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Commit 960e710c authored by Egor Sobolev's avatar Egor Sobolev
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Apply suggestions: (i) check settings instead of catching exception, fix NameError

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notebooks/AGIPD/AGIPD_Correct_and_Verify_Summary_NBC.ipynb
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%% Cell type:markdown id: tags:
# Summary of the AGIPD offline correction #
%% Cell type:code id: tags:
``` python
run = 11 # runs to process, required
out_folder = "/gpfs/exfel/data/scratch/ahmedk/test/AGIPD_Corr" # path to output to, required
metadata_folder = "" # Directory containing calibration_metadata.yml when run by xfel-calibrate
karabo_id = "SPB_DET_AGIPD1M-1" # karabo karabo_id
modules = [-1]
karabo_da = ['-1'] # a list of data aggregators names, Default [-1] for selecting all data aggregators
rel_gain_mode = "off" # Select relative gain correction. Choices [`PC`, `CS`, `off`]. (`PC`: Pulse Capacitor, `CS`: Current Source, `off`: Disable relative gain correction). Default: off.
# Additional processing
count_lit_pixels = False # Count the number of pixels registering photons
spi_hitfinding = False # Find hits using lit-pixel counter
# SPI hit-finder parameters
spi_hf_modules = [3, 4, 8, 15] # Use specified modules for SPI hitfinding
spi_hf_mode = "adaptive" # The method to compute threshold for hitscores in SPI hitfinding: `fixed` or `adaptive`
spi_hf_snr = 4.0 # Siginal-to-noise ration for adaptive threshold in SPI hitfinding
spi_hf_min_scores = 100 # The minimal size of events to compute adaptive threshold in SPI hitfinding
spi_hf_fixed_threshold = 0 # The fixed threshold value
spi_hf_hitrate_window_size = 200 # The window size for runnig average of hitrate in trains
spi_hf_miss_fraction = 1 # The fraction of misses to select along with hits
spi_hf_miss_fraction_base = "hit" # The base to compute the number of misses to select: the number of hits (`hit`) or misses (`miss`)
```
%% Cell type:code id: tags:
``` python
from pathlib import Path
from logging import warning
import yaml
import tabulate
from cal_tools.tools import CalibrationMetadata
from IPython.display import Latex, display, Markdown
import matplotlib
import matplotlib.pyplot as plt
matplotlib.use("agg")
%matplotlib inline
from extra_data import RunDirectory
from extra_redu.fileutils import StackedPulseSource, exdf_save
from extra_redu.spi import SpiHitfinder
```
%% Cell type:code id: tags:
``` python
out_folder = Path(out_folder)
metadata = CalibrationMetadata(metadata_folder or out_folder)
const_dict = metadata.setdefault("retrieved-constants", {})
time_dict = const_dict.setdefault("time-summary", {})
# Extracting Instrument string
instrument = karabo_id.split("_")[0]
# Evaluate detector instance for mapping
if instrument == "SPB":
dinstance = "AGIPD1M1"
nmods = 16
elif instrument == "MID":
dinstance = "AGIPD1M2"
nmods = 16
elif instrument == "HED":
dinstance = "AGIPD500K"
nmods = 8
if karabo_da[0] == '-1':
if modules[0] == -1:
modules = list(range(nmods))
karabo_da = ["AGIPD{:02d}".format(i) for i in modules]
else:
modules = [int(x[-2:]) for x in karabo_da]
# This is needed only if AGIPD Correction notebook had no precorrection notebooks for retrieving constants
# gather all generated sequence yml files for time summary of retrieved constant under retrieved-constants in metadata.yml
for fn in sorted(out_folder.glob("retrieved_constants_*.yml")):
with fn.open("r") as fd:
fdict = yaml.safe_load(fd)
# append different sequences' time summary to the main yaml
time_dict.update(fdict["time-summary"])
fn.unlink()
metadata.save()
```
%% Cell type:code id: tags:
``` python
def print_const_table(const):
print(f"{const} constants were injected on:")
table_data = {}
for seq, mod_data in time_dict.items():
for mod, const_data in mod_data.items():
timestamp = const_data[const]
table_data.setdefault(timestamp, []).append(f"{seq}:{mod}")
table = []
if not len(table_data):
table.append(["No constants retrieved"])
elif len(table_data) == 1:
table.append([[*table_data][0], "All modules"])
else:
for timestamp, seqmod in table_data.items():
table.append([timestamp, seqmod[0]])
for further_module in seqmod[1:]:
table.append(["", further_module])
display(Latex(tabulate.tabulate(table,
tablefmt="latex",
headers=["Timestamps", "Modules and sequences"])))
rel_gain_alias = "CS" if rel_gain_mode.lower() == "cs" else "PC" # 'off' or 'pc'
for const in ['Offset', f'Slopes{rel_gain_alias}', 'SlopesFF']:
print_const_table(const)
```
%% Cell type:code id: tags:
``` python
if spi_hitfinding:
if spi_hitfinding and not count_lit_pixels:
# Operators are not expected to enable SPI hitfidnig without lit-pixels
# counting. If this happens, warn on the mismatch in the configuration.
warning("SPI hitfinding will be skipped because the required lit-pixel "
"counting is disabled. To run hitfinding, enable also lit-pixel "
"counting with the `--count-lit-pixels` option.")
if spi_hitfinding and count_lit_pixels:
display(Markdown("# SPI hit finding"))
try:
dc = RunDirectory(out_folder)
litpx_src = StackedPulseSource.from_datacollection(
dc, f"{karabo_id}/CORR/(?P<key>\d+)CH0:output", "litpx")
except ValueError:
litpx_src = None
warning("The data sources of the number of lit-pixels are not found. "
"Use `count_lit_pixels = True`")
if litpx_src is not None:
dc = RunDirectory(out_folder)
litpx_src = StackedPulseSource.from_datacollection(
dc, f"{karabo_id}/CORR/(?P<key>\d+)CH0:output", "litpx")
hitfinder = SpiHitfinder(
modules=spi_hf_modules,
mode=spi_hf_mode,
snr=spi_hf_snr,
min_scores=spi_hf_min_scores,
fixed_threshold=spi_hf_fixed_threshold,
hitrate_window_size=spi_hf_hitrate_window_size,
miss_fraction=spi_hf_miss_fraction,
miss_fraction_base=spi_hf_miss_fraction_base,
)
hitfinder.find_hits(litpx_src)
# write hit-finder data in file
sources = {
f"{karabo_id}/REDU/SPI_HITFINDER": hitfinder,
}
exdf_save(out_folder, "REDU00", run, sources, sequence_size=3500)
# draw plots
display(Markdown("## Hit-rate plot"))
hitfinder.plot_hitrate()
plt.show()
display(Markdown("## Hitscore histogram"))
hitfinder.plot_hitscore_hist()
plt.show()
display(Markdown("## Hitscore plots"))
hitfinder.plot_hitscore()
```
......
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