def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
reader_factory = EventFileReaderFactory(**kwargs)
reader_class = reader_factory.get_class()
self.reader = reader_class(**kwargs)
r1_factory = CameraR1CalibratorFactory(origin=self.reader.origin,
**kwargs)
r1_class = r1_factory.get_class()
self.r1 = r1_class(**kwargs)
self.cleaner = CHECMWaveformCleanerAverage(**kwargs)
self.extractor = AverageWfPeakIntegrator(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=self.extractor,
cleaner=self.cleaner,
**kwargs)
self.dead = Dead()
fitter_factory = SPEFitterFactory(**kwargs)
fitter_class = fitter_factory.get_class()
self.fitter = fitter_class(**kwargs)
self.output_dir = join(self.reader.output_directory, "extract_spe")
if not exists(self.output_dir):
self.log.info("Creating directory: {}".format(self.output_dir))
makedirs(self.output_dir)
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._active_pixel = None
self.dead = Dead()
self.charge = None
self.charge_error = None
self.hv = None
self.n_hv = None
self.n_pixels = None
self.n_tmpix = 64
self.modules = None
self.tmpix = None
self.n_tm = None
self.m_pix = None
self.c_pix = None
self.m_tm = None
self.c_tm = None
self.m_tm2048 = None
self.c_tm2048 = None
self.p_camera_pix = None
self.p_plotter_pix = None
self.p_camera_tm = None
self.p_plotter_tm = None
self.w_view_radio = None
self.layout = None
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
self.dead = Dead()
file = np.load(self.input_path)
self.spe = file['spe']
self.spe_sigma = file['spe_sigma']
self.hist = file['hist']
self.edges = file['edges']
self.between = file['between']
self.output_dir = join(dirname(self.input_path),
"plot_adc2pe_residuals")
if not exists(self.output_dir):
self.log.info("Creating directory: {}".format(self.output_dir))
makedirs(self.output_dir)
# Create figures
sns.set_style("whitegrid")
sns.despine()
self.fig_spectrum_all = plt.figure(figsize=(13, 6))
self.fig_spectrum_all.suptitle("SPE Spectrum, All Pixels")
self.fig_spectrum_tm_list = []
for i in range(32):
fig = plt.figure(figsize=(13, 6))
self.fig_spectrum_tm_list.append(plt.figure(figsize=(13, 6)))
self.fig_combgaus = plt.figure(figsize=(13, 6))
self.fig_combgaus.suptitle("Combined 1pe fit, All Pixels")
self.fig_kde = plt.figure(figsize=(13, 6))
self.fig_kde.suptitle("Distribution of SPE, Kernel density estimate")
self.fig_hist = plt.figure(figsize=(13, 6))
self.fig_hist.suptitle("Distribution of SPE, Histogram")
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
reader_factory = EventFileReaderFactory(**kwargs)
reader_class = reader_factory.get_class()
self.reader = reader_class(**kwargs)
r1_factory = CameraR1CalibratorFactory(origin=self.reader.origin,
**kwargs)
r1_class = r1_factory.get_class()
self.r1 = r1_class(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.cleaner = CHECMWaveformCleanerAverage(**kwargs)
self.extractor = AverageWfPeakIntegrator(**kwargs)
self.extractor_height = SimpleIntegrator(window_shift=0,
window_width=1,
**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=self.extractor,
cleaner=self.cleaner,
**kwargs)
self.dl1_height = CameraDL1Calibrator(extractor=self.extractor_height,
cleaner=self.cleaner,
**kwargs)
self.dead = Dead()
fitter_factory = ChargeFitterFactory(**kwargs)
fitter_class = fitter_factory.get_class()
self.fitter = fitter_class(**kwargs)
self.n_events = self.reader.num_events
first_event = self.reader.get_event(0)
self.n_pixels = first_event.inst.num_pixels[0]
self.n_samples = first_event.r0.tel[0].num_samples
geom = CameraGeometry.guess(*first_event.inst.pixel_pos[0],
first_event.inst.optical_foclen[0])
self.neighbours2d = get_neighbours_2d(geom.pix_x, geom.pix_y)
# Get stage names
self.stage_names = [
'0: raw', '1: baseline_sub', '2: no_pulse', '3: smooth_baseline',
'4: smooth_wf', '5: cleaned'
]
# Init Plots
self.p_camera_area = Camera(self, self.neighbours2d, "Area", geom)
self.p_camera_fit_gain = Camera(self, self.neighbours2d, "Gain", geom)
self.p_camera_fit_brightness = Camera(self, self.neighbours2d,
"Brightness", geom)
self.p_fitter = FitterWidget(fitter=self.fitter, **kwargs)
self.p_stage_viewer = StageViewer(**kwargs)
self.p_fit_viewer = FitViewer(**kwargs)
self.p_fit_table = FitTable(**kwargs)
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
self.a2p = TargetioADC2PECalibrator(**kwargs)
self.dead = Dead()
self.cfmaker = CfMaker(32)
# Save figures
self.output_dir = join(self.output_dir, "plot_adc2pe_vs_hv")
if not exists(self.output_dir):
self.log.info("Creating directory: {}".format(self.output_dir))
makedirs(self.output_dir)
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
self.file_looper = TargetioFileLooper(**kwargs)
r1_factory = CameraR1CalibratorFactory(origin='targetio', **kwargs)
r1_class = r1_factory.get_class()
self.r1 = r1_class(**kwargs)
self.cleaner = CHECMWaveformCleanerAverage(**kwargs)
self.extractor = AverageWfPeakIntegrator(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=self.extractor,
cleaner=self.cleaner,
**kwargs)
self.dead = Dead()
self.fitter_bright = CHECBrightFitter(**kwargs)
fitter_factory = SPEFitterFactory(**kwargs)
fitter_class = fitter_factory.get_class()
self.fitter_spe = fitter_class(**kwargs)
file_reader_list = self.file_looper.file_reader_list
self.n_runs = len(file_reader_list)
first_event = file_reader_list[0].get_event(0)
self.n_pixels = first_event.inst.num_pixels[0]
self.n_samples = first_event.r0.tel[0].num_samples
self.fw_list = self.fw_list[:self.file_looper.num_readers]
assert (self.n_runs == len(self.fw_list))
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
reader_factory = EventFileReaderFactory(**kwargs)
reader_class = reader_factory.get_class()
self.reader = reader_class(**kwargs)
r1_factory = CameraR1CalibratorFactory(origin=self.reader.origin,
**kwargs)
r1_class = r1_factory.get_class()
self.r1 = r1_class(**kwargs)
self.cleaner = CHECMWaveformCleanerLocal(**kwargs)
extractor_factory = ChargeExtractorFactory(**kwargs)
extractor_class = extractor_factory.get_class()
self.extractor = extractor_class(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=self.extractor,
cleaner=self.cleaner,
**kwargs)
self.fitter = CHECMSPEFitter(**kwargs)
self.dead = Dead()
self.animator = Animator(**kwargs)
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
for key, path in self.event_path_dict.items():
self.reader_dict[key] = TargetioFileReader(input_path=path,
**kwargs)
cleaner = CHECMWaveformCleanerAverage(**kwargs)
extractor = AverageWfPeakIntegrator(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=extractor,
cleaner=cleaner,
**kwargs)
self.fitter = CHECBrightFitter(**kwargs)
self.dead = Dead()
first_event = list(self.reader_dict.values())[0].get_event(0)
telid = list(first_event.r0.tels_with_data)[0]
r1 = first_event.r1.tel[telid].pe_samples[0]
self.n_pixels, self.n_samples = r1.shape
script = "checm_paper_adc2pe_application"
self.p_comparison = ADC2PEPlotter(**kwargs,
script=script,
figure_name="adc2pe_comparison",
shape='wide')
self.p_tmspread = TMSpreadPlotter(**kwargs,
script=script,
figure_name="tmspread",
shape='wide')
self.p_dist = GMDistributionPlotter(**kwargs,
script=script,
figure_name="gm_distribution",
shape='wide')
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
reader_factory = EventFileReaderFactory(**kwargs)
reader_class = reader_factory.get_class()
self.reader = reader_class(**kwargs)
r1_factory = CameraR1CalibratorFactory(origin=self.reader.origin,
**kwargs)
r1_class = r1_factory.get_class()
self.r1 = r1_class(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.cleaner = CHECMWaveformCleaner(**kwargs)
self.extractor = CHECMExtractor(**kwargs)
self.fitter = CHECMSPEFitter(**kwargs)
self.dead = Dead()
self.output_dir = join(self.reader.output_directory, "extract_adc2pe")
if not exists(self.output_dir):
self.log.info("Creating directory: {}".format(self.output_dir))
makedirs(self.output_dir)
n_events = self.reader.num_events
first_event = self.reader.get_event(0)
n_pixels, n_samples = first_event.r0.tel[0].adc_samples[0].shape
self.charge = np.zeros((n_events, n_pixels))
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
self.reader_uncal = TargetioFileReader(input_path=self.path_uncal,
**kwargs)
self.reader_cal = TargetioFileReader(input_path=self.path_cal,
**kwargs)
cleaner = CHECMWaveformCleanerAverage(**kwargs)
extractor = AverageWfPeakIntegrator(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=extractor,
cleaner=cleaner,
**kwargs)
self.dead = Dead()
self.fitter_uncal = CHECMSPEFitter(**kwargs)
self.fitter_cal = CHECMSPEFitter(**kwargs)
self.fitter_cal.range = [-3, 6]
self.fitter_cal.initial = dict(norm=None,
eped=0,
eped_sigma=0.2,
spe=1,
spe_sigma=0.5,
lambda_=0.2)
first_event = self.reader_uncal.get_event(0)
telid = list(first_event.r0.tels_with_data)[0]
r1 = first_event.r1.tel[telid].pe_samples[0]
self.n_pixels, self.n_samples = r1.shape
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
self.fw_calibrator = FWCalibrator(**kwargs)
r1_0320 = TargetioR1Calibrator(pedestal_path='/Volumes/gct-jason/data/170320/pedestal/Run04109_ped.tcal',
tf_path='/Volumes/gct-jason/data/170320/tf/Run04110-04159_tf.tcal',
pe_path='/Users/Jason/Software/CHECAnalysis/targetpipe/adc2pe/adc2pe_1100.tcal',
**kwargs)
r1_0319 = TargetioR1Calibrator(pedestal_path='/Volumes/gct-jason/data/170319/linearity/pedestal/Run04051_ped.tcal',
tf_path='/Volumes/gct-jason/data/170319/linearity/tf/Run04001-04050_tf.tcal',
pe_path='/Users/Jason/Software/CHECAnalysis/targetpipe/adc2pe/adc2pe_1100.tcal',
**kwargs)
dfl = []
base_path = "/Volumes/gct-jason/data/170320/linearity/Run{:05}_r0.tio"
dfl.append(dict(path=base_path.format(4160), type="LS64", cal=True, level=1250, r1=r1_0320))
dfl.append(dict(path=base_path.format(4161), type="LS64", cal=True, level=1450, r1=r1_0320))
dfl.append(dict(path=base_path.format(4162), type="LS64", cal=True, level=1650, r1=r1_0320))
dfl.append(dict(path=base_path.format(4163), type="LS64", cal=True, level=1850, r1=r1_0320))
dfl.append(dict(path=base_path.format(4164), type="LS64", cal=True, level=2050, r1=r1_0320))
dfl.append(dict(path=base_path.format(4165), type="LS64", cal=True, level=2250, r1=r1_0320))
dfl.append(dict(path=base_path.format(4166), type="LS64", cal=True, level=2450, r1=r1_0320))
dfl.append(dict(path=base_path.format(4167), type="LS64", cal=True, level=2650, r1=r1_0320))
dfl.append(dict(path=base_path.format(4168), type="LS64", cal=True, level=2850, r1=r1_0320))
dfl.append(dict(path=base_path.format(4169), type="LS64", cal=True, level=3050, r1=r1_0320))
base_path = "/Volumes/gct-jason/data/170319/linearity/linearity/Run{:05}_r0.tio"
dfl.append(dict(path=base_path.format(3986), type="LS62", cal=True, level=1250, r1=r1_0319))
dfl.append(dict(path=base_path.format(3987), type="LS62", cal=True, level=1450, r1=r1_0319))
dfl.append(dict(path=base_path.format(3988), type="LS62", cal=True, level=1650, r1=r1_0319))
dfl.append(dict(path=base_path.format(3989), type="LS62", cal=True, level=1850, r1=r1_0319))
dfl.append(dict(path=base_path.format(3990), type="LS62", cal=True, level=2050, r1=r1_0319))
dfl.append(dict(path=base_path.format(3991), type="LS62", cal=True, level=2250, r1=r1_0319))
dfl.append(dict(path=base_path.format(3992), type="LS62", cal=True, level=2450, r1=r1_0319))
dfl.append(dict(path=base_path.format(3993), type="LS62", cal=True, level=2650, r1=r1_0319))
dfl.append(dict(path=base_path.format(3994), type="LS62", cal=True, level=2850, r1=r1_0319))
dfl.append(dict(path=base_path.format(3995), type="LS62", cal=True, level=3050, r1=r1_0319))
for d in dfl:
d['reader'] = TargetioFileReader(input_path=d['path'], **kwargs)
self.df_file = pd.DataFrame(dfl)
cleaner = CHECMWaveformCleanerAverage(**kwargs)
extractor = AverageWfPeakIntegrator(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=extractor,
cleaner=cleaner,
**kwargs)
self.dead = Dead()
self.dummy_event = dfl[0]['reader'].get_event(0)
telid = list(self.dummy_event.r0.tels_with_data)[0]
r1 = self.dummy_event.r1.tel[telid].pe_samples[0]
self.n_pixels, self.n_samples = r1.shape
script = "checm_paper_recovery"
self.p_scatter_pix = Scatter(**kwargs, script=script, figure_name="recovery_lookup")
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
self.path_dict[
'800'] = '/Volumes/gct-jason/data/170310/hv/Run00904_r1_adc.tio'
self.path_dict[
'900'] = '/Volumes/gct-jason/data/170310/hv/Run00914_r1_adc.tio'
self.path_dict[
'1000'] = '/Volumes/gct-jason/data/170310/hv/Run00924_r1_adc.tio'
self.path_dict[
'1100'] = '/Volumes/gct-jason/data/170310/hv/Run00934_r1_adc.tio'
self.path_dict[
'800gm'] = '/Volumes/gct-jason/data/170319/gainmatching/gainmatched/Run03983_r1_adc.tio'
self.path_dict[
'900gm'] = '/Volumes/gct-jason/data/170319/gainmatching/gainmatched/Run03984_r1_adc.tio'
self.path_dict[
'1000gm'] = '/Volumes/gct-jason/data/170320/linearity/Run04174_r1_adc.tio' #'/Volumes/gct-jason/data/170319/gainmatching/gainmatched/Run03985_r1_adc.tio'
# self.path_dict['800'] = '/Volumes/gct-jason/data/170310/hv/Run00904_r1.tio'
# self.path_dict['900'] = '/Volumes/gct-jason/data/170310/hv/Run00914_r1.tio'
# self.path_dict['1000'] = '/Volumes/gct-jason/data/170310/hv/Run00924_r1.tio'
# self.path_dict['1100'] = '/Volumes/gct-jason/data/170310/hv/Run00934_r1.tio'
# self.path_dict['800gm'] = '/Volumes/gct-jason/data/170319/gainmatching/gainmatched/Run03983_r1.tio'
# self.path_dict['900gm'] = '/Volumes/gct-jason/data/170319/gainmatching/gainmatched/Run03984_r1.tio'
# self.path_dict['1000gm'] = '/Volumes/gct-jason/data/170320/linearity/Run04174_r1.tio'
ped = '/Volumes/gct-jason/data/170310/pedestal/Run00843_ped.tcal'
pedgm = '/Volumes/gct-jason/data/170319/gainmatching/pedestal/Run03932_ped.tcal'
tf = '/Volumes/gct-jason/data/170310/tf/Run00844-00893_tf.tcal'
for key, val in self.path_dict.items():
# p = ped
# if 'gm' in key:
# p = pedgm
# adc2pe = '/Users/Jason/Software/CHECAnalysis/targetpipe/adc2pe/adc2pe_{}.tcal'.format(key)
# self.r1_dict[key] = TargetioR1Calibrator(pedestal_path=p,
# tf_path=tf,
# pe_path='',
# **kwargs)
self.reader_dict[key] = TargetioFileReader(input_path=val,
**kwargs)
cleaner = CHECMWaveformCleanerAverage(**kwargs)
extractor = AverageWfPeakIntegrator(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=extractor,
cleaner=cleaner,
**kwargs)
self.fitter = CHECBrightFitter(**kwargs)
self.dead = Dead()
first_event = list(self.reader_dict.values())[0].get_event(0)
telid = list(first_event.r0.tels_with_data)[0]
r1 = first_event.r1.tel[telid].pe_samples[0]
self.n_pixels, self.n_samples = r1.shape
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
filepath = '/Volumes/gct-jason/data/170314/spe/Run00073_r1_adc.tio'
self.reader = TargetioFileReader(input_path=filepath, **kwargs)
cleaner = CHECMWaveformCleanerAverage(**kwargs)
extractor = AverageWfPeakIntegrator(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=extractor,
cleaner=cleaner,
**kwargs)
self.fitter = CHECMSPEFitter(**kwargs)
self.fitter.range = [-30, 160]
self.dead = Dead()
self.fw_calibrator = FWCalibrator(**kwargs)
script = "filter_wheel"
self.p_attenuation = Scatter(**kwargs, script=script, figure_name="attenuation")
self.p_pe = Scatter(**kwargs, script=script, figure_name="pe")
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
cleaner_factory = WaveformCleanerFactory(**kwargs)
cleaner_class = cleaner_factory.get_class()
self.cleaner = cleaner_class(**kwargs)
reader_factory = EventFileReaderFactory(**kwargs)
reader_class = reader_factory.get_class()
self.reader = reader_class(**kwargs)
extractor_factory = ChargeExtractorFactory(**kwargs)
extractor_class = extractor_factory.get_class()
self.extractor = extractor_class(**kwargs)
r1_factory = CameraR1CalibratorFactory(origin=self.reader.origin,
**kwargs)
r1_class = r1_factory.get_class()
self.r1 = r1_class(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=self.extractor,
cleaner=self.cleaner,
**kwargs)
self.dead = Dead()
n_events = self.reader.num_events
first_event = self.reader.get_event(0)
r1 = first_event.r1.tel[0].pe_samples[0]
n_pixels, n_samples = r1.shape
self.tack = np.zeros(n_events)
self.sec = np.zeros(n_events)
self.ns = np.zeros(n_events)
self.fci = np.zeros((n_events, n_pixels))
self.charge = np.zeros((n_events, n_pixels))
self.t0 = np.zeros((n_events, n_pixels))
self.baseline_mean_start = np.zeros((n_events, n_pixels))
self.baseline_mean_end = np.zeros((n_events, n_pixels))
self.baseline_mean_full = np.zeros((n_events, n_pixels))
self.baseline_rms_start = np.zeros((n_events, n_pixels))
self.baseline_rms_end = np.zeros((n_events, n_pixels))
self.baseline_rms_full = np.zeros((n_events, n_pixels))
self.peak_time = np.zeros((n_events, n_pixels))
self.fwhm = np.zeros((n_events, n_pixels))
self.rise_time = np.zeros((n_events, n_pixels))
self.n_saturated = np.zeros((n_events, n_pixels))
# Justus:
self.n_1pe = np.zeros((n_events, n_pixels))
self.peak_height = np.zeros((n_events, n_pixels))
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._active_pixel = None
self._active_run = None
self.dead = Dead()
self.charge = None
self.charge_error = None
self.rundesc = None
self.charge_tm = None
self.charge_error_tm = None
self.mean_tm2048 = None
self.tmpixspread_tm2048 = None
self.n_run = None
self.n_pixels = None
self.n_tmpix = 64
self.modules = None
self.tmpix = None
self.n_tm = None
self.p_c_pix = None
self.p_p_pix = None
self.p_c_tm = None
self.p_p_tm = None
self.p_c_tmpixspread = None
self.p_b_tmpixspread = None
self.p_b_tmspread = None
self.p_b_pixspread = None
self.p_c_pix_title = 'Charge Across Pixels, Run: {}'
self.p_c_tm_title = 'Mean Charge Across TMs, Run: {}'
self.p_c_tmpixspread_title = 'Median Charge Across TMs, Run: {}'
self.p_p_pix_title = 'Charge vs Runs, Error: fit stddev, Pixel: {}'
self.p_p_tm_title = 'Charge vs Runs, Error: combined pixel, TM: {}'
self.p_b_tmpixspread_title = 'Charge Spread vs Runs, TM: {}'
self.layout = None
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
self.file_looper = TargetioFileLooper(**kwargs)
r1_factory = CameraR1CalibratorFactory(origin='targetio', **kwargs)
r1_class = r1_factory.get_class()
self.r1 = r1_class(**kwargs)
self.cleaner = CHECMWaveformCleanerAverage(**kwargs)
self.extractor = AverageWfPeakIntegrator(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.fitter = CHECBrightFitter(**kwargs)
self.dead = Dead()
file_reader_list = self.file_looper.file_reader_list
first_event = file_reader_list[0].get_event(0)
telid = list(first_event.r0.tels_with_data)[0]
r0 = first_event.r0.tel[telid].adc_samples[0]
self.n_pixels, self.n_samples = r0.shape
self.rundesc_list = self.rundesc_list[:self.file_looper.num_readers]
assert (len(file_reader_list) == len(self.rundesc_list))
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
filepath = '/Volumes/gct-jason/data/170319/linearity/linearity/Run03991_r1_pe.tio'
# filepath = '/Volumes/gct-jason/data/170320/linearity/Run04164_r1_pe.tio'
self.reader = TargetioFileReader(input_path=filepath, **kwargs)
cleaner = CHECMWaveformCleanerAverage(**kwargs)
extractor = AverageWfPeakIntegrator(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=extractor,
cleaner=cleaner,
**kwargs)
self.dead = Dead()
self.fw_calibrator = FWCalibrator(**kwargs)
self.cfmaker = CfMaker(32)
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
reader_factory = EventFileReaderFactory(**kwargs)
reader_class = reader_factory.get_class()
self.reader = reader_class(**kwargs)
r1_factory = CameraR1CalibratorFactory(origin=self.reader.origin,
**kwargs)
r1_class = r1_factory.get_class()
self.r1 = r1_class(**kwargs)
self.cleaner = CHECMWaveformCleanerLocal(**kwargs)
extractor_factory = ChargeExtractorFactory(**kwargs)
extractor_class = extractor_factory.get_class()
self.extractor = extractor_class(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=self.extractor,
cleaner=self.cleaner,
**kwargs)
self.fitter = CHECMSPEFitter(**kwargs)
self.dead = Dead()
self.n_events = self.reader.num_events
self.time = np.ma.zeros(self.n_events)
self.size = np.ma.zeros(self.n_events)
self.cen_x = np.ma.zeros(self.n_events)
self.cen_y = np.ma.zeros(self.n_events)
self.length = np.ma.zeros(self.n_events)
self.width = np.ma.zeros(self.n_events)
self.r = np.ma.zeros(self.n_events)
self.phi = np.ma.zeros(self.n_events)
self.psi = np.ma.zeros(self.n_events)
self.miss = np.ma.zeros(self.n_events)
self.skewness = np.ma.zeros(self.n_events)
self.kurtosis = np.ma.zeros(self.n_events)
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
reader_factory = EventFileReaderFactory(**kwargs)
reader_class = reader_factory.get_class()
self.reader = reader_class(**kwargs)
r1_factory = CameraR1CalibratorFactory(origin=self.reader.origin,
**kwargs)
r1_class = r1_factory.get_class()
self.r1 = r1_class(**kwargs)
self.cleaner = CHECMWaveformCleanerLocal(**kwargs)
self.extractor = AverageWfPeakIntegrator(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=self.extractor,
cleaner=self.cleaner,
**kwargs)
self.fitter = CHECMSPEFitter(**kwargs)
# self.fitter.nbins = 60
self.fitter.range = [-3, 6]
self.fitter.initial = dict(norm=None,
eped=0,
eped_sigma=0.2,
spe=1,
spe_sigma=0.5,
lambda_=0.2)
self.dead = Dead()
self.output_dir = join(self.reader.output_directory,
"extract_adc2pe_residuals")
if not exists(self.output_dir):
self.log.info("Creating directory: {}".format(self.output_dir))
makedirs(self.output_dir)
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
filepath = '/Volumes/gct-jason/data/170314/spe/Run00073_r1_adc.tio'
self.reader = TargetioFileReader(input_path=filepath, **kwargs)
filepath = '/Volumes/gct-jason/data/170314/spe/Run00073_r1.tio'
self.reader_pe = TargetioFileReader(input_path=filepath, **kwargs)
cleaner = CHECMWaveformCleanerAverage(**kwargs)
extractor = AverageWfPeakIntegrator(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=extractor,
cleaner=cleaner,
**kwargs)
self.fitter = CHECMSPEFitter(**kwargs)
self.fitter.range = [-30, 160]
self.fitter_pe = CHECMSPEFitter(**kwargs)
self.fitter_pe.range = [-1, 6]
self.fitter_pe.initial = dict(norm=None,
eped=0,
eped_sigma=0.2,
spe=1,
spe_sigma=0.5,
lambda_=0.2)
self.dead = Dead()
script = "checm_paper_adc2pe"
self.p_pixelspe = PixelSPEFitPlotter(**kwargs,
script=script,
figure_name="spe_fit_pixel1559")
self.p_tmspe = TMSPEFitPlotter(**kwargs,
script=script,
figure_name="spe_fit_tm24")
self.p_tmspe_pe = TMSPEFitPlotter(**kwargs,
script=script,
figure_name="spe_fit_tm24_pe")
self.p_adc2pe = ADC2PEPlotter(**kwargs,
script=script,
figure_name="adc2pe",
shape='square')
self.p_adc2pe_1100tm = ADC2PE1100VTMPlotter(
**kwargs,
script=script,
figure_name="adc2pe_1100V_tms",
shape='wide')
self.p_adc2pe_1100tm_stats = ADC2PE1100VTMStatsPlotter(
**kwargs,
script=script,
figure_name="adc2pe_1100V_tms_stats",
shape='wide')
self.p_eped = Hist(**kwargs,
script=script,
figure_name="f_eped",
shape='square')
self.p_eped_sigma = Hist(**kwargs,
script=script,
figure_name="f_eped_sigma",
shape='square')
self.p_spe = Hist(**kwargs,
script=script,
figure_name="f_spe",
shape='square')
self.p_spe_sigma = Hist(**kwargs,
script=script,
figure_name="f_spe_sigma",
shape='square')
self.p_lambda = Hist(**kwargs,
script=script,
figure_name="f_lambda",
shape='square')
self.p_enf = Hist(**kwargs,
script=script,
figure_name="f_enf",
shape='square')
class ADC2PEPlots(Tool):
name = "ADC2PEPlots"
description = "Create plots related to adc2pe"
aliases = Dict(dict(max_events='TargetioFileReader.max_events'))
classes = List([
TargetioFileReader,
TargetioR1Calibrator,
])
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.reader = None
self.reader_pe = None
self.dl0 = None
self.dl1 = None
self.fitter = None
self.fitter_pe = None
self.dead = None
self.n_pixels = None
self.n_samples = None
self.spe_path = "/Volumes/gct-jason/data/170314/spe/Run00073_r0/extract_spe/spe.npy"
self.gm_path = "/Volumes/gct-jason/data/170310/hv/gain_matching_coeff.npz"
self.p_pixelspe = None
self.p_tmspe = None
self.p_tmspe_pe = None
self.p_adc2pe = None
self.p_adc2pe_1100tm = None
self.p_adc2pe_1100tm_stats = None
self.p_eped = None
self.p_eped_sigma = None
self.p_spe = None
self.p_spe_sigma = None
self.p_lambda = None
self.p_enf = None
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
filepath = '/Volumes/gct-jason/data/170314/spe/Run00073_r1_adc.tio'
self.reader = TargetioFileReader(input_path=filepath, **kwargs)
filepath = '/Volumes/gct-jason/data/170314/spe/Run00073_r1.tio'
self.reader_pe = TargetioFileReader(input_path=filepath, **kwargs)
cleaner = CHECMWaveformCleanerAverage(**kwargs)
extractor = AverageWfPeakIntegrator(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=extractor,
cleaner=cleaner,
**kwargs)
self.fitter = CHECMSPEFitter(**kwargs)
self.fitter.range = [-30, 160]
self.fitter_pe = CHECMSPEFitter(**kwargs)
self.fitter_pe.range = [-1, 6]
self.fitter_pe.initial = dict(norm=None,
eped=0,
eped_sigma=0.2,
spe=1,
spe_sigma=0.5,
lambda_=0.2)
self.dead = Dead()
script = "checm_paper_adc2pe"
self.p_pixelspe = PixelSPEFitPlotter(**kwargs,
script=script,
figure_name="spe_fit_pixel1559")
self.p_tmspe = TMSPEFitPlotter(**kwargs,
script=script,
figure_name="spe_fit_tm24")
self.p_tmspe_pe = TMSPEFitPlotter(**kwargs,
script=script,
figure_name="spe_fit_tm24_pe")
self.p_adc2pe = ADC2PEPlotter(**kwargs,
script=script,
figure_name="adc2pe",
shape='square')
self.p_adc2pe_1100tm = ADC2PE1100VTMPlotter(
**kwargs,
script=script,
figure_name="adc2pe_1100V_tms",
shape='wide')
self.p_adc2pe_1100tm_stats = ADC2PE1100VTMStatsPlotter(
**kwargs,
script=script,
figure_name="adc2pe_1100V_tms_stats",
shape='wide')
self.p_eped = Hist(**kwargs,
script=script,
figure_name="f_eped",
shape='square')
self.p_eped_sigma = Hist(**kwargs,
script=script,
figure_name="f_eped_sigma",
shape='square')
self.p_spe = Hist(**kwargs,
script=script,
figure_name="f_spe",
shape='square')
self.p_spe_sigma = Hist(**kwargs,
script=script,
figure_name="f_spe_sigma",
shape='square')
self.p_lambda = Hist(**kwargs,
script=script,
figure_name="f_lambda",
shape='square')
self.p_enf = Hist(**kwargs,
script=script,
figure_name="f_enf",
shape='square')
def start(self):
n_events = self.reader.num_events
first_event = self.reader.get_event(0)
telid = list(first_event.r0.tels_with_data)[0]
n_pixels, n_samples = first_event.r1.tel[telid].pe_samples[0].shape
### SPE values from fit _______________________________________________
# Prepare storage array
dl1 = np.zeros((n_events, n_pixels))
dl1_pe = np.zeros((n_events, n_pixels))
hist_pix1559 = None
edges_pix1559 = None
between_pix1559 = None
fit_pix1559 = None
fitx_pix1559 = None
hist_tm24 = np.zeros((64, self.fitter.nbins))
edges_tm24 = np.zeros((64, self.fitter.nbins + 1))
between_tm24 = np.zeros((64, self.fitter.nbins))
hist_tm24_pe = np.zeros((64, self.fitter.nbins))
edges_tm24_pe = np.zeros((64, self.fitter.nbins + 1))
between_tm24_pe = np.zeros((64, self.fitter.nbins))
f_eped = np.zeros(n_pixels)
f_eped_sigma = np.zeros(n_pixels)
f_spe = np.zeros(n_pixels)
f_spe_sigma = np.zeros(n_pixels)
f_lambda = np.zeros(n_pixels)
source = self.reader.read()
desc = "Looping through file"
for event in tqdm(source, total=n_events, desc=desc):
index = event.count
self.dl0.reduce(event)
self.dl1.calibrate(event)
dl1[index] = event.dl1.tel[telid].image
source = self.reader_pe.read()
desc = "Looping through file (pe)"
for event in tqdm(source, total=n_events, desc=desc):
index = event.count
self.dl0.reduce(event)
self.dl1.calibrate(event)
dl1_pe[index] = event.dl1.tel[telid].image
desc = "Fitting pixels"
for pix in trange(n_pixels, desc=desc):
tm = pix // 64
tmpix = pix % 64
if not self.fitter.apply(dl1[:, pix]):
self.log.warning("Pixel {} couldn't be fit".format(pix))
continue
if pix == 1559:
hist_pix1559 = self.fitter.hist
edges_pix1559 = self.fitter.edges
between_pix1559 = self.fitter.between
fit_pix1559 = self.fitter.fit
fitx_pix1559 = self.fitter.fit_x
if tm == 24:
hist_tm24[tmpix] = self.fitter.hist
edges_tm24[tmpix] = self.fitter.edges
between_tm24[tmpix] = self.fitter.between
f_eped[pix] = self.fitter.coeff['eped']
f_eped_sigma[pix] = self.fitter.coeff['eped_sigma']
f_spe[pix] = self.fitter.coeff['spe']
f_spe_sigma[pix] = self.fitter.coeff['spe_sigma']
f_lambda[pix] = self.fitter.coeff['lambda_']
f_eped = checm_dac_to_volts(f_eped)
f_eped_sigma = checm_dac_to_volts(f_eped_sigma)
f_spe = checm_dac_to_volts(f_spe)
f_spe_sigma = checm_dac_to_volts(f_spe_sigma)
f_eped = self.dead.mask1d(f_eped).compressed()
f_eped_sigma = self.dead.mask1d(f_eped_sigma).compressed()
f_spe = self.dead.mask1d(f_spe).compressed()
f_spe_sigma = self.dead.mask1d(f_spe_sigma).compressed()
f_lambda = self.dead.mask1d(f_lambda).compressed()
edges_pix1559 = checm_dac_to_volts(edges_pix1559)
between_pix1559 = checm_dac_to_volts(between_pix1559)
fitx_pix1559 = checm_dac_to_volts(fitx_pix1559)
edges_tm24 = checm_dac_to_volts(edges_tm24)
between_tm24 = checm_dac_to_volts(edges_tm24)
desc = "Fitting pixels (pe)"
for pix in trange(n_pixels, desc=desc):
tm = pix // 64
tmpix = pix % 64
if tm != 24:
continue
if not self.fitter_pe.apply(dl1_pe[:, pix]):
self.log.warning("Pixel {} couldn't be fit".format(pix))
continue
hist_tm24_pe[tmpix] = self.fitter_pe.hist
edges_tm24_pe[tmpix] = self.fitter_pe.edges
between_tm24_pe[tmpix] = self.fitter_pe.between
### SPE values for each hv setting ____________________________________
kwargs = dict(config=self.config,
tool=self,
spe_path=self.spe_path,
gain_matching_path=self.gm_path)
a2p = TargetioADC2PECalibrator(**kwargs)
hv_dict = dict()
hv_dict['800'] = [800] * 2048
hv_dict['900'] = [900] * 2048
hv_dict['1000'] = [1000] * 2048
hv_dict['1100'] = [1100] * 2048
hv_dict['800gm'] = [a2p.gm800[i // 64] for i in range(2048)]
hv_dict['900gm'] = [a2p.gm900[i // 64] for i in range(2048)]
hv_dict['1000gm'] = [a2p.gm1000[i // 64] for i in range(2048)]
df_list = []
for key, l in hv_dict.items():
hv = int(key.replace("gm", ""))
gm = 'gm' in key
gm_t = 'Gain-matched' if 'gm' in key else 'Non-gain-matched'
for pix in range(n_pixels):
if pix in self.dead.dead_pixels:
continue
adc2pe = a2p.get_adc2pe_at_hv(l[pix], pix)
df_list.append(
dict(key=key,
hv=hv,
gm=gm,
gm_t=gm_t,
pixel=pix,
tm=pix // 64,
spe=1 / adc2pe))
df = pd.DataFrame(df_list)
df = df.sort_values(by='gm', ascending=True)
df = df.assign(spe_mv=checm_dac_to_volts(df['spe']))
# Create figures
self.p_pixelspe.create(hist_pix1559, edges_pix1559, between_pix1559,
fit_pix1559, fitx_pix1559)
self.p_tmspe.create(hist_tm24, edges_tm24, between_tm24, "V ns")
self.p_tmspe_pe.create(hist_tm24_pe, edges_tm24_pe, between_tm24_pe,
"p.e.", 1)
self.p_adc2pe.create(df)
self.p_adc2pe_1100tm.create(df)
self.p_adc2pe_1100tm_stats.create(df)
self.p_eped.create(f_eped, "Pedestal (V ns)")
self.p_eped_sigma.create(f_eped_sigma, "Pedestal Sigma (V ns)")
self.p_spe.create(f_spe, "SPE (V ns)")
self.p_spe_sigma.create(f_spe_sigma, "SPE Sigma (V ns)")
self.p_lambda.create(f_lambda, "Illumination (Photoelectrons)")
enf = np.sqrt(f_spe_sigma**2 - f_eped_sigma**2) / f_spe
self.p_enf.create(enf, "Relative SPE Width")
def finish(self):
# Save figures
self.p_pixelspe.save()
self.p_tmspe.save()
self.p_tmspe_pe.save()
self.p_adc2pe.save()
self.p_adc2pe_1100tm.save()
self.p_adc2pe_1100tm_stats.save()
self.p_eped.save()
self.p_eped_sigma.save()
self.p_spe.save()
self.p_spe_sigma.save()
self.p_lambda.save()
self.p_enf.save()
class BokehGainMatching(Tool):
name = "BokehGainMatching"
description = "Interactively explore the steps in obtaining charge vs hv"
input_path = Unicode('',
help='Path to the numpy array containing the '
'gain and hv').tag(config=True)
aliases = Dict(dict(f='BokehGainMatching.input_path'))
classes = List([])
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._active_pixel = None
self._active_run = None
self.dead = Dead()
self.charge = None
self.charge_error = None
self.rundesc = None
self.charge_tm = None
self.charge_error_tm = None
self.mean_tm2048 = None
self.tmpixspread_tm2048 = None
self.n_run = None
self.n_pixels = None
self.n_tmpix = 64
self.modules = None
self.tmpix = None
self.n_tm = None
self.p_c_pix = None
self.p_p_pix = None
self.p_c_tm = None
self.p_p_tm = None
self.p_c_tmpixspread = None
self.p_b_tmpixspread = None
self.p_b_tmspread = None
self.p_b_pixspread = None
self.p_c_pix_title = 'Charge Across Pixels, Run: {}'
self.p_c_tm_title = 'Mean Charge Across TMs, Run: {}'
self.p_c_tmpixspread_title = 'Median Charge Across TMs, Run: {}'
self.p_p_pix_title = 'Charge vs Runs, Error: fit stddev, Pixel: {}'
self.p_p_tm_title = 'Charge vs Runs, Error: combined pixel, TM: {}'
self.p_b_tmpixspread_title = 'Charge Spread vs Runs, TM: {}'
self.layout = None
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
arrays = np.load(self.input_path)
self.charge = self.dead.mask2d(arrays['charge'])
self.charge_error = self.dead.mask2d(arrays['charge_error'])
self.rundesc = arrays['rundesc']
self.n_run, self.n_pixels = self.charge.shape
assert (self.n_run == self.rundesc.size)
geom = CameraGeometry.guess(*checm_pixel_pos * u.m,
optical_foclen * u.m)
self.modules = np.arange(self.n_pixels) // self.n_tmpix
self.tmpix = np.arange(self.n_pixels) % self.n_tmpix
self.n_tm = np.unique(self.modules).size
# Init Plots
self.p_c_pix = Camera(self, "", geom)
self.p_c_tm = Camera(self, "", geom)
self.p_c_tmpixspread = Camera(self, "", geom)
self.p_p_pix = Plotter(**kwargs)
self.p_p_tm = Plotter(**kwargs)
self.p_b_tmpixspread = BoxPlotter(**kwargs)
self.p_b_tmspread = BoxPlotter(**kwargs)
self.p_b_pixspread = BoxPlotter(**kwargs)
def start(self):
shape_tm = (self.n_run, self.n_tm, self.n_tmpix)
shape_4d = (self.n_run, self.n_tm, self.n_tmpix, self.n_tmpix)
shape_pix = (self.n_run, self.n_pixels, self.n_tmpix)
self.charge_tm = np.reshape(self.charge, shape_tm)
self.charge_error_tm = np.reshape(self.charge_error, shape_tm)
charge_tm_mean = np.mean(self.charge_tm, axis=2)
charge_error_tm_mean = np.sqrt(np.sum(self.charge_error_tm**2, axis=2))
self.mean_tm2048 = charge_tm_mean[..., None] * np.ones(shape_tm)
tm_spread = self.charge_tm[:, :, None, :] * np.ones(shape_4d)
self.tmpixspread_tm2048 = np.reshape(tm_spread, shape_pix)
# Setup Plots
self.p_c_pix.enable_pixel_picker()
self.p_c_pix.add_colorbar()
self.p_c_tm.enable_pixel_picker()
self.p_c_tm.add_colorbar()
self.p_c_tmpixspread.enable_pixel_picker()
self.p_c_tmpixspread.add_colorbar()
self.p_p_pix.create(self.rundesc, self.charge, self.charge_error)
self.p_p_tm.create(self.rundesc, charge_tm_mean, charge_error_tm_mean)
self.p_b_tmpixspread.create()
self.p_b_tmspread.create()
self.p_b_pixspread.create()
self.p_b_tmspread.fig.title.text = 'Mean TM Charge Spread vs Runs'
self.p_b_pixspread.fig.title.text = 'Pixel Spread vs Runs'
self.p_b_tmspread.update(self.rundesc, charge_tm_mean)
self.p_b_pixspread.update(self.rundesc, self.charge)
self.p_p_pix.enable_run_picker()
self.p_p_tm.enable_run_picker()
self.p_b_tmpixspread.enable_run_picker()
self.p_b_tmspread.enable_run_picker()
self.p_b_pixspread.enable_run_picker()
# Setup widgets
self.active_pixel = 0
self.active_run = 0
# Get bokeh layouts
l_camera_pix = self.p_c_pix.layout
l_camera_tm = self.p_c_tm.layout
l_camera_tmpixspread = self.p_c_tmpixspread.layout
l_plotter_pix = self.p_p_pix.layout
l_plotter_tm = self.p_p_tm.layout
l_boxplotter_tmpixspread = self.p_b_tmpixspread.layout
l_boxplotter_tmspread = self.p_b_tmspread.layout
l_boxplotter_pixspread = self.p_b_pixspread.layout
# Setup layout
self.layout = layout([[l_camera_pix, l_plotter_pix],
[l_camera_tm, l_plotter_tm],
[l_camera_tmpixspread, l_boxplotter_tmpixspread],
[l_boxplotter_tmspread, l_boxplotter_pixspread]])
def finish(self):
curdoc().add_root(self.layout)
curdoc().title = "Charge Vs Run"
@property
def active_pixel(self):
return self._active_pixel
@active_pixel.setter
def active_pixel(self, val):
if not self._active_pixel == val:
self._active_pixel = val
self.p_c_pix.active_pixel = val
self.p_c_tm.active_pixel = val
self.p_c_tmpixspread.active_pixel = val
self.p_p_pix.active_pixel = val
self.p_p_pix.fig.title.text = self.p_p_pix_title.format(val)
module = self.modules[val]
self.p_p_tm.active_pixel = module
self.p_p_tm.fig.title.text = self.p_p_tm_title.format(module)
self.p_b_tmpixspread.update(self.rundesc,
self.tmpixspread_tm2048[:, val])
t = self.p_b_tmpixspread_title
self.p_b_tmpixspread.fig.title.text = t.format(module)
@property
def active_run(self):
return self._active_run
@active_run.setter
def active_run(self, val):
if not self._active_run == val:
self._active_run = val
self.p_p_pix.active_run = val
self.p_p_tm.active_run = val
self.p_b_tmpixspread.active_run = val
self.p_b_tmspread.active_run = val
self.p_b_pixspread.active_run = val
self.set_camera_image()
self.p_c_pix.fig.title.text = self.p_c_pix_title.format(val)
self.p_c_tm.fig.title.text = self.p_c_tm_title.format(val)
t = self.p_c_tmpixspread_title
self.p_c_tmpixspread.fig.title.text = t.format(val)
def set_camera_image(self):
r = self.active_run
self.p_c_pix.image = self.charge[r]
self.p_c_tm.image = self.mean_tm2048[r]
self.p_c_tmpixspread.image = np.median(self.tmpixspread_tm2048[r],
axis=1)
class ADC2PEPlots(Tool):
name = "ADC2PEPlots"
description = "Create plots related to adc2pe"
aliases = Dict(dict(max_events='TargetioFileReader.max_events'))
classes = List([
TargetioFileReader,
TargetioR1Calibrator,
])
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.reader_dict = dict()
self.dl0 = None
self.dl1 = None
self.dead = None
self.dummy_event = None
self.fw_calibrator = None
self.n_pixels = None
self.n_samples = None
self.df_file = None
self.poi = [1825, 1203]
self.p_scatter_led = None
self.p_scatter_led_width = None
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
self.fw_calibrator = FWCalibrator(**kwargs)
dfl = []
base_path = "/Volumes/gct-jason/data/170322/led/Run{:05}_r1_adc.tio"
base_path_pe = "/Volumes/gct-jason/data/170322/led/Run{:05}_r1_pe.tio"
dfl.append(
dict(path=base_path.format(4333), type="LED", cal=False, level=0))
dfl.append(
dict(path=base_path.format(4334), type="LED", cal=False, level=1))
dfl.append(
dict(path=base_path.format(4335), type="LED", cal=False, level=2))
dfl.append(
dict(path=base_path.format(4336), type="LED", cal=False, level=3))
dfl.append(
dict(path=base_path.format(4337), type="LED", cal=False, level=4))
dfl.append(
dict(path=base_path.format(4338), type="LED", cal=False, level=5))
dfl.append(
dict(path=base_path.format(4339), type="LED", cal=False, level=6))
dfl.append(
dict(path=base_path.format(4340), type="LED", cal=False, level=7))
dfl.append(
dict(path=base_path.format(4341), type="LED", cal=False, level=8))
dfl.append(
dict(path=base_path.format(4342), type="LED", cal=False, level=9))
dfl.append(
dict(path=base_path.format(4343), type="LED", cal=False, level=10))
dfl.append(
dict(path=base_path.format(4344), type="LED", cal=False, level=11))
dfl.append(
dict(path=base_path.format(4345), type="LED", cal=False, level=12))
dfl.append(
dict(path=base_path.format(4346), type="LED", cal=False, level=13))
dfl.append(
dict(path=base_path.format(4347), type="LED", cal=False, level=14))
dfl.append(
dict(path=base_path.format(4348), type="LED", cal=False, level=15))
dfl.append(
dict(path=base_path.format(4349), type="LED", cal=False, level=16))
dfl.append(
dict(path=base_path.format(4350), type="LED", cal=False, level=17))
dfl.append(
dict(path=base_path.format(4351), type="LED", cal=False, level=18))
dfl.append(
dict(path=base_path.format(4352), type="LED", cal=False, level=19))
dfl.append(
dict(path=base_path.format(4353), type="LED", cal=False, level=20))
dfl.append(
dict(path=base_path.format(4354), type="LED", cal=False, level=21))
dfl.append(
dict(path=base_path.format(4355), type="LED", cal=False, level=22))
dfl.append(
dict(path=base_path.format(4356), type="LED", cal=False, level=23))
dfl.append(
dict(path=base_path.format(4357), type="LED", cal=False, level=24))
dfl.append(
dict(path=base_path.format(4358), type="LED", cal=False, level=25))
dfl.append(
dict(path=base_path.format(4359), type="LED", cal=False, level=26))
dfl.append(
dict(path=base_path.format(4360), type="LED", cal=False, level=27))
dfl.append(
dict(path=base_path.format(4361), type="LED", cal=False, level=28))
dfl.append(
dict(path=base_path.format(4362), type="LED", cal=False, level=29))
dfl.append(
dict(path=base_path.format(4363), type="LED", cal=False, level=30))
dfl.append(
dict(path=base_path.format(4364), type="LED", cal=False, level=31))
dfl.append(
dict(path=base_path.format(4365), type="LED", cal=False, level=32))
dfl.append(
dict(path=base_path.format(4366), type="LED", cal=False, level=33))
dfl.append(
dict(path=base_path.format(4367), type="LED", cal=False, level=34))
dfl.append(
dict(path=base_path.format(4368), type="LED", cal=False, level=35))
dfl.append(
dict(path=base_path.format(4369), type="LED", cal=False, level=36))
dfl.append(
dict(path=base_path.format(4370), type="LED", cal=False, level=37))
dfl.append(
dict(path=base_path.format(4371), type="LED", cal=False, level=38))
dfl.append(
dict(path=base_path.format(4372), type="LED", cal=False, level=39))
dfl.append(
dict(path=base_path_pe.format(4333), type="LED", cal=True,
level=0))
dfl.append(
dict(path=base_path_pe.format(4334), type="LED", cal=True,
level=1))
dfl.append(
dict(path=base_path_pe.format(4335), type="LED", cal=True,
level=2))
dfl.append(
dict(path=base_path_pe.format(4336), type="LED", cal=True,
level=3))
dfl.append(
dict(path=base_path_pe.format(4337), type="LED", cal=True,
level=4))
dfl.append(
dict(path=base_path_pe.format(4338), type="LED", cal=True,
level=5))
dfl.append(
dict(path=base_path_pe.format(4339), type="LED", cal=True,
level=6))
dfl.append(
dict(path=base_path_pe.format(4340), type="LED", cal=True,
level=7))
dfl.append(
dict(path=base_path_pe.format(4341), type="LED", cal=True,
level=8))
dfl.append(
dict(path=base_path_pe.format(4342), type="LED", cal=True,
level=9))
dfl.append(
dict(path=base_path_pe.format(4343),
type="LED",
cal=True,
level=10))
dfl.append(
dict(path=base_path_pe.format(4344),
type="LED",
cal=True,
level=11))
dfl.append(
dict(path=base_path_pe.format(4345),
type="LED",
cal=True,
level=12))
dfl.append(
dict(path=base_path_pe.format(4346),
type="LED",
cal=True,
level=13))
dfl.append(
dict(path=base_path_pe.format(4347),
type="LED",
cal=True,
level=14))
dfl.append(
dict(path=base_path_pe.format(4348),
type="LED",
cal=True,
level=15))
dfl.append(
dict(path=base_path_pe.format(4349),
type="LED",
cal=True,
level=16))
dfl.append(
dict(path=base_path_pe.format(4350),
type="LED",
cal=True,
level=17))
dfl.append(
dict(path=base_path_pe.format(4351),
type="LED",
cal=True,
level=18))
dfl.append(
dict(path=base_path_pe.format(4352),
type="LED",
cal=True,
level=19))
dfl.append(
dict(path=base_path_pe.format(4353),
type="LED",
cal=True,
level=20))
dfl.append(
dict(path=base_path_pe.format(4354),
type="LED",
cal=True,
level=21))
dfl.append(
dict(path=base_path_pe.format(4355),
type="LED",
cal=True,
level=22))
dfl.append(
dict(path=base_path_pe.format(4356),
type="LED",
cal=True,
level=23))
dfl.append(
dict(path=base_path_pe.format(4357),
type="LED",
cal=True,
level=24))
dfl.append(
dict(path=base_path_pe.format(4358),
type="LED",
cal=True,
level=25))
dfl.append(
dict(path=base_path_pe.format(4359),
type="LED",
cal=True,
level=26))
dfl.append(
dict(path=base_path_pe.format(4360),
type="LED",
cal=True,
level=27))
dfl.append(
dict(path=base_path_pe.format(4361),
type="LED",
cal=True,
level=28))
dfl.append(
dict(path=base_path_pe.format(4362),
type="LED",
cal=True,
level=29))
dfl.append(
dict(path=base_path_pe.format(4363),
type="LED",
cal=True,
level=30))
dfl.append(
dict(path=base_path_pe.format(4364),
type="LED",
cal=True,
level=31))
dfl.append(
dict(path=base_path_pe.format(4365),
type="LED",
cal=True,
level=32))
dfl.append(
dict(path=base_path_pe.format(4366),
type="LED",
cal=True,
level=33))
dfl.append(
dict(path=base_path_pe.format(4367),
type="LED",
cal=True,
level=34))
dfl.append(
dict(path=base_path_pe.format(4368),
type="LED",
cal=True,
level=35))
dfl.append(
dict(path=base_path_pe.format(4369),
type="LED",
cal=True,
level=36))
dfl.append(
dict(path=base_path_pe.format(4370),
type="LED",
cal=True,
level=37))
dfl.append(
dict(path=base_path_pe.format(4371),
type="LED",
cal=True,
level=38))
dfl.append(
dict(path=base_path_pe.format(4372),
type="LED",
cal=True,
level=39))
for d in dfl:
d['reader'] = TargetioFileReader(input_path=d['path'], **kwargs)
self.df_file = pd.DataFrame(dfl)
cleaner = CHECMWaveformCleanerAverage(**kwargs)
extractor = AverageWfPeakIntegrator(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=extractor,
cleaner=cleaner,
**kwargs)
self.dead = Dead()
self.dummy_event = dfl[0]['reader'].get_event(0)
telid = list(self.dummy_event.r0.tels_with_data)[0]
r1 = self.dummy_event.r1.tel[telid].pe_samples[0]
self.n_pixels, self.n_samples = r1.shape
script = "checm_paper_led"
self.p_scatter_led = Scatter(**kwargs,
script=script,
figure_name="scatter_led",
shape='wide')
self.p_scatter_led_width = Scatter(**kwargs,
script=script,
figure_name="scatter_led_width",
shape='wide')
def start(self):
df_list = []
dead = self.dead.get_pixel_mask()
kernel = general_gaussian(3, p=1.0, sig=1)
x_base = np.arange(self.n_samples)
x_interp = np.linspace(0, self.n_samples - 1, 300)
ind = np.indices((self.n_pixels, x_interp.size))[1]
r_ind = ind[:, ::-1]
ind_x = x_interp[ind]
r_ind_x = x_interp[r_ind]
saturation_recovery_file = np.load(
"/Volumes/gct-jason/plots/checm_paper/checm_paper_recovery/saturation_recovery.npz"
)
gradient = saturation_recovery_file['gradient']
intercept = saturation_recovery_file['intercept']
desc1 = 'Looping through files'
n_rows = len(self.df_file.index)
for index, row in tqdm(self.df_file.iterrows(),
total=n_rows,
desc=desc1):
path = row['path']
reader = row['reader']
type_ = row['type']
cal = row['cal']
level = row['level']
cal_t = 'Calibrated' if cal else 'Uncalibrated'
source = reader.read()
n_events = reader.num_events
dl1 = np.zeros((n_events, self.n_pixels))
width = np.zeros((n_events, self.n_pixels))
low_max = np.zeros((n_events, self.n_pixels), dtype=np.bool)
desc2 = "Extracting Charge"
for event in tqdm(source, desc=desc2, total=n_events):
ev = event.count
self.dl0.reduce(event)
self.dl1.calibrate(event)
dl1[ev] = event.dl1.tel[0].image[0]
dl0 = event.dl0.tel[0].pe_samples[0]
cleaned = event.dl1.tel[0].cleaned[0]
smooth_flat = np.convolve(dl0.ravel(), kernel, "same")
smoothed = np.reshape(smooth_flat, dl0.shape)
samples_std = np.std(dl0, axis=1)
smooth_baseline_std = np.std(smoothed, axis=1)
with np.errstate(divide='ignore', invalid='ignore'):
smoothed *= (samples_std / smooth_baseline_std)[:, None]
smoothed[~np.isfinite(smoothed)] = 0
dl0 = smoothed
f = interpolate.interp1d(x_base, dl0, kind=3, axis=1)
dl0 = f(x_interp)
grad = np.gradient(dl0)[1]
t_max = x_interp[np.argmax(dl0, 1)]
t_start = t_max - 2
t_end = t_max + 2
t_window = (ind_x >= t_start[..., None]) & (ind_x <
t_end[..., None])
t_windowed = np.ma.array(dl0, mask=~t_window)
t_windowed_ind = np.ma.array(ind_x, mask=~t_window)
max_ = np.max(dl0, axis=1)
reversed_ = dl0[:, ::-1]
peak_time_i = np.ones(dl0.shape) * t_max[:, None]
mask_before = np.ma.masked_less(ind_x, peak_time_i).mask
mask_after = np.ma.masked_greater(r_ind_x, peak_time_i).mask
masked_bef = np.ma.masked_array(dl0, mask_before)
masked_aft = np.ma.masked_array(reversed_, mask_after)
pe_width = 20
d_l = np.diff(np.sign(pe_width - masked_aft))
d_r = np.diff(np.sign(pe_width - masked_bef))
t_l = x_interp[r_ind[0, np.argmax(d_l, axis=1) + 1]]
t_r = x_interp[ind[0, np.argmax(d_r, axis=1) + 1]]
width[ev] = t_r - t_l
low_max[ev] = (max_ < pe_width)
width[ev, low_max[ev]] = 0
charge_masked = self.dead.mask2d(dl1).compressed()
charge_camera = np.mean(charge_masked)
q75, q25 = np.percentile(charge_masked, [75, 25])
charge_err_top_camera = q75 - charge_camera
charge_err_bottom_camera = charge_camera - q25
width = np.ma.masked_array(width, mask=low_max)
ch = gradient[None, :] * width + intercept[None, :]
recovered_charge = 10**(ch**2)
desc3 = "Aggregate charge per pixel"
for pix in trange(self.n_pixels, desc=desc3):
pixel_area = dl1[:, pix]
pixel_width = width[:, pix]
pixel_low_max = low_max[:, pix].all()
pixel_rec_ch = recovered_charge[:, pix]
if pix in self.dead.dead_pixels:
continue
charge = np.mean(pixel_area)
q75, q25 = np.percentile(pixel_area, [75, 25])
charge_err_top = q75 - charge
charge_err_bottom = charge - q25
w = np.mean(pixel_width)
w_err = np.std(pixel_width)
rec_charge = np.mean(pixel_rec_ch)
q75, q25 = np.percentile(pixel_rec_ch, [75, 25])
rec_charge_err_top = q75 - rec_charge
rec_charge_err_bottom = rec_charge - q25
df_list.append(
dict(type=type_,
level=level,
cal=cal,
cal_t=cal_t,
pixel=pix,
tm=pix // 64,
charge=charge,
charge_err_top=charge_err_top,
charge_err_bottom=charge_err_bottom,
charge_camera=charge_camera,
charge_err_top_camera=charge_err_top_camera,
charge_err_bottom_camera=charge_err_bottom_camera,
width=w,
width_err=w_err,
low_max=pixel_low_max,
recovered_charge=rec_charge,
rec_charge_err_top=rec_charge_err_top,
rec_charge_err_bottom=rec_charge_err_bottom))
df = pd.DataFrame(df_list)
store = pd.HDFStore('/Volumes/gct-jason/plots/checm_paper/df/led.h5')
store['df'] = df
store = pd.HDFStore('/Volumes/gct-jason/plots/checm_paper/df/led.h5')
df = store['df']
# Scale ADC values to match p.e.
type_list = np.unique(df['type'])
for t in type_list:
df_t = df.loc[df['type'] == t]
level_list = np.unique(df_t['level'])
for l in level_list:
df_l = df_t.loc[df_t['level'] == l]
median_cal = np.median(df_l.loc[df_l['cal'], 'charge'])
median_uncal = np.median(df_l.loc[~df_l['cal'], 'charge'])
ratio = median_cal / median_uncal
b = (df['type'] == t) & (df['level'] == l) & (~df['cal'])
df.loc[b, 'charge'] *= ratio
df_led = df.loc[(df['type'] == 'LED') & (df['cal'])]
# Create figures
self.p_scatter_led.create("LED", "Charge (p.e.)", "LED Distribution")
self.p_scatter_led.set_y_log()
output_np = join(self.p_scatter_led.output_dir, "pix{}_dr_led.npz")
for ip, p in enumerate(self.poi):
df_pix = df_led.loc[df_led['pixel'] == p]
x = df_pix['level']
y = df_pix['charge']
y_err = [df_pix['charge_err_bottom'], df_pix['charge_err_top']]
label = "Pixel {}".format(p)
self.p_scatter_led.add(x, y, None, y_err, label)
self.log.info("Saving numpy array: {}".format(output_np.format(p)))
np.savez(output_np.format(p), x=x, y=y, x_err=None, y_err=y_err)
self.p_scatter_led.add_legend()
self.p_scatter_led_width.create("Width (ns)", "Charge (p.e.)",
"LED Saturation Recovery")
for ip, p in enumerate(self.poi):
df_pix = df_led.loc[df_led['pixel'] == p]
x = df_pix['width']
y = df_pix['charge']
x_err = df_pix['width_err']
y_err = [df_pix['charge_err_bottom'], df_pix['charge_err_top']]
label = "Pixel {}, Pulse Integration".format(p)
self.p_scatter_led_width.add(x, y, x_err, y_err, label)
x = df_pix['width']
y = df_pix['recovered_charge']
x_err = df_pix['width_err']
y_err = [
df_pix['rec_charge_err_bottom'], df_pix['rec_charge_err_top']
]
label = "Pixel {}, Saturation Recovery".format(p)
self.p_scatter_led_width.add(x, y, x_err, y_err, label)
self.p_scatter_led_width.set_y_log()
self.p_scatter_led_width.add_legend()
def finish(self):
# Save figures
self.p_scatter_led.save()
self.p_scatter_led_width.save()
class ADC2PEvsHVPlotter(Tool):
name = "ADC2PEvsHVPlotter"
description = "For a given hv values, get the conversion from adc " \
"to pe for each pixel."
output_dir = Unicode('', help='Directory to store output').tag(config=True)
aliases = Dict(
dict(spe='TargetioADC2PECalibrator.spe_path',
gm='TargetioADC2PECalibrator.gain_matching_path',
o='ADC2PEvsHVPlotter.output_dir'))
classes = List([
TargetioADC2PECalibrator,
])
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.a2p = None
self.dead = None
sns.set_style("whitegrid")
self.cfmaker = None
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
self.a2p = TargetioADC2PECalibrator(**kwargs)
self.dead = Dead()
self.cfmaker = CfMaker(32)
# Save figures
self.output_dir = join(self.output_dir, "plot_adc2pe_vs_hv")
if not exists(self.output_dir):
self.log.info("Creating directory: {}".format(self.output_dir))
makedirs(self.output_dir)
def start(self):
hv_dict = dict()
hv_dict['800'] = [800] * 2048
hv_dict['900'] = [900] * 2048
hv_dict['1000'] = [1000] * 2048
hv_dict['1100'] = [1100] * 2048
hv_dict['800gm'] = [self.a2p.gm800[i // 64] for i in range(2048)]
hv_dict['900gm'] = [self.a2p.gm900[i // 64] for i in range(2048)]
hv_dict['1000gm'] = [self.a2p.gm1000[i // 64] for i in range(2048)]
hv_dict['800gm_c1'] = [self.a2p.gm800_c1[i // 64] for i in range(2048)]
df_list = []
for key, hv in hv_dict.items():
hv_group = int(key.replace("gm", "").replace("_c1", ""))
gm = 'gm' in key
gm_t = 'Gain-matched' if 'gm' in key else 'Non-gain-matched'
adc2pe = self.a2p.get_adc2pe_at_hv(hv, np.arange(2048))
adc2pe = self.dead.mask1d(adc2pe)
spe = 1 / adc2pe
self.cfmaker.SetAll(np.ma.filled(adc2pe, 0).astype(np.float32))
path = join(self.output_dir, "adc2pe_{}.tcal".format(key))
self.cfmaker.Save(path, False)
self.log.info("ADC2PE tcal created: {}".format(path))
self.cfmaker.Clear()
for pix in range(2048):
if pix in self.dead.dead_pixels:
continue
df_list.append(
dict(pixel=pix,
key=key,
hv_group=hv_group,
gm=gm,
gm_t=gm_t,
hv=hv[pix],
adc2pe=adc2pe[pix],
spe=spe[pix]))
df = DataFrame(df_list)
df = df.loc[df['key'] != '800gm_c1']
# Create Plot
fig = plt.figure(figsize=(13, 6))
ax = fig.add_subplot(1, 1, 1)
sns.violinplot(ax=ax,
data=df,
x='hv_group',
y='spe',
hue='gm_t',
split=True,
scale='count',
inner='quartile')
ax.set_xlabel('HV')
ax.set_ylabel('SPE Value (ADC)')
fig_path = join(self.output_dir, "spe_vs_hv.pdf")
fig.savefig(fig_path)
def finish(self):
pass
class SPEExtractor(Tool):
name = "SPEExtractor"
description = "Extract the conversion from adc to pe and save as a " \
"numpy array"
aliases = Dict(
dict(
r='EventFileReaderFactory.reader',
f='EventFileReaderFactory.input_path',
max_events='EventFileReaderFactory.max_events',
ped='CameraR1CalibratorFactory.pedestal_path',
tf='CameraR1CalibratorFactory.tf_path',
fitter='SPEFitterFactory.fitter',
))
classes = List([
EventFileReaderFactory,
CameraR1CalibratorFactory,
SPEFitterFactory,
])
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.reader = None
self.r1 = None
self.dl0 = None
self.cleaner = None
self.extractor = None
self.dl1 = None
self.fitter = None
self.dead = None
self.output_dir = None
self.spe = None
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
reader_factory = EventFileReaderFactory(**kwargs)
reader_class = reader_factory.get_class()
self.reader = reader_class(**kwargs)
r1_factory = CameraR1CalibratorFactory(origin=self.reader.origin,
**kwargs)
r1_class = r1_factory.get_class()
self.r1 = r1_class(**kwargs)
self.cleaner = CHECMWaveformCleanerAverage(**kwargs)
self.extractor = AverageWfPeakIntegrator(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=self.extractor,
cleaner=self.cleaner,
**kwargs)
self.dead = Dead()
fitter_factory = SPEFitterFactory(**kwargs)
fitter_class = fitter_factory.get_class()
self.fitter = fitter_class(**kwargs)
self.output_dir = join(self.reader.output_directory, "extract_spe")
if not exists(self.output_dir):
self.log.info("Creating directory: {}".format(self.output_dir))
makedirs(self.output_dir)
def start(self):
n_events = self.reader.num_events
first_event = self.reader.get_event(0)
telid = list(first_event.r0.tels_with_data)[0]
n_pixels, n_samples = first_event.r0.tel[telid].adc_samples[0].shape
# Prepare storage array
area = np.zeros((n_events, n_pixels))
spe = np.ma.zeros(n_pixels)
spe.mask = np.zeros(spe.shape, dtype=np.bool)
spe.fill_value = 0
source = self.reader.read()
desc = "Looping through file"
with tqdm(total=n_events, desc=desc) as pbar:
for event in source:
pbar.update(1)
index = event.count
self.r1.calibrate(event)
self.dl0.reduce(event)
self.dl1.calibrate(event)
# Perform CHECM Charge Extraction
peak_area = event.dl1.tel[telid].image
area[index] = peak_area
desc = "Fitting pixels"
with tqdm(total=n_pixels, desc=desc) as pbar:
for pix in range(n_pixels):
pbar.update(1)
if not self.fitter.apply(area[:, pix]):
self.log.warning("Pixel {} couldn't be fit".format(pix))
spe.mask = True
continue
spe[pix] = self.fitter.coeff['spe']
self.spe = np.ma.filled(self.dead.mask1d(spe))
def finish(self):
output_path = join(self.output_dir, "spe.npy")
np.save(output_path, self.spe)
self.log.info("spe array saved: {}".format(output_path))
class BokehGainMatching(Tool):
name = "BokehGainMatching"
description = "Interactively explore the steps in obtaining charge vs hv"
input_path = Unicode('',
help='Path to the numpy array containing the '
'gain and hv').tag(config=True)
aliases = Dict(dict(f='BokehGainMatching.input_path'))
classes = List([])
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._active_pixel = None
self.dead = Dead()
self.charge = None
self.charge_error = None
self.hv = None
self.n_hv = None
self.n_pixels = None
self.n_tmpix = 64
self.modules = None
self.tmpix = None
self.n_tm = None
self.m_pix = None
self.c_pix = None
self.m_tm = None
self.c_tm = None
self.m_tm2048 = None
self.c_tm2048 = None
self.p_camera_pix = None
self.p_plotter_pix = None
self.p_camera_tm = None
self.p_plotter_tm = None
self.w_view_radio = None
self.layout = None
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
arrays = np.load(self.input_path)
self.charge = self.dead.mask2d(arrays['charge'])
self.charge = np.ma.masked_where(self.charge <= 0, self.charge)
self.charge_error = np.ma.array(arrays['charge_error'],
mask=self.charge.mask)
self.hv = arrays['rundesc']
self.n_hv, self.n_pixels = self.charge.shape
assert (self.n_hv == self.hv.size)
geom = CameraGeometry.guess(*checm_pixel_pos * u.m,
optical_foclen * u.m)
self.modules = np.arange(self.n_pixels) // self.n_tmpix
self.tmpix = np.arange(self.n_pixels) % self.n_tmpix
self.n_tm = np.unique(self.modules).size
# Init Plots
self.p_camera_pix = Camera(self, "Gain Matching Pixels", geom)
self.p_camera_tm = Camera(self, "Gain Matching TMs", geom)
self.p_plotter_pix = Plotter(**kwargs)
self.p_plotter_tm = Plotter(**kwargs)
def start(self):
# Overcomplicated method instead of just reshaping...
# gain_modules = np.zeros((self.n_hv, self.n_tm, self.n_tmpix))
# hv_r = np.arange(self.n_hv, dtype=np.int)[:, None]
# hv_z = np.zeros(self.n_hv, dtype=np.int)[:, None]
# tm_r = np.arange(self.n_tm, dtype=np.int)[None, :]
# tm_z = np.zeros(self.n_tm, dtype=np.int)[None, :]
# tmpix_r = np.arange(self.n_tmpix, dtype=np.int)[None, :]
# tmpix_z = np.zeros(self.n_tmpix, dtype=np.int)[None, :]
# hv_i = (hv_r + tm_z)[..., None] + tmpix_z
# tm_i = (hv_z + tm_r)[..., None] + tmpix_z
# tmpix_i = (hv_z + tm_z)[..., None] + tmpix_r
# gain_rs = np.reshape(self.charge, (self.n_hv, self.n_tm, self.n_tmpix))
# modules_rs = np.reshape(self.modules, (self.n_tm, self.n_tmpix))
# tmpix_rs = np.reshape(self.tmpix, (self.n_tm, self.n_tmpix))
# tm_j = hv_z[..., None] + modules_rs[None, ...]
# tmpix_j = hv_z[..., None] + tmpix_rs[None, ...]
# gain_modules[hv_i, tm_i, tmpix_i] = gain_rs[hv_i, tm_j, tmpix_j]
# gain_modules_mean = np.mean(gain_modules, axis=2)
shape = (self.n_hv, self.n_tm, self.n_tmpix)
gain_tm = np.reshape(self.charge, shape)
gain_error_tm = np.reshape(self.charge_error, shape)
gain_tm_mean = np.mean(gain_tm, axis=2)
gain_error_tm_mean = np.sqrt(np.sum(gain_error_tm**2, axis=2))
self.m_pix = np.ma.zeros(self.n_pixels, fill_value=0)
self.c_pix = np.ma.zeros(self.n_pixels, fill_value=0)
self.m_tm = np.ma.zeros(self.n_tm, fill_value=0)
self.c_tm = np.ma.zeros(self.n_tm, fill_value=0)
p0 = [0, 5]
bounds = (-np.inf, np.inf) # ([-2000, -10], [2000, 10])
for pix in range(self.n_pixels):
x = self.hv[~self.charge.mask[:, pix]]
y = self.charge[:, pix][~self.charge.mask[:, pix]]
if x.size == 0:
continue
try:
coeff, _ = curve_fit(
gain_func,
x,
y,
p0=p0,
bounds=bounds,
# sigma=y_err[:, pix],
# absolute_sigma=True
)
self.c_pix[pix], self.m_pix[pix] = coeff
except RuntimeError:
self.log.warning("Unable to fit pixel: {}".format(pix))
for tm in range(self.n_tm):
x = self.hv
y = gain_tm_mean[:, tm]
try:
coeff, _ = curve_fit(
gain_func,
x,
y,
p0=p0,
bounds=bounds,
# sigma=y_err_tm[:, tm],
# absolute_sigma=True
)
self.c_tm[tm], self.m_tm[tm] = coeff
except RuntimeError:
self.log.warning("Unable to fit tm: {}".format(tm))
self.m_tm2048 = self.m_tm[:, None] * np.ones((self.n_tm, self.n_tmpix))
self.c_tm2048 = self.c_tm[:, None] * np.ones((self.n_tm, self.n_tmpix))
self.m_pix = self.dead.mask1d(self.m_pix)
self.c_pix = self.dead.mask1d(self.c_pix)
self.m_tm2048 = self.dead.mask1d(self.m_tm2048.ravel())
self.c_tm2048 = self.dead.mask1d(self.c_tm2048.ravel())
# Setup Plots
self.p_camera_pix.enable_pixel_picker()
self.p_camera_pix.add_colorbar()
self.p_camera_tm.enable_pixel_picker()
self.p_camera_tm.add_colorbar()
self.p_plotter_pix.create(self.hv, self.charge, self.charge_error,
self.m_pix, self.c_pix)
self.p_plotter_tm.create(self.hv, gain_tm_mean, gain_error_tm_mean,
self.m_tm, self.c_tm)
# Setup widgets
self.create_view_radio_widget()
self.set_camera_image()
self.active_pixel = 0
# Get bokeh layouts
l_camera_pix = self.p_camera_pix.layout
l_camera_tm = self.p_camera_tm.layout
l_plotter_pix = self.p_plotter_pix.layout
l_plotter_tm = self.p_plotter_tm.layout
# Setup layout
self.layout = layout([[self.w_view_radio],
[l_camera_pix, l_plotter_pix],
[l_camera_tm, l_plotter_tm]])
def finish(self):
curdoc().add_root(self.layout)
curdoc().title = "Charge Vs HV"
output_dir = dirname(self.input_path)
output_path = join(output_dir, 'gain_matching_coeff.npz')
np.savez(output_path,
alpha_pix=np.ma.filled(self.m_pix),
C_pix=np.ma.filled(self.c_pix),
alpha_tm=np.ma.filled(self.m_tm),
C_tm=np.ma.filled(self.c_tm))
self.log.info("Numpy array saved to: {}".format(output_path))
@property
def active_pixel(self):
return self._active_pixel
@active_pixel.setter
def active_pixel(self, val):
if not self._active_pixel == val:
self._active_pixel = val
self.p_camera_pix.active_pixel = val
self.p_camera_tm.active_pixel = val
self.p_plotter_pix.active_pixel = val
self.p_plotter_pix.fig.title.text = 'Pixel {}'.format(val)
module = self.modules[val]
self.p_plotter_tm.active_pixel = module
self.p_plotter_tm.fig.title.text = 'TM {}'.format(module)
def set_camera_image(self):
if self.w_view_radio.active == 0:
self.p_camera_pix.image = self.m_pix
self.p_camera_tm.image = self.m_tm2048
self.p_camera_pix.fig.title.text = 'Gain Matching Pixels (gradient)'
self.p_camera_tm.fig.title.text = 'Gain Matching TMs (gradient)'
elif self.w_view_radio.active == 1:
self.p_camera_pix.image = self.c_pix
self.p_camera_tm.image = self.c_tm2048
self.p_camera_pix.fig.title.text = 'Gain Matching Pixels (intercept)'
self.p_camera_tm.fig.title.text = 'Gain Matching TMs (intercept)'
def create_view_radio_widget(self):
self.w_view_radio = RadioButtonGroup(labels=["gradient", "intercept"],
active=0)
self.w_view_radio.on_click(self.on_view_radio_widget_click)
def on_view_radio_widget_click(self, active):
self.set_camera_image()
class ADC2PEResidualsPlotter(Tool):
name = "ADC2PEResidualsPlotter"
description = "Plot the residuals from the adc2pe calibration"
input_path = Unicode("", help="Path to the adc2pe_residuals numpy "
"file").tag(config=True)
aliases = Dict(dict(i='ADC2PEResidualsPlotter.input_path', ))
classes = List([])
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.dead = None
self.output_dir = None
self.spe = None
self.spe_sigma = None
self.hist = None
self.edges = None
self.between = None
self.fig_spectrum_all = None
self.fig_spectrum_tm_list = None
self.fig_combgaus = None
self.fig_kde = None
self.fig_hist = None
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
self.dead = Dead()
file = np.load(self.input_path)
self.spe = file['spe']
self.spe_sigma = file['spe_sigma']
self.hist = file['hist']
self.edges = file['edges']
self.between = file['between']
self.output_dir = join(dirname(self.input_path),
"plot_adc2pe_residuals")
if not exists(self.output_dir):
self.log.info("Creating directory: {}".format(self.output_dir))
makedirs(self.output_dir)
# Create figures
sns.set_style("whitegrid")
sns.despine()
self.fig_spectrum_all = plt.figure(figsize=(13, 6))
self.fig_spectrum_all.suptitle("SPE Spectrum, All Pixels")
self.fig_spectrum_tm_list = []
for i in range(32):
fig = plt.figure(figsize=(13, 6))
self.fig_spectrum_tm_list.append(plt.figure(figsize=(13, 6)))
self.fig_combgaus = plt.figure(figsize=(13, 6))
self.fig_combgaus.suptitle("Combined 1pe fit, All Pixels")
self.fig_kde = plt.figure(figsize=(13, 6))
self.fig_kde.suptitle("Distribution of SPE, Kernel density estimate")
self.fig_hist = plt.figure(figsize=(13, 6))
self.fig_hist.suptitle("Distribution of SPE, Histogram")
def start(self):
# Normalise histogram
norm = np.sum(np.diff(self.edges, axis=1) * self.hist, axis=1)
hist = self.hist / norm[:, None]
# Roll axis for easier plotting
hist_r = np.rollaxis(hist, 1)
nbins, npix = hist_r.shape
e = self.edges[0]
hist_tops = np.insert(hist_r, np.arange(nbins), hist_r, axis=0)
edges_tops = np.insert(e, np.arange(e.shape[0]), e, axis=0)[1:-1]
# Mask dead pixels
spe = self.dead.mask1d(self.spe)
spe_sigma = self.dead.mask1d(self.spe_sigma)
hist_tops = self.dead.mask2d(hist_tops)
# Spectrum with all pixels
self.log.info("Plotting: spectrum_all")
ax_spectrum_all = self.fig_spectrum_all.add_subplot(1, 1, 1)
ax_spectrum_all.semilogy(edges_tops, hist_tops, color='b', alpha=0.2)
ax_spectrum_all.set_xlabel("Amplitude (p.e.)")
ax_spectrum_all.set_ylabel("Probability")
# Sprectrum for each tm
self.log.info("Plotting: spectrum_tm")
hist_tops_tm = np.reshape(hist_tops, (hist_tops.shape[0], 32, 64))
for tm, fig in enumerate(self.fig_spectrum_tm_list):
ax = fig.add_subplot(1, 1, 1)
ax.set_title("SPE Spectrum, TM {}".format(tm))
ax.semilogy(edges_tops, hist_tops_tm[:, tm], color='b', alpha=0.2)
ax.set_xlabel("Amplitude (p.e.)")
ax.set_ylabel("Probability")
# Combined gaussian of each spe value
self.log.info("Plotting: combined_gaussian")
ax_comgaus = self.fig_combgaus.add_subplot(1, 1, 1)
x = np.linspace(-1, 4, 200)
kernels = []
for val, sigma in zip(spe.compressed(), spe_sigma.compressed()):
kernel = stats.norm(val, sigma).pdf(x)
kernels.append(kernel)
# plt.plot(x, kernel, color="r")
sns.rugplot(spe.compressed(), color=".2", linewidth=1, ax=ax_comgaus)
density = np.sum(kernels, axis=0)
density /= integrate.trapz(density, x)
ax_comgaus.plot(x, density)
ax_comgaus.set_xlabel("SPE Fit Value (p.e.)")
ax_comgaus.set_ylabel("Sum")
# Kernel density estimate
self.log.info("Plotting: spe_kde")
ax_kde = self.fig_kde.add_subplot(1, 1, 1)
sns.rugplot(spe.compressed(), color=".2", linewidth=1, ax=ax_kde)
sns.kdeplot(spe.compressed(), shade=True, ax=ax_kde)
ax_kde.set_xlabel("SPE Fit Value (p.e.)")
ax_kde.set_ylabel("KDE")
# Histogram
self.log.info("Plotting: histogram")
ax_hist = self.fig_hist.add_subplot(1, 1, 1)
sns.distplot(spe.compressed(), kde=False, rug=True, ax=ax_hist)
ax_hist.set_xlabel("SPE Fit Value (p.e.)")
ax_hist.set_ylabel("N")
def finish(self):
output_path = join(self.output_dir, "spectrum_all.png")
self.fig_spectrum_all.savefig(output_path)
self.log.info("Created figure: {}".format(output_path))
output_path = join(self.output_dir, "spectrum_tm{}.png")
for tm, fig in enumerate(self.fig_spectrum_tm_list):
p = output_path.format(tm)
fig.savefig(p)
self.log.info("Created figure: {}".format(p))
output_path = join(self.output_dir, "combined_gaussian.png")
self.fig_combgaus.savefig(output_path)
self.log.info("Created figure: {}".format(output_path))
output_path = join(self.output_dir, "kde.png")
self.fig_kde.savefig(output_path)
self.log.info("Created figure: {}".format(output_path))
output_path = join(self.output_dir, "hist.png")
self.fig_hist.savefig(output_path)
self.log.info("Created figure: {}".format(output_path))
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
self.fw_calibrator = FWCalibrator(**kwargs)
dfl = []
base_path = "/Volumes/gct-jason/data/170322/led/Run{:05}_r1_adc.tio"
base_path_pe = "/Volumes/gct-jason/data/170322/led/Run{:05}_r1_pe.tio"
dfl.append(
dict(path=base_path.format(4333), type="LED", cal=False, level=0))
dfl.append(
dict(path=base_path.format(4334), type="LED", cal=False, level=1))
dfl.append(
dict(path=base_path.format(4335), type="LED", cal=False, level=2))
dfl.append(
dict(path=base_path.format(4336), type="LED", cal=False, level=3))
dfl.append(
dict(path=base_path.format(4337), type="LED", cal=False, level=4))
dfl.append(
dict(path=base_path.format(4338), type="LED", cal=False, level=5))
dfl.append(
dict(path=base_path.format(4339), type="LED", cal=False, level=6))
dfl.append(
dict(path=base_path.format(4340), type="LED", cal=False, level=7))
dfl.append(
dict(path=base_path.format(4341), type="LED", cal=False, level=8))
dfl.append(
dict(path=base_path.format(4342), type="LED", cal=False, level=9))
dfl.append(
dict(path=base_path.format(4343), type="LED", cal=False, level=10))
dfl.append(
dict(path=base_path.format(4344), type="LED", cal=False, level=11))
dfl.append(
dict(path=base_path.format(4345), type="LED", cal=False, level=12))
dfl.append(
dict(path=base_path.format(4346), type="LED", cal=False, level=13))
dfl.append(
dict(path=base_path.format(4347), type="LED", cal=False, level=14))
dfl.append(
dict(path=base_path.format(4348), type="LED", cal=False, level=15))
dfl.append(
dict(path=base_path.format(4349), type="LED", cal=False, level=16))
dfl.append(
dict(path=base_path.format(4350), type="LED", cal=False, level=17))
dfl.append(
dict(path=base_path.format(4351), type="LED", cal=False, level=18))
dfl.append(
dict(path=base_path.format(4352), type="LED", cal=False, level=19))
dfl.append(
dict(path=base_path.format(4353), type="LED", cal=False, level=20))
dfl.append(
dict(path=base_path.format(4354), type="LED", cal=False, level=21))
dfl.append(
dict(path=base_path.format(4355), type="LED", cal=False, level=22))
dfl.append(
dict(path=base_path.format(4356), type="LED", cal=False, level=23))
dfl.append(
dict(path=base_path.format(4357), type="LED", cal=False, level=24))
dfl.append(
dict(path=base_path.format(4358), type="LED", cal=False, level=25))
dfl.append(
dict(path=base_path.format(4359), type="LED", cal=False, level=26))
dfl.append(
dict(path=base_path.format(4360), type="LED", cal=False, level=27))
dfl.append(
dict(path=base_path.format(4361), type="LED", cal=False, level=28))
dfl.append(
dict(path=base_path.format(4362), type="LED", cal=False, level=29))
dfl.append(
dict(path=base_path.format(4363), type="LED", cal=False, level=30))
dfl.append(
dict(path=base_path.format(4364), type="LED", cal=False, level=31))
dfl.append(
dict(path=base_path.format(4365), type="LED", cal=False, level=32))
dfl.append(
dict(path=base_path.format(4366), type="LED", cal=False, level=33))
dfl.append(
dict(path=base_path.format(4367), type="LED", cal=False, level=34))
dfl.append(
dict(path=base_path.format(4368), type="LED", cal=False, level=35))
dfl.append(
dict(path=base_path.format(4369), type="LED", cal=False, level=36))
dfl.append(
dict(path=base_path.format(4370), type="LED", cal=False, level=37))
dfl.append(
dict(path=base_path.format(4371), type="LED", cal=False, level=38))
dfl.append(
dict(path=base_path.format(4372), type="LED", cal=False, level=39))
dfl.append(
dict(path=base_path_pe.format(4333), type="LED", cal=True,
level=0))
dfl.append(
dict(path=base_path_pe.format(4334), type="LED", cal=True,
level=1))
dfl.append(
dict(path=base_path_pe.format(4335), type="LED", cal=True,
level=2))
dfl.append(
dict(path=base_path_pe.format(4336), type="LED", cal=True,
level=3))
dfl.append(
dict(path=base_path_pe.format(4337), type="LED", cal=True,
level=4))
dfl.append(
dict(path=base_path_pe.format(4338), type="LED", cal=True,
level=5))
dfl.append(
dict(path=base_path_pe.format(4339), type="LED", cal=True,
level=6))
dfl.append(
dict(path=base_path_pe.format(4340), type="LED", cal=True,
level=7))
dfl.append(
dict(path=base_path_pe.format(4341), type="LED", cal=True,
level=8))
dfl.append(
dict(path=base_path_pe.format(4342), type="LED", cal=True,
level=9))
dfl.append(
dict(path=base_path_pe.format(4343),
type="LED",
cal=True,
level=10))
dfl.append(
dict(path=base_path_pe.format(4344),
type="LED",
cal=True,
level=11))
dfl.append(
dict(path=base_path_pe.format(4345),
type="LED",
cal=True,
level=12))
dfl.append(
dict(path=base_path_pe.format(4346),
type="LED",
cal=True,
level=13))
dfl.append(
dict(path=base_path_pe.format(4347),
type="LED",
cal=True,
level=14))
dfl.append(
dict(path=base_path_pe.format(4348),
type="LED",
cal=True,
level=15))
dfl.append(
dict(path=base_path_pe.format(4349),
type="LED",
cal=True,
level=16))
dfl.append(
dict(path=base_path_pe.format(4350),
type="LED",
cal=True,
level=17))
dfl.append(
dict(path=base_path_pe.format(4351),
type="LED",
cal=True,
level=18))
dfl.append(
dict(path=base_path_pe.format(4352),
type="LED",
cal=True,
level=19))
dfl.append(
dict(path=base_path_pe.format(4353),
type="LED",
cal=True,
level=20))
dfl.append(
dict(path=base_path_pe.format(4354),
type="LED",
cal=True,
level=21))
dfl.append(
dict(path=base_path_pe.format(4355),
type="LED",
cal=True,
level=22))
dfl.append(
dict(path=base_path_pe.format(4356),
type="LED",
cal=True,
level=23))
dfl.append(
dict(path=base_path_pe.format(4357),
type="LED",
cal=True,
level=24))
dfl.append(
dict(path=base_path_pe.format(4358),
type="LED",
cal=True,
level=25))
dfl.append(
dict(path=base_path_pe.format(4359),
type="LED",
cal=True,
level=26))
dfl.append(
dict(path=base_path_pe.format(4360),
type="LED",
cal=True,
level=27))
dfl.append(
dict(path=base_path_pe.format(4361),
type="LED",
cal=True,
level=28))
dfl.append(
dict(path=base_path_pe.format(4362),
type="LED",
cal=True,
level=29))
dfl.append(
dict(path=base_path_pe.format(4363),
type="LED",
cal=True,
level=30))
dfl.append(
dict(path=base_path_pe.format(4364),
type="LED",
cal=True,
level=31))
dfl.append(
dict(path=base_path_pe.format(4365),
type="LED",
cal=True,
level=32))
dfl.append(
dict(path=base_path_pe.format(4366),
type="LED",
cal=True,
level=33))
dfl.append(
dict(path=base_path_pe.format(4367),
type="LED",
cal=True,
level=34))
dfl.append(
dict(path=base_path_pe.format(4368),
type="LED",
cal=True,
level=35))
dfl.append(
dict(path=base_path_pe.format(4369),
type="LED",
cal=True,
level=36))
dfl.append(
dict(path=base_path_pe.format(4370),
type="LED",
cal=True,
level=37))
dfl.append(
dict(path=base_path_pe.format(4371),
type="LED",
cal=True,
level=38))
dfl.append(
dict(path=base_path_pe.format(4372),
type="LED",
cal=True,
level=39))
for d in dfl:
d['reader'] = TargetioFileReader(input_path=d['path'], **kwargs)
self.df_file = pd.DataFrame(dfl)
cleaner = CHECMWaveformCleanerAverage(**kwargs)
extractor = AverageWfPeakIntegrator(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=extractor,
cleaner=cleaner,
**kwargs)
self.dead = Dead()
self.dummy_event = dfl[0]['reader'].get_event(0)
telid = list(self.dummy_event.r0.tels_with_data)[0]
r1 = self.dummy_event.r1.tel[telid].pe_samples[0]
self.n_pixels, self.n_samples = r1.shape
script = "checm_paper_led"
self.p_scatter_led = Scatter(**kwargs,
script=script,
figure_name="scatter_led",
shape='wide')
self.p_scatter_led_width = Scatter(**kwargs,
script=script,
figure_name="scatter_led_width",
shape='wide')
class FWInvestigator(Tool):
name = "FWInvestigator"
description = "Investigate the FW"
aliases = Dict(dict())
classes = List([])
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.reader = None
self.dl0 = None
self.dl1 = None
self.fitter = None
self.dead = None
self.fw_calibrator = None
directory = join(realpath(dirname(__file__)), "../targetpipe/io")
self.fw_txt_path = join(directory, "FW.txt")
self.fw_storage_path = join(directory, "FW.h5")
self.fw_storage_path_spe = join(directory, "FW_spe_LS62.h5")
self.spe_fw = 1210
self.p_attenuation = None
self.p_pe = None
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
filepath = '/Volumes/gct-jason/data/170314/spe/Run00073_r1_adc.tio'
self.reader = TargetioFileReader(input_path=filepath, **kwargs)
cleaner = CHECMWaveformCleanerAverage(**kwargs)
extractor = AverageWfPeakIntegrator(**kwargs)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=extractor,
cleaner=cleaner,
**kwargs)
self.fitter = CHECMSPEFitter(**kwargs)
self.fitter.range = [-30, 160]
self.dead = Dead()
self.fw_calibrator = FWCalibrator(**kwargs)
script = "filter_wheel"
self.p_attenuation = Scatter(**kwargs, script=script, figure_name="attenuation")
self.p_pe = Scatter(**kwargs, script=script, figure_name="pe")
# self.p_tmspe = TMSPEFitPlotter(**kwargs, script=script, figure_name="spe_fit_tm24")
# self.p_tmspe_pe = TMSPEFitPlotter(**kwargs, script=script, figure_name="spe_fit_tm24_pe")
# self.p_adc2pe = ADC2PEPlotter(**kwargs, script=script, figure_name="adc2pe", shape='wide')
# self.p_adc2pe_1100tm = ADC2PE1100VTMPlotter(**kwargs, script=script, figure_name="adc2pe_1100V_tms", shape='wide')
# self.p_adc2pe_1100tm_stats = ADC2PE1100VTMStatsPlotter(**kwargs, script=script, figure_name="adc2pe_1100V_tms_stats", shape='wide')
def start(self):
n_events = self.reader.num_events
first_event = self.reader.get_event(0)
telid = list(first_event.r0.tels_with_data)[0]
n_pixels, n_samples = first_event.r1.tel[telid].pe_samples[0].shape
dl1 = np.zeros((n_events, n_pixels))
lambda_ = np.zeros(n_pixels)
source = self.reader.read()
desc = "Looping through file"
for event in tqdm(source, total=n_events, desc=desc):
index = event.count
self.dl0.reduce(event)
self.dl1.calibrate(event)
dl1[index] = event.dl1.tel[telid].image
desc = "Fitting pixels"
for pix in trange(n_pixels, desc=desc):
if not self.fitter.apply(dl1[:, pix]):
self.log.warning("Pixel {} couldn't be fit".format(pix))
continue
lambda_[pix] = self.fitter.coeff['lambda_']
lambda_ = self.dead.mask1d(lambda_)
avg_lamda = np.mean(lambda_)
columns = ['position', 'attenuation_mean', 'attenuation_rms']
df = pd.read_table(self.fw_txt_path, sep=' ', names=columns, usecols=[0, 1, 2], skiprows=1)
df = df.groupby('position').apply(np.mean)
self.fw_calibrator.df = df
self.fw_calibrator.save(self.fw_storage_path)
self.fw_calibrator.set_calibration(self.spe_fw, avg_lamda)
df = self.fw_calibrator.df
x = df['position']
y = df['attenuation_mean']
y_err = df['attenuation_rms']
self.p_attenuation.create(x, y, y_err, '', "Postion", "Attenuation", "Filter Wheel Attenuation")
x = df['position']
y = df['pe']
y_err = df['pe_err']
self.p_pe.create(x, y, y_err, '', "Postion", "Illumination (p.e.)", "Filter Wheel Calibrated")
self.p_pe.ax.set_yscale('log')
self.p_pe.ax.get_yaxis().set_major_formatter(FuncFormatter(lambda y, _: '{:g}'.format(y)))
def finish(self):
# Save figures
self.p_attenuation.save()
self.p_pe.save()
self.fw_calibrator.save(self.fw_storage_path_spe)
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
filepath = '/Volumes/gct-jason/data/170322/led/Run04345_r0.tio'
self.reader_led = TargetioFileReader(input_path=filepath, **kwargs)
filepath = '/Volumes/gct-jason/data/170320/linearity/Run04167_r0.tio'
self.reader_laser = TargetioFileReader(input_path=filepath, **kwargs)
extractor = LocalPeakIntegrator(**kwargs)
self.r1_led = TargetioR1Calibrator(
pedestal_path=
'/Volumes/gct-jason/data/170322/pedestal/Run04240_ped.tcal',
tf_path='/Volumes/gct-jason/data/170322/tf/Run04277-04327_tf.tcal',
pe_path=
'/Users/Jason/Software/CHECAnalysis/targetpipe/adc2pe/adc2pe_800gm_c1.tcal',
**kwargs,
)
self.r1_laser = TargetioR1Calibrator(
pedestal_path=
'/Volumes/gct-jason/data/170320/pedestal/Run04109_ped.tcal',
tf_path='/Volumes/gct-jason/data/170320/tf/Run04110-04159_tf.tcal',
pe_path=
'/Users/Jason/Software/CHECAnalysis/targetpipe/adc2pe/adc2pe_1100.tcal',
**kwargs,
)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(extractor=extractor, **kwargs)
self.dead = Dead()
self.n_events_led = self.reader_led.num_events
first_event = self.reader_led.get_event(0)
telid = list(first_event.r0.tels_with_data)[0]
r1 = first_event.r1.tel[telid].pe_samples[0]
self.n_pixels, self.n_samples = r1.shape
p_kwargs = kwargs
p_kwargs['script'] = "checm_paper_timing"
p_kwargs['figure_name'] = "led_eid_vs_fci"
self.p_led_eidvsfci = Scatter(**p_kwargs, shape='wide')
p_kwargs['figure_name'] = "led_time_vs_tack"
self.p_led_timevstack = Scatter(**p_kwargs, shape='wide')
p_kwargs['figure_name'] = "led_bp_vs_tack"
self.p_led_bpvstack = Hist2D(**p_kwargs, shape='wide')
p_kwargs['figure_name'] = "led_eid_vs_t"
self.p_led_eidvst = Hist2D(**p_kwargs, shape='wide')
p_kwargs['figure_name'] = "led_eid_vs_tgrad"
self.p_led_eidvstgrad = Hist2D(**p_kwargs, shape='wide')
p_kwargs['figure_name'] = "led_t_vs_tgrad"
self.p_led_tvstgrad = Hist2D(**p_kwargs, shape='wide')
p_kwargs['figure_name'] = "led_t_vs_charge"
self.p_led_tvscharge = Hist2D(**p_kwargs, shape='wide')
p_kwargs['figure_name'] = "led_tgrad_vs_charge"
self.p_led_tgradvscharge = Hist2D(**p_kwargs, shape='wide')
p_kwargs['figure_name'] = "led_1D_comparison_eid{}_wavg".format(
self.eoi)
self.p_led_1deoicomp_wavg = WaveformHist1D(**p_kwargs, shape='wide')
p_kwargs['figure_name'] = "led_1D_comparison_allevents_wavg".format(
self.eoi)
self.p_led_1dcomp_wavg = WaveformHist1D(**p_kwargs, shape='wide')
p_kwargs['figure_name'] = "led_1D_comparison_eid{}".format(self.eoi)
self.p_led_1deoicomp = WaveformHist1DInt(**p_kwargs, shape='wide')
p_kwargs['figure_name'] = "led_1D_comparison_allevents".format(
self.eoi)
self.p_led_1dcomp = WaveformHist1D(**p_kwargs, shape='wide')
p_kwargs['figure_name'] = "led_image_tgrad_eid{}".format(self.eoi)
self.p_led_imageeoitgrad = ImagePlotter(**p_kwargs)
p_kwargs['figure_name'] = "laser_1D_comparison_eid{}_wavg".format(
self.eoi)
self.p_laser_1deoicomp_wavg = WaveformHist1D(**p_kwargs, shape='wide')
p_kwargs['figure_name'] = "laser_1D_comparison_allevents_wavg".format(
self.eoi)
self.p_laser_1dcomp_wavg = WaveformHist1D(**p_kwargs, shape='wide')
p_kwargs['figure_name'] = "laser_1D_comparison_eid{}".format(self.eoi)
self.p_laser_1deoicomp = WaveformHist1DInt(**p_kwargs, shape='wide')
p_kwargs['figure_name'] = "laser_1D_comparison_allevents".format(
self.eoi)
self.p_laser_1dcomp = WaveformHist1D(**p_kwargs, shape='wide')
p_kwargs['figure_name'] = "laser_1D_finalmethod"
self.p_laser_1d_final = WaveformHist1D(**p_kwargs, shape='square')
p_kwargs['figure_name'] = "laser_1D_finalmethod_pix"
self.p_laser_1d_final_pix = WaveformHist1D(**p_kwargs, shape='square')
p_kwargs['figure_name'] = "laser_image_tgrad_eid{}".format(self.eoi)
self.p_laser_imageeoitgrad = ImagePlotter(**p_kwargs)
p_kwargs['figure_name'] = "laser_fwhm_allevents"
self.p_laser_fwhm = WaveformHist1DInt(**p_kwargs, shape='wide')
