Merge pull request PyDMD#25 from mathLab/plot_warning

improved coverage

tests/test_dmd.py

@@ -1,7 +1,8 @@
 from unittest import TestCase
 from pydmd.dmd import DMD
-
+import matplotlib.pyplot as plt
 import numpy as np
+import os
 
 # 15 snapshot with 400 data. The matrix is 400x15 and it contains
 # the following data: f1 + f2 where
@@ -41,6 +42,25 @@ def test_truncation_shape(self):
 		dmd.fit(X=sample_data)
 		assert dmd.modes.shape[1] == 3
 
+	def test_Atilde_shape(self):
+		dmd = DMD(svd_rank=3)
+		dmd.fit(X=sample_data)
+		assert dmd.atilde.shape == (dmd.svd_rank, dmd.svd_rank)
+
+	def test_Atilde_values(self):
+		dmd = DMD(svd_rank=2)
+		dmd.fit(X=sample_data)
+		exact_atilde = np.array([[-0.70558526 + 0.67815084j, 0.22914898 + 0.20020143j], 
+			[0.10459069 + 0.09137814j, -0.57730040 + 0.79022994j]])
+		np.testing.assert_allclose(exact_atilde, dmd.atilde)
+
+	def test_fit_given_Y(self):
+		dmd = DMD(svd_rank=2)
+		X = np.copy(sample_data[:, :-1])
+		Y = np.copy(sample_data[:, 1:])
+		dmd.fit(X=X, Y=Y)
+		np.testing.assert_allclose(Y, dmd._Y)
+
 	def test_eigs_1(self):
 		dmd = DMD(svd_rank=-1)
 		dmd.fit(X=sample_data)
@@ -60,12 +80,12 @@ def test_reconstructed_data(self):
 		dmd = DMD()
 		dmd.fit(X=sample_data)
 		dmd_data = dmd.reconstructed_data
-		assert np.allclose(dmd_data, sample_data)
+		np.testing.assert_allclose(dmd_data, sample_data)
 
 	def test_original_timesteps(self):
 		dmd = DMD()
 		dmd.fit(X=sample_data)
-		assert np.array_equal(
+		np.testing.assert_allclose(
 			dmd.original_timesteps, np.arange(sample_data.shape[1])
 		)
 
@@ -75,53 +95,90 @@ def test_dmd_time(self):
 		dmd.dmd_time['t0'] = 10
 		dmd.dmd_time['tend'] = 14
 		expected_data = sample_data[:, -5:]
-		assert np.allclose(dmd.reconstructed_data, expected_data)
+		np.testing.assert_allclose(dmd.reconstructed_data, expected_data)
 
 	def test_plot_eigs_1(self):
 		dmd = DMD()
 		dmd.fit(X=sample_data)
 		dmd.plot_eigs(show_axes=True, show_unit_circle=True)
+		plt.close()
 
 	def test_plot_eigs_2(self):
 		dmd = DMD()
 		dmd.fit(X=sample_data)
 		dmd.plot_eigs(show_axes=False, show_unit_circle=False)
+		plt.close()
 
 	def test_plot_modes_1(self):
 		dmd = DMD()
 		dmd.fit(X=sample_data)
 		with self.assertRaises(ValueError):
 			dmd.plot_modes_2D()
+			plt.close()
 
 	def test_plot_modes_2(self):
 		dmd = DMD(svd_rank=-1)
 		dmd.fit(X=sample_data)
 		dmd.plot_modes_2D((1, 2, 5), x=np.arange(20), y=np.arange(20))
+		plt.close()
 
 	def test_plot_modes_3(self):
 		dmd = DMD()
 		snapshots = [snap.reshape(20, 20) for snap in sample_data.T]
 		dmd.fit(X=snapshots)
 		dmd.plot_modes_2D()
+		plt.close()
+
+	def test_plot_modes_4(self):
+		dmd = DMD()
+		snapshots = [snap.reshape(20, 20) for snap in sample_data.T]
+		dmd.fit(X=snapshots)
+		dmd.plot_modes_2D(index_mode=1)
+		plt.close()
+
+	def test_plot_modes_5(self):
+		dmd = DMD()
+		snapshots = [snap.reshape(20, 20) for snap in sample_data.T]
+		dmd.fit(X=snapshots)
+		dmd.plot_modes_2D(index_mode=1, filename='tmp.png')
+		self.addCleanup(os.remove, 'tmp.1.png')
 
 	def test_plot_snapshots_1(self):
 		dmd = DMD()
 		dmd.fit(X=sample_data)
 		with self.assertRaises(ValueError):
 			dmd.plot_snapshots_2D()
+			plt.close()
 
 	def test_plot_snapshots_2(self):
 		dmd = DMD(svd_rank=-1)
 		dmd.fit(X=sample_data)
 		dmd.plot_snapshots_2D((1, 2, 5), x=np.arange(20), y=np.arange(20))
+		plt.close()
 
 	def test_plot_snapshots_3(self):
 		dmd = DMD()
 		snapshots = [snap.reshape(20, 20) for snap in sample_data.T]
 		dmd.fit(X=snapshots)
 		dmd.plot_snapshots_2D()
+		plt.close()
+
+	def test_plot_snapshots_4(self):
+		dmd = DMD()
+		snapshots = [snap.reshape(20, 20) for snap in sample_data.T]
+		dmd.fit(X=snapshots)
+		dmd.plot_snapshots_2D(index_snap=2)
+		plt.close()
+
+	def test_plot_snapshots_5(self):
+		dmd = DMD()
+		snapshots = [snap.reshape(20, 20) for snap in sample_data.T]
+		dmd.fit(X=snapshots)
+		dmd.plot_snapshots_2D(index_snap=2, filename='tmp.png')
+		self.addCleanup(os.remove, 'tmp.2.png')
 
 	def test_tdmd_plot(self):
 		dmd = DMD(tlsq_rank=3)
 		dmd.fit(X=sample_data)
 		dmd.plot_eigs(show_axes=False, show_unit_circle=False)
+		plt.close()

tests/test_dmdbase.py

@@ -1,6 +1,6 @@
 from unittest import TestCase
 from pydmd.dmdbase import DMDBase
-
+import matplotlib.pyplot as plt
 import numpy as np
 
 # 15 snapshot with 400 data. The matrix is 400x15 and it contains
@@ -44,13 +44,16 @@ def test_plot_eigs(self):
 		dmd = DMDBase()
 		with self.assertRaises(ValueError):
 			dmd.plot_eigs(show_axes=True, show_unit_circle=True)
+			plt.close()
 
 	def test_plot_modes_2D(self):
 		dmd = DMDBase()
 		with self.assertRaises(ValueError):
 			dmd.plot_modes_2D()
+			plt.close()
 
 	def test_plot_snaps_2D(self):
 		dmd = DMDBase()
 		with self.assertRaises(ValueError):
 			dmd.plot_snapshots_2D()
+			plt.close()

tests/test_fbdmd.py

@@ -1,5 +1,6 @@
 from unittest import TestCase
 from pydmd.fbdmd import FbDMD
+import matplotlib.pyplot as plt
 import numpy as np
 
 
@@ -74,8 +75,10 @@ def test_plot_eigs_1(self):
 		dmd = FbDMD(svd_rank=-1)
 		dmd.fit(X=sample_data)
 		dmd.plot_eigs(show_axes=True, show_unit_circle=True)
+		plt.close()
 
 	def test_plot_eigs_2(self):
 		dmd = FbDMD(svd_rank=-1)
 		dmd.fit(X=sample_data)
 		dmd.plot_eigs(show_axes=False, show_unit_circle=False)
+		plt.close()

tests/test_mrdmd.py

@@ -1,6 +1,6 @@
 from unittest import TestCase
 from pydmd.mrdmd import MrDMD
-
+import matplotlib.pyplot as plt
 import numpy as np
 
 
@@ -171,23 +171,28 @@ def test_wrong_plot_eig1(self):
 			dmd.plot_eigs(
 				show_axes=True, show_unit_circle=True, figsize=(8, 8), level=7
 			)
+			plt.close()
 
 	def test_wrong_plot_eig2(self):
 		dmd = MrDMD(svd_rank=1, max_level=7, max_cycles=1)
 		with self.assertRaises(ValueError):
 			dmd.plot_eigs()
+			plt.close()
 
 	def test_plot_eig1(self):
 		dmd = MrDMD(svd_rank=-1, max_level=7, max_cycles=1)
 		dmd.fit(X=sample_data)
 		dmd.plot_eigs(show_axes=True, show_unit_circle=True, figsize=(8, 8))
+		plt.close()
 
 	def test_plot_eig2(self):
 		dmd = MrDMD(svd_rank=-1, max_level=7, max_cycles=1)
 		dmd.fit(X=sample_data)
 		dmd.plot_eigs(show_axes=True, show_unit_circle=False, title='Title')
+		plt.close()
 
 	def test_plot_eig3(self):
 		dmd = MrDMD(svd_rank=-1, max_level=7, max_cycles=1)
 		dmd.fit(X=sample_data)
 		dmd.plot_eigs(show_axes=False, show_unit_circle=False, level=1, node=0)
+		plt.close()