Cloogy
Python client for Cloogy
See Demo.ipynb for a working Jupyter Notebook
0. Install cloogy
In your terminal: pip3 install cloogy
or in a python shell or notebook:
import pip
pip.main(['install', 'cloogy'])import yaml
from cloogy import CloogyClient1. Get your credentials
Get your login and password.
In this example we'll load it from a YAML file.
with open('credentials.yaml', 'r') as f:
credentials = yaml.load(f)login = credentials['login']
password = credentials['password']2. Create a CloogyClient
If you supply a login and password, authentication will be handled for you.
client = CloogyClient(login=login, password=password)3. List your Units
units = client.get_units()units4. Get a specific Unit by ID
# Lets grab the first ID from the list above
unit_id = units[0]['Id']
print(unit_id)unit = client.get_unit(unit_id=unit_id)unit5. Find out some stuff about your unit
The Unit class is an extension to the regular python dict. This means it behaves like a normal dict, but adds some extra features.
# Get date and time of the last communication
unit.get_last_communication_date()6. List all available Tags for your login
client.get_tags()7. List available Tags for a Unit
tags = unit.get_tags()tags[tag['Id'] for tag in tags]8. Get a specific Tag directly
tag_id = tags[0]['Id']
print(tag_id)tag = client.get_tag(tag_id=tag_id)tagtag.get_last_communication_date()9. Get consumptions
# pick a start and end time, as POSIX timestamp
import pandas as pd
start = int(pd.Timestamp('20180414').timestamp() * 1000)
end = int(pd.Timestamp('20180416').timestamp() * 1000)
print(start, end)client.get_consumptions(
granularity='hourly', # can be Instant, Hourly, Daily, Monthly, Yearly
start=start,
end=end,
tags=[tag_id], # List of tag Id's
instants_type=None # How instant measurements should be aggregated. Can be Avg, Max, Min, Stdev. Default is Avg.
)10. Get consumptions as a DataFrame
For some easy analysis, methods to get data as a Pandas DataFrame are included
Let's say we want to analyse the active energy consumption, which has TagTypeId 20001
tags = client.get_tags(where='TagTypeId=20001')
tag_ids = [tag['Id'] for tag in tags]
start = pd.Timestamp('20180101')
end = pd.Timestamp('20180417')client.get_consumptions_dataframe(granularity='monthly', start=start, end=end, tags=tag_ids)A flat table like this is nice, but it can contain multiple TagIds, and it has way to many columns we don't need.
We can also get a table for only the readings:
df = client.get_readings_dataframe(granularity='monthly', start=start, end=end, tags=tag_ids, metric='Read')df# make a plot!
%matplotlib inline
df.plot.bar()
