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Getting Started

wellplot has two primary workflows:

  • YAML workflow: good when the layout itself is a maintained artifact
  • Python API workflow: good when the log is part of a notebook, script, or automated pipeline

Both workflows converge on the same architecture:

  1. normalize data into WellDataset
  2. define the layout as a document/report
  3. render full or partial outputs

Choose a workflow

Use the YAML workflow when you want:

  • reusable templates and savefiles
  • operator-editable job definitions
  • report layouts stored as versioned assets

Use the Python API when you want:

  • computed channels from numpy / pandas
  • notebook-driven analysis
  • automated report generation
  • partial renders during research

Minimal path

The shortest path into the library is:

  1. install the package and optional extras you need
  2. choose YAML or Python API authoring
  3. render a single example
  4. adapt that example to your own data

Base install:

python -m pip install wellplot

Notebook and data-source extras:

python -m pip install "wellplot[las,dlis,pandas,notebook]"

Start with examples

Recommended first examples:

  • YAML/report example: examples/cbl_job_demo.log.yaml
  • Python API example: examples/api_end_to_end_demo.py
  • Dataset ingestion example: examples/api_dataset_ingest_demo.py
  • Partial rendering example: examples/api_partial_render_demo.py

Basic mental model

Keep these concerns separate:

  • WellDataset owns normalized data, axes, and units
  • LogBuilder / LogDocument own layout and report structure
  • render helpers own output generation
  • YAML is serialization, not the only authoring path