def distances(self):
"""
Squared distances
"""
if self._distances is None:
self._distances = distance_matrix(self.data, self.params.X)**2
return self._distances
def find_TADs_py(x, a, cutoff, offset):
d = distance_matrix(x)
c = cgen_ss(d, a, cutoff, offset)
j = np.arange(len(x))
counts = np.array([c[:i, :i].sum() + c[i:, i:].sum() for i in j])
areas = j**2 + (len(x) - j)**2
return np.argmax(counts.astype('d') / areas)
def find_TADs(x, cutoff=1.5, offset=(3,10)[1]):
d = distance_matrix(x)
a = p['smooth_steepness'].value
c = cgen_ss(d, a, cutoff, offset)
j = np.arange(len(x))
counts = np.array([c[:i,:i].sum() + c[i:,i:].sum() for i in j])
areas = j**2 + (len(x) - j)**2
return np.argmax(counts.astype('d') / areas)
def testDistanceMatrix(self):
d = cbu.distance_matrix(X1)
s2 = 2.0**0.5
d_test = [
[ 0., 1., 1. ],
[ 1., 0., s2 ],
[ 1., s2, 0. ]]
self.assertArrayEqual(d, d_test)
def average_distances(self, burnin=0, thining=1):
d = 0.
n = 0
for x in self.samples[burnin::thining]:
d += distance_matrix(x)
n += 1
d/= n
return d
for i, j in pairs:
c[i,j] = 1
c[j,i] = 1
return c
if __name__ == '__main__':
n_particles = 1000
box_length = 10 / 2.1
cellsize = 2. / 10 * box_length
n_cells = 500
n_per_cell = 500
coords = np.random.rand(n_particles,3) * box_length
nblist = isdhic.NBList(cellsize, n_cells, n_per_cell, n_particles)
universe = isdhic.Universe(n_particles)
universe.coords[...] = coords
pairs = nblist_pairs(nblist, universe)
d = distance_matrix(universe.coords)
A = pairs_to_matrix(pairs, n_particles)
B = pairs_to_matrix(kd_pairs(coords, cellsize), n_particles)
print 'Are contact matrices identical? ---',
print np.all(squareform(A,checks=False) ==
squareform((d<nblist.cellsize).astype('i'),checks=False))
if False:
## create data by adding Gaussian noise
g = MultivariateGaussian(mu=coords.ravel(), sigma=sigma * numpy.eye(n_beads*3))
ensemble1 = g.random(size=ensemble_size).reshape(ensemble_size, n_beads, 3)
g = MultivariateGaussian(mu=coords2.ravel(), sigma=sigma * numpy.eye(n_beads*3))
ensemble2 = g.random(size=ensemble_size).reshape(ensemble_size, n_beads, 3)
dms1 = numpy.array(map(distance_matrix, ensemble1))
dms2 = numpy.array(map(distance_matrix, ensemble2))
frequencies1 = numpy.sum(dms1 < cutoff, 0)
frequencies2 = numpy.sum(dms2 < cutoff, 0)
summed_frequencies = frequencies1 + frequencies2
if False:
## create Poisson-distributed data
dm1 = distance_matrix(coords)
dm2 = distance_matrix(coords2)
cs1 = ensemble_size * (dm1 < cutoff)
cs2 = ensemble_size * (dm2 < cutoff)
summed_frequencies = numpy.random.poisson(cs1 + cs2)
if True:
## create Poisson-distributed data based on probabilites obtained form
## non-central Maxwell distribution
sys.path.append(os.path.expanduser('~/projects/hic/py/'))
from maxwell import prob_contact
from scipy.spatial.distance import pdist
probs1 = numpy.zeros((len(coords), len(coords)))
probs2 = numpy.zeros((len(coords), len(coords)))
dm1 = distance_matrix(coords)