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conformer_search_type flex | |
write_fragment_libraries no | |
user_specified_anchor no | |
limit_max_anchors no | |
min_anchor_size 5 | |
pruning_use_clustering yes | |
pruning_max_orients 1000 | |
pruning_clustering_cutoff 100 | |
pruning_conformer_score_cutoff 100.0 | |
pruning_conformer_score_scaling_factor 1.0 |
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for f in *.logs; | |
do echo $f; | |
echo "compound,score" > `basename $f .logs`.csv; | |
cat $f | egrep "Molecule:|Grid_Score" | tr -s " " | sed -e ':a;N;$!ba;s/\n Grid_Score: / /g' -e 's/Molecule: //g' | \ | |
tr " " "," >> `basename $f .logs`.csv; | |
done |
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#!/bin/bash | |
# direcotry with log files from autoweka | |
autowekadir="./" | |
# weka.classifiers.meta.FilteredClassifier -F "weka.filters.supervised.attribute.AttributeSelection -E \"$eval\" -S \"$selection\"" -S 1 -W $classifier | |
# best classifier: weka.classifiers.lazy.IBk | |
# arguments: [-K, 4, -F] | |
# attribute search: weka.attributeSelection.GreedyStepwise | |
# attribute search arguments: [-B, -R] |
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import time | |
from time import strftime | |
import subprocess | |
def runForDuration(cmdline, duration, logFile): | |
teeCommand = ['tee', '-a', logFile ] # quick and dirty hack to get log to file | |
try: | |
p1 = subprocess.Popen(cmdline, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) | |
p2 = subprocess.Popen(teeCommand, stdin=p1.stdout) |
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#!/bin/bash | |
### list files bigger than the given size | |
### and optionally compress them | |
filesize=100M # files bigger than this will be listed; default is 100M | |
for f in `find . -type f -size +$filesize` | |
do |
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#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
from re import sub | |
fname = "FAQ.html" | |
header="<h4>" | |
outfile = "FAQ+TOC.html" | |
#-----------------------------------------# |
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#!/bin/bash | |
for ses in `screen -r | grep "(Detached)"| cut -f 2`; do screen -r "$ses"; done |
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#!/bin/bash | |
DATA=`date` | |
DIR=/usr/local/bin/rrd/pi/rrd/ | |
OUTDIR=/var/www/pi/sysinfo/img/ | |
BAZA=$DIR/temperatura.rrd | |
var=cisnienie |
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#!/bin/bash | |
rrdtool create airq.rrd --step 3600 \ | |
DS:pm10:GAUGE:1800:0:500 \ | |
DS:pm25:GAUGE:1800:0:500 \ | |
RRA:AVERAGE:0.5:1:144 \ | |
RRA:AVERAGE:0.5:3:336 \ | |
RRA:AVERAGE:0.5:144:367 \ | |
RRA:AVERAGE:0.5:1008:250 \ |
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gua | |
OpenBabel11131516582D | |
7 7 0 0 0 0 0 0 0 0999 V2000 | |
-0.3090 -0.9511 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 | |
-1.3090 -0.9511 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 | |
-1.8968 -1.7601 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 | |
-1.6180 -0.0000 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 | |
-0.8090 0.5878 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 | |
-0.0000 -0.0000 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 |