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Commit 23a7e3f2 authored by Thomas Kluyver's avatar Thomas Kluyver
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Add boolean option to exclude memory cell order from detector conditions

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%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
# LPD Offline Correction # # LPD Offline Correction #
Author: European XFEL Data Analysis Group Author: European XFEL Data Analysis Group
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# Input parameters # Input parameters
in_folder = "/gpfs/exfel/exp/FXE/202201/p003073/raw/" # the folder to read data from, required in_folder = "/gpfs/exfel/exp/FXE/202201/p003073/raw/" # the folder to read data from, required
out_folder = "/gpfs/exfel/data/scratch/schmidtp/random/LPD_test" # the folder to output to, required out_folder = "/gpfs/exfel/data/scratch/schmidtp/random/LPD_test" # the folder to output to, required
metadata_folder = '' # Directory containing calibration_metadata.yml when run by xfel-calibrate. metadata_folder = '' # Directory containing calibration_metadata.yml when run by xfel-calibrate.
sequences = [-1] # Sequences to correct, use [-1] for all sequences = [-1] # Sequences to correct, use [-1] for all
modules = [-1] # Modules indices to correct, use [-1] for all, only used when karabo_da is empty modules = [-1] # Modules indices to correct, use [-1] for all, only used when karabo_da is empty
karabo_da = [''] # Data aggregators names to correct, use [''] for all karabo_da = [''] # Data aggregators names to correct, use [''] for all
run = 10 # run to process, required run = 10 # run to process, required
# Source parameters # Source parameters
karabo_id = 'FXE_DET_LPD1M-1' # Karabo domain for detector. karabo_id = 'FXE_DET_LPD1M-1' # Karabo domain for detector.
input_source = '{karabo_id}/DET/{module_index}CH0:xtdf' # Input fast data source. input_source = '{karabo_id}/DET/{module_index}CH0:xtdf' # Input fast data source.
output_source = '' # Output fast data source, empty to use same as input. output_source = '' # Output fast data source, empty to use same as input.
# CalCat parameters # CalCat parameters
creation_time = "" # The timestamp to use with Calibration DB. Required Format: "YYYY-MM-DD hh:mm:ss" e.g. 2019-07-04 11:02:41 creation_time = "" # The timestamp to use with Calibration DB. Required Format: "YYYY-MM-DD hh:mm:ss" e.g. 2019-07-04 11:02:41
cal_db_interface = '' # Not needed, compatibility with current webservice. cal_db_interface = '' # Not needed, compatibility with current webservice.
cal_db_timeout = 0 # Not needed, compatbility with current webservice. cal_db_timeout = 0 # Not needed, compatbility with current webservice.
cal_db_root = '/gpfs/exfel/d/cal/caldb_store' cal_db_root = '/gpfs/exfel/d/cal/caldb_store'
# Operating conditions # Operating conditions
mem_cells = 512 # Memory cells, LPD constants are always taken with 512 cells. mem_cells = 512 # Memory cells, LPD constants are always taken with 512 cells.
bias_voltage = 250.0 # Detector bias voltage. bias_voltage = 250.0 # Detector bias voltage.
capacitor = '5pF' # Capacitor setting: 5pF or 50pF capacitor = '5pF' # Capacitor setting: 5pF or 50pF
photon_energy = 9.2 # Photon energy in keV. photon_energy = 9.2 # Photon energy in keV.
category = 0 # Whom to blame. category = 0 # Whom to blame.
use_cell_order = True # Whether to use memory cell order as a detector condition (not stored for older constants)
# Correction parameters # Correction parameters
offset_corr = True # Offset correction. offset_corr = True # Offset correction.
rel_gain = True # Gain correction based on RelativeGain constant. rel_gain = True # Gain correction based on RelativeGain constant.
ff_map = True # Gain correction based on FFMap constant. ff_map = True # Gain correction based on FFMap constant.
gain_amp_map = True # Gain correction based on GainAmpMap constant. gain_amp_map = True # Gain correction based on GainAmpMap constant.
# Output options # Output options
overwrite = True # set to True if existing data should be overwritten overwrite = True # set to True if existing data should be overwritten
chunks_data = 1 # HDF chunk size for pixel data in number of frames. chunks_data = 1 # HDF chunk size for pixel data in number of frames.
chunks_ids = 32 # HDF chunk size for cellId and pulseId datasets. chunks_ids = 32 # HDF chunk size for cellId and pulseId datasets.
create_virtual_cxi_in = '' # Folder to create virtual CXI files in (for each sequence). create_virtual_cxi_in = '' # Folder to create virtual CXI files in (for each sequence).
# Parallelization options # Parallelization options
sequences_per_node = 1 # Sequence files to process per node sequences_per_node = 1 # Sequence files to process per node
max_nodes = 8 # Maximum number of SLURM jobs to split correction work into max_nodes = 8 # Maximum number of SLURM jobs to split correction work into
num_workers = 8 # Worker processes per node, 8 is safe on 768G nodes but won't work on 512G. num_workers = 8 # Worker processes per node, 8 is safe on 768G nodes but won't work on 512G.
num_threads_per_worker = 32 # Number of threads per worker. num_threads_per_worker = 32 # Number of threads per worker.
def balance_sequences(in_folder, run, sequences, sequences_per_node, karabo_da, max_nodes): def balance_sequences(in_folder, run, sequences, sequences_per_node, karabo_da, max_nodes):
from xfel_calibrate.calibrate import balance_sequences as bs from xfel_calibrate.calibrate import balance_sequences as bs
return bs(in_folder, run, sequences, sequences_per_node, karabo_da, max_nodes=max_nodes) return bs(in_folder, run, sequences, sequences_per_node, karabo_da, max_nodes=max_nodes)
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
from collections import OrderedDict from collections import OrderedDict
from pathlib import Path from pathlib import Path
from time import perf_counter from time import perf_counter
import gc import gc
import re import re
import warnings import warnings
import numpy as np import numpy as np
import h5py import h5py
import matplotlib import matplotlib
matplotlib.use('agg') matplotlib.use('agg')
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
%matplotlib inline %matplotlib inline
from calibration_client import CalibrationClient from calibration_client import CalibrationClient
from calibration_client.modules import CalibrationConstantVersion from calibration_client.modules import CalibrationConstantVersion
import extra_data as xd import extra_data as xd
import extra_geom as xg import extra_geom as xg
import pasha as psh import pasha as psh
from extra_data.components import LPD1M from extra_data.components import LPD1M
from cal_tools.lpdalgs import correct_lpd_frames from cal_tools.lpdalgs import correct_lpd_frames
from cal_tools.tools import CalibrationMetadata, calcat_creation_time from cal_tools.tools import CalibrationMetadata, calcat_creation_time
from cal_tools.files import DataFile from cal_tools.files import DataFile
from cal_tools.restful_config import restful_config from cal_tools.restful_config import restful_config
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
# Prepare environment # Prepare environment
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
file_re = re.compile(r'^RAW-R(\d{4})-(\w+\d+)-S(\d{5})$') # This should probably move to cal_tools file_re = re.compile(r'^RAW-R(\d{4})-(\w+\d+)-S(\d{5})$') # This should probably move to cal_tools
run_folder = Path(in_folder) / f'r{run:04d}' run_folder = Path(in_folder) / f'r{run:04d}'
out_folder = Path(out_folder) out_folder = Path(out_folder)
out_folder.mkdir(exist_ok=True) out_folder.mkdir(exist_ok=True)
output_source = output_source or input_source output_source = output_source or input_source
cal_db_root = Path(cal_db_root) cal_db_root = Path(cal_db_root)
metadata = CalibrationMetadata(metadata_folder or out_folder) metadata = CalibrationMetadata(metadata_folder or out_folder)
creation_time = calcat_creation_time(in_folder, run, creation_time) creation_time = calcat_creation_time(in_folder, run, creation_time)
print(f'Using {creation_time.isoformat()} as creation time') print(f'Using {creation_time.isoformat()} as creation time')
# Pick all modules/aggregators or those selected. # Pick all modules/aggregators or those selected.
if not karabo_da or karabo_da == ['']: if not karabo_da or karabo_da == ['']:
if not modules or modules == [-1]: if not modules or modules == [-1]:
modules = list(range(16)) modules = list(range(16))
karabo_da = [f'LPD{i:02d}' for i in modules] karabo_da = [f'LPD{i:02d}' for i in modules]
# Pick all sequences or those selected. # Pick all sequences or those selected.
if not sequences or sequences == [-1]: if not sequences or sequences == [-1]:
do_sequence = lambda seq: True do_sequence = lambda seq: True
else: else:
do_sequence = [int(x) for x in sequences].__contains__ do_sequence = [int(x) for x in sequences].__contains__
# List of detector sources. # List of detector sources.
det_inp_sources = [input_source.format(karabo_id=karabo_id, module_index=int(da[-2:])) for da in karabo_da] det_inp_sources = [input_source.format(karabo_id=karabo_id, module_index=int(da[-2:])) for da in karabo_da]
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
# Select data to process # Select data to process
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
data_to_process = [] data_to_process = []
for inp_path in run_folder.glob('RAW-*.h5'): for inp_path in run_folder.glob('RAW-*.h5'):
match = file_re.match(inp_path.stem) match = file_re.match(inp_path.stem)
if match[2] not in karabo_da or not do_sequence(int(match[3])): if match[2] not in karabo_da or not do_sequence(int(match[3])):
continue continue
outp_path = out_folder / 'CORR-R{run:04d}-{aggregator}-S{seq:05d}.h5'.format( outp_path = out_folder / 'CORR-R{run:04d}-{aggregator}-S{seq:05d}.h5'.format(
run=int(match[1]), aggregator=match[2], seq=int(match[3])) run=int(match[1]), aggregator=match[2], seq=int(match[3]))
data_to_process.append((match[2], inp_path, outp_path)) data_to_process.append((match[2], inp_path, outp_path))
print('Files to process:') print('Files to process:')
for data_descr in sorted(data_to_process, key=lambda x: f'{x[0]}{x[1]}'): for data_descr in sorted(data_to_process, key=lambda x: f'{x[0]}{x[1]}'):
print(f'{data_descr[0]}\t{data_descr[1]}') print(f'{data_descr[0]}\t{data_descr[1]}')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
# Obtain and prepare calibration constants # Obtain and prepare calibration constants
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# Read the order of memory cells used # Read the order of memory cells used
raw_data = xd.DataCollection.from_paths([e[1] for e in data_to_process]) raw_data = xd.DataCollection.from_paths([e[1] for e in data_to_process])
cell_ids = raw_data[det_inp_sources[0], 'image.cellId'].drop_empty_trains() cell_ids = raw_data[det_inp_sources[0], 'image.cellId'].drop_empty_trains()
cell_ids_pattern = np.squeeze(cell_ids[0].ndarray()) cell_ids_pattern = np.squeeze(cell_ids[0].ndarray())
cell_ids_pattern_s = ",".join([str(c) for c in cell_ids_pattern]) + "," cell_ids_pattern_s = ",".join([str(c) for c in cell_ids_pattern]) + ","
print("Memory cells order:", cell_ids_pattern_s) print("Memory cells order:", cell_ids_pattern_s)
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# Connect to CalCat. # Connect to CalCat.
calcat_config = restful_config['calcat'] calcat_config = restful_config['calcat']
client = CalibrationClient( client = CalibrationClient(
base_api_url=calcat_config['base-api-url'], base_api_url=calcat_config['base-api-url'],
use_oauth2=calcat_config['use-oauth2'], use_oauth2=calcat_config['use-oauth2'],
client_id=calcat_config['user-id'], client_id=calcat_config['user-id'],
client_secret=calcat_config['user-secret'], client_secret=calcat_config['user-secret'],
user_email=calcat_config['user-email'], user_email=calcat_config['user-email'],
token_url=calcat_config['token-url'], token_url=calcat_config['token-url'],
refresh_url=calcat_config['refresh-url'], refresh_url=calcat_config['refresh-url'],
auth_url=calcat_config['auth-url'], auth_url=calcat_config['auth-url'],
scope='') scope='')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
metadata = CalibrationMetadata(metadata_folder or out_folder) metadata = CalibrationMetadata(metadata_folder or out_folder)
# Constant paths & timestamps are saved under retrieved-constants in calibration_metadata.yml # Constant paths & timestamps are saved under retrieved-constants in calibration_metadata.yml
const_yaml = metadata.setdefault("retrieved-constants", {}) const_yaml = metadata.setdefault("retrieved-constants", {})
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
const_data = {} const_data = {}
const_load_mp = psh.ProcessContext(num_workers=24) const_load_mp = psh.ProcessContext(num_workers=24)
if const_yaml: # Read constants from YAML file. if const_yaml: # Read constants from YAML file.
start = perf_counter() start = perf_counter()
for da, ccvs in const_yaml.items(): for da, ccvs in const_yaml.items():
for calibration_name, ccv in ccvs['constants'].items(): for calibration_name, ccv in ccvs['constants'].items():
if ccv['file-path'] is None: if ccv['file-path'] is None:
continue continue
dtype = np.uint32 if calibration_name.startswith('BadPixels') else np.float32 dtype = np.uint32 if calibration_name.startswith('BadPixels') else np.float32
const_data[(da, calibration_name)] = dict( const_data[(da, calibration_name)] = dict(
path=Path(ccv['file-path']), path=Path(ccv['file-path']),
dataset=ccv['dataset-name'], dataset=ccv['dataset-name'],
data=const_load_mp.alloc(shape=(256, 256, mem_cells, 3), dtype=dtype) data=const_load_mp.alloc(shape=(256, 256, mem_cells, 3), dtype=dtype)
) )
else: # Retrieve constants from CALCAT. else: # Retrieve constants from CALCAT.
dark_calibrations = { dark_calibrations = {
1: 'Offset', # np.float32 1: 'Offset', # np.float32
14: 'BadPixelsDark' # should be np.uint32, but is np.float64 14: 'BadPixelsDark' # should be np.uint32, but is np.float64
} }
base_condition = [ base_condition = [
dict(parameter_id=1, value=bias_voltage), # Sensor bias voltage dict(parameter_id=1, value=bias_voltage), # Sensor bias voltage
dict(parameter_id=7, value=mem_cells), # Memory cells dict(parameter_id=7, value=mem_cells), # Memory cells
dict(parameter_id=15, value=capacitor), # Feedback capacitor dict(parameter_id=15, value=capacitor), # Feedback capacitor
dict(parameter_id=13, value=256), # Pixels X dict(parameter_id=13, value=256), # Pixels X
dict(parameter_id=14, value=256), # Pixels Y dict(parameter_id=14, value=256), # Pixels Y
] ]
dark_condition = base_condition + [ dark_condition = base_condition + ([
dict(parameter_id=142, value=cell_ids_pattern_s), # Memory cell order dict(parameter_id=142, value=cell_ids_pattern_s), # Memory cell order
] ] if use_cell_order else [])
illuminated_calibrations = { illuminated_calibrations = {
20: 'BadPixelsFF', # np.uint32 20: 'BadPixelsFF', # np.uint32
42: 'GainAmpMap', # np.float32 42: 'GainAmpMap', # np.float32
43: 'FFMap', # np.float32 43: 'FFMap', # np.float32
44: 'RelativeGain' # np.float32 44: 'RelativeGain' # np.float32
} }
illuminated_condition = base_condition + [ illuminated_condition = base_condition + [
dict(parameter_id=3, value=photon_energy), # Source energy dict(parameter_id=3, value=photon_energy), # Source energy
dict(parameter_id=25, value=category) # category dict(parameter_id=25, value=category) # category
] ]
print('Querying calibration database', end='', flush=True) print('Querying calibration database', end='', flush=True)
start = perf_counter() start = perf_counter()
for calibrations, condition in [ for calibrations, condition in [
(dark_calibrations, dark_condition), (dark_calibrations, dark_condition),
(illuminated_calibrations, illuminated_condition) (illuminated_calibrations, illuminated_condition)
]: ]:
resp = CalibrationConstantVersion.get_closest_by_time_by_detector_conditions( resp = CalibrationConstantVersion.get_closest_by_time_by_detector_conditions(
client, karabo_id, list(calibrations.keys()), client, karabo_id, list(calibrations.keys()),
{'parameters_conditions_attributes': condition}, {'parameters_conditions_attributes': condition},
karabo_da='', event_at=creation_time.isoformat(), snapshot_at=None) karabo_da='', event_at=creation_time.isoformat(), snapshot_at=None)
if not resp['success']: if not resp['success']:
raise RuntimeError(resp) raise RuntimeError(resp)
for ccv in resp['data']: for ccv in resp['data']:
cc = ccv['calibration_constant'] cc = ccv['calibration_constant']
da = ccv['physical_detector_unit']['karabo_da'] da = ccv['physical_detector_unit']['karabo_da']
calibration_name = calibrations[cc['calibration_id']] calibration_name = calibrations[cc['calibration_id']]
dtype = np.uint32 if calibration_name.startswith('BadPixels') else np.float32 dtype = np.uint32 if calibration_name.startswith('BadPixels') else np.float32
const_data[(da, calibration_name)] = dict( const_data[(da, calibration_name)] = dict(
path=Path(ccv['path_to_file']) / ccv['file_name'], path=Path(ccv['path_to_file']) / ccv['file_name'],
dataset=ccv['data_set_name'], dataset=ccv['data_set_name'],
data=const_load_mp.alloc(shape=(256, 256, mem_cells, 3), dtype=dtype) data=const_load_mp.alloc(shape=(256, 256, mem_cells, 3), dtype=dtype)
) )
print('.', end='', flush=True) print('.', end='', flush=True)
total_time = perf_counter() - start total_time = perf_counter() - start
print(f'{total_time:.1f}s') print(f'{total_time:.1f}s')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
def load_constant_dataset(wid, index, const_descr): def load_constant_dataset(wid, index, const_descr):
ccv_entry = const_data[const_descr] ccv_entry = const_data[const_descr]
with h5py.File(cal_db_root / ccv_entry['path'], 'r') as fp: with h5py.File(cal_db_root / ccv_entry['path'], 'r') as fp:
fp[ccv_entry['dataset'] + '/data'].read_direct(ccv_entry['data']) fp[ccv_entry['dataset'] + '/data'].read_direct(ccv_entry['data'])
print('.', end='', flush=True) print('.', end='', flush=True)
print('Loading calibration data', end='', flush=True) print('Loading calibration data', end='', flush=True)
start = perf_counter() start = perf_counter()
const_load_mp.map(load_constant_dataset, list(const_data.keys())) const_load_mp.map(load_constant_dataset, list(const_data.keys()))
total_time = perf_counter() - start total_time = perf_counter() - start
print(f'{total_time:.1f}s') print(f'{total_time:.1f}s')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# These are intended in order cell, X, Y, gain # These are intended in order cell, X, Y, gain
ccv_offsets = {} ccv_offsets = {}
ccv_gains = {} ccv_gains = {}
ccv_masks = {} ccv_masks = {}
ccv_shape = (mem_cells, 256, 256, 3) ccv_shape = (mem_cells, 256, 256, 3)
constant_order = { constant_order = {
'Offset': (2, 1, 0, 3), 'Offset': (2, 1, 0, 3),
'BadPixelsDark': (2, 1, 0, 3), 'BadPixelsDark': (2, 1, 0, 3),
'RelativeGain': (2, 1, 0, 3), 'RelativeGain': (2, 1, 0, 3),
'FFMap': (2, 0, 1, 3), 'FFMap': (2, 0, 1, 3),
'BadPixelsFF': (2, 0, 1, 3), 'BadPixelsFF': (2, 0, 1, 3),
'GainAmpMap': (2, 0, 1, 3), 'GainAmpMap': (2, 0, 1, 3),
} }
def prepare_constants(wid, index, aggregator): def prepare_constants(wid, index, aggregator):
consts = {calibration_name: entry['data'] consts = {calibration_name: entry['data']
for (aggregator_, calibration_name), entry for (aggregator_, calibration_name), entry
in const_data.items() in const_data.items()
if aggregator == aggregator_} if aggregator == aggregator_}
def _prepare_data(calibration_name, dtype): def _prepare_data(calibration_name, dtype):
return consts[calibration_name] \ return consts[calibration_name] \
.transpose(constant_order[calibration_name]) \ .transpose(constant_order[calibration_name]) \
.astype(dtype, copy=True) # Make sure array is contiguous. .astype(dtype, copy=True) # Make sure array is contiguous.
if offset_corr and 'Offset' in consts: if offset_corr and 'Offset' in consts:
ccv_offsets[aggregator] = _prepare_data('Offset', np.float32) ccv_offsets[aggregator] = _prepare_data('Offset', np.float32)
else: else:
ccv_offsets[aggregator] = np.zeros(ccv_shape, dtype=np.float32) ccv_offsets[aggregator] = np.zeros(ccv_shape, dtype=np.float32)
ccv_gains[aggregator] = np.ones(ccv_shape, dtype=np.float32) ccv_gains[aggregator] = np.ones(ccv_shape, dtype=np.float32)
if 'BadPixelsDark' in consts: if 'BadPixelsDark' in consts:
ccv_masks[aggregator] = _prepare_data('BadPixelsDark', np.uint32) ccv_masks[aggregator] = _prepare_data('BadPixelsDark', np.uint32)
else: else:
ccv_masks[aggregator] = np.zeros(ccv_shape, dtype=np.uint32) ccv_masks[aggregator] = np.zeros(ccv_shape, dtype=np.uint32)
if rel_gain and 'RelativeGain' in consts: if rel_gain and 'RelativeGain' in consts:
ccv_gains[aggregator] *= _prepare_data('RelativeGain', np.float32) ccv_gains[aggregator] *= _prepare_data('RelativeGain', np.float32)
if ff_map and 'FFMap' in consts: if ff_map and 'FFMap' in consts:
ccv_gains[aggregator] *= _prepare_data('FFMap', np.float32) ccv_gains[aggregator] *= _prepare_data('FFMap', np.float32)
if 'BadPixelsFF' in consts: if 'BadPixelsFF' in consts:
np.bitwise_or(ccv_masks[aggregator], _prepare_data('BadPixelsFF', np.uint32), np.bitwise_or(ccv_masks[aggregator], _prepare_data('BadPixelsFF', np.uint32),
out=ccv_masks[aggregator]) out=ccv_masks[aggregator])
if gain_amp_map and 'GainAmpMap' in consts: if gain_amp_map and 'GainAmpMap' in consts:
ccv_gains[aggregator] *= _prepare_data('GainAmpMap', np.float32) ccv_gains[aggregator] *= _prepare_data('GainAmpMap', np.float32)
print('.', end='', flush=True) print('.', end='', flush=True)
print('Preparing constants', end='', flush=True) print('Preparing constants', end='', flush=True)
start = perf_counter() start = perf_counter()
psh.ThreadContext(num_workers=len(karabo_da)).map(prepare_constants, karabo_da) psh.ThreadContext(num_workers=len(karabo_da)).map(prepare_constants, karabo_da)
total_time = perf_counter() - start total_time = perf_counter() - start
print(f'{total_time:.1f}s') print(f'{total_time:.1f}s')
const_data.clear() # Clear raw constants data now to save memory. const_data.clear() # Clear raw constants data now to save memory.
gc.collect(); gc.collect();
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
def correct_file(wid, index, work): def correct_file(wid, index, work):
aggregator, inp_path, outp_path = work aggregator, inp_path, outp_path = work
module_index = int(aggregator[-2:]) module_index = int(aggregator[-2:])
start = perf_counter() start = perf_counter()
dc = xd.H5File(inp_path, inc_suspect_trains=False).select('*', 'image.*', require_all=True) dc = xd.H5File(inp_path, inc_suspect_trains=False).select('*', 'image.*', require_all=True)
inp_source = dc[input_source.format(karabo_id=karabo_id, module_index=module_index)] inp_source = dc[input_source.format(karabo_id=karabo_id, module_index=module_index)]
open_time = perf_counter() - start open_time = perf_counter() - start
# Load raw data for this file. # Load raw data for this file.
# Reshaping gets rid of the extra 1-len dimensions without # Reshaping gets rid of the extra 1-len dimensions without
# mangling the frame axis for an actual frame count of 1. # mangling the frame axis for an actual frame count of 1.
start = perf_counter() start = perf_counter()
in_raw = inp_source['image.data'].ndarray().reshape(-1, 256, 256) in_raw = inp_source['image.data'].ndarray().reshape(-1, 256, 256)
in_cell = inp_source['image.cellId'].ndarray().reshape(-1) in_cell = inp_source['image.cellId'].ndarray().reshape(-1)
in_pulse = inp_source['image.pulseId'].ndarray().reshape(-1) in_pulse = inp_source['image.pulseId'].ndarray().reshape(-1)
read_time = perf_counter() - start read_time = perf_counter() - start
# Allocate output arrays. # Allocate output arrays.
out_data = np.zeros((in_raw.shape[0], 256, 256), dtype=np.float32) out_data = np.zeros((in_raw.shape[0], 256, 256), dtype=np.float32)
out_gain = np.zeros((in_raw.shape[0], 256, 256), dtype=np.uint8) out_gain = np.zeros((in_raw.shape[0], 256, 256), dtype=np.uint8)
out_mask = np.zeros((in_raw.shape[0], 256, 256), dtype=np.uint32) out_mask = np.zeros((in_raw.shape[0], 256, 256), dtype=np.uint32)
start = perf_counter() start = perf_counter()
correct_lpd_frames(in_raw, in_cell, correct_lpd_frames(in_raw, in_cell,
out_data, out_gain, out_mask, out_data, out_gain, out_mask,
ccv_offsets[aggregator], ccv_gains[aggregator], ccv_masks[aggregator], ccv_offsets[aggregator], ccv_gains[aggregator], ccv_masks[aggregator],
num_threads=num_threads_per_worker) num_threads=num_threads_per_worker)
correct_time = perf_counter() - start correct_time = perf_counter() - start
image_counts = inp_source['image.data'].data_counts(labelled=False) image_counts = inp_source['image.data'].data_counts(labelled=False)
start = perf_counter() start = perf_counter()
if (not outp_path.exists() or overwrite) and image_counts.sum() > 0: if (not outp_path.exists() or overwrite) and image_counts.sum() > 0:
outp_source_name = output_source.format(karabo_id=karabo_id, module_index=module_index) outp_source_name = output_source.format(karabo_id=karabo_id, module_index=module_index)
with DataFile(outp_path, 'w') as outp_file: with DataFile(outp_path, 'w') as outp_file:
outp_file.create_index(dc.train_ids, from_file=dc.files[0]) outp_file.create_index(dc.train_ids, from_file=dc.files[0])
outp_file.create_metadata(like=dc, instrument_channels=(f'{outp_source_name}/image',)) outp_file.create_metadata(like=dc, instrument_channels=(f'{outp_source_name}/image',))
outp_source = outp_file.create_instrument_source(outp_source_name) outp_source = outp_file.create_instrument_source(outp_source_name)
outp_source.create_index(image=image_counts) outp_source.create_index(image=image_counts)
outp_source.create_key('image.cellId', data=in_cell, outp_source.create_key('image.cellId', data=in_cell,
chunks=(min(chunks_ids, in_cell.shape[0]),)) chunks=(min(chunks_ids, in_cell.shape[0]),))
outp_source.create_key('image.pulseId', data=in_pulse, outp_source.create_key('image.pulseId', data=in_pulse,
chunks=(min(chunks_ids, in_pulse.shape[0]),)) chunks=(min(chunks_ids, in_pulse.shape[0]),))
outp_source.create_key('image.data', data=out_data, outp_source.create_key('image.data', data=out_data,
chunks=(min(chunks_data, out_data.shape[0]), 256, 256)) chunks=(min(chunks_data, out_data.shape[0]), 256, 256))
outp_source.create_compressed_key('image.gain', data=out_gain) outp_source.create_compressed_key('image.gain', data=out_gain)
outp_source.create_compressed_key('image.mask', data=out_mask) outp_source.create_compressed_key('image.mask', data=out_mask)
write_time = perf_counter() - start write_time = perf_counter() - start
total_time = open_time + read_time + correct_time + write_time total_time = open_time + read_time + correct_time + write_time
frame_rate = in_raw.shape[0] / total_time frame_rate = in_raw.shape[0] / total_time
print('{}\t{}\t{:.3f}\t{:.3f}\t{:.3f}\t{:.3f}\t{:.3f}\t{}\t{:.1f}'.format( print('{}\t{}\t{:.3f}\t{:.3f}\t{:.3f}\t{:.3f}\t{:.3f}\t{}\t{:.1f}'.format(
wid, aggregator, open_time, read_time, correct_time, write_time, total_time, wid, aggregator, open_time, read_time, correct_time, write_time, total_time,
in_raw.shape[0], frame_rate)) in_raw.shape[0], frame_rate))
in_raw = None in_raw = None
in_cell = None in_cell = None
in_pulse = None in_pulse = None
out_data = None out_data = None
out_gain = None out_gain = None
out_mask = None out_mask = None
gc.collect() gc.collect()
print('worker\tDA\topen\tread\tcorrect\twrite\ttotal\tframes\trate') print('worker\tDA\topen\tread\tcorrect\twrite\ttotal\tframes\trate')
start = perf_counter() start = perf_counter()
psh.ProcessContext(num_workers=num_workers).map(correct_file, data_to_process) psh.ProcessContext(num_workers=num_workers).map(correct_file, data_to_process)
total_time = perf_counter() - start total_time = perf_counter() - start
print(f'Total time: {total_time:.1f}s') print(f'Total time: {total_time:.1f}s')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
# Data preview for first train # Data preview for first train
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
geom = xg.LPD_1MGeometry.from_quad_positions( geom = xg.LPD_1MGeometry.from_quad_positions(
[(11.4, 299), (-11.5, 8), (254.5, -16), (278.5, 275)]) [(11.4, 299), (-11.5, 8), (254.5, -16), (278.5, 275)])
output_paths = [outp_path for _, _, outp_path in data_to_process if outp_path.exists()] output_paths = [outp_path for _, _, outp_path in data_to_process if outp_path.exists()]
dc = xd.DataCollection.from_paths(output_paths).select_trains(np.s_[0]) dc = xd.DataCollection.from_paths(output_paths).select_trains(np.s_[0])
det = LPD1M(dc, detector_name=karabo_id) det = LPD1M(dc, detector_name=karabo_id)
data = det.get_array('image.data') data = det.get_array('image.data')
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Intensity histogram across all cells ### Intensity histogram across all cells
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
left_edge_ratio = 0.01 left_edge_ratio = 0.01
right_edge_ratio = 0.99 right_edge_ratio = 0.99
fig, ax = plt.subplots(num=1, clear=True, figsize=(15, 6)) fig, ax = plt.subplots(num=1, clear=True, figsize=(15, 6))
values, bins, _ = ax.hist(np.ravel(data.data), bins=2000, range=(-1500, 2000)) values, bins, _ = ax.hist(np.ravel(data.data), bins=2000, range=(-1500, 2000))
def find_nearest_index(array, value): def find_nearest_index(array, value):
return (np.abs(array - value)).argmin() return (np.abs(array - value)).argmin()
cum_values = np.cumsum(values) cum_values = np.cumsum(values)
vmin = bins[find_nearest_index(cum_values, cum_values[-1]*left_edge_ratio)] vmin = bins[find_nearest_index(cum_values, cum_values[-1]*left_edge_ratio)]
vmax = bins[find_nearest_index(cum_values, cum_values[-1]*right_edge_ratio)] vmax = bins[find_nearest_index(cum_values, cum_values[-1]*right_edge_ratio)]
max_value = values.max() max_value = values.max()
ax.vlines([vmin, vmax], 0, max_value, color='red', linewidth=5, alpha=0.2) ax.vlines([vmin, vmax], 0, max_value, color='red', linewidth=5, alpha=0.2)
ax.text(vmin, max_value, f'{left_edge_ratio*100:.0f}%', ax.text(vmin, max_value, f'{left_edge_ratio*100:.0f}%',
color='red', ha='center', va='bottom', size='large') color='red', ha='center', va='bottom', size='large')
ax.text(vmax, max_value, f'{right_edge_ratio*100:.0f}%', ax.text(vmax, max_value, f'{right_edge_ratio*100:.0f}%',
color='red', ha='center', va='bottom', size='large') color='red', ha='center', va='bottom', size='large')
ax.text(vmax+(vmax-vmin)*0.01, max_value/2, 'Colormap interval', ax.text(vmax+(vmax-vmin)*0.01, max_value/2, 'Colormap interval',
color='red', rotation=90, ha='left', va='center', size='x-large') color='red', rotation=90, ha='left', va='center', size='x-large')
ax.set_xlim(vmin-(vmax-vmin)*0.1, vmax+(vmax-vmin)*0.1) ax.set_xlim(vmin-(vmax-vmin)*0.1, vmax+(vmax-vmin)*0.1)
ax.set_ylim(0, max_value*1.1) ax.set_ylim(0, max_value*1.1)
pass pass
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### First memory cell ### First memory cell
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
fig, ax = plt.subplots(num=2, figsize=(15, 15), clear=True, nrows=1, ncols=1) fig, ax = plt.subplots(num=2, figsize=(15, 15), clear=True, nrows=1, ncols=1)
geom.plot_data_fast(data[:, 0, 0], ax=ax, vmin=vmin, vmax=vmax) geom.plot_data_fast(data[:, 0, 0], ax=ax, vmin=vmin, vmax=vmax)
pass pass
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Train average ### Train average
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
fig, ax = plt.subplots(num=3, figsize=(15, 15), clear=True, nrows=1, ncols=1) fig, ax = plt.subplots(num=3, figsize=(15, 15), clear=True, nrows=1, ncols=1)
geom.plot_data_fast(data[:, 0].mean(axis=1), ax=ax, vmin=vmin, vmax=vmax) geom.plot_data_fast(data[:, 0].mean(axis=1), ax=ax, vmin=vmin, vmax=vmax)
pass pass
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Lowest gain stage per pixel ### Lowest gain stage per pixel
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
highest_gain_stage = det.get_array('image.gain', pulses=np.s_[:]).max(axis=(1, 2)) highest_gain_stage = det.get_array('image.gain', pulses=np.s_[:]).max(axis=(1, 2))
fig, ax = plt.subplots(num=4, figsize=(15, 15), clear=True, nrows=1, ncols=1) fig, ax = plt.subplots(num=4, figsize=(15, 15), clear=True, nrows=1, ncols=1)
p = geom.plot_data_fast(highest_gain_stage, ax=ax, vmin=0, vmax=2); p = geom.plot_data_fast(highest_gain_stage, ax=ax, vmin=0, vmax=2);
cb = ax.images[0].colorbar cb = ax.images[0].colorbar
cb.set_ticks([0, 1, 2]) cb.set_ticks([0, 1, 2])
cb.set_ticklabels(['High gain', 'Medium gain', 'Low gain']) cb.set_ticklabels(['High gain', 'Medium gain', 'Low gain'])
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
### Create virtual CXI file ### Create virtual CXI file
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
if create_virtual_cxi_in: if create_virtual_cxi_in:
vcxi_folder = Path(create_virtual_cxi_in.format( vcxi_folder = Path(create_virtual_cxi_in.format(
run=run, proposal_folder=str(Path(in_folder).parent))) run=run, proposal_folder=str(Path(in_folder).parent)))
vcxi_folder.mkdir(parents=True, exist_ok=True) vcxi_folder.mkdir(parents=True, exist_ok=True)
def sort_files_by_seq(by_seq, outp_path): def sort_files_by_seq(by_seq, outp_path):
by_seq.setdefault(int(outp_path.stem[-5:]), []).append(outp_path) by_seq.setdefault(int(outp_path.stem[-5:]), []).append(outp_path)
return by_seq return by_seq
from functools import reduce from functools import reduce
reduce(sort_files_by_seq, output_paths, output_by_seq := {}) reduce(sort_files_by_seq, output_paths, output_by_seq := {})
for seq_number, seq_output_paths in output_by_seq.items(): for seq_number, seq_output_paths in output_by_seq.items():
# Create data collection and detector components only for this sequence. # Create data collection and detector components only for this sequence.
try: try:
det = LPD1M(xd.DataCollection.from_paths(seq_output_paths), detector_name=karabo_id, min_modules=4) det = LPD1M(xd.DataCollection.from_paths(seq_output_paths), detector_name=karabo_id, min_modules=4)
except ValueError: # Couldn't find enough data for min_modules except ValueError: # Couldn't find enough data for min_modules
continue continue
det.write_virtual_cxi(vcxi_folder / f'VCXI-LPD-R{run:04d}-S{seq_number:05d}.cxi') det.write_virtual_cxi(vcxi_folder / f'VCXI-LPD-R{run:04d}-S{seq_number:05d}.cxi')
``` ```
......
notebooks/LPD/LPD_retrieve_constants_precorrection.ipynb
+ 4
− 3
View file @ 23a7e3f2
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
# LPD Retrieving Constants Pre-correction # # LPD Retrieving Constants Pre-correction #
Author: European XFEL Detector Group, Version: 1.0 Author: European XFEL Detector Group, Version: 1.0
The following notebook provides a constants metadata in a YAML file to use while correcting LPD images. The following notebook provides a constants metadata in a YAML file to use while correcting LPD images.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# Input parameters # Input parameters
in_folder = "/gpfs/exfel/exp/FXE/202201/p003073/raw/" # the folder to read data from, required in_folder = "/gpfs/exfel/exp/FXE/202201/p003073/raw/" # the folder to read data from, required
out_folder = "/gpfs/exfel/data/scratch/ahmedk/test/remove/LPD_test" # the folder to output to, required out_folder = "/gpfs/exfel/data/scratch/ahmedk/test/remove/LPD_test" # the folder to output to, required
metadata_folder = '' # Directory containing calibration_metadata.yml when run by xfel-calibrate. metadata_folder = '' # Directory containing calibration_metadata.yml when run by xfel-calibrate.
modules = [-1] # Modules indices to correct, use [-1] for all, only used when karabo_da is empty modules = [-1] # Modules indices to correct, use [-1] for all, only used when karabo_da is empty
karabo_da = [''] # Data aggregators names to correct, use [''] for all karabo_da = [''] # Data aggregators names to correct, use [''] for all
run = 10 # run to process, required run = 10 # run to process, required
# Source parameters # Source parameters
karabo_id = 'FXE_DET_LPD1M-1' # Karabo domain for detector. karabo_id = 'FXE_DET_LPD1M-1' # Karabo domain for detector.
input_source = '{karabo_id}/DET/{module_index}CH0:xtdf' # Input fast data source. input_source = '{karabo_id}/DET/{module_index}CH0:xtdf' # Input fast data source.
# CalCat parameters # CalCat parameters
creation_time = "" # The timestamp to use with Calibration DB. Required Format: "YYYY-MM-DD hh:mm:ss" e.g. 2019-07-04 11:02:41 creation_time = "" # The timestamp to use with Calibration DB. Required Format: "YYYY-MM-DD hh:mm:ss" e.g. 2019-07-04 11:02:41
# Operating conditions # Operating conditions
mem_cells = 512 # Memory cells, LPD constants are always taken with 512 cells. mem_cells = 512 # Memory cells, LPD constants are always taken with 512 cells.
bias_voltage = 250.0 # Detector bias voltage. bias_voltage = 250.0 # Detector bias voltage.
capacitor = '5pF' # Capacitor setting: 5pF or 50pF capacitor = '5pF' # Capacitor setting: 5pF or 50pF
photon_energy = 9.2 # Photon energy in keV. photon_energy = 9.2 # Photon energy in keV.
category = 0 # Whom to blame. category = 0 # Whom to blame.
use_cell_order = True # Whether to use memory cell order as a detector condition (not stored for older constants)
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
from pathlib import Path from pathlib import Path
from time import perf_counter from time import perf_counter
import numpy as np import numpy as np
from calibration_client import CalibrationClient from calibration_client import CalibrationClient
from calibration_client.modules import CalibrationConstantVersion from calibration_client.modules import CalibrationConstantVersion
import extra_data as xd import extra_data as xd
from cal_tools.tools import ( from cal_tools.tools import (
CalibrationMetadata, CalibrationMetadata,
calcat_creation_time, calcat_creation_time,
save_constant_metadata, save_constant_metadata,
) )
from cal_tools.restful_config import restful_config from cal_tools.restful_config import restful_config
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
out_folder = Path(out_folder) out_folder = Path(out_folder)
out_folder.mkdir(exist_ok=True) out_folder.mkdir(exist_ok=True)
metadata = CalibrationMetadata(metadata_folder or out_folder) metadata = CalibrationMetadata(metadata_folder or out_folder)
# Constant paths & timestamps are saved under retrieved-constants in calibration_metadata.yml # Constant paths & timestamps are saved under retrieved-constants in calibration_metadata.yml
retrieved_constants = metadata.setdefault("retrieved-constants", {}) retrieved_constants = metadata.setdefault("retrieved-constants", {})
creation_time = calcat_creation_time(in_folder, run, creation_time) creation_time = calcat_creation_time(in_folder, run, creation_time)
print(f'Using {creation_time.isoformat()} as creation time') print(f'Using {creation_time.isoformat()} as creation time')
# Pick all modules/aggregators or those selected. # Pick all modules/aggregators or those selected.
if not karabo_da or karabo_da == ['']: if not karabo_da or karabo_da == ['']:
if not modules or modules == [-1]: if not modules or modules == [-1]:
modules = list(range(16)) modules = list(range(16))
karabo_da = [f'LPD{i:02d}' for i in modules] karabo_da = [f'LPD{i:02d}' for i in modules]
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# Read the order of memory cells used # Read the order of memory cells used
raw_data = xd.RunDirectory(Path(in_folder, f'r{run:04d}')) raw_data = xd.RunDirectory(Path(in_folder, f'r{run:04d}'))
src = input_source.format(karabo_id=karabo_id, module_index=int(karabo_da[0][-2:])) src = input_source.format(karabo_id=karabo_id, module_index=int(karabo_da[0][-2:]))
cell_ids = raw_data[src, 'image.cellId'].drop_empty_trains() cell_ids = raw_data[src, 'image.cellId'].drop_empty_trains()
cell_ids_pattern = np.squeeze(cell_ids[0].ndarray()) cell_ids_pattern = np.squeeze(cell_ids[0].ndarray())
cell_ids_pattern_s = ",".join([str(c) for c in cell_ids_pattern]) + "," cell_ids_pattern_s = ",".join([str(c) for c in cell_ids_pattern]) + ","
print("Memory cells order:", cell_ids_pattern_s) print("Memory cells order:", cell_ids_pattern_s)
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# Connect to CalCat. # Connect to CalCat.
calcat_config = restful_config['calcat'] calcat_config = restful_config['calcat']
client = CalibrationClient( client = CalibrationClient(
base_api_url=calcat_config['base-api-url'], base_api_url=calcat_config['base-api-url'],
use_oauth2=calcat_config['use-oauth2'], use_oauth2=calcat_config['use-oauth2'],
client_id=calcat_config['user-id'], client_id=calcat_config['user-id'],
client_secret=calcat_config['user-secret'], client_secret=calcat_config['user-secret'],
user_email=calcat_config['user-email'], user_email=calcat_config['user-email'],
token_url=calcat_config['token-url'], token_url=calcat_config['token-url'],
refresh_url=calcat_config['refresh-url'], refresh_url=calcat_config['refresh-url'],
auth_url=calcat_config['auth-url'], auth_url=calcat_config['auth-url'],
scope='') scope='')
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
dark_calibrations = { dark_calibrations = {
1: 'Offset', 1: 'Offset',
14: 'BadPixelsDark', 14: 'BadPixelsDark',
} }
base_condition = [ base_condition = [
dict(parameter_id=1, value=bias_voltage), # Sensor bias voltage dict(parameter_id=1, value=bias_voltage), # Sensor bias voltage
dict(parameter_id=7, value=mem_cells), # Memory cells dict(parameter_id=7, value=mem_cells), # Memory cells
dict(parameter_id=15, value=capacitor), # Feedback capacitor dict(parameter_id=15, value=capacitor), # Feedback capacitor
dict(parameter_id=13, value=256), # Pixels X dict(parameter_id=13, value=256), # Pixels X
dict(parameter_id=14, value=256), # Pixels Y dict(parameter_id=14, value=256), # Pixels Y
] ]
dark_condition = base_condition + [ dark_condition = base_condition + ([
dict(parameter_id=142, value=cell_ids_pattern_s), # Memory cell order dict(parameter_id=142, value=cell_ids_pattern_s), # Memory cell order
] ] if use_cell_order else [])
illuminated_calibrations = { illuminated_calibrations = {
20: 'BadPixelsFF', 20: 'BadPixelsFF',
42: 'GainAmpMap', 42: 'GainAmpMap',
43: 'FFMap', 43: 'FFMap',
44: 'RelativeGain', 44: 'RelativeGain',
} }
illuminated_condition = base_condition + [ illuminated_condition = base_condition + [
dict(parameter_id=3, value=photon_energy), # Source energy dict(parameter_id=3, value=photon_energy), # Source energy
dict(parameter_id=25, value=category) # category dict(parameter_id=25, value=category) # category
] ]
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
const_data = {} const_data = {}
print('Querying calibration database', end='', flush=True) print('Querying calibration database', end='', flush=True)
start = perf_counter() start = perf_counter()
for k_da in karabo_da: for k_da in karabo_da:
pdu = None pdu = None
if k_da in retrieved_constants: if k_da in retrieved_constants:
print(f"Constant for {k_da} already in {metadata.filename}, won't query again.") # noqa print(f"Constant for {k_da} already in {metadata.filename}, won't query again.") # noqa
continue continue
retrieved_constants[k_da] = dict() retrieved_constants[k_da] = dict()
const_mdata = retrieved_constants[k_da]["constants"] = dict() const_mdata = retrieved_constants[k_da]["constants"] = dict()
for calibrations, condition in [ for calibrations, condition in [
(dark_calibrations, dark_condition), (dark_calibrations, dark_condition),
(illuminated_calibrations, illuminated_condition) (illuminated_calibrations, illuminated_condition)
]: ]:
resp = CalibrationConstantVersion.get_closest_by_time_by_detector_conditions( resp = CalibrationConstantVersion.get_closest_by_time_by_detector_conditions(
client, karabo_id, list(calibrations.keys()), client, karabo_id, list(calibrations.keys()),
{'parameters_conditions_attributes': condition}, {'parameters_conditions_attributes': condition},
karabo_da=k_da, event_at=creation_time.isoformat(), snapshot_at=None) karabo_da=k_da, event_at=creation_time.isoformat(), snapshot_at=None)
if not resp["success"]: if not resp["success"]:
print(f"ERROR: Constants {list(calibrations.values())} " print(f"ERROR: Constants {list(calibrations.values())} "
f"were not retrieved, {resp['app_info']}") f"were not retrieved, {resp['app_info']}")
for cname in calibrations.values(): for cname in calibrations.values():
const_mdata[cname] = dict() const_mdata[cname] = dict()
const_mdata[cname]["file-path"] = None const_mdata[cname]["file-path"] = None
const_mdata[cname]["dataset-name"] = None const_mdata[cname]["dataset-name"] = None
const_mdata[cname]["creation-time"] = None const_mdata[cname]["creation-time"] = None
continue continue
for ccv in resp["data"]: for ccv in resp["data"]:
cc = ccv['calibration_constant'] cc = ccv['calibration_constant']
cname = calibrations[cc['calibration_id']] cname = calibrations[cc['calibration_id']]
const_mdata[cname] = dict() const_mdata[cname] = dict()
const_mdata[cname]["file-path"] = str(Path(ccv['path_to_file']) / ccv['file_name']) const_mdata[cname]["file-path"] = str(Path(ccv['path_to_file']) / ccv['file_name'])
const_mdata[cname]["dataset-name"] = ccv['data_set_name'] const_mdata[cname]["dataset-name"] = ccv['data_set_name']
const_mdata[cname]["creation-time"] = ccv['begin_at'] const_mdata[cname]["creation-time"] = ccv['begin_at']
pdu = ccv['physical_detector_unit']['physical_name'] pdu = ccv['physical_detector_unit']['physical_name']
print('.', end='', flush=True) print('.', end='', flush=True)
retrieved_constants[k_da]["physical-detector-unit"] = pdu retrieved_constants[k_da]["physical-detector-unit"] = pdu
metadata.save() metadata.save()
total_time = perf_counter() - start total_time = perf_counter() - start
print(f'{total_time:.1f}s') print(f'{total_time:.1f}s')
print(f"Stored retrieved constants in {metadata.filename}") print(f"Stored retrieved constants in {metadata.filename}")
``` ```
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