class ByteportPandas:
"""
Provides base functionality for connecting and loading data into Pandas data formats
Extend at will!
"""
def __init__(self, username, password):
self.client = ByteportHttpClient()
self.client.login(username, password)
print "Successfully logged in to Byteport!"
def load_to_series(self, namespace, device_uid, field_name, from_time, to_time):
timeseries_data = self.client.load_timeseries_data_range(namespace, device_uid, field_name, from_time, to_time)
# create pandas data-frame
timestamps = list()
values = list()
# Prepare by splitting the time series data into two arrays
for row in timeseries_data['data']['ts_data']:
try:
dt = datetime.datetime.strptime(row['t'], ISO8601)
fv = float(row['v'])
timestamps.append(dt)
values.append(fv)
except Exception:
print "Failed to parse data (%s), ignoring" % row
return pandas.Series(values, timestamps)
def __init__(self, username, password):
self.client = ByteportHttpClient()
self.client.login(username, password)
print "Successfully logged in to Byteport!"
from byteport.http_clients import ByteportHttpClient # # NOTE: Needs a proxy set up on port 5000, first to this from shell: # # (venv-byteport-api) hans@Hanss-MacBook-Pro:~/Development/igw/git_clones/byteport-api$ ssh -i ./id_rsa -D 5000 -N [email protected] & # byteport_client = ByteportHttpClient('mloc', 'f5770ca3ac1ff64de7f0bc70', 'locator8', proxy_port=5000) byteport_client.store({'eth0_rx_mb': '10'})
