311 lines
7.6 KiB
Cython
311 lines
7.6 KiB
Cython
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import operator
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from cpython.object cimport (
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Py_EQ,
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Py_GE,
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Py_GT,
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Py_LE,
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Py_LT,
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Py_NE,
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PyObject_RichCompareBool,
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)
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import cython
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from cython import Py_ssize_t
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import numpy as np
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from numpy cimport (
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import_array,
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ndarray,
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uint8_t,
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)
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import_array()
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from pandas._libs.missing cimport checknull
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from pandas._libs.util cimport is_nan
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def scalar_compare(object[:] values, object val, object op) -> ndarray:
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"""
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Compare each element of `values` array with the scalar `val`, with
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the comparison operation described by `op`.
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Parameters
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----------
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values : ndarray[object]
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val : object
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op : {operator.eq, operator.ne,
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operator.le, operator.lt,
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operator.ge, operator.gt}
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Returns
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-------
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result : ndarray[bool]
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"""
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cdef:
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Py_ssize_t i, n = len(values)
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ndarray[uint8_t, cast=True] result
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bint isnull_val
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int flag
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object x
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if op is operator.lt:
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flag = Py_LT
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elif op is operator.le:
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flag = Py_LE
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elif op is operator.gt:
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flag = Py_GT
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elif op is operator.ge:
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flag = Py_GE
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elif op is operator.eq:
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flag = Py_EQ
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elif op is operator.ne:
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flag = Py_NE
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else:
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raise ValueError('Unrecognized operator')
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result = np.empty(n, dtype=bool).view(np.uint8)
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isnull_val = checknull(val)
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if flag == Py_NE:
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for i in range(n):
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x = values[i]
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if checknull(x):
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result[i] = True
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elif isnull_val:
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result[i] = True
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else:
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try:
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result[i] = PyObject_RichCompareBool(x, val, flag)
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except TypeError:
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result[i] = True
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elif flag == Py_EQ:
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for i in range(n):
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x = values[i]
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if checknull(x):
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result[i] = False
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elif isnull_val:
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result[i] = False
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else:
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try:
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result[i] = PyObject_RichCompareBool(x, val, flag)
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except TypeError:
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result[i] = False
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else:
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for i in range(n):
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x = values[i]
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if checknull(x):
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result[i] = False
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elif isnull_val:
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result[i] = False
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else:
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result[i] = PyObject_RichCompareBool(x, val, flag)
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return result.view(bool)
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def vec_compare(ndarray[object] left, ndarray[object] right, object op) -> ndarray:
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"""
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Compare the elements of `left` with the elements of `right` pointwise,
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with the comparison operation described by `op`.
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Parameters
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----------
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left : ndarray[object]
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right : ndarray[object]
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op : {operator.eq, operator.ne,
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operator.le, operator.lt,
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operator.ge, operator.gt}
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Returns
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-------
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result : ndarray[bool]
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"""
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cdef:
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Py_ssize_t i, n = len(left)
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ndarray[uint8_t, cast=True] result
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int flag
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if n != <Py_ssize_t>len(right):
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raise ValueError(f'Arrays were different lengths: {n} vs {len(right)}')
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if op is operator.lt:
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flag = Py_LT
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elif op is operator.le:
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flag = Py_LE
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elif op is operator.gt:
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flag = Py_GT
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elif op is operator.ge:
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flag = Py_GE
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elif op is operator.eq:
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flag = Py_EQ
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elif op is operator.ne:
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flag = Py_NE
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else:
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raise ValueError('Unrecognized operator')
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result = np.empty(n, dtype=bool).view(np.uint8)
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if flag == Py_NE:
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for i in range(n):
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x = left[i]
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y = right[i]
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if checknull(x) or checknull(y):
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result[i] = True
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else:
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result[i] = PyObject_RichCompareBool(x, y, flag)
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else:
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for i in range(n):
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x = left[i]
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y = right[i]
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if checknull(x) or checknull(y):
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result[i] = False
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else:
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result[i] = PyObject_RichCompareBool(x, y, flag)
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return result.view(bool)
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def scalar_binop(object[:] values, object val, object op) -> ndarray:
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"""
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Apply the given binary operator `op` between each element of the array
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`values` and the scalar `val`.
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Parameters
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----------
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values : ndarray[object]
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val : object
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op : binary operator
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Returns
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-------
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result : ndarray[object]
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"""
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cdef:
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Py_ssize_t i, n = len(values)
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object[:] result
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object x
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result = np.empty(n, dtype=object)
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if val is None or is_nan(val):
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result[:] = val
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return result.base # `.base` to access underlying np.ndarray
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for i in range(n):
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x = values[i]
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if x is None or is_nan(x):
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result[i] = x
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else:
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result[i] = op(x, val)
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return maybe_convert_bool(result.base)[0]
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def vec_binop(object[:] left, object[:] right, object op) -> ndarray:
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"""
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Apply the given binary operator `op` pointwise to the elements of
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arrays `left` and `right`.
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Parameters
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----------
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left : ndarray[object]
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right : ndarray[object]
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op : binary operator
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Returns
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-------
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result : ndarray[object]
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"""
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cdef:
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Py_ssize_t i, n = len(left)
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object[:] result
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if n != <Py_ssize_t>len(right):
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raise ValueError(f'Arrays were different lengths: {n} vs {len(right)}')
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result = np.empty(n, dtype=object)
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for i in range(n):
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x = left[i]
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y = right[i]
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try:
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result[i] = op(x, y)
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except TypeError:
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if x is None or is_nan(x):
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result[i] = x
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elif y is None or is_nan(y):
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result[i] = y
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else:
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raise
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return maybe_convert_bool(result.base)[0] # `.base` to access np.ndarray
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def maybe_convert_bool(ndarray[object] arr,
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true_values=None,
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false_values=None,
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convert_to_masked_nullable=False
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) -> tuple[np.ndarray, np.ndarray | None]:
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cdef:
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Py_ssize_t i, n
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ndarray[uint8_t] result
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ndarray[uint8_t] mask
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object val
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set true_vals, false_vals
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bint has_na = False
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n = len(arr)
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result = np.empty(n, dtype=np.uint8)
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mask = np.zeros(n, dtype=np.uint8)
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# the defaults
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true_vals = {'True', 'TRUE', 'true'}
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false_vals = {'False', 'FALSE', 'false'}
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if true_values is not None:
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true_vals = true_vals | set(true_values)
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if false_values is not None:
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false_vals = false_vals | set(false_values)
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for i in range(n):
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val = arr[i]
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if isinstance(val, bool):
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if val is True:
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result[i] = 1
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else:
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result[i] = 0
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elif val in true_vals:
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result[i] = 1
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elif val in false_vals:
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result[i] = 0
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elif is_nan(val):
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mask[i] = 1
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result[i] = 0 # Value here doesn't matter, will be replaced w/ nan
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has_na = True
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else:
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return (arr, None)
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if has_na:
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if convert_to_masked_nullable:
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return (result.view(np.bool_), mask.view(np.bool_))
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else:
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arr = result.view(np.bool_).astype(object)
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np.putmask(arr, mask, np.nan)
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return (arr, None)
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else:
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return (result.view(np.bool_), None)
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