I am using quite a lot of fortran libraries to do some mathematical computation. So all the arrays in numpy need to be Fortran-contiguous.
Currently I accomplish this with numpy.asfortranarray().
My questions are:
Fortran order/ array is a special case in which all elements of an array are stored in column-major order. Sometimes we need to display array in fortran order, for this numpy has a function known as numpy. nditer().
Various NumPy modules use FORTRAN 77 libraries, so you'll also need a FORTRAN 77 compiler installed. Note that NumPy is developed mainly using GNU compilers.
arrange() It creates an array by using the evenly spaced values over the given interval. The interval mentioned is half opened i.e. [Start, Stop]).
Ordered sequence is any sequence that has an order corresponding to elements, like numeric or alphabetical, ascending or descending. The NumPy ndarray object has a function called sort() , that will sort a specified array.
Use optional argument order='F' (default 'C'), when generating numpy.array objects. This is the way I do it, probably does the same thing that you are doing. About number 2, I am not aware of setting default order, but it's easy enough to just include order optional argument when generating arrays.
Regarding question 2: you may be concerned about retaining Fortran ordering after performing array transformations and operations. I had a similar issue with endianness. I loaded a big-endian raw array from file, but when I applied a log transformation, the resultant array would be little-endian. I got around the problem by first allocating a second big-endian array, then performing an in-place log:
b=np.zeros(a.shape,dtype=a.dtype)
np.log10(1+100*a,b)
In your case you would allocate b
with Fortran ordering.
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