diff --git a/README.rst b/README.rst
index cccdad8..7f613f0 100644
--- a/README.rst
+++ b/README.rst
@@ -7,7 +7,7 @@ Introduction
 
 :globalemu: Robust Global 21-cm Signal Emulation
 :Author: Harry Thomas Jones Bevins
-:Version: 1.9.1
+:Version: 1.10.0
 :Homepage: https://github.com/htjb/globalemu
 :Documentation: https://globalemu.readthedocs.io/
 
diff --git a/globalemu/network.py b/globalemu/network.py
index c54167f..652c5cc 100644
--- a/globalemu/network.py
+++ b/globalemu/network.py
@@ -35,6 +35,9 @@ class nn():
                 large for every sample to have influenced an update of the
                 network hyperparameters.
 
+        save_after **int / default: 10**
+            | After how many epochs do you want to save the trained model?
+
         activation: **string / default: 'tanh'**
             | The type of activation function used in the neural networks
                 hidden layers. The activation function effects the way that the
@@ -140,7 +143,7 @@ def __init__(self, **kwargs):
 
         for key, values in kwargs.items():
             if key not in set(
-                    ['batch_size', 'activation', 'epochs',
+                    ['batch_size', 'activation', 'epochs','save_after',
                         'lr', 'dropout', 'input_shape',
                         'output_shape', 'layer_sizes', 'base_dir',
                         'early_stop', 'xHI', 'resume',
@@ -166,6 +169,7 @@ def __init__(self, **kwargs):
         if type(self.activation) is not str:
             raise TypeError("'activation' must be a string.")
         self.epochs = kwargs.pop('epochs', 10)
+        self.save_after = kwargs.pop('save_after',10)
         self.lr = kwargs.pop('lr', 1e-3)
         self.drop_val = kwargs.pop('dropout', 0)
         self.input_shape = kwargs.pop('input_shape', 8)
@@ -321,7 +325,7 @@ def grad(model, inputs, targets):
                               ' Epochs used = ' + str(epoch))
                         break
 
-            if (epoch + 1) % 10 == 0:
+            if (epoch + 1) % self.save_after == 0:
                 model.save(self.base_dir + 'model.h5')
                 np.savetxt(
                     self.base_dir + 'loss_history.txt', train_loss_results)
diff --git a/globalemu/plotter.py b/globalemu/plotter.py
index 0d9dc91..00980e5 100644
--- a/globalemu/plotter.py
+++ b/globalemu/plotter.py
@@ -11,7 +11,7 @@
 import numpy as np
 from globalemu.losses import loss_functions
 import matplotlib.pyplot as plt
-
+import os
 
 class signal_plot():
 
@@ -209,3 +209,23 @@ def __init__(self, parameters, labels, loss_type,
         plt.subplots_adjust(hspace=0, wspace=0)
         plt.savefig(self.base_dir + 'eval_plot.pdf')
         plt.close()
+
+        #---------------------------------------------
+        #Shikhar: saving the data for the quality figure for customisation purpose.
+        print('\nSaving the predicted and true signals.\n')
+        save_dir = self.base_dir + '/checks/'
+        if not os.path.isdir(save_dir):
+            os.makedirs(save_dir)
+        np.save(save_dir+'mean_true',mean_label)
+        np.save(save_dir+'mean_pred',mean_pred)
+        np.save(save_dir+'95_true',limit_label)
+        np.save(save_dir+'95_pred',limit_pred)
+        np.save(save_dir+'worst_true',worst_label)
+        np.save(save_dir+'worst_pred',worst_pred)
+
+
+        filenm = open(save_dir+'loss_quality.txt', "w")
+        filenm.write(f'{loss[np.where(np.isclose(loss, loss.mean(), rtol=self.rtol, atol=self.atol))[0][0]]}\n')
+        filenm.write(f'{limit95}\n')
+        filenm.write(f'{loss.max()}\n')
+        filenm.close()
diff --git a/pyproject.toml b/pyproject.toml
index 326149b..7ce9f12 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "globalemu"
-version = "1.9.1"
+version = "1.10.0"
 description = "globalemu: Robust and Fast Global 21-cm Signal Emulation."
 readme = "README.rst"
 authors = [