kiendang

#!/bin/bash

jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=-1 "$1" --output "$1"

kiendang

#!/bin/bash

function split_colon {
    awk '{split($0, a, ": "); print a[2]}'
}

function get_field {
    spotify status | grep "$1" | split_colon
}

kiendang

# display file, stream audio only
vlc -vvv <file> \
--sout '#duplicate{dst=display,dst="transcode{vcodec=none}:standard{access=http,mux=ts,dst=:8080}"}'

kiendang

bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" --incompatible_load_argument_is_label=false

kiendang

#!/bin/sh
{ head -n1 file1.csv; for f in file*.csv; do tail -n+2 "$f"; done; } > results.csv 

kiendang

import os, sys

spark_home = os.getenv('SPARK_HOME')
sys.path.insert(0, spark_home + '/python')

sys.path.insert(0, os.path.join(spark_home, 'python/lib/py4j-0.10.3-src.zip'))

exec(open(os.path.join(spark_home, 'python/pyspark/shell.py')).read())

kiendang

= Location and infection tracking in a hospital

== Data model

=== People and hospital rooms

People and places inside a hospital are represented as nodes with labels _Person_ and _Facility_ respectively. _Person_ nodes have extra labels _Doctor_, _Nurse_, or _Patient_,... and properties _name_, _age_. _Facility_ nodes have properties _type_ (_eg_ _Ward_) and _name_ (_eg_ _Ward 01_).

[source,cypher]
----