py>: Python scripts

py> operator runs a Python script using python command.

See Python API documents for details including variable mappings to keyword arguments.

+step1:
  py>: my_step1_method
+step2:
  py>: tasks.MyWorkflow.step2

Options

• py> : [PACKAGE.CLASS.] METHOD

  Name of a method to run.

  Examples:

  Copy

  Copied

  # sample.dig
  py>: tasks.MyWorkflow.my_task

  This example assume the following directory structure:

  .
  ├── sample.dig
  └── tasks
      └── __init__.py

You can write __init__.py like:

# __init__.py
class MyWorkflow(object):
    def my_task(self):
        print("awesome execution")

Or, you can create put a Python script named tasks.py in a same directory as dig file.

  .
  ├── sample.dig
  └── tasks.py

Here is the example of tasks.py:

# tasks.py
class MyWorkflow(object):
    def my_task(self):
        print("awesome execution")

You can write a function without creating a class as the following:

# simple_sample.dig
py>: simple_tasks.my_func

  .
  ├── simple_sample.dig
  └── simple_tasks.py

# simple_tasks.py
def my_func():
  print("simple execution")

You can pass arguments to class for initialization　by defining arguments under the py>: operation as the following:

# sample.dig
+some_task:
  py>: tasks.MyWorkflow.my_task
  required1_1: awesome execution
  required1_2: "awesome execution"
  required2: {a: "a"}
  required3: 1
  required4: 1.0
  required5: [a, 1, 1.0, "a"]

Also, you can do the same thing using _export as the following:

# sample.dig
+some_task:
  _export:
    required1_1: awesome execution
    required1_2: "awesome execution"
    required2: {a: "a"}
    required3: 1
    required4: 1.0
    required5: [a, 1, 1.0, "a"]
  py>: tasks.MyWorkflow.my_task

This example assume following Python script:

# tasks.py
from typing import Union


class MyWorkflow(object):
    def __init__(
      self,
      required1_1: str,
      required1_2: str,
      required2: dict[str, str],
      required3: int,
      required4: float,
      required5: list[Union[str, int, float]]
    ):
        print(f"{required1_1} same as {required1_2}")
        self.arg2 = required2
        print(f"{float(required3)} same as {required4}")
        self.arg5 = required5

    def my_task(self):
        pass

Or, you can pass arguments to function as the following:

# sample.dig
+some_task:
  py>: simple_tasks.my_func
  required1: simple execution
  required2: {a: "a"}

# simple_sample.dig
+some_task:
  _export:
    required1: simple execution
    required2: {a: "a"}
  py>: simple_tasks.my_func

# simple_tasks.py
def my_func(required1: str, required2: dict[str, str]):
  print(f"{required1}: {required2}")

Finally, you can pass combination (must have different names) of class and mehtod arguments to Python script as the following:

# sample.dig
+some_task:
  py>: tasks.MyWorkflow.my_task
  required_class_arg: awesome execution
  required_method_arg: ["a", "b"]

# sample.dig
+some_task:
  _export:
    required_class_arg: awesome execution
    required_method_arg: ["a", "b"]
  py>: tasks.MyWorkflow.my_task

# tasks.py
class MyWorkflow:
  def __init__(self, required_class_arg: str):
    self.arg = required_class_arg

  def my_task(self, required_method_arg: list[str]):
    print(f"{self.arg}: {required_method_arg}")

• python : PATH STRING or COMMAND ARGUMENTS LIST

  The python defaults to python. If an alternate python and options are desired, use the python option.

  Examples:

  yamlyaml

  Copy

  Copied

  python: /opt/conda/bin/python

  Copy

  Copied

  python: ["python", "-v"]

  It is also possible to configure in _export section.

  Examples:

  Copy

  Copied

  _export:
  py:
    python: /opt/conda/bin/python