semvec/pypi-packages huggingface dataset:
aceso 0.1.0
Lightweight package to calculate 2SFCA and other measures of spatial accessibility
License: MIT
Requires Python: >=2.7
Keywords: geospatial,geo,gis,2SFCA,gravity,access,catchment-area

# aceso [![PyPI version](https://badge.fury.io/py/aceso.svg)](https://badge.fury.io/py/aceso) [![Build Status](https://travis-ci.org/tetraptych/aceso.svg?branch=master)](https://travis-ci.org/tetraptych/unrasterize) [![Coverage Status](https://coveralls.io/repos/github/tetraptych/aceso/badge.svg?branch=master)](https://coveralls.io/github/tetraptych/aceso?branch=master) [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
Aceso is a lightweight package to calculate 2SFCA and other measures of spatial accessibility.

This package shares a name with [the Greek goddess of the healing process](https://en.wikipedia.org/wiki/Aceso). The two go hand-in-hand, since the most common applications of 2SFCA and gravity models of spatial interaction are for measuring spatial access to healthcare services.

## Installation

`aceso` is available on [PyPI](https://pypi.org/project/aceso/) and can be installed via the following command:

```
pip install aceso
```

The only dependency is `numpy`.

## Gallery

![Potential spatial access to abortion clinics in Texas (2017)](https://farm1.staticflickr.com/902/39848822400_197406d944_z_d.jpg "Gravity model using raised cosine decay with 3 hour driving radius")
Deep-Models 0.1.5
Keras deep learning model implementations
License: MIT

# DeepModels
Implementations of various deep learning models using Keras.

## Requirements
An existing installation of either Tensorflow or Theano.

## Installation
```
pip install deep-models
```

## Usage

The models are implemented using Keras and instantiation returns a Keras Model object unless otherwise noted.

### Wide Residual Network
```python3
from deep_models import wide_residual_network as wrn

# Load your data
trainX = ...
trainY = ...
img_shape = (32, 32, 3)

# Create the model
# k is the width, 6 * n + 4 is the depth
model = wrn.build_model(img_shape, classes=10, n=4, k=10, dropout=0.3)

# Train the model
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["acc"])
model.fit(
    trainX, trainY,
    batch_size=128,
    epochs=100,
    validation_split=0.2)
```

## Examples
Some working examples are available in the notebooks directory.

## License
See LICENSE file