diff --git a/chemplot/chemplot.py b/chemplot/chemplot.py
index c15618d..3213f70 100644
--- a/chemplot/chemplot.py
+++ b/chemplot/chemplot.py
@@ -75,7 +75,7 @@ class Plotter(object):
     
     _target_types = {'R', 'C'}
 
-    def __init__(self, encoding_list, target, target_type, sim_type, get_desc, get_fingerprints):
+    def __init__(self, encoding_list, target, target_type, sim_type, get_desc, get_fingerprints, radius, nBits):
            
         # Error handeling sym_type
         if sim_type not in self._sim_types:
@@ -136,7 +136,7 @@ def __init__(self, encoding_list, target, target_type, sim_type, get_desc, get_f
                 raise Exception("Descriptors could not be computed for given molecules")
             self.__df_descriptors, self.__target = desc.select_descriptors_lasso(df_descriptors,target,kind=self.__target_type)
         elif self.__sim_type == "structural":
-            self.__mols, self.__df_descriptors, self.__target = get_fingerprints(encoding_list,target,2,2048)
+            self.__mols, self.__df_descriptors, self.__target = get_fingerprints(encoding_list,target, radius, nBits)
             
         if len(self.__mols) < 2 or len(self.__df_descriptors.columns) < 2:
             raise Exception("Plotter object cannot be instantiated for given molecules")
@@ -146,14 +146,16 @@ def __init__(self, encoding_list, target, target_type, sim_type, get_desc, get_f
             
     
     @classmethod        
-    def from_smiles(cls, smiles_list, target=[], target_type=None, sim_type=None):
+    def from_smiles(cls, smiles_list, target=[], target_type=None, sim_type=None, radius=2, nBits=2048):
         """
         Class method to construct a Plotter object from a list of SMILES.
         
         :param smile_list: List of the SMILES representation of the molecules to plot.       
         :param target: target values       
         :param target_type: target type R (regression) or C (classificatino)      
-        :param sim_type: similarity type structural or tailored     
+        :param sim_type: similarity type structural or tailored 
+        :param radius: The ECPF fingerprints radius.
+        :param nBits: The number of bits of the fingerprint vector.    
         :type smile_list: list
         :type target: list
         :type target_type: string
@@ -162,11 +164,11 @@ def from_smiles(cls, smiles_list, target=[], target_type=None, sim_type=None):
         :rtype: Plotter
         """
             
-        return cls(smiles_list, target, target_type, sim_type, desc.get_mordred_descriptors, desc.get_ecfp)
+        return cls(smiles_list, target, target_type, sim_type, desc.get_mordred_descriptors, desc.get_ecfp, radius, nBits)
             
 
     @classmethod
-    def from_inchi(cls, inchi_list, target=[], target_type=None, sim_type=None):
+    def from_inchi(cls, inchi_list, target=[], target_type=None, sim_type=None, radius=2, nBits=2048):
         """
         Class method to construct a Plotter object from a list of InChi.
         
@@ -178,11 +180,15 @@ def from_inchi(cls, inchi_list, target=[], target_type=None, sim_type=None):
         :type target_type: string
         :param sim_type: similarity type structural or tailored 
         :type sim_type: string
+        :param radius: The ECPF fingerprints radius.
+        :type radius: int
+        :param nBits: The number of bits of the fingerprint vector. 
+        :type nBits: int
         :returns: A Plotter object for the molecules given as input.
         :rtype: Plotter
         """
                 
-        return cls(inchi_list, target, target_type, sim_type, desc.get_mordred_descriptors_from_inchi, desc.get_ecfp_from_inchi)
+        return cls(inchi_list, target, target_type, sim_type, desc.get_mordred_descriptors_from_inchi, desc.get_ecfp_from_inchi, radius, nBits)
         
     
     def pca(self, **kwargs):
@@ -257,7 +263,7 @@ def tsne(self, perplexity=None, pca=False, random_state=None, **kwargs):
         
         # Embed the data in two dimensions
         self.tsne_fit = TSNE(n_components=2, perplexity=perplexity, random_state=random_state, **kwargs)
-        ecfp_tsne_embedding = self.tsne_fit.fit_transform(self.__data)
+        ecfp_tsne_embedding = self.tsne_fit.fit_transform(np.array(self.__data))
         # Create a dataframe containinting the first 2 TSNE components of ECFP 
         self.__df_2_components = pd.DataFrame(data = ecfp_tsne_embedding
              , columns = ['t-SNE-1', 't-SNE-2'])
@@ -722,4 +728,4 @@ def __open_plot(self, p):
         show(p)
     
     def get_target(self):
-        return self.__target
\ No newline at end of file
+        return self.__target
diff --git a/setup.py b/setup.py
index baa1478..2bc2e6d 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setup(
     name="chemplot", 
-    version="1.2.1",
+    version="1.2.2",
     author="Murat Cihan Sorkun, Dajt Mullaj",
     author_email="mcsorkun@gmail.com, dajt.mullai@gmail.com",
     description="A python library for chemical space visualization.",
@@ -35,10 +35,10 @@
     install_requires=[
 	"pandas>=1.1.3",
 	"numpy>=1.19.2",
-	"matplotlib==3.3.2",
-	"seaborn==0.11.1",
+	"matplotlib>=3.3.2",
+	"seaborn>=0.11.1",
 	"umap-learn>=0.5.1",
-	"scikit-learn==0.24.2",
+	"scikit-learn>=0.24.2",
 	"bokeh>=2.2.3",
 	"scipy>=1.5.2",
 	"mordred>=1.2.0",