def get_arguments():
info_txt = 'Usage: python plot_activity_as_colormap.py FOLDER_NAME CELL_TYPE [PATTERN_NR]'
try:
folder = sys.argv[1]
params_fn = os.path.abspath(
folder) + '/Parameters/simulation_parameters.json'
param_tool = simulation_parameters.parameter_storage(
params_fn=params_fn)
except:
print info_txt
print 'Taking the parameters currently in simulation_parameters.py'
param_tool = simulation_parameters.parameter_storage()
params = param_tool.params
print 'debug n_cells', params['n_cells']
try:
cell_type = sys.argv[2]
except:
print 'Missing cell_type argument'
print info_txt
exit(1)
try:
pn = int(sys.argv[3])
except:
print info_txt
print 'Plotting pattern 0'
pn = 0
return params, cell_type, pn
def get_arguments():
info_txt = 'Usage: python plot_activity_as_colormap.py FOLDER_NAME CELL_TYPE [PATTERN_NR]'
try:
folder = sys.argv[1]
params_fn = os.path.abspath(folder) + '/Parameters/simulation_parameters.json'
param_tool = simulation_parameters.parameter_storage(params_fn=params_fn)
except:
print info_txt
print 'Taking the parameters currently in simulation_parameters.py'
param_tool = simulation_parameters.parameter_storage()
params = param_tool.params
print 'debug n_cells', params['n_cells']
try:
cell_type = sys.argv[2]
except:
print 'Missing cell_type argument'
print info_txt
exit(1)
try:
pn = int(sys.argv[3])
except:
print info_txt
print 'Plotting pattern 0'
pn = 0
return params, cell_type, pn
def __init__(self, params=None, **kwargs):
print 'BasicPlotter'
if params == None:
self.network_params = simulation_parameters.parameter_storage() # network_params class containing the simulation parameters
self.params = self.network_params.load_params() # params stores cell numbers, etc as a dictionary
else:
self.params = params
self.subfig_cnt = 1
self.n_fig_x = kwargs.get('n_fig_x', 2)
self.n_fig_y = kwargs.get('n_fig_y', 2)
self.tuning_prop = np.loadtxt(self.params['tuning_prop_means_fn'])
assert (self.tuning_prop[:, 0].size == self.params['n_exc']), 'Number of cells does not match in %s and simulation_parameters!\n Wrong tuning_prop file?' % self.params['tuning_prop_means_fn']
# figure details
fig_width_pt = 800.0 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inch
golden_mean = (np.sqrt(5)-1.0)/2.0 # Aesthetic ratio
fig_width = fig_width_pt*inches_per_pt # width in inches
fig_height = fig_width*golden_mean # height in inches
# fig_size = [fig_width,fig_height]
fig_size = [fig_height,fig_width]
params = {#'backend': 'png',
'titel.fontsize': 16,
'axes.labelsize' : 12,
'text.fontsize': 12,
'figure.figsize': fig_size}
pylab.rcParams.update(params)
def return_plot(cell_gids=[], subplot_code=111, fig=None, input_fn_base=None, motion_params=None):
network_params = simulation_parameters.parameter_storage() # network_params class containing the simulation parameters
params = network_params.load_params() # params stores cell numbers, etc as a dictionary
if input_fn_base == None:
input_fn_base = params['input_rate_fn_base']
fn = params['tuning_prop_means_fn']
if motion_params == None:
mp = params['motion_params']
else:
mp = motion_params
tp = np.loadtxt(fn)
if len(cell_gids) == 0:
n_cells =15
cell_gids = np.random.randint(0, params['n_exc'], n_cells)
n_cells = len(cell_gids)
ms = 5# markersize for scatterplots
bg_color = 'w'
pylab.rcParams['lines.markeredgewidth'] = 0
# input_sum = np.zeros(n_cells)
# for i, gid in enumerate(cell_gids):
# input_fn = input_fn_base + str(gid) + '.dat'
# rate = np.loadtxt(input_fn)
# input_sum[i] = rate.sum()
# input_max = input_sum.max()
if fig == None:
fig = pylab.figure(facecolor=bg_color)
ax = fig.add_subplot(subplot_code)
colors = ['b', 'g']
for i, gid in enumerate(cell_gids):
x, y, u, v = tp[gid, :]
# print 'tp[%d]:' % (gid), tp[gid, :]
# h = 240.
# l = 1. - 0.5 * input_sum[i] / input_max
# s = 1. # saturation
# assert (0 <= h and h < 360)
# assert (0 <= l and l <= 1)
# assert (0 <= s and s <= 1)
# (r, g, b) = utils.convert_hsl_to_rgb(h, s, l)
# ax.plot(x, y, 'o', c=(r,g,b), markersize=ms)
ax.plot(x, y, 'o', c=colors[i%len(colors)], markersize=ms)
ax.quiver(x, y, u, v, angles='xy', scale_units='xy', scale=1, width=0.02)#, headwidth=6)
# plot stimulus
stim_color = 'y'
ax.quiver(mp[0], mp[1], mp[2], mp[3], angles='xy', scale_units='xy', scale=1, color=stim_color, headwidth=6, width=0.02)
ax.annotate('Stimulus', (mp[0]+.5*mp[2], mp[1]+0.1), fontsize=12, color=stim_color)
ax.set_xlim((0, 1))
ax.set_ylim((0, 1))
#output_fn_fig = 'delme_test.png'
#print "Saving figure: ", output_fn_fig
#pylab.savefig(output_fn_fig)#, facecolor=bg_color)
return ax
def __init__(self, params=None, comm=None, data_fn=None, sim_cnt=0):
if params == None:
self.network_params = simulation_parameters.parameter_storage() # network_params class containing the simulation parameters
self.params = self.network_params.load_params() # params stores cell numbers, etc as a dictionary
else:
self.params = params
self.no_spikes = False
self.comm = comm
self.n_fig_x = 2
self.n_fig_y = 2
self.tuning_params = np.loadtxt(self.params['tuning_prop_means_fn'])
# define parameters
self.n_cells = self.params['n_exc']
self.time_binsize = 20 # [ms]
self.n_bins = int((self.params['t_sim'] / self.time_binsize) )
self.time_bins = [self.time_binsize * i for i in xrange(self.n_bins)]
self.t_axis = np.arange(0, self.n_bins * self.time_binsize, self.time_binsize)
self.n_good = self.params['n_exc'] * .10 # fraction of 'interesting' cells
print 'Number of cells with \'good\' tuning_properties = ', self.n_good
# create data structures
self.nspikes = np.zeros(self.n_cells) # summed activity
self.nspikes_binned = np.zeros((self.n_cells, self.n_bins)) # binned activity over time
self.spiketrains = [[] for i in xrange(self.n_cells)]
self.tuning_prop = np.loadtxt(self.params['tuning_prop_means_fn'])
# sort the cells by their proximity to the stimulus into 'good_gids' and the 'rest'
# cell in 'good_gids' should have the highest response to the stimulus
print 'utils.sort_gids_by_distance_to_stimulus'
all_gids, all_distances = utils.sort_gids_by_distance_to_stimulus(self.tuning_prop, self.params['motion_params'], self.params)
self.good_gids, self.good_distances = all_gids[0:self.n_good], all_distances[0:self.n_good]
print 'Saving gids to record to', self.params['gids_to_record_fn']
np.savetxt(self.params['gids_to_record_fn'], np.array(self.good_gids), fmt='%d')
self.rest_gids = range(self.n_cells)
for gid in self.good_gids:
self.rest_gids.remove(gid)
fig_width_pt = 800.0 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inch
golden_mean = (np.sqrt(5)-1.0)/2.0 # Aesthetic ratio
fig_width = fig_width_pt*inches_per_pt # width in inches
fig_height = fig_width*golden_mean # height in inches
fig_size = [fig_width,fig_height]
params = {#'backend': 'png',
# 'axes.labelsize': 10,
# 'text.fontsize': 10,
'legend.fontsize': 10,
# 'xtick.labelsize': 8,
# 'ytick.labelsize': 8,
# 'text.usetex': True,
'figure.figsize': fig_size}
pylab.rcParams.update(params)
def plot_conductances_vs_time(params=None, comm=None, data_fn=None, inh_spikes = None):
t_start = time.time()
if params== None:
network_params = simulation_parameters.parameter_storage() # network_params class containing the simulation parameters
# P = network_params.load_params() # params stores cell numbers, etc as a dictionary
params = network_params.params
sim_cnt = 0
if data_fn == None:
data_fn = params['exc_spiketimes_fn_merged'] + '%d.ras' % (sim_cnt)
# if inh_spikes == None:
# inh_spikes = params['inh_spiketimes_fn_merged'] + '%d.ras' % (sim_cnt)
plotter = P.PlotConductances(params, comm, data_fn)
plotter.load_spiketimes()
if plotter.no_spikes:
return
output_fn_base = '%s%s_wsigmaX_%.2f_wsigmaV%.2f_wthresh%.1e' % (params['grouped_actitivty_fig_fn_base'], params['connectivity_code'], \
params['w_sigma_x'], params['w_sigma_v'], params['w_thresh_connection'])
# fig 1
# neuronal level
plotter.create_fig() # create an empty figure
plotter.plot_rasterplot('exc', 1) # 1
plotter.plot_rasterplot('inh', 2) # 2
plotter.plot_group_spikes_vs_time(3) # 3
output_fn = output_fn_base + '_0.png'
print 'Saving figure to:', output_fn
pylab.savefig(output_fn)
# output_fn = '%sgoodcell_connections_%s_wsigmaX_%.2f_wsigmaV%.2f_wthresh%.1e.png' % (params['figures_folder'], params['connectivity_code'], \
# params['w_sigma_x'], params['w_sigma_v'], params['w_thresh_connection'])
# plotter.create_fig() # create an empty figure
# plotter.plot_good_cell_connections(1) # subplot 1 + 2
# print 'Saving figure to:', output_fn
# pylab.savefig(output_fn)
# fig 2
plotter.create_fig() # create an empty figure
plotter.plot_input_cond(1) # subplot 1 + 2
plotter.plot_conductances()
output_fn = output_fn_base + '_1.png'
print 'Saving figure to:', output_fn
pylab.savefig(output_fn)
plotter.create_fig() # create an empty figure
plotter.plot_input_cond()
t_stop = time.time()
t_run = t_stop - t_start
print "PlotConductance duration: %d sec or %.1f min for %d cells (%d exc, %d inh)" % (t_run, (t_run)/60., \
params['n_cells'], params['n_exc'], params['n_inh'])
def sort_cells_by_distance_to_stimulus(n_cells, verbose=True):
import simulation_parameters
network_params = simulation_parameters.parameter_storage() # network_params class containing the simulation parameters
params = network_params.load_params() # params stores cell numbers, etc as a dictionary
tp = np.loadtxt(params['tuning_prop_means_fn'])
mp = params['motion_params']
indices, distances = sort_gids_by_distance_to_stimulus(tp , mp, params) # cells in indices should have the highest response to the stimulus
print 'Motion parameters', mp
print 'GID\tdist_to_stim\tx\ty\tu\tv\t\t'
if verbose:
for i in xrange(n_cells):
gid = indices[i]
print gid, '\t', distances[i], tp[gid, :]
return indices, distances
def __init__(self, param_fn=None, spiketimes_fn=None):
"""
params : dictionary or NeuroTools.parameters ParameterSet
"""
if params == None:
self.network_params = simulation_parameters.parameter_storage() # network_params class containing the simulation parameters
self.params = self.network_params.load_params() # params stores cell numbers, etc as a dictionary
else:
self.params = params
self.no_spikes = False
self.n_fig_x = 2
self.n_fig_y = 2
self.n_cells = self.params['n_exc']
self.nspikes = np.zeros(self.n_cells) # summed activity
self.spiketrains = [[] for i in xrange(self.n_cells)]
self.load_spiketimes(data_fn)
def plot_input_colormap(params=None, data_fn=None, inh_spikes = None):
if params== None:
network_params = simulation_parameters.parameter_storage() # network_params class containing the simulation parameters
# P = network_params.load_params() # params stores cell numbers, etc as a dictionary
params = network_params.params
if data_fn == None:
if params.has_key('merged_input_spiketrains_fn'):
output_fn = params['merged_input_spiketrains_fn']
else:
params['merged_input_spiketrains_fn'] = "%sinput_spiketrain_merged.dat" % (params['input_folder'])
data_fn = params['merged_input_spiketrains_fn']
if not os.path.exists(data_fn):
merge_input_spiketrains(params)
plotter = P.PlotPrediction(params, data_fn)
pylab.rcParams['axes.labelsize'] = 14
pylab.rcParams['axes.titlesize'] = 16
if plotter.no_spikes:
return
plotter.compute_v_estimates()
# plotter.compute_position_estimates()
# plotter.compute_theta_estimates()
# fig 1
# neuronal level
output_fn_base = params['figures_folder'] + 'input_colormap.png'
plotter.create_fig() # create an empty figure
pylab.subplots_adjust(left=0.07, bottom=0.07, right=0.97, top=0.93, wspace=0.3, hspace=.2)
plotter.n_fig_x = 2
plotter.n_fig_y = 2
# plotter.plot_rasterplot('exc', 1) # 1
# plotter.plot_rasterplot('inh', 2) # 2
plotter.plot_vx_grid_vs_time(1) # 3
plotter.plot_vy_grid_vs_time(2) # 4
plotter.plot_x_grid_vs_time(3, ylabel='x-position of stimulus')
plotter.plot_y_grid_vs_time(4, ylabel='y-position of stimulus')
output_fn = output_fn_base + '_0.png'
print 'Saving figure to:', output_fn
pylab.savefig(output_fn)
def __init__(self, params=None):
if params == None:
network_params = simulation_parameters.parameter_storage() # network_params class containing the simulation parameters
# P = network_params.load_params() # params stores cell numbers, etc as a dictionary
self.params = network_params.params
else:
self.params = params
self.n_exc = self.params['n_exc']
self.output = []
self.g_in_histograms = []
self.output_fig = self.params['conductances_fig_fn_base']
self.n_good = self.params['n_exc'] * .05 # fraction of 'good' (well-tuned) cells
print 'Number of \'good\' (well-tuned) cells:', self.n_good
self.no_spikes = False
self.load_nspikes()
self.conn_dict = {}
for conn_type in self.params['conn_types']:
print 'Calling utils.get_conn_dict(..., %s)' % conn_fn
conn_fn = self.params['conn_list_%s_fn' % conn_type]
self.conn_dict[conn_type] = utils.get_conn_dict(self.params, conn_fn)
fig_width_pt = 800.0 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inch
golden_mean = (np.sqrt(5)-1.0)/2.0 # Aesthetic ratio
fig_width = fig_width_pt*inches_per_pt # width in inches
fig_height = fig_width*golden_mean # height in inches
fig_size = [fig_width,fig_height]
params = {#'backend': 'png',
'axes.labelsize': 12,
# 'text.fontsize': 14,
# 'legend.fontsize': 10,
# 'xtick.labelsize': 8,
# 'ytick.labelsize': 8,
# 'text.usetex': True,
'figure.figsize': fig_size}
pylab.rcParams.update(params)
pylab.subplots_adjust(bottom=0.30)
def __init__(self, argv):
if len(argv) > 1:
if argv[1].isdigit():
gid = int(argv[1])
else:
param_fn = argv[1]
if os.path.isdir(param_fn):
param_fn += '/Parameters/simulation_parameters.json'
print '\nLoading parameters from %s\n' % (param_fn)
f = file(param_fn, 'r')
params = json.load(f)
else:
print '\nLoading the default paremters...\n'
import simulation_parameters
ps = simulation_parameters.parameter_storage()
params = ps.params
self.params = params
self.conn_list_loaded = [False, False, False, False]
self.conn_mat_loaded = [False, False, False, False]
self.conn_lists = {}
def __init__(self, params=None, comm=None):
if params == None:
network_params = simulation_parameters.parameter_storage() # network_params class containing the simulation parameters
params = network_params.load_params() # params stores cell numbers, etc as a dictionary
print 'Merging connlists ...'
os.system('python merge_connlists.py')
else:
self.params = params
print 'Assuming that \n\tpython merge_connlists.py \nhas been called before in the directory %s' % params['folder_name']
self.comm = comm
if comm != None:
self.pc_id, self.n_proc = comm.rank, comm.size
self.conn_lists = {}
self.n_fig_x = 1
self.n_fig_y = 1
# cell markers
self.markersize_cell = 10
self.markersize_min = 3
self.markersize_max = 12
self.shaft_width = 0.005
self.conn_type_dict = {'e' : 'excitatory', 'i' : 'inhibitory'}
def __init__(self, param_fn=None, spiketimes_fn=None):
"""
params : dictionary or NeuroTools.parameters ParameterSet
"""
print "debug", type(param_fn)
if param_fn == None:
print "Loading default parameters stored in simulation_parameters.py"
self.network_params = simulation_parameters.parameter_storage() # network_params class containing the simulation parameters
self.params = self.network_params.load_params() # params stores cell numbers, etc as a dictionary
self.params_fn = self.params['params_fn']
else:
if type(param_fn) != type(""): raise TypeError("File name expected for param_fn")
self.params_fn = param_fn
self.params = ntp.ParameterSet(param_fn)
self.params
self.spiketimes_fn = spiketimes_fn
print os.path.abspath(self.params_fn)
print os.path.abspath(self.spiketimes_fn)
fig_width_pt = 800.0 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inch
golden_mean = (np.sqrt(5)-1.0)/2.0 # Aesthetic ratio
fig_width = fig_width_pt*inches_per_pt # width in inches
fig_height = fig_width*golden_mean # height in inches
fig_size = [fig_width,fig_height]
params = {#'backend': 'png',
# 'axes.labelsize': 10,
# 'text.fontsize': 10,
# 'legend.fontsize': 10,
# 'xtick.labelsize': 8,
# 'ytick.labelsize': 8,
# 'text.usetex': True,
'figure.figsize': fig_size}
pylab.rcParams.update(params)
if __name__ == '__main__':
info_txt = \
"""
Usage:
python plot_pattern_completion_rivalry.py [PATTERN_NUMBER]
"""
# python plot_pattern_completion_rivalry.py [TRAINING_FOLDER] [TEST_FOLDER] [PATTERN_NUMBER_MIN] [PATTERN_NUMBER_MAX]
assert (len(sys.argv) > 1), 'ERROR: pattern number not given\n' + info_txt
pn_max = int(sys.argv[1])
training_folder = 'Cluster_OcOcLearning_nGlom40_nHC12_nMC30_vqOvrlp4_np50_OcOnly/'
plot_folder = 'Cluster_PatternCompletionTestPostLearningWithSniff_fOR0.50_nGlom40_nHC12_nMC30_vqOvrlp4_np50_FullSystem/'
params_fn = os.path.abspath(plot_folder) + '/Parameters/simulation_parameters.json'
param_tool = simulation_parameters.parameter_storage(params_fn=params_fn)
params = param_tool.params
training_params_fn = os.path.abspath(training_folder) + '/Parameters/simulation_parameters.json'
training_param_tool = simulation_parameters.parameter_storage(params_fn=training_params_fn)
training_params = training_param_tool.params
cell_type = 'readout'
# cell_type = 'pyr'
# cell_type = 'mit'
for pn in xrange(pn_max):
training_fn = training_params['%s_spiketimes_merged_fn_base' % cell_type] + str(pn) + '.dat'
test_fn = params['%s_spiketimes_merged_fn_base' % cell_type] + str(pn) + '.dat'
plot_params['figure.subplot.left'] = .11
plot_params['figure.subplot.top'] = .92
plot_params['figure.subplot.right'] = .98
def return_plot(iteration=None, fig=None):
network_params = simulation_parameters.parameter_storage() # network_params class containing the simulation parameters
params = network_params.load_params() # params stores cell numbers, etc as a dictionary
fn = params['tuning_prop_means_fn']
tp = np.loadtxt(fn)
if iteration == None:
mp = params['motion_params']
else:
motion_params_fn = params['parameters_folder'] + 'input_params.txt'
all_mp = np.loadtxt(motion_params_fn)
mp = all_mp[iteration, :]
n_cells = tp[:, 0].size
#n_cells = 10
ms = 10 # markersize for scatterplots
bg_color = 'w'
pylab.rcParams['lines.markeredgewidth'] = 0
input_sum = np.zeros(n_cells)
if iteration == None:
input_fn_base = params['input_rate_fn_base']
fn_ending = '.npy'
else:
input_fn_base = params['folder_name'] + 'TrainingInput_%d/abstract_input_' % iteration
fn_ending = '.dat'
for i in xrange(n_cells):
input_fn = input_fn_base + str(i) + fn_ending
if iteration == None:
rate = np.load(input_fn)
else:
rate = np.loadtxt(input_fn)
input_sum[i] = rate.sum()
input_max = input_sum.max()
print 'input_max', input_max
idx = input_sum.argsort()
n_mac = int(round(params['n_exc'] * 0.05))
mac = idx[-n_mac:]
print 'motion stimulus', mp
print 'most activated cells:'
for i in mac:
dist = tp[i, :] - mp
# print i, tp[i, :], np.sqrt(np.dot(dist, dist)), input_sum[i], input_max
if fig == None:
fig = pylab.figure(facecolor=bg_color)
ax = fig.add_subplot(211)
# h = 240.
# s = 1. # saturation
o_min = 0.
o_max = input_max
norm = matplotlib.mpl.colors.Normalize(vmin=o_min, vmax=o_max)
m = matplotlib.cm.ScalarMappable(norm=norm, cmap=cm.binary)#PuBu)#jet)
m.set_array(np.arange(o_min, o_max, (o_max-o_min)/1000.))
for i in idx:
if i in mac:
print 'debug', i, input_sum[i], input_max, input_sum[i] / input_max
c = m.to_rgba(input_sum[i])
# l = 1. - 0.5 * input_sum[i] / input_max
# assert (0 <= h and h < 360)
# assert (0 <= l and l <= 1)
# assert (0 <= s and s <= 1)
# (r, g, b) = utils.convert_hsl_to_rgb(h, s, l)
x, y, u, v = tp[i, :]
ax.plot(x, y, 'o', c=c, markersize=ms)
# ax.plot(x, y, 'o', c=(r,g,b), markersize=ms)
# if l < .7:
if i in mac:
ax.annotate('%d' % i, (x + np.random.rand() * 0.02, y + np.random.rand() * 0.02), fontsize=10)
stim_color = 'k'
ax.quiver(mp[0], mp[1], mp[2], mp[3], angles='xy', scale_units='xy', scale=1, color=stim_color, headwidth=4, pivot='tail')
ax.annotate('Stimulus', (mp[0]+.5*mp[2], mp[1]+0.1), fontsize=12, color=stim_color)
fig.colorbar(m)
ax.set_xlim((-.05, 1.05))
ax.set_ylim((-.05, 1.05))
if iteration == None:
iteration = 0
ax.set_title('Abstract activtation iteration %d' % iteration)
ax = fig.add_subplot(212)
ax.bar(range(params['n_exc']), input_sum, width=1)
output_fn = params['figures_folder'] + 'abstract_activation_%d.png' % (iteration)
print "Saving figure: ", output_fn
pylab.savefig(output_fn)#, facecolor=bg_color)
return ax
colors = m.to_rgba(prj_matrix[:, hc])
cax = ax.scatter(mt_pos[:, 0],
mt_pos[:, 1],
mt_pos[:, 2],
c=colors,
marker='o',
linewidth='5',
edgecolor=colors)
output_fig = self.params[
'figure_folder'] + '/prj_mt_hc%d_ovrlp%d.png' % (
hc, self.params['vq_ob_oc_overlap'])
print 'Saving to:', output_fig
pylab.savefig(output_fig)
pylab.show()
if __name__ == '__main__':
try:
folder = sys.argv[1]
params_fn = os.path.abspath(
folder) + '/Parameters/simulation_parameters.json'
param_tool = simulation_parameters.parameter_storage(
params_fn=params_fn)
except:
param_tool = simulation_parameters.parameter_storage()
params = param_tool.params
P = Plotter(params)
P.plot_mt_cells_to_hc(hc=2)
# EPTH -> OB connections are not affected by the pattern
print "Creating connections: orn -> mit"
ConnectionClass = CreateObConnections.CreateObConnections(params)
ConnectionClass.connect_orn_mit()
ConnectionClass.connect_orn_pg()
ConnectionClass.connect_pg_mit_serial()
ConnectionClass.connect_pg_mit_reciprocal()
ConnectionClass.connect_mt_gran_local()
ConnectionClass.connect_mt_gran_global()
if __name__ == '__main__':
print info_txt
# ------------ I N I T -----------------------------
# The simulation_parameters module defines a class for simulation parameter storage
param_tool = simulation_parameters.parameter_storage()
# params is the dictionary with all parameters
params = param_tool.params
if not (params['test_pattern_rivalry']):
print '\n\n\tThis scipt is not intended to be used with the train_pattern_rivalry flag!\nWill now quit'
exit(1)
print 'New folder:', params['folder_name']
ok = raw_input('\nContinue to create this folder structure? Parameters therein will be overwritten\n\ty / Y / blank = OK; anything else --> exit\n')
if not ((ok == '') or (ok.capitalize() == 'Y')):
print 'quit'
exit(1)
OrnParamClass = CreateOrnParameters.CreateOrnParameters(params) # patterns for ORN activation must be recreated to add noise
"""
Make sure that prepare_epth_response_curve.py was run successfully before
calling this script.
"""
import simulation_parameters
import os
import time
import MergeSpikefiles
import SetOfCurvesPlotter
import CreateOrnParameters
param_tool = simulation_parameters.parameter_storage()
params = param_tool.params
param_tool.hoc_export()
prepare = True
if prepare:
OrnParamClass = CreateOrnParameters.CreateOrnParameters(params)
OrnParamClass.create_params_for_response_curve()
t1 = time.time()
sim_cnt = 0
pn = sim_cnt
param_file_orns = params['orn_params_fn_base'] + '%d.dat' % pn
assert os.path.exists(
param_file_orns
), 'File does not exist: %s\nPlease run prepare_epth_response_curve.py before!' % param_file_orns
if params['with_artificial_orns']:
def plot_prediction(params=None, data_fn=None, inh_spikes = None):
if params== None:
network_params = simulation_parameters.parameter_storage() # network_params class containing the simulation parameters
# P = network_params.load_params() # params stores cell numbers, etc as a dictionary
params = network_params.params
if data_fn == None:
data_fn = params['exc_spiketimes_fn_merged'] + '.ras'
# if inh_spikes == None:
# inh_spikes = params['inh_spiketimes_fn_merged'] + '.ras'
# params['t_sim'] = 1200
plotter = P.PlotPrediction(params, data_fn)
pylab.rcParams['axes.labelsize'] = 14
pylab.rcParams['axes.titlesize'] = 16
if plotter.no_spikes:
return
plotter.compute_v_estimates()
# plotter.compute_position_estimates() --> happening in compute_v_estimates
plotter.compute_theta_estimates()
plotter.compute_orientation_estimates()
# fig 1
# neuronal level
output_fn_base = '%s%s_wsigmaX_%.2f_wsigmaV%.2f_delayScale%d_connRadius%.2f_wee%.2f' % (params['prediction_fig_fn_base'], params['connectivity_code'], \
params['w_sigma_x'], params['w_sigma_v'], params['delay_scale'], params['connectivity_radius'], params['w_tgt_in_per_cell_ee'])
plotter.create_fig() # create an empty figure
pylab.subplots_adjust(left=0.07, bottom=0.07, right=0.97, top=0.93, wspace=0.3, hspace=.2)
plotter.n_fig_x = 2
plotter.n_fig_y = 3
plotter.plot_rasterplot('exc', 1) # 1
plotter.plot_rasterplot('inh', 2) # 2
plotter.plot_vx_grid_vs_time(3) # 3
plotter.plot_vy_grid_vs_time(4) # 4
plotter.plot_x_grid_vs_time(5)
plotter.plot_y_grid_vs_time(6)
output_fn = output_fn_base + '_0.png'
print 'Saving figure to:', output_fn
pylab.savefig(output_fn, dpi=200)
# output_fn = output_fn_base + '_0.pdf'
# print 'Saving figure to:', output_fn
# pylab.savefig(output_fn, dpi=200)
# orientation figure
plotter.create_fig() # create an empty figure
plotter.n_fig_x = 3
plotter.n_fig_y = 2 # to have the ~same figure sizes as for the others
plotter.plot_orientation_grid_vs_time(1)
plotter.plot_orientation_estimates(2)
plotter.plot_orientation_diff(3)
output_fn = output_fn_base + '_orientation.png'
print 'Saving figure to:', output_fn
pylab.savefig(output_fn, dpi=200)
# output_fn = output_fn_base + '_0.eps'
# print 'Saving figure to:', output_fn
# pylab.savefig(output_fn, dpi=200)
# poplation level, short time-scale
plotter.n_fig_x = 3
plotter.n_fig_y = 2
plotter.create_fig()
pylab.rcParams['legend.fontsize'] = 12
pylab.subplots_adjust(left=0.07, bottom=0.07, right=0.97, top=0.93, wspace=0.3, hspace=.3)
plotter.plot_vx_estimates(1)
plotter.plot_vy_estimates(2)
plotter.plot_vdiff(3)
plotter.plot_x_estimates(4)
plotter.plot_y_estimates(5)
plotter.plot_xdiff(6)
output_fn = output_fn_base + '_1.png'
print 'Saving figure to:', output_fn
pylab.savefig(output_fn, dpi=200)
# output_fn = output_fn_base + '_1.pdf'
# print 'Saving figure to:', output_fn
# pylab.savefig(output_fn, dpi=200)
# output_fn = output_fn_base + '_1.eps'
# print 'Saving figure to:', output_fn
# pylab.savefig(output_fn, dpi=200)
# plotter.plot_theta_estimates(5)
# fig 3
# population level, long time-scale
# plotter.n_fig_x = 1
# plotter.n_fig_y = 4
# pylab.rcParams['legend.fontsize'] = 10
# pylab.subplots_adjust(hspace=0.5)
# plotter.create_fig()
# plotter.plot_fullrun_estimates_vx(1)
# plotter.plot_fullrun_estimates_vy(2)
# plotter.plot_fullrun_estimates_theta(3)
# plotter.plot_nspike_histogram(4)
# output_fn = output_fn_base + '_2.png'
# print 'Saving figure to:', output_fn
# pylab.savefig(output_fn, dpi=200)
# output_fn = output_fn_base + '_2.pdf'
# print 'Saving figure to:', output_fn
# pylab.savefig(output_fn, dpi=200)
# output_fn = output_fn_base + '_2.eps'
# print 'Saving figure to:', output_fn
# pylab.savefig(output_fn, dpi=200)
# fig 4
plotter.n_fig_x = 1
plotter.n_fig_y = 2
plotter.create_fig() # create an empty figure
plotter.plot_network_activity('exc', 1)
plotter.plot_network_activity('inh', 2)
output_fn = output_fn_base + '_4.png'
print 'Saving figure to:', output_fn
pylab.savefig(output_fn, dpi=200)
# output_fn = output_fn_base + '_4.pdf'
# print 'Saving figure to:', output_fn
# pylab.savefig(output_fn, dpi=200)
# output_fn = output_fn_base + '_4.eps'
# print 'Saving figure to:', output_fn
# pylab.savefig(output_fn, dpi=200)
plotter.n_fig_x = 1
plotter.n_fig_y = 1
plotter.create_fig()
weights = [plotter.nspikes_binned_normalized[i, :].sum() / plotter.n_bins for i in xrange(plotter.n_cells)]
plotter.quiver_plot(weights, fig_cnt=1)
output_fn = output_fn_base + '_quiver.png'
print 'Saving figure to:', output_fn
pylab.savefig(output_fn, dpi=200)
# output_fn = output_fn_base + '_quiver.pdf'
# print 'Saving figure to:', output_fn
# pylab.savefig(output_fn, dpi=200)
# output_fn = output_fn_base + '_quiver.eps'
# print 'Saving figure to:', output_fn
# pylab.savefig(output_fn, dpi=200)
plotter.save_data()
Usage:
python plot_response_curve.py [FOLDER] [CELLTYPE]
or
python plot_response_curve.py [FOLDER] [CELLTYPE] [PATTERN_NUMBER]
"""
assert (len(sys.argv) > 2), 'ERROR: folder and cell_type not given\n' + info_txt
folder = sys.argv[1]
cell_type = sys.argv[2]
try:
pn = int(sys.argv[3])
except:
print 'WARNING: Using the default pattern number 0'
pn = 0
params_fn = os.path.abspath(folder) + '/Parameters/simulation_parameters.json'
param_tool = simulation_parameters.parameter_storage(params_fn=params_fn)
params = param_tool.params
if cell_type == 'all':
# cell_types = params['cell_types']
cell_types = ['mit', 'pg', 'gran']
else:
cell_types = [cell_type]
for cell_type in cell_types:
print 'Plotting raster for:', cell_type
plot_raster_for_celltype(params, cell_type, title='%s spikes pattern %d' % (cell_type.upper(), pn))
# plot_raster_for_celltype(params, cell_type, title='%s spikes pattern %d' % (cell_type.upper(), pn))
pylab.show()
import sys
if len(sys.argv) > 1:
param_fn = sys.argv[1]
if os.path.isdir(param_fn):
param_fn += '/Parameters/simulation_parameters.json'
import json
f = file(param_fn, 'r')
print 'Loading parameters from', param_fn
params = json.load(f)
else:
print '\n NOT successfull\nLoading the parameters currently in simulation_parameters.py\n'
import simulation_parameters
network_params = simulation_parameters.parameter_storage() # network_params class containing the simulation parameters
params = network_params.load_params() # params stores cell numbers, etc as a dictionary
# E -> E
tmp_fn = 'delme_tmp_%d' % (np.random.randint(0, 1e8))
cat_cmd = 'cat %s* > %s' % (params['conn_list_ee_fn_base'], tmp_fn)
sort_cmd = 'sort -gk 1 -gk 2 %s > %s' % (tmp_fn, params['merged_conn_list_ee'])
rm_cmd = 'rm %s' % (tmp_fn)
print cat_cmd
os.system(cat_cmd)
print sort_cmd
os.system(sort_cmd)
print rm_cmd
os.system(rm_cmd)
times = []
times.append(time.time())
full_system = False
if len(sys.argv) > 1:
if sys.argv[1] == 'full':
full_system = True
tau_dict = {'tau_zi' : 50., 'tau_zj' : 5.,
'tau_ei' : 50., 'tau_ej' : 50., 'tau_eij' : 50.,
'tau_pi' : 500., 'tau_pj' : 500., 'tau_pij' : 500.,
}
PS = simulation_parameters.parameter_storage()
params = PS.params
PS.create_folders()
PS.write_parameters_to_file()
n_cells = params['n_exc']
my_units = utils.distribute_n(n_cells, n_proc, pc_id)
mp = params['motion_params']
# P R E P A R E T U N I N G P R O P E R T I E S
tuning_prop = utils.set_tuning_prop(params, mode='hexgrid', v_max=params['v_max'])
np.savetxt(params['tuning_prop_means_fn'],tuning_prop)
#exit(1)
# load
import pylab import numpy as np import simulation_parameters as sp import matplotlib from matplotlib import cm import time PS = sp.parameter_storage() params = PS.load_params() tp = np.loadtxt(params['tuning_prop_means_fn']) input_params = np.loadtxt(params['parameters_folder'] + 'input_params.txt') #network_activity_fn = 'AndersWij/activity_test.dat' #output_folder_fig = 'AndersWij/test_activity/' #network_activity_fn = 'Abstract/ANNActivity/ann_activity_40iterations_no_rec.dat' network_activity_fn = 'Abstract/ANNActivity/ann_activity_40iterations.dat' output_folder_fig = params['figures_folder'] print 'Loading ', network_activity_fn network_activity = np.loadtxt(network_activity_fn) network_activity = np.exp(network_activity) scale = 3 #n_time_steps = d[:, 0].size n_time_steps_per_iteration = 300 n_cells = params['n_exc'] n_iteration = 40
