def create_plot_shape(rotation, view):
h.load_file("../NEURON_color_maps/TColorMap.hoc")
h.load_file("movierun.hoc")
ps = h.PlotShape()
ps.exec_menu("View = plot")
ps.variable("v")
cm1 = h.TColorMap("../NEURON_color_maps/jet.cm")
cm1.set_color_map(ps, -90, -10)
h.fast_flush_list.append(ps)
ps.exec_menu("Shape Plot")
ps.exec_menu("Show Diam")
ps.exec_menu("Variable Scale")
try:
ps.rotate(rotation[0], rotation[1], rotation[2], rotation[3],
rotation[4], rotation[5])
ps.view(view[0], view[1], view[2], view[3], view[4], view[5], view[6],
view[7])
except:
import pdb
pdb.set_trace()
ps.exec_menu("View Box")
def get_shape_plot(variable, min_val=-70, max_val=40):
ps = h.PlotShape(True)
ps.variable(variable)
ps.scale(min_val, max_val)
ps.show(0)
h.fast_flush_list.append(ps)
ps.exec_menu('Shape Plot')
return ps
def make_shape_plot(variable: str = None, min_val=-70, max_val=40):
"""
Create a shape plot in NEURON GUI
:param variable:
variable name to show on the neural shape. By default (None) it will show voltage
:param min_val:
min value of variable specified
:param max_val:
max valie of the variable specified
:return:
HOC's plot shape object
"""
ps = h.PlotShape(True)
if variable:
ps.variable(variable)
ps.scale(min_val, max_val)
ps.show(0)
h.fast_flush_list.append(ps)
ps.exec_menu('Shape Plot')
return ps
# -*- coding: utf-8 -*- """ Created on Mon Apr 25 14:12:00 2016 @author: Radu """ from neuron import h, gui from Ia_LFPy import Ia bla = Ia(n_nodes=43) shape_window = h.PlotShape()
def show_geometry(obj):
sections = obj.all
ps = h.PlotShape(sections)
ps.exec_menu('Shape Plot')
def show(self):
"""
This method allows to view the Ring network.
"""
graph = h.PlotShape()
graph.exec_menu('Show Diam')
def show_cell_geometry():
"""Shows the 3D cell morphology"""
h.define_shape()
shape_window = h.PlotShape()
shape_window.exec_menu('Show Diam')
# record
v = h.Vector().record(soma(0.5)._ref_v) # Membrane potential vector
t = h.Vector().record(h._ref_t) # Time stamp vector
# run
h.load_file('stdrun.hoc')
#h.fcurrent() # Make all assigned variables (currents, conductances, etc) consistent with the values of the states. Useful in combination with finitialize().
h.finitialize(-64.97 * mV)
h.continuerun(300 * ms)
# plot graph
import matplotlib.pyplot as plt
plt.figure()
plt.plot(t, v)
plt.xlabel('t (ms)')
plt.ylabel('v (mV)')
#plt.show()
# plot shape
#plt.figure()
h.PlotShape(False).plot(plt)
#ps = h.PlotShape(True)
# show plots
#ps.show()
plt.show()
#input("Press Enter to continue...")
#h('objref cell')
#h('cell = new CellSwc("./SWC/070224_SN-23-R.swc")')
#h('forall insert hh')
#h('forall insert k_ion')
#h('forall insert na_ion')
cell = h.CellSwc("./SWC/070224_SN-23-R.swc")
for sec in h.allsec():
sec.insert('hh')
for seg in chain(sec):
seg.hh.gnabar = 0.0001
seg.hh.gkbar = 0.01
sh = h.PlotShape(1)
sh.scale(0, 0)
sh.size(-140, 140, -140, 140)
sh.exec_menu("Shape Plot")
STIM_POINT = 187
STOPTIME = 8000
TIMESTEP = 0.025
LENGTH = 0.5
AMPLITUDE = 15
FREQUENCY = 300
L_RATIO = 2.0 # ratio of length (pre+post)/pre
A_RATIO = 0.3 # ratio of amplitude
POLAR_T = 0.0
volt_time = h.Vector()
#topology
dend.connect(soma(1))
h.psection(sec=dend)
h.topology()
#geometry
## soma as a square cylinder such that diam = h
## radius = 500 micron
soma.L = soma.diam = 12.6157
dend.L = 200
dend.diam = 1
print("Surface area of soma = {}".format(soma(0.5).area()))
#plot shape
shape_window = h.PlotShape() #by default, no diam is shown
shape_window.exec_menu('Show Diam')
#biophysics
for sec in h.allsec(): #iterate over all sections
sec.Ra = 100 ## axial resistance in Ohm*cm
sec.cm = 1 ## membrane capacitance in microFarad/cm2
soma.insert('hh') #insert active Hodgkin-Huxley current in the soma
for seg in soma:
seg.hh.gnabar = 0.12 ## sodium conductance in S/cm2
seg.hh.gkbar = 0.036 # potassium conductance in S/cm2
seg.hh.gl = 0.0003 # leak conductance in S/cm2
seg.hh.el = -54.3 # reversal potential in mV
dend.insert('pas')
conf = bionet.Config.from_json(config_file, validate=True)
conf.build_env()
graph = bionet.BioNetwork.from_config(conf)
sim = bionet.BioSimulator.from_config(conf, network=graph)
cells = graph.get_local_cells()
cell = cells[list(cells.keys())[0]]
psoma = cell.morphology.psoma
import matplotlib.pyplot as plt
ps = h.PlotShape(False)
ax = ps.plot(plt)
#import pdb; pdb.set_trace()
for con in cell._connections:
#print(con._connector.postseg())
#print(p.mark)
#import pdb; pdb.set_trace()
ax.mark(con._connector.postseg())
import pickle
f = open('groups.pkl', 'rb')
groups = pickle.load(f)
f.close()
from clustering import *
cell.add_sec(name="neck", diam=0.5, l=0.5, nseg=50)
cell.add_sec(name="dend", diam=0.5, l=5, nseg=100)
cell.connect(fr='head', to='neck')
cell.connect(fr='neck', to='dend', to_loc=0.5)
cell.add_rxd()
# init
h.finitialize(-65 * mV)
#TODO how to check if mM concentration diffuse rapidly or there is a "wall" on dendrite
head_last = cell.secs['head'].nseg + cell.secs['neck'].nseg + cell.secs[
'dend'].nseg - 1
cell.ca.nodes[head_last].concentration = 0.5
h.cvode.re_init()
# plot shape
ps = h.PlotShape(True)
ps.variable('cai')
ps.scale(0, 0.01)
ps.show(0)
h.fast_flush_list.append(ps)
ps.exec_menu('Shape Plot')
#h.PlotShape(False).plot(plt)
# run
sleep = 3
print("sleep before run for: %s seconds", sleep)
time.sleep(sleep)
before = time.time()
const_delay = DELAY / 1000 # in seconds
for i in np.arange(0, RUNTIME, STEPSIZE):
h.continuerun(i * ms)
if tar[:4] == 'dend':
h.dend[int(tar[5:-1])](.5).v = 35
elif tar[:4] == 'soma':
soma_col = 'cyan'
elif tar[:4] == 'axon':
h.axon[int(tar[5:-1])](.5).v = 35
# visualisation ==========
# plots dendrites
'''
sections = h.SectionList([sec for sec in h.allsec() if 'dend' in str(sec)])
ps = h.PlotShape(sections,False).plot(plt)
ps._do_plot(0,1,h.dend,'v',cmap=vis_cmap)
'''
ps = h.PlotShape(h.SectionList(), False).plot(plt)
ps._do_plot(0, 1, h.allsec(), 'v', cmap=vis_cmap)
# Make sphere to mimic soma
u = np.linspace(0, 2 * np.pi, 100)
v = np.linspace(0, np.pi, 100)
x = 12 * np.outer(np.cos(u), np.sin(v))
y = 12 * np.outer(np.sin(u), np.sin(v))
z = 12 * np.outer(np.ones(np.size(u)), np.cos(v))
ps.plot_surface(x, y + 5, z + 10, color=soma_col)
ps.plot_surface(x, y + 5, z + 14, color=soma_col)
ps.plot_surface(x, y + 5, z + 17, color=soma_col)
# removes plot background
ps.xaxis.pane.fill = False
ps.yaxis.pane.fill = False
conf = bionet.Config.from_json(config_file, validate=True)
conf.build_env()
graph = bionet.BioNetwork.from_config(conf)
sim = bionet.BioSimulator.from_config(conf, network=graph)
dic = {}
dic["high"] = {"dist": [], "Ih": [], "LVAst": [], "HVA": [], "diam": []}
dic["low"] = {"dist": [], "Ih": [], "LVAst": [], "HVA": [], "diam": []}
#segs = list(graph.get_local_cells().values())[0]._morph.seg_prop
#import pdb; pdb.set_trace()
cell = list(graph.get_local_cells().values())[0]
hobj = cell.hobj
import matplotlib.pyplot as plt
ps = h.PlotShape(False) # False tells h.PlotShape not to use NEURON's gui
h.distance(sec=cell.hobj.soma[0])
diams1 = []
diams2 = []
for sec in hobj.all:
fullsecname = sec.name()
sec_type = fullsecname.split(".")[1][:4]
sec_id = int(fullsecname.split("[")[-1].split("]")[0])
#sec.v = np.random.random(1)[0]*1
#print(sec.v)
for seg in sec:
#import pdb; pdb.set_trace()
dist = h.distance(seg)
if sec_type == "soma": #light blue
seg.v = -20
#%%
h.load_file(biophysicalModelFilename)
h.load_file(biophysicalModelTemplateFilename)
L5PC = h.L5PCtemplate(morphologyFilename)
#%% set dendritic VDCC g=0
##secs = h.allsec
#for sec in h.allsec():
# if hasattr(sec, 'gCa_HVAbar_Ca_HVA'):
# sec.gCa_HVAbar_Ca_HVA = 0
#%% inspect the created shape
shapeWindow = h.PlotShape()
shapeWindow.exec_menu('Show Diam')
#%% helper functions
def Add_NMDA_SingleSynapticEventToSegment(segment, activationTime,
synapseWeight, exc_inh):
# synapse = h.ProbAMPANMDA2(segment)
# synapse = h.ProbAMPANMDA_EMS(segLoc,sec=section)
if exc_inh == 0: # inhibitory
synapse = h.ProbGABAAB_EMS(segment) #GABAA/B
synapse.tau_r_GABAA = 0.2
synapse.tau_d_GABAA = 8
synapse.tau_r_GABAB = 3.5
print("baba")
def set_plotshape_colormap(plotshape, cmap='jet'):
import matplotlib.cm
s = matplotlib.cm.ScalarMappable(cmap=cmap)
cmap = s.get_cmap()
s.set_clim(0, cmap.N)
rs, gs, bs = itertools.islice(zip(*s.to_rgba(list(range(cmap.N)))), 0, 3)
plotshape.colormap(cmap.N)
for i, r, g, b in zip(list(range(cmap.N)), rs, gs, bs):
plotshape.colormap(i, r * 255, g * 255, b * 255)
# call s.scale(lo, hi) to replot the legend
s = h.PlotShape()
s.exec_menu('Shape Plot')
set_plotshape_colormap(s)
# show the diameters
s.show(0)
# cytoplasmic, er volume fractions
fc, fe = 0.83, 0.17
# parameters
caDiff = 0.016
#caDiff =0
ip3Diff = 0.283
#ip3Diff = 0
def show_architecture(self):
shape_window = h.PlotShape()
shape_window.exec_menu("Show Diam")
