def func_callback( query, user ):
from fatools.lib.analytics.summary import summarize_alleles, plot_alleles
analytical_sets = query.get_filtered_analytical_sets()
report = summarize_alleles( analytical_sets )
options={}
fso_dir = None
if True:
# create plot file
if fso_dir is None:
fso_dir = get_fso_temp_dir(user.login)
plotfile = fso_dir.abspath + '/' + PLOTFILE
plot_alleles(report, plotfile, dbh=get_dbhandler())
options['plotfile'] = fso.get_urlpath(plotfile)
if False:
# create tab-delimited text file
if fso_dir is None:
fso_dir = get_fso_temp_dir(user.login)
tabfile = fso_dir.abspath + '/' + TABFILE
options['tabfile'] = fso.get_urlpath(tabfile)
html, code = format_output(report, options)
return { 'custom': None,
'options': options,
'title': 'Allele Summary Result',
'html': html,
'jscode': code,
}
def do_nj(query, userinstance, ns, *args, **kwargs): #q, user, ns=None):
dbh = get_dbhandler()
analytical_sets = query.get_filtered_analytical_sets()
dm = dist.get_distance_matrix(analytical_sets)
dbh = query.dbh
fso_dir = get_fso_temp_dir(userinstance.login)
tip_label = query.specs['options'].get('tip_label', 'S')
label_callback = {
'S': lambda x: dbh.get_sample_by_id(x).code,
'I': None,
'C': lambda x: dbh.get_sample_by_id(x).location.country,
'1': lambda x: dbh.get_sample_by_id(x).location.level1,
'2': lambda x: dbh.get_sample_by_id(x).location.level2,
'3': lambda x: dbh.get_sample_by_id(x).location.level3,
'4': lambda x: dbh.get_sample_by_id(x).location.level4,
'-': lambda x: '-',
}
tree_type = {
'F': 'fan',
'R': 'radial',
'U': 'unrooted',
'P': 'phylogram'
}[query.specs['options'].get('tree_type', 'F')]
branch_coloring = True #query.options.get('branch_coloring', 'Y') == 'Y'
njplot_png = plot_nj(dm,
fso_dir.abspath,
'png',
label_callback=label_callback[tip_label],
tree_type=tree_type,
branch_coloring=branch_coloring)
njplot_pdf = plot_nj(dm,
fso_dir.abspath,
'pdf',
label_callback=label_callback[tip_label],
tree_type=tree_type,
branch_coloring=branch_coloring)
ns.result = {
'png_plot': fso.get_urlpath(njplot_png),
'pdf_plot': fso.get_urlpath(njplot_pdf)
}
return True
def func_callback( query, user ):
from fatools.lib.analytics.dist import get_distance_matrix
from fatools.lib.analytics.nj import plot_nj
haplotype_sets = query.get_filtered_haplotype_sets()
dm = get_distance_matrix(haplotype_sets)
dbh = query.dbh
fso_dir = get_fso_temp_dir(user.login)
tip_label = query.options.get('tip_label', 'S')
label_callback = {
'S': lambda x: dbh.get_sample_by_id(x).code,
'I': None,
'C': lambda x: dbh.get_sample_by_id(x).location.country,
'1': lambda x: dbh.get_sample_by_id(x).location.level1,
'2': lambda x: dbh.get_sample_by_id(x).location.level2,
'3': lambda x: dbh.get_sample_by_id(x).location.level3,
'4': lambda x: dbh.get_sample_by_id(x).location.level4,
}
tree_type = { 'F': 'fan', 'R': 'radial', 'U': 'unrooted'}[query.options.get('tree_type', 'F')]
njplot_png = plot_nj(dm, fso_dir.abspath, 'png',
label_callback = label_callback[tip_label], tree_type=tree_type)
njplot_pdf = plot_nj(dm, fso_dir.abspath, 'pdf',
label_callback = label_callback[tip_label], tree_type=tree_type)
options = { 'png_plot': fso.get_urlpath(njplot_png),
'pdf_plot': fso.get_urlpath(njplot_pdf) }
html, code = format_output( options )
return { 'custom': None,
'options': None,
'title': 'Neighbor-Joining (NJ) Tree Result',
'html': html,
'jscode': code,
'refs': [
'Paradis E., Claude J. & Strimmer K. (2004) '
'APE: analyses of phylogenetics and evolution in R language. '
'<em>Bioinformatics</em>, <strong>20</strong>: 289-290',
'<a href="http://ape-package.ird.fr/">APE website</a>'
],
}
def index(request):
if not request.GET.get('_method', None) in ['_exec', '_yamlexec']:
queries = get_queries()
return render_to_response('msaf:templates/tools/allele/index.mako',
{ 'queries': queries, 'markers': get_marker_list() },
request = request)
if request.GET.get('_method') == '_exec':
#parse form
baseparams = parse_base_params( request.GET )
spatial_differentiation = int(request.GET.get('spatial_differentiation', -1))
temporal_differentiation = int(request.GET.get('temporal_differentiation', 0))
sample_sets = parse_advquerycmd( baseparams.queryset )
else:
selector, filter, differentiation = parse_yaml_params( request.GET )
baseparams = set_base_params(selector, filter)
spatial_differentiation = differentiation.spatial
temporal_differentiation = differentiation.temporal
sample_sets = selector.get_sample_sets()
#sample_ids = parse_querycmd( baseparams.queryset )
#diff_analytical_sets, sample_report, marker_report = get_filtered_analytical_sets(
# sample_ids = sample_ids, marker_ids = baseparams.marker_ids,
# allele_absolute_threshold = baseparams.allele_absolute_threshold,
# allele_relative_threshold = baseparams.allele_relative_threshold,
# sample_quality_threshold = baseparams.sample_quality_threshold,
# marker_quality_threshold = baseparams.marker_quality_threshold,
# spatial_differentiation = spatial_differentiation,
# temporal_differentiation = temporal_differentiation)
#analytical_set = diff_analytical_sets[0]
diff_analytical_sets, sample_report, marker_report = get_filtered_analytical_sets2(
sample_sets = sample_sets,
baseparams = baseparams,
spatial_differentiation = spatial_differentiation,
temporal_differentiation = temporal_differentiation )
# need to get all alleles from all peaks, including size & height
temp_dir = fso.mkranddir( '/temps' )
results, plot_file = summarize_alleles2( diff_analytical_sets, temp_dir.rpath )
ploturl = fso.get_urlpath( plot_file )
return render_to_response('msaf:templates/tools/allele/report.mako',
{ 'results': results,
'ploturl': ploturl
},
request = request )
def do_pca(query, userinstance, ns, *args, **kwargs): #q, user, ns=None):
dbh = get_dbhandler()
#df = dbh.get_allele_dataframe(None, None, None)
#initial_sample_sets = query.get_sample_sets()
#analytical_sets = query.get_analytical_sets()
analytical_sets = query.get_filtered_analytical_sets()
#variant_df = pd.pivot_table( df, index = 'sample_id', columns = 'locus_id', values='call',
# aggfunc = lambda x: x )
#haplotype_sets = query.get_filtered_haplotype_sets()
dimension = 3
dm = dist.get_distance_matrix(analytical_sets)
pca_res = ca.pcoa(dm, dim=dimension)
fso_dir = get_fso_temp_dir(userinstance.login)
plotfile_urls = []
for (ax, ay) in combinations(range(dimension), 2):
plotfile = fso_dir.abspath + '/' + 'pcoa-%d-%d' % (ax, ay)
plot_png = ca.plot_pca(pca_res, dm, ax, ay, plotfile + '.png',
query.specs['options']['symbol'],
query.specs['options']['symbol_size'],
query.specs['options']['symbol_alpha'])
plot_pdf = ca.plot_pca(pca_res, dm, ax, ay, plotfile + '.pdf',
query.specs['options']['symbol'],
query.specs['options']['symbol_size'],
query.specs['options']['symbol_alpha'])
plotfile_urls.append(
(fso.get_urlpath(plot_png), fso.get_urlpath(plot_pdf)))
pca_data = ca.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')
ns.result = {
'plotfile_urls': plotfile_urls,
'data_file': fso.get_urlpath(data_file)
}
return True
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)
def format_output( djost ):
body = div()
body.add( h4('D-Jost') )
body.add( create_table( djost['M'] ) )
if djost['msg']:
body.add(br(), b('Warning: '), djost['msg'])
body.add(
br(),
a('Data download link', href=fso.get_urlpath( djost['data_file'] )),
)
return (body, '')
def index(request):
if not request.GET.get('_method', None) == '_exec':
queries = get_queries()
return render_to_response('msaf:templates/tools/haplotype/index.mako',
{ 'queries': queries, 'markers': get_marker_list() },
request = request)
#parse form
baseparams = parse_base_params( request.GET )
spatial_differentiation = int(request.GET.get('spatial_differentiation', 4))
temporal_differentiation = int(request.GET.get('temporal_differentiation', 0))
sample_ids = parse_querycmd( baseparams.queryset )
diff_analytical_sets, sample_report, marker_report = get_filtered_analytical_sets(
sample_ids = sample_ids, marker_ids = baseparams.marker_ids,
allele_absolute_threshold = baseparams.allele_absolute_threshold,
allele_relative_threshold = baseparams.allele_relative_threshold,
allele_relative_cutoff = baseparams.allele_relative_cutoff,
sample_quality_threshold = baseparams.sample_quality_threshold,
marker_quality_threshold = baseparams.marker_quality_threshold,
spatial_differentiation = spatial_differentiation,
temporal_differentiation = temporal_differentiation )
(unique_haplotype, haplotype_freqs, total_freqs, haplotype_df) = summarize_haplotypes(
diff_analytical_sets )
# create the histogram
temp_dir = fso.mkranddir('/temps')
plot_file = plot_haplotype( haplotype_df, temp_dir.rpath, 'haplotypes_stacked.png' )
vpath_plot = fso.get_urlpath( plot_file )
return render_to_response('msaf:templates/tools/haplotype/report.mako',
{ 'unique_haplotype': unique_haplotype,
'haplotype_freqs': haplotype_freqs,
'vpath_plot': vpath_plot },
request = request )
def index(request):
if not request.GET.get('_method', None) == '_exec':
queries = get_queries()
return render_to_response('msaf:templates/tools/structure/index.mako',
{ 'queries': queries, 'markers': get_marker_list() },
request = request)
#parse form
baseparams = parse_base_params( request.GET )
spatial_differentiation = int(request.GET.get('spatial_differentiation', 4))
temporal_differentiation = int(request.GET.get('temporal_differentiation', 0))
# filter samples
sample_ids = parse_querycmd( baseparams.queryset )
sample_sets, sample_df = group_samples( sample_ids,
spatial_differentiation = spatial_differentiation,
temporal_differentiation = temporal_differentiation )
base_analytical_sets = create_analytical_sets( sample_sets,
marker_ids = baseparams.marker_ids,
allele_absolute_threshold = baseparams.allele_absolute_threshold,
allele_relative_threshold = baseparams.allele_relative_threshold )
sample_report, filtered_analytical_sets = assess_sample_quality( base_analytical_sets,
sample_quality_threshold = baseparams.sample_quality_threshold )
marker_report, filtered_marker_ids = assess_marker_quality( filtered_analytical_sets,
marker_quality_threshold = baseparams.marker_quality_threshold )
diff_sample_sets, diff_sample_df = group_samples( get_sample_ids(filtered_analytical_sets),
spatial_differentiation = spatial_differentiation,
temporal_differentiation = temporal_differentiation )
diff_analytical_sets = create_analytical_sets( diff_sample_sets,
marker_ids = filtered_marker_ids,
allele_absolute_threshold = baseparams.allele_absolute_threshold,
allele_relative_threshold = baseparams.allele_relative_threshold )
# start analysis here
(databuf, parambuf) = export_structure( diff_analytical_sets )
temp_dir = fso.mkranddir( '/temps' )
infile = temp_dir.rpath + '/infile.txt'
paramfile = temp_dir.rpath + '/baseparams.txt'
with open( infile, 'w' ) as f:
f.write( databuf )
with open( paramfile, 'w') as f:
f.write( parambuf )
#pca_file = plot_pca( base_pca, base_pwdist, temp_dir.rpath, "base-pca.png" )
#vpath_basepca = fso.get_urlpath( pca_file )
return render_to_response('msaf:templates/tools/structure/report.mako',
{ 'infile': fso.get_urlpath( infile ), 'paramfile': fso.get_urlpath( paramfile ) },
request = request )
def index(request):
if not request.params.get('_method', None) == '_exec':
queries = get_queries()
return render_to_response('msaf:templates/tools/nj/index.mako',
{ 'queries': queries, 'markers': get_marker_list() },
request = request)
#parse form
#baseparams = parse_base_params( request.params )
#spatial_differentiation = int(request.params.get('spatial_differentiation', 4))
#temporal_differentiation = int(request.params.get('temporal_differentiation', 0))
label_modifier_file = request.params.get('labelfile', None)
label_modifier = {}
if label_modifier_file:
buf = label_modifier_file.file.read().decode('UTF-8')
#lines = label_modifier_file.read().split('\n')
for line in buf.split('\n'):
if not line: continue
old_label, new_label = line.split('\t')
label_modifier[old_label] = new_label
baseparams = parse_base_params( request.params )
spatial_differentiation = int(request.params.get('spatial_differentiation', -1))
temporal_differentiation = int(request.params.get('temporal_differentiation', 0))
sample_sets = parse_advquerycmd( baseparams.queryset )
diff_analytical_sets, sample_report, marker_report = get_filtered_analytical_sets2(
sample_sets = sample_sets,
baseparams = baseparams,
spatial_differentiation = spatial_differentiation,
temporal_differentiation = temporal_differentiation )
#sample_ids = parse_querycmd( baseparams.queryset )
#diff_analytical_sets, sample_report, marker_report = get_filtered_analytical_sets(
# sample_ids = sample_ids, marker_ids = baseparams.marker_ids,
# allele_absolute_threshold = baseparams.allele_absolute_threshold,
# allele_relative_threshold = baseparams.allele_relative_threshold,
# sample_quality_threshold = baseparams.sample_quality_threshold,
# marker_quality_threshold = baseparams.marker_quality_threshold,
# spatial_differentiation = spatial_differentiation,
# temporal_differentiation = temporal_differentiation )
base_pwdist = pw_distance( diff_analytical_sets )
if label_modifier:
base_pwdist.modify_labels( label_modifier )
temp_dir = fso.mkranddir('/temps')
nj_file = nj_tree( base_pwdist, temp_dir.rpath, 'png')
nj_pdf = nj_tree( base_pwdist, temp_dir.rpath, 'pdf')
vpath_nj = fso.get_urlpath( nj_file )
vpath_nj_pdf = fso.get_urlpath( nj_pdf )
return render_to_response('msaf:templates/tools/nj/report.mako',
{ 'vpath_nj': vpath_nj, 'vpath_nj_pdf': vpath_nj_pdf },
request = request )
def index(request):
if not request.GET.get('_method', None) == '_exec':
queries = get_queries()
return render_to_response('msaf:templates/tools/genotype/index.mako',
{ 'queries': queries, 'markers': get_marker_list() },
request = request)
#parse form
baseparams = parse_base_params( request.GET )
spatial_differentiation = int(request.GET.get('spatial_differentiation', -1))
temporal_differentiation = int(request.GET.get('temporal_differentiation', 0))
# filter samples
#sample_ids = parse_querycmd( baseparams.queryset )
sample_sets = parse_advquerycmd( baseparams.queryset )
diff_analytical_sets, sample_report, marker_report = get_filtered_analytical_sets2(
sample_sets = sample_sets,
baseparams = baseparams,
spatial_differentiation = spatial_differentiation,
temporal_differentiation = temporal_differentiation )
#raise RuntimeError
#diff_analytical_sets, sample_report, marker_report = get_filtered_analytical_sets2(
# sample_ids = sample_ids, marker_ids = baseparams.marker_ids,
# allele_absolute_threshold = baseparams.allele_absolute_threshold,
# allele_relative_threshold = baseparams.allele_relative_threshold,
# allele_relative_cutoff = baseparams.allele_relative_cutoff,
# sample_quality_threshold = baseparams.sample_quality_threshold,
# marker_quality_threshold = baseparams.marker_quality_threshold,
# spatial_differentiation = spatial_differentiation,
# temporal_differentiation = temporal_differentiation )
#sample_sets, sample_df = group_samples( sample_ids,
# spatial_differentiation = spatial_differentiation,
# temporal_differentiation = temporal_differentiation )
#base_analytical_sets = create_analytical_sets( sample_sets,
# marker_ids = baseparams.marker_ids,
# allele_absolute_threshold = baseparams.allele_absolute_threshold,
# allele_relative_threshold = baseparams.allele_relative_threshold,
# allele_relative_cutoff = baseparams.allele_relative_cutoff)
#sample_report, filtered_analytical_sets = assess_sample_quality( base_analytical_sets,
# sample_quality_threshold = baseparams.sample_quality_threshold )
#marker_report, filtered_marker_ids = assess_marker_quality( filtered_analytical_sets,
# marker_quality_threshold = baseparams.marker_quality_threshold )
#diff_sample_sets, diff_sample_df = group_samples( get_sample_ids(filtered_analytical_sets),
# spatial_differentiation = spatial_differentiation,
# temporal_differentiation = temporal_differentiation )
#diff_analytical_sets = create_analytical_sets( diff_sample_sets,
# marker_ids = filtered_marker_ids,
# allele_absolute_threshold = baseparams.allele_absolute_threshold,
# allele_relative_threshold = baseparams.allele_relative_threshold )
# start analysis here
#genotype_summary = summarize_genotypes( diff_analytical_sets )
reports = []
for analytical_set in diff_analytical_sets:
data, aux_data, assay_data = prepare_data( analytical_set.get_allele_df() )
reports.append( ( analytical_set.get_label(), data, aux_data, assay_data ))
temp_dir = fso.mkranddir( '/temps' )
tabfile = temp_dir.rpath + '/genotypes.tab'
with open(tabfile, 'w') as f:
writer = csv.writer(f, delimiter='\t')
for (label, rows, aux_rows, assay_rows) in reports:
writer.writerow(('Label: %s' % label,))
writer.writerows( rows )
return render_to_response('msaf:templates/tools/genotype/report.mako',
{ 'reports': reports, 'tabfile': fso.get_urlpath(tabfile) },
request = request )
def format_output(result_data, user, options):
results, stats = result_data
dbh = get_dbhandler()
html = div()
# construct table header
table_header = tr( th('') )
labels = results.keys()
for label in labels:
table_header.add( th(label) )
# construct table body
table_body = tbody()
for row_label, row_key in ( ('Mean', 'mean'), ('Median', 'med'),
('Max', 'max'), ('Std Dev', 'std')):
cerr('formating table')
table_row = tr( td(row_label) )
table_row.add( * tuple( td('%4.3f' % getattr(results[l], row_key))
for l in labels ))
table_body.add( table_row )
table_row = tr( td('# of polyclonal samples)') )
for l in labels:
res = results[l]
table_row.add( td('%d (%3.2f)' % (res.M, res.M/res.N)) )
table_body.add( table_row )
# construct histonum
table_row = tr( td('# of MoI') )
for l in labels:
res = results[l]
txt = "<table><tr><td>MoI | </td><td># samples</td></tr>"
for r in res.histogram.items():
txt += '<tr><td> %d </td><td> %d </td></tr>' % r
txt += '</table>'
table_row.add( td( literal(txt) ))
table_body.add( table_row )
if stats:
table_row = tr( td('Statistical test') )
table_row.add( td( '%s -- p-value: %4.3f' % (stats['test'], stats['stats'].pvalue),
colspan = len(results) - 1 ))
table_body.add( table_row )
table_row = tr( td('# of polyclonal markers') )
for l in labels:
res = results[l]
txt = "<table><tr><td># markers | </td><td># samples</td></tr>"
for r in res.alleles.items():
txt += '<tr><td> %d </td><td> %d </td></tr>' % r
txt += '</table>'
table_row.add( td( literal(txt) ))
table_body.add( table_row )
table_row = tr( td('Polyclonality by markers') )
for l in labels:
res = results[l]
txt = "<table><tr><td>Marker | </td><td>Proportion</td></tr>"
for r in res.markers.items():
txt += '<tr><td>%s</td><td>%4.3f</td></tr>' % (
dbh.get_marker_by_id(r[0]).code, r[1]/res.N)
txt += '</table>'
table_row.add( td( literal(txt) ))
table_body.add( table_row )
table_row = tr( td('Polyclonality rank') )
for l in labels:
res = results[l]
txt = "<table><tr><td>Marker | </td><td>Proportion</td></tr>"
for r in res.markers_rank:
txt += '<tr><td>+ %s</td><td>%4.3f</td></tr>' % (
dbh.get_marker_by_id(r[0]).code, r[1]/res.M)
txt += '</table>'
table_row.add( td( literal(txt) ))
table_body.add( table_row )
fso_dir = get_fso_temp_dir(user.login)
table_row = tr( td('Data file') )
for l in labels:
res = results[l]
data_file = 'moi-%s.txt' % l.replace('|','_').replace('/','_').replace(' ', '_')
data_path = fso_dir.abspath + '/' + data_file
outstream = open(data_path, 'w')
outstream.write('SAMPLE_ID\tSAMPLE\tMOI\tMLOCI\n')
for t in res.sample_dist.itertuples():
(sample_id, moi_number, mloci_number) = t
sample_code = dbh.get_sample_by_id(sample_id).code
outstream.write('%d\t%s\t%d\t%d\n' %
(sample_id, sample_code, moi_number, mloci_number))
outstream.close()
#res.sample_dist.to_csv(data_path)
table_row.add( td(
literal('<a href="%s">%s</a>' % (fso.get_urlpath(data_path), data_file)) )
)
table_body.add( table_row )
# construct full table
cerr('constructing table')
moi_table = table(class_='table table-condensed table-striped')[
table_header, table_body
]
html.add( moi_table )
return (html, '')
def index(request):
if not request.GET.get('_method', None) == '_exec':
queries = get_queries()
return render_to_response('msaf:templates/tools/pca/index.mako',
{ 'queries': queries, 'markers': get_marker_list() },
request = request)
#parse form
#baseparams = parse_base_params( request.GET )
#spatial_differentiation = int(request.GET.get('spatial_differentiation', 4))
#temporal_differentiation = int(request.GET.get('temporal_differentiation', 0))
dimension = int(request.GET.get('dimension', 2))
baseparams = parse_base_params( request.GET )
spatial_differentiation = int(request.GET.get('spatial_differentiation', -1))
temporal_differentiation = int(request.GET.get('temporal_differentiation', 0))
sample_sets = parse_advquerycmd( baseparams.queryset )
diff_analytical_sets, sample_report, marker_report = get_filtered_analytical_sets2(
sample_sets = sample_sets,
baseparams = baseparams,
spatial_differentiation = spatial_differentiation,
temporal_differentiation = temporal_differentiation )
#sample_ids = parse_querycmd( baseparams.queryset )
#sample_sets, sample_df = group_samples( sample_ids,
# spatial_differentiation = spatial_differentiation,
# temporal_differentiation = temporal_differentiation )
#base_analytical_sets = create_analytical_sets( sample_sets,
# marker_ids = baseparams.marker_ids,
# allele_absolute_threshold = baseparams.allele_absolute_threshold,
# allele_relative_threshold = baseparams.allele_relative_threshold )
#sample_report, filtered_analytical_sets = assess_sample_quality( base_analytical_sets,
# sample_quality_threshold = baseparams.sample_quality_threshold )
#marker_report, filtered_marker_ids = assess_marker_quality( filtered_analytical_sets,
# marker_quality_threshold = baseparams.marker_quality_threshold )
#diff_sample_sets, diff_sample_df = group_samples( get_sample_ids(filtered_analytical_sets),
# spatial_differentiation = spatial_differentiation,
# temporal_differentiation = temporal_differentiation )
#diff_analytical_sets = create_analytical_sets( diff_sample_sets,
# marker_ids = filtered_marker_ids,
# allele_absolute_threshold = baseparams.allele_absolute_threshold,
# allele_relative_threshold = baseparams.allele_relative_threshold )
base_pwdist = pw_distance( diff_analytical_sets )
base_pca = pca_distance2( base_pwdist, dimension )
temp_dir = fso.mkranddir( '/temps' )
vpaths_basepca = []
for (ax, ay) in combinations( range( dimension ), 2 ):
filename = 'base-pca-%d-%d' % (ax, ay)
pca_file = plot_pca2( base_pca, base_pwdist, ax, ay, temp_dir.rpath, filename + '.png' )
pca_pdf = plot_pca2( base_pca, base_pwdist, ax, ay, temp_dir.rpath, filename + '.pdf' )
vpaths_basepca.append( fso.get_urlpath( pca_file ) )
return render_to_response('msaf:templates/tools/pca/report.mako',
{ 'vpaths_basepca': vpaths_basepca },
request = request )
