semvec/pypi-packages huggingface dataset:
itreport 0.0.2
Data gatherer and final data report generator.
License: MIT
Keywords: jira atlassian it report

Quick SetUp
===========

ITReport uses python-jira, so it can use the `.netrc` file too.

The easiest setup is:

1. Get an api token. If you are using Jira Cloud, you can get it at
   https://id.atlassian.com/manage/api-tokens

2. Edit the `~/.netrc` file with an entry like::

       machine <uri to your jira instance>
         login <your username>
         password <the jira token>

   example::

       machine example.atlassian.net
         login fubar@example.org
         password ASDFHUdflasfj867ua

3. Install itreport::

    pip install itreport


This installs some scripts:

jiradump
--------

Dumps jira issues, authors and more into yaml files.

Example:

    itr-jiradump --server example.atlassian.net --project EX

itreport
--------

Applies some templates in order to generate a final report.

Example:

    itreport -t issue.rst -vvv
flatto 0.0.1
Easy flattening with detailed setting
License: MIT

Easy flattening with detailed setting
-------------------------------------
Powered by [Yamato Nagata](https://twitter.com/514YJ)

[GitHub](https://github.com/delta114514/flatto)

```python
>>> list(flatten(["12", (3, [4]), [5, 6, {7, "8"}]]))
['12', 3, 4, 5, 6, 7, '8']
>>> list(flatten(["12", (3, [4]), [5, 6, {7, "8"}]], ignore=(), peep=()))
['1', '2', 3, 4, 5, 6, 7, '8']
>>> list(flatten(["12", (3, [4]), [5, 6, {7, "8"}]], ignore=(), peep=(tuple,)))
['1', '2', (3, 4), 5, 6, 7, '8']
>>> list(flatten(["12", (3, [4]), [5, 6, {7, "8"}]], ignore=(), peep=(tuple,), depth=1))
['1', '2', (3, [4]), 5, 6, {7, '8'}]
>>> list(flatten(["12", (3, [4]), [5, 6, {7, "8"}]], ignore=(), peep=(tuple,), depth=0))
['12', (3, [4]), [5, 6, {7, '8'}]]
```
torchacg 0.1.0
TorchACG is a Pytorch based framework for GAN based ACG applications.
License: MIT
Keywords: pytorch

TorchACG
==========

TorchACG is a Pytorch based framework for designing and developing GAN based ACG applications. This framework has been designed to provide
building blocks for popular GANs and also to allow customization for
cutting edge research.

System requirements
~~~~~~~~~~~~~~~~~~~

TorchACG should work on

-  all Linux distributions no earlier than Ubuntu 16.04
-  macOS X
-  Windows 10

Installation
~~~~~~~~~~~~

Using pip (for stable release):

::

    pip install torchacg

Using pip (for latest master):

::

    conda install -c tczhangzhi torchacg

From source:

::

    git clone https://github.com/tczhangzhi/torchacg.git
    cd torchacg
    python setup.py install

Getting Started
~~~~~~~~~~~~~~~

The network architectures of popular GANs and cutting edge GAN based
Neural Style Transfer methods are out of the box:

::

    from torchacg.model.acgan import Generator, Discriminator

    g_model = Generator()
    d_model = Discriminator()

The diverse loss functions and complicated adversarial training methods
are packed in different learners. It is convenient for users to
reproduce the state-of-the-art training methods quickly without knowing
implementation details:

::

    from torchacg.trainer.acgan import ACGANTrainer

    trainer = ACGANTrainer(g_model, d_model, dataloader, device=device)
    trainer.start(epoch=10)

Test
~~~~

Setup environments:

::

    export PYTHONPATH=./

Run the test case:

::

    python tests/test_acgan.py

Documentation
~~~~~~~~~~~~~

Coming soon.

License
~~~~~~~

PyTorch is `MIT <http://opensource.org/licenses/MIT>`__-style licensed,
as found in the LICENSE file.

Copyright (c) 2020-present, Zhang Zhi