semvec/pypi-packages huggingface dataset:
rustxes 0.2.11
Rust-based XES/OCEL2 event log importer
Requires Python: >=3.7
Keywords: process-mining, process, mining, OC-DECLARE, object-centric, DECLARE

# rustxes
A Python package to efficiently import XES or OCEL2 event logs using Rust.

For traditional event data, it supports parsing data both from `.xes` XML files and `.xes.gz` archives (and also from strings directly if needed). 

For object-centric event data, it supports parsing OCEL2 XML or JSON files (`.xml` or `.json`).



## Usage
### XES Import

The `import_xes` returns a tuple of 1) the XES log polars dataframe and 2) JSON-encoding of global log attributes.

```python
import rustxes

[xes,log_attrs_json] = rustxes.import_xes("path/to/file.xes")
print(xes.shape)
```

#### Options
The following parameters can be passed to the `import_xes_rs` or the python wrapper (`import_xes`):
- `path` - The filepath of the .xes or .xes.gz file to import
- `date_format` - Optional date format to use for parsing `<date>` tags (See https://docs.rs/chrono/latest/chrono/format/strftime/index.html)
- `print_debug` - Optional flag to enable debug print outputs


### XES Export

The `export_xes` exports the passed polars DataFrame to the given path (either `.xes` or `.xes.gz`).

```python
import rustxes

[xes,log_attrs_json] = rustxes.import_xes("path/to/file.xes")
rustxes.export_xes(xes,"path/to/export-file.xes")
```

#### Options
The following parameters can be passed to the `export_xes_rs` or the python wrapper (`export_xes`):
- `df` - The polars DataFrame representing the event log
- `path` - The filepath the .xes or .xes.gz file should be written to



### OCEL Import
The `import_ocel_xml` and `import_ocel_json` functions return a dict of polars DataFrames with the following keys: 'objects', 'events', 'o2o', 'object_changes', 'relations'.

```python
import rustxes

ocel = rustxes.import_ocel_xml("path/to/ocel.xml")
print(ocel['objects'].shape)
```
If you want to use PM4Py's OCEL data structure, you can use the `import_ocel_xml_pm4py` or `import_ocel_json_pm4py` functions, which return a `pm4py.ocel.OCEL` objects.
Note: PM4Py must be installed for this to work!


## Development
This package was scaffolded using [maturin](https://www.maturin.rs/).
The most important commands are:
- `maturin build --release` Builds the Rust code and python package (in release mode), producing the build artifacts (wheels)
- `maturin develop --release` Builds the Rust code and python package (in release mode) and automatically installs/updates it in the corresponding (virtual) python env

  Building this package requires [Rust](https://www.rust-lang.org/), which can be installed using Rustup (see [https://www.rust-lang.org/learn/get-started](https://www.rust-lang.org/learn/get-started)).
  The Rust part on its own can be build using `cargo build --release`.


## LICENSE
This package is licensed under either Apache License Version 2.0 or MIT License at your option.
dnplot 0.4.3
Plotting of data imported by the dnora package
Requires Python: >=3.9

# dnplot 💻

**dnplot** is a tool designed for the visualization of [**dnora**](https://github.com/MET-OM/dnora) objects, such as 🌬️ wind, 🌊 wave and ocean current data.

![dnplot Visualization](https://github.com/bjorkqvi/dnplot/blob/doc/docs/files/spectra_plotly.gif)


## Quick Installation 🚀 

Ready to dive in? You can easily install **dnplot** using pip:

```shell
$ pip install dnplot 
```

## Examples

To quickly visualize the wave data outputted by SWAN to a netcdf file:

```python
import dnplot
from dnora.wavegrid import WaveGrid

data = WaveGrid.from_netcdf('model_output.nc')
point = data.sel(lon=5.25, lat=62.3, method='nearest') # Pick just one point

# The dict could be a dnora ModelRun object containing the data
plot = dnplot.Matplotlib({'wavegrid': data, 'waveseries': point})

# default value for var=["hs", ("tm01", "tm02"), "dirm"] 
plot.waveseries(var=['hs', ('tp','tm01'), 'dirp']) 
plot.wavegrid('hs', contour=False)
```

<img width="924" height="754" alt="Screenshot from 2025-10-14 14-09-08" src="https://github.com/user-attachments/assets/4bddfb55-9cf2-43e0-9a50-8b9431fa5991" />
<img width="997" height="786" alt="Screenshot from 2025-10-14 12-49-05" src="https://github.com/user-attachments/assets/1fc94da7-4345-4208-b63b-f20f8b198087" />


To interactively compare spectra from a model with several locations with buoy measurements:

```python
import dnora as dn
import dnplot

model = dn.modelrun.ModelRun(year=2020)
model.import_spectra(filename="nora3.nc", name="NORA3")
model.spectra_to_1d()

buoy = dn.modelrun.ModelRun(year=2020, month=2, day=5)
buoy.import_spectra1d(filename="spec_E39_D_1h.nc", name="Breisundet")

plot = dnplot.Plotly(buoy, model)
plot.spectra()
```
<img width="3377" height="1299" alt="Screenshot from 2025-10-14 12-34-48" src="https://github.com/user-attachments/assets/88fcddd5-52da-4d90-851e-b1ce7d6a127c" />

## Docs 📚
Looking for more details? Our documentation (currently under development) provides information about the package, its features, and how to use it. Explore it [here](https://dnora.readthedocs.io/en/latest/index.html).
listings-db-api-client 1.0.6
Python client library for the Finder property listings database API
Requires Python: >=3.12

# Listings DB API Client

A Python client library for the Finder property listings database API.

## Installation

```bash
pip install listings-db-api-client
```

## Usage

```python
from listings_db_api_client import ListingsDBAPIClient

# Initialize client
client = ListingsDBAPIClient(
    base_url="http://localhost:8000",
    api_key="your-api-key"
)

# Get listings
listings = client.get_listings(limit=10)

# Create a listing
# ... (see examples for more details)
```

## Features

- Full CRUD operations for listings
- Image management
- Estate agent management
- Authentication support
- Error handling and validation
FastKmedoids 0.0.49
For more information, check out the official documentation of `FastKmedoids` at: https://fabioscielzoortiz.github.io/FastKmedoids-book/intro.html
Requires Python: >=3.7

# FastKmedoids

For more information, check out the official documentation of `FastKmedoids` at: https://fabioscielzoortiz.github.io/FastKmedoids-book/intro.html
gettex-exchange 1.4.0
A Python client library for Gettex exchange
Requires Python: <4.0,>=3.13

[![Test](https://github.com/escalate/gettex-exchange/actions/workflows/test.yml/badge.svg?branch=master&event=push)](https://github.com/escalate/gettex-exchange/actions/workflows/test.yml)

# 📈 Gettex exchange

A Python client library for [Gettex exchange](https://www.gettex.de).

## ✨ Features

- Stock data from overview e.g. https://www.gettex.de/en/stock/US88160R1014/

## ⚠️ Disclaimer

This Python library is unofficial and not affiliated with, endorsed, or maintained by Gettex exchange.

Users are responsible for ensuring compliance with the legal terms and conditions of Gettex exchange when accessing, processing, or distributing any data obtained through this library.

Please review the official [legal notices](https://www.gettex.de/en/legal-notice/) of Gettex exchange before use.

## 📦 Installation

You can install [gettex-exchange](https://pypi.org/project/gettex-exchange/) using [pip](https://pypi.org/project/pip/):

```bash
pip install gettex-exchange
```

## 🚀 Quick Start

Here is a minimal example to get you started:

```python
from gettex_exchange import Stock

# Create an Stock instance
# URL: https://www.gettex.de/en/stock/US88160R1014/
isin = "US88160R1014"
s = Stock(isin)

# Print basic information of stock
print(s.bid_price)
print(s.ask_price)
print(s.bid_size)
print(s.ask_size)
print(s.ticker)
print(s.display_name)
print(s.wkn)
print(s.open_price)
print(s.high_price)
print(s.low_price)
print(s.last_price)
print(s.turnover)
print(s.percent_change)
print(s.price_change)
print(s.trade_date_time)
print(s.taxonomy)
```

## 🤝 Contributing

We welcome contributions of all kinds 🎉.

Please read our [Contributing](https://github.com/escalate/gettex-exchange/blob/master/CONTRIBUTING.md) guide to learn how to get started, submit changes, and follow our contribution standards.

## 🌐 Code of Conduct

This project follows a [Code of Conduct](https://github.com/escalate/gettex-exchange/blob/master/CODE_OF_CONDUCT.md) to ensure a welcoming and respectful community.

By participating, you agree to uphold this standard.

## 🐛 Issues

Found a bug or want to request a feature?

Open an issue here: [GitHub Issues](https://github.com/escalate/gettex-exchange/issues)

## 🧪 Development

Development is possible via an interactive Docker container in [VSCode](https://code.visualstudio.com/).

1. Build and launch the [DevContainer](https://code.visualstudio.com/docs/devcontainers/containers) in VSCode.

2. Initiate the Python Virtual Environment via `poetry env activate` in the terminal.

3. Run test suite via `pytest` in the terminal.

## 📜 License

This project is licensed under the **MIT License**.

See the [LICENSE](https://github.com/escalate/gettex-exchange/blob/master/LICENSE) file for details.