So what is the best way, to perform the desired reordering? It seems very uncomfortable to create all the intermediate arrays and broadcast them correctly. This seems to be quite efficient, so I looked at Numpy advanced indexing and found this solution: out = mask[Īn answer to a related question suggests the use of ogrid: ogrid = np.ogrid The straight forward way to do this, is by applying a for loop: out = np.empty_like(mask) Mask = np.random.randint(4, size=(T, K, F)) We may assume, that the mapping matrix comes from some other algorithm. This means that requesting eachindex from an array that is IndexCartesian will return a range of CartesianIndices.Lets assume, I have two given ndarrays, where the matrix mapping contains information, of how row of the matrix mask should be permuted. This is the default for new custom AbstractArray subtypes.Ī Cartesian indexing style uses multiple integer indices to describe the position in a multidimensional array, with exactly one index per dimension. Subtype of IndexStyle used to describe arrays which are optimally indexed by a Cartesian index. In particular, eachindex creates an iterator whose type depends on the setting of this trait. Some methods check this trait on their inputs, and dispatch to different algorithms depending on the most efficient access pattern. If you define both styles of indexing for your AbstractArray, this trait can be used to select the most performant indexing style. This allows users to access elements of your array using any indexing style, even when explicit methods have not been provided. Julia's internal indexing machinery will automatically (and invisibly) recompute all indexing operations into the preferred style. If they do not specialize this method, the default is an Array) = IndexLinear()
#Permute a matrix python series#
The dimensions may be specified either as a single tuple argument or as a series of integer arguments.Ĭustom AbstractArray subtypes may choose which specific array type is best-suited to return for the given element type and dimensionality. The second and third arguments are both optional, defaulting to the given array's eltype and size. Similar(array,, )Ĭreate an uninitialized mutable array with the given element type and size, based upon the given source array. Julia> A # both A and A are the very same vector Julia> A = 42 # modifies the filled value to be Julia> A = fill(zeros(2), 2) # sets both elements to the same vector This creates a new and distinct array on each iteration of the loop: julia> v2 = for _ in 1:3] To create an array of many independent inner arrays, use a comprehension instead. For example, fill(, 3) places the very same empty array in all three locations of the returned vector: julia> v = fill(, 3) This is of no concern with fill(1.0, (5,5)) as the value 1.0 is immutable and cannot itself be modified, but can be unexpected with mutable values like - most commonly - arrays. Thus, a common idiom for creating a zero-dimensional array with its only location set to x is fill(x).Įvery location of the returned array is set to (and is thus = to) the value that was passed this means that if the value is itself modified, all elements of the filled array will reflect that modification because they're still that very value. An N-length tuple or N arguments following the value specify an N-dimensional array. The dimension lengths dims may be specified as either a tuple or a sequence of arguments.
#Permute a matrix python code#
Static analyzer annotations for GC correctness in C code.Proper maintenance and care of multi-threading locks.printf() and stdio in the Julia runtime.Talking to the compiler (the :meta mechanism).High-level Overview of the Native-Code Generation Process.Noteworthy Differences from other Languages.
Mathematical Operations and Elementary Functions.
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