Merge branch 'mrcosts-clean' of https://github.com/klapo/PyDMD into mrcosts-clean

Author: klapo

LICENSE.rst

@@ -1,6 +1,6 @@
 The MIT License (MIT)
 
-Copyright (c) 2017-2023 PyDMD contributors
+Copyright (c) 2017-2024 PyDMD contributors
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -7,20 +7,20 @@
     <a href="http://pydmd.github.io/PyDMD" target="_blank">
         <img alt="Docs" src="https://img.shields.io/badge/PyDMD-docs-blue?style=for-the-badge"/>
     </a>
-    <a href="https://doi.org/10.21105/joss.00530" target="_blank">
-        <img alt="JOSS DOI" src="https://img.shields.io/badge/JOSS-10.21105/joss.00530-blue?style=for-the-badge">
+    <a href="https://pypi.org/project/pydmd/" target="_blank">
+        <img alt="PyPI version" src="https://img.shields.io/pypi/v/pydmd?style=for-the-badge">
+    </a>
+    <a href="#dependencies-and-installation" target="_blank">
+        <img alt="Python versions" src="https://img.shields.io/pypi/pyversions/PyDMD?style=for-the-badge">
     </a>
     <a href="https://github.com/PyDMD/PyDMD/blob/master/LICENSE" target="_blank">
         <img alt="Software License" src="https://img.shields.io/badge/license-MIT-brightgreen.svg?style=for-the-badge">
     </a>
-    <a href="https://pypi.org/project/pydmd/"  target="_blank">
-        <img alt="PyPI version" src="https://img.shields.io/pypi/v/pydmd?style=for-the-badge">
-    </a>
     <br>
     <a href="https://github.com/PyDMD/PyDMD/actions/workflows/deploy_after_push.yml" target="_blank">
         <img alt="CI Status" src="https://img.shields.io/github/actions/workflow/status/PyDMD/PyDMD/deploy_after_push.yml?style=for-the-badge">
     </a>
-    <a href="https://www.codacy.com/gh/PyDMD/PyDMD/dashboard?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=PyDMD/PyDMD&amp;utm_campaign=Badge_Coverage" target="_blank">
+    <a href="https://app.codacy.com/gh/PyDMD/PyDMD/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_coverage" target="_blank">
       <img src="https://img.shields.io/codacy/coverage/3d8b278a835e402c86cac9625bb4912f/master?style=for-the-badge"/>
     </a>
     <a href="https://app.codacy.com/gh/PyDMD/PyDMD/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade" target="_blank">
@@ -31,12 +31,17 @@
     </a>
     <br>
     <a href="#developers-and-contributors">
-      <img alt="All Contributors" src="https://img.shields.io/badge/all_contributors-25-orange.svg?style=for-the-badge"/>
+      <img alt="All Contributors" src="https://img.shields.io/github/contributors-anon/PyDMD/PyDMD?style=for-the-badge"/>
     </a>
     <a href="#stargazers">
       <img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/PyDMD/PyDMD?style=for-the-badge">
     </a>
-
+    <a href="#dependencies-and-installation" target="_blank">
+        <img alt="PyPI downloads per month" src="https://img.shields.io/pypi/dm/PyDMD?label=pypi&logo=python&logoColor=yellow&style=for-the-badge">
+    </a>
+    <a href="https://doi.org/10.21105/joss.00530" target="_blank">
+        <img alt="JOSS DOI" src="https://img.shields.io/badge/JOSS-10.21105/joss.00530-blue?style=for-the-badge">
+    </a>
 </p>
 
 ## Table of contents
@@ -48,6 +53,7 @@
 * [References](#references)
 * [Developers and contributors](#developers-and-contributors)
 * [Funding](#funding)
+* [Affiliations](#affiliations)
 
 ## Description
 
@@ -202,3 +208,10 @@ Beyond this, PyDMD has also been supported by some dedicated projects that have
 <p align="center">
     <img src="readme/logos_funding.png" width="800" />
 </p>
+
+## Affiliations
+<p align="center">
+    <a href="https://numfocus.org/sponsored-projects/affiliated-projects">
+        <img src="readme/numfocus-affiliated-project.png" width="300" />
+    </a>
+</p>

pydmd/mrdmd.py

@@ -386,6 +386,26 @@ def partial_eigs(self, level, node=None):
             [self.dmd_tree[level, leaf].eigs for leaf in leaves]
         )
 
+    def partial_amplitudes(self, level, node=None):
+        """
+        Return the amplitudes of the specific `level` and of the specific
+        `node`; if `node` is not specified, the method returns the amplitudes
+        of the given `level` (all the nodes).
+
+        :param int level: the index of the level from where the amplitudes are
+            extracted.
+        :param int node: the index of the node from where the amplitudes are
+            extracted; if None, the amplitudes are extracted from all the
+            nodes of the given level. Default is None.
+
+        :return: the selected eigs
+        :rtype: numpy.ndarray
+        """
+        leaves = self.dmd_tree.index_leaves(level) if node is None else [node]
+        return np.concatenate(
+            [self.dmd_tree[level, leaf].amplitudes for leaf in leaves]
+        )
+
     def partial_reconstructed_data(self, level, node=None):
         """
         Return the reconstructed data computed using the modes and the time

pydmd/rdmd.py

@@ -7,7 +7,6 @@
 Systems, 18, 2019.
 """
 
-
 from .cdmd import CDMD
 from .utils import compute_rqb
 

pydmd/utils.py

@@ -277,19 +277,20 @@ def pseudo_hankel_matrix(X: np.ndarray, d: int) -> np.ndarray:
     :Example:
 
         >>> a = np.array([[1, 2, 3, 4, 5]])
-        >>> _hankel_pre_processing(a, d=2)
+        >>> pseudo_hankel_matrix(a, d=2)
         array([[1, 2, 3, 4],
                [2, 3, 4, 5]])
-        >>> _hankel_pre_processing(a, d=4)
+        >>> pseudo_hankel_matrix(a, d=4)
         array([[1, 2],
                [2, 3],
                [3, 4],
                [4, 5]])
 
         >>> a = np.array([1,2,3,4,5,6]).reshape(2,3)
-        array([[1, 2, 3],
-               [4, 5, 6]])
-        >>> _hankel_pre_processing(a, d=2)
+        >>> print(a)
+        [[1 2 3]
+         [4 5 6]]
+        >>> pseudo_hankel_matrix(a, d=2)
         array([[1, 2],
                [4, 5],
                [2, 3],

readme/numfocus-affiliated-project.png

@@ -233,6 +233,28 @@ def test_partial_eigs2():
     assert peigs.shape == (rank,)
 
 
+def test_partial_eigs1():
+    max_level = 5
+    level = 2
+    rank = 2
+    dmd = DMD(svd_rank=rank)
+    mrdmd = MrDMD(dmd, max_level=max_level, max_cycles=2)
+    mrdmd.fit(X=sample_data)
+    pamps = mrdmd.partial_amplitudes(level)
+    assert pamps.shape == (rank * 2**level,)
+
+
+def test_partial_eigs2():
+    max_level = 5
+    level = 2
+    rank = 2
+    dmd = DMD(svd_rank=rank)
+    mrdmd = MrDMD(dmd, max_level=6, max_cycles=2)
+    mrdmd.fit(X=sample_data)
+    pamps = mrdmd.partial_amplitudes(level, 3)
+    assert pamps.shape == (rank,)
+
+
 def test_partial_reconstructed1():
     max_level = 5
     level = 2

tests/test_mrdmd.py

@@ -3,30 +3,29 @@
 In this folder we collect several useful tutorials in order to understand the principles and the potential of **PyDMD**. Please read the following table for details about the tutorials.
 An additional PDF tutorial ([DSWeb contest winner](https://dsweb.siam.org/The-Magazine/All-Issues/dsweb-2019-contest-tutorials-on-dynamical-systems-software)) is available [here](tutorial_dsweb.pdf).
 
-
-| 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)]                                        | 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`                |
-| Tutorial5 [[.ipynb](tutorial5/tutorial-5-fbdmd.ipynb), [.py](tutorial5/tutorial-5-fbdmd.py), [.html](http://pydmd.github.io/PyDMD/tutorial5fbdmd.html)]                                  | forward-backward DMD for CFD model analysis                     | `pydmd.FbDMD`               |
-| Tutorial6 [[.ipynb](tutorial6/tutorial-6-hodmd.ipynb), [.py](tutorial6/tutorial-6-hodmd.py), [.html](http://pydmd.github.io/PyDMD/tutorial6hodmd.html)]                                  | higher-order DMD applied to scalar time-series                  | `pydmd.HODMD`               |
-| Tutorial7 [[.ipynb](tutorial7/tutorial-7-dmdc.ipynb), [.py](tutorial7/tutorial-7-dmdc.py), [.html](http://pydmd.github.io/PyDMD/tutorial7dmdc.html)]                                     | DMD with control                                                | `pydmd.DMDC`                |
-| Tutorial8 [[.ipynb](tutorial8/tutorial-8-comparisons.ipynb), [.py](tutorial8/tutorial-8-comparisons.py), [.html](http://pydmd.github.io/PyDMD/tutorial8comparison.html)]                 | comparison between DMD and optimal closed-form DMD              | `pydmd.OptDMD`              |
-| Tutorial9 [[.ipynb](tutorial9/tutorial-9-spdmd.ipynb), [.py](tutorial9/tutorial-9-spdmd.py), [.html](http://pydmd.github.io/PyDMD/tutorial9spdmd.html)]                                  | sparsity-promoting DMD                                          | `pydmd.SpDMD`               |
-| Tutorial10 [[.ipynb](tutorial10/tutorial-10-paramdmd.ipynb), [.py](tutorial10/tutorial-10-paramdmd.py), [.html](http://pydmd.github.io/PyDMD/tutorial10paramdmd.html)]                   | parametric DMD                                                  | `pydmd.ParametricDMD`       |
-| Tutorial11 [[.ipynb](tutorial10/tutorial-11-regularization.ipynb), [.py](tutorial11/tutorial-11-regularization.py), [.html](http://pydmd.github.io/PyDMD/tutorial11regularization.html)] | Tikhonov regularization                                         | `pydmd.DMDBase`             |
-| Tutorial12 [[.ipynb](tutorial12/tutorial-12-cdmd.ipynb), [.py](tutorial12/tutorial-12-cdmd.py)]                                                                                               | cDMD for background modeling                                    | `pydmd.CDMD`                |
-| Tutorial13 [[.ipynb](tutorial13/tutorial-13-subspacedmd.ipynb), [.py](tutorial13/tutorial-13-subspacedmd.py)]                                                                                 | SubspaceDMD for locating eigenvalues of stochastic systems      | `pydmd.SubspaceDMD`         |
-| Tutorial14 [[.ipynb](tutorial14/tutorial-14-bop-dmd.ipynb), [.py](tutorial14/tutorial-14-bop-dmd.py), [.html](http://pydmd.github.io/PyDMD/tutorial14-bop-dmd.html)]                     | Comparison between Bagging-/ Optimized DMD and exact DMD        | `pydmd.BOPDMD`              |
-| Tutorial15 [[.ipynb](tutorial15/tutorial-15-pidmd.ipynb), [.py](tutorial15/tutorial-15-pidmd.py), [.html](http://pydmd.github.io/PyDMD/tutorial15-pidmd.html)]                           | Physics-informed DMD for manifold enforcement                   | `pydmd.PiDMD`               |
-| Tutorial16 [[.ipynb](tutorial16/tutorial-16-rdmd.ipynb), [.py](tutorial16/tutorial-16-rdmd.py), [.html](http://pydmd.github.io/PyDMD/tutorial16-rdmd.html)]                              | Randomized DMD for greater computation speedup                  | `pydmd.RDMD`                |
-| Tutorial17 [[.ipynb](tutorial17/tutorial-17-edmd.ipynb), [.py](tutorial17/tutorial-17-edmd.py), [.html](http://pydmd.github.io/PyDMD/tutorial17-edmd.html)]                              | Extended DMD for nonlinear eigenfunction discovery              | `pydmd.EDMD`                |
-| Tutorial18 [[.ipynb](tutorial18/tutorial-18-lando.ipynb), [.py](tutorial18/tutorial-18-lando.py), [.html](http://pydmd.github.io/PyDMD/tutorial18-lando.html)]                           | LANDO for nonlinear system modeling                             | `pydmd.LANDO`               |
-| Tutorial19 [[.ipynb](tutorial19/tutorial-19-havok.ipynb), [.py](tutorial19/tutorial-19-havok.py), [.html](http://pydmd.github.io/PyDMD/tutorial19-havok.html)]                           | HAVOK for modeling chaos with partial measurements              | `pydmd.HAVOK`               |
-| Tutorial20a&#160;[[.ipynb](tutorial20/costs-tutorial_toy-data.ipynb)]     <br/><br/><br/><br/>                                                                                                          | COSTS for decomposing toy data                                  | `pydmd.COSTS`             |
-| Tutorial20b&#160;[[.ipynb](tutorial20/costs-tutorial_real-data.ipynb)]     <br/><br/><br/>                                                                                                              | mrCOSTS for decomposing multi-scale physics of real, noisy data | `pydmd.mrCOSTS`             |
+| Name                                                                                                                                                                                      | Description                                                        | PyDMD used classes    |
+|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------|-----------------------|
+| Tutorial1&#160;[[.ipynb](tutorial1/tutorial-1-dmd.ipynb),&#160;[.py](tutorial1/tutorial-1-dmd.py),&#160;[.html](http://pydmd.github.io/PyDMD/tutorial1dmd.html)]                        | Analyzing real, simple data sets with PyDMD                                        | `pydmd.DMD`, `pydmd.BOPDMD`           |
+| Tutorial2&#160;[[.ipynb](tutorial2/tutorial-2-adv-dmd.ipynb),&#160;[.py](tutorial2/tutorial-2-adv-dmd.py),&#160;[.html](http://pydmd.github.io/PyDMD/tutorial2advdmd.html)]             | advanced features of standard DMD                                  | `pydmd.DMD`           |
+| Tutorial3&#160;[[.ipynb](tutorial3/tutorial-3-mrdmd.ipynb),&#160;[.py](tutorial3/tutorial-3-mrdmd.py),&#160;[.html](http://pydmd.github.io/PyDMD/tutorial3mrdmd.html)]                  | multi-resolution DMD for transient phenomena                       | `pydmd.MrDMD`         |
+| Tutorial4&#160;[[.ipynb](tutorial4/tutorial-4-cdmd.ipynb),&#160;[.py](tutorial4/tutorial-4-cdmd.py),&#160;[.html](http://pydmd.github.io/PyDMD/tutorial4cdmd.html)]                     | compress DMD for computation speedup                               | `pydmd.CDMD`          |
+| Tutorial5&#160;[[.ipynb](tutorial5/tutorial-5-fbdmd.ipynb),&#160;[.py](tutorial5/tutorial-5-fbdmd.py),&#160;[.html](http://pydmd.github.io/PyDMD/tutorial5fbdmd.html)]                  | forward-backward DMD for CFD model analysis                        | `pydmd.FbDMD`         |
+| Tutorial6&#160;[[.ipynb](tutorial6/tutorial-6-hodmd.ipynb),&#160;[.py](tutorial6/tutorial-6-hodmd.py),&#160;[.html](http://pydmd.github.io/PyDMD/tutorial6hodmd.html)]                  | higher-order DMD applied to scalar time-series                     | `pydmd.HODMD`         |
+| Tutorial7&#160;[[.ipynb](tutorial7/tutorial-7-dmdc.ipynb),&#160;[.py](tutorial7/tutorial-7-dmdc.py),&#160;[.html](http://pydmd.github.io/PyDMD/tutorial7dmdc.html)]                     | DMD with control                                                   | `pydmd.DMDC`          |
+| Tutorial8&#160;[[.ipynb](tutorial8/tutorial-8-comparisons.ipynb),&#160;[.py](tutorial8/tutorial-8-comparisons.py),&#160;[.html](http://pydmd.github.io/PyDMD/tutorial8comparison.html)] | comparison between DMD and optimal closed-form DMD                 | `pydmd.OptDMD`        |
+| Tutorial9&#160;[[.ipynb](tutorial9/tutorial-9-spdmd.ipynb),&#160;[.py](tutorial9/tutorial-9-spdmd.py),&#160;[.html](http://pydmd.github.io/PyDMD/tutorial9spdmd.html)]                  | sparsity-promoting DMD                                             | `pydmd.SpDMD`         |
+| Tutorial10&#160;[[.ipynb](tutorial10/tutorial-10-paramdmd.ipynb),&#160;[.py](tutorial10/tutorial-10-paramdmd.py),&#160;[.html](http://pydmd.github.io/PyDMD/tutorial10paramdmd.html)]   | parametric DMD                                                     | `pydmd.ParametricDMD` |
+| Tutorial11&#160;[[.ipynb](tutorial11/tutorial-11-regularization.ipynb),&#160;[.py](tutorial11/tutorial-11-regularization.py),&#160;[.html](http://pydmd.github.io/PyDMD/tutorial11regularization.html)]   | Tikhonov regularization                                      | `pydmd.DMDBase` |
+| Tutorial12&#160;[[.ipynb](tutorial12/tutorial-12-cdmd.ipynb),&#160;[.py](tutorial12/tutorial-12-cdmd.py)]                                                                                 | cDMD for background modeling                                       | `pydmd.CDMD`          |
+| Tutorial13&#160;[[.ipynb](tutorial13/tutorial-13-subspacedmd.ipynb),&#160;[.py](tutorial13/tutorial-13-subspacedmd.py)]                                                                   | SubspaceDMD for locating eigenvalues of stochastic systems         | `pydmd.SubspaceDMD`   |
+| Tutorial14&#160;[[.ipynb](tutorial14/tutorial-14-bop-dmd.ipynb),&#160;[.py](tutorial14/tutorial-14-bop-dmd.py),&#160;[.html](http://pydmd.github.io/PyDMD/tutorial14-bop-dmd.html)]     | Comparison between Bagging-/ Optimized DMD and exact DMD | `pydmd.BOPDMD`        |
+| Tutorial15&#160;[[.ipynb](tutorial15/tutorial-15-pidmd.ipynb),&#160;[.py](tutorial15/tutorial-15-pidmd.py),&#160;[.html](http://pydmd.github.io/PyDMD/tutorial15-pidmd.html)]     | Physics-informed DMD for manifold enforcement | `pydmd.PiDMD`        |
+| Tutorial16&#160;[[.ipynb](tutorial16/tutorial-16-rdmd.ipynb),&#160;[.py](tutorial16/tutorial-16-rdmd.py),&#160;[.html](http://pydmd.github.io/PyDMD/tutorial16-rdmd.html)]     | Randomized DMD for greater computation speedup | `pydmd.RDMD`        |
+| Tutorial17&#160;[[.ipynb](tutorial17/tutorial-17-edmd.ipynb),&#160;[.py](tutorial17/tutorial-17-edmd.py),&#160;[.html](http://pydmd.github.io/PyDMD/tutorial17-edmd.html)]     | Extended DMD for nonlinear eigenfunction discovery | `pydmd.EDMD`        |
+| Tutorial18&#160;[[.ipynb](tutorial18/tutorial-18-lando.ipynb),&#160;[.py](tutorial18/tutorial-18-lando.py),&#160;[.html](http://pydmd.github.io/PyDMD/tutorial18-lando.html)]     | LANDO for nonlinear system modeling | `pydmd.LANDO`        |
+| Tutorial19&#160;[[.ipynb](tutorial19/tutorial-19-havok.ipynb),&#160;[.py](tutorial19/tutorial-19-havok.py),&#160;[.html](http://pydmd.github.io/PyDMD/tutorial19-havok.html)]     | HAVOK for modeling chaos with partial measurements | `pydmd.HAVOK`        |
+| Tutorial20a&#160;[[.ipynb](tutorial20/costs-tutorial_toy-data.ipynb)]                                                                                                               | COSTS for decomposing toy data                                  | `pydmd.COSTS`             |
+| Tutorial20b&#160;[[.ipynb](tutorial20/costs-tutorial_real-data.ipynb)]                                                                                                                  | mrCOSTS for decomposing multi-scale physics of real, noisy data | `pydmd.mrCOSTS`             |