diff --git a/doc/notebooks/07_tutorial_shanchen_twophase.ipynb b/doc/notebooks/07_tutorial_shanchen_twophase.ipynb
index b6dbc8cae666eb2d98eaaa5d80c7eb16144fdeca..6f0be3f51fbc6191109eb07df8ce1b857a14842e 100644
--- a/doc/notebooks/07_tutorial_shanchen_twophase.ipynb
+++ b/doc/notebooks/07_tutorial_shanchen_twophase.ipynb
@@ -15,7 +15,8 @@
    "source": [
     "from lbmpy.session import *\n",
     "from lbmpy.updatekernels import create_stream_pull_with_output_kernel\n",
-    "from lbmpy.macroscopic_value_kernels import macroscopic_values_getter, macroscopic_values_setter"
+    "from lbmpy.macroscopic_value_kernels import macroscopic_values_getter, macroscopic_values_setter\n",
+    "from lbmpy.maxwellian_equilibrium import get_weights"
    ]
   },
   {
@@ -42,7 +43,10 @@
     "N = 64\n",
     "omega_a = 1.\n",
     "g_aa = -4.7\n",
-    "rho0 = 1."
+    "rho0 = 1.\n",
+    "\n",
+    "stencil = get_stencil(\"D2Q9\")\n",
+    "weights = get_weights(stencil, c_s_sq=sp.Rational(1,3))"
    ]
   },
   {
@@ -58,14 +62,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dh = ps.create_data_handling((N, N), periodicity=True, default_target='cpu')\n",
+    "dim = len(stencil[0])\n",
     "\n",
-    "method_a = create_lb_method(relaxation_rate=omega_a, compressible=True)\n",
+    "dh = ps.create_data_handling((N,)*dim, periodicity=True, default_target='cpu')\n",
     "\n",
-    "src_a = dh.add_array('src_a', values_per_cell=len(method_a.stencil))\n",
-    "dst_a = dh.add_array_like('dst_a', 'src_a')\n",
+    "src = dh.add_array('src', values_per_cell=len(stencil))\n",
+    "dst = dh.add_array_like('dst', 'src')\n",
     "\n",
-    "ρ_a = dh.add_array('rho_a')"
+    "ρ = dh.add_array('rho')"
    ]
   },
   {
@@ -103,10 +107,8 @@
    "source": [
     "zero_vec = sp.Matrix([0] * dh.dim) \n",
     "\n",
-    "stencil, weights = method_a.stencil, method_a.weights\n",
-    "force_a = sum((psi(ρ_a[d]) * w_d * sp.Matrix(d)\n",
-    "                for d, w_d in zip(stencil, weights)), \n",
-    "               zero_vec) * psi(ρ_a.center) * -1 * g_aa"
+    "force = sum((psi(ρ[d]) * w_d * sp.Matrix(d)\n",
+    "            for d, w_d in zip(stencil, weights)), zero_vec) * psi(ρ.center) * -1 * g_aa"
    ]
   },
   {
@@ -122,19 +124,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "stream_a = create_stream_pull_with_output_kernel(method_a, src_a, dst_a, {'density': ρ_a})\n",
+    "collision = create_lb_update_rule(stencil=stencil,\n",
+    "                                  relaxation_rate=omega_a, \n",
+    "                                  compressible=True,\n",
+    "                                  force_model='guo', \n",
+    "                                  force=force,\n",
+    "                                  kernel_type='collide_only',\n",
+    "                                  optimization={'symbolic_field': src})\n",
+    "\n",
+    "stream = create_stream_pull_with_output_kernel(collision.method, src, dst, {'density': ρ})\n",
+    "\n",
     "\n",
-    "# TODO use method above\n",
-    "collision_a = create_lb_update_rule(relaxation_rate=omega_a, \n",
-    "                                    compressible=True,\n",
-    "                                    force_model='guo', \n",
-    "                                    force=force_a,\n",
-    "                                    kernel_type='collide_only',\n",
-    "                                    optimization={'symbolic_field': src_a})\n",
+    "opts = {'cpu_openmp': False, \n",
+    "        'target': dh.default_target}\n",
     "\n",
-    "opts = {'cpu_openmp': False}\n",
-    "stream_a_kernel = ps.create_kernel(stream_a, **opts).compile()\n",
-    "collision_a_kernel = ps.create_kernel(collision_a, **opts).compile()"
+    "stream_kernel = ps.create_kernel(stream, **opts).compile()\n",
+    "collision_kernel = ps.create_kernel(collision, **opts).compile()"
    ]
   },
   {
@@ -150,9 +155,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "init_a = macroscopic_values_setter(method_a, velocity=(0, 0), \n",
-    "                                   pdfs=src_a.center_vector, density=ρ_a.center)\n",
-    "init_a_kernel = ps.create_kernel(init_a, ghost_layers=0).compile()"
+    "method_without_force = create_lb_method(stencil=stencil, relaxation_rate=omega_a, compressible=True)\n",
+    "init_assignments = macroscopic_values_setter(method_without_force, velocity=(0, 0), \n",
+    "                                             pdfs=src.center_vector, density=ρ.center)\n",
+    "\n",
+    "\n",
+    "init_kernel = ps.create_kernel(init_assignments, ghost_layers=0).compile()"
    ]
   },
   {
@@ -165,11 +173,11 @@
     "    for x in range(N):\n",
     "        for y in range(N):\n",
     "            if (x-N/2)**2 + (y-N/2)**2 <= 15**2:\n",
-    "                dh.fill(ρ_a.name, 2.1, slice_obj=[x,y])\n",
+    "                dh.fill(ρ.name, 2.1, slice_obj=[x,y])\n",
     "            else:\n",
-    "                dh.fill(ρ_a.name, 0.15, slice_obj=[x,y])\n",
+    "                dh.fill(ρ.name, 0.15, slice_obj=[x,y])\n",
     "\n",
-    "    dh.run_kernel(init_a_kernel)"
+    "    dh.run_kernel(init_kernel)"
    ]
   },
   {
@@ -185,31 +193,31 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sync_pdfs = dh.synchronization_function([src_a.name])\n",
-    "sync_ρs = dh.synchronization_function([ρ_a.name])\n",
+    "sync_pdfs = dh.synchronization_function([src.name])\n",
+    "sync_ρs = dh.synchronization_function([ρ.name])\n",
     "\n",
     "def time_loop(steps):\n",
     "    dh.all_to_gpu()\n",
     "    for i in range(steps):\n",
     "        sync_ρs()\n",
-    "        dh.run_kernel(collision_a_kernel)\n",
+    "        dh.run_kernel(collision_kernel)\n",
     "        \n",
     "        sync_pdfs()\n",
-    "        dh.run_kernel(stream_a_kernel)\n",
+    "        dh.run_kernel(stream_kernel)\n",
     "        \n",
-    "        dh.swap(src_a.name, dst_a.name)\n",
+    "        dh.swap(src.name, dst.name)\n",
     "    dh.all_to_cpu()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
     "def plot_ρs():\n",
-    "    plt.title(\"$\\\\rho_A$\")\n",
-    "    plt.scalar_field(dh.gather_array(ρ_a.name), vmin=0, vmax=2.5)\n",
+    "    plt.title(\"$\\\\rho$\")\n",
+    "    plt.scalar_field(dh.gather_array(ρ.name), vmin=0, vmax=2.5)\n",
     "    plt.colorbar()"
    ]
   },
@@ -223,12 +231,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1152x432 with 2 Axes>"
       ]
@@ -261,14 +269,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
     "init()\n",
     "time_loop(1)\n",
-    "ref_a = np.array([0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.136756, 0.220324, 1.2382, 2.26247, 2.26183, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.26183, 2.26247, 1.2382, 0.220324, 0.136756, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15])\n",
-    "assert np.allclose(dh.gather_array(ρ_a.name)[N//2], ref_a)"
+    "ref = np.array([0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.136756, 0.220324, 1.2382, 2.26247, 2.26183, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.26183, 2.26247, 1.2382, 0.220324, 0.136756, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15])\n",
+    "\n",
+    "assert np.allclose(dh.gather_array(ρ.name)[N//2], ref)"
    ]
   },
   {
@@ -280,12 +289,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1152x432 with 2 Axes>"
       ]
@@ -301,13 +310,6 @@
     "time_loop(1000)\n",
     "plot_ρs()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -326,7 +328,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.2"
   }
  },
  "nbformat": 4,
diff --git a/doc/notebooks/08_tutorial_shanchen_twocomponent.ipynb b/doc/notebooks/08_tutorial_shanchen_twocomponent.ipynb
index 81329d401fceb34b3b35ad69af670d2b1907827c..4d2d79f922af595ea07f3e80adfef9714995fded 100644
--- a/doc/notebooks/08_tutorial_shanchen_twocomponent.ipynb
+++ b/doc/notebooks/08_tutorial_shanchen_twocomponent.ipynb
@@ -15,7 +15,8 @@
    "source": [
     "from lbmpy.session import *\n",
     "from lbmpy.updatekernels import create_stream_pull_with_output_kernel\n",
-    "from lbmpy.macroscopic_value_kernels import macroscopic_values_getter, macroscopic_values_setter"
+    "from lbmpy.macroscopic_value_kernels import macroscopic_values_getter, macroscopic_values_setter\n",
+    "from lbmpy.maxwellian_equilibrium import get_weights"
    ]
   },
   {
@@ -39,20 +40,30 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "N = 64\n",
-    "omega_a = 1.\n",
-    "omega_b = 1.\n",
+    "N = 64       # domain size\n",
+    "omega_a = 1. # relaxation rate of first component\n",
+    "omega_b = 1. # relaxation rate of second component\n",
+    "\n",
+    "# interaction strength\n",
     "g_aa = 0.\n",
     "g_ab = g_ba = 6.\n",
     "g_bb = 0.\n",
-    "rho0 = 1."
+    "\n",
+    "rho0 = 1.\n",
+    "\n",
+    "stencil = get_stencil(\"D2Q9\")\n",
+    "weights = get_weights(stencil, c_s_sq=sp.Rational(1,3))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Data structures"
+    "## Data structures\n",
+    "\n",
+    "We allocate two sets of PDF's, one for each phase. Additionally, for each phase there is one field to store its density and velocity.\n",
+    "\n",
+    "To run the simulation on GPU, change the `default_target` to gpu"
    ]
   },
   {
@@ -61,15 +72,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dh = ps.create_data_handling((N, N), periodicity=True, default_target='cpu')\n",
-    "\n",
-    "method_a = create_lb_method(relaxation_rate=omega_a, compressible=True)\n",
-    "method_b = create_lb_method(relaxation_rate=omega_b, compressible=True)\n",
+    "dim = len(stencil[0])\n",
+    "dh = ps.create_data_handling((N, ) * dim, periodicity=True, default_target='cpu')\n",
     "\n",
-    "src_a = dh.add_array('src_a', values_per_cell=len(method_a.stencil))\n",
+    "src_a = dh.add_array('src_a', values_per_cell=len(stencil))\n",
     "dst_a = dh.add_array_like('dst_a', 'src_a')\n",
     "\n",
-    "src_b = dh.add_array('src_b', values_per_cell=len(method_b.stencil))\n",
+    "src_b = dh.add_array('src_b', values_per_cell=len(stencil))\n",
     "dst_b = dh.add_array_like('dst_b', 'src_b')\n",
     "\n",
     "ρ_a = dh.add_array('rho_a')\n",
@@ -82,7 +91,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Force & combined velocity"
+    "## Force & combined velocity\n",
+    "\n",
+    "The two LB methods are coupled using a force term. Its symbolic representation is created in the next cells.\n",
+    "The force value is not written to a field, but directly evaluated inside the collision kernel."
    ]
   },
   {
@@ -116,14 +128,12 @@
     "\n",
     "force_a = zero_vec\n",
     "for factor, ρ in zip([g_aa, g_ab], [ρ_a, ρ_b]):\n",
-    "    stencil, weights = method_a.stencil, method_a.weights\n",
     "    force_a += sum((psi(ρ[d]) * w_d * sp.Matrix(d)\n",
     "                    for d, w_d in zip(stencil, weights)), \n",
     "                   zero_vec) * psi(ρ_a.center) * -1 * factor\n",
     "\n",
     "force_b = zero_vec\n",
     "for factor, ρ in zip([g_ba, g_bb], [ρ_a, ρ_b]):\n",
-    "    stencil, weights = method_b.stencil, method_b.weights\n",
     "    force_b += sum((psi(ρ[d]) * w_d * sp.Matrix(d)\n",
     "                    for d, w_d in zip(stencil, weights)), \n",
     "                   zero_vec) * psi(ρ_b.center) * -1 * factor"
@@ -161,11 +171,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "stream_a = create_stream_pull_with_output_kernel(method_a, src_a, dst_a, {'density': ρ_a, 'velocity': u_a})\n",
-    "stream_b = create_stream_pull_with_output_kernel(method_b, src_b, dst_b, {'density': ρ_b, 'velocity': u_b})\n",
-    "\n",
-    "# TODO use method above\n",
-    "collision_a = create_lb_update_rule(relaxation_rate=omega_a, \n",
+    "collision_a = create_lb_update_rule(stencil=stencil,\n",
+    "                                    relaxation_rate=omega_a, \n",
     "                                    compressible=True,\n",
     "                                    velocity_input=u_full, density_input=ρ_a,\n",
     "                                    force_model='guo', \n",
@@ -173,15 +180,29 @@
     "                                    kernel_type='collide_only',\n",
     "                                    optimization={'symbolic_field': src_a})\n",
     "\n",
-    "collision_b = create_lb_update_rule(relaxation_rate=omega_b,\n",
+    "collision_b = create_lb_update_rule(stencil=stencil,\n",
+    "                                    relaxation_rate=omega_b,\n",
     "                                    compressible=True,\n",
     "                                    velocity_input=u_full, density_input=ρ_b,\n",
     "                                    force_model='guo', \n",
     "                                    force=force_b,\n",
     "                                    kernel_type='collide_only',\n",
-    "                                    optimization={'symbolic_field': src_b})\n",
+    "                                    optimization={'symbolic_field': src_b})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stream_a = create_stream_pull_with_output_kernel(collision_a.method, src_a, dst_a, \n",
+    "                                                 {'density': ρ_a, 'velocity': u_a})\n",
+    "stream_b = create_stream_pull_with_output_kernel(collision_b.method, src_b, dst_b, \n",
+    "                                                 {'density': ρ_b, 'velocity': u_b})\n",
     "\n",
-    "opts = {'cpu_openmp': False}\n",
+    "opts = {'cpu_openmp': 1,  # number of threads when running on CPU\n",
+    "        'target': dh.default_target}\n",
     "stream_a_kernel = ps.create_kernel(stream_a, **opts).compile()\n",
     "stream_b_kernel = ps.create_kernel(stream_b, **opts).compile()\n",
     "collision_a_kernel = ps.create_kernel(collision_a, **opts).compile()\n",
@@ -197,13 +218,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
-    "init_a = macroscopic_values_setter(method_a, velocity=(0, 0), \n",
+    "init_a = macroscopic_values_setter(collision_a.method, velocity=(0, 0), \n",
     "                                   pdfs=src_a.center_vector, density=ρ_a.center)\n",
-    "init_b = macroscopic_values_setter(method_b, velocity=(0, 0), \n",
+    "init_b = macroscopic_values_setter(collision_b.method, velocity=(0, 0), \n",
     "                                   pdfs=src_b.center_vector, density=ρ_b.center)\n",
     "init_a_kernel = ps.create_kernel(init_a, ghost_layers=0).compile()\n",
     "init_b_kernel = ps.create_kernel(init_b, ghost_layers=0).compile()"
@@ -211,7 +232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -238,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -248,7 +269,7 @@
     "def time_loop(steps):\n",
     "    dh.all_to_gpu()\n",
     "    for i in range(steps):\n",
-    "        sync_ρs()\n",
+    "        sync_ρs()  # collision kernel evaluates force values, that depend on neighboring ρ's\n",
     "        dh.run_kernel(collision_a_kernel)\n",
     "        dh.run_kernel(collision_b_kernel)\n",
     "        \n",
@@ -263,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -288,17 +309,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f1214072438>"
+       "<Figure size 1152x432 with 4 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -326,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -347,17 +370,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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2WzxKUiGVrm5KklRBpWz2UR2SJEmSpJ7seZSkIsY9Hbim2OzsuFsgSSVVy2aLR0kqpFJASZJUQaVstniUpCKSWjO6SZI07apls8WjJBVSaUY3SZIqqJTNTpgjSZIkSepp9D2PdQpvSZosWWtchSRJU69YNnvbqiQVUW1GN0mSpl21bLZ4lKRCKgWUJEkVVMpmxzxKkiRJknqy51GSiqg2HbgkSdOuWjY7YY4kFZKFAkqSpAoqZbM9j5JUSKVnSUmSVEGlbLZ4lKQisth04JIkTbtq2eyEOZIkSZKknux5lKRCKo2rkCSpgkrZPNLiMZtFkla6dv4W1prRTZKk6Vcrm+15lKRCKl3dlCSpgkrZbPEoSUUktQblS5I07aplsxPmSJIkSZJ6sudRkqrIzpTgkiRpQhTL5tEXj3V6bSVp4lR6ELFGqNK/bCRpwlTKZnseJamIpNagfEmSpl21bHbMoyRJkiSpJ3seJamMWs+SkiRp+tXKZotHSSrEoWuSJE2WStnc922rEbE6Ir4aEX/brB8dETdGxN0R8bGIWNdeMyVJ/ciMZS+aPmazJE2+Stm8lJ7HtwF3Ak9r1t8FvDszL4+I9wPnAe9b9BMC0lGWktTKzNOZtQblqy+DZ7MkqTXVsrmvUi4iNgI/B3ywWQ/gFcAVzSGXAme10UBJkvREZrMkadT67Qf8M+D3gNlm/RnAw5m5r1nfARwx3xsj4vyI2BYR22YefXSgxkqSFjebseyll4jYEhG7IuK2Bfa/MSJubZYvRsTxXfvujYivRcQtEbFtiKe8kg0lm/fm4+23VJJWsErZ3LN4jIjXAbsy8+buzfMcOu9Q0MzcnJmbMnPT6vXr+2mTJGmZOrfHLG/pwyXA6Yvs/yfgZZl5HPBOYPOc/S/PzBMyc9Nyzk0/MsxsXhcHttJGSVJHpWzuZ8zjycAZEfFa4EA64yr+DDgoItY0Vzg3Ajv7+UJJUnvaHFeRmTdExFGL7P9i1+qX6WSD2mE2S9KUqJTNPYvHzHw78HaAiDgF+J3MfGNE/A3weuBy4Fzgqr6+MQrNVStJEyQZeGa2DXNuW9mcmXOvUPbrPODTXesJfDYiEvjAAJ8rWshmSVIrqmXzIM95/H3g8oj4Q+CrwIcG+CxJ0vg9OIxbSiPi5XQC6me7Np+cmTsj4lDg2oj4embeMOh36QnMZkmqZaKyeUnFY2ZeD1zfvL4HOGkp75cktWvc93ZExHF0Zv98TWZ+Z//2zNzZ/NwVEVfSyQ+LxyEwmyVpslXKZp+6KElV5HgfRBwRzwY+CbwpM7/RtX19RDx1/2vgNGDeWeEkSSqlWDYPctuqJGnStHh5MyIuA06hM/5iB3AhsBYgM98PvIPO4yL+svPIQfY1t9ocBlzZbFsDfDQzP9NeSyVJmiCFsnnkxWPa1ylJUykzz+mx/1eAX5ln+z3A8U98hyZCJszO9j5Okqrr89kYk2TU2WzPoyQV0uZ04JIkaekqZbPFoyQVMoUXTSVJKq1SNls8SlIRSa2rm5IkTbtq2WzxKElVJFAooCRJmnrFsnm0xWM0iyStdP4tlCRJU8aeR0kqpNK4CkmSKqiUzRaPklRJoYCSJKmEQtls8ShJZUSpQfmSJE2/Wtls8ShJlRS6uilJUgmFsnn0xWOdwluSJEmSVgx7HiWpiqz1LClJkqZesWy2eJSkSgrdGiNJUgmFstniUZJKqXN1U5KkGupk86pxN0CSJEmSNPnseZSkSgrdGiNJUgmFstniUZIqKRRQkiSVUCibLR4lqYoECs3oJknS1CuWzRaPklRIFrq6KUlSBZWy2QlzJEmSJEk9jbbnMSH2jfQbJWkytXUVstDVTUmSSiiUzd62KkmVFBpXIUlSCYWy2eJRkgqJQlc3JUmqoFI2WzxKUhVJqVtjJEmaesWy2QlzJEmSJEk9jbTncdU+OGC39aokrWpl8rAoNa5CI7IqiHXrxt0KSRq/vW1kaK1s9rZVSaqk0K0xkiSVUCibLR4lqZJCASVJUgmFstl7SCVJkiRJPdnzKEmVFLq6KUlSCYWyeaTF4+o9cPBdM6P8SkmaSPftaeFDk1KD8jUia9bAYRvG3QpJGr/HWyiNimWzPY+SVEilBxFLklRBpWx2zKMkVZIDLD1ExJaI2BURty2wPyLizyNie0TcGhEv7Np3bkTc3SznDnKKkiRNlULZbPEoSerXJcDpi+x/DXBMs5wPvA8gIg4BLgReDJwEXBgRB7faUkmSVoZLGGE2WzxKkvqSmTcAuxc55Ezgw9nxZeCgiDgceDVwbWbuzsyHgGtZPOgkSVIfRp3NjnmUpELGPK7iCOC+rvUdzbaFtkuSVF6lbB7tbKuP/ZCn3frtUX6lJE2k1Y/9sJ0PHmxGtw0Rsa1rfXNmbl7C++f78lxkuybAzJPX8shxzxx3MyRp7Ga+vbadDy6UzfY8SlIVfQ6uX8SDmblpgPfvAI7sWt8I7Gy2nzJn+/UDfI8kSdOhWDY75lGSNCxbgV9qZnZ7CfDdzLwfuAY4LSIObgbjn9ZskyRJ7RpqNtvzKEmVtHgzaERcRucq5YaI2EFnlra1AJn5fuBq4LXAduAx4M3Nvt0R8U7gpuajLsrMxQb3S5JUR6Fs7lk8RsSBwA3AAc3xV2TmhRFxNHA5cAjwFeBNmbm3/1OVJA1bm4PyM/OcHvsTeMsC+7YAW9po10pkNkvS9KiUzf30PO4BXpGZ34+ItcAXIuLTwG8B787MyyPi/cB5NM8NWdC+ffDAg0tpnyTVtG9fO5/rNDQrxdCyeeYAeOh5q9tvsSRNuJkvtPTBhbK555jH5pkg329W1zZLAq8Armi2Xwqc1UoLJUnSjzGbJUnj0NeEORGxOiJuAXbReYDkN4GHM3P/pfMFnwsSEedHxLaI2LZ39vFhtFmStJAcYNFUGVY273vs0dE0WJJWqkLZ3FfxmJkzmXkCnSlcTwKOne+wBd67OTM3ZeamdasOXH5LJUmLihxs0XQZVjavefL6NpspSStatWxe0myrmflwRFwPvAQ4KCLWNFc49z8vRJI0ToM9iFhTyGyWpAlXKJv7mW31mcAPm3B6EvBK4F3A54HX05nV7Vzgqp7fNpvkXid9kyRmW7qcOIFXKTV8w8zm2TWw55DZNpsrSVNhtq2HGBbK5n7+JzocuDQiVtO5zfXjmfm3EXEHcHlE/CHwVeBDLbZTkiT9iNksSRq5nsVjZt4KnDjP9nvojLGQJE2ISRwfoeEzmyVpelTK5rY6ZyVJ41AooCRJKqFQNls8SlIVEzozmyRJK1axbLZ4lCRpJQtI/zUgSVBnUtTWGBeSVEmhq5uSJJVQKJstHiWpkkIBJUlSCYWy2eJRkgqpNK5CkqQKKmXzqnE3QJIkSZI0+SweJUmSJEk9eduqJFVS6NYYSZJKKJTNFo+SVEWxZ0lJkjT1imWzxaMkVVIooCRJKqFQNls8SlIlhQJKkqQSCmWzxaMkSStdoX/YSJLaY/EoSUUEtcZVSJI07apls8WjJFVSKKAkSSqhUDZbPEpSFcVmdJMkaeoVy+ZV426AJEmSJGny2fMoSZUUurqpEUn8vZEkaO9vYaG/sRaPklRJoYCSJKmEQtls8ShJhVQaVyFJUgWVstniUZIqKRRQkiSVUCibnTBHkiRJktTTaHseI2CV9aokETH8z3TiEy1TzI67BZJUVLFs9rZVSSqk0rgKSZIqqJTNdgNKUiU5wNKHiDg9Iu6KiO0RccE8+98dEbc0yzci4uGufTNd+7YOcpqSJE2NQtlsz6MkFdLm1c2IWA28F3gVsAO4KSK2ZuYd+4/JzN/sOv7XgRO7PuIHmXlCey2UJGnyVMpmex4lSf06Cdiemfdk5l7gcuDMRY4/B7hsJC2TJGllGmk22/MoSZUMdnVzQ0Rs61rfnJmbu9aPAO7rWt8BvHi+D4qI5wBHA5/r2nxg8/n7gIsz81MDtVbDky1M4CRJ6iiUzRaPklTF4DO6PZiZmxbZP1+FsdA3ng1ckZkzXduenZk7I+K5wOci4muZ+c3lNlaSpIlXLJu9bVWSiogBlz7sAI7sWt8I7Fzg2LOZc1tMZu5sft4DXM+Pj7mQJKmcatls8ShJ6tdNwDERcXRErKMTQk+YmS0ingccDHypa9vBEXFA83oDcDJwx9z3SpKkJRlpNnvbqiRV0uKMbpm5LyLeClwDrAa2ZObtEXERsC0z94fVOcDlmdndmmOBD0TELJ0Llxd3zwQnSVJZhbLZ4lGSCmn7QcSZeTVw9Zxt75iz/j/med8XgZ9utXFanoSYHXcjJGkCtJShlbLZ4lGSKmk5oCRJ0hIVymaLR0mqpFBASZJUQqFsdsIcSZIkSVJP9jxKUhXZ/rgKSZK0BMWy2eJRkiopFFCSJJVQKJtHXzxGn4+7lCQtWaWrmxohf28kqTWVstmeR0mqpFBASZJUQqFsdsIcSZIkSVJP9jxKUiGVbo2RJKmCStncs+cxIo6MiM9HxJ0RcXtEvK3ZfkhEXBsRdzc/D26/uZKkBeWAi6aG2SxJU6JYNvfT87gP+O3M/EpEPBW4OSKuBX4ZuC4zL46IC4ALgN9vr6mSpJ4mMGjUiqFlczSLJK10rf0tLJTNPXseM/P+zPxK8/p7wJ3AEcCZwKXNYZcCZ7XVSEmS9CNmsyRpHJY05jEijgJOBG4EDsvM+6ETYhFx6ALvOR84H+DAWD9IWyVJiwhqjatQfwbN5jUHeWerJLWlWjb3PdtqRDwF+ATwG5n5SL/vy8zNmbkpMzetiwOX00ZJUr8KjatQb8PI5tXrvbArSa0qlM199TxGxFo64fSRzPxks/mBiDi8ubJ5OLCrrUZKkvoTOYFJo1aYzZI0HSplc8/iMSIC+BBwZ2b+adeurcC5wMXNz6taaaEkqT8TepVSwzf0bPb3RpLaUSyb++l5PBl4E/C1iLil2fYHdILp4xFxHvAt4A3tNFGSJM1hNkuSRq5n8ZiZX2DhmWtPHW5zJEmDqDQoXwszmyVpelTK5iXNtipJmnCFAkqSpBIKZbPFoyQVUunqpiRJFVTKZotHSaqkUEBphPy9kaT2FPob2/dzHiVJkiRJK5c9j5JURda6NUaSpKlXLJstHiWpkkIBJUlSCYWy2eJRkooIal3dlCRp2lXL5tEXj6scZilJkiRJ08aeR0mqJAtd3pQkqYJC2WzxKEmFVLo1RpKkCipls8WjJFWRlBqUL0nS1CuWzRaPklRIzI67BZIkqVulbLZ4lCRphat0S5UkqT0Wj5JUiUWAJEmTpVA2+9wMSSokcvlLX58fcXpE3BUR2yPignn2/3JEfDsibmmWX+nad25E3N0s5w7vrCVJmlyVstmeR0mqIml1OvCIWA28F3gVsAO4KSK2ZuYdcw79WGa+dc57DwEuBDY1Lb25ee9DrTVYkqRxK5bN9jxKUiEtX908Cdiemfdk5l7gcuDMPpv2auDazNzdhNK1wOnLOUdJkqZJpWy2eJQk7bchIrZ1LefP2X8EcF/X+o5m21z/KSJujYgrIuLIJb5XkiT9yERls7etSlIlg90Z82Bmblpkf/Txjf8HuCwz90TErwKXAq/o872SJNVTKJvteZSkIoLWb43ZARzZtb4R2Nl9QGZ+JzP3NKv/G3hRv++VJKmaatls8ShJVWQOtvR2E3BMRBwdEeuAs4Gt3QdExOFdq2cAdzavrwFOi4iDI+Jg4LRmmyRJdRXLZm9blST1JTP3RcRb6QTLamBLZt4eERcB2zJzK/BfI+IMYB+wG/jl5r27I+KddEIO4KLM3D3yk5AkqZBRZ7PFoyQV0u8zoZYrM68Grp6z7R1dr98OvH2B924BtrTaQC1LzjfqRZI0FJWy2eJRkipxChpJkiZLoWy2eJSkQtq+uilJkpamUjZbPEpSFQnMFkooSZKmXbFsdrZVSZIkSVJPo+95nJ0d+VdK0opR5+KmJEk1FMpmb1uVpEIqjauQJKmCStls8ShJlfT3QGFJkjQqhbLZ4lGSCql0dVOSpAoqZbMT5kiSJEmSerLnUZKqSEoNypckaeoVy2aLR0kqIoAoNK5CIxTjboAk1VQtmy0eJakSn4YkSdJkKZTNjnmUJEmSJPVkz6MkFVLp1hhJkiqolM0Wj5JURbE6xIOdAAAM2ElEQVRB+ZIkTb1i2WzxKEllZKkHEWuEnDBHklpSK5stHiWpkEoPIpYkqYJK2eyEOZIkSZKknux5lKRKCt0aI0lSCYWyuWfPY0RsiYhdEXFb17ZDIuLaiLi7+Xlwu82UJPWUELPLXzQ9zGZJmhLFsrmf21YvAU6fs+0C4LrMPAa4rlmXJI1b5vIXTZNLGFI2p4uLi4vLvy2tKJTNPYvHzLwB2D1n85nApc3rS4GzhtwuSZK0ALNZkjQOyx3zeFhm3g+QmfdHxKELHRgR5wPnAxwY65f5dZKkvkzeRUqNzrKyec1B3t0qSa0qlM2tT5iTmZuBzQBPX72h0P90kjR5YgJvcdHk6c7mAzYe6S+NJLWoUjYv91EdD0TE4QDNz13Da5IkadkKjavQkpnNkjSJCmXzcnsetwLnAhc3P6/q+50T+D+CJJWQwATOzKaRWX42R0stkqSVrlg29/OojsuALwHPi4gdEXEenWB6VUTcDbyqWZckSSNgNkuSxqFnz2NmnrPArlOH3BZJ0gCCLDWuQgszmyVpOlTL5tYnzJEkjVChgJIkqYRC2WzxKEmVFAooSZJKKJTNFo+SVEWxQfkakYBc7tzrklRJG5OHFctm40KSJEmS1JPFoyQVEpnLXvr6/IjTI+KuiNgeERfMs/+3IuKOiLg1Iq6LiOd07ZuJiFuaZesQT1uSpIlVKZu9bVWSKmlxXEVErAbeS+cxEDuAmyJia2be0XXYV4FNmflYRPwa8MfAf272/SAzT2itgZIkTaJC2WzPoySVkZ2AWu7S20nA9sy8JzP3ApcDZ/5YCzI/n5mPNatfBjYO9RQlSZoqtbLZnkdJ0n4bImJb1/rmzNzctX4EcF/X+g7gxYt83nnAp7vWD2w+fx9wcWZ+atAGa0iizkyAklTMRGWzxaMkVZEMemvMg5m5aZH9881DN+8XRsQvApuAl3VtfnZm7oyI5wKfi4ivZeY3l99cSZImXLFstniUpEranQ58B3Bk1/pGYOfcgyLilcB/A16WmXv2b8/Mnc3PeyLieuBEwOJRklRboWx2zKMkFdLyjG43AcdExNERsQ44G/ixmdki4kTgA8AZmbmra/vBEXFA83oDcDLQPZhfkqSSKmWzPY+SVEmLM7pl5r6IeCtwDbAa2JKZt0fERcC2zNwK/AnwFOBvIgLgW5l5BnAs8IGImKVz4fLiOTPBSZJUU6FsHm3xmAmz7fbbStJUaDFI2pSZVwNXz9n2jq7Xr1zgfV8Efrrd1mm50vuQJGlqjTKb7XmUpCoSmJ3OolSSpJKKZbPFoySV0fczoSRJ0kjUymaLR0mqpFBASZJUQqFstniUpEoKBZQkSSUUymaLR0mSVrJg/kdMS9JK49/CniweJamKYoPyJUmaesWy2eJRkspISB+HJEnS5KiVzRaPklRJoXEVkiSVUCibfSywJEmSJKknex4lqYpi4yo0Qk4SIUntKJbNFo+SVEmhW2MkSSqhUDZbPEpSJYUCSpKkEgpls8WjJJWRpQJKkqTpVyubnTBHkiRJktSTPY+SVEUCs3WeJSVJ0tQrls0Wj5JUSaFbYyRJKqFQNls8SlIlhQJKkqQSCmWzxaMklZGlniUlSdL0q5XNTpgjSZIkSerJnkdJqiIhs86gfEmSpl6xbLZ4lKRKCt0aoxFJiH3jboQkTYC2IrRQNls8SlIlhQblS5JUQqFsdsyjJEmSJKknex4lqYrMUg8iliRp6hXLZotHSaqk0K0xkiSVUCibR1s8rgpi3bqRfqUkTaS90crHZqGrmxqNVfvggN2OYpGkVS1NHlYpm+15lKQystTVTUmSpl+tbPZSoyRJkiSpJ3seJamKpNSzpCRJmnrFsnmgnseIOD0i7oqI7RFxwbAaJUlappxd/qISzGZJmjCFsnnZPY8RsRp4L/AqYAdwU0Rszcw7Fv62NXDYhuV+pSTV8fjwb/xIIAtd3dTSLSebV++Bg++aGVUTJWli3bdn+J9ZLZsH6Xk8Cdiemfdk5l7gcuDM4TRLkrRkma1f3ezVqxURB0TEx5r9N0bEUV373t5svysiXj2081Y3s1mSJkmxbB6keDwCuK9rfUezbW5jz4+IbRGxbe/MDwb4OknSOHX1ar0GeD5wTkQ8f85h5wEPZea/B94NvKt57/OBs4EXAKcDf9l8noZrydn8wz3fH1njJEnDNepsHqR4nO8hZU/ok83MzZm5KTM3rVv9pAG+TpLUS87mspc+9NOrdSZwafP6CuDUiIhm++WZuScz/wnY3nyehmvJ2bz2gKeMoFmStHJVyuZBiscdwJFd6xuBnQN8niRpUO3eGtNPr9a/HZOZ+4DvAs/o870anNksSZOmUDYPMmPDTcAxEXE08C90ujx/YbE3PLLngQev+cYf/zOwAXhwgO+eJJ7LZKpyLlXOAzyXuZ4zjIZ0+x4PXfN/84pBZiU7MCK2da1vzszNXev99GotdExfPWIa2JKz+dGHdjz4pSt+12yeXJ7L5KlyHuC5zGU298jmZRePmbkvIt4KXAOsBrZk5u093vNMgIjYlpmblvvdk8RzmUxVzqXKeYDnMgqZeXrLX9FPr9b+Y3ZExBrg6cDuPt+rAZnNHZ7LZKpyLlXOAzyXUaiWzQM95zEzr87Mn8jMf5eZfzTIZ0mSJt6/9WpFxDo6vVpb5xyzFTi3ef164HOZmc32s5sZ344GjgH+YUTtXlHMZklaUUaazcN/0JgkqaSFerUi4iJgW2ZuBT4E/HVEbKdzVfPs5r23R8THgTuAfcBbMtOHC0qSNIBRZ/O4isfNvQ+ZGp7LZKpyLlXOAzyXEjLzauDqOdve0fX6ceANC7z3jwB7wiZXpd9rz2UyVTmXKucBnksJo8zm6PRYSpIkSZK0sIHGPEqSJEmSVoaRF48RcXpE3BUR2yPiglF//yAiYktE7IqI27q2HRIR10bE3c3Pg8fZxn5ExJER8fmIuDMibo+ItzXbp/FcDoyIf4iIf2zO5X8224+OiBubc/lYM4B44kXE6oj4akT8bbM+redxb0R8LSJu2T+99DT+fgFExEERcUVEfL35b+ZnpvVcpIWYzeNnNk8us3nymM3jM9LiMSJWA+8FXgM8HzgnIp4/yjYM6BJg7nS7FwDXZeYxwHXN+qTbB/x2Zh4LvAR4S/P/wzSeyx7gFZl5PHACcHpEvAR4F/Du5lweAs4bYxuX4m3AnV3r03oeAC/PzBO6ps2ext8vgPcAn8nMnwSOp/P/z7Sei/QEZvPEMJsnl9k8eczmMRl1z+NJwPbMvCcz9wKXA2eOuA3Llpk30JmhqNuZwKXN60uBs0baqGXIzPsz8yvN6+/R+Q/uCKbzXDIzv9+srm2WBF4BXNFsn4pziYiNwM8BH2zWgyk8j0VM3e9XRDwNeCmdWcrIzL2Z+TBTeC7SIszmCWA2TyazefKYzeM16uLxCOC+rvUdzbZpdlhm3g+dP/zAoWNuz5JExFHAicCNTOm5NLeT3ALsAq4Fvgk8nJn7mkOm5ffsz4DfA2ab9WcwnecBnX8kfDYibo6I85tt0/j79Vzg28BfNbcsfTAi1jOd5yItxGyeMGbzRDGbJ4/ZPEajLh5jnm1O9zomEfEU4BPAb2TmI+Nuz3Jl5kxmngBspHMF/dj5Dhttq5YmIl4H7MrMm7s3z3PoRJ9Hl5Mz84V0boN7S0S8dNwNWqY1wAuB92XmicCjeBuM6pnmvzXlmM2Tw2yeWGbzGI26eNwBHNm1vhHYOeI2DNsDEXE4QPNz15jb05eIWEsnnD6SmZ9sNk/luezX3LJwPZ2xIgdFxP7nmE7D79nJwBkRcS+dW8ZeQedq57SdBwCZubP5uQu4ks4/HKbx92sHsCMzb2zWr6ATWNN4LtJCzOYJYTZPHLN5MpnNYzTq4vEm4Jhmlqp1wNnA1hG3Ydi2Auc2r88FrhpjW/rS3K//IeDOzPzTrl3TeC7PjIiDmtdPAl5JZ5zI54HXN4dN/Llk5tszc2NmHkXnv4vPZeYbmbLzAIiI9RHx1P2vgdOA25jC36/M/Ffgvoh4XrPpVOAOpvBcpEWYzRPAbJ48ZvNkMpvHKzJH29MeEa+lc9VmNbAlM/9opA0YQERcBpwCbAAeAC4EPgV8HHg28C3gDZk5d+D+RImInwX+DvgaP7qH/w/ojK2YtnM5js6g6NV0LoZ8PDMviojn0rlKeAjwVeAXM3PP+Frav4g4BfidzHzdNJ5H0+Yrm9U1wEcz848i4hlM2e8XQEScQGeihHXAPcCbaX7XmLJzkRZiNo+f2TzZzObJYjaPz8iLR0mSJEnS9Bn1bauSJEmSpClk8ShJkiRJ6sniUZIkSZLUk8WjJEmSJKkni0dJkiRJUk8Wj5IkSZKkniweJUmSJEk9WTxKkiRJknr6/+XiTYfc+O0IAAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f120f9267b8>"
+       "<Figure size 1152x432 with 4 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -384,7 +409,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.2"
   }
  },
  "nbformat": 4,