ravila4

#!/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?\""

ravila4

"""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']

ravila4

#!/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'`

ravila4

#!/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

ravila4

ravila4

Databricks Programming Guidance

This document contains lessons learned with regard to Databricks programming, but also contains some best practices

Mapping to a Azure Data Lake Generation 2

blobname = "miraw"  
storageaccount = "rdmidlgen2"  
mountname = "/rdmi"

configs = {"fs.azure.account.auth.type": "OAuth",

ravila4

#!/usr/bin/env python

# Sequence alignment using PyMOL
# The purpose of this script is to generate a sequence alignment between
# the original crystal structure of the apo and holo models, and the sequence
# of the finalised, ungapped Rosetta models. This allows us to get a 1 to 1
# corresponcence between the residue numberings in both structures.

# USAGE: Run once from the project root.
# "pockets.csv" contains the information about apo holo pairs.

ravila4

ravila4

def flatten_json(y):
    out = {}

    def flatten(x, name=''):
        if type(x) is dict:
            for a in x:
                flatten(x[a], name + a + '_')
        elif type(x) is list:
            i = 0
            for a in x:

ravila4

# List unique values in a DataFrame column
pd.unique(df.column_name.ravel())

# Convert Series datatype to numeric, getting rid of any non-numeric values
df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

# Grab DataFrame rows where column has certain values
valuelist = ['value1', 'value2', 'value3']
df = df[df.column.isin(valuelist)]