def getAppxCoef2d_significant(self, arr2d, maxNumCoef, alpha):
"""Returns[:,j] approx. coeffcients with p-value < alpha; subsequent coef. are equal to 0.
Reference: Ott, pp.606.
The significance of the coef. estimated by comparing the variance drop of the model2 with the variance of the model1, where:
model 1: complete model with maxNumCoef coef. different from 0
model 2: reduced model with coef. in range (0,k) different from 0
null hypothesis (H0): coef. k,k+1,...,maxNumCoef are equal to 0
if H0 rejected (pval below some alpha (e.g. 0.05) -> there exist an important coef. among coef. k,k+1,...,maxNumCoef
repeat the test for k=0,1,...maxNumCoef-1
"""
assert len(arr2d.shape) == 2, "2d array expected"
assert 0 < maxNumCoef <= self.k
coefMax = self.getAppxCoef(arr2d, maxNumCoef)
curveMax = self.getAppxCurve(coefMax)
SSE1 = Numeric.add.reduce((arr2d - curveMax)**2, 1)
MSE1 = SSE1 / (arr2d.shape[1] - maxNumCoef)
#print "SSE1[0], MSE1[0]",SSE1[0], MSE1[0]
pvals = Numeric.zeros((arr2d.shape[0], maxNumCoef), Numeric.Float)
for k in range(
maxNumCoef): # test cofInd: [maxNum-1, maxNum-2, ..., minNum]
#print "Keeping %i coeff" % (k)
shpk = list(coefMax.shape)
shpk[1] -= k
coefk = Numeric.concatenate(
(coefMax[:, :k], Numeric.zeros((shpk), Numeric.Float)), 1)
curvek = self.getAppxCurve(coefk)
SSE2 = Numeric.add.reduce((arr2d - curvek)**2, 1)
MSdrop = (SSE2 - SSE1) / (maxNumCoef - k)
F = MSdrop / MSE1
#2007-10-11: F -> F.filled(???)
pvals[:, k] = scipy.stats.fprob(
(maxNumCoef - k), arr2d.shape[1] - maxNumCoef,
F.filled(ApproxOrthPolyBasis._F_fillValue))
pvals = Numeric.where(
pvals > alpha,
Numeric.resize(Numeric.arange(pvals.shape[1]), pvals.shape),
pvals.shape[1]) # MAX where significant, idx where nonsignificant
firstNonSignIdx = MLab.min(pvals,
1) # idx of the first non-significant coef.
coefSign = Numeric.zeros(coefMax.shape, Numeric.Float)
for idx in range(coefSign.shape[1]):
coefSign[:, idx] = Numeric.where(idx < firstNonSignIdx,
coefMax[:, idx], 0)
return coefSign
def arc_by_radian(x, y, height, radian_range, thickness, gaussian_width):
"""
Radial arc with Gaussian fall-off after the solid ring-shaped
region with the given thickness, with shape specified by the
(start,end) radian_range.
"""
# Create a circular ring (copied from the ring function)
radius = height/2.0
half_thickness = thickness/2.0
distance_from_origin = sqrt(x**2+y**2)
distance_outside_outer_disk = distance_from_origin - radius - half_thickness
distance_inside_inner_disk = radius - half_thickness - distance_from_origin
ring = 1.0-bitwise_xor(greater_equal(distance_inside_inner_disk,0.0),greater_equal(distance_outside_outer_disk,0.0))
sigmasq = gaussian_width*gaussian_width
if sigmasq==0.0:
inner_falloff = x*0.0
outer_falloff = x*0.0
else:
with float_error_ignore():
inner_falloff = exp(divide(-distance_inside_inner_disk*distance_inside_inner_disk, 2.0*sigmasq))
outer_falloff = exp(divide(-distance_outside_outer_disk*distance_outside_outer_disk, 2.0*sigmasq))
output_ring = maximum(inner_falloff,maximum(outer_falloff,ring))
# Calculate radians (in 4 phases) and cut according to the set range)
# RZHACKALERT:
# Function float_error_ignore() cannot catch the exception when
# both dividend and divisor are 0.0, and when only divisor is 0.0
# it returns 'Inf' rather than 0.0. In x, y and
# distance_from_origin, only one point in distance_from_origin can
# be 0.0 (circle center) and in this point x and y must be 0.0 as
# well. So here is a hack to avoid the 'invalid value encountered
# in divide' error by turning 0.0 to 1e-5 in distance_from_origin.
distance_from_origin += where(distance_from_origin == 0.0, 1e-5, 0)
with float_error_ignore():
sines = divide(y, distance_from_origin)
cosines = divide(x, distance_from_origin)
arcsines = arcsin(sines)
phase_1 = where(logical_and(sines >= 0, cosines >= 0), 2*pi-arcsines, 0)
phase_2 = where(logical_and(sines >= 0, cosines < 0), pi+arcsines, 0)
phase_3 = where(logical_and(sines < 0, cosines < 0), pi+arcsines, 0)
phase_4 = where(logical_and(sines < 0, cosines >= 0), -arcsines, 0)
arcsines = phase_1 + phase_2 + phase_3 + phase_4
if radian_range[0] <= radian_range[1]:
return where(logical_and(arcsines >= radian_range[0], arcsines <= radian_range[1]),
output_ring, 0.0)
else:
return where(logical_or(arcsines >= radian_range[0], arcsines <= radian_range[1]),
output_ring, 0.0)
def arc_by_radian(x, y, height, radian_range, thickness, gaussian_width):
"""
Radial arc with Gaussian fall-off after the solid ring-shaped
region with the given thickness, with shape specified by the
(start,end) radian_range.
"""
# Create a circular ring (copied from the ring function)
radius = height/2.0
half_thickness = thickness/2.0
distance_from_origin = sqrt(x**2+y**2)
distance_outside_outer_disk = distance_from_origin - radius - half_thickness
distance_inside_inner_disk = radius - half_thickness - distance_from_origin
ring = 1.0-bitwise_xor(greater_equal(distance_inside_inner_disk,0.0),greater_equal(distance_outside_outer_disk,0.0))
sigmasq = gaussian_width*gaussian_width
if sigmasq==0.0:
inner_falloff = x*0.0
outer_falloff = x*0.0
else:
with float_error_ignore():
inner_falloff = exp(divide(-distance_inside_inner_disk*distance_inside_inner_disk, 2.0*sigmasq))
outer_falloff = exp(divide(-distance_outside_outer_disk*distance_outside_outer_disk, 2.0*sigmasq))
output_ring = maximum(inner_falloff,maximum(outer_falloff,ring))
# Calculate radians (in 4 phases) and cut according to the set range)
# RZHACKALERT:
# Function float_error_ignore() cannot catch the exception when
# both dividend and divisor are 0.0, and when only divisor is 0.0
# it returns 'Inf' rather than 0.0. In x, y and
# distance_from_origin, only one point in distance_from_origin can
# be 0.0 (circle center) and in this point x and y must be 0.0 as
# well. So here is a hack to avoid the 'invalid value encountered
# in divide' error by turning 0.0 to 1e-5 in distance_from_origin.
distance_from_origin += where(distance_from_origin == 0.0, 1e-5, 0)
with float_error_ignore():
sines = divide(y, distance_from_origin)
cosines = divide(x, distance_from_origin)
arcsines = arcsin(sines)
phase_1 = where(logical_and(sines >= 0, cosines >= 0), 2*pi-arcsines, 0)
phase_2 = where(logical_and(sines >= 0, cosines < 0), pi+arcsines, 0)
phase_3 = where(logical_and(sines < 0, cosines < 0), pi+arcsines, 0)
phase_4 = where(logical_and(sines < 0, cosines >= 0), -arcsines, 0)
arcsines = phase_1 + phase_2 + phase_3 + phase_4
if radian_range[0] <= radian_range[1]:
return where(logical_and(arcsines >= radian_range[0], arcsines <= radian_range[1]),
output_ring, 0.0)
else:
return where(logical_or(arcsines >= radian_range[0], arcsines <= radian_range[1]),
output_ring, 0.0)
def data(self, data):
if data != None:
self._data = data
## self._dataMA = chipstat.orng2ma(data)
self._dataMA = data.toNumpyMA("a")[0]
# info text
self.infoa.setText("Examples: %i profiles on %i points" %
(self._dataMA.shape[0], self._dataMA.shape[1]))
numTotalMissing = int(
Numeric.multiply.reduce(self._dataMA.shape) -
MA.count(self._dataMA))
if numTotalMissing > 0:
numValsByCol = MA.count(self._dataMA, 0)
numEmptyCol = Numeric.add.reduce(
Numeric.where(numValsByCol == 0, 1, 0))
colNonEmpty = Numeric.compress(
numValsByCol != 0, Numeric.arange(self._dataMA.shape[1]))
dataRemEmptyCol = self._dataMA.take(colNonEmpty, 1)
self.numRowsMissing = Numeric.add.reduce(
Numeric.where(
MA.count(dataRemEmptyCol, 1) <
dataRemEmptyCol.shape[1], 1, 0))
s1 = ""
s2 = ""
if numEmptyCol > 0: s1 = "s"
if self.numRowsMissing > 0: s2 = "s"
self.infob.setText(
"missing %i values, %i column%s completely, %i row%s partially"
% (numTotalMissing, numEmptyCol, s1, self.numRowsMissing,
s2))
else:
self.infob.setText("")
else:
self._data = None
self._dataMA = None
self.infoa.setText("No examples on input")
self.infob.setText("")
self.numRowsMissing = 0
self.setGuiCommonExpChip()
if self.commitOnChange:
self.senddata(1)
def line(y, thickness, gaussian_width):
"""
Infinite-length line with a solid central region, then Gaussian fall-off at the edges.
"""
distance_from_line = abs(y)
gaussian_y_coord = distance_from_line - thickness/2.0
sigmasq = gaussian_width*gaussian_width
if sigmasq==0.0:
falloff = y*0.0
else:
with float_error_ignore():
falloff = exp(divide(-gaussian_y_coord*gaussian_y_coord,2*sigmasq))
return where(gaussian_y_coord<=0, 1.0, falloff)
def line(y, thickness, gaussian_width):
"""
Infinite-length line with a solid central region, then Gaussian fall-off at the edges.
"""
distance_from_line = abs(y)
gaussian_y_coord = distance_from_line - thickness/2.0
sigmasq = gaussian_width*gaussian_width
if sigmasq==0.0:
falloff = x*0.0
else:
with float_error_ignore():
falloff = exp(divide(-gaussian_y_coord*gaussian_y_coord,2*sigmasq))
return where(gaussian_y_coord<=0, 1.0, falloff)
def getAppxCoef2d_significant(self, arr2d, maxNumCoef, alpha):
"""Returns[:,j] approx. coeffcients with p-value < alpha; subsequent coef. are equal to 0.
Reference: Ott, pp.606.
The significance of the coef. estimated by comparing the variance drop of the model2 with the variance of the model1, where:
model 1: complete model with maxNumCoef coef. different from 0
model 2: reduced model with coef. in range (0,k) different from 0
null hypothesis (H0): coef. k,k+1,...,maxNumCoef are equal to 0
if H0 rejected (pval below some alpha (e.g. 0.05) -> there exist an important coef. among coef. k,k+1,...,maxNumCoef
repeat the test for k=0,1,...maxNumCoef-1
"""
assert len(arr2d.shape) == 2, "2d array expected"
assert 0 < maxNumCoef <= self.k
coefMax = self.getAppxCoef(arr2d, maxNumCoef)
curveMax = self.getAppxCurve(coefMax)
SSE1 = Numeric.add.reduce((arr2d-curveMax)**2,1)
MSE1 = SSE1 / (arr2d.shape[1]-maxNumCoef)
#print "SSE1[0], MSE1[0]",SSE1[0], MSE1[0]
pvals = Numeric.zeros((arr2d.shape[0], maxNumCoef), Numeric.Float)
for k in range(maxNumCoef): # test cofInd: [maxNum-1, maxNum-2, ..., minNum]
#print "Keeping %i coeff" % (k)
shpk = list(coefMax.shape)
shpk[1] -= k
coefk = Numeric.concatenate((coefMax[:,:k], Numeric.zeros((shpk), Numeric.Float)),1)
curvek = self.getAppxCurve(coefk)
SSE2 = Numeric.add.reduce((arr2d-curvek)**2,1)
MSdrop =(SSE2-SSE1) / (maxNumCoef-k)
F = MSdrop / MSE1
#2007-10-11: F -> F.filled(???)
pvals[:,k] = scipy.stats.fprob((maxNumCoef-k), arr2d.shape[1]-maxNumCoef, F.filled(ApproxOrthPolyBasis._F_fillValue))
pvals = Numeric.where(pvals > alpha, Numeric.resize(Numeric.arange(pvals.shape[1]),pvals.shape), pvals.shape[1]) # MAX where significant, idx where nonsignificant
firstNonSignIdx = MLab.min(pvals, 1) # idx of the first non-significant coef.
coefSign = Numeric.zeros(coefMax.shape, Numeric.Float)
for idx in range(coefSign.shape[1]):
coefSign[:,idx] = Numeric.where(idx<firstNonSignIdx, coefMax[:,idx], 0)
return coefSign
def __call__(self,**params_to_override):
p = ParamOverrides(self,params_to_override)
xsize,ysize = SheetCoordinateSystem(p.bounds,p.xdensity,p.ydensity).shape
xsize,ysize = int(round(xsize)),int(round(ysize))
xdisparity = int(round(xsize*p.xdisparity))
ydisparity = int(round(xsize*p.ydisparity))
dotsize = int(round(xsize*p.dotsize))
bigxsize = 2*xsize
bigysize = 2*ysize
ndots=int(round(p.dotdensity * (bigxsize+2*dotsize) * (bigysize+2*dotsize) /
min(dotsize,xsize) / min(dotsize,ysize)))
halfdot = floor(dotsize/2)
# Choose random colors and locations of square dots
random_seed = p.random_seed
random_array.seed(random_seed*12,random_seed*99)
col=where(random_array.random((ndots))>=0.5, 1.0, -1.0)
random_array.seed(random_seed*122,random_seed*799)
xpos=floor(random_array.random((ndots))*(bigxsize+2*dotsize)) - halfdot
random_array.seed(random_seed*1243,random_seed*9349)
ypos=floor(random_array.random((ndots))*(bigysize+2*dotsize)) - halfdot
# Construct arrays of points specifying the boundaries of each
# dot, cropping them by the big image size (0,0) to (bigxsize,bigysize)
x1=xpos.astype(Int) ; x1=choose(less(x1,0),(x1,0))
y1=ypos.astype(Int) ; y1=choose(less(y1,0),(y1,0))
x2=(xpos+(dotsize-1)).astype(Int) ; x2=choose(greater(x2,bigxsize),(x2,bigxsize))
y2=(ypos+(dotsize-1)).astype(Int) ; y2=choose(greater(y2,bigysize),(y2,bigysize))
# Draw each dot in the big image, on a blank background
bigimage = zeros((bigysize,bigxsize))
for i in range(ndots):
bigimage[y1[i]:y2[i]+1,x1[i]:x2[i]+1] = col[i]
result = p.offset + p.scale*bigimage[ (ysize/2)+ydisparity:(3*ysize/2)+ydisparity ,
(xsize/2)+xdisparity:(3*xsize/2)+xdisparity ]
for of in p.output_fns:
of(result)
return result
def __call__(self,**params_to_override):
p = ParamOverrides(self,params_to_override)
xsize,ysize = SheetCoordinateSystem(p.bounds,p.xdensity,p.ydensity).shape
xsize,ysize = int(round(xsize)),int(round(ysize))
xdisparity = int(round(xsize*p.xdisparity))
ydisparity = int(round(xsize*p.ydisparity))
dotsize = int(round(xsize*p.dotsize))
bigxsize = 2*xsize
bigysize = 2*ysize
ndots=int(round(p.dotdensity * (bigxsize+2*dotsize) * (bigysize+2*dotsize) /
min(dotsize,xsize) / min(dotsize,ysize)))
halfdot = floor(dotsize/2)
# Choose random colors and locations of square dots
random_seed = p.random_seed
random_array.seed(random_seed*12,random_seed*99)
col=where(random_array.random((ndots))>=0.5, 1.0, -1.0)
random_array.seed(random_seed*122,random_seed*799)
xpos=floor(random_array.random((ndots))*(bigxsize+2*dotsize)) - halfdot
random_array.seed(random_seed*1243,random_seed*9349)
ypos=floor(random_array.random((ndots))*(bigysize+2*dotsize)) - halfdot
# Construct arrays of points specifying the boundaries of each
# dot, cropping them by the big image size (0,0) to (bigxsize,bigysize)
x1=xpos.astype(Int) ; x1=choose(less(x1,0),(x1,0))
y1=ypos.astype(Int) ; y1=choose(less(y1,0),(y1,0))
x2=(xpos+(dotsize-1)).astype(Int) ; x2=choose(greater(x2,bigxsize),(x2,bigxsize))
y2=(ypos+(dotsize-1)).astype(Int) ; y2=choose(greater(y2,bigysize),(y2,bigysize))
# Draw each dot in the big image, on a blank background
bigimage = zeros((bigysize,bigxsize))
for i in range(ndots):
bigimage[y1[i]:y2[i]+1,x1[i]:x2[i]+1] = col[i]
result = p.offset + p.scale*bigimage[ (ysize/2)+ydisparity:(3*ysize/2)+ydisparity ,
(xsize/2)+xdisparity:(3*xsize/2)+xdisparity ]
for of in p.output_fns:
of(result)
return result
def Translate(self, t):
## #scl = 0.25
## scl = 1
## print "t:", t
## t1 = int(t[0]*scl)
## t2 = int(t[1]*scl)
## t3 = int(t[2]*scl)
t1, t2, t3 = Numeric.where(Numeric.less(t, 0), -2, 2)
xmin = self.xmin
xmax = self.xmax
ymin = self.ymin
ymax = self.ymax
zmin = self.zmin
zmax = self.zmax
xmin = xmin + t1
xmax = xmax + t1
nx, ny, nz = self.volSize
if xmin < 0:
xmax = xmax - xmin
xmin = 0
if xmax > nx:
xmin = xmin - (xmax - nx)
xmax = nx
ymin = ymin + t2
ymax = ymax + t2
if ymin < 0:
ymax = ymax - ymin
ymin = 0
if ymax > ny:
ymin = ymin - (ymax - ny)
ymax = ny
zmin = zmin + t3
zmax = zmax + t3
if zmin < 0:
zmax = zmax - zmin
zmin = 0
if zmax > nz:
zmin = zmin - (zmax - nz)
zmax = nz
self.xmin = xmin
self.xmax = xmax
self.ymin = ymin
self.ymax = ymax
self.zmin = zmin
self.zmax = zmax
self.update()
def disk(x, y, height, gaussian_width):
"""
Circular disk with Gaussian fall-off after the solid central region.
"""
disk_radius = height/2.0
distance_from_origin = sqrt(x**2+y**2)
distance_outside_disk = distance_from_origin - disk_radius
sigmasq = gaussian_width*gaussian_width
if sigmasq==0.0:
falloff = x*0.0
else:
with float_error_ignore():
falloff = exp(divide(-distance_outside_disk*distance_outside_disk,
2*sigmasq))
return where(distance_outside_disk<=0,1.0,falloff)
def disk(x, y, height, gaussian_width):
"""
Circular disk with Gaussian fall-off after the solid central region.
"""
disk_radius = height/2.0
distance_from_origin = sqrt(x**2+y**2)
distance_outside_disk = distance_from_origin - disk_radius
sigmasq = gaussian_width*gaussian_width
if sigmasq==0.0:
falloff = x*0.0
else:
with float_error_ignore():
falloff = exp(divide(-distance_outside_disk*distance_outside_disk,
2*sigmasq))
return where(distance_outside_disk<=0,1.0,falloff)
def pca(M):
"Perform PCA on M, return eigenvectors and eigenvalues, sorted."
T, N = shape(M)
# if there are fewer rows T than columns N, use snapshot method
if T < N:
C = dot(M, t(M))
evals, evecsC = eigenvectors(C)
# HACK: make sure evals are all positive
evals = where(evals < 0, 0, evals)
evecs = 1. / sqrt(evals) * dot(t(M), t(evecsC))
else:
# calculate covariance matrix
K = 1. / T * dot(t(M), M)
evals, evecs = eigenvectors(K)
# sort the eigenvalues and eigenvectors, descending order
order = (argsort(evals)[::-1])
evecs = take(evecs, order, 1)
evals = take(evals, order)
return evals, t(evecs)
def LoadUnknowns(self, u):
"""Load the unknown variables. Returns False is there are not enough known variables"""
#Load vars in a convenient way
p, m, d = self._matProcess, self._matMotive, self._matDischarge
#Load everything into lists (in order to allow for None values) which then will be converted to Numeric vectors
unkNamLst = self._unkName
unkValLst = self._nuUnk*[None]
unkInitValLst = self._nuUnk*[None]
unkIsFixLst = self._nuUnk*[False]
unkScaleFact = self._nuUnk*[1.0]
#First load everytihng that is already known!!
#Load some unknown vals
unkValLst[w1Idx] = w1Val = p.GetPropValue(MASSFLOW_VAR)
unkValLst[w2Idx] = w2Val = m.GetPropValue(MASSFLOW_VAR)
unkValLst[w3Idx] = w3Val = d.GetPropValue(MASSFLOW_VAR)
#Can not use three mass flows as specs
self._canBeSpec[w3Idx] = where((w1Val!=None and w2Val!=None), False, True)
unkValLst[p1Idx] = p1Val = p.GetPropValue(P_VAR)
unkValLst[p2Idx] = p2Val = m.GetPropValue(P_VAR)
unkValLst[p3Idx] = p3Val = d.GetPropValue(P_VAR)
unkValLst[d1Idx] = d1Val = p.GetPropValue(MASSDEN_VAR)
unkValLst[d2Idx] = d2Val = m.GetPropValue(MASSDEN_VAR)
unkValLst[d3Idx] = d3Val = d.GetPropValue(MASSDEN_VAR)
nozzDiamVal = self._sigNozzDiam.GetValue()
thrDiamVal = self._sigThrDiam.GetValue()
if nozzDiamVal != None and thrDiamVal != None:
if thrDiamVal <= nozzDiamVal or nozzDiamVal <= 0.0 or thrDiamVal <= 0.0:
self.InfoMessage('WrongDiamEjector', (self.GetPath(), nozzDiamVal, thrDiamVal),addToUnitOpMsg=1)
return False
_H1MolVal = p.GetPropValue(H_VAR)
_H2MolVal = m.GetPropValue(H_VAR)
_H3MolVal = d.GetPropValue(H_VAR)
_MW1Val = p.GetPropValue(MOLEWT_VAR)
_MW2Val = m.GetPropValue(MOLEWT_VAR)
_MW3Val = d.GetPropValue(MOLEWT_VAR)
self._pFracs = pFracs = p.GetCompositionValues()
self._mFracs = mFracs = m.GetCompositionValues()
self._dFracs = dFracs = d.GetCompositionValues()
nuCmps = len(pFracs)
#Two ports must have full known fracs. Make fractions Numeric arrays
if None in pFracs:
self._unkFracs = _P_FRACS
if None in mFracs or None in dFracs:
return False #Can not solve
else:
self._mFracs = array(self._mFracs, Float)
self._dFracs = array(self._dFracs, Float)
elif None in mFracs:
self._unkFracs = _M_FRACS
if None in dFracs:
return False #Can not solve
self._pFracs = array(self._pFracs, Float)
self._dFracs = array(self._dFracs, Float)
elif None in dFracs:
self._unkFracs = _D_FRACS
self._pFracs = array(self._pFracs, Float)
self._mFracs = array(self._mFracs, Float)
else:
self._unkFracs = None
self._pFracs = array(self._pFracs, Float)
self._mFracs = array(self._mFracs, Float)
self._dFracs = array(self._dFracs, Float)
#In case ports were flashed but some vars are not there (only density in this case)
if d1Val == None and p1Val != None and _H1MolVal != None and self._unkFracs != _P_FRACS:
unkValLst[d1Idx] = d1Val = self.GetDensity(p1Val, _H1MolVal, pFracs)
if d2Val == None and p2Val != None and _H2MolVal != None and self._unkFracs != _M_FRACS:
unkValLst[d2Idx] = d2Val = self.GetDensity(p2Val, _H2MolVal, mFracs)
if d3Val == None and p3Val != None and _H3MolVal != None and self._unkFracs != _D_FRACS:
unkValLst[d3Idx] = d3Val = self.GetDensity(p3Val, _H3MolVal, dFracs)
#Calculate surfaces
S1Val = None
if nozzDiamVal != None: S2Val = (PI/4.0) * (nozzDiamVal**2)
else: S2Val = None
if thrDiamVal != None: S3Val = (PI/4.0) * (thrDiamVal**2)
else: S3Val = None
unkValLst[S1Idx] = S1Val
unkValLst[S2Idx] = S2Val
unkValLst[S3Idx] = S3Val
#Finally try to see if v are known
if not None in (w1Val, d1Val, S1Val):
unkValLst[v1Idx] = v1Val = w1Val/d1Val/S1Val
else:
unkValLst[v1Idx] = v1Val = None
if not None in (w2Val, d2Val, S2Val):
unkValLst[v2Idx] = v2Val = w2Val/d2Val/S2Val
else:
unkValLst[v2Idx] = v2Val = None
if not None in (w3Val, d3Val, S3Val):
unkValLst[v3Idx] = v3Val = w3Val/d3Val/S3Val
else:
unkValLst[v3Idx] = v3Val = None
#Load Mass enthalpies
if _H1MolVal != None and _MW1Val != None:
unkValLst[H1Idx] = H1Val = _H1MolVal/_MW1Val
else:
unkValLst[H1Idx] = H1Val = None
if _H2MolVal != None and _MW2Val != None:
unkValLst[H2Idx] = H2Val = _H2MolVal/_MW2Val
else:
unkValLst[H2Idx] = H2Val = None
if _H3MolVal != None and _MW3Val != None:
unkValLst[H3Idx] = H3Val = _H3MolVal/_MW3Val
else:
unkValLst[H3Idx] = H3Val = None
#If not enough info is known then return False
cnt = 0
for i in range(self._nuUnk):
if unkValLst[i] != None and self._canBeSpec[i]:
cnt += 1
if self._nuUnk - self._nuEqns > cnt:
return False
#Load lists with info on which vals are knwon(fixed) already
for i in range(self._nuUnk):
v = unkValLst[i]
if v != None and self._canBeSpec[i]:
unkIsFixLst[i] = True
#Now create necessary estimates
#Mass flows
lst = [w1Val, w2Val, w3Val]
nuNones = lst.count(None)
if nuNones == 1:
if w1Val == None: w1Val = -w2Val + w3Val
elif w2Val == None: w2Val = -w1Val + w3Val
elif w3Val == None: w3Val = w1Val + w2Val
elif nuNones == 2:
if w1Val != None:
w2Val = w1Val*10.0
w3Val = w1Val + w2Val
elif w2Val != None:
w1Val = w2Val/10.0
w3Val = w1Val + w2Val
elif w3Val != None:
w1Val = w3Val*1.0/10.0
w2Val = w3Val*9.0/10.0
elif nuNones == 3:
#What to do?
w1Val = 20.0
w2Val = 30.0
w3Val = w1Val + w2Val
unkValLst[w1Idx] = w1Val
unkValLst[w2Idx] = w2Val
unkValLst[w3Idx] = w3Val
#Do fractions
if self._unkFracs != None:
if self._unkFracs == _P_FRACS:
mFlows = self._mFracs*(w2Val/Numeric.sum(self._pureCmpMW*self._mFracs))
dFlows = self._dFracs*(w3Val/Numeric.sum(self._pureCmpMW*self._dFracs))
self._pFracs = -mFlows+dFlows
self._pFracs = self._pFracs/Numeric.sum(self._pFracs) #Normalize
elif self._unkFracs == _M_FRACS:
pFlows = self._pFracs*(w1Val/Numeric.sum(self._pureCmpMW*self._pFracs))
dFlows = self._dFracs*(w3Val/Numeric.sum(self._pureCmpMW*self._dFracs))
self._mFracs = -pFlows+dFlows
self._mFracs = self._mFracs/Numeric.sum(self._mFracs) #Normalize
elif self._unkFracs == _D_FRACS:
pFlows = self._pFracs*(w1Val/Numeric.sum(self._pureCmpMW*self._pFracs))
mFlows = self._mFracs*(w2Val/Numeric.sum(self._pureCmpMW*self._mFracs))
self._dFracs = pFlows+mFlows
self._dFracs = self._dFracs/Numeric.sum(self._dFracs) #Normalize
#Pressures
lst = [p1Val, p2Val, p3Val]
nuNones = lst.count(None)
if nuNones == 1:
if p1Val == None: p1Val = p3Val/5
elif p2Val == None: p2Val = p1Val*10.0
elif p3Val == None: p3Val = p1Val*5.0
elif nuNones == 2:
if p1Val != None:
p2Val = p1Val*10.0
p3Val = p1Val*5.0
elif p2Val != None:
p1Val = p2Val/10.0
p3Val = p1Val*5.0
elif p3Val != None:
p1Val = p3Val/5.0
p2Val = p1Val*10.0
elif nuNones == 3:
#What to do?
p1Val = 101.0
p2Val = 800.0
p3Val = 500.0
unkValLst[p1Idx] = p1Val
unkValLst[p2Idx] = p2Val
unkValLst[p3Idx] = p3Val
#Enthalpies
if None in (H1Val, H2Val, H3Val):
(H1Val, H2Val, H3Val) = self.FillInWithAverage((H1Val, H2Val, H3Val))
if None in (H1Val, H2Val, H3Val):
#What to do??
#Could estimate a T and get H from there
H1Val = H2Val = H3Val = 10000.0
unkValLst[H1Idx] = H1Val
unkValLst[H2Idx] = H2Val
unkValLst[H3Idx] = H3Val
#Densities
if None in (d1Val, d2Val, d3Val):
(d1Val, d2Val, d3Val) = self.FillInWithAverage((d1Val, d2Val, d3Val))
if None in (d1Val, d2Val, d3Val):
#What to do??
#Could get it from flashing the estimates of H and P
d1Val = d2Val = d3Val = 1000.0
unkValLst[d1Idx] = d1Val
unkValLst[d2Idx] = d2Val
unkValLst[d3Idx] = d3Val
#Surfaces
lst = [S1Val, S2Val, S3Val]
nuNones = lst.count(None)
if nuNones == 1:
if S1Val == None: S1Val = -S2Val + S3Val
elif S2Val == None: S2Val = -S1Val + S3Val
elif S3Val == None: S3Val = S1Val + S2Val
elif nuNones == 2:
if S1Val != None:
S2Val = S1Val/2.0
S3Val = S1Val + S2Val
elif S2Val != None:
S1Val = S2Val*2.0
S3Val = S1Val + S2Val
elif S3Val != None:
S1Val = S3Val*2.0/3.0
S2Val = S3Val/3.0
elif nuNones == 3:
#What to do?
S1Val = 20.0
S2Val = 10.0
S3Val = S1Val + S2Val
unkValLst[S1Idx] = S1Val
unkValLst[S2Idx] = S2Val
unkValLst[S3Idx] = S3Val
#Velocities. Estimate based in known or newly estimated values
unkValLst[v1Idx] = v1Val = w1Val/d1Val/S1Val
unkValLst[v2Idx] = v2Val = w2Val/d2Val/S2Val
unkValLst[v3Idx] = v3Val = w3Val/d3Val/S3Val
#Load scale factors based on the numbers that the solver will be working with
self.scaleFactorW = min(abs(w1Val), abs(w2Val), abs(w3Val))
if not self.scaleFactorW: self.scaleFactorW = 1.0
self.scaleFactorS = S2Val
self.scaleFactorP = min(p1Val, p2Val, p3Val)
self.scaleFactorPS = self.scaleFactorP*self.scaleFactorS
self.scaleFactorH = min(abs(H1Val), abs(H2Val), abs(H3Val))
if not self.scaleFactorH: self.scaleFactorH = 10.0
self.scaleFactorD = min(d1Val, d2Val, d3Val)
self.scaleFactorv = min(abs(v1Val), abs(v2Val), abs(v3Val))
if not self.scaleFactorv: self.scaleFactorv = 10.0
#Put them in place for solver
unkScaleFact[p1Idx] = self.scaleFactorP
unkScaleFact[w1Idx] = self.scaleFactorW
unkScaleFact[d1Idx] = self.scaleFactorD
unkScaleFact[S1Idx] = self.scaleFactorS
unkScaleFact[v1Idx] = self.scaleFactorv
unkScaleFact[H1Idx] = self.scaleFactorH
#Do unit conversion
unkValLst = self.ConvertToMKS(unkValLst)
unkScaleFact = self.ConvertToMKS(unkScaleFact)
self.scaleFactorP = unkScaleFact[p1Idx]
self.scaleFactorW = unkScaleFact[w1Idx]
self.scaleFactorD = unkScaleFact[d1Idx]
self.scaleFactorS = unkScaleFact[S1Idx]
self.scaleFactorv = unkScaleFact[v1Idx]
self.scaleFactorH = unkScaleFact[H1Idx]
self.scaleFactorPS = self.scaleFactorP*self.scaleFactorS
#Load the unknowns object
unkInitValLst = list(unkValLst)
self._unknowns.SetNames(unkNamLst)
self._unknowns.SetValues(unkValLst)
self._unknowns.SetInitValues(unkInitValLst)
self._unknowns.SetIsFixed(unkIsFixLst)
self._unknowns.SetScaleFactors(unkScaleFact)
return True
def deNAN(a, value=0.0):
nans = Numeric.logical_not(
Numeric.less(a, 0.0) + Numeric.greater_equal(a, 0.0))
return Numeric.where(nans, value, a)
def Solve(self):
"""solve for controllers in controllerList"""
from sim.solver import Flowsheet
flowsheet = self.flowsheet
flPath = flowsheet.GetPath()
details = flowsheet.GetParameterValue(Flowsheet.RECYCLE_DETAILS_VAR)
# solve the controllers
self.activeControllers = []
self.activeControllersBisect = []
for cont in self.controllerList:
error = cont.Error()
if error != None and cont.SaveBase() and cont.GetParameterValue(IGNORED_PAR) == None:
#solMeth = cont.GetParameterValue(SOLUTION_METH_PAR)
solMeth = BROYDEN_METH
if solMeth == BROYDEN_METH or solMeth == None:
self.activeControllers.append(cont)
elif solMeth == BISECTION_METH:
self.activeControllersBisect.append(cont)
#For broyden
numA = self.numberActive = len(self.activeControllers)
#For bisection
numABisect = self.numberActiveBisect = len(self.activeControllersBisect)
if self.numberActive + self.numberActiveBisect == 0: return
#For broyden
maxErr1 = 0.0
totalError = 0.0
if numA > 0:
self.errors = self.GetErrors(BROYDEN_METH)
tolerances = self.GetTolerances(BROYDEN_METH)
maxErr1 = max(Numeric.absolute(self.errors)/tolerances)
totalError = Numeric.sum(Numeric.absolute(self.errors))
#For bisection
maxErr2 = 0.0
totalErrorBisect = 0.0
if numABisect > 0:
self.errorsBisect = self.GetErrors(BISECTION_METH)
tolerancesBisect = self.GetTolerances(BISECTION_METH)
maxErr2 = max(Numeric.absolute(self.errorsBisect)/tolerancesBisect)
totalErrorBisect = Numeric.sum(Numeric.absolute(self.errorsBisect))
if max(maxErr1, maxErr2) <= 1.0:
return
if numA > 0:
# calculate a jacobian if necessary
newJacobian = 0
if self.jacobian == None or self.jacobian.shape[0] != numA:
self.CalcJacobian()
newJacobian = 1
#For broyden
if numA > 0:
oldErrors = zeros(self.numberActive, Float)
oldVars = zeros(self.numberActive, Float)
vars = zeros(self.numberActive, Float) # using scaled variables so base is 0
minV = self.GetMinimums(BROYDEN_METH)
maxV = self.GetMaximums(BROYDEN_METH)
#For bisection
if numABisect > 0:
oldErrorsBisect = zeros(numABisect, Float)
oldVarsBisect = zeros(numABisect, Float)
varsBisect = zeros(numABisect, Float) # using scaled variables so base is 0
lowBound = self.GetMinimums(BISECTION_METH)
upBound = self.GetMaximums(BISECTION_METH)
minStepSize = 0.0001
#Initialize boundaries
if numABisect > 0:
sign = 1.0
#We already have errors @ varsBisect, lets get errors in upBound
# adjust the output
self.SetOutput(upBound, BISECTION_METH)
# recalculate flowsheet
self.flowsheet.InnerSolve()
changeInSign = self.GetErrors(BISECTION_METH)*self.errorsBisect
for i in range(numABisect):
#Didn't change sign
if sign*changeInSign[i] > 0.0:
upBound[i] = varsBisect[i]
sign = 1.0
#sign changed
else:
lowBound[i] = varsBisect[i]
sign = -1.0
iter = 0
maxIter = flowsheet.GetParameterValue(MAXITERCONT_PAR)
if not maxIter: maxIter = 1E10 #A very large number
while iter < maxIter: #main loop
converged = 0
iter += 1
#First bisect
if numABisect > 0:
oldErrorsBisect[:] = self.errorsBisect[:]
oldTotalErrorBisect = totalErrorBisect
varsBisect[:] = lowBound + (upBound-lowBound)/2.0
# adjust the output
self.SetOutput(varsBisect, BISECTION_METH)
# recalculate flowsheet
flowsheet.InnerSolve()
self.errorsBisect = self.GetErrors(BISECTION_METH)
changeInSign = self.errorsBisect*oldErrorsBisect
for i in range(numABisect):
#Didn't change sign
if sign*changeInSign[i] > 0.0:
upBound[i] = varsBisect[i]
sign = 1.0
#sign changed
else:
lowBound[i] = varsBisect[i]
sign = -1.0
maxGap = max(upBound - lowBound)
if maxGap < minStepSize:
# step size too small - bail
break
totalErrorBisect = Numeric.sum(Numeric.absolute(self.errorsBisect))
#Do broyden
if numA > 0:
oldErrors[:] = self.errors[:]
oldTotalError = totalError
oldVars[:] = vars[:]
adjustment = -dot(self.jacobian, self.errors)
# initial step length limited so no controller exceeds its max step
maxAdjustment = max(Numeric.absolute(adjustment))
if maxAdjustment > 1.0:
stepLength = 1.0 / maxAdjustment
else:
stepLength = 1.0
adj = stepLength * adjustment
newvars = vars + adj
minlimit = min(where(newvars < minV, (minV - vars)/adj, 1.0))
maxlimit = min(where(newvars > maxV, (maxV - vars)/adj, 1.0))
stepLength *= min(minlimit, maxlimit)
# loop until reduction in errors or step length become too small
while 1:
actualAdjustment = stepLength * adjustment
vars += actualAdjustment
# adjust the output
self.SetOutput(vars)
# recalculate flowsheet
flowsheet.InnerSolve()
# check errors
self.errors = self.GetErrors()
totalError = Numeric.sum(Numeric.absolute(self.errors))
if totalError < oldTotalError:
break
# errors did not go down - back down step size
vars[:] = oldVars[:]
if stepLength < minStepSize:
# step size too small - bail
break
stepLength /= 4
if details:
try:
for cont in self.activeControllers + self.activeControllersBisect:
target = cont.targetPort.GetValue()
current = cont.monitoredPort.GetValue()
flowsheet.InfoMessage('RecycleErrorDetail',(cont.GetPath(), cont.targetPort.GetType().name, current, target))
except:
pass
flowsheet.InfoMessage('ControllerTotalError', (flPath, totalError+totalErrorBisect))
#Broyden
if numA > 0:
maxErr1 = max(Numeric.absolute(self.errors)/tolerances)
#Bisect
if numABisect > 0:
maxErr2 = max(Numeric.absolute(self.errorsBisect)/tolerancesBisect)
if max(maxErr1, maxErr2) <= 1.0:
converged = 1
break
if numA > 0:
if totalError >= oldTotalError:
# smallest step didn't work
# if already has new jacobian, exit
if newJacobian:
break
else:
# other wise try a fresh one
self.CalcJacobian()
newJacobian = 1
minV = self.GetMinimums()
maxV = self.GetMaximums()
else:
# update jacobian
self.jacobian = self.UpdateJacobian(self.jacobian, actualAdjustment,
(self.errors - oldErrors))
newJacobian = 0
if not converged:
flowsheet.InfoMessage('ControllerConvergeFail', flPath)
def _magFix(self, catalogFile):
"""This private method receives a path to a catalog file and sifts through the
MAGERR field looking for values > 10. It sets the corresponding MAG field = -99 and
sets that object's MAGERR field to 0.0. catalogFile is a path not a file object."""
# fillHeader will return a list of tuples where which looks like
#
# [(1, 'NUMBER'),
# (2, 'X_IMAGE'),
# (3, 'Y_IMAGE'),
# ...
# (12, 'MAG_ISOCOR'),
# (13, 'MAGERR_ISOCOR'),
# (14, 'FLUX_APER', 1)
# (15, 'FLUX_APER', 2),
# (16, 'FLUX_APER', 3),
# ...
# ]
#
# The tuples are either of length 2 or 3. If len is 3, the 3rd item of the
# tuple is the nth occurance of that column identifier. This occurs on those
# columns of MAGs and MAGERRs for a series of increasingly larger apertures.
# newFieldList will be a list of Numeric arrays containing the columns of the catalogs.
# This list will contain fields which have not been altered, i.e. all fields other than
# MAG_* and MAGERR_*, and the new MAG and MAGERR fields which have been corrected.
# Once the list is complete, it is tuple-ized and send to the tableio pu_data function.
newFieldList = []
newMagsList = []
newMagErrsList = []
newMagHeaders = []
newMagErrHeaders = []
newHeaders = []
magCols = []
magErrCols = []
selectSet = fillHeader(catalogFile)
print "Searching catalog for required columns, MAG, MAGERR"
for i in range(len(selectSet)):
if len(selectSet[i]) == 2:
column, name = selectSet[i]
paramNames = name.split("_")
if "MAG" in paramNames:
magCols.append((column, name))
elif "MAGERR" in paramNames:
magErrCols.append((column, name))
else:
oldField = tableio.get_data(catalogFile, (column - 1))
newFieldList.append(oldField)
newHeaders.append(name)
continue
else:
column, name, id = selectSet[i]
paramNames = name.split("_")
if "MAG" in paramNames:
magCols.append((column, name, id))
elif "MAGERR" in paramNames:
magErrCols.append((column, name, id))
else:
oldField = tableio.get_data(catalogFile, (column - 1))
newFieldList.append(oldField)
newHeaders.append(name)
continue
# We now have
# catalog field --> list
# --------------------------------
# MAG_* --> magCols
# MAGERR_* --> magErrCols
#
# The algorithm will be to step through the magErrCols columns, extracting those fields
# via get_data and getting Numeric arrays. The matching mag columns are slurped as well.
# We search the magErrCols arrays looking for >= 10 values and then marking the those mags
# as -99.0 and the matching magerrs as 0.0
# See Bugzilla bug #2700
for item in magErrCols:
magErrAperId = None
# item may be of len 2 or 3
if len(item) == 2:
magErrColId, magErrColName = item
else:
magErrColId, magErrColName, magErrAperId = item
magErrKind = magErrColName.split("_")[1] # ISO, ISOCORR, etc.
print "\n\nMAG type:", magErrKind
if magErrAperId: print magErrColName, "Aper id is", magErrAperId
print "Getting\t", magErrColName, "\tfield", magErrColId
# MAGERR array:
magErrs = tableio.get_data(catalogFile, magErrColId - 1)
matchingMagColName = None
matchingMagColId = None
#----------------------- Search for matching MAG_* field -----------------------#
for magitems in magCols:
# We know that the magErrColName is MAGERR and if magErrNameId is true then
# the tuple is of len 3, i.e. a MAGERR_APER field. We look for the matching
# MAG_APER field id, 1, 2, 3... etc.
if len(magitems) == 3:
magColId, magColName, magAperId = magitems
if magColName == "MAG_" + magErrKind:
matchingMagColName = magColName
#print "Found matching field type:",magColName,"in field",magColId
if magAperId == magErrAperId:
print "Found matching aperture id."
print "MAG_APER id: ", magAperId, "MAGERR_APER id: ", magErrAperId
matchingMagColId = magColId
matchingMags = tableio.get_data(
catalogFile, magColId - 1)
break
else:
continue
else:
magColId, magColName = magitems
if magColName == "MAG_" + magErrKind:
print "Found matching field type:", magColName, "in field", magColId
matchingMagColName = magColName
matchingMagColId = magColId
matchingMags = tableio.get_data(
catalogFile, magColId - 1)
break
else:
continue
#--------------------------------------------------------------------------------#
print " MAG err field:", magErrColName, magErrColId
print " Mag field:", matchingMagColName, matchingMagColId
# Now the grunt work on the arrays,
# magErrs, matchingMags
#
# update: flagging all MAGs as -99 when the corresponding MAGERR > 10
# introduced a bug which unintentionally reset the magnitudes
# SExtractor had flagged with a MAG = 99.0 and a MAGERR = 99.0
# This now checks for a MAGERR of 99 and does not reset the MAG value
# if MAGERR = 99.0 but does for all other MAGERRS > 10.0
badMagErrs1 = Numeric.where(magErrs >= 10, 1, 0)
badMagErrs2 = Numeric.where(magErrs != 99.0, 1, 0)
badMagErrs = badMagErrs1 * badMagErrs2
del badMagErrs1, badMagErrs2
newMags = Numeric.where(badMagErrs, -99.0, matchingMags)
newMagErrs = Numeric.where(badMagErrs, 0.0, magErrs)
newMagsList.append(newMags)
newMagHeaders.append(matchingMagColName)
newMagErrsList.append(newMagErrs)
newMagErrHeaders.append(magErrColName)
# concatenate the lists. This is done to preserve the MAG_APER and MAGERR_APER
# grouping of the original SExtractor catalog.
newFieldList = newFieldList + newMagsList
newFieldList = newFieldList + newMagErrsList
newHeaders = newHeaders + newMagHeaders
newHeaders = newHeaders + newMagErrHeaders
newVariables = tuple(newFieldList)
# rename the old catalog file as catalogFile.old
os.rename(catalogFile, catalogFile + ".old")
self.outputList[os.path.basename(catalogFile) +
".old"] = [os.path.basename(catalogFile)]
fob = open(catalogFile, 'w')
fob.write("## " + ptime() + "\n")
fob.write("## " + self.modName +
" catalog regenerated by _magFix method.\n")
fob.write(
'## (This file was generated automatically by the ACS Pipeline.)\n##\n'
)
fob.write(
"## This catalog has been photometrically corrected to remove\n")
fob.write("## 'bad' magnitude values.\n")
fob.write("##\n")
for i in range(len(newHeaders)):
fob.write("# " + str(i + 1) + "\t" + newHeaders[i] + "\n")
fob.close()
tableio.put_data(catalogFile, newVariables, append="yes")
return
def __call__(self, data, data_min=None, data_max=None,
val_min=None, val_max=None, map_type='linear', powerOf2=0):
""" (data, data_min=None, data_max=None, val_min=None, val_max=None,
map_type = 'linear', powerOf2=0)
Maps an array of floats to integer values.
data -- 3D numeric array;
data_min, data_max -- min and max data values(other than actual
array minimum/maximum) to map to integer values -
val_min and val_max - in range (0 ... int_limit);
map_type -- can be 'linear' or 'log';
powerOf2 -- if set to 1, then if the data array dimensions are not
power of 2 - the returned array will be padded with zeros
so that its dims are power of 2.
"""
# if data_min/max and val_min/max specified then the mapping
# will proceed as follows:
# [arr_min, data_min] is mapped to [int_min, val_min],
# [data_min, data_max] is maped to [val_min, val_max],
# [data_max, arr_max] is mapped to [val_max, int_limit].
int_limit = self.int_limit
int_min = self.int_min
shape = data.shape
assert len(shape)==3
nx, ny, nz = shape
arrsize = nx*ny*nz
#arr_max = Numeric.maximum.reduce(data.ravel())
#arr_min = Numeric.minimum.reduce(data.ravel())
maxif = Numeric.maximum.reduce
arr_max = maxif(maxif(maxif(data)))
minif = Numeric.minimum.reduce
arr_min = minif(minif(minif(data)))
#print "min(arr)=%f" % arr_min
#print "max(arr)=%f" % arr_max
if val_min != None:
assert val_min >= 0 and val_min < int_limit
else:
val_min = int_min
if val_max != None:
assert val_max <= int_limit and val_max > 0
else:
val_max = int_limit
if data_min != None:
if data_min < arr_min: data_min = arr_min
else:
data_min = arr_min
if data_max != None:
if data_max > arr_max: data_max = arr_max
else:
data_max = arr_max
print "mapping data_min %4f to val_min %d, data_max %4f to val_max %d"\
% (data_min, val_min, data_max, val_max)
if map_type == 'linear':
k2,c2 = self.ScaleMap((val_min, val_max), (data_min, data_max))
n_intervals = 3
if abs(data_min-arr_min) < 0.00001: # data_min==arr_min
k1,c1 = k2, c2
n_intervals = n_intervals-1
else :
k1, c1 = self.ScaleMap((int_min, val_min),
(arr_min, data_min))
if abs(data_max-arr_max) < 0.00001: # data_max == arr_max
k3, c3 = k2, c2
n_intervals = n_intervals-1
else:
k3, c3 = self.ScaleMap((val_max, int_limit),
(data_max, arr_max))
t1 = time()
#print "n_intervals = ", n_intervals
if n_intervals == 2:
if data_max == arr_max:
#print "data_max == arr_max"
new_arr = Numeric.where(Numeric.less(data, data_min),
k1*data+c1, k2*data+c2 )
elif data_min == arr_min:
#print "data_min == arr_min"
new_arr = Numeric.where(Numeric.greater_equal(data,
data_max), k3*data+c3, k2*data+c2)
elif n_intervals == 3:
new_arr1 = Numeric.where(Numeric.less(data, data_min),
k1*data+c1, k2*data+c2)
new_arr = Numeric.where(Numeric.greater_equal(data, data_max),
k3*data+c3, new_arr1)
del(new_arr1)
else :
new_arr = k2*data+c2
arr = Numeric.transpose(new_arr).astype(self.int_type)
del(new_arr)
t2 = time()
print "time to map : ", t2-t1
elif map_type == 'log':
if arr_min < 0:
diff = abs(arr_min)+1.0
elif arr_min >= 0 and arr_min < 1.0:
diff = 1.0
elif arr_min >= 1.0:
diff=0
k1, c1 = self.ScaleMap( (int_min, int_limit),
(log(arr_min+diff), log(arr_max+diff)) )
arr=Numeric.transpose(k1*Numeric.log10(data+diff)+c1).astype(self.int_type)
self.data_min = data_min
self.data_max = data_max
self.val_min = val_min
self.val_max = val_max
if powerOf2:
nx1, ny1, nz1 = nx, ny, nz
res, power = isPowerOf2(nx)
if not res:
nx1 = 2**power
res, power = isPowerOf2(ny)
if not res:
ny1 = 2**power
res, power = isPowerOf2(nz)
if not res:
nz1 = 2**power
dx, dy, dz = 0, 0, 0
if nx1 != nx or ny1 != ny or nz1 != nz:
#print "new data size: ", nx1,ny1,nz1
dx = (nx1-nx)/2. ; dy = (ny1-ny)/2. ; dz = (nz1-nz)/2.
#narr = Numeric.zeros((nx1,ny1,nz1), self.int_type)
#narr[:nx,:ny,:nz] = arr[:,:,:]
narr = Numeric.zeros((nz1,ny1,nx1), self.int_type)
narr[:nz,:ny,:nx] = arr[:,:,:]
self.arr = narr
#arr = Numeric.zeros((nx1,ny1,nz1), self.int_type)
#arr[:nx,:ny,:nz] = new_arr[:,:,:]
#arr = Numeric.transpose(arr).astype(self.int_type)
#self.arr = arr
return narr
self.arr = arr
return arr
def rgrd(self, dataIn, missingValueIn, missingMatch, logYes = 'yes', positionIn = None, missingValueOut = None):
""" #---------------------------------------------------------------------------------
#
# PURPOSE: To perform all the tasks required to regrid the input data, dataIn, into the ouput data,
# dataout along the level dimension only.
#
# DEFINITION:
#
# def rgrd(self, dataIn, missingValueIn, missingMatch, positionIn = None, missingValueOut = None):
#
#
# PASSED : dataIn -- data to regrid
#
# missingValueIn -- the missing data value to use in setting missing in the mask. It is required
# and there are two choices:
# None -- there is no missing data
# A number -- the value to use in the search for possible missing data.
# The presence of missing data at a grid point leads to recording 0.0 in the mask.
#
# missingMatch -- the comparison scheme used in searching for missing data in dataIn using the value passed
# in as missingValueIn. The choices are:
# None -- used if None is the entry for missingValueIn
# exact -- used if missingValue is the exact value from the file
# greater -- the missing data value is equal to or greater than missingValueIn
# less -- the missing data value is equal to or less than missingValueIn
#
# logYes -- choose the level regrid as linear in log of level or linear in level. Set to
# 'yes' for log. Anything else is linear in level.
#
#
#
# positionIn -- a tuple with the numerical position of the dimensions
# in C or Python order specified in the sequence longitude,
# latitude, level and time. Longitude, latitude and level are
# required. If time is missing submit None in its slot in the
# tuple. Notice that the length of the tuple is always four.
#
# Explicitly, in terms of the shape of dataIn as returned by Python's shape function
#
# positionIn[0] contains the position of longitude in dataIn
# positionIn[1] contains the position of latitude in dataIn
# positionIn[2] contains the position of level in dataIn or None
# positionIn[3] contains the position of time in dataIn or None
#
# As examples:
# If the C order shape of 4D data is
# (number of longitudes, number of times, number of levels, number of latitudes)
# submit
# (0, 3, 2, 1)
#
# If the C order shape of 3D data is
# (number of longitudes, number of times, number oflatitudes)
# submit
# (0, 2, 1, None)
#
# Send in None if the shape is a subset of (time, level,
# latitude, longitude) which is evaluated as follows:
# 3D -- code assumes (2,1,0,None)
# 4D -- code assumes (3,2,1,0)
#
# missingValueOut -- the value for the missing data used in writing the output data. If left at the
# default entry, None, the code uses missingValueIn if present or as a last resort
# 1.0e20
#
#
# RETURNED : dataOut -- the regridded data
#
#
# USAGE:
#
# Example 1. To regrid dataIn into dataOut using all the defaults where None, None signifies no
# missing data.
# dataOut = x.rgrd(dataIn, None, None)
#
# Example 2. To regrid dataIn into dataOut using 1.0e20 and greater as the missing data
#
# dataOut = x.rgrd(dataIn, 1.e20, 'greater')
#
#---------------------------------------------------------------------------------------------------------------------"""
# check the required input -- dataIn, missingValueIn and missingMatch
# make sure that dataIn is an array
try:
z = len(dataIn)
except TypeError:
sendmsg('Error in calling the rgrd method -- dataIn must be an array')
raise TypeError
# check the missingValueIn pass
if missingValueIn != None:
try:
z = abs(missingValueIn)
except TypeError:
sendmsg('Error in calling the rgrd method -- missingvalueIn must be None or a number. Now it is ', missingValueIn)
raise TypeError
# check the missingMatch pass
missingPossibilities = ['greater', 'equal', 'less', None]
if missingMatch not in missingPossibilities:
msg = 'Error in missingMatch -- it must be None or the string greater, equal, or less. Now it is '
sendmsg(msg, missingMatch)
raise ValueError
# --- Check data type and change to float if necessary ----
if dataIn.dtype.char != 'f':
dataIn = dataIn.astype(Numeric.Float32)
dataShape = dataIn.shape
numberDim = len(dataShape)
if numberDim < 2:
msg = 'Error in call to rgrd -- data must have at least 2 dimensions'
sendmsg(msg)
raise TypeError
# --- evaluate positionIn ----
# --- make standard positionIn as a check----
positionList =[]
for n in range(numberDim): # insert a sequence of numbers
positionList.append(n)
positionList.reverse()
for n in range(numberDim, 4): # fill end of list with Nones
positionList.append(None)
positionCheck = tuple(positionList)
standardPosition = 0 # transpose required
if positionIn == None: # construct the default positionIn tuple
positionIn = positionCheck
standardPosition = 1 # no need for a transpose with this data
else:
if positionIn == positionCheck: # compare to the standard
standardPosition = 1 # no need for a transpose with this data
if len(positionIn) != 4:
msg = 'Error in call to rgrd -- positionIn must be a tuple of length 4'
sendmsg(msg)
raise TypeError
if standardPosition == 0: # transpose data to the standard order (t,z,y,x)
newOrder, inverseOrder = checkorder(positionIn)
dataIn = Numeric.transpose(dataIn, newOrder) # transpose data to standard order (t,z,y,x)
dataIn = Numeric.array(dataIn.astype(Numeric.Float32), Numeric.Float32) # make contiguous
# set dimension sizes and check for consistency
if positionIn[0] != None:
self.nlon = (dataShape[ positionIn[0] ])
else:
self.nlon = 0
if positionIn[1] != None:
self.nlat = (dataShape[ positionIn[1] ])
else:
self.nlat = 0
if positionIn[2] != None:
if self.nlevi != (dataShape[ positionIn[2] ]):
msg = 'Level size is inconsistent with input data'
sendmsg(msg)
raise ValueError
if positionIn[3] != None:
self.ntime = (dataShape[ positionIn[3] ])
else:
self.ntime = 0
# allocate memory for dataOut -- the array with new number of levels
outList = list(dataIn.shape)
for i in range(len(outList)):
if outList[i] == self.nlevi:
outList[i] = self.nlevo
break
dataOut = Numeric.zeros(tuple(outList), Numeric.Float32) # memory for aout
if missingMatch == None: # if no missing do not pass None
missingMatch = 'none'
if missingValueIn == None: # if no missing do not pass None
missingValueIn = 1.333e33
if logYes != 'yes':
logYes = 'no'
levIn = self.axisIn[:].astype(Numeric.Float64)
levOut = self.axisOut[:].astype(Numeric.Float64)
_regrid.rgdpressure(self.nlevi, self.nlevo, self.nlat, self.nlon, self.ntime, missingValueIn, missingMatch, logYes, levIn, levOut, dataIn, dataOut)
if missingMatch == 'none': # if no missing do not pass None
missingMatch = None
if missingValueIn == 1.333e33:
missingValueIn = None
if standardPosition == 0:
dataOut = Numeric.transpose(dataOut, inverseOrder) # transpose data to original order
dataOut = Numeric.array(dataOut.astype(Numeric.Float32), Numeric.Float32) # make contiguous
if missingValueOut != None: # set the missing value in data to missingValueOut
if missingMatch == 'greater':
if missingValueIn > 0.0:
missing = 0.99*missingValueIn
else:
missing = 1.01*missingValueIn
dataOut = Numeric.where(Numeric.greater(dataOut,missing), missingValueOut, dataOut)
elif missingMatch == 'equal':
missing = missingValueIn
dataOut = Numeric.where(Numeric.equal(dataOut,missing), missingValueOut, dataOut)
elif missingMatch == 'less':
if missingValueIn < 0.0:
missing = 0.99*missingValueIn
else:
missing = 1.01*missingValueIn
dataOut = Numeric.where(Numeric.less(dataOut,missing), missingValueOut, dataOut)
return dataOut
# Those objects with flux equal or less than 0 are assigned a magnitude of 99
# and a limiting magnitude equal to their SExtractor photometric error. This
# is interpreted by BPZ as a nondetection with zero flux and 1-sigma error
# equal to the limiting magnitude
nondetected = Numeric.less_equal(
flux[i, :], 0.0) * Numeric.greater(fluxerr[i, :], 0.0)
# Those objects with error flux and flux equal to 0 are assigned a magnitude of -99
# and a flux of 0, which is interpreted by SExtractor as a non-observed object
nonobserved = Numeric.less_equal(fluxerr[i, :], 0.0)
# When flux error > 100*(flux), mark as nonobserved (Benitez, 24-Oct-03).
nonobserved = Numeric.where(
fluxerr[i, :] > 100 * (abs(flux[i, :])), 1.0, nonobserved[:])
detected = Numeric.logical_not(nonobserved + nondetected)
# Get the zero point for the final magnitudes
zpoint = fUtil.zeroPoint(
fitsfile) # pass the fits file to zeroPoint func
pardict["Zeropoint"] = str(zpoint)
pardict["Zeropoint_Error"] = zpoint_err
self.logfile.write("Photometric ZeroPoint of " +
os.path.basename(fitsfile) + ": " + str(zpoint))
#---------------- Temporary zero point correction --------------------#
## Commented 24-Sep-2002 as per Bugzilla bug #1800
##
def LoadUnknowns(self, u):
"""Load the unknown variables. Returns False is there are not enough known variables"""
#Load vars in a convenient way
p, m, d = self._matProcess, self._matMotive, self._matDischarge
#Load everything into lists (in order to allow for None values) which then will be converted to Numeric vectors
unkNamLst = self._unkName
unkValLst = self._nuUnk * [None]
unkInitValLst = self._nuUnk * [None]
unkIsFixLst = self._nuUnk * [False]
unkScaleFact = self._nuUnk * [1.0]
#First load everytihng that is already known!!
#Load some unknown vals
unkValLst[w1Idx] = w1Val = p.GetPropValue(MASSFLOW_VAR)
unkValLst[w2Idx] = w2Val = m.GetPropValue(MASSFLOW_VAR)
unkValLst[w3Idx] = w3Val = d.GetPropValue(MASSFLOW_VAR)
#Can not use three mass flows as specs
self._canBeSpec[w3Idx] = where((w1Val != None and w2Val != None),
False, True)
unkValLst[p1Idx] = p1Val = p.GetPropValue(P_VAR)
unkValLst[p2Idx] = p2Val = m.GetPropValue(P_VAR)
unkValLst[p3Idx] = p3Val = d.GetPropValue(P_VAR)
unkValLst[d1Idx] = d1Val = p.GetPropValue(MASSDEN_VAR)
unkValLst[d2Idx] = d2Val = m.GetPropValue(MASSDEN_VAR)
unkValLst[d3Idx] = d3Val = d.GetPropValue(MASSDEN_VAR)
nozzDiamVal = self._sigNozzDiam.GetValue()
thrDiamVal = self._sigThrDiam.GetValue()
if nozzDiamVal != None and thrDiamVal != None:
if thrDiamVal <= nozzDiamVal or nozzDiamVal <= 0.0 or thrDiamVal <= 0.0:
self.InfoMessage('WrongDiamEjector',
(self.GetPath(), nozzDiamVal, thrDiamVal),
addToUnitOpMsg=1)
return False
_H1MolVal = p.GetPropValue(H_VAR)
_H2MolVal = m.GetPropValue(H_VAR)
_H3MolVal = d.GetPropValue(H_VAR)
_MW1Val = p.GetPropValue(MOLEWT_VAR)
_MW2Val = m.GetPropValue(MOLEWT_VAR)
_MW3Val = d.GetPropValue(MOLEWT_VAR)
self._pFracs = pFracs = p.GetCompositionValues()
self._mFracs = mFracs = m.GetCompositionValues()
self._dFracs = dFracs = d.GetCompositionValues()
nuCmps = len(pFracs)
#Two ports must have full known fracs. Make fractions Numeric arrays
if None in pFracs:
self._unkFracs = _P_FRACS
if None in mFracs or None in dFracs:
return False #Can not solve
else:
self._mFracs = array(self._mFracs, Float)
self._dFracs = array(self._dFracs, Float)
elif None in mFracs:
self._unkFracs = _M_FRACS
if None in dFracs:
return False #Can not solve
self._pFracs = array(self._pFracs, Float)
self._dFracs = array(self._dFracs, Float)
elif None in dFracs:
self._unkFracs = _D_FRACS
self._pFracs = array(self._pFracs, Float)
self._mFracs = array(self._mFracs, Float)
else:
self._unkFracs = None
self._pFracs = array(self._pFracs, Float)
self._mFracs = array(self._mFracs, Float)
self._dFracs = array(self._dFracs, Float)
#In case ports were flashed but some vars are not there (only density in this case)
if d1Val == None and p1Val != None and _H1MolVal != None and self._unkFracs != _P_FRACS:
unkValLst[d1Idx] = d1Val = self.GetDensity(p1Val, _H1MolVal,
pFracs)
if d2Val == None and p2Val != None and _H2MolVal != None and self._unkFracs != _M_FRACS:
unkValLst[d2Idx] = d2Val = self.GetDensity(p2Val, _H2MolVal,
mFracs)
if d3Val == None and p3Val != None and _H3MolVal != None and self._unkFracs != _D_FRACS:
unkValLst[d3Idx] = d3Val = self.GetDensity(p3Val, _H3MolVal,
dFracs)
#Calculate surfaces
S1Val = None
if nozzDiamVal != None: S2Val = (PI / 4.0) * (nozzDiamVal**2)
else: S2Val = None
if thrDiamVal != None: S3Val = (PI / 4.0) * (thrDiamVal**2)
else: S3Val = None
unkValLst[S1Idx] = S1Val
unkValLst[S2Idx] = S2Val
unkValLst[S3Idx] = S3Val
#Finally try to see if v are known
if not None in (w1Val, d1Val, S1Val):
unkValLst[v1Idx] = v1Val = w1Val / d1Val / S1Val
else:
unkValLst[v1Idx] = v1Val = None
if not None in (w2Val, d2Val, S2Val):
unkValLst[v2Idx] = v2Val = w2Val / d2Val / S2Val
else:
unkValLst[v2Idx] = v2Val = None
if not None in (w3Val, d3Val, S3Val):
unkValLst[v3Idx] = v3Val = w3Val / d3Val / S3Val
else:
unkValLst[v3Idx] = v3Val = None
#Load Mass enthalpies
if _H1MolVal != None and _MW1Val != None:
unkValLst[H1Idx] = H1Val = _H1MolVal / _MW1Val
else:
unkValLst[H1Idx] = H1Val = None
if _H2MolVal != None and _MW2Val != None:
unkValLst[H2Idx] = H2Val = _H2MolVal / _MW2Val
else:
unkValLst[H2Idx] = H2Val = None
if _H3MolVal != None and _MW3Val != None:
unkValLst[H3Idx] = H3Val = _H3MolVal / _MW3Val
else:
unkValLst[H3Idx] = H3Val = None
#If not enough info is known then return False
cnt = 0
for i in range(self._nuUnk):
if unkValLst[i] != None and self._canBeSpec[i]:
cnt += 1
if self._nuUnk - self._nuEqns > cnt:
return False
#Load lists with info on which vals are knwon(fixed) already
for i in range(self._nuUnk):
v = unkValLst[i]
if v != None and self._canBeSpec[i]:
unkIsFixLst[i] = True
#Now create necessary estimates
#Mass flows
lst = [w1Val, w2Val, w3Val]
nuNones = lst.count(None)
if nuNones == 1:
if w1Val == None: w1Val = -w2Val + w3Val
elif w2Val == None: w2Val = -w1Val + w3Val
elif w3Val == None: w3Val = w1Val + w2Val
elif nuNones == 2:
if w1Val != None:
w2Val = w1Val * 10.0
w3Val = w1Val + w2Val
elif w2Val != None:
w1Val = w2Val / 10.0
w3Val = w1Val + w2Val
elif w3Val != None:
w1Val = w3Val * 1.0 / 10.0
w2Val = w3Val * 9.0 / 10.0
elif nuNones == 3:
#What to do?
w1Val = 20.0
w2Val = 30.0
w3Val = w1Val + w2Val
unkValLst[w1Idx] = w1Val
unkValLst[w2Idx] = w2Val
unkValLst[w3Idx] = w3Val
#Do fractions
if self._unkFracs != None:
if self._unkFracs == _P_FRACS:
mFlows = self._mFracs * (
w2Val / Numeric.sum(self._pureCmpMW * self._mFracs))
dFlows = self._dFracs * (
w3Val / Numeric.sum(self._pureCmpMW * self._dFracs))
self._pFracs = -mFlows + dFlows
self._pFracs = self._pFracs / Numeric.sum(
self._pFracs) #Normalize
elif self._unkFracs == _M_FRACS:
pFlows = self._pFracs * (
w1Val / Numeric.sum(self._pureCmpMW * self._pFracs))
dFlows = self._dFracs * (
w3Val / Numeric.sum(self._pureCmpMW * self._dFracs))
self._mFracs = -pFlows + dFlows
self._mFracs = self._mFracs / Numeric.sum(
self._mFracs) #Normalize
elif self._unkFracs == _D_FRACS:
pFlows = self._pFracs * (
w1Val / Numeric.sum(self._pureCmpMW * self._pFracs))
mFlows = self._mFracs * (
w2Val / Numeric.sum(self._pureCmpMW * self._mFracs))
self._dFracs = pFlows + mFlows
self._dFracs = self._dFracs / Numeric.sum(
self._dFracs) #Normalize
#Pressures
lst = [p1Val, p2Val, p3Val]
nuNones = lst.count(None)
if nuNones == 1:
if p1Val == None: p1Val = p3Val / 5
elif p2Val == None: p2Val = p1Val * 10.0
elif p3Val == None: p3Val = p1Val * 5.0
elif nuNones == 2:
if p1Val != None:
p2Val = p1Val * 10.0
p3Val = p1Val * 5.0
elif p2Val != None:
p1Val = p2Val / 10.0
p3Val = p1Val * 5.0
elif p3Val != None:
p1Val = p3Val / 5.0
p2Val = p1Val * 10.0
elif nuNones == 3:
#What to do?
p1Val = 101.0
p2Val = 800.0
p3Val = 500.0
unkValLst[p1Idx] = p1Val
unkValLst[p2Idx] = p2Val
unkValLst[p3Idx] = p3Val
#Enthalpies
if None in (H1Val, H2Val, H3Val):
(H1Val, H2Val, H3Val) = self.FillInWithAverage(
(H1Val, H2Val, H3Val))
if None in (H1Val, H2Val, H3Val):
#What to do??
#Could estimate a T and get H from there
H1Val = H2Val = H3Val = 10000.0
unkValLst[H1Idx] = H1Val
unkValLst[H2Idx] = H2Val
unkValLst[H3Idx] = H3Val
#Densities
if None in (d1Val, d2Val, d3Val):
(d1Val, d2Val, d3Val) = self.FillInWithAverage(
(d1Val, d2Val, d3Val))
if None in (d1Val, d2Val, d3Val):
#What to do??
#Could get it from flashing the estimates of H and P
d1Val = d2Val = d3Val = 1000.0
unkValLst[d1Idx] = d1Val
unkValLst[d2Idx] = d2Val
unkValLst[d3Idx] = d3Val
#Surfaces
lst = [S1Val, S2Val, S3Val]
nuNones = lst.count(None)
if nuNones == 1:
if S1Val == None: S1Val = -S2Val + S3Val
elif S2Val == None: S2Val = -S1Val + S3Val
elif S3Val == None: S3Val = S1Val + S2Val
elif nuNones == 2:
if S1Val != None:
S2Val = S1Val / 2.0
S3Val = S1Val + S2Val
elif S2Val != None:
S1Val = S2Val * 2.0
S3Val = S1Val + S2Val
elif S3Val != None:
S1Val = S3Val * 2.0 / 3.0
S2Val = S3Val / 3.0
elif nuNones == 3:
#What to do?
S1Val = 20.0
S2Val = 10.0
S3Val = S1Val + S2Val
unkValLst[S1Idx] = S1Val
unkValLst[S2Idx] = S2Val
unkValLst[S3Idx] = S3Val
#Velocities. Estimate based in known or newly estimated values
unkValLst[v1Idx] = v1Val = w1Val / d1Val / S1Val
unkValLst[v2Idx] = v2Val = w2Val / d2Val / S2Val
unkValLst[v3Idx] = v3Val = w3Val / d3Val / S3Val
#Load scale factors based on the numbers that the solver will be working with
self.scaleFactorW = min(abs(w1Val), abs(w2Val), abs(w3Val))
if not self.scaleFactorW: self.scaleFactorW = 1.0
self.scaleFactorS = S2Val
self.scaleFactorP = min(p1Val, p2Val, p3Val)
self.scaleFactorPS = self.scaleFactorP * self.scaleFactorS
self.scaleFactorH = min(abs(H1Val), abs(H2Val), abs(H3Val))
if not self.scaleFactorH: self.scaleFactorH = 10.0
self.scaleFactorD = min(d1Val, d2Val, d3Val)
self.scaleFactorv = min(abs(v1Val), abs(v2Val), abs(v3Val))
if not self.scaleFactorv: self.scaleFactorv = 10.0
#Put them in place for solver
unkScaleFact[p1Idx] = self.scaleFactorP
unkScaleFact[w1Idx] = self.scaleFactorW
unkScaleFact[d1Idx] = self.scaleFactorD
unkScaleFact[S1Idx] = self.scaleFactorS
unkScaleFact[v1Idx] = self.scaleFactorv
unkScaleFact[H1Idx] = self.scaleFactorH
#Do unit conversion
unkValLst = self.ConvertToMKS(unkValLst)
unkScaleFact = self.ConvertToMKS(unkScaleFact)
self.scaleFactorP = unkScaleFact[p1Idx]
self.scaleFactorW = unkScaleFact[w1Idx]
self.scaleFactorD = unkScaleFact[d1Idx]
self.scaleFactorS = unkScaleFact[S1Idx]
self.scaleFactorv = unkScaleFact[v1Idx]
self.scaleFactorH = unkScaleFact[H1Idx]
self.scaleFactorPS = self.scaleFactorP * self.scaleFactorS
#Load the unknowns object
unkInitValLst = list(unkValLst)
self._unknowns.SetNames(unkNamLst)
self._unknowns.SetValues(unkValLst)
self._unknowns.SetInitValues(unkInitValLst)
self._unknowns.SetIsFixed(unkIsFixLst)
self._unknowns.SetScaleFactors(unkScaleFact)
return True