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Commit ee43d523 authored by Karim Ahmed's avatar Karim Ahmed
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remove unneeded code from SUMMARY NBs and improve the BadPixel Plots

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notebooks/Jungfrau/Jungfrau_gain_Spectra_Fit_Summary_NBC.ipynb
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%% Cell type:markdown id: tags:
# Jungfrau Spectra Fit Summary
Author: European XFEL Detector Department, Version: 1.0
Summary for plotting Spectra Fit results for Jungfrau FF histogram and fitting notebook
%% Cell type:code id: tags:
``` python
in_folder = '/gpfs/exfel/exp/SPB/202330/p900343/raw' # RAW data path, required
out_folder = "/gpfs/exfel/data/scratch/ahmedk/test/remove/JF4M_SPB_gain/r66/second/" # Output path for gain data, required
metadata_folder = "" # Directory containing calibration_metadata.yml when run by xfel-calibrate.
runs = [66]
# Parameters used to access raw data.
karabo_da = [] # list of data aggregators, which corresponds to different JF modules. This is only needed for the detectors of one module.
karabo_id = "SPB_IRDA_JF4M" # detector identifier.
creation_time = "" # To overwrite the measured creation_time. Required Format: YYYY-MM-DD HR:MN:SC e.g. "2022-06-28 13:00:00"
_fit_func = 'CHARGE_SHARING' # function used to fit the single photon peak\
g0_fit_dataset = 'gainMap_fit' # name of the data structure in the fit files
spectra_fit_temp = 'R{:04d}_{}_Gain_Spectra_{}_{}_Fit.h5'
```
%% Cell type:code id: tags:
``` python
import warnings
from pathlib import Path
warnings.filterwarnings('ignore')
from h5py import File as h5file
import matplotlib
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import yaml
from IPython.display import Markdown, display
matplotlib.use("agg")
%matplotlib inline
from cal_tools.plotting import init_jungfrau_geom, show_processed_modules_jungfrau
from cal_tools.plotting import init_jungfrau_geom
from cal_tools.tools import CalibrationMetadata
from XFELDetAna.plotting.simpleplot import simplePlot
from cal_tools.restful_config import calibration_client
from cal_tools.calcat_interface import CalCatApi
```
%% Cell type:code id: tags:
``` python
expected_modules, geom = init_jungfrau_geom(karabo_id=karabo_id, karabo_da=karabo_da)
nmods = len(expected_modules)
```
%% Cell type:code id: tags:
``` python
calcat_client = calibration_client()
calcat = CalCatApi(client=calcat_client)
detector_id = calcat.detector(karabo_id)['id']
da_mapping = calcat.physical_detector_units(detector_id, pdu_snapshot_at=creation_time)
da_to_pdu = {k: v["physical_name"] for k, v in da_mapping.items()}
```
%% Cell type:code id: tags:
``` python
# display(Markdown('## Processed modules'))
# processed_modules = list(da_to_pdu.values())
# processed_pdus = list(da_to_pdu.keys())
# show_processed_modules_jungfrau(
# jungfrau_geom=geom,
# constants=["RelativeGain10Hz", "BadPixelsFF10Hz"],
# processed_modules=processed_modules,
# expected_modules=expected_modules,
# display_module_names=processed_pdus,
# )
```
%% Cell type:code id: tags:
``` python
proposal = list(filter(None, in_folder.strip('/').split('/')))[-2]
run = runs[0] # TODO this will need to be fixed when I start implementing multiple runs.
stacked_constants = np.full(geom.expected_data_shape, np.nan) # nmods, 512, 1024
for i, da in enumerate (da_to_pdu.keys()):
with h5file(
Path(out_folder) / spectra_fit_temp.format(run, proposal.upper(), da, _fit_func),
'r'
) as f:
stacked_constants[i] = np.moveaxis(
np.mean(
np.array(f[g0_fit_dataset]),
axis=-1
), 0, 1)
fig, ax = plt.subplots(figsize=(18, 10))
vmin, vmax = np.percentile(stacked_constants, [5, 95])
geom.plot_data_fast(
stacked_constants,
ax=ax,
vmin=vmin,
vmax=vmax,
cmap="jet",
colorbar={'shrink': 1, 'pad': 0.01},
)
ax.set_title(f'{karabo_id} - Mean RAW', size=18)
plt.show()
```
%% Cell type:code id: tags:
``` python
proposal = list(filter(None, in_folder.strip('/').split('/')))[-2]
run = runs[0] # TODO this will need to be fixed when I start implementing multiple runs.
stacked_constants = np.full(geom.expected_data_shape, np.nan) # nmods, 512, 1024
for i, da in enumerate (da_to_pdu.keys()):
with h5file(
Path(out_folder) / spectra_fit_temp.format(run, proposal.upper(), da, _fit_func),
'r'
) as f:
stacked_constants[i] = np.moveaxis(
np.mean(
np.array(f[g0_fit_dataset]),
axis=-1
), 0, 1)
fig, ax = plt.subplots(figsize=(18, 10))
geom.plot_data_fast(
stacked_constants,
ax=ax,
vmin=200,
vmax=450,
cmap="jet",
colorbar={'shrink': 1, 'pad': 0.01},
)
ax.set_title(f'{karabo_id} - Mean RAW', size=18)
plt.show()
```
......
notebooks/Jungfrau/Jungfrau_gain_map_Summary_NBC.ipynb
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− 27
View file @ ee43d523
%% Cell type:markdown id: tags:
# Jungfrau Gain Map Summary
Author: European XFEL Detector Department, Version: 1.0
Summary for plotting Gain map results for Jungfrau after creating and injecting it.
%% Cell type:code id: tags:
``` python
in_folder = '/gpfs/exfel/exp/SPB/202330/p900343/raw' # RAW data path, required
out_folder = "/gpfs/exfel/data/scratch/ahmedk/test/remove/JF4M_SPB_gain/r66/second/" # Output path for gain data, required
metadata_folder = "" # Directory containing calibration_metadata.yml when run by xfel-calibrate.
runs = [66]
# Parameters used to access raw data.
karabo_da = [] # list of data aggregators, which corresponds to different JF modules. This is only needed for the detectors of one module.
karabo_id = "SPB_IRDA_JF4M" # detector identifier.
creation_time = "" # To overwrite the measured creation_time. Required Format: YYYY-MM-DD HR:MN:SC e.g. "2022-06-28 13:00:00"
_fit_func = 'CHARGE_SHARING' # function used to fit the single photon peak
g0_fit_dataset = 'gainMap_fit' # name of the data structure in the fit files
```
%% Cell type:code id: tags:
``` python
import warnings
from pathlib import Path
warnings.filterwarnings('ignore')
from h5py import File as h5file
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import Markdown, display
import tabulate
from IPython.display import Latex, Markdown, display
matplotlib.use("agg")
%matplotlib inline
from cal_tools.plotting import init_jungfrau_geom, show_processed_modules_jungfrau
from cal_tools.enums import BadPixels
from cal_tools.plotting import init_jungfrau_geom
from cal_tools.restful_config import calibration_client
from cal_tools.calcat_interface import CalCatApi
```
%% Cell type:code id: tags:
``` python
expected_modules, geom = init_jungfrau_geom(karabo_id=karabo_id, karabo_da=karabo_da)
nmods = len(expected_modules)
```
%% Cell type:code id: tags:
``` python
calcat_client = calibration_client()
calcat = CalCatApi(client=calcat_client)
detector_id = calcat.detector(karabo_id)['id']
da_mapping = calcat.physical_detector_units(detector_id, pdu_snapshot_at=creation_time)
da_to_pdu = {k: v["physical_name"] for k, v in da_mapping.items()}
```
%% Cell type:markdown id: tags:
## Created gain calibration constants
%% Cell type:code id: tags:
``` python
# display(Markdown('## Processed modules'))
# processed_modules = list(da_to_pdu.values())
# processed_pdus = list(da_to_pdu.keys())
const_names_dict = {
"RelativeGain10Hz": "RelativeGain", # TODO: This name is not relevant should we display gain instead of Relative??
"BadPixelsFF10Hz": "BadPixelsFF",
}
# show_processed_modules_jungfrau(
# jungfrau_geom=geom,
# constants=list(const_names_dict.keys()),
# processed_modules=processed_modules,
# expected_modules=expected_modules,
# display_module_names=processed_pdus,
# )
```
%% Cell type:markdown id: tags:
gains = ["High gain", "Medium gain", "Low gain"]
stacked_constants = {g: np.full(geom.expected_data_shape, np.nan) for g in gains}
constants = dict()
## Created gain calibration constants
def badpx(constant_name):
return True if "bad" in constant_name.lower() else False
def bp_entry(bp):
return [f"{bp.name:<30s}", f"{bp.value:032b}", f"{int(bp.value)}"]
badpixels = [
BadPixels.FF_NO_ENTRIES,
BadPixels.FF_GAIN_EVAL_ERROR,
BadPixels.FF_GAIN_DEVIATION,
]
```
%% Cell type:code id: tags:
``` python
gains = ["High gain", "Medium gain", "Low gain"]
stacked_constants = {g: np.full(geom.expected_data_shape, np.nan) for g in gains}
constants = dict()
for cname in const_names_dict.values():
if badpx(cname):
table = [bp_entry(bp) for bp in badpixels]
display(Markdown("""**The bad pixel** mask is encoded as a bit mask."""))
display(Latex(
tabulate.tabulate(
table,
tablefmt='latex',
headers=["Name", "bit value", "integer value"]
)))
for i, (da, pdu) in enumerate(da_to_pdu.items()):
with h5file(
Path(out_folder) / f"const_{cname}_{pdu}.h5",
'r'
) as f:
for j, g in enumerate(gains):
stacked_constants[g][i] = np.moveaxis(
np.mean(
f["data"][..., j],
axis=-1
), 0, 1).astype(np.float32 if cname == "RelativeGain" else np.uint32)
display(Markdown(f"### {cname} map per gain"))
for g in gains:
fig, ax = plt.subplots(figsize=(18, 10))
vmin, vmax = np.percentile(stacked_constants[g], [5, 95])
if badpx(cname):
vmin, vmax = (0, sorted([bp.value for bp in badpixels])[-2])
else:
vmin, vmax = np.percentile(stacked_constants[g], [5, 95])
geom.plot_data_fast(
stacked_constants[g],
ax=ax,
vmin=vmin,
vmax=vmax,
cmap="jet",
colorbar={'shrink': 1, 'pad': 0.01},
)
ax.set_title(f'{karabo_id} - Mean across cells {g} {cname} map', size=18)
plt.show()
```
......
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