Skip to content
Navigation Menu
{{ message }}
forked from xianhu/LearnPython
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathpython_visual.py
More file actions
502 lines (391 loc) · 12.6 KB
/
Copy pathpython_visual.py
File metadata and controls
502 lines (391 loc) · 12.6 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
# _*_ coding: utf-8 _*_
"""
python_visual.py by xianhu
"""
import numpy as np
import matplotlib
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
from mpl_toolkits.mplot3d import Axes3D
# 解决中文乱码问题
myfont = fm.FontProperties(fname="/Library/Fonts/Songti.ttc", size=14)
matplotlib.rcParams["axes.unicode_minus"] = False
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，并设定标题
plt.figure(figsize=(8, 6), dpi=80)
plt.title("简单曲线图", fontproperties=myfont)
plt.grid(True)
# 设置X轴
plt.xlabel("X轴", fontproperties=myfont)
plt.xlim(-4.0, 4.0)
plt.xticks(np.linspace(-4, 4, 9, endpoint=True))
# 设置Y轴
plt.ylabel("Y轴", fontproperties=myfont)
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示例")
plt.plot(x, y_sin, "g-", linewidth=2.0, label="sin示例")
# 设置图例位置,loc可以为[upper, lower, left, right, center]
plt.legend(loc="upper left", prop=myfont, 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, 并设定标题
plt.figure(figsize=(8, 6), dpi=80)
plt.title("复杂曲线图", fontproperties=myfont)
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="左cos")
ax_1.legend(loc="upper left", prop=myfont, shadow=True)
# 设置Y轴(左边)
ax_1.set_ylabel("左cos的y轴", fontproperties=myfont)
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="右sin")
ax_2.legend(loc="upper right", prop=myfont, shadow=True)
# 设置Y轴(右边)
ax_2.set_ylabel("右sin的y轴", fontproperties=myfont)
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_1.set_xlabel("x轴", fontproperties=myfont)
ax_1.set_xlim(-4.0, 4.0)
ax_1.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.title("子图 %d" % (num+1), fontproperties=myfont)
plt.plot(x, y, style_list[num])
# 图形显示
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)
# 设置标题
plt.title("柱状图", fontproperties=myfont)
# 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="男生")
plt.bar(index+bar_width, means_women, width=bar_width, alpha=0.8, color="r", label="女生")
plt.legend(loc="upper right", prop=myfont, shadow=True)
# 设置柱状图标示
for x, y in zip(index, means_men):
plt.text(x, y+0.3, y, ha="center", va="bottom")
for x, y in zip(index, means_women):
plt.text(x+bar_width, 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("分组Group", fontproperties=myfont)
plt.ylabel("得分Scores", fontproperties=myfont)
plt.xticks(index+(bar_width/2), ("A组", "B组", "C组", "D组", "E组"), fontproperties=myfont)
# 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)
# 设置标题
plt.title("横向柱状图", fontproperties=myfont)
# 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, y, ha="left", va="center")
for x, y in zip(index, means_women):
plt.text(y+0.3, x+bar_height, 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/2), ("A", "B", "C", "D", "E"))
# 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))
# 设置标题
plt.title("高级柱状图", fontproperties=myfont)
# 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, means_men, marker="o", linestyle="-", color="r", label="Men line")
plt.plot(index, -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, y+1, y, ha="center", va="bottom")
for x, y in zip(index, means_women):
plt.text(x, -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]
])
# 设置标题
plt.title("层次柱状图", fontproperties=myfont)
# 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, data[i], width=0.5, color=color_index[i], bottom=bottom, alpha=0.7, label="标签 %d" % i)
bottom += data[i]
# 设置图例位置
plt.legend(loc="upper left", prop=myfont, 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)
# 设置标题
plt.title("直方图", fontproperties=myfont)
# 画直方图, 并返回相关结果
n, bins, patches = plt.hist(x, bins=50, normed=1, cumulative=False, color="green", alpha=0.6, label="直方图")
# 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="线条")
# 设置图例位置
plt.legend(loc="upper left", prop=myfont, 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]
labels = ["Frogs", "中文", "Dogs", "Logs"]
colors = ["yellowgreen", "gold", "lightskyblue", "lightcoral"]
# 设置标题
plt.title("饼图", fontproperties=myfont)
# 设置突出参数
explode = [0, 0.05, 0, 0]
# 画饼状图
patches, l_text, p_text = plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct="%1.1f%%", shadow=True, startangle=90)
for text in l_text:
text.set_fontproperties(myfont)
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)
# 设置标题
plt.title("散点图", fontproperties=myfont)
# 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)
# 设置标题
plt.title("填充图", fontproperties=myfont)
# 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, where=(y > 0), color="blue", alpha=0.25)
plt.fill_between(x, 0, y, where=(y < 0), color="red", alpha=0.25)
# 图形显示
plt.show()
return
# fill_plot()
def radar_plot():
"""
radar plot
"""
# 生成测试数据
labels = np.array(["A组", "B组", "C组", "D组", "E组", "F组"])
data = np.array([68, 83, 90, 77, 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)
plt.title("雷达图", fontproperties=myfont)
# 设置"theta grid"/"radar grid"
plt.thetagrids(theta*(180/np.pi), labels=labels, fontproperties=myfont)
plt.rgrids(np.arange(20, 100, 20), labels=np.arange(20, 100, 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
x = np.random.random(100)
y = np.random.random(100)
z = np.random.random(100)
color = np.random.random(100)
scale = np.random.random(100) * 100
# 生成画布(两种形式)
fig = plt.figure()
fig.suptitle("三维散点图", fontproperties=myfont)
# 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
x = np.linspace(0, 1, 1000)
y = np.linspace(0, 1, 1000)
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", title="plot title")
# ax = fig.add_subplot(111, projection="3d", title="plot title")
# 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", title="plot title")
# 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()
You can’t perform that action at this time.