def xt_array(self, lexeme):
data = self._nextExprData(lexeme) # converts lexeme into a numpy array
if lexeme.hasAttr and lexeme.attrTypeCode == XT_LIST_TAG:
for tag, value in lexeme.attr:
if tag == 'dim':
# the array has a defined shape
data.shape = value
elif tag == 'names':
# convert numpy-vector 'value' into list to make TaggedArray work properly:
data = asTaggedArray(data, list(value))
else:
# there are additional tags in the attribute, just collect them in a dictionary
# attached to the array.
try:
data.attr[tag] = value
except AttributeError:
data = asAttrArray(data, {tag: value})
return data
def xt_array(self, lexeme):
data = self._nextExprData(lexeme) # converts lexeme into a numpy array
if lexeme.hasAttr and lexeme.attrTypeCode == XT_LIST_TAG:
for tag, value in lexeme.attr:
if tag == 'dim':
# the array has a defined shape
data.shape = value
elif tag == 'names':
# convert numpy-vector 'value' into list to make TaggedArray work properly:
data = asTaggedArray(data, list(value))
else:
# there are additional tags in the attribute, just collect them in a dictionary
# attached to the array.
try:
data.attr[tag] = value
except AttributeError:
data = asAttrArray(data, {tag: value})
return data
# String vector:
('c("abc", "defghi")', array(["abc", "defghi"])),
("seq(1, 5)", array(range(1, 6), dtype=numpy.int32)),
("polyroot(c(-39.141,151.469,401.045))", array([0.1762039 + 1.26217745e-29j, -0.5538897 - 1.26217745e-29j])),
# An explicit R list with only one item remains a list on the python side:
('list("otto")', ["otto"]),
('list("otto", "gustav")', ["otto", "gustav"]),
# tagged lists:
('list(husband="otto")', TaggedList([("husband", "otto")])),
('list(husband="otto", wife="erna")', TaggedList([("husband", "otto"), ("wife", "erna")])),
(
'list(n="Fred", no_c=2, c_ages=c(4,7))',
TaggedList([("n", "Fred"), ("no_c", 2.0), ("c_ages", array([4.0, 7.0]))]),
),
# tagged array:
("c(a=1.,b=2.,c=3.)", asTaggedArray(array([1.0, 2.0, 3.0]), ["a", "b", "c"])),
# tagged single item array should remain an array on the python side in order to preserve the tag:
("c(a=1)", asTaggedArray(array([1.0]), ["a"])),
# multi-dim array (internally also a tagged array) gets translated into a shaped numpy array:
("array(1:20, dim=c(4, 5))", shaped_array(range(1, 21), numpy.int32, (4, 5))),
#
# ('x<-1:20; y<-x*2; lm(y~x)', ????),
# Environment
# ('parent.env', [1,2]),
]
###############################################3
def test_rExprGenerator():
# String vector:
('c("abc", "defghi")', array(["abc", "defghi"])),
('seq(1, 5)', array(range(1, 6), dtype=numpy.int32)),
('polyroot(c(-39.141,151.469,401.045))',
array([0.1762039 + 1.26217745e-29j, -0.5538897 - 1.26217745e-29j])),
# An explicit R list with only one item remains a list on the python side:
('list("otto")', ["otto"]),
('list("otto", "gustav")', ["otto", "gustav"]),
# tagged lists:
('list(husband="otto")', TaggedList([("husband", "otto")])),
('list(husband="otto", wife="erna")',
TaggedList([("husband", "otto"), ("wife", "erna")])),
('list(n="Fred", no_c=2, c_ages=c(4,7))',
TaggedList([("n", "Fred"), ("no_c", 2.), ("c_ages", array([4., 7.]))])),
# tagged array:
('c(a=1.,b=2.,c=3.)', asTaggedArray(array([1., 2., 3.]), ['a', 'b', 'c'])),
# tagged single item array should remain an array on the python side in order to preserve the tag:
('c(a=1)', asTaggedArray(array([1.]), ['a'])),
# multi-dim array (internally also a tagged array) gets translated into a shaped numpy array:
('array(1:20, dim=c(4, 5))', shaped_array(range(1, 21), numpy.int32,
(4, 5))),
#
#('x<-1:20; y<-x*2; lm(y~x)', ????),
# Environment
#('parent.env', [1,2]),
]
###############################################3
def test_rExprGenerator():
