"""
Module that simulates smearing effects of the light incident on each
photodetector
"""
import numba as nb
from numba import cuda
from numba import float32
import numpy as np
import cupy as cp
from math import ceil, floor, exp, sqrt, sin
import h5py
from .consts import light
#from .consts.light import LIGHT_TICK_SIZE, LIGHT_WINDOW, SINGLET_FRACTION, TAU_S, TAU_T, LIGHT_GAIN, LIGHT_OSCILLATION_PERIOD, LIGHT_RESPONSE_TIME, LIGHT_DET_NOISE_SAMPLE_SPACING, LIGHT_TRIG_MODE, LIGHT_TRIG_THRESHOLD, LIGHT_TRIG_WINDOW, LIGHT_DIGIT_SAMPLE_SPACING, LIGHT_NBIT, OP_CHANNEL_TO_TPC, OP_CHANNEL_PER_TRIG, TPC_TO_OP_CHANNEL, SIPM_RESPONSE_MODEL, IMPULSE_TICK_SIZE, IMPULSE_MODEL, MC_TRUTH_THRESHOLD, ENABLE_LUT_SMEARING
#from .consts.detector import TPC_BORDERS, MODULE_TO_TPCS, TPC_TO_MODULE
from .consts import units
from .consts import sim
from .consts import detector
[docs]
def get_nticks(light_incidence):
"""
Calculates the number of time ticks needed to simulate light signals of the
event (plus the desired pre- and post-intervals)
Args:
light_incidence(array): shape `(ntracks, ndet)`, containing first hit time and number of photons on each detector
Returns:
tuple: number of time ticks (`int`), time of first tick (`float`) [in microseconds]
"""
mask = light_incidence['n_photons_det'] > 0
# only use the first photon arrival time if it's threshold trigger
if np.any(mask) and light.LIGHT_TRIG_MODE == 0:
start_time = np.min(light_incidence['t0_det'][mask]) - light.LIGHT_WINDOW[0]
end_time = np.max(light_incidence['t0_det'][mask]) + light.LIGHT_WINDOW[1]
return int(np.ceil((end_time - start_time)/light.LIGHT_TICK_SIZE)), start_time
return int((light.LIGHT_WINDOW[1] + light.LIGHT_WINDOW[0])/light.LIGHT_TICK_SIZE), 0
[docs]
def get_active_op_channel(light_incidence):
"""
Returns an array of optical channels that need to be simulated
Args:
light_incidence(array): shape `(ntracks, ndet)`, containing first hit time and number of photons on each detector
Returns:
array: shape `(ndet_active,)` op detector index of each active channel (`int`)
"""
mask = light_incidence['n_photons_det'] > 0
if np.any(mask):
return cp.array(np.where(np.any(mask, axis=0))[0], dtype='i4')
return cp.empty((0,), dtype='i4')
@cuda.jit
def sum_light_signals(segments, segment_voxel, segment_track_id, light_inc, op_channel, lut, start_time, light_sample_inc, light_sample_inc_true_track_id, light_sample_inc_true_photons, sorted_indices, t0_profile_length):
"""
Sums the number of photons observed by each light detector at each time tick
Args:
segments(array): shape `(ntracks,)`, edep-sim tracks to simulate
segment_voxel(array): shape `(ntracks, 3)`, LUT voxel for eack edep-sim track
segment_track_id(array): shape `(ntracks,)`, unique id for each track segment (for MC truth backtracking)
light_inc(array): shape `(ntracks, ndet)`, number of photons incident on each detector and voxel id
op_channel(array): shape `(ntracks, ndet_active)`, optical channel index, will use lut[:,:,:,op_channel%lut.shape[3]] to look up timing information
lut(array): shape `(nx,ny,nz,ndet_tpc)`, light look up table
start_time(float): start time of light simulation in microseconds
light_sample_inc(array): output array, shape `(ndet, nticks)`, number of photons incident on each detector at each time tick (propogation delay only)
light_sample_inc_true_track_id(array): output array, shape `(ndet, nticks, maxtracks)`, true track ids on each detector at each time tick (propogation delay only)
light_sample_inc_true_photons(array): output array, shape `(ndet, nticks, maxtracks)`, number of photons incident on each detector at each time tick from each track
sorted_indices(array): shape `(maxtracks,)`, indices of segments sorted by how much light they contribute
"""
idet,itick = cuda.grid(2)
if idet < light_sample_inc.shape[0]:
if itick < light_sample_inc.shape[1]:
start_tick_time = itick * light.LIGHT_TICK_SIZE + start_time
end_tick_time = start_tick_time + light.LIGHT_TICK_SIZE
# find tracks that contribute light to this time tick
idet_lut = op_channel[idet] % lut.shape[3]
for itrk in sorted_indices[idet]:
if light_inc[itrk,op_channel[idet]]['n_photons_det'] > 0:
voxel = segment_voxel[itrk]
track_time = segments[itrk]['t0']
track_end_time = track_time + t0_profile_length * units.ns / units.mus # FIXME: assumes light LUT time profile bins are 1ns (might not be true in general)
if track_end_time < start_tick_time or track_time > end_tick_time:
continue
# use LUT time smearing
if light.ENABLE_LUT_SMEARING:
time_profile = lut[voxel[0],voxel[1],voxel[2],idet_lut]['time_dist'] # normalised
# add photons to time tick
for iprof in range(time_profile.shape[0]):
profile_time = track_time + iprof * units.ns / units.mus # FIXME: assumes light LUT time profile bins are 1ns (might not be true in general)
if profile_time < end_tick_time and profile_time > start_tick_time:
photons = light_inc['n_photons_det'][itrk,op_channel[idet]] * float32(time_profile[iprof]) / light.LIGHT_TICK_SIZE
light_sample_inc[idet,itick] += photons
if photons > sim.MC_TRUTH_THRESHOLD:
# get truth information for time tick
for itrue in range(light_sample_inc_true_track_id.shape[-1]):
if light_sample_inc_true_track_id[idet,itick,itrue] == -1 or light_sample_inc_true_track_id[idet,itick,itrue] == segment_track_id[itrk]:
light_sample_inc_true_track_id[idet,itick,itrue] = segment_track_id[itrk]
light_sample_inc_true_photons[idet,itick,itrue] += photons
break
# use average time only
else:
# calculate average delay time
t0_avg = lut[voxel[0],voxel[1],voxel[2],idet_lut]['t0_avg'] * units.ns / units.mus # normalised averagein us
# add photons to time tick
profile_time = track_time + t0_avg
if profile_time < end_tick_time and profile_time > start_tick_time:
photons = light_inc['n_photons_det'][itrk,op_channel[idet]] / light.LIGHT_TICK_SIZE
light_sample_inc[idet,itick] += photons
if photons > sim.MC_TRUTH_THRESHOLD:
# get truth information for time tick
for itrue in range(light_sample_inc_true_track_id.shape[-1]):
if light_sample_inc_true_track_id[idet,itick,itrue] == -1 or light_sample_inc_true_track_id[idet,itick,itrue] == segment_track_id[itrk]:
light_sample_inc_true_track_id[idet,itick,itrue] = segment_track_id[itrk]
light_sample_inc_true_photons[idet,itick,itrue] += photons
break
@nb.njit
def scintillation_model(time_tick):
"""
Calculates the fraction of scintillation photons emitted
during time interval `time_tick` to `time_tick + 1`
Args:
time_tick(int): time tick relative to t0
Returns:
float: fraction of scintillation photons
"""
p1 = light.SINGLET_FRACTION * exp(-time_tick * light.LIGHT_TICK_SIZE / light.TAU_S) * (1 - exp(-light.LIGHT_TICK_SIZE / light.TAU_S))
p3 = (1 - light.SINGLET_FRACTION) * exp(-time_tick * light.LIGHT_TICK_SIZE / light.TAU_T) * (1 - exp(-light.LIGHT_TICK_SIZE / light.TAU_T))
return (p1 + p3) * (time_tick >= 0)
@nb.njit
def scintillation_array(scint_model):
"""
Calculates the fraction of scintillation photons emitted
during time interval `time_tick` to `time_tick + 1` for
the entire input array.
Args:
scint_model: array to store result
"""
for time_tick in range(scint_model.shape[0]):
p1 = light.SINGLET_FRACTION * exp(-time_tick * light.LIGHT_TICK_SIZE / light.TAU_S) * (1 - exp(-light.LIGHT_TICK_SIZE / light.TAU_S))
p3 = (1 - light.SINGLET_FRACTION) * exp(-time_tick * light.LIGHT_TICK_SIZE / light.TAU_T) * (1 - exp(-light.LIGHT_TICK_SIZE / light.TAU_T))
scint_model[time_tick] = p1 + p3
@cuda.jit
def calc_scintillation_effect(light_sample_inc, light_sample_inc_true_track_id, light_sample_inc_true_photons, light_sample_inc_scint, light_sample_inc_scint_true_track_id, light_sample_inc_scint_true_photons, scint_model):
"""
Applies a smearing effect due to the liquid argon scintillation time profile using
a two decay component scintillation model.
Args:
light_sample_inc(array): shape `(ndet, ntick)`, light incident on each detector
light_sample_inc_scint(array): output array, shape `(ndet, ntick)`, light incident on each detector after accounting for scintillation time
"""
idet,itick = cuda.grid(2)
if idet < light_sample_inc.shape[0]:
if itick < light_sample_inc.shape[1]:
conv_ticks = ceil((light.LIGHT_WINDOW[1] - light.LIGHT_WINDOW[0])/light.LIGHT_TICK_SIZE)
for jtick in range(max(itick - conv_ticks, 0), itick+1):
if light_sample_inc[idet,jtick] == 0:
continue
tick_weight = scint_model[itick-jtick]
light_sample_inc_scint[idet,itick] += tick_weight * light_sample_inc[idet,jtick]
# loop over convolution tick truth
for itrue in range(light_sample_inc_true_track_id.shape[-1]):
if light_sample_inc_true_track_id[idet,jtick,itrue] == -1:
break
if tick_weight * light_sample_inc_true_photons[idet,jtick,itrue] < sim.MC_TRUTH_THRESHOLD:
continue
# loop over current tick truth
for jtrue in range(light_sample_inc_scint_true_track_id.shape[-1]):
if light_sample_inc_scint_true_track_id[idet,itick,jtrue] == light_sample_inc_true_track_id[idet,jtick,itrue] or light_sample_inc_scint_true_track_id[idet,itick,jtrue] == -1:
light_sample_inc_scint_true_track_id[idet,itick,jtrue] = light_sample_inc_true_track_id[idet,jtick,itrue]
light_sample_inc_scint_true_photons[idet,itick,jtrue] += tick_weight * light_sample_inc_true_photons[idet,jtick,itrue]
break
@nb.njit
def xoroshiro128p_poisson_int32(mean, states, index):
"""
Return poisson distributed int32 and advance `states[index]`. For efficiency,
if `mean > 30`, returns a gaussian distributed int32 with `mean == mean`
and `std = sqrt(mean)` truncated at 0 (approximately equivalent to a poisson-
distributed number)
[DOI:10.1007/978-1-4613-8643-8_10]
Args:
mean(float): mean of poisson distribution
states(array): array of RNG states
index(int): offset in states to update
"""
if mean <= 0:
return 0
if mean < 30: # poisson statistics are important
u = cuda.random.xoroshiro128p_uniform_float32(states, index)
x = 0
p = exp(-mean)
s = p
prev_s = s
while u > s:
x += 1
p = p * mean / x
prev_s = s
s = s + p
if s == prev_s: # break if machine precision reached
break
return x
return max(int(cuda.random.xoroshiro128p_normal_float32(states, index) * sqrt(mean) + mean),0)
@cuda.jit
def calc_stat_fluctuations(light_sample_inc, light_sample_inc_disc, rng_states):
"""
Simulates Poisson fluctuations in the number of PE per time tick.
Args:
light_sample_inc(array): shape `(ndet, ntick)`, effective photocurrent on each detector
light_sample_inc_disc(array): output array, shape `(ndet, ntick)`, effective photocurrent on each detector (with stocastic fluctuations)
rng_states(array): shape `(>ndet*ntick,)`, random number states
"""
idet,itick = cuda.grid(2)
if idet < light_sample_inc.shape[0]:
if itick < light_sample_inc.shape[1]:
if light_sample_inc[idet,itick] > 0:
light_sample_inc_disc[idet,itick] = 1. / light.LIGHT_TICK_SIZE * xoroshiro128p_poisson_int32(
light_sample_inc[idet,itick] * light.LIGHT_TICK_SIZE, rng_states, idet*light_sample_inc.shape[1] + itick)
else:
light_sample_inc_disc[idet,itick] = 0.
@nb.njit
def interp(idx, arr, low, high):
"""
Performs a simple linear interpolation of an array at a given floating point
index
Args:
idx(float): index into array to interpolate
arr(array): 1D array of values to interpolate
low(float): value to return if index is less than 0
high(float): value to return if index is above `len(arr)-1`
Returns:
float: interpolated array value
"""
i0 = int(floor(idx))
if i0 < 0:
return low
if i0 > len(arr)-1:
return high
if i0 == idx:
return arr[i0]
if i0 > len(arr)-2:
return high
i1 = i0 + 1
v0 = arr[i0]
v1 = arr[i1]
return v0 + (v1 - v0) * (idx - i0)
@nb.njit()
def sipm_response_model(time_tick):
"""
Calculates the SiPM response from a PE at `time_tick` relative to the PE time
Args:
idet(int): SiPM index
time_tick(int): time tick relative to t0
Returns:
float: response
"""
# use RLC response model
if light.SIPM_RESPONSE_MODEL == 0:
t = time_tick * light.LIGHT_TICK_SIZE
impulse = (t>=0) * exp(-t/light.LIGHT_RESPONSE_TIME) * sin(t/light.LIGHT_OSCILLATION_PERIOD)
# normalize to 1
impulse /= light.LIGHT_OSCILLATION_PERIOD * light.LIGHT_RESPONSE_TIME**2
impulse *= light.LIGHT_OSCILLATION_PERIOD**2 + light.LIGHT_RESPONSE_TIME**2
return impulse * light.LIGHT_TICK_SIZE
# use measured response model
if light.SIPM_RESPONSE_MODEL == 1:
impulse = interp(time_tick * light.LIGHT_TICK_SIZE / light.IMPULSE_TICK_SIZE, light.IMPULSE_MODEL, 0, 0)
# normalize to 1
impulse /= light.IMPULSE_TICK_SIZE/light.LIGHT_TICK_SIZE
return impulse
@nb.njit()
def sipm_response_array(sipm_response):
"""
Calculates the SiPM response from a PE at every `time_tick` relative to the PE time
for the given array
Args:
sipm_response: array to store response
"""
if light.SIPM_RESPONSE_MODEL == 0:
for time_tick in range(sipm_response.shape[0]):
t = time_tick * light.LIGHT_TICK_SIZE
sipm_response[time_tick] = (t>=0) * exp(-t/light.LIGHT_RESPONSE_TIME) * sin(t/light.LIGHT_OSCILLATION_PERIOD)
sipm_response /= light.LIGHT_OSCILLATION_PERIOD * light.LIGHT_RESPONSE_TIME**2
sipm_response *= light.LIGHT_OSCILLATION_PERIOD**2 + light.LIGHT_RESPONSE_TIME**2
if light.SIPM_RESPONSE_MODEL == 1:
for time_tick in range(sipm_response.shape[0]):
sipm_response[time_tick] = interp(time_tick * light.LIGHT_TICK_SIZE / light.IMPULSE_TICK_SIZE, light.IMPULSE_MODEL, 0, 0)
sipm_response /= light.IMPULSE_TICK_SIZE/light.LIGHT_TICK_SIZE
@cuda.jit
def calc_light_detector_response(light_sample_inc, light_sample_inc_true_track_id, light_sample_inc_true_photons, light_response, light_response_true_track_id, light_response_true_photons, light_gain, sipm_response):
"""
Simulates the SiPM reponse and digit
Args:
light_sample_inc(array): shape `(ndet, ntick)`, PE produced on each SiPM at each time tick
light_response(array): shape `(ndet, ntick)`, ADC value at each time tick
"""
idet,itick = cuda.grid(2)
if idet < light_sample_inc.shape[0]:
if itick < light_sample_inc.shape[1]:
conv_ticks = ceil((light.LIGHT_WINDOW[1] - light.LIGHT_WINDOW[0])/light.LIGHT_TICK_SIZE)
for jtick in range(max(itick - conv_ticks, 0), itick+1):
tick_weight = sipm_response[itick-jtick]
light_response[idet,itick] += light_gain[idet] * tick_weight * light_sample_inc[idet,jtick]
# loop over convolution tick truth
for itrue in range(light_sample_inc_true_track_id.shape[-1]):
if light_sample_inc_true_track_id[idet,jtick,itrue] == -1:
break
if abs(tick_weight * light_sample_inc_true_photons[idet,jtick,itrue]) < sim.MC_TRUTH_THRESHOLD:
continue
# loop over current tick truth
for jtrue in range(light_response_true_track_id.shape[-1]):
# apply convolution if convolution tick matches or if available truth slot
if light_sample_inc_true_track_id[idet,itick,jtrue] == light_sample_inc_true_track_id[idet,itick,itrue] or light_sample_inc_true_track_id[idet,itick,jtrue] == -1:
light_response_true_track_id[idet,itick,jtrue] = light_sample_inc_true_track_id[idet,itick,itrue]
light_response_true_photons[idet,itick,jtrue] += tick_weight * light_sample_inc_true_photons[idet,jtick,itrue]
break
[docs]
def gen_light_detector_noise(shape, light_det_noise):
"""
Generates uncorrelated noise with a defined frequency spectrum
Args:
shape(tuple): desired shape of output noise, `shape[0]` must equal `light_det_noise.shape[0]`
light_det_noise(array): FFT of noise, `light_det_noise.ndim == 2`
Returns:
array: shape `(shape[0], shape[1])`, randomly generated sample noise
"""
if not shape[0]:
return cp.empty(shape)
noise_freq = cp.fft.rfftfreq((light_det_noise.shape[-1]-1)*2, d=light.LIGHT_DET_NOISE_SAMPLE_SPACING)
desired_freq = cp.fft.rfftfreq(shape[-1], d=light.LIGHT_TICK_SIZE)
bin_size = cp.diff(desired_freq).mean()
noise_spectrum = cp.zeros((shape[0], desired_freq.shape[0]))
for idet in range(shape[0]):
noise_spectrum[idet] = cp.interp(desired_freq, noise_freq, light_det_noise[idet], left=0, right=0)
# rescale noise spectrum to have constant noise power with digitizer sample spacing
noise_spectrum *= cp.sqrt(cp.diff(noise_freq, axis=-1).mean()/bin_size) * light.LIGHT_DIGIT_SAMPLE_SPACING / light.LIGHT_TICK_SIZE
# generate an FFT with the same frequency power, but with random phase
noise = noise_spectrum * cp.exp(2j * cp.pi * cp.random.uniform(size=noise_spectrum.shape))
if shape[1] < 2:
# special case where inverse FFT does not exist - just generate one sample
noise = cp.round(cp.real(noise)) * 2**(16-light.LIGHT_NBIT)
else:
# invert FFT to create a noise waveform
noise = cp.round(cp.fft.irfft(noise, axis=-1)) * 2**(16-light.LIGHT_NBIT)
if noise.shape[1] < shape[1]:
# FFT must have even samples, so append 0 if an odd number of samples is requested
noise = cp.concatenate([noise, cp.zeros((noise.shape[0],shape[1]-noise.shape[1]))],axis=-1)
return noise[:,:shape[1]]
[docs]
def get_triggers(signal, group_threshold, op_channel_idx, i_subbatch):
"""
Identifies each simulated ticks that would initiate a trigger taking into account the ADC digitization window
Args:
signal(array): shape `(ndet, nticks)`, simulated signal on each channel
group_threshold(array): shape `(ngrp,)`, threshold on group sum (requires `ndet/ngrp == OP_CHANNEL_PER_TRIG`)
op_channel_idx(array): shape `(ndet,)`, optical channel index for each signal
i_subbatch(int): index of the sub_batch numbering ("itrk in the batch for loop")
Returns:
tuple: array of tick indices at each trigger (shape `(ntrigs,)`) and array of op channel index (shape `(ntrigs, ndet_module)`)
"""
shape = signal.shape
# sum over all signals on a single detector (shape: (ndet, nticks) -> (ngrp, ndetpergrp, nticks) -> (ngrp, 1, nticks))
signal_sum = signal.reshape(shape[0]//light.OP_CHANNEL_PER_TRIG, light.OP_CHANNEL_PER_TRIG, shape[-1]).sum(axis=1, keepdims=True)
# reduce to approximate ADC sample rate (padded with zeros)
sample_factor = round(light.LIGHT_DIGIT_SAMPLE_SPACING / light.LIGHT_TICK_SIZE)
padding = sample_factor - shape[-1] % sample_factor
if padding > 0:
signal_sum = cp.concatenate((signal_sum, cp.zeros((shape[0]//light.OP_CHANNEL_PER_TRIG, 1, padding))), axis=-1)
signal_sum = signal_sum.reshape(-1, 1, signal_sum.shape[-1]//sample_factor, sample_factor).mean(axis=-1, keepdims=True)
signal_sum = cp.broadcast_to(signal_sum, signal_sum.shape[:3] + (sample_factor,))
signal_sum = signal_sum.reshape(-1, 1, shape[-1] + padding)[...,:(-padding if padding > 0 else shape[-1])]
# apply trigger threshold
sample_above_thresh = cp.broadcast_to(signal_sum < group_threshold[:,cp.newaxis,cp.newaxis], (shape[0]//light.OP_CHANNEL_PER_TRIG, light.OP_CHANNEL_PER_TRIG, shape[-1]))
# cast back into the original signal array
sample_above_thresh = sample_above_thresh.reshape(signal.shape)
# calculate the minimum number of ticks between two triggers on the same module
digit_ticks = ceil((light.LIGHT_TRIG_WINDOW[1] + light.LIGHT_TRIG_WINDOW[0])/light.LIGHT_TICK_SIZE)
tpc_ids = np.unique(light.OP_CHANNEL_TO_TPC[op_channel_idx.get()])
mod_ids = np.unique([detector.TPC_TO_MODULE[tpc_id] for tpc_id in tpc_ids])
trigger_idx_list = []
op_channel_idx_list = []
trigger_type_list = [] # 0 --> threshold, # 1 --> beam
if light.LIGHT_TRIG_MODE == 0:
# treat each module independently
for mod_id, tpc_ids in [(mod_id, detector.MODULE_TO_TPCS[mod_id]) for mod_id in mod_ids]:
# get active channels for the module
op_channels = light.TPC_TO_OP_CHANNEL[tpc_ids].ravel()
op_channel_mask = np.isin(op_channel_idx.get(), op_channels)
#module_above_thresh = cp.any(sample_above_thresh[op_channels], axis=0)
module_above_thresh = np.any(sample_above_thresh[op_channel_mask], axis=0)
last_trigger = 0
while cp.any(module_above_thresh):
# find next time signal goes above threshold
next_idx = cp.sort(cp.nonzero(module_above_thresh)[0])[0] + (last_trigger if last_trigger != 0 else 0)
next_trig_type = cp.asarray(0)
# keep track of trigger time
trigger_idx_list.append(next_idx)
trigger_type_list.append(next_trig_type)
op_channel_idx_list.append(op_channels)
# ignore samples during digitization window
module_above_thresh = module_above_thresh[next_idx+digit_ticks:]
last_trigger = next_idx + digit_ticks
# if i_subbatch > 0, means this batch/event should already have a trigger
# then return an empty list
elif light.LIGHT_TRIG_MODE == 1 and i_subbatch == 0:
# always add a trigger for a simulated spill/event
# this function is called in the batch script
# which means it is executed per event
# keep track of trigger time (initial comment)
trigger_idx_list.append(cp.asarray(0)) # the first trigger in the event
op_channel_idx_list.append(op_channel_idx)
trigger_type_list.append(cp.asarray(1)) # beam
## would we ever get these secondary triggers? -- Not at the moment
## 1. currently the internal light simulation window is the same as the light readout window,
## and 16us is large enough (for NuMI at least)
## 2. potentially an off-beam event if ever simulated together with the beam, will be considered as a separate event
## therefore, likely will not be in the same batch
## if we ever overlay beam and off-beam, we should consider if we should compare the two signals and overlay the two light signals
## wether they would be in the same readout window; how to deal with the dead time between the two triggers etc...
#module_above_thresh = np.any(sample_above_thresh, axis=0)
#module_above_thresh = module_above_thresh[digit_ticks:]
#last_trigger = digit_ticks
#while cp.any(module_above_thresh):
# # find next time signal goes above threshold
# next_idx = cp.sort(cp.nonzero(module_above_thresh)[0])[0] + (last_trigger if last_trigger != 0 else 0)
# next_trig_type = cp.asarray(0)
# # keep track of trigger time
# trigger_idx_list.append(next_idx)
# trigger_type_list.append(next_trig_type)
# op_channel_idx_list.append(op_channel_idx)
# # ignore samples during digitization window
# module_above_thresh = module_above_thresh[next_idx+digit_ticks:]
# last_trigger = next_idx + digit_ticks
if len(trigger_idx_list):
return cp.array(trigger_idx_list), cp.array(op_channel_idx_list), cp.array(trigger_type_list)
return cp.empty((0,), dtype=int), cp.empty((0, len(op_channel_idx)), dtype=int), cp.empty((0,), dtype=int)
@cuda.jit
def digitize_signal(signal, signal_op_channel_idx, trigger_idx, trigger_op_channel_idx, signal_true_track_id, signal_true_photons, digit_signal, digit_signal_true_track_id, digit_signal_true_photons):
"""
Interpolate signal to the appropriate sampling frequency
Args:
signal(array): shape `(ndet, nticks)`, simulated signal on each channel
signal_op_channel_idx(array): shape `(ndet,)`, optical channel index for each simulated signal
trigger_idx(array): shape `(ntrigs,)`, tick index for each trigger
trigger_op_channel_idx(array): shape `(ntrigs, ndet_module)`, optical channel index for each trigger
digit_signal(array): output array, shape `(ntrigs, ndet_module, nsamples)`, digitized signal
"""
itrig,idet_module,isample = cuda.grid(3)
if itrig < digit_signal.shape[0]:
if idet_module < digit_signal.shape[1]:
if isample < digit_signal.shape[2]:
#sample_tick = isample * light.LIGHT_DIGIT_SAMPLE_SPACING / light.LIGHT_TICK_SIZE - light.LIGHT_TRIG_WINDOW[0] / light.LIGHT_TICK_SIZE + trigger_idx[itrig]
sample_tick = isample * light.LIGHT_DIGIT_SAMPLE_SPACING / light.LIGHT_TICK_SIZE
idet = trigger_op_channel_idx[itrig, idet_module]
idet_signal = 0
for idet_signal in range(signal.shape[0]):
if idet == signal_op_channel_idx[idet_signal]:
break
if idet_signal == signal.shape[0]:
return
digit_signal[itrig,idet_module,isample] = interp(sample_tick, signal[idet_signal], 0, 0)
itick0 = int(floor(sample_tick))
itick1 = int(ceil(sample_tick))
itrue = 0
# loop over previous tick truth
for jtrue in range(signal_true_track_id.shape[-1]):
if itrue >= digit_signal_true_track_id.shape[-1]:
break
if signal_true_track_id[idet_signal,itick0,jtrue] == -1:
break
photons0, photons1 = 0, 0
# if matches the current sample track or we have empty truth slot, add truth info
if signal_true_track_id[idet_signal,itick0,jtrue] == digit_signal_true_track_id[itrig,idet_module,isample,itrue] or digit_signal_true_track_id[itrig,idet_module,isample,itrue] == -1:
digit_signal_true_track_id[itrig,idet_module,isample,itrue] = signal_true_track_id[idet_signal,itick0,jtrue]
itrue += 1
# interpolate true photons
photons0 = signal_true_photons[idet,itick0,jtrue]
if abs(photons0) < sim.MC_TRUTH_THRESHOLD:
continue
# loop over next tick
# first try same position (for speed-up)
if signal_true_track_id[idet_signal,itick0,jtrue] == signal_true_track_id[idet_signal,itick1,jtrue]:
photons1 = signal_true_photons[idet_signal,itick1,jtrue]
else:
for ktrue in range(signal_true_track_id.shape[-1]):
if signal_true_track_id[idet_signal,itick0,jtrue] == signal_true_track_id[idet_signal,itick1,ktrue]:
photons1 = signal_true_photons[idet_signal,itick1,ktrue]
break
# if a valid truth entry was found, do interpolation
if digit_signal_true_track_id[itrig,idet_module,isample,itrue-1] != -1:
digit_signal_true_photons[itrig,idet_module,isample,itrue-1] = interp(sample_tick-itick0, (photons0,photons1), 0, 0)
[docs]
def sim_triggers(bpg, tpb, signal, signal_op_channel_idx, signal_true_track_id, signal_true_photons, trigger_idx, op_channel_idx, digit_samples, light_det_noise):
"""
Generates digitized waveforms at specified simulation tick indices
Args:
bpg(tuple): blocks per grid used to generate digitized waveforms, `len(bpg) == 3`, `prod(bpg) * prod(tpb) >= digit_samples.size`
tpb(tuple): threads per grid used to generate digitized waveforms, `len(bpg) == 3`, `bpg[i] * tpb[i] >= digit_samples.shape[i]`
signal(array): shape `(ndet, nticks)`, simulated signal on each channel
signal_op_channel_idx(array): shape `(ndet,)`, optical channel index for each simulated signal
signal_true_track_id(array): shape `(ndet, nticks, ntruth)`, true segments associated with each tick
signal_true_photons(array): shape `(ndet, nticks, ntruth)`, true photons associated with each tick from each track
trigger_idx(array): shape `(ntrigs,)`, tick index for each trigger to digitize
op_channel_idx(array): shape `(ntrigs, ndet_module)`, optical channel indices for each trigger
digit_samples(int): number of digitizations per waveform
light_det_noise(array): shape `(ndet, nnoise_bins)`, noise spectrum for each channel (only used if waveforms extend past simulated signal)
Returns:
array: shape `(ntrigs, ndet_module, digit_samples)`, digitized waveform on each channel for each trigger
"""
digit_signal = cp.zeros((trigger_idx.shape[0], op_channel_idx.shape[-1], digit_samples), dtype='f8')
digit_signal_true_track_id = cp.full((trigger_idx.shape[0], op_channel_idx.shape[-1], digit_samples, signal_true_track_id.shape[-1]), -1, dtype=signal_true_track_id.dtype)
digit_signal_true_photons = cp.zeros((trigger_idx.shape[0], op_channel_idx.shape[-1], digit_samples, signal_true_photons.shape[-1]), dtype=signal_true_photons.dtype)
# exit if no triggers
if digit_signal.shape[0] == 0:
return digit_signal, digit_signal_true_track_id, digit_signal_true_photons
padded_trigger_idx = trigger_idx.copy()
# pad front of simulation with noise, if trigger close to start of simulation window
pre_digit_ticks = int(ceil(light.LIGHT_TRIG_WINDOW[0]/light.LIGHT_TICK_SIZE))
if trigger_idx.min() - pre_digit_ticks < 0:
pad_shape = (signal.shape[0], int(pre_digit_ticks - trigger_idx.min()))
pre_trig_readout = cp.zeros(pad_shape)
signal = cp.concatenate([pre_trig_readout, signal], axis=-1)
#signal = cp.concatenate([gen_light_detector_noise(pad_shape, light_det_noise[signal_op_channel_idx]), signal], axis=-1)
signal_true_track_id = cp.concatenate([cp.full(pad_shape + signal_true_track_id.shape[-1:], -1, dtype=signal_true_track_id.dtype), signal_true_track_id], axis=-2)
signal_true_photons = cp.concatenate([cp.zeros(pad_shape + signal_true_photons.shape[-1:], signal_true_photons.dtype), signal_true_photons], axis=-2)
padded_trigger_idx += pad_shape[1]
# pad end of simulation with noise, if trigger close to end of simulation window
post_digit_ticks = int(ceil(light.LIGHT_TRIG_WINDOW[1]/light.LIGHT_TICK_SIZE))
if post_digit_ticks + padded_trigger_idx.max() > signal.shape[1]:
pad_shape = (signal.shape[0], int(post_digit_ticks + padded_trigger_idx.max() - signal.shape[1]))
post_trig_readout = cp.zeros(pad_shape)
signal = cp.concatenate([signal, post_trig_readout], axis=-1)
#signal = cp.concatenate([signal, gen_light_detector_noise(pad_shape, light_det_noise[signal_op_channel_idx])], axis=-1)
signal_true_track_id = cp.concatenate([signal_true_track_id, cp.full(pad_shape + signal_true_track_id.shape[-1:], -1, dtype=signal_true_track_id.dtype)], axis=-2)
signal_true_photons = cp.concatenate([signal_true_photons, cp.zeros(pad_shape + signal_true_photons.shape[-1:], dtype=signal_true_photons.dtype)], axis=-2)
# add noise to padded (in readout time) signal
signal += cp.array(gen_light_detector_noise(signal.shape, light_det_noise[signal_op_channel_idx]))
# add noise for any channels that had no signal
if cp.any(~cp.isin(op_channel_idx, signal_op_channel_idx)):
missing = cp.unique(op_channel_idx[~cp.isin(op_channel_idx, signal_op_channel_idx)])
pad_shape = (missing.shape[0], signal.shape[-1])
signal = cp.concatenate([signal, gen_light_detector_noise(pad_shape, light_det_noise[missing])], axis=0)
signal_op_channel_idx = cp.concatenate([signal_op_channel_idx, missing], axis=0)
signal_true_track_id = cp.concatenate([signal_true_track_id, cp.full(pad_shape + signal_true_track_id.shape[-1:], -1, dtype=signal_true_track_id.dtype)], axis=0)
signal_true_photons = cp.concatenate([signal_true_photons, cp.zeros(pad_shape + signal_true_photons.shape[-1:], dtype=signal_true_photons.dtype)], axis=0)
# sort to keep consistency across events
order = cp.argsort(signal_op_channel_idx, axis=-1)
signal = cp.take_along_axis(signal, order[...,np.newaxis], axis=0)
signal_op_channel_idx = cp.take_along_axis(signal_op_channel_idx, order, axis=0)
signal_true_track_id = cp.take_along_axis(signal_true_track_id, order[...,np.newaxis,np.newaxis], axis=0)
signal_true_photons = cp.take_along_axis(signal_true_photons, order[...,np.newaxis,np.newaxis], axis=0)
digitize_signal[bpg,tpb](signal, signal_op_channel_idx, padded_trigger_idx, op_channel_idx, signal_true_track_id, signal_true_photons,
digit_signal, digit_signal_true_track_id, digit_signal_true_photons)
# truncate to correct number of bits
digit_signal = cp.round(digit_signal / 2**(16-light.LIGHT_NBIT)) * 2**(16-light.LIGHT_NBIT)
return digit_signal, digit_signal_true_track_id, digit_signal_true_photons
[docs]
def export_light_wvfm_to_hdf5(event_id, waveforms, output_filename, waveforms_true_track_id, waveforms_true_photons, i_trig, i_mod=-1, compression=None):
"""
Saves waveforms to output file
Args:
event_id(array): shape `(ntrigs,)`, event id for each trigger
waveforms(array): shape `(ntrigs, ndet_module, nsamples)`, simulated waveforms to save
output_filename(str): output hdf5 file path
waveforms_true_track_id(array): shape `(ntrigs, ndet, nsamples)`, segment ids contributing to each sample
waveforms_true_photons(array): shape `(ntrigs, ndet, nsamples)`, true photocurrent at each sample
i_mod(int): module id. The default value is -1 which indicates that there is no modular variation activated.
"""
if event_id.shape[0] == 0:
return
with h5py.File(output_filename, 'a') as f:
# the final dataset will be (n_triggers, all op channels in the detector, waveform samples)
# it would take too much memory if we hold the information until all the modules been simulated
# therefore, let's store the intermediate data with (n_triggers, op channels in a module, waveform samples) per module
# and we will cast it into the final shape
# FIXME currently this does not support threshold triggering
if sim.MOD2MOD_VARIATION and light.LIGHT_TRIG_MODE == 1:
if i_mod > 0:
if f'light_wvfm/light_wvfm_mod{i_mod-1}' not in f:
f.create_dataset(f'light_wvfm/light_wvfm_mod{i_mod-1}', data=waveforms, maxshape=(None,None,None), compression=compression)
else:
f[f'light_wvfm/light_wvfm_mod{i_mod-1}'].resize(f[f'light_wvfm/light_wvfm_mod{i_mod-1}'].shape[0] + waveforms.shape[0], axis=0)
f[f'light_wvfm/light_wvfm_mod{i_mod-1}'][-waveforms.shape[0]:] = waveforms
else:
raise ValueError("Mod2mod variation is activated, but the module id is not provided correctly.")
else:
if 'light_wvfm' not in f:
f.create_dataset('light_wvfm', data=waveforms, maxshape=(None,None,None), compression=compression)
else:
f['light_wvfm'].resize(f['light_wvfm'].shape[0] + waveforms.shape[0], axis=0)
f['light_wvfm'][-waveforms.shape[0]:] = waveforms
# Store the light truth backtracking, in the same way for module variation turned on and off
# skip creating the truth dataset if there is no truth information to store
truth_data=None
if sim.MAX_MC_TRUTH_IDS > 0:
truth_data = zero_suppress_waveform_truth(waveforms_true_track_id, waveforms_true_photons, event_id[0], i_trig, i_mod)
if truth_data.shape[0] > 0:
if f'light_wvfm_mc_assn' not in f:
f.create_dataset(f'light_wvfm_mc_assn', data=truth_data, maxshape=(None,), compression=compression)
else:
f[f'light_wvfm_mc_assn'].resize(f[f'light_wvfm_mc_assn'].shape[0] + truth_data.shape[0], axis=0)
f[f'light_wvfm_mc_assn'][-truth_data.shape[0]:] = truth_data
[docs]
def export_light_trig_to_hdf5(event_id, start_times, trigger_idx, op_channel_idx, output_filename, event_times, compression=None):
"""
Saves light trigger to output file
Args:
event_id(array): shape `(ntrigs,)`, event id for each trigger
start_times(array): shape `(ntrigs,)`, simulation time offset for each trigger [microseconds]
trigger_idx(array): shape `(ntrigs,)`, simulation time tick of each trigger
op_channel_idx(array): shape `(ntrigs, ndet_module)`, optical channel index for each trigger
output_filename(str): output hdf5 file path
event_times(array): shape `(nevents,)`, global event t0 for each unique event [microseconds]
"""
if event_id.shape[0] == 0:
return
unique_events, unique_events_inv = np.unique(event_id, return_inverse=True)
event_start_times = event_times[unique_events_inv]
event_sync_times = (event_times[unique_events_inv] / detector.CLOCK_CYCLE).astype(int) % detector.CLOCK_RESET_PERIOD
with h5py.File(output_filename, 'a') as f:
trig_data = np.empty(trigger_idx.shape[0], dtype=np.dtype([('op_channel','i4',(op_channel_idx.shape[-1])), ('ts_s','f8'), ('ts_sync','u8')]))
trig_data['op_channel'] = op_channel_idx
trig_data['ts_s'] = ((start_times + trigger_idx * light.LIGHT_TICK_SIZE + event_start_times) * units.mus / units.s)
trig_data['ts_sync'] = (((start_times + trigger_idx * light.LIGHT_TICK_SIZE)/detector.CLOCK_CYCLE + event_sync_times).astype(int) % detector.CLOCK_RESET_PERIOD)
if 'light_trig' not in f:
f.create_dataset('light_trig', data=trig_data, maxshape=(None,), compression=compression)
else:
f['light_trig'].resize(f['light_trig'].shape[0] + trigger_idx.shape[0], axis=0)
f['light_trig'][-trigger_idx.shape[0]:] = trig_data
[docs]
def export_to_hdf5(event_id, start_times, trigger_idx, op_channel_idx, waveforms, output_filename, event_times, waveforms_true_track_id, waveforms_true_photons, i_trig, i_mod, compression=None):
"""
Saves waveforms to output file
Args:
event_id(array): shape `(ntrigs,)`, event id for each trigger
start_times(array): shape `(ntrigs,)`, simulation time offset for each trigger [microseconds]
trigger_idx(array): shape `(ntrigs,)`, simulation time tick of each trigger
op_channel_idx(array): shape `(ntrigs, ndet_module)`, optical channel index for each trigger
waveforms(array): shape `(ntrigs, ndet_module, nsamples)`, simulated waveforms to save
output_filename(str): output hdf5 file path
event_times(array): shape `(nevents,)`, global event t0 for each unique event [microseconds]
waveforms_true_track_id(array): shape `(ntrigs, ndet, nsamples)`, segment ids contributing to each sample
waveforms_true_photons(array): shape `(ntrigs, ndet, nsamples)`, true photocurrent at each sample
"""
export_light_trig_to_hdf5(event_id, start_times, trigger_idx, op_channel_idx, output_filename, event_times, compression)
export_light_wvfm_to_hdf5(event_id, waveforms, output_filename, waveforms_true_track_id, waveforms_true_photons, i_trig, i_mod, compression)
[docs]
def merge_module_light_wvfm_same_trigger(output_filename, compression=None):
"""
Merge light waveforms for each module if the modular variation is activated
"""
with h5py.File(output_filename, 'a') as f:
for i_, i_mod in enumerate(detector.MOD_IDS):
if i_ == 0:
merged_wvfm = f[f'light_wvfm/light_wvfm_mod{i_mod-1}']
else:
mod_wvfm = f[f'light_wvfm/light_wvfm_mod{i_mod-1}']
if mod_wvfm.shape[0] != merged_wvfm.shape[0]:
raise ValueError("The number of triggers should be the same in each module with light trigger mode 1 (light waveform).")
merged_wvfm = np.append(merged_wvfm, mod_wvfm, axis=1)
del f['light_wvfm']
f.create_dataset(f'light_wvfm', data=merged_wvfm, maxshape=(None,None,None), compression=compression)
