def optimize_with_new_images(h5file: File,
calibration: Calibration,
gonioref,
calibrant: pyFAI.calibrant.Calibrant,
indexes: Iterable[int],
pts_per_deg: float = 1) -> None:
"""This function adds new images to the pool of data used for the
refinement. A set of new control points are extractred and a
refinement step is performed at each iteration The last image of
the serie is displayed
"""
sg = None
for frame in gen_metadata_idx(h5file, calibration, indexes):
print()
if frame.label in gonioref.single_geometries:
continue
print(frame.label)
sg = gonioref.new_geometry(frame.label,
image=frame.image,
metadata=frame,
calibrant=calibrant)
print(sg.extract_cp(pts_per_deg=pts_per_deg))
print("*" * 50)
gonioref.refine2()
if sg:
sg.geometry_refinement.set_param(
gonioref.get_ai(sg.get_position()).param) # noqa
jupyter.display(sg=sg)
def optimize_with_new_images(h5file: File,
calibration: Calibration,
gonioref,
calibrant: pyFAI.calibrant.Calibrant,
pts_per_deg: float=1) -> None:
"""This function adds new images to the pool of data used for the
refinement. A set of new control points are extractred and a
refinement step is performed at each iteration The last image of
the serie is displayed
"""
sg = None
for n, frame in enumerate(gen_metadata_all(h5file, calibration)):
print()
base = os.path.splitext(os.path.basename(calibration.filename))[0]
label = base + "_%d" % (frame.idx,)
if label in gonioref.single_geometries:
continue
print(label)
sg = gonioref.new_geometry(label, image=frame.image, metadata=frame,
calibrant=calibrant)
print(sg.extract_cp(pts_per_deg=pts_per_deg))
print("*"*50)
gonioref.refine2()
if sg:
sg.geometry_refinement.set_param(gonioref.get_ai(sg.get_position()).param) # noqa
jupyter.display(sg=sg)
def optimize_with_new_images(h5file: File,
calibration: Calibration,
gonioref,
calibrant: pyFAI.calibrant.Calibrant,
pts_per_deg: float = 1) -> None:
"""This function adds new images to the pool of data used for the
refinement. A set of new control points are extractred and a
refinement step is performed at each iteration The last image of
the serie is displayed
"""
sg = None
for _, frame in enumerate(gen_metadata_all(h5file, calibration)):
print()
base = os.path.splitext(os.path.basename(calibration.filename))[0]
label = base + "_%d" % (frame.idx, )
if label in gonioref.single_geometries:
continue
print(label)
sg = gonioref.new_geometry(label,
image=frame.image,
metadata=frame,
calibrant=calibrant)
print(sg.extract_cp(pts_per_deg=pts_per_deg))
print("*" * 50)
gonioref.refine2()
if sg:
sg.geometry_refinement.set_param(
gonioref.get_ai(sg.get_position()).param) # noqa
jupyter.display(sg=sg)
def show_pyfai_improvement(
self,
before_geometry: pfSingleGeometry,
after_geometry: pfSingleGeometry,
show: bool = True,
):
fig, (ax1, ax2) = plt.subplots(1, 2)
fig.set_size_inches(20, 8)
pfjupyter.display(sg=before_geometry, label="Initial geometry", ax=ax1)
pfjupyter.display(sg=after_geometry, label="After pyFAI fit", ax=ax2)
if show:
plt.show()
else:
fig.savefig(self.user_args.pyfai_improvement, bbox_inches="tight")
plt.close(fig)
def optimize_with_new_images_mars_tx_tz(h5file: File,
calibration: CalibrationMarsTxTz,
gonioref,
calibrant: pyFAI.calibrant.Calibrant,
indexes: Iterable[int],
pts_per_deg: float = 1) -> None:
"""This function adds new images to the pool of data used for the
refinement. A set of new control points are extractred and a
refinement step is performed at each iteration The last image of
the serie is displayed
"""
sg = None
for frame in gen_metadata_idx_mars_tx_tz(h5file, calibration, indexes):
print()
if frame.label in gonioref.single_geometries:
continue
print(frame.label)
sg = gonioref.new_geometry(frame.label,
image=frame.image,
metadata=frame,
calibrant=calibrant)
print(
sg.extract_cp(max_rings=calibration.max_rings,
pts_per_deg=pts_per_deg))
# filter the cp to remove unwanted values.
cp = sg.control_points
cp.pop(0)
sg.geometry_refinement.data = numpy.asarray(cp.getList(),
dtype=numpy.float64)
sg.control_points = cp
print("*" * 50)
gonioref.refine2()
if sg:
sg.geometry_refinement.set_param(
gonioref.get_ai(sg.get_position()).param) # noqa
jupyter.display(sg=sg)
def set_up_figure(self):
"""
Add title and label axes
"""
fig, ax = plt.subplots()
ax = pfjupyter.display(
self.image,
label="Calibrant overlay on experimental image",
ax=ax,
)
ax.set_xlabel("slow position [pixels]")
ax.set_ylabel("fast position [pixels]")
fig.set_size_inches(10, 10)
return fig, ax
def calibration(json: str,
params: Calibration,
indexes: Optional[Iterable[int]] = None) -> None:
"""Do a calibration with a bunch of images"""
# Definition of the geometry refinement: the parameter order is
# the same as the param_names
calibrant = get_calibrant(params.calibrant, params.wavelength)
detector = get_detector(params.detector)
(functions, initial_parameters) = params.functions
parameters = {p.name: p.value for p in initial_parameters}
bounds = {p.name: p.bounds for p in initial_parameters}
param_names = [p.name for p in initial_parameters]
# Let's refine poni1 and poni2 also as function of the distance:
trans_function = GeometryTransformation(param_names=param_names,
pos_names=["delta"],
dist_expr=functions.distance,
poni1_expr=functions.poni1,
poni2_expr=functions.poni2,
rot1_expr=functions.rot1,
rot2_expr=functions.rot2,
rot3_expr=functions.rot3)
def pos_function(frame: CalibrationFrame) -> Tuple[float]:
"""Definition of the function reading the detector position from the
header of the image."""
return (frame.delta, )
gonioref = GoniometerRefinement(
parameters, # initial guess
bounds=bounds,
pos_function=pos_function,
trans_function=trans_function,
detector=detector,
wavelength=params.wavelength)
print("Empty refinement object:")
print(gonioref)
# Let's populate the goniometer refinement object with the know poni
with File(params.filename, mode='r') as h5file:
for frame in gen_metadata_idx(h5file, params, params.idxs):
base = os.path.basename(params.filename)
control_points = os.path.join(
params.basedir, base + "_{:02d}.npt".format(frame.idx)) # noqa
ai = pyFAI.load(
os.path.join(params.basedir,
base + "_{:02d}.poni".format(frame.idx))) # noqa
print(ai)
gonioref.new_geometry(frame.label, frame.image, frame,
control_points, calibrant, ai)
print("Filled refinement object:")
print(gonioref)
print(os.linesep + "\tlabel \t tx")
for k, v in gonioref.single_geometries.items():
print(k, v.get_position())
for g in gonioref.single_geometries.values():
ai = gonioref.get_ai(g.get_position())
print(ai)
for sg in gonioref.single_geometries.values():
jupyter.display(sg=sg)
gonioref.refine2()
for multi in [params]:
with File(multi.filename, mode='r') as h5file:
optimize_with_new_images(h5file,
multi,
gonioref,
calibrant,
indexes,
pts_per_deg=10)
for idx, sg in enumerate(gonioref.single_geometries.values()):
sg.geometry_refinement.set_param(
gonioref.get_ai(sg.get_position()).param) # noqa
jupyter.display(sg=sg)
gonioref.save(json)
def calibration(json: str, params: Calibration) -> None:
"""Do a calibration with a bunch of images"""
# Definition of the geometry refinement: the parameter order is
# the same as the param_names
calibrant = get_calibrant(params.calibrant,
params.wavelength)
detector = get_detector(params.detector)
parameters = {p.name: p.value for p in params.initial_parameters}
bounds = {p.name: p.bounds for p in params.initial_parameters}
param_names = [p.name for p in params.initial_parameters]
# Let's refine poni1 and poni2 also as function of the distance:
trans_function = GeometryTransformation(param_names=param_names,
pos_names=["delta"],
dist_expr="dist",
poni1_expr="poni1", # noqa
poni2_expr="poni2", # noqa
rot1_expr="rot1",
rot2_expr="rot2_scale * delta + rot2_offset", # noqa
rot3_expr="rot3")
def pos_function(frame: CalibrationFrame) -> Tuple[float]:
"""Definition of the function reading the detector position from the
header of the image."""
return (frame.delta,)
gonioref = GoniometerRefinement(parameters, # initial guess
bounds=bounds,
pos_function=pos_function,
trans_function=trans_function,
detector=detector,
wavelength=params.wavelength)
print("Empty refinement object:")
print(gonioref)
# Let's populate the goniometer refinement object with the know poni
with File(params.filename, mode='r') as h5file:
for frame in gen_metadata_idx(h5file, params):
base = os.path.splitext(os.path.basename(params.filename))[0]
label = base + "_%d" % (frame.idx,)
control_points = params.filename + "_{:02d}.npt".format(frame.idx)
ai = pyFAI.load(params.filename + "_{:02d}.poni".format(frame.idx))
print(ai)
gonioref.new_geometry(label, frame.image, frame,
control_points, calibrant, ai)
print("Filled refinement object:")
print(gonioref)
print(os.linesep + "\tlabel \t tx")
for k, v in gonioref.single_geometries.items():
print(k, v.get_position())
for g in gonioref.single_geometries.values():
ai = gonioref.get_ai(g.get_position())
print(ai)
for sg in gonioref.single_geometries.values():
jupyter.display(sg=sg)
gonioref.refine2()
for multi in [params]:
with File(multi.filename, mode='r') as h5file:
optimize_with_new_images(h5file, multi, gonioref, calibrant)
for idx, sg in enumerate(gonioref.single_geometries.values()):
sg.geometry_refinement.set_param(gonioref.get_ai(sg.get_position()).param) # noqa
jupyter.display(sg=sg)
gonioref.save(json)
import time
from matplotlib.pyplot import subplots
from pyFAI.gui import jupyter
import pyFAI
import fabio
from pyFAI.test.utilstest import UtilsTest
from pyFAI.calibrant import CALIBRANT_FACTORY
from pyFAI.goniometer import SingleGeometry
print(pyFAI.version)
start_time = time.perf_counter()
# %%
# In this example, we will re-use one of the image used int the test-suite
filename = '/Users/rbelisle/Desktop/LaB6_det315_3s_01221009_0001.tif'
frame = fabio.open(filename).data
# and now display the image
ax = jupyter.display(frame)
# %%
# This allow to measure approximatively the position of the beam center ...
x = 1500 # x-coordinate of the beam-center in pixels
y = 2750 # y-coordinate of the beam-center in pixels
d = 315 # This is the distance in mm (unit used by Fit2d)
wl = 0.9762e-10 # The wavelength is 1 Å
# %%
# Definition of the detector and of the calibrant:
pilatus = pyFAI.detectors.Detector(pixel1=73.242e-6,
pixel2=73.242e-6,
max_shape=(.225, .225))
behenate = CALIBRANT_FACTORY("LaB6")
behenate.wavelength = wl
behenate
# %%