def __init__(self,<EOL>activationtimes=[<NUM_LIT:200>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT>, <NUM_LIT:1000>],<EOL>autocollect=False,<EOL>**kwargs):
CachedNetwork.__init__(self, autocollect=autocollect, **kwargs)<EOL>self.activationtimes = activationtimes<EOL>if len(activationtimes) != len(self.N_X):<EOL><INDENT>raise Exception('<STR_LIT>')<EOL><DEDENT>"""<STR_LIT>"""<EOL>if RANK == <NUM_LIT:0>:<EOL><INDENT>for i, N in enumerate(self.N_X):<EOL><INDENT>nodes = self.nodes[self.X[i]]<EOL>cell_spt = list(zip(nodes, [self.activationtimes[i]<EOL>for x in range(nodes.size)]))<EOL>cell_spt = np.array(cell_spt, dtype=[('<STR_LIT:a>', int), ('<STR_LIT:b>', float)])<EOL>np.savetxt(os.path.join(self.spike_output_path,<EOL>self.label + '<STR_LIT>'.format(self.X[i])),<EOL>cell_spt, fmt=['<STR_LIT>', '<STR_LIT>'])<EOL><DEDENT><DEDENT>COMM.barrier()<EOL>self.collect_gdf()<EOL>
Subclass of CachedNetwork

Fake nest output, where each cell in a subpopulation spike
simultaneously, and each subpopulation is activated at times given in
kwarg activationtimes.

Parameters
----------
activationtimes : list
    Each entry set spike times of all cells in each population
autocollect : bool
    whether or not to automatically gather gdf file output
**kwargs : see parent class `hybridLFPy.cachednetworks.CachedNetwork`


Returns
-------
`hybridLFPy.cachednetworks.CachedFixedSpikesNetwork` object


See also
--------
CachedNetwork, CachedNoiseNetwork,