diff --git a/Venus_all_band donwnload data.from Google Earth Engine.py b/Venus_all_band donwnload data.from Google Earth Engine.py
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+++ b/Venus_all_band donwnload data.from Google Earth Engine.py	
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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Oct  1 15:16:23 2019
+
+@author: yaron
+"""
+
+import ee
+import os
+from ipygee import *
+
+import pandas as pd 
+from openpyxl import load_workbook
+from datetime import datetime
+ee.Initialize()
+
+
+
+
+
+
+
+feature_to_use ='users/yaron1205/Mizam_Israeli_Wheat_Adaptation_to_Climate_Change/LR2019_GEE'
+#feature_to_use ='users/yaron1205/Mizam_Israeli_Wheat_Adaptation_to_Climate_Change/PW2019_genus'
+
+#fid_st of
+id_colum_to_use = 'st_fid'
+#id_colum_to_use = 'cv'
+path_to_xls = 'D:/F.xlsx'
+Scale = 0.001
+
+
+name_of_columns = {
+         'b1': 'VE1',
+         'b2': 'VE2',
+         'b3': 'VE3',
+         'b4': 'VE4',
+         'b5': 'VE5',
+         'b6': 'VE6',
+         'b7': 'VE7',
+         'b8': 'VE8',
+         'b9': 'VE9',
+         'b10': 'VE10',
+         'b11': 'VE11',
+         'b12': 'VE12',
+         'b15': 'CloudMask',
+ }  
+    
+
+
+
+
+def Scale_function(x):
+    x= x*Scale
+    return x
+
+
+feature = ee.FeatureCollection(feature_to_use)
+fc_temp = feature.getInfo()
+fc_info =fc_temp['features']
+
+
+
+list_of_vale_to_loop =[]
+# Using for loop 
+
+for i in fc_info: 
+
+    print(i['properties'][id_colum_to_use]) 
+
+    list_of_vale_to_loop.append(i['properties'][id_colum_to_use])
+
+
+# start the work
+list_of_sheet =[]
+list_of_sheet_name =[]
+for vale in  list_of_vale_to_loop:
+    print (vale)
+    FC = (ee.FeatureCollection(feature_to_use).filter(ee.Filter().eq(id_colum_to_use, vale)))
+    print(FC)  
+    col = ee.ImageCollection('users/venusdataw10/VEw10').filterBounds(feature)
+    time_series = col.filterDate('2018-07-11', '2019-05-31')
+    bands = ['b1', 'b2', 'b3','b4', 'b5', 'b6','b7', 'b8', 'b9','b10', 'b11', 'b12','b15']
+    chart_ts = chart.Image.series(**{
+    'imageCollection': time_series, 
+    'region': FC,
+    'scale': 5,
+    'bands': bands,
+    'reducer': ee.Reducer.mean()}) 
+    print(chart_ts.dataframe)   
+ 
+    dataframe = chart_ts.dataframe.apply(Scale_function, axis=1)
+ 
+   
+        
+    
+    #chart_ts.dataframe['DATE'] = chart_ts.dataframe.index
+    #chart_ts.dataframe = chart_ts.dataframe.reindex(columns=['DATE','B1', 'B2', 'B3','B4', 'B5', 'B6','B7', 'B8', 'B8A','B9', 'B10', 'B11','B12'])
+    list_of_sheet.append(dataframe)
+    list_of_sheet_name.append(vale)
+    
+    
+# save the data
+for sheet_data, sheet in zip(list_of_sheet, list_of_sheet_name):
+    print(sheet_data, sheet)
+    
+    sheet_data['DATE'] = sheet_data.index
+    sheet_data = sheet_data.reindex(columns=['DATE','b1', 'b2', 'b3','b4', 'b5', 'b6','b7', 'b8', 'b9','b10', 'b11', 'b12','b15'])
+    sheet_data['b15'] = sheet_data['b15'].apply(lambda x: x* 1000) #cloud mask info
+    sheet_data = sheet_data.rename(name_of_columns, axis='columns')
+    sheet_data['DATE'] = sheet_data['DATE'].apply(lambda x: x.strftime("%d/%m/%Y"))
+    writer = pd.ExcelWriter(path_to_xls, engine='openpyxl')
+    book = load_workbook(path_to_xls)
+    writer.book = book
+    writer.sheets = dict((ws.title, ws) for ws in book.worksheets)
+
+    sheet_data.to_excel(writer, sheet_name=sheet, index=False )
+    writer.save()
+    
+    
+#columnsTitles = ['ID','Vb1', 'Vb2', 'Vb3','Vb4', 'Vb5', 'Vb6','Vb7', 'Vb8', 'Vb9','Vb10', 'Vb11', 'Vb12']
+
+
+
+#df = df.reindex(columns=columnsTitles)   
+    
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