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main_sim.py
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main_sim.py
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import math
import numpy as np
import sys
import matplotlib.pyplot as plt
from pylab import *
muscle_row_count = 24
default_time_per_step = 0.000001 # s
time_per_step = default_time_per_step # s
quadrant0 = 'MDR'
quadrant1 = 'MVR'
quadrant2 = 'MVL'
quadrant3 = 'MDL'
colours = {}
colours[quadrant0] = '#000000'
colours[quadrant1] = '#00ff00'
colours[quadrant2] = '#0000ff'
colours[quadrant3] = '#ff0000'
def print_(msg):
pre = "Python >> "
print('%s %s'%(pre,msg.replace('\n','\n'+pre)))
"""
Get list of muscle names in same order as waves generated below.
Based on info here:
https://github.com/openworm/Smoothed-Particle-Hydrodynamics/blob/3da1edc3b018c2e5c7c1a25e2f8d44b54b1a1c47/src/owWorldSimulation.cpp#L475
"""
def get_muscle_names():
names = []
for i in range(muscle_row_count):
names.append(get_muscle_name(quadrant0, i))
names.append(get_muscle_name(quadrant1, i))
names.append(get_muscle_name(quadrant2, i))
names.append(get_muscle_name(quadrant3, i))
return names
def get_muscle_name(quadrant, index):
return "%s%s"%(quadrant, index+1 if index>8 else ("0%i"%(index+1)))
def parallel_waves(n=muscle_row_count, #24 for our first test?
step=0,
phi=math.pi,
amplitude=1,
#velocity=0.000008):
velocity =0.000015):
"""
Array of two travelling waves, second one starts
half way through the array
"""
if n % 2 != 0:
raise NotImplementedError("Currently only supports even number of muscles!")
j = n/2
row_positions = np.linspace(0,0.75*1.5*2*math.pi,j)
wave_1 = (map(math.sin,(row_positions - velocity*step)))
wave_2 = (map(math.sin,(row_positions + (math.pi) - velocity*step)))
normalize_sine = lambda x : (x + 1)/2
wave_1 = map(normalize_sine, wave_1)
wave_2 = map(normalize_sine, wave_2)
double_wave_1 = []
double_wave_2 = []
for i in wave_1:
double_wave_1.append(i)
double_wave_1.append(i)
for i in wave_2:
double_wave_2.append(i)
double_wave_2.append(i)
return (double_wave_1,double_wave_2)
class MuscleSimulation():
def __init__(self,increment=1.0):
self.increment = increment
self.step = 0
def run(self, skip_to_time=0, do_plot = True):
"""
if(iterationCount<400000)
{
//muscle_activation_signal_cpp[0*24+20] = 0;
//muscle_activation_signal_cpp[0*24+21] = 0;
muscle_activation_signal_cpp[0*24+22] = 0;
muscle_activation_signal_cpp[0*24+23] = 0;
//muscle_activation_signal_cpp[1*24+20] = 0;
//muscle_activation_signal_cpp[1*24+21] = 0;
muscle_activation_signal_cpp[1*24+22] = 0;
muscle_activation_signal_cpp[1*24+23] = 0;
//muscle_activation_signal_cpp[2*24+20] = 0;
//muscle_activation_signal_cpp[2*24+21] = 0;
muscle_activation_signal_cpp[2*24+22] = 0;
muscle_activation_signal_cpp[2*24+23] = 0;
//muscle_activation_signal_cpp[3*24+20] = 0;
//muscle_activation_signal_cpp[3*24+21] = 0;
muscle_activation_signal_cpp[3*24+22] = 0;
muscle_activation_signal_cpp[3*24+23] = 0;
}
"""
self.contraction_array = parallel_waves(step = self.step)
self.step += self.increment
# for reversal movment after 40000 steps it will switch sinusoid
self.contraction_array[0][muscle_row_count - 2] = 0
self.contraction_array[0][muscle_row_count - 1] = 0
self.contraction_array[1][muscle_row_count - 2] = 0
self.contraction_array[1][muscle_row_count - 1] = 0
return list(np.concatenate([self.contraction_array[0],
self.contraction_array[1],
self.contraction_array[1],
self.contraction_array[0]]))
def save_results(self):
print_("> NOT saving results for MuscleSimulation")
class C302Simulation():
values = []
def __init__(self,
activity_file='configuration/test/c302/c302_C1_Muscles.muscles.activity.dat',
dt=0.0001,
scale_to_max=True):
self.step = 0
self.dt = dt
data = open(activity_file, 'r')
min_ = 1e19
max_ = -1e19
for line in data:
vv = []
vs = line.strip().split('\t')
for v in vs:
vf = float(v)
vv.append(vf)
min_ = min(min_,vf)
max_ = max(max_,vf)
self.values.append(vv)
print_("Loaded a list of %i activity traces (values %s->%s) at %i time points from %s"%(len(self.values[0]), min_, max_, len(self.values), activity_file))
max_ = 4e-7
if scale_to_max:
vals_scaled = []
for vv in self.values:
vv2 = [v/max_ for v in vv]
vals_scaled.append(vv2)
self.values = vals_scaled
def run(self, skip_to_time=0.05):
t = skip_to_time + self.step*time_per_step
index = int(t/self.dt)
if (index<len(self.values)):
v = self.values[index][1:48]
v.append(0)
v.extend(self.values[index][48:])
else:
v = np.zeros(96)
print_("Returning %i values at time: %f s, step: %i (index %i): [%f, %f, %f, ...]"%(len(v), t, self.step, index, v[0], v[1], v[2]))
#print v
self.step += 1
return list(v)
class C302NRNSimulation():
max_ca = 4e-7
max_ca_found = -1
def __init__(self, tstop=100, dt=0.005, activity_file=None, verbose=True):
#from LEMS_c302_C1_Full_nrn import NeuronSimulation
from LEMS_c302_nrn import NeuronSimulation
import neuron
self.h = neuron.h
self.verbose = verbose
self.ns = NeuronSimulation(tstop, dt)
print_("Initialised C302NRNSimulation of length %s ms and dt = %s ms..."%(tstop,dt))
def save_results(self):
print_("> Saving results at time: %s"%self.h.t)
self.ns.save_results()
def run(self, skip_to_time=-1):
print_("> Current NEURON time: %s ms"%self.h.t)
self.ns.advance()
print_("< Current NEURON time: %s ms"%self.h.t)
values = list([self._scale(self.h.a_MDR01[0].soma.cai,print_it=True), \
self._scale(self.h.a_MVR01[0].soma.cai), \
self._scale(self.h.a_MVL01[0].soma.cai), \
self._scale(self.h.a_MDL01[0].soma.cai), \
self._scale(self.h.a_MDR02[0].soma.cai), \
self._scale(self.h.a_MVR02[0].soma.cai), \
self._scale(self.h.a_MVL02[0].soma.cai), \
self._scale(self.h.a_MDL02[0].soma.cai), \
self._scale(self.h.a_MDR03[0].soma.cai), \
self._scale(self.h.a_MVR03[0].soma.cai), \
self._scale(self.h.a_MVL03[0].soma.cai), \
self._scale(self.h.a_MDL03[0].soma.cai), \
self._scale(self.h.a_MDR04[0].soma.cai), \
self._scale(self.h.a_MVR04[0].soma.cai), \
self._scale(self.h.a_MVL04[0].soma.cai), \
self._scale(self.h.a_MDL04[0].soma.cai), \
self._scale(self.h.a_MDR05[0].soma.cai), \
self._scale(self.h.a_MVR05[0].soma.cai), \
self._scale(self.h.a_MVL05[0].soma.cai), \
self._scale(self.h.a_MDL05[0].soma.cai), \
self._scale(self.h.a_MDR06[0].soma.cai), \
self._scale(self.h.a_MVR06[0].soma.cai), \
self._scale(self.h.a_MVL06[0].soma.cai), \
self._scale(self.h.a_MDL06[0].soma.cai), \
self._scale(self.h.a_MDR07[0].soma.cai), \
self._scale(self.h.a_MVR07[0].soma.cai), \
self._scale(self.h.a_MVL07[0].soma.cai), \
self._scale(self.h.a_MDL07[0].soma.cai), \
self._scale(self.h.a_MDR08[0].soma.cai), \
self._scale(self.h.a_MVR08[0].soma.cai), \
self._scale(self.h.a_MVL08[0].soma.cai), \
self._scale(self.h.a_MDL08[0].soma.cai), \
self._scale(self.h.a_MDR09[0].soma.cai), \
self._scale(self.h.a_MVR09[0].soma.cai), \
self._scale(self.h.a_MVL09[0].soma.cai), \
self._scale(self.h.a_MDL09[0].soma.cai), \
self._scale(self.h.a_MDR10[0].soma.cai), \
self._scale(self.h.a_MVR10[0].soma.cai), \
self._scale(self.h.a_MVL10[0].soma.cai), \
self._scale(self.h.a_MDL10[0].soma.cai), \
self._scale(self.h.a_MDR11[0].soma.cai), \
self._scale(self.h.a_MVR11[0].soma.cai), \
self._scale(self.h.a_MVL11[0].soma.cai), \
self._scale(self.h.a_MDL11[0].soma.cai), \
self._scale(self.h.a_MDR12[0].soma.cai), \
self._scale(self.h.a_MVR12[0].soma.cai), \
self._scale(self.h.a_MVL12[0].soma.cai), \
self._scale(self.h.a_MDL12[0].soma.cai), \
self._scale(self.h.a_MDR13[0].soma.cai), \
self._scale(self.h.a_MVR13[0].soma.cai), \
self._scale(self.h.a_MVL13[0].soma.cai), \
self._scale(self.h.a_MDL13[0].soma.cai), \
self._scale(self.h.a_MDR14[0].soma.cai), \
self._scale(self.h.a_MVR14[0].soma.cai), \
self._scale(self.h.a_MVL14[0].soma.cai), \
self._scale(self.h.a_MDL14[0].soma.cai), \
self._scale(self.h.a_MDR15[0].soma.cai), \
self._scale(self.h.a_MVR15[0].soma.cai), \
self._scale(self.h.a_MVL15[0].soma.cai), \
self._scale(self.h.a_MDL15[0].soma.cai), \
self._scale(self.h.a_MDR16[0].soma.cai), \
self._scale(self.h.a_MVR16[0].soma.cai), \
self._scale(self.h.a_MVL16[0].soma.cai), \
self._scale(self.h.a_MDL16[0].soma.cai), \
self._scale(self.h.a_MDR17[0].soma.cai), \
self._scale(self.h.a_MVR17[0].soma.cai), \
self._scale(self.h.a_MVL17[0].soma.cai), \
self._scale(self.h.a_MDL17[0].soma.cai), \
self._scale(self.h.a_MDR18[0].soma.cai), \
self._scale(self.h.a_MVR18[0].soma.cai), \
self._scale(self.h.a_MVL18[0].soma.cai), \
self._scale(self.h.a_MDL18[0].soma.cai), \
self._scale(self.h.a_MDR19[0].soma.cai), \
self._scale(self.h.a_MVR19[0].soma.cai), \
self._scale(self.h.a_MVL19[0].soma.cai), \
self._scale(self.h.a_MDL19[0].soma.cai), \
self._scale(self.h.a_MDR20[0].soma.cai), \
self._scale(self.h.a_MVR20[0].soma.cai), \
self._scale(self.h.a_MVL20[0].soma.cai), \
self._scale(self.h.a_MDL20[0].soma.cai), \
self._scale(self.h.a_MDR21[0].soma.cai), \
self._scale(self.h.a_MVR21[0].soma.cai), \
self._scale(self.h.a_MVL21[0].soma.cai), \
self._scale(self.h.a_MDL21[0].soma.cai), \
self._scale(self.h.a_MDR22[0].soma.cai), \
self._scale(self.h.a_MVR22[0].soma.cai), \
self._scale(self.h.a_MVL22[0].soma.cai), \
self._scale(self.h.a_MDL22[0].soma.cai), \
self._scale(self.h.a_MDR23[0].soma.cai), \
self._scale(self.h.a_MVR23[0].soma.cai), \
self._scale(self.h.a_MVL23[0].soma.cai), \
self._scale(self.h.a_MDL23[0].soma.cai), \
self._scale(self.h.a_MDR24[0].soma.cai), \
self._scale(0), \
self._scale(self.h.a_MVL24[0].soma.cai), \
self._scale(self.h.a_MDL24[0].soma.cai)])
if self.verbose:
print_("Returning %s values: %s"%(len(values),values))
return values
def _scale(self,ca,print_it=False):
self.max_ca_found = max(ca,self.max_ca_found)
scaled = min(1,(ca/self.max_ca))
if print_it:
print_("- Scaling %s to %s (max found: %s)"%(ca,scaled,self.max_ca_found))
return scaled
if __name__ == '__main__':
print_("This script is used by the Sibernetic C++ application")
print_("Running it directly in Python will only plot the waves being generated for sending to the muscle cells...")
try_c302 = '-c302dat' in sys.argv
try_c302_nrn = '-c302nrn' in sys.argv
testnrn = '-testnrn' in sys.argv
skip_to_time = 0
max_time = 1.0 # s
time_per_step = 0.001 # s
increment = time_per_step/default_time_per_step
num_plots = 1
ms = MuscleSimulation(increment=increment)
if try_c302:
#ms = C302Simulation('configuration/test/c302/c302_B_Muscles.muscles.activity.dat', scale_to_max=True)
ms = C302Simulation('configuration/test/c302/c302_C1_Muscles.muscles.activity.dat', scale_to_max=True)
#ms = C302Simulation('../../../neuroConstruct/osb/invertebrate/celegans/CElegansNeuroML/CElegans/pythonScripts/c302/TestMuscles.activity.dat')
#ms = C302Simulation('../../neuroConstruct/osb/invertebrate/celegans/CElegansNeuroML/CElegans/pythonScripts/c302/c302_B_Oscillator.muscles.activity.dat')
skip_to_time = 0.05
max_time = 0.2
elif try_c302_nrn or testnrn:
dt = 0.1 # ms
max_time = .5 # s
maxt = max_time*1000
time_per_step = dt/1000 # s
increment = time_per_step/default_time_per_step
ms = C302NRNSimulation(tstop=maxt, dt=dt, verbose=False)
activation = {}
row = '11'
row_int=int(row)
m0='%s%s'%(quadrant0,row)
m1='%s%s'%(quadrant1,row)
m2='%s%s'%(quadrant2,row)
m3='%s%s'%(quadrant3,row)
for m in get_muscle_names():
activation[m] = []
times = []
num_steps = int(max_time/time_per_step)
steps_between_plots = int(num_steps/num_plots)
show_all = True
if testnrn:
for step in range(num_steps):
ms.run()
ms.save_results()
quit()
for step in range(num_steps):
t = step*time_per_step
l = ms.run(skip_to_time=skip_to_time)
for i in range(muscle_row_count):
mq0=get_muscle_name(quadrant0, i)
activation[mq0].append(l[i])
mq1=get_muscle_name(quadrant1, i)
activation[mq1].append(l[i+muscle_row_count])
mq2=get_muscle_name(quadrant2, i)
activation[mq2].append(l[i+muscle_row_count*2])
mq3=get_muscle_name(quadrant3, i)
activation[mq3].append(l[i+muscle_row_count*3])
times.append(t)
if step==0 or step%steps_between_plots == 0:
print_("At step %s (%s s)"%(step, t))
if show_all:
figV = plt.figure()
figV.suptitle("Muscle activation waves at step %s (%s s)"%(step, t))
plV = figV.add_subplot(111, autoscale_on=True)
plV.plot(l[0:muscle_row_count], label='%s*'%quadrant0, color=colours[quadrant0], linestyle='-', marker='o')
plV.plot(l[muscle_row_count:2*muscle_row_count], label='%s*'%quadrant1, color=colours[quadrant1], linestyle='-', marker='o')
plV.plot(l[2*muscle_row_count:3*muscle_row_count], label='%s*'%quadrant2, color=colours[quadrant2], linestyle='-')
plV.plot(l[3*muscle_row_count:4*muscle_row_count], label='%s*'%quadrant3, color=colours[quadrant3], linestyle='-')
plV.legend()
if show_all:
fig0 = plt.figure()
fig0.suptitle("Muscle activation waves of [%s, %s, %s, %s] vs time"%(m0,m1,m2,m3))
pl0 = fig0.add_subplot(111, autoscale_on=True)
pl0.plot(times, activation[m0], label=m0, color=colours[quadrant0], linestyle='-')
pl0.plot(times, activation[m1], label=m1, color=colours[quadrant1], linestyle='-')
pl0.plot(times, activation[m2], label=m2, color=colours[quadrant2], linestyle='--')
pl0.plot(times, activation[m3], label=m3, color=colours[quadrant3], linestyle='--')
pl0.legend()
f, a = plt.subplots(4, sharex=True, sharey=True)
a[0].set_title(quadrant0)
a[0].set_ylabel('muscle #')
a[1].set_title(quadrant3)
a[1].set_ylabel('muscle #')
a[2].set_title(quadrant1)
a[2].set_ylabel('muscle #')
a[3].set_title(quadrant2)
a[3].set_ylabel('muscle #')
a[3].set_xlabel('time step (%s s -> %s s)'%(skip_to_time, max_time))
arr0 = []
arr1 = []
arr2 = []
arr3 = []
for i in range(muscle_row_count):
arr0.append(activation[get_muscle_name(quadrant0, i)])
arr1.append(activation[get_muscle_name(quadrant3, i)])
arr2.append(activation[get_muscle_name(quadrant1, i)])
arr3.append(activation[get_muscle_name(quadrant2, i)])
a[0].imshow(arr0, interpolation='none', aspect='auto')
a[1].imshow(arr1, interpolation='none', aspect='auto')
a[2].imshow(arr2, interpolation='none', aspect='auto')
a[3].imshow(arr3, interpolation='none', aspect='auto')
#plt.colorbar()
plt.show()