.png%3Ftable%3Dblock%26id%3D99172ae2-ee44-4671-9354-b020520c82ba%26cache%3Dv2&w=3840&q=75)
案例1
代码
import numpy as np import matplotlib.pyplot as plt x = np.arange(11) y = np.zeros(11) y2 = np.ones(11) * 20 for i in range(11): if i % 3 == 0 or i % 4 == 0 or i % 8 == 0: y[i] = 25 else: y[i] = 10 fig, ax = plt.subplots() ax.plot(x, y, color='black', alpha=0.5) ax.fill_between(x, y, color='red', alpha=0.5) ax.plot(x, y2, color='red', alpha=0.5) ax.set_xlim(0, 10) ax.set_ylim(-10, 30) plt.show()
效果
案例2：
import numpy as np import matplotlib.pyplot as plt x = np.arange(0, 11) y = np.where((x % 3 == 0) | (x % 4 == 0) | (x % 8 == 0), 25, 10) y2 = np.full_like(x, 20) # 添加子图 fig, ax = plt.subplots() # 绘制第一个图层 ax.plot(x, y, color='black') ax.fill_between(x, y, alpha=0.5, color='white') # 绘制第二个图层 ax.plot(x, y2, color='red') # 图层间填充 ax.fill_between(x, y, y2, alpha=0.5, color='red') # 设置坐标轴范围 ax.set_xlim(0, 10) ax.set_ylim(-10, 30) # 显示图形 plt.show()
案例4：指定数区域大于指定值
import numpy as np import matplotlib.pyplot as plt x = np.arange(0, 11) y = np.where((x % 3 == 0) | (x % 4 == 0) | (x % 8 == 0), 25, 10) y2 = np.full_like(x, 20) # 添加子图 fig, ax = plt.subplots() # 绘制第一个图层 ax.plot(x, y2, color='red') # 绘制大于20的部分为红色，小于20的部分为蓝色 ax.fill_between(x, y, y2, where=(y >= 20), interpolate=True, color='red') ax.fill_between(x, y, y2, where=(y < 20), interpolate=True, color='blue') # 设置坐标轴范围 ax.set_xlim(0, 10) ax.set_ylim(-10, 30) # 显示图形 plt.show()
案例5：旋转坐标轴
import numpy as np import matplotlib.pyplot as plt x = np.arange(0, 11) y = np.where((x % 3 == 0) | (x % 4 == 0) | (x % 8 == 0), 25, 10) y2 = np.full_like(x, 20) # 添加子图 fig, ax = plt.subplots() # 绘制第一个图层 ax.plot(y2, x, color='red') # 绘制大于20的部分为红色，小于20的部分为蓝色 ax.fill_betweenx(x, y, y2, where=(y >= 20), interpolate=True, color='red') # ax.fill_betweenx(x, y, y2, where=(y < 20), interpolate=True, color='blue') # 设置刻度标签 ax.set_yticks(x) ax.set_xticks([]) # 清空x轴刻度 # 设置刻度标签文本 ax.set_yticklabels(x) ax.set_xticklabels([]) # 清空x轴刻度文本 # 调整图形布局 plt.subplots_adjust(left=0.15, bottom=0.15) # 显示图形 plt.show()
案例6：多条数据
import numpy as np import matplotlib.pyplot as plt import random x = np.arange(0, 20) # y = np.where((x % 3 == 0) | (x % 4 == 0) | (x % 8 == 0), 25, 10) y_threshold_list = np.arange(0, 20) # print(y_threshold_list) # 添加子图 fig, ax = plt.subplots() for y_threshold in y_threshold_list: # y = random.choices(range(0, 10), k=len(x)) # 获取ntrace的值 y = [random.random() for _ in range(len(x))]+y_threshold print(y) y_threshold_temp = np.full_like(x, y_threshold) # 绘制第一个图层 ax.plot(y, x, color='black',linewidth=0.2) ax.plot(y_threshold_temp+0.5, x, color='black', linestyle='dashed', linewidth=0.2) # 填充 ax.fill_betweenx(x, y, y_threshold_temp+0.5, where=( y >= y_threshold_temp+0.5), interpolate=True, color='red') # ax.fill_betweenx(x, y, y_threshold_temp+0.5, where=( # y < y_threshold_temp+0.5), interpolate=True, color='blue') # 设置刻度标签 ax.set_yticks(x) ax.set_xticks([]) # 清空x轴刻度 # 设置刻度标签文本 ax.set_yticklabels(x) ax.set_xticklabels([]) # 清空x轴刻度文本 # 调整图形布局 plt.subplots_adjust(left=0.15, bottom=0.15) # 显示图形 plt.show()
案例7：读取segy数据，这个加载的太慢了
这个数据加载的就很慢了
import numpy as np import matplotlib.pyplot as plt import random import segyio # 添加子图 fig, ax = plt.subplots() # 使用seyio读取数据 with segyio.open(".\data\LX_SEGY005.segy") as f: # print(f.trace[1]) for iline in range(0, len(f.ilines)): # print(f.trace[iline]) # f.trace[iline] # x坐标的个数，对应xline xline = np.arange(0, len(f.trace[iline])) # 获取到每一道，y坐标的个数，对应iline # for iline_threshold in range(0,len(f.trace[iline])): for iline_threshold in range(0, 10): iline_value = f.trace[iline_threshold] # print(iline_value) # 阈值 y = iline_value/10**5 +iline_threshold # 填充list iline_threshold_temp = np.full_like(xline, iline_threshold) # 绘制第一个图层 ax.plot(y, xline, color='black', linewidth=0.2) ax.plot(iline_threshold_temp+0.5, xline, color='black', linestyle='dashed', linewidth=0.2) # 填充 ax.fill_betweenx(xline, y, iline_threshold_temp+0.5, where=( y >= iline_threshold_temp+0.5), interpolate=True, color='red') # print(iline_threshold_temp) # 显示图形 plt.show()
案例:计算交叉点个数
from typing import List, Tuple import numpy as np def get_fill_polygon(line_data: List[Tuple[float, float]], segment: Tuple[int, int]) -> List[Tuple[float, float]]: if segment[1] - segment[0] < 1: return [] result = [(line_data[0][0], segment[0])] dividing = 20 start = (line_data[0][0], dividing) end = (line_data[-1][0], dividing) line1 = (start, end) po = (0, 0) point_count = 0 out = [] for i in range(len(line_data) - 1): line2 = (line_data[i], line_data[i+1]) out.append(line_data[i]) if line_intersects(line2, line1, po): out.append(po) point_count += 1 out.append(line_data[-1]) result.extend([(p[0], start[1]) if p[1] > start[1] else p for p in out]) result.append(end) print("总的点数:", len(result), "交点数量:", point_count) return result def line_intersects(line1, line2, intersection): xdiff = (line1[0][0] - line1[1][0], line2[0][0] - line2[1][0]) ydiff = (line1[0][1] - line1[1][1], line2[0][1] - line2[1][1]) def det(a, b): return a[0] * b[1] - a[1] * b[0] div = det(xdiff, ydiff) if div == 0: return False d = (det(*line1), det(*line2)) intersection = (det(d, xdiff) / div, det(d, ydiff) / div) return True # Example usage line_data = [(40.2451, 542), (41.123, 543), (42.345, 541), (43.789, 539)] segment = (0, len(line_data) - 1) fill_polygon = get_fill_polygon(line_data, segment) print(fill_polygon) import numpy as np import matplotlib.pyplot as plt x = np.arange(11) y = np.zeros(11) y2 = np.ones(11) * 20 for i in range(11): if i % 3 == 0 or i % 4 == 0 or i % 8 == 0: y[i] = 25 else: y[i] = 10 fig, ax = plt.subplots() ax.plot(x, y, color='black', alpha=0.5) ax.fill_between(x, y, color='red', alpha=0.5) ax.plot(x, y2, color='red', alpha=0.5) ax.set_xlim(0, 10) ax.set_ylim(-10, 30) plt.show()
