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Markevery示例此示例演示了使用Line2D对象的markevery属性在数据点子集上显示标记的各种选项。 整数参数非常直观。例如 markevery = 5 将从第一个数据点开始绘制每个第5个标记。 浮点参数允许标记沿着线以大致相等的距离间隔开。沿着标记之间的线的理论距离通过将轴边界对角线的显示坐标距离乘以 markevery 值来确定。将显示最接近理论距离的数据点。 切片或列表/数组也可以与 markevery 一起使用以指定要显示的标记。 12345678910111213141516171819202122import numpy as npimport matplotlib.pyplot as pltimport matplotlib.gridspec as gridspec# define a list of markevery cases to plotcases = [None, 8, (30, 8), [16, 24, 30], [0, -1], slice(100, 20...

在rcParams中实现了对prop_cycle属性markevery的支持此示例演示了一个发布 #8576 的工作解决方案，通过rcParams为axes.prop_cycle分配提供对markevery属性的完全支持。从markevery演示中使用相同的markevery案例列表。 使用每列的移位正弦曲线渲染绘图，每个正弦曲线都有一个唯一的市场价值。 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849from cycler import cyclerimport numpy as npimport matplotlib as mplimport matplotlib.pyplot as plt# Define a list of markevery cases and color cases to plotcases = [None, 8, (30, 8), [16, 24, 30], ...

五彩线条此示例显示如何制作多色线。 在此示例中，线条基于其衍生物着色。 123456789101112131415161718192021222324252627282930313233343536373839import numpy as npimport matplotlib.pyplot as pltfrom matplotlib.collections import LineCollectionfrom matplotlib.colors import ListedColormap, BoundaryNormx = np.linspace(0, 3 * np.pi, 500)y = np.sin(x)dydx = np.cos(0.5 * (x[:-1] + x[1:])) # first derivative# Create a set of line segments so that we can color them individually# This creates the points as a N x 1 x 2 array so that we can...

遮盖示例绘制带有点的线条。 这通常与gappy数据一起使用，以打破数据空白处的界限。 12345678910111213141516171819import matplotlib.pyplot as pltimport numpy as npx = np.arange(0, 2*np.pi, 0.02)y = np.sin(x)y1 = np.sin(2*x)y2 = np.sin(3*x)ym1 = np.ma.masked_where(y1 > 0.5, y1)ym2 = np.ma.masked_where(y2 < -0.5, y2)lines = plt.plot(x, y, x, ym1, x, ym2, 'o')plt.setp(lines[0], linewidth=4)plt.setp(lines[1], linewidth=2)plt.setp(lines[2], markersize=10)plt.legend(('No mask', 'Masked if > 0.5', &#x...

Nan测试示例：插入Nan的简单线条图。 123456789101112131415161718192021222324252627import numpy as npimport matplotlib.pyplot as pltt = np.arange(0.0, 1.0 + 0.01, 0.01)s = np.cos(2 * 2*np.pi * t)t[41:60] = np.nanplt.subplot(2, 1, 1)plt.plot(t, s, '-', lw=2)plt.xlabel('time (s)')plt.ylabel('voltage (mV)')plt.title('A sine wave with a gap of NaNs between 0.4 and 0.6')plt.grid(True)plt.subplot(2, 1, 2)t[0] = np.nant[-1] = np.nanplt.plot(t, s, '-', lw=2)plt.title...

功率谱密度图示例在Matplotlib中绘制功率谱密度(PSD)。 PSD是信号处理领域中常见的图形。NumPy有许多用于计算PSD的有用库。下面，我们演示一些如何使用Matplotlib实现和可视化这一点的示例。 1234567891011121314151617181920212223import matplotlib.pyplot as pltimport numpy as npimport matplotlib.mlab as mlabimport matplotlib.gridspec as gridspec# Fixing random state for reproducibilitynp.random.seed(19680801)dt = 0.01t = np.arange(0, 10, dt)nse = np.random.randn(len(t))r = np.exp(-t / 0.05)cnse = np.convolve(nse, r) * dtcnse = cnse[:len(t)]s = 0.1 * np.sin(2 * np.pi * t) + cn...

散点图自定义符号在散点图中创建自定义椭圆符号。 12345678910111213141516import matplotlib.pyplot as pltimport numpy as np# unit area ellipserx, ry = 3., 1.area = rx * ry * np.pitheta = np.arange(0, 2 * np.pi + 0.01, 0.1)verts = np.column_stack([rx / area * np.cos(theta), ry / area * np.sin(theta)])x, y, s, c = np.random.rand(4, 30)s *= 10**2.fig, ax = plt.subplots()ax.scatter(x, y, s, c, marker=verts)plt.show() 下载这个示例 下载python源码: scatter_custom_symbol.py 下载Jupyter notebook: scatter_custom_symbol.ipynb

散点图自定义样式演示散点图与不同的标记颜色和大小。 12345678910111213141516171819202122232425262728import numpy as npimport matplotlib.pyplot as pltimport matplotlib.cbook as cbook# Load a numpy record array from yahoo csv data with fields date, open, close,# volume, adj_close from the mpl-data/example directory. The record array# stores the date as an np.datetime64 with a day unit ('D') in the date column.with cbook.get_sample_data('goog.npz') as datafile: price_data = np.load(datafile)['...

散点图遮盖屏蔽一些数据点，并添加一条线去标记掩码区域。 1234567891011121314151617181920212223import matplotlib.pyplot as pltimport numpy as np# Fixing random state for reproducibilitynp.random.seed(19680801)N = 100r0 = 0.6x = 0.9 * np.random.rand(N)y = 0.9 * np.random.rand(N)area = (20 * np.random.rand(N))**2 # 0 to 10 point radiic = np.sqrt(area)r = np.sqrt(x * x + y * y)area1 = np.ma.masked_where(r < r0, area)area2 = np.ma.masked_where(r >= r0, area)plt.scatter(x, y, s=area1, marker='^', c=c)plt.scat...

散点图视图拆解从散点图创建直方图，并将其添加到散点图的两侧。 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253import numpy as npimport matplotlib.pyplot as pltfrom matplotlib.ticker import NullFormatter# Fixing random state for reproducibilitynp.random.seed(19680801)# the random datax = np.random.randn(1000)y = np.random.randn(1000)nullfmt = NullFormatter() # no labels# definitions for the axesleft, width = 0.1, 0.65bottom, height = 0.1, 0.65bottom_h = left_h = left...