# The histogram scaling isn't dD/df, though, it's dD/d(bin)
# dD/d(bin) = dD/df df/d(bin) = binwidth P0 z e^{bf + z/b(1-e^{bf})}
#
-# test with lots of runs of v-dx=1
-# sawsim -v1 -mhooke -a1 -kbell -K1,1 -T7.24296e22 -F1
-# ^- v=1 ^- k=1 ^-a=1 ^-1/kB
+# test with lots of runs of
+# v- T=1/kB v- v=1 v- k=1
+# sawsim -T7.24296e22 -v1 -s folded,hooke,1 -N1 -s unfolded,null \
+# -k "folded,unfolded,bell,{1,1}" -q folded
+# ^- {a,dx}={1,1}
# (kB = 1.3806503 10-23 J/K, we use T=1/kB to get the bell K=e^{-f})
#
-# 70 runs of that takes ~1 second:
-# time xtimes 200 sawsim -v1 -mhooke -a1 -kconst -K1 -F1 > /dev/null
+# 60 runs of that takes ~1 second:
+# time xtimes 60 sawsim -T7.24296e22 -v1 -s folded,hooke,1 -N1 \
+# -s unfolded,null -k "folded,unfolded,bell,{1,1}" -q folded \
+# > /dev/null
#
# usage: bell_rate.sh
# dD/d(bin) = binwidth P0 z e^{bf + z/b(1-e^{bf})}
# where z ≡ a/kv, and b = Δx/kBT
KB=1.3806503e-23 # J/K
+#STYLE="ones"
STYLE="protein"
if [ "$STYLE" == "ones" ]
then
> $DATA
if [ "$ISACLUSTER" -eq 1 ]
then
- qcmd "xtimes $N $SAWSIM -v$V -mhooke -a$K -kbell -K$A,$DX -T$T -F1 | grep -v '^#' >> $DATA"
+ # Sawsim <= 0.5
+ #qcmd "xtimes $N $SAWSIM -T$T -v$V -mhooke -a$K -kbell -K$A,$DX -F1 | grep -v '^#' >> $DATA"
+ # Sawsim >= 0.6
+ qcmd "xtimes $N $SAWSIM -T$T -v$V -s folded,hooke,$K -N1 -s unfolded,null -k 'folded,unfolded,bell,{$A,$DX}' -q folded | grep -v '^#' | cut -f1 >> $DATA"
else
- xtimes $N $SAWSIM -v$V -mhooke -a$K -kbell -K$A,$DX -T$T -F1 | grep -v '^#' >> $DATA
+ # Sawsim <= 0.5
+ #xtimes $N $SAWSIM -T$T -v$V -mhooke -a$K -kbell -K$A,$DX -F1 | grep -v '^#' >> $DATA
+ # Sawsim >= 0.6
+ xtimes $N $SAWSIM -T$T -v$V -s folded,hooke,$K -N1 -s unfolded,null -k "folded,unfolded,bell,{$A,$DX}" -q folded | grep -v '^#' | cut -f1 >> $DATA
fi
# histogram the data
#echo "$FIT"
FITZ=`echo "$FIT" | sed -n 's/a:[[:space:]]*//p'`
FITB=`echo "$FIT" | sed -n 's/b:[[:space:]]*//p'`
-echo -e "a:\t$FITZ\t(expected $Z)"
+echo -e "z:\t$FITZ\t(expected $Z)"
echo -e "b:\t$FITB\t(expected $B)"
# generate gnuplot script