diff --git a/parcels/examples/example_globcurrent.py b/parcels/examples/example_globcurrent.py
index 7bd0206f3..8d76d38b4 100644
--- a/parcels/examples/example_globcurrent.py
+++ b/parcels/examples/example_globcurrent.py
@@ -9,24 +9,22 @@
 ptype = {'scipy': ScipyParticle, 'jit': JITParticle}
 
 
-def set_globcurrent_fieldset(filename=None, indices={}):
+def set_globcurrent_fieldset(filename=None, indices={}, full_load=False):
     if filename is None:
         filename = path.join(path.dirname(__file__), 'GlobCurrent_example_data',
                              '20*-GLOBCURRENT-L4-CUReul_hs-ALT_SUM-v02.0-fv01.0.nc')
     filenames = {'U': filename, 'V': filename}
     variables = {'U': 'eastward_eulerian_current_velocity', 'V': 'northward_eulerian_current_velocity'}
     dimensions = {'lat': 'lat', 'lon': 'lon', 'time': 'time'}
-    return FieldSet.from_netcdf(filenames, variables, dimensions, indices)
+    return FieldSet.from_netcdf(filenames, variables, dimensions, indices, full_load=full_load)
 
 
 def test_globcurrent_fieldset():
     fieldset = set_globcurrent_fieldset()
     assert(fieldset.U.lon.size == 81)
     assert(fieldset.U.lat.size == 41)
-    assert(fieldset.U.data.shape == (365, 41, 81))
     assert(fieldset.V.lon.size == 81)
     assert(fieldset.V.lat.size == 41)
-    assert(fieldset.V.data.shape == (365, 41, 81))
 
     indices = {'lon': [5], 'lat': range(20, 30)}
     fieldsetsub = set_globcurrent_fieldset(indices=indices)
@@ -46,10 +44,10 @@ def test_globcurrent_fieldset_advancetime(mode, dt, substart, subend, lonstart,
                          '20*-GLOBCURRENT-L4-CUReul_hs-ALT_SUM-v02.0-fv01.0.nc')
     files = sorted(glob(str(basepath)))
 
-    fieldsetsub = set_globcurrent_fieldset(files[substart:subend])
+    fieldsetsub = set_globcurrent_fieldset(files[0:10])
     psetsub = ParticleSet.from_list(fieldset=fieldsetsub, pclass=ptype[mode], lon=[lonstart], lat=[latstart])
 
-    fieldsetall = set_globcurrent_fieldset(files[0:10])
+    fieldsetall = set_globcurrent_fieldset(files[0:10], full_load=True)
     psetall = ParticleSet.from_list(fieldset=fieldsetall, pclass=ptype[mode], lon=[lonstart], lat=[latstart])
     if dt < 0:
         psetsub[0].time = fieldsetsub.U.time[-1]
@@ -57,8 +55,6 @@ def test_globcurrent_fieldset_advancetime(mode, dt, substart, subend, lonstart,
 
     for i in irange:
         psetsub.execute(AdvectionRK4, runtime=delta(days=1), dt=dt)
-        fieldsetsub.advancetime(set_globcurrent_fieldset(files[i]))
-
         psetall.execute(AdvectionRK4, runtime=delta(days=1), dt=dt)
 
     assert abs(psetsub[0].lon - psetall[0].lon) < 1e-4
diff --git a/parcels/examples/example_moving_eddies.py b/parcels/examples/example_moving_eddies.py
index b6a875496..b1f7c0280 100644
--- a/parcels/examples/example_moving_eddies.py
+++ b/parcels/examples/example_moving_eddies.py
@@ -148,6 +148,34 @@ def test_moving_eddies_file(fieldsetfile, mode):
     assert(pset[1].lon < 2.0 and 48.8 < pset[1].lat < 48.85)
 
 
+@pytest.mark.parametrize('mode', ['scipy', 'jit'])
+def test_periodic_and_computeTimeChunk_eddies(mode):
+    filename = path.join(path.dirname(__file__), 'MovingEddies_data', 'moving_eddies')
+    fieldset = FieldSet.from_parcels(filename)
+    fieldset.add_periodic_halo(zonal=True, meridional=True)
+    pset = ParticleSet.from_list(fieldset=fieldset,
+                                 pclass=ptype[mode],
+                                 lon=[3.3, 3.3],
+                                 lat=[46.0, 47.8])
+
+    def periodicBC(particle, fieldset, time, dt):
+        if particle.lon < fieldset.halo_west:
+            particle.lon += fieldset.halo_east - fieldset.halo_west
+        elif particle.lon > fieldset.halo_east:
+            particle.lon -= fieldset.halo_east - fieldset.halo_west
+        if particle.lat < fieldset.halo_south:
+            particle.lat += fieldset.halo_north - fieldset.halo_south
+        elif particle.lat > fieldset.halo_north:
+            particle.lat -= fieldset.halo_north - fieldset.halo_south
+
+    def slowlySouthWestward(particle, fieldset, time, dt):
+        particle.lon = particle.lon - 5 * dt / 1e5
+        particle.lat -= 3 * dt / 1e5
+
+    kernels = pset.Kernel(AdvectionRK4)+slowlySouthWestward+periodicBC
+    pset.execute(kernels, runtime=delta(days=6), dt=delta(hours=1))
+
+
 if __name__ == "__main__":
     p = ArgumentParser(description="""
 Example of particle advection around an idealised peninsula""")
diff --git a/parcels/examples/example_ofam.py b/parcels/examples/example_ofam.py
index 1ed2758ff..f7c318cdc 100644
--- a/parcels/examples/example_ofam.py
+++ b/parcels/examples/example_ofam.py
@@ -7,27 +7,17 @@
 ptype = {'scipy': ScipyParticle, 'jit': JITParticle}
 
 
-def set_ofam_fieldset():
+def set_ofam_fieldset(full_load=False):
     filenames = {'U': path.join(path.dirname(__file__), 'OFAM_example_data', 'OFAM_simple_U.nc'),
                  'V': path.join(path.dirname(__file__), 'OFAM_example_data', 'OFAM_simple_V.nc')}
     variables = {'U': 'u', 'V': 'v'}
     dimensions = {'lat': 'yu_ocean', 'lon': 'xu_ocean', 'depth': 'st_ocean',
                   'time': 'Time'}
-    return FieldSet.from_netcdf(filenames, variables, dimensions, allow_time_extrapolation=True)
-
-
-def test_ofam_fieldset():
-    fieldset = set_ofam_fieldset()
-    assert(fieldset.U.lon.size == 2001)
-    assert(fieldset.U.lat.size == 601)
-    assert(fieldset.U.data.shape == (4, 601, 2001))
-    assert(fieldset.V.lon.size == 2001)
-    assert(fieldset.V.lat.size == 601)
-    assert(fieldset.V.data.shape == (4, 601, 2001))
+    return FieldSet.from_netcdf(filenames, variables, dimensions, allow_time_extrapolation=True, full_load=full_load)
 
 
 def test_ofam_fieldset_fillvalues():
-    fieldset = set_ofam_fieldset()
+    fieldset = set_ofam_fieldset(full_load=True)
     # V.data[0, 0, 150] is a landpoint, that makes NetCDF4 generate a masked array, instead of an ndarray
     assert(fieldset.V.data[0, 0, 150] == 0)
 
diff --git a/parcels/examples/parcels_tutorial.ipynb b/parcels/examples/parcels_tutorial.ipynb
index bbeb94ad7..e8916bc4a 100644
--- a/parcels/examples/parcels_tutorial.ipynb
+++ b/parcels/examples/parcels_tutorial.ipynb
@@ -2,40 +2,28 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# Parcels Tutorial"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Welcome to a quick tutorial on Parcels. This is meant to get you started with the code, and give you a flavour of some of the key features of Parcels."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "In this tutorial, we will first cover how to run a set of particles [from a very simple idealised field](#Running-particles-in-an-idealised-field). We will show how easy it is to run particles in [time-backward mode](#Running-particles-in-backward-time). Then, we will show how to [add custom behaviour](#Adding-a-custom-behaviour-kernel) to the particles. Then we will show how to [run particles in a set of NetCDF files from external data](#Reading-in-data-from-arbritrary-NetCDF-files). Then we will show how to use particles to [sample a field](#Sampling-a-Field-with-Particles) such as temperature or sea surface height. And finally, we will show how to [write a kernel that tracks the distance travelled by the particles](#A-second-example-kernel:-calculating-distance-travelled)."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Let's start with importing the relevant modules. The key ones are all in the `parcels` directory."
    ]
@@ -43,11 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from parcels import FieldSet, ParticleSet, Variable, JITParticle, AdvectionRK4, plotTrajectoriesFile\n",
@@ -59,20 +43,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Running particles in an idealised field"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The first step to running particles with Parcels is to define a `FieldSet` object, which is simply a collection of hydrodynamic fields. In this first case, we use a simple flow of two idealised moving eddies. That field is saved in NetCDF format in the directory `examples/MovingEddies_data`. Since we know that the files are in what's called `Parcels FieldSet` format, we can call these files using the function `FieldSet.from_parcels()`."
    ]
@@ -80,11 +58,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "fieldset = FieldSet.from_parcels(\"MovingEddies_data/moving_eddies\")"
@@ -92,170 +66,34 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
-    "The `fieldset` can then be visualised with the `show()` function. To plot an animation of the zonal velocity (`U`), give the following command"
+    "The `fieldset` can then be visualised with the `show()` function. To show the zonal velocity (`U`), give the following command"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
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mtEsA7wZIpVhz9WY5StPYtenFpxW+cmM9zFdZ5+n7WQJ5sY0zsM7L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\n/FIQobOzgqwAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.animation.FuncAnimation at 0x10dfc1510>"
+       "<matplotlib.figure.Figure at 0x11055d650>"
       ]
      },
-     "execution_count": 3,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "fieldset.U.show(animation=True)"
+    "fieldset.U.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The next step is to define a `ParticleSet`. In this case, we start 2 particles at (3.3E, 46N) and (3.3E, 47.8N) using the `from_list` constructor method, that are advected on the `fieldset` we defined above. Note that we use `JITParticle` as `pclass`, because we will be executing the advection in JIT (Just-In-Time) mode. The alternative is to run in `scipy` mode, in which case `pclass` is `ScipyParticle`"
    ]
@@ -263,11 +101,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pset = ParticleSet.from_list(fieldset=fieldset,   # the fields on which the particles are advected\n",
@@ -278,10 +112,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Print the `ParticleSet` to see where they start"
    ]
@@ -289,11 +120,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -310,20 +137,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This output shows for each particle the (longitude, latitude, depth, time). Note that in this case the time is `not_yet_set`, that is because we didn't specify a `time` when we defined the `pset`."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "To plot the positions of these particles on the zonal velocity, use the following command"
    ]
@@ -331,17 +152,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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OMurG5+in6rL3YwVhzsyoN9FDgIujl+kPA0cBsRgcBfxt2P8o8JawgtlRwIfN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\nAR4Z67kB+Lakx8eyJOm+8ZpLgMfF41lBzS4GTpF0m1jGrSTdOV7/IEm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OsP3Y5B5/Bvxgs3XNnkEmk8l0sLUoz+DewIW2L7K9G/hb4MROnjsC/xLu6wuAoyUdDiDp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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x110726350>"
+       "<matplotlib.figure.Figure at 0x1243b6490>"
       ]
      },
      "metadata": {},
@@ -354,10 +171,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The final step is to run (or 'execute') the `ParticelSet`. We run the particles using the `AdvectionRK4` kernel, which is a 4th order Runge-Kutte implementation that comes with Parcels. We run the particles for 6 days (using the `timedelta` function from `datetime`), at an RK4 timestep of 5 minutes. We store the trajectory information at an interval of 1 hour in a file called `EddyParticles.nc`. Because `time` was `not_yet_set`, the particles will be advected from the first date available in the `fieldset`, which is the default behaviour."
    ]
@@ -365,17 +179,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO: Compiled JITParticleAdvectionRK4 ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gn/T/parcels-501/218f0b1f004549f5b8077878203091fd.so\n"
+      "INFO: Compiled JITParticleAdvectionRK4 ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gr/T/parcels-504/6f9e3d09880cfb05f9eb5a91ed0b634d.so\n"
      ]
     }
    ],
@@ -388,10 +198,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The code should have run, which can be confirmed by printing and plotting the `ParticleSet` again"
    ]
@@ -399,25 +206,21 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "P[0](lon=2.024544, lat=46.088627, depth=0.000000, time=518400.000000)\n",
-      "P[1](lon=2.017195, lat=48.305283, depth=0.000000, time=518400.000000)\n"
+      "P[0](lon=2.024550, lat=46.088692, depth=0.000000, time=518400.000000)\n",
+      "P[1](lon=2.017325, lat=48.305149, depth=0.000000, time=518400.000000)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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TzeycVTfSI2kGCQkJCQ2Y0TyD+wIXm9klZrYV+BBwRK3O3YDP+fQXQrmZbTWzLT5/M3OW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\nBP/xYeNG4O/HVTQXAl8ys88ethCS/lryw0q6gPD/9MUDvoaAXwPuMrP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9DHgo8FN//ofAhcDWkv7OzL4+pv4RwMWUzo7jgNeZ2bclvQh4PfAPE7c8ohsW\nSwQNx2OJoE0bCIV6Q2+/kQQqmzWnpfV8rynke7OSELwmUTxPYCIaMp9U/ATywjjv8Xty6HnBX5CA\nF/K9OglY5ZyAGAqNwagK7yTY+1dd+BQSX5aiie5YYXppNpqIELpg1RPCdOYZ+Em6r8AtHdwDPmlm\nF0l6qc8/Brdy5F9LmgfuBP7K+2cb6/pL/w3wIUl9YD1w+GLb2oUMrgFenDfCxyZ6B/AG4EtAKxlI\n2gXnDHkXzu8AsBfwHX98Ju5BIxksBaahEdTKTUoEFQdvzbxTN/u0kkBakkCS1gggtZIE0pAMvODP\nj81QmmsHVpJASA7h4+ZEkOC0AE8CSFjPE0GSH6sQ9Dk5ZL0aMfSqpJD1HLmoTgq5ppA3MSQEKIkg\nq2kJSUAIVMkB0+SEEE1GQ38bC76M2enA6bW0Y4Lj9wDv6VrXp38XeNh0WujQhQz2CtgIM/uZpAeY\n2WW1BW+a8EEcaYR+h4tww6O+DPwlVZtYxHJi3I99UtNQi1moTRMohH8trdLTT2skMHD74jwDDawQ\n+sVxak4zMIPUUJYFhBBsuDbUoUL4qthMTtgXhJA4CWz9xPf4VQh99fxxDyx1wjrr5+QglBlZLyeQ\n8t1hniAozwt/ARQHpqBcYGbyst9dByIhLBRGnIHcgIsk/RvwOX/+V8DPJK3FL4XZBEkHATeY2XmS\n9g+yXgR8WNI/4GY2b2ypfzhe9eltu22HZkZUsIBY7J2dxV00ggYiaDQLWSj8q0SQpPm5kQzKnn+S\n5kLfpSWDzJuE8vTMk0G5Lwghq5FBZjQah3M2SDwZJAlKSiJQkjgy6Amb9/te4gnAbU47ENZ3pKDM\naQRm3s9gYD1zzeip0AYyvAzOncqUcjnXCkhcs0NCKL4LpVAvTEZ1gd9F0Ic3nkWsQo4bhS5kcBjw\nMty4VoDvAa/DEcHjRtR7FPA0PztuHbCVpBPM7HnAkwEk7UU5prYCP1b3WIC1u+06Y59lGTDKPNRq\nKiqlwjgfQaWnWzcVjSGCJLXq8SBPcwRBaiUhpBnJwGsBgwylaUkEgwzSzJNB5qRnmtXIIINRQ0uT\npCADEkHSQ73EkYMX/jkxqJ+UWkI/QT2R9RMsM0cQfRU9zqxvJPlxDxKs6IkmBE0aRwiBBtBqJiI4\naRtlFP0HDZgtFuwytPROSR8DTjOzn9eyb2uq4+sdCRwJ4DWD15nZ8yTdw8xu8KEu3oIbWRQxTUxr\nhaa6VhBBC1xLAAAgAElEQVSkjaoTEoVqJDDONDSOCBwBOG1AaU4ATvBrkFVJIE39lhNC6qRnmmEW\naAlZg2ZQkICQciJIyq2XOGLo99xxP4HMb72eJz85gd9XYYkqR5A6c1KGkZDnl1LZROFQL/wGuWWr\nTrpQFeZBUiHvC1JQ7YN2wKxqB9MZTbTZoMvQ0qcB7wPWAHv4AEnvMLOnLfCeh0p6uT/+Em6thIjl\nxEI6cR21gpGzf/NzAjKo7ENHcUcimPfaQEgEA08Ag0FJAlmGBcRgOTFkvuGhqajBRESvBxLqeeGf\n9SDx2kbWQ1kP6+N8FN4pbZa4bJKwgx8cy5Gjv00xbDUrFZOiB5/bjqwU+Pl7zY8rmkDjNyzzR/kH\nFqQdrDZ4Mp8ldDETvRUXI+NsADM734977QwzOzuo/yFcFL6IFYKJev6jMMr0VC/XsA05nsN9WnMM\np85EVPEP1Ikg0AzMHxckkKZY4C+wrNpY5x9wfgJT4q6fJJgZWOKEfpJU6wBGDylzl029WSYzSEH5\niKTc1m+GMjf6SCZn8snMMUGgCeTCWUHakAN4jDBvFfCz2uvvgFkLvpOML8K8mdXXL5ix1xQxEUaR\nS8svp2l4Z1udcBZxc/mqfm8W+Amg1AaK4oF2EJKDZUHeqPuN+XPIpxvX0upkOan1JmKJ0dJpqWyr\nCF1HEz0H6EnaE/h74PtL26yI5UTFjBx6ISdFzSlZRN20Wpl8H2xuHL9VInuGgd3U8z3nxIrx/Fji\nujM9Q5a4colBv48L8R7cUhkmoTSFJMGyzJt0DAUEoCToJue+gtw85H0FxXG/7/0F/dKPkA859SON\nyIec1sJTFENRi3kMVMJc5FthOhr1zsM9zeVnzOIxHczYS+tCBq/ErX+8AfgsbpLYO5eyURFLjIUI\n/LqdQVULgwVFcoel8nKBTTyPy1OEWrBgj7wZBjeypm9kyNvYc0drgmQkheOBYmKY/OQvpd7528sg\nTSDt+WGmzndQDCetOZArf/q5CaggAv8QASmQ5M7jniOAfDRRr4f1HZFkfT/ktO+HmvZKEmjeVM44\nTsp3Fs5YLgm0JIzhB2j6huWh1euMIZOZg/dlzRK6jCa6A0cGRy19cyKmgsQmHlHUqB2EPfwgr3Be\nNiHPV+nQLMbCh5f3QyOLBL/PEPJCXml+rIJMlF8vSZxzOZEr10tQP0NpDxtkaC4nAGscXlqZhVyY\nj/xff0AExeaHlRajinrlPAOSfEhp0kgC+VyDzM9EzvLJaL08jcqs5MyfD4WsyEmgRgoFcRCQQ12z\nKN5x8CW6/kRmkhzGqWSrD61kIOm/GNF/XMRooojlwDhC6KodhIRQHlTHtXuysLw3lZt2clO5ldpC\nRVPIR8jkaZmTtZa5nnOS5A5Wl2cD/LBTkaTmJl0NDGVJdRaydy6T+hnIOSnkWkAegiIcTQT+AYJ3\nlk8ykwrtgyQpZiIXk86SssfviCApZhoXmxf21ne9/1zgWy83GRHEMwqEf40Q8mB19XDZBO93iAiK\nb2llufo3zl/BiLxWrMo5Bqw6n8A4jNIM3u/3fwHcEzjBnx8KXL+UjYpYJtQIoR4v3yWW5QpC8JUr\nRJAX8yaf0FRked1A+BczaLOyt4+36uTDS13YBryAF1nfEYGbkRzkBfti1JGfnVzEJQqC1rkRSqFD\nueX9FPZ8/zJCG38RqbT0CRQEUPgEKENSBKahLDgnSCMkgUADCKOfVvb1nn8o/Bs0giHTUO04EkEN\nq/jRmtBKBmb2bQBJ/1xbvOG/aiv7RKxU5H+o4zQEGCKFRpNRUEyBxLewXD4UMh9tkZuC/HERQsFr\nEYU2YSUJFBPWiklpaoxrVM3LCSCv64V+fZGbMJR1fV2DAPXIpYVgTUJnb3lcCWOdhOlUyCEU+PXz\nugmoEgK7i0mI4LhmFprYRzDrRACRDBqwhaT7BOtw7oFbpzNic8ECSKFRSwjQSAoBIRSF6ltOBAFh\nDMUxMobDXVfKqJioNpyWC3sN1S2epz6sc4T/I3wXlQVu6vuEILx1mdco2KHd3KOGdNXaslACqB1H\nEhgBo+UFTReS7g58Htgdt77LIWZ2c0O5A3Dzs3rAcWZ29Lj6kvYB/h23fEAG/JGZrW9rSxcyeDVw\ntqTLcD+VezOF2NkRmwCTkAKQm4Yas1XLD3rZzk+gMLkU/uFxKKD9/UZFP+2U7x0a9ThJlXPU7ABv\nI76aUK2P4CkIIS8zyoTTJZ/afWr3He7ldxP+lTIt+a2YBQKoYZlGE70JOMvMjpb0Jn/+xko73AJh\nHwWehFsH+UeSTjWzn7XV9+scnAA838x+Imk7RgQWhW6jib7q5xc8wCddYmYbJnnaiBWGpj/sJoJo\nE4w2nF8sDFMUDO1OQRULy1RtT1WBPSzQK2mMKDsqvdbeLujkcG0ijEnKjqjX6PidROC3lGvFDAr+\nTYiDgf398fG4SA1vrJXZD7g0sM58ztf72Yj6TwYuMLOfAJjZTeMaMmo00UPN7H/9hTYAPxlVJmIz\nxyIFwFDtFu3DRpx1zWrDSpjBuyDLwjSsEVGATx3L9Hva0cyu9cfXATs2lLkXcGVwfhXwiDH19wJM\n0teAHYDPmdl7RzVklGbwKR9tdNRP9RPAQ0bdIGLzw1RMpf2a2B/qXQf5Tb3chp5xmN40ZLKiqYy6\nX9N9R2FIG6lWrESjsOFyzetEaDitkt9+z1YT1yKwEoh0RcHoOldn+9qAmmN9+P0Ckr6BG5FZR2Xu\nll8DecFfola/Dzwa+CPgDuAsSeeZ2Vlt9UeRwdbAeYz+k/nNhO2NWCFYlMBvM0s0Cfgm+3bFPFIK\ncqma7/bm84J9rbx8GAsYLieCOsHx+EX6gucLLV6FL8Tv82Mr892o1fIhc/9IXi4sXzgbAoel1c4r\nsZjy0VuB6axoCNBKGmNEzKjfw8wSRbfnvrE22nL4MmZPbMuTdL2knczsWkk7ATc0FLua6oqQu/g0\ngLb6VwHfMbMb/X1Ox61lPzkZmNnubXkRmycWTABNdm8Y7pGH+yahXxP4ahH2SiiEfJIE6UCSZEhw\ny3cu4Pr/+CbzN/6eue23YpfD9mf7x/8BSV4v2CdYQQKJT8uRjJF0mX/gcG8mMlQeV/ZuFrWZSDN5\ncqgeZwEpZJmGCSPLyaJKFJaTQoUkgo+Ss0RBTMFHU7u/ZRyGBhHMCDks03OeCrwAONrvv9JQ5kfA\nnn4k59XAs4HnjKn/NeANku6KW03yscC/jGpIl9FEEZs5FmvHHksAtV5/Xfjngr7S00+sUfArKYV+\nLynTe0lGzwvy337rQq756FfJNrhgdPO/uYUrPnw6W6+7k92evBf9JCPB6CdpQQaOBPw1yImhHC5S\nJ4VS+LtB/RkiNZFZQhaQQWYJgzzNxCArzwdZQuqP0yxxW0AOWebrZSWhWOY1iyzx+5I4lGsUnhBK\nDSIghvyDmX/fRuG1d87/gEjy7zah0KsMO17NWJ7RREcDJ0t6MXAFcAiApJ1xQ0gPNLOBpFfgBHwP\n+GSwLn1jfTO7WdIHcERiwOlm9t+jGhLJYBVjWUggSG/UABJoI4BC+Oc9eC/0Q+HfSzJ6ScZcktJP\nMvpJxgWf/nZBBDmyDQMu/fj3+eOn34M5ORII9z05gugpc6SAVcigF0jENHgJmSXFeWYJ89YrBPy8\n9chImM8Sn5cwsB7zWY+BJQyKvds2Zr2CIApyyOTPHTmUJOEEeJbJE4OVxJDVNQNcrKdC0FtVY6jA\n/P/TIYXVSgiy5Xk2P8rnCQ3p1wAHBuenA6d3re/zTqCMHDEWkQxWKaZGBAvQBAoTkYZJQN7MoyQn\nAKcB5Pu+F/4hAawJiGBNMuDOG25tbP5t19/OPdfcwlwyYE5psa3RoCCCOaX0yAoi6PnuXy/4y0/9\nw6d++rQjBNerzygJYd56zFvf7/2Wuf2GzKUPMnc8sISNab8gh5wY5rNeQQzzfp9lmd/LLa6TmSMF\nJWXE15wcJDBDBBFf3WwLchKumJEq2oKaZ5tPgNVMCMsx6WwlocuylwKeC9zHzN4haTfgnmb2P0ve\nuogFYZMSQZL7ClyvvyCDxGsCcse9MSSwppfSV8aaXurJIGVNMmBtMuBu97wrt157x9AjbLvTWnac\n+z3rknlPBAPW+H2PnAiMhIxeoR20S7KsIAUxb31SXC9+nioZbLQeG2yOjZ4YNmRzzFuP9dlcQQob\nsj4be302eELYmPXZmPYYWMrG1JFILyCFxBPAIE3ISPy7zhxBiGL9Hst9C0kedsORR0kI7luUDhwi\nIXTFanymEeiiGXwMZz17PPAO4Fbgi7ghSxGrAW2moZYyYzWCNiJo0Ab6PWcOmuulzCUpvaQkgDVJ\nypregL4y1noiWNsb8LhX7s0Z7/gx8+vToklr1iUc+tp7sUP/lkIbmFPKOs3Twzw5OAJw595vgItC\nXUfqBUGGI4PMYCPOPLSRHvOWFEQwjxP889Znvc0xn7jztckcG7I51mdzzCllgxyhbcz69NOMvvps\nzHqFT2OghHn1fLuMeXr0ydzqmZlI/VrKZkaxCESWy6zA1uNXFDKCmdaKhDApVtvzjEMXMniEmT1U\n0o+hcEysWeJ2RSwQU9NsG8bu10cJ5Wl1Isg1CGcewg/9LLdQI8iJINcK6kSQawPhtt/TdmSL/gP5\n6gd/yc3XbmC7ndbwnNftxJMO3oJ1Ws8aTwI5AawhY07GnKCHWyxnzgtddz780jL//ClGhpGaMU9K\najBPyryJeZtnIz3WZ33W9eZZb3PMZQPW2xp37yz1fovM+yqMJBstYczkXIT0yMgXfcgwS0hkZAmQ\nCZMX+gJh3nykggtyz0ChIYS3bRH2q1GgLwoz9i66kMG8j43hFFJpB5bLzx6xMtBAMJU5ArVyudB3\nJ1YuTENIDBQCUnL2/H6S0c/3iTMTzeVb4rSDdck8a5N5/uTPd+DxB2/DWs2zLplnncrNkcGAOWWs\nU8acYA7Rk5gjIfHHSXGcUEfq7TAZRkbGPBlzGPOU2oUzOTmT13rzf0o+FDesIZXzNazToLLaeGYi\nk0iTYFRSeJzJvxPD5EJPF6Tq37N7p056mx86WlqDrL1XEBJBTTtoLTeLMOJKZw34MHAKcA9J7wKe\nBbxlSVsVsXzoYiKa5DrBNeoTu8LJYbmtPh9J1ITEj/xJlJH4/kcS2PqdU9jo5T4Av5WjiMyZgWpE\nMKdehQQShskgUeJ65n5JtjlBGX+7DJmamtMcCvMTqSeBAanciKNEGT0Lh7S69ufDXPPzhOo+9e8H\nROJJofEjFYK7NAV5v3Kt3AiSiBjGjJFhl0B1J0o6Dzd8ScDTzezirjfwWsW5wNVmdpCkBwPHAOtw\nq5a/LDqjVz/GzbKvE0ITQfQaumqOCKwYFTQpmoigvaxIR0gIR1i9avuUVYTKQtsZsfyYNZPZqEB1\ndw9ObwA+G+aZ2W873uMI4GJcTG2A9wJvN7MzJB3oz/efpNERU0Q4znzK1wtXkbQ8jIL/PzMVjtt8\nwlaIfEJXPukL8OP7E9JA5ciHfKZKmCMt0ubIyMxN5k0xEvM9clTa4i0r2hqSQuYFdmkqygoNwG3O\nwZzPPyjmIZA4Uw+unRluTkFeJkPFcFV3/XDyWnAcjgOy5uMFIWoFESMwSjM4j/JPezfgZn+8DfBr\nYI9xF5e0C/BU4F3Aa3yyURLD1sA1C2l4xPSxKCtCjVTM1KgNlHF7rAjbEIZpKLeEzDIyvKnFjMSM\nOUuLyV89MnqW0aNPz0LtYB7ogwb5bCz/cBmZjMTEHEZPYmD5GJ200s6SELwDGWPeXOpGM+YR8ybW\n+/kF622OjdZjox9htDGce2A9R2J+Utogc9vwbObqVoQjCsgyfF+Vd+++oD/XsIlo5LeLJNGIqBk4\nmNkeAJI+DpziZ8Ah6SnA0zte/4PAG4C7BWmvAr4m6f04t9qfLKDdES1YErNwZZaqE+Th//lAlvzA\nzI1wKWPqWNEuJ9jcUMlELiyDJAaWuLWN/YibREZiCYkfetnz/oENWfCTzZ21CW7ilsS6ZJ7UEtZp\nnkxiXilzZKwxN6IoMaMnZ65JDHpey6iPKMq8JHBkgCeDfM6Bm22cDzFdb35ega1hYz6/wNyQ0vVW\nDi/dkPWZz5KifjFLOUuKyWipJT5chdMyyphGBDGN/KewkhzKUBQdvmXTMTWzyIwJwiFYNBM14ZFm\n9jf5iTfvjIyLDSDpIOAGMzvPh8LO8XfAq83si5IOwYXBHorqJ+lw/IpqvW237dDMiKkiH2dYIQLa\nTUqeJHJnpuEFlBlkzhmbgB8aSUEEJEkxaiORsTFtvrwzsbgROKk3x6SITE5LWJfMe4dtn3XJPPP0\nmLO0mHQ2J6dFzHlnrnNCu/b1WiRfPr8g9eadfI5BZirmF4QkMG/9ggDyyWc5CWywOTakfeYtcRPQ\nsj4bUzfPoAhTEW45IQQhKkKtIA9NUdEKCvKlOFZwXH6rsV9/YqxKwTlj7p0uZHCNpLdQxrh4Lt1M\nO48Cnub9AuuArSSdAPw5zo8A8J/AcU2VfUzwYwHW7rbravyprRwEQr5Vs+iiHaDSUZCHRsjkZiXn\nQipzhJBmotK1z9GjIITMRNYrg8GlgQkljwE0nwyYSwZOEGuOdYkf778SZyBnTTOQEzamPT/zuFcL\nS1ENaGdZGeU0f7dOK6gRQ0gE9W9Y1yDCwHXU8mYYTSOnVzu6kMGhwFtxw0sBvuPTRsLMjgSOBPCa\nwevM7HmSLsaFUz0bN6v5lxO3OmIkFmQqaiKEunYQjk/3dYYIIRf+kh+JaUVgtQzHE85C74KvJSbM\nO2pzs0e/l2ImBonzGQyyhDW9PBJoj0GvRz9LmU8SNtgc/Sxlgw9BcUeWFkRQxicauMltnhTckM48\nJlE1RlGO3NGbFo7gXDtI2JgHqPM+gmpson5BADkJ5PGJNnpfQa4JhIHr5j0hpOZDUFSC1lWJwLKQ\nCAi2Bl9BrhU0CfvoKxiNSAZV+FFDR4wrNwH+BviQX7B5Pd4UFDFdTM130EYIlOmTEAJSxWRkabkY\nTNZzeZmJNHFmncxEP8mcTT1J6CcZG7M+a5IBG5I+c3IT1O7UnJuYpvkiUmkesC6PUJoHqcsjmEJJ\nBklABhm1EUxeI8hNRakngqzQDsoAdXlwujyC6YYiOF1JAoU2UIteOp/1vDaQlGGtvdB30UtHaASj\nzENj/AmL1QpWZQ86+gyGIelbNPxEzOzxXW9iZmfjNAHM7LvAwzq3MGL50GYuWighyCBxhFBOSjMg\nIfWjgxxROC0hn4mbJkbP5AK3JQk9PzN5Y9IrAthtTHp+tnJa7B0xzNHD/LmbfNbDBb8LQ1gDQ2Gs\n68iHtTrfQVIhhnoI65wEMjQUwjo3B+WhrLuFsHZCvAxhTS2EdUkEpf8gMPm0EcGUzUOrWmAuw7P5\nIfyfB3YHLgcOMbOba2XW4Swya3Ey+wtm9laf9z6c6X0j8H/AC83sdz7vSODFuKFyf29mXxvVli5m\notcFx+uAZ+Imi0WscEzNXASjCcFXbPQhZFZMNrAUN7zf3ETezIQsQxKWmCOETCSJVUghSRMGPtLp\nfNYrQ1d4kkhkQSiLtFjYJp/h209K7SD3EYSmIhixuE1hLiqHvKaUx/N+7kO5oE0p+J1vIymcwwMb\ndhCbUXUSQ2ESCh3FVbPQGGcxDBNBRehHIuiCZQpH8SbgLDM7WtKb/Pkba2U2AI83s9skzQHflXSG\nmZ0DnAkc6RfAeQ/ONP9GSXvjVkT7A2Bn4BuS9jKzliEa3cxE59WSvicpzhjeTDANQoAxPgRXkpwK\n8iiYeTC1MpBdoCVYfrnEh9jJMAklRhaQQqrELXWZUVnwJlG+AE5JBomsIIfqcVYJ8wDlKme9MRIt\nX9ugaYWz8DwU/vm5+fM0y4eLllqA1cigogmEJBCOHAq0gbGjhkY4i8PvWsmfAKudCIDl8hkcTDnp\n9nicBaVCBubC1N7mT+f8Zj7v60HRc3DhgvLrfs7MNgC/knQpsB/wg7aGdDEThTORE5yJZ+tx9SJW\nDhZMCDDaqUxQJhRWqmkJRkAKFNpBEymQuXDXJiPN8IHskioxqOcIASuXxsxJgvI8J4h6TKCSEMb/\ntTetgdw0USyfF5AvX5l6AR8uc1knAKMU9uFSl0OaQBcSgE5mofxbDn3nCTAzRNDtObeXdG5wfqwf\nCdkVO5rZtf74OmDHpkI+rM95wP2Aj5rZDxuKvQhncgK4F44cclzl01rRxUwUzkQeAL/C2aEiNiMs\n2KHcZDayMKHxboBfbEXyxc13ZVSG1hdYPlstJwXhJ6KBEq8t1IjBhcFOKhFQXeC3rDjOCSEMhJcT\nBVSJoIkUwvAY4exfoCL0q3uKWdV59NFKXhMBWDBCKB8y2pUEKM/L4yAdmCYJDF1jlaPjs95oZg8f\neR3pG8A9G7KOCk/MzNQSxMubdx4saRvgFEkPMrMLg3schZPPJ3ZqdQO6kMEDzWx9mCBp7UJvGLHp\nMC1CgBopNEkWE3k45QopqK4p5OYjr0V44W8CZW6Fr7xcJqc1IEgzp3fUCaF+ji+Tawf1qKl5/lDz\nay+qJASK2EGhoLcaKRSTw6gK+UkJIL/nWBJo3EciWBSm9LxmNjSpNoek6yXtZGbXStoJFwdu1LV+\n5wf1HABc6K9xGHAQ8ARvUgK4Gtg1qLqLT2tFFzL4PvDQWtoPGtIiNgNUhPkkaDAbFddpMh0V9VpI\nwaj6FPJrB9pChRh8epaW5JALfck8aeTLvFdJAqrEQFCm06PXNYNASId5oeAv0kLhn5uA2gggf8+h\nFuDf4ZA5CFguEhi6zoxgmZ75VOAFwNF+/5Whdrg1ZOY9EdwFeBLwHp93AC7kz2PNLFwL9lTgJEkf\nwDmQ9wRG+npHRS29J87GdBdJD6EcNrIVcNcODxmxgrEoLQEmNx3lpGCBEPbEYHm9VmLwRiYv8N2t\nFJBA+VAhAZT3qZ2XSRM/dj1AnDUQQ12YV4R/cbFAA2ioM6QF+LQhwV/Pz5+v/g0iEUwGY7nCURwN\nnCzpxcAVwCEAknYGjjOzA4GdgOO93yABTjaz03z9j+CGnJ7plqvnHDN7qZldJOlk4Gc489HLR40k\ngtGawZ8Bh+HUiw8E6bcCb57gYSNWKBZMCFAhhVBYWEXM1qRIbm6qE4NPqxMDlFqAu1FJDrnm4MqU\n5DJMALUHzg8nlHBVEgjMN8FjhsJ+SPDnx3XhD+MJoHJe24dliCQwLfi+yJLDzG7CrRVTT78GONAf\nXwA8pKX+/UZc+124iNGdMCpq6fE4NnqmmX2x6wUjNi8s2GyUY5z5qFKoqb4Y6rnn2kBgRRkS7sHe\nUCBv27WBoaXX6vn1pjW9FGvIr1yiQXi3kMfQeP8FEkDlGg3tnBSzTAIVzNh7GGUmep6ZnQDsLuk1\n9Xwz+0BDtYjNFMtDCkFBK8s2aQzISqE0RA6lpkC1SLU7lxNFPT1oi43t/40mhMp58JxjBX9TvTb7\n/jJoAa3XnGHM2rsYZSbawu+3bMibsdc0O5gaKcCwCamNGOr1Kz6IsljZiw80gYo1JmQYhuT4sGKw\nsIdsDAZXHI9x3tZ8DuOPx2gA9fILxKwJvk6YsXcyykz07/7wG2b2vTBP0qOWtFURmxyLJgUYSQwu\nuyYYi5tS1Rp8/UaCCBvbogU0WnPqGGrc6AfvJJTbBH/9fFS9rvdaICIJjMCMvZsuQ0v/leFhpE1p\nEasQUyEFGCKG8NrF9evCs+qZHiaI/Fr1xrWdjpR8Ezxg28voRBDjr7WUwr/1+hFVGMsVm2jFYJTP\n4I9xS1LuUPMZbAX0lrphESsLQ4J7MagLowatwRWrd/GbGlO9TuP187xFN7yhLZPkdRX8466/QEQS\nmAyz9r5GaQZrcP6CPtU1jG+hDIYUMYOYKjFAq/Bu+2Nsdfo2kcWoe0wTCzErFXWn25TO940YjRl7\nd6N8Bt8Gvi3p02Z2xTK2KWIzwtSJobjYiHuOraumw6ljIkG7TIIlCv/pYdbeZRefwR1+AYU/wK1n\nADDR4jYRs4HGDvlSCOMJ/kjVZEZapnsvNWZNWC0rjBX1rZcDyfginAhcAuwBvB23Gs+PlrBNEasI\nstHbssKmsC0zVsy7m0WswN/DUqKLZrCdmX1C0hGB6SiSQcRUsFxCbZoaShTEqx8ijiZqwrzfXyvp\nqcA1wN1HlI+IWHGIAjxiUmhoduHqRhcy+CdJWwOvxc0v2Ap41ZK2KmK2kC1HSLDNDMlsCaIVh1Vo\nBhqHsT4DMzvNzH5vZhea2ePM7GHAfZehbRGrBZlGb5NgGnb/YFM2epv2/TbJO4tYEMb5u6ahbUq6\nu6QzJf3S77dtKbeNpC9IukTSxX4eGJLeKekCSedL+roPfR3W203SbZJeN64tXRzITRgKXBcRASy9\nsPfo8oc6tC1A2DeSxIT3XcjzLfl7jhiP5XEgvwk4y8z2BM7y5034EPBVM3sAsC9wsU9/n5ntY2YP\nBk4D/rFW7wPAGV0a0sVM1ITOvzy/IMO5wNVmdpCkzwP399nbAL/zDxKxOWIxQmjMH9PYntdi8xeK\n8LodHn9UkSHHdlObu77i+reIpqZFYZkcyAcD+/vj44GzgTdW2uHM9H+KW18GM9sIbPTHtwRFtyD4\nBUl6OvAr4PYuDVkoGUzyKzsCx2JbAZjZX+UZkv4Z+P0C2xCxqbAQAlio4J8wfTyBLIK8xsxsnvjS\nbWE4xhHEQsghEsNk6G4G2l7SucH5sWZ27AR32tHMrvXH1wE7NpTZA/gN8ClJ+wLnAUeY2e0Akt4F\n/DVOlj7Op22JI5UnAWNNRDA6NtGttPdT7tLl4pJ2AZ6KW23nNbU84ZZ4i5PXNhcsxOTTgs7Cv6Hc\nsOmlw5oDXe49BtawOI67YMN1G+IndSKLpuiuo8hhUmKIpNAd3V7VjWb28FEFJH0DuGdD1lGV25mZ\nmldb6uMCg77SzH4o6UM4c9I/+HpHAUdJOhJ4BfBW4G3Av5jZbWr73TbcpBFmdre2vAnwQdxizU3X\net71aVgAACAASURBVAxwvZn9sqmipMOBwwF62zb6VCKWC5OQwKRCeIzwr64J0BK4runaXUhkQrTG\numsKmR0WDMmiRhJjCWIcOUxKDFFb6AQxveHIZvbE1vtI10vaycyulbQTcENDsauAq8zsh/78CzT7\nFk4ETseRwSOAZ0l6L84cn0lab2YfaWvLQs1EYyHpIOAGMztP0v4NRQ4FPttW36taxwKs3W3X+Kvd\nFJgCCYwV0m0CvWUtgMYFY0akT4sUwqUUygvVL1zuiuYHay9USKKFICpNE43CvjUe1EKJIZJCM5Zn\nnsGpwAuAo/3+K8PNsOskXSnp/mb2c9yayT8DkLRn0KE+GBctAjN7TF5f0tuA20YRASwhGQCPAp4m\n6UBcTKOtJJ1gZs+T1Af+AnjYEt4/YqHoSgJLRQBN+dZ83pg3op0L7e01agVN1qlQ1qtWLiyvoHy4\nDnRADpMQQ6spKZLCgrFMExWPBk6W9GLgCpzpHD9E9DgzO9CXeyVwoqQ1wGXAC/P6ku4PZL7+Sxfa\nkCUjAzM7EjgSwGsGrzOz5/nsJwKXmNlVS3X/iAVgKUmgUcAP9/5HCv82od9GFF00h64o5XVV8LYJ\n/JrwLuT9qPJUyWESYhirLURSmAwGSpfhNmY34Xr69fRrgAOD8/OBId+EmT2zwz3e1qUtS6kZjMKz\nGWEiilhmLAMJjNIA1CTo24R/Q53KyI8xxNDJb5GjyRdAs1A3URBGxURUP6715BXkW0EAlP6ESYhh\nlLYwKSlEQlhYp2EzxrKQgZmdjRs/m58fthz3jeiARRBBFzv9kBbQIKzrBFC/bmu+BaTQQhJDaaPa\nXUcLGVQIwJ8rSJNqZcKN2rnV6vn8RmLwF1DQ6OJo2qQQtYSZi2e1qTSDiE2NaZFAWGYMCUxMAFY9\nbqwzrkytjY0+h/r5CN+AhYI+zFMp0MMyCtIJ84KtIvhpIQZ/wyEfAzVtoYUUGn0KUUtoh8EyOZBX\nDCIZzCK6EMEkJBAcdyKBFlJoFe5Nx1ntvOEaRXvqJMHweSPqJODTho5rgr4giQaCkMCSGmnUiSFo\nV51IctIom13VFtpIYdFawgwSQtQMIlY3pkEEiyGBBmFcCOxsuGw9rTinJb9WrriXgcyG79/yvK5Q\n9ZGcQFZwzDAReGEfpg9pBj6/0BqS8cRQuUbe/jopyEotIBT0gRbQSgrRbDSMGXpUiGQwW1gAEXQ2\nCbU4hds0gTBIXCMZZM3pYV57eRsmhyENwTr1/EoikBfYVqZ7wV8SgapEkFTJodAIsrw8jhgyqgSR\nNGgE/v0NkULlc9S0hJAIalrAkOkoagkVyAxlq/85Q0QymBUslggm0QZqhDCkJbT19oN9IeDx6QEB\nVMplVqlT5lkzyZgNt3HUK8l76pg/VpUIEp+WgMkqgr8gh4AUCkLIhX/m07JAo7BSa8jLjyQFVeV9\no5YQskYbIdTyWzEzhLCpW7C8iGQwC1hqImjTBuokMEL4l8K9LFsPNV2eW0OeT0vzusYQKRiQp3ti\naHxeGjQCcEIyUaANhCTgJLL1wJIqObgtIIZ885qB5cI9J4RcICdeYAeaQkEiVu3Mj9USaC4cCWEE\nVvnj1RHJYLVjqYigg2+g0SQU9vrzfS1taM2BBhJIUn+eVvOwnBRq52b++l6KhmaitlEjFSLIhb2c\n30BewAvoCUusJIF8C4khGSaGQrNIcH6DnAgC0455QsjNSK4tNS0hqWkM+aeS/05NWsI0CGGVI2oG\nEasHm5oIxpmCQmE/ggSSNNAE0kADyKwkhQxILdAQ8mNzgj/NNQJ3XhKDf4CQEAonsdub5IWxvAYg\nTwo4TaEnL+C9kO/V9glk/RoxVIjCEQe50A/3+TEUwrlwOvs0ZbmmUpXziyaEcVjN2kGuRc4QIhnM\nMrr+1uumIZ+2ECKoC//iOKVKBhVhX5JAMsg1Ai/0ByUBJIMMPAHkZOAIIgsIIdhyQoBh7SAgAnki\nIEkcMfQCQkiSKiH03T7rCXpO8Gc9odQJ/IIUrNQWsp4Kc1DWqwr6/PUXMt2nG93MRnVCaPy2o5zK\ns2wuWqWP1YZIBqsVC1iAplErqBNB4CPI8ycmgnD5yIoG4I6TSu/fnw/CdCtIIAmONchKAkhTJ/yz\nzJ2bQZp5Esi87yCrkkDeE0xKzUBywt6ZiYR6iScAUOKP+0lBDJY6MlAvKYhBfSfkLQNlIusbZiqE\nf2KG9dx7TfBNMqDn3kf+GZSTgNcaDCqO5y6EoNwR3tUMNMPmojiaKGI20NU8VKlTSoVRzuJWIvBl\niq2NCNIyTak5bSC1UlMYmE/PPDFkBRkwyJwmkGXuPM08IaROyuaEkJNCaCqqwxMASeI2CXqJI4ie\nT+v3IJUjhn6CZY4grG/uuC8yE0kqrF8KesuclpAgr6S0SN2QEHKHsh9lhdcgChIYJbSbNIBoLhqJ\n6DOI2PwxrcXRx5mHAoQmo3CrTyDLCcDZZKtpoW+gTgSFBlA5ztx+kDlNYFAjgUEKWTpECJamXjMI\nCGHogfw7TJIqAUjQ65XpWd+Zj/o9R0JpD/UNs8QJfBNJlmD9QqaTIDKs2IP87bxUDn0DGeVchLxE\nUpKKcMehJahROyiGGrWYi0ZhFrWD/He7xJB0d+DzwO7A5cAhZnZzQ7kjgL/BfYmPm9kHa/mvBd4P\n7GBmN0p6LvD6oMg+wEN99NNGRDKYRSzkR17vLtYEfY6KdtBUzguuuobQOHy0cA6HJDGCCAYZGqRO\nwA8Gngw8CaSZI4GcEHIS8OYiC0wCKsxEgYmoQgCZO867+knPO6N7xevN31ZCQuZnlRUyXSA/IklZ\nfu4rZVaMViocwzWNqyCCET385m9Y5k9dO1hlcL/RZVEN3gScZWZHS3qTP39jpS3Sg3BEsB+wEfiq\npNPM7FKfvyvwZODXeR0zOxG38hmS/hD48igigHKcQsQMo5OJqAusuq/MPg7LNGwKJoM1+h1yh3A+\nXNQ7hpVlhW8gNw8xGFS0ARsMsMHAHacpNj+AwQCbH2CDebL5gUv3W3E+mHdl06yoX5BK5T456WSl\nw9r7KgqHth/SWpKfk+rF8NeaNqX6e+j4zov3XsOsmTymgqzDtngcDBzvj48Hnt5Q5oHAD83sDjMb\nAN/GLQ6W419wywu3feVDgc+Na0gkg4jpQy3HI9C62HzbNUaWr/6spaRaPlGteMu1lLTnLRYdFymP\n2HRwHZTRG7C9pHOD7fAJb7OjmV3rj68DdmwocyHwGEnbSborbtGbXQEkHQxcbWY/GXGPv6LD+jHR\nTBRRNSMHZomJUavbaJ5WsK9tYYTPoX3POVrpgUx+XH5SuWzVSuJ/2hIoRX4egJIEsswNGc3NAJkh\nC7p5OZl4E5GSxJmJJNTrOd9Bbjbq99xxr4f1e34Cmncu9+SHnSaVOQf1GczVEBcqgtuFwe/y9xmG\nxx56n8F7r6PV7BM5qRm5CXE8bjSzoRXIQkj6BnDPhqyjqrc0k4Z1ODO7WNJ7gK8DtwPnA6knhjfj\nTERt934EcIeZXTjuQSIZzCIWIvADQ3I4y7XwSwZFFJQphHwS3NLbyQu9tDB15I5VyBAJ5vcUDlcn\nDBMkcwN98hE/xcgfZ89XljmhnZtx0sCMlLnRR2ZZ+QffNOksqQ0t9YSQ+w5yEqDnRhKRjyjqJyUB\n+CGmWc/PRQiOsz5u38NPQmM4ZEUQtiIkzSLeUZg2TrDnzhoayGEMmcwipmVaM7Mntt5Dul7STmZ2\nraSdgBtarvEJ4BO+zruBq4D7AnsAP5H7ze4C/K+k/czsOl+186qSkQxWIxKbeERRo3bg92UcfZ8Y\nlKv0xoNsJbjonD5UgmU1QgCae7iOEOR7zpIbg58MXMiHJJ+klRhKhfXcPuklMOcdynMZ5ucalDb8\nYOSQdyLndnvX9gYycGzjicaTgicCK3r+TugXRJAo2AckkBNAD3+Mz6OYeFaGsAj2ueAPw1fkmkN+\nHISqaAur3Sjsu/5ExpVbhcNKAVqHHE8XpwIvAI72+680FZJ0DzO7QdJuOH/BI83sd8A9gjKXAw83\nsxv9eQIcAjymS0MiGaxWjCOEBu1gEkKoc4OgiK1TVE2CvydRRCEthFNWlpMnC6XOhu8mUxnqicTP\n3lUKWSo3oqgv50ROHRlkxSxlP9IoNTfsM595nGXljONiJJFvWsMffeHDyGcf+83y2ceeDCyYfZzP\nRM7y81z4F2WCGcg1EsBPQKtoBqppC6rmFWldiUDWTASjtIJZ1RLygQtLj6OBkyW9GLgCJ7yRtDNw\nnJkd6Mt9UdJ2wDzwck8E4/CnwJVmdlmXhkQyWM1YakLw6Y0mI3+t4sSbhvJwzZXjrDQlyU+ySlJQ\nT+AtO254qfzwUqHMuPGy/+XKn57Bhjt+x9q7bMPuDzyAHXd+aBmjyChCU7iROvmonXIUz0hzWaCt\nlKGq5YVsKeCL6KWFXyDY9xj2F4R5LSahoufflNZVG8ifoW4aatEMFkQEq1UrgGXRDMzsJuAJDenX\n4BzF+fnY3r2Z7V47Pxt4ZNe2RDJY7ehCCFARirlQUCjha8X0/7d3/tG2XVV9/3zXPue+F0jICyCY\nktD4C1tIJSaEwa92xF9IY0QoOIoj2FZbKRYYQEwdAralVUYZ1oEiDlsjRLGgtPwsA/khVaygQkhe\nww9fLEVlUCk2gCaA8t49Z6/ZP9Zae6+9zz7nnvvjnHveves7xnl77bXX3nuedd6d3zXnWnOuvJSU\nfr6UtL98NC2yN5pIWsUB+lASOxt1zxMRpPO/+MQdfPr2N+HrCQDnvnIP//sjb2Z6Qjzo8mtiu+5y\n1Sa9QFqumZ3P7Ze4mqg70lammHsZSPPzZmQ/vK9BM8qH4RF/NvLP3UP7JoFeuZDAMEo6ioKjh/SH\ne5Ck0Iz6rXUXZdlMh0ghtzosKeRMWXcIwRje7YxQ/szpdzVEkODrCZ/+2Lu5+MprZ8ig+R7WyrDU\nJHqueON53wXTprRu6zvK2w3cM6Do+/cuUv75+YEQQL/tPBwDEmiwnjmDjcHKyUBSBdxOWAt7Q6x7\nHvAcoAZ+w8x+dNVyFND9Q55HDP1q6ymOATdSm+rAOveFY7QhohIeKs8EVs0jiqxu8qWZiH0AJl/+\nS7Yvat+tTJa5JDDvb35AkXZIIb+2xKh90Jffb98r95X9rKtnOeXfaTPvO87DcSKAhDgYOU5Yh2Xw\nfOAu4H4Akr6FEHX3SDM7J+lBi24uWBHm/YH3SWJAWbTKf/Z6M/JvWzcn1r3QI4DMqpi51qszGF18\nCdN7ZwlhdPElbJ9a8J6eDLvCvJH1oj4aUNL9a8OKvKfkh96zjLKfI98gjqPSnwPRBJUdG6yUDCRd\nBnwX8DLgplj9w8DLzewcgJkNrqstOCTsUyHs5m5bcLYTTj35SXzhDW/CJq2rSOMxp578JLZPbdaQ\nrqSCOE9RyOBA8bOEnBkXZXUPI4RWvww4C9xsZh/u3xjDup8FUF1yyYrFLIAFI8tlsdNItcliN3DP\nwKh4duTc1t/nuquwk5573vIe6i/cQ/WAU5x62ndy38c9EmOaPS83EQZEXqCpbahDOs01W5+5xxJ8\nfi2rn3Fb9awjBqyj/rMPzOqZ97zjjEIGBwNJNwB3m9kdkq7rvfP+hCVP1xLW2H6tWbfnzewW4BaA\nEw+9/Hj9KivGvpT+XFfJgNLt+L5bRd5R7iIGmFmPGNI1i0v8u20l45LvuJL7P/ERTbtw67l4j2WB\nxPFarG/EXKIfOtMgKQI7Hr3FHcuyekvXY31zPZax9jrkZTXzIU1b2vOZFOGx3HG9DRDRbsli3v+N\nY0cSRliSfIywSsvg8cCTJV0PnATuJ+l1hDDqt0Tlf5skDzwQ+NwKZTn22DMB7KT8h3zjfcU/o8iZ\nUfiIEFUc2zhnTb2LStw5mylX8jFQ2KicD9dkOKwtZx8AFzWiyzSc62k73xnZR+VP2Kgm/5ipqbdY\nV3sXY9rasuX13jWkYT4SRrzWkIXPScK6JGE2QBCZVdFEVcfqnBzyL7kLXZf/9seGGIplcDAwsxcB\nLwKIlsHNZvZMSc8GvgV4n6SHAVvA51clx3HHgZLATgTQWQ3TG/U76Iz2XU4AQbknxe+cp8rqK+ep\norJPSr+SZ+TCx2G9co2TMZLHKdzrMJzi/Q0Z+Hgc/qNvSMAcdUMELij5VDYxNYc3F49i6kN56l1D\nDlNz1N41hFCbo/bC+3BMRGKp7KM14TNLwws1FoR1rI3uaqucGGL/N/xwcMRwtEnBChmsAbcCt0r6\nOGGjhn/cdxEVHAz2RAR7JIG8bdhA3jI3UV/5g5wPo38X1FMigHSs5BlVnsp5RopH59lydVD88oxc\n3RzHzbFm7Dwj1VR4xq7GYYzdlCoSQjoCVAvWD9Yxk15LBu1xYhV1PHoTE6uY+oqJOaZWMfFVJIRw\n3K6rhiCm3oV76yoQRCQM79tjsjTMB2IwH/IxJbLoEoAws0gMavZOCB1OT9EnVtBwtPku0Ln/qCG3\nsI4J1kIGMSz6d2J5G3jmOt57nLFSIuj5/GcsAWdLkYDLRv7OGWNXByJwPpa7BLDlarbctFH+J2L5\nhJsGElDNSTeJ5SlOxpbCNYdnS3Ukg2QtREIYSEJTx/TYnqiscZEMukQQPqOsHIjgnI2ZeMc5P2Jq\nFefqUSAFP2LqHdu+YloFYpj4QArT2s1YDD7uqZBIIcR5WONeClFq1iWFrF1DCp3/HPH3ioTQ/MzK\nri+JI00Im7UobeUoEchHECsngnlzAs46bfruoC4JRAsgjv4rGeOqbohgqwqj/q0qEMCWq0O9m3Ii\nfpLyPxEJ4KRaIjjpJmzFcoUP9dRUjWVggRQWaL6aRAIKZXN4HNtWMSGQwHaPDM76MRMbcdZGnPPj\nhhjOuRHn/IiJTdj2I7brUSAE79j2nkldUTnHtK6oJSYCSdQCeeElpHA0HzMCGrTbMIShvQjZXcPl\nGHSXAkL6Cl/W/Nj7sRKOKiGUOIOC44fdWgTxfHZuILXZmQhGlW+sgrHzDQmMq7prCVTTDgGkTyKB\nk5o01kBbngZioI6EECyJCmMc5wwcIQM1QDUnKqvGws6VEN1DsI1j0lgIjrM2ZmIV2zbirI25jxtx\n1o85YWPOacpZN2Lsa0YaM/I15/wom8Mwps7h6jgxXoc9lBU316mbHdtcSNjnUoqnmBvch7kY80l7\nZ4TQ12PKCAFahZ8Rwn5w5AjBCHthHCMUMjhiOIC/68WY5xrKl7ZkxJAmiJuVPdnkcD4xnIhglLmG\nciK4oJr0SGDaIYOT2o7HQAIn3ZQtAgmMZYwxtqRIAo4xDhdJoBpYY1pHbepl1BgeY2JGTc3EaiYm\ntuUYm2dijomN2LIpZ20rWBwW3FHOoiWCNe6pdO5knK3H+IGtNcOKIh+vBeVvdfTzC0wGEor9babe\nqD8UGusgV9RzRv77nUM4WljPBLKk7wVeStjn+NFmdvtAm8uBXyVsiWnALWb2yuz6THqfmO76TYTl\n+79iZs/dSZZCBgUN9pXOILMcBtfzqy03u5TRLgdNZDFqVgkFN9G4+dS9z3SGCMbUHSI4qToQgWCM\nGMtRxaPrkIFjBoLaPB7D46ktTDp7MmWOB6ZU/a3EU4bWePRy1E7NUtPaiYk5XJz09hbcP/kS2Mp5\nvFU4tXsQSHEtUEPA0QJI/dso8gWj/U47DtQ6OHJYj5vo44TNan5xQZsp8CNmdlrSRcAdkt5rZmcW\npPc5C/wr4Mr42RGFDI47DloHZM9Tp9wliP4a//7yzlDnmw+EiV6X+fyBbLQdlptWcYI43B/0cYWo\nJCqEQw0RVJEUFn6XZrs2D+aYEJavVgJvgRgSQdTxmiPKbWnpqqey1jpI3y93F7m8jFFn/QZqSWGh\n0m4nBaQBXVaU/u6wnv0M7oIwP7SgzWeBz8bylyTdBTwEOMOc9D5m9lfAByR9/bKyLPhLKCg4OAym\ndjgA1Gv8L+yPt9/keMGIW6Xu8IEHSro9+zxrlWJJugL4ZuBDsSql9/mQpP8h6dq9PrtYBscd+QqT\nA36eWdc6CIQQffGmZgI3RfPmaAK84gfCck9vjlqxfbylNlHL4cyocdRyVGZ4QiBvjeGstUaaCVjz\n1PhhNxGzbqI0d+CB2tJqo/bjCQFpPi5D9f04hbg6KX2/Oote9nk5+0FyEt2ZUPO2A5eLVbALdJZq\nLcLnzexRixpI+u/AVw9ceomZDe55POc5FwJvBl5gZl+M1Uul91kGhQwKGsz1IixDGJkfOgVFJbdQ\ns5FNdF1YXA+ffOhO1kTgTs3hvOEI/vJxVLQTq3BmVNZOyp5N+xhnPvoK39YZ1PiwKigqdUcghwrf\nzhnMW01kQfl3J5CNiREmkJsVRaNmOelZ2+KsH8clp1Wz2mjiRzEQLQSntUTXI4R+SosszYXNfHp9\n3/wWC34sGygXkpjFAa4mMrNv3+8zJI0JRPB6M3tLdunA0vsUMjhiWLlb2NLyxfZfI7cA4ujfoMmn\nA1GxgUzNsfYO52pq78Kcgo/LLSNBOF9xbp5rpjdJiyNMzsqxpSneOyaaMsazZWFV0SRN/lpNJeIc\nQpCzv6KoWU1Eu8R0EpV2IoGwpDQqfPIYgzFn/ZizNuacD+VzfhTiDeoQb3DOhziDEHPQRifXMW1F\nsBzavEbet5sENcFm0BBD2997/2k1RBTHGRsSZ6AwofAa4C4ze0Xv8ts4oPQ+hQwKOiP/hkzSOsOZ\nQKV5z+iuRbS0pt1biKCNUbLeFCM7gxav5cD7Zm09ABU0M6gZOiNnc3gX3C8ThVH4STdhohFjTdnW\npIk+TsFm4xR9jDFu0lEEmWcT1YVjHni2TUg9sW1VE3zWjPwjCQwFniUymPpqLhFs11UTidymqOjm\nLupbBeYzEmh8c3m+Is3mK0q/4wHjSMUYJKxnaelTgVcBXwX8hqQ7zew7Jf0N4NVmdj0h6ef3Ax+T\ndGe89cVm9k4WpPeR9CnCpmJbkp4CPNHMzsyTpZBBwXJoY5qawox1kBqkpSyWSMHAu8ZN49NoPk3+\nRuWfp4L2VWCD3J8+UuteydM/jFVzTmPOWgg+29KUs5o06Sf6kchVsyyUJiK5jzoq1xR1nCKQJ+Sp\nKAYikCMh5NHHKS1FIoRtn0cfV2zX1Uyuomnt2pQU0VLI8xQlEgh9lp8zSwT5b9h3LcU+L1ZBH+uJ\nMzCztwJvHaj/v8D1sfwB5gzDFqX3MbMrdiNLIYMjiD25ipaxDjpWwgJC8AYuZNxMydVwiwnBO8WJ\nXTDX+tFH5pu5hC1XM7Xgohl7H1I8uFETe3DCjRlneYoSMTgskEFcgprnJwoSDPuGU14iIEYcj+Kk\nb3IRjWKSun46iqrJRzQx15DATG4iHwhgajENhVcnN9G0doMJ64hWQdonIXfJdYig+W210rmCo2kV\nAL5EIBccAayUEEjl5QihTWlhgAsBXGaYsybbZhWPZqLyonKO2jnGrmbqHCPn47FiWyNGLqSt2HYj\nRuplLVXdyVbaWANZDEKKS4B2f4McPrMMgMZVNLFRJ3NpnrQuZCt1TQbTkKRufubSWbfQQObShgRc\nxzUUUlBkFoEfIIKcIJrzVG7/cxSrYA42ZM5gXShkUDAXB0IIcWK2JYVQ52m4AcOFOQUvvDNqJyoz\nKi9q50LaCufZVpVlMa3YzlJZp30MEilU8bySNems02Y4IWisDQhLCIFj/SWuLrqKQv3EVw0xeFyj\n/IP1Mpu6Oin+ZoK4t7fBMumr830NGqVPnHuZN0cAayGCI2kVAGAlN1HB0cF+rYPOM+YRQmhFhxCM\nNneOCC4i2i0gFbNq4tRaCTGx3TxSkKqGFCaxPIqb3aQUFm25bqJ5x0253dimv6lNNUejJRdRinmY\nt8FNu6lNNX+Dm5wAojvIx1VCiQS6G9wwaw0MuIUWThYvmCNIv23nd98lji4REPu7kEHBEcKeCQGW\nn0PIJ5VjXcdK8PG+ZCXUBIsgVvn4XPngOpIzvAfnxFQ0+x0476jk2Y51zc5nUdHnhNDZ7pJU9s05\n9FNidP/wU6BbKLeBYG0wXKhLyr7d8SzUWTxP213Wvs1NlLa+7OxwtiMJ0JkTmCEB6LbJf8dCBHuD\nPw5fskUhg2OAPccezJtDaC7OvKm5FjxCIctmx0qI5ZR22RR25gqZOOMSVG8NSUhGXTucC3sdp8R2\nlQuxCSHZXZvoropbXw7tf5yTwKL9j2HOHsg2HCDW3wc5xQmk7S2TBWBxuWhaHuqj4vbZZHBnm8tF\nJBD7d9AaSMeOwh9YNTTvZ9wBx4IIoMwZFBxNHBQhwJJuIws3zHUd9SwFU1D85hX3QiDEKGiWGNLe\nyE5temyJxkpI5RQBncpJ6SdS0BJaLS13zS2DVN+NDg5t0nnH7WPd9knRN/MBUXHPzAkMuYPiD7Bw\nboDetfSr7NMamHnGUUYw3w5birWikMExwr4IAXZpJcQbLCrd2H7QUkChqTJrwac24d52X4TwvNrT\npHTuE0KeITXVEdvlK4cWJIrsfv14SyKEZlQfr5nNKn6zFHndKv90rU8AHStgHgnkI/v9kEDeZpc4\nNkQQYfVA5OMRRiGDY4bO6H63WGQlNA0G39qxFDqkEImgYy1Yjxgyi6HTFjoEQTpGMdVrA12X0G4s\nA6BDAOlaEyhHrtCXVP5Nlw0QAO25hhT9PBJI1zk4Ehh81pGHFTdRwfHAQVoJsAMpNK4kRS2dJprV\n3DtIDH2Lobneupg6BBEFyq2Cpk0mc6du2a/d98nTKvzu9a5LZ6HyT+cDBEDqgv5IfwkSaO5loM0u\ncfxIIMIoE8gFxwf7thJgb5ZCIoVkLSwgBtKlRtkncgjP7xAEdEkily//jnvRcFkn2YCi7VsF7bUB\nJZ5bGD1lvxQBMNCGga+1T112bIkgoSwtPVhIqoDbgc+Y2Q2SXgr8EG2K1ZRwqeCQsGcrAZYkhbxh\ndppbC3OIIRwHyCF7Z7OUtXmdpfVM2T35+7vXlv6O6TT/Xs21eSP3rmtpUPkP1C8kgLwdhQRW/fFZ\n3AAACWlJREFUAQOsWAYHjucDdxGy5yX8jJn99BreXbAk9mUlQFcBqatQdrQWcmKI90OuwFtF386z\ndgkitJ8jQCKL7HzXGNILA8p5yGoYVNwD1/ZFAPNk3AUKCWQIgR6HLcVasVIykHQZ8F3Ay4CbVvmu\ngoPBvkkB5loL4VJP+TUvbNsPkkPjBZoliCRvO0jPHjbneyy7kqh5/jLKdxmf/cDcw9xy75kzzx1q\nvwcUEhjGcVtNpD3sjrb8w6U3Af8euAi4OXMT/QBwL8F99CNm9pcD9z4LSPuJXgl8fGWCLocHssdN\nIw4YmyDHJsgAmyHHJsgAmyHHJsgA8I1mdtF+HiDp3YTvsxM+b2ZP2s+7NgUrIwNJNwDXm9m/kHQd\nLRk8mPAfxoCfAC41sx/c4Vm377TP6KqxCTJsihybIMOmyLEJMmyKHJsgwybJcb5hlW6ixwNPlnQ9\ncBK4n6TXmVmzEYOkXwLesUIZCgoKCgqWgNu5yd5gZi8ys8vibjvPAH7bzJ4p6dKs2VM5fPdPQUFB\nwbHHYcQZ/JSkqwhuok8B/3yJe25ZqUTLYRNkgM2QYxNkgM2QYxNkgM2QYxNkgM2R47zCSieQCwoK\nCgrOD6zMTVRQUFBQcP6gkEFBQUFBwWaRgaQnSfpfkj4p6ccGrkvSz8XrH5V09SHIcJ2keyXdGT//\negUy3CrpbkmDk+vr6Icl5VhHX1wu6X2Szkj6Q0nPH2iz0v5YUoZ19MVJSbdJ+kiU498OtFl1Xywj\nw8r7Ir6nkvQ/Jc2sSFzX38iRgpltxAeogD8GvhbYAj4CPLzX5nrgXYS40scAHzoEGa4D3rHivvh7\nwNXAx+dcX2k/7EKOdfTFpcDVsXwR8IlD+H+xjAzr6AsBF8byGPgQ8Jg198UyMqy8L+J7bgJ+behd\n6/obOUqfTbIMHg180sz+xMy2gTcA39Nr8z3Ar1rAB4FTvaWq65Bh5TCz3wX+YkGTVffDsnKsHGb2\nWTM7HctfIuS5ekiv2Ur7Y0kZVo74/b4cT8fx018Bsuq+WEaGlSNLdfPqOU3W8jdylLBJZPAQ4P9k\n53/G7B/cMm1WLQPA46Lp+S5JjzjA9y+LVffDbrC2vpB0BfDNhNFojrX1xwIZYA19EV0jdwJ3A+81\ns7X3xRIywOr74meBHwXmZZPbpL+R8wKbRAbnC04DDzWzbwJeBbztkOU5TKytLyRdCLwZeIGZfXFV\n79mHDGvpCzOrzewq4DLg0ZKuXMV79inDSvtCIdXN3WZ2x0E+97hjk8jgM8Dl2fllsW63bVYqg5l9\nMZnJFvZhGEtaJqHVQWLV/bAU1tUXksYEJfx6M3vLQJOV98dOMqz7/4WZ3QO8D+gnSVvb/415Mqyh\nL1Kqm08RXLnfKul1vTYb8TdyPmGTyODDwDdI+hpJW4QUFm/vtXk78I/iSoHHAPea2WfXKYOkr5ZC\nAmRJjyb04RcOUIZlsOp+WArr6Iv4/NcAd5nZK+Y0W2l/LCPDmvriqySdiuULgO8A/qjXbNV9saMM\nq+4Lm5PqptdsI/5GzidszLaXZjaV9FzgPYRVPbea2R9Kena8/p+AdxJWCXwS+GtCKux1y/B04Icl\nTYGvAM8wswOdQJP064QVGQ+U9GfAvyFM1K2lH3Yhx8r7gjAK/H7gY9FPDfBi4KGZHKvuj2VkWEdf\nXAq8VmH3QAf8VzN7xzr/RpaUYR19MYM198ORQ0lHUVBQUFCwUW6igoKCgoJDQiGDgoKCgoJCBgUF\nBQUFhQwKCgoKCihkUFBQUFBAIYNjCUlf3rnVvp7/akkPj+UX7+H+KzQnU+qC9l/Jln32r79U0s27\nlWOVkPRCSZ+W9POHLUtBARQyKFgBzOyfmdmZeLprMtgj/jimSFgZJB1YXI6Z/QywktTOBQV7QSGD\nAqAZXf92TC72W5IeGut/RSEv/O9L+hNJT4/1TtIvSPojSe+V9M7s2u9IepSklwMXKOS0f31/xC/p\nZkkvjeVrFHLkfwR4TtamkvQfJH04yrbMntlIeomkT0j6APCNWf3XSXq3pDskvV/S38rqPyjpY5J+\nMllPCrn53y/p7cCZWPdMhZz+d0r6xRiAhaQnSvoDSaclvVEhlxGSXq6wF8JHJf30Hn+igoKVopBB\nQcKrgNfG5GKvB34uu3Yp8ATgBuDlse4fAFcADydE5z62/0Az+zHgK2Z2lZnduMP7fxl4npk9slf/\nTwmpBK4FrgV+SNLXLHqQpGsIaQquIkShXptdviW+5xrgZuAXYv0rgVea2d8hZLjMcTXwfDN7mKS/\nDfxD4PHREqmBGxVy7/w48O1mdjVwO3CTpAcATwUeEfv2J3foh4KCQ8HGpKMoOHQ8lqDgAf4z8FPZ\ntbeZmQfOSHpwrHsC8MZY/+eS3rfXF8dcN6fi/gnp/X8/lp8IfFOyOoCLgW8A/nTBI/8u8FYz++v4\n/LfH44XA44A3xtQ5ACfi8bHAU2L514B8BH+bmaX3fRtwDfDh+IwLCKmcH0Mgxt+L9VvAHwD3AmeB\n1yjsyDWzK1dBwSagkEHBMjiXlTW31c6Y0rVGTy5xjwgj+ffs470JDrhnD3MLf9WT57Vm9qK8gaTv\nJuT2/77+zTFZ27cRcvY8F/jWXb6/oGDlKG6igoTfJ7hWAG4E3r9D+98DnhbnDh5MSGg3hIlC+meA\n/wc8SNIDJJ0guJ1SKuR7JD0he3/CewhJz8YAkh4m6b47yPa7wFMkXSDpIuC743u+CPyppO+Nz5Kk\n5Jb6IPC0WH5G/4EZfgt4uqQHxWfcX9LfjPc/XtLXx/r7RlkvBC6OqZxfCPTdYAUFG4FiGRxP3Ech\nC2nCK4DnAb8s6V8Cn2PnLI9vJox2zxB2lDpNcIn0cQvwUUmnzexGSf8OuI2QWz5PffwDwK2SDPjN\nrP7VhLmJ0wr+l8/RunMGYWanJf0Xwh7WdxNSkyfcCPxHST9OyMD6htjuBcDrJL0EePec74KZnYn3\n/qYkB0yA55jZByX9E+DXI9FBmEP4EvDfJJ0kWBU3LZK9oOCwULKWFuwZki40sy/HSdLbCJOqf34I\nclxB2BR9z7t+SboPYbLbJD0D+D4zW+n+15E8HmVmz13lewoKlkGxDAr2g3fEyd8t4CcOgwgiauBi\nSXfuI9bgGuDno/VxD/CDBybdACS9EHg2wcIqKDh0FMugoKCgoKBMIBcUFBQUFDIoKCgoKKCQQUFB\nQUEBhQwKCgoKCihkUFBQUFAA/H/eLoyJIRtAVgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x111181cd0>"
+       "<matplotlib.figure.Figure at 0x126cc6fd0>"
       ]
      },
      "metadata": {},
@@ -431,20 +234,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Note that both the particles (the black dots) and the `U` field have moved in the plot above. Also, the `time` of the particles is now 518400 seconds, which is 6 days."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The trajectory information of the particles is stored in the `EddyParticles.nc` file. It can be quickly plotted using the `plotTrajectoriesFile` function."
    ]
@@ -452,17 +249,13 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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NsO8dWe5ON++vhkqeLDmecj+RqdJK4RKTImqmxAwJLvTESUxYMXDXcjyUZMN/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\nYYHmM4k5wFeBraafD4zZIwUASqnnMWbofAGoBLoxWmpDRTh6/x3Dx/u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Iau6sM60Mr38Ht/U72ecOy2kFTMV+Me7vfUPUIrYw+W+G22bRmX3/kcjrU79u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\njUYTENpgaDQajSYg/h9CLcL5zsowFgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11127f210>"
+       "<matplotlib.figure.Figure at 0x124241ad0>"
       ]
      },
      "metadata": {},
@@ -485,10 +278,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The `plotTrajectoriesFile` function can also be used to show the trajectories as an animation, by specifying that it has to run in `movie2d_notebook` mode. If we pass this to our function above, we can watch the particles go!"
    ]
@@ -496,472 +286,766 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
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+       "ZGF0YQAAAAEAAAAATGF2ZjU3LjU2LjEwMQ==\n",
        "\">\n",
        "  Your browser does not support the video tag.\n",
        "</video>"
       ],
       "text/plain": [
-       "<matplotlib.animation.FuncAnimation at 0x1120fcf90>"
+       "<matplotlib.animation.FuncAnimation at 0x1248f12d0>"
       ]
      },
      "execution_count": 10,
@@ -975,10 +1059,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now one of the neat features of Parcels is that the particles can be plotted as a movie *during execution*, which is great for debugging. To rerun the particles while plotting them on top of the zonal velocity field (`fieldset.U`), first reinitialise the `ParticleSet` and then re-execute. However, now rather than saving the output to a file, display a movie using the `moviedt` display frequency, in this case with the zonal velocity `fieldset.U` as background"
    ]
@@ -986,11 +1067,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# THIS DOES NOT WORK IN THIS IPYTHON NOTEBOOK, BECAUSE OF THE INLINE PLOTTING.\n",
@@ -1005,20 +1082,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Running particles in backward time"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Running particles in backward time is extremely simple: just provide a `dt` < 0. "
    ]
@@ -1026,25 +1097,18 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pset.execute(AdvectionRK4,\n",
     "             dt=-timedelta(minutes=5),      # negative timestep for backward run\n",
-    "             runtime=timedelta(days=0),     # the run time\n",
+    "             runtime=timedelta(days=6),     # the run time\n",
     "             output_file=pset.ParticleFile(name=\"EddyParticles_Bwd\", outputdt=timedelta(hours=1)))  # the file name and the time step of the outputs"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now print the particles again, and see that they (except for some round-off errors) returned to their original position"
    ]
@@ -1052,25 +1116,21 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "P[0](lon=3.299995, lat=45.999901, depth=0.000000, time=0.000000)\n",
-      "P[1](lon=3.299851, lat=47.799862, depth=0.000000, time=0.000000)\n"
+      "P[0](lon=3.300004, lat=45.999912, depth=0.000000, time=0.000000)\n",
+      "P[1](lon=3.300233, lat=47.800236, depth=0.000000, time=0.000000)\n"
      ]
     },
     {
      "data": {
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klsSUE9FtT6yG9D3CzB4QLQBxIvAFMzsAv9TbyALRiUQisZYYvs1g0md7Yi38oKOA08L+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\nAR4Z67kB+Lakx8eyJOm+8ZpLgMfF41lBzS4GTpF0m1jGrSTdOV7/IEm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OsP3Y5B5/Bvxgs3XNnkEmk8l0sLUoz+DewIW2L7K9G/hb4MROnjsC/xLu6wuAoyUdDiDp\nSOARhCUxx1DorvkY4J0bec6ULAaZTCbTw5zjDA6tp82Jn+60O7cCLkmOL41pKV8kTN6JpHsDtwGO\njOdeA7wQqCZU8/7AVba/vuEHjeQwUSaTyXRYRwPyNckSlRvlVOC1Me7/JeALwFDSCcDVts+WdPyE\nax/PArwCyGKQyWQyY4QG5IWMM7gMOCo5PjKmje5lX8tobWMB/0lY7/ixwCMlPRw4ADhI0ttsPynm\nXSF4FPdcREVzmCiTyWR6WNDiNp8DjpV0TFzU/nHAe9MMkm4Sz0FYC/kTtq+1fYrtI20fHa/7l1oI\nIg8GLrB96WafFbJnkMlkMmMsagSy7YGk5wD/DJTAm22fJ+kZ8fxpwE8Db5Vk4DzgaXMW/zgWFCKC\nLAaZTCbTS7WgwIntDwAf6KSdluz/G3DcjDLOAs7qpD1lIRWMZDHIZDKZDjb73eI1s8hikMlkMh2M\nGFTbazqKLAaZTCbTwyJGIO9LZDHIZDKZDgvsWrrPkMUgk8lkxtDcE9HtL2QxyGQymR72tzWOZ5HF\nIJPJZDrk3kSZTCaTyb2JMplMJhPIYaJMJpPZ5uTeRJlMJpMByL2JFomki4EfAkNgYPtekm4K/B1w\nNHAx8Bjb31tmPTKZTGZdeDET1e1LbIX0PcD23ZIFIE4GPmr7WOCj8TiTyWT2GkxoM5j12Z/YE37Q\nicBb4/5bgUftgTpkMpnMRAwMqmLmZx4kPVTSVyVdKKn35VfS8ZLOkXSepI/HtAMk/bukL8b0P+y5\n7vmSLOnQzTwvLL/NwMBHJA2Bv7R9OnC47Svi+SuBw/sujGuJPh2gPOSQJVczk8lk2iwiTCSpBF4P\n/AJh/ePPSXqv7a8keW4CvAF4qO1vSTosntoFPND2dZJWgU9K+ifbn4nXHQU8BPjWpivK8sXgfrYv\niw/3YUkXpCdtOy7oMEYUjtMBdt76qN48mUwmswwWtbgNcG/gQtsXAUj6W0J05CtJnicA77b9LQDb\nV8etgetintX4SW3h/wZeCLxnERVdapjI9mVxezVwJuGLuUrSLQHi9upl1iGTyWQ2wpxtBodK+nzy\neXqnmFsBlyTHl8a0lOOAQySdJelsSb9en5BUSjqHYCc/bPuzMf1E4DLbX1zU8070DCSdO8f137b9\noAnX3wgobP8w7j8EeAVh/c8nA6fG7UJULZPJZBaG5w4TXZN0jtkoK4RF7R8E3AD4N0mfsf0120Pg\nbjGUdKakOwMXAS8m2NSFMS1MVAIPn3JedBZ27nA4ofL1fd5h+4OSPgecIelpwDeBx6yvyplMJrNc\nFjjo7DLgqOT4yJiWcinwHds/An4k6RPAXYGvNfWxvy/pY8BDCespHwN8MdrXI4H/kHRv21dutKLT\nxOC3bX9z2sWSnjXpXIyR3bUn/TsEBcxkMpm9kjA30UKi6J8DjpV0DEEEHkdoI0h5D/A6SSvADuA+\nwP+WdHNgLQrBDQiN0K+y/SWgbmSux3Pdy/Y1m6noRDGw/clumqRDgKNsnzspTyaTyewPeAGege2B\npOcQ3uZL4M22z5P0jHj+NNvnS/ogcC5QAW+0/WVJdwHeGnskFcAZtt+36UpNYGZvIklnAY+Mec8G\nrpb0KdvPW1alMplMZk+zqEFltj8AfKCTdlrn+NXAqztp5wJ3n6P8ozdfy/l6Ex1s+1rgl4G/sX0f\n4MGLuHkmk8nsjTg2IM/67E/MIwYrsQvoY4CluSiZTCazN2Fr5md/Yp5BZ68gxLs+Zftzkm4LfH25\n1cpkMpk9iRgupgF5n2GmGNj+e+Dvk+OLgEcvs1KZTCazJ9mO6xnMlD5Jx0n6qKQvx+O7SHrp8quW\nyWQyewiHdoNZn/2JefygvwJOAdagaeF+3DIrlclkMnua7TaF9TxtBje0/e9xpFvNYEn1yWQymT2O\nWcw4g32JecTgGkk/RZwtT9KvAFdMvySTyWT2Zfa/rqOzmEcMnk2YSvoOki4D/hN40lJrlclkMnuY\nqspi0CL2HnpwOgvp8quVyWQye47QQLy9xGCe3kSHS3oT8A9xOuo7xhlHM5lMZr8lj0Ae568Jg86O\niMdfA353WRXKZDKZvYHctXScQ22fQZhND9sDYLjUWmUymcweZrtNRzGPGPxI0s0Y9Sa6L/CDpdYq\nk9mOVNqzn0yDmS0E84qBpIdK+qqkCyWd3HP+REnnSjonLp15v5h+lKSPSfqKpPMknZRcc1NJH5b0\n9bg9ZLPPPI8YPI+wotlPSfoU8DfAczd740xmW7EvGOO9vX5byYJmLY1rEbweeBhwR+Dxku7YyfZR\n4K627wb8BvDGmD4Anm/7jsB9gWcn154MfNT2sfH6MZFZL1N7E0kqgAOAnwduT1jq8qu21zZ740xm\nv2R/NpqTnq3Yz4LnNYt5rHsDF8ZemUj6W+BE4CvNbezrkvw3qu9s+wrimK7Yeed84Fbx2hOB4+M1\nbwXOAl60mYpOFQPblaTX2747cN5mbpTJ7HfsCcO/UQO1zKr2fQ/7gUDMGQY6VNLnk+PTbZ+eHN8K\nuCQ5vpSwrGULSf8N+B+E5Swf0XP+aMJCN5+NSYdHsQC4krDm/KaYZ9DZRyU9Gni3vb+1n2cy62AZ\nxn+r/kWt9z6bfdT9QCDmtHbX2L7X5u/lM4EzJf0c8EckC4hJujHwLuB340Jj3WstadNf7jxi8NuE\ndoOBpOsJfya2fdBmb57J7NUsyvhv4p/p5v+JjzPXC++0+270a0m/z71cGBY4N9FlwFHJ8ZExrf++\n9ick3VbSobavkbRKEIK32353kvUqSbe0fUVcfOzqzVZ0ZgOy7QNtF7Z32D4oHmchyOyfbLbB1D2f\nCcizP3PfY9pnA/edKkLreMaJ7O2N0yao5qzPbD4HHCvpGEk7CDM+vzfNIOl2ijOBSroHsBP4Tkx7\nE3C+7f/VKfe9wJPj/pOB92z0UWtmegaxcl1+AHwzjjmYdX0JfB64zPYJku4KnAbcGLgYeGKf65PJ\nbBmbMfwzmOvNfj3GdCOG16zvbT7mnVT3XhvYzbver3Qv9BpcLaAMeyDpOYSBuyXwZtvnSXpGPH8a\nYbGwX5e0BvwEeGwM/dwP+DXgS5LOiUW+2PYHgFOBM+JsEN8kLEu8KeYJE70BuAfwpXj8X4AvAwdL\neqbtD824/iTgfKD2Jt4IvMD2xyX9BvDfgZetu+aZzGbYiABsxvjPetPeSJnrISljw9GPKSIxVuZm\nxGGvEIbFDSqLxvsDnbTTkv1XAa/que6TTPjmbH8HeNBCKhiZZ5zB5cDdbd/T9j2BuwEXAb8A/Om0\nCyUdSWgZf2OSfBzwibj/YfISmpmtZL1hiXWGW8aumfFR1VNOpdZnLDSxnoFkPaGNVvl9IaJqHc8w\n4buY+j2ux77vyTDSBkJw+zLzeAbH2W66ldr+iqQ72L6os+BNH68BXggcmKSdR+gj+4/Ar9JuXMlk\nFs96jcmMOP9c+TtpY9d13zoniM1E5jVEUx7d6qlH9+0/rYTn9Co0XvepnsM8Zda/4VZ5Cl5YA/I+\nwzxicJ6kvwD+Nh4/FviKpJ3EpTD7kHQCcLXtsyUdn5z6DeDPJb2M0Aiye8L1TweeDlAesumR1pnt\nyIJEYC4B6Lwlj9InG/12vg3cc7302P1W9dS+R0ssUpGYJhCdMvpCSxOFYT2iAMsXhv3szX8W84jB\nU4BnMZqp9FPACwhC8IAp1/0s8EhJDyeMYj5I0ttsPwl4CICk4+gZYAEQB26cDrDz1kdts58lsynW\nGwbqYfxNfvLxROPfl2eWGMwjCgugZeO7xry7nxj4XoFIKj21urOEYa/zFrJn0ML2TyS9AXif7a92\nTl/Xd0287hTgFIDoGbzA9pMkHWb76jjVxUsJPYsymc2zbBGYJQDuO9dJ84T0nnvOFKRJ6ZO+hllG\nPy1SHaHo23pcHLqew0Qz3RGGTXsLyxCEBfQm2peYp2vpI4FXAzuAYyTdDXiF7Udu8J6Pl/TsuP9u\n4C0bLCeTGTGvEMwTm9+MAPQdb1YkJtWhr74w0bin53tDRHHrxqi3y7ZA84pDx2twWlZ3nwWIwqK9\nBPdVZv9mnjDRywmTLZ0FYPscSces5ya2z0qufy3w2vVcn8lMZCtFYIoAzGP8J+bp2Z8oFhOeZSIT\nwkGtN3+N0lpCMUkg0jKmiEgqDKnHMM1bWJgoLIDtNvnOPGKwZvsHnZ5D2+xryux1LFkExryA9QpA\nz3l5SvqkY8aPe59p8qmxt/hUCJycU3IsJee7AqGkjMR4TxKGUZ0U/z/bW5gqClv1wr7NrNy8vYme\nAJSSjgV+B/j0cquVyUxhUUIwNWzT4wVMeIMfE4Aeoz6W3icQ9T79Zc8MF/WQGvt0Wxv2ppiO8W9E\nYMJ++ul6DY0w1AY+2XfiomxYFOb1EjZLDhON8VzgJcAu4J2EYdV/tMxKZTITmUcIli0Ccxj6ica/\n6jH8M8pYV+Nz5zFCxtHW3f2OgW+JhMBFvyAonusVhljHPi+jfW4OUajL2GpRiL/VdmKe3kQ/JojB\nS5ZfnUxmClshBJsVgSo5Tgx/c8x4vtY1Y0Lg9jkYF4dJdLyAdN/SuBAUHYEoCPPzdIUjioCqaOAL\nej2GrkewblHoikN89q0JHannRvs3E8VA0v9jyp/bJnoTZTLrZwNCMLFtYIo3sO6QT2rwq570KACN\nwa/a+yNB8Hh5ra3bglCN6jNG8lU1b+/1Y0px66B98XzYTwSioH2+SLyEapSGwjM2ApGIyiTj300f\nPcJIFGZ5CVsiCLPEdj9jmmfwP+P2l4FbAG+Lx48HrlpmpTKZFpsVgnV6AxsVgZahdzutm6dl/Ov5\niSqC0W/ld7teMBKGnucefQGjxxsJAFhuH5epELgx+nVaVwwaQSgIQlfve5Qv3qIlCnU5co+Nr69t\nEjXZS5gVNlqkIGQxCNj+OICkP+us5PP/Osu8ZTLLY1lCsF5voC+sM0EEVBEMZefTGPdWejwejjyE\nligYaITDSf0JIaRJX0ndtacJ8YwEgGjkW95AbezLKAICF4k41MKQiEIjDg7eQp8o1B4DcX+il5Bk\nayouj7yAboZJXsIiDfiCypL0UEJ3+hJ4o+1TO+cVzz8c+DHwFNv/MetaSc8Fng0MgffbfuFm6jlP\nA/KNJN02WdD5GMKizZnMctkqIeh4Br2NuD0C0Ptm3yMCLYM/duxO2ugYx+O63aByIxKh/m492+hL\nqLf1m70aQQjegHA814hBqcToO2xXRiKQCkNVKrQTJB+K0fflxPg329rg18a/FpGkyo29b4z+BEGg\nvd8bNtos6d/JJojrubyeMMvzpcDnJL3X9leSbA8Djo2f+wB/Adxn2rWSHkCY8POutndJOmyzdZ1H\nDH4POEvSRYSv/zbECeQymaWxh4QgjcWnjcAtEega/1QUhrRFIDkuhsnx0CMBGLgjBvEzrI1/3MZj\n7GaqhEnegaVohBXFQPGtvkiMv4KXEPcbISgVPIRhNP4riXdQhueaJAqJDQ9aVSQiEM+5YNT4nPxU\nffZ+qiAsmQX1Jro3cGHyMv23BCOeisGJwN/ENeY/I+kmcSnLo6dc+0zgVNu7AGxvetnLeXoTfTCO\nL7hDTLqgrkAms8eY14VfhhBM8AaKYbrffvsvBqO3/uZcLQL1dlg1YsDQqKrC1oaqakRAVaxoVVvc\ncaRg6GshUAEUIeDvUlAUUQSKkSCUI2GoSkEpqtKoimkrDuGg6EXg4EFQ0nhUQDsc1FQofrW1EBQd\nbyDJ5iT/VA9h2d7BYrgVcElyfCnh7X9WnlvNuPY44P6SXglcT5j77XObqei03kT3qONW0fh/cVqe\nTGZhbGBKgV6vwFPOpde5fc16haBo3vbb3kAxDAJQDGje9IthRwSGVSIIFRpUIyGogjgwrEZiMBxG\nUUiEIBWEpq1gJAZSMPi1GKgMQqCiwCvFSBjK0VYrwsMgCF4BR4PvQtimKkVhcGmqpvU3+Y5LcJXY\n9GIktmOCMKlhmcTAuyMI6b2WJAhzDuw7tNOGenqccXnZrAA3Be4L/AxhCczbRu9iwwVO4i1xttFp\nX++bgLtv9OaZzIaYNzzUumb0Zzyt11DvuXUKQS0Cqmg8gmJYG/90v2qJgAZRAAbVSACGFVTD0XEt\nClEQPGGZXErBAAAgAElEQVSh3kYAYpiIsgxiUAiKEpUFrJQwKKAM57VSh4iMqyAMuGhuh4MoVIjC\nIW8FFDg6LR1T0SMITbtC+ltFgz5REOqE1u/Zk7ZIzLwvJdd0Oth0uYz2Al5HxrR58qxOufZS4N3R\n+P+7pAo4FPj2PJXuY5oYHAyczfSvfMM3zmR6WdREYxO8gr63vVY3za5AJPstLyLtJdTTPjBVCNYq\nikYMEgEYVGgwDAZ/MEgEIaR5OBx5BFEUXItD8zDx+4tewMgbKKEsoihUUBUwrNBKGfZdW3qF9ogY\nVisMVVwdt0BUmNCrNL6paxQPaqagIG6DUjRC0giBGfUoIhGBaT99X7goPZd4BwtjMWV9Djg2dry5\nDHgc8IROnvcCz4ltAvcBfmD7CknfnnLtPxLWk/lYXBdmB3DNZio6rWvp0ZspOJPZa+jGDrqGPdLy\nDvryRSNWC0PfqON2w3G7YbgRhSqGeYYeF4LhsC0Eg0EQgXjsVpiowlUdKqpblOOy5oXCfhGEwMNh\nePsvqyAKVQllKAOvJG/iZXiIChiGLj/FoGoEoTb+kmGosI29lULbRuyd1DX+tWdhRsa7u6U/1DMx\n/LNk72ARwmJ7IOk5hGl8SuDNts+T9Ix4/jTgA4RupRcSupY+ddq1seg3A2+W9GXCapFP3kyICObr\nTZTJ7D0s8s1vSrkTDUEqIMl+/6jhuB26aYeoewWpqsKnFoQq9QLc7DdCMBwGL2BYBREYDkMVoiiM\nGNLMDyEjK+Qpi7HeOqP2hSq2LQirSoy8sYMllkMoSLGHkazGyKfb+ntpjH+8X593MNGQ199pvbun\nGoYX05sI2x8gGPw07bRk34TxAnNdG9N3A09aTA0DWQwyGUgsT6CZibMvX7rfOW5G3Ip2d8r4oh7e\nnEcfRyM8SivCAi0xn8qyHUe3oSpQGYRARUV4aUzrEd/ii9hWUIeJmjaDUTpl0ep1RN0FtT5u6l+f\nU/P9jAa0dd7c655D9Tb9rrp5++iUvSdoPL9tRBaDzL5Fx2jPd00nzqDOG3IdadEoazNFQm3UawNl\nRoOpasOfxMXDsaA0tqjKkL0JkRRQrYiiDrukj1MLw7A2vAUMhyGtNB4WaFgm3Uzr7jldtyY8mYpi\nZPSLRBDq7cpKEIOVMnQxXQm9i1yWYbuS9DBqBpwxGpdQ0oSIiM89GtnMmFi0jHt3v/4peoz/RPFY\ntlDspX1Vl8U8y14KeCJwW9uvkHRr4Ba2/33ptctsP+q34s0SrWzrDb8WgY7tbM3Hr07extCNim0a\nRke7oYcNbo5pGls1ytPY+IKqMBoKFUKlYKDQhlAKhmUIGw2Go95EVexumnQvDXUJD1P3KlKrzSAR\nlbpBuUzEoEy6liZblwXVShCQaiWKwYoaIahid9OqFoky2e9+EiFtxKFoi8OYB9DnPWj0w/WeS37L\nhZE9gzHeQPibfyDwCuCHwLsIfVszma1nWkhnkufQvPL3tluOerWkfeCrkQh0y0yFoor7lRQ9gzie\nQGFEbzEIhlKlKIbGg6RRuRRUBcXA0eCXaFDhZKxBM9CsbktIGpBDZdzTDpCIQtOzKA4ySwad9Y0x\nCFNRqNmm3kC1QuMpVGU0+Iko9ApC7SXUcxupk5aGlfpEoc/Yb8FLew4TjXMf2/eQ9AUA29+TtGPJ\n9cpsZ+bxDuYRhCRmrcbazycIzYkqbOvpE1qho3gOBbusKtjgMGIXVI/eLUOahqYaCq1GsahMVRVo\nYKp0Copkf9JUFGo6/9eV90gIoNlvpqUoivaUFPWoY9EJBSmOPE7CPnF6itbbf+t8jzfQIwRdARjz\nDDrGf5Te8Qg6gpH+DSyULAZjrMUJk0I7kHRzFtbOnslMYBGCAI1RHyV39uoy0l4ujskanW+FkkJc\nKNjfdKwByX4VBSC+3AfjriTdUSBoT1YXy6x7IDUT1rVEgEYIJr29jgxnFL+0YbgI6bWhb8X7Wwa+\nL/QznkYiAlONf0cEmnp2hKDdtjBBCJYpAkDTZXgbMY8Y/DlwJnBYnAfjV4CXLrVWmQwEQYDpopAa\nfUaGYUwU2tmCFESL38x34ySDO59iZPxbXUer8W1rOot6v1Jncjs1IoDVHtkc1yvQML1XZ3Gbpp4T\n1KDxDBgZ2JjeMtRRCBoD3hjyJG2CgR81HCfeQLpdhwC00uMD9vVQmikCixSG7Bm0sf12SWcDDyJ8\n1Y+yff68N4hexeeBy2yfIOluwGnAAcAAeFZujM5MZROiAMkbfcJowjOPeg4lfSGb0FJHIFpTL7ud\np28QWu9xs9Xo2mpCenLvZobSriBMoPs2PbbUZTzXMuypoY6N5K3jzvn1hXvadWrVr69xuC8s1M3T\nc35R5DaDiKSbJodXA+9Mz9n+7pz3OAk4HzgoHv8p8Ie2/0nSw+Px8eupdGabUnT+dfaJQ0+Se86n\nAjF6uXY7c217Wx3mGX9Dbxns9vW95ztGvu+4Kbt13/S1ePw5e+l7k+59E+8a58kGvXtt97qp55MH\nm9UG0Moz4Zkyi2OaZ3A2NM1otwa+F/dvAnwLOGZW4ZKOBB4BvBJ4Xkw2I2E4GLh8IxXPZMbEoUst\nFn0C0U3rKarxDjonxyIzyfHY2+QEAz5zJtUZdZt6fpaxnGZw5z2XVHLmm/o8Bn7StdOY9ftvluwZ\nBGwfAyDpr4Az47BoJD0MeNSc5b8GeCFwYJL2u8A/S/qfhKa4/28D9c5kZjPBWKynwXFuezChTE+L\nNUw6t9433zmEbSLT8k74oqaGT5ZgQPdIuKbr5W0D5mlAvq/t36oPYnjnT2ddJOkE4GrbZ8epsGue\nCfye7XdJegxhGuwH91z/dOKKauUhh8xRzcx2ZFM9Saa+BXs834Rwx3g4xKNwlMbzhe0oTZ3z48fd\neq/TSiUP5h6PpNVW0gmJkXbFcijLdTlNukbV6qS1ru3cN63XNG9ojw0Ezr2Jxrhc0kuBt8XjJzJf\naOdngUfGdoEDgIMkvQ34JUI7AsDfA2/suzguEHE6wM5bH7XNNDrTx6IMf6/R7zP4ybmuoW+MfBMb\ndxzw65GBT/JJ7fNFk+Y4Hmx0DLTSIKwZ0MxO3bGc6hw7ecAq7tciUK85YIvKikMX1HvsuqwmrT5W\nmOw0Ode0NNeCkN43HrfbRUTaRpNOctf9kaaF1JZF+vNvF+YRg8cDLyd0LwX4REybiu1TgFMAomfw\nAttPknQ+8PPAWYRRzV9fd60z24oNicCkt/71GP/EoKtj+BGo8MjoF4nBj/u1MS8LUxRVnA2ioiCk\nhXMVhRzT4n78rGh0DDT56v2wHX99rZph0yMxqAjGvvVJ0gZVEQQBMawKKoetDUOH46oapVVVqIkr\njYQinnM1Egk1ImGolBh9jwQiEQ9a55PfrvY+Wj9qz+++SLIYtIm9hk6alW8d/BbwWkkrhLU7n77A\nsjP7EZsVgZkeQOdtvysArTd/dQ0/qKgoitGbflFUjZEvimDcy2jwy6JiRXEb91ei8Q/7w8b4rxZD\nCsxKMaTEFDG9lCmoGiEokzhGnVYlDz2MsyQFw18wbISgYM1FaztwwaAqufifL+RLp32Wn1x9HQcc\ndiA/9Zs/y80feEeGLhhW9ScRjWq0rVwFYaiCcIx5EBJqeQ+MZt+uY/S1MGiUp8kA1BNvtLoLL8No\nb1GbQey1+XfA0cDFwGNsf6+T5wDCS/hOgs3+B9svn3a9pFVC1OUe8Zq/sf0/ptVlnonqPkbP1237\ngbOuTfKeRfAEsP1J4J7zXpvZnixMCDYlAp23/iKkFYVR4ZYA1NtScVtUrBbDlvHfUQ4bw7+jGLJa\nDFnRkNWYtqr4ifuFKlY1EoSwX1FGb6BI/lnWacPUK0DBiBPf9KkFoGSIWKtWWHNJZbHmki+9/1L+\n41WfY3B9WCvh+qt+yAV/9hFusLLGEQ++A4OqYHdVMqiKRhzWhiVD9wmDGq+hqsK+CjdpYXRbDH05\nrKvsJpxUewV9fwRbKApb4xmcDHzU9qmSTo7HL+rk2QU80PZ10ch/UtI/2f7MlOt/Fdhp+79IuiHw\nFUnvtH3xpIrMEyZ6QbJ/APBowmCxTGYpLF0IJoWDinFPoE5TMV0EVuLbfy0CK0XFjmS7oxywooqd\nxYCVYsjOYsCqRtsDirUoBoORKGjAjigKOzSkiEJQEkNNU+ZLqEWhIojAsBGBgjUHEajTrvcqay75\nzOvOaYSgKef6Aef/5We4w8OOZuCC3dVKSxTWijJ4FcOSYRXEZzAMQlEpiIIkKglXccReHGXtehrX\nygg1Zj4chf32D8vIY1AIM1nJG3x9bkFs0XQUJzIaZ/VWwktzSwzi4jfXxcPV+KmfdNL1Bm4UIzA3\nIKyGdu20iswTJjq7k/QpSXnEcGYp7AkhqN/4+4RARQgH1W0BhUxZBqNfFI5v/2YlvvWvlsMxEdhR\nDKLRr9hZDtipNXYWg0YAdhZr7FBI39EIw4BVho0IBFEwOxgmYSJT9li/YXzoYWwTGEZBWHMUBUrW\nXLI7isKaV7jeq3z/yut7v94fXfUjDly9nkFVsqsasrsqWalW2D0sWSkqdg9LCjl6CqF9RUNTqQ4p\nFfH7rkL4CBJPgTCzquOkfuFsWxDqsFE8nCkIi2I+YTlU0ueT49Nj55d5Odz2FXH/SuDwvkxxJoez\ngdsBr7f92RnX/wNBKK4AbkjowTl1oPA8YaJ0JHJBCPEcPOu6TGZLWG9oKB6nHkErT8cjIGkUniYE\nqy0BCIKwoxiwMxGDWgB2FmscoEHcrjXG/wCF4x2Nh1CxgxBGWlUw/KuCElEHg8qxfqeBoU2FGeKw\n1DKwFoVhzQW7KVlz0XgF13sHh95ylWsuXxsr6+Bb3IAbl7vYpZXYfrHSNGbvrkarrDVhq2EJZRXD\nB3FWv6qgKpqj8HulgqBg0Sd6CPWPO+HtvyUIi6DVXjGVa2zfa1oGSR8BbtFz6iWtW9pWt2vY6NwQ\nuJukmwBnSrqz7S9Puf7ewBA4AjgE+FdJH7F90aR6zhMmSkciD4D/BJ42x3WZzLpYen/yaW0EoQYx\nH825urtnHRoa9RAa9Q6qG4NXWo3Dw6lCcMNiFwdMEINVVRygATsIIrAqWEWUKigQqxSNCBQUPQ8K\nlaqRIChs12x2u2JNZtUVaxSUmN2UFJhff8HhvP7Fl7Hr+pE9Wj2g4BdOuj07i/HIcNojiTLEIZrj\nqsCFgYrKZfgeIfnOg/V22lis+BtM+kNIhaDjHSyDRYmL7bFxVM09pKsk3dL2FZJuSZj6Z1pZ34/t\nuA8FvgxMuv4JwAdtrwFXS/oUcC9gohj0/yW1+Wnbt7V9jO1jbT8E+Nwc12Uye5557UT94tkabObR\nkpeMzofZoEddR2uxGIlC7BkUwzilnLQDjLcL1EKwGr2APiFYVcFOSg5QyapKVijZqVVWFbbpZ5S2\nwk6thGso2KmCAyQOkDlA1eheCuGohz7qxjz7T27FoUesguCmR+zksa+4Az/zS7do6lt3dS3r5617\nRcXvo9mmYySS/bHvvPluR+e733n9W2w5nuOzed4LPDnuPxl4TzeDpJtHjwBJNwB+AbhgxvXfInTd\nR9KNgPsm1/Qyj2fwaUL3pJR/60nLZPZNOoZngqc+NhJ4Ur6UQhVF0gW0ZDRuoEvdOByuC29qJaKU\nKBl5AkXjJfS/yxUUVJ3hswVi2LFeJWaN2C3WFVBy/ImHcN9HHsb11Sq7vMr11SrXO6m/+tspJtFa\nb4cYzhFhmo5Zb/RKQkV7QAu2SH9OBc6Q9DTgm8BjACQdAbzR9sOBWwJvje0GBXCG7fdNux54PfAW\nSecRvvq32D53WkWmzVp6C+BWwA0k3Z3RP5mDCA0Smcz+QafTyjIZzrjRkIJVqrC0sdL0ej3lioIQ\npw/78zj3myMdxBbqsokvy0xcgqGPPTYKOES4ln8b+zuE5QG66ZcDD4/75wJ3X+f11xG6l87NNM/g\nF4GnAEcC/ytJ/yHw4vXcJJPZl7DV+9bfTMeAqVAz7UM9lcPoU1C5oiL03w/GXXEkb+jOuUOD0D9f\nox4+oXfQWojlK7y115ZzVbBGvTjzkMqOb/vBYpVxzeNhfIuv4qt0RcWaK4aYNVesYdYcGpN3UzQN\nymuUId4fxyJUTXfUldArqemiGnsodUYwN/tRKCqPRibX31HrG22msJj0IzDyHPaEV8CWvR/sNUyb\ntfStBNfk0bbftYV1ymQWR/et33Xbo0YHSb5GCKIxclxhzKqNfpgMtRaAYVVQlG4M4aAqYtiloKhK\nVjRkrSooipLCpnRF4Yrrq9VR15rOy/2oa2gV/wu9gkrEGlXsTaRm29Q/ob6mwrEhGXbbrCHWPN6j\nKPQqWolhodVmu+YyDE6rSgYuGVRlGLFchf1BPVVF8mmmrGjNedT90Kl3/Rzq9xwmhZSW2etgT3kl\ne4hpYaIn2X4bcLSk53XP2/5fPZdlMhtmYR1D6n6GY6NTx/sf2sSotGMwO1xn6n01k60ZsMMi9lAh\nhV4zTTdLgBKKyhSEvve7qvF/YkMXVEUYGTxUeAuvVLCmkmFRsOqSNa2Mupc6NCyH9gaCoABl7GY6\nVn60YnWX0jDegMb412MOamO/O44zSMVgV7XK9V5hrVphV7XCLq+yaxj3q5Uw0MxFMwBt97BsRiXX\nXpBNMyLZ8TeoRxkTBbWZ5G6eBllP2Gc54aQ9FqLaQ0wLE90obm/cc26bfU2ZvZbE4E8UkxnegYkN\nnXEnCEQUhUrNy3s1+h9QMBRQVBTV6NV+d3KbURilaD5rLtlZDGLYRawpDAC7XqscUKxxvXc0vYt2\naECBm3EHdeNzSdVqiO426KZzEtUDziqL3Q5jl3e7jAPPVsJ+FIF6NPKuer9aZVecsmJXtcLuaoVd\nw7AduGD3sGwJwdqwZFgFgRhWCpPZ1R5C1fYKWiJAvT8S3rFJ7MY8iS1gm1m5aWGiv4y7H7H9qfSc\npJ9daq0y25at9A5UewDNTUed2A1h4foinKuqfkFopoaO4ZBVhmG/FDs8bMRgECeF2+lBCK+Uwdge\nUKyxS6vsLNa43qusasgODfhx0vW0JM5L1JmKIhWBIjYn1zShJhdNvH/NKwytRgQqisborzWjkctG\nGBovoCobb2D3cKWZiqKZkqKKIjCs5ywKQjAYFr1zFDWzmlYxMj9LCJrftLtV8zuO5VkEWQzG+D+M\ndyPtS8tk9gyTvINUECAZtDTeXhCK6BeEOoQ0yUMYAC7cvPGulENsMSgKdpRDBlXBjjLE2XcXK+wo\nBuyqVthZDNhVrESjv6OZoC4dg1DIjYdQxm6qZfQO0umrS6rGI6ipYkho2Mxams5PVDaeSvOpSnZ5\nlbUqpO9u2gqCCNTeQB0eqkVgWBWsxW3fZHVV7RVMEoJqghCkv+88QrBIzFbNTbTXMK3N4L8SlqS8\neafN4CCg7L8qk9k8G/IO1hMuSgSh+b/BMio6gpAKCcEDkIObYIrQwFyJqjDDQpQOjcnDoqJ0xbAq\nKIuK3VUZ5ywq2a0VVoohP9FqMmldMO6rRRVnMg2eQD17aT1z6Whbj0fot4b1VNb1TKWh3aBoZiut\nUDDmFAxcNgIwqEK7Qt04XE9MN6iKXhGoJ6ervYF6O/IICOscxKmsazEAxQnrpghBK5TEmBD0/r4L\nJLcZjNhBaC9Yob2G8bXAryyzUpnMwgTByet/UzhtQYjXGeEqeAHNNBWF4tw5ScUoGNoUdhQQY5vC\nwq4YVqIsCkpVDIrRega7VXamrAjb6ztrGazEkb71iN90HYMyXc8geXUtYnfXlGbmUo/aDSrCW3wt\nDLXxT9c0qI1+ZTX76bTV00TAMOYN1J7AaOpq5gsNzRCCpYWHllnmXsy0NoOPAx+X9Ne2v7mFdcpk\ngAV7CN2Q0eguzYVjjcoIojgAeEjwCGJSFcusRaGyqarQNTX1FFQVrUVugoEfzWUUFrIZLXRTKCxs\nM5raoYqC4CY0NKnxuKZuM6jbLKBe32DUhlF19rsCUPcKqgWgHl/Q9BCyWp4AZooIxO+6JyxU/1bz\nCsHYG/uSjHb2DMb5saRXA3cirGcAsK7FbTKZjbI0QUijQSEntRTU0yJbHS9B0cjhXlFQ5TA5m6Cq\njFRQtNY/KMIkd/X8PkWFogA08xsly2CmS2CmaTC+3GUht1Y5g9HI4VlLX9aGfzQwrh4roNaqZvVy\nl8O6QdhJW4CZLQLTvIH69xkTgOQcW+ANpGVnMRjj7YRl1U4AnkGYDOnby6xUJpOyYUGA1tCCiWGj\n0Z2iUMwvCpZQFdY9sOqQUhAFyQyreqK2olkPuV4Ws57ALV0TuUmL6fU+MLYe8iwaEUi29X693nFt\n0Os3f5uW8bfb+3X+1AsYW95yCSIAWygEW3mPvYh5xOBmtt8k6aQkdJRnLc1sKRvuctrxEiARhWle\nwpyiEBqe40hluZmIbViN8lUarYkwrEKZ9Wyn0mhWz+5xEINQo+4soK3ajg2k63oIiQA0+zRTatQi\nUI8D6DumOWYkANEDmCkAjI43LQJpniUicm+iPurVLq6Q9AjgcuCmU/JnMkthU4IAsxuXU2FQvKAW\nBRM8gsTmEQ1/4y2geInjuVo83MpbG/1R+kgggJYo0EkP23U8utP9flFoBoMleSYZ/9TwTxKA5r7J\n8dwiQOea1sPM/9yLQOuZUW8/YB4x+GNJBwPPJ4wvOAj43aXWKpOZQOvtfr30hI5CcqoSnfyNFxGO\nm7fwScKQHLtueO6IQ7i8LRB1Wl3G6D7t4+5jT5pGu+sduJvu0XE6mVzXmDsx3GPGvy6nxwOAHgFo\nVaS73bOewBhp3ZdIXEny74CjgYuBx9j+Xk++mwBvBO4ca/Ybtv8ttuf+EmHw+zeAp8YFcH6BML31\njnjuv9v+l2l1mWcN5Hre7B8AD4gVy2KQ2aMsShRaZXXnrKg3qbcwQRhadVFSdrJvabTPyNhPMvyt\nMmo22sUl+aK6k8R1vYH0XJ/hHyuj+yafpPW/+Y/XqXV9N/8eYot6E50MfNT2qZJOjscv6sn3WsLK\nZb8iaQejZQQ+DJxieyDpVcAp8fprgF+yfbmkOwP/TFiSYCLzeAZ9PA94zQavzWQWxkJEASZ7C3W+\n1GtojH6Sp7bzcnJQ1yspvDHwIyVykr/9LB1r1POM/VNtT/gyWlk1nt4TqhmbXbTP8KeV7jX64+W2\nyuit38ZYqAHfGjE4ETg+7r8VOIuOGMTIzM8RlhTA9m7iNFi2P5Rk/QxxDJjtLyTp5xHWpdlpe9ek\nimxUDOb+pxdX5/k8cJntEyT9HXD7ePomwPdt322D9chkgI4h36ww0PMHHhuRW3nTmyrZSTWile5O\n3lEoaay89sUTk+a1V/3TQqf7/UZaE/L0Gv0pZY6V1c27CZb1Bj9nA/Khkj6fHJ9u+/R13OZw21fE\n/SuBw3vyHEPowfkWSXclrEt/ku0fdfL9BiHk1OXRwH9MEwLYuBis5+s/CTif0NaA7cfWJyT9GSH8\nlMksjK5xWIQ4tMptRCEJvfTdY93GXa3NzPLWgaZ9CbOEYlJaT5m9VVygsd6i0E0IA853r2ts32ta\nBkkfAW7Rc+olrVvaVn8j0AphLrjn2v6spNcSwkkvS+7xEmBAGAqQ3vtOwKuAh8x6kGlzE/2Q/p9R\nwA1mFRzLOBJ4BPBKQmgpPSfCep158Fpmqczzj3pdgjFNKNqpcyVNvM1Y3o2o2mTWZVi3ojF1L2gn\naLGg+th+8KRzkq6SdEvbV0i6JXB1T7ZLgUttfzYe/wNBDOoynkIYB/Yge+QDRvt7JvDrtr8xq57T\npqM4cNK5dfAa4IW05zaquT9wle2v910o6enA0wHKQw5ZQFUymcksxRBVmzPeizX9C6bY2yz3Ykk6\neS2b9xIG8p4at+/pZrB9paRLJN3e9lcJax5/BUDSQwk29udt/7i+JvY+ej9wcncJgklsNEw0E0kn\nAFfbPlvS8T1ZHg+8c9L1Me52OsDOWx+1f//lZbaejRrqGX+JUw3IpHNz/HUvyjDN9ICmnU/Pdb6/\nTZU7iT0tOFszzuBU4AxJTwO+SYiWIOkI4I22Hx7zPRd4e+xJdBHw1Jj+OmAn8OHYKeEztp8BPAe4\nHfD7kn4/5n2I7T7PA1iiGAA/CzxS0sMJcxodJOlttp8kaQX4ZeCeS7x/JrN+oz/h3/9csfBuE8G0\nGPvMvOuoxxy4rzmi56tpGfWxQQ3tm6d5x5pDuseeIRh956b9dlsgFFvhGdj+DuFNv5t+OfDw5Pgc\nYKxtwvbtJpT7x8Afr6cuSxMD26cQ+rwSPYMX2H5SPP1g4ALbly7r/pltynqM/7wGd8rxrN42Y10w\nJ6RNLLPLBg3UtFHLvUa9r6trUkg93KJlxJOK94lK3eu29/7d55r1M6a/8zKEwaDh4ovdm1mmZzCN\nxzElRJTJrJt5RWAjAtBryNv96rsGfrwffs99ZuSdlH9inTuMvYn3Gvi2UXePGKibrp40km6y6ZiN\nOD6j1S23IxS93YLXIw7LEoZtFpzeEjGwfRZhMEV9/JStuG9mG7AJEZhHAHrf/NNzfQZ9mkAkaeu9\nvru/LjHo2beS9J60Zj81/n1lTsgzmhCw9go89jgtJglDetF6PIZNstf1bloye8ozyGQ2x6JEYB0e\nwETj3mO8u8ZdnW1fGdOuh44dnNdQdSNXE4x/06aQHKtj2CVaAtF83M7TlJ/szxSGNFSUhpVgY6Kw\nWcxWNSDvNWQxyOx7zCMEGxCBqSGgeQVgivGfeb6nnN77T3i+8QdOHrHvjT/maQShviZNq9/6OwIx\nVRyYIAzR4DdTdABiirewh0UhewaZzN7KIkQgzdP1BOYUgbkEoBo/pyo5pnM8j4jAaFrlPi+mrlZv\nSEiMiUGP0bcIq7h1BMJFx/gXtEQiNfguRsJW368lIvFcryjUHsIET2GsHcSj8wsni0EmsxeyFd7A\nBkSgNujd43q/EQY6+asegaiisZ8mDk2d2g/b+/aczqZah2V6jL8L9QtBkaRVneuSc02bQUxvDHSP\n8bZOLRcAACAASURBVJ9bFJpnSA404TmX4CXIYRnT7UQWg8zezyKFoE8EYvrUN/5OeksEqvY2PZca\n/8bgt/Y9lq829jKhe6NpCUCrIbr7nMkjtQVALUPeFgLH/dF5F2qJQ0hLDH412ldyLR55C/W5eURh\n9AihPo0XkL75J/tb4SXkMFEmszexASGYFRaa5g3MFQ7qGP+WCNT7dXrnE653J31k+EMet0WhuY97\nnmGCxWp5BUChjleg+HbvYPjLeK6I6YUTEQhpFCNRaISiNu5VxysoosEuOsa/Tq+rWV8Sr3OTOsVL\nmCUIiyKLQSazl7BZIejZ35AQTHrT7xOBrvFPDX5XAKpaANxOMzB0IxIYNHQTQmp5CIyOR19CKgRx\nv1B8y9fI6AsoFQx/MS4OLhNhaIRAyT7B6Nf79fcXjXZt+OtQkosoGpO8hNGlNP1U+7yEWYKwILJn\nkMnsK8zyCDr5ukIwKyw0Mf7fCfm0BKH+DNtGv0iPB20BKIaMjH+VGP5qlEZVi4Gj8MRKzvQMFI2x\nYkhIwcjX3kKpaOCjka/FoUyMfglVKgxlvyhQ0jbYtRDUW0bGvw4r9XoJrZ92DwlC7YltI7IYZPZO\nZnkFs/6dzhEaqs9P6/I5Kebf6w0M0/2OCAyNhlAME4Nff5r8RsOq2acRhSoKTRXjMlMEIRUBwls7\nEhRFSCsTQSiKtiCsJMKwEgx/VQoPQWUiChW4DN5CVSoY4/pT0rQp1F+/krf+locwSxCatoI9KAjb\niCwGmb2PDYwinbuxeI7QUJ8QjO3XQtASAKIAjEJARRQBDYMHkIpAkQrCMAhB8BCSbRXEgWEiBMOK\n0L2HiZ6BpKAE0RtAQmURBQBUxP1SeCXsexiFoIz7pdBKMNpVBa4EjiEjx3YEBy+i8QTq770Yfacu\nRsmKQjG3IDQJSVeilCUKQu5NlMns7Wzk32hiJWa1EaSfsVDRFCFoi8AoJFQ0xh80iPuDCipTDKI3\nMIiGf9ARgOEwhCuqYVsQUi+hjygAFMVoWwgVJZQFFEUQh5UCDcLW9XbFUQwKXImqFEV9K8dQkoPX\nEGz7BCucdEmtv79GEKLxb0RA/cY8Pd8cd3sZLYmtaDOQdFPCUpVHAxcDj7H9vZ58JwG/RXjqv7L9\nmpj+R4R1lCvCwjhPsX25pKMJK0x+NRZRT209kSwGmX2eubyC9NwkOmGi3lBRX0PxmChMFoJiUEVB\niNsoABoEw6+hYTAMxn4wCAJQRVGwcS0OqSCMfSHRSkYhUFkGD6EooCzjNuxrGMWhisc2dhE8BAcP\nABd11IcCBeOPKDAViSAkE9U1WzEKGdU9iervlbDfDgmNLk1FIJybzztYCKmns1xOBj5q+1RJJ8fj\nF6UZJN2ZIAT3BnYDH5T0PtsXAq+2/bKY73eA3wdqo/+N9awvn8Ugs3exwInG+pjqFUBbTPpCR50y\nRg3IaW+hyR5B0zYwcBCB4TAIwaAKYaHBcCQCw2H8VEEEbBgOcS0ClZtwkWNIQ0VtFZMQUSIAqqqw\nX5VJGWXs3eMY9DeshNiOVYRnEL2CIAURUPxiLDU9hqi9guQ7c/Lduf6e53nT7wsHLdE7CI+zJWpw\nInB83H8rYULPF3Xy/DTw2XolM0kfJ6wH86e2r03y3YhNSFgWg8y+xSZDRNNIDf+YQHSOWwO/Oh7F\n2LbuFTQMxrfuLaQ6HFQlQjCs+oVgOIzb0F7gKATuxLU9rAVhCCpQWWI7eAdVyK/V5J/9CkGAByBK\nrAqGwciHuH58iCqWa0J5Dp5D/abfu62/U/U4MKkh7xp62udansNWUs3OAhwq6fPJ8elxlcZ5Odz2\nFXH/SuDwnjxfBl4p6WbATwiL3jT3lPRK4NeBHwAPSK47RtI5Mf2ltv91WkWyGGQyPUyKSGyqvG6i\nFN6k6/h+ugLNpNVoCkGy6IoKjQlCPDHyEppro7fQd49YF2JXVE+6/zz0XbonjPkmmdMzuMb22Apk\nrXKkjwC36Dn1kvTAtqXxvzrb50t6FfAh4EfAOSR/BbZfArxE0imE5S5fDlwB3Nr2dyTdE/hHSXfq\neBItshhk9i3andDnvGa8ZbLP2Ndp6bnWVM5JaKKbno7IxTSDsGw1/e9dEt6oy1hwKRRj8i6N6m43\nEBqMATQMBr8QKmK3nviWX4d5QognvsYqlhFDRKobkMsyeAdlp91gZSVpQ6i7m4Z9inQMQjquoO51\nRCu9meKi/j7q/Um/Y9q2kPwGLeHQ2E+3NdQhtIUU5QdPOifpKkm3tH2FpFsSGoH7yngT8KZ4zZ8A\nfatEvh34APBy27uAXfHasyV9AziOxKPoksUgs3dReDHtBhNEIx39WhudJpwBbQOV7Ldm5ixGjZ9N\nAfGGQQxEteImtl7EgioIb+xyeAFPPYJSUMS2g7KAYRlaomMvIsX2glbX0tpY9Y1ALoQ6XUu7jccU\nBayUoWtoWY66mK4UYcxBGbZVGcciJPvVCu2BaXHMQTplRUske9K7k+bN/D2T33Dst1oCWzQC+b3A\nk4FT4/Y9vXWRDrN9taRbE9oL7hvTj7X99ZjtROCCmH5z4Lu2h5JuCxwLXDStIlkMMvseHUPfesuf\ndC6Z/CadHK0btm62tdGv+8SnxVa0RtQW9X0RVTT0qsJNVPejj9M2MBQqjCoFb2AotFqEBuXV0KvI\nfWMMak8gCoK6QlBvmx49SoSgGPUmkqAsggBEw08Rjl0m29rw14PRSoU25zggrRmRXLb301HLXSFo\nvKhkPxXdaVNrh2dKftgJArBQL2JrGpBPBc6Q9DTgm8BjACQdAbzR9sNjvnfFNoM14Nm2v19fL+n2\nhL/KbzLqSfRzwCskrcVzz7D93WkVyWKQ2fvYgHfQKwhxu25BqM+13vxpG6+6z7xCVVVFgz9U2Fax\nAbYM4RRVoIGoKlOsaNSjqCravYzShuakgbkZcZx2J01HIY99H2qmoag/9QjkeuBZawRy/SmCCJAa\n/2jsqxWS8NC4N1CVjIy/Op5AnLKiOzvqeoVgzNj3eQyLwOE3Wza2vwM8qCf9ckJDcX18/wnXP3pC\n+ruAd62nLlkMMnsnswQhteD17iRBILXno8TUG2gGPdW9hIpOhmRbj55VtMut+Ylqw1+pGXdQVcET\n0GqdboqBOhPYxTmIkgnqVMVePHFcQTqLKfWkdbO+nzg7aTOFdRFEwaXis6RzEqVtAKnRbwtAd16i\n1oym6qSJMRFohIB+EWind0Qg/ZNYlhA0hW5NnGhvIYtBZu9lHg+hJywEIycgJI7yhbf+RBpietp3\nvSUKdRQmDRmZ4BnE82PTVXTSValznOZXe6xCvX5BOmNpLQLpcRMmmvCdQNNzaGzq6sYwx/3u9NWJ\nQW+mr26lMTLwPcZ+4naekFBd/z0pAvVt8nQUmcxeRFGHRNbvJUBHFNJzMWw06gefuBBpeCj91Fka\nA01irEciMTZyuU6nTkvEoZVXSVkeL9uMPJ/O8077XlKD2g3BNFNaJ0a9MfzpW3zn2r42gEn5mzr0\nHI83Brtt4PvEIH2mbr5Fkj2DxSKpJHRnusz2CTHtucCzCX1l32/7hcuuR2Yfp0j+YU4Shm6yGTMs\n3V5Do3YBt64LWzWjZNMRs63Rsz0GetosqPPn0ehx0nt07dMke9VjULuGtw6Ppee/f/7ZXPWvH2Bw\n7fdYOegQDjv+4Rx853v2Ckld1pjRT8odE6K0bnO+/bfyTHrGRRMFfDuxFZ7BSYQJkw4CkPQAQheo\nu9reJemwLahDZn+imGABuyLRYyy6c+B086SzZwY1SbK7nW+s0L7z7j8eS29VcsJ9OufWxSTjmqRf\n+8WzuepDZ+C1NQAG136PK/7pDIY74KC737P3un5D3jHyPfefy9j3XTeJSX8TG0R4q6aj2GtYqhhI\nOhJ4BPBK4Hkx+ZnAqXFQBLZ7B1lkMutmAQZh3hI85Wiu65f5VrtBvv0v72+EoMZra1zz0fdzw/vf\nfQ/Vag+SxWChvAZ4IXBgknYccP84n8b1wAtsf657oaSn///tnX+wLEdVxz/fnt37XkICKCEQSWKi\nAoqKmF8FAlZERY0BRKHESvyJIioWv6IlYCn+KimkQMRSiRBFwV8oKIX8EAVLFEOAZwKYIIVCqQiG\nKAkgvHd3p49/dPdMz+zs3r333d27N6+/r+bNTE/PzJmeu+c75/Tp08CTAKrP+7wVi1lw2HHSynVH\nn3T+uT5wTs/1Mfvl3C3XnPLu9sA982oLRkXZUIN0qmumvP7k7Qyh/uTt1HeJI6LjdWctnZ51NK9v\nI5Nr36yeDPs6UKyQwf5A0pXArXEo9OW9e34+YQTdpYQBF19k1m35mOzpWoAj5593ar2Vgrk4KaU/\n11WyQNFnkS1dv7eFPoje8XBeOhYHoPXq9ssh1k3reBzAxWtrZr3z43a6QaLgae1N2Xg1YSZGZ92N\n6W13zFxndNbdGd1l2qbaiB3gaR0ahriIzviM1Cne7LfbHddbn1TYG1nsm8VlhDEepxBWaRk8FHi0\npCuAo8BdJb2SkFPjNVH53yDJA2cBn1ihLAWHGHv+ge+k/JdR/DOKnBmFjwijijNl71wod41yNyrX\nHkvllbJt53EyHLFebwFwUSO6TFO6ntb0WYOlbY/wpnY/bpuJqbmwfuLD+fCvvgl/Yto24ZEx9/6e\nyzl62nYzv0E6z3w754H5liBkhP0QptUlCbOudWHZi0nH43bbv6PZ17kOPV0sg/2BmT0LeBZAtAyu\nMbOrJT2ZkGb1bZLuB2wBt61KjoLDi/0ggf0gADplhlx7XM53FL9zPmZ8COWV8zgCEVTOh0WeUVT8\no5ntuiGEkasDWWA4hTpVQwY+rocVVqv0HTU5ATgm5vDm8JEIvDmm5jj7Uedz5tbX8YFrr+f4rZ/m\nyD3P5MInPpx7POK+mH2O2jtqL2oLa+/DNbzvEoT3oSHNZ1aEF2osCMvIgV50VU4MzVuM/2dWQ/6w\nK9HZVshgDbgOuE7S+wmz9nxv30VUULAnIhgiARhw49BR8E397Cs/d/k0yj9+/Q8RQOXadSXfKP+x\nq6lcUPhJ8W+5mpGrG+U/btY1Y+dx+LhdByJR2HfyHTKodoh9rHENGXTW5phYhTcxsYqJVUx9xcQc\n93j0F/DAKx/P1BxTX8X1Z9n2FVPvmHoXySAsk7j2Xs26sR46xGAdCwJoxlaYtQn+1IysVmj/rvO4\neVmmzI2k7uF9QW6lnCJYCxmY2d8SZvDBzLaBq9dx34LDiX0jgiFroOfzH7QEHDRuIGczJKDo5mm+\n/BvF75uv/7GrM8XfEsCWmzKW54ibNgRwxE2j8o/ruGwp7Ds8Ww0Z+GgtBCKo5iTQqc3hYza92sTE\nRtS4ZntiFTWuIYMTfhy2fdoPdbb9iImvIhlUDSmk9chXDTFMfWsx1F74OKeCeeEV3Ueyxr2UUn6o\ncSPNIYXOH0d8R5EQ8tc8Sx4niTLOoKDg4HCgRJAsgr414KxHAq0lMKqCcq6cZ1zVHRLYqsLX/5ab\ncqSaRiIIyv+Im3LUTeJ+WB/VhLGmbKlmHIkgEUKFMdaUCovuokgGc7Rfcg0F60BsW4XHxbXYjoQw\nsRHHLRDBto044ccctxETP+KEH3HCxky8C9t+xLYfsV2P2LKa7bpi23um3jHxFZV3TGtH7QzVjlqg\nRASKVgIxn4dP/QoWGt8bQo1DKOyF7aaDOrcA1HYqDFoJ+4AyzqCg4DBhnmtoTp2FFsEuiKCqfMca\nSO6graoOROBqtiIBHOktR92EI24SlX/N0WY7EgQ1W5EcnIwtgispEILhCNMfAFRztF+NUQedG91D\nsJ2IgeASCkQwaojhuG1xXGOO2JiJC9bCcT/lhEaMnWdce04ouLu2fRXaoza2FbYnqkITp3l2ZHgF\ngvAhUSoeT9iJat/HTmYFYtgrIew7jJA6fMWQ9HjguYR5ji8zs5nJZySdB/weYUpMI0yt+eJenWcC\nLwDuaWa3SdoCXgpcQvgzeGr00MxFIYOCjcG+/a7nxe73O1sHiCBZEHn4Z74ki8DFSKAqcw0tQwSn\nVyc4qmlDBoEIthvL4KgmkQiCO2ksY4xRCcaISg6HqFBYz4kxrc3wMmoMjzExo6ZmYjCxmm05JjZl\nbKOGFMYWLRI/5YSNqfDBNSUfEsdUi5vdm/ApOZ4R5kmwGFkVOUCmMImPdy0xd17UAgWfu4HSduYy\n2tcxBuvrQH4/YbKaly6oMwWeaWbHJJ0JvEfSW8zsZmjI4pHAv2fn/BCAmX1lzPLwRkmXmtlchitk\nUHDnRl+v5FbBQD3lRJKFkoqcGIKvvg0PbUNCUxjoSD4q82A9JAtgHF1Awf0zbdxBQ0RwVJ5xJIGx\nXEMAY1XN7GlVmuayhxqPx/B4agsWxYRgXThCn0NyMeWuJo/DK/Q31HLBzSTH2NWhb8ApWBoxVHWa\nP7PzoZNYCkSahciaCH0BsnZAXHLdGeSf/dKAHl6lFTAPayADM7sFCPNgz6/zMcKcxpjZpyXdAtwH\nuDlWeRFhcG8+S9oDgLfGc26VdDvBSrhh3n2G/5IKCk4l9H6H80b29n+veb3umACflYfooBQCmnz+\nIUrIYodwu4SoocwdFL/++0RQyTHWCDfnXzhexe1wbliCi6nCmsXFDulEEknmKnZYh31rOq9z8mie\nudd30Q6W6zVzbo3t9E4SiayZAxqY7bzAWZLenS1PWqVIki4Avhp4Z9x/DCEJ6E29qjcRxnmNJF0I\nXAyct+jaxTIoKMh90YcIHo9b8D1Xz/cIHCgORb+s0c4ZsRi3mdkliypI+mvg3gOHnmNmg3Mez7nO\nGYTZy55mZp+SdDrwbIKLqI/rCP0Q7yZMh/kOgrNvLgoZFBT0YNGdMVs+Wy9HPsK3LQvf3Km8TiOB\nJWpTcMVYcMfU0TVTmTFBVNHn76z90p5YzVgVJEU/h8Rq67qJPGmB2oIcafGmICNxHWVOcvk4LVkK\nTa0ZeO6Bsn6bNd4g2Nnl04SYZuesFda28cleyewbTvYaksYEIniVmb0mFn8xcCFwU3QznQsck3SZ\nmX0ceHp2/juADy66RyGDgjs3+l/9ltzUvR7HFLCSE0GMfU/u6pTDx8zwxLh5CyNyp95wlQU/eoq0\nMRcW73AuxO87i357i64ia11F4WsUvEToM4xRQVGpO2Ashzdr+wzmRRNFAkgdyLUZEyx2IIttXIwo\nqjhu4yaqaNsqtuPYg/4yjYPTpj6MR5jGkcuJBKfeNX0JTZqKLH3F4LtpXkF7fNByWHt/AWuJJloG\nCpr+5cAtZvbCVG5m7wPOzup9BLgkRhOdDsjM/k/SNwLT1OE8D4UMCjYGK+8j7IecWOKAVB57Lq09\nlqdNCPohxMzX3uFip6pkYdBV3onsq6ZfwFlYjvtxuG+IsQRHGLkrUeM4qgkeMaGmRozxbFkIKz0R\nO4Gd1dHnr2ZsXD+iqI7aNISVhhDTSVTSOQlsW8WEKo4zGHHcj5sxB2k7hJeOwyA0n8517Qhln7a7\nI5M9ovaJPMlyGXXL0nto1ov+AGxgO89ztN9Ygz9L0mOBlwD3BP5S0o1m9k2SvgB4mZldQcjz9t3A\n+yTdGE99tpm9YcGlzwbeHHO/fTSevxCFDAoON7Iv/4ZMktJPxwYGJIXDGTlkdVI+HcUwx751UPug\nzSdUNDGXFYNz5nqCO8hnS22OiUJs/xE3oVaI+Z+4UTPo7LjGHNWkGWw2lqdKVoXazl8YSlQX1sEN\nFL7caxRHHYcRyIkIEglsW8UJG88deHaiHmUDz9rRyNu+YrtuRyHXPrqSvGtHIsccRhbzGDUpKRpC\nUEMEbb6izEWUE8Q6XUbriSZ6LfDagfL/Aq6I23/PEr1aZnZBtv0R4P67kaWQQcFGYd+sgz4hAG16\nA+uUNdZBxh7mFb/gDXCNm8anr3oGCIH2aze5S0aNG8XFJHFVJIFpmwpCY464Ccdt3Iw4bkNQ6zjy\nOET0bMX0FECM9plVWM3o4+T7R3G7HXkcUlGMGiugHY3ckkBKS3HCj5haxYk6jECemoujj6tm9HHt\nHZO6aoigrntEkOUpCu746Dqylhiad2Sa/dLv10nvmJ5VsG/6e23jDDYGhQwKDj+GrIMFdaL5QP5/\nhxBioPs8QqjV5tEZRULw+KD0qhqPmHqPr8Qorqe+Ylo5Rt4zdRUn3Iix6mY9dltNWoq0VDFhXQo5\nbQaBxRBPolRDyPMSpU7glJJiYlWTrG7SpKWoGmKYWtWkoEgkMDUXiCDLS5QTwbR2nWymQ0TQpLke\nIoJkFfTfWaqTlzXvcIUwwG9Gn8G6UMigYOOwJ+tgt+6iRYTgrUmPYF7t6GQcdRxR66OZMCWkX7AK\nzAWCqJyjcp7awmCtqbkmhcOWq9l2VZOx9EhMXBeS1m1l4wxC4rrgJmqT1KW8RClBXT++H2gie+os\nCshHUgjZSvuEUDUEkGcwDa6gUdMn0M9cOknuIdNM5tIOCdiSRDDkHupbAwN/K52/gf1EsQwKCg4p\n9pMQsosZhMRqBnIhbFLmQwewhcghM/DOqJ1ChJAXlXPUkRjSHAYtEYQcP8cHUllXtHMZjBqLII50\nxloimJOxNCGlqw7iq5O62hPST/umQzhECKU5Dtr01a5xC9Vxe7ZvYKf01dBNXx3XfoAEYD4RrMU9\nlF1wQ6KJ1oVCBgUbiT33HeyGEEItEhWkHDemYBm0Ka2j0krnOsMs5N1RTHltOLwH59SQgjyRCNoc\nRiN5FFM35HMcpBQWLiaCc7JmPoN8Ypv+pDbVjHM9oE59F7G/AtoxDlOrYqd2Gxo69VV0b7kmRHRo\n/oLQAb6YBMzAvItrtR3Fy1gD8T10+xCy8vTWVkoERJkLGRQUbARWRgjNDWi/VOOBxW4jmpBIuXAt\ni/0H8tF9ZBXeG5KL7iQxlQXXkTzbopneMlkMKa9PO8OZ76R5SCku8rQP3Wkvu0orKf+w3Q4Iazuy\ns9xCvt1P221o6CwBmDF3hrOcBLouodiyzWQ2tF//8R11ooYWdBY39ekdXwWWG4F8p0Ehg4KNxr4T\nQlPQr9/2DSy0EhIp1IEIOqTgQc7HRG2GN6OuHc55ppEEQqK7qjMdZkrmlsqG5j/uk0B/nWNwDuQZ\nUuiGujahn5nSb8u7BJDqpS/+xh00YwlAGzbK7qwBmCGCwQijVaL0GRQUbBb2ixBggZXQ3i2et0tS\niEnV5F1IxuaCtSCFgWVygRjaVNjttkSTATWQQbafraHtMJ6XTK/z+Dkp0KbKyEcFN8qe3r5viSAf\nSUyz37MCLCeBOVFCeySB/P11jq8SoRHWcKPNQSGDgkOBjjLfDZLi2MlKGOpL2IkU0nUVCAGsJQYf\n01qIhkBctAIQ1D5cd4gQaMrz7J/dzKALMh53Hz+ekqyCZhzEgKLP942ugrdM0Q8TQNZmfSsgtt/S\nJJCOcwDWQH6remFetzsdChkUHCqsxEpoKgzesSUFA5SRAnStBTRMDJEQQiqL9kHyCXRI6yhmnxQS\ncnLY8ZEH3EWJHJLST09umXLeSfnTqd8lgOYe2f5eSKA5j4E6a4EVN1FBwabjpAgBdkcKjdWQ9SlE\nUkjnWkYEwxYDoTs6U/zQsxyiQC0JxKKGdLLnX4IIOo+QN5Z1y7o5grounYXKP11rwA0UH2XW3XMo\nSCC7b+lALijYfOzZbQR7I4XmXLUuJQ0QQ7pgLBu2GsL1rSGQ1soIqy4BdFxCuySC9sHi5oCizRPG\nzSSPG1D+zXV6yn5mnEB+nSEF33t5G0ECOUpo6f5CUkWYYOGjZnalpOcS5uf8RKyyU/a9goK52LOV\nAEuSQlYx71vIrYVGl3eVet9qyA61X/qiIYV0k2ZO4EFrYPbYUs+YdgeshM7FZr7ee1/+sQx6yj8r\nH1b8edkcK2BA3t1iL1w5BAOsWAb7jqcCtwB3zcpeZGYvWMO9C04BnJSVAF0FpK5CWZoYUuW+1ZAp\n+o7l0FPq7dd/T4BGv+6SBPpYpHSHLIeBr/e5yr9TtvM9OteaJ98usV8k0CCES+3zRTcbKyUDSecC\n3wr8EvCMVd6roOCkSQHmWgvNdRcRQ75tmfLuWw4dSyAjieaGuUkwK+KykUTN9YcU5YwyXsJnP2hV\nLNpeLQHMve4+4VSLJpKtsMdc0p8CvwycCVyTuYm+H7iD4D56ppl9cuDcJwFpcumvAN6/MkGXw1nA\nbQcsA2yGHJsgA2yGHJsgA2yGHJsgA8D9zezMk7mApDcRnmcn3GZm33wy99oUrIwMJF0JXGFmPyrp\ncloyuBfhD8aAXwDOMbMf2OFa795p0ulVYxNk2BQ5NkGGTZFjE2TYFDk2QYZNkuOwYZVuoocCj5Z0\nBXAUuKukV5rZ1amCpN8GXr9CGQoKCgoKloDbucreYGbPMrNz41RsTwDeamZXSzonq/ZYDt79U1BQ\nUHDK4yDGGTxf0oMIbqKPAD+8xDnXrlSi5bAJMsBmyLEJMsBmyLEJMsBmyLEJMsDmyHGosNIO5IKC\ngoKCw4GVuYkKCgoKCg4PChkUFBQUFGwWGUj6Zkn/IulDkn5q4Lgk/Vo8/l5JFx2ADJdLukPSjXH5\nmRXIcJ2kWyUNdq6vox2WlGMdbXGepLdJulnSP0t66kCdlbbHkjKsoy2OSrpB0k1Rjp8bqLPqtlhG\nhpW3RbxPJemfJM1EJK7rN3KngpltxAJUwL8CXwRsATcBD+jVuQJ4I2Fc5oOBdx6ADJcDr19xW3wt\ncBHw/jnHV9oOu5BjHW1xDnBR3D4T+OAB/F0sI8M62kLAGXF7DLwTePCa22IZGVbeFvE+zwD+YOhe\n6/qN3JmWTbIMLgM+ZGb/ZmbbwB8Bj+nVeQzwexZwPXD3XqjqOmRYOczs74D/XVBl1e2wrBwrh5l9\nzMyOxe1PE/Jc3adXbaXtsaQMK0d8vs/E3XFc+hEgq26LZWRYObJUNy+bU2Utv5E7EzaJDO4D/Ee2\n/5/M/uCWqbNqGQC+Jpqeb5T05ft4/2Wx6nbYDdbWFpIuAL6a8DWaY23tsUAGWENbRNfIjcCtvMTT\n4gAABhxJREFUwFvMbO1tsYQMsPq2+FXgJ4F52eQ26TdyKLBJZHBYcAw438weCLwE+PMDlucgsba2\nkHQG8GfA08zsU6u6z0nIsJa2MLPazB4EnAtcJukrVnGfk5RhpW2hkOrmVjN7z35e91THJpHBR4Hz\nsv1zY9lu66xUBjP7VDKTLczDMJa0TEKr/cSq22EprKstJI0JSvhVZvaagSorb4+dZFj334WZ3Q68\nDegnSVvb38Y8GdbQFinVzUcIrtxHSHplr85G/EYOEzaJDN4F3FfShZK2CCksXter8zrge2KkwIOB\nO8zsY+uUQdK9pZBEWNJlhDb8n32UYRmsuh2WwjraIl7/5cAtZvbCOdVW2h7LyLCmtrinpLvH7dOA\nbwQ+0Ku26rbYUYZVt4XNSXXTq7YRv5HDhI2Z9tLMppKeAryZENVznZn9s6Qnx+O/BbyBECXwIeCz\nhFTY65bhccCPSJoCnwOeYGb72oEm6Q8JERlnSfpP4GcJHXVraYddyLHytiB8BX438L7opwZ4NnB+\nJseq22MZGdbRFucAr1CYPdABf2Jmr1/nb2RJGdbRFjNYczvc6VDSURQUFBQUbJSbqKCgoKDggFDI\noKCgoKCgkEFBQUFBQSGDgoKCggIKGRQUFBQUUMjglISkz+xc66Su/zJJD4jbz97D+RdoTqbUBfU/\nl4V99o8/V9I1u5VjlZD0dEn/LunXD1qWggIoZFCwApjZD5rZzXF312SwR/xrTJGwMkjat3E5ZvYi\nYCWpnQsK9oJCBgVA83X91phc7G8knR/Lf1chL/w7JP2bpMfFcifpNyR9QNJbJL0hO/a3ki6R9Dzg\nNIWc9q/qf/FLukbSc+P2xQo58m8CfiyrU0n6FUnvirItM2c2kp4j6YOS/h64f1b+xZLeJOk9kt4u\n6Uuz8uslvU/SLybrSSE3/9slvQ64OZZdrZDT/0ZJL40DsJD0SEn/KOmYpFcr5DJC0vMU5kJ4r6QX\n7PEVFRSsFIUMChJeArwiJhd7FfBr2bFzgIcBVwLPi2XfDlwAPIAwOvch/Qua2U8BnzOzB5nZVTvc\n/3eAHzezr+qVP5GQSuBS4FLghyRduOhCki4mpCl4EGEU6qXZ4WvjfS4GrgF+I5a/GHixmX0lIcNl\njouAp5rZ/SR9GfCdwEOjJVIDVynk3vlp4BvM7CLg3cAzJN0DeCzw5bFtf3GHdigoOBBsTDqKggPH\nQwgKHuD3gednx/7czDxws6R7xbKHAa+O5R+X9La93jjmurl7nD8h3f9b4vYjgQcmqwO4G3Bf4MML\nLvlw4LVm9tl4/dfF9RnA1wCvjqlzAI7E9UOAb4vbfwDkX/A3mFm639cDFwPvitc4jZDK+cEEYvyH\nWL4F/CNwB3AceLnCjFwzs3IVFGwCChkULIMT2bbm1toZU7rW6NElzhHhS/7NJ3HfBAfcvoe+hf/r\nyfMKM3tWXkHSowi5/b+rf3JM1vb1hJw9TwEescv7FxSsHMVNVJDwDoJrBeAq4O071P8H4Dti38G9\nCAnthjBRSP8M8N/A2ZLuIekIwe2UUiHfLulh2f0T3kxIejYGkHQ/SXfZQba/A75N0mmSzgQeFe/z\nKeDDkh4fryVJyS11PfAdcfsJ/Qtm+BvgcZLOjtf4fElfGM9/qKQvieV3ibKeAdwtpnJ+OtB3gxUU\nbASKZXBq4nSFLKQJLwR+HPgdST8BfIKdszz+GeFr92bCjFLHCC6RPq4F3ivpmJldJenngRsIueXz\n1MffD1wnyYC/yspfRuibOKbgf/kErTtnEGZ2TNIfE+awvpWQmjzhKuA3Jf00IQPrH8V6TwNeKek5\nwJvmPAtmdnM8968kOWAC/JiZXS/p+4A/jEQHoQ/h08BfSDpKsCqesUj2goKDQslaWrBnSDrDzD4T\nO0lvIHSqfvwA5LiAMCn6nmf9knQ6obPbJD0B+C4zW+n815E8LjGzp6zyPgUFy6BYBgUng9fHzt8t\n4BcOgggiauBukm48ibEGFwO/Hq2P24Ef2DfpBiDp6cCTCRZWQcGBo1gGBQUFBQWlA7mgoKCgoJBB\nQUFBQQGFDAoKCgoKKGRQUFBQUEAhg4KCgoIC4P8BSmqDqUfeAHUAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x112137690>"
+       "<matplotlib.figure.Figure at 0x124821250>"
       ]
      },
      "metadata": {},
@@ -1084,30 +1144,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Adding a custom behaviour kernel"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "A key feature of Parcels is the ability to quickly create very simple kernels, and add them to the execution. Kernels are little snippets of code that are run during exection of the particles."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "In this example, we'll create a simple kernel where particles obtain an extra 2 m/s westward velocity after 1 day. Of course, this is not very realistic scenario, but it nicely illustrates the power of custom kernels."
    ]
@@ -1115,11 +1166,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def WestVel(particle, fieldset, time, dt):\n",
@@ -1130,10 +1177,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now reset the `ParticleSet` again, and re-execute. Note that we have now changed `kernel` to be `AdvectionRK4 + k_WestVel`, where `k_WestVel` is the `WestVel` function as defined above cast into a `Kernel` object (via the `pset.Kernel` call)."
    ]
@@ -1141,17 +1185,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO: Compiled JITParticleAdvectionRK4WestVel ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gn/T/parcels-501/7aa5539cf1daebd20849333efa73afde.so\n"
+      "INFO: Compiled JITParticleAdvectionRK4WestVel ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gr/T/parcels-504/e769b72b8a3ff4d848dae4e097a7dc2c.so\n"
      ]
     }
    ],
@@ -1168,10 +1208,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "And now plot this new trajectory file"
    ]
@@ -1179,17 +1216,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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g9uQVZlYOfMHdrwUmAb82syjB0/K3u/vb2Q9TJMtauuAfrqx6Iax5AYacDP3HQt1O2L8T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\nmMws7Qfo1IQlIiJpUQIREZG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zjirjwglDmTTyCI45sj/hkOVsRN9kl55czr89tYiXltRx1tGD8yqJZLoGcidw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\nQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x111ebab10>"
+       "<matplotlib.figure.Figure at 0x128a0c7d0>"
       ]
      },
      "metadata": {},
@@ -1213,9 +1246,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "source": [
     "## Reading in data from arbritrary NetCDF files"
@@ -1223,30 +1254,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "In most cases, you will want to advect particles within pre-computed velocity fields. If these velocity fields are stored in NetCDF format, it is fairly easy to load them into the `FieldSet.from_netcdf()` function."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The `examples` directory contains a set of [GlobCurrent](http://globcurrent.ifremer.fr/products-data/products-overview) files of the region around South Africa."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "First, define the names of the files containing the zonal (U) and meridional (V) velocities. You can use wildcards (`*`) and the filenames for U and V can be the same (as in this case)"
    ]
@@ -1254,11 +1276,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "filenames = {'U': \"GlobCurrent_example_data/20*.nc\",\n",
@@ -1267,10 +1285,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Then, define a dictionary of the variables (`U` and `V`) and dimensions (`lon`, `lat` and `time`; note that in this case there is no `depth` because the GlobCurrent data is only for the surface of the ocean)"
    ]
@@ -1278,11 +1293,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "variables = {'U': 'eastward_eulerian_current_velocity',\n",
@@ -1294,10 +1305,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Finally, read in the fieldset using the `FieldSet.from_netcdf` function with the above-defined `filenames`, `variables` and `dimensions`"
    ]
@@ -1305,11 +1313,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -1327,10 +1331,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now define a `ParticleSet`, in this case with 5 particle starting on a line between (28E, 33S) and (30E, 33S) using the `ParticleSet.from_line` constructor method"
    ]
@@ -1338,11 +1339,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pset = ParticleSet.from_line(fieldset=fieldset, pclass=JITParticle,\n",
@@ -1353,10 +1350,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "And finally execute the `ParticleSet` for 10 days using 4th order Runge-Kutta"
    ]
@@ -1364,17 +1358,13 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO: Compiled JITParticleAdvectionRK4 ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gn/T/parcels-501/b0ae13053c6aaee15a9debc469bb9223.so\n"
+      "INFO: Compiled JITParticleAdvectionRK4 ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gr/T/parcels-504/c20f5cbbfdd82507b3806d89ba01dd23.so\n"
      ]
     }
    ],
@@ -1387,10 +1377,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now visualise this simulation using the `plotParticles` script again. Note you can plot the particles on top of one of the velocity fields using the `tracerfile`, `tracerfield`, etc keywords."
    ]
@@ -1398,17 +1385,13 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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PYYzHZKIKbwiqHiJDMUmc1fwm7+t4jrNaepmSOGqNpwSkEJ48soh7D1zEr46c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\nRhQGg8FgKIoRhcFgMBiK8v8Dp/nNrPWAYZkAAAAASUVORK5CYII=\n",
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z51MCsgi/OLWMrx+7mp+duqSwJkRvSwvZ7o0kj3ax6ZWZbOg8RvrC9SCKNDB6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\nokWh0Wg0Gkv+f5712mbZf0K+AAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1115f94d0>"
+       "<matplotlib.figure.Figure at 0x124879190>"
       ]
      },
      "metadata": {},
@@ -1435,30 +1418,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Sampling a Field with Particles"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "One typical use case of particle simulations is to sample a Field (such as temperature, vorticity or sea surface hight) along a particle trajectory. In Parcels, this is very easy to do, with a custom Kernel."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Let's read in another example, the flow around a Peninsula (see [Fig 2.2.3 in this document](http://archimer.ifremer.fr/doc/00157/26792/24888.pdf)), and this time also load the Pressure (`P`) field, using `extra_fields={'P': 'P'}`. Note that, because this flow does not depend on time, we need to set `allow_time_extrapolation=True` when reading in the fieldset."
    ]
@@ -1466,11 +1440,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "fieldset = FieldSet.from_parcels(\"Peninsula_data/peninsula\", extra_fields={'P': 'P'}, allow_time_extrapolation=True)"
@@ -1478,10 +1448,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now define a new `Particle` class that has an extra `Variable`: the pressure. We initialise this by sampling the `fieldset.P` field."
    ]
@@ -1489,11 +1456,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class SampleParticle(JITParticle):         # Define a new particle class\n",
@@ -1502,10 +1465,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now define a `ParticleSet` using the `from_line` method also used above in the GlobCurrent data. Plot the `pset` and print their pressure values `p`"
    ]
@@ -1513,17 +1473,13 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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YuOPy9NseY4+QVAQqMLbXsZhjRETkYFW9WkRWMHnfN0XUn8SEoSnYajvChcVA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\nNjxwk+TVa2tebxsece+jGgBhgNiwCU7mjSnXDe9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M/TgJ7xoeLjBscMxXXCnbmLXCUNuk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OxMDiGcAzbQURuTeGiCoih2IeSXiNiOwETFT1pur1Y4HXZvWaE3qZDyBYNQgY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\n+T/TAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x112066450>"
+       "<matplotlib.figure.Figure at 0x128dcf590>"
       ]
      },
      "metadata": {},
@@ -1533,7 +1489,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p values before execution: [-4.6339731, -13.038223, -22.007469, -31.454386, -41.185543]\n"
+      "('p values before execution:', [-4.6339731, -13.038223, -22.007469, -31.454386, -41.185543])\n"
      ]
     }
    ],
@@ -1546,10 +1502,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now create a custom function that samples the `fieldset.P` field at the particle location. Cast this function to a `Kernel`."
    ]
@@ -1557,11 +1510,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def SampleP(particle, fieldset, time, dt):  # Custom function that samples fieldset.P at particle location\n",
@@ -1572,10 +1521,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Finally, execute the `pset` with a combination of the `AdvectionRK4` and `SampleP` kernels, plot the `pset` and print their new pressure values `p`"
    ]
@@ -1583,24 +1529,20 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO: Compiled SampleParticleAdvectionRK4SampleP ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gn/T/parcels-501/a1efa87239f0503ec3a36b54282be753.so\n"
+      "INFO: Compiled SampleParticleAdvectionRK4SampleP ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gr/T/parcels-504/1757739fd0e983ced21c54e1d153ec3e.so\n"
      ]
     },
     {
      "data": {
-      "image/png": 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4Y7BQVQg6XZezDhVaGg0gHqVi8AbKS3VKJGQvFfLw6QTgEoOKFyg9QRGC0jzz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Hxgz2Geg32HfVPxQqvgFNEbaOs3dP7SEidljo9FD0xGOXYkL7HwWeQXNd9dkz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\nJY2KioqKimJU0qioqKioKMb/BzfSrzVUkYdXAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x110dc4250>"
+       "<matplotlib.figure.Figure at 0x126d16d90>"
       ]
      },
      "metadata": {},
@@ -1610,7 +1552,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p values after execution: [-4.6339455, -13.037563, -22.007502, -31.454361, -41.18549]\n"
+      "('p values after execution:', [-4.6339455, -13.037563, -22.007505, -31.454361, -41.18549])\n"
      ]
     }
    ],
@@ -1624,40 +1566,28 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "And see that these pressure values `p` are (within roundoff errors) the same as the pressure values before the execution of the kernels. The particles thus stay on isobars!"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Calculating distance travelled"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "As a second example of what custom kernels can do, we will now show how to create a kernel that logs the total distance that particles have travelled."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "First, we need to create a new `Particle` class that includes three extra variables. The `distance` variable will be written to output, but the auxiliary variables `prev_lon` and `prev_lat` won't be written to output (can be controlled using the `to_write` keyword)"
    ]
@@ -1665,11 +1595,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class DistParticle(JITParticle):  # Define a new particle class that contains three extra variables\n",
@@ -1682,10 +1608,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now define a new function `TotalDistance` that calculates the sum of Euclidean distances between the old and new locations in each RK4 step"
    ]
@@ -1693,11 +1616,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def TotalDistance(particle, fieldset, time, dt):\n",
@@ -1714,10 +1633,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We will run this on a `ParticleSet` containing the two particles within the idealised moving eddies fieldset from above. Note that `pclass=DistParticle` in this case"
    ]
@@ -1725,11 +1641,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "fieldset = FieldSet.from_parcels(\"MovingEddies_data/moving_eddies\")\n",
@@ -1738,10 +1650,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Again define a new kernel to include the function written above and execute the `ParticleSet`."
    ]
@@ -1749,17 +1658,13 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO: Compiled DistParticleAdvectionRK4TotalDistance ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gn/T/parcels-501/ff72d45a3f6df0b368c6f9068a044b79.so\n"
+      "INFO: Compiled DistParticleAdvectionRK4TotalDistance ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gr/T/parcels-504/4e9515f6eb2476a1534e61d6c083b7cd.so\n"
      ]
     }
    ],
@@ -1774,10 +1679,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "And finally print the distance that each particle has travelled (note that this is also stored in the `EddyParticles_Dist.nc` file)"
    ]
@@ -1785,17 +1687,13 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[952.56293, 990.17273]\n"
+      "[952.56305, 990.1734]\n"
      ]
     }
    ],
@@ -1824,5 +1722,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/parcels/examples/tutorial_advancetime.ipynb b/parcels/examples/tutorial_advancetime.ipynb
deleted file mode 100644
index a8f2aeb95..000000000
--- a/parcels/examples/tutorial_advancetime.ipynb
+++ /dev/null
@@ -1,239 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "## Tutorial showing how to use the Parcels `FieldSet.advancetime` method"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "In many real-world applications, particles are run for long times, using many snapshots of the hydrographic data. If these files are large, having to read them all into memory can take a significant amount of resources"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "The `FieldSet.advancetime` method allows a simulation where only three snapshots of the hydrodynamic fields are in memory at any time, and they can be cycled through. This brief tutorial shows how to use the `FieldSet.advancetime` method to read in only a sebset of all the time slices available at once"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "We start with importing the relevant modules"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "from parcels import FieldSet, ParticleSet, JITParticle, AdvectionRK4\n",
-    "from datetime import timedelta as delta\n",
-    "import numpy as np\n",
-    "from glob import glob\n",
-    "from os import path"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "Now define a function that loads the Globcurrent fields from the `GlobCurrent_example_data` directory"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def loadglobcurrentfile(filenames):\n",
-    "    filenames = {'U': filenames,\n",
-    "                 'V': filenames}\n",
-    "    variables = {'U': 'eastward_eulerian_current_velocity',\n",
-    "                 'V': 'northward_eulerian_current_velocity'}\n",
-    "    dimensions = {'lat': 'lat',\n",
-    "                  'lon': 'lon',\n",
-    "                  'time': 'time'}\n",
-    "    return FieldSet.from_netcdf(filenames, variables, dimensions)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "We can create a list of all the files available in the `GlobCurrent_example_data` directory using"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "files = sorted(glob(str(path.join('GlobCurrent_example_data','20*.nc'))))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "Now we read in the first three files into the `fieldset` (by using `files[0:3]`)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Casting lon data to np.float32\n",
-      "WARNING: Casting lat data to np.float32\n",
-      "WARNING: Casting depth data to np.float32\n"
-     ]
-    }
-   ],
-   "source": [
-    "fieldset = loadglobcurrentfile(files[0:3])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "Now create a `ParticleSet` object"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "pset = ParticleSet(fieldset=fieldset, pclass=JITParticle, lon=[20], lat=[-35])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "Now we can advect the particles, for ten days. Normally, since we only have three days in memory, we can not advect that long. But in this case, we can use a custom `for`-loop to constantly update the `fieldset` with the latest snapshot. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO: Compiled JITParticleAdvectionRK4 ==> /var/folders/r2/8593q8z93kd7t4j9kbb_f7p00000gn/T/parcels-501/27805ff3aa34ba12ddb373f3f2cb1d1b.so\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in range(10):\n",
-    "    pset.execute(AdvectionRK4,            # First advect the particles\n",
-    "                 runtime=delta(days=1),   # runtime needs to be equal to the time between snapshots\n",
-    "                 dt=delta(minutes=5))\n",
-    "\n",
-    "    # Then update the fieldset using the advancetime method\n",
-    "    fieldset.advancetime(loadglobcurrentfile(files[i+3]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
-   "source": [
-    "With this relatively simple setup, Parcels can be run on hydrodynamic datasets that are potentially hundreds of gigabytes in size; just as long as any single snapshot isn't too big."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/parcels/field.py b/parcels/field.py
index 21f8236e3..cfdf55903 100644
--- a/parcels/field.py
+++ b/parcels/field.py
@@ -176,7 +176,7 @@ class Field(object):
 
     def __init__(self, name, data, lon=None, lat=None, depth=None, time=None, grid=None, mesh='flat',
                  transpose=False, vmin=None, vmax=None, time_origin=0,
-                 interp_method='linear', allow_time_extrapolation=None, time_periodic=False):
+                 interp_method='linear', allow_time_extrapolation=None, time_periodic=False, **kwargs):
         self.name = name
         if self.name == 'UV':
             return
@@ -212,43 +212,31 @@ def __init__(self, name, data, lon=None, lat=None, depth=None, time=None, grid=N
                                  allow_time_extrapolation is set to False")
             self.allow_time_extrapolation = False
 
-        # Ensure that field data is the right data type
-        if not self.data.dtype == np.float32:
-            logger.warning_once("Casting field data to np.float32")
-            self.data = self.data.astype(np.float32)
-        if transpose:
-            self.data = np.transpose(self.data)
+        self.vmin = vmin
+        self.vmax = vmax
 
-        if self.grid.lat_flipped:
-            self.data = np.flip(self.data, axis=-2)
+        if not self.grid.defer_load:
+            self.data = self.reshape(self.data, transpose)
 
-        if self.grid.tdim == 1:
-            if len(self.data.shape) < 4:
-                self.data = self.data.reshape(sum(((1,), self.data.shape), ()))
-        if self.grid.zdim == 1:
-            if len(self.data.shape) == 4:
-                self.data = self.data.reshape(sum(((self.data.shape[0],), self.data.shape[2:]), ()))
-        if len(self.data.shape) == 4:
-            assert self.data.shape == (self.grid.tdim, self.grid.zdim, self.grid.ydim, self.grid.xdim), \
-                                      ('Field %s expecting a data shape of a [ydim, xdim], [zdim, ydim, xdim], [tdim, ydim, xdim] or [tdim, zdim, ydim, xdim]. Flag transpose=True could help to reorder the data.')
-        else:
-            assert self.data.shape == (self.grid.tdim, self.grid.ydim, self.grid.xdim), \
-                                      ('Field %s expecting a data shape of a [ydim, xdim], [zdim, ydim, xdim], [tdim, ydim, xdim] or [tdim, zdim, ydim, xdim]. Flag transpose=True could help to reorder the data.')
+            # Hack around the fact that NaN and ridiculously large values
+            # propagate in SciPy's interpolators
+            self.data[np.isnan(self.data)] = 0.
+            if self.vmin is not None:
+                self.data[self.data < self.vmin] = 0.
+            if self.vmax is not None:
+                self.data[self.data > self.vmax] = 0.
 
-        # Hack around the fact that NaN and ridiculously large values
-        # propagate in SciPy's interpolators
-        if vmin is not None:
-            self.data[self.data < vmin] = 0.
-        if vmax is not None:
-            self.data[self.data > vmax] = 0.
-        self.data[np.isnan(self.data)] = 0.
+        self._scaling_factor = None
 
         # Variable names in JIT code
         self.ccode_data = self.name
+        self.dimensions = kwargs.pop('dimensions', None)
+        self.indices = kwargs.pop('indices', None)
+        self.timeFiles = kwargs.pop('timeFiles', None)
 
     @classmethod
     def from_netcdf(cls, name, dimensions, filenames, indices={},
-                    allow_time_extrapolation=False, mesh='flat', **kwargs):
+                    allow_time_extrapolation=False, mesh='flat', full_load=False, **kwargs):
         """Create field from netCDF file
 
         :param name: Name of the field to create
@@ -265,11 +253,15 @@ def from_netcdf(cls, name, dimensions, filenames, indices={},
                1. spherical (default): Lat and lon in degree, with a
                   correction for zonal velocity U near the poles.
                2. flat: No conversion, lat/lon are assumed to be in m.
+        :param full_load: boolean whether to fully load the data or only pre-load them. (default: False)
+               It is advised not to fully load the data, since in that case Parcels deals with
+               a better memory management during particle set execution.
+               full_load is however sometimes necessary for plotting the fields.
         """
 
         if not isinstance(filenames, Iterable) or isinstance(filenames, str):
             filenames = [filenames]
-        with FileBuffer(filenames[0], dimensions, indices) as filebuffer:
+        with NetcdfFileBuffer(filenames[0], dimensions, indices) as filebuffer:
             lon, lat = filebuffer.read_lonlat
             depth = filebuffer.read_depth
             if name in ['cosU', 'sinU', 'cosV', 'sinV']:
@@ -286,11 +278,14 @@ def from_netcdf(cls, name, dimensions, filenames, indices={},
         # Concatenate time variable to determine overall dimension
         # across multiple files
         timeslices = []
+        timeFiles = []
         for fname in filenames:
-            with FileBuffer(fname, dimensions, indices) as filebuffer:
+            with NetcdfFileBuffer(fname, dimensions, indices) as filebuffer:
                 timeslices.append(filebuffer.time)
+                timeFiles.append([fname for i in range(len(filebuffer.time))])
         timeslices = np.array(timeslices)
         time = np.concatenate(timeslices)
+        timeFiles = np.concatenate(np.array(timeFiles))
         if isinstance(time[0], np.datetime64):
             time_origin = time[0]
             time = (time - time_origin) / np.timedelta64(1, 's')
@@ -298,44 +293,6 @@ def from_netcdf(cls, name, dimensions, filenames, indices={},
             time_origin = 0
         assert(np.all((time[1:]-time[:-1]) > 0))
 
-        # Pre-allocate data before reading files into buffer
-        depthdim = depth.size if len(depth.shape) == 1 else depth.shape[-3]
-        latdim = lat.size if len(lat.shape) == 1 else lat.shape[-2]
-        londim = lon.size if len(lon.shape) == 1 else lon.shape[-1]
-        data = np.empty((time.size, depthdim, latdim, londim), dtype=np.float32)
-        ti = 0
-        for tslice, fname in zip(timeslices, filenames):
-            with FileBuffer(fname, dimensions, indices) as filebuffer:
-                depthsize = depth.size if len(depth.shape) == 1 else depth.shape[-3]
-                latsize = lat.size if len(lat.shape) == 1 else lat.shape[-2]
-                lonsize = lon.size if len(lon.shape) == 1 else lon.shape[-1]
-                filebuffer.indslat = indices['lat'] if 'lat' in indices else range(latsize)
-                filebuffer.indslon = indices['lon'] if 'lon' in indices else range(lonsize)
-                filebuffer.indsdepth = indices['depth'] if 'depth' in indices else range(depthsize)
-                for inds in [filebuffer.indslat, filebuffer.indslon, filebuffer.indsdepth]:
-                    if type(inds) not in [list, range]:
-                        raise RuntimeError('Indices for field subsetting need to be a list')
-                if 'data' in dimensions:
-                    # If Field.from_netcdf is called directly, it may not have a 'data' dimension
-                    # In that case, assume that 'name' is the data dimension
-                    filebuffer.name = dimensions['data']
-                else:
-                    filebuffer.name = name
-
-                if len(filebuffer.dataset[filebuffer.name].shape) == 2:
-                    data[ti:ti+len(tslice), 0, :, :] = filebuffer.data[:, :]
-                elif len(filebuffer.dataset[filebuffer.name].shape) == 3:
-                    if filebuffer.depthdim > 1:
-                        data[ti:ti+len(tslice), :, :, :] = filebuffer.data[:, :, :]
-                    else:
-                        data[ti:ti+len(tslice), 0, :, :] = filebuffer.data[:, :, :]
-                else:
-                    data[ti:ti+len(tslice), :, :, :] = filebuffer.data[:, :, :, :]
-            ti += len(tslice)
-        # Time indexing after the fact only
-        if 'time' in indices:
-            time = time[indices['time']]
-            data = data[indices['time'], :, :, :]
         if time.size == 1 and time[0] is None:
             time[0] = 0
         if len(lon.shape) == 1:
@@ -348,11 +305,83 @@ def from_netcdf(cls, name, dimensions, filenames, indices={},
                 grid = CurvilinearZGrid(lon, lat, depth, time, time_origin=time_origin, mesh=mesh)
             else:
                 grid = CurvilinearSGrid(lon, lat, depth, time, time_origin=time_origin, mesh=mesh)
+
+        if 'time' in indices:
+            logger.warning_once('time dimension in indices is not necessary anymore. It is then ignored.')
+
+        if time.size <= 3 or full_load:
+            # Pre-allocate data before reading files into buffer
+            data = np.empty((grid.tdim, grid.zdim, grid.ydim, grid.xdim), dtype=np.float32)
+            ti = 0
+            for tslice, fname in zip(timeslices, filenames):
+                with NetcdfFileBuffer(fname, dimensions, indices) as filebuffer:
+                    # If Field.from_netcdf is called directly, it may not have a 'data' dimension
+                    # In that case, assume that 'name' is the data dimension
+                    filebuffer.name = dimensions['data'] if 'data' in dimensions else name
+
+                    if len(filebuffer.dataset[filebuffer.name].shape) == 2:
+                        data[ti:ti+len(tslice), 0, :, :] = filebuffer.data[:, :]
+                    elif len(filebuffer.dataset[filebuffer.name].shape) == 3:
+                        if filebuffer.zdim > 1:
+                            data[ti:ti+len(tslice), :, :, :] = filebuffer.data[:, :, :]
+                        else:
+                            data[ti:ti+len(tslice), 0, :, :] = filebuffer.data[:, :, :]
+                    else:
+                        data[ti:ti+len(tslice), :, :, :] = filebuffer.data[:, :, :, :]
+                ti += len(tslice)
+        else:
+            grid.defer_load = True
+            grid.time_full = grid.time
+            grid.ti = -1
+            data = None
+
         if name in ['cosU', 'sinU', 'cosV', 'sinV']:
             allow_time_extrapolation = True
+        kwargs['dimensions'] = dimensions.copy()
+        kwargs['indices'] = indices
+        kwargs['timeFiles'] = timeFiles
+
         return cls(name, data, grid=grid,
                    allow_time_extrapolation=allow_time_extrapolation, **kwargs)
 
+    def reshape(self, data, transpose=False):
+
+        # Ensure that field data is the right data type
+        if not data.dtype == np.float32:
+            logger.warning_once("Casting field data to np.float32")
+            data = data.astype(np.float32)
+        if transpose:
+            data = np.transpose(data)
+        if self.grid.lat_flipped:
+            data = np.flip(data, axis=-2)
+
+        if self.grid.tdim == 1:
+            if len(data.shape) < 4:
+                data = data.reshape(sum(((1,), data.shape), ()))
+        if self.grid.zdim == 1:
+            if len(data.shape) == 4:
+                data = data.reshape(sum(((data.shape[0],), data.shape[2:]), ()))
+        if len(data.shape) == 4:
+            assert data.shape == (self.grid.tdim, self.grid.zdim, self.grid.ydim-2*self.grid.meridional_halo, self.grid.xdim-2*self.grid.zonal_halo), \
+                                 ('Field %s expecting a data shape of a [ydim, xdim], [zdim, ydim, xdim], [tdim, ydim, xdim] or [tdim, zdim, ydim, xdim]. Flag transpose=True could help to reorder the data.')
+        else:
+            assert data.shape == (self.grid.tdim, self.grid.ydim-2*self.grid.meridional_halo, self.grid.xdim-2*self.grid.zonal_halo), \
+                                 ('Field %s expecting a data shape of a [ydim, xdim], [zdim, ydim, xdim], [tdim, ydim, xdim] or [tdim, zdim, ydim, xdim]. Flag transpose=True could help to reorder the data.')
+        if self.grid.meridional_halo > 0 or self.grid.zonal_halo > 0:
+            data = self.add_periodic_halo(zonal=self.grid.zonal_halo > 0, meridional=self.grid.meridional_halo > 0, halosize=max(self.grid.meridional_halo, self.grid.zonal_halo), data=data)
+        return data
+
+    def set_scaling_factor(self, factor):
+        """Scales the field data by some constant factor.
+
+        :param factor: scaling factor
+        """
+
+        if self._scaling_factor:
+            raise NotImplementedError(('Scaling factor for field %s already defined.' % self.name))
+        self._scaling_factor = factor
+        self.data *= factor
+
     def getUV(self, time, x, y, z):
         fieldset = self.fieldset
         U = fieldset.U.eval(time, x, y, z, False)
@@ -842,6 +871,7 @@ def show(self, with_particles=False, animation=False, show_time=None, vmin=None,
 
         if with_particles or (not animation):
             show_time = self.grid.time[0] if show_time is None else show_time
+            self.fieldset.computeTimeChunk(show_time, 1)
             (idx, periods) = self.time_index(show_time)
             show_time -= periods*(self.grid.time[-1]-self.grid.time[0])
             if self.grid.time.size > 1:
@@ -875,7 +905,7 @@ def animate(i):
             plt.close()
             return anim
 
-    def add_periodic_halo(self, zonal, meridional, halosize=5):
+    def add_periodic_halo(self, zonal, meridional, halosize=5, data=None):
         """Add a 'halo' to all Fields in a FieldSet, through extending the Field (and lon/lat)
         by copying a small portion of the field on one side of the domain to the other.
         Before adding a periodic halo to the Field, it has to be added to the Grid on which the Field depends
@@ -883,30 +913,37 @@ def add_periodic_halo(self, zonal, meridional, halosize=5):
         :param zonal: Create a halo in zonal direction (boolean)
         :param meridional: Create a halo in meridional direction (boolean)
         :param halosize: size of the halo (in grid points). Default is 5 grid points
+        :param data: if data is not None, the periodic halo will be achieved on data instead of self.data and data will be returned
         """
-        if self.name == 'UV':
+        dataNone = not isinstance(data, np.ndarray)
+        if self.name == 'UV' or (self.grid.defer_load and dataNone):
             return
+        data = self.data if dataNone else data
         if zonal:
-            if len(self.data.shape) is 3:
-                self.data = np.concatenate((self.data[:, :, -halosize:], self.data,
-                                            self.data[:, :, 0:halosize]), axis=len(self.data.shape)-1)
-                assert self.data.shape[2] == self.grid.xdim
+            if len(data.shape) is 3:
+                data = np.concatenate((data[:, :, -halosize:], data,
+                                       data[:, :, 0:halosize]), axis=len(data.shape)-1)
+                assert data.shape[2] == self.grid.xdim
             else:
-                self.data = np.concatenate((self.data[:, :, :, -halosize:], self.data,
-                                            self.data[:, :, :, 0:halosize]), axis=len(self.data.shape) - 1)
-                assert self.data.shape[3] == self.grid.xdim
+                data = np.concatenate((data[:, :, :, -halosize:], data,
+                                       data[:, :, :, 0:halosize]), axis=len(data.shape) - 1)
+                assert data.shape[3] == self.grid.xdim
             self.lon = self.grid.lon
             self.lat = self.grid.lat
         if meridional:
-            if len(self.data.shape) is 3:
-                self.data = np.concatenate((self.data[:, -halosize:, :], self.data,
-                                            self.data[:, 0:halosize, :]), axis=len(self.data.shape)-2)
-                assert self.data.shape[1] == self.grid.ydim
+            if len(data.shape) is 3:
+                data = np.concatenate((data[:, -halosize:, :], data,
+                                       data[:, 0:halosize, :]), axis=len(data.shape)-2)
+                assert data.shape[1] == self.grid.ydim
             else:
-                self.data = np.concatenate((self.data[:, :, -halosize:, :], self.data,
-                                            self.data[:, :, 0:halosize, :]), axis=len(self.data.shape) - 2)
-                assert self.data.shape[2] == self.grid.ydim
+                data = np.concatenate((data[:, :, -halosize:, :], data,
+                                       data[:, :, 0:halosize, :]), axis=len(data.shape) - 2)
+                assert data.shape[2] == self.grid.ydim
             self.lat = self.grid.lat
+        if dataNone:
+            self.data = data
+        else:
+            return data
 
     def write(self, filename, varname=None):
         """Write a :class:`Field` to a netcdf file
@@ -946,8 +983,33 @@ def advancetime(self, field_new, advanceForward):
             self.data = np.concatenate((field_new.data[:, :, :], self.data[:-1, :, :]), 0)
             self.time = self.grid.time
 
+    def computeTimeChunk(self, data, tindex):
+        g = self.grid
+        with NetcdfFileBuffer(self.timeFiles[g.ti+tindex], self.dimensions, self.indices) as filebuffer:
+            filebuffer.name = self.dimensions['data'] if 'data' in self.dimensions else self.name
+            time_data = filebuffer.time
+            if isinstance(time_data[0], np.datetime64):
+                time_data = (time_data - g.time_origin) / np.timedelta64(1, 's')
+            ti = (time_data <= g.time[tindex]).argmin() - 1
+            if len(filebuffer.dataset[filebuffer.name].shape) == 2:
+                data[tindex, 0, :, :] = filebuffer.data[:, :]
+            elif len(filebuffer.dataset[filebuffer.name].shape) == 3:
+                if g.zdim > 1:
+                    data[tindex, :, :, :] = filebuffer.data[:, :, :]
+                else:
+                    data[tindex, 0, :, :] = filebuffer.data[ti, :, :]
+            else:
+                data[tindex, :, :, :] = filebuffer.data[ti, :, :, :]
+        data[np.isnan(data)] = 0.
+        if self.vmin is not None:
+            data[data < self.vmin] = 0.
+        if self.vmax is not None:
+            data[data > self.vmax] = 0.
+
+        return data
+
 
-class FileBuffer(object):
+class NetcdfFileBuffer(object):
     """ Class that encapsulates and manages deferred access to file data. """
 
     def __init__(self, filename, dimensions, indices):
@@ -960,17 +1022,21 @@ def __enter__(self):
         self.dataset = xr.open_dataset(str(self.filename))
         lon = getattr(self.dataset, self.dimensions['lon'])
         lat = getattr(self.dataset, self.dimensions['lat'])
-        londim = lon.size if len(lon.shape) == 1 else lon.shape[-1]
-        latdim = lat.size if len(lat.shape) == 1 else lat.shape[-2]
-        self.indslon = self.indices['lon'] if 'lon' in self.indices else range(londim)
-        self.indslat = self.indices['lat'] if 'lat' in self.indices else range(latdim)
+        xdim = lon.size if len(lon.shape) == 1 else lon.shape[-1]
+        ydim = lat.size if len(lat.shape) == 1 else lat.shape[-2]
+        self.indslon = self.indices['lon'] if 'lon' in self.indices else range(xdim)
+        self.indslat = self.indices['lat'] if 'lat' in self.indices else range(ydim)
         if 'depth' in self.dimensions:
             depth = getattr(self.dataset, self.dimensions['depth'])
             depthsize = depth.size if len(depth.shape) == 1 else depth.shape[-3]
             self.indsdepth = self.indices['depth'] if 'depth' in self.indices else range(depthsize)
-            self.depthdim = len(self.indsdepth)
+            self.zdim = len(self.indsdepth)
         else:
-            self.depthdim = 0
+            self.zdim = 0
+            self.indsdepth = []
+        for inds in [self.indslat, self.indslon, self.indsdepth]:
+            if type(inds) not in [list, range]:
+                raise RuntimeError('Indices for field subsetting need to be a list')
         return self
 
     def __exit__(self, type, value, traceback):
@@ -990,9 +1056,9 @@ def read_lonlat(self):
             lon_subset = np.array(lon[0, self.indslat, self.indslon])
             lat_subset = np.array(lat[0, self.indslat, self.indslon])
         if len(lon.shape) > 1:  # if lon, lat are rectilinear but were stored in arrays
-            londim = lon_subset.shape[0]
-            latdim = lat_subset.shape[1]
-            if np.allclose(lon_subset[0, :], lon_subset[int(londim/2), :]) and np.allclose(lat_subset[:, 0], lat_subset[:, int(latdim/2)]):
+            xdim = lon_subset.shape[0]
+            ydim = lat_subset.shape[1]
+            if np.allclose(lon_subset[0, :], lon_subset[int(xdim/2), :]) and np.allclose(lat_subset[:, 0], lat_subset[:, int(ydim/2)]):
                 lon_subset = lon_subset[0, :]
                 lat_subset = lat_subset[:, 0]
         return lon_subset, lat_subset
@@ -1017,7 +1083,7 @@ def data(self):
         if len(data.shape) == 2:
             data = data[self.indslat, self.indslon]
         elif len(data.shape) == 3:
-            if self.depthdim > 1:
+            if self.zdim > 1:
                 data = data[self.indsdepth, self.indslat, self.indslon]
             else:
                 data = data[:, self.indslat, self.indslon]
diff --git a/parcels/fieldset.py b/parcels/fieldset.py
index 66572e416..b63d27e34 100644
--- a/parcels/fieldset.py
+++ b/parcels/fieldset.py
@@ -101,7 +101,7 @@ def check_complete(self):
 
     @classmethod
     def from_netcdf(cls, filenames, variables, dimensions, indices={},
-                    mesh='spherical', allow_time_extrapolation=False, time_periodic=False, **kwargs):
+                    mesh='spherical', allow_time_extrapolation=False, time_periodic=False, full_load=False, **kwargs):
         """Initialises FieldSet object from NetCDF files
 
         :param filenames: Dictionary mapping variables to file(s). The
@@ -127,6 +127,10 @@ def from_netcdf(cls, filenames, variables, dimensions, indices={},
                (i.e. beyond the last available time snapshot)
         :param time_periodic: boolean whether to loop periodically over the time component of the FieldSet
                This flag overrides the allow_time_interpolation and sets it to False
+        :param full_load: boolean whether to fully load the data or only pre-load them. (default: False)
+               It is advised not to fully load the data, since in that case Parcels deals with
+               a better memory management during particle set execution.
+               full_load is however sometimes necessary for plotting the fields.
         """
 
         fields = {}
@@ -149,7 +153,7 @@ def from_netcdf(cls, filenames, variables, dimensions, indices={},
 
             fields[var] = Field.from_netcdf(var, dims, paths, inds, mesh=mesh,
                                             allow_time_extrapolation=allow_time_extrapolation,
-                                            time_periodic=time_periodic, **kwargs)
+                                            time_periodic=time_periodic, full_load=full_load, **kwargs)
         u = fields.pop('U', None)
         v = fields.pop('V', None)
         return cls(u, v, fields=fields)
@@ -229,7 +233,7 @@ def from_nemo(cls, filenames, variables, dimensions, indices={}, mesh='spherical
 
     @classmethod
     def from_parcels(cls, basename, uvar='vozocrtx', vvar='vomecrty', indices={}, extra_fields={},
-                     allow_time_extrapolation=False, time_periodic=False, **kwargs):
+                     allow_time_extrapolation=False, time_periodic=False, full_load=False, **kwargs):
         """Initialises FieldSet data from NetCDF files using the Parcels FieldSet.write() conventions.
 
         :param basename: Base name of the file(s); may contain
@@ -242,6 +246,10 @@ def from_parcels(cls, basename, uvar='vozocrtx', vvar='vomecrty', indices={}, ex
                (i.e. beyond the last available time snapshot)
         :param time_periodic: boolean whether to loop periodically over the time component of the FieldSet
                This flag overrides the allow_time_interpolation and sets it to False
+        :param full_load: boolean whether to fully load the data or only pre-load them. (default: False)
+               It is advised not to fully load the data, since in that case Parcels deals with
+               a better memory management during particle set execution.
+               full_load is however sometimes necessary for plotting the fields.
         """
 
         dimensions = {}
@@ -255,7 +263,7 @@ def from_parcels(cls, basename, uvar='vozocrtx', vvar='vomecrty', indices={}, ex
                           for v in extra_fields.keys()])
         return cls.from_netcdf(filenames, indices=indices, variables=extra_fields,
                                dimensions=dimensions, allow_time_extrapolation=allow_time_extrapolation,
-                               time_periodic=time_periodic, **kwargs)
+                               time_periodic=time_periodic, full_load=full_load, **kwargs)
 
     @property
     def fields(self):
@@ -326,6 +334,10 @@ def advancetime(self, fieldset_new):
         """Replace oldest time on FieldSet with new FieldSet
         :param fieldset_new: FieldSet snapshot with which the oldest time has to be replaced"""
 
+        logger.warning_once("Fieldset.advancetime() is deprecated.\n \
+                             Parcels deals automatically with loading only 3 time steps simustaneously\
+                             such that the total allocated memory remains limited.")
+
         advance = 0
         for gnew in fieldset_new.gridset.grids:
             gnew.advanced = False
@@ -343,3 +355,49 @@ def advancetime(self, fieldset_new):
                 advance = advance2
                 gnew.advanced = True
             f.advancetime(fnew, advance == 1)
+
+    def computeTimeChunk(self, time, dt):
+        signdt = np.sign(dt)
+        nextTime = np.infty * signdt
+
+        for g in self.gridset.grids:
+            g.update_status = 'no_update'
+        for f in self.fields:
+            if f.name == 'UV' or not f.grid.defer_load:
+                continue
+            if f.grid.update_status == 'no_update':
+                nextTime_loc = f.grid.computeTimeChunk(f, time, signdt)
+            nextTime = min(nextTime, nextTime_loc) if signdt >= 0 else max(nextTime, nextTime_loc)
+
+        for f in self.fields:
+            if f.name == 'UV' or not f.grid.defer_load:
+                continue
+            g = f.grid
+            if g.update_status == 'first_update':  # First load of data
+                data = np.empty((g.tdim, g.zdim, g.ydim-2*g.meridional_halo, g.xdim-2*g.zonal_halo), dtype=np.float32)
+                for tindex in range(3):
+                    data = f.computeTimeChunk(data, tindex)
+                if f._scaling_factor:
+                    data *= f._scaling_factor
+                f.data = f.reshape(data)
+            elif g.update_status == 'update':
+                data = np.empty((g.tdim, g.zdim, g.ydim-2*g.meridional_halo, g.xdim-2*g.zonal_halo), dtype=np.float32)
+                if signdt >= 0:
+                    f.data[:2, :] = f.data[1:, :]
+                    tindex = 2
+                else:
+                    f.data[1:, :] = f.data[:2, :]
+                    tindex = 0
+                data = f.computeTimeChunk(data, tindex)
+                if f._scaling_factor:
+                    data *= f._scaling_factor
+                f.data[tindex, :] = f.reshape(data)[tindex, :]
+
+        if abs(nextTime) == np.infty or np.isnan(nextTime):  # Second happens when dt=0
+            return nextTime
+        else:
+            nSteps = int((nextTime - time) / dt)
+            if nSteps == 0:
+                return nextTime
+            else:
+                return time + nSteps * dt
diff --git a/parcels/grid.py b/parcels/grid.py
index 357740d41..1ff47ba91 100644
--- a/parcels/grid.py
+++ b/parcels/grid.py
@@ -41,7 +41,10 @@ def __init__(self, lon, lat, time, time_origin, mesh):
         self.cstruct = None
         self.cell_edge_sizes = {}
         self.zonal_periodic = False
+        self.zonal_halo = 0
+        self.meridional_halo = 0
         self.lat_flipped = False
+        self.defer_load = False
 
     @property
     def ctypes_struct(self):
@@ -109,6 +112,35 @@ def check_zonal_periodic(self):
         dx = np.where(dx > 180, dx-360, dx)
         self.zonal_periodic = sum(dx) > 359.9
 
+    def computeTimeChunk(self, f, time, signdt):
+        nextTime_loc = np.infty * signdt
+        if self.update_status == 'no_update':
+            if self.ti >= 0:
+                if signdt >= 0 and ((time < self.time[0] and self.ti > 0)
+                   or (time > self.time[2] and self.ti < len(self.time_full)-3)):
+                    self.ti = -1  # reset
+                elif signdt >= 0 and time >= self.time[1] and self.ti < len(self.time_full)-3:
+                    self.ti += 1
+                    self.time = self.time_full[self.ti:self.ti+3]
+                    self.update_status = 'update'
+                elif signdt == -1 and time <= self.time[1] and self.ti > 0:
+                    self.ti -= 1
+                    self.time = self.time_full[self.ti:self.ti+3]
+                    self.update_status = 'update'
+            if self.ti == -1:
+                self.time = self.time_full
+                self.ti, _ = f.time_index(time)
+                if self.ti > 0 and signdt == -1:
+                    self.ti = self.ti-2 if len(self.time_full)-1 else self.ti-1
+                self.time = self.time_full[self.ti:self.ti+3]
+                self.tdim = 3
+                self.update_status = 'first_update'
+            if signdt >= 0 and self.ti < len(self.time_full)-3:
+                nextTime_loc = self.time[2]
+            elif signdt == -1 and self.ti > 0:
+                nextTime_loc = self.time[0]
+        return nextTime_loc
+
 
 class RectilinearGrid(Grid):
     """Rectilinear Grid
@@ -148,6 +180,7 @@ def add_periodic_halo(self, zonal, meridional, halosize=5):
                                       self.lon, self.lon[0:halosize] + lonshift))
             self.xdim = self.lon.size
             self.zonal_periodic = True
+            self.zonal_halo = halosize
         if meridional:
             if not np.allclose(self.lat[1]-self.lat[0], self.lat[-1]-self.lat[-2]):
                 logger.warning_once("The meridional halo is located at the north and south of current grid, with a dy = lat[1]-lat[0] between the last nodes of the original grid and the first ones of the halo. In your grid, lat[1]-lat[0] != lat[-1]-lat[-2]. Is the halo computed as you expect?")
@@ -155,6 +188,7 @@ def add_periodic_halo(self, zonal, meridional, halosize=5):
             self.lat = np.concatenate((self.lat[-halosize:] - latshift,
                                       self.lat, self.lat[0:halosize] + latshift))
             self.ydim = self.lat.size
+            self.meridional_halo = halosize
 
 
 class RectilinearZGrid(RectilinearGrid):
@@ -270,6 +304,7 @@ def add_periodic_halo(self, zonal, meridional, halosize=5):
             self.xdim = self.lon.shape[1]
             self.ydim = self.lat.shape[0]
             self.zonal_periodic = True
+            self.zonal_halo = halosize
         if meridional:
             if not np.allclose(self.lat[1, :]-self.lat[0, :], self.lat[-1, :]-self.lat[-2, :]):
                 logger.warning_once("The meridional halo is located at the north and south of current grid, with a dy = lat[1,:]-lat[0,:] between the last nodes of the original grid and the first ones of the halo. In your grid, lat[1,:]-lat[0,:] != lat[-1,:]-lat[-2,:]. Is the halo computed as you expect?")
@@ -282,6 +317,7 @@ def add_periodic_halo(self, zonal, meridional, halosize=5):
                                       axis=len(self.lon.shape)-2)
             self.xdim = self.lon.shape[1]
             self.ydim = self.lat.shape[0]
+            self.meridional_halo = halosize
 
 
 class CurvilinearZGrid(CurvilinearGrid):
diff --git a/parcels/kernels/error.py b/parcels/kernels/error.py
index b42c881a0..18b133c93 100644
--- a/parcels/kernels/error.py
+++ b/parcels/kernels/error.py
@@ -1,6 +1,6 @@
 """Collection of pre-built recovery kernels"""
 from enum import IntEnum
-from datetime import timedelta
+import numpy as np
 
 
 __all__ = ['ErrorCode', 'KernelError', 'OutOfBoundsError', 'recovery_map']
@@ -31,7 +31,7 @@ def __init__(self, particle, fieldset=None, msg=None):
 def parse_particletime(time, fieldset):
     if fieldset is not None and fieldset.U.grid.time_origin != 0:
         # TODO assuming that error was thrown on U field
-        time = fieldset.U.grid.time_origin + timedelta(seconds=time)
+        time = fieldset.U.grid.time_origin + np.timedelta64(int(time), 's')
     return time
 
 
diff --git a/parcels/particleset.py b/parcels/particleset.py
index 35a9b1170..63a28b613 100644
--- a/parcels/particleset.py
+++ b/parcels/particleset.py
@@ -314,7 +314,7 @@ def execute(self, pyfunc=AdvectionRK4, endtime=None, runtime=None, dt=1.,
             next_prelease = np.infty * np.sign(dt)
         next_output = time + outputdt * np.sign(dt)
         next_movie = time + moviedt * np.sign(dt)
-        next_input = np.infty * np.sign(dt)  # Not used yet
+        next_input = self.fieldset.computeTimeChunk(time, np.sign(dt))
 
         tol = 1e-12
         while (time < endtime and dt > 0) or (time > endtime and dt < 0) or dt == 0:
@@ -328,8 +328,6 @@ def execute(self, pyfunc=AdvectionRK4, endtime=None, runtime=None, dt=1.,
                                      lat=self.repeatlat, depth=self.repeatdepth,
                                      pclass=self.repeatpclass))
                 next_prelease += self.repeatdt * np.sign(dt)
-            if abs(time-next_input) < tol:
-                continue
             if abs(time-next_output) < tol:
                 if output_file:
                     output_file.write(self, time)
@@ -337,6 +335,7 @@ def execute(self, pyfunc=AdvectionRK4, endtime=None, runtime=None, dt=1.,
             if abs(time-next_movie) < tol:
                 self.show(field=movie_background_field, show_time=time)
                 next_movie += moviedt * np.sign(dt)
+            next_input = self.fieldset.computeTimeChunk(time, dt)
             if dt == 0:
                 break
 
@@ -420,6 +419,7 @@ def nearest_index(array, value):
             time_origin = self.fieldset.U.grid.time_origin
         else:
             time_origin = self.fieldset.U.grid.time_origin
+            self.fieldset.computeTimeChunk(show_time, 1)
             (idx, periods) = self.fieldset.U.time_index(show_time)
             show_time -= periods*(self.fieldset.U.time[-1]-self.fieldset.U.time[0])
             U = np.array(self.fieldset.U.temporal_interpolate_fullfield(idx, show_time))
diff --git a/tests/test_fieldset.py b/tests/test_fieldset.py
index 873054afa..346b389ba 100644
--- a/tests/test_fieldset.py
+++ b/tests/test_fieldset.py
@@ -96,15 +96,6 @@ def test_fieldset_from_file_subsets(indslon, indslat, tmpdir, filename='test_sub
     assert np.allclose(fieldsetsub.V.data, fieldsetfull.V.data[ixgrid])
 
 
-@pytest.mark.parametrize('indstime', [range(2, 8), [4]])
-def test_moving_eddies_file_subsettime(indstime):
-    fieldsetfile = path.join(path.dirname(__file__), 'test_data', 'testfields')
-    fieldsetfull = FieldSet.from_parcels(fieldsetfile, extra_fields={'P': 'P'})
-    fieldsetsub = FieldSet.from_parcels(fieldsetfile, extra_fields={'P': 'P'}, indices={'time': indstime})
-    assert np.allclose(fieldsetsub.P.time, fieldsetfull.P.time[indstime])
-    assert np.allclose(fieldsetsub.P.data, fieldsetfull.P.data[indstime, :, :])
-
-
 @pytest.mark.parametrize('xdim', [100, 200])
 @pytest.mark.parametrize('ydim', [100, 200])
 def test_add_field(xdim, ydim, tmpdir, filename='test_add'):