semvec/pypi-packages huggingface dataset:
stdb-driver 0.3.2
Mireo SpaceTime database connector
Requires Python: >=3.10
Keywords: mireo, spacetime, database, connector, table, schema, ingest, spatiotemporal

# Mireo SpaceTime database remote connector

**stdb-driver** is a Python module for managing remote [Mireo SpaceTime database](https://www.mireo.com/spacetime).

## Features

- Both **blocking** and **asynchronous** modes of operation are available.
- Supports listing **nodes**; listing, creating and droping **tables**; inserting **rows**; and performing **range queries**.
- Range query results are convertible to **Python lists**, **NumPy arrays** and **Pandas dataframes**.
- Requires **CPython 3.10+**.
- Available for **Linux** (**glibc** 2.17+ and **musl** 1.2+; x86-64) and **Windows** (x86-64).

## Installation

```
pip install -U stdb-driver -i https://pypi.mireo.local
```

Conversions to NumPy and Pandas data types require those packages that can be installed as optional dependencies:
```
pip install -U stdb-driver[numpy]
pip install -U stdb-driver[pandas]
pip install -U stdb-driver[all]
```

## Example

```
import stdb_driver

# Connect
session = stdb_driver.SessionConfig().zk_hosts(<address>).build()
print(session.status())  # SessionStatus.Connected

# List nodes and tables
print(session.list_nodes(), session.list_tables())

# Run range query
RALL = stdb_driver.RANGE_ALL
res = session.range_query('segment', [[(1, 10000), RALL, RALL, RALL, RALL, RALL, RALL, RALL, RALL]])

# Print column names and their MSQL data types
print(res.row_type.columns)

# Iterating over rows converts each lazily to a Python list of appropriate Python data types
for row in res:
	print(row)

# Query result can be converted to:
rows = res.to_list()  # Python list of appropriate Python data types
arr = res.to_numpy()  # NumPy array of appropriate NumPy data types (requires numpy)
df = res.to_pandas()  # Pandas dataframe of appropriate NumPy/Pandas data types (requires pandas)

# Disconnect
session.stop()
print(session.status())  # SessionStatus.Stopped
```
lpsd 1.0.6
Python and C implementation for logarithmical power spectral density (LPSD) calculation.
Requires Python: <3.16,>=3.10

# Python 3 LPSD algorithm

## Overview

This repository contains a Python 3 implementation of the LPSD algorithm.
The implementation is similiar to the LPSD implementation in the Matlab package LTPDA.
The core of the algorithm can be run as Python 3 or as (faster) C code.
Both deliver the *same results*.
To run the C core the file ltpda_dft.c has to be compiled to a shared library.


## Installation

Install directly with pip:
```bash
pip install lpsd
```


## Usage

Fully working examples can be found in [/doc/examples](https://git.physnet.uni-hamburg.de/gwd/lpsd/-/tree/main/doc/examples).

### With a DataFrame

Recommended interface, direct usage of a `DataFrame`

```python
import pandas as pd
from lpsd import lpsd
# read time series
data = pd.read_csv("time_series.csv.gz", index_col=0)
# select column and calculate
spectrum = lpsd(data["column"])
# plot PSD
spectrum["psd"].plot(logx=True, logy=True)
```

### Using numpy arrays

Use the traditional method `lpsd_trad`, which uses simple numpy arrays.


## References

- [Improved spectrum estimation from digitized time series on a logarithmic frequency axis](https://doi.org/10.1016/j.measurement.2005.10.010)
Authors: Michael Tröbs and Gerhard Heinzel
- [Spectrum and spectral density estimation by the Discrete Fourier transform (DFT), including a comprehensive list of window functions and some new flat-top windows](http://hdl.handle.net/11858/00-001M-0000-0013-557A-5)
Authors: Gerhard Heinzel, Albrecht Rüdiger and Roland Schilling
- [MATLAB Toolbox LTPDA](https://www.elisascience.org/ltpda/)
Author:  Martin Hewitson
graph-db-interface 1.1.0
A simple interface to interact with a GraphDB instance
Requires Python: <4.0,>=3.11

# graph_db_interface

[![PyPI version](https://badge.fury.io/py/graph-db-interface.svg)](https://badge.fury.io/py/graph-db-interface)
![Python](https://img.shields.io/badge/python-%3E=3.9-blue)
![License](https://img.shields.io/github/license/JaFeKl/graph_db_interface)


This repository acts as an interface to abstract SPARQL queries to callable methods to interact with a running GraphDB instance in an easier way.

# Installation


To install the current PyPI release, simply run the following command using your preferred python interpreter: 

```bash
pip install graph-db-interface
```

Or after cloning this repository you can also use poetry to install the package:
```bash
poetry install
```


# Getting Started
The package uses a single class named `GraphDB`. To use the interface, simply generate an object from this class:

```python
from graph_db_interface import GraphDB

credentials = GraphDBCredentials(
    base_url=<your_graph_db_url>,
    username=<your_graph_db_user>
    password=<your_graph_db_password>
    repository=<your_selected_repository_id>
)

myDB = GraphDB(
    credentials=credentials
)
```

# License

The package is licensed under the [MIT license](LICENSE).


# Acknowledgements
This package is developed as part of the INF subproject of the [CRC 1574: Circular Factory for the Perpetual Product](https://www.sfb1574.kit.edu/english/index.php). This work is therefore supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [grant-number: SFB-1574-471687386]