importmatplotlib.pyplotaspltimportnumpyasnp x=np.random.rand(100)y=np.random.rand(100)colors=x+y plt.figure(figsize=(8,6))scatter=plt.scatter(x,y,c=colors,cmap='viridis')plt.colorbar(scatter)plt.title('Color-coded Scatter Plot - how2matplotlib.com')plt.xlabel('X-axis')plt.ylabel('Y...
= ax.plot(dat) Ln2, = ax.plot(dat) plt.ion() plt.show() for i in range(1, 40): ax.set_xlim(int(len(X[:i])*0.8), len(X[:i])) #show last 20% data of X Ln.set_ydata(X[:i]) Ln.set_xdata(range(len(X[:i]))) Ln2.set_ydata(Y[:i]) Ln2.set_xdata(range(le...
y):returnnp.sin(x)*np.cos(y)# 生成网格数据x=np.linspace(-5,5,100)y=np.linspace(-5,5,100)X,Y=np.meshgrid(x,y)# 使用自定义函数计算Z值Z=custom_function(X,Y)# 创建3D图形fig=plt.figure(figsize=(12,9))ax=fig.add_subplot(111,projection='3d')# 绘制表面surf=ax.plot_...
import numpy as np import matplotlib.pyplot as plt from matplotlib.collections import LineCollection from matplotlib.colors import ListedColormap, BoundaryNorm x = np.linspace(0, 3*np.pi, 500) y = np.sin(x) dydx= np.cos(0.5*(x[:-1]+x[1:])) # 两点之间的中点的导数 """ 这里的目的...
# As many colors as there are unique midwest['category'] categories = np.unique(midwest['category']) colors = [plt.cm.tab10(i / float(len(categories) - 1)) for i in range(len(categories))] # Step 2: Draw Scatterplot with unique color for each category fig = plt.figure(figsize=...
colors = [plt.cm.tab10(i / float(len(categories) - 1)) for i in range(len(categories))] # Step 2: Draw Scatterplot with unique color for each category fig = plt.figure(figsize=(16, 10), dpi=80, facecolor='w', edgecolor='k') for i, category in enumerate(categories): plt.sca...
#plot some shapefile on the basemap as symbols #get the centriod points of thegeomerties data = pd.read_csv('stars.xy', sep=" ", header=None) data.columns = ["LONGITUDE", "LATITUDE"] #print(data) lats = data['LATITUDE'].values ...
Bubble plot with Encircling # df <- fread("https://raw.githubusercontent.com/selva86/datasets/master/midwest_filter.csv") df <- fread("data1_midwest.csv") # 将category定义为具有顺序的因子类型 df_class <- unique(df$category) %>% .[order(.)] df[, category:=factor(category, levels =...
colors = [plt.cm.tab10(i/float(len(categories)-1))foriinrange(len(categories))] # Step 2: Draw Scatterplot with unique color for each category fig = plt.figure(figsize=(16,10), dpi=80, facecolor='w', edgecolor='k') fori, ...
我想生成包含大量曲线的两个子图,所以我定义了一个生成色条的函数,以避免出现不可读的超长图例。这是创建色条的函数: import matplotlib as mpl, matplotlib.pyplot as plt def colorbar (cmap, vmin, vmax, label, ax=None, **cbar_opts): norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax, clip=False...