raprasad

(1) We have a pod with multiple containers. Rough example:

- Pod

-- container 1:  webapp
      exposes port: 80

-- container 2: r-service
 exposes port: 8070

raprasad

import os
import tempfile

def dashes(): print('-' * 40)
def msgt(amsg): dashes(); print(amsg); dashes()

# ---------------------------
# Create the temp file object
# ---------------------------
named_temp_file = tempfile.NamedTemporaryFile(delete=False)

raprasad

source: http://guides.dataverse.org/en/latest/api/native-api.html

import os
from datetime import datetime
import json
import requests  # http://docs.python-requests.org/en/master/

# --------------------------------------------------
# Update the 3 params below to run this code

raprasad

example of putting file contents in a secrets

@cloudshell:~ (raven2-186120)$ echo husky > dogs.txt

@cloudshell:~ (raven2-186120)$  kubectl create secret generic test-env-variable --from-file=TEST_ENV_VARIABLE=./dogs.txt                                                      
secret "test-env-variable" created

raprasad

one suggestion to get used to the process:

Don't change anything yet

branch from master

1. add this to settings/base.py:

raprasad

{
   "type":"object",
   "properties":{
      "dataset":{
         "type":"object",
         "properties":{
            "description":{
               "type":"string"
            },
            "unitOfAnalysis":{

raprasad

This describes a single variable within the variables section.

Note: The interpretation and tworavens sections are placeholders in this version

{
   "type":"object",
   "patternProperties":{
      "^[_a-zA-Z0-9]+$":{
         "type":"object",

raprasad

{
   "success":true,
   "message":"Metadata retrieved",
   "data":{
      "self":{
         "description":"Generated by https://github.com/TwoRavens/raven-metadata-service",
         "created":"2018-05-04 13:12:12",
         "preprocess_id":1782,
         "version":1

raprasad

def get_dataframe(input_file, start_idx=0, num_rows=100, delimiter=None, is_excel=False):
    """Retrieve a pandas dataframe based on format"""
    try:
        if is_excel:
            dataframe = pd.read_excel(input_file) # add nrows, skiprows + lineterminator, if needed
        else:
            dataframe = pd.read_csv(input_file,
                                     delimiter=delimiter) # add nrows, skiprows + lineterminator, if needed
        return ok_resp(dataframe)
    except pd.errors.ParserError as err_obj:

raprasad

[{"responseInfo": {"status": {"code": "OK", "details": "Pipeline Submitted"}}, "progressInfo": "SUBMITTED", "pipelineId": "27a0f05d-fc46-4497-bc55-ecfd27e52e83"}, {"responseInfo": {"status": {"code": "OK", "details": "Pipeline Submitted"}}, "progressInfo": "SUBMITTED", "pipelineId": "22548ad8-b38c-4145-9656-f8e7d555c4b4"}, {"responseInfo": {"status": {"code": "OK", "details": "Pipeline Submitted"}}, "progressInfo": "SUBMITTED", "pipelineId": "2f7688a0-e121-463c-89bf-585b1848ff3d"}, {"responseInfo": {"status": {"code": "OK", "details": "Pipeline Submitted"}}, "progressInfo": "SUBMITTED", "pipelineId": "244ce547-5ec3-4f5e-a0c3-1a0efff6e269"}, {"responseInfo": {"status": {"code": "OK", "details": "Pipeline Submitted"}}, "progressInfo": "SUBMITTED", "pipelineId": "de7ed878-e475-454a-93b3-a6d1ba95757f"}, {"responseInfo": {"status": {"code": "OK", "details": "Pipeline Submitted"}}, "progressInfo": "SUBMITTED", "pipelineId": "27b5f716-8275-465d-819e-a6c135a14e47"}, {"responseInfo": {"status": {"code": "OK",