import numpy as np
import models
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from matplotlib.gridspec import GridSpec
from corner import hist2d
import data
from data import molarConv
import sympy as sp
# Long PRL
model, blockSize = models.exclusiveActinModelRuleFactory(0, 4, sides='left')
act = models.actin(model, [0,1], sp.symbols('a b c'), blockSize=blockSize)
# Read samples
samples = np.loadtxt('Output/d_0_4_exclusive_left')
# Cut out J
samples = samples[:,(0,1)]
# Make convention match PRL.
samples *= -1
# Make corner plot
fig = plt.figure(figsize=(5,7))
modelSTR = 'Output/' + fname
# Threads
threads = int(sys.argv[4])
# Build the model
if s2 == 'inclusive':
fname = fname + '_inclusive'
modelSTR = modelSTR + '_inclusive'
model, blockSize = models.actinModelRuleFactory(left, right)
elif s2 == 'exclusive':
s3 = sys.argv[6]
fname = fname + '_exclusive_' + s3
modelSTR = modelSTR + '_exclusive_' + s3
model, blockSize = models.exclusiveActinModelRuleFactory(left,
right,
sides=s3)
act = models.actin(model, [0, 1], sp.symbols('a b c'), blockSize=blockSize)
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)
# Can specify s = 'h' or 'd' for the two kinds of actin
# The models we use are:
# short - eFuncTwoPlaneVeryShortActinModel
# medium - eFuncTwoPlaneShortActinModel
# long - eFuncTwoPlaneActinModel
s = 'b'
lefts = [-1,-2,-1,0,-4]
rights = [1,1,3,2,0]
# Build the models
acts = []
for i in range(len(lefts)):
model, blockSize = models.exclusiveActinModelRuleFactory(lefts[i], rights[i], sides='left')
act = models.actin(model, [0,1], sp.symbols('a b c'), blockSize=blockSize)
acts.append(act)
strs = ['Output/' + s + '_' + str(l) + '_' + str(r) for l,r in zip(*(lefts,rights))]
labels = [str(r) + ',' + str(l) for l,r in zip(*(lefts,rights))]
fits = []
for st in strs:
f = np.loadtxt(st + '_inclusive_summary.txt')
q = f[0][0]
w = f[1][0]
j = f[2][0]
fits.append((q,w,j))
c,bindingF,length,dl,name = data(s)