semvec/pypi-packages huggingface dataset:
sylte 0.0.3
Pickling and unpickling of function arguments
License: MIT
Requires Python: >=3.8,<4.0
Keywords: pickle,pickling,arguments,debug,cache

# sylte

![Testing and linting](https://github.com/danhje/sylte/workflows/Test%20And%20Lint/badge.svg)
[![codecov](https://codecov.io/gh/danhje/sylte/branch/main/graph/badge.svg)](https://codecov.io/gh/danhje/sylte)
![GitHub release (latest by date including pre-releases)](https://img.shields.io/github/v/release/danhje/sylte?include_prereleases)
![PyPI](https://img.shields.io/pypi/v/sylte)

Sylte provides a decorator that stores function arguments as a pickle file in a central location,
and functions for retrieving those arguments later on, so that the function can be re-invoked
with the same args. Useful when writing or debugging a function that exists somewhere near the end
of a long-running pipeline.

## Installation

Using poetry:

```shell
poetry add sylte
```

Using pipenv:

```shell
pipenv install sylte
```

Using pip:

```shell
pip install sylte
```

## Usage

Let's say we have a function `transform` that we want to debug or modify. We will have to call it
several times to see how it behaves with real data. It's normally called as part of a long-running
pipeline, and we don't want to have to wait for this pipeline every time. We could write a unit-test,
but perhaps we aren't yet sure how the output will look, and perhaps the input-data is complex
and time-consuming to recreate in a test, and we'd prefer to do that when the function is finalized,
to avoid having to repeatedly modify the test as the function is modified.

Enter sylte. By applying the `@sylt` decorator to the function and running the pipeline once,
the args are recorded, and can be retrieved later.

```python
from sylte import sylt

@sylt
def transform(this_huge_df, this_other_huge_df, this_object_with_lots_of_attributes):
    ...
```

The arg set will be stored in a pickle file in the default cache location for the os.
The location can be seen by running `from sylte import CACHE_DIR; print(CACHE_DIR)`.
To use a different location than the default, specify the location with the environment variable `SYLTE_CACHE_DIR`.

The file name will have the format
`<file name>-<function name>-<timestamp>.pickle`.

The function `latest` will retrieve an unsylt the latest arg set, returning a tuple with args and kwargs.

```python
>>> from sylte import latest
...
>>> args, kwargs = latest()
>>> transform(*args, **kwargs)
```

`show` returns a list of all sylted arg sets:

```python
>>> from sylte import show
...
>>> show()
['demo-transform-2022-01-14-15-08-59',
 'demo-transform-2022-01-14-15-12-33',]
```

`unsylt` unsylts and returns the arg set with the specified name as output by `show`, i.e. the filename with the extension omitted:

```python
>>> from sylte import unsylt
...
>>> args, kwargs = unsylt('demo-add-2022-01-14-15-08-59')
>>> transform(*args, **kwargs)
```

`clear` deletes all previously sylted arg sets:

```python
>>> from sylte import clear, show
...
>>> clear()
>>> show()
[]
```
django-tiny-erp 0.4.2
Enterprise Resource Planning (ERP) for tiny companies
License: MIT

# tiny-erp

[![Build Status](https://api.travis-ci.com/moshthepitt/tiny-erp.svg?branch=master)](https://travis-ci.com/moshthepitt/tiny-erp)

Enterprise Resource Planning (ERP) for tiny companies

## Installation

1. Install the app: `pip install django-tiny-erp`
2. Add the tiny-erp apps to `INSTALLED_APPS`.

   ```py
   INSTALLED_APPS = [
       # the usual django stuff
       "tiny_erp",
       "tiny_erp.apps.locations",
       "tiny_erp.apps.products",
       "tiny_erp.apps.purchases",
       "small_small_hr",
       'model_reviews',
       'django_comments',
   ]
   ```

3. Run `manage.py migrate` so that Django will create the model_review tables.
4. Set up the [django-model-reviews](https://github.com/moshthepitt/django-model-reviews) and [small-small-hr](https://github.com/moshthepitt/small-small-hr) apps.
small-small-hr 0.4.2
Minimal human resource management app for Django
License: MIT

# small-small-hr

[![Build Status](https://travis-ci.org/moshthepitt/small-small-hr.svg?branch=master)](https://travis-ci.org/moshthepitt/small-small-hr)

`small-small-hr` is a really really small and light-weight Django application that helps you (yes you!) to manage certain aspects of HR (human resource) in your small or large business/organization/whatever.

## Installation

```sh
pip install small-small-hr
```

## Features

At this time, `small-small-hr` supports the following:

### Employee records

You can keep track of all your employee's details.  This is achieved through a custom `StaffProfile` model attached to your `User` model via a `one-to-one` relationship.  Some of the available fields on this model are:

* first name
* last name
* gender
* birth day
* photo
* number of allowed leave days per year
* whether or not overtime is allowed for the employee
* data - a JSON field that allows you to store any extra information

### Employee Documents

You can keep track of an unlimited number of employee documents (think employment contracts, performance reviews, scans of identification documents, etc).  This is achieved by an `StaffDocument` model that has a `one-to-many` relationship with the `StaffProfile` model (above).

The fields on this model are

* staffprofile_id
* name of document
* description of document
* dcoument file

### Leave management

All employees can log in and make a request for leave.  To achieve this, there exists a `LeaveRequest` model with these fields:

* staffprofile_id
* request date
* start date
* end date
* reason for leave
* status (pending approval/approved/rejected)
* comments (made by the admin, e.g. reasons for refusal)

Once a LeaveRequest object is created, an administrator should review it and approve/reject it.

### Overtime hours tracking

Employees who are allowed overtime can log in and record overtime hours.  This is done by an `OvertimeHour` model with these fields:

* staffprofile_id
* date
* start time
* end time
* reason for overtime
* status (pending approval/approved/rejected)
* comments (made by the admin, e.g. reasons for refusal)

Admins can download overtime hours reports for a particular period.

## Contribution

All contributions are welcome.

To set up the project:

1. Clone this repo
2. `pip install -r requirements/dev.txt`
3. `pre-commit install`

## Testing

```sh

pip install -U tox

tox
```
adifa 0.0.2
Adifa is a framework for visualising single-cell gene expression data in a web browser
License: MIT
Requires Python: >=3.8


[![tests](https://github.com/haniffalab/sci-adifa/actions/workflows/test-coverage.yml/badge.svg)](https://github.com/haniffalab/sci-adifa/actions/workflows/test-coverage.yml)
[![build](https://github.com/haniffalab/sci-adifa/actions/workflows/docker-release.yml/badge.svg)](https://github.com/haniffalab/sci-adifa/actions/workflows/docker-release.yml)
[![docker](https://github.com/haniffalab/sci-adifa/actions/workflows/docker-latest.yml/badge.svg)](https://github.com/haniffalab/sci-adifa/actions/workflows/docker-latest.yml)
[![sphinx](https://github.com/haniffalab/sci-adifa/actions/workflows/sphinx-build.yml/badge.svg)](https://github.com/haniffalab/sci-adifa/actions/workflows/sphinx-build.yml)
[![codecov](https://codecov.io/gh/haniffalab/sci-adifa/branch/main/graph/badge.svg?token=RQLL0HKQ5W)](https://codecov.io/gh/haniffalab/sci-adifa)
[![python](https://img.shields.io/badge/python-3.8-blue)](https://python.org)

## SINGLE CELL INSIGHTS

# Adifa - Annotated Data in Flask App

[![docs](https://img.shields.io/badge/Documentation-online-blue)](https://haniffalab.github.io/sci-adifa)
[![doi](https://zenodo.org/badge/DOI/10.5281/zenodo.5824895.svg)](https://doi.org/10.5281/zenodo.5824895)

Adifa is a framework for visualising single-cell gene expression data in a web browser. It is built on [Flask](https://flask.palletsprojects.com/), a micro web framework, and ingests [Annotated Data](https://anndata.readthedocs.io/) objects in the `.h5ad` file format. It includes dimensionality reduction visualisation, heatmaps and dotplots, with the ability to explore gene expression and disease markers. The Python-based implementation and usage of the [deck.gl](https://deck.gl/) framework allows efficient handling of datasets up to one million cells.

Discuss usage on [Discourse]. Read the [documentation](https://haniffalab.github.io/sci-adifa). If you’d like to contribute by opening an issue or creating a pull request, please take a look at our [contributing guide](CONTRIBUTING.md). If Adifa is useful for your research, consider [citing the software](https://haniffalab.com/sci-adifa/citing.html).
django-koldar-utils 2.111.14
Some stuff that i used when developing with django_toolbox
License: MIT
Requires Python: >=3.6
Keywords: utils


Introduction
============

Generic django providing models and function used to better developer other apps.
In itself, this is **not** a django app 

.. code-block::

   pip install django-koldar-utils

Capabilities
============


* ``AbstractModelMetaclass`` which can be used to create django models with abc capabilities;
* ``AbstractPermissionMixin`` which does not rely on ``groups`` and ``permissions`` relationships;
* ``Orm`` class that provides the creation of popular django fields, just for your convenience;
* ``ArrowAuditMixin`` for providing ``created_at`` and  ``updated_at`` fields using ``arrow`` project;
* convenience class to easily create django backends, middlesware, validators;
* ``ArrowField`` implementation (taken from ``django-arrow-field``\ );
* convenience methods to interact with django framework without remembering how to do certain tasks;
* classes used to create graphql CRUD operation easily;

Upload new version
==================

Use setup.py directly:

.. code-block::

   # create .pypirc in your home directory
   python setup.py update_version_patch bdist_wheel upload

Or ``pmake``\ :

.. code-block::

   # pmakeup is a Massimo Bono project used to automatize the build; per se it is not necessary
   pip install pmakeup 
   # add in TWINE_PYPI_PASSWORD the pypi password
   pmakeup update-version-patch build upload-to-pypi

Build documentation
===================

.. code-block::

   python setup.py build_sphinx
koldar-utils 2.104.5
Some stuff that i used when developing with python. You can use this library pretty much anywhere
License: MIT
Keywords: utils


Introduction
============

Generic django providing models and function used to better developer other apps.
In itself, this is **not** a django app 

.. code-block::

   pip install django-koldar-utils

Capabilities
============


* ``AbstractModelMetaclass`` which can be used to create django models with abc capabilities;
* ``AbstractPermissionMixin`` which does not rely on ``groups`` and ``permissions`` relationships;
* ``Orm`` class that provides the creation of popular django fields, just for your convenience;
* ``ArrowAuditMixin`` for providing ``created_at`` and  ``updated_at`` fields using ``arrow`` project;
* convenience class to easily create django backends, middlesware, validators;
* ``ArrowField`` implementation (taken from ``django-arrow-field``\ );
* convenience methods to interact with django framework without remembering how to do certain tasks;
* classes used to create graphql CRUD operation easily;

Upload new version
==================

Use setup.py directly:

.. code-block::

   # create .pypirc in your home directory
   python setup.py update_version_patch bdist_wheel upload

Generate new doc
================

.. code-block::

   python setup.py build_sphinx

Or ``pmake``\ :

.. code-block::

   # pmakeup is a Massimo Bono project used to automatize the build; per se it is not necessary
   pip install pmakeup 
   # add in TWINE_PYPI_PASSWORD the pypi password
   pmakeup update-version-patch build upload-to-pypi