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Commit 48f8d849 authored by Karim Ahmed's avatar Karim Ahmed
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merge notebooks and update notebooks.py

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notebooks/Jungfrau/gainCal_JF_Fit_Spectra_Histos.ipynb deleted 100644 → 0
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%% Cell type:markdown id: tags:
# G0 map from single photon spectra
Author: European XFEL Detector Group, Version: 1.0
Converts single photon flat fields into a G0 map
%% Cell type:code id: tags:
``` python
in_folder = "/gpfs/exfel/exp/HED/202231/p900297/raw"
out_folder = "/gpfs/exfel/data/scratch/ahmedk/test/FFDATA/HED/p900297/gain_fit"
runs = [26] # can be a list of runs
dir_date_iso = "" ## string to save the date of the creation of the run
# to be used when injecting constants (third separate nb)
sensor_size = [512, 1024] # size of the array in the 'row' and 'col' dimensions
chunk_size = 10 # n of trains per chunk
block_size = [128, 64] # dimension of the chunks in 'row' and 'col'
mod_n = 1 # module number
karabo_id = "HED_IA1_JF500K1" # karabo prefix of Jungfrau devices
da_name = f"JNGFR{mod_n:02d}"
da_control = "JNGFRCTRL00"
ctrl_source_template = "{}/DET/CONTROL" # template for control source name (filled with karabo_id_control)
karabo_id_control = "" # if control is on a different ID, set to empty string if it is the same a karabo-id
bias_voltage = 180.0 # bias voltage - should be derived from CONTROL file
integration_time = 10.0 # integration time - should be derived from CONTROL file.
memory_cells = 1 # number of memory cells - should be derived from CONTROL file
sc_start = 15 # storage cell start value - should be derived from CONTROL file
gain_mode = 0 # number of memory cells - Set to -1 to derive from CONTROL file.
_fit_func = "CHARGE_SHARING" ## which function will be used to fit the histogram
_h_range = (200.0, 350.0) # range of the histogram in x-axis units
rebin = 1
# parameters for the peak finder
n_sigma = 20.0 # n of sigma abov pedestal threshold
ratio = 0.99 # ratio of the next peak amplitude in the peak_finder
```
%% Cell type:code id: tags:
``` python
import multiprocessing
import time
from functools import partial
from logging import warning
from pathlib import Path
import numpy as np
from extra_data import RunDirectory
from h5py import File as h5file
from cal_tools.jungfrau import jungfrau_ff
from cal_tools.jungfraulib import JungfrauCtrl
```
%% Cell type:code id: tags:
``` python
out_folder = Path(out_folder)
out_folder.mkdir(parents=True, exist_ok=True)
in_folder = Path(in_folder)
if karabo_id_control == "":
karabo_id_control = karabo_id
```
%% Cell type:markdown id: tags:
### Opening log file
%% Cell type:code id: tags:
``` python
h_spectra = None
edges = None
noise_map = None
ctrl_src = ctrl_source_template.format(karabo_id_control)
run_dc = RunDirectory(in_folder / f"r{runs[0]:04d}")
ctrl_data = JungfrauCtrl(run_dc, ctrl_src)
if memory_cells < 0:
memory_cells, sc_start = ctrl_data.get_memory_cells()
mem_cells_name = "single cell" if memory_cells == 1 else "burst"
print(f"Run is in {mem_cells_name} mode.\nStorage cell start: {sc_start:02d}")
else:
mem_cells_name = "single cell" if memory_cells == 1 else "burst"
print(
f"Run is in manually set to {mem_cells_name} mode. With {memory_cells} memory cells"
)
mode = "Single"
if memory_cells > 1:
mode = "Burst"
file_h_name = (
f"R{runs[0]:04d}_Gain_{mode}_Spectra_{da_name}_Histo.h5" # histogram file name
)
begin_stuff = time.localtime()
i_cut = file_h_name.find("_Histo")
fout_temp = file_h_name[:i_cut]
fout_temp += f"_{_fit_func}_Fit"
print(f"block_size: {block_size}")
if integration_time < 0:
integration_time = ctrl_data.get_integration_time()
if bias_voltage < 0:
bias_voltage = ctrl_data.get_bias_voltage()
# if gain_setting < 0:
# gain_setting = ctrl_data.get_gain_setting()
if gain_mode < 0:
gain_mode = ctrl_data.get_gain_mode()
print(f"Integration time is {integration_time} us")
# print(f"Gain setting is {gain_setting} (run settings: {ctrl_data.run_settings})")
print(f"Gain mode is {gain_mode} ({ctrl_data.run_mode})")
print(f"Bias voltage is {bias_voltage} V")
print(f"Number of memory cells are {memory_cells}")
```
%% Cell type:markdown id: tags:
## Opening Histo File
%% Cell type:code id: tags:
``` python
# transposition here is just to make saved histo compatible with my other notebooks
with h5file(out_folder / file_h_name, "r") as f:
print(f"opening histo in file {file_h_name}")
if "histos" in f.keys():
h_spectra = np.transpose(np.array(f["histos"]))
print(f"histogram found: {h_spectra.shape}")
else:
raise AttributeError("No histo in file!")
edges = np.array(f["edges"])
x = (edges[1:] + edges[:-1]) / 2.0
if "noise_map" in f.keys():
noise_map = np.array(f["noise_map"])
print(f"noise map found: {noise_map.shape}")
else:
warning("noise map not found!")
print("Reading control data from histogram files.")
if "integration_time" in f.attrs.keys():
integration_time = np.float32(f.attrs["integration_time"])
else:
print(f"integration_time is not found! using default value{integration_time}")
if "bias_voltage" in f.attrs.keys():
bias_voltage = np.float32(f.attrs["bias_voltage"])
else:
warning(f"bias_voltage not found! using default value: {bias_voltage}")
if "creation_time" in f.attrs.keys():
dir_date_iso = str(f.attrs["creation_time"])
if len(dir_date_iso) == 0:
warning("Dir date is empty string")
else:
print("dir_date not found!")
```
%% Cell type:markdown id: tags:
## Fitting histograms
%% Cell type:code id: tags:
``` python
chunks = jungfrau_ff.chunk_Multi([h_spectra], block_size)
pool = multiprocessing.Pool()
st = time.perf_counter()
partial_fit = partial(
jungfrau_ff.fit_histogram,
x,
_fit_func,
n_sigma,
rebin,
ratio,
noise_map,
)
print("starting spectra fit")
r_maps = pool.map(partial_fit, chunks)
print("r_maps calculation", time.perf_counter() - st)
g0_map = np.zeros((memory_cells, sensor_size[0], sensor_size[1]), dtype=np.float32)
sigma_map = np.zeros((memory_cells, sensor_size[0], sensor_size[1]), dtype=np.float32)
chi2ndf_map = np.zeros((memory_cells, sensor_size[0], sensor_size[1]), dtype=np.float32)
alpha_map = np.zeros((memory_cells, sensor_size[0], sensor_size[1]), dtype=np.float32)
for i, r in enumerate(r_maps):
g0_chk, sigma_chk, chi2ndf_chk, alpha_chk = r
n_blocks_col = int(g0_map.shape[-1] / block_size[1])
irow = int(np.floor(i / n_blocks_col)) * block_size[0]
icol = i % n_blocks_col * block_size[1]
g0_map[..., irow : irow + block_size[0], icol : icol + block_size[1]] = g0_chk
sigma_map[..., irow : irow + block_size[0], icol : icol + block_size[1]] = sigma_chk
chi2ndf_map[
..., irow : irow + block_size[0], icol : icol + block_size[1]
] = chi2ndf_chk
alpha_map[..., irow : irow + block_size[0], icol : icol + block_size[1]] = alpha_chk
print("loading r_maps calculation results", time.perf_counter() - st)
pool.close()
print("... done")
```
%% Cell type:markdown id: tags:
## Final steps
### Saving fit results
%% Cell type:code id: tags:
``` python
fout_path = f"{out_folder}/{fout_temp}.h5"
with h5file(fout_path, "w") as fout:
print("saving noise map ...")
##trasposition is to make it compatible with my other nb
dset_noi = fout.create_dataset("noise_map", data=np.transpose(noise_map))
print("saving fit results ...")
dset_chi2 = fout.create_dataset("chi2map", data=np.transpose(chi2ndf_map))
dset_gmap_fit = fout.create_dataset("gainMap_fit", data=np.transpose(g0_map))
dset_std = fout.create_dataset("sigmamap", data=np.transpose(sigma_map))
dset_alpha = fout.create_dataset("alphamap", data=np.transpose(alpha_map))
fout.attrs["memory_cells"] = memory_cells # TODO: Why memory cells are not saved here. What about the other conditions??
fout.attrs["integration_time"] = integration_time
fout.attrs["bias_voltage"] = bias_voltage
fout.attrs["dir_date_iso"] = dir_date_iso
fout.attrs["karabo_id"] = karabo_id
fout.attrs["da_name"] = da_name
print("closing")
```
src/xfel_calibrate/notebooks.py
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− 19
View file @ 48f8d849
......@@ -206,30 +206,15 @@ notebooks = {
"FF_HISTS": {
"notebook":
"notebooks/Jungfrau/gainCal_JF_Create_Spectra_Histos.ipynb",
"dep_notebooks": [
"notebooks/Jungfrau/gainCal_JF_Fit_Spectra_Histos.ipynb"],
"concurrency": {"parameter": None,
"default concurrency": None,
"concurrency": {"parameter": "karabo_da",
"default concurrency": list(range(8)),
"cluster cores": 4},
},
"FF": {
"notebook":
"notebooks/Jungfrau/create_gain_map.ipynb",
"dep_notebooks": [
"notebooks/Jungfrau/gainCal_JF_Fit_sendDB_NBC.ipynb"],
"concurrency": {"parameter": None,
"default concurrency": None,
"cluster cores": 4},
},
"FF_ALL": {
"notebook":
"notebooks/Jungfrau/gainCal_JF_Create_Spectra_Histos.ipynb",
"dep_notebooks": [
"notebooks/Jungfrau/gainCal_JF_Fit_Spectra_Histos.ipynb",
"notebooks/Jungfrau/create_gain_map.ipynb",
"notebooks/Jungfrau/gainCal_JF_Fit_sendDB_NBC.ipynb",],
"concurrency": {"parameter": None,
"default concurrency": None,
"concurrency": {"parameter": "karabo_da",
"default concurrency": list(range(8)),
"cluster cores": 4},
},
},
......
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