semvec/pypi-packages huggingface dataset:
andrew-confidence-intervals 0.0.6
Package for calculation of confidence intervals.
License: MIT
Keywords: confidence intervals

This library allows to calculate confidence intervals.

Change Log
===============

0.0.1 (23/08/2020)
-------------------
- First Release

0.0.5 (28/08/2020)
-------------------
- Second Release. New confidence intervals have been added.


0.0.6 (05/09/2020)
-------------------
- Third Release. More calculating details have been added to the functions. Print statements have become optional (verbose paramater added).
nim-install 0.6.0
install nim compiler in Linux virtualenv assumes gcc
License: MIT


This package can be used to install the nim compiler in a virtualenv::

   virtualenv /your/venv
   source /your/venv/bin/activate
   pip install nim-install
   nim_install
   nim --version

installs nim version 1.2.6, nimble, nimgrep and nimsuggest

If you want to install a different version run e.g.:

   nim_install 1.0.8

The compilation of nim is executed in parallel. Total installation time is about
40 seconds on my machine (including download time).
mynameis 2020.1
Simply returns either notebook filename or the full path to the notebook when run from Jupyter notebook in browser.
License: MIT
Requires Python: >=3.4, <4
Keywords: jupyter,notebook,filename

# mynameis

When run in a Jupyter notebook, simply returns the notebook filename or the full path to the notebook.

## Examples

Get the notebook name:

```python
import mynameis
nb_fname = mynameis.what()
```

Get the full path to the notebook:

```python
import mynameis
nb_path = mynameis.what(path=True)
```

You would think there was already some built-in way to access the current notebook name, but it took many hours
of searching for a way to do it. As it seems many others did, I tried using Javascript, but the async nature of
JS meant that it was unreliable. Finally I stumbled on this [post](https://forums.fast.ai/t/jupyter-notebook-enhancements-tips-and-tricks/17064/39).
Credit for most of the code goes to the author of this post, thanks!
django_react_templatetags 6.0.2
This django library allows you to add React components into your django templates.
License: MIT

|Build Status| |PyPI version|

.. figure:: https://raw.githubusercontent.com/frojd/django-react-templatetags/develop/img/django-react-templatetags-logo.png
   :alt: Django-React-Templatetags

   Django-React-Templatetags

Django-React-Templatetags
=========================

This django library allows you to add React (16+) components into your
django templates.

Features
--------

-  Include react components using django templatetags
-  Unlimited amount of components in one view
-  Support custom models (that is from the beginning not
   json-serializable)
-  Server side rendering with
   `Hypernova <https://github.com/airbnb/hypernova>`__ or
   `Hastur <https://github.com/frojd/Hastur>`__

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

Install the library with pip:

::

    $ pip install django_react_templatetags

Where to go from here?
----------------------

You should first read `Getting
started <https://github.com/Frojd/django-react-templatetags/blob/develop/docs/getting-started.md>`__,
then go through these topics: -
`Settings <https://github.com/Frojd/django-react-templatetags/blob/develop/docs/settings.md>`__
- `How to use the templatetags included in this
library <https://github.com/Frojd/django-react-templatetags/blob/develop/docs/templatetags-params.md>`__
- `Adding a single
component <https://github.com/Frojd/django-react-templatetags/blob/develop/docs/example-single-component.md>`__
- `Adding multiple
components <https://github.com/Frojd/django-react-templatetags/blob/develop/docs/example-multiple-components.md>`__
-
`Examples <https://github.com/Frojd/django-react-templatetags/blob/develop/docs/examples.md>`__
- `Working with
models <https://github.com/Frojd/django-react-templatetags/blob/develop/docs/working-with-models.md>`__
- `Server side
rendering <https://github.com/Frojd/django-react-templatetags/blob/develop/docs/server-side-rendering.md>`__
-
`FAQ <https://github.com/Frojd/django-react-templatetags/blob/develop/docs/faq.md>`__

Tests
-----

This library include tests, just run ``python runtests.py``

You can also run separate test cases:
``python runtests.py tests.test_filters.ReactIncludeComponentTest``

Coverage
--------

Make sure you have Coverage.py installed, then run
``coverage run runtests.py`` to measure coverage. We are currently at
95%.

Contributing
------------

Want to contribute? Awesome. Just send a pull request.

Security
--------

If you believe you have found a security issue with any of our projects
please email us at `security@frojd.se <security@frojd.se>`__.

License
-------

Django-React-Templatetags is released under the `MIT
License <http://www.opensource.org/licenses/MIT>`__.

.. |Build Status| image:: https://travis-ci.org/Frojd/django-react-templatetags.svg?branch=master
   :target: https://travis-ci.org/Frojd/django-react-templatetags
.. |PyPI version| image:: https://badge.fury.io/py/django_react_templatetags.svg
   :target: https://badge.fury.io/py/django_react_templatetags
key-value-ds 1.0.3
A key value datastore which stores data locally in a memory-mapped file.
License: MIT

# [key-value-ds](https://pypi.org/project/key-value-ds/)

##### File based Key-Value datastore

   Supports basic CRD (Create, Read, Delete)

**Functionalities:**
  1. It can be initialized using an optional file path. If one is not provided, it will reliably create itself using `uuid`.
  2. Key string capped at 32 characters and Value must be a JSON object capped at 16KB. (These values can be changed in config.py)
  3. Every key supports setting a Time-To-Live property when it is created. This property is optional. If provided, it will be evaluated as an integer defining the number of seconds. Once the Time-To-Live for a key has expired, the key will no longer be available for Read or Delete operations.
  4. Only one process can access the datastore (local file) at a time.
  5. Thread safe.


**Usage:**

###### Creating an instance
```
import key_value_ds
ds_instance = key_value_ds.get_instance()
```

Note: If `file_path` is provided in the `get_instance()` call, it will obtain lock on that file using `fcntl`. If object is created for the same file path twice, `BlockingIOError` is thrown.

###### Creating an data
```
data_key = 'test_key'
data_value = {"value": 1}  # must be a JSON
time_to_live = 5*1000  # in milliseconds
ds_instance.create(data_key, data_value, ttl=time_to_live)
```

###### Retrieving data
```
retrieve_key = 'test_key'
ds_instance.get(retrieve_key)   # returns {"value": 1} if retrieved within 5 seconds else ValueError
```

###### Deleting data
```
key_to_delete = 'test_key'
ds_instance.delete(key_to_delete)  # key-value is removed from the datastore
```

###### Delete all data
```
ds_instance.delete_all()
```
OreoML 1.0.0
Provide an input CSV and a target field to predict, generate a model.
License: MIT
Requires Python: >=3.5
Keywords: deep learning,tensorflow,keras,automl,xgboost

Give an input CSV file and a target field you want to predict to OreoML, and get a trained high-performing machine learning.OreoML is an AutoML tool which, offers a *zero code/model definition interface* to getting an high performance machine learning model
load-bar 0.0.7
Python Librairy for a loading bar in the console
License: MIT
Requires Python: >=3.6

# LoadBar

This is a very simple python library to create a loading bar on the shell

## Install it

```
pip install load-bar
```

## Use it

``` python
import time
from loadbar import LoadBar
bar = LoadBar(max=100)
bar.start()
for i in range(100):
    time.sleep(0.05)
    bar.update(step=i)
bar.end()
```

![image](https://github.com/ValentinVignal/LoadBar/blob/master/images/loadbar.gif)

[See the documentation](https://github.com/ValentinVignal/LoadBar/blob/master/doc/Loadbar.md)
cam-motion-field 1.4
This package provides quick and easy way to compute camera motion field given two consecutive images.
License: MIT
Keywords: video,optical-flow,camera,camera-motion,motion field

# Cam_motion_field

### Description
This package provides quick and easy way to compute camera motion field given
two consecutive images.

### Install
```
pip install cam-motion-field
```
 
### Usage
```python
from cam_motion_field import get_cam_flow
import cv2
image1_file = "PATH/TO/THE/FIRST/IMAGE/FILE"
image2_file = "PATH/TO/THE/SECOND/IMAGE/FILE"
ncol = 10
nrow = 10
grid_size = (ncol, nrow)

img1 = cv2.imread(image1_file, 0)
img2 = cv2.imread(image2_file, 0)
# origins.shape = (10,10,2)
# displacements.shape = (10,10,2)
origins, displacements = get_cam_flow(img1, img2, grid_size[0], grid_size[1])
```
The above example defines a 10x10 grid and returns 100 origins and
displacement points. Both origins and displacements are absolute coordinates!

######  Parameters:
    Parameters
    ----------
    image1 : numpy array
        image1 a grayscale image
    image2 : numpy array
        image2 a grayscale image
    n_rows : int
        number of rows in the grid
    n_cols : int
        number of columns in the grid

## Example
Examples where generated using the example.py. This gif shows two images where either the camera moved forward or some zoom effect took place

<img src="https://github.com/stamas02/cam_motion_field/blob/master/data/image_anim.gif" width="400"/>

The resuts are:
5x5 Grid

<img src="https://github.com/stamas02/cam_motion_field/blob/master/data/motion_filed_5x5.jpg" width="400"/>

10x10 Grid

<img src="https://github.com/stamas02/cam_motion_field/blob/master/data/motion_filed_10x10.jpg" width="400"/>

20x20 Grid

<img src="https://github.com/stamas02/cam_motion_field/blob/master/data/motion_filed_20x20.jpg" width="400"/>


Notice that 20x20 has many zero displacements. This happens when the grid is too fine and no good features are detected
in the cell.
passthesalt 3.2.2
Deterministic password generation and password storage.
License: MIT
Requires Python: >=3.6
Keywords: password manager pbkdf2

# PassTheSalt

[![PyPI](https://img.shields.io/pypi/v/passthesalt)](https://pypi.org/project/passthesalt/)
![PyPI: supported Python](https://img.shields.io/pypi/pyversions/passthesalt)
[![Build status](https://img.shields.io/github/workflow/status/rossmacarthur/passthesalt/build)](https://github.com/rossmacarthur/passthesalt/actions?query=workflow%3Abuild)
[![Code coverage](https://img.shields.io/codecov/c/github/rossmacarthur/passthesalt/master.svg)](https://codecov.io/gh/rossmacarthur/passthesalt)
[![Code style](https://img.shields.io/badge/code%20style-black-101010.svg)](https://github.com/psf/black)

A command line application for deterministic password generation and password
storage.

## Getting started

Install it using

```sh
pip install passthesalt
```

and start storing secrets with

```sh
pts add
```

## How does it work?

Secrets are generated on the fly using the same secure algorithm each time which
uses a *master password* and a *description* of the password. Only the
description of the secret is stored. This means the secrets are not stored
anywhere.

The generation algorithm is PBKDF2 using 2048 iterations of HMAC-SHA-256,
applied to (*full name* + *master password*) as the key and the *description* as
the salt.

Since sometimes you cannot choose your passwords the application also has
provision to securely encrypt secrets with the master password.

## Usage


The command-line interface has the following commands

```
add      Add a secret.
diff     Compare two stores.
edit     Edit a secret.
encrypt  Encrypt a secret.
get      Retrieve a secret.
ls       List the secrets.
mv       Relabel a secret.
pull     Retrieve a remote store.
push     Update the remote store.
rm       Remove a secret.
```

All commands and options are documented in the cli. You can use the `--help`
option with any command.

## License

This project is licensed under the MIT license ([LICENSE](LICENSE) or
http://opensource.org/licenses/MIT).