Added Pandas Exp, Log, Sin, Cos, Tan nodes and updated Pandas DataFrame Summary node to output a DataFrame instead of a string. Added node reference pages.
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HowToSD
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"""
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Generate node reference documents.
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Run this at the project root if you update a docstring of the node source file.
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To skip the python file from included in the reference, put
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```
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pragma: skip_doc
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```
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in the docstring for the class.
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To use a custom node name, put the node name on the first line of the docstring for the class with ":".
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e.g.
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```
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CDA JSON Create:
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Creates a serialized JSON object using values entered in the text field.
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```
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"""
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import os
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import re
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import ast
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from collections import defaultdict
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from typing import Dict, List
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# Mapping for data type conversion
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data_type_mapping = {
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"DATAFRAME": "DataFrame",
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"PDSERIES": "Series",
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"PDINDEX": "Index"
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}
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def convert_data_type(data_type: str) -> str:
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"""Convert type to the required format."""
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if data_type in data_type_mapping:
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return data_type_mapping[data_type]
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return data_type.lower().capitalize()
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def extract_class_info(class_def: ast.ClassDef) -> Dict[str, str]:
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"""Extract class name, title, category, and docstring."""
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class_name = class_def.name
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docstring = ast.get_docstring(class_def) or ""
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if "pragma: skip_doc" in docstring:
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return None
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lines = docstring.split("\n")
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title = None
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category = "Miscellaneous"
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if len(lines) > 1 and lines[0].strip().endswith(":") and lines[0].strip()[0].isupper():
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title = lines.pop(0).strip().rstrip(":")
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if not title:
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title = "# " + " ".join(re.findall(r'[A-Z][a-z]*', class_name))
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else:
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title = f"# {title}"
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description_lines = []
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for line in lines:
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if line.strip().startswith("category:"):
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category = line.split(":", 1)[-1].strip()
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else:
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description_lines.append(line)
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description = "\n".join(description_lines).strip()
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return {"title": title, "description": description, "class_name": class_name, "first_sentence": description_lines[0] if description_lines else "", "category": category}
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def extract_input_types(class_def: ast.ClassDef) -> str:
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"""Extract INPUT_TYPES definition."""
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input_table = ""
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for node in class_def.body:
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if isinstance(node, ast.FunctionDef) and node.name == "INPUT_TYPES":
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for stmt in ast.walk(node):
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if isinstance(stmt, ast.Dict):
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for key, value in zip(stmt.keys, stmt.values):
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if isinstance(key, ast.Constant) and isinstance(value, ast.Tuple) and len(value.elts) > 0:
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param_name = key.value
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data_type = value.elts[0].value if isinstance(value.elts[0], ast.Constant) else ""
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input_table += f"| {param_name} | {convert_data_type(data_type)} |\n"
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return "## Input\n| Name | Data type |\n|---|---|\n" + input_table if input_table else ""
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def extract_return_types(class_def: ast.ClassDef) -> str:
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"""Extract RETURN_TYPES value."""
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for node in class_def.body:
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# Look for assignment or annotated assignment (type hint) statement.
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# Node target is the RHS.
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if (
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isinstance(node, ast.Assign) and
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any(target.id == "RETURN_TYPES" for target in node.targets if isinstance(target, ast.Name))
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) or (
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isinstance(node, ast.AnnAssign) and
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isinstance(node.target, ast.Name) and
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node.target.id == "RETURN_TYPES"
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):
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if isinstance(node.value, ast.Tuple):
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return_types = [convert_data_type(elt.value) for elt in node.value.elts if isinstance(elt, ast.Constant)]
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return "## Output\n| Data type |\n|---|\n" + "".join(f"| {rtype} |\n" for rtype in return_types)
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return ""
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def process_python_file(file_path: str, output_dir: str, node_list: Dict[str, List[str]]):
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"""Process a single Python file and generate corresponding Markdown."""
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with open(file_path, "r", encoding="utf-8") as f:
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tree = ast.parse(f.read(), filename=file_path)
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for node in tree.body:
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if isinstance(node, ast.ClassDef):
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class_info = extract_class_info(node)
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if not class_info:
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return
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input_section = extract_input_types(node)
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output_section = extract_return_types(node)
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category = class_info["category"]
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file_name = os.path.splitext(os.path.basename(file_path))[0] + ".md"
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output_path = os.path.join(output_dir, file_name)
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with open(output_path, "w", encoding="utf-8") as md_file:
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md_file.write(f"{class_info['title']}\n{class_info['description']}\n\n")
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if input_section:
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md_file.write(input_section + "\n")
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if output_section:
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md_file.write(output_section + "\n")
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md_file.write("<HR>\n")
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md_file.write(f"Category: {category}\n\n")
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md_file.write(f"ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.\n")
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node_list[category].append((class_info['title'][2:], file_name, class_info['first_sentence']))
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def generate_node_list(output_dir: str, node_list: Dict[str, List[str]]):
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"""Generate node_reference.md file."""
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node_list_path = os.path.join(output_dir, "node_reference.md")
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with open(node_list_path, "w", encoding="utf-8") as md_file:
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md_file.write("# Node Reference\n")
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for category in sorted(node_list.keys()):
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md_file.write(f"## {category}\n| Node | Description |\n| --- | --- |\n")
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for title, file_name, description in sorted(node_list[category]):
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md_file.write(f"| [{title}]({file_name}) | {description} |\n")
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def main():
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"""Main function to process all Python files under modules/**.py."""
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source_dir = "modules"
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output_dir = "docs/reference"
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os.makedirs(output_dir, exist_ok=True)
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node_list = defaultdict(list)
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for root, _, files in os.walk(source_dir):
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for file in files:
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if file.endswith(".py"):
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file_path = os.path.join(root, file)
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process_python_file(file_path, output_dir, node_list)
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generate_node_list(output_dir, node_list)
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if __name__ == "__main__":
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main()
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