philerooski

"""
Compute MD5 hashes of files in a local directory or in an S3 bucket.
Outputs a CSV file with columns `path` and `md5`.
When computing MD5 hashes of objects in an S3 bucket, `path` corresponds
to the S3 URI.
"""

import multiprocessing as mp
import boto3 as boto
import pandas as pd

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import bridgeclient
import hashlib
import pandas as pd
import synapseclient as sc
import argparse

SYNAPSE_TABLES = {
        'journey-pro': 'syn11439373',
        'elevate-ms': 'syn11439398',
        'lilly-presence': 'syn11445782'}

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philerooski

import pandas as pd
import argparse
import hashlib
import re
import os


def read_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("target_dir",

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philerooski

library(mhealthtools)
head(mhealthtools::accelerometer_data)
cleaned_sensor_data <- mhealthtools:::preprocess_sensor_data(
  mhealthtools::accelerometer_data,
  window_length = 256,
  sampling_rate = 100,
  frequency_range = c(1,25),
  time_range = c(1,9))
cleaned_sensor_data
transformation <- transformation_window(

philerooski

from __future__ import division, print_function
import synapseclient as sc
import pandas as pd
import numpy as np
import argparse
import LDopaScorer


TRAINING_TABLE = 'syn10495809'
TESTING_TABLE = 'syn10701954'

philerooski

Mounting an EFS Instance From an External Account

Only First time:

1. Send Thaneer your AWS account ID and he will add you to the trusted entities for the IAM role that he has created.
2. If you don't have one already, create an IAM user. Permissions aren't relevant since you will be switching roles as soon as you sign in as the IAM user, but you may want to omit any permissions so that you don't accidentally create an instance before switching roles.

Every time

1. When you are signed in as an IAM user under your root account, go to this link, enter a display name, (pick your favorite color), and click "Switch Role" https://signin.aws.amazon.com/switchrole?roleName=mPowerEFSRole&account=389689814525

philerooski

AWS Batch Tips and Tricks

Working with AWS Batch works roughly like this:

​ Compute Environments > Job Queues ⊥ Job Definitions > Jobs

Meaning compute environments and job queues are configured independently of job definitions and jobs — though if you're going to create a job, you're going to need a job definition, and if you're going to create a job queue, it's going to need a compute environment to run within.

Job Definitions > Jobs

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---
title: "JourneyPRO - on-going summary"
output:
  html_document: default
  html_notebook: default
  pdf_document: default
date: "`r format(Sys.time(), '%d %B, %Y')`"
---

```{r global_options, include=FALSE}