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()
#!/usr/bin/env python-i
# use summatrix to visualize a dense matrix
#
# load summon matrix library
import summon
from summon import matrix, util
# read in matrix (stored in Index Format 'imat')
mat = matrix.Matrix()
matrix.open_matrix("data.mat", mat, format="dmat")
# set colormap
mat.colormap = summon.PosNegColorMap()
# read row and column labels
mat.rowlabels = util.read_strings("data.rlabels")
mat.collabels = util.read_strings("data.clabels")
# create and show matrix viewer
viewer = matrix.MatrixViewer(mat, title="unclustered data",
show_labels=True,
bgcolor=(1,1,1),
style="quads",
winsize=(400, 600))
viewer.show()
