pyTSI 0.1.0

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pyTSI

pyTSI is a Python SDK for Microsoft Azure time series insights. It provides methods to conveniently retrieve your data and is designed for analysts, data scientists and developers working with time series data in Azure TSI.

Documentation

• Azure time series REST APIs: https://docs.microsoft.com/en-us/rest/api/time-series-insights/

Quickstart

Instantiate the TSIClient to query your TSI environment. Use the credentials from your service principal in Azure that has access to the TSI environment (you can also use environment variables to instantiate the pyTSI or provide a specific TSI API version, check the documentation).

from pyTSI import TSIClient as tsi

client = tsi.TSIClient(
    enviroment='<your-tsi-env-name>',
    client_id='<your-client-id>',
    client_secret='<your-client-secret>',
    tenant_id='<your-tenant-id>',
    application_name='<your-app-name>')

# List the instances in the TSI, also list their types
# and variables.
for instance in client.time_series:
    print(f'\t{instance}')
    print('\tInstance type description:')
    print(f'\t\t{instance.series_type}')
    print('\t\tType vars:')
    for v in instance.series_type.type_vars:
        print(f'\t\t\t{v}')

You can now query each instance

You can query your timeseries data by timeseries id, timeseries name or timeseries description. The Microsoft TSI apis support aggregation, so you can specify a sampling freqency and an aggregation method. Refer to the documentation for detailed information.

import datetime

t0 = datetime.datetime(year=2020, month=10, day=22, hour=10, minute=53, second=00,
                       tzinfo=datetime.timezone.utc)
t1 = datetime.datetime(year=2020, month=10, day=22, hour=11, minute=53, second=30,
                       tzinfo=datetime.timezone.utc)
# Get raw event data
raw_data = client.query.get_events({ts: [v for v in ts.series_type.type_vars
                                         if v.var_name in ('temperature', 'humidity')]},
                                   t0, t1)
# Get series data for raw events & composed variables
series_data = client.query.get_series({ts: [v for v in ts.series_type.type_vars
                                         if v.var_name in ('temperature', 'humidity', 'series_sum')]},
                                   t0, t1)
# Aggregate series
aggregated_data = client.query.aggregate_series({ts: [v for v in ts.series_type.type_vars
                                                      if v.var_name in ('temperature', 'EventCount')]},
                                                 t0, t1, 'PT1M')

Each of these functions return a DataFrame, which can be used for analysis.

Contributing

Contributions are welcome. See the developer reference for details.

License

pyTSI is licensed under the MIT license. See LICENSE file for details.