semvec/pypi-packages huggingface dataset:
abi-fetcher 0.1.1
A Python package to fetch smart contract ABIs from all EVM compatible blockchains
Requires Python: >=3.7

This is a python module aiming to allow the user to quickly fetch any ABI from a verified smartcontract on any EVM compatible blockchain.

This is a beta version, improvements might be added over time. if you notice anything not working appropriately or have any suggestions, feel free to report them.

commands:<br>
abi-fetcher help or h => display this message<br>
abi-fetcher list => display all available networks<br>
abi-fetcher fetch --contract xxx --network xxx --api-key xxx --output xxx<br>

--contract = the contract address on the blockchain<br>
--network = network name as displayed in the list (use quotation marks)<br>
--api-key = your personnal etherscan api key. If you don't have one, just go there : https://etherscan.io/ and sign up to get one.<br>
--output(optional) = output directory for the generated ABI. Default directory is /ABI from the root<br>

example :
abi-fetcher fetch --contract 0xXXXXXX --network "base mainnet" --api-key XXXXXXXXXXXX

If you are unsure about the network name you can browse the abi_fetcher/chainlist.json file to search via chain ID. If the network isn't listed, you can either add a network entry into the json abi_fetcher/chainlist.json or try inputing the beginning of the explorer API Url like this :

*https://api.etherscan.io/v2/api
   ?chainid=1*
   &module=contract
   &action=getabi
   &address=0xBB9bc244D798123fDe783fCc1C72d3Bb8C189413
   &apikey=YourApiKeyToken

   replace the part between * * with your own url.


The abi json should be in the ABI folder. If anything went wrong, you will find the recorded error message in the created json file.
twinlab 2.21.0
twinLab - Probabilistic Machine Learning for Engineers
Requires Python: <3.13,>=3.9
Keywords: machine-learning, AI, cloud, twinLab, digiLab

![twinLab Banner](https://hackmd.io/_uploads/BkQro2CgR.png)

# twinLab - Probabilistic Machine Learning for Engineers

twinLab is a tool for augmenting engineering workflows with Probabilistic Machine Learning. It enables users to quickly and easily build real-time emulators of their simulations, experimental set-ups, or sensor networks. Then they can make predictions, make recommendations, perform optimisations, and calibrate physics parameters from data.

twinLab comes with built-in uncertainty quantification (UQ), which means that even with sparse or noisy data, users can maximise their understanding of the design space and surrogate model with confidence.

For help, or to arrange a trial, please email: [twinlab@digilab.co.uk](mailto:twinlab@digilab.co.uk) or fill in the contact form [here](https://www.digilab.co.uk/contact).

## Getting Started

**Step 1**: Install the Python Interface

```shell
pip install twinlab
```

**Step 2**: Configure your user details

If you don't yet have one, you'll need to request a trial. Please email [twinlab@digilab.co.uk](mailto:twinlab@digilab.co.uk) or fill in the contact form [here](https://www.digilab.co.uk/contact).

Method 1: Use `tl.set_user` and `tl.set_api_key` within a script. Be careful not to publicly expose your API key if sharing files.

```python
import twinlab as tl

tl.set_user("<your_username>")
tl.set_api_key("<your_api_key>")
```

Method 2: Create a `.env` file containing `TWINLAB_USER` and `TWINLAB_API_KEY` in your working directory. You can then `import twinlab as tl` in your Python script / notebook as normal. The API key will be read from `.env` automatically.

```shell
echo "TWINLAB_USER=<your_username>" >> .env
echo "TWINLAB_API_KEY=<your_api_key>" >> .env
```

**Step 3**: Run an Example

Here’s an example script to get you started:

```python
import pandas as pd
import twinlab as tl

# Load an example dataset and upload to twinLab
dataset = tl.Dataset("quickstart")
df = tl.load_example_dataset("quickstart")
dataset.upload(df)

# Train a machine-learning emulator for the data
emulator = tl.Emulator("test-emulator")
emulator.train(dataset, ["x"], ["y"])

# Evaluate the emulator on some unseen data
sample_points = pd.DataFrame({"x": [0.25, 0.5, 0.75]})
predict_mean, predict_std = emulator.predict(sample_points)

# Explore the results
print(predict_mean)
print(predict_std)
```

## Documentation

Find more examples, tutorials, and the full reference guide for our Python Interface in our [documentation](https://twinlab.ai).

## Speak to an Expert

Our Solution Engineers are here to provide technical support and help you maximise the value of twinLab. Please email [twinlab@digilab.co.uk](mailto:twinlab@digilab.co.uk) or fill in the contact form [here](https://www.digilab.co.uk/contact).
xlsxparse-joeypas 0.1.2
A .xlsx parser
Requires Python: >=3.8

# xlsxparse

xlsxparse is a Python command-line application that extracts cell references from an Excel file and outputs their locations to a JSON file. The tool provides two main commands:

- `create`: Parses an Excel (`.xlsx`) file for references and exports them to a JSON file.
- `search`: Searches the generated JSON file for specific references.

## Installation

You can install xlsxparse via pip:

```sh
pip install xlsxparse-joeypas
```

Ensure you have Python 3.8+ installed.

## Usage

### Create Command

Extracts references from an Excel file and saves them to a JSON file.

```sh
xlsxparse create <xlsx_file> [--sheet <sheet_name>] --output <json_file>
```

#### Parameters:
- `<xlsx_file>`: Path to the Excel file.
- `--sheet <sheet_name>` (optional): Name of the specific sheet to parse. If omitted, all sheets are parsed.
- `--output <json_file>`: (optional): Path to save the extracted references in JSON format.

#### Example:
```sh
xlsxparse create data.xlsx --sheet Sheet1 --output refs.json
```

### Search Command

Searches the JSON file for specific references.

```sh
xlsxparse search [OPTIONS] STRING [FILE]
```

#### Arguments:
- `STRING` (required): String to search for.
- `FILE` (optional): Path to the file. Defaults to `output.json`.

#### Options:
- `--stype [metric|sheet-metric|ref-file|ref-file-sheet]` (default: `metric`): Search type.
- `--help`: Show help message and exit.

#### Example:
```sh
xlsxparse search "Book2.xlsx, Sheet2" refs.json --stype ref-file-sheet
```

## Output Format

The output JSON file follows this structure:

```json
[
  {
    "Sheet": "Sheet1",
    "Metric": [
      "Test",
      "Three"
    ],
    "Cell": "B4",
    "Formula": "=B2+B3",
    "References": [
      {
        "sheet": "Sheet1",
        "cell": "B2"
      },
      {
        "sheet": "Sheet1",
        "cell": "B3"
      }
    ]
  },
  {
    "Sheet": "Sheet2",
    "Metric": [
      null,
      null
    ],
    "Cell": "A2",
    "Formula": "=SUM(30,[1]Sheet1!D12)",
    "References": [
      {
        "file": "Book2.xlsx",
        "sheet": "Sheet1",
        "cell": "D12"
      }
    ]
  }
]
```

## Dependencies

- Python 3.8+
- openpyxl
- typer

## Contributing

1. Fork the repository.
2. Create a new branch (`feature-branch`).
3. Commit your changes.
4. Push to your branch and open a Pull Request.

## License

This project is licensed under the MIT License.
opsml-cli 0.7.1
Rust-based CLI for interacting with OpsML
Requires Python: >=3.10

# Opsml-cli

[![Lints-Tests](https://github.com/shipt/opsml-cli/actions/workflows/lint-testing.yml/badge.svg)](https://github.com/shipt/opsml-cli/actions/workflows/lint-testing.yml)

This package provides a python-installable CLI written in `Rust` for interacting with [opsml](https://github.com/shipt/opsml).

## Installation

### Using Poetry

```bash
poetry add opsml-cli
```

### Using Pip

```bash
pip install opsml-cli
```

## Commands

To get a list of commands, run `opsml-cli help`.

### Listing Cards

```console

$ opsml-cli list-cards --registry model
```

### Downloading Model

```console
# Download model only

$ opsml-cli download-model --name {{model}} --repository {{repository}} --version {{version}}  # name repository version
$ opsml-cli download-model --uid {{uid}} # model from uid

# Download onnx version of model

$ opsml-cli download-model --name model --repository {{repository}} --version 1.0.0  --onnx

# Download model and preprocessor (if available)
opsml-cli download-model --name model -- version 1.0.0 --repository {{repository}} --onnx --preprocessor
```

## Contributing
If you'd like to contribute, be sure to check out our [contributing guide](./CONTRIBUTING.md)!

Thanks goes to these phenomenal [projects and people](./ATTRIBUTIONS.md) and people for creating a great foundation to build from!

<a href="https://github.com/demml/opsml-cli/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=demml/opsml-cli" />
</a>
project-euler-utils 0.3.2
Utility functions to solve project-euler and math problems
Requires Python: >=3.1

## project-euler-utils

Hi, I made this package to provide utility functions for solving project euler problems. It can be extended to solve math problems

Back story - It has been some time I have been solving math problems from different available problem sets. 
A lot of these functions were frequently needed to solve the problems. I compiled the ones frequently used and decided to publish them. 


## Installation

```
pip install project-euler-utils
```

## Usage

Import the package

```
import project_euler_utils
```

## Contributing

I will massively appreciate your contributions in developing this package alongside.
Please refer to CONTRIBUTING.md for the details


- Email - shlok.kothari@gmail.com

or open an issue on GitHub if you encounter any problems or have any suggestions
pytobase 0.0.2
This module will help you encrypt a python file using base64 encoding
Requires Python: >=3.6
Keywords: files speedfiles

<img src="https://i.ibb.co/ZTq7C5V/260.png" align="center" style="object-fit: scale-down"/>

<p align="center" style="font-size: 25">This module will help you encrypt a python file using base64 encoding</p>

<h3 align="center">To encrypt a file you can enter the command 'python3 -m pytobase encode file.py' or add 'from pytobase import encode' to the beginning of the file</h3>

<h4 align="center" style="font-size: 15">ATTENTION!!! Be sure to save a backup copy of the file somewhere else, as the original file will be overwritten!</h4>

<h5 style="font-size: 10">By Zailox</h5>