def q_integrate(
integrator: AzimuthalIntegrator,
data: np.ndarray,
npt: int = 1000,
polz_factor: float = 0,
unit: Union[str, units.Unit] = "q_A^-1",
radial_range: Tuple[float, float] = None,
azimuth_range: Tuple[float, float] = None,
mask: np.ndarray = None,
dark: np.ndarray = None,
flat: np.ndarray = None,
method: str = 'splitbbox',
normalization_factor: float = 1,
) -> Tuple[np.ndarray, np.ndarray]:
q, I = integrator.integrate1d(data=data,
npt=npt,
radial_range=radial_range,
azimuth_range=azimuth_range,
mask=mask,
polarization_factor=polz_factor,
dark=dark,
flat=flat,
method=method,
unit=unit,
normalization_factor=normalization_factor)
return q, I
def get_soq(Isaxs, mask, setup, Vsaxs=None, nbins=1000):
ai = AzimuthalIntegrator(dist=setup['distance'],
pixel1=setup['pix_size'][0] * 1e-6,
pixel2=setup['pix_size'][1] * 1e-6)
ai.setFit2D(setup['distance'] * 1000, setup['ctr'][0], setup['ctr'][1])
ai.wavelength = setup['lambda'] * 1e-10
if Vsaxs is None:
q, ii, e = ai.integrate1d(Isaxs,
nbins,
mask=~(mask.astype(bool)),
unit='q_nm^-1',
error_model='poisson')
else:
q, ii, e = ai.integrate1d(Isaxs,
nbins,
mask=~(mask.astype(bool)),
unit='q_nm^-1',
variance=Vsaxs)
return q, ii, e
def naive_sdd(data: np.ndarray,
azimuthal_integrator: AzimuthalIntegrator,
calibrant: calibrants = calibrant.get_calibrant("AgBh"),
mask: np.ndarray=None,
wavelength_override: float = None,
npts: int = 2000) -> AzimuthalIntegrator:
kwargs = {}
if mask is not None:
kwargs['mask'] = mask
# TODO: add a type that is a special parameter-tree item allowing enable/disable the parameter in the gui
if wavelength_override:
azimuthal_integrator.set_wavelength(wavelength_override)
# slice into the first index as long as there's higher dimensionality
while len(data.shape) > 2:
data = data[0]
# Un-calibrated azimuthal integration
r, radialprofile = azimuthal_integrator.integrate1d(np.asarray(data), npts, unit='r_mm', **kwargs)
# find peaks
peaks = np.array(find_peaks(np.arange(len(radialprofile)), radialprofile)).T
# get best peak
bestpeak = None
for peak in peaks:
if peak[0] > 15 and not np.isinf(peak[1]): ####This thresholds the minimum sdd which is acceptable
bestpeak = peak[0]
# print peak
break
# identify order of selected peak
best_order = (0, 0)
for i in range(1, 6):
peak_ratios = ((peaks[:, 0] / (np.arange(len(peaks)))) / (bestpeak / (i + 1)))
order = np.sum(np.logical_and(peak_ratios < 1.1, 0.9 < peak_ratios))
if order > best_order[0]:
best_order = (order, i)
calibrant1stpeak = calibrant.dSpacing[best_order[1]]
# Calculate sample to detector distance for lowest q peak
tth = 2 * np.arcsin(0.5 * azimuthal_integrator.wavelength / calibrant1stpeak / 1.e-10)
tantth = np.tan(tth)
sdd = r[int(round(bestpeak))] / tantth
# set sdd back on azimuthal integrator
fit2dcal = azimuthal_integrator.getFit2D()
fit2dcal['directDist'] = sdd
azimuthal_integrator.setFit2D(**fit2dcal)
return azimuthal_integrator
def run(self):
ai = AzimuthalIntegrator(dist=self.__distance,
poni1=self.__poni1,
poni2=self.__poni2,
rot1=self.__rotation1,
rot2=self.__rotation2,
rot3=self.__rotation3,
detector=self.__detector,
wavelength=self.__wavelength)
# FIXME error model, method
self.__result1d = ai.integrate1d(
data=self.__image,
npt=self.__nPointsRadial,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
self.__result2d = ai.integrate2d(
data=self.__image,
npt_rad=self.__nPointsRadial,
npt_azim=self.__nPointsAzimuthal,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
try:
self.__directDist = ai.getFit2D()["directDist"]
except Exception:
# The geometry could not fit this param
_logger.debug("Backtrace", exc_info=True)
self.__directDist = None
if self.__calibrant:
rings = self.__calibrant.get_2th()
try:
rings = utils.from2ThRad(rings, self.__radialUnit,
self.__wavelength, self.__directDist)
except ValueError:
message = "Convertion to unit %s not supported. Ring marks ignored"
_logger.warning(message, self.__radialUnit)
rings = []
# Filter the rings which are not part of the result
rings = filter(
lambda x: self.__result1d.radial[0] <= x <= self.__result1d.
radial[-1], rings)
rings = list(rings)
else:
rings = []
self.__ringAngles = rings
self.__ai = ai
def azimuthal_integrate(
z,
origin,
detector_distance,
detector,
wavelength,
size_1d,
unit,
kwargs_for_integrator,
kwargs_for_integrate1d,
):
"""Calculate the azimuthal integral of z around a determined origin.
This method is used for signals where the origin is iterated, compared to
azimuthal_integrate_fast which is used when the origin in the data is
constant.
Parameters
----------
z : np.array()
Two-dimensional data array containing the signal.
origin : np.array()
A size 2 numpy array containing the position of the origin.
detector_distance : float
Detector distance in meters passed to pyFAI AzimuthalIntegrator.
detector : pyFAI.detectors.Detector object
A pyFAI detector used for the AzimuthalIntegrator.
wavelength : float
The electron wavelength in meters. Used by pyFAI AzimuthalIntegrator.
size_1d : int
The size of the returned 1D signal. (i.e. number of pixels in the 1D
azimuthal integral.)
unit : str
The unit for for PyFAI integrate1d.
*args :
Arguments to be passed to AzimuthalIntegrator.
**kwargs :
Keyword arguments to be passed to AzimuthalIntegrator.
Returns
-------
tth : np.array()
One-dimensional scattering vector axis of z.
I : np.array()
One-dimensional azimuthal integral of z.
"""
p1, p2 = origin[0] * detector.pixel1, origin[1] * detector.pixel2
ai = AzimuthalIntegrator(dist=detector_distance,
poni1=p1,
poni2=p2,
detector=detector,
wavelength=wavelength,
**kwargs_for_integrator)
tth, I = ai.integrate1d(z, size_1d, unit=unit, **kwargs_for_integrate1d)
return tth, I
def process(
*,
raw_img: np.ndarray,
ai: AzimuthalIntegrator,
dk_img: np.ndarray = None,
dk_sub_bg_img: np.ndarray = None,
integ_setting: dict = None,
mask_setting: dict = None,
pdfgetx_setting: dict = None,
) -> dict:
"""The function to process the data from event."""
data = dict()
# dark subtraction
if dk_img is None:
dk_img = np.zeros_like(raw_img)
dk_sub_img = np.subtract(raw_img, dk_img)
data.update({"dk_sub_image": dk_sub_img})
# background subtraction
if dk_sub_bg_img is None:
dk_sub_bg_img = np.zeros_like(dk_sub_img)
bg_sub_img = np.subtract(dk_sub_img, dk_sub_bg_img)
data.update({"bg_sub_image": bg_sub_img})
# auto masking
mask, _ = integ.auto_mask(bg_sub_img, ai, mask_setting=mask_setting)
data.update({"mask": mask})
# integration
x, y = ai.integrate1d(bg_sub_img, mask=mask, **integ_setting)
chi_max_ind = np.argmax(y)
data.update({
"chi_Q": x,
"chi_I": y,
"chi_max": y[chi_max_ind],
"chi_argmax": x[chi_max_ind]
})
# transformation
pdfconfig = PDFConfig(dataformat="QA", **pdfgetx_setting)
pdfgetter = PDFGetter(pdfconfig)
pdfgetter(x, y)
iq, sq, fq, gr = pdfgetter.iq, pdfgetter.sq, pdfgetter.fq, pdfgetter.gr
gr_max_ind = np.argmax(gr[1])
data.update({
"iq_Q": iq[0],
"iq_I": iq[1],
"sq_Q": sq[0],
"sq_S": sq[1],
"fq_Q": fq[0],
"fq_F": fq[1],
"gr_r": gr[0],
"gr_G": gr[1],
"gr_max": gr[1][gr_max_ind],
"gr_argmax": gr[0][gr_max_ind]
})
return data
def test_AzimuthalIntegrator_pickle():
import dill
import numpy as np
from pyFAI.azimuthalIntegrator import AzimuthalIntegrator
det = pyFAI.detectors.detector_factory('pilatus2m')
ai = AzimuthalIntegrator(detector=det)
ai.set_wavelength(.1)
spectra = ai.integrate1d(np.ones(det.shape), 1000) # force lut generation
dump = dumps(ai)
newai = loads(dump)
assert np.array_equal(newai.integrate1d(np.ones(det.shape), 1000), spectra)
assert newai.detector.shape == (1679, 1475)
def naive_sdd(data: np.ndarray,
mask: np.ndarray,
calibrant: calibrant.Calibrant,
azimuthal_integrator: AzimuthalIntegrator,
npts: int = 2000) -> AzimuthalIntegrator:
# Un-calibrated azimuthal integration
r, radialprofile = azimuthal_integrator.integrate1d(data,
npts,
unit='r_mm',
mask=mask)
# find peaks
peaks = np.array(find_peaks(np.arange(len(radialprofile)),
radialprofile)).T
# get best peak
bestpeak = None
for peak in peaks:
if peak[0] > 15 and not np.isinf(
peak[1]
): ####This thresholds the minimum sdd which is acceptable
bestpeak = peak[0]
# print peak
break
# identify order of selected peak
best_order = (0, 0)
for i in range(1, 6):
peak_ratios = ((peaks[:, 0] / (np.arange(len(peaks)))) / (bestpeak /
(i + 1)))
order = np.sum(np.logical_and(peak_ratios < 1.1, 0.9 < peak_ratios))
if order > best_order[0]:
best_order = (order, i)
calibrant1stpeak = calibrant.dSpacing[best_order[1]]
# Calculate sample to detector distance for lowest q peak
tth = 2 * np.arcsin(
0.5 * azimuthal_integrator.wavelength / calibrant1stpeak / 1.e-10)
tantth = np.tan(tth)
sdd = r[int(round(bestpeak))] / tantth
# set sdd back on azimuthal integrator
fit2dcal = azimuthal_integrator.getFit2D()
fit2dcal['directDist'] = sdd
azimuthal_integrator.setFit2D(**fit2dcal)
return azimuthal_integrator
def get_soq(Isaxs, mask, setup, Vsaxs=None, nbins=1000):
ai = AzimuthalIntegrator(
dist=setup["distance"],
pixel1=setup["pix_size"][0] * 1e-6,
pixel2=setup["pix_size"][1] * 1e-6,
)
ai.setFit2D(setup["distance"] * 1000, setup["ctr"][0], setup["ctr"][1])
ai.wavelength = setup["lambda"] * 1e-10
if Vsaxs is None:
q, ii, e = ai.integrate1d(
Isaxs,
nbins,
mask=~(mask.astype(bool)),
unit="q_nm^-1",
error_model="poisson",
)
else:
q, ii, e = ai.integrate1d(Isaxs,
nbins,
mask=~(mask.astype(bool)),
unit="q_nm^-1",
variance=Vsaxs)
return q, ii, e
def run(self):
ai = AzimuthalIntegrator(
dist=self.__distance,
poni1=self.__poni1,
poni2=self.__poni2,
rot1=self.__rotation1,
rot2=self.__rotation2,
rot3=self.__rotation3,
detector=self.__detector,
wavelength=self.__wavelength)
numberPoint1D = 1024
numberPointRadial = 400
numberPointAzimuthal = 360
# FIXME error model, method
self.__result1d = ai.integrate1d(
data=self.__image,
npt=numberPoint1D,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
self.__result2d = ai.integrate2d(
data=self.__image,
npt_rad=numberPointRadial,
npt_azim=numberPointAzimuthal,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
if self.__calibrant:
rings = self.__calibrant.get_2th()
rings = filter(lambda x: x <= self.__result1d.radial[-1], rings)
rings = list(rings)
try:
rings = utils.from2ThRad(rings, self.__radialUnit, self.__wavelength, ai)
except ValueError:
message = "Convertion to unit %s not supported. Ring marks ignored"
_logger.warning(message, self.__radialUnit)
rings = []
else:
rings = []
self.__ringAngles = rings
def run(self):
ai = AzimuthalIntegrator(dist=self.__distance,
poni1=self.__poni1,
poni2=self.__poni2,
rot1=self.__rotation1,
rot2=self.__rotation2,
rot3=self.__rotation3,
detector=self.__detector,
wavelength=self.__wavelength)
numberPoint1D = 1024
numberPointRadial = 400
numberPointAzimuthal = 360
# FIXME error model, method
self.__result1d = ai.integrate1d(
data=self.__image,
npt=numberPoint1D,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
self.__result2d = ai.integrate2d(
data=self.__image,
npt_rad=numberPointRadial,
npt_azim=numberPointAzimuthal,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
if self.__calibrant:
rings = self.__calibrant.get_2th()
rings = filter(lambda x: x <= self.__result1d.radial[-1], rings)
rings = list(rings)
try:
rings = utils.from2ThRad(rings, self.__radialUnit,
self.__wavelength, ai)
except ValueError:
message = "Convertion to unit %s not supported. Ring marks ignored"
_logger.warning(message, self.__radialUnit)
rings = []
else:
rings = []
self.__ringAngles = rings
def integrate(img: ndarray,
ai: AzimuthalIntegrator,
mask: ndarray = None,
integ_setting: dict = None) -> Tuple[ndarray, dict]:
"""Use AzimuthalIntegrator to integrate the image.
Parameters
----------
img : ndarray
The 2D diffraction image array.
ai : AzimuthalIntegrator
The AzimuthalIntegrator instance.
mask : ndarray
The mask as a 0 and 1 array. 0 pixels are good pixels, 1 pixels are masked out.
integ_setting : dict
The user's modification to integration settings.
Returns
-------
chi : ndarray
The chi data. The first row is bin centers and the second row is the average intensity in bins.
_integ_setting: dict
The whole integration setting.
"""
# merge integrate setting
_integ_setting = INTEG_SETTING.copy()
if integ_setting is not None:
_integ_setting.update(integ_setting)
# integrate
xy = ai.integrate1d(img, mask=mask, **_integ_setting)
chi = np.stack(xy)
return chi, _integ_setting
def run(args):
FILE = args.INPUT
if np.shape(FILE) == (1, ):
FILE = np.sort(glob.glob(str(FILE[0])))
SAVE_PATH = os.getcwd()
if args.OUTPUT:
SAVE_PATH = args.OUTPUT
else:
print(
'!!! Warning files will be saved in the current folder because no output was defined.'
)
### Read json file ###
jsonParam = readJson(args.JSON)
### Integration of FILE ###
azimutalIntegrator = AzimuthalIntegrator(
dist=jsonParam['dist'],
poni1=jsonParam['poni1'],
poni2=jsonParam['poni2'],
rot1=jsonParam['rot1'],
rot2=jsonParam['rot2'],
rot3=jsonParam['rot3'],
pixel1=jsonParam['detector_config']['pixel1'],
pixel2=jsonParam['detector_config']['pixel2'],
detector=jsonParam['detector'],
wavelength=jsonParam['wavelength'])
#dark = np.array(fabio.open(jsonParam['dark_current']).data)
#flat = np.array(fabio.open(jsonParam['flat_field']).data)
mask = np.array(fabio.open(jsonParam['mask_file']).data)
for i in range(len(FILE)):
dataFile = np.array(fabio.open(FILE[i]).data)
dataBragg, dataPowder = azimutalIntegrator.separate(
dataFile,
npt_rad=1024,
npt_azim=512,
unit=jsonParam['unit'],
method='splitpixel',
percentile=Threshold,
mask=None,
restore_mask=True)
dataXP, dataYP = azimutalIntegrator.integrate1d(
dataPowder,
int(jsonParam['nbpt_rad']),
filename=None,
correctSolidAngle=jsonParam['do_solid_angle'],
variance=None,
error_model=None,
radial_range=(float(jsonParam['radial_range_min']),
float(jsonParam['radial_range_max'])),
azimuth_range=None,
mask=mask,
dummy=jsonParam['do_dummy'],
delta_dummy=jsonParam['delta_dummy'],
polarization_factor=None,
method=jsonParam['method'],
unit=jsonParam['unit'],
safe=True,
profile=False,
all=False,
metadata=None)
dataXB, dataYB = azimutalIntegrator.integrate1d(
dataBragg,
int(jsonParam['nbpt_rad']),
filename=None,
correctSolidAngle=jsonParam['do_solid_angle'],
variance=None,
error_model=None,
radial_range=(float(jsonParam['radial_range_min']),
float(jsonParam['radial_range_max'])),
azimuth_range=None,
mask=mask,
dummy=jsonParam['do_dummy'],
delta_dummy=jsonParam['delta_dummy'],
polarization_factor=None,
method=jsonParam['method'],
unit=jsonParam['unit'],
safe=True,
profile=False,
all=False,
metadata=None)
dataXO, dataYO = azimutalIntegrator.integrate1d(
dataFile,
int(jsonParam['nbpt_rad']),
filename=None,
correctSolidAngle=jsonParam['do_solid_angle'],
variance=None,
error_model=None,
radial_range=(float(jsonParam['radial_range_min']),
float(jsonParam['radial_range_max'])),
azimuth_range=None,
mask=mask,
dummy=jsonParam['do_dummy'],
delta_dummy=jsonParam['delta_dummy'],
polarization_factor=None,
method=jsonParam['method'],
unit=jsonParam['unit'],
safe=True,
profile=False,
all=False,
metadata=None)
# Display #
currentFILE = FILE[i]
if args.DISPLAY:
ax1 = plt.subplot(2, 3, 1)
ax1.imshow(dataFile, interpolation=None)
ax1.set_title('Full data')
ax2 = plt.subplot(2, 3, 2, sharex=ax1, sharey=ax1)
ax2.imshow(dataBragg, interpolation=None)
ax2.set_title('Bragg contribution')
ax3 = plt.subplot(2, 3, 3, sharex=ax1, sharey=ax1)
ax3.imshow(dataPowder, interpolation=None)
ax3.set_title('Powder contribution')
ax4 = plt.subplot(2, 1, 2)
ax4.plot(dataXP, dataYP, label='Powder contribution')
ax4.plot(dataXB, dataYB, label='Bragg contribution')
ax4.plot(dataXO, dataYO, label='Full')
ax4.set_title('Integrated data')
ax4.set_xlabel(r'$Q (\AA^{-1})$', fontsize=11)
ax4.set_ylabel(r'$I(A.U.)$', fontsize=11)
plt.legend()
plt.show()
# Save Tif File #
if args.TIFSAVE:
Image.fromarray(dataBragg).save(currentFILE[:-4] + '_THR' +
str(Threshold) + '_bragg.tif')
Image.fromarray(dataPowder).save(currentFILE[:-4] + '_THR' +
str(Threshold) + '_powder.tif')
# Save integrated data #
f_out_dataPowder = open(
currentFILE[:-4] + '_THR' + str(Threshold) + '_powder.dat', 'w')
for j in range(0, np.size(dataXP, 0)):
f_out_dataPowder.writelines('%07f' % dataXP[j] + ' ' +
'%04f' % dataYP[j] + '\n')
f_out_dataPowder.close()
f_out_dataBragg = open(
currentFILE[:-4] + '_THR' + str(Threshold) + '_bragg.dat', 'w')
for j in range(0, np.size(dataXB, 0)):
f_out_dataBragg.writelines('%07f' % dataXB[j] + ' ' +
'%04f' % dataYB[j] + '\n')
f_out_dataBragg.close()
f_out_dataFull = open(
currentFILE[:-4] + '_THR' + str(Threshold) + '_full.dat', 'w')
for j in range(0, np.size(dataXO, 0)):
f_out_dataFull.writelines('%07f' % dataXO[j] + ' ' +
'%04f' % dataYO[j] + '\n')
f_out_dataFull.close()
class CalibrationModel(QtCore.QObject):
def __init__(self, img_model=None):
super(CalibrationModel, self).__init__()
"""
:param img_model:
:type img_model: ImgModel
"""
self.img_model = img_model
self.points = []
self.points_index = []
self.pattern_geometry = AzimuthalIntegrator()
self.pattern_geometry_img_shape = None
self.cake_geometry = None
self.cake_geometry_img_shape = None
self.calibrant = Calibrant()
self.pattern_geometry.wavelength = 0.3344e-10
self.start_values = {'dist': 200e-3,
'wavelength': 0.3344e-10,
'pixel_width': 79e-6,
'pixel_height': 79e-6,
'polarization_factor': 0.99}
self.orig_pixel1 = 79e-6
self.orig_pixel2 = 79e-6
self.fit_wavelength = False
self.fit_distance = True
self.fit_poni1 = True
self.fit_poni2 = True
self.fit_rot1 = True
self.fit_rot2 = True
self.fit_rot3 = True
self.is_calibrated = False
self.use_mask = False
self.filename = ''
self.calibration_name = 'None'
self.polarization_factor = 0.99
self.supersampling_factor = 1
self.correct_solid_angle = True
self._calibrants_working_dir = calibrants_path
self.distortion_spline_filename = None
self.tth = np.linspace(0, 25)
self.int = np.sin(self.tth)
self.num_points = len(self.int)
self.cake_img = np.zeros((2048, 2048))
self.cake_tth = None
self.cake_azi = None
self.peak_search_algorithm = None
def find_peaks_automatic(self, x, y, peak_ind):
"""
Searches peaks by using the Massif algorithm
:param float x:
x-coordinate in pixel - should be from original image (not supersampled x-coordinate)
:param float y:
y-coordinate in pixel - should be from original image (not supersampled y-coordinate)
:param peak_ind:
peak/ring index to which the found points will be added
:return:
array of points found
"""
massif = Massif(self.img_model._img_data)
cur_peak_points = massif.find_peaks((int(np.round(x)), int(np.round(y))), stdout=DummyStdOut())
if len(cur_peak_points):
self.points.append(np.array(cur_peak_points))
self.points_index.append(peak_ind)
return np.array(cur_peak_points)
def find_peak(self, x, y, search_size, peak_ind):
"""
Searches a peak around the x,y position. It just searches for the maximum value in a specific search size.
:param int x:
x-coordinate in pixel - should be from original image (not supersampled x-coordinate)
:param int y:
y-coordinate in pixel - should be form original image (not supersampled y-coordinate)
:param search_size:
the length of the search rectangle in pixels in all direction in which the algorithm searches for
the maximum peak
:param peak_ind:
peak/ring index to which the found points will be added
:return:
point found (as array)
"""
left_ind = int(np.round(x - search_size * 0.5))
if left_ind < 0:
left_ind = 0
top_ind = int(np.round(y - search_size * 0.5))
if top_ind < 0:
top_ind = 0
search_array = self.img_model.img_data[left_ind:(left_ind + search_size), top_ind:(top_ind + search_size)]
x_ind, y_ind = np.where(search_array == search_array.max())
x_ind = x_ind[0] + left_ind
y_ind = y_ind[0] + top_ind
self.points.append(np.array([x_ind, y_ind]))
self.points_index.append(peak_ind)
return np.array([np.array((x_ind, y_ind))])
def clear_peaks(self):
self.points = []
self.points_index = []
def remove_last_peak(self):
if self.points:
num_points = int(self.points[-1].size/2) # each peak is x, y so length is twice as number of peaks
self.points.pop(-1)
self.points_index.pop(-1)
return num_points
def create_cake_geometry(self):
self.cake_geometry = AzimuthalIntegrator(splineFile=self.distortion_spline_filename)
pyFAI_parameter = self.pattern_geometry.getPyFAI()
pyFAI_parameter['polarization_factor'] = self.polarization_factor
pyFAI_parameter['wavelength'] = self.pattern_geometry.wavelength
self.cake_geometry.setPyFAI(dist=pyFAI_parameter['dist'],
poni1=pyFAI_parameter['poni1'],
poni2=pyFAI_parameter['poni2'],
rot1=pyFAI_parameter['rot1'],
rot2=pyFAI_parameter['rot2'],
rot3=pyFAI_parameter['rot3'],
pixel1=pyFAI_parameter['pixel1'],
pixel2=pyFAI_parameter['pixel2'])
self.cake_geometry.wavelength = pyFAI_parameter['wavelength']
def setup_peak_search_algorithm(self, algorithm, mask=None):
"""
Initializes the peak search algorithm on the current image
:param algorithm:
peak search algorithm used. Possible algorithms are 'Massif' and 'Blob'
:param mask:
if a mask is used during the process this is provided here as a 2d array for the image.
"""
if algorithm == 'Massif':
self.peak_search_algorithm = Massif(self.img_model.raw_img_data)
elif algorithm == 'Blob':
if mask is not None:
self.peak_search_algorithm = BlobDetection(self.img_model.raw_img_data * mask)
else:
self.peak_search_algorithm = BlobDetection(self.img_model.raw_img_data)
self.peak_search_algorithm.process()
else:
return
def search_peaks_on_ring(self, ring_index, delta_tth=0.1, min_mean_factor=1,
upper_limit=55000, mask=None):
"""
This function is searching for peaks on an expected ring. It needs an initial calibration
before. Then it will search for the ring within some delta_tth and other parameters to get
peaks from the calibrant.
:param ring_index: the index of the ring for the search
:param delta_tth: search space around the expected position in two theta
:param min_mean_factor: a factor determining the minimum peak intensity to be picked up. it is based
on the mean value of the search area defined by delta_tth. Pick a large value
for larger minimum value and lower for lower minimum value. Therefore, a smaller
number is more prone to picking up noise. typical values like between 1 and 3.
:param upper_limit: maximum intensity for the peaks to be picked
:param mask: in case the image has to be masked from certain areas, it need to be given here. Default is None.
The mask should be given as an 2d array with the same dimensions as the image, where 1 denotes a
masked pixel and all others should be 0.
"""
self.reset_supersampling()
if not self.is_calibrated:
return
# transform delta from degree into radians
delta_tth = delta_tth / 180.0 * np.pi
# get appropriate two theta value for the ring number
tth_calibrant_list = self.calibrant.get_2th()
if ring_index >= len(tth_calibrant_list):
raise NotEnoughSpacingsInCalibrant()
tth_calibrant = np.float(tth_calibrant_list[ring_index])
# get the calculated two theta values for the whole image
tth_array = self.pattern_geometry.twoThetaArray(self.img_model._img_data.shape)
# create mask based on two_theta position
ring_mask = abs(tth_array - tth_calibrant) <= delta_tth
if mask is not None:
mask = np.logical_and(ring_mask, np.logical_not(mask))
else:
mask = ring_mask
# calculate the mean and standard deviation of this area
sub_data = np.array(self.img_model._img_data.ravel()[np.where(mask.ravel())], dtype=np.float64)
sub_data[np.where(sub_data > upper_limit)] = np.NaN
mean = np.nanmean(sub_data)
std = np.nanstd(sub_data)
# set the threshold into the mask (don't detect very low intensity peaks)
threshold = min_mean_factor * mean + std
mask2 = np.logical_and(self.img_model._img_data > threshold, mask)
mask2[np.where(self.img_model._img_data > upper_limit)] = False
size2 = mask2.sum(dtype=int)
keep = int(np.ceil(np.sqrt(size2)))
try:
sys.stdout = DummyStdOut
res = self.peak_search_algorithm.peaks_from_area(mask2, Imin=mean - std, keep=keep)
sys.stdout = sys.__stdout__
except IndexError:
res = []
# Store the result
if len(res):
self.points.append(np.array(res))
self.points_index.append(ring_index)
self.set_supersampling()
self.pattern_geometry.reset()
def set_calibrant(self, filename):
self.calibrant = Calibrant()
self.calibrant.load_file(filename)
self.pattern_geometry.calibrant = self.calibrant
def set_start_values(self, start_values):
self.start_values = start_values
self.polarization_factor = start_values['polarization_factor']
def calibrate(self):
self.pattern_geometry = GeometryRefinement(self.create_point_array(self.points, self.points_index),
dist=self.start_values['dist'],
wavelength=self.start_values['wavelength'],
pixel1=self.start_values['pixel_width'],
pixel2=self.start_values['pixel_height'],
calibrant=self.calibrant,
splineFile=self.distortion_spline_filename)
self.orig_pixel1 = self.start_values['pixel_width']
self.orig_pixel2 = self.start_values['pixel_height']
self.refine()
self.create_cake_geometry()
self.is_calibrated = True
self.calibration_name = 'current'
self.set_supersampling()
# reset the integrator (not the geometric parameters)
self.pattern_geometry.reset()
def refine(self):
self.reset_supersampling()
self.pattern_geometry.data = self.create_point_array(self.points, self.points_index)
fix = ['wavelength']
if self.fit_wavelength:
fix = []
if not self.fit_distance:
fix.append('dist')
if not self.fit_poni1:
fix.append('poni1')
if not self.fit_poni2:
fix.append('poni2')
if not self.fit_rot1:
fix.append('rot1')
if not self.fit_rot2:
fix.append('rot2')
if not self.fit_rot3:
fix.append('rot3')
if self.fit_wavelength:
self.pattern_geometry.refine2()
self.pattern_geometry.refine2_wavelength(fix=fix)
self.create_cake_geometry()
self.set_supersampling()
# reset the integrator (not the geometric parameters)
self.pattern_geometry.reset()
def integrate_1d(self, num_points=None, mask=None, polarization_factor=None, filename=None,
unit='2th_deg', method='csr'):
if np.sum(mask) == self.img_model.img_data.shape[0] * self.img_model.img_data.shape[1]:
# do not perform integration if the image is completely masked...
return self.tth, self.int
if self.pattern_geometry_img_shape != self.img_model.img_data.shape:
# if cake geometry was used on differently shaped image before the azimuthal integrator needs to be reset
self.pattern_geometry.reset()
self.pattern_geometry_img_shape = self.img_model.img_data.shape
if polarization_factor is None:
polarization_factor = self.polarization_factor
if num_points is None:
num_points = self.calculate_number_of_pattern_points(2)
self.num_points = num_points
t1 = time.time()
if unit is 'd_A':
try:
self.tth, self.int = self.pattern_geometry.integrate1d(self.img_model.img_data, num_points,
method=method,
unit='2th_deg',
mask=mask,
polarization_factor=polarization_factor,
correctSolidAngle=self.correct_solid_angle,
filename=filename)
except NameError:
self.tth, self.int = self.pattern_geometry.integrate1d(self.img_model.img_data, num_points,
method='csr',
unit='2th_deg',
mask=mask,
polarization_factor=polarization_factor,
correctSolidAngle=self.correct_solid_angle,
filename=filename)
self.tth = self.pattern_geometry.wavelength / (2 * np.sin(self.tth / 360 * np.pi)) * 1e10
self.int = self.int
else:
try:
self.tth, self.int = self.pattern_geometry.integrate1d(self.img_model.img_data, num_points,
method=method,
unit=unit,
mask=mask,
polarization_factor=polarization_factor,
correctSolidAngle=self.correct_solid_angle,
filename=filename)
except NameError:
self.tth, self.int = self.pattern_geometry.integrate1d(self.img_model.img_data, num_points,
method='csr',
unit=unit,
mask=mask,
polarization_factor=polarization_factor,
correctSolidAngle=self.correct_solid_angle,
filename=filename)
logger.info('1d integration of {0}: {1}s.'.format(os.path.basename(self.img_model.filename), time.time() - t1))
ind = np.where((self.int > 0) & (~np.isnan(self.int)))
self.tth = self.tth[ind]
self.int = self.int[ind]
return self.tth, self.int
def integrate_2d(self, mask=None, polarization_factor=None, unit='2th_deg', method='csr',
rad_points=None, azimuth_points=360,
azimuth_range=None):
if polarization_factor is None:
polarization_factor = self.polarization_factor
if self.cake_geometry_img_shape != self.img_model.img_data.shape:
# if cake geometry was used on differently shaped image before the azimuthal integrator needs to be reset
self.cake_geometry.reset()
self.cake_geometry_img_shape = self.img_model.img_data.shape
if rad_points is None:
rad_points = self.calculate_number_of_pattern_points(2)
self.num_points = rad_points
t1 = time.time()
res = self.cake_geometry.integrate2d(self.img_model.img_data, rad_points, azimuth_points,
azimuth_range=azimuth_range,
method=method,
mask=mask,
unit=unit,
polarization_factor=polarization_factor,
correctSolidAngle=self.correct_solid_angle)
logger.info('2d integration of {0}: {1}s.'.format(os.path.basename(self.img_model.filename), time.time() - t1))
self.cake_img = res[0]
self.cake_tth = res[1]
self.cake_azi = res[2]
return self.cake_img
def create_point_array(self, points, points_ind):
res = []
for i, point_list in enumerate(points):
if point_list.shape == (2,):
res.append([point_list[0], point_list[1], points_ind[i]])
else:
for point in point_list:
res.append([point[0], point[1], points_ind[i]])
return np.array(res)
def get_point_array(self):
return self.create_point_array(self.points, self.points_index)
def get_calibration_parameter(self):
pyFAI_parameter = self.pattern_geometry.getPyFAI()
pyFAI_parameter['polarization_factor'] = self.polarization_factor
try:
fit2d_parameter = self.pattern_geometry.getFit2D()
fit2d_parameter['polarization_factor'] = self.polarization_factor
except TypeError:
fit2d_parameter = None
pyFAI_parameter['wavelength'] = self.pattern_geometry.wavelength
if fit2d_parameter:
fit2d_parameter['wavelength'] = self.pattern_geometry.wavelength
return pyFAI_parameter, fit2d_parameter
def calculate_number_of_pattern_points(self, max_dist_factor=1.5):
# calculates the number of points for an integrated pattern, based on the distance of the beam center to the the
# image corners. Maximum value is determined by the shape of the image.
fit2d_parameter = self.pattern_geometry.getFit2D()
center_x = fit2d_parameter['centerX']
center_y = fit2d_parameter['centerY']
width, height = self.img_model.img_data.shape
if width > center_x > 0:
side1 = np.max([abs(width - center_x), center_x])
else:
side1 = width
if center_y < height and center_y > 0:
side2 = np.max([abs(height - center_y), center_y])
else:
side2 = height
max_dist = np.sqrt(side1 ** 2 + side2 ** 2)
return int(max_dist * max_dist_factor)
def load(self, filename):
"""
Loads a calibration file and and sets all the calibration parameter.
:param filename: filename for a *.poni calibration file
"""
self.pattern_geometry = AzimuthalIntegrator()
self.pattern_geometry.load(filename)
self.orig_pixel1 = self.pattern_geometry.pixel1
self.orig_pixel2 = self.pattern_geometry.pixel2
self.calibration_name = get_base_name(filename)
self.filename = filename
self.is_calibrated = True
self.create_cake_geometry()
self.set_supersampling()
def save(self, filename):
"""
Saves the current calibration parameters into a a text file. Default extension is
*.poni
"""
self.cake_geometry.save(filename)
self.calibration_name = get_base_name(filename)
self.filename = filename
def create_file_header(self):
try:
# pyFAI version 0.12.0
return self.pattern_geometry.makeHeaders(polarization_factor=self.polarization_factor)
except AttributeError:
# pyFAI after version 0.12.0
from pyFAI.io import DefaultAiWriter
return DefaultAiWriter(None, self.pattern_geometry).make_headers()
def set_fit2d(self, fit2d_parameter):
"""
Reads in a dictionary with fit2d parameters where the fields of the dictionary are:
'directDist', 'centerX', 'centerY', 'tilt', 'tiltPlanRotation', 'pixelX', pixelY',
'polarization_factor', 'wavelength'
"""
self.pattern_geometry.setFit2D(directDist=fit2d_parameter['directDist'],
centerX=fit2d_parameter['centerX'],
centerY=fit2d_parameter['centerY'],
tilt=fit2d_parameter['tilt'],
tiltPlanRotation=fit2d_parameter['tiltPlanRotation'],
pixelX=fit2d_parameter['pixelX'],
pixelY=fit2d_parameter['pixelY'])
self.pattern_geometry.wavelength = fit2d_parameter['wavelength']
self.create_cake_geometry()
self.polarization_factor = fit2d_parameter['polarization_factor']
self.orig_pixel1 = fit2d_parameter['pixelX'] * 1e-6
self.orig_pixel2 = fit2d_parameter['pixelY'] * 1e-6
self.is_calibrated = True
self.set_supersampling()
def set_pyFAI(self, pyFAI_parameter):
"""
Reads in a dictionary with pyFAI parameters where the fields of dictionary are:
'dist', 'poni1', 'poni2', 'rot1', 'rot2', 'rot3', 'pixel1', 'pixel2', 'wavelength',
'polarization_factor'
"""
self.pattern_geometry.setPyFAI(dist=pyFAI_parameter['dist'],
poni1=pyFAI_parameter['poni1'],
poni2=pyFAI_parameter['poni2'],
rot1=pyFAI_parameter['rot1'],
rot2=pyFAI_parameter['rot2'],
rot3=pyFAI_parameter['rot3'],
pixel1=pyFAI_parameter['pixel1'],
pixel2=pyFAI_parameter['pixel2'])
self.pattern_geometry.wavelength = pyFAI_parameter['wavelength']
self.create_cake_geometry()
self.polarization_factor = pyFAI_parameter['polarization_factor']
self.orig_pixel1 = pyFAI_parameter['pixel1']
self.orig_pixel2 = pyFAI_parameter['pixel2']
self.is_calibrated = True
self.set_supersampling()
def load_distortion(self, spline_filename):
self.distortion_spline_filename = spline_filename
self.pattern_geometry.set_splineFile(spline_filename)
if self.cake_geometry:
self.cake_geometry.set_splineFile(spline_filename)
def reset_distortion_correction(self):
self.distortion_spline_filename = None
self.pattern_geometry.set_splineFile(None)
if self.cake_geometry:
self.cake_geometry.set_splineFile(None)
def set_supersampling(self, factor=None):
"""
Sets the supersampling to a specific factor. Whereby the factor determines in how many artificial pixel the
original pixel is split. (factor^2)
factor n_pixel
1 1
2 4
3 9
4 16
"""
if factor is None:
factor = self.supersampling_factor
self.pattern_geometry.pixel1 = self.orig_pixel1 / float(factor)
self.pattern_geometry.pixel2 = self.orig_pixel2 / float(factor)
if factor != self.supersampling_factor:
self.pattern_geometry.reset()
self.supersampling_factor = factor
def reset_supersampling(self):
self.pattern_geometry.pixel1 = self.orig_pixel1
self.pattern_geometry.pixel2 = self.orig_pixel2
def get_two_theta_img(self, x, y):
"""
Gives the two_theta value for the x,y coordinates on the image. Be aware that this function will be incorrect
for pixel indices, since it does not correct for center of the pixel.
:param x: x-coordinate in pixel on the image
:type x: ndarray
:param y: y-coordinate in pixel on the image
:type y: ndarray
:return : two theta in radians
"""
x *= self.supersampling_factor
y *= self.supersampling_factor
return self.pattern_geometry.tth(x - 0.5, y - 0.5)[0] # deletes 0.5 because tth function uses pixel indices
def get_azi_img(self, x, y):
"""
Gives chi for position on image.
:param x: x-coordinate in pixel on the image
:type x: ndarray
:param y: y-coordinate in pixel on the image
:type y: ndarray
:return : azimuth in radians
"""
x *= self.supersampling_factor
y *= self.supersampling_factor
return self.pattern_geometry.chi(x - 0.5, y - 0.5)[0]
def get_two_theta_array(self):
return self.pattern_geometry.twoThetaArray(self.img_model.img_data.shape)[::self.supersampling_factor,
::self.supersampling_factor]
def get_pixel_ind(self, tth, azi):
"""
Calculates pixel index for a specfic two theta and azimutal value.
:param tth:
two theta in radians
:param azi:
azimuth in radians
:return:
tuple of index 1 and 2
"""
tth_ind = find_contours(self.pattern_geometry.ttha, tth)
if len(tth_ind) == 0:
return []
tth_ind = np.vstack(tth_ind)
azi_values = self.pattern_geometry.chi(tth_ind[:, 0], tth_ind[:, 1])
min_index = np.argmin(np.abs(azi_values - azi))
return tth_ind[min_index, 0], tth_ind[min_index, 1]
@property
def wavelength(self):
return self.pattern_geometry.wavelength
class TestMultiGeometry(unittest.TestCase):
def setUp(self):
unittest.TestCase.setUp(self)
self.data = fabio.open(UtilsTest.getimage("1788/moke.tif")).data
self.lst_data = [self.data[:250, :300], self.data[250:, :300], self.data[:250, 300:], self.data[250:, 300:]]
self.det = Detector(1e-4, 1e-4)
self.det.max_shape = (500, 600)
self.sub_det = Detector(1e-4, 1e-4)
self.sub_det.max_shape = (250, 300)
self.ai = AzimuthalIntegrator(0.1, 0.03, 0.03, detector=self.det)
self.range = (0, 23)
self.ais = [AzimuthalIntegrator(0.1, 0.030, 0.03, detector=self.sub_det),
AzimuthalIntegrator(0.1, 0.005, 0.03, detector=self.sub_det),
AzimuthalIntegrator(0.1, 0.030, 0.00, detector=self.sub_det),
AzimuthalIntegrator(0.1, 0.005, 0.00, detector=self.sub_det),
]
self.mg = MultiGeometry(self.ais, radial_range=self.range, unit="2th_deg")
self.N = 390
def tearDown(self):
unittest.TestCase.tearDown(self)
self.data = None
self.lst_data = None
self.det = None
self.sub_det = None
self.ai = None
self.ais = None
self.mg = None
def test_integrate1d(self):
tth_ref, I_ref = self.ai.integrate1d(self.data, radial_range=self.range, npt=self.N, unit="2th_deg", method="splitpixel")
obt = self.mg.integrate1d(self.lst_data, self.N)
tth_obt, I_obt = obt
self.assertEqual(abs(tth_ref - tth_obt).max(), 0, "Bin position is the same")
# intensity need to be scaled by solid angle 1e-4*1e-4/0.1**2 = 1e-6
delta = (abs(I_obt * 1e6 - I_ref).max())
self.assertLessEqual(delta, 5e-5, "Intensity is the same delta=%s" % delta)
def test_integrate2d(self):
ref = self.ai.integrate2d(self.data, self.N, 360, radial_range=self.range, azimuth_range=(-180, 180), unit="2th_deg", method="splitpixel", all=True)
obt = self.mg.integrate2d(self.lst_data, self.N, 360, all=True)
self.assertEqual(abs(ref["radial"] - obt["radial"]).max(), 0, "Bin position is the same")
self.assertEqual(abs(ref["azimuthal"] - obt["azimuthal"]).max(), 0, "Bin position is the same")
# intensity need to be scaled by solid angle 1e-4*1e-4/0.1**2 = 1e-6
delta = abs(obt["I"] * 1e6 - ref["I"])[obt["count"] >= 1] # restict on valid pixel
delta_cnt = abs(obt["count"] - ref["count"])
delta_sum = abs(obt["sum"] * 1e6 - ref["sum"])
if delta.max() > 1:
logger.warning("TestMultiGeometry.test_integrate2d gave intensity difference of %s" % delta.max())
if logger.level <= logging.DEBUG:
from matplotlib import pyplot as plt
f = plt.figure()
a1 = f.add_subplot(2, 2, 1)
a1.imshow(ref["sum"])
a2 = f.add_subplot(2, 2, 2)
a2.imshow(obt["sum"])
a3 = f.add_subplot(2, 2, 3)
a3.imshow(delta_sum)
a4 = f.add_subplot(2, 2, 4)
a4.plot(delta_sum.sum(axis=0))
f.show()
raw_input()
self.assertLess(delta_cnt.max(), 0.001, "pixel count is the same delta=%s" % delta_cnt.max())
self.assertLess(delta_sum.max(), 0.03, "pixel sum is the same delta=%s" % delta_sum.max())
self.assertLess(delta.max(), 0.004, "pixel intensity is the same (for populated pixels) delta=%s" % delta.max())
def run(self):
ai = AzimuthalIntegrator(
dist=self.__distance,
poni1=self.__poni1,
poni2=self.__poni2,
rot1=self.__rotation1,
rot2=self.__rotation2,
rot3=self.__rotation3,
detector=self.__detector,
wavelength=self.__wavelength)
# FIXME Add error model
method1d = method_registry.Method(1, self.__method.split, self.__method.algo, self.__method.impl, None)
methods = method_registry.IntegrationMethod.select_method(method=method1d)
if len(methods) == 0:
method1d = method_registry.Method(1, method1d.split, "*", "*", None)
_logger.warning("Downgrade 1D integration method to %s", method1d)
else:
method1d = methods[0].method
method2d = method_registry.Method(2, self.__method.split, self.__method.algo, self.__method.impl, None)
methods = method_registry.IntegrationMethod.select_method(method=method2d)
if len(methods) == 0:
method2d = method_registry.Method(2, method2d.split, "*", "*", None)
_logger.warning("Downgrade 2D integration method to %s", method2d)
else:
method2d = methods[0].method
self.__result1d = ai.integrate1d(
method=method1d,
data=self.__image,
npt=self.__nPointsRadial,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
self.__result2d = ai.integrate2d(
method=method2d,
data=self.__image,
npt_rad=self.__nPointsRadial,
npt_azim=self.__nPointsAzimuthal,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
# Create an image masked where data exists
self.__resultMask2d = None
if self.__mask is not None:
if self.__mask.shape == self.__image.shape:
maskData = numpy.ones(shape=self.__image.shape, dtype=numpy.float32)
maskData[self.__mask == 0] = float("NaN")
if self.__displayMask:
self.__resultMask2d = ai.integrate2d(
method=method2d,
data=maskData,
npt_rad=self.__nPointsRadial,
npt_azim=self.__nPointsAzimuthal,
unit=self.__radialUnit,
polarization_factor=self.__polarizationFactor)
else:
_logger.warning("Inconsistency between image and mask sizes. %s != %s", self.__image.shape, self.__mask.shape)
try:
self.__directDist = ai.getFit2D()["directDist"]
except Exception:
# The geometry could not fit this param
_logger.debug("Backtrace", exc_info=True)
self.__directDist = None
if self.__calibrant:
rings = self.__calibrant.get_2th()
try:
rings = unitutils.from2ThRad(rings, self.__radialUnit, self.__wavelength, self.__directDist)
except ValueError:
message = "Convertion to unit %s not supported. Ring marks ignored"
_logger.warning(message, self.__radialUnit)
rings = []
# Filter the rings which are not part of the result
rings = filter(lambda x: self.__result1d.radial[0] <= x <= self.__result1d.radial[-1], rings)
rings = list(rings)
else:
rings = []
self.__ringAngles = rings
self.__ai = ai
class Data_Reducer(QWidget):
"""
This widget is developed to reduce on the fly 2D SAXS data to azimuthally averaged 1D SAXS data
"""
def __init__(self,poniFile=None,dataFile=None, darkFile=None, maskFile=None,extractedFolder='/tmp', npt=1000, transmission_corr=True, azimuthRange=(-180.0,180.0), parent=None):
"""
poniFile is the calibration file obtained after Q-calibration
"""
QWidget.__init__(self,parent)
self.layout=QGridLayout(self)
self.setup_dict=json.load(open('./SetupData/reducer_setup.txt','r'))
if poniFile is not None:
self.poniFile=poniFile
else:
self.poniFile=self.setup_dict['poniFile']
if maskFile is not None:
self.maskFile=maskFile
else:
self.maskFile=self.setup_dict['maskFile']
self.dataFile=dataFile
if darkFile is None:
self.dark_corrected=False
self.darkFile=''
else:
self.darkFile=darkFile
self.dark_corrected=True
self.curDir=os.getcwd()
self.extractedFolder=extractedFolder
self.npt=npt
self.set_externally=False
self.transmission_corr=transmission_corr
#ai=AIWidget()
#self.layout.addWidget(ai)
self.azimuthRange=azimuthRange
self.create_UI()
if os.path.exists(self.poniFile):
self.openPoniFile(file=self.poniFile)
if os.path.exists(self.maskFile):
self.openMaskFile(file=self.maskFile)
def create_UI(self):
"""
Creates the widget user interface
"""
row=0
col=0
dataFileLabel=QLabel('Data file')
self.layout.addWidget(dataFileLabel,row,col)
col+=1
self.dataFileLineEdit=QLineEdit(self.dataFile)
self.layout.addWidget(self.dataFileLineEdit,row,col,1,2)
col+=2
self.openDataPushButton=QPushButton('Select')
self.openDataPushButton.clicked.connect(self.openDataFiles)
self.layout.addWidget(self.openDataPushButton,row,col)
col+=1
self.reducePushButton=QPushButton('Reduce data')
self.reducePushButton.clicked.connect(self.reduce_multiple)
self.layout.addWidget(self.reducePushButton,row,col,1,1)
row+=1
col=0
darkFileLabel=QLabel('Dark file')
self.layout.addWidget(darkFileLabel,row,col)
col+=1
self.darkFileLineEdit=QLineEdit(self.darkFile)
self.layout.addWidget(self.darkFileLineEdit,row,col,1,2)
col+=2
self.openDarkPushButton=QPushButton('Select')
self.openDarkPushButton.clicked.connect(self.openDarkFile)
self.layout.addWidget(self.openDarkPushButton,row,col)
col+=1
self.darkCheckBox=QCheckBox('Dark Correction')
self.layout.addWidget(self.darkCheckBox,row,col)
row+=1
col=0
poniFileLabel=QLabel('Calibration file')
self.layout.addWidget(poniFileLabel,row,col)
col+=1
self.poniFileLineEdit=QLineEdit(self.poniFile)
self.layout.addWidget(self.poniFileLineEdit,row,col,1,2)
col+=2
self.openPoniPushButton=QPushButton('Select')
self.openPoniPushButton.clicked.connect(lambda x: self.openPoniFile(file=None))
self.layout.addWidget(self.openPoniPushButton,row,col)
col+=1
self.calibratePushButton=QPushButton('Calibrate')
self.calibratePushButton.clicked.connect(self.calibrate)
self.layout.addWidget(self.calibratePushButton,row,col)
row+=1
col=0
maskFileLabel=QLabel('Mask file')
self.layout.addWidget(maskFileLabel,row,col)
col+=1
self.maskFileLineEdit=QLineEdit(self.maskFile)
self.maskFileLineEdit.returnPressed.connect(self.maskFileChanged)
self.layout.addWidget(self.maskFileLineEdit,row,col,1,2)
col+=2
self.openMaskPushButton=QPushButton('Select')
self.openMaskPushButton.clicked.connect(lambda x: self.openMaskFile(file=None))
self.layout.addWidget(self.openMaskPushButton,row,col)
col+=1
self.createMaskPushButton=QPushButton('Create mask')
self.createMaskPushButton.clicked.connect(self.createMask)
self.layout.addWidget(self.createMaskPushButton,row,col)
row+=1
col=0
extractedFolderLabel=QLabel('Extracted folder')
self.layout.addWidget(extractedFolderLabel,row,col)
col+=1
self.extractedFolderLineEdit=QLineEdit()
self.layout.addWidget(self.extractedFolderLineEdit,row,col,1,2)
col+=2
self.extractedFolderPushButton=QPushButton('Select')
self.extractedFolderPushButton.clicked.connect(self.openFolder)
self.layout.addWidget(self.extractedFolderPushButton,row,col)
row+=1
col=0
radialPointsLabel=QLabel('Radial Points')
self.layout.addWidget(radialPointsLabel,row,col)
col+=1
self.radialPointsLineEdit=QLineEdit(str(self.npt))
self.radialPointsLineEdit.returnPressed.connect(self.nptChanged)
self.layout.addWidget(self.radialPointsLineEdit,row,col)
col+=1
azimuthRangeLabel=QLabel('Azimuthal Range (min:max)')
self.layout.addWidget(azimuthRangeLabel,row,col)
col+=1
self.azimuthRangeLineEdit=QLineEdit('%.2f:%.2f'%(self.azimuthRange[0],self.azimuthRange[1]))
self.azimuthRangeLineEdit.returnPressed.connect(self.azimuthRangeChanged)
self.layout.addWidget(self.azimuthRangeLineEdit,row,col)
col+=1
self.transCorrCheckBox=QCheckBox('Transmission Correction')
self.transCorrCheckBox.setTristate(False)
self.transCorrCheckBox.setChecked(self.transmission_corr)
self.layout.addWidget(self.transCorrCheckBox)
row+=1
col=0
progressLabel=QLabel('Status')
self.layout.addWidget(progressLabel,row,col,1,1)
col+=1
self.progressBar=QProgressBar()
self.layout.addWidget(self.progressBar,row,col,1,1)
col+=1
self.statusLabel=QLabel('Idle')
self.layout.addWidget(self.statusLabel,row,col,1,1)
col+=1
normLabel=QLabel('Normalized by')
self.normComboBox=QComboBox()
self.normComboBox.addItems(['BSDiode','Image sum','Monitor'])
self.layout.addWidget(normLabel,row,col,1,1)
col+=1
self.layout.addWidget(self.normComboBox,row,col,1,1)
row+=1
col=0
self.tabWidget=QTabWidget(self)
self.layout.addWidget(self.tabWidget,row,col,20,5)
self.imageWidget=Image_Widget(zeros((100,100)))
imgNumberLabel=QLabel('Image number')
self.imgNumberSpinBox=QSpinBox()
self.imgNumberSpinBox.setSingleStep(1)
self.imageWidget.imageLayout.addWidget(imgNumberLabel,row=2,col=1)
self.imageWidget.imageLayout.addWidget(self.imgNumberSpinBox,row=2,col=2)
self.imageView=self.imageWidget.imageView.getView()
self.plotWidget=PlotWidget()
self.plotWidget.setXLabel('Q, Å<sup>-1</sup>',fontsize=5)
self.plotWidget.setYLabel('Intensity',fontsize=5)
self.tabWidget.addTab(self.plotWidget,'Reduced 1D-data')
self.tabWidget.addTab(self.imageWidget,'Masked 2D-data')
def createMask(self):
"""
Opens a mask-widget to create mask file
"""
fname=str(QFileDialog.getOpenFileName(self,'Select an image file', directory=self.curDir,filter='Image file (*.edf *.tif)')[0])
if fname is not None or fname!='':
img=fb.open(fname).data
self.maskWidget=MaskWidget(img)
self.maskWidget.saveMaskPushButton.clicked.disconnect()
self.maskWidget.saveMaskPushButton.clicked.connect(self.save_mask)
self.maskWidget.show()
else:
QMessageBox.warning(self,'File error','Please import a data file first for creating the mask',QMessageBox.Ok)
def maskFileChanged(self):
"""
Changes the mask file
"""
maskFile=str(self.maskFileLineEdit.text())
if str(maskFile)=='':
self.maskFile=None
elif os.path.exists(maskFile):
self.maskFile=maskFile
else:
self.maskFile=None
def save_mask(self):
"""
Saves the entire mask combining all the shape ROIs
"""
fname=str(QFileDialog.getSaveFileName(filter='Mask Files (*.msk)')[0])
name,extn=os.path.splitext(fname)
if extn=='':
fname=name+'.msk'
elif extn!='.msk':
QMessageBox.warning(self,'File extension error','Please donot provide file extension other than ".msk". Thank you!')
return
else:
tmpfile=fb.edfimage.EdfImage(data=self.maskWidget.full_mask_data.T,header=None)
tmpfile.save(fname)
self.maskFile=fname
self.maskFileLineEdit.setText(self.maskFile)
def calibrate(self):
"""
Opens a calibartion widget to create calibration file
"""
fname=str(QFileDialog.getOpenFileName(self,'Select calibration image',directory=self.curDir, filter='Calibration image (*.edf *.tif)')[0])
if fname is not None:
img=fb.open(fname).data
pixel1=79.0
pixel2=79.0
self.calWidget=CalibrationWidget(img,pixel1,pixel2)
self.calWidget.saveCalibrationPushButton.clicked.disconnect()
self.calWidget.saveCalibrationPushButton.clicked.connect(self.save_calibration)
self.calWidget.show()
else:
QMessageBox.warning(self,'File error','Please import a data file first for creating the calibration file',QMessageBox.Ok)
def save_calibration(self):
fname=str(QFileDialog.getSaveFileName(self,'Calibration file',directory=self.curDir,filter='Clibration files (*.poni)')[0])
tfname=os.path.splitext(fname)[0]+'.poni'
self.calWidget.applyPyFAI()
self.calWidget.geo.save(tfname)
self.poniFile=tfname
self.poniFileLineEdit.setText(self.poniFile)
def openPoniFile(self,file=None):
"""
Select and imports the calibration file
"""
if file is None:
self.poniFile=QFileDialog.getOpenFileName(self,'Select calibration file',directory=self.curDir,filter='Calibration file (*.poni)')[0]
self.poniFileLineEdit.setText(self.poniFile)
else:
self.poniFile=file
if os.path.exists(self.poniFile):
self.setup_dict['poniFile']=self.poniFile
json.dump(self.setup_dict,open('./SetupData/reducer_setup.txt','w'))
fh=open(self.poniFile,'r')
lines=fh.readlines()
self.calib_data={}
for line in lines:
if line[0]!='#':
key,val=line.split(': ')
self.calib_data[key]=float(val)
self.dist=self.calib_data['Distance']
self.pixel1=self.calib_data['PixelSize1']
self.pixel2=self.calib_data['PixelSize2']
self.poni1=self.calib_data['Poni1']
self.poni2=self.calib_data['Poni2']
self.rot1=self.calib_data['Rot1']
self.rot2=self.calib_data['Rot2']
self.rot3=self.calib_data['Rot3']
self.wavelength=self.calib_data['Wavelength']
self.ai=AzimuthalIntegrator(dist=self.dist,poni1=self.poni1,poni2=self.poni2,pixel1=self.pixel1,pixel2=self.pixel2,rot1=self.rot1,rot2=self.rot2,rot3=self.rot3,wavelength=self.wavelength)
#pos=[self.poni2/self.pixel2,self.poni1/self.pixel1]
#self.roi=cake(pos,movable=False)
#self.roi.sigRegionChangeStarted.connect(self.endAngleChanged)
#self.imageView.addItem(self.roi)
else:
QMessageBox.warning(self,'File error','The calibration file '+self.poniFile+' doesnot exists.',QMessageBox.Ok)
def endAngleChanged(self,evt):
print(evt.pos())
def nptChanged(self):
"""
Changes the number of radial points
"""
try:
self.npt=int(self.radialPointsLineEdit.text())
except:
QMessageBox.warning(self,'Value error', 'Please input positive integers only.',QMessageBox.Ok)
def azimuthRangeChanged(self):
"""
Changes the azimuth angular range
"""
try:
self.azimuthRange=tuple(map(float, self.azimuthRangeLineEdit.text().split(':')))
except:
QMessageBox.warning(self,'Value error','Please input min:max angles in floating point numbers',QMessageBox.Ok)
def openDataFile(self):
"""
Select and imports one data file
"""
dataFile=QFileDialog.getOpenFileName(self,'Select data file',directory=self.curDir,filter='Data file (*.edf *.tif)')[0]
if dataFile!='':
self.dataFile=dataFile
self.curDir=os.path.dirname(self.dataFile)
self.dataFileLineEdit.setText(self.dataFile)
self.data2d=fb.open(self.dataFile).data
if self.darkFile is not None:
self.applyDark()
if self.maskFile is not None:
self.applyMask()
self.imageWidget.setImage(self.data2d,transpose=True)
self.tabWidget.setCurrentWidget(self.imageWidget)
if not self.set_externally:
self.extractedFolder=os.path.join(self.curDir,'extracted_pyFAI')
if not os.path.exists(self.extractedFolder):
os.makedirs(self.extractedFolder)
def openDataFiles(self):
"""
Selects and imports multiple data files
"""
self.dataFiles=QFileDialog.getOpenFileNames(self,'Select data files', directory=self.curDir,filter='Data files (*.edf *.tif)')[0]
if len(self.dataFiles)!=0:
self.imgNumberSpinBox.valueChanged.connect(self.imageChanged)
self.imgNumberSpinBox.setMinimum(0)
self.imgNumberSpinBox.setMaximum(len(self.dataFiles)-1)
self.dataFileLineEdit.setText(str(self.dataFiles))
self.curDir=os.path.dirname(self.dataFiles[0])
self.extractedFolder=os.path.join(self.curDir,'extracted_pyFAI')
if not os.path.exists(self.extractedFolder):
os.makedirs(self.extractedFolder)
self.extractedFolderLineEdit.setText(self.extractedFolder)
self.imgNumberSpinBox.setValue(0)
self.imageChanged()
def imageChanged(self):
self.data2d=fb.open(self.dataFiles[self.imgNumberSpinBox.value()]).data
if self.darkFile is not None:
self.applyDark()
if self.maskFile is not None:
self.applyMask()
self.imageWidget.setImage(self.data2d,transpose=True)
def applyDark(self):
if not self.dark_corrected and self.darkFile!='':
self.dark2d=fb.open(self.darkFile).data
self.data2d=self.data2d-self.dark2d
self.dark_corrected=True
def applyMask(self):
self.mask2d=fb.open(self.maskFile).data
self.data2d=self.data2d*(1+self.mask2d)/2.0
self.mask_applied=True
def openDarkFile(self):
"""
Select and imports the dark file
"""
self.darkFile=QFileDialog.getOpenFileName(self,'Select dark file',directory=self.curDir,filter='Dark file (*.edf)')[0]
if self.darkFile!='':
self.dark_corrected=False
self.darkFileLineEdit.setText(self.darkFile)
if self.dataFile is not None:
self.data2d=fb.open(self.dataFile).data
self.applyDark()
def openMaskFile(self,file=None):
"""
Select and imports the Mask file
"""
if file is None:
self.maskFile=QFileDialog.getOpenFileName(self,'Select mask file',directory=self.curDir,filter='Mask file (*.msk)')[0]
else:
self.maskFile=file
if self.maskFile!='':
self.mask_applied=False
if os.path.exists(self.maskFile):
self.curDir=os.path.dirname(self.maskFile)
self.maskFileLineEdit.setText(self.maskFile)
self.setup_dict['maskFile']=self.maskFile
self.setup_dict['poniFile']=self.poniFile
json.dump(self.setup_dict,open('./SetupData/reducer_setup.txt','w'))
else:
self.openMaskFile(file=None)
if self.dataFile is not None:
self.applyMask()
else:
self.maskFile=None
self.maskFileLineEdit.clear()
def openFolder(self):
"""
Select the folder to save the reduce data
"""
self.extractedFolder=QFileDialog.getExistingDirectory(self,'Select extracted directory',directory=self.curDir)
if self.extractedFolder!='':
self.extractedFolderLineEdit.setText(self.extractedFolder)
self.set_externally=True
def reduceData(self):
"""
Reduces the 2d data to 1d data
"""
print('Iloveu', self.darkFile)
if (self.dataFile is not None) and (os.path.exists(self.dataFile)):
if (self.poniFile is not None) and (os.path.exists(self.poniFile)):
# self.statusLabel.setText('Busy')
# self.progressBar.setRange(0, 0)
imageData=fb.open(self.dataFile)
#self.data2d=imageData.data
#if self.maskFile is not None:
# self.applyMask()
#self.imageWidget.setImage(self.data2d,transpose=True)
#self.tabWidget.setCurrentWidget(self.imageWidget)
self.header=imageData.header
try:
self.ai.set_wavelength(float(self.header['Wavelength'])*1e-10)
except:
self.ai.set_wavelength(self.wavelength)
print('Iloveu',self.darkFile)
if os.path.exists(self.dataFile.split('.')[0]+'_dark.edf') and self.darkCheckBox.isChecked():
self.darkFile=self.dataFile.split('.')[0]+'_dark.edf'
dark=fb.open(self.darkFile)
self.darkFileLineEdit.setText(self.darkFile)
imageDark=dark.data
self.header['BSDiode_corr']=max([1.0,(float(imageData.header['BSDiode'])-float(dark.header['BSDiode']))])
self.header['Monitor_corr']=max([1.0,(float(imageData.header['Monitor'])-float(dark.header['Monitor']))])
print("Dark File read from existing dark files")
elif self.darkFile is not None and self.darkFile!='' and self.darkCheckBox.isChecked():
dark=fb.open(self.darkFile)
imageDark=dark.data
self.header['BSDiode_corr']=max([1.0,(float(imageData.header['BSDiode'])-float(dark.header['BSDiode']))])
self.header['Monitor_corr']=max([1.0,(float(imageData.header['Monitor'])-float(dark.header['Monitor']))])
print("Dark File from memory subtracted")
else:
imageDark=None
try:
self.header['BSDiode_corr']=float(imageData.header['BSDiode'])
self.header['Monitor_corr']=float(imageData.header['Monitor'])
except:
self.transCorrCheckBox.setCheckState(Qt.Unchecked)
print("No dark correction done")
if self.transCorrCheckBox.isChecked():
if str(self.normComboBox.currentText())=='BSDiode':
norm_factor=self.header['BSDiode_corr']#/float(self.header['count_time'])
elif str(self.normComboBox.currentText())=='Monitor':
norm_factor=self.header['Monitor_corr']
else:
norm_factor=sum(imageData.data)
else:
norm_factor=1.0
if self.maskFile is not None:
imageMask=fb.open(self.maskFile).data
else:
imageMask=None
print(self.maskFile)
# QApplication.processEvents()
#print(self.azimuthRange)
self.q,self.I,self.Ierr=self.ai.integrate1d(imageData.data,self.npt,error_model='poisson',mask=imageMask,dark=imageDark,unit='q_A^-1',normalization_factor=norm_factor,azimuth_range=self.azimuthRange)
self.plotWidget.add_data(self.q,self.I,yerr=self.Ierr,name='Reduced data')
self.plotWidget.setTitle(self.dataFile,fontsize=2)
# self.progressBar.setRange(0,100)
# self.progressBar.setValue(100)
# self.statusLabel.setText('Idle')
# QApplication.processEvents()
self.saveData()
self.tabWidget.setCurrentWidget(self.plotWidget)
else:
QMessageBox.warning(self,'Calibration File Error','Data reduction failed because either no calibration file provided or the provided file or path do not exists',QMessageBox.Ok)
else:
QMessageBox.warning(self,'Data File Error','No data file provided', QMessageBox.Ok)
def reduce_multiple(self):
"""
Reduce multiple files
"""
#try:
i=0
self.progressBar.setRange(0,len(self.dataFiles))
self.progressBar.setValue(i)
self.statusLabel.setText('Busy')
for file in self.dataFiles:
self.dataFile=file
QApplication.processEvents()
self.reduceData()
i=i+1
self.progressBar.setValue(i)
QApplication.processEvents()
self.statusLabel.setText('Idle')
#except:
# QMessageBox.warning(self,'File error','No data files to reduce',QMessageBox.Ok)
def saveData(self):
"""
saves the extracted data into a file
"""
if not os.path.exists(self.extractedFolder):
os.makedirs(self.extractedFolder)
filename=os.path.join(self.extractedFolder,os.path.splitext(os.path.basename(self.dataFile))[0]+'.txt')
headers='File extracted on '+time.asctime()+'\n'
headers='Files used for extraction are:\n'
headers+='Data file: '+self.dataFile+'\n'
if self.darkFile is not None:
headers+='Dark file: '+self.darkFile+'\n'
else:
headers+='Dark file: None\n'
headers+='Poni file: '+self.poniFile+'\n'
if self.maskFile is not None:
headers+='mask file: '+self.maskFile+'\n'
else:
headers+='mask file: None\n'
for key in self.header.keys():
headers+=key+'='+str(self.header[key])+'\n'
headers+='Q (A^-1)\t\tIntensity\t\tIntensity_err'
data=vstack((self.q,self.I,self.Ierr)).T
savetxt(filename,data,header=headers,comments='#')
class EwaldArch(PawsPlugin):
"""Class for storing area detector data collected in
X-ray diffraction experiments.
Attributes:
idx: integer name of arch
map_raw: numpy 2d array of the unprocessed image data
poni: poni data for integration
mask: map of pixels to be masked out of integration
scan_info: information from any relevant motors and sensors
ai_args: arguments passed to AzimuthalIntegrator
file_lock: lock to ensure only one writer to data file
integrator: AzimuthalIntegrator object from pyFAI
arch_lock: threading lock used to ensure only one process can
access data at a time
map_norm: normalized image data
map_q: reciprocal space coordinates for data
int_1d: int_1d_data object from containers
int_2d: int_2d_data object from containers
Methods:
integrate_1d: integrate the image data to create I, 2theta, q,
and normalization arrays
integrate_2d: not implemented
set_integrator: set new integrator
set_map_raw: replace raw data
set_poni: replace poni object
set_mask: replace mask data
set_scan_info: replace scan_info
save_to_h5: save data to hdf5 file
load_from_h5: load data from hdf5 file
copy: create copy of arch
"""
# pylint: disable=too-many-instance-attributes
def __init__(self,
idx=None,
map_raw=None,
poni=PONI(),
mask=None,
scan_info={},
ai_args={},
file_lock=Condition()):
# pylint: disable=too-many-arguments
super(EwaldArch, self).__init__()
self.idx = idx
self.map_raw = map_raw
self.poni = poni
if mask is None and map_raw is not None:
self.mask = np.where(map_raw < 0, 1, 0)
else:
self.mask = mask
self.scan_info = scan_info
self.ai_args = ai_args
self.file_lock = file_lock
self.integrator = AzimuthalIntegrator(dist=self.poni.dist,
poni1=self.poni.poni1,
poni2=self.poni.poni2,
rot1=self.poni.rot1,
rot2=self.poni.rot2,
rot3=self.poni.rot3,
wavelength=self.poni.wavelength,
detector=self.poni.detector,
**ai_args)
self.arch_lock = Condition()
self.map_norm = 0
self.map_q = 0
self.int_1d = int_1d_data()
self.int_2d = int_2d_data()
self.xyz = None # TODO: implement rotations to generate pixel coords
self.tcr = None
self.qchi = None
def integrate_1d(self,
numpoints=10000,
radial_range=[0, 180],
monitor=None,
unit=units.TTH_DEG,
**kwargs):
"""Wrapper for integrate1d method of AzimuthalIntegrator from pyFAI.
Sets 1d integration variables for object instance.
args:
numpoints: int, number of points in final array
radial_range: tuple or list, lower and upper end of integration
monitor: str, keyword for normalization counter in scan_info
unit: pyFAI unit for integration, units.TTH_DEG or units.Q_A
kwargs: other keywords to be passed to integrate1d, see pyFAI docs.
returns:
result: integrate1d result from pyFAI.
"""
with self.arch_lock:
if monitor is not None:
self.map_norm = self.map_raw / self.scan_info[monitor]
else:
self.map_norm = self.map_raw
if self.mask is None:
self.mask = np.where(self.map_raw < 0, 1, 0)
result = self.integrator.integrate1d(self.map_norm,
numpoints,
unit=unit,
radial_range=radial_range,
mask=self.mask,
**kwargs)
self.int_1d.ttheta, self.int_1d.q = parse_unit(
result, self.poni.wavelength)
self.int_1d.pcount = result._count
self.int_1d.raw = result._sum_signal
self.int_1d.norm = pawstools.div0(self.int_1d.raw,
self.int_1d.pcount)
return result
def integrate_2d(self):
"""Not implemented.
"""
with self.arch_lock:
pass
def set_integrator(self, **args):
"""Sets AzimuthalIntegrator with new arguments and instances poni
attribute.
args:
args: see pyFAI for acceptable arguments for the integrator
constructor.
returns:
None
"""
with self.arch_lock:
self.ai_args = args
self.integrator = AzimuthalIntegrator(
dist=self.poni.dist,
poni1=self.poni.poni1,
poni2=self.poni.poni2,
rot1=self.poni.rot1,
rot2=self.poni.rot2,
rot3=self.poni.rot3,
wavelength=self.poni.wavelength,
detector=self.poni.detector,
**args)
def set_map_raw(self, new_data):
with self.arch_lock:
self.map_raw = new_data
if self.mask is None:
self.mask = np.where(self.map_raw < 0, 1, 0)
def set_poni(self, new_data):
with self.arch_lock:
self.poni = new_data
def set_mask(self, new_data):
with self.arch_lock:
self.mask = new_data
def set_scan_info(self, new_data):
with self.arch_lock:
self.scan_info = new_data
def save_to_h5(self, file):
"""Saves data to hdf5 file using h5py as backend.
args:
file: h5py group or file object.
returns:
None
"""
with self.file_lock:
if str(self.idx) in file:
del (file[str(self.idx)])
grp = file.create_group(str(self.idx))
lst_attr = [
"map_raw", "mask", "map_norm", "map_q", "xyz", "tcr", "qchi",
"scan_info", "ai_args"
]
pawstools.attributes_to_h5(self, grp, lst_attr)
grp.create_group('int_1d')
pawstools.attributes_to_h5(self.int_1d, grp['int_1d'])
grp.create_group('int_2d')
pawstools.attributes_to_h5(self.int_2d, grp['int_2d'])
grp.create_group('poni')
pawstools.dict_to_h5(self.poni.to_dict(), grp['poni'])
def load_from_h5(self, file):
"""Loads data from hdf5 file and sets attributes.
args:
file: h5py file or group object
returns:
None
"""
with self.file_lock:
with self.arch_lock:
if str(self.idx) not in file:
print("No data can be found")
grp = file[str(self.idx)]
lst_attr = [
"map_raw", "mask", "map_norm", "map_q", "xyz", "tcr",
"qchi", "scan_info", "ai_args"
]
pawstools.h5_to_attributes(self, grp, lst_attr)
pawstools.h5_to_attributes(self.int_1d, grp['int_1d'])
pawstools.h5_to_attributes(self.int_2d, grp['int_2d'])
self.poni = PONI.from_yamdict(pawstools.h5_to_dict(
grp['poni']))
self.integrator = AzimuthalIntegrator(
dist=self.poni.dist,
poni1=self.poni.poni1,
poni2=self.poni.poni2,
rot1=self.poni.rot1,
rot2=self.poni.rot2,
rot3=self.poni.rot3,
wavelength=self.poni.wavelength,
detector=self.poni.detector,
**self.ai_args)
def copy(self):
arch_copy = EwaldArch(copy.deepcopy(self.idx),
copy.deepcopy(self.map_raw),
copy.deepcopy(self.poni),
copy.deepcopy(self.mask),
copy.deepcopy(self.scan_info),
copy.deepcopy(self.ai_args), self.file_lock)
arch_copy.integrator = copy.deepcopy(self.integrator)
arch_copy.arch_lock = Condition()
arch_copy.map_norm = copy.deepcopy(self.map_norm)
arch_copy.map_q = copy.deepcopy(self.map_q)
arch_copy.int_1d = copy.deepcopy(self.int_1d)
arch_copy.int_2d = copy.deepcopy(self.int_2d)
arch_copy.xyz = copy.deepcopy(self.xyz)
arch_copy.tcr = copy.deepcopy(self.tcr)
arch_copy.qchi = copy.deepcopy(self.qchi)
return arch_copy
def integrate_them(o):
"""
Process a series of files
o = options object
o.parfile gives name of parameter file (fit2d or poni format)
o.dark overrides to supply dark filename
o.flood overrides to supply flood filename
o.mask overrides to supply mask filename
o.backcalc asks for the back computation of the image
o.npts gives number of output points
"""
# pyFAI.load( ponifile )
integrator = AzimuthalIntegrator()
#integrator.tth = integrator.newtth
# integrator.setChiDiscAtZero()
ptype = determineparfile(o.parfile)
if ptype == "pyfai":
integrator.load(o.parfile)
if o.dark is not None:
print("Using dark from command line", o.dark)
if o.flood is not None:
print("Using dark from command line", o.flood)
elif ptype == "fit2d":
f2d = fit2dcakepars(o.parfile)
if f2d["SPATIAL DIS."][0] not in ["Y", "y"]:
# Set to None. Spatial is from parfile
f2d["SD FILE"] = None
integrator.setFit2D(float(f2d["DISTANCE"]),
float(f2d["X-BEAM CENTRE"]),
float(f2d["Y-BEAM CENTRE"]),
tilt=float(f2d["ANGLE OF TILT"]),
tiltPlanRotation=float(f2d["TILT ROTATION"]),
pixelX=float(f2d["X-PIXEL SIZE"]),
pixelY=float(f2d["Y-BEAM CENTRE"]),
splineFile=f2d["SD FILE"])
integrator.rot3 = 0
integrator.reset()
print(integrator.param, integrator.detector.pixel1)
# First choice is command line. Then from pars if supplied
if o.dark is None:
if f2d["DARK CURRENT"][0] in ["Y", "y"]:
o.dark = f2d["DC FILE"]
print("Using dark from fit2d parameter file", o.dark)
else:
print("Using dark from command line", o.dark)
if o.flood is None:
if f2d["FLAT-FIELD"][0] in ["Y", "y"]:
o.flood = f2d["FF FILE"]
print("Using flood from fit2d parameter file", o.flood)
else:
print("Using flood from command line", o.flood)
# Should be in fabio utilities
df = darkflood(o.dark, o.flood)
# Should be in fabio
fs = edffilenameseries(o.stem, o.first, o.last, o.glob, o.extn)
# integrator.polarization( factor = 1, shape=(2048,2048) )
# Command line is first priority for make
if o.mask is not None:
mask = fabio.open(o.mask).data
print("Using mask", o.mask)
# assume poni file deals with this independently?
elif ptype == "fit2d": # try in fit2d parfile
if f2d["USE MASK"][0] in ['y', 'Y']:
mask = fabio.open(f2d["MASK FILE"]).data
print("Using mask", f2d["MASK FILE"])
else:
mask = None
if mask is not None:
print("mask mean:", mask.mean())
integrator.write(os.path.splitext(o.parfile)[0] + ".poni")
for f in fs:
print("Processing", f, end=' ')
try:
fo = df.correct(fabio.open(f))
except:
continue
if ptype == "fit2d":
outFile = f.replace(f2d["input_extn"], f2d["output_extn"])
else:
outFile = f.replace(o.extn, ".dat")
global SOLID_ANGLE
if 0:
from matplotlib.pylab import imshow, figure, show, log, plot
#imshow(log(fo.data))
#figure()
if mask is not None:
imshow(log(fo.data * (1 - mask)),
vmin=log(10),
vmax=log(30000))
else:
imshow(log(fo.data), vmin=log(100), vmax=log(3000))
# show()
if o.npts is None:
npts = min(fo.data.shape)
else:
npts = int(o.npts)
tth, I = integrator.integrate1d(
fo.data,
nbPt=npts,
filename=outFile,
correctSolidAngle=SOLID_ANGLE,
mask=mask, # 1 for valid
unit="q_A^-1",
#dummy=dummy, # mask pixels == dummy
#delta_dummy=delta_dummy # precision of dummy
)
print("wrote", outFile)
if o.backcalc:
calcimage = calcfrom1d(integrator, tth, I,
fo.data.shape) * integrator._polarization
err = (calcimage - fo.data) * (1 - mask) / (calcimage + mask)
e = fabio.edfimage.edfimage(data=err.astype(numpy.float32))
e.write(outFile + ".edf")
fitcen(fo.data, calcimage, (1 - mask))
# from matplotlib.pylab import imshow, show
# imshow( integrator._polarization )
# show()
if o.display:
if mask is not None:
display(tth, I,
(calcimage - fo.data) * (1 - mask) / (calcimage + 1))
else:
display(tth, I, (calcimage - fo.data) / (calcimage + 1))
def run(args):
FILE = args.INPUT
if np.shape(FILE) == (1, ):
FILE = np.sort(glob.glob(str(FILE[0])))
sample_name = str(input("Enter sample name for saving: "))
SAVE_PATH = os.getcwd()
if args.OUTPUT:
SAVE_PATH = args.OUTPUT
else:
print(
'!!! Warning files will be saved in the current folder because no output was defined.'
)
currentFile = np.zeros((len(FILE), 2))
### Read json file ###
jsonParam = readJson(args.JSON)
### Grabbing first file to check matrix size ###
for i in range(0, len(FILE)):
currentFile[i] = re.findall(r'\d{3,7}', FILE[i])
progression("Cheking files, matrix size definition ..... ", len(FILE),
i)
pattern = np.zeros(
(int(np.max(currentFile[:, 0])), int(np.max(currentFile[:, 1]) + 1),
int(jsonParam['nbpt_rad'])))
### Normalisation matrix ###
pico = np.zeros((len(FILE), 1))
for i in range(len(FILE)):
image = fabio.open(FILE[i])
counter = image.header["counter_pos"].split(" ")
pico[i] = float(counter[7]) * 0.0000001
progression("Importing counter ..... ", len(FILE), i)
picoCorrected = median_filter(pico.flat, 9, mode='constant')
### Integration of FILE ###
azimutalIntegrator = AzimuthalIntegrator(
dist=jsonParam['dist'],
poni1=jsonParam['poni1'],
poni2=jsonParam['poni2'],
rot1=jsonParam['rot1'],
rot2=jsonParam['rot2'],
rot3=jsonParam['rot3'],
pixel1=jsonParam['pixel1'],
pixel2=jsonParam['pixel2'],
splineFile=jsonParam['splineFile'],
detector=jsonParam['detector'],
wavelength=jsonParam['wavelength'])
dark = np.array(fabio.open(jsonParam['dark_current']).data)
flat = np.array(fabio.open(jsonParam['flat_field']).data)
mask = np.array(fabio.open(jsonParam['mask_file']).data)
offset_rot = int(np.min(currentFile[:, 0]))
offset_trans = int(np.min(currentFile[:, 1]))
for i in range(len(FILE)):
dataFile = np.array(fabio.open(FILE[i]).data)
if args.SEPARATE:
bragg, amorphous = azimutalIntegrator.separate(
dataFile,
npt_rad=1024,
npt_azim=512,
unit=jsonParam['unit'],
method='splitpixel',
percentile=50,
mask=None,
restore_mask=True)
if args.SEPARATE == 'Amorphous':
dataFile = amorphous
elif args.SEPARATE == 'Bragg':
dataFile = bragg
dataX, dataY = azimutalIntegrator.integrate1d(
dataFile,
int(jsonParam['nbpt_rad']),
filename=None,
correctSolidAngle=jsonParam['do_solid_angle'],
variance=None,
error_model=None,
radial_range=(float(jsonParam['radial_range_min']),
float(jsonParam['radial_range_max'])),
azimuth_range=None,
mask=mask,
dummy=jsonParam['do_dummy'],
delta_dummy=jsonParam['delta_dummy'],
polarization_factor=None,
method='csr',
dark=dark,
flat=flat,
unit=jsonParam['unit'],
safe=True,
normalization_factor=picoCorrected[i],
profile=False,
all=False,
metadata=None)
currentFile[i] = re.findall(r'\d{3,7}', FILE[i])
pattern[int(currentFile[i, 0]) - offset_rot,
int(currentFile[i, 1]) - offset_trans, :] = dataY
progression("Integrating data............. ", len(FILE), i)
print()
### Storing special 2-theta ###
theta = np.linspace(int(np.min(currentFile[:, 0])),
int(np.max(currentFile[:, 0])),
int(np.max(currentFile[:, 0])))
if args.THETA:
special_theta = np.fromstring(args.THETA, dtype=int, sep=',')
for i in range(0, np.size(theta, 0)):
theta[i] = i * (int(special_theta[0])) % int(special_theta[1])
### Multiplier ###
if args.MULTIPLIER:
pattern = pattern * int(args.MULTIPLIER)
### Saving ###
if args.OUTPUT:
f = h5py.File(args.OUTPUT + sample_name + '_sinogram.xrdct', 'w')
else:
f = h5py.File(sample_name + '_sinogram.xrdct', 'w')
grp = f.create_group("data")
dset = f.create_dataset('data/data', np.shape(pattern), dtype='f')
dset[:, :, :] = pattern[:, :, :]
dset_theta = f.create_dataset('data/theta', np.shape(theta), dtype='f')
dset_theta[:] = theta[:]
dset_dataX = f.create_dataset('data/dataX', np.shape(dataX), dtype='f')
dset_dataX[:] = dataX[:]
class CalibrationModel(object):
def __init__(self, img_model=None):
"""
:param img_model:
:type img_model: ImgModel
"""
self.img_model = img_model
self.points = []
self.points_index = []
self.spectrum_geometry = AzimuthalIntegrator()
self.cake_geometry = None
self.calibrant = Calibrant()
self.start_values = {'dist': 200e-3,
'wavelength': 0.3344e-10,
'pixel_width': 79e-6,
'pixel_height': 79e-6,
'polarization_factor': 0.99}
self.orig_pixel1 = 79e-6
self.orig_pixel2 = 79e-6
self.fit_wavelength = False
self.fit_distance = True
self.is_calibrated = False
self.use_mask = False
self.filename = ''
self.calibration_name = 'None'
self.polarization_factor = 0.99
self.supersampling_factor = 1
self._calibrants_working_dir = os.path.dirname(calibrants.__file__)
self.cake_img = np.zeros((2048, 2048))
self.tth = np.linspace(0, 25)
self.int = np.sin(self.tth)
self.num_points = len(self.int)
self.peak_search_algorithm = None
def find_peaks_automatic(self, x, y, peak_ind):
"""
Searches peaks by using the Massif algorithm
:param x:
x-coordinate in pixel - should be from original image (not supersampled x-coordinate)
:param y:
y-coordinate in pixel - should be from original image (not supersampled y-coordinate)
:param peak_ind:
peak/ring index to which the found points will be added
:return:
array of points found
"""
massif = Massif(self.img_model._img_data)
cur_peak_points = massif.find_peaks([x, y], stdout=DummyStdOut())
if len(cur_peak_points):
self.points.append(np.array(cur_peak_points))
self.points_index.append(peak_ind)
return np.array(cur_peak_points)
def find_peak(self, x, y, search_size, peak_ind):
"""
Searches a peak around the x,y position. It just searches for the maximum value in a specific search size.
:param x:
x-coordinate in pixel - should be from original image (not supersampled x-coordinate)
:param y:
y-coordinate in pixel - should be form original image (not supersampled y-coordinate)
:param search_size:
the length of the search rectangle in pixels in all direction in which the algorithm searches for
the maximum peak
:param peak_ind:
peak/ring index to which the found points will be added
:return:
point found (as array)
"""
left_ind = np.round(x - search_size * 0.5)
if left_ind < 0:
left_ind = 0
top_ind = np.round(y - search_size * 0.5)
if top_ind < 0:
top_ind = 0
search_array = self.img_model.img_data[left_ind:(left_ind + search_size),
top_ind:(top_ind + search_size)]
x_ind, y_ind = np.where(search_array == search_array.max())
x_ind = x_ind[0] + left_ind
y_ind = y_ind[0] + top_ind
self.points.append(np.array([x_ind, y_ind]))
self.points_index.append(peak_ind)
return np.array([np.array((x_ind, y_ind))])
def clear_peaks(self):
self.points = []
self.points_index = []
def create_cake_geometry(self):
self.cake_geometry = AzimuthalIntegrator()
pyFAI_parameter = self.spectrum_geometry.getPyFAI()
pyFAI_parameter['polarization_factor'] = self.polarization_factor
pyFAI_parameter['wavelength'] = self.spectrum_geometry.wavelength
self.cake_geometry.setPyFAI(dist=pyFAI_parameter['dist'],
poni1=pyFAI_parameter['poni1'],
poni2=pyFAI_parameter['poni2'],
rot1=pyFAI_parameter['rot1'],
rot2=pyFAI_parameter['rot2'],
rot3=pyFAI_parameter['rot3'],
pixel1=pyFAI_parameter['pixel1'],
pixel2=pyFAI_parameter['pixel2'])
self.cake_geometry.wavelength = pyFAI_parameter['wavelength']
def setup_peak_search_algorithm(self, algorithm, mask=None):
"""
Initializes the peak search algorithm on the current image
:param algorithm:
peak search algorithm used. Possible algorithms are 'Massif' and 'Blob'
:param mask:
if a mask is used during the process this is provided here as a 2d array for the image.
"""
if algorithm == 'Massif':
self.peak_search_algorithm = Massif(self.img_model.get_raw_img_data())
elif algorithm == 'Blob':
if mask is not None:
self.peak_search_algorithm = BlobDetection(self.img_model.get_raw_img_data() * mask)
else:
self.peak_search_algorithm = BlobDetection(self.img_model.get_raw_img_data())
self.peak_search_algorithm.process()
else:
return
def search_peaks_on_ring(self, ring_index, delta_tth=0.1, min_mean_factor=1,
upper_limit=55000, mask=None):
"""
This function is searching for peaks on an expected ring. It needs an initial calibration
before. Then it will search for the ring within some delta_tth and other parameters to get
peaks from the calibrant.
:param ring_index: the index of the ring for the search
:param delta_tth: search space around the expected position in two theta
:param min_mean_factor: a factor determining the minimum peak intensity to be picked up. it is based
on the mean value of the search area defined by delta_tth. Pick a large value
for larger minimum value and lower for lower minimum value. Therefore, a smaller
number is more prone to picking up noise. typical values like between 1 and 3.
:param upper_limit: maximum intensity for the peaks to be picked
:param mask: in case the image has to be masked from certain areas, it need to be given here. Default is None.
The mask should be given as an 2d array with the same dimensions as the image, where 1 denotes a
masked pixel and all others should be 0.
"""
self.reset_supersampling()
if not self.is_calibrated:
return
# transform delta from degree into radians
delta_tth = delta_tth / 180.0 * np.pi
# get appropriate two theta value for the ring number
tth_calibrant_list = self.calibrant.get_2th()
tth_calibrant = np.float(tth_calibrant_list[ring_index])
# get the calculated two theta values for the whole image
if self.spectrum_geometry._ttha is None:
tth_array = self.spectrum_geometry.twoThetaArray(self.img_model._img_data.shape)
else:
tth_array = self.spectrum_geometry._ttha
# create mask based on two_theta position
ring_mask = abs(tth_array - tth_calibrant) <= delta_tth
if mask is not None:
mask = np.logical_and(ring_mask, np.logical_not(mask))
else:
mask = ring_mask
# calculate the mean and standard deviation of this area
sub_data = np.array(self.img_model._img_data.ravel()[np.where(mask.ravel())], dtype=np.float64)
sub_data[np.where(sub_data > upper_limit)] = np.NaN
mean = np.nanmean(sub_data)
std = np.nanstd(sub_data)
# set the threshold into the mask (don't detect very low intensity peaks)
threshold = min_mean_factor * mean + std
mask2 = np.logical_and(self.img_model._img_data > threshold, mask)
mask2[np.where(self.img_model._img_data > upper_limit)] = False
size2 = mask2.sum(dtype=int)
keep = int(np.ceil(np.sqrt(size2)))
try:
sys.stdout = DummyStdOut
res = self.peak_search_algorithm.peaks_from_area(mask2, Imin=mean - std, keep=keep)
sys.stdout = sys.__stdout__
except IndexError:
res = []
# Store the result
if len(res):
self.points.append(np.array(res))
self.points_index.append(ring_index)
self.set_supersampling()
self.spectrum_geometry.reset()
def set_calibrant(self, filename):
self.calibrant = Calibrant()
self.calibrant.load_file(filename)
self.spectrum_geometry.calibrant = self.calibrant
def set_start_values(self, start_values):
self.start_values = start_values
self.polarization_factor = start_values['polarization_factor']
def calibrate(self):
self.spectrum_geometry = GeometryRefinement(self.create_point_array(self.points, self.points_index),
dist=self.start_values['dist'],
wavelength=self.start_values['wavelength'],
pixel1=self.start_values['pixel_width'],
pixel2=self.start_values['pixel_height'],
calibrant=self.calibrant)
self.orig_pixel1 = self.start_values['pixel_width']
self.orig_pixel2 = self.start_values['pixel_height']
self.refine()
self.create_cake_geometry()
self.is_calibrated = True
self.calibration_name = 'current'
self.set_supersampling()
# reset the integrator (not the geometric parameters)
self.spectrum_geometry.reset()
def refine(self):
self.reset_supersampling()
self.spectrum_geometry.data = self.create_point_array(self.points, self.points_index)
fix = ['wavelength']
if self.fit_wavelength:
fix = []
if not self.fit_distance:
fix.append('dist')
if self.fit_wavelength:
self.spectrum_geometry.refine2()
self.spectrum_geometry.refine2_wavelength(fix=fix)
self.create_cake_geometry()
self.set_supersampling()
# reset the integrator (not the geometric parameters)
self.spectrum_geometry.reset()
def integrate_1d(self, num_points=None, mask=None, polarization_factor=None, filename=None,
unit='2th_deg', method='csr'):
if np.sum(mask) == self.img_model.img_data.shape[0] * self.img_model.img_data.shape[1]:
# do not perform integration if the image is completely masked...
return self.tth, self.int
if self.spectrum_geometry._polarization is not None:
if self.img_model.img_data.shape != self.spectrum_geometry._polarization.shape:
# resetting the integrator if the polarization correction matrix has not the correct shape
self.spectrum_geometry.reset()
if polarization_factor is None:
polarization_factor = self.polarization_factor
if num_points is None:
num_points = self.calculate_number_of_spectrum_points(2)
self.num_points = num_points
t1 = time.time()
if unit is 'd_A':
try:
self.tth, self.int = self.spectrum_geometry.integrate1d(self.img_model.img_data, num_points,
method=method,
unit='2th_deg',
mask=mask,
polarization_factor=polarization_factor,
filename=filename)
except NameError:
self.tth, self.int = self.spectrum_geometry.integrate1d(self.img_model.img_data, num_points,
method='csr',
unit='2th_deg',
mask=mask,
polarization_factor=polarization_factor,
filename=filename)
self.tth = self.spectrum_geometry.wavelength / (2 * np.sin(self.tth / 360 * np.pi)) * 1e10
self.int = self.int
else:
try:
self.tth, self.int = self.spectrum_geometry.integrate1d(self.img_model.img_data, num_points,
method=method,
unit=unit,
mask=mask,
polarization_factor=polarization_factor,
filename=filename)
except NameError:
self.tth, self.int = self.spectrum_geometry.integrate1d(self.img_model.img_data, num_points,
method='csr',
unit=unit,
mask=mask,
polarization_factor=polarization_factor,
filename=filename)
logger.info('1d integration of {0}: {1}s.'.format(os.path.basename(self.img_model.filename), time.time() - t1))
ind = np.where((self.int > 0) & (~np.isnan(self.int)))
self.tth = self.tth[ind]
self.int = self.int[ind]
return self.tth, self.int
def integrate_2d(self, mask=None, polarization_factor=None, unit='2th_deg', method='csr', dimensions=(2048, 2048)):
if polarization_factor is None:
polarization_factor = self.polarization_factor
if self.cake_geometry._polarization is not None:
if self.img_model.img_data.shape != self.cake_geometry._polarization.shape:
# resetting the integrator if the polarization correction matrix has not the same shape as the image
self.cake_geometry.reset()
t1 = time.time()
res = self.cake_geometry.integrate2d(self.img_model._img_data, dimensions[0], dimensions[1], method=method,
mask=mask,
unit=unit, polarization_factor=polarization_factor)
logger.info('2d integration of {0}: {1}s.'.format(os.path.basename(self.img_model.filename), time.time() - t1))
self.cake_img = res[0]
self.cake_tth = res[1]
self.cake_azi = res[2]
return self.cake_img
def create_point_array(self, points, points_ind):
res = []
for i, point_list in enumerate(points):
if point_list.shape == (2,):
res.append([point_list[0], point_list[1], points_ind[i]])
else:
for point in point_list:
res.append([point[0], point[1], points_ind[i]])
return np.array(res)
def get_point_array(self):
return self.create_point_array(self.points, self.points_index)
def get_calibration_parameter(self):
pyFAI_parameter = self.cake_geometry.getPyFAI()
pyFAI_parameter['polarization_factor'] = self.polarization_factor
try:
fit2d_parameter = self.cake_geometry.getFit2D()
fit2d_parameter['polarization_factor'] = self.polarization_factor
except TypeError:
fit2d_parameter = None
try:
pyFAI_parameter['wavelength'] = self.spectrum_geometry.wavelength
fit2d_parameter['wavelength'] = self.spectrum_geometry.wavelength
except RuntimeWarning:
pyFAI_parameter['wavelength'] = 0
return pyFAI_parameter, fit2d_parameter
def calculate_number_of_spectrum_points(self, max_dist_factor=1.5):
# calculates the number of points for an integrated spectrum, based on the distance of the beam center to the the
# image corners. Maximum value is determined by the shape of the image.
fit2d_parameter = self.spectrum_geometry.getFit2D()
center_x = fit2d_parameter['centerX']
center_y = fit2d_parameter['centerY']
width, height = self.img_model.img_data.shape
if center_x < width and center_x > 0:
side1 = np.max([abs(width - center_x), center_x])
else:
side1 = width
if center_y < height and center_y > 0:
side2 = np.max([abs(height - center_y), center_y])
else:
side2 = height
max_dist = np.sqrt(side1 ** 2 + side2 ** 2)
return int(max_dist * max_dist_factor)
def load(self, filename):
"""
Loads a calibration file and and sets all the calibration parameter.
:param filename: filename for a *.poni calibration file
"""
self.spectrum_geometry = AzimuthalIntegrator()
self.spectrum_geometry.load(filename)
self.orig_pixel1 = self.spectrum_geometry.pixel1
self.orig_pixel2 = self.spectrum_geometry.pixel2
self.calibration_name = get_base_name(filename)
self.filename = filename
self.is_calibrated = True
self.create_cake_geometry()
self.set_supersampling()
def save(self, filename):
"""
Saves the current calibration parameters into a a text file. Default extension is
*.poni
"""
self.cake_geometry.save(filename)
self.calibration_name = get_base_name(filename)
self.filename = filename
def create_file_header(self):
return self.cake_geometry.makeHeaders(polarization_factor=self.polarization_factor)
def set_fit2d(self, fit2d_parameter):
"""
Reads in a dictionary with fit2d parameters where the fields of the dictionary are:
'directDist', 'centerX', 'centerY', 'tilt', 'tiltPlanRotation', 'pixelX', pixelY',
'polarization_factor', 'wavelength'
"""
self.spectrum_geometry.setFit2D(directDist=fit2d_parameter['directDist'],
centerX=fit2d_parameter['centerX'],
centerY=fit2d_parameter['centerY'],
tilt=fit2d_parameter['tilt'],
tiltPlanRotation=fit2d_parameter['tiltPlanRotation'],
pixelX=fit2d_parameter['pixelX'],
pixelY=fit2d_parameter['pixelY'])
self.spectrum_geometry.wavelength = fit2d_parameter['wavelength']
self.create_cake_geometry()
self.polarization_factor = fit2d_parameter['polarization_factor']
self.orig_pixel1 = fit2d_parameter['pixelX'] * 1e-6
self.orig_pixel2 = fit2d_parameter['pixelY'] * 1e-6
self.is_calibrated = True
self.set_supersampling()
def set_pyFAI(self, pyFAI_parameter):
"""
Reads in a dictionary with pyFAI parameters where the fields of dictionary are:
'dist', 'poni1', 'poni2', 'rot1', 'rot2', 'rot3', 'pixel1', 'pixel2', 'wavelength',
'polarization_factor'
"""
self.spectrum_geometry.setPyFAI(dist=pyFAI_parameter['dist'],
poni1=pyFAI_parameter['poni1'],
poni2=pyFAI_parameter['poni2'],
rot1=pyFAI_parameter['rot1'],
rot2=pyFAI_parameter['rot2'],
rot3=pyFAI_parameter['rot3'],
pixel1=pyFAI_parameter['pixel1'],
pixel2=pyFAI_parameter['pixel2'])
self.spectrum_geometry.wavelength = pyFAI_parameter['wavelength']
self.create_cake_geometry()
self.polarization_factor = pyFAI_parameter['polarization_factor']
self.orig_pixel1 = pyFAI_parameter['pixel1']
self.orig_pixel2 = pyFAI_parameter['pixel2']
self.is_calibrated = True
self.set_supersampling()
def set_supersampling(self, factor=None):
"""
Sets the supersampling to a specific factor. Whereby the factor determines in how many artificial pixel the
original pixel is split. (factor^2)
factor n_pixel
1 1
2 4
3 9
4 16
"""
if factor is None:
factor = self.supersampling_factor
self.spectrum_geometry.pixel1 = self.orig_pixel1 / float(factor)
self.spectrum_geometry.pixel2 = self.orig_pixel2 / float(factor)
if factor != self.supersampling_factor:
self.spectrum_geometry.reset()
self.supersampling_factor = factor
def reset_supersampling(self):
self.spectrum_geometry.pixel1 = self.orig_pixel1
self.spectrum_geometry.pixel2 = self.orig_pixel2
def get_two_theta_img(self, x, y):
"""
Gives the two_theta value for the x,y coordinates on the image. Be aware that this function will be incorrect
for pixel indices, since it does not correct for center of the pixel.
:return:
two theta in radians
"""
x = np.array([x]) * self.supersampling_factor
y = np.array([y]) * self.supersampling_factor
return self.spectrum_geometry.tth(x - 0.5, y - 0.5)[0] # deletes 0.5 because tth function uses pixel indices
def get_azi_img(self, x, y):
"""
Gives chi for position on image.
:param x:
x-coordinate in pixel
:param y:
y-coordinate in pixel
:return:
azimuth in radians
"""
x *= self.supersampling_factor
y *= self.supersampling_factor
return self.spectrum_geometry.chi(x, y)[0]
def get_two_theta_cake(self, x):
"""
Gives the two_theta value for the x coordinate in the cake
:param x:
x-coordinate on image
:return:
two theta in degree
"""
x -= 0.5
cake_step = self.cake_tth[1] - self.cake_tth[0]
tth = self.cake_tth[int(np.floor(x))] + (x - np.floor(x)) * cake_step
return tth
def get_azi_cake(self, x):
"""
Gives the azimuth value for a cake.
:param x:
x-coordinate in pixel
:return:
azimuth in degree
"""
x -= 0.5
azi_step = self.cake_azi[1] - self.cake_azi[0]
azi = self.cake_azi[int(np.floor(x))] + (x - np.floor(x)) * azi_step
return azi
def get_two_theta_array(self):
return self.spectrum_geometry._ttha[::self.supersampling_factor, ::self.supersampling_factor]
def get_pixel_ind(self, tth, azi):
"""
Calculates pixel index for a specfic two theta and azimutal value.
:param tth:
two theta in radians
:param azi:
azimuth in radians
:return:
tuple of index 1 and 2
"""
tth_ind = find_contours(self.spectrum_geometry.ttha, tth)
tth_ind = np.vstack(tth_ind)
azi_values = self.spectrum_geometry.chi(tth_ind[:, 0], tth_ind[:, 1])
min_index = np.argmin(np.abs(azi_values - azi))
return tth_ind[min_index, 0], tth_ind[min_index, 1]
@property
def wavelength(self):
return self.spectrum_geometry.wavelength
def run(self):
ai = AzimuthalIntegrator(dist=self.__distance,
poni1=self.__poni1,
poni2=self.__poni2,
rot1=self.__rotation1,
rot2=self.__rotation2,
rot3=self.__rotation3,
detector=self.__detector,
wavelength=self.__wavelength)
# FIXME Add error model
method1d = method_registry.Method(1, self.__method.split,
self.__method.algo,
self.__method.impl, None)
methods = method_registry.IntegrationMethod.select_method(
method=method1d)
if len(methods) == 0:
method1d = method_registry.Method(1, method1d.split, "*", "*",
None)
_logger.warning("Downgrade 1D integration method to %s", method1d)
else:
method1d = methods[0].method
method2d = method_registry.Method(2, self.__method.split,
self.__method.algo,
self.__method.impl, None)
methods = method_registry.IntegrationMethod.select_method(
method=method2d)
if len(methods) == 0:
method2d = method_registry.Method(2, method2d.split, "*", "*",
None)
_logger.warning("Downgrade 2D integration method to %s", method2d)
else:
method2d = methods[0].method
self.__result1d = ai.integrate1d(
method=method1d,
data=self.__image,
npt=self.__nPointsRadial,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
self.__result2d = ai.integrate2d(
method=method2d,
data=self.__image,
npt_rad=self.__nPointsRadial,
npt_azim=self.__nPointsAzimuthal,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
# Create an image masked where data exists
self.__resultMask2d = None
if self.__mask is not None:
if self.__mask.shape == self.__image.shape:
maskData = numpy.ones(shape=self.__image.shape,
dtype=numpy.float32)
maskData[self.__mask == 0] = float("NaN")
if self.__displayMask:
self.__resultMask2d = ai.integrate2d(
method=method2d,
data=maskData,
npt_rad=self.__nPointsRadial,
npt_azim=self.__nPointsAzimuthal,
unit=self.__radialUnit,
polarization_factor=self.__polarizationFactor)
else:
_logger.warning(
"Inconsistency between image and mask sizes. %s != %s",
self.__image.shape, self.__mask.shape)
try:
self.__directDist = ai.getFit2D()["directDist"]
except Exception:
# The geometry could not fit this param
_logger.debug("Backtrace", exc_info=True)
self.__directDist = None
if self.__calibrant:
rings = self.__calibrant.get_2th()
try:
rings = unitutils.from2ThRad(rings, self.__radialUnit,
self.__wavelength,
self.__directDist)
except ValueError:
message = "Convertion to unit %s not supported. Ring marks ignored"
_logger.warning(message, self.__radialUnit)
rings = []
# Filter the rings which are not part of the result
rings = filter(
lambda x: self.__result1d.radial[0] <= x <= self.__result1d.
radial[-1], rings)
rings = list(rings)
else:
rings = []
self.__ringAngles = rings
self.__ai = ai
class EigerFrame(wx.Frame):
"""AreaDetector Display """
img_attrs = ('ArrayData', 'UniqueId_RBV')
cam_attrs = ('Acquire', 'DetectorState_RBV', 'ArrayCounter',
'ArrayCounter_RBV', 'ThresholdEnergy', 'ThresholdEnergy_RBV',
'PhotonEnergy', 'PhotonEnergy_RBV', 'NumImages',
'NumImages_RBV', 'AcquireTime', 'AcquireTime_RBV',
'AcquirePeriod', 'AcquirePeriod_RBV', 'TriggerMode',
'TriggerMode_RBV')
# plugins to enable
enabled_plugins = ('image1', 'Over1', 'ROI1', 'JPEG1', 'TIFF1')
def __init__(self, prefix=None, url=None, scale=1.0):
self.ad_img = None
self.ad_cam = None
if prefix is None:
dlg = SavedParameterDialog(label='Detector Prefix',
title='Connect to Eiger Detector',
configfile='.ad_eigerdisplay.dat')
res = dlg.GetResponse()
dlg.Destroy()
if res.ok:
prefix = res.value
self.prefix = prefix
self.fname = 'Eiger.tif'
self.esimplon = None
if url is not None and HAS_SIMPLON:
self.esimplon = EigerSimplon(url, prefix=prefix + 'cam1:')
self.lineplotter = None
self.calib = {}
self.integrator = None
self.int_panel = None
self.int_lastid = None
self.contrast_levels = None
self.scandb = None
wx.Frame.__init__(self,
None,
-1,
"Eiger500K Area Detector Display",
style=wx.DEFAULT_FRAME_STYLE)
self.buildMenus()
self.buildFrame()
wx.CallAfter(self.connect_escandb)
def connect_escandb(self):
if HAS_ESCAN and os.environ.get('ESCAN_CREDENTIALS', None) is not None:
self.scandb = ScanDB()
calib_loc = self.scandb.get_info('eiger_calibration')
cal = self.scandb.get_detectorconfig(calib_loc)
self.setup_calibration(json.loads(cal.text))
def buildFrame(self):
sbar = self.CreateStatusBar(3, wx.CAPTION) # |wx.THICK_FRAME)
self.SetStatusWidths([-1, -1, -1])
sfont = sbar.GetFont()
sfont.SetWeight(wx.BOLD)
sfont.SetPointSize(10)
sbar.SetFont(sfont)
self.SetStatusText('', 0)
sizer = wx.GridBagSizer(3, 3)
panel = self.panel = wx.Panel(self)
pvpanel = PVConfigPanel(panel, self.prefix, display_pvs)
wsize = (100, -1)
lsize = (250, -1)
start_btn = wx.Button(panel, label='Start', size=wsize)
stop_btn = wx.Button(panel, label='Stop', size=wsize)
free_btn = wx.Button(panel, label='Free Run', size=wsize)
start_btn.Bind(wx.EVT_BUTTON, partial(self.onButton, key='start'))
stop_btn.Bind(wx.EVT_BUTTON, partial(self.onButton, key='stop'))
free_btn.Bind(wx.EVT_BUTTON, partial(self.onButton, key='free'))
self.cmap_choice = wx.Choice(panel, size=(80, -1), choices=colormaps)
self.cmap_choice.SetSelection(0)
self.cmap_choice.Bind(wx.EVT_CHOICE, self.onColorMap)
self.cmap_reverse = wx.CheckBox(panel, label='Reverse', size=(60, -1))
self.cmap_reverse.Bind(wx.EVT_CHECKBOX, self.onColorMap)
self.show1d_btn = wx.Button(panel,
label='Show 1D Integration',
size=(200, -1))
self.show1d_btn.Bind(wx.EVT_BUTTON, self.onShowIntegration)
self.show1d_btn.Disable()
self.imagesize = wx.StaticText(panel,
label='? x ?',
size=(250, 30),
style=txtstyle)
self.contrast = ContrastChoice(panel, callback=self.set_contrast_level)
def lin(len=200, wid=2, style=wx.LI_HORIZONTAL):
return wx.StaticLine(panel, size=(len, wid), style=style)
irow = 0
sizer.Add(pvpanel, (irow, 0), (1, 3), labstyle)
irow += 1
sizer.Add(start_btn, (irow, 0), (1, 1), labstyle)
sizer.Add(stop_btn, (irow, 1), (1, 1), labstyle)
sizer.Add(free_btn, (irow, 2), (1, 1), labstyle)
irow += 1
sizer.Add(lin(300), (irow, 0), (1, 3), labstyle)
irow += 1
sizer.Add(self.imagesize, (irow, 0), (1, 3), labstyle)
irow += 1
sizer.Add(wx.StaticText(panel, label='Color Map: '), (irow, 0), (1, 1),
labstyle)
sizer.Add(self.cmap_choice, (irow, 1), (1, 1), labstyle)
sizer.Add(self.cmap_reverse, (irow, 2), (1, 1), labstyle)
irow += 1
sizer.Add(self.contrast.label, (irow, 0), (1, 1), labstyle)
sizer.Add(self.contrast.choice, (irow, 1), (1, 1), labstyle)
irow += 1
sizer.Add(self.show1d_btn, (irow, 0), (1, 2), labstyle)
panel.SetSizer(sizer)
sizer.Fit(panel)
# image panel
self.image = ADMonoImagePanel(self,
prefix=self.prefix,
rot90=DEFAULT_ROTATION,
size=(400, 750),
writer=partial(self.write, panel=2))
mainsizer = wx.BoxSizer(wx.HORIZONTAL)
mainsizer.Add(panel, 0, wx.LEFT | wx.GROW | wx.ALL)
mainsizer.Add(self.image, 1, wx.CENTER | wx.GROW | wx.ALL)
self.SetSizer(mainsizer)
mainsizer.Fit(self)
self.SetAutoLayout(True)
try:
self.SetIcon(wx.Icon(ICONFILE, wx.BITMAP_TYPE_ICO))
except:
pass
wx.CallAfter(self.connect_pvs)
def onColorMap(self, event=None):
cmap_name = self.cmap_choice.GetStringSelection()
if self.cmap_reverse.IsChecked():
cmap_name = cmap_name + '_r'
self.image.colormap = getattr(colormap, cmap_name)
self.image.Refresh()
def onCopyImage(self, event=None):
"copy bitmap of canvas to system clipboard"
bmp = wx.BitmapDataObject()
bmp.SetBitmap(wx.Bitmap(self.image.GrabWxImage()))
wx.TheClipboard.Open()
wx.TheClipboard.SetData(bmp)
wx.TheClipboard.Close()
wx.TheClipboard.Flush()
def onReadCalibFile(self, event=None):
"read calibration file"
wcards = "Poni Files(*.poni)|*.poni|All files (*.*)|*.*"
dlg = wx.FileDialog(None,
message='Read Calibration File',
defaultDir=os.getcwd(),
wildcard=wcards,
style=wx.FD_OPEN)
ppath = None
if dlg.ShowModal() == wx.ID_OK:
ppath = os.path.abspath(dlg.GetPath())
if os.path.exists(ppath):
if self.scandb is not None:
CalibrationDialog(self, ppath).Show()
else:
self.setup_calibration(read_poni(ppath))
def setup_calibration(self, calib):
"""set up calibration from calibration dict"""
if self.image.rot90 in (1, 3):
calib['rot3'] = np.pi / 2.0
self.calib = calib
if HAS_PYFAI:
self.integrator = AzimuthalIntegrator(**calib)
self.show1d_btn.Enable()
def onShowIntegration(self, event=None):
if self.calib is None or 'poni1' not in self.calib:
return
shown = False
try:
self.int_panel.Raise()
shown = True
except:
self.int_panel = None
if not shown:
self.int_panel = PlotFrame(self)
self.show_1dpattern(init=True)
else:
self.show_1dpattern()
def onAutoIntegration(self, event=None):
if not event.IsChecked():
self.int_timer.Stop()
return
if self.calib is None or 'poni1' not in self.calib:
return
shown = False
try:
self.int_panel.Raise()
shown = True
except:
self.int_panel = None
if not shown:
self.int_panel = PlotFrame(self)
self.show_1dpattern(init=True)
else:
self.show_1dpattern()
self.int_timer.Start(500)
def show_1dpattern(self, init=False):
if self.calib is None or not HAS_PYFAI:
return
img = self.ad_img.PV('ArrayData').get()
h, w = self.image.GetImageSize()
img.shape = (w, h)
img = img[3:-3, 1:-1][::-1, :]
img_id = self.ad_cam.ArrayCounter_RBV
q, xi = self.integrator.integrate1d(img,
2048,
unit='q_A^-1',
correctSolidAngle=True,
polarization_factor=0.999)
if init:
self.int_panel.plot(q,
xi,
xlabel=r'$Q (\rm\AA^{-1})$',
marker='+',
title='Image %d' % img_id)
self.int_panel.Raise()
self.int_panel.Show()
else:
self.int_panel.update_line(0, q, xi, draw=True)
self.int_panel.set_title('Image %d' % img_id)
@EpicsFunction
def onSaveImage(self, event=None):
"prompts for and save image to file"
defdir = os.getcwd()
self.fname = "Image_%i.tiff" % self.ad_cam.ArrayCounter_RBV
dlg = wx.FileDialog(None,
message='Save Image as',
defaultDir=os.getcwd(),
defaultFile=self.fname,
style=wx.FD_SAVE)
path = None
if dlg.ShowModal() == wx.ID_OK:
path = os.path.abspath(dlg.GetPath())
root, fname = os.path.split(path)
epics.caput("%sTIFF1:FileName" % self.prefix, fname)
epics.caput("%sTIFF1:FileWriteMode" % self.prefix, 0)
time.sleep(0.05)
epics.caput("%sTIFF1:WriteFile" % self.prefix, 1)
time.sleep(0.05)
print(
"Saved TIFF File ",
epics.caget("%sTIFF1:FullFileName_RBV" % self.prefix,
as_string=True))
def onExit(self, event=None):
try:
wx.Yield()
except:
pass
self.Destroy()
def onAbout(self, event=None):
msg = """Eiger Image Display version 0.1
Matt Newville <*****@*****.**>"""
dlg = wx.MessageDialog(self, msg, "About Epics Image Display",
wx.OK | wx.ICON_INFORMATION)
dlg.ShowModal()
dlg.Destroy()
def buildMenus(self):
fmenu = wx.Menu()
MenuItem(self, fmenu, "&Save\tCtrl+S", "Save Image", self.onSaveImage)
MenuItem(self, fmenu, "&Copy\tCtrl+C", "Copy Image to Clipboard",
self.onCopyImage)
MenuItem(self, fmenu, "Read Calibration File", "Read PONI Calibration",
self.onReadCalibFile)
fmenu.AppendSeparator()
MenuItem(self, fmenu, "E&xit\tCtrl+Q", "Exit Program", self.onExit)
omenu = wx.Menu()
MenuItem(self, omenu, "&Rotate CCW\tCtrl+R",
"Rotate Counter Clockwise", self.onRot90)
MenuItem(self, omenu, "Flip Up/Down\tCtrl+T", "Flip Up/Down",
self.onFlipV)
MenuItem(self, omenu, "Flip Left/Right\tCtrl+F", "Flip Left/Right",
self.onFlipH)
MenuItem(self, omenu, "Reset Rotations and Flips", "Reset",
self.onResetRotFlips)
omenu.AppendSeparator()
hmenu = wx.Menu()
MenuItem(self, hmenu, "About", "About Epics AreadDetector Display",
self.onAbout)
mbar = wx.MenuBar()
mbar.Append(fmenu, "File")
mbar.Append(omenu, "Options")
mbar.Append(hmenu, "&Help")
self.SetMenuBar(mbar)
def onResetRotFlips(self, event):
self.image.rot90 = DEFAULT_ROTATION
self.image.flipv = self.fliph = False
def onRot90(self, event):
self.image.rot90 = (self.image.rot90 - 1) % 4
def onFlipV(self, event):
self.image.flipv = not self.image.flipv
def onFlipH(self, event):
self.image.fliph = not self.image.fliph
def set_contrast_level(self, contrast_level=0):
self.image.contrast_levels = [contrast_level, 100.0 - contrast_level]
def write(self, s, panel=0):
"""write a message to the Status Bar"""
self.SetStatusText(text=s, number=panel)
@EpicsFunction
def onButton(self, event=None, key='free'):
key = key.lower()
if key.startswith('free'):
self.image.restart_fps_counter()
self.ad_cam.AcquireTime = 0.25
self.ad_cam.AcquirePeriod = 0.25
self.ad_cam.NumImages = 345600
self.ad_cam.Acquire = 1
elif key.startswith('start'):
self.image.restart_fps_counter()
self.ad_cam.Acquire = 1
elif key.startswith('stop'):
self.ad_cam.Acquire = 0
@EpicsFunction
def connect_pvs(self, verbose=True):
if self.prefix is None or len(self.prefix) < 2:
return
if self.prefix.endswith(':'):
self.prefix = self.prefix[:-1]
if self.prefix.endswith(':image1'):
self.prefix = self.prefix[:-7]
if self.prefix.endswith(':cam1'):
self.prefix = self.prefix[:-5]
self.write('Connecting to AD %s' % self.prefix)
self.ad_img = epics.Device(self.prefix + ':image1:',
delim='',
attrs=self.img_attrs)
self.ad_cam = epics.Device(self.prefix + ':cam1:',
delim='',
attrs=self.cam_attrs)
epics.caput("%s:TIFF1:EnableCallbacks" % self.prefix, 1)
epics.caput("%s:TIFF1:AutoSave" % self.prefix, 0)
epics.caput("%s:TIFF1:AutoIncrement" % self.prefix, 0)
epics.caput("%s:TIFF1:FileWriteMode" % self.prefix, 0)
time.sleep(0.002)
if not self.ad_img.PV('UniqueId_RBV').connected:
epics.poll()
if not self.ad_img.PV('UniqueId_RBV').connected:
self.write('Warning: Camera seems to not be connected!')
return
if verbose:
self.write('Connected to AD %s' % self.prefix)
self.SetTitle("Epics Image Display: %s" % self.prefix)
sizex = self.ad_cam.MaxSizeX_RBV
sizey = self.ad_cam.MaxSizeY_RBV
sizelabel = 'Image Size: %i x %i pixels'
try:
sizelabel = sizelabel % (sizex, sizey)
except:
sizelabel = sizelabel % (0, 0)
self.imagesize.SetLabel(sizelabel)
self.ad_cam.add_callback('DetectorState_RBV', self.onDetState)
self.contrast.set_level_str('0.05')
@DelayedEpicsCallback
def onDetState(self, pvname=None, value=None, char_value=None, **kw):
self.write(char_value, panel=1)
class ADFrame(wx.Frame):
"""
AreaDetector Display Frame
"""
def __init__(self, configfile=None):
wx.Frame.__init__(self,
None,
-1,
'AreaDetector Viewer',
style=wx.DEFAULT_FRAME_STYLE)
if configfile is None:
wcard = 'Detector Config Files (*.yaml)|*.yaml|All files (*.*)|*.*'
configfile = FileOpen(self,
"Read Detector Configuration File",
default_file='det.yaml',
wildcard=wcard)
if configfile is None:
sys.exit()
self.config = read_adconfig(configfile)
self.prefix = self.config['general']['prefix']
self.fname = self.config['general']['name']
self.colormode = self.config['general']['colormode'].lower()
self.cam_attrs = self.config['cam_attributes']
self.img_attrs = self.config['img_attributes']
self.fsaver = self.config['general']['filesaver']
self.SetTitle(self.config['general']['title'])
self.scandb = None
if ScanDB is not None:
self.scandb = ScanDB()
if self.scandb.engine is None: # not connected to running scandb server
self.scandb = None
self.calib = None
self.ad_img = None
self.ad_cam = None
self.lineplotter = None
self.integrator = None
self.int_panel = None
self.int_lastid = None
self.contrast_levels = None
self.thumbnail = None
self.buildMenus()
self.buildFrame()
def buildFrame(self):
self.SetFont(Font(11))
sbar = self.CreateStatusBar(3, wx.CAPTION)
self.SetStatusWidths([-1, -1, -1])
self.SetStatusText('', 0)
sizer = wx.GridBagSizer(3, 3)
panel = self.panel = wx.Panel(self)
pvpanel = PVConfigPanel(panel, self.prefix, self.config['controls'])
wsize = (100, -1)
lsize = (250, -1)
start_btn = wx.Button(panel, label='Start', size=wsize)
stop_btn = wx.Button(panel, label='Stop', size=wsize)
start_btn.Bind(wx.EVT_BUTTON, partial(self.onButton, key='start'))
stop_btn.Bind(wx.EVT_BUTTON, partial(self.onButton, key='stop'))
self.contrast = ContrastControl(panel,
callback=self.set_contrast_level)
self.imagesize = wx.StaticText(panel,
label='? x ?',
size=(150, 30),
style=txtstyle)
def lin(len=200, wid=2, style=wx.LI_HORIZONTAL):
return wx.StaticLine(panel, size=(len, wid), style=style)
irow = 0
sizer.Add(pvpanel, (irow, 0), (1, 3), labstyle)
irow += 1
sizer.Add(start_btn, (irow, 0), (1, 1), labstyle)
sizer.Add(stop_btn, (irow, 1), (1, 1), labstyle)
if self.config['general'].get('show_free_run', False):
free_btn = wx.Button(panel, label='Free Run', size=wsize)
free_btn.Bind(wx.EVT_BUTTON, partial(self.onButton, key='free'))
irow += 1
sizer.Add(free_btn, (irow, 0), (1, 2), labstyle)
irow += 1
sizer.Add(lin(200, wid=4), (irow, 0), (1, 3), labstyle)
irow += 1
sizer.Add(self.imagesize, (irow, 0), (1, 3), labstyle)
if self.colormode.startswith('mono'):
self.cmap_choice = wx.Choice(panel,
size=(80, -1),
choices=self.config['colormaps'])
self.cmap_choice.SetSelection(0)
self.cmap_choice.Bind(wx.EVT_CHOICE, self.onColorMap)
self.cmap_reverse = wx.CheckBox(panel,
label='Reverse',
size=(60, -1))
self.cmap_reverse.Bind(wx.EVT_CHECKBOX, self.onColorMap)
irow += 1
sizer.Add(wx.StaticText(panel, label='Color Map: '), (irow, 0),
(1, 1), labstyle)
sizer.Add(self.cmap_choice, (irow, 1), (1, 1), labstyle)
sizer.Add(self.cmap_reverse, (irow, 2), (1, 1), labstyle)
irow += 1
sizer.Add(self.contrast.label, (irow, 0), (1, 1), labstyle)
sizer.Add(self.contrast.choice, (irow, 1), (1, 1), labstyle)
if self.config['general']['show_1dintegration']:
self.show1d_btn = wx.Button(panel,
label='Show 1D Integration',
size=(200, -1))
self.show1d_btn.Bind(wx.EVT_BUTTON, self.onShowIntegration)
self.show1d_btn.Disable()
irow += 1
sizer.Add(self.show1d_btn, (irow, 0), (1, 2), labstyle)
if self.config['general']['show_thumbnail']:
t_size = self.config['general'].get('thumbnail_size', 100)
self.thumbnail = ThumbNailImagePanel(panel,
imgsize=t_size,
size=(350, 350),
motion_writer=partial(
self.write, panel=0))
label = wx.StaticText(panel,
label='Thumbnail size (pixels): ',
size=(200, -1),
style=txtstyle)
self.thumbsize = FloatSpin(panel,
value=100,
min_val=10,
increment=5,
action=self.onThumbSize,
size=(150, -1),
style=txtstyle)
irow += 1
sizer.Add(label, (irow, 0), (1, 1), labstyle)
sizer.Add(self.thumbsize, (irow, 1), (1, 1), labstyle)
irow += 1
sizer.Add(self.thumbnail, (irow, 0), (1, 2), labstyle)
panel.SetSizer(sizer)
sizer.Fit(panel)
# image panel
self.image = ADMonoImagePanel(
self,
prefix=self.prefix,
rot90=self.config['general']['default_rotation'],
size=(750, 750),
writer=partial(self.write, panel=1),
thumbnail=self.thumbnail,
motion_writer=partial(self.write, panel=2))
mainsizer = wx.BoxSizer(wx.HORIZONTAL)
mainsizer.Add(panel, 0, wx.LEFT | wx.GROW | wx.ALL)
mainsizer.Add(self.image, 1, wx.CENTER | wx.GROW | wx.ALL)
self.SetSizer(mainsizer)
mainsizer.Fit(self)
self.SetAutoLayout(True)
iconfile = self.config['general'].get('iconfile', None)
if iconfile is None or not os.path.exists(iconfile):
iconfile = DEFAULT_ICONFILE
try:
self.SetIcon(wx.Icon(iconfile, wx.BITMAP_TYPE_ICO))
except:
pass
self.connect_pvs()
def onThumbSize(self, event=None):
self.thumbnail.imgsize = int(self.thumbsize.GetValue())
def onColorMap(self, event=None):
cmap_name = self.cmap_choice.GetStringSelection()
if self.cmap_reverse.IsChecked():
cmap_name = cmap_name + '_r'
self.image.colormap = getattr(colormap, cmap_name)
self.image.Refresh()
def onCopyImage(self, event=None):
"copy bitmap of canvas to system clipboard"
bmp = wx.BitmapDataObject()
bmp.SetBitmap(wx.Bitmap(self.image.GrabWxImage()))
wx.TheClipboard.Open()
wx.TheClipboard.SetData(bmp)
wx.TheClipboard.Close()
wx.TheClipboard.Flush()
def onReadCalibFile(self, event=None):
"read calibration file"
wcards = "Poni Files(*.poni)|*.poni|All files (*.*)|*.*"
dlg = wx.FileDialog(None,
message='Read Calibration File',
defaultDir=os.getcwd(),
wildcard=wcards,
style=wx.FD_OPEN)
ppath = None
if dlg.ShowModal() == wx.ID_OK:
ppath = os.path.abspath(dlg.GetPath())
if os.path.exists(ppath):
self.setup_calibration(ppath)
def setup_calibration(self, ponifile):
"""set up calibration from PONI file"""
calib = read_poni(ponifile)
# if self.image.rot90 in (1, 3):
# calib['rot3'] = np.pi/2.0
self.calib = calib
if HAS_PYFAI:
self.integrator = AzimuthalIntegrator(**calib)
self.show1d_btn.Enable()
else:
self.write('Warning: PyFAI is not installed')
if self.scandb is not None:
_, calname = os.path.split(ponifile)
self.scandb.set_detectorconfig(calname, json.dumps(calib))
self.scandb.set_info('xrd_calibration', calname)
def onShowIntegration(self, event=None):
if self.calib is None or 'poni1' not in self.calib:
return
shown = False
try:
self.int_panel.Raise()
shown = True
except:
self.int_panel = None
if not shown:
self.int_panel = PlotFrame(self)
self.show_1dpattern(init=True)
else:
self.show_1dpattern()
def onAutoIntegration(self, event=None):
if not event.IsChecked():
self.int_timer.Stop()
return
if self.calib is None or 'poni1' not in self.calib:
return
shown = False
try:
self.int_panel.Raise()
shown = True
except:
self.int_panel = None
if not shown:
self.int_panel = PlotFrame(self)
self.show_1dpattern(init=True)
else:
self.show_1dpattern()
self.int_timer.Start(500)
def show_1dpattern(self, init=False):
if self.calib is None or not HAS_PYFAI:
return
img = self.ad_img.PV('ArrayData').get()
h, w = self.image.GetImageSize()
img.shape = (w, h)
# may need to trim outer pixels (int1d_trimx/int1d_trimy in config)
xstride = 1
if self.config['general'].get('int1d_flipx', False):
xstride = -1
xslice = slice(None, None, xstride)
trimx = int(self.config['general'].get('int1d_trimx', 0))
if trimx != 0:
xslice = slice(trimx * xstride, -trimx * xstride, xstride)
ystride = 1
if self.config['general'].get('int1d_flipy', True):
ystride = -1
yslice = slice(None, None, ystride)
trimy = int(self.config['general'].get('int1d_trimy', 0))
if trimy > 0:
yslice = slice(trimy * ystride, -trimy * ystride, ystride)
img = img[yslice, xslice]
img_id = self.ad_cam.ArrayCounter_RBV
q, xi = self.integrator.integrate1d(img,
2048,
unit='q_A^-1',
correctSolidAngle=True,
polarization_factor=0.999)
if init:
self.int_panel.plot(q,
xi,
xlabel=r'$Q (\rm\AA^{-1})$',
marker='+',
title='Image %d' % img_id)
self.int_panel.Raise()
self.int_panel.Show()
else:
self.int_panel.update_line(0, q, xi, draw=True)
self.int_panel.set_title('Image %d' % img_id)
@EpicsFunction
def onSaveImage(self, event=None):
"prompts for and save image to file"
defdir = os.getcwd()
self.fname = "Image_%i.tiff" % self.ad_cam.ArrayCounter_RBV
dlg = wx.FileDialog(None,
message='Save Image as',
defaultDir=os.getcwd(),
defaultFile=self.fname,
style=wx.FD_SAVE)
path = None
if dlg.ShowModal() == wx.ID_OK:
path = os.path.abspath(dlg.GetPath())
root, fname = os.path.split(path)
epics.caput("%s%sFileName" % self.prefix, self.fsaver, fname)
epics.caput("%s%sFileWriteMode" % self.prefix, self.fsaver, 0)
time.sleep(0.05)
epics.caput("%s%sWriteFile" % self.prefix, self.fsaver, 1)
time.sleep(0.05)
file_pv = "%s%sFullFileName_RBV" % (self.prefix, self.prefix)
print("Saved image File ", epics.caget(file_pv, as_string=True))
def onExit(self, event=None):
try:
wx.Yield()
except:
pass
self.Destroy()
def onAbout(self, event=None):
msg = """areaDetector Display version 0.2
Matt Newville <*****@*****.**>"""
dlg = wx.MessageDialog(self, msg, "About areaDetector Display",
wx.OK | wx.ICON_INFORMATION)
dlg.ShowModal()
dlg.Destroy()
def buildMenus(self):
fmenu = wx.Menu()
MenuItem(self, fmenu, "&Save\tCtrl+S", "Save Image", self.onSaveImage)
MenuItem(self, fmenu, "&Copy\tCtrl+C", "Copy Image to Clipboard",
self.onCopyImage)
MenuItem(self, fmenu, "Read Calibration File", "Read PONI Calibration",
self.onReadCalibFile)
fmenu.AppendSeparator()
MenuItem(self, fmenu, "E&xit\tCtrl+Q", "Exit Program", self.onExit)
omenu = wx.Menu()
MenuItem(self, omenu, "&Rotate CCW\tCtrl+R",
"Rotate Counter Clockwise", self.onRot90)
MenuItem(self, omenu, "Flip Up/Down\tCtrl+T", "Flip Up/Down",
self.onFlipV)
MenuItem(self, omenu, "Flip Left/Right\tCtrl+F", "Flip Left/Right",
self.onFlipH)
MenuItem(self, omenu, "Reset Rotations and Flips", "Reset",
self.onResetRotFlips)
omenu.AppendSeparator()
hmenu = wx.Menu()
MenuItem(self, hmenu, "About", "About areaDetector Display",
self.onAbout)
mbar = wx.MenuBar()
mbar.Append(fmenu, "File")
mbar.Append(omenu, "Options")
mbar.Append(hmenu, "&Help")
self.SetMenuBar(mbar)
def onResetRotFlips(self, event):
self.image.rot90 = 0
self.image.flipv = self.image.fliph = False
def onRot90(self, event):
self.image.rot90 = (self.image.rot90 - 1) % 4
def onFlipV(self, event):
self.image.flipv = not self.image.flipv
def onFlipH(self, event):
self.image.fliph = not self.image.fliph
def set_contrast_level(self, contrast_level=0):
self.image.contrast_levels = [contrast_level, 100.0 - contrast_level]
self.image.Refresh()
def write(self, s, panel=0):
"""write a message to the Status Bar"""
self.SetStatusText(text=s, number=panel)
@EpicsFunction
def onButton(self, event=None, key='free'):
key = key.lower()
if key.startswith('free'):
ftime = self.config['general']['free_run_time']
self.image.restart_fps_counter()
self.ad_cam.AcquireTime = ftime
self.ad_cam.AcquirePeriod = ftime
self.ad_cam.NumImages = int((3 * 86400.) / ftime)
self.ad_cam.Acquire = 1
elif key.startswith('start'):
self.image.restart_fps_counter()
self.ad_cam.Acquire = 1
elif key.startswith('stop'):
self.ad_cam.Acquire = 0
@EpicsFunction
def connect_pvs(self, verbose=True):
if self.prefix is None or len(self.prefix) < 2:
return
self.write('Connecting to areaDetector %s' % self.prefix)
self.ad_img = epics.Device(self.prefix + 'image1:',
delim='',
attrs=self.img_attrs)
self.ad_cam = epics.Device(self.prefix + 'cam1:',
delim='',
attrs=self.cam_attrs)
if self.config['general']['use_filesaver']:
epics.caput("%s%sEnableCallbacks" % (self.prefix, self.fsaver), 1)
epics.caput("%s%sAutoSave" % (self.prefix, self.fsaver), 0)
epics.caput("%s%sAutoIncrement" % (self.prefix, self.fsaver), 0)
epics.caput("%s%sFileWriteMode" % (self.prefix, self.fsaver), 0)
time.sleep(0.002)
if not self.ad_img.PV('UniqueId_RBV').connected:
epics.poll()
if not self.ad_img.PV('UniqueId_RBV').connected:
self.write('Warning: detector seems to not be connected!')
return
if verbose:
self.write('Connected to detector %s' % self.prefix)
self.SetTitle("Epics areaDetector Display: %s" % self.prefix)
sizex = self.ad_cam.MaxSizeX_RBV
sizey = self.ad_cam.MaxSizeY_RBV
sizelabel = 'Image Size: %i x %i pixels'
try:
sizelabel = sizelabel % (sizex, sizey)
except:
sizelabel = sizelabel % (0, 0)
self.imagesize.SetLabel(sizelabel)
self.ad_cam.add_callback('DetectorState_RBV', self.onDetState)
self.contrast.set_level_str('0.01')
@DelayedEpicsCallback
def onDetState(self, pvname=None, value=None, char_value=None, **kw):
self.write(char_value, panel=0)
from pyFAI.azimuthalIntegrator import AzimuthalIntegrator
import pyFAI.opencl.peak_finder
shape = 2048, 2048
npeaks = 100
nbins = 512
numpy.random.seed(0)
img = numpy.ones(shape, dtype="float32")
variance = img.copy()
peaks = numpy.random.randint(0, shape[0] * shape[1], size=npeaks)
img.ravel()[peaks] = 4e9
print(img.shape, img.mean(), img.std())
# or a in zip(peaks//shape[1], peaks%shape[1]): print(a)
JF4 = Detector(pixel1=75e-6, pixel2=75e-6, max_shape=shape)
ai = AzimuthalIntegrator(detector=JF4)
ai.setFit2D(100, shape[1] // 2, shape[0] // 2)
csr = ai.setup_CSR(None, nbins, unit="r_m", split="no").lut
r2 = ai.array_from_unit(unit="r_m")
res = ai.integrate1d(img, nbins, unit="r_m")
pf = pyFAI.opencl.peak_finder.OCL_PeakFinder(csr,
img.size,
bin_centers=res[0],
radius=r2,
profile=True)
print(pf.count(img, error_model="azimuthal", cutoff_clip=6), npeaks)
# res = pf(img, variance=variance)
# for a in zip(res[0] // shape[1], res[0] % shape[1], res[1]): print(a)
pf.log_profile(stats=True)
class ADFrame(wx.Frame):
"""
AreaDetector Display Frame
"""
def __init__(self, configfile=None):
wx.Frame.__init__(self, None, -1, 'AreaDetector Viewer',
style=wx.DEFAULT_FRAME_STYLE)
if configfile is None:
wcard = 'Detector Config Files (*.yaml)|*.yaml|All files (*.*)|*.*'
configfile = FileOpen(self, "Read Detector Configuration File",
default_file='det.yaml',
wildcard=wcard)
if configfile is None:
sys.exit()
self.config = read_adconfig(configfile)
self.prefix = self.config['general']['prefix']
self.fname = self.config['general']['name']
self.colormode = self.config['general']['colormode'].lower()
self.cam_attrs = self.config['cam_attributes']
self.img_attrs = self.config['img_attributes']
self.fsaver = self.config['general']['filesaver']
self.SetTitle(self.config['general']['title'])
self.scandb = None
if ScanDB is not None:
self.scandb = ScanDB()
if self.scandb.engine is None: # not connected to running scandb server
self.scandb = None
self.calib = None
self.ad_img = None
self.ad_cam = None
self.lineplotter = None
self.integrator = None
self.int_panel = None
self.int_lastid = None
self.contrast_levels = None
self.thumbnail = None
self.buildMenus()
self.buildFrame()
def buildFrame(self):
self.SetFont(Font(11))
sbar = self.CreateStatusBar(3, wx.CAPTION)
self.SetStatusWidths([-1, -1, -1])
self.SetStatusText('',0)
sizer = wx.GridBagSizer(3, 3)
panel = self.panel = wx.Panel(self)
pvpanel = PVConfigPanel(panel, self.prefix, self.config['controls'])
wsize = (100, -1)
lsize = (250, -1)
start_btn = wx.Button(panel, label='Start', size=wsize)
stop_btn = wx.Button(panel, label='Stop', size=wsize)
start_btn.Bind(wx.EVT_BUTTON, partial(self.onButton, key='start'))
stop_btn.Bind(wx.EVT_BUTTON, partial(self.onButton, key='stop'))
self.contrast = ContrastControl(panel, callback=self.set_contrast_level)
self.imagesize = wx.StaticText(panel, label='? x ?',
size=(150, 30), style=txtstyle)
def lin(len=200, wid=2, style=wx.LI_HORIZONTAL):
return wx.StaticLine(panel, size=(len, wid), style=style)
irow = 0
sizer.Add(pvpanel, (irow, 0), (1, 3), labstyle)
irow += 1
sizer.Add(start_btn, (irow, 0), (1, 1), labstyle)
sizer.Add(stop_btn, (irow, 1), (1, 1), labstyle)
if self.config['general'].get('show_free_run', False):
free_btn = wx.Button(panel, label='Free Run', size=wsize)
free_btn.Bind(wx.EVT_BUTTON, partial(self.onButton, key='free'))
irow += 1
sizer.Add(free_btn, (irow, 0), (1, 2), labstyle)
irow += 1
sizer.Add(lin(200, wid=4), (irow, 0), (1, 3), labstyle)
irow += 1
sizer.Add(self.imagesize, (irow, 0), (1, 3), labstyle)
if self.colormode.startswith('mono'):
self.cmap_choice = wx.Choice(panel, size=(80, -1),
choices=self.config['colormaps'])
self.cmap_choice.SetSelection(0)
self.cmap_choice.Bind(wx.EVT_CHOICE, self.onColorMap)
self.cmap_reverse = wx.CheckBox(panel, label='Reverse', size=(60, -1))
self.cmap_reverse.Bind(wx.EVT_CHECKBOX, self.onColorMap)
irow += 1
sizer.Add(wx.StaticText(panel, label='Color Map: '),
(irow, 0), (1, 1), labstyle)
sizer.Add(self.cmap_choice, (irow, 1), (1, 1), labstyle)
sizer.Add(self.cmap_reverse, (irow, 2), (1, 1), labstyle)
irow += 1
sizer.Add(self.contrast.label, (irow, 0), (1, 1), labstyle)
sizer.Add(self.contrast.choice, (irow, 1), (1, 1), labstyle)
if self.config['general']['show_1dintegration']:
self.show1d_btn = wx.Button(panel, label='Show 1D Integration',
size=(200, -1))
self.show1d_btn.Bind(wx.EVT_BUTTON, self.onShowIntegration)
self.show1d_btn.Disable()
irow += 1
sizer.Add(self.show1d_btn, (irow, 0), (1, 2), labstyle)
if self.config['general']['show_thumbnail']:
t_size=self.config['general'].get('thumbnail_size', 100)
self.thumbnail = ThumbNailImagePanel(panel, imgsize=t_size,
size=(350, 350),
motion_writer=partial(self.write, panel=0))
label = wx.StaticText(panel, label='Thumbnail size (pixels): ',
size=(200, -1), style=txtstyle)
self.thumbsize = FloatSpin(panel, value=100, min_val=10, increment=5,
action=self.onThumbSize,
size=(150, -1), style=txtstyle)
irow += 1
sizer.Add(label, (irow, 0), (1, 1), labstyle)
sizer.Add(self.thumbsize, (irow, 1), (1, 1), labstyle)
irow += 1
sizer.Add(self.thumbnail, (irow, 0), (1, 2), labstyle)
panel.SetSizer(sizer)
sizer.Fit(panel)
# image panel
self.image = ADMonoImagePanel(self, prefix=self.prefix,
rot90=self.config['general']['default_rotation'],
size=(750, 750),
writer=partial(self.write, panel=1),
thumbnail=self.thumbnail,
motion_writer=partial(self.write, panel=2))
mainsizer = wx.BoxSizer(wx.HORIZONTAL)
mainsizer.Add(panel, 0, wx.LEFT|wx.GROW|wx.ALL)
mainsizer.Add(self.image, 1, wx.CENTER|wx.GROW|wx.ALL)
self.SetSizer(mainsizer)
mainsizer.Fit(self)
self.SetAutoLayout(True)
iconfile = self.config['general'].get('iconfile', None)
if iconfile is None or not os.path.exists(iconfile):
iconfile = DEFAULT_ICONFILE
try:
self.SetIcon(wx.Icon(iconfile, wx.BITMAP_TYPE_ICO))
except:
pass
self.connect_pvs()
def onThumbSize(self, event=None):
self.thumbnail.imgsize = int(self.thumbsize.GetValue())
def onColorMap(self, event=None):
cmap_name = self.cmap_choice.GetStringSelection()
if self.cmap_reverse.IsChecked():
cmap_name = cmap_name + '_r'
self.image.colormap = getattr(colormap, cmap_name)
self.image.Refresh()
def onCopyImage(self, event=None):
"copy bitmap of canvas to system clipboard"
bmp = wx.BitmapDataObject()
bmp.SetBitmap(wx.Bitmap(self.image.GrabWxImage()))
wx.TheClipboard.Open()
wx.TheClipboard.SetData(bmp)
wx.TheClipboard.Close()
wx.TheClipboard.Flush()
def onReadCalibFile(self, event=None):
"read calibration file"
wcards = "Poni Files(*.poni)|*.poni|All files (*.*)|*.*"
dlg = wx.FileDialog(None, message='Read Calibration File',
defaultDir=os.getcwd(),
wildcard=wcards,
style=wx.FD_OPEN)
ppath = None
if dlg.ShowModal() == wx.ID_OK:
ppath = os.path.abspath(dlg.GetPath())
if os.path.exists(ppath):
self.setup_calibration(ppath)
def setup_calibration(self, ponifile):
"""set up calibration from PONI file"""
calib = read_poni(ponifile)
# if self.image.rot90 in (1, 3):
# calib['rot3'] = np.pi/2.0
self.calib = calib
if HAS_PYFAI:
self.integrator = AzimuthalIntegrator(**calib)
self.show1d_btn.Enable()
else:
self.write('Warning: PyFAI is not installed')
if self.scandb is not None:
_, calname = os.path.split(ponifile)
self.scandb.set_detectorconfig(calname, json.dumps(calib))
self.scandb.set_info('xrd_calibration', calname)
def onShowIntegration(self, event=None):
if self.calib is None or 'poni1' not in self.calib:
return
shown = False
try:
self.int_panel.Raise()
shown = True
except:
self.int_panel = None
if not shown:
self.int_panel = PlotFrame(self)
self.show_1dpattern(init=True)
else:
self.show_1dpattern()
def onAutoIntegration(self, event=None):
if not event.IsChecked():
self.int_timer.Stop()
return
if self.calib is None or 'poni1' not in self.calib:
return
shown = False
try:
self.int_panel.Raise()
shown = True
except:
self.int_panel = None
if not shown:
self.int_panel = PlotFrame(self)
self.show_1dpattern(init=True)
else:
self.show_1dpattern()
self.int_timer.Start(500)
def show_1dpattern(self, init=False):
if self.calib is None or not HAS_PYFAI:
return
img = self.ad_img.PV('ArrayData').get()
h, w = self.image.GetImageSize()
img.shape = (w, h)
# may need to trim outer pixels (int1d_trimx/int1d_trimy in config)
xstride = 1
if self.config['general'].get('int1d_flipx', False):
xstride = -1
xslice = slice(None, None, xstride)
trimx = int(self.config['general'].get('int1d_trimx', 0))
if trimx != 0:
xslice = slice(trimx*xstride, -trimx*xstride, xstride)
ystride = 1
if self.config['general'].get('int1d_flipy', True):
ystride = -1
yslice = slice(None, None, ystride)
trimy = int(self.config['general'].get('int1d_trimy', 0))
if trimy > 0:
yslice = slice(trimy*ystride, -trimy*ystride, ystride)
img = img[yslice, xslice]
img_id = self.ad_cam.ArrayCounter_RBV
q, xi = self.integrator.integrate1d(img, 2048, unit='q_A^-1',
correctSolidAngle=True,
polarization_factor=0.999)
if init:
self.int_panel.plot(q, xi, xlabel=r'$Q (\rm\AA^{-1})$', marker='+',
title='Image %d' % img_id)
self.int_panel.Raise()
self.int_panel.Show()
else:
self.int_panel.update_line(0, q, xi, draw=True)
self.int_panel.set_title('Image %d' % img_id)
@EpicsFunction
def onSaveImage(self, event=None):
"prompts for and save image to file"
defdir = os.getcwd()
self.fname = "Image_%i.tiff" % self.ad_cam.ArrayCounter_RBV
dlg = wx.FileDialog(None, message='Save Image as',
defaultDir=os.getcwd(),
defaultFile=self.fname,
style=wx.FD_SAVE)
path = None
if dlg.ShowModal() == wx.ID_OK:
path = os.path.abspath(dlg.GetPath())
root, fname = os.path.split(path)
epics.caput("%s%sFileName" % self.prefix, self.fsaver, fname)
epics.caput("%s%sFileWriteMode" % self.prefix, self.fsaver, 0)
time.sleep(0.05)
epics.caput("%s%sWriteFile" % self.prefix, self.fsaver, 1)
time.sleep(0.05)
file_pv = "%s%sFullFileName_RBV" % (self.prefix, self.prefix)
print("Saved image File ", epics.caget(file_pv, as_string=True))
def onExit(self, event=None):
try:
wx.Yield()
except:
pass
self.Destroy()
def onAbout(self, event=None):
msg = """areaDetector Display version 0.2
Matt Newville <*****@*****.**>"""
dlg = wx.MessageDialog(self, msg, "About areaDetector Display",
wx.OK | wx.ICON_INFORMATION)
dlg.ShowModal()
dlg.Destroy()
def buildMenus(self):
fmenu = wx.Menu()
MenuItem(self, fmenu, "&Save\tCtrl+S", "Save Image", self.onSaveImage)
MenuItem(self, fmenu, "&Copy\tCtrl+C", "Copy Image to Clipboard",
self.onCopyImage)
MenuItem(self, fmenu, "Read Calibration File", "Read PONI Calibration",
self.onReadCalibFile)
fmenu.AppendSeparator()
MenuItem(self, fmenu, "E&xit\tCtrl+Q", "Exit Program", self.onExit)
omenu = wx.Menu()
MenuItem(self, omenu, "&Rotate CCW\tCtrl+R", "Rotate Counter Clockwise", self.onRot90)
MenuItem(self, omenu, "Flip Up/Down\tCtrl+T", "Flip Up/Down", self.onFlipV)
MenuItem(self, omenu, "Flip Left/Right\tCtrl+F", "Flip Left/Right", self.onFlipH)
MenuItem(self, omenu, "Reset Rotations and Flips", "Reset", self.onResetRotFlips)
omenu.AppendSeparator()
hmenu = wx.Menu()
MenuItem(self, hmenu, "About", "About areaDetector Display", self.onAbout)
mbar = wx.MenuBar()
mbar.Append(fmenu, "File")
mbar.Append(omenu, "Options")
mbar.Append(hmenu, "&Help")
self.SetMenuBar(mbar)
def onResetRotFlips(self, event):
self.image.rot90 = 0
self.image.flipv = self.image.fliph = False
def onRot90(self, event):
self.image.rot90 = (self.image.rot90 - 1) % 4
def onFlipV(self, event):
self.image.flipv= not self.image.flipv
def onFlipH(self, event):
self.image.fliph = not self.image.fliph
def set_contrast_level(self, contrast_level=0):
self.image.contrast_levels = [contrast_level, 100.0-contrast_level]
self.image.Refresh()
def write(self, s, panel=0):
"""write a message to the Status Bar"""
self.SetStatusText(text=s, number=panel)
@EpicsFunction
def onButton(self, event=None, key='free'):
key = key.lower()
if key.startswith('free'):
ftime = self.config['general']['free_run_time']
self.image.restart_fps_counter()
self.ad_cam.AcquireTime = ftime
self.ad_cam.AcquirePeriod = ftime
self.ad_cam.NumImages = int((3*86400.)/ftime)
self.ad_cam.Acquire = 1
elif key.startswith('start'):
self.image.restart_fps_counter()
self.ad_cam.Acquire = 1
elif key.startswith('stop'):
self.ad_cam.Acquire = 0
@EpicsFunction
def connect_pvs(self, verbose=True):
if self.prefix is None or len(self.prefix) < 2:
return
self.write('Connecting to areaDetector %s' % self.prefix)
self.ad_img = epics.Device(self.prefix + 'image1:', delim='',
attrs=self.img_attrs)
self.ad_cam = epics.Device(self.prefix + 'cam1:', delim='',
attrs=self.cam_attrs)
if self.config['general']['use_filesaver']:
epics.caput("%s%sEnableCallbacks" % (self.prefix, self.fsaver), 1)
epics.caput("%s%sAutoSave" % (self.prefix, self.fsaver), 0)
epics.caput("%s%sAutoIncrement" % (self.prefix, self.fsaver), 0)
epics.caput("%s%sFileWriteMode" % (self.prefix, self.fsaver), 0)
time.sleep(0.002)
if not self.ad_img.PV('UniqueId_RBV').connected:
epics.poll()
if not self.ad_img.PV('UniqueId_RBV').connected:
self.write('Warning: detector seems to not be connected!')
return
if verbose:
self.write('Connected to detector %s' % self.prefix)
self.SetTitle("Epics areaDetector Display: %s" % self.prefix)
sizex = self.ad_cam.MaxSizeX_RBV
sizey = self.ad_cam.MaxSizeY_RBV
sizelabel = 'Image Size: %i x %i pixels'
try:
sizelabel = sizelabel % (sizex, sizey)
except:
sizelabel = sizelabel % (0, 0)
self.imagesize.SetLabel(sizelabel)
self.ad_cam.add_callback('DetectorState_RBV', self.onDetState)
self.contrast.set_level_str('0.01')
@DelayedEpicsCallback
def onDetState(self, pvname=None, value=None, char_value=None, **kw):
self.write(char_value, panel=0)
class TestMultiGeometry(unittest.TestCase):
def setUp(self):
unittest.TestCase.setUp(self)
self.data = fabio.open(UtilsTest.getimage("1788/moke.tif")).data
self.lst_data = [
self.data[:250, :300], self.data[250:, :300],
self.data[:250, 300:], self.data[250:, 300:]
]
self.det = Detector(1e-4, 1e-4)
self.det.max_shape = (500, 600)
self.sub_det = Detector(1e-4, 1e-4)
self.sub_det.max_shape = (250, 300)
self.ai = AzimuthalIntegrator(0.1, 0.03, 0.03, detector=self.det)
self.range = (0, 23)
self.ais = [
AzimuthalIntegrator(0.1, 0.030, 0.03, detector=self.sub_det),
AzimuthalIntegrator(0.1, 0.005, 0.03, detector=self.sub_det),
AzimuthalIntegrator(0.1, 0.030, 0.00, detector=self.sub_det),
AzimuthalIntegrator(0.1, 0.005, 0.00, detector=self.sub_det),
]
self.mg = MultiGeometry(self.ais,
radial_range=self.range,
unit="2th_deg")
self.N = 390
def tearDown(self):
unittest.TestCase.tearDown(self)
self.data = None
self.lst_data = None
self.det = None
self.sub_det = None
self.ai = None
self.ais = None
self.mg = None
def test_integrate1d(self):
tth_ref, I_ref = self.ai.integrate1d(self.data,
radial_range=self.range,
npt=self.N,
unit="2th_deg",
method="splitpixel")
obt = self.mg.integrate1d(self.lst_data, self.N)
tth_obt, I_obt = obt
self.assertEqual(
abs(tth_ref - tth_obt).max(), 0, "Bin position is the same")
# intensity need to be scaled by solid angle 1e-4*1e-4/0.1**2 = 1e-6
delta = (abs(I_obt * 1e6 - I_ref).max())
self.assert_(delta < 5e-5, "Intensity is the same delta=%s" % delta)
def test_integrate2d(self):
ref = self.ai.integrate2d(self.data,
self.N,
360,
radial_range=self.range,
azimuth_range=(-180, 180),
unit="2th_deg",
method="splitpixel",
all=True)
obt = self.mg.integrate2d(self.lst_data, self.N, 360, all=True)
self.assertEqual(
abs(ref["radial"] - obt["radial"]).max(), 0,
"Bin position is the same")
self.assertEqual(
abs(ref["azimuthal"] - obt["azimuthal"]).max(), 0,
"Bin position is the same")
# intensity need to be scaled by solid angle 1e-4*1e-4/0.1**2 = 1e-6
delta = abs(obt["I"] * 1e6 -
ref["I"])[obt["count"] >= 1e-6] # restrict on valid pixel
delta_cnt = abs(obt["count"] - ref["count"])
delta_sum = abs(obt["sum"] * 1e6 - ref["sum"])
if delta.max() > 0:
logger.warning(
"TestMultiGeometry.test_integrate2d gave intensity difference of %s"
% delta.max())
if logger.level <= logging.DEBUG:
from matplotlib import pyplot as plt
f = plt.figure()
a1 = f.add_subplot(2, 2, 1)
a1.imshow(ref["sum"])
a2 = f.add_subplot(2, 2, 2)
a2.imshow(obt["sum"])
a3 = f.add_subplot(2, 2, 3)
a3.imshow(delta_sum)
a4 = f.add_subplot(2, 2, 4)
a4.plot(delta_sum.sum(axis=0))
f.show()
raw_input()
self.assert_(delta_cnt.max() < 0.001,
"pixel count is the same delta=%s" % delta_cnt.max())
self.assert_(delta_sum.max() < 0.03,
"pixel sum is the same delta=%s" % delta_sum.max())
self.assert_(
delta.max() < 0.004,
"pixel intensity is the same (for populated pixels) delta=%s" %
delta.max())
class CalibrationData(object):
def __init__(self, img_data=None):
self.img_data = img_data
self.points = []
self.points_index = []
self.spectrum_geometry = AzimuthalIntegrator()
self.calibrant = Calibrant()
self.start_values = {'dist': 200e-3,
'wavelength': 0.3344e-10,
'pixel_width': 79e-6,
'pixel_height': 79e-6,
'polarization_factor': 0.99}
self.orig_pixel1 = 79e-6
self.orig_pixel2 = 79e-6
self.fit_wavelength = False
self.fit_distance = True
self.is_calibrated = False
self.use_mask = False
self.filename = ''
self.calibration_name = 'None'
self.polarization_factor = 0.99
self.supersampling_factor = 1
self._calibrants_working_dir = os.path.dirname(Calibrants.__file__)
self.cake_img = np.zeros((2048, 2048))
self.tth = np.linspace(0, 25)
self.int = np.sin(self.tth)
def find_peaks_automatic(self, x, y, peak_ind):
"""
Searches peaks by using the Massif algorithm
:param x:
x-coordinate in pixel - should be from original image (not supersampled x-coordinate)
:param y:
y-coordinate in pixel - should be from original image (not supersampled y-coordinate)
:param peak_ind:
peak/ring index to which the found points will be added
:return:
array of points found
"""
massif = Massif(self.img_data._img_data)
cur_peak_points = massif.find_peaks([x, y])
if len(cur_peak_points):
self.points.append(np.array(cur_peak_points))
self.points_index.append(peak_ind)
return np.array(cur_peak_points)
def find_peak(self, x, y, search_size, peak_ind):
"""
Searches a peak around the x,y position. It just searches for the maximum value in a specific search size.
:param x:
x-coordinate in pixel - should be from original image (not supersampled x-coordinate)
:param y:
y-coordinate in pixel - should be form original image (not supersampled y-coordinate)
:param search_size:
the amount of pixels in all direction in which the algorithm searches for the maximum peak
:param peak_ind:
peak/ring index to which the found points will be added
:return:
point found (as array)
"""
left_ind = np.round(x - search_size * 0.5)
top_ind = np.round(y - search_size * 0.5)
x_ind, y_ind = np.where(self.img_data._img_data[left_ind:(left_ind + search_size),
top_ind:(top_ind + search_size)] == \
self.img_data._img_data[left_ind:(left_ind + search_size),
top_ind:(top_ind + search_size)].max())
x_ind = x_ind[0] + left_ind
y_ind = y_ind[0] + top_ind
self.points.append(np.array([x_ind, y_ind]))
self.points_index.append(peak_ind)
return np.array([np.array((x_ind, y_ind))])
def clear_peaks(self):
self.points = []
self.points_index = []
def create_cake_geometry(self):
self.cake_geometry = AzimuthalIntegrator()
pyFAI_parameter = self.spectrum_geometry.getPyFAI()
pyFAI_parameter['polarization_factor'] = self.polarization_factor
pyFAI_parameter['wavelength'] = self.spectrum_geometry.wavelength
self.cake_geometry.setPyFAI(dist=pyFAI_parameter['dist'],
poni1=pyFAI_parameter['poni1'],
poni2=pyFAI_parameter['poni2'],
rot1=pyFAI_parameter['rot1'],
rot2=pyFAI_parameter['rot2'],
rot3=pyFAI_parameter['rot3'],
pixel1=pyFAI_parameter['pixel1'],
pixel2=pyFAI_parameter['pixel2'])
self.cake_geometry.wavelength = pyFAI_parameter['wavelength']
def setup_peak_search_algorithm(self, algorithm, mask=None):
"""
Initializes the peak search algorithm on the current image
:param algorithm:
peak search algorithm used. Possible algorithms are 'Massif' and 'Blob'
:param mask:
if a mask is used during the process this is provided here as a 2d array for the image.
"""
if algorithm == 'Massif':
self.peak_search_algorithm = Massif(self.img_data._img_data)
elif algorithm == 'Blob':
if mask is not None:
self.peak_search_algorithm = BlobDetection(self.img_data._img_data * mask)
else:
self.peak_search_algorithm = BlobDetection(self.img_data._img_data)
self.peak_search_algorithm.process()
else:
return
def search_peaks_on_ring(self, peak_index, delta_tth=0.1, min_mean_factor=1,
upper_limit=55000, mask=None):
self.reset_supersampling()
if not self.is_calibrated:
return
# transform delta from degree into radians
delta_tth = delta_tth / 180.0 * np.pi
# get appropriate two theta value for the ring number
tth_calibrant_list = self.calibrant.get_2th()
tth_calibrant = np.float(tth_calibrant_list[peak_index])
# get the calculated two theta values for the whole image
if self.spectrum_geometry._ttha is None:
tth_array = self.spectrum_geometry.twoThetaArray(self.img_data._img_data.shape)
else:
tth_array = self.spectrum_geometry._ttha
# create mask based on two_theta position
ring_mask = abs(tth_array - tth_calibrant) <= delta_tth
if mask is not None:
mask = np.logical_and(ring_mask, np.logical_not(mask))
else:
mask = ring_mask
# calculate the mean and standard deviation of this area
sub_data = np.array(self.img_data._img_data.ravel()[np.where(mask.ravel())], dtype=np.float64)
sub_data[np.where(sub_data > upper_limit)] = np.NaN
mean = np.nanmean(sub_data)
std = np.nanstd(sub_data)
# set the threshold into the mask (don't detect very low intensity peaks)
threshold = min_mean_factor * mean + std
mask2 = np.logical_and(self.img_data._img_data > threshold, mask)
mask2[np.where(self.img_data._img_data > upper_limit)] = False
size2 = mask2.sum(dtype=int)
keep = int(np.ceil(np.sqrt(size2)))
try:
res = self.peak_search_algorithm.peaks_from_area(mask2, Imin=mean - std, keep=keep)
except IndexError:
res = []
# Store the result
if len(res):
self.points.append(np.array(res))
self.points_index.append(peak_index)
self.set_supersampling()
self.spectrum_geometry.reset()
def set_calibrant(self, filename):
self.calibrant = Calibrant()
self.calibrant.load_file(filename)
self.spectrum_geometry.calibrant = self.calibrant
def set_start_values(self, start_values):
self.start_values = start_values
self.polarization_factor = start_values['polarization_factor']
def calibrate(self):
self.spectrum_geometry = GeometryRefinement(self.create_point_array(self.points, self.points_index),
dist=self.start_values['dist'],
wavelength=self.start_values['wavelength'],
pixel1=self.start_values['pixel_width'],
pixel2=self.start_values['pixel_height'],
calibrant=self.calibrant)
self.orig_pixel1 = self.start_values['pixel_width']
self.orig_pixel2 = self.start_values['pixel_height']
self.refine()
self.create_cake_geometry()
self.is_calibrated = True
self.calibration_name = 'current'
self.set_supersampling()
# reset the integrator (not the geometric parameters)
self.spectrum_geometry.reset()
def refine(self):
self.reset_supersampling()
self.spectrum_geometry.data = self.create_point_array(self.points, self.points_index)
fix = ['wavelength']
if self.fit_wavelength:
fix = []
if not self.fit_distance:
fix.append('dist')
if self.fit_wavelength:
self.spectrum_geometry.refine2()
self.spectrum_geometry.refine2_wavelength(fix=fix)
self.create_cake_geometry()
self.set_supersampling()
# reset the integrator (not the geometric parameters)
self.spectrum_geometry.reset()
def integrate_1d(self, num_points=None, mask=None, polarization_factor=None, filename=None,
unit='2th_deg', method='csr'):
if np.sum(mask) == self.img_data.img_data.shape[0] * self.img_data.img_data.shape[1]:
# do not perform integration if the image is completely masked...
return self.tth, self.int
if self.spectrum_geometry._polarization is not None:
if self.img_data.img_data.shape != self.spectrum_geometry._polarization.shape:
# resetting the integrator if the polarization correction matrix has not the correct shape
self.spectrum_geometry.reset()
if polarization_factor is None:
polarization_factor = self.polarization_factor
if num_points is None:
num_points = self.calculate_number_of_spectrum_points(2)
self.num_points = num_points
t1 = time.time()
if unit is 'd_A':
try:
self.tth, self.int = self.spectrum_geometry.integrate1d(self.img_data.img_data, num_points,
method=method,
unit='2th_deg',
mask=mask,
polarization_factor=polarization_factor,
filename=filename)
except NameError:
self.tth, self.int = self.spectrum_geometry.integrate1d(self.img_data.img_data, num_points,
method=method,
unit='2th_deg',
mask=mask,
polarization_factor=polarization_factor,
filename=filename)
self.tth = self.spectrum_geometry.wavelength / (2 * np.sin(self.tth / 360 * np.pi)) * 1e10
self.int = self.int
else:
try:
self.tth, self.int = self.spectrum_geometry.integrate1d(self.img_data.img_data, num_points,
method=method,
unit=unit,
mask=mask,
polarization_factor=polarization_factor,
filename=filename)
except NameError:
self.tth, self.int = self.spectrum_geometry.integrate1d(self.img_data.img_data, num_points,
method='lut',
unit=unit,
mask=mask,
polarization_factor=polarization_factor,
filename=filename)
logger.info('1d integration of {}: {}s.'.format(os.path.basename(self.img_data.filename), time.time() - t1))
ind = np.where((self.int > 0) & (~np.isnan(self.int)))
self.tth = self.tth[ind]
self.int = self.int[ind]
return self.tth, self.int
def integrate_2d(self, mask=None, polarization_factor=None, unit='2th_deg', method='csr', dimensions=(2048, 2048)):
if polarization_factor is None:
polarization_factor = self.polarization_factor
if self.cake_geometry._polarization is not None:
if self.img_data.img_data.shape != self.cake_geometry._polarization.shape:
# resetting the integrator if the polarization correction matrix has not the same shape as the image
self.cake_geometry.reset()
t1 = time.time()
res = self.cake_geometry.integrate2d(self.img_data._img_data, dimensions[0], dimensions[1], method=method,
mask=mask,
unit=unit, polarization_factor=polarization_factor)
logger.info('2d integration of {}: {}s.'.format(os.path.basename(self.img_data.filename), time.time() - t1))
self.cake_img = res[0]
self.cake_tth = res[1]
self.cake_azi = res[2]
return self.cake_img
def create_point_array(self, points, points_ind):
res = []
for i, point_list in enumerate(points):
if point_list.shape == (2,):
res.append([point_list[0], point_list[1], points_ind[i]])
else:
for point in point_list:
res.append([point[0], point[1], points_ind[i]])
return np.array(res)
def get_point_array(self):
return self.create_point_array(self.points, self.points_index)
def get_calibration_parameter(self):
pyFAI_parameter = self.cake_geometry.getPyFAI()
pyFAI_parameter['polarization_factor'] = self.polarization_factor
try:
fit2d_parameter = self.cake_geometry.getFit2D()
fit2d_parameter['polarization_factor'] = self.polarization_factor
except TypeError:
fit2d_parameter = None
try:
pyFAI_parameter['wavelength'] = self.spectrum_geometry.wavelength
fit2d_parameter['wavelength'] = self.spectrum_geometry.wavelength
except RuntimeWarning:
pyFAI_parameter['wavelength'] = 0
return pyFAI_parameter, fit2d_parameter
def calculate_number_of_spectrum_points(self, max_dist_factor=1.5):
# calculates the number of points for an integrated spectrum, based on the distance of the beam center to the the
#image corners. Maximum value is determined by the shape of the image.
fit2d_parameter = self.spectrum_geometry.getFit2D()
center_x = fit2d_parameter['centerX']
center_y = fit2d_parameter['centerY']
width, height = self.img_data.img_data.shape
if center_x < width and center_x > 0:
side1 = np.max([abs(width - center_x), center_x])
else:
side1 = width
if center_y < height and center_y > 0:
side2 = np.max([abs(height - center_y), center_y])
else:
side2 = height
max_dist = np.sqrt(side1 ** 2 + side2 ** 2)
return int(max_dist * max_dist_factor)
def load(self, filename):
self.spectrum_geometry = AzimuthalIntegrator()
self.spectrum_geometry.load(filename)
self.orig_pixel1 = self.spectrum_geometry.pixel1
self.orig_pixel2 = self.spectrum_geometry.pixel2
self.calibration_name = get_base_name(filename)
self.filename = filename
self.is_calibrated = True
self.create_cake_geometry()
self.set_supersampling()
def save(self, filename):
self.cake_geometry.save(filename)
self.calibration_name = get_base_name(filename)
self.filename = filename
def create_file_header(self):
return self.cake_geometry.makeHeaders(polarization_factor=self.polarization_factor)
def set_fit2d(self, fit2d_parameter):
print fit2d_parameter
self.spectrum_geometry.setFit2D(directDist=fit2d_parameter['directDist'],
centerX=fit2d_parameter['centerX'],
centerY=fit2d_parameter['centerY'],
tilt=fit2d_parameter['tilt'],
tiltPlanRotation=fit2d_parameter['tiltPlanRotation'],
pixelX=fit2d_parameter['pixelX'],
pixelY=fit2d_parameter['pixelY'])
self.spectrum_geometry.wavelength = fit2d_parameter['wavelength']
self.create_cake_geometry()
self.polarization_factor = fit2d_parameter['polarization_factor']
self.orig_pixel1 = fit2d_parameter['pixelX'] * 1e-6
self.orig_pixel2 = fit2d_parameter['pixelY'] * 1e-6
self.is_calibrated = True
self.set_supersampling()
def set_pyFAI(self, pyFAI_parameter):
self.spectrum_geometry.setPyFAI(dist=pyFAI_parameter['dist'],
poni1=pyFAI_parameter['poni1'],
poni2=pyFAI_parameter['poni2'],
rot1=pyFAI_parameter['rot1'],
rot2=pyFAI_parameter['rot2'],
rot3=pyFAI_parameter['rot3'],
pixel1=pyFAI_parameter['pixel1'],
pixel2=pyFAI_parameter['pixel2'])
self.spectrum_geometry.wavelength = pyFAI_parameter['wavelength']
self.create_cake_geometry()
self.polarization_factor = pyFAI_parameter['polarization_factor']
self.orig_pixel1 = pyFAI_parameter['pixel1']
self.orig_pixel2 = pyFAI_parameter['pixel2']
self.is_calibrated = True
self.set_supersampling()
def set_supersampling(self, factor=None):
if factor is None:
factor = self.supersampling_factor
self.spectrum_geometry.pixel1 = self.orig_pixel1 / float(factor)
self.spectrum_geometry.pixel2 = self.orig_pixel2 / float(factor)
if factor != self.supersampling_factor:
self.spectrum_geometry.reset()
self.supersampling_factor = factor
def reset_supersampling(self):
self.spectrum_geometry.pixel1 = self.orig_pixel1
self.spectrum_geometry.pixel2 = self.orig_pixel2
def get_two_theta_img(self, x, y):
"""
Gives the two_theta value for the x,y coordinates on the image
:return:
two theta in radians
"""
x = np.array([x]) * self.supersampling_factor
y = np.array([y]) * self.supersampling_factor
return self.spectrum_geometry.tth(x, y)[0]
def get_azi_img(self, x, y):
"""
Gives chi for position on image.
:param x:
x-coordinate in pixel
:param y:
y-coordinate in pixel
:return:
azimuth in radians
"""
x *= self.supersampling_factor
y *= self.supersampling_factor
return self.spectrum_geometry.chi(x, y)[0]
def get_two_theta_cake(self, y):
"""
Gives the two_theta value for the x coordinate in the cake
:param x:
y-coordinate on image
:return:
two theta in degree
"""
return self.cake_tth[np.round(y[0])]
def get_azi_cake(self, x):
"""
Gives the azimuth value for a cake.
:param x:
x-coordinate in pixel
:return:
azimuth in degree
"""
return self.cake_azi[np.round(x[0])]
def get_two_theta_array(self):
return self.spectrum_geometry._ttha[::self.supersampling_factor, ::self.supersampling_factor]
@property
def wavelength(self):
return self.spectrum_geometry.wavelength
class XAnoS_Reducer(QWidget):
"""
This widget is developed to reduce on the fly 2D SAXS data to azimuthally averaged 1D SAXS data
"""
def __init__(self,poniFile=None,dataFile=None, darkFile=None, maskFile=None,extractedFolder='/tmp', npt=1000, azimuthalRange=(-180.0,180.0), parent=None):
"""
poniFile is the calibration file obtained after Q-calibration
"""
QWidget.__init__(self,parent)
self.setup_dict=json.load(open('./SetupData/reducer_setup.txt','r'))
if poniFile is not None:
self.poniFile=poniFile
else:
self.poniFile=self.setup_dict['poniFile']
if maskFile is not None:
self.maskFile=maskFile
else:
self.maskFile=self.setup_dict['maskFile']
self.dataFile=dataFile
if darkFile is None:
self.dark_corrected=False
self.darkFile=''
else:
self.darkFile=darkFile
self.dark_corrected=True
self.curDir=os.getcwd()
self.extractedBaseFolder=extractedFolder
self.npt=npt
self.set_externally=False
#ai=AIWidget()
#self.layout.addWidget(ai)
self.azimuthalRange=azimuthalRange
self.create_UI()
if os.path.exists(self.poniFile):
self.openPoniFile(file=self.poniFile)
if os.path.exists(self.maskFile):
self.openMaskFile(file=self.maskFile)
self.clientRunning=False
def create_UI(self):
"""
Creates the widget user interface
"""
loadUi('UI_Forms/Data_Reduction_Client.ui',self)
self.poniFileLineEdit.setText(str(self.poniFile))
self.maskFileLineEdit.setText(str(self.maskFile))
self.darkFileLineEdit.setText(str(self.darkFile))
self.extractedBaseFolderLineEdit.setText(self.extractedBaseFolder)
self.radialPointsLineEdit.setText(str(self.npt))
self.openDataPushButton.clicked.connect(self.openDataFiles)
self.reducePushButton.clicked.connect(self.reduce_multiple)
self.openDarkPushButton.clicked.connect(self.openDarkFile)
self.openPoniPushButton.clicked.connect(lambda x: self.openPoniFile(file=None))
self.calibratePushButton.clicked.connect(self.calibrate)
self.maskFileLineEdit.returnPressed.connect(self.maskFileChanged)
self.openMaskPushButton.clicked.connect(lambda x: self.openMaskFile(file=None))
self.createMaskPushButton.clicked.connect(self.createMask)
self.extractedFolderPushButton.clicked.connect(self.openFolder)
self.extractedFolderLineEdit.textChanged.connect(self.extractedFolderChanged)
self.polCorrComboBox.currentIndexChanged.connect(self.polarizationChanged)
self.polarizationChanged()
self.radialPointsLineEdit.returnPressed.connect(self.nptChanged)
self.azimuthalRangeLineEdit.returnPressed.connect(self.azimuthalRangeChanged)
self.azimuthalRangeChanged()
#self.statusLabel.setStyleSheet("color:rgba(0,1,0,0)")
self.imageWidget=Image_Widget(zeros((100,100)))
self.cakedImageWidget=Image_Widget(zeros((100,100)))
imgNumberLabel=QLabel('Image number')
self.imgNumberSpinBox=QSpinBox()
self.imgNumberSpinBox.setSingleStep(1)
self.imageWidget.imageLayout.addWidget(imgNumberLabel,row=2,col=1)
self.imageWidget.imageLayout.addWidget(self.imgNumberSpinBox,row=2,col=2)
self.imageView=self.imageWidget.imageView.getView()
self.plotWidget=PlotWidget()
self.plotWidget.setXLabel('Q, Å<sup>-1</sup>',fontsize=5)
self.plotWidget.setYLabel('Intensity',fontsize=5)
self.tabWidget.addTab(self.plotWidget,'Reduced 1D-data')
self.tabWidget.addTab(self.imageWidget,'Masked 2D-data')
self.tabWidget.addTab(self.cakedImageWidget,'Reduced Caked Data')
self.serverAddress=self.serverAddressLineEdit.text()
self.startClientPushButton.clicked.connect(self.startClient)
self.stopClientPushButton.clicked.connect(self.stopClient)
self.serverAddressLineEdit.returnPressed.connect(self.serverAddressChanged)
self.startServerPushButton.clicked.connect(self.startServer)
self.stopServerPushButton.clicked.connect(self.stopServer)
def startServer(self):
serverAddr=self.serverAddressLineEdit.text()
dataDir=QFileDialog.getExistingDirectory(self,'Select data folder',options=QFileDialog.ShowDirsOnly)
self.serverStatusLabel.setText('<font color="Red">Transmitting</font>')
QApplication.processEvents()
self.serverThread=QThread()
self.zeromq_server=ZeroMQ_Server(serverAddr,dataDir)
self.zeromq_server.moveToThread(self.serverThread)
self.serverThread.started.connect(self.zeromq_server.loop)
self.zeromq_server.messageEmitted.connect(self.updateServerMessage)
self.zeromq_server.folderFinished.connect(self.serverDone)
QTimer.singleShot(0,self.serverThread.start)
def updateServerMessage(self,mesg):
#self.serverStatusLabel.setText('<font color="Red">Transmitting</font>')
self.serverMessageLabel.setText('Server sends: %s'%mesg)
QApplication.processEvents()
def serverDone(self):
self.serverStatusLabel.setText('<font color="Green">Idle</font>')
self.zeromq_server.socket.unbind(self.zeromq_server.socket.last_endpoint)
self.serverThread.quit()
self.serverThread.wait()
self.serverThread.deleteLater()
self.zeromq_server.deleteLater()
def stopServer(self):
try:
self.zeromq_server.running=False
self.serverStatusLabel.setText('<font color="Green">Idle</font>')
self.zeromq_server.socket.unbind(self.zeromq_server.socket.last_endpoint)
self.serverThread.quit()
self.serverThread.wait()
self.serverThread.deleteLater()
self.zeromq_server.deleteLater()
except:
QMessageBox.warning(self,'Server Error','Start the server before stopping it')
def enableClient(self,enable=True):
self.startClientPushButton.setEnabled(enable)
self.stopClientPushButton.setEnabled(enable)
def enableServer(self,enable=True):
self.startServerPushButton.setEnabled(enable)
self.stopServerPushButton.setEnabled(enable)
def startClient(self):
if self.clientRunning:
self.stopClient()
else:
self.clientFree=True
self.clientRunning=True
self.files=[]
self.listenerThread = QThread()
addr=self.clientAddressLineEdit.text()
self.zeromq_listener = ZeroMQ_Listener(addr)
self.zeromq_listener.moveToThread(self.listenerThread)
self.listenerThread.started.connect(self.zeromq_listener.loop)
self.zeromq_listener.messageReceived.connect(self.signal_received)
QTimer.singleShot(0, self.listenerThread.start)
QTimer.singleShot(0,self.clientReduce)
self.clientStatusLabel.setText('<font color="red">Connected</font>')
def stopClient(self):
try:
self.clientRunning=False
self.clientFree=False
self.zeromq_listener.messageReceived.disconnect()
self.zeromq_listener.running=False
self.listenerThread.quit()
self.listenerThread.wait()
self.listenerThread.deleteLater()
self.zeromq_listener.deleteLater()
self.clientStatusLabel.setText('<font color="green">Idle</font>')
except:
QMessageBox.warning(self,'Client Error', 'Please start the client first before closing.',QMessageBox.Ok)
def serverAddressChanged(self):
if self.clientRunning:
self.startClient()
def signal_received(self, message):
self.clientMessageLabel.setText('Client receives: %s'%message)
if 'dark.edf' not in message:
self.files.append(message)
def clientReduce(self):
while self.clientFree:
QApplication.processEvents()
if len(self.files)>0:
message=self.files[0]
self.dataFiles=[message]
self.dataFileLineEdit.setText(str(self.dataFiles))
self.extractedBaseFolder=os.path.dirname(message)
self.extractedFolder=os.path.join(self.extractedBaseFolder,self.extractedFolderLineEdit.text())
if not os.path.exists(self.extractedFolder):
os.makedirs(self.extractedFolder)
self.extractedBaseFolderLineEdit.setText(self.extractedBaseFolder)
self.set_externally=True
self.reduce_multiple()
self.set_externally=False
self.files.pop(0)
def closeEvent(self, event):
if self.clientRunning:
self.stopClient()
event.accept()
def polarizationChanged(self):
if self.polCorrComboBox.currentText()=='Horizontal':
self.polarization_factor=1
elif self.polCorrComboBox.currentText()=='Vertical':
self.polarization_factor=-1
elif self.polCorrComboBox.currentText()=='Circular':
self.polarization_factor=0
else:
self.polarization_factor=None
def createMask(self):
"""
Opens a mask-widget to create mask file
"""
fname=str(QFileDialog.getOpenFileName(self,'Select an image file', directory=self.curDir,filter='Image file (*.edf *.tif)')[0])
if fname is not None or fname!='':
img=fb.open(fname).data
self.maskWidget=MaskWidget(img)
self.maskWidget.saveMaskPushButton.clicked.disconnect()
self.maskWidget.saveMaskPushButton.clicked.connect(self.save_mask)
self.maskWidget.show()
else:
QMessageBox.warning(self,'File error','Please import a data file first for creating the mask',QMessageBox.Ok)
def maskFileChanged(self):
"""
Changes the mask file
"""
maskFile=str(self.maskFileLineEdit.text())
if str(maskFile)=='':
self.maskFile=None
elif os.path.exists(maskFile):
self.maskFile=maskFile
else:
self.maskFile=None
def save_mask(self):
"""
Saves the entire mask combining all the shape ROIs
"""
fname=str(QFileDialog.getSaveFileName(filter='Mask Files (*.msk)')[0])
name,extn=os.path.splitext(fname)
if extn=='':
fname=name+'.msk'
elif extn!='.msk':
QMessageBox.warning(self,'File extension error','Please donot provide file extension other than ".msk". Thank you!')
return
else:
tmpfile=fb.edfimage.EdfImage(data=self.maskWidget.full_mask_data.T,header=None)
tmpfile.save(fname)
self.maskFile=fname
self.maskFileLineEdit.setText(self.maskFile)
def calibrate(self):
"""
Opens a calibartion widget to create calibration file
"""
fname=str(QFileDialog.getOpenFileName(self,'Select calibration image',directory=self.curDir, filter='Calibration image (*.edf *.tif)')[0])
if fname is not None:
img=fb.open(fname).data
if self.maskFile is not None:
try:
mask=fb.open(self.maskFile).data
except:
QMessageBox.warning(self,'Mask File Error','Cannot open %s.\n No masking will be done.'%self.maskFile)
mask=None
else:
mask=None
pixel1=79.0
pixel2=79.0
self.calWidget=CalibrationWidget(img,pixel1,pixel2,mask=mask)
self.calWidget.saveCalibrationPushButton.clicked.disconnect()
self.calWidget.saveCalibrationPushButton.clicked.connect(self.save_calibration)
self.calWidget.show()
else:
QMessageBox.warning(self,'File error','Please import a data file first for creating the calibration file',QMessageBox.Ok)
def save_calibration(self):
fname=str(QFileDialog.getSaveFileName(self,'Calibration file',directory=self.curDir,filter='Clibration files (*.poni)')[0])
tfname=os.path.splitext(fname)[0]+'.poni'
self.calWidget.applyPyFAI()
self.calWidget.geo.save(tfname)
self.poniFile=tfname
self.poniFileLineEdit.setText(self.poniFile)
self.openPoniFile(file=self.poniFile)
def openPoniFile(self,file=None):
"""
Select and imports the calibration file
"""
if file is None:
self.poniFile=QFileDialog.getOpenFileName(self,'Select calibration file',directory=self.curDir,filter='Calibration file (*.poni)')[0]
self.poniFileLineEdit.setText(self.poniFile)
else:
self.poniFile=file
if os.path.exists(self.poniFile):
self.setup_dict['poniFile']=self.poniFile
json.dump(self.setup_dict,open('./SetupData/reducer_setup.txt','w'))
fh=open(self.poniFile,'r')
lines=fh.readlines()
self.calib_data={}
for line in lines:
if line[0]!='#':
key,val=line.split(': ')
self.calib_data[key]=float(val)
self.dist=self.calib_data['Distance']
self.pixel1=self.calib_data['PixelSize1']
self.pixel2=self.calib_data['PixelSize2']
self.poni1=self.calib_data['Poni1']
self.poni2=self.calib_data['Poni2']
self.rot1=self.calib_data['Rot1']
self.rot2=self.calib_data['Rot2']
self.rot3=self.calib_data['Rot3']
self.wavelength=self.calib_data['Wavelength']
self.ai=AzimuthalIntegrator(dist=self.dist,poni1=self.poni1,poni2=self.poni2,pixel1=self.pixel1,pixel2=self.pixel2,rot1=self.rot1,rot2=self.rot2,rot3=self.rot3,wavelength=self.wavelength)
#pos=[self.poni2/self.pixel2,self.poni1/self.pixel1]
#self.roi=cake(pos,movable=False)
#self.roi.sigRegionChangeStarted.connect(self.endAngleChanged)
#self.imageView.addItem(self.roi)
else:
QMessageBox.warning(self,'File error','The calibration file '+self.poniFile+' doesnot exists.',QMessageBox.Ok)
def endAngleChanged(self,evt):
print(evt.pos())
def nptChanged(self):
"""
Changes the number of radial points
"""
try:
self.npt=int(self.radialPointsLineEdit.text())
except:
QMessageBox.warning(self,'Value error', 'Please input positive integers only.',QMessageBox.Ok)
def azimuthalRangeChanged(self):
"""
Changes the azimuth angular range
"""
try:
self.azimuthalRange=tuple(map(float, self.azimuthalRangeLineEdit.text().split(':')))
except:
QMessageBox.warning(self,'Value error','Please input min:max angles in floating point numbers',QMessageBox.Ok)
def openDataFile(self):
"""
Select and imports one data file
"""
dataFile=QFileDialog.getOpenFileName(self,'Select data file',directory=self.curDir,filter='Data file (*.edf *.tif)')[0]
if dataFile!='':
self.dataFile=dataFile
self.curDir=os.path.dirname(self.dataFile)
self.dataFileLineEdit.setText(self.dataFile)
self.data2d=fb.open(self.dataFile).data
if self.darkFile is not None:
self.applyDark()
if self.maskFile is not None:
self.applyMask()
self.imageWidget.setImage(self.data2d,transpose=True)
self.tabWidget.setCurrentWidget(self.imageWidget)
if not self.set_externally:
self.extractedFolder=os.path.join(self.curDir,self.extractedFolderLineEdit.text())
if not os.path.exists(self.extractedFolder):
os.makedirs(self.extractedFolder)
def openDataFiles(self):
"""
Selects and imports multiple data files
"""
self.dataFiles=QFileDialog.getOpenFileNames(self,'Select data files', directory=self.curDir,filter='Data files (*.edf *.tif)')[0]
if len(self.dataFiles)!=0:
self.imgNumberSpinBox.valueChanged.connect(self.imageChanged)
self.imgNumberSpinBox.setMinimum(0)
self.imgNumberSpinBox.setMaximum(len(self.dataFiles)-1)
self.dataFileLineEdit.setText(str(self.dataFiles))
self.curDir=os.path.dirname(self.dataFiles[0])
self.extractedBaseFolder=self.curDir
self.extractedFolder=os.path.abspath(os.path.join(self.extractedBaseFolder,self.extractedFolderLineEdit.text()))
if not os.path.exists(self.extractedFolder):
os.makedirs(self.extractedFolder)
self.extractedBaseFolderLineEdit.setText(self.extractedBaseFolder)
self.imgNumberSpinBox.setValue(0)
self.imageChanged()
def imageChanged(self):
self.data2d=fb.open(self.dataFiles[self.imgNumberSpinBox.value()]).data
if self.darkFile is not None:
self.applyDark()
if self.maskFile is not None:
self.applyMask()
self.imageWidget.setImage(self.data2d,transpose=True)
def applyDark(self):
if not self.dark_corrected and self.darkFile!='':
self.dark2d=fb.open(self.darkFile).data
self.data2d=self.data2d-self.dark2d
self.dark_corrected=True
def applyMask(self):
self.mask2d=fb.open(self.maskFile).data
self.data2d=self.data2d*(1+self.mask2d)/2.0
self.mask_applied=True
def openDarkFile(self):
"""
Select and imports the dark file
"""
self.darkFile=QFileDialog.getOpenFileName(self,'Select dark file',directory=self.curDir,filter='Dark file (*.edf)')[0]
if self.darkFile!='':
self.dark_corrected=False
self.darkFileLineEdit.setText(self.darkFile)
if self.dataFile is not None:
self.data2d=fb.open(self.dataFile).data
self.applyDark()
def openMaskFile(self,file=None):
"""
Select and imports the Mask file
"""
if file is None:
self.maskFile=QFileDialog.getOpenFileName(self,'Select mask file',directory=self.curDir,filter='Mask file (*.msk)')[0]
else:
self.maskFile=file
if self.maskFile!='':
self.mask_applied=False
if os.path.exists(self.maskFile):
self.curDir=os.path.dirname(self.maskFile)
self.maskFileLineEdit.setText(self.maskFile)
self.setup_dict['maskFile']=self.maskFile
self.setup_dict['poniFile']=self.poniFile
json.dump(self.setup_dict,open('./SetupData/reducer_setup.txt','w'))
else:
self.openMaskFile(file=None)
if self.dataFile is not None:
self.applyMask()
else:
self.maskFile=None
self.maskFileLineEdit.clear()
def openFolder(self):
"""
Select the folder to save the reduce data
"""
oldfolder=self.extractedBaseFolder.text()
folder=QFileDialog.getExistingDirectory(self,'Select extracted directory',directory=self.curDir)
if folder!='':
self.extractedBaseFolder=folder
self.extractedBaseFolderLineEdit.setText(folder)
self.extractedFolder=os.path.join(folder,self.extractedFolderLineEdit.text())
self.set_externally=True
else:
self.extractedBaseFolder=oldfolder
self.extractedBaseFolderLineEdit.setText(oldfolder)
self.extractedFolder = os.path.join(oldfolder, self.extractedFolderLineEdit.text())
self.set_externally = True
def extractedFolderChanged(self,txt):
self.extractedFolder=os.path.join(self.extractedBaseFolder,txt)
self.set_externally=True
def reduceData(self):
"""
Reduces the 2d data to 1d data
"""
if (self.dataFile is not None) and (os.path.exists(self.dataFile)):
if (self.poniFile is not None) and (os.path.exists(self.poniFile)):
# self.statusLabel.setText('Busy')
# self.progressBar.setRange(0, 0)
imageData=fb.open(self.dataFile)
#self.data2d=imageData.data
#if self.maskFile is not None:
# self.applyMask()
#self.imageWidget.setImage(self.data2d,transpose=True)
#self.tabWidget.setCurrentWidget(self.imageWidget)
self.header=imageData.header
try:
self.ai.set_wavelength(float(self.header['Wavelength'])*1e-10)
except:
self.ai.set_wavelength(self.wavelength)
#print(self.darkFile)
if os.path.exists(self.dataFile.split('.')[0]+'_dark.edf') and self.darkCheckBox.isChecked():
self.darkFile=self.dataFile.split('.')[0]+'_dark.edf'
dark=fb.open(self.darkFile)
self.darkFileLineEdit.setText(self.darkFile)
imageDark=dark.data
self.header['BSDiode_corr']=max([1.0,(float(imageData.header['BSDiode'])-float(dark.header['BSDiode']))])
self.header['Monitor_corr']=max([1.0,(float(imageData.header['Monitor'])-float(dark.header['Monitor']))])
print("Dark File read from existing dark files")
elif self.darkFile is not None and self.darkFile!='' and self.darkCheckBox.isChecked():
dark=fb.open(self.darkFile)
imageDark=dark.data
self.header['BSDiode_corr']=max([1.0,(float(imageData.header['BSDiode'])-float(dark.header['BSDiode']))])
self.header['Monitor_corr']=max([1.0,(float(imageData.header['Monitor'])-float(dark.header['Monitor']))])
print("Dark File from memory subtracted")
else:
imageDark=None
try:
self.header['BSDiode_corr']=float(imageData.header['BSDiode'])
self.header['Monitor_corr']=float(imageData.header['Monitor'])
self.header['Transmission'] = float(imageData.header['Transmission'])
except:
self.normComboBox.setCurrentText('None')
print("No dark correction done")
if str(self.normComboBox.currentText())=='BSDiode':
norm_factor=self.header['BSDiode_corr']#/self.header['Monitor_corr']#float(self.header[
# 'count_time'])
elif str(self.normComboBox.currentText())=='TransDiode':
norm_factor=self.header['Transmission']*self.header['Monitor_corr']
elif str(self.normComboBox.currentText())=='Monitor':
norm_factor=self.header['Monitor_corr']
elif str(self.normComboBox.currentText())=='Image Sum':
norm_factor=sum(imageData.data)
else:
norm_factor=1.0
if self.maskFile is not None:
imageMask=fb.open(self.maskFile).data
else:
imageMask=None
# QApplication.processEvents()
#print(self.azimuthalRange)
self.q,self.I,self.Ierr=self.ai.integrate1d(imageData.data,self.npt,error_model='poisson',mask=imageMask,dark=imageDark,unit='q_A^-1',normalization_factor=norm_factor,azimuth_range=self.azimuthalRange,polarization_factor=self.polarization_factor)
self.plotWidget.add_data(self.q,self.I,yerr=self.Ierr,name='Reduced data')
if not self.set_externally:
cakedI,qr,phir=self.ai.integrate2d(imageData.data,self.npt,mask=imageMask,dark=imageDark,unit='q_A^-1',normalization_factor=norm_factor,polarization_factor=self.polarization_factor)
self.cakedImageWidget.setImage(cakedI,xmin=qr[0],xmax=qr[-1],ymin=phir[0],ymax=phir[-1],transpose=True,xlabel='Q ', ylabel='phi ',unit=['Å<sup>-1</sup>','degree'])
self.cakedImageWidget.imageView.view.setAspectLocked(False)
try:
self.azimuthalRegion.setRegion(self.azimuthalRange)
except:
self.azimuthalRegion=pg.LinearRegionItem(values=self.azimuthalRange,orientation=pg.LinearRegionItem.Horizontal,movable=True,bounds=[-180,180])
self.cakedImageWidget.imageView.getView().addItem(self.azimuthalRegion)
self.azimuthalRegion.sigRegionChanged.connect(self.azimuthalRegionChanged)
self.plotWidget.setTitle(self.dataFile,fontsize=3)
# self.progressBar.setRange(0,100)
# self.progressBar.setValue(100)
# self.statusLabel.setText('Idle')
# QApplication.processEvents()
self.saveData()
#self.tabWidget.setCurrentWidget(self.plotWidget)
else:
QMessageBox.warning(self,'Calibration File Error','Data reduction failed because either no calibration file provided or the provided file or path do not exists',QMessageBox.Ok)
else:
QMessageBox.warning(self,'Data File Error','No data file provided', QMessageBox.Ok)
def azimuthalRegionChanged(self):
minp,maxp=self.azimuthalRegion.getRegion()
self.azimuthalRangeLineEdit.setText('%.1f:%.1f'%(minp,maxp))
self.azimuthalRange=[minp,maxp]
self.set_externally=True
def reduce_multiple(self):
"""
Reduce multiple files
"""
try:
i=0
self.progressBar.setRange(0,len(self.dataFiles))
self.progressBar.setValue(i)
self.statusLabel.setText('<font color="red">Busy</font>')
for file in self.dataFiles:
self.dataFile=file
QApplication.processEvents()
self.reduceData()
i=i+1
self.progressBar.setValue(i)
QApplication.processEvents()
self.statusLabel.setText('<font color="green">Idle</font>')
self.progressBar.setValue(0)
except:
QMessageBox.warning(self,'File error','No data files to reduce',QMessageBox.Ok)
def saveData(self):
"""
saves the extracted data into a file
"""
if not os.path.exists(self.extractedFolder):
os.makedirs(self.extractedFolder)
filename=os.path.join(self.extractedFolder,os.path.splitext(os.path.basename(self.dataFile))[0]+'.txt')
headers='File extracted on '+time.asctime()+'\n'
headers='Files used for extraction are:\n'
headers+='Data file: '+self.dataFile+'\n'
if self.darkFile is not None:
headers+='Dark file: '+self.darkFile+'\n'
else:
headers+='Dark file: None\n'
headers+='Poni file: '+self.poniFile+'\n'
if self.maskFile is not None:
headers+='mask file: '+self.maskFile+'\n'
else:
headers+='mask file: None\n'
for key in self.header.keys():
headers+=key+'='+str(self.header[key])+'\n'
headers+="col_names=['Q (inv Angs)','Int','Int_err']\n"
headers+='Q (inv Angs)\tInt\tInt_err'
data=vstack((self.q,self.I,self.Ierr)).T
savetxt(filename,data,header=headers,comments='#')
class EigerFrame(wx.Frame):
"""AreaDetector Display """
img_attrs = ('ArrayData', 'UniqueId_RBV')
cam_attrs = ('Acquire', 'DetectorState_RBV',
'ArrayCounter', 'ArrayCounter_RBV',
'ThresholdEnergy', 'ThresholdEnergy_RBV',
'PhotonEnergy', 'PhotonEnergy_RBV',
'NumImages', 'NumImages_RBV',
'AcquireTime', 'AcquireTime_RBV',
'AcquirePeriod', 'AcquirePeriod_RBV',
'TriggerMode', 'TriggerMode_RBV')
# plugins to enable
enabled_plugins = ('image1', 'Over1', 'ROI1', 'JPEG1', 'TIFF1')
def __init__(self, prefix=None, url=None, scale=1.0):
self.ad_img = None
self.ad_cam = None
if prefix is None:
dlg = SavedParameterDialog(label='Detector Prefix',
title='Connect to Eiger Detector',
configfile='.ad_eigerdisplay.dat')
res = dlg.GetResponse()
dlg.Destroy()
if res.ok:
prefix = res.value
self.prefix = prefix
self.fname = 'Eiger.tif'
self.esimplon = None
if url is not None and HAS_SIMPLON:
self.esimplon = EigerSimplon(url, prefix=prefix+'cam1:')
self.lineplotter = None
self.calib = {}
self.integrator = None
self.int_panel = None
self.int_lastid = None
self.contrast_levels = None
self.scandb = None
wx.Frame.__init__(self, None, -1, "Eiger500K Area Detector Display",
style=wx.DEFAULT_FRAME_STYLE)
self.buildMenus()
self.buildFrame()
wx.CallAfter(self.connect_escandb)
def connect_escandb(self):
if HAS_ESCAN and os.environ.get('ESCAN_CREDENTIALS', None) is not None:
self.scandb = ScanDB()
calib_loc = self.scandb.get_info('eiger_calibration')
cal = self.scandb.get_detectorconfig(calib_loc)
self.setup_calibration(json.loads(cal.text))
def buildFrame(self):
sbar = self.CreateStatusBar(3, wx.CAPTION) # |wx.THICK_FRAME)
self.SetStatusWidths([-1, -1, -1])
sfont = sbar.GetFont()
sfont.SetWeight(wx.BOLD)
sfont.SetPointSize(10)
sbar.SetFont(sfont)
self.SetStatusText('',0)
sizer = wx.GridBagSizer(3, 3)
panel = self.panel = wx.Panel(self)
pvpanel = PVConfigPanel(panel, self.prefix, display_pvs)
wsize = (100, -1)
lsize = (250, -1)
start_btn = wx.Button(panel, label='Start', size=wsize)
stop_btn = wx.Button(panel, label='Stop', size=wsize)
free_btn = wx.Button(panel, label='Free Run', size=wsize)
start_btn.Bind(wx.EVT_BUTTON, partial(self.onButton, key='start'))
stop_btn.Bind(wx.EVT_BUTTON, partial(self.onButton, key='stop'))
free_btn.Bind(wx.EVT_BUTTON, partial(self.onButton, key='free'))
self.cmap_choice = wx.Choice(panel, size=(80, -1),
choices=colormaps)
self.cmap_choice.SetSelection(0)
self.cmap_choice.Bind(wx.EVT_CHOICE, self.onColorMap)
self.cmap_reverse = wx.CheckBox(panel, label='Reverse', size=(60, -1))
self.cmap_reverse.Bind(wx.EVT_CHECKBOX, self.onColorMap)
self.show1d_btn = wx.Button(panel, label='Show 1D Integration', size=(200, -1))
self.show1d_btn.Bind(wx.EVT_BUTTON, self.onShowIntegration)
self.show1d_btn.Disable()
self.imagesize = wx.StaticText(panel, label='? x ?',
size=(250, 30), style=txtstyle)
self.contrast = ContrastChoice(panel, callback=self.set_contrast_level)
def lin(len=200, wid=2, style=wx.LI_HORIZONTAL):
return wx.StaticLine(panel, size=(len, wid), style=style)
irow = 0
sizer.Add(pvpanel, (irow, 0), (1, 3), labstyle)
irow += 1
sizer.Add(start_btn, (irow, 0), (1, 1), labstyle)
sizer.Add(stop_btn, (irow, 1), (1, 1), labstyle)
sizer.Add(free_btn, (irow, 2), (1, 1), labstyle)
irow += 1
sizer.Add(lin(300), (irow, 0), (1, 3), labstyle)
irow += 1
sizer.Add(self.imagesize, (irow, 0), (1, 3), labstyle)
irow += 1
sizer.Add(wx.StaticText(panel, label='Color Map: '),
(irow, 0), (1, 1), labstyle)
sizer.Add(self.cmap_choice, (irow, 1), (1, 1), labstyle)
sizer.Add(self.cmap_reverse, (irow, 2), (1, 1), labstyle)
irow += 1
sizer.Add(self.contrast.label, (irow, 0), (1, 1), labstyle)
sizer.Add(self.contrast.choice, (irow, 1), (1, 1), labstyle)
irow += 1
sizer.Add(self.show1d_btn, (irow, 0), (1, 2), labstyle)
panel.SetSizer(sizer)
sizer.Fit(panel)
# image panel
self.image = ADMonoImagePanel(self, prefix=self.prefix,
rot90=DEFAULT_ROTATION,
size=(400, 750),
writer=partial(self.write, panel=2))
mainsizer = wx.BoxSizer(wx.HORIZONTAL)
mainsizer.Add(panel, 0, wx.LEFT|wx.GROW|wx.ALL)
mainsizer.Add(self.image, 1, wx.CENTER|wx.GROW|wx.ALL)
self.SetSizer(mainsizer)
mainsizer.Fit(self)
self.SetAutoLayout(True)
try:
self.SetIcon(wx.Icon(ICONFILE, wx.BITMAP_TYPE_ICO))
except:
pass
wx.CallAfter(self.connect_pvs )
def onColorMap(self, event=None):
cmap_name = self.cmap_choice.GetStringSelection()
if self.cmap_reverse.IsChecked():
cmap_name = cmap_name + '_r'
self.image.colormap = getattr(colormap, cmap_name)
self.image.Refresh()
def onCopyImage(self, event=None):
"copy bitmap of canvas to system clipboard"
bmp = wx.BitmapDataObject()
bmp.SetBitmap(wx.Bitmap(self.image.GrabWxImage()))
wx.TheClipboard.Open()
wx.TheClipboard.SetData(bmp)
wx.TheClipboard.Close()
wx.TheClipboard.Flush()
def onReadCalibFile(self, event=None):
"read calibration file"
wcards = "Poni Files(*.poni)|*.poni|All files (*.*)|*.*"
dlg = wx.FileDialog(None, message='Read Calibration File',
defaultDir=os.getcwd(),
wildcard=wcards,
style=wx.FD_OPEN)
ppath = None
if dlg.ShowModal() == wx.ID_OK:
ppath = os.path.abspath(dlg.GetPath())
if os.path.exists(ppath):
if self.scandb is not None:
CalibrationDialog(self, ppath).Show()
else:
self.setup_calibration(read_poni(ppath))
def setup_calibration(self, calib):
"""set up calibration from calibration dict"""
if self.image.rot90 in (1, 3):
calib['rot3'] = np.pi/2.0
self.calib = calib
if HAS_PYFAI:
self.integrator = AzimuthalIntegrator(**calib)
self.show1d_btn.Enable()
def onShowIntegration(self, event=None):
if self.calib is None or 'poni1' not in self.calib:
return
shown = False
try:
self.int_panel.Raise()
shown = True
except:
self.int_panel = None
if not shown:
self.int_panel = PlotFrame(self)
self.show_1dpattern(init=True)
else:
self.show_1dpattern()
def onAutoIntegration(self, event=None):
if not event.IsChecked():
self.int_timer.Stop()
return
if self.calib is None or 'poni1' not in self.calib:
return
shown = False
try:
self.int_panel.Raise()
shown = True
except:
self.int_panel = None
if not shown:
self.int_panel = PlotFrame(self)
self.show_1dpattern(init=True)
else:
self.show_1dpattern()
self.int_timer.Start(500)
def show_1dpattern(self, init=False):
if self.calib is None or not HAS_PYFAI:
return
img = self.ad_img.PV('ArrayData').get()
h, w = self.image.GetImageSize()
img.shape = (w, h)
img = img[3:-3, 1:-1][::-1, :]
img_id = self.ad_cam.ArrayCounter_RBV
q, xi = self.integrator.integrate1d(img, 2048, unit='q_A^-1',
correctSolidAngle=True,
polarization_factor=0.999)
if init:
self.int_panel.plot(q, xi, xlabel=r'$Q (\rm\AA^{-1})$', marker='+',
title='Image %d' % img_id)
self.int_panel.Raise()
self.int_panel.Show()
else:
self.int_panel.update_line(0, q, xi, draw=True)
self.int_panel.set_title('Image %d' % img_id)
@EpicsFunction
def onSaveImage(self, event=None):
"prompts for and save image to file"
defdir = os.getcwd()
self.fname = "Image_%i.tiff" % self.ad_cam.ArrayCounter_RBV
dlg = wx.FileDialog(None, message='Save Image as',
defaultDir=os.getcwd(),
defaultFile=self.fname,
style=wx.FD_SAVE)
path = None
if dlg.ShowModal() == wx.ID_OK:
path = os.path.abspath(dlg.GetPath())
root, fname = os.path.split(path)
epics.caput("%sTIFF1:FileName" % self.prefix, fname)
epics.caput("%sTIFF1:FileWriteMode" % self.prefix, 0)
time.sleep(0.05)
epics.caput("%sTIFF1:WriteFile" % self.prefix, 1)
time.sleep(0.05)
print("Saved TIFF File ",
epics.caget("%sTIFF1:FullFileName_RBV" % self.prefix, as_string=True))
def onExit(self, event=None):
try:
wx.Yield()
except:
pass
self.Destroy()
def onAbout(self, event=None):
msg = """Eiger Image Display version 0.1
Matt Newville <*****@*****.**>"""
dlg = wx.MessageDialog(self, msg, "About Epics Image Display",
wx.OK | wx.ICON_INFORMATION)
dlg.ShowModal()
dlg.Destroy()
def buildMenus(self):
fmenu = wx.Menu()
MenuItem(self, fmenu, "&Save\tCtrl+S", "Save Image", self.onSaveImage)
MenuItem(self, fmenu, "&Copy\tCtrl+C", "Copy Image to Clipboard",
self.onCopyImage)
MenuItem(self, fmenu, "Read Calibration File", "Read PONI Calibration",
self.onReadCalibFile)
fmenu.AppendSeparator()
MenuItem(self, fmenu, "E&xit\tCtrl+Q", "Exit Program", self.onExit)
omenu = wx.Menu()
MenuItem(self, omenu, "&Rotate CCW\tCtrl+R", "Rotate Counter Clockwise", self.onRot90)
MenuItem(self, omenu, "Flip Up/Down\tCtrl+T", "Flip Up/Down", self.onFlipV)
MenuItem(self, omenu, "Flip Left/Right\tCtrl+F", "Flip Left/Right", self.onFlipH)
MenuItem(self, omenu, "Reset Rotations and Flips", "Reset", self.onResetRotFlips)
omenu.AppendSeparator()
hmenu = wx.Menu()
MenuItem(self, hmenu, "About", "About Epics AreadDetector Display", self.onAbout)
mbar = wx.MenuBar()
mbar.Append(fmenu, "File")
mbar.Append(omenu, "Options")
mbar.Append(hmenu, "&Help")
self.SetMenuBar(mbar)
def onResetRotFlips(self, event):
self.image.rot90 = DEFAULT_ROTATION
self.image.flipv = self.fliph = False
def onRot90(self, event):
self.image.rot90 = (self.image.rot90 - 1) % 4
def onFlipV(self, event):
self.image.flipv= not self.image.flipv
def onFlipH(self, event):
self.image.fliph = not self.image.fliph
def set_contrast_level(self, contrast_level=0):
self.image.contrast_levels = [contrast_level, 100.0-contrast_level]
def write(self, s, panel=0):
"""write a message to the Status Bar"""
self.SetStatusText(text=s, number=panel)
@EpicsFunction
def onButton(self, event=None, key='free'):
key = key.lower()
if key.startswith('free'):
self.image.restart_fps_counter()
self.ad_cam.AcquireTime = 0.25
self.ad_cam.AcquirePeriod = 0.25
self.ad_cam.NumImages = 345600
self.ad_cam.Acquire = 1
elif key.startswith('start'):
self.image.restart_fps_counter()
self.ad_cam.Acquire = 1
elif key.startswith('stop'):
self.ad_cam.Acquire = 0
@EpicsFunction
def connect_pvs(self, verbose=True):
if self.prefix is None or len(self.prefix) < 2:
return
if self.prefix.endswith(':'):
self.prefix = self.prefix[:-1]
if self.prefix.endswith(':image1'):
self.prefix = self.prefix[:-7]
if self.prefix.endswith(':cam1'):
self.prefix = self.prefix[:-5]
self.write('Connecting to AD %s' % self.prefix)
self.ad_img = epics.Device(self.prefix + ':image1:', delim='',
attrs=self.img_attrs)
self.ad_cam = epics.Device(self.prefix + ':cam1:', delim='',
attrs=self.cam_attrs)
epics.caput("%s:TIFF1:EnableCallbacks" % self.prefix, 1)
epics.caput("%s:TIFF1:AutoSave" % self.prefix, 0)
epics.caput("%s:TIFF1:AutoIncrement" % self.prefix, 0)
epics.caput("%s:TIFF1:FileWriteMode" % self.prefix, 0)
time.sleep(0.002)
if not self.ad_img.PV('UniqueId_RBV').connected:
epics.poll()
if not self.ad_img.PV('UniqueId_RBV').connected:
self.write('Warning: Camera seems to not be connected!')
return
if verbose:
self.write('Connected to AD %s' % self.prefix)
self.SetTitle("Epics Image Display: %s" % self.prefix)
sizex = self.ad_cam.MaxSizeX_RBV
sizey = self.ad_cam.MaxSizeY_RBV
sizelabel = 'Image Size: %i x %i pixels'
try:
sizelabel = sizelabel % (sizex, sizey)
except:
sizelabel = sizelabel % (0, 0)
self.imagesize.SetLabel(sizelabel)
self.ad_cam.add_callback('DetectorState_RBV', self.onDetState)
self.contrast.set_level_str('0.05')
@DelayedEpicsCallback
def onDetState(self, pvname=None, value=None, char_value=None, **kw):
self.write(char_value, panel=1)
