ImXPAD S540 detector at D2AM

This tutorial corresponds to the calibration the goniometer an ImXPAD detector composed of 8 stripes of 7 modules, many of which are defective, on a goniometer.

This detector is mounted on the goniometer 2theta arm at the D2AM beam-line, French CRG at the ESRF synchrotron.

The raw data files are available at: http://www.silx.org/pub/pyFAI/gonio/D2AM-15/

[1]:

%matplotlib inline
# %pylab nbagg
import numpy
from matplotlib.pyplot import subplots

[2]:

import os, time
start_time = time.perf_counter()
import fabio, pyFAI
print("Working with pyFAI version", pyFAI.version)
from pyFAI.goniometer import GeometryTransformation, GoniometerRefinement, Goniometer
from pyFAI.gui import jupyter
from math import ceil

Working with pyFAI version 0.21.0-dev1

[3]:

#Download all data

from silx.resources import ExternalResources

#Comment out and configure the proxy if you are behind a firewall
#os.environ["http_proxy"] = "http://proxy.company.com:3128"

downloader = ExternalResources("pyFAI", "http://www.silx.org/pub/pyFAI/testimages", "PYFAI_DATA")
all_files = downloader.getdir("LaB6_gonio_D2AM.tar.bz2")

print("List of files downloaded:")
for i in all_files:
    print("    "+os.path.basename(i))

detector_file = [i for i in all_files if i.endswith("D5Geom-2018.h5")][0]
images = [i for i in all_files if i.endswith(".edf")]
npt_files = [i for i in all_files if i.endswith(".npt")]

List of files downloaded:
    LaB6_gonio_D2AM
    16Dec08D5_1777-rsz.npt
    16Dec08D5_1791-rsz.npt
    16Dec08D5_1729-rsz.edf
    16Dec08D5_1763-rsz.npt
    D5Geom-2018.h5
    16Dec08D5_1725-rsz.edf
    16Dec08D5_1728-rsz.npt
    16Dec08D5_1784-rsz.npt
    16Dec08D5_1763-rsz.edf
    16Dec08D5_1728-rsz.edf
    16Dec08D5_1735-rsz.npt
    16Dec08D5_1727-rsz.npt
    16Dec08D5_1729-rsz.npt
    16Dec08D5_1770-rsz.edf
    16Dec08D5_1784-rsz.edf
    16Dec08D5_1730-rsz.npt
    16Dec08D5_1742-rsz.npt
    16Dec08D5_1770-rsz.npt
    16Dec08D5_1725-rsz.npt
    16Dec08D5_1726-rsz.npt
    16Dec08D5_1777-rsz.edf
    16Dec08D5_1756-rsz.edf
    16Dec08D5_1735-rsz.edf
    16Dec08D5_1730-rsz.edf
    16Dec08D5_1742-rsz.edf
    16Dec08D5_1791-rsz.edf
    16Dec08D5_1749-rsz.npt
    16Dec08D5_1726-rsz.edf
    16Dec08D5_1756-rsz.npt
    16Dec08D5_1727-rsz.edf
    16Dec08D5_1749-rsz.edf

[4]:

#Definition of the detector and deplay of an image and its mask:

d5 = pyFAI.detector_factory(detector_file)
print("Detector shape: ",d5.shape)

fimg = fabio.open(images[-1])

for k,v in fimg.header.items():
    print(k, ": ", v)

f, ax = subplots(1, 2)
jupyter.display(d5.mask, label="mask", ax=ax[0])
jupyter.display(fimg.data, label=os.path.basename(fimg.filename), ax=ax[1])
pass

Detector shape:  (960, 578)
EDF_DataBlockID :  0.Image.Psd
EDF_BinarySize :  4439040
EDF_HeaderSize :  1536
ByteOrder :  LowByteFirst
DataType :  DoubleValue
Dim_1 :  578
Dim_2 :  960
Image :  0
HeaderID :  EH:000000:000000:000000
Size :  4439040
VersionNumber :  1
Epoch :  1481327234.3919599056
det_sample_dist :  0
y_beam :  0
x_beam :  0
Lambda :  0.495938
offset :  0
count_time :  120
point_no :  24
scan_no :  906
preset :  0
col_end :  559
col_beg :  0
row_end :  959
row_beg :  0
counter_pos :  120 2689 156 15.3097 0 90.3692 15.3097 0 25 25 1749 5.65726e+09 0 0 0 174.925 0 0 15.3097
counter_mne :  sec vct1 vct2 vct3 vct4 Imach pseudoC pfoil Emono Ecod img roi1 roi2 roi3 roi4 pico1 pico2 pico3 pico4
motor_pos :  24.0001 0.077502 89.9912 -89.9921 -0.0032 0.0022 57.1197 134.748 -32.9504 0.16656 -5 0.47558 -1.5 0 0 0 4.53604 0.1416 1.04 1.04022 1.04022 -4.4 -1.10211 -0.543725 -9.962 -14.038 -16.865 -7.195 24 -2.038 24.06 4.835 1
motor_mne :  del eta chi phi nu mu keta kap kphi tsx tsy tsz rox roy tox toy mono inc1 courb courbb courbf omega khimono gamma su6 sd6 sf6 sb6 vg6 vo6 hg6 ho6 rien
suffix :  .edf
prefix :  16Dec08D5_
dir :  /users/opd02/raw
run :  1749
title :  CCD Image

../../../../_images/usage_tutorial_Goniometer_Rotation-XPADS540_D2AM-15_4_1.png

[5]:

# Define wavelength and create our "large" LaB6 calibrant

wavelength = 0.495938 * 1e-10
from pyFAI.calibrant import get_calibrant
LaB6 = get_calibrant("LaB6")
LaB6.wavelength = wavelength
print("2theta max: ", numpy.degrees(LaB6.get_2th()[-1]))
print("Number of reflections: ", len(LaB6.get_2th()))

2theta max:  179.17349767223402
Number of reflections:  236

[6]:

#Use a few manually calibrated images:

npt_files.sort()
print("Number of hand-calibrated images :",len(npt_files))

Number of hand-calibrated images : 15

[7]:

# Definition of the goniometer translation function:
# The detector rotates vertically, around the horizontal axis, i.e. rot2.
# Rotation both around axis 1 and axis 2 are allowed

goniotrans = GeometryTransformation(param_names = ["dist", "poni1", "poni2",
                                                   "rot1", "rot2", "rot3", "scale1", "scale2" ],
                                    dist_expr="dist",
                                    poni1_expr="poni1",
                                    poni2_expr="poni2",
                                    rot1_expr="scale1 * pos +rot1",
                                    rot2_expr="scale2 * pos + rot2",
                                    rot3_expr="rot3")


#Definition of the function reading the goniometer angle from the filename of the image.

def get_angle(metadata):
    """Takes the angle from the first motor position and returns the angle of the goniometer arm"""
    return float(metadata["motor_pos"].split()[0])

print('filename', os.path.basename(fimg.filename), "angle:",get_angle(fimg.header))

filename 16Dec08D5_1749-rsz.edf angle: 24.0001

[8]:

# Definition of the geometry refinement: the parameter order is the same as the param_names

rot3 = numpy.pi/2
scale1 = -numpy.pi/180
scale2 = 0
param = {"dist":0.5,
         "poni1":0.05,
         "poni2":0.05,
         "rot1":0,
         "rot2":0,
         "rot3": rot3,
         "scale1": scale1,
         "scale2": scale2,
        }
#Defines the bounds for some variables
bounds = {"dist": (0.2, 0.8),
          "poni1": (0, 0.1),
          "poni2": (0, 0.1),
          "rot1": (-0.1, 0.1),
          "rot2": (-0.1, 0.1),
          "rot3": (rot3, rot3), #strict bounds on rot3
          "scale1": (scale1, scale1),
          "scale2": (scale2, scale2),
         }
gonioref = GoniometerRefinement(param, #initial guess
                                bounds=bounds,
                                pos_function=get_angle,
                                trans_function=goniotrans,
                                detector=d5, wavelength=wavelength)
print("Empty refinement object:", gonioref)

#Let's populate the goniometer refinement object with all control point files:

for fn in npt_files[:]:
    base = os.path.splitext(fn)[0]
    fimg = fabio.open(base + ".edf")
    basename = os.path.basename(base)
    sg =gonioref.new_geometry(basename, image=fimg.data, metadata=fimg.header, control_points=fn, calibrant=LaB6)
    print(basename, "Angle:", sg.get_position())


print("Filled refinement object:")
print(gonioref)

Empty refinement object: GoniometerRefinement with 0 geometries labeled: .
16Dec08D5_1725-rsz Angle: -0.003
16Dec08D5_1726-rsz Angle: 0.9998
16Dec08D5_1727-rsz Angle: 2.0
16Dec08D5_1728-rsz Angle: 2.9998
16Dec08D5_1729-rsz Angle: 4.0002
16Dec08D5_1730-rsz Angle: 4.9998
16Dec08D5_1735-rsz Angle: 10.0001
16Dec08D5_1742-rsz Angle: 16.9996
16Dec08D5_1749-rsz Angle: 24.0001
16Dec08D5_1756-rsz Angle: 30.9997
16Dec08D5_1763-rsz Angle: 37.9999
16Dec08D5_1770-rsz Angle: 44.9997
16Dec08D5_1777-rsz Angle: 52.0
16Dec08D5_1784-rsz Angle: 58.9995
16Dec08D5_1791-rsz Angle: 65.9999
Filled refinement object:
GoniometerRefinement with 15 geometries labeled: 16Dec08D5_1725-rsz, 16Dec08D5_1726-rsz, 16Dec08D5_1727-rsz, 16Dec08D5_1728-rsz, 16Dec08D5_1729-rsz, 16Dec08D5_1730-rsz, 16Dec08D5_1735-rsz, 16Dec08D5_1742-rsz, 16Dec08D5_1749-rsz, 16Dec08D5_1756-rsz, 16Dec08D5_1763-rsz, 16Dec08D5_1770-rsz, 16Dec08D5_1777-rsz, 16Dec08D5_1784-rsz, 16Dec08D5_1791-rsz.

[9]:

# Initial refinement of the goniometer model with 5 dof

gonioref.refine3()

Free parameters: ['dist', 'poni1', 'poni2', 'rot1', 'rot2']
Fixed: {'rot3': 1.5707963267948966, 'scale1': -0.017453292519943295, 'scale2': 0}
     fun: 3.46909192001821e-07
     jac: array([ 2.27544206e-08,  5.31854027e-08, -2.20583935e-07, -1.00096365e-07,
        2.83131989e-08])
 message: 'Optimization terminated successfully'
    nfev: 151
     nit: 25
    njev: 25
  status: 0
 success: True
       x: array([ 0.52250238,  0.08731299,  0.045737  ,  0.00326958, -0.03864791])
Constrained Least square 0.00030766121608429307 --> 3.46909192001821e-07
maxdelta on rot2: 0.0 --> -0.038647906288874545

[9]:

3.46909192001821e-07

[10]:

width = 3
height = int(ceil(len(gonioref.single_geometries) / width))
fig,ax = subplots(height, width,figsize=(10, 25))
for idx, sg in enumerate(gonioref.single_geometries.values()):
    sg.geometry_refinement.set_param(gonioref.get_ai(sg.get_position()).param)
    jupyter.display(sg=sg, ax=ax[idx//width, idx%width])
pass

WARNING:matplotlib.legend:No handles with labels found to put in legend.
WARNING:matplotlib.legend:No handles with labels found to put in legend.
WARNING:matplotlib.legend:No handles with labels found to put in legend.
WARNING:matplotlib.legend:No handles with labels found to put in legend.
WARNING:matplotlib.legend:No handles with labels found to put in legend.
WARNING:matplotlib.legend:No handles with labels found to put in legend.
WARNING:matplotlib.legend:No handles with labels found to put in legend.
WARNING:matplotlib.legend:No handles with labels found to put in legend.
WARNING:matplotlib.legend:No handles with labels found to put in legend.
WARNING:matplotlib.legend:No handles with labels found to put in legend.
WARNING:matplotlib.legend:No handles with labels found to put in legend.
WARNING:matplotlib.legend:No handles with labels found to put in legend.
WARNING:matplotlib.legend:No handles with labels found to put in legend.
WARNING:matplotlib.legend:No handles with labels found to put in legend.

../../../../_images/usage_tutorial_Goniometer_Rotation-XPADS540_D2AM-15_10_1.png

[11]:

# Final pass of refinement with all constrains removed, very fine refinement

gonioref.bounds = None
gonioref.refine3(fix=["rot3"], method="slsqp", eps=1e-13, maxiter=10000, ftol=1e-12)
gonioref.refine3(fix=["rot3"], method="simplex", maxiter=10000, ftol=1e-12)

Free parameters: ['dist', 'poni1', 'poni2', 'rot1', 'rot2', 'scale1', 'scale2']
Fixed: {'rot3': 1.5707963267948966}

WARNING:pyFAI.goniometer:No bounds for optimization method Nelder-Mead

     fun: 1.8635199252706352e-07
     jac: array([ 1.01347742e-07,  0.00000000e+00,  0.00000000e+00,  1.59877677e-07,
        2.72629627e-08, -1.55139581e-06, -8.49679940e-08])
 message: 'Optimization terminated successfully'
    nfev: 102
     nit: 12
    njev: 12
  status: 0
 success: True
       x: array([ 5.21700583e-01,  8.73129939e-02,  4.57369953e-02,  4.50190970e-03,
       -3.88258933e-02, -1.74767899e-02, -4.28916991e-05])
Constrained Least square 3.46909192001821e-07 --> 1.8635199252706352e-07
maxdelta on rot1: 0.0032695821333764115 --> 0.004501909698808229
Free parameters: ['dist', 'poni1', 'poni2', 'rot1', 'rot2', 'scale1', 'scale2']
Fixed: {'rot3': 1.5707963267948966}

/usr/lib/python3/dist-packages/scipy/optimize/_minimize.py:535: RuntimeWarning: Method Nelder-Mead cannot handle constraints nor bounds.
  warn('Method %s cannot handle constraints nor bounds.' % method,

 final_simplex: (array([[ 5.20151434e-01,  5.79083022e-02,  4.04753294e-02,
        -4.77679221e-03,  1.83032901e-02, -1.74728002e-02,
        -4.65095105e-04],
       [ 5.20151434e-01,  5.79083022e-02,  4.04753294e-02,
        -4.77679221e-03,  1.83032901e-02, -1.74728002e-02,
        -4.65095105e-04],
       [ 5.20151434e-01,  5.79083022e-02,  4.04753294e-02,
        -4.77679221e-03,  1.83032901e-02, -1.74728002e-02,
        -4.65095105e-04],
       [ 5.20151434e-01,  5.79083022e-02,  4.04753294e-02,
        -4.77679221e-03,  1.83032901e-02, -1.74728002e-02,
        -4.65095105e-04],
       [ 5.20151434e-01,  5.79083022e-02,  4.04753294e-02,
        -4.77679221e-03,  1.83032901e-02, -1.74728002e-02,
        -4.65095105e-04],
       [ 5.20151434e-01,  5.79083022e-02,  4.04753294e-02,
        -4.77679221e-03,  1.83032901e-02, -1.74728002e-02,
        -4.65095105e-04],
       [ 5.20151434e-01,  5.79083022e-02,  4.04753294e-02,
        -4.77679221e-03,  1.83032901e-02, -1.74728002e-02,
        -4.65095105e-04],
       [ 5.20151434e-01,  5.79083022e-02,  4.04753294e-02,
        -4.77679221e-03,  1.83032901e-02, -1.74728002e-02,
        -4.65095105e-04]]), array([1.38937271e-08, 1.38937271e-08, 1.38937271e-08, 1.38937271e-08,
       1.38937271e-08, 1.38937271e-08, 1.38937271e-08, 1.38937271e-08]))
           fun: 1.389372707379124e-08
       message: 'Optimization terminated successfully.'
          nfev: 1638
           nit: 938
        status: 0
       success: True
             x: array([ 5.20151434e-01,  5.79083022e-02,  4.04753294e-02, -4.77679221e-03,
        1.83032901e-02, -1.74728002e-02, -4.65095105e-04])
Constrained Least square 1.8635199252706352e-07 --> 1.389372707379124e-08
maxdelta on rot2: -0.03882589330648216 --> 0.01830329012475576

[11]:

1.389372707379124e-08

[12]:

#Create a MultiGeometry integrator from the refined geometry:

angles = []
images = []
for sg in gonioref.single_geometries.values():
    angles.append(sg.get_position())
    images.append(sg.image)

multigeo = gonioref.get_mg(angles)
multigeo.radial_range=(0, 80)
print(multigeo)

MultiGeometry integrator with 15 geometries on (0, 80) radial range (2th_deg) and (-180, 180) azimuthal range (deg)

[13]:

# Integrate the whole set of images in a single run:

res = multigeo.integrate1d(images, 10000)
jupyter.plot1d(res)

#Note the large number of peaks due to hot pixels ....

[13]:

<AxesSubplot:title={'center':'1D integration'}, xlabel='Scattering angle $2\\theta$ ($^{o}$)', ylabel='Intensity'>

../../../../_images/usage_tutorial_Goniometer_Rotation-XPADS540_D2AM-15_13_1.png

[14]:

#Add hot pixels to the mask: pixel which are 15x more intense than the median in their ring.

thres = 15

old_mask = d5.mask.astype("bool", copy=True)
new_mask = d5.mask.astype("bool", copy=True)

for ai,img in zip(multigeo.ais,images):
    b,a = ai.separate(img, 1000, restore_mask=0)
    b[old_mask] = 0
    b[b<0] = 0
    print(sum(b>thres*a))
    new_mask = numpy.logical_or(new_mask, (b>thres*a))

print(" Size of old mask", sum(old_mask),
      " Size of new mask",sum(new_mask),
      " Number of pixel discarded", sum(new_mask)-sum(old_mask))

WARNING:pyFAI.DEPRECATION:Function integrate2d_legacy is deprecated since pyFAI version 0.21.
  File "/usr/lib/python3/dist-packages/pyFAI/azimuthalIntegrator.py", line 2742, in medfilt1d
    res2d = self.integrate2d(data, npt_rad, npt_azim, mask=mask,

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[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 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 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 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 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 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 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 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 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]
 Size of old mask [960 302 346 280 262 270 259 272 259 258 262 259 259 264 254 253 262 254
 258 259 257 279 255 256 256 363 261 256 253 256 257 263 256 256 255 260
 253 257 260 258 258 261 257 260 255 256 256 256 262 261 257 256 259 257
 274 264 255 258 256 254 259 257 257 256 258 260 256 261 258 259 258 265
 260 263 257 257 256 252 258 960 960 960 960 960 252 253 253 253 253 254
 253 254 254 252 253 333 253 254 253 252 252 255 252 252 253 253 253 252
 284 252 259 253 252 253 253 315 252 253 252 253 253 252 277 253 252 262
 261 252 254 252 290 252 252 252 255 253 252 253 252 252 253 253 254 253
 252 252 252 252 252 252 253 253 321 252 283 252 252 253 254 252 252 253
 960 960 960 960 960 370 370 370 370 370 372 398 371 370 370 371 370 370
 370 370 370 370 373 370 370 370 371 370 370 388 370 372 372 371 370 370
 372 370 370 370 370 370 370 371 370 370 370 370 370 370 371 370 370 370
 370 370 370 370 370 370 370 370 370 370 371 370 370 370 370 370 370 371
 370 370 370 370 370 489 371 370 370 370 371 960 960 960 960 960 488 488
 488 488 488 488 488 488 488 488 488 488 489 488 502 488 488 488 488 488
 508 488 488 488 494 489 488 488 488 488 488 489 488 488 489 488 489 489
 489 490 489 488 489 488 488 488 488 488 488 488 488 488 488 489 491 488
 489 488 488 489 488 488 489 488 490 488 507 488 488 488 488 488 488 489
 491 488 488 488 960 960 960 960 960 254 253 253 253 254 252 252 254 252
 253 252 253 253 252 252 253 253 252 252 252 252 252 252 252 253 253 252
 252 253 252 253 252 253 253 252 253 254 253 252 253 253 254 252 252 252
 252 253 252 253 252 252 254 252 252 252 253 254 252 253 253 254 253 253
 253 252 253 342 252 255 252 252 253 256 257 253 253 252 255 960 960 960
 960 960 259 253 253 253 252 253 253 252 252 253 260 252 253 252 253 252
 253 252 252 252 253 252 253 252 253 254 254 253 252 262 252 252 252 253
 253 253 253 254 261 252 268 252 252 252 253 254 252 254 254 255 253 252
 252 254 252 252 255 252 252 252 253 253 253 253 254 365 252 252 252 252
 252 252 252 252 252 253 252 252 960 960 960 960 960 253 252 253 252 253
 255 254 255 255 252 254 253 255 254 256 252 253 252 253 252 252 254 252
 252 252 254 252 252 254 255 252 256 258 252 253 256 257 253 254 287 254
 254 253 252 253 254 256 254 254 252 252 253 253 254 253 254 253 255 254
 253 254 877 254 255 254 253 254 252 254 258 288 254 370 254 252 253 252
 254 960]  Size of new mask [960 302 346 280 262 270 259 272 259 258 262 259 259 264 254 253 262 254
 258 259 257 279 255 256 256 363 261 256 253 256 257 263 256 256 255 260
 253 257 260 258 258 261 257 260 255 256 256 256 262 261 257 256 259 257
 274 264 255 258 256 254 259 257 257 256 258 260 256 261 258 259 258 265
 260 263 257 257 256 252 258 960 960 960 960 960 252 253 253 253 253 254
 253 254 254 252 253 333 253 254 254 252 252 255 252 252 253 253 253 252
 284 252 259 253 252 253 253 315 252 253 252 253 253 252 277 253 252 262
 261 252 254 252 290 252 252 252 255 253 252 253 252 252 253 254 254 253
 252 252 252 252 252 252 253 253 321 252 283 252 252 253 254 252 252 253
 960 960 960 960 960 370 370 370 370 370 372 398 372 370 370 371 370 370
 370 370 370 370 373 370 370 370 371 370 370 388 370 372 372 371 370 370
 372 370 370 370 370 370 370 371 370 370 370 370 370 370 371 370 370 370
 370 370 370 370 370 370 370 370 370 370 371 370 370 370 370 370 370 371
 370 370 370 370 370 489 371 370 370 370 371 960 960 960 960 960 488 488
 488 488 488 488 488 488 488 488 488 488 489 488 502 488 488 488 488 488
 508 488 488 488 494 489 488 488 488 488 488 489 488 488 489 488 489 489
 489 490 489 488 489 488 488 488 488 488 488 488 488 488 488 489 491 488
 489 488 488 489 488 488 489 488 490 488 507 488 488 488 488 488 488 489
 491 488 488 488 960 960 960 960 960 254 253 253 253 254 252 252 254 252
 253 252 253 253 252 252 253 253 252 252 252 252 252 252 252 253 253 252
 252 253 252 253 252 253 253 252 253 254 253 252 253 253 254 252 252 252
 252 253 252 253 252 252 254 252 252 252 253 254 252 253 253 254 253 253
 253 252 253 342 252 255 252 252 253 256 257 253 253 252 255 960 960 960
 960 960 259 253 253 253 252 253 253 252 252 253 260 252 253 252 253 252
 253 252 252 252 253 252 253 252 253 254 254 253 252 262 252 252 252 253
 253 253 253 254 261 252 268 252 252 252 253 254 252 254 256 255 253 252
 252 254 252 252 255 252 252 252 253 253 253 253 254 365 252 252 252 252
 252 252 252 252 252 253 252 252 960 960 960 960 960 253 252 253 252 253
 255 254 255 255 252 254 253 255 254 256 252 253 252 253 252 252 254 252
 252 252 254 252 252 254 255 252 256 258 252 253 256 257 253 254 287 254
 254 253 252 253 254 256 254 254 252 252 253 253 254 253 254 253 255 254
 253 254 877 254 255 254 253 254 252 254 258 288 254 370 254 252 253 252
 254 960]  Number of pixel discarded [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 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 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 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 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 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 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 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 2 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 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]

[15]:

# Update the mask
for ai in multigeo.ais:
    ai.detector.mask = new_mask

# Integrate the whole set of images in a single run:
res2 = multigeo.integrate1d(images, 10000)
ax = jupyter.plot1d(res, label="Before hot-pixel removal")
ax.plot(*res2, label="After hot-pixel removal")
ax.legend()
pass

../../../../_images/usage_tutorial_Goniometer_Rotation-XPADS540_D2AM-15_15_0.png

[16]:

# Integrate the whole set of images in 2D:

res2d = multigeo.integrate2d(images, 1000, 360)
jupyter.plot2d(res2d)
pass

../../../../_images/usage_tutorial_Goniometer_Rotation-XPADS540_D2AM-15_16_0.png

[17]:

print(f"Total execution time {time.perf_counter()-start_time:.3f} s")

Total execution time 44.993 s