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148 | class CylindricArray:
def __init__(self, rust_obj: _CylindricArray):
self._rust_obj = rust_obj
def __repr__(self) -> str:
return f"CylindricArray({self._rust_obj.asarray()}, nrise={self.nrise})"
@property
def nrise(self) -> int:
"""The number of rise of the cylindric structure."""
return self._rust_obj.nrise()
def asarray(self, dtype=None) -> NDArray[np.float32]:
"""As a 2D numpy array."""
out = self._rust_obj.asarray()
if dtype is not None:
out = out.astype(dtype, copy=False)
return out
def as1d(self, dtype=None) -> NDArray[np.float32]:
out = self._rust_obj.as1d()
if dtype is not None:
out = out.astype(dtype, copy=False)
return out
def as_series(self, name: str = "", dtype=None) -> pl.Series:
if type(dtype) is type and dtype in pl.DataType:
pl_dtype = dtype
np_dtype = None
else:
pl_dtype = None
np_dtype = dtype
return pl.Series(name, self.as1d(np_dtype), dtype=pl_dtype)
def with_values(self, values: ArrayLike) -> Self:
return CylindricArray(
self._rust_obj.with_values(np.asarray(values, dtype=np.float32))
)
__array__ = asarray
@classmethod
def from_sequences(
cls, nth: ArrayLike, npf: ArrayLike, value: ArrayLike, nrise: int
) -> Self:
nth = np.asarray(nth, dtype=np.int32)
npf = np.asarray(npf, dtype=np.int32)
value = np.asarray(value, dtype=np.float32)
nrise = int(nrise)
return cls(_CylindricArray(nth, npf, value, nrise))
@classmethod
def zeros_like(self, df: pl.DataFrame, nrise: int) -> Self:
nth = df[Mole.nth].to_numpy()
npf = df[Mole.pf].to_numpy()
value = np.zeros(len(df), dtype=np.float32)
return CylindricArray.from_sequences(nth, npf, value, nrise)
@classmethod
def from_dataframe(self, df: pl.DataFrame, target: str, nrise: int) -> Self:
nth = df[Mole.nth].to_numpy()
npf = df[Mole.pf].to_numpy()
value = df[target].to_numpy()
return CylindricArray.from_sequences(nth, npf, value, nrise)
def convolve(self, kernel: ArrayLike) -> Self:
ker = np.asarray(kernel, dtype=np.float32)
return CylindricArray(self._rust_obj.convolve(ker))
def mean_filter(self, kernel: ArrayLike) -> Self:
ker = np.asarray(kernel, dtype=np.bool_)
return CylindricArray(self._rust_obj.mean_filter(ker))
def count_neighbors(self, kernel: ArrayLike) -> Self:
ker = np.asarray(kernel, dtype=np.bool_)
return CylindricArray(self._rust_obj.count_neighbors(ker))
def max_filter(self, kernel: ArrayLike) -> Self:
ker = np.asarray(kernel, dtype=np.bool_)
return CylindricArray(self._rust_obj.max_filter(ker))
def min_filter(self, kernel: ArrayLike) -> Self:
ker = np.asarray(kernel, dtype=np.bool_)
return CylindricArray(self._rust_obj.min_filter(ker))
def median_filter(self, kernel: ArrayLike) -> Self:
ker = np.asarray(kernel, dtype=np.bool_)
return CylindricArray(self._rust_obj.median_filter(ker))
def build_binary_heatmap(self, footprint: ArrayLike) -> NDArray[np.float32]:
footprint = np.asarray(footprint, dtype=np.bool_)
return self._rust_obj.build_binary_heatmap(footprint)
def build_correlation_heatmap(self, footprint: ArrayLike) -> NDArray[np.float32]:
footprint = np.asarray(footprint, dtype=np.bool_)
return self._rust_obj.build_correlation_heatmap(footprint)
def binarize(self, threshold: float) -> Self:
value = self.as1d()
new_value = value >= threshold
return self.with_values(new_value)
def label(self) -> Self:
return CylindricArray(self._rust_obj.label())
def __neg__(self) -> Self:
return self.with_values(-self.as1d())
@staticmethod
def _make_operator(op) -> Callable[[CylindricArray, Any], CylindricArray]:
def _method(self: CylindricArray, value) -> CylindricArray:
if isinstance(value, CylindricArray):
value = value.as1d()
return self.with_values(op(self.as1d(), value))
_method.__name__ = op.__name__
_method.__qualname__ = f"CylindricArray.{op.__name__}"
return _method
__eq__ = _make_operator(operator.__eq__)
__ne__ = _make_operator(operator.__ne__)
__lt__ = _make_operator(operator.__lt__)
__le__ = _make_operator(operator.__le__)
__gt__ = _make_operator(operator.__gt__)
__ge__ = _make_operator(operator.__ge__)
__add__ = _make_operator(operator.__add__)
__sub__ = _make_operator(operator.__sub__)
__mul__ = _make_operator(operator.__mul__)
__truediv__ = _make_operator(operator.__truediv__)
__pow__ = _make_operator(operator.__pow__)
__and__ = _make_operator(operator.__and__)
__or__ = _make_operator(operator.__or__)
__xor__ = _make_operator(operator.__xor__)
|