reshape_helper.pxi.in

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

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

# ----------------------------------------------------------------------
# reshape
# ----------------------------------------------------------------------

{{py:

# name, c_type
dtypes = [('uint8', 'uint8_t'),
          ('uint16', 'uint16_t'),
          ('uint32', 'uint32_t'),
          ('uint64', 'uint64_t'),
          ('int8', 'int8_t'),
          ('int16', 'int16_t'),
          ('int32', 'int32_t'),
          ('int64', 'int64_t'),
          ('float32', 'float32_t'),
          ('float64', 'float64_t'),
          ('object', 'object')]
}}

{{for dtype, c_type in dtypes}}


@cython.wraparound(False)
@cython.boundscheck(False)
def unstack_{{dtype}}(ndarray[{{c_type}}, ndim=2] values,
                      ndarray[uint8_t, ndim=1] mask,
                      Py_ssize_t stride,
                      Py_ssize_t length,
                      Py_ssize_t width,
                      ndarray[{{c_type}}, ndim=2] new_values,
                      ndarray[uint8_t, ndim=2] new_mask):
    """
    transform long sorted_values to wide new_values

    Parameters
    ----------
    values : typed ndarray
    mask : boolean ndarray
    stride : int
    length : int
    width : int
    new_values : typed ndarray
        result array
    new_mask : boolean ndarray
        result mask

    """

    cdef:
        Py_ssize_t i, j, w, nulls, s, offset

    {{if dtype == 'object'}}
    if True:
    {{else}}
    with nogil:
    {{endif}}

        for i in range(stride):

            nulls = 0
            for j in range(length):

                for w in range(width):

                    offset = j * width + w

                    if mask[offset]:
                        s = i * width + w
                        new_values[j, s] = values[offset - nulls, i]
                        new_mask[j, s] = 1
                    else:
                        nulls += 1

{{endfor}}