def create_html_output(dataset_name, motifs, occurrences, by_motif, seq_infos,
options):
"""Create HTML output.
"""
from jinja2 import Environment, PackageLoader
env = Environment(loader=PackageLoader('stempy', 'templates'))
template = env.get_template('scan-stats.html')
# copy the static info
static_dir = os.path.join(options.results_dir, 'static')
html_copy_static(static_dir)
# write the HTML
filename = os.path.join(options.results_dir, 'scan-stats.html')
logger.info('Writing STEME scan statistics as HTML to %s', filename)
num_bases = sum(info.length for info in seq_infos)
with open(filename, 'w') as f:
variables = {
'dataset_name': dataset_name,
'num_sites': len(occurrences),
'num_motifs': len(motifs),
'num_seqs': len(seq_infos),
'num_bases': num_bases,
'options': options,
'num_seq_clusters': num_seq_clusters(len(seq_infos)),
}
f.write(template.render(**variables))
# create the figures
if len(occurrences):
with pylab_context_ioff():
create_figures(motifs, occurrences, by_motif, seq_infos, options)
def dpmeans(x, lambda_, progress_plots=False):
"""
The DP-means algorithm by `Kulis et al.`_
.. _Kulis et al.: http://arxiv.org/abs/1111.0352
:parameters:
- :math:`x` : input data, a sequence of length :math:`N`
- :math:`\lambda` : cluster penalty parameter
- progress_plots : Scatter plot clusters at every iteration
:returns: Cluster indicator variables
Algorithm:
#. Initialise:
* Number of clusters :math:`K=1`
* Global cluster mean :math:`\\mu_1 = \\frac{1}{n} \\sum_n x_n`
* Cluster indicator variables :math:`z_n = 0 \\quad \\forall n`
#. Repeat until convergence:
* For each point :math:`n`:
- Compute distance to each cluster :math:`d_{nk} = ||x_n - \\mu_k||^2`
- If :math:`\\min d_{nk} > \lambda` then set :math:`K=K+1, z_n=K, \\mu_k=x_n`
- Otherwise set :math:`z_n= \\arg\!\\min_k d_{nk}`
* For each cluster :math:`k`, compute :math:`\\mu_k = \\frac{1}{|\{n: z_n = k\}|}\sum_{n: z_n = k} x_n`
"""
if progress_plots:
import pylab as P
from cookbook.pylab_utils import pylab_context_ioff, \
create_format_cycler, simple_marker_styles, simple_colours
format_cycler = create_format_cycler(marker=simple_marker_styles, color=simple_colours)
N = len(x)
logging.info('Got %d data', N)
lambda2 = lambda_ ** 2
z = numpy.zeros(N, dtype=numpy.int) # initialise cluster indicators
last_z = None
for i in count(1):
#for i in xrange(1,21):
logging.info('Iteration %d: have %d cluster(s)', i, int(z.max() + 1))
# calculate cluster means
mu = [numpy.mean(x[z==k], axis=0) for k in xrange(int(z.max() + 1))]
for n, xn in enumerate(x):
d2 = numpy.array([((xn - muk)**2).sum() for muk in mu])
closest_k = d2.argmin()
if d2[closest_k] > lambda2:
mu.append(xn)
else:
z[n] = closest_k
# make clusters contiguous from 0
cluster_map = dict((c, k) for k, c in enumerate(set(z)))
if len(cluster_map) < int(z.max() + 1):
logging.warning('Reducing cluster indices')
for n, c in enumerate(z):
z[n] = cluster_map[c]
# check if we have converged by testing if no z have changed
if None != last_z and (z == last_z).all():
break
last_z = z.copy()
if progress_plots:
with pylab_context_ioff():
P.figure()
plot_clusters(x, z, format_cycler)
P.savefig('dpmeans-%04d.png' % i)
P.close()
num_clusters = len(set(z))
logging.info('Have %d cluster(s)', num_clusters)
return z
