def test_averagewf_peak_integration(camera_waveforms):
waveforms, _ = camera_waveforms
integrator = AverageWfPeakIntegrator()
charge, _, _ = integrator.extract_charge(waveforms)
assert_allclose(charge[0][0], 232.559, rtol=1e-3)
assert_allclose(charge[1][0], 425.406, rtol=1e-3)
def test_averagewf_peak_integration(example_event):
telid = list(example_event.r0.tel)[0]
data = example_event.r0.tel[telid].waveform
nsamples = data.shape[2]
ped = example_event.mc.tel[telid].pedestal
data_ped = data - np.atleast_3d(ped / nsamples)
data_ped = np.array([data_ped[0], data_ped[0]]) # Test LG functionality
integrator = AverageWfPeakIntegrator()
integration, peakpos, window = integrator.extract_charge(data_ped)
assert_almost_equal(integration[0][0], 73, 0)
assert_almost_equal(integration[1][0], 73, 0)
def test_averagewf_peak_integration():
telid = 11
event = get_test_event()
data = event.r0.tel[telid].adc_samples
nsamples = data.shape[2]
ped = event.mc.tel[telid].pedestal
data_ped = data - np.atleast_3d(ped/nsamples)
data_ped = np.array([data_ped[0], data_ped[0]]) # Test LG functionality
integrator = AverageWfPeakIntegrator(None, None)
integration, peakpos, window = integrator.extract_charge(data_ped)
assert_almost_equal(integration[0][0], 73, 0)
assert_almost_equal(integration[1][0], 73, 0)
assert peakpos[0][0] == 10
assert peakpos[1][0] == 10
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)
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.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 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 test_averagewf_peak_integration():
telid = 11
event = get_test_event()
data = event.r0.tel[telid].adc_samples
nsamples = data.shape[2]
ped = event.mc.tel[telid].pedestal
data_ped = data - np.atleast_3d(ped/nsamples)
data_ped = np.array([data_ped[0], data_ped[0]]) # Test LG functionality
integrator = AverageWfPeakIntegrator(None, None)
integration, peakpos, window = integrator.extract_charge(data_ped)
assert_almost_equal(integration[0][0], 73, 0)
assert_almost_equal(integration[1][0], 73, 0)
assert peakpos[0][0] == 10
assert peakpos[1][0] == 10
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)
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.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):
kwargs = dict(config=self.config, tool=self)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(**kwargs)
self.cal = CameraCalibrator(r1_product=self.calibrator)
self.cross = CrossCorrelation()
self.glob_peak = GlobalPeakIntegrator()
self.local_peak = LocalPeakIntegrator()
self.neighbour = NeighbourPeakIntegrator()
self.aver = AverageWfPeakIntegrator()
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)
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)
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)
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)
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 get_extractor(self):
return AverageWfPeakIntegrator(None, self.parent,
window_width=self.window_width,
window_shift=self.window_shift)
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')
