Hi all, to try and make some progress towards a decision since the broad design is pretty much settling from my side. I am thinking about making a meeting, and suggest Monday at 11am Pacific Time (I am open to other times though). My hope is to get everyone interested on board, so that we can make an informed decision about the general direction very soon. So just reach out, or discuss on the mailing list as well. The current draft for an NEP is here: https://hackmd.io/kxuh15QGSjueEKft5SaMug?both There are some design goals that I would like to clear up. I would prefer to avoid deep discussions of some specific issues, since I think the important decision right now is that my general start is in the right direction. It is not an easy topic, so my plan would be try and briefly summarize that and then hopefully clarify any questions and then we can discuss why alternatives are rejected. The most important thing is maybe gathering concerns which need to be clarified before we can go towards accepting the general design ideas. The main point of the NEP draft is actually captured by the picture in the linked document: DTypes are classes (such as Float64) and what is attached to the array is an instance of that class "<float64" or ">float64". Additionally, we would have AbstractDType classes which cannot be instantiated but define a type hierarchy. To list the main points: * DTypes are classes (corresponding to the current type number) * `arr.dtype` is an instances of its class, allowing to store additional information such as a physical unit, the string length. * Most things are defined in special dtype slots similar to Pythons type and number slots. They will be hidden and can be set through an init function similar to `PyType_FromSpec` [1]. * Promotion is defined primarily on the DType classes * Casting from one DType to another DType is defined by a new CastingImpl object (should become a special ufunc) - e.g. for strings, the CastingImpl is in charge of finding the correct string length * The AbstractDType hierarchy will be used to decide the signature when calling UFuncs. The main iffier points I can think of are: * NumPy currently uses value based promotion in some cases, which requires special AbstractDTypes to describe (and some legacy paths). (They are used use more like instances than typical classes) * Casting between flexible dtypes (such as strings) is a multi-step process to figure out the actual output dtype. - An example is: `np.can_cast("float64", "S3")` first finding that `Float64->String` is possible in principle and then asking the CastingImpl to find that `float64->S3` is not. * We have to break ABI compatibility in very minor, back-portable way. More smaller incompatibilities are likely [2]. * Since it is a major redesign, a lot of code has to be added/touched, although it is possible to channel much of it back into the old machinery. * A largish amount of new API around new DType type objects and also DTypeMeta type objects, which users can (although usually do not have to) subclass. However, most other designs will have similar issues. Basically, I currently really think this is "right", even if some details may end up a tricky. Best, Sebastian PS: The one thing outside the more general list above that I may want to discuss is how acceptable a global dict/mapping for dtype discovery during `np.array` coercion is (mapping python type -> dtype)... [1] https://docs.python.org/3/c-api/type.html#c.PyType_FromSpec [2] One possible issue may be "S0" which is normally used to denote what in the new API would be the `String` DType class.