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pipeline_creation.py
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# 1. IMPORTING SCRIPTS
import cbgt as cbgt
from frontendhelpers import *
from tracetype import *
import init_params as par
import popconstruct as popconstruct
import qvalues as qval
import generateepochs as gen
import generate_stop_dataframe as gen_stop
from agentmatrixinit import *
from agent_timestep import timestep_mutator, multitimestep_mutator
import mega_loop as ml
# 2. NETWORK PIPELINE
# 2.1. Defining necessary codeblocks:
#MODIFIERS:
#init_params.py: to modify the neuronal default values
def codeblock_modifycelldefaults(self):
self.celldefaults = par.helper_cellparams(self.params)
def codeblock_modifypopspecific(self):
self.popspecific = par.helper_popspecific(self.pops)
def codeblock_modifyreceptordefaults(self):
self.receptordefaults = par.helper_receptor(self.receps)
def codeblock_modifybasestim(self):
self.basestim = par.helper_basestim(self.base)
def codeblock_modifydpmndefaults(self):
self.dpmndefaults = par.helper_dpmn(self.dpmns)
def codeblock_modifyd1defaults(self):
self.d1defaults = par.helper_d1(self.d1)
def codeblock_modifyd2defaults(self):
self.d2defaults = par.helper_d2(self.d2)
def codeblock_modifyactionchannels(self):
self.actionchannels = par.helper_actionchannels(self.channels)
#popconstruct.py: to modify population parameters
def codeblock_popconstruct(self):
self.popdata = popconstruct.helper_popconstruct(self.actionchannels, self.popspecific, self.celldefaults, self.receptordefaults, self.basestim, self.dpmndefaults, self.d1defaults, self.d2defaults)
def codeblock_poppathways(self):
self.pathways = popconstruct.helper_poppathways(self.popdata, self.newpathways)
#init_params.py: Q-values initialization and update
def codeblock_init_Q_support_params(self):
self.Q_support_params = qval.helper_init_Q_support_params()
def codeblock_update_Q_support_params(self,reward_val, chosen_action):
self.Q_support_params = qval.helper_update_Q_support_params(self.Q_support_params,self.reward_val,pl.chosen_action)
def codeblock_init_Q_df(self):
self.Q_df = qval.helper_init_Q_df(self.channels)
def codeblock_update_Q_df(self):
self.Q_df, self.Q_support_params, self.dpmndefaults = qval.helper_update_Q_df(self.Q_df, self.Q_support_params, self.dpmndefaults,pl.trial_num)
# 2.2 Create reward pipeline
def create_reward_pipeline(pl):
rsg = cbgt.Pipeline() #rsg is short for 'reward schedule generator'
(rsg.volatile_pattern, rsg.cp_idx, rsg.cp_indicator, rsg.noisy_pattern, rsg.t_epochs, rsg.block) = rsg[gen.GenRewardSchedule](
rsg.n_trials,
rsg.volatility,
rsg.conflict,
rsg.reward_mu,
rsg.reward_std, pl.actionchannels
).shape(6)
return rsg
def create_stop_pipeline(pl):
stop = cbgt.Pipeline() #rsg is short for 'reward schedule generator'
(stop.stop_df, stop.stop_channels_df) = stop[gen_stop.GenStopSchedule](
stop.stop_signal_probability,
pl.actionchannels,
stop.n_trials,
stop.stop_signal_channel,
stop.stop_signal_amplitude,
stop.stop_signal_onset,
stop.stop_signal_present
).shape(2)
return stop
# 2.3 Create q-values pipeline
def create_q_val_pipeline(pl):
q_val_pipe = cbgt.Pipeline()
#Defining necessary function modules:
#qvalues.py
q_val_pipe.Q_support_params = q_val_pipe[qval.helper_init_Q_support_params]()
q_val_pipe.Q_df = q_val_pipe[qval.helper_init_Q_df](pl.actionchannels)
#rsg.reward_val = q_val_pipe[qval.get_reward_value](rsg.t1_epochs,rsg.t2_epochs,pl.chosen_action,pl.trial_num)
#q_val_pipe.Q_support_params = q_val_pipe[qval.helper_update_Q_support_params](q_val_pipe.Q_support_params,rsg.reward_val,pl.chosen_action)
#(q_val_pipe.Q_df, q_val_pipe.Q_support_params, pl.dpmndefaults) = q_val_pipe[qval.helper_update_Q_df](q_val_pipe.Q_df,q_val_pipe.Q_support_params,pl.dpmndefaults,pl.trial_num).shape(3)
return q_val_pipe
# 3. CREATE CBGT PIPELINE - MAIN
def create_main_pipeline():
pl = cbgt.Pipeline()
pl.add(codeblock_modifyactionchannels)
rsg = create_reward_pipeline(pl)
stop = create_stop_pipeline(pl)
#Adding rsg pipeline to the network pipeline:
pl.add(rsg)
pl.add(stop)
#to update the Q-values
pl.trial_num = 0 #first row of Q-values df - initialization data
pl.chosen_action = None # 2 #chosen action for the current trial
#Defining necessary function modules:
#init_params.py: default neuronal values
pl.celldefaults = par.helper_cellparams()
pl.popspecific = par.helper_popspecific()
pl.receptordefaults = par.helper_receptor()
pl.basestim = par.helper_basestim()
pl.dpmndefaults = par.helper_dpmn()
pl.d1defaults = par.helper_d1()
pl.d2defaults = par.helper_d2()
#pl.actionchannels = pl[par.helper_actionchannels]()
#popconstruct.py: default population parameters
pl.popdata = pl[popconstruct.helper_popconstruct](pl.actionchannels, pl.popspecific, pl.celldefaults, pl.receptordefaults, pl.basestim, pl.dpmndefaults, pl.d1defaults, pl.d2defaults)
pl.pathways = pl[popconstruct.helper_poppathways](pl.popdata)
#popconstruct.py: to create connectivity grids
pl.connectivity_AMPA, pl.meaneff_AMPA, pl.plastic_AMPA = pl[popconstruct.helper_connectivity]('AMPA', pl.popdata, pl.pathways).shape(3)
pl.connectivity_GABA, pl.meaneff_GABA, pl.plastic_GABA = pl[popconstruct.helper_connectivity]('GABA', pl.popdata, pl.pathways).shape(3)
pl.connectivity_NMDA, pl.meaneff_NMDA, pl.plastic_NMDA = pl[popconstruct.helper_connectivity]('NMDA', pl.popdata, pl.pathways).shape(3)
#Adding codeblocks to the newtork pipeline:
pl.add(codeblock_modifycelldefaults)
pl.add(codeblock_modifypopspecific)
pl.add(codeblock_modifyreceptordefaults)
pl.add(codeblock_modifybasestim)
pl.add(codeblock_modifydpmndefaults)
pl.add(codeblock_modifyd1defaults)
pl.add(codeblock_modifyd2defaults)
pl.add(codeblock_popconstruct)
pl.add(codeblock_poppathways)
q_val_pipe = create_q_val_pipeline(pl)
#Adding the q_val_pipe to the main network pipeline pl:
pl.add(q_val_pipe)
# Agent mega loop - updated trial wise qvalues and chosen action
#mega_loop = ml.mega_loop(pl)
#pl.add(mega_loop)
return pl