fix matplotlib version and complex warning issues

dynamicslab · Nov 5, 2024 · 2a859ba · 2a859ba
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diff --git a/README.rst b/README.rst
@@ -221,15 +221,15 @@ Citing PyKoopman
 
 .. code-block:: text
 
-    @article{Pan2024, doi = {10.21105/joss.05881}, 
-    url = {https://doi.org/10.21105/joss.05881}, 
-    year = {2024}, 
-    publisher = {The Open Journal}, 
-    volume = {9}, 
-    number = {94}, 
-    pages = {5881}, 
-    author = {Shaowu Pan and Eurika Kaiser and Brian M. de Silva and J. Nathan Kutz and Steven L. Brunton}, 
-    title = {PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator}, 
+    @article{Pan2024, doi = {10.21105/joss.05881},
+    url = {https://doi.org/10.21105/joss.05881},
+    year = {2024},
+    publisher = {The Open Journal},
+    volume = {9},
+    number = {94},
+    pages = {5881},
+    author = {Shaowu Pan and Eurika Kaiser and Brian M. de Silva and J. Nathan Kutz and Steven L. Brunton},
+    title = {PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator},
     journal = {Journal of Open Source Software}}
 
 Related packages

diff --git a/test/regression/test_regressors.py b/test/regression/test_regressors.py
@@ -107,7 +107,7 @@ def test_fit_regressors(data_xy, regressor):
             lbfgs=True,
             normalize=False,
             normalize_mode="max",
-            trainer_kwargs=dict(max_epochs=1,accelerator="cpu"),
+            trainer_kwargs=dict(max_epochs=1, accelerator="cpu"),
         )
     ],
 )
@@ -161,7 +161,7 @@ def test_fit_nndmd_regressor(data_xy, regressor):
             lbfgs=True,
             normalize=False,
             normalize_mode="max",
-            trainer_kwargs=dict(max_epochs=1,accelerator="cpu"),
+            trainer_kwargs=dict(max_epochs=1, accelerator="cpu"),
         )
     ],
 )

diff --git a/test/test_koopman.py b/test/test_koopman.py
@@ -92,7 +92,7 @@ def test_fit_koopman_nndmd(data_xy):
             lbfgs=True,
             normalize=False,
             normalize_mode="max",
-            trainer_kwargs=dict(max_epochs=1,accelerator="cpu"),
+            trainer_kwargs=dict(max_epochs=1, accelerator="cpu"),
         ),
     ],
 )
@@ -176,7 +176,7 @@ def test_dmd_on_nonconsecutive_data_accuracy(data_2D_linear_real_system, regress
             lbfgs=True,
             normalize=False,
             normalize_mode="max",
-            trainer_kwargs=dict(max_epochs=1,accelerator="cpu"),
+            trainer_kwargs=dict(max_epochs=1, accelerator="cpu"),
         ),
     ],
 )
@@ -351,7 +351,7 @@ def test_observables_integration_with_nndmd(data_random, observables):
         lbfgs=True,
         normalize=False,
         normalize_mode="max",
-        trainer_kwargs=dict(max_epochs=1,accelerator="cpu"),
+        trainer_kwargs=dict(max_epochs=1, accelerator="cpu"),
     )
     model = Koopman(observables=observables, regressor=regressor).fit(x)
     check_is_fitted(model)
@@ -432,7 +432,7 @@ def test_observables_integration_accuracy_with_nndmd(data_1D_cosine, observables
         lbfgs=True,
         normalize=False,
         normalize_mode="max",
-        trainer_kwargs=dict(max_epochs=2,accelerator="cpu"),
+        trainer_kwargs=dict(max_epochs=2, accelerator="cpu"),
     )
     model = Koopman(observables=observables, regressor=regressor, quiet=True).fit(x)
     assert model.score(x) > 0.95
@@ -814,7 +814,7 @@ def test_accuracy_koopman_nndmd_validity_check(data_for_validty_check):
             normalize=True,
             normalize_mode="equal",
             std_koopman=1 / dt,
-            trainer_kwargs=dict(max_epochs=10,accelerator="cpu"),
+            trainer_kwargs=dict(max_epochs=10, accelerator="cpu"),
         )
         model = Koopman(regressor=regressor)
         model.fit(X, dt=1)
@@ -843,7 +843,7 @@ def test_accuracy_nndmd_linear_system():
             lbfgs=True,
             normalize=True,
             normalize_mode="max",
-            trainer_kwargs=dict(max_epochs=3,accelerator="cpu"),
+            trainer_kwargs=dict(max_epochs=3, accelerator="cpu"),
         ),
         NNDMD(
             look_forward=1,
@@ -857,7 +857,7 @@ def test_accuracy_nndmd_linear_system():
             lbfgs=True,
             normalize=True,
             normalize_mode="equal",
-            trainer_kwargs=dict(max_epochs=3,accelerator="cpu"),
+            trainer_kwargs=dict(max_epochs=3, accelerator="cpu"),
         ),
         NNDMD(
             look_forward=1,
@@ -871,7 +871,7 @@ def test_accuracy_nndmd_linear_system():
             lbfgs=True,
             normalize=False,
             normalize_mode="max",
-            trainer_kwargs=dict(max_epochs=3,accelerator="cpu"),
+            trainer_kwargs=dict(max_epochs=3, accelerator="cpu"),
         ),
         NNDMD(
             mode="Dissipative",
@@ -886,7 +886,7 @@ def test_accuracy_nndmd_linear_system():
             lbfgs=True,
             normalize=True,
             normalize_mode="max",
-            trainer_kwargs=dict(max_epochs=3,accelerator="cpu"),
+            trainer_kwargs=dict(max_epochs=3, accelerator="cpu"),
         ),
         NNDMD(
             mode="Dissipative",
@@ -901,7 +901,7 @@ def test_accuracy_nndmd_linear_system():
             lbfgs=True,
             normalize=True,
             normalize_mode="equal",
-            trainer_kwargs=dict(max_epochs=3,accelerator="cpu"),
+            trainer_kwargs=dict(max_epochs=3, accelerator="cpu"),
         ),
         NNDMD(
             mode="Dissipative",
@@ -917,7 +917,7 @@ def test_accuracy_nndmd_linear_system():
             normalize=True,
             normalize_mode="equal",
             std_koopman=1.0 / 1.0,
-            trainer_kwargs=dict(max_epochs=3,accelerator="cpu"),
+            trainer_kwargs=dict(max_epochs=3, accelerator="cpu"),
         ),
     ]