scout-rig

scout-rig provides the language interop “rigging” for the Scout ecosystem. It currently focuses on Python: executing Python from Ruby, round‑tripping data (TSV ↔ pandas), and running Scout Workflows from Python code. It builds on the low-level/core packages:

• scout-essentials — low level utilities (Annotation, CMD, ConcurrentStream, IndiferentHash, Log, Open, Path, Persist, TmpFile)
• scout-gear — data and workflow primitives (TSV, Workflow, KnowledgeBase, Association, Entity, WorkQueue, Semaphore)
• scout-rig — interop with other languages (currently Python)
• scout-camp — remote servers, cloud deployments, web interfaces, cross-site operations
• scout-ai — model training and agentic tools

All packages are available on GitHub under https://github.com/mikisvaz (for example, https://github.com/mikisvaz/scout-gear).

For broader background and many real workflow examples, see Rbbt (the bioinformatics framework from which Scout was refactored) and the Rbbt-Workflows organization:

• https://github.com/mikisvaz/rbbt
• https://github.com/Rbbt-Workflows

This README focuses on the Python bridge in scout-rig (ScoutPython). See the docs in doc/ for reference material.

• doc/Python.md — ScoutPython user guide

ScoutPython (Ruby) and a companion Python package (python/scout) provide:

• Safe, ergonomic execution of Python code from Ruby (PyCall-based), with:
  • Simple import helpers and localized bindings
  • Synchronous, direct, or background-thread execution
  • Logging wrappers that capture Python stdout/stderr
• Scripting to run ad‑hoc Python text with Ruby variables (including TSV) injected, and results returned
• Data conversion helpers:
  • numpy arrays → Ruby Arrays
  • pandas DataFrame ↔ TSV (key_field, fields, type respected)
• Python path management (expose package python/ dirs to sys.path)
• Python‑side helpers to:
  • Read/write TSVs with headers (pandas)
  • Run Ruby Workflows from Python
  • Call remote Workflow services over HTTP

Ruby

• Ruby 2.6+ (or compatible with PyCall)
• Gems:
  • pycall (PyCall)
  • json (standard)
  • Optional for script result loading:
    • python/pickle (gem) for loading pickle from Python scripts

Python

• Python 3
• Packages:
  • pandas
  • numpy
  • requests (only for remote workflow client)
• Ensure python3 is in PATH

Add scout-rig to your Ruby project (Gemfile or local checkout), then ensure Python dependencies are installed in your Python environment.

Execute Python directly from Ruby:

require 'scout_python'

# Sum with numpy
arr_sum = ScoutPython.run 'numpy', as: :np do
  np.array([1,2,3]).sum
end
# => PyObject (to_i if needed)

# Background thread execution
ScoutPython.run_threaded :sys do
  sys.path.append('/opt/my_py_pkg')
end
ScoutPython.stop_thread

Run an ad‑hoc Python script, returning a result value:

tsv = TSV.setup({}, "Key~ValueA,ValueB#:type=:list")
tsv["k1"] = %w[a1 b1]; tsv["k2"] = %w[a2 b2]

TmpFile.with_file do |target|
  result = ScoutPython.script <<~PY, df: tsv, target: target
    import scout
    # df is a pandas DataFrame (tsv injected)
    result = df.loc["k2", "ValueB"]
    scout.save_tsv(target, df)  # save as TSV with header
  PY

  # result is "b2"; target holds a TSV round-tripped from pandas
end

Convert between TSV and pandas:

df = ScoutPython.tsv2df(tsv)      # TSV -> pandas DataFrame
tsv2 = ScoutPython.df2tsv(df)     # pandas DataFrame -> TSV

Run a Workflow from Python:

import sys
sys.path.append('python')  # add this repo's python/ on dev checkouts

import scout.workflow as sw

wf = sw.Workflow('Baking')
print(wf.tasks())
step = wf.fork('bake_muffin_tray', add_blueberries=True, clean='recursive')
step.join()
print(step.load())         # load Ruby job result

ScoutPython tracks Python directories to add to sys.path:

• ScoutPython.add_path(path) / add_paths(paths)
• ScoutPython.process_paths # idempotent; run before/inside sessions

These are applied in Python contexts by run/run_simple/run_direct.

Pick the execution model that fits:

• run(mod = nil, imports = nil) { ... }
  • Initialize PyCall if needed, set up paths, run block; GC after run
• run_simple(mod = nil, imports = nil) { ... }
  • Lightweight; process_paths, then run block
• run_direct(mod = nil, imports = nil) { ... }
  • Minimal overhead: optional single pyimport/pyfrom, then evaluate
• run_threaded(mod = nil, imports = nil) { ... }
  • Queue work into a dedicated Python thread; stop with stop_thread

Logging wrappers capture Python’s stdout/stderr via the Scout Log:

• run_log(mod=nil, imports=nil, severity=Log::LOW, severity_err=nil) { ... }
• run_log_stderr(mod=nil, imports=nil, severity=Log::LOW) { ... }

Imports

• Pass 'numpy', as: :np or "module.submodule", import: [:Class, :func]

Keep imports local to a binding:

ScoutPython.binding_run do
  pyimport :torch
  pyfrom :torch, import: ['nn']
  # torch and nn available here only
end

Helpers

• new_binding, binding_run
• import_method, call_method
• get_module, get_class, class_new_obj
• exec(script) → PyCall.exec

Run arbitrary Python text with Ruby variables injected:

• ScoutPython.script(text, variables = {}) → result
  • Ruby primitives → Python literals
  • Arrays/Hashes → recursively converted
  • TSV variables → materialized to temp file and loaded into pandas via the python/scout helper
  • result is read back via pickle (default) or JSON (configurable)

Swap result serializer if desired:

class << ScoutPython
  alias save_script_result save_script_result_json
  alias load_result        load_json
end

Traverse Python iterables with optional progress bars:

• iterate(iterator, bar: nil|true|String) { |elem| ... }
• iterate_index(sequence, bar: ...) { |elem| ... }
• collect(iterator, bar: ...) { |elem| ... } → Array

• numpy2ruby(numpy_array)
• to_a/py2ruby_a(py_list)
• obj2hash(py_mapping)
• tsv2df(tsv) / df2tsv(df, options={type: :list, key_field: ...})

The included Python package is importable as scout and provides:

General utilities

• scout.libdir(), scout.add_libdir()
• scout.path(), scout.read()
• scout.inspect(obj), scout.rich(obj)

TSV IO (pandas-aware)

• scout.tsv(tsv_path_or_stream, ...) → pandas.DataFrame (Scout headers respected)
• scout.save_tsv(filename, df, key=None)

Workflow wrappers

• scout.run_job(workflow, task, name='Default', fork=False, clean=False, **inputs)
  • Shells out to the Ruby CLI to execute/fork jobs
• scout.workflow.Workflow(name).run/fork/tasks/task_info
• scout.workflow.Step(path).info/status/join/load

Remote workflows (HTTP)

• scout.workflow.remote.RemoteWorkflow(url).job/task_info
• scout.workflow.remote.RemoteStep(url).status/wait/raw/json

• Python process errors from script are surfaced as ConcurrentStreamProcessFailed (non‑zero exit), with stderr logged via Log if a logging wrapper is used
• Background thread execution must be stopped explicitly:
  • ScoutPython.stop_thread — sends a sentinel, tries to join/kill, GCs, and finalizes PyCall if available

Scout commands are discovered under scout_commands across installed packages using the Path subsystem. The dispatcher resolves nested commands by adding terms until a file is found to execute; if you stop on a directory, it lists available subcommands.

• General pattern:
  • scout [ ...] [options] [args...]
• Examples relevant to Python integration (executed from Ruby CLI but callable from Python via scout.run_job):
  • scout workflow task [task-input-options...]
  • scout workflow prov <step_path>
  • scout workflow info <step_path>

Notes

• The bin/scout launcher walks scout_commands/… across packages; Workflows and other packages can add their own commands and they will be discovered
• See the Workflow, TSV, and KnowledgeBase docs for their CLI suites:
  • TSV: scout tsv …
  • Workflow: scout workflow …
  • KnowledgeBase: scout kb …

scout-rig itself does not register standalone CLI commands; instead, its Python wrapper invokes the existing Ruby CLI to run jobs from Python.

Read the full module guide in doc/Python.md. For core building blocks referenced above, see these docs in scout-essentials and scout-gear:

• Annotation.md, CMD.md, ConcurrentStream.md, IndiferentHash.md, Log.md, Open.md, Path.md, Persist.md, TmpFile.md
• TSV.md, Workflow.md, KnowledgeBase.md, Association.md, Entity.md, WorkQueue.md, Semaphore.md

Direct PyCall with imports:

ScoutPython.run 'numpy', as: :np do
  a = np.array([1,2,3])
  a.sum            # PyObject; convert with to_i if needed
end

Script with a returned value and TSV round‑trip:

tsv = TSV.setup({}, "Key~ValueA,ValueB#:type=:list")
tsv["k1"] = ["a1", "b1"]; tsv["k2"] = ["a2", "b2"]

TmpFile.with_file do |target|
  result = ScoutPython.script <<~PY, df: tsv, target: target
    import scout
    result = df.loc["k2", "ValueB"]
    scout.save_tsv(target, df)
  PY
  # result == "b2"; target contains the saved TSV
end

numpy conversion:

ra = ScoutPython.run :numpy, as: :np do
  na = np.array([[[1,2,3], [4,5,6]]])
  ScoutPython.numpy2ruby(na)
end
ra[0][1][2] # => 6

Run workflows from Python:

import scout.workflow as sw

wf = sw.Workflow('Baking')
step = wf.fork('bake_muffin_tray', add_blueberries=True, clean='recursive')
step.join()
print(step.load())

• scout-essentials — https://github.com/mikisvaz/scout-essentials
• scout-gear — https://github.com/mikisvaz/scout-gear
• scout-rig — https://github.com/mikisvaz/scout-rig
• scout-camp — https://github.com/mikisvaz/scout-camp
• scout-ai — https://github.com/mikisvaz/scout-ai
• Rbbt — https://github.com/mikisvaz/rbbt
• Rbbt-Workflows — https://github.com/Rbbt-Workflows

Contributions and issues are welcome in their respective GitHub repositories.