semvec/pypi-packages huggingface dataset:
sgqlc-schemas 0.1.0
A set of schemas for sgqlc package
License: MIT
Requires Python: >=3.6
Keywords: sgqlc,schema,schemas,graphql,github,monday

[![PyPI version](https://badge.fury.io/py/sgqlc-schemas.svg)](https://badge.fury.io/py/sgqlc-schemas)
## About

This package was created so that when using [sgqlc (Simple GraphQL Client)](https://github.com/profusion/sgqlc "sgqlc (Simple GraphQL Client)") you do not need to create schemas for popular APIs.

## How to use

#### Installation

You can install this package either via the Python Package Index (PyPI) or from source.

To install using ``pip``:
```
$ pip install -U sgqlc-schemas
```

#### Import schema and simple query
```python
from sgqlc.endpoint.http import HTTPEndpoint
from sgqlc.operation import Operation
from sgqlc_schemas import github_schema

op = Operation(github_schema.query_type)
op.viewer().login()
endpoint = HTTPEndpoint('https://api.github.com/graphql', {'Authorization': f'bearer {token}'})
data = endpoint(op)
```

## Current schemas

- [GitHub](http://github.com "GitHub")
- [Monday.com](http://monday.com "Monday.com")

## Contribution

This package welcomes any new contribution. Please create [pull requests on GitHub](https://github.com/Mogost/sgqlc-schemas/pulls "pull requests on GitHub").
g2pg 1.3.0
Google Sheets to Postgres DB using gspread and env files.
License: MIT
Keywords: pandas,postgres,gspread,gsheets

# g2pg
g2pg takes a Google Sheet, converts it to a DataFrame, which you can then manipulate as you need to.

This DataFrame can then be written to a PostgreSQL database table.

This makes use of a `.env` file, which after much trial and error I finally got to work with gspread.

Can install using `pip install g2pg`.

To use:

`import g2pg`

### How does it work
g2pg uses the `gspread` package to extract data from the Google Sheet. 

Follow these directions to get the json credentials file that can  be used with `gspread` <https://gspread.readthedocs.io/en/latest/oauth2.html#for-bots-using-service-account>

Make sure you share any Gsheets with the email that is specified in your credentials file.

The json credentials need to be stored in a `.env` file. Don't upload the contents of your .env or json_credentials file to github unless you want everyone to know your secrets.

If using this package you need to have a `.env` file or enviroment variables set in the following way.

    DB_USER= 'username'
    DB_PW = 'super_secret_password'
    DB_URL = 'db_address'
    DB_NAME = 'db_name'

    SHEET_TYPE= 'service_account'
    SHEET_PROJECT_ID= 'api-project-XXX'
    SHEET_PRIVATE_KEY_ID= '2cd … ba4'
    SHEET_PRIVATE_KEY="-----BEGIN PRIVATE KEY-----\nNrDyLw … jINQh/9\n-----END PRIVATE KEY-----\n"
    SHEET_CLIENT_EMAIL= 'yoursisdifferent@developer.gserviceaccount.com'
    SHEET_CLIENT_ID= '1234567890'
    SHEET_AUTH_URI= 'https://accounts.google.com/o/oauth2/auth'
    SHEET_TOKEN_URI= 'https://oauth2.googleapis.com/token'
    SHEET_AUTH_PROVIDER_X509_CERT_URL= 'https://www.googleapis.com/oauth2/v1/certs'
    SHEET_CLIENT_X509_CERT_URL= 'https://www.googleapis.com/robot/v1/metadata/bla...bla..bla.iam.gserviceaccount.com'

There are 2 methods available:
* `def get_df_from_gsheet(gsheet_name,worksheet_name)`
    This returns a datframe from the specified Google Sheet and worksheet.The worksheet name is optional and will default to `Sheet1`.
    The dataframe will have all columns and rows removed, where there is no data, and the column names will be converted to a database friendly format.
* `def df_to_db(df, table_name,schema, index_name)`
    This writes the specified `df` to the `table_name` in the DB that is specified in the `.env` file.
    `schema` is optional and will default to `public` in postgres if not specified.
    `index_name` is the index of your df. If not specified, it will default to index (this is used as the primary key in your DB table).
GPGO 0.1.2
Bayesian Optimization with Gaussian Process as surrogate model
License: MIT
Requires Python: >=3.6

# GPGO - Gaussian Process GO
My own implementation of a Bayesian Black box Optimization with Gaussian Process as a surrogate model.
It is still in development as I'm using it for my Master degree thesis to achieve a bottom up optimization of the Dissipative
Particle Dynamics force field for a complex system of polymers chains functionalized gold nanoparticles in a water solvent. 

# Hyperparameters
The Hyperparameters of the GP are optimized by the common technique of maximizing the Log Marginal Likelihood. In this repository this is achieved by using a search grid (although not in an efficient way) or by using the scipy optimizer module (L-BFGS-B, TNC, SLSCP).
The analytical gradient is implemented for the Radial Basis Function kernel and it is possible to use the derivate of the Log Marginal Likelihood to optimize the hyperparameters.
<a href="https://ibb.co/D8yvW3x"><img src="https://i.ibb.co/pR8MwCt/Figure-6.png" alt="Figure-6" border="0"></a>

# Acquisition function
As it is there are two different acquisition function implemented right now:

-Expected Improvement (EI)

-UCB (Upper Confidence Bound)

# Maximizing the Acquisition function 
In this little package right now there are 3 ways to run an optimization task with Gaussian Processes:

-NAIVE : AkA sampling the acquisition function with a grid of some kind or a quasi random methods as LHS (require smt package)

-BFGS : optimize the Acquisition function by using the L-BFGS-B optimizer

-DIRECT : optimize the Acquisition function by using the DIRECT optimizer (require DIRECT python package)
<a href="https://ibb.co/GPSM0cm"><img src="https://i.ibb.co/f0wN24J/Figure-7.png" alt="Figure-7" border="0"></a>

# TODO

-Tutorials and Examples

-Good code practice maybe 

-An integration with LAMMPS using the pyLammps routine
text2features 0.0.1
A Python package to get basic features from the text data.
License: MIT

# **Introduction: text2features**
When we handle the text data, we always have concerns about the data features, pre-processing of data and more likely the predictions. To improve our model, it is important to understand the data and find the more interesting features in the data like hashtags, links and many more.

## **What is text2features?**

It is a python package which helps you to extract the basic features from the text data such as hashtags, stopwords, numerics which will help you to understand the data and improve your model more effectively.

## **Function structure:**

## function_name(dataframe, ”text_column”, ”new_column”)

**dataframe:- name of dataframe**

**text_column:- name of the column from which features are to be extracted.**

**new_column:- new column derived by feature extraction from text_column.**

# **What will text2features serve you?**

**1. word_count():-** give the total words count present in text data.

**2. char_count():-** give the characters count.

**3. avg_word_length():-** give the average word length.

**4. stopwords_count()**:- give the stopwords count.

**5. stopwords()**:- extract the stopwords from the text data.

**6. hashtags_count():-** give the hashtags count.

**7. hashtags():-** extract the hashtags from text data.

**8. links_count():-** give the embedded links count from text data.

**9. links():-** extract the links from the text data.

**10. numeric_count():-** give the numeric digits count.

**11. user_mentions_count():-** give the user mentions count from text data.

**12. user_mentions():-** extract the user mentions from text data.

**13. clean():-** give the pre-processed data after removal for unnecessary material in text data.
gauss-bino 1.1
Gaussian and Binomial distributions Utility Package from Majf Technologies.
License: MIT
Keywords: gaussian,distribution,binomaial,statistics,scikit,python,package,mathematics,majftech,india,aligarh university,owasp,ieee,anwar jamal faiz,ghazipur,lucknow

Copyright: Majf Technologies (www.majftech.com)
Author: Mohd Anwar Jamal Faiz
Email: Toughjamy@yahoo.com


Gaussian and Binomial distributions Utility Package from Majf Technologies.