def revb_extracluster_peaks(well, channel_num, threshold=None, pct_boundary=0.3, exclude_min_amplitude_peaks=True):
"""
Return the peaks that are outside the clusters.
A superset of polydispersity peaks, meant primarily for dye wells,
where there should be no biological basis for rain.
Returns a 3-tuple: peaks, rain gates, width gates
"""
if not threshold:
threshold = well.channels[channel_num].statistics.threshold
if not threshold:
threshold = None
if exclude_min_amplitude_peaks:
peaks = above_min_amplitude_peaks(well)
else:
peaks = well.peaks
# get rain_pvalues
p_plus, p, p_minus, pos, middle_high, middle_low, neg = \
rain_pvalues_thresholds(peaks,
channel_num=channel_num,
threshold=threshold,
pct_boundary=pct_boundary)
binned_peaks = bin_peaks_by_amplitude(peaks, well.sum_amplitude_bins)
extra_peaks = np.ndarray([0], dtype=peak_dtype(2))
for bin, (min_gate, max_gate, boundary) in zip(binned_peaks, well.sum_amplitude_bins):
if middle_high and middle_low:
extra_peaks = np.hstack([extra_peaks, np.extract(np.logical_not(
np.logical_or(
reduce(np.logical_and,
(channel_widths(bin, channel_num) > min_gate,
channel_widths(bin, channel_num) < max_gate,
channel_amplitudes(bin, channel_num) > middle_high,
channel_amplitudes(bin, channel_num) < pos)),
reduce(np.logical_and,
(channel_widths(bin, channel_num) > min_gate,
channel_widths(bin, channel_num) < max_gate,
channel_amplitudes(bin, channel_num) > neg,
channel_amplitudes(bin, channel_num) < middle_low))
)
), bin)])
else:
extra_peaks = np.hstack([extra_peaks, np.extract(np.logical_not(
reduce(np.logical_and,
(channel_widths(bin, channel_num) > min_gate,
channel_widths(bin, channel_num) < max_gate,
channel_amplitudes(bin, channel_num) > neg,
channel_amplitudes(bin, channel_num) < pos)
)
), bin)])
return (extra_peaks,
(pos, middle_high, middle_low, neg),
(np.mean(fam_amplitudes(peaks)), np.mean(vic_amplitudes(peaks))))
def extracluster_peaks(well, channel_num, threshold=None, pct_boundary=0.3, exclude_min_amplitude_peaks=True):
"""
Return the peaks that are outside the clusters.
A superset of polydispersity peaks, meant primarily for dye wells,
where there should be no biological basis for rain.
Returns a 3-tuple: peaks, rain gates, width gates
"""
if not threshold:
threshold = well.channels[channel_num].statistics.threshold
if not threshold:
threshold = None
if exclude_min_amplitude_peaks:
peaks = above_min_amplitude_peaks(well)
else:
peaks = well.peaks
# get rain_pvalues
p_plus, p, p_minus, pos, middle_high, middle_low, neg = \
rain_pvalues_thresholds(peaks,
channel_num=channel_num,
threshold=threshold,
pct_boundary=pct_boundary)
min_gate, max_gate = well_static_width_gates(well)
if middle_high and middle_low:
extracluster_peaks = np.extract(np.logical_not(
np.logical_or(
reduce(np.logical_and,
(channel_widths(peaks, channel_num) > min_gate,
channel_widths(peaks, channel_num) < max_gate,
channel_amplitudes(peaks, channel_num) > middle_high,
channel_amplitudes(peaks, channel_num) < pos)),
reduce(np.logical_and,
(channel_widths(peaks, channel_num) > min_gate,
channel_widths(peaks, channel_num) < max_gate,
channel_amplitudes(peaks, channel_num) > neg,
channel_amplitudes(peaks, channel_num) < middle_low))
)
), peaks)
else:
extracluster_peaks = np.extract(np.logical_not(
reduce(np.logical_and,
(channel_widths(peaks, channel_num) > min_gate,
channel_widths(peaks, channel_num) < max_gate,
channel_amplitudes(peaks, channel_num) > neg,
channel_amplitudes(peaks, channel_num) < pos)
)
), peaks)
return (extracluster_peaks,
(pos, middle_high, middle_low, neg),
(min_gate, max_gate))
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 revb_polydisperse_peaks(well, channel_num, threshold=None, pct_boundary=0.3, exclude_min_amplitude_peaks=True):
"""
Computes polydispersity for a well which has amplitude bins defined.
Returns a 3-tuple (4-tuple, 4-tuple, 2-tuple). The first 4-tuple is:
* positive droplets, with widths above the width gate set for that droplet's amplitude bin.
* middle rain, with widths above the bin width gate.
* middle rain, with width below the bin width gate.
* negative rain, with width below the bin width gate.
The second 4-tuple is:
* positive rain boundary
* middle rain upper boundary (can be None)
* middle rain lower boundary (can be None)
* negative rain boundary
The third 2-tuple is:
* mean FAM amplitude
* mean VIC amplitude
This is for being able to draw approximate single-channel polydispersity graphs
down the line (this does beg the question, is there a better 2D definition of
polydispersity?)
Will raise an error if amplitude bins are not defined on the well.
"""
if not hasattr(well, 'sum_amplitude_bins') or len(well.sum_amplitude_bins) == 0:
raise ValueError("No amplitude bins for this well.")
if not threshold:
threshold = well.channels[channel_num].statistics.threshold
if not threshold:
threshold = None
if exclude_min_amplitude_peaks:
peaks = above_min_amplitude_peaks(well)
else:
peaks = well.peaks
p_plus, p, p_minus, pos, middle_high, middle_low, neg = \
rain_pvalues_thresholds(peaks,
channel_num=channel_num,
threshold=threshold,
pct_boundary=pct_boundary)
binned_peaks = bin_peaks_by_amplitude(peaks, well.sum_amplitude_bins)
pos_peaks = np.ndarray([0], dtype=peak_dtype(2))
midhigh_peaks = np.ndarray([0], dtype=peak_dtype(2))
midlow_peaks = np.ndarray([0], dtype=peak_dtype(2))
neg_peaks = np.ndarray([0], dtype=peak_dtype(2))
for bin, (min_gate, max_gate, boundary) in zip(binned_peaks, well.sum_amplitude_bins):
pos_peaks = np.hstack([pos_peaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) > max_gate,
channel_amplitudes(bin, channel_num) > pos)),
bin)])
if middle_high and middle_low:
midhigh_peaks = np.hstack([midhigh_peaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) > max_gate,
reduce(np.logical_and,
(channel_amplitudes(bin, channel_num) < middle_high,
channel_amplitudes(bin, channel_num) > middle_low)))),
bin)])
midlow_peaks = np.hstack([midlow_peaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) < min_gate,
reduce(np.logical_and,
(channel_amplitudes(bin, channel_num) < middle_high,
channel_amplitudes(bin, channel_num) > middle_low)))),
bin)])
neg_peaks = np.hstack([neg_peaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) < min_gate,
channel_amplitudes(bin, channel_num) < neg)),
bin)])
return ((pos_peaks, midhigh_peaks, midlow_peaks, neg_peaks),
(pos, middle_high, middle_low, neg),
(np.mean(fam_amplitudes(peaks)), np.mean(vic_amplitudes(peaks))))
def revb_extracluster_peaks_by_region(well, channel_num, threshold=None, pct_boundary=0.3, exclude_min_amplitude_peaks=True):
"""
Return the peaks that are not desired (outside clusters)
and separate them by region. The region order is:
-- positive large peaks
-- positive rain
-- positive small peaks
-- positive wide peaks (directly above positive cluster)
-- positive narrow peaks (directly below positive cluster)
-- middle large peaks
-- middle rain
-- middle small peaks
-- negative large peaks
-- negative rain
-- negative small peaks
-- negative wide peaks (directly above positive cluster)
-- negative narrow peaks (directly below positive cluster)
Returns this 9-tuple, then rain gates, then mean of FAM and VIC.
"""
extra_peaks, rain_gates, means = \
revb_extracluster_peaks(well, channel_num,
threshold=threshold,
pct_boundary=pct_boundary,
exclude_min_amplitude_peaks=exclude_min_amplitude_peaks)
pos_gate, midhigh_gate, midlow_gate, neg_gate = rain_gates
binned_peaks = bin_peaks_by_amplitude(extra_peaks, well.sum_amplitude_bins)
plpeaks = np.ndarray([0], dtype=peak_dtype(2))
prpeaks = np.ndarray([0], dtype=peak_dtype(2))
pspeaks = np.ndarray([0], dtype=peak_dtype(2))
pwpeaks = np.ndarray([0], dtype=peak_dtype(2))
pnpeaks = np.ndarray([0], dtype=peak_dtype(2))
mlpeaks = np.ndarray([0], dtype=peak_dtype(2))
mrpeaks = np.ndarray([0], dtype=peak_dtype(2))
mspeaks = np.ndarray([0], dtype=peak_dtype(2))
nlpeaks = np.ndarray([0], dtype=peak_dtype(2))
nrpeaks = np.ndarray([0], dtype=peak_dtype(2))
nspeaks = np.ndarray([0], dtype=peak_dtype(2))
nwpeaks = np.ndarray([0], dtype=peak_dtype(2))
nnpeaks = np.ndarray([0], dtype=peak_dtype(2))
for bin, (min_gate, max_gate, boundary) in zip(binned_peaks, well.sum_amplitude_bins):
plpeaks = np.hstack([plpeaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) > max_gate,
channel_amplitudes(bin, channel_num) > pos_gate)
), bin)])
prpeaks = np.hstack([prpeaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) >= min_gate,
channel_widths(bin, channel_num) <= max_gate,
channel_amplitudes(bin, channel_num) > pos_gate)
), bin)])
pspeaks = np.hstack([pspeaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) < min_gate,
channel_amplitudes(bin, channel_num) > pos_gate)
), bin)])
if midhigh_gate and midlow_gate:
mlpeaks = np.hstack([mlpeaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) > max_gate,
channel_amplitudes(bin, channel_num) < midhigh_gate,
channel_amplitudes(bin, channel_num) > midlow_gate)
), bin)])
mrpeaks = np.hstack([mrpeaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) >= min_gate,
channel_widths(bin, channel_num) <= max_gate,
channel_amplitudes(bin, channel_num) < midhigh_gate,
channel_amplitudes(bin, channel_num) > midlow_gate)
), bin)])
mspeaks = np.hstack([mspeaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) < min_gate,
channel_amplitudes(bin, channel_num) < midhigh_gate,
channel_amplitudes(bin, channel_num) > midlow_gate)
), bin)])
# this means there are positives
pwpeaks = np.hstack([pwpeaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) > max_gate,
channel_amplitudes(bin, channel_num) >= midhigh_gate,
channel_amplitudes(bin, channel_num) <= pos_gate)
), bin)])
pnpeaks = np.hstack([pnpeaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) < min_gate,
channel_amplitudes(bin, channel_num) >= midhigh_gate,
channel_amplitudes(bin, channel_num) <= pos_gate)
), bin)])
nwpeaks = np.hstack([nwpeaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) > max_gate,
channel_amplitudes(bin, channel_num) >= neg_gate,
channel_amplitudes(bin, channel_num) <= midlow_gate)
), bin)])
nnpeaks = np.hstack([nnpeaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) < min_gate,
channel_amplitudes(bin, channel_num) >= neg_gate,
channel_amplitudes(bin, channel_num) <= midlow_gate)
), bin)])
else:
nwpeaks = np.hstack([nwpeaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) > max_gate,
channel_amplitudes(bin, channel_num) >= neg_gate,
channel_amplitudes(bin, channel_num) <= pos_gate)
), bin)])
nnpeaks = np.hstack([nnpeaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) < min_gate,
channel_amplitudes(bin, channel_num) >= neg_gate,
channel_amplitudes(bin, channel_num) <= pos_gate)
), bin)])
nlpeaks = np.hstack([nlpeaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) > max_gate,
channel_amplitudes(bin, channel_num) < neg_gate)
), bin)])
nrpeaks = np.hstack([nrpeaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) >= min_gate,
channel_widths(bin, channel_num) <= max_gate,
channel_amplitudes(bin, channel_num) < neg_gate)
), bin)])
pbpeaks = np.hstack([pspeaks, np.extract(
reduce(np.logical_and,
(channel_widths(bin, channel_num) < min_gate,
channel_amplitudes(bin, channel_num) >= midhigh_gate,
channel_amplitudes(bin, channel_num) <= pos_gate)
), bin)])
return ((plpeaks, prpeaks, pspeaks,
pwpeaks, pnpeaks,
mlpeaks, mrpeaks, mspeaks,
nlpeaks, nrpeaks, nspeaks,
nwpeaks, nnpeaks),
rain_gates,
means)
def polydisperse_peaks(well, channel_num, threshold=None, pct_boundary=0.3, exclude_min_amplitude_peaks=True):
"""
Returns a 3-tuple (4-tuple, 4-tuple, 2-tuple). The first 4-tuple is:
* positive rain above the width gates.
* middle rain above the width gates.
* middle rain below the width gates.
* negative rain below the width gates.
The second 4-tuple is:
* positive rain boundary
* middle rain upper boundary (can be None)
* middle rain lower boundary (can be None)
* negative rain boundary
The last 2-tuple is:
* computed min width gate
* computed max width gate
Positives & negatives are computed on the specified channel number.
"""
if not threshold:
threshold = well.channels[channel_num].statistics.threshold
if not threshold:
threshold = None
# filter out min_amplitude_peaks
if exclude_min_amplitude_peaks:
peaks = above_min_amplitude_peaks(well)
else:
peaks = well.peaks
p_plus, p, p_minus, pos, middle_high, middle_low, neg = \
rain_pvalues_thresholds(peaks,
channel_num=channel_num,
threshold=threshold,
pct_boundary=pct_boundary)
min_gate, max_gate = well_static_width_gates(well)
pos_peaks = np.extract(
reduce(np.logical_and,
(channel_widths(peaks, channel_num) > max_gate,
channel_amplitudes(peaks, channel_num) > pos)),
peaks)
if middle_high and middle_low:
midhigh_peaks = np.extract(
reduce(np.logical_and,
(channel_widths(peaks, channel_num) > max_gate,
reduce(np.logical_and,
(channel_amplitudes(peaks, channel_num) < middle_high,
channel_amplitudes(peaks, channel_num) > middle_low)))),
peaks)
midlow_peaks = np.extract(
reduce(np.logical_and,
(channel_widths(peaks, channel_num) < min_gate,
reduce(np.logical_and,
(channel_amplitudes(peaks, channel_num) < middle_high,
channel_amplitudes(peaks, channel_num) > middle_low)))),
peaks)
else:
midhigh_peaks = np.ndarray([0],dtype=peak_dtype(2))
midlow_peaks = np.ndarray([0],dtype=peak_dtype(2))
neg_peaks = np.extract(
reduce(np.logical_and,
(channel_widths(peaks, channel_num) < min_gate,
channel_amplitudes(peaks, channel_num) < neg)),
peaks)
return ((pos_peaks, midhigh_peaks, midlow_peaks, neg_peaks),
(pos, middle_high, middle_low, neg),
(min_gate, max_gate))
