This document contains lessons learned with regard to Databricks programming, but also contains some best practices
blobname = "miraw"
storageaccount = "rdmidlgen2"
mountname = "/rdmi"
configs = {"fs.azure.account.auth.type": "OAuth",
#!/bin/bash | |
set -euo pipefail | |
# This script sends a request to the OpenAI completions endpoint and parses the output using jq. | |
# It is recommended that the API key is stored in an environment variable. | |
usage() { | |
echo "Usage: $0 [-k OPENAI_API_KEY] [-m MODEL] [-t MAX_TOKENS] PROMPT" | |
echo "Example: $0 -m text-davinci-002 -t 500 \"What is the capital of France?\"" |
"""Web config to run MyGeneset API on GitHub Actions""" | |
import os | |
ES_INDEX = 'user_genesets' | |
COOKIE_SECRET = "JKA#9%Wc4ofuqM@C!&yLFsYE" | |
# ORCID keys | |
ORCID_CLIENT_ID = os.environ['ORCID_CLIENT_ID'] |
This document contains lessons learned with regard to Databricks programming, but also contains some best practices
blobname = "miraw"
storageaccount = "rdmidlgen2"
mountname = "/rdmi"
configs = {"fs.azure.account.auth.type": "OAuth",
# Utility script for feature generation | |
# Md Mahmudulla Hassan | |
# The University of Texas at El Paso | |
# Last Modified: 12/19/2018 | |
import os | |
from rdkit import Chem | |
from rdkit.Chem import AllChem | |
import tempfile | |
import shutil |
#!/bin/bash | |
# Genesets aggregated by taxid | |
aggs=`curl -s "https://mygeneset.info/v1/query?q=*&facets=taxid&facet_size=100"` | |
taxids=`echo $aggs | jq -r '.facets.taxid.terms | map(.term) | @csv'` | |
counts=`echo $aggs | jq -r '.facets.taxid.terms | map(.count) | @csv'` | |
# Query scientific name for each taxid | |
resp=`curl -s -X POST -d "q=${taxids}" "http://t.biothings.io/v1/query"` | |
species=`echo $resp | jq -r 'map(.scientific_name) | @csv'` |
#!/usr/bin/env bash | |
set -Eeuo pipefail | |
cd "$(dirname "${BASH_SOURCE[0]}")" >/dev/null 2>&1 | |
trap cleanup SIGINT SIGTERM ERR EXIT | |
usage() { | |
cat <<EOF |