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()],
"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
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
def make_html_map(state: SimulationState, base_path: Path) -> dict:
html_map = {
"css_style":
Util.get_css_content(SimulationHTMLBuilder.CSS_PATH),
"name":
state.name,
'immuneML_version':
MLUtil.get_immuneML_version(),
"full_specs":
Util.get_full_specs_path(base_path),
"dataset_name":
state.resulting_dataset.name if state.resulting_dataset.name
is not None else state.resulting_dataset.identifier,
"dataset_type":
StringHelper.camel_case_to_word_string(
type(state.resulting_dataset).__name__),
"example_count":
state.resulting_dataset.get_example_count(),
"dataset_size":
f"{state.resulting_dataset.get_example_count()} {type(state.resulting_dataset).__name__.replace('Dataset', 's').lower()}",
"labels": [{
"label_name": label
} for label in state.resulting_dataset.get_label_names()],
"formats": [{
"format_name":
format_name,
"dataset_download_link":
os.path.relpath(path=Util.make_downloadable_zip(
state.result_path,
state.paths[state.resulting_dataset.name][format_name]),
start=base_path)
} for format_name in state.formats],
"implantings": [
Util.to_dict_recursive(implanting, base_path)
for implanting in state.simulation.implantings
]
}
return html_map