def dependencies_by_kind(self):
"""Return dependencies grouped by data kind
i.e. {kind1: [dep0, dep1], kind2: [dep, dep]}
:param require_time: If True, one dependency of each kind
must provide time information. It will be put first in the list.
If require_time is omitted, we will require time only if there is
more than one data kind in the dependencies.
"""
return strax.group_by_kind(self.depends_on, plugins=self.deps)
def wrapped_f(context: strax.Context, run_id: str, **kwargs):
# Validate arguments
known_kwargs = (
'time_range seconds_range time_within time_selection '
'ignore_time_warning '
'selection_str t_reference to_pe config').split()
for k in kwargs:
if k not in known_kwargs and k not in parameters:
# Python itself also raises TypeError for invalid kwargs
raise TypeError(f"Unknown argument {k} for {f.__name__}")
if 'config' in kwargs:
context = context.new_context(config=kwargs['config'])
if 'config' in parameters:
kwargs['config'] = context.config
# Say magic words to enable holoviews
if hv_bokeh:
global _hv_bokeh_initialized
if not _hv_bokeh_initialized:
import holoviews
holoviews.extension('bokeh')
_hv_bokeh_initialized = True
# TODO: This is a placeholder until the corrections system
# is more fully developed
if 'to_pe' in parameters and 'to_pe' not in kwargs:
kwargs['to_pe'] = straxen.get_to_pe(
run_id, context.config['gain_model'],
context.config['n_tpc_pmts'])
# Prepare selection arguments
kwargs['time_range'] = context.to_absolute_time_range(
run_id,
targets=requires,
**{
k: kwargs.get(k)
for k in ('time_range seconds_range time_within'.split())
})
kwargs.setdefault('time_selection', default_time_selection)
kwargs.setdefault('selection_str', None)
kwargs['t_reference'] = context.estimate_run_start(
run_id, requires)
if warn_beyond_sec is not None and not kwargs.get(
'ignore_time_warning'):
tr = kwargs['time_range']
if tr is None:
sec_requested = float('inf')
else:
sec_requested = (tr[1] - tr[0]) / int(1e9)
if sec_requested > warn_beyond_sec:
tr_str = "the entire run" if tr is None else f"{sec_requested} seconds"
raise ValueError(
f"The author of this mini analysis recommends "
f"not requesting more than {warn_beyond_sec} seconds. "
f"You are requesting {tr_str}. If you wish to proceed, "
"pass ignore_time_warning = True.")
# Load required data, if any
if len(requires):
deps_by_kind = strax.group_by_kind(requires, context=context)
for dkind, dtypes in deps_by_kind.items():
if dkind in kwargs:
# Already have data, just apply cuts
kwargs[dkind] = context.apply_selection(
kwargs[dkind],
selection_str=kwargs['selection_str'],
time_range=kwargs['time_range'],
time_selection=kwargs['time_selection'])
else:
kwargs[dkind] = context.get_array(
run_id,
dtypes,
selection_str=kwargs['selection_str'],
time_range=kwargs['time_range'],
time_selection=kwargs['time_selection'],
# Arguments for new context, if needed
config=kwargs.get('config'),
register=kwargs.get('register'),
storage=kwargs.get('storage', tuple()))
# If user did not give time kwargs, but the function expects
# a time_range, try to add one based on the time range of the data
base_dkind = list(deps_by_kind.keys())[0]
x = kwargs[base_dkind]
if len(x) and kwargs.get('time_range') is None:
x0 = x.iloc[0] if isinstance(x, pd.DataFrame) else x[0]
try:
kwargs.setdefault('time_range',
(x0['time'], strax.endtime(x).max()))
except AttributeError:
# If x is a holoviews dataset, this will fail.
pass
if 'seconds_range' in parameters:
if kwargs.get('time_range') is None:
scr = None
else:
scr = tuple([(t - kwargs['t_reference']) / int(1e9)
for t in kwargs['time_range']])
kwargs.setdefault('seconds_range', scr)
kwargs.setdefault('run_id', run_id)
kwargs.setdefault('context', context)
if 'kwargs' in parameters:
# Likely this will be passed to another mini-analysis
to_pass = kwargs
# Do not pass time_range and seconds_range both (unless explicitly requested)
# strax does not like that
if 'seconds_range' in to_pass and not 'seconds_range' in parameters:
del to_pass['seconds_range']
if 'time_within' in to_pass and not 'time_within' in parameters:
del to_pass['time_within']
else:
# Pass only arguments the function wants
to_pass = {k: v for k, v in kwargs.items() if k in parameters}
return f(**to_pass)
def dependencies_by_kind(self, require_time=None):
return strax.group_by_kind(self.depends_on,
plugins=self.deps,
require_time=require_time)
def wrapped_f(context: strax.Context, run_id: str, **kwargs):
# Validate arguments
known_kwargs = (
'time_range seconds_range time_within time_selection '
'selection_str t_reference to_pe config').split()
for k in kwargs:
if k not in known_kwargs and k not in parameters:
# Python itself also raises TypeError for invalid kwargs
raise TypeError(f"Unknown argument {k} for {f.__name__}")
if 'config' in kwargs:
context = context.new_context(config=kwargs['config'])
# Say magic words to enable holoviews
if hv_bokeh:
global _hv_bokeh_initialized
if not _hv_bokeh_initialized:
hv.extension('bokeh')
_hv_bokeh_initialized = True
# TODO: This is a placeholder until the corrections system
# is more fully developed
if 'to_pe' in parameters and 'to_pe' not in kwargs:
kwargs['to_pe'] = nEXO_strax.get_to_pe(
run_id,
'https://raw.githubusercontent.com/XENONnT/'
'strax_auxiliary_files/master/to_pe.npy')
# Prepare selection arguments
kwargs['time_range'] = context.to_absolute_time_range(
run_id,
targets=requires,
**{k: kwargs.get(k)
for k in ('time_range seconds_range time_within'.split())})
kwargs.setdefault('time_selection', 'fully_contained')
kwargs.setdefault('selection_str', None)
if ('t_reference' in parameters
and kwargs.get('t_reference') is None):
kwargs['t_reference'] = context.estimate_run_start(
run_id, requires)
# Load required data
deps_by_kind = strax.group_by_kind(
requires, context=context, require_time=False)
for dkind, dtypes in deps_by_kind.items():
if dkind in kwargs:
# Already have data, just apply cuts
kwargs[dkind] = context.apply_selection(
kwargs[dkind],
selection_str=kwargs['selection_str'],
time_range=kwargs['time_range'],
time_selection=kwargs['time_selection'])
else:
kwargs[dkind] = context.get_array(
run_id,
dtypes,
selection_str=kwargs['selection_str'],
time_range=kwargs['time_range'],
time_selection=kwargs['time_selection'])
# If user did not give time kwargs, but the function expects
# a time_range, add them based on the time range of the data
# (if there is no data, otherwise give (NaN, NaN))
if kwargs.get('time_range') is None:
base_dkind = list(deps_by_kind.keys())[0]
x = kwargs[base_dkind]
x0 = x.iloc[0] if isinstance(x, pd.DataFrame) else x[0]
kwargs['time_range'] = (
(x0['time'], strax.endtime(x).max()) if len(x)
else (float('nan'), float('nan')))
# Pass only the arguments the function wants
to_pass = dict()
for k in parameters:
if k == 'run_id':
to_pass['run_id'] = run_id
elif k == 'context':
to_pass['context'] = context
elif k in kwargs:
to_pass[k] = kwargs[k]
# If we get here, let's hope the function defines a default...
return f(**to_pass)
