semvec/pypi-packages huggingface dataset:
django-payments-mollie 0.0.1
Mollie integration for django-payments
Requires Python: >=3.7

# django-payments-mollie

[![PyPI - Version](https://img.shields.io/pypi/v/django-payments-mollie.svg)](https://pypi.org/project/django-payments-mollie)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/django-payments-mollie.svg)](https://pypi.org/project/django-payments-mollie)

-----

Django Payments Mollie is a Django app that adds support for the [Mollie payment provider](https://www.mollie.com) to [Django Payments](https://django-payments.readthedocs.io/).

**Table of Contents**

- [Installation](#installation)
- [Configuration](#configuration)
- [Sandbox](#sandbox)
- [License](#license)

## Installation

```console
pip install django-payments-mollie
```

## Configuration

You should follow the configration guide in the Django Payments documentation. To setup this package as a payment variant, use the following `PAYMENT_VARIANTS` in the Django settings file:

```python
PAYMENT_VARIANTS = {
    "mollie": (
        "django_payments_mollie.provider.MollieProvider",
        {
            # For api key authentication
            "api_key": "test_example-api-key",

            # For access token authentication
            "access_token": "access_example-token",
            "testmode": True,

            # For OAuth2 authentication
            "client_id": "example-client-id",
            "client_secret": "example-client-secret",
            "testmode": True,
        }
    )
}
```

### Available configuration options

- `api_key`: A [Mollie API key](https://docs.mollie.com/overview/authentication#creating-api-keys), this is the simplest way to configure access to the Mollie API. Use the test key for development or testing. This also allows you to use payment methods that aren't enabled for live payments yet.

### Configuration helpers

#### Payment model

Django Payments docs will instruct you to create a Payment model that subclasses `BasePayment`. This package also provides a base model that you can use (optionally). The abstract model class `BaseMolliePayment` is a subclass of `BasePayment`, and it configures some of the fields as `required=True`, since Mollie requires them to be filled. Use it just like you would use Django payments' `BasePayment`:

```python
from django_mollie_payments.models import BaseMolliePayment

class Payment(BaseMolliePayment):
    ...
    # Add custom fields and methods
```

## Sandbox

The project contains a sandbox that shows a very simple implementation of Django Payments with the Mollie payment variant. You can use it to see how implementation could be done, or to actually run an application against your own Mollie account. See the [Sandbox README](sandbox/README.md) for details.

## License

`django-payments-mollie` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
micronets 0.1.2
A simple autograd engine along with a PyTorch/Tensorflow like API module to easily train neural nets
Requires Python: >=3.7

# Micronets

Micronets is a python framework for the simple implementation and training of deep neural networks. The project consists of a small auto-gradient engine module which implements back propagation through a DAG (Directed Acyclic Graph) and a module resembling the PyTorch/Tensorflow APIs to create and train dense neural networks on top of it.

The package also provides additional functionalities such as plotting of the training curve, make predictions and visualisation of computational graphs after training

## Installation 

```bash
pip install micronets
```

## Usage

### 1. Building Computational Graphs

```py
from micronets.autograd_engine import Value
from micronets.graph import draw

#inputs
x1 = Value(2.0,label='x1')
x2 = Value(4.0, label='x2')

#weights
w1 = Value(-3.0,label='w1')
w2 = Value(1.0,label='w2')

#bias
b = Value(3.3,label='b')

x1w1=x1*w1; x1w1.label='x1*w1'
x2w2=x2*w2; x2w2.label='x2*w2'

x1w1x2w2 = x1w1+x2w2; x1w1x2w2.label='x1w1 + x2w2'

z = x1w1x2w2 + b; z.label='z'

output = z.tanh(); output.label='output'

output.backward()

draw(output)
```
<br>

### 2. Neural Net Implementation

```py
from micronets.nn import DNN  # Dense Neural Network
from micronets.losses import binary_cross_entropy


xs = [
    [2.0, 3.0, -1.0],
    [3.0, -1.0, 0.5],
    [-0.5, -1.0, -1.0],
    [-1.0, -1.0, 0.5]
]

ys = [1.0, 0.0, 1.0, 0.0]

model = DNN(input_features=3, layers=[2,2,1], activations=['relu', 'relu', 'sigmoid'])  # creates model architecture

history, graph = model.train(X=xs, Y=ys, iterations=500, loss_function=binary_cross_entropy, learning_rate=0.1)  # trains network and stores loss function history and computational graph

predictions = model.predict([  # outputs predictions
    [2.0, -1.0, 3.5],
    [-1.0, -3.0, 0.5]
]) 

model.plot(history)  # plots learning curve
```

Additionally the `playground.ipynb` file demonstrates functionality through a more complex example along with its outputs.
<br>
<br>

Lastly, a big thank you to [Andrej Karpathy](https://github.com/karpathy) for inspiring this project :)

## License

MIT