Python SDK

cfdl_sdk compiles and runs CFDL models from Python with pandas accessors over the results. The compiler and engine are embedded in-process as a Rust extension module — no separate binary or server, and results are byte-identical to the CLI's.

Install

See Install for Python. Short version, from a checkout: pip install -e "python/[dev,viz]".

Quickstart

import cfdl_sdk

results = cfdl_sdk.run(
    "examples/cre_developer",
    packs_dir="packs",
    config="examples/cre_developer/run.base.json",
)

results.cashflows()      # wide DataFrame: one column per stream, PeriodIndex
results.metrics()        # flat Series of core + domain metrics
results.metrics_frame()  # metric / value / currency / source(core|domain:<pack>)
results.scenarios()      # one row per scenario (when the run declares them)
results.monte_carlo()    # per-metric summary stats (mean/stdev/percentiles)
results.annual()         # annual rollup, when present

config accepts a dict, a path to a run-config JSON file, or a raw JSON string. pack (for domain metrics) is auto-detected from a pack file in the model directory, or can be passed explicitly. compile() and Model.run() are available separately when you want to reuse compiled IR.

Compile problems raise CompileError with structured .diagnostics (code, message, span); runtime problems raise RunError.

Notebooks

Four industry notebooks are built on the benchmark models — the same models CFDL validates against an independent reference. Each is published here with the outputs and chart it actually produced, so you can read the SDK's DataFrame surface on a realistic model before installing anything:

• Solar PPA microgrid
• CRE office acquisition
• Credit loan pool
• Operating company LBO

To run them yourself, install the notebooks extra and open the folder from a checkout — they read their models from benchmarks/:

pip install -e "python/[notebooks]"
jupyter lab examples/notebooks