def func_callback( query, user ):
from fatools.lib.analytics.dist import get_distance_matrix, null_distance
from fatools.lib.analytics.ca import mca, plot_pca, format_data
dimension = 2
haplotype_sets = query.get_filtered_haplotype_sets()
dm = get_distance_matrix(haplotype_sets, null_distance)
mca_res = mca(dm)
fso_dir = get_fso_temp_dir(user.login)
plotfile_urls = []
for (ax, ay) in combinations(range( dimension ), 2):
plotfile = fso_dir.abspath + '/' + 'pcoa-%d-%d' % (ax, ay)
plot_png = plot_pca(mca_res, dm, ax, ay, plotfile + '.png')
plot_pdf = plot_pca(mca_res, dm, ax, ay, plotfile + '.pdf')
plotfile_urls.append( (fso.get_urlpath(plot_png), fso.get_urlpath(plot_pdf)) )
mca_data = format_data(mca_res, dm)
data_file = fso_dir.abspath + '/' + 'mca-data.txt'
with open(data_file, 'w') as outfile:
for r in mca_data:
outfile.write( '\t'.join( r ) )
outfile.write( '\n' )
options = { 'plotfile_urls': plotfile_urls, 'data_file': fso.get_urlpath(data_file) }
html, code = format_output( (mca_res, dm), options )
return { 'custom': None,
'options': None,
'title': 'Multiple Correspondence Analysis (MCA) Result',
'html': html,
'jscode': code,
'refs': [
'Lê, S., Josse, J. & Husson, F. (2008). '
'FactoMineR: An R Package for Multivariate Analysis. '
'<em>Journal of Statistical Software</em>. <strong>25(1)</strong>. pp. 1-18.',
'<a href="http://factominer.free.fr/">FactoMineR website</a>'
],
}
def func_callback( query, user ):
from fatools.lib.analytics.dist import get_distance_matrix
from fatools.lib.analytics.ca import pcoa, plot_pca, format_data
dimension = 2
haplotype_sets = query.get_filtered_haplotype_sets()
dm = get_distance_matrix(haplotype_sets)
pca_res = pcoa(dm, dim = dimension)
fso_dir = get_fso_temp_dir(user.login)
plotfile_urls = []
for (ax, ay) in combinations(range( dimension ), 2):
plotfile = fso_dir.abspath + '/' + 'pcoa-%d-%d' % (ax, ay)
plot_png = plot_pca(pca_res, dm, ax, ay, plotfile + '.png')
plot_pdf = plot_pca(pca_res, dm, ax, ay, plotfile + '.pdf')
plotfile_urls.append( (fso.get_urlpath(plot_png), fso.get_urlpath(plot_pdf)) )
pca_data = format_data(pca_res, dm)
data_file = fso_dir.abspath + '/' + 'pcoa-data.txt'
with open(data_file, 'w') as outfile:
for r in pca_data:
outfile.write( '\t'.join( r ) )
outfile.write( '\n' )
options = { 'plotfile_urls': plotfile_urls, 'data_file': fso.get_urlpath(data_file) }
html, code = format_output( (pca_res, dm), options )
return { 'custom': None,
'options': None,
'title': 'Principal Coordinate Analysis (PCoA) Result',
'html': html,
'jscode': code,
}
return ('Principal Coordinate Analysis (PCoA) Result', html, code)