Spec-Zone.ru › Matplotlib 3.5
matplotlib.axes.Axes.errorbar Axes. errorbar ( x , y , yerr = None , xerr = None , fmt = '' , ecolor = None , elinewidth = None , capsize = None , barsabove = False , lolims = False , uplims = False , xlolims = False , xuplims = False , errorevery = 1 , capthick = None , * , data = None , ** kwargs ) [source]
Строит график y по оси x с указанием погрешностей.
x , y определяют положение данных, xerr , yerr определяют размеры погрешностей. По умолчанию, это рисует маркеры/линии данных, а также погрешности. Используйте fmt='none', чтобы нарисовать погрешности без маркеров данных.
Параметры
x, y float или массив
Положение данных.
xerr, yerr float или массив, shape(N,) или shape(2, N), необязательно
Размеры погрешностей:
скаляр: Симметричные +/- значения для всех точек данных. shape(N,): Симметричные +/- значения для каждой точки данных. shape(2, N): Отдельные - и + значения для каждой погрешности. Первая строка содержит нижние погрешности, вторая строка содержит верхние погрешности.
None : Без погрешностей. Обратите внимание, что все массивы погрешностей должны иметь положительные значения.
См. Различные способы задания погрешностей для примера использования xerr и yerr.
fmt строка, по умолчанию: ''
Формат для точек/линий данных. См. plot для деталей.
Используйте 'none' (регистронезависимо) для построения погрешностей без маркеров.
ecolor цвет, по умолчанию: None
Цвет линий погрешностей. Если None, используется цвет линии, соединяющей маркеры.
elinewidth вещественное число, по умолчанию: None
Толщина линий погрешностей. Если None, используется толщина линии текущего стиля.
capsize вещественное число, по умолчанию: rcParams["errorbar.capsize"] (по умолчанию: 0.0)
Длина колпачков погрешностей в пунктах.
capthick вещественное число, по умолчанию: None
Псевдоним для ключевого аргумента markeredgewidth (также известного как mew ). Эта настройка — более осмысленное название для свойства, которое контролирует толщину колпачков погрешностей в пунктах. Для обратной совместимости, если заданы mew или markeredgewidth , они переопределят capthick . Это может измениться в будущих выпусках.
barsabove bool, по умолчанию: False
Если True, то погрешности будут нарисованы над символами графика. По умолчанию — под.
lolims, uplims, xlolims, xuplims bool, по умолчанию: False
Эти аргументы могут быть использованы для указания того, что значение задает только верхние/нижние пределы. В этом случае используется символ каретки, чтобы указать это. lims -аргументы могут быть скалярами или массивами той же длины, что и xerr и yerr . Чтобы использовать пределы с инвертированными осями, нужно вызвать set_xlim или set_ylim до errorbar() . Обратите внимание на хитрые имена параметров: установка, например, lolims в True означает, что значение y является нижним пределом истинного значения, поэтому будет нарисован только стрелка направленная вверх !
errorevery int или (int, int), по умолчанию: 1
Рисует погрешности для подмножества данных. errorevery =N рисует погрешности для точек (x[::N], y[::N]). errorevery =(start, N) рисует погрешности для точек (x[start::N], y[start::N]). Например, errorevery=(6, 3) добавляет погрешности к данным в (x[6], x[9], x[12], x[15], ...). Используется для предотвращения перекрывания погрешностей при использовании двух рядов данных с одинаковыми значениями на оси x .
Возвращаемое значение
ErrorbarContainer
Контейнер содержит:
plotline: Экземпляр Line2D для маркеров и/или линии x , y . caplines: Кортеж экземпляров Line2D колпачков погрешностей. barlinecols: Кортеж LineCollection с горизонтальными и вертикальными диапазонами погрешностей. Другие параметры
data индексируемый объект, необязательно
Если задано, следующие параметры также принимают строку s, которая интерпретируется как data[s] (если это не приводит к исключению):
x , y , xerr , yerr
**kwargs
Все остальные ключевые аргументы передаются в вызов plot для рисования маркеров. Например, этот код создает большие красные квадраты с толстыми зелеными границами:
x, y, yerr = rand(3, 10)
errorbar(x, y, yerr, marker='s', mfc='red',
mec='green', ms=20, mew=4)
где mfc , mec , ms и mew являются псевдонимами для более длинных имен свойств, markerfacecolor , markeredgecolor , markersize и markeredgewidth .
Допустимые значения kwargs для свойств маркеров — это свойства Line2D .
Свойство
Описание
agg_filter
функция-фильтр, которая принимает (m, n, 3) массив чисел с плавающей точкой и значение dpi, и возвращает массив (m, n, 3)
alpha
скаляр или None
animated
bool
antialiased или aa
bool
clip_box
Bbox
clip_on
bool
clip_path
Объект Patch или (Path, Transform) или None
color или c
цвет
dash_capstyle
CapStyle или {'butt', 'projecting', 'round'}
dash_joinstyle
JoinStyle или {'miter', 'round', 'bevel'}
dashes
последовательность чисел с плавающей точкой (чернила/пробел в точках) или (None, None)
data
(2, N) массив или два одномерных массива
drawstyle или ds
{'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, по умолчанию: 'default'
figure
Figure
fillstyle
{'full', 'left', 'right', 'bottom', 'top', 'none'}
gid
строка
in_layout
bool
label
объект
linestyle или ls
{'-', '--', '-.', ':', '', (смещение, последовательность включения/выключения), ...}
linewidth или lw
число с плавающей точкой
marker
строка стиля маркера, Path или MarkerStyle
markeredgecolor или mec
цвет
markeredgewidth или mew
число с плавающей точкой
markerfacecolor или mfc
цвет
markerfacecoloralt или mfcalt
цвет
markersize или ms
число с плавающей точкой
markevery
None или целое число или (целое число, целое число) или срез или список [целое число] или число с плавающей точкой или (число с плавающей точкой, число с плавающей точкой) или список [bool]
path_effects
AbstractPathEffect
picker
число с плавающей точкой или вызываемый объект [[Художник, Событие], кортеж [bool, dict]]
pickradius
число с плавающей точкой
rasterized
bool
sketch_params
(масштаб: число с плавающей точкой, длина: число с плавающей точкой, случайность: число с плавающей точкой)
snap
bool или None
solid_capstyle
CapStyle или {'butt', 'projecting', 'round'}
solid_joinstyle
JoinStyle или {'miter', 'round', 'bevel'}
transform
неизвестно
url
строка
visible
bool
xdata
одномерный массив
ydata
одномерный массив
zorder
число с плавающей точкой
Примеры использования matplotlib.axes.Axes.errorbar
<img alt="Демонстрация легенды" 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
<img alt="3D errorbars" 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
<img alt="Демонстрация логарифмических осей" src="data:image/png;base64,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