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RFC: Reverse Array dims in no-copy conversion from Julia to Numpy #85

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RFC: Reverse Array dims in no-copy conversion from Julia to Numpy #85

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12 changes: 9 additions & 3 deletions src/numpy.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -175,14 +175,20 @@ const npy_typestrs = Dict( "b1"=>Bool,
#########################################################################
# no-copy conversion of Julia arrays to NumPy arrays.

function PyObject{T<:NPY_TYPES}(a::StridedArray{T})
# In some cases, an array is already in row-major order (e.g., RGB
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I would say rather than "Julia arrays are in column-major order, but in some cases it is useful to pass them to Python as row-major arrays simply by reversing the dimensions. For example, although NumPy works with both row-major and column-major data, some Python libraries like OpenCV seem to require row-major data (the default in NumPy). In such cases, use PyObject(array, true)."

And then add a similar comment to the README.md file.

# images) and it is useful to reverse the order of dimensions passed
# to PyArray_New. In these cases, set revdims=true.

function PyObject{T<:NPY_TYPES}(a::StridedArray{T}, revdims::Bool=false)
try
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This should not silently fall back on failure to array2py(a) if revdims=true. It produces inconsistent behavior. Is there some way array2py could take revdims as an extra arg as well?

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@stevengj stevengj Apr 21, 2016 •
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Good catch. For now, it can throw an error if revdims=true but in the future array2py could implement this too.

@npyinitialize
size_a = revdims ? reverse(size(a)) : size(a)
strides_a = revdims ? reverse(strides(a)) : strides(a)
p = @pycheck ccall(npy_api[:PyArray_New], PyPtr,
(PyPtr,Cint,Ptr{Int},Cint, Ptr{Int},Ptr{T}, Cint,Cint,PyPtr),
npy_api[:PyArray_Type],
ndims(a), Int[size(a)...], npy_type(T),
Int[strides(a)...] * sizeof(eltype(a)), a, sizeof(eltype(a)),
ndims(a), Int[size_a...], npy_type(T),
Int[strides_a...] * sizeof(eltype(a)), a, sizeof(eltype(a)),
NPY_ARRAY_ALIGNED | NPY_ARRAY_WRITEABLE,
C_NULL)
return PyObject(p, a)
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