def test_fromrecords(self):
"Test construction from records."
(mrec, nrec, ddtype) = self.data
#......
palist = [(1, 'abc', 3.7000002861022949, 0),
(2, 'xy', 6.6999998092651367, 1),
(0, ' ', 0.40000000596046448, 0)]
pa = recfromrecords(palist, names='c1, c2, c3, c4')
mpa = fromrecords(palist, names='c1, c2, c3, c4')
assert_equal_records(pa, mpa)
#.....
_mrec = fromrecords(nrec)
assert_equal(_mrec.dtype, mrec.dtype)
for field in _mrec.dtype.names:
assert_equal(getattr(_mrec, field), getattr(mrec._data, field))
#
_mrec = fromrecords(nrec.tolist(), names='c1,c2,c3')
assert_equal(_mrec.dtype, [('c1', int), ('c2', float), ('c3', '|S5')])
for (f, n) in zip(('c1', 'c2', 'c3'), ('a', 'b', 'c')):
assert_equal(getattr(_mrec, f), getattr(mrec._data, n))
#
_mrec = fromrecords(mrec)
assert_equal(_mrec.dtype, mrec.dtype)
assert_equal_records(_mrec._data, mrec.filled())
assert_equal_records(_mrec._mask, mrec._mask)
def test_fromrecords(self):
# Test construction from records.
(mrec, nrec, ddtype) = self.data
#......
palist = [(1, 'abc', 3.7000002861022949, 0),
(2, 'xy', 6.6999998092651367, 1),
(0, ' ', 0.40000000596046448, 0)]
pa = recfromrecords(palist, names='c1, c2, c3, c4')
mpa = fromrecords(palist, names='c1, c2, c3, c4')
assert_equal_records(pa, mpa)
#.....
_mrec = fromrecords(nrec)
assert_equal(_mrec.dtype, mrec.dtype)
for field in _mrec.dtype.names:
assert_equal(getattr(_mrec, field), getattr(mrec._data, field))
_mrec = fromrecords(nrec.tolist(), names='c1,c2,c3')
assert_equal(_mrec.dtype, [('c1', int), ('c2', float), ('c3', '|S5')])
for (f, n) in zip(('c1', 'c2', 'c3'), ('a', 'b', 'c')):
assert_equal(getattr(_mrec, f), getattr(mrec._data, n))
_mrec = fromrecords(mrec)
assert_equal(_mrec.dtype, mrec.dtype)
assert_equal_records(_mrec._data, mrec.filled())
assert_equal_records(_mrec._mask, mrec._mask)
def test_fromrecords(self):
# Test construction from records.
(mrec, nrec, ddtype) = self.data
# ......
palist = [
(1, "abc", 3.7000002861022949, 0),
(2, "xy", 6.6999998092651367, 1),
(0, " ", 0.40000000596046448, 0),
]
pa = recfromrecords(palist, names="c1, c2, c3, c4")
mpa = fromrecords(palist, names="c1, c2, c3, c4")
assert_equal_records(pa, mpa)
# .....
_mrec = fromrecords(nrec)
assert_equal(_mrec.dtype, mrec.dtype)
for field in _mrec.dtype.names:
assert_equal(getattr(_mrec, field), getattr(mrec._data, field))
_mrec = fromrecords(nrec.tolist(), names="c1,c2,c3")
assert_equal(_mrec.dtype, [("c1", int), ("c2", float), ("c3", "|S5")])
for (f, n) in zip(("c1", "c2", "c3"), ("a", "b", "c")):
assert_equal(getattr(_mrec, f), getattr(mrec._data, n))
_mrec = fromrecords(mrec)
assert_equal(_mrec.dtype, mrec.dtype)
assert_equal_records(_mrec._data, mrec.filled())
assert_equal_records(_mrec._mask, mrec._mask)
def test_fromrecords(self):
# Test construction from records.
(mrec, nrec, ddtype) = self.data
# ......
palist = [(1, "abc", 3.7000002861022949, 0), (2, "xy", 6.6999998092651367, 1), (0, " ", 0.40000000596046448, 0)]
pa = recfromrecords(palist, names="c1, c2, c3, c4")
mpa = fromrecords(palist, names="c1, c2, c3, c4")
assert_equal_records(pa, mpa)
# .....
_mrec = fromrecords(nrec)
assert_equal(_mrec.dtype, mrec.dtype)
for field in _mrec.dtype.names:
assert_equal(getattr(_mrec, field), getattr(mrec._data, field))
#
_mrec = fromrecords(nrec.tolist(), names="c1,c2,c3")
assert_equal(_mrec.dtype, [("c1", int), ("c2", float), ("c3", "|S5")])
for (f, n) in zip(("c1", "c2", "c3"), ("a", "b", "c")):
assert_equal(getattr(_mrec, f), getattr(mrec._data, n))
#
_mrec = fromrecords(mrec)
assert_equal(_mrec.dtype, mrec.dtype)
assert_equal_records(_mrec._data, mrec.filled())
assert_equal_records(_mrec._mask, mrec._mask)
def fromrecords(reclist, dtype=None, shape=None, formats=None, names=None,
titles=None, aligned=False, byteorder=None,
fill_value=None, mask=nomask):
"""Creates a MaskedRecords from a list of records.
Parameters
----------
reclist : sequence
A list of records. Each element of the sequence is first converted
to a masked array if needed. If a 2D array is passed as argument, it is
processed line by line
dtype : {None, dtype}, optional
Data type descriptor.
shape : {None,int}, optional
Number of records. If None, ``shape`` is defined from the shape of the
first array in the list.
formats : {None, sequence}, optional
Sequence of formats for each individual field. If None, the formats will
be autodetected by inspecting the fields and selecting the highest dtype
possible.
names : {None, sequence}, optional
Sequence of the names of each field.
fill_value : {None, sequence}, optional
Sequence of data to be used as filling values.
mask : {nomask, sequence}, optional.
External mask to apply on the data.
Notes
-----
Lists of tuples should be preferred over lists of lists for faster processing.
"""
# Grab the initial _fieldmask, if needed:
_mask = getattr(reclist, '_mask', None)
# Get the list of records.....
try:
nfields = len(reclist[0])
except TypeError:
nfields = len(reclist[0].dtype)
if isinstance(reclist, ndarray):
# Make sure we don't have some hidden mask
if isinstance(reclist, MaskedArray):
reclist = reclist.filled().view(ndarray)
# Grab the initial dtype, just in case
if dtype is None:
dtype = reclist.dtype
reclist = reclist.tolist()
mrec = recfromrecords(reclist, dtype=dtype, shape=shape, formats=formats,
names=names, titles=titles,
aligned=aligned, byteorder=byteorder).view(mrecarray)
# Set the fill_value if needed
if fill_value is not None:
mrec.fill_value = fill_value
# Now, let's deal w/ the mask
if mask is not nomask:
mask = np.array(mask, copy=False)
maskrecordlength = len(mask.dtype)
if maskrecordlength:
mrec._mask.flat = mask
elif len(mask.shape) == 2:
mrec._mask.flat = [tuple(m) for m in mask]
else:
mrec.__setmask__(mask)
if _mask is not None:
mrec._mask[:] = _mask
return mrec
def fromrecords(reclist,
dtype=None,
shape=None,
formats=None,
names=None,
titles=None,
aligned=False,
byteorder=None,
fill_value=None,
mask=nomask):
"""
Creates a MaskedRecords from a list of records.
Parameters
----------
reclist : sequence
A list of records. Each element of the sequence is first converted
to a masked array if needed. If a 2D array is passed as argument, it is
processed line by line
dtype : {None, dtype}, optional
Data type descriptor.
shape : {None,int}, optional
Number of records. If None, ``shape`` is defined from the shape of the
first array in the list.
formats : {None, sequence}, optional
Sequence of formats for each individual field. If None, the formats will
be autodetected by inspecting the fields and selecting the highest dtype
possible.
names : {None, sequence}, optional
Sequence of the names of each field.
fill_value : {None, sequence}, optional
Sequence of data to be used as filling values.
mask : {nomask, sequence}, optional.
External mask to apply on the data.
Notes
-----
Lists of tuples should be preferred over lists of lists for faster processing.
"""
# Grab the initial _fieldmask, if needed:
_mask = getattr(reclist, '_mask', None)
# Get the list of records.
if isinstance(reclist, ndarray):
# Make sure we don't have some hidden mask
if isinstance(reclist, MaskedArray):
reclist = reclist.filled().view(ndarray)
# Grab the initial dtype, just in case
if dtype is None:
dtype = reclist.dtype
reclist = reclist.tolist()
mrec = recfromrecords(reclist,
dtype=dtype,
shape=shape,
formats=formats,
names=names,
titles=titles,
aligned=aligned,
byteorder=byteorder).view(mrecarray)
# Set the fill_value if needed
if fill_value is not None:
mrec.fill_value = fill_value
# Now, let's deal w/ the mask
if mask is not nomask:
mask = np.array(mask, copy=False)
maskrecordlength = len(mask.dtype)
if maskrecordlength:
mrec._mask.flat = mask
elif mask.ndim == 2:
mrec._mask.flat = [tuple(m) for m in mask]
else:
mrec.__setmask__(mask)
if _mask is not None:
mrec._mask[:] = _mask
return mrec
