semvec/pypi-packages huggingface dataset:
hdx-data-freshness 2.0.0
HDX Data Freshness analyses freshness of data on HDX
License: MIT
Requires Python: >=3.8
Keywords: HDX,fresh,freshness,data freshness

[![Build Status](https://github.com/OCHA-DAP/hdx-data-freshness/actions/workflows/run-python-tests.yml/badge.svg)](https://github.com/OCHA-DAP/hdx-data-freshness/actions/workflows/run-python-tests.yml)
[![Coverage Status](https://codecov.io/gh/OCHA-DAP/hdx-data-freshness/branch/main/graph/badge.svg?token=JpWZc5js4y)](https://codecov.io/gh/OCHA-DAP/hdx-data-freshness)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)

# PyPI Package DEPRECATED - code from 
https://github.com/OCHA-DAP/hdx-data-freshness now directly copied to Docker 
image rather than installing from PyPI package 

HDX data freshness is a process that runs against the [Humanitarian Data Exchange](https://data.humdata.org/)
portal every day. It attempts to determine the freshness of datasets on the platform (ie. how up to date they
are) and updates the last modified field of resources held externally to HDX that have changed.

For more information, please read the [documentation](https://hdx-data-freshness.readthedocs.io/en/latest/). 

This library is part of the [Humanitarian Data Exchange](https://data.humdata.org/) (HDX) project. If you have 
humanitarian related data, please upload your datasets to HDX.
bqhus 0.1.11
bqhus: BigQuery helper utils.
License: MIT
Keywords: bigquery

# bqhus
[![Maintainability](https://api.codeclimate.com/v1/badges/102f4aceaa6a1c544f52/maintainability)](https://codeclimate.com/github/yokoe/bqhus/maintainability)

BigQuery helper utils for Python

## How to use
### Select from table
```
import bqhus
bqhus.select("SELECT * FROM `foo.bar.purchases` LIMIT 20").as_dicts()
bqhus.select("SELECT * FROM `foo.bar.purchases` ORDER BY created_at LIMIT 1", client=bigquery.Client(project="foobar")).first_as_dict()
bqhus.select("SELECT * FROM `foo.bar.purchases` LIMIT 20").to_dataframe()
```

### Create table from query
```
bqhus.select("SELECT * FROM `some.source.table`").to_table("foo.bar.new_table")
```

### Create temp table from query
```
bqhus.select("SELECT * FROM `some.source.table`").to_table("foo.bar.new_table").expires_in(days=7)
```

### Export table to GCS as csv
```
bqhus.export_table("foo.bar.sample").as_csv(gzip=True).to_gcs("my-bucket", "exported-table.csv.gz")
```


### Generate random table name
```
bqhus.random_table_name("prefix-here")
```

## Development
### Run tests
```
docker compose run bqhus
```
hdx-data-freshness-dbclean 1.0.2
HDX Data Freshness Database Clean
License: MIT
Requires Python: >=3.8
Keywords: HDX,fresh,freshness,database

### Utility to clean Freshness Database
[![Build Status](https://github.com/OCHA-DAP/hdx-data-freshness-dbclean/actions/workflows/run-python-tests.yml/badge.svg)](https://github.com/OCHA-DAP/hdx-data-freshness-dbclean/actions/workflows/run-python-tests.yml)
[![Coverage Status](https://codecov.io/gh/OCHA-DAP/hdx-data-freshness-dbclean/branch/main/graph/badge.svg?token=JpWZc5js4y)](https://codecov.io/gh/OCHA-DAP/hdx-data-freshness-dbclean)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)

# DEPRECATED - code moved to https://github.com/OCHA-DAP/hdx-data-freshness

This script cleans the freshness database.


### Usage

    python -m hdx.freshness.dbactions [-db/--db_uri=] [-dp/--db_params=] [action]

Either db_uri or db_params must be provided or the environment variable DB_URI
must be set. db_uri or DB_URI are of form: 
`postgresql+psycopg://user:password@host:port/database`

db_params is of form:
`database=XXX,host=X.X.X.X,username=XXX,password=XXX,port=1234,
ssh_host=X.X.X.X,ssh_port=1234,ssh_username=XXX,
ssh_private_key=/home/XXX/.ssh/keyfile`

action: 

- "clone" which creates a shallow clone of the database which only
has all the runs and one dataset and its resources per run for testing 
purposes.

- "clean" (the default) cleans the database by removing runs according to these 
rules:
  1. Keep a handful of runs around the end of each quarter all the way back to 
  the first run in 2017
  2. Keep daily runs going back 2 years
  3. Keep weekly runs from 2 to 4 years back
  4. Keep monthly runs for 4 years back and earlier
spins-utilz 1.0.1
Utilities for differentiable computational photonics
License: MIT

# Utilities for differentiable computational photonics

General functionality for building structures and using input/output modes on finite-difference Yee cell grids.
Intended for use with simulation engines such as, but not limited to, [fdtd-z](https://github.com/spinsphotonics/fdtdz).
Built on top of [JAX](https://github.com/google/jax) to enable optimization and design use cases.
llama-agi 0.2.0
Building AGI loops using LlamaIndex and Langchain
License: MIT
Requires Python: >=3.8.1,<4.0
Keywords: LLM,LlamaIndex,Langchain,AGI

# 🤖 Llama AGI 🦙

This python package allows you to quickly create Auto-GPT-like agents, using LlamaIndex and Langchain.

## Setup

Install using pip:

```bash
pip install llama-agi
```

Or install from source:

```bash
git clone https://github.com/run-llama/llama-lab.git
cd llama-lab/llama_agi
pip install -e .
```

## Example Usage

The following shows an example of setting up the `AutoAGIRunner`, which will continue completing tasks (nearly) indefinitely, trying to achieve it's initial objective of "Solve world hunger."

```python
from langchain.agents import load_tools
from langchain.llms import OpenAI

from llama_agi.execution_agent import ToolExecutionAgent
from llama_agi.runners import AutoAGIRunner
from llama_agi.task_manager import LlamaTaskManager
from llama_agi.tools import search_notes, record_note, search_webpage

from llama_index import ServiceContext, LLMPredictor

# LLM setup
llm = OpenAI(temperature=0, model_name='text-davinci-003')
service_context = ServiceContext.from_defaults(llm_predictor=LLMPredictor(llm=llm), chunk_size_limit=512)

# llama_agi setup
task_manager = LlamaTaskManager([args.initial_task], task_service_context=service_context)

tools = load_tools(["google-search-results-json"])
tools = tools + [search_notes, record_note, search_webpage]
execution_agent = ToolExecutionAgent(llm=llm, tools=tools)

# launch the auto runner
runner = AutoAGIRunner(task_manager, execution_agent)
objective = "Solve world hunger"
initial_task = "Create a list of tasks"
sleep_time = 2 
runner.run(objective, initial_task, sleep_time)
```

More examples can be found in the `examples` folder!

## Llama Ecosystem

- LlamaIndex (connecting your LLMs to data): https://github.com/jerryjliu/llama_index
- LlamaHub (community library of data loaders): https://llamahub.ai
hdx-data-freshness-emailer 1.7.9
HDX Data Freshness Emailer
License: MIT
Requires Python: >=3.8
Keywords: HDX,data freshness,freshness emailer

[![Build Status](https://github.com/OCHA-DAP/hdx-data-freshness-emailer/actions/workflows/run-python-tests.yml/badge.svg)](https://github.com/OCHA-DAP/hdx-data-freshness-emailer/actions/workflows/run-python-tests.yml)
[![Coverage Status](https://codecov.io/gh/OCHA-DAP/hdx-data-freshness-emailer/branch/main/graph/badge.svg?token=JpWZc5js4y)](https://codecov.io/gh/OCHA-DAP/hdx-data-freshness-emailer)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)

# DEPRECATED - code moved to https://github.com/OCHA-DAP/hdx-data-freshness

The HDX freshness emailer reads the HDX data freshness database and finds datasets whose status has changed. 
It sends emails to system administrators if the change is from overdue to delinquent or to maintainers if 
from due to overdue. It also alerts when there are new candidates for the data grid and reports datasets that 
are broken or which have invalid maintainers. An email is also sent for organisations with invalid 
administrators. 

For more information, please read the [documentation](https://hdx-data-freshness-emailer.readthedocs.io/en/latest/). 

This library is part of the [Humanitarian Data Exchange](https://data.humdata.org/) (HDX) project. If you have 
humanitarian related data, please upload your datasets to HDX.