Repository: OceanParcels/parcels
Pull request #555 (2019-03-04 10:34:35)

Summedfields fix ccode names
This PR fixes a bug in `SummedFields` where fields with duplicate names were not parsed well in JIT mode. See also the discussion in #547 



Patch:
diff --git a/parcels/codegenerator.py b/parcels/codegenerator.py
index 3b38e0f1..ec0fbabf 100644
--- a/parcels/codegenerator.py
+++ b/parcels/codegenerator.py
@@ -416,8 +416,8 @@ class KernelGenerator(ast.NodeVisitor):
             args += [c.Pointer(c.Value("CField", "%s" % field_name))]
         for field_name, field in self.vector_field_args.items():
             fieldset = field.fieldset
-            Wname = field.W.name if field.W else 'not_defined'
-            for f in [field.U.name, field.V.name, Wname]:
+            Wname = field.W.ccode_name if field.W else 'not_defined'
+            for f in [field.U.ccode_name, field.V.ccode_name, Wname]:
                 try:
                     # Next line will break for example if field.U was created but not added to the fieldset
                     getattr(fieldset, f)
@@ -678,36 +678,36 @@ class KernelGenerator(ast.NodeVisitor):
 
     def visit_FieldNode(self, node):
         """Record intrinsic fields used in kernel"""
-        self.field_args[node.obj.name] = node.obj
+        self.field_args[node.obj.ccode_name] = node.obj
 
     def visit_SummedFieldNode(self, node):
         """Record intrinsic fields used in kernel"""
         for fld in node.obj:
-            self.field_args[fld.name] = fld
+            self.field_args[fld.ccode_name] = fld
 
     def visit_NestedFieldNode(self, node):
         """Record intrinsic fields used in kernel"""
         for fld in node.obj:
-            self.field_args[fld.name] = fld
+            self.field_args[fld.ccode_name] = fld
 
     def visit_VectorFieldNode(self, node):
         """Record intrinsic fields used in kernel"""
-        self.vector_field_args[node.obj.name] = node.obj
+        self.vector_field_args[node.obj.ccode_name] = node.obj
 
     def visit_SummedVectorFieldNode(self, node):
         """Record intrinsic fields used in kernel"""
         for fld in node.obj.U:
-            self.field_args[fld.name] = fld
+            self.field_args[fld.ccode_name] = fld
         for fld in node.obj.V:
-            self.field_args[fld.name] = fld
+            self.field_args[fld.ccode_name] = fld
         if hasattr(node.obj, 'W') and node.obj.W:
             for fld in node.obj.W:
-                self.field_args[fld.name] = fld
+                self.field_args[fld.ccode_name] = fld
 
     def visit_NestedVectorFieldNode(self, node):
         """Record intrinsic fields used in kernel"""
         for fld in node.obj:
-            self.vector_field_args[fld.name] = fld
+            self.vector_field_args[fld.ccode_name] = fld
 
     def visit_ConstNode(self, node):
         self.const_args[node.ccode] = node.obj
diff --git a/parcels/examples/tutorial_SummedFields.ipynb b/parcels/examples/tutorial_SummedFields.ipynb
index f34cd305..7461f884 100644
--- a/parcels/examples/tutorial_SummedFields.ipynb
+++ b/parcels/examples/tutorial_SummedFields.ipynb
@@ -43,11 +43,11 @@
    "outputs": [],
    "source": [
     "xdim, ydim = (10, 20)\n",
-    "U = Field('U', np.ones((ydim, xdim), dtype=np.float32),\n",
-    "          lon=np.linspace(0., 1e3, xdim, dtype=np.float32),\n",
-    "          lat=np.linspace(0., 1e3, ydim, dtype=np.float32))\n",
-    "V = Field('V', np.zeros((ydim, xdim), dtype=np.float32), grid=U.grid)\n",
-    "fieldset = FieldSet(U, V)"
+    "Uflow = Field('U', np.ones((ydim, xdim), dtype=np.float32),\n",
+    "              lon=np.linspace(0., 1e3, xdim, dtype=np.float32),\n",
+    "              lat=np.linspace(0., 1e3, ydim, dtype=np.float32))\n",
+    "Vflow = Field('V', np.zeros((ydim, xdim), dtype=np.float32), grid=Uflow.grid)\n",
+    "fieldset_flow = FieldSet(Uflow, Vflow)"
    ]
   },
   {
@@ -66,15 +66,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO: Compiled JITParticleAdvectionRK4 ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gr/T/parcels-504/95f68bdd3e2c61236f4287b88f438342.so\n",
-      "100% (10.0 of 10.0) |####################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
+      "INFO: Compiled JITParticleAdvectionRK4 ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gn/T/parcels-501/f4a8b4db3aa8d583976f0284a7a13f1c.so\n"
      ]
     },
     {
      "data": {
       "image/png": 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AX3ZT3gRsqKqnAB8CPjiIwiVJ/el362YesHuSecAewG1AAY/tHn9cdwzgFOCT3e1LgeOTZHrKlSRtq1TV5IOSc4DzgAeBr1TV6UmOBb7QHfsVsKSqfpVkNXBCVa3v5t4EHFNVvxhzzmXAMoD58+cffckll0ypgY0bN7LXXntNae5sZc9zgz3PDdvT83HHHbeqqhZPOrCqJvwC9gW+BjwB2IVeuL8O+By9AAf4M+Ci7vaNwIJR828C9ptojaOPPrqm6utf//qU585W9jw32PPcsD09Aytrkgyvqr62bl4E3FxVd1XVpi7gnwccWVXXdmM+Azy3u70eOASg2+p5HHBPH+tIkgagn6C/FViSZI9ur/144PvA45L8VjfmxcCa7vblwBnd7T8Avta98kiShmDeZAOq6toklwLXAZuB7wIX0rtyvyzJFmAD8MZuykeBf06ylt6V/GmDKFyS1J9Jgx6gqs4Fzh1z+PPd19ixvwZO3f7SJEnTwZ+MlaTGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGtdX0Cd5R5Ibk6xOsjzJbuk5L8mPkqxJcnY3Nkk+nGRtku8lOWqwLUiSJjJvsgFJDgbOBp5RVQ8m+SxwGhDgEOBpVbUlyQHdlBOBRd3XMcD53XdJ0hD0u3UzD9g9yTxgD+A24D8D/7WqtgBU1Z3d2FOAT1XPNcA+SQ6c5rolSX1KVU0+KDkHOA94EPhKVZ2e5G7g74BXAncBZ1fVj5NcAXygqr7Vzb0aeFdVrRxzzmXAMoD58+cffckll0ypgY0bN7LXXntNae5sZc9zgz3PDdvT83HHHbeqqhZPNq6frZt96V2lHw7cC/xrktcBuwK/rqrFSV4FfAw4lt6WzliPejWpqguBCwEWL15cS5cunayUca1YsYKpzp2t7HlusOe5YSZ67mfr5kXAzVV1V1VtAj4HPBdYD1zWjfk88Kzu9np6e/cjFtDb6pEkDUE/QX8rsCTJHkkCHA+sAb4A/F435oXAj7rblwOv7z59swT4ZVXdPs11S5L6NOnWTVVdm+RS4DpgM/BdelsuuwMXJ3kHsBF4czflSuAkYC3wAHDmAOqWJPVp0qAHqKpzgXPHHH4IeOk4Yws4a/tLkyRNB38yVpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMb19Z+DD7yI5C7glilO3x/4xTSWMxvY89xgz3PD9vR8WFU9YbJBO0TQb48kK/v5X9BbYs9zgz3PDTPRs1s3ktQ4g16SGtdC0F847AKGwJ7nBnueGwbe86zfo5ckTayFK3pJ0gQMeklq3KwO+iQnJPlhkrVJ3j3segYtySFJvp5kTZIbk5wz7JpmQpKdk3w3yRXDrmWmJNknyaVJftD9ev/usGsapCTv6H5Pr06yPMluw65pEJJ8LMmdSVaPOvb4JFcl+XH3fd/pXnfWBn2SnYF/BE4EngG8NskzhlvVwG0G3llVTweWAGfNgZ4BzgHWDLuIGfYPwJeq6mnAkTTcf5KDgbOBxVV1BLAzcNpwqxqYTwAnjDn2buDqqloEXN3dn1azNuiB5wBrq+onVfUwcAlwypBrGqiqur2qrutu30fvD//Bw61qsJIsAF4KXDTsWmZKkscCLwA+ClBVD1fVvcOtauDmAbsnmQfsAdw25HoGoqq+Cdwz5vApwCe7258EXjHd687moD8YWDfq/noaD73RkiwEfhu4driVDNzfA38ObBl2ITPoScBdwMe7LauLkuw57KIGpap+BvwtcCtwO/DLqvrKcKuaUfOr6nboXcwBB0z3ArM56DPOsTnxWdEkewGXAW+vql8Nu55BSXIycGdVrRp2LTNsHnAUcH5V/TZwPwP46/yOotuTPgU4HDgI2DPJ64ZbVVtmc9CvBw4ZdX8Bjf51b7Qku9AL+Yur6nPDrmfAnge8PMlP6W3N/V6STw+3pBmxHlhfVSN/W7uUXvC36kXAzVV1V1VtAj4HPHfINc2knyc5EKD7fud0LzCbg/47wKIkhyd5DL03by4fck0DlST09m3XVNXfDbueQauq91TVgqpaSO/X92tV1fyVXlXdAaxL8tTu0PHA94dY0qDdCixJskf3e/x4Gn7zeRyXA2d0t88A/m26F5g33SecKVW1OckfA1+m9y79x6rqxiGXNWjPA/4Q+D9Jru+OvbeqrhxiTRqMPwEu7i5ifgKcOeR6Bqaqrk1yKXAdvU+WfZdG/ymEJMuBpcD+SdYD5wIfAD6b5E30XvROnfZ1/ScQJKlts3nrRpLUB4Nekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNe7/AuHu7lXMa6xGAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x105845050>"
+       "<matplotlib.figure.Figure at 0x11041fa90>"
       ]
      },
      "metadata": {},
@@ -82,10 +81,10 @@
     }
    ],
    "source": [
-    "pset = ParticleSet(fieldset, pclass=JITParticle, lon=[0], lat=[900])\n",
-    "output_file = pset.ParticleFile(name='FieldListParticle_adv.nc', outputdt=1)\n",
+    "pset = ParticleSet(fieldset_flow, pclass=JITParticle, lon=[0], lat=[900])\n",
+    "output_file = pset.ParticleFile(name='SummedFieldParticle_flow.nc', outputdt=1)\n",
     "pset.execute(AdvectionRK4, runtime=10, dt=1, output_file=output_file)\n",
-    "plotTrajectoriesFile('FieldListParticle_adv.nc');"
+    "plotTrajectoriesFile('SummedFieldParticle_flow.nc');"
    ]
   },
   {
@@ -99,9 +98,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, let's define another set of velocities (`Ustokes, Vstokes`) on a different, higher-resolution grid. This flow is southwestward at -0.2 m/s in each direction.\n",
-    "\n",
-    "Note that it is **very important to specify the `fieldtype` of the fields, as Parcels will otherwise not perform the unit conversion correctly**."
+    "Now, let's define another set of velocities (`Ustokes, Vstokes`) on a different, higher-resolution grid. This flow is southward at -0.2 m/s."
    ]
   },
   {
@@ -111,55 +108,90 @@
    "outputs": [],
    "source": [
     "gf = 10  # factor by which the resolution of this grid is higher than of the original one.\n",
-    "Ustokes = Field('Ustokes', -0.2*np.ones((ydim*gf, xdim*gf), dtype=np.float32),\n",
+    "Ustokes = Field('U', np.zeros((ydim*gf, xdim*gf), dtype=np.float32),\n",
     "                lon=np.linspace(0., 1e3, xdim*gf, dtype=np.float32),\n",
-    "                lat=np.linspace(0., 1e3, ydim*gf, dtype=np.float32),\n",
-    "                fieldtype='U')\n",
-    "Vstokes = Field('Vstokes', -0.2*np.ones((ydim*gf, xdim*gf), dtype=np.float32), grid=Ustokes.grid, \n",
-    "                fieldtype='V')"
+    "                lat=np.linspace(0., 1e3, ydim*gf, dtype=np.float32))\n",
+    "Vstokes = Field('V', -0.2*np.ones((ydim*gf, xdim*gf), dtype=np.float32), grid=Ustokes.grid)\n",
+    "fieldset_stokes=FieldSet(Ustokes, Vstokes)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now comes the trick of the `SummedFields`. We can simply define a new `FieldSet` with a summation of different `Fields`, as in `U=U+Ustokes`."
+    "We run a particle in this `FieldSet` and also plot its trajectory"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: Compiled JITParticleAdvectionRK4 ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gn/T/parcels-501/4d88daba7f9f4523f96fbc7c30b967ee.so\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1131bf2d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "fieldset = FieldSet(U=U+Ustokes, V=V+Vstokes)"
+    "pset = ParticleSet(fieldset_stokes, pclass=JITParticle, lon=[0], lat=[900])\n",
+    "output_file = pset.ParticleFile(name='SummedFieldParticle_stokes.nc', outputdt=1)\n",
+    "pset.execute(AdvectionRK4, runtime=10, dt=1, output_file=output_file)\n",
+    "plotTrajectoriesFile('SummedFieldParticle_stokes.nc');"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And if we then run the particle again and plot its trajectory, we see that it moves slightly southward too (and less far eastward), because of the Stokes drift."
+    "Now comes the trick of the `SummedFields`. We can simply define a new `FieldSet` with a summation of different `Fields`, as in `U=fieldset_flow.U+fieldset_stokes.U`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "fieldset_sum = FieldSet(U=fieldset_flow.U+fieldset_stokes.U, V=fieldset_flow.V+fieldset_stokes.V)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And if we then run the particle again and plot its trajectory, we see that it moves southeastward!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO: Compiled JITParticleAdvectionRK4 ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gr/T/parcels-504/8ba0de8db83dec9865083d5c33acfdb2.so\n",
-      "100% (10.0 of 10.0) |####################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
+      "INFO: Compiled JITParticleAdvectionRK4 ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gn/T/parcels-501/b144599600769c9fe6b24339a7949645.so\n"
      ]
     },
     {
      "data": {
-      "image/png": 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cm5TAA+OWc8fLn7H9m+8jHZpzrgQIawgPMxsKDM20+YFgyVx2HtAwm3auyPS6UxZlngaeDieukuac6gm81a8NbyzYxt+nruXSEan84dJG3NGmHjEx0XOz2zlXvPg3s4uYmBhxR5t6fDioAxfXq8KfJ63mhufnsfHrtEiH5pwrpjxRFFG1E8vxap+LGXbDBWzck8YVT8zmmZkbOXbiZKRDc84VM54oijBJXHtRbaYP6kj3Jkn834fruObpuazc+W2kQ3POFSOeKIqB6hVK88ytF/HcbcnsSTvCNc/M5e8frPVBBp1zBcITRTFyWdMz+GhQR3pfVItnP9nEFU/M5vOtPsigc+7UeKIoZiqVi+cf113AG79qzdETJ7n+uXn86T8rSTtyPNKhOeeKKE8UxdQlDasxbVAHftnubF6fv40ew2Yxc93XkQ7LOVcE+VSoxVi5UnH86aomXNn8TIaMX06fVz6n47nVaFarMp0b1/DpV51zYfErihIguW4i7w+4hOuTazFr/V6enrmRm0bOY5Hfv3DOhcETRQlROi6WetUSfpx+9dgJY8iEFXx90AcZdM7lzBNFCZJSvyql4mKIFcTFiG37DtF12Cze/twHGXTOZc/vUZQg6XN1z9+8j5T6ValSvhRDxi/nD+OXM3HZLv72i2acVbVcpMN0zkUZv6IoYZLrJtK/cwOS6yZydrXyjL07hUd6NWXp9gNcOiKVl+Zs4cRJv7pwzv2XJ4oSLiZG3JZSl2mDOpBSvwoPT17Ndc99yoavvot0aM65KOGJwgFQs3JZXr7rYkbc2IKtew9x5ZNzePLjDRw97oMMOlfShZUoJA2StErSSkljJJWR1EXS4mDbKElxQdlESe9KWi7pM0lNs2nzVUlbJC0NlhbBdkl6UtLGoI2LCu50XU4k0evCWkwf3JFLm57BsOnrufrpOSzfcSDSoTnnIijXRCGpFjAAaGlmTYFY4BZgFKHZ55oC24A7gyoPAUvNrDmhObOfyKH5B8ysRbAsDbZdTmjio4ZAP+DZvJ+WOxXVEkrz1M0X8sIdLdn//VF6PTOXx6as8UEGnSuhwu16igPKBlcN5YBDwBEzWx/snw70DtabAB8DmNlaoJ6kpDzEdA3wmoXMBypLOjMP9V0B6d4kiemDO3LjxXV4PnUzl41IZf7mEjt9uXMlVjhzZu8E/gl8AewGvgXeBuIltQyKXQfUCdaXAdcCSGoF1AVqZ9P8o0H30nBJpYNttYDtGcrsCLa5CKhYJp7Hrm3Om31bc9LgppHzGbXqCN/9cCzSoTnnThPl9kUrSYnAeOBG4ADwDjAO2AT8AygNTAOuNLMLJVUk1N10IbACaAz0NbNlmdo9E/gSKAWMBDaZ2V8lvQ88ZmZzgnIfA38ws0WZ6vcj1DVFUlJS8tixY/P1BqSlpZGQkJCvuoUpGuM6csKYsOEo07YeI7FMDHc0KUWLGtHzVZxofM/A48orjytvTiWuzp07LzKzlrkWNLMcF+B64KUMr+8A/p2pTA/g7SzqCtgKVMzlGJ2AycH688DNGfatA87MqX5ycrLl18yZM/NdtzBFa1xmZi+++5F1H/aJ1R0y2QaOWWz70o5EOiQzi973zOPKG48rb04lLmCh5ZIDzCysexRfACmSykkS0BVYI6kGQNBlNAR4LnhdWVKpoG5fINXMDmZuNP2+Q9BmL2BlsGsicEfw9FMK8K2Z7Q4jTneanFM5lsm/bc/Arg15f8Vuug2bxcRlu3wYEOeKqXDuUSwg1NW0mFBXUgyhrqIHJK0BlgOTzGxGUOU8YJWktYSeYBqY3pakKZJqBi9HS1oRtFkNeCTYPgXYDGwEXgB+c0pn6ApFqbgYBnU/l0m/vYQ6iWUZMGYJd7+2iC+/9UEGnStuwupgNrOhwNBMmx8Ilsxl5xF6tDWrdq7IsN4lmzIG9A8nLhd5jc+oyITftOOVuVv457R1dB82i4euPI+bLq5D6GLROVfU+Tez3SmLjRF929fnw/s70LRWJR6csIJbXljAtn2HIh2ac64AeKJwBaZu1fK8eXdrHru2GSt3fsulI1J5cfZmH2TQuSLOE4UrUJK4udVZTB/ckUsaVOOR99dw7bOfsu5LH2TQuaIqeh6Cd8XKGZXK8MIdLZm0fDd/nriKnk/N5hcX1qJ2YjnaNajm83U7V4R4onCFRhJXX1CTSxpUY+DYJby9cAcAT8/cyJi7UzxZOFdEeNeTK3RVypcipX5V0p+BOnr8JI9PXcPhoz7IoHNFgScKd1qk1K9K6fjQfN2xMeLzrfu5dEQqn27aG+nQnHO58K4nd1pknq/72ImT/HH8cm55YQE3tzqLB69oTMUy8ZEO0zmXBU8U7rRJrpv4k/sSUwd2YMRH63lh9mZmrP2KR3s1o1uTvIxI75w7HbzryUVM2VKxPHjFebzXvx2J5UrR97WF/HbMEvalHYl0aM65DDxRuIhrXrsyE++7hMHdz+WDlaFBBv+zdKcPMuhclPBE4aJCqbgYBnRtyPsD2lO3ankGjl3Kr0YtZNeBw5EOzbkSzxOFiyrnJlVg/L1t+d+eTZi3aR89hqcyesE2TvowIM5FjCcKF3ViY8SvLjmbD+/vwAV1KvE/767k5hfms2WvDzLoXCR4onBR66yq5XjjV635R+/mrN59kMtGpPL8rE0cP3Ey0qE5V6J4onBRTRI3XFyHjwZ3pMO51Xls6lquffZT1uz+2aSJzrlCElaikDRI0ipJKyWNkVRGUhdJi4NtoyTFBWUTJb0rabmkzyQ1zabN0ZLWBfVflhQfbO8k6VtJS4PlTwV3uq6oSqpYhpG3J/PMLRex68BhrnpqDsOmrePIcR8GxLnClmuikFQLGAC0NLOmQCxwCzAKuCnYtg24M6jyELDUzJoDdwBPZNP0aKAx0AwoS2h+7XSzzaxFsPw176fliiNJXNn8TKYP6sjVF9TkyRkb6fnkHBZ/sT/SoTlXrIXb9RQHlA2uGsoBh4AjZrY+2D8d6B2sNwE+BjCztUA9ST/7uq2ZTbEA8BlQO/+n4UqSxPKlGHZjC17pczGHjhyn97Of8tdJqzly3J+Mcq4wKJwvNUkaCDwKHAamAbcBW4HeZrZQ0hNAFzNrJulvQBkzGyypFfAp0NrMFmXTdjywABhoZrMldQLGAzuAXcDvzWxVFvX6Af0AkpKSkseOHZu3Mw+kpaWRkJCQr7qFKVrjguiK7fBxY9z6o3z8xXGqljZ+2aws51eLjXRYPxFN71dGHlfeFMe4OnfuvMjMWuZa0MxyXIBEYAZQHYgH3iOUKNoAswldDTwCLAnKVwReAZYCrwOfAxfk0P4LwIgMrysCCcH6FcCG3GJMTk62/Jo5c2a+6xamaI3LLDpjW7B5n7X6y/tWd8hke+CdpXbg+6ORDulH0fh+mXlceVUc4wIWWi6fr2YW1qCA3YAtZrYHQNIEoK2ZvQG0D7b1AM4NEs9BoE+wXcCWYPkZSUODBHRPhsR1MMP6FEn/llTNzHw8apetVmdX4eF2ZVly7ExGpm7mk3V7eLhXUy49/4xIh+ZckRfOPYovgBRJ5YIP/q7AGkk1ACSVBoYAzwWvK0sqFdTtC6Rm/PBPJ6kvcClws5mdzLD9jOA4BF1XMcC+/J6gKzlKxYohlzXmvd+0o2pCae55fRH9Ry/m4zVf8czMjSza5je9ncuPXK8ozGyBpHHAYuA4sAQYCTwiqSehD/JnzWxGUOU84DVJJ4DVwK/S25I0BehrZrsIJZZtwLwgL0yw0BNO1wH3SjpO6J7ITcElknNhaVa7EhPva8fI1M0Mn76e91fsRkDp+BhG9/UpWJ3Lq7DmozCzocDQTJsfCJbMZecBDbNp54oM61ke28yeBp4OJy7nshMfG0P/zg3Yd+gIL8/ZigE/HDvJtNVfeqJwLo/8m9muWLuyWU3KxMf8OF/3qLlbeX3eVh9k0Lk88BnuXLGWcQrW+tXK8+ZnX/C//1nFxGW7eLx3c86pHn2POzoXbTxRuGIv4xSslzU9g3GLdvDw5NVc/sRs7u/WkH7t6xMX6xfXzmXHfztciSKJ61vW4aPfdaRLoxr844N19Pr3XFbt+jbSoTkXtTxRuBKpRoUyPHd7Ms/eehFffnuEq5+ey/99uJYfjvkgg85l5onClWiXNzuTjwZ3oFeLWjwzcxNXPDmbhVu/iXRYzkUVTxSuxKtcrhT/uuECRv2yFUeOneT65+fx54mrOHTkeKRDcy4qeKJwLtDx3OpMG9SBO9vUY9S8rfQYnkrq+j2RDsu5iPNE4VwG5UvH8eerz+ede9pQOj6GO17+jN+/s4wD3x+NdGjORYwnCuey0LJeFaYMaE//zufw7pKddBuWytQVuyMdlnMR4YnCuWyUiY/lgUsbM/G+diRVLM29oxfz69cX8fXBHyIdmnOnlScK53Jxfs1KvNe/HX+4rBEz1n1Nt2GzeGfhdnysSldSeKJwLgzxsTH8plMDpg5sT6MzKvDAuOXc8fJnbP/m+0iH5lyh80ThXB6cUz2Bt/q14eFrzmfxtv1cOiKVV+du4YQPMuiKMU8UzuVRTIy4vU09PhzUgYvrVeHPk1Zzw/Pz2Pj1d5EOzblC4YnCuXyqnViOV/tczLAbLmDTnjSueGIOT8/YwLETJ3Ov7FwRElaikDRI0ipJKyWNkVRGUhdJi4NtoyTFBWUTJb0rabmkzyQ1zabNsyUtkLRB0lvp06dKKh283hjsr1dQJ+tcQZPEtRfVZvqgjnRvksQ/p63n6qfnsnKnDzLoio9cE4WkWsAAoKWZNQVigVuAUYSmKW1KaErTO4MqDwFLzaw5cAfwRDZN/x0YbmYNgf38d8rUXwH7zawBMDwo51xUq16hNM/cehHP3ZbM3rQjXPPMXB6fupZ5m/YyedNRn6/bFWnhdj3FAWWDq4ZywCHgiJmtD/ZPB3oH602AjwHMbC1QT1JSxsYUmiS7CzAu2DQK6BWsXxO8JtjfNSjvXNS7rOkZfDSoI70vqsVzszZxywsLGL/hGLe+ON+ThSuyFM6z4JIGAo8Ch4FpwG3AVqC3mS2U9ATQxcyaSfobUMbMBktqBXwKtDazRRnaqwbMD64akFQHmGpmTSWtBC4zsx3Bvk1B/b2ZYuoH9ANISkpKHjt2bL7egLS0NBISom+Ws2iNC6I3tmiL66UVR5i9878DC15zTjy/aFgqghH9VLS9X+k8rrw5lbg6d+68yMxa5lYu1xnuJCUS+iv/bOAA8A5wK3ATMFxSaULJI/034nHgCUlLgRXAkgz7fmw2i0NZGPv+u8FsJDASoGXLltapU6fcTiVLn3zyCfmtW5iiNS6I3tiiLa4KZ+/n8xfm88Px0M3tuV+KXh2a0LlRjQhHFhJt71c6jytvTkdc4XQ9dQO2mNkeMzsGTADamtk8M2tvZq2AVGADgJkdNLM+ZtaC0D2K6sCWTG3uBSqn3wAHagO7gvUdQB2AYH8lwCcIcEVOct1ERt+dwnUN4/nbL5pRuXwp+rzyOYPfWsr+Qz7IoCs6wkkUXwApksoF9wq6Amsk1YDQU0rAEOC54HXl9CeYgL5AqpkdzNighfq7ZgLXBZvuBP4TrE/kvzfGrwNmmI+V4Iqo5LqJ9DynFLe0Pov3B1zCgC4NmLhsF92GzWLy8l0+DIgrEnJNFGa2gNBN5cWEupJiCHX5PCBpDbAcmGRmM4Iq5wGrJK0FLgcGprclaYqkmsHLIcBgSRuBqsBLwfaXgKrB9sHAH0/tFJ2LDqXjYhncoxET77uEmpXLct+bS7jn9UV85YMMuiiX6z0KADMbCgzNtPmBYMlcdh7QMJt2rsiwvhlolUWZH4Drw4nLuaKoSc2KvPubtrw0ZwvDpq+n27BZ/L8rz+OGlnXwB/xcNPJvZjsXAXGxMdzT8Rw+uL8D551ZkSHjV3DbSwv4Yp8PMuiijycK5yLo7GrlGXt3Co/0asqy7d9y6YhUXprjgwy66OKJwrkIi4kRt6XUZdqgDqTUr8LDk1fT+9lPWf+VDzLoooMnCueiRM3KZXn5rosZcWMLtu07xJUOy4AhAAAZ5ElEQVRPzubJjzdw9LgPMugiyxOFc1FEEr0urMX0wR25rOmZDJu+nqufnsOy7QciHZorwTxROBeFqiWU5qmbL+SFO1qy//uj/OLfc3lsyhoOHz0R6dBcCeSJwrko1r1JEtMHd+TGi+vwfOpmLn8ilfmb90U6LFfCeKJwLspVLBPPY9c2582+rTlpcNPI+Tz07goO/nAs0qG5EsIThXNFRNsG1fjw/g7c3f5sxn72BT2GpTJj7VeRDsuVAJ4onCtCypaK5X+ubMKE37SjUtl4fvnqQgaOXcK+tCORDs0VY54onCuCWtSpzKTfXsL93RoyZcVuug9PZeIyH2TQFQ5PFM4VUaXiYri/27lM/m176lQpx4AxS7j7tYV8+a0PMugKVliDAjrnolejMyow4d62vDJ3C/+cto7uw2Zxa8pZVCgTR0r9aiTXTYx0iK6I80ThXDEQGyP6tq9P9yZJ9B+9mOdmbQagdNxG3rw7xZOFOyXe9eRcMVK3ankub3bGj/MJHzl+kqc+3uCDDLpT4onCuWImpX41SsfHECuIEXyyfg/X/nsu6770QQZd/oSVKCQNkrRK0kpJYySVkdRF0uJg26j0+a8lVZI0SdKyoE6fLNqrIGlphmWvpBHBvrsk7cmwr2/BnrJzxVty3URG901hcI9GvHNPG566+UJ27D9Mz6dmM3z6eh9k0OVZrvcoJNUCBgBNzOywpLeBW4C/AF3NbL2kvxKa5/oloD+w2syuklQdWCdptJn9OJu8mX0HtMhwjEXAhAyHfcvM7iuA83OuREqum/jjfYlkoF2Davx10iqe+HgDU1fu5h/XXRDZAF2REm7XUxxQNrhqKAccAo6Y2fpg/3Sgd7BuQAWF5nRMAL4BjmfXsKSGQA1gdt7Dd86Fo0r5Uoy46UJevqsl3/1wnGv/PZcxa4/w/dFsfzWd+5HC+YKOpIHAo8BhYBpwG7AV6G1mCyU9AXQxs2aSKgATgcZABeBGM3s/h7b/BFQ0s98Hr+8CHgP2AOuBQWa2PYt6/YB+AElJScljx44N95x/Ii0tjYSEhHzVLUzRGhdEb2weV3gOHzfeXneUmduPU72s6NO0NE2qxkY6rB9F2/uVrjjG1blz50Vm1jLXgmaW4wIkAjOA6kA88B6hRNGG0FXAZ8AjwJKg/HXAcEBAA2ALoUSQXfurgeQMr6sCpYP1XwMzcosxOTnZ8mvmzJn5rluYojUus+iNzePKm2fHf2Qd/zHD6g6ZbEPGLbMD3x+NdEhmFr3vV3GMC1houXy+mllYXU/dgC1mtsfMjhG6l9DWzOaZWXszawWkAhuC8n2ACUEcG4NE0TirhiVdAMSZ2aIMiWufmaUPXPMCoS5W51wBa1wllg/u78A9Hevz9sLt9Bg+i+mrfZBB93PhJIovgBRJ5YL7Dl2BNZJqAEgqDQwBnstQvmuwLwloBGzOpu2bgTEZN0g6M8PLq4E14Z2Kcy6vysTH8uDl5/Fe/3YklivF3a8t5L43F7PXBxl0GeSaKMxsATAOWAysCOqMBB6QtAZYDkwysxlBlYeBtpJWAB8DQ8xsL4CkpZmav4FMiQIYEDxWu4zQ01Z35efEnHPha167MhPvu4TfdT+Xaau+ovuwWby3ZKcPMuiAMIfwMLOhwNBMmx8IlsxldwE9smmnRabX9bMo8yDwYDhxOecKTqm4GH7btSGXNT2DP4xfzv1vLeU/S3fy6C+aUbNy2UiH5yLIv5ntnPuJhkkVGPfrtvypZxPmb/6GHsNTeX3+Nk76MCAllicK59zPxMaIX15yNtMGdaBFncr873sruemF+WzZeyjSobkI8EThnMtWnSrleP1XrfhH7+as2X2Qy0ak8tysTRw/4cOAlCSeKJxzOZLEDRfX4aPBHel4bnUen7qWXv+ey+pdByMdmjtNPFE458KSVLEMz9+ezDO3XMSX3/7A1U/P4V/T1nHk+IlIh+YKmScK51zYJHFl8zOZPqgjV7eoyVMzNnLlk3NYtG1/pENzhcgThXMuzxLLl2LYDS14tc/FHD56guue+5S/TFrFoSM+yGBx5InCOZdvnRrV4MNBHbg9pS6vzN1Kp3/O5IFxy/wKo5jxROGcOyUJpeP46zVNeeSapuz97ijvLNzBDc/NI3XdnkiH5gqIJwrnXIH49odjKJis+4QZ945exAcrv4xsUK5AeKJwzhWIlPpVKRUXmqu7VFwM1SuU5tdvLOI3oxfx9Xc/RDo8dwrCGuvJOedykz5X9/zN+0ipX5XmtSsxMnUzT3y0gbkb9/Gnnk249qJaKP2ywxUZfkXhnCswyXUT6d+5Acl1E4mPjaF/5wZMGdieBjUS+N07y7jzlc/Zsf/7SIfp8sgThXOuUDWokcA797ThL1efz8Kt33Dp8FRem7fVBxksQjxROOcKXUyMuLNtPT68vwMX1U3kT/9ZxY0j57FpT1qkQ3NhCCtRSBoUTCa0UtIYSWUkdZG0ONg2SlJcULaSpEmSlgV1+mTT5ieS1klaGiw/zpgn6S1JGyUtkFSvoE7WORdZdaqU47VftuKf11/A+q/SuPyJ2fz7k40c80EGo1quiUJSLUIzzbU0s6ZALHALMAq4Kdi2DbgzqNIfWG1mFwCdgH9JKpVN87eaWYtg+TrY9itgv5k1AIYDf8/fqTnnopEkrkuuzfTBHejauAb/+GAdvZ6Zy8qd30Y6NJeNcLue4oCywVVDOeAQcMTM1gf7pwO9g3UDKgTzaycA3wB5+V7/NYSSEISmYO0qf0zCuWKnRoUyPHtbMs/eehFfHTzCNc/M5f8+XMvRE37vItoonDlxJQ0EHgUOA9OA24CtQG8zWyjpCaCLmTWTVAGYCDQGKgA3mtn7WbT5CVAVOAGMBx4xM5O0ErjMzHYE5TYBrdPn3c5Qvx/QDyApKSl57Nix+Th9SEtLIyEhIV91C1O0xgXRG5vHlTfRFFfaUWPsuqPM2XmcpLJG3+ZlaZgYG+mwfiKa3q+MTiWuzp07LzKzlrkWNLMcFyARmAFUB+KB9wglijbAbOAz4BFgSVD+OkJdRgIaAFuAilm0Wyv4WYFQ8rkjeL0KqJ2h3Cagak4xJicnW37NnDkz33ULU7TGZRa9sXlceRONcc1a97VdNPR9q/fHyfan91bYdz8ci3RIP4rG98vs1OICFlouOcDMwup66gZsMbM9ZnYMmAC0NbN5ZtbezFoBqcCGoHwfYEIQx8YgUTTOIkHtDH5+B7wJtAp27QDqAARdXZUIdV8554q5DudW59FLynJnm3q8Nn8blw5PZdZ6HzMq0sJJFF8AKZLKBfcKugJrMj6lBAwBnstQvmuwLwloBGzO2KCkOEnVgvV4oCewMtg9kf/eGL8OmBFkPudcCVAmTvz56vN55542lImP4c6XP+N3by/jwPdHIx1aiZVrojCzBYRuKi8GVgR1RgIPSFoDLAcmmdmMoMrDQFtJK4CPgSEW3F+QtDQoUxr4UNJyYCmwE3gh2PcSUFXSRmAw8MdTPkvnXJHTsl4V3h/Qnvs6N+C9pTvpNiyVqSt2RzqsEimssZ7MbCgwNNPmB4Ilc9ldQI9s2mkR/DwEJGdT5gfg+nDics4Vb2XiY/n9pY24vNkZ/GHccu4dvZjLzj+Dv15zPjUqlol0eCWGfzPbORf1zq9Zif/0b8eQyxozY93XdBs2i3cWbsd7pU8PTxTOuSIhLjaGezudw9SB7Wl0RgUeGLecO17+jO3f+CCDhc0ThXOuSDmnegJv9WvDw9ecz+Jt+7l0RCqvzt3Cwq3f8MzMjT4NayHw+Sicc0VOTIy4vU09upyXxEMTVvDnSauRQl/eKhUXw+i+KSTXTYx0mMWGX1E454qsWpXL8mqfi7mi2RmYwUmDI8dO8ummvblXdmHzROGcK9Ik8atL6lM6LvRxZsC4RTt8kMEC5InCOVfkJddN5M27U3jg0kb84bJGfH/0BNc8M5fHp67lh2MnIh1ekef3KJxzxUJy3cQf70vc2qouf5uyhudmbWLaqi95vHdzWp1dJcIRFl1+ReGcK3YqlYvn79c1Z3Tf1hw7eZIbnp/H/763ku9+OBbp0IokTxTOuWKrXYNqfHh/B37Z7mzeWBAaZHDmuq9zr+h+whOFc65YK1cqjj9d1YTx97alfOk4+rzyOYPfWsr+Qz7IYLg8UTjnSoSLzkpk8oBLGNClAROX7aLbsFlMXr7LhwEJgycK51yJUToulsE9GjHpt5dQK7Es9725hHteX8RXB3+IdGhRzROFc67EOe/Miky4ty0PXdGYWev30G3YLN76/Au/usiGJwrnXIkUFxtDvw7n8OH9HWhyZkWGjF/BrS8u4It9PshgZp4onHMlWr1q5RlzdwqP/qIpy3d8y6UjUnlpzhZOnPSri3RhJQpJgyStkrRS0hhJZSR1kbQ42DYqmN8aSZUkTZK0LKjTJ4v2ykl6X9LaoMzjGfbdJWmPpKXB0rfgTtc5534uJkbc2rou0wd3oM05VXl48mp6P/sp67/6LtKhRYVcE4WkWsAAoKWZNQVigVuAUcBNwbZt/Hee6/7AajO7AOgE/EtSqSya/qeZNQYuBNpJujzDvrfMrEWwvJjPc3POuTw5s1JZXrqzJU/c1IIvvvmeK5+czRMfbeDo8ZORDi2iwu16igPKBlcN5YBDwBEzWx/snw70DtYNqCBJQALwDXA8Y2Nm9r2ZzQzWjxKaj7v2qZyIc84VBElc06IW0wd14PKmZzL8o/Vc/fQcNn9bcseMUjh3+SUNBB4FDgPTgNuArUBvM1so6Qmgi5k1k1QBmAg0BioAN5rZ+zm0XZlQouhmZpsl3QU8BuwB1gODzGx7FvX6Af0AkpKSkseOHRv2SWeUlpZGQkJCvuoWpmiNC6I3No8rbzyu8Cz5+jivrTrKgSMnubReKX7RMJ7SsYp0WD86lferc+fOi8ysZa4FzSzHBUgEZgDVgXjgPUKJog0wG/gMeARYEpS/DhhOaA6RBsAWoGI2bccBU4H7M2yrCpQO1n8NzMgtxuTkZMuvmTNn5rtuYYrWuMyiNzaPK288rvB9e/io3fnUB1Z3yGTr+I8Z9unGvZEO6Uen8n4BCy2Xz1czC6vrqRuwxcz2mNkxYALQ1szmmVl7M2sFpAIbgvJ9gAlBHBuDRNE4m7ZHAhvMbESGxLXPzI4EL18AksOI0TnnCk3FMvHc1bQ0b97dGgNufmE+D727goMlZJDBcIYZ/wJIkVSOUNdTV2ChpBpm9rWk0sAQQl1T6eW7ArMlJQGNgM2ZG5X0CFAJ6Jtp+5lmtjt4eTWwJu+n5ZxzBa/tOdX4YGAHhn+0nhdnb2bGmq/p064ex08aKfWrFtvpV3NNFGa2QNI4QvcRjgNLCF0JPCKpJ6Eb4s+a2YygysPAq5JWEOp+GmJmewEkLTWzFpJqA/8DrAUWh+5787SFnnAaIOnq4FjfAHcV2Nk659wpKlsqloeuOI8rmp3JgDeX8NjUtQCUjovhzbuL51zdYU1cZGZDgaGZNj8QLJnL7gJ6ZNNOi+DnDkJJJKsyDwIPhhOXc85FSos6lbmuZW2GT1+PAUeOn+SVuVu46KzKBH/8Fhv+zWznnMundg2qUTo+hhiBBJOX76bvqIXs/vZwpEMrUD4VqnPO5VNy3URG901h/uZ9tDq7Csu2H+Cf09bRY1gqD15xHjddXIeYmKJ/deGJwjnnTkHGubovrleFHk3O4I8TlvPQuyuYuGwnj1/bnHrVykc4ylPjXU/OOVeAzqpajtF9W/P4tc1YtfMgl45IZWTqJo6fKLrDgHiicM65AiaJm1qdxfTBHWnfsDp/m7KW3s9+ytovD0Y6tHzxROGcc4XkjEpleOGOZJ66+UJ27D9MzyfnMGz6eo4cL1rjRnmicM65QiSJqy6oyfTBHbnqgpo8+fEGrnpqDku+2B/p0MLmicI5506DKuVLMfzGFrxy18V898Nxrn32Ux6evJrvjx7PvXKEeaJwzrnTqHPjGkwb1IFbW5/FS3O2cOmIVOZu3BvpsHLkicI5506zCmXieaRXM97ql0JcTAy3vriAP45fzreHo3OQQU8UzjkXIa3rV2XqwPbc07E+by/cTvdhs5i26stIh/Uzniiccy6CysTH8uDl5/Fe/3ZUKV+Kfq8v4r43F7M37UjulU8TTxTOORcFmteuzKTfXsLve5zLtFVf0W3YLN5dsiN9QreI8kThnHNRIj42hvu6NGTKwEuoX608g95axi9f/ZxdByI7yKAnCuecizINalTgnV+3ZehVTZi/+Ru6D5vF6/O3cfJkZK4uPFE451wUio0RfdqdzbRBHbjwrET+972V3DRyPpv3pJ32WMJKFJIGSVolaaWkMZLKSOoiaXGwbZSkuKBsJUmTJC0L6vTJps1kSSskbZT0pIKZPiRVkTRd0obgZ/GbLso558JUp0o5Xv9VK/5xXXPWfnmQy5+YzXOzTu8gg7kmCkm1gAFASzNrCsQCtwCjgJuCbduAO4Mq/YHVZnYB0An4l6RSWTT9LNAPaBgslwXb/wh8bGYNgY+D1845V2JJ4oaWdfhocEc6NarO41PX0uvfcxm/aAeTNx1l0bbCHQ4k3K6nOKBscNVQDjgEHDGz9cH+6UDvYN2ACsEVQgKhea9/8h11SWcCFc1snoVu6b8G9Ap2X0MoCRH87IVzzjlqVCzD87e35NlbL2L7N4f53TvLGLfhGLe+OL9Qk4XCefRK0kDgUeAwMA24DdgK9DazhZKeALqYWTNJFYCJQGOgAnCjmb2fqb2WwONm1i143R4YYmY9JR0ws8oZyu43s591P0nqR+iKhKSkpOSxY8fm/eyBtLQ0EhIS8lW3MEVrXBC9sXlceeNx5U20xTV+/VEmbQ59kzsGuLZhPD3PyarzJnudO3deZGYtcyuX6wx3wT2Ca4CzgQPAO8CtwE3AcEmlCSWP9KuGS4GlQBfgHGC6pNlmlnEg9qzmBszT7XwzGwmMBGjZsqV16tQpL9V/9Mknn5DfuoUpWuOC6I3N48objytvoi2uCmfvZ/qL8zl67CSl4mO4udvFP860V9DC6XrqBmwxsz1mdgyYALQNuo3am1krIBXYEJTvA0ywkI3AFkJXFxntAGpneF0b2BWsfxV0TaV3UX2dnxNzzrniLH2+7msbxjO6b0qhJQkIL1F8AaRIKhfcd+gKrJFUAyC4ohgCPJehfNdgXxLQCNicsUEz2w18JyklaPMO4D/B7on898b4nRm2O+ecyyC5biI9zylVqEkCwkgUZrYAGAcsBlYEdUYCD0haAywHJpnZjKDKw0BbSSsIPbU0xMz2AkhamqHpe4EXgY3AJmBqsP1xoLukDUD34LVzzrkIyfUeBYCZDQWGZtr8QLBkLrsL6JFNOy0yrC8EmmZRZh/BFYlzzrnI829mO+ecy5EnCueccznyROGccy5Hniicc87lKKxvZkc7SXsIjTeVH9WAaJzZPFrjguiNzePKG48rb4pjXHXNrHpuhYpFojgVkhaG8xX20y1a44Lojc3jyhuPK29Kclze9eSccy5Hniicc87lyBNFMLBgFIrWuCB6Y/O48sbjypsSG1eJv0fhnHMuZ35F4ZxzLkeeKJxzzuWoRCcKSZdJWidpo6SomJtb0suSvpa0MtKxZCSpjqSZktZIWhXMehhxkspI+kzSsiCuv0Q6powkxUpaImlypGNJJ2mrpBWSlkpaGOl40kmqLGmcpLXB/7M2URBTo+B9Sl8OSro/0nEBSBoU/J9fKWmMpDKFdqySeo9CUiywntBQ5juAz4GbzWx1hOPqAKQBr5nZz0bXjZRgEqkzzWxxMN3tIqBXFLxfAsqbWZqkeGAOMNDM5kcyrnSSBgMtCc0R3zPS8UAoUQAt04f/jxaSRgGzzexFSaWAcmZ2INJxpQs+M3YCrc0sv1/wLahYahH6v97EzA5LehuYYmavFsbxSvIVRStgo5ltNrOjwFhCU75GlJmlAt9EOo7MzGy3mS0O1r8D1gC1IhsVBDMppgUv44MlKv76kVQbuJLQvCsuB5IqAh2AlwDM7Gg0JYlAV2BTpJNEBnFAWUlxQDn+O0togSvJiaIWsD3D6x1EwQdfUSCpHnAhsCCykYQE3TtLCU2bOz2YbCsajAD+AJyMdCCZGDBN0iJJ/SIdTKA+sAd4Jeiqe1FS+UgHlclNwJhIBwFgZjuBfxKaUXQ38K2ZTSus45XkRKEstkXFX6LRTFICMB6438wORjoeADM7EUyKVRtoJSniXXaSegJfm9miSMeShXZmdhFwOdA/6O6MtDjgIuBZM7sQOARExX1DgKAr7GrgnUjHAiApkVAPyNlATaC8pNsK63glOVHsAOpkeF2bQrx0Kw6CewDjgdFmNiHS8WQWdFV8AlwW4VAA2gFXB/cDxgJdJL0R2ZBCglkoMbOvgXcJdcNG2g5gR4arwXGEEke0uBxYbGZfRTqQQDdgi5ntMbNjwASgbWEdrCQnis+BhpLODv5auAmYGOGYolZw0/glYI2ZDYt0POkkVZdUOVgvS+gXaG1kowIze9DMaptZPUL/t2aYWaH9xRcuSeWDhxEIunZ6ABF/ws7MvgS2S2oUbOoKRPRBiUxuJkq6nQJfACmSygW/m10J3TcsFGHNmV0cmdlxSfcBHwKxwMtmtirCYSFpDNAJqCZpBzDUzF6KbFRA6C/k24EVwf0AgIfMbEoEYwI4ExgVPJESA7xtZlHzKGoUSgLeDX22EAe8aWYfRDakH/0WGB384bYZ6BPheACQVI7Q05H3RDqWdGa2QNI4YDFwHFhCIQ7lUWIfj3XOOReektz15JxzLgyeKJxzzuXIE4VzzrkceaJwzjmXI08UzjnncuSJwjnnXI48UTjnnMvR/wePyZuDIFy9MAAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10b6e52d0>"
+       "<matplotlib.figure.Figure at 0x108165890>"
       ]
      },
      "metadata": {},
@@ -167,27 +199,20 @@
     }
    ],
    "source": [
-    "pset = ParticleSet(fieldset, pclass=JITParticle, lon=[0], lat=[900])\n",
-    "output_file = pset.ParticleFile(name='FieldListParticle_adv_stokes.nc', outputdt=1)\n",
+    "pset = ParticleSet(fieldset_sum, pclass=JITParticle, lon=[0], lat=[900])\n",
+    "output_file = pset.ParticleFile(name='SummedFieldParticle_sum.nc', outputdt=1)\n",
     "pset.execute(AdvectionRK4, runtime=10, dt=1, output_file=output_file)\n",
-    "plotTrajectoriesFile('FieldListParticle_adv_stokes.nc');"
+    "plotTrajectoriesFile('SummedFieldParticle_sum.nc');"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "What happens under the hood is that each `Field` in the `SummedField` is interpolated separately to the particle location, and that the different velocities are added in each step of the RK4 advection. So `SummedFields` are effortless to users\n",
+    "What happens under the hood is that each `Field` in the `SummedField` is interpolated separately to the particle location, and that the different velocities are added in each step of the RK4 advection. So `SummedFields` are effortless to users.\n",
     "\n",
     "Note that `SummedFields` work for any type of `Field`, not only for velocities. Any call to a `Field` interpolation (`fieldset.fld[time, lon, lat, depth]`) will return the sum of all `Fields` in the list if `fld` is a `SummedField`."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/parcels/field.py b/parcels/field.py
index 51a29c2e..a2ac0a60 100644
--- a/parcels/field.py
+++ b/parcels/field.py
@@ -121,7 +121,6 @@ class Field(object):
         self.is_gradient = False
 
         # Variable names in JIT code
-        self.ccode_data = self.name
         self.dimensions = kwargs.pop('dimensions', None)
         self.indices = kwargs.pop('indices', None)
         self.dataFiles = kwargs.pop('dataFiles', None)
@@ -783,7 +782,7 @@ class Field(object):
     def ccode_eval(self, var, t, z, y, x):
         # Casting interp_methd to int as easier to pass on in C-code
         return "temporal_interpolation(%s, %s, %s, %s, %s, particle->cxi, particle->cyi, particle->czi, particle->cti, &%s, %s)" \
-            % (x, y, z, t, self.name, var, self.interp_method.upper())
+            % (x, y, z, t, self.ccode_name, var, self.interp_method.upper())
 
     def ccode_convert(self, _, z, y, x):
         return self.units.ccode_to_target(x, y, z)
@@ -1233,12 +1232,12 @@ class VectorField(object):
         # Casting interp_methd to int as easier to pass on in C-code
         if varW:
             return "temporal_interpolationUVW(%s, %s, %s, %s, %s, %s, %s, " \
-                   % (x, y, z, t, U.name, V.name, W.name) + \
+                   % (x, y, z, t, U.ccode_name, V.ccode_name, W.ccode_name) + \
                    "particle->cxi, particle->cyi, particle->czi, particle->cti, &%s, &%s, &%s, %s)" \
                    % (varU, varV, varW, U.interp_method.upper())
         else:
             return "temporal_interpolationUV(%s, %s, %s, %s, %s, %s, " \
-                   % (x, y, z, t, U.name, V.name) + \
+                   % (x, y, z, t, U.ccode_name, V.ccode_name) + \
                    "particle->cxi, particle->cyi, particle->czi, particle->cti, &%s, &%s, %s)" \
                    % (varU, varV, U.interp_method.upper())
 
diff --git a/parcels/fieldset.py b/parcels/fieldset.py
index 7bae5b5b..627f00eb 100644
--- a/parcels/fieldset.py
+++ b/parcels/fieldset.py
@@ -164,6 +164,16 @@ class FieldSet(object):
             else:
                 self.add_vector_field(VectorField('UVW', self.U, self.V, self.W))
 
+        ccode_fieldnames = []
+        counter = 1
+        for fld in self.fields:
+                if fld.name not in ccode_fieldnames:
+                    fld.ccode_name = fld.name
+                else:
+                    fld.ccode_name = fld.name + str(counter)
+                    counter += 1
+                ccode_fieldnames.append(fld.ccode_name)
+
     @classmethod
     def parse_wildcards(cls, paths, filenames, var):
         if not isinstance(paths, list):
@@ -488,16 +498,15 @@ class FieldSet(object):
 
     @property
     def fields(self):
-        """Returns a list of all the :class:`parcels.field.Field` objects
-        associated with this FieldSet"""
+        """Returns a list of all the :class:`parcels.field.Field` and :class:`parcels.field.VectorField`
+        objects associated with this FieldSet"""
         fields = []
         for v in self.__dict__.values():
-            if isinstance(v, Field):
+            if type(v) in [Field, VectorField]:
                 fields.append(v)
             elif isinstance(v, SummedField):
                 for v2 in v:
-                    if v2 not in fields:
-                        fields.append(v2)
+                    fields.append(v2)
         return fields
 
     def add_constant(self, name, value):


Test patch:
diff --git a/tests/test_fieldset_sampling.py b/tests/test_fieldset_sampling.py
index 190c6ad5..051c4bb3 100644
--- a/tests/test_fieldset_sampling.py
+++ b/tests/test_fieldset_sampling.py
@@ -414,41 +414,40 @@ def test_summedfields(mode, with_W, k_sample_p, mesh):
     ydim = 20
     zdim = 4
     gf = 10  # factor by which the resolution of grid1 is higher than of grid2
-    U1 = Field('U1', 0.2*np.ones((zdim*gf, ydim*gf, xdim*gf), dtype=np.float32),
+    U1 = Field('U', 0.2*np.ones((zdim*gf, ydim*gf, xdim*gf), dtype=np.float32),
                lon=np.linspace(0., 1., xdim*gf, dtype=np.float32),
                lat=np.linspace(0., 1., ydim*gf, dtype=np.float32),
                depth=np.linspace(0., 20., zdim*gf, dtype=np.float32),
-               mesh=mesh, fieldtype='U')
-    U2 = Field('U2', 0.1*np.ones((zdim, ydim, xdim), dtype=np.float32),
+               mesh=mesh)
+    U2 = Field('U', 0.1*np.ones((zdim, ydim, xdim), dtype=np.float32),
                lon=np.linspace(0., 1., xdim, dtype=np.float32),
                lat=np.linspace(0., 1., ydim, dtype=np.float32),
                depth=np.linspace(0., 20., zdim, dtype=np.float32),
-               mesh=mesh, fieldtype='U')
-    V1 = Field('V1', np.zeros((zdim*gf, ydim*gf, xdim*gf), dtype=np.float32), grid=U1.grid, fieldtype='V')
-    V2 = Field('V2', np.zeros((zdim, ydim, xdim), dtype=np.float32), grid=U2.grid, fieldtype='V')
-    fieldset = FieldSet(U1+U2, V1+V2)
+               mesh=mesh)
+    V1 = Field('V', np.zeros((zdim*gf, ydim*gf, xdim*gf), dtype=np.float32), grid=U1.grid, fieldtype='V')
+    V2 = Field('V', np.zeros((zdim, ydim, xdim), dtype=np.float32), grid=U2.grid, fieldtype='V')
+    fieldsetS = FieldSet(U1+U2, V1+V2)
 
     conv = 1852*60 if mesh == 'spherical' else 1.
-    assert np.allclose(fieldset.U.eval(0, 0, 0, 0)*conv, 0.3)
-    assert np.allclose(fieldset.U[0, 0, 0, 0]*conv, 0.3)
+    assert np.allclose(fieldsetS.U.eval(0, 0, 0, 0)*conv, 0.3)
+    assert np.allclose(fieldsetS.U[0, 0, 0, 0]*conv, 0.3)
 
-    P1 = Field('P1', 30*np.ones((zdim*gf, ydim*gf, xdim*gf), dtype=np.float32), grid=U1.grid)
-    P2 = Field('P2', 20*np.ones((zdim, ydim, xdim), dtype=np.float32), grid=U2.grid)
-    P3 = Field('P3', 10*np.ones((zdim, ydim, xdim), dtype=np.float32), grid=U2.grid)
-    P4 = Field('P4', 0*np.ones((zdim, ydim, xdim), dtype=np.float32), grid=U2.grid)
-    fieldset.add_field((P1+P4)+(P2+P3), name='P')
-    assert fieldset.P[0].name == 'P1'
-    assert np.allclose(fieldset.P[0, 0, 0, 0], 60)
+    P1 = Field('P', 30*np.ones((zdim*gf, ydim*gf, xdim*gf), dtype=np.float32), grid=U1.grid)
+    P2 = Field('P', 20*np.ones((zdim, ydim, xdim), dtype=np.float32), grid=U2.grid)
+    P3 = Field('P', 10*np.ones((zdim, ydim, xdim), dtype=np.float32), grid=U2.grid)
+    P4 = Field('P', 0*np.ones((zdim, ydim, xdim), dtype=np.float32), grid=U2.grid)
+    fieldsetS.add_field((P1+P4)+(P2+P3), name='P')
+    assert np.allclose(fieldsetS.P[0, 0, 0, 0], 60)
 
     if with_W:
-        W1 = Field('W1', 2*np.ones((zdim * gf, ydim * gf, xdim * gf), dtype=np.float32), grid=U1.grid)
-        W2 = Field('W2', np.ones((zdim, ydim, xdim), dtype=np.float32), grid=U2.grid)
-        fieldset.add_field(W1+W2, name='W')
-        pset = ParticleSet(fieldset, pclass=pclass(mode), lon=[0], lat=[0.9])
+        W1 = Field('W', 2*np.ones((zdim * gf, ydim * gf, xdim * gf), dtype=np.float32), grid=U1.grid)
+        W2 = Field('W', np.ones((zdim, ydim, xdim), dtype=np.float32), grid=U2.grid)
+        fieldsetS.add_field(W1+W2, name='W')
+        pset = ParticleSet(fieldsetS, pclass=pclass(mode), lon=[0], lat=[0.9])
         pset.execute(AdvectionRK4_3D+pset.Kernel(k_sample_p), runtime=2, dt=1)
         assert np.isclose(pset[0].depth, 6)
     else:
-        pset = ParticleSet(fieldset, pclass=pclass(mode), lon=[0], lat=[0.9])
+        pset = ParticleSet(fieldsetS, pclass=pclass(mode), lon=[0], lat=[0.9])
         pset.execute(AdvectionRK4+pset.Kernel(k_sample_p), runtime=2, dt=1)
     assert np.isclose(pset[0].p, 60)
     assert np.isclose(pset[0].lon*conv, 0.6, atol=1e-3)