Call the Predix Time Series REST APIs to retrieve data and show on a chart

px-timeseries-demo.py

"""
Call the Predix Time Series REST APIs to retrieve data and show on a chart
"""

import pandas as pd
import requests
import json
import base64
from bokeh.io import output_notebook
from bokeh.charts import TimeSeries, output_file, show

output_notebook()

uaaUrl = "<uaa-issuer-id-url-here>"
tsUrl = "<predix-time-series-query-url-here>"
payload_last = "{\n \"start\": \"50y-ago\",\n \"tags\": [\n {\n \"name\": \"TAG NAME\",\n \"order\": \"desc\",\n \"limit\": 1\n }\n]\n}"
payload_first = "{\n \"start\": \"50y-ago\",\n \"tags\": [\n {\n \"name\": \"TAG NAME\",\n \"order\": \"asc\",\n \"limit\": 1\n }\n]\n}"
zoneId = "<your-time-series-zone-id-here>"
token = base64.b64encode('<your-client-id>:<your-client-secret>')

def doQuery(payload, tsUrl, uaaUrl, token, zoneId):
    headers = {
        'authorization': "Basic " + token,
        'cache-control': "no-cache",
        'content-type': "application/x-www-form-urlencoded"
    }
    response = requests.request('POST', uaaUrl, data="grant_type=client_credentials", headers=headers)
    token = json.loads(response.text)['access_token']
    headers = {
        'authorization': "Bearer " + token,
        'predix-zone-id': zoneId,
        'content-type': "application/json",
        'cache-control': "no-cache"
    }
    response = requests.request("POST", tsUrl, data=payload, headers=headers)
    data = json.loads(response.text)['tags'][0]['results'][0]['values']
    column_labels = ['timestamp', 'values', 'quality']
    series = pd.DataFrame(data, columns=column_labels)
    series['timestamp'] = pd.to_datetime(series['timestamp'], unit='ms')
    return series

firstPoint = doQuery(payload_first, tsUrl, uaaUrl, token, zoneId)
startDate =  pd.Timestamp(firstPoint['timestamp'][0])
startDateOrigin = startDate = int(startDate.strftime("%s")) * 1000

lastPoint = doQuery(payload_last, tsUrl, uaaUrl, token, zoneId)
endDate =  pd.Timestamp(lastPoint['timestamp'][0])
endDate = int(endDate.strftime("%s")) * 1000
pdArray = []

while (startDate < endDate ):
    payload = { 'cache_time': 0, 'tags': [{'name': '<TAG-NAME>', 'order': 'asc'}], 'start': startDate, 'end': startDate + 10000000}
    startDate = startDate + 100000000
    series = doQuery(json.dumps(payload), tsUrl, uaaUrl, token, zoneId)
    pdArray.append(series)

fullseries = pd.concat(pdArray)

data = dict(values = fullseries['values'], Date=fullseries['timestamp'])
p = TimeSeries(data, x='Date', y='values')
show(p)