semvec/pypi-packages huggingface dataset:
smacom 1.0.2
SamaCOM a Python Package extends main framework functionalities
Requires Python: >=3.6, <4
Keywords: smaPython development environment


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## SmaCOM

PythonCLI
dynamatic 1.0.0b5
A DynamoDB library based on boto3
Requires Python: >=3.6

# Dynamatic

Dynamatic is a python library for working with DynamoDB. It is NOT an ORM. It sits somewhere between the low-level [boto3](https://github.com/boto/boto3) library (on which Dynamatic is based) and high-level ORMs like [PynamoDB](https://github.com/pynamodb/PynamoDB).

## Motivation

First, I was inspired by [this blog post](https://www.trek10.com/blog/dynamodb-single-table-relational-modeling/) which was inspired by [this excellent re:Invent 2018 session by Rick Houlihan](https://youtu.be/HaEPXoXVf2k) about how to properly model data in DynamoDB for efficiency and scale.

Second, I did not want an ORM. ORMs like PynamoDB package their own methods of defining object schemas but there are already excellent libraries out there like [Pydantic](https://github.com/samuelcolvin/pydantic) or [Marshmallow](https://github.com/marshmallow-code/marshmallow). They also tend to impose traditional relational database patterns (like Active Record) that do not scale well.

Third, boto3 is a great library but its DynamoDB API is painfully low-level and not particularly "Pythonic". Working with advanced features like [Update Expressions](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Expressions.UpdateExpressions.html) and [Projection Expressions](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Expressions.ProjectionExpressions.html) is complex and unintutive.

So this library aims to be a happy medium: Providing enough abstraction and "developer ergonomics" to get up and running with DynamoDB quickly, while not getting in the way with high-level ORM features.

## Installation

Dynamatic requires Python 3.6 or later. You can install it via pip.

```bash
pip install dynamatic
```

## Usage

TODO! Documentation and usage examples coming soon!

## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License

[MIT](https://choosealicense.com/licenses/mit/)
Py-FCM 0.2.21
Fuzzy cognitive maps python library
Requires Python: >=3.6

# Py_FCM
Fuzzy cognitive maps python library

###Example usage

```
from Py_FCM import from_json

fcm_json = """{
         "iter": 500,
         "activation_function": "sigmoid",
         "actv_func_params": {"lambda_val":1},
         "memory_influence": false,
         "result": "last",
         "concepts" :
          [
            {"id": "concept_1", "type": "SIMPLE", "activation": 0.5},
            {"id": "concept_2", "type": "DECISION", "custom_function": "sum_w", "custom_func_args": {"weight":0.3}},
            {"id": "concept_3", "type": "SIMPLE", "memory_influence":true },
            {"id": "concept_4", "type": "SIMPLE", "custom_function": "saturation", "activation": 0.3}
          ],
         "relations":
          [
            {"origin": "concept_4", "destiny": "concept_2", "weight": -0.1},
            {"origin": "concept_1", "destiny": "concept_3", "weight": 0.59},
            {"origin": "concept_3", "destiny": "concept_2", "weight": 0.8911}
          ]
        }
        """
my_fcm = from_json(fcm_json)
my_fcm.run_inference()
result = my_fcm.get_result_by_type(node_type='any')
print(result)
```