Python mlab.apply_window方法代碼示例

本文整理匯總了Python中matplotlib.mlab.apply_window方法的典型用法代碼示例。如果您正苦於以下問題：Python mlab.apply_window方法的具體用法？Python mlab.apply_window怎麽用？Python mlab.apply_window使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類matplotlib.mlab的用法示例。

在下文中一共展示了mlab.apply_window方法的15個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: test_apply_window_hanning_2D_stack_windows_axis1_unflatten

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import apply_window [as 別名]
def test_apply_window_hanning_2D_stack_windows_axis1_unflatten(self):
        n = 32
        ydata = np.arange(n)
        ydata1 = ydata+5
        ydata2 = ydata+3.3
        ycontrol1 = mlab.apply_window(ydata1, mlab.window_hanning)
        ycontrol2 = mlab.window_hanning(ydata2)
        ydata = np.vstack([ydata1, ydata2])
        ycontrol = np.vstack([ycontrol1, ycontrol2])
        ydata = np.tile(ydata, (20, 1))
        ycontrol = np.tile(ycontrol, (20, 1))
        ydata = ydata.flatten()
        ydata1 = mlab.stride_windows(ydata, 32, noverlap=0, axis=0)
        result = mlab.apply_window(ydata1, mlab.window_hanning, axis=0,
                                   return_window=False)
        assert_allclose(ycontrol.T, result, atol=1e-08)

開發者ID:miloharper，項目名稱:neural-network-animation，代碼行數:18，代碼來源:test_mlab.py

示例2: test_apply_window_1D_axis1_ValueError

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import apply_window [as 別名]
def test_apply_window_1D_axis1_ValueError(self):
        x = self.sig_rand
        window = mlab.window_hanning
        assert_raises(ValueError, mlab.apply_window, x, window, axis=1,
                      return_window=False)

開發者ID:miloharper，項目名稱:neural-network-animation，代碼行數:7，代碼來源:test_mlab.py

示例3: test_apply_window_1D_els_wrongsize_ValueError

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import apply_window [as 別名]
def test_apply_window_1D_els_wrongsize_ValueError(self):
        x = self.sig_rand
        window = mlab.window_hanning(np.ones(x.shape[0]-1))
        assert_raises(ValueError, mlab.apply_window, x, window)

開發者ID:miloharper，項目名稱:neural-network-animation，代碼行數:6，代碼來源:test_mlab.py

示例4: test_apply_window_0D_ValueError

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import apply_window [as 別名]
def test_apply_window_0D_ValueError(self):
        x = np.array(0)
        window = mlab.window_hanning
        assert_raises(ValueError, mlab.apply_window, x, window, axis=1,
                      return_window=False)

開發者ID:miloharper，項目名稱:neural-network-animation，代碼行數:7，代碼來源:test_mlab.py

示例5: test_apply_window_3D_ValueError

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import apply_window [as 別名]
def test_apply_window_3D_ValueError(self):
        x = self.sig_rand[np.newaxis][np.newaxis]
        window = mlab.window_hanning
        assert_raises(ValueError, mlab.apply_window, x, window, axis=1,
                      return_window=False)

開發者ID:miloharper，項目名稱:neural-network-animation，代碼行數:7，代碼來源:test_mlab.py

示例6: test_apply_window_hanning_1D

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import apply_window [as 別名]
def test_apply_window_hanning_1D(self):
        x = self.sig_rand
        window = mlab.window_hanning
        window1 = mlab.window_hanning(np.ones(x.shape[0]))
        y, window2 = mlab.apply_window(x, window, return_window=True)
        yt = window(x)
        assert_equal(yt.shape, y.shape)
        assert_equal(x.shape, y.shape)
        assert_allclose(yt, y, atol=1e-06)
        assert_array_equal(window1, window2)

開發者ID:miloharper，項目名稱:neural-network-animation，代碼行數:12，代碼來源:test_mlab.py

示例7: test_apply_window_hanning_els_1D_axis0

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import apply_window [as 別名]
def test_apply_window_hanning_els_1D_axis0(self):
        x = self.sig_rand
        window = mlab.window_hanning(np.ones(x.shape[0]))
        window1 = mlab.window_hanning
        y = mlab.apply_window(x, window, axis=0, return_window=False)
        yt = window1(x)
        assert_equal(yt.shape, y.shape)
        assert_equal(x.shape, y.shape)
        assert_allclose(yt, y, atol=1e-06)

開發者ID:miloharper，項目名稱:neural-network-animation，代碼行數:11，代碼來源:test_mlab.py

示例8: test_apply_window_hanning_2D_axis0

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import apply_window [as 別名]
def test_apply_window_hanning_2D_axis0(self):
        x = np.random.standard_normal([1000, 10]) + 100.
        window = mlab.window_hanning
        y = mlab.apply_window(x, window, axis=0, return_window=False)
        yt = np.zeros_like(x)
        for i in range(x.shape[1]):
            yt[:, i] = window(x[:, i])
        assert_equal(yt.shape, y.shape)
        assert_equal(x.shape, y.shape)
        assert_allclose(yt, y, atol=1e-06)

開發者ID:miloharper，項目名稱:neural-network-animation，代碼行數:12，代碼來源:test_mlab.py

示例9: test_apply_window_hanning_els1_2D_axis0

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import apply_window [as 別名]
def test_apply_window_hanning_els1_2D_axis0(self):
        x = np.random.standard_normal([1000, 10]) + 100.
        window = mlab.window_hanning(np.ones(x.shape[0]))
        window1 = mlab.window_hanning
        y = mlab.apply_window(x, window, axis=0, return_window=False)
        yt = np.zeros_like(x)
        for i in range(x.shape[1]):
            yt[:, i] = window1(x[:, i])
        assert_equal(yt.shape, y.shape)
        assert_equal(x.shape, y.shape)
        assert_allclose(yt, y, atol=1e-06)

開發者ID:miloharper，項目名稱:neural-network-animation，代碼行數:13，代碼來源:test_mlab.py

示例10: test_apply_window_hanning_els2_2D_axis0

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import apply_window [as 別名]
def test_apply_window_hanning_els2_2D_axis0(self):
        x = np.random.standard_normal([1000, 10]) + 100.
        window = mlab.window_hanning
        window1 = mlab.window_hanning(np.ones(x.shape[0]))
        y, window2 = mlab.apply_window(x, window, axis=0, return_window=True)
        yt = np.zeros_like(x)
        for i in range(x.shape[1]):
            yt[:, i] = window1*x[:, i]
        assert_equal(yt.shape, y.shape)
        assert_equal(x.shape, y.shape)
        assert_allclose(yt, y, atol=1e-06)
        assert_array_equal(window1, window2)

開發者ID:miloharper，項目名稱:neural-network-animation，代碼行數:14，代碼來源:test_mlab.py

示例11: test_apply_window_hanning_els3_2D_axis0

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import apply_window [as 別名]
def test_apply_window_hanning_els3_2D_axis0(self):
        x = np.random.standard_normal([1000, 10]) + 100.
        window = mlab.window_hanning
        window1 = mlab.window_hanning(np.ones(x.shape[0]))
        y, window2 = mlab.apply_window(x, window, axis=0, return_window=True)
        yt = mlab.apply_window(x, window1, axis=0, return_window=False)
        assert_equal(yt.shape, y.shape)
        assert_equal(x.shape, y.shape)
        assert_allclose(yt, y, atol=1e-06)
        assert_array_equal(window1, window2)

開發者ID:miloharper，項目名稱:neural-network-animation，代碼行數:12，代碼來源:test_mlab.py

示例12: test_apply_window_hanning_2D__els1_axis1

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import apply_window [as 別名]
def test_apply_window_hanning_2D__els1_axis1(self):
        x = np.random.standard_normal([10, 1000]) + 100.
        window = mlab.window_hanning(np.ones(x.shape[1]))
        window1 = mlab.window_hanning
        y = mlab.apply_window(x, window, axis=1, return_window=False)
        yt = np.zeros_like(x)
        for i in range(x.shape[0]):
            yt[i, :] = window1(x[i, :])
        assert_equal(yt.shape, y.shape)
        assert_equal(x.shape, y.shape)
        assert_allclose(yt, y, atol=1e-06)

開發者ID:miloharper，項目名稱:neural-network-animation，代碼行數:13，代碼來源:test_mlab.py

示例13: test_apply_window_hanning_2D_els2_axis1

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import apply_window [as 別名]
def test_apply_window_hanning_2D_els2_axis1(self):
        x = np.random.standard_normal([10, 1000]) + 100.
        window = mlab.window_hanning
        window1 = mlab.window_hanning(np.ones(x.shape[1]))
        y, window2 = mlab.apply_window(x, window, axis=1, return_window=True)
        yt = np.zeros_like(x)
        for i in range(x.shape[0]):
            yt[i, :] = window1 * x[i, :]
        assert_equal(yt.shape, y.shape)
        assert_equal(x.shape, y.shape)
        assert_allclose(yt, y, atol=1e-06)
        assert_array_equal(window1, window2)

開發者ID:miloharper，項目名稱:neural-network-animation，代碼行數:14，代碼來源:test_mlab.py

示例14: test_apply_window_hanning_2D_els3_axis1

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import apply_window [as 別名]
def test_apply_window_hanning_2D_els3_axis1(self):
        x = np.random.standard_normal([10, 1000]) + 100.
        window = mlab.window_hanning
        window1 = mlab.window_hanning(np.ones(x.shape[1]))
        y = mlab.apply_window(x, window, axis=1, return_window=False)
        yt = mlab.apply_window(x, window1, axis=1, return_window=False)
        assert_equal(yt.shape, y.shape)
        assert_equal(x.shape, y.shape)
        assert_allclose(yt, y, atol=1e-06)

開發者ID:miloharper，項目名稱:neural-network-animation，代碼行數:11，代碼來源:test_mlab.py

示例15: test_apply_window_stride_windows_hanning_2D_n13_noverlapn3_axis0

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import apply_window [as 別名]
def test_apply_window_stride_windows_hanning_2D_n13_noverlapn3_axis0(self):
        x = self.sig_rand
        window = mlab.window_hanning
        yi = mlab.stride_windows(x, n=13, noverlap=2, axis=0)
        y = mlab.apply_window(yi, window, axis=0, return_window=False)
        yt = self.check_window_apply_repeat(x, window, 13, 2)
        assert_equal(yt.shape, y.shape)
        assert_not_equal(x.shape, y.shape)
        assert_allclose(yt, y, atol=1e-06)

開發者ID:miloharper，項目名稱:neural-network-animation，代碼行數:11，代碼來源:test_mlab.py

注：本文中的matplotlib.mlab.apply_window方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。