lightning-node-prediction 0.0.4

predictive analysis

Lightning Node Prediction Analysis

This package contains useful functionalities to aid in analyzing lightning transactions.

Install

pip install lightning_node_prediction

How to use

Provide a path to the lightning transactions. Kindly note that the file should be in a CSV format

light = LnPreds('path to csv file')

Plot a chart showing the number of merchants you want to analyze the trendlines by using the num_merch parameter. You can also specify the number of columns you want to display the merchants by using the cols parameter. Merchants are sorted based on their overall transactions amounts with the merchant with the hightest transactions in the first place.

light.plt_yrs_trnd(num_merch=4,cols=2)

You can also zoom-in on a particular merchant by specifying a merchant and a year using the merch_id and yr parameters. Moreover, you can also specify the number of months and the columns for the display using the num_mnths and cols parameters.

merchant_id=10
yr='2019'

light.plt_wks_trnd(merch_id=merchant_id,yr=yr,num_mnths=4,cols=2)

You can also determine the number of outputs and inputs for each day based on the merchant,year,number of months and the column display. Use the merch_id parameter for the merchant id. yr for the year of interest and num_mnths for the number of months needed. cols is used for the display output of your charts

light.plt_wks_stack(merch_id=merchant_id,yr=yr,num_mnths=4,cols=2)

The plt_dy_mnth method takes as input the start year:strt_yr, Number of years:numb_yrs, start month:strt_mnth and number of months:numb_mnths. This methods plots a comparative balance for each year based on the selected months

light.plt_dy_mnth(strt_yr=1,numb_yrs=3,strt_mnth=1,numb_mnths=3)