Stuck on an issue?
Lightrun Answers was designed to reduce the constant googling that comes with debugging 3rd party libraries. It collects links to all the places you might be looking at while hunting down a tough bug.
And, if you’re still stuck at the end, we’re happy to hop on a call to see how we can help out.
Questions about representation of numbers
See original GitHub issueIssue Description
Dear devs, Example schema: number1:int() number2:float()
1.Why 1e4 or 1.1e4 are not recognized as valid int. Should be possible imho. 2. Why build in float validator brings error on numbers without decimal point. I.e. 10.0 is valid but 10 is not valid double. Seems illogical, since ppl never add “.” to such numbers
I have created own validator, which did the job. But may be support is natively? Thanks you very much for great tool!
`#custom type int64
class int64_type_validator(Validator):
“”" Custom Int64 validator “”"
tag = ‘int64’
def _is_valid(self, value):
strg=str(value)
try:
f=float(value)
i=np.int64(f)
return (f-i)==0
#Notes:
# TBD:May be add epsilon?
# Is 10.1e9 valid input of int64. So far yes. or do we want to limit AeB
except:
return False
class float_type_validator(Validator): “”" Custom float validator “”" tag = ‘float’
def _is_valid(self, value):
strg=str(value)
try:
f:float=float(strg);
return True
except:
return False`
Issue Analytics
- State:
- Created a year ago
- Comments:10
Top Results From Across the Web
Top Related Medium Post
Top Related StackOverflow Question
Troubleshoot Live Code
Top Related Reddit Thread
Top Related Hackernoon Post
Top Related Tweet
Top Related Dev.to Post
Top Related Hashnode Post
Yamale leaves the parsing up to the Python library being used. Yamale validates the values based on the Python types which the parser assigned. I don’t see it as a weakness of either the parser (ruamel) or the validator (yamale). Both try to be explicit in what they do. The ruamel parser assigns
1e4to a float, and yamale correctly says a float is not an integer. For example, I’d also expect1.0to fail a validation if an integer was required since1.0is a float.I can see why someone would think the other way. You’re saying that if I can cast/convert a float to an integer successfully, then yamale should validate as an integer. I could also cast
1.0as a string…but one would expect the validator to fail if the user provided1.0and the schema expected a string instead. For that reason, yamale validates against the actual types that the parser gives it instead of what it can be cast/converted into.Both
make_data()andmake_schema()have aparserparameter that you can set. There’s an example in the README here: https://github.com/23andMe/Yamale#api