semvec/pypi-packages huggingface dataset:
ninebot-ble-jekson 0.1.0
Ninebot scooter BLE client
Requires Python: >=3.9

# Ninebot Scooter BLE Python client

Python client for interfacing with a Ninebot scooter using bluetooth low energy (BLE).

It is primarely using the BLE UART characteristic for communication with the scooter. It is also
using the new encrypted protocol using [miauth](https://github.com/dnandha/miauth) library. Old
scooter firmwares might therefore not work.

The projects primary objective is to support Home Assistant integration but will work for more
use-cases as well.

## Usage

Installation:

```
pip install ninebot-ble
```

A command-line client for testing purposes are shipped:

```
ninebot-ble --help
```

## Troubleshoot

I only have access to a Ninebot F-series scooter, if you have problems with other models, I will
need help with debugging.
ddr-analysis-tools 0.0.14
Package for data analysis tools
Requires Python: <4,>=3.10
Keywords: ddr, time series data analysis, data analysis, POD, SPOD, FFT, ddr_FFT, ddr_SPOD, ddr_PODddr_analysis_tools

# ddr_analysis_tools

This project contains multiple analysis tools. Some of these tools may be derrived from some other libraries but are adjusted to use in other conditions.

## ddr_POD

This code contains two major class which could perfor POD. 1- POD using singular value decomposition and 2- snapshot POD method. I have varified in certain cases that these two methods results in almost similar eigenmodes, however use these method judiciously. The data to these classes should be provided in 2D matrix form where the time axis lies in first dimention.

## ddr_SPOD

The SPOD class in this module is derrived from pyspod library available online.

## ddr_PSD_welch

This is the class made to use welch's method in performing FFT. The welch's method is already availalbe in scipy. The class in this library uses that.

## ddr_FFT

numpy FFT method is used to perform FFt on the data. The class contains several other methods which could be usefull in plotting and filtering.


---
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icoscp_stilt 0.1.3
Access to ICOS STILT calculation results available from https://stilt.icos-cp.eu/viewer/
Requires Python: >=3.10

# icoscp_stilt

Python API for access to STILT calculation results from a service hosted by
ICOS Carbon Portal (browser access requires ATMO ACCESS login): [https://stilt.icos-cp.eu/viewer/](https://stilt.icos-cp.eu/viewer/)

## Installation
The latest release is available on [https://pypi.org/project/icoscp_stilt/](
https://pypi.org/project/icoscp_stilt/). You can simply run:

```shell
pip install icoscp_stilt
```

We would encourage you to use a virtual environment for Python to test this
library.  
For example with [Miniconda](https://docs.conda.io/en/latest/miniconda.html)
you can create a new environment with:

```shell
conda create -n icos python
activate icos
pip install icoscp_stilt
```

or with Python

```shell
python3 -m venv icos
source venv/bin/activate
pip install icoscp_stilt
```

## Documentation
The full documentation about the library and all the modules are available at
[https://icos-carbon-portal.github.io/pylib/icoscp_stilt/](
https://icos-carbon-portal.github.io/pylib/icoscp_stilt/)
icoscp 0.2.2
Access to ICOS data objects hosted at https://data.icos-cp.eu
Requires Python: >=3.10

# icoscp
| Latest Release | [![](https://badge.fury.io/py/icoscp.svg)](https://pypi.org/project/icoscp/)                                                                                                                |
|----------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| PyPI Downloads | [![](https://static.pepy.tech/personalized-badge/icoscp?period=total&units=international_system&left_color=black&right_color=orange&left_text=Downloads)](https://pepy.tech/project/icoscp) |

## Installation
The latest release is available on [https://pypi.org/project/icoscp/](
https://pypi.org/project/icoscp/). You can simply run:

```shell
pip install icoscp
```

If you need the cutting edge version you may install the library directly from
GitHub with:

```shell
pip install git+https://github.com/ICOS-Carbon-Portal/pylib.git
```

We would encourage you to use a virtual environment for python to test this
library.  
For example with [Miniconda](https://docs.conda.io/en/latest/miniconda.html)
you can create a new environment with:

```shell
conda create -n icos python
activate icos
pip install icoscp
```

or with python

```shell
python3 -m venv icos
source venv/bin/activate
pip install icoscp
```

## Documentation
The full documentation about the library and all the modules are available at
[https://icos-carbon-portal.github.io/pylib/icoscp/](
https://icos-carbon-portal.github.io/pylib/icoscp/)

## Development
For instructions about how to go about extending and testing this software,
please see <development.md>