km3modules.k40

A collection of k40 related functions and modules.

Module Contents

Classes

K40BackgroundSubtractor	Subtracts random coincidence background from K40 data
IntraDOMCalibrator	Intra DOM calibrator which performs the calibration from K40Counts.
TwofoldCounter	Counts twofold coincidences in timeslice hits per PMT combination.
HRVFIFOTimesliceFilter	Creat a frame index lookup table which holds DOM IDs of frames with
SummaryMedianPMTRateService	The module which can be attached to the pipeline
MedianPMTRatesService	The module which can be attached to the pipeline
ResetTwofoldCounts	The module which can be attached to the pipeline

Functions

calibrate_dom(dom_id, data, detector[, livetime, ...])	Calibrate intra DOM PMT time offsets, efficiencies and sigmas
calculate_weights(fitted_rates, data)
load_k40_coincidences_from_hdf5(filename, dom_id)	Load k40 coincidences from hdf5 file
load_k40_coincidences_from_rootfile(filename, dom_id)	Load k40 coincidences from JMonitorK40 ROOT file
gaussian(x, mean, sigma, rate, offset)
gaussian_wo_offset(x, mean, sigma, rate)
fit_delta_ts(data, livetime[, fit_background])	Fits gaussians to delta t for each PMT pair.
calculate_angles(detector, combs)	Calculates angles between PMT combinations according to positions in
exponential_polinomial(x, p1, p2, p3, p4)
exponential(x, a, b)
fit_angular_distribution(angles, rates, rate_errors[, ...])	Fits angular distribution of rates.
minimize_t0s(means, weights, combs)	Varies t0s to minimize the deviation of the gaussian means from zero.
minimize_sigmas(sigmas, weights, combs)	Varies sigmas to minimize gaussian sigma12 - sqrt(sigma1² + sigma2²).
minimize_qes(fitted_rates, rates, weights, combs)	Varies QEs to minimize the deviation of the rates from the fitted_rates.
correct_means(means, opt_t0s, combs)	Applies optimal t0s to gaussians means.
correct_rates(rates, opt_qes, combs)	Applies optimal qes to rates.
calculate_rms_means(means, corrected_means)	Calculates RMS of means from zero before and after correction
calculate_rms_rates(rates, fitted_rates, corrected_rates)	Calculates RMS of rates from fitted_rates before and after correction
get_comb_index(i, j)	Return the index of PMT pair combinations
add_to_twofold_matrix(times, tdcs, mat[, tmax])	Add counts to twofold coincidences for a given tmax.

Attributes

log
jit
TIMESLICE_LENGTH
MC_ANG_DIST

km3modules.k40.log

km3modules.k40.jit

km3modules.k40.TIMESLICE_LENGTH = 0.1

km3modules.k40.MC_ANG_DIST

class km3modules.k40.K40BackgroundSubtractor(name=None, **parameters)

Subtracts random coincidence background from K40 data

Notes

Requires servce ‘MedianPMTRates()’ Writes ‘K40Counts’ into the Blob: dict, Corrected K40 counts

configure()

Configure module, like instance variables etc.

process(blob)

Knead the blob and return it

get_corrected_counts()

subtract_background()

finish()

Clean everything up.

dump(mean_rates, corrected_counts, livetime)

class km3modules.k40.IntraDOMCalibrator(name=None, **parameters)

Intra DOM calibrator which performs the calibration from K40Counts.

Parameters:

det_id: int

Detector ID [default: 14]

ctmin: float

Minimum cos(angle)

mode: str (‘offline’ | ‘online’)

Calibration mode [default: ‘online’]

Notes

Requires ‘TwofoldCounts’: dict

(key=dom_id, value=matrix of k40 counts 465x(dt*2+1))

Requires ‘CorrectedTwofoldCounts’: dict

(key=dom_id, value=matrix of k40 counts 465x(dt*2+1))

Writes ‘IntraDOMCalibration’ into the blob: dict (key=dom_id, value=calibration)

configure()

Configure module, like instance variables etc.

process(blob)

Knead the blob and return it

calibrate(twofold_counts, fit_background=False)

finish()

Clean everything up.

class km3modules.k40.TwofoldCounter(name=None, **parameters)

Counts twofold coincidences in timeslice hits per PMT combination.

Parameters:

‘tmax’: int

time window of twofold coincidences [ns]

‘dump_filename’: str

name for the dump file

Notes

Requires the key ‘TSHits’: RawHitSeries Provides the following services: ‘TwofoldCounts’: dict (key=dom_id, value=matrix (465,(dt*2+1))) ‘ResetTwofoldCounts’: reset the TwofoldCounts dict ‘GetLivetime()’: dict (key=dom_id, value=float) ‘DumpTwofoldCounts’: Writes twofold counts into ‘dump_filename’

configure()

Configure module, like instance variables etc.

reset()

Reset coincidence counter

get_livetime()

process(blob)

Knead the blob and return it

dump()

Write coincidence counts into a Python pickle

class km3modules.k40.HRVFIFOTimesliceFilter(name=None, **parameters)

Creat a frame index lookup table which holds DOM IDs of frames with at least one PMT in HRV.

configure()

Configure module, like instance variables etc.

get_skipped_frames()

class km3modules.k40.SummaryMedianPMTRateService(name=None, **parameters)

The module which can be attached to the pipeline

configure()

Configure module, like instance variables etc.

get_median_rates()

class km3modules.k40.MedianPMTRatesService(name=None, **parameters)

The module which can be attached to the pipeline

configure()

Configure module, like instance variables etc.

process(blob)

Knead the blob and return it

get_median_rates()

class km3modules.k40.ResetTwofoldCounts(name=None, **parameters)

The module which can be attached to the pipeline

process(blob)

Knead the blob and return it

km3modules.k40.calibrate_dom(dom_id, data, detector, livetime=None, fit_ang_dist=False, scale_mc_to_data=True, ad_fit_shape='pexp', fit_background=True, ctmin=-1.0)

Calibrate intra DOM PMT time offsets, efficiencies and sigmas

Parameters:

dom_id: DOM ID

data: dict of coincidences or root or hdf5 file

detector: instance of detector class

livetime: data-taking duration [s]

fixed_ang_dist: fixing angular distribution e.g. for data mc comparison

auto_scale: auto scales the fixed angular distribution to the data

Returns:

return_data: dictionary with fit results

km3modules.k40.calculate_weights(fitted_rates, data)

km3modules.k40.load_k40_coincidences_from_hdf5(filename, dom_id)

Load k40 coincidences from hdf5 file

Parameters:

filename: filename of hdf5 file

dom_id: DOM ID

Returns:

data: numpy array of coincidences

livetime: duration of data-taking

km3modules.k40.load_k40_coincidences_from_rootfile(filename, dom_id)

Load k40 coincidences from JMonitorK40 ROOT file

Parameters:

filename: root file produced by JMonitorK40

dom_id: DOM ID

Returns:

data: numpy array of coincidences

dom_weight: weight to apply to coincidences to get rate in Hz

km3modules.k40.gaussian(x, mean, sigma, rate, offset)

km3modules.k40.gaussian_wo_offset(x, mean, sigma, rate)

km3modules.k40.fit_delta_ts(data, livetime, fit_background=True)

Fits gaussians to delta t for each PMT pair.

Parameters:

data: 2d np.array: x = PMT combinations (465), y = time, entry = frequency

livetime: length of data taking in seconds

fit_background: if True: fits gaussian with offset, else without offset

Returns:

numpy arrays with rates and means for all PMT combinations

km3modules.k40.calculate_angles(detector, combs)

Calculates angles between PMT combinations according to positions in detector_file

Parameters:

detector: detector description (kp.hardware.Detector)

combs: pmt combinations

Returns:

angles: numpy array of angles between all PMT combinations

km3modules.k40.exponential_polinomial(x, p1, p2, p3, p4)

km3modules.k40.exponential(x, a, b)

km3modules.k40.fit_angular_distribution(angles, rates, rate_errors, shape='pexp')

Fits angular distribution of rates.

Parameters:

rates: numpy array

with rates for all PMT combinations

angles: numpy array

with angles for all PMT combinations

shape:

which function to fit; exp for exponential or pexp for exponential_polinomial

Returns:

fitted_rates: numpy array of fitted rates (fit_function(angles, popt…))

km3modules.k40.minimize_t0s(means, weights, combs)

Varies t0s to minimize the deviation of the gaussian means from zero.

Parameters:

means: numpy array of means of all PMT combinations

weights: numpy array of weights for the squared sum

combs: pmt combinations to use for minimization

Returns:

opt_t0s: optimal t0 values for all PMTs

km3modules.k40.minimize_sigmas(sigmas, weights, combs)

Varies sigmas to minimize gaussian sigma12 - sqrt(sigma1² + sigma2²).

Parameters:

sigmas: numpy array of fitted sigmas of gaussians

weights: numpy array of weights for the squared sum

combs: pmt combinations to use for minimization

Returns:

opt_sigmas: optimal sigma values for all PMTs

km3modules.k40.minimize_qes(fitted_rates, rates, weights, combs)

Varies QEs to minimize the deviation of the rates from the fitted_rates.

Parameters:

fitted_rates: numpy array of fitted rates from fit_angular_distribution

rates: numpy array of rates of all PMT combinations

weights: numpy array of weights for the squared sum

combs: pmt combinations to use for minimization

Returns:

opt_qes: optimal qe values for all PMTs

km3modules.k40.correct_means(means, opt_t0s, combs)

Applies optimal t0s to gaussians means.

Should be around zero afterwards.

Parameters:

means: numpy array of means of gaussians of all PMT combinations

opt_t0s: numpy array of optimal t0 values for all PMTs

combs: pmt combinations used to correct

Returns:

corrected_means: numpy array of corrected gaussian means for all PMT combs

km3modules.k40.correct_rates(rates, opt_qes, combs)

Applies optimal qes to rates.

Should be closer to fitted_rates afterwards.

Parameters:

rates: numpy array of rates of all PMT combinations

opt_qes: numpy array of optimal qe values for all PMTs

combs: pmt combinations used to correct

Returns:

corrected_rates: numpy array of corrected rates for all PMT combinations

km3modules.k40.calculate_rms_means(means, corrected_means)

Calculates RMS of means from zero before and after correction

Parameters:

means: numpy array of means of gaussians of all PMT combinations

corrected_means: numpy array of corrected gaussian means for all PMT combs

Returns:

rms_means: RMS of means from zero

rms_corrected_means: RMS of corrected_means from zero

km3modules.k40.calculate_rms_rates(rates, fitted_rates, corrected_rates)

Calculates RMS of rates from fitted_rates before and after correction

Parameters:

rates: numpy array of rates of all PMT combinations

corrected_rates: numpy array of corrected rates for all PMT combinations

Returns:

rms_rates: RMS of rates from fitted_rates

rms_corrected_rates: RMS of corrected_ratesrates from fitted_rates

km3modules.k40.get_comb_index(i, j)

Return the index of PMT pair combinations

km3modules.k40.add_to_twofold_matrix(times, tdcs, mat, tmax=10)

Add counts to twofold coincidences for a given tmax.

Parameters:

times: np.ndarray of hit times (int32)

tdcs: np.ndarray of channel_ids (uint8)

mat: ref to a np.array((465, tmax * 2 + 1))

tmax: int (time window)

Returns:

mat: coincidence matrix (np.array((465, tmax * 2 + 1)))