# _*_ coding: utf-8 _*_ """ python_visual.py by xianhu """ import numpy as np import matplotlib.mlab as mlab import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D def simple_plot(): """ simple plot """ # 生成测试数据 x = np.linspace(-np.pi, np.pi, 256, endpoint=True) y_cos, y_sin = np.cos(x), np.sin(x) # 生成画布 plt.figure(figsize=(8, 6), dpi=80) plt.title("plot title") plt.grid(True) # 设置X轴 plt.xlabel("x label") plt.xlim(-4.0, 4.0) plt.xticks(np.linspace(-4, 4, 9, endpoint=True)) # 设置Y轴 plt.ylabel("y label") plt.ylim(-1.0, 1.0) plt.yticks(np.linspace(-1, 1, 9, endpoint=True)) # 画两条曲线 plt.plot(x, y_cos, "b--", linewidth=2.0, label="cos") plt.plot(x, y_sin, "g-", linewidth=2.0, label="sin") # 设置图例位置,loc可以为[upper, lower, left, right, center] plt.legend(loc="upper left", shadow=True) # 图形显示 plt.show() return # simple_plot() def simple_advanced_plot(): """ simple advanced plot """ # 生成测试数据 x = np.linspace(-np.pi, np.pi, 256, endpoint=True) y_cos, y_sin = np.cos(x), np.sin(x) # 生成画布 plt.figure(figsize=(8, 6), dpi=80) plt.title("plot title") plt.grid(True) # 画图的另外一种方式 ax_1 = plt.subplot(111) ax_1.plot(x, y_cos, color="blue", linewidth=2.0, linestyle="--", label="cos in left") ax_1.legend(loc="upper left", shadow=True) # 设置Y轴(左边) ax_1.set_ylabel("y label for cos in left") ax_1.set_ylim(-1.0, 1.0) ax_1.set_yticks(np.linspace(-1, 1, 9, endpoint=True)) # 画图的另外一种方式 ax_2 = ax_1.twinx() ax_2.plot(x, y_sin, color="green", linewidth=2.0, linestyle="-", label="sin in right") ax_2.legend(loc="upper right", shadow=True) # 设置Y轴(右边) ax_2.set_ylabel("y label for sin in right") ax_2.set_ylim(-2.0, 2.0) ax_2.set_yticks(np.linspace(-2, 2, 9, endpoint=True)) # 设置X轴(共同) ax_2.set_xlabel("x label") ax_2.set_xlim(-4.0, 4.0) ax_2.set_xticks(np.linspace(-4, 4, 9, endpoint=True)) # 图形显示 plt.show() return # simple_advanced_plot() def subplot_plot(): """ subplot plot """ # 子图的style列表 style_list = ["g+-", "r*-", "b.-", "yo-"] # 依次画图 for num in range(4): # 生成测试数据 x = np.linspace(0.0, 2+num, num=10*(num+1)) y = np.sin((5-num) * np.pi * x) # 子图的生成方式 plt.subplot(2, 2, num+1) plt.plot(x, y, style_list[num]) # 图形显示 plt.grid(True) plt.show() return # subplot_plot() def bar_plot(): """ bar plot """ # 生成测试数据 means_men = (20, 35, 30, 35, 27) means_women = (25, 32, 34, 20, 25) # 设置相关参数 index = np.arange(len(means_men)) bar_width = 0.35 # 画柱状图 plt.bar(index, means_men, width=bar_width, alpha=0.2, color="b", label="Men") plt.bar(index+bar_width, means_women, width=bar_width, alpha=0.8, color="r", label="Women") plt.legend(loc="upper right", shadow=True) # 设置柱状图标示 for x, y in zip(index, means_men): plt.text(x+(bar_width/2), y+0.3, y, ha="center", va="bottom") for x, y in zip(index, means_women): plt.text(x+bar_width+(bar_width/2), y+0.3, y, ha="center", va="bottom") # 设置刻度范围/坐标轴名称等 plt.ylim(0, 45) plt.xlabel("Group") plt.ylabel("Scores") plt.xticks(index+bar_width, ("A组", "B组", "C组", "D组", "E组")) # 图形显示 plt.show() return # bar_plot() def barh_plot(): """ barh plot """ # 生成测试数据 means_men = (20, 35, 30, 35, 27) means_women = (25, 32, 34, 20, 25) # 设置相关参数 index = np.arange(len(means_men)) bar_height = 0.35 # 画柱状图(水平方向) plt.barh(index, means_men, height=bar_height, alpha=0.2, color="b", label="Men") plt.barh(index+bar_height, means_women, height=bar_height, alpha=0.8, color="r", label="Women") plt.legend(loc="upper right", shadow=True) # 设置柱状图标示 for x, y in zip(index, means_men): plt.text(y+0.3, x+(bar_height/2), y, ha="left", va="center") for x, y in zip(index, means_women): plt.text(y+0.3, x+bar_height+(bar_height/2), y, ha="left", va="center") # 设置刻度范围/坐标轴名称等 plt.xlim(0, 45) plt.xlabel("Scores") plt.ylabel("Group") plt.yticks(index+bar_height, ("A组", "B组", "C组", "D组", "E组")) # 图形显示 plt.show() return # barh_plot() def bar_advanced_plot(): """ bar advanced plot """ # 生成测试数据 means_men = np.array((20, 35, 30, 35, 27, 25, 32, 34, 20, 25)) means_women = np.array((25, 32, 34, 20, 25, 20, 35, 30, 35, 27)) # 设置相关参数 index = np.arange(len(means_men)) bar_width = 0.8 # 画柱状图(两种:X轴以上/X轴以下) plt.bar(index, means_men, width=bar_width, alpha=0.4, color="b", label="Men") plt.bar(index, -means_women, width=bar_width, alpha=0.4, color="r", label="Women") # 画折线图(两种,和柱状图对应) plt.plot(index+(bar_width/2), means_men, marker="o", linestyle="-", color="r", label="Men line") plt.plot(index+(bar_width/2), -means_women, marker=".", linestyle="--", color="b", label="Women line") # 设置图形标示(两种,和柱状图对应) for x, y in zip(index, means_men): plt.text(x+(bar_width/2), y+1, y, ha="center", va="bottom") for x, y in zip(index, means_women): plt.text(x+(bar_width/2), -y-1, y, ha="center", va="top") # 设置Y轴和图例位置 plt.ylim(-45, 80) plt.legend(loc="upper left", shadow=True) # 图形显示 plt.show() return # bar_advanced_plot() def table_plot(): """ table plot """ # 生成测试数据 data = np.array([ [1, 4, 2, 5, 2], [2, 1, 1, 3, 6], [5, 3, 6, 4, 1] ]) # 设置相关参数 index = np.arange(len(data[0])) color_index = ["r", "g", "b"] # 声明底部位置 bottom = np.array([0, 0, 0, 0, 0]) # 依次画图,并更新底部位置 for i in range(len(data)): plt.bar(index+0.25, data[i], width=0.5, color=color_index[i], bottom=bottom, alpha=0.7, label="label %d" % i) bottom += data[i] # 设置图例位置 plt.legend(loc="upper left", shadow=True) # 图形显示 plt.show() return # table_plot() def histograms_plot(): """ histograms plot """ # 生成测试数据 mu, sigma = 100, 15 x = mu + sigma * np.random.randn(10000) # 设置相关参数 num_bins = 50 # 画直方图,并返回相关结果 n, bins, patches = plt.hist(x, bins=num_bins, normed=1, color="green", alpha=0.6, label="hist") # 根据直方图返回的结果,画折线图 y = mlab.normpdf(bins, mu, sigma) plt.plot(bins, y, "r--", label="line") # 设置图例位置 plt.legend(loc="upper left", shadow=True) # 图形显示 plt.show() return # histograms_plot() def pie_plot(): """ pie plot """ # 生成测试数据 sizes = [15, 30, 45, 10] explode = [0, 0.05, 0, 0] labels = ["Frogs", "Hogs", "Dogs", "Logs"] colors = ["yellowgreen", "gold", "lightskyblue", "lightcoral"] # 画饼状图 plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct="%1.1f%%", shadow=True, startangle=90) plt.axis("equal") # 图形显示 plt.show() return # pie_plot() def scatter_plot(): """ scatter plot """ # 生成测试数据 point_count = 1000 x_index = np.random.random(point_count) y_index = np.random.random(point_count) # 设置相关参数 color_list = np.random.random(point_count) scale_list = np.random.random(point_count) * 100 # 画散点图 plt.scatter(x_index, y_index, s=scale_list, c=color_list, marker="o") # 图形显示 plt.show() return # scatter_plot() def fill_plot(): """ fill plot """ # 生成测试数据 x = np.linspace(-2*np.pi, 2*np.pi, 1000, endpoint=True) y = np.sin(x) # 画图 plt.plot(x, y, color="blue", alpha=1.00) # 填充图形 # plt.fill_between(x, y1, y2, where=None, *kwargs) plt.fill_between(x, 0, y, y > 0, color="blue", alpha=.25) plt.fill_between(x, 0, y, y < 0, color="red", alpha=.25) # 图形显示 plt.show() return # fill_plot() def radar_plot(): """ radar plot """ # 生成测试数据 labels = np.array(["A", "B", "C", "D", "E", "F"]) data = np.array([38, 43, 90, 67, 89, 73]) theta = np.linspace(0, 2*np.pi, len(data), endpoint=False) # 数据预处理 data = np.concatenate((data, [data[0]])) theta = np.concatenate((theta, [theta[0]])) # 画图方式 plt.subplot(111, polar=True) # 设置"theta grid"/"radar grid" plt.thetagrids(theta*(180/np.pi), labels=labels) plt.rgrids(np.arange(20, 101, 20), labels=np.arange(20, 101, 20), angle=0) plt.ylim(0, 100) # 画雷达图,并填充雷达图内部区域 plt.plot(theta, data, "bo-", linewidth=2) plt.fill(theta, data, color="red", alpha=0.25) # 图形显示 plt.show() return # radar_plot() def three_dimension_scatter(): """ 3d scatter plot """ # 生成测试数据 number = 1000 x = np.random.random(number) y = np.random.random(number) z = np.random.random(number) color = np.random.random(number) scale = np.random.random(number) * 100 # 生成画布(两种形式) fig = plt.figure() # ax = fig.gca(projection="3d") ax = fig.add_subplot(111, projection="3d") # 画三维散点图 ax.scatter(x, y, z, s=scale, c=color, marker=".") # 设置坐标轴图标 ax.set_xlabel("X Label") ax.set_ylabel("Y Label") ax.set_zlabel("Z Label") # 设置坐标轴范围 ax.set_xlim(0, 1) ax.set_ylim(0, 1) ax.set_zlim(0, 1) # 图形显示 plt.show() return # three_dimension_scatter() def three_dimension_line(): """ 3d line plot """ # 生成测试数据 number = 1000 x = np.linspace(0, 1, number) y = np.linspace(0, 1, number) z = np.sin(x * 2 * np.pi) / (y + 0.1) # 生成画布(两种形式) fig = plt.figure() ax = fig.gca(projection="3d") # ax = fig.add_subplot(111, projection="3d") # 画三维折线图 ax.plot(x, y, z, color="red", linestyle="-") # 设置坐标轴图标 ax.set_xlabel("X Label") ax.set_ylabel("Y Label") ax.set_zlabel("Z Label") # 图形显示 plt.show() return # three_dimension_line() def three_dimension_bar(): """ 3d bar plot """ # 生成测试数据(位置数据) xpos = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] ypos = [2, 3, 4, 5, 1, 6, 2, 1, 7, 2] zpos = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] # 生成测试数据(柱形参数) dx = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] dy = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] dz = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] # 生成画布(两种形式) fig = plt.figure() ax = fig.gca(projection="3d") # ax = fig.add_subplot(111, projection="3d") # 画三维柱状图 ax.bar3d(xpos, ypos, zpos, dx, dy, dz, alpha=0.5) # 设置坐标轴图标 ax.set_xlabel("X Label") ax.set_ylabel("Y Label") ax.set_zlabel("Z Label") # 图形显示 plt.show() return # three_dimension_bar()