def rain_split(qlwell, channel_num=0, threshold=None, pct_boundary=0.3, split_all_peaks=False):
"""
Splits between rain and non-rain. If you want the well's auto threshold to be used,
use None as a threshold parameter (the default).
If you do not want a threshold to be calculated, use '0'. (little unclear from the spec)
Returns tuple (rain, non-rain)
"""
if threshold is None:
threshold = qlwell.channels[channel_num].statistics.threshold
ok_peaks = accepted_peaks(qlwell)
prain, rain, nrain, p_thresh, mh_thresh, ml_thresh, l_thresh = \
rain_pvalues_thresholds(ok_peaks, channel_num=channel_num, threshold=threshold, pct_boundary=pct_boundary)
if split_all_peaks:
peaks = qlwell.peaks
else:
peaks = ok_peaks
# this would be useful as a standalone, but for efficiency's sake will cut out for now
rain_condition_arr = [channel_amplitudes(peaks, channel_num) > p_thresh]
if mh_thresh and ml_thresh:
rain_condition_arr.append(np.logical_and(channel_amplitudes(peaks, channel_num) > ml_thresh,
channel_amplitudes(peaks, channel_num) < mh_thresh))
rain_condition_arr.append(channel_amplitudes(peaks, channel_num) < l_thresh)
rain_condition = reduce(np.logical_or, rain_condition_arr)
nonrain_condition = np.logical_not(rain_condition)
rain = np.extract(rain_condition, peaks)
nonrain = np.extract(nonrain_condition, peaks)
return rain, nonrain
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 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 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))