Update tutorials 1 through 12 to current version 0.2

tutorials/tutorial1/tutorial.py

@@ -53,7 +53,7 @@
 # What if our equation is also time-dependent? In this case, our `class` will inherit from both `SpatialProblem` and `TimeDependentProblem`:
 # 
 
-# In[ ]:
+# In[1]:
 
 
 ## routine needed to run the notebook on Google Colab
@@ -89,11 +89,11 @@ 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**:
 
-# In[2]:
+# In[ ]:
 
 
 from pina.problem import SpatialProblem
-from pina.operators import grad
+from pina.operator import grad
 from pina import Condition
 from pina.domain import CartesianDomain
 from pina.equation import Equation, FixedValue
@@ -106,6 +106,11 @@ class SimpleODE(SpatialProblem):
     output_variables = ['u']
     spatial_domain = CartesianDomain({'x': [0, 1]})
 
+    domains ={
+        'x0': CartesianDomain({'x': 0.}),
+        'D': CartesianDomain({'x': [0, 1]})
+    }
+
     # defining the ode equation
     def ode_equation(input_, output_):
 
@@ -120,13 +125,10 @@ class SimpleODE(SpatialProblem):
 
     # conditions to hold
     conditions = {
-        'x0': Condition(location=CartesianDomain({'x': 0.}), equation=FixedValue(1)),             # We fix initial condition to value 1
-        'D': Condition(location=CartesianDomain({'x': [0, 1]}), equation=Equation(ode_equation)), # We wrap the python equation using Equation
+        'bound_cond': Condition(domain='x0', equation=FixedValue(1.)),
+        'phys_cond': Condition(domain='D', equation=Equation(ode_equation))
     }
 
-    # sampled points (see below)
-    input_pts = None
-
     # defining the true solution
     def truth_solution(self, pts):
         return torch.exp(pts.extract(['x']))
@@ -149,14 +151,14 @@ problem = SimpleODE()
 
 
 # sampling 20 points in [0, 1] through discretization in all locations
-problem.discretise_domain(n=20, mode='grid', variables=['x'], locations='all')
+problem.discretise_domain(n=20, mode='grid', domains='all')
 
 # sampling 20 points in (0, 1) through latin hypercube sampling in D, and 1 point in x0
-problem.discretise_domain(n=20, mode='latin', variables=['x'], locations=['D'])
-problem.discretise_domain(n=1, mode='random', variables=['x'], locations=['x0'])
+problem.discretise_domain(n=20, mode='latin', domains=['D'])
+problem.discretise_domain(n=1, mode='random', domains=['x0'])
 
 # sampling 20 points in (0, 1) randomly
-problem.discretise_domain(n=20, mode='random', variables=['x'])
+problem.discretise_domain(n=20, mode='random')
 
 
 # We are going to use latin hypercube points for sampling. We need to sample in all the conditions domains. In our case we sample in `D` and `x0`.
@@ -165,8 +167,8 @@ problem.discretise_domain(n=20, mode='random', variables=['x'])
 
 
 # sampling for training
-problem.discretise_domain(1, 'random', locations=['x0'])
-problem.discretise_domain(20, 'lh', locations=['D'])
+problem.discretise_domain(20, 'random', domains=['x0']) # TODO check
+problem.discretise_domain(20, 'lh', domains=['D'])
 
 
 # The points are saved in a python `dict`, and can be accessed by calling the attribute `input_pts` of the problem 
@@ -174,26 +176,26 @@ problem.discretise_domain(20, 'lh', locations=['D'])
 # In[5]:
 
 
-print('Input points:', problem.input_pts)
-print('Input points labels:', problem.input_pts['D'].labels)
+print('Input points:', problem.discretised_domains)
+print('Input points labels:', problem.discretised_domains['D'].labels)
 
 
 # To visualize the sampled points we can use the `.plot_samples` method of the `Plotter` class
 
-# In[5]:
+# In[6]:
 
 
-from pina import Plotter
+#from pina import Plotter
 
-pl = Plotter()
-pl.plot_samples(problem=problem)
+#pl = Plotter()
+#pl.plot_samples(problem=problem)
 
 
 # ## Perform a small training
 
 # Once we have defined the problem and generated the data we can start the modelling. Here we will choose a `FeedForward` neural network available in `pina.model`, and we will train using the `PINN` solver from `pina.solvers`. We highlight that this training is fairly simple, for more advanced stuff consider the tutorials in the ***Physics Informed Neural Networks*** section of ***Tutorials***. For training we use the `Trainer` class from `pina.trainer`. Here we show a very short training and some method for plotting the results. Notice that by default all relevant metrics (e.g. MSE error during training) are going to be tracked using a `lightining` logger, by default `CSVLogger`. If you want to track the metric by yourself without a logger, use `pina.callbacks.MetricTracker`.
 
-# In[ ]:
+# In[7]:
 
 
 from pina import Trainer
@@ -222,7 +224,7 @@ trainer.train()
 
 # After the training we can inspect trainer logged metrics (by default **PINA** logs mean square error residual loss). The logged metrics can be accessed online using one of the `Lightinig` loggers. The final loss can be accessed by `trainer.logged_metrics`
 
-# In[7]:
+# In[8]:
 
 
 # inspecting final loss
@@ -231,19 +233,19 @@ trainer.logged_metrics
 
 # By using the `Plotter` class from **PINA** we can also do some quatitative plots of the solution. 
 
-# In[8]:
+# In[9]:
 
 
 # plotting the solution
-pl.plot(solver=pinn)
+#pl.plot(solver=pinn)
 
 
 # The solution is overlapped with the actual one, and they are barely indistinguishable. We can also plot easily the loss:
 
-# In[9]:
+# In[10]:
 
 
-pl.plot_loss(trainer=trainer, label = 'mean_loss', logy=True)
+#pl.plot_loss(trainer=trainer, label = 'mean_loss', logy=True)
 
 
 # As we can see the loss has not reached a minimum, suggesting that we could train for longer