agentlab.analyze.agent_xray

Functions

`add_patch`(ax, start, stop, color, label[, edge])
`clean_column_names`(col_list)
`code`(txt)
`display_table`(df)
`fig_to_pil`(fig)
`format_constant_and_variables`()
`gallery_step_change`(evt, episode_id)
`generate_profiling`(progress_fn)
`get_action_info`(info)
`get_agent_report`(result_df)
`get_directory_contents`(results_dir)
`get_episode_info`(info)
`get_obs`(key[, default])
`get_screenshot`(info[, step, som_or_not])
`get_seeds_df`(result_df, task_name)
`get_state_error`(state)
`if_active`(tab_name[, n_out])
`main`()
`most_recent_folder`(results_dir)
`new_agent_id`(agent_id)
`new_episode`(episode_id[, progress])
`new_exp_dir`(exp_dir[, progress, just_refresh])
`on_select_agent`(evt, df)
`on_select_seed`(evt, df, agent_task_id)
`on_select_task`(evt, df, agent_id)
`plot_profiling`(ax, step_info_list, ...)
`refresh_exp_dir_choices`(exp_dir_choice)
`remove_args_from_col`(df)
`run_gradio`(results_dir)	Run Gradio on the selected experiments saved at savedir_base.
`select_step`(episode_id, evt)
`submit_action`(input_text)
`tab_select`(evt)
`update_agent_info_html`()
`update_agent_info_md`()
`update_axtree`()
`update_chat_messages`()
`update_error_report`()
`update_global_stats`()
`update_html`()
`update_logs`()
`update_prompt_tests`()
`update_pruned_html`()
`update_screenshot`(som_or_not)
`update_screenshot_gallery`(som_or_not)
`update_screenshot_pair`(som_or_not)
`update_seeds`(agent_task_id)
`update_stats`()
`update_step_info`()
`update_task_error`()

Classes

`ClickMapper`(ax, step_times)
`EpisodeId`([agent_id, task_name, seed])
`Info`([results_dir, exp_list_dir, result_df, ...])
`StepId`([episode_id, step])

class agentlab.analyze.agent_xray.ClickMapper(ax: Axes, step_times: list[float])

Bases: object

to_step(x_pix_coord)

to_time(x_pix_coord)

class agentlab.analyze.agent_xray.EpisodeId(agent_id: str = None, task_name: str = None, seed: int = None)

Bases: object

agent_id: str

seed: int

task_name: str

class agentlab.analyze.agent_xray.Info(results_dir: Path = None, exp_list_dir: Path = None, result_df: DataFrame = None, agent_df: DataFrame = None, tasks_df: DataFrame = None, exp_result: ExpResult = None, click_mapper: ClickMapper = None, step: int = None, active_tab: str = 'Screenshot', agent_id_keys: list[str] = None)

Bases: object

active_tab: str

agent_df: DataFrame

agent_id_keys: list[str]

click_mapper: ClickMapper

exp_list_dir: Path

exp_result: ExpResult

filter_agent_id(agent_id: list[tuple])

get_agent_id(row: Series)

result_df: DataFrame

results_dir: Path

step: int

tasks_df: DataFrame

update_exp_result(episode_id: EpisodeId)

class agentlab.analyze.agent_xray.StepId(episode_id: EpisodeId = None, step: int = None)

Bases: object

episode_id: EpisodeId

step: int

agentlab.analyze.agent_xray.add_patch(ax, start, stop, color, label, edge=False)

agentlab.analyze.agent_xray.clean_column_names(col_list)

agentlab.analyze.agent_xray.code(txt)

agentlab.analyze.agent_xray.display_table(df: DataFrame)

agentlab.analyze.agent_xray.fig_to_pil(fig)

agentlab.analyze.agent_xray.format_constant_and_variables()

agentlab.analyze.agent_xray.gallery_step_change(evt: SelectData, episode_id: EpisodeId)

agentlab.analyze.agent_xray.generate_profiling(progress_fn)

agentlab.analyze.agent_xray.get_action_info(info: Info)

agentlab.analyze.agent_xray.get_agent_report(result_df: DataFrame)

agentlab.analyze.agent_xray.get_directory_contents(results_dir: Path)

agentlab.analyze.agent_xray.get_episode_info(info: Info)

agentlab.analyze.agent_xray.get_obs(key: str, default=None)

agentlab.analyze.agent_xray.get_screenshot(info: Info, step: int = None, som_or_not: str = 'Raw Screenshots')

agentlab.analyze.agent_xray.get_seeds_df(result_df: DataFrame, task_name: str)

agentlab.analyze.agent_xray.get_state_error(state: Info)

agentlab.analyze.agent_xray.if_active(tab_name, n_out=1)

agentlab.analyze.agent_xray.main()

agentlab.analyze.agent_xray.most_recent_folder(results_dir: Path)

agentlab.analyze.agent_xray.new_agent_id(agent_id: list[tuple])

agentlab.analyze.agent_xray.new_episode(episode_id: ~agentlab.analyze.agent_xray.EpisodeId, progress=<gradio.helpers.Progress object>)

agentlab.analyze.agent_xray.new_exp_dir(exp_dir, progress=<gradio.helpers.Progress object>, just_refresh=False)

agentlab.analyze.agent_xray.on_select_agent(evt: SelectData, df: DataFrame)

agentlab.analyze.agent_xray.on_select_seed(evt: SelectData, df: DataFrame, agent_task_id: tuple)

agentlab.analyze.agent_xray.on_select_task(evt: SelectData, df: DataFrame, agent_id: list[tuple])

agentlab.analyze.agent_xray.plot_profiling(ax, step_info_list: list[StepInfo], summary_info: dict, progress_fn)

agentlab.analyze.agent_xray.refresh_exp_dir_choices(exp_dir_choice)

agentlab.analyze.agent_xray.remove_args_from_col(df: DataFrame)

agentlab.analyze.agent_xray.run_gradio(results_dir: Path): Run Gradio on the selected experiments saved at savedir_base.

agentlab.analyze.agent_xray.select_step(episode_id: EpisodeId, evt: SelectData)

agentlab.analyze.agent_xray.submit_action(input_text)

agentlab.analyze.agent_xray.tab_select(evt: SelectData)

agentlab.analyze.agent_xray.update_agent_info_html()

agentlab.analyze.agent_xray.update_agent_info_md()

agentlab.analyze.agent_xray.update_axtree()

agentlab.analyze.agent_xray.update_chat_messages()

agentlab.analyze.agent_xray.update_error_report()

agentlab.analyze.agent_xray.update_global_stats()

agentlab.analyze.agent_xray.update_html()

agentlab.analyze.agent_xray.update_logs()

agentlab.analyze.agent_xray.update_prompt_tests()

agentlab.analyze.agent_xray.update_pruned_html()

agentlab.analyze.agent_xray.update_screenshot(som_or_not: str)

agentlab.analyze.agent_xray.update_screenshot_gallery(som_or_not: str)

agentlab.analyze.agent_xray.update_screenshot_pair(som_or_not: str)

agentlab.analyze.agent_xray.update_seeds(agent_task_id: tuple)

agentlab.analyze.agent_xray.update_stats()

agentlab.analyze.agent_xray.update_step_info()

agentlab.analyze.agent_xray.update_task_error()