def process_lmat(infile, mat, filename=None):
"""Processes an lmat so that it returns as an imat"""
if filename:
util.tic("reading '%s'" % filename)
rows, cols, vals = matrixlib.transpose(list(matrixlib.iter_lmat(infile)))
# determine labels
rowlabels = util.unique(rows)
collabels = util.unique(cols)
nrows = len(rowlabels)
ncols = len(collabels)
nnz = len(vals)
# determine order
if mat.order is not None:
order = util.read_strings(mat.order)
rowlookup = util.list2lookup(order)
collookup = util.list2lookup(order)
rowlabels.sort(key=lambda x: rowlookup[x])
collabels.sort(key=lambda x: collookup[x])
else:
rowlookup = util.list2lookup(rowlabels)
collookup = util.list2lookup(collabels)
mat.rowlabels = rowlabels
mat.collabels = collabels
# iterate with an imat, then post process
def func():
ilmat = itertools.izip(rows, cols, vals)
imat = matrixlib.ilmat2imat(ilmat, rowlabels, collabels)
for entry in imat:
yield entry
# also store entries by label
for i, j, v in itertools.izip(mat.rows, mat.cols, mat.vals):
mat[rowlabels[i]][collabels[j]] = v
if filename:
util.toc()
return nrows, ncols, nnz, func()
def load_matrix(nrows, ncols, nnz, imat, mat,
loadvals=False, minval=-util.INF,
sample=False, rowsample=False, colsample=False,
filename=None):
"""Load matrix from an index matrix iterator"""
mat.setup(nrows, ncols, nnz, rowsample=rowsample, colsample=colsample)
rows, cols, vals = (mat.rows, mat.cols, mat.vals)
# clear matrix
rows[:] = []
cols[:] = []
vals[:] = []
if filename:
util.tic("reading '%s'" % filename)
util.log("%s: %d nrows, %d ncols, %d non-zeros" %
(filename, nrows, ncols, nnz))
try:
for i, j, v in imat:
# filtering: 1. random sample sample
# 2. row/col filtering
# 3. value cutoff
if (sample and random.random() > sample) or \
i not in mat.rshow or j not in mat.cshow or \
v < minval:
continue
rows.append(i)
cols.append(j)
vals.append(v)
if loadvals:
mat[i][j] = v
except Exception, e:
if filename:
util.toc()
raise e
#!/usr/bin/env python-i
# a very large hierarchical clustering of all human and dog genes
#
print """
NOTE: use Ctrl+right drag and Shift+right drag to zoom the
x- and y-axis independently
"""
# import necessary libraries
from summon import sumtree, util, treelib
# read tree in parent tree format
util.tic("read tree")
tree = treelib.Tree()
tree.read_parent_tree("dog-human-genes.ptree", "dog-human-genes.label")
util.toc()
# read colormap
colormap = treelib.read_tree_color_map("dog-human.colormap")
# Provide a customization of the node labels.
# To view node labels, press "L"
def nodelabel(node):
if isinstance(node.name, str):
if node.name.startswith("ENS"):
return ""
return node.name
win = summon.Window("11_fractal")
def fractal(depth, offset):
if depth == 0:
return group()
branches = 5
shrink = .38
step = 2 * pi / 10
c = 1 - depth / 7.0
c2 = 1 - (depth - 1) / 7.0
vis = []
for i in xrange(branches):
angle = i * 2 * pi / branches + offset
vis.append(group(
lines(color(1, c, 0), 0,0, color(1, c2, 0), cos(angle), sin(angle)),
translate(cos(angle), sin(angle),
scale(shrink, shrink, fractal(depth - 1, offset + step)))))
return group(*vis)
util.tic("draw")
win.add_group(fractal(6, 0))
win.home()
util.toc()
