semvec/pypi-packages huggingface dataset:
tjwb 3.1.0
Python library for reservoir water balance calculations.
Requires Python: <4.0,>=3.9

# tjwb

![PyPI - Version](https://img.shields.io/pypi/v/tjwb)

`tjwb` is a Python library designed for water balance management in reservoirs. The library provides methods to
calculate inflow and outflow speeds of various components, such as pumps, box culverts, and valve overflows, based on
water level and configuration...

## Overview

```
Subsequent Capacity = Previous Capacity + (Inflow Speed * ΔT) - (Outflow Speed * ΔT)
```

Where:

- **Subsequent Capacity**: The capacity of the reservoir after the current time step.
- **Previous Capacity**: The capacity of the reservoir before the current time step.
- **Inflow Speed**: The rate at which water is entering the reservoir.
- **Outflow Speed**: The rate at which water is leaving the reservoir.
- **ΔT**: The time step or interval over which the inflow and outflow are measured.

If the difference between the subsequent capacity and the previous capacity is zero, then the inflow speed and outflow
speed
are equal:

```
Subsequent Capacity - Previous Capacity = 0
```

In this case:

```
Inflow Speed = Outflow Speed
```

If the difference between the subsequent capacity and the previous capacity is negative, then the outflow speed is
greater
than the inflow speed:

```
Subsequent Capacity - Previous Capacity < 0
```

In this case:

```
Outflow Speed > Inflow Speed
```

Conversely, if the difference between the subsequent capacity and the previous capacity is positive, then the inflow
speed
is greater than the outflow speed:

```
Subsequent Capacity - Previous Capacity > 0
```

In this case:

```
Inflow Speed > Outflow Speed
```

## Installation

To install the library, use pip:

```bash
pip install tjwb
```

## Usage

### Basic Example

```python
from datetime import datetime
from tjwb.dataset import Dataset, ComponentConfig
from tjwb.tjwb import calculate

dataset = (
    Dataset()
    .time_series([datetime(2023, 1, 1), datetime(2023, 1, 2)])
    .water_level([2.0, 3.0])
    .pump("pump1", [0.5, 0.6]) # (m3/s)
    .pump("pump2", [0.4, 0.4]) # (m3/s)
    .box_culvert("culvert1", ComponentConfig(elevation=1.0, height=2.0), [0.7, 0.8]) # (m)
    .valve_overflow(
        "overflow1", ComponentConfig(elevation=1.0, height=2.0), [
            [0.4, 0.3],  # port 0 (m)
            [0.4, 0.3],  # port 1 (m)
            [0.4, 0.3],  # port 2 (m)
        ]
    )
    .custom_outflows('custom1', [0.4, 0.7]) # v3.1.0 (m3/s)
    .capacity([2.0, 3.0]) # v3.1.0
)

result_df = calculate(
    dataset=dataset,
    water_level_capacity_map={2.0: 100, 3.0: 200},
    round_to=None,
    nearest_mapping=False,
    capacity_from_dataset_first=True  # v3.1.0 set True if you want to use the capacity in dataset instead of water_level_capacity_map
)
```
## License

This library is released under the MIT License.

## Contact

If you have any questions or issues, please open an issue on [GitHub](https://github.com/duynguyen02/tjwb/issues) or
email us at [duynguyen02.dev@gmail.com](mailto:duynguyen02.dev@gmail.com).
pydashi 2.0.1
TODO
Requires Python: >=3.10

# dashi 


Elaborate data analyses are possible with the functionality offered by the great
`numpy <http://numpy.scipy.org/>`_, `matplotlib
<http://matplotlib.sourceforge.net/>`_, and `pytables
<http://www.pytables.org/moin>`_ libraries. However their support for
HEP-typical problems (histograms, fitting routines,visualization of those,..) is
limited. Dashi is intended to be a thin wrapper around these libraries to
provide some useful tools for these problems without obstructing the user the access
to the underlying libraries and without being a dependency sink.

# Installation

The easiest way to install dashi is with `pip`::

	pip install pydashi

# Documentation
circaPy 0.1.5
For analysing circadian activity data
Requires Python: >=3.9
Keywords: circadian

[![Build Status](https://travis-ci.org/A-Fisk/actigraphy_analysis.png?branch=master)](https://travis-ci.org/A-Fisk/actigraphy_analysis)  


# CircaPy

CircaPy is a python module for circadian analysis of activity data.
It was developed using laboratory
rodents data but is applicable across species and monitoring devices.


## Getting Started

Before you continue you need an installation of Anaconda, available 
[here](https://www.anaconda.com/download).

Then in a terminal of your choice run the following 

```
pip install git+https://github.com/A-Fisk/circaPy.git@main
```

This will install circaPy in your current python environment.
Package dependencies are listed in the environment.yml file 


## Using circaPy

circaPy provides a set of functions to analyse and plot the most common
methods of circadian analysis.

Create some test data 
```
import pandas as pd
import numpy as np
import circaPy.activity as act

# Create a sample dataset with time-series activity data
index = pd.date_range(start='2024-01-01', periods=86400, freq="10s")
values = np.random.randint(0, 100, size=(len(index),2))

df = pd.DataFrame(values, index=index)
``` 

Calculate IV 
```
# Use circaPy's calculate_IV function to compute Interdaily Variability
iv = act.calculate_IV(df)

# Print the result
print(f"Interdaily Variability (IV): {iv:.4f}")
```

Plot actogram 
```
# Use circaPy plot_actogram
import circaPy.actogram_plot as actp

actp.plot_actogram(df, showfig=True)
```



## Contributing 

1. Fork this repository
2. Create branch `git checkout -b <branch-name>
3. Create conda environment
```
conda env create -f environment.yml
conda activate actipy_env
```
4. Make your changes and commit them `git commit -m <commit-message>
    - ensure test suite is passing by running `make all`
5. Push to the original branch `git push origin <project_name>/<location>`
6. Create pull request 


## Authors  

- Angus Fisk 
    - [angus_fisk@hotmail.com](angus_fisk@hotmail.com)
    

## Licence 

- Available under GNU general public licence
rs-trader 0.2.0
Add your description here
Requires Python: >=3.9

# rs_trader

```py
from rs_trader import Exchange
from rs_trader.interfaces import DatabaseInterface
from rs_trader.storage import JsonDatabase
from rs_trader.structs import Order, OrderStatus, OrderType

# Initialize the database and exchange
database = JsonDatabase()
exchange = Exchange(database=database)

# Place a sell order
exchange.place_order(
    Order(user_id=2, item_id=123, order_type=OrderType.SELL, quantity=1000, price=1)
)
exchange.place_order(
    Order(user_id=2, item_id=123, order_type=OrderType.SELL, quantity=100, price=3)
)

# Place a buy order
buy_order = Order(user_id=1, item_id=123, order_type=OrderType.BUY, quantity=150, price=5)
exchange.place_order(buy_order)

orders = exchange.get_orders(item_id=123)
_ = [print(o, database.get_order_remaining_quantity(o.order_id)) for o in orders]

order_parts = exchange.get_order_parts(order_id=buy_order.order_id)
_ = [print(o)for o in order_parts]

median_price = exchange.get_median_price(item_id=123)
print(median_price)
```
mdfiletree 0.1.0
Package for generating file trees in Markdown
Requires Python: >=3.9

# MDFT - Markdown File Trees

Package for generating file trees in Markdown

## Example

The following file tree is automatically generated based on the contents of the specified path.

<!-- mdft src -->
- [mdft/](src/mdft)
	- [converter/](src/mdft/converter)
		- [converter.py](src/mdft/converter/converter.py)
		- [\_\_init\_\_.py](src/mdft/converter/__init__.py)
	- [util/](src/mdft/util)
		- [options.py](src/mdft/util/options.py)
		- [command.py](src/mdft/util/command.py)
		- [blueprint.py](src/mdft/util/blueprint.py)
		- [line.py](src/mdft/util/line.py)
		- [\_\_init\_\_.py](src/mdft/util/__init__.py)
	- [file/](src/mdft/file)
		- [\_\_init\_\_.py](src/mdft/file/__init__.py)
		- [file.py](src/mdft/file/file.py)
	- [scanner/](src/mdft/scanner)
		- [file\_scanner.py](src/mdft/scanner/file_scanner.py)
		- [\_\_init\_\_.py](src/mdft/scanner/__init__.py)
		- [file\_filter.py](src/mdft/scanner/file_filter.py)
	- [\_\_main\_\_.py](src/mdft/__main__.py)
	- [injector/](src/mdft/injector)
		- [\_\_init\_\_.py](src/mdft/injector/__init__.py)
		- [injector.py](src/mdft/injector/injector.py)
ibb-veri-portali 0.2.0
Ibb Portal
Requires Python: >=3.6

# Ä°BB Veri PortalÄ±  

**Ä°BB Veri PortalÄ±**, Ä°stanbul BÃ¼yÃ¼kÅŸehir Belediyesiâ€™nin (Ä°BB) Ä°SPARK verilerine kolayca eriÅŸmek ve bu verileri analiz etmek iÃ§in geliÅŸtirilmiÅŸ bir Python kÃ¼tÃ¼phanesidir. KÃ¼tÃ¼phane, Ä°BBâ€™nin saÄŸladÄ±ÄŸÄ± otopark API'sine baÄŸlanmayÄ± kolaylaÅŸtÄ±rÄ±r ve veri iÅŸleme araÃ§larÄ±yla kullanÄ±cÄ± dostu bir deneyim sunar.  

---

## Ã–zellikler  

- **Genel park bilgilerini Ã§ekme:** TÃ¼m Ä°SPARK verilerini tek bir API Ã§aÄŸrÄ±sÄ± ile elde edin.  
- **Belirli bir parkÄ±n detaylarÄ±na eriÅŸim:** Ä°stediÄŸiniz park ID'sini belirterek detaylÄ± bilgilere ulaÅŸÄ±n.  
- **Kolay veri analizi:** Verileri `pandas` DataFrame veya Series formatÄ±nda iÅŸleyin.  

---

## Kurulum  

KÃ¼tÃ¼phaneyi PyPI Ã¼zerinden kolayca kurabilirsiniz:  
```bash
pip install ibb-veri-portali
```  

---

## KullanÄ±m  

### 1. Genel Park Verilerini Ã‡ekme  
TÃ¼m park bilgilerini JSON formatÄ±nda almak iÃ§in:  
```python
from ibb_veri_portali import json

veriler = json()
print(veriler)
```  

### 2. Belirli Bir ParkÄ±n DetaylarÄ±nÄ± Ã‡ekme  
Park ID'sine gÃ¶re detaylÄ± bilgi almak iÃ§in:  
```
from ibb_veri_portali import series

park_bilgisi = series(882)  # Ã–rnek: Park ID'si 882
print(park_bilgisi)
```  

### 3. Verileri DataFrame FormatÄ±nda Kullanma  
TÃ¼m park verilerini `pandas.DataFrame` olarak almak iÃ§in:  
```
from ibb_veri_portali import dataframe

df = dataframe()
print(df.head())
```  

---

## Gereksinimler  

- Python 3.8 veya Ã¼stÃ¼  
- `requests` kÃ¼tÃ¼phanesi  
- `pandas` kÃ¼tÃ¼phanesi  

---

## KatkÄ±da Bulunma  

Bu projeye katkÄ±da bulunmak isterseniz, GitHub Ã¼zerinden bir **pull request** oluÅŸturabilirsiniz. HatalarÄ± veya iyileÅŸtirme Ã¶nerilerinizi **issues** bÃ¶lÃ¼mÃ¼nde paylaÅŸabilirsiniz.  

---

## Lisans  

Bu proje MIT LisansÄ± ile lisanslanmÄ±ÅŸtÄ±r. Daha fazla bilgi iÃ§in `LICENSE` dosyasÄ±na bakabilirsiniz.  

---