Wrapping and Bias issues

[desvdp]

Hi Franco,
thanks for fixing the previous issues so quickly! Looking a bit closer, I did find some extra:

(1) Bias and scaling don't seem to play very wel with wrapping/saturation:

>>> b= Fxp(20.5,False, n_word=5 ,scaling=1, bias=8)
>>> b
fxp-u5/1(20.5)  ----> Ok
>>> b.bin() 
'11001'               ----> Ok

>>> b= Fxp(20.5,False, n_word=4 ,scaling=1, bias=8)
>>> b
fxp-u4/0(20.0)    ---> Had expected a saturation ie 15/2+8 = 15.5
>>> b.bin()
'1100'                  ---> Saturation? 1111

(2) There seems to be an issue with zero values:

>>> zero = Fxp(0.0,False, n_word=5 ,overflow='wrap', scaling=1, bias=8)     --> The range is [8, 23.5] ie 0 is out of range 
>>> zero
fxp-u5/0(32.0)        --> But so is 32 ... 
>>> zero.bin()
'11000'
>>> zero.info(verbose=3)
	dtype		=	fxp-u5/0
	Value		=	32.0
	Scaling		=	1 * val + 8
	underflow	=	True
	inaccuracy	=	True

	Signed		=	False
	Word bits	=	5
	Fract bits	=	0
	Int bits	=	5
	Val data type	=	<class 'float'>

	Upper		=	39.0
	Lower		=	8.0
	Precision	=	1.0
	Overflow	=	wrap
	Rounding	=	trunc
	Shifting	=	expand

Saturation does deal with it:

>>> zero = Fxp(0,False, n_word=5 ,overflow='saturate', scaling=1, bias=8)
>>> zero
fxp-u5/0(8.0) --> Ok 

(3) Last but not least, some long values still throw an error:
b= Fxp(0,False,64,0,overflow='wrap', bias = 8)

Let me know if you need further help testing ...

Thanks !

[francof2a]

Hello @desvdp
Thank you for the feedback.

I'm going to answer point by point while I review them.

1

In this case:

b = Fxp(20.5, False, n_word=4, scaling=1, bias=8)

fxp-u4/0(20.0)

fxpmath should define n_frac and prioritize fit the value instead of keeping enough resolution. In other words, it defines first n_int (bits for integer part). If n_int = 4, scaled range will be from 8 to 23 (the original value is in the range), but if we want to keep precision n_frac = 1 therefore n_int = 3, in this last case scaled range is from 8.0 to 15.5, saturating 20.5 to highest value of the range.

You can check this doing:

b = Fxp(20.5, False, n_word=4, n_frac=1, scaling=1, bias=8)

fxp-u4/1(15.5)

[francof2a]

2

In this case

zero = Fxp(0.0, False, n_word=5, overflow='wrap', scaling=1, bias=8)

fxp-u5/0(32.0)

The behavior is similar what explained in the previous reply. As 0.0 is outside the range, fxpmath choose n_int = 5 (highest possible size) and n_frac = 0 trying to fit the number. That's the reason range is 8 up to 39 (8 + 2**5 - 1). Considering this range, the value 0.0 is wrapped to 32; the value 7 will be wrapped to 39, and so on.

If you force n_frac = 1 you will get:

zero = Fxp(0.0, False, n_word=5, n_frac=1, overflow='wrap', scaling=1, bias=8)

fxp-u5/1(16.0)

The result is ok.

zero.info(verbose=3)

dtype = fxp-u5/1
Value = 16.0
Scaling = 1 * val + 8
underflow = True
inaccuracy = True

Signed = False
Word bits = 5
Fract bits = 1
Int bits = 4
Val data type = <class 'float'>

Upper = 15.5
Lower = 0.0
Precision = 0.5
Overflow = wrap
Rounding = trunc
Shifting = expand

The info is not ok in values of Upper and Lower (should be 23.5 and 8.0). So, there is a bug here.

[francof2a]

3

Crossing the portal to the "beyond 64 bits" always have surprises with Numpy. I've already reproduced the error and found the way to fix. So, in the next release will be included. By the way, I think more bugs are hide in scaling methods, even more using more than 64 bits, I guess. fxpmath is poorly tested in scaled arithmetic.

Thanks for your revision.

[francof2a]

Closing the issue because version 0.4.4 should solve it!
Feel free to reopen the issue if the problem remains.
Thanks!