old_contents: #@author: Amol Kapoor
  #date: 3-13-15
  #Visualizer for simpleLPU stuff
  
  import matplotlib as mpl
  mpl.use('agg')
  
  import neurokernel.LPU.utils.visualizer as vis
  import networkx as nx
  
  # Temporary fix for bug in networkx 1.8:
  nx.readwrite.gexf.GEXF.convert_bool = {'false':False, 'False':False,
                                         'true':True, 'True':True}
  #starts up the visualizer code
  V = vis.visualizer()
  
  #takes in the input file as defined in data, and plots it
  V.add_LPU('./data/simple_input.h5', LPU='Input')
  V.add_plot({'type': 'waveform', 'ids': [[0]]}, 'input_Input')
  
  #takes in the spike data/potential from the neuron output and plots it
  V.add_LPU('simple_output_spike.h5',
            './data/simple_lpu.gexf.gz', 'Simple LPU (Spikes)')
  
  #the [0,1] under ids should print both hh and leaky
  V.add_plot({'type':'raster', 'ids': {0: [0]},
              'yticks': [0], 'yticklabels': [0]},
              'Simple LPU (Spikes)','Output')
  
  V.add_LPU('simple_output_gpot.h5',
            './data/simple_lpu.gexf.gz', 'Simple LPU (Graded Potential)')
  
  V.add_plot({'type': 'waveform', 'ids': {0:[0]}},
              'Simple LPU (Graded Potential)', 'Output')
  
  V.add_plot({'type': 'waveform', 'ids': {0:[1]}},
              'Simple LPU (Graded Potential)', 'Output')
  
  V.add_plot({'type': 'waveform', 'ids': {0:[2]}},
              'Simple LPU (Graded Potential)', 'Output')
  
  #vars for plots
  
  #how often it updates
  V._update_interval = 50
  
  #rows and colums to plot in terms of size
  V.rows = 5
  V.cols = 1 
  
  #self explantory
  V.fontsize = 10
  #V.out_filename = 'simple_output.avi'
  #V.codec = 'libtheora'
  
  #time step
  V.dt = 0.0001
  
  #Changes the sizes on the axis
  V.xlim = [0, 1.0]
  V.ylim = [-70.0, 10.0]
  
  #figure size
  V.figsize = (16, 9)
  
  V.title = "Simple LPU Testing RK4 Models"
  
  #runs the visualizer
  V.run('simple_output.png', 120)
  
  
new_contents: #@author: Amol Kapoor
  #date: 3-13-15
  #Visualizer for simpleLPU stuff
  
  import matplotlib as mpl
  mpl.use('agg')
  
  import neurokernel.LPU.utils.visualizer as vis
  import networkx as nx
  
  # Temporary fix for bug in networkx 1.8:
  nx.readwrite.gexf.GEXF.convert_bool = {'false':False, 'False':False,
                                         'true':True, 'True':True}
  #starts up the visualizer code
  V = vis.visualizer()
  
  #takes in the input file as defined in data, and plots it
  V.add_LPU('./data/simple_input.h5', LPU='Input')
  V.add_plot({'type': 'waveform', 'ids': [[0]]}, 'input_Input')
  
  #takes in the spike data/potential from the neuron output and plots it
  V.add_LPU('simple_output_spike.h5',
            './data/simple_lpu.gexf.gz', 'Simple LPU (Spikes)')
  
  #the [0,1] under ids should print both hh and leaky
  V.add_plot({'type':'raster', 'ids': {0: [0]},
              'yticks': [0], 'yticklabels': [0]},
              'Simple LPU (Spikes)','Output')
  
  V.add_LPU('simple_output_gpot.h5',
            './data/simple_lpu.gexf.gz', 'Simple LPU (Graded Potential)')
  
  V.add_plot({'type': 'waveform', 'ids': {0:[0]}},
              'Simple LPU (Graded Potential)', 'Output')
  
  V.add_plot({'type': 'waveform', 'ids': {0:[1]}},
              'Simple LPU (Graded Potential)', 'Output')
  
  V.add_plot({'type': 'waveform', 'ids': {0:[2]}},
              'Simple LPU (Graded Potential)', 'Output')
  
  #vars for plots
  
  #how often it updates
  V._update_interval = 50
  
  #rows and colums to plot in terms of size
  V.rows = 5
  V.cols = 1
  
  #self explantory
  V.fontsize = 10
  #V.out_filename = 'simple_output.avi'
  #V.codec = 'libtheora'
  
  #time step
  V.dt = 0.0001
  
  #Changes the sizes on the axis
  V.xlim = [0, 1.0]
  V.ylim = [-70.0, 10.0]
  
  #figure size
  V.figsize = (16, 9)
  
  V.title = "Simple LPU Testing RK4 Models"
  
  #runs the visualizer
  V.run('simple_output.png', 120)
  
  
subject: Update parameters for Morris-Lecar testing