Source code for immuneML.presentation.html.DatasetExportHTMLBuilder

import os
from pathlib import Path

from immuneML.environment.EnvironmentSettings import EnvironmentSettings
from immuneML.ml_methods.util.Util import Util as MLUtil
from immuneML.presentation.TemplateParser import TemplateParser
from immuneML.presentation.html.Util import Util
from immuneML.util.PathBuilder import PathBuilder
from immuneML.util.StringHelper import StringHelper
from immuneML.workflows.instructions.dataset_generation.DatasetExportState import DatasetExportState


[docs] class DatasetExportHTMLBuilder: CSS_PATH = EnvironmentSettings.html_templates_path / "css/custom.css"
[docs] @staticmethod def build(state: DatasetExportState) -> Path: """ Function that builds the HTML files based on the Simulation state. Arguments: state: SimulationState object including all details of the Simulation instruction Returns: path to the main HTML file (which is located under state.result_path) """ base_path = PathBuilder.build(state.result_path / "../HTML_output/") html_map = DatasetExportHTMLBuilder.make_html_map(state, base_path) result_file = base_path / f"DatasetExport_{state.name}.html" TemplateParser.parse(template_path=EnvironmentSettings.html_templates_path / "DatasetExport.html", template_map=html_map, result_path=result_file) return result_file
[docs] @staticmethod def make_html_map(state: DatasetExportState, base_path: Path) -> dict: html_map = { "css_style": Util.get_css_content(DatasetExportHTMLBuilder.CSS_PATH), "name": state.name, 'immuneML_version': MLUtil.get_immuneML_version(), "full_specs": Util.get_full_specs_path(base_path), "datasets": [ { "dataset_name": dataset.name, "dataset_type": StringHelper.camel_case_to_word_string(type(dataset).__name__), "dataset_size": f"{dataset.get_example_count()} {type(dataset).__name__.replace('Dataset', 's').lower()}", "labels": [{"label_name": label} for label in dataset.get_label_names()], "preprocessing_sequence": [ { "preprocessing_name": preprocessing.__class__.__name__, "preprocessing_params": ", ".join([f"{key}: {value}" for key, value in vars(preprocessing).items()]) } for preprocessing in state.preprocessing_sequence ] if state.preprocessing_sequence is not None else [], "show_preprocessing": state.preprocessing_sequence is not None and len(state.preprocessing_sequence) > 0, "formats": [ { "format_name": format_name, "dataset_download_link": os.path.relpath(path=Util.make_downloadable_zip(state.result_path, state.paths[dataset.name][format_name]), start=base_path) } for format_name in state.formats ] } for dataset in state.datasets ] } return html_map