def put(arr, ids, values):
"""
The opposite of `take`. The values of `arr` at the locations
specified by `ids` are set to the corresponding value of `values`.
The following is to test improvments to puts with masked arrays.
Places in the code were assuming incorrect put behavior.
>>> maskValue = 999999
>>> arr = zeros(3, 'l')
>>> ids = MA.masked_values((2, maskValue), maskValue)
>>> values = MA.masked_values((4, maskValue), maskValue)
>>> put(arr, ids, values) ## this should work
>>> print arr
[0 0 4]
>>> arr = MA.masked_values((maskValue, 5, 10), maskValue)
>>> ids = MA.masked_values((2, maskValue), maskValue)
>>> values = MA.masked_values((4, maskValue), maskValue)
>>> put(arr, ids, values)
>>> print arr ## works as expected
[-- 5 4]
>>> arr = MA.masked_values((maskValue, 5, 10), maskValue)
>>> ids = MA.masked_values((maskValue, 2), maskValue)
>>> values = MA.masked_values((4, maskValue), maskValue)
>>> put(arr, ids, values)
>>> print arr ## should be [-- 5 --] maybe??
[-- 5 999999]
"""
if _isPhysical(arr):
arr.put(ids, values)
elif MA.isMaskedArray(arr):
if NUMERIX.sometrue(MA.getmaskarray(ids)):
if numpy_version == 'old':
pvalues = MA.array(values, mask=MA.getmaskarray(ids))
else:
pvalues = MA.array(values.filled(), mask=MA.getmaskarray(ids))
MA.put(arr, ids.compressed(), pvalues.compressed())
else:
MA.put(arr, ids, values)
elif MA.isMaskedArray(ids):
NUMERIX.put(arr, ids.compressed(),
MA.array(values, mask=MA.getmaskarray(ids)).compressed())
else:
NUMERIX.put(arr, ids, values)
def put(arr, ids, values):
"""
The opposite of `take`. The values of `arr` at the locations
specified by `ids` are set to the corresponding value of `values`.
The following is to test improvments to puts with masked arrays.
Places in the code were assuming incorrect put behavior.
>>> maskValue = 999999
>>> arr = zeros(3, 'l')
>>> ids = MA.masked_values((2, maskValue), maskValue)
>>> values = MA.masked_values((4, maskValue), maskValue)
>>> put(arr, ids, values) ## this should work
>>> print arr
[0 0 4]
>>> arr = MA.masked_values((maskValue, 5, 10), maskValue)
>>> ids = MA.masked_values((2, maskValue), maskValue)
>>> values = MA.masked_values((4, maskValue), maskValue)
>>> put(arr, ids, values)
>>> print arr ## works as expected
[-- 5 4]
>>> arr = MA.masked_values((maskValue, 5, 10), maskValue)
>>> ids = MA.masked_values((maskValue, 2), maskValue)
>>> values = MA.masked_values((4, maskValue), maskValue)
>>> put(arr, ids, values)
>>> print arr ## should be [-- 5 --] maybe??
[-- 5 999999]
"""
if _isPhysical(arr):
arr.put(ids, values)
elif MA.isMaskedArray(arr):
if NUMERIX.sometrue(MA.getmaskarray(ids)):
if numpy_version == 'old':
pvalues = MA.array(values, mask=MA.getmaskarray(ids))
else:
pvalues = MA.array(values.filled(), mask=MA.getmaskarray(ids))
MA.put(arr, ids.compressed(), pvalues.compressed())
else:
MA.put(arr, ids, values)
elif MA.isMaskedArray(ids):
NUMERIX.put(arr, ids.compressed(), MA.array(values, mask=MA.getmaskarray(ids)).compressed())
else:
NUMERIX.put(arr, ids, values)
def reshape(arr, shape):
"""
Change the shape of `arr` to `shape`, as long as the product of all the
lenghts of all the axes is constant (the total number of elements does not
change).
"""
if -1 in shape:
# e.g., NUMERIX.reshape(a, (-1, N)) fails if N == 0
oldShape = array(getShape(arr))
oldShape[oldShape == 0] = 1
if hasattr(shape, 'index'):
index = shape.index(-1)
else:
index = list(shape).index(-1)
left = shape[:index]
right = shape[index + 1:]
newShape = array(left + right)
newShape[newShape == 0] = 1
shape = left + (oldShape.prod() / newShape.prod(), ) + right
if _isPhysical(arr):
return arr.reshape(shape)
elif type(arr) is type(array((0))):
return NUMERIX.reshape(arr, tuple(shape))
elif type(arr) is type(MA.array((0))):
return MA.reshape(arr, shape)
else:
return NUMERIX.reshape(array(arr), tuple(shape))
def reshape(arr, shape):
"""
Change the shape of `arr` to `shape`, as long as the product of all the
lenghts of all the axes is constant (the total number of elements does not
change).
"""
if -1 in shape:
# e.g., NUMERIX.reshape(a, (-1, N)) fails if N == 0
oldShape = array(getShape(arr))
oldShape[oldShape == 0] = 1
if hasattr(shape, 'index'):
index = shape.index(-1)
else:
index = list(shape).index(-1)
left = shape[:index]
right = shape[index+1:]
newShape = array(left + right)
newShape[newShape == 0] = 1
shape = left + (oldShape.prod() / newShape.prod(),) + right
if _isPhysical(arr):
return arr.reshape(shape)
elif type(arr) is type(array((0))):
return NUMERIX.reshape(arr, tuple(shape))
elif type(arr) is type(MA.array((0))):
return MA.reshape(arr, shape)
else:
return NUMERIX.reshape(array(arr), tuple(shape))
def take(a, indices, axis=0, fill_value=None):
"""
Selects the elements of `a` corresponding to `indices`.
"""
if _isPhysical(a):
taken = a.take(indices, axis=axis)
elif type(indices) is type(MA.array((0))):
## Replaces `MA.take`. `MA.take` does not always work when
## `indices` is a masked array.
##
taken = MA.take(a, MA.filled(indices, 0), axis=axis)
mask = MA.getmask(indices)
if mask is not MA.nomask:
mask = MA.getmaskarray(indices)
if taken.shape != mask.shape:
mask = MA.repeat(mask[NewAxis, ...], taken.shape[0], axis=0)
mask = MA.mask_or(MA.getmask(taken), mask)
if mask is not MA.nomask:
taken = MA.array(data=taken, mask=mask)
else:
if MA.getmask(taken) is MA.nomask and numpy_version == 'old':
# numpy 1.1 returns normal array when masked array is filled
taken = taken.filled()
elif type(a) in (type(array((0))), type(()), type([])):
taken = NUMERIX.take(a, indices, axis=axis)
elif type(a) is type(MA.array((0))):
taken = MA.take(a, indices, axis=axis)
else:
raise TypeError, 'cannot take from %s object: %s' % (type(a), `a`)
if fill_value is not None and type(taken) is type(MA.array((0))):
taken = taken.filled(fill_value=fill_value)
return taken
def take(a, indices, axis=0, fill_value=None):
"""
Selects the elements of `a` corresponding to `indices`.
"""
if _isPhysical(a):
taken = a.take(indices, axis=axis)
elif type(indices) is type(MA.array((0))):
## Replaces `MA.take`. `MA.take` does not always work when
## `indices` is a masked array.
##
taken = MA.take(a, MA.filled(indices, 0), axis=axis)
mask = MA.getmask(indices)
if mask is not MA.nomask:
mask = MA.getmaskarray(indices)
if taken.shape != mask.shape:
mask = MA.repeat(mask[NewAxis, ...], taken.shape[0], axis=0)
mask = MA.mask_or(MA.getmask(taken), mask)
if mask is not MA.nomask:
taken = MA.array(data=taken, mask=mask)
else:
if MA.getmask(taken) is MA.nomask and numpy_version == 'old':
# numpy 1.1 returns normal array when masked array is filled
taken = taken.filled()
elif type(a) in (type(array((0))), type(()), type([])):
taken = NUMERIX.take(a, indices, axis=axis)
elif type(a) is type(MA.array((0))):
taken = MA.take(a, indices, axis=axis)
else:
raise TypeError, 'cannot take from %s object: %s' % (type(a), ` a `)
if fill_value is not None and type(taken) is type(MA.array((0))):
taken = taken.filled(fill_value=fill_value)
return taken
def sqrtDot(a1, a2):
"""Return array of square roots of vector dot-products
for arrays a1 and a2 of vectors v1 and v2
Usually used with v1==v2 to return magnitude of v1.
"""
from fipy.tools.dimensions import physicalField
unit1 = unit2 = physicalField._unity
def dimensionlessUnmasked(a):
unit = physicalField._unity
mask = False
if _isPhysical(a):
unit = a.inBaseUnits().unit
a = a.numericValue
if MA.isMaskedArray(a):
mask = a.mask
a = a.filled(fill_value=1)
if not a.flags['C_CONTIGUOUS']:
a = a.copy('C')
return (a, unit, mask)
a1, unit1, mask1 = dimensionlessUnmasked(a1)
a2, unit2, mask2 = dimensionlessUnmasked(a2)
NJ, ni = NUMERIX.shape(a1)
result1 = NUMERIX.zeros((ni, ), 'd')
inline._runInline("""
int j;
result1[i] = 0.;
for (j = 0; j < NJ; j++)
{
result1[i] += a1[i + j * ni] * a2[i + j * ni];
}
result1[i] = sqrt(result1[i]);
""",
result1=result1,
a1=a1,
a2=a2,
ni=ni,
NJ=NJ)
if NUMERIX.any(mask1) or NUMERIX.any(mask2):
result1 = MA.array(result1, mask=NUMERIX.logical_or(mask1, mask2))
if unit1 != physicalField._unity or unit2 != physicalField._unity:
from fipy.tools.dimensions.physicalField import PhysicalField
result1 = PhysicalField(value=result, unit=(unit1 * unit2)**0.5)
return result1
def sum(arr, axis=0):
"""
The sum of all the elements of `arr` along the specified axis.
"""
if _isPhysical(arr):
return arr.sum(axis)
elif type(arr) is type(MA.array((0))):
return MA.sum(arr, axis)
else:
if type(arr) in (float, int) or len(arr) == 0 or 0 in arr.shape:
return NUMERIX.sum(arr, axis)
else:
if axis is None:
axis = 0
return NUMERIX.tensordot(NUMERIX.ones(arr.shape[axis], 'l'), arr, (0, axis))
def sum(arr, axis=0):
"""
The sum of all the elements of `arr` along the specified axis.
"""
if _isPhysical(arr):
return arr.sum(axis)
elif type(arr) is type(MA.array((0))):
return MA.sum(arr, axis)
else:
if type(arr) in (float, int) or len(arr) == 0 or 0 in arr.shape:
return NUMERIX.sum(arr, axis)
else:
if axis is None:
axis = 0
return NUMERIX.tensordot(NUMERIX.ones(arr.shape[axis], 'l'), arr,
(0, axis))
def sqrtDot(a1, a2):
"""Return array of square roots of vector dot-products
for arrays a1 and a2 of vectors v1 and v2
Usually used with v1==v2 to return magnitude of v1.
"""
from fipy.tools.dimensions import physicalField
unit1 = unit2 = physicalField._unity
def dimensionlessUnmasked(a):
unit = physicalField._unity
mask = False
if _isPhysical(a):
unit = a.inBaseUnits().unit
a = a.numericValue
if MA.isMaskedArray(a):
mask = a.mask
a = a.filled(fill_value=1)
if not a.flags['C_CONTIGUOUS']:
a = a.copy('C')
return (a, unit, mask)
a1, unit1, mask1 = dimensionlessUnmasked(a1)
a2, unit2, mask2 = dimensionlessUnmasked(a2)
NJ, ni = NUMERIX.shape(a1)
result1 = NUMERIX.zeros((ni,), 'd')
inline._runInline("""
int j;
result1[i] = 0.;
for (j = 0; j < NJ; j++)
{
result1[i] += a1[i + j * ni] * a2[i + j * ni];
}
result1[i] = sqrt(result1[i]);
""", result1=result1, a1=a1, a2=a2, ni=ni, NJ=NJ)
if NUMERIX.any(mask1) or NUMERIX.any(mask2):
result1 = MA.array(result1, mask=NUMERIX.logical_or(mask1, mask2))
if unit1 != physicalField._unity or unit2 != physicalField._unity:
from fipy.tools.dimensions.physicalField import PhysicalField
result1 = PhysicalField(value=result, unit=(unit1 * unit2)**0.5)
return result1
def isNumpy(aa):
return type(aa) in (type(MA.array(0)), type(NUMERIX.array(0)))
print "--------------------"
chi = NglA.chiinv([0.99], [2])
print chi, type(chi)
print "\nchiinv (tuple input)"
print "---------------------"
chi = NglA.chiinv((0.99), (2))
print chi, type(chi)
#
# linmsg
#
x = MA.array([ \
[ 1190., 1455., 1550., -999., 1745., 1770., \
1900., -999., -999., -999., 2335., 2490., \
2720., 2710., 2530., 2900., 2760., -999.], \
[ 1115., -999., 1515., 1794., -999. ,1710., \
1830., 1920., 1970., 2300., 2280., 2520., \
2630., -999. ,-999. ,2800., -999.,-999. ] \
],fill_value=-999.)
ix = MA.array([ \
[ 1190, 1455, 1550, -999, 1745, 1770, \
1900, -999, -999, -999, 2335, 2490, \
2720, 2710, 2530, 2900, 2760, -999], \
[ 1115, -999, 1515, 1794, -999 ,1710, \
1830, 1920, 1970, 2300, 2280, 2520, \
2630, -999 ,-999 ,2800, -999,-999 ] \
],fill_value=-999)
lx =[ \
[ 1190, 1455, 1550, -999, 1745, 1770, \
def normToPoints(self,
minMJD=None,
maxMJD=None,
zerotype="mean",
clipsigma=3.):
import numpy.core.ma as ma
zerodates = None
# Make sure things that should be floats if defined are floats
if minMJD is not None: minMJD = float(minMJD)
if maxMJD is not None: maxMJD = float(maxMJD)
if clipsigma is not None:
clipsigma = float(clipsigma) # should always be defined
if minMJD is None and maxMJD is None:
self.normToLastPoint()
return
else:
if minMJD is not None and maxMJD is not None:
if minMJD < maxMJD:
zerodates = [
i for (i, d) in enumerate(self.dates)
if (d >= minMJD) and (d <= maxMJD)
]
else:
zerodates = [
i for (i, d) in enumerate(self.dates)
if (d >= minMJD) or (d <= maxMJD)
]
elif minMJD is not None:
zerodates = [
i for (i, d) in enumerate(self.dates) if d >= minMJD
]
elif maxMJD is not None:
zerodates = [
i for (i, d) in enumerate(self.dates) if d <= maxMJD
]
if zerodates is None or len(zerodates) <= 0:
print(
"NN2: No points found in given MJD range to use as zero points for this filter."
)
print("NN2: Leaving points unchanged.")
return
# There's got to be a better way of doing the following masked array stuff
zeroflux_mask = [not isfinite(f) for f in self.V[zerodates]]
zerofluxerr_mask = [not isfinite(df) for df in self.Verr[zerodates]]
zeroflux = ma.array(self.V[zerodates], mask=zeroflux_mask)
zerofluxerr = ma.array(self.Verr[zerodates], mask=zerofluxerr_mask)
# Now finally adjust the flux by the zeroflux calculated.
# If there's only one entry then we just take it
if len(zeroflux) == 1:
zerofluxnorm = zeroflux[0]
elif zerotype == "mean":
# zerofluxnorm = ma.average(zeroflux, weights=1./(zerofluxerr*zerofluxerr))
zerofluxnorm = ma.average(zeroflux,
weights=1. / zerofluxerr * zerofluxerr)
elif zerotype == "clipmean":
zeroavg = ma.average(zeroflux,
weights=1. / (zerofluxerr * zerofluxerr))
residual = zeroflux - zeroavg
residual /= zerofluxerr # error-weighted
# 2006/02/13 - MWV:
# Need the additional check to make sure the value isn't masked
w = [i for (i,f) in enumerate(ma.fabs(residual)) \
if f is not ma.masked and f < clipsigma]
if len(w) <= 0:
print("No good zero flux points")
print("No flux zero applied to points.")
try:
zerofluxnorm = ma.average(zeroflux[w],
weights=1. /
(zerofluxerr[w] * zerofluxerr[w]))
this_resid = residual[w] * residual[w]
zerofluxchisq = this_resid.sum()
print(zerofluxchisq)
dof = len(residual) - 1
zerofluxchisqnu = zerofluxchisq / dof
print("Chi^2/DoF of zero points : %f / %f = %f " %
(zerofluxchisq, dof, zerofluxchisqnu))
except 'DIE':
zerofluxnorm = 0.
zerofluxchisq = 0.
dof = 0.
else:
print("zerotype == '%s' unknown or not yet supported." %
(zerotype))
print("No flux zero applied to points.")
zerofluxnorm = 0.
# Subtract the fiducial flux
self.V -= zerofluxnorm
def isNumpy(aa):
return type(aa) in (type(MA.array(0)), type(NUMERIX.array(0)))