semvec/pypi-packages huggingface dataset:
pygdatax 0.2.12
Python libray for Small Angle Neutron (SANS) and X-ray (SAXS) scattering data treatment and visualization.
Requires Python: >=3.10
Keywords: SAXS, SANS, XRR, data reduction

# PYGDATAX
Python libray for Small Angle Neutron (SANS) and X-ray (SAXS) scattering data treatment and visualization.
This package can be used for the following instruments : 
 - Xeuss 2.0 SAXS instrument
 - SANS_LLB instrument installed at the Paul Scherrer Institut (PSI)
 - Xeuss 2.0 in reflectivity mode
## Installation
To install pygdatax, run:
```
pip install pygdatax
```
Depending on your OS and python distribution, you may need to install PyQt5 and Ipython
```
pip install pyqt5 Ipython
```
## Upgrade Version
pygdatax package is up to now often upgraded. In order to get the last version type the following command in your
 terminal or acaconda prompt
```
pip install --upgrade pygdatax
```
## Launch Graphical User Interface for Xeuss data treatment and visualization
To launch the GUI, run in you terminal (or conda prompt):
````
pygdatax_gui
````
This GUI can also be used to visualize nexus files from SANS_LLB.

![alt text](pygdatax_screenshot_2.png)



## Run Graphical User Interface for scan log file visualization
From version 0.1.8, a new application  is available allowing to preview and fit the scan logfile of the Xeuss spectrometer

To launch it , run in your terminal the command:
````
pygdatax_log
````
![alt text](pygdatax_log_screenshot.png)

The two curves plotted per scans corresponds to the **roi0** (integration over the instrument's roi) signal and **ct0** signal (full detector sum)

To fit one curve, you can use the fit widget present within the toolbar. The fit will be performed on the current 
plot window which means that one can fit a speific part of the curve by zooming on it.

## Run Gaphical User Interface for X-Ray reflectivity treatment and visualzation
from version 2.0.0, a new application allows to treat and  vizualize X ray reflectiviy measurements performed on the Xeuss 2.0
apparatus. The command to run it is:
````
pygdatax_rx
````
financedatascraperonline 0.1.2
Library for scraping financial data from online sources

Hello! This is a Python library for scraping various forms of public finance data in the USA. The code is shown in this [GitHub repository](https://github.com/AvilashaHaldar/ajh_finance_scraper_python_library). Currently, this library can:

1. Scrape forward 1 and 3 month SOFR curves

This can be done by doing:

```
from financedatascraperonline import InterestRatesScrapedData
forward_SOFR_curves_df, allowed_SOFR_lengths_months = InterestRatesScrapedData.InterestRatesScrapedData().get_scraped_SOFR_curves()
```

forward_SOFR_curves_df is a Pandas DataFrame containing the 1-month and 3-month forward SOFR rates for the next 10 years. allowed_SOFR_lengths_months is a NumPy array containg the allowed month lengths for the SOFR curves (currently only 1 and 3). We scrape the data from [Pensford](https://www.pensford.com/resources/forward-curve).

2. Getting daily closing stock prices for the past 3 years

```
from financedatascraperonline import HistoricalDailyStockPricesAPI
closing_prices_df = HistoricalDailyStockPricesAPI.HistoricalDailyStockPricesAPI().get_historical_prices_df(ticker_symbol)
```

closing_prices_df is a Pandas DataFrame of daily closing prices over the past 3 years containing the columns [Close, Log_Close, Daily_Returns, Annualized_Hist_Volatility, Annualized_Mean_Daily_Return]. Volatility and mean are rolling values calculated over a 252-day period. (We assume 252 business days in a year.)

3. Compute a few key statistics from the past day

```
from financedatascraperonline import HistoricalDailyStockPricesAPI
mu, sigma, latest_price = HistoricalDailyStockPricesAPI.HistoricalDailyStockPricesAPI().get_main_stats_from_hist_prices(ticker_symbol)
```

mu is the annualized mean daily log return of the last business day calculated for the last 252 days, sigma is the annualized standard deviation of daily log returns over the last 252 days, and latest_price is the last closing price.

4. Scrape US treasury bill rates

```
from financedatascraperonline import InterestRatesScrapedData
treasury_bill_rates_df = InterestRatesScrapedData.InterestRatesScrapedData().get_scraped_treasury_bill_rates()
```

Here, we scrape US treasury bill rates from the (US Treasury website)[https://home.treasury.gov/resource-center/data-chart-center/interest-rates/TextView?type=daily_treasury_bill_rates&field_tdr_date_value=2025] and return the rates in a Pandas DataFrame.