semvec/pypi-packages huggingface dataset:
ukhotides 1.1.2
Python library for interfacing with the UKHO Admiralty Tidal API
License: MIT
Requires Python: <4.0,>=3.10

# UKHO Tides

This package provides a client wrapper for the [Admiralty Tidal API](https://admiraltyapi.portal.azure-api.net/) endpoints.

# Installation

Install via [pip](https://pypi.org/project/ukhotides/).

```
pip install ukhotides
```

# API Key

To call the API you will need an API key from the Admiralty Maritime Data Solutions developer portal. Follow [their guide](https://admiraltyapi.portal.azure-api.net/docs/startup) on how to do so and select one of the **UK Tidal API** products - the **Discovery** tier is free (the paid APIs have not been tested for this package).

# Usage

```python
from ukhotides import UkhoTides
from aiohttp import ClientSession

session = ClientSession()
client = UkhoTides(session, "<api_key>")

# Get all stations
stations = await client.async_get_stations()

# Get stations with name filter
stations = await client.async_get_stations("StationName")

# Get station by id
station = await client.async_get_station("0001")

# Get tidal events for station by id
events = await client.async_get_tidal_events("0001")

# Get 4 days of tidal events for station by id
events = await client.async_get_tidal_events("0001", 4)
```

There are also several premium endpoints exposed, however these are built based on the documentation and have not been tested.

```python
### Premium API Endpoints ###
### UNTESTED ###
from ukhotides import UkhoTides, ApiLevel
from aiohttp import ClientSession
from datetime import datetime

session = ClientSession()
client = UkhoTides(session, "<api_key>", ApiLevel.Premium)

# Get tidal events for a station between a given date range
start_date = datetime(2020,1,1)
end_date = datetime(2020,1,30)
events = await client.async_get_tidal_events_for_date_range("0001", start_date, end_date)

# Get tide height for a station for a given datetime
date_time = datetime(2020,1,1)
height = await client.async_get_tidal_height("0001", date_time)

# Get tide heights for a station between a given date range and at a given interval
interval_in_minutes = 60
start_date = datetime(2020,1,1)
end_date = datetime(2020,1,1,2)
heights = await client.async_get_tidal_heights("0001", start_date, end_date, interval_in_minutes)
```

# Publish to PyPi

Update the version number in `pyproject.toml` and then run:

```sh
poetry config pypi-token.pypi $PYPI_TOKEN
poetry publish --build
```

# TODO

- Better docs (sorry)
- Webhooks to automate distribution and versioning

# Attribution

Contains ADMIRALTY® Tidal Data: © Crown copyright and database right
nextnum 1.2.4
A Python package for replacing '+' with a more meaningful symbol.
License: MIT
Requires Python: >=3.8

# next-num-package
Takes a number 0-1000 as an input and gives out the next number. 
Be careful When 
- nextnuming a number greater than 1000
- nextnuming a negative number
- nextnuming a non-integer

# Installation
```bash
pip install nextnum
```

# Usage
```python
from nextnum.functions import nextnum

print(nextnum(1000)) # 1001
print(nextnum(0)) # 1
print(nextnum(1001)) # "Number should be between 0 and 1000"
print(nextnum(-1)) # "Number should be between 0 and 1000"
print(nextnum(1.1)) # "Number should be an integer"
```

# Author
 [Nischal Lamichhane](https://github.com/hehenischal) Built this Package with the best language (c++) in the world. You cad find the source for it at [repository](https://github.com/hehenischal/next-num-package).

```python version 3.8```
tire-cpd 0.1.3
This is an unsupervised change point detection toolbox, including time-invariant representation (TIRE) model with diamond loss, multi-channel time-invariant representation (MC-TIRE) model, and multi-view time-invariant representation (multiview TIRE) model.
License: MIT
Requires Python: <4.0,>=3.10

TIRE-cpd toolbox 
===============================

Toolbox for time-invariant representation autoencoder approach (TIRE) for change point detection (CPD) task. Including three models: TIRE model with diamond loss [1], multi-channel TIRE model [2], and multi-view TIRE model [3].

The authors of these papers are:

- [Zhenxiang Cao](https://www.esat.kuleuven.be/stadius/person.php?id=2380) ([STADIUS](https://www.esat.kuleuven.be/stadius/), Dept. Electrical Engineering, KU Leuven)
- [Nick Seeuws](https://www.esat.kuleuven.be/stadius/person.php?id=2318) ([STADIUS](https://www.esat.kuleuven.be/stadius/), Dept. Electrical Engineering, KU Leuven)
- [Maarten De Vos](https://www.esat.kuleuven.be/stadius/person.php?id=203) ([STADIUS](https://www.esat.kuleuven.be/stadius/), Dept. Electrical Engineering, KU Leuven and Dept. Development and Regeneration, KU Leuven)
- [Alexander Bertrand](https://www.esat.kuleuven.be/stadius/person.php?id=331) ([STADIUS](https://www.esat.kuleuven.be/stadius/), Dept. Electrical Engineering, KU Leuven)

All authors are affiliated to [LEUVEN.AI - KU Leuven Institute for AI](https://ai.kuleuven.be). 

### Use examples and function explanations can be found in GitHub repository: [tire-cpd_toolbox_example](https://github.com/caozhenxiang/tire-cpd_toolbox_examples).

References
------------
- [1] Cao, Z., Seeuws, N., De Vos, M. and Bertrand, A., 2023. A novel loss for change point detection models with time-invariant representations. IEEE Signal Processing Letters, 30, pp.1737-1741.
- [2] Cao, Z., Seeuws, N., De Vos, M. and Bertrand, A., 2023. Change Point Detection in Multi-Channel Time Series Via a Time-Invariant Representation. IEEE Transactions on Knowledge and Data Engineering.
- [3] Cao, Z., Seeuws, N., De Vos, M. and Bertrand, A., 2024. A Multi-view Extension for Change Point Detection via Time-invariant Representations. Proceedings of EUSIPCO 2024.