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Jupyter Notebooks

This guide explains the two main example notebooks included with PyETM and how to use them effectively for scenario analysis and data manipulation.

Overview​

The PyETM package includes two comprehensive Jupyter notebooks that demonstrate different ways of working with scenarios via the API:

  1. scenarios_with_excel.ipynb - Complete workflow from Excel input to API interaction
  2. scenarios_with_packer.ipynb - Programmatic scenario management and data export

Both notebooks are designed to help you understand PyETM's capabilities and provide starting points for your own workflows.

1. Scenarios with Excel Notebook​

Notebook: scenarios_with_excel.ipynb

Purpose​

This notebook demonstrates the complete end-to-end workflow for working with ETM scenarios using Excel as the primary interface. It shows how to load existing scenarios, create new ones, and export results back to Excel format.

Key Functionalities​

Excel-Based Scenario Management​

  • Load scenarios from Excel: Import scenario configurations, inputs, and metadata from structured Excel files
  • Create new scenarios: Automatically create scenarios in the ETM based on Excel specifications
  • Update existing scenarios: Modify loaded scenarios with new inputs and parameters

Data Exploration and Analysis​

  • Scenario metadata inspection: View and analyze scenario properties (title, ID, area, end year, version)
  • Input parameter analysis: Examine user inputs, defaults, min/max values, and permitted values
  • Coupling relationships: Explore interdependent input parameters
  • Sortable data: Access and analyze merit order and priority queue data
  • Custom curves: View and manipulate time-series data and custom profiles
  • Query results: Access calculated outputs and model results
  • Warning system: Identify and troubleshoot scenario issues

Export Capabilities​

  • Comprehensive Excel export: Generate detailed Excel workbooks with all scenario data
  • Separate export files: Automatically create additional files for complex data (e.g., exports by carrier type)
  • Configurable output: Control which data types are included in exports

Typical Use Cases​

  • Batch scenario processing: Work with multiple scenarios simultaneously
  • Data validation: Verify scenario configurations before API submission
  • Results analysis: Export and analyze scenario outcomes in familiar Excel format
  • Template-based workflow: Use Excel templates for consistent scenario creation

Getting Started​

  1. Place your Excel input file in the inputs folder (use example_input_excel.xlsx as a template)
  2. Configure scenario IDs for scenarios you own
  3. Run through the notebook cells to see the complete workflow
  4. Specify where to export your 'outputs'.

Note: The exports and curves contribute to longer processing times. If you are only interested in updating inputs then exclude them from your outputs.

2. Scenarios with packer (Development-Focused)​

Notebook: scenarios_with_packer.ipynb

Purpose​

This notebook provides a more programmatic approach to scenario management, focusing on bulk operations and data manipulation using Python code rather than Excel interfaces.

Key Functionalities​

Programmatic Scenario Management​

  • Bulk loading: Load multiple scenarios by ID in a single operation
  • Batch creation: Create multiple scenarios with different parameters programmatically
  • Scenario collections: Manage groups of scenarios as unified collections

Data Processing with ScenarioPacker​

  • Unified data structure: Convert all scenario data into pandas DataFrames for analysis
  • Flexible data views: Access different aspects of scenario data (inputs, curves, sortables, exports)
  • Column customization: Choose specific data columns for focused analysis
  • Export control: Fine-tune what data gets included in Excel exports

Developer-Oriented Features​

  • Direct API interaction: Work directly with PyETM's programmatic interfaces
  • Data transformation: Leverage pandas for advanced data manipulation
  • Batch processing: Efficiently handle large numbers of scenarios
  • Custom analysis workflows: Build specialized analysis pipelines

ScenarioPacker Capabilities​

The ScenarioPacker class is the central tool in this notebook, providing:

  • inputs(): Access input parameters with customizable column selection
  • custom_curves(): Retrieve time-series and profile data
  • sortables(): Get merit order and queue data
  • exports(): Access output carrier data
  • to_excel(): Export with granular control over included data types

Typical Use Cases​

  • Large-scale analysis: Process many scenarios efficiently
  • Custom data pipelines: Build specialized analysis workflows
  • API integration: Integrate PyETM into larger analyses
  • Performance optimization: Handle large datasets with optimized data structures

Getting Started​

  1. Modify the scenario IDs in the loading examples to use scenarios you own
  2. Experiment with different create_params for scenario creation
  3. Explore the ScenarioPacker methods to understand data access patterns
  4. Try different export configurations to see how they affect output files

Use scenarios_with_excel.ipynb when:​

You prefer working with Excel for scenario configuration or want to create/update batches of scenarios via Excel.

Use scenarios_with_packer.ipynb when:​

You want to understand the core PyETM development functionalities so you can use the package in your own development project and take advantage of the API tools.