Spec-Zone.ru › Matplotlib 3.6

matplotlib.axes.Axes.locator_params

Axes.locator_params(axis='both', tight=None, **kwargs)[source]

Управление поведением локаторов основных делений.

Поскольку локатор участвует в автоматическом масштабировании, autoscale_view вызывается автоматически после изменения параметров.

Параметры:
axis{'both', 'x', 'y'}, по умолчанию: 'both'

Ось, на которой нужно выполнить операцию. (Для 3D осей, axis также может быть 'z', и 'both' относится ко всем трём осям.)

tightbool или None, необязательно

Параметр, передаваемый в autoscale_view. По умолчанию None, никаких изменений.

Другие параметры:
**kwargs

Остальные ключевые параметры передаются напрямую в метод set_params() локатора. Поддерживаемые ключевые слова зависят от типа локатора. Например, см. set_params для ticker.MaxNLocator, используемого по умолчанию для линейных.

Примеры

При построении небольших подграфиков можно уменьшить максимальное количество делений и использовать жёсткие границы, например:

ax.locator_params(tight=True, nbins=4)

Примеры использования matplotlib.axes.Axes.locator_params

<img alt="Contourf Demo" src="data:image/png;base64,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

Демонстрация Contourf

Демонстрация Contourf
Руководство по ограниченной компоновке

Руководство по ограниченной компоновке

Руководство по ограниченной компоновке
Руководство по Tight Layout

Руководство по Tight Layout

Руководство по Tight Layout

© 2012–2023 Matplotlib Development Team. All rights reserved.
Licensed under the Matplotlib License Agreement.
https://matplotlib.org/3.6.0/api/_as_gen/matplotlib.axes.Axes.locator_params.html

Spec-Zone.ru

Настройки Оффлайн Что нового Помощь О нас
Spec-Zone .ru
спецификации, руководства, описания, API