Reference

Application

class prompt_toolkit.application.Application(layout: Optional[prompt_toolkit.layout.layout.Layout] = None, style: Optional[prompt_toolkit.styles.base.BaseStyle] = None, include_default_pygments_style: Union[prompt_toolkit.filters.base.Filter, bool] = True, style_transformation: Optional[prompt_toolkit.styles.style_transformation.StyleTransformation] = None, key_bindings: Optional[prompt_toolkit.key_binding.key_bindings.KeyBindingsBase] = None, clipboard: Optional[prompt_toolkit.clipboard.base.Clipboard] = None, full_screen: bool = False, color_depth: Union[prompt_toolkit.output.color_depth.ColorDepth, Callable[[], Optional[prompt_toolkit.output.color_depth.ColorDepth]], None] = None, mouse_support: Union[prompt_toolkit.filters.base.Filter, bool] = False, enable_page_navigation_bindings: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, paste_mode: Union[prompt_toolkit.filters.base.Filter, bool] = False, editing_mode: prompt_toolkit.enums.EditingMode = <EditingMode.EMACS: 'EMACS'>, erase_when_done: bool = False, reverse_vi_search_direction: Union[prompt_toolkit.filters.base.Filter, bool] = False, min_redraw_interval: Union[float, int, None] = None, max_render_postpone_time: Union[float, int, None] = 0.01, refresh_interval: Optional[float] = None, on_reset: Optional[Callable[[Application[_AppResult]], None]] = None, on_invalidate: Optional[Callable[[Application[_AppResult]], None]] = None, before_render: Optional[Callable[[Application[_AppResult]], None]] = None, after_render: Optional[Callable[[Application[_AppResult]], None]] = None, input: Optional[prompt_toolkit.input.base.Input] = None, output: Optional[prompt_toolkit.output.base.Output] = None)

The main Application class! This glues everything together.

Parameters:
  • layout – A Layout instance.
  • key_bindingsKeyBindingsBase instance for the key bindings.
  • clipboardClipboard to use.
  • on_abort – What to do when Control-C is pressed.
  • on_exit – What to do when Control-D is pressed.
  • full_screen – When True, run the application on the alternate screen buffer.
  • color_depth – Any ColorDepth value, a callable that returns a ColorDepth or None for default.
  • erase_when_done – (bool) Clear the application output when it finishes.
  • reverse_vi_search_direction – Normally, in Vi mode, a ‘/’ searches forward and a ‘?’ searches backward. In Readline mode, this is usually reversed.
  • min_redraw_interval

    Number of seconds to wait between redraws. Use this for applications where invalidate is called a lot. This could cause a lot of terminal output, which some terminals are not able to process.

    None means that every invalidate will be scheduled right away (which is usually fine).

    When one invalidate is called, but a scheduled redraw of a previous invalidate call has not been executed yet, nothing will happen in any case.

  • max_render_postpone_time – When there is high CPU (a lot of other scheduled calls), postpone the rendering max x seconds. ‘0’ means: don’t postpone. ‘.5’ means: try to draw at least twice a second.
  • refresh_interval – Automatically invalidate the UI every so many seconds. When None (the default), only invalidate when invalidate has been called.

Filters:

Parameters:
  • mouse_support – (Filter or boolean). When True, enable mouse support.
  • paste_modeFilter or boolean.
  • editing_modeEditingMode.
  • enable_page_navigation_bindings – When True, enable the page navigation key bindings. These include both Emacs and Vi bindings like page-up, page-down and so on to scroll through pages. Mostly useful for creating an editor or other full screen applications. Probably, you don’t want this for the implementation of a REPL. By default, this is enabled if full_screen is set.

Callbacks (all of these should accept a Application object as input.)

Parameters:
  • on_reset – Called during reset.
  • on_invalidate – Called when the UI has been invalidated.
  • before_render – Called right before rendering.
  • after_render – Called right after rendering.

I/O: (Note that the preferred way to change the input/output is by creating an AppSession with the required input/output objects. If you need multiple applications running at the same time, you have to create a separate AppSession using a with create_app_session(): block.

Parameters:
  • inputInput instance.
  • outputOutput instance. (Probably Vt100_Output or Win32Output.)

Usage:

app = Application(…) app.run()

# Or await app.run_async()

cancel_and_wait_for_background_tasks() → None

Cancel all background tasks, and wait for the cancellation to be done. If any of the background tasks raised an exception, this will also propagate the exception.

(If we had nurseries like Trio, this would be the __aexit__ of a nursery.)

color_depth

Active ColorDepth.

cpr_not_supported_callback() → None

Called when we don’t receive the cursor position response in time.

create_background_task(coroutine: Awaitable[None]) → asyncio.Task[None]

Start a background task (coroutine) for the running application. If asyncio had nurseries like Trio, we would create a nursery in Application.run_async, and run the given coroutine in that nursery.

current_buffer

The currently focused Buffer.

(This returns a dummy Buffer when none of the actual buffers has the focus. In this case, it’s really not practical to check for None values or catch exceptions every time.)

current_search_state

Return the current SearchState. (The one for the focused BufferControl.)

exit(result: Optional[_AppResult] = None, exception: Union[BaseException, Type[BaseException], None] = None, style: str = '') → None

Exit application.

Parameters:
  • result – Set this result for the application.
  • exception – Set this exception as the result for an application. For a prompt, this is often EOFError or KeyboardInterrupt.
  • style – Apply this style on the whole content when quitting, often this is ‘class:exiting’ for a prompt. (Used when erase_when_done is not set.)
get_used_style_strings() → List[str]

Return a list of used style strings. This is helpful for debugging, and for writing a new Style.

invalidate() → None

Thread safe way of sending a repaint trigger to the input event loop.

invalidated

True when a redraw operation has been scheduled.

is_running

True when the application is currently active/running.

key_processor = None

The InputProcessor instance.

print_text(text: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None], style: Optional[prompt_toolkit.styles.base.BaseStyle] = None) → None

Print a list of (style_str, text) tuples to the output. (When the UI is running, this method has to be called through run_in_terminal, otherwise it will destroy the UI.)

Parameters:
  • text – List of (style_str, text) tuples.
  • style – Style class to use. Defaults to the active style in the CLI.
quoted_insert = None

Quoted insert. This flag is set if we go into quoted insert mode.

render_counter = None

Render counter. This one is increased every time the UI is rendered. It can be used as a key for caching certain information during one rendering.

reset() → None

Reset everything, for reading the next input.

run(pre_run: Optional[Callable[[], None]] = None, set_exception_handler: bool = True) → _AppResult

A blocking ‘run’ call that waits until the UI is finished.

This will start the current asyncio event loop. If no loop is set for the current thread, then it will create a new loop.

Parameters:
  • pre_run – Optional callable, which is called right after the “reset” of the application.
  • set_exception_handler – When set, in case of an exception, go out of the alternate screen and hide the application, display the exception, and wait for the user to press ENTER.
run_async(pre_run: Optional[Callable[[], None]] = None, set_exception_handler: bool = True) → _AppResult

Run the prompt_toolkit Application until exit() has been called. Return the value that was passed to exit().

This is the main entry point for a prompt_toolkit Application and usually the only place where the event loop is actually running.

Parameters:
  • pre_run – Optional callable, which is called right after the “reset” of the application.
  • set_exception_handler – When set, in case of an exception, go out of the alternate screen and hide the application, display the exception, and wait for the user to press ENTER.
run_system_command(command: str, wait_for_enter: bool = True, display_before_text: str = '', wait_text: str = 'Press ENTER to continue...') → None

Run system command (While hiding the prompt. When finished, all the output will scroll above the prompt.)

Parameters:
  • command – Shell command to be executed.
  • wait_for_enter – FWait for the user to press enter, when the command is finished.
  • display_before_text – If given, text to be displayed before the command executes.
Returns:

A Future object.

suspend_to_background(suspend_group: bool = True) → None

(Not thread safe – to be called from inside the key bindings.) Suspend process.

Parameters:suspend_group – When true, suspend the whole process group. (This is the default, and probably what you want.)
timeoutlen = None

Like Vim’s timeoutlen option. This can be None or a float. For instance, suppose that we have a key binding AB and a second key binding A. If the uses presses A and then waits, we don’t handle this binding yet (unless it was marked ‘eager’), because we don’t know what will follow. This timeout is the maximum amount of time that we wait until we call the handlers anyway. Pass None to disable this timeout.

ttimeoutlen = None

When to flush the input (For flushing escape keys.) This is important on terminals that use vt100 input. We can’t distinguish the escape key from for instance the left-arrow key, if we don’t know what follows after “x1b”. This little timer will consider “x1b” to be escape if nothing did follow in this time span. This seems to work like the ttimeoutlen option in Vim.

vi_state = None

Vi state. (For Vi key bindings.)

prompt_toolkit.application.get_app() → Application[Any]

Get the current active (running) Application. An Application is active during the Application.run_async() call.

We assume that there can only be one Application active at the same time. There is only one terminal window, with only one stdin and stdout. This makes the code significantly easier than passing around the Application everywhere.

If no Application is running, then return by default a DummyApplication. For practical reasons, we prefer to not raise an exception. This way, we don’t have to check all over the place whether an actual Application was returned.

(For applications like pymux where we can have more than one Application, we’ll use a work-around to handle that.)

prompt_toolkit.application.set_app(app: Application[Any]) → Generator[None, None, None]

Context manager that sets the given Application active in an AppSession.

This should only be called by the Application itself. The application will automatically be active while its running. If you want the application to be active in other threads/coroutines, where that’s not the case, use contextvars.copy_context(), or use Application.context to run it in the appropriate context.

class prompt_toolkit.application.DummyApplication

When no Application is running, get_app() will run an instance of this DummyApplication instead.

run(pre_run: Optional[Callable[[], None]] = None, set_exception_handler: bool = True) → None

A blocking ‘run’ call that waits until the UI is finished.

This will start the current asyncio event loop. If no loop is set for the current thread, then it will create a new loop.

Parameters:
  • pre_run – Optional callable, which is called right after the “reset” of the application.
  • set_exception_handler – When set, in case of an exception, go out of the alternate screen and hide the application, display the exception, and wait for the user to press ENTER.
run_async(pre_run: Optional[Callable[[], None]] = None, set_exception_handler: bool = True) → None

Run the prompt_toolkit Application until exit() has been called. Return the value that was passed to exit().

This is the main entry point for a prompt_toolkit Application and usually the only place where the event loop is actually running.

Parameters:
  • pre_run – Optional callable, which is called right after the “reset” of the application.
  • set_exception_handler – When set, in case of an exception, go out of the alternate screen and hide the application, display the exception, and wait for the user to press ENTER.
run_system_command(command: str, wait_for_enter: bool = True, display_before_text: str = '', wait_text: str = '') → None

Run system command (While hiding the prompt. When finished, all the output will scroll above the prompt.)

Parameters:
  • command – Shell command to be executed.
  • wait_for_enter – FWait for the user to press enter, when the command is finished.
  • display_before_text – If given, text to be displayed before the command executes.
Returns:

A Future object.

suspend_to_background(suspend_group: bool = True) → None

(Not thread safe – to be called from inside the key bindings.) Suspend process.

Parameters:suspend_group – When true, suspend the whole process group. (This is the default, and probably what you want.)
prompt_toolkit.application.run_in_terminal(func: Callable[[], _T], render_cli_done: bool = False, in_executor: bool = False) → Awaitable[_T]

Run function on the terminal above the current application or prompt.

What this does is first hiding the prompt, then running this callable (which can safely output to the terminal), and then again rendering the prompt which causes the output of this function to scroll above the prompt.

func is supposed to be a synchronous function. If you need an asynchronous version of this function, use the in_terminal context manager directly.

Parameters:
  • func – The callable to execute.
  • render_cli_done – When True, render the interface in the ‘Done’ state first, then execute the function. If False, erase the interface first.
  • in_executor – When True, run in executor. (Use this for long blocking functions, when you don’t want to block the event loop.)
Returns:

A Future.

Formatted text

Many places in prompt_toolkit can take either plain text, or formatted text. For instance the prompt() function takes either plain text or formatted text for the prompt. The FormattedTextControl can also take either plain text or formatted text.

In any case, there is an input that can either be just plain text (a string), an HTML object, an ANSI object or a sequence of (style_string, text) tuples. The to_formatted_text() conversion function takes any of these and turns all of them into such a tuple sequence.

prompt_toolkit.formatted_text.to_formatted_text(value: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None], style: str = '', auto_convert: bool = False) → FormattedText

Convert the given value (which can be formatted text) into a list of text fragments. (Which is the canonical form of formatted text.) The outcome is always a FormattedText instance, which is a list of (style, text) tuples.

It can take an HTML object, a plain text string, or anything that implements __pt_formatted_text__.

Parameters:
  • style – An additional style string which is applied to all text fragments.
  • auto_convert – If True, also accept other types, and convert them to a string first.
prompt_toolkit.formatted_text.is_formatted_text(value: object) → bool

Check whether the input is valid formatted text (for use in assert statements). In case of a callable, it doesn’t check the return type.

class prompt_toolkit.formatted_text.Template(text: str)

Template for string interpolation with formatted text.

Example:

Template(' ... {} ... ').format(HTML(...))
Parameters:text – Plain text.
prompt_toolkit.formatted_text.merge_formatted_text(items: Iterable[Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None]]) → Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None]

Merge (Concatenate) several pieces of formatted text together.

class prompt_toolkit.formatted_text.FormattedText

A list of (style, text) tuples.

(In some situations, this can also be (style, text, mouse_handler) tuples.)

class prompt_toolkit.formatted_text.HTML(value: str)

HTML formatted text. Take something HTML-like, for use as a formatted string.

# Turn something into red.
HTML('<style fg="ansired" bg="#00ff44">...</style>')

# Italic, bold and underline.
HTML('<i>...</i>')
HTML('<b>...</b>')
HTML('<u>...</u>')

All HTML elements become available as a “class” in the style sheet. E.g. <username>...</username> can be styled, by setting a style for username.

format(*args, **kwargs) → prompt_toolkit.formatted_text.html.HTML

Like str.format, but make sure that the arguments are properly escaped.

class prompt_toolkit.formatted_text.ANSI(value: str)

ANSI formatted text. Take something ANSI escaped text, for use as a formatted string. E.g.

ANSI('\x1b[31mhello \x1b[32mworld')

Characters between \001 and \002 are supposed to have a zero width when printed, but these are literally sent to the terminal output. This can be used for instance, for inserting Final Term prompt commands. They will be translated into a prompt_toolkit ‘[ZeroWidthEscape]’ fragment.

format(*args, **kwargs) → prompt_toolkit.formatted_text.ansi.ANSI

Like str.format, but make sure that the arguments are properly escaped. (No ANSI escapes can be injected.)

class prompt_toolkit.formatted_text.PygmentsTokens(token_list: List[Tuple[Token, str]])

Turn a pygments token list into a list of prompt_toolkit text fragments ((style_str, text) tuples).

prompt_toolkit.formatted_text.fragment_list_len(fragments: List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]]) → int

Return the amount of characters in this text fragment list.

Parameters:fragments – List of (style_str, text) or (style_str, text, mouse_handler) tuples.
prompt_toolkit.formatted_text.fragment_list_width(fragments: List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]]) → int

Return the character width of this text fragment list. (Take double width characters into account.)

Parameters:fragments – List of (style_str, text) or (style_str, text, mouse_handler) tuples.
prompt_toolkit.formatted_text.fragment_list_to_text(fragments: List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]]) → str

Concatenate all the text parts again.

Parameters:fragments – List of (style_str, text) or (style_str, text, mouse_handler) tuples.
prompt_toolkit.formatted_text.split_lines(fragments: List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]]) → Iterable[List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]]]

Take a single list of (style_str, text) tuples and yield one such list for each line. Just like str.split, this will yield at least one item.

Parameters:fragments – List of (style_str, text) or (style_str, text, mouse_handler) tuples.

Buffer

Data structures for the Buffer. It holds the text, cursor position, history, etc…

exception prompt_toolkit.buffer.EditReadOnlyBuffer

Attempt editing of read-only Buffer.

class prompt_toolkit.buffer.Buffer(completer: Optional[prompt_toolkit.completion.base.Completer] = None, auto_suggest: Optional[prompt_toolkit.auto_suggest.AutoSuggest] = None, history: Optional[prompt_toolkit.history.History] = None, validator: Optional[prompt_toolkit.validation.Validator] = None, tempfile_suffix: Union[str, Callable[[], str]] = '', tempfile: Union[str, Callable[[], str]] = '', name: str = '', complete_while_typing: Union[prompt_toolkit.filters.base.Filter, bool] = False, validate_while_typing: Union[prompt_toolkit.filters.base.Filter, bool] = False, enable_history_search: Union[prompt_toolkit.filters.base.Filter, bool] = False, document: Optional[prompt_toolkit.document.Document] = None, accept_handler: Optional[Callable[[Buffer], bool]] = None, read_only: Union[prompt_toolkit.filters.base.Filter, bool] = False, multiline: Union[prompt_toolkit.filters.base.Filter, bool] = True, on_text_changed: Optional[Callable[[Buffer], None]] = None, on_text_insert: Optional[Callable[[Buffer], None]] = None, on_cursor_position_changed: Optional[Callable[[Buffer], None]] = None, on_completions_changed: Optional[Callable[[Buffer], None]] = None, on_suggestion_set: Optional[Callable[[Buffer], None]] = None)

The core data structure that holds the text and cursor position of the current input line and implements all text manipulations on top of it. It also implements the history, undo stack and the completion state.

Parameters:
  • completerCompleter instance.
  • historyHistory instance.
  • tempfile_suffix – The tempfile suffix (extension) to be used for the “open in editor” function. For a Python REPL, this would be “.py”, so that the editor knows the syntax highlighting to use. This can also be a callable that returns a string.
  • tempfile – For more advanced tempfile situations where you need control over the subdirectories and filename. For a Git Commit Message, this would be “.git/COMMIT_EDITMSG”, so that the editor knows the syntax highlighting to use. This can also be a callable that returns a string.
  • name – Name for this buffer. E.g. DEFAULT_BUFFER. This is mostly useful for key bindings where we sometimes prefer to refer to a buffer by their name instead of by reference.
  • accept_handler

    Called when the buffer input is accepted. (Usually when the user presses enter.) The accept handler receives this Buffer as input and should return True when the buffer text should be kept instead of calling reset.

    In case of a PromptSession for instance, we want to keep the text, because we will exit the application, and only reset it during the next run.

Events:

Parameters:
  • on_text_changed – When the buffer text changes. (Callable on None.)
  • on_text_insert – When new text is inserted. (Callable on None.)
  • on_cursor_position_changed – When the cursor moves. (Callable on None.)
  • on_completions_changed – When the completions were changed. (Callable on None.)
  • on_suggestion_set – When an auto-suggestion text has been set. (Callable on None.)

Filters:

Parameters:
  • complete_while_typingFilter or bool. Decide whether or not to do asynchronous autocompleting while typing.
  • validate_while_typingFilter or bool. Decide whether or not to do asynchronous validation while typing.
  • enable_history_searchFilter or bool to indicate when up-arrow partial string matching is enabled. It is advised to not enable this at the same time as complete_while_typing, because when there is an autocompletion found, the up arrows usually browse through the completions, rather than through the history.
  • read_onlyFilter. When True, changes will not be allowed.
  • multilineFilter or bool. When not set, pressing Enter will call the accept_handler. Otherwise, pressing Esc-Enter is required.
append_to_history() → None

Append the current input to the history.

apply_completion(completion: prompt_toolkit.completion.base.Completion) → None

Insert a given completion.

Apply search. If something is found, set working_index and cursor_position.

auto_down(count: int = 1, go_to_start_of_line_if_history_changes: bool = False) → None

If we’re not on the last line (of a multiline input) go a line down, otherwise go forward in history. (If nothing is selected.)

auto_up(count: int = 1, go_to_start_of_line_if_history_changes: bool = False) → None

If we’re not on the first line (of a multiline input) go a line up, otherwise go back in history. (If nothing is selected.)

cancel_completion() → None

Cancel completion, go back to the original text.

complete_next(count: int = 1, disable_wrap_around: bool = False) → None

Browse to the next completions. (Does nothing if there are no completion.)

complete_previous(count: int = 1, disable_wrap_around: bool = False) → None

Browse to the previous completions. (Does nothing if there are no completion.)

copy_selection(_cut: bool = False) → prompt_toolkit.clipboard.base.ClipboardData

Copy selected text and return ClipboardData instance.

Notice that this doesn’t store the copied data on the clipboard yet. You can store it like this:

data = buffer.copy_selection()
get_app().clipboard.set_data(data)
cursor_down(count: int = 1) → None

(for multiline edit). Move cursor to the next line.

cursor_up(count: int = 1) → None

(for multiline edit). Move cursor to the previous line.

cut_selection() → prompt_toolkit.clipboard.base.ClipboardData

Delete selected text and return ClipboardData instance.

delete(count: int = 1) → str

Delete specified number of characters and Return the deleted text.

delete_before_cursor(count: int = 1) → str

Delete specified number of characters before cursor and return the deleted text.

document

Return Document instance from the current text, cursor position and selection state.

Return a Document instance that has the text/cursor position for this search, if we would apply it. This will be used in the BufferControl to display feedback while searching.

get_search_position(search_state: prompt_toolkit.search.SearchState, include_current_position: bool = True, count: int = 1) → int

Get the cursor position for this search. (This operation won’t change the working_index. It’s won’t go through the history. Vi text objects can’t span multiple items.)

go_to_completion(index: Optional[int]) → None

Select a completion from the list of current completions.

go_to_history(index: int) → None

Go to this item in the history.

history_backward(count: int = 1) → None

Move backwards through history.

history_forward(count: int = 1) → None

Move forwards through the history.

Parameters:count – Amount of items to move forward.
insert_line_above(copy_margin: bool = True) → None

Insert a new line above the current one.

insert_line_below(copy_margin: bool = True) → None

Insert a new line below the current one.

insert_text(data: str, overwrite: bool = False, move_cursor: bool = True, fire_event: bool = True) → None

Insert characters at cursor position.

Parameters:fire_event – Fire on_text_insert event. This is mainly used to trigger autocompletion while typing.
is_returnable

True when there is something handling accept.

join_next_line(separator: str = ' ') → None

Join the next line to the current one by deleting the line ending after the current line.

join_selected_lines(separator: str = ' ') → None

Join the selected lines.

newline(copy_margin: bool = True) → None

Insert a line ending at the current position.

open_in_editor(validate_and_handle: bool = False) → asyncio.Task[None]

Open code in editor.

This returns a future, and runs in a thread executor.

paste_clipboard_data(data: prompt_toolkit.clipboard.base.ClipboardData, paste_mode: prompt_toolkit.selection.PasteMode = <PasteMode.EMACS: 'EMACS'>, count: int = 1) → None

Insert the data from the clipboard.

reset(document: Optional[prompt_toolkit.document.Document] = None, append_to_history: bool = False) → None
Parameters:append_to_history – Append current input to history first.
save_to_undo_stack(clear_redo_stack: bool = True) → None

Safe current state (input text and cursor position), so that we can restore it by calling undo.

set_document(value: prompt_toolkit.document.Document, bypass_readonly: bool = False) → None

Set Document instance. Like the document property, but accept an bypass_readonly argument.

Parameters:bypass_readonly – When True, don’t raise an EditReadOnlyBuffer exception, even when the buffer is read-only.

Warning

When this buffer is read-only and bypass_readonly was not passed, the EditReadOnlyBuffer exception will be caught by the KeyProcessor and is silently suppressed. This is important to keep in mind when writing key bindings, because it won’t do what you expect, and there won’t be a stack trace. Use try/finally around this function if you need some cleanup code.

start_completion(select_first: bool = False, select_last: bool = False, insert_common_part: bool = False, complete_event: Optional[prompt_toolkit.completion.base.CompleteEvent] = None) → None

Start asynchronous autocompletion of this buffer. (This will do nothing if a previous completion was still in progress.)

start_history_lines_completion() → None

Start a completion based on all the other lines in the document and the history.

start_selection(selection_type: prompt_toolkit.selection.SelectionType = <SelectionType.CHARACTERS: 'CHARACTERS'>) → None

Take the current cursor position as the start of this selection.

swap_characters_before_cursor() → None

Swap the last two characters before the cursor.

transform_current_line(transform_callback: Callable[[str], str]) → None

Apply the given transformation function to the current line.

Parameters:transform_callback – callable that takes a string and return a new string.
transform_lines(line_index_iterator: Iterable[int], transform_callback: Callable[[str], str]) → str

Transforms the text on a range of lines. When the iterator yield an index not in the range of lines that the document contains, it skips them silently.

To uppercase some lines:

new_text = transform_lines(range(5,10), lambda text: text.upper())
Parameters:
  • line_index_iterator – Iterator of line numbers (int)
  • transform_callback – callable that takes the original text of a line, and return the new text for this line.
Returns:

The new text.

transform_region(from_: int, to: int, transform_callback: Callable[[str], str]) → None

Transform a part of the input string.

Parameters:
  • from – (int) start position.
  • to – (int) end position.
  • transform_callback – Callable which accepts a string and returns the transformed string.
validate(set_cursor: bool = False) → bool

Returns True if valid.

Parameters:set_cursor – Set the cursor position, if an error was found.
validate_and_handle() → None

Validate buffer and handle the accept action.

yank_last_arg(n: Optional[int] = None) → None

Like yank_nth_arg, but if no argument has been given, yank the last word by default.

yank_nth_arg(n: Optional[int] = None, _yank_last_arg: bool = False) → None

Pick nth word from previous history entry (depending on current yank_nth_arg_state) and insert it at current position. Rotate through history if called repeatedly. If no n has been given, take the first argument. (The second word.)

Parameters:n – (None or int), The index of the word from the previous line to take.
class prompt_toolkit.buffer.CompletionState(original_document: prompt_toolkit.document.Document, completions: Optional[List[Completion]] = None, complete_index: Optional[int] = None)

Immutable class that contains a completion state.

complete_index = None

Position in the completions array. This can be None to indicate “no completion”, the original text.

completions = None

List of all the current Completion instances which are possible at this point.

current_completion

Return the current completion, or return None when no completion is selected.

go_to_index(index: Optional[int]) → None

Create a new CompletionState object with the new index.

When index is None deselect the completion.

new_text_and_position() → Tuple[str, int]

Return (new_text, new_cursor_position) for this completion.

original_document = None

Document as it was when the completion started.

prompt_toolkit.buffer.indent(buffer: prompt_toolkit.buffer.Buffer, from_row: int, to_row: int, count: int = 1) → None

Indent text of a Buffer object.

prompt_toolkit.buffer.unindent(buffer: prompt_toolkit.buffer.Buffer, from_row: int, to_row: int, count: int = 1) → None

Unindent text of a Buffer object.

prompt_toolkit.buffer.reshape_text(buffer: prompt_toolkit.buffer.Buffer, from_row: int, to_row: int) → None

Reformat text, taking the width into account. to_row is included. (Vi ‘gq’ operator.)

Selection

Data structures for the selection.

class prompt_toolkit.selection.SelectionType

Type of selection.

BLOCK = 'BLOCK'

A block selection. (Visual-Block in Vi.)

CHARACTERS = 'CHARACTERS'

Characters. (Visual in Vi.)

LINES = 'LINES'

Whole lines. (Visual-Line in Vi.)

class prompt_toolkit.selection.PasteMode

An enumeration.

class prompt_toolkit.selection.SelectionState(original_cursor_position: int = 0, type: prompt_toolkit.selection.SelectionType = <SelectionType.CHARACTERS: 'CHARACTERS'>)

State of the current selection.

Parameters:

Clipboard

class prompt_toolkit.clipboard.Clipboard

Abstract baseclass for clipboards. (An implementation can be in memory, it can share the X11 or Windows keyboard, or can be persistent.)

get_data() → prompt_toolkit.clipboard.base.ClipboardData

Return clipboard data.

rotate() → None

For Emacs mode, rotate the kill ring.

set_data(data: prompt_toolkit.clipboard.base.ClipboardData) → None

Set data to the clipboard.

Parameters:dataClipboardData instance.
set_text(text: str) → None

Shortcut for setting plain text on clipboard.

class prompt_toolkit.clipboard.ClipboardData(text: str = '', type: prompt_toolkit.selection.SelectionType = <SelectionType.CHARACTERS: 'CHARACTERS'>)

Text on the clipboard.

Parameters:
class prompt_toolkit.clipboard.DummyClipboard

Clipboard implementation that doesn’t remember anything.

get_data() → prompt_toolkit.clipboard.base.ClipboardData

Return clipboard data.

rotate() → None

For Emacs mode, rotate the kill ring.

set_data(data: prompt_toolkit.clipboard.base.ClipboardData) → None

Set data to the clipboard.

Parameters:dataClipboardData instance.
set_text(text: str) → None

Shortcut for setting plain text on clipboard.

class prompt_toolkit.clipboard.DynamicClipboard(get_clipboard: Callable[[], Optional[prompt_toolkit.clipboard.base.Clipboard]])

Clipboard class that can dynamically returns any Clipboard.

Parameters:get_clipboard – Callable that returns a Clipboard instance.
get_data() → prompt_toolkit.clipboard.base.ClipboardData

Return clipboard data.

rotate() → None

For Emacs mode, rotate the kill ring.

set_data(data: prompt_toolkit.clipboard.base.ClipboardData) → None

Set data to the clipboard.

Parameters:dataClipboardData instance.
set_text(text: str) → None

Shortcut for setting plain text on clipboard.

class prompt_toolkit.clipboard.InMemoryClipboard(data: Optional[prompt_toolkit.clipboard.base.ClipboardData] = None, max_size: int = 60)

Default clipboard implementation. Just keep the data in memory.

This implements a kill-ring, for Emacs mode.

get_data() → prompt_toolkit.clipboard.base.ClipboardData

Return clipboard data.

rotate() → None

For Emacs mode, rotate the kill ring.

set_data(data: prompt_toolkit.clipboard.base.ClipboardData) → None

Set data to the clipboard.

Parameters:dataClipboardData instance.

Auto completion

class prompt_toolkit.completion.Completion(text: str, start_position: int = 0, display: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = None, display_meta: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = None, style: str = '', selected_style: str = '')
Parameters:
  • text – The new string that will be inserted into the document.
  • start_position – Position relative to the cursor_position where the new text will start. The text will be inserted between the start_position and the original cursor position.
  • display – (optional string or formatted text) If the completion has to be displayed differently in the completion menu.
  • display_meta – (Optional string or formatted text) Meta information about the completion, e.g. the path or source where it’s coming from. This can also be a callable that returns a string.
  • style – Style string.
  • selected_style – Style string, used for a selected completion. This can override the style parameter.
display_meta

Return meta-text. (This is lazy when using a callable).

display_meta_text

The ‘meta’ field as plain text.

display_text

The ‘display’ field as plain text.

new_completion_from_position(position: int) → prompt_toolkit.completion.base.Completion

(Only for internal use!) Get a new completion by splitting this one. Used by Application when it needs to have a list of new completions after inserting the common prefix.

class prompt_toolkit.completion.Completer

Base class for completer implementations.

get_completions(document: prompt_toolkit.document.Document, complete_event: prompt_toolkit.completion.base.CompleteEvent) → Iterable[prompt_toolkit.completion.base.Completion]

This should be a generator that yields Completion instances.

If the generation of completions is something expensive (that takes a lot of time), consider wrapping this Completer class in a ThreadedCompleter. In that case, the completer algorithm runs in a background thread and completions will be displayed as soon as they arrive.

Parameters:
get_completions_async(document: prompt_toolkit.document.Document, complete_event: prompt_toolkit.completion.base.CompleteEvent) → AsyncGenerator[prompt_toolkit.completion.base.Completion, None]

Asynchronous generator for completions. (Probably, you won’t have to override this.)

Asynchronous generator of Completion objects.

class prompt_toolkit.completion.ThreadedCompleter(completer: prompt_toolkit.completion.base.Completer)

Wrapper that runs the get_completions generator in a thread.

(Use this to prevent the user interface from becoming unresponsive if the generation of completions takes too much time.)

The completions will be displayed as soon as they are produced. The user can already select a completion, even if not all completions are displayed.

get_completions(document: prompt_toolkit.document.Document, complete_event: prompt_toolkit.completion.base.CompleteEvent) → Iterable[prompt_toolkit.completion.base.Completion]

This should be a generator that yields Completion instances.

If the generation of completions is something expensive (that takes a lot of time), consider wrapping this Completer class in a ThreadedCompleter. In that case, the completer algorithm runs in a background thread and completions will be displayed as soon as they arrive.

Parameters:
get_completions_async(document: prompt_toolkit.document.Document, complete_event: prompt_toolkit.completion.base.CompleteEvent) → AsyncGenerator[prompt_toolkit.completion.base.Completion, None]

Asynchronous generator of completions.

class prompt_toolkit.completion.DummyCompleter

A completer that doesn’t return any completion.

get_completions(document: prompt_toolkit.document.Document, complete_event: prompt_toolkit.completion.base.CompleteEvent) → Iterable[prompt_toolkit.completion.base.Completion]

This should be a generator that yields Completion instances.

If the generation of completions is something expensive (that takes a lot of time), consider wrapping this Completer class in a ThreadedCompleter. In that case, the completer algorithm runs in a background thread and completions will be displayed as soon as they arrive.

Parameters:
class prompt_toolkit.completion.DynamicCompleter(get_completer: Callable[[], Optional[prompt_toolkit.completion.base.Completer]])

Completer class that can dynamically returns any Completer.

Parameters:get_completer – Callable that returns a Completer instance.
get_completions(document: prompt_toolkit.document.Document, complete_event: prompt_toolkit.completion.base.CompleteEvent) → Iterable[prompt_toolkit.completion.base.Completion]

This should be a generator that yields Completion instances.

If the generation of completions is something expensive (that takes a lot of time), consider wrapping this Completer class in a ThreadedCompleter. In that case, the completer algorithm runs in a background thread and completions will be displayed as soon as they arrive.

Parameters:
get_completions_async(document: prompt_toolkit.document.Document, complete_event: prompt_toolkit.completion.base.CompleteEvent) → AsyncGenerator[prompt_toolkit.completion.base.Completion, None]

Asynchronous generator for completions. (Probably, you won’t have to override this.)

Asynchronous generator of Completion objects.

class prompt_toolkit.completion.CompleteEvent(text_inserted: bool = False, completion_requested: bool = False)

Event that called the completer.

Parameters:
  • text_inserted – When True, it means that completions are requested because of a text insert. (Buffer.complete_while_typing.)
  • completion_requested – When True, it means that the user explicitly pressed the Tab key in order to view the completions.

These two flags can be used for instance to implement a completer that shows some completions when Tab has been pressed, but not automatically when the user presses a space. (Because of complete_while_typing.)

completion_requested = None

Used explicitly requested completion by pressing ‘tab’.

text_inserted = None

Automatic completion while typing.

prompt_toolkit.completion.merge_completers(completers: Sequence[prompt_toolkit.completion.base.Completer]) → prompt_toolkit.completion.base._MergedCompleter

Combine several completers into one.

prompt_toolkit.completion.get_common_complete_suffix(document: prompt_toolkit.document.Document, completions: Sequence[prompt_toolkit.completion.base.Completion]) → str

Return the common prefix for all completions.

class prompt_toolkit.completion.PathCompleter(only_directories: bool = False, get_paths: Optional[Callable[[], List[str]]] = None, file_filter: Optional[Callable[[str], bool]] = None, min_input_len: int = 0, expanduser: bool = False)

Complete for Path variables.

Parameters:
  • get_paths – Callable which returns a list of directories to look into when the user enters a relative path.
  • file_filter – Callable which takes a filename and returns whether this file should show up in the completion. None when no filtering has to be done.
  • min_input_len – Don’t do autocompletion when the input string is shorter.
get_completions(document: prompt_toolkit.document.Document, complete_event: prompt_toolkit.completion.base.CompleteEvent) → Iterable[prompt_toolkit.completion.base.Completion]

This should be a generator that yields Completion instances.

If the generation of completions is something expensive (that takes a lot of time), consider wrapping this Completer class in a ThreadedCompleter. In that case, the completer algorithm runs in a background thread and completions will be displayed as soon as they arrive.

Parameters:
class prompt_toolkit.completion.ExecutableCompleter

Complete only executable files in the current path.

class prompt_toolkit.completion.FuzzyCompleter(completer: prompt_toolkit.completion.base.Completer, WORD: bool = False, pattern: Optional[str] = None, enable_fuzzy: Union[prompt_toolkit.filters.base.Filter, bool] = True)

Fuzzy completion. This wraps any other completer and turns it into a fuzzy completer.

If the list of words is: [“leopard” , “gorilla”, “dinosaur”, “cat”, “bee”] Then trying to complete “oar” would yield “leopard” and “dinosaur”, but not the others, because they match the regular expression ‘o.*a.*r’. Similar, in another application “djm” could expand to “django_migrations”.

The results are sorted by relevance, which is defined as the start position and the length of the match.

Notice that this is not really a tool to work around spelling mistakes, like what would be possible with difflib. The purpose is rather to have a quicker or more intuitive way to filter the given completions, especially when many completions have a common prefix.

Fuzzy algorithm is based on this post: https://blog.amjith.com/fuzzyfinder-in-10-lines-of-python

Parameters:
  • completer – A Completer instance.
  • WORD – When True, use WORD characters.
  • pattern – Regex pattern which selects the characters before the cursor that are considered for the fuzzy matching.
  • enable_fuzzy – (bool or Filter) Enabled the fuzzy behavior. For easily turning fuzzyness on or off according to a certain condition.
get_completions(document: prompt_toolkit.document.Document, complete_event: prompt_toolkit.completion.base.CompleteEvent) → Iterable[prompt_toolkit.completion.base.Completion]

This should be a generator that yields Completion instances.

If the generation of completions is something expensive (that takes a lot of time), consider wrapping this Completer class in a ThreadedCompleter. In that case, the completer algorithm runs in a background thread and completions will be displayed as soon as they arrive.

Parameters:
class prompt_toolkit.completion.FuzzyWordCompleter(words: Union[List[str], Callable[[], List[str]]], meta_dict: Optional[Dict[str, str]] = None, WORD: bool = False)

Fuzzy completion on a list of words.

(This is basically a WordCompleter wrapped in a FuzzyCompleter.)

Parameters:
  • words – List of words or callable that returns a list of words.
  • meta_dict – Optional dict mapping words to their meta-information.
  • WORD – When True, use WORD characters.
get_completions(document: prompt_toolkit.document.Document, complete_event: prompt_toolkit.completion.base.CompleteEvent) → Iterable[prompt_toolkit.completion.base.Completion]

This should be a generator that yields Completion instances.

If the generation of completions is something expensive (that takes a lot of time), consider wrapping this Completer class in a ThreadedCompleter. In that case, the completer algorithm runs in a background thread and completions will be displayed as soon as they arrive.

Parameters:
class prompt_toolkit.completion.NestedCompleter(options: Dict[str, Optional[prompt_toolkit.completion.base.Completer]], ignore_case: bool = True)

Completer which wraps around several other completers, and calls any the one that corresponds with the first word of the input.

By combining multiple NestedCompleter instances, we can achieve multiple hierarchical levels of autocompletion. This is useful when WordCompleter is not sufficient.

If you need multiple levels, check out the from_nested_dict classmethod.

classmethod from_nested_dict(data: Mapping[str, Union[Any, Set[str], None, prompt_toolkit.completion.base.Completer]]) → prompt_toolkit.completion.nested.NestedCompleter

Create a NestedCompleter, starting from a nested dictionary data structure, like this:

data = {
    'show': {
        'version': None,
        'interfaces': None,
        'clock': None,
        'ip': {'interface': {'brief'}}
    },
    'exit': None
    'enable': None
}

The value should be None if there is no further completion at some point. If all values in the dictionary are None, it is also possible to use a set instead.

Values in this data structure can be a completers as well.

get_completions(document: prompt_toolkit.document.Document, complete_event: prompt_toolkit.completion.base.CompleteEvent) → Iterable[prompt_toolkit.completion.base.Completion]

This should be a generator that yields Completion instances.

If the generation of completions is something expensive (that takes a lot of time), consider wrapping this Completer class in a ThreadedCompleter. In that case, the completer algorithm runs in a background thread and completions will be displayed as soon as they arrive.

Parameters:
class prompt_toolkit.completion.WordCompleter(words: Union[List[str], Callable[[], List[str]]], ignore_case: bool = False, meta_dict: Optional[Dict[str, str]] = None, WORD: bool = False, sentence: bool = False, match_middle: bool = False, pattern: Optional[Pattern[str]] = None)

Simple autocompletion on a list of words.

Parameters:
  • words – List of words or callable that returns a list of words.
  • ignore_case – If True, case-insensitive completion.
  • meta_dict – Optional dict mapping words to their meta-text. (This should map strings to strings or formatted text.)
  • WORD – When True, use WORD characters.
  • sentence – When True, don’t complete by comparing the word before the cursor, but by comparing all the text before the cursor. In this case, the list of words is just a list of strings, where each string can contain spaces. (Can not be used together with the WORD option.)
  • match_middle – When True, match not only the start, but also in the middle of the word.
  • pattern – Optional regex. When given, use this regex pattern instead of default one.
get_completions(document: prompt_toolkit.document.Document, complete_event: prompt_toolkit.completion.base.CompleteEvent) → Iterable[prompt_toolkit.completion.base.Completion]

This should be a generator that yields Completion instances.

If the generation of completions is something expensive (that takes a lot of time), consider wrapping this Completer class in a ThreadedCompleter. In that case, the completer algorithm runs in a background thread and completions will be displayed as soon as they arrive.

Parameters:

Document

The Document that implements all the text operations/querying.

class prompt_toolkit.document.Document(text: str = '', cursor_position: Optional[int] = None, selection: Optional[prompt_toolkit.selection.SelectionState] = None)

This is a immutable class around the text and cursor position, and contains methods for querying this data, e.g. to give the text before the cursor.

This class is usually instantiated by a Buffer object, and accessed as the document property of that class.

Parameters:
  • text – string
  • cursor_position – int
  • selectionSelectionState
char_before_cursor

Return character before the cursor or an empty string.

current_char

Return character under cursor or an empty string.

current_line

Return the text on the line where the cursor is. (when the input consists of just one line, it equals text.

current_line_after_cursor

Text from the cursor until the end of the line.

current_line_before_cursor

Text from the start of the line until the cursor.

cursor_position

The document cursor position.

cursor_position_col

Current column. (0-based.)

cursor_position_row

Current row. (0-based.)

cut_selection() → Tuple[prompt_toolkit.document.Document, prompt_toolkit.clipboard.base.ClipboardData]

Return a (Document, ClipboardData) tuple, where the document represents the new document when the selection is cut, and the clipboard data, represents whatever has to be put on the clipboard.

empty_line_count_at_the_end() → int

Return number of empty lines at the end of the document.

end_of_paragraph(count: int = 1, after: bool = False) → int

Return the end of the current paragraph. (Relative cursor position.)

find(sub: str, in_current_line: bool = False, include_current_position: bool = False, ignore_case: bool = False, count: int = 1) → Optional[int]

Find text after the cursor, return position relative to the cursor position. Return None if nothing was found.

Parameters:count – Find the n-th occurrence.
find_all(sub: str, ignore_case: bool = False) → List[int]

Find all occurrences of the substring. Return a list of absolute positions in the document.

find_backwards(sub: str, in_current_line: bool = False, ignore_case: bool = False, count: int = 1) → Optional[int]

Find text before the cursor, return position relative to the cursor position. Return None if nothing was found.

Parameters:count – Find the n-th occurrence.
find_boundaries_of_current_word(WORD: bool = False, include_leading_whitespace: bool = False, include_trailing_whitespace: bool = False) → Tuple[int, int]

Return the relative boundaries (startpos, endpos) of the current word under the cursor. (This is at the current line, because line boundaries obviously don’t belong to any word.) If not on a word, this returns (0,0)

find_enclosing_bracket_left(left_ch: str, right_ch: str, start_pos: Optional[int] = None) → Optional[int]

Find the left bracket enclosing current position. Return the relative position to the cursor position.

When start_pos is given, don’t look past the position.

find_enclosing_bracket_right(left_ch: str, right_ch: str, end_pos: Optional[int] = None) → Optional[int]

Find the right bracket enclosing current position. Return the relative position to the cursor position.

When end_pos is given, don’t look past the position.

find_matching_bracket_position(start_pos: Optional[int] = None, end_pos: Optional[int] = None) → int

Return relative cursor position of matching [, (, { or < bracket.

When start_pos or end_pos are given. Don’t look past the positions.

find_next_matching_line(match_func: Callable[[str], bool], count: int = 1) → Optional[int]

Look downwards for empty lines. Return the line index, relative to the current line.

find_next_word_beginning(count: int = 1, WORD: bool = False) → Optional[int]

Return an index relative to the cursor position pointing to the start of the next word. Return None if nothing was found.

find_next_word_ending(include_current_position: bool = False, count: int = 1, WORD: bool = False) → Optional[int]

Return an index relative to the cursor position pointing to the end of the next word. Return None if nothing was found.

find_previous_matching_line(match_func: Callable[[str], bool], count: int = 1) → Optional[int]

Look upwards for empty lines. Return the line index, relative to the current line.

find_previous_word_beginning(count: int = 1, WORD: bool = False) → Optional[int]

Return an index relative to the cursor position pointing to the start of the previous word. Return None if nothing was found.

find_previous_word_ending(count: int = 1, WORD: bool = False) → Optional[int]

Return an index relative to the cursor position pointing to the end of the previous word. Return None if nothing was found.

find_start_of_previous_word(count: int = 1, WORD: bool = False, pattern: Optional[Pattern[str]] = None) → Optional[int]

Return an index relative to the cursor position pointing to the start of the previous word. Return None if nothing was found.

Parameters:pattern – (None or compiled regex). When given, use this regex pattern.
get_column_cursor_position(column: int) → int

Return the relative cursor position for this column at the current line. (It will stay between the boundaries of the line in case of a larger number.)

get_cursor_down_position(count: int = 1, preferred_column: Optional[int] = None) → int

Return the relative cursor position (character index) where we would be if the user pressed the arrow-down button.

Parameters:preferred_column – When given, go to this column instead of staying at the current column.
get_cursor_left_position(count: int = 1) → int

Relative position for cursor left.

get_cursor_right_position(count: int = 1) → int

Relative position for cursor_right.

get_cursor_up_position(count: int = 1, preferred_column: Optional[int] = None) → int

Return the relative cursor position (character index) where we would be if the user pressed the arrow-up button.

Parameters:preferred_column – When given, go to this column instead of staying at the current column.
get_end_of_document_position() → int

Relative position for the end of the document.

get_end_of_line_position() → int

Relative position for the end of this line.

get_start_of_document_position() → int

Relative position for the start of the document.

get_start_of_line_position(after_whitespace: bool = False) → int

Relative position for the start of this line.

get_word_before_cursor(WORD: bool = False, pattern: Optional[Pattern[str]] = None) → str

Give the word before the cursor. If we have whitespace before the cursor this returns an empty string.

Parameters:pattern – (None or compiled regex). When given, use this regex pattern.
get_word_under_cursor(WORD: bool = False) → str

Return the word, currently below the cursor. This returns an empty string when the cursor is on a whitespace region.

has_match_at_current_position(sub: str) → bool

True when this substring is found at the cursor position.

insert_after(text: str) → prompt_toolkit.document.Document

Create a new document, with this text inserted after the buffer. It keeps selection ranges and cursor position in sync.

insert_before(text: str) → prompt_toolkit.document.Document

Create a new document, with this text inserted before the buffer. It keeps selection ranges and cursor position in sync.

is_cursor_at_the_end

True when the cursor is at the end of the text.

is_cursor_at_the_end_of_line

True when the cursor is at the end of this line.

last_non_blank_of_current_line_position() → int

Relative position for the last non blank character of this line.

leading_whitespace_in_current_line

The leading whitespace in the left margin of the current line.

line_count

Return the number of lines in this document. If the document ends with a trailing n, that counts as the beginning of a new line.

lines

Array of all the lines.

lines_from_current

Array of the lines starting from the current line, until the last line.

on_first_line

True when we are at the first line.

on_last_line

True when we are at the last line.

paste_clipboard_data(data: prompt_toolkit.clipboard.base.ClipboardData, paste_mode: prompt_toolkit.selection.PasteMode = <PasteMode.EMACS: 'EMACS'>, count: int = 1) → prompt_toolkit.document.Document

Return a new Document instance which contains the result if we would paste this data at the current cursor position.

Parameters:
  • paste_mode – Where to paste. (Before/after/emacs.)
  • count – When >1, Paste multiple times.
selection

SelectionState object.

selection_range() → Tuple[int, int]

Return (from, to) tuple of the selection. start and end position are included.

This doesn’t take the selection type into account. Use selection_ranges instead.

selection_range_at_line(row: int) → Optional[Tuple[int, int]]

If the selection spans a portion of the given line, return a (from, to) tuple.

The returned upper boundary is not included in the selection, so (0, 0) is an empty selection. (0, 1), is a one character selection.

Returns None if the selection doesn’t cover this line at all.

selection_ranges() → Iterable[Tuple[int, int]]

Return a list of (from, to) tuples for the selection or none if nothing was selected. The upper boundary is not included.

This will yield several (from, to) tuples in case of a BLOCK selection. This will return zero ranges, like (8,8) for empty lines in a block selection.

start_of_paragraph(count: int = 1, before: bool = False) → int

Return the start of the current paragraph. (Relative cursor position.)

text

The document text.

translate_index_to_position(index: int) → Tuple[int, int]

Given an index for the text, return the corresponding (row, col) tuple. (0-based. Returns (0, 0) for index=0.)

translate_row_col_to_index(row: int, col: int) → int

Given a (row, col) tuple, return the corresponding index. (Row and col params are 0-based.)

Negative row/col values are turned into zero.

Enums

prompt_toolkit.enums.DEFAULT_BUFFER = 'DEFAULT_BUFFER'

Name of the default buffer.

class prompt_toolkit.enums.EditingMode

An enumeration.

prompt_toolkit.enums.SEARCH_BUFFER = 'SEARCH_BUFFER'

Name of the search buffer.

prompt_toolkit.enums.SYSTEM_BUFFER = 'SYSTEM_BUFFER'

Name of the system buffer.

History

Implementations for the history of a Buffer.

NOTE: Notice that there is no DynamicHistory. This doesn’t work well, because
the Buffer needs to be able to attach an event handler to the event when a history entry is loaded. This loading can be done asynchronously and making the history swappable would probably break this.
class prompt_toolkit.history.History

Base History class.

This also includes abstract methods for loading/storing history.

append_string(string: str) → None

Add string to the history.

get_strings() → List[str]

Get the strings from the history that are loaded so far.

load(item_loaded_callback: Callable[[str], None]) → None

Load the history and call the callback for every entry in the history.

XXX: The callback can be called from another thread, which happens in

case of ThreadedHistory.

We can’t assume that an asyncio event loop is running, and schedule the insertion into the Buffer using the event loop.

The reason is that the creation of the History object as well as the start of the loading happens before Application.run() is called, and it can continue even after Application.run() terminates. (Which is useful to have a complete history during the next prompt.)

Calling get_event_loop() right here is also not guaranteed to return the same event loop which is used in Application.run, because a new event loop can be created during the run. This is useful in Python REPLs, where we want to use one event loop for the prompt, and have another one active during the eval of the commands. (Otherwise, the user can schedule a while/true loop and freeze the UI.)

load_history_strings() → Iterable[str]

This should be a generator that yields str instances.

It should yield the most recent items first, because they are the most important. (The history can already be used, even when it’s only partially loaded.)

store_string(string: str) → None

Store the string in persistent storage.

class prompt_toolkit.history.ThreadedHistory(history: prompt_toolkit.history.History)

Wrapper that runs the load_history_strings generator in a thread.

Use this to increase the start-up time of prompt_toolkit applications. History entries are available as soon as they are loaded. We don’t have to wait for everything to be loaded.

append_string(string: str) → None

Add string to the history.

get_strings() → List[str]

Get the strings from the history that are loaded so far.

load(item_loaded_callback: Callable[[str], None]) → None

Load the history and call the callback for every entry in the history.

XXX: The callback can be called from another thread, which happens in

case of ThreadedHistory.

We can’t assume that an asyncio event loop is running, and schedule the insertion into the Buffer using the event loop.

The reason is that the creation of the History object as well as the start of the loading happens before Application.run() is called, and it can continue even after Application.run() terminates. (Which is useful to have a complete history during the next prompt.)

Calling get_event_loop() right here is also not guaranteed to return the same event loop which is used in Application.run, because a new event loop can be created during the run. This is useful in Python REPLs, where we want to use one event loop for the prompt, and have another one active during the eval of the commands. (Otherwise, the user can schedule a while/true loop and freeze the UI.)

load_history_strings() → Iterable[str]

This should be a generator that yields str instances.

It should yield the most recent items first, because they are the most important. (The history can already be used, even when it’s only partially loaded.)

store_string(string: str) → None

Store the string in persistent storage.

class prompt_toolkit.history.DummyHistory

History object that doesn’t remember anything.

append_string(string: str) → None

Add string to the history.

load_history_strings() → Iterable[str]

This should be a generator that yields str instances.

It should yield the most recent items first, because they are the most important. (The history can already be used, even when it’s only partially loaded.)

store_string(string: str) → None

Store the string in persistent storage.

class prompt_toolkit.history.FileHistory(filename: str)

History class that stores all strings in a file.

load_history_strings() → Iterable[str]

This should be a generator that yields str instances.

It should yield the most recent items first, because they are the most important. (The history can already be used, even when it’s only partially loaded.)

store_string(string: str) → None

Store the string in persistent storage.

class prompt_toolkit.history.InMemoryHistory

History class that keeps a list of all strings in memory.

load_history_strings() → Iterable[str]

This should be a generator that yields str instances.

It should yield the most recent items first, because they are the most important. (The history can already be used, even when it’s only partially loaded.)

store_string(string: str) → None

Store the string in persistent storage.

Keys

class prompt_toolkit.keys.Keys

List of keys for use in key bindings.

Note that this is an “StrEnum”, all values can be compared against strings.

Style

Styling for prompt_toolkit applications.

class prompt_toolkit.styles.Attrs(color, bgcolor, bold, underline, italic, blink, reverse, hidden)
Parameters:
  • color – Hexadecimal string. E.g. ‘000000’ or Ansi color name: e.g. ‘ansiblue’
  • bgcolor – Hexadecimal string. E.g. ‘ffffff’ or Ansi color name: e.g. ‘ansired’
  • bold – Boolean
  • underline – Boolean
  • italic – Boolean
  • blink – Boolean
  • reverse – Boolean
  • hidden – Boolean
bgcolor

Alias for field number 1

Alias for field number 5

bold

Alias for field number 2

color

Alias for field number 0

hidden

Alias for field number 7

italic

Alias for field number 4

reverse

Alias for field number 6

underline

Alias for field number 3

class prompt_toolkit.styles.BaseStyle

Abstract base class for prompt_toolkit styles.

get_attrs_for_style_str(style_str: str, default: prompt_toolkit.styles.base.Attrs = Attrs(color='', bgcolor='', bold=False, underline=False, italic=False, blink=False, reverse=False, hidden=False)) → prompt_toolkit.styles.base.Attrs

Return Attrs for the given style string.

Parameters:
  • style_str – The style string. This can contain inline styling as well as classnames (e.g. “class:title”).
  • defaultAttrs to be used if no styling was defined.
invalidation_hash() → Hashable

Invalidation hash for the style. When this changes over time, the renderer knows that something in the style changed, and that everything has to be redrawn.

style_rules

The list of style rules, used to create this style. (Required for DynamicStyle and _MergedStyle to work.)

class prompt_toolkit.styles.DummyStyle

A style that doesn’t style anything.

get_attrs_for_style_str(style_str: str, default: prompt_toolkit.styles.base.Attrs = Attrs(color='', bgcolor='', bold=False, underline=False, italic=False, blink=False, reverse=False, hidden=False)) → prompt_toolkit.styles.base.Attrs

Return Attrs for the given style string.

Parameters:
  • style_str – The style string. This can contain inline styling as well as classnames (e.g. “class:title”).
  • defaultAttrs to be used if no styling was defined.
invalidation_hash() → Hashable

Invalidation hash for the style. When this changes over time, the renderer knows that something in the style changed, and that everything has to be redrawn.

style_rules

The list of style rules, used to create this style. (Required for DynamicStyle and _MergedStyle to work.)

class prompt_toolkit.styles.DynamicStyle(get_style: Callable[[], Optional[prompt_toolkit.styles.base.BaseStyle]])

Style class that can dynamically returns an other Style.

Parameters:get_style – Callable that returns a Style instance.
get_attrs_for_style_str(style_str: str, default: prompt_toolkit.styles.base.Attrs = Attrs(color='', bgcolor='', bold=False, underline=False, italic=False, blink=False, reverse=False, hidden=False)) → prompt_toolkit.styles.base.Attrs

Return Attrs for the given style string.

Parameters:
  • style_str – The style string. This can contain inline styling as well as classnames (e.g. “class:title”).
  • defaultAttrs to be used if no styling was defined.
invalidation_hash() → Hashable

Invalidation hash for the style. When this changes over time, the renderer knows that something in the style changed, and that everything has to be redrawn.

style_rules

The list of style rules, used to create this style. (Required for DynamicStyle and _MergedStyle to work.)

class prompt_toolkit.styles.Style(style_rules: List[Tuple[str, str]])

Create a Style instance from a list of style rules.

The style_rules is supposed to be a list of (‘classnames’, ‘style’) tuples. The classnames are a whitespace separated string of class names and the style string is just like a Pygments style definition, but with a few additions: it supports ‘reverse’ and ‘blink’.

Later rules always override previous rules.

Usage:

Style([
    ('title', '#ff0000 bold underline'),
    ('something-else', 'reverse'),
    ('class1 class2', 'reverse'),
])

The from_dict classmethod is similar, but takes a dictionary as input.

classmethod from_dict(style_dict: Dict[str, str], priority: prompt_toolkit.styles.style.Priority = <Priority.DICT_KEY_ORDER: 'KEY_ORDER'>) → prompt_toolkit.styles.style.Style
Parameters:
  • style_dict – Style dictionary.
  • priorityPriority value.
get_attrs_for_style_str(style_str: str, default: prompt_toolkit.styles.base.Attrs = Attrs(color='', bgcolor='', bold=False, underline=False, italic=False, blink=False, reverse=False, hidden=False)) → prompt_toolkit.styles.base.Attrs

Get Attrs for the given style string.

invalidation_hash() → Hashable

Invalidation hash for the style. When this changes over time, the renderer knows that something in the style changed, and that everything has to be redrawn.

style_rules

The list of style rules, used to create this style. (Required for DynamicStyle and _MergedStyle to work.)

class prompt_toolkit.styles.Priority

The priority of the rules, when a style is created from a dictionary.

In a Style, rules that are defined later will always override previous defined rules, however in a dictionary, the key order was arbitrary before Python 3.6. This means that the style could change at random between rules.

We have two options:

  • DICT_KEY_ORDER: This means, iterate through the dictionary, and take
    the key/value pairs in order as they come. This is a good option if you have Python >3.6. Rules at the end will override rules at the beginning.
  • MOST_PRECISE: keys that are defined with most precision will get higher priority. (More precise means: more elements.)
prompt_toolkit.styles.merge_styles(styles: List[prompt_toolkit.styles.base.BaseStyle]) → prompt_toolkit.styles.style._MergedStyle

Merge multiple Style objects.

prompt_toolkit.styles.style_from_pygments_cls(pygments_style_cls: Type[PygmentsStyle]) → prompt_toolkit.styles.style.Style

Shortcut to create a Style instance from a Pygments style class and a style dictionary.

Example:

from prompt_toolkit.styles.from_pygments import style_from_pygments_cls
from pygments.styles import get_style_by_name
style = style_from_pygments_cls(get_style_by_name('monokai'))
Parameters:pygments_style_cls – Pygments style class to start from.
prompt_toolkit.styles.style_from_pygments_dict(pygments_dict: Dict[Token, str]) → prompt_toolkit.styles.style.Style

Create a Style instance from a Pygments style dictionary. (One that maps Token objects to style strings.)

prompt_toolkit.styles.pygments_token_to_classname(token: Token) → str

Turn e.g. Token.Name.Exception into ‘pygments.name.exception’.

(Our Pygments lexer will also turn the tokens that pygments produces in a prompt_toolkit list of fragments that match these styling rules.)

Styling for prompt_toolkit applications.

class prompt_toolkit.styles.StyleTransformation

Base class for any style transformation.

invalidation_hash() → Hashable

When this changes, the cache should be invalidated.

transform_attrs(attrs: prompt_toolkit.styles.base.Attrs) → prompt_toolkit.styles.base.Attrs

Take an Attrs object and return a new Attrs object.

Remember that the color formats can be either “ansi…” or a 6 digit lowercase hexadecimal color (without ‘#’ prefix).

class prompt_toolkit.styles.SwapLightAndDarkStyleTransformation

Turn dark colors into light colors and the other way around.

This is meant to make color schemes that work on a dark background usable on a light background (and the other way around).

Notice that this doesn’t swap foreground and background like “reverse” does. It turns light green into dark green and the other way around. Foreground and background colors are considered individually.

Also notice that when <reverse> is used somewhere and no colors are given in particular (like what is the default for the bottom toolbar), then this doesn’t change anything. This is what makes sense, because when the ‘default’ color is chosen, it’s what works best for the terminal, and reverse works good with that.

transform_attrs(attrs: prompt_toolkit.styles.base.Attrs) → prompt_toolkit.styles.base.Attrs

Return the Attrs used when opposite luminosity should be used.

class prompt_toolkit.styles.AdjustBrightnessStyleTransformation(min_brightness: Union[Callable[[], float], float] = 0.0, max_brightness: Union[Callable[[], float], float] = 1.0)

Adjust the brightness to improve the rendering on either dark or light backgrounds.

For dark backgrounds, it’s best to increase min_brightness. For light backgrounds it’s best to decrease max_brightness. Usually, only one setting is adjusted.

This will only change the brightness for text that has a foreground color defined, but no background color. It works best for 256 or true color output.

Note

Notice that there is no universal way to detect whether the application is running in a light or dark terminal. As a developer of an command line application, you’ll have to make this configurable for the user.

Parameters:
  • min_brightness – Float between 0.0 and 1.0 or a callable that returns a float.
  • max_brightness – Float between 0.0 and 1.0 or a callable that returns a float.
invalidation_hash() → Hashable

When this changes, the cache should be invalidated.

transform_attrs(attrs: prompt_toolkit.styles.base.Attrs) → prompt_toolkit.styles.base.Attrs

Take an Attrs object and return a new Attrs object.

Remember that the color formats can be either “ansi…” or a 6 digit lowercase hexadecimal color (without ‘#’ prefix).

prompt_toolkit.styles.merge_style_transformations(style_transformations: Sequence[prompt_toolkit.styles.style_transformation.StyleTransformation]) → prompt_toolkit.styles.style_transformation.StyleTransformation

Merge multiple transformations together.

class prompt_toolkit.styles.DummyStyleTransformation

Don’t transform anything at all.

invalidation_hash() → Hashable

When this changes, the cache should be invalidated.

transform_attrs(attrs: prompt_toolkit.styles.base.Attrs) → prompt_toolkit.styles.base.Attrs

Take an Attrs object and return a new Attrs object.

Remember that the color formats can be either “ansi…” or a 6 digit lowercase hexadecimal color (without ‘#’ prefix).

class prompt_toolkit.styles.ConditionalStyleTransformation(style_transformation: prompt_toolkit.styles.style_transformation.StyleTransformation, filter: Union[prompt_toolkit.filters.base.Filter, bool])

Apply the style transformation depending on a condition.

invalidation_hash() → Hashable

When this changes, the cache should be invalidated.

transform_attrs(attrs: prompt_toolkit.styles.base.Attrs) → prompt_toolkit.styles.base.Attrs

Take an Attrs object and return a new Attrs object.

Remember that the color formats can be either “ansi…” or a 6 digit lowercase hexadecimal color (without ‘#’ prefix).

class prompt_toolkit.styles.DynamicStyleTransformation(get_style_transformation: Callable[[], Optional[prompt_toolkit.styles.style_transformation.StyleTransformation]])

StyleTransformation class that can dynamically returns any StyleTransformation.

Parameters:get_style_transformation – Callable that returns a StyleTransformation instance.
invalidation_hash() → Hashable

When this changes, the cache should be invalidated.

transform_attrs(attrs: prompt_toolkit.styles.base.Attrs) → prompt_toolkit.styles.base.Attrs

Take an Attrs object and return a new Attrs object.

Remember that the color formats can be either “ansi…” or a 6 digit lowercase hexadecimal color (without ‘#’ prefix).

Shortcuts

prompt_toolkit.shortcuts.prompt(message: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = None, *, history: Optional[prompt_toolkit.history.History] = None, editing_mode: Optional[prompt_toolkit.enums.EditingMode] = None, refresh_interval: Optional[float] = None, vi_mode: Optional[bool] = None, lexer: Optional[prompt_toolkit.lexers.base.Lexer] = None, completer: Optional[prompt_toolkit.completion.base.Completer] = None, complete_in_thread: Optional[bool] = None, is_password: Optional[bool] = None, key_bindings: Optional[prompt_toolkit.key_binding.key_bindings.KeyBindingsBase] = None, bottom_toolbar: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = None, style: Optional[prompt_toolkit.styles.base.BaseStyle] = None, color_depth: Optional[prompt_toolkit.output.color_depth.ColorDepth] = None, include_default_pygments_style: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, style_transformation: Optional[prompt_toolkit.styles.style_transformation.StyleTransformation] = None, swap_light_and_dark_colors: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, rprompt: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = None, multiline: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, prompt_continuation: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[int, int, int], Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None]], None] = None, wrap_lines: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, enable_history_search: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, search_ignore_case: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, complete_while_typing: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, validate_while_typing: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, complete_style: Optional[prompt_toolkit.shortcuts.prompt.CompleteStyle] = None, auto_suggest: Optional[prompt_toolkit.auto_suggest.AutoSuggest] = None, validator: Optional[prompt_toolkit.validation.Validator] = None, clipboard: Optional[prompt_toolkit.clipboard.base.Clipboard] = None, mouse_support: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, input_processors: Optional[List[prompt_toolkit.layout.processors.Processor]] = None, reserve_space_for_menu: Optional[int] = None, enable_system_prompt: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, enable_suspend: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, enable_open_in_editor: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, tempfile_suffix: Union[str, Callable[[], str], None] = None, tempfile: Union[str, Callable[[], str], None] = None, default: str = '', accept_default: bool = False, pre_run: Optional[Callable[[], None]] = None) → str

Display the prompt.

The first set of arguments is a subset of the PromptSession class itself. For these, passing in None will keep the current values that are active in the session. Passing in a value will set the attribute for the session, which means that it applies to the current, but also to the next prompts.

Note that in order to erase a Completer, Validator or AutoSuggest, you can’t use None. Instead pass in a DummyCompleter, DummyValidator or DummyAutoSuggest instance respectively. For a Lexer you can pass in an empty SimpleLexer.

Additional arguments, specific for this prompt:

Parameters:
  • _async – (internal, please call prompt_async instead). When True return a Future instead of waiting for the prompt to finish.
  • default – The default input text to be shown. (This can be edited by the user).
  • accept_default – When True, automatically accept the default value without allowing the user to edit the input.
  • pre_run – Callable, called at the start of Application.run.

This method will raise KeyboardInterrupt when control-c has been pressed (for abort) and EOFError when control-d has been pressed (for exit).

class prompt_toolkit.shortcuts.PromptSession(message: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = '', *, multiline: Union[prompt_toolkit.filters.base.Filter, bool] = False, wrap_lines: Union[prompt_toolkit.filters.base.Filter, bool] = True, is_password: Union[prompt_toolkit.filters.base.Filter, bool] = False, vi_mode: bool = False, editing_mode: prompt_toolkit.enums.EditingMode = <EditingMode.EMACS: 'EMACS'>, complete_while_typing: Union[prompt_toolkit.filters.base.Filter, bool] = True, validate_while_typing: Union[prompt_toolkit.filters.base.Filter, bool] = True, enable_history_search: Union[prompt_toolkit.filters.base.Filter, bool] = False, search_ignore_case: Union[prompt_toolkit.filters.base.Filter, bool] = False, lexer: Optional[prompt_toolkit.lexers.base.Lexer] = None, enable_system_prompt: Union[prompt_toolkit.filters.base.Filter, bool] = False, enable_suspend: Union[prompt_toolkit.filters.base.Filter, bool] = False, enable_open_in_editor: Union[prompt_toolkit.filters.base.Filter, bool] = False, validator: Optional[prompt_toolkit.validation.Validator] = None, completer: Optional[prompt_toolkit.completion.base.Completer] = None, complete_in_thread: bool = False, reserve_space_for_menu: int = 8, complete_style: prompt_toolkit.shortcuts.prompt.CompleteStyle = <CompleteStyle.COLUMN: 'COLUMN'>, auto_suggest: Optional[prompt_toolkit.auto_suggest.AutoSuggest] = None, style: Optional[prompt_toolkit.styles.base.BaseStyle] = None, style_transformation: Optional[prompt_toolkit.styles.style_transformation.StyleTransformation] = None, swap_light_and_dark_colors: Union[prompt_toolkit.filters.base.Filter, bool] = False, color_depth: Optional[prompt_toolkit.output.color_depth.ColorDepth] = None, include_default_pygments_style: Union[prompt_toolkit.filters.base.Filter, bool] = True, history: Optional[prompt_toolkit.history.History] = None, clipboard: Optional[prompt_toolkit.clipboard.base.Clipboard] = None, prompt_continuation: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[int, int, int], Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None]], None] = None, rprompt: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = None, bottom_toolbar: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = None, mouse_support: Union[prompt_toolkit.filters.base.Filter, bool] = False, input_processors: Optional[List[prompt_toolkit.layout.processors.Processor]] = None, key_bindings: Optional[prompt_toolkit.key_binding.key_bindings.KeyBindingsBase] = None, erase_when_done: bool = False, tempfile_suffix: Union[str, Callable[[], str], None] = '.txt', tempfile: Union[str, Callable[[], str], None] = None, refresh_interval: float = 0, input: Optional[prompt_toolkit.input.base.Input] = None, output: Optional[prompt_toolkit.output.base.Output] = None)

PromptSession for a prompt application, which can be used as a GNU Readline replacement.

This is a wrapper around a lot of prompt_toolkit functionality and can be a replacement for raw_input.

All parameters that expect “formatted text” can take either just plain text (a unicode object), a list of (style_str, text) tuples or an HTML object.

Example usage:

s = PromptSession(message='>')
text = s.prompt()
Parameters:
  • message – Plain text or formatted text to be shown before the prompt. This can also be a callable that returns formatted text.
  • multilinebool or Filter. When True, prefer a layout that is more adapted for multiline input. Text after newlines is automatically indented, and search/arg input is shown below the input, instead of replacing the prompt.
  • wrap_linesbool or Filter. When True (the default), automatically wrap long lines instead of scrolling horizontally.
  • is_password – Show asterisks instead of the actual typed characters.
  • editing_modeEditingMode.VI or EditingMode.EMACS.
  • vi_modebool, if True, Identical to editing_mode=EditingMode.VI.
  • complete_while_typingbool or Filter. Enable autocompletion while typing.
  • validate_while_typingbool or Filter. Enable input validation while typing.
  • enable_history_searchbool or Filter. Enable up-arrow parting string matching.
  • search_ignore_caseFilter. Search case insensitive.
  • lexerLexer to be used for the syntax highlighting.
  • validatorValidator instance for input validation.
  • completerCompleter instance for input completion.
  • complete_in_threadbool or Filter. Run the completer code in a background thread in order to avoid blocking the user interface. For CompleteStyle.READLINE_LIKE, this setting has no effect. There we always run the completions in the main thread.
  • reserve_space_for_menu – Space to be reserved for displaying the menu. (0 means that no space needs to be reserved.)
  • auto_suggestAutoSuggest instance for input suggestions.
  • styleStyle instance for the color scheme.
  • include_default_pygments_stylebool or Filter. Tell whether the default styling for Pygments lexers has to be included. By default, this is true, but it is recommended to be disabled if another Pygments style is passed as the style argument, otherwise, two Pygments styles will be merged.
  • style_transformationStyleTransformation instance.
  • swap_light_and_dark_colorsbool or Filter. When enabled, apply SwapLightAndDarkStyleTransformation. This is useful for switching between dark and light terminal backgrounds.
  • enable_system_promptbool or Filter. Pressing Meta+’!’ will show a system prompt.
  • enable_suspendbool or Filter. Enable Control-Z style suspension.
  • enable_open_in_editorbool or Filter. Pressing ‘v’ in Vi mode or C-X C-E in emacs mode will open an external editor.
  • historyHistory instance.
  • clipboardClipboard instance. (e.g. InMemoryClipboard)
  • rprompt – Text or formatted text to be displayed on the right side. This can also be a callable that returns (formatted) text.
  • bottom_toolbar – Formatted text or callable which is supposed to return formatted text.
  • prompt_continuation – Text that needs to be displayed for a multiline prompt continuation. This can either be formatted text or a callable that takes a prompt_width, line_number and wrap_count as input and returns formatted text. When this is None (the default), then prompt_width spaces will be used.
  • complete_styleCompleteStyle.COLUMN, CompleteStyle.MULTI_COLUMN or CompleteStyle.READLINE_LIKE.
  • mouse_supportbool or Filter to enable mouse support.
  • refresh_interval – (number; in seconds) When given, refresh the UI every so many seconds.
  • inputInput object. (Note that the preferred way to change the input/output is by creating an AppSession.)
  • outputOutput object.
prompt(message: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = None, *, editing_mode: Optional[prompt_toolkit.enums.EditingMode] = None, refresh_interval: Optional[float] = None, vi_mode: Optional[bool] = None, lexer: Optional[prompt_toolkit.lexers.base.Lexer] = None, completer: Optional[prompt_toolkit.completion.base.Completer] = None, complete_in_thread: Optional[bool] = None, is_password: Optional[bool] = None, key_bindings: Optional[prompt_toolkit.key_binding.key_bindings.KeyBindingsBase] = None, bottom_toolbar: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = None, style: Optional[prompt_toolkit.styles.base.BaseStyle] = None, color_depth: Optional[prompt_toolkit.output.color_depth.ColorDepth] = None, include_default_pygments_style: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, style_transformation: Optional[prompt_toolkit.styles.style_transformation.StyleTransformation] = None, swap_light_and_dark_colors: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, rprompt: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = None, multiline: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, prompt_continuation: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[int, int, int], Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None]], None] = None, wrap_lines: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, enable_history_search: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, search_ignore_case: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, complete_while_typing: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, validate_while_typing: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, complete_style: Optional[prompt_toolkit.shortcuts.prompt.CompleteStyle] = None, auto_suggest: Optional[prompt_toolkit.auto_suggest.AutoSuggest] = None, validator: Optional[prompt_toolkit.validation.Validator] = None, clipboard: Optional[prompt_toolkit.clipboard.base.Clipboard] = None, mouse_support: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, input_processors: Optional[List[prompt_toolkit.layout.processors.Processor]] = None, reserve_space_for_menu: Optional[int] = None, enable_system_prompt: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, enable_suspend: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, enable_open_in_editor: Union[prompt_toolkit.filters.base.Filter, bool, None] = None, tempfile_suffix: Union[str, Callable[[], str], None] = None, tempfile: Union[str, Callable[[], str], None] = None, default: Union[str, prompt_toolkit.document.Document] = '', accept_default: bool = False, pre_run: Optional[Callable[[], None]] = None) → _T

Display the prompt.

The first set of arguments is a subset of the PromptSession class itself. For these, passing in None will keep the current values that are active in the session. Passing in a value will set the attribute for the session, which means that it applies to the current, but also to the next prompts.

Note that in order to erase a Completer, Validator or AutoSuggest, you can’t use None. Instead pass in a DummyCompleter, DummyValidator or DummyAutoSuggest instance respectively. For a Lexer you can pass in an empty SimpleLexer.

Additional arguments, specific for this prompt:

Parameters:
  • _async – (internal, please call prompt_async instead). When True return a Future instead of waiting for the prompt to finish.
  • default – The default input text to be shown. (This can be edited by the user).
  • accept_default – When True, automatically accept the default value without allowing the user to edit the input.
  • pre_run – Callable, called at the start of Application.run.

This method will raise KeyboardInterrupt when control-c has been pressed (for abort) and EOFError when control-d has been pressed (for exit).

prompt_toolkit.shortcuts.confirm(message: str = 'Confirm?', suffix: str = ' (y/n) ') → bool

Display a confirmation prompt that returns True/False.

class prompt_toolkit.shortcuts.CompleteStyle

How to display autocompletions for the prompt.

prompt_toolkit.shortcuts.create_confirm_session(message: str, suffix: str = ' (y/n) ') → prompt_toolkit.shortcuts.prompt.PromptSession[bool][bool]

Create a PromptSession object for the ‘confirm’ function.

prompt_toolkit.shortcuts.clear() → None

Clear the screen.

prompt_toolkit.shortcuts.clear_title() → None

Erase the current title.

prompt_toolkit.shortcuts.print_formatted_text(*values, sep: str = ' ', end: str = '\n', file: Optional[TextIO] = None, flush: bool = False, style: Optional[prompt_toolkit.styles.base.BaseStyle] = None, output: Optional[prompt_toolkit.output.base.Output] = None, color_depth: Optional[prompt_toolkit.output.color_depth.ColorDepth] = None, style_transformation: Optional[prompt_toolkit.styles.style_transformation.StyleTransformation] = None, include_default_pygments_style: bool = True) → None
print_formatted_text(*values, sep=' ', end='\n', file=None, flush=False, style=None, output=None)

Print text to stdout. This is supposed to be compatible with Python’s print function, but supports printing of formatted text. You can pass a FormattedText, HTML or ANSI object to print formatted text.

  • Print HTML as follows:

    print_formatted_text(HTML('<i>Some italic text</i> <ansired>This is red!</ansired>'))
    
    style = Style.from_dict({
        'hello': '#ff0066',
        'world': '#884444 italic',
    })
    print_formatted_text(HTML('<hello>Hello</hello> <world>world</world>!'), style=style)
    
  • Print a list of (style_str, text) tuples in the given style to the output. E.g.:

    style = Style.from_dict({
        'hello': '#ff0066',
        'world': '#884444 italic',
    })
    fragments = FormattedText([
        ('class:hello', 'Hello'),
        ('class:world', 'World'),
    ])
    print_formatted_text(fragments, style=style)
    

If you want to print a list of Pygments tokens, wrap it in PygmentsTokens to do the conversion.

Parameters:
  • values – Any kind of printable object, or formatted string.
  • sep – String inserted between values, default a space.
  • end – String appended after the last value, default a newline.
  • styleStyle instance for the color scheme.
  • include_default_pygments_stylebool. Include the default Pygments style when set to True (the default).
prompt_toolkit.shortcuts.set_title(text: str) → None

Set the terminal title.

class prompt_toolkit.shortcuts.ProgressBar(title: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = None, formatters: Optional[Sequence[prompt_toolkit.shortcuts.progress_bar.formatters.Formatter]] = None, bottom_toolbar: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = None, style: Optional[prompt_toolkit.styles.base.BaseStyle] = None, key_bindings: Optional[prompt_toolkit.key_binding.key_bindings.KeyBindings] = None, file: Optional[TextIO] = None, color_depth: Optional[prompt_toolkit.output.color_depth.ColorDepth] = None, output: Optional[prompt_toolkit.output.base.Output] = None, input: Optional[prompt_toolkit.input.base.Input] = None)

Progress bar context manager.

Usage

with ProgressBar(...) as pb:
    for item in pb(data):
        ...
Parameters:
  • title – Text to be displayed above the progress bars. This can be a callable or formatted text as well.
  • formatters – List of Formatter instances.
  • bottom_toolbar – Text to be displayed in the bottom toolbar. This can be a callable or formatted text.
  • styleprompt_toolkit.styles.BaseStyle instance.
  • key_bindingsKeyBindings instance.
  • file – The file object used for rendering, by default sys.stderr is used.
  • color_depthprompt_toolkit ColorDepth instance.
  • outputOutput instance.
  • inputInput instance.
prompt_toolkit.shortcuts.input_dialog(title: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = '', text: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = '', ok_text: str = 'OK', cancel_text: str = 'Cancel', completer: Optional[prompt_toolkit.completion.base.Completer] = None, password: Union[prompt_toolkit.filters.base.Filter, bool] = False, style: Optional[prompt_toolkit.styles.base.BaseStyle] = None) → prompt_toolkit.application.application.Application[str][str]

Display a text input box. Return the given text, or None when cancelled.

prompt_toolkit.shortcuts.message_dialog(title: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = '', text: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = '', ok_text: str = 'Ok', style: Optional[prompt_toolkit.styles.base.BaseStyle] = None) → prompt_toolkit.application.application.Application[NoneType][None]

Display a simple message box and wait until the user presses enter.

prompt_toolkit.shortcuts.progress_dialog(title: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = '', text: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = '', run_callback: Callable[[Callable[[int], None], Callable[[str], None]], None] = <function <lambda>>, style: Optional[prompt_toolkit.styles.base.BaseStyle] = None) → prompt_toolkit.application.application.Application[NoneType][None]
Parameters:run_callback – A function that receives as input a set_percentage function and it does the work.
prompt_toolkit.shortcuts.radiolist_dialog(title: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = '', text: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = '', ok_text: str = 'Ok', cancel_text: str = 'Cancel', values: Optional[List[Tuple[_T, Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None]]]] = None, style: Optional[prompt_toolkit.styles.base.BaseStyle] = None) → prompt_toolkit.application.application.Application[~_T][_T]

Display a simple list of element the user can choose amongst.

Only one element can be selected at a time using Arrow keys and Enter. The focus can be moved between the list and the Ok/Cancel button with tab.

prompt_toolkit.shortcuts.yes_no_dialog(title: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = '', text: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = '', yes_text: str = 'Yes', no_text: str = 'No', style: Optional[prompt_toolkit.styles.base.BaseStyle] = None) → prompt_toolkit.application.application.Application[bool][bool]

Display a Yes/No dialog. Return a boolean.

prompt_toolkit.shortcuts.button_dialog(title: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = '', text: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = '', buttons: List[Tuple[str, _T]] = [], style: Optional[prompt_toolkit.styles.base.BaseStyle] = None) → prompt_toolkit.application.application.Application[~_T][_T]

Display a dialog with button choices (given as a list of tuples). Return the value associated with button.

Formatter classes for the progress bar. Each progress bar consists of a list of these formatters.

class prompt_toolkit.shortcuts.progress_bar.formatters.Formatter

Base class for any formatter.

class prompt_toolkit.shortcuts.progress_bar.formatters.Text(text: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None], style: str = '')

Display plain text.

class prompt_toolkit.shortcuts.progress_bar.formatters.Label(width: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, suffix: str = '')

Display the name of the current task.

Parameters:
  • width – If a width is given, use this width. Scroll the text if it doesn’t fit in this width.
  • suffix – String suffix to be added after the task name, e.g. ‘: ‘. If no task name was given, no suffix will be added.
class prompt_toolkit.shortcuts.progress_bar.formatters.Percentage

Display the progress as a percentage.

class prompt_toolkit.shortcuts.progress_bar.formatters.Bar(start: str = '[', end: str = ']', sym_a: str = '=', sym_b: str = '>', sym_c: str = ' ', unknown: str = '#')

Display the progress bar itself.

class prompt_toolkit.shortcuts.progress_bar.formatters.Progress

Display the progress as text. E.g. “8/20”

class prompt_toolkit.shortcuts.progress_bar.formatters.TimeElapsed

Display the elapsed time.

class prompt_toolkit.shortcuts.progress_bar.formatters.TimeLeft

Display the time left.

class prompt_toolkit.shortcuts.progress_bar.formatters.IterationsPerSecond

Display the iterations per second.

class prompt_toolkit.shortcuts.progress_bar.formatters.SpinningWheel

Display a spinning wheel.

class prompt_toolkit.shortcuts.progress_bar.formatters.Rainbow(formatter: prompt_toolkit.shortcuts.progress_bar.formatters.Formatter)

For the fun. Add rainbow colors to any of the other formatters.

prompt_toolkit.shortcuts.progress_bar.formatters.create_default_formatters() → List[prompt_toolkit.shortcuts.progress_bar.formatters.Formatter]

Return the list of default formatters.

Validation

Input validation for a Buffer. (Validators will be called before accepting input.)

class prompt_toolkit.validation.ConditionalValidator(validator: prompt_toolkit.validation.Validator, filter: Union[prompt_toolkit.filters.base.Filter, bool])

Validator that can be switched on/off according to a filter. (This wraps around another validator.)

validate(document: prompt_toolkit.document.Document) → None

Validate the input. If invalid, this should raise a ValidationError.

Parameters:documentDocument instance.
exception prompt_toolkit.validation.ValidationError(cursor_position: int = 0, message: str = '')

Error raised by Validator.validate().

Parameters:
  • cursor_position – The cursor position where the error occurred.
  • message – Text.
class prompt_toolkit.validation.Validator

Abstract base class for an input validator.

A validator is typically created in one of the following two ways:

  • Either by overriding this class and implementing the validate method.
  • Or by passing a callable to Validator.from_callable.

If the validation takes some time and needs to happen in a background thread, this can be wrapped in a ThreadedValidator.

classmethod from_callable(validate_func: Callable[[str], bool], error_message: str = 'Invalid input', move_cursor_to_end: bool = False) → prompt_toolkit.validation.Validator

Create a validator from a simple validate callable. E.g.:

def is_valid(text):
    return text in ['hello', 'world']
Validator.from_callable(is_valid, error_message='Invalid input')
Parameters:
  • validate_func – Callable that takes the input string, and returns True if the input is valid input.
  • error_message – Message to be displayed if the input is invalid.
  • move_cursor_to_end – Move the cursor to the end of the input, if the input is invalid.
validate(document: prompt_toolkit.document.Document) → None

Validate the input. If invalid, this should raise a ValidationError.

Parameters:documentDocument instance.
validate_async(document: prompt_toolkit.document.Document) → None

Return a Future which is set when the validation is ready. This function can be overloaded in order to provide an asynchronous implementation.

class prompt_toolkit.validation.ThreadedValidator(validator: prompt_toolkit.validation.Validator)

Wrapper that runs input validation in a thread. (Use this to prevent the user interface from becoming unresponsive if the input validation takes too much time.)

validate(document: prompt_toolkit.document.Document) → None

Validate the input. If invalid, this should raise a ValidationError.

Parameters:documentDocument instance.
validate_async(document: prompt_toolkit.document.Document) → None

Run the validate function in a thread.

class prompt_toolkit.validation.DummyValidator

Validator class that accepts any input.

validate(document: prompt_toolkit.document.Document) → None

Validate the input. If invalid, this should raise a ValidationError.

Parameters:documentDocument instance.
class prompt_toolkit.validation.DynamicValidator(get_validator: Callable[[], Optional[prompt_toolkit.validation.Validator]])

Validator class that can dynamically returns any Validator.

Parameters:get_validator – Callable that returns a Validator instance.
validate(document: prompt_toolkit.document.Document) → None

Validate the input. If invalid, this should raise a ValidationError.

Parameters:documentDocument instance.
validate_async(document: prompt_toolkit.document.Document) → None

Return a Future which is set when the validation is ready. This function can be overloaded in order to provide an asynchronous implementation.

Auto suggestion

Fish-style like auto-suggestion.

While a user types input in a certain buffer, suggestions are generated (asynchronously.) Usually, they are displayed after the input. When the cursor presses the right arrow and the cursor is at the end of the input, the suggestion will be inserted.

If you want the auto suggestions to be asynchronous (in a background thread), because they take too much time, and could potentially block the event loop, then wrap the AutoSuggest instance into a ThreadedAutoSuggest.

class prompt_toolkit.auto_suggest.Suggestion(text: str)

Suggestion returned by an auto-suggest algorithm.

Parameters:text – The suggestion text.
class prompt_toolkit.auto_suggest.AutoSuggest

Base class for auto suggestion implementations.

get_suggestion(buffer: Buffer, document: prompt_toolkit.document.Document) → Optional[prompt_toolkit.auto_suggest.Suggestion]

Return None or a Suggestion instance.

We receive both Buffer and Document. The reason is that auto suggestions are retrieved asynchronously. (Like completions.) The buffer text could be changed in the meantime, but document contains the buffer document like it was at the start of the auto suggestion call. So, from here, don’t access buffer.text, but use document.text instead.

Parameters:
get_suggestion_async(buff: Buffer, document: prompt_toolkit.document.Document) → Optional[prompt_toolkit.auto_suggest.Suggestion]

Return a Future which is set when the suggestions are ready. This function can be overloaded in order to provide an asynchronous implementation.

class prompt_toolkit.auto_suggest.ThreadedAutoSuggest(auto_suggest: prompt_toolkit.auto_suggest.AutoSuggest)

Wrapper that runs auto suggestions in a thread. (Use this to prevent the user interface from becoming unresponsive if the generation of suggestions takes too much time.)

get_suggestion(buff: Buffer, document: prompt_toolkit.document.Document) → Optional[prompt_toolkit.auto_suggest.Suggestion]

Return None or a Suggestion instance.

We receive both Buffer and Document. The reason is that auto suggestions are retrieved asynchronously. (Like completions.) The buffer text could be changed in the meantime, but document contains the buffer document like it was at the start of the auto suggestion call. So, from here, don’t access buffer.text, but use document.text instead.

Parameters:
get_suggestion_async(buff: Buffer, document: prompt_toolkit.document.Document) → Optional[prompt_toolkit.auto_suggest.Suggestion]

Run the get_suggestion function in a thread.

class prompt_toolkit.auto_suggest.DummyAutoSuggest

AutoSuggest class that doesn’t return any suggestion.

get_suggestion(buffer: Buffer, document: prompt_toolkit.document.Document) → Optional[prompt_toolkit.auto_suggest.Suggestion]

Return None or a Suggestion instance.

We receive both Buffer and Document. The reason is that auto suggestions are retrieved asynchronously. (Like completions.) The buffer text could be changed in the meantime, but document contains the buffer document like it was at the start of the auto suggestion call. So, from here, don’t access buffer.text, but use document.text instead.

Parameters:
class prompt_toolkit.auto_suggest.AutoSuggestFromHistory

Give suggestions based on the lines in the history.

get_suggestion(buffer: Buffer, document: prompt_toolkit.document.Document) → Optional[prompt_toolkit.auto_suggest.Suggestion]

Return None or a Suggestion instance.

We receive both Buffer and Document. The reason is that auto suggestions are retrieved asynchronously. (Like completions.) The buffer text could be changed in the meantime, but document contains the buffer document like it was at the start of the auto suggestion call. So, from here, don’t access buffer.text, but use document.text instead.

Parameters:
class prompt_toolkit.auto_suggest.ConditionalAutoSuggest(auto_suggest: prompt_toolkit.auto_suggest.AutoSuggest, filter: Union[bool, prompt_toolkit.filters.base.Filter])

Auto suggest that can be turned on and of according to a certain condition.

get_suggestion(buffer: Buffer, document: prompt_toolkit.document.Document) → Optional[prompt_toolkit.auto_suggest.Suggestion]

Return None or a Suggestion instance.

We receive both Buffer and Document. The reason is that auto suggestions are retrieved asynchronously. (Like completions.) The buffer text could be changed in the meantime, but document contains the buffer document like it was at the start of the auto suggestion call. So, from here, don’t access buffer.text, but use document.text instead.

Parameters:
class prompt_toolkit.auto_suggest.DynamicAutoSuggest(get_auto_suggest: Callable[[], Optional[prompt_toolkit.auto_suggest.AutoSuggest]])

Validator class that can dynamically returns any Validator.

Parameters:get_validator – Callable that returns a Validator instance.
get_suggestion(buff: Buffer, document: prompt_toolkit.document.Document) → Optional[prompt_toolkit.auto_suggest.Suggestion]

Return None or a Suggestion instance.

We receive both Buffer and Document. The reason is that auto suggestions are retrieved asynchronously. (Like completions.) The buffer text could be changed in the meantime, but document contains the buffer document like it was at the start of the auto suggestion call. So, from here, don’t access buffer.text, but use document.text instead.

Parameters:
get_suggestion_async(buff: Buffer, document: prompt_toolkit.document.Document) → Optional[prompt_toolkit.auto_suggest.Suggestion]

Return a Future which is set when the suggestions are ready. This function can be overloaded in order to provide an asynchronous implementation.

Renderer

Renders the command line on the console. (Redraws parts of the input line that were changed.)

class prompt_toolkit.renderer.Renderer(style: prompt_toolkit.styles.base.BaseStyle, output: prompt_toolkit.output.base.Output, input: prompt_toolkit.input.base.Input, full_screen: bool = False, mouse_support: Union[prompt_toolkit.filters.base.Filter, bool] = False, cpr_not_supported_callback: Optional[Callable[[], None]] = None)

Typical usage:

output = Vt100_Output.from_pty(sys.stdout)
r = Renderer(style, output)
r.render(app, layout=...)
clear() → None

Clear screen and go to 0,0

erase(leave_alternate_screen: bool = True) → None

Hide all output and put the cursor back at the first line. This is for instance used for running a system command (while hiding the CLI) and later resuming the same CLI.)

Parameters:leave_alternate_screen – When True, and when inside an alternate screen buffer, quit the alternate screen.
height_is_known

True when the height from the cursor until the bottom of the terminal is known. (It’s often nicer to draw bottom toolbars only if the height is known, in order to avoid flickering when the CPR response arrives.)

last_rendered_screen

The Screen class that was generated during the last rendering. This can be None.

render(app: Application[Any], layout: Layout, is_done: bool = False) → None

Render the current interface to the output.

Parameters:is_done – When True, put the cursor at the end of the interface. We won’t print any changes to this part.
report_absolute_cursor_row(row: int) → None

To be called when we know the absolute cursor position. (As an answer of a “Cursor Position Request” response.)

request_absolute_cursor_position() → None

Get current cursor position.

We do this to calculate the minimum available height that we can consume for rendering the prompt. This is the available space below te cursor.

For vt100: Do CPR request. (answer will arrive later.) For win32: Do API call. (Answer comes immediately.)

rows_above_layout

Return the number of rows visible in the terminal above the layout.

wait_for_cpr_responses(timeout: int = 1) → None

Wait for a CPR response.

waiting_for_cpr

Waiting for CPR flag. True when we send the request, but didn’t got a response.

prompt_toolkit.renderer.print_formatted_text(output: prompt_toolkit.output.base.Output, formatted_text: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None], style: prompt_toolkit.styles.base.BaseStyle, style_transformation: Optional[prompt_toolkit.styles.style_transformation.StyleTransformation] = None, color_depth: Optional[prompt_toolkit.output.color_depth.ColorDepth] = None) → None

Print a list of (style_str, text) tuples in the given style to the output.

Lexers

Lexer interface and implementations. Used for syntax highlighting.

class prompt_toolkit.lexers.Lexer

Base class for all lexers.

invalidation_hash() → Hashable

When this changes, lex_document could give a different output. (Only used for DynamicLexer.)

lex_document(document: prompt_toolkit.document.Document) → Callable[[int], List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]]]

Takes a Document and returns a callable that takes a line number and returns a list of (style_str, text) tuples for that line.

XXX: Note that in the past, this was supposed to return a list
of (Token, text) tuples, just like a Pygments lexer.
class prompt_toolkit.lexers.SimpleLexer(style: str = '')

Lexer that doesn’t do any tokenizing and returns the whole input as one token.

Parameters:style – The style string for this lexer.
lex_document(document: prompt_toolkit.document.Document) → Callable[[int], List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]]]

Takes a Document and returns a callable that takes a line number and returns a list of (style_str, text) tuples for that line.

XXX: Note that in the past, this was supposed to return a list
of (Token, text) tuples, just like a Pygments lexer.
class prompt_toolkit.lexers.DynamicLexer(get_lexer: Callable[[], Optional[prompt_toolkit.lexers.base.Lexer]])

Lexer class that can dynamically returns any Lexer.

Parameters:get_lexer – Callable that returns a Lexer instance.
invalidation_hash() → Hashable

When this changes, lex_document could give a different output. (Only used for DynamicLexer.)

lex_document(document: prompt_toolkit.document.Document) → Callable[[int], List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]]]

Takes a Document and returns a callable that takes a line number and returns a list of (style_str, text) tuples for that line.

XXX: Note that in the past, this was supposed to return a list
of (Token, text) tuples, just like a Pygments lexer.
class prompt_toolkit.lexers.PygmentsLexer(pygments_lexer_cls: Type[PygmentsLexerCls], sync_from_start: Union[prompt_toolkit.filters.base.Filter, bool] = True, syntax_sync: Optional[prompt_toolkit.lexers.pygments.SyntaxSync] = None)

Lexer that calls a pygments lexer.

Example:

from pygments.lexers.html import HtmlLexer
lexer = PygmentsLexer(HtmlLexer)

Note: Don’t forget to also load a Pygments compatible style. E.g.:

from prompt_toolkit.styles.from_pygments import style_from_pygments_cls
from pygments.styles import get_style_by_name
style = style_from_pygments_cls(get_style_by_name('monokai'))
Parameters:
  • pygments_lexer_cls – A Lexer from Pygments.
  • sync_from_start – Start lexing at the start of the document. This will always give the best results, but it will be slow for bigger documents. (When the last part of the document is display, then the whole document will be lexed by Pygments on every key stroke.) It is recommended to disable this for inputs that are expected to be more than 1,000 lines.
  • syntax_syncSyntaxSync object.
classmethod from_filename(filename: str, sync_from_start: Union[prompt_toolkit.filters.base.Filter, bool] = True) → prompt_toolkit.lexers.base.Lexer

Create a Lexer from a filename.

lex_document(document: prompt_toolkit.document.Document) → Callable[[int], List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]]]

Create a lexer function that takes a line number and returns the list of (style_str, text) tuples as the Pygments lexer returns for that line.

class prompt_toolkit.lexers.RegexSync(pattern: str)

Synchronize by starting at a line that matches the given regex pattern.

classmethod from_pygments_lexer_cls(lexer_cls: PygmentsLexerCls) → RegexSync

Create a RegexSync instance for this Pygments lexer class.

get_sync_start_position(document: prompt_toolkit.document.Document, lineno: int) → Tuple[int, int]

Scan backwards, and find a possible position to start.

class prompt_toolkit.lexers.SyncFromStart

Always start the syntax highlighting from the beginning.

get_sync_start_position(document: prompt_toolkit.document.Document, lineno: int) → Tuple[int, int]

Return the position from where we can start lexing as a (row, column) tuple.

Parameters:
  • documentDocument instance that contains all the lines.
  • lineno – The line that we want to highlight. (We need to return this line, or an earlier position.)
class prompt_toolkit.lexers.SyntaxSync

Syntax synchroniser. This is a tool that finds a start position for the lexer. This is especially important when editing big documents; we don’t want to start the highlighting by running the lexer from the beginning of the file. That is very slow when editing.

get_sync_start_position(document: prompt_toolkit.document.Document, lineno: int) → Tuple[int, int]

Return the position from where we can start lexing as a (row, column) tuple.

Parameters:
  • documentDocument instance that contains all the lines.
  • lineno – The line that we want to highlight. (We need to return this line, or an earlier position.)

Layout

The layout class itself

Command line layout definitions

The layout of a command line interface is defined by a Container instance. There are two main groups of classes here. Containers and controls:

  • A container can contain other containers or controls, it can have multiple children and it decides about the dimensions.
  • A control is responsible for rendering the actual content to a screen. A control can propose some dimensions, but it’s the container who decides about the dimensions – or when the control consumes more space – which part of the control will be visible.

Container classes:

- Container (Abstract base class)
   |- HSplit (Horizontal split)
   |- VSplit (Vertical split)
   |- FloatContainer (Container which can also contain menus and other floats)
   `- Window (Container which contains one actual control

Control classes:

- UIControl (Abstract base class)
   |- FormattedTextControl (Renders formatted text, or a simple list of text fragments)
   `- BufferControl (Renders an input buffer.)

Usually, you end up wrapping every control inside a Window object, because that’s the only way to render it in a layout.

There are some prepared toolbars which are ready to use:

- SystemToolbar (Shows the 'system' input buffer, for entering system commands.)
- ArgToolbar (Shows the input 'arg', for repetition of input commands.)
- SearchToolbar (Shows the 'search' input buffer, for incremental search.)
- CompletionsToolbar (Shows the completions of the current buffer.)
- ValidationToolbar (Shows validation errors of the current buffer.)

And one prepared menu:

  • CompletionsMenu
class prompt_toolkit.layout.Layout(container: Union[prompt_toolkit.layout.containers.Container, MagicContainer], focused_element: Union[str, prompt_toolkit.buffer.Buffer, prompt_toolkit.layout.controls.UIControl, prompt_toolkit.layout.containers.Container, MagicContainer, None] = None)

The layout for a prompt_toolkit Application. This also keeps track of which user control is focused.

Parameters:
  • container – The “root” container for the layout.
  • focused_element – element to be focused initially. (Can be anything the focus function accepts.)
buffer_has_focus

Return True if the currently focused control is a BufferControl. (For instance, used to determine whether the default key bindings should be active or not.)

current_buffer

The currently focused Buffer or None.

current_control

Get the UIControl to currently has the focus.

current_window

Return the Window object that is currently focused.

find_all_windows() → Generator[prompt_toolkit.layout.containers.Window, None, None]

Find all the UIControl objects in this layout.

focus(value: Union[str, prompt_toolkit.buffer.Buffer, prompt_toolkit.layout.controls.UIControl, prompt_toolkit.layout.containers.Container, MagicContainer]) → None

Focus the given UI element.

value can be either:

  • a UIControl
  • a Buffer instance or the name of a Buffer
  • a Window
  • Any container object. In this case we will focus the Window from this container that was focused most recent, or the very first focusable Window of the container.
focus_last() → None

Give the focus to the last focused control.

focus_next() → None

Focus the next visible/focusable Window.

focus_previous() → None

Focus the previous visible/focusable Window.

get_buffer_by_name(buffer_name: str) → Optional[prompt_toolkit.buffer.Buffer]

Look in the layout for a buffer with the given name. Return None when nothing was found.

get_focusable_windows() → Iterable[prompt_toolkit.layout.containers.Window]

Return all the Window objects which are focusable (in the ‘modal’ area).

get_parent(container: prompt_toolkit.layout.containers.Container) → Optional[prompt_toolkit.layout.containers.Container]

Return the parent container for the given container, or None, if it wasn’t found.

get_visible_focusable_windows() → List[prompt_toolkit.layout.containers.Window]

Return a list of Window objects that are focusable.

has_focus(value: Union[str, prompt_toolkit.buffer.Buffer, prompt_toolkit.layout.controls.UIControl, prompt_toolkit.layout.containers.Container, MagicContainer]) → bool

Check whether the given control has the focus. :param value: UIControl or Window instance.

is_searching

True if we are searching right now.

previous_control

Get the UIControl to previously had the focus.

search_target_buffer_control

Return the BufferControl in which we are searching or None.

update_parents_relations() → None

Update child->parent relationships mapping.

walk() → Iterable[prompt_toolkit.layout.containers.Container]

Walk through all the layout nodes (and their children) and yield them.

walk_through_modal_area() → Iterable[prompt_toolkit.layout.containers.Container]

Walk through all the containers which are in the current ‘modal’ part of the layout.

exception prompt_toolkit.layout.InvalidLayoutError
prompt_toolkit.layout.walk(container: prompt_toolkit.layout.containers.Container, skip_hidden: bool = False) → Iterable[prompt_toolkit.layout.containers.Container]

Walk through layout, starting at this container.

Containers

Command line layout definitions

The layout of a command line interface is defined by a Container instance. There are two main groups of classes here. Containers and controls:

  • A container can contain other containers or controls, it can have multiple children and it decides about the dimensions.
  • A control is responsible for rendering the actual content to a screen. A control can propose some dimensions, but it’s the container who decides about the dimensions – or when the control consumes more space – which part of the control will be visible.

Container classes:

- Container (Abstract base class)
   |- HSplit (Horizontal split)
   |- VSplit (Vertical split)
   |- FloatContainer (Container which can also contain menus and other floats)
   `- Window (Container which contains one actual control

Control classes:

- UIControl (Abstract base class)
   |- FormattedTextControl (Renders formatted text, or a simple list of text fragments)
   `- BufferControl (Renders an input buffer.)

Usually, you end up wrapping every control inside a Window object, because that’s the only way to render it in a layout.

There are some prepared toolbars which are ready to use:

- SystemToolbar (Shows the 'system' input buffer, for entering system commands.)
- ArgToolbar (Shows the input 'arg', for repetition of input commands.)
- SearchToolbar (Shows the 'search' input buffer, for incremental search.)
- CompletionsToolbar (Shows the completions of the current buffer.)
- ValidationToolbar (Shows validation errors of the current buffer.)

And one prepared menu:

  • CompletionsMenu
class prompt_toolkit.layout.Container

Base class for user interface layout.

get_children() → List[prompt_toolkit.layout.containers.Container]

Return the list of child Container objects.

get_key_bindings() → Optional[prompt_toolkit.key_binding.key_bindings.KeyBindingsBase]

Returns a KeyBindings object. These bindings become active when any user control in this container has the focus, except if any containers between this container and the focused user control is modal.

is_modal() → bool

When this container is modal, key bindings from parent containers are not taken into account if a user control in this container is focused.

preferred_height(width: int, max_available_height: int) → prompt_toolkit.layout.dimension.Dimension

Return a Dimension that represents the desired height for this container.

preferred_width(max_available_width: int) → prompt_toolkit.layout.dimension.Dimension

Return a Dimension that represents the desired width for this container.

reset() → None

Reset the state of this container and all the children. (E.g. reset scroll offsets, etc…)

write_to_screen(screen: prompt_toolkit.layout.screen.Screen, mouse_handlers: prompt_toolkit.layout.mouse_handlers.MouseHandlers, write_position: prompt_toolkit.layout.screen.WritePosition, parent_style: str, erase_bg: bool, z_index: Optional[int]) → None

Write the actual content to the screen.

Parameters:
  • screenScreen
  • mouse_handlersMouseHandlers.
  • parent_style – Style string to pass to the Window object. This will be applied to all content of the windows. VSplit and HSplit can use it to pass their style down to the windows that they contain.
  • z_index – Used for propagating z_index from parent to child.
class prompt_toolkit.layout.HSplit(children: Sequence[Union[prompt_toolkit.layout.containers.Container, MagicContainer]], window_too_small: Optional[prompt_toolkit.layout.containers.Container] = None, align: prompt_toolkit.layout.containers.VerticalAlign = <VerticalAlign.JUSTIFY: 'JUSTIFY'>, padding: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = 0, padding_char: Optional[str] = None, padding_style: str = '', width: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, height: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, z_index: Optional[int] = None, modal: bool = False, key_bindings: Optional[prompt_toolkit.key_binding.key_bindings.KeyBindingsBase] = None, style: Union[str, Callable[[], str]] = '')

Several layouts, one stacked above/under the other.

+--------------------+
|                    |
+--------------------+
|                    |
+--------------------+

By default, this doesn’t display a horizontal line between the children, but if this is something you need, then create a HSplit as follows:

HSplit(children=[ ... ], padding_char='-',
       padding=1, padding_style='#ffff00')
Parameters:
  • children – List of child Container objects.
  • window_too_small – A Container object that is displayed if there is not enough space for all the children. By default, this is a “Window too small” message.
  • alignVerticalAlign value.
  • width – When given, use this width instead of looking at the children.
  • height – When given, use this height instead of looking at the children.
  • z_index – (int or None) When specified, this can be used to bring element in front of floating elements. None means: inherit from parent.
  • style – A style string.
  • modalTrue or False.
  • key_bindingsNone or a KeyBindings object.
  • padding – (Dimension or int), size to be used for the padding.
  • padding_char – Character to be used for filling in the padding.
  • padding_style – Style to applied to the padding.
preferred_height(width: int, max_available_height: int) → prompt_toolkit.layout.dimension.Dimension

Return a Dimension that represents the desired height for this container.

preferred_width(max_available_width: int) → prompt_toolkit.layout.dimension.Dimension

Return a Dimension that represents the desired width for this container.

reset() → None

Reset the state of this container and all the children. (E.g. reset scroll offsets, etc…)

write_to_screen(screen: prompt_toolkit.layout.screen.Screen, mouse_handlers: prompt_toolkit.layout.mouse_handlers.MouseHandlers, write_position: prompt_toolkit.layout.screen.WritePosition, parent_style: str, erase_bg: bool, z_index: Optional[int]) → None

Render the prompt to a Screen instance.

Parameters:screen – The Screen class to which the output has to be written.
class prompt_toolkit.layout.VSplit(children: Sequence[Union[prompt_toolkit.layout.containers.Container, MagicContainer]], window_too_small: Optional[prompt_toolkit.layout.containers.Container] = None, align: prompt_toolkit.layout.containers.HorizontalAlign = <HorizontalAlign.JUSTIFY: 'JUSTIFY'>, padding: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = 0, padding_char: Optional[str] = None, padding_style: str = '', width: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, height: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, z_index: Optional[int] = None, modal: bool = False, key_bindings: Optional[prompt_toolkit.key_binding.key_bindings.KeyBindingsBase] = None, style: Union[str, Callable[[], str]] = '')

Several layouts, one stacked left/right of the other.

+---------+----------+
|         |          |
|         |          |
+---------+----------+

By default, this doesn’t display a vertical line between the children, but if this is something you need, then create a HSplit as follows:

VSplit(children=[ ... ], padding_char='|',
       padding=1, padding_style='#ffff00')
Parameters:
  • children – List of child Container objects.
  • window_too_small – A Container object that is displayed if there is not enough space for all the children. By default, this is a “Window too small” message.
  • alignHorizontalAlign value.
  • width – When given, use this width instead of looking at the children.
  • height – When given, use this height instead of looking at the children.
  • z_index – (int or None) When specified, this can be used to bring element in front of floating elements. None means: inherit from parent.
  • style – A style string.
  • modalTrue or False.
  • key_bindingsNone or a KeyBindings object.
  • padding – (Dimension or int), size to be used for the padding.
  • padding_char – Character to be used for filling in the padding.
  • padding_style – Style to applied to the padding.
preferred_height(width: int, max_available_height: int) → prompt_toolkit.layout.dimension.Dimension

Return a Dimension that represents the desired height for this container.

preferred_width(max_available_width: int) → prompt_toolkit.layout.dimension.Dimension

Return a Dimension that represents the desired width for this container.

reset() → None

Reset the state of this container and all the children. (E.g. reset scroll offsets, etc…)

write_to_screen(screen: prompt_toolkit.layout.screen.Screen, mouse_handlers: prompt_toolkit.layout.mouse_handlers.MouseHandlers, write_position: prompt_toolkit.layout.screen.WritePosition, parent_style: str, erase_bg: bool, z_index: Optional[int]) → None

Render the prompt to a Screen instance.

Parameters:screen – The Screen class to which the output has to be written.
class prompt_toolkit.layout.FloatContainer(content: Union[prompt_toolkit.layout.containers.Container, MagicContainer], floats: List[Float], modal: bool = False, key_bindings: Optional[prompt_toolkit.key_binding.key_bindings.KeyBindingsBase] = None, style: Union[str, Callable[[], str]] = '', z_index: Optional[int] = None)

Container which can contain another container for the background, as well as a list of floating containers on top of it.

Example Usage:

FloatContainer(content=Window(...),
               floats=[
                   Float(xcursor=True,
                        ycursor=True,
                        layout=CompletionMenu(...))
               ])
Parameters:z_index – (int or None) When specified, this can be used to bring element in front of floating elements. None means: inherit from parent. This is the z_index for the whole Float container as a whole.
get_children() → List[prompt_toolkit.layout.containers.Container]

Return the list of child Container objects.

get_key_bindings() → Optional[prompt_toolkit.key_binding.key_bindings.KeyBindingsBase]

Returns a KeyBindings object. These bindings become active when any user control in this container has the focus, except if any containers between this container and the focused user control is modal.

is_modal() → bool

When this container is modal, key bindings from parent containers are not taken into account if a user control in this container is focused.

preferred_height(width: int, max_available_height: int) → prompt_toolkit.layout.dimension.Dimension

Return the preferred height of the float container. (We don’t care about the height of the floats, they should always fit into the dimensions provided by the container.)

preferred_width(max_available_width: int) → prompt_toolkit.layout.dimension.Dimension

Return a Dimension that represents the desired width for this container.

reset() → None

Reset the state of this container and all the children. (E.g. reset scroll offsets, etc…)

write_to_screen(screen: prompt_toolkit.layout.screen.Screen, mouse_handlers: prompt_toolkit.layout.mouse_handlers.MouseHandlers, write_position: prompt_toolkit.layout.screen.WritePosition, parent_style: str, erase_bg: bool, z_index: Optional[int]) → None

Write the actual content to the screen.

Parameters:
  • screenScreen
  • mouse_handlersMouseHandlers.
  • parent_style – Style string to pass to the Window object. This will be applied to all content of the windows. VSplit and HSplit can use it to pass their style down to the windows that they contain.
  • z_index – Used for propagating z_index from parent to child.
class prompt_toolkit.layout.Float(content: Union[prompt_toolkit.layout.containers.Container, MagicContainer], top: Optional[int] = None, right: Optional[int] = None, bottom: Optional[int] = None, left: Optional[int] = None, width: Union[int, Callable[[], int], None] = None, height: Union[int, Callable[[], int], None] = None, xcursor: bool = False, ycursor: bool = False, attach_to_window: Union[prompt_toolkit.layout.containers.Container, MagicContainer, None] = None, hide_when_covering_content: bool = False, allow_cover_cursor: bool = False, z_index: int = 1, transparent: bool = False)

Float for use in a FloatContainer. Except for the content parameter, all other options are optional.

Parameters:
  • contentContainer instance.
  • widthDimension or callable which returns a Dimension.
  • heightDimension or callable which returns a Dimension.
  • left – Distance to the left edge of the FloatContainer.
  • right – Distance to the right edge of the FloatContainer.
  • top – Distance to the top of the FloatContainer.
  • bottom – Distance to the bottom of the FloatContainer.
  • attach_to_window – Attach to the cursor from this window, instead of the current window.
  • hide_when_covering_content – Hide the float when it covers content underneath.
  • allow_cover_cursor – When False, make sure to display the float below the cursor. Not on top of the indicated position.
  • z_index – Z-index position. For a Float, this needs to be at least one. It is relative to the z_index of the parent container.
  • transparentFilter indicating whether this float needs to be drawn transparently.
class prompt_toolkit.layout.Window(content: Optional[prompt_toolkit.layout.controls.UIControl] = None, width: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, height: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, z_index: Optional[int] = None, dont_extend_width: Union[prompt_toolkit.filters.base.Filter, bool] = False, dont_extend_height: Union[prompt_toolkit.filters.base.Filter, bool] = False, ignore_content_width: Union[prompt_toolkit.filters.base.Filter, bool] = False, ignore_content_height: Union[prompt_toolkit.filters.base.Filter, bool] = False, left_margins: Optional[Sequence[prompt_toolkit.layout.margins.Margin]] = None, right_margins: Optional[Sequence[prompt_toolkit.layout.margins.Margin]] = None, scroll_offsets: Optional[prompt_toolkit.layout.containers.ScrollOffsets] = None, allow_scroll_beyond_bottom: Union[prompt_toolkit.filters.base.Filter, bool] = False, wrap_lines: Union[prompt_toolkit.filters.base.Filter, bool] = False, get_vertical_scroll: Optional[Callable[[Window], int]] = None, get_horizontal_scroll: Optional[Callable[[Window], int]] = None, always_hide_cursor: Union[prompt_toolkit.filters.base.Filter, bool] = False, cursorline: Union[prompt_toolkit.filters.base.Filter, bool] = False, cursorcolumn: Union[prompt_toolkit.filters.base.Filter, bool] = False, colorcolumns: Union[None, List[prompt_toolkit.layout.containers.ColorColumn], Callable[[], List[prompt_toolkit.layout.containers.ColorColumn]]] = None, align: Union[prompt_toolkit.layout.containers.WindowAlign, Callable[[], prompt_toolkit.layout.containers.WindowAlign]] = <WindowAlign.LEFT: 'LEFT'>, style: Union[str, Callable[[], str]] = '', char: Union[None, str, Callable[[], str]] = None, get_line_prefix: Optional[Callable[[int, int], Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None]]] = None)

Container that holds a control.

Parameters:
  • contentUIControl instance.
  • widthDimension instance or callable.
  • heightDimension instance or callable.
  • z_index – When specified, this can be used to bring element in front of floating elements.
  • dont_extend_width – When True, don’t take up more width then the preferred width reported by the control.
  • dont_extend_height – When True, don’t take up more width then the preferred height reported by the control.
  • ignore_content_width – A bool or Filter instance. Ignore the UIContent width when calculating the dimensions.
  • ignore_content_height – A bool or Filter instance. Ignore the UIContent height when calculating the dimensions.
  • left_margins – A list of Margin instance to be displayed on the left. For instance: NumberedMargin can be one of them in order to show line numbers.
  • right_margins – Like left_margins, but on the other side.
  • scroll_offsetsScrollOffsets instance, representing the preferred amount of lines/columns to be always visible before/after the cursor. When both top and bottom are a very high number, the cursor will be centered vertically most of the time.
  • allow_scroll_beyond_bottom – A bool or Filter instance. When True, allow scrolling so far, that the top part of the content is not visible anymore, while there is still empty space available at the bottom of the window. In the Vi editor for instance, this is possible. You will see tildes while the top part of the body is hidden.
  • wrap_lines – A bool or Filter instance. When True, don’t scroll horizontally, but wrap lines instead.
  • get_vertical_scroll – Callable that takes this window instance as input and returns a preferred vertical scroll. (When this is None, the scroll is only determined by the last and current cursor position.)
  • get_horizontal_scroll – Callable that takes this window instance as input and returns a preferred vertical scroll.
  • always_hide_cursor – A bool or Filter instance. When True, never display the cursor, even when the user control specifies a cursor position.
  • cursorline – A bool or Filter instance. When True, display a cursorline.
  • cursorcolumn – A bool or Filter instance. When True, display a cursorcolumn.
  • colorcolumns – A list of ColorColumn instances that describe the columns to be highlighted, or a callable that returns such a list.
  • alignWindowAlign value or callable that returns an WindowAlign value. alignment of content.
  • style – A style string. Style to be applied to all the cells in this window. (This can be a callable that returns a string.)
  • char – (string) Character to be used for filling the background. This can also be a callable that returns a character.
  • get_line_prefix – None or a callable that returns formatted text to be inserted before a line. It takes a line number (int) and a wrap_count and returns formatted text. This can be used for implementation of line continuations, things like Vim “breakindent” and so on.
get_children() → List[prompt_toolkit.layout.containers.Container]

Return the list of child Container objects.

get_key_bindings() → Optional[prompt_toolkit.key_binding.key_bindings.KeyBindingsBase]

Returns a KeyBindings object. These bindings become active when any user control in this container has the focus, except if any containers between this container and the focused user control is modal.

preferred_height(width: int, max_available_height: int) → prompt_toolkit.layout.dimension.Dimension

Calculate the preferred height for this window.

preferred_width(max_available_width: int) → prompt_toolkit.layout.dimension.Dimension

Calculate the preferred width for this window.

reset() → None

Reset the state of this container and all the children. (E.g. reset scroll offsets, etc…)

write_to_screen(screen: prompt_toolkit.layout.screen.Screen, mouse_handlers: prompt_toolkit.layout.mouse_handlers.MouseHandlers, write_position: prompt_toolkit.layout.screen.WritePosition, parent_style: str, erase_bg: bool, z_index: Optional[int]) → None

Write window to screen. This renders the user control, the margins and copies everything over to the absolute position at the given screen.

class prompt_toolkit.layout.WindowAlign

Alignment of the Window content.

Note that this is different from HorizontalAlign and VerticalAlign, which are used for the alignment of the child containers in respectively VSplit and HSplit.

class prompt_toolkit.layout.ConditionalContainer(content: Union[prompt_toolkit.layout.containers.Container, MagicContainer], filter: Union[prompt_toolkit.filters.base.Filter, bool])

Wrapper around any other container that can change the visibility. The received filter determines whether the given container should be displayed or not.

Parameters:
get_children() → List[prompt_toolkit.layout.containers.Container]

Return the list of child Container objects.

preferred_height(width: int, max_available_height: int) → prompt_toolkit.layout.dimension.Dimension

Return a Dimension that represents the desired height for this container.

preferred_width(max_available_width: int) → prompt_toolkit.layout.dimension.Dimension

Return a Dimension that represents the desired width for this container.

reset() → None

Reset the state of this container and all the children. (E.g. reset scroll offsets, etc…)

write_to_screen(screen: prompt_toolkit.layout.screen.Screen, mouse_handlers: prompt_toolkit.layout.mouse_handlers.MouseHandlers, write_position: prompt_toolkit.layout.screen.WritePosition, parent_style: str, erase_bg: bool, z_index: Optional[int]) → None

Write the actual content to the screen.

Parameters:
  • screenScreen
  • mouse_handlersMouseHandlers.
  • parent_style – Style string to pass to the Window object. This will be applied to all content of the windows. VSplit and HSplit can use it to pass their style down to the windows that they contain.
  • z_index – Used for propagating z_index from parent to child.
class prompt_toolkit.layout.ScrollOffsets(top: Union[int, Callable[[], int]] = 0, bottom: Union[int, Callable[[], int]] = 0, left: Union[int, Callable[[], int]] = 0, right: Union[int, Callable[[], int]] = 0)

Scroll offsets for the Window class.

Note that left/right offsets only make sense if line wrapping is disabled.

class prompt_toolkit.layout.ColorColumn(position: int, style: str = 'class:color-column')

Column for a Window to be colored.

prompt_toolkit.layout.to_container(container: Union[prompt_toolkit.layout.containers.Container, MagicContainer]) → prompt_toolkit.layout.containers.Container

Make sure that the given object is a Container.

prompt_toolkit.layout.to_window(container: Union[prompt_toolkit.layout.containers.Container, MagicContainer]) → prompt_toolkit.layout.containers.Window

Make sure that the given argument is a Window.

prompt_toolkit.layout.is_container(value: object) → bool

Checks whether the given value is a container object (for use in assert statements).

class prompt_toolkit.layout.HorizontalAlign

Alignment for VSplit.

class prompt_toolkit.layout.VerticalAlign

Alignment for HSplit.

Controls

Command line layout definitions

The layout of a command line interface is defined by a Container instance. There are two main groups of classes here. Containers and controls:

  • A container can contain other containers or controls, it can have multiple children and it decides about the dimensions.
  • A control is responsible for rendering the actual content to a screen. A control can propose some dimensions, but it’s the container who decides about the dimensions – or when the control consumes more space – which part of the control will be visible.

Container classes:

- Container (Abstract base class)
   |- HSplit (Horizontal split)
   |- VSplit (Vertical split)
   |- FloatContainer (Container which can also contain menus and other floats)
   `- Window (Container which contains one actual control

Control classes:

- UIControl (Abstract base class)
   |- FormattedTextControl (Renders formatted text, or a simple list of text fragments)
   `- BufferControl (Renders an input buffer.)

Usually, you end up wrapping every control inside a Window object, because that’s the only way to render it in a layout.

There are some prepared toolbars which are ready to use:

- SystemToolbar (Shows the 'system' input buffer, for entering system commands.)
- ArgToolbar (Shows the input 'arg', for repetition of input commands.)
- SearchToolbar (Shows the 'search' input buffer, for incremental search.)
- CompletionsToolbar (Shows the completions of the current buffer.)
- ValidationToolbar (Shows validation errors of the current buffer.)

And one prepared menu:

  • CompletionsMenu
class prompt_toolkit.layout.BufferControl(buffer: Optional[prompt_toolkit.buffer.Buffer] = None, input_processors: Optional[List[prompt_toolkit.layout.processors.Processor]] = None, include_default_input_processors: bool = True, lexer: Optional[prompt_toolkit.lexers.base.Lexer] = None, preview_search: Union[prompt_toolkit.filters.base.Filter, bool] = False, focusable: Union[prompt_toolkit.filters.base.Filter, bool] = True, search_buffer_control: Union[None, SearchBufferControl, Callable[[], SearchBufferControl]] = None, menu_position: Optional[Callable] = None, focus_on_click: Union[prompt_toolkit.filters.base.Filter, bool] = False, key_bindings: Optional[KeyBindingsBase] = None)

Control for visualising the content of a Buffer.

Parameters:
  • buffer – The Buffer object to be displayed.
  • input_processors – A list of Processor objects.
  • include_default_input_processors – When True, include the default processors for highlighting of selection, search and displaying of multiple cursors.
  • lexerLexer instance for syntax highlighting.
  • preview_searchbool or Filter: Show search while typing. When this is True, probably you want to add a HighlightIncrementalSearchProcessor as well. Otherwise only the cursor position will move, but the text won’t be highlighted.
  • focusablebool or Filter: Tell whether this control is focusable.
  • focus_on_click – Focus this buffer when it’s click, but not yet focused.
  • key_bindings – a KeyBindings object.
create_content(width: int, height: int, preview_search: bool = False) → prompt_toolkit.layout.controls.UIContent

Create a UIContent.

get_invalidate_events() → Iterable[Event[object]]

Return the Window invalidate events.

get_key_bindings() → Optional[KeyBindingsBase]

When additional key bindings are given. Return these.

is_focusable() → bool

Tell whether this user control is focusable.

mouse_handler(mouse_event: prompt_toolkit.mouse_events.MouseEvent) → NotImplementedOrNone

Mouse handler for this control.

move_cursor_down() → None

Request to move the cursor down. This happens when scrolling down and the cursor is completely at the top.

move_cursor_up() → None

Request to move the cursor up.

preferred_width(max_available_width: int) → Optional[int]

This should return the preferred width.

Note: We don’t specify a preferred width according to the content,
because it would be too expensive. Calculating the preferred width can be done by calculating the longest line, but this would require applying all the processors to each line. This is unfeasible for a larger document, and doing it for small documents only would result in inconsistent behaviour.
search_state

Return the SearchState for searching this BufferControl. This is always associated with the search control. If one search bar is used for searching multiple BufferControls, then they share the same SearchState.

class prompt_toolkit.layout.SearchBufferControl(buffer: Optional[prompt_toolkit.buffer.Buffer] = None, input_processors: Optional[List[prompt_toolkit.layout.processors.Processor]] = None, lexer: Optional[prompt_toolkit.lexers.base.Lexer] = None, focus_on_click: Union[prompt_toolkit.filters.base.Filter, bool] = False, key_bindings: Optional[KeyBindingsBase] = None, ignore_case: Union[prompt_toolkit.filters.base.Filter, bool] = False)

BufferControl which is used for searching another BufferControl.

Parameters:ignore_case – Search case insensitive.
class prompt_toolkit.layout.DummyControl

A dummy control object that doesn’t paint any content.

Useful for filling a Window. (The fragment and char attributes of the Window class can be used to define the filling.)

create_content(width: int, height: int) → prompt_toolkit.layout.controls.UIContent

Generate the content for this user control.

Returns a UIContent instance.

is_focusable() → bool

Tell whether this user control is focusable.

class prompt_toolkit.layout.FormattedTextControl(text: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = '', style: str = '', focusable: Union[prompt_toolkit.filters.base.Filter, bool] = False, key_bindings: Optional[KeyBindingsBase] = None, show_cursor: bool = True, modal: bool = False, get_cursor_position: Optional[Callable[[], Optional[prompt_toolkit.data_structures.Point]]] = None)

Control that displays formatted text. This can be either plain text, an HTML object an ANSI object or a list of (style_str, text) tuples, depending on how you prefer to do the formatting. See prompt_toolkit.layout.formatted_text for more information.

(It’s mostly optimized for rather small widgets, like toolbars, menus, etc…)

When this UI control has the focus, the cursor will be shown in the upper left corner of this control by default. There are two ways for specifying the cursor position:

  • Pass a get_cursor_position function which returns a Point instance with the current cursor position.
  • If the (formatted) text is passed as a list of (style, text) tuples and there is one that looks like ('[SetCursorPosition]', ''), then this will specify the cursor position.

Mouse support:

The list of fragments can also contain tuples of three items, looking like: (style_str, text, handler). When mouse support is enabled and the user clicks on this fragment, then the given handler is called. That handler should accept two inputs: (Application, MouseEvent) and it should either handle the event or return NotImplemented in case we want the containing Window to handle this event.
Parameters:
  • focusablebool or Filter: Tell whether this control is focusable.
  • text – Text or formatted text to be displayed.
  • style – Style string applied to the content. (If you want to style the whole Window, pass the style to the Window instead.)
  • key_bindings – a KeyBindings object.
  • get_cursor_position – A callable that returns the cursor position as a Point instance.
create_content(width: int, height: Optional[int]) → prompt_toolkit.layout.controls.UIContent

Generate the content for this user control.

Returns a UIContent instance.

get_key_bindings() → Optional[KeyBindingsBase]

The key bindings that are specific for this user control.

Return a KeyBindings object if some key bindings are specified, or None otherwise.

is_focusable() → bool

Tell whether this user control is focusable.

mouse_handler(mouse_event: prompt_toolkit.mouse_events.MouseEvent) → NotImplementedOrNone

Handle mouse events.

(When the fragment list contained mouse handlers and the user clicked on on any of these, the matching handler is called. This handler can still return NotImplemented in case we want the Window to handle this particular event.)

preferred_width(max_available_width: int) → int

Return the preferred width for this control. That is the width of the longest line.

class prompt_toolkit.layout.UIControl

Base class for all user interface controls.

create_content(width: int, height: int) → prompt_toolkit.layout.controls.UIContent

Generate the content for this user control.

Returns a UIContent instance.

get_invalidate_events() → Iterable[Event[object]]

Return a list of Event objects. This can be a generator. (The application collects all these events, in order to bind redraw handlers to these events.)

get_key_bindings() → Optional[KeyBindingsBase]

The key bindings that are specific for this user control.

Return a KeyBindings object if some key bindings are specified, or None otherwise.

is_focusable() → bool

Tell whether this user control is focusable.

mouse_handler(mouse_event: prompt_toolkit.mouse_events.MouseEvent) → NotImplementedOrNone

Handle mouse events.

When NotImplemented is returned, it means that the given event is not handled by the UIControl itself. The Window or key bindings can decide to handle this event as scrolling or changing focus.

Parameters:mouse_eventMouseEvent instance.
move_cursor_down() → None

Request to move the cursor down. This happens when scrolling down and the cursor is completely at the top.

move_cursor_up() → None

Request to move the cursor up.

class prompt_toolkit.layout.UIContent(get_line: Callable[[int], List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]]] = <function UIContent.<lambda>>, line_count: int = 0, cursor_position: Optional[prompt_toolkit.data_structures.Point] = None, menu_position: Optional[prompt_toolkit.data_structures.Point] = None, show_cursor: bool = True)

Content generated by a user control. This content consists of a list of lines.

Parameters:
  • get_line – Callable that takes a line number and returns the current line. This is a list of (style_str, text) tuples.
  • line_count – The number of lines.
  • cursor_position – a Point for the cursor position.
  • menu_position – a Point for the menu position.
  • show_cursor – Make the cursor visible.
get_height_for_line(lineno: int, width: int, get_line_prefix: Optional[Callable[[int, int], Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None]]], slice_stop: Optional[int] = None) → int

Return the height that a given line would need if it is rendered in a space with the given width (using line wrapping).

Parameters:
  • get_line_prefix – None or a Window.get_line_prefix callable that returns the prefix to be inserted before this line.
  • slice_stop – Wrap only “line[:slice_stop]” and return that partial result. This is needed for scrolling the window correctly when line wrapping.
Returns:

The computed height.

Other

Command line layout definitions

The layout of a command line interface is defined by a Container instance. There are two main groups of classes here. Containers and controls:

  • A container can contain other containers or controls, it can have multiple children and it decides about the dimensions.
  • A control is responsible for rendering the actual content to a screen. A control can propose some dimensions, but it’s the container who decides about the dimensions – or when the control consumes more space – which part of the control will be visible.

Container classes:

- Container (Abstract base class)
   |- HSplit (Horizontal split)
   |- VSplit (Vertical split)
   |- FloatContainer (Container which can also contain menus and other floats)
   `- Window (Container which contains one actual control

Control classes:

- UIControl (Abstract base class)
   |- FormattedTextControl (Renders formatted text, or a simple list of text fragments)
   `- BufferControl (Renders an input buffer.)

Usually, you end up wrapping every control inside a Window object, because that’s the only way to render it in a layout.

There are some prepared toolbars which are ready to use:

- SystemToolbar (Shows the 'system' input buffer, for entering system commands.)
- ArgToolbar (Shows the input 'arg', for repetition of input commands.)
- SearchToolbar (Shows the 'search' input buffer, for incremental search.)
- CompletionsToolbar (Shows the completions of the current buffer.)
- ValidationToolbar (Shows validation errors of the current buffer.)

And one prepared menu:

  • CompletionsMenu
class prompt_toolkit.layout.Dimension(min: Optional[int] = None, max: Optional[int] = None, weight: Optional[int] = None, preferred: Optional[int] = None)

Specified dimension (width/height) of a user control or window.

The layout engine tries to honor the preferred size. If that is not possible, because the terminal is larger or smaller, it tries to keep in between min and max.

Parameters:
  • min – Minimum size.
  • max – Maximum size.
  • weight – For a VSplit/HSplit, the actual size will be determined by taking the proportion of weights from all the children. E.g. When there are two children, one width a weight of 1, and the other with a weight of 2. The second will always be twice as big as the first, if the min/max values allow it.
  • preferred – Preferred size.
classmethod exact(amount: int) → prompt_toolkit.layout.dimension.Dimension

Return a Dimension with an exact size. (min, max and preferred set to amount).

is_zero() → bool

True if this Dimension represents a zero size.

classmethod zero() → prompt_toolkit.layout.dimension.Dimension

Create a dimension that represents a zero size. (Used for ‘invisible’ controls.)

class prompt_toolkit.layout.Margin

Base interface for a margin.

create_margin(window_render_info: WindowRenderInfo, width: int, height: int) → List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]]

Creates a margin. This should return a list of (style_str, text) tuples.

Parameters:
  • window_render_infoWindowRenderInfo instance, generated after rendering and copying the visible part of the UIControl into the Window.
  • width – The width that’s available for this margin. (As reported by get_width().)
  • height – The height that’s available for this margin. (The height of the Window.)
get_width(get_ui_content: Callable[[], prompt_toolkit.layout.controls.UIContent]) → int

Return the width that this margin is going to consume.

Parameters:get_ui_content – Callable that asks the user control to create a UIContent instance. This can be used for instance to obtain the number of lines.
class prompt_toolkit.layout.NumberedMargin(relative: Union[prompt_toolkit.filters.base.Filter, bool] = False, display_tildes: Union[prompt_toolkit.filters.base.Filter, bool] = False)

Margin that displays the line numbers.

Parameters:
  • relative – Number relative to the cursor position. Similar to the Vi ‘relativenumber’ option.
  • display_tildes – Display tildes after the end of the document, just like Vi does.
create_margin(window_render_info: WindowRenderInfo, width: int, height: int) → List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]]

Creates a margin. This should return a list of (style_str, text) tuples.

Parameters:
  • window_render_infoWindowRenderInfo instance, generated after rendering and copying the visible part of the UIControl into the Window.
  • width – The width that’s available for this margin. (As reported by get_width().)
  • height – The height that’s available for this margin. (The height of the Window.)
get_width(get_ui_content: Callable[[], prompt_toolkit.layout.controls.UIContent]) → int

Return the width that this margin is going to consume.

Parameters:get_ui_content – Callable that asks the user control to create a UIContent instance. This can be used for instance to obtain the number of lines.
class prompt_toolkit.layout.ScrollbarMargin(display_arrows: Union[prompt_toolkit.filters.base.Filter, bool] = False, up_arrow_symbol: str = '^', down_arrow_symbol: str = 'v')

Margin displaying a scrollbar.

Parameters:display_arrows – Display scroll up/down arrows.
create_margin(window_render_info: WindowRenderInfo, width: int, height: int) → List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]]

Creates a margin. This should return a list of (style_str, text) tuples.

Parameters:
  • window_render_infoWindowRenderInfo instance, generated after rendering and copying the visible part of the UIControl into the Window.
  • width – The width that’s available for this margin. (As reported by get_width().)
  • height – The height that’s available for this margin. (The height of the Window.)
get_width(get_ui_content: Callable[[], prompt_toolkit.layout.controls.UIContent]) → int

Return the width that this margin is going to consume.

Parameters:get_ui_content – Callable that asks the user control to create a UIContent instance. This can be used for instance to obtain the number of lines.
class prompt_toolkit.layout.ConditionalMargin(margin: prompt_toolkit.layout.margins.Margin, filter: Union[prompt_toolkit.filters.base.Filter, bool])

Wrapper around other Margin classes to show/hide them.

create_margin(window_render_info: WindowRenderInfo, width: int, height: int) → List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]]

Creates a margin. This should return a list of (style_str, text) tuples.

Parameters:
  • window_render_infoWindowRenderInfo instance, generated after rendering and copying the visible part of the UIControl into the Window.
  • width – The width that’s available for this margin. (As reported by get_width().)
  • height – The height that’s available for this margin. (The height of the Window.)
get_width(get_ui_content: Callable[[], prompt_toolkit.layout.controls.UIContent]) → int

Return the width that this margin is going to consume.

Parameters:get_ui_content – Callable that asks the user control to create a UIContent instance. This can be used for instance to obtain the number of lines.
class prompt_toolkit.layout.PromptMargin(get_prompt: Callable[[], List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]]], get_continuation: Optional[Callable[[int, int, bool], List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]]]] = None)

[Deprecated]

Create margin that displays a prompt. This can display one prompt at the first line, and a continuation prompt (e.g, just dots) on all the following lines.

This PromptMargin implementation has been largely superseded in favor of the get_line_prefix attribute of Window. The reason is that a margin is always a fixed width, while get_line_prefix can return a variable width prefix in front of every line, making it more powerful, especially for line continuations.

Parameters:
  • get_prompt – Callable returns formatted text or a list of (style_str, type) tuples to be shown as the prompt at the first line.
  • get_continuation – Callable that takes three inputs. The width (int), line_number (int), and is_soft_wrap (bool). It should return formatted text or a list of (style_str, type) tuples for the next lines of the input.
create_margin(window_render_info: WindowRenderInfo, width: int, height: int) → List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]]

Creates a margin. This should return a list of (style_str, text) tuples.

Parameters:
  • window_render_infoWindowRenderInfo instance, generated after rendering and copying the visible part of the UIControl into the Window.
  • width – The width that’s available for this margin. (As reported by get_width().)
  • height – The height that’s available for this margin. (The height of the Window.)
get_width(get_ui_content: Callable[[], prompt_toolkit.layout.controls.UIContent]) → int

Width to report to the Window.

class prompt_toolkit.layout.CompletionsMenu(max_height: Optional[int] = None, scroll_offset: Union[int, Callable[[], int]] = 0, extra_filter: Union[prompt_toolkit.filters.base.Filter, bool] = True, display_arrows: Union[prompt_toolkit.filters.base.Filter, bool] = False, z_index: int = 100000000)
class prompt_toolkit.layout.MultiColumnCompletionsMenu(min_rows: int = 3, suggested_max_column_width: int = 30, show_meta: Union[prompt_toolkit.filters.base.Filter, bool] = True, extra_filter: Union[prompt_toolkit.filters.base.Filter, bool] = True, z_index: int = 100000000)

Container that displays the completions in several columns. When show_meta (a Filter) evaluates to True, it shows the meta information at the bottom.

Processors are little transformation blocks that transform the fragments list from a buffer before the BufferControl will render it to the screen.

They can insert fragments before or after, or highlight fragments by replacing the fragment types.

class prompt_toolkit.layout.processors.Processor

Manipulate the fragments for a given line in a BufferControl.

apply_transformation(transformation_input: prompt_toolkit.layout.processors.TransformationInput) → prompt_toolkit.layout.processors.Transformation

Apply transformation. Returns a Transformation instance.

Parameters:transformation_inputTransformationInput object.
class prompt_toolkit.layout.processors.TransformationInput(buffer_control: BufferControl, document: prompt_toolkit.document.Document, lineno: int, source_to_display: Callable[[int], int], fragments: List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], width: int, height: int)
Parameters:
  • controlBufferControl instance.
  • lineno – The number of the line to which we apply the processor.
  • source_to_display – A function that returns the position in the fragments for any position in the source string. (This takes previous processors into account.)
  • fragments – List of fragments that we can transform. (Received from the previous processor.)
class prompt_toolkit.layout.processors.Transformation(fragments: List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], source_to_display: Optional[Callable[[int], int]] = None, display_to_source: Optional[Callable[[int], int]] = None)

Transformation result, as returned by Processor.apply_transformation().

Important: Always make sure that the length of document.text is equal to
the length of all the text in fragments!
Parameters:
  • fragments – The transformed fragments. To be displayed, or to pass to the next processor.
  • source_to_display – Cursor position transformation from original string to transformed string.
  • display_to_source – Cursor position transformed from source string to original string.
class prompt_toolkit.layout.processors.DummyProcessor

A Processor that doesn’t do anything.

apply_transformation(transformation_input: prompt_toolkit.layout.processors.TransformationInput) → prompt_toolkit.layout.processors.Transformation

Apply transformation. Returns a Transformation instance.

Parameters:transformation_inputTransformationInput object.
class prompt_toolkit.layout.processors.HighlightSearchProcessor

Processor that highlights search matches in the document. Note that this doesn’t support multiline search matches yet.

The style classes ‘search’ and ‘search.current’ will be applied to the content.

apply_transformation(transformation_input: prompt_toolkit.layout.processors.TransformationInput) → prompt_toolkit.layout.processors.Transformation

Apply transformation. Returns a Transformation instance.

Parameters:transformation_inputTransformationInput object.
class prompt_toolkit.layout.processors.HighlightIncrementalSearchProcessor

Highlight the search terms that are used for highlighting the incremental search. The style class ‘incsearch’ will be applied to the content.

Important: this requires the preview_search=True flag to be set for the BufferControl. Otherwise, the cursor position won’t be set to the search match while searching, and nothing happens.

class prompt_toolkit.layout.processors.HighlightSelectionProcessor

Processor that highlights the selection in the document.

apply_transformation(transformation_input: prompt_toolkit.layout.processors.TransformationInput) → prompt_toolkit.layout.processors.Transformation

Apply transformation. Returns a Transformation instance.

Parameters:transformation_inputTransformationInput object.
class prompt_toolkit.layout.processors.PasswordProcessor(char: str = '*')

Processor that turns masks the input. (For passwords.)

Parameters:char – (string) Character to be used. “*” by default.
apply_transformation(ti: prompt_toolkit.layout.processors.TransformationInput) → prompt_toolkit.layout.processors.Transformation

Apply transformation. Returns a Transformation instance.

Parameters:transformation_inputTransformationInput object.
class prompt_toolkit.layout.processors.HighlightMatchingBracketProcessor(chars: str = '[](){}<>', max_cursor_distance: int = 1000)

When the cursor is on or right after a bracket, it highlights the matching bracket.

Parameters:max_cursor_distance – Only highlight matching brackets when the cursor is within this distance. (From inside a Processor, we can’t know which lines will be visible on the screen. But we also don’t want to scan the whole document for matching brackets on each key press, so we limit to this value.)
apply_transformation(transformation_input: prompt_toolkit.layout.processors.TransformationInput) → prompt_toolkit.layout.processors.Transformation

Apply transformation. Returns a Transformation instance.

Parameters:transformation_inputTransformationInput object.
class prompt_toolkit.layout.processors.DisplayMultipleCursors

When we’re in Vi block insert mode, display all the cursors.

apply_transformation(transformation_input: prompt_toolkit.layout.processors.TransformationInput) → prompt_toolkit.layout.processors.Transformation

Apply transformation. Returns a Transformation instance.

Parameters:transformation_inputTransformationInput object.
class prompt_toolkit.layout.processors.BeforeInput(text: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None], style: str = '')

Insert text before the input.

Parameters:
  • text – This can be either plain text or formatted text (or a callable that returns any of those).
  • style – style to be applied to this prompt/prefix.
apply_transformation(ti: prompt_toolkit.layout.processors.TransformationInput) → prompt_toolkit.layout.processors.Transformation

Apply transformation. Returns a Transformation instance.

Parameters:transformation_inputTransformationInput object.
class prompt_toolkit.layout.processors.ShowArg

Display the ‘arg’ in front of the input.

This was used by the PromptSession, but now it uses the Window.get_line_prefix function instead.

class prompt_toolkit.layout.processors.AfterInput(text: str, style: str = '')

Insert text after the input.

Parameters:
  • text – This can be either plain text or formatted text (or a callable that returns any of those).
  • style – style to be applied to this prompt/prefix.
apply_transformation(ti: prompt_toolkit.layout.processors.TransformationInput) → prompt_toolkit.layout.processors.Transformation

Apply transformation. Returns a Transformation instance.

Parameters:transformation_inputTransformationInput object.
class prompt_toolkit.layout.processors.AppendAutoSuggestion(style: str = 'class:auto-suggestion')

Append the auto suggestion to the input. (The user can then press the right arrow the insert the suggestion.)

apply_transformation(ti: prompt_toolkit.layout.processors.TransformationInput) → prompt_toolkit.layout.processors.Transformation

Apply transformation. Returns a Transformation instance.

Parameters:transformation_inputTransformationInput object.
class prompt_toolkit.layout.processors.ConditionalProcessor(processor: prompt_toolkit.layout.processors.Processor, filter: Union[prompt_toolkit.filters.base.Filter, bool])

Processor that applies another processor, according to a certain condition. Example:

# Create a function that returns whether or not the processor should
# currently be applied.
def highlight_enabled():
    return true_or_false

# Wrapped it in a `ConditionalProcessor` for usage in a `BufferControl`.
BufferControl(input_processors=[
    ConditionalProcessor(HighlightSearchProcessor(),
                         Condition(highlight_enabled))])
Parameters:
apply_transformation(transformation_input: prompt_toolkit.layout.processors.TransformationInput) → prompt_toolkit.layout.processors.Transformation

Apply transformation. Returns a Transformation instance.

Parameters:transformation_inputTransformationInput object.
class prompt_toolkit.layout.processors.ShowLeadingWhiteSpaceProcessor(get_char: Optional[Callable[[], str]] = None, style: str = 'class:leading-whitespace')

Make leading whitespace visible.

Parameters:get_char – Callable that returns one character.
apply_transformation(ti: prompt_toolkit.layout.processors.TransformationInput) → prompt_toolkit.layout.processors.Transformation

Apply transformation. Returns a Transformation instance.

Parameters:transformation_inputTransformationInput object.
class prompt_toolkit.layout.processors.ShowTrailingWhiteSpaceProcessor(get_char: Optional[Callable[[], str]] = None, style: str = 'class:training-whitespace')

Make trailing whitespace visible.

Parameters:get_char – Callable that returns one character.
apply_transformation(ti: prompt_toolkit.layout.processors.TransformationInput) → prompt_toolkit.layout.processors.Transformation

Apply transformation. Returns a Transformation instance.

Parameters:transformation_inputTransformationInput object.
class prompt_toolkit.layout.processors.TabsProcessor(tabstop: Union[int, Callable[[], int]] = 4, char1: Union[str, Callable[[], str]] = '|', char2: Union[str, Callable[[], str]] = '┈', style: str = 'class:tab')

Render tabs as spaces (instead of ^I) or make them visible (for instance, by replacing them with dots.)

Parameters:
  • tabstop – Horizontal space taken by a tab. (int or callable that returns an int).
  • char1 – Character or callable that returns a character (text of length one). This one is used for the first space taken by the tab.
  • char2 – Like char1, but for the rest of the space.
apply_transformation(ti: prompt_toolkit.layout.processors.TransformationInput) → prompt_toolkit.layout.processors.Transformation

Apply transformation. Returns a Transformation instance.

Parameters:transformation_inputTransformationInput object.
class prompt_toolkit.layout.processors.ReverseSearchProcessor

Process to display the “(reverse-i-search)`…`:…” stuff around the search buffer.

Note: This processor is meant to be applied to the BufferControl that contains the search buffer, it’s not meant for the original input.

apply_transformation(ti: prompt_toolkit.layout.processors.TransformationInput) → prompt_toolkit.layout.processors.Transformation

Apply transformation. Returns a Transformation instance.

Parameters:transformation_inputTransformationInput object.
class prompt_toolkit.layout.processors.DynamicProcessor(get_processor: Callable[[], Optional[prompt_toolkit.layout.processors.Processor]])

Processor class that can dynamically returns any Processor.

Parameters:get_processor – Callable that returns a Processor instance.
apply_transformation(ti: prompt_toolkit.layout.processors.TransformationInput) → prompt_toolkit.layout.processors.Transformation

Apply transformation. Returns a Transformation instance.

Parameters:transformation_inputTransformationInput object.
prompt_toolkit.layout.processors.merge_processors(processors: List[prompt_toolkit.layout.processors.Processor]) → prompt_toolkit.layout.processors.Processor

Merge multiple Processor objects into one.

prompt_toolkit.layout.utils.explode_text_fragments(fragments: Iterable[_T]) → prompt_toolkit.layout.utils._ExplodedList[~_T][_T]

Turn a list of (style_str, text) tuples into another list where each string is exactly one character.

It should be fine to call this function several times. Calling this on a list that is already exploded, is a null operation.

Parameters:fragments – List of (style, text) tuples.
class prompt_toolkit.layout.screen.Screen(default_char: Optional[prompt_toolkit.layout.screen.Char] = None, initial_width: int = 0, initial_height: int = 0)

Two dimensional buffer of Char instances.

append_style_to_content(style_str: str) → None

For all the characters in the screen. Set the style string to the given style_str.

cursor_positions = None

Position of the cursor.

draw_all_floats() → None

Draw all float functions in order of z-index.

draw_with_z_index(z_index: int, draw_func: Callable[[], None]) → None

Add a draw-function for a Window which has a >= 0 z_index. This will be postponed until draw_all_floats is called.

fill_area(write_position: prompt_toolkit.layout.screen.WritePosition, style: str = '', after: bool = False) → None

Fill the content of this area, using the given style. The style is prepended before whatever was here before.

get_cursor_position(window: Window) → prompt_toolkit.data_structures.Point

Get the cursor position for a given window. Returns a Point.

get_menu_position(window: Window) → prompt_toolkit.data_structures.Point

Get the menu position for a given window. (This falls back to the cursor position if no menu position was set.)

menu_positions = None

(Optional) Where to position the menu. E.g. at the start of a completion. (We can’t use the cursor position, because we don’t want the completion menu to change its position when we browse through all the completions.)

set_cursor_position(window: Window, position: prompt_toolkit.data_structures.Point) → None

Set the cursor position for a given window.

set_menu_position(window: Window, position: prompt_toolkit.data_structures.Point) → None

Set the cursor position for a given window.

show_cursor = None

Visibility of the cursor.

width = None

Currently used width/height of the screen. This will increase when data is written to the screen.

zero_width_escapes = None

Escape sequences to be injected.

class prompt_toolkit.layout.screen.Char(char: str = ' ', style: str = '')

Represent a single character in a Screen.

This should be considered immutable.

Parameters:
  • char – A single character (can be a double-width character).
  • style – A style string. (Can contain classnames.)

Widgets

Collection of reusable components for building full screen applications. These are higher level abstractions on top of the prompt_toolkit.layout module.

Most of these widgets implement the __pt_container__ method, which makes it possible to embed these in the layout like any other container.

class prompt_toolkit.widgets.TextArea(text: str = '', multiline: Union[prompt_toolkit.filters.base.Filter, bool] = True, password: Union[prompt_toolkit.filters.base.Filter, bool] = False, lexer: Optional[prompt_toolkit.lexers.base.Lexer] = None, auto_suggest: Optional[prompt_toolkit.auto_suggest.AutoSuggest] = None, completer: Optional[prompt_toolkit.completion.base.Completer] = None, complete_while_typing: Union[prompt_toolkit.filters.base.Filter, bool] = True, accept_handler: Optional[Callable[[Buffer], bool]] = None, history: Optional[prompt_toolkit.history.History] = None, focusable: Union[prompt_toolkit.filters.base.Filter, bool] = True, focus_on_click: Union[prompt_toolkit.filters.base.Filter, bool] = False, wrap_lines: Union[prompt_toolkit.filters.base.Filter, bool] = True, read_only: Union[prompt_toolkit.filters.base.Filter, bool] = False, width: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, height: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, dont_extend_height: Union[prompt_toolkit.filters.base.Filter, bool] = False, dont_extend_width: Union[prompt_toolkit.filters.base.Filter, bool] = False, line_numbers: bool = False, get_line_prefix: Optional[Callable[[int, int], Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None]]] = None, scrollbar: bool = False, style: str = '', search_field: Optional[prompt_toolkit.widgets.toolbars.SearchToolbar] = None, preview_search: Union[prompt_toolkit.filters.base.Filter, bool] = True, prompt: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = '', input_processors: Optional[List[prompt_toolkit.layout.processors.Processor]] = None)

A simple input field.

This is a higher level abstraction on top of several other classes with sane defaults.

This widget does have the most common options, but it does not intend to cover every single use case. For more configurations options, you can always build a text area manually, using a Buffer, BufferControl and Window.

Buffer attributes:

Parameters:
  • text – The initial text.
  • multiline – If True, allow multiline input.
  • completerCompleter instance for auto completion.
  • complete_while_typing – Boolean.
  • accept_handler – Called when Enter is pressed (This should be a callable that takes a buffer as input).
  • historyHistory instance.
  • auto_suggestAutoSuggest instance for input suggestions.

BufferControl attributes:

Parameters:
  • password – When True, display using asterisks.
  • focusable – When True, allow this widget to receive the focus.
  • focus_on_click – When True, focus after mouse click.
  • input_processorsNone or a list of Processor objects.

Window attributes:

Parameters:
  • lexerLexer instance for syntax highlighting.
  • wrap_lines – When True, don’t scroll horizontally, but wrap lines.
  • width – Window width. (Dimension object.)
  • height – Window height. (Dimension object.)
  • scrollbar – When True, display a scroll bar.
  • style – A style string.
  • dont_extend_width – When True, don’t take up more width then the preferred width reported by the control.
  • dont_extend_height – When True, don’t take up more width then the preferred height reported by the control.
  • get_line_prefix – None or a callable that returns formatted text to be inserted before a line. It takes a line number (int) and a wrap_count and returns formatted text. This can be used for implementation of line continuations, things like Vim “breakindent” and so on.

Other attributes:

Parameters:search_field – An optional SearchToolbar object.
accept_handler

The accept handler. Called when the user accepts the input.

document

The Buffer document (text + cursor position).

text

The Buffer text.

class prompt_toolkit.widgets.Label(text: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None], style: str = '', width: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, dont_extend_height: bool = True, dont_extend_width: bool = False)

Widget that displays the given text. It is not editable or focusable.

Parameters:
  • text – The text to be displayed. (This can be multiline. This can be formatted text as well.)
  • style – A style string.
  • width – When given, use this width, rather than calculating it from the text size.
class prompt_toolkit.widgets.Button(text: str, handler: Optional[Callable[[], None]] = None, width: int = 12)

Clickable button.

Parameters:
  • text – The caption for the button.
  • handlerNone or callable. Called when the button is clicked.
  • width – Width of the button.
class prompt_toolkit.widgets.Frame(body: Union[prompt_toolkit.layout.containers.Container, MagicContainer], title: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = '', style: str = '', width: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, height: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, key_bindings: Optional[prompt_toolkit.key_binding.key_bindings.KeyBindings] = None, modal: bool = False)

Draw a border around any container, optionally with a title text.

Changing the title and body of the frame is possible at runtime by assigning to the body and title attributes of this class.

Parameters:
  • body – Another container object.
  • title – Text to be displayed in the top of the frame (can be formatted text).
  • style – Style string to be applied to this widget.
class prompt_toolkit.widgets.Shadow(body: Union[prompt_toolkit.layout.containers.Container, MagicContainer])

Draw a shadow underneath/behind this container. (This applies class:shadow the the cells under the shadow. The Style should define the colors for the shadow.)

Parameters:body – Another container object.
class prompt_toolkit.widgets.Box(body: Union[prompt_toolkit.layout.containers.Container, MagicContainer], padding: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, padding_left: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, padding_right: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, padding_top: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, padding_bottom: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, width: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, height: Union[None, int, prompt_toolkit.layout.dimension.Dimension, Callable[[], Any]] = None, style: str = '', char: Union[None, str, Callable[[], str]] = None, modal: bool = False, key_bindings: Optional[prompt_toolkit.key_binding.key_bindings.KeyBindings] = None)

Add padding around a container.

This also makes sure that the parent can provide more space than required by the child. This is very useful when wrapping a small element with a fixed size into a VSplit or HSplit object. The HSplit and VSplit try to make sure to adapt respectively the width and height, possibly shrinking other elements. Wrapping something in a Box makes it flexible.

Parameters:
  • body – Another container object.
  • padding – The margin to be used around the body. This can be overridden by padding_left, padding_right`, padding_top and padding_bottom.
  • style – A style string.
  • char – Character to be used for filling the space around the body. (This is supposed to be a character with a terminal width of 1.)
class prompt_toolkit.widgets.VerticalLine

A simple vertical line with a width of 1.

class prompt_toolkit.widgets.HorizontalLine

A simple horizontal line with a height of 1.

class prompt_toolkit.widgets.RadioList(values: Sequence[Tuple[_T, Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None]]])

List of radio buttons. Only one can be checked at the same time.

Parameters:values – List of (value, label) tuples.
class prompt_toolkit.widgets.Checkbox(text: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = '')

Backward compatibility util: creates a 1-sized CheckboxList

Parameters:text – the text
class prompt_toolkit.widgets.FormattedTextToolbar(text: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None], style: str = '', **kw)
class prompt_toolkit.widgets.SearchToolbar(search_buffer: Optional[prompt_toolkit.buffer.Buffer] = None, vi_mode: bool = False, text_if_not_searching: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = '', forward_search_prompt: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = 'I-search: ', backward_search_prompt: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = 'I-search backward: ', ignore_case: Union[prompt_toolkit.filters.base.Filter, bool] = False)
Parameters:
  • vi_mode – Display ‘/’ and ‘?’ instead of I-search.
  • ignore_case – Search case insensitive.
class prompt_toolkit.widgets.SystemToolbar(prompt: Union[str, MagicFormattedText, List[Union[Tuple[str, str], Tuple[str, str, Callable[[prompt_toolkit.mouse_events.MouseEvent], None]]]], Callable[[], Any], None] = 'Shell command: ', enable_global_bindings: Union[prompt_toolkit.filters.base.Filter, bool] = True)

Toolbar for a system prompt.

Parameters:prompt – Prompt to be displayed to the user.
class prompt_toolkit.widgets.MenuContainer(body: Union[prompt_toolkit.layout.containers.Container, MagicContainer], menu_items: List[MenuItem], floats: Optional[List[prompt_toolkit.layout.containers.Float]] = None, key_bindings: Optional[prompt_toolkit.key_binding.key_bindings.KeyBindingsBase] = None)
Parameters:
  • floats – List of extra Float objects to display.
  • menu_items – List of MenuItem objects.

Filters

Filters decide whether something is active or not (they decide about a boolean state). This is used to enable/disable features, like key bindings, parts of the layout and other stuff. For instance, we could have a HasSearch filter attached to some part of the layout, in order to show that part of the user interface only while the user is searching.

Filters are made to avoid having to attach callbacks to all event in order to propagate state. However, they are lazy, they don’t automatically propagate the state of what they are observing. Only when a filter is called (it’s actually a callable), it will calculate its value. So, its not really reactive programming, but it’s made to fit for this framework.

Filters can be chained using & and | operations, and inverted using the ~ operator, for instance:

filter = has_focus('default') & ~ has_selection
class prompt_toolkit.filters.Filter

Base class for any filter to activate/deactivate a feature, depending on a condition.

The return value of __call__ will tell if the feature should be active.

class prompt_toolkit.filters.Condition(func: Callable[[], bool])

Turn any callable into a Filter. The callable is supposed to not take any arguments.

This can be used as a decorator:

@Condition
def feature_is_active():  # `feature_is_active` becomes a Filter.
    return True
Parameters:func – Callable which takes no inputs and returns a boolean.
prompt_toolkit.filters.utils.to_filter(bool_or_filter: Union[prompt_toolkit.filters.base.Filter, bool]) → prompt_toolkit.filters.base.Filter

Accept both booleans and Filters as input and turn it into a Filter.

prompt_toolkit.filters.utils.is_true(value: Union[prompt_toolkit.filters.base.Filter, bool]) → bool

Test whether value is True. In case of a Filter, call it.

Parameters:value – Boolean or Filter instance.

Filters that accept a Application as argument.

prompt_toolkit.filters.app.has_focus(value: FocusableElement) → prompt_toolkit.filters.base.Condition

Enable when this buffer has the focus.

prompt_toolkit.filters.app.in_editing_mode(editing_mode: prompt_toolkit.enums.EditingMode) → prompt_toolkit.filters.base.Condition

Check whether a given editing mode is active. (Vi or Emacs.)

Key binding

class prompt_toolkit.key_binding.KeyBindingsBase

Interface for a KeyBindings.

bindings

List of Binding objects. (These need to be exposed, so that KeyBindings objects can be merged together.)

get_bindings_for_keys(keys: Tuple[Union[prompt_toolkit.keys.Keys, str], ...]) → List[prompt_toolkit.key_binding.key_bindings.Binding]

Return a list of key bindings that can handle these keys. (This return also inactive bindings, so the filter still has to be called, for checking it.)

Parameters:keys – tuple of keys.
get_bindings_starting_with_keys(keys: Tuple[Union[prompt_toolkit.keys.Keys, str], ...]) → List[prompt_toolkit.key_binding.key_bindings.Binding]

Return a list of key bindings that handle a key sequence starting with keys. (It does only return bindings for which the sequences are longer than keys. And like get_bindings_for_keys, it also includes inactive bindings.)

Parameters:keys – tuple of keys.
class prompt_toolkit.key_binding.KeyBindings

A container for a set of key bindings.

Example usage:

kb = KeyBindings()

@kb.add('c-t')
def _(event):
    print('Control-T pressed')

@kb.add('c-a', 'c-b')
def _(event):
    print('Control-A pressed, followed by Control-B')

@kb.add('c-x', filter=is_searching)
def _(event):
    print('Control-X pressed')  # Works only if we are searching.
add(*keys, filter: Union[prompt_toolkit.filters.base.Filter, bool] = True, eager: Union[prompt_toolkit.filters.base.Filter, bool] = False, is_global: Union[prompt_toolkit.filters.base.Filter, bool] = False, save_before: Callable[[KeyPressEvent], bool] = <function KeyBindings.<lambda>>, record_in_macro: Union[prompt_toolkit.filters.base.Filter, bool] = True) → Callable[[T], T]

Decorator for adding a key bindings.

Parameters:
  • filterFilter to determine when this key binding is active.
  • eagerFilter or bool. When True, ignore potential longer matches when this key binding is hit. E.g. when there is an active eager key binding for Ctrl-X, execute the handler immediately and ignore the key binding for Ctrl-X Ctrl-E of which it is a prefix.
  • is_global – When this key bindings is added to a Container or Control, make it a global (always active) binding.
  • save_before – Callable that takes an Event and returns True if we should save the current buffer, before handling the event. (That’s the default.)
  • record_in_macro – Record these key bindings when a macro is being recorded. (True by default.)
add_binding(*keys, filter: Union[prompt_toolkit.filters.base.Filter, bool] = True, eager: Union[prompt_toolkit.filters.base.Filter, bool] = False, is_global: Union[prompt_toolkit.filters.base.Filter, bool] = False, save_before: Callable[[KeyPressEvent], bool] = <function KeyBindings.<lambda>>, record_in_macro: Union[prompt_toolkit.filters.base.Filter, bool] = True) → Callable[[T], T]

Decorator for adding a key bindings.

Parameters:
  • filterFilter to determine when this key binding is active.
  • eagerFilter or bool. When True, ignore potential longer matches when this key binding is hit. E.g. when there is an active eager key binding for Ctrl-X, execute the handler immediately and ignore the key binding for Ctrl-X Ctrl-E of which it is a prefix.
  • is_global – When this key bindings is added to a Container or Control, make it a global (always active) binding.
  • save_before – Callable that takes an Event and returns True if we should save the current buffer, before handling the event. (That’s the default.)
  • record_in_macro – Record these key bindings when a macro is being recorded. (True by default.)
bindings

List of Binding objects. (These need to be exposed, so that KeyBindings objects can be merged together.)

get_bindings_for_keys(keys: Tuple[Union[prompt_toolkit.keys.Keys, str], ...]) → List[prompt_toolkit.key_binding.key_bindings.Binding]

Return a list of key bindings that can handle this key. (This return also inactive bindings, so the filter still has to be called, for checking it.)

Parameters:keys – tuple of keys.
get_bindings_starting_with_keys(keys: Tuple[Union[prompt_toolkit.keys.Keys, str], ...]) → List[prompt_toolkit.key_binding.key_bindings.Binding]

Return a list of key bindings that handle a key sequence starting with keys. (It does only return bindings for which the sequences are longer than keys. And like get_bindings_for_keys, it also includes inactive bindings.)

Parameters:keys – tuple of keys.
remove(*args) → None

Remove a key binding.

This expects either a function that was given to add method as parameter or a sequence of key bindings.

Raises ValueError when no bindings was found.

Usage:

remove(handler)  # Pass handler.
remove('c-x', 'c-a')  # Or pass the key bindings.
remove_binding(*args) → None

Remove a key binding.

This expects either a function that was given to add method as parameter or a sequence of key bindings.

Raises ValueError when no bindings was found.

Usage:

remove(handler)  # Pass handler.
remove('c-x', 'c-a')  # Or pass the key bindings.
class prompt_toolkit.key_binding.ConditionalKeyBindings(key_bindings: prompt_toolkit.key_binding.key_bindings.KeyBindingsBase, filter: Union[prompt_toolkit.filters.base.Filter, bool] = True)

Wraps around a KeyBindings. Disable/enable all the key bindings according to the given (additional) filter.:

@Condition
def setting_is_true():
    return True  # or False

registry = ConditionalKeyBindings(key_bindings, setting_is_true)

When new key bindings are added to this object. They are also enable/disabled according to the given filter.

Parameters:
prompt_toolkit.key_binding.merge_key_bindings(bindings: Sequence[prompt_toolkit.key_binding.key_bindings.KeyBindingsBase]) → prompt_toolkit.key_binding.key_bindings._MergedKeyBindings

Merge multiple Keybinding objects together.

Usage:

bindings = merge_key_bindings([bindings1, bindings2, ...])
class prompt_toolkit.key_binding.DynamicKeyBindings(get_key_bindings: Callable[[], Optional[prompt_toolkit.key_binding.key_bindings.KeyBindingsBase]])

KeyBindings class that can dynamically returns any KeyBindings.

Parameters:get_key_bindings – Callable that returns a KeyBindings instance.

Default key bindings.:

key_bindings = load_key_bindings()
app = Application(key_bindings=key_bindings)
prompt_toolkit.key_binding.defaults.load_key_bindings() → prompt_toolkit.key_binding.key_bindings.KeyBindingsBase

Create a KeyBindings object that contains the default key bindings.

class prompt_toolkit.key_binding.vi_state.InputMode

An enumeration.

class prompt_toolkit.key_binding.vi_state.ViState

Mutable class to hold the state of the Vi navigation.

input_mode

Get InputMode.

last_character_find = None

None or CharacterFind instance. (This is used to repeat the last search in Vi mode, by pressing the ‘n’ or ‘N’ in navigation mode.)

named_registers = None

Named registers. Maps register name (e.g. ‘a’) to ClipboardData instances.

reset() → None

Reset state, go back to the given mode. INSERT by default.

tilde_operator = None

When true, make ~ act as an operator.

waiting_for_digraph = None

Waiting for digraph.

An KeyProcessor receives callbacks for the keystrokes parsed from the input in the InputStream instance.

The KeyProcessor will according to the implemented keybindings call the correct callbacks when new key presses are feed through feed.

class prompt_toolkit.key_binding.key_processor.KeyProcessor(key_bindings: prompt_toolkit.key_binding.key_bindings.KeyBindingsBase)

Statemachine that receives KeyPress instances and according to the key bindings in the given KeyBindings, calls the matching handlers.

p = KeyProcessor(key_bindings)

# Send keys into the processor.
p.feed(KeyPress(Keys.ControlX, ''))
p.feed(KeyPress(Keys.ControlC, '')

# Process all the keys in the queue.
p.process_keys()

# Now the ControlX-ControlC callback will be called if this sequence is
# registered in the key bindings.
Parameters:key_bindingsKeyBindingsBase instance.
empty_queue() → List[prompt_toolkit.key_binding.key_processor.KeyPress]

Empty the input queue. Return the unprocessed input.

feed(key_press: prompt_toolkit.key_binding.key_processor.KeyPress, first: bool = False) → None

Add a new KeyPress to the input queue. (Don’t forget to call process_keys in order to process the queue.)

Parameters:first – If true, insert before everything else.
feed_multiple(key_presses: List[prompt_toolkit.key_binding.key_processor.KeyPress], first: bool = False) → None
Parameters:first – If true, insert before everything else.
process_keys() → None

Process all the keys in the input_queue. (To be called after feed.)

Note: because of the feed/process_keys separation, it is
possible to call feed from inside a key binding. This function keeps looping until the queue is empty.
class prompt_toolkit.key_binding.key_processor.KeyPress(key: Union[prompt_toolkit.keys.Keys, str], data: Optional[str] = None)
Parameters:
  • key – A Keys instance or text (one character).
  • data – The received string on stdin. (Often vt100 escape codes.)
class prompt_toolkit.key_binding.key_processor.KeyPressEvent(key_processor_ref: weakref.ReferenceType[KeyProcessor], arg: Optional[str], key_sequence: List[prompt_toolkit.key_binding.key_processor.KeyPress], previous_key_sequence: List[prompt_toolkit.key_binding.key_processor.KeyPress], is_repeat: bool)

Key press event, delivered to key bindings.

Parameters:
  • key_processor_ref – Weak reference to the KeyProcessor.
  • arg – Repetition argument.
  • key_sequence – List of KeyPress instances.
  • previouskey_sequence – Previous list of KeyPress instances.
  • is_repeat – True when the previous event was delivered to the same handler.
app

The current Application object.

append_to_arg_count(data: str) → None

Add digit to the input argument.

Parameters:data – the typed digit as string
arg

Repetition argument.

arg_present

True if repetition argument was explicitly provided.

cli

For backward-compatibility.

current_buffer

The current buffer.

is_repeat = None

True when the previous key sequence was handled by the same handler.

Eventloop

prompt_toolkit.eventloop.get_traceback_from_context(context: Dict[str, Any]) → Optional[traceback]

Get the traceback object from the context.

Input

class prompt_toolkit.input.Input

Abstraction for any input.

An instance of this class can be given to the constructor of a Application and will also be passed to the EventLoop.

attach(input_ready_callback: Callable[[], None]) → AbstractContextManager[None]

Return a context manager that makes this input active in the current event loop.

close() → None

Close input.

closed

Should be true when the input stream is closed.

cooked_mode() → AbstractContextManager[None]

Context manager that turns the input into cooked mode.

detach() → AbstractContextManager[None]

Return a context manager that makes sure that this input is not active in the current event loop.

fileno() → int

Fileno for putting this in an event loop.

flush() → None

The event loop can call this when the input has to be flushed.

flush_keys() → List[prompt_toolkit.key_binding.key_processor.KeyPress]

Flush the underlying parser. and return the pending keys. (Used for vt100 input.)

raw_mode() → AbstractContextManager[None]

Context manager that turns the input into raw mode.

read_keys() → List[prompt_toolkit.key_binding.key_processor.KeyPress]

Return a list of Key objects which are read/parsed from the input.

responds_to_cpr

True if the Application can expect to receive a CPR response from here.

typeahead_hash() → str

Identifier for storing type ahead key presses.

class prompt_toolkit.input.DummyInput

Input for use in a DummyApplication

attach(input_ready_callback: Callable[[], None]) → AbstractContextManager[None]

Return a context manager that makes this input active in the current event loop.

closed

Should be true when the input stream is closed.

cooked_mode() → AbstractContextManager[None]

Context manager that turns the input into cooked mode.

detach() → AbstractContextManager[None]

Return a context manager that makes sure that this input is not active in the current event loop.

fileno() → int

Fileno for putting this in an event loop.

raw_mode() → AbstractContextManager[None]

Context manager that turns the input into raw mode.

read_keys() → List[prompt_toolkit.key_binding.key_processor.KeyPress]

Return a list of Key objects which are read/parsed from the input.

typeahead_hash() → str

Identifier for storing type ahead key presses.

prompt_toolkit.input.create_input(stdin: Optional[TextIO] = None) → prompt_toolkit.input.base.Input

Create the appropriate Input object for the current os/environment.

class prompt_toolkit.input.vt100.Vt100Input(stdin: TextIO)

Vt100 input for Posix systems. (This uses a posix file descriptor that can be registered in the event loop.)

attach(input_ready_callback: Callable[[], None]) → AbstractContextManager[None]

Return a context manager that makes this input active in the current event loop.

closed

Should be true when the input stream is closed.

cooked_mode() → AbstractContextManager[None]

Context manager that turns the input into cooked mode.

detach() → AbstractContextManager[None]

Return a context manager that makes sure that this input is not active in the current event loop.

fileno() → int

Fileno for putting this in an event loop.

flush_keys() → List[prompt_toolkit.key_binding.key_processor.KeyPress]

Flush pending keys and return them. (Used for flushing the ‘escape’ key.)

raw_mode() → AbstractContextManager[None]

Context manager that turns the input into raw mode.

read_keys() → List[prompt_toolkit.key_binding.key_processor.KeyPress]

Read list of KeyPress.

responds_to_cpr

True if the Application can expect to receive a CPR response from here.

typeahead_hash() → str

Identifier for storing type ahead key presses.

class prompt_toolkit.input.vt100.raw_mode(fileno: int)
with raw_mode(stdin):
    ''' the pseudo-terminal stdin is now used in raw mode '''

We ignore errors when executing tcgetattr fails.

class prompt_toolkit.input.vt100.cooked_mode(fileno: int)

The opposite of raw_mode, used when we need cooked mode inside a raw_mode block. Used in Application.run_in_terminal.:

with cooked_mode(stdin):
    ''' the pseudo-terminal stdin is now used in cooked mode. '''

Parser for VT100 input stream.

class prompt_toolkit.input.vt100_parser.Vt100Parser(feed_key_callback: Callable[[prompt_toolkit.key_binding.key_processor.KeyPress], None])

Parser for VT100 input stream. Data can be fed through the feed method and the given callback will be called with KeyPress objects.

def callback(key):
    pass
i = Vt100Parser(callback)
i.feed('data...')
Attr feed_key_callback:
 Function that will be called when a key is parsed.
feed(data: str) → None

Feed the input stream.

Parameters:data – Input string (unicode).
feed_and_flush(data: str) → None

Wrapper around feed and flush.

flush() → None

Flush the buffer of the input stream.

This will allow us to handle the escape key (or maybe meta) sooner. The input received by the escape key is actually the same as the first characters of e.g. Arrow-Up, so without knowing what follows the escape sequence, we don’t know whether escape has been pressed, or whether it’s something else. This flush function should be called after a timeout, and processes everything that’s still in the buffer as-is, so without assuming any characters will follow.

Mappings from VT100 (ANSI) escape sequences to the corresponding prompt_toolkit keys.

Output

class prompt_toolkit.output.Output

Base class defining the output interface for a Renderer.

Actual implementations are Vt100_Output and Win32Output.

ask_for_cpr() → None

Asks for a cursor position report (CPR). (VT100 only.)

bell() → None

Sound bell.

clear_title() → None

Clear title again. (or restore previous title.)

cursor_backward(amount: int) → None

Move cursor amount place backward.

cursor_down(amount: int) → None

Move cursor amount place down.

cursor_forward(amount: int) → None

Move cursor amount place forward.

cursor_goto(row: int = 0, column: int = 0) → None

Move cursor position.

cursor_up(amount: int) → None

Move cursor amount place up.

disable_autowrap() → None

Disable auto line wrapping.

disable_bracketed_paste() → None

For vt100 only.

disable_mouse_support() → None

Disable mouse.

enable_autowrap() → None

Enable auto line wrapping.

enable_bracketed_paste() → None

For vt100 only.

enable_mouse_support() → None

Enable mouse.

encoding() → str

Return the encoding for this output, e.g. ‘utf-8’. (This is used mainly to know which characters are supported by the output the data, so that the UI can provide alternatives, when required.)

enter_alternate_screen() → None

Go to the alternate screen buffer. (For full screen applications).

erase_down() → None

Erases the screen from the current line down to the bottom of the screen.

erase_end_of_line() → None

Erases from the current cursor position to the end of the current line.

erase_screen() → None

Erases the screen with the background colour and moves the cursor to home.

fileno() → int

Return the file descriptor to which we can write for the output.

flush() → None

Write to output stream and flush.

get_rows_below_cursor_position() → int

For Windows only.

get_size() → prompt_toolkit.data_structures.Size

Return the size of the output window.

hide_cursor() → None

Hide cursor.

quit_alternate_screen() → None

Leave the alternate screen buffer.

reset_attributes() → None

Reset color and styling attributes.

scroll_buffer_to_prompt() → None

For Win32 only.

set_attributes(attrs: prompt_toolkit.styles.base.Attrs, color_depth: prompt_toolkit.output.color_depth.ColorDepth) → None

Set new color and styling attributes.

set_title(title: str) → None

Set terminal title.

show_cursor() → None

Show cursor.

write(data: str) → None

Write text (Terminal escape sequences will be removed/escaped.)

write_raw(data: str) → None

Write text.

class prompt_toolkit.output.DummyOutput

For testing. An output class that doesn’t render anything.

ask_for_cpr() → None

Asks for a cursor position report (CPR). (VT100 only.)

bell() → None

Sound bell.

clear_title() → None

Clear title again. (or restore previous title.)

cursor_backward(amount: int) → None

Move cursor amount place backward.

cursor_down(amount: int) → None

Move cursor amount place down.

cursor_forward(amount: int) → None

Move cursor amount place forward.

cursor_goto(row: int = 0, column: int = 0) → None

Move cursor position.

cursor_up(amount: int) → None

Move cursor amount place up.

disable_autowrap() → None

Disable auto line wrapping.

disable_bracketed_paste() → None

For vt100 only.

disable_mouse_support() → None

Disable mouse.

enable_autowrap() → None

Enable auto line wrapping.

enable_bracketed_paste() → None

For vt100 only.

enable_mouse_support() → None

Enable mouse.

encoding() → str

Return the encoding for this output, e.g. ‘utf-8’. (This is used mainly to know which characters are supported by the output the data, so that the UI can provide alternatives, when required.)

enter_alternate_screen() → None

Go to the alternate screen buffer. (For full screen applications).

erase_down() → None

Erases the screen from the current line down to the bottom of the screen.

erase_end_of_line() → None

Erases from the current cursor position to the end of the current line.

erase_screen() → None

Erases the screen with the background colour and moves the cursor to home.

fileno() → int

There is no sensible default for fileno().

flush() → None

Write to output stream and flush.

get_rows_below_cursor_position() → int

For Windows only.

get_size() → prompt_toolkit.data_structures.Size

Return the size of the output window.

hide_cursor() → None

Hide cursor.

quit_alternate_screen() → None

Leave the alternate screen buffer.

reset_attributes() → None

Reset color and styling attributes.

scroll_buffer_to_prompt() → None

For Win32 only.

set_attributes(attrs: prompt_toolkit.styles.base.Attrs, color_depth: prompt_toolkit.output.color_depth.ColorDepth) → None

Set new color and styling attributes.

set_title(title: str) → None

Set terminal title.

show_cursor() → None

Show cursor.

write(data: str) → None

Write text (Terminal escape sequences will be removed/escaped.)

write_raw(data: str) → None

Write text.

class prompt_toolkit.output.ColorDepth

Possible color depth values for the output.

DEPTH_1_BIT = 'DEPTH_1_BIT'

One color only.

DEPTH_24_BIT = 'DEPTH_24_BIT'

24 bit True color.

DEPTH_4_BIT = 'DEPTH_4_BIT'

ANSI Colors.

DEPTH_8_BIT = 'DEPTH_8_BIT'

The default.

prompt_toolkit.output.create_output(stdout: Optional[TextIO] = None) → prompt_toolkit.output.base.Output

Return an Output instance for the command line.

Parameters:stdout – The stdout object

Output for vt100 terminals.

A lot of thanks, regarding outputting of colors, goes to the Pygments project: (We don’t rely on Pygments anymore, because many things are very custom, and everything has been highly optimized.) http://pygments.org/

class prompt_toolkit.output.vt100.Vt100_Output(stdout: TextIO, get_size: Callable[[], prompt_toolkit.data_structures.Size], term: Optional[str] = None, write_binary: bool = True)
Parameters:
  • get_size – A callable which returns the Size of the output terminal.
  • stdout – Any object with has a write and flush method + an ‘encoding’ property.
  • term – The terminal environment variable. (xterm, xterm-256color, linux, …)
  • write_binary – Encode the output before writing it. If True (the default), the stdout object is supposed to expose an encoding attribute.
ask_for_cpr() → None

Asks for a cursor position report (CPR).

bell() → None

Sound bell.

clear_title() → None

Clear title again. (or restore previous title.)

cursor_backward(amount: int) → None

Move cursor amount place backward.

cursor_down(amount: int) → None

Move cursor amount place down.

cursor_forward(amount: int) → None

Move cursor amount place forward.

cursor_goto(row: int = 0, column: int = 0) → None

Move cursor position.

cursor_up(amount: int) → None

Move cursor amount place up.

disable_autowrap() → None

Disable auto line wrapping.

disable_bracketed_paste() → None

For vt100 only.

disable_mouse_support() → None

Disable mouse.

enable_autowrap() → None

Enable auto line wrapping.

enable_bracketed_paste() → None

For vt100 only.

enable_mouse_support() → None

Enable mouse.

encoding() → str

Return encoding used for stdout.

enter_alternate_screen() → None

Go to the alternate screen buffer. (For full screen applications).

erase_down() → None

Erases the screen from the current line down to the bottom of the screen.

erase_end_of_line() → None

Erases from the current cursor position to the end of the current line.

erase_screen() → None

Erases the screen with the background colour and moves the cursor to home.

fileno() → int

Return file descriptor.

flush() → None

Write to output stream and flush.

classmethod from_pty(stdout: TextIO, term: Optional[str] = None) → prompt_toolkit.output.vt100.Vt100_Output

Create an Output class from a pseudo terminal. (This will take the dimensions by reading the pseudo terminal attributes.)

get_size() → prompt_toolkit.data_structures.Size

Return the size of the output window.

hide_cursor() → None

Hide cursor.

quit_alternate_screen() → None

Leave the alternate screen buffer.

reset_attributes() → None

Reset color and styling attributes.

set_attributes(attrs: prompt_toolkit.styles.base.Attrs, color_depth: prompt_toolkit.output.color_depth.ColorDepth) → None

Create new style and output.

Parameters:attrsAttrs instance.
set_title(title: str) → None

Set terminal title.

show_cursor() → None

Show cursor.

write(data: str) → None

Write text to output. (Removes vt100 escape codes. – used for safely writing text.)

write_raw(data: str) → None

Write raw data to output.

Patch stdout

patch_stdout

This implements a context manager that ensures that print statements within it won’t destroy the user interface. The context manager will replace sys.stdout by something that draws the output above the current prompt, rather than overwriting the UI.

Usage:

with patch_stdout(application):
    ...
    application.run()
    ...

Multiple applications can run in the body of the context manager, one after the other.

prompt_toolkit.patch_stdout.patch_stdout(raw: bool = False) → Generator[None, None, None]

Replace sys.stdout by an _StdoutProxy instance.

Writing to this proxy will make sure that the text appears above the prompt, and that it doesn’t destroy the output from the renderer. If no application is curring, the behaviour should be identical to writing to sys.stdout directly.

Warning: If a new event loop is installed using asyncio.set_event_loop(),
then make sure that the context manager is applied after the event loop is changed. Printing to stdout will be scheduled in the event loop that’s active when the context manager is created.
Parameters:raw – (bool) When True, vt100 terminal escape sequences are not removed/escaped.
class prompt_toolkit.patch_stdout.StdoutProxy(raw: bool = False, original_stdout: Optional[TextIO] = None)

Proxy object for stdout which captures everything and prints output above the current application.

fileno() → int

Return file descriptor.

flush() → None

Flush buffered output.