diff --git a/examples/PyMPDATA_examples/Jaruga_et_al_2015/fig.ipynb b/examples/PyMPDATA_examples/Jaruga_et_al_2015/fig.ipynb deleted file mode 100644 index aa261a5e..00000000 --- a/examples/PyMPDATA_examples/Jaruga_et_al_2015/fig.ipynb +++ /dev/null @@ -1,623 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "854fc1cb-5e7c-424e-a09a-ace10e519c3a", - "metadata": {}, - "source": [ - "## 2D Boussinesq (buoyancy-driven) flow example\n", - "\n", - "### equations solved:\n", - "conservation of momentum (inviscid and incompressible form of Navier-Stokes equation)\n", - "$$\n", - "\\partial_t v+\\mathrm{\\nabla}\\cdot\\left(\\vec{v}\\otimes \\vec{v}\\right)=-\\mathrm{\\nabla\\pi}-\\vec{g}\\frac{\\theta^\\prime}{\\theta_0}\n", - "$$\n", - "conservation of energy\n", - "$$\n", - "\\partial_t \\theta+ \\mathrm{\\nabla}\\cdot\\left(\\vec{v}\\theta\\right)=0\n", - "$$\n", - "continuity equation(conservation of mass)\n", - "$$\n", - "\\mathrm{\\nabla}\\cdot\\ \\vec{v}=0\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9ef2a5c3-3ac9-418b-92dd-e422ba66af66", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "if 'google.colab' in sys.modules:\n", - " !pip --quiet install open-atmos-jupyter-utils\n", - " from open_atmos_jupyter_utils import pip_install_on_colab\n", - " pip_install_on_colab('PyMPDATA-examples')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "69583de6-8b30-4a74-8cd0-d3ade00ec579", - "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[2], line 7\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcollections\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m namedtuple\n\u001b[1;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumba\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m jit\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumba\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01merrors\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m NumbaExperimentalFeatureWarning\n\u001b[0;32m 10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n", - "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\numba\\__init__.py:92\u001b[0m\n\u001b[0;32m 89\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumba\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtypes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[0;32m 91\u001b[0m \u001b[38;5;66;03m# Re-export decorators\u001b[39;00m\n\u001b[1;32m---> 92\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumba\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdecorators\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (cfunc, jit, njit, stencil,\n\u001b[0;32m 93\u001b[0m jit_module)\n\u001b[0;32m 95\u001b[0m \u001b[38;5;66;03m# Re-export vectorize decorators and the thread layer querying function\u001b[39;00m\n\u001b[0;32m 96\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumba\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnp\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mufunc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (vectorize, guvectorize, threading_layer,\n\u001b[0;32m 97\u001b[0m get_num_threads, set_num_threads,\n\u001b[0;32m 98\u001b[0m set_parallel_chunksize, get_parallel_chunksize,\n\u001b[0;32m 99\u001b[0m get_thread_id)\n", - 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"File \u001b[1;32m:1354\u001b[0m, in \u001b[0;36m_find_and_load\u001b[1;34m(name, import_)\u001b[0m\n", - "File \u001b[1;32m:1325\u001b[0m, in \u001b[0;36m_find_and_load_unlocked\u001b[1;34m(name, import_)\u001b[0m\n", - "File \u001b[1;32m:929\u001b[0m, in \u001b[0;36m_load_unlocked\u001b[1;34m(spec)\u001b[0m\n", - "File \u001b[1;32m:990\u001b[0m, in \u001b[0;36mexec_module\u001b[1;34m(self, module)\u001b[0m\n", - "File \u001b[1;32m:1086\u001b[0m, in \u001b[0;36mget_code\u001b[1;34m(self, fullname)\u001b[0m\n", - "File \u001b[1;32m:1185\u001b[0m, in \u001b[0;36mget_data\u001b[1;34m(self, path)\u001b[0m\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "import os\n", - "os.environ[\"NUMBA_DISABLE_JIT\"]=\"1\"\n", - "\n", - "import warnings\n", - "from collections import namedtuple\n", - "\n", - "from numba import jit\n", - "from numba.core.errors import NumbaExperimentalFeatureWarning\n", - "\n", - "import numpy as np\n", - "from matplotlib import colors, pyplot\n", - "import ipywidgets\n", - "from IPython.display import display\n", - "\n", - "from open_atmos_jupyter_utils import show_plot\n", - "from open_atmos_jupyter_utils.show_anim import show_anim\n", - "\n", - "from PyMPDATA import Options, ScalarField, VectorField, Solver, Stepper\n", - "from PyMPDATA.boundary_conditions import Periodic\n", - "from PyMPDATA.impl.enumerations import IMPL_META_AND_DATA, META_AND_DATA_DATA, IMPL_BC\n", - "from PyMPDATA.impl.clock import clock" - ] - }, - { - "cell_type": "markdown", - "id": "8eb61fd1-2b1d-4eb1-afd8-551899676947", - "metadata": {}, - "source": [ - "## Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "813a03e0-9804-4ba2-8620-6835ec4074e2", - "metadata": {}, - "outputs": [], - "source": [ - "OPTIONS = Options(n_iters=2, infinite_gauge=True, nonoscillatory=True)\n", - "\n", - "SETUP = {\n", - " \"outfreq\": 1,\n", - " \"N\": 200,\n", - " \"M\": 200,\n", - " \"Tht_ref\": 300.,\n", - " \"Tht_dlt\": .5,\n", - " \"g\": 9.81,\n", - " \"r0\": 250.,\n", - " \"x0\": 1000,\n", - " \"y0\": 260,\n", - " \"dt\": 0.75,\n", - " \"prs_tol\": 1e-7,\n", - " \"k_iters\": 4,\n", - "}\n", - "\n", - "SETUP[\"nt\"] = int(600//SETUP[\"dt\"])\n", - "SETUP[\"err_tol\"] = SETUP[\"prs_tol\"] / SETUP[\"dt\"]\n", - "SETUP[\"grid\"] = SETUP[\"N\"], SETUP[\"M\"]\n", - "SETUP[\"dxy\"] = (2000/SETUP[\"N\"], 2000/SETUP[\"M\"])\n", - "SETUP[\"halo\"] = OPTIONS.n_halo\n", - "SETUP[\"grid_with_halo\"] = SETUP[\"N\"] + SETUP[\"halo\"]*2, SETUP[\"M\"] + SETUP[\"halo\"]*2\n", - "\n", - "SETUP = namedtuple(\"Setup\", SETUP.keys())(**SETUP)\n", - "\n", - "assert SETUP.dt * SETUP.nt == 600" - ] - }, - { - "cell_type": "markdown", - "id": "01add99e-16cf-47a6-8eef-8f55b7e3b088", - "metadata": {}, - "source": [ - "## initial condition" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7ce51f39-0c3a-47e6-84fd-6dc618a40394", - "metadata": {}, - "outputs": [], - "source": [ - "xi, yi = np.indices(SETUP.grid)\n", - "\n", - "mesh = np.full(SETUP.grid, fill_value=SETUP.Tht_ref, dtype=float)\n", - "mesh += np.where(\n", - " np.sqrt(\n", - " ((xi+1/2) * SETUP.dxy[0] - SETUP.x0)**2 +\n", - " ((yi+1/2) * SETUP.dxy[1] - SETUP.y0)**2\n", - " ) < SETUP.r0,\n", - " SETUP.Tht_dlt,\n", - " 0\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "c784fff7-1ad7-478c-8434-3e5815a3d30a", - "metadata": {}, - "source": [ - "## PyMPDATA setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "849497c5-8731-492e-843b-1a575d34e503", - "metadata": {}, - "outputs": [], - "source": [ - "_U, _W, _T = 0, 1, 2\n", - "\n", - "bcond = (Periodic(), Periodic())\n", - "field_ctor_kwargs = {'halo': SETUP.halo, 'boundary_conditions': bcond}\n", - "\n", - "advector = VectorField(data=(\n", - " np.zeros((SETUP.N+1, SETUP.M)),\n", - " np.zeros((SETUP.N, SETUP.M+1))\n", - "), **field_ctor_kwargs)\n", - "stepper = Stepper(options=OPTIONS, grid=SETUP.grid,n_threads = 1)\n", - "advector.assemble(stepper.traversals)\n", - "\n", - "def new_sf(g):\n", - " field = ScalarField(data=np.zeros(g), **field_ctor_kwargs)\n", - " field.assemble(stepper.traversals)\n", - " return field\n", - "\n", - "solver_ctor_kwargs = {'stepper': stepper, 'advector': advector}\n", - "solvers = {\n", - " _U: Solver(advectee=new_sf(SETUP.grid), **solver_ctor_kwargs),\n", - " _W: Solver(advectee=new_sf(SETUP.grid), **solver_ctor_kwargs),\n", - " _T: Solver(advectee=ScalarField(data=mesh, **field_ctor_kwargs), **solver_ctor_kwargs),\n", - "}\n", - "\n", - "ADVECTEE_DATA = tuple(\n", - " solvers[idx].advectee.data for idx in range(3)\n", - ")\n", - "\n", - "ADVECTOR_DATA = tuple(\n", - " datum for datum in advector.data\n", - ")\n", - "\n", - "ARRAYS = {\n", - " \"rhs_w\": np.zeros(SETUP.grid),\n", - " \"stash\": np.zeros((len(SETUP.grid), *SETUP.grid_with_halo)),\n", - " \"vip_rhs\": np.zeros((len(SETUP.grid), *SETUP.grid_with_halo)),\n", - " \"Phi\": np.zeros(SETUP.grid_with_halo),\n", - " \"tmp_uvw\": np.zeros((len(SETUP.grid), *SETUP.grid_with_halo)),\n", - " \"lap_tmp\": np.zeros((len(SETUP.grid), *SETUP.grid_with_halo)),\n", - " \"lap_err\": np.zeros(SETUP.grid),\n", - " \"err\": np.zeros(SETUP.grid_with_halo),\n", - " \"p_err\": np.zeros((SETUP.k_iters, *SETUP.grid_with_halo)),\n", - " \"lap_p_err\": np.zeros((SETUP.k_iters, *SETUP.grid)),\n", - "}\n", - "ARRAYS = namedtuple(\"Arrays\", ARRAYS.keys())(**ARRAYS)\n", - "\n", - "IDX = {\n", - " \"interior\": (slice(SETUP.halo, -SETUP.halo), slice(SETUP.halo, -SETUP.halo)),\n", - " \"grad_left\": (\n", - " (slice(SETUP.halo + 1, None if SETUP.halo==1 else (-SETUP.halo+1)), slice(SETUP.halo,-SETUP.halo)),\n", - " (slice(SETUP.halo,-SETUP.halo), slice(SETUP.halo+1,None if SETUP.halo==1 else (-SETUP.halo+1)))\n", - " ),\n", - " \"grad_right\": (\n", - " (slice(None if SETUP.halo==1 else (SETUP.halo-1),-SETUP.halo-1), slice(SETUP.halo,-SETUP.halo)),\n", - " (slice(SETUP.halo,-SETUP.halo), slice(None if SETUP.halo==1 else (SETUP.halo-1), -SETUP.halo-1))\n", - " ),\n", - " \"intrp_left\": ( \n", - " (slice(None, -1), slice(None, None)),\n", - " (slice(None, None), slice(None, -1)),\n", - " ),\n", - " \"intrp_right\": ( \n", - " (slice(1, None), slice(None, None)),\n", - " (slice(None, None), slice(1, None)),\n", - " )\n", - "}\n", - "IDX = namedtuple(\"Idx\", IDX.keys())(**IDX)" - ] - }, - { - "cell_type": "markdown", - "id": "615f8939-191e-4335-be45-1d6cc53a2223", - "metadata": {}, - "source": [ - "## JIT-compilable functions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c0ce0be0-346e-4074-9a01-72d635844903", - "metadata": {}, - "outputs": [], - "source": [ - "fill_halos_scalar = stepper.traversals._code[\"fill_halos_scalar\"]\n", - "\n", - "@jit(**OPTIONS.jit_flags)\n", - "def grad(psi, axis):\n", - " return (psi[IDX.grad_left[axis]] - psi[IDX.grad_right[axis]]) / 2 / SETUP.dxy[axis]\n", - " \n", - "@jit(**OPTIONS.jit_flags)\n", - "def calc_grad_Phi(arg, phi):\n", - " for k in (0,1):\n", - " arg[k][IDX.interior] = grad(phi, axis=k)\n", - "\n", - "@jit(**OPTIONS.jit_flags)\n", - "def data(impl):\n", - " return impl[IMPL_META_AND_DATA][META_AND_DATA_DATA]\n", - "\n", - "@jit(**OPTIONS.jit_flags)\n", - "def pressure_solver_loop_body(ARRAYS,converged):\n", - " tmp_den = [1.] * SETUP.k_iters\n", - " alpha = [1.] * SETUP.k_iters\n", - " for v in range(0, SETUP.k_iters):\n", - " tmp_den[v] = np.sum(ARRAYS.lap_p_err[v]**2)\n", - " if tmp_den[v] != 0:\n", - " beta = - np.dot(\n", - " ARRAYS.err[IDX.interior].ravel(),\n", - " ARRAYS.lap_p_err[v].ravel()\n", - " ) / tmp_den[v]\n", - " else:\n", - " beta = .25\n", - " ARRAYS.Phi[IDX.interior][:] += beta * ARRAYS.p_err[v][IDX.interior]\n", - " ARRAYS.err[IDX.interior][:] += beta * ARRAYS.lap_p_err[v]\n", - "\n", - " error = max(\n", - " abs(np.amax(ARRAYS.err[IDX.interior])),\n", - " abs(np.amin(ARRAYS.err[IDX.interior]))\n", - " )\n", - "\n", - " if error <= SETUP.err_tol: \n", - " converged = True\n", - "\n", - " ARRAYS.lap_err[:] = lap(ARRAYS.err,False,ARRAYS)\n", - "\n", - " for l in range(v):\n", - " if tmp_den[l] != 0:\n", - " alpha[l] = - np.dot(ARRAYS.lap_err.ravel(), ARRAYS.lap_p_err[l].ravel()) / tmp_den[l]\n", - " if v < SETUP.k_iters - 1:\n", - " ARRAYS.p_err[v + 1][IDX.interior][:] = ARRAYS.err[IDX.interior]\n", - " ARRAYS.lap_p_err[v + 1][:] = ARRAYS.lap_err\n", - " for l in range(v):\n", - " ARRAYS.p_err[v + 1][IDX.interior][:] += alpha[l] * ARRAYS.p_err[l][IDX.interior]\n", - " ARRAYS.lap_p_err[v + 1][:] += alpha[l] * ARRAYS.lap_p_err[l]\n", - " else:\n", - " ARRAYS.p_err[0][IDX.interior][:] = ARRAYS.err[IDX.interior][:] + alpha[0] * ARRAYS.p_err[0][IDX.interior]\n", - " ARRAYS.lap_p_err[0][:] = ARRAYS.lap_err[:] + alpha[0] * ARRAYS.lap_p_err[0]\n", - " for l in range(1, v+1):\n", - " ARRAYS.p_err[0][IDX.interior][:] += alpha[l] * ARRAYS.p_err[l][IDX.interior]\n", - " ARRAYS.lap_p_err[0][:] += alpha[l] * ARRAYS.lap_p_err[l]\n", - " return converged\n", - "\n", - "@jit(**OPTIONS.jit_flags)\n", - "def calc_gc_interpolate_in_space(ARRAYS,ADVECTOR_DATA):\n", - " for axis in (_U, _W):\n", - " ADVECTOR_DATA[axis][:] = SETUP.dt / SETUP.dxy[axis] * (\n", - " (\n", - " ARRAYS.stash[axis][IDX.intrp_right[axis]] - \n", - " ARRAYS.stash[axis][IDX.intrp_left[axis]]\n", - " ) / 2 + ARRAYS.stash[axis][IDX.intrp_left[axis]]\n", - " )\n", - "\n", - "@jit(**OPTIONS.jit_flags)\n", - "def ini_pressure(ARRAYS,ADVECTEE_DATA):\n", - " for k in (_U, _W):\n", - " ARRAYS.Phi[IDX.interior][:] -= 0.5 * np.power(ADVECTEE_DATA[k][IDX.interior][:], 2)\n", - " ARRAYS.Phi[IDX.interior][:] -= np.sum(ARRAYS.Phi[IDX.interior]) / (SETUP.N * SETUP.M)\n", - "\n", - "@jit(**OPTIONS.jit_flags)\n", - "def lap(Phi, err_init,ARRAYS): \n", - " xchng_pres_old(Phi)\n", - " calc_grad_Phi(ARRAYS.lap_tmp, Phi)\n", - "\n", - " if err_init:\n", - " for k in (_U, _W):\n", - " ARRAYS.lap_tmp[k][:] -= ARRAYS.tmp_uvw[k]\n", - "\n", - " for k in (_U, _W):\n", - " xchng_pres_old(ARRAYS.lap_tmp[k])\n", - " \n", - " return (\n", - " grad(ARRAYS.lap_tmp[0], axis=0) +\n", - " grad(ARRAYS.lap_tmp[1], axis=1)\n", - " )\n", - "\n", - "@jit(**OPTIONS.jit_flags)\n", - "def pressure_solver_loop_init(ARRAYS):\n", - " ARRAYS.p_err[0][IDX.interior][:] = ARRAYS.err[IDX.interior]\n", - " ARRAYS.lap_p_err[0][:] = lap(ARRAYS.p_err[0], False,ARRAYS)\n", - "\n", - "@jit(**OPTIONS.jit_flags)\n", - "def pressure_solver_update(ARRAYS,ADVECTEE_DATA):\n", - " for k in (_U, _W):\n", - " ARRAYS.tmp_uvw[k][:] = ADVECTEE_DATA[k]\n", - " ARRAYS.err[IDX.interior][:] = lap(ARRAYS.Phi, True,ARRAYS)\n", - " pressure_solver_loop_init(ARRAYS)\n", - "\n", - " iters, converged = 0, False\n", - " while not converged: \n", - " converged = pressure_solver_loop_body(ARRAYS,converged)\n", - " iters += 1\n", - " if iters > 10000:\n", - " raise ArithmeticError(\"stuck in pressure solver\")\n", - " \n", - " xchng_pres_old(ARRAYS.Phi)\n", - " calc_grad_Phi(ARRAYS.tmp_uvw, ARRAYS.Phi)\n", - "\n", - "@jit(**OPTIONS.jit_flags)\n", - "def calc_gc_extrapolate_in_time(ARRAYS,ADVECTEE_DATA):\n", - " for k in (_U, _W):\n", - " ARRAYS.stash[k][IDX.interior] = -.5 * ARRAYS.stash[k][IDX.interior] + 1.5 * ADVECTEE_DATA[k][IDX.interior]\n", - " ARRAYS.stash[k][IDX.interior] = -.5 * ARRAYS.stash[k][IDX.interior] + 1.5 * ADVECTEE_DATA[k][IDX.interior]\n", - " xchng_pres_old(ARRAYS.stash[k])\n", - "\n", - "@jit(**OPTIONS.jit_flags)\n", - "def fill_stash(ARRAYS,ADVECTEE_DATA):\n", - " for k in (_U, _W):\n", - " ARRAYS.stash[k][IDX.interior] = ADVECTEE_DATA[k][IDX.interior]\n", - " xchng_pres_old(ARRAYS.stash[k])\n", - " \n", - "@jit(**OPTIONS.jit_flags)\n", - "def xchng_pres_old(data):\n", - " data[0:SETUP.halo, :] = data[-2*SETUP.halo:-SETUP.halo,:]\n", - " data[-SETUP.halo:, :] = data[SETUP.halo:2*SETUP.halo, :]\n", - " data[:,0:SETUP.halo] = data[:,-2*SETUP.halo : -SETUP.halo]\n", - " data[:,-SETUP.halo:] = data[:,SETUP.halo : 2*SETUP.halo]\n", - "\n", - "@jit(**OPTIONS.jit_flags)\n", - "def vip_rhs_apply(ARRAYS,ADVECTEE_DATA):\n", - " for k in (_U, _W):\n", - " ADVECTEE_DATA[k][:] += 0.5 * SETUP.dt * ARRAYS.vip_rhs[k]\n", - " ARRAYS.vip_rhs[k][:] = 0\n", - "\n", - "@jit(**OPTIONS.jit_flags)\n", - "def apply_half_rhs(ARRAYS,ADVECTEE_DATA): \n", - " ADVECTEE_DATA[_W][IDX.interior] += ARRAYS.rhs_w * SETUP.dt/2\n", - "\n", - "@jit(**OPTIONS.jit_flags)\n", - "def finilize_rhs(ARRAYS,ADVECTEE_DATA):\n", - " for k in (_U, _W):\n", - " ARRAYS.vip_rhs[k][:] = -ADVECTEE_DATA[k]\n", - "\n", - "@jit(**OPTIONS.jit_flags)\n", - "def update_rhs(ARRAYS,ADVECTEE_DATA):\n", - " ARRAYS.rhs_w[:] = SETUP.g * (ADVECTEE_DATA[_T][IDX.interior] - SETUP.Tht_ref) / SETUP.Tht_ref\n", - "\n", - "@jit(**OPTIONS.jit_flags)\n", - "def pressure_solver_apply(ARRAYS,ADVECTEE_DATA):\n", - " for k in (_U, _W):\n", - " ADVECTEE_DATA[k][:] -= ARRAYS.tmp_uvw[k]\n", - " ARRAYS.vip_rhs[k][:] += ADVECTEE_DATA[k]\n", - " ARRAYS.vip_rhs[k][:] /= 0.5 * SETUP.dt\n", - "\n", - "\n", - "# note: all code below will be replaced by an implementation of PostStep hook referring to above methods\n", - "MPDATA_STEPPERS = tuple([\n", - " solvers[k]._Solver__stepper._Stepper__call for k in (_U, _W, _T)\n", - "])\n", - "\n", - "from PyMPDATA import solver\n", - "MPDATA_POST_STEP = solver.PostStepNull() \n", - "MPDATA_POST_ITER = solver.PostIterNull()\n", - "\n", - "from PyMPDATA.impl.meta import _Impl\n", - "\n", - "MPDATA_TRAVERSALS_DATA = tuple([\n", - " solvers[k]._Solver__stepper.traversals.data for k in (_U, _W, _T)\n", - "])\n", - "\n", - "MPDATA_FIELDS = tuple([\n", - " tuple([\n", - " _Impl(field=v.impl[IMPL_META_AND_DATA], bc=v.impl[IMPL_BC])\n", - " for v in solvers[k]._Solver__fields.values()\n", - " ]) for k in (_U, _W, _T)\n", - "])\n", - "\n", - "@jit(**OPTIONS.jit_flags)\n", - "def mpdata_advance(steppers, post_step, post_iter, fields, traversals_data):\n", - " n_steps = 1\n", - " mu_coeff = (0,0,0)\n", - " wall_time = 0\n", - " for k in (_U, _W, _T):\n", - " wall_time += steppers[k](\n", - " n_steps, \n", - " mu_coeff, \n", - " post_step,\n", - " post_iter,\n", - " *fields[k],\n", - " traversals_data[k]\n", - " )\n", - " return wall_time\n", - "@jit(**OPTIONS.jit_flags)\n", - "def sstep(ARRAYS,ADVECTOR_DATA, ADVECTEE_DATA,MPDATA_STEPPERS, MPDATA_POST_STEP, MPDATA_POST_ITER, MPDATA_FIELDS, MPDATA_TRAVERSALS_DATA):\n", - " t0 = clock()\n", - " calc_gc_extrapolate_in_time(ARRAYS, ADVECTEE_DATA) \n", - " calc_gc_interpolate_in_space(ARRAYS, ADVECTOR_DATA) \n", - " fill_stash(ARRAYS, ADVECTEE_DATA)\n", - " apply_half_rhs(ARRAYS, ADVECTEE_DATA)\n", - " vip_rhs_apply(ARRAYS, ADVECTEE_DATA)\n", - " mpdata_advance(MPDATA_STEPPERS, MPDATA_POST_STEP, MPDATA_POST_ITER, MPDATA_FIELDS, MPDATA_TRAVERSALS_DATA)\n", - " update_rhs(ARRAYS, ADVECTEE_DATA)\n", - " apply_half_rhs(ARRAYS, ADVECTEE_DATA)\n", - " finilize_rhs(ARRAYS, ADVECTEE_DATA)\n", - " pressure_solver_update(ARRAYS, ADVECTEE_DATA)\n", - " pressure_solver_apply(ARRAYS, ADVECTEE_DATA)\n", - " return clock()-t0" - ] - }, - { - "cell_type": "markdown", - "id": "229c52f6-8072-45fb-a0dd-335c7045583c", - "metadata": {}, - "source": [ - "## Memory allocations, initial condition, and simulation loop\n", - "Progress bar stays at 0 until all functions compile" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1052e101-e702-4bb7-8b3b-708bc7fe47aa", - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "progress = ipywidgets.FloatProgress(max=SETUP.nt, description=\"starting ...\")\n", - "display(progress)\n", - "\n", - "times = np.empty(SETUP.nt)\n", - "\n", - "with warnings.catch_warnings():\n", - " warnings.simplefilter(action=\"ignore\",category=NumbaExperimentalFeatureWarning)\n", - "\n", - " output = np.empty((SETUP.nt//SETUP.outfreq+1, *SETUP.grid))\n", - " for step in range(SETUP.nt + 1):\n", - " if step != 0:\n", - " times[step-1] = sstep(ARRAYS,ADVECTOR_DATA, ADVECTEE_DATA,MPDATA_STEPPERS, MPDATA_POST_STEP, \n", - " MPDATA_POST_ITER, MPDATA_FIELDS, MPDATA_TRAVERSALS_DATA)\n", - " progress.description = 'running ...'\n", - " if step % SETUP.outfreq == 0:\n", - " output[step//SETUP.outfreq] = ADVECTEE_DATA[_T][IDX.interior]\n", - " progress.value = step\n", - " \n", - "print(f\"{np.min(times)=} {np.mean(times)=} {np.max(times)=}\")\n", - "with open(\"output.npy\", 'wb') as f:\n", - " np.save(f, np.array(output))" - ] - }, - { - "cell_type": "markdown", - "id": "2ab14876-a416-4ba8-bd14-eeb6abad82ef", - "metadata": {}, - "source": [ - "## Visualisation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1ab53227-a29b-4df8-86d7-77c4fd1e5584", - "metadata": {}, - "outputs": [], - "source": [ - "freq = int(10)\n", - "progress = ipywidgets.FloatProgress(max=len(output)/freq)\n", - "\n", - "def plot(step,plotflag=False):\n", - " data = output[step]\n", - " grid = data.shape\n", - " x, y = np.indices(grid)\n", - " colorticks = np.linspace(299.95, 300.55, 7, endpoint=True)\n", - " cmap = colors.ListedColormap([\"white\", \"purple\", \"blue\", \"green\",\"orange\",\"yellow\"])\n", - "\n", - " fig = pyplot.figure(figsize=(5,6))\n", - " pyplot.title(\"t/dt=\"+str(step))\n", - " pyplot.xlabel(\"x/dx\")\n", - " pyplot.ylabel(\"y/dy\")\n", - " pyplot.contour(x+1/2, y+1/2, data, levels=colorticks[1:], colors='k',linewidths=0.3)\n", - " pyplot.imshow(data.T, origin=\"lower\", extent=(0, grid[0], 0, grid[1]),\n", - " cmap=cmap, vmin=colorticks[0], vmax=colorticks[-1])\n", - " pyplot.grid()\n", - " cax = pyplot.axes([0.95, 0.262, 0.05,0.466])\n", - " if plotflag:\n", - " pyplot.colorbar(ticks=colorticks,label = \"Temperature[K]\",cax = cax)\n", - " else:\n", - " pyplot.colorbar(ticks=colorticks,label = \"Temperature[K]\")\n", - " progress.value += 1\n", - " return fig\n", - " \n", - "display(progress)\n", - "show_anim(plot, frame_range=range(len(output))[::freq])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e19fba6c-c5b1-43ba-8f99-a23222e0afd1", - "metadata": {}, - "outputs": [], - "source": [ - "show_plot(fig=plot(0,True))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6ea87f1c-e52a-4e41-a594-5ad1f13c51d8", - "metadata": {}, - "outputs": [], - "source": [ - "show_plot(fig=plot(800,True))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "22a790d5-89e6-43d8-986b-d04a50c842ba", - "metadata": {}, - "outputs": [], - "source": [ - "print(times)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2c52ab6e-2c89-4686-8a6b-43e253d6011f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/PyMPDATA_examples/Jaruga_et_al_2015/fig19.ipynb b/examples/PyMPDATA_examples/Jaruga_et_al_2015/fig19.ipynb index be1f33b3..2f94106d 100644 --- a/examples/PyMPDATA_examples/Jaruga_et_al_2015/fig19.ipynb +++ b/examples/PyMPDATA_examples/Jaruga_et_al_2015/fig19.ipynb @@ -53,7 +53,7 @@ "outputs": [], "source": [ "import os\n", - "#os.environ[\"NUMBA_DISABLE_JIT\"]=\"1\"\n", + "os.environ[\"NUMBA_DISABLE_JIT\"]=\"0\"\n", "\n", "import warnings\n", "from collections import namedtuple\n", @@ -247,9 +247,9 @@ "\n", "ARRAYS, ADVECTOR_DATA, ADVECTEE_DATA, solvers = initialize()\n", "# note: all code below will be replaced by an implementation of PostStep hook referring to above methods\n", - "MPDATA_STEPPERS = tuple([\n", - " solvers[k]._Solver__stepper._Stepper__call for k in (_U, _W, _T)\n", - "])\n", + "MPDATA_STEPPERS = tuple(\n", + " solvers[k]._Solver__stepper._Stepper__call for k in (_U, _W, _T)#pylint: disable=protected-access\n", + ")\n", "\n", "from PyMPDATA import solver\n", "MPDATA_POST_STEP = solver.PostStepNull() \n", @@ -257,16 +257,16 @@ "\n", "from PyMPDATA.impl.meta import _Impl\n", "\n", - "MPDATA_TRAVERSALS_DATA = tuple([\n", - " solvers[k]._Solver__stepper.traversals.data for k in (_U, _W, _T)\n", - "])\n", + "MPDATA_TRAVERSALS_DATA = tuple(\n", + " solvers[k]._Solver__stepper.traversals.data for k in (_U, _W, _T)#pylint: disable=protected-access\n", + ")\n", "\n", - "MPDATA_FIELDS = tuple([\n", - " tuple([\n", - " _Impl(field=v.impl[IMPL_META_AND_DATA], bc=v.impl[IMPL_BC])\n", - " for v in solvers[k]._Solver__fields.values()\n", - " ]) for k in (_U, _W, _T)\n", - "])" + "MPDATA_FIELDS = tuple(\n", + " tuple(\n", + " _Impl(field=v.impl[IMPL_META_AND_DATA], bc=v.impl[IMPL_BC])#pylint: disable=protected-access\n", + " for v in solvers[k]._Solver__fields.values()#pylint: disable=protected-access\n", + " ) for k in (_U, _W, _T)\n", + ")" ] }, { @@ -284,7 +284,7 @@ "metadata": {}, "outputs": [], "source": [ - "fill_halos_scalar = stepper.traversals._code[\"fill_halos_scalar\"]\n", + "fill_halos_scalar = stepper.traversals._code[\"fill_halos_scalar\"]#pylint: disable=protected-access\n", "\n", "@jit(**OPTIONS.jit_flags)\n", "def grad(psi, axis):\n", @@ -503,9 +503,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "np.min(times)=0.0 np.mean(times)=356736821801.375 np.max(times)=822045740000.0\n", - "CPU times: total: 13min 32s\n", - "Wall time: 15min 53s\n" + "np.min(times)=0.0 np.mean(times)=591214069245.125 np.max(times)=1542881119200.0\n", + "CPU times: total: 22min 23s\n", + "Wall time: 27min 56s\n" ] } ], @@ -539,7 +539,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "355f7fce056a4481a58269546f5c0a39", + "model_id": "0af5c6af735a4b87a27f19193d01b4f5", "version_major": 2, "version_minor": 0 }, @@ -565,12 +565,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b099c274ca68417dbdd2ec6b5f67128b", + "model_id": "3a717d6c9b3d483ba469b19892dd0080", "version_major": 2, "version_minor": 0 }, "text/plain": [ - 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stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2763,11 +2763,11 @@ " \n", " \n", + "\" clip-path=\"url(#p3d4e041663)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2809,11 +2809,11 @@ " \n", " \n", + "\" clip-path=\"url(#p3d4e041663)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2845,11 +2845,11 @@ " \n", " \n", + "\" clip-path=\"url(#p3d4e041663)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2865,11 +2865,11 @@ " \n", " \n", + "\" clip-path=\"url(#p3d4e041663)\" style=\"fill: none; stroke: #b0b0b0; stroke-width: 0.8; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2972,16 +2972,16 @@ " \n", " \n", + "\" clip-path=\"url(#p3d4e041663)\" style=\"fill: none; 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