straxen.plugins.led_cal package

Submodules

straxen.plugins.led_cal.led_calibration module

Dear nT analyser, if you want to complain please contact:

class straxen.plugins.led_cal.led_calibration.LEDCalibration[source]

Bases: Plugin

Preliminary version, several parameters to set during commissioning. LEDCalibration returns: channel, time, dt, length, Area, amplitudeLED and amplitudeNOISE.

The new variables are:

  • Area: Area computed in the given window, averaged over 6 windows that have the same starting sample and different end samples.

  • amplitudeLED: peak amplitude of the LED on run in the given window.

  • amplitudeNOISE: amplitude of the LED on run in a window far from the signal one.

baseline_window

Dispatch on URL protocol.

unrecognized protocol returns identity inspired by dasks Dispatch and fsspec fs protocols.

channel_list

Dispatch on URL protocol.

unrecognized protocol returns identity inspired by dasks Dispatch and fsspec fs protocols.

compressor = 'zstd'
compute(raw_records)[source]

The data for LED calibration are build for those PMT which belongs to channel list.

This is used for the different ligh levels. As defaul value all the PMTs are considered.

config: Dict
data_kind: str | immutabledict | dict = 'led_cal'
depends_on: tuple = 'raw_records'
deps: Dict
dtype: tuple | dtype | immutabledict | dict = [('area', <class 'numpy.float32'>, 'Area averaged in integration windows'), ('amplitude_led', <class 'numpy.float32'>, 'Amplitude in LED window'), ('amplitude_noise', <class 'numpy.float32'>, 'Amplitude in off LED window'), ('channel', <class 'numpy.int16'>, 'Channel'), ('time', <class 'numpy.int64'>, 'Start time of the interval (ns since unix epoch)'), ('dt', <class 'numpy.int16'>, 'Time resolution in ns'), ('length', <class 'numpy.int32'>, 'Length of the interval in samples')]
input_buffer: Dict[str, Chunk]
led_window

Dispatch on URL protocol.

unrecognized protocol returns identity inspired by dasks Dispatch and fsspec fs protocols.

noise_window

Dispatch on URL protocol.

unrecognized protocol returns identity inspired by dasks Dispatch and fsspec fs protocols.

parallel: str | bool = 'process'
provides: tuple = ('led_calibration',)
rechunk_on_save = False
run_i: int
run_id: str
takes_config = immutabledict({'baseline_window': <straxen.url_config.URLConfig object>, 'led_window': <straxen.url_config.URLConfig object>, 'noise_window': <straxen.url_config.URLConfig object>, 'channel_list': <straxen.url_config.URLConfig object>})
class straxen.plugins.led_cal.led_calibration.nVetoExtTimings[source]

Bases: Plugin

Plugin which computes the time difference delta_time from pulse timing of hitlets_nv to start time of raw_records which belong the hitlets_nv.

They are used as the external trigger timings.

static calc_delta_time(ext_timings_nv_delta_time, pulses, hitlets_nv, nv_pmt_start, nv_pmt_stop)[source]

Numpy access with fancy index returns copy, not view This for-loop is required to substitute in one by one.

channel_map

Dispatch on URL protocol.

unrecognized protocol returns identity inspired by dasks Dispatch and fsspec fs protocols.

compressor = 'zstd'
compute(hitlets_nv, raw_records_nv)[source]
config: Dict
data_kind: str | immutabledict | dict = 'hitlets_nv'
depends_on: tuple = ('raw_records_nv', 'hitlets_nv')
deps: Dict
dtype: tuple | dtype | immutabledict | dict
infer_dtype()[source]

Return dtype of computed data; used only if no dtype attribute defined.

input_buffer: Dict[str, Chunk]
provides: tuple = ('ext_timings_nv',)
static pulse_dtype()[source]
run_i: int
run_id: str
setup()[source]

Hook if plugin wants to do something on initialization.

takes_config = immutabledict({'channel_map': <straxen.url_config.URLConfig object>})

Module contents