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Overview

Jupyter Notebooks provide an interactive environment for interacting with the Falkonry TSI Platform. By leveraging API v1.3, these notebooks enable data scientists and engineers to automate common workflows, perform bulk operations, and integrate TSI into external data pipelines.

While the Falkonry UI is optimized for discovery and model building, this notebook suite is designed for automation, reproducibility, and custom reporting.

Core Use Cases

The notebook suite is organized into modular categories based on your operational goals:

Data Access & Asset Management

  • Asset Hierarchies: Programmatically retrieve signals and asset structures.

  • Output Extraction: Export anomaly detections, pattern labels, and confidence scores.

  • Explainability: Pull "explaining signals" and contribution percentages for specific events.

Model Operations & Evaluation

  • Performance Audits: Batch query model metadata to compare detection quality across different assets.

  • Bulk Management: List and filter all active anomaly and pattern models within a tenant.

Advanced Analytics & Reporting

  • KPI Dashboards: Generate stability indices and alert-ratio summaries.

  • Signal Analysis: Compute median/mean signal importance across event windows.

  • BI Integration: Export structured DataFrames formatted specifically for tools like Power BI or Tableau.

Notebook Structure

Each notebook follows a consistent structure:

  1. Authentication Setup Configure API endpoint and credentials

  2. Environment Initialization Import libraries and helper functions

  3. API Interaction Execute Falkonry API v1.3 calls

  4. Data Processing Transform responses into structured DataFrames

  5. Analysis / Computation Perform calculations and aggregations

  6. Output Generation Export CSV, tables, or visualization-ready data

Prerequisites

Before running the notebooks, ensure the following:

  • Access to Falkonry TSI platform
  • API v1.3 credentials
  • Python 3.8+ environment

  • Required Python libraries:

  • requests

  • pandas
  • numpy
  • matplotlib (optional for visualization)

Benefits

  • Reduces manual data extraction
  • Enables reproducible analytics workflows
  • Accelerates model evaluation and tuning
  • Supports integration with enterprise reporting tools
  • Provides flexibility for advanced users

Intended Users

These notebooks are designed for:

  • Data Scientists & Power Users: For custom model tuning and advanced signal processing.

  • Reliability & Operations Engineers: For automated daily reporting and alert monitoring.

  • Integration Developers: For building automated data pipelines between Falkonry and downstream systems.

Note

This notebook suite is built for Falkonry API Version: v1.3

Downloads

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FOR FALKONRY's CUSTOMER USE ONLY.

THIS SAMPLE CODE FROM FALKONRY IS PROVIDED "AS IS" AND WARRANTIES OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL FALKONRY INC. BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) SUSTAINED BY YOU OR A THIRD PARTY, HOWEVER CAUSED AND UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT ARISING IN ANY WAY OUT OF THE USE OF OR INABILITY TO USE THIS SAMPLE CODE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Jupyter Notebooks

Download ready-to-run Jupyter Notebooks for interacting with Falkonry TSI using API v1.3. Each notebook is self-contained and can be executed independently.

1. Rules & Automation

2. Calculations

3. Anomaly Detection & Pattern Recognition

📥 All notebooks are compatible with Falkonry TSI API v1.3 and designed for direct execution.