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Commit 2e5918f0 authored by Karim Ahmed's avatar Karim Ahmed
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Change func name to sort_runs_by_gain, fix docstring, use sorted runs, and fix... 

Change func name to sort_runs_by_gain, fix docstring, use sorted runs, and fix high gain run check condition
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%% Cell type:markdown id: tags:
# Jungfrau Dark Image Characterization #
Author: European XFEL Detector Group, Version: 2.0
Analyzes Jungfrau dark image data to deduce offset, noise and resulting bad pixel maps
%% Cell type:code id: tags:
``` python
in_folder = '/gpfs/exfel/exp/SPB/202130/p900204/raw/' # folder under which runs are located, required
out_folder = '/gpfs/exfel/data/scratch/ahmedk/test/remove' # path to place reports at, required
metadata_folder = '' # Directory containing calibration_metadata.yml when run by xfel-calibrate
run_high = 141 # run number for G0 dark run, required
run_med = 142 # run number for G1 dark run, required
run_low = 143 # run number for G2 dark run, required
# Parameters used to access raw data.
karabo_da = ['JNGFR01', 'JNGFR02','JNGFR03','JNGFR04', 'JNGFR05', 'JNGFR06','JNGFR07','JNGFR08'] # list of data aggregators, which corresponds to different JF modules
karabo_id = 'SPB_IRDA_JF4M' # karabo_id (detector identifier) prefix of Jungfrau detector to process.
karabo_id_control = '' # if control is on a different ID, set to empty string if it is the same a karabo-id
receiver_template = 'JNGFR{:02}' # inset for receiver devices
instrument_source_template = '{}/DET/{}:daqOutput' # template for instrument source name (filled with karabo_id & receiver_id). e.g. 'SPB_IRDA_JF4M/DET/JNGFR01:daqOutput'
ctrl_source_template = '{}/DET/CONTROL' # template for control source name (filled with karabo_id_control)
# Parameters for calibration database and storing constants.
use_dir_creation_date = True # use dir creation date
cal_db_interface = 'tcp://max-exfl-cal001:8016#8045' # calibrate db interface to connect to
cal_db_timeout = 300000 # timeout on caldb requests
local_output = True # output constants locally
db_output = False # output constants to database
# Parameters affecting creating dark calibration constants.
badpixel_threshold_sigma = 5. # bad pixels defined by values outside n times this std from median
offset_abs_threshold_low = [1000, 10000, 10000] # absolute bad pixel threshold in terms of offset, lower values
offset_abs_threshold_high = [8000, 15000, 15000] # absolute bad pixel threshold in terms of offset, upper values
max_trains = 1000 # Maximum trains to process darks. Set to 0 to process all available train images. 1000 trains is enough resolution to create the dark constants
min_trains = 100 # Minimum number of trains to process dark constants. Raise a warning if the run has fewer trains.
manual_slow_data = False # if true, use manually entered bias_voltage and integration_time values
time_limits = 0.025 # to find calibration constants later on, the integration time is allowed to vary by 0.5 us
# Parameters to be used for injecting dark calibration constants.
integration_time = 1000 # integration time in us, will be overwritten by value in file
gain_setting = 0 # 0 for dynamic, forceswitchg1, forceswitchg2, 1 for dynamichg0, fixgain1, fixgain2. Will be overwritten by value in file
gain_mode = 0 # 1 if medium and low runs are fixgain1 and fixgain2, otherwise 0. It will be overwritten by value in file, if manual_slow_data
bias_voltage = 90 # sensor bias voltage in V, will be overwritten by value in file
memory_cells = 16 # number of memory cells
# Parameters used for plotting
detailed_report = False
# TODO: this is used for only Warning check at AGIPD dark.
# Need to rethink if it makes sense to use it here as well.
operation_mode = 'ADAPTIVE_GAIN' # Detector operation mode, optional
```
%% Cell type:code id: tags:
``` python
import os
import warnings
from logging import warning
warnings.filterwarnings('ignore')
import matplotlib
import matplotlib.pyplot as plt
import multiprocessing
import numpy as np
import pasha as psh
import yaml
from IPython.display import Markdown, display
from extra_data import RunDirectory
matplotlib.use('agg')
%matplotlib inline
from XFELDetAna.plotting.heatmap import heatmapPlot
from XFELDetAna.plotting.histogram import histPlot
from cal_tools import jungfraulib, step_timing
from cal_tools.enums import BadPixels, JungfrauGainMode
from cal_tools.tools import (
get_dir_creation_date,
get_pdu_from_db,
get_random_db_interface,
get_report,
save_const_to_h5,
send_to_db,
)
from iCalibrationDB import Conditions, Constants
```
%% Cell type:code id: tags:
``` python
# Constants relevant for the analysis
run_nums = [run_high, run_med, run_low] # run number for G0/HG0, G1, G2
sensor_size = (1024, 512)
gains = [0, 1, 2]
fixed_settings = [
JungfrauGainMode.FIX_GAIN_1.value, JungfrauGainMode.FIX_GAIN_2.value]
dynamic_settings = [
JungfrauGainMode.FORCE_SWITCH_HG1.value, JungfrauGainMode.FORCE_SWITCH_HG2.value]
old_fixed_settings = ["fixgain1", "fixgain2"]
creation_time = None
if use_dir_creation_date:
creation_time = get_dir_creation_date(in_folder, run_high)
print(f"Using {creation_time} as creation time")
os.makedirs(out_folder, exist_ok=True)
cal_db_interface = get_random_db_interface(cal_db_interface)
print(f'Calibration database interface: {cal_db_interface}')
if karabo_id_control == "":
karabo_id_control = karabo_id
```
%% Cell type:code id: tags:
``` python
proposal = list(filter(None, in_folder.strip('/').split('/')))[-2]
file_loc = f"proposal:{proposal} runs:{run_high} {run_med} {run_low}"
report = get_report(metadata_folder)
step_timer = step_timing.StepTimer()
```
%% Cell type:markdown id: tags:
## Reading control data
%% Cell type:code id: tags:
``` python
step_timer.start()
gain_runs = dict()
med_low_settings = []
ctrl_src = ctrl_source_template.format(karabo_id_control)
jungfraulib.validate_dark_runs_order(
run_nums = jungfraulib.sort_runs_by_gain(
raw_folder=in_folder,
runs=run_nums,
ctrl_src=ctrl_src,
)
for gain, run_n in enumerate(run_nums):
run_dc = RunDirectory(f"{in_folder}/r{run_n:04d}/")
gain_runs[run_n] = [gain, run_dc]
ctrl_data = jungfraulib.JungfrauCtrl(run_dc, ctrl_src)
# Read control data for the high gain run only.
if run_n == run_high:
if gain == 0:
run_mcells, sc_start = ctrl_data.get_memory_cells()
if not manual_slow_data:
integration_time = ctrl_data.get_integration_time()
bias_voltage = ctrl_data.get_bias_voltage()
gain_setting = ctrl_data.get_gain_setting()
print(f"Gain setting is {gain_setting} ({ctrl_data.run_settings})")
print(f"Integration time is {integration_time} us")
print(f"Bias voltage is {bias_voltage} V")
if run_mcells == 1:
memory_cells = 1
print('Dark runs in single cell mode, '
f'storage cell start: {sc_start:02d}')
else:
memory_cells = 16
print('Dark runs in burst mode, '
f'storage cell start: {sc_start:02d}')
else:
gain_mode = ctrl_data.get_gain_mode()
med_low_settings.append(ctrl_data.run_mode)
print(f"Gain mode is {gain_mode} ({med_low_settings})")
step_timer.done_step(f'Reading control data.')
```
%% Cell type:code id: tags:
``` python
# set the operating condition
condition = Conditions.Dark.jungfrau(
memory_cells=memory_cells,
bias_voltage=bias_voltage,
integration_time=integration_time,
gain_setting=gain_setting,
gain_mode=gain_mode,
)
db_modules = get_pdu_from_db(
karabo_id=karabo_id,
karabo_da=karabo_da,
constant=Constants.jungfrau.Offset(),
condition=condition,
cal_db_interface=cal_db_interface,
snapshot_at=creation_time)
```
%% Cell type:code id: tags:
``` python
# Start retrieving existing constants for comparison
mod_x_const = [(mod, const) for const in ["Offset", "Noise", "BadPixelsDark"] for mod in karabo_da]
from cal_tools.tools import get_from_db
from datetime import timedelta
def retrieve_old_constant(mod, const):
dconst = getattr(Constants.jungfrau, const)()
data, mdata = get_from_db(
karabo_id=karabo_id,
karabo_da=mod,
constant=dconst,
condition=condition,
empty_constant=None,
cal_db_interface=cal_db_interface,
creation_time=creation_time-timedelta(seconds=60) if creation_time else None,
strategy="pdu_prior_in_time",
verbosity=1,
timeout=cal_db_timeout
)
if mdata is None or data is None:
timestamp = "Not found"
filepath = None
h5path = None
else:
timestamp = mdata.calibration_constant_version.begin_at.isoformat()
filepath = os.path.join(
mdata.calibration_constant_version.hdf5path,
mdata.calibration_constant_version.filename
)
h5path = mdata.calibration_constant_version.h5path
return data, timestamp, filepath, h5path
old_retrieval_pool = multiprocessing.Pool()
old_retrieval_res = old_retrieval_pool.starmap_async(
retrieve_old_constant, mod_x_const
)
old_retrieval_pool.close()
```
%% Cell type:code id: tags:
``` python
# Use only high gain threshold for all gains in case of fixed_gain.
if gain_mode: # fixed_gain
offset_abs_threshold = [[offset_abs_threshold_low[0]]*3, [offset_abs_threshold_high[0]]*3]
else:
offset_abs_threshold = [offset_abs_threshold_low, offset_abs_threshold_high]
```
%% Cell type:code id: tags:
``` python
context = psh.context.ThreadContext(num_workers=memory_cells)
```
%% Cell type:code id: tags:
``` python
"""
All jungfrau runs are taken through one acquisition, except for the forceswitch runs.
While taking non-fixed dark runs, a procedure of multiple acquisitions is used to switch the storage cell indices.
This is done for medium and low gain dark dynamic runs, only [forceswitchg1, forceswitchg2]:
Switching the cell indices in burst mode is a work around for hardware procedure
deficiency that produces wrong data for dark runs except for the first storage cell.
This is why multiple acquisitions are taken to switch the used storage cells and
acquire data through two cells for each of the 16 cells instead of acquiring darks through all 16 cells.
"""
print(f"Maximum trains to process is set to {max_trains}")
noise_map = dict()
offset_map = dict()
bad_pixels_map = dict()
for mod in karabo_da:
step_timer.start()
instrument_src = instrument_source_template.format(
karabo_id, receiver_template.format(int(mod[-2:])))
print(f"\n- Instrument data path for {mod} is {instrument_src}.")
offset_map[mod] = context.alloc(
shape=(sensor_size+(memory_cells, 3)), fill=0, dtype=np.float32)
noise_map[mod] = context.alloc(like=offset_map[mod], fill=0)
bad_pixels_map[mod] = context.alloc(like=offset_map[mod], dtype=np.uint32, fill=0)
for run_n, [gain, run_dc] in gain_runs.items():
def process_cell(worker_id, array_index, cell_number):
cell_slice_idx = acelltable == cell_number
thiscell = images[..., cell_slice_idx] # [1024, 512, n_trains]
# Identify cells/trains with images of 0 pixels.
# TODO: An investigation is ongoing by DET to identify reason for these empty images.
nonzero_adc = np.any(thiscell != 0 , axis=(0, 1)) # [n_trains]
# Exclude empty images with 0 pixels, before calculating offset and noise
thiscell = thiscell[..., nonzero_adc]
offset_map[mod][..., cell_number, gain] = np.mean( # [1024, 512]
thiscell, axis=2, dtype=np.float32)
noise_map[mod][..., cell_number, gain] = np.std( # [1024, 512]
thiscell, axis=2, dtype=np.float32)
del thiscell
# Check if there are wrong bad gain values.
# 1. Exclude empty images.
# 2. Indicate pixels with wrong gain value for any train for each cell.
# TODO: mean is used to use thresholds for accepting gain values, even if not 0 mean value.
gain_avg = np.mean( # [1024, 512]
gain_vals[..., cell_slice_idx][..., nonzero_adc],
axis=2, dtype=np.float32
)
# [1024, 512]
bad_pixels_map[mod][..., cell_number, gain][gain_avg != raw_g] |= BadPixels.WRONG_GAIN_VALUE.value
print(f"Gain stage {gain}, run {run_n}")
# load shape of data for memory cells, and detector size (imgs, cells, x, y)
n_trains = run_dc[instrument_src, "data.adc"].shape[0]
# load number of data available, including trains with empty data.
all_trains = len(run_dc.train_ids)
instr_dc = run_dc.select(instrument_src, require_all=True)
empty_trains = all_trains - n_trains
if empty_trains != 0:
print(f"{mod} has {empty_trains} empty trains out of {all_trains} trains")
if max_trains > 0:
n_trains = min(n_trains, max_trains)
print(f"Processing {n_trains} images.")
if n_trains == 0:
raise ValueError(f"{run_n} has no trains to process.")
if n_trains < min_trains:
warning(f"Less than {min_trains} trains are available in RAW data.")
# Select only requested number of images to process darks.
instr_dc = instr_dc.select_trains(np.s_[:n_trains])
images = np.transpose(
instr_dc[instrument_src, "data.adc"].ndarray(), (3, 2, 1, 0))
acelltable = np.transpose(instr_dc[instrument_src, "data.memoryCell"].ndarray())
gain_vals = np.transpose(
instr_dc[instrument_src, "data.gain"].ndarray(), (3, 2, 1, 0))
# define gain value as saved in raw gain map
raw_g = 3 if gain == 2 else gain
if memory_cells == 1:
acelltable -= sc_start
# Only for dynamic medium and low gain runs [forceswitchg1, forceswitchg2] in burst mode.
if gain_mode == 0 and gain > 0 and memory_cells == 16:
# 255 similar to the receiver which uses the 255
# value to indicate a cell without an image.
# image shape for forceswitchg1 and forceswitchg2 = (1024, 512, 2, trains)
# compared to expected shape of (1024, 512, 16, trains) for high gain run.
acelltable[1:] = 255
# Calculate offset and noise maps
context.map(process_cell, range(memory_cells))
del images
del acelltable
del gain_vals
step_timer.done_step(f'Creating Offset and noise constants for a module.')
```
%% Cell type:code id: tags:
``` python
if detailed_report:
display(Markdown("## Offset and Noise Maps:"))
display(Markdown(
"Below offset and noise maps for the high ($g_0$) gain stage are shown, "
"alongside the distribution of these values. One expects block-like "
"structures mapping to the ASICs of the detector"))
g_name = ['G0', 'G1', 'G2']
g_range = [(0, 8000), (8000, 16000), (8000, 16000)]
n_range = [(0., 50.), (0., 50.), (0., 50.)]
unit = '[ADCu]'
# TODO: Fix plots arrangment and speed for Jungfrau burst mode.
step_timer.start()
for pdu, mod in zip(db_modules, karabo_da):
for g_idx in gains:
for cell in range(0, memory_cells):
f_o0 = heatmapPlot(
np.swapaxes(offset_map[mod][..., cell, g_idx], 0, 1),
y_label="Row",
x_label="Column",
lut_label=unit,
aspect=1.,
vmin=g_range[g_idx][0],
vmax=g_range[g_idx][1],
title=f'Pedestal {g_name[g_idx]} - Cell {cell:02d} - Module {mod} ({pdu})')
fo0, ax_o0 = plt.subplots()
res_o0 = histPlot(
ax_o0, offset_map[mod][..., cell, g_idx],
bins=800,
range=g_range[g_idx],
facecolor='b',
histotype='stepfilled',
)
ax_o0.tick_params(axis='both',which='major',labelsize=15)
ax_o0.set_title(
f'Module pedestal distribution - Cell {cell:02d} - Module {mod} ({pdu})',
fontsize=15)
ax_o0.set_xlabel(f'Pedestal {g_name[g_idx]} {unit}',fontsize=15)
ax_o0.set_yscale('log')
f_n0 = heatmapPlot(
np.swapaxes(noise_map[mod][..., cell, g_idx], 0, 1),
y_label="Row",
x_label="Column",
lut_label= unit,
aspect=1.,
vmin=n_range[g_idx][0],
vmax=n_range[g_idx][1],
title=f"RMS noise {g_name[g_idx]} - Cell {cell:02d} - Module {mod} ({pdu})",
)
fn0, ax_n0 = plt.subplots()
res_n0 = histPlot(
ax_n0,
noise_map[mod][..., cell, g_idx],
bins=100,
range=n_range[g_idx],
facecolor='b',
histotype='stepfilled',
)
ax_n0.tick_params(axis='both', which='major', labelsize=15)
ax_n0.set_title(
f'Module noise distribution - Cell {cell:02d} - Module {mod} ({pdu})',
fontsize=15)
ax_n0.set_xlabel(
f'RMS noise {g_name[g_idx]} ' + unit, fontsize=15)
plt.show()
step_timer.done_step(f'Plotting offset and noise maps.')
```
%% Cell type:markdown id: tags:
## Bad Pixel Map ###
The bad pixel map is deduced by comparing offset and noise of each pixel ($v_i$) and each gain ($g$) against the median value for that gain stage:
$$
v_i > \mathrm{median}(v_{k,g}) + n \sigma_{v_{k,g}}
$$
or
$$
v_i < \mathrm{median}(v_{k,g}) - n \sigma_{v_{k,g}}
$$
Values are encoded in a 32 bit mask, where for the dark image deduced bad pixels the following non-zero entries are relevant:
%% Cell type:code id: tags:
``` python
def print_bp_entry(bp):
print("{:<30s} {:032b} -> {}".format(bp.name, bp.value, int(bp.value)))
print_bp_entry(BadPixels.OFFSET_OUT_OF_THRESHOLD)
print_bp_entry(BadPixels.NOISE_OUT_OF_THRESHOLD)
print_bp_entry(BadPixels.OFFSET_NOISE_EVAL_ERROR)
print_bp_entry(BadPixels.WRONG_GAIN_VALUE)
def eval_bpidx(d):
mdn = np.nanmedian(d, axis=(0, 1))[None, None, :, :]
std = np.nanstd(d, axis=(0, 1))[None, None, :, :]
idx = (d > badpixel_threshold_sigma*std+mdn) | (d < (-badpixel_threshold_sigma)*std+mdn)
return idx
```
%% Cell type:code id: tags:
``` python
step_timer.start()
for pdu, mod in zip(db_modules, karabo_da):
display(Markdown(f"### Badpixels for module {mod} ({pdu}):"))
offset_abs_threshold = np.array(offset_abs_threshold)
bad_pixels_map[mod][eval_bpidx(offset_map[mod])] |= BadPixels.OFFSET_OUT_OF_THRESHOLD.value
bad_pixels_map[mod][~np.isfinite(offset_map[mod])] |= BadPixels.OFFSET_NOISE_EVAL_ERROR.value
bad_pixels_map[mod][eval_bpidx(noise_map[mod])] |= BadPixels.NOISE_OUT_OF_THRESHOLD.value
bad_pixels_map[mod][~np.isfinite(noise_map[mod])] |= BadPixels.OFFSET_NOISE_EVAL_ERROR.value
bad_pixels_map[mod][(offset_map[mod] < offset_abs_threshold[0][None, None, None, :]) | (offset_map[mod] > offset_abs_threshold[1][None, None, None, :])] |= BadPixels.OFFSET_OUT_OF_THRESHOLD.value # noqa
if detailed_report:
for g_idx in gains:
for cell in range(memory_cells):
bad_pixels = bad_pixels_map[mod][:, :, cell, g_idx]
fn_0 = heatmapPlot(
np.swapaxes(bad_pixels, 0, 1),
y_label="Row",
x_label="Column",
lut_label=f"Badpixels {g_name[g_idx]} [ADCu]",
aspect=1.,
vmin=0, vmax=5,
title=f'G{g_idx} Bad pixel map - Cell {cell:02d} - Module {mod} ({pdu})')
step_timer.done_step(f'Creating bad pixels constant')
```
%% Cell type:markdown id: tags:
## Inject and save calibration constants
%% Cell type:code id: tags:
``` python
step_timer.start()
for mod, db_mod in zip(karabo_da, db_modules):
constants = {
'Offset': np.moveaxis(offset_map[mod], 0, 1),
'Noise': np.moveaxis(noise_map[mod], 0, 1),
'BadPixelsDark': np.moveaxis(bad_pixels_map[mod], 0, 1),
}
md = None
for key, const_data in constants.items():
const = getattr(Constants.jungfrau, key)()
const.data = const_data
for parm in condition.parameters:
if parm.name == "Integration Time":
parm.lower_deviation = time_limits
parm.upper_deviation = time_limits
if db_output:
md = send_to_db(
db_module=db_mod,
karabo_id=karabo_id,
constant=const,
condition=condition,
file_loc=file_loc,
report_path=report,
cal_db_interface=cal_db_interface,
creation_time=creation_time,
timeout=cal_db_timeout,
)
if local_output:
md = save_const_to_h5(
db_module=db_mod,
karabo_id=karabo_id,
constant=const,
condition=condition,
data=const.data,
file_loc=file_loc,
report=report,
creation_time=creation_time,
out_folder=out_folder,
)
print(f"Calibration constant {key} is stored locally at {out_folder}.\n")
print("Constants parameter conditions are:\n")
print(
f"• Bias voltage: {bias_voltage}\n"
f"• Memory cells: {memory_cells}\n"
f"• Integration time: {integration_time}\n"
f"• Gain setting: {gain_setting}\n"
f"• Gain mode: {gain_mode}\n"
f"• Creation time: {md.calibration_constant_version.begin_at if md is not None else creation_time}\n") # noqa
step_timer.done_step("Injecting constants.")
```
%% Cell type:code id: tags:
``` python
print(f"Total processing time {step_timer.timespan():.01f} s")
step_timer.print_summary()
```
%% Cell type:code id: tags:
``` python
# now we need the old constants
old_const = {}
old_mdata = {}
old_retrieval_res.wait()
for (mod, const), (data, timestamp, filepath, h5path) in zip(
mod_x_const, old_retrieval_res.get()):
old_const.setdefault(mod, {})[const] = data
old_mdata.setdefault(mod, {})[const] = {
"timestamp": timestamp,
"filepath": filepath,
"h5path": h5path,
}
```
%% Cell type:code id: tags:
``` python
display(Markdown("## The following pre-existing constants are used for comparison:"))
for mod, consts in old_mdata.items():
pdu = db_modules[karabo_da.index(mod)]
display(Markdown(f"- {mod} ({pdu})"))
for const in consts:
display(Markdown(f" - {const} at {consts[const]['timestamp']}"))
# saving locations of old constants for summary notebook
with open(f"{metadata_folder or out_folder}/module_metadata_{mod}.yml", "w") as fd:
yaml.safe_dump(
{
"module": mod,
"pdu": pdu,
"old-constants": old_mdata[mod],
},
fd,
)
```
......
src/cal_tools/jungfraulib.py
+ 2
− 2
View file @ 2e5918f0
......@@ -142,7 +142,7 @@ EXPECTED_RUN_ORDER = [
]
def validate_dark_runs_order(
def sort_runs_by_gain(
raw_folder,
runs,
ctrl_src,
......@@ -153,7 +153,7 @@ def validate_dark_runs_order(
Args:
raw_folder (str): RAW folder for the validated dark runs.
runs (list, optional): [High run, Medium run, Low run]. Defaults to [].
runs (list, optional): [High run, Medium run, Low run].
ctrl_src (str): Control source path for slow data.
modes_order (dict): Gain modes order to sort the runs by.
expected_run_order (list):Expected dark runs order to process.
......
tests/test_jungfraulib.py
+ 3
− 3
View file @ 2e5918f0
import pytest
from extra_data import RunDirectory
from cal_tools.jungfraulib import JungfrauCtrl, validate_dark_runs_order
from cal_tools.jungfraulib import JungfrauCtrl, sort_runs_by_gain
# TODO: replace with mocked RAW data as in tests/test_agipdlib.py
JF = JungfrauCtrl(
......@@ -57,9 +57,9 @@ def test_get_gain_mode(mode, result):
)
# TODO: missing fixed gain dark runs for JUNGFRAU from test proposal.
# TODO: missing fixed and adaptive runs after the JF control updated.
def test_validate_dark_runs_order(original_runs, sorted_runs):
def test_sort_runs_by_gain(original_runs, sorted_runs):
raw_folder = "/gpfs/exfel/exp/CALLAB/202130/p900203/raw"
validated_runs = validate_dark_runs_order(
validated_runs = sort_runs_by_gain(
raw_folder=raw_folder,
runs=original_runs,
ctrl_src="FXE_XAD_JF1M/DET/CONTROL")
......
tests/test_reference_runs/callab_tests.py
+ 4
− 3
View file @ 2e5918f0
......@@ -379,9 +379,10 @@ automated_test_config = {
"out-folder": "{}/{}/{}",
# "/gpfs/exfel/exp/HED/202102/p002656/raw"
"in-folder": "/gpfs/exfel/exp/CALLAB/202130/p900203/raw",
"run-high": "9039", # Original run: "219",
"run-med": "9040", # Original run: "220",
"run-low": "9041", # Original run: "221",
# The 3 runs are arranged in a wrong way on purpose.
"run-high": "9040", # Original run: "219",
"run-med": "9041", # Original run: "220",
"run-low": "9039", # Original run: "221",
"karabo-id": "HED_IA1_JF500K2",
"karabo-da": "JNGFR02",
},
......
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