def run(out_name):
V = vis.visualizer()
# Assumes that generic_lpu_0_input.h5 and generic_lpu_1_input.h5
# contain the same data:
V.add_LPU('./data/generic_lpu_0_input.h5', LPU='Sensory')
V.add_plot({'type': 'waveform', 'ids': [[0]]}, 'input_Sensory')
for i in [0, 1]:
G = nx.read_gexf('./data/generic_lpu_%s.gexf.gz' % i)
neu_pub = sorted([int(n) for n, d in G.nodes_iter(True) \
if d['public'] == True])
V.add_LPU('generic_lpu_%s_%s_output_spike.h5' % (i, out_name),
'./data/generic_lpu_%s.gexf.gz' % i,
'Generic LPU %s' % i)
V.add_plot({'type': 'raster',
'ids': {0: neu_pub},
#'yticks': range(1, 1+len(neu_out)),
#'yticklabels': range(len(neu_out))
},
'Generic LPU %s' % i, 'Output')
V._update_interval = 50
V.rows = 3
V.cols = 1
V.fontsize = 18
V.out_filename = '%s.avi' % out_name
V.codec = 'libtheora'
V.dt = 0.0001
V.xlim = [0, 1.0]
V.run()
def run(out_name):
V = vis.visualizer()
V.add_LPU('./data/generic_input_0.h5', LPU='Sensory')
V.add_plot({'type':'waveform', 'ids': [[0]]}, 'input_Sensory')
for i in [0, 1]:
G = nx.read_gexf('./data/generic_lpu_%s.gexf.gz' % i)
neu_out = [k for k, n in G.node.items() if n['name'][:3] == 'out']
V.add_LPU('generic_output_%s_%s_spike.h5' % (i, out_name),
'./data/generic_lpu_%s.gexf.gz' % i,
'Generic LPU %s' % i)
V.add_plot({'type': 'raster',
'ids': {0: range(len(neu_out))},
#'yticks': range(1, 1+len(neu_out)),
#'yticklabels': range(len(neu_out))
},
'Generic LPU %s' % i, 'Output')
V._update_interval = 50
V.rows = 3
V.cols = 1
V.fontsize = 18
V.out_filename = 'generic_output_%s.avi' % out_name
V.codec = 'libtheora'
V.dt = 0.0001
V.xlim = [0, 1.0]
V.run()
import neurokernel.LPU.utils.visualizer as vis
import networkx as nx
nx.readwrite.gexf.GEXF.convert_bool = {
'false': False,
'False': False,
'true': True,
'True': True
}
# Select IDs of projection neurons:
G = nx.read_gexf('./data/generic_lpu.gexf.gz')
neu_proj = sorted(
[int(k) for k, n in G.node.items() if n['name'][:4] == 'proj'])
V = vis.visualizer()
V.add_LPU('./data/generic_input.h5', LPU='Sensory')
V.add_plot({'type': 'waveform', 'ids': [[0]]}, 'input_Sensory')
V.add_LPU('generic_output_spike.h5', './data/generic_lpu.gexf.gz',
'Generic LPU')
V.add_plot(
{
'type': 'raster',
'ids': {
0: neu_proj
},
'yticks': range(1, 1 + len(neu_proj)),
'yticklabels': range(len(neu_proj))
}, 'Generic LPU', 'Output')
LAM_GEXF_FILE = 'lamina.gexf.gz'
INPUT_FILE = 'vision_input.h5'
IMAGE_FILE = 'image1.mat'
RET_OUTPUT_FILE = 'retina_output'
LAM_OUTPUT_FILE = 'lamina_output'
RET_OUTPUT_GPOT = RET_OUTPUT_FILE + '_gpot.h5'
LAM_OUTPUT_GPOT = LAM_OUTPUT_FILE + '_gpot.h5'
RET_OUTPUT_PNG = 'retina_output.png'
LAM_OUTPUT_PNG = 'lamina_output.png'
RET_OUTPUT_AVI = 'retina_output.avi'
LAM_OUTPUT_AVI = 'lamina_output.avi'
RET_OUTPUT_MPEG = 'retina_output.mp4'
LAM_OUTPUT_MPEG = 'lamina_output.mp4'
V = vis.visualizer()
n = Normalize(vmin=0, vmax=2000, clip=True)
conf_input = {}
conf_input['norm'] = n
conf_input['type'] = 'dome'
V.add_LPU('intensities.h5','retina.gexf.gz', LPU='Vision', is_input=True)
V.add_plot(conf_input, 'input_Vision')
conf_R1 = {}
conf_R1['type'] = 'dome'
V.add_LPU(RET_OUTPUT_GPOT,'retina.gexf.gz', LPU='Retina')
V.add_plot(conf_R1, 'Retina', 'R1')
conf_L1 = {}
conf_L1['type'] = 'dome'
V.add_LPU(LAM_OUTPUT_GPOT, 'lamina.gexf.gz', LPU='Lamina')
V.add_plot(conf_L1, 'Lamina', 'L1')
position_PEN = list(uids).index(sort_list_dict_PEN[i])
id_positions_PEN.append(position_PEN)
sort_list['PEN'] = sort_list_list_PEN
data_out = data_T[id_positions_PEN, :].T
with h5py.File('%s_output_pick.h5' % 'PEN', 'w') as f:
f.create_dataset('/array', data=data_out)
logger = setup_logger(screen=True)
dt = 1e-4
update_interval = None
fontsize = 16
xlim = [0, 1.0]
v_BU = vis.visualizer()
v_BU.add_LPU('BU_output.h5', LPU='BU')
v_BU.add_plot(
{
'type': 'raster',
'uids': [sort_list['BU']],
'variable': 'spike_state',
'yticks': range(
1,
1 + len(sort_list['BU'])), #'yticklabels': range(len(bu_neurons))
'yticklabels': []
},
'BU')
#[list(np.arange(753,785, dtype=np.int32))]
v_BU.add_LPU('bu_output.h5', LPU='bu')