Parse input parameters to dataframe

In Azure, the HTTP-request is automatically transformed to a Pandas Dataframe. In this environment we have to prepare that frame.



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import pandas as pd

def inputFrame(d):
    """
        transforms a dictionary with parameters into a single line dataframe
    """
    df = pd.DataFrame.from_dict(d, orient='index').T
    return df

Get SensorID's and Tokens

We need some test data, so we'll take some Fluksos from Open Grid

Import Houseprint module



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import os, sys, inspect



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script_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
sys.path.append(os.path.join(script_dir, os.pardir, os.pardir))



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from opengrid.library import config
c = config.Config()
sys.path.append(c.get('tmpo', 'folder'))
from opengrid.library.houseprint import houseprint









    



---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
<ipython-input-7-7babd5769312> in <module>()
----> 1 from opengrid.library import config
      2 c = config.Config()
      3 sys.path.append(c.get('tmpo', 'folder'))
      4 from opengrid.library.houseprint import houseprint
      5 from configparser import SafeConfigParser

/Users/Jan/git_energyid/opengrid/scripts/../../opengrid/library/config.py in <module>()
     21 """
     22 
---> 23 from ConfigParser import SafeConfigParser
     24 import inspect
     25 import os, sys

ImportError: No module named 'ConfigParser'



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from configparser import SafeConfigParser

Create Houseprint



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hp = houseprint.Houseprint(gjson = c.get('houseprint','json'))

Create a list of SensorID's en Tokens



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#get all sensors from Houseprint
sensors = hp.get_sensors()

id_token = [{'sensorId':sensor.key, 'token':sensor.token} for sensor in sensors]

Define Azure Method

Azure Python modules are defined by two Pandas Dataframes as input (one can be optional) and a Pandas Dataframe as output. Azure then automatically transcribes the output to JSON.



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import requests
import io



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HTTP_ACCEPT = {
    "json": "application/json",
    "gz": "application/gzip"}

FLUKSO_CRT = """
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----"""

def AzureMethod(dataframe1=None, dataframe2=None):
    
    def getMetadata():
        headers = {
            "Accept": HTTP_ACCEPT["json"]
        }
        url = "https://api.flukso.net/sensor/{}".format(sensorId)
        parameters = {
            "param": "all",
            "token" : token,
            "version" : "1.0"
        }
        
        f = requests.get(
            url,
            params=parameters,
            headers=headers,
            verify=crt
        )
    
        print f.json()
        
    def 
    
    #unpack parameters
    sensorId = dataframe1['sensorId'][0]
    token = dataframe1['token'][0]
    
    crt = "flukso.crt"
    with io.open(crt, "wb") as f:
        f.write(FLUKSO_CRT.encode("ascii"))
    
    
    
    getMetadata()
    return dataframe1

Test



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import datetime as dt

for sensorparam in id_token:
    
    #add extra parameters
    sensorparam['resolution'] = 'd'
    sensorparam['start'] = dt.datetime.utcnow().isoformat()
    
    #create dataframe
    df = inputFrame(sensorparam)
    
    #print result of Azure Method
    AzureMethod(dataframe1 = df)



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