semvec/pypi-packages huggingface dataset:
sensitive-test 1.0.3
A small, simple, and fast ordered failfast test-suite for Python.
License: MIT
Requires Python: >=3.6
Keywords: test,tests,ci,debug

# tests

```
pip install sensitive-test
```

A small, simple, and fast ordered fail-fast tests suite for Python.

```py
import sensitive

# any Exception thrown will make the test running it fail,
# and the program will immediately return exit code 1.

with sensitive.test("test name 1"):
  print("Oh yes")

with sensitive.test("test name 2"):
  raise Exception("Trolled")

with sensitive.test("test name 3"):
  print("oh no")
```
pycache3 0.1.1
A safe and lightweight cache library, written in pure-Python.
License: MIT

pycache3: Python3 cache library
===============================

**PyCache3** is a MIT licensed  safe and lightweight cache library, written
in pure-Python.

.. image:: https://img.shields.io/badge/LICENSE-MIT-green
    :target: https://github.com/VenusMonkey/cache3/blob/master/LICENSE
.. image:: https://img.shields.io/badge/version-0.1.2-informational
    :target: https://pypi.org/project/pycache3
.. image:: https://img.shields.io/badge/python-3.5+-blueviolet
    :target: https://www.python.org

cache3 is very tiny and completely implemented by the Python standard library without any third-party dependencies, so it can be easily embedded in any Python program or script.

Installing
==========

pycache3 is available from PyPI and can be installed by running:

.. code:: shell

    pip install -U pycache3

or install from github

.. code:: shell

    pip install git+https://github.com/VenusMonkey/cache3.git

or download the file and install it locally

.. code-block:: shell

    $ git clone https://github.com/VenusMonkey/cache3.git
    $ cd cache3
    $ python setup.py install

Feature
=======

- Pure-Python
- Thread-safe and process-safe
- High performance
- 100% test coverage
- Extension friendly
- Supports multiple eviction policies and is easy to scale
- Supports multiple data types
- Compatible with all platforms


Links
=====

- Documentation: https://venusmonkey.github.io/cache3

- PyPI Releases: https://pypi.org/project/pycache3

- Source Code: https://github.com/VenusMonkey/cache3

- Issue Tracker: https://github.com/VenusMonkey/cache3/issues

License
=======

Copyright (c) 2020-2021 clarkmonkey@163.com
Licensed under the MIT License.
filtercss 0.0.2
Tool for filtering unused CSS.
License: MIT
Requires Python: >=3.8

# filtercss

**filtercss** lets you filter unused rules from CSS files, like <https://purgecss.com/> but in Python.

## Installation

```shell
pip install filtercss
```

## Example

Filtering is simple, just use `filter_css`.

```python
from filtercss import filter_css

css = """
.test1, .test3 {
    color: red;
}
.test2 {
    color: blue;
}
"""

html = """
<a class="test1 some-other-cls">test</a>
"""

res_css = filter_css(css, html)
assert "test1" in res_css
assert "test2" not in res_css
```

## Limitations

**filtercss** uses [tinycss2](https://doc.courtbouillon.org/tinycss2/stable/). Also **filtercss** will not be able
to anticipate what DOM elements might be inserted by any JavaScript and thus will happily filter out the unused
CSS rules they might want to use. **filtercss** uses BeautifulSoup4 to parse HTML and to detect whether a
CSS selector in a rule matches against a given file. However, BeautifulSoup4 might not support all of the CSS
selectors, in which case **filtercss** is conservative and includes the given CSS rule. **filtercss** is also
not optimal in that it includes a full CSS rule if any of its selectors matches and it does not filter out
unmatched selectors from the rule. Note that this may change in the future.

## License

MIT License
ncov-parser 0.7.0
A nCoV package for parsing analysis files
License: MIT
Requires Python: >=3.6

# ncov_parser

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

The `ncov_parser` package provides a suite of tools to parse the files generated
in the Nextflow workflow and provide a QC summary file.  The package requires
several files including:
* <sample>.variants.tsv
* <sample>.variants.norm.vcf
* <sample>.pass.vcf
* <sample>.per_base_coverage.bed
* <sample>.primertrimmed.consensus.fa
* <sample>.consensus.fasta
* alleles.tsv

An optional metadata file with qPCR ct and collection date values can be
included.

In addition, `bedtools` should be run to generate a
`<sample>.per_base_coverage.bed` file to generate mean and median depth of
coverage statistics.


## Installation
After downloading the repository, the package can be installed using `pip`:
```
git clone https://github.com/jts/ncov-tools
cd ncov-tools/parser
pip install .
```


## Usage
The library consists of several functions that can be imported.
```
import ncov.parser
```
Several classes are available representing the different files that can
be processed.
```
ncov.parser.Alleles
ncov.parser.Consensus
ncov.parser.Lineage
ncov.parser.Meta
ncov.parser.PerBaseCoverage
ncov.parser.Snpeff
ncov.parser.Variants
ncov.parser.Vcf
ncov.parser.primers
```

Similarly, wrapper scripts for creating a standard format output can be found in
`ncov.parser.qc`
```
import ncov.parser.qc as qc
qc.write_qc_summary_header()
qc.write_qc_summary()
```

### Top levels scripts
In the `bin` directory, several wrapper scripts exist to assist in generating
QC metrics.

To create sample level summary qc files, use the `get_qc.py` script:
```
get_qc.py --variants <sample>.variants.tsv or <sample>.pass.vcf
--coverage <sample>.per_base_coverage.bed --meta <metadata>.tsv
--consensus <sample>.primertrimmed.consensus.fa [--indel] --sample <samplename>
--platform <illumina or oxford-nanopore> --run_name <run_name> --alleles alleles.tsv
--indel --lineage <Pangolin lineage report> --aa_table <SNPEff annotation table>
```

Note the `--indel` flag should only be present if indels will be used in the
calculation of variants.

Once this is complete, we can use the `collect_qc_summary.py` script to
aggregate the sample level summary files into a single run tab-separate file.
```
collect_qc_summary.py --path <path to sample.summary.qc.tsv files>
```

To create an amplicon BED file from a primer scheme BED file:
```
primers_to_amplicons.py --primers <path to primer scheme BED file>
--offset <number of bases to offset> --bed_type <full or no_primers or unique_amplicons>
--output <full path to file to write BED data to>
```

## Credit and Acknowledgements
Note that this tool has been used in conjunction with the [@jts `ncov-tools`](https://github.com/jts/ncov-tools)
suite of tools. 

BED file importing and amplicon site merging obtained from the ARTIC pipeline:
`https://github.com/artic-network/fieldbioinformatics/blob/master/artic/vcftagprimersites.py`

## License
MIT
modoboa-pdfcredentials 1.5.0
Generate PDF documents containing user credentials
License: MIT
Keywords: email

PDF credentials for Modoboa
===========================

|gha| |codecov|

A simple `Modoboa <http://modoboa.org/>`_ extension which provides a
way to download PDF documents containing users credentials.

Installation
------------

Install this extension system-wide or inside a virtual environment by
running the following command::

  $ python setup.py install

Then, edit the ``settings.py`` file of your modoboa instance and
add ``modoboa_pdfcredentials`` inside the ``MODOBOA_APPS`` variable
like this::

  MODOBOA_APPS = (
    # ...
    'modoboa_pdfcredentials',
  )

Restart the python process running modoboa (uwsgi, gunicorn, apache,
whatever).

Configuration
-------------

All the configuration is done from the admin panel (*Modoboa >
Parameters > PDF Credentials*).

.. |gha| image:: https://github.com/modoboa/modoboa-pdfcredentials/actions/workflows/plugin.yml/badge.svg
   :target: https://github.com/modoboa/modoboa-pdfcredentials/actions/workflows/plugin.yml
.. |codecov| image:: https://codecov.io/gh/modoboa/modoboa-pdfcredentials/branch/master/graph/badge.svg
   :target: https://codecov.io/gh/modoboa/modoboa-pdfcredentials