add synapses, fixed ez_record segment locations

PyNeuronToolbox/morphology.py

@@ -176,19 +176,19 @@ def shapeplot(h,ax,sections=None,order=None,cvals=None,\
     return lines
 
 def shapeplot_animate(v,lines,nframes,tscale='linear',\
-                      x_min=-80,x_max=50,cmap=cm.YlOrBr_r):
+                      clim=[-80,50],cmap=cm.YlOrBr_r):
     """ Returns animate function which updates color of shapeplot """
 
     if tscale == 'linear':
         def animate(i):
             i_t = int((i/nframes)*v.shape[0])
             for i_seg in range(v.shape[1]):
-                lines[i_seg].set_color(cmap(int((v[i_t,i_seg]-x_min)*255/(x_max-x_min))))
+                lines[i_seg].set_color(cmap(int((v[i_t,i_seg]-clim[0])*255/(clim[1]-clim[0]))))
     elif tscale == 'log':
         def animate(i):
             i_t = int(np.round((v.shape[0] ** (1.0/(nframes-1))) ** i - 1))
             for i_seg in range(v.shape[1]):
-                lines[i_seg].set_color(cmap(int((v[i_t,i_seg]-x_min)*255/(x_max-x_min))))
+                lines[i_seg].set_color(cmap(int((v[i_t,i_seg]-clim[0])*255/(clim[1]-clim[0]))))
     else:
         raise ValueError("Unrecognized option '%s' for tscale" % tscale)
 
@@ -254,9 +254,19 @@ def get_all_sections(h):
 def add_pre(h,sec_list,section):
     """
     A helper function that traverses a neuron's morphology (or a sub-tree)
-    of the morphology in pre-order.
+    of the morphology in pre-order. This is usually not necessary for the
+    user to import.
     """
     sec_list.append(section)
     sref = h.SectionRef(sec=section)
     for next_node in sref.child:
-        add_pre(h,sec_list,next_node)
+        add_pre(h,sec_list,next_node)
+
+def dist_between(h,seg1,seg2):
+    """
+    Calculates the distance between two segments. I stole this function from
+    a post by Michael Hines on the NEURON forum
+    (www.neuron.yale.edu/phpbb/viewtopic.php?f=2&t=2114)
+    """
+    h.distance(0, seg1.x, sec=seg1.sec)
+    return h.distance(seg2.x, sec=seg2.sec)

PyNeuronToolbox/record.py

@@ -4,7 +4,7 @@
 def ez_record(h,var='v',sections=None,order=None,\
               targ_names=None,cust_labels=None):
     """
-    Plots a 3D shapeplot
+    Records state variables across segments
 
     Args:
         h = hocObject to interface with neuron
@@ -37,14 +37,15 @@ def ez_record(h,var='v',sections=None,order=None,\
     data, labels = [], []
     for i in range(len(sections)):
         sec = sections[i]
-        for position in np.linspace(0,1,sec.nseg):
+        positions = np.linspace(0,1,sec.nseg+2)
+        for position in positions[1:-1]:
             # record data
             data.append(h.Vector())
             if var is 'v':
                 data[-1].record(sec(position)._ref_v)
             elif var is 'cai':
                 data[-1].record(sec(position)._ref_cai)
-            # determine label
+            # determine labels
             if cust_labels is None:
                 lab = sec.name()+'_'+str(round(position,5))
             else: 

PyNeuronToolbox/synapses.py

@@ -0,0 +1,30 @@
+from __future__ import division
+import numpy as np
+
+def add_exp2(h,seg,spktimes,e=0,tau1=0.5,tau2=20,weight=0.001):
+    """
+    Adds a double-exponential synapse at seg, with spikes
+    specified by the list/np.array spktimes. Optional arguments
+    specify parameters for the synapse. Returns a list of
+    hocObjects that must be kept in memory (see below).
+
+    IMPORTANT: This function requires that you have the vecevent.mod
+               file compiled and loaded into python. This file can
+               be found in nrn/share/examples/nrniv/netcon/
+
+    IMPORTANT: Neuron requires that you keep the Exp2Syn, VecStim,
+               NetCon, and Vector objects in memory during the
+               simulation. You will also need these objects if
+               you wish to alter the weight of the synapse
+               or other parameters during the simulation.
+    """
+    syn = h.Exp2Syn(seg)
+    syn.e = 0
+    syn.tau1 = 0.5
+    syn.tau2 = 20
+    vs = h.VecStim()
+    vec = h.Vector(np.sort(spktimes)) # Spend a bit of overhead to make sure spktimes are sorted
+    vs.play(vec)
+    nc = h.NetCon(vs,syn)
+    nc.weight[0] = weight
+    return [syn,vs,nc,vec]            # All these things need to be kept in memory

build/lib.linux-x86_64-2.7/PyNeuronToolbox/morphology.py

@@ -176,19 +176,19 @@ def shapeplot(h,ax,sections=None,order=None,cvals=None,\
     return lines
 
 def shapeplot_animate(v,lines,nframes,tscale='linear',\
-                      x_min=-80,x_max=50,cmap=cm.YlOrBr_r):
+                      clim=[-80,50],cmap=cm.YlOrBr_r):
     """ Returns animate function which updates color of shapeplot """
 
     if tscale == 'linear':
         def animate(i):
             i_t = int((i/nframes)*v.shape[0])
             for i_seg in range(v.shape[1]):
-                lines[i_seg].set_color(cmap(int((v[i_t,i_seg]-x_min)*255/(x_max-x_min))))
+                lines[i_seg].set_color(cmap(int((v[i_t,i_seg]-clim[0])*255/(clim[1]-clim[0]))))
     elif tscale == 'log':
         def animate(i):
             i_t = int(np.round((v.shape[0] ** (1.0/(nframes-1))) ** i - 1))
             for i_seg in range(v.shape[1]):
-                lines[i_seg].set_color(cmap(int((v[i_t,i_seg]-x_min)*255/(x_max-x_min))))
+                lines[i_seg].set_color(cmap(int((v[i_t,i_seg]-clim[0])*255/(clim[1]-clim[0]))))
     else:
         raise ValueError("Unrecognized option '%s' for tscale" % tscale)
 
@@ -254,9 +254,19 @@ def get_all_sections(h):
 def add_pre(h,sec_list,section):
     """
     A helper function that traverses a neuron's morphology (or a sub-tree)
-    of the morphology in pre-order.
+    of the morphology in pre-order. This is usually not necessary for the
+    user to import.
     """
     sec_list.append(section)
     sref = h.SectionRef(sec=section)
     for next_node in sref.child:
-        add_pre(h,sec_list,next_node)
+        add_pre(h,sec_list,next_node)
+
+def dist_between(h,seg1,seg2):
+    """
+    Calculates the distance between two segments. I stole this function from
+    a post by Michael Hines on the NEURON forum
+    (www.neuron.yale.edu/phpbb/viewtopic.php?f=2&t=2114)
+    """
+    h.distance(0, seg1.x, sec=seg1.sec)
+    return h.distance(seg2.x, sec=seg2.sec)

build/lib.linux-x86_64-2.7/PyNeuronToolbox/record.py

@@ -4,7 +4,7 @@
 def ez_record(h,var='v',sections=None,order=None,\
               targ_names=None,cust_labels=None):
     """
-    Plots a 3D shapeplot
+    Records state variables across segments
 
     Args:
         h = hocObject to interface with neuron
@@ -38,6 +38,11 @@ def ez_record(h,var='v',sections=None,order=None,\
     for i in range(len(sections)):
         sec = sections[i]
         for position in np.linspace(0,1,sec.nseg):
+            # ugly fix for single-segment compartments
+            if sec.nseg == 1:
+                print 'yeah'
+                position = 0.5
+            print position
             # record data
             data.append(h.Vector())
             if var is 'v':

build/lib.linux-x86_64-2.7/PyNeuronToolbox/synapses.py

@@ -0,0 +1,30 @@
+from __future__ import division
+import numpy as np
+
+def add_exp2(h,seg,spktimes,e=0,tau1=0.5,tau2=20,weight=0.001):
+    """
+    Adds a double-exponential synapse at seg, with spikes
+    specified by the list/np.array spktimes. Optional arguments
+    specify parameters for the synapse. Returns a list of
+    hocObjects that must be kept in memory (see below).
+
+    IMPORTANT: This function requires that you have the vecevent.mod
+               file compiled and loaded into python. This file can
+               be found in nrn/share/examples/nrniv/netcon/
+
+    IMPORTANT: Neuron requires that you keep the Exp2Syn, VecStim,
+               NetCon, and Vector objects in memory during the
+               simulation. You will also need these objects if
+               you wish to alter the weight of the synapse
+               or other parameters during the simulation.
+    """
+    syn = h.Exp2Syn(seg)
+    syn.e = 0
+    syn.tau1 = 0.5
+    syn.tau2 = 20
+    vs = h.VecStim()
+    vec = h.Vector(np.sort(spktimes)) # Spend a bit of overhead to make sure spktimes are sorted
+    vs.play(vec)
+    nc = h.NetCon(vs,syn)
+    nc.weight[0] = weight
+    return [syn,vs,nc,vec]            # All these things need to be kept in memory