def compute(self, qlwell, qlwell_channel, well_channel_metric, dyeset=None):
if dyeset:
blue_hi, blue_lo, green_hi, green_lo = single_well_calibration_clusters(qlwell, dyeset)
elif qlwell.sample_name == DYES_FAM_VIC_LABEL:
blue_hi, blue_lo, green_hi, green_lo = single_well_calibration_clusters(qlwell, DYES_FAM_VIC)
elif qlwell.sample_name == DYES_FAM_HEX_LABEL:
blue_hi, blue_lo, green_hi, green_lo = single_well_calibration_clusters(qlwell, DYES_FAM_HEX)
else:
# do not know how to compute, return wcm
return well_channel_metric
if well_channel_metric.channel_num == 0:
hi_amplitudes = fam_amplitudes(blue_hi)
lo_amplitudes = fam_amplitudes(blue_lo)
well_channel_metric.positive_peaks = len(blue_hi)
well_channel_metric.positive_mean = np.mean(hi_amplitudes)
well_channel_metric.positive_stdev = np.std(hi_amplitudes)
well_channel_metric.negative_peaks = len(blue_lo)
well_channel_metric.negative_mean = np.mean(lo_amplitudes)
well_channel_metric.negative_stdev = np.std(lo_amplitudes)
well_channel_metric.width_mean_hi = np.mean( fam_widths( blue_hi ) )
elif well_channel_metric.channel_num == 1:
hi_amplitudes = vic_amplitudes(green_hi)
lo_amplitudes = vic_amplitudes(green_lo)
well_channel_metric.positive_peaks = len(green_hi)
well_channel_metric.positive_mean = np.mean(hi_amplitudes)
well_channel_metric.positive_stdev = np.std(hi_amplitudes)
well_channel_metric.negative_peaks = len(green_lo)
well_channel_metric.negative_mean = np.mean(lo_amplitudes)
well_channel_metric.negative_stdev = np.std(lo_amplitudes)
well_channel_metric.width_mean_hi = np.mean( fam_widths( green_hi ))
return well_channel_metric
def cluster_csv(self, id=None, show_only_gated=True, *args, **kwargs):
from pyqlb.nstats.well import accepted_peaks
qlwell = self.__qlwell_from_threshold_form(id)
if show_only_gated != 'False':
peaks = accepted_peaks(qlwell)
else:
peaks = qlwell.peaks
from pyqlb.nstats.peaks import fam_amplitudes, fam_widths, vic_amplitudes, vic_widths, peak_times
from pyqlb.nstats.well import well_observed_cluster_assignments
response.headers['Content-Type'] = 'text/csv'
h.set_download_response_header(request, response, "%s_%s%s.csv" % \
(str(c.well.plate.plate.name), str(c.well.well_name), '' if show_only_gated != 'False' else '_all'))
out = StringIO.StringIO()
csvwriter = csv_pkg.writer(out)
csvwriter.writerow(['Plate',c.well.plate.plate.name])
csvwriter.writerow(['Well',c.well.well_name])
csvwriter.writerow([])
csvwriter.writerow(['Time','FAMAmplitude','FAMWidth','VICAmplitude','VICWidth','Cluster'])
csvwriter.writerow([])
pts = peak_times(peaks)
fas = fam_amplitudes(peaks)
fws = fam_widths(peaks)
vas = vic_amplitudes(peaks)
vws = vic_widths(peaks)
cls = well_observed_cluster_assignments(qlwell, peaks)
for row in zip(pts, fas, fws, vas, vws, cls):
csvwriter.writerow(row)
csv = out.getvalue()
out.close()
return csv
def svilen(self, id=None, *args, **kwargs):
from pyqlb.nstats.well import accepted_peaks
from pyqlb.nstats.peaks import cluster_2d, peak_times, fam_widths
from pyqlb.factory import QLNumpyObjectFactory
from qtools.lib.mplot import svilen, cleanup, render as plt_render
qlwell = self.__qlwell_from_threshold_form(id)
self.__set_threshold_context(qlwell)
well_path = self.__well_path()
# oh shit
factory = QLNumpyObjectFactory()
raw_well = factory.parse_well(well_path)
crap, crap, gold, crap = cluster_2d(accepted_peaks(qlwell), c.fam_threshold,
c.vic_threshold)
times = peak_times(gold)
widths = fam_widths(gold)
title = "VIC+/FAM- droplet traces (accepted events)"
ranges = [(int(t-(w*2)), int(t+(w*2))) for t, w in zip(times, widths)]
if c.fam_threshold == 0 or c.vic_threshold == 0:
ranges = []
title = "%s (no events in quadrant)" % title
elif len(ranges) > 100:
ranges = ranges[:100]
title = "%s (truncated at first 100)" % title
fig = svilen(title, raw_well.samples, ranges, widths)
response.content_type = 'image/png'
imgdata = plt_render(fig, dpi=72)
cleanup(fig)
return imgdata
def well_mean_sigma_closure(well):
qplate = plate_objects[(well.plate.file.dirname, well.plate.file.basename)]
if not qplate:
return None
well = qplate.wells[well.well_name]
widths = fam_widths(well.peaks)
mean = np.mean(widths)
stddev = np.std(widths)
return (mean, mean-stddev, mean+stddev)
def peak_csv(self, id=None, show_only_gated=True, *args, **kwargs):
from qtools.lib.nstats.peaks import accepted_peaks
qlwell = self.__qlwell_from_threshold_form(id)
if show_only_gated != 'False':
peaks = accepted_peaks(qlwell)
else:
peaks = qlwell.peaks
from pyqlb.nstats.peaks import fam_amplitudes, fam_widths, fam_quality, vic_amplitudes, vic_widths, vic_quality, peak_times
response.headers['Content-Type'] = 'text/csv'
h.set_download_response_header(request, response, "%s_%s%s.csv" % \
(str(c.well.plate.plate.name), str(c.well.well_name), '' if show_only_gated != 'False' else '_all'))
out = StringIO.StringIO()
csvwriter = csv_pkg.writer(out)
csvwriter.writerow(['Plate',c.well.plate.plate.name])
csvwriter.writerow(['Well',c.well.well_name])
csvwriter.writerow([])
csvwriter.writerow(['FAMThreshold',qlwell.channels[0].statistics.threshold])
csvwriter.writerow(['VICThreshold',qlwell.channels[1].statistics.threshold])
csvwriter.writerow(['WidthGate',qlwell.channels[0].statistics.min_width_gate,qlwell.channels[0].statistics.max_width_gate])
csvwriter.writerow(['MinQualityGate',qlwell.channels[0].statistics.min_quality_gate])
csvwriter.writerow([])
csvwriter.writerow(['Time','FAMAmplitude','FAMWidth','FAMQuality','VICAmplitude','VICWidth','VICQuality'])
csvwriter.writerow([])
pts = peak_times(peaks)
fas = fam_amplitudes(peaks)
fws = fam_widths(peaks)
fqs = fam_quality(peaks)
vas = vic_amplitudes(peaks)
vws = vic_widths(peaks)
vqs = vic_quality(peaks)
for row in zip(pts, fas, fws, fqs, vas, vws, vqs):
csvwriter.writerow(row)
csv = out.getvalue()
out.close()
return csv
