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Commit 0a292786 authored by Karim Ahmed's avatar Karim Ahmed
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fix: add gh2_detector in the first notebook cell

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1 merge request!1107[GH2] fix: enable manual edit for gh2_detector type and add validation
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%% Cell type:markdown id:49b6577f-96a5-4dd2-bdd9-da661b2c4619 tags:
# Gotthard2 Dark Image Characterization #
Author: European XFEL Detector Group, Version: 1.0
The following is a processing for the dark constants (`Offset`, `Noise`, and `BadPixelsDark`) maps using dark images taken with Gotthard2 detector (GH2 50um or 25um).
All constants are evaluated per strip, per pulse, and per memory cell. The maps are calculated for each gain stage that is acquired in 3 separate runs.
The three maps are of shape (stripes, cells, gains): (1280, 2, 3). They can be injected to the database (`db_output`) and/or stored locally (`local_output`).
%% Cell type:code id:818e24e8 tags:
``` python
in_folder = "/gpfs/exfel/exp/FXE/202231/p900298/raw" # the folder to read data from, required
out_folder = "/gpfs/exfel/data/scratch/ahmedk/test/gotthard2/darks" # the folder to output to, required
metadata_folder = '' # Directory containing calibration_metadata.yml when run by xfel-calibrate
run_high = 7 # run number for G0 dark run, required
run_med = 8 # run number for G1 dark run, required
run_low = 9 # run number for G2 dark run, required
# Parameters used to access raw data.
input_source_template = "{karabo_id}/DET/RECEIVER{input_source_affixes}:daqOutput" # data source template used to read INSTRUMENT keys.
ctrl_source_template = "{karabo_id_control}/DET/{control_template}" # template for control source name (filled with karabo_id_control)
karabo_id = "FXE_XAD_G2XES" # karabo prefix of Gotthard-II devices
karabo_da = [""] # data aggregators
input_source_affixes = [""] # The affix to format into the input source template to be able to load the correct module's data.
control_template = "CONTROL" # control template used to read CONTROL keys.
karabo_id_control = "" # Control karabo ID. Set to empty string to use the karabo-id
# Parameters for the calibration database.
cal_db_interface = "tcp://max-exfl-cal001:8020" # calibration DB interface to use
cal_db_timeout = 300000 # 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"
db_output = False # Output constants to the calibration database
local_output = True # Output constants locally
# Conditions used for injected calibration constants.
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.
# This is used for the summary notebook
gh2_detector = "" # Specifies the GH2 detector type ('50um' or '25um'). This parameter can be used to manually set the detector type if the code cannot automatically determine it from the control data.
# Parameters used for calculating calibration constants
bpix_noise_nitrs = 5 # number of maximum iterations to actively try to reach correct number of badpixels of NOISE_OUT_OF_THRESHOLD type. Leave -1 to keep iterating until finding maximum number of badpixels.
# Parameters used during selecting raw data trains.
min_trains = 1 # Minimum number of trains that should be available to process dark constants. Default 1.
max_trains = 1000 # Maximum number of trains to use for processing dark constants. Set to 0 to use all available trains.
badpixel_threshold_sigma = 5. # bad pixels defined by values outside n times this std from median
# Don't delete! myMDC sends this by default.
operation_mode = '' # Detector dark run acquiring operation mode, optional
```
%% Cell type:code id:8085f9aa tags:
``` python
import numpy as np
import matplotlib.pyplot as plt
import pasha as psh
from IPython.display import Markdown, display
from extra_data import RunDirectory
from pathlib import Path
import yaml
from cal_tools.calcat_interface import CalCatApi
from cal_tools.enums import BadPixels
from cal_tools.gotthard2 import gotthard2algs, gotthard2lib
from cal_tools.step_timing import StepTimer
from cal_tools.restful_config import calibration_client
from cal_tools.tools import (
calcat_creation_time,
get_constant_from_db_and_time,
get_report,
raw_data_location_string,
save_const_to_h5,
send_to_db,
)
from iCalibrationDB import Conditions, Constants
%matplotlib inline
```
%% Cell type:code id:18fe4379 tags:
``` python
run_nums = [run_high, run_med, run_low]
in_folder = Path(in_folder)
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=karabo_id_control,
control_template=control_template,
)
run_nums = gotthard2lib.sort_dark_runs_by_gain(
raw_folder=in_folder,
runs=run_nums,
ctrl_src=ctrl_src,
)
# Read report path to associate it later with injected constants.
report = get_report(metadata_folder)
# Run's creation time:
creation_time = calcat_creation_time(in_folder, run_nums[0], creation_time)
print(f"Creation time: {creation_time}")
```
%% Cell type:code id:c176a86f tags:
``` python
run_dc = RunDirectory(in_folder / f"r{run_nums[0]:04d}")
proposal = list(filter(None, str(in_folder).strip('/').split('/')))[-2]
file_loc = raw_data_location_string(proposal, run_nums)
```
%% Cell type:code id:108be688 tags:
``` python
step_timer = StepTimer()
```
%% Cell type:code id:ff9149fc tags:
``` python
# Read parameter conditions and validate the values for the three runs.
step_timer.start()
run_dcs_dict = dict()
conditions = {
"bias_voltage": set(),
"exposure_time": set(),
"exposure_period": set(),
"acquisition_rate": set(),
"single_photon": set(),
}
for gain, run in enumerate(run_nums):
run_dc = RunDirectory(in_folder / f"r{run:04d}/")
run_dcs_dict[run] = [gain, run_dc]
# Read slow data.
g2ctrl = gotthard2lib.Gotthard2Ctrl(run_dc=run_dc, ctrl_src=ctrl_src)
conditions["bias_voltage"].add(
g2ctrl.get_bias_voltage() if bias_voltage == -1 else bias_voltage
)
conditions["exposure_time"].add(
g2ctrl.get_exposure_time() if exposure_time == -1 else exposure_time
)
conditions["exposure_period"].add(
g2ctrl.get_exposure_period() if exposure_period == -1 else exposure_period
)
conditions["single_photon"].add(
g2ctrl.get_single_photon() if single_photon == -1 else single_photon
)
conditions["acquisition_rate"].add(
g2ctrl.get_acquisition_rate() if acquisition_rate == -1 else acquisition_rate
)
for c, v in conditions.items():
assert len(v) == 1, f"{c} value is not the same for the three runs."
bias_voltage = conditions["bias_voltage"].pop()
print("Bias voltage: ", bias_voltage)
exposure_time = conditions["exposure_time"].pop()
print("Exposure time: ", exposure_time)
exposure_period = conditions["exposure_period"].pop()
print("Exposure period: ", exposure_period)
single_photon = conditions["single_photon"].pop()
print("Single photon: ", single_photon)
acquisition_rate = conditions["acquisition_rate"].pop()
print("Acquisition rate: ", acquisition_rate)
gh2_detector = g2ctrl.get_det_type()
if not gh2_detector:
gh2_detector = g2ctrl.get_det_type()
print(f"Processing {gh2_detector} Gotthard2.")
```
%% Cell type:code id:f64bc150-cfcd-4f98-83f9-a982fdacedd7 tags:
``` python
calcat = CalCatApi(client=calibration_client())
detector_id = calcat.detector(karabo_id)['id']
pdus_by_da = calcat.physical_detector_units(detector_id, pdu_snapshot_at=creation_time)
# Module index is stored in the da_to_pdu dict to be used
# later in selecting the correct data source.
# TODO: This can be improved later by getting the module index and even data source
# when we start storing in the CALCAT.
da_to_pdu = dict()
for i, (da, p) in enumerate(sorted(pdus_by_da.items())):
da_to_pdu[da] = p['physical_name']
calcat_das = list(da_to_pdu.keys())
if karabo_da != [""]:
# Filter DA connected to detector in CALCAT
karabo_da = [da for da in karabo_da if da in calcat_das]
else:
karabo_da = calcat_das
print(f"Processing {karabo_da}")
da_to_source = gotthard2lib.map_da_to_source(
run_dc,
calcat_das,
input_source_template,
karabo_id,
input_source_affixes,
)
```
%% Cell type:code id:ac9c5dc3-bc66-4e7e-b6a1-360259be535c tags:
``` python
def specify_trains_to_process(
img_key_data: "extra_data.KeyData",
run_num: int,
src: str,
):
"""Specify total number of trains to process.
Based on given min_trains and max_trains, if given.
Print number of trains to process and number of empty trains.
Raise ValueError if specified trains are less than min_trains.
"""
# Specifies total number of trains to process.
n_trains = img_key_data.shape[0]
all_trains = len(img_key_data.train_ids)
print(
f"{src} for run {run} has {all_trains - n_trains} "
f"trains with empty frames out of {all_trains} trains"
)
if n_trains < min_trains:
raise ValueError(
f"Less than {min_trains} trains are available in RAW data."
" Not enough data to process darks."
)
if max_trains > 0:
n_trains = min(n_trains, max_trains)
print(f"Processing {n_trains} trains.")
return n_trains
```
%% Cell type:code id:3c59c11d tags:
``` python
# set the operating condition
condition = Conditions.Dark.Gotthard2(
bias_voltage=bias_voltage,
exposure_time=exposure_time,
exposure_period=exposure_period,
acquisition_rate=acquisition_rate,
single_photon=single_photon,
)
```
%% Cell type:code id:e2eb2fc0-df9c-4887-9691-f81474f8c131 tags:
``` python
def convert_train(wid, index, tid, d):
"""Convert a Gotthard2 train from 12bit to 10bit."""
gotthard2algs.convert_to_10bit(
d[src]["data.adc"], lut, data_10bit[index, ...]
)
```
%% Cell type:code id:4e8ffeae tags:
``` python
# Calculate noise and offset per pixel and global average, std and median
noise_map = dict()
offset_map = dict()
badpixels_map = dict()
context = psh.ProcessContext(num_workers=25)
empty_lut = (np.arange(2 ** 12).astype(np.float64) * 2 ** 10 / 2 ** 12).astype(
np.uint16
)
empty_lut = np.stack(1280 * [np.stack([empty_lut] * 2)], axis=0)
for mod in karabo_da:
# For 50um idx always 0. For 25um pick the right data source
# from list of 2 sources.
src = da_to_source[mod]
# Retrieve LUT constant
lut, time = get_constant_from_db_and_time(
constant=Constants.Gotthard2.LUT(),
condition=condition,
empty_constant=empty_lut,
karabo_id=karabo_id,
karabo_da=mod,
cal_db_interface=cal_db_interface,
creation_time=creation_time,
timeout=cal_db_timeout,
print_once=False,
)
print(f"Retrieved LUT constant with creation-time {time}")
cshape = (1280, 2, 3)
offset_map[mod] = context.alloc(shape=cshape, dtype=np.float32)
noise_map[mod] = context.alloc(like=offset_map[mod])
badpixels_map[mod] = context.alloc(like=offset_map[mod], dtype=np.uint32)
for run_num, [gain, run_dc] in run_dcs_dict.items():
step_timer.start()
n_trains = specify_trains_to_process(run_dc[src, "data.adc"], run_num, src)
# Select requested number of trains to process.
dc = run_dc.select(src, require_all=True).select_trains(
np.s_[:n_trains]
)
step_timer.done_step("preparing raw data")
step_timer.start()
# Convert 12bit data to 10bit
data_10bit = context.alloc(
shape=dc[src, "data.adc"].shape, dtype=np.float32
)
context.map(convert_train, dc)
step_timer.done_step("convert to 10bit")
step_timer.start()
# The first ~20 frames of each train are excluded.
# The electronics needs some time to reach stable operational conditions
# at the beginning of each acquisition cycle,
# hence the first ~20 images have lower quality and should not be used.
# The rough number of 20 is correct at 4.5 MHz acquisition rate,
# 5 should be enough at 1.1 MHz. Sticking with 20 excluded frames, as there isn't
# much of expected difference.
# Split even and odd data to calculate the two storage cell constants.
# Detector always operates in burst mode.
even_data = data_10bit[:, 20::2, :]
odd_data = data_10bit[:, 21::2, :]
def offset_noise_cell(wid, index, d):
offset_map[mod][:, index, gain] = np.mean(d, axis=(0, 1))
noise_map[mod][:, index, gain] = np.std(d, axis=(0, 1))
# Calculate Offset and Noise Maps.
context.map(offset_noise_cell, (even_data, odd_data))
# Split even and odd gain data.
data_gain = dc[src, "data.gain"].ndarray()
even_gain = data_gain[:, 20::2, :]
odd_gain = data_gain[:, 21::2, :]
raw_g = 3 if gain == 2 else gain
def badpixels_cell(wid, index, g):
"""Check if there are wrong bad gain values.
Indicate pixels with wrong gain value across all trains for each cell."""
badpixels_map[mod][
np.mean(g, axis=(0, 1)) != raw_g, index, :
] |= BadPixels.WRONG_GAIN_VALUE.value
context.map(badpixels_cell, (even_gain, odd_gain))
step_timer.done_step("Processing darks")
```
%% Cell type:code id:8e410ca2 tags:
``` python
print(f"Total processing time {step_timer.timespan():.01f} s")
step_timer.print_summary()
```
%% Cell type:code id:3fc17e05-17ab-4ac4-9e79-c95399278bb9 tags:
``` python
def print_bp_entry(bp):
print(f"{bp.name:<30s} {bp.value:032b} -> {int(bp.value)}")
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))[None, :, :]
std = np.nanstd(d, axis=(0))[None, :, :]
idx = (d > badpixel_threshold_sigma * std + mdn) | (
d < (-badpixel_threshold_sigma) * std + mdn
)
return idx
```
%% Cell type:code id:40c34cc5-fe93-4b83-bf39-f465f37c40b4 tags:
``` python
step_timer.start()
g_name = ["G0", "G1", "G2"]
for mod in karabo_da:
pdu = da_to_pdu[mod]
display(Markdown(f"### Badpixels for module {mod}:"))
badpixels_map[mod][
~np.isfinite(offset_map[mod])
] |= BadPixels.OFFSET_NOISE_EVAL_ERROR.value
# Iteratively identify noisy pixels (OFFSET_NOISE_EVAL_ERROR & NOISE_OUT_OF_THRESHOLD)
# Due to the low number of strips (1280), outliers can significantly impact
# the badpixel calculation. We use multiple iterations to gradually refine
# the sigma threshold, as a single fixed threshold may not be suitable for
# different Gotthard2 detectors.
last_nbpix = 0
nmap = noise_map[mod].copy()
for itr in range(bpix_noise_nitrs):
badpixels_map[mod][
~np.isfinite(nmap)
] |= BadPixels.OFFSET_NOISE_EVAL_ERROR.value
badpixels_map[mod][
eval_bpidx(nmap)
] |= BadPixels.NOISE_OUT_OF_THRESHOLD.value
nmap[badpixels_map[mod] > 0] = np.nan
recent_nbpix = np.count_nonzero(badpixels_map[mod])
if last_nbpix == recent_nbpix:
# stop iterating if no more badpixels difference
break
if not local_output:
for cell in [0, 1]:
fig, ax = plt.subplots(figsize=(10, 5))
for g_idx in [0, 1, 2]:
ax.plot(badpixels_map[mod][:, cell, g_idx], label=f"G{g_idx} Bad pixel map")
ax.set_xticks(np.arange(0, 1281, 80))
ax.set_xlabel("Stripes #")
ax.set_xlabel("BadPixels")
ax.set_title(f"BadPixels map - Cell {cell} - Module {mod} ({pdu})")
ax.set_ylim([0, 5])
ax.legend()
plt.show()
step_timer.done_step(f"Creating bad pixels constant.")
```
%% Cell type:code id:c8777cfe tags:
``` python
if not local_output:
for cons, cname in zip([offset_map, noise_map], ["Offset", "Noise"]):
for mod in karabo_da:
pdu = da_to_pdu[mod]
display(Markdown(f"### {cname} for module {mod}:"))
for cell in [0, 1]:
fig, ax = plt.subplots(figsize=(10, 5))
for g_idx in [0, 1, 2]:
ax.plot(cons[mod][:, cell, g_idx], label=f"G{g_idx} {cname} map")
ax.set_xlabel("Stripes #")
ax.set_xlabel(cname)
ax.set_title(f"{cname} map - Cell {cell} - Module {mod} ({pdu})")
ax.legend()
plt.show()
```
%% Cell type:code id:1c4eddf7-7d6e-49f4-8cbb-12d2bc496a8f tags:
``` python
step_timer.start()
for mod in karabo_da:
db_mod = da_to_pdu[mod]
constants = {
"Offset": offset_map[mod],
"Noise": noise_map[mod],
"BadPixelsDark": badpixels_map[mod],
}
md = None
for key, const_data in constants.items():
const = getattr(Constants.Gotthard2, key)()
const.data = const_data
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"• Exposure time: {exposure_time}\n"
f"• Exposure period: {exposure_period}\n"
f"• Acquisition rate: {acquisition_rate}\n"
f"• Single photon: {single_photon}\n"
f"• Creation time: {md.calibration_constant_version.begin_at if md is not None else creation_time}\n"
)
step_timer.done_step("Injecting constants.")
```
%% Cell type:code id:98ca9486 tags:
``` python
# TODO: store old constants for comparison.
for mod in karabo_da:
mod_file = mod.replace("/", "-")
with open(f"{metadata_folder or out_folder}/module_metadata_{mod_file}.yml", "w") as fd:
yaml.safe_dump(
{
"module": mod,
"pdu": da_to_pdu[mod],
},
fd,
)
```
......
notebooks/Gotthard2/Summary_Darks_Gotthard2_NBC.ipynb
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%% Cell type:markdown id: tags:
# Gotthard2 Dark Summary
Author: European XFEL Detector Department, Version: 1.0
Summary for process dark constants and a comparison with previously injected constants with the same conditions.
%% Cell type:code id: tags:
``` python
in_folder = "/gpfs/exfel/exp/DETLAB/202330/p900326/raw" # the folder to read data from, required
out_folder = "/gpfs/exfel/data/scratch/ahmedk/test/gotthard2" # path to output to, required
metadata_folder = "" # Directory containing calibration_metadata.yml when run by xfel-calibrate.
run_high = 20 # run number for G0 dark run, required
run_med = 21 # run number for G1 dark run, required
run_low = 22 # run number for G2 dark run, required
# Parameters used to access raw data.
karabo_id = "DETLAB_25UM_GH2" # detector identifier.
karabo_da = ["DA01/1", "DA01/2"] # list of data aggregators, which corresponds to different JF modules. This is only needed for the detectors of one module.
control_template = "CONTROL" # control template used to read CONTROL keys.
ctrl_source_template = '{karabo_id}/DET/{control_template}' # 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
gh2_detector = "" # Specifies the GH2 detector type ('50um' or '25um'). This parameter can be used to manually set the detector type if the code cannot automatically determine it from the control data.
# Parameters to be used for injecting dark calibration constants.
local_output = True # Boolean indicating that local constants were stored in the out_folder
# Skip the whole notebook if local_output is false in the preceding notebooks.
if not local_output:
from cal_tools.tools import exit_notebook
exit_notebook('No local constants saved. Skipping summary plots')
```
%% Cell type:code id: tags:
``` python
import warnings
from pathlib import Path
warnings.filterwarnings('ignore')
import h5py
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import yaml
from extra_data import RunDirectory
from IPython.display import Markdown, display
from cal_tools.gotthard2 import gotthard2lib
matplotlib.use("agg")
%matplotlib inline
from cal_tools.tools import CalibrationMetadata
```
%% Cell type:code id: tags:
``` python
out_folder = Path(out_folder)
metadata = CalibrationMetadata(metadata_folder or out_folder)
```
%% Cell type:code id: tags:
``` python
if not karabo_id_control:
karabo_id_control = karabo_id
g2ctrl = gotthard2lib.Gotthard2Ctrl(
run_dc=RunDirectory(Path(in_folder) / f"r{run_high:04d}"),
ctrl_src=ctrl_source_template.format(
karabo_id_control=karabo_id_control,
control_template=control_template
))
gh2_detector = g2ctrl.get_det_type()
if not gh2_detector:
# Unfortunately we can't check if we can't validate this value
# as the notebook will have only one karabo_da anyway.
# Dark processing split nodes over each karabo_da
gh2_detector = g2ctrl.get_det_type()
print(f"Processing {gh2_detector} Gotthard2.")
```
%% Cell type:code id: tags:
``` python
mod_mapping = dict()
for fn in Path(metadata_folder or out_folder).glob("module_metadata_*.yml"):
with fn.open("r") as fd:
fdict = yaml.safe_load(fd)
mod_mapping[fdict["module"].replace("-", "/")] = fdict["pdu"]
mod_mapping = dict(sorted(mod_mapping.items(), key=lambda item: item[0]))
```
%% Cell type:code id: tags:
``` python
plt_map = dict()
dark_constants = ["Offset", "Noise", "BadPixelsDark"]
if gh2_detector == "25um":
for cname in dark_constants:
plt_map[cname] = np.zeros(
(1280 * 2, 2, 3),
dtype=np.uint32 if cname == "BadPixelsDark" else np.float32
)
```
%% Cell type:code id: tags:
``` python
for cname in dark_constants:
for i, (mod, pdu) in enumerate(mod_mapping.items()):
module = mod.replace("-", "/")
with h5py.File(out_folder / f"const_{cname}_{pdu}.h5", 'r') as f:
if gh2_detector == "25um":
plt_map[cname][i::2] = f["data"][()]
else:
plt_map[cname] = f["data"][()]
```
%% Cell type:code id: tags:
``` python
if gh2_detector == "50um":
title = (
f"{{}} data for "
f"{[f'{mod}({pdu})' for mod, pdu in mod_mapping.items()]}"
)
else:
title = (
f"Interleaved {{}} data for "
f"{[f'{mod}({pdu})' for mod, pdu in mod_mapping.items()]}"
)
for cname in dark_constants:
display(Markdown(f"### {cname}"))
for cell in [0, 1]:
fig, ax = plt.subplots(figsize=(10, 5))
for g_idx in [0, 1, 2]:
ax.plot(plt_map[cname][:, cell, g_idx], label=f"G{g_idx} {cname} map")
ax.set_xticks(
np.arange(0, plt_map[cname].shape[0]+1, plt_map[cname].shape[0]//16)
)
ax.set_xlabel("Stripes #", fontsize=15)
ax.set_ylabel("ADU", fontsize=15)
ax.set_title(title.format(f"{cname} map - Cell {cell}"), fontsize=15)
if cname == "BadPixelsDark":
ax.set_ylim([0, 5])
ax.legend()
plt.show()
```
......
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