semvec/pypi-packages huggingface dataset:
near-seed-phrase-py 0.1.2
Python tool for creating and converting mnemonic-phrases, public key and private key for NEAR accounts.
License: MIT
Requires Python: >=3.9,<4.0
Keywords: near,seedphrase,seed,phrase,python

# NEAR Seed Phrase

Python tool for creating and converting mnemonic-phrases, public key and private key for NEAR accounts.

### 🚨 Status: BETA, contributions welcome!

#### Ported to Python from https://github.com/near/near-seed-phrase
### Install

#### Poetry
```py
poetry shell
poetry show -v # copy this virtualenv path and set it as your Python interpreter 
poetry add near-seed-phrase-py
```

#### Pip
```py
pip install near-seed-phrase-py
```

### Usage
```py
from near_seed_phrase.main import generate_seed_phrase, parse_seed_phrase

# Generate a BIP39 seed phrase with its corresponding Keys

generate_seed_phrase()

Returns:

{
    seed_phrase: str # BIP39 seed phrase
    secret_key: str # ed25519 secret/private key, formatted for NEAR (e.g. "ed25519:[SECRET_KEY]")
    public_key: str # ed25519 public key, formatted for NEAR (e.g. "ed25519:[PUBLIC_KEY]")
    public_key_hex: str # lowercase hex representation of public_key that can be used as an implicit account ID; see https://docs.near.org/integrator/implicit-accounts
} 

# Recover keys from a BIP39 seed phrase (returns same response as generate_seed_phrase())

parse_seed_phrase(seed_phrase)

```
mwaah 0.1.1
Python Client for Apache CLI on AWS Managed Airflow
License: MIT
Requires Python: >=3.7

# AWS MWAA Helper (Managed Workflows for Apache Airflow) - Python
Python Client for running Apache Airflow CLI commands on AWS MWAA (Managed Workflows for Apache Airflow) instances.  
Built to give a client like experience for MWAA, utilizing the Apache Python Client objects.

https://docs.aws.amazon.com/mwaa/latest/userguide/airflow-cli-command-reference.html

# (currently) Supported Apache Airflow CLI commands
| Version | Command                  | API         | 
|---------|--------------------------|-------------|
| v2.2.2  | dags list                | get_dags    |
| v2.0+   | dags pause               | pause_dag   |
| v2.0+   | dags unpause             | unpause_dag |
| v2.0+   | dags show                | show_dag    |
| v2.0+   | dags state               | get_dag_state|
| v2.0+   | dags trigger             | new_dagrun  |
| v2.0+   | version                  | get_version |


# Examples
## Running CLI on a private VPC instance
Test locally using the following ssh tunnel configuration  
```shell
ssh -D 8080 -C -N  user@example.com
```
Create a client with proxy config session  
```python
from mwaah import MWAAH

cli = MWAAH(
    'example-mwaa-environment',
    boto3.client('mwaa'),
    proxies={'https': 'socks5://0:8080'}
)
```

## Setting up a new CLI session
Create a client passing in your own session  
```python
from mwaah import MWAAH
cli = MWAAH(
    'example-mwaa-environment',
    boto3.client('mwaa', region_name='example-region-1')
)
```

## Get Version
```python
print(cli.get_version())
```
```
2.2.2
```

## Triggering a New DAG Run
```python
from airflow_client.client.model.dag_run import DAGRun
from dateteim import datetime

date = datetime.now()
run = DAGRun()._from_openapi_data(
    dag_id='example_dag_id',
    execution_date=date,
    dag_run_id="dag_run_id_example"+date.__str__(),
    conf={'key': 'val'},
)
cli.new_dagrun(run)
```