--- a
+++ b/Serialized/helper/miscellaneous.py
@@ -0,0 +1,106 @@
+import torch
+import smtplib
+import pandas as pd
+import torch
+
+def device_by_name(name):
+    ''' Return reference to cuda device by using Part of it's name
+        
+        Args:
+            name: part of the cuda device name (shuuld be distinct
+            
+        Return:
+            Reference to cuda device
+            
+        Updated: Yuval 12/10/19
+    '''
+    assert torch.cuda.is_available(),"No cuda device"
+    device=None
+    for i in range(torch.cuda.device_count()):
+        dv=torch.device("cuda:{}".format(i))
+        if name in torch.cuda.get_device_name(dv):
+            device=dv
+            break
+    assert device, "device {} not found".format(name)
+    return device
+
+def gmean_torch(t,w,op = None):
+    # todo Doc
+    wn = w/w.sum()
+    tr = t.unsqueeze(-1).transpose(0,-1).squeeze(0)
+    if op is not None:
+        tr=op(tr)
+    tt=torch.ones_like(tr[...,0])
+    for i in range(tr.shape[-1]):
+        tt=tt*torch.pow(tr[...,i],wn[i])
+        
+class Email_Progress():
+    ''' class  - Email progress to myself
+    
+        Args:
+            source email    : Gmail user name, don't need the @gmail.com (don't use your mail account - risky)
+            source_password : Gmail Password (RiSK!!!!!!!!!!!)
+            target_email    : The recipt email
+            title           : Email's Title
+            
+       Methods:
+           __call__
+           Args:
+               history - directory with the data to send
+               
+       Update: Yuval 12/10/19
+    '''
+    
+    def __init__(self,source_email,source_password,target_email,title):
+        self.source_email=source_email
+        self.source_password=source_password
+        self.target_email=target_email
+        self.title=title
+        
+    def __call__(self,history):
+        str_list=['Subject:{}\n'.format(self.title)]+[str(d)[1:-2].replace("'",'').replace(':','')+'\n' for d in history]
+        email_text=''.join(str_list)            
+        try:
+            server = smtplib.SMTP_SSL('smtp.gmail.com', 465)
+            server.ehlo()
+            server.login(self.source_email, self.source_password)
+            server.sendmail(self.source_email, self.target_email, email_text)
+            server.close()
+            return 0
+        except Exception as e:
+            print(e)
+            print ('Something went wrong...')
+            return e
+        
+def get_submission(test_df,pred):
+    epidural_df=pd.DataFrame(data={'ID':'ID_'+test_df.PatientID.values+'_epidural','Label':torch.sigmoid(pred[:,0])})
+    intraparenchymal_df=pd.DataFrame(data={'ID':'ID_'+test_df.PatientID.values+'_intraparenchymal','Label':torch.sigmoid(pred[:,1])})
+    intraventricular_df=pd.DataFrame(data={'ID':'ID_'+test_df.PatientID.values+'_intraventricular','Label':torch.sigmoid(pred[:,2])})
+    subarachnoid_df=pd.DataFrame(data={'ID':'ID_'+test_df.PatientID.values+'_subarachnoid','Label':torch.sigmoid(pred[:,3])})
+    subdural_df=pd.DataFrame(data={'ID':'ID_'+test_df.PatientID.values+'_subdural','Label':torch.sigmoid(pred[:,4])})
+    any_df=pd.DataFrame(data={'ID':'ID_'+test_df.PatientID.values+'_any','Label':torch.sigmoid(pred[:,5])}) 
+    return pd.concat([epidural_df,
+                        intraparenchymal_df,
+                        intraventricular_df,
+                        subarachnoid_df,
+                        subdural_df,
+                        any_df]).sort_values('ID').reset_index(drop=True)
+
+
+def get_submission_ids(image_ids,pred,do_sigmoid=True):
+    if do_sigmoid:
+        func = lambda x:torch.sigmoid(x)
+    else:
+        func = lambda x:x
+    epidural_df=pd.DataFrame(data={'ID':'ID_'+image_ids+'_epidural','Label':func(pred[:,0])})
+    intraparenchymal_df=pd.DataFrame(data={'ID':'ID_'+image_ids+'_intraparenchymal','Label':func(pred[:,1])})
+    intraventricular_df=pd.DataFrame(data={'ID':'ID_'+image_ids+'_intraventricular','Label':func(pred[:,2])})
+    subarachnoid_df=pd.DataFrame(data={'ID':'ID_'+image_ids+'_subarachnoid','Label':func(pred[:,3])})
+    subdural_df=pd.DataFrame(data={'ID':'ID_'+image_ids+'_subdural','Label':func(pred[:,4])})
+    any_df=pd.DataFrame(data={'ID':'ID_'+image_ids+'_any','Label':func(pred[:,5])}) 
+    return pd.concat([epidural_df,
+                        intraparenchymal_df,
+                        intraventricular_df,
+                        subarachnoid_df,
+                        subdural_df,
+                        any_df]).sort_values('ID').reset_index(drop=True)
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