[AGIPD][TESTs]test_agipdlib AGIPDCtrl and get_bias_voltage for AGIPD1M and AGIPD500K

Based on https://git.xfel.eu/detectors/pycalibration/-/merge_requests/651

• Test AGIPDCtrl() methods to read conditions from Fast or slow data.
• differentiate between AGIPD1M and AGIPD500K sources and keys to read bias_voltage. (temporary unbreakable change.)

Description

Latest changes to agipdlib had a class AGIPDCtrl() created collecting all methods to read AGIPD parameter conditions from fast and slow data.

1. I have added tests for these methods
2. I have split some methods based on their functionality.
3. As bias_voltage is stored in different paths for AGIPD1M and AGIPD500K, a condition was added to select the right source.

• This is a temporary change and I think one should give the correct source and key through get_bias_voltage arguments.

4. All of the tests are based on EXtra-data mocked data. I have added mock_agipd1m_run, mock_agipd500K_run and mock_agipd1m_run_old at :

How Has This Been Tested?

Unit tests.

Relevant Documents (optional)

Types of changes

• Test (additional or refactored tests)

Checklist:

• My code follows the code style of this project.
• I have updated the documentation accordingly.
• I added tests where appropriate.

Reviewers

@calibration

notebooks/LPD/LPD_Correct_Fast.ipynb

 %% Cell type:markdown id: tags:
 
 # LPD Offline Correction #
 
 Author: European XFEL Data Analysis Group
 
 %% Cell type:code id: tags:
 
 ``` python
 # Input parameters
 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
 metadata_folder = ''  # Directory containing calibration_metadata.yml when run by xfel-calibrate.
 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
 karabo_da = ['']  # Data aggregators names to correct, use [''] for all
 run = 10 # runs to process, required
 
 # Source parameters
 karabo_id = 'FXE_DET_LPD1M-1'  # Karabo domain for detector.
 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.
 
 # CalCat parameters
 use_dir_creation_date = True  # Use the creation date of the directory for database time derivation.
 cal_db_interface = ''  # Not needed, compatibility with current webservice.
 cal_db_timeout = 0  # Not needed, compatbility with current webservice.
 cal_db_root = '/gpfs/exfel/d/cal/caldb_store'
 
 # Operating conditions
 mem_cells = 512  # Memory cells, LPD constants are always taken with 512 cells.
 bias_voltage = 250.0  # Detector bias voltage.
 capacitor = '5pF'  # Capacitor setting: 5pF or 50pF
 photon_energy = 9.2  # Photon energy in keV.
 category = 0  # Whom to blame.
 
 # Correction parameters
 offset_corr = True  # Offset correction.
 rel_gain = True  # Gain correction based on RelativeGain constant.
 ff_map = True  # Gain correction based on FFMap constant.
 gain_amp_map = True  # Gain correction based on GainAmpMap constant.
 
 # Output options
 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_ids = 32  # HDF chunk size for cellId and pulseId datasets.
 create_virtual_cxi_in = ''  # Folder to create virtual CXI files in (for each sequence).
 
 # Parallelization options
 sequences_per_node = 1  # Sequence files to process per node
 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_threads_per_worker = 32  # Number of threads per worker.
 
 def balance_sequences(in_folder, run, sequences, sequences_per_node, karabo_da, max_nodes):
     from xfel_calibrate.calibrate import balance_sequences as bs
     return bs(in_folder, run, sequences, sequences_per_node, karabo_da, max_nodes=max_nodes)
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 from collections import OrderedDict
 from pathlib import Path
 from time import perf_counter
 import gc
 import re
 import warnings
 
 import numpy as np
 import h5py
 
 import matplotlib
 matplotlib.use('agg')
 import matplotlib.pyplot as plt
 %matplotlib inline
 
 from calibration_client import CalibrationClient
 from calibration_client.modules import CalibrationConstantVersion
 import extra_data as xd
 import extra_geom as xg
 import pasha as psh
 
 from extra_data.components import LPD1M
 
 from cal_tools.lpdalgs import correct_lpd_frames
 from cal_tools.tools import CalibrationMetadata, get_dir_creation_date, write_compressed_frames
 from cal_tools.files import DataFile
 from cal_tools.restful_config import restful_config
 ```
 
 %% Cell type:markdown id: tags:
 
 # Prepare environment
 
 %% Cell type:code id: tags:
 
 ``` python
 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}'
 out_folder = Path(out_folder)
 out_folder.mkdir(exist_ok=True)
 
 output_source = output_source or input_source
 
 cal_db_root = Path(cal_db_root)
 
 metadata = CalibrationMetadata(metadata_folder or out_folder)
 
 if use_dir_creation_date:
     creation_time = get_dir_creation_date(in_folder, run)
 else:
     from datetime import datetime
     creation_time = datetime.now()
 
 print(f'Using {creation_time.isoformat()} as creation time')
 
 # Pick all modules/aggregators or those selected.
 if not karabo_da or karabo_da == ['']:
     if not modules or modules == [-1]:
         modules = list(range(16))
 
     karabo_da = [f'LPD{i:02d}' for i in modules]
 
 # Pick all sequences or those selected.
 if not sequences or sequences == [-1]:
     do_sequence = lambda seq: True
 else:
     do_sequence = [int(x) for x in sequences].__contains__
 
 # List of detector sources.
 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:
 
 # Select data to process
 
 %% Cell type:code id: tags:
 
 ``` python
 data_to_process = []
 
 for inp_path in run_folder.glob('RAW-*.h5'):
     match = file_re.match(inp_path.stem)
 
     if match[2] not in karabo_da or not do_sequence(int(match[3])):
         continue
 
     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]))
 
     data_to_process.append((match[2], inp_path, outp_path))
 
 print('Files to process:')
 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]}')
 ```
 
 %% Cell type:markdown id: tags:
 
 # Obtain and prepare calibration constants
 
 %% Cell type:code id: tags:
 
 ``` python
 # Connect to CalCat.
 calcat_config = restful_config['calcat']
 client = CalibrationClient(
     client_id=calcat_config['user-id'],
     client_secret=calcat_config['user-secret'],
     user_email=calcat_config['user-email'],
     base_api_url=calcat_config['base-api-url'],
     token_url=calcat_config['token-url'],
     refresh_url=calcat_config['refresh-url'],
     auth_url=calcat_config['auth-url'],
     scope='')
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 dark_calibrations = {
     1: 'Offset',  # np.float32
     14: 'BadPixelsDark'  # should be np.uint32, but is np.float64
 }
 
 dark_condition = [
     dict(parameter_id=1, value=bias_voltage),  # Sensor bias voltage
     dict(parameter_id=7, value=mem_cells),  # Memory cells
     dict(parameter_id=15, value=capacitor),  # Feedback capacitor
     dict(parameter_id=13, value=256),  # Pixels X
     dict(parameter_id=14, value=256),  # Pixels Y
 ]
 
 illuminated_calibrations = {
     20: 'BadPixelsFF',  # np.uint32
     42: 'GainAmpMap',  # np.float32
     43: 'FFMap',  # np.float32
     44: 'RelativeGain'  # np.float32
 }
 
 illuminated_condition = dark_condition.copy()
 illuminated_condition += [
     dict(parameter_id=3, value=photon_energy),  # Source energy
     dict(parameter_id=25, value=category)  # category
 ]
 
 const_data = {}
 const_load_mp = psh.ProcessContext(num_workers=24)
 
 print('Querying calibration database', end='', flush=True)
 start = perf_counter()
 for calibrations, condition in [
     (dark_calibrations, dark_condition),
     (illuminated_calibrations, illuminated_condition)
 ]:
     resp = CalibrationConstantVersion.get_closest_by_time_by_detector_conditions(
         client, karabo_id, list(calibrations.keys()),
         {'parameters_conditions_attributes': condition},
         karabo_da='', event_at=creation_time.isoformat(), snapshot_at=None)
 
     if not resp['success']:
         raise RuntimeError(resp)
 
     for ccv in resp['data']:
         cc = ccv['calibration_constant']
         da = ccv['physical_detector_unit']['karabo_da']
         calibration_name = calibrations[cc['calibration_id']]
 
         dtype = np.uint32 if calibration_name.startswith('BadPixels') else np.float32
 
         const_data[(da, calibration_name)] = dict(
             path=Path(ccv['path_to_file']) / ccv['file_name'],
             dataset=ccv['data_set_name'],
             data=const_load_mp.alloc(shape=(256, 256, mem_cells, 3), dtype=dtype)
         )
     print('.', end='', flush=True)
 
 total_time = perf_counter() - start
 print(f'{total_time:.1f}s')
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 def load_constant_dataset(wid, index, const_descr):
     ccv_entry = const_data[const_descr]
 
     with h5py.File(cal_db_root / ccv_entry['path'], 'r') as fp:
         fp[ccv_entry['dataset'] + '/data'].read_direct(ccv_entry['data'])
 
     print('.', end='', flush=True)
 
 print('Loading calibration data', end='', flush=True)
 start = perf_counter()
 const_load_mp.map(load_constant_dataset, list(const_data.keys()))
 total_time = perf_counter() - start
 
 print(f'{total_time:.1f}s')
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # These are intended in order cell, X, Y, gain
 ccv_offsets = {}
 ccv_gains = {}
 ccv_masks = {}
 
 ccv_shape = (mem_cells, 256, 256, 3)
 
 constant_order = {
     'Offset':        (2, 1, 0, 3),
     'BadPixelsDark': (2, 1, 0, 3),
     'RelativeGain':  (2, 1, 0, 3),
     'FFMap':         (2, 0, 1, 3),
     'BadPixelsFF':   (2, 0, 1, 3),
     'GainAmpMap':    (2, 0, 1, 3),
 }
 
 def prepare_constants(wid, index, aggregator):
     consts = {calibration_name: entry['data']
               for (aggregator_, calibration_name), entry
               in const_data.items()
               if aggregator == aggregator_}
 
     def _prepare_data(calibration_name, dtype):
         return consts[calibration_name] \
             .transpose(constant_order[calibration_name]) \
             .astype(dtype, copy=True)  # Make sure array is contiguous.
 
     if offset_corr and 'Offset' in consts:
         ccv_offsets[aggregator] = _prepare_data('Offset', np.float32)
     else:
         ccv_offsets[aggregator] = np.zeros(ccv_shape, dtype=np.float32)
 
     ccv_gains[aggregator] = np.ones(ccv_shape, dtype=np.float32)
 
     if 'BadPixelsDark' in consts:
         ccv_masks[aggregator] = _prepare_data('BadPixelsDark', np.uint32)
     else:
         ccv_masks[aggregator] = np.zeros(ccv_shape, dtype=np.uint32)
 
     if rel_gain and 'RelativeGain' in consts:
         ccv_gains[aggregator] *= _prepare_data('RelativeGain', np.float32)
 
     if ff_map and 'FFMap' in consts:
         ccv_gains[aggregator] *= _prepare_data('FFMap', np.float32)
 
         if 'BadPixelsFF' in consts:
             np.bitwise_or(ccv_masks[aggregator], _prepare_data('BadPixelsFF', np.uint32),
                           out=ccv_masks[aggregator])
 
     if gain_amp_map and 'GainAmpMap' in consts:
         ccv_gains[aggregator] *= _prepare_data('GainAmpMap', np.float32)
 
     print('.', end='', flush=True)
 
 
 print('Preparing constants', end='', flush=True)
 start = perf_counter()
 psh.ThreadContext(num_workers=len(karabo_da)).map(prepare_constants, karabo_da)
 total_time = perf_counter() - start
 print(f'{total_time:.1f}s')
 
 const_data.clear()  # Clear raw constants data now to save memory.
 gc.collect();
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 def correct_file(wid, index, work):
     aggregator, inp_path, outp_path = work
     module_index = int(aggregator[-2:])
 
     start = perf_counter()
     dc = xd.H5File(inp_path).select('*', 'image.*', require_all=True)
     inp_source = dc[input_source.format(karabo_id=karabo_id, module_index=module_index)]
     open_time = perf_counter() - start
 
     # Load raw data for this file.
+    # Reshaping gets rid of the extra 1-len dimensions without
+    # mangling the frame axis for an actual frame count of 1.
     start = perf_counter()
-    in_raw = inp_source['image.data'].ndarray().squeeze()
-    in_cell = inp_source['image.cellId'].ndarray().squeeze()
-    in_pulse = inp_source['image.pulseId'].ndarray().squeeze()
+    in_raw = inp_source['image.data'].ndarray().reshape(-1, 256, 256)
+    in_cell = inp_source['image.cellId'].ndarray().reshape(-1)
+    in_pulse = inp_source['image.pulseId'].ndarray().reshape(-1)
     read_time = perf_counter() - start
 
     # Allocate output arrays.
     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_mask = np.zeros((in_raw.shape[0], 256, 256), dtype=np.uint32)
 
     start = perf_counter()
     correct_lpd_frames(in_raw, in_cell,
                        out_data, out_gain, out_mask,
                        ccv_offsets[aggregator], ccv_gains[aggregator], ccv_masks[aggregator],
                        num_threads=num_threads_per_worker)
     correct_time = perf_counter() - start
 
     image_counts = inp_source['image.data'].data_counts(labelled=False)
 
     start = perf_counter()
     if (not outp_path.exists() or overwrite) and image_counts.sum() > 0:
         fa = dc.files[0]
         sel_trains = np.isin(fa.train_ids, dc.train_ids)
 
         outp_source_name = output_source.format(karabo_id=karabo_id, module_index=module_index)
 
         with DataFile(outp_path, 'w') as outp_file:
             outp_file.create_index(
                 train_ids=dc.train_ids,
                 timestamps=fa.file['INDEX/timestamp'][sel_trains],
                 flags=fa.validity_flag[sel_trains])
             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.create_index(image=image_counts)
             outp_source.create_key('image.cellId', data=in_cell,
-                                   chunks=(chunks_ids,))
+                                   chunks=(min(chunks_ids, in_cell.shape[0]),))
             outp_source.create_key('image.pulseId', data=in_pulse,
-                                   chunks=(chunks_ids,))
+                                   chunks=(min(chunks_ids, in_pulse.shape[0]),))
             outp_source.create_key('image.data', data=out_data,
-                                   chunks=(chunks_data, 256, 256))
+                                   chunks=(min(chunks_data, out_data.shape[0]), 256, 256))
             write_compressed_frames(
                 out_gain, outp_file, f'INSTRUMENT/{outp_source_name}/image/gain', comp_threads=8)
             write_compressed_frames(
                 out_mask, outp_file, f'INSTRUMENT/{outp_source_name}/image/mask', comp_threads=8)
     write_time = perf_counter() - start
 
     total_time = open_time + read_time + correct_time + write_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(
         wid, aggregator, open_time, read_time, correct_time, write_time, total_time,
         in_raw.shape[0], frame_rate))
 
     in_raw = None
     in_cell = None
     in_pulse = None
     out_data = None
     out_gain = None
     out_mask = None
     gc.collect()
 
 print('worker\tDA\topen\tread\tcorrect\twrite\ttotal\tframes\trate')
 start = perf_counter()
 psh.ProcessContext(num_workers=num_workers).map(correct_file, data_to_process)
 total_time = perf_counter() - start
 print(f'Total time: {total_time:.1f}s')
 ```
 
 %% Cell type:markdown id: tags:
 
 # Data preview for first train
 
 %% Cell type:code id: tags:
 
 ``` python
 geom = xg.LPD_1MGeometry.from_quad_positions(
     [(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()]
 dc = xd.DataCollection.from_paths(output_paths).select_trains(np.s_[0])
 
 det = LPD1M(dc, detector_name=karabo_id)
 data = det.get_array('image.data')
 ```
 
 %% Cell type:markdown id: tags:
 
 ### Intensity histogram across all cells
 
 %% Cell type:code id: tags:
 
 ``` python
 left_edge_ratio = 0.01
 right_edge_ratio = 0.99
 
 fig, ax = plt.subplots(num=1, clear=True, figsize=(15, 6))
 values, bins, _ = ax.hist(np.ravel(data.data), bins=2000, range=(-1500, 2000))
 
 def find_nearest_index(array, value):
     return (np.abs(array - value)).argmin()
 
 cum_values = np.cumsum(values)
 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)]
 
 max_value = values.max()
 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}%',
         color='red', ha='center', va='bottom', size='large')
 ax.text(vmax, max_value, f'{right_edge_ratio*100:.0f}%',
         color='red', ha='center', va='bottom', size='large')
 ax.text(vmax+(vmax-vmin)*0.01, max_value/2, 'Colormap interval',
         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_ylim(0, max_value*1.1)
 pass
 ```
 
 %% Cell type:markdown id: tags:
 
 ### First memory cell
 
 %% Cell type:code id: tags:
 
 ``` python
 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)
 pass
 ```
 
 %% Cell type:markdown id: tags:
 
 ### Train average
 
 %% Cell type:code id: tags:
 
 ``` python
 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)
 pass
 ```
 
 %% Cell type:markdown id: tags:
 
 ### Lowest gain stage per pixel
 
 %% Cell type:code id: tags:
 
 ``` python
 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)
 p = geom.plot_data_fast(highest_gain_stage, ax=ax, vmin=0, vmax=2);
 
 cb = ax.images[0].colorbar
 cb.set_ticks([0, 1, 2])
 cb.set_ticklabels(['High gain', 'Medium gain', 'Low gain'])
 ```
 
 %% Cell type:markdown id: tags:
 
 ### Create virtual CXI file
 
 %% Cell type:code id: tags:
 
 ``` python
 if create_virtual_cxi_in:
     vcxi_folder = Path(create_virtual_cxi_in.format(
         run=run, proposal_folder=str(Path(in_folder).parent)))
     vcxi_folder.mkdir(parents=True, exist_ok=True)
 
     def sort_files_by_seq(by_seq, outp_path):
         by_seq.setdefault(int(outp_path.stem[-5:]), []).append(outp_path)
         return by_seq
 
     from functools import reduce
     reduce(sort_files_by_seq, output_paths, output_by_seq := {})
 
     for seq_number, seq_output_paths in output_by_seq.items():
         # Create data collection and detector components only for this sequence.
         det = LPD1M(xd.DataCollection.from_paths(seq_output_paths), detector_name=karabo_id)
         det.write_virtual_cxi(vcxi_folder / f'VCXI-LPD-R{run:04d}-S{seq_number:05d}.cxi')
 ```