semvec/pypi-packages huggingface dataset:
pumpkin_py 0.0.1
Top level package
Requires Python: >=3.8

PumpkinPy - Semantic similarity implemented in python

##### About

PumpkinPy uses IC ordered bitmaps for fast ranking of genes and diseases.  This is useful for larger ontologies such as Upheno and large datasets such as ranking all mouse genes given a set of input HPO terms.  This approach was first used in OWLTools and OwlSim-v3.

The goal of this project was to build an implementation of the PhenoDigm algorithm in python. There are also implementations for common measures for distance and similarity (euclidean, cosine, Jin-Conrath, Resnik, jaccard)

*Disclaimer*: This is a side project and has little documetation and only a modest amount of testing

##### Getting Started

Requires python 3.8+ and python3-dev to install pyroaring

Installing from pypi

```
pip install pumpkin_py
```

Running make will install locally and run the tests
```
make
```

###### Fetching annotations and closures

Uses robot and sparql to generate closures and class labels

Annotation data is fetched from the latest Monarch release
 - Requires >Java 8
 
```cd data/monarch/ && make```


PhenoDigm Reference: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3649640/  
Exomiser: https://github.com/exomiser/Exomiser  
OWLTools: https://github.com/owlcollab/owltools  
OWLSim-v3: https://github.com/monarch-initiative/owlsim-v3
onelearn 0.3.0
onelearn is a small python package for online learning
Requires Python: >=3.7,<4.0
Keywords: machine-learning,online-learning-algorithms,python,random-forest,classification,regression


[![Build Status](https://travis-ci.com/onelearn/onelearn.svg?branch=master)](https://travis-ci.com/onelearn/onelearn)
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![PyPI - Wheel](https://img.shields.io/pypi/wheel/onelearn)
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# onelearn: Online learning in Python

[Documentation](https://onelearn.readthedocs.io) | [Reproduce experiments](https://onelearn.readthedocs.io/en/latest/experiments.html) |

onelearn stands for ONE-shot LEARNning. It is a small python package for **online learning** 
with Python. It provides :

- **online** (or **one-shot**) learning algorithms: each sample is processed **once**, only a 
  single pass is performed on the data
- including **multi-class classification** and regression algorithms
- For now, only *ensemble* methods, namely **Random Forests**

## Installation

The easiest way to install onelearn is using pip

    pip install onelearn


But you can also use the latest development from github directly with

    pip install git+https://github.com/onelearn/onelearn.git

## References

    @article{mourtada2019amf,
      title={AMF: Aggregated Mondrian Forests for Online Learning},
      author={Mourtada, Jaouad and Ga{\"\i}ffas, St{\'e}phane and Scornet, Erwan},
      journal={arXiv preprint arXiv:1906.10529},
      year={2019}
    }
tap-freshworkscrm 0.1.0
Singer.io tap for extracting FreshWorks CRM Data

# tap_freshworkscrm

  

This is a [Singer](https://singer.io) tap that produces JSON-formatted data following the [Singer spec](https://github.com/singer-io/getting-started/blob/master/SPEC.md).

  

  

This tap:

  

- Pulls raw data from [FreshWorks CRM REST API](https://developers.freshworks.com/crm/api)

- Extracts the following resources:

-  [Contacts](https://developers.freshworks.com/crm/api/#contacts)

-  [Accounts](https://developers.freshworks.com/crm/api/#accounts)

-  [Deals](https://developers.freshworks.com/crm/api/#deals)

-  [Tasks](https://developers.freshworks.com/crm/api/#tasks)

-  [Appointments](https://developers.freshworks.com/crm/api/#appointments)

-  [Sales](https://developers.freshworks.com/crm/api/#sales-activities)

- Outputs the schema for each resource

- Pulls all data

  

# Installation

- Clone the app

- Create virtual env `virtualenv test` then `source test/bin/activate`

- Install dependencies i.e `pip install -e .`

- Run Tests `pytest tests`

  

# How to run the tap

- Create a config file from the `sample_config` already provided

```

{

"api_key": "your-api-token",

"domain": "subdomain",

"start_date": "2021-09-26T00:00:00Z"

}

```

- Run the command below

```

python tap_freshworkscrm/__init__.py --config config.json >> state.json

```

- Where state.json - a file where the tap writes all data pulled from freshsales.

If successful, state.json should have this format.

- STATE : `{ item_id: updated_at }` - Adds the bookmapping function where only sync if a record has been updated from previous sync.

- SCHEMA : Generated schema - automates the `Data insert ` process

- RECORD : Actual data for each record in json format.

  

# Running tap to Postgres Database

- To push data from tap_freshsale to postgres db using the target-postgres

- Add db_config

```

{

  

"host": "localhost",

"port": 5432,

"dbname": "local freshsales db",

"user": "db user",

"password": "db password",

"schema": "tap_freshworkscrm"

}

```

- Run this command `python your_virtual_env/bin/tap_freshworkscrm --config ../config.json | your_virtual_env/bin/target-postgres -c ../db_config.json`

  

# Running tap to Redshift Database

- To push data from tap_freshsale to Redshift db using the target-redshift

-  `Pip install target-redshift`

- Add db_config

```

{

"redshift_username": "username",

"redshift_schema": "tap_freshworkscrm",

"redshift_host": "your redshift.amazonaws.com",

"redshift_password": "password",

"redshift_port": 5439,

"redshift_database": "your db",

"default_column_length": 1000,

"target_s3":{

"aws_access_key_id": "AKIA...",

"aws_secret_access_key": "secrete key",

"bucket": "bucket name",

"key_prefix": "temp_"

  

}

}

```

- Run this command `python your_virtual_env/bin/tap_freshworkscrm --config ../config.json | your_virtual_env/bin/target-redshift -c ../db_config.json`

  

Since python2.7 has been deprecated, you might have to

-  `pip3 install target-redshift` or copy the target-redshift file to `your_env/bin/`

---
rplanpy 0.1.1
Set of python utilities to work with the RPLAN dataset from "[Data-driven Interior Plan Generation for Residential Buildings](https://doi.org/10.1145/3355089.3356556)" paper.

# rplanpy

Set of python utilities to work with the RPLAN dataset from 
"[Data-driven Interior Plan Generation for Residential Buildings](https://doi.org/10.1145/3355089.3356556)" paper.


## Data

This package works with the RPLAN image dataset, which consists of more than 80k floorplan 
images of real residential buildings, and can be obtained from the following 
[link](https://docs.google.com/forms/d/e/1FAIpQLSfwteilXzURRKDI5QopWCyOGkeb_CFFbRwtQ0SOPhEg0KGSfw/viewform)

## Examples

### Floorplan image to relation graph

Check an example converting a floorplan to a relation graph with node and edge attibutes in
[create_graph.py](create_graph.py). `True` in an edge means there is a door in the connection between two rooms.

![Graph example image](example_graph.png)

## Citation

If you use this work please cite the original paper which collected the data.

```
@article{wu2019data,
  title={Data-driven interior plan generation for residential buildings},
  author={Wu, Wenming and Fu, Xiao-Ming and Tang, Rui and Wang, Yuhan and Qi, Yu-Hao and Liu, Ligang},
  journal={ACM Transactions on Graphics (TOG)},
  volume={38},
  number={6},
  pages={1--12},
  year={2019},
  publisher={ACM New York, NY, USA}
}

```
tendenci-group-on-email 0.1.9
Apply discounts and permissions to account groups based on email suffix eg. .edu.
Requires Python: >=3.6

=======================
Tendenci Group on Email
=======================


Add automatic groups based on email suffix, eg. ".edu" for use with discounts and such.

* Free software: GNU General Public License v3

To install, add ``group_on_email`` to Django's ``INSTALLED_APPS`` setting.
Thereafter, members are added to and removed from the ".edu" group when their
profile is updated. The group can be entirely regenerated with the
``group_on_email`` management command.

*For commercial support and extensions to your Tendenci system, visit https://www.sturm.com.au/.*

=======
History
=======

0.1.9 (2021-09-29)
------------------

* Switch to setup.cfg.


0.1.0 (2018-03-01)
------------------

* First release on PyPI.