diff --git a/docs/bibliography.json b/docs/bibliography.json index be4a10a54..67c47f43c 100644 --- a/docs/bibliography.json +++ b/docs/bibliography.json @@ -217,5 +217,12 @@ ], "label": "Magnuszewski & Arabas 2025 (arXiv)", "title": "Path-dependent option pricing with two-dimensional PDE using MPDATA" + }, + "https://doi.org/10.1016/S0065-2156(08)70100-5": { + "usages": [ + "examples/PyMPDATA_examples/burgers_equation/burgers_equation.ipynb" + ], + "label": "Burgers 1948 (Adv. Appl. Mech. 1)", + "title": "A Mathematical Model Illustrating the Theory of Turbulence" } } diff --git a/examples/PyMPDATA_examples/burgers_equation/__init__.py b/examples/PyMPDATA_examples/burgers_equation/__init__.py index 7908197af..8a8876563 100644 --- a/examples/PyMPDATA_examples/burgers_equation/__init__.py +++ b/examples/PyMPDATA_examples/burgers_equation/__init__.py @@ -4,5 +4,3 @@ burgers-equation.ipynb: .. include:: ./burgers_equation.ipynb.badges.md """ - -from .burgers_equation import run_numerical_simulation diff --git a/examples/PyMPDATA_examples/burgers_equation/burgers_equation.ipynb b/examples/PyMPDATA_examples/burgers_equation/burgers_equation.ipynb index 135767d5c..c787b4fd8 100644 --- a/examples/PyMPDATA_examples/burgers_equation/burgers_equation.ipynb +++ b/examples/PyMPDATA_examples/burgers_equation/burgers_equation.ipynb @@ -15,15 +15,25 @@ "id": "18560cdd-64ec-43e4-afd4-4d516ce6a3b1", "metadata": {}, "source": [ - "Solution to the [Burgers' equation](https://en.wikipedia.org/wiki/Burgers%27_equation) using MPDATA compared against analytic results \n", - "(students' project by: Wojciech Neuman, Paulina Pojda, Michał Szczygieł, Joanna Wójcicka & Antoni Zięciak)\n", + "Solution to the [Burgers' equation](https://en.wikipedia.org/wiki/Burgers%27_equation)\n", + "([Burgers 1948](https://doi.org/10.1016/S0065-2156(08)70100-5)) using MPDATA compared against analytic results \n", + "(based on students' project by: Wojciech Neuman, Paulina Pojda, Michał Szczygieł, Joanna Wójcicka & Antoni Zięciak)\n", + "$$ \\partial_t u + u\\partial_x u = \\partial_t u + \\frac{1}{2} \\partial_x u^2 = \\frac{1}{\\text{Re}} \\partial^2_x u $$\n", + "where Re is the Reynolds number, $u$ is the velocity, $x$ is the spatial coordinate and $t$ is time.\n", "\n", - "$$ \\frac{\\partial u}{\\partial t} = -\\frac{1}{2} \\frac{\\partial u^2}{\\partial x} $$" + "Initial and boundary conditions:\n", + "- **domain**: \n", + " $-1 \\le x \\le 1$\n", + "- **boundary condition**: \n", + " $u(-1, t) = u(1, t) = 0$\n", + "- **initial condition**:\n", + " - **case A**: $u(x, 0) = -\\sin(\\pi \\cdot x)$\n", + " - **case B** $u(x, 0) = \\sin(\\pi \\cdot x)$ " ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "id": "745955e4-6f48-44ba-a9f4-bab1493a996e", "metadata": {}, "outputs": [], @@ -37,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "id": "b6651fc5", "metadata": { "ExecuteTime": { @@ -47,2024 +57,175 @@ }, "outputs": [], "source": [ + "import os\n", "import numpy as np\n", "from functools import partial\n", "from matplotlib import pyplot\n", - "from open_atmos_jupyter_utils import show_anim, show_plot\n", + "from open_atmos_jupyter_utils import show_anim\n", + "from ipywidgets import FloatProgress\n", + "from IPython.display import display\n", "from scipy.optimize import root_scalar\n", - "from PyMPDATA_examples.burgers_equation import run_numerical_simulation" + "from PyMPDATA import Options, ScalarField, Solver, Stepper, VectorField\n", + "from PyMPDATA.boundary_conditions import Constant" ] }, { - "cell_type": "code", - "execution_count": 11, - "id": "a786e106-86c1-4b06-b474-198f709520fc", + "cell_type": "markdown", + "id": "b0d6cec1-e9ee-4130-9303-30734ea602c2", "metadata": {}, - "outputs": [], "source": [ - "T_MAX = 1\n", - "T_SHOCK = 1 / np.pi\n", - "T_RANGE = [0, 0.1, 0.3, 0.5, 0.7, 1]\n", - "\n", - "NT = 400\n", - "NX = 100\n", - "\n", - "X_ANALYTIC = np.linspace(-1, 1, NX)" + "## Numerical solver" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "227d698d-0533-472c-9be1-bec14e6dabff", + "execution_count": 3, + "id": "da9db73a-5c0d-460a-b283-9c2649ea64d0", "metadata": {}, "outputs": [], "source": [ - "def f(x0, t, xi):\n", - " \"\"\"\n", - " The function to solve: x0 - sin(pi*x0)*t - xi = 0\n", - " where xi is the initial condition at x0.\n", - " \"\"\"\n", - " return x0 - np.sin(np.pi * x0) * t - xi\n", - "\n", + "def interpolate_in_space_and_multiply(*, vector_out, scalar_in, multiplier):\n", + " vector_out[:] = multiplier * (scalar_in[1:] + scalar_in[:-1]) / 2\n", "\n", - "def df(x0, t, _):\n", - " \"\"\"\n", - " The derivative of the function f with respect to x0.\n", - " \"\"\"\n", - " return 1 - np.cos(np.pi * x0) * np.pi * t\n", + "def extrapolate_in_time(*, vectors_in, vector_out):\n", + " vector_out[:] = 0.5 * (3 * vectors_in[0] - vectors_in[1])\n", "\n", + "def advector_view_no_edges(solver):\n", + " return solver.advector.get_component(0)[1:-1]\n", "\n", - "def df2(x0, t, _):\n", - " \"\"\"\n", - " The 2nd derivative of the function f with respect to x0.\n", - " \"\"\"\n", - " return np.sin(np.pi * x0) * np.pi**2 * t\n", + "def check_cfl_condition(solver):\n", + " assert np.all(abs(solver.advector.get_component(0)) <= 1), np.amax(abs(solver.advector.get_component(0)))\n", "\n", + "def run_numerical_simulation(*, nt, t_max, dx, psi0, reynolds_number, output_interval, progbar):\n", + " dt = t_max / nt\n", + " boundary_conditions = Constant(0),\n", + " options = Options(nonoscillatory=True, infinite_gauge=True, n_iters=3, non_zero_mu_coeff=reynolds_number != np.inf,)\n", + " solver = Solver(\n", + " stepper=Stepper(\n", + " options=options,\n", + " grid=psi0.shape\n", + " ),\n", + " advectee=ScalarField(\n", + " data=psi0,\n", + " halo=options.n_halo,\n", + " boundary_conditions=boundary_conditions,\n", + " ),\n", + " advector=VectorField(\n", + " data=(np.zeros(len(psi0)+1),),\n", + " halo=options.n_halo,\n", + " boundary_conditions=boundary_conditions,\n", + " ), \n", + " )\n", "\n", - "def find_root(x0, t, xi):\n", - " \"\"\"Find the root of the equation f(x0, t, xi) = 0 \"\"\"\n", - " return root_scalar(f, args=(t, xi), x0=x0, fprime=df, fprime2=df2, method='halley', maxiter=100).root\n", - "\n", - "\n", - "def analytical_solution(x, t):\n", - " \"\"\"\n", - " Analytical solution for the wave equation\n", - " \"\"\"\n", - " u = np.zeros(len(x))\n", - " for i, xi in enumerate(x):\n", - " if t < T_SHOCK:\n", - " x0 = find_root(x0=0, t=t, xi=xi)\n", - " u[i] = -np.sin(np.pi * x0)\n", - " else:\n", - " if xi == 0:\n", - " u[i] = 0\n", - " else:\n", - " # After the schock occurs, we have discontinuity at the x=0\n", - " # so we have to start finding roots from some other arbitraty point\n", - " # from which we have continuous function, we are starting from the -1\n", - " # for the negative x values and from the 1 for the positive x values\n", - " x0 = find_root(x0=xi / abs(xi), t=t, xi=xi)\n", - " u[i] = -np.sin(np.pi * x0)\n", - " return u\n", - "\n", - "def calculate_analytical_solutions():\n", - " \"\"\" \n", - " Calculate the analytical solutions for the given time range.\n", - " Initial and boundary conditions:\n", - " - -1 <= x <= 1\n", - " - u(x, 0) = -sin(pi * x)\n", - " - u(-1, t) = u(1, t) = 0\n", - " \"\"\"\n", - " solutions = np.zeros((len(X_ANALYTIC), len(T_RANGE)))\n", - "\n", - " for j, t in enumerate(T_RANGE):\n", - " solutions[:, j] = analytical_solution(X_ANALYTIC, t)\n", - "\n", - " return solutions" + " compute_advector = partial(\n", + " interpolate_in_space_and_multiply,\n", + " scalar_in=solver.advectee.get(),\n", + " multiplier=.5 * dt / dx\n", + " )\n", + " \n", + " interpolated_advectors = tuple(\n", + " np.empty_like(advector_view_no_edges(solver))\n", + " for _ in (0,1)\n", + " )\n", + " \n", + " states = []\n", + " compute_advector(vector_out=interpolated_advectors[1])\n", + " for step in range(nt + 1):\n", + " if step != 0:\n", + " compute_advector(vector_out=interpolated_advectors[0])\n", + " extrapolate_in_time(vector_out=advector_view_no_edges(solver), vectors_in=interpolated_advectors)\n", + " check_cfl_condition(solver)\n", + " solver.advance(n_steps=1, mu_coeff=(1 / reynolds_number,) if reynolds_number != np.inf else None)\n", + " interpolated_advectors = interpolated_advectors[::-1]\n", + " if step % output_interval == 0:\n", + " states.append(solver.advectee.get().copy())\n", + " progbar.value = 100 * step / nt\n", + " return states, x, dt" + ] + }, + { + "cell_type": "markdown", + "id": "1c2d7ad3-dadf-411b-ba26-c26d720bbeab", + "metadata": {}, + "source": [ + "## Initial condition and parameters" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "ce086840-57f2-41f8-8654-8cca1a29067d", + "execution_count": 4, + "id": "809c6f35-2bf8-4fc7-aa83-6fb4ed8c4917", "metadata": {}, "outputs": [], "source": [ - "def plot_commons():\n", - " pyplot.xlabel(\"x\")\n", - " pyplot.ylabel(\"u\")\n", - " pyplot.ylim([-1.05, 1.05])\n", - " pyplot.grid()\n", + "def sign(case):\n", + " return {'A': -1, 'B': 1}[case]\n", + "\n", + "def initial_condition(x, case):\n", + " return sign(case) * np.sin(np.pi * x)\n", "\n", - "def plot_numerical_vs_analytical(states, x, t, t_max, nt):\n", - " analytical = analytical_solution(x, t)\n", - " time_index = int((t / t_max) * nt) \n", - " time_index = min(time_index, len(states) - 1)\n", - " numerical = states[time_index, :]\n", - " pyplot.step(x, numerical, label=\"Numerical\", where='mid')\n", - " pyplot.plot(x, analytical, label=\"Analytical\")\n", - " pyplot.title(f\"t={t:.3f}\")\n", - " plot_commons()\n", - " pyplot.legend()\n", - " show_plot(filename=\"numeric\")\n", + "reynolds_numbers = np.inf, 1e16\n", + "cases = 'A', 'B'\n", "\n", - "def plot_gif(step, states, x, dt):\n", - " fig = pyplot.figure()\n", - " pyplot.plot(x, analytical_solution(x, 0), label=\"Initial condition\")\n", - " pyplot.step(x, states[step], label=\"Numerical\", where='mid')\n", - " pyplot.plot(x, analytical_solution(x, step * dt), label=\"Analytical\")\n", - " pyplot.title(f\"t={step * dt:.3f}\")\n", - " plot_commons()\n", - " pyplot.legend()\n", - " return fig \n", + "T_MAX = {'A': 1, 'B': .3}\n", + "NT = 4000 if 'CI' not in os.environ else 800\n", + "NX = 100 if 'CI' not in os.environ else 20\n", + "OUTPUT_INTERVAL = 10\n", "\n", - "def plot_analytical_solutions(solutions, t_range):\n", - " pyplot.plot(X_ANALYTIC, solutions)\n", - " pyplot.xlim([-1, 1]) \n", - " pyplot.title(\"Analytical Solution to the Burgers Equation\")\n", - " pyplot.legend([f\"t={t}\" for t in t_range])\n", - " plot_commons()\n", - " show_plot(filename=\"analytical\")" + "x, dx = np.linspace(-1 + 1/NX, 1 - 1/NX, NX, retstep=True)" + ] + }, + { + "cell_type": "markdown", + "id": "6f39dffb-6735-46f2-b257-5824ab0bb239", + "metadata": {}, + "source": [ + "## Numerical solution" ] }, { "cell_type": "code", - 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" + "FloatProgress(value=0.0)" ] }, "metadata": {}, @@ -2073,12 +234,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e184eabb630493cadba6c0dd22f1ea0", + "model_id": "7d90a7387a6640a6a15baa0708ca5052", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "HBox(children=(HTML(value=\"./analytical.pdf
\"), HTML(value=\"…" + "FloatProgress(value=0.0)" ] }, "metadata": {}, @@ -2086,14 +247,63 @@ } ], "source": [ - "analytical_solutions = calculate_analytical_solutions()\n", - "plot_analytical_solutions(analytical_solutions, T_RANGE)" + "states_num = {case:{} for case in cases}\n", + "x_num = {}\n", + "dt_num = {}\n", + "\n", + "for case in cases:\n", + " for reynolds_number in reynolds_numbers:\n", + " display(progbar := FloatProgress())\n", + " states_num[case][reynolds_number], x_num[case], dt_num[case] = run_numerical_simulation(\n", + " nt=NT,\n", + " t_max=T_MAX[case],\n", + " dx=dx,\n", + " psi0=initial_condition(x, case),\n", + " reynolds_number=reynolds_number,\n", + " output_interval=OUTPUT_INTERVAL,\n", + " progbar=progbar,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "43f93c5e-3bd2-4225-8c65-d400050aa663", + "metadata": {}, + "source": [ + "## Semi-analytic solver" ] }, { "cell_type": "code", - "execution_count": 19, - "id": "e1bc2d61d23d9b3d", + "execution_count": 9, + "id": "227d698d-0533-472c-9be1-bec14e6dabff", + "metadata": {}, + "outputs": [], + "source": [ + "function = lambda x, t, sign, initial: x + sign * np.sin(np.pi * x) * t - initial\n", + "fprime = lambda x, t, sign, _: 1 + sign * np.cos(np.pi * x) * np.pi * t\n", + "fprime2 = lambda x, t, sign, _: -1 * sign * np.sin(np.pi * x) * np.pi**2 * t\n", + "\n", + "def analytical_solution(x, t, case): \n", + " u = np.zeros_like(x)\n", + " for i, xi in enumerate(x):\n", + " res = root_scalar(\n", + " f=function,\n", + " args=(t, sign(case), xi),\n", + " x0=xi / abs(xi),\n", + " fprime=fprime,\n", + " fprime2=fprime2,\n", + " method='halley',\n", + " maxiter=10000\n", + " ) \n", + " assert res.converged\n", + " u[i] = initial_condition(res.root, case)\n", + " return u" + ] + }, + { + "cell_type": "markdown", + "id": "e6d18b6c-32d0-4f57-9001-69b29eb259de", "metadata": { "ExecuteTime": { "end_time": "2025-06-08T10:14:03.595403Z", @@ -2104,1343 +314,42 @@ "outputs_hidden": false } }, + "source": [ + "## Animation" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "735a6191", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-08T10:14:11.937164Z", + "start_time": "2025-06-08T10:14:03.595403Z" + } + }, "outputs": [ { "data": { - 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"
" + "" ] }, "metadata": {}, @@ -3449,39 +358,35 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "05b6f66bff5c4169a6649a44c21c2ca9", + "model_id": "c79394885bb94cc28b1e6cd8e507e57e", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "HBox(children=(HTML(value=\"./numeric.pdf
\"), HTML(value=\"./burgers-case-A.gif
\")" ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "states_num, x_num, dt_num, dx_num = run_numerical_simulation(nt=NT, nx=NX, t_max=T_MAX)\n", - "plot_numerical_vs_analytical(states_num, x_num, T_SHOCK, T_MAX, NT)\n", - "plot_partial = partial(plot_gif, states=states_num, x=x_num, dt=dt_num)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "735a6191", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-08T10:14:11.937164Z", - "start_time": "2025-06-08T10:14:03.595403Z" - } - }, - "outputs": [ + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a550f704a80b4d7d902231ddaaaa0ca8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -3493,12 +398,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3713f3d551fb4e29af8f87661cdaa174", + "model_id": "f3269b48df614d6896cc0d1da413c427", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "HTML(value=\"./burgers.gif
\")" + "HTML(value=\"./burgers-case-B.gif
\")" ] }, "metadata": {}, @@ -3506,13 +411,43 @@ } ], "source": [ - "show_anim(plot_partial, range(0, int(T_SHOCK/(T_MAX/NT)) - 10), gif_file=\"burgers.gif\")" + "sci_label = lambda v: r\"\\infty\" if v == np.inf else rf\"{float(f'{v:.2e}'.split('e')[0])}\\times10^{{{int(f'{v:.2e}'.split('e')[1])}}}\"\n", + "\n", + "def plot_gif(frame, states, x, dt, case, progbar):\n", + " time = frame * OUTPUT_INTERVAL * dt\n", + " fig, ax = pyplot.subplots(figsize=(12,6))\n", + " for t, y, label, style in [\n", + " (0, analytical_solution(x, 0, case), \"Initial condition\", '-'),\n", + " *[\n", + " (time, states[reynolds_number][frame], f\"Numerical (Re=${sci_label(reynolds_number)}$)\", 'step') \n", + " for reynolds_number in reynolds_numbers\n", + " ],\n", + " (time, analytical_solution(x, time, case), r\"Analytical inviscid (Re$\\rightarrow\\infty$)\", '-'),\n", + " ]:\n", + " if style == 'step':\n", + " ax.step(x, y, where='mid', label=label, linewidth=2.5)\n", + " else:\n", + " ax.plot(x, y, label=label)\n", + " ax.set(xlabel=\"x\", ylabel=\"u\", ylim=(-1.35, 1.35), title=f\"case {case} t={time:.3f}\")\n", + " ax.grid();\n", + " ax.legend()\n", + " pyplot.savefig(f\"{frame:03d}.pdf\")\n", + " progbar.value = 100 * frame / len(states[reynolds_number])\n", + " return fig\n", + "\n", + "for case in cases:\n", + " display(progbar := FloatProgress())\n", + " show_anim(\n", + " partial(plot_gif, states=states_num[case], x=x_num[case], dt=dt_num[case], case=case, progbar=progbar),\n", + " range(NT // OUTPUT_INTERVAL + 1),\n", + " gif_file=f\"burgers-case-{case}.gif\"\n", + " )" ] }, { "cell_type": "code", "execution_count": null, - "id": "8ffa5c6d-ef5f-4015-9c7c-034aa0e99e70", + "id": "91c2743f-dd61-4ffa-b26a-d6f7e1e2dce4", "metadata": {}, "outputs": [], "source": [] @@ -3534,7 +469,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.2" + "version": "3.13.5" } }, "nbformat": 4, diff --git a/examples/PyMPDATA_examples/burgers_equation/burgers_equation.py b/examples/PyMPDATA_examples/burgers_equation/burgers_equation.py deleted file mode 100644 index 312d17b2f..000000000 --- a/examples/PyMPDATA_examples/burgers_equation/burgers_equation.py +++ /dev/null @@ -1,64 +0,0 @@ -""" -Solution for the Burgers equation solution with MPDATA -""" - -import numpy as np - -from PyMPDATA import Options, ScalarField, Solver, Stepper, VectorField -from PyMPDATA.boundary_conditions import Constant - -OPTIONS = Options(nonoscillatory=False, infinite_gauge=True) - - -def initialize_simulation(nt, nx, t_max): - """ - Initializes simulation variables and returns them. - """ - dt = t_max / nt - courants_x, dx = np.linspace(-1, 1, nx + 1, endpoint=True, retstep=True) - x = courants_x[:-1] + dx / 2 - u0 = -np.sin(np.pi * x) - - stepper = Stepper(options=OPTIONS, n_dims=1) - advectee = ScalarField( - data=u0, halo=OPTIONS.n_halo, boundary_conditions=(Constant(0), Constant(0)) - ) - advector = VectorField( - data=(np.full(courants_x.shape, 0.0),), - halo=OPTIONS.n_halo, - boundary_conditions=(Constant(0), Constant(0)), - ) - solver = Solver(stepper=stepper, advectee=advectee, advector=advector) - return dt, dx, x, advectee, advector, solver - - -def update_advector_n(vel, dt, dx, slice_idx): - """ - Computes and returns the updated advector_n. - """ - indices = np.arange(slice_idx.start, slice_idx.stop) - return 0.5 * ((vel[indices] - vel[indices - 1]) / 2 + vel[:-1]) * dt / dx - - -def run_numerical_simulation(*, nt, nx, t_max): - """ - Runs the numerical simulation and returns (states, x, dt, dx). - """ - dt, dx, x, advectee, advector, solver = initialize_simulation(nt, nx, t_max) - states = [] - vel = advectee.get() - advector_n_1 = 0.5 * (vel[:-1] + np.diff(vel) / 2) * dt / dx - assert np.all(advector_n_1 <= 1) - i = slice(1, len(vel)) - - for _ in range(nt): - vel = advectee.get() - advector_n = update_advector_n(vel, dt, dx, i) - advector.get_component(0)[1:-1] = 0.5 * (3 * advector_n - advector_n_1) - assert np.all(advector.get_component(0) <= 1) - - solver.advance(n_steps=1) - advector_n_1 = advector_n.copy() - states.append(solver.advectee.get().copy()) - - return np.array(states), x, dt, dx diff --git a/tests/smoke_tests/burgers_equation/test_burgers_equation.py b/tests/smoke_tests/burgers_equation/test_burgers_equation.py index 3b4a8df6b..c8f9ac9a6 100644 --- a/tests/smoke_tests/burgers_equation/test_burgers_equation.py +++ b/tests/smoke_tests/burgers_equation/test_burgers_equation.py @@ -1,58 +1,26 @@ -"""Unit tests for the Burgers' equation numerical simulation.""" +"""smoke tests for the Burgers' equation numerical simulation.""" + +from pathlib import Path import numpy as np import pytest -from PyMPDATA_examples.burgers_equation import run_numerical_simulation - +from open_atmos_jupyter_utils import notebook_vars +from PyMPDATA_examples import burgers_equation -@pytest.fixture(name="states") -def states_fixture(): - """Run the simulation once for all tests.""" - return run_numerical_simulation(nt=400, nx=100, t_max=1 / np.pi)[0] +PLOT = False -class TestBurgersEquation: - """Test suite for general numerical verification of Burgers' equation simulation.""" +@pytest.fixture(scope="session", name="variables") +def _variables_fixture(): + return notebook_vars( + file=Path(burgers_equation.__file__).parent / "burgers_equation.ipynb", + plot=PLOT, + ) - @staticmethod - def test_total_momentum_conservation(states): - """Verify total momentum remains approximately constant over time.""" - sum_initial_state = np.sum(states[0]) - eps = 1e-5 - - for state in states: - sum_state = np.sum(state) - np.testing.assert_allclose( - desired=sum_initial_state, - actual=sum_state, - atol=eps, - ) - @staticmethod - def test_solution_within_bounds(states): - """Ensure numerical solution u(i, j) stays within expected bounds.""" - eps = 1e-2 - min_val = np.min(states) + eps - max_val = np.max(states) - eps - assert min_val >= -1.0 - assert max_val <= 1.0 +class TestBurgersEquation: + """assertions on the final notebook state""" @staticmethod - def test_zero_constant_boundary_conditions(states): - """Verify zero-constant boundary conditions are satisfied at all time steps.""" - eps = 5e-2 - - for state in states: - left_boundary = state[0] - right_boundary = state[-1] - - np.testing.assert_allclose( - desired=0, - actual=left_boundary, - atol=eps, - ) - np.testing.assert_allclose( - desired=0, - actual=right_boundary, - atol=eps, - ) + def test_vs_analytic(variables): + pass