def calc_timedependent_D_fromdir(dirname,dtc,figname,zoom=100,shift=1):
x = np.array(read_coor(dirname+"/smoothcrd-x/out"))
y = np.array(read_coor(dirname+"/smoothcrd-y/out"))
z = np.array(read_coor(dirname+"/smoothcrd-z/out"))
ddr,weight = calc_timedependent_D(x,y,z,dtc,shift=shift)
meanddr = np.mean(ddr,axis=1) # average over atoms
plot_vs_time(meanddr,dtc,figname+".png")
plot_vs_time(meanddr[:zoom],dtc,figname+".zoom.png")
return ddr,weight
def analyze_dist_multi_1D(list_x,nstep,outdir,dtc):
"Take the average over the D estimates in each of these files"""
assert len(list_x) > 0
nfiles = len(list_x)
t = np.arange(0,nstep*dtc,dtc)
alldist = np.zeros((nstep,nfiles),float)
allD = np.zeros((nfiles),float)
print("="*5)
print("Results")
print("making %i fits" %nfiles)
print("fit from %i steps, i.e. time %f ps, actually time %f ps" %(nstep,nstep*dtc,(nstep-1)*dtc))
for i in range(nfiles):
dist = calc_dist_1D(np.array(read_coor(list_x[i])))
d = dist[:nstep,:] # only fit the first nstep time steps (use all atoms)
average = np.mean(d,1)
alldist[:,i] = average
p = np.polyfit(t,average**2,1)
allD[i] = p[0] # a_1 this is in angstrom**2/ps = 1e-20/1e-12 meter**2/second
# = 1e-8 meter**2/second = 1e-4 cm**2/s
# if I want many figures:
#fit_sqrt_vs_time(np.mean(dist_xy,1),dtc,outdir+"/fig_dist.xy.%i.png"%i,title=str(i))
#fit_sqrt_vs_time(np.mean(dist_z,1), dtc,outdir+"/fig_dist.z.%i.png"%i,title=str(i))
#fit_sqrt_vs_time(np.mean(dist_r,1), dtc,outdir+"/fig_dist.r.%i.png"%i,title=str(i))
m = np.mean(alldist,axis=1)
#s = np.std(alldist,axis=1)
fit_sqrt_vs_time(m,dtc,outdir+"/fig_dist.average.png",title="average of %i"%nfiles)
print_output_D_ave_1D(allD)
def analyze_dist_conditionalz(list_x,list_y,list_z,dtc,zpbc,lt,figname):
"Collect distances in all these files"""
assert len(list_x) > 0
assert len(list_x) == len(list_y)
assert len(list_x) == len(list_z)
nfiles = len(list_x)
nstep = 1000
print("="*5)
print("Results")
edges = np.arange(-26.,26.1,1)
nbins = len(edges)-1
redges = np.arange(0,50,0.2)
alldxy = []
alldz = []
alldr = []
allz = []
for i in range(nfiles):
d_xy,d_z,d_r = calc_dist_lt_from3files(list_x[i],list_y[i],list_z[i],lt)
z = np.array(read_coor(list_z[i]))
z = z[:-lt] # initz
z -= zpbc*np.floor(z/zpbc+0.5)
alldxy.append(d_xy)
alldz.append(d_z)
alldr.append(d_r)
allz.append(z)
#alldxy = np.array(alldxy)
print("XXXXXX lt*dtc",lt*dtc)
if lt*dtc < 0.6:
distmax = 2.
elif lt*dtc < 6.: #ps
distmax = 5.
elif lt*dtc < 60.:
distmax = 10.
else:
distmax = 25.
for i,alld in enumerate([alldxy,alldz,alldr]):
label = ["xy","z","r"][i]
factor = [np.sqrt(2),1.,np.sqrt(3)][i]
k,xedges,yedges = np.histogram2d(np.array(alld).ravel(),np.array(allz).ravel(),[150,50])
print(d_r.shape,z.shape)
print(k.shape, xedges.shape, yedges.shape)
# norm
xedges /= factor
k = k / np.sum(k,axis=0)
import matplotlib.pyplot as plt
plt.figure()
plt.contourf(xedges[:-1],yedges[:-1],k.transpose()) #locator=ticker.LogLocator()
plt.xlabel("distance [A]")
plt.xlim(xmax=distmax)
plt.ylabel("z [A]")
plt.title("root mean square distance: "+label)
plt.colorbar()
plt.savefig(figname+"_hist2d.%s.lt%i.png"%(label,lt))
def analyze_dist_multi_1D(list_x, nstep, outdir, dtc):
"Take the average over the D estimates in each of these files" ""
assert len(list_x) > 0
nfiles = len(list_x)
t = np.arange(0, nstep * dtc, dtc)
alldist = np.zeros((nstep, nfiles), float)
allD = np.zeros((nfiles), float)
print("=" * 5)
print("Results")
print("making %i fits" % nfiles)
print("fit from %i steps, i.e. time %f ps, actually time %f ps" %
(nstep, nstep * dtc, (nstep - 1) * dtc))
for i in range(nfiles):
dist = calc_dist_1D(np.array(read_coor(list_x[i])))
d = dist[:
nstep, :] # only fit the first nstep time steps (use all atoms)
average = np.mean(d, 1)
alldist[:, i] = average
p = np.polyfit(t, average**2, 1)
allD[i] = p[
0] # a_1 this is in angstrom**2/ps = 1e-20/1e-12 meter**2/second
# = 1e-8 meter**2/second = 1e-4 cm**2/s
# if I want many figures:
#fit_sqrt_vs_time(np.mean(dist_xy,1),dtc,outdir+"/fig_dist.xy.%i.png"%i,title=str(i))
#fit_sqrt_vs_time(np.mean(dist_z,1), dtc,outdir+"/fig_dist.z.%i.png"%i,title=str(i))
#fit_sqrt_vs_time(np.mean(dist_r,1), dtc,outdir+"/fig_dist.r.%i.png"%i,title=str(i))
m = np.mean(alldist, axis=1)
#s = np.std(alldist,axis=1)
fit_sqrt_vs_time(m,
dtc,
outdir + "/fig_dist.average.png",
title="average of %i" % nfiles)
print_output_D_ave_1D(allD)
def calc_dist_from3files(xfile, yfile, zfile):
x = np.array(read_coor(xfile))
y = np.array(read_coor(yfile))
z = np.array(read_coor(zfile))
dist_xy, dist_z, dist_r = calc_dist(x, y, z) # use ALL data in the file!
return dist_xy, dist_z, dist_r
def calc_dist_fromdir(dirname):
x = np.array(read_coor(dirname + "/smoothcrd-x/out"))
y = np.array(read_coor(dirname + "/smoothcrd-y/out"))
z = np.array(read_coor(dirname + "/smoothcrd-z/out"))
dist_xy, dist_z, dist_r = calc_dist(x, y, z)
return dist_xy, dist_z, dist_r
def collect_dist_from3files(xfile,yfile,zfile,dtc):
x = np.array(read_coor(xfile))
y = np.array(read_coor(yfile))
z = np.array(read_coor(zfile))
lagtimes,dist2_xy,dist2_z,dist2_r = collect_dist(x,y,z,dtc)
return lagtimes,dist2_xy,dist2_z,dist2_r
def calc_dist_lt_from3files(xfile,yfile,zfile,dn,rv=False):
x = np.array(read_coor(xfile,rv=rv,axis=0))
y = np.array(read_coor(yfile,rv=rv,axis=1))
z = np.array(read_coor(zfile,rv=rv,axis=2))
d2_xy,d2_z,d2_r = calc_dist_lt(x,y,z,dn)
return d2_xy,d2_z,d2_r
def calc_dist_from3files(xfile,yfile,zfile,rv=False):
x = np.array(read_coor(xfile,rv=rv,axis=0))
y = np.array(read_coor(yfile,rv=rv,axis=1))
z = np.array(read_coor(zfile,rv=rv,axis=2))
dist2_xy,dist2_z,dist2_r,weight = calc_dist(x,y,z)
return dist2_xy,dist2_z,dist2_r,weight
def calc_dist_fromdir(dirname):
x = np.array(read_coor(dirname+"/smoothcrd-x/out"))
y = np.array(read_coor(dirname+"/smoothcrd-y/out"))
z = np.array(read_coor(dirname+"/smoothcrd-z/out"))
dist2_xy,dist2_z,dist2_r,weight = calc_dist(x,y,z)
return dist2_xy,dist2_z,dist2_r,weight
def calc_timedependent_D_from3files(xfile,yfile,zfile,dtc,shift=1):
x = np.array(read_coor(xfile))
y = np.array(read_coor(yfile))
z = np.array(read_coor(zfile))
ddr,weight = calc_timedependent_D(x,y,z,dtc,shift=shift)
return ddr,weight
def calc_dist_from3files(xfile,yfile,zfile):
x = np.array(read_coor(xfile))
y = np.array(read_coor(yfile))
z = np.array(read_coor(zfile))
dist_xy,dist_z,dist_r = calc_dist(x,y,z) # use ALL data in the file!
return dist_xy,dist_z,dist_r
def calc_dist_lt_from3files(xfile,yfile,zfile,lt):
x = np.array(read_coor(xfile))
y = np.array(read_coor(yfile))
z = np.array(read_coor(zfile))
d_xy,d_z,d_r = calc_dist_lt(x,y,z,lt)
return d_xy,d_z,d_r
