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Commit aad5841

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toddrjentoddrjen
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add unit tests for specgram, psd, and csd plot types
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‎lib/matplotlib/tests/test_axes.py

Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_axes.py
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@@ -1280,6 +1280,224 @@ def test_mixed_collection():
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ax.set_ylim(0, 16)
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@image_comparison(baseline_images=['specgram_freqs'], remove_text=True,
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extensions=['png'])
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def test_specgram_freqs():
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n = 10000
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Fs = 100.
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fstims1 = [Fs/4, Fs/5, Fs/11]
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fstims2 = [Fs/4.7, Fs/5.6, Fs/11.9]
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NFFT = int(1000 * Fs / min(fstims1 + fstims2))
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noverlap = int(NFFT / 2)
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pad_to = int(2 ** np.ceil(np.log2(NFFT)))
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x = np.arange(0, n, 1/Fs)
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y1 = np.zeros(x.size)
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y2 = np.zeros(x.size)
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for fstim1, fstim2 in zip(fstims1, fstims2):
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y1 += np.sin(fstim1 * x * np.pi * 2)
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y2 += np.sin(fstim2 * x * np.pi * 2)
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y = np.hstack([y1, y2])
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fig = plt.figure()
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ax1 = fig.add_subplot(3, 1, 1)
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ax2 = fig.add_subplot(3, 1, 2)
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ax3 = fig.add_subplot(3, 1, 3)
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spec1 = ax1.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to,
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sides='default')
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spec2 = ax2.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to,
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sides='onesided')
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spec3 = ax3.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to,
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sides='twosided')
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@image_comparison(baseline_images=['specgram_noise'], remove_text=True,
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extensions=['png'])
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def test_specgram_noise():
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np.random.seed(0)
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n = 10000
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Fs = 100.
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NFFT = int(1000 * Fs / 11)
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noverlap = int(NFFT / 2)
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pad_to = int(2 ** np.ceil(np.log2(NFFT)))
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y1 = np.random.standard_normal(n)
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y2 = np.random.rand(n)
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y = np.hstack([y1, y2])
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fig = plt.figure()
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ax1 = fig.add_subplot(3, 1, 1)
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ax2 = fig.add_subplot(3, 1, 2)
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ax3 = fig.add_subplot(3, 1, 3)
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spec1 = ax1.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to,
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sides='default')
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spec2 = ax2.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to,
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sides='onesided')
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spec3 = ax3.specgram(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to,
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sides='twosided')
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@image_comparison(baseline_images=['psd_freqs'], remove_text=True,
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extensions=['png'])
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def test_psd_freqs():
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n = 10000
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Fs = 100.
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fstims1 = [Fs/4, Fs/5, Fs/11]
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fstims2 = [Fs/4.7, Fs/5.6, Fs/11.9]
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NFFT = int(1000 * Fs / min(fstims1 + fstims2))
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noverlap = int(NFFT / 2)
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pad_to = int(2 ** np.ceil(np.log2(NFFT)))
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x = np.arange(0, n, 1/Fs)
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y1 = np.zeros(x.size)
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y2 = np.zeros(x.size)
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for fstim1, fstim2 in zip(fstims1, fstims2):
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y1 += np.sin(fstim1 * x * np.pi * 2)
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y2 += np.sin(fstim2 * x * np.pi * 2)
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y = np.hstack([y1, y2])
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fig = plt.figure()
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ax1 = fig.add_subplot(3, 1, 1)
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ax2 = fig.add_subplot(3, 1, 2)
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ax3 = fig.add_subplot(3, 1, 3)
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psd1 = ax1.psd(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to,
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sides='default')
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psd2 = ax2.psd(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to,
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sides='onesided')
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psd3 = ax3.psd(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to,
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sides='twosided')
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ax1.set_xlabel('')
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ax2.set_xlabel('')
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ax3.set_xlabel('')
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ax1.set_ylabel('')
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ax2.set_ylabel('')
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ax3.set_ylabel('')
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@image_comparison(baseline_images=['psd_noise'], remove_text=True,
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extensions=['png'])
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def test_psd_noise():
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np.random.seed(0)
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n = 10000
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Fs = 100.
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NFFT = int(1000 * Fs / 11)
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noverlap = int(NFFT / 2)
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pad_to = int(2 ** np.ceil(np.log2(NFFT)))
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y1 = np.random.standard_normal(n)
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y2 = np.random.rand(n)
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y = np.hstack([y1, y2])
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fig = plt.figure()
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ax1 = fig.add_subplot(3, 1, 1)
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ax2 = fig.add_subplot(3, 1, 2)
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ax3 = fig.add_subplot(3, 1, 3)
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psd1 = ax1.psd(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to,
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sides='default')
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psd2 = ax2.psd(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to,
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sides='onesided')
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psd3 = ax3.psd(y, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to,
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sides='twosided')
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ax1.set_xlabel('')
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ax2.set_xlabel('')
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ax3.set_xlabel('')
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ax1.set_ylabel('')
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ax2.set_ylabel('')
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ax3.set_ylabel('')
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@image_comparison(baseline_images=['csd_freqs'], remove_text=True,
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extensions=['png'])
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def test_csd_freqs():
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n = 10000
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Fs = 100.
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fstims1 = [Fs/4, Fs/5, Fs/11]
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fstims2 = [Fs/4.7, Fs/5.6, Fs/11.9]
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NFFT = int(1000 * Fs / min(fstims1 + fstims2))
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noverlap = int(NFFT / 2)
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pad_to = int(2 ** np.ceil(np.log2(NFFT)))
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x = np.arange(0, n, 1/Fs)
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y1 = np.zeros(x.size)
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y2 = np.zeros(x.size)
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for fstim1, fstim2 in zip(fstims1, fstims2):
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y1 += np.sin(fstim1 * x * np.pi * 2)
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y2 += np.sin(fstim2 * x * np.pi * 2)
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fig = plt.figure()
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ax1 = fig.add_subplot(3, 1, 1)
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ax2 = fig.add_subplot(3, 1, 2)
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ax3 = fig.add_subplot(3, 1, 3)
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csd1 = ax1.csd(y1, y2, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to,
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sides='default')
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csd2 = ax2.csd(y1, y2, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to,
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sides='onesided')
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csd3 = ax3.csd(y1, y2, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to,
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sides='twosided')
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ax1.set_xlabel('')
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ax2.set_xlabel('')
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ax3.set_xlabel('')
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ax1.set_ylabel('')
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ax2.set_ylabel('')
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ax3.set_ylabel('')
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@image_comparison(baseline_images=['csd_noise'], remove_text=True,
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extensions=['png'])
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def test_csd_noise():
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np.random.seed(0)
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n = 10000
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Fs = 100.
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NFFT = int(1000 * Fs / 11)
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noverlap = int(NFFT / 2)
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pad_to = int(2 ** np.ceil(np.log2(NFFT)))
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y1 = np.random.standard_normal(n)
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y2 = np.random.rand(n)
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fig = plt.figure()
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ax1 = fig.add_subplot(3, 1, 1)
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ax2 = fig.add_subplot(3, 1, 2)
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ax3 = fig.add_subplot(3, 1, 3)
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csd1 = ax1.csd(y1, y2, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to,
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sides='default')
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csd2 = ax2.csd(y1, y2, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to,
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sides='onesided')
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csd3 = ax3.csd(y1, y2, NFFT=NFFT, Fs=Fs, noverlap=noverlap, pad_to=pad_to,
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sides='twosided')
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ax1.set_xlabel('')
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ax2.set_xlabel('')
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ax3.set_xlabel('')
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ax1.set_ylabel('')
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ax2.set_ylabel('')
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ax3.set_ylabel('')
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if __name__ == '__main__':
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import nose
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nose.runmodule(argv=['-s', '--with-doctest'], exit=False)

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