Module pmt_analysis.plotting.model_independent
Expand source code
import os
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import gridspec
from typing import Optional
class PlottingGainModelIndependent:
"""
Class for plots related to the model independent occupancy and gain calculation.
Attributes:
input_dict: Dictionary with output results from the
`pmt_analysis.analysis.model_independent.GainModelIndependent.compute` method.
save_plots: Bool defining if plots are saved (as png and pdf).
show_plots: Bool defining if plots are displayed.
save_dir: Target directory for saving the plots.
save_name_suffix: Measurement specific figure name suffix.
"""
def __init__(self, input_dict: dict, save_plots: bool = False, show_plots: bool = True,
save_dir: Optional[str] = None, save_name_suffix: Optional[str] = None):
"""Init of the GainModelIndependent class.
Generation of standard plots with respect to the model independent occupancy
and gain calculation.
Args:
input_dict: Dictionary with output results from the
`pmt_analysis.analysis.model_independent.GainModelIndependent.compute` method.
save_plots: Bool defining if plots are saved (as png and pdf).
show_plots: Bool defining if plots are displayed.
save_dir: Target directory for saving the plots.
save_name_suffix: Measurement specific figure name suffix.
"""
self.input_dict = input_dict
self.save_plots = save_plots
self.show_plots = show_plots
self.save_dir = save_dir
self.save_name_suffix = save_name_suffix
if save_plots & (save_dir is None):
raise NameError('save_dir must be defined if save_plots is True.')
if save_plots & (save_name_suffix is None):
raise NameError('save_name_suffix must be defined if save_plots is True.')
def plot_occupancy_vs_threshold(self):
"""Plot occupancy as a function of integration threshold."""
thresholds_list = np.asarray(self.input_dict['iterations']['threshold'])
occupancy_list = np.asarray(self.input_dict['iterations']['occupancy'])
occupancy_err_list = np.asarray(self.input_dict['iterations']['occupancy_err'])
occupancy_list_smooth = np.asarray(self.input_dict['iterations']['occupancy_smoothed'])
occupancy = self.input_dict['occupancy']
threshold_occ_det = self.input_dict['threshold_occupancy_determination']
plt.plot(thresholds_list, occupancy_list, color='C0')
plt.fill_between(thresholds_list, occupancy_list - occupancy_err_list,
occupancy_list + occupancy_err_list,
alpha=0.3, color='C0', linewidth=0)
plt.plot(thresholds_list[occupancy_list_smooth > 0.1],
occupancy_list_smooth[occupancy_list_smooth > 0.1],
color='C1', linewidth=1)
plt.axhline(y=occupancy, color='gray', linestyle='dotted')
plt.axvline(x=threshold_occ_det, color='gray', linestyle='dotted')
plt.xlim(thresholds_list[0], thresholds_list[-1])
plt.xlabel('Area Threshold [ADC Units]')
plt.ylabel('Occupancy [PE/Trigger]')
filename = 'occupancy_vs_threshold_{}'.format(self.save_name_suffix)
if self.save_plots:
plt.savefig(os.path.join(self.save_dir, filename + '.png'))
plt.savefig(os.path.join(self.save_dir, filename + '.pdf'))
if self.show_plots:
plt.show()
else:
plt.close()
def plot_rel_occ_err_vs_threshold(self):
"""Plot relative occupancy error as a function of integration threshold."""
thresholds_list = np.asarray(self.input_dict['iterations']['threshold'])
occupancy_list = np.asarray(self.input_dict['iterations']['occupancy'])
occupancy_err_list = np.asarray(self.input_dict['iterations']['occupancy_err'])
occupancy = self.input_dict['occupancy']
occupancy_err = self.input_dict['occupancy_err']
threshold_occ_det = self.input_dict['threshold_occupancy_determination']
plt.plot(thresholds_list, occupancy_err_list / occupancy_list, linewidth=1)
plt.axhline(y=0.01, color='gray', linestyle='dashed')
plt.axhline(y=occupancy_err / occupancy, color='gray', linestyle='dotted')
plt.axvline(x=threshold_occ_det, color='gray', linestyle='dotted')
plt.xlim(thresholds_list[0], thresholds_list[-1])
plt.yscale('log')
plt.xlabel('Area Threshold [ADC Units]')
plt.ylabel('Relative Error Occupancy')
filename = 'rel_occ_err_vs_threshold_{}'.format(self.save_name_suffix)
if self.save_plots:
plt.savefig(os.path.join(self.save_dir, filename + '.png'))
plt.savefig(os.path.join(self.save_dir, filename + '.pdf'))
if self.show_plots:
plt.show()
else:
plt.close()
def plot_occ_and_rel_err_vs_threshold(self):
"""Plot occupancy and relative occupancy error as a function of integration threshold
in two vertically stacked subplots."""
thresholds_list = np.asarray(self.input_dict['iterations']['threshold'])
occupancy_list = np.asarray(self.input_dict['iterations']['occupancy'])
occupancy_err_list = np.asarray(self.input_dict['iterations']['occupancy_err'])
occupancy_list_smooth = np.asarray(self.input_dict['iterations']['occupancy_smoothed'])
occupancy = self.input_dict['occupancy']
occupancy_err = self.input_dict['occupancy_err']
threshold_occ_det = self.input_dict['threshold_occupancy_determination']
height = matplotlib.rcParams['figure.figsize'][-1]
gs = gridspec.GridSpec(2, 1, height_ratios=[0.65 * height, 0.35 * height])
ax0 = plt.subplot(gs[0])
ax0.plot(thresholds_list, occupancy_list)
ax0.fill_between(thresholds_list, occupancy_list - occupancy_err_list, occupancy_list + occupancy_err_list,
alpha=0.3, color='C0', linewidth=0)
ax0.plot(thresholds_list[occupancy_list_smooth > 0.1],
occupancy_list_smooth[occupancy_list_smooth > 0.1], linewidth=1)
ax0.axhline(y=occupancy, color='gray', linestyle='dotted')
ax0.axvline(x=threshold_occ_det, color='gray', linestyle='dotted')
ax0.set_ylabel('Occupancy [PE/Trigger]')
ax0.tick_params(axis='x', labelbottom=False)
ax1 = plt.subplot(gs[1], sharex=ax0)
ax1.plot(thresholds_list, occupancy_err_list / occupancy_list, linewidth=1)
ax1.axhline(y=0.01, color='gray', linestyle='dashed')
ax1.axhline(y=occupancy_err / occupancy, color='gray', linestyle='dotted')
ax1.axvline(x=threshold_occ_det, color='gray', linestyle='dotted')
ax1.set_xlabel('Area Threshold [ADC Units]')
ax1.set_ylabel('Rel. Error')
ax1.set_yscale('log')
plt.xlim(thresholds_list[0], thresholds_list[-1])
plt.subplots_adjust(hspace=.0)
filename = 'occ_and_rel_err_vs_threshold_{}'.format(self.save_name_suffix)
if self.save_plots:
plt.savefig(os.path.join(self.save_dir, filename + '.png'))
plt.savefig(os.path.join(self.save_dir, filename + '.pdf'))
if self.show_plots:
plt.show()
else:
plt.close()
def plot_area_hist_occ_threshold_det(self):
"""Area histogram illustrating the threshold for the occupancy determination
and resulting 'LED on' and 'LED off' integrals."""
hist_bin_centers = np.asarray(self.input_dict['histograms']['bin_centers'])
hist_counts_led_on = np.asarray(self.input_dict['histograms']['counts_led_on'])
hist_counts_led_off = np.asarray(self.input_dict['histograms']['counts_led_off'])
threshold_occ_det = self.input_dict['threshold_occupancy_determination']
outlier_threshold_lower = self.input_dict['outlier_thresholds'][0]
plt.fill_between(hist_bin_centers,
hist_counts_led_off - (np.sqrt(hist_counts_led_off)),
hist_counts_led_off + (np.sqrt(hist_counts_led_off)),
step='mid', color='C0', alpha=0.4, linewidth=0)
plt.fill_between(hist_bin_centers,
hist_counts_led_on - (np.sqrt(hist_counts_led_on)),
hist_counts_led_on + (np.sqrt(hist_counts_led_on)),
step='mid', color='C1', alpha=0.4, linewidth=0)
mask = hist_bin_centers <= threshold_occ_det + 1
plt.fill_between(hist_bin_centers[mask], hist_counts_led_off[mask],
step='mid', hatch=5 * '/', color='none',
edgecolor='C0', alpha=0.3)
plt.fill_between(hist_bin_centers[mask], hist_counts_led_on[mask],
step='mid', hatch=5 * '\\', color='none',
edgecolor='C1', alpha=0.3)
plt.step(hist_bin_centers, hist_counts_led_off, where='mid',
color='C0', label='LED Off')
plt.step(hist_bin_centers, hist_counts_led_on, where='mid',
color='C1', label='LED On')
plt.axvline(x=threshold_occ_det, color='gray', linestyle='dashed',
label='Threshold Occupancy\nCalculation: {}'.format(threshold_occ_det))
plt.axvline(x=outlier_threshold_lower / 2, color='gray', linestyle='dotted',
label='Range Probed\nThresholds')
plt.axvline(x=-outlier_threshold_lower / 2, color='gray', linestyle='dotted')
plt.xlim(outlier_threshold_lower, -outlier_threshold_lower)
plt.yscale('log')
plt.xlabel('Area [ADC Units]')
plt.ylabel('Entries')
legend = plt.legend(loc=4, bbox_to_anchor=(0.99, 0.025),
framealpha=1)
legend.get_frame().set_linewidth(matplotlib.rcParams['axes.linewidth'])
filename = 'area_hist_occ_threshold_det_{}'.format(self.save_name_suffix)
if self.save_plots:
plt.savefig(os.path.join(self.save_dir, filename + '.png'))
plt.savefig(os.path.join(self.save_dir, filename + '.pdf'))
if self.show_plots:
plt.show()
else:
plt.close()
def plot_area_hist_model_independent_gain(self):
"""Area histogram of the 'LED on' and 'LED off' data illustrating the
model independent gain determination."""
hist_bin_centers = np.asarray(self.input_dict['histograms']['bin_centers'])
hist_counts_led_on = np.asarray(self.input_dict['histograms']['counts_led_on'])
hist_counts_led_off = np.asarray(self.input_dict['histograms']['counts_led_off'])
mean_s = self.input_dict['moments_s']['mean']
mean_b = self.input_dict['moments_b']['mean']
mean_psi = self.input_dict['mean_psi']
plt.fill_between(hist_bin_centers,
hist_counts_led_off - (np.sqrt(hist_counts_led_off)),
hist_counts_led_off + (np.sqrt(hist_counts_led_off)),
step='mid', color='C0', alpha=0.4, linewidth=0)
plt.fill_between(hist_bin_centers,
hist_counts_led_on - (np.sqrt(hist_counts_led_on)),
hist_counts_led_on + (np.sqrt(hist_counts_led_on)),
step='mid', color='C1', alpha=0.4, linewidth=0)
plt.step(hist_bin_centers, hist_counts_led_off, where='mid',
color='C0', label='LED Off')
plt.step(hist_bin_centers, hist_counts_led_on, where='mid',
color='C1', label='LED On')
plt.axvline(x=mean_b, color='C0', linestyle='dotted',
linewidth=1, label='Mean LED Off')
plt.axvline(x=mean_s, color='C1', linestyle='dotted',
linewidth=1, label='Mean LED On')
plt.axvline(x=mean_psi, color='gray', linestyle='dashed',
linewidth=1, label='Mean Single\nPhotoelectron\nResponse')
plt.xlim(min(hist_bin_centers), max(hist_bin_centers))
plt.yscale('log')
plt.xlabel('Area [ADC Units]')
plt.ylabel('Entries')
legend = plt.legend(loc=1, bbox_to_anchor=(0.99, 0.99))
legend.get_frame().set_linewidth(matplotlib.rcParams['axes.linewidth'])
filename = 'area_hist_model_independent_gain_{}'.format(self.save_name_suffix)
if self.save_plots:
plt.savefig(os.path.join(self.save_dir, filename + '.png'))
plt.savefig(os.path.join(self.save_dir, filename + '.pdf'))
if self.show_plots:
plt.show()
else:
plt.close()
def plot_essentials(self):
"""Plot essential plots for model independent gain and occupancy estimation."""
self.plot_occ_and_rel_err_vs_threshold()
self.plot_area_hist_occ_threshold_det()
self.plot_area_hist_model_independent_gain()Classes
class PlottingGainModelIndependent (input_dict: dict, save_plots: bool = False, show_plots: bool = True, save_dir: Optional[str] = None, save_name_suffix: Optional[str] = None)-
Class for plots related to the model independent occupancy and gain calculation.
Attributes
input_dict- Dictionary with output results from the
GainModelIndependent.compute()method. save_plots- Bool defining if plots are saved (as png and pdf).
show_plots- Bool defining if plots are displayed.
save_dir- Target directory for saving the plots.
save_name_suffix- Measurement specific figure name suffix.
Init of the GainModelIndependent class.
Generation of standard plots with respect to the model independent occupancy and gain calculation.
Args
input_dict- Dictionary with output results from the
GainModelIndependent.compute()method. save_plots- Bool defining if plots are saved (as png and pdf).
show_plots- Bool defining if plots are displayed.
save_dir- Target directory for saving the plots.
save_name_suffix- Measurement specific figure name suffix.
Expand source code
class PlottingGainModelIndependent: """ Class for plots related to the model independent occupancy and gain calculation. Attributes: input_dict: Dictionary with output results from the `pmt_analysis.analysis.model_independent.GainModelIndependent.compute` method. save_plots: Bool defining if plots are saved (as png and pdf). show_plots: Bool defining if plots are displayed. save_dir: Target directory for saving the plots. save_name_suffix: Measurement specific figure name suffix. """ def __init__(self, input_dict: dict, save_plots: bool = False, show_plots: bool = True, save_dir: Optional[str] = None, save_name_suffix: Optional[str] = None): """Init of the GainModelIndependent class. Generation of standard plots with respect to the model independent occupancy and gain calculation. Args: input_dict: Dictionary with output results from the `pmt_analysis.analysis.model_independent.GainModelIndependent.compute` method. save_plots: Bool defining if plots are saved (as png and pdf). show_plots: Bool defining if plots are displayed. save_dir: Target directory for saving the plots. save_name_suffix: Measurement specific figure name suffix. """ self.input_dict = input_dict self.save_plots = save_plots self.show_plots = show_plots self.save_dir = save_dir self.save_name_suffix = save_name_suffix if save_plots & (save_dir is None): raise NameError('save_dir must be defined if save_plots is True.') if save_plots & (save_name_suffix is None): raise NameError('save_name_suffix must be defined if save_plots is True.') def plot_occupancy_vs_threshold(self): """Plot occupancy as a function of integration threshold.""" thresholds_list = np.asarray(self.input_dict['iterations']['threshold']) occupancy_list = np.asarray(self.input_dict['iterations']['occupancy']) occupancy_err_list = np.asarray(self.input_dict['iterations']['occupancy_err']) occupancy_list_smooth = np.asarray(self.input_dict['iterations']['occupancy_smoothed']) occupancy = self.input_dict['occupancy'] threshold_occ_det = self.input_dict['threshold_occupancy_determination'] plt.plot(thresholds_list, occupancy_list, color='C0') plt.fill_between(thresholds_list, occupancy_list - occupancy_err_list, occupancy_list + occupancy_err_list, alpha=0.3, color='C0', linewidth=0) plt.plot(thresholds_list[occupancy_list_smooth > 0.1], occupancy_list_smooth[occupancy_list_smooth > 0.1], color='C1', linewidth=1) plt.axhline(y=occupancy, color='gray', linestyle='dotted') plt.axvline(x=threshold_occ_det, color='gray', linestyle='dotted') plt.xlim(thresholds_list[0], thresholds_list[-1]) plt.xlabel('Area Threshold [ADC Units]') plt.ylabel('Occupancy [PE/Trigger]') filename = 'occupancy_vs_threshold_{}'.format(self.save_name_suffix) if self.save_plots: plt.savefig(os.path.join(self.save_dir, filename + '.png')) plt.savefig(os.path.join(self.save_dir, filename + '.pdf')) if self.show_plots: plt.show() else: plt.close() def plot_rel_occ_err_vs_threshold(self): """Plot relative occupancy error as a function of integration threshold.""" thresholds_list = np.asarray(self.input_dict['iterations']['threshold']) occupancy_list = np.asarray(self.input_dict['iterations']['occupancy']) occupancy_err_list = np.asarray(self.input_dict['iterations']['occupancy_err']) occupancy = self.input_dict['occupancy'] occupancy_err = self.input_dict['occupancy_err'] threshold_occ_det = self.input_dict['threshold_occupancy_determination'] plt.plot(thresholds_list, occupancy_err_list / occupancy_list, linewidth=1) plt.axhline(y=0.01, color='gray', linestyle='dashed') plt.axhline(y=occupancy_err / occupancy, color='gray', linestyle='dotted') plt.axvline(x=threshold_occ_det, color='gray', linestyle='dotted') plt.xlim(thresholds_list[0], thresholds_list[-1]) plt.yscale('log') plt.xlabel('Area Threshold [ADC Units]') plt.ylabel('Relative Error Occupancy') filename = 'rel_occ_err_vs_threshold_{}'.format(self.save_name_suffix) if self.save_plots: plt.savefig(os.path.join(self.save_dir, filename + '.png')) plt.savefig(os.path.join(self.save_dir, filename + '.pdf')) if self.show_plots: plt.show() else: plt.close() def plot_occ_and_rel_err_vs_threshold(self): """Plot occupancy and relative occupancy error as a function of integration threshold in two vertically stacked subplots.""" thresholds_list = np.asarray(self.input_dict['iterations']['threshold']) occupancy_list = np.asarray(self.input_dict['iterations']['occupancy']) occupancy_err_list = np.asarray(self.input_dict['iterations']['occupancy_err']) occupancy_list_smooth = np.asarray(self.input_dict['iterations']['occupancy_smoothed']) occupancy = self.input_dict['occupancy'] occupancy_err = self.input_dict['occupancy_err'] threshold_occ_det = self.input_dict['threshold_occupancy_determination'] height = matplotlib.rcParams['figure.figsize'][-1] gs = gridspec.GridSpec(2, 1, height_ratios=[0.65 * height, 0.35 * height]) ax0 = plt.subplot(gs[0]) ax0.plot(thresholds_list, occupancy_list) ax0.fill_between(thresholds_list, occupancy_list - occupancy_err_list, occupancy_list + occupancy_err_list, alpha=0.3, color='C0', linewidth=0) ax0.plot(thresholds_list[occupancy_list_smooth > 0.1], occupancy_list_smooth[occupancy_list_smooth > 0.1], linewidth=1) ax0.axhline(y=occupancy, color='gray', linestyle='dotted') ax0.axvline(x=threshold_occ_det, color='gray', linestyle='dotted') ax0.set_ylabel('Occupancy [PE/Trigger]') ax0.tick_params(axis='x', labelbottom=False) ax1 = plt.subplot(gs[1], sharex=ax0) ax1.plot(thresholds_list, occupancy_err_list / occupancy_list, linewidth=1) ax1.axhline(y=0.01, color='gray', linestyle='dashed') ax1.axhline(y=occupancy_err / occupancy, color='gray', linestyle='dotted') ax1.axvline(x=threshold_occ_det, color='gray', linestyle='dotted') ax1.set_xlabel('Area Threshold [ADC Units]') ax1.set_ylabel('Rel. Error') ax1.set_yscale('log') plt.xlim(thresholds_list[0], thresholds_list[-1]) plt.subplots_adjust(hspace=.0) filename = 'occ_and_rel_err_vs_threshold_{}'.format(self.save_name_suffix) if self.save_plots: plt.savefig(os.path.join(self.save_dir, filename + '.png')) plt.savefig(os.path.join(self.save_dir, filename + '.pdf')) if self.show_plots: plt.show() else: plt.close() def plot_area_hist_occ_threshold_det(self): """Area histogram illustrating the threshold for the occupancy determination and resulting 'LED on' and 'LED off' integrals.""" hist_bin_centers = np.asarray(self.input_dict['histograms']['bin_centers']) hist_counts_led_on = np.asarray(self.input_dict['histograms']['counts_led_on']) hist_counts_led_off = np.asarray(self.input_dict['histograms']['counts_led_off']) threshold_occ_det = self.input_dict['threshold_occupancy_determination'] outlier_threshold_lower = self.input_dict['outlier_thresholds'][0] plt.fill_between(hist_bin_centers, hist_counts_led_off - (np.sqrt(hist_counts_led_off)), hist_counts_led_off + (np.sqrt(hist_counts_led_off)), step='mid', color='C0', alpha=0.4, linewidth=0) plt.fill_between(hist_bin_centers, hist_counts_led_on - (np.sqrt(hist_counts_led_on)), hist_counts_led_on + (np.sqrt(hist_counts_led_on)), step='mid', color='C1', alpha=0.4, linewidth=0) mask = hist_bin_centers <= threshold_occ_det + 1 plt.fill_between(hist_bin_centers[mask], hist_counts_led_off[mask], step='mid', hatch=5 * '/', color='none', edgecolor='C0', alpha=0.3) plt.fill_between(hist_bin_centers[mask], hist_counts_led_on[mask], step='mid', hatch=5 * '\\', color='none', edgecolor='C1', alpha=0.3) plt.step(hist_bin_centers, hist_counts_led_off, where='mid', color='C0', label='LED Off') plt.step(hist_bin_centers, hist_counts_led_on, where='mid', color='C1', label='LED On') plt.axvline(x=threshold_occ_det, color='gray', linestyle='dashed', label='Threshold Occupancy\nCalculation: {}'.format(threshold_occ_det)) plt.axvline(x=outlier_threshold_lower / 2, color='gray', linestyle='dotted', label='Range Probed\nThresholds') plt.axvline(x=-outlier_threshold_lower / 2, color='gray', linestyle='dotted') plt.xlim(outlier_threshold_lower, -outlier_threshold_lower) plt.yscale('log') plt.xlabel('Area [ADC Units]') plt.ylabel('Entries') legend = plt.legend(loc=4, bbox_to_anchor=(0.99, 0.025), framealpha=1) legend.get_frame().set_linewidth(matplotlib.rcParams['axes.linewidth']) filename = 'area_hist_occ_threshold_det_{}'.format(self.save_name_suffix) if self.save_plots: plt.savefig(os.path.join(self.save_dir, filename + '.png')) plt.savefig(os.path.join(self.save_dir, filename + '.pdf')) if self.show_plots: plt.show() else: plt.close() def plot_area_hist_model_independent_gain(self): """Area histogram of the 'LED on' and 'LED off' data illustrating the model independent gain determination.""" hist_bin_centers = np.asarray(self.input_dict['histograms']['bin_centers']) hist_counts_led_on = np.asarray(self.input_dict['histograms']['counts_led_on']) hist_counts_led_off = np.asarray(self.input_dict['histograms']['counts_led_off']) mean_s = self.input_dict['moments_s']['mean'] mean_b = self.input_dict['moments_b']['mean'] mean_psi = self.input_dict['mean_psi'] plt.fill_between(hist_bin_centers, hist_counts_led_off - (np.sqrt(hist_counts_led_off)), hist_counts_led_off + (np.sqrt(hist_counts_led_off)), step='mid', color='C0', alpha=0.4, linewidth=0) plt.fill_between(hist_bin_centers, hist_counts_led_on - (np.sqrt(hist_counts_led_on)), hist_counts_led_on + (np.sqrt(hist_counts_led_on)), step='mid', color='C1', alpha=0.4, linewidth=0) plt.step(hist_bin_centers, hist_counts_led_off, where='mid', color='C0', label='LED Off') plt.step(hist_bin_centers, hist_counts_led_on, where='mid', color='C1', label='LED On') plt.axvline(x=mean_b, color='C0', linestyle='dotted', linewidth=1, label='Mean LED Off') plt.axvline(x=mean_s, color='C1', linestyle='dotted', linewidth=1, label='Mean LED On') plt.axvline(x=mean_psi, color='gray', linestyle='dashed', linewidth=1, label='Mean Single\nPhotoelectron\nResponse') plt.xlim(min(hist_bin_centers), max(hist_bin_centers)) plt.yscale('log') plt.xlabel('Area [ADC Units]') plt.ylabel('Entries') legend = plt.legend(loc=1, bbox_to_anchor=(0.99, 0.99)) legend.get_frame().set_linewidth(matplotlib.rcParams['axes.linewidth']) filename = 'area_hist_model_independent_gain_{}'.format(self.save_name_suffix) if self.save_plots: plt.savefig(os.path.join(self.save_dir, filename + '.png')) plt.savefig(os.path.join(self.save_dir, filename + '.pdf')) if self.show_plots: plt.show() else: plt.close() def plot_essentials(self): """Plot essential plots for model independent gain and occupancy estimation.""" self.plot_occ_and_rel_err_vs_threshold() self.plot_area_hist_occ_threshold_det() self.plot_area_hist_model_independent_gain()Methods
def plot_area_hist_model_independent_gain(self)-
Area histogram of the 'LED on' and 'LED off' data illustrating the model independent gain determination.
Expand source code
def plot_area_hist_model_independent_gain(self): """Area histogram of the 'LED on' and 'LED off' data illustrating the model independent gain determination.""" hist_bin_centers = np.asarray(self.input_dict['histograms']['bin_centers']) hist_counts_led_on = np.asarray(self.input_dict['histograms']['counts_led_on']) hist_counts_led_off = np.asarray(self.input_dict['histograms']['counts_led_off']) mean_s = self.input_dict['moments_s']['mean'] mean_b = self.input_dict['moments_b']['mean'] mean_psi = self.input_dict['mean_psi'] plt.fill_between(hist_bin_centers, hist_counts_led_off - (np.sqrt(hist_counts_led_off)), hist_counts_led_off + (np.sqrt(hist_counts_led_off)), step='mid', color='C0', alpha=0.4, linewidth=0) plt.fill_between(hist_bin_centers, hist_counts_led_on - (np.sqrt(hist_counts_led_on)), hist_counts_led_on + (np.sqrt(hist_counts_led_on)), step='mid', color='C1', alpha=0.4, linewidth=0) plt.step(hist_bin_centers, hist_counts_led_off, where='mid', color='C0', label='LED Off') plt.step(hist_bin_centers, hist_counts_led_on, where='mid', color='C1', label='LED On') plt.axvline(x=mean_b, color='C0', linestyle='dotted', linewidth=1, label='Mean LED Off') plt.axvline(x=mean_s, color='C1', linestyle='dotted', linewidth=1, label='Mean LED On') plt.axvline(x=mean_psi, color='gray', linestyle='dashed', linewidth=1, label='Mean Single\nPhotoelectron\nResponse') plt.xlim(min(hist_bin_centers), max(hist_bin_centers)) plt.yscale('log') plt.xlabel('Area [ADC Units]') plt.ylabel('Entries') legend = plt.legend(loc=1, bbox_to_anchor=(0.99, 0.99)) legend.get_frame().set_linewidth(matplotlib.rcParams['axes.linewidth']) filename = 'area_hist_model_independent_gain_{}'.format(self.save_name_suffix) if self.save_plots: plt.savefig(os.path.join(self.save_dir, filename + '.png')) plt.savefig(os.path.join(self.save_dir, filename + '.pdf')) if self.show_plots: plt.show() else: plt.close() def plot_area_hist_occ_threshold_det(self)-
Area histogram illustrating the threshold for the occupancy determination and resulting 'LED on' and 'LED off' integrals.
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def plot_area_hist_occ_threshold_det(self): """Area histogram illustrating the threshold for the occupancy determination and resulting 'LED on' and 'LED off' integrals.""" hist_bin_centers = np.asarray(self.input_dict['histograms']['bin_centers']) hist_counts_led_on = np.asarray(self.input_dict['histograms']['counts_led_on']) hist_counts_led_off = np.asarray(self.input_dict['histograms']['counts_led_off']) threshold_occ_det = self.input_dict['threshold_occupancy_determination'] outlier_threshold_lower = self.input_dict['outlier_thresholds'][0] plt.fill_between(hist_bin_centers, hist_counts_led_off - (np.sqrt(hist_counts_led_off)), hist_counts_led_off + (np.sqrt(hist_counts_led_off)), step='mid', color='C0', alpha=0.4, linewidth=0) plt.fill_between(hist_bin_centers, hist_counts_led_on - (np.sqrt(hist_counts_led_on)), hist_counts_led_on + (np.sqrt(hist_counts_led_on)), step='mid', color='C1', alpha=0.4, linewidth=0) mask = hist_bin_centers <= threshold_occ_det + 1 plt.fill_between(hist_bin_centers[mask], hist_counts_led_off[mask], step='mid', hatch=5 * '/', color='none', edgecolor='C0', alpha=0.3) plt.fill_between(hist_bin_centers[mask], hist_counts_led_on[mask], step='mid', hatch=5 * '\\', color='none', edgecolor='C1', alpha=0.3) plt.step(hist_bin_centers, hist_counts_led_off, where='mid', color='C0', label='LED Off') plt.step(hist_bin_centers, hist_counts_led_on, where='mid', color='C1', label='LED On') plt.axvline(x=threshold_occ_det, color='gray', linestyle='dashed', label='Threshold Occupancy\nCalculation: {}'.format(threshold_occ_det)) plt.axvline(x=outlier_threshold_lower / 2, color='gray', linestyle='dotted', label='Range Probed\nThresholds') plt.axvline(x=-outlier_threshold_lower / 2, color='gray', linestyle='dotted') plt.xlim(outlier_threshold_lower, -outlier_threshold_lower) plt.yscale('log') plt.xlabel('Area [ADC Units]') plt.ylabel('Entries') legend = plt.legend(loc=4, bbox_to_anchor=(0.99, 0.025), framealpha=1) legend.get_frame().set_linewidth(matplotlib.rcParams['axes.linewidth']) filename = 'area_hist_occ_threshold_det_{}'.format(self.save_name_suffix) if self.save_plots: plt.savefig(os.path.join(self.save_dir, filename + '.png')) plt.savefig(os.path.join(self.save_dir, filename + '.pdf')) if self.show_plots: plt.show() else: plt.close() def plot_essentials(self)-
Plot essential plots for model independent gain and occupancy estimation.
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def plot_essentials(self): """Plot essential plots for model independent gain and occupancy estimation.""" self.plot_occ_and_rel_err_vs_threshold() self.plot_area_hist_occ_threshold_det() self.plot_area_hist_model_independent_gain() def plot_occ_and_rel_err_vs_threshold(self)-
Plot occupancy and relative occupancy error as a function of integration threshold in two vertically stacked subplots.
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def plot_occ_and_rel_err_vs_threshold(self): """Plot occupancy and relative occupancy error as a function of integration threshold in two vertically stacked subplots.""" thresholds_list = np.asarray(self.input_dict['iterations']['threshold']) occupancy_list = np.asarray(self.input_dict['iterations']['occupancy']) occupancy_err_list = np.asarray(self.input_dict['iterations']['occupancy_err']) occupancy_list_smooth = np.asarray(self.input_dict['iterations']['occupancy_smoothed']) occupancy = self.input_dict['occupancy'] occupancy_err = self.input_dict['occupancy_err'] threshold_occ_det = self.input_dict['threshold_occupancy_determination'] height = matplotlib.rcParams['figure.figsize'][-1] gs = gridspec.GridSpec(2, 1, height_ratios=[0.65 * height, 0.35 * height]) ax0 = plt.subplot(gs[0]) ax0.plot(thresholds_list, occupancy_list) ax0.fill_between(thresholds_list, occupancy_list - occupancy_err_list, occupancy_list + occupancy_err_list, alpha=0.3, color='C0', linewidth=0) ax0.plot(thresholds_list[occupancy_list_smooth > 0.1], occupancy_list_smooth[occupancy_list_smooth > 0.1], linewidth=1) ax0.axhline(y=occupancy, color='gray', linestyle='dotted') ax0.axvline(x=threshold_occ_det, color='gray', linestyle='dotted') ax0.set_ylabel('Occupancy [PE/Trigger]') ax0.tick_params(axis='x', labelbottom=False) ax1 = plt.subplot(gs[1], sharex=ax0) ax1.plot(thresholds_list, occupancy_err_list / occupancy_list, linewidth=1) ax1.axhline(y=0.01, color='gray', linestyle='dashed') ax1.axhline(y=occupancy_err / occupancy, color='gray', linestyle='dotted') ax1.axvline(x=threshold_occ_det, color='gray', linestyle='dotted') ax1.set_xlabel('Area Threshold [ADC Units]') ax1.set_ylabel('Rel. Error') ax1.set_yscale('log') plt.xlim(thresholds_list[0], thresholds_list[-1]) plt.subplots_adjust(hspace=.0) filename = 'occ_and_rel_err_vs_threshold_{}'.format(self.save_name_suffix) if self.save_plots: plt.savefig(os.path.join(self.save_dir, filename + '.png')) plt.savefig(os.path.join(self.save_dir, filename + '.pdf')) if self.show_plots: plt.show() else: plt.close() def plot_occupancy_vs_threshold(self)-
Plot occupancy as a function of integration threshold.
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def plot_occupancy_vs_threshold(self): """Plot occupancy as a function of integration threshold.""" thresholds_list = np.asarray(self.input_dict['iterations']['threshold']) occupancy_list = np.asarray(self.input_dict['iterations']['occupancy']) occupancy_err_list = np.asarray(self.input_dict['iterations']['occupancy_err']) occupancy_list_smooth = np.asarray(self.input_dict['iterations']['occupancy_smoothed']) occupancy = self.input_dict['occupancy'] threshold_occ_det = self.input_dict['threshold_occupancy_determination'] plt.plot(thresholds_list, occupancy_list, color='C0') plt.fill_between(thresholds_list, occupancy_list - occupancy_err_list, occupancy_list + occupancy_err_list, alpha=0.3, color='C0', linewidth=0) plt.plot(thresholds_list[occupancy_list_smooth > 0.1], occupancy_list_smooth[occupancy_list_smooth > 0.1], color='C1', linewidth=1) plt.axhline(y=occupancy, color='gray', linestyle='dotted') plt.axvline(x=threshold_occ_det, color='gray', linestyle='dotted') plt.xlim(thresholds_list[0], thresholds_list[-1]) plt.xlabel('Area Threshold [ADC Units]') plt.ylabel('Occupancy [PE/Trigger]') filename = 'occupancy_vs_threshold_{}'.format(self.save_name_suffix) if self.save_plots: plt.savefig(os.path.join(self.save_dir, filename + '.png')) plt.savefig(os.path.join(self.save_dir, filename + '.pdf')) if self.show_plots: plt.show() else: plt.close() def plot_rel_occ_err_vs_threshold(self)-
Plot relative occupancy error as a function of integration threshold.
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def plot_rel_occ_err_vs_threshold(self): """Plot relative occupancy error as a function of integration threshold.""" thresholds_list = np.asarray(self.input_dict['iterations']['threshold']) occupancy_list = np.asarray(self.input_dict['iterations']['occupancy']) occupancy_err_list = np.asarray(self.input_dict['iterations']['occupancy_err']) occupancy = self.input_dict['occupancy'] occupancy_err = self.input_dict['occupancy_err'] threshold_occ_det = self.input_dict['threshold_occupancy_determination'] plt.plot(thresholds_list, occupancy_err_list / occupancy_list, linewidth=1) plt.axhline(y=0.01, color='gray', linestyle='dashed') plt.axhline(y=occupancy_err / occupancy, color='gray', linestyle='dotted') plt.axvline(x=threshold_occ_det, color='gray', linestyle='dotted') plt.xlim(thresholds_list[0], thresholds_list[-1]) plt.yscale('log') plt.xlabel('Area Threshold [ADC Units]') plt.ylabel('Relative Error Occupancy') filename = 'rel_occ_err_vs_threshold_{}'.format(self.save_name_suffix) if self.save_plots: plt.savefig(os.path.join(self.save_dir, filename + '.png')) plt.savefig(os.path.join(self.save_dir, filename + '.pdf')) if self.show_plots: plt.show() else: plt.close()