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public-database-snapshot
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public-database-snapshot
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#!/bin/bash
export PATH="$PATH:.:scripts"
phenotype_step=true
report_step=true
clean_step=true
test_run=false
## debug
#phenotype_step=true
#report_step=true
#clean_step=true
#test_run=true
set -e
set -o pipefail
if [ $clean_step = true ] ; then
rm -rf stage
rm -rf out-data
rm -rf scratch
fi
mkdir -p stage
mkdir -p out-data
mkdir -p scratch
if [ $phenotype_step = true ]
then
# collect survey data
#
echo "collecting survey data"
./scripts/grab_tap_survey.sh
./scripts/create_phenotype.py > stage/survey.tsv
# get user list
#
echo "getting user list"
./scripts/grab_user.sh
# scrape Tapestry for phenotype information
# and file locations
#
echo "scraping tapestry for phenotype info"
if [ $test_run = true ]
then
./scripts/grab_profile_data.sh <( head -n10 scratch/hid.list )
else
./scripts/grab_profile_data.sh scratch/hid.list
fi
#################################
#################################
# get specimen list
#
#echo "getting specimens list"
#./scripts/grab_specimens.sh
#mv ./scratch/specimens.tsv ./stage/specimens.tsv
echo -e "human_id\thref\ttext\tdescription\tlog_date\tlog_text\tlog_description" > ./scratch/specimens.tsv
if [ $test_run = true ]
then
while read huid ; do
./scripts/collect_samples_from_profile_data.py scratch/$huid/$huid.html $huid >> ./scratch/specimens.tsv
done < <( head -n10 scratch/hid.list )
else
while read huid ; do
./scripts/collect_samples_from_profile_data.py scratch/$huid/$huid.html $huid >> ./scratch/specimens.tsv
done < scratch/hid.list
fi
mv ./scratch/specimens.tsv ./stage/specimens.tsv
#################################
#################################
# collect them into tsv files
#
echo "collecting into tsv files"
./scripts/collect_profile_data.py
# scrape Tapestry for enrollment dates
#
echo "grabbing enrollment dates"
./scripts/grab_enrollment_date.sh
cp ./scratch/hid-enrolldate.list ./stage/enrollment_date.tsv
# insert into the untap.db. Use it to
# generate the list of download urls for
# the report collection step.
#
#cd stage
#cat ../db/untap-db.sql | sqlite3 untap.db
#echo "select id, human_id, report_url from uploaded_data where length(report_url)>0;" | sqlite3 untap.db > huid_report_url.csv
#cd ..
echo "inserting into untap.db"
./scripts/export_surveys_to_sqlite3.py ./stage/untap.db ./stage
fi
if [ $report_step = true ]
then
echo "select id, human_id, report_url from uploaded_data where length(report_url)>0;" | sqlite3 stage/untap.db > stage/huid_report_url.csv
# download, collect and de-duplicate the report data
# and insert into untap.db
#
echo "downloading report data"
mv stage/huid_report_url.csv ge-report
cd ge-report
./generate
cd ..
# collect results from the report aggregation step.
#
echo "collecting results"
mv ge-report/out-data/* stage/
# create final database
#
echo "creating final database"
cd stage
cat ../db/untap-db-report.sql | sqlite3 untap.db
gzip -c untap.db > untap.sqlite3.gz
cd ..
fi
if [ $clean_step = true ]
then
# finally collect everything into the out-data directory
#
rm -f ./hu-pgp.sqlite3.gz
echo "cleaning up into out-data directory"
ln -s ./stage/untap.sqlite3.gz ./hu-pgp.sqlite3.gz
mv stage/* out-data/
fi