diff --git a/examples/PyMPDATA_examples/Jaruga_et_al_2015/fig19.ipynb b/examples/PyMPDATA_examples/Jaruga_et_al_2015/fig19.ipynb
index 220d0798..3c3ce2c4 100644
--- a/examples/PyMPDATA_examples/Jaruga_et_al_2015/fig19.ipynb
+++ b/examples/PyMPDATA_examples/Jaruga_et_al_2015/fig19.ipynb
@@ -10,7 +10,14 @@
     "### equations solved:\n",
     "\n",
     "$$\n",
-    "\\partial_t \\theta + ...$$"
+    "\\partial_t \\theta + \n",
+    "$$\n",
+    "$$\n",
+    "\\partial_t \\theta + \n",
+    "$$\n",
+    "$$\n",
+    "\\partial_t \\theta + \n",
+    "$$"
    ]
   },
   {
@@ -39,13 +46,13 @@
     "from PyMPDATA import Options,ScalarField,VectorField,Solver,Stepper\n",
     "from matplotlib import colors, pyplot\n",
     "from PyMPDATA.boundary_conditions import Periodic\n",
-    "from PyMPDATA_examples.Jaruga_et_al_2015.temp import *\n",
     "import numpy as np\n",
     "from numba import jit\n",
     "import warnings\n",
     "import ipywidgets\n",
     "from numba.core.errors import NumbaExperimentalFeatureWarning\n",
-    "from PyMPDATA.impl.enumerations import IMPL_META_AND_DATA, META_AND_DATA_DATA, IMPL_BC"
+    "from PyMPDATA.impl.enumerations import IMPL_META_AND_DATA, META_AND_DATA_DATA, IMPL_BC\n",
+    "from IPython.display import display"
    ]
   },
   {
@@ -115,7 +122,7 @@
     "def lap(Phi, dxy, err_init, lap_tmp, tmp_uvw = None):\n",
     "    xchng_pres(Phi.impl)\n",
     "    calc_grad([x.impl for x in lap_tmp], Phi, dxy)\n",
-    "    if (err_init):\n",
+    "    if err_init:\n",
     "        for k in (0, 1):\n",
     "            lap_tmp[k].data[:] -= data(tmp_uvw[k])\n",
     "\n",
@@ -128,54 +135,54 @@
     "def pressure_solver_loop_init(err,p_err,lap_p_err,dxy,lap_tmp,tmp_uvw):\n",
     "    p_err[0].get()[:] = err.get()[:]\n",
     "    lap_p_err[0][:] = lap(p_err[0], dxy, False, lap_tmp,tmp_uvw)\n",
-    "    \n",
-    "##@njit\n",
+    "\n",
+    "#@njit\n",
     "def pressure_solver_loop_body(Phi,beta,converged,err,p_err,lap_p_err,dxy,\n",
     "                              k_iters,err_tol,lap_err,lap_tmp):\n",
     "    tmp_den = [1.]*k_iters\n",
     "    alpha = [1.]*k_iters\n",
     "    for v in range(0,k_iters):\n",
-    "          tmp_den[v] = np.sum(lap_p_err[v]**2)\n",
-    "          if (tmp_den[v] != 0):\n",
-    "              beta = - np.dot(\n",
-    "                  err.get().ravel(),\n",
-    "                  lap_p_err[v].ravel()\n",
-    "              ) / tmp_den[v]\n",
-    "          Phi.get()[:] += beta * p_err[v].get()\n",
-    "          err.get()[:] += beta * lap_p_err[v]\n",
-    "\n",
-    "          error = max(\n",
-    "             abs(np.amax(err.get())),\n",
-    "             abs(np.amin(err.get()))\n",
-    "          )\n",
-    "\n",
-    "          if (error <= err_tol): \n",
-    "              converged = True\n",
-    "\n",
-    "          lap_err[:] = lap(err, dxy, False, lap_tmp)\n",
-    "          \n",
-    "          for l in range(v):\n",
-    "              if (tmp_den[l] != 0):\n",
-    "                  alpha[l] = - np.dot(lap_err.ravel(), lap_p_err[l].ravel()) / tmp_den[l]\n",
-    "          if (v < (k_iters - 1)):\n",
-    "              p_err[v + 1].get()[:] = err.get()\n",
-    "              lap_p_err[v + 1][:] = lap_err[:]\n",
-    "              for l in range(v):\n",
-    "                  p_err[v + 1].get()[:] += alpha[l] * p_err[l].get()\n",
-    "                  lap_p_err[v + 1][:] += alpha[l] * lap_p_err[l]\n",
-    "          else:\n",
-    "              p_err[0].get()[:] = err.get()[:] + alpha[0] * p_err[0].get()\n",
-    "              lap_p_err[0][:] = lap_err[:] + alpha[0] * lap_p_err[0]\n",
-    "              for l in range(1,v+1):\n",
-    "                  p_err[0].get()[:] += alpha[l] * p_err[l].get()\n",
-    "                  lap_p_err[0][:] += alpha[l] * lap_p_err[l]\n",
+    "        tmp_den[v] = np.sum(lap_p_err[v]**2)\n",
+    "        if tmp_den[v] != 0:\n",
+    "            beta = - np.dot(\n",
+    "            err.get().ravel(),\n",
+    "            lap_p_err[v].ravel()\n",
+    "            ) / tmp_den[v]\n",
+    "        Phi.get()[:] += beta * p_err[v].get()\n",
+    "        err.get()[:] += beta * lap_p_err[v]\n",
+    "\n",
+    "        error = max(\n",
+    "            abs(np.amax(err.get())),\n",
+    "            abs(np.amin(err.get()))\n",
+    "        )\n",
+    "\n",
+    "        if error <= err_tol: \n",
+    "            converged = True\n",
+    "\n",
+    "        lap_err[:] = lap(err, dxy, False, lap_tmp)\n",
+    "\n",
+    "        for l in range(v):\n",
+    "            if tmp_den[l] != 0:\n",
+    "                alpha[l] = - np.dot(lap_err.ravel(), lap_p_err[l].ravel()) / tmp_den[l]\n",
+    "        if v < k_iters - 1:\n",
+    "            p_err[v + 1].get()[:] = err.get()\n",
+    "            lap_p_err[v + 1][:] = lap_err[:]\n",
+    "            for l in range(v):\n",
+    "                p_err[v + 1].get()[:] += alpha[l] * p_err[l].get()\n",
+    "                lap_p_err[v + 1][:] += alpha[l] * lap_p_err[l]\n",
+    "        else:\n",
+    "            p_err[0].get()[:] = err.get()[:] + alpha[0] * p_err[0].get()\n",
+    "            lap_p_err[0][:] = lap_err[:] + alpha[0] * lap_p_err[0]\n",
+    "            for l in range(1,v+1):\n",
+    "                p_err[0].get()[:] += alpha[l] * p_err[l].get()\n",
+    "                lap_p_err[0][:] += alpha[l] * lap_p_err[l]\n",
     "    return converged\n",
     "\n",
     "#@njit\n",
-    "def pressure_solver_update(solvers,Phi,beta,lap_tmp,tmp_uvw,err,p_err,lap_p_err,dxy,\n",
-    "                           k_iters,err_tol,lap_err,simple = False):\n",
+    "def pressure_solver_update(solvers,Phi,beta,lap_tmp,tmp_uvw,err,p_err,\n",
+    "                           lap_p_err,dxy,k_iters,err_tol,lap_err):\n",
     "    for k in (0, 1):\n",
-    "         data(tmp_uvw[k])[:] = solvers[k].advectee.data\n",
+    "        data(tmp_uvw[k])[:] = solvers[k].advectee.data\n",
     "    \n",
     "    #initial error\n",
     "    err.get()[:] = lap(Phi, dxy, True,lap_tmp,tmp_uvw)\n",
@@ -186,16 +193,15 @@
     "    pressure_solver_loop_init(err,p_err,lap_p_err,dxy,lap_tmp,tmp_uvw)\n",
     "    #pseudo-time loop\n",
     "    while not converged:        \n",
-    "         converged = pressure_solver_loop_body(Phi,beta,converged,err,p_err,lap_p_err,\n",
+    "        converged = pressure_solver_loop_body(Phi,beta,converged,err,p_err,lap_p_err,\n",
     "                                               dxy,k_iters,err_tol,lap_err,lap_tmp)\n",
-    "         iters += 1\n",
-    "\n",
-    "         if (iters > 10000): # going beyond 10000 iters means something is really wrong,\n",
-    "             # usually boundary conditions but not always !\n",
-    "             raise Exception(\"stuck in pressure solver\")\n",
+    "        iters += 1\n",
     "\n",
+    "        if (iters > 10000): # going beyond 10000 iters means something is really wrong,\n",
+    "            # usually boundary conditions but not always !\n",
+    "            raise ArithmeticError(\"stuck in pressure solver\")\n",
+    "             \n",
     "    xchng_pres(Phi.impl)\n",
-    "\n",
     "    calc_grad(tmp_uvw, Phi, dxy)\n",
     "\n",
     "#@njit\n",
@@ -292,7 +298,7 @@
     "    return field\n",
     "\n",
     "def new_sfs(N, M, impl=False):\n",
-    "    return tuple([new_sf(N,M).impl if impl else new_sf(N,M) for _ in ('u', 'w')])\n",
+    "    return tuple(new_sf(N,M).impl if impl else new_sf(N,M) for _ in ('u', 'w'))\n",
     "\n",
     "solver_ctor_kwargs = {'stepper': stepper, 'advector': advector}\n",
     "solvers = {\n",
@@ -300,9 +306,9 @@
     "    _W: Solver(advectee=new_sf(N,M), **solver_ctor_kwargs),\n",
     "    _T: Solver(advectee=ScalarField(data=mesh, **field_ctor_kwargs), **solver_ctor_kwargs),\n",
     "}\n",
-    "advectee_data = tuple([\n",
+    "advectee_data = tuple(\n",
     "    solvers[idx].advectee.data for idx in range(3)\n",
-    "])\n",
+    ")\n",
     "\n",
     "rhs_w = np.zeros((N,M))\n",
     "stash = new_sfs(N, M)\n",
@@ -339,9 +345,9 @@
     "\n",
     "@jit(**options.jit_flags)\n",
     "def vip_rhs_apply(dt, vip_rhs, advectee_data):\n",
-    "     for k in (0, 1):\n",
-    "         advectee_data[k][:] += 0.5 * dt * data(vip_rhs[k])\n",
-    "         data(vip_rhs[k])[:] = 0\n",
+    "    for k in (0, 1):\n",
+    "        advectee_data[k][:] += 0.5 * dt * data(vip_rhs[k])\n",
+    "        data(vip_rhs[k])[:] = 0\n",
     "\n",
     "@jit(**options.jit_flags)\n",
     "def apply_rhs(w, rhs_w : np.ndarray, dt : float):\n",
@@ -374,7 +380,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8b84b7919b314b9383b748fd32ec0d70",
+       "model_id": "a3a66e7d490249288d4b38f9cc3efa5c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -389,8 +395,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: total: 1min 36s\n",
-      "Wall time: 1min 46s\n"
+      "CPU times: total: 1min 31s\n",
+      "Wall time: 1min 41s\n"
      ]
     }
    ],
@@ -406,7 +412,7 @@
     "\n",
     "    Phi.get()[:] = 0\n",
     "    pressure_solver_update(solvers,Phi,beta,lap_tmp,tmp_uvw,err,p_err,lap_p_err,dxy,\n",
-    "                           k_iters,err_tol,lap_err,simple = True)\n",
+    "                           k_iters,err_tol,lap_err)\n",
     "    xchng_pres(Phi.impl)\n",
     "    calc_grad(tmp_uvw, Phi, dxy)\n",
     "    pressure_solver_apply(advectee_data, tmp_uvw)\n",
@@ -470,7 +476,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0a3762134cfa452cacece883690eabc7",
+       "model_id": "baa8f55f01f5441bb96110e9b0d523b6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -496,12 +502,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "56da990921454749b499d3d9f2e94776",
+       "model_id": "e1864a55f28a46f5850e0e2b10732fb9",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "HTML(value=\"<a href='.\\\\tmpgsxuio24.gif' target='_blank'>.\\\\tmpgsxuio24.gif</a><br>\")"
+       "HTML(value=\"<a href='.\\\\tmp531j16v4.gif' target='_blank'>.\\\\tmp531j16v4.gif</a><br>\")"
       ]
      },
      "metadata": {},
@@ -533,6 +539,22 @@
     "display(progress)\n",
     "show_anim(plot, frame_range=range(len(output)))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58f716ed-b17f-49de-9bdf-c655942e63bd",
+   "metadata": {},
+   "source": [
+    "add showplot\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e19fba6c-c5b1-43ba-8f99-a23222e0afd1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {