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Module: Profile>>> # Note that by default Profile() uses the number of atoms as an observable >>> Layers = [] >>> for n in range(5): ->>> Layers.append(Profile()) +... Layers.append(Profile()) >>> >>> # Go through the trajectory, center the liquid slab and sample the profiles >>> for ts in u.trajectory[::50]: ->>> print ts.frame, ->>> # this shifts the system so that the center of mass of the liquid slab ->>> # is in the middle of the box ->>> inter.center() ->>> ->>> Layers[0].sample(g) ->>> Layers[1].sample(u.atoms[u.atoms.layers == 1 ]) ->>> Layers[2].sample(u.atoms[u.atoms.layers == 2 ]) ->>> Layers[3].sample(u.atoms[u.atoms.layers == 3 ]) ->>> Layers[4].sample(u.atoms[u.atoms.layers == 4 ]) +... # this shifts the system so that the center of mass of the liquid slab +... # is in the middle of the box +... inter.center() +... +... Layers[0].sample(g) +... Layers[1].sample(u.atoms[u.atoms.layers == 1 ]) +... Layers[2].sample(u.atoms[u.atoms.layers == 2 ]) +... Layers[3].sample(u.atoms[u.atoms.layers == 3 ]) +... Layers[4].sample(u.atoms[u.atoms.layers == 4 ]) >>> >>> density=[] >>> for L in Layers: ->>> low,up,avg = L.get_values(binwidth=0.5) ->>> density.append(avg) +... low,up,avg = L.get_values(binwidth=0.5) +... density.append(avg) >>> >>> # (low + up )/2 is the middle of the bin >>> np.savetxt('profile.dat',list(zip(low,up,density[0],density[1],density[2],density[3],density[4]))) @@ -211,7 +210,11 @@

Module: Profile
>>> XTC = pytim.datafiles.pytim_data.fetch('LJ_BIG_XTC',tmpdir='./')
->>> u = mda.Universe(LJ_GRO,XTC)
+checking presence of a cached copy... not found. Fetching remote file... done.
+
+ +
>>> u = mda.Universe(LJ_GRO,XTC)
+>>> g = u.select_atoms('all')
 >>>
 >>> inter = pytim.ITIM(u,alpha=2.5,cluster_cut=4.5)
 >>> profile = Profile(interface=inter)
@@ -326,10 +329,10 @@ 
Time Correlation Functions is considered
+only if the particle did not leave the
+reference group between t_0 and
+t_1. If False, the intermittent
 correlation is calculated, and the
 above restriction is released.
 
@@ -374,10 +377,11 @@ 
Time Correlation Functions is done over all trajectories, including those
+that start with h=0.
+The correlation \langle h(t)h(0) \rangle is divided
+by the average \langle h \rangle computed over all
+trajectores that extend up to a time lag t. The
 normalize switch has no effect.

 
>>> # normalized, continuous
 >>> corr = nn.correlation()
@@ -391,11 +395,11 @@ 
Time Correlation Functions (i.e., which start within the reference
 group). The normalization is done by dividing the
-correlation at time lag $t$ by its value at time lag 0
+correlation at time lag t by its value at time lag 0
 computed over all trajectories that extend up to time
-lag $t$ and do not start with $h=0$.

+lag t and do not start with h=0.

 
>>> # not normalizd, intermittent
 >>> corr = vv.correlation(normalized=False,continuous=False)
 >>> c0 = (1+1+1+0.25+1+1+0.25)/7
diff --git a/searchindex.js b/searchindex.js
index b7e19ada..2e6363d3 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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