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Commit 692e7528 authored by Philipp Schmidt's avatar Philipp Schmidt
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Add support to reconstruct PPT based on some trigger edges for REMI

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1 merge request!811[REMI] Add PPT reconstruction from trigger signal
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notebooks/REMI/REMI_Digitize_and_Transform.ipynb
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%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# Data selection parameters. # Data selection parameters.
run = 104 # Run ID. run = 104 # Run ID.
in_folder = '/gpfs/exfel/exp/SQS/202101/p002535/raw' # Partial input path appended with run ID. in_folder = '/gpfs/exfel/exp/SQS/202101/p002535/raw' # Partial input path appended with run ID.
out_folder = '/gpfs/exfel/exp/SQS/202101/p002535/scratch/cal_test' # Full path to output folder. out_folder = '/gpfs/exfel/exp/SQS/202101/p002535/scratch/cal_test' # Full path to output folder.
calib_config_path = '/gpfs/exfel/exp/SQS/202101/p002535/usr/config_board2+4.yaml' # Path to correction and transform configuration calib_config_path = '/gpfs/exfel/exp/SQS/202101/p002535/usr/config_board2+4.yaml' # Path to correction and transform configuration
# These parameters are required by xfel-calibrate but ignored in this notebook. # These parameters are required by xfel-calibrate but ignored in this notebook.
cycle = '' # Proposal cycle, currently not used. cycle = '' # Proposal cycle, currently not used.
cal_db_timeout = 0 # Calibration DB timeout, currently not used. cal_db_timeout = 0 # Calibration DB timeout, currently not used.
cal_db_interface = 'foo' # Calibration DB interface, currently not used. cal_db_interface = 'foo' # Calibration DB interface, currently not used.
karabo_da = 'bar' # Karabo data aggregator name, currently not used karabo_da = 'bar' # Karabo data aggregator name, currently not used
# Output parameters. # Output parameters.
karabo_id = 'SQS_REMI_DLD6' # Karabo device ID root for virtual output device. karabo_id = 'SQS_REMI_DLD6' # Karabo device ID root for virtual output device.
proposal = '' # Proposal, leave empty for auto detection based on in_folder proposal = '' # Proposal, leave empty for auto detection based on in_folder
out_aggregator = 'REMI01' # Aggregator name for output files. out_aggregator = 'REMI01' # Aggregator name for output files.
out_seq_len = 5000 # Number of trains per sequence file in output. out_seq_len = 5000 # Number of trains per sequence file in output.
det_device_id = '{karabo_id}/DET/{det_name}' # Karabo device ID for virtual output device. det_device_id = '{karabo_id}/DET/{det_name}' # Karabo device ID for virtual output device.
det_output_key = 'output' # Pipeline name for fast data output. det_output_key = 'output' # Pipeline name for fast data output.
save_raw_triggers = True # Whether to save trigger position in files. save_raw_triggers = True # Whether to save trigger position in files.
save_raw_edges = True # Whether to save digitized edge positions in files. save_raw_edges = True # Whether to save digitized edge positions in files.
save_rec_signals = True # Whether to save reconstructed signals (u1-w2, mcp) in files. save_rec_signals = True # Whether to save reconstructed signals (u1-w2, mcp) in files.
save_rec_hits = True # Whether to save reoncstructed hits (x,y,t,m) in files. save_rec_hits = True # Whether to save reoncstructed hits (x,y,t,m) in files.
chunks_triggers = [500] # HDF chunk size for triggers. chunks_triggers = [500] # HDF chunk size for triggers.
chunks_edges = [500, 7, 50] # HDF chunk size for edges. chunks_edges = [500, 7, 50] # HDF chunk size for edges.
chunks_hits = [50, 50] # HDF chunk size for hits. chunks_hits = [50, 50] # HDF chunk size for hits.
chunks_signals = [50, 50] # HDF chunk size for signals. chunks_signals = [50, 50] # HDF chunk size for signals.
dataset_compression = 'gzip' # HDF compression method. dataset_compression = 'gzip' # HDF compression method.
dataset_compression_opts = 3 # HDF GZIP compression level. dataset_compression_opts = 3 # HDF GZIP compression level.
# Detector parameters. # Detector parameters.
quad_anode = False # Reconstruction assumes a hex anode by default, change for quad anodes. quad_anode = False # Reconstruction assumes a hex anode by default, change for quad anodes.
# Trigger parameters. # Trigger parameters.
ppt_source = 'SQS_RR_UTC/TSYS/TIMESERVER:outputBunchPattern' ppt_source = 'SQS_RR_UTC/TSYS/TIMESERVER:outputBunchPattern'
ignore_fel = False # Ignore any FEL entries in the PPT. ignore_fel = False # Ignore any FEL entries in the PPT.
ignore_ppl = False # Ignore any PPL entries in the PPT. ignore_ppl = False # Ignore any PPL entries in the PPT.
ppl_offset = 0 # In units of the PPT. ppl_offset = 0 # In units of the PPT.
laser_ppt_mask = -1 # Bit mask for used laser, negative to auto-detect from instrument. laser_ppt_mask = -1 # Bit mask for used laser, negative to auto-detect from instrument.
instrument_sase = 3 instrument_sase = 3 # Which SASE we're running at for PPT decoding.
first_pulse_offset = 1000 first_pulse_offset = 10000 # Sample position where the first pulse begins, ignored when PPT is reconstructed.
single_pulse_length = 25000 single_pulse_length = 25000 # How many samples if there's only one pulse.
pulse_start_offset = 0 # Signal offset at the start of each pulse.
pulse_end_offset = 0 # Signal offset at the end of each pulse.
# PPT reconstruction parameters.
reconstruct_ppt = False # Reconstruct PPT from some trigger edges.
trigger_edge_channel = '4_D' # Channel to use for triggering.
trigger_edge_offset = 0 # Offset to apply to the first trigger edge position to compute first pulse offset.
fake_ppt_offset = 0 # Offset in reconstructed PPT for pulses.
# Parallelization parameters. # Parallelization parameters.
mp_find_triggers = 0.5 # Parallelization for finding triggers. mp_find_triggers = 0.5 # Parallelization for finding triggers.
mp_find_edges = 0.5 # Parallelization for digitizing analog signal. mp_find_edges = 0.5 # Parallelization for digitizing analog signal.
mt_avg_trace = 2 # Parallelization for trace averaging. mt_avg_trace = 2 # Parallelization for trace averaging.
mp_rec_hits = 1.0 # Parallelization for hit reconstruction. mp_rec_hits = 1.0 # Parallelization for hit reconstruction.
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm from matplotlib.colors import LogNorm
from threadpoolctl import threadpool_limits from threadpoolctl import threadpool_limits
import re import re
import h5py import h5py
from pathlib import Path from pathlib import Path
from datetime import datetime from datetime import datetime
import pasha as psh import pasha as psh
from euxfel_bunch_pattern import indices_at_sase, indices_at_laser from euxfel_bunch_pattern import indices_at_sase, indices_at_laser
from extra_data import RunDirectory from extra_data import RunDirectory
from extra_remi import Analysis, trigger_dt from extra_remi import Analysis, trigger_dt
from extra_remi.util import timing from extra_remi.util import timing
from extra_remi.rd_resort import signal_dt, hit_dt from extra_remi.rd_resort import signal_dt, hit_dt
from extra_remi.files import DataFile, sequence_pulses from extra_remi.files import DataFile, sequence_pulses
if quad_anode: if quad_anode:
from extra_remi.plots import plot_detector_diagnostics_quad as plot_detector_diagnostics from extra_remi.plots import plot_detector_diagnostics_quad as plot_detector_diagnostics
else: else:
from extra_remi.plots import plot_detector_diagnostics_hex as plot_detector_diagnostics from extra_remi.plots import plot_detector_diagnostics_hex as plot_detector_diagnostics
%matplotlib inline %matplotlib inline
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
calib_config_path = Path(calib_config_path) calib_config_path = Path(calib_config_path)
if not calib_config_path.is_file(): if not calib_config_path.is_file():
# If the path cannot be resolved right now, try the same path relative to in_folder. # If the path cannot be resolved right now, try the same path relative to in_folder.
calib_config_path = Path(in_folder) / calib_config_path calib_config_path = Path(in_folder) / calib_config_path
if not calib_config_path.is_file(): if not calib_config_path.is_file():
# Disallow implicit config file creation. # Disallow implicit config file creation.
raise ValueError('calib_config_path not found - neither absolute nor relative to in_folder') raise ValueError('calib_config_path not found - neither absolute nor relative to in_folder')
remi = Analysis(calib_config_path, use_hex=not quad_anode) remi = Analysis(calib_config_path, use_hex=not quad_anode)
with timing('open_run'): with timing('open_run'):
dc = remi.prepare_dc(RunDirectory(Path(in_folder) / f'r{run:04d}', inc_suspect_trains=True)) dc = remi.prepare_dc(RunDirectory(Path(in_folder) / f'r{run:04d}', inc_suspect_trains=True),
require_ppt=not reconstruct_ppt)
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
# Transformation parameters # Transformation parameters
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
def print_leaf(leaf, indent=0): def print_leaf(leaf, indent=0):
for key, value in leaf.items(): for key, value in leaf.items():
if isinstance(value, dict): if isinstance(value, dict):
print(indent * 4 * ' ' + key) print(indent * 4 * ' ' + key)
print_leaf(value, indent=indent+1) print_leaf(value, indent=indent+1)
else: else:
print(indent * 4 * ' ' + f'{key}: {value}') print(indent * 4 * ' ' + f'{key}: {value}')
print_leaf(remi.tree) print_leaf(remi.tree)
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
# Pulse and trigger information # Pulse and trigger information
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Read PPT from file or reconstruct PPT for older data
%% Cell type:code id: tags:
``` python
if reconstruct_ppt:
# Take up to the first hundred trains for now.
# Could be done for each train individually, but likely not necessary for now.
trigger_trace = dc[remi['digitizer']['source'], remi['digitizer']['key_pattern'].format(trigger_edge_channel)] \
[:100].ndarray().mean(axis=0).astype(np.float64)
trigger_trace -= trigger_trace[0] # Use simple offset correction.
fake_ppt = np.zeros(2700, dtype=np.uint32)
discr_func, discr_params = remi.get_discriminator([trigger_edge_channel])
edges = np.zeros(1000, dtype=np.float64)
num_pulses = discr_func(trigger_trace, edges, **discr_params[0])
edges = edges[:num_pulses]
first_edge = edges[0]
rel_edges = np.round(edges - first_edge)
edge_diff = rel_edges[1] - rel_edges[0]
if not np.allclose(rel_edges[1:] - rel_edges[:-1], edge_diff):
raise ValueError('PPT reconstruction for unstable edge intervals not supported')
pulse_spacing = edge_diff / (2 * remi['digitizer']['clock_factor']) # In units of PPT
if not float.is_integer(pulse_spacing):
raise ValueError('PPT reconstruction encountered non-integer pulse spacing')
pulse_spacing = int(pulse_spacing)
# Taken from euxfel_bunch_pattern/__init__.py
from euxfel_bunch_pattern import DESTINATION_T4D, DESTINATION_T5D, PHOTON_LINE_DEFLECTION
if instrument_sase == 1:
flag = DESTINATION_T4D
elif instrument_sase == 2:
flag = DESTINATION_T5D
elif instrument_sase == 3:
flag = DESTINATION_T4D | PHOTON_LINE_DEFLECTION
first_pulse_offset = int(first_edge + trigger_edge_offset) # Overwrite notebook argument.
fake_ppt[fake_ppt_offset:fake_ppt_offset + (pulse_spacing * num_pulses):pulse_spacing] = flag
from pasha.functor import Functor, gen_split_slices
class FakeKeyDataFunctor(Functor):
"""Functor appearing KeyData-like with constant data.
This functor serves a constant data row for a given number
of train IDs the same way a KeyData object would.
"""
def __init__(self, row, train_ids):
self.row = row
self.train_ids = train_ids
def split(self, num_workers):
return gen_split_slices(len(self.train_ids), n_parts=num_workers)
def iterate(self, share):
it = zip(range(*share.indices(len(self.train_ids))), self.train_ids)
for index, train_id in it:
yield index, train_id, self.row
ppt_data = FakeKeyDataFunctor(fake_ppt, dc.train_ids)
fig, ax = plt.subplots(num=99, figsize=(9, 6), clear=True, ncols=1, nrows=1)
ax.set_title('Edge trigger signal')
ax.plot(trigger_trace, lw=1, label=f'Mean {trigger_edge_channel} trace')
ax.vlines(edges, trigger_trace.min()*1.1, trigger_trace.max()*1.1,
color='red', linewidth=3, alpha=0.3, label='Edge positions')
ax.set_xlabel('Samples')
ax.set_ylabel('Intensity / ADU')
ax.legend()
else:
ppt_data = dc[ppt_source, 'data.bunchPatternTable']
```
%% Cell type:markdown id: tags:
### Count pulses per train and compute offsets ### Count pulses per train and compute offsets
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# Based on the pulse pattern tables, three global variables are obtained: # Based on the pulse pattern tables, three global variables are obtained:
# #
# * `pulse_counts [int32: len(dc.train_ids)]` containing the number of pulses per train. # * `pulse_counts [int32: len(dc.train_ids)]` containing the number of pulses per train.
# * `pulse_offsets [int32: len(dc.train_ids)]` containing the global offset for the first pulse of each train. # * `pulse_offsets [int32: len(dc.train_ids)]` containing the global offset for the first pulse of each train.
# * `num_pulses = pulse_counts.sum(axis=0)` # * `num_pulses = pulse_counts.sum(axis=0)`
ppt_data = dc[ppt_source, 'data.bunchPatternTable']
def get_pulse_positions(ppt, sase, laser, ppl_offset): def get_pulse_positions(ppt, sase, laser, ppl_offset):
# Combine FEL and PPL positions. # Combine FEL and PPL positions.
fel_pos = indices_at_sase(ppt, sase) if not ignore_fel else np.array([]) fel_pos = indices_at_sase(ppt, sase) if not ignore_fel else np.array([])
ppl_pos = indices_at_laser(ppt, laser) if not ignore_ppl else np.array([]) ppl_pos = indices_at_laser(ppt, laser) if not ignore_ppl else np.array([])
if len(fel_pos) > 0: if len(fel_pos) > 0:
# Move PPL up to the FEL position. # Move PPL up to the FEL position.
ppl_pos += fel_pos[0] + ppl_offset ppl_pos += fel_pos[0] + ppl_offset
return np.union1d(fel_pos, ppl_pos), fel_pos, ppl_pos return np.union1d(fel_pos, ppl_pos), fel_pos, ppl_pos
if laser_ppt_mask < 0: if laser_ppt_mask < 0:
# If laser PPT mask is not specified, try to figure it out from device IDs. # If laser PPT mask is not specified, try to figure it out from device IDs.
from euxfel_bunch_pattern import PPL_BITS from euxfel_bunch_pattern import PPL_BITS
instrument = karabo_id[:karabo_id.index('_')] instrument = karabo_id[:karabo_id.index('_')]
try: try:
laser_ppt_mask = PPL_BITS[f'LP_{instrument}'] laser_ppt_mask = PPL_BITS[f'LP_{instrument}']
except KeyError: except KeyError:
raise ValueError(f'Laser PPT mask unknown for instrument `{instrument}`') raise ValueError(f'Laser PPT mask unknown for instrument `{instrument}`')
with timing('pulse_info'): with timing('pulse_info'):
psh.set_default_context('processes', num_workers=remi.get_num_workers(mp_find_triggers)) psh.set_default_context('processes', num_workers=remi.get_num_workers(mp_find_triggers))
# Build the pulse index # Build the pulse index
pulse_counts = psh.alloc(shape=len(dc.train_ids), dtype=np.uint64) pulse_counts = psh.alloc(shape=len(dc.train_ids), dtype=np.uint64)
has_ppt = psh.alloc(shape=len(dc.train_ids), dtype=bool, fill=False) has_ppt = psh.alloc(shape=len(dc.train_ids), dtype=bool, fill=False)
def count_pulses(wid, index, tid, ppt): def count_pulses(wid, index, tid, ppt):
pulse_counts[index] = len(get_pulse_positions(ppt, instrument_sase, laser_ppt_mask, ppl_offset)[0]) pulse_counts[index] = len(get_pulse_positions(ppt, instrument_sase, laser_ppt_mask, ppl_offset)[0])
has_ppt[index] = True has_ppt[index] = True
psh.map(count_pulses, ppt_data) psh.map(count_pulses, ppt_data)
# Fill any missing values with the highest. # Fill any missing values with the highest.
pulse_counts[has_ppt == False] = pulse_counts.max() pulse_counts[has_ppt == False] = pulse_counts.max()
# Compute offsets based on pulse counts. # Compute offsets based on pulse counts.
pulse_offsets = np.zeros_like(pulse_counts) pulse_offsets = np.zeros_like(pulse_counts)
np.cumsum(pulse_counts[:-1], out=pulse_offsets[1:]) np.cumsum(pulse_counts[:-1], out=pulse_offsets[1:])
# Total number of pulses. # Total number of pulses.
num_pulses = int(pulse_counts.sum()) num_pulses = int(pulse_counts.sum())
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
fig, ax = plt.subplots(num=1, ncols=1, nrows=1, figsize=(9, 4), clear=True) fig, ax = plt.subplots(num=1, ncols=1, nrows=1, figsize=(9, 4), clear=True)
ax.set_title('Pulse count') ax.set_title('Pulse count')
ax.plot(dc.train_ids, pulse_counts, lw=1) ax.plot(dc.train_ids, pulse_counts, lw=1)
ax.set_xlabel('Train ID') ax.set_xlabel('Train ID')
ax.set_ylabel('Number of pulses') ax.set_ylabel('Number of pulses')
ax.set_ylim(0, max(300, pulse_counts.max() + 10)) ax.set_ylim(0, max(300, pulse_counts.max() + 10))
ax.ticklabel_format(style='plain') ax.ticklabel_format(style='plain')
pass pass
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Find triggers ### Find triggers
The trigger defines the boundary of a pulse on the digitizer trace, which is stored per train. The trigger defines the boundary of a pulse on the digitizer trace, which is stored per train.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# A trigger defines the boundary of a pulse on the digitizer trace stored per train. This cell creates a # A trigger defines the boundary of a pulse on the digitizer trace stored per train. This cell creates a
# global variable: # global variable:
# * `triggers [(start: int32, stop: int32, offset: float64, fel: bool, ppl: bool): num_pulses]` # * `triggers [(start: int32, stop: int32, offset: float64, fel: bool, ppl: bool): num_pulses]`
# containing the triggers for each pulse. # containing the triggers for each pulse.
# #
# This uses the pulse puttern table to locate the pulse positions on the trace. Only number of pulses and # This uses the pulse puttern table to locate the pulse positions on the trace. Only number of pulses and
# their distance can be drawn this way, leaving the absolute offset for the very first pulse to be # their distance can be drawn this way, leaving the absolute offset for the very first pulse to be
# configured via `trigger/ppt/first_pulse_offset`. If a PPL is used, it will be included in the trigger # configured via `trigger/ppt/first_pulse_offset`. If a PPL is used, it will be included in the trigger
# pattern. The ppt_offset parameter allows taking into account an offset betwen PPL and FEL. # pattern. The ppt_offset parameter allows taking into account an offset betwen PPL and FEL.
psh.set_default_context('processes', num_workers=remi.get_num_workers(mp_find_triggers)) psh.set_default_context('processes', num_workers=remi.get_num_workers(mp_find_triggers))
triggers = psh.alloc(shape=(num_pulses,), dtype=trigger_dt, fill=(-1, -1, np.nan, -1, 0, 0)) triggers = psh.alloc(shape=(num_pulses,), dtype=trigger_dt, fill=(-1, -1, np.nan, -1, 0, 0))
clock_factor = remi['digitizer']['clock_factor'] clock_factor = remi['digitizer']['clock_factor']
def trigger_by_ppt(worker_id, index, train_id, ppt): def trigger_by_ppt(worker_id, index, train_id, ppt):
all_pos, fel_pos, ppl_pos = get_pulse_positions(ppt, instrument_sase, laser_ppt_mask, ppl_offset) all_pos, fel_pos, ppl_pos = get_pulse_positions(ppt, instrument_sase, laser_ppt_mask, ppl_offset)
num_pulses = len(all_pos) num_pulses = len(all_pos)
if num_pulses == 0: if num_pulses == 0:
return return
elif len(ppl_pos) == 0 and ppl_offset < 0: elif len(ppl_pos) == 0 and ppl_offset < 0:
# No PPL pulses, but a negative offset is configured. This will cause # No PPL pulses, but a negative offset is configured. This will cause
# first_pulse_offset to start early and most likely miss pulses at the # first_pulse_offset to start early and most likely miss pulses at the
# end, so we correct by adding the ppl_offset to relative positions # end, so we correct by adding the ppl_offset to relative positions
# when computing trace positions. # when computing trace positions.
pos_corr = abs(ppl_offset) pos_corr = abs(ppl_offset)
else: else:
pos_corr = 0 pos_corr = 0
rel_pos = all_pos - all_pos[0] rel_pos = all_pos - all_pos[0]
if num_pulses > 1: if num_pulses > 1:
pulse_len = np.unique(rel_pos[1:] - rel_pos[:-1]).min() pulse_len = np.unique(rel_pos[1:] - rel_pos[:-1]).min()
elif num_pulses == 1: elif num_pulses == 1:
pulse_len = single_pulse_length pulse_len = single_pulse_length
start_frac = first_pulse_offset + (rel_pos + pos_corr) * 2 * clock_factor start_frac = first_pulse_offset + (rel_pos + pos_corr) * 2 * clock_factor
start_int = start_frac.astype(int) start_int = start_frac.astype(int)
pulse_offset = pulse_offsets[index] pulse_offset = pulse_offsets[index]
pulse_count = pulse_counts[index] pulse_count = pulse_counts[index]
train_triggers = triggers[pulse_offset:pulse_offset+pulse_count] train_triggers = triggers[pulse_offset:pulse_offset+pulse_count]
train_triggers['start'] = start_int train_triggers['start'] = start_int + pulse_start_offset
train_triggers['stop'] = start_int + int(pulse_len * 2 * clock_factor) - 1 train_triggers['stop'] = start_int + int(pulse_len * 2 * clock_factor) - 1 + pulse_end_offset
train_triggers['offset'] = start_frac - start_int train_triggers['offset'] = start_frac - start_int
train_triggers['pulse'] = all_pos.astype(np.int16) train_triggers['pulse'] = all_pos.astype(np.int16)
train_triggers['fel'] = [pos in fel_pos for pos in all_pos] train_triggers['fel'] = [pos in fel_pos for pos in all_pos]
train_triggers['ppl'] = [pos in ppl_pos for pos in all_pos] train_triggers['ppl'] = [pos in ppl_pos for pos in all_pos]
with timing('find_triggers'): with timing('find_triggers'):
psh.map(trigger_by_ppt, ppt_data) psh.map(trigger_by_ppt, ppt_data)
if (np.unique(triggers['pulse'][1:] - triggers['pulse'][:-1]) > 0).sum() > 1: if (np.unique(triggers['pulse'][1:] - triggers['pulse'][:-1]) > 0).sum() > 1:
# There is more than one delta between pulse entries across all pulses. This is not # There is more than one delta between pulse entries across all pulses. This is not
# necessarily a problem, as the pattern could simply have changed in between trains # necessarily a problem, as the pattern could simply have changed in between trains
# with each train being split properly. # with each train being split properly.
# If there's more than one delta in a single train, this likely points to a mismatch # If there's more than one delta in a single train, this likely points to a mismatch
# of FEL and PPL repetition rate. This is most likely not intended. # of FEL and PPL repetition rate. This is most likely not intended.
one = np.uint64(1) # Because np.uint64 + int = np.float64 one = np.uint64(1) # Because np.uint64 + int = np.float64
pulse_deltas = set() pulse_deltas = set()
for pulse_id, (offset, count) in enumerate(zip(pulse_offsets, pulse_counts)): for pulse_id, (offset, count) in enumerate(zip(pulse_offsets, pulse_counts)):
deltas = triggers['pulse'][offset+one:offset+count] - triggers['pulse'][offset:offset+count-one] deltas = triggers['pulse'][offset+one:offset+count] - triggers['pulse'][offset:offset+count-one]
if len(np.unique(deltas)) > 1: if len(np.unique(deltas)) > 1:
for delta in deltas: for delta in deltas:
pulse_deltas.add(delta) pulse_deltas.add(delta)
if len(pulse_deltas) > 1: if len(pulse_deltas) > 1:
delta_str = ', '.join([str(x) for x in sorted(pulse_deltas)]) delta_str = ', '.join([str(x) for x in sorted(pulse_deltas)])
print(f'WARNING: Different pulse lengths (PPT: {delta_str}) encountered within single trains, ' print(f'WARNING: Different pulse lengths (PPT: {delta_str}) encountered within single trains, '
f'separated pulse spectra may split up signals!') f'separated pulse spectra may split up signals!')
else: else:
print('WARNING: Different pulse lengths encountered across trains, separation may be unstable!') print('WARNING: Different pulse lengths encountered across trains, separation may be unstable!')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
fig, (lx, rx) = plt.subplots(num=2, ncols=2, nrows=1, figsize=(9, 4), clear=True, fig, (lx, rx) = plt.subplots(num=2, ncols=2, nrows=1, figsize=(9, 4), clear=True,
gridspec_kw=dict(top=0.75)) gridspec_kw=dict(top=0.75))
# Display ~400 pulses or 10 trains, whatever is lower # Display ~400 pulses or 10 trains, whatever is lower
n_trains = max(abs(pulse_offsets - 200).argmin(), 5) n_trains = max(abs(pulse_offsets - 200).argmin(), 5)
visible_triggers = triggers[:pulse_offsets[n_trains]] visible_triggers = triggers[:pulse_offsets[n_trains]]
pulse_index = np.arange(len(visible_triggers)) pulse_index = np.arange(len(visible_triggers))
pumped = visible_triggers['fel'] & visible_triggers['ppl'] pumped = visible_triggers['fel'] & visible_triggers['ppl']
fel_only = visible_triggers['fel'] & ~pumped fel_only = visible_triggers['fel'] & ~pumped
ppl_only = visible_triggers['ppl'] & ~pumped ppl_only = visible_triggers['ppl'] & ~pumped
lx.plot(pulse_index[pumped], visible_triggers[pumped]['start'], ' .', ms=3, c='C0', label='FEL+PPL') lx.plot(pulse_index[pumped], visible_triggers[pumped]['start'], ' .', ms=3, c='C0', label='FEL+PPL')
lx.plot(pulse_index[fel_only], visible_triggers[fel_only]['start'], '.', ms=3, c='C1', label='FEL-only') lx.plot(pulse_index[fel_only], visible_triggers[fel_only]['start'], '.', ms=3, c='C1', label='FEL-only')
lx.plot(pulse_index[ppl_only], visible_triggers[ppl_only]['start'], '.', ms=2, c='C2', label='PPL-only') lx.plot(pulse_index[ppl_only], visible_triggers[ppl_only]['start'], '.', ms=2, c='C2', label='PPL-only')
max_start = visible_triggers['start'].max() max_start = visible_triggers['start'].max()
lx.vlines(pulse_offsets[:n_trains], 0, max_start, color='grey', linewidth=1, alpha=0.2) lx.vlines(pulse_offsets[:n_trains], 0, max_start, color='grey', linewidth=1, alpha=0.2)
lx.tick_params(right=True) lx.tick_params(right=True)
lx.set_xlabel('Pulse index') lx.set_xlabel('Pulse index')
lx.set_xlim(-15, pulse_offsets[n_trains]+15) lx.set_xlim(-15, pulse_offsets[n_trains]+15)
lx.set_ylabel('Trigger position') lx.set_ylabel('Trigger position')
lx.set_ylim(-max_start // 20, max_start + max_start // 20) lx.set_ylim(-max_start // 20, max_start + max_start // 20)
lx.legend(fontsize='small', loc='lower right') lx.legend(fontsize='small', loc='lower right')
train_lx = lx.twiny() train_lx = lx.twiny()
train_lx.set_xlabel('Train ID', labelpad=8) train_lx.set_xlabel('Train ID', labelpad=8)
train_lx.set_xlim(lx.get_xlim()) train_lx.set_xlim(lx.get_xlim())
train_lx.set_xticks(pulse_offsets[:n_trains]) train_lx.set_xticks(pulse_offsets[:n_trains])
train_lx.set_xticklabels([str(int(x)) for x in dc.train_ids[:n_trains]], train_lx.set_xticklabels([str(int(x)) for x in dc.train_ids[:n_trains]],
rotation=-45, fontsize='x-small') rotation=-45, fontsize='x-small')
rx.plot(triggers['start'], lw=0.2) rx.plot(triggers['start'], lw=0.2)
rx.set_xlabel('Pulse index') rx.set_xlabel('Pulse index')
rx.tick_params(left=False, labelleft=False, right=True, labelright=True) rx.tick_params(left=False, labelleft=False, right=True, labelright=True)
pass pass
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
# Analog signal to digital edges # Analog signal to digital edges
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Find edges in analog signal ### Find edges in analog signal
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
psh.set_default_context('processes', num_workers=remi.get_num_workers(mp_find_edges)) psh.set_default_context('processes', num_workers=remi.get_num_workers(mp_find_edges))
threadpool_limits(limits=remi.get_num_workers(mt_avg_trace)) threadpool_limits(limits=remi.get_num_workers(mt_avg_trace))
edges_by_det = {} edges_by_det = {}
avg_traces_by_det = {} avg_traces_by_det = {}
for det_name, det in remi['detector'].items(): for det_name, det in remi['detector'].items():
det_sourcekeys = remi.get_detector_sourcekeys(det_name) det_sourcekeys = remi.get_detector_sourcekeys(det_name)
det_get_traces = remi.get_traces_getter(det_name) det_get_traces = remi.get_traces_getter(det_name)
trace_len = dc[next(iter(det_sourcekeys))].entry_shape[0] trace_len = dc[next(iter(det_sourcekeys))].entry_shape[0]
edges = psh.alloc(shape=(num_pulses, 7, det['max_hits']), edges = psh.alloc(shape=(num_pulses, 7, det['max_hits']),
dtype=np.float64, fill=np.nan) dtype=np.float64, fill=np.nan)
avg_traces = psh.alloc_per_worker(shape=(7, trace_len), dtype=np.float64) avg_traces = psh.alloc_per_worker(shape=(7, trace_len), dtype=np.float64)
def prepare_edge_worker(worker_id): def prepare_edge_worker(worker_id):
correct_func = remi.get_baseline_corrector() correct_func = remi.get_baseline_corrector()
discr_func, discr_params = remi.get_discriminator(det['channels']) discr_func, discr_params = remi.get_discriminator(det['channels'])
source_name = remi['digitizer']['source'] source_name = remi['digitizer']['source']
bl_start, bl_stop, _ = remi.get_baseline_limits(trace_len) bl_start, bl_stop, _ = remi.get_baseline_limits(trace_len)
bl_sym = remi['digitizer']['baseline_symmetry'] bl_sym = remi['digitizer']['baseline_symmetry']
time_cal = remi.get_time_calibration() time_cal = remi.get_time_calibration()
traces_corr = np.empty((7, trace_len), dtype=np.float64) traces_corr = np.empty((7, trace_len), dtype=np.float64)
baselines = np.empty(bl_sym, dtype=np.float64) baselines = np.empty(bl_sym, dtype=np.float64)
yield yield
@psh.with_init(prepare_edge_worker) @psh.with_init(prepare_edge_worker)
def find_edges(worker_id, index, train_id, data): def find_edges(worker_id, index, train_id, data):
try: try:
data = det_get_traces(data[source_name]) data = det_get_traces(data[source_name])
except KeyError: except KeyError:
return return
for channel_idx in range(7): for channel_idx in range(7):
correct_func(data[channel_idx], traces_corr[channel_idx], correct_func(data[channel_idx], traces_corr[channel_idx],
baselines, bl_start, bl_stop) baselines, bl_start, bl_stop)
avg_traces[worker_id] += traces_corr avg_traces[worker_id] += traces_corr
pulses_slice = np.s_[pulse_offsets[index]:pulse_offsets[index]+pulse_counts[index]] pulses_slice = np.s_[pulse_offsets[index]:pulse_offsets[index]+pulse_counts[index]]
for trigger, pulse_edges in zip(triggers[pulses_slice], edges[pulses_slice]): for trigger, pulse_edges in zip(triggers[pulses_slice], edges[pulses_slice]):
trigger_slice = np.s_[trigger['start']:trigger['stop']] trigger_slice = np.s_[trigger['start']:trigger['stop']]
for trace, channel_params, channel_edges in zip(traces_corr, discr_params, pulse_edges): for trace, channel_params, channel_edges in zip(traces_corr, discr_params, pulse_edges):
discr_func(trace[trigger_slice], channel_edges, **channel_params) discr_func(trace[trigger_slice], channel_edges, **channel_params)
pulse_edges += trigger['offset'] pulse_edges += trigger['offset']
pulse_edges *= time_cal pulse_edges *= time_cal
with timing(f'find_edges, {det_name}'): with timing(f'find_edges, {det_name}'):
psh.map(find_edges, dc.select(det_sourcekeys)) psh.map(find_edges, dc.select(det_sourcekeys))
edges_by_det[det_name] = edges edges_by_det[det_name] = edges
avg_traces_by_det[det_name] = avg_traces.sum(axis=0) / len(dc.train_ids) avg_traces_by_det[det_name] = avg_traces.sum(axis=0) / len(dc.train_ids)
with np.printoptions(precision=2, suppress=True): with np.printoptions(precision=2, suppress=True):
print(edges[:5, :, :8]) print(edges[:5, :, :8])
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Global average of analog signals ### Global average of analog signals
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
for i, det_name in enumerate(remi['detector'].keys()): for i, det_name in enumerate(remi['detector'].keys()):
fig, axs = plt.subplots(num=10+i, nrows=7, figsize=(9.5, 8), clear=True, fig, axs = plt.subplots(num=10+i, nrows=7, figsize=(9.5, 8), clear=True,
gridspec_kw=dict(left=0.1, right=0.98, top=0.98, bottom=0.1)) gridspec_kw=dict(left=0.1, right=0.98, top=0.98, bottom=0.1))
fig.text(0.02, 0.98, det_name.upper(), rotation=90, ha='left', va='top', size='x-large') fig.text(0.02, 0.98, det_name.upper(), rotation=90, ha='left', va='top', size='x-large')
for edge_idx, edge_name in enumerate(['u1', 'u2', 'v1', 'v2', 'w1', 'w2', 'mcp']): for edge_idx, edge_name in enumerate(['u1', 'u2', 'v1', 'v2', 'w1', 'w2', 'mcp']):
axs[edge_idx].plot(avg_traces_by_det[det_name][edge_idx], lw=1) axs[edge_idx].plot(avg_traces_by_det[det_name][edge_idx], lw=1)
axs[edge_idx].tick_params(labelbottom=False) axs[edge_idx].tick_params(labelbottom=False)
axs[edge_idx].set_ylabel(edge_name) axs[edge_idx].set_ylabel(edge_name)
axs[-1].tick_params(labelbottom=True) axs[-1].tick_params(labelbottom=True)
pass pass
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Sample for digitized traces ### Sample for digitized traces
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
for i, det_name in enumerate(remi['detector'].keys()): for i, det_name in enumerate(remi['detector'].keys()):
edges = edges_by_det[det_name] edges = edges_by_det[det_name]
fig = plt.figure(num=100+i, figsize=(9.5, 8)) fig = plt.figure(num=100+i, figsize=(9.5, 8))
grid = fig.add_gridspec(ncols=2, nrows=4, left=0.1, right=0.98, top=0.98, bottom=0.1) grid = fig.add_gridspec(ncols=2, nrows=4, left=0.1, right=0.98, top=0.98, bottom=0.1)
fig.text(0.02, 0.98, det_name.upper(), rotation=90, ha='left', va='top', size='x-large') fig.text(0.02, 0.98, det_name.upper(), rotation=90, ha='left', va='top', size='x-large')
for signal_idx, signal_name in enumerate(['u1', 'u2', 'v1', 'v2', 'w1', 'w2', 'mcp']): for signal_idx, signal_name in enumerate(['u1', 'u2', 'v1', 'v2', 'w1', 'w2', 'mcp']):
row = (1 + signal_idx // 2) if signal_idx < 6 else 0 row = (1 + signal_idx // 2) if signal_idx < 6 else 0
col = (signal_idx % 2) if signal_idx < 6 else np.s_[:] col = (signal_idx % 2) if signal_idx < 6 else np.s_[:]
ax = fig.add_subplot(grid[row, col]) ax = fig.add_subplot(grid[row, col])
finite_edges = np.isfinite(edges[:, signal_idx, 0]) finite_edges = np.isfinite(edges[:, signal_idx, 0])
if not finite_edges.any(): if not finite_edges.any():
continue continue
pulse_idx = np.uint64(finite_edges.nonzero()[0][0]) # Is combined with other uint64 values below. pulse_idx = np.uint64(finite_edges.nonzero()[0][0]) # Is combined with other uint64 values below.
train_idx = (pulse_idx >= pulse_offsets).nonzero()[0][-1] train_idx = (pulse_idx >= pulse_offsets).nonzero()[0][-1]
trigger = triggers[pulse_idx] trigger = triggers[pulse_idx]
sourcekey = remi.get_channel_sourcekey( sourcekey = remi.get_channel_sourcekey(
remi['detector'][det_name]['channels'][signal_idx]) remi['detector'][det_name]['channels'][signal_idx])
train_trace = dc[sourcekey].select_trains(np.s_[train_idx:train_idx+1]).ndarray()[0] train_trace = dc[sourcekey].select_trains(np.s_[train_idx:train_idx+1]).ndarray()[0]
corr_trace = np.zeros_like(train_trace, dtype=np.float64) corr_trace = np.zeros_like(train_trace, dtype=np.float64)
remi.get_baseline_corrector()( remi.get_baseline_corrector()(
train_trace, corr_trace, train_trace, corr_trace,
np.empty(remi['digitizer']['baseline_symmetry'], dtype=np.float64), np.empty(remi['digitizer']['baseline_symmetry'], dtype=np.float64),
*remi.get_baseline_limits(len(train_trace))[:2]) *remi.get_baseline_limits(len(train_trace))[:2])
pulse_trace = corr_trace[np.s_[trigger['start']:trigger['stop']]] pulse_trace = corr_trace[np.s_[trigger['start']:trigger['stop']]]
x_time = remi.get_time_calibration() * (np.arange(len(pulse_trace)) + trigger['offset']) x_time = remi.get_time_calibration() * (np.arange(len(pulse_trace)) + trigger['offset'])
ax.plot(x_time, pulse_trace, lw=1) ax.plot(x_time, pulse_trace, lw=1)
ax.set_xlim(x_time[0], x_time[-1]) ax.set_xlim(x_time[0], x_time[-1])
ax.set_ylim(-200, pulse_trace.max()*1.1) ax.set_ylim(-200, pulse_trace.max()*1.1)
ax.text(x_time[-1], pulse_trace.max(), ax.text(x_time[-1], pulse_trace.max(),
f'T{train_idx} P{pulse_idx - pulse_offsets[train_idx]} ', f'T{train_idx} P{pulse_idx - pulse_offsets[train_idx]} ',
va='top', ha='right') va='top', ha='right')
ax.tick_params(labelbottom=False) ax.tick_params(labelbottom=False)
ax.set_ylabel(signal_name) ax.set_ylabel(signal_name)
ax.vlines(edges[pulse_idx, signal_idx, :], *ax.get_ylim(), color='red', linewidth=1) ax.vlines(edges[pulse_idx, signal_idx, :], *ax.get_ylim(), color='red', linewidth=1)
pass pass
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Digitized channel spectra ### Digitized channel spectra
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
for i, det_name in enumerate(remi['detector'].keys()): for i, det_name in enumerate(remi['detector'].keys()):
fig = plt.figure(num=20+i, figsize=(9.5, 6)) fig = plt.figure(num=20+i, figsize=(9.5, 6))
edges = edges_by_det[det_name] edges = edges_by_det[det_name]
min_edge = edges[np.isfinite(edges)].min() min_edge = edges[np.isfinite(edges)].min()
max_edge = edges[np.isfinite(edges)].max() max_edge = edges[np.isfinite(edges)].max()
grid = fig.add_gridspec(ncols=3, nrows=3, left=0.08, right=0.98, top=0.95, hspace=0.4) grid = fig.add_gridspec(ncols=3, nrows=3, left=0.08, right=0.98, top=0.95, hspace=0.4)
fig.text(0.02, 0.98, det_name.upper(), rotation=90, ha='left', va='top', size='x-large') fig.text(0.02, 0.98, det_name.upper(), rotation=90, ha='left', va='top', size='x-large')
numx = fig.add_subplot(grid[0, 0]) numx = fig.add_subplot(grid[0, 0])
numx.set_title('Edges per pulse') numx.set_title('Edges per pulse')
agg_window = num_pulses // 60 agg_window = num_pulses // 60
max_num_edges = 0.0 max_num_edges = 0.0
max_spectral_intensity = 0 max_spectral_intensity = 0
hist_axs = [] hist_axs = []
for edge_idx, edge_name in enumerate(['u1', 'u2', 'v1', 'v2', 'w1', 'w2', 'mcp']): for edge_idx, edge_name in enumerate(['u1', 'u2', 'v1', 'v2', 'w1', 'w2', 'mcp']):
num_edges = np.isfinite(edges[:, edge_idx, :]).sum(axis=1) num_edges = np.isfinite(edges[:, edge_idx, :]).sum(axis=1)
num_edges = num_edges[:((len(num_edges) // agg_window) * agg_window)] num_edges = num_edges[:((len(num_edges) // agg_window) * agg_window)]
num_edges = num_edges.reshape(-1, agg_window).mean(axis=1) num_edges = num_edges.reshape(-1, agg_window).mean(axis=1)
if edge_idx < 6: if edge_idx < 6:
plot_kwargs = dict(c=f'C{edge_idx}', ls='solid', lw=1.0) plot_kwargs = dict(c=f'C{edge_idx}', ls='solid', lw=1.0)
else: else:
plot_kwargs = dict(c='k', ls='dashed', lw=1.0) plot_kwargs = dict(c='k', ls='dashed', lw=1.0)
numx.plot(np.arange(len(num_edges)) * agg_window, num_edges, label=edge_name, **plot_kwargs) numx.plot(np.arange(len(num_edges)) * agg_window, num_edges, label=edge_name, **plot_kwargs)
max_num_edges = max(max_num_edges, num_edges.max()) max_num_edges = max(max_num_edges, num_edges.max())
cur_edges = edges[:, edge_idx, :].flatten() cur_edges = edges[:, edge_idx, :].flatten()
if edge_idx < 6: if edge_idx < 6:
row = 1 + edge_idx % 2 row = 1 + edge_idx % 2
col = edge_idx // 2 col = edge_idx // 2
else: else:
row = 0 row = 0
col = np.s_[1:3] col = np.s_[1:3]
ax = fig.add_subplot(grid[row, col]) ax = fig.add_subplot(grid[row, col])
ax.set_title(f'TOF spectrum: {edge_name}') ax.set_title(f'TOF spectrum: {edge_name}')
y, _, _ = ax.hist(cur_edges[np.isfinite(cur_edges)], bins=int((max_edge - min_edge) // 5), y, _, _ = ax.hist(cur_edges[np.isfinite(cur_edges)], bins=int((max_edge - min_edge) // 5),
range=(min_edge, max_edge), color=plot_kwargs['c'], histtype='step', linewidth=1) range=(min_edge, max_edge), color=plot_kwargs['c'], histtype='step', linewidth=1)
hist_axs.append(ax) hist_axs.append(ax)
max_spectral_intensity = max(max_spectral_intensity, y.max()) max_spectral_intensity = max(max_spectral_intensity, y.max())
numx.tick_params(labelbottom=False) numx.tick_params(labelbottom=False)
numx.set_ylim(0, 1.2*max_num_edges) numx.set_ylim(0, 1.2*max_num_edges)
for ax in hist_axs: for ax in hist_axs:
ax.set_ylim(0, max_spectral_intensity*1.1) ax.set_ylim(0, max_spectral_intensity*1.1)
ax.ticklabel_format(axis='y', style='sci', scilimits=(0, 3)) ax.ticklabel_format(axis='y', style='sci', scilimits=(0, 3))
pass pass
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
# Detector diagnostics # Detector diagnostics
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
for i, det_name in enumerate(remi['detector'].keys()): for i, det_name in enumerate(remi['detector'].keys()):
edges = edges_by_det[det_name] edges = edges_by_det[det_name]
sort = remi.get_dld_sorter(det_name) sort = remi.get_dld_sorter(det_name)
sum_shifts = sort.sum_shifts if sort.sum_shifts != (0.0, 0.0, 0.0) else None sum_shifts = sort.sum_shifts if sort.sum_shifts != (0.0, 0.0, 0.0) else None
is_valid = remi.get_presort_mask(edges, edge_idx=0, w=not quad_anode, is_valid = remi.get_presort_mask(edges, edge_idx=0, w=not quad_anode,
sum_limit=max(sort.uncorrected_time_sum_half_widths), sum_limit=max(sort.uncorrected_time_sum_half_widths),
sum_shifts=sum_shifts) sum_shifts=sum_shifts)
signals, sums = remi.get_signals_and_sums(edges, indices=sort.channel_indices, sum_shifts=sum_shifts, signals, sums = remi.get_signals_and_sums(edges, indices=sort.channel_indices, sum_shifts=sum_shifts,
mask=is_valid) mask=is_valid)
fig = plot_detector_diagnostics(signals=signals, sums=sums, fig_num=30+i, im_scale=1.5, fig = plot_detector_diagnostics(signals=signals, sums=sums, fig_num=30+i, im_scale=1.5,
sum_range=max(sort.uncorrected_time_sum_half_widths), sum_range=max(sort.uncorrected_time_sum_half_widths),
sorter=sort) sorter=sort)
fig.text(0.02, 0.98, det_name.upper() + ' before corrections', rotation=90, ha='left', va='top', size='x-large') fig.text(0.02, 0.98, det_name.upper() + ' before corrections', rotation=90, ha='left', va='top', size='x-large')
if remi['detector'][det_name]['use_sum_correction'] or remi['detector'][det_name]['use_pos_correction']: if remi['detector'][det_name]['use_sum_correction'] or remi['detector'][det_name]['use_pos_correction']:
n_masked = is_valid.sum() n_masked = is_valid.sum()
signals = np.full((n_masked, 3), np.nan, dtype=np.float64) signals = np.full((n_masked, 3), np.nan, dtype=np.float64)
sums = np.full((n_masked, 3), np.nan, dtype=np.float64) sums = np.full((n_masked, 3), np.nan, dtype=np.float64)
sort.correct(edges[is_valid], signals, sums) sort.correct(edges[is_valid], signals, sums)
fig = plot_detector_diagnostics(signals=signals, sums=sums, fig_num=40+i, im_scale=1.5, fig = plot_detector_diagnostics(signals=signals, sums=sums, fig_num=40+i, im_scale=1.5,
sum_range=max(sort.uncorrected_time_sum_half_widths), sum_range=max(sort.uncorrected_time_sum_half_widths),
sorter=sort) sorter=sort)
fig.text(0.02, 0.98, det_name.upper() + ' after corrections', rotation=90, ha='left', va='top', size='x-large') fig.text(0.02, 0.98, det_name.upper() + ' after corrections', rotation=90, ha='left', va='top', size='x-large')
pass pass
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
# Hit reconstruction # Hit reconstruction
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
psh.set_default_context('processes', num_workers=remi.get_num_workers(mp_rec_hits)) psh.set_default_context('processes', num_workers=remi.get_num_workers(mp_rec_hits))
signals_by_det = {} signals_by_det = {}
hits_by_det = {} hits_by_det = {}
hit_counts_by_det = {} hit_counts_by_det = {}
for det_name, det in remi['detector'].items(): for det_name, det in remi['detector'].items():
edges = edges_by_det[det_name] edges = edges_by_det[det_name]
signals = psh.alloc(shape=(num_pulses, 50), dtype=signal_dt, fill=np.nan) signals = psh.alloc(shape=(num_pulses, 50), dtype=signal_dt, fill=np.nan)
hits = psh.alloc(shape=(num_pulses, 50), dtype=hit_dt, fill=(np.nan, np.nan, np.nan, -1)) hits = psh.alloc(shape=(num_pulses, 50), dtype=hit_dt, fill=(np.nan, np.nan, np.nan, -1))
hit_counts = psh.alloc(shape=len(dc.train_ids), dtype=np.uint32) hit_counts = psh.alloc(shape=len(dc.train_ids), dtype=np.uint32)
def prepare_hit_worker(worker_id): def prepare_hit_worker(worker_id):
sort = remi.get_dld_sorter(det_name) sort = remi.get_dld_sorter(det_name)
yield yield
@psh.with_init(prepare_hit_worker) @psh.with_init(prepare_hit_worker)
def reconstruct_hits(worker_id, index, train_id): def reconstruct_hits(worker_id, index, train_id):
hit_counts[index] += sort.run_on_train( hit_counts[index] += sort.run_on_train(
edges, signals, hits, pulse_offsets[index], pulse_offsets[index] + pulse_counts[index]) edges, signals, hits, pulse_offsets[index], pulse_offsets[index] + pulse_counts[index])
with timing(f'rec_hits, {det_name}'): with timing(f'rec_hits, {det_name}'):
psh.map(reconstruct_hits, dc.train_ids) psh.map(reconstruct_hits, dc.train_ids)
signals_by_det[det_name] = signals signals_by_det[det_name] = signals
hits_by_det[det_name] = hits hits_by_det[det_name] = hits
hit_counts_by_det[det_name] = hit_counts hit_counts_by_det[det_name] = hit_counts
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
fig, ax = plt.subplots(num=50+i, figsize=(9.5, 4), ncols=1, clear=True, fig, ax = plt.subplots(num=50+i, figsize=(9.5, 4), ncols=1, clear=True,
gridspec_kw=dict(top=0.92, right=0.98, left=0.05, bottom=0.12)) gridspec_kw=dict(top=0.92, right=0.98, left=0.05, bottom=0.12))
max_num_hits = 0.0 max_num_hits = 0.0
for det_name in remi['detector'].keys(): for det_name in remi['detector'].keys():
agg_window = num_pulses // 1000 agg_window = num_pulses // 1000
num_hits = np.isfinite(hits_by_det[det_name]['x']).sum(axis=1) num_hits = np.isfinite(hits_by_det[det_name]['x']).sum(axis=1)
num_hits = num_hits[:(len(num_hits) // agg_window) * agg_window] num_hits = num_hits[:(len(num_hits) // agg_window) * agg_window]
num_hits = num_hits.reshape(-1, agg_window).mean(axis=1) num_hits = num_hits.reshape(-1, agg_window).mean(axis=1)
max_num_hits = max(max_num_hits, num_hits.max()) max_num_hits = max(max_num_hits, num_hits.max())
ax.plot(np.arange(0, (num_pulses // agg_window) * agg_window, agg_window), num_hits, ax.plot(np.arange(0, (num_pulses // agg_window) * agg_window, agg_window), num_hits,
lw=1, label=det_name.upper()) lw=1, label=det_name.upper())
ax.set_title('Hits per pulse') ax.set_title('Hits per pulse')
ax.set_xlabel('Pulse index') ax.set_xlabel('Pulse index')
ax.set_ylim(0, max_num_hits*1.1) ax.set_ylim(0, max_num_hits*1.1)
ax.legend() ax.legend()
pass pass
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Reconstruction methods ### Reconstruction methods
Each hit may be reconstructed by one of 19 different methods. These differ by the number of real signals across the channels, which could be combined to form the hit. Each of these methods is designed by a number between `0` and `19` (with empty hits using `-1`), which can be found in the `m` key of a hit, e.g.: Each hit may be reconstructed by one of 19 different methods. These differ by the number of real signals across the channels, which could be combined to form the hit. Each of these methods is designed by a number between `0` and `19` (with empty hits using `-1`), which can be found in the `m` key of a hit, e.g.:
* `0`: All six anode signals and the corresponding MCP signal were found. * `0`: All six anode signals and the corresponding MCP signal were found.
* `4`: One signal on layer `u` is missing, all other signals for this event were found. * `4`: One signal on layer `u` is missing, all other signals for this event were found.
* `18`: Only one anode signal on each layer was found and the MCP signal is missing. There is no way to check whether this combination of signals is actually valid. * `18`: Only one anode signal on each layer was found and the MCP signal is missing. There is no way to check whether this combination of signals is actually valid.
| Method | `u+v+w +mcp` | | Method | `u+v+w +mcp` |
| - | - | | - | - |
| 0 | `2+2+2 +1` | | 0 | `2+2+2 +1` |
| 1 | `0+2+2 +1` | | 1 | `0+2+2 +1` |
| 2 | `2+0+2 +1` | | 2 | `2+0+2 +1` |
| 3 | `2+2+0 +1` | | 3 | `2+2+0 +1` |
| 4 | `1+2+2 +1` (2 permutations) | | 4 | `1+2+2 +1` (2 permutations) |
| 5 | `2+1+2 +1` (2 permutations) | | 5 | `2+1+2 +1` (2 permutations) |
| 6 | `2+2+1 +1` (2 permutations) | | 6 | `2+2+1 +1` (2 permutations) |
| 7 | `2+2+2 +0` | | 7 | `2+2+2 +0` |
| 8 | `0+2+2 +0` | | 8 | `0+2+2 +0` |
| 9 | `2+0+2 +0` | | 9 | `2+0+2 +0` |
| 10 | `2+2+0 +0` | | 10 | `2+2+0 +0` |
| 11 | `1+2+2 +0` (2 permutations) | | 11 | `1+2+2 +0` (2 permutations) |
| 12 | `2+1+2 +0` (2 permutations) | | 12 | `2+1+2 +0` (2 permutations) |
| 13 | `2+2+1 +0` (2 permutations) | | 13 | `2+2+1 +0` (2 permutations) |
| 14 | `2+1+1 +1` `1+2+1 +1` `1+1+2 +1` (12 permutations) | | 14 | `2+1+1 +1` `1+2+1 +1` `1+1+2 +1` (12 permutations) |
| 15 | `2+1+0 +1` `2+0+1 +1` `1+2+0 +1` `1+0+2 +1` `0+2+1 +1` `0+1+2 +1` (12 permutations) | | 15 | `2+1+0 +1` `2+0+1 +1` `1+2+0 +1` `1+0+2 +1` `0+2+1 +1` `0+1+2 +1` (12 permutations) |
| 16 | `1+1+1 +1` (8 permutations) | | 16 | `1+1+1 +1` (8 permutations) |
| 17 | `2+1+1 +0` `1+2+1 +0` `1+1+2 +0` (12 permutations) | | 17 | `2+1+1 +0` `1+2+1 +0` `1+1+2 +0` (12 permutations) |
| 18 | `1+1+1 +0` (8 permutations) | | 18 | `1+1+1 +0` (8 permutations) |
| 19 | `2+1+0 +0` `2+0+1 +0` `1+2+0 +0` `1+0+2 +0` `0+1+2 +0` `0+2+1 +0` (12 permutations) | | 19 | `2+1+0 +0` `2+0+1 +0` `1+2+0 +0` `1+0+2 +0` `0+1+2 +0` `0+2+1 +0` (12 permutations) |
* For hits reconstructed with method `> 10`, extra attention should be given to ensure they add meaningful signal. * For hits reconstructed with method `> 10`, extra attention should be given to ensure they add meaningful signal.
* Any method `> 14` has to considered risky, because neither a time sum nor the position can be checked. If the scale factors and/or `w` shift are not correct, then the number of events reconstructed with the risky methods will increase. They will most likely be *ghost hits*, which do not correspond to actual impacts on the detector. * Any method `> 14` has to considered risky, because neither a time sum nor the position can be checked. If the scale factors and/or `w` shift are not correct, then the number of events reconstructed with the risky methods will increase. They will most likely be *ghost hits*, which do not correspond to actual impacts on the detector.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
for i, det_name in enumerate(remi['detector'].keys()): for i, det_name in enumerate(remi['detector'].keys()):
hits = hits_by_det[det_name] hits = hits_by_det[det_name]
fig, ax = plt.subplots(num=60+i, figsize=(9.5, 5), ncols=1, clear=True, fig, ax = plt.subplots(num=60+i, figsize=(9.5, 5), ncols=1, clear=True,
gridspec_kw=dict(left=0.08, right=0.91, top=0.8)) gridspec_kw=dict(left=0.08, right=0.91, top=0.8))
fig.text(0.02, 0.98, det_name.upper(), rotation=90, ha='left', va='top', size='x-large') fig.text(0.02, 0.98, det_name.upper(), rotation=90, ha='left', va='top', size='x-large')
method_bins = np.bincount(hits['m'][hits['m'] >= 0], minlength=20) method_bins = np.bincount(hits['m'][hits['m'] >= 0], minlength=20)
ax.bar(np.arange(20), method_bins, width=0.5) ax.bar(np.arange(20), method_bins, width=0.5)
ax.set_xlabel('Reconstruction method') ax.set_xlabel('Reconstruction method')
ax.set_xlim(-0.5, 19.5) ax.set_xlim(-0.5, 19.5)
ax.set_xticks(np.arange(20)) ax.set_xticks(np.arange(20))
ax.set_ylabel('Number of hits') ax.set_ylabel('Number of hits')
ax.set_ylim(0, method_bins.max()*1.05) ax.set_ylim(0, method_bins.max()*1.05)
ylims = ax.get_ylim() ylims = ax.get_ylim()
ax.tick_params(which='both', right=True, labelright=True) ax.tick_params(which='both', right=True, labelright=True)
num_risky = method_bins[15:].sum() num_risky = method_bins[15:].sum()
num_total = method_bins.sum() num_total = method_bins.sum()
ax.text(14.2, method_bins.max(), f'{(100*(num_total-num_risky)/num_total):.2g}%', ax.text(14.2, method_bins.max(), f'{(100*(num_total-num_risky)/num_total):.2g}%',
va='top', ha='right', color='black') va='top', ha='right', color='black')
ax.text(14.8, method_bins.max(), f'{(100*num_risky/num_total):.2g}%', ax.text(14.8, method_bins.max(), f'{(100*num_risky/num_total):.2g}%',
va='top', ha='left', color='red') va='top', ha='left', color='red')
ax.fill([14.5, 19.5, 19.5, 14.5], [ylims[0], ylims[0], ylims[1], ylims[1]], c='r', alpha=0.2) ax.fill([14.5, 19.5, 19.5, 14.5], [ylims[0], ylims[0], ylims[1], ylims[1]], c='r', alpha=0.2)
labelx = ax.twiny() labelx = ax.twiny()
labelx.set_xlim(*ax.get_xlim()) labelx.set_xlim(*ax.get_xlim())
labelx.set_xticks(ax.get_xticks()) labelx.set_xticks(ax.get_xticks())
labelx.set_xticklabels([ labelx.set_xticklabels([
'2+2+2 +1', '2+2+2 +1',
'0+2+2 +1', '2+0+2 +1', '2+2+0 +1', '0+2+2 +1', '2+0+2 +1', '2+2+0 +1',
'1+2+2 +1', '2+1+2 +1', '2+2+1 +1', '1+2+2 +1', '2+1+2 +1', '2+2+1 +1',
'2+2+2 +0', '2+2+2 +0',
'0+2+2 +0', '2+0+2 +0', '2+2+0 +0', '1+2+2 +0', '2+1+2 +0', '2+2+1 +0', '0+2+2 +0', '2+0+2 +0', '2+2+0 +0', '1+2+2 +0', '2+1+2 +0', '2+2+1 +0',
'2+1+1 +1', '2+1+1 +1',
'2+1+0 +1', '2+1+0 +1',
'1+1+1 +1', '1+1+1 +1',
'2+1+1 +0', '2+1+1 +0',
'1+1+1 +0', '1+1+1 +0',
'2+1+0 +0', '2+1+0 +0',
], rotation=90) ], rotation=90)
min_rel_tick = np.ceil((ax.get_ylim()[0] / num_total) / 0.1) * 0.1 min_rel_tick = np.ceil((ax.get_ylim()[0] / num_total) / 0.1) * 0.1
max_rel_tick = np.floor((method_bins.max() / num_total) / 0.1) * 0.1 max_rel_tick = np.floor((method_bins.max() / num_total) / 0.1) * 0.1
rely = ax.twinx() rely = ax.twinx()
rely.set_ylim(*ax.get_ylim()) rely.set_ylim(*ax.get_ylim())
rely.set_yticks(np.arange(0.0, max_rel_tick+0.01, 0.1)*num_total) rely.set_yticks(np.arange(0.0, max_rel_tick+0.01, 0.1)*num_total)
rely.set_yticks(np.arange(0.0, ylims[1]/num_total, 0.02)*num_total, minor=True) rely.set_yticks(np.arange(0.0, ylims[1]/num_total, 0.02)*num_total, minor=True)
rely.set_yticklabels([f'{(y/num_total)*100:.0f}%' for y in rely.get_yticks()]) rely.set_yticklabels([f'{(y/num_total)*100:.0f}%' for y in rely.get_yticks()])
rely.set_ylabel('Percentage of total hits') rely.set_ylabel('Percentage of total hits')
pass pass
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Detector image and fishes ### Detector image and fishes
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
for i, det_name in enumerate(remi['detector'].keys()): for i, det_name in enumerate(remi['detector'].keys()):
flat_hits = hits_by_det[det_name].reshape(-1) flat_hits = hits_by_det[det_name].reshape(-1)
flat_hits = flat_hits[np.isfinite(flat_hits[:]['x'])] flat_hits = flat_hits[np.isfinite(flat_hits[:]['x'])]
flat_hits = flat_hits[flat_hits['m'] < 10] flat_hits = flat_hits[flat_hits['m'] < 10]
fig = plt.figure(num=70+i, figsize=(9, 13.5)) fig = plt.figure(num=70+i, figsize=(9, 13.5))
fig.text(0.02, 0.98, det_name.upper(), rotation=90, ha='left', va='top', size='x-large') fig.text(0.02, 0.98, det_name.upper(), rotation=90, ha='left', va='top', size='x-large')
fig.text(0.02, 0.02, det_name.upper(), rotation=90, ha='left', va='bottom', size='x-large') fig.text(0.02, 0.02, det_name.upper(), rotation=90, ha='left', va='bottom', size='x-large')
imp = fig.add_axes([0.1 + 0.25/2, 0.56, 0.6, 0.4]) imp = fig.add_axes([0.1 + 0.25/2, 0.56, 0.6, 0.4])
txp = fig.add_axes([0.1, 0.28, 0.85, 0.22]) txp = fig.add_axes([0.1, 0.28, 0.85, 0.22])
typ = fig.add_axes([0.1, 0.04, 0.85, 0.22]) typ = fig.add_axes([0.1, 0.04, 0.85, 0.22])
im_radius = remi['detector'][det_name]['mcp_radius']*1.1 im_radius = remi['detector'][det_name]['mcp_radius']*1.1
min_tof = flat_hits['t'].min() min_tof = flat_hits['t'].min()
max_tof = flat_hits['t'].max() max_tof = flat_hits['t'].max()
imp.hist2d(flat_hits['x'], flat_hits['y'], bins=(256, 256), imp.hist2d(flat_hits['x'], flat_hits['y'], bins=(256, 256),
range=[[-im_radius, im_radius], [-im_radius, im_radius]], norm=LogNorm()) range=[[-im_radius, im_radius], [-im_radius, im_radius]], norm=LogNorm())
imp.xaxis.set_label_position('top') imp.xaxis.set_label_position('top')
imp.set_xlabel('X / mm') imp.set_xlabel('X / mm')
imp.set_ylabel('Y / mm') imp.set_ylabel('Y / mm')
imp.tick_params(right=True, labelright=True, top=True, labeltop=True) imp.tick_params(right=True, labelright=True, top=True, labeltop=True)
imp.grid() imp.grid()
for ax, dim_label in zip([txp, typ], ['x', 'y']): for ax, dim_label in zip([txp, typ], ['x', 'y']):
ax.hist2d(flat_hits['t'], flat_hits[dim_label], bins=(int((max_tof - min_tof) // 5), 256), ax.hist2d(flat_hits['t'], flat_hits[dim_label], bins=(int((max_tof - min_tof) // 5), 256),
range=[[min_tof, max_tof], [-im_radius, im_radius]], norm=LogNorm()) range=[[min_tof, max_tof], [-im_radius, im_radius]], norm=LogNorm())
ax.set_ylabel(f'{dim_label.upper()} / mm') ax.set_ylabel(f'{dim_label.upper()} / mm')
typ.set_xlabel('Time-of-flight / ns') typ.set_xlabel('Time-of-flight / ns')
txp.tick_params(bottom=True, labelbottom=False, top=True, labeltop=True, right=True, labelright=True) txp.tick_params(bottom=True, labelbottom=False, top=True, labeltop=True, right=True, labelright=True)
typ.tick_params(right=True, labelright=True, top=True) typ.tick_params(right=True, labelright=True, top=True)
pass pass
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
# Transformed data files # Transformed data files
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
seq_len = out_seq_len if out_seq_len > 0 else len(dc.files[0].train_ids) seq_len = out_seq_len if out_seq_len > 0 else len(dc.files[0].train_ids)
dataset_kwargs = {k[8:]: v for k, v in locals().items() if k.startswith('dataset_compression')} dataset_kwargs = {k[8:]: v for k, v in locals().items() if k.startswith('dataset_compression')}
Path(out_folder).mkdir(parents=True, exist_ok=True) Path(out_folder).mkdir(parents=True, exist_ok=True)
print('Writing sequence files', flush=True, end='') print('Writing sequence files', flush=True, end='')
t_write = timing('write_files') t_write = timing('write_files')
t_write.__enter__() t_write.__enter__()
for seq_id, train_mask, pulse_mask in sequence_pulses(dc.train_ids, pulse_counts, pulse_offsets, seq_len): for seq_id, train_mask, pulse_mask in sequence_pulses(dc.train_ids, pulse_counts, pulse_offsets, seq_len):
seq_train_ids = dc.train_ids[train_mask] seq_train_ids = dc.train_ids[train_mask]
with DataFile.from_details(out_folder, out_aggregator, run, seq_id) as outp: with DataFile.from_details(out_folder, out_aggregator, run, seq_id) as outp:
outp.create_index(seq_train_ids) outp.create_index(seq_train_ids)
for det_name in remi['detector']: for det_name in remi['detector']:
cur_device_id = det_device_id.format(karabo_id=karabo_id, det_name=det_name.upper()) cur_device_id = det_device_id.format(karabo_id=karabo_id, det_name=det_name.upper())
cur_control_data = outp.create_control_source(cur_device_id) cur_control_data = outp.create_control_source(cur_device_id)
# Manually manipulate the file here, still creates the index properly. # Manually manipulate the file here, still creates the index properly.
remi.attach_detector_config(det_name, cur_control_data.get_run_group()) remi.attach_detector_config(det_name, cur_control_data.get_run_group())
cur_control_data.create_index(len(seq_train_ids)) cur_control_data.create_index(len(seq_train_ids))
cur_fast_data = outp.create_instrument_source(f'{cur_device_id}:{det_output_key}') cur_fast_data = outp.create_instrument_source(f'{cur_device_id}:{det_output_key}')
if save_raw_triggers: if save_raw_triggers:
cur_fast_data.create_key('raw.triggers', triggers[pulse_mask], cur_fast_data.create_key('raw.triggers', triggers[pulse_mask],
chunks=tuple(chunks_triggers), **dataset_kwargs) chunks=tuple(chunks_triggers), **dataset_kwargs)
if save_raw_edges: if save_raw_edges:
cur_fast_data.create_key('raw.edges', edges_by_det[det_name][pulse_mask], cur_fast_data.create_key('raw.edges', edges_by_det[det_name][pulse_mask],
chunks=tuple(chunks_edges), **dataset_kwargs) chunks=tuple(chunks_edges), **dataset_kwargs)
if save_rec_signals: if save_rec_signals:
cur_fast_data.create_key('rec.signals', signals_by_det[det_name][pulse_mask], cur_fast_data.create_key('rec.signals', signals_by_det[det_name][pulse_mask],
chunks=tuple(chunks_signals), **dataset_kwargs) chunks=tuple(chunks_signals), **dataset_kwargs)
if save_rec_hits: if save_rec_hits:
cur_fast_data.create_key('rec.hits', hits_by_det[det_name][pulse_mask], cur_fast_data.create_key('rec.hits', hits_by_det[det_name][pulse_mask],
chunks=tuple(chunks_hits), **dataset_kwargs) chunks=tuple(chunks_hits), **dataset_kwargs)
cur_fast_data.create_index(raw=pulse_counts[train_mask], rec=pulse_counts[train_mask]) cur_fast_data.create_index(raw=pulse_counts[train_mask], rec=pulse_counts[train_mask])
outp.create_metadata(like=dc) outp.create_metadata(like=dc)
print('.', flush=True, end='') print('.', flush=True, end='')
print('') print('')
t_write.__exit__() t_write.__exit__()
``` ```
......
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