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Update tutorials 1 through 12 to current version 0.2

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This commit is contained in:
Matteo Bertocchi
2025-02-26 16:21:12 +01:00
committed by Nicola Demo
parent 10ea59e15a
commit 14a6008437
4 changed files with 70 additions and 62 deletions
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10
tutorials/tutorial1/tutorial.ipynb vendored
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@@ -137,7 +137,15 @@
"execution_count": 2, "execution_count": 2,
"id": "f2608e2e", "id": "f2608e2e",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/matte_b/PINA/pina/operators.py: DeprecationWarning: 'pina.operators' is deprecated and will be removed in future versions. Please use 'pina.operator' instead.\n"
]
}
],
"source": [ "source": [
"from pina.problem import SpatialProblem\n", "from pina.problem import SpatialProblem\n",
"from pina.operator import grad\n", "from pina.operator import grad\n",

2
tutorials/tutorial1/tutorial.py vendored
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@@ -89,7 +89,7 @@ class TimeSpaceODE(SpatialProblem, TimeDependentProblem):
# #
# Once the `Problem` class is initialized, we need to represent the differential equation in **PINA**. In order to do this, we need to load the **PINA** operators from `pina.operators` module. Again, we'll consider Equation (1) and represent it in **PINA**: # Once the `Problem` class is initialized, we need to represent the differential equation in **PINA**. In order to do this, we need to load the **PINA** operators from `pina.operators` module. Again, we'll consider Equation (1) and represent it in **PINA**:
# In[2]: # In[ ]:
from pina.problem import SpatialProblem from pina.problem import SpatialProblem

60
tutorials/tutorial4/tutorial.ipynb vendored
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@@ -869,16 +869,16 @@
"output = net(input_data).detach()\n", "output = net(input_data).detach()\n",
"\n", "\n",
"# visualize data\n", "# visualize data\n",
"fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n", "#fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n",
"pic1 = axes[0].scatter(grid[:, 0], grid[:, 1], c=input_data[0, 0, :, -1])\n", "#pic1 = axes[0].scatter(grid[:, 0], grid[:, 1], c=input_data[0, 0, :, -1])\n",
"axes[0].set_title(\"Real\")\n", "#axes[0].set_title(\"Real\")\n",
"fig.colorbar(pic1)\n", "#fig.colorbar(pic1)\n",
"plt.subplot(1, 2, 2)\n", "#plt.subplot(1, 2, 2)\n",
"pic2 = axes[1].scatter(grid[:, 0], grid[:, 1], c=output[0, 0, :, -1])\n", "#pic2 = axes[1].scatter(grid[:, 0], grid[:, 1], c=output[0, 0, :, -1])\n",
"axes[1].set_title(\"Autoencoder\")\n", "#axes[1].set_title(\"Autoencoder\")\n",
"fig.colorbar(pic2)\n", "#fig.colorbar(pic2)\n",
"plt.tight_layout()\n", "#plt.tight_layout()\n",
"plt.show()\n" "#plt.show()\n"
] ]
}, },
{ {
@@ -963,16 +963,16 @@
"output = net.decoder(latent, input_data2).detach()\n", "output = net.decoder(latent, input_data2).detach()\n",
"\n", "\n",
"# show the picture\n", "# show the picture\n",
"fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n", "#fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n",
"pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1])\n", "#pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1])\n",
"axes[0].set_title(\"Real\")\n", "#axes[0].set_title(\"Real\")\n",
"fig.colorbar(pic1)\n", "#fig.colorbar(pic1)\n",
"plt.subplot(1, 2, 2)\n", "#plt.subplot(1, 2, 2)\n",
"pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1])\n", "#pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1])\n",
"axes[1].set_title(\"Up-sampling\")\n", "# axes[1].set_title(\"Up-sampling\")\n",
"fig.colorbar(pic2)\n", "#fig.colorbar(pic2)\n",
"plt.tight_layout()\n", "#plt.tight_layout()\n",
"plt.show()\n" "#plt.show()\n"
] ]
}, },
{ {
@@ -1051,16 +1051,16 @@
"output = net.decoder(latent, input_data2).detach()\n", "output = net.decoder(latent, input_data2).detach()\n",
"\n", "\n",
"# show the picture\n", "# show the picture\n",
"fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n", "#fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))\n",
"pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1])\n", "#pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1])\n",
"axes[0].set_title(\"Real\")\n", "#axes[0].set_title(\"Real\")\n",
"fig.colorbar(pic1)\n", "#fig.colorbar(pic1)\n",
"plt.subplot(1, 2, 2)\n", "#plt.subplot(1, 2, 2)\n",
"pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1])\n", "#pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1])\n",
"axes[1].set_title(\"Autoencoder not re-trained\")\n", "#axes[1].set_title(\"Autoencoder not re-trained\")\n",
"fig.colorbar(pic2)\n", "#fig.colorbar(pic2)\n",
"plt.tight_layout()\n", "#plt.tight_layout()\n",
"plt.show()\n", "#plt.show()\n",
"\n", "\n",
"# calculate l2 error\n", "# calculate l2 error\n",
"print(f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}')" "print(f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}')"

60
tutorials/tutorial4/tutorial.py vendored
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@@ -530,16 +530,16 @@ net.eval()
output = net(input_data).detach() output = net(input_data).detach()
# visualize data # visualize data
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3)) #fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))
pic1 = axes[0].scatter(grid[:, 0], grid[:, 1], c=input_data[0, 0, :, -1]) #pic1 = axes[0].scatter(grid[:, 0], grid[:, 1], c=input_data[0, 0, :, -1])
axes[0].set_title("Real") #axes[0].set_title("Real")
fig.colorbar(pic1) #fig.colorbar(pic1)
plt.subplot(1, 2, 2) #plt.subplot(1, 2, 2)
pic2 = axes[1].scatter(grid[:, 0], grid[:, 1], c=output[0, 0, :, -1]) #pic2 = axes[1].scatter(grid[:, 0], grid[:, 1], c=output[0, 0, :, -1])
axes[1].set_title("Autoencoder") #axes[1].set_title("Autoencoder")
fig.colorbar(pic2) #fig.colorbar(pic2)
plt.tight_layout() #plt.tight_layout()
plt.show() #plt.show()
# As we can see, the two solutions are really similar! We can compute the $l_2$ error quite easily as well: # As we can see, the two solutions are really similar! We can compute the $l_2$ error quite easily as well:
@@ -579,16 +579,16 @@ latent = net.encoder(input_data)
output = net.decoder(latent, input_data2).detach() output = net.decoder(latent, input_data2).detach()
# show the picture # show the picture
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3)) #fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))
pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1]) #pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1])
axes[0].set_title("Real") #axes[0].set_title("Real")
fig.colorbar(pic1) #fig.colorbar(pic1)
plt.subplot(1, 2, 2) #plt.subplot(1, 2, 2)
pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1]) #pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1])
axes[1].set_title("Up-sampling") # axes[1].set_title("Up-sampling")
fig.colorbar(pic2) #fig.colorbar(pic2)
plt.tight_layout() #plt.tight_layout()
plt.show() #plt.show()
# As we can see we have a very good approximation of the original function, even thought some noise is present. Let's calculate the error now: # As we can see we have a very good approximation of the original function, even thought some noise is present. Let's calculate the error now:
@@ -621,16 +621,16 @@ latent = net.encoder(input_data2)
output = net.decoder(latent, input_data2).detach() output = net.decoder(latent, input_data2).detach()
# show the picture # show the picture
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3)) #fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 3))
pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1]) #pic1 = axes[0].scatter(grid2[:, 0], grid2[:, 1], c=input_data2[0, 0, :, -1])
axes[0].set_title("Real") #axes[0].set_title("Real")
fig.colorbar(pic1) #fig.colorbar(pic1)
plt.subplot(1, 2, 2) #plt.subplot(1, 2, 2)
pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1]) #pic2 = axes[1].scatter(grid2[:, 0], grid2[:, 1], c=output[0, 0, :, -1])
axes[1].set_title("Autoencoder not re-trained") #axes[1].set_title("Autoencoder not re-trained")
fig.colorbar(pic2) #fig.colorbar(pic2)
plt.tight_layout() #plt.tight_layout()
plt.show() #plt.show()
# calculate l2 error # calculate l2 error
print(f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}') print(f'l2 error: {l2_error(input_data2[0, 0, :, -1], output[0, 0, :, -1]):.2%}')