@whatever cheatsheet?

[Steve Litt]

Hi all,

Does there currently exist a cheatsheet showing the purpose of all the
various @whatever nodes (@file, @test, @button for instance)? If not, I
think it would be an easy and excellent document enhancement.

SteveT

Steve Litt 
Spring 2021 featured book: Troubleshooting Techniques of the Successful 
Technologist
http://www.troubleshooters.com/techniques

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Re: Tools of Interest: Hypothesis and Pydantic for Property-based Testing

[David Szent-Györgyi]

 

The Overview page for Pydantic offers the following Rationale 
, which makes me think that 
it might be of interest to Leo's users and Leo's developers: 

«So *pydantic* uses some cool new language features, but why should I 
actually go and use it?

*plays nicely with your IDE/linter/brain *There's no new schema definition 
micro-language to learn. If you know how to use python type hints, you know 
how to use *pydantic*. Data structures are just instances of classes you 
define with type annotations, so auto-completion, linting, mypy 
, IDEs (especially PyCharm 
), and your intuition 
should all work properly with your validated data.

*dual use **pydantic's* BaseSettings 
 class allows 
*pydantic* to be used in both a "validate this request data" context and in 
a "load my system settings" context. The main differences are that system 
settings can be read from environment variables, and more complex objects 
like DSNs and python objects are often required.

*fast *In benchmarks  
*pydantic* is faster than all other tested libraries.

*validate complex structures *use of recursive *pydantic* models 

, typing's standard types 
 (e.g. 
List, Tuple, Dictetc.) 
and validators  allow 
complex data schemas to be clearly and easily defined, validated, and 
parsed.

*extensible **pydantic* allows custom data types 
 to be 
defined or you can extend validation with methods on a model decorated with 
the validator 
 decorator.

*dataclasses integration *As well as BaseModel, *pydantic* provides a 
dataclass  decorator 
which creates (almost) vanilla python dataclasses with input data parsing 
and validation.»  

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Re: Tools of Interest: Hypothesis and Pydantic for Property-based Testing

[David Szent-Györgyi]

 

The Overview page for Pydantic offers the following Rationale 
, which makes me think that 
it might be of interest to Leo's users and Leo's developers: 

«So *pydantic* uses some cool new language features, but why should I 
actually go and use it?

*plays nicely with your IDE/linter/brain *There's no new schema definition 
micro-language to learn. If you know how to use python type hints, you know 
how to use *pydantic*. Data structures are just instances of classes you 
define with type annotations, so auto-completion, linting, mypy 
, IDEs (especially PyCharm 
), and your intuition 
should all work properly with your validated data.

*dual use **pydantic's* BaseSettings 
 class allows 
*pydantic* to be used in both a "validate this request data" context and in 
a "load my system settings" context. The main differences are that system 
settings can be read from environment variables, and more complex objects 
like DSNs and python objects are often required.*fast *In benchmarks 
 *pydantic* is faster than 
all other tested libraries.

*validate complex structures *use of recursive *pydantic* models 

, typing's standard types 
 (e.g. 
List, Tuple, Dictetc.) 
and validators  allow 
complex data schemas to be clearly and easily defined, validated, and 
parsed.

*extensible**pydantic* allows custom data types 
 to be 
defined or you can extend validation with methods on a model decorated with 
the validator 
 decorator.

*dataclasses integration *As well as BaseModel, *pydantic* provides a 
dataclass  decorator 
which creates (almost) vanilla python dataclasses with input data parsing 
and validation.»

>

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Re: Tools of Interest: Hypothesis and Pydantic for Property-based Testing

[David Szent-Györgyi]

 

The Overview page for Pydantic offers the following Rationale 
, which makes me think that 
it might be of interest to Leo's users and Leo's developers: 

«So *pydantic* uses some cool new language features, but why should I 
actually go and use it?

*plays nicely with your IDE/linter/brain *There's no new schema definition 
micro-language to learn. If you know how to use python type hints, you know 
how to use *pydantic*. Data structures are just instances of classes you 
define with type annotations, so auto-completion, linting, mypy 
, IDEs (especially PyCharm 
), and your intuition 
should all work properly with your validated data.

*dual use **pydantic's* BaseSettings 
 class allows 
*pydantic* to be used in both a "validate this request data" context and in 
a "load my system settings" context. The main differences are that system 
settings can be read from environment variables, and more complex objects 
like DSNs and python objects are often required.*fast *In benchmarks 
 *pydantic* is faster than 
all other tested libraries.

*validate complex structures *use of recursive *pydantic* models 

, typing's standard types 
 (e.g. 
List, Tuple, Dictetc.) 
and validators  allow 
complex data schemas to be clearly and easily defined, validated, and 
parsed.

*extensible**pydantic* allows custom data types 
 to be 
defined or you can extend validation with methods on a model decorated with 
the validator 
 decorator.

*dataclasses integration *As well as BaseModel, *pydantic* provides a 
dataclass  decorator 
which creates (almost) vanilla python dataclasses with input data parsing 
and validation.»
On Sunday, April 25, 2021 at 8:18:57 AM UTC-4 David Szent-Györgyi wrote:

> These links might be of interest to Edward and others developing Python 
> code, including Leo. 
>
> First, a praise of property-based testing 
> , 
> with examples that differentiate it from example-based testing. 
>
> Second, Hypothesis, «a Python library for creating unit tests which are 
> simpler to write and more powerful when run, finding edge cases in your 
> code you wouldn’t have thought to look for. It is stable, powerful and easy 
> to add to any existing test suite.
>
> It works by letting you write tests that assert that something should be 
> true for every case, not just the ones you happen to think of.»
>
> Third, Pydantic, «Data validation and settings management using python 
> type annotations. *pydantic* enforces type hints at runtime, and provides 
> user friendly errors when data is invalid. Define how data should be in 
> pure, canonical python; validate it with *pydantic*.» This includes 
> support for "custom" (user-defined) types. Pydantic uses type hinting from 
> Versions 3.6 and later of Python. 
>
> A plug-in for Pydantic supports Hypothesis. Used together, Pydantic 1.8 
> and Hypothesis v5.29.0 allow Hypothesis to automatically load support for 
> custom types like PaymentCardNumber (a credit-card number) and 
> PositiveFloat. Current releases are Pydantic 1.8.1 and Hypothesis 6.10.0. 
>
> Documentation for Hypothesis on Read the Docs 
> 
> Hypothesis on PyPi 
> Installing Hypothesis from source 
> 
>
> Documentation for Pydantic on Help Manual 
> 
> Documentation for Pydantic's Hypothesis plug-in 
> 
> Pydantic on PyPi 
> Pydantic on GitHub 
>

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Tools of Interest: Hypothesis and Pydantic for Property-based Testing

[David Szent-Györgyi]

These links might be of interest to Edward and others developing Python 
code, including Leo. 

First, a praise of property-based testing 
, with 
examples that differentiate it from example-based testing. 

Second, Hypothesis, «a Python library for creating unit tests which are 
simpler to write and more powerful when run, finding edge cases in your 
code you wouldn’t have thought to look for. It is stable, powerful and easy 
to add to any existing test suite.

It works by letting you write tests that assert that something should be 
true for every case, not just the ones you happen to think of.»

Third, Pydantic, «Data validation and settings management using python type 
annotations. *pydantic* enforces type hints at runtime, and provides user 
friendly errors when data is invalid. Define how data should be in pure, 
canonical python; validate it with *pydantic*.» This includes support for 
"custom" (user-defined) types. Pydantic uses type hinting from Versions 3.6 
and later of Python. 

A plug-in for Pydantic supports Hypothesis. Used together, Pydantic 1.8 and 
Hypothesis v5.29.0 allow Hypothesis to automatically load support for 
custom types like PaymentCardNumber (a credit-card number) and 
PositiveFloat. Current releases are Pydantic 1.8.1 and Hypothesis 6.10.0. 

Documentation for Hypothesis on Read the Docs 

Hypothesis on PyPi 
Installing Hypothesis from source 


Documentation for Pydantic on Help Manual 

Documentation for Pydantic's Hypothesis plug-in 

Pydantic on PyPi 
Pydantic on GitHub 

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