def infer_dtype(self):
self.input_dtype = (
strax.unpack_dtype(self.deps['event_info'].dtype)
+ [strax.unpack_dtype(self.deps['distinct_channels'].dtype)[0]])
return [
((comment, self.rename_field(name)), dt)
for (comment, name), dt in self.input_dtype]
def infer_dtype(self):
dtype = strax.unpack_dtype(
self.deps['online_peak_monitor'].dtype_for('online_peak_monitor'))
dtype += [
(('Live time', 'live_time'), np.float64),
]
return dtype
def infer_dtype(self):
return strax.unpack_dtype(
self.deps['peaklets_he'].dtype_for('peaklets_he'))
def apply_selection(self,
x,
selection_str=None,
keep_columns=None,
time_range=None,
time_selection='fully_contained'):
"""Return x after applying selections
:param x: Numpy structured array
:param selection_str: Query string or sequence of strings to apply.
:param time_range: (start, stop) range to load, in ns since the epoch
:param time_selection: Kind of time selectoin to apply:
- skip: Do not select a time range, even if other arguments say so
- touching: select things that (partially) overlap with the range
- fully_contained: (default) select things fully contained in the range
The right bound is, as always in strax, considered exclusive.
Thus, data that ends (exclusively) exactly at the right edge of a
fully_contained selection is returned.
"""
# Apply the time selections
if time_range is None or time_selection == 'skip':
pass
elif time_selection == 'fully_contained':
x = x[(time_range[0] <= x['time'])
& (strax.endtime(x) <= time_range[1])]
elif time_selection == 'touching':
x = x[(strax.endtime(x) > time_range[0])
& (x['time'] < time_range[1])]
else:
raise ValueError(f"Unknown time_selection {time_selection}")
if selection_str:
if isinstance(selection_str, (list, tuple)):
selection_str = ' & '.join(f'({x})' for x in selection_str)
mask = numexpr.evaluate(
selection_str, local_dict={fn: x[fn]
for fn in x.dtype.names})
x = x[mask]
if keep_columns:
keep_columns = strax.to_str_tuple(keep_columns)
# Construct the new dtype
new_dtype = []
for unpacked_dtype in strax.unpack_dtype(x.dtype):
field_name = unpacked_dtype[0]
if isinstance(field_name, tuple):
field_name = field_name[1]
if field_name in keep_columns:
new_dtype.append(unpacked_dtype)
# Copy over the data
x2 = np.zeros(len(x), dtype=new_dtype)
for field_name in keep_columns:
x2[field_name] = x[field_name]
x = x2
del x2
return x
