algos_take_helper.pxi.in

"""
Template for each `dtype` helper function for take

WARNING: DO NOT edit .pxi FILE directly, .pxi is generated from .pxi.in
"""

# ----------------------------------------------------------------------
# take_1d, take_2d
# ----------------------------------------------------------------------


{{py:

# c_type_in, c_type_out
dtypes = [
    ('uint8_t', 'uint8_t'),
    ('uint8_t', 'object'),
    ('int8_t', 'int8_t'),
    ('int8_t', 'int32_t'),
    ('int8_t', 'int64_t'),
    ('int8_t', 'float64_t'),
    ('int16_t', 'int16_t'),
    ('int16_t', 'int32_t'),
    ('int16_t', 'int64_t'),
    ('int16_t', 'float64_t'),
    ('int32_t', 'int32_t'),
    ('int32_t', 'int64_t'),
    ('int32_t', 'float64_t'),
    ('int64_t', 'int64_t'),
    ('int64_t', 'float64_t'),
    ('float32_t', 'float32_t'),
    ('float32_t', 'float64_t'),
    ('float64_t', 'float64_t'),
    ('object', 'object'),
]


def get_dispatch(dtypes):

    for (c_type_in, c_type_out) in dtypes:

        def get_name(dtype_name):
            if dtype_name == "object":
                return "object"
            if dtype_name == "uint8_t":
                return "bool"
            return dtype_name[:-2]

        name = get_name(c_type_in)
        dest = get_name(c_type_out)

        args = dict(name=name, dest=dest, c_type_in=c_type_in,
                    c_type_out=c_type_out)

        yield (name, dest, c_type_in, c_type_out)

}}


{{for name, dest, c_type_in, c_type_out in get_dispatch(dtypes)}}


@cython.wraparound(False)
@cython.boundscheck(False)
{{if c_type_in != "object"}}
def take_1d_{{name}}_{{dest}}(const {{c_type_in}}[:] values,
{{else}}
def take_1d_{{name}}_{{dest}}(ndarray[{{c_type_in}}, ndim=1] values,
{{endif}}
                              const intp_t[:] indexer,
                              {{c_type_out}}[:] out,
                              fill_value=np.nan):

    cdef:
        Py_ssize_t i, n, idx
        {{c_type_out}} fv

    n = indexer.shape[0]

    fv = fill_value

    {{if c_type_out != "object"}}
    with nogil:
    {{else}}
    if True:
    {{endif}}
        for i in range(n):
            idx = indexer[i]
            if idx == -1:
                out[i] = fv
            else:
                {{if c_type_in == "uint8_t" and c_type_out == "object"}}
                out[i] = True if values[idx] > 0 else False
                {{else}}
                out[i] = values[idx]
                {{endif}}


@cython.wraparound(False)
@cython.boundscheck(False)
{{if c_type_in != "object"}}
def take_2d_axis0_{{name}}_{{dest}}(const {{c_type_in}}[:, :] values,
{{else}}
def take_2d_axis0_{{name}}_{{dest}}(ndarray[{{c_type_in}}, ndim=2] values,
{{endif}}
                                    ndarray[intp_t, ndim=1] indexer,
                                    {{c_type_out}}[:, :] out,
                                    fill_value=np.nan):
    cdef:
        Py_ssize_t i, j, k, n, idx
        {{c_type_out}} fv
        {{if c_type_in == c_type_out != "object"}}
        const {{c_type_out}} *v
        {{c_type_out}} *o
        {{endif}}

    n = len(indexer)
    k = values.shape[1]

    fv = fill_value

    {{if c_type_in == c_type_out != "object"}}
    # GH#3130
    with nogil:
        if (values.strides[1] == out.strides[1] and
            values.strides[1] == sizeof({{c_type_out}}) and
            sizeof({{c_type_out}}) * n >= 256):

            for i in range(n):
                idx = indexer[i]
                if idx == -1:
                    for j in range(k):
                        out[i, j] = fv
                else:
                    v = &values[idx, 0]
                    o = &out[i, 0]
                    memmove(o, v, <size_t>(sizeof({{c_type_out}}) * k))
        else:
            for i in range(n):
                idx = indexer[i]
                if idx == -1:
                    for j in range(k):
                        out[i, j] = fv
                else:
                    for j in range(k):
                        out[i, j] = values[idx, j]
    {{else}}

    for i in range(n):
        idx = indexer[i]
        if idx == -1:
            for j in range(k):
                out[i, j] = fv
        else:
            for j in range(k):
                {{if c_type_in == "uint8_t" and c_type_out == "object"}}
                out[i, j] = True if values[idx, j] > 0 else False
                {{else}}
                out[i, j] = values[idx, j]
                {{endif}}
    {{endif}}


@cython.wraparound(False)
@cython.boundscheck(False)
{{if c_type_in != "object"}}
def take_2d_axis1_{{name}}_{{dest}}(const {{c_type_in}}[:, :] values,
{{else}}
def take_2d_axis1_{{name}}_{{dest}}(ndarray[{{c_type_in}}, ndim=2] values,
{{endif}}
                                    ndarray[intp_t, ndim=1] indexer,
                                    {{c_type_out}}[:, :] out,
                                    fill_value=np.nan):

    cdef:
        Py_ssize_t i, j, k, n, idx
        {{c_type_out}} fv

    n = len(values)
    k = len(indexer)

    if n == 0 or k == 0:
        return

    fv = fill_value

    {{if c_type_in == c_type_out != "object"}}
    with nogil:
        for i in range(n):
            for j in range(k):
                idx = indexer[j]
                if idx == -1:
                    out[i, j] = fv
                else:
                    out[i, j] = values[i, idx]

    {{else}}
    for i in range(n):
        for j in range(k):
            idx = indexer[j]
            if idx == -1:
                out[i, j] = fv
            else:
                {{if c_type_in == "uint8_t" and c_type_out == "object"}}
                out[i, j] = True if values[i, idx] > 0 else False
                {{else}}
                out[i, j] = values[i, idx]
                {{endif}}
    {{endif}}


@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_multi_{{name}}_{{dest}}(ndarray[{{c_type_in}}, ndim=2] values,
                                    indexer,
                                    ndarray[{{c_type_out}}, ndim=2] out,
                                    fill_value=np.nan):
    cdef:
        Py_ssize_t i, j, k, n, idx
        ndarray[intp_t, ndim=1] idx0 = indexer[0]
        ndarray[intp_t, ndim=1] idx1 = indexer[1]
        {{c_type_out}} fv

    n = len(idx0)
    k = len(idx1)

    fv = fill_value
    for i in range(n):
        idx = idx0[i]
        if idx == -1:
            for j in range(k):
                out[i, j] = fv
        else:
            for j in range(k):
                if idx1[j] == -1:
                    out[i, j] = fv
                else:
                    {{if c_type_in == "uint8_t" and c_type_out == "object"}}
                    out[i, j] = True if values[idx, idx1[j]] > 0 else False
                    {{else}}
                    out[i, j] = values[idx, idx1[j]]
                    {{endif}}

{{endfor}}