Documentation for TrendMiner Python SDK Core¶
The TrendMiner Python SDK Core allows you to interact with TrendMiner via a high-level API from any Python runtime, e.g. from a Python notebook embedded within TrendMiner, custom python calculations etc. Most boilerplate code has been written for you, so you can focus on what is important: getting valuable insights from your time series data.
To interact with TrendMiner via this SDK, you will first have to create a TrendMinerClient object.
The client creation requires the URL of your TrendMiner installation and an OAuth2 token.
However, if you are working from a TrendMiner embedded notebook, we simplified this for you and creating a
TrendMinerClient is as simple as (note os.environ["KERNEL_USER_TOKEN”] is detected and replaced with
your current OAuth2 token):
from trendminer import TrendMinerClient
client = TrendMinerClient.from_token(token=os.environ['KERNEL_USER_TOKEN'])
If however, you are working from an external environment (or an environment with SSL enabled), you must provide a valid TrendMiner URL and OAuth2 token:
from trendminer import TrendMinerClient
client = TrendMinerClient.from_token(url='https://trendminer.yourcompany.com',token='<oauth2_token>')
Loading a tag¶
tag = client.tag.get_by_name("<tag_name>")
Loading data of a tag¶
tag = client.tag.get_by_name("<tag_name>")
period = ("2019-03-25", "2019-03-26")
data = tag.get_data(period, resolution="1m")
Performing a Value Based Search¶
from trendminer.sdk.search import ValueBasedSearchOperators
from trendminer.sdk.search import LogicalOperators
level = client.tag.get_by_name("<tag_name>")
vbs = client.search.value(queries=[
(level, ValueBasedSearchOperators.GREATER_THAN, 35),
("<tag2_name>", ValueBasedSearchOperators.EQUAL_TO, "<value>"),
],
operator=LogicalOperators.AND,
duration="2m"
)
results = vbs.get_results(("2019-01-20", "2019-01-29"))
Loading a TrendHub view as a pandas dataframe¶
view = client.view.get_by_name("<view_name>")
df = view.get_data()[0]
Loading Context items as a pandas dataframe¶
client = TrendMinerClient(url='https://trendminer.yourcompany.com',token='<oauth2_token>')
chv = client.context.view.get_by_identifier("<context_view_identifier>")
items = chv.search_items()
df = pd.DataFrame(
[{
**item,
"start_date": item.interval.start,
"end_date": item.interval.end,
"total_duration": item.interval.duration,
"type": item.context_type,
"last_state": item.events[-1].state,
} for item in items]
)