diff --git a/inst/python/rse_disc.py b/inst/python/rse_disc.py
deleted file mode 100644
index ac888d0f..00000000
--- a/inst/python/rse_disc.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# -*- coding: utf-8 -*-
-# author: Wenbo Lv
-# email: lyu.geosocial@gmail.com
-# date: 2025-01-06
-
-import numpy as np
-import pandas as pd
-import ruptures as rpt
-from joblib import Parallel, delayed
-
-def rse_disc(df,y,xvars,groups,orders,minsizes,cores = 1):
-    def rse_disc1(dt,y,x,gs,os,min_size):
-        df = dt.loc[:,[y,x]]
-        IndusFile = df.values
-        y = IndusFile[:, 0].astype(float)
-        x = IndusFile[:, 1].astype(float)
-        os = os.astype(int)
-        ranks = pd.Series(os, name='ranks')
-        b = np.array([y, x]).transpose()
-        sorted_b = b[os-1]
-        signals = np.array(sorted_b[:, 0])
-        algo = rpt.Dynp(model = "l2", min_size = min_size, jump = 1).fit(signals)
-        result = algo.predict(n_bkps = gs - 1)
-        result.insert(0, 0)
-        labels = ["empty"] * gs
-        for i in range(0, gs):
-            labels[i] = str("group" + str(i + 1))
-        df['_category'] = pd.cut(ranks, bins=result, labels=labels)  
-        return df.loc[:,'_category'].values
-      
-    def scalar4list(x):
-        if isinstance(x, list):
-            return(x)
-        else:
-            return([x])  
-          
-    xvars = scalar4list(xvars)
-    groups = scalar4list(groups)
-    minsizes = scalar4list(minsizes)
-      
-    if cores < 1:
-        raise ValueError("Cores must be greater than or equal to 1")
-    elif cores == 1:
-        res = []
-        for i in range(len(xvars)):
-            result = rse_disc1(df,y,xvars[i],groups[i],orders[:,i],minsizes[i])
-            res.append(result)
-        res = pd.DataFrame(np.array(res).transpose(), columns=xvars)
-    else:
-      args_list = [(df,y,xvars[i],groups[i],orders[:,i],minsizes[i]) for i in range(len(xvars))]
-      res = Parallel(n_jobs=cores)(delayed(rse_disc1)(d,y,x,g,o,m) for d,y,x,g,o,m in args_list)
-      res = pd.DataFrame(np.array(res).transpose(), columns=xvars)
-      
-    return res