Пример #1
0
def exercise_two_means_clustering():
    data = flex.double((0.21947204867684716, 0.21947204867684716,
                        0.21947204867684716, 0.21947204867684714))
    c = clustering.two_means(data)
    assert c.cut == 4  # precision limit

    data = flex.double((1, 1, 1, 1))
    c = clustering.two_means(data)
    assert c.cut == 4

    data = flex.double((10, 4, 3, 2, 2, 1))
    c = clustering.two_means(data)
    assert c.cut == 1

    data = flex.double((10, 8, 5, 4, 4, 3))
    assert clustering.two_means(data).cut == 2

    data = flex.double((10, 9, 9, 8, 8, 8))
    assert clustering.two_means(data).cut == 3

    data = flex.double((1000, 900, 800, 500, 200, 200, 100))
    assert clustering.two_means(data).cut == 3

    data = flex.double((0.91, 0.29, 0.29, 0.27, 0.27, 0.27, 0.27))
    assert clustering.two_means(data).cut == 1

    data = flex.double((0.998, 0.449, 0.152, 0.152, 0.151))
    clusters = clustering.two_means(data)
    assert clusters.cut == 2

    data = flex.double((0.998, 0.449, 0.152))
    clusters = clustering.two_means(data)
    assert clusters.cut == 1
Пример #2
0
def exercise_two_means_clustering():
    data = flex.double((0.21947204867684716, 0.21947204867684716, 0.21947204867684716, 0.21947204867684714))
    c = clustering.two_means(data)
    assert c.cut == 4  # precision limit

    data = flex.double((1, 1, 1, 1))
    c = clustering.two_means(data)
    assert c.cut == 4

    data = flex.double((10, 4, 3, 2, 2, 1))
    c = clustering.two_means(data)
    assert c.cut == 1

    data = flex.double((10, 8, 5, 4, 4, 3))
    assert clustering.two_means(data).cut == 2

    data = flex.double((10, 9, 9, 8, 8, 8))
    assert clustering.two_means(data).cut == 3

    data = flex.double((1000, 900, 800, 500, 200, 200, 100))
    assert clustering.two_means(data).cut == 3

    data = flex.double((0.91, 0.29, 0.29, 0.27, 0.27, 0.27, 0.27))
    assert clustering.two_means(data).cut == 1

    data = flex.double((0.998, 0.449, 0.152, 0.152, 0.151))
    clusters = clustering.two_means(data)
    assert clusters.cut == 2

    data = flex.double((0.998, 0.449, 0.152))
    clusters = clustering.two_means(data)
    assert clusters.cut == 1
Пример #3
0
    def centring_translation_peak_sites(self):
        f = self.f_in_p1
        cc_sf = f * f.conjugate().data() / f.sum_sq()
        cc_map = cc_sf.fft_map(symmetry_flags=maptbx.use_space_group_symmetry,
                               resolution_factor=self.grid_resolution_factor)

        heights = flex.double()
        sites = []
        cc_map_peaks = cc_map.peak_search(self.search_parameters)
        zero = mat.zeros(3)
        for cc_peak_info in itertools.islice(cc_map_peaks, 5):
            if abs(mat.col(cc_peak_info.site) - zero) < 0.01: continue
            heights.append(cc_peak_info.height)
            sites.append(cc_peak_info.site)

        if not heights: return ()
        clusters = clustering.two_means(heights)
        if clusters.highest_stat < self.cross_correlation_cutoff_for_centring:
            return ()
        return sites[:clusters.cut]
Пример #4
0
  def centring_translation_peak_sites(self):
    f = self.f_in_p1
    cc_sf = f * f.conjugate().data() / f.sum_sq()
    cc_map = cc_sf.fft_map(
      symmetry_flags=maptbx.use_space_group_symmetry,
      resolution_factor=self.grid_resolution_factor)

    heights = flex.double()
    sites = []
    cc_map_peaks = cc_map.peak_search(self.search_parameters)
    zero = mat.zeros(3)
    for cc_peak_info in itertools.islice(cc_map_peaks, 5):
      if abs(mat.col(cc_peak_info.site) - zero) < 0.01: continue
      heights.append(cc_peak_info.height)
      sites.append(cc_peak_info.site)

    if not heights: return ()
    clusters = clustering.two_means(heights)
    if clusters.highest_stat < self.cross_correlation_cutoff_for_centring:
      return ()
    return sites[:clusters.cut]