semvec/pypi-packages huggingface dataset:
cardanomsg 0.1.5
A module to send Cardano ADA with a message in the metadata.
Requires Python: >=3.6

# cardanomsg

Store and retrieve text or JSON in the Cardano blockchain metadata.

## Installation

You can install the module using pip:

```sh
pip install cardanomsg
```

## Usage

A [BlockFrost](https://blockfrost.io/) API account is required *(to prevent needing to run the blockchain locally on your PC)*.

## Send message

Send a message in a transaction.

```python
from cardanomsg.transaction import send_message
transaction_hash = send_message("<BLOCKFROST_PROJECT_ID>", "wallet.skey", "<RECIPIENT_ADDRESS>", 1000000, "Hello World")
```

## Send message with label

Send a message with a searchable label in a transaction.

```
label = uuid.uuid4().int & (1<<32)-1
payload = {
    "id": 12345,
    "text": "Hello World"
}
transaction_hash = send_message("<BLOCKFROST_PROJECT_ID>", "wallet.skey", "<RECIPIENT_ADDRESS>", 1000000, payload, label)
```

*The label must be numeric. Wait 1-3 minutes after submitting the transaction to find your label.*

## Get message

Get a message from a transaction.

```python
from cardanomsg.transaction import get_message
message = get_message("<BLOCKFROST_PROJECT_ID>", "079112f6a5192c6eeae57de0607d61e07dea864efc2bbad7aa953795a5c56aae")[0].json_metadata
```

You can also view the message on the blockchain using Cardanoscan.

https://preview.cardanoscan.io/transaction/079112f6a5192c6eeae57de0607d61e07dea864efc2bbad7aa953795a5c56aae?tab=metadata

```
Summary | UTXOs | Metadata (1)

Metadata Hash: 2f86fa9fdfcb606ab2b5f060bd125848e45187cf2c798ab389e6a9af98ba8ad1
Public Label: 1
Value: "Hello World"
```

## Find message

Find messages using a label.

```python
from cardanomsg.transaction import find_message
messages = find_message(<BLOCKFROST_PROJECT_ID>, 1782959986)
for message in messages:
    print(message.json_metadata)
```

Find messages using a label on Cardano mainnet, count 10, page 2, sorted by most recent.

```python
from blockfrost import ApiUrls
messages = find_message(<BLOCKFROST_PROJECT_ID>, 123, api=ApiUrls.mainnet, count=10, page=2, order='desc')
```

*The label must be numeric.*

## Create wallet

Create a wallet.

```
from cardanomsg.wallet import create
result = create()
```

Two files will be created: `wallet.skey` and `wallet.addr`.

The contents of `wallet.skey` is the secret key with the following format.

```json
{
  "type": "PaymentSigningKeyShelley_ed25519",
  "description": "Payment Signing Key",
  "cborHex": "<SENDER_SECRET_KEY>"
}
```

## Publishing to PyPi

Use the following steps to publish a new version to PyPi.

1. Update the version number in [pyproject.toml](https://github.com/primaryobjects/cardanomsg/blob/main/pyproject.toml#L7) and [setup.py](https://github.com/primaryobjects/cardanomsg/blob/main/setup.py#L7).
2. Package the distributable: `py -m build`
3. Upload the package: `py -m twine upload dist/*`
4. Confirm the new version at https://pypi.org/project/cardanomsg

## License

MIT

## Author

Kory Becker
http://primaryobjects.com
npu-easy 0.1.0
A zero-dependency wrapper for easy NPU usage in Python on Windows.

## Installation

```bash
pip install .
```

`npu-easy` has **zero hard dependencies**. It will install quickly without downloading large packages. 

### Usage Acceleration
To actually use NPU acceleration, you should install the appropriate ONNX Runtime version for your hardware:

- **Intel**: `pip install onnxruntime-openvino`
- **AMD/Generic Windows**: `pip install onnxruntime-directml`
- **Qualcomm**: Install `onnxruntime` and the QNN SDK.

## Quick Start

### Basic NPU Usage
```python
from npu_easy import NPUModel
import numpy as np

# Automatically selects NPU if found
model = NPUModel("path/to/model.onnx")
results = model.run(np.random.randn(1, 10).astype(np.float32))
```

### Bonus: Multi-Hardware Bonus Feature
Run on NPU, GPU, and CPU all at once!
```python
from npu_easy import MultiRunner

multi = MultiRunner("path/to/model.onnx")
# Runs on all backends in parallel threads
all_results = multi.run_all(input_data)
```

### Multi-Threading
Set the number of internal threads for the engine:
```python
model = NPUModel("path/to/model.onnx", intra_op_num_threads=4)
```

## Hardware Support
- **Intel**: via OpenVINO Execution Provider.
- **AMD/NVIDIA/Integrated**: via DirectML Execution Provider.
- **Qualcomm**: via QNN Execution Provider.
WorkingWithFiles 0.1.3
Functions to find and list files
Requires Python: >=3.8
Keywords: files, library

# WorkingWithFiles

Functions to find and list files

## 1. Installing

To install the package from [PyPI](https://pypi.org/project/WorkingWithFiles/), follow the instructions below:


```bash
pip install --upgrade WorkingWithFiles
```

Execute `pip show WorkingWithFiles` to see where it was installed, probably in `/home/USERNAME/.local/lib/python3.10/site-packages/WorkingWithFiles`.

### Using

To start, use the command below:

```bash
import WorkingWithFiles as wwf

input_diretory_1 = "/some/path/with/images"
file_format_1    = ".png"

list_files = wwf.get_all_files_in_dir_list([input_diretory_1],formats_search=[file_format_1]);
```
## 2. More information

If you want more information go to [doc](https://github.com/trucomanx/WorkingWithFiles/blob/main/doc) directory.

## 3. Buy me a coffee

If you find this tool useful and would like to support its development, you can buy me a coffee!  
Your donations help keep the project running and improve future updates.  

[☕ Buy me a coffee](https://ko-fi.com/trucomanx) 

## 4. License

This project is licensed under the GPL license. See the `LICENSE` file for more details.