def test_SAXSWorkflow():
# create processes
thresholdmask = ThresholdMaskPlugin()
qintegrate = QIntegratePlugin()
# set values
AI = AzimuthalIntegrator(.283,
5.24e-3,
4.085e-3,
0,
0,
0,
1.72e-4,
1.72e-4,
detector=Pilatus2M(),
wavelength=1.23984e-10)
thresholdmask.data.value = fabio.open(
'/Users/hari/Downloads/AGB_5S_USE_2_2m.edf').data
def AI_func():
from pyFAI.detectors import Pilatus2M
from pyFAI import AzimuthalIntegrator, units
return AzimuthalIntegrator(.283,
5.24e-3,
4.085e-3,
0,
0,
0,
1.72e-4,
1.72e-4,
detector=Pilatus2M(),
wavelength=1.23984e-10)
qintegrate.integrator.value = AI_func
qintegrate.npt.value = 1000
thresholdmask.minimum.value = 30
thresholdmask.maximum.value = 1e12
qintegrate.data.value = fabio.open(
'/Users/hari/Downloads/AGB_5S_USE_2_2m.edf').data
thresholdmask.neighborhood.value = 1
qintegrate.normalization_factor.value = 0.5
qintegrate.method.value = "numpy"
# connect processes
thresholdmask.mask.connect(qintegrate.mask)
# add processes to workflow
wf = Workflow('QIntegrate')
wf.addProcess(thresholdmask)
wf.addProcess(qintegrate)
dsk = DaskExecutor()
result = dsk.execute(wf)
print(result)
def test_tomoworkflow():
read = read_APS2BM()
read.path.value = '/Users/hari/test.hdf'
read.sino.value = (1050, 1051)
norm = Normalize()
read.arr.connect(norm.arr)
read.flat.connect(norm.flats)
read.dark.connect(norm.darks)
outliers = RemoveOutlier()
norm.normalized.connect(outliers.arr)
outliers.dif.value = 500
outliers.size.value = 5
# maximum = ArrayMax()
# outliers.corrected.connect(maximum.arr)
# maximum.floor.value = 1e-16
# neglog = MinusLog()
# maximum.out.connect(neglog.arr)
# phase = RetrievePhase()
# maximum.out.connect(phase.arr)
# phase.pixel_size.value=6.5e-5
# phase.dist.value=3
# phase.energy.value=27
stripe = RemoveStripeFw()
outliers.corrected.connect(stripe.tomo)
stripe.level.value = 8
stripe.wname.value = 'db5'
stripe.sigma.value = 4
stripe.pad.value = True
padding = Pad()
stripe.corrected.connect(padding.arr)
padding.axis.value = 2
padding.npad.value = 448
padding.mode.value = 'edge'
# angles = Angles()
# angles.nang.value=0
# angles.ang1.value=90
# angles.ang2.value=180
gridrec = Recon()
padding.padded.connect(gridrec.tomo)
read.angles.connect(gridrec.theta)
gridrec.filter_name.value = 'butterworth'
gridrec.algorithm.value = 'gridrec'
gridrec.center.value = np.array([1295 + 448]) # 1295
gridrec.filter_par.value = np.array([0.2, 2])
# gridrec.sinogram_order.value = True
crop = Crop()
gridrec.reconstructed.connect(crop.arr)
crop.p11.value = 448
crop.p22.value = 448
crop.p12.value = 448
crop.p21.value = 448
crop.axis.value = 0
divide = ArrayDivide()
circularmask = CircMask()
crop.croppedarr.connect(circularmask.arr)
circularmask.val.value = 0
circularmask.axis.value = 0
circularmask.ratio.value = 1
writetiff = WriteTiffStack()
workflow = Workflow('Tomography')
for process in [read,
norm,
outliers,
# maximum,
# neglog,
# phase,
stripe,
padding,
# angles,
gridrec,
crop,
# divide,
circularmask,
# writetiff
]:
workflow.addProcess(process)
dsk = DaskExecutor()
result = dsk.execute(workflow)
print(result)
import pyqtgraph as pg
pg.image(result[0]['normalized'].value.squeeze())
from qtpy.QtWidgets import QApplication
app = QApplication([])
app.exec_()
