def Simulate():
record_path = '/Users/matthewbakalar/projects/VASP/latticerecord2.dat'
simulation_steps = 100
initial_size = 10
max_size = 10000
c_steps = 10
c_multiplier = 10
c_range = sp.zeros(c_steps)
c_range[0] = .0001
for i in range(1,c_steps):
c_range[i] = c_range[i-1] * c_multiplier
error = sp.zeros(c_range.size)
a = sp.zeros(c_range.size)
b = sp.zeros(c_range.size)
count = 0
for conc in c_range:
lattice = LatticeModel(initial_size, max_size)
simulation = VaspSimulation(lattice)
record = SimulationRecord(record_path, simulation, 'w')
record.open_file()
VaspSimulation.VASP_ON = VaspSimulation.VASP_ON_BASE * conc
for i in range(0, simulation_steps):
simulation.advance_simulation()
record.save_state()
record.close_file()
record = SimulationRecord(record_path, simulation, 'r')
time_recon = TimeReconstruction(record)
lvt = time_recon.length_vs_time()
#Linear regressison -polyfit - polyfit can be used other orders polys
n = lvt[0].size
(ar,br)=polyfit(lvt[0],lvt[1],1)
xr=polyval([ar,br],lvt[0])
#compute the mean square error
err=sp.sqrt(sum((xr-lvt[1])**2)/n)
print('Linear regression using polyfit')
print('parameters: regression: a=%.2f b=%.2f, ms error= %.3f' % (ar,br,err))
pyplot.plot(lvt[0], lvt[1], 'g')
pyplot.plot(lvt[0], xr, 'r')
pyplot.show()
a[count] = ar
b[count] = br
error[count] = err
count+=1
print 'Count = %(count)d' % {'count':count}
def Report():
imgname = 'polymethodsgrowth.png'
slopename = 'slopes.txt'
slopes = sp.zeros([len(Experiment.runs), len(Experiment.conc)])
i = 0
j = 0
pyplot.figure(1)
for sim, fname in Experiment.runs:
for conc in Experiment.conc[0:6]:
print 'Concentration: ', str(conc)
fname_conc = fname + '_c=' + str(conc)
pth = os.path.join(Experiment.datafolder, fname_conc)
record = SimulationRecord(pth, sim, 'r')
recon = TimeReconstruction(record)
data = recon.length_vs_time()
if fname == 'processive.txt':
color = 'r'
elif fname == 'non_processive.txt':
color = 'g'
else:
color = 'b'
pyplot.plot(data[0], data[1], color)
(ar,br)=polyfit(data[0],data[1],1)
slopes[i, j] = ar
j += 1
j = 0
i += 1
# length versus time
pyplot.legend(('Processive', 'Non-processive', 'Detach'),
'upper left', shadow=True, fancybox=False)
pyplot.xlabel('Time (s)')
pyplot.ylabel('Filament length')
pyplot.savefig(os.path.join(Experiment.datafolder, imgname))
slope_fname = os.path.join(Experiment.datafolder, slopename)
sp.savetxt(slopename, slopes)
# slope vs concentration
pyplot.figure(2)
pyplot.plot(Experiment.conc, slopes[0])
pyplot.plot(Experiment.conc, slopes[1])
pyplot.plot(Experiment.conc, slopes[2])
pyplot.legend(('Processive', 'Non-processive', 'Detach'),
'upper left', shadow=True, fancybox=False)
# pyplot.xscale('log')
pyplot.xlabel('Concentration')
pyplot.ylabel('Average Growth Rate')
pyplot.show()
return slopes
