def evaluate(theta, x, y, c):
bindingF = x
q, w, j = theta
pVals = np.zeros(bindingF.shape)
fVals = np.zeros(bindingF.shape)
for i, bf in enumerate(bindingF):
p, _, _ = act.bindingFinder((0, q, w), bf)
pVals[i] = p
fVals[i] = act.fN((p, q, w))[1]
model = models.meanL(c, fVals, j)
return model
def plotterCran(minB,maxB,model,cRan,q,w,j):
for c in cRan:
bFhighRes = np.linspace(minB,maxB,num=100,endpoint=True)
fHR = np.zeros(100)
for i,x in enumerate(bFhighRes):
p = model.bindingFinder((0,q,w),x)[0]
fHR[i] = model.fN((p,q,w))[1]
mlHighRes = models.meanL(c*molarConv,fHR,j)
plt.plot(bFhighRes,mlHighRes/100,label=str(c)+'$\mu M$')
plt.xlabel('Cofilin Binding Fraction')
plt.ylabel('Filament length ($\mu m$)')
plt.legend(loc=0)
plt.show()
def plotterModels(ax, minB,maxB,model,c,qR,wR,jR,modelStr,bindF): for aa in range(len(qR)): q = qR[aa] j = jR[aa] w = wR[aa] q *= -1 w *= -1 bFhighRes = np.linspace(minB,maxB,num=100,endpoint=True) fHR = np.zeros(100) for i,x in enumerate(bFhighRes): p = model.bindingFinder((0,q,w),x)[0] fHR[i] = model.fN((p,q,w))[1] mlHighRes = models.meanL(c*molarConv,fHR,j) ax.plot(bFhighRes,mlHighRes,label=modelStr[aa])
def plottermodelRan(minB,maxB,model,c,qR,wR,jR):
for aa in range(len(qR)):
q = qR[aa]
j = jR[aa]
w = wR[aa]
bFhighRes = np.linspace(minB,maxB,num=100,endpoint=True)
fHR = np.zeros(100)
for i,x in enumerate(bFhighRes):
p = model.bindingFinder((0,q,w),x)[0]
fHR[i] = model.fN((p,q,w))[1]
mlHighRes = models.meanL(c*molarConv,fHR,j)
plt.plot(bFhighRes,mlHighRes/100,label='('+str(q)+','+str(w)+','+str(j)+')')
plt.xlabel('Cofilin Binding Fraction')
plt.ylabel('Filament length ($\mu m$)')
plt.legend(loc=0)
plt.show()
def plotterModels(minB,maxB,modelss,c,qR,wR,jR,modelStr,bindF,length,dl):
for aa in range(len(qR)):
q = qR[aa]
j = jR[aa]
w = wR[aa]
bFhighRes = np.linspace(minB,maxB,num=100,endpoint=True)
fHR = np.zeros(100)
for i,x in enumerate(bFhighRes):
p = modelss[aa].bindingFinder((0,q,w),x)[0]
fHR[i] = modelss[aa].fN((p,q,w))[1]
mlHighRes = models.meanL(c*molarConv,fHR,j)
plt.plot(bFhighRes,mlHighRes/100,label=modelStr[aa]+' ('+str(q)+','+str(w)+','+str(j)+')')
plt.scatter(bindingF,length/100)
plt.errorbar(bindingF,length/100,yerr=dl/100,ls='dotted')
plt.xlabel('Cofilin Binding Fraction')
plt.ylabel('Filament length ($\mu m$)')
plt.legend(loc=0)
plt.show()
rights)):
if l != -3 or r != 0:
continue
q, w, j = params
bFhighRes = np.linspace(min(bindingF),
max(bindingF),
num=100,
endpoint=True)
fHR = np.zeros(100)
for i, x in enumerate(bFhighRes):
p = act.bindingFinder((0, q, w), x)[0]
fHR[i] = act.fN((p, q, w))[1]
print(x, p, q, w, act.fN((p, q, w)))
if i % 10 == 0:
print(p)
mlHighRes = models.meanL(c, fHR, j)
if abs(l) < abs(r):
line, = ax.plot(bFhighRes,
mlHighRes,
linewidth=1,
label=label,
linestyle='-')
elif abs(l) == abs(r):
line, = ax.plot(bFhighRes,
mlHighRes,
linewidth=1,
label=label,
linestyle='-.')
else:
line, = ax.plot(bFhighRes,
mlHighRes,
q_mcmc, w_mcmc, j_mcmc,
[
0, 0,
reducedChiSquared((q_mcmc[0], w_mcmc[0], j_mcmc[0]), bindingF,
length, dl, c)
]
]))
fig = corner(samples, labels=["$Q$", "$W$", "$J$", "$\ln\,f$"])
fig.savefig(modelSTR + '_' + 'triangle' + s + '.png')
plt.close('all')
plt.scatter(bindingF, length)
plt.errorbar(bindingF, length, yerr=dl)
bFhighRes = np.linspace(min(bindingF),
max(bindingF),
num=100,
endpoint=True)
fHR = np.zeros(100)
for i, x in enumerate(bFhighRes):
p = act.bindingFinder((0, q_mcmc[0], w_mcmc[0]), x)[0]
fHR[i] = act.fN((p, q_mcmc[0], w_mcmc[0]))[1]
if i % 10 == 0:
print(p)
mlHighRes = models.meanL(c, fHR, j_mcmc[0])
plt.plot(bFhighRes, mlHighRes)
plt.xlabel('Cofilin Binding Fraction')
plt.ylabel('Filament length ($\mu m$)')
plt.title(name)
plt.savefig(modelSTR + '_' + 'fig.pdf', dpi=100)
exit()