def center_check(data_list):
x_axis_mm = np.linspace(-23, 23, 2**9)
r_axis_mm = np.linspace(0, 23, 2**9 + 1)[1::2]
th_axis_rad = np.linspace(0, np.pi * 2, 2**9 + 1)[1::2]
xy_center_slice = slice(x_axis_mm.searchsorted(-1),
x_axis_mm.searchsorted(1, side='right'))
x = []
y = []
r = []
th = []
for data in data_list:
x.extend(data.electrons.pos_x.value)
y.extend(data.electrons.pos_y.value)
if np.any(data.electrons.pos_t.value > np.pi * 2):
data.electrons.recalculate_polar_coordinates()
r.extend(data.electrons.pos_r.value)
th.extend(data.electrons.pos_t.value)
image_xy = epicea.center_histogram_2d(x, y, x_axis_mm)
x_slice = image_xy[xy_center_slice, :].sum(axis=0)
y_slice = image_xy[:, xy_center_slice].sum(axis=1)
image_rt = epicea.center_histogram_2d(r, th, r_axis_mm, th_axis_rad)
plt_func.figure_wrapper('Electron data {} eV pass'.format(
data_list[0].electron_center_energy()))
plt.subplot(231)
plt_func.imshow_wrapper(image_xy, x_axis_mm)
plt.subplot(234)
plt_func.imshow_wrapper(image_rt,
r_axis_mm,
th_axis_rad,
kw_args={'aspect': 'auto'})
plt.subplot(132)
plt.plot(x_axis_mm, x_slice, label='normal')
plt.plot(x_axis_mm[::-1], x_slice, label='flipped')
plt_func.title_wrapper('x slice')
plt_func.legend_wrapper()
plt.xlim(xmin=7)
plt.subplot(133)
plt.plot(x_axis_mm, y_slice, label='normal')
plt.plot(x_axis_mm[::-1], y_slice, label='flipped')
plt_func.title_wrapper('y slice')
plt_func.legend_wrapper()
plt.xlim(xmin=7)
def get_r_theta_image(dataset,
r_axis_mm,
th_axis_rad,
gas,
*,
plot=False,
verbose=False,
fig=None):
data_name = dataset.name()
if verbose:
print('Get the theta-r spectrum for {}.'.format(data_name), flush=True)
# Get the image fron the data in polar coordinates
e_rth_image, e_rth_image_time_stamp = \
dataset.get_e_rth_image(r_axis_mm, th_axis_rad, verbose=False)
if plot:
if verbose:
print('Plot the raw theta r image of', data_name)
if fig is None:
# Make the figure
rth_fig = plt_func.figure_wrapper(
'e spectra theta-r {}'.format(data_name))
else:
rth_fig = fig
ax1 = rth_fig.add_subplot(131)
# Show the image
plt_func.imshow_wrapper(e_rth_image,
r_axis_mm,
th_axis_rad,
kw_args={'aspect': 'auto'})
# Make the image look nice
plt_func.tick_fontsize()
plt_func.title_wrapper('original ' + dataset.name())
plt_func.xlabel_wrapper('Position (mm)')
plt_func.ylabel_wrapper('Angle (rad)')
# plt_func.colorbar_wrapper()
rth_fig.add_subplot(132, sharex=ax1, sharey=ax1)
ax3 = rth_fig.add_subplot(133, sharex=ax1)
ax3.plot(r_axis_mm, e_rth_image.sum(axis=0))
else:
rth_fig = None
return e_rth_image, e_rth_image_time_stamp, rth_fig
# data = data_list[0]
for data in data_list:
r_th_img = data.get_e_rth_image(r_axis_mm, th_axis_rad)[0]
r_proj = r_th_img.sum(axis=0)
proj_params_initial = epicea.electron_calibration_helper.start_params(
r_axis_mm, r_proj, n_lines=2)
proj_result = lmfit.minimize(line_model,
proj_params_initial,
args=(r_axis_mm, r_proj),
kws={'line_type': line})
r_th_fig = plt_func.figure_wrapper('theta - r ' + data.name())
ax_origaninl = plt.subplot(221)
plt_func.imshow_wrapper(r_th_img,
r_axis_mm,
th_axis_rad,
kw_args={'aspect': 'auto'})
plt_func.colorbar_wrapper()
ax_origaninl.autoscale(False)
plt.subplot(223)
plt.plot(r_axis_mm, r_proj)
plt.plot(r_axis_mm,
line_model(proj_result.params, r_axis_mm, line_type=line), '--')
centers = (r_th_img * r_axis_mm).sum(axis=1) / r_th_img.sum(axis=1)
# radial_factors = centers.mean()/centers
# Find the center of the first line
low_radius_centers = np.empty_like(centers)
for i_th in range(len(th_axis_rad)):
def plot_e_spec(data, verbose=False):
"""First view of the electron spectra."""
if verbose:
print('Plotting electron spectra for {}.'.format(data.name()))
plt_func.figure_wrapper('Electron data {}'.format(data.name()))
valid_pos = data.get_filter('has_position_electrons', verbose=verbose)
NN_O_events_electrons = data.get_filter('NN_O_events_electrons',
verbose=verbose)
NO_N_events_electrons = data.get_filter('NO_N_events_electrons',
verbose=verbose)
if verbose:
print('Valid positions for {} electrons.'.format(valid_pos.sum()))
x_axis_mm = np.linspace(-23, 23, 512)
r_axis_mm = np.linspace(0, 23, 513)[1::2]
th_axis_rad = np.linspace(0, 2 * np.pi, 513)[1::2]
xy_center_slice = slice(x_axis_mm.searchsorted(-3),
x_axis_mm.searchsorted(3, side='right'))
e_axis_ev = np.linspace(320, 380, 2**8 + 1)[1::2]
# e_all_image_xy = data.get_e_xy_image(x_axis_mm, verbose=verbose)
e_all_image_xy, _ = data.get_e_xy_image(
x_axis_mm,
verbose=verbose,
electrons_filter=data.electrons.event_id.value >
0.9 * data.events.len())
e_all_x_slice = e_all_image_xy[xy_center_slice, :].sum(axis=0)
e_all_y_slice = e_all_image_xy[:, xy_center_slice].sum(axis=1)
# e_all_image_rth = data.get_e_rth_image(r_axis_mm, th_axis_rad,
# verbose=verbose)
e_all_image_rth, _ = data.get_e_rth_image(
r_axis_mm,
th_axis_rad,
verbose=verbose,
electrons_filter=data.electrons.event_id.value >
0.9 * data.events.len())
e_all_radial_dist = e_all_image_rth.sum(axis=0)
# e_NN_O_image_xy = data.get_e_xy_image(
# x_axis_mm, electrons_filter=NN_O_events_electrons)
# e_NO_N_image_xy = data.get_e_xy_image(
# x_axis_mm, electrons_filter=NO_N_events_electrons)
e_NN_O_image_rth, _ = data.get_e_rth_image(
r_axis_mm,
th_axis_rad,
electrons_filter=NN_O_events_electrons,
verbose=verbose)
e_NN_O_radial_dist = e_NN_O_image_rth.sum(axis=0)
e_NO_N_image_rth, _ = data.get_e_rth_image(
r_axis_mm,
th_axis_rad,
electrons_filter=NO_N_events_electrons,
verbose=verbose)
e_NO_N_radial_dist = e_NO_N_image_rth.sum(axis=0)
e_NN_O_energy_dist, _ = data.get_e_spectrum(
e_axis_ev, electrons_filter=NN_O_events_electrons, verbose=verbose)
e_NO_N_energy_dist, _ = data.get_e_spectrum(
e_axis_ev, electrons_filter=NO_N_events_electrons, verbose=verbose)
plt.subplot(231)
plt_func.imshow_wrapper(e_all_image_xy, x_axis_mm)
plt_func.title_wrapper('2D image')
plt_func.xlabel_wrapper('Position (mm)')
plt_func.ylabel_wrapper('Position (mm)')
plt_func.tick_fontsize()
plt.subplot(437)
plt.plot(x_axis_mm, e_all_x_slice, label='normal')
plt.plot(x_axis_mm[::-1], e_all_x_slice, label='flipped')
plt_func.title_wrapper('x slice')
plt_func.xlabel_wrapper('Position (mm)')
plt_func.ylabel_wrapper('Number of ions')
plt_func.tick_fontsize()
plt_func.legend_wrapper()
plt.subplot(4, 3, 10)
plt.plot(x_axis_mm, e_all_y_slice, label='normal')
plt.plot(x_axis_mm[::-1], e_all_y_slice, label='flipped')
plt_func.title_wrapper('y slice')
plt_func.xlabel_wrapper('Position (mm)')
plt_func.ylabel_wrapper('Number of ions')
plt_func.tick_fontsize()
plt_func.legend_wrapper()
plt.subplot(232)
plt_func.imshow_wrapper(e_all_image_rth,
r_axis_mm,
th_axis_rad,
kw_args={'aspect': 'auto'})
plt_func.title_wrapper('2D image')
plt_func.xlabel_wrapper('Position (mm)')
plt_func.ylabel_wrapper('Angle (rad)')
plt_func.tick_fontsize()
plt.subplot(235)
plt.plot(r_axis_mm, e_all_radial_dist)
plt_func.title_wrapper('Radial distribution')
plt_func.xlabel_wrapper('Position (mm)')
plt_func.ylabel_wrapper('Number of electrons')
plt_func.tick_fontsize()
# plt.subplot(233)
# plt_func.imshow_wrapper(e_NN_O_image_xy, x_axis_mm)
# plt_func.title_wrapper('NN+ O+ events')
# plt_func.xlabel_wrapper('Position (mm)')
# plt_func.ylabel_wrapper('Position (mm)')
# plt_func.tick_fontsize()
#
# plt.subplot(236)
# plt_func.imshow_wrapper(e_NO_N_image_xy, x_axis_mm)
# plt_func.title_wrapper('NO+ N+ events')
# plt_func.xlabel_wrapper('Position (mm)')
# plt_func.ylabel_wrapper('Position (mm)')
# plt_func.tick_fontsize()
#
# plt.subplot(233)
# plt_func.imshow_wrapper(e_NN_O_image_rth, r_axis_mm, th_axis_rad)
# plt_func.title_wrapper('NN+ O+ events')
# plt_func.xlabel_wrapper('Position (mm)')
# plt_func.ylabel_wrapper('Angle (rad)')
# plt_func.tick_fontsize()
#
# plt.subplot(236)
# plt_func.imshow_wrapper(e_NO_N_image_rth, r_axis_mm, th_axis_rad)
# plt_func.title_wrapper('NO+ N+ events')
# plt_func.xlabel_wrapper('Position (mm)')
# plt_func.ylabel_wrapper('Angle (rad)')
# plt_func.tick_fontsize()
plt.subplot(233)
plt.plot(r_axis_mm, e_NN_O_radial_dist, 'y', label=r'N$_2$$^+$ + O$^+$')
plt.plot(r_axis_mm, e_NO_N_radial_dist, 'm', label=r'NO$^+$ + N$^+$')
plt_func.title_wrapper('on radial grid')
plt_func.xlabel_wrapper('Position (mm)')
plt_func.ylabel_wrapper('Number of electrons')
plt_func.tick_fontsize()
plt_func.legend_wrapper()
plt.subplot(236)
plt.plot(e_axis_ev, e_NN_O_energy_dist, 'y', label=r'N$_2$$^+$ + O$^+$')
plt.plot(e_axis_ev, e_NO_N_energy_dist, 'm', label=r'NO$^+$ + N$^+$')
plt_func.title_wrapper('On an energy grid')
plt_func.xlabel_wrapper('Energy (eV)')
plt_func.ylabel_wrapper('Number of electrons')
plt_func.tick_fontsize()
plt_func.legend_wrapper()
plt.tight_layout()
plt_func.savefig_wrapper()
def plot_two_ion_corelations(data, verbose=False):
if verbose:
print('In plot_two_ion_corelations.')
# Get some ions filters
data.get_filter('two_ions_events_ions')
double_ions_e_start = data.get_filter('two_ion_e_start_events_ions')
data.get_filter('two_ion_rand_start_events_ions')
NN_O_events_ions = data.get_filter('NN_O_events_ions', verbose=verbose)
NO_N_events_ions = data.get_filter('NO_N_events_ions', verbose=verbose)
# Plot distribution of nomber of ions
num_ions = data.events.num_i.value
num_ions_axis = np.arange(5)
hist_ions_all = epicea.center_histogram(num_ions, num_ions_axis)
hist_ions_e_start = epicea.center_histogram(
num_ions[data.get_filter('e_start_events')], num_ions_axis)
hist_ions_rand_start = epicea.center_histogram(
num_ions[data.get_filter('rand_start_events')], num_ions_axis)
plt_func.figure_wrapper('Two-ion correlations {}'.format(data.name()))
plt.subplot(221)
plt.plot(num_ions_axis, hist_ions_all, 'o-', label='All events')
plt.plot(num_ions_axis, hist_ions_rand_start, 'o-', label='random start')
plt.plot(num_ions_axis, hist_ions_e_start, 'o-', label='e start')
plt_func.legend_wrapper()
plt_func.title_wrapper('Ion number distributions.')
plt_func.xlabel_wrapper('Number of ions per event')
plt_func.ylabel_wrapper('Number of events')
plt_func.tick_fontsize()
# Look at some two ions, e start event correlations
# Angle information
# plt_func.figure_wrapper('two ion angles {}'.format(data.name()))
plt.subplot(222)
if verbose:
print('Get angles.')
th_e_start = data.ions.pos_t[double_ions_e_start]
th_NN_O = data.ions.pos_t[NN_O_events_ions]
th_NO_N = data.ions.pos_t[NO_N_events_ions]
th_axis_rad = np.linspace(0, np.pi, 513)[1::2]
th_hist_e_start, th_diff_e_start = calc_angle_diffs_hist(
th_e_start, th_axis_rad)
th_hist_NN_O, _ = calc_angle_diffs_hist(th_NN_O, th_axis_rad)
th_hist_NO_N, _ = calc_angle_diffs_hist(th_NO_N, th_axis_rad)
plt_func.bar_wrapper(th_axis_rad,
th_hist_e_start,
label='e start ion pairs')
sl = slice(th_axis_rad.searchsorted(ANGLE_CUT), None)
plt_func.bar_wrapper(th_axis_rad[sl],
th_hist_e_start[sl],
'r',
label='selection')
plt_func.bar_wrapper(th_axis_rad,
th_hist_NN_O,
'y',
label='NN+ O+ ion pairs')
plt_func.bar_wrapper(th_axis_rad,
th_hist_NO_N,
'm',
label='NO+ N+ ion pairs')
# plt.hist(angle_diff[np.isfinite(angle_diff)],
# bins=np.linspace(-0.1, np.pi, 256))
plt_func.xlabel_wrapper('Angle difference between tow ions (rad)')
plt_func.ylabel_wrapper('Number of ion pairs')
plt_func.title_wrapper('Two ion angles.')
plt_func.legend_wrapper()
plt_func.tick_fontsize()
if verbose:
print('{} events with two ions and electron start identified.'.format(
data.get_filter('two_ion_e_start_events').sum()))
print('{} valid angle diffs found.'.format(th_hist_e_start.sum()))
# Radial information
# plt_func.figure_wrapper('radii {}'.format(data.name()))
plt.subplot(223)
if verbose:
print('Get radii.')
r_frac_axis = np.linspace(1., 3.5, 257)[1::2]
r_e_start = data.ions.pos_r[double_ions_e_start]
r_NN_O = data.ions.pos_r[NN_O_events_ions]
r_NO_N = data.ions.pos_r[NO_N_events_ions]
r_frac_e_start_hist = calc_radius_fraction_hist(r_e_start, r_frac_axis)
r_frac_selection_hist = calc_radius_fraction_hist(
r_e_start.reshape(-1, 2)[th_diff_e_start > ANGLE_CUT, :], r_frac_axis)
r_frac_NN_O_hist = calc_radius_fraction_hist(r_NN_O, r_frac_axis)
r_frac_NO_N_hist = calc_radius_fraction_hist(r_NO_N, r_frac_axis)
plt_func.bar_wrapper(r_frac_axis, r_frac_e_start_hist, label='e start')
plt_func.bar_wrapper(r_frac_axis,
r_frac_selection_hist,
'r',
label='selection')
plt_func.bar_wrapper(r_frac_axis,
r_frac_NN_O_hist,
'y',
label='NN+ O+ ions')
plt_func.bar_wrapper(r_frac_axis,
r_frac_NO_N_hist,
'm',
label='NO+ N+ ions')
# plt.hist(r_frac, bins=np.linspace(0.9, 3., 256))
plt_func.xlabel_wrapper('Radial quotient for two ions r1/r2')
plt_func.ylabel_wrapper('Number of ion pairs')
plt_func.title_wrapper('Radius quotient')
plt_func.legend_wrapper()
plt_func.tick_fontsize()
plt_func.savefig_wrapper()
###########################
# TOF-TOF correlation plot
plt_func.figure_wrapper('tof-tof hist {}'.format(data.name()))
t_axis_us = np.linspace(3.3, 5.3, 512)
t_axis = t_axis_us * 1e6
# names = ['e start', 'all', 'random start']
# for i, ions_filter in enumerate([double_ions_e_start,
# double_ions,
# double_ions_rand_start]):
# plt.subplot(2, 2, i+1)
names = ['electrons']
for i, ions_filter in enumerate([double_ions_e_start]):
i_tof = data.ions.tof_falling_edge[ions_filter]
i_tof = i_tof.reshape(-1, 2)
i_tof.sort(axis=1)
i_tof = i_tof[np.isfinite(i_tof).sum(1) == 2, :]
tof_tof_hist = epicea.center_histogram_2d(i_tof[:, 0], i_tof[:, 1],
t_axis)
tof_tof_sym_hist = tof_tof_hist + tof_tof_hist.T
# plt_func.imshow_wrapper(tof_tof_sym_hist, t_axis_us)
plt_func.imshow_wrapper(np.log(tof_tof_sym_hist + 1), t_axis_us)
# plt.plot(t_axis_us,
# glob.NN_O_TIME_SUM_RANGE_US[data.name()][0] - t_axis_us,
# 'y', label='NN+ O+ selection')
# plt.plot(t_axis_us,
# glob.NN_O_TIME_SUM_RANGE_US[data.name()][1] - t_axis_us,
# 'y')
# plt.plot(t_axis_us,
# glob.NN_O_TIME_DIFF_RANGE_US[data.name()][0] + t_axis_us,
# 'y')
# plt.plot(t_axis_us,
# glob.NN_O_TIME_DIFF_RANGE_US[data.name()][1] + t_axis_us,
# 'y')
for sum_range, diff_range, label, c in zip([
glob.NO_N_TIME_SUM_RANGE_US[data.name()],
glob.NN_O_TIME_SUM_RANGE_US[data.name()]
], [
glob.NO_N_TIME_DIFF_RANGE_US[data.name()],
glob.NN_O_TIME_DIFF_RANGE_US[data.name()]
], ['NO+ N+ selection', 'NN+ O+ selection'], ['m', 'y']):
s = sum_range[[0, 1, 1, 0, 0]]
d = diff_range[[1, 1, 0, 0, 1]]
x = (s - d) / 2
y = (s + d) / 2
plt.plot(x, y, c, label=label)
plt_func.title_wrapper(names[i])
plt.axis([
t_axis_us.min(),
t_axis_us.max(),
t_axis_us.min(),
t_axis_us.max()
])
plt_func.ylabel_wrapper('Time of flight (us)')
plt_func.xlabel_wrapper('Time of flight (us)')
plt_func.legend_wrapper(loc='center right')
plt_func.colorbar_wrapper('log(counts + 1)')
plt_func.tick_fontsize()
# if i == 0:
# tof_tof_hist_e_start = tof_tof_sym_hist.copy()
# if i == 2:
# tof_tof_hist_random = tof_tof_sym_hist.copy()
#
# sl = slice(t_axis.searchsorted(5.5e6))
# factor = (tof_tof_hist_e_start[sl, :].sum() /
# tof_tof_hist_random[sl, :].sum())
# pure = tof_tof_hist_e_start - tof_tof_hist_random * factor
#
# plt.subplot(224)
# plt_func.imshow_wrapper(pure, t_axis_us)
# plt_func.tick_fontsize()
plt_func.savefig_wrapper()
def plot_ion_image(data, verbose=False):
"""Plot some ion images."""
if verbose:
print('\nPlot some ion image data.')
x_axis_mm = np.arange(-40, 40, 0.1)
if verbose:
print('Get full image.')
i_image = data.get_i_xy_image(x_axis_mm)
if verbose:
print('Get e start image.')
i_image_e_start = data.get_i_xy_image(
x_axis_mm, ions_filter=data.get_filter('e_start_ions'))
if verbose:
print('Get rand start iage.')
i_image_random_start = data.get_i_xy_image(
x_axis_mm, ions_filter=~data.get_filter('e_start_ions'))
plt_func.figure_wrapper('Ion image {}'.format(data.name()))
plt.subplot(331)
plt_func.imshow_wrapper(i_image, x_axis_mm)
plt_func.title_wrapper('all ions')
plt_func.xlabel_wrapper('Position (mm)')
plt_func.ylabel_wrapper('Position (mm)')
plt_func.colorbar_wrapper()
plt_func.tick_fontsize()
plt.subplot(332)
plt_func.imshow_wrapper(i_image_e_start, x_axis_mm)
plt_func.title_wrapper('electron start')
plt_func.xlabel_wrapper('Position (mm)')
plt_func.ylabel_wrapper('Position (mm)')
plt_func.colorbar_wrapper()
plt_func.tick_fontsize()
plt.subplot(334)
plt_func.imshow_wrapper(i_image_random_start, x_axis_mm)
plt_func.title_wrapper('random start')
plt_func.xlabel_wrapper('Position (mm)')
plt_func.ylabel_wrapper('Position (mm)')
plt_func.colorbar_wrapper()
plt_func.tick_fontsize()
# Make a slice selection around the center of the image
selection = slice(x_axis_mm.searchsorted(-5), x_axis_mm.searchsorted(5))
# Project along x and y
x_projection = i_image_e_start[selection, :].sum(axis=0)
y_projection = i_image_e_start[:, selection].sum(axis=1)
plt.subplot(638)
plt.plot(x_axis_mm, x_projection, label='normal')
plt.plot(x_axis_mm[::-1], x_projection, label='reverse')
plt_func.title_wrapper('x slice')
plt_func.xlabel_wrapper('Position (mm)')
plt_func.legend_wrapper()
plt_func.tick_fontsize()
plt.subplot(6, 3, 11)
plt.plot(x_axis_mm, y_projection, label='nomral')
plt.plot(x_axis_mm[::-1], y_projection, label='reversed')
plt_func.title_wrapper('y slice')
plt_func.xlabel_wrapper('Position (mm)')
plt_func.legend_wrapper()
plt_func.tick_fontsize()
r_axis_mm = np.linspace(0, 40, 201)[1::2]
th_axis_rad = np.linspace(0, 2 * np.pi, 513)[1::2]
rt_image = data.get_i_rth_image(r_axis_mm=r_axis_mm,
th_axis_rad=th_axis_rad)
NN_O_ions = data.get_filter('NN_O_events_ions')
NO_N_ions = data.get_filter('NO_N_events_ions')
NN_O_rth_image = data.get_i_rth_image(r_axis_mm,
th_axis_rad,
ions_filter=NN_O_ions)
NO_N_rth_image = data.get_i_rth_image(r_axis_mm,
th_axis_rad,
ions_filter=NO_N_ions)
NN_O_r_proj = NN_O_rth_image.sum(axis=0)
NO_N_r_proj = NO_N_rth_image.sum(axis=0)
plt.subplot(333)
plt_func.imshow_wrapper(rt_image,
r_axis_mm,
th_axis_rad,
kw_args={'aspect': 'auto'})
plt_func.tick_fontsize()
plt_func.title_wrapper('electron start polar')
plt_func.xlabel_wrapper('Radius (mm)')
plt_func.ylabel_wrapper('Angle (rad)')
plt.subplot(336)
r_projection = rt_image.sum(axis=0)
plt.plot(r_axis_mm, r_projection, label='e start')
plt_func.xlabel_wrapper('Radius (mm)')
plt_func.ylabel_wrapper('Number of counts')
plt_func.legend_wrapper()
plt_func.tick_fontsize()
plt.subplot(337)
plt.plot(r_axis_mm, NN_O_r_proj, 'y', label='NN+ O+ ions')
plt_func.xlabel_wrapper('Radius (mm)')
plt_func.ylabel_wrapper('Number of counts')
plt_func.legend_wrapper()
plt_func.tick_fontsize()
plt.subplot(338)
r_projection = rt_image.sum(axis=0)
plt.plot(r_axis_mm, NO_N_r_proj, 'm', label='NO+ N+ ions')
plt_func.xlabel_wrapper('Radius (mm)')
plt_func.ylabel_wrapper('Number of counts')
plt_func.legend_wrapper()
plt_func.tick_fontsize()
plt_func.savefig_wrapper()
def make_calibration(setting, plot=True, verbose=False):
"""Make the electron calibration for a given center energy setting."""
# The Kr binding energies are needed for Kr based calibrations
calibration_file_name = 'h5_data/calib_{}.h5'.format(setting)
# Get the list of data sets
calib_data_list = load_data_for_calibration(setting, verbose=verbose)
if 'Kr' in calib_data_list[0].name():
gas = 'Kr'
n_lines = 2
elif 'N2' in calib_data_list[0].name():
gas = 'N2'
n_lines = 1
else:
print('Unknown gas.',
'Dataset names must specify the used gas (N2 or Kr).')
# Create an empty calibration object
calibration = epicea.ElectronEnergyCalibration()
# # Make an x axis
# x_axis_mm = np.linspace(-23, 23, 2**8)
# # Define polar coordinate axis vectors
# r_axis_mm = np.linspace(0, 25, 2**7+1)[1::2]
# th_axis_rad = np.linspace(0, 2*np.pi, 2**9+1)[1::2]
if plot:
# Create a plot of all the spectra in the data list
if verbose:
print('Plot all the raw spectra.')
# Create the figure
plt_func.figure_wrapper('Calibration spectra {} eV'.format(setting))
# One subplot for each data set...
n_subplots = calib_data_list.len()
# ... nicely set in collumns and rows
n_rows = np.floor(np.sqrt(n_subplots))
n_cols = np.ceil(float(n_subplots) / n_rows)
# Iterate over the datasets
for i, c_data in enumerate(calib_data_list):
plt.subplot(n_rows, n_cols, i + 1) # Make the subplot
# Show the electron figure
plt_func.imshow_wrapper(
c_data.get_e_xy_image(x_axis_mm)[0], x_axis_mm)
# Adjust the figure for good looks
plt_func.tick_fontsize()
plt_func.title_wrapper(c_data.name())
plt_func.xlabel_wrapper('Position (mm)')
plt_func.ylabel_wrapper('Position (mm)')
# plt.tight_layout()
# Keep track of the latest time stamp of the lines
latest_lines = 0
rth_fig = {}
straight_img = {}
straight_img_time_stamp = {}
radial_factors = {}
# Iterate over the datasets and make images for each set.
# Also find the lines in each of the spectra
for i, c_data in enumerate(calib_data_list):
d_name = c_data.name()
#######################################
# Raw theta vs. r spectrum
e_rth_image, e_rth_image_time_stamp, rth_fig[d_name] = \
get_r_theta_image(c_data, r_axis_mm, th_axis_rad, gas,
plot=True, verbose=verbose, fig=None)
#######################################
# radial factors
radial_factors[d_name], radial_factors_time_stamp, _ = \
get_radial_factors(c_data, r_axis_mm, th_axis_rad,
e_rth_image_time_stamp, gas,
e_rth_image=e_rth_image,
verbose=verbose)
#######################################
# Straight image
straight_img[d_name], straight_img_time_stamp[d_name], _= \
get_straight_image(c_data, r_axis_mm, th_axis_rad,
radial_factors_time_stamp, gas,
radial_factors=radial_factors[d_name],
verbose=verbose, plot=plot,
fig=rth_fig[d_name])
# Make sure the straight image is avaliable for all the calibration data
# sets before making the background.
# This is achieved by exiting the loop and starting a neew one.
for i, c_data in enumerate(calib_data_list):
d_name = c_data.name()
#######################################
# N_2 background
if gas == 'N2':
if not background.check_bg_valid(
setting, len(r_axis_mm), verbose=verbose):
if verbose:
print('Make new N2 background.', flush=True)
background.make_new_bg(setting=setting,
plot=plot,
verbose=verbose)
elif verbose:
print('Use old N2 background.')
n2_bg = (background.load_background(setting, len(r_axis_mm)) *
c_data.data_scaling)
kws = {'bg': n2_bg}
else:
n2_bg = None
kws = {}
########
# do some plotting of the straight image projection and a fit to it
if plot:
plot_projections(rth_fig[d_name],
c_data,
straight_img[d_name],
r_axis_mm,
th_axis_rad,
gas,
n_lines,
setting,
verbose=verbose,
**kws)
#######################################
# Find lines
r, w, a, red_chi2, lines_time_stamp = get_lines(
c_data,
r_axis_mm,
th_axis_rad,
n_lines,
setting,
max(straight_img_time_stamp.values()),
gas,
verbose=verbose,
plot=plot,
fig=rth_fig[d_name],
straight_img=straight_img[d_name],
radial_factors=radial_factors[d_name],
n2_bg=n2_bg,
calibration=calibration)
# Keep the latest time stamp
latest_lines = max(lines_time_stamp, latest_lines)
print('Create calibration', flush=True)
# calibration.create_conversion(poly_order=2)
calibration.create_or_load_conversion(calibration_file_name,
compare_time_stmp=max(
latest_lines, 1444746720),
poly_order=2,
verbose=verbose)
print('Check the calibration', flush=True)
plt_func.figure_wrapper('Calib data check')
theta_list, data_list = calibration.get_data_copy()
r_min = data_list[:, :, 0].min()
r_max = data_list[:, :, 0].max()
r_axis_for_calib_check_mm = np.linspace(r_min + (r_min - r_max) * 0.0,
r_max + (r_max - r_min) * 0.0, 256)
for idx in range(0, len(theta_list),
int(np.ceil(float(len(theta_list)) / 20))):
plt.subplot(121)
plt.plot(data_list[:, idx, 0], data_list[:, idx, 1], '.:')
# plt.plot(r_axis_for_calib_check_mm,
# epicea.poly_line(calibration._energy_params_list[idx],
# r_axis_for_calib_check_mm), 'r')
plt.plot(
r_axis_for_calib_check_mm,
epicea.electron_calibration_helper.r_to_e_conversion(
calibration._energy_params_list[idx],
r_axis_for_calib_check_mm), 'r')
plt.subplot(122)
shift = 0.1 * idx
plt.plot(data_list[:, idx, 0], 1. / data_list[:, idx, 3] + shift, '.:')
# plt.plot(r_axis_for_calib_check_mm,
# epicea.poly_line(calibration._weights_params_list[idx],
# r_axis_for_calib_check_mm) + shift, 'r')
plt.plot(data_list[..., 0].mean(1),
1. / data_list[..., 3].mean(1),
'k',
lw=2)
a, b = ((338, 368) if setting == 357 else (359, 368) if setting == 366 else
(366, 376) if setting == 373 else (480, 508))
E_axis_eV = np.linspace(a, b, 2**9 + 1)[1::2]
E_all = []
err_all = []
theta_all = []
weight_all = []
for c_data in calib_data_list:
print('Get the calibrated energies, {} eV.'.format(
c_data.photon_energy()),
flush=True)
c_data.calculate_electron_energy(calibration, verbose=verbose)
# E, err, weigths = calibration.get_energies(c_data.electrons.pos_r,
# c_data.electrons.pos_t)
E_all.extend(c_data.electrons.energy.value)
err_all.extend(c_data.electrons.energy_uncertainty.value)
theta_all.extend(c_data.electrons.pos_t.value)
weight_all.extend(c_data.electrons.spectral_weight.value)
plt_func.figure_wrapper('Energy domain all calibration data')
ax = plt.subplot(211)
E_image = epicea.center_histogram_2d(E_all,
theta_all,
E_axis_eV,
th_axis_rad,
weights=weight_all)
plt_func.imshow_wrapper(E_image,
E_axis_eV,
th_axis_rad,
kw_args={'aspect': 'auto'})
plt.subplot(212, sharex=ax)
plt.plot(E_axis_eV, E_image.sum(0))
plt.grid(True)
calibration.save_to_file(calibration_file_name)
def get_straight_image(data,
r_axis_mm,
th_axis_rad,
compare_time_stamp,
gas,
*,
verbose=False,
plot=False,
fig=None,
radial_factors=None):
d_name = data.name()
if radial_factors is None:
radial_factors, r_f_time_stamp, fig = get_radial_factors(
data, r_axis_mm, th_axis_rad, 0, gas, verbose=verbose, plot=plot)
compare_time_stamp = max(compare_time_stamp, r_f_time_stamp)
compare_time_stamp = max(compare_time_stamp, 144178446)
th_limits = epicea.limits_from_centers(th_axis_rad)
# Get the corrected image with some defined properties
straight_img_name = 'e_rth_image_straight'
filter_sum_string = 'no_filter'
match_data_dict = {'r_axis_mm': r_axis_mm, 'th_axis_rad': th_axis_rad}
# With the radial factors done, now look for the corrected image
match_data_dict['radial_factors'] = radial_factors
straight_img, straight_img_time_stamp = \
data.load_derived_data(
straight_img_name, filter_sum_string,
compare_time_stamp=compare_time_stamp,
verbose=verbose,
match_data_dict=match_data_dict)
# If there is no data it needs to be made
if straight_img.size > 0:
if verbose:
print('Use old straight image.', flush=True)
else:
if verbose:
print('Make new straight image.', flush=True)
# Get and adjust the coordinates
th = data.electrons.pos_t.value
r = data.electrons.pos_r.value
for i_th in range(len(th_axis_rad)):
try:
I = (th_limits[i_th] <= th) & (th < th_limits[i_th + 1])
except RuntimeWarning as W:
print(W.args)
print(W.with_traceback)
print('I.sum() =', I.sum(), 'i_th =', i_th, th_limits[i_th])
r[I] *= radial_factors[i_th]
straight_img = epicea.center_histogram_2d(r, th, r_axis_mm,
th_axis_rad)
# Save the straight image
straight_img_time_stamp = data.store_derived_data(
straight_img,
straight_img_name,
filter_sum_string,
match_data_dict=match_data_dict,
verbose=verbose)
if (fig is not None) & (plot == True):
if verbose:
print('Plot the straight image of', d_name, flush=True)
plt.sca(fig.axes[1])
img_axis = plt_func.imshow_wrapper(straight_img,
r_axis_mm,
th_axis_rad,
kw_args={'aspect': 'auto'})
plt_func.tick_fontsize()
plt_func.title_wrapper('straight ' + d_name)
plt_func.xlabel_wrapper('Position (mm)')
plt_func.ylabel_wrapper('Angle (rad)')
plt_func.colorbar_wrapper(mappable=img_axis)
return straight_img, straight_img_time_stamp, fig
# Do once to chenge the data in the hdf5 file
for dataset in data_list:
dataset.ions.recalculate_polar_coordinates()
x_axis_mm = np.linspace(-40, 40, 2**10)
r_axis_mm = np.linspace(0, 40, 2**10)[1::2]
th_axis_rad = np.linspace(0, 2 * np.pi, 2**10 + 1)[1::2]
for dataset in data_list:
xy_img = dataset.get_i_xy_image(x_axis_mm)
rth_img = dataset.get_i_rth_image(r_axis_mm, th_axis_rad)
r_dist_th_sum = rth_img.sum(axis=0)
r_dist = r_dist_th_sum / r_axis_mm
fig = plt_func.figure_wrapper('Ion image of "{}'.format(dataset.name()))
ax1 = fig.add_subplot(221)
plt_func.imshow_wrapper(xy_img, x_axis_mm, axes=ax1)
ax2 = fig.add_subplot(222)
plt_func.imshow_wrapper(rth_img,
r_axis_mm,
th_axis_rad,
axes=ax2,
kw_args={'aspect': 'auto'})
ax4 = fig.add_subplot(224)
ax4.plot(r_axis_mm, r_dist_th_sum)
ax3 = fig.add_subplot(223)
ax3.plot(r_axis_mm, r_dist)
for data in [data_list[0]]:
#for data in data_list:
r_th_img = data.get_e_rth_image(r_axis_mm, th_axis_rad)[0]
r_proj = r_th_img.sum(axis=0)
th_sums = r_th_img.sum(axis=1)
max_line_number = np.argmax(th_sums)
norm_img = (r_th_img.T / th_sums).T
fig_img = plt_func.figure_wrapper('normalized image')
ax_img = fig_img.add_subplot(211)
ax_img_straight = fig_img.add_subplot(212, sharex=ax_img, sharey=ax_img)
plt_func.imshow_wrapper(norm_img,
r_axis_mm,
th_axis_rad,
kw_args={'aspect': 'auto'},
ax=ax_img)
# max_line = norm_img[max_line_number, :]
max_line = norm_img.mean(axis=0)
corr_img = np.array(
[np.correlate(line, max_line, mode='same') for line in norm_img])
fig_corr = plt_func.figure_wrapper('correlation')
ax_corr = fig_corr.add_subplot(111)
plt_func.imshow_wrapper(corr_img,
r_axis_mm,
th_axis_rad,
kw_args={'aspect': 'auto'},
ax=ax_corr)