semvec/pypi-packages huggingface dataset:
paneltime 1.2.69
Panel regression simultaneously solving for ARIMA/GARCH and random/fixed effects.
Requires Python: >=3.8
Keywords: econometrics




**Paneltime** is a statistical tool for estimating regressions on datasets that:  

- Are **panels** (have both a time and a group dimension)  
- Are **non-stationary in means** (ARIMA)  
- Are **non-stationary in variance** (GARCH)  

Unlike any other statistical tool currently available, **Paneltime** simultaneously handles common timeseries problems 
(**ARIMA**/**GARCH**)  and  *panel data sets* consisting of several groups/individuals 
 (**random/fixed effects**).  

The package can also be used on pure **panel data** sets without ARIMA/GARCH or single time series data with ARIMA/GARCH problems. 

**Author:** Espen Sirnes  
**Current version:** 1.2.68  


# Installation


"pip install paneltime" for installation


# Usage

Datasets are estimated with 

```
paneltime.execute(model_string, dataframe, T,
						ID=None, HF=None,
						instruments=None)
```
It takes the following arguments:

- `model_string`: A string on the form 'Y ~ X1 + X2 + X3', where Y is the dependent and X1-X3 are the independents, as named in the dataframe (required)
- `dataframe`: a dataframe consisting of variables with the names used in `model_string` (required)
- `T`: the time identifier (required)
- `ID`: The group identifier (optional)
- `HF`: list with names of heteroskedasticity factors (optional)
- `instruments`: list with names of instruments (optional)
  
## The model string

The model string can contain operations supported by the `numpy` package, using `np` as alias for numpy. For example `np.abs(x)` will result in a 
variable that is the absolute value of x. 


# Example using world bank data
```python
{{< include example.py >}}
```

[Download working example zip](working_example.zip)
oops-datedir-repo 0.1.0
OOPS disk serialisation and repository management.
Requires Python: >=3.8

*************************************************************************
python-oops-datedir-repo: A simple disk repository for OOPS Error reports
*************************************************************************

    Copyright (c) 2011, Canonical Ltd

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU Lesser General Public License as published by
    the Free Software Foundation, version 3 only.
   
    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU Lesser General Public License for more details.
   
    You should have received a copy of the GNU Lesser General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
    GNU Lesser General Public License version 3 (see the file LICENSE).

This is a component of the python-oops project:
https://launchpad.net/python-oops. An OOPS report is a report
about something going wrong in a piece of software... thus, an 'oops' :)

This package provides disk storage, management, and a serialisation format for
OOPSes stored in the repository. Programs or services that are generating OOPS
reports need this package or other similar ones, if they want to persist the
reports.

Dependencies
============

* Python 3.8+
* The oops package (https://launchpad.net/python-oops or 'oops' on pypi).

Testing Dependencies
====================

* fixtures (http://pypi.python.org/pypi/fixtures)

* subunit (http://pypi.python.org/pypi/python-subunit) (optional)

* testtools (http://pypi.python.org/pypi/testtools)

Usage
=====

oops_datedir_repo is an extension package for the oops package.

The DateDirRepo class provides an OOPS publisher (``DateDirRepo.publish``)
which will write OOPSes into the repository.

Retrieving OOPSes can be done by using the low level serializer_rfc822
functions : an OOPS report can be written to a disk file via the
serializer_rfc822.write() function, and read via the matching read() function.

Typical usage::

  >>> config = oops.Config()
  >>> with fixtures.TempDir() as tempdir:
  ...    repo = oops_datedir_repo.DateDirRepo('/tmp/demo')
  ...    config.publishers.append(repo.publish)
  ...    ids = config.publish({'oops': '!!!'})

For more information see the oops package documentation or the api docs.

Installation
============

oops_datedir_repo can be installed into an existing Python environment in
the usual way using ``pip``, or it can be installed as a standalone package
in order to use its ``prune`` tool.  To install it as a standalone package,
run ``make`` to create a suitable virtual environment; you can then run
``bin/prune`` to delete unreferenced OOPSes.


Development
===========

Upstream development takes place at https://launchpad.net/python-oops-datedir-repo.

To run this project's tests, use `tox <https://tox.readthedocs.io/en/latest/>`.
semimutable 0.1.0
Drop-in replacement for Python's built-in `dataclasses` module, allowing for certain fields to be mutable while others remain immutable.
Requires Python: >=3.12
Keywords: dataclasses

# Semimutable

[![Tests](https://github.com/Glinte/semimutable/actions/workflows/ci.yml/badge.svg)](https://github.com/Glinte/semimutable/actions/workflows/ci.yml)
[![codecov](https://codecov.io/gh/Glinte/semimutable/branch/main/graph/badge.svg)](https://codecov.io/gh/Glinte/semimutable)

A dataclass drop-in that allows you to define individual fields as immutable.

## Usage

Simply replace all your `dataclasses` imports with `semimutable`, and use `field(frozen=True)` for the fields you want to be immutable.

`field` takes in the same parameters as `dataclasses.field`, with an extra `frozen` boolean flag that defaults to `False`. You can specify default values, default factories, and other options just like you would with a regular dataclass field.

Here is one example from our tests.

```python
from semimutable import dataclass, field

@dataclass
class Simple:
    x: int = field(frozen=True)
    y: int = 0 # normal, mutable field

def test_frozen_field_is_immutable():
    obj = Simple(x=1, y=2)
    with pytest.raises(TypeError):
        obj.x = 99

def test_non_frozen_field_is_mutable():
    obj = Simple(x=1, y=2)
    obj.y = 42
    assert obj.y == 42
```

## Credits

Parts of this library are derived from Python's standard library `dataclasses` module. The original implementation is distributed under the Python Software Foundation License. See `LICENSE.PSF` for the full license text.
katcp-codec 0.2.0
Fast encoding and decoding of katcp messages
Requires Python: >=3.8
Keywords: katcp

# Fast katcp encoding and decoding

This is a library for efficiently encoding and decoding the
[katcp](https://katcp-python.readthedocs.io/en/latest/_downloads/361189acb383a294be20d6c10c257cb4/NRF-KAT7-6.0-IFCE-002-Rev5-1.pdf)
protocol. Refer to the [online
manual](https://katcp-codec.readthedocs.io/en/latest) for more information.
pyexecutable-gen 0.0.3
A Python wrapper for PyInstaller to programmatically generate an executable from a Python script
Requires Python: >=3.6
Keywords: pyinstaller, exe, executable, generate


# pyexecutable_gen

A lightweight, configurable **PyInstaller wrapper** for Python developers who want to package their scripts into standalone executables using Python code instead of CLI.
 
> Author: [its-me-abi](https://github.com/its-me-abi)  
> Date: May 14, 2025

---

## 📦 What is this?

`pyexecutable_gen.py` provides a `builder` class to programmatically invoke PyInstaller with custom configurations,  
by doing it we can turn a python script into a executable file   
supporting:

- One-file / one-dir builds
- Hidden imports
- Data folder mapping
- Custom icons and console toggling
- Clean builds and custom PyInstaller flags

---
## 🤝 Documentation
[ full documentation is available in wiki](https://github.com/its-me-abi/pyexecutable/wiki)  
## 🛠 Installation


```bash
pip install pyexecutable_gen
```
or download this project by git comand (or download by browser as zip and extract )
```
git clone https://github.com/its-me-abi/pyexecutable_gen.git
```

---

## 🚀 Quick Start

```python
from pyexecutable_gen import builder

b = builder("your_script.py")
b.set_console(True)
b.set_onedir(True)
b.set_icon("icon.ico")
b.set_loglevel("DEBUG")
b.set_data_folders("assets", "assets")
b.set_hidden_import("your_dynamic_module")
# b.set_extra_args("--heloo its_extra_argument")  # you can set more argumnts as string.only use when functionality not available by api
b.set_collect_all("a_big_package_with_submodules")
if b.build_executable():
    print("✅ Build succeeded!")
    print(f"✅ Build executable path is {b.get_executable_path()}")
else:
    print("❌ Build failed.")

```

---

## 🔧 Features

- ✅ No CLI required
- 📂 Cross-platform data folder support
- 🪄 Hidden import handling
- 🖼 Set icon with `.ico` file
- 🧵 Toggle between GUI and console mode
- 🔍 Control build verbosity via log levels
- 🧹 Optional clean builds


---



## 🪪 License

MIT License – See `LICENSE.md` for details.
ebrains-validation-framework 0.9.3
Python client for the EBRAINS Validation Framework web services.
Requires Python: >=3.8
Keywords: ebrains, hbp, model validation

A Python package for working with the EBRAINS Model Validation Framework
========================================================================

Andrew Davison and Shailesh Appukuttan, CNRS, 2017-2025

Documentation: http://ebrains-validation-client.readthedocs.io

The "ebrains_validation_framework" package replaces "hbp_validation_framework".

Licence: BSD 3-clause, see LICENSE.txt

<div><img src="https://raw.githubusercontent.com/HumanBrainProject/ebrains-validation-client/master/eu_logo.jpg" alt="EU Logo" width="15%" align="right"></div>

### ACKNOWLEDGEMENTS
This open source software code was developed in part or in whole in the Human Brain Project, funded from the European Union's Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No. 720270, No. 785907 and No. 945539 (Human Brain Project SGA1, SGA2 and SGA3).
flexible-dict 2.2.0
A flexible way to access dict data instead of built-in dict.

# Flexible Dict

A flexible way to access dict data instead of built-in dict.

## Installation

```shell
pip install flexible-dict
```

## Usage

### Define a dict class and access value

Use a decorator `json_object` to make class to be a flexible dict.

```python
from flexible_dict import json_object, MISSING

@json_object
class A:
    i: int = 3
    j: str = "init value"
    s: float
    g: int = MISSING

a = A()
print(a)  # actual is a dict

print(a.i)  # access value via x.y
print(a.j)

a.j = "update value"  # set value

print(a['j'])  # access value via native dict way
```

### Build a json_object class from json data

Suppose there is a file named `a.py` with content

```json
{
  "a": 1,
  "b": "two",
  "c": {
    "d": 4,
    "e": "li"
  }
}
```

Run the script bellow
```shell
python -m flexible_dict build_class --name A --file a.json --output a.py
```

Then a file named `a.py` will be generated

```python
from flexible_dict import json_object

@json_object
class A:
    a: int
    b: str
    c: dict
```
pyedm4hep 0.1.1
A Python library for working with EDM4hep data, providing an object-oriented interface on top of Pandas DataFrames.
Requires Python: >=3.8

# PyEDM4hep

[![codecov](https://codecov.io/gh/murnanedaniel/pyEDM4hep/graph/badge.svg?token=JD9R3LYXH0)](https://codecov.io/gh/murnanedaniel/pyEDM4hep)
<!-- Add other badges here, e.g., PyPI version, license, build status -->

**PyEDM4hep** is a Pythonic interface for EDM4hep event data, combining the power of Pandas DataFrames with an intuitive object-oriented API. It enables fast, flexible analysis of HEP event data stored in ROOT files, with seamless navigation between particles, hits, and decay trees.

---

## Features
- **Object-oriented access** to particles, hits, and calorimeter data
- **Bulk operations** via Pandas DataFrames for high performance
- **ROOT file support** using `uproot`
- **Relationship navigation** (parents, daughters, hits, decay trees)
- **Built-in plotting** for event overviews, decay trees, and kinematics
- **Detector geometry awareness** for realistic analysis
- **Easy integration** with Jupyter and scientific Python stack

---

## Installation

PyEDM4hep requires Python 3.8+ and can be installed via pip:

    pip install pyedm4hep

Or from source:

    git clone https://github.com/murnanedaniel/pyEDM4hep.git
    cd pyEDM4hep
    pip install .

---

## Quickstart

```python
from pyedm4hep import EDM4hepEvent

# Define detector parameters (example values)
detector_params = {
    'tracking_radius': 1100.0,
    'tracking_z_max': 2500.0,
    'energy_threshold': 0.05
}

# Load an event from a ROOT file
file_path = 'path/to/events.root'
event = EDM4hepEvent(file_path, event_index=0, detector_params=detector_params)

# Access particles as a DataFrame
particles_df = event.get_particles_df()

# Work with a single particle
particle = event.get_particle(0)
print(particle.pdg, particle.pt, particle.vertex)

# Plot event overview
fig = event.plot.event_overview()
fig.suptitle("Event Overview")
```

---

## Documentation

- [API Reference](https://github.com/murnanedaniel/pyEDM4hep)
- [Examples & Tutorials](https://github.com/murnanedaniel/pyEDM4hep/tree/main/examples)

---

## Contributing

Contributions are welcome! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
- Run tests with `pytest`.
- Ensure code coverage remains high.
- Document new features and update the changelog.

---

## License

This project is licensed under the MIT License. See [LICENSE](LICENSE) for details.

---

## Acknowledgments

- Built on top of [EDM4hep](https://github.com/key4hep/EDM4hep) and [uproot](https://github.com/scikit-hep/uproot).
- Inspired by the needs of the HEP analysis community.
electrum-ecc 0.0.6
Pure python ctypes wrapper for libsecp256k1
Requires Python: >=3.10
Keywords: libsecp256k1, ecc

# electrum-ecc

```
Licence: MIT Licence
Author: The Electrum developers
Language: Python (>= 3.10)
```

[![Latest PyPI package](https://badge.fury.io/py/electrum_ecc.svg)](https://pypi.org/project/electrum-ecc/)
[![Build Status](https://api.cirrus-ci.com/github/spesmilo/electrum-ecc.svg)](https://cirrus-ci.com/github/spesmilo/electrum-ecc)


This package provides a pure python interface to
[libsecp256k1](https://github.com/bitcoin-core/secp256k1).

Unlike Coincurve, it uses ctypes, and has no dependency.


### Tests

```
$ python3 -m unittest discover -s tests -t .
```
Or
```
$ pytest tests -v
```


### Maintainer notes

Release checklist:
- bump `__version__` in `__init__.py`
- write changelog?
- `$ git tag -s "$VERSION" -m "$VERSION"`
- `$ git push "$REMOTE_ORIGIN" tag "$VERSION"`
- build sdist (see [`contrib/sdist/`](contrib/sdist)):
  - `$ ELECBUILD_COMMIT=HEAD ELECBUILD_NOCACHE=1 ./contrib/sdist/build.sh`
- `$ python3 -m twine upload dist/$DISTNAME`
oops-timeline 0.1.0
Integrate timelines with OOPS error reports.
Requires Python: >=3.8

*************************************************************
python-oops-timeline: Error report integration with timelines
*************************************************************

    Copyright (c) 2010, 2011, Canonical Ltd
   
    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU Lesser General Public License as published by
    the Free Software Foundation, version 3 only.
   
    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU Lesser General Public License for more details.
   
    You should have received a copy of the GNU Lesser General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.

The oops_timeline package provides integration glue between timeline objects
(http://pypi.python.org/pypi/timeline) and the oops error reporting system
(http://pypi.python.org/pypi/oops).

Dependencies
============

* Python 3.8+

* oops (http://pypi.python.org/pypi/oops)

* timeline (http://pypi.python.org/pypi/timeline)

Testing Dependencies
====================

* subunit (http://pypi.python.org/pypi/python-subunit) (optional)

* testtools (http://pypi.python.org/pypi/testtools)

Usage
=====

oops_timeline provides an oops on_create hook to extract timeline data from
oops_context['timeline'] and inject it into the oops report as primitive data.

* Setup your configuration::

  >>> from oops import Config
  >>> config = Config()

Note that you will probably want at least one publisher, or your reports will
be discarded.

* Add in the timeline hook to the config::

  >>> oops_timeline.install_hooks(config)

This is a convenience function in case additional hooks are needed in future.

For more information see pydoc oops_timeline.

Installation
============

python-oops-timeline can be installed into an existing Python environment in
the usual way using ``pip``, or it can be installed as a standalone package.
To install it as a standalone package, run ``make`` to create a suitable virtual
environment.

Development
===========

Upstream development takes place at https://launchpad.net/python-oops-timeline.
To setup a working area for development, if the dependencies are not
immediately available, you can use `make` to create a virtual environment, then
env/bin/python3 to get a python interpreter with the dependencies available.

To run the tests use tox.

For instance::

  $ tox
spectrumlab 0.1.2
A pioneering unified platform designed to systematize and accelerate deep learning research in spectroscopy.
Requires Python: >=3.10
Keywords: benchmark, chemistry, evaluation, spectroscopy

<!-- This README is automatically synced from GitHub repository -->

<!-- # SpectrumLab -->

<div align="center">
  <img src="https://raw.githubusercontent.com/little1d/SpectrumLab/main/docs/public/spectrumlab.svg" alt="SpectrumLab" width="600"/>
  
  <p><strong>A pioneering unified platform designed to systematize and accelerate deep learning research in spectroscopy.</strong></p>
</div>

## 🚀 Quick Start

### Environment Setup

We recommend using conda and uv for environment management:

```bash
# Clone the repository
git clone https://github.com/little1d/SpectrumLab.git
cd SpectrumLab

# Create conda environment
conda create -n spectrumlab python=3.10
conda activate spectrumlab

pip install uv
uv pip install -e .
```

### Data Setup

Download benchmark data from Hugging Face:

- [SpectrumBench v1.0](https://huggingface.co/datasets/SpectrumWorld/spectrumbench_v_1.0)

Extract the data to the `data` directory in the project root.

### API Keys Configuration

```bash
# Copy and edit environment configuration
cp .env.example .env
# Configure your API keys in the .env file
```

## 💻 Usage

### Python API

```python
from spectrumlab.benchmark import get_benchmark_group
from spectrumlab.models import GPT4o
from spectrumlab.evaluator import get_evaluator

# Load benchmark data
benchmark = get_benchmark_group("perception")
data = benchmark.get_data_by_subcategories("all")

# Initialize model
model = GPT4o()

# Get evaluator
evaluator = get_evaluator("perception")

# Run evaluation
results = evaluator.evaluate(
    data_items=data,
    model=model,
    save_path="./results"
)

print(f"Overall accuracy: {results['metrics']['overall']['accuracy']:.2f}%")
```

### Command Line Interface

The CLI provides a simple way to run evaluations:

```bash
# Basic evaluation
spectrumlab eval --model gpt4o --level perception

# Specify data path and output directory
spectrumlab eval --model claude --level signal --data-path ./data --output ./my_results

# Evaluate specific subcategories
spectrumlab eval --model deepseek --level semantic --subcategories "IR_spectroscopy" "Raman_spectroscopy"

# Customize output length
spectrumlab eval --model internvl --level generation --max-length 1024

# Get help
spectrumlab eval --help
```

## 🤝 Contributing

We welcome community contributions! Please see [CONTRIBUTING.md](https://github.com/little1d/SpectrumLab/blob/main/CONTRIBUTING.md) for detailed guidelines.

## Acknowledgments

- **Experiment Tracking**: [SwanLab](https://github.com/SwanHubX/SwanLab/) for experiment management and visualization
- **Choice Evaluator Framework**: Inspired by [MMAR](https://github.com/ddlBoJack/MMAR)