# ***************************************************************************** # Copyright (c) 2020, Intel Corporation All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; # OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR # OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, # EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # ***************************************************************************** """ | This file contains function templates used by the auto-generation script """ # below imports are copied into the auto-generated source file as-is # for the auto-generation script to work ensure they are not mixed up with code import numba import numpy import operator import pandas from numba.core.errors import TypingError from numba import types from sdc.utilities.sdc_typing_utils import (TypeChecker, check_index_is_numeric, check_types_comparable, find_common_dtype_from_numpy_dtypes) from sdc.datatypes.common_functions import (sdc_join_series_indexes, ) from sdc.hiframes.pd_series_type import SeriesType from sdc.str_arr_ext import (string_array_type, str_arr_is_na) from sdc.utilities.utils import sdc_overload, sdc_overload_method from sdc.functions import numpy_like from sdc.datatypes.range_index_type import RangeIndexType def sdc_pandas_series_binop(self, other, level=None, fill_value=None, axis=0): """ Intel Scalable Dataframe Compiler User Guide ******************************************** Pandas API: pandas.Series.binop Limitations ----------- Parameters ``level`` and ``axis`` are currently unsupported by Intel Scalable Dataframe Compiler Examples -------- .. literalinclude:: ../../../examples/series/series_binop.py :language: python :lines: 27- :caption: :name: ex_series_binop .. command-output:: python ./series/series_binop.py :cwd: ../../../examples Intel Scalable Dataframe Compiler Developer Guide ************************************************* Pandas Series method :meth:`pandas.Series.binop` implementation. .. only:: developer Test: python -m sdc.runtests sdc.tests.test_series.TestSeries.test_series_op5 """ _func_name = 'Method binop().' ty_checker = TypeChecker(_func_name) self_is_series, other_is_series = isinstance(self, SeriesType), isinstance(other, SeriesType) if not (self_is_series or other_is_series): return None # this overload is not for string series self_is_string_series = self_is_series and isinstance(self.dtype, types.UnicodeType) other_is_string_series = other_is_series and isinstance(other.dtype, types.UnicodeType) if self_is_string_series or other_is_string_series: return None if not isinstance(self, (SeriesType, types.Number)): ty_checker.raise_exc(self, 'pandas.series or scalar', 'self') if not isinstance(other, (SeriesType, types.Number)): ty_checker.raise_exc(other, 'pandas.series or scalar', 'other') operands_are_series = self_is_series and other_is_series if operands_are_series: none_or_numeric_indexes = ((isinstance(self.index, types.NoneType) or check_index_is_numeric(self)) and (isinstance(other.index, types.NoneType) or check_index_is_numeric(other))) series_indexes_comparable = check_types_comparable(self.index, other.index) or none_or_numeric_indexes if not series_indexes_comparable: raise TypingError('{} Not implemented for series with not-comparable indexes. \ Given: self.index={}, other.index={}'.format(_func_name, self.index, other.index)) series_data_comparable = check_types_comparable(self, other) if not series_data_comparable: raise TypingError('{} Not supported for not-comparable operands. \ Given: self={}, other={}'.format(_func_name, self, other)) if not isinstance(level, types.Omitted) and level is not None: ty_checker.raise_exc(level, 'None', 'level') if not isinstance(fill_value, (types.Omitted, types.Number, types.NoneType)) and fill_value is not None: ty_checker.raise_exc(fill_value, 'number', 'fill_value') fill_value_is_none = isinstance(fill_value, (types.NoneType, types.Omitted)) or fill_value is None if not isinstance(axis, types.Omitted) and axis != 0: ty_checker.raise_exc(axis, 'int', 'axis') # specializations for numeric series only if not operands_are_series: def _series_binop_scalar_impl(self, other, level=None, fill_value=None, axis=0): if self_is_series == True: # noqa numpy_like.fillna(self._data, inplace=True, value=fill_value) result_data = numpy.empty(len(self._data), dtype=numpy.float64) result_data[:] = self._data + numpy.float64(other) return pandas.Series(result_data, index=self._index, name=self._name) else: numpy_like.fillna(other._data, inplace=True, value=fill_value) result_data = numpy.empty(len(other._data), dtype=numpy.float64) result_data[:] = numpy.float64(self) + other._data return pandas.Series(result_data, index=other._index, name=other._name) return _series_binop_scalar_impl else: # both operands are numeric series # optimization for series with default indexes, that can be aligned differently if (isinstance(self.index, types.NoneType) and isinstance(other.index, types.NoneType)): def _series_binop_none_indexes_impl(self, other, level=None, fill_value=None, axis=0): numpy_like.fillna(self._data, inplace=True, value=fill_value) numpy_like.fillna(other._data, inplace=True, value=fill_value) if (len(self._data) == len(other._data)): result_data = numpy_like.astype(self._data, numpy.float64) result_data = result_data + other._data return pandas.Series(result_data) else: left_size, right_size = len(self._data), len(other._data) min_data_size = min(left_size, right_size) max_data_size = max(left_size, right_size) result_data = numpy.empty(max_data_size, dtype=numpy.float64) _fill_value = numpy.nan if fill_value_is_none == True else fill_value # noqa if (left_size == min_data_size): result_data[:min_data_size] = self._data for i in range(min_data_size, len(result_data)): result_data[i] = _fill_value result_data = result_data + other._data else: result_data[:min_data_size] = other._data for i in range(min_data_size, len(result_data)): result_data[i] = _fill_value result_data = self._data + result_data return pandas.Series(result_data) return _series_binop_none_indexes_impl else: left_index_is_range = isinstance(self.index, (RangeIndexType, types.NoneType)) right_index_is_range = isinstance(other.index, (RangeIndexType, types.NoneType)) check_index_equal = left_index_is_range and right_index_is_range self_index_dtype = RangeIndexType.dtype if isinstance(self.index, types.NoneType) else self.index.dtype other_index_dtype = RangeIndexType.dtype if isinstance(other.index, types.NoneType) else other.index.dtype index_dtypes_match = self_index_dtype == other_index_dtype if not index_dtypes_match: numba_index_common_dtype = find_common_dtype_from_numpy_dtypes( [self_index_dtype, other_index_dtype], []) else: numba_index_common_dtype = self_index_dtype def _series_binop_common_impl(self, other, level=None, fill_value=None, axis=0): left_index, right_index = self.index, other.index numpy_like.fillna(self._data, inplace=True, value=fill_value) numpy_like.fillna(other._data, inplace=True, value=fill_value) if check_index_equal == True: # noqa equal_indexes = numpy_like.array_equal(left_index, right_index) else: equal_indexes = False if (left_index is right_index or equal_indexes): result_data = numpy.empty(len(self._data), dtype=numpy.float64) result_data[:] = self._data + other._data if index_dtypes_match == False: # noqa result_index = numpy_like.astype(left_index, numba_index_common_dtype) else: result_index = left_index.values if left_index_is_range == True else left_index # noqa return pandas.Series(result_data, index=result_index) # TODO: replace below with core join(how='outer', return_indexers=True) when implemented joined_index, left_indexer, right_indexer = sdc_join_series_indexes(left_index, right_index) result_size = len(joined_index) left_values = numpy.empty(result_size, dtype=numpy.float64) right_values = numpy.empty(result_size, dtype=numpy.float64) _fill_value = numpy.nan if fill_value_is_none == True else fill_value # noqa for i in range(result_size): left_pos, right_pos = left_indexer[i], right_indexer[i] left_values[i] = self._data[left_pos] if left_pos != -1 else _fill_value right_values[i] = other._data[right_pos] if right_pos != -1 else _fill_value result_data = left_values + right_values return pandas.Series(result_data, joined_index) return _series_binop_common_impl return None def sdc_pandas_series_comp_binop(self, other, level=None, fill_value=None, axis=0): """ Intel Scalable Dataframe Compiler User Guide ******************************************** Pandas API: pandas.Series.comp_binop Limitations ----------- Parameters ``level`` and ``axis`` are currently unsupported by Intel Scalable Dataframe Compiler Examples -------- .. literalinclude:: ../../../examples/series/series_comp_binop.py :language: python :lines: 27- :caption: :name: ex_series_comp_binop .. command-output:: python ./series/series_comp_binop.py :cwd: ../../../examples Intel Scalable Dataframe Compiler Developer Guide ************************************************* Pandas Series method :meth:`pandas.Series.comp_binop` implementation. .. only:: developer Test: python -m sdc.runtests -k sdc.tests.test_series.TestSeries.test_series_op8 """ _func_name = 'Method comp_binop().' ty_checker = TypeChecker(_func_name) ty_checker.check(self, SeriesType) if not (isinstance(level, types.Omitted) or level is None): ty_checker.raise_exc(level, 'None', 'level') if not isinstance(fill_value, (types.Omitted, types.Number, types.NoneType)) and fill_value is not None: ty_checker.raise_exc(fill_value, 'number', 'fill_value') if not (isinstance(axis, types.Omitted) or axis == 0): ty_checker.raise_exc(axis, 'int', 'axis') self_is_series, other_is_series = isinstance(self, SeriesType), isinstance(other, SeriesType) if not (self_is_series or other_is_series): return None if not isinstance(self, (SeriesType, types.Number, types.UnicodeType)): ty_checker.raise_exc(self, 'pandas.series or scalar', 'self') if not isinstance(other, (SeriesType, types.Number, types.UnicodeType)): ty_checker.raise_exc(other, 'pandas.series or scalar', 'other') operands_are_series = self_is_series and other_is_series if operands_are_series: none_or_numeric_indexes = ((isinstance(self.index, types.NoneType) or check_index_is_numeric(self)) and (isinstance(other.index, types.NoneType) or check_index_is_numeric(other))) series_indexes_comparable = check_types_comparable(self.index, other.index) or none_or_numeric_indexes if not series_indexes_comparable: raise TypingError('{} Not implemented for series with not-comparable indexes. \ Given: self.index={}, other.index={}'.format(_func_name, self.index, other.index)) series_data_comparable = check_types_comparable(self, other) if not series_data_comparable: raise TypingError('{} Not supported for not-comparable operands. \ Given: self={}, other={}'.format(_func_name, self, other)) fill_value_is_none = isinstance(fill_value, (types.NoneType, types.Omitted)) or fill_value is None if not operands_are_series: def _series_comp_binop_scalar_impl(self, other, level=None, fill_value=None, axis=0): if self_is_series == True: # noqa numpy_like.fillna(self._data, inplace=True, value=fill_value) return pandas.Series(self._data < other, index=self._index, name=self._name) else: numpy_like.fillna(other._data, inplace=True, value=fill_value) return pandas.Series(self < other._data, index=other._index, name=other._name) return _series_comp_binop_scalar_impl else: # optimization for series with default indexes, that can be aligned differently if (isinstance(self.index, types.NoneType) and isinstance(other.index, types.NoneType)): def _series_comp_binop_none_indexes_impl(self, other, level=None, fill_value=None, axis=0): numpy_like.fillna(self._data, inplace=True, value=fill_value) numpy_like.fillna(other._data, inplace=True, value=fill_value) left_size, right_size = len(self._data), len(other._data) if (left_size == right_size): return pandas.Series(self._data < other._data) else: raise ValueError("Can only compare identically-labeled Series objects") return _series_comp_binop_none_indexes_impl else: left_index_is_range = isinstance(self.index, (RangeIndexType, types.NoneType)) index_dtypes_match = self.index.dtype == other.index.dtype if not index_dtypes_match: numba_index_common_dtype = find_common_dtype_from_numpy_dtypes( [self.index.dtype, other.index.dtype], []) else: numba_index_common_dtype = self.index.dtype def _series_comp_binop_common_impl(self, other, level=None, fill_value=None, axis=0): numpy_like.fillna(self._data, inplace=True, value=fill_value) numpy_like.fillna(other._data, inplace=True, value=fill_value) left_index, right_index = self.index, other.index if (left_index is right_index or numpy_like.array_equal(left_index, right_index)): if index_dtypes_match == False: # noqa new_index = numpy_like.astype(left_index, numba_index_common_dtype) else: new_index = left_index.values if left_index_is_range == True else left_index # noqa return pandas.Series(self._data < other._data, new_index) else: raise ValueError("Can only compare identically-labeled Series objects") return _series_comp_binop_common_impl return None def sdc_pandas_series_operator_binop(self, other): """ Pandas Series operator :attr:`pandas.Series.binop` implementation Note: Currently implemented for numeric Series only. Differs from Pandas in returning Series with fixed dtype :obj:`float64` .. only:: developer **Test**: python -m sdc.runtests -k sdc.tests.test_series.TestSeries.test_series_op1* python -m sdc.runtests -k sdc.tests.test_series.TestSeries.test_series_op2* python -m sdc.runtests -k sdc.tests.test_series.TestSeries.test_series_operator_binop* Parameters ---------- series: :obj:`pandas.Series` Input series other: :obj:`pandas.Series` or :obj:`scalar` Series or scalar value to be used as a second argument of binary operation Returns ------- :obj:`pandas.Series` The result of the operation """ _func_name = 'Operator binop().' ty_checker = TypeChecker(_func_name) self_is_series, other_is_series = isinstance(self, SeriesType), isinstance(other, SeriesType) if not (self_is_series or other_is_series): return None # this overload is not for string series self_is_string_series = self_is_series and isinstance(self.dtype, types.UnicodeType) other_is_string_series = other_is_series and isinstance(other.dtype, types.UnicodeType) if self_is_string_series or other_is_string_series: return None if not isinstance(self, (SeriesType, types.Number)): ty_checker.raise_exc(self, 'pandas.series or scalar', 'self') if not isinstance(other, (SeriesType, types.Number)): ty_checker.raise_exc(other, 'pandas.series or scalar', 'other') operands_are_series = self_is_series and other_is_series if operands_are_series: none_or_numeric_indexes = ((isinstance(self.index, types.NoneType) or check_index_is_numeric(self)) and (isinstance(other.index, types.NoneType) or check_index_is_numeric(other))) series_indexes_comparable = check_types_comparable(self.index, other.index) or none_or_numeric_indexes if not series_indexes_comparable: raise TypingError('{} Not implemented for series with not-comparable indexes. \ Given: self.index={}, other.index={}'.format(_func_name, self.index, other.index)) series_data_comparable = check_types_comparable(self, other) if not series_data_comparable: raise TypingError('{} Not supported for not-comparable operands. \ Given: self={}, other={}'.format(_func_name, self, other)) def sdc_pandas_series_operator_binop_impl(self, other): return self.binop(other) return sdc_pandas_series_operator_binop_impl def sdc_pandas_series_operator_comp_binop(self, other): """ Pandas Series operator :attr:`pandas.Series.comp_binop` implementation .. only:: developer **Test**: python -m sdc.runtests -k sdc.tests.test_series.TestSeries.test_series_op7* python -m sdc.runtests -k sdc.tests.test_series.TestSeries.test_series_operator_comp_binop* Parameters ---------- series: :obj:`pandas.Series` Input series other: :obj:`pandas.Series` or :obj:`scalar` Series or scalar value to be used as a second argument of binary operation Returns ------- :obj:`pandas.Series` The result of the operation """ _func_name = 'Operator comp_binop().' ty_checker = TypeChecker(_func_name) self_is_series, other_is_series = isinstance(self, SeriesType), isinstance(other, SeriesType) if not (self_is_series or other_is_series): return None if not isinstance(self, (SeriesType, types.Number, types.UnicodeType)): ty_checker.raise_exc(self, 'pandas.series or scalar', 'self') if not isinstance(other, (SeriesType, types.Number, types.UnicodeType)): ty_checker.raise_exc(other, 'pandas.series or scalar', 'other') operands_are_series = self_is_series and other_is_series if operands_are_series: none_or_numeric_indexes = ((isinstance(self.index, types.NoneType) or check_index_is_numeric(self)) and (isinstance(other.index, types.NoneType) or check_index_is_numeric(other))) series_indexes_comparable = check_types_comparable(self.index, other.index) or none_or_numeric_indexes if not series_indexes_comparable: raise TypingError('{} Not implemented for series with not-comparable indexes. \ Given: self.index={}, other.index={}'.format(_func_name, self.index, other.index)) series_data_comparable = check_types_comparable(self, other) if not series_data_comparable: raise TypingError('{} Not supported for not-comparable operands. \ Given: self={}, other={}'.format(_func_name, self, other)) def sdc_pandas_series_operator_comp_binop_impl(self, other): return self.comp_binop(other) return sdc_pandas_series_operator_comp_binop_impl def sdc_str_arr_operator_comp_binop(self, other): self_is_str_arr = self == string_array_type other_is_str_arr = other == string_array_type operands_are_arrays = self_is_str_arr and other_is_str_arr if not (operands_are_arrays or (self_is_str_arr and isinstance(other, types.UnicodeType)) or (isinstance(self, types.UnicodeType) and other_is_str_arr)): return None if operands_are_arrays: def _sdc_str_arr_operator_comp_binop_impl(self, other): if len(self) != len(other): raise ValueError("Mismatch of String Arrays sizes in operator.comp_binop") n = len(self) out_list = [False] * n for i in numba.prange(n): out_list[i] = (self[i] < other[i] and not (str_arr_is_na(self, i) or str_arr_is_na(other, i))) return out_list elif self_is_str_arr: def _sdc_str_arr_operator_comp_binop_impl(self, other): n = len(self) out_list = [False] * n for i in numba.prange(n): out_list[i] = (self[i] < other and not (str_arr_is_na(self, i))) return out_list elif other_is_str_arr: def _sdc_str_arr_operator_comp_binop_impl(self, other): n = len(other) out_list = [False] * n for i in numba.prange(n): out_list[i] = (self < other[i] and not (str_arr_is_na(other, i))) return out_list else: return None return _sdc_str_arr_operator_comp_binop_impl