Adding initial analysis of cells

.gitignore

@@ -37,3 +37,15 @@ ChannelTest/*.log
 /NMC/NeuroML2/ManyCells.net.png
 /NMC/Connectome/BBP.net.png
 /NMC/Connectome/cons_locs_pathways_mc0_Column.tar
+/NMC/NeuroML2/nmllite/*.mod
+*.gv
+*.gv.png
+/NMC/NeuroML2/nmllite/IClamp_*.json
+/NMC/NeuroML2/nmllite/ParamSweep_*
+/NMC/NeuroML2/nmllite/IClamp_*.net.nml
+/NMC/NeuroML2/nmllite/*.hoc
+/NMC/NeuroML2/nmllite/*.dat
+/NMC/NeuroML2/nmllite/LEMS_Sim_IClamp_*.xml
+/NMC/NeuroML2/nmllite/Sim_IClamp_*.json
+report.*.txt
+*_nrn.py

NMC/NeuroML2/nmllite/GenerateExamples.py

@@ -0,0 +1,78 @@
+from neuromllite import *
+from neuromllite.NetworkGenerator import *
+from neuromllite.utils import create_new_model
+import sys
+
+sys.path.append("..")
+
+'''
+bAC217_L4_MC_5fa0a62bd0_0_0.cell.nml      cADpyr229_L23_PC_c292d67a2e_0_0.cell.nml     cADpyr231_L6_TPC_L4_117b9dfb71_0_0.cell.nml  cNAC187_L1_HAC_f8c9772d9d_0_0.cell.nml    Soma_AllNML2.cell.nml
+bNAC219_L1_DAC_a9ae5cbbf5_0_0.cell.nml    cADpyr230_L4_SS_1afeb14f17_0_0.cell.nml      cADpyr232_L5_TTPC1_0fb1ca4724_0_0.cell.nml   cNAC187_L23_NBC_9d37c4b1f8_0_0.cell.nml
+cADpyr229_L23_PC_5ecbf9b163_0_0.cell.nml  cADpyr231_L6_TPC_L1_44f2206f70_0_0.cell.nml  cADpyr232_L5_UTPC_d736225429_0_0.cell.nml    cSTUT189_L23_LBC_e6e8f83407_0_0.cell.nml
+
+'''
+colors = {'RS':'0 0 0.8', 'FS':'0.8 0 0', 'LTS':'0 0 0.8', 'IB':'0.8 0 0', 'IBR':'0.8 0 0'}
+colors = {'cADpyr229_L23_PC_c292d67a2e_0_0':'0 0 0.8', 'cNAC187_L23_NBC_9d37c4b1f8_0_0':'0.8 0 0'}
+
+def generate(cell, duration=3000, config='IClamp',parameters = None):
+
+    reference = "%s_%s"%(config, cell)
+
+    cell_id = '%s'%cell
+    cell_nmll = Cell(id=cell_id, neuroml2_source_file='../%s.cell.nml'%(cell))
+
+    ################################################################################
+    ###   Add some inputs
+
+    if 'IClamp' in config:
+
+        if not parameters:
+            parameters = {}
+            parameters['stim_amp'] = '350pA'
+
+        input_source = InputSource(id='iclamp_0', 
+                                   neuroml2_input='PulseGenerator', 
+                                   parameters={'amplitude':'stim_amp', 'delay':'500ms', 'duration':'2000ms'})
+
+
+    else:
+
+        if not parameters:
+            parameters = {}
+            parameters['average_rate'] = '100 Hz'
+            parameters['number_per_cell'] = '10'
+
+        input_source = InputSource(id='pfs0', 
+                                   neuroml2_input='PoissonFiringSynapse', 
+                                   parameters={'average_rate':'average_rate', 
+                                               'synapse':syn_exc.id, 
+                                               'spike_target':"./%s"%syn_exc.id})
+
+    sim, net = create_new_model(reference,
+                     duration, 
+                     dt=0.025, # ms 
+                     temperature=34, # degC
+                     default_region='Cortex',
+                     parameters = parameters,
+                     cell_for_default_population=cell_nmll,
+                     color_for_default_population=colors[cell],
+                     input_for_default_population=input_source)
+
+    return sim, net
+
+
+
+if __name__ == "__main__":
+
+    if '-all' in sys.argv:
+        for cell in colors:
+            generate(cell, 3000, config="IClamp")
+
+
+    else:
+
+        #sim, net = generate('cADpyr229_L23_PC_c292d67a2e_0_0', 3000, config="IClamp")
+        sim, net = generate('cNAC187_L23_NBC_9d37c4b1f8_0_0', 3000, config="IClamp",parameters={'stim_amp':'30pA'})
+
+        check_to_generate_or_run(sys.argv, sim)
+

NMC/NeuroML2/nmllite/README.md

@@ -0,0 +1,44 @@
+### BBP cell summary 
+<table>
+<tr>
+  <td width=30><b>cADpyr229_L23_PC_c292d67a2e_0_0</b></td>
+  <td><a href="mean_spike_frequency_cADpyr229_L23_PC_c292d67a2e_0_0.png">
+    <img alt="?" src="mean_spike_frequency_cADpyr229_L23_PC_c292d67a2e_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="firing_rates_cADpyr229_L23_PC_c292d67a2e_0_0.png">
+    <img alt="?" src="firing_rates_cADpyr229_L23_PC_c292d67a2e_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="heatmap_cADpyr229_L23_PC_c292d67a2e_0_0.png">
+    <img alt="?" src="heatmap_cADpyr229_L23_PC_c292d67a2e_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="dt_traces_cADpyr229_L23_PC_c292d67a2e_0_0.png">
+    <img alt="?" src="dt_traces_cADpyr229_L23_PC_c292d67a2e_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="heatmap_dt_cADpyr229_L23_PC_c292d67a2e_0_0.png">
+    <img alt="?" src="heatmap_dt_cADpyr229_L23_PC_c292d67a2e_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="mean_spike_frequency_dt_cADpyr229_L23_PC_c292d67a2e_0_0.png">
+    <img alt="?" src="mean_spike_frequency_dt_cADpyr229_L23_PC_c292d67a2e_0_0.png" height="160"/></a>
+  </td>
+<tr>
+<tr>
+  <td width=30><b>cNAC187_L23_NBC_9d37c4b1f8_0_0</b></td>
+  <td><a href="mean_spike_frequency_cNAC187_L23_NBC_9d37c4b1f8_0_0.png">
+    <img alt="?" src="mean_spike_frequency_cNAC187_L23_NBC_9d37c4b1f8_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="firing_rates_cNAC187_L23_NBC_9d37c4b1f8_0_0.png">
+    <img alt="?" src="firing_rates_cNAC187_L23_NBC_9d37c4b1f8_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="heatmap_cNAC187_L23_NBC_9d37c4b1f8_0_0.png">
+    <img alt="?" src="heatmap_cNAC187_L23_NBC_9d37c4b1f8_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="dt_traces_cNAC187_L23_NBC_9d37c4b1f8_0_0.png">
+    <img alt="?" src="dt_traces_cNAC187_L23_NBC_9d37c4b1f8_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="heatmap_dt_cNAC187_L23_NBC_9d37c4b1f8_0_0.png">
+    <img alt="?" src="heatmap_dt_cNAC187_L23_NBC_9d37c4b1f8_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="mean_spike_frequency_dt_cNAC187_L23_NBC_9d37c4b1f8_0_0.png">
+    <img alt="?" src="mean_spike_frequency_dt_cNAC187_L23_NBC_9d37c4b1f8_0_0.png" height="160"/></a>
+  </td>
+<tr>

NMC/NeuroML2/nmllite/Sweep.py

@@ -0,0 +1,188 @@
+import sys
+
+import pprint; pp = pprint.PrettyPrinter(depth=6)
+
+from neuromllite.sweep.ParameterSweep import ParameterSweep
+from neuromllite.sweep.ParameterSweep import NeuroMLliteRunner
+
+
+
+from GenerateExamples import colors
+
+if __name__ == '__main__':
+
+    heatmap_lims=[-110,-20]
+
+    if '-all' in sys.argv:
+
+        print('Generating all plots')
+        save_fig_dir = './'
+        html = '<table>\n'
+
+        fixed = {'dt':0.025, 'duration':3000}
+
+
+        vary = {'stim_amp':['%spA'%(i) for i in xrange(-100,1000,50)]}
+        #vary = {'stim_amp':['%spA'%(i/10.0) for i in xrange(-10,20,5)]}
+        #vary = {'stim_amp':['-100pA','0pA','100pA','200pA','300pA','400pA']}
+
+        cells = colors.keys()
+
+        for type in cells:
+            if type!='ec' and type !='ca3':
+
+                run = True
+                run = False
+
+                if run:
+
+                    nmllr = NeuroMLliteRunner('Sim_IClamp_%s.json'%type,
+                                              simulator='jNeuroML_NEURON')
+                    ps = ParameterSweep(nmllr, 
+                                        vary, 
+                                        fixed,
+                                        num_parallel_runs=16,
+                                        save_plot_all_to='firing_rates_%s.png'%type,
+                                        heatmap_all=True,
+                                        save_heatmap_to='heatmap_%s.png'%type,
+                                        heatmap_lims=heatmap_lims,
+                                        plot_all=True, 
+                                        show_plot_already=False)
+
+                    report = ps.run()
+
+                    #ps.plotLines('stim_amp','average_last_1percent',save_figure_to='average_last_1percent_%s.png'%type)
+                    ps.plotLines('stim_amp','mean_spike_frequency',save_figure_to='mean_spike_frequency_%s.png'%type)
+
+                height = '160'
+                html+='<tr>\n'
+                html+='  <td width=30><b>'+type+'</b></td>\n'
+                html+='  <td><a href="mean_spike_frequency_%s.png'%type+'">\n'
+                html+='    <img alt="?" src="mean_spike_frequency_%s.png'%type+'" height="'+height+'"/></a>\n'
+                html+='  </td>\n'
+                html+='  <td><a href="firing_rates_%s.png'%type+'">\n'
+                html+='    <img alt="?" src="firing_rates_%s.png'%type+'" height="'+height+'"/></a>\n'
+                html+='  </td>\n'
+                html+='  <td><a href="heatmap_%s.png'%type+'">\n'
+                html+='    <img alt="?" src="heatmap_%s.png'%type+'" height="'+height+'"/></a>\n'
+                html+='  </td>\n'
+                html+='  <td><a href="dt_traces_%s.png'%type+'">\n'
+                html+='    <img alt="?" src="dt_traces_%s.png'%type+'" height="'+height+'"/></a>\n'
+                html+='  </td>\n'
+                html+='  <td><a href="heatmap_dt_%s.png'%type+'">\n'
+                html+='    <img alt="?" src="heatmap_dt_%s.png'%type+'" height="'+height+'"/></a>\n'
+                html+='  </td>\n'
+                html+='  <td><a href="mean_spike_frequency_dt_%s.png'%type+'">\n'
+                html+='    <img alt="?" src="mean_spike_frequency_dt_%s.png'%type+'" height="'+height+'"/></a>\n'
+                html+='  </td>\n'
+                html+='<tr>\n'
+
+        import matplotlib.pyplot as plt
+        if not '-nogui' in sys.argv:
+            print("Showing plots")
+            plt.show()
+
+
+        with open(save_fig_dir+'info.html','w') as f:
+            f.write('<html><body>\n%s\n</body></html>'%html)
+        with open(save_fig_dir+'README.md','w') as f2:
+            f2.write('### BBP cell summary \n%s'%(html.replace('.html','.md')))
+
+    elif '-dt' in sys.argv:
+
+
+        optimal_stim = {'cADpyr229_L23_PC_c292d67a2e_0_0':'500',
+                        'cNAC187_L23_NBC_9d37c4b1f8_0_0':'30'}
+
+        vary = {'dt':[0.1,0.05,0.025,0.01,0.005,0.0025,0.001,0.0005,0.00025,0.0001]}
+        vary = {'dt':[0.1,0.05,0.025,0.01,0.005,0.0025,0.001]}
+        vary = {'dt':[0.05,0.025,0.01,0.005,0.0025]}
+        #vary = {'dt':[0.05,0.025,0.01,0.005]}
+        #vary = {'dt':[0.05,0.025,0.01]}
+        #vary = {'dt':[0.05,0.025,0.01]}
+
+        for type in optimal_stim:
+            if type!='ec' and type !='ca3':
+
+                run = True
+
+                if run:
+
+                    fixed = {'duration':3000, 'stim_amp':'%spA'%optimal_stim[type]}
+
+                    nmllr = NeuroMLliteRunner('Sim_IClamp_%s.json'%type,
+                                              simulator='jNeuroML_NEURON')
+                    ps = ParameterSweep(nmllr, 
+                                        vary, 
+                                        fixed,
+                                        num_parallel_runs=16,
+                                        save_plot_all_to='dt_traces_%s.png'%type,
+                                        heatmap_all=True,
+                                        save_heatmap_to='heatmap_dt_%s.png'%type,
+                                        heatmap_lims=heatmap_lims,
+                                        plot_all=True, 
+                                        show_plot_already=False)
+
+                    report = ps.run()
+
+                    #ps.plotLines('stim_amp','average_last_1percent',save_figure_to='average_last_1percent_%s.png'%type)
+                    ps.plotLines('dt','mean_spike_frequency',save_figure_to='mean_spike_frequency_dt_%s.png'%type, logx=True)
+
+
+
+        import matplotlib.pyplot as plt
+        if not '-nogui' in sys.argv:
+            print("Showing plots")
+            plt.show()
+
+
+    else:
+
+        fixed = {'dt':0.025, 'duration':3000}
+
+        quick = False
+        #quick=True
+
+        vary = {'stim_amp':['%spA'%(i/10.0) for i in xrange(-10,20,2)]}
+        vary = {'dt':[0.1,0.05,0.025,0.01,0.005,0.0025,0.001,0.0005,0.00025,0.0001]}
+        vary = {'dt':[0.1,0.05,0.025,0.01,0.005,0.0025,0.001]}
+        vary = {'dt':[0.1,0.05,0.025,0.01,0.005]}
+
+        #vary = {'number_per_cell':[i for i in xrange(0,250,10)]}
+        #vary = {'stim_amp':['1pA','1.5pA','2pA']}
+        vary = {'stim_amp':['%spA'%(i) for i in xrange(-100,1000,50)]}
+
+        type = 'RS'
+        type = 'cADpyr229_L23_PC_c292d67a2e_0_0'
+        type = 'cNAC187_L23_NBC_9d37c4b1f8_0_0'
+        #type='poolosyn'
+        config = 'IClamp'
+        #config = 'PoissonFiringSynapse'
+
+        nmllr = NeuroMLliteRunner('Sim_%s_%s.json'%(config,type),
+                                  simulator='jNeuroML_NEURON')
+
+        if quick:
+            pass
+
+        ps = ParameterSweep(nmllr, vary, fixed,
+                            num_parallel_runs=16,
+                                  plot_all=True, 
+                                  save_plot_all_to='firing_rates_%s.png'%type,
+                                  heatmap_all=True,
+                                  save_heatmap_to='heatmap_%s.png'%type,
+                                  heatmap_lims=heatmap_lims,
+                                  show_plot_already=False)
+
+        report = ps.run()
+        ps.print_report()
+
+        #ps.plotLines('stim_amp','average_last_1percent',save_figure_to='average_last_1percent_%s.png'%type)
+        ps.plotLines('stim_amp','mean_spike_frequency',save_figure_to='mean_spike_frequency_%s.png'%type)
+        #ps.plotLines('dt','mean_spike_frequency',save_figure_to='mean_spike_frequency_%s.png'%type, logx=True)
+        #ps.plotLines('number_per_cell','mean_spike_frequency',save_figure_to='poisson_mean_spike_frequency_%s.png'%type)
+
+        import matplotlib.pyplot as plt
+        if not '-nogui' in sys.argv:
+            print("Showing plots")
+            plt.show()

NMC/NeuroML2/nmllite/info.html

@@ -0,0 +1,46 @@
+<html><body>
+<table>
+<tr>
+  <td width=30><b>cADpyr229_L23_PC_c292d67a2e_0_0</b></td>
+  <td><a href="mean_spike_frequency_cADpyr229_L23_PC_c292d67a2e_0_0.png">
+    <img alt="?" src="mean_spike_frequency_cADpyr229_L23_PC_c292d67a2e_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="firing_rates_cADpyr229_L23_PC_c292d67a2e_0_0.png">
+    <img alt="?" src="firing_rates_cADpyr229_L23_PC_c292d67a2e_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="heatmap_cADpyr229_L23_PC_c292d67a2e_0_0.png">
+    <img alt="?" src="heatmap_cADpyr229_L23_PC_c292d67a2e_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="dt_traces_cADpyr229_L23_PC_c292d67a2e_0_0.png">
+    <img alt="?" src="dt_traces_cADpyr229_L23_PC_c292d67a2e_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="heatmap_dt_cADpyr229_L23_PC_c292d67a2e_0_0.png">
+    <img alt="?" src="heatmap_dt_cADpyr229_L23_PC_c292d67a2e_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="mean_spike_frequency_dt_cADpyr229_L23_PC_c292d67a2e_0_0.png">
+    <img alt="?" src="mean_spike_frequency_dt_cADpyr229_L23_PC_c292d67a2e_0_0.png" height="160"/></a>
+  </td>
+<tr>
+<tr>
+  <td width=30><b>cNAC187_L23_NBC_9d37c4b1f8_0_0</b></td>
+  <td><a href="mean_spike_frequency_cNAC187_L23_NBC_9d37c4b1f8_0_0.png">
+    <img alt="?" src="mean_spike_frequency_cNAC187_L23_NBC_9d37c4b1f8_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="firing_rates_cNAC187_L23_NBC_9d37c4b1f8_0_0.png">
+    <img alt="?" src="firing_rates_cNAC187_L23_NBC_9d37c4b1f8_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="heatmap_cNAC187_L23_NBC_9d37c4b1f8_0_0.png">
+    <img alt="?" src="heatmap_cNAC187_L23_NBC_9d37c4b1f8_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="dt_traces_cNAC187_L23_NBC_9d37c4b1f8_0_0.png">
+    <img alt="?" src="dt_traces_cNAC187_L23_NBC_9d37c4b1f8_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="heatmap_dt_cNAC187_L23_NBC_9d37c4b1f8_0_0.png">
+    <img alt="?" src="heatmap_dt_cNAC187_L23_NBC_9d37c4b1f8_0_0.png" height="160"/></a>
+  </td>
+  <td><a href="mean_spike_frequency_dt_cNAC187_L23_NBC_9d37c4b1f8_0_0.png">
+    <img alt="?" src="mean_spike_frequency_dt_cNAC187_L23_NBC_9d37c4b1f8_0_0.png" height="160"/></a>
+  </td>
+<tr>
+
+</body></html>