matplotlib 中的平滑贝塞尔等效绘图
smooth bezier equivalent plotting in matplotlib
我需要帮助在 matplotlib 中使用 plt.steps 函数绘制光谱。由于数据嘈杂，在 gnuplot 中我使用平滑贝塞尔选项来平滑。 matplotlib 中有类似的选项吗？样条曲线无法平滑所需级别的数据。这是一个示例数据集 https://drive.google.com/open?id=0B4shfFfM7MOqV2h3ZDA0RXlOa2M
我用 plt.steps(data[:,0],data[:,1]) 绘制了 log x 值。
剧情附后matplotlib image
.如何使用 matplotlib 平滑整个数据。（我对 python 绘图很陌生）
一个普通的低通滤波器似乎很有效。根据需要调整 alpha。
#! /usr/bin/env python3
import matplotlib.pyplot as plt
def smooth(y, alpha=0.1):
ret = []
sm = y[0] # smoothed value, a moving average
for val in y:
ret.append(sm)
sm = alpha * val + (1 - alpha) * sm
return ret
def plot(points):
x = [a for a, b in points]
y = [b for a, b in points]
plt.subplot(211)
plt.semilogx(x, y)
plt.subplot(212)
plt.semilogx(x, smooth(y))
plt.savefig('/tmp/smoothed.png')
if __name__ == '__main__':
plot([
(0.25, 0.000),
(0.35, 0.055),
(0.45, 0.103),
(0.55, 0.104),
(0.65, 0.143),
(0.75, 0.140),
(0.85, 0.143),
(0.95, 0.143),
(1.05, 0.126),
(1.15, 0.223),
(1.25, 0.217),
(1.35, 0.232),
(1.45, 0.225),
(1.55, 0.219),
(1.65, 0.223),
(1.75, 0.236),
(1.85, 0.216),
(1.95, 0.222),
(2.05, 0.244),
(2.15, 0.244),
(2.25, 0.257),
(2.35, 0.221),
(2.45, 0.221),
(2.55, 0.213),
(2.65, 0.238),
(2.75, 0.209),
(2.85, 0.223),
(2.95, 0.226),
(3.05, 0.212),
(3.15, 0.247),
(3.25, 0.247),
(3.35, 0.236),
(3.45, 0.215),
(3.55, 0.218),
(3.65, 0.241),
(3.75, 0.209),
(3.85, 0.239),
(3.95, 0.221),
(4.05, 0.169),
(4.15, 0.246),
(4.25, 0.230),
(4.35, 0.229),
(4.45, 0.242),
(4.55, 0.264),
(4.65, 0.188),
(4.75, 0.182),
(4.85, 0.248),
(4.95, 0.172),
(5.05, 0.189),
(5.15, 0.228),
(5.25, 0.183),
(5.35, 0.272),
(5.45, 0.201),
(5.55, 0.204),
(5.65, 0.203),
(5.75, 0.198),
(5.85, 0.187),
(5.95, 0.244),
(6.05, 0.229),
(6.15, 0.202),
(6.25, 0.234),
(6.35, 0.231),
(6.45, 0.173),
(6.55, 0.206),
(6.65, 0.173),
(6.75, 0.178),
(6.85, 0.183),
(6.95, 0.188),
(7.05, 0.181),
(7.15, 0.153),
(7.25, 0.150),
(7.35, 0.183),
(7.45, 0.188),
(7.55, 0.111),
(7.65, 0.145),
(7.75, 0.195),
(7.85, 0.192),
(7.95, 0.156),
(8.05, 0.126),
(8.15, 0.095),
(8.25, 0.151),
(8.35, 0.127),
(8.45, 0.130),
(8.55, 0.095),
(8.65, 0.127),
(8.75, 0.219),
(8.85, 0.122),
(8.95, 0.094),
(9.05, 0.128),
(9.15, 0.054),
(9.25, 0.122),
(9.35, 0.080),
(9.45, 0.139),
(9.55, 0.107),
(9.65, 0.097),
(9.75, 0.087),
(9.85, 0.050),
(9.95, 0.090),
(10.05, 0.053),
(10.15, 0.121),
(10.25, 0.055),
(10.35, 0.056),
(10.45, 0.014),
(10.55, 0.087),
(10.65, 0.044),
(10.75, 0.150),
(10.85, 0.077),
(10.95, 0.140),
(11.05, 0.064),
(11.15, 0.065),
(11.25, 0.132),
(11.35, 0.050),
(11.45, 0.068),
(11.55, 0.017),
(11.65, 0.000),
(11.75, 0.072),
(11.85, 0.110),
(11.95, 0.056),
(12.05, 0.057),
(12.15, 0.115),
(12.25, 0.098),
(12.35, 0.060),
(12.45, 0.101),
(12.55, 0.041),
(12.65, 0.062),
(12.75, 0.063),
(12.85, 0.064),
(12.95, 0.065),
(13.05, 0.066),
(13.15, 0.157),
(13.25, 0.023),
(13.35, 0.093),
(13.45, 0.094),
(13.55, 0.072),
(13.65, 0.048),
(13.75, 0.098),
(13.85, 0.125),
(13.95, 0.101),
(14.05, 0.051),
(14.15, 0.104),
(14.25, 0.053),
(14.35, 0.054),
(14.45, 0.054),
(14.55, 0.083),
(14.65, 0.112),
(14.75, 0.113),
(14.85, 0.115),
(14.95, 0.087),
(15.05, 0.029),
(15.15, 0.000),
(15.25, 0.091),
(15.35, 0.031),
(15.45, 0.124),
(15.55, 0.031),
(15.65, 0.032),
(15.75, 0.065),
(15.85, 0.033),
(15.95, 0.033),
(16.05, 0.000),
(16.15, 0.068),
(16.25, 0.000),
(16.35, 0.070),
(16.45, 0.141),
(16.55, 0.143),
(16.65, 0.072),
(16.75, 0.073),
(16.85, 0.000),
(16.95, 0.037),
(17.05, 0.113),
(17.15, 0.077),
(17.25, 0.039),
(17.35, 0.078),
(17.45, 0.079),
(17.55, 0.040),
(17.65, 0.041),
(17.75, 0.082),
(17.85, 0.041),
(17.95, 0.042),
(18.05, 0.042),
(18.15, 0.043),
(18.25, 0.043),
(18.35, 0.000),
(18.45, 0.133),
(18.55, 0.134),
(18.65, 0.045),
(18.75, 0.091),
(18.85, 0.046),
(18.95, 0.093),
(19.05, 0.236),
(19.15, 0.048),
(19.25, 0.145),
(19.35, 0.049),
(19.45, 0.000),
(19.55, 0.050),
(19.65, 0.000),
(19.75, 0.101),
(19.85, 0.205),
(19.95, 0.155),
(20.05, 0.052),
(20.250, 0.034),
(21.750, 0.029),
(23.250, 0.036),
(24.750, 0.033),
(26.250, 0.048),
(27.750, 0.051),
(29.250, 0.033),
(30.750, 0.033),
(32.250, 0.089),
(33.750, 0.084),
(35.250, 0.068),
(36.750, 0.089),
(38.250, 0.017),
(39.750, 0.049),
(41.250, 0.093),
(42.750, 0.043),
(44.250, 0.069),
(45.750, 0.049),
(47.250, 0.096),
(48.750, 0.065),
(50.250, 0.098),
(51.750, 0.042),
(53.250, 0.088),
(54.750, 0.105),
(56.250, 0.074),
(57.750, 0.065),
(59.250, 0.096),
(60.750, 0.129),
(62.250, 0.075),
(63.750, 0.142),
(65.250, 0.116),
(66.750, 0.035),
(68.250, 0.091),
(69.750, 0.170),
(71.250, 0.119),
(72.750, 0.082),
(74.250, 0.086),
(75.750, 0.223),
(77.250, 0.163),
(78.750, 0.097),
(80.250, 0.175),
(81.750, 0.182),
(83.250, 0.108),
(84.750, 0.196),
(86.250, 0.145),
(87.750, 0.090),
(89.250, 0.372),
(90.750, 0.224),
(92.250, 0.132),
(93.750, 0.171),
(95.250, 0.141),
(96.750, 0.146),
(98.250, 0.225),
(99.750, 0.503),
(101.250, 0.199),
(102.750, 0.123),
(104.250, 0.169),
(105.750, 0.174),
(107.250, 0.224),
(108.750, 0.368),
(110.250, 0.284),
(111.750, 0.243),
(113.250, 0.150),
(114.750, 0.256),
(116.250, 0.263),
(117.750, 0.540),
(119.250, 0.332),
(120.750, 0.567),
(122.250, 0.407),
(123.750, 0.477),
(125.250, 0.122),
(126.750, 0.313),
(128.250, 0.384),
(129.750, 0.328),
(131.250, 0.335),
(132.750, 0.411),
(134.250, 0.351),
(135.750, 0.574),
(137.250, 0.367),
(138.750, 0.150),
(140.250, 0.536),
(141.750, 0.391),
(143.250, 0.479),
(144.750, 0.489),
(146.250, 0.333),
(147.750, 0.510),
(149.250, 0.520),
(150.750, 0.796),
(152.250, 0.090),
(153.750, 0.092),
(155.250, 0.563),
(156.750, 0.478),
(158.250, 0.487),
(159.750, 0.298),
(161.250, 0.405),
(162.750, 0.206),
(164.250, 0.735),
(165.750, 0.428),
(167.250, 0.653),
(168.750, 0.332),
(170.250, 0.113),
(171.750, 0.459),
(173.250, 0.117),
])
我需要帮助在 matplotlib 中使用 plt.steps 函数绘制光谱。由于数据嘈杂，在 gnuplot 中我使用平滑贝塞尔选项来平滑。 matplotlib 中有类似的选项吗？样条曲线无法平滑所需级别的数据。这是一个示例数据集 https://drive.google.com/open?id=0B4shfFfM7MOqV2h3ZDA0RXlOa2M
我用 plt.steps(data[:,0],data[:,1]) 绘制了 log x 值。 剧情附后matplotlib image .如何使用 matplotlib 平滑整个数据。（我对 python 绘图很陌生）
一个普通的低通滤波器似乎很有效。根据需要调整 alpha。
#! /usr/bin/env python3
import matplotlib.pyplot as plt
def smooth(y, alpha=0.1):
ret = []
sm = y[0] # smoothed value, a moving average
for val in y:
ret.append(sm)
sm = alpha * val + (1 - alpha) * sm
return ret
def plot(points):
x = [a for a, b in points]
y = [b for a, b in points]
plt.subplot(211)
plt.semilogx(x, y)
plt.subplot(212)
plt.semilogx(x, smooth(y))
plt.savefig('/tmp/smoothed.png')
if __name__ == '__main__':
plot([
(0.25, 0.000),
(0.35, 0.055),
(0.45, 0.103),
(0.55, 0.104),
(0.65, 0.143),
(0.75, 0.140),
(0.85, 0.143),
(0.95, 0.143),
(1.05, 0.126),
(1.15, 0.223),
(1.25, 0.217),
(1.35, 0.232),
(1.45, 0.225),
(1.55, 0.219),
(1.65, 0.223),
(1.75, 0.236),
(1.85, 0.216),
(1.95, 0.222),
(2.05, 0.244),
(2.15, 0.244),
(2.25, 0.257),
(2.35, 0.221),
(2.45, 0.221),
(2.55, 0.213),
(2.65, 0.238),
(2.75, 0.209),
(2.85, 0.223),
(2.95, 0.226),
(3.05, 0.212),
(3.15, 0.247),
(3.25, 0.247),
(3.35, 0.236),
(3.45, 0.215),
(3.55, 0.218),
(3.65, 0.241),
(3.75, 0.209),
(3.85, 0.239),
(3.95, 0.221),
(4.05, 0.169),
(4.15, 0.246),
(4.25, 0.230),
(4.35, 0.229),
(4.45, 0.242),
(4.55, 0.264),
(4.65, 0.188),
(4.75, 0.182),
(4.85, 0.248),
(4.95, 0.172),
(5.05, 0.189),
(5.15, 0.228),
(5.25, 0.183),
(5.35, 0.272),
(5.45, 0.201),
(5.55, 0.204),
(5.65, 0.203),
(5.75, 0.198),
(5.85, 0.187),
(5.95, 0.244),
(6.05, 0.229),
(6.15, 0.202),
(6.25, 0.234),
(6.35, 0.231),
(6.45, 0.173),
(6.55, 0.206),
(6.65, 0.173),
(6.75, 0.178),
(6.85, 0.183),
(6.95, 0.188),
(7.05, 0.181),
(7.15, 0.153),
(7.25, 0.150),
(7.35, 0.183),
(7.45, 0.188),
(7.55, 0.111),
(7.65, 0.145),
(7.75, 0.195),
(7.85, 0.192),
(7.95, 0.156),
(8.05, 0.126),
(8.15, 0.095),
(8.25, 0.151),
(8.35, 0.127),
(8.45, 0.130),
(8.55, 0.095),
(8.65, 0.127),
(8.75, 0.219),
(8.85, 0.122),
(8.95, 0.094),
(9.05, 0.128),
(9.15, 0.054),
(9.25, 0.122),
(9.35, 0.080),
(9.45, 0.139),
(9.55, 0.107),
(9.65, 0.097),
(9.75, 0.087),
(9.85, 0.050),
(9.95, 0.090),
(10.05, 0.053),
(10.15, 0.121),
(10.25, 0.055),
(10.35, 0.056),
(10.45, 0.014),
(10.55, 0.087),
(10.65, 0.044),
(10.75, 0.150),
(10.85, 0.077),
(10.95, 0.140),
(11.05, 0.064),
(11.15, 0.065),
(11.25, 0.132),
(11.35, 0.050),
(11.45, 0.068),
(11.55, 0.017),
(11.65, 0.000),
(11.75, 0.072),
(11.85, 0.110),
(11.95, 0.056),
(12.05, 0.057),
(12.15, 0.115),
(12.25, 0.098),
(12.35, 0.060),
(12.45, 0.101),
(12.55, 0.041),
(12.65, 0.062),
(12.75, 0.063),
(12.85, 0.064),
(12.95, 0.065),
(13.05, 0.066),
(13.15, 0.157),
(13.25, 0.023),
(13.35, 0.093),
(13.45, 0.094),
(13.55, 0.072),
(13.65, 0.048),
(13.75, 0.098),
(13.85, 0.125),
(13.95, 0.101),
(14.05, 0.051),
(14.15, 0.104),
(14.25, 0.053),
(14.35, 0.054),
(14.45, 0.054),
(14.55, 0.083),
(14.65, 0.112),
(14.75, 0.113),
(14.85, 0.115),
(14.95, 0.087),
(15.05, 0.029),
(15.15, 0.000),
(15.25, 0.091),
(15.35, 0.031),
(15.45, 0.124),
(15.55, 0.031),
(15.65, 0.032),
(15.75, 0.065),
(15.85, 0.033),
(15.95, 0.033),
(16.05, 0.000),
(16.15, 0.068),
(16.25, 0.000),
(16.35, 0.070),
(16.45, 0.141),
(16.55, 0.143),
(16.65, 0.072),
(16.75, 0.073),
(16.85, 0.000),
(16.95, 0.037),
(17.05, 0.113),
(17.15, 0.077),
(17.25, 0.039),
(17.35, 0.078),
(17.45, 0.079),
(17.55, 0.040),
(17.65, 0.041),
(17.75, 0.082),
(17.85, 0.041),
(17.95, 0.042),
(18.05, 0.042),
(18.15, 0.043),
(18.25, 0.043),
(18.35, 0.000),
(18.45, 0.133),
(18.55, 0.134),
(18.65, 0.045),
(18.75, 0.091),
(18.85, 0.046),
(18.95, 0.093),
(19.05, 0.236),
(19.15, 0.048),
(19.25, 0.145),
(19.35, 0.049),
(19.45, 0.000),
(19.55, 0.050),
(19.65, 0.000),
(19.75, 0.101),
(19.85, 0.205),
(19.95, 0.155),
(20.05, 0.052),
(20.250, 0.034),
(21.750, 0.029),
(23.250, 0.036),
(24.750, 0.033),
(26.250, 0.048),
(27.750, 0.051),
(29.250, 0.033),
(30.750, 0.033),
(32.250, 0.089),
(33.750, 0.084),
(35.250, 0.068),
(36.750, 0.089),
(38.250, 0.017),
(39.750, 0.049),
(41.250, 0.093),
(42.750, 0.043),
(44.250, 0.069),
(45.750, 0.049),
(47.250, 0.096),
(48.750, 0.065),
(50.250, 0.098),
(51.750, 0.042),
(53.250, 0.088),
(54.750, 0.105),
(56.250, 0.074),
(57.750, 0.065),
(59.250, 0.096),
(60.750, 0.129),
(62.250, 0.075),
(63.750, 0.142),
(65.250, 0.116),
(66.750, 0.035),
(68.250, 0.091),
(69.750, 0.170),
(71.250, 0.119),
(72.750, 0.082),
(74.250, 0.086),
(75.750, 0.223),
(77.250, 0.163),
(78.750, 0.097),
(80.250, 0.175),
(81.750, 0.182),
(83.250, 0.108),
(84.750, 0.196),
(86.250, 0.145),
(87.750, 0.090),
(89.250, 0.372),
(90.750, 0.224),
(92.250, 0.132),
(93.750, 0.171),
(95.250, 0.141),
(96.750, 0.146),
(98.250, 0.225),
(99.750, 0.503),
(101.250, 0.199),
(102.750, 0.123),
(104.250, 0.169),
(105.750, 0.174),
(107.250, 0.224),
(108.750, 0.368),
(110.250, 0.284),
(111.750, 0.243),
(113.250, 0.150),
(114.750, 0.256),
(116.250, 0.263),
(117.750, 0.540),
(119.250, 0.332),
(120.750, 0.567),
(122.250, 0.407),
(123.750, 0.477),
(125.250, 0.122),
(126.750, 0.313),
(128.250, 0.384),
(129.750, 0.328),
(131.250, 0.335),
(132.750, 0.411),
(134.250, 0.351),
(135.750, 0.574),
(137.250, 0.367),
(138.750, 0.150),
(140.250, 0.536),
(141.750, 0.391),
(143.250, 0.479),
(144.750, 0.489),
(146.250, 0.333),
(147.750, 0.510),
(149.250, 0.520),
(150.750, 0.796),
(152.250, 0.090),
(153.750, 0.092),
(155.250, 0.563),
(156.750, 0.478),
(158.250, 0.487),
(159.750, 0.298),
(161.250, 0.405),
(162.750, 0.206),
(164.250, 0.735),
(165.750, 0.428),
(167.250, 0.653),
(168.750, 0.332),
(170.250, 0.113),
(171.750, 0.459),
(173.250, 0.117),
])