def air_hist(self, id=None, channel_num=0, *args, **kwargs): from qtools.lib.nstats.peaks import gap_air from pyqlb.nstats.well import accepted_peaks from pyqlb.nstats.peaks import color_uncorrected_peaks, channel_amplitudes, peak_times from qtools.lib.mplot import air_hist, cleanup, render as plt_render qlwell = self.__qlwell_from_threshold_form(id) self.__set_threshold_context(qlwell) c.channel_num = int(channel_num) threshold = c.vic_threshold if c.channel_num == 1 else c.fam_threshold cutoff = request.params.get('cutoff', 500) # can detect air on either channel (especially if VICs super low) # but always report VIC amplitude air_drops = gap_air(qlwell, c.channel_num, threshold=threshold) uncorrected_air = color_uncorrected_peaks(air_drops, qlwell.color_compensation_matrix) # count number of accepted peak times air_drop_times = peak_times(air_drops) accepted_times = peak_times(accepted_peaks(qlwell)) num_air_accepted = len([t for t in air_drop_times if t in accepted_times]) # always gate on VIC air_amps = channel_amplitudes(uncorrected_air, 1) title = 'Air Droplet Histogram - %s, %s (%s)' % (c.well.plate.plate.name, c.well.well_name, 'VIC' if c.channel_num == 1 else 'FAM') fig = air_hist(title, air_amps, cutoff=cutoff, num_accepted=num_air_accepted) response.content_type = 'image/png' imgdata = plt_render(fig, dpi=72) cleanup(fig) return imgdata
def gap_rain(qlwell, channel_num=0, threshold=None, pct_boundary=0.3, gap_size=10000): """ Return the rain in the gaps between non-rain droplets. """ rain, nonrain = rain_split(qlwell, channel_num=channel_num, threshold=threshold, pct_boundary=pct_boundary) # ok, now identify the gaps in the gates. times = peak_times(nonrain) if nonrain is None or len(nonrain) < 2: return np.ndarray([0],dtype=peak_dtype(2)) intervals = np.ediff1d(times, to_begin=0, to_end=0) big_intervals = intervals > gap_size # find beginning of gaps with extract beginnings = np.extract(big_intervals[1:], times) ends = np.extract(big_intervals[:-1], times) gap_intervals = zip(beginnings, ends) gap_intervals.insert(0, (0, times[0])) gap_intervals.append((times[-1], times[-1]*100)) # count the rain in the intervals gap_drops = np.extract(reduce(np.logical_or, [np.logical_and(peak_times(rain) > b, peak_times(rain) < e) for b, e in gap_intervals]), rain) return gap_drops
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 temporal2d(self, id=None, *args, **kwargs): from qtools.lib.nstats.peaks import accepted_peaks from pyqlb.nstats.peaks import peak_times, fam_amplitudes, vic_amplitudes qlwell = self.__qlwell_from_threshold_form(id) self.__set_threshold_context(qlwell) ok_peaks = accepted_peaks(qlwell) c.tvf = zip(peak_times(ok_peaks), vic_amplitudes(ok_peaks), fam_amplitudes(ok_peaks)) return render('/well/temporal2d.html')
def gap_air(qlwell, channel_num=0, threshold=None, pct_boundary=0.3, gap_size=10000, gap_buffer=250, max_amp=1000): """ Return the air (gap rain < max_amp) in the gaps between non-rain droplets. :param channel_num: The channel on which to detect air droplets. :param threshold: The threshold dividing positives and negatives (used to detect 'rain') :param pct_boundary: The percentage outside of which a droplet is classified as rain. :param gap_size: The minimum size (in samples) of a gap. Default 0.1s. :param gap_buffer: The distance a droplet must be from the main population to be considered an air droplet, in samples. Default 0.0025s. :param max_amp: The maximum color-corrected amplitude of an air droplet. Default 1000 RFU. """ rain, nonrain = rain_split(qlwell, channel_num=channel_num, threshold=threshold, pct_boundary=pct_boundary, split_all_peaks=True) low_amp = np.extract(channel_amplitudes(rain, channel_num) < max_amp, rain) times = peak_times(nonrain) if nonrain is None or len(nonrain) < 2: return np.ndarray([0], dtype=peak_dtype(2)) intervals = np.ediff1d(times, to_begin=0, to_end=0) big_intervals = intervals > gap_size # find beginning of gaps with extract beginnings = [b+gap_buffer for b in np.extract(big_intervals[1:], times)] ends = [e-gap_buffer for e in np.extract(big_intervals[:-1], times)] gap_intervals = zip(beginnings, ends) gap_intervals.insert(0, (0, times[0]-gap_buffer)) gap_intervals.append((times[-1]+gap_buffer, times[-1]*100)) # count the rain in the intervals gap_drops = np.extract(reduce(np.logical_or, [np.logical_and(peak_times(low_amp) > b, peak_times(low_amp) < e) for b, e in gap_intervals]), low_amp) return gap_drops
def temporal_galaxy(self, id=None, channel_num=0, *args, **kwargs): from qtools.lib.nstats.peaks import above_min_amplitude_peaks from pyqlb.nstats.peaks import peak_times, channel_amplitudes, channel_widths qlwell = self.__qlwell_from_threshold_form(id) self.__set_threshold_context(qlwell) c.channel_num = int(channel_num) ok_peaks = above_min_amplitude_peaks(qlwell) c.taw = zip(peak_times(ok_peaks), channel_amplitudes(ok_peaks, c.channel_num), channel_widths(ok_peaks, c.channel_num)) if c.channel_num == 0: c.channel_name = 'FAM' else: c.channel_name = 'VIC' return render('/well/temporal_galaxy.html')
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