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bad indentation fixed
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Castlefox committed Jan 6, 2025
1 parent 57a029b commit 640c42a
Showing 1 changed file with 52 additions and 44 deletions.
96 changes: 52 additions & 44 deletions examples/PyMPDATA_examples/Jaruga_et_al_2015/fig19.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -128,47 +128,47 @@
"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",
"\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",
Expand Down Expand Up @@ -339,9 +339,9 @@
"\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",
Expand Down Expand Up @@ -374,7 +374,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8b84b7919b314b9383b748fd32ec0d70",
"model_id": "199b35ae3a8f4e8e81d0e751aa39f900",
"version_major": 2,
"version_minor": 0
},
Expand All @@ -389,8 +389,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: total: 1min 36s\n",
"Wall time: 1min 46s\n"
"CPU times: total: 1min 33s\n",
"Wall time: 1min 42s\n"
]
}
],
Expand Down Expand Up @@ -470,7 +470,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0a3762134cfa452cacece883690eabc7",
"model_id": "cb9cbca6a63b46d89e03ae9f2ae40154",
"version_major": 2,
"version_minor": 0
},
Expand All @@ -496,12 +496,12 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "56da990921454749b499d3d9f2e94776",
"model_id": "12be6dee3b0c42458b6269d92cf01483",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HTML(value=\"<a href='.\\\\tmpgsxuio24.gif' target='_blank'>.\\\\tmpgsxuio24.gif</a><br>\")"
"HTML(value=\"<a href='.\\\\tmp17ey2xuu.gif' target='_blank'>.\\\\tmp17ey2xuu.gif</a><br>\")"
]
},
"metadata": {},
Expand Down Expand Up @@ -533,6 +533,14 @@
"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"
]
}
],
"metadata": {
Expand Down

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