[1]:

import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['figure.constrained_layout.use'] = True

import dask
print(f'dask: {dask.__version__}')
import dask.array
dask.config.set({'array.chunk-size': '512MiB'})

from psutil import virtual_memory

mem = virtual_memory()
print(f'Physical memory: {mem.total/1024/1024/1024:.0f} Gb')  # total physical memory available

import logging
logging.basicConfig(filename='example.log', level=logging.DEBUG)

import toolbox_scs as tb
print(tb.__file__)
import toolbox_scs.routines.boz as boz

import os

dask: 2023.3.2
Physical memory: 754 Gb
/home/lleguy/notebooks/ToolBox-git/src/toolbox_scs/__init__.py

[2]:

import sys
print(sys.executable)

/gpfs/exfel/u/scratch/SCS/202301/p003423/envs/toolbox_p003423-gpu/bin/python

[3]:

%load_ext autoreload

%autoreload 2

Create parameters object¶

[4]:

proposal = 2937
darkrun = 615
run = 614
module = 15
gain = 3
drop_intra_darks = True
sat_level = 490
rois_th = 0.08
ff_prod_th = 350
ff_ratio_th = 0.75

[5]:

# Parameters
proposal = 2937
darkrun = 615
run = 614
module = 15
gain = 3.0
rois_th = 0.18
sat_level = 500

[6]:

params = boz.parameters(proposal=proposal, darkrun=darkrun, run=run, module=module, gain=gain, drop_intra_darks=drop_intra_darks)

[7]:

from extra_data.read_machinery import find_proposal

root = find_proposal(f'p{proposal:06d}')
path = root + f'/usr/processed_runs/r{params.run:04d}/'
print(path)
os.makedirs(path, exist_ok=True)
prefix = f'p{proposal}-r{run}-d{darkrun}-BOZ-Ia'

/gpfs/exfel/exp/SCS/202122/p002937/usr/processed_runs/r0614/

[8]:

print(params)

proposal:2937 darkrun:615 run:614 module:15 gain:3.0 ph/bin
drop intra darks:True
mask:None
rois threshold: None
rois: None
flat-field p: None prod:(5.0, inf) ratio:(-inf, 1.2)
plane guess fit: None
use hexagons: False
enforce mirror symmetry: True
ff alpha: None, max. iter.: None
Fnl: None

Load data persistently¶

[9]:

params.dask_load_persistently()

/gpfs/exfel/u/scratch/SCS/202301/p003423/envs/toolbox_p003423-gpu/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3460: PerformanceWarning: Reshaping is producing a large chunk. To accept the large
chunk and silence this warning, set the option
    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
    ...     array.reshape(shape)

To avoid creating the large chunks, set the option
    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
    ...     array.reshape(shape)Explictly passing ``limit`` to ``reshape`` will also silence this warning
    >>> array.reshape(shape, limit='128 MiB')
  exec(code_obj, self.user_global_ns, self.user_ns)
/gpfs/exfel/u/scratch/SCS/202301/p003423/envs/toolbox_p003423-gpu/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3460: PerformanceWarning: Reshaping is producing a large chunk. To accept the large
chunk and silence this warning, set the option
    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
    ...     array.reshape(shape)

To avoid creating the large chunks, set the option
    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
    ...     array.reshape(shape)Explictly passing ``limit`` to ``reshape`` will also silence this warning
    >>> array.reshape(shape, limit='128 MiB')
  exec(code_obj, self.user_global_ns, self.user_ns)

Slice the data by trainId if the number of pulses is large¶

[10]:

params.arr_dark

[10]:

	Array	Chunk
Bytes	1.74 GiB	510.00 MiB
Shape	(712, 20, 128, 512)	(204, 20, 128, 512)
Dask graph	4 chunks in 2 graph layers
Data type	uint16 numpy.ndarray

[11]:

# keep only 1Gb of data
N = int(1*1024**3/(params.arr_dark.shape[1]*128*512*2))
SLICE = slice(0, N)
params.arr_dark = params.arr_dark[SLICE]
params.tid_dark = params.tid_dark[SLICE]

[12]:

params.arr

[12]:

	Array	Chunk
Bytes	5.19 GiB	510.00 MiB
Shape	(2124, 20, 128, 512)	(204, 20, 128, 512)
Dask graph	11 chunks in 2 graph layers
Data type	uint16 numpy.ndarray

[13]:

# keep only 4Gb of data
N = int(4*1024**3/(params.arr.shape[1]*128*512*2))
SLICE = slice(0, N)
params.arr = params.arr[SLICE]
params.tid = params.tid[SLICE]

[14]:

params.arr

[14]:

	Array	Chunk
Bytes	4.00 GiB	510.00 MiB
Shape	(1638, 20, 128, 512)	(204, 20, 128, 512)
Dask graph	9 chunks in 3 graph layers
Data type	uint16 numpy.ndarray

Use GPU¶

[15]:

dask.config.set(scheduler="single-threaded")
params.use_gpu()

Dark run inspection¶

The aim is to check dark level and extract bad pixel map.

[16]:

dark = boz.average_module(params.arr_dark).compute()

[17]:

pedestal = boz.ensure_on_host(np.mean(dark))
pedestal

[17]:

array(40.97503102)

[18]:

mean_th = (pedestal-12, pedestal+15)
f = boz.inspect_dark(boz.ensure_on_host(params.arr_dark),
                     mean_th=mean_th)
f.suptitle(f'p:{params.proposal} d:{params.darkrun}')
fname = path + prefix + '-inspect_dark.png'
f.savefig(fname, dpi=300)

_images/BOZ_analysis_part_I.a_Correction_determination_23_0.png

[19]:

params.mean_th = mean_th
params.set_mask(boz.bad_pixel_map(params))

# bad pixel: 12

[20]:

print(params)

proposal:2937 darkrun:615 run:614 module:15 gain:3.0 ph/bin
drop intra darks:True
mean threshold:(28.975031015633952, 55.97503101563395) std threshold:(None, None)
mask:(#12) [[15, 437], [36, 477], [43, 506], [59, 215], [61, 215], [63, 28], [71, 451], [76, 302], [80, 223], [80, 224], [108, 185], [125, 296]]
rois threshold: None
rois: None
flat-field p: None prod:(5.0, inf) ratio:(-inf, 1.2)
plane guess fit: None
use hexagons: False
enforce mirror symmetry: True
ff alpha: None, max. iter.: None
Fnl: None

Veto pattern check¶

Check potential veto pattern issue

[21]:

data = boz.average_module(params.arr, dark=dark).compute()
pp = boz.ensure_on_host(data.mean(axis=(1,2))) # pulseId resolved mean
dataM = boz.ensure_on_host(data.mean(axis=0)) # mean over pulseId

[22]:

plt.figure()
plt.plot(pp)
plt.xlabel('pulseId')
plt.ylabel('dark corrected module mean')
plt.title(f'p:{params.proposal} r:{params.run} d:{params.darkrun}')
plt.savefig(path+prefix+'-veto_inspect.png', dpi=300)

_images/BOZ_analysis_part_I.a_Correction_determination_29_0.png

[23]:

# Thresholding out bad veto pulse
"""
threshold = 5
if False:
    params.arr = params.arr[:, pp > threshold, :, :]
    params.arr_dark = params.arr_dark[:, pp > threshold, :, :]
    dark = boz.average_module(params.arr_dark).compute()
    data = boz.average_module(params.arr, dark=dark).compute()
    dataM = data.mean(axis=0) # mean over pulseId
"""

[23]:

'\nthreshold = 5\nif False:\n    params.arr = params.arr[:, pp > threshold, :, :]\n    params.arr_dark = params.arr_dark[:, pp > threshold, :, :]\n    dark = boz.average_module(params.arr_dark).compute()\n    data = boz.average_module(params.arr, dark=dark).compute()\n    dataM = data.mean(axis=0) # mean over pulseId\n'

Histogram¶

[24]:

h, f = boz.inspect_histogram(boz.ensure_on_host(params.arr),
                             boz.ensure_on_host(params.arr_dark),
                             mask=boz.ensure_on_host(params.get_mask())
                             #, extra_lines=True
                         )
f.suptitle(f'p:{params.proposal} r:{params.run} d:{params.darkrun}')
f.savefig(path+prefix+'-histogram.png', dpi=300)

/home/lleguy/notebooks/ToolBox-git/src/toolbox_scs/routines/boz.py:598: PerformanceWarning: Reshaping is producing a large chunk. To accept the large
chunk and silence this warning, set the option
    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):
    ...     array.reshape(shape)

To avoid creating the large chunks, set the option
    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):
    ...     array.reshape(shape)Explictly passing ``limit`` to ``reshape`` will also silence this warning
    >>> array.reshape(shape, limit='128 MiB')
  h = ensure_on_host(histogram_module(arr, mask=mask))

_images/BOZ_analysis_part_I.a_Correction_determination_32_1.png

ROIs extraction¶

[25]:

params.rois_th = rois_th
params.rois = boz.find_rois_from_params(params)

[26]:

print(params)

proposal:2937 darkrun:615 run:614 module:15 gain:3.0 ph/bin
drop intra darks:True
mean threshold:(28.975031015633952, 55.97503101563395) std threshold:(None, None)
mask:(#12) [[15, 437], [36, 477], [43, 506], [59, 215], [61, 215], [63, 28], [71, 451], [76, 302], [80, 223], [80, 224], [108, 185], [125, 296]]
rois threshold: 0.18
rois: {'n': {'xl': 12, 'xh': 89, 'yl': 22, 'yh': 105}, '0': {'xl': 89, 'xh': 167, 'yl': 22, 'yh': 105}, 'p': {'xl': 167, 'xh': 244, 'yl': 22, 'yh': 105}, 'sat': {'xl': 12, 'xh': 244, 'yl': 22, 'yh': 105}}
flat-field p: None prod:(5.0, inf) ratio:(-inf, 1.2)
plane guess fit: None
use hexagons: False
enforce mirror symmetry: True
ff alpha: None, max. iter.: None
Fnl: None

[27]:

f = boz.inspect_rois(dataM, params.rois, params.rois_th)
f.savefig(path+prefix+f'-rois.png', dpi=300)

/home/lleguy/notebooks/ToolBox-git/src/toolbox_scs/routines/boz.py:767: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.
  main_ax.pcolormesh(Xs, Ys, np.flipud(data_mean[:, :256]),

_images/BOZ_analysis_part_I.a_Correction_determination_36_1.png

Flat field extraction¶

The first step is to compute a good average image, this mean remove saturated shots and ignoring bad pixels

[28]:

params.sat_level = sat_level
res = boz.average_module(params.arr, dark=dark,
                         ret='mean', mask=params.get_mask(), sat_roi=params.rois['sat'],
                         sat_level=params.sat_level)
avg = res.mean(axis=0).compute()

The second step is from that good average image to fit the plane field on n/0 and p/0 rois. We have to make sure that the rois have same width.

[29]:

# default values
params.flat_field_prod_th = (5.0, np.PINF)
params.flat_field_ratio_th = (np.NINF, 1.2)
params.use_hex = False
params.force_mirror = False
params.ff_alpha = 0.1
params.ff_max_iter = 25

print(params)

proposal:2937 darkrun:615 run:614 module:15 gain:3.0 ph/bin
drop intra darks:True
mean threshold:(28.975031015633952, 55.97503101563395) std threshold:(None, None)
mask:(#12) [[15, 437], [36, 477], [43, 506], [59, 215], [61, 215], [63, 28], [71, 451], [76, 302], [80, 223], [80, 224], [108, 185], [125, 296]]
rois threshold: 0.18
rois: {'n': {'xl': 12, 'xh': 89, 'yl': 22, 'yh': 105}, '0': {'xl': 89, 'xh': 167, 'yl': 22, 'yh': 105}, 'p': {'xl': 167, 'xh': 244, 'yl': 22, 'yh': 105}, 'sat': {'xl': 12, 'xh': 244, 'yl': 22, 'yh': 105}}
flat-field p: None prod:(5.0, inf) ratio:(-inf, 1.2)
plane guess fit: None
use hexagons: False
enforce mirror symmetry: False
ff alpha: 0.1, max. iter.: 25
Fnl: None

Refining flat field¶

[30]:

res, cb = boz.ff_refine_fit(params)

0: 0:00:00.000001 (18.4836060294627, 95.86388810686377, 792.2864268034733), [-0.2, -0.1, 1, -0.54, 0.2, -0.1, 1, -0.54]
1: 0:00:14.281071 (1.704098033186518, 74.25427490721434, 727.2058667734647), [-0.2058043  -0.0792378   1.00268546 -0.53672204  0.20203086 -0.09360214
  0.97615395 -0.58459197]
2: 0:00:18.655748 (11.890682695560002, 61.75454580368649, 510.5293137768249), [-0.22922627 -0.00316114  0.97621938 -0.58932566  0.21332385 -0.07818025
  0.96348728 -0.60881437]
3: 0:00:23.169202 (1.9664154222668786, 54.483411566268536, 527.1363768622834), [-0.26789167  0.08104171  0.991437   -0.5666215   0.23390899 -0.02913895
  0.97200164 -0.59716311]
4: 0:00:30.855880 (2.788852315822956, 51.780828173217316, 492.7086108897666), [-0.28686793  0.08023693  0.99309416 -0.5720163   0.24892017  0.0086582
  0.9721102  -0.59781099]
5: 0:00:35.233928 (2.8306980133010997, 46.731008152166524, 441.83379940195533), [-0.33479122  0.073223    1.00089164 -0.57662417  0.2996213   0.01636881
  0.96678814 -0.59674371]
6: 0:00:39.613031 (1.1027609383335018, 41.17392391325677, 401.8143906875661), [-0.42657317  0.07059582  1.41240491 -0.79234128  0.29854325  0.01741615
  0.8727304  -0.5401786 ]
7: 0:00:47.271427 (0.3834769179951083, 40.067238110104064, 397.22108883908464), [-0.34358714  0.0842115   1.49996193 -0.83727507  0.39646279  0.02845304
  0.8808928  -0.54309903]
8: 0:00:55.050496 (0.4623140913753959, 31.66381594753332, 312.47733265295466), [-0.56869177  0.03071662  1.43181094 -0.80317794  0.37921352  0.00833382
  0.93026185 -0.5700529 ]
9: 0:01:02.729413 (0.5262076728723595, 31.533228320912848, 310.59641415327724), [-0.56596605  0.03091362  1.42287508 -0.79821606  0.38315469  0.01139978
  0.89660161 -0.55178684]
10: 0:01:10.393711 (0.40577343836681873, 30.558773621350866, 301.9357752682073), [-0.60374124  0.01543549  1.40900101 -0.79059803  0.38721309  0.00676208
  0.92332579 -0.56538049]
11: 0:01:18.169954 (0.29536178209128877, 30.14022919095299, 298.74403587070833), [-0.59873252  0.01126187  1.47905137 -0.82735865  0.41319924  0.00458773
  0.91818125 -0.56084676]
12: 0:01:22.553112 (0.23277723937779787, 29.98455620216637, 297.7505668672635), [-0.62677837 -0.00429811  1.46602808 -0.81904516  0.41077136  0.00368263
  0.95237687 -0.57894441]
13: 0:01:26.940119 (0.1677490401115567, 29.89336741828346, 297.4239328218306), [-6.20967484e-01  4.92248974e-04  1.48469078e+00 -8.27985442e-01
  4.08252795e-01  2.84273532e-03  9.38604555e-01 -5.71401832e-01]
14: 0:01:31.327848 (0.12004223960690431, 29.88067993575002, 297.726419201038), [-6.20858884e-01  1.11764583e-03  1.49346862e+00 -8.31514626e-01
  4.06801459e-01  3.23657214e-03  9.32085950e-01 -5.67486100e-01]
15: 0:01:35.715057 (0.08534285020804122, 29.876609006905095, 297.99800441717855), [-6.22344600e-01  7.21760918e-04  1.49956749e+00 -8.33734042e-01
  4.04782012e-01  3.07071239e-03  9.29454644e-01 -5.65835079e-01]
16: 0:01:40.104267 (0.08380257947297409, 29.87653945624043, 298.0111713471475), [-6.22203211e-01  7.85738897e-04  1.50051239e+00 -8.34194153e-01
  4.05073545e-01  3.25776827e-03  9.29479050e-01 -5.65778142e-01]
17: 0:01:44.495705 (0.08425002000016209, 29.876523092452047, 298.006980744519), [-6.22301296e-01  7.26689476e-04  1.50018252e+00 -8.34031747e-01
  4.05003387e-01  3.19353498e-03  9.29539604e-01 -5.65831164e-01]
18: 0:01:48.998946 (0.08427220167345736, 29.87652308110631, 298.00678099600196), [-6.22303819e-01  7.24886121e-04  1.50017694e+00 -8.34029572e-01
  4.05002711e-01  3.19264427e-03  9.29552380e-01 -5.65838672e-01]
19: 0:01:53.396445 (0.08427383467812025, 29.87652308099577, 298.0067662978546), [-6.22303994e-01  7.24728531e-04  1.50017648e+00 -8.34029378e-01
  4.05002952e-01  3.19271474e-03  9.29552217e-01 -5.65838631e-01]
20: 0:01:57.800381 (0.08427416822406841, 29.876523080994907, 298.00676329593244), [-6.22304067e-01  7.24733676e-04  1.50017671e+00 -8.34029520e-01
  4.05003057e-01  3.19272047e-03  9.29552429e-01 -5.65838772e-01]
21: 0:02:22.053462 (0.08427417717855114, 29.87652308099486, 298.00676321534166), [-6.22304070e-01  7.24733782e-04  1.50017672e+00 -8.34029526e-01
  4.05003061e-01  3.19272061e-03  9.29552438e-01 -5.65838778e-01]
Warning: Desired error not necessarily achieved due to precision loss.
         Current function value: 29.876523
         Iterations: 21
         Function evaluations: 651
         Gradient evaluations: 71

[31]:

params.set_flat_field(res.x)

[32]:

ff = boz.compute_flat_field_correction(params.rois, params)
f = boz.inspect_plane_fitting(boz.ensure_on_host(avg)/ff, params.rois)
f.savefig(path+prefix+'-inspect-withflatfield-refined.png', dpi=300)

_images/BOZ_analysis_part_I.a_Correction_determination_45_0.png

Non-linearities correction extraction¶

To speed up online analysis, we save the corrections with a dummy non-linearity correction. The saved file can be used for online analysis as soon as it appears.

[33]:

params.set_Fnl(np.arange(2**9))
params.save(path=path)

/gpfs/exfel/exp/SCS/202122/p002937/usr/processed_runs/r0614/parameters_p2937_d615_r614.json

[34]:

N = 80
domain = boz.nl_domain(N, 40, 511)
params.nl_alpha = 0.5
params.nl_max_iter = 25

minimizing¶

[35]:

res, fit_res = boz.nl_fit(params, domain)

0: 0:00:00.000001 (3086.0699271427047, 1543.0349635713524, 0.0), [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0]
1: 0:00:58.777819 (1486.0545571870425, 746.0852336389034, 6.115910090764297), [-1.57636853e-01 -4.99656139e-01 -4.12595060e-02  4.91009113e-01
  4.91730605e-01  3.52349007e-01  9.33380898e-02 -4.54187036e-02
 -1.40888962e-01 -1.91877987e-01 -1.62450269e-01 -1.31042085e-01
 -1.10913045e-01 -8.06552446e-02 -4.61741230e-02 -3.09095183e-02
 -1.50260207e-02  8.84763575e-04  1.29982137e-02  1.83006270e-02
  2.49125057e-02  2.18491230e-02  2.26397447e-02  2.27667555e-02
  2.00809841e-02  1.76044770e-02  1.32664669e-02  9.73182337e-03
  8.40726981e-03  4.19531553e-03  3.66141318e-03  2.65210996e-03
  1.38896229e-03  9.11011464e-04  4.56409049e-04  2.92753558e-04
  1.26513645e-04  7.69714922e-05  3.40700753e-05  1.52505553e-05
  1.91493415e-06  0.00000000e+00  0.00000000e+00 -1.01063234e-05
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]
2: 0:01:57.609617 (1240.9171005959702, 624.2149507031482, 7.512800810326353), [-2.68572233e-01 -3.59878600e-01  6.13434905e-02  5.44656994e-01
  5.59683714e-01  4.31540425e-01  1.32802698e-01 -3.87940851e-02
 -1.64732311e-01 -2.43392693e-01 -2.18942805e-01 -1.86267432e-01
 -1.66367657e-01 -1.28463788e-01 -7.84566497e-02 -5.69307934e-02
 -3.33554472e-02 -5.89918240e-03  1.42538832e-02  2.53876360e-02
  3.73202443e-02  3.40889060e-02  3.62029912e-02  3.71500569e-02
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14: 0:08:56.712505 (442.1896165874871, 230.28499118125276, 18.380365775018426), [-5.94735802e-02  1.19482034e-03  3.71908465e-01  4.49464956e-01
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15: 0:09:55.741792 (442.55772594069373, 229.90635865183347, 17.254991362973197), [-5.75971085e-02  9.71157523e-03  3.81207565e-01  4.64101877e-01
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16: 0:10:54.781969 (442.70188344589843, 229.87218627370297, 17.04248910150751), [-6.58721316e-02  8.17370651e-03  3.76028800e-01  4.63618398e-01
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17: 0:11:24.842809 (442.88782239485795, 229.83843273571858, 16.789043076579198), [-7.86656716e-02  7.69939304e-03  3.68956799e-01  4.65456855e-01
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18: 0:12:23.875630 (442.921245449471, 229.83468615095325, 16.74812685243551), [-8.01093743e-02  7.88368564e-03  3.68280488e-01  4.66091447e-01
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19: 0:13:22.988183 (442.92547055591086, 229.83458975919572, 16.743708962480607), [-8.01228164e-02  7.85617819e-03  3.68280852e-01  4.66017877e-01
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20: 0:14:22.050028 (442.9256335662375, 229.83457951935975, 16.743525472482048), [-8.01074332e-02  7.73854829e-03  3.68183213e-01  4.65866447e-01
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21: 0:14:52.122790 (442.92622105130977, 229.83457222022807, 16.74292338914637), [-8.00202484e-02  7.61243743e-03  3.68111842e-01  4.65694212e-01
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22: 0:15:51.534856 (442.9263038626892, 229.83457168941098, 16.74283951613272), [-8.00308336e-02  7.60088489e-03  3.68097512e-01  4.65680601e-01
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23: 0:16:21.768297 (442.92614847467667, 229.8345713195211, 16.742994164365566), [-8.00517912e-02  7.62697997e-03  3.68110588e-01  4.65718230e-01
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24: 0:17:20.876679 (442.92613151978617, 229.83457126777625, 16.74301101576635), [-8.00504605e-02  7.63158019e-03  3.68115028e-01  4.65724350e-01
  8.17296382e-01  5.92798854e-01  3.85097399e-01  2.43348603e-01
  1.48216549e-01  1.09516180e-01  2.64243301e-02 -1.26262656e-01
 -1.39369575e-01 -1.22862792e-01 -1.66406179e-01 -2.26673272e-01
 -3.43199728e-01 -4.03156687e-01 -4.54841974e-01 -3.63261010e-01
 -3.57541461e-01 -2.65797407e-01 -1.66415868e-01 -8.14295427e-02
  3.67354874e-02  1.43650636e-01  1.99970413e-01  2.06115597e-01
  2.31513982e-01  1.57051727e-01  1.34967377e-01  1.07854745e-01
  6.15203833e-02  4.21601706e-02  2.18012491e-02  1.46733476e-02
  7.35782828e-03  4.15011235e-03  1.77358434e-03  7.86479355e-04
  7.21457653e-05  0.00000000e+00  0.00000000e+00 -7.22671285e-03
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]
25: 0:17:50.966772 (442.92620525360365, 229.83457125323253, 16.742937252861374), [-8.00453334e-02  7.62612083e-03  3.68112793e-01  4.65716488e-01
  8.17287237e-01  5.92789810e-01  3.85099098e-01  2.43318538e-01
  1.48217035e-01  1.09508744e-01  2.64204350e-02 -1.26275695e-01
 -1.39399786e-01 -1.22860948e-01 -1.66420706e-01 -2.26657858e-01
 -3.43191302e-01 -4.03181635e-01 -4.54859594e-01 -3.63298806e-01
 -3.57576383e-01 -2.65715514e-01 -1.66431840e-01 -8.14228426e-02
  3.67432700e-02  1.43658157e-01  1.99958967e-01  2.06103849e-01
  2.31494991e-01  1.57034560e-01  1.34961857e-01  1.07848008e-01
  6.15195621e-02  4.21554264e-02  2.18073533e-02  1.46723419e-02
  7.35637424e-03  4.15214780e-03  1.78038360e-03  7.85856408e-04
  7.76728798e-05  0.00000000e+00  0.00000000e+00 -7.22107260e-03
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]
Warning: Maximum number of iterations has been exceeded.
         Current function value: 229.834571
         Iterations: 25
         Function evaluations: 2916
         Gradient evaluations: 36

[36]:

params.set_Fnl(boz.nl_lut(domain, res.x))

[37]:

print(params)

proposal:2937 darkrun:615 run:614 module:15 gain:3.0 ph/bin
drop intra darks:True
mean threshold:(28.975031015633952, 55.97503101563395) std threshold:(None, None)
mask:(#12) [[15, 437], [36, 477], [43, 506], [59, 215], [61, 215], [63, 28], [71, 451], [76, 302], [80, 223], [80, 224], [108, 185], [125, 296]]
rois threshold: 0.18
rois: {'n': {'xl': 12, 'xh': 89, 'yl': 22, 'yh': 105}, '0': {'xl': 89, 'xh': 167, 'yl': 22, 'yh': 105}, 'p': {'xl': 167, 'xh': 244, 'yl': 22, 'yh': 105}, 'sat': {'xl': 12, 'xh': 244, 'yl': 22, 'yh': 105}}
flat-field p: [-0.6223040701072852, 0.000724733782145572, 1.500176720279694, -0.8340295260244752, 0.4050030612762583, 0.003192720612202234, 0.9295524383664061, -0.5658387778696685] prod:(5.0, inf) ratio:(-inf, 1.2)
plane guess fit: [-0.2, -0.1, 1, -0.54, 0.2, -0.1, 1, -0.54]
use hexagons: False
enforce mirror symmetry: False
ff alpha: 0.1, max. iter.: 25
dFnl: [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00 -8.00453334e-02 -8.00453334e-02 -8.00453334e-02
 -8.00453334e-02 -8.00453334e-02 -8.00453334e-02  7.62612083e-03
  7.62612083e-03  7.62612083e-03  7.62612083e-03  7.62612083e-03
  7.62612083e-03  3.68112793e-01  3.68112793e-01  3.68112793e-01
  3.68112793e-01  3.68112793e-01  3.68112793e-01  4.65716488e-01
  4.65716488e-01  4.65716488e-01  4.65716488e-01  4.65716488e-01
  4.65716488e-01  8.17287237e-01  8.17287237e-01  8.17287237e-01
  8.17287237e-01  8.17287237e-01  8.17287237e-01  5.92789810e-01
  5.92789810e-01  5.92789810e-01  5.92789810e-01  5.92789810e-01
  5.92789810e-01  3.85099098e-01  3.85099098e-01  3.85099098e-01
  3.85099098e-01  3.85099098e-01  3.85099098e-01  2.43318538e-01
  2.43318538e-01  2.43318538e-01  2.43318538e-01  2.43318538e-01
  1.48217035e-01  1.48217035e-01  1.48217035e-01  1.48217035e-01
  1.48217035e-01  1.48217035e-01  1.09508744e-01  1.09508744e-01
  1.09508744e-01  1.09508744e-01  1.09508744e-01  1.09508744e-01
  2.64204350e-02  2.64204350e-02  2.64204350e-02  2.64204350e-02
  2.64204350e-02  2.64204350e-02 -1.26275695e-01 -1.26275695e-01
 -1.26275695e-01 -1.26275695e-01 -1.26275695e-01 -1.26275695e-01
 -1.39399786e-01 -1.39399786e-01 -1.39399786e-01 -1.39399786e-01
 -1.39399786e-01 -1.39399786e-01 -1.22860948e-01 -1.22860948e-01
 -1.22860948e-01 -1.22860948e-01 -1.22860948e-01 -1.22860948e-01
 -1.66420706e-01 -1.66420706e-01 -1.66420706e-01 -1.66420706e-01
 -1.66420706e-01 -2.26657858e-01 -2.26657858e-01 -2.26657858e-01
 -2.26657858e-01 -2.26657858e-01 -2.26657858e-01 -3.43191302e-01
 -3.43191302e-01 -3.43191302e-01 -3.43191302e-01 -3.43191302e-01
 -3.43191302e-01 -4.03181635e-01 -4.03181635e-01 -4.03181635e-01
 -4.03181635e-01 -4.03181635e-01 -4.03181635e-01 -4.54859594e-01
 -4.54859594e-01 -4.54859594e-01 -4.54859594e-01 -4.54859594e-01
 -4.54859594e-01 -3.63298806e-01 -3.63298806e-01 -3.63298806e-01
 -3.63298806e-01 -3.63298806e-01 -3.63298806e-01 -3.57576383e-01
 -3.57576383e-01 -3.57576383e-01 -3.57576383e-01 -3.57576383e-01
 -3.57576383e-01 -2.65715514e-01 -2.65715514e-01 -2.65715514e-01
 -2.65715514e-01 -2.65715514e-01 -1.66431840e-01 -1.66431840e-01
 -1.66431840e-01 -1.66431840e-01 -1.66431840e-01 -1.66431840e-01
 -8.14228426e-02 -8.14228426e-02 -8.14228426e-02 -8.14228426e-02
 -8.14228426e-02 -8.14228426e-02  3.67432700e-02  3.67432700e-02
  3.67432700e-02  3.67432700e-02  3.67432700e-02  3.67432700e-02
  1.43658157e-01  1.43658157e-01  1.43658157e-01  1.43658157e-01
  1.43658157e-01  1.43658157e-01  1.99958967e-01  1.99958967e-01
  1.99958967e-01  1.99958967e-01  1.99958967e-01  1.99958967e-01
  2.06103849e-01  2.06103849e-01  2.06103849e-01  2.06103849e-01
  2.06103849e-01  2.06103849e-01  2.31494991e-01  2.31494991e-01
  2.31494991e-01  2.31494991e-01  2.31494991e-01  2.31494991e-01
  1.57034560e-01  1.57034560e-01  1.57034560e-01  1.57034560e-01
  1.57034560e-01  1.34961857e-01  1.34961857e-01  1.34961857e-01
  1.34961857e-01  1.34961857e-01  1.34961857e-01  1.07848008e-01
  1.07848008e-01  1.07848008e-01  1.07848008e-01  1.07848008e-01
  1.07848008e-01  6.15195621e-02  6.15195621e-02  6.15195621e-02
  6.15195621e-02  6.15195621e-02  6.15195621e-02  4.21554264e-02
  4.21554264e-02  4.21554264e-02  4.21554264e-02  4.21554264e-02
  4.21554264e-02  2.18073533e-02  2.18073533e-02  2.18073533e-02
  2.18073533e-02  2.18073533e-02  2.18073533e-02  1.46723419e-02
  1.46723419e-02  1.46723419e-02  1.46723419e-02  1.46723419e-02
  1.46723419e-02  7.35637424e-03  7.35637424e-03  7.35637424e-03
  7.35637424e-03  7.35637424e-03  4.15214780e-03  4.15214780e-03
  4.15214780e-03  4.15214780e-03  4.15214780e-03  4.15214780e-03
  1.78038360e-03  1.78038360e-03  1.78038360e-03  1.78038360e-03
  1.78038360e-03  1.78038360e-03  7.85856408e-04  7.85856408e-04
  7.85856408e-04  7.85856408e-04  7.85856408e-04  7.85856408e-04
  7.76728797e-05  7.76728797e-05  7.76728797e-05  7.76728797e-05
  7.76728797e-05  7.76728797e-05  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00 -7.22107260e-03 -7.22107260e-03
 -7.22107260e-03 -7.22107260e-03 -7.22107260e-03  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]
nl alpha:0.5, sat. level:500,  nl max. iter.:25

[38]:

f = boz.inspect_correction(params, gain=params.gain)

mu: 0.5548827255333549, s: 0.005084289575244334, snr: 109.13672742699538
weighted mu: 0.5548458130246263, s: 0.0044434740849424535, snr: 124.86757037805747
diff mu: 0.5548827255333549, s: 0.004872386726144649, snr: 113.8831453086267
mu: 0.5976615112755194, s: 0.005382491809662051, snr: 111.03807166091065
weighted mu: 0.597788040850602, s: 0.0047850701948281725, snr: 124.92774745430208
diff mu: 0.5976615112755194, s: 0.005185181280175686, snr: 115.26337826616337
mu: 0.9285018768837483, s: 0.012200618602811707, snr: 76.10285241354643
weighted mu: 0.9281647927167087, s: 0.011232131843254469, snr: 82.63478435521786
diff mu: 0.9285018768837483, s: 0.011195132904929328, snr: 82.93799499914108
mu: 0.989131458619956, s: 0.0070755493535068804, snr: 139.79571185235378
weighted mu: 0.9884902174393602, s: 0.0054195731639837316, snr: 182.39263269079237
diff mu: 0.989131458619956, s: 0.007101403322843433, snr: 139.2867597645339
mu: 0.9920652557682886, s: 0.0066457310959286905, snr: 149.27857318452863
weighted mu: 0.9923455021819694, s: 0.005687673097417032, snr: 174.47302001808572
diff mu: 0.9920652557682886, s: 0.0066754073354702865, snr: 148.61493927072797
mu: 0.9970452858694611, s: 0.0033459909372665136, snr: 297.982064076955
weighted mu: 0.9961149773600704, s: 0.0029137435593010515, snr: 341.86775777859407
diff mu: 0.9970452858694611, s: 0.003264412741680508, snr: 305.4286834317972
mu: 0.9969613645782567, s: 0.002526641087343409, snr: 394.5797325834251
weighted mu: 0.9967346659245049, s: 0.0021407377892240045, snr: 465.60334055942974
diff mu: 0.9969613645782567, s: 0.002537198385132514, snr: 392.9378839353892
mu: 0.9990953203422078, s: 0.002431422225116063, snr: 410.9098411710522
weighted mu: 0.9993800049741802, s: 0.0020677924999526415, snr: 483.3076844011519
diff mu: 0.9990953203422078, s: 0.002429860501200696, snr: 411.1739418162125
mu: 0.9978674182119845, s: 0.0026633668482404335, snr: 374.6639028984049
weighted mu: 0.9973530198357894, s: 0.0023843840153367555, snr: 418.2853992564321
diff mu: 0.9978674182119845, s: 0.0025864547554682957, snr: 385.8050932854279

_images/BOZ_analysis_part_I.a_Correction_determination_54_1.png

[39]:

f.savefig(path+prefix+'-inspect-correction.png', dpi=300)

plotting the fitted correction¶

[40]:

f = boz.inspect_Fnl(boz.ensure_on_host(params.get_Fnl()))

_images/BOZ_analysis_part_I.a_Correction_determination_57_0.png

[41]:

f.savefig(path+prefix+'-inspect-Fnl.png', dpi=300)

plotting the fit progresion¶

[42]:

f = boz.inspect_nl_fit(fit_res)

_images/BOZ_analysis_part_I.a_Correction_determination_60_0.png

[43]:

f.savefig(path+prefix+'-inspect-nl-fit.png', dpi=300)

Save the analysis parameters¶

[44]:

params.save(path=path)

/gpfs/exfel/exp/SCS/202122/p002937/usr/processed_runs/r0614/parameters_p2937_d615_r614.json