Spec-Zone.ru › Matplotlib 3.3

matplotlib.pyplot.contour

matplotlib.pyplot.contour(*args, data=None, **kwargs) [source]

Строить контурные линии.

Вызов:

contour([X, Y,] Z, [levels], **kwargs)

contour и contourf строят, соответственно, контурные линии и заполненные контурные области. За исключением отмеченных случаев, сигнатуры функций и возвращаемые значения одинаковы для обеих версий.

Параметры:
X, Yarray-like, optional

Координаты значений в Z.

X и Y должны быть двумерными массивами с той же формой, что и Z (например, созданные с помощью numpy.meshgrid), или они должны быть одномерными, так что len(X) == M — это количество столбцов в Z, а len(Y) == N — количество строк в Z.

Если не указаны, они предполагаются целочисленными индексами, т.е. X = range(M), Y = range(N).

Zarray-like(N, M)

Значения высот, на которых строится контур.

levelsint or array-like, optional

Определяет количество и позиции контурных линий/областей.

Если целое число n, используйте MaxNLocator, которая пытается автоматически выбрать не более n+1 «красивых» уровней контура между vmin и vmax.

Если массив, стройте контурные линии на указанных уровнях. Значения должны быть упорядочены по возрастанию.

Возвращает:
QuadContourSet
Другие параметры:
corner_maskbool, default: rcParams["contour.corner_mask"] (default: True)

Включает/выключает маскирование углов, что имеет эффект только если Z является массивкой с пропускаемыми значениями. Если False, любой четырехугольник, соприкасающийся с пропускаемым значением, маскируется. Если True, только треугольные углы четырехугольников, ближайшие к этим точкам, всегда маскируются, другие треугольные углы, состоящие из трёх немаскированных точек, отображаются как обычно.

colorscolor string or sequence of colors, optional

Цвета уровней, т.е. линии для contour и области для contourf.

Последовательность циклически используется для уровней в порядке возрастания. Если последовательность короче количества уровней, она повторяется.

В качестве сокращения могут использоваться одиночные строковые цвета вместо списков с одним элементом, т.е. 'red' вместо ['red'], чтобы окрасить все уровни в один цвет. Это сокращение работает только для цветовых строк, а не для других способов задания цветов.

По умолчанию (значение None) используется цветовая карта, заданная параметром cmap.

alphafloat, default: 1

Значение альфа-смешивания, от 0 (прозрачный) до 1 (непрозрачный).

cmapstr or Colormap, default: rcParams["image.cmap"] (default: 'viridis')

Экземпляр Colormap или зарегистрированное имя цветовой карты. Цветовая карта сопоставляет значения уровней с цветами.

Если заданы и colors, и cmap, произойдёт ошибка.

normNormalize, optional

Если используется цветовая карта, экземпляр Normalize масштабирует значения уровней до канонического диапазона цветовой карты [0, 1] для сопоставления с цветами. Если не указан, используется линейное масштабирование по умолчанию.

vmin, vmaxfloat, optional

Если не None, эти значения будут предоставлены экземпляру Normalize, переопределяя масштабирование цветов по умолчанию, основанное на levels.

origin{None, 'upper', 'lower', 'image'}, default: None

Определяет ориентацию и точное положение Z, задавая положение Z[0, 0]. Это актуально только если X, Y не указаны.

  • None: Z[0, 0] находится в левом нижнем углу при X=0, Y=0.
  • 'lower': Z[0, 0] находится в левом нижнем углу при X=0.5, Y=0.5.
  • 'upper': Z[0, 0] находится в левом верхнем углу при X=N+0.5, Y=0.5.
  • 'image': Используйте значение из rcParams["image.origin"] (по умолчанию: 'upper').
extent(x0, x1, y0, y1), optional

Если origin не None, то extent интерпретируется как в imshow: он задаёт границы внешних пикселей. В этом случае положение Z[0, 0] — центр пикселя, а не угол. Если origin равно None, то (x0, y0) — положение Z[0, 0], а (x1, y1) — положение Z[-1, -1].

Этот аргумент игнорируется, если X и Y заданы при вызове contour.

locatorticker.Locator subclass, optional

Локатор используется для определения уровней контура, если они не заданы явно через levels. По умолчанию MaxNLocator.

extend{'neither', 'both', 'min', 'max'}, default: 'neither'

Определяет contourf окраску значений, которые находятся вне диапазона levels.

Если 'neither', значения вне диапазона levels не окрашиваются. Если 'min', 'max' или 'both', окрашиваются значения ниже, выше или ниже и выше диапазона levels.

Значения ниже min(levels) и выше max(levels) сопоставляются с под/над значениями цветовой карты Colormap. Обратите внимание, что большинство цветовых карт по умолчанию не имеют выделенных цветов для этих значений, поэтому значения «над» и «под» — это крайние значения цветовой карты. Вы можете явно задать эти значения, используя Colormap.set_under и Colormap.set_over.

Примечание

Существующий QuadContourSet не получает уведомлений о изменениях свойств его цветовой карты. Поэтому требуется явный вызов QuadContourSet.changed() после изменения цветовой карты. Явный вызов можно опустить, если цветовая шкала назначена объекту QuadContourSet, поскольку он внутренне вызывает QuadContourSet.changed().

Пример:

x = np.arange(1, 10)
y = x.reshape(-1, 1)
h = x * y

cs = plt.contourf(h, levels=[10, 30, 50],
    colors=['#808080', '#A0A0A0', '#C0C0C0'], extend='both')
cs.cmap.set_over('red')
cs.cmap.set_under('blue')
cs.changed()
xunits, yunitsregistered units, optional

Переопределяет единицы осей, указывая экземпляр matplotlib.units.ConversionInterface.

antialiasedbool, optional

Включает/отключает сглаживание, переопределяя значения по умолчанию. Для заполненных контуров значение по умолчанию True. Для контурных линий оно берётся из rcParams["lines.antialiased"] (по умолчанию: True).

nchunkint >= 0, optional

Если 0, нет разбиения области. Укажите положительное целое число для деления области на поддомены размера nchunk × nchunk четырехугольников. Разбиение уменьшает максимальную длину полигонов, генерируемых алгоритмом контурирования, что уменьшает нагрузку на бэкэнд при отрисовке и потребляет немного меньше ОЗУ. Однако это может привести к артефактам отрисовки на границах поддоменов, в зависимости от бэкэнда, флага antialiased и значения alpha.

linewidthsfloat or array-like, default: rcParams["contour.linewidth"] (default: None)

Применяется только к contour.

Ширина линий контурных линий.

Если число, все уровни будут нарисованы с этой шириной линии.

Если последовательность, уровни в порядке возрастания будут нарисованы с шириной линий, указанной в порядке.

Если None, это возвращается к rcParams["lines.linewidth"] (по умолчанию: 1.5).

linestyles{None, 'solid', 'dashed', 'dashdot', 'dotted'}, optional

Применяется только к contour.

Если linestyles равно None, по умолчанию «сплошная», если линии не монохромные. В этом случае отрицательные контуры получат стиль линии из rcParams["contour.negative_linestyle"] (по умолчанию: 'dashed') настройки.

linestyles также может быть итерируемым объектом указанных выше строк, определяющим набор стилей линий, которые будут использоваться. Если этот итерируемый объект короче, чем количество уровней контура, он будет повторяться по необходимости.

hatchesList[str], optional

Применяется только к contourf.

Список узоров штриховки, которые будут использоваться для заполненных областей. Если None, штриховка не будет добавлена к контуру. Штриховка поддерживается только в PostScript, PDF, SVG и Agg бэкендах.

Примечания

  1. contourf отличается от версии MATLAB тем, что не рисует рёбра полигонов. Чтобы нарисовать рёбра, добавьте контурные линии с вызовами contour.
  2. contourf заполняет интервалы, которые закрыты сверху; то есть, для границ z1 и z2, заполненная область:

    z1 < Z <= z2
    

    за исключением самого нижнего интервала, который закрыт с обеих сторон (т.е. включает самое низкое значение).

Примеры использования matplotlib.pyplot.contour

<img alt="Маска угла контура" 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

Маска углов контура

Демонстрация контуров

Демонстрация контуров

<img alt="Изображение контура" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAUAAAADgCAMAAABFJU/CAAADAFBMVEX29vb7+/vx9fD9/f3+/v729fb09vTm8+P////u4+719vXe3d319vSNjY35+fnz8PN9fX2Dg4O0tLSxsLHz9vLf39/w5fDw8PDw6PHs3+z18/Xp9ObExMS6urrz8/OlpaTU1NTq9Oh7eXvt7e2SkpLh4eHu/Ouqq6qhoaHn5+ft4e3t+umFjYT06fXY2Njk8uHi8t7r6+tPTk/08fTa2tqWlpYjJSPBwcFtbm1lHHGDiYFUVFO5sLm96bby7fNwcHDKysqdnp2Ghobs9ukUIxWAgIDUud1jY2PQttpeXl50dHR3dniurq6amZpYWVg7OTzu9eu7t7fp6ek1NDWJiYllaGW45rLMstbq2uzz5fPj5OPp+OVHSEfOz84nLCZpaWlzKIDx++5kuGpCQEOdc6+04a4ZdDWop6m+vr4HGQuieLSMjIsHfwcaLBvHx8ePkY/d9NcvLTB6gHhLR00BDAUSaC3My8z37fdetGXDpNG+pcfX8NEgGSI6AET19PUmHyn25/YcPSE0YznR0dHXvd9ZPWMIIg/Grc+RaKKi3J0RBBOMZZxlWWguBzWNUpxYo13m1Oidh6UeBSMtITKXbqklSyorWTFcqmKkzJ4ISB0PYikrNCoaeTdadFev2aoPVCXD7b1zZndPolV3KoVXF2Lf1N/l5eVID1Eeih6FYJQKLxWId40aEhy3ncIVbDCpkrI3Jz1NOFbbxOFbT19kgmFQlFZsYG+o0qNCMUkFOxhuem5Dfkd8WorX5dSjo6OOt4uHqIM9l06/n8xKiU9rrm2ogLlqIXeESJJ3l3NwjWzgzORMYEpjRm49cUF9tX1OH1dSaFDv3PAwPC7Bzr5DVkKxkL+Se5tzVX+Ae4HP6coyiUWxn7bf7Nv5/fhrT3e2ybM9SjuUypGyvLBxv3bm3OZ+nnqAcIabwJeb1Jc3RTXSy9I8EkN2QoTD3sCExYaUhpfO28sykzJAm0CpwaZ7aYPt4u2jlKbj+t3HusiVoJIxej+o4KPQvdSWq5O/scH79f3vOCr1AAAgAElEQVR42uzaf2ySZx4AcBhkPBvhDT8O3hj5NX5YegYqpJXXoUFw/C4gFCT9ga856iCU0bQ920vDwKTX1NQZ09ZFo5mZ4rBGemq6TLP0qn+ouWSLNdPY3ea57a7ncprbRc8l3iVL7nnBqq39wTnAuvTrH7zwtuV5P3yf5/l+XyGB5fhZQVomWAZcBlwGXAZcjhcASEIQhEx68myJXylZ+PTDiwbsYRoNzo0VYbFp+pUa9jN/urNcNkFtAT+kYuQeuMbpFyIN8/wkW116QIO9R9WAGhQ9doD0ZACoVPlq4ctUsYoMqqsBpVKuAsGmIIL0VAKhFEiFqFdMygTVpQFU+JBKegNVLAUksQoAijjjBUBMf3RW2CMFqFfllYOk2CtFuLnBwZBslAqBF5F6xUng9YrhDKpWAWqlvAFTlR5QgWmrWwUiRY+D5JBYgnSNIwQzEGH6JRmFzeZEQnYdR9DGkutxnZjTCmSccIjFsTHDpQEUyardJh3TbkvymbiTzLL4XRSGw1+RO6m2Ya6MIeRjO0gWvcbIxZJuu474tA30Fg7Qq4wavQXBXZgeEekxGeLGBTXy0gOO65xaBgHIjGg8DolRBHAOnNgu+HlrpKhO3iFVYRkbiOCAI8kBihzA4+oBpQJU6YDMAfydCF+ho+uoPS5VE0emyQ/VBGpqDEwQdEQwYDDRcWGTUIzlThkhoNhUAzCtnwMsEY0oHJKHvGVZAw1OAHwEoImjq6gQ2wWABT/UuB8AVEehulQ2khzn2qAe6NRDQAZnvAZQPRiTXCpAPzDIgH6jj6nQ9NhAg39jU0VFfmWUOUGtD443KGEzAdtIx1AbQsfzgGyAi+HAmWwsDhzhkCgsULuSZQF0whXZ6FEYgnauKxKhcywCN8xAta5CoMZqnDiqI3EtXg2/WufBwz2uSIhjaAUqAUdXmq1awRDbiCHhcUykcKO2sE+D2mr5ccCGW25Q53HxnTLQick1HouJ7kd1SLUlD68X1KlMLo+mwY+xNRm9Ia5FNcKyAHbyAdBWBzszHhA0tlQDEUNErLwqowyVtrR4kTAirQVslrTTJCJTRa1hVTAOqn0+VWmmcJDvrSWGxJFzjeNhKrdFZgHyFl9cqkHhWYGJA+JxkGGDOIPJkHKQMFldm7/4GqdIbWQZtQBjsDqBVGYUICKkLIBLOvi1elnuwDuzkKKyK1zPFjwSov6yPHfJtSQBSU4ZBZa8nJbn23XEBs7ca63WyZ8jfYk9l+8tGSDl6Zj9vFjxdMMAnyBBC5xIHjtbWmiXMSOIUZLnijlP5H575p8oIqAab386rl73fykpejAxT+7NWq9/dt3GImpFih6OS4IzDIVdBEPCLGo4KEUD5Joa11RN/6uinR0e/qZ+RbGDx1Hk3sx/8A8HbVRi8FQ9mSgyklhhF2GSJ4VFDNSBFA1Q/uZq2nRU0Wib/3w/dr6+/rXihnK9KPdm2I63d/iJA8QQEnn41XZmRWEXwcqQihlJZmkAidi05mJs/1R9iQEpXLocTQJ1oS3VSwRIW7npu9i94M5SAFIfA/6/8TIBwmm8ZXj43zt5pczAXzYgbfMfj8auvLbINOYplfXLGTg3IK9R9rfYUfrO+e26lMqph5zOKSU84j1HBiYZxF0HvslY+csEpK3cfCB279rOefimHjrtTW1ENOGM2juLZ+LjDDz49g4b3DwAmeuH4/Ki486XFpCqVYjnB6TRVv3zXuz7uRbCro8sa9vco7dPdfeNnDr5XqguZbnTxSssAz/971df/cNHlC5kopAGJMnCrRxZME5fqoBkjxYn7r9n5gGkbbq5N3Zxxezk4vGcWztOdQfM1nyYzX2nOtYap5SFAe79zd5P82+PIQgiNEYWHjvCjuC5u3fGpTiFk3oUIGLBfIC0xjd+iN1XzZrGv7alblitgUDAau4b6TOb4aHVOlBXt76rIMCjv8sDIrImJ5sv0rTGFxm9mshUudheufQAk6xOANBxX/t8gHA3PgwbuxmCXYxUt5nQ6x4YbUu0dRwZGAmYA9bAkYRsoc3kGUAqKkQRMoKqF1mhpUziBqPAYPMWTw8hF2cKWxxaYnZUvjk/4KPG7olM/Z21A9Cr+4ZmKHFucvdk7+lEqm50xBowD6T8K3iFAxY6dh2LjxR5CpO54aIAUiICQXKBTSQfb1RdjO190tgpbR3WgPVIKnVmrPlSdFf0UrT5xK3TKQJ1pE73I6/YgGSBIIIUdxMhZ9w4UvoyZu7GTlmbGLGaj2THmqOX9t0ayg71noBH0d7sbcjaV2fj8QoHzF0BgpS7kIZ+FlI5AWm0V7ZPN3a8qboHZuvt7Ilo867exODxdccHs6kzk/uiY9nRPjix2/CCAUk+PZv49oOEX17AvB+prIBPGjulIhWwfp2djDbvSw1ObEtvS6cvT+wZTO2K7kqNwhwcSTmViwIe9XNh96H1CTEhMCgqasoK+MivzIC0TbQrucau/k7qlLU7caK5eXf2cnpbLtLpbYNnotETie5AwHxE17UwYPuV737QEN+xqRABhxfwcZxdTsBpv3IDEo3d8DBs7Loso+aA+3Polb0M8fKI6WPZXc3NQyfhRjLQ9hFvYcCzW8625/5bjaXGMkkWX+woI+Bjv7IDwmmca+x2PkyNmN87F4WLHoSb6D+eFxzsjUZ7Q7Ck7kutVy4MeGB7HhCR6bWCjWKJJF4+QOjnz/u9AEDY2O1PXfyxS/PJ708ONUcnByHd4GD/nukUhHN4BNbXHV92FQYIS+li3c56fVYsnn9lB1y9uqpx1epNsLGjr0/1jSR2Xfp8MJ0+1v/uu/2D6x6lYPPQbXPAfLuJVyhg0e4HvjorXl/cr6yAjatu/tV2yNV6reovsJ75ZtQ90jG0b6x/Ij3xW5iC8BEC7oGz+vQncBG84Z7iLU3AGX5lBFy18lp73aEPLnz87VY3VnvtfuwCnroxmhibzN7dNrHn7jFiL0lPZHdHxxJfw4ZuFC90CiP5O6kopTyAT61/5QRsXPOfprXX/354w4cfbjh74eoXW9svxvYbUw8eZG/tTg1eTudjXf8tmIDvmeEmMsQpcBNBmY5x4htMTfKyAJIbZviVC7DxpubQxwe2bNhw/uoHfzp/4PD27w+5zg/fN9SN/it1Zt+5/oljE8eP373bD+vAsdwesngZAwH9cRUAglYKhgJvjZ37cwBzm8avcvEKEbmjV5/dSmb7lQlw5c2mbw9v2L7lwNW33n//nbfe2frFZxeuu3+6N/yTxj0QGtp9aygxdPr0uXP/o95eYJrK0gAAO6tjL1ZZiBFhqshLfFTloVYmbEXCBaQ8itZe0JKyFgrW8cpbu8CirVJEmVQWaIECxck4Kkrx0cqjAkWcCVhxCCDjOKiIskA2RHayWbOjk91zLsosY8tLtDM/CTRQ0psv59zz/+f8NzM/enQA+uUETJJI+w6nfR8QuwVBgkAi7Yb4erOXzwxwwUIQ8wk1CgySCwziZSjxW/j3hcb8PhAg9MP4WD8q0vB4PKG0WStDW9S0OFDYMY5051y6sX8s7sBCBKSBsZOUcpb1nHpLovvQYpMX2W3fiqTYdwckwa8JAH91//tggNQVXODHL0TbeDCE8FszWtpPYzw9II07cuZi+pVHN05FR4O65HJ6N9yOiUp6ZjU5IPHYxEcNDsf2HQIl3c7ZGoEkkhFAA34fBtA5oJTO52OlIh5PLi4oEIlwHG7T4r26IffG8//8mZmUe42Zl56eV1NzKScVrMBZNRHPJtuNGQN81zRmPKC9jzOVxKFy7A0Avj1/PxAgpUfRCCZwIdrM48kEtbVFRUqlUlAgl9F07ayffzzfGJBQFZaVWnXxek6Vn9/2yjMJ7sY7at4noD2V2jPSwPAMWdfZ47zXx348oEG/2QH8aIWT4wSA1CBuGRiA7WAAagUpJaOhkuDSNrS1lbZOeOCWZQLrWneq39Xjx7O6k464B9vsNpt1wDnewRmGAEdXXeJC0x6zWbSOLnXpEI3l+Z+0713hOAydDxfkBUb8ZgcwaFHQZxOciTiz28EdcADV8KQCZUlMTIxKWSFQxtQKmjWoXsdl3z3w48EfPJgJzDO5OQms+4esDk/hUOkXQMdjsEuUTJ7k4lcHN2w2dCZC+IXGx8e7vtDhel11WXb2y+zBRjWu56e5grAnMpoF0M/c/D2dyu06ZMtAMras8DUM6MrSYXw6vAMWVAC/EmUyy8GbVgR+NstFvYVDIWXnz3f+1+ygU0iC/2Kbw7undC48BriTYek0D9kXyJhkR9qC/JUDghxbHrnGECCkqkNb6HQ6PxvES3p59dDQMGkMEPhFGhh/swTotMWNgdjuCDZyrMmJagW3wFaRkCeWQMAUgactkqAsKZG08aToQBmtLw2e2K1fv95s/fqp9sZAQAv41OeOzYiXI2Kxyc5yknufHdkCQbYtZu80AshWDAC/6n69Yqh1sJxOx4bqOG8AHxqev7MFeMzLYfFEUzixi0hiNDy5BE7hkpgKVqAETGWJlifE+8tL3feOP7GbEuAm62eesEc6qAFhOCKBeyDPRLGMEbjP+BQGfqyRcqylF+T4LWoFV11ILx+gjZBGAR+uNOY3S6vw6BONRheRPmIR6RXzRocgICxKhpICsCwXlNILaZ3OU27FHAM8a3WWmMJ2gcsC7fZ4r/aerMmc/FZvDFGp/SEUBCWtjtuZ9kQvabt19xYIrQhtfzLYkfgiPp6y4PX4y8j4mAgTpDH1RBrTggvBKlJMCI6GANQlbR0YpvenkkKHp9aK+RbgvB2frdxmZx7sPWfaaQyxY0BkfBwdOkDniyQPmppUkmRBUYlQI5EJW7jDaWkkYv3N+Hjjxo2fmAiQRPVU00fnME8miQE5DMFXohJIhTwNXogNcEc4JJ+ptGIaAHyHPPA1IIniM0JrofPbJQ8eyFBBhbI2uUIjbBZoyyIS49NI88D6YQ79TAjoDZT4mB6X8qQ4uEAVTARjlAJcLuRp0QGsjNYA/tP+9YndBwekUPZ6ddHp/XhTkwQX/5SiUhVXoFKhTMSPG4pP84F+5rYmBSSRIhXVGL9MD2oRebMMF0iKiosEqBgOQEErhqmZPaMndtUHznceNcEIpESABEZfKpehTU0PUlQpqmL0C6GsgB9SFx8/Av3mmBiQQwrogNsxragYbiZo2kQorhWCV80CNYYV0vqo/39iZzUDwE3LiYvbQJ7JCPSpBxkMn6v7okIGAZXFqhRUIxeJnygevxjugH6mBiRRXDw74CxupIm0GrgbI4d6MhRXg192BVDHndjZ7J42INnLadc8kFSH7Jou4EKKiwvnMXfwpY77JCY5JSYmpSJZUqTENUJU0xr1cAkrJxzGWtjQuBUqbjQJIIniyqaBegSrLu3AUZFYK9UWoFx9P5jYmI4GlpCx98FWTLujUwMMXANK8LmObm6OK2IRj6Wg3A1ymgkgdV1Xdnbp0KBIokpJqU2WJNcCQDkqFwXvZEZanQ4PP3063MbGJtz6jzBMA0iiUOPS1XzgxS9sVHd1oIp2XRkG8musmhvnPHErpkHA+6nbs9wXBSHI0kWWlk6bg2A5siXAwdNxJoD+dS+zh0oH0SKVSpksAIuwEtW04c20ZUyPudavnv/79mniOT2TAoKCpI/ZoSPM6FhZIaHHx7B+WoDLxK2YRgArw1Lvj320HWOVRcYO2w2rPcxnApjYnp3d3kuMwOJaASAUFGhRMeoR5fXVyrPMqITnp5/tXmtyQBK1Zx2tqxHIvQ46Vt3YxfWmUH7duuAKC7ujk43AsFSHXz77YIPjnC0I4mg3o3tgn2IwuxG9q0pWghSmthYMQa1EjMoUkZYsy8Ovwm/f/oG5+ZmZzVYTA5IoziNsrqKXOJerbmzVdyi47HrnSVoxpwb4DmkMyEDruY2g1ARpjED8EwhJgZhWx/VQXEnK606tzL2Ye+/rvydEvbpqs9Xa2nprBgzTAIJ8hjoSFBcZwVJwFQkRdd6dPVSKwfftBfmM4cJu1gF94D5+RGt5uY4rlMIjQxwt0NIsWYFR/kl5j2oy8y+nn7yTvz2sO8wv3BrGJzBMBQgJqc7Unm19wM6Z6kMx8i4XjrHC7n0AUvYmdpWDRLBNKJRLpXK5VqIHfow912rSM29En8h/9F1+lt/V5/+ysba2Mjng6BzlGLd705MOC7tNR6dxD4T7aqvtZgRI7UR15fR+gVgul0s1YlSMg/HnS76efuLr/dH5+dHRNc+PH889cnbrbwVwKsF53Yo5AeD4VXguspO9jO04E0B7Tj86UE5vRnERKsALxKg+yj8k5FjSd48y8y7XnIiOvhJ13C8s70+HfwNTeBphsLB7A2hRmVUZabcEQczJGzaQVxN5YAbD0mG6acz8UHsQLuVqWmvZU7m4TXPraTMuxq/7RwWvqbp05cadzMwLV05duFTl53ct9rCVldXvCNBgYfcGMPDE/nx37+UIYhscF+cdvApWImSLDYErpwE4bkd6RYQicfhF/ONEZoJv1LX9N2qOXEj/27lz577886cn//HNndy/3Lx522zt2rVmsw940Ndp6XsBJHF83irsxgBPRZ8IHFcLZ8Qu8QjyMPbUsFuc76EJATmuqyK47F6uZ5Adkx1yJzP9VPS3n5/8/K/nPv3m25Nf3rtXebMq5+z/2Du70DayKwCPkENuqnqQZKSxuxpp0E9Gyo+6UqxKjsDSqMgTyTMy+jFWtJblRQaJCuSkBi1u7MmDbBycyDhuX0qflk1UwiY4TcDkIfghsE5ZDH2Ks2B2C4Wm0GZpoU3IQyidkWRbtmdEZEvr2vhgCNHYM1ffnHPvPeeee8+ppgCMhWylL2VqOMCTH+1y7KoB+qvi9SgEY0ASFFRARrFxjF1RaFXukyv/Js99fzrU5/eER767+Yenw7MLkwsLXV0rhT+9CK+NL15/d7nRAGM4d3AcLM2zPiml/2XDAe527AQA1hI4yLZSDUqBBlWKFFqV+/jja1dY+VfYoU6+vXnzq4erwwsLC7O/X+liEa5dfzF+e6jxGhhCUTTHRLjMMihHeZsA8OTPf7rNsdsLQBXbSgbl4l3AnHWYhAFeu3bl+98lYJH77sPpr55MDxcWFgp3Vlb+URhe7Xs2vua73Jw+kLbaSrvFixebAXCHY7cHgGUbT3pSvCb8o9JYXMmq/FYRJqPR1PtHEy9/Mb36mtW9ruHnz+9Pz63dvjQwMN/enFFYbTKomzOIbM5ntlbs+AFCpUVNyDgqOJGGQzGTcHLRj0vpB5DYEHZ0zz+6uj5w/x47fy4BfD23ev/53IueS5duvTt/yKYxW2a85dhVTWOqRmE4yB0zybhzCbjeaUwVQLEi7IC7o+tDX79/+ZuRl09WVrpWuoZfv56em7460/Ng4vACPPnJRxsrdpsA792Ye5xmuw44a7dnYaSsjXGpeO8Av+X4we3z78LvHt1bHbn5ZLbQdacw+3BubnX2y56B5ONo+6EFyJpxZcVuA+DnXcMrY/Isq3b48jIOq0sAMSkC9gywzA9qn59/Pz/1dPa7kZHpQlehcGdu7vmvp8enZm796lADLEW4/nZZWQWw6/MtE6aTFEMXkx65qG6AlQT9Mj+GBRid711/8Z/70y9uPGflxr3ppyMjPW/ffj3VUQpnHWg8cP+O3WdVAJe2UjwxzCwqQlgeq9uEK8L1fydOXLig7u7oUPoeP5paX59JLi0+W+y73nd7aio18VZ58BHp/XaE3Ipd53/5AO4jxXfj/AhW/7j8jRLAdxOLU1NTA2tPJl49+PLBQE/P7QlaGY22H3KA5RW7v6aaALA0/sIXKgA7lN/8kwXYM/CM9YBZfDMzi38vR6PR7kMPkHPsftsEDSzzgzYBdkR7lpbWZ8afjbM/M4t9t3qiHdGjoIGcY/fHZR6ATKaUkpCPaPbSB7L8XFz+RhngmTdvTkUfLY4NPb60dvvVq4FbQ0sDU9EzLNaDzExomJxFNzTwi5WqUdjNHXiPJOoL6W/1fyV+UCn15SeclnW3n//s7t+Vjqvhb95gd8+3K0vWe4C5MQ0E+JcKwDtdd8a4vXJMG463lSfSnX0al6lugKKy/e4AyMr5U8q7rPvTzs3+jh7ApcLk5JjbzDbKjqJ2GHGxamgggTdXL8AtftsAKlmCp86cYn+UyqMJcHJy8taWCbcmjcworJfKmDoBbtrvLoDKdi6Cf2QBJrYBFJtCIbEatBThOvtAsWSLXzXAErUywJIcdQ3c6zRGVM2vDPAE5+pe6N4hF7hPTxxdDdwjQNhcza9KTjA75AAPof1hNLBl2z8fClCk5ecHldSwWpoL0OArHrAGSmTcwUUi2tUm2HixVLPbhHUC+49+WICQnizldpvOfXq2Sk6frSWn93jx0z/zaSCD5NmJIKbVOQSr0QX95XOSPZaWjcokLazwFCzhPtxZSISrY8Jb3UTk3RdAic1mk9O41g2AjpY5z1WL9+I5Ybm454v9xtKT/YVCIcmd3qYmbDYCAqOs7gVpIOWpxARrbDba7sU93NZEvdPTuiGeSCufeDJ1fJzxifYDEMnn8wpjJGljm6mQKCRVYui1b/v/NlHIvQbhi5RH+KIBN1ZWskymmIT1O2Asn8cgmxGIkLiUcfCcnCAy5/PmrKefy582VT9H1sbXQkWvnffJjjjvx/5GDCK85f3oYo1bmY01LmK1DuDI8dUNyQ16cMQG612aGn+4664afs9ZoOGt/DXQIo0bhXdmo9S6M1wrDQ1C6r0ohhkIiOttIQLV03ALv62GWpoF8Fj2C1AdDAp6pqqgYN/bkqVq5aiJ0kjDvpCCcvNlmwV5VFCsovgr5GL5pgFUUITQzqu0dFRwfzSMyx01GMmTWMMA2uW9u09aUGn1PIUhILZVfOoQCsiaAVBUjMWKLSArdPNMDqmR2AI5hGtW5jWNqRnJxGIxds4Y2V29UK9iXHxxbTUfQIhu0zcDoFofiYxCnVrLXgAyMrfwnaXSMX0jAOYjkQgFcB5dEwCo5g01un3+MUuzTFgyqMH2YsKkNyc8gFskDrRhJrzstIf4TJjnSGrYJ4vztAoy4C5RswAWcUolPIgIMhJRlLzWk8yNqxzvpnCe8Sor52kbLNQqpPN4GvP/Oo05lmOAxwCPAR4DPJZjgIcDoKXsoVVv2ldY9n3XSqwJKzvX5s0bmtD9hSTUfP4knBc3B2DqQ2qlVKKo5uVNlyrk4Vsiimvq8IVc/WVvkigfyJPaeDuiftv+AFb2je2gahM1B+AoEUpTaROBAYURh0CMSGeBySivvK4cnQf5NGXOg5zRbtbFobQc51wBhyEjB2lCAdtBulOtAnE6B7A0RXk+PGDgbLPYElAaTccRMSVPq/MWs32Zc7TRMNVp11iytBm4VUZFzsjhzKdxJk2Xo1NFlIphdBakIeA2MakUBKNtcTMhF8O4BgEKQsO68nBaBFQ61hfBiRhAEQhlsgAjgqLGA9RrsmGN1UE7ETfujagDtkRAndD0lj3OnJN2SmSDckqqsBKD+pw/NET0c1oWRCJo0Ec7Y6Ql6bF74wnch0WSbZjqg5+bc7LfclDS53U7MDpBXDVIs/qk0VlkvbW+tCaswQO00+Ty0Ul9L/dWpINymlwue9roUCqYSPncfrs4INHSEa/uZ3qVE6cgmV7vtwRoMgGAmMSQAMP2NDhBwoQ3o0d8BvZXmGYAVDmAhwY+s0GvDcRJEHep+lpdZOmiRwP0RGQZpCOaVkDY4lpTALjLW2g8qCsLelGP0eXNBD2BVqdmlKjnuXYfa6xWLKADrpwvBnwSWdZmA1wIEPGBlBRI24C3TYsCq8XA/iaQaYBvw8aDDtDqHA3QaRnmN4QjXqfEChgygiF9MpmVcgGVi+sRbPIMEjeoiUgyBgJjYh0ZIjNm0BSAfpDRABIjg2gg7wNZhyrQKSmn8mUI0GrMUCDtSUWAngNIgpy/AtBvBy67fDDlHbTIPBIJk0nV81zDoBp0Wk0+NXDFSQkIsABpAnD103QBkMoALw60cq0dBHQSDqCHAuRGyArVglatRIKoyd420yAmMRV9IgCj1uxYTmKyJwDK9YEhksTyXnlrBLMadE6rDiFFMDrW2YQ+cJl9YR4j+379XpcV6pf+j7izj2rqvON4IqCXZoQCQsAGSHlZjUcoKK8ngEghBCJhvCgQFTpJJ+pByGEFAwEqJiZNIE1IxNpqBRfhxEBJwDk7OD0VxVQGozitUixq2TidsvqHiu7Uut3nhsQEEoqaXO9f/gHeL5/7+z2vv+f7pKZiilJc3LGgGI1ESaGQ3XZCvq9nU5K2ZVSmt8OANyC/55ZFpKSkYXCdoZE5EIlRlsTc4/9cL45Mc2G4Yyh+UAzpLUZuTnDK3sgMCP4oUDgF8k+CUzyFQdhQAlHCcQz4p2GeHoxcD7wbBmzDQ7g0lyQSRC8kYFPoZTvJFMfwpEhGcNCGoIy6tBQGcjn0hm2gHV+9eU+OJ33TpvTwNCb4EdsDDCYTIiBPMkTC+PmSQiFCyUY6hCkpCSfFgAbX050J4bKhQBwUHB3g7xeBJxmWiOBhDCkrEMJX4sPh1GJm7cUHk5/vzQfcIyAsCQuWwCqJDDKOwGyHIghgMxkie0JQRFY2FBEIkfAYkLqe2fDnzAo+ABb4wmEF5CxiHdQOxySW6MuEfwNbmeUJdkxwUKCv3mdPv3CIJSaGErY7OlbWheITs4LtPw78MoCi7wgI5nub+D10BtNew0zfDZvdlrYBSVjyAMeD0v5KBtIEkpU4aif52Ytf4OZUkoOt/1NmNoQKQPy8AgrPYCuVG8FM68Ugpg/+uV6fzWQy/Ta6u4DZDs6mj6cnjol8FYdsMjkbazeAialBLjZ8gjaXPM/bHfxzc/d4pAWAO5eiIxkBZbbUUpaG5Ht76alTydvtBpDob1IZBv4ky3ViFirKLD7QpuffVc

Изображение контура

Демонстрация подписей контура

Демонстрация подписей контура

<img alt="Демонстрация Contourf" 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

Демонстрация Contourf

<img alt="Контурное заполнение штриховкой" 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kHkS9zv6SCpxRM11OYrBHQUE23uW8bkdbpmm1iwP8J9De6rgZ832/LypZf70fzrHLRFWuXtNt0UrTgWMVhdJEpW+CnKPuLlzNbgflNJ8nF+/UrQfNStUUv4PQRbUVCh8LMcgSZyfqYOJLtNRg+fUWjb3hy235Eon21JYfmD+p2q+naRitQhBKsiBqvV00NTlIfI5m8WGmSZRCJ+5Gbh/L0T3hA0HwN9jNOZaL/oVTyQaU9hEzaIn/JzgOZo2bH94iPR/OFJezF+OzvUj9YvDm6+LhPkMZiAXMxjsHoszXaJapJsRpfBzfzZLABBSAXHj7YeIKnkdxBvkY2HZw8ATsay81MjEVxIB7ZlerI0xU2K+8mNWjl/5H4JS7ZUUFQveS6OSzFY5BLa+j0DTC34SJ6Lm0mSGfTIHA3JYiwXPxmGSTcLl/z4G7TDoXrywXTWjvj9FODMCEYINylysIj3f3LjOv8Sv03OxGOQIaanFape9YNVXtBsUOcAZln30vNU3Ayn5ceXeohDLsRD3CCwA3vdYYedvvZLEgC40H5wLDz/8OlnmRb93+SWXx5/m5h3dFQwTWP8O3Urkq7GZHGb14OICW5yv3U3UVRQzoQ+9AtfeqRo97Xh43hs+tKgww5LDG/61Wwy2CkBGj85mWCCgzFf+bH4cxZFNxdTwZN79GOazOUVOdj3y+0ueD0IBJmfctzDFWMvvxnJI8QPO9Y/TPJDA1oDdlhH3j3zzdZgglu1h23Sl/h/yWzMxL/wkzxJrAjx+g/7Jf1WKdkG0nmqVpgfO+psGVX3OgZzPzK9BVabJJTHH/Ab0z9Z/Mkg/qQGWMxifn8VoFTys4ZgUXJ8JM8a0fZ7au8Obh5/xKhy3rVzP/bTCqwlDkfXTWMYg9WTl/thQRuueA4nov0yP7pgPsgPO/H+b6KJV+0G0a7NxkJ/D2DJD3myWBX3EClgcr9N7GZUMB/DcT8E/SqVal1hJU4Rg/ahyCVODS7NvyLoF+V+s86F3xI8EJINFn9/FyDPv9jP78LF30z4OSTZZnkMMr9zya/H/NKB7qblfhBMz5xVeCDC+9pvWPR/Exm0+zNuv/m73QJs/Qxg2a9xkUAKP5oeeAy6135G7kdWxnBmBjG4ANNb+r60nV8Xfq2yX6uIP/ACNeB+SuJdzwf6bHPRghw/iLPsPotKkw5ov363tL9K+CmnfNBBBXHd0i78nhfXfpVKksFWzId0jqaVN2Fq38SfIcHUjZ7FZO1JugR0ahMagYo2QZVK9ODf52cmTHfQr7Q7LRN+G55cs13L/YMf3VxRceaNlBXcVDB2uJ+TlQ5IdMmw7sJPBu239EDduirGNnELDuWOWXZsm6/aIatUQhQP3Iqlvd4HUPOL9iujUvztz8LPySerSAy2g2/9kvmhUuoHaSN2iJ+jtKGJ2vX+1P+VGwRqg7FhCgDjAYtQfybjvGZ3U82tVBeRfLc6kPgdPFTeJV4TfscTn3OOS+13VfKrFu133qWrVqAVb3I/8mblIxKt1+ZXfoOL7exn4efEEHAx2NPLpPeXe92nbOFsmv8v4HHUcos6kMSfNyufU5hkDo8/9FAFs1dfxZ/wO7CPFYtWHDuFH+6qtuUDEcOwddNvNS0/EQKzO6dqKQJ3PXJZA5pE0jDayI4c/c99YOPwRr9DS5/5XcRft8b9FGXxZhTlXwz9Trf8nNxvr+WtngqeFNp+6dW4OBBBp/mv/LTL0zwZmB2LqzcisGHet4zpHcjPD1wj6kemr+CvxnETPed+STVlgnT88U38cb/p8RcY1bkW3yOLJOWbyZ506fdB+r/SA6nAD9dU5T6Q9pW10V0BffKBF5uByfwuto6rGx5/Cnnc5pJF4Lf5d1r4KZVfcGbhDOZXr3aqS1rZ7/X94fJr9iIAnRo511WKwMb9AUdriR13fVle++ESUPg5dOTBBF31wq9/6dflfpVKWsyx/kLTI9j+cb66nemX/aIr45rwc8LtIr2Vhe8KeG4xwPmghaSrD9QWfrQGxIJvWDA9T79ov7f88CcrGj6OQVHDJQfz0odObxXjj9f1tR8pqordm4YTPi7+/XQE8jLGQ3v14tS+qhrPosUt8tEvFkzN9n/xG7XSvPbBgs3iDZNux1evflKAhPj4bd1rX/1748j95r3UDf5l7ly7U+WZMKyIpwqeUFELloOiKFWRjUp93Uv9/z/qJQkkE8G2z4du66fd5eouXs5kJpOZO7vgBCvS5LGnTynpN5SMdoSqzRk/sgtOCAaf8lvc8Zuz/DFOwC8pv414yZGrGM0S+zv21MzbXofyW1fRsZY7l4EL7/BcnqA9ZdBmvcvqZGzPMGdNU8DoZfvf+AUFmn4X0/1czE/2b93s39yNcWl32kKzZ5mXqFN+IVpTTPUIAM7wYadrPwPgoNPMvkDKQGJIkkgLiJjFx4+H/hvz+4iYCRKAPuJXLjfbOX+148ix/45y3mmWbqn/VsMyKQyNJr/DhVenvEFLwO8K+zb+G79CVEoDcUAAhoRf+YFSgGW8GrlvHc5Js83QLxOA0emXBJE8gCzkxTlX3QRH6CB/qRB+8wf8dmi5NLuGSXfRIVn/cDrzSGth1W/nA7xgfgvELwEo/WKAdQowbM8GbRPsg+Vv2t8OK8B+6IsJ5a8zfo8AGor4QA7kwviRtODpeeCnAOusf3zSeW9SDkDTurO/z/mZ2nlJCKJvIGT8HrjwxBNkq1PKt8B4/SP8fp0FNtv3rzSIhO1X1b8eXtvpQchtnM8vE38Jv5dhFF3XqQ36jF85+zfbzZKBtZSFTjvvXcYvB+Azg4hdbWQECyou41fWo3pig0HxWvwOP2MeEH7VD9S7hQnGQYTx05vbrEjCdku0vEfzYVZMpiKdKT+yNeJceD1Br+lTJtbtfreReeARWgTD0quB12yzjm0wDcNfxY+E38dLFRdVE4LRpUXtTy9lADYGrSrmp71p227mO3131pQfAhj8IoDxNv7eZ5qlNuNXvhYTgigMfxQK1ZQfkRkuzx7ZH64m4M6jmGDkyKycUMr6b3WU8FMkQY5zU/79m6OFfGb6e1zYet8L2dCH/JfwK4fFIrNBwE+946dn+CXhI7rGkSQAw02XQ0YjZdBP+OEufkvKZlbs18/n6BcFkYF9nIjOIRP12i3DZ184JRjd8dMz/Irp+kfLWQGywQAU5NuHe36rIeQnDeadR4kBcmFNMH8PwPXfqZCjxial9pecCScEo1uf89+UX4f6L+DHWpGia08CADP2Zw+p/2J+f6by6WFy71u9mWA+PQ80SWNaYY5zB3nEp18ly/D5NSe1wQ8Sf7/iV0jrqUkvfwg2N7fD3f5jd88vXpdl+YEJ+lbrcNsLhccWGOFFJPjhY+HLmpxhrBwsjLO3Dlw1oXkD9QSye08Imt+wPzwrrl8jehRVhOOIbb6SIOwrUoafKFi1El9PuFB+oX+ZnTo5QQR/JH81PsXP1LV/9jICs4M+Zfm2cxC/lix6fBWpW2cVQeEKCEZfx18yay/2rrSXfwmqE8qar2JtXiRu/Uuv3NgN+UJbn/KLv9LzosIupQoWrqK5rlcZ3ML4MRy/axa6vqP+rAnWkSaD5L1aQsxPjH1Gc2EdU+2c6Zq9CElVuf5aQtnM+Iv4i/mZUeT10l5+0KAVagqYzUM3tbzn89uL06EHi6rTEuVXLhfLGzBos3NsTbbk0aJ2SDsTxlHnZyfnzNN7RFyY8BMF2RaanC5Rwk/Z35KqNCHodzh+nbv4WyH2F6MzMUG0BF4Zv0YXluubYqvxiF/8nYrtNtdtR/mVQ1MAEqDrCtrFCBp2YUu5rfziquk6PwqwPF5LJA9M+bXmlpCNe5gfiSNBQtA0v8UPEVxjG1wCfgsufsibh/aHfpxuuTDSdlJ+5TI8Ewk2ZBc4xDuRYt8+O+XzavhvrgSyZ1RYSLoXZzun/ND2KQDroBnzO+TyI/77kbYW1bANhoAflJ/5jF96ZdgJPpHdof+VL4IgkpzGL55xGcFsw/jdbQEO9XPKjzYIEoJxXsfzUzn7M+mBcEzwGl0ZvzXHzzl+Zn9E8tbhCLLTda5DdYdPUwaVp5zKTQA/wViI8HlvMb8Q5jLpOrgsf8KvELEJ4pgglScL33uAX6muHr+wPyK5x2WoIJ3mAQrT1UR7TjEB8Jsc3y3QU3YQxqwlTw+KIBZHKB/M59fsmPQUBdVg03wofN+VYLOQ9/aV/5Iu/rEEf+uctifMJxCgMLUN6VkAAb84JMrjFmspG4M9fFiENoiaVddZfh9RdI43WuAwLk2hw5ddBza2zY98/F2l/DjJfkE9Dl3Y5DYl/MpdcDnpcof4CcKTAAJ+WNhF9sBXzlYd/WYsExssEYL+9eOD8YsqfYQPiZnuBTM9DqYDhzG/NuxX094e8Nvz/N6GkjgRYd/dGfEbD6ALz6crpPP1JICc/aGS8MaBS/0lbQmY9ZNRuSAliDa5pjQm/Pp9M1n7CMEiOwwm/C5Q0c3L8LOwqhHiJ9zxQ++DykPprOtjm5vWnK2wUMtTAHaV/U4SAL/4I4lcceaStqT4tFcaECzqQRSZ58nyElEdAGqD0P4uMBaob/f583Q60RSluos3KhPLapEpxISfVN3L3PTF2NZ9CLBFmoRfnpHGdIdVRTFkidof+kiydXcmS/gljTLMi+nwZrC1o4j9nNggy184+6ufavz6Jwz+V3U1F1+lW6u5rttriZDf8OjB4kxpa+t3aQwZfp89xQI1Q3Xj7z4VtiIfSdRg+nVJ+XH5YMkEnS/RtgvmhYsokkQP+Xk8P6fftd9G7PY1VfVeW8pUqFF+qiBaMnsg6abn5IFuZfiskf/4mbV312H8JHtjwZmvJuUXE0xS5NSLU/2TahdqGEXnXgn47xlqgnrQ2EVHGbnGxP47gvo/3ubVUzbVU8IPxzaDzny1Sxf9HqDgruTnrIHk0hrVcEeeSPnFIdECg4GrIajnhcWIeXHAjK6a2iBp5zpfKL/5LTYgOi8I+QlWjA8rZd0RrG/2qqZNGD9pPANDh9bB5wHG/CzpmQCRFbrVPy4TFhL6bMp0YZ0BQKl5H4szNhhcyzB+qEDVd/iH8ZNr9O7IHIITT3Nl7L94CtaSx3s29trk10B3YD0pD6QAkeNoWkyOCuOsZDZ4zXq8fadVvy6ZFwd020EIon8CuQkcfw95/utJjqYxxZMcgobqDZMrT8kUMbsIq16CsidzxO9ZDZZ9+NSKrZyYMI4jZfNBxC8s6zglfGCDS+jvcf4MAvpJZfwEdd+DN6vlEqxpHvpOkyls+YXGNij9FLwSqeWnAFz8hYVoVVnsW1RYiC9v3Rg/3DfIZTPJ2VHVXgbw+OOOnwz4iSj07zilrDyCcYpgCHSKvUdLCzxA8sj2v8wDoymZZrRnR4WzweMaPywRdrHA6eKhCfhh9cBlscDbYFTcSuH3+E1j7zXcbxCsqRpUAUjLW5z4GMkDR71/aYGqohkYoDvnCMYfao9UABJhl1kNEtQBP7ylPy1Lr4e0ehUE9cpN5/j16vn8FFXzyB+jBFXVUKerv30DaDCmBLGicKICkEjy8QARv/529C/zwJVeHhQKl/pi6O6OLvze3ePCZcJMccbAOljEGc9v/qJfOxP/il6+7p82Imd/7zumANqG/FwlvfGPEES5s6Y0Ki/b2Z+tprkpQ1UlBL2RQ1UUJl2sAcVJgKI8sN/4t2vg4HyuFAodZ2OvKq3YBlVIcD8HKpZTl4p+jZQbxw8J3tARuPC0Efj1b8MkxxQv5peq+PZduu5igqrbHwy6/1tXt9vqtjEYVAZVxTAAwVpNc4GKpaHcqfjuEL9/HESk1QpPtXUrw+p2vGsA/a/4Q/XcqUBvYdkx2Tl70oT8OHu746e/rBX6e+PdC+O3HdVUuGT0KvZg2/i7JxebIEnpbWU1b3gq8+Kauu3hNQWp+Araes6r+O4Qv1yA5s8JJ9zI0BZSMh/2q5XBSlOBWfS0/zN3dm1tMkEYthgh8mEsAXYhNMGQQBBMEFONRmPr//9R7+6yX0Dsaw40zkF70F5XyJ2Zndlhd56E8jOLQSLOv+E5+nv53bb5QZvP4b68+GkmfLMdxtKiizaR9tKAbon4aZLYV/UceC6RFGMEiBa88Oz85RWCGN+CKBYA691G32/XxU8//Q60KO/ezlIiwZB2lIqNm5PlnJ4IBXh//nfWTSUKiDTEs0etYjSCSUIgJ/HpavBFj+3es1dTfewNgKLYpD5Vs0PS8ZajTJmYNQ3lbS/dyxHzv7ETydv99O7LwKoufOdFxt8cjMi6CbSYDD5zc7DxShW0UcAABKRSURBVEvH/8wmv5etVIOfYn7M/7wMlSuUIMjQR6KI3cPPI2oEeUymXDOC7orym4SWIgOcA5y7x+XIR8vSbRaGiXF5FTye3D0A4+lrACJ+hqt5KpNuw4t7MeSDcRTfkgmevnX4mRK/FzxG/z1+ZERgTdBFCzDEoiF7+RE1oLjE0gUrIlAMhh6RaCKSiZYhF9Lkh4/q15o/3i4uXrLpWfx4d3VyW119CUDMD9b6J3SBRz5YZcNsQwe7rPuN97OmL0/Tb/F7O28IsphW1OJXEwReLWnzPj/8jzp2wj4lWPhTyq+h6nW3sjHBSBdJ5K9nX56Nz9R4+yUhTPmRqNEzieBKDMZJTPlM89vl26/

Штриховка контуров

<img alt="Смешивание прозрачности с цветом в 2-мерных изображениях" 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

Смешивание прозрачности с цветом в 2-мерных изображениях

<img alt="Контурный график неравномерно расположенных данных" 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

Контурный график неравномерно расположенных данных

Интерактивные функции

Интерактивные функции

Примеры графиков в Matplotlib

Примеры графиков в Matplotlib

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Licensed under the Matplotlib License Agreement.
https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.contour.html

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Spec-Zone .ru
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