Improve basic datatypes #1756
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Improve basic datatypes #1756
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…to devel-issue1729
This reverts commit 3903d8e.
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Add missing cast when creating an array from an object with a different datatype. Fixes #1785 In order to call the cast on the argument passed to `np.array` (which may be a `InhomogeneousTupleType`) fix the type of the cast functions. Modify `InhomogeneousTupleType.datatype` to return a `FixedSizeType` if the datatypes of all elements are equivalent. Also fix some minor bugs after #1756
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This PR rewrites the datatyping system. Fixes #1729.
As described in the issue, the existing superclass
Datatypeis replaced with 2 superclasses representing slightly different concepts:PrimitiveType: Representing the category of datatype (integer/floating point/etc)PyccelType: Representing the actual type of the object in PythonSubclasses of
PyccelTypegenerally fall into one of two categories:FixedSizeTypeContainerTypeThese types are described in
developer_docs/type_inference.md(it is recommended to read this file first before reviewing this pull request.The
precisionis removed fromast.basic.TypedAstNodeand is stored in theFixedSizeTypes where it is relevant. This allows us to remove the necessity for negative precision values as defaults, as the types can now be easily differentiated using the differentFixedSizeTypes.The attribute
_dtypeis removed fromast.basic.TypedAstNodeinstances. It is still accessible via thedtypeproperty but is extracted from the class type. This ensures that the two do not deviate from one another.The NumPy type deduction is significantly improved due to two major improvements. Firstly an assertion is added to
NumpyNDArrayTypeto ensure that the internal datatypes are NumPy types. This uncovered a few bugs where the native Python types were used in our current code. Thus the wrong type would be returned if an element of one of these objects was returned.Secondly
np.result_typeis now used to deduce the result type of NumPy functions. This function is implemented by NumPy which guarantees that it handles all corner cases and matches the expected behaviour of NumPy. Fixes #1763.The
CustomDatatypeis simplified slightly to remove the unused parameters.The printing of floats/complexes in Fortran is improved to stop printing decimal digits beyond the precision of the number. Additionally printing the return value of functions returning multiple results of different types is fixed.
Unfortunately these changes have a huge effect on the code as datatypes appear almost everywhere. I have tried my best to find all the places where the code has changed however it is possible that some places were missed if coverage is missing.