Kevin Jacobs:
#- Decimal('2.4000', precision=2, scale=1) == Decimal('2.4')
#- Decimal('2.4', precision=5, scale=4) == Decimal('2.4000')
#-
#- Remember, these literals are frequently coming from an
#- external source that must be constrained to a given schema.
Facundo Batista:
> I like it a lot, but not for Decimal.
This is another face of "what to do with float"
Even if your default context is n digits, there is no reason
to assume that all your inputs will be measured that precisely.
If someone sends me a list of weights:
PartA 1105 kg
PartB 3 kg
then I don't want to pretend that the 3kg part was weighed
any more precisely just because the total is smaller.
On the other hand, if the weights are:
PartA 3 kg
PartB 3.0 kg
then I do want to assume that the second weight is more precise.
As an example
3000 g 3001 g
+ 2 g + 1 g
------ ------
3kg 3002 g
#- I assume that a new Decimal would normally be created
#- with as much precision as the context would need for
#- calculations. By passing a context/precision/position,
#- the user is saying "yeah, but this measurement wasn't
#- that precise in the first place. Use zeros for the
#- rest, no matter what this number claims."
I don't get to understand you, and I'm not sure if you have the right
concept. Several examples may help:
>>> getcontext().prec = 5
>>> Decimal(124)
Decimal( (0, (1, 2, 4), 0) )
>>> +Decimal(124)
Decimal( (0, (1, 2, 4), 0) )
>>> Decimal('258547.368')
Decimal( (0, (2, 5, 8, 5, 4, 7, 3, 6, 8), -3) )
>>> +Decimal('258547.368')
Decimal( (0, (2, 5, 8, 5, 5), 1L) )
>>> Decimal.from_float(1.1)
Decimal( (0, (1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 1,
7, 8, 4, 1, 9, 7, 0, 0, 1, 2, 5, 2, 3, 2, 3, 3, 8, 9, 0, 5, 3, 3, 4, 4, 7,
2, 6, 5, 6, 2, 5), -51L) )
>>> +Decimal.from_float(1.1)
Decimal( (0, (1, 1, 0, 0, 0), -4L) )
. Facundo
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