How to use the netpyne.specs.NetParams function in netpyne

from netpyne import specs, sim

# Network parameters
netParams = specs.NetParams()  # object of class NetParams to store the network parameters

netParams.sizeX = 100 # x-dimension (horizontal length) size in um
netParams.sizeY = 1000 # y-dimension (vertical height or cortical depth) size in um
netParams.sizeZ = 100 # z-dimension (horizontal length) size in um
netParams.propVelocity = 100.0 # propagation velocity (um/ms)
netParams.probLengthConst = 150.0 # length constant for conn probability (um)


## Population parameters
netParams.popParams['E2'] = {'cellType': 'E', 'numCells': 50, 'yRange': [100,300], 'cellModel': 'HH'}
netParams.popParams['I2'] = {'cellType': 'I', 'numCells': 50, 'yRange': [100,300], 'cellModel': 'HH'}
netParams.popParams['E4'] = {'cellType': 'E', 'numCells': 50, 'yRange': [300,600], 'cellModel': 'HH'}
netParams.popParams['I4'] = {'cellType': 'I', 'numCells': 50, 'yRange': [300,600], 'cellModel': 'HH'}
netParams.popParams['E5'] = {'cellType': 'E', 'numCells': 50, 'ynormRange': [0.6,1.0], 'cellModel': 'HH'}
netParams.popParams['I5'] = {'cellType': 'I', 'numCells': 50, 'ynormRange': [0.6,1.0], 'cellModel': 'HH'}

def __init__(self):
        self.model_interpreter = NetPyNEModelInterpreter()
        self.client = None
        self.netParams = specs.NetParams()
        self.simConfig = specs.SimConfig()
        synchronization.startSynchronization(self.__dict__)
        logging.debug("Initializing the original model")

        jupyter_geppetto.context = {'netpyne_geppetto': self}

"""
netParams.py 

High-level specifications for M1 network model using NetPyNE

Contributors: salvadordura@gmail.com
"""

from netpyne import specs
import pickle, json

netParams = specs.NetParams()   # object of class NetParams to store the network parameters

netParams.version = 49

try:
    from __main__ import cfg  # import SimConfig object with params from parent module
except:
    from cfg import cfg

#------------------------------------------------------------------------------
#
# NETWORK PARAMETERS
#
#------------------------------------------------------------------------------

#------------------------------------------------------------------------------
# General connectivity parameters

# execfile('hopbrodnetpyne.py')
# notebook: ~/nrniv/notebooks/nbnetpyne.dol
from netpyne import specs, sim

netParams = specs.NetParams()   # object of class NetParams to store the network parameters
simConfig = specs.SimConfig()   # object of class SimConfig to store the simulation configuration


# Network and connections
netParams.addPopParams('hop', {'cellType': 'PYR', 'cellModel': 'HH', 'numCells': 1})
netParams.addConnParams('hop->hop', {'preConds': {'pop': 'hop'}, 'postConds': {'pop': 'hop'}, 'weight': 0.0, 'synMech': 'inh', 'delay': 5})
netParams.addStimSourceParams('bg', {'type': 'IClamp', 'delay': 10, 'dur': int(1000), 'amp': 0.5})

# cells
netParams.addCellParams('hh_PYR',
  {'conds': {'cellType': 'PYR'}, # could have complex rule here for eg PYR cells in certain loc with particular implementation
   'secs': {'soma': {'geom' :  {'diam': 5, 'L': 5}, 'vinit' : -70.6, 
                         'mechs':  {'hh' : {'gnabar': 0.10, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}}}}}) 
netParams.addSynMechParams('exc', {'mod': 'Exp2Syn', 'tau2': 1.0, 'e': 0})
netParams.addSynMechParams('inh', {'mod': 'Exp2Syn', 'tau2': 1.0, 'e': -80})
netParams.addStimTargetParams('bg->hop', {'source': 'bg', 'sec':'soma', 'loc': 0.5, 'conds': {'pop':'hop'}})

def setNetParams (params):
    from .. import sim

    if params and isinstance(params, specs.NetParams):
        paramsDict = utils.replaceKeys(params.todict(), 'popLabel', 'pop')  # for backward compatibility
        sim.net.params = specs.NetParams(paramsDict)  # convert back to NetParams obj
    elif params and isinstance(params, dict):
        params = utils.replaceKeys(params, 'popLabel', 'pop')  # for backward compatibility
        sim.net.params = specs.NetParams(params)
    else:
        sim.net.params = specs.NetParams()

def deleteModel(self, modelParams):
        try:
            with redirect_stdout(sys.__stdout__):       
                self.netParams = specs.NetParams()
                self.simConfig = specs.SimConfig()
                self.netParams.todict()
                self.netParams.todict()
                if self.doIhaveInstOrSimData()['haveInstance']: sim.clearAll()
                self.geppetto_model = None
            return utils.getJSONReply()

        except:
            return utils.getJSONError("Error while exporting the NetPyNE model", sys.exc_info())

"""
params.py 

netParams is a dict containing a set of network parameters using a standardized structure

simConfig is a dict containing a set of simulation configurations using a standardized structure

Contributors: salvadordura@gmail.com
"""

from netpyne import specs

netParams = specs.NetParams()   # object of class NetParams to store the network parameters
simConfig = specs.SimConfig()   # object of class SimConfig to store the simulation configuration


###############################################################################
#
# MPI HH TUTORIAL PARAMS
#
###############################################################################

###############################################################################
# NETWORK PARAMETERS
###############################################################################

# Population parameters
netParams.popParams['PYR'] = {'cellModel': 'HH', 'cellType': 'PYR', 'numCells': 200} # add dict with params for this pop 

def importNeuroML2(fileName, simConfig, simulate=True, analyze=True):

        from .. import sim

        netParams = specs.NetParams()

        import pprint
        pp = pprint.PrettyPrinter(indent=4)

        print("Importing NeuroML 2 network from: %s"%fileName)

        nmlHandler = None
        
        verbose = False

        if fileName.endswith(".nml"):

            import logging
            logging.basicConfig(level=logging.WARNING, format="%(name)-19s %(levelname)-5s - %(message)s")

            from neuroml.hdf5.NeuroMLXMLParser import NeuroMLXMLParser