Python library for Netdata 'Insights' features.
Project description
Netdata Insights
Netdata Insights.
Install
pip install netdata_insights
Quickstart
import random
import pandas as pd
import numpy as np
from netdata_insights.correlations.core import run_model_dict
from netdata_insights.data.utils import get_data_dict, get_chart_list
# pick some charts
#charts = random.sample(get_chart_list(host='london.my-netdata.io'),2)
charts = ['system.cpu']
# get some data
dict_baseline = get_data_dict(after=-120, before=0, host='london.my-netdata.io', charts=charts)
dict_highlight = get_data_dict(after=-560, before=-120, host='london.my-netdata.io', charts=charts)
# run correlations
results = run_model_dict('ks', dict_baseline, dict_highlight)
results
{'data': {'system.cpu': {'softirq': {'score': 0.0661},
'iowait': {'score': 0.0071},
'guest': {'score': 0.0},
'nice': {'score': 0.0},
'steal': {'score': 0.0405},
'system': {'score': 0.1518},
'irq': {'score': 0.0},
'guest_nice': {'score': 0.0},
'user': {'score': 0.2244}}},
'summary': {'model_level': 'dim',
'n_charts': 1,
'n_dims': 9,
'n_bad_data': 0,
'fit_success': 9,
'fit_default': 0,
'fit_fail': 0,
'success_rate': 1.0,
'bad_data_rate': 0.0,
'n_lags': 0,
'fit_manual': 0}}
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