# -*- coding: utf-8 -*-

"""

This is

"""

from pyNN import nest

import sys

import mozaik

import mozaik.controller

from mozaik.controller import run_workflow, setup_logging

from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore

from parameters import ParameterSet

from model_V1_full import ThalamoCorticalModel

from experiments import create_experiments_size_V1_inactivated_overlapping

from analysis_and_visualization import perform_analysis_test

from analysis_and_visualization import perform_analysis_and_visualization

from analysis_and_visualization import perform_analysis_and_visualization_radius

try:

from mpi4py import MPI

except ImportError:

MPI = None

if MPI:

mpi_comm = MPI.COMM_WORLD

MPI_ROOT = 0

logger = mozaik.getMozaikLogger()

# Manage what is executed

# a set of variable here to manage the type of experiment and whether the pgn, cortex are there or not.

withPGN = True #

withV1 = True # open-loop

withFeedback_CxPGN = True # closed loop

withFeedback_CxLGN = True # closed loop

# Model execution

if True:

data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_size_V1_inactivated_overlapping )

data_store.save()

# or only load pickled data

else:

setup_logging()

# data_store = PickledDataStore(load=True,parameters=ParameterSet({'root_directory':'Deliverable/ThalamoCorticalModel_data_size_overlapping_____', 'store_stimuli' : False}),replace=True)

data_store = PickledDataStore(load=True,parameters=ParameterSet({'root_directory':'ThalamoCorticalModel_data_size_overlapping_____', 'store_stimuli' : False}),replace=True)

logger.info('Loaded data store')

# Analysis and Plotting

if mpi_comm.rank == MPI_ROOT:

# perform_analysis_test( data_store )

# perform_analysis_and_visualization( data_store, 'luminance', withPGN, withV1 )

# perform_analysis_and_visualization( data_store, 'contrast', withPGN, withV1 )

# perform_analysis_and_visualization( data_store, 'spatial_frequency', withPGN, withV1 )

# perform_analysis_and_visualization( data_store, 'temporal_frequency', withPGN, withV1 )

perform_analysis_and_visualization( data_store, 'size', withPGN, withV1 )

# perform_analysis_and_visualization( data_store, 'size_radius', withPGN, withV1 )

# perform_analysis_and_visualization( data_store, 'orientation', withPGN, withV1 )

# import numpy

# step = .2

# for i in numpy.arange(step, 2.+step, step):

# perform_analysis_and_visualization_radius( data_store, 'size_radius', [i-step,i], withPGN, withV1 )

data_store.save()