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Gaussian picking uncertainty #13

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Gaussian picking uncertainty #13

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18 changes: 16 additions & 2 deletions run.py
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Original file line number Diff line number Diff line change
Expand Up @@ -505,7 +505,14 @@ def pred_fn(args, data_reader, figure_dir=None, result_dir=None, log_dir=None):
row = "{},[{}],[{}],[{}],[{}]".format(fname_batch[i].decode(), " ".join(map(str,picks_batch[i][0][0])), " ".join(map(str,picks_batch[i][0][1])),
" ".join(map(str,picks_batch[i][1][0])), " ".join(map(str,picks_batch[i][1][1])))
fclog.write(row+"\n")
picks[fname_batch[i].decode()]={"itp":picks_batch[i][0][0], "tp_prob":picks_batch[i][0][1], "its":picks_batch[i][1][0], "ts_prob":picks_batch[i][1][1]}
picks[fname_batch[i].decode()]={
"itp":picks_batch[i][0][0],
"tp_prob":picks_batch[i][0][1],
"its":picks_batch[i][1][0],
"ts_prob":picks_batch[i][1][1],
"sigma_itp":picks_batch[i][0][2],
"sigma_its":picks_batch[i][1][2],
}

if last_batch:
break
Expand All @@ -531,7 +538,14 @@ def pred_fn(args, data_reader, figure_dir=None, result_dir=None, log_dir=None):
row = "{},[{}],[{}],[{}],[{}]".format(fname_batch[i].decode(), " ".join(map(str,picks_batch[i][0][0])), " ".join(map(str,picks_batch[i][0][1])),
" ".join(map(str,picks_batch[i][1][0])), " ".join(map(str,picks_batch[i][1][1])))
fclog.write(row+"\n")
picks[fname_batch[i].decode()]={"itp":picks_batch[i][0][0], "tp_prob":picks_batch[i][0][1], "its":picks_batch[i][1][0], "ts_prob":picks_batch[i][1][1]}
picks[fname_batch[i].decode()]={
"itp":picks_batch[i][0][0],
"tp_prob":picks_batch[i][0][1],
"its":picks_batch[i][1][0],
"ts_prob":picks_batch[i][1][1],
"sigma_itp":picks_batch[i][0][2],
"sigma_its":picks_batch[i][1][2],
}
# fclog.flush()

fclog.close()
Expand Down
28 changes: 25 additions & 3 deletions util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,26 @@
from detect_peaks import detect_peaks
import logging


def gauss_sigma(pred, it):
if len(it) == 0:
return np.empty(0)
# get peak widths to cut `pred` aroud each pick
from scipy.signal import peak_widths
_, _, left, right = peak_widths(pred, it, rel_height=0.8)
sigma = []
for ii, li, ri in zip(it, left, right):
li = int(li)
ri = int(ri)
pred_cut = pred.copy()
pred_cut[:li] = 0
pred_cut[ri:] = 0
samples = np.arange(len(pred_cut))
# get gaussian sigma of `pred_cut`
sigma.append((sum(((samples-ii) * pred_cut)**2)/sum(pred_cut))**0.5)
return np.array(sigma)


def detect_peaks_thread(i, pred, fname=None, result_dir=None, args=None):
if args is None:
itp = detect_peaks(pred[i,:,0,1], mph=0.5, mpd=0.5/Config().dt, show=False)
Expand All @@ -17,6 +37,8 @@ def detect_peaks_thread(i, pred, fname=None, result_dir=None, args=None):
its = detect_peaks(pred[i,:,0,2], mph=args.ts_prob, mpd=0.5/Config().dt, show=False)
prob_p = pred[i,itp,0,1]
prob_s = pred[i,its,0,2]
sigma_itp = gauss_sigma(pred[i,:,0,1], itp)
sigma_its = gauss_sigma(pred[i,:,0,2], its)
if (fname is not None) and (result_dir is not None):
# np.savez(os.path.join(result_dir, fname[i].decode().split('/')[-1]), pred=pred[i], itp=itp, its=its, prob_p=prob_p, prob_s=prob_s)
try:
Expand All @@ -25,7 +47,7 @@ def detect_peaks_thread(i, pred, fname=None, result_dir=None, args=None):
#if not os.path.exists(os.path.dirname(os.path.join(result_dir, fname[i].decode()))):
os.makedirs(os.path.dirname(os.path.join(result_dir, fname[i].decode())), exist_ok=True)
np.savez(os.path.join(result_dir, fname[i].decode()), pred=pred[i], itp=itp, its=its, prob_p=prob_p, prob_s=prob_s)
return [(itp, prob_p), (its, prob_s)]
return [(itp, prob_p, sigma_itp), (its, prob_s, sigma_its)]

def plot_result_thread(i, pred, X, Y=None, itp=None, its=None,
itp_pred=None, its_pred=None, fname=None, figure_dir=None):
Expand Down Expand Up @@ -130,10 +152,10 @@ def plot_result_thread(i, pred, X, Y=None, itp=None, its=None,
return 0

def postprocessing_thread(i, pred, X, Y=None, itp=None, its=None, fname=None, result_dir=None, figure_dir=None, args=None):
(itp_pred, prob_p), (its_pred, prob_s) = detect_peaks_thread(i, pred, fname, result_dir, args)
(itp_pred, prob_p, sigma_itp), (its_pred, prob_s, sigma_its) = detect_peaks_thread(i, pred, fname, result_dir, args)
if (fname is not None) and (figure_dir is not None):
plot_result_thread(i, pred, X, Y, itp, its, itp_pred, its_pred, fname, figure_dir)
return [(itp_pred, prob_p), (its_pred, prob_s)]
return [(itp_pred, prob_p, sigma_itp), (its_pred, prob_s, sigma_its)]


def clean_queue(picks):
Expand Down