248 lines
8.8 KiB
Python
248 lines
8.8 KiB
Python
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import numpy as np
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from numpy.testing import assert_almost_equal, assert_equal
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import pytest
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from statsmodels.datasets import elnino
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from statsmodels.graphics.functional import (
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banddepth,
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fboxplot,
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hdrboxplot,
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rainbowplot,
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)
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try:
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import matplotlib.pyplot as plt
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except ImportError:
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pass
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data = elnino.load()
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data.raw_data = np.asarray(data.raw_data)
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labels = data.raw_data[:, 0].astype(int)
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data = data.raw_data[:, 1:]
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@pytest.mark.matplotlib
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def test_hdr_basic(close_figures):
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try:
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_, hdr = hdrboxplot(data, labels=labels, seed=12345)
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assert len(hdr.extra_quantiles) == 0
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median_t = [24.247, 25.625, 25.964, 24.999, 23.648, 22.302,
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21.231, 20.366, 20.168, 20.434, 21.111, 22.299]
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assert_almost_equal(hdr.median, median_t, decimal=2)
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quant = np.vstack([hdr.outliers, hdr.hdr_90, hdr.hdr_50])
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quant_t = np.vstack([[24.36, 25.42, 25.40, 24.96, 24.21, 23.35,
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22.50, 21.89, 22.04, 22.88, 24.57, 25.89],
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[27.25, 28.23, 28.85, 28.82, 28.37, 27.43,
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25.73, 23.88, 22.26, 22.22, 22.21, 23.19],
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[23.70, 26.08, 27.17, 26.74, 26.77, 26.15,
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25.59, 24.95, 24.69, 24.64, 25.85, 27.08],
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[28.12, 28.82, 29.24, 28.45, 27.36, 25.19,
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23.61, 22.27, 21.31, 21.37, 21.60, 22.81],
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[25.48, 26.99, 27.51, 27.04, 26.23, 24.94,
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23.69, 22.72, 22.26, 22.64, 23.33, 24.44],
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[23.11, 24.50, 24.66, 23.44, 21.74, 20.58,
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19.68, 18.84, 18.76, 18.99, 19.66, 20.86],
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[24.84, 26.23, 26.67, 25.93, 24.87, 23.57,
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22.46, 21.45, 21.26, 21.57, 22.14, 23.41],
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[23.62, 25.10, 25.34, 24.22, 22.74, 21.52,
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20.40, 19.56, 19.63, 19.67, 20.37, 21.76]])
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assert_almost_equal(quant, quant_t, decimal=0)
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labels_pos = np.all(np.isin(data, hdr.outliers).reshape(data.shape),
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axis=1)
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outliers = labels[labels_pos]
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assert_equal([1982, 1983, 1997, 1998], outliers)
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assert_equal(labels[hdr.outliers_idx], outliers)
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except OSError:
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pytest.xfail('Multiprocess randomly crashes in Windows testing')
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@pytest.mark.slow
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@pytest.mark.matplotlib
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def test_hdr_basic_brute(close_figures, reset_randomstate):
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try:
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_, hdr = hdrboxplot(data, ncomp=2, labels=labels, use_brute=True)
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assert len(hdr.extra_quantiles) == 0
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median_t = [24.247, 25.625, 25.964, 24.999, 23.648, 22.302,
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21.231, 20.366, 20.168, 20.434, 21.111, 22.299]
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assert_almost_equal(hdr.median, median_t, decimal=2)
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except OSError:
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pytest.xfail('Multiprocess randomly crashes in Windows testing')
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@pytest.mark.slow
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@pytest.mark.matplotlib
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def test_hdr_plot(close_figures):
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fig = plt.figure()
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ax = fig.add_subplot(111)
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try:
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hdrboxplot(data, labels=labels.tolist(), ax=ax, threshold=1,
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seed=12345)
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ax.set_xlabel("Month of the year")
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ax.set_ylabel("Sea surface temperature (C)")
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ax.set_xticks(np.arange(13, step=3) - 1)
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ax.set_xticklabels(["", "Mar", "Jun", "Sep", "Dec"])
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ax.set_xlim([-0.2, 11.2])
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except OSError:
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pytest.xfail('Multiprocess randomly crashes in Windows testing')
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@pytest.mark.slow
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@pytest.mark.matplotlib
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def test_hdr_alpha(close_figures):
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try:
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_, hdr = hdrboxplot(data, alpha=[0.7], seed=12345)
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extra_quant_t = np.vstack([[25.1, 26.5, 27.0, 26.4, 25.4, 24.1,
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23.0, 22.0, 21.7, 22.1, 22.7, 23.8],
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[23.4, 24.8, 25.0, 23.9, 22.4, 21.1,
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20.0, 19.3, 19.2, 19.4, 20.1, 21.3]])
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assert_almost_equal(hdr.extra_quantiles, extra_quant_t, decimal=0)
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except OSError:
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pytest.xfail('Multiprocess randomly crashes in Windows testing')
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@pytest.mark.slow
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@pytest.mark.matplotlib
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def test_hdr_multiple_alpha(close_figures):
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try:
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_, hdr = hdrboxplot(data, alpha=[0.4, 0.92], seed=12345)
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extra_quant_t = [[25.712, 27.052, 27.711, 27.200,
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26.162, 24.833, 23.639, 22.378,
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22.250, 22.640, 23.472, 24.649],
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[22.973, 24.526, 24.608, 23.343,
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21.908, 20.655, 19.750, 19.046,
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18.812, 18.989, 19.520, 20.685],
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[24.667, 26.033, 26.416, 25.584,
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24.308, 22.849, 21.684, 20.948,
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20.483, 21.019, 21.751, 22.890],
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[23.873, 25.371, 25.667, 24.644,
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23.177, 21.923, 20.791, 20.015,
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19.697, 19.951, 20.622, 21.858]]
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assert_almost_equal(hdr.extra_quantiles, np.vstack(extra_quant_t),
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decimal=0)
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except OSError:
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pytest.xfail('Multiprocess randomly crashes in Windows testing')
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@pytest.mark.slow
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@pytest.mark.matplotlib
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def test_hdr_threshold(close_figures):
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try:
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_, hdr = hdrboxplot(data, alpha=[0.8], threshold=0.93,
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seed=12345)
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labels_pos = np.all(np.isin(data, hdr.outliers).reshape(data.shape),
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axis=1)
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outliers = labels[labels_pos]
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assert_equal([1968, 1982, 1983, 1997, 1998], outliers)
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except OSError:
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pytest.xfail('Multiprocess randomly crashes in Windows testing')
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@pytest.mark.matplotlib
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def test_hdr_bw(close_figures):
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try:
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_, hdr = hdrboxplot(data, bw='cv_ml', seed=12345)
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median_t = [24.25, 25.64, 25.99, 25.04, 23.71, 22.38,
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21.31, 20.44, 20.24, 20.51, 21.19, 22.38]
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assert_almost_equal(hdr.median, median_t, decimal=2)
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except OSError:
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pytest.xfail('Multiprocess randomly crashes in Windows testing')
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@pytest.mark.slow
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@pytest.mark.matplotlib
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def test_hdr_ncomp(close_figures):
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try:
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_, hdr = hdrboxplot(data, ncomp=3, seed=12345)
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median_t = [24.33, 25.71, 26.04, 25.08, 23.74, 22.40,
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21.32, 20.45, 20.25, 20.53, 21.20, 22.39]
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assert_almost_equal(hdr.median, median_t, decimal=2)
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except OSError:
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pytest.xfail('Multiprocess randomly crashes in Windows testing')
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def test_banddepth_BD2():
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xx = np.arange(500) / 150.
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y1 = 1 + 0.5 * np.sin(xx)
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y2 = 0.3 + np.sin(xx + np.pi/6)
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y3 = -0.5 + np.sin(xx + np.pi/6)
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y4 = -1 + 0.3 * np.cos(xx + np.pi/6)
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data = np.asarray([y1, y2, y3, y4])
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depth = banddepth(data, method='BD2')
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expected_depth = [0.5, 5./6, 5./6, 0.5]
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assert_almost_equal(depth, expected_depth)
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# Plot to visualize why we expect this output
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# fig = plt.figure()
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# ax = fig.add_subplot(111)
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# for ii, yy in enumerate([y1, y2, y3, y4]):
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# ax.plot(xx, yy, label="y%s" % ii)
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# ax.legend()
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# plt.close(fig)
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def test_banddepth_MBD():
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xx = np.arange(5001) / 5000.
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y1 = np.zeros(xx.shape)
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y2 = 2 * xx - 1
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y3 = np.ones(xx.shape) * 0.5
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y4 = np.ones(xx.shape) * -0.25
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data = np.asarray([y1, y2, y3, y4])
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depth = banddepth(data, method='MBD')
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expected_depth = [5./6, (2*(0.75-3./8)+3)/6, 3.5/6, (2*3./8+3)/6]
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assert_almost_equal(depth, expected_depth, decimal=4)
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@pytest.mark.matplotlib
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def test_fboxplot_rainbowplot(close_figures):
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# Test fboxplot and rainbowplot together, is much faster.
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def harmfunc(t):
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"""Test function, combination of a few harmonic terms."""
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# Constant, 0 with p=0.9, 1 with p=1 - for creating outliers
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ci = int(np.random.random() > 0.9)
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a1i = np.random.random() * 0.05
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a2i = np.random.random() * 0.05
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b1i = (0.15 - 0.1) * np.random.random() + 0.1
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b2i = (0.15 - 0.1) * np.random.random() + 0.1
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func = (1 - ci) * (a1i * np.sin(t) + a2i * np.cos(t)) + \
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ci * (b1i * np.sin(t) + b2i * np.cos(t))
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return func
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np.random.seed(1234567)
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# Some basic test data, Model 6 from Sun and Genton.
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t = np.linspace(0, 2 * np.pi, 250)
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data = [harmfunc(t) for _ in range(20)]
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# fboxplot test
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fig = plt.figure()
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ax = fig.add_subplot(111)
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_, depth, ix_depth, ix_outliers = fboxplot(data, wfactor=2, ax=ax)
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ix_expected = np.array([13, 4, 15, 19, 8, 6, 3, 16, 9, 7, 1, 5, 2,
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12, 17, 11, 14, 10, 0, 18])
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assert_equal(ix_depth, ix_expected)
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ix_expected2 = np.array([2, 11, 17, 18])
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assert_equal(ix_outliers, ix_expected2)
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# rainbowplot test (re-uses depth variable)
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xdata = np.arange(data[0].size)
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fig = rainbowplot(data, xdata=xdata, depth=depth, cmap=plt.cm.rainbow)
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