semvec/pypi-packages huggingface dataset:
pyqtx-widgets 0.1.2
pyqtx Widgets
Requires Python: >=3.4

pyqtx_widgets
=======================

This lib is in active use and makes knocking up PyQt5 apps a little quicker.

It's just a bunch of constructors and Xtended widgets to add some functionality.
Now some of you may scream about adding another layer!!
Instead is viewed as  DRY, as same stuff gets copied across projects


.. code-block:: python

    ## Layout
    lay = QtWidgets.QVBoxLayout()
    lay.setContentMargins(10,10,10,10)
    lay.setSpacing(20)

    # vs
    lay = xwidgets.vlayout(margin=10. spacing=20)

    ## Toolbutton
    butt = QtWidgets.QToolButton()
    butt.setAutoRaise(True)
    butt.setText("Foo")
    butt.clicked.connect(self.on_button)

    # vs
    butt = xwidgets.XToolButton(text="Foo", autoRaise=True, clicked=self.on_button)
excel2wisxml 3.5.7
Generation of WMO Core 1.3 profile metadata

# excel2wisxml, an excel to WMO XML conversion tool

excel2wisxml tool has been developed by Météo-France and allows the generation of large set of metadata WMO Core 1.3 profile issued from an excel document. The python package is under the free open source G

For any information, please contact: gisc_support@meteo.fr

Official documentation for excel2wis can be found in the [exce2wis github repository](https://github.com/OpenWIS/excel2wis):

* [User Manual](https://github.com/OpenWIS/excel2wis/blob/master/Excel2wisxml_manual.pdf "excel2wisxml User Manual")

* [Developer Manual](https://github.com/OpenWIS/excel2wis/blob/master/excel2wisxml_developer_manual.md "excel2wisxml Developer Manual")
alephmarcreader 1.1.0
A package to read Marc data obtained from Aleph, the catalogue of the library of the University of Basel

# alephmarcreader
## General
Python library to read Marc obtained from Aleph, the catalogue of the library of the University of Basel.

This library supports Marc21, MARCXML, and AlephX.

## Documentation
The docstrings can be displayed with pydoc (from the project root): `pydoc alephmarcreader.abstractalephmarcreader.AbstractAlephMarcReader
`. For the inner classes such as `Person`, run `pydoc alephmarcreader.abstractalephmarcreader.AbstractAlephMarcReader.Person`.

## Design
`alephmarcreader.abstractalephmarcreader.AbstractAlephMarcReader` provides methods to access Marc data.
It is an abstract class that has two abstract methods `__get_field` and `__get_subfield_text` that have to be implemented in the subclass for the file format at hand.

## Unit Tests

From the project root, run `python -m unittest alephmarcreader.tests.test_[Marc[21|XML]|X]Reader`.

## Dependencies

- `pymarc`: install with pip
- `lxml`: install with pip

The library works both with python2 and python3.

## Usage
Install the package with `pip install alephmarcreader`.

Example usage:
```python
# import
from alephmarcreader import AlephMarcXMLReader

# Read data from local file
marc = AlephMarcXMLReader('example_file.xml')

# get some fields
author = marc.get_author()[0]
recipient = marc.get_recipient()[0]
date = marc.get_date()[0]

# print it
print(author.name)
print(recipient.name)
print(date)
```

For an exhaustive list of the API, use `pydoc`, as described above.
hypia 0.0.3
A package for doing hyper-spectral image augmentation for deep learning.

# HYPer-spectral Image Augmentation (hypia)

The following is a Python package (available through the Python Package Manager (pip)) for the augmentation of hyper-spectral images for uses in deep learning.

### Why does this exist?

* Other image augmentation libraries often only accept RGB or grayscale images (with options for RGBA images usually too). However, in a scientific context, we often have images with more than 3/4 channels (so-called hyper-spectral imaging) and it seemed like a good idea to have an augmentation library which deals with all channels in parallel.
* Another problem is that some image augmentation frameworks can accept more than 3/4 channels but convert the data to unsigned 8-bit integers (see PyTorch's [torchvision](https://pytorch.org/docs/stable/torchvision/transforms.html)) which is damaging for scientific data where we care about the actual numbers of the data. This can lead to the images losing some of the features and relative contrasts of features which is important for our science.

I had a look at several image augmentation packages but none of them seemed to satisfy both of these criteria so here we are.

### Installation

There are two ways to install this package: either from pip or from source from GitHub.

From pip:

```bash
>>> pip install hypia
```

From GitHub:
```bash
>>> git clone https://github.com/rhero12/hypia
>>> cd hypia
>>> python setup.py install
```

### Documentation

The documentation (including examples) is available [here](https://rhero12.github.io/hypia/).