def fix_dtype(self):
if not hasattr(self, 'dtype'):
self.dtype = self.infer_dtype()
if self.multi_output:
# Convert to a dict of numpy dtypes
if (not hasattr(self, 'data_kind')
or not isinstance(self.data_kind, (dict, immutabledict))):
raise ValueError(
f"{self.__class__.__name__} has multiple outputs and "
"must declare its data kind as a dict: "
"{dtypename: data kind}.")
if not isinstance(self.dtype, dict):
raise ValueError(
f"{self.__class__.__name__} has multiple outputs, so its "
"dtype must be specified as a dict: {output: dtype}.")
self.dtype = {
k: strax.to_numpy_dtype(dt)
for k, dt in self.dtype.items()
}
else:
# Convert to a numpy dtype
self.dtype = strax.to_numpy_dtype(self.dtype)
# Check required time information is present
for d in self.provides:
fieldnames = self.dtype_for(d).names
ok = 'time' in fieldnames and (
('dt' in fieldnames and 'length' in fieldnames)
or 'endtime' in fieldnames)
if not ok:
raise ValueError(
f"Missing time and endtime information for {d}")
def get_plugin(d):
nonlocal plugins
if d not in self._plugin_class_registry:
raise KeyError(f"No plugin class registered that provides {d}")
p = self._plugin_class_registry[d]()
for d in p.provides:
plugins[d] = p
p.run_id = run_id
# The plugin may not get all the required options here
# but we don't know if we need the plugin yet
self._set_plugin_config(p, run_id, tolerant=True)
p.deps = {d: get_plugin(d) for d in p.depends_on}
p.lineage = {
d: (p.__class__.__name__, p.version(run_id), {
q: v
for q, v in p.config.items() if p.takes_config[q].track
})
}
for d in p.depends_on:
p.lineage.update(p.deps[d].lineage)
if not hasattr(p, 'data_kind') and not p.multi_output:
if len(p.depends_on):
# Assume data kind is the same as the first dependency
p.data_kind = p.deps[p.depends_on[0]].data_kind
else:
# No dependencies: assume provided data kind and
# data type are synonymous
p.data_kind = p.provides[0]
if not hasattr(p, 'dtype'):
p.dtype = p.infer_dtype()
if p.multi_output:
if (not hasattr(p, 'data_kind')
or not isinstance(p.data_kind, dict)):
raise ValueError(
f"{p.__class__.__name__} has multiple outputs and "
"must declare its data kind as a dict: "
"{dtypename: data kind}.")
if not isinstance(p.dtype, dict):
raise ValueError(
f"{p.__class__.__name__} has multiple outputs, so its "
"dtype must be specified as a dict: {output: dtype}.")
p.dtype = {
k: strax.to_numpy_dtype(dt)
for k, dt in p.dtype.items()
}
else:
p.dtype = strax.to_numpy_dtype(p.dtype)
return p
