def find_gr(M,L,x_target,x_edges,delta=5):
#finds end to end distances and plots a histogram with that data
print "############# FINDING GR ##############"
distance=particle_distance(M,M,L,rmax=L[0]/2)
bin_set = np.linspace(.8,2.2,75)
hist_s, xs = np.histogram(distance,bins=bin_set)
r_new = np.zeros(xs.shape[0]-1)
for i in range(r_new.shape[0]):
r_new[i] = (xs[i]+xs[i+1])/2
hist = np.zeros(hist_s.shape)
delta_r = xs[1] - xs[0]
#normalize the function with respect to an ideal gas
for i in range(hist_s.shape[0]):
r = r_new[i]
hist[i] = hist_s[i] / ((delta*M.shape[1])*(4*math.pi*delta_r*r**2*(M.shape[1]/L[0]**3)))
#interpolate g(r) for very smooth plot
fhist = interpolate.interp1d(r_new, Smooth(hist))
shist = []
for i in x_target:
try:
shist.append(fhist(i))
except:
try:
shist.append(fhist(i+.0001))
except:
shist.append(fhist(i-.0001))
return x_target,shist
def find_gr(M, L, x_target, x_edges, delta=5):
#finds end to end distances and plots a histogram with that data
print "############# FINDING GR ##############"
distance = particle_distance(M, M, L, rmax=L[0] / 2)
bin_set = np.linspace(.8, 2.2, 75)
hist_s, xs = np.histogram(distance, bins=bin_set)
r_new = np.zeros(xs.shape[0] - 1)
for i in range(r_new.shape[0]):
r_new[i] = (xs[i] + xs[i + 1]) / 2
hist = np.zeros(hist_s.shape)
delta_r = xs[1] - xs[0]
#normalize the function with respect to an ideal gas
for i in range(hist_s.shape[0]):
r = r_new[i]
hist[i] = hist_s[i] / ((delta * M.shape[1]) *
(4 * math.pi * delta_r * r**2 *
(M.shape[1] / L[0]**3)))
#interpolate g(r) for very smooth plot
fhist = interpolate.interp1d(r_new, Smooth(hist))
shist = []
for i in x_target:
try:
shist.append(fhist(i))
except:
try:
shist.append(fhist(i + .0001))
except:
shist.append(fhist(i - .0001))
return x_target, shist
def hist(M1,M2,save_name,label):
distance=particle_distance(M1,M2,L)
dist = []
for d in distance:
if d>1 and d<25:
dist.append(d)
hist_s,xs=histogram_reg(dist,bins=25)
return xs,hist_s
def hist(M1,M2,L,save_name,label,end=1):
distance=particle_distance(M1,M2,L)
hist_s,xs,max_hist=histogram_normal(distance,bins=60,end=end)
#normalize the function with respect to an ideal gas
delta_r = xs[1] - xs[0]
for i in range(len(hist_s)):
r = (xs[i])
hist_s[i] /= (4*math.pi*delta_r*r*(M1.shape[1]/L[0]**3))
return xs,hist_s
def PMF(hpy, V,L,n_frames=15,n_start=1,end=1):
start=n_start
finish=n_start+n_frames
time_start = time.clock()
distance=particle_distance(V[start:finish],V[start:finish],L)
print "g(r) Elapsed Time -" , time.clock()-time_start
time_start = time.clock()
dset = hpy.create_dataset("%i"%n_start, data = np.array(distance))
dset.attrs.create("N_atoms",V.shape[1])
dset.attrs.create("V",L[0]*L[1]*L[2])
dset.attrs.create("L",L[0])
dset.attrs.create("n_frames",n_frames)
#util.marshal_dump([V.shape[1],L[0],n_frames,distance], 'pmf_dist%i.pkl'%n_start)
print "Marshal Elapsed Time -" , time.clock()-time_start
def hist(M1,M2,save_name,label):
distance=particle_distance(M1,M2,L)
hist_s,xs,max_hist=histogram(distance,bins=50)
return xs,hist_s
