbert(mean_y_bunch))
signal_reversed = np.flip(signal)
return None
def plot_beam_risetime_fit(ax, filename, folder, smoothing_window_size = 2000, N=0):
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(0, filename, folder, N)
size = mean_y_bunch.shape[0]
mean_y_beam = np.empty(shape=(size*H_RF), dtype=np.float64)
turns = np.linspace(0, size, H_RF*size)
for bunchnumber in range(0, H_RF):
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(bunchnumber, filename, folder, N)
mean_y_bunch -= np.mean(mean_y_bunch)
mean_y_beam[bunchnumber::H_RF] = mean_y_bunch
ax.axvline(t0, linestyle='dashed', color='black')
ax.axvline(t1, linestyle='dashdot', color='black')
ax.set_xlim(0,)
ax.set_xlabel('Time (turns)')
ax.set_ylabel('Beam c.\,m. offset (arb. units)')
ax.text(mean_y_beam.shape[0]/10/H_RF, np.min(mean_y_beam)/2, '$\\varepsilon_i={:.1e}$,\n $\\varepsilon_f={:.1e}$'.format(emitYC16before, emitYC16after), fontsize=12)
signal = np.abs(hilbert(mean_y_beam))
min_level = np.min((5*np.mean(signal[:200]), 5*np.mean(signal[-200:])))
damping_time = fit_risetime(signal_reversed,
risetime = fit_risetime(signal,
min_level=min_level,
smoothing_window_size=smoothing_window_size,
matplotlib_axis=None)
risetime = fit_risetime(signal_reversed,
matplotlib_axis=ax)
return risetime/H_RF
def plot_beam_damptime_fit(ax, filename, folder, smoothing_window_size = 2000, N=0):
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(0, filename, folder, N)
size = mean_y_bunch.shape[0]
mean_y_beam = np.empty(shape=(size*H_RF), dtype=np.float64)
turns = np.linspace(0, size, H_RF*size)
for bunchnumber in range(0, H_RF):
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(bunchnumber, filename, folder, N)
mean_y_bunch -= np.mean(mean_y_bunch)
mean_y_beam[bunchnumber::H_RF] = mean_y_bunch
ax.axvline(t0, linestyle='dashed', color='black')
ax.axvline(t1, linestyle='dashdot', color='black')
ax.set_xlim(0,)
ax.set_xlabel('Time (turns)')
ax.set_ylabel('Beam c.\,m. offset (arb. units)')
ax.text(mean_y_beam.shape[0]/10/H_RF, np.min(mean_y_beam)/2, '$\\varepsilon_i={:.1e}$,\n $\\varepsilon_f={:.1e}$'.format(emitYC16before, emitYC16after), fontsize=12)
signal = np.flip(np.abs(hilbert(mean_y_beam)))
min_level = np.min((10*np.mean(signal[:200]), 10*np.mean(signal[-200:])))
damptime = fit_risetime(signal,
min_level=min_level,
smoothing_window_size=smoothing_window_size,
matplotlib_axis=None)
return risetime/H_RF, damping_time/H_RF
matplotlib_axis=ax)
return damptime/H_RF
def plot_beam_spectrum(ax, filename, folder):
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(0, filename, folder)
def plot_beam_spectrum(ax, filename, folder, N=0):
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(0, filename, folder, N)
size = mean_y_bunch.shape[0]
mean_y_beam = np.empty(shape=(size*H_RF), dtype=np.float64)
turns = np.linspace(0, size, H_RF*size)
for bunchnumber in range(0, H_RF):
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(bunchnumber, filename, folder)
mean_y_bunch, t0, t1, emitYC02before, emitYC16before, emitYC02after, emitYC16after, emitXC02before, emitXC02after = read_bunch_data(bunchnumber, filename, folder, N)
mean_y_bunch -= np.mean(mean_y_bunch)
mean_y_beam[bunchnumber::H_RF] = mean_y_bunch
fftfreq = rfftfreq(mean_y_bunch.shape[0])
absfft = np.abs(rfft(mean_y_bunch))
ax.plot(fftfreq, absfft/np.max(absfft))
fftfreq = H_RF*rfftfreq(mean_y_beam.shape[0])
absfft = np.abs(rfft(mean_y_beam))
ax.semilogy(fftfreq, absfft/np.max(absfft))
beta_x = CIRCUMFERENCE/(2*pi)/Q_X
beta_y = CIRCUMFERENCE/(2*pi)/Q_Y
......@@ -149,9 +192,8 @@ def plot_beam_spectrum(ax, filename, folder):
print('Ion frequencies H {:.1f}, {:.1f}, CO {:.1f}, {:.1f}, CO2 {:.1f}, {:.1f}'.format(omega_Hy/OMEGA_REV, omega_H/OMEGA_REV,
omega_COy/OMEGA_REV, omega_CO/OMEGA_REV,
omega_CO2y/OMEGA_REV, omega_CO2/OMEGA_REV))
# ax.set_xlim(0, H_RF/2)
ax.set_xlim(0, .5)
ax.set_ylim(0, 1.05)
ax.set_xlim(0, H_RF/2)
ax.set_ylim(1e-3, 1.05)
ax.set_xlabel('Coherent frequency, $\omega/\omega_0$')
ax.set_ylabel('Power spectrum (arb. units)')
# ax.text(mean_y_beam.shape[0]/15/H_RF, np.min(mean_y_beam)/2, '$\\varepsilon_i={:.1e}$,\n $\\varepsilon_f={:.1e}$'.format(emitYC16before, emitYC16after), fontsize=12)
......
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