reduction.pyx

from copy import copy

from cython import Py_ssize_t
from cpython.ref cimport Py_INCREF

from libc.stdlib cimport malloc, free

import numpy as np
cimport numpy as cnp
from numpy cimport (ndarray,
                    int64_t,
                    PyArray_SETITEM)

cnp.import_array()

cimport pandas._libs.util as util
from pandas._libs.lib import maybe_convert_objects, is_scalar


cdef _check_result_array(object obj, Py_ssize_t cnt):

    if (util.is_array(obj) or
            (isinstance(obj, list) and len(obj) == cnt) or
            getattr(obj, 'shape', None) == (cnt,)):
        raise ValueError('Function does not reduce')


cdef class Reducer:
    """
    Performs generic reduction operation on a C or Fortran-contiguous ndarray
    while avoiding ndarray construction overhead
    """
    cdef:
        Py_ssize_t increment, chunksize, nresults
        object dummy, f, labels, typ, ityp, index
        ndarray arr

    def __init__(
        self, ndarray arr, object f, int axis=1, object dummy=None, object labels=None
    ):
        cdef:
            Py_ssize_t n, k

        n, k = (<object>arr).shape

        if axis == 0:
            self.nresults = k
            self.chunksize = n
            self.increment = n * arr.dtype.itemsize
        else:
            arr = arr.T
            self.nresults = n
            self.chunksize = k
            self.increment = k * arr.dtype.itemsize

        self.f = f
        self.arr = arr
        self.labels = labels

        # TODO: do we still need this?
        self._check_dummy(dummy=dummy)

    cdef _check_dummy(self, object dummy=None):
        if dummy is not None:
            if dummy.dtype != self.arr.dtype:
                raise ValueError('Dummy array must be same dtype')
            if len(dummy) != self.chunksize:
                raise ValueError(f'Dummy array must be length {self.chunksize}')


    def get_result(self):
        cdef:
            ndarray arr, result
            Py_ssize_t i
            object res, name, labels
            object cached_typ = None

        arr = self.arr
        labels = self.labels

        result = np.empty(self.nresults, dtype='O')

        with np.nditer([arr, result], flags=["reduce_ok", "external_loop", "refs_ok"], op_flags=[["readonly"], ["readwrite"]], order="F") as it: 
            for i, (x, y) in enumerate(it):
                if i == 0:

                    if self.typ is not None:
                        # In this case, we also have self.index
                        name = labels[i]
                        cached_typ = self.typ(
                            x, index=self.index, name=name, dtype=arr.dtype)

                # use the cached_typ if possible
                if cached_typ is not None:
                    # In this case, we also have non-None labels
                    name = labels[i]

                    object.__setattr__(
                        cached_typ._mgr._block, 'values', x)
                    object.__setattr__(cached_typ, 'name', name)
                    res = self.f(cached_typ)
                else:
                    res = self.f(x)

                # TODO: reason for not squeezing here?
                res = _extract_result(res, squeeze=False)
                if i == 0:
                    # On the first pass, we check the output shape to see
                    #  if this looks like a reduction.
                    _check_result_array(res, len(x))

                y[...] = res

        result = maybe_convert_objects(result)
        return result


cdef class _BaseGrouper:
    cdef _check_dummy(self, object dummy):
        # both values and index must be an ndarray!

        values = dummy.values
        # GH 23683: datetimetz types are equivalent to datetime types here
        if (dummy.dtype != self.arr.dtype
                and values.dtype != self.arr.dtype):
            raise ValueError('Dummy array must be same dtype')
        if util.is_array(values) and not values.flags.contiguous:
            # e.g. Categorical has no `flags` attribute
            values = values.copy()
        index = dummy.index.values
        if not index.flags.contiguous:
            index = index.copy()

        return values, index

    cdef inline _update_cached_objs(self, object cached_typ, object cached_ityp,
                                    Slider islider, Slider vslider):
        if cached_typ is None:
            cached_ityp = self.ityp(islider.buf)
            cached_typ = self.typ(vslider.buf, index=cached_ityp, name=self.name)
        else:
            # See the comment in indexes/base.py about _index_data.
            # We need this for EA-backed indexes that have a reference
            # to a 1-d ndarray like datetime / timedelta / period.
            object.__setattr__(cached_ityp, '_index_data', islider.buf)
            cached_ityp._engine.clear_mapping()
            object.__setattr__(cached_typ._mgr._block, 'values', vslider.buf)
            object.__setattr__(cached_typ._mgr._block, 'mgr_locs',
                               slice(len(vslider.buf)))
            object.__setattr__(cached_typ, '_index', cached_ityp)
            object.__setattr__(cached_typ, 'name', self.name)

        return cached_typ, cached_ityp

    cdef inline object _apply_to_group(self,
                                       object cached_typ, object cached_ityp,
                                       Slider islider, Slider vslider,
                                       Py_ssize_t group_size, bint initialized):
        """
        Call self.f on our new group, then update to the next group.
        """
        cdef:
            object res

        cached_ityp._engine.clear_mapping()
        res = self.f(cached_typ)
        res = _extract_result(res)
        if not initialized:
            # On the first pass, we check the output shape to see
            #  if this looks like a reduction.
            initialized = True
            _check_result_array(res, len(self.dummy_arr))

        islider.advance(group_size)
        vslider.advance(group_size)

        return res, initialized


cdef class SeriesBinGrouper(_BaseGrouper):
    """
    Performs grouping operation according to bin edges, rather than labels
    """
    cdef:
        Py_ssize_t nresults, ngroups

    cdef public:
        ndarray arr, index, dummy_arr, dummy_index
        object values, f, bins, typ, ityp, name

    def __init__(self, object series, object f, object bins, object dummy):

        assert dummy is not None  # always obj[:0]
        assert len(bins) > 0  # otherwise we get IndexError in get_result

        self.bins = bins
        self.f = f

        values = series.values
        if util.is_array(values) and not values.flags.c_contiguous:
            # e.g. Categorical has no `flags` attribute
            values = values.copy('C')
        self.arr = values
        self.typ = series._constructor
        self.ityp = series.index._constructor
        self.index = series.index.values
        self.name = series.name

        self.dummy_arr, self.dummy_index = self._check_dummy(dummy)

        # kludge for #1688
        if len(bins) > 0 and bins[-1] == len(series):
            self.ngroups = len(bins)
        else:
            self.ngroups = len(bins) + 1

    def get_result(self):
        cdef:
            ndarray arr, result
            ndarray[int64_t] counts
            Py_ssize_t i, n, group_size
            object res
            bint initialized = 0
            Slider vslider, islider
            object cached_typ = None, cached_ityp = None

        counts = np.zeros(self.ngroups, dtype=np.int64)

        if self.ngroups > 0:
            counts[0] = self.bins[0]
            for i in range(1, self.ngroups):
                if i == self.ngroups - 1:
                    counts[i] = len(self.arr) - self.bins[i - 1]
                else:
                    counts[i] = self.bins[i] - self.bins[i - 1]

        group_size = 0
        n = len(self.arr)

        vslider = Slider(self.arr, self.dummy_arr)
        islider = Slider(self.index, self.dummy_index)

        result = np.empty(self.ngroups, dtype='O')

        try:
            for i in range(self.ngroups):
                group_size = counts[i]

                islider.set_length(group_size)
                vslider.set_length(group_size)

                cached_typ, cached_ityp = self._update_cached_objs(
                    cached_typ, cached_ityp, islider, vslider)

                res, initialized = self._apply_to_group(cached_typ, cached_ityp,
                                                        islider, vslider,
                                                        group_size, initialized)

                result[i] = res

        finally:
            # so we don't free the wrong memory
            islider.reset()
            vslider.reset()

        result = maybe_convert_objects(result)
        return result, counts


cdef class SeriesGrouper(_BaseGrouper):
    """
    Performs generic grouping operation while avoiding ndarray construction
    overhead
    """
    cdef:
        Py_ssize_t nresults, ngroups

    cdef public:
        ndarray arr, index, dummy_arr, dummy_index
        object f, labels, values, typ, ityp, name

    def __init__(self, object series, object f, object labels,
                 Py_ssize_t ngroups, object dummy):

        # in practice we always pass obj.iloc[:0] or equivalent
        assert dummy is not None

        if len(series) == 0:
            # get_result would never assign `result`
            raise ValueError("SeriesGrouper requires non-empty `series`")

        self.labels = labels
        self.f = f

        values = series.values
        if util.is_array(values) and not values.flags.c_contiguous:
            # e.g. Categorical has no `flags` attribute
            values = values.copy('C')
        self.arr = values
        self.typ = series._constructor
        self.ityp = series.index._constructor
        self.index = series.index.values
        self.name = series.name

        self.dummy_arr, self.dummy_index = self._check_dummy(dummy)
        self.ngroups = ngroups

    def get_result(self):
        cdef:
            # Define result to avoid UnboundLocalError
            ndarray arr, result = None
            ndarray[int64_t] labels, counts
            Py_ssize_t i, n, group_size, lab
            object res
            bint initialized = 0
            Slider vslider, islider
            object cached_typ = None, cached_ityp = None

        labels = self.labels
        counts = np.zeros(self.ngroups, dtype=np.int64)
        group_size = 0
        n = len(self.arr)

        vslider = Slider(self.arr, self.dummy_arr)
        islider = Slider(self.index, self.dummy_index)

        result = np.empty(self.ngroups, dtype='O')

        try:
            for i in range(n):
                group_size += 1

                lab = labels[i]

                if i == n - 1 or lab != labels[i + 1]:
                    if lab == -1:
                        islider.advance(group_size)
                        vslider.advance(group_size)
                        group_size = 0
                        continue

                    islider.set_length(group_size)
                    vslider.set_length(group_size)

                    cached_typ, cached_ityp = self._update_cached_objs(
                        cached_typ, cached_ityp, islider, vslider)

                    res, initialized = self._apply_to_group(cached_typ, cached_ityp,
                                                            islider, vslider,
                                                            group_size, initialized)

                    result[lab] = res
                    counts[lab] = group_size
                    group_size = 0

        finally:
            # so we don't free the wrong memory
            islider.reset()
            vslider.reset()

        # We check for empty series in the constructor, so should always
        #  have result initialized by this point.
        assert initialized, "`result` has not been initialized."

        result = maybe_convert_objects(result)

        return result, counts


cdef inline _extract_result(object res, bint squeeze=True):
    """ extract the result object, it might be a 0-dim ndarray
        or a len-1 0-dim, or a scalar """
    if hasattr(res, 'values') and util.is_array(res.values):
        res = res.values
    if util.is_array(res):
        if res.ndim == 0:
            res = res.item()
        elif squeeze and res.ndim == 1 and len(res) == 1:
            res = res[0]
    return res


cdef class Slider:
    """
    Only handles contiguous data for now
    """
    cdef:
        ndarray values, buf
        Py_ssize_t stride, orig_len, orig_stride
        char *orig_data

    def __init__(self, ndarray values, ndarray buf):
        assert values.ndim == 1
        assert values.dtype == buf.dtype

        if not values.flags.contiguous:
            values = values.copy()

        self.values = values
        self.buf = buf
        self.stride = values.strides[0]

        self.orig_data = self.buf.data
        self.orig_len = self.buf.shape[0]
        self.orig_stride = self.buf.strides[0]

        self.buf.data = self.values.data
        self.buf.strides[0] = self.stride

    cdef advance(self, Py_ssize_t k):
        self.buf.data = <char*>self.buf.data + self.stride * k

    cdef move(self, int start, int end):
        """
        For slicing
        """
        self.buf.data = self.values.data + self.stride * start
        self.buf.shape[0] = end - start

    cdef set_length(self, Py_ssize_t length):
        self.buf.shape[0] = length

    cdef reset(self):

        self.buf.shape[0] = self.orig_len
        self.buf.data = self.orig_data
        self.buf.strides[0] = self.orig_stride


class InvalidApply(Exception):
    pass


def apply_frame_axis0(object frame, object f, object names,
                      const int64_t[:] starts, const int64_t[:] ends):
    cdef:
        BlockSlider slider
        Py_ssize_t i, n = len(starts)
        list results
        object piece
        dict item_cache

    # We have already checked that we don't have a MultiIndex before calling
    assert frame.index.nlevels == 1

    results = []

    slider = BlockSlider(frame)

    mutated = False
    item_cache = slider.dummy._item_cache
    try:
        for i in range(n):
            slider.move(starts[i], ends[i])

            item_cache.clear()  # ugh
            chunk = slider.dummy
            object.__setattr__(chunk, 'name', names[i])

            try:
                piece = f(chunk)
            except Exception:
                # We can't be more specific without knowing something about `f`
                raise InvalidApply('Let this error raise above us')

            # Need to infer if low level index slider will cause segfaults
            require_slow_apply = i == 0 and piece is chunk
            try:
                if piece.index is not chunk.index:
                    mutated = True
            except AttributeError:
                # `piece` might not have an index, could be e.g. an int
                pass

            if not is_scalar(piece):
                # Need to copy data to avoid appending references
                try:
                    piece = piece.copy(deep="all")
                except (TypeError, AttributeError):
                    piece = copy(piece)

            results.append(piece)

            # If the data was modified inplace we need to
            # take the slow path to not risk segfaults
            # we have already computed the first piece
            if require_slow_apply:
                break
    finally:
        slider.reset()

    return results, mutated


cdef class BlockSlider:
    """
    Only capable of sliding on axis=0
    """

    cdef public:
        object frame, dummy, index
        int nblocks
        Slider idx_slider
        list blocks

    cdef:
        char **base_ptrs

    def __init__(self, object frame):
        cdef:
            Py_ssize_t i
            object b

        self.frame = frame
        self.dummy = frame[:0]
        self.index = self.dummy.index

        self.blocks = [b.values for b in self.dummy._mgr.blocks]

        for x in self.blocks:
            util.set_array_not_contiguous(x)

        self.nblocks = len(self.blocks)
        # See the comment in indexes/base.py about _index_data.
        # We need this for EA-backed indexes that have a reference to a 1-d
        # ndarray like datetime / timedelta / period.
        self.idx_slider = Slider(
            self.frame.index._index_data, self.dummy.index._index_data)

        self.base_ptrs = <char**>malloc(sizeof(char*) * len(self.blocks))
        for i, block in enumerate(self.blocks):
            self.base_ptrs[i] = (<ndarray>block).data

    def __dealloc__(self):
        free(self.base_ptrs)

    cdef move(self, int start, int end):
        cdef:
            ndarray arr
            Py_ssize_t i

        # move blocks
        for i in range(self.nblocks):
            arr = self.blocks[i]

            # axis=1 is the frame's axis=0
            arr.data = self.base_ptrs[i] + arr.strides[1] * start
            arr.shape[1] = end - start

        # move and set the index
        self.idx_slider.move(start, end)

        object.__setattr__(self.index, '_index_data', self.idx_slider.buf)
        self.index._engine.clear_mapping()

    cdef reset(self):
        cdef:
            ndarray arr
            Py_ssize_t i

        # reset blocks
        for i in range(self.nblocks):
            arr = self.blocks[i]

            # axis=1 is the frame's axis=0
            arr.data = self.base_ptrs[i]
            arr.shape[1] = 0


def compute_reduction(arr: np.ndarray, f, axis: int = 0, dummy=None, labels=None):
    """

    Parameters
    -----------
    arr : np.ndarray
    f : function
    axis : integer axis
    dummy : type of reduced output (series)
    labels : Index or None
    """

    # We either have both dummy and labels, or neither of them
    if (labels is None) ^ (dummy is None):
        raise ValueError("Must pass either dummy and labels, or neither")

    if labels is not None:
        # Caller is responsible for ensuring we don't have MultiIndex
        assert labels.nlevels == 1

        # pass as an ndarray/ExtensionArray
        labels = labels._values

    reducer = Reducer(arr, f, axis=axis, dummy=dummy, labels=labels)
    return reducer.get_result()