semvec/pypi-packages huggingface dataset:
redcapy 0.4.1
Python package to access REDCAP API

# redcapy

Library for accessing RedCap API from Python

## Installation

Install the package with pip directly with:

```console
pip install redcapy
```

## Implemented functionalities

### Get data from API

By now the implemented functionalities are for obtaining data from the REDCAP server, no API calls related with saving new data data or deleting data has been implemented. Available API calls are:
```python
import redcapy
import datetime
redcap_handler = redcapy.request.APIHandler(api_url=api_url, token=token)
metadata = redcap_handler.get_metadata()
redcap_version = redcap_handler.get_redcap_version()
users = redcap_handler.get_users()
arms = redcap_handler.get_arms()
events = redcap_handler.get_events()
field_names = redcap_handler.get_field_names()
file = redcap_handler.get_file(record=1)
form_event_mapping = redcap_handler.get_form_event_mapping()
instruments_pdf = redcap_handler.get_instruments_pdf(output_file="./instruments.pdf")
instruments = redcap_handler.get_instruments()
project = redcap_handler.get_project_info()
report = redcap_handler.get_reports(1)
participant_list = redcap_handler.get_participant_list(instrument=instruments[-1]['instrument_name'])
records = redcap_handler.get_records()
date_range_begin = datetime.datetime.strptime("2019-10-10", "%Y-%m-%d")
date_range_end = datetime.datetime.strptime("2019-12-10", "%Y-%m-%d")
records = redcap_handler.get_records(date_range_begin=date_range_begin, date_range_end=date_range_end)
```
binalyzer-cli 1.0.0
Binary Data Analyzer

# Binalyzer Command Line Interface (CLI)

This repository implements Binalyzer's CLI. It is part of the overall [Binalyzer]
project and comes with its installation.

[Binalyzer]: https://github.com/denisvasilik/binalyzer

## Installation

Binalyzer is published on [PyPI] and can be installed from there:

```sh
~$ pip3 install --upgrade binalyzer
```

If you wish to install [Binalyzer] for development purposes, refer to [the
developer's guide].

[PyPI]: https://pypi.org/project/Binalyzer/
[Binalyzer]: https://pypi.org/project/Binalyzer/
[the developer's guide]: DEVELOPMENT.md

## Documentation

Documentation is available from [binalyzer.readthedocs.io].

[binalyzer.readthedocs.io]: https://binalyzer.readthedocs.io/en/latest/

## Get in touch

- Report bugs, suggest features or view the source code [on GitHub].
- For contributions refer to [the contributor's guide].

[on GitHub]: https://github.com/denisvasilik/binalyzer
[the contributor's guide]: CONTRIBUTING.md

# License

Licensed under the MIT license ([LICENSE-MIT] or http://opensource.org/licenses/MIT).

[LICENSE-MIT]: LICENSE.rst
binalyzer-data-provider 1.0.0
Binary Data Analyzer

# Binalyzer Data Provider

This repository implements data providers. It is part of the overall [Binalyzer]
project and comes with its installation.

[Binalyzer]: https://github.com/denisvasilik/binalyzer

## Installation

Binalyzer is published on [PyPI] and can be installed from there:

```sh
~$ pip3 install --upgrade binalyzer
```

If you wish to install [Binalyzer] for development purposes, refer to [the
developer's guide].

[PyPI]: https://pypi.org/project/Binalyzer/
[Binalyzer]: https://pypi.org/project/Binalyzer/
[the developer's guide]: DEVELOPMENT.md

## Documentation

Documentation is available from [binalyzer.readthedocs.io].

[binalyzer.readthedocs.io]: https://binalyzer.readthedocs.io/en/latest/

## Get in touch

- Report bugs, suggest features or view the source code [on GitHub].
- For contributions refer to [the contributor's guide].

[on GitHub]: https://github.com/denisvasilik/binalyzer
[the contributor's guide]: CONTRIBUTING.md

# License

Licensed under the MIT license ([LICENSE-MIT] or http://opensource.org/licenses/MIT).

[LICENSE-MIT]: LICENSE.rst
binalyzer-rest 1.0.0
Binary Data Analyzer

# Binalyzer REST

This repository implements Binalyzer's REST interface. It is part of the overall
[Binalyzer] project and comes with its installation.

[Binalyzer]: https://github.com/denisvasilik/binalyzer

## Installation

Binalyzer is published on [PyPI] and can be installed from there:

```sh
~$ pip3 install --upgrade binalyzer
```

If you wish to install [Binalyzer] for development purposes, refer to [the
developer's guide].

[PyPI]: https://pypi.org/project/Binalyzer/
[Binalyzer]: https://pypi.org/project/Binalyzer/
[the developer's guide]: DEVELOPMENT.md

## Documentation

Documentation is available from [binalyzer.readthedocs.io].

[binalyzer.readthedocs.io]: https://binalyzer.readthedocs.io/en/latest/

## Get in touch

- Report bugs, suggest features or view the source code [on GitHub].
- For contributions refer to [the contributor's guide].

[on GitHub]: https://github.com/denisvasilik/binalyzer
[the contributor's guide]: CONTRIBUTING.md

# License

Licensed under the MIT license ([LICENSE-MIT] or http://opensource.org/licenses/MIT).

[LICENSE-MIT]: LICENSE.rst
quants 2.0.1
ANN based learning of quantifiers
Requires Python: >=3.8

# quants package

Package for Artifical Neural Network (**ANN**) based learning of quantifiers.

It contains definition of scenes of elements and classes for both quantifier sampling and NN simple example package.


## Classes

### Quantifer classes
These classes are a quantifier hierarchy with methods and definitions for:
* *Scenes* that are composed of world *elements* pertaining to use of a sentence of the form "**q as are bs**".
* Construction of quantifier if parameters (such as in the case of ExactlyN for instance) are required.
* Static method for generation of a completely random scene.
* Generation of permuted prototype scenes that are evaluated as true under the given quantifier q.
* Evaluation of a given scene (returns **TRUE** for scenes generated by the same quantifier q in the previous method).

### Classifier class approach
This approach assumes that quantifiers are learned as a group, essentially each quantifier q **TRUE** *scene* is a negative example for all other quantifiers q'.
There is of course many scenes for which more than one quantifier is evaluated as **TRUE**, for instance if "Both students are eating" is **TRUE** then "Some students are eating" is also **TRUE**, this can be mitigated by implicatures which we do not address.

The classifier is in effect a solver for which q makes the sentence "**q as are bs**" most likely given an input *scene* s.

This enables us to use not only the quantifier quantify evaluation methods but the classifier in order to generate a teacher-student scheme.

### AE approach
The AE approach tries to let an AE look at *scenes* where a given quantifier q was used by a teacher (language speaker), this is repeated many times till whatever structure typical to scenes **TRUE** under the quantifier q are encoded in the AE hidden values' represented structure. When learning is complete and when we are given a scene we use the AE as an anomaly detector to decide whether the scene is True under the quantifier q. The idea is that after seeing many q **TRUE** scenes a non q **TRUE** scene will have relatively high reconstruction errors.
binalyzer-wasm 1.0.0
Binary Data Analyzer

# Binalyzer WebAssembly Extension

This repository provides the WebAssembly extension for [Binalyzer]. It implements
value providers (e.g. for LEB128) that are used to parse WebAssembly's
binary module format. It is part of the overall [Binalyzer] project and comes with
its installation.

[Binalyzer]: https://github.com/denisvasilik/binalyzer

## Installation

Binalyzer is published on [PyPI] and can be installed from there:

```sh
~$ pip3 install --upgrade binalyzer
```

If you wish to install [Binalyzer] for development purposes, refer to [the
developer's guide].

[PyPI]: https://pypi.org/project/Binalyzer/
[Binalyzer]: https://pypi.org/project/Binalyzer/
[the developer's guide]: DEVELOPMENT.md

## Documentation

Documentation is available from [binalyzer.readthedocs.io].

[binalyzer.readthedocs.io]: https://binalyzer.readthedocs.io/en/latest/

## Get in touch

- Report bugs, suggest features or view the source code [on GitHub].
- For contributions refer to [the contributor's guide].

[on GitHub]: https://github.com/denisvasilik/binalyzer
[the contributor's guide]: CONTRIBUTING.md

# License

Licensed under the MIT license ([LICENSE-MIT] or http://opensource.org/licenses/MIT).

[LICENSE-MIT]: LICENSE.rst