semvec/pypi-packages huggingface dataset:
calib-rt 0.0.4
calib_rt
Requires Python: >=3

# Calib-RT
Calib-RT is an open-source Python software package designed for RT (retention time) calibration. 
This package provides a flexible and robust solution for achieving accurate RT calibration across various data scales while handling a certain level of noise interference. 

The workflow diagram is below, providing an overview of the process. For a comprehensive and in-depth explanation, please refer to the associated paper for detailed insights and analysis.

<div align=center>
<img src="https://raw.githubusercontent.com/chenghui03/Calib_RT/93750fced36ee36786e61817d71d6ec5e2c5fdd3/img/workflow.svg" alt="Your Image" width="500">
</div>

# Installation
You can install the calib_rt package using pip:
```bash
pip install calib_rt 
```

# Usage
Here is an example of how to use Calib-RT for retention time calibration:

```python
import calib_rt
# basic information of all built-in datasets 
calib_rt.RTdatasets.get_datasets_list()  
         sample_type  datasets_num
   0   distort_left             2
   1  distort_right             2
   2            exp             2
   3         linear             2
   4              S             2
# use first of "S" type datasets
datasets = calib_rt.RTdatasets.get_pandas(sample_type="S",1)   
x = datasets["Spectral library RT"]
y = datasets["Measured RT"]
# fit and predict
model = calib_rt.Calib_RT() 
model.fit(x,y)                  
y_pred = model.predict(x)        
```

# Performance test

<div align=center>
<img src="https://raw.githubusercontent.com/chenghui03/Calib_RT/main/img/performance.jpg" alt="Your Image" width="500">
</div>

For a detailed analysis of the test conclusion and all performance test results, please refer to the full paper.

# References
[link of paper]()

# License
This project is licensed under the MIT License. See the LICENSE file for details.
optionsPricingAndGreeks 0.0.6
A package to calculate the price of options and the greeks
Requires Python: >=3.11

# Option pricing methods

This package aims to provide 4 different methods to estimate the price of options.

The estimator don't allow to set all the parameters but just accept a stock, in the constructor, and then evaluate the price of an option of that stock.
This lead to be able to estimate real world data, but doesn't allow to create custom environment with fake option price, volatility etc. etc.

Instead the estimators takes the last prices (for the period of this prices go to the "setting parameter" section) of the stock and calculate volatility and the drift of the stock.

The 4 model to estimate a the price of an option are the most popular one:
- binomial model
- monte carlo simulation
- black-scholes formula
- bootstrap

## Setting paramter
The only (financial) paramter that should be set is the annual risk free rate. As a default it is set to 0.04 in the super class of the estimator, so it's not a compulsary argument in the
estimate methods.

Another parameter that can be set is the period that the estimators take into consideration to calculate the volatility and drift of the stock. Also this parameter is set
in the superclass of the estimators and its default value is six months. If we want to change it we should set the estimate method parameter periodForPrices to a value of the period
enumeration that is defined in the utilities directory.

### Monte carlo estimator
For the monte carlo estimator there are another couple of customizable parameter in the estimate method:

- <ins>numberOfSimulations</ins>: the number of simulations of the asset price path that are run to evaluate the option price
- <ins>showSimulationPaths</ins>: plot a chart with the paths of the stock price for each simulation

## American option
For american options has been implemented a method in the binomial estimator (*estimateAmericanOption*). In all the other methods we suppose to work with european options
Holiday-Manager 0.2
A package for planning holidays


# Holiday Package

## Introduction
This package is designed to help you plan your holidays. It includes functionalities for finding flights, hotels, and attractions.

## Installation
```
pip install Holiday
```

## Usage
Here are some example usages of the package.

### Find Flights
```python
from Holiday import Flight, setup

setup(<your amadeus client id>, <your amadeus client secret>, <your google place api key>)


get_flights = Flight('LHR', 'JFK')
budget = 1000
flight_offers = get_flights.find_flights()
for i in flight_offers:
    print("\n")
    print(f"Airline: {i['airline']}")
    print(f"Departure time: {i['departure_time']}")
    print(f"Arrival time: {i['arrival_time']}")
    if int(float(i['adult_ticket_price'])) < budget: 
        print(f"This offer fits your budget as one ticket costs {i['adult_ticket_price']}")
    else:
        print(f"This offer does not fit your budget as one ticket costs {i['adult_ticket_price']}")

```

### Find Hotels
```python
from Holiday import Hotel, setup

setup(<your amadeus client id>, <your amadeus client secret>, <your google place api key>)


hotel = Hotel('2023-11-31', '2023-12-05', 'LON', 1)
rooms = hotel.find_hotel_rooms()
print(rooms)

```

### Find Attractions
```python
from Holiday import Attractions, setup

setup(<your amadeus client id>, <your amadeus client secret>, <your google place api key>)

places = Attractions.Functions('Pensylvania, USA', 'ta')
output = places.get_attractions()


for i in output:
    print('\n')
    print(f"{i['name']} ({i['rating']}⋆)")
    print(f"Address: {i['address']}")
    print(f"Type: {i['type']}")
```

### Find Food
```python
from Holiday import Attractions, setup

setup(<your amadeus client id>, <your amadeus client secret>, <your google place api key>)

places = Attractions.Functions('London', 'food')
output = places.get_attractions()


for i in output:
    print('\n')
    print(f"{i['name']} ({i['rating']}⋆)")
    print(f"Address: {i['address']}")
    print(f"Type: {i['type']}")
```


## Contribution
Feel free to contribute to this package. For any bugs, please open an issue.

## License
MIT