semvec/pypi-packages huggingface dataset:
EasyDataPy 1.4
EasyDataPy through EasyData API key of State Bank of Pakistan helps to obtain information on and download a series of interest in Python for further analysis
Keywords: EasyData, EasyDataPy

# EasyDataPy

EasyDataPy aKa EDpy for short, is a unofficial Python library/package to read in data from EasyData platform of the State Bank of Pakistan

# Details about EasyData Database of the State Bank of Pakistan

EasyData platform of the State Bank of Pakistan is the largest repository of Macroeconomic time-series. It has more than 2 million observations covering more than 13,000 time-series related to economy of Pakistan.

# About the Library/Package

This package is intended to identify a session with EasyData API key, obtain information about a particular series of interest, and download a series of interest to Python for further analysis. Although, I could have performed basic time-series in Python such as:

1. Unit-Root tests
2. Seasonality tests
3. Cointegration tests and Cointegrated vector autoregressive model
4. Autoregressive and Vector Autoregressive models
5. Forecasting using Machine Learning and Dynamic Factor models (Rolling and Fixed Window Forecast)
6. In and Out-of-Sample Forecasts

But this package is not intended to conduct these analysis but I am programming another one, which will be able to perform all of these operations. Stay Tuned!

# How to install and use

Inside the Python you just need to type "pip install EasyDataPy"

# How to use the functions inside this library/package

## Verifying EasyData API Key

EasyData_key_setup("C10D3D29160CE5693F56AA9846ABB2C423D8B123") <- type in/paste your EasyData API Key!

## Finding if the EasyData API Key has been verified

session_has_key()

## Getting the entered key for further use

get_Easydata_key()

##  Downloads Weighted-average Overnight Repo Rate series as a Pandas dataframe 

data_frame = download_series("TS_GP_IR_REPOMR_D.ORR",Easydata_key, "2015-05-25" ,"2023-12-20", "csv")

## Tranforming output of download_series function, that is object called data_frame into a usable time-series

build_time_series(data_frame)

## Plot Time-Series Graph for the downloaded time-series

plot_time_series(data_frame)

## To download a dataset containing multiple time-series

# We present an example that downloads three time-series from Easydata database. It is assumed that a researcher is
# expected to know the sample period and variables needed for the study. Just remove the #(hash) below:

# series_ids = ["TS_GP_BOP_BPM6SUM_M.P00010", "TS_GP_RL_LSM1516_M.LSM000160000", "TS_GP_PT_CPI_M.P00011516"]
# start_date = "2016-07-31"
# end_date = "2023-11-30"

# The data has to be saved in an object, we call that object combined_dataframe below:

# combined_dataframe = download_dataset(series_ids, Easydata_key, start_date, end_date)

# combined_dataframe
b-fade 0.0.0
B-FADE: Bayesian FAtigue moDel Estimator
Requires Python: >=3.6

# B-FADE: Bayesian FAtigue moDel Estimator

The package implements Maximum a Posteriori Estimation (MAP) to accomplish the estimation of fatigue models' parameters. Currently the package is designed to identify the El Haddad (EH) curve given a fatigue & defectivity characterisation dataset. Other curves are foreseen in future developments.

## Features

- Maximum a Posteriori Estimation and computation of the predictive posterior.
- Monte Carlo Estimation of the prediction interval for the considered curve.
- Data pre-processing & visualisation.

## Quick Setup

B-FADE is available at [PyPI](https://pypi.org/project/b-fade/), so it can be installed using common package managers, such as `pip` or `conda`:

```
pip install --user b-fade
```

```
conda install b-fade
```

For further instructions, please take a look at the documentation.

## Documentation

Please refer to [ReadTheDocs](https://b-fade.readthedocs.io/en/latest/) for detailed instructions about installing, utilising B-FADE and working examples.