semvec/pypi-packages huggingface dataset:
pyavl3 1.0.0
Pure python implementation of avl trees.
License: MIT
Keywords: avl,avltree,balancedtree,bst

# pyavl3

A python dictionary alternative implemented with an AVL Tree.

# Quick Start

```bash
pip install pyavl3
```

```python
from pyavl3 import AVLTree

a = AVLTree()
a[1] = "Hello"
a[2] = "world!"

print(", ".join(a.values()))
```

## Why?

Python dictionaries are implemented on hashmaps. Hashmaps, besides being awesome, are a balancing
act between efficiency and memory utilization. Python's builtin algorithm is solid and probably the
correct choice 99 times out 100. Not really a supprise. But, hashmaps suffers from the need to
resize and resizing is pretty expensive. For those few cases where resizing large in-memory blocks
of sequentional memory is not going to work, AVLTrees might be a better option. Oh, and AVLTrees are
effectivly sorted. So, iterators are deterministic and always in order.

This table compairs the runtime characteristics of Hashtables and AVLTrees

|        	| Hashtable 	| Hashtable(worst case) 	| AVLTree 	| AVLTree(worst case) 	|
|--------	|-----------	|-----------------------	|---------	|---------------------	|
| Space  	| O(n)      	| O(n)                  	| O(n)    	| O(n)                	|
| Search 	| O(1)      	| O(n)                  	| O(logn) 	| O(logn)             	|
| Insert 	| O(1)      	| O(n)                  	| O(logn) 	| O(logn)             	|
| Delete 	| O(1)      	| O(n)                  	| O(logn) 	| O(logn)             	|
| Resize 	| O(n)      	| O(n)                  	| N/A     	| N/A                 	|
tcrsampler 0.1.9
Sample TCRs according to VJ gene usage frequency
License: MIT

# tcrsampler

Sample TCRs according to VDJ gene usage frequency

[![Build Status](https://travis-ci.com/kmayerb/tcrsampler.svg?branch=master)](https://travis-ci.com/kmayerb/tcrsampler)
[![Coverage Status](https://coveralls.io/repos/github/kmayerb/tcrsampler/badge.svg?branch=master)](https://coveralls.io/github/kmayerb/tcrsampler?branch=master)
[![PyPI version](https://badge.fury.io/py/tcrsampler.svg)](https://badge.fury.io/py/tcrsampler)

## Install 

```
pip install tcrsampler
```

### Example

```python
import os
import pandas as pd
from tcrsampler.sampler import TCRsampler
t = TCRsampler()
fn= os.path.join('tcrsampler' ,'tests', 'pmbc_mixcr_example_data.txt')
t.clean_mixcr(filename = fn)
t.build_background(stratify_by_subject = True, use_frequency = True)
t.sample([['TRBV9*01','TRBJ2-7*01', 2],['TRBV7-7*01', 'TRBJ2-4*01', 4] ], depth = 1, use_frequency = True)
```

```
[['CASSRTGSLADEQYF', 'CASSATGVVSAQYF'],
 ['CASSLGQAARGIQYF', 'CASSLGQAARGIQYF', 'CASSLGQAARGIQYF', 'CASSLGQAARGIQYF']]
```

## Download Default Background

```python
python -c "from tcrsampler.setup_db import install_all_next_gen; install_all_next_gen(dry_run = False)"
```
django-static-html5shiv 3.7.3.3
Django application contain html5shiv static files.
License: MIT
Keywords: django-static-html5shiv

# django-static-html5shiv


Django application contain html5shiv static files.


## Install

```
pip install django-static-html5shiv
```

## About static resources of html5shiv

- We get all static resources of html5shiv from https://github.com/aFarkas/html5shiv/releases without any changes.
- The license about all the static resources of html5shiv obey html5shiv's license.
- We don't guarantee the latest html5shiv version.

## Settings

```
INSTALLED_APPS = [
    ...
    "django_static_html5shiv",
    ...
]
```

## Use static resource

```
{% load staticfiles %}

<script src="{% static "html5shiv/html5shiv.min.js" %}"></script>
<script src="{% static "html5shiv/html5shiv-printshiv.min.js" %}"></script>
```

## About package version

- django-static-html5shiv Uses version number like v3.7.3.1.
- The first three numbers are the version number of html5shiv.
- the fourth number is our release number.

## Releases

### v3.7.3.3 2020/09/02

- No depends on django.
- Turn to the package as a pure static wrapper package.

### v3.7.3.2 2020/03/09

- Use python standard package structure.
- Use markdown for README.md.

### v3.7.3.1 2018/03/28

- First release.
neural-map-franco-bobadilla 0.0.1
NeuralMap is a data analysis tool based on Self-Organizing Maps
License: MIT
Requires Python: >=3.6
Keywords: som,self-organizing maps,dimensionality reduction,machine learning,clustering,hdbscan

NeuralMap is a data analysis tool based on Self-Organizing Maps

https://www.makeareadme.com/

What does this project do?
Why is this project useful?
How do I get started?
Where can I get more help, if I need it?