def __init__(self,<EOL>cellParams={<EOL>'<STR_LIT>': '<STR_LIT>',<EOL>'<STR_LIT>': <NUM_LIT>,<EOL>'<STR_LIT>': <NUM_LIT:1.0>,<EOL>'<STR_LIT>': <NUM_LIT:0.0>,<EOL>'<STR_LIT>': <NUM_LIT:100>,<EOL>'<STR_LIT>': '<STR_LIT>',<EOL>'<STR_LIT>': <NUM_LIT>,<EOL>'<STR_LIT>': <NUM_LIT:0.1>,<EOL>'<STR_LIT>': <NUM_LIT:0.1>,<EOL>'<STR_LIT>': <NUM_LIT:0>,<EOL>'<STR_LIT>': <NUM_LIT>,<EOL>'<STR_LIT>': <NUM_LIT:0.0>,<EOL>'<STR_LIT>': False},<EOL>rand_rot_axis=[],<EOL>simulationParams={},<EOL>populationParams={<EOL>'<STR_LIT>': <NUM_LIT:1.0>,<EOL>'<STR_LIT>': <NUM_LIT>,<EOL>'<STR_LIT>': <NUM_LIT:100>,<EOL>'<STR_LIT>': -<NUM_LIT>,<EOL>'<STR_LIT>': -<NUM_LIT>},<EOL>y = '<STR_LIT>',<EOL>layerBoundaries = [[<NUM_LIT:0.0>, -<NUM_LIT>], [-<NUM_LIT>, -<NUM_LIT>]],<EOL>electrodeParams={<EOL>'<STR_LIT:N>': [[<NUM_LIT:1>, <NUM_LIT:0>, <NUM_LIT:0>], [<NUM_LIT:1>, <NUM_LIT:0>, <NUM_LIT:0>], [<NUM_LIT:1>, <NUM_LIT:0>, <NUM_LIT:0>],<EOL>[<NUM_LIT:1>, <NUM_LIT:0>, <NUM_LIT:0>], [<NUM_LIT:1>, <NUM_LIT:0>, <NUM_LIT:0>], [<NUM_LIT:1>, <NUM_LIT:0>, <NUM_LIT:0>]],<EOL>'<STR_LIT>': '<STR_LIT>',<EOL>'<STR_LIT:n>': <NUM_LIT:20>,<EOL>'<STR_LIT:r>': <NUM_LIT:5>,<EOL>'<STR_LIT>': [[-<NUM_LIT>, -<NUM_LIT>, -<NUM_LIT>, <NUM_LIT>], [<NUM_LIT:0>, <NUM_LIT:0>, <NUM_LIT:10>, <NUM_LIT:10>]],<EOL>'<STR_LIT>': None,<EOL>'<STR_LIT>': <NUM_LIT>,<EOL>'<STR_LIT:x>': [<NUM_LIT:0>, <NUM_LIT:0>, <NUM_LIT:0>, <NUM_LIT:0>, <NUM_LIT:0>, <NUM_LIT:0>],<EOL>'<STR_LIT:y>': [<NUM_LIT:0>, <NUM_LIT:0>, <NUM_LIT:0>, <NUM_LIT:0>, <NUM_LIT:0>, <NUM_LIT:0>],<EOL>'<STR_LIT:z>': [-<NUM_LIT:0.0>, -<NUM_LIT>, -<NUM_LIT>, -<NUM_LIT>, -<NUM_LIT>, -<NUM_LIT>]},<EOL>savelist=['<STR_LIT>', '<STR_LIT:x>', '<STR_LIT:y>', '<STR_LIT:z>', '<STR_LIT>', '<STR_LIT>'],<EOL>savefolder='<STR_LIT>',<EOL>calculateCSD=True,<EOL>dt_output=<NUM_LIT:1.>,<EOL>recordSingleContribFrac=<NUM_LIT:0>,<EOL>POPULATIONSEED=<NUM_LIT>,<EOL>verbose=False,<EOL>output_file='<STR_LIT>'<EOL>):
self.cellParams = cellParams<EOL>self.dt = self.cellParams['<STR_LIT>']<EOL>self.rand_rot_axis = rand_rot_axis<EOL>self.simulationParams = simulationParams<EOL>self.populationParams = populationParams<EOL>self.POPULATION_SIZE = populationParams['<STR_LIT>']<EOL>self.y = y<EOL>self.layerBoundaries = np.array(layerBoundaries)<EOL>self.electrodeParams = electrodeParams<EOL>self.savelist = savelist<EOL>self.savefolder = savefolder<EOL>self.calculateCSD = calculateCSD<EOL>self.dt_output = dt_output<EOL>self.recordSingleContribFrac = recordSingleContribFrac<EOL>self.output_file = output_file<EOL>try:<EOL><INDENT>assert int(self.dt_output / self.dt) == self.dt_output / self.dt<EOL><DEDENT>except AssertionError as ae:<EOL><INDENT>raise ae('<STR_LIT>')<EOL><DEDENT>self.decimatefrac = int(self.dt_output / self.dt)<EOL>self.POPULATIONSEED = POPULATIONSEED<EOL>self.verbose = verbose<EOL>if self.savefolder is not None:<EOL><INDENT>os.system('<STR_LIT>' %self.savefolder)<EOL><DEDENT>np.random.seed(self.POPULATIONSEED)<EOL>self.colors = []<EOL>for i in range(self.POPULATION_SIZE):<EOL><INDENT>i *= <NUM_LIT><EOL>if self.POPULATION_SIZE > <NUM_LIT:1>:<EOL><INDENT>i /= self.POPULATION_SIZE - <NUM_LIT:1.><EOL><DEDENT>else:<EOL><INDENT>i /= self.POPULATION_SIZE<EOL><DEDENT>try:<EOL><INDENT>self.colors.append(plt.cm.rainbow(int(i)))<EOL><DEDENT>except:<EOL><INDENT>self.colors.append(plt.cm.gist_rainbow(int(i)))<EOL><DEDENT><DEDENT>self.alphas = np.ones(self.POPULATION_SIZE)<EOL>self.pop_soma_pos = self.set_pop_soma_pos()<EOL>self.rotations = self.set_rotations()<EOL>self._set_up_savefolder()<EOL>self.CELLINDICES = np.arange(self.POPULATION_SIZE)<EOL>self.RANK_CELLINDICES = self.CELLINDICES[self.CELLINDICES % SIZE<EOL>== RANK]<EOL>self.output = dict((i, {}) for i in self.RANK_CELLINDICES)<EOL>
Main population class object, let one set up simulations, execute, and
compile the results. This class is suitable for subclassing for
custom cell simulation procedures, inherit things like gathering of
data written to disk.

Note that `PopulationSuper.cellsim` do not have any stimuli,
its main purpose is to gather common methods for inherited Population
objects.


Parameters
----------
cellParams : dict
    Parameters for class `LFPy.Cell`
rand_rot_axis : list
    Axis of which to randomly rotate morphs.
simulationParams : dict
    Additional args for `LFPy.Cell.simulate()`.
populationParams : dict
    Constraints for population and cell number.
y : str
    Population identifier string.
layerBoundaries : list of lists
    Each element is a list setting upper and lower layer boundary as
    floats
electrodeParams : dict
    parameters for class `LFPy.RecExtElectrode`
savelist : list
    `LFPy.Cell` arguments to save for each single-cell simulation.
savefolder : str
    path to where simulation results are stored.
calculateCSD : bool
    Exctract laminar CSD from transmembrane currents
dt_output : float
    Time resolution of output, e.g., LFP, CSD etc.
recordSingleContribFrac : float
    fraction  in [0, 1] of individual neurons in population which output
    will be stored
POPULATIONSEED : int/float
    Random seed for population, for positions etc.
verbose : bool
    Verbosity flag.
output_file : str
    formattable string for population output, e.g., '{}_population_{}'


Returns
-------
hybridLFPy.population.PopulationSuper object


See also
--------
Population, LFPy.Cell, LFPy.RecExtElectrode