semvec/pypi-packages huggingface dataset:
hexpy 0.7.3
Python Client for Crimson Hexagon API
Requires Python: >=3.6
Keywords: hexpy

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    <img src="https://travis-ci.com/sullivancolin/hexpy.svg?branch=master" alt="Build Status">
</a>
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    <img src="https://badge.fury.io/py/hexpy.svg" alt="PyPI version">
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A Python Client for the Crimson Hexagon API
===========================================

**hexpy** is a simple python package for working with the [Crimson Hexagon API](https://apidocs.crimsonhexagon.com/)

---
## Project Homepage

**Documentation**: [hexpy](https://sullivancolin.github.io/hexpy/)

**Source Code**: [Github](https://github.com/sullivancolin/hexpy)

---

## Why use this client?

* Easily and securely manage account authentication.
* Automatically abide by Crimson Hexagon's rate limits.
* Automatically convert python data to/from JSON strings.
* Automatically check requests success.
* Make it easy to do common tasks like exporting and uploading content.
* Easily create shell scripts to work with API data.

## Requirements
**hexpy** is compatible with Python 3.6 and higher

## Installation
To install the most recent stable release run `pip install hexpy`.

To install the latest version:
```bash
$ git clone https://github.com/sullivancolin/hexpy.git
$ pip install hexpy/
```

## Quick Start

```python
>>> from hexpy import HexpySession, MonitorAPI
>>> session = HexpySession.login(username="user@email.com", password="crimson_login")
>>> monitor_results_client = MonitorAPI(session)
>>> monitor_results_client.details(monitor_id)
{'categories': [{'hidden': False,
   'id': 6054759055,
   'name': 'Basic Positive',
   'sortOrder': 100,
   'status': 'red',
   'trainingDocs': 0},
  {'hidden': False,
   'id': 6054759059,
   'name': 'Basic Neutral',
   'sortOrder': 101,
   'status': 'red',
   'trainingDocs': 0},
  {'hidden': False,
   'id': 6054759051,
   'name': 'Basic Negative',
   'sortOrder': 102,
   ...
}
```
hypercluster 0.1.13
A package for automatic clustering hyperparameter optmization

# Hypercluster
A package for clustering optimization with sklearn. 

### Requirements:  
pandas  
numpy  
scipy  
matplotlib  
seaborn  
scikit-learn  
hdbscan  

Optional:
snakemake


### Install  
With pip:
```
pip install hypercluster
```

or with conda:
```
conda install hypercluster
# or
conda install -c conda-forge -c bioconda hypercluster
```
If you are having problems installing with conda, try changing your channel priority. Priority of conda-forge > bioconda > defaults is recommended. 
To check channel priority: `conda config --get channels`
It should look like:
```
--add channels 'defaults'   # lowest priority
--add channels 'bioconda'
--add channels 'conda-forge'   # highest priority
```

If it doesn't look like that, try:
```
conda config --add channels bioconda
conda config --add channels conda-forge
```

### Docs 
https://hypercluster.readthedocs.io/en/latest/index.html  

It will also be useful to check out sklearn's page on [clustering](https://scikit-learn.org/stable/modules/clustering.html) 
and [evaluation metrics](https://scikit-learn.org/stable/modules/clustering.html#clustering-performance-evaluation) 

### Examples
https://github.com/liliblu/hypercluster/tree/dev/examples

### Quickstart with SnakeMake

Default `config.yml` and `hypercluster.smk` are in the snakemake repo above.  
Edit the `config.yml` file or arguments.
```bash
snakemake -s hypercluster.smk --configfile config.yml --config input_data_files=test_data input_data_folder=. 
```

Example editing with python:
```python
import yaml

with open('config.yml', 'r') as fh:
    config = yaml.load(fh)

input_data_prefix = 'test_data'
config['input_data_folder'] = os.path.abspath('.')
config['input_data_files'] = [input_data_prefix]
config['read_csv_kwargs'] = {input_data_prefix:{'index_col': [0]}}

with open('config.yml', 'w') as fh:
    yaml.dump(config, stream=fh)
```

Then call snakemake. 
```bash
snakemake -s hypercluster.smk
```

Or submit the snakemake scheduler as an sbatch job e.g. with BigPurple Slurm:
```bash
module add slurm
sbatch snakemake_submit.sh
```
Examples for `snakemake_submit.sh` and `cluster.json` is in the scRNA-seq example. 

### Quickstart with python
```python
import pandas as pd
from sklearn.datasets import make_blobs
import hypercluster

data, labels = make_blobs()
data = pd.DataFrame(data)
labels = pd.Series(labels, index=data.index, name='labels')

# With a single clustering algorithm
clusterer = hypercluster.AutoClusterer()
clusterer.fit(data).evaluate(
  methods = hypercluster.constants.need_ground_truth+hypercluster.constants.inherent_metrics, 
  gold_standard = labels
  )

clusterer.visualize_evaluations()

# With a range of algorithms

clusterer = hypercluster.MultiAutoClusterer()
clusterer.fit(data).evaluate(
  methods = hypercluster.constants.need_ground_truth+hypercluster.constants.inherent_metrics, 
  gold_standard = labels
  )

clusterer.visualize_evaluations()
```
graph-plot 0.0.3
Plot (di)graphs using a timestep simulation of charged particles and springs
Requires Python: >=3.0.0
Keywords: Graph

# graph-plot

A python module for plotting (directed) Graphs using a simulation of springs and charged particles

You can import this module with PIP.

>`pip install graph-plot --upgrade`

or

>`python3 -m pip install --user graph-plot --upgrade`

PyPi release page: https://pypi.org/project/graph-plot/.

See [here](https://share.cocalc.com/share/f6aee960-eb32-4259-a1ff-cddafc5864a0/New%20Projects%20and%20Problems/Springboard%20Graphs.ipynb?viewer=embed) for examples and a mathematical explanation.
diGraph 0.1.4
For plotting DiGraphs
Requires Python: >=3.0.0
Keywords: DiGraph

# diGraph

A python module for plotting Directed Graphs using a simulation of springs and charged particles.

You can import this module with PIP.

>`pip install ifsFractals --upgrade`

or

>`python3 -m pip install --user ifsFractals --upgrade`

PyPi release page: https://pypi.org/project/diGraph/.

See https://share.cocalc.com/share/f6aee960-eb32-4259-a1ff-cddafc5864a0/New%20Projects%20and%20Problems/Springboard%20Graphs.ipynb?viewer=embed for examples and a mathematical explanation.