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Automatize Tutorials html, py files creation (#496)

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* workflow to export tutorials

---------
This commit is contained in:
Dario Coscia
2025-03-15 11:01:19 +01:00
committed by Nicola Demo
parent aea24d0bee
commit 0146155c9b
51 changed files with 140529 additions and 440 deletions
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126
tutorials/tutorial10/tutorial.ipynb vendored
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@@ -25,16 +25,17 @@
"source": [
"## routine needed to run the notebook on Google Colab\n",
"try:\n",
" import google.colab\n",
" IN_COLAB = True\n",
" import google.colab\n",
"\n",
" IN_COLAB = True\n",
"except:\n",
" IN_COLAB = False\n",
" IN_COLAB = False\n",
"if IN_COLAB:\n",
" !pip install \"pina-mathlab\"\n",
" # get the data\n",
" !mkdir \"data\"\n",
" !wget \"https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial10/data/Data_KS.mat\" -O \"data/Data_KS.mat\"\n",
" !wget \"https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial10/data/Data_KS2.mat\" -O \"data/Data_KS2.mat\"\n",
" !pip install \"pina-mathlab\"\n",
" # get the data\n",
" !mkdir \"data\"\n",
" !wget \"https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial10/data/Data_KS.mat\" -O \"data/Data_KS.mat\"\n",
" !wget \"https://github.com/mathLab/PINA/raw/refs/heads/master/tutorials/tutorial10/data/Data_KS2.mat\" -O \"data/Data_KS2.mat\"\n",
"\n",
"import torch\n",
"import matplotlib.pyplot as plt\n",
@@ -46,7 +47,7 @@
"from pina.solver import SupervisedSolver\n",
"from pina.problem.zoo import SupervisedProblem\n",
"\n",
"warnings.filterwarnings('ignore')"
"warnings.filterwarnings(\"ignore\")"
]
},
{
@@ -106,17 +107,24 @@
],
"source": [
"# load data\n",
"data=io.loadmat(\"data/Data_KS.mat\")\n",
"data = io.loadmat(\"data/Data_KS.mat\")\n",
"\n",
"# converting to label tensor\n",
"initial_cond_train = LabelTensor(torch.tensor(data['initial_cond_train'], dtype=torch.float), ['t','x','u0'])\n",
"initial_cond_test = LabelTensor(torch.tensor(data['initial_cond_test'], dtype=torch.float), ['t','x','u0'])\n",
"sol_train = LabelTensor(torch.tensor(data['sol_train'], dtype=torch.float), ['u'])\n",
"sol_test = LabelTensor(torch.tensor(data['sol_test'], dtype=torch.float), ['u'])\n",
"initial_cond_train = LabelTensor(\n",
" torch.tensor(data[\"initial_cond_train\"], dtype=torch.float),\n",
" [\"t\", \"x\", \"u0\"],\n",
")\n",
"initial_cond_test = LabelTensor(\n",
" torch.tensor(data[\"initial_cond_test\"], dtype=torch.float), [\"t\", \"x\", \"u0\"]\n",
")\n",
"sol_train = LabelTensor(\n",
" torch.tensor(data[\"sol_train\"], dtype=torch.float), [\"u\"]\n",
")\n",
"sol_test = LabelTensor(torch.tensor(data[\"sol_test\"], dtype=torch.float), [\"u\"])\n",
"\n",
"print('Data Loaded')\n",
"print(f' shape initial condition: {initial_cond_train.shape}')\n",
"print(f' shape solution: {sol_train.shape}')"
"print(\"Data Loaded\")\n",
"print(f\" shape initial condition: {initial_cond_train.shape}\")\n",
"print(f\" shape solution: {sol_train.shape}\")"
]
},
{
@@ -151,35 +159,58 @@
"# helper function\n",
"def plot_trajectory(coords, real, no_sol=None):\n",
" # find the x-t shapes\n",
" dim_x = len(torch.unique(coords.extract('x')))\n",
" dim_t = len(torch.unique(coords.extract('t')))\n",
" dim_x = len(torch.unique(coords.extract(\"x\")))\n",
" dim_t = len(torch.unique(coords.extract(\"t\")))\n",
" # if we don't have the Neural Operator solution we simply plot the real one\n",
" if no_sol is None:\n",
" fig, axs = plt.subplots(1, 1, figsize=(15, 5), sharex=True, sharey=True)\n",
" c = axs.imshow(real.reshape(dim_t, dim_x).T.detach(),extent=[0, 50, 0, 64], cmap='PuOr_r', aspect='auto')\n",
" axs.set_title('Real solution')\n",
" c = axs.imshow(\n",
" real.reshape(dim_t, dim_x).T.detach(),\n",
" extent=[0, 50, 0, 64],\n",
" cmap=\"PuOr_r\",\n",
" aspect=\"auto\",\n",
" )\n",
" axs.set_title(\"Real solution\")\n",
" fig.colorbar(c, ax=axs)\n",
" axs.set_xlabel('t')\n",
" axs.set_ylabel('x')\n",
" axs.set_xlabel(\"t\")\n",
" axs.set_ylabel(\"x\")\n",
" # otherwise we plot the real one, the Neural Operator one, and their difference\n",
" else:\n",
" fig, axs = plt.subplots(1, 3, figsize=(15, 5), sharex=True, sharey=True)\n",
" axs[0].imshow(real.reshape(dim_t, dim_x).T.detach(),extent=[0, 50, 0, 64], cmap='PuOr_r', aspect='auto')\n",
" axs[0].set_title('Real solution')\n",
" axs[1].imshow(no_sol.reshape(dim_t, dim_x).T.detach(),extent=[0, 50, 0, 64], cmap='PuOr_r', aspect='auto')\n",
" axs[1].set_title('NO solution')\n",
" c = axs[2].imshow((real - no_sol).abs().reshape(dim_t, dim_x).T.detach(),extent=[0, 50, 0, 64], cmap='PuOr_r', aspect='auto')\n",
" axs[2].set_title('Absolute difference')\n",
" axs[0].imshow(\n",
" real.reshape(dim_t, dim_x).T.detach(),\n",
" extent=[0, 50, 0, 64],\n",
" cmap=\"PuOr_r\",\n",
" aspect=\"auto\",\n",
" )\n",
" axs[0].set_title(\"Real solution\")\n",
" axs[1].imshow(\n",
" no_sol.reshape(dim_t, dim_x).T.detach(),\n",
" extent=[0, 50, 0, 64],\n",
" cmap=\"PuOr_r\",\n",
" aspect=\"auto\",\n",
" )\n",
" axs[1].set_title(\"NO solution\")\n",
" c = axs[2].imshow(\n",
" (real - no_sol).abs().reshape(dim_t, dim_x).T.detach(),\n",
" extent=[0, 50, 0, 64],\n",
" cmap=\"PuOr_r\",\n",
" aspect=\"auto\",\n",
" )\n",
" axs[2].set_title(\"Absolute difference\")\n",
" fig.colorbar(c, ax=axs.ravel().tolist())\n",
" for ax in axs:\n",
" ax.set_xlabel('t')\n",
" ax.set_ylabel('x')\n",
" ax.set_xlabel(\"t\")\n",
" ax.set_ylabel(\"x\")\n",
" plt.show()\n",
"\n",
"\n",
"# a sample trajectory (we use the sample 5, feel free to change)\n",
"sample_number = 20\n",
"plot_trajectory(coords=initial_cond_train[sample_number].extract(['x', 't']),\n",
" real=sol_train[sample_number].extract('u'))\n"
"plot_trajectory(\n",
" coords=initial_cond_train[sample_number].extract([\"x\", \"t\"]),\n",
" real=sol_train[sample_number].extract(\"u\"),\n",
")"
]
},
{
@@ -300,7 +331,12 @@
],
"source": [
"# initialize problem\n",
"problem = SupervisedProblem(initial_cond_train, sol_train, input_variables=initial_cond_train.labels, output_variables=sol_train.labels)\n",
"problem = SupervisedProblem(\n",
" initial_cond_train,\n",
" sol_train,\n",
" input_variables=initial_cond_train.labels,\n",
" output_variables=sol_train.labels,\n",
")\n",
"# initialize solver\n",
"solver = SupervisedSolver(problem=problem, model=model)\n",
"# train, only CPU and avoid model summary at beginning of training (optional)\n",
@@ -343,9 +379,11 @@
"source": [
"sample_number = 2\n",
"no_sol = solver(initial_cond_test)\n",
"plot_trajectory(coords=initial_cond_test[sample_number].extract(['x', 't']),\n",
" real=sol_test[sample_number].extract('u'),\n",
" no_sol=no_sol[5])"
"plot_trajectory(\n",
" coords=initial_cond_test[sample_number].extract([\"x\", \"t\"]),\n",
" real=sol_test[sample_number].extract(\"u\"),\n",
" no_sol=no_sol[5],\n",
")"
]
},
{
@@ -373,15 +411,19 @@
"source": [
"from pina.loss import PowerLoss\n",
"\n",
"error_metric = PowerLoss(p=2) # we use the MSE loss\n",
"error_metric = PowerLoss(p=2) # we use the MSE loss\n",
"\n",
"with torch.no_grad():\n",
" no_sol_train = solver(initial_cond_train)\n",
" err_train = error_metric(sol_train.extract('u'), no_sol_train).mean() # we average the error over trajectories\n",
" err_train = error_metric(\n",
" sol_train.extract(\"u\"), no_sol_train\n",
" ).mean() # we average the error over trajectories\n",
" no_sol_test = solver(initial_cond_test)\n",
" err_test = error_metric(sol_test.extract('u'),no_sol_test).mean() # we average the error over trajectories\n",
" print(f'Training error: {float(err_train):.3f}')\n",
" print(f'Testing error: {float(err_test):.3f}')"
" err_test = error_metric(\n",
" sol_test.extract(\"u\"), no_sol_test\n",
" ).mean() # we average the error over trajectories\n",
" print(f\"Training error: {float(err_train):.3f}\")\n",
" print(f\"Testing error: {float(err_test):.3f}\")"
]
},
{