# -*- coding: utf-8 -*-
"""Selection Functions.
Examples
--------
do some examples here
"""
__author__ = "Nathaniel Starkman"
__all__ = [
"inRange",
"outRange",
"ioRange",
"ellipse",
"circle",
]
#############################################################################
# IMPORTS
# BUILT-IN
import typing as T
# THIRD PARTY
import numpy as np
# PROJECT-SPECIFIC
from .decorators import idxDecorator
from utilipy.utils.typing import EllipsisType
#############################################################################
# Functions
def _inRange(
x: np.array, rng: T.Sequence, lbi: bool = True, ubi: bool = False
) -> np.array:
"""`inRange` helper function.
Parameters
----------
x : array_like
the array on which to test for membership in the range
supports multiple dimension
rng : list
the range (lower, upper)
when applied to ND array, must match the number of rows
ex : for x [NxM], rng must be [Nx2]
lbi : bool
(default True)
Lower Bound Inclusive, whether to be inclusive on the lower bound
ubi : bool
(default False)
Upper Bound Inclusive, whether to be inclusive on the upper bound
Returns
-------
idx: bool array
bool index array
shape matches `x`
Examples
--------
>>> x = np.array([[ 0, 1],
... [10, 11]])
>>> rng = [[0, 3], [9, 11]]
>>> _inRange(x, rng) # doctest: +SKIP
array([[ True, True],
[ True, False]])
"""
if len(x.shape) == 1: # 1D
if lbi and ubi: # both true
return (rng[0] <= x) & (x <= rng[1])
elif lbi: # only lbi is true
return (rng[0] <= x) & (x < rng[1])
elif ubi: # only ubi is true
return (rng[0] < x) & (x <= rng[1])
else: # neither true
return (rng[0] < x) & (x < rng[1])
else: # ND
# iterate over rows
out = np.zeros_like(x, dtype=bool)
for i, (row, lu) in enumerate(zip(x, rng)):
out[i, :] = _inRange(row, lu, lbi=lbi, ubi=ubi)
return out
# /def
# -----------------------------------------------------------------------------
[docs]@idxDecorator(_doc_style="numpy")
def inRange(
*args: T.Union[np.array, T.Sequence],
rng: T.Union[T.Sequence, EllipsisType] = Ellipsis,
lbi: bool = True,
ubi: bool = False
) -> np.array:
"""Multidimensional box selection.
Parameters
----------
args : Sequence
list of values along each dimension
must be the same length
can be same-shaped ND arrays, each treated as a series of rows.
rng : Sequence or Ellipsis, optional
(default Ellipsis)
the domain for each argument in `args`::
args = [[x1], [x2], ...]
rng = [1st [lower, upper],
2nd [lower, upper],
...]
if each 'xn' is multidimensional
lbi : bool, optional
(default True)
Lower Bound Inclusive, whether to be inclusive on the lower bound
ubi : bool, optional
(default False)
Upper Bound Inclusive, whether to be inclusive on the upper bound
as_ind : bool, optional
(default False)
whether to return bool array or the indices (where(bool array == True))
sets the default behavior for the wrapped fnction *func*
Returns
-------
inrange : bool ndarray
boolean array to select values in box selection
See Also
--------
outRange : multidimensional box exclusion
ioRange : `inRange` and `outRange` combined
Examples
--------
list of args:
>>> x = np.arange(5)
>>> y = np.arange(5) + 10
>>> inRange(x, y, rng=[[0, 3], [10, 15]]) # doctest: +SKIP
array([ True, True, True, False, False])
multidimensional arg:
>>> x = np.array([[ 0, 1], [10, 11]])
>>> rng = [[0, 3], [9, 11]]
>>> inRange(x, rng=rng) # doctest: +SKIP
array([[ True, True],
[ True, False]])
.. todo::
allow lbi & rbi to be lists, matching args, for individual adjustment
"""
# if only one arg
if len(args) == 1:
rng = (rng,)
rowbool = np.array(
[_inRange(v, lu, lbi=lbi, ubi=ubi) for v, lu in zip(args, rng)]
)
numtrues = len(args)
# now getting where all the dimensions are inside the bounds
# collapses column to 1 row
# check where all rows=True for each collapsed column
allbool = rowbool.sum(axis=0)
inrange = allbool == numtrues
return inrange
# /def
# -----------------------------------------------------------------------------
[docs]@idxDecorator(_doc_style="numpy")
def outRange(
*args: T.Union[np.array, T.Sequence],
rng: T.Union[T.Sequence, EllipsisType] = Ellipsis,
lbi: bool = True,
ubi: bool = False
) -> np.array:
"""Multidimensional box exclusion.
equivelent to ~inRange
Parameters
----------
args : Sequence
either list of values along each dimension or list of values & bounds
the input type depends on `rng`
rng : Ellipsis or Sequence, optional
(default Ellipsis)
if rng is not Ellipsis::
args = [[x1], [x2], ...]
rng = [1st [lower, upper],
2nd [lower, upper],
...]
else, args are the list of (x, [lower bound, upper. bound])
lbi : bool, optional
(default False)
Lower Bound Inclusive, whether to be inclusive on the lower bound
ubi : bool, optional
(default True)
Upper Bound Inclusive, whether to be inclusive on the upper bound
as_ind : bool, optional
(default False)
whether to return bool array or the indices (where(bool array == True))
sets the default behavior for the wrapped fnction *func*
Returns
-------
outrange : ndarray of bool or ndarray of int
boolean array to select values outside box selection
if as_ind, then index array of same
.. todo::
allow `lbi` & `rbi` to be lists, matching `args`, for indiv adjustment
"""
return ~inRange(*args, rng=rng, lbi=lbi, ubi=ubi)
# /def
# -----------------------------------------------------------------------------
[docs]@idxDecorator(_doc_style="numpy")
def ioRange(
incl: T.Union[None, tuple, np.array] = None,
excl: T.Union[None, tuple, np.array] = None,
rng: T.Union[T.Sequence, EllipsisType] = Ellipsis,
) -> np.array:
"""Supports inRange and outRange.
Parameters
----------
incl : array_like, optional
args into `inRange`
must be a tuple if many args, not a tuple else
excl : array_like, optional
args into `outRange`
must be a tuple if many args, not a tuple else
rng : Sequence or Ellipsis, optional
concatenated list of (in)outRange rng
must be in orgder of [inRange rng, outRange rng]
as_ind : bool
(default False)
whether to return bool array or the indices (``where(bool array == True``))
sets the default behavior for the wrapped function
Returns
-------
out : ndarray of bool or ndarray of ints
boolean array to select values inside / outside box selection
if as_ind, then index array of same
"""
# Nothing passed. Raise error
if (incl is None) & (excl is None):
raise ValueError("incl and excl are None")
# Only inclusion passed
elif excl is None:
if isinstance(incl, tuple):
out = inRange(*incl, rng=rng)
else:
out = inRange(incl, rng=rng)
# Only exclusion passed
elif incl is None:
if isinstance(excl, tuple):
out = outRange(*excl, rng=rng)
else:
out = outRange(excl, rng=rng)
# Both inclusion and exclusion
else:
if isinstance(incl, tuple):
inclrng = rng[: len(incl)] if rng is not ... else ...
out = inRange(*incl, rng=inclrng)
else:
inclrng = rng[: np.shape(incl)[0] - 1] if rng is not ... else ...
if len(inclrng) == 1:
inclrng = inclrng[0]
out = inRange(incl, rng=inclrng)
if isinstance(excl, tuple):
exclrng = rng[len(excl) :] if rng is not ... else ...
out &= outRange(*excl, rng=exclrng)
else:
exclrng = rng[np.shape(excl)[0] - 1 :] if rng is not ... else ...
if len(exclrng) == 1:
exclrng = exclrng[0]
out &= outRange(excl, rng=exclrng)
return out
# /def
# -----------------------------------------------------------------------------
[docs]@idxDecorator(_doc_style="numpy")
def ellipse(
*x: T.Union[np.array, T.Sequence],
x0: T.Union[float, T.Sequence] = 0.0,
dx: T.Union[float, T.Sequence] = 1.0
) -> np.array:
r"""Elliptical selection of data in many dimensions.
Supports selection with variable center and radius::
np.sum((x[i] - x0[i]) / dx[i])^2) < 1^2
Parameters
----------
x: m x (n, 1) array_like
values along each dimension
x0: scalar or (m, 1) array, optional
(default = 0.)
the center position of each x.
can broadcast a scalar to apply to all
dx: scalar or (m, 1) array, optional
(default = 0.)
the radius in each dimension
Returns
-------
sel: array_like of bool
bool array selecting data w/in ellipse
if as_ind is True, then array_like of indices
"""
shape = (len(x[0]), len(x))
x0 = np.broadcast_to(x0, shape).T # reshape x0 correctly
dx = np.broadcast_to(dx, shape).T # reshape x0 correctly
arr = np.divide(np.subtract(x, x0), dx)
return np.sqrt(np.sum(np.square(arr), axis=0)) < 1
# /def
# -----------------------------------------------------------------------------
[docs]@idxDecorator(_doc_style="numpy")
def circle(
*x: T.Union[np.array, T.Sequence],
x0: T.Union[float, T.Sequence] = 0.0,
radius: T.Union[float, T.Sequence] = 1.0
) -> np.array:
"""Circular selection of data in many dimensions.
Elliptical selection with fixed radius::
np.sum((x[i] - x0[i]) / radius)^2) < 1^2
where the summation is over the dimensions.
Parameters
----------
x : array_like
shape (m x (n, 1))
values along each dimension
x0 : array_like, optional
scalar or (m, 1) array
the center position of each x, [default 0.]
can broadcast a scalar to apply to all
radius : float, optional
the radius
Returns
-------
sel : array_like of bool
bool array selecting data within circle
if as_ind is True, then array_like of indices
"""
return ellipse(*x, x0=x0, dx=radius)
# /def
#############################################################################
# END