def test_condition_pass(self):
matrix_type = MatrixDfBuilder("T").verify(
lambda d: len(d) == 2).build()
df = pd.DataFrame([[11, float("NaN")], [21, 22]],
columns=["b", "a"],
index=["b", "a"])
matrix_type.convert(df)
def test_condition_fail(self):
matrix_type = MatrixDfBuilder("T").verify(
lambda d: len(d) == 4).build()
df = pd.DataFrame([[11, float("NaN")], [21, 22]],
columns=["b", "a"],
index=["b", "a"])
with pytest.raises(VerificationFailedError):
matrix_type.convert(df)
def test_matrix_bad_inf(self):
matrix_type = MatrixDfBuilder("T").dtype(np.int32).build()
df = pd.DataFrame([[11, float("inf")], [21, 22]],
columns=["b", "a"],
index=["b", "a"])
matrix_type(df)
with pytest.raises(IntCastingNaNError):
matrix_type.convert(df)
def test_io(self):
matrix_type = MatrixDfBuilder("T").build()
df = pd.DataFrame([[11, 12], [21, 22]],
columns=["b", "a"],
index=["b", "a"])
df = matrix_type.convert(df)
for s in [".feather", ".snappy", ".csv.gz", ".tsv"]:
with tmpfile(s) as path:
df.write_file(path)
df2 = matrix_type.read_file(path)
assert df2.flatten().tolist() == [11, 12, 21, 22]
def test_shuffle(self):
matrix_type = MatrixDfBuilder("T").build()
df: MatrixDf = matrix_type.of([[11, 12], [21, 22]],
columns=["b", "a"],
index=["b", "a"])
for _ in range(10):
df2 = df.shuffle(rand=0)
assert df2.values.tolist() == [[21, 22], [12, 11]]
df2 = df.shuffle(rand=1)
assert df2.values.tolist() == [[22, 21], [11, 12]]
df2 = df.shuffle(rand=None)
assert len(df2.values.tolist()) == 2
def test_matrix_dtype(self):
matrix_type = (MatrixDfBuilder("T").dtype(np.float16)).build()
df = pd.DataFrame([[11, 12], [21, 22]],
columns=["b", "a"],
index=["b", "a"])
df = matrix_type.convert(df)
assert isinstance(df, MatrixDf)
assert df.dtypes.tolist() == [np.float16, np.float16]
def matrix(cls, name: str, doc: Optional[str] = None) -> MatrixDfBuilder:
"""
Creates a new subclass of an :class:`typeddfs.matrix_dfs.MatrixDf`.
Args:
name: The name that will be used for the new class
doc: The docstring for the new class
Returns:
A builder instance (builder pattern) to be used with chained calls
"""
return MatrixDfBuilder(name, doc)
def test_matrix(self):
matrix_type = MatrixDfBuilder("T").build()
df = pd.DataFrame([[11, 12], [21, 22]],
columns=["b", "a"],
index=["b", "a"])
df = matrix_type.convert(df)
assert isinstance(df, MatrixDf)
assert len(df) == 2
assert df.cols == ["b", "a"]
assert df.rows == ["b", "a"]
assert not df.is_symmetric()
ltr = df.triangle()
assert [str(x) for x in ltr.flatten().tolist()
] == [str(x) for x in [11.0, np.nan, 21.0, 22.0]]
srt = df.sort_alphabetical()
assert srt.cols == ["a", "b"]
assert srt.rows == ["a", "b"]
long = df.long_form()
assert long.columns.tolist() == ["row", "column", "value"]
assert long["value"].map(str).tolist() == [
str(x) for x in [11, 12, 21, 22]
]
def test_matrix_subclass(self):
t1 = MatrixDfBuilder("t1").build()
t2 = MatrixDfBuilder("t2").subclass(t1).build()
assert issubclass(t2, t1)
assert not issubclass(t1, t2)