Flatfield calibration#
Inspiration from: https://scripts.iucr.org/cgi-bin/paper?S1600577523001157
Work done for ID31: Scattering of amorphous carbon on a well defined position using a Pilatus CdTe 2M.
There are 9 positions investigated on the detector each of them contains calibration data and a flatfield image. First of all define an object container containing position, calibration, …
[1]:
%matplotlib inline
# Switch from widget <-> inline for documentation purposes
import copy, time
from dataclasses import dataclass
import numpy
import h5py
from matplotlib.pyplot import subplots
import fabio
from silx.resources import ExternalResources
import pyFAI
from pyFAI.gui import jupyter
from pyFAI.gui.jupyter.calib import Calibration
from pyFAI.gui.cli_calibration import AbstractCalibration
t0 = time.perf_counter()
print(f"Running pyFAI version {pyFAI.version}")
WARNING:pyFAI.gui.matplotlib:matplotlib already loaded, setting its backend may not work
Running pyFAI version 2025.3.0
[2]:
polarization = 0.999
npt = 512
energy = 75 #keV
wavelength = 1e-10*pyFAI.units.hc/energy
detector = pyFAI.detector_factory("Pilatus2M_CdTe")
calibrant = pyFAI.calibrant.CALIBRANT_FACTORY("AgBh")
calibrant.wavelength = wavelength
[3]:
# Here we download the test data
downloader = ExternalResources("flatfield", "http://www.silx.org/pub/pyFAI/testimages")
all_files = downloader.getdir("flatfield_ID31.tar.bz2")
master_file = [i for i in all_files if i.endswith("calibration_0001.h5")][0]
print(master_file)
/tmp/flatfield_testdata_jerome/flatfield_ID31.tar.bz2__content/flatfield_ID31/calibration_0001.h5
[4]:
@dataclass
class Position:
"""All data related to one of the position"""
position: int
calibration_idx: int
scattering_idx: int
coordinates: tuple=tuple()
calibration_data: object=None
scattering_data: object=None
poni: object=None
ai: object=None
control_points: object=None
flatfield: object=None
@classmethod
def init(cls, h5_file, position, calibration_idx, scattering_idx, detector_name="p3", positioners=("cncx","cncy","cncz")):
with h5py.File(h5_file) as h:
calibration_str = f"{calibration_idx}."
scattering_str = f"{scattering_idx}."
keys = list(h.keys())
ids = [i for i in keys if i.startswith(calibration_str)]
if ids:
entry = h[ids[0]]
calibration_data = entry[f"measurement/{detector_name}"][0]
coordinates = tuple(entry[f"instrument/positioners/{positioner}"][()] for positioner in positioners)
else:
raise IndexError(f"no such Entry {calibration_idx}")
ids = [i for i in keys if i.startswith(scattering_str)]
if ids:
entry = h[ids[0]]
scattering_data = entry[f"measurement/{detector_name}"][0]
coordinates = tuple(entry[f"instrument/positioners/{positioner}"][()] for positioner in positioners)
else:
raise IndexError(f"no such Entry {calibration_idx}")
return cls(position, calibration_idx, scattering_idx, coordinates, calibration_data, scattering_data)
center = Position.init(master_file, "CC", 14, 13)
center
[4]:
Position(position='CC', calibration_idx=14, scattering_idx=13, coordinates=(np.float64(6489.605), np.float64(20.0), np.float64(20.0)), calibration_data=array([[2728, 2784, 2791, ..., 1582, 1636, 1544],
[2664, 2663, 2829, ..., 1542, 1485, 1533],
[2839, 2739, 2674, ..., 1542, 1581, 1478],
...,
[3216, 2998, 3165, ..., 3048, 2992, 3125],
[3121, 3252, 3299, ..., 3086, 3110, 2913],
[3231, 3261, 3414, ..., 3099, 3039, 3020]],
shape=(1679, 1475), dtype=int32), scattering_data=array([[102929, 101856, 105155, ..., 36466, 36234, 35175],
[100320, 98901, 104158, ..., 35047, 34531, 35871],
[102334, 101772, 98380, ..., 35634, 35428, 34703],
...,
[ 96866, 94780, 96978, ..., 95870, 94463, 97045],
[ 97101, 99105, 99604, ..., 97634, 97246, 94603],
[100027, 99620, 102607, ..., 95336, 96377, 94539]],
shape=(1679, 1475), dtype=int32), poni=None, ai=None, control_points=None, flatfield=None)
[5]:
# This contains which scan correspond to what position and if it contains amorphous scattering or calibration data.
data =[None,
Position.init(master_file, 1, 1, 5),
Position.init(master_file, 2, 7, 6),
Position.init(master_file, 3, 8, 9),
Position.init(master_file, 4, 11, 12),
Position.init(master_file, 5, 14, 13),
Position.init(master_file, 6, 15, 16),
Position.init(master_file, 7, 18, 17),
Position.init(master_file, 8, 19, 20),
Position.init(master_file, 9, 22, 21)]
[6]:
#calculate the mask:
mask = -detector.mask.astype(int)
for p in data[1:]:
numpy.minimum(mask, p.scattering_data, out=mask)
numpy.minimum(mask, p.calibration_data, out=mask)
detector.mask = (mask<0).astype(numpy.int8)
[7]:
#display calibraation scattering:
fig, ax = subplots(3,3, figsize=(12,12))
jupyter.display(data[1].calibration_data, ax=ax[0,2])
jupyter.display(data[2].calibration_data, ax=ax[0,1])
jupyter.display(data[3].calibration_data, ax=ax[0,0])
jupyter.display(data[4].calibration_data, ax=ax[1,0])
jupyter.display(data[5].calibration_data, ax=ax[1,1])
jupyter.display(data[6].calibration_data, ax=ax[1,2])
jupyter.display(data[7].calibration_data, ax=ax[2,2])
jupyter.display(data[8].calibration_data, ax=ax[2,1])
jupyter.display(data[9].calibration_data, ax=ax[2,0])
pass
[8]:
#display amorphous scattering:
fig, ax = subplots(3,3, figsize=(12,12))
jupyter.display(data[1].scattering_data, ax=ax[0,2])
jupyter.display(data[2].scattering_data, ax=ax[0,1])
jupyter.display(data[3].scattering_data, ax=ax[0,0])
jupyter.display(data[4].scattering_data, ax=ax[1,0])
jupyter.display(data[5].scattering_data, ax=ax[1,1])
jupyter.display(data[6].scattering_data, ax=ax[1,2])
jupyter.display(data[7].scattering_data, ax=ax[2,2])
jupyter.display(data[8].scattering_data, ax=ax[2,1])
jupyter.display(data[9].scattering_data, ax=ax[2,0])
pass
Calibration of the central position#
[9]:
%matplotlib widget
extra_mask = center.calibration_data<5000
# switch to widget mode ... for calibration purpose. Use right click.
calib = Calibration(center.calibration_data,
calibrant=calibrant,
wavelength=calibrant.wavelength,
detector=detector,
mask=extra_mask)
[ ]:
input("Please perform the calibration in the previous cell before going on ... use the right-click")
%matplotlib inline
fig, ax = subplots()
[ ]:
calib.mask=None
print(calib.geoRef)
f2d = calib.geoRef.getFit2D()
f2d["tilt"] = 0
calib.geoRef.setFit2D(**f2d)
print(calib.geoRef)
calib.fixed+=["rot1", "rot2"]
print(f"Fixed parameters: {calib.fixed}")
[ ]:
logger = pyFAI.gui.cli_calibration.logger
from silx.image import marchingsquares
def extract_cpt(self, method="massif", pts_per_deg=1.0, max_rings=numpy.iinfo(int).max):
"""
Performs an automatic keypoint extraction:
Can be used in recalib or in calib after a first calibration has been performed.
:param method: method for keypoint extraction
:param pts_per_deg: number of control points per azimuthal degree (increase for better precision)
:param max_rings: extract at most max_rings
"""
logger.info("in extract_cpt with method %s", method)
assert self.ai
assert self.calibrant
assert self.peakPicker
self.peakPicker.reset()
self.peakPicker.init(method, False)
if self.geoRef:
self.ai.setPyFAI(**self.geoRef.getPyFAI())
tth = numpy.array([i for i in self.calibrant.get_2th() if i is not None])
tth = numpy.unique(tth)
tth_min = numpy.zeros_like(tth)
tth_max = numpy.zeros_like(tth)
delta = (tth[1:] - tth[:-1]) / 4.0
tth_max[:-1] = delta
tth_max[-1] = delta[-1]
tth_min[1:] = -delta
tth_min[0] = -delta[0]
tth_max += tth
tth_min += tth
if self.geoRef:
ttha = self.geoRef.get_ttha()
chia = self.geoRef.get_chia()
if (ttha is None) or (ttha.shape != self.peakPicker.data.shape):
ttha = self.geoRef.twoThetaArray(self.peakPicker.data.shape)
if (chia is None) or (chia.shape != self.peakPicker.data.shape):
chia = self.geoRef.chiArray(self.peakPicker.data.shape)
else:
ttha = self.ai.twoThetaArray(self.peakPicker.data.shape)
chia = self.ai.chiArray(self.peakPicker.data.shape)
rings = 0
self.peakPicker.sync_init()
if self.max_rings is None:
self.max_rings = tth.size
ms = marchingsquares.MarchingSquaresMergeImpl(ttha, self.mask, use_minmax_cache=True)
for i in range(tth.size):
if rings >= min(self.max_rings, max_rings):
break
mask = numpy.logical_and(ttha >= tth_min[i], ttha < tth_max[i])
if self.mask is not None:
mask = numpy.logical_and(mask, numpy.logical_not(self.mask))
size = mask.sum(dtype=int)
if (size > 0):
rings += 1
self.peakPicker.massif_contour(mask)
# if self.gui:
# self.peakPicker.widget.update()
sub_data = self.peakPicker.data.ravel()[numpy.where(mask.ravel())]
mean = sub_data.mean(dtype=numpy.float64)
std = sub_data.std(dtype=numpy.float64)
upper_limit = mean + std
mask2 = numpy.logical_and(self.peakPicker.data > upper_limit, mask)
size2 = mask2.sum(dtype=int)
if size2 < 1000:
upper_limit = mean
mask2 = numpy.logical_and(self.peakPicker.data > upper_limit, mask)
size2 = mask2.sum()
# length of the arc:
points = ms.find_pixels(tth[i])
seeds = set((i[0], i[1]) for i in points if mask2[i[0], i[1]])
# max number of points: 360 points for a full circle
azimuthal = chia[points[:, 0].clip(0, self.peakPicker.data.shape[0]), points[:, 1].clip(0, self.peakPicker.data.shape[1])]
nb_deg_azim = numpy.unique(numpy.rad2deg(azimuthal).round()).size
keep = int(nb_deg_azim * pts_per_deg)
if keep == 0:
continue
dist_min = len(seeds) / 2.0 / keep
# why 3.0, why not ?
logger.info("Extracting datapoint for ring %s (2theta = %.2f deg); "
"searching for %i pts out of %i with I>%.1f, dmin=%.1f" %
(i, numpy.degrees(tth[i]), keep, size2, upper_limit, dist_min))
_res = self.peakPicker.peaks_from_area(mask=mask2, Imin=upper_limit, keep=keep, method=method, ring=i, dmin=dist_min, seed=seeds)
if self.basename:
self.peakPicker.points.save(self.basename + ".npt")
if self.weighted:
self.data = self.peakPicker.points.getWeightedList(self.peakPicker.data)
else:
self.data = self.peakPicker.points.getList()
if self.geoRef:
self.geoRef.data = numpy.array(self.data, dtype=numpy.float64)
Calibration.extract_cpt = extract_cpt
[ ]:
calib.extract_cpt(max_rings=4)
[ ]:
calib.refine()
[ ]:
ai = calib.geoRef.promote("pyFAI.integrator.azimuthal.AzimuthalIntegrator")
it = ai.integrate1d(center.scattering_data, npt, polarization_factor=polarization, error_model="no", method=("no", "csr", "opencl"))
sc = ai.sigma_clip(center.scattering_data, npt, polarization_factor=polarization, error_model="azimuthal", method=("no", "csr", "opencl"),
thres=0, max_iter=3)
md = ai.medfilt1d_ng(center.scattering_data, npt, polarization_factor=polarization, method=("full", "csr", "opencl"))
[ ]:
fig, ax = subplots()
ax = jupyter.plot1d(it, label="integrate", ax=ax)
ax.errorbar(*sc, alpha=0.8, label="sigma-clip")
ax.plot(*md, alpha=0.8, label="median")
ax.legend()
[ ]:
# Approximate polarization correction needed:
print(f"Approximate polarization factor: {ai.guess_polarization(center.scattering_data, unit='q_nm^-1', target_rad=10):.4f}")
[ ]:
# median filter provides the smoothest curve achievable
rebuilt = ai.calcfrom1d(md.radial,
md.intensity,
detector.shape,
dim1_unit=pyFAI.units.Q_NM,
polarization_factor=polarization)
flat = rebuilt/center.scattering_data
flat[numpy.where(detector.mask)] = numpy.nan
flat[center.scattering_data<=0] = numpy.nan
jupyter.display(flat)
Calculate the approximate correction of the other positions#
[ ]:
dx,dy,dz = numpy.array(data[1].coordinates)-center.coordinates
ai1 = copy.copy(ai)
ai1.poni1 += dz*0.001
ai1.poni2 += dy*0.001
ai1
[ ]:
AbstractCalibration.extract_cpt = extract_cpt
[ ]:
calib1 = AbstractCalibration(data[1].calibration_data, detector.mask, detector, wavelength=wavelength, calibrant=calibrant)
calib1.preprocess()
calib1.data = []
calib1.geoRef = calib1.initgeoRef()
calib1.geoRef.setPyFAI(**ai1.getPyFAI())
[ ]:
calib1.extract_cpt(max_rings=4)
[ ]:
calib1.fixed+=["rot1", "rot2"]
calib1.geoRef.refine3(fix=calib1.fixed)
[ ]:
calib1.geoRef
[ ]:
data[1].ai = pyFAI.load(calib1.geoRef)
data[1].control_points = calib1.peakPicker.points
data[5].ai = pyFAI.load(calib.geoRef)
data[5].control_points = calib.peakPicker.points
Perform the geometry extraction for each of the position:#
[ ]:
for idx in [2,3,4,6,7,8,9]:
dx,dy,dz = numpy.array(data[idx].coordinates)-center.coordinates
ain = copy.copy(ai)
ain.poni1 += dz*0.001
ain.poni2 += dy*0.001
calibn = AbstractCalibration(data[idx].calibration_data, detector.mask, detector, wavelength=wavelength, calibrant=calibrant)
calibn.preprocess()
calibn.data = []
calibn.geoRef = calib1.initgeoRef()
calibn.geoRef.setPyFAI(**ain.getPyFAI())
calibn.extract_cpt(max_rings=4)
calibn.fixed+=["rot1", "rot2"]
calibn.geoRef.refine3(fix=calibn.fixed)
print(f"#### Position {idx} ####")
print(calibn.geoRef)
data[idx].ai = pyFAI.load(calibn.geoRef)
data[idx].control_points = calibn.peakPicker.points
[ ]:
#display scattering:
fig, ax = subplots(3,3, figsize=(12,12))
jupyter.display(data[1].calibration_data, ai=data[1].ai, cp=data[1].control_points, ax=ax[0,2])
jupyter.display(data[2].calibration_data, ai=data[2].ai, cp=data[2].control_points, ax=ax[0,1])
jupyter.display(data[3].calibration_data, ai=data[3].ai, cp=data[3].control_points, ax=ax[0,0])
jupyter.display(data[4].calibration_data, ai=data[4].ai, cp=data[4].control_points, ax=ax[1,0])
jupyter.display(data[5].calibration_data, ai=data[5].ai, cp=data[5].control_points, ax=ax[1,1])
jupyter.display(data[6].calibration_data, ai=data[6].ai, cp=data[6].control_points, ax=ax[1,2])
jupyter.display(data[7].calibration_data, ai=data[7].ai, cp=data[7].control_points, ax=ax[2,2])
jupyter.display(data[8].calibration_data, ai=data[8].ai, cp=data[8].control_points, ax=ax[2,1])
jupyter.display(data[9].calibration_data, ai=data[9].ai, cp=data[9].control_points, ax=ax[2,0])
pass
Extract the flatfield for all positions#
[ ]:
for p in data[1:]:
md = p.ai.medfilt1d(p.scattering_data, npt, polarization_factor=polarization, method=("full", "csr", "opencl"))
rebuilt = p.ai.calcfrom1d(md.radial, md.intensity, detector.shape, dim1_unit=pyFAI.units.Q_NM, polarization_factor=polarization)
flat = rebuilt/p.scattering_data
flat[numpy.where(detector.mask)] = numpy.nan
flat[p.scattering_data<=0] = numpy.nan
p.flat = flat
[ ]:
#display flat:
fig, ax = subplots(3,3, figsize=(12,12))
jupyter.display(data[1].flat, ax=ax[0,2])
jupyter.display(data[2].flat, ax=ax[0,1])
jupyter.display(data[3].flat, ax=ax[0,0])
jupyter.display(data[4].flat, ax=ax[1,0])
jupyter.display(data[5].flat, ax=ax[1,1])
jupyter.display(data[6].flat, ax=ax[1,2])
jupyter.display(data[7].flat, ax=ax[2,2])
jupyter.display(data[8].flat, ax=ax[2,1])
jupyter.display(data[9].flat, ax=ax[2,0])
pass
The final Flatfield is the median of the flats calculated on the 9 positions#
[ ]:
flat_stack = numpy.array([p.flat for p in data[1:]])
flat = numpy.nanmedian(flat_stack, axis=0)
[ ]:
jupyter.display(flat)
[ ]:
fabio.edfimage.EdfImage(data=flat.astype("float32")).write("flat.edf")
[ ]:
numpy.nanmean(flat)
[ ]:
print(f"Total run time: {time.perf_counter()-t0:.3f}s.")