webinterface.pages.base_pages.quant module

Streamlit-based web interface for ProteoBench.

class webinterface.pages.base_pages.quant.QuantUIObjects(variables_quant: VariablesDDAQuant, ionmodule: DDAQuantIonModuleQExactive, parsesettingsbuilder: ParseSettingsBuilder, page_name: str = '/')

Bases: object

Main class for the Streamlit interface of ProteoBench quantification. This class handles the creation of the Streamlit UI elements, including the main page layout, input forms, results display, and data submission elements.

Parameters:

• variables_quant (VariablesDDAQuant) – The variables for the quantification module.

• ionmodule (IonModule) – The quantification module.

• parsesettingsbuilder (ParseSettingsBuilder) – The parse settings builder.

clear_highlight_column() → None

Remove the highlight column from the submission data if it exists.

copy_dataframes_for_submission() → None

Create copies of the dataframes before submission.

create_pull_request(params: Any) → str | None

Submit the pull request with the benchmark results and returns the PR URL.

Parameters:

params (Any) – The parameters object.

Returns:

The URL of the pull request.

Return type:

Optional[str]

display_all_data_results_main() → None

Display the results for all data in Tab 1.

display_all_data_results_submitted() → None

Display the results for all data in Tab 3.

display_download_section(reset_uuid=False) → None

Render the selector and area for raw data download.

Parameters:

reset_uuid (bool, optional) – Whether to reset the UUID, by default False.

display_existing_results() → None

Display the results section of the page for existing data.

display_indepth_plots() → None

Display the dataset selection dropdown and plot the selected dataset.

display_public_submission_ui() → None

Display the public submission section of the page in Tab 4.

display_submission_form() → None

Create the main submission form for the Streamlit UI in Tab 2.

display_submitted_results() → None

Display the results section of the page for submitted data.

execute_proteobench() → None

Execute the ProteoBench benchmarking process.

filter_data_main_slider() → DataFrame

Filter the data points based on the slider value.

Returns:

The filtered data points.

Return type:

pandas.DataFrame

filter_data_submitted_slider() → DataFrame

Filter the data points based on the slider value.

Returns:

The filtered data points.

Return type:

pd.DataFrame

generate_additional_parameters_fields() → None

Create the additional parameters section of the form and initializes the parameter fields.

generate_additional_parameters_fields_submission() → None

Create the additional parameters section of the form and initializes the parameter fields.

generate_comments_section() → None

Create the text area for submission comments.

generate_confirmation_checkbox() → None

Create the confirmation checkbox for metadata correctness.

generate_indepth_plots(recalculate: bool, public_id: str | None, public_hash: str | None) → Figure

Generate and return plots based on the current benchmark data in Tab 2.5.

Parameters:

• recalculate (bool) – Whether to recalculate the plots.

• public_id (Optional[str], optional) – The dataset to plot, either “Uploaded dataset” or name of public run.

• public_hash (Optional[str], optional) – The hash of the selected public dataset. If None, the uploaded dataset is displayed.

Returns:

The generated plots for the selected dataset.

Return type:

go.Figure

generate_input_fields() → None

Create the input section of the form.

generate_input_widget(input_format: str, content: dict, key: str = '', editable: bool = True) → Any

Generate input fields in the Streamlit UI based on the specified format and content.

Parameters:

• input_format (str) – The input format.

• content (dict) – The content of the input fields.

• key (str) – The key of the input fields.

• editable (bool) – Whether the input fields are editable.

Returns:

The input fields.

Return type:

Any

generate_main_selectbox() → None

Create the selectbox for the Streamlit UI.

generate_main_slider() → None

Create a slider input.

generate_metadata_uploader() → None

Create the file uploader for meta data.

generate_sample_name() → str

Generate a unique sample name based on the input format, software version, and the current timestamp.

Returns:

The generated sample name.

Return type:

str

generate_submission_button() → str | None

Create a button for public submission and returns the PR URL if the button is pressed.

Returns:

The URL of the pull request.

Return type:

Optional[str]

generate_submission_ui_elements() → None

Create the UI elements necessary for data submission, including metadata uploader and comments section.

generate_submitted_selectbox() → None

Create the selectbox for the Streamlit UI.

generate_submitted_slider() → None

Create a slider input.

generate_text_area_widget(input_format: str, content: dict, editable: bool = True) → Any

Generate a text area input field.

Parameters:

• input_format (str) – The input format.

• content (dict) – The content of the text area.

• editable (bool) – Whether the text area is editable.

Returns:

The text area input field.

Return type:

Any

get_form_values() → Dict[str, Any]

Retrieve all user inputs from Streamlit session state and returns them as a dictionary.

Returns:

A dictionary containing all user inputs.

Return type:

Dict[str, Any]

handle_submitted_table_edits() → None

Callback function for handling edits made to the data table in the UI.

initialize_main_data_points() → None

Initialize the all_datapoints variable in the session state.

initialize_main_slider() → None

Initialize the slider for the main data.

initialize_submitted_data_points() → None

Initialize the all_datapoints variable in the session state.

initialize_submitted_slider() → None

Initialize the slider for the submitted data.

load_user_parameters() → Any

Read and process the parameter files provided by the user.

Returns:

The parsed parameters.

Return type:

Any

process_submission_form() → None

Handle the form submission logic.

Returns:

Whether the submission was handled unsuccessfully.

Return type:

bool, optional

run_benchmarking_process()

Execute the benchmarking process and returns the results.

Returns:

The benchmarking results, all data points, and the input data frame.

Return type:

Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]

save_intermediate_submission_data() → None

Store intermediate and input data to the storage directory if available.

set_highlight_column_in_submitted_data() → None

Initialize the highlight column in the data points.

show_submission_success_message(pr_url) → None

Handle the UI updates and notifications after a successful submission.

Parameters:

pr_url (str) – The URL of the pull request.

submit_to_repository(params) → str | None

Handle the submission process of the benchmark results to the ProteoBench repository.

Parameters:

params (ProteoBenchParameters) – The parameters for the submission.

Returns:

The URL of the pull request if the submission was successful.

Return type:

str, optional

update_parameters_submission_form(field, value) → None

Update the session state dictionary with the specified field and value.

Parameters:

• field (str) – The field to update.

• value (Any) – The value to update the field with.

webinterface.pages.base_pages.quant.compare_dictionaries(old_dict, new_dict)

Generate a human-readable string describing differences between two dictionaries.

Parameters:

• old_dict (dict) – The old dictionary.

• new_dict (dict) – The new dictionary.

Returns:

The human-readable string describing the differences between the two dictionaries.

Return type:

str