pyecsca.sca.trace_set.hdf5 module

Provides a traceset implemented on top of the Hierarchical Data Format (HDF5).

This traceset can be loaded “inplace” which means that it is not fully loaded into memory, and only parts of traces that are operated on are in memory. This is very useful for working with huge sets of traces that do not fit in memory.

class pyecsca.sca.trace_set.hdf5.HDF5Meta(attrs)

Bases: MutableMapping

Metadata mapping that is HDF5-compatible (items are picklable).

clear() → None.  Remove all items from D.

get(k[, d]) → D[k] if k in D, else d.  d defaults to None.

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D

update([E, ]**F) → None.  Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values() → an object providing a view on D's values

class pyecsca.sca.trace_set.hdf5.HDF5TraceSet(*traces, _file=None, _ordering=None, **kwargs)

Bases: TraceSet

Traceset based on the HDF5 (Hierarchical Data Format).

classmethod read(input)

Return type:

HDF5TraceSet

classmethod inplace(input)

Return type:

HDF5TraceSet

append(value)

Return type:

Trace

get(index)

Return type:

Trace

remove(value)

save()

close()

write(output)