diff --git a/Calibration/HcalCalibAlgos/macros/CalibCorr.C b/Calibration/HcalCalibAlgos/macros/CalibCorr.C
index 98c60921e3cd8..6374f6d76b01b 100644
--- a/Calibration/HcalCalibAlgos/macros/CalibCorr.C
+++ b/Calibration/HcalCalibAlgos/macros/CalibCorr.C
@@ -7,8 +7,16 @@
 //      Defines depth of the DetId guided by the value of truncateFlag
 // double puFactor(type, ieta, pmom, eHcal, ediff, debug)
 //      Returns a multiplicative factor to the energy as PU correction
+//        int type = The type of data being considered
+//                   (1: Run 1 old; 2: Run1 new; 3: Run2 2016;
+//                    4: Run 2 2017; 5: Run2 2018; 6: Run3 Old;
+//                    7: Run 3 June 2021; 97: dlphin Try 3;
+//                    98: dlphin Try 2; 99: dlphin Try 1)
 // double puFactorRho(type, ieta, rho, double eHcal)
 //      Returns a multiplicative factor as PU correction evaluated from rho
+//        int type = The type of data being considered
+//                   (1: 2017 Data;  2: 2017 MC; 3: 2018 MC; 4: 2018AB;
+//                    5: 2018BC; 6: 2016 MC)
 // double puweight(vtx)
 //      Return PU weight for QCD PU sample
 // bool fillChain(chain, inputFileList)
diff --git a/Calibration/HcalCalibAlgos/macros/CalibSort.C b/Calibration/HcalCalibAlgos/macros/CalibSort.C
index 2db36a495f84c..2a6092a8b9166 100644
--- a/Calibration/HcalCalibAlgos/macros/CalibSort.C
+++ b/Calibration/HcalCalibAlgos/macros/CalibSort.C
@@ -1635,6 +1635,11 @@ void CalibFitPU::Loop(bool extract_PU_parameters, std::string fileName) {
 
     }  //End of Event Loop to extract PU correction parameters
 
+    for (int k = 0; k < n; k++)
+      std::cout << "Bin " << k << " for 2D Hist " << vec_h2[k] << ":" << vec_h2[k]->GetEntries() << " Graph "
+                << vec_gr[k] << ":" << points[k] << " Profile " << vec_hp[k] << ":" << vec_hp[k]->GetEntries()
+                << std::endl;
+
     std::ofstream myfile0, myfile1, myfile2;
     sprintf(filename, "%s_par2d.txt", fileName.c_str());
     myfile0.open(filename);
@@ -1677,7 +1682,7 @@ void CalibFitPU::Loop(bool extract_PU_parameters, std::string fileName) {
       sprintf(namepng, "%s.png", pad1->GetName());
       pad1->Print(namepng);
 
-      TF1 *f2 = ((k < 2) ? (new TF1("f2", "[0]+[1]*x", 0, 5)) : (new TF1("f1", "[0]+[1]*x+[2]*x*x", 0, 5)));
+      TF1 *f2 = ((k < 2) ? (new TF1("f2", "[0]+[1]*x", 0, 5)) : (new TF1("f2", "[0]+[1]*x+[2]*x*x", 0, 5)));
       sprintf(name, "c_ieta%dPr", k);
       TCanvas *pad2 = new TCanvas(name, name, 500, 500);
       pad2->SetLeftMargin(0.10);
@@ -1706,7 +1711,7 @@ void CalibFitPU::Loop(bool extract_PU_parameters, std::string fileName) {
       sprintf(namepng, "%s.png", pad2->GetName());
       pad2->Print(namepng);
 
-      TF1 *f3 = ((k < 2) ? (new TF1("f3", "[0]+[1]*x", 0, 5)) : (new TF1("f1", "[0]+[1]*x+[2]*x*x", 0, 5)));
+      TF1 *f3 = ((k < 2) ? (new TF1("f3", "[0]+[1]*x", 0, 5)) : (new TF1("f3", "[0]+[1]*x+[2]*x*x", 0, 5)));
       sprintf(name, "c_ieta%dGr", k);
       TCanvas *pad3 = new TCanvas(name, name, 500, 500);
       pad3->SetLeftMargin(0.10);
diff --git a/Calibration/HcalCalibAlgos/macros/isotrackApplyRegressor.py b/Calibration/HcalCalibAlgos/macros/isotrackApplyRegressor.py
index af41cb0f0ec1b..f2aea052e93fb 100644
--- a/Calibration/HcalCalibAlgos/macros/isotrackApplyRegressor.py
+++ b/Calibration/HcalCalibAlgos/macros/isotrackApplyRegressor.py
@@ -1,14 +1,14 @@
 ######################################################################################
 # Evaluates regressor from loaded model
 # Usage:
-# python3 isotrackApplyRegressor.py -PU root://cmseos.fnal.gov//store/user/sghosh/ISOTRACK/DIPI_2021_PUpart.root -M ./models/model1.h5 -B endcap -O corrfac1.txt
-# python3 isotrackApplyRegressor.py -PU root://cmseos.fnal.gov//store/user/sghosh/ISOTRACK/DIPI_2021_PUpart.root -M ./models/model2.h5 -B barrel -O corrfac2.txt
+# source /cvmfs/sft.cern.ch/lcg/views/LCG_97apython3/x86_64-centos7-gcc8-opt/setup.csh
+# python3 isotrackApplyRegressor.py -PU root://eoscms.cern.ch//eos/cms/store/group/dpg_hcal/comm_hcal/ISOTRACK/SinglePion_E-50_Eta-0to3_Run3Winter21_112X_PU.root -M ./models/model1.h5 -B endcap -O corrfac1.txt
+# python3 isotrackApplyRegressor.py -PU root://eoscms.cern.ch//eos/cms/store/group/dpg_hcal/comm_hcal/ISOTRACK/SinglePion_E-50_Eta-0to3_Run3Winter21_112X_PU.root -M ./models/model2.h5 -B barrel -O corrfac2.txt
 ######################################################################################
 # coding: utf-8
 
 # In[1]:
 
-
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
@@ -30,8 +30,7 @@
 # In[2]:
 
 parser = argparse.ArgumentParser()
-parser.add_argument("-PU", "--filePU",help="input PU file",default="root://cmseos.fnal.gov//store/user/sghosh/ISOTRACK/DIPI_2021_PUpart.root")
-#parser.add_argument("-PU", "--filePU",help="input PU file",default="/eos/uscms/store/user/sghosh/ISOTRACK/DIPI_2021_noPU.root")
+parser.add_argument("-PU", "--filePU",help="input PU file",default="root://eoscms.cern.ch//eos/cms/store/group/dpg_hcal/comm_hcal/ISOTRACK/SinglePion_E-50_Eta-0to3_Run3Winter21_112X_PU.root")
 parser.add_argument("-B", "--ifbarrel",help="barrel / endcap",default='barrel')
 parser.add_argument("-M", "--modelname",help="model file name",default="./models/model.h5")
 parser.add_argument("-O", "--opfilename",help="output text file name",default="corrfac_regression.txt")
diff --git a/Calibration/HcalCalibAlgos/macros/isotrackNtupler.py b/Calibration/HcalCalibAlgos/macros/isotrackNtupler.py
index de33ab542d99f..c44a1f492cfb5 100644
--- a/Calibration/HcalCalibAlgos/macros/isotrackNtupler.py
+++ b/Calibration/HcalCalibAlgos/macros/isotrackNtupler.py
@@ -1,7 +1,8 @@
 ######################################################################################
 # Makes pkl and text files comparing PU and noPU samples for training regressor and other stuff
 # Usage:
-# python3 isotrackNtupler.py -PU root://cmseos.fnal.gov//store/user/sghosh/ISOTRACK/DIPI_2021_PUpart.root -NPU root://cmseos.fnal.gov//store/user/sghosh/ISOTRACK/DIPI_2021_noPU.root -O isotk_relval 
+# source /cvmfs/sft.cern.ch/lcg/views/LCG_97apython3/x86_64-centos7-gcc8-opt/setup.csh
+# python3 isotrackNtupler.py -PU root://eoscms.cern.ch//eos/cms/store/group/dpg_hcal/comm_hcal/ISOTRACK/SinglePion_E-50_Eta-0to3_Run3Winter21_112X_PU.root -NPU root://eoscms.cern.ch//eos/cms/store/group/dpg_hcal/comm_hcal/ISOTRACK/SinglePion_E-50_Eta-0to3_Run3Winter21_112X_PU.root -O isotk_relval 
 ######################################################################################
 
 
@@ -13,8 +14,8 @@
 import matplotlib.pyplot as plt
 
 parser = argparse.ArgumentParser()
-parser.add_argument("-PU", "--filePU",help="input PU file",default="root://cmseos.fnal.gov//store/user/sghosh/ISOTRACK/DIPI_2021_PUpart.root")
-parser.add_argument("-NPU", "--fileNPU",help="input no PU file",default="root://cmseos.fnal.gov//store/user/sghosh/ISOTRACK/DIPI_2021_noPU.root")
+parser.add_argument("-PU", "--filePU",help="input PU file",default="root://eoscms.cern.ch//eos/cms/store/group/dpg_hcal/comm_hcal/ISOTRACK/SinglePion_E-50_Eta-0to3_Run3Winter21_112X_PU.root")
+parser.add_argument("-NPU", "--fileNPU",help="input no PU file",default="//eoscms.cern.ch//eos/cms/store/group/dpg_hcal/comm_hcal/ISOTRACK/SinglePion_E-50_Eta-0to3_Run3Winter21_112X_PU.root")
 parser.add_argument("-O", "--opfilename",help="ouput file name",default="isotk_relval")
 
 
diff --git a/Calibration/HcalCalibAlgos/macros/isotrackRootTreeMaker.py b/Calibration/HcalCalibAlgos/macros/isotrackRootTreeMaker.py
index 96c5efc4f63f9..0e911628efa59 100644
--- a/Calibration/HcalCalibAlgos/macros/isotrackRootTreeMaker.py
+++ b/Calibration/HcalCalibAlgos/macros/isotrackRootTreeMaker.py
@@ -1,7 +1,8 @@
 ######################################################################################
 # Makes pkl, root and text files comparing PU and noPU samples for training regressor and other stuff
 # Usage:
-# python3 isotrackRootTreeMaker.py -PU root://cmseos.fnal.gov//store/user/sghosh/ISOTRACK/DIPI_2021_PUpart.root -NPU root://cmseos.fnal.gov//store/user/sghosh/ISOTRACK/DIPI_2021_noPU.root -O isotrackRelval 
+# source /cvmfs/sft.cern.ch/lcg/views/LCG_97apython3/x86_64-centos7-gcc8-opt/setup.csh
+# python3 isotrackRootTreeMaker.py -PU root://eoscms.cern.ch//eos/cms/store/group/dpg_hcal/comm_hcal/ISOTRACK/SinglePion_E-50_Eta-0to3_Run3Winter21_112X_PU.root -NPU root://eoscms.cern.ch//eos/cms/store/group/dpg_hcal/comm_hcal/ISOTRACK/SinglePion_E-50_Eta-0to3_Run3Winter21_112X_PU.root -O isotrackRelval 
 ######################################################################################
 
 import uproot
diff --git a/Calibration/HcalCalibAlgos/macros/isotrackTrainRegressor.py b/Calibration/HcalCalibAlgos/macros/isotrackTrainRegressor.py
index 4abcc261de16a..309e47196d272 100644
--- a/Calibration/HcalCalibAlgos/macros/isotrackTrainRegressor.py
+++ b/Calibration/HcalCalibAlgos/macros/isotrackTrainRegressor.py
@@ -143,7 +143,7 @@ def propweights(y_true):
 # In[6]:
 
 
-from keras import optimizers
+from tensorflow.keras import optimizers
 print ("creating model=========>")
 model = Sequential()
 model.add(Dense(128, input_shape=(X_train.shape[1],), activation='relu'))
@@ -153,7 +153,7 @@ def propweights(y_true):
 model.add(Dense(60, activation='relu',kernel_regularizer=regularizers.l2(0.01)))
 model.add(Dense(1))
 
-RMS = keras.optimizers.RMSprop(lr=0.001, rho=0.9)
+RMS = tensorflow.keras.optimizers.RMSprop(learning_rate=0.001, rho=0.9)
 # Compile model
 print ("compilation up next=======>")
 model.compile(loss='logcosh',optimizer='adam')
diff --git a/Calibration/HcalCalibAlgos/macros/isotrackTrainRegressorNoOptimization.py b/Calibration/HcalCalibAlgos/macros/isotrackTrainRegressorNoOptimization.py
new file mode 100644
index 0000000000000..08b8817ebc6e6
--- /dev/null
+++ b/Calibration/HcalCalibAlgos/macros/isotrackTrainRegressorNoOptimization.py
@@ -0,0 +1,310 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+######################################################################################
+# Trains regressor and saves model for evaluation
+# Usage:
+# python3 isotrackTrainRegressorNoOptimization.py -I isotk_relval_hi.pkl  -V 1 
+# python3 isotrackTrainRegressorNoOptimization.py -I isotk_relval_lo.pkl  -V 2
+######################################################################################
+
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import argparse
+import tensorflow.keras
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.layers import Dropout
+from tensorflow.keras.utils import to_categorical
+from tensorflow.keras.utils import plot_model
+from tensorflow.keras import regularizers
+from tensorflow.keras.wrappers.scikit_learn import KerasRegressor
+from sklearn.model_selection import GridSearchCV, RandomizedSearchCV,train_test_split, cross_val_score
+from sklearn.metrics import roc_curve, auc
+from tensorflow.keras.layers import Activation
+from tensorflow.keras import backend as K
+from tensorflow.keras.models import save_model
+import tensorflow as tf
+from tensorflow import keras
+
+
+# In[37]:
+
+
+parser1 = argparse.ArgumentParser()
+parser1.add_argument("-I", "--input",help="input file",default="isotk_relval_lowinter1.pkl")
+parser1.add_argument("-V", "--modelv",help="model version (any number) ",default="2")
+
+args = parser1.parse_args(args=[])
+
+fName = args.input
+modv = args.modelv
+
+
+# In[38]:
+
+
+print ("file Name:", fName)
+print ("modv :", modv)
+
+
+# In[39]:
+
+
+df = pd.read_pickle(fName)
+print ("vars in file:",df.keys())
+print("events in df original:",df.shape[0])
+df = df.loc[df['t_eHcal_y'] > 20]
+print("events in df after energy cut:",df.shape[0])
+df['t_eHcal_xun'] = df['t_eHcal_x']
+df['t_delta_un'] = df['t_delta']
+df['t_ieta_un'] = df['t_ieta']
+
+
+# In[40]:
+
+
+mina = []
+maxa = []
+keya = []
+
+
+for i in df.keys():
+    keya.append(i)
+    mina.append(df[i].min())
+    maxa.append(df[i].max())
+
+print('var = ',keya)
+print('mina = ',mina)
+print('maxa = ',maxa)
+
+
+# In[41]:
+
+
+cols_to_minmax = ['t_delta', 't_hmaxNearP','t_emaxNearP', 't_hAnnular', 't_eAnnular','t_pt','t_nVtx','t_ieta','t_eHcal10', 't_eHcal30','t_rhoh','t_eHcal_x']
+#df[cols_to_stand] = df[cols_to_stand].apply(lambda x: (x - x.mean()) /(x.std()))
+#df[cols_to_minmax] = df[cols_to_minmax].apply(lambda x: (x - x.mean()) /  (x.max() - x.min()))
+#                                            #(x.max() - x.min()))
+df[cols_to_minmax] = df[cols_to_minmax].apply(lambda x: (x - x.min()) /  (x.max() - x.min()))
+
+data = df.values
+print ('data shape:',data.shape)
+targets = data[:,12]
+targets.shape
+print ('targets shape:', targets.shape)
+print ("vars in file:",df.keys())
+
+
+# In[42]:
+
+
+data = df.values
+ntest = 20000
+testindx = data.shape[0] - ntest
+# data.shape[0]: 438118  is no of events 
+# data.shape[1] : 18 ==> columns 
+# testindx = 438118-20000
+print ('data shape:',data.shape[0])
+print ('testindx: ' ,testindx)
+# this :testindx = 438118-20000 = 418118
+X_train = data[:testindx,0:12]
+Y_train = data[:testindx,12]
+X_test = data[testindx:,:]
+print ("shape of X_train:",X_train.shape)
+print ("shape of Y_train:",Y_train.shape)
+print ("shape of X_test:",X_test.shape)
+meany = np.mean(Y_train)
+print ("mean y:",meany)
+stdy = np.std(Y_train)
+print ("std y:", stdy)
+
+
+# In[43]:
+
+
+############################################# marinas correction  form
+a0 = [0.973, 0.998,  0.992,  0.965 ]
+a1 =  [0,    -0.318, -0.261, -0.406]
+a2 = [0,     0,      0.047,  0.089]
+def fac0(jeta):
+    PU_IETA_1 = 9
+    PU_IETA_2 = 16
+    PU_IETA_3 = 25
+    idx = (int(jeta >= PU_IETA_1) + int(jeta >= PU_IETA_2) + int(jeta >= PU_IETA_3))
+    return a0[idx]
+def fac1(jeta):
+    PU_IETA_1 = 9
+    PU_IETA_2 = 16
+    PU_IETA_3 = 25
+    idx = (int(jeta >= PU_IETA_1) + int(jeta >= PU_IETA_2) + int(jeta >= PU_IETA_3))
+    return a1[idx]
+def fac2(jeta):
+    PU_IETA_1 = 9
+    PU_IETA_2 = 16
+    PU_IETA_3 = 25
+    idx = (int(jeta >= PU_IETA_1) + int(jeta >= PU_IETA_2) + int(jeta >= PU_IETA_3))
+    return a2[idx]
+
+
+# In[44]:
+
+
+vec0 = np.vectorize(fac0)
+vec1 = np.vectorize(fac1)
+vec2 = np.vectorize(fac2)
+
+X_test[:,17]
+eop = (X_test[:,15]/X_test[:,13])
+dop = (X_test[:,16]/X_test[:,13])
+print ("eop: ", eop)
+#mcorrval = vec0(abs(X_test[:,17])) + vec1(abs(X_test[:,17]))*(X_test[:,15]/X_test[:,13])*(X_test[:,16]/X_test[:,13])*( 1 + vec2(fac(abs(X_test[:,17])))*(X_test[:,16]/X_test[:,13]))
+
+mcorrval = vec0(abs(X_test[:,17]))   +  vec1(abs(X_test[:,17]))*eop*dop*( 1   +  vec2(abs(X_test[:,17]))*dop)
+
+
+# In[45]:
+
+
+def propweights(y_true):
+    weight = np.copy(y_true)
+    weight[abs(y_true - meany) > 0] = 1.90*abs(y_true - meany)/stdy  #1.25
+#    weight[abs(y_true - meany) > stdy] = 1.75*abs((weight[abs(y_true - meany) > stdy]) - meany)/(stdy)
+    weight[abs(y_true - meany) < stdy] =  1
+    print ("wieght : ", weight)
+    return weight
+
+
+# In[46]:
+
+
+from keras import optimizers
+print ("creating model=========>")
+model = Sequential()
+model.add(Dense(128, input_shape=(X_train.shape[1],), activation='relu'))
+#model.add(Dropout(0.2))
+model.add(Dense(271, activation='relu',kernel_regularizer=regularizers.l2(0.01)))
+model.add(Dense(301, activation='relu',kernel_regularizer=regularizers.l2(0.01)))
+model.add(Dense(241, activation='relu',kernel_regularizer=regularizers.l2(0.01)))
+model.add(Dense(1))
+
+
+# In[47]:
+
+
+print ("compilation up next=======>")
+model.compile(loss='logcosh',optimizer='adam')
+model.summary()
+#print ("Y_train : ", Y_train)
+#fitting
+print ("fitting now=========>")
+history = model.fit(X_train,Y_train , batch_size=5000, epochs=50, validation_split=0.2, verbose=1,sample_weight=propweights(Y_train))
+
+
+# In[48]:
+
+
+plt.plot(history.history['loss'])
+plt.plot(history.history['val_loss'])
+plt.title('model loss')
+plt.ylabel('loss')
+plt.xlabel('epoch')
+plt.legend(['train', 'validation'], loc='upper left')
+plt.savefig(modv+'_loss_distributions_lowWinter.png')
+#plt.show() 
+plt.close()
+
+
+# In[49]:
+
+
+preds = model.predict(X_test[:,0:12])
+targets = X_test[:,12]
+uncorrected = X_test[:,15]
+marinascorr = X_test[:,15]*mcorrval
+plt.hist(preds, bins =100, range=(0,200),label='PU regression',alpha=0.6)
+plt.hist(targets, bins =100, range=(0,200),label='no PU',alpha=0.6)
+plt.hist(uncorrected, bins =100, range=(0,200),label='uncorrected',alpha=0.6)
+#plt.hist(marinascorr, bins =100, range=(0,200),label='marinas correction',alpha=0.6)
+plt.yscale('log')
+plt.title("Energy distribution")
+plt.legend(loc='upper right')
+plt.savefig(modv+'_energy_distributions_lowWinter.png')
+#plt.show() 
+plt.close()
+
+preds = preds.reshape(preds.shape[0])
+print (preds.shape)
+
+
+# In[50]:
+
+
+plt.hist(abs(uncorrected -targets)/targets*100, bins =100, range=(0,100),label='uncorrected',alpha=0.6)
+#plt.hist(abs(marinascorr -targets)/targets*100, bins =100, range=(0,100),label='marinas correction',alpha=0.6)
+plt.hist(100*abs(preds -targets)/targets, bins =100, range=(0,100),label='PU correction',alpha=0.6)
+#plt.yscale('log')
+plt.title("error distribution")
+plt.legend(loc='upper right')
+plt.savefig(modv+'_errors_low.png')
+#plt.show() 
+plt.close()
+
+
+# In[51]:
+
+
+plt.scatter(targets, uncorrected,alpha=0.3,label='uncorr')
+plt.scatter(targets, preds,alpha=0.3,label='PUcorr')
+plt.xlabel('True Values ')
+plt.ylabel('Predictions ')
+plt.legend(loc='upper right')
+lims = [0, 200]
+plt.xlim(lims)
+plt.ylim(lims)
+_ = plt.plot(lims, lims)
+plt.savefig(modv+'_energyscatt_lowWinter.png')
+#plt.show() 
+plt.close()
+
+
+# In[52]:
+
+
+pmom= X_test[:,13]
+#get_ipython().run_line_magic('matplotlib', 'inline')
+plt.hist(targets/pmom, bins =100, range=(0,5),label='E/p noPU',alpha=0.6)
+#plt.hist(preds/pmom, bins =100, range=(0,5),label='E/p PUcorr',alpha=0.6)
+#plt.hist(uncorrected/pmom, bins =100, range=(0,5),label='E/p uncorr',alpha=0.6)
+plt.hist(marinascorr/pmom, bins =100, range=(0,5),label='E/p marina corr',alpha=0.6)
+#plt.hist(np.exp(preds.reshape((preds.shape[0])))/pmom[0:n_test_events], bins =100, range=(0,5),label='corrEnp/p',alpha=0.3)
+#plt.hist(preds.reshape((preds.shape[0]))/pmom[0:n_test_events], bins =100, range=(0,5),label='corrEnp/p',alpha=0.3)
+plt.legend(loc='upper right')
+plt.yscale('log')
+plt.title("E/p distributions") 
+plt.savefig(modv+'_eopdist_lowWinter.png')
+#plt.show() 
+plt.close()
+
+
+# In[53]:
+
+
+import os
+############## save model
+if not os.path.exists('models'):
+    os.makedirs('models')
+model.save('models/model_BarrelWinter'+modv+'.h5')
+#new_model_2 = load_model('my_model.h5')
+
+
+# In[ ]:
+
+
+
+