def loessMA(m, windowSize, axis=0, approxMasked=True, verbose=False, callback=None):
"""Returns a new array with values at the given axis smoothed by loess;
if approxMasked==True: the masked values are approximated by loess;
assumes equidistant spacing of points on the given axis.
"""
assert 0 < windowSize <= m.shape[axis]+0.1, "0 < windowSize[%s] <= 1 OR windowSize in range(1.1,m.shape[axis]+1) expected, got %f" % ("%", windowSize)
m = MA.asarray(m)
if m.dtype.char <> Numeric.Float:
m = m.astype(Numeric.Float)
shp_other = list(m.shape)
shp_other.pop(axis)
# get a transposed and reshaped mask and data from m; if m.mask() == None, construct a new array of zeros
mask = Numeric.reshape(Numeric.transpose(MA.getmaskarray(m), [axis] + range(0,axis) + range(axis+1,len(m.shape))), (m.shape[axis], Numeric.multiply.reduce(shp_other)))
data = MA.reshape(MA.transpose(m, [axis] + range(0,axis) + range(axis+1,len(m.shape))), (m.shape[axis], Numeric.multiply.reduce(shp_other)))
maskInv = -1*(mask-1)
xall = Numeric.arange(data.shape[0])
xallList = xall.tolist()
for ii in Numeric.compress(Numeric.add.reduce(maskInv,0) > 1, range(data.shape[1])): # run loess if the profile contains more than 2 values
try:
data[:,ii] = MA.array(statc.loess(zip(MA.compress(maskInv[:,ii], xall).tolist(), MA.compress(maskInv[:,ii], data[:,ii]).tolist()), xallList, windowSize))[:,1]
except:
if verbose:
print "Warning: loessMA: could not loess axis %i index %i" % (axis, ii)
if callback:
callback()
if not approxMasked:
data = MA.array(data, mask=mask)
return MA.transpose(MA.reshape(data, [m.shape[axis]] + shp_other), [axis] + range(0,axis) + range(axis+1,len(m.shape)))
def loessMA(m,
windowSize,
axis=0,
approxMasked=True,
verbose=False,
callback=None):
"""Returns a new array with values at the given axis smoothed by loess;
if approxMasked==True: the masked values are approximated by loess;
assumes equidistant spacing of points on the given axis.
"""
assert 0 < windowSize <= m.shape[
axis] + 0.1, "0 < windowSize[%s] <= 1 OR windowSize in range(1.1,m.shape[axis]+1) expected, got %f" % (
"%", windowSize)
m = MA.asarray(m)
if m.dtype.char <> Numeric.Float:
m = m.astype(Numeric.Float)
shp_other = list(m.shape)
shp_other.pop(axis)
# get a transposed and reshaped mask and data from m; if m.mask() == None, construct a new array of zeros
mask = Numeric.reshape(
Numeric.transpose(MA.getmaskarray(m), [axis] + range(0, axis) +
range(axis + 1, len(m.shape))),
(m.shape[axis], Numeric.multiply.reduce(shp_other)))
data = MA.reshape(
MA.transpose(m,
[axis] + range(0, axis) + range(axis + 1, len(m.shape))),
(m.shape[axis], Numeric.multiply.reduce(shp_other)))
maskInv = -1 * (mask - 1)
xall = Numeric.arange(data.shape[0])
xallList = xall.tolist()
for ii in Numeric.compress(
Numeric.add.reduce(maskInv, 0) > 1, range(data.shape[1])
): # run loess if the profile contains more than 2 values
try:
data[:, ii] = MA.array(
statc.loess(
zip(
MA.compress(maskInv[:, ii], xall).tolist(),
MA.compress(maskInv[:, ii], data[:, ii]).tolist()),
xallList, windowSize))[:, 1]
except:
if verbose:
print "Warning: loessMA: could not loess axis %i index %i" % (
axis, ii)
if callback:
callback()
if not approxMasked:
data = MA.array(data, mask=mask)
return MA.transpose(MA.reshape(data, [m.shape[axis]] + shp_other), [axis] +
range(0, axis) + range(axis + 1, len(m.shape)))
def triangularPut(m1d, upper=1, lower=0):
"""Returns 2D masked array with elements of the given 1D array in the strictly upper (lower) triangle.
Elements of the 1D array should be ordered according to the upper triangular part of the 2D matrix.
The lower triangular part (if requested) equals to the transposed upper triangular part.
If upper == lower == 1 a symetric matrix is returned.
"""
assert upper in [0,1] and lower in [0,1], "[0|1] expected for upper / lower"
m1d = MA.asarray(m1d)
assert MA.rank(m1d) == 1, "1D masked array expected"
m2dShape0 = math.ceil(math.sqrt(2*m1d.shape[0]))
assert m1d.shape[0] == m2dShape0*(m2dShape0-1)/2, "the length of m1d does not correspond to n(n-1)/2"
if upper:
if lower:
mask = Numeric.fromfunction(lambda i,j: i==j, (m2dShape0, m2dShape0))
else:
mask = Numeric.fromfunction(lambda i,j: i>=j, (m2dShape0, m2dShape0))
else:
if lower:
mask = Numeric.fromfunction(lambda i,j: i<=j, (m2dShape0, m2dShape0))
else:
mask = Numeric.ones((m2dShape0, m2dShape0))
m2d = MA.ravel(MA.zeros((m2dShape0, m2dShape0), m1d.dtype.char))
condUpperTriang = Numeric.fromfunction(lambda i,j: i<j, (m2dShape0, m2dShape0))
putIndices = Numeric.compress(Numeric.ravel(condUpperTriang), Numeric.arange(0, m2dShape0**2, typecode=Numeric.Int))
MA.put(m2d, putIndices, m1d)
m2d = MA.reshape(m2d, (m2dShape0, m2dShape0))
m2d = MA.where(condUpperTriang, m2d, MA.transpose(m2d))
return MA.array(m2d, mask=Numeric.logical_or(mask, MA.getmaskarray(m2d)))
def diagonalPut(m1d, m2d):
"""Puts the given 1D masked array into the diagonal of the given 2D masked array and returns a new copy of the 2D array.
"""
m1d = MA.asarray(m1d)
m2d = MA.asarray(m2d)
assert MA.rank(m1d) == 1 and MA.rank(m2d) == 2, "1D and 2D masked array expected"
assert m1d.shape[0] == m2d.shape[0] == m2d.shape[1], "the shape of the given arrays does not match"
putIndices = Numeric.compress(Numeric.ravel(Numeric.fromfunction(lambda i,j: i==j, m2d.shape)), Numeric.arange(0, Numeric.multiply.reduce(m2d.shape), typecode=Numeric.Int))
m2dShape = m2d.shape
m2d = MA.ravel(m2d)
MA.put(m2d, putIndices, m1d)
return MA.reshape(m2d, m2dShape)
