Hi! Problem: Using Fortran routines from Python C/API is "tricky" when multi-dimensional arrays are passed in. Cause: Arrays in Fortran are stored in column-wise order while arrays in C are stored in row-wise order. Standard solutions: 1) Create a new C array; copy the data from the old one in column-wise order; pass the new array to fortran; copy changed array back to old one in row-wise order; deallocate the array. 2) Change the storage order of an array in place: element-wise swapping; pass the array to fortran; change the storage order back with element-wise swapping Why standard solutions are not good? 1) Additional memory allocation, that is problem for large arrays; Element-wise copying is time consuming (2 times). 2) It is good as no extra memory is needed but element-wise swapping (2 times) is approx. equivalent with the element-wise copying (4 times). Proclamation: Introduce a column-wise array to Numeric Python where data is stored in column-wise order that can be used specifically for fortran routines. Proposal sketch: 1) Introduce a new flag `row_order'to PyArrayObject structure: row_order == 1 -> the data is stored in row-wise order (default, as it is now) row_order == 0 -> the data is stored in column-wise order Note that now the concept of contiguousness depends on this flag. 2) Introduce new array "constructors" such as PyArray_CW_FromDims, PyArray_CW_FromDimsAndData, PyArray_CW_ContiguousFromObject, PyArray_CW_CopyFromObject, PyArray_CW_FromObject, etc. that all return arrays with row_order=0 and data stored in column-wise order (that is in case of contiguous results, otherwise strides feature is employd). 3) In order to operations between arrays (possibly with different storage order) would work correctly, many internal functions of NumPy C/API need to be modifyied. 4) anything else? What is the good of this? 1) The fact is that there is a large number of very good scietific tools freely available written in Fortran (Netlib, for instance). And I don't mean only Fortran 77 codes but also Fortran 90/95 codes. 2) Having Numeric Python arrays with data stored in column-wise order, calling Fortran routines from Python becomes really efficient and space-saving. 3) There should be little performance hit if, say, two arrays with different storage order are multiplied (compared to the operations between non-contiguous arrays in the current implementation). 4) I don't see any reason why older C/API modules would broke because of this change if it is carried out carefully enough. So, back-ward compability should be there. 5) anything else? What are against of this? 1) Lots of work but with current experience it should not be a problem. 2) The size of the code will grow. 3) I suppose that most people using Numerical Python will not care of calling Fortran routines from Python. Possible reasons: too "tricky" or no need. In the first case, the answer is that there are tools such as PyFort, f2py that solve this problem. In the later case, there is no problem:-) 4) anything else? I understand that my proposal is quite radical but taking into account that we want to use Python for many years to come, the use would be more pleasing if one cause of (constant) confusion would be less during this time. Best regards, Pearu