import numpy as np

import utils

import simulation_parameters

PS = simulation_parameters.parameter_storage()

params = PS.load_params() # params stores cell numbers, etc as a dictionary

tp = np.loadtxt(params['tuning_prop_means_fn'])

# spatial distribution of inhibitory cells

hexgrid_pos = utils.set_hexgrid_positions(params, params['N_RF_X_INH'], params['N_RF_Y_INH'])

output_fn = params['inh_cell_pos_fn']

print 'debug hexgrid pos shape', hexgrid_pos.shape

print 'printing inh cell positions to:', output_fn

np.savetxt(output_fn, hexgrid_pos)

# for each exc cell calculate the predicted position of the dot from the cell's tuning prop

predicted_positions = utils.get_predicted_stim_pos(tp) # x_predicted = x + v [a.u.]

print "Predicted positions: ", params['predicted_positions_fn']

np.savetxt(params['predicted_positions_fn'], predicted_positions)

# calculate the ring of excitatory source cells for each target inhibitory cell

src_pos = predicted_positions

tgt_pos = hexgrid_pos

import CreateConnections as CC

n_ei_per_cell = int(round(params['p_ei'] * params['n_exc']))

print 'Each inh cell receives input from %d exc cells (p_ei = %.3f)' % (n_ei_per_cell, params['p_ei'])

output_indices, output_distances = CC.get_exc_inh_connections(src_pos, tgt_pos, tp, n=n_ei_per_cell)

output_fn1 = params['exc_inh_adjacency_list_fn']

output_fn2 = params['exc_inh_distances_fn']

print 'saving exc-inh connections', output_fn1

print 'saving exc-inh distances ', output_fn2

np.savetxt(output_fn1, output_indices)

np.savetxt(output_fn2, output_distances)

# TODO: convert

#self.params['exc_inh_weights_fn'] = '%sexc_to_inh_indices.dat' % (self.params['connections_folder']) # same format as exc_inh_distances_fn, containing the exc - inh weights