| Name | valueparser JSON |
| Version |
0.1.5
JSON |
| download |
| home_page | |
| Summary | Python package for quick creation of tools to parse a value |
| upload_time | 2023-01-17 16:08:10 |
| maintainer | |
| docs_url | None |
| author | Sylvain Guieu |
| requires_python | >=3.7,<4.0 |
| license | |
| keywords |
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|
| bugtrack_url |
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| requirements |
No requirements were recorded.
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|
Parser
======
Python package for quick creation/ combination of value parser object for muly-puposes
Install
=======
```shell
> pip install valueparser
```
Usage
=====
```python
from valueparser.parsers import Bounded
value_parser = Bounded(min=0, max=1)
value_parser.parse(4.0)
# ParseError: 4.0 is higher than 1.0
```
Several parsers can be combined to one single parser object, they share however the same name space for configuration
parameters
```python
from valueparser import parser, Clipped, Rounded
ratio_parser = parser( (float, Clipped, Rounded), min=0, max=1.0, ndigits=2 )
assert ratio_parser.parse( 0.231234) == 0.23
assert ratio_parser.parse( 4.5) == 1.0
assert ratio_parser.parse( "0.12345") == 0.12
```
Equivalent can be done by creating a new class
```python
from valueparser import parser_class, Clipped, Rounded
MyParser = parser_class( (float, Clipped, Rounded), "MyParser" )
ratio_parser = MyParser( min=0, max=1, ndigits=2)
```
`conparser` works the same way than `parser` except it construct a typing object to be use inside pydantic BaseModel in
a compact way.
```python
from valueparser import conparser, Bounded
from pydantic import BaseModel
Pixel = conparser( (int, Bounded), min=0, max=1023 )
class Data(BaseModel):
x: Pixel = 512
y: Pixel = 512
Data(x=-200)
#
#pydantic.error_wrappers.ValidationError: 1 validation error for Data
#x
# -200.0 is lower than 0.0 (type=value_error.parse; error_code=Errors.OUT_OF_BOUND)
```
to make any function a `parser` (e.g. an object with `parse` method) one can use the `parser` function as well :
```python
from valueparser import parser
float_parser = parser(float)
assert float_parser.parse("1.234") == 1.234
force_int_parser = parser( (float, int)) # parse to float then int
assert force_int_parser.parse( "1.234") == 1
```
Actually the `parser` function accepts :
- A Parser Class iddentified as a class with the `parse` method
- A callable
- An instance of a Parser Class
- an mix inside an iterable
Plus any kwargs accepted by the combination of parsers
Builtin Parsers
===============
| class name | kwargs | comment |
|------------|---------|---------|
| Bounded | min=-inf, max=+inf | raise an error if value outside interval else return value |
| Clipped | min=-inf, max=+inf | clip silently the value to inferior and superior bounds |
| Rounded | ndigits=0 | round the numerical value to ndigits |
| Formated | format="%s" | convert to string with the given format |
| Listed | items=[], default_item(optional) | raise error if value not in items list else return value a
| | | default_item can be set to be returned instead of error raised |
| Enumerated | enumerator | return enumerator(value) or raise error |
Create a custom parser
======================
To create a parser one need to create a new class from BaseParser, declare any configurable argument
inside the child class ``Config`` and define the static or classmethod `__parse__`
For instance a parser adding some noise to a value ( can be usefull for e.i. a simulator)
```python
from valueparser import BaseParser
import random
class Noisier(BaseParser):
class Config:
noise_scale = 1.0
@staticmethod
def __parse__( value, config):
return value + (random.random()-0.5) * config.noise_scale
```
Usage :
```python
noisier = Noisier( noise_scale=100)
x = noisier.parse(0.0)
x
36.700125482238036
```
Or use conparser in pydantic Model:
```python
from valueparser import conparser
from pydantic import BaseModel
class MyData(BaseModel):
x: conparser(Noisier, noise_scale=100)
y: conparser(Noisier, noise_scale=100)
my_data = MyData(x=0, y=0)
my_data
MyData(x=32.819723479459284, y=-25.95893228872207)
```
Parser and Systemy
==================
Parsers are beased on :class:`systemy.System` class. One can include a parser factory in
a systemy class and expose the parser configuration to user.
```python
from valueparser import ParserFactory , Bounded
from systemy import BaseSystem
from pydantic import AnyUrl
dummy = lambda x:x
class Node(BaseSystem):
class Config:
url: AnyUrl = "http://localhost:4840"
parser: ParserFactory = ParserFactory(type=dummy)
def set(self, value):
value = self.parser.parse(value)
# set value on server
return value
node = Node( parser={'type':(float,Bounded), 'min':0, 'max':10} )
```
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