Merge pull request PyDMD#475 from sichinaga/tutorial1-updates

Updated tutorial 1

Author: mtezzele

pydmd/bopdmd.py

@@ -748,7 +748,7 @@ def compute_operator(self, H, t):
                 "Consider re-adjusting your variable projection parameters "
                 "with the varpro_opts_dict and consider setting verbose=True."
             )
-            print(msg)
+            warnings.warn(msg)
 
         # If num_trials isn't a positive int, perform standard optimized dmd.
         if self._num_trials <= 0 or not isinstance(self._num_trials, int):
@@ -948,7 +948,7 @@ def __init__(
         init_alpha=None,
         proj_basis=None,
         num_trials=0,
-        trial_size=0.2,
+        trial_size=0.6,
         eig_sort="auto",
         eig_constraints=None,
         bag_warning=100,

pydmd/plotter.py

@@ -535,6 +535,7 @@ def plot_snapshots_2D(
 def plot_summary(
     dmd,
     *,
+    d=1,
     continuous=False,
     snapshots_shape=None,
     index_modes=None,
@@ -567,6 +568,10 @@ def plot_summary(
 
     :param dmd: DMD instance.
     :type dmd: pydmd.DMDBase
+    :param d: Number of delays applied to the data passed to the DMD instance.
+        If `d` is greater than 1, then each plotted mode will be the average
+        mode taken across all `d` delays.
+    :type d: int
     :param continuous: Whether or not the eigenvalues of the given DMD instance
         are continuous-time. If `False`, the eigenvalues are assumed to be the
         discrete-time eigenvalues. If `True`, the eigenvalues are taken to be
@@ -715,6 +720,15 @@ def plot_summary(
             disc_eigs = lead_eigs
             cont_eigs = np.log(lead_eigs) / dt
 
+    # Get mode averages across delays if time-delay was used.
+    if d > 1:
+        lead_modes = np.average(
+            lead_modes.reshape(
+                d, lead_modes.shape[0] // d, lead_modes.shape[1]
+            ),
+            axis=0,
+        )
+
     # Compute the singular values of the data matrix.
     if isinstance(dmd, HankelDMD):
         # Use time-delay data matrix to compute singular values.

pydmd/preprocessing/pre_post_processing.py

@@ -79,6 +79,16 @@ def __getattribute__(self, name):
             return object.__getattribute__(self._pre_post_processed_dmd, name)
         return None
 
+    @property
+    def pre_post_processed_dmd(self):
+        """
+        Return the pre/post-processed DMD instance.
+
+        :return: decorated DMD instance.
+        :rtype: pydmd.DMDBase
+        """
+        return self._pre_post_processed_dmd
+
     @property
     def modes_activation_bitmask(self):
         return self._pre_post_processed_dmd.modes_activation_bitmask

tests/test_bopdmd.py

@@ -361,4 +361,4 @@ def relative_error(x, x_true):
         bopdmd_error = relative_error(bopdmd.A, expected_A)
         bop_success += bopdmd_error < optdmd_error
 
-    assert bop_success >= 0.75 * test_trials
+    assert bop_success >= 0.7 * test_trials

tutorials/README.md

@@ -6,7 +6,7 @@ An additional PDF tutorial ([DSWeb contest winner](https://dsweb.siam.org/The-Ma
 
 | Name                                                                                                                                                                                      | Description                                                        | PyDMD used classes    |
 |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------|-----------------------|
-| Tutorial1 [[.ipynb](tutorial1/tutorial-1-dmd.ipynb), [.py](tutorial1/tutorial-1-dmd.py), [.html](http://pydmd.github.io/PyDMD/tutorial1dmd.html)]                        | basic usage of standard DMD                                        | `pydmd.DMD`           |
+| Tutorial1 [[.ipynb](tutorial1/tutorial-1-dmd.ipynb), [.py](tutorial1/tutorial-1-dmd.py), [.html](http://pydmd.github.io/PyDMD/tutorial1dmd.html)]                        | Analyzing real, simple data sets with PyDMD                                        | `pydmd.DMD`, `pydmd.BOPDMD`           |
 | Tutorial2 [[.ipynb](tutorial2/tutorial-2-adv-dmd.ipynb), [.py](tutorial2/tutorial-2-adv-dmd.py), [.html](http://pydmd.github.io/PyDMD/tutorial2advdmd.html)]             | advanced features of standard DMD                                  | `pydmd.DMD`           |
 | Tutorial3 [[.ipynb](tutorial3/tutorial-3-mrdmd.ipynb), [.py](tutorial3/tutorial-3-mrdmd.py), [.html](http://pydmd.github.io/PyDMD/tutorial3mrdmd.html)]                  | multi-resolution DMD for transient phenomena                       | `pydmd.MrDMD`         |
 | Tutorial4 [[.ipynb](tutorial4/tutorial-4-cdmd.ipynb), [.py](tutorial4/tutorial-4-cdmd.py), [.html](http://pydmd.github.io/PyDMD/tutorial4cdmd.html)]                     | compress DMD for computation speedup                               | `pydmd.CDMD`          |

tutorials/tutorial1/tutorial-1-dmd.ipynb

@@ -291,18 +291,20 @@
     }
    ],
    "source": [
-    "t1 = time.time()\n",
-    "edmd = EDMD(\n",
-    "    svd_rank=18,\n",
-    "    kernel_metric=\"poly\",\n",
-    "    kernel_params={\"degree\": 3, \"coef0\": 1.0, \"gamma\": 1.0},\n",
-    ")\n",
-    "edmd.fit(X)\n",
-    "t2 = time.time()\n",
-    "print(\"EDMD Fitting Time: {}\".format(t2 - t1))\n",
-    "\n",
-    "edmd_recon = edmd.reconstructed_data.real\n",
-    "plot_lorenz_reconstruction(X, edmd_recon, \"EDMD reconstruction\", \"tab:orange\")"
+    "# Note: This cell may take a while to run (approximately 10 minutes).\n",
+    "\n",
+    "# t1 = time.time()\n",
+    "# edmd = EDMD(\n",
+    "#     svd_rank=18,\n",
+    "#     kernel_metric=\"poly\",\n",
+    "#     kernel_params={\"degree\": 3, \"coef0\": 1.0, \"gamma\": 1.0},\n",
+    "# )\n",
+    "# edmd.fit(X)\n",
+    "# t2 = time.time()\n",
+    "# print(\"EDMD Fitting Time: {}\".format(t2 - t1))\n",
+    "\n",
+    "# edmd_recon = edmd.reconstructed_data.real\n",
+    "# plot_lorenz_reconstruction(X, edmd_recon, \"EDMD reconstruction\", \"tab:orange\")"
    ]
   },
   {