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
