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 single_well_calibration_clusters(qlwell, dye_cal_props):
"""
Returns the clusters for the specified color calibration well.
Returns them in ch0-HI, ch0-LO, ch1-HI, ch1-LO order.
:param qlwell: The well to analyze
:param dye_cal_props: A list of dye properties representing the calibration
properties on the dyes for each channel. (Should be a 2-tuple.)
"""
ok_peaks = accepted_peaks(qlwell)
if ( len( ok_peaks ) < 1 ):
#pass back a 4-tuple of empty peaks
return ( ok_peaks,ok_peaks,ok_peaks,ok_peaks)
peaks = color_uncorrected_peaks(accepted_peaks(qlwell), qlwell.color_compensation_matrix)
# FAM is y, VIC is x.
polars = np.array([cmath.polar(complex(f, v)) for f, v in zip(vic_amplitudes(peaks), fam_amplitudes(peaks))])
blue_hi = np.extract(reduce(np.logical_and,
(polars[...,1] >= THETA_THRESHOLD,
polars[...,0] >= dye_cal_props[0].expected_magnitude_threshold)),
ok_peaks)
blue_lo = np.extract(reduce(np.logical_and,
(polars[...,1] >= THETA_THRESHOLD,
polars[...,0] < dye_cal_props[0].expected_magnitude_threshold)),
ok_peaks)
green_hi = np.extract(reduce(np.logical_and,
(polars[...,1] < THETA_THRESHOLD,
polars[...,0] >= dye_cal_props[1].expected_magnitude_threshold)),
ok_peaks)
green_lo = np.extract(reduce(np.logical_and,
(polars[...,1] < THETA_THRESHOLD,
polars[...,0] < dye_cal_props[1].expected_magnitude_threshold)),
ok_peaks)
return blue_hi, blue_lo, green_hi, green_lo
