diff --git a/CIM outputs.ipynb b/CIM outputs.ipynb
deleted file mode 100755
index 2d64da5..0000000
--- a/CIM outputs.ipynb	
+++ /dev/null
@@ -1,105 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "   Script prepared by Dr. Dasaraden Mauree                 \n",
-    "   Contact: EPFL                                           \n",
-    "   dasaraden.mauree@epfl.ch / dasaraden.mauree@gmail.com  \n",
-    "                                                         \n",
-    "   Use this script to read Profils.csv from CIM, select \n",
-    "   specific rows and create new file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#importing necessary packages\n",
-    "\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import time\n",
-    "import math\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.dates as dates\n",
-    "%matplotlib inline \n",
-    "from datetime import datetime, timedelta\n",
-    "from IPython.core.interactiveshell import InteractiveShell\n",
-    "InteractiveShell.ast_node_interactivity = \"all\"\n",
-    "\n",
-    "#Opening file and saving in array\n",
-    "profils=pd.read_csv(\"confluence/v2/Profils_july.csv\", header=\"infer\", sep=\";\", names = [\"z\", \"u\", \"v\", \"pt\", \"tke\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Selecting data\n",
-    "cf=profils[profils['z']==7.5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "#Computing horizontal wind speed and air temperature (°C)\n",
-    "cf['U (m s$^{-1}$)']=(cf['u']**(2)+cf['v']**(2))**(1/2)\n",
-    "cf['Air Temp. (°C)']=cf['pt']-273.15"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cf=cf.drop(['u', 'v','pt', 'tke'], axis=1)\n",
-    "cf=cf.round(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "#Printing output in txt file\n",
-    "cf.to_csv(r'cf_results_july.txt', header=None, sep=' ', mode='a')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [default]",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/CIM/CIM outputs.ipynb b/CIM/CIM outputs.ipynb
deleted file mode 100755
index 2d64da5..0000000
--- a/CIM/CIM outputs.ipynb	
+++ /dev/null
@@ -1,105 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "   Script prepared by Dr. Dasaraden Mauree                 \n",
-    "   Contact: EPFL                                           \n",
-    "   dasaraden.mauree@epfl.ch / dasaraden.mauree@gmail.com  \n",
-    "                                                         \n",
-    "   Use this script to read Profils.csv from CIM, select \n",
-    "   specific rows and create new file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#importing necessary packages\n",
-    "\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import time\n",
-    "import math\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.dates as dates\n",
-    "%matplotlib inline \n",
-    "from datetime import datetime, timedelta\n",
-    "from IPython.core.interactiveshell import InteractiveShell\n",
-    "InteractiveShell.ast_node_interactivity = \"all\"\n",
-    "\n",
-    "#Opening file and saving in array\n",
-    "profils=pd.read_csv(\"confluence/v2/Profils_july.csv\", header=\"infer\", sep=\";\", names = [\"z\", \"u\", \"v\", \"pt\", \"tke\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Selecting data\n",
-    "cf=profils[profils['z']==7.5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "#Computing horizontal wind speed and air temperature (°C)\n",
-    "cf['U (m s$^{-1}$)']=(cf['u']**(2)+cf['v']**(2))**(1/2)\n",
-    "cf['Air Temp. (°C)']=cf['pt']-273.15"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cf=cf.drop(['u', 'v','pt', 'tke'], axis=1)\n",
-    "cf=cf.round(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "#Printing output in txt file\n",
-    "cf.to_csv(r'cf_results_july.txt', header=None, sep=' ', mode='a')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [default]",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Read_cli_citysim_v1.ipynb b/Read_cli_citysim_v1.ipynb
new file mode 100644
index 0000000..5ccc6ec
--- /dev/null
+++ b/Read_cli_citysim_v1.ipynb
@@ -0,0 +1,76 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Preparing climate file for CIM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  Script prepared by Dr. Dasaraden Mauree                 \n",
+    "  Contact: EPFL                                           \n",
+    "  dasaraden.mauree@epfl.ch / dasaraden.mauree@gmail.com \n",
+    "                                                         \n",
+    "   use this script to read the standard climate file from\n",
+    "   Meteonorm for CitySim and save new file for CIM. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing necessary packages\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Opening file and saving in array\n",
+    "meteonorm=pd.read_csv(\"Ecublens.cli\", header=\"infer\", sep=\"\\s+\", encoding = \"ISO-8859-1\", skiprows=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Selecting columns and saving in file for CIM\n",
+    "cim=meteonorm[['Ta','FF','DD','RH']]\n",
+    "cim.to_csv(r'for_cim.cli', header=None, sep=' ', mode='a', index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/canopy.dbx b/canopy.dbx
new file mode 100755
index 0000000..5f36c3c
Binary files /dev/null and b/canopy.dbx differ
diff --git a/citysim-SW_buil_surf_v1.ipynb b/citysim-SW_buil_surf_v1.ipynb
new file mode 100644
index 0000000..4f94e9e
--- /dev/null
+++ b/citysim-SW_buil_surf_v1.ipynb
@@ -0,0 +1,403 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  Script prepared by Dr. Dasaraden Mauree                 \n",
+    "  Contact: EPFL                                           \n",
+    "  dasaraden.mauree@epfl.ch / dasaraden.mauree@gmail.com \n",
+    "                                                         \n",
+    "   use this script to read surface temperature data      \n",
+    "   from CitySim and calculate the hourly mean data       \n",
+    "   for each direction and then used as input for CIM.    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#importing necessary packages\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import time\n",
+    "import math\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as dates\n",
+    "%matplotlib inline \n",
+    "from datetime import datetime, timedelta\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\"\n",
+    "\n",
+    "#Opening file and saving in array\n",
+    "buil_surf=pd.read_csv(\"jonction/jonction_vitrage_standard_VF.out\", header=\"infer\", sep=\"\t\", encoding = \"ISO-8859-1\")\n",
+    "buil_surf=buil_surf.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "buil_surf.index.name = 'ID'\n",
+    "buil_surf=buil_surf.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#selecting only the columns corresponding to building surfaces\n",
+    "buil_surf=buil_surf[buil_surf['ID'].str.contains(\"NA\")==False]\n",
+    "buil_surf=buil_surf[buil_surf['ID'].str.contains(\"TREE\")==False]\n",
+    "buil_surf=buil_surf[buil_surf['ID'].str.contains(\"Pedestrian\")==False]\n",
+    "buil_surf=buil_surf.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "buil_surf.columns = buil_surf.iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "buil_surf.columns=buil_surf.columns.str.extract('(\\d+)',expand=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "buil_surf=buil_surf.reindex(buil_surf.index.drop('ID'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "azim=pd.DataFrame(buil_surf.iloc[1])\n",
+    "azim=azim.iloc[1:-1] #this is included to remove unnecessary lines in the dataframe\n",
+    "azim.columns = [\"Angle\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "direction=[]\n",
+    "azimuth=azim.iloc[:,0]\n",
+    "#azimuth=azimuth.iloc[1:-1] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Calculating direction from Azimuth given by CitySim (1 - East, 2 - South, 3 - West, 4 - North, 5 - Roof)\n",
+    "\n",
+    "i=0\n",
+    "while i < len(azimuth):\n",
+    "    if(azimuth.iloc[i]>45 and azimuth.iloc[i]<135):\n",
+    "        dir=1\n",
+    "    if(azimuth.iloc[i]>135 and azimuth.iloc[i]<225):\n",
+    "        dir=2\n",
+    "    if(azimuth.iloc[i]>225 and azimuth.iloc[i]<315):\n",
+    "        dir=3\n",
+    "    if(azimuth.iloc[i]>315 and azimuth.iloc[i]<360 or azimuth.iloc[i]>0 and azimuth.iloc[i]<45):\n",
+    "        dir=4\n",
+    "    if(azimuth.iloc[i]==0):\n",
+    "        dir=5\n",
+    "    direction.append(dir)\n",
+    "    i=i+1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "azim['Dir']=direction\n",
+    "#azim['Faces'] = azim.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "viewT=buil_surf.T\n",
+    "viewT=viewT[1:-1]\n",
+    "azim['Area']=viewT[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "azim=azim.drop(['Angle'],axis=1)  #to remove Angles from dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#reading file for surface temperature from CitySim\n",
+    "sw=pd.read_csv(\"jonction/jonction_vitrage_standard_SW.out\", header=\"infer\", sep=\"\t\", encoding = \"ISO-8859-1\")\n",
+    "\n",
+    "#dropping columns for Ke\n",
+    "sw.drop([col for col in sw.columns if \"#Igh\" in col], axis=1, inplace=True)\n",
+    "\n",
+    "#Transforming header to keep only face id\n",
+    "#surf_temp.columns=surf_temp.columns.str.extract('(\\d\\d\\d\\d\\d)',expand=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "sw=sw.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "sw.index.name = 'ID'\n",
+    "sw=sw.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\dmauree\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  from ipykernel import kernelapp as app\n",
+      "C:\\Users\\dmauree\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  app.launch_new_instance()\n"
+     ]
+    }
+   ],
+   "source": [
+    "buil_sw=sw[sw['ID'].str.contains(\"NA\")== False][:-1] #added to remove line from file (EPFL campus file)\n",
+    "buil_sw=buil_sw[sw['ID'].str.contains(\"TREE\")== False]\n",
+    "buil_sw=buil_sw[sw['ID'].str.contains(\"Pedestrian\")== False]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "buil_sw=buil_sw.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "buil_sw.columns = buil_sw.iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "buil_sw=buil_sw.reindex(buil_sw.index.drop('ID'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "buil_sw.columns=buil_sw.columns.str.extract('(\\d+)',expand=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "buil_sw=buil_sw.T\n",
+    "#buil_surf_temp=buil_surf_temp[:-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#concatenating both direction dataframe and shortwave dataframe\n",
+    "surf_dir=pd.concat([buil_sw, azim],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "meansurf=surf_dir.groupby(\"Dir\").apply(lambda surf_dir: np.average(surf_dir, weights=surf_dir['Area'], axis=0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "fi=meansurf.tolist()\n",
+    "fi=pd.DataFrame(fi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "fi=fi.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fi.to_csv(r'sw_jonctio_vitr_std.txt', header=None, sep=' ', mode='a')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "area_dir=[]\n",
+    "area_dir=azim.groupby(\"Dir\").sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "area_dir.to_csv(r'area_dir.txt', header=None, sep=' ', mode='a')"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/citysim-SW_buil_surf_v1.py b/citysim-SW_buil_surf_v1.py
new file mode 100644
index 0000000..13ce450
--- /dev/null
+++ b/citysim-SW_buil_surf_v1.py
@@ -0,0 +1,206 @@
+
+# coding: utf-8
+
+#   Script prepared by Dr. Dasaraden Mauree                 
+#   Contact: EPFL                                           
+#   dasaraden.mauree@epfl.ch / dasaraden.mauree@gmail.com 
+#                                                          
+#    use this script to read surface temperature data      
+#    from CitySim and calculate the hourly mean data       
+#    for each direction and then used as input for CIM.    
+
+# In[1]:
+
+#importing necessary packages
+
+import pandas as pd
+import numpy as np
+import time
+import math
+import matplotlib.pyplot as plt
+import matplotlib.dates as dates
+get_ipython().magic('matplotlib inline')
+from datetime import datetime, timedelta
+from IPython.core.interactiveshell import InteractiveShell
+InteractiveShell.ast_node_interactivity = "all"
+
+#Opening file and saving in array
+buil_surf=pd.read_csv("jonction/jonction_vitrage_standard_VF.out", header="infer", sep="	", encoding = "ISO-8859-1")
+buil_surf=buil_surf.T
+
+
+# In[2]:
+
+buil_surf.index.name = 'ID'
+buil_surf=buil_surf.reset_index()
+
+
+# In[3]:
+
+#selecting only the columns corresponding to building surfaces
+buil_surf=buil_surf[buil_surf['ID'].str.contains("NA")==False]
+buil_surf=buil_surf[buil_surf['ID'].str.contains("TREE")==False]
+buil_surf=buil_surf[buil_surf['ID'].str.contains("Pedestrian")==False]
+buil_surf=buil_surf.T
+
+
+# In[4]:
+
+buil_surf.columns = buil_surf.iloc[0]
+
+
+# In[5]:
+
+buil_surf.columns=buil_surf.columns.str.extract('(\d+)',expand=False)
+
+
+# In[6]:
+
+buil_surf=buil_surf.reindex(buil_surf.index.drop('ID'))
+
+
+# In[7]:
+
+azim=pd.DataFrame(buil_surf.iloc[1])
+azim=azim.iloc[1:-1] #this is included to remove unnecessary lines in the dataframe
+azim.columns = ["Angle"]
+
+
+# In[8]:
+
+direction=[]
+azimuth=azim.iloc[:,0]
+#azimuth=azimuth.iloc[1:-1] 
+
+
+# In[9]:
+
+# Calculating direction from Azimuth given by CitySim (1 - East, 2 - South, 3 - West, 4 - North, 5 - Roof)
+
+i=0
+while i < len(azimuth):
+    if(azimuth.iloc[i]>45 and azimuth.iloc[i]<135):
+        dir=1
+    if(azimuth.iloc[i]>135 and azimuth.iloc[i]<225):
+        dir=2
+    if(azimuth.iloc[i]>225 and azimuth.iloc[i]<315):
+        dir=3
+    if(azimuth.iloc[i]>315 and azimuth.iloc[i]<360 or azimuth.iloc[i]>0 and azimuth.iloc[i]<45):
+        dir=4
+    if(azimuth.iloc[i]==0):
+        dir=5
+    direction.append(dir)
+    i=i+1
+
+
+# In[10]:
+
+azim['Dir']=direction
+#azim['Faces'] = azim.index
+
+
+# In[11]:
+
+viewT=buil_surf.T
+viewT=viewT[1:-1]
+azim['Area']=viewT[0]
+
+
+# In[12]:
+
+azim=azim.drop(['Angle'],axis=1)  #to remove Angles from dataframe
+
+
+# In[14]:
+
+#reading file for surface temperature from CitySim
+sw=pd.read_csv("jonction/jonction_vitrage_standard_SW.out", header="infer", sep="	", encoding = "ISO-8859-1")
+
+#dropping columns for Ke
+sw.drop([col for col in sw.columns if "#Igh" in col], axis=1, inplace=True)
+
+#Transforming header to keep only face id
+#surf_temp.columns=surf_temp.columns.str.extract('(\d\d\d\d\d)',expand=False)
+
+
+# In[15]:
+
+sw=sw.T
+
+
+# In[16]:
+
+sw.index.name = 'ID'
+sw=sw.reset_index()
+
+
+# In[17]:
+
+buil_sw=sw[sw['ID'].str.contains("NA")== False][:-1] #added to remove line from file (EPFL campus file)
+buil_sw=buil_sw[sw['ID'].str.contains("TREE")== False]
+buil_sw=buil_sw[sw['ID'].str.contains("Pedestrian")== False]
+
+
+# In[19]:
+
+buil_sw=buil_sw.T
+
+
+# In[20]:
+
+buil_sw.columns = buil_sw.iloc[0]
+
+
+# In[22]:
+
+buil_sw=buil_sw.reindex(buil_sw.index.drop('ID'))
+
+
+# In[23]:
+
+buil_sw.columns=buil_sw.columns.str.extract('(\d+)',expand=False)
+
+
+# In[24]:
+
+buil_sw=buil_sw.T
+#buil_surf_temp=buil_surf_temp[:-1]
+
+
+# In[25]:
+
+#concatenating both direction dataframe and shortwave dataframe
+surf_dir=pd.concat([buil_sw, azim],axis=1)
+
+
+# In[26]:
+
+meansurf=surf_dir.groupby("Dir").apply(lambda surf_dir: np.average(surf_dir, weights=surf_dir['Area'], axis=0))
+
+
+# In[29]:
+
+fi=meansurf.tolist()
+fi=pd.DataFrame(fi)
+
+
+# In[30]:
+
+fi=fi.T
+
+
+# In[33]:
+
+fi.to_csv(r'sw_jonctio_vitr_std.txt', header=None, sep=' ', mode='a')
+
+
+# In[45]:
+
+area_dir=[]
+area_dir=azim.groupby("Dir").sum()
+
+
+# In[48]:
+
+area_dir.to_csv(r'area_dir.txt', header=None, sep=' ', mode='a')
+
diff --git a/citysim-hum_surf_v2.ipynb b/citysim-hum_surf_v2.ipynb
new file mode 100644
index 0000000..81bded2
--- /dev/null
+++ b/citysim-hum_surf_v2.ipynb
@@ -0,0 +1,221 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Preparing evapotranspiration flux for CIM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  Script prepared by Dr. Dasaraden Mauree                 \n",
+    "  Contact: EPFL                                           \n",
+    "  dasaraden.mauree@epfl.ch / dasaraden.mauree@gmail.com \n",
+    "                                                         \n",
+    "   use this script to read evapotranspiration ata      \n",
+    "   from CitySim and calculate the hourly mean data       \n",
+    "   for the ground surface and then used as input for CIM. \n",
+    "   \n",
+    "   This file originates from citysim_hum_surf-v1.\n",
+    "   \n",
+    "   This version works for CitySim version April 2020 output files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing necessary packages\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import time\n",
+    "import math\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as dates\n",
+    "%matplotlib inline \n",
+    "from datetime import datetime, timedelta\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Opening file and saving in array\n",
+    "all_surf=pd.read_csv(\"building_VF.out\", header=\"infer\", sep=\"\t\", encoding = \"ISO-8859-1\")\n",
+    "#all_surf=all_surf.T\n",
+    "\n",
+    "header=['ID', 'Type','Area', 'Azimuth', 'Altitude','SVF','GVF']\n",
+    "#header=['Area', 'Azimuth','SVF','GVF']\n",
+    "all_surf.columns=header\n",
+    "\n",
+    "#Creating an index\n",
+    "#all_surf=all_surf.rename(columns={'#':'ID'})\n",
+    "#all_surf.index.name = 'ID'\n",
+    "#all_surf=all_surf.reset_index()[1:-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Ground surfaces"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grd_surf=pd.DataFrame()\n",
+    "grd_surf=all_surf[all_surf['ID'].str.contains(\"NA\")]\n",
+    "grd_surf=grd_surf.T\n",
+    "\n",
+    "grd_surf.columns=grd_surf.iloc[0].str.split(':').str[-1]\n",
+    "grd_surf=grd_surf.reindex(grd_surf.index.drop('ID'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "azim_grd=pd.DataFrame(index=grd_surf.T.index)\n",
+    "azim_grd['Area']=grd_surf.iloc[0].values\n",
+    "azim_grd['Dir']=6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ET rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#reading file for surface temperature from CitySim\n",
+    "all_surf_et=pd.read_csv(\"building_ET.out\", header=\"infer\", sep=\"\t\", encoding = \"ISO-8859-1\")\n",
+    "all_surf_et=all_surf_et.T\n",
+    "\n",
+    "#Creating an index\n",
+    "all_surf_et.index.name = 'ID'\n",
+    "all_surf_et=all_surf_et.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ground ET"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#selecting only the columns corresponding to ground surfaces\n",
+    "grd_surf_et=pd.DataFrame()\n",
+    "grd_surf_et=all_surf_et[all_surf_et['ID'].str.contains(\"NA\")]\n",
+    "\n",
+    "grd_surf_et['ID']=grd_surf_et['ID'].str.split(':').str[-2]\n",
+    "grd_surf_et=grd_surf_et.set_index('ID')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#concatenating both direction dataframe and ground surface temp dataframe\n",
+    "grd_surf_dir=pd.merge(grd_surf_et, azim_grd, on='ID')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Computing weighted ET rates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataFrame is empty!\n"
+     ]
+    }
+   ],
+   "source": [
+    "if len(grd_surf_et.columns)<=1:\n",
+    "    print('ET is equal to zero!')\n",
+    "    grd_surf_et_dir = pd.DataFrame(np.zeros((8760, 1)))\n",
+    "else:\n",
+    "    meansurf_grd=grd_surf_dir.groupby(\"Dir\").apply(lambda grd_surf_dir: np.average(grd_surf_dir, weights=grd_surf_dir['Area'], axis=0))\n",
+    "\n",
+    "    grd_surf_et_dir=pd.DataFrame(meansurf_grd.tolist()).T[:-2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving file to CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grd_surf_et_dir.to_csv(r'surf_hum.txt', header=None, sep=' ', mode='a', index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/citysim-hum_surf_v2.py b/citysim-hum_surf_v2.py
new file mode 100644
index 0000000..57cfa49
--- /dev/null
+++ b/citysim-hum_surf_v2.py
@@ -0,0 +1,127 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Preparing evapotranspiration flux for CIM
+
+#   Script prepared by Dr. Dasaraden Mauree                 
+#   Contact: EPFL                                           
+#   dasaraden.mauree@epfl.ch / dasaraden.mauree@gmail.com 
+#                                                          
+#    use this script to read evapotranspiration ata      
+#    from CitySim and calculate the hourly mean data       
+#    for the ground surface and then used as input for CIM. 
+#    
+#    This file originates from citysim_hum_surf-v1.
+#    
+#    This version works for CitySim version April 2020 output files.
+
+# In[1]:
+
+
+#importing necessary packages
+
+import pandas as pd
+import numpy as np
+import time
+import math
+import matplotlib.pyplot as plt
+import matplotlib.dates as dates
+get_ipython().run_line_magic('matplotlib', 'inline')
+from datetime import datetime, timedelta
+from IPython.core.interactiveshell import InteractiveShell
+InteractiveShell.ast_node_interactivity = "all"
+
+
+# In[2]:
+
+
+#Opening file and saving in array
+all_surf=pd.read_csv("building_VF.out", header="infer", sep="	", encoding = "ISO-8859-1")
+#all_surf=all_surf.T
+
+header=['ID', 'Type','Area', 'Azimuth', 'Altitude','SVF','GVF']
+#header=['Area', 'Azimuth','SVF','GVF']
+all_surf.columns=header
+
+#Creating an index
+#all_surf=all_surf.rename(columns={'#':'ID'})
+#all_surf.index.name = 'ID'
+#all_surf=all_surf.reset_index()[1:-1]
+
+
+# ##  Ground surfaces
+
+# In[3]:
+
+
+grd_surf=pd.DataFrame()
+grd_surf=all_surf[all_surf['ID'].str.contains("NA")]
+grd_surf=grd_surf.T
+
+grd_surf.columns=grd_surf.iloc[0].str.split(':').str[-1]
+grd_surf=grd_surf.reindex(grd_surf.index.drop('ID'))
+
+
+# In[4]:
+
+
+azim_grd=pd.DataFrame(index=grd_surf.T.index)
+azim_grd['Area']=grd_surf.iloc[0].values
+azim_grd['Dir']=6
+
+
+# ## ET rate
+
+# In[29]:
+
+
+#reading file for surface temperature from CitySim
+all_surf_et=pd.read_csv("building_ET.out", header="infer", sep="	", encoding = "ISO-8859-1")
+all_surf_et=all_surf_et.T
+
+#Creating an index
+all_surf_et.index.name = 'ID'
+all_surf_et=all_surf_et.reset_index()
+
+
+# ## Ground ET
+
+# In[40]:
+
+
+#selecting only the columns corresponding to ground surfaces
+grd_surf_et=pd.DataFrame()
+grd_surf_et=all_surf_et[all_surf_et['ID'].str.contains("NA")]
+
+grd_surf_et['ID']=grd_surf_et['ID'].str.split(':').str[-2]
+grd_surf_et=grd_surf_et.set_index('ID')
+
+
+# In[41]:
+
+
+#concatenating both direction dataframe and ground surface temp dataframe
+grd_surf_dir=pd.merge(grd_surf_et, azim_grd, on='ID')
+
+
+# ## Computing weighted ET rates
+
+# In[42]:
+
+
+if len(grd_surf_et.columns)<=1:
+    print('ET is equal to zero!')
+    grd_surf_et_dir = pd.DataFrame(np.zeros((8760, 1)))
+else:
+    meansurf_grd=grd_surf_dir.groupby("Dir").apply(lambda grd_surf_dir: np.average(grd_surf_dir, weights=grd_surf_dir['Area'], axis=0))
+
+    grd_surf_et_dir=pd.DataFrame(meansurf_grd.tolist()).T[:-2]
+
+
+# ## Saving file to CSV
+
+# In[44]:
+
+
+grd_surf_et_dir.to_csv(r'surf_hum.txt', header=None, sep=' ', mode='a', index=False)
+
diff --git a/cim-citysim/citysim-surf_temp_buil_grd_surf_v1.py b/citysim-surf_temp_buil_grd_surf_v1.py
similarity index 100%
copy from cim-citysim/citysim-surf_temp_buil_grd_surf_v1.py
copy to citysim-surf_temp_buil_grd_surf_v1.py
diff --git a/citysim-surf_temp_buil_grd_surf_v2.ipynb b/citysim-surf_temp_buil_grd_surf_v2.ipynb
new file mode 100644
index 0000000..a044a8f
--- /dev/null
+++ b/citysim-surf_temp_buil_grd_surf_v2.ipynb
@@ -0,0 +1,377 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Preparing surface temperature for CIM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  Script prepared by Dr. Dasaraden Mauree                 \n",
+    "  Contact: EPFL                                           \n",
+    "  dasaraden.mauree@epfl.ch / dasaraden.mauree@gmail.com \n",
+    "                                                         \n",
+    "   use this script to read surface temperature data      \n",
+    "   from CitySim and calculate the hourly mean data       \n",
+    "   for each direction and then used as input for CIM. \n",
+    "   \n",
+    "   This file originates from citysim-surf_temp_buil_grd_surf-v1.\n",
+    "   \n",
+    "   This version works for CitySim version April 2020 output files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing necessary packages\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import time\n",
+    "import math\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as dates\n",
+    "%matplotlib inline \n",
+    "from datetime import datetime, timedelta\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Opening file and saving in array\n",
+    "all_surf=pd.read_csv(\"bee_minergie.tsv_VF.tsv\", header=\"infer\", sep=\"\t\", encoding = \"ISO-8859-1\")\n",
+    "#all_surf=all_surf.T\n",
+    "\n",
+    "header=['ID', 'Type','Area', 'Azimuth', 'Altitude','SVF','GVF']\n",
+    "all_surf.columns=header\n",
+    "\n",
+    "#Creating an index\n",
+    "#all_surf=all_surf.rename(columns={'#':'ID'})\n",
+    "#all_surf.index.name = 'ID'\n",
+    "#all_surf=all_surf.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Building surfaces"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#selecting only the columns corresponding to building surfaces\n",
+    "buil_surf=pd.DataFrame()\n",
+    "buil_surf=all_surf[all_surf['ID'].str.contains(\"NA\")==False]\n",
+    "buil_surf=buil_surf[buil_surf['ID'].str.contains(\"TREE\")==False]\n",
+    "buil_surf=buil_surf[buil_surf['ID'].str.contains(\"Pedestrian\")==False]\n",
+    "buil_surf=buil_surf.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Indexing the table with surface IDs.\n",
+    "buil_surf.columns=buil_surf.iloc[0].str.split(':').str[-1]\n",
+    "buil_surf=buil_surf.reindex(buil_surf.index.drop('ID'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Creating new series with values of Azimuth and SVF.\n",
+    "direction=[]\n",
+    "azimuth=buil_surf.iloc[2]\n",
+    "svf=buil_surf.iloc[4]\n",
+    "#azimuth=azimuth.iloc[1:-1] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculating direction from Azimuth given by CitySim (1 - East, 2 - South, 3 - West, 4 - North, 5 - Roof)\n",
+    "\n",
+    "i=0\n",
+    "while i < len(azimuth):\n",
+    "    if(azimuth.iloc[i]>45 and azimuth.iloc[i]<135):\n",
+    "        dir=1\n",
+    "    if(azimuth.iloc[i]>135 and azimuth.iloc[i]<225):\n",
+    "        dir=2\n",
+    "    if(azimuth.iloc[i]>225 and azimuth.iloc[i]<315):\n",
+    "        dir=3\n",
+    "    if(azimuth.iloc[i]>315 and azimuth.iloc[i]<360 and svf.iloc[i]<=0.9 or azimuth.iloc[i]>0 and azimuth.iloc[i]<45 and svf.iloc[i]<=0.9):\n",
+    "        dir=4\n",
+    "    if(azimuth.iloc[i]==0 and svf.iloc[i]>0.9):\n",
+    "        dir=5\n",
+    "    direction.append(dir)\n",
+    "    i=i+1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "azim=pd.DataFrame()\n",
+    "azim['Dir']=direction\n",
+    "azim['Area']=buil_surf.iloc[1].values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Ground surfaces"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grd_surf=pd.DataFrame()\n",
+    "grd_surf=all_surf[all_surf['ID'].str.contains(\"NA\")]\n",
+    "grd_surf=grd_surf.T\n",
+    "\n",
+    "grd_surf.columns=grd_surf.iloc[0].str.split(':').str[-1]\n",
+    "grd_surf=grd_surf.reindex(grd_surf.index.drop('ID'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "azim_grd=pd.DataFrame()\n",
+    "azim_grd['Area']=grd_surf.iloc[1].values\n",
+    "azim_grd['Dir']=6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Surface temperature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#reading file for surface temperature from CitySim\n",
+    "all_surf_temp=pd.read_csv(\"bee_minergie.tsv_TS.tsv\", header=\"infer\", sep=\"\t\", encoding = \"ISO-8859-1\")\n",
+    "\n",
+    "#dropping columns for Ke\n",
+    "all_surf_temp.drop([col for col in all_surf_temp.columns if \"Ke\" in col], axis=1, inplace=True)\n",
+    "\n",
+    "all_surf_temp=all_surf_temp.T\n",
+    "\n",
+    "#Creating an index\n",
+    "all_surf_temp.index.name = 'ID'\n",
+    "all_surf_temp=all_surf_temp.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Building surface temperature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#selecting only the columns corresponding to building surfaces\n",
+    "buil_surf_temp=pd.DataFrame()\n",
+    "buil_surf_temp=all_surf_temp[all_surf_temp['ID'].str.contains(\"NA\")== False][:-1] #added to remove line from file (EPFL campus file)\n",
+    "buil_surf_temp=buil_surf_temp[buil_surf_temp['ID'].str.contains(\"TREE\")== False]\n",
+    "buil_surf_temp=buil_surf_temp[buil_surf_temp['ID'].str.contains(\"Pedestrian\")== False]\n",
+    "\n",
+    "#Removing Index for concat\n",
+    "buil_surf_temp=buil_surf_temp.drop(['ID'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#concatenating both direction dataframe and building surface temp dataframe\n",
+    "surf_dir=pd.concat([buil_surf_temp, azim],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ground surface temperature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#selecting only the columns corresponding to ground surfaces\n",
+    "grd_surf_temp=pd.DataFrame()\n",
+    "grd_surf_temp=all_surf_temp[all_surf_temp['ID'].str.contains(\"NA\")]\n",
+    "\n",
+    "#Removing Index for concat\n",
+    "grd_surf_temp=grd_surf_temp.drop(['ID'],axis=1)\n",
+    "grd_surf_temp.reset_index(drop=True, inplace=True)\n",
+    "azim_grd.reset_index(drop=True, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#concatenating both direction dataframe and ground surface temp dataframe\n",
+    "grd_surf_dir=pd.concat([grd_surf_temp, azim_grd],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Computing weighted building surface temperatures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "meansurf=surf_dir.groupby(\"Dir\").apply(lambda surf_dir: np.average(surf_dir, weights=surf_dir['Area'], axis=0))\n",
+    "\n",
+    "surf_temp_dir=pd.DataFrame(meansurf.tolist()).T[:-2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meansurf_grd=grd_surf_dir.groupby(\"Dir\").apply(lambda grd_surf_dir: np.average(grd_surf_dir, weights=grd_surf_dir['Area'], axis=0))\n",
+    "\n",
+    "grd_surf_temp_dir=pd.DataFrame(meansurf_grd.tolist()).T[:-2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving file to CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_surf_temp_dir=pd.concat([surf_temp_dir, grd_surf_temp_dir],axis=1)\n",
+    "header=['East', 'South','West', 'North', 'Roof','Ground']\n",
+    "all_surf_temp_dir.columns=header"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "all_surf_temp_dir.to_csv(r'Confluence_surf_temp.txt', header=None, sep=' ', mode='a')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#In a previous version of the script the following lines where used instead of the one above.\n",
+    "\n",
+    "#buil_surf_temp=all_surf_temp[all_surf_temp['ID'].str.contains(\"NA\")== False][:-1] #added to remove line from file (EPFL campus file)\n",
+    "#buil_surf_temp=buil_surf_temp[buil_surf_temp['ID'].str.contains(\"TREE\")== False]\n",
+    "#buil_surf_temp=buil_surf_temp[buil_surf_temp['ID'].str.contains(\"Pedestrian\")== False]\n",
+    "#buil_surf_temp=buil_surf_temp.T\n",
+    "\n",
+    "#buil_surf_temp.columns = buil_surf_temp.iloc[0]\n",
+    "#buil_surf_temp=buil_surf_temp.reindex(buil_surf_temp.index.drop('ID'))\n",
+    "#buil_surf_temp.columns=buil_surf_temp.columns.str.split(':').str[-2]\n",
+    "#buil_surf_temp=buil_surf_temp.T\n",
+    "\n",
+    "#buil_surf_temp.reset_index(drop=True, inplace=True)\n",
+    "#azim.reset_index(drop=True, inplace=True)\n",
+    "\n",
+    "#surf_dir=pd.concat([buil_surf_temp, azim],axis=1)"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/cim-citysim/citysim-surf_temp_buil_grd_surf_v1.py b/citysim-surf_temp_buil_grd_surf_v2.py
old mode 100755
new mode 100644
similarity index 86%
rename from cim-citysim/citysim-surf_temp_buil_grd_surf_v1.py
rename to citysim-surf_temp_buil_grd_surf_v2.py
index 97e3080..e9a85df
--- a/cim-citysim/citysim-surf_temp_buil_grd_surf_v1.py
+++ b/citysim-surf_temp_buil_grd_surf_v2.py
@@ -1,241 +1,247 @@
 #!/usr/bin/env python
 # coding: utf-8
 
 # # Preparing surface temperature for CIM
 
 #   Script prepared by Dr. Dasaraden Mauree                 
 #   Contact: EPFL                                           
 #   dasaraden.mauree@epfl.ch / dasaraden.mauree@gmail.com 
 #                                                          
 #    use this script to read surface temperature data      
 #    from CitySim and calculate the hourly mean data       
 #    for each direction and then used as input for CIM. 
 #    
-#    This file originates from citysim-surf_temp_buil_surf-v5
+#    This file originates from citysim-surf_temp_buil_surf-v5.
+#    
+#    This version works for CitySim version April 2020 output files.
 
-# In[45]:
+# In[1]:
 
 
 #importing necessary packages
 
 import pandas as pd
 import numpy as np
 import time
 import math
 import matplotlib.pyplot as plt
 import matplotlib.dates as dates
 get_ipython().run_line_magic('matplotlib', 'inline')
 from datetime import datetime, timedelta
 from IPython.core.interactiveshell import InteractiveShell
 InteractiveShell.ast_node_interactivity = "all"
 
 
-# In[46]:
+# In[34]:
 
 
 #Opening file and saving in array
-all_surf=pd.read_csv("Confluence_V1_VF.out", header="infer", sep="	", encoding = "ISO-8859-1")
-all_surf=all_surf.T
+all_surf=pd.read_csv("bee_minergie.tsv_VF.tsv", header="infer", sep="	", encoding = "ISO-8859-1")
+#all_surf=all_surf.T
+
+header=['ID', 'Type','Area', 'Azimuth', 'Altitude','SVF','GVF']
+all_surf.columns=header
 
 #Creating an index
-all_surf.index.name = 'ID'
-all_surf=all_surf.reset_index()
+#all_surf=all_surf.rename(columns={'#':'ID'})
+#all_surf.index.name = 'ID'
+#all_surf=all_surf.reset_index()
 
 
 # ## Building surfaces
 
-# In[91]:
+# In[44]:
 
 
 #selecting only the columns corresponding to building surfaces
 buil_surf=pd.DataFrame()
 buil_surf=all_surf[all_surf['ID'].str.contains("NA")==False]
 buil_surf=buil_surf[buil_surf['ID'].str.contains("TREE")==False]
 buil_surf=buil_surf[buil_surf['ID'].str.contains("Pedestrian")==False]
 buil_surf=buil_surf.T
 
 
-# In[92]:
+# In[47]:
 
 
 #Indexing the table with surface IDs.
 buil_surf.columns=buil_surf.iloc[0].str.split(':').str[-1]
 buil_surf=buil_surf.reindex(buil_surf.index.drop('ID'))
 
 
-# In[93]:
+# In[57]:
 
 
 #Creating new series with values of Azimuth and SVF.
 direction=[]
-azimuth=buil_surf.iloc[1][1:-1]
-svf=buil_surf.iloc[2][1:-1]
+azimuth=buil_surf.iloc[2]
+svf=buil_surf.iloc[4]
 #azimuth=azimuth.iloc[1:-1] 
 
 
-# In[94]:
+# In[59]:
 
 
 # Calculating direction from Azimuth given by CitySim (1 - East, 2 - South, 3 - West, 4 - North, 5 - Roof)
 
 i=0
 while i < len(azimuth):
     if(azimuth.iloc[i]>45 and azimuth.iloc[i]<135):
         dir=1
     if(azimuth.iloc[i]>135 and azimuth.iloc[i]<225):
         dir=2
     if(azimuth.iloc[i]>225 and azimuth.iloc[i]<315):
         dir=3
     if(azimuth.iloc[i]>315 and azimuth.iloc[i]<360 and svf.iloc[i]<=0.9 or azimuth.iloc[i]>0 and azimuth.iloc[i]<45 and svf.iloc[i]<=0.9):
         dir=4
     if(azimuth.iloc[i]==0 and svf.iloc[i]>0.9):
         dir=5
     direction.append(dir)
     i=i+1
 
 
-# In[108]:
+# In[65]:
 
 
 azim=pd.DataFrame()
 azim['Dir']=direction
-azim['Area']=buil_surf.iloc[0][1:-1].values
+azim['Area']=buil_surf.iloc[1].values
 
 
 # ##  Ground surfaces
 
-# In[228]:
+# In[67]:
 
 
 grd_surf=pd.DataFrame()
 grd_surf=all_surf[all_surf['ID'].str.contains("NA")]
 grd_surf=grd_surf.T
 
 grd_surf.columns=grd_surf.iloc[0].str.split(':').str[-1]
 grd_surf=grd_surf.reindex(grd_surf.index.drop('ID'))
 
 
-# In[235]:
+# In[68]:
 
 
 azim_grd=pd.DataFrame()
-azim_grd['Area']=grd_surf.iloc[0].values
+azim_grd['Area']=grd_surf.iloc[1].values
 azim_grd['Dir']=6
 
 
 # ## Surface temperature
 
-# In[112]:
+# In[70]:
 
 
 #reading file for surface temperature from CitySim
-all_surf_temp=pd.read_csv("Confluence_V1_TS.out", header="infer", sep="	", encoding = "ISO-8859-1")
+all_surf_temp=pd.read_csv("bee_minergie.tsv_TS.tsv", header="infer", sep="	", encoding = "ISO-8859-1")
 
 #dropping columns for Ke
 all_surf_temp.drop([col for col in all_surf_temp.columns if "Ke" in col], axis=1, inplace=True)
 
 all_surf_temp=all_surf_temp.T
 
 #Creating an index
 all_surf_temp.index.name = 'ID'
 all_surf_temp=all_surf_temp.reset_index()
 
 
 # ## Building surface temperature
 
-# In[245]:
+# In[73]:
 
 
 #selecting only the columns corresponding to building surfaces
 buil_surf_temp=pd.DataFrame()
 buil_surf_temp=all_surf_temp[all_surf_temp['ID'].str.contains("NA")== False][:-1] #added to remove line from file (EPFL campus file)
 buil_surf_temp=buil_surf_temp[buil_surf_temp['ID'].str.contains("TREE")== False]
 buil_surf_temp=buil_surf_temp[buil_surf_temp['ID'].str.contains("Pedestrian")== False]
 
 #Removing Index for concat
 buil_surf_temp=buil_surf_temp.drop(['ID'],axis=1)
 
 
-# In[247]:
+# In[74]:
 
 
 #concatenating both direction dataframe and building surface temp dataframe
 surf_dir=pd.concat([buil_surf_temp, azim],axis=1)
 
 
 # ## Ground surface temperature
 
-# In[253]:
+# In[76]:
 
 
 #selecting only the columns corresponding to ground surfaces
 grd_surf_temp=pd.DataFrame()
 grd_surf_temp=all_surf_temp[all_surf_temp['ID'].str.contains("NA")]
 
 #Removing Index for concat
 grd_surf_temp=grd_surf_temp.drop(['ID'],axis=1)
 grd_surf_temp.reset_index(drop=True, inplace=True)
 azim_grd.reset_index(drop=True, inplace=True)
 
 
-# In[254]:
+# In[77]:
 
 
 #concatenating both direction dataframe and ground surface temp dataframe
 grd_surf_dir=pd.concat([grd_surf_temp, azim_grd],axis=1)
 
 
 # ## Computing weighted building surface temperatures
 
-# In[200]:
+# In[78]:
 
 
 meansurf=surf_dir.groupby("Dir").apply(lambda surf_dir: np.average(surf_dir, weights=surf_dir['Area'], axis=0))
 
 surf_temp_dir=pd.DataFrame(meansurf.tolist()).T[:-2]
 
 
-# In[266]:
+# In[79]:
 
 
 meansurf_grd=grd_surf_dir.groupby("Dir").apply(lambda grd_surf_dir: np.average(grd_surf_dir, weights=grd_surf_dir['Area'], axis=0))
 
 grd_surf_temp_dir=pd.DataFrame(meansurf_grd.tolist()).T[:-2]
 
 
 # ## Saving file to CSV
 
-# In[267]:
+# In[80]:
 
 
 all_surf_temp_dir=pd.concat([surf_temp_dir, grd_surf_temp_dir],axis=1)
 header=['East', 'South','West', 'North', 'Roof','Ground']
 all_surf_temp_dir.columns=header
 
 
 # In[112]:
 
 
 all_surf_temp_dir.to_csv(r'Confluence_surf_temp.txt', header=None, sep=' ', mode='a')
 
 
 # In[198]:
 
 
 #In a previous version of the script the following lines where used instead of the one above.
 
 #buil_surf_temp=all_surf_temp[all_surf_temp['ID'].str.contains("NA")== False][:-1] #added to remove line from file (EPFL campus file)
 #buil_surf_temp=buil_surf_temp[buil_surf_temp['ID'].str.contains("TREE")== False]
 #buil_surf_temp=buil_surf_temp[buil_surf_temp['ID'].str.contains("Pedestrian")== False]
 #buil_surf_temp=buil_surf_temp.T
 
 #buil_surf_temp.columns = buil_surf_temp.iloc[0]
 #buil_surf_temp=buil_surf_temp.reindex(buil_surf_temp.index.drop('ID'))
 #buil_surf_temp.columns=buil_surf_temp.columns.str.split(':').str[-2]
 #buil_surf_temp=buil_surf_temp.T
 
 #buil_surf_temp.reset_index(drop=True, inplace=True)
 #azim.reset_index(drop=True, inplace=True)
 
 #surf_dir=pd.concat([buil_surf_temp, azim],axis=1)
 
diff --git a/create_citysim_cli_v2.ipynb b/create_citysim_cli_v2.ipynb
new file mode 100644
index 0000000..2cc553b
--- /dev/null
+++ b/create_citysim_cli_v2.ipynb
@@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Preparing climate file for CIM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  Script prepared by Dr. Dasaraden Mauree                 \n",
+    "  Contact: EPFL                                           \n",
+    "  dasaraden.mauree@epfl.ch / dasaraden.mauree@gmail.com \n",
+    "                                                         \n",
+    "   use this script to read the output from CIM and save \n",
+    "   it as a standard file for CitySim."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing necessary packages\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Opening file and saving in array\n",
+    "cim=pd.read_csv(\"Citysim.cli\", header=None, sep=\";\", encoding = \"ISO-8859-1\")\n",
+    "\n",
+    "header=['Ta','FF','DD','RH']\n",
+    "cim.columns=header\n",
+    "\n",
+    "cim[['Ta','FF']]=cim[['Ta','FF']].round(1)\n",
+    "cim[['DD','RH']]=cim[['DD','RH']].astype(int)\n",
+    "\n",
+    "meteonorm=pd.read_csv(\"Ecublens.cli\", header=\"infer\", sep=\"\\s+\", encoding = \"ISO-8859-1\", skiprows=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# replacing values of Meteonorm file with values from CIM \n",
+    "meteonorm.loc[:len(cim)-1,('Ta')]=cim.loc[:len(cim)-1,('Ta')]\n",
+    "meteonorm.loc[:len(cim)-1,('FF')]=cim.loc[:len(cim)-1,('FF')]\n",
+    "meteonorm.loc[:len(cim)-1,('DD')]=cim.loc[:len(cim)-1,('DD')]\n",
+    "meteonorm.loc[:len(cim)-1,('RH')]=cim.loc[:len(cim)-1,('RH')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# open file in read mode \n",
+    "fn = open(\"Ecublens.cli\", 'r') \n",
+    "\n",
+    "# open other file in write mode \n",
+    "fn1 = open('Ecublens_cim.cli', 'w') \n",
+    "\n",
+    "# read the content of the file line by line \n",
+    "cont = fn.readlines() \n",
+    "for i in range(0, 3): \n",
+    "        fn1.write(cont[i]) \n",
+    "        \n",
+    "# close the file \n",
+    "fn.close() \n",
+    "fn1.close() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Selecting columns and saving in file for Citysim\n",
+    "meteonorm.to_csv(r'Ecublens_cim.cli', sep=' ', mode='a', index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/create_citysim_cli_v2.py b/create_citysim_cli_v2.py
new file mode 100644
index 0000000..0115d53
--- /dev/null
+++ b/create_citysim_cli_v2.py
@@ -0,0 +1,69 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Preparing climate file for CIM
+
+#   Script prepared by Dr. Dasaraden Mauree                 
+#   Contact: EPFL                                           
+#   dasaraden.mauree@epfl.ch / dasaraden.mauree@gmail.com 
+#                                                          
+#    use this script to read the output from CIM and save 
+#    it as a standard file for CitySim.
+
+# In[1]:
+
+
+#importing necessary packages
+import pandas as pd
+
+
+# In[46]:
+
+
+#Opening file and saving in array
+cim=pd.read_csv("Citysim.cli", header=None, sep=";", encoding = "ISO-8859-1")
+
+header=['Ta','FF','DD','RH']
+cim.columns=header
+
+cim[['Ta','FF']]=cim[['Ta','FF']].round(1)
+cim[['DD','RH']]=cim[['DD','RH']].astype(int)
+
+meteonorm=pd.read_csv("Ecublens.cli", header="infer", sep="\s+", encoding = "ISO-8859-1", skiprows=3)
+
+
+# In[49]:
+
+
+# replacing values of Meteonorm file with values from CIM 
+meteonorm.loc[:len(cim)-1,('Ta')]=cim.loc[:len(cim)-1,('Ta')]
+meteonorm.loc[:len(cim)-1,('FF')]=cim.loc[:len(cim)-1,('FF')]
+meteonorm.loc[:len(cim)-1,('DD')]=cim.loc[:len(cim)-1,('DD')]
+meteonorm.loc[:len(cim)-1,('RH')]=cim.loc[:len(cim)-1,('RH')]
+
+
+# In[51]:
+
+
+# open file in read mode 
+fn = open("Ecublens.cli", 'r') 
+
+# open other file in write mode 
+fn1 = open('Ecublens_cim.cli', 'w') 
+
+# read the content of the file line by line 
+cont = fn.readlines() 
+for i in range(0, 3): 
+        fn1.write(cont[i]) 
+        
+# close the file 
+fn.close() 
+fn1.close() 
+
+
+# In[52]:
+
+
+#Selecting columns and saving in file for Citysim
+meteonorm.to_csv(r'Ecublens_cim.cli', sep=' ', mode='a', index=False)
+
diff --git a/create_cli_from_vahid_file_v1.ipynb b/create_cli_from_vahid_file_v1.ipynb
new file mode 100644
index 0000000..00a3190
--- /dev/null
+++ b/create_cli_from_vahid_file_v1.ipynb
@@ -0,0 +1,201 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Preparing climate file for CIM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  Script prepared by Dr. Dasaraden Mauree                 \n",
+    "  Contact: EPFL                                           \n",
+    "  dasaraden.mauree@epfl.ch / dasaraden.mauree@gmail.com \n",
+    "                                                         \n",
+    "   use this script to read the standard Meteonorm file\n",
+    "   and replace some values from file obtained from \n",
+    "   Vahid Nik file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing necessary packages\n",
+    "import pandas as pd\n",
+    "import os as os\n",
+    "import re"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def swap_columns(df, c1, c2):\n",
+    "    df['temp'] = df[c1]\n",
+    "    df[c1] = df[c2]\n",
+    "    df[c2] = df['temp']\n",
+    "    df.drop(columns=['temp'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def new_col(df,df1):\n",
+    "    header_met=df.columns\n",
+    "    df1.columns=header_met\n",
+    "    \n",
+    "    df1[['dm', 'm', 'h', 'G_Dh', 'G_Bn', 'DD', 'RH', 'N']]= df1[['dm', 'm', 'h', 'G_Dh', 'G_Bn', 'DD', 'RH', 'N']].astype(int)\n",
+    "    df1['h']=df1['h']+1\n",
+    "    df1[['Ta', 'FF', 'RR']]=df1[['Ta', 'FF', 'RR']].round(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def remove(string): \n",
+    "    pattern = re.compile(r'\\s+') \n",
+    "    return re.sub(pattern, ',', string) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "extreme=\"data\\Vahid_files\"\n",
+    "path=\"data\\Meteonorm_files\"\n",
+    "out=\"data\\Merged_files\"\n",
+    "\n",
+    "filelist = os.listdir(path)\n",
+    "header_vf=['YY','m','dm','h','G_Dh','G_Bn','Ta','Ts','DD','FF','RH','HOR','N']\n",
+    "\n",
+    "for i in filelist:\n",
+    "    city=i.split('.')[0]\n",
+    "    #print(\"Reading \"+city)\n",
+    "    meteonorm=pd.read_csv(path+\"/\"+i, header=\"infer\", sep=\"\\s+\", encoding = \"ISO-8859-1\", skiprows=3)\n",
+    "    ecy=pd.read_csv(extreme+\"/\"+city+\"_ECY_multiple_13Scs_2070_2099\", header=None, names=header_vf, sep=\"\\s+\", encoding = \"ISO-8859-1\")\n",
+    "    ewy=pd.read_csv(extreme+\"/\"+city+\"_EWY_multiple_13Scs_2070_2099\", header=None, names=header_vf, sep=\"\\s+\", encoding = \"ISO-8859-1\")\n",
+    "    #print(\"Done reading \"+city)\n",
+    "    \n",
+    "    ecy=ecy.drop(['YY','Ts'], axis=1)\n",
+    "    ewy=ewy.drop(['YY','Ts'], axis=1)\n",
+    "    ecy['HOR']=meteonorm['RR']\n",
+    "    ewy['HOR']=meteonorm['RR']\n",
+    "    swap_columns(ecy,'DD','FF')\n",
+    "    swap_columns(ewy,'DD','FF')\n",
+    "    swap_columns(ecy,'m','dm')\n",
+    "    swap_columns(ewy,'m','dm')\n",
+    "    \n",
+    "    new_col(meteonorm,ecy)\n",
+    "    new_col(meteonorm,ewy)\n",
+    "    \n",
+    "    # open file in read mode \n",
+    "    fn = open(path+\"/\"+i, 'r') \n",
+    "\n",
+    "    # open other file in write mode \n",
+    "    fn1 = open(out+\"/\"+city+'_ECY.cli', 'w')\n",
+    "    fn2 = open(out+\"/\"+city+'_EWY.cli', 'w') \n",
+    "\n",
+    "    # read the content of the file line by line \n",
+    "    cont = fn.readlines() \n",
+    "    for i in range(0, 3): \n",
+    "        cont[i]=(cont[i].replace(',','.'))\n",
+    "        if(i==1):\n",
+    "            cont[i]=remove(cont[i])\n",
+    "            cont[i]=cont[i][1:-1]\n",
+    "        fn1.write(cont[i])\n",
+    "        fn2.write(cont[i])\n",
+    "    fn1.write('\\n')\n",
+    "    fn2.write('\\n')\n",
+    "\n",
+    "    # close the file \n",
+    "    fn.close() \n",
+    "    fn1.close()\n",
+    "    fn2.close()\n",
+    "    \n",
+    "    #Selecting columns and saving in file for Citysim\n",
+    "    ecy.to_csv(out+\"/\"+city+'_ECY.cli', sep=' ', mode='a', index=False)\n",
+    "    ewy.to_csv(out+\"/\"+city+'_EWY.cli', sep=' ', mode='a', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 52.517        13.3889       43            1 \n",
+      "\n",
+      "52.517,13.3889,43,1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# open file in read mode \n",
+    "fn = open(path+\"/Berlin.cli\", 'r') \n",
+    "\n",
+    "    # open other file in write mode \n",
+    "fn1 = open(\"test.cli\", 'w')\n",
+    "\n",
+    "    # read the content of the file line by line \n",
+    "cont = fn.readlines() \n",
+    "for i in range(0, 3):\n",
+    "    cont[i]=(cont[i].replace(',','.'))\n",
+    "    if(i==1):\n",
+    "        print(cont[i])\n",
+    "        cont[i]=remove(cont[i])\n",
+    "        cont[i]=cont[i][1:-1]\n",
+    "        print(cont[i])\n",
+    "    fn1.write(cont[i])\n",
+    "fn1.write('\\n')\n",
+    "\n",
+    "    # close the file \n",
+    "fn.close() \n",
+    "fn1.close()\n",
+    "    \n",
+    "    #Selecting columns and saving in file for Citysim\n",
+    "ecy.to_csv(\"test.cli\", sep=' ', mode='a', index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/epfl_geo_char.dat b/epfl_geo_char.dat
new file mode 100644
index 0000000..5e140a1
--- /dev/null
+++ b/epfl_geo_char.dat
@@ -0,0 +1,20 @@
+Geometry file for CIM
+===========================
+
+  Size of each level
+	3.
+    ==========
+
+  Number of levels where obstacles are present
+	5
+    ==========
+
+ Obstacles sizes (widobsx, widobsy, widairx, widairy)
+73	63	43	40
+73	63	43	40
+73	63	43	40
+63	53	53	50
+53	43	63	60
+
+
+END of file
diff --git a/geo_char.gh b/geo_char.gh
new file mode 100644
index 0000000..a8c7c99
Binary files /dev/null and b/geo_char.gh differ
diff --git a/read.me b/read.me
new file mode 100644
index 0000000..c89d26d
--- /dev/null
+++ b/read.me
@@ -0,0 +1,8 @@
+in this repository you can find some scripts for processing the CitySim output to run CIM.
+"canopy.dbx" is the executable for CIM and you have to download CitySim.
+The "citysim-surf_temp_buil_grd_surf_v2.py" script read the VF and TS file from CitySim and creates a file with surface temperatures for each direction, the roof and the ground.
+The "citysim-hum_surf_v2.py" is the same as the surface temperature file but read the ET file instead.
+The "create_citysim_cli_v2.py" uses the output from CIM to create a new CLI file for CitySim.
+The "read_cli_citysim_v1.py" reads the standard CLI file used by CiySim to create a file used as input for CIM.
+The "geo_char.gh" grasshopper script is run with Rhino and Grasshopper to create a file with width of the obstacles present in the scene for every 3m. You should use this output to create a file like the example given in "epfl_geo_char.dat"
+The "run-vDM.01-ECY" is a bash script that processes all the files and runs all the scripts needed to run a full CIM - CitySim simulation.
diff --git a/read_cli_citysim_v1.py b/read_cli_citysim_v1.py
new file mode 100644
index 0000000..be51c0e
--- /dev/null
+++ b/read_cli_citysim_v1.py
@@ -0,0 +1,33 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Preparing climate file for CIM
+
+#   Script prepared by Dr. Dasaraden Mauree                 
+#   Contact: EPFL                                           
+#   dasaraden.mauree@epfl.ch / dasaraden.mauree@gmail.com 
+#                                                          
+#    use this script to read the standard climate file from
+#    Meteonorm for CitySim and save new file for CIM. 
+
+# In[1]:
+
+
+#importing necessary packages
+import pandas as pd
+
+
+# In[2]:
+
+
+#Opening file and saving in array
+meteonorm=pd.read_csv("Ecublens.cli", header="infer", sep="\s+", encoding = "ISO-8859-1", skiprows=3)
+
+
+# In[3]:
+
+
+#Selecting columns and saving in file for CIM
+cim=meteonorm[['Ta','FF','DD','RH']]
+cim.to_csv(r'for_cim.cli', header=None, sep=' ', mode='a', index=False)
+
diff --git a/run-vDM.01-ECY b/run-vDM.01-ECY
new file mode 100755
index 0000000..3ccefc8
--- /dev/null
+++ b/run-vDM.01-ECY
@@ -0,0 +1,85 @@
+#!/bin/bash
+
+###############################################################
+# Prepared by D. Mauree, EPFL                                 #
+# dasaraden.mauree@epfl.ch                                    #
+###############################################################
+
+# dasaraden.mauree@epfl.ch                                    #
+###############################################################
+
+List="London"
+
+script=/home/mauree/program/scripts/cim-citysim
+inputdir=/home/mauree/program/citysim/study\ case/climate_cim_citysim/ECY/Inputs
+canopydir=/home/mauree/program/CANOPY/Program/versionDM.28
+
+for rundir in $List; 
+  do
+    echo $rundir
+
+#First CitySim simulation
+	echo --------------First CitySim simulation--------------
+
+	mkdir ECY/$rundir
+	cd ECY/$rundir
+
+#Creating local directories
+	mkdir meteonorm
+	mkdir input_cim
+	mkdir cim
+	
+	cd meteonorm
+	
+	ln -fs "$inputdir/$rundir/$rundir"_"ECY.cli" city.cli
+	ln -fs "$inputdir/$rundir/$rundir.xml" building.xml
+
+	CitySim building.xml
+
+	echo --------------Done with first CitySim simulation--------------
+	
+	echo --------------Preparing data CIM--------------
+	
+	cd ../input_cim
+
+    rm *
+	
+    ln -fs "$script/citysim-surf_temp_buil_grd_surf_v2.py" .
+    ln -fs "$script/citysim-hum_surf_v2.py" .
+    ln -fs "$script/read_cli_citysim_v1.py" .
+
+    ln -fs "../meteonorm/building_VF.out" .
+    ln -fs "../meteonorm/building_TS.out" .
+	ln -fs "../meteonorm/building_ET.out" .
+
+
+	python citysim-surf_temp_buil_grd_surf_v2.py
+	python citysim-hum_surf_v2.py
+	python read_cli_citysim_v1.py
+	
+	echo --------------CIM simulation--------------
+	
+	ln -fs "$inputdir/$rundir/$rundir"_geo_char.dat city_geo_char.dat
+	ln -fs "$inputdir/canopy.par" .
+
+	
+	$canopydir/canopy.dbx canopy.par
+
+	echo --------------Done with CIM simulation--------------
+
+	ln -fs "$script/create_citysim_cli_v2.py" .
+
+	python create_citysim_cli_v2.py
+	
+	echo --------------Second CitySim simulation--------------
+
+	cd ../cim
+
+	ln -fs "$inputdir/$rundir/$rundir.xml" building.xml
+	CitySim building.xml
+
+	echo --------------Done with second CitySim simulation--------------
+	
+	cd ../../../
+	
+  done