From a352b010ade8d0656e45ba3c202ba151d188ab35 Mon Sep 17 00:00:00 2001
From: Giovanni Bussi <giovanni.bussi@gmail.com>
Date: Tue, 18 Dec 2018 13:34:27 +0100
Subject: [PATCH] Fixed verbatim -> plumedfile
(@gtribello noticed this is #423)
---
user-doc/Analysis.md | 16 ++++++++--------
user-doc/Performances.md | 4 ++--
2 files changed, 10 insertions(+), 10 deletions(-)
diff --git a/user-doc/Analysis.md b/user-doc/Analysis.md
index ae07956d7..87c43f751 100644
--- a/user-doc/Analysis.md
+++ b/user-doc/Analysis.md
@@ -107,11 +107,11 @@ within colvars and functions. One place where this is very useful is when you a
not you have implemented the derivatives of a new collective variables correctly. So for example if
we wanted to do such a test on the distance CV we would employ an input file something like this:
-\verbatim
+\plumedfile
d1: DISTANCE ATOMS=1,2
d1n: DISTANCE ATOMS=1,2 NUMERICAL_DERIVATIVES
DUMPDERIVATIVES ARG=d1,d1n FILE=derivatives
-\endverbatim
+\endplumedfile
The first of these two distance commands calculates the analytical derivtives of the distance
while the second calculates these derivatives numerically. Obviously, if your CV is implemented
@@ -170,12 +170,12 @@ that are available in PLUMED are as follows
In general most of these landmark selection algorithms must be used in tandem with a \ref dissimilaritym "dissimilarity matrix" object as as follows:
-\verbatim
+\plumedfile
data: COLLECT_FRAMES ARG=d1 STRIDE=1
ss1: EUCLIDEAN_DISSIMILARITIES USE_OUTPUT_DATA_FROM=data
ll2: LANDMARK_SELECT_FPS USE_OUTPUT_DATA_FROM=ss1 NLANDMARKS=300
OUTPUT_COLVAR_FILE USE_OUTPUT_DATA_FROM=ll2 FILE=mylandmarks
-\endverbatim
+\endplumedfile
When landmark selection is performed in this way a weight is ascribed to each of the landmark configurations. This weight is
calculated by summing the weights of all the trajectory frames in each of the landmarks Voronoi polyhedra
@@ -207,22 +207,22 @@ the following <a href="https://www.youtube.com/watch?v=ofC2qz0_9_A&feature=youtu
Within PLUMED running an input to run a dimensionality reduction algorithm can be as simple as:
-\verbatim
+\plumedfile
data: COLLECT_FRAMES STRIDE=1 ARG=d1
ss1: EUCLIDEAN_DISSIMILARITIES USE_OUTPUT_DATA_FROM=data
mds: CLASSICAL_MDS USE_OUTPUT_DATA_FROM=ss1 NLOW_DIM=2
-\endverbatim
+\endplumedfile
Where we have to use the \ref EUCLIDEAN_DISSIMILARITIES action here in order to calculate the matrix of dissimilarities between trajectory frames.
We can even throw some landmark selection into this procedure and perform
-\verbatim
+\plumedfile
data: COLLECT_FRAMES STRIDE=1 ARG=d1
ss1: EUCLIDEAN_DISSIMILARITIES USE_OUTPUT_DATA_FROM=data
ll2: LANDMARK_SELECT_FPS USE_OUTPUT_DATA_FROM=ss1 NLANDMARKS=300
mds: CLASSICAL_MDS USE_OUTPUT_DATA_FROM=ll2 NLOW_DIM=2
osample: PROJECT_ALL_ANALYSIS_DATA USE_OUTPUT_DATA_FROM=ss1 PROJECTION=smap
-\endverbatim
+\endplumedfile
Notice here that the final command allows us to caluclate the projections of all the non-landmark points that were collected by the action with
label ss1.
diff --git a/user-doc/Performances.md b/user-doc/Performances.md
index 87afac91f..88d235c8e 100644
--- a/user-doc/Performances.md
+++ b/user-doc/Performances.md
@@ -271,12 +271,12 @@ You are done!
In some case using a custom expression is almost as fast as using a hard-coded
function. For instance, with an input like this one:
-\verbatim
+\plumedfile
...
c: COORDINATION GROUPA=1-108 GROUPB=1-108 R_0=1
dfast: COORDINATION GROUPA=1-108 GROUPB=1-108 SWITCH={CUSTOM FUNC=1/(1+x2^3) R_0=1}
...
-\endverbatim
+\endplumedfile
I (GB) obtained the following timings (on a Macbook laptop):
\verbatim
...
--
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