def integrate(json: str,
params: Calibration,
f: Callable[[ndarray], ndarray],
plot_calibrant: bool = False,
save: bool = False,
n: int = 10000,
lst_mask: ndarray = None,
lst_flat: ndarray = None,
to_use: bool = False) -> None:
"""Integrate a file with a json calibration file"""
gonio = pyFAI.goniometer.Goniometer.sload(json)
with File(params.filename, mode='r') as h5file:
images = []
deltas = []
for frame in gen_metadata_idx(h5file, params, to_use=to_use):
images.append(f(frame.image))
deltas.append((frame.delta, ))
mai = gonio.get_mg(deltas)
res = mai.integrate1d(images, n, lst_mask=lst_mask, lst_flat=lst_flat)
if save is True:
try:
os.makedirs(params.basedir)
except os.error:
pass
numpy.savetxt(
os.path.join(params.basedir,
os.path.basename(params.filename) + '.txt'),
numpy.vstack([res.radial, res.intensity]).T)
if plot_calibrant:
calibrant = get_calibrant(params.calibrant, params.wavelength)
jupyter.plot1d(res, calibrant)
else:
jupyter.plot1d(res)
return res
def integrate_mars_tx_tz(json: str,
paramss: List[CalibrationMarsTxTz],
f: Callable[[ndarray], ndarray],
plot_calibrant: bool = False,
save: bool = False,
n: int = 10000,
lst_mask: ndarray = None,
lst_flat: ndarray = None,
to_use: bool = False,
no_plot: bool = False) -> None:
"""Integrate a file with a json calibration file"""
gonio = pyFAI.goniometer.Goniometer.sload(json)
images = []
positions = []
for params in paramss:
with File(params.filename, mode='r') as h5file:
for frame in gen_metadata_idx_mars_tx_tz(h5file,
params,
to_use=to_use):
images.append(f(frame.image))
positions.append((frame.tx, frame.tz))
mai = gonio.get_mg(positions)
res = mai.integrate1d(images, n, lst_mask=lst_mask, lst_flat=lst_flat)
if save is True:
try:
os.makedirs(params.basedir)
except os.error:
pass
ofilename = os.path.join(params.basedir,
os.path.basename(params.filename) + '.txt')
numpy.savetxt(ofilename, numpy.vstack([res.radial, res.intensity]).T)
print("Saves as: {}".format(ofilename))
if no_plot is False:
if plot_calibrant:
calibrant = get_calibrant(params.calibrant, params.wavelength)
jupyter.plot1d(res, calibrant)
else:
jupyter.plot1d(res)
return res
def add_module(name,
zero_pos,
frame_start,
frame_stop):
ds = data[name]
param = {"dist": 0.72,
"poni1": 640*50e-6,
"poni2": 4e-3,
"rot1": 0,
"offset": -get_position(zero_pos),
"scale": 1,
"nrj": nrj}
# Lock enegy for now and a couple of other parameters
bounds = {"nrj": (nrj, nrj),
"dist": (0.7, 0.8),
"poni2": (4e-3, 4e-3),
"rot1": (0, 0),
"scale": (1, 1)}
gonioref = GoniometerRefinement(param,
get_position,
trans,
detector=modules[name],
wavelength=wl,
bounds=bounds)
goniometers[name] = gonioref
for i in range(frame_start, frame_stop):
cp = ControlPoints(calibrant=LaB6, wavelength=wl)
peak = peak_picking(name, i)
if len(peak) != 1:
continue
cp.append([peak[0]], ring=0)
img = (ds[i]).reshape((-1, 1))
sg = gonioref.new_geometry("%s_%04i" % (name, i),
image=img,
metadata=i,
control_points=cp,
calibrant=LaB6)
sg.geometry_refinement.data = numpy.array(cp.getList())
print(gonioref.chi2())
gonioref.refine2()
tths = LaB6.get_2th()
# print(tths)
for i in range(frame_stop, ds.shape[0]):
frame_name = "%s_%04i" % (name, i)
if frame_name in gonioref.single_geometries:
continue
peak = peak_picking(name, i)
ai = gonioref.get_ai(get_position(i))
tth = ai.array_from_unit(unit="2th_rad", scale=False)
tth_low = tth[20]
tth_hi = tth[-20]
ttmin, ttmax = min(tth_low, tth_hi), max(tth_low, tth_hi)
valid_peaks = numpy.logical_and(ttmin <= tths, tths < ttmax)
cnt = valid_peaks.sum()
if (len(peak) == cnt) and cnt > 0:
cp = ControlPoints(calibrant=LaB6, wavelength=wl)
# revert the order of assignment if needed !!
if tth_hi < tth_low:
peak = peak[-1::-1]
for p, r in zip(peak, numpy.where(valid_peaks)[0]):
cp.append([p], ring=r)
img = (ds[i]).reshape((-1, 1))
sg = gonioref.new_geometry(frame_name,
image=img,
metadata=i,
control_points=cp,
calibrant=LaB6)
sg.geometry_refinement.data = numpy.array(cp.getList())
# print(frame_name, len(sg.geometry_refinement.data))
print(" Number of peaks found and used for refinement")
print(sum([len(sg.geometry_refinement.data)
for sg in gonioref.single_geometries.values()]))
gonioref.refine2()
gonioref.set_bounds("poni1", -1, 1)
gonioref.set_bounds("poni2", -1, 1)
gonioref.set_bounds("rot1", -1, 1)
gonioref.set_bounds("scale", 0.9, 1.1)
gonioref.refine2()
mg = gonioref.get_mg(position)
mg.radial_range = (0, 95)
images = [i.reshape(-1, 1) for i in ds]
res_mg = mg.integrate1d(images, 50000)
results[name] = res_mg
LaB6_new = get_calibrant("LaB6")
LaB6_new.wavelength = hc/gonioref.param[-1]*1e-10
p = jupyter.plot1d(res_mg, calibrant=LaB6_new)
p.figure.show()
multigonio.refine2()
# In[65]:
LaB6_new = get_calibrant("LaB6")
LaB6_new.wavelength = 1e-10 * hc / multigonio.param[-1]
print(LaB6, "\n", LaB6_new)
# In[66]:
res = multigonio.integrate([data[ds_names[i]] for i in range(9)])
# In[67]:
ax = jupyter.plot1d(res, calibrant=LaB6_new)
ax.figure.show()
ax.figure.savefig("toto.svg")
# In[68]:
# fig, ax = plt.subplots()
# ax.plot(*calc_fwhm(res, LaB6_new, 10, 95), "o", label="FWHM")
# ax.plot(*calc_peak_error(res, LaB6_new, 10, 95), "o", label="error")
# ax.set_title("Peak shape & error as function of the angle")
# ax.set_xlabel(res.unit.label)
# ax.legend()
# fig.show()
# In[69]:
print("total run time: ", time.time() - start_time)
mg = gonioref0.get_mg(position)
mg.radial_range = (0, 95)
images = [i.reshape(-1, 1) for i in ds]
res_mg = mg.integrate1d(images, 50000)
results = {name: res_mg}
print(results)
# In[20]:
# Plot the integrated pattern vs expected peak positions:
LaB6_new = get_calibrant("LaB6")
LaB6_new.wavelength = hc/gonioref0.param[-1]*1e-10
p = jupyter.plot1d(res_mg, calibrant=LaB6_new)
p.figure.show()
# In[21]:
# Peak profile function based on a bilinear interpolations:
def calc_fwhm(integrate_result, calibrant, tth_min=None, tth_max=None):
"calculate the tth position and FWHM for each peak"
delta = integrate_result.intensity[1:] - integrate_result.intensity[:-1]
maxima = numpy.where(numpy.logical_and(delta[:-1] > 0, delta[1:] < 0))[0]
minima = numpy.where(numpy.logical_and(delta[:-1] < 0, delta[1:] > 0))[0]
maxima += 1
minima += 1
tth = []