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Port new masking to Cython, 2D as well

This commit is contained in:
Jerome Kieffer 2012-06-28 21:53:30 +02:00
parent 3cf889159a
commit 4bb8a0884e
4 changed files with 3572 additions and 2860 deletions
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22
pyFAI-src/azimuthalIntegrator.py
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@ -585,21 +585,21 @@ class AzimuthalIntegrator(Geometry):
return self.xrpd2_histogram(data=data, nbPt2Th=nbPt2Th, nbPtChi=nbPtChi,
filename=filename, correctSolidAngle=correctSolidAngle,
tthRange=tthRange, chiRange=chiRange, mask=mask, dummy=dummy, delta_dummy=delta_dummy)
mask = self.makeMask(data, mask, dummy, delta_dummy)
tth = self.twoThetaArray(data.shape)[mask]
chi = self.chiArray(data.shape)[mask]
dtth = self.delta2Theta(data.shape)[mask]
dchi = self.deltaChi(data.shape)[mask]
# mask = self.makeMask(data, mask, dummy, delta_dummy)
tth = self.twoThetaArray(data.shape)#[mask]
chi = self.chiArray(data.shape)#[mask]
dtth = self.delta2Theta(data.shape)#[mask]
dchi = self.deltaChi(data.shape)#[mask]
if tthRange is not None:
tthRange = tuple([numpy.deg2rad(i) for i in tthRange])
if chiRange is not None:
chiRange = tuple([numpy.deg2rad(i) for i in chiRange])
if correctSolidAngle:
data = (data / self.solidAngleArray(data.shape))[mask]
data = (data / self.solidAngleArray(data.shape))#[mask]
else:
data = data[mask]
if dummy is None:
dummy = 0.0
data = data#[mask]
# if dummy is None:
# dummy = 0.0
I, bins2Th, binsChi, a, b = splitBBox.histoBBox2d(weights=data,
pos0=tth,
delta_pos0=dtth,
@ -608,7 +608,9 @@ class AzimuthalIntegrator(Geometry):
bins=(nbPt2Th, nbPtChi),
pos0Range=tthRange,
pos1Range=chiRange,
dummy=dummy)
dummy=dummy,
delta_dummy=delta_dummy,
mask=mask)
bins2Th = numpy.degrees(bins2Th)
binsChi = numpy.degrees(binsChi)
if filename:

2604
src/splitBBox.c
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3758
src/splitBBox.html
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48
src/splitBBox.pyx
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@ -72,7 +72,9 @@ def histoBBox1d(numpy.ndarray weights not None,
@param bins: number of output bins
@param pos0Range: minimum and maximum of the 2th range
@param pos1Range: minimum and maximum of the chi range
@param dummy: value for bins without pixels
@param dummy: value for bins without pixels & value of "no good" pixels
@param delta_dummy: precision of dummy value
@param mask: array (of int8) with masked pixels with 1 (0=not masked)
@return 2theta, I, weighted histogram, unweighted histogram
"""
cdef long size = weights.size
@ -82,7 +84,7 @@ def histoBBox1d(numpy.ndarray weights not None,
cdef long bin0_max, bin0_min, bin = 0
cdef float data, deltaR, deltaL, deltaA,p1, epsilon = 1e-10, cdummy, ddummy
cdef float pos0_min, pos0_max, pos0_maxin, pos1_min, pos1_max, pos1_maxin, min0, max0, fbin0_min, fbin0_max
cdef long checkpos1 = 0, check_mask = 0, check_dummy = 0
cdef int checkpos1 = 0, check_mask = 0, check_dummy = 0
cdef numpy.ndarray[numpy.float32_t, ndim = 1] cdata = numpy.ascontiguousarray(weights.ravel(),dtype=numpy.float32)
cdef numpy.ndarray[numpy.float32_t, ndim = 1] cpos0, dpos0, cpos1, dpos1,cpos0_lower, cpos0_upper
@ -150,11 +152,11 @@ def histoBBox1d(numpy.ndarray weights not None,
outPos[i] = pos0_min + (0.5 +< float > i) * delta
for idx in range(size):
if (check_mask) and cmask[idx]:
if (check_mask) and (cmask[idx]):
continue
data = cdata[idx]
if check_dummy and fabs(data-cdummy)<ddummy:
if check_dummy and (fabs(data-cdummy)<=ddummy):
continue
min0 = cpos0_lower[idx]
@ -220,7 +222,9 @@ def histoBBox2d(numpy.ndarray weights not None,
bins=(100, 36),
pos0Range=None,
pos1Range=None,
float dummy=0.0):
dummy=None,
delta_dummy=None,
mask=None):
"""
Calculate 2D histogram of pos0(tth),pos1(chi) weighted by weights
@ -235,8 +239,9 @@ def histoBBox2d(numpy.ndarray weights not None,
@param bins: number of output bins (tth=100, chi=36 by default)
@param pos0Range: minimum and maximum of the 2th range
@param pos1Range: minimum and maximum of the chi range
@param dummy: value for bins without pixels
@return 2theta, I, weighted histogram, unweighted histogram
@param dummy: value for bins without pixels & value of "no good" pixels
@param delta_dummy: precision of dummy value
@param mask: array (of int8) with masked pixels with 1 (0=not masked)
@return I, edges0, edges1, weighted histogram(2D), unweighted histogram (2D)
"""
@ -269,8 +274,25 @@ def histoBBox2d(numpy.ndarray weights not None,
cdef float min0, max0, min1, max1, deltaR, deltaL, deltaU, deltaD, deltaA, tmp, delta0, delta1
cdef float pos0_min, pos0_max, pos1_min, pos1_max, pos0_maxin, pos1_maxin
cdef float fbin0_min, fbin0_max, fbin1_min, fbin1_max, data, epsilon = 1e-10
cdef float fbin0_min, fbin0_max, fbin1_min, fbin1_max, data, epsilon = 1e-10, cdummy, ddummy
cdef long bin0_max, bin0_min, bin1_max, bin1_min
cdef int check_mask = 0, check_dummy = 0
cdef numpy.ndarray[numpy.int8_t, ndim = 1] cmask
if mask is not None:
assert mask.size == size
check_mask = 1
cmask = numpy.ascontiguousarray(mask.ravel(),dtype="int8")
if (dummy is not None) and delta_dummy is not None:
check_dummy = 1
cdummy = float(dummy)
ddummy = float(delta_dummy)
elif (dummy is not None):
cdummy = float(dummy)
else:
cdummy=0.0
pos0_min=cpos0[0]
pos0_max=cpos0[0]
@ -313,6 +335,14 @@ def histoBBox2d(numpy.ndarray weights not None,
edges1[i] = pos1_min + (0.5 + i) * delta1
for idx in range(size):
if (check_mask) and cmask[idx]:
continue
data = cdata[idx]
if (check_dummy) and (fabs(data-cdummy)<=ddummy):
continue
data = cdata[idx]
min0 = cpos0_lower[idx]
max0 = cpos0_upper[idx]
@ -415,6 +445,6 @@ def histoBBox2d(numpy.ndarray weights not None,
if outCount[i, j] > epsilon:
outMerge[i, j] = <float> (outData[i, j] / outCount[i, j])
else:
outMerge[i, j] = dummy
outMerge[i, j] = cdummy
return outMerge.T, edges0, edges1, outData.T, outCount.T