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
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
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]
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]