{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pystencils as ps\n", "import pystencils.plot as psplot\n", "import numpy as np\n", "import sympy as sp\n", "\n", "frac = sp.Rational" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial 06: Phase-field simulation of dentritic solidification\n", "\n", "This is the second tutorial on phase field methods with pystencils. Make sure to read the previous tutorial first. \n", "\n", "In this tutorial we again implement a model described in **Programming Phase-Field Modelling** by S. Bulent Biner.\n", "This time we implement the model from chapter 4.7 that describes dentritic growth. So get ready for some beautiful snowflake pictures.\n", "\n", "We start again by adding all required arrays fields. This time we explicitly store the change of the phase variable φ in time, since the dynamics is calculated using an Allen-Cahn formulation where a term $\\partial_t \\phi$ occurs." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "dh = ps.create_data_handling(domain_size=(300, 300), periodicity=True, \n", " default_target=ps.Target.CPU)\n", "φ_field = dh.add_array('phi', latex_name='φ')\n", "φ_field_tmp = dh.add_array('phi_temp', latex_name='φ_temp')\n", "φ_delta_field = dh.add_array('phidelta', latex_name='φ_D')\n", "t_field = dh.add_array('T')\n", "t_field_tmp = dh.add_array('T_tmp')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This model has a lot of parameters that are created here in a symbolic fashion. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle \\frac{{φ}_{(0,0)}^{4}}{4} - {φ}_{(0,0)}^{3} \\left(\\frac{1}{2} - \\frac{m}{3}\\right) + {φ}_{(0,0)}^{2} \\left(\\frac{1}{4} - \\frac{m}{2}\\right) + \\frac{ε^{2} \\left({\\partial_{0} {φ}_{(0,0)}}^{2} + {\\partial_{1} {φ}_{(0,0)}}^{2}\\right)}{2}$" ], "text/plain": [ "phi_C**4/4 - phi_C**3*(1/2 - m/3) + phi_C**2*(1/4 - m/2) + ε**2*(Diff(phi_C, 0, -1)**2 + Diff(phi_C, 1, -1)**2)/2" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ε, m, δ, j, θzero, α, γ, Teq, κ, τ = sp.symbols(\"ε m δ j θ_0 α γ T_eq κ τ\")\n", "εb = sp.Symbol(\"\\\\bar{\\\\epsilon}\")\n", "\n", "φ = φ_field.center\n", "φ_tmp = φ_field_tmp.center\n", "T = t_field.center\n", "\n", "def f(φ, m):\n", " return φ**4 / 4 - (frac(1, 2) - m/3) * φ**3 + (frac(1,4)-m/2)*φ**2\n", "\n", "free_energy_density = ε**2 / 2 * (ps.fd.Diff(φ,0)**2 + ps.fd.Diff(φ,1)**2 ) + f(φ, m)\n", "free_energy_density" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The free energy is again composed of a bulk and interface part." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "psplot.figure(figsize=(7,4))\n", "psplot.sympy_function(f(φ, m=1), x_values=(-1.05, 1.5) )\n", "psplot.xlabel(\"φ\")\n", "psplot.title(\"Bulk free energy\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compared to last tutorial, this bulk free energy has also two minima, but at different values. \n", "\n", "Another complexity of the model is its anisotropy. The gradient parameter $\\epsilon$ depends on the interface normal." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle \\bar{\\epsilon} \\left(δ \\cos{\\left(j \\left(- θ_{0} + \\operatorname{atan}_{2}{\\left({\\partial_{1} {φ}_{(0,0)}},{\\partial_{0} {φ}_{(0,0)}} \\right)}\\right) \\right)} + 1\\right)$" ], "text/plain": [ "\\bar{\\epsilon}*(δ*cos(j*(-θ_0 + atan2(Diff(phi_C, 1, -1), Diff(phi_C, 0, -1)))) + 1)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def σ(θ):\n", " return 1 + δ * sp.cos(j * (θ - θzero))\n", "\n", "θ = sp.atan2(ps.fd.Diff(φ, 1), ps.fd.Diff(φ, 0))\n", "\n", "ε_val = εb * σ(θ)\n", "ε_val" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def m_func(T):\n", " return (α / sp.pi) * sp.atan(γ * (Teq - T))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, we just insert these parameters into the free energy formulation before doing the functional derivative, to make the dependence of $\\epsilon$ on $\\nabla \\phi$ explicit." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle {φ}_{(0,0)}^{3} - \\frac{{φ}_{(0,0)}^{2} α \\operatorname{atan}{\\left({T}_{(0,0)} γ - T_{eq} γ \\right)}}{\\pi} - \\frac{3 {φ}_{(0,0)}^{2}}{2} + \\frac{{φ}_{(0,0)} α \\operatorname{atan}{\\left({T}_{(0,0)} γ - T_{eq} γ \\right)}}{\\pi} + \\frac{{φ}_{(0,0)}}{2} - \\bar{\\epsilon}^{2} δ^{2} \\cos^{2}{\\left(j θ_{0} - j \\operatorname{atan}_{2}{\\left({\\partial_{1} {φ}_{(0,0)}},{\\partial_{0} {φ}_{(0,0)}} \\right)} \\right)} {\\partial_{0} {\\partial_{0} {φ}_{(0,0)}}} - \\bar{\\epsilon}^{2} δ^{2} \\cos^{2}{\\left(j θ_{0} - j \\operatorname{atan}_{2}{\\left({\\partial_{1} {φ}_{(0,0)}},{\\partial_{0} {φ}_{(0,0)}} \\right)} \\right)} {\\partial_{1} {\\partial_{1} {φ}_{(0,0)}}} - 2 \\bar{\\epsilon}^{2} δ \\cos{\\left(j θ_{0} - j \\operatorname{atan}_{2}{\\left({\\partial_{1} {φ}_{(0,0)}},{\\partial_{0} {φ}_{(0,0)}} \\right)} \\right)} {\\partial_{0} {\\partial_{0} {φ}_{(0,0)}}} - 2 \\bar{\\epsilon}^{2} δ \\cos{\\left(j θ_{0} - j \\operatorname{atan}_{2}{\\left({\\partial_{1} {φ}_{(0,0)}},{\\partial_{0} {φ}_{(0,0)}} \\right)} \\right)} {\\partial_{1} {\\partial_{1} {φ}_{(0,0)}}} - \\bar{\\epsilon}^{2} {\\partial_{0} {\\partial_{0} {φ}_{(0,0)}}} - \\bar{\\epsilon}^{2} {\\partial_{1} {\\partial_{1} {φ}_{(0,0)}}}$" ], "text/plain": [ "phi_C**3 - phi_C**2*α*atan(T_C*γ - T_eq*γ)/pi - 3*phi_C**2/2 + phi_C*α*atan(T_C*γ - T_eq*γ)/pi + phi_C/2 - \\bar{\\epsilon}**2*δ**2*cos(j*θ_0 - j*atan2(Diff(phi_C, 1, -1), Diff(phi_C, 0, -1)))**2*Diff(Diff(phi_C, 0, -1), 0, -1) - \\bar{\\epsilon}**2*δ**2*cos(j*θ_0 - j*atan2(Diff(phi_C, 1, -1), Diff(phi_C, 0, -1)))**2*Diff(Diff(phi_C, 1, -1), 1, -1) - 2*\\bar{\\epsilon}**2*δ*cos(j*θ_0 - j*atan2(Diff(phi_C, 1, -1), Diff(phi_C, 0, -1)))*Diff(Diff(phi_C, 0, -1), 0, -1) - 2*\\bar{\\epsilon}**2*δ*cos(j*θ_0 - j*atan2(Diff(phi_C, 1, -1), Diff(phi_C, 0, -1)))*Diff(Diff(phi_C, 1, -1), 1, -1) - \\bar{\\epsilon}**2*Diff(Diff(phi_C, 0, -1), 0, -1) - \\bar{\\epsilon}**2*Diff(Diff(phi_C, 1, -1), 1, -1)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fe = free_energy_density.subs({\n", " m: m_func(T),\n", " ε: εb * σ(θ),\n", "})\n", "\n", "dF_dφ = ps.fd.functional_derivative(fe, φ)\n", "dF_dφ = ps.fd.expand_diff_full(dF_dφ, functions=[φ])\n", "dF_dφ" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we insert all the numeric parameters and discretize:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{τ: 0.0003,\n", " κ: 1.8,\n", " \\bar{\\epsilon}: 0.01,\n", " δ: 0.02,\n", " γ: 10,\n", " j: 6,\n", " α: 0.9,\n", " T_eq: 1.0,\n", " θ_0: 0.2,\n", " pi: 3.14159265358979}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "discretize = ps.fd.Discretization2ndOrder(dx=0.03, dt=1e-5)\n", "parameters = {\n", " τ: 0.0003,\n", " κ: 1.8,\n", " εb: 0.01,\n", " δ: 0.02,\n", " γ: 10,\n", " j: 6,\n", " α: 0.9,\n", " Teq: 1.0,\n", " θzero: 0.2,\n", " sp.pi: sp.pi.evalf()\n", "}\n", "parameters" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "dφ_dt = - dF_dφ / τ\n", "φ_eqs = ps.simp.sympy_cse_on_assignment_list([ps.Assignment(φ_delta_field.center, \n", " discretize(dφ_dt.subs(parameters)))])\n", "φ_eqs.append(ps.Assignment(φ_tmp, discretize(ps.fd.transient(φ) - φ_delta_field.center)))\n", "\n", "temperature_evolution = -ps.fd.transient(T) + ps.fd.diffusion(T, 1) + κ * φ_delta_field.center\n", "temperature_eqs = [\n", " ps.Assignment(t_field_tmp.center, discretize(temperature_evolution.subs(parameters)))\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When creating the kernels we pass as target (which may be 'Target.CPU' or 'Target.GPU') the default target of the target handling. This enables to switch to a GPU simulation just by changing the parameter of the data handling.\n", "\n", "The rest is similar to the previous tutorial." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "cfg = ps.CreateKernelConfig(target=dh.default_target)\n", "cfg.cpu.openmp.enable = True\n", "\n", "φ_kernel = ps.create_kernel(φ_eqs, config=cfg).compile()\n", "temperature_kernel = ps.create_kernel(temperature_eqs, target=dh.default_target).compile()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "def timeloop(steps=200):\n", " φ_sync = dh.synchronization_function(['phi'])\n", " temperature_sync = dh.synchronization_function(['T'])\n", " dh.all_to_gpu() # this does nothing when running on CPU\n", " for t in range(steps):\n", " φ_sync()\n", " dh.run_kernel(φ_kernel)\n", " dh.swap(φ_field.name, φ_field_tmp.name)\n", " temperature_sync()\n", " dh.run_kernel(temperature_kernel)\n", " dh.swap(t_field.name, t_field_tmp.name)\n", " dh.all_to_cpu()\n", " return dh.gather_array('phi')\n", " \n", "def init(nucleus_size=np.sqrt(5)):\n", " for b in dh.iterate():\n", " x, y = b.cell_index_arrays\n", " x, y = x-b.shape[0]//2, y-b.shape[0]//2\n", " bArr = (x**2 + y**2) < nucleus_size**2\n", " b['phi'].fill(0)\n", " b['phi'][(x**2 + y**2) < nucleus_size**2] = 1.0\n", " b['T'].fill(0.0)\n", " \n", "def plot():\n", " psplot.subplot(1,3,1)\n", " psplot.scalar_field(dh.gather_array('phi'))\n", " psplot.title(\"φ\")\n", " psplot.colorbar()\n", " psplot.subplot(1,3,2)\n", " psplot.title(\"T\")\n", " psplot.scalar_field(dh.gather_array('T'))\n", " psplot.colorbar()\n", " psplot.subplot(1,3,3)\n", " psplot.title(\"∂φ\")\n", " psplot.scalar_field(dh.gather_array('phidelta'))\n", " psplot.colorbar()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
void kernel (const double * RESTRICT const _data_T, const double * RESTRICT const _data_phi, double * RESTRICT const _data_phi_temp, double * RESTRICT const _data_phidelta)\n",
       "{\n",
       "   #pragma omp parallel\n",
       "   {\n",
       "      #pragma omp for schedule(static)\n",
       "      for(ctr_1: int64 = [1: int64]; ctr_1 < [301: int64]; ctr_1 += [1: int64])\n",
       "      {\n",
       "         for(ctr_0: int64 = [1: int64]; ctr_0 < [301: int64]; ctr_0 += [1: int64])\n",
       "         {\n",
       "            xi_0: const float64 = _data_phi[ctr_0 + ctr_1 * [302: int64]] * _data_phi[ctr_0 + ctr_1 * [302: int64]];\n",
       "            xi_1: const float64 = [954.9296585513721: float64] * atan([-10.0: float64] + [10.0: float64] * _data_T[ctr_0 + ctr_1 * [302: int64]]);\n",
       "            xi_2: const float64 = [-2222.222222222222: float64] * _data_phi[ctr_0 + ctr_1 * [302: int64]];\n",
       "            xi_3: const float64 = xi_2 + [1111.111111111111: float64] * _data_phi[ctr_0 + (ctr_1 + [1: int64]) * [302: int64]] + [1111.111111111111: float64] * _data_phi[ctr_0 + (ctr_1 + [-1: int64]) * [302: int64]];\n",
       "            xi_4: const float64 = cos([-1.2000000000000002: float64] + [6.0: float64] * atan2([16.666666666666668: float64] * _data_phi[ctr_0 + (ctr_1 + [1: int64]) * [302: int64]] + [-16.666666666666668: float64] * _data_phi[ctr_0 + (ctr_1 + [-1: int64]) * [302: int64]], [16.666666666666668: float64] * _data_phi[ctr_0 + [1: int64] + ctr_1 * [302: int64]] + [-16.666666666666668: float64] * _data_phi[ctr_0 + [-1: int64] + ctr_1 * [302: int64]]));\n",
       "            xi_5: const float64 = [0.013333333333333336: float64] * xi_4;\n",
       "            xi_6: const float64 = xi_2 + [1111.111111111111: float64] * _data_phi[ctr_0 + [1: int64] + ctr_1 * [302: int64]] + [1111.111111111111: float64] * _data_phi[ctr_0 + [-1: int64] + ctr_1 * [302: int64]];\n",
       "            xi_7: const float64 = [0.00013333333333333334: float64] * (xi_4 * xi_4);\n",
       "            _data_phidelta[ctr_0 + ctr_1 * [302: int64]] = [5000.0: float64] * xi_0 + [370.3703703703704: float64] * _data_phi[ctr_0 + [1: int64] + ctr_1 * [302: int64]] + [370.3703703703704: float64] * _data_phi[ctr_0 + (ctr_1 + [1: int64]) * [302: int64]] + [370.3703703703704: float64] * _data_phi[ctr_0 + (ctr_1 + [-1: int64]) * [302: int64]] + [370.3703703703704: float64] * _data_phi[ctr_0 + [-1: int64] + ctr_1 * [302: int64]] + [-3148.1481481481483: float64] * _data_phi[ctr_0 + ctr_1 * [302: int64]] + [-3333.3333333333335: float64] * (_data_phi[ctr_0 + ctr_1 * [302: int64]] * _data_phi[ctr_0 + ctr_1 * [302: int64]] * _data_phi[ctr_0 + ctr_1 * [302: int64]]) + xi_0 * xi_1 + xi_3 * xi_5 + xi_3 * xi_7 + xi_5 * xi_6 + xi_6 * xi_7 - xi_1 * _data_phi[ctr_0 + ctr_1 * [302: int64]];\n",
       "            _data_phi_temp[ctr_0 + ctr_1 * [302: int64]] = [1e-05: float64] * _data_phidelta[ctr_0 + ctr_1 * [302: int64]] + _data_phi[ctr_0 + ctr_1 * [302: int64]];\n",
       "         }\n",
       "      }\n",
       "   }\n",
       "}\n",
       "
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}\\PY{n}{ctr\\PYZus{}1}\\PY{+w}{ }\\PY{o}{*}\\PY{+w}{ }\\PY{p}{[}\\PY{l+m+mi}{302}\\PY{o}{:}\\PY{+w}{ }\\PY{n}{int64}\\PY{p}{]}\\PY{p}{]}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{\\PYZus{}data\\PYZus{}phi}\\PY{p}{[}\\PY{n}{ctr\\PYZus{}0}\\PY{+w}{ }\\PY{o}{+}\\PY{+w}{ }\\PY{n}{ctr\\PYZus{}1}\\PY{+w}{ }\\PY{o}{*}\\PY{+w}{ }\\PY{p}{[}\\PY{l+m+mi}{302}\\PY{o}{:}\\PY{+w}{ }\\PY{n}{int64}\\PY{p}{]}\\PY{p}{]}\\PY{p}{;}\n", "\\PY{+w}{ }\\PY{p}{\\PYZcb{}}\n", "\\PY{+w}{ }\\PY{p}{\\PYZcb{}}\n", "\\PY{+w}{ }\\PY{p}{\\PYZcb{}}\n", "\\PY{p}{\\PYZcb{}}\n", "\\end{Verbatim}\n" ], "text/plain": [ "void kernel (const double * RESTRICT const _data_T, const double * RESTRICT const _data_phi, double * RESTRICT const _data_phi_temp, double * RESTRICT const _data_phidelta)\n", "{\n", " #pragma omp parallel\n", " {\n", " #pragma omp for schedule(static)\n", " for(ctr_1: int64 = [1: int64]; ctr_1 < [301: int64]; ctr_1 += [1: int64])\n", " {\n", " for(ctr_0: int64 = [1: int64]; ctr_0 < [301: int64]; ctr_0 += [1: int64])\n", " {\n", " xi_0: const float64 = _data_phi[ctr_0 + ctr_1 * [302: int64]] * _data_phi[ctr_0 + ctr_1 * [302: int64]];\n", " xi_1: const float64 = [954.9296585513721: float64] * atan([-10.0: float64] + [10.0: float64] * _data_T[ctr_0 + ctr_1 * [302: int64]]);\n", " xi_2: const float64 = [-2222.222222222222: float64] * _data_phi[ctr_0 + ctr_1 * [302: int64]];\n", " xi_3: const float64 = xi_2 + [1111.111111111111: float64] * _data_phi[ctr_0 + (ctr_1 + [1: int64]) * [302: int64]] + [1111.111111111111: float64] * _data_phi[ctr_0 + (ctr_1 + [-1: int64]) * [302: int64]];\n", " xi_4: const float64 = cos([-1.2000000000000002: float64] + [6.0: float64] * atan2([16.666666666666668: float64] * _data_phi[ctr_0 + (ctr_1 + [1: int64]) * [302: int64]] + [-16.666666666666668: float64] * _data_phi[ctr_0 + (ctr_1 + [-1: int64]) * [302: int64]], [16.666666666666668: float64] * _data_phi[ctr_0 + [1: int64] + ctr_1 * [302: int64]] + [-16.666666666666668: float64] * _data_phi[ctr_0 + [-1: int64] + ctr_1 * [302: int64]]));\n", " xi_5: const float64 = [0.013333333333333336: float64] * xi_4;\n", " xi_6: const float64 = xi_2 + [1111.111111111111: float64] * _data_phi[ctr_0 + [1: int64] + ctr_1 * [302: int64]] + [1111.111111111111: float64] * _data_phi[ctr_0 + [-1: int64] + ctr_1 * [302: int64]];\n", " xi_7: const float64 = [0.00013333333333333334: float64] * (xi_4 * xi_4);\n", " _data_phidelta[ctr_0 + ctr_1 * [302: int64]] = [5000.0: float64] * xi_0 + [370.3703703703704: float64] * _data_phi[ctr_0 + [1: int64] + ctr_1 * [302: int64]] + [370.3703703703704: float64] * _data_phi[ctr_0 + (ctr_1 + [1: int64]) * [302: int64]] + [370.3703703703704: float64] * _data_phi[ctr_0 + (ctr_1 + [-1: int64]) * [302: int64]] + [370.3703703703704: float64] * _data_phi[ctr_0 + [-1: int64] + ctr_1 * [302: int64]] + [-3148.1481481481483: float64] * _data_phi[ctr_0 + ctr_1 * [302: int64]] + [-3333.3333333333335: float64] * (_data_phi[ctr_0 + ctr_1 * [302: int64]] * _data_phi[ctr_0 + ctr_1 * [302: int64]] * _data_phi[ctr_0 + ctr_1 * [302: int64]]) + xi_0 * xi_1 + xi_3 * xi_5 + xi_3 * xi_7 + xi_5 * xi_6 + xi_6 * xi_7 - xi_1 * _data_phi[ctr_0 + ctr_1 * [302: int64]];\n", " _data_phi_temp[ctr_0 + ctr_1 * [302: int64]] = [1e-05: float64] * _data_phidelta[ctr_0 + ctr_1 * [302: int64]] + _data_phi[ctr_0 + ctr_1 * [302: int64]];\n", " }\n", " }\n", " }\n", "}" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ps.inspect(φ_kernel)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Name| Inner (min/max)| WithGl (min/max)\n", "----------------------------------------------------\n", " T| ( 0, 0)| ( 0, 0)\n", " T_tmp|(-5.18e-320,4.31e-319)|(-5.18e-320,4.31e-319)\n", " phi| ( 0, 1)| ( 0, 1)\n", "phi_temp| ( 0,2.08e-319)| ( 0,2.08e-319)\n", "phidelta|(-3.56e-316,6.57e-317)| (nan,nan)\n", "\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "timeloop(10)\n", "init()\n", "plot()\n", "print(dh)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result = None\n", "if 'is_test_run' not in globals():\n", " ani = psplot.scalar_field_animation(timeloop, rescale=True, frames=600)\n", "\n", " from pystencils.jupyter import display_as_html_video\n", " result = display_as_html_video(ani)\n", "result" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "assert np.isfinite(dh.max('phi'))\n", "assert np.isfinite(dh.max('T'))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 4 }