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#!/bin/bash | |
jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=-1 "$1" --output "$1" |
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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 |
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# display file, stream audio only | |
vlc -vvv <file> \ | |
--sout '#duplicate{dst=display,dst="transcode{vcodec=none}:standard{access=http,mux=ts,dst=:8080}"}' |
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#!/bin/bash | |
function split_colon { | |
awk '{split($0, a, ": "); print a[2]}' | |
} | |
function get_field { | |
spotify status | grep "$1" | split_colon | |
} |
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#!/bin/sh | |
{ head -n1 file1.csv; for f in file*.csv; do tail -n+2 "$f"; done; } > results.csv |
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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()) |
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= 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] | |
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