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import numpy as np
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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from scipy.stats import gaussian_kde
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from scipy.spatial.distance import cdist
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import copy
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class create():
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def __init__(self):
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"""
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data is the x and y coordinate of data.pos
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contours is the edge points
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f is the kernel density value
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levels is the levels for contours
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x_range is the range of x label
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y_range is the range of y label
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"""
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self.data=[]
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self.contours = []
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self.f = []
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self.levels = []
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self.x_range = []
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self.y_range = []
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def data_pre(self, data_name):
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with open(data_name, 'r') as f:
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lines = f.readlines()
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L_en=len(lines)
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lines= lines[1:L_en]
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data = []
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for line in lines:
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x, y,z,t = line.strip().split("\t")
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data.append(list(map(float, [x,y])))
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data = np.array(data)
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self.data = data
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self.x_range = [min(data[:, 0]), max(data[:,0])]
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self.y_range = [min(data[:, 1]), max(data[:,1])]
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def contours_pre(self, level_nums, gWeight):
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x = self.data[:, 0]
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y = self.data[:, 1]
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# 使用scipy库中的gaussian_kde函数计算密度估计
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kde = gaussian_kde(self.data.T)
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# 生成网格点坐标
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xx, yy = np.mgrid[x.min():x.max():200j, y.min():y.max():200j]
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positions = np.vstack([xx.ravel(), yy.ravel()])
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# 计算网格点上的密度估计值
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f = np.reshape(kde(positions).T, xx.shape)
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# 绘制等高线图
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levels = []
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for i in range(level_nums):
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levels.append(f.max() - (level_nums - i - 1) * (f.max() - f.min()) / gWeight)
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self.levels = levels
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contours = plt.contour(xx, yy, f, levels=[levels[0], levels[level_nums - 1]], cmap='coolwarm', alpha=0)
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self_contours = []
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for i in range(len(contours.allsegs[0])):
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self_contours += contours.allsegs[0][i].tolist()
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for i in range(len(self_contours)):
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if self_contours[i] not in self.contours:
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self.contours += [self_contours[i]]
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self.f = f
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class well_to_edge():
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def __init__(self):
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"""
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name is used to store the well names
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type is the types of the wells
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position is the coordinates of wells
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min_distance is the minimum distances between wells and edge
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welltoedge_points is the points responding to the min_distance
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angle is the angles between the shortest distance direction vector from the well to the edge and the positive direction of the y-axis during clockwise rotation;
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wells_num: the number of wells
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"""
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self.name = []
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self.type = []
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self.position = []
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self.min_distance = []
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self.welltoedge_points = []
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self.angle = []
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self.wells_num = 0
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def wells_name_and_position(self, wells_name):
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# 读取井位信息
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with open(wells_name, 'r') as f_j:
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j_ing = f_j.readlines()
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points = []
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typee = []
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namee = []
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for line in j_ing:
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x, y, type, name = line.strip().split("\t")
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points.append(list(map(float, [x, y])))
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typee.append(list(map(int, [type])))
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namee.append(name.split('\n'))
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self.position = points
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self.name = namee
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self.type = typee
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self.wells_num = len(points)
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def welltoedge_distance(self, contour_points):
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min_distance = []
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contours_p=[]
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angles=[]
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wells_num = self.wells_num
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points = self.position
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# 定义待计算距离的点
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for i in range(wells_num):
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point = np.array([points[i][0], points[i][1]])
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# 计算点到等高线上所有点之间的距离
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distances = cdist(point.reshape(1, -1), contour_points)
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# 取距离的最小值
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min_distance.append(distances.min())
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# 记录最短距离对应的点
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for ii in range(distances.size):
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if distances[0][ii] == min_distance[-1]:
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contours_p.append(contour_points[ii])
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# 计算角度（y轴为正北方向）
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direct = contours_p[-1] - point
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if direct[0] < 0:
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angles.append(360-np.arccos(np.dot(direct, np.array(([0, 1]))) / np.linalg.norm(
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direct)) / np.pi * 180)
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else:
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angles.append(np.arccos(np.dot(direct, np.array(([0, 1]))) / np.linalg.norm(
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direct)) / np.pi * 180)
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self.min_distance = min_distance
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self.welltoedge_points = contours_p
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self.angle = angles
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data_name = ['data.pos', 'data1.pos']
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level_nums = 15
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gWeight = 16
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data = create()
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fig, ax = plt.subplots()
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zmin=1
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zmax=0
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for i in range(len(data_name)):
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data.data_pre(data_name[i])
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data.contours_pre(level_nums, gWeight)
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"""画出密度等高线"""
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f = data.f
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x_min, x_max = data.x_range[0], data.x_range[1]
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y_min, y_max = data.y_range[0], data.y_range[1]
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levels = data.levels
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xx, yy = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
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# 填充等高线图中间的区域
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cmap = mpl.colormaps.get_cmap('jet')
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colors = cmap(np.linspace(0, 1, len(data_name)))
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level = [levels[0], levels[-1]]
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ff = ax.contourf(xx, yy, f, levels=level, colors=colors[len(data_name)-i-1:len(data_name)-i], alpha=1, zorder=len(data_name)-i)
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zmin = min(zmin, ff.zmin)
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zmax = max(zmax, ff.zmax)
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plt.xlabel('X')
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plt.ylabel('Y')
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plt.title('Density Distribution')
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# 生成colorbar
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sm = mpl.cm.ScalarMappable(cmap=cmap, norm=mpl.colors.Normalize(vmin=zmin, vmax=zmax))
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sm.set_array([])
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cbar = fig.colorbar(sm, ax=ax)
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wells_name = 'well.txt'
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wells = well_to_edge()
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wells.wells_name_and_position(wells_name)
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wells.welltoedge_distance(data.contours)
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"""画井位信息"""
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typee = wells.type
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points = wells.position
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namee = wells.name
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min_distance = wells.min_distance
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contours_p = wells.welltoedge_points
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for i in range(len(points)):
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if typee[i][0] == 0:
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ax.scatter(points[i][0], points[i][1], marker='o', edgecolors='black', facecolors='none', s=100)
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ax.scatter(points[i][0], points[i][1], marker='o', edgecolors='black', facecolors='none', s=50,
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linewidths=1)
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ax.scatter(points[i][0], points[i][1], marker='o', edgecolors='black', facecolors='none', s=30)
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else:
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ax.scatter(points[i][0], points[i][1], marker='o', edgecolors='black', facecolors='black', s=100, zorder=len(data_name)+1)
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ax.annotate(namee[i][0], (points[i][0], points[i][1]), xytext=(5, 5), textcoords='offset points')
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# 绘制油井边缘最短距离点的箭头并标出距离
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ax.quiver(points[i][0], points[i][1], contours_p[i][0] - points[i][0], contours_p[i][1] - points[i][1],
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angles='xy', scale=1.03,
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scale_units='xy', width=0.002, zorder=len(data_name)+1) # 绘制箭头
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ax.text(points[i][0] / 2 + contours_p[i][0] / 2 + min_distance[i] / 20,
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points[i][1] / 2 + contours_p[i][1] / 2 - min_distance[i] / 20, f'{round(min_distance[i], 2)}',
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fontdict={'size': '10', 'color': 'g'}) # 标出距离
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ax.text(points[i][0] * 3 / 4 + contours_p[i][0] / 4 + min_distance[i] / 20,
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points[i][1] * 3 / 4 + contours_p[i][1] / 4 - min_distance[i] / 20,
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r'$\theta$=' f'{round(wells.angle[i], 2)}', fontdict={'size': '10', 'color': 'y'}) # 标出角度
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plt.show()
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冯冬卫
2 years ago