@bedhad
Address issues raised in #1403
I do think setting the dummy CFF2 PrivateDict nominalWidthX and defaultWidthX to None, which leads to the charstring.width also being None, is a good idea. I originally set them to 0, which produces a charstring width of 0, in order to avoid problems with logic that assumes that the field is good for math. However, I now think that it is better to find errors around charstring type assumptions earlier than later.
"drop_hints()" is actually not wrong - I did look at this when making the changes. For CFF2 charstrings, self.width is always equal to self.private.defaultWidthX, so the width is never inserted. This is because in psCharstrings.py::T2WidthExtractor.popallWidth(), the test "evenOdd ^ (len(args) % 2)" is alway False. Left to myself, I would not change this code. If the CFF2 charstring is correct, there is not a problem. if the CFF2 charstring is not correct, then both in drop_hints() and in T2WidthExtractor.popallWidth(), the logic will stack dump. I did add asserts, but am not totally sure it is worth the extra calls.
Fixed psCharstrings so that calc_bounds will run. I would guess no-one has tried to use a BoundsPen on a CFF2 VF before - thanks to Chris Chapman. It now returns a result only for the default instance.
Fixed bug in subsetting: removed assert that a Subr is not empty after subsetting or de-hinting. CFF2 Charstrings do not have terminal "return' op.
Removed check_program functions. Supporting these requires knowledge of CFF vs CFF2 state, whci is leads to wide-spread diffuse changes. Also, not needed - the endchar/return opcodes are removed when compiling for CFF2.
Removed CFF2Subr class. This was used for CFF2 CharStrings, and allowed avoiding referencing the width fields. I worked around this by providing dummy values for the Private.nominalWidthX and defaultWidthX.
Added a public method PrivateDict.in_cff2.
It is useful to re-order the CFF2 master font list to match the sorted location order, but doing so means touching internal fields of the model, so we'll move this into the VariationModel class.
