diff --git a/hysop/core/graph/computational_graph.py b/hysop/core/graph/computational_graph.py
index ac384366bd95c7158d7cfb9e1e9140dc2fd9b775..3b00209401a24691d75712d1de6896b69641221c 100644
--- a/hysop/core/graph/computational_graph.py
+++ b/hysop/core/graph/computational_graph.py
@@ -11,6 +11,7 @@ from hysop.core.graph.computational_operator import ComputationalGraphOperator
 from hysop.core.graph.node_generator import ComputationalGraphNodeGenerator
 from hysop.core.memory.memory_request import MultipleOperatorMemoryRequests
 from hysop.fields.field_requirements import MultiFieldRequirements
+from hysop.topology.topology import Topology
 
 from abc import ABCMeta, abstractmethod
 
@@ -122,7 +123,10 @@ class ComputationalGraph(ComputationalGraphNode):
         ss= '>INPUTS:'
         if sinputs:
             for (td, sreqs) in sinputs.iteritems():
-                ss+='\n {}'.format(td)
+                if isinstance(td, Topology):
+                    ss+='\n {}'.format(td.short_description())
+                else:
+                    ss+='\n {}'.format(td)
                 ss+= template.format(*titles[0][0],
                         name_size=name_size, field_size=field_size, ghosts_size=ghosts_size, 
                         basis_size=basis_size, tstates_size=tstates_size)
@@ -136,7 +140,10 @@ class ComputationalGraph(ComputationalGraphNode):
         ss+= '\n>OUTPUTS:'
         if soutputs:
             for (td, sreqs) in soutputs.iteritems():
-                ss+='\n {}'.format(td)
+                if isinstance(td, Topology):
+                    ss+='\n {}'.format(td.short_description())
+                else:
+                    ss+='\n {}'.format(td)
                 ss+= template.format(*titles[0][0],
                         name_size=name_size, field_size=field_size, ghosts_size=ghosts_size, 
                         basis_size=basis_size, tstates_size=tstates_size)
@@ -171,7 +178,7 @@ class ComputationalGraph(ComputationalGraphNode):
                 outputs = '{}{}{}'.format(outfields, 'x' if outfields and outparams else '', outparams)
                 
                 if inputs == '':
-                    outputs='no inputs'
+                    inputs='no inputs'
                 if outputs == '':
                     outputs='no outputs'
                 ops.setdefault(domain, []).append( (op.name, inputs, outputs, type(op).__name__) )
@@ -187,7 +194,7 @@ class ComputationalGraph(ComputationalGraphNode):
                 inputs='{}'.format(inparams)
                 outputs='{}'.format(outparams)
                 if inputs == '':
-                    outputs='no inputs'
+                    inputs='no inputs'
                 if outputs == '':
                     outputs='no outputs'
                 ops.setdefault(None, []).append( (op.name, inputs, outputs, type(op).__name__) )
@@ -257,7 +264,7 @@ class ComputationalGraph(ComputationalGraphNode):
             inputs  = '{}{}{}'.format(infields,  'x' if infields  and inparams  else '', inparams)
             outputs = '{}{}{}'.format(outfields, 'x' if outfields and outparams else '', outparams)
             if inputs == '':
-                outputs='no inputs'
+                inputs='no inputs'
             if outputs == '':
                 outputs='no outputs'
 
@@ -281,7 +288,7 @@ class ComputationalGraph(ComputationalGraphNode):
                     name_size=name_size, in_size=in_size,
                     out_size=out_size, type_size=type_size)
         
-        title = ' ComputationalGraph {} final operator report '.format(self.name)
+        title = ' ComputationalGraph {} discrete operator report '.format(self.name)
         return '\n{}\n'.format(framed_str(title=title, msg=ss))
 
     def get_domains(self):
diff --git a/hysop/core/memory/memory_request.py b/hysop/core/memory/memory_request.py
index 69a98381270fb565a22e8b0509b4e7f5c6da6feb..d8b1e47e3021105ddf2ddfcab6abe7be0f24ea0d 100644
--- a/hysop/core/memory/memory_request.py
+++ b/hysop/core/memory/memory_request.py
@@ -395,34 +395,37 @@ class MultipleOperatorMemoryRequests(object):
                 if local_total>total:
                     total=local_total
         
-        sizes = {}
-        template = '\n'
-        titles=('OPERATOR', 'REQUEST_ID', 'SIZE', 'COMPONENTS', 'SHAPE', 'DTYPE', 'ALIGNMENT')
-        for (i,k) in enumerate(titles): 
-            k=k.lower()
-            template += '    '
-            size = max(len(req[i]) for breqs in all_requests.values() for reqs in breqs.values() for req in reqs)
-            size = max(size, len(k))
-            name=k+'_len'
-            sizes[name] = size
-            template += '{:'+('<' if i==0 else '^')+'{'+name+'}}'
-        
-        ss=''
-        for (backend, backend_srequests) in all_requests.iteritems():
-            kind = backend.kind 
-            if (kind == Backend.OPENCL):
-                precision = ' on device {}'.format(backend.device.name.strip())
-            else:
-                precision = ''
-            ss+= '\n {}{}:'.format(backend.full_tag, precision)
-            ss+= template.format(*titles, **sizes)
-            for op in sorted(backend_requests.keys(), key=lambda op: op.name):
-                sop_reqs = backend_srequests[op]
-                for sreq in sop_reqs:
-                    ss+= template.format(*sreq, **sizes)
-            ss +='\n  Total extra work buffers requested: {} ({})'.format(
-                    bytes2str(total,decimal=False),
-                    bytes2str(total,decimal=True))
-            ss += '\n'
-        return ss[1:-1]
+        if len(all_requests):
+            sizes = {}
+            template = '\n'
+            titles=('OPERATOR', 'REQUEST_ID', 'SIZE', 'COMPONENTS', 'SHAPE', 'DTYPE', 'ALIGNMENT')
+            for (i,k) in enumerate(titles): 
+                k=k.lower()
+                template += '    '
+                size = max(len(req[i]) for breqs in all_requests.values() for reqs in breqs.values() for req in reqs)
+                size = max(size, len(k))
+                name=k+'_len'
+                sizes[name] = size
+                template += '{:'+('<' if i==0 else '^')+'{'+name+'}}'
+            
+            ss=''
+            for (backend, backend_srequests) in all_requests.iteritems():
+                kind = backend.kind 
+                if (kind == Backend.OPENCL):
+                    precision = ' on device {}'.format(backend.device.name.strip())
+                else:
+                    precision = ''
+                ss+= '\n {}{}:'.format(backend.full_tag, precision)
+                ss+= template.format(*titles, **sizes)
+                for op in sorted(backend_requests.keys(), key=lambda op: op.name):
+                    sop_reqs = backend_srequests[op]
+                    for sreq in sop_reqs:
+                        ss+= template.format(*sreq, **sizes)
+                ss +='\n  Total extra work buffers requested: {} ({})'.format(
+                        bytes2str(total,decimal=False),
+                        bytes2str(total,decimal=True))
+                ss += '\n'
+            return ss[1:-1]
+        else:
+            return ' No extra buffers have been requested.'
         
diff --git a/hysop/fields/field_requirements.py b/hysop/fields/field_requirements.py
index 417bf2557f396b217931109b2612898963d16b61..48c9d26417de411c1db39131f0a4a21b5c098b06 100644
--- a/hysop/fields/field_requirements.py
+++ b/hysop/fields/field_requirements.py
@@ -372,11 +372,17 @@ class MultiFieldRequirements(object):
     def _build_compatible_topologies(self):
         assert self.all_compatible()
         for topology_descriptor, reqs in self.requirements.iteritems():
-            ghosts    = npw.integer_zeros(shape=(topology_descriptor.dim,))
-            can_split = npw.integer_ones(shape=(topology_descriptor.dim,))
-            known_topologies = []
+            if isinstance(topology_descriptor, Topology):
+                dim = topology_descriptor.domain_dim
+                known_topologies = [topology_descriptor]
+            else:
+                dim = topology_descriptor.dim
+                known_topologies = []
             unknown_topologies = []
 
+            ghosts    = npw.integer_zeros(shape=(dim,))
+            can_split = npw.integer_ones(shape=(dim,))
+
             for req in reqs:
                 if isinstance(req.topology_descriptor, Topology):
                     req.check_topology()
diff --git a/hysop/operator/analytic.py b/hysop/operator/analytic.py
index d28856cbbec643e57b9b640c6cefe8b72e495817..d1f4a38ce2b029359154abad29af0ce88493950b 100644
--- a/hysop/operator/analytic.py
+++ b/hysop/operator/analytic.py
@@ -19,7 +19,7 @@ class AnalyticField(ComputationalGraphNodeFrontend):
         from hysop.backend.host.python.operator.analytic   import PythonAnalyticField
         from hysop.backend.device.opencl.operator.analytic import OpenClAnalyticField
         __implementations = {
-                Implementation.PYTHON:         PythonAnalyticField,
+                Implementation.PYTHON: PythonAnalyticField,
                 Implementation.OPENCL: OpenClAnalyticField
         }
         return __implementations
diff --git a/hysop/topology/cartesian_topology.py b/hysop/topology/cartesian_topology.py
index f18248f408efd79967d3be7c60431b5359374395..08d55a1338ec4f1f80177b0d04b1e2fb3ac53a8d 100644
--- a/hysop/topology/cartesian_topology.py
+++ b/hysop/topology/cartesian_topology.py
@@ -360,15 +360,12 @@ class CartesianTopologyView(TopologyView):
         Returns a short description of the current TopologyView.
         Short version of long_description().
         """
-        s='CartesianTopology[tag={}, domain={}, task_id={}, pcoords={}, pshape={}, '
-        s+='distr.={}, periods={}, shape={}, ghosts={}]'
+        s='CartesianTopology[tag={}, domain={}, pcoords={}, pshape={}, '
+        s+='shape={}, ghosts={}]'
         s = s.format(
                 self.tag, 
                 self.domain.domain.full_tag, 
-                self.task_id, 
                 self.proc_coords, self.proc_shape, 
-                '[{}]'.format(','.join('T' if per else 'F' for per in self.is_distributed)),
-                '[{}]'.format(','.join('T' if per else 'F' for per in self.is_periodic)),
                 '[{}]'.format(','.join(str(s) for s in self.global_resolution)),
                 '[{}]'.format(','.join(str(g) for g in self.ghosts)))
         return s
diff --git a/notebooks/.gitignore b/notebooks/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..61c6e23f8b321e032095a2f4649de66da9168ebb
--- /dev/null
+++ b/notebooks/.gitignore
@@ -0,0 +1,2 @@
+.ipynb_checkpoints
+interactive
diff --git a/notebooks/intro.ipynb b/notebooks/00_introduction.ipynb
similarity index 99%
rename from notebooks/intro.ipynb
rename to notebooks/00_introduction.ipynb
index 39dd78452d50c7aa027ea36028a9443e73fd5a0b..daee36a7c9f44ef02d1bd02e935864d739e081a3 100644
--- a/notebooks/intro.ipynb
+++ b/notebooks/00_introduction.ipynb
@@ -19,7 +19,7 @@
     "Interactive session is very useful for tests, basic understanding of hysop functionnalities but real simulation must be executed by using a script. \n",
     "\n",
     "\n",
-    "## Quick start\n",
+    "## Quick introduction\n",
     "\n",
     "In this quick introduction we will introduce some of the main data structures and types of HySoP.\n",
     "At the end of this notebook, you will be able to define and discretize Fields on topologies and initialize them.\n"
@@ -429,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 7,
    "metadata": {
     "scrolled": true
    },
@@ -440,7 +440,7 @@
      "text": [
       "HYSOP_REAL is set to float64.\n",
       "\n",
-      "Field::f8\n",
+      "Field::f0\n",
       "          *name:           F0\n",
       "          *pname:          F0\n",
       "          *dim:            2\n",
@@ -449,7 +449,7 @@
       "          *initial values: (0, 0)\n",
       "          *topology tags:  []\n",
       "        \n",
-      "Field::f9\n",
+      "Field::f1\n",
       "          *name:           F1\n",
       "          *pname:          F1\n",
       "          *dim:            2\n",
@@ -947,14 +947,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd67f6e7f10>"
+       "<matplotlib.figure.Figure at 0x7ff9cd265bd0>"
       ]
      },
      "metadata": {},
@@ -987,7 +987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1601,7 +1601,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1609,7 +1609,7 @@
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd673acc590>"
+       "<matplotlib.figure.Figure at 0x7ff9c85be1d0>"
       ]
      },
      "metadata": {},
@@ -1662,7 +1662,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the next notebook we will see how to do this the HySoP way by introducing operators."
+    "In the next notebook we will see how to do this the HySoP way by introducing operators and computational graphs."
    ]
   }
  ],
diff --git a/notebooks/01_analytical.ipynb b/notebooks/01_analytical.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a1974e9e06d4089b1d6ed339fc8b5cafab3f9941
--- /dev/null
+++ b/notebooks/01_analytical.ipynb
@@ -0,0 +1,678 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# HySoP: Time dependent analytical fields\n",
+    "\n",
+    "In this notebook we will see how to define analytical fields that evolve with time\n",
+    "and how to dump their values to disk at a given dump frequency.\n",
+    "\n",
+    "* __Level:__ easy\n",
+    "* __Recommended lecture:__ 00_introduction notebook\n",
+    "\n",
+    "In this notebook you will learn how to create, setup and apply operators embedded into a computational graph.\n",
+    "\n",
+    "Two operators are discussed here:\n",
+    "* __PythonAnalyticField:__ update a field with a python method at each simulation step.\n",
+    "* __HDF_Writer:__ write a field to disk as an hdf file.\n",
+    "\n",
+    "## Setting up HySoP\n",
+    "\n",
+    "Like in the last notebook we need to import the library and \n",
+    "required types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Starting hysop version 2.0.0-rc.1 with 1 mpi process(es) on 1 host(s) providing 1 shared memory node(s).\n",
+      "\n",
+      "*Default path for all i/o is '/home/poulpy/Documents/hysop/notebooks/interactive/p1'.\n",
+      "*Default path for caching is '/home/poulpy/.cache/hysop'.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import hysop\n",
+    "from hysop.deps import np\n",
+    "from hysop import Box, Discretization, CartesianTopology, \\\n",
+    "                  Field, Simulation, Problem\n",
+    "from hysop.defaults import TimeParameters\n",
+    "from hysop.operators import HDF_Writer\n",
+    "from hysop.backend.host.python.operator.analytic import PythonAnalyticField"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Domain and topology\n",
+    "First we define a domain, a discretization and finally our topology."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======== CartesianTopology::t0 ========\n",
+      "  *on task: 999\n",
+      "  *backend: HostArrayBackend::bk1\n",
+      "  *shape: [1 1]\n",
+      "  *process of coords [0 0] and of ranks cart_rank=0, parent_rank=0\n",
+      "  *cartesian ranks map:\n",
+      "    [0]\n",
+      "  *cartesian to parent comm ranks mapping:\n",
+      "    [[0]]\n",
+      "  *neighbours ranks (left, right) x direction \n",
+      "    [[-1  0]\n",
+      "     [-1  0]]\n",
+      "  *BoxView::d0 | 2D rectangular box domain:\n",
+      "    *origin:  [0. 0.]\n",
+      "    *max_pos: [6.28318531 6.28318531]\n",
+      "    *length:  [6.28318531 6.28318531]\n",
+      "    *left  boundary conditions: [PERIODIC(1), PERIODIC(1)]\n",
+      "    *right boundary conditions: [PERIODIC(1), PERIODIC(1)]\n",
+      "  *CartesianMeshView::m0:\n",
+      "    *proc coords:        [0 0]\n",
+      "    *global start:       [0 0]\n",
+      "    *local resolution:   [64 64]\n",
+      "    *compute resolution: [64 64]\n",
+      "    *ghosts:             [0 0]\n",
+      "    *local boundaries:   left  => [PERIODIC(1) PERIODIC(1)]\n",
+      "                         right => [PERIODIC(1) PERIODIC(1)]\n",
+      "=================================\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# chose the dimensionality of the domain\n",
+    "dim = 2\n",
+    "\n",
+    "# define the domain\n",
+    "box = Box(length=(2*np.pi,)*dim)\n",
+    "\n",
+    "# discretization parameters (here we choose 64^dim, without ghosts)\n",
+    "discretization = Discretization((65,)*dim)\n",
+    "\n",
+    "# finally create a cartesian topology\n",
+    "topo = CartesianTopology(domain=box, discretization=discretization)\n",
+    "print topo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define the scalar field and time parameters\n",
+    "Here we will define the field that will be updated at each time step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t,dt = TimeParameters(dtype=np.float32)\n",
+    "f0   = Field(name='F0', domain=box, dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define all required operators\n",
+    "### Python analytic operator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_scalar(data, coords, t):\n",
+    "    data[0][...] = (1.0/(1.0+0.1*t()))\n",
+    "    for x in coords:\n",
+    "        data[0][...] *= np.cos(x-t())\n",
+    "        \n",
+    "# Analytic operator\n",
+    "op0 = PythonAnalyticField(name='analytic', field=f0,\n",
+    "                    formula=compute_scalar,\n",
+    "                    variables={f0: topo},\n",
+    "                    extra_input_kwds={'t':t}) # <= here we pass all extra keyword arguments required by formula"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The HDF writer operator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# HDF Writer\n",
+    "# by default fields will be dumps to /tmp/\n",
+    "op1 = HDF_Writer(name='write', \n",
+    "                 variables={f0:topo})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### And finally build the operator graph\n",
+    "Build computational operator graph:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Initializing problem...\n",
+      "\n",
+      "== ComputationalGraph Problem domain and operator report ==================\n",
+      ">Box::d0 (O=[0.0,0.0], L=[6.3,6.3], BC=[PER/PER,PER/PER], current_task=999)\n",
+      "   OPERATOR  INPUTS       OUTPUTS       OPERATOR TYPE      \n",
+      "   analytic  [t]   ->   [F0]          PythonAnalyticField\n",
+      "   write     [F0]  ->   no outputs    HDF_Writer         \n",
+      "===========================================================================\n",
+      "\n",
+      "\n",
+      "== ComputationalGraph Problem field requirements report ============================================\n",
+      ">INPUTS:\n",
+      " CartesianTopology[tag=t0, domain=Box::d0, pcoords=[0 0], pshape=[1 1], shape=[65,65], ghosts=[0,0]]\n",
+      "   OPERATOR   FIELD            GHOSTS              BASIS      TSTATES\n",
+      "   write       F0       [0,0]<=ghosts<[+∞,+∞]       ANY         YX   \n",
+      ">OUTPUTS:\n",
+      " CartesianTopology[tag=t0, domain=Box::d0, pcoords=[0 0], pshape=[1 1], shape=[65,65], ghosts=[0,0]]\n",
+      "   OPERATOR   FIELD            GHOSTS              BASIS      TSTATES\n",
+      "   analytic    F0       [0,0]<=ghosts<[+∞,+∞]       ANY         ANY  \n",
+      "====================================================================================================\n",
+      "\n",
+      "\n",
+      "== ComputationalGraph Problem topology report ========================================================\n",
+      " :HostBackend:  tag=bk1, allocator=HostAllocator::al0:\n",
+      "  *CartesianTopology[tag=t0, domain=Box::d0, pcoords=[0 0], pshape=[1 1], shape=[65,65], ghosts=[0,0]]\n",
+      "======================================================================================================\n",
+      "\n",
+      "\n",
+      "== ComputationalGraph Problem discrete operator report ========\n",
+      "  ID  OPERATOR  INPUTS        OUTPUTS       OPERATOR TYPE      \n",
+      "  0  analytic  [t]      ->   [F0.t0]       PythonAnalyticField\n",
+      "  1  write     [F0.t0]  ->   no outputs    HDF_Writer         \n",
+      "===============================================================\n",
+      "\n",
+      "\n",
+      "Discretizing problem...\n",
+      "\n",
+      "Allocation of discrete field F00 (CartesianDiscreteField::df0) on CartesianTopology::t0\n",
+      "  (compute_res=[64 64], ghosts=[0 0], nb_comp=1, dtype=float32, size=16.38kB)\n",
+      "\n",
+      "Getting work properties...\n",
+      "\n",
+      "== ComputationalGraph Problem work properties report ==\n",
+      " No extra buffers have been requested.\n",
+      "=======================================================\n",
+      "\n",
+      "\n",
+      "Allocating work...\n",
+      "\n",
+      "Setting up problem...\n",
+      "\n",
+      "***********************************************\n",
+      "** Problem building took 0:00:00 (0.016592s) **\n",
+      "***********************************************\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create a problem and build it\n",
+    "problem = Problem()\n",
+    "problem.insert(op0, op1)\n",
+    "problem.build()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Solve and finalize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ">Parameter dt set to 0.1.\n",
+      ">Parameter t set to 0.0.\n",
+      "\n",
+      "Solving problem...\n",
+      ">Parameter t set to 0.0.\n",
+      ">Parameter dt set to 0.1.\n",
+      "\n",
+      "== Iteration :   0, from t =    0.0 to t = 0.10000 ==\n",
+      ">Parameter t set to 0.10000000149.\n",
+      "\n",
+      "== Iteration :   1, from t =    0.1 to t = 0.20000 ==\n",
+      ">Parameter t set to 0.20000000298.\n",
+      "\n",
+      "== Iteration :   2, from t =    0.2 to t = 0.30000 ==\n",
+      ">Parameter t set to 0.300000011921.\n",
+      "\n",
+      "== Iteration :   3, from t =    0.3 to t = 0.40000 ==\n",
+      ">Parameter t set to 0.40000000596.\n",
+      "\n",
+      "== Iteration :   4, from t =    0.4 to t = 0.50000 ==\n",
+      ">Parameter t set to 0.5.\n",
+      "\n",
+      "== Iteration :   5, from t =    0.5 to t = 0.60000 ==\n",
+      ">Parameter t set to 0.600000023842.\n",
+      "\n",
+      "== Iteration :   6, from t =    0.6 to t = 0.70000 ==\n",
+      ">Parameter t set to 0.700000047684.\n",
+      "\n",
+      "== Iteration :   7, from t =    0.7 to t = 0.80000 ==\n",
+      ">Parameter t set to 0.800000071526.\n",
+      "\n",
+      "== Iteration :   8, from t =    0.8 to t = 0.90000 ==\n",
+      ">Parameter t set to 0.900000095367.\n",
+      "\n",
+      "== Iteration :   9, from t =    0.9 to t = 1.00000 ==\n",
+      ">Parameter t set to 1.00000011921.\n",
+      "\n",
+      ">Problem profiler report\n",
+      "  >write::apply ncalls=10, total= 20.7ms, mean=  2.1ms\n",
+      "  >analytic::apply ncalls=10, total= 12.5ms, mean=  1.2ms\n",
+      "\n",
+      "== Iteration :  10, from t =    1.0 to t = 1.10000 ==\n",
+      ">Parameter t set to 1.10000014305.\n",
+      "\n",
+      "== Iteration :  11, from t =    1.1 to t = 1.20000 ==\n",
+      ">Parameter t set to 1.20000016689.\n",
+      "\n",
+      "== Iteration :  12, from t =    1.2 to t = 1.30000 ==\n",
+      ">Parameter t set to 1.30000019073.\n",
+      "\n",
+      "== Iteration :  13, from t =    1.3 to t = 1.40000 ==\n",
+      ">Parameter t set to 1.40000021458.\n",
+      "\n",
+      "== Iteration :  14, from t =    1.4 to t = 1.50000 ==\n",
+      ">Parameter t set to 1.50000023842.\n",
+      "\n",
+      "== Iteration :  15, from t =    1.5 to t = 1.60000 ==\n",
+      ">Parameter t set to 1.60000026226.\n",
+      "\n",
+      "== Iteration :  16, from t =    1.6 to t = 1.70000 ==\n",
+      ">Parameter t set to 1.7000002861.\n",
+      "\n",
+      "== Iteration :  17, from t =    1.7 to t = 1.80000 ==\n",
+      ">Parameter t set to 1.80000030994.\n",
+      "\n",
+      "== Iteration :  18, from t =    1.8 to t = 1.90000 ==\n",
+      ">Parameter t set to 1.90000033379.\n",
+      "\n",
+      "== Iteration :  19, from t =    1.9 to t = 2.00000 ==\n",
+      ">Parameter t set to 2.00000023842.\n",
+      "\n",
+      ">Problem profiler report\n",
+      "  >write::apply ncalls=20, total= 40.4ms, mean=  2.0ms\n",
+      "  >analytic::apply ncalls=20, total= 25.6ms, mean=  1.3ms\n",
+      "\n",
+      "== Iteration :  20, from t =    2.0 to t = 2.10000 ==\n",
+      ">Parameter t set to 2.10000014305.\n",
+      "\n",
+      "== Iteration :  21, from t =    2.1 to t = 2.20000 ==\n",
+      ">Parameter t set to 2.20000004768.\n",
+      "\n",
+      "== Iteration :  22, from t =    2.2 to t = 2.30000 ==\n",
+      ">Parameter t set to 2.29999995232.\n",
+      "\n",
+      "== Iteration :  23, from t =    2.3 to t = 2.40000 ==\n",
+      ">Parameter t set to 2.39999985695.\n",
+      "\n",
+      "== Iteration :  24, from t =    2.4 to t = 2.50000 ==\n",
+      ">Parameter t set to 2.49999976158.\n",
+      "\n",
+      "== Iteration :  25, from t =    2.5 to t = 2.60000 ==\n",
+      ">Parameter t set to 2.59999966621.\n",
+      "\n",
+      "== Iteration :  26, from t =    2.6 to t = 2.70000 ==\n",
+      ">Parameter t set to 2.69999957085.\n",
+      "\n",
+      "== Iteration :  27, from t =    2.7 to t = 2.80000 ==\n",
+      ">Parameter t set to 2.79999947548.\n",
+      "\n",
+      "== Iteration :  28, from t =    2.8 to t = 2.90000 ==\n",
+      ">Parameter t set to 2.89999938011.\n",
+      "\n",
+      "== Iteration :  29, from t =    2.9 to t = 3.00000 ==\n",
+      ">Parameter t set to 2.99999928474.\n",
+      "\n",
+      ">Problem profiler report\n",
+      "  >write::apply ncalls=30, total= 56.1ms, mean=  1.9ms\n",
+      "  >analytic::apply ncalls=30, total= 36.3ms, mean=  1.2ms\n",
+      "\n",
+      "== Iteration :  30, from t =    3.0 to t = 3.10000 ==\n",
+      ">Parameter t set to 3.09999918938.\n",
+      "\n",
+      "== Iteration :  31, from t =    3.1 to t = 3.20000 ==\n",
+      ">Parameter t set to 3.19999909401.\n",
+      "\n",
+      "== Iteration :  32, from t =    3.2 to t = 3.30000 ==\n",
+      ">Parameter t set to 3.29999899864.\n",
+      "\n",
+      "== Iteration :  33, from t =    3.3 to t = 3.40000 ==\n",
+      ">Parameter t set to 3.39999890327.\n",
+      "\n",
+      "== Iteration :  34, from t =    3.4 to t = 3.50000 ==\n",
+      ">Parameter t set to 3.49999880791.\n",
+      "\n",
+      "== Iteration :  35, from t =    3.5 to t = 3.60000 ==\n",
+      ">Parameter t set to 3.59999871254.\n",
+      "\n",
+      "== Iteration :  36, from t =    3.6 to t = 3.70000 ==\n",
+      ">Parameter t set to 3.69999861717.\n",
+      "\n",
+      "== Iteration :  37, from t =    3.7 to t = 3.80000 ==\n",
+      ">Parameter t set to 3.7999985218.\n",
+      "\n",
+      "== Iteration :  38, from t =    3.8 to t = 3.90000 ==\n",
+      ">Parameter t set to 3.89999842644.\n",
+      "\n",
+      "== Iteration :  39, from t =    3.9 to t = 4.00000 ==\n",
+      ">Parameter t set to 3.99999833107.\n",
+      "\n",
+      ">Problem profiler report\n",
+      "  >write::apply ncalls=40, total= 71.5ms, mean=  1.8ms\n",
+      "  >analytic::apply ncalls=40, total= 47.2ms, mean=  1.2ms\n",
+      "\n",
+      "== Iteration :  40, from t =    4.0 to t = 4.10000 ==\n",
+      ">Parameter t set to 4.09999847412.\n",
+      "\n",
+      "== Iteration :  41, from t =    4.1 to t = 4.20000 ==\n",
+      ">Parameter t set to 4.19999837875.\n",
+      "\n",
+      "== Iteration :  42, from t =    4.2 to t = 4.30000 ==\n",
+      ">Parameter t set to 4.29999828339.\n",
+      "\n",
+      "== Iteration :  43, from t =    4.3 to t = 4.40000 ==\n",
+      ">Parameter t set to 4.39999818802.\n",
+      "\n",
+      "== Iteration :  44, from t =    4.4 to t = 4.50000 ==\n",
+      ">Parameter t set to 4.49999809265.\n",
+      "\n",
+      "== Iteration :  45, from t =    4.5 to t = 4.60000 ==\n",
+      ">Parameter t set to 4.59999799728.\n",
+      "\n",
+      "== Iteration :  46, from t =    4.6 to t = 4.70000 ==\n",
+      ">Parameter t set to 4.69999790192.\n",
+      "\n",
+      "== Iteration :  47, from t =    4.7 to t = 4.80000 ==\n",
+      ">Parameter t set to 4.79999780655.\n",
+      "\n",
+      "== Iteration :  48, from t =    4.8 to t = 4.90000 ==\n",
+      ">Parameter t set to 4.89999771118.\n",
+      "\n",
+      "== Iteration :  49, from t =    4.9 to t = 5.00000 ==\n",
+      ">Parameter t set to 4.99999761581.\n",
+      "\n",
+      ">Problem profiler report\n",
+      "  >write::apply ncalls=50, total= 94.3ms, mean=  1.9ms\n",
+      "  >analytic::apply ncalls=50, total= 60.7ms, mean=  1.2ms\n",
+      "\n",
+      "== Iteration :  50, from t =    5.0 to t = 5.10000 ==\n",
+      ">Parameter t set to 5.09999752045.\n",
+      "\n",
+      "== Iteration :  51, from t =    5.1 to t = 5.20000 ==\n",
+      ">Parameter t set to 5.19999742508.\n",
+      "\n",
+      "== Iteration :  52, from t =    5.2 to t = 5.30000 ==\n",
+      ">Parameter t set to 5.29999732971.\n",
+      "\n",
+      "== Iteration :  53, from t =    5.3 to t = 5.40000 ==\n",
+      ">Parameter t set to 5.39999723434.\n",
+      "\n",
+      "== Iteration :  54, from t =    5.4 to t = 5.50000 ==\n",
+      ">Parameter t set to 5.49999713898.\n",
+      "\n",
+      "== Iteration :  55, from t =    5.5 to t = 5.60000 ==\n",
+      ">Parameter t set to 5.59999704361.\n",
+      "\n",
+      "== Iteration :  56, from t =    5.6 to t = 5.70000 ==\n",
+      ">Parameter t set to 5.69999694824.\n",
+      "\n",
+      "== Iteration :  57, from t =    5.7 to t = 5.80000 ==\n",
+      ">Parameter t set to 5.79999685287.\n",
+      "\n",
+      "== Iteration :  58, from t =    5.8 to t = 5.90000 ==\n",
+      ">Parameter t set to 5.89999675751.\n",
+      "\n",
+      "== Iteration :  59, from t =    5.9 to t = 6.00000 ==\n",
+      ">Parameter t set to 5.99999666214.\n",
+      "\n",
+      ">Problem profiler report\n",
+      "  >write::apply ncalls=60, total=110.8ms, mean=  1.8ms\n",
+      "  >analytic::apply ncalls=60, total= 72.5ms, mean=  1.2ms\n",
+      "\n",
+      "== Iteration :  60, from t =    6.0 to t = 6.10000 ==\n",
+      ">Parameter t set to 6.09999656677.\n",
+      "\n",
+      "== Iteration :  61, from t =    6.1 to t = 6.20000 ==\n",
+      ">Parameter t set to 6.19999647141.\n",
+      "\n",
+      "== Iteration :  62, from t =    6.2 to t = 6.30000 ==\n",
+      ">Parameter t set to 6.29999637604.\n",
+      "\n",
+      "== Iteration :  63, from t =    6.3 to t = 6.40000 ==\n",
+      ">Parameter t set to 6.39999628067.\n",
+      "\n",
+      "== Iteration :  64, from t =    6.4 to t = 6.50000 ==\n",
+      ">Parameter t set to 6.4999961853.\n",
+      "\n",
+      "== Iteration :  65, from t =    6.5 to t = 6.60000 ==\n",
+      ">Parameter t set to 6.59999608994.\n",
+      "\n",
+      "== Iteration :  66, from t =    6.6 to t = 6.70000 ==\n",
+      ">Parameter t set to 6.69999599457.\n",
+      "\n",
+      "== Iteration :  67, from t =    6.7 to t = 6.80000 ==\n",
+      ">Parameter t set to 6.7999958992.\n",
+      "\n",
+      "== Iteration :  68, from t =    6.8 to t = 6.90000 ==\n",
+      ">Parameter t set to 6.89999580383.\n",
+      "\n",
+      "== Iteration :  69, from t =    6.9 to t = 7.00000 ==\n",
+      ">Parameter t set to 6.99999570847.\n",
+      "\n",
+      ">Problem profiler report\n",
+      "  >write::apply ncalls=70, total=128.6ms, mean=  1.8ms\n",
+      "  >analytic::apply ncalls=70, total= 84.8ms, mean=  1.2ms\n",
+      "\n",
+      "== Iteration :  70, from t =    7.0 to t = 7.10000 ==\n",
+      ">Parameter t set to 7.0999956131.\n",
+      "\n",
+      "== Iteration :  71, from t =    7.1 to t = 7.20000 ==\n",
+      ">Parameter t set to 7.19999551773.\n",
+      "\n",
+      "== Iteration :  72, from t =    7.2 to t = 7.30000 ==\n",
+      ">Parameter t set to 7.29999542236.\n",
+      "\n",
+      "== Iteration :  73, from t =    7.3 to t = 7.40000 ==\n",
+      ">Parameter t set to 7.399995327.\n",
+      "\n",
+      "== Iteration :  74, from t =    7.4 to t = 7.50000 ==\n",
+      ">Parameter t set to 7.49999523163.\n",
+      "\n",
+      "== Iteration :  75, from t =    7.5 to t = 7.60000 ==\n",
+      ">Parameter t set to 7.59999513626.\n",
+      "\n",
+      "== Iteration :  76, from t =    7.6 to t = 7.70000 ==\n",
+      ">Parameter t set to 7.69999504089.\n",
+      "\n",
+      "== Iteration :  77, from t =    7.7 to t = 7.79999 ==\n",
+      ">Parameter t set to 7.79999494553.\n",
+      "\n",
+      "== Iteration :  78, from t =    7.8 to t = 7.89999 ==\n",
+      ">Parameter t set to 7.89999485016.\n",
+      "\n",
+      "== Iteration :  79, from t =    7.9 to t = 7.99999 ==\n",
+      ">Parameter t set to 7.99999475479.\n",
+      "\n",
+      ">Problem profiler report\n",
+      "  >write::apply ncalls=80, total=145.8ms, mean=  1.8ms\n",
+      "  >analytic::apply ncalls=80, total= 96.2ms, mean=  1.2ms\n",
+      "\n",
+      "== Iteration :  80, from t =    8.0 to t = 8.09999 ==\n",
+      ">Parameter t set to 8.09999465942.\n",
+      "\n",
+      "== Iteration :  81, from t =    8.1 to t = 8.20000 ==\n",
+      ">Parameter t set to 8.19999504089.\n",
+      "\n",
+      "== Iteration :  82, from t =    8.2 to t = 8.30000 ==\n",
+      ">Parameter t set to 8.29999542236.\n",
+      "\n",
+      "== Iteration :  83, from t =    8.3 to t = 8.40000 ==\n",
+      ">Parameter t set to 8.39999580383.\n",
+      "\n",
+      "== Iteration :  84, from t =    8.4 to t = 8.50000 ==\n",
+      ">Parameter t set to 8.4999961853.\n",
+      "\n",
+      "== Iteration :  85, from t =    8.5 to t = 8.60000 ==\n",
+      ">Parameter t set to 8.59999656677.\n",
+      "\n",
+      "== Iteration :  86, from t =    8.6 to t = 8.70000 ==\n",
+      ">Parameter t set to 8.69999694824.\n",
+      "\n",
+      "== Iteration :  87, from t =    8.7 to t = 8.80000 ==\n",
+      ">Parameter t set to 8.79999732971.\n",
+      "\n",
+      "== Iteration :  88, from t =    8.8 to t = 8.90000 ==\n",
+      ">Parameter t set to 8.89999771118.\n",
+      "\n",
+      "== Iteration :  89, from t =    8.9 to t = 9.00000 ==\n",
+      ">Parameter t set to 8.99999809265.\n",
+      "\n",
+      ">Problem profiler report\n",
+      "  >write::apply ncalls=90, total=160.5ms, mean=  1.8ms\n",
+      "  >analytic::apply ncalls=90, total=106.6ms, mean=  1.2ms\n",
+      "\n",
+      "== Iteration :  90, from t =    9.0 to t = 9.10000 ==\n",
+      ">Parameter t set to 9.09999847412.\n",
+      "\n",
+      "== Iteration :  91, from t =    9.1 to t = 9.20000 ==\n",
+      ">Parameter t set to 9.19999885559.\n",
+      "\n",
+      "== Iteration :  92, from t =    9.2 to t = 9.30000 ==\n",
+      ">Parameter t set to 9.29999923706.\n",
+      "\n",
+      "== Iteration :  93, from t =    9.3 to t = 9.40000 ==\n",
+      ">Parameter t set to 9.39999961853.\n",
+      "\n",
+      "== Iteration :  94, from t =    9.4 to t = 9.50000 ==\n",
+      ">Parameter t set to 9.5.\n",
+      "\n",
+      "== Iteration :  95, from t =    9.5 to t = 9.60000 ==\n",
+      ">Parameter t set to 9.60000038147.\n",
+      "\n",
+      "== Iteration :  96, from t =    9.6 to t = 9.70000 ==\n",
+      ">Parameter t set to 9.70000076294.\n",
+      "\n",
+      "== Iteration :  97, from t =    9.7 to t = 9.80000 ==\n",
+      ">Parameter t set to 9.80000114441.\n",
+      "\n",
+      "== Iteration :  98, from t =    9.8 to t = 9.90000 ==\n",
+      ">Parameter t set to 9.90000152588.\n",
+      "\n",
+      "**********************************************************************\n",
+      "** Next iteration is last iteration, clamping dt to achieve t=10.0. **\n",
+      "**********************************************************************\n",
+      ">Parameter dt set to 0.0999984741211.\n",
+      "\n",
+      "== Iteration :  99, from t =    9.9 to t = 10.00000 ==\n",
+      "\n",
+      "*****************************************************\n",
+      "** Simulation took 0:00:00 (0.329768s)             **\n",
+      "**  for 100 iterations (0.00329768s per iteration) **\n",
+      "*****************************************************\n",
+      "\n",
+      "\n",
+      ">Problem profiler report\n",
+      "  >write::apply ncalls=100, total=175.5ms, mean=  1.8ms\n",
+      "  >analytic::apply ncalls=100, total=117.5ms, mean=  1.2ms\n",
+      "Finalizing problem...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create a simulation and solve the problem \n",
+    "# (do not forget to specify the time parameter here)\n",
+    "simu = Simulation(start=0.0, end=10.0, \n",
+    "                  nb_iter=100, t=t, dt=dt)\n",
+    "\n",
+    "# Finally solve the problem \n",
+    "problem.solve(simu)\n",
+    "\n",
+    "# Finalize\n",
+    "problem.finalize()"
+   ]
+  }
+ ],
+ "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": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}