# MWE for compiling functions into standalone DLL and calling them in Python?

I have read calling-a-compiledfunction-dll-from-outside-mathematica and the CodeGeneration tutorial and scanned other related questions too numerous to mention, but I am still unclear about how to generate a DLL containing one or more compiled functions that I can call in Python.

The functions concerned take as parameters 1, 2 or 3D real valued arrays and various int/real scalars, and may also return arrays, multiple values (for assignment to multiple variables in Python).

(I also looked at pjlink @b3m2a1, but was concerned about overheads as the compiled functions will be heavily used, and then noticed it has been superseded by WolframClientForPython - I installed it anyway with pip but then noticed it says is requires Wolfram Language 11.3 or higher - alas I only have MMA 11.0.1 - is that an absolute pre-requisitive or recommendation? Update Looks absolute: evaluation generated error

WolframKernelException: Failed to communicate with kernel: C:\Program Files\Wolfram Research\Mathematica\11.0\WolframKernel.exe

)

Given that my C & compilation skills are negligible, and my ignorance of Windows executables, shells, etc. vast, can anyone provide or direct me to a minimum working example of: compiling >2 such functions into a DLL for the environment below?

The functions concerned do compile to C, do not call MainEvaluate and have been used successfully within MMA, I just need to re-use them in a Python environment.

Environment: MMA 11.0.1.0; Win 10 64-bit, VS Studio 2017, Python 3.6/7, Jupyterlab front-end.

Although useless on their own, here are examples of the compiled function definitions

pearsonRtoRefVector = Compile[{{dataVec, _Real,1}, {corrVecLen, _Integer},{corrVecMean, _Real}, {corrVecMeanDiffs, _Real, 1}, {corrVecMeanDiffsSquaredSummed, _Real}},
Module[
{dataVecLen,dataVecMean, dataVecMeanDiffs,dataVecMeanDiffsSquaredSummed},
dataVecLen = Length[dataVec];
If[dataVecLen !=corrVecLen,
-11, (* just a number that is not in the range[-1,1] *)
dataVecMean = Last[Accumulate[dataVec]]/dataVecLen;
dataVecMeanDiffs= dataVec - dataVecMean;
dataVecMeanDiffsSquaredSummed = dataVecMeanDiffs.dataVecMeanDiffs;
If[dataVecMeanDiffsSquaredSummed <= 2.2250738585072014*^-308 (*\$MinMachineNumber*), (* 2019-06-04 Why not just Chop, which is compilable *)
0, (* clamp value to zero for effectively zero floats - can increase the size of "effectively zero" as required *)
dataVecMeanDiffs.corrVecMeanDiffs/(Sqrt[dataVecMeanDiffsSquaredSummed]*Sqrt[corrVecMeanDiffsSquaredSummed])
]
]
], CompilationTarget -> "C", "RuntimeOptions" -> "Speed"];

(* COMPILATION TESTED OK 2017-02-12 Needed "InlineExternalDefinitions"\[Rule]True *)
(* It is assumed the following have been precomputed: the common correlation vector, vOscTable...
To see the effect of the Vosc table (which is assumed to have the right dimensions) pass a table filled with zeroes
Recall  that vOscTable has n columns and nc rows, i.e. indexes as [[nc, n]]*)
buildTAMSDTable =
Compile[{{aPQTable, _Real, 3},{gCorrVecLen, _Real}, {gCorrVecMean, _Real}, {gCorrVecMeanDiffs, _Real, 1}, {gCorrVecMeanDiffsSquaredSummed, _Real},{aVoscTable, _Real, 2}},
Module[{pTable, qTable, ps, qs, pDiffs, qDiffs, bigN, intNcut,n,nc,cs,commonPQDims},
pTable = aPQTable[[1]];
qTable = aPQTable[[2]];
commonPQDims = Dimensions[pTable];
nc = commonPQDims[[1]]; (* #random c values = [[1]] because I am working on one of the translation variable sub-tables extracted *)
bigN = Last[commonPQDims]; (* Number of points in data series *)
intNcut = IntegerPart[bigN/10];

Table[ (* Outer Table makes a vector of the correlations, but built-in Correlation is not compilable :( *)
pearsonRtoRefVector[
Table[ (* This makes the mean squared deviations for correlation *)
ps = pTable[[cs]]; (* Take one set of p values for the series *)
qs = qTable[[cs]]; (* Take one set of q values for the series *)
pDiffs = Take[ps, n - bigN] - Take[ps, bigN - n]; (* Calc the p diffs *)
qDiffs = Take[qs, n - bigN] - Take[qs, bigN - n]; (* Calc the q diffs *)
(1/(bigN - n) (pDiffs.pDiffs + qDiffs.qDiffs)) - aVoscTable[[cs, n]], (* The Mean of squared diffs summed - Vosc Continue to be wary of Vosc index order - ok but you never know!   *)
{n, 1, intNcut}],
gCorrVecLen,gCorrVecMean,gCorrVecMeanDiffs,gCorrVecMeanDiffsSquaredSummed],
{cs,1,nc}]
],
CompilationTarget -> "C", "RuntimeOptions" -> "Speed", CompilationOptions->{"InlineCompiledFunctions"->True, "InlineExternalDefinitions"->True}];
`
• A successful alternative approach using Wolfram Engine, Wolfram Client for Python and Mathematica source code compiling with Visual Studio 2017 is described in this answer: mathematica.stackexchange.com/a/210215/12858 – Julian Moore Nov 25 '19 at 20:31