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Commit 047b20c9 authored by Karim Ahmed's avatar Karim Ahmed
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update correction to use virtual karabo da

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%% Cell type:markdown id:bed7bd15-21d9-4735-82c1-c27c1a5e3346 tags:
# Gotthard2 Offline Correction #
Author: European XFEL Detector Group, Version: 1.0
Offline Calibration for the Gothard2 Detector
%% Cell type:code id:570322ed-f611-4fd1-b2ec-c12c13d55843 tags:
``` python
in_folder = "/gpfs/exfel/exp/FXE/202221/p003225/raw" # the folder to read data from, required
out_folder = "/gpfs/exfel/data/scratch/ahmedk/test/gotthard2" # the folder to output to, required
metadata_folder = "" # Directory containing calibration_metadata.yml when run by xfel-calibrate
run = 50 # run to process, required
sequences = [-1] # sequences to correct, set to [-1] for all, range allowed
sequences_per_node = 1 # number of sequence files per node if notebook executed through xfel-calibrate, set to 0 to not run SLURM parallel
# Parameters used to access raw data.
karabo_id = "FXE_XAD_G2XES" # karabo prefix of Gotthard-II devices
karabo_da = ["GH201"] # data aggregators
receiver_template = "RECEIVER" # receiver template used to read INSTRUMENT keys.
control_template = "CONTROL" # control template used to read CONTROL keys.
instrument_source_template = "{}/DET/{}:daqOutput" # template for source name (filled with karabo_id & receiver_id). e.g. 'SPB_IRDA_JF4M/DET/JNGFR01:daqOutput'
ctrl_source_template = "{}/DET/{}" # template for control source name (filled with karabo_id_control)
karabo_id_control = "" # Control karabo ID. Set to empty string to use the karabo-id
# Parameters for calibration database.
cal_db_interface = "tcp://max-exfl-cal001:8016#8025" # the database interface to use.
cal_db_timeout = 180000 # timeout on caldb requests.
creation_time = "" # To overwrite the measured creation_time. Required Format: YYYY-MM-DD HR:MN:SC e.g. "2022-06-28 13:00:00"
# Parameters affecting corrected data.
constants_file = "" # Use constants in given constant file path. /gpfs/exfel/data/scratch/ahmedk/dont_remove/gotthard2/constants/calibration_constants_GH2.h5
offset_correction = True # apply offset correction. This can be disabled to only apply LUT or apply LUT and gain correction for non-linear differential results.
gain_correction = True # apply gain correction.
chunks_data = 1 # HDF chunk size for pixel data in number of frames.
# Parameter conditions.
bias_voltage = -1 # Detector bias voltage, set to -1 to use value in raw file.
exposure_time = -1. # Detector exposure time, set to -1 to use value in raw file.
exposure_period = -1. # Detector exposure period, set to -1 to use value in raw file.
acquisition_rate = -1. # Detector acquisition rate (1.1/4.5), set to -1 to use value in raw file.
single_photon = -1 # Detector single photon mode (High/Low CDS), set to -1 to use value in raw file.
# Parameters for plotting
skip_plots = False # exit after writing corrected files
pulse_idx_preview = 3 # pulse index to preview. The following even/odd pulse index is used for preview. # TODO: update to pulseId preview.
def balance_sequences(in_folder, run, sequences, sequences_per_node, karabo_da):
from xfel_calibrate.calibrate import balance_sequences as bs
return bs(in_folder, run, sequences, sequences_per_node, karabo_da)
```
%% Cell type:code id:6e9730d8-3908-41d7-abe2-d78e046d5de2 tags:
``` python
import warnings
from logging import warning
import h5py
import pasha as psh
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import Markdown, display
from extra_data import RunDirectory, H5File
from pathlib import Path
import cal_tools.restful_config as rest_cfg
from cal_tools.calcat_interface import CalCatError, GOTTHARD2_CalibrationData
from cal_tools.files import DataFile
from cal_tools.gotthard2 import gotthard2algs, gotthard2lib
from cal_tools.step_timing import StepTimer
from cal_tools.tools import (
calcat_creation_time,
write_constants_fragment,
)
from XFELDetAna.plotting.heatmap import heatmapPlot
warnings.filterwarnings('ignore')
%matplotlib inline
```
%% Cell type:code id:d7c02c48-4429-42ea-a42e-de45366d7fa3 tags:
``` python
in_folder = Path(in_folder)
run_folder = in_folder / f"r{run:04d}"
out_folder = Path(out_folder)
out_folder.mkdir(parents=True, exist_ok=True)
if not karabo_id_control:
karabo_id_control = karabo_id
ctrl_src = ctrl_source_template.format(karabo_id_control, control_template)
print(f"Process modules: {karabo_da} for run {run}")
# Run's creation time:
creation_time = calcat_creation_time(in_folder, run, creation_time)
print(f"Creation time: {creation_time}")
```
%% Cell type:code id:b5eb816e-b5f2-44ce-9907-0273d82341b6 tags:
``` python
# Select only sequence files to process for the selected detector.
if sequences == [-1]:
possible_patterns = list(f"*{mod}*.h5" for mod in karabo_da)
else:
possible_patterns = list(
f"*{mod}-S{s:05d}.h5" for mod in karabo_da for s in sequences
)
run_folder = Path(in_folder / f"r{run:04d}")
seq_files = [
f for f in run_folder.glob("*.h5") if any(f.match(p) for p in possible_patterns)
]
seq_files = sorted(seq_files)
if not seq_files:
raise IndexError("No sequence files available for the selected sequences.")
print(f"Processing a total of {len(seq_files)} sequence files")
```
%% Cell type:code id:f9a8d1eb-ce6a-4ed0-abf4-4a6029734672 tags:
``` python
step_timer = StepTimer()
```
%% Cell type:code id:7bf7fb32 tags:
%% Cell type:code id:892172d8 tags:
``` python
run_dc = RunDirectory(run_folder)
# Decide if we are processing 2 25um or 1 50um Gotthard-II
receivers = list(run_dc.select(f'{karabo_id}/DET/{receiver_template}*').all_sources)
corr_karabo_da = receivers[0].split("/")[-1].split(":")[0][:-2]
```
%% Cell type:code id:892172d8 tags:
``` python
# Read slow data
g2ctrl = gotthard2lib.Gotthard2Ctrl(run_dc=run_dc, ctrl_src=ctrl_src)
if bias_voltage == -1:
bias_voltage = g2ctrl.get_bias_voltage()
if exposure_time == -1:
exposure_time = g2ctrl.get_exposure_time()
if exposure_period == -1:
exposure_period = g2ctrl.get_exposure_period()
if acquisition_rate == -1:
acquisition_rate = g2ctrl.get_acquisition_rate()
if single_photon == -1:
single_photon = g2ctrl.get_single_photon()
print("Bias Voltage:", bias_voltage)
print("Exposure Time:", exposure_time)
print("Exposure Period:", exposure_period)
print("Acquisition Rate:", acquisition_rate)
print("Single Photon:", single_photon)
# Decide if GH2 is 25um or 50um
gh2_hostname = run_dc.get_run_value(ctrl_src, "rxHostname")
# gh2_hostname is a vector of bytes objects.
# GH2 25um has two hostnames unlike 50um which has one.
gh2_detector = "25um" if gh2_hostname[1].decode("utf-8") else "50um"
# Decide if we are processing 2 25um or 1 50um Gotthard-II
receivers = sorted(list(run_dc.select(f'{karabo_id}/DET/{receiver_template}*').all_sources))
if gh2_detector == "25um": # For 25um use virtual karabo_das for CALCAT data mapping.
karabo_da = [f"{karabo_da[0]}/1", f"{karabo_da[0]}/2"]
print("Processing 25um Gotthard2.")
corr_karabo_da = receivers[0].split("/")[-1].split(":")[0][:-2]
else:
corr_karabo_da = receivers[0].split("/")[-1].split(":")[0]
```
%% Cell type:markdown id:8c852392-bb19-4c40-b2ce-3b787538a92d tags:
### Retrieving calibration constants
%% Cell type:code id:5717d722 tags:
``` python
da_to_pdu = {}
# Used for old FXE (p003225) runs before adding Gotthard2 to CALCAT
const_data = dict()
g2_cal = GOTTHARD2_CalibrationData(
detector_name=karabo_id,
sensor_bias_voltage=bias_voltage,
exposure_time=exposure_time,
exposure_period=exposure_period,
acquisition_rate=acquisition_rate,
single_photon=single_photon,
event_at=creation_time,
client=rest_cfg.calibration_client(),
)
# Keep as long as it is essential to correct
# RAW data (FXE p003225) before the data mapping was added to CALCAT.
try: # in case local constants are used with old RAW data. This can be removed in the future.
for mod_info in g2_cal.physical_detector_units.values():
da_to_pdu[mod_info["karabo_da"]] = mod_info["physical_name"]
db_modules = [da_to_pdu[da] for da in karabo_da]
except CalCatError as e:
print(e)
db_modules = [None] * len(karabo_da)
if constants_file:
for mod in receivers:
for mod in karabo_da:
const_data[mod] = dict()
# load constants temporarily using defined local paths.
with h5py.File(constants_file, "r") as cfile:
const_data[mod]["LUTGotthard2"] = cfile["LUT"][()]
const_data[mod]["OffsetGotthard2"] = cfile["offset_map"][()].astype(np.float32)
const_data[mod]["RelativeGainGotthard2"] = cfile["gain_map"][()].astype(np.float32)
const_data[mod]["Mask"] = cfile["bpix_ff"][()].astype(np.uint32)
else:
constant_names = ["LUTGotthard2", "OffsetGotthard2", "BadPixelsDarkGotthard2"]
if gain_correction:
constant_names += ["RelativeGainGotthard2", "BadPixelsFFGotthard2"]
g2_metadata = g2_cal.metadata(calibrations=constant_names)
# Validate the constants availability and raise/warn correspondingly.
for mod, calibrations in g2_metadata.items():
dark_constants = {"LUTGotthard2"}
if offset_correction:
dark_constants |= {"OffsetGotthard2", "BadPixelsDarkGotthard2"}
missing_dark_constants = dark_constants - set(calibrations)
if missing_dark_constants:
karabo_da.remove(mod)
warning(f"Dark constants {missing_dark_constants} are not available to correct {mod}.") # noqa
missing_gain_constants = {
"BadPixelsFFGotthard2", "RelativeGainGotthard2"} - set(calibrations)
if gain_correction and missing_gain_constants:
warning(f"Gain constants {missing_gain_constants} are not retrieved for mod {mod}.")
if not karabo_da:
raise ValueError("Dark constants are not available for all modules.")
```
%% Cell type:code id:ac1cdec5 tags:
``` python
# Record constant details in YAML metadata.
write_constants_fragment(
out_folder=(metadata_folder or out_folder),
det_metadata=g2_metadata,
caldb_root=g2_cal.caldb_root)
# Load constants data for all constants.
const_data = g2_cal.ndarray_map(metadata=g2_metadata)
# Prepare constant arrays.
if not constants_file:
# Create the mask array.
bpix = const_data[mod].get("BadPixelsDarkGotthard2")
if bpix is None:
bpix = np.zeros((1280, 2, 3), dtype=np.uint32)
if const_data[mod].get("BadPixelsFFGotthard2") is not None:
bpix |= const_data[mod]["BadPixelsFFGotthard2"]
const_data[mod]["Mask"] = bpix
# Prepare empty arrays for missing constants.
if const_data[mod].get("OffsetGotthard2") is None:
const_data[mod]["OffsetGotthard2"] = np.zeros(
(1280, 2, 3), dtype=np.float32)
if const_data[mod].get("RelativeGainGotthard2") is None:
const_data[mod]["RelativeGainGotthard2"] = np.ones(
(1280, 2, 3), dtype=np.float32)
const_data[mod]["RelativeGainGotthard2"] = const_data[mod]["RelativeGainGotthard2"].astype( # noqa
np.float32, copy=False) # Old gain constants are not float32.
```
%% Cell type:code id:23fcf7f4-351a-4df7-8829-d8497d94fecc tags:
``` python
context = psh.ProcessContext(num_workers=23)
```
%% Cell type:code id:daecd662-26d2-4cb8-aa70-383a579cf9f9 tags:
``` python
def correct_train(wid, index, d):
g = gain[index]
gotthard2algs.convert_to_10bit(d, const_data[mod]["LUTGotthard2"], data_corr[index, ...])
gotthard2algs.correct_train(
data_corr[index, ...],
mask[index, ...],
g,
const_data[rec_mod]["OffsetGotthard2"],
const_data[rec_mod]["RelativeGainGotthard2"],
const_data[rec_mod]["Mask"],
apply_offset=offset_correction,
apply_gain=gain_correction,
)
```
%% Cell type:code id:f88c1aa6-a735-4b72-adce-b30162f5daea tags:
``` python
corr_data_source = f"{karabo_id}/DET/{corr_karabo_da}:daqOutput"
for raw_file in seq_files:
out_file = out_folder / raw_file.name.replace("RAW", "CORR")
# Select module INSTRUMENT sources and deselect empty trains.
dc = H5File(raw_file).select(receivers, require_all=True)
n_trains = len(dc.train_ids)
# Initialize GH2 data and gain arrays to store in corrected files.
if gh2_detector == "25um":
data_stored = np.zeros((dc[receivers[0], "data.adc"].shape[:2] + (1280 * 2,)), dtype=np.float32)
gain_stored = np.zeros((dc[receivers[0], "data.adc"].shape[:2] + (1280 * 2,)), dtype=np.uint8)
else:
data_stored = None
gain_stored = None
for i, rec_mod in enumerate(receivers):
for i, (receiver, mod) in enumerate(zip(receivers, karabo_da)):
step_timer.start()
print(f"Correcting {rec_mod} for {raw_file}")
print(f"Correcting {receiver} for {raw_file}")
data = dc[rec_mod, "data.adc"].ndarray()
gain = dc[rec_mod, "data.gain"].ndarray()
data = dc[receiver, "data.adc"].ndarray()
gain = dc[receiver, "data.gain"].ndarray()
step_timer.done_step("Preparing raw data")
dshape = data.shape
step_timer.start()
# Allocate shared arrays.
data_corr = context.alloc(shape=dshape, dtype=np.float32)
mask = context.alloc(shape=dshape, dtype=np.uint32)
context.map(correct_train, data)
step_timer.done_step(f"Correcting one receiver in one sequence file")
step_timer.start()
# Provided PSI gain map has 0 values. Set inf values to nan.
# TODO: This can maybe be removed after creating XFEL gain maps.?
data_corr[np.isinf(data_corr)] = np.nan
# Create CORR files and add corrected data sections.
image_counts = dc[rec_mod, "data.adc"].data_counts(labelled=False)
image_counts = dc[receiver, "data.adc"].data_counts(labelled=False)
if gh2_detector == "25um":
data_stored[..., i::2] = data_corr.copy()
gain_stored[..., i::2] = gain.copy()
else: # "50um"
data_stored = data_corr
gain_stored = gain
with DataFile(out_file, "w") as ofile:
# Create INDEX datasets.
ofile.create_index(dc.train_ids, from_file=dc.files[0])
ofile.create_metadata(
like=dc,
sequence=dc.run_metadata()["sequenceNumber"],
instrument_channels=(f"{instrument_src}/data",)
)
# Create Instrument section to later add corrected datasets.
outp_source = ofile.create_instrument_source(f"{karabo_id}/DET/{corr_karabo_da}:daqOutput")
outp_source = ofile.create_instrument_source(corr_data_source)
# Create count/first datasets at INDEX source.
outp_source.create_index(data=image_counts)
# Store uncorrected trainId in the corrected file.
outp_source.create_key(
f"data.trainId", data=dc.train_ids,
chunks=min(50, len(dc.train_ids))
)
# Create datasets with the available corrected data
for field_name, field_data in {
"adc": data_stored,
"gain": gain_stored,
}.items():
outp_source.create_key(
f"data.{field_name}", data=field_data,
chunks=((chunk_size_idim,) + data_corr.shape[1:])
)
# For GH2 25um, the data of the second receiver is
# stored in the corrected file.
for field in ["bunchId", "memoryCell", "frameNumber", "timestamp"]:
outp_source.create_key(
f"data.{field}", data=dc[instr_mod_src, f"data.{field}"].ndarray(),
chunks=(chunks_data, data_corr.shape[1])
f"data.{field}", data=dc[receiver, f"data.{field}"].ndarray(),
chunks=(chunk_size_idim, data_corr.shape[1])
)
outp_source.create_compressed_key(f"data.mask", data=mask)
step_timer.done_step("Storing data")
```
%% Cell type:code id:94b8e4d2-9f8c-4c23-a509-39238dd8435c tags:
``` python
print(f"Total processing time {step_timer.timespan():.01f} s")
step_timer.print_summary()
```
%% Cell type:code id:7f366352 tags:
``` python
corr_data_source = f"{karabo_id}/DET/{corr_karabo_da}:daqOutput"
```
%% Cell type:code id:0ccc7f7e-2a3f-4ac0-b854-7d505410d2fd tags:
``` python
if skip_plots:
print("Skipping plots")
import sys
sys.exit(0)
```
%% Cell type:code id:ff203f77-3811-46f3-bf7d-226d2dcab13f tags:
``` python
mod_dcs = {}
first_seq_raw = seq_files[0]
first_seq_corr = out_folder / first_seq_raw.name.replace("RAW", "CORR")
mod_dcs[corr_data_source] = {}
with H5File(first_seq_corr) as out_dc:
print(out_dc.all_sources)
tid, mod_dcs[corr_data_source]["train_corr_data"] = next(
out_dc[corr_data_source, "data.adc"].trains()
)
if gh2_detector == "25um":
mod_dcs[corr_data_source]["train_raw_data"] = np.zeros((data_corr.shape[1], 1280 * 2), dtype=np.float32)
mod_dcs[corr_data_source]["train_raw_gain"] = np.zeros((data_corr.shape[1], 1280 * 2), dtype=np.uint8)
for i, rec_mod in enumerate(receivers):
with H5File(first_seq_raw) as in_dc:
train_dict = in_dc.train_from_id(tid)[1][rec_mod]
if gh2_detector == "25um":
mod_dcs[corr_data_source]["train_raw_data"][..., i::2] = train_dict["data.adc"]
mod_dcs[corr_data_source]["train_raw_gain"][..., i::2] = train_dict["data.gain"]
else:
mod_dcs[corr_data_source]["train_raw_data"] = train_dict["data.adc"]
mod_dcs[corr_data_source]["train_raw_gain"] = train_dict["data.gain"]
```
%% Cell type:code id:1b379438-eb1d-42b2-ac83-eb8cf88c46db tags:
``` python
display(Markdown("### Mean RAW and CORRECTED across pulses for one train:"))
display(Markdown(f"Train: {tid}"))
if gh2_detector == "50um":
title = f"{{}} data for {karabo_da} ({db_modules})"
else:
title = f"Interleaved {{}} data for {karabo_da} ({db_modules})"
step_timer.start()
fig, ax = plt.subplots(figsize=(20, 10))
raw_data = mod_dcs[corr_data_source]["train_raw_data"]
im = ax.plot(np.mean(raw_data, axis=0))
ax.set_title(f"RAW module {receivers} ({db_modules})")
ax.set_title(title.format("RAW"))
ax.set_xlabel("Strip #", size=20)
ax.set_ylabel("12-bit ADC output", size=20)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
pass
fig, ax = plt.subplots(figsize=(20, 10))
corr_data = mod_dcs[corr_data_source]["train_corr_data"]
im = ax.plot(np.mean(corr_data, axis=0))
ax.set_title(f"CORRECTED module {receivers} ({db_modules})")
ax.set_title(title.format("CORRECTED"))
ax.set_xlabel("Strip #", size=20)
ax.set_ylabel("10-bit KeV. output", size=20)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
pass
step_timer.done_step("Plotting mean data")
```
%% Cell type:code id:58a6a276 tags:
``` python
display(Markdown(f"### RAW and CORRECTED strips across pulses for train {tid}"))
step_timer.start()
for plt_data, dname in zip(
["train_raw_data", "train_corr_data"], ["RAW", "CORRECTED"]
):
fig, ax = plt.subplots(figsize=(15, 20))
plt.rcParams.update({"font.size": 20})
heatmapPlot(
mod_dcs[corr_data_source][plt_data],
y_label="Pulses",
x_label="Strips",
title=f"{dname} module {receivers} ({db_modules})",
title=title.format(dname),
use_axis=ax,
)
pass
step_timer.done_step("Plotting RAW and CORRECTED data for one train")
```
%% Cell type:code id:cd8f5e08-fcee-4bff-ba63-6452b3d892a2 tags:
``` python
# Validate given "pulse_idx_preview"
if pulse_idx_preview + 1 > data.shape[1]:
print(
f"WARNING: selected pulse_idx_preview {pulse_idx_preview} is not available in data."
" Previewing 1st pulse."
)
pulse_idx_preview = 1
if data.shape[1] == 1:
odd_pulse = 1
even_pulse = None
else:
odd_pulse = pulse_idx_preview if pulse_idx_preview % 2 else pulse_idx_preview + 1
even_pulse = (
pulse_idx_preview if not (pulse_idx_preview % 2) else pulse_idx_preview + 1
)
if pulse_idx_preview + 1 > data.shape[1]:
pulse_idx_preview = 1
if data.shape[1] > 1:
pulse_idx_preview = 2
```
%% Cell type:code id:e5f0d4d8-e32c-4f2c-8469-4ebbfd3f644c tags:
``` python
display(Markdown("### RAW and CORRECTED even/odd pulses for one train:"))
display(Markdown(f"Train: {tid}"))
for mod, pdu in zip(karabo_da, db_modules):
fig, ax = plt.subplots(figsize=(20, 20))
raw_data = mod_dcs[mod]["train_raw_data"]
corr_data = mod_dcs[mod]["train_corr_data"]
ax.plot(raw_data[odd_pulse], label=f"Odd Pulse {odd_pulse}")
if even_pulse:
ax.plot(raw_data[even_pulse], label=f"Even Pulse {even_pulse}")
ax.set_title(f"RAW module {mod} ({pdu})")
ax.set_xlabel("Strip #", size=20)
ax.set_ylabel("12-bit ADC RAW", size=20)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
ax.legend()
pass
fig, ax = plt.subplots(figsize=(20, 20))
ax.plot(corr_data[odd_pulse], label=f"Odd Pulse {odd_pulse}")
if even_pulse:
ax.plot(corr_data[even_pulse], label=f"Even Pulse {even_pulse}")
ax.set_title(f"CORRECTED module {mod} ({pdu})")
ax.set_xlabel("Strip #", size=20)
ax.set_ylabel("10-bit KeV CORRECTED", size=20)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
ax.legend()
pass
step_timer.done_step("Plotting RAW and CORRECTED odd/even pulses.")
```
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