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Python mlab.window_none方法代碼示例

本文整理匯總了Python中matplotlib.mlab.window_none方法的典型用法代碼示例。如果您正苦於以下問題:Python mlab.window_none方法的具體用法?Python mlab.window_none怎麽用?Python mlab.window_none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matplotlib.mlab的用法示例。


在下文中一共展示了mlab.window_none方法的8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_window_none_rand

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import window_none [as 別名]
def test_window_none_rand(self):
        res = mlab.window_none(self.sig_ones)
        assert_array_equal(res, self.sig_ones) 
開發者ID:miloharper,項目名稱:neural-network-animation,代碼行數:5,代碼來源:test_mlab.py

示例2: test_window_none_ones

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import window_none [as 別名]
def test_window_none_ones(self):
        res = mlab.window_none(self.sig_rand)
        assert_array_equal(res, self.sig_rand) 
開發者ID:miloharper,項目名稱:neural-network-animation,代碼行數:5,代碼來源:test_mlab.py

示例3: test_csd_padding

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import window_none [as 別名]
def test_csd_padding(self):
        """Test zero padding of csd(). """
        if self.NFFT_density is None:  # for derived classes
            return
        sargs = dict(x=self.y, y=self.y+1, Fs=self.Fs, window=mlab.window_none,
                     sides=self.sides)

        spec0, _ = mlab.csd(NFFT=self.NFFT_density, **sargs)
        spec1, _ = mlab.csd(NFFT=self.NFFT_density*2, **sargs)
        assert_almost_equal(np.sum(np.conjugate(spec0)*spec0).real,
                            np.sum(np.conjugate(spec1/2)*spec1/2).real) 
開發者ID:holzschu,項目名稱:python3_ios,代碼行數:13,代碼來源:test_mlab.py

示例4: test_psd_oversampling

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import window_none [as 別名]
def test_psd_oversampling():
    """Test the case len(x) < NFFT for psd()."""
    u = np.array([0, 1, 2, 3, 1, 2, 1])
    dt = 1.0
    Su = np.abs(np.fft.fft(u) * dt)**2 / (dt * u.size)
    P, f = mlab.psd(u, NFFT=u.size*2, Fs=1/dt, window=mlab.window_none,
                    detrend=mlab.detrend_none, noverlap=0, pad_to=None,
                    scale_by_freq=None,
                    sides='onesided')
    Su_1side = np.append([Su[0]], Su[1:4] + Su[4:][::-1])
    assert_almost_equal(np.sum(P), np.sum(Su_1side))  # same energy 
開發者ID:holzschu,項目名稱:python3_ios,代碼行數:13,代碼來源:test_mlab.py

示例5: test_psd_window_hanning

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import window_none [as 別名]
def test_psd_window_hanning(self):
        if self.NFFT_density is None:
            return
        freqs = self.freqs_density
        ydata = np.arange(self.NFFT_density)
        ydata1 = ydata+5
        ydata2 = ydata+3.3
        ycontrol1, windowVals = mlab.apply_window(ydata1,
                                                  mlab.window_hanning,
                                                  return_window=True)
        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))
        ydatab = ydata.T.flatten()
        ydataf = ydata.flatten()
        ycontrol = ycontrol.flatten()
        spec_g, fsp_g = mlab.psd(x=ydataf,
                                 NFFT=self.NFFT_density,
                                 Fs=self.Fs,
                                 noverlap=0,
                                 sides=self.sides,
                                 window=mlab.window_hanning)
        spec_b, fsp_b = mlab.psd(x=ydatab,
                                 NFFT=self.NFFT_density,
                                 Fs=self.Fs,
                                 noverlap=0,
                                 sides=self.sides,
                                 window=mlab.window_hanning)
        spec_c, fsp_c = mlab.psd(x=ycontrol,
                                 NFFT=self.NFFT_density,
                                 Fs=self.Fs,
                                 noverlap=0,
                                 sides=self.sides,
                                 window=mlab.window_none)
        spec_c *= len(ycontrol1)/(np.abs(windowVals)**2).sum()
        assert_array_equal(fsp_g, fsp_c)
        assert_array_equal(fsp_b, fsp_c)
        assert_allclose(spec_g, spec_c, atol=1e-08)
        # these should not be almost equal
        assert_raises(AssertionError,
                      assert_allclose, spec_b, spec_c, atol=1e-08) 
開發者ID:miloharper,項目名稱:neural-network-animation,代碼行數:45,代碼來源:test_mlab.py

示例6: test_psd_window_hanning_detrend_linear

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import window_none [as 別名]
def test_psd_window_hanning_detrend_linear(self):
        if self.NFFT_density is None:
            return
        freqs = self.freqs_density
        ydata = np.arange(self.NFFT_density)
        ycontrol = np.zeros(self.NFFT_density)
        ydata1 = ydata+5
        ydata2 = ydata+3.3
        ycontrol1 = ycontrol
        ycontrol2 = ycontrol
        ycontrol1, windowVals = mlab.apply_window(ycontrol1,
                                                  mlab.window_hanning,
                                                  return_window=True)
        ycontrol2 = mlab.window_hanning(ycontrol2)
        ydata = np.vstack([ydata1, ydata2])
        ycontrol = np.vstack([ycontrol1, ycontrol2])
        ydata = np.tile(ydata, (20, 1))
        ycontrol = np.tile(ycontrol, (20, 1))
        ydatab = ydata.T.flatten()
        ydataf = ydata.flatten()
        ycontrol = ycontrol.flatten()
        spec_g, fsp_g = mlab.psd(x=ydataf,
                                 NFFT=self.NFFT_density,
                                 Fs=self.Fs,
                                 noverlap=0,
                                 sides=self.sides,
                                 detrend=mlab.detrend_linear,
                                 window=mlab.window_hanning)
        spec_b, fsp_b = mlab.psd(x=ydatab,
                                 NFFT=self.NFFT_density,
                                 Fs=self.Fs,
                                 noverlap=0,
                                 sides=self.sides,
                                 detrend=mlab.detrend_linear,
                                 window=mlab.window_hanning)
        spec_c, fsp_c = mlab.psd(x=ycontrol,
                                 NFFT=self.NFFT_density,
                                 Fs=self.Fs,
                                 noverlap=0,
                                 sides=self.sides,
                                 window=mlab.window_none)
        spec_c *= len(ycontrol1)/(np.abs(windowVals)**2).sum()
        assert_array_equal(fsp_g, fsp_c)
        assert_array_equal(fsp_b, fsp_c)
        assert_allclose(spec_g, spec_c, atol=1e-08)
        # these should not be almost equal
        assert_raises(AssertionError,
                      assert_allclose, spec_b, spec_c, atol=1e-08) 
開發者ID:miloharper,項目名稱:neural-network-animation,代碼行數:50,代碼來源:test_mlab.py

示例7: test_psd_window_hanning

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import window_none [as 別名]
def test_psd_window_hanning(self):
        if self.NFFT_density is None:
            return
        freqs = self.freqs_density
        ydata = np.arange(self.NFFT_density)
        ydata1 = ydata+5
        ydata2 = ydata+3.3
        ycontrol1, windowVals = mlab.apply_window(ydata1,
                                                  mlab.window_hanning,
                                                  return_window=True)
        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))
        ydatab = ydata.T.flatten()
        ydataf = ydata.flatten()
        ycontrol = ycontrol.flatten()
        spec_g, fsp_g = mlab.psd(x=ydataf,
                                 NFFT=self.NFFT_density,
                                 Fs=self.Fs,
                                 noverlap=0,
                                 sides=self.sides,
                                 window=mlab.window_hanning)
        spec_b, fsp_b = mlab.psd(x=ydatab,
                                 NFFT=self.NFFT_density,
                                 Fs=self.Fs,
                                 noverlap=0,
                                 sides=self.sides,
                                 window=mlab.window_hanning)
        spec_c, fsp_c = mlab.psd(x=ycontrol,
                                 NFFT=self.NFFT_density,
                                 Fs=self.Fs,
                                 noverlap=0,
                                 sides=self.sides,
                                 window=mlab.window_none)
        spec_c *= len(ycontrol1)/(np.abs(windowVals)**2).sum()
        assert_array_equal(fsp_g, fsp_c)
        assert_array_equal(fsp_b, fsp_c)
        assert_allclose(spec_g, spec_c, atol=1e-08)
        # these should not be almost equal
        with pytest.raises(AssertionError):
            assert_allclose(spec_b, spec_c, atol=1e-08) 
開發者ID:holzschu,項目名稱:python3_ios,代碼行數:45,代碼來源:test_mlab.py

示例8: test_psd_window_hanning_detrend_linear

# 需要導入模塊: from matplotlib import mlab [as 別名]
# 或者: from matplotlib.mlab import window_none [as 別名]
def test_psd_window_hanning_detrend_linear(self):
        if self.NFFT_density is None:
            return
        freqs = self.freqs_density
        ydata = np.arange(self.NFFT_density)
        ycontrol = np.zeros(self.NFFT_density)
        ydata1 = ydata+5
        ydata2 = ydata+3.3
        ycontrol1 = ycontrol
        ycontrol2 = ycontrol
        ycontrol1, windowVals = mlab.apply_window(ycontrol1,
                                                  mlab.window_hanning,
                                                  return_window=True)
        ycontrol2 = mlab.window_hanning(ycontrol2)
        ydata = np.vstack([ydata1, ydata2])
        ycontrol = np.vstack([ycontrol1, ycontrol2])
        ydata = np.tile(ydata, (20, 1))
        ycontrol = np.tile(ycontrol, (20, 1))
        ydatab = ydata.T.flatten()
        ydataf = ydata.flatten()
        ycontrol = ycontrol.flatten()
        spec_g, fsp_g = mlab.psd(x=ydataf,
                                 NFFT=self.NFFT_density,
                                 Fs=self.Fs,
                                 noverlap=0,
                                 sides=self.sides,
                                 detrend=mlab.detrend_linear,
                                 window=mlab.window_hanning)
        spec_b, fsp_b = mlab.psd(x=ydatab,
                                 NFFT=self.NFFT_density,
                                 Fs=self.Fs,
                                 noverlap=0,
                                 sides=self.sides,
                                 detrend=mlab.detrend_linear,
                                 window=mlab.window_hanning)
        spec_c, fsp_c = mlab.psd(x=ycontrol,
                                 NFFT=self.NFFT_density,
                                 Fs=self.Fs,
                                 noverlap=0,
                                 sides=self.sides,
                                 window=mlab.window_none)
        spec_c *= len(ycontrol1)/(np.abs(windowVals)**2).sum()
        assert_array_equal(fsp_g, fsp_c)
        assert_array_equal(fsp_b, fsp_c)
        assert_allclose(spec_g, spec_c, atol=1e-08)
        # these should not be almost equal
        with pytest.raises(AssertionError):
            assert_allclose(spec_b, spec_c, atol=1e-08) 
開發者ID:holzschu,項目名稱:python3_ios,代碼行數:50,代碼來源:test_mlab.py


注:本文中的matplotlib.mlab.window_none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。