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Commit 2add8f72 authored by Thomas Kluyver's avatar Thomas Kluyver
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Pass most parameters to write_ccv() as keyword arguments

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1 merge request!1026Feat[Jungfrau]: Inject CCVs using RESTful API
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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.
cal_db_interface = '' # calibrate db interface to connect to # KEEP FOR THE WEBSERVICE
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.
skip_first_ntrains = 0 # Skip first number of trains and don't include them in dark processing.
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
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 to be used for injecting dark calibration constants.
integration_time = -1 # Integration time in us. Set to -1 to overwrite by value in file.
exposure_timeout = -1 # Exposure timeout. Set to -1 to overwrite by value in file.
gain_setting = -1 # 0 for dynamic, forceswitchg1, forceswitchg2, 1 for dynamichg0, fixgain1, fixgain2. Set to overwrite by value in file.
gain_mode = -1 # 1 if medium and low runs are fixgain1 and fixgain2, otherwise 0. Set to -1 to overwrite by value in file.
bias_voltage = -1 # sensor bias voltage in V, will be overwritten by value in file
memory_cells = -1 # 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
from datetime import timedelta
from logging import warning
from tempfile import NamedTemporaryFile
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pasha as psh
import yaml
from IPython.display import Markdown, display
from extra_data import RunDirectory
from XFELDetAna.plotting.heatmap import heatmapPlot
from XFELDetAna.plotting.histogram import histPlot
from cal_tools import step_timing
from cal_tools.calcat_interface2 import (
CalibrationData,
JUNGFRAUConditions,
)
from cal_tools.constants import (
CCVAlreadyInjectedError,
inject_ccv,
write_ccv,
)
from cal_tools.enums import BadPixels, JungfrauGainMode
from cal_tools.jungfrau import jungfraulib
from cal_tools.restful_config import (
extra_calibration_client,
)
from cal_tools.tools import calcat_creation_time, pdus_by_detector_id
matplotlib.use('agg')
%matplotlib inline
```
%% 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 = calcat_creation_time(in_folder, run_high, creation_time)
print(f"Using {creation_time} as creation time")
os.makedirs(out_folder, exist_ok=True)
if karabo_id_control == "":
karabo_id_control = karabo_id
```
%% Cell type:code id: tags:
``` python
proposal = list(filter(None, in_folder.strip('/').split('/')))[-2]
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)
run_nums = jungfraulib.sort_runs_by_gain(
raw_folder=in_folder,
runs=run_nums,
ctrl_src=ctrl_src,
)
_gain_mode = None
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 gain == 0:
run_mcells, sc_start = ctrl_data.get_memory_cells()
if integration_time < 0:
integration_time = ctrl_data.get_integration_time()
print(f"Integration time is {integration_time} us.")
else:
print(f"Integration time is manually set to {integration_time} us.")
if exposure_timeout < 0:
exposure_timeout = ctrl_data.get_exposure_timeout()
print(f"Exposure timeout is {exposure_timeout}.")
else:
print(f"Exposure timeout is manually set to {exposure_timeout}.")
if bias_voltage < 0:
bias_voltage = ctrl_data.get_bias_voltage()
print(f"Bias voltage is {bias_voltage} V.")
else:
print(f"Bias voltage is manually set to {bias_voltage} V.")
if gain_setting < 0:
gain_setting = ctrl_data.get_gain_setting()
print(f"Gain setting is {gain_setting} ({ctrl_data.run_settings})")
else:
print(f"Gain setting is manually set to {gain_setting}.")
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: # medium and low gain
_gain_mode = ctrl_data.get_gain_mode()
med_low_settings.append(ctrl_data.run_mode)
# TODO: consider updating this cell into something similar to agipdlib.AgipdCtrlsRuns()
if gain_mode < 0:
gain_mode = _gain_mode
print(f"Gain mode is {gain_mode} ({med_low_settings})")
else:
print(f"Gain mode is manually set to {gain_mode}.")
step_timer.done_step(f'Reading control data.')
```
%% Cell type:code id: tags:
``` python
step_timer.start()
# set the operating condition
conditions = JUNGFRAUConditions(
sensor_bias_voltage=bias_voltage,
memory_cells=memory_cells,
exposure_timeout=exposure_timeout,
integration_time=integration_time,
gain_setting=gain_setting,
gain_mode=gain_mode,
)
cc = extra_calibration_client(inject=True)
det_id = cc.detector_by_identifier(karabo_id)['id']
pdus = pdus_by_detector_id(cc, det_id, snapshot_at=creation_time)
da_to_pdu = dict()
pdu_to_uuid = dict()
for pdu in pdus:
if pdu['karabo_da'] in karabo_da: # exclude unselected das
da_to_pdu[pdu['karabo_da']] = pdu['physical_name']
pdu_to_uuid[pdu['physical_name']] = pdu['uuid']
first_pdu = pdus[0]
detector_info = first_pdu['detector']
detector_info['detector_type'] = first_pdu['detector_type']['name']
step_timer.done_step('Set conditions and get PDU names from CalCat.')
```
%% 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}.")
# (1024, 512, 1 or 16, 3)
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(shape=offset_map[mod].shape, 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
if cell_slice_idx.sum() == 0:
# This cell is not in the data (or it's deliberated excluded)
bad_pixels_map[mod][..., cell_number, gain] = BadPixels.NO_DARK_DATA.value
offset_map[mod][..., cell_number, gain] = np.nan
noise_map[mod][..., cell_number, gain] = np.nan
return
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
)
# Assign WRONG_GAIN_VALUE for a pixel in a badpixel map for all gains.
bad_pixels_map[mod][:, :,cell_number][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)
empty_trains = all_trains - n_trains
if empty_trains != 0:
print(f"{mod} has {empty_trains} empty trains out of {all_trains} trains")
if skip_first_ntrains:
print(f"Skip first {skip_first_ntrains} trains from processing as configured.")
instr_dc = run_dc.select(
instrument_src, require_all=True).select_trains(np.s_[skip_first_ntrains:])
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 # dynamic gain mode
gain > 0 and # Medium and low runs
memory_cells == 16 and # Burst mode
acelltable.shape[0] == 2 # forceswitchg1 and forceswitchg2 acquired with the MDL device.
):
# 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))
cells_missing = (bad_pixels_map[mod][0, 0, :, gain] & BadPixels.NO_DARK_DATA) > 0
if np.any(cells_missing):
print(f"No dark data in gain stage {gain} found for cells", np.nonzero(cells_missing)[0])
del images
del acelltable
del gain_vals
step_timer.done_step('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 mod, pdu in da_to_pdu.items():
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('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.NO_DARK_DATA)
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 mod, pdu in da_to_pdu.items():
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('Creating bad pixels constant')
```
%% Cell type:markdown id: tags:
## Inject and save calibration constants
%% Cell type:code id: tags:
``` python
step_timer.start()
constants = {}
for mod, pdu in da_to_pdu.items():
constants['Offset10Hz'] = np.moveaxis(offset_map[mod], 0, 1)
constants['Noise10Hz'] = np.moveaxis(noise_map[mod], 0, 1)
constants['BadPixelsDark10Hz'] = np.moveaxis(bad_pixels_map[mod], 0, 1)
for const_name, const_data in constants.items():
with NamedTemporaryFile(dir=out_folder) as tempf:
ccv_root = write_ccv(
tempf.name,
pdu,
pdu_to_uuid[pdu],
detector_info["detector_type"],
const_name,
conditions,
creation_time,
proposal,[run_high, run_med, run_low],
const_data,
pdu_name=pdu,
pdu_uuid=pdu_to_uuid[pdu],
detector_type=detector_info["detector_type"],
calibration=const_name,
conditions=conditions,
created_at=creation_time,
proposal=proposal,
runs=[run_high, run_med, run_low],
data=const_data,
dims=["fast_scan", "slow_scan", "cell", "gain"],
deviations={"integration_time": time_limits},
)
if db_output:
try:
inject_ccv(tempf.name, ccv_root, metadata_folder)
print(f"{const_name} for {mod}({pdu}) has been injected to the database.")
except CCVAlreadyInjectedError:
warning(
f"{const_name} calibration constant version for {pdu}"
" has been already injected.\n")
if local_output:
ofile = f"{out_folder}/const_{const_name}_{pdu}.h5"
if os.path.isfile(ofile):
print(f'File {ofile} already exists and will be overwritten\n')
from shutil import copyfile
copyfile(tempf.name, ofile)
print(f"Calibration constant {const_name} 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"• Exposure timeout: {exposure_timeout}\n"
f"• Gain setting: {gain_setting}\n"
f"• Gain mode: {gain_mode}\n"
f"• Creation time: {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
# Start retrieving existing constants for comparison
step_timer.start()
old_const = {}
old_mdata = {}
# set the operating condition
conditions = JUNGFRAUConditions(
sensor_bias_voltage=bias_voltage,
memory_cells=memory_cells,
integration_time=integration_time,
exposure_timeout=exposure_timeout,
gain_setting=gain_setting,
gain_mode=gain_mode,
)
jf_caldata = CalibrationData.from_condition(
conditions,
karabo_id,
event_at=creation_time-timedelta(seconds=60),
begin_at_strategy="prior",
)
for mod in karabo_da:
old_const[mod] = {}
old_mdata[mod] = {}
for cname in constants.keys():
cmdata = jf_caldata.get(cname, None)
data_found = cmdata and mod in cmdata.aggregator_names
if data_found:
old_const[mod][cname] = cmdata[mod].ndarray()
old_mdata[mod][cname] = {
"timestamp": cmdata[mod].metadata("begin_validity_at"),
"filepath": str(cmdata[mod].file_path()),
"dataset": cmdata[mod].dataset,
}
else:
old_const[mod][cname] = None
old_mdata[mod][cname] = {
"timestamp": "Not found",
"filepath": None,
"dataset": None,
}
step_timer.done_step('Retrieved old dark constants for comparison.')
```
%% 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 = da_to_pdu[mod]
display(Markdown(f"- {mod} ({pdu})"))
for const in consts:
display(Markdown(f" - {const}: {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,
)
```
......
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