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 kNNimputeMA(arr2d, K=20, callback=None):
"""Returns a new 2D MA.array with missing values imputed from K nearest neighbours.
Find K rows (axis 0) with the most similar values where similarity measure corresponds to weighted Euclidean distance.
Imputed value = weighted average of the corresponding values of K nearest neighbours,
where weights equal to tricubic distribution of distances to all rows.
Impute missing rows by average over all rows.
Version: 30.8.2005
"""
arr2d = MA.asarray(arr2d)
assert len(arr2d.shape) == 2, "2D array expected"
# make a copy for imputation
aImp2 = MA.array(arr2d)
# leave out columns with 0 known values (columnInd: non-zero columns)
columnCond = Numeric.greater(MA.count(arr2d, axis=0), 0)
columnIndAll = Numeric.arange(arr2d.shape[1])
columnInd = Numeric.compress(columnCond, columnIndAll)
# impute the rows where 0 < #known_values < #non_zero_columns, i.e. exclude the rows with 0 and all (non-zero-column) values
countByRows = MA.count(arr2d, axis=1)
for rowIdx in Numeric.compress(Numeric.logical_and(Numeric.greater(countByRows, 0), Numeric.less(countByRows, columnInd.shape[0])), Numeric.arange(arr2d.shape[0])):
rowResized = MA.resize(arr2d[rowIdx], arr2d.shape)
diff = arr2d - rowResized
distances = MA.sqrt(MA.add.reduce((diff)**2, 1) / MA.count(diff, axis=1))
# nearest neighbours row indices (without the current row index)
indSorted = MA.argsort(distances)[1:]
distSorted = distances.take(indSorted)
# number of distances different from MA.masked
numNonMasked = distSorted.shape[0] - Numeric.add.reduce(Numeric.asarray(MA.getmaskarray(distSorted), Numeric.Int))
# number of distances to account for (K or less)
if numNonMasked > 1:
weightsSorted = MA.power(1-MA.power(distSorted/distSorted[numNonMasked-1],3),3) # tricubic distribution of all weights
else:
weightsSorted = Numeric.ones(distSorted.shape[0])
# compute average for each column separately in order to account for K non-masked values
colInd4CurrRow = Numeric.compress(Numeric.logical_and(MA.getmaskarray(arr2d[rowIdx]), columnCond), columnIndAll)
for colIdx in colInd4CurrRow:
# column values sorted by distances
columnVals = arr2d[:,colIdx].take(indSorted)
# take only those weights where columnVals does not equal MA.masked
weightsSortedCompressed = MA.compress(1-MA.getmaskarray(columnVals), weightsSorted)
# impute from K (or possibly less) values
aImp2[rowIdx,colIdx] = MA.average(columnVals.compressed()[:K], weights=weightsSortedCompressed[:K])
if callback:
callback()
# impute the unknown rows with average profile
avrgRow = MA.average(arr2d, 0)
for rowIdx in Numeric.compress(Numeric.equal(countByRows, 0), Numeric.arange(arr2d.shape[0])):
aImp2[rowIdx] = avrgRow
if callback:
callback()
return aImp2
def histogram(data, nbins, range=None):
"""
Create a histogram.
Comes from Konrad Hinsen: Scientific Python
@param data: data list or array
@type data: [any]
@param nbins: number of bins
@type nbins: int
@param range: data range to create histogram from (min val, max val)
@type range: (float, float) OR None
@return: array (2 x len(data) ) with start of bin and witdh of bin.
@rtype: array
"""
data = Numeric.array(data, Numeric.Float)
if range is None:
min = Numeric.minimum.reduce(data)
max = Numeric.maximum.reduce(data)
else:
min, max = range
data = Numeric.repeat(
data,
Numeric.logical_and(Numeric.less_equal(data, max),
Numeric.greater_equal(data, min)))
bin_width = (max - min) / nbins
data = Numeric.floor((data - min) / bin_width).astype(Numeric.Int)
histo = Numeric.add.reduce(
Numeric.equal(Numeric.arange(nbins)[:, Numeric.NewAxis], data), -1)
histo[-1] = histo[-1] + Numeric.add.reduce(Numeric.equal(nbins, data))
bins = min + bin_width * (Numeric.arange(nbins) + 0.5)
return Numeric.transpose(Numeric.array([bins, histo]))
def histogram(data, nbins, range = None):
"""
Comes from Konrad Hinsen: Scientific Python
"""
data = Numeric.array(data, Numeric.Float)
if range is None:
min = Numeric.minimum.reduce(data)
max = Numeric.maximum.reduce(data)
else:
min, max = range
data = Numeric.repeat(data,
Numeric.logical_and(Numeric.less_equal(data, max),
Numeric.greater_equal(data,
min)))
# end if
bin_width = (max-min)/nbins
data = Numeric.floor((data - min)/bin_width).astype(Numeric.Int)
histo = Numeric.add.reduce(Numeric.equal(
Numeric.arange(nbins)[:,Numeric.NewAxis], data), -1)
histo[-1] = histo[-1] + Numeric.add.reduce(Numeric.equal(nbins, data))
bins = min + bin_width*(Numeric.arange(nbins)+0.5)
return Numeric.transpose(Numeric.array([bins, histo]))
def histogram(data, nbins, range=None):
"""
Comes from Konrad Hinsen: Scientific Python
"""
data = Numeric.array(data, Numeric.Float)
if range is None:
min = Numeric.minimum.reduce(data)
max = Numeric.maximum.reduce(data)
else:
min, max = range
data = Numeric.repeat(
data,
Numeric.logical_and(Numeric.less_equal(data, max),
Numeric.greater_equal(data, min)))
# end if
bin_width = (max - min) / nbins
data = Numeric.floor((data - min) / bin_width).astype(Numeric.Int)
histo = Numeric.add.reduce(
Numeric.equal(Numeric.arange(nbins)[:, Numeric.NewAxis], data), -1)
histo[-1] = histo[-1] + Numeric.add.reduce(Numeric.equal(nbins, data))
bins = min + bin_width * (Numeric.arange(nbins) + 0.5)
return Numeric.transpose(Numeric.array([bins, histo]))
def histogram(data, nbins, range = None):
"""
Create a histogram.
Comes from Konrad Hinsen: Scientific Python
@param data: data list or array
@type data: [any]
@param nbins: number of bins
@type nbins: int
@param range: data range to create histogram from (min val, max val)
@type range: (float, float) OR None
@return: array (2 x len(data) ) with start of bin and witdh of bin.
@rtype: array
"""
data = Numeric.array(data, Numeric.Float)
if range is None:
min = Numeric.minimum.reduce(data)
max = Numeric.maximum.reduce(data)
else:
min, max = range
data = Numeric.repeat(data,
Numeric.logical_and(Numeric.less_equal(data, max),
Numeric.greater_equal(data, min)))
bin_width = (max-min)/nbins
data = Numeric.floor((data - min)/bin_width).astype(Numeric.Int)
histo = Numeric.add.reduce(Numeric.equal(
Numeric.arange(nbins)[:,Numeric.NewAxis], data), -1)
histo[-1] = histo[-1] + Numeric.add.reduce(Numeric.equal(nbins, data))
bins = min + bin_width*(Numeric.arange(nbins)+0.5)
return Numeric.transpose(Numeric.array([bins, histo]))
def divide_unary(a, b):
"""Returns a*b with masked values only in places where both a and b are masked.
"""
a = MA.asarray(a)
b = MA.asarray(b)
el = MA.divide(a.filled(1), b.filled(1))
mask = Numeric.logical_and(MA.getmaskarray(a), MA.getmaskarray(b))
return MA.array(el, mask=mask)
def subtract_unary(a, b):
"""Returns a-b with masked values only in places where both a and b are masked.
"""
a = MA.asarray(a)
b = MA.asarray(b)
el = MA.subtract(a.filled(0), b.filled(0))
mask = Numeric.logical_and(MA.getmaskarray(a), MA.getmaskarray(b))
return MA.array(el, mask=mask)
def fractionNativeSurface(self, cont, contRef ):
"""
fraction of atoms/residues that are involved in B{any} contacts
in both complexes.
@param cont: contact matrix
@type cont: matrix
@param contRef: reference contact matrix
@type contRef: matrix
@return: (fractRec, fractLig), fraction of atoms/residues that
are involved in any contacts in both complexes
@rtype: (float, float)
"""
lig, ligRef = N.clip( N.sum(cont),0,1), N.clip( N.sum(contRef), 0,1)
rec = N.clip( N.sum(cont, 1),0,1)
recRef = N.clip( N.sum(contRef, 1), 0,1)
fLig = N.sum( N.logical_and( lig, ligRef )) *1./ N.sum( ligRef )
fRec = N.sum( N.logical_and( rec, recRef )) *1./ N.sum( recRef )
return (fRec, fLig)
def contactsShared(self, reference, cutoff=None):
"""
Number of equal B{residue-residue} contacts in this and
reference complex.
@param reference: reference complex
@type reference: Complex
@param cutoff: cutoff for atom-atom contact to be counted
@type cutoff: float
@return: the number or residue-residue contacts that are common to
both this and reference::
abs( N.sum( N.sum( contactMatrix_a - contactMatrix_b )))
@rtype: int
"""
equality = N.logical_and(self.resContacts( cutoff=cutoff ),
reference.resContacts( cutoff=cutoff ) )
return abs(N.sum(N.sum( equality )))
def contactsOverlap(self, ref, cutoff=None):
"""
Fraction of overlapping B{residue-residue} contacts between this and
reference complex.
@param ref: reference complex
@type ref: Complex
@param cutoff: maximal atom-atom distance, None .. previous setting
@type cutoff: float
@return: fraction of contacts shared between this and ref
(normalized to number of all contacts)
@rtype: float
"""
equal = N.logical_and(self.resContacts( cutoff=cutoff ),
ref.resContacts( cutoff=cutoff ) )
total = N.logical_or( self.resContacts(cutoff),
ref.resContacts(cutoff) )
return N.sum(N.sum( equal )) * 1.0 / N.sum(N.sum( total ))
def filter(self, dlg):
fptr = open(dlg)
dlg_lines = fptr.readlines()
fptr.close()
#STEP 1:accumulate lines of various poses
model_lines = []
#keep all of them
all_models = []
in_model = False
for ll in dlg_lines:
if ll.find("DOCKED:") == 0:
#check for a new model
if ll.find("DOCKED: MODEL") == 0:
model_lines = []
in_model = True
model_lines.append(ll)
if ll.find("_") == 0 and in_model:
all_models.append(model_lines)
model_lines = []
in_model = False
#initialize this ligand
# loop over the models:
for model_lines in all_models:
self.setup_ligand(model_lines)
bigR = self.bigRC[:self.lenK]
bigM = self.bigC[:self.lenK]
cutoff = bigR + self.keyRadii
d = bigM - self.smallM
dSQ = d * d
dSQMAT = Numeric.sum(dSQ, 2)
cutoffSQMAT = cutoff * cutoff
ansMat = Numeric.logical_and(Numeric.less(dSQMAT, cutoffSQMAT),
Numeric.not_equal(dSQMAT, 0.))
rowIndices = Numeric.nonzero(Numeric.sum(ansMat, 1))
num_contacts = 0
for ind in rowIndices:
for j in ansMat[ind]:
if j: num_contacts += 1
if num_contacts > 0:
break
return num_contacts
def filter(self, dlg):
fptr = open(dlg)
dlg_lines = fptr.readlines()
fptr.close()
#STEP 1:accumulate lines of various poses
model_lines = []
#keep all of them
all_models = []
in_model = False
for ll in dlg_lines:
if ll.find("DOCKED:")==0:
#check for a new model
if ll.find("DOCKED: MODEL")==0:
model_lines = []
in_model = True
model_lines.append(ll)
if ll.find("_")==0 and in_model:
all_models.append(model_lines)
model_lines = []
in_model = False
#initialize this ligand
# loop over the models:
for model_lines in all_models:
self.setup_ligand(model_lines)
bigR = self.bigRC[:self.lenK]
bigM = self.bigC[:self.lenK]
cutoff = bigR + self.keyRadii
d = bigM - self.smallM
dSQ = d*d
dSQMAT = Numeric.sum(dSQ,2)
cutoffSQMAT = cutoff*cutoff
ansMat = Numeric.logical_and(Numeric.less(dSQMAT, cutoffSQMAT),Numeric.not_equal(dSQMAT, 0.))
rowIndices = Numeric.nonzero(Numeric.sum(ansMat,1))
num_contacts = 0
for ind in rowIndices:
for j in ansMat[ind]:
if j: num_contacts+=1
if num_contacts > 0:
break
return num_contacts
Y = Numeric.ones(N).astype('f')
for i in range(numproc):
Y = Y*Numeric.array(range(N))*(i+1)
#print X_float
#print Y
assert Numeric.allclose(X_float, Y)
print "Raw reduce using pypar.PROD OK"
else:
if myid == 0:
print "Skipping product-reduce - try again with numproc < 20"
pypar.raw_reduce(testArray, X, pypar.LAND, 0, 0)
if myid == 0:
Y = Numeric.ones(N)
for i in range(numproc):
Y = Numeric.logical_and(Y, Numeric.array(range(N))*(i+1))
assert Numeric.allclose(X, Y)
print "Raw reduce using pypar.LAND OK"
pypar.raw_reduce(testArray, X, pypar.BAND, 0, 0)
if myid == 0:
Y = Numeric.ones(N)*255 #Neutral element for &
for i in range(numproc):
Y = Numeric.bitwise_and(Y, Numeric.array(range(N))*(i+1))
assert Numeric.allclose(X, Y)
print "Raw reduce using pypar.BAND OK"
pypar.raw_reduce(testArray, X, pypar.LOR, 0, 0)
if myid == 0:
Y = Numeric.zeros(N)
for i in range(numproc):
def logical_unary_and(m1, m2):
el = Numeric.logical_and(m1.filled(1), m2.filled(1))
mask = Numeric.logical_and(MA.getmaskarray(m1), MA.getmaskarray(m2))
return MA.array(el, mask=mask)
def match(x, y, n_iterations=1, z=2, eps_rmsd=0.5, eps_stdv=0.05):
"""
Matches two arrays onto each other, while iteratively removing outliers.
Superimposed array y would be C{ N.dot(y, N.transpose(r)) + t }.
@param n_iterations: number of calculations::
1 .. no iteration
0 .. until convergence
@type n_iterations: 1|0
@param z: number of standard deviations for outlier definition (default: 2)
@type z: float
@param eps_rmsd: tolerance in rmsd (default: 0.5)
@type eps_rmsd: float
@param eps_stdv: tolerance in standard deviations (default: 0.05)
@type eps_stdv: float
@return: (r,t), [ [percent_considered, rmsd_for_it, outliers] ]
@rtype: (array, array), [float, float, int]
"""
iter_trace = []
rmsd_old = 0
stdv_old = 0
n = 0
converged = 0
mask = N.ones(len(y), N.int32)
while not converged:
## find transformation for best match
r, t = findTransformation(N.compress(mask, x, 0),
N.compress(mask, y, 0))
## transform coordinates
xt = N.dot(y, N.transpose(r)) + t
## calculate row distances
d = N.sqrt(N.sum(N.power(x - xt, 2), 1)) * mask
## calculate rmsd and stdv
rmsd = N.sqrt(N.average(N.compress(mask, d)**2))
stdv = MU.SD(N.compress(mask, d))
## check conditions for convergence
d_rmsd = abs(rmsd - rmsd_old)
d_stdv = abs(1 - stdv_old / stdv)
if d_rmsd < eps_rmsd and d_stdv < eps_stdv:
converged = 1
else:
rmsd_old = rmsd
stdv_old = stdv
## store result
perc = round(float(N.sum(mask)) / float(len(mask)), 2)
## throw out non-matching rows
mask = N.logical_and(mask, N.less(d, rmsd + z * stdv))
outliers = N.nonzero(N.logical_not(mask))
iter_trace.append([perc, round(rmsd, 3), outliers])
n += 1
if n_iterations and n >= n_iterations:
break
return (r, t), iter_trace
def senddata(self):
"""computes selectionList, partitions the examples and updates infoc;
sends out selectionList and selected/other dataStructure or None;
"""
if self.dataStructure and self.ps.shape[1]:
# set selectionList
alphas = [self.alphaA, self.alphaB, self.alphaI]
selectors = [self.selectorA, self.selectorB, self.selectorI]
selectionList = Numeric.ones((self.numExamples,))
boxSelectors = [self.boxSelectorA, self.boxSelectorB, self.boxSelectorI]
for si in range(3):
try:
## if selectors[si] and self.anovaType in [[0,1,3,4],[2,3,4],[4]][si]:
if selectors[si] and boxSelectors[si].isEnabled():
selectionList = Numeric.logical_and(selectionList, Numeric.less(self.ps[si], float(alphas[si])))
except ValueError:
print "Warning: cannot convert %s to float" % str(alphas[si])
pass
self.infoc.setText('Sending out data...')
if self.sendProbabilities:
# create example table with probabilities
## print self.ps
## print Numeric.transpose(self.ps).shape
etProb = orange.ExampleTable(orange.Domain([orange.FloatVariable("Factor A p-val"),orange.FloatVariable("Factor B p-val"),orange.FloatVariable("Interaction p-val")]), Numeric.transpose(self.ps))
# in etProb, convert p-val to meta attribute
domProb = orange.Domain([])
domProb.addmetas(dict(zip([orange.newmetaid(),orange.newmetaid(),orange.newmetaid()], etProb.domain.variables)))
etProb = orange.ExampleTable(domProb, etProb)
else:
# create new etProb without attributes/metas and of length equal to etProb
etProb = orange.ExampleTable(orange.Domain([]), Numeric.zeros((selectionList.shape[0],0)))
# partition dataStructure and send out data
selectionList = selectionList.tolist()
self.send("Example Selection", (self.selectorName, selectionList))
dataStructS = []
dataStructN = []
self.progressBarInit()
if self.sendNotSelectedData:
pbStep = 50./len(self.dataStructure)
else:
pbStep = 100./len(self.dataStructure)
for (dsName, etList) in self.dataStructure:
etListS = [et.select(selectionList) for et in etList]
for i in range(len(etList)):
# append probabilities (if etProb not empty)
etListS[i] = orange.ExampleTable([etListS[i], etProb.select(selectionList)])
# add name
etListS[i].name = etList[i].name
dataStructS.append((dsName, etListS))
self.progressBarAdvance(pbStep)
self.send("Selected Structured Data", dataStructS)
if self.sendNotSelectedData:
for (dsName, etList) in self.dataStructure:
etListN = [et.select(selectionList, negate=1) for et in etList]
for i in range(len(etList)):
# append probabilities (if etProb not empty)
etListN[i] = orange.ExampleTable([etListN[i], etProb.select(selectionList, negate=1)])
# add name
etListN[i].name = etList[i].name
dataStructN.append((dsName, etListN))
self.progressBarAdvance(pbStep)
self.send("Other Structured Data", dataStructN)
else:
self.send("Other Structured Data", None)
self.progressBarFinished()
# report the number of selected examples
numExamples = Numeric.add.reduce(Numeric.greater(selectionList, 0))
self.infoc.setText('Total of %d example%s match criteria.' % (numExamples, ['', 's'][int(numExamples!=1)]))
else:
self.send("Example Selection", None)
self.send("Selected Structured Data", None)
self.send("Other Structured Data", None)
def select(self, keyAts, checkAts, cutoff=3.0, percentCutoff=1.0,
keyMat=None, checkMat=None):
""" keyAts, checkAts, cutoff, percentCutoff
keyAts: first set of atoms
checkAts: a second set of atoms which is checked vs. keyAts
cutoff:
either a single float by default 3.0
or a matrix with shape:
(max(len(keyAts),len(checkAts)), min(len(keyAts),len(checkAts)))
percentCutoff: by default 1.0 (cutoff is multiplied by this value)
keyMat: transformation of keyAts
checkMat: transformation of checkAts
returns 'pairDict' whose keys are atoms used as reference points and whose
values are atoms within cutoff distance of corresponding key.
If 'return_dist' flag is set,
'distDict' is returned also, whose keys are
the same atoms which are used as reference points and whose values are
lists of distances to atoms within cutoff distance of corresponding key
"""
lenK = len(keyAts)
lenC = len(checkAts)
#data arrays are used to find atoms with given indices quickly
atar = Numeric.array(checkAts.data)
keyAtar = Numeric.array(keyAts.data)
#basic arrays of coords used to build others
c = Numeric.array(checkAts.coords, 'f')
if checkMat:
c = self.mul(c, checkMat)
k = Numeric.array(keyAts.coords, 'f')
if keyMat:
k = self.mul(k, keyMat)
# first build matrix of distances between all pairs of ats
# rows correspond to ats in larger set, columns to those in smaller
# first build square matrix
if lenC >= lenK:
bigC = Numeric.resize(c, (lenC, lenC, 3))
k.shape = (lenK,1,3)
bigM = bigC[:lenK]
smallM = k
cutoff = self.setupCutoff(checkAts, keyAts, cutoff)
#print "0a:cutoff[0][0]=", cutoff[0][0]
cutoff.shape = (lenK, -1)
else:
bigK = Numeric.resize(k, (lenK, lenK, 3))
c.shape = (lenC,1,3)
bigM = bigK[:lenC]
smallM = c
cutoff = self.setupCutoff(keyAts, checkAts, cutoff)
#print "0b:cutoff[0][0]=", cutoff[0][0]
cutoff.shape = (lenC, -1)
# distance matrix
d = bigM - smallM
# distance squared matrix
dSQ = d * d
# next step sums deltaX**2, deltaY**2, deltaZ**2
dSQMAT = Numeric.sum(dSQ,2)
#percentCutoff lets user relax sum of radii
#the smaller the percentCutoff the smaller the key
#dSQ has to be less than
cutoff = cutoff * percentCutoff
cutoffSQMAT = cutoff * cutoff
#cutoffSQMAT = cutoffSQMAT * percentCutoff
# ansMat has 1 where sq dist. is smaller than cutoff
ansMat = Numeric.logical_and(self.func(dSQMAT, cutoffSQMAT) , \
Numeric.not_equal(dSQMAT, 0.))
if lenK > lenC:
# in this case need to rearrange matrix
# which got shuffled in if-else above
ansMat = Numeric.swapaxes(ansMat, 0, 1)
dSQMAT = Numeric.swapaxes(dSQMAT, 0, 1)
# finally, build result dictionaries which have atom keys:
# pairDict has values which are lists of close atoms
# distDict has values which are lists of distances
pairDict = {}
distDict = {}
# get a list of rows which have non-zero entries
# to loop over in next section
rowIndices = Numeric.nonzero(Numeric.sum(ansMat,1))
# rows correspond to ats in keyAts
# columns correspond to ats in checkAts
for i in rowIndices:
# atindex is a list [7 8 9] indexing into checkAts
atindex = Numeric.nonzero(ansMat[i])
# keyAtar[i] is ith atom in keyAts
keyAt = keyAtar[i]
pairDict[keyAt] = Numeric.take(atar, atindex)
if self.return_dist:
distDict[keyAt] = []
for ind in atindex:
distDict[keyAt].append(math.sqrt(dSQMAT[i][ind]))
#getting distDict back is optional
if self.return_dist: return pairDict, distDict
else: return pairDict
def kNNimputeMA(arr2d, K=20, callback=None):
"""Returns a new 2D MA.array with missing values imputed from K nearest neighbours.
Find K rows (axis 0) with the most similar values where similarity measure corresponds to weighted Euclidean distance.
Imputed value = weighted average of the corresponding values of K nearest neighbours,
where weights equal to tricubic distribution of distances to all rows.
Impute missing rows by average over all rows.
Version: 30.8.2005
"""
arr2d = MA.asarray(arr2d)
assert len(arr2d.shape) == 2, "2D array expected"
# make a copy for imputation
aImp2 = MA.array(arr2d)
# leave out columns with 0 known values (columnInd: non-zero columns)
columnCond = Numeric.greater(MA.count(arr2d, axis=0), 0)
columnIndAll = Numeric.arange(arr2d.shape[1])
columnInd = Numeric.compress(columnCond, columnIndAll)
# impute the rows where 0 < #known_values < #non_zero_columns, i.e. exclude the rows with 0 and all (non-zero-column) values
countByRows = MA.count(arr2d, axis=1)
for rowIdx in Numeric.compress(
Numeric.logical_and(Numeric.greater(countByRows, 0),
Numeric.less(countByRows, columnInd.shape[0])),
Numeric.arange(arr2d.shape[0])):
rowResized = MA.resize(arr2d[rowIdx], arr2d.shape)
diff = arr2d - rowResized
distances = MA.sqrt(
MA.add.reduce((diff)**2, 1) / MA.count(diff, axis=1))
# nearest neighbours row indices (without the current row index)
indSorted = MA.argsort(distances)[1:]
distSorted = distances.take(indSorted)
# number of distances different from MA.masked
numNonMasked = distSorted.shape[0] - Numeric.add.reduce(
Numeric.asarray(MA.getmaskarray(distSorted), Numeric.Int))
# number of distances to account for (K or less)
if numNonMasked > 1:
weightsSorted = MA.power(
1 - MA.power(distSorted / distSorted[numNonMasked - 1], 3),
3) # tricubic distribution of all weights
else:
weightsSorted = Numeric.ones(distSorted.shape[0])
# compute average for each column separately in order to account for K non-masked values
colInd4CurrRow = Numeric.compress(
Numeric.logical_and(MA.getmaskarray(arr2d[rowIdx]), columnCond),
columnIndAll)
for colIdx in colInd4CurrRow:
# column values sorted by distances
columnVals = arr2d[:, colIdx].take(indSorted)
# take only those weights where columnVals does not equal MA.masked
weightsSortedCompressed = MA.compress(
1 - MA.getmaskarray(columnVals), weightsSorted)
# impute from K (or possibly less) values
aImp2[rowIdx,
colIdx] = MA.average(columnVals.compressed()[:K],
weights=weightsSortedCompressed[:K])
if callback:
callback()
# impute the unknown rows with average profile
avrgRow = MA.average(arr2d, 0)
for rowIdx in Numeric.compress(Numeric.equal(countByRows, 0),
Numeric.arange(arr2d.shape[0])):
aImp2[rowIdx] = avrgRow
if callback:
callback()
return aImp2
def match(x, y, n_iterations=1, z=2, eps_rmsd=0.5, eps_stdv=0.05):
"""
Matches two arrays onto each other, while iteratively removing outliers.
Superimposed array y would be C{ N.dot(y, N.transpose(r)) + t }.
@param n_iterations: number of calculations::
1 .. no iteration
0 .. until convergence
@type n_iterations: 1|0
@param z: number of standard deviations for outlier definition (default: 2)
@type z: float
@param eps_rmsd: tolerance in rmsd (default: 0.5)
@type eps_rmsd: float
@param eps_stdv: tolerance in standard deviations (default: 0.05)
@type eps_stdv: float
@return: (r,t), [ [percent_considered, rmsd_for_it, outliers] ]
@rtype: (array, array), [float, float, int]
"""
iter_trace = []
rmsd_old = 0
stdv_old = 0
n = 0
converged = 0
mask = N.ones(len(y), N.int32 )
while not converged:
## find transformation for best match
r, t = findTransformation(N.compress(mask, x, 0),
N.compress(mask, y, 0))
## transform coordinates
xt = N.dot(y, N.transpose(r)) + t
## calculate row distances
d = N.sqrt(N.sum(N.power(x - xt, 2), 1)) * mask
## calculate rmsd and stdv
rmsd = N.sqrt(N.average(N.compress(mask, d)**2))
stdv = MU.SD(N.compress(mask, d))
## check conditions for convergence
d_rmsd = abs(rmsd - rmsd_old)
d_stdv = abs(1 - stdv_old / stdv)
if d_rmsd < eps_rmsd and d_stdv < eps_stdv:
converged = 1
else:
rmsd_old = rmsd
stdv_old = stdv
## store result
perc = round(float(N.sum(mask)) / float(len(mask)), 2)
## throw out non-matching rows
mask = N.logical_and(mask, N.less(d, rmsd + z * stdv))
outliers = N.nonzero( N.logical_not( mask ) )
iter_trace.append([perc, round(rmsd, 3), outliers])
n += 1
if n_iterations and n >= n_iterations:
break
return (r, t), iter_trace