def __init__(self, observed, expected=None):
"""Parameters
-------------
observed
a DictArray instance, or something that can be converted to one.
Values must be integers.
expected
provide in the case where you know the prior proportions, otherwise
calculated from marginal frequencies
"""
if not isinstance(observed, DictArray):
observed = DictArray(observed)
# make sure values are int
observed.array = _astype(observed.array, int)
if observed.array.sum() == 0:
raise ValueError("at least one value must be > 0")
if observed.array.min() < 0:
raise ValueError("negative values encountered")
if observed.array.ndim > 2:
raise NotImplementedError("not designed for >2D")
self._observed = observed
self.expected = expected
self._residuals = None
self._df = None
self.shape = observed.shape
def test_to_dict_roundtrip(self):
"""roundtrip of DictArray.to_dict() should produce same order."""
d1 = dict(a=dict(k=1, l=2, m=3), b=dict(k=4, l=5, m=6))
darr1 = DictArray(d1)
d2 = darr1.to_dict()
darr2 = DictArray(d2)
self.assertEqual(d1, d2)
d3 = DictArray(d2)
self.assertEqual(d1, d3)
def test_to_dict_1d(self):
"""should successfully produce a 1D dict"""
data = {
"ABAYE2984": {
"ABAYE2984": 0,
"Atu3667": None,
"Avin_42730": 0.6381173875591908,
"BAA10469": None,
},
"Atu3667": {
"ABAYE2984": None,
"Atu3667": 0,
"Avin_42730": 2.3682377869318993,
"BAA10469": None,
},
"Avin_42730": {
"ABAYE2984": 0.6381173875591908,
"Atu3667": 2.3682377869318993,
"Avin_42730": 0,
"BAA10469": 1.8515731266342546,
},
"BAA10469": {
"ABAYE2984": None,
"Atu3667": None,
"Avin_42730": 1.8515731266342546,
"BAA10469": 0,
},
}
darr = DictArray(data, dtype="O")
expect = {
(n1, n2): darr[n1, n2]
for n1 in darr.template.names[0]
for n2 in darr.template.names[1]
}
self.assertEqual(darr.to_dict(flatten=True), expect)
darr = DictArrayTemplate(["s1", "s2"], ["s1", "s2"]).wrap(
[[0.0, 0.25], [0.25, 0.0]]
)
self.assertEqual(
darr.to_dict(flatten=True),
{
("s1", "s2"): 0.25,
("s2", "s1"): 0.25,
("s1", "s1"): 0.0,
("s2", "s2"): 0.0,
},
)
def test_inputs_from_dict_array(self):
"""inputs_from_dict_array makes an array object and PhyloNode list"""
twod = {
"1": {
"1": 0,
"2": 0.86,
"3": 0.92
},
"2": {
"1": 0.86,
"2": 0,
"3": 0.67
},
"3": {
"1": 0.92,
"2": 0.67,
"3": 0
},
}
matrix_d2d = DictArray(twod)
matrix_array, PhyloNode_order = inputs_from_dict_array(matrix_d2d)
self.assertEqual(PhyloNode_order[0].name, "1")
self.assertEqual(PhyloNode_order[2].name, "3")
assert_allclose(matrix_array[0][2], 0.92)
assert_allclose(matrix_array[1][0], 0.86)
def __init__(self, observed, expected=None):
"""Parameters
-------------
observed
a DictArray instance, or something that can be converted to one
expected
provide in the case where you know the prior proportions, otherwise
calculated from marginal frequencies
"""
if not isinstance(observed, DictArray):
observed = DictArray(observed)
if observed.array.sum() == 0:
raise ValueError("at least one value must be > 0")
if expected:
expected = observed.template.wrap(expected)
if observed.array.min() < 0 or expected and expected.array.min() < 0:
raise ValueError("negative values encountered")
if expected:
assert_allclose(observed.array.sum(), expected.array.sum()
), "unequal totals of observed and expected"
self._observed = observed
self._expected = expected
self._residuals = None
self._df = None
self.shape = observed.shape
def test_valid_setitem(self):
"""tabular_result works when set correct item type"""
tr = tabular_result("null")
tr["result"] = make_table(data={"A": [0, 1]})
darr = DictArray({"A": [0, 1]})
tr["result2"] = darr
js = tr.to_json()
self.assertIsInstance(js, str)
def test_direct_construction(self):
"""directly construct a dict array"""
b = DictArrayTemplate("abc", "ABC").wrap(self.a)
data_types = (
[[245, 599]],
dict(a=dict(b=4, c=5)),
{("a", "b"): 4, ("a", "c"): 5},
dict(a=0, b=35, c=45),
b,
)
for data in data_types:
g = DictArray(data)
def upgma(pairwise_distances):
"""Uses the UPGMA algorithm to cluster sequences
pairwise_distances: a dictionary with pair tuples mapped to a distance
returns a PhyloNode object of the UPGMA cluster
"""
darr = DictArray(pairwise_distances)
matrix_a, node_order = inputs_from_dict_array(darr)
tree = UPGMA_cluster(matrix_a, node_order, BIG_NUM)
index = 0
for node in tree.traverse():
if not node.parent:
node.name = "root"
elif not node.name:
node.name = "edge." + str(index)
index += 1
return tree
def __init__(
self,
header=None,
rows=None,
row_order=None,
digits=4,
space=4,
title="",
missing_data="",
max_width=1e100,
row_ids=None,
legend="",
column_templates=None,
dtype=None,
data_frame=None,
format="simple",
):
"""
Parameters
----------
header
column headings
rows
a 2D dict, list or tuple. If a dict, it must have column
headings as top level keys, and common row labels as keys in each
column.
row_order
the order in which rows will be pulled from the twoDdict
digits
floating point resolution
space
number of spaces between columns or a string
title
as implied
missing_data
character assigned if a row has no entry for a column
max_width
maximum column width for printing
row_ids
if True, the 0'th column is used as row identifiers and keys
for slicing.
legend
table legend
column_templates
dict of column headings
or a function that will handle the formatting.
dtype
optional numpy array typecode.
data_frame
pandas DataFrame, Table will be created from this
format
output format when using str(Table)
"""
if data_frame is not None and not _pandas_available:
raise ValueError("data_frame provided when pandas not installed")
elif data_frame is not None:
if rows or header:
warnings.warn("provided rows/header will be over ridden by "
"DataFrame")
rows = data_frame.to_records(index=False).tolist()
header = data_frame.columns.tolist()
if type(header) == numpy.ndarray:
header = header.tolist()
if not header:
raise ValueError("header must be provided to Table")
elif rows is None:
raise ValueError("rows cannot be None")
if len(rows) == 0:
rows = numpy.empty((0, len(header)))
try:
num_cols = len(header)
assert num_cols > 0
if type(rows) == numpy.ndarray:
assert num_cols == rows.shape[1]
elif type(rows) == dict:
assert num_cols == len(rows)
else:
assert num_cols == len(rows[0])
except (IndexError, TypeError, AssertionError):
raise RuntimeError("header and rows must be provided to Table")
header = [str(head) for head in header]
if isinstance(rows, dict):
rows = convert2DDict(rows, header=header, row_order=row_order)
# if row_ids, we select that column as the row identifiers
if row_ids is not None:
identifiers = [row[0] for row in rows]
else:
identifiers = len(rows)
if not dtype:
dtype = "O"
DictArray.__init__(self, rows, identifiers, header, dtype=dtype)
# forcing all column headings to be strings
self._header = _Header([str(head) for head in header])
self._missing_data = missing_data
# default title / legend to be empty strings
self.title = str(title) if title else ""
self.legend = str(legend) if legend else ""
try:
self.space = " " * space
except TypeError:
self.space = space
self._digits = digits
self._row_ids = row_ids
self._max_width = max_width
# some attributes are not preserved in any file format, so always based
# on args
self._column_templates = column_templates or {}
self.format = format
# define the repr() display policy
random = 0
if self.shape[0] < 50:
head = self.shape[0]
tail = None
else:
head, tail = 5, 5
self._repr_policy = dict(head=tail, tail=tail, random=random)
def row_totals(self):
"""returns totalled row values"""
row_sums = self.array.sum(axis=1)
template = DictArray(1, row_sums.shape[0])
return template.wrap(row_sums)
