def onclick(event): import math ts = event.xdata if ts is None: return diffs = flex.abs(t - ts) ts = t[flex.first_index(diffs, flex.min(diffs))] print(get_paths_from_timestamps([ts], tag="shot", ext=ext)[0])
def _analyse_periodogram(self, pgram):
"""Use the periodogram pgram to suggest a suitable interval width for
scan-varying refinement to account for the major variation in residuals"""
if pgram is None:
return None
# determine a baseline from the high frequency noise
bl = flex.median(pgram.spec.select(pgram.freq > 0.25))
# look for peaks greater than 5 times this baseline. We expect one at
# low frequency
cutoff = 5 * bl
peaks = pgram.spec > cutoff
# find where this peak falls off below the cutoff and return the cycle
# period at half that frequency (this is a heuristic that often seems to
# give sensible results)
pk_start = flex.first_index(peaks, True)
if pk_start is None:
return None
peaks = peaks[pk_start:]
idx = pk_start + flex.first_index(peaks, False) - 1
if idx is not None:
f1 = pgram.freq[idx]
s1 = pgram.spec[idx]
try:
f2 = pgram.freq[idx + 1]
s2 = pgram.spec[idx + 1]
ds = cutoff - s1
df = (f2 - f1) * ds / (s2 - s1)
freq = f1 + df
except IndexError:
freq = f1
period = 2.0 * 1.0 / freq
else:
period = None
return period
def onclick(event): import math ts = event.xdata diffs = flex.abs(t - ts) ts = t[flex.first_index(diffs, flex.min(diffs))] print get_paths_from_timestamps([ts], tag="shot")[0]
