def compute(self, qlwell, well_metric):
# do not compute a metric if neither well has a threshold set
channels = qlwell.channels
if not channels or len(channels) != 2:
return None
for c in channels:
if c.statistics.threshold == 0 or not c.statistics.threshold:
return None
# get clusters
peaks = accepted_peaks(qlwell)
fampos_vicpos, fampos_vicneg, famneg_vicpos, famneg_vicneg = \
cluster_2d(peaks, channels[0].statistics.threshold, channels[1].statistics.threshold)
well_metric.null_linkage = linkage_2d(len(fampos_vicpos), len(fampos_vicneg),
len(famneg_vicpos), len(famneg_vicneg))
return well_metric
def view(self, id=None, *args, **kwargs):
file_ok = False
qlwell = None
try:
qlwell = self.__qlwell_from_threshold_form(id)
file_ok = True
except IOError:
path = self.__plate_path()
session['flash'] = "Could not read file: %s" % path
session['flash_class'] = 'error'
# this may be easier/more efficient to ascertain via direct SQL, but going ORM route
well_ids = Session.query(QLBWell.id, QLBWell.well_name).filter(and_(QLBWell.plate_id == c.well.plate_id,
QLBWell.file_id != None)).\
order_by(QLBWell.id).all()
current_idx = -1
for idx, (id, well_name) in enumerate(well_ids):
if c.well.id == id:
current_idx = idx
break
if current_idx == 0:
c.prev_well_id = None
c.prev_well_name = None
else:
c.prev_well_id, c.prev_well_name = well_ids[current_idx-1]
if current_idx == len(well_ids)-1:
c.next_well_id = None
c.next_well_name = None
else:
c.next_well_id, c.next_well_name = well_ids[current_idx+1]
thresholds = [self.form_result['fam_threshold'], self.form_result['vic_threshold']]
c.fam_threshold, c.vic_threshold = thresholds
for i, t in enumerate(thresholds):
if t == 0:
thresholds[i] = None
max_amplitudes = [self.form_result['max_fam_amplitude'], self.form_result['max_vic_amplitude']]
c.max_fam_amplitude, c.max_vic_amplitude = max_amplitudes
if file_ok and qlwell:
# get at well first
statistics, cluster_data = stats_for_qlp_well(qlwell, compute_clusters=True, override_thresholds=thresholds)
fam_cnv = get_cnv(statistics[0].positives, statistics[0].negatives,
statistics[1].positives, statistics[1].negatives,
reference_copies=statistics.ref_copy_num)
vic_cnv = get_cnv(statistics[1].positives, statistics[1].negatives,
statistics[0].positives, statistics[0].negatives,
reference_copies=statistics.ref_copy_num)
c.cluster_data = cluster_data
c.statistics = statistics
c.fam_cnv = fam_cnv
c.vic_cnv = vic_cnv
c.alg_version = statistics.alg_version
if h.wowo('numpy.well_frag') and hasattr(c, 'cluster_data'):
from qtools.lib.nstats import frag
from pyqlb.nstats import chisquare_2d, zscore_2d, linkage_2d, balance_score_2d
from pyqlb.nstats.well import BSCORE_MINIMUM_CLUSTER_SIZE
n00 = len(c.cluster_data['negative_peaks']['negative_peaks'])
n01 = len(c.cluster_data['negative_peaks']['positive_peaks'])
n10 = len(c.cluster_data['positive_peaks']['negative_peaks'])
n11 = len(c.cluster_data['positive_peaks']['positive_peaks'])
frag_stats = frag.prob_of_frag(n10,n11,n00,n01,
statistics[0].positives+statistics[0].negatives)
c.frag_stats = frag_stats
chi_stat, chi_p = chisquare_2d(n11,n10,n01,n00,
yates_correction=True)
c.chi_stat = chi_stat
c.chi_p = chi_p
z_fpvn, z_fpvp_vn, z_fpvp_fn, z_fnvp = \
zscore_2d(n11,n10,n01,n00)
c.zscores = (z_fpvn, z_fpvp_vn, z_fpvp_fn, z_fnvp)
c.linkage = linkage_2d(n11,n10,n01,n00)
c.bscore = balance_score_2d(n00,n01,n10,n11)[0]
#if n00 >= BSCORE_MINIMUM_CLUSTER_SIZE \
# and n01 >= BSCORE_MINIMUM_CLUSTER_SIZE \
# and n10 >= BSCORE_MINIMUM_CLUSTER_SIZE \
# and n11 >= BSCORE_MINIMUM_CLUSTER_SIZE:
return render('/well/view.html')
