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134
CREAT.py
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import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde
from scipy.spatial.distance import cdist
import copy
class create():
def __init__(self):
"""
data is the x and y coordinate of data.pos
contours is the edge points
f is the kernel density value
levels is the levels for contours
x_range is the range of x label
y_range is the range of y label
"""
self.data = []
self.contours = []
self.f = []
self.levels = []
self.x_range = []
self.y_range = []
def data_pre(self, data_name):
with open(data_name, 'r') as f:
lines = f.readlines()
L_en=len(lines)
lines= lines[1:L_en]
data = []
for line in lines:
x, y,z,t = line.strip().split("\t")
data.append(list(map(float, [x,y])))
data = np.array(data)
self.data = data
self.x_range = [min(data[:, 0]), max(data[:,0])]
self.y_range = [min(data[:, 1]), max(data[:,1])]
def contours_pre(self, level_nums, gWeight):
x = self.data[:, 0]
y = self.data[:, 1]
# 使用scipy库中的gaussian_kde函数计算密度估计
kde = gaussian_kde(self.data.T)
# 生成网格点坐标
xx, yy = np.mgrid[x.min():x.max():200j, y.min():y.max():200j]
positions = np.vstack([xx.ravel(), yy.ravel()])
# 计算网格点上的密度估计值
f = np.reshape(kde(positions).T, xx.shape)
# 绘制等高线图
levels = []
for i in range(level_nums):
levels.append(f.max() - (level_nums - i - 1) * (f.max() - f.min()) / gWeight)
self.levels = levels
contours = plt.contour(xx, yy, f, levels=[levels[0], levels[level_nums - 1]], cmap='coolwarm', alpha=0)
plt.close()
self_contours = []
for i in range(len(contours.allsegs[0])):
self_contours += contours.allsegs[0][i].tolist()
for i in range(len(self_contours)):
if self_contours[i] not in self.contours:
self.contours += [self_contours[i]]
self.f = f
class well_to_edge():
def __init__(self):
"""
name is used to store the well names
type is the types of the wells
position is the coordinates of wells
min_distance is the minimum distances between wells and edge
welltoedge_points is the points responding to the min_distance
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;
wells_num: the number of wells
"""
self.name = []
self.type = []
self.position = []
self.min_distance = []
self.welltoedge_points = []
self.angle = []
self.wells_num = 0
def wells_name_and_position(self, wells_name):
# 读取井位信息
with open(wells_name, 'r') as f_j:
j_ing = f_j.readlines()
points = []
typee = []
namee = []
for line in j_ing:
x, y, type, name = line.strip().split("\t")
points.append(list(map(float, [x, y])))
typee.append(list(map(int, [type])))
namee.append(name.split('\n'))
self.position = points
self.name = namee
self.type = typee
self.wells_num = len(points)
def welltoedge_distance(self, contour_points):
min_distance = []
contours_p=[]
angles=[]
wells_num = self.wells_num
points = self.position
# 定义待计算距离的点
for i in range(wells_num):
point = np.array([points[i][0], points[i][1]])
# 计算点到等高线上所有点之间的距离
distances = cdist(point.reshape(1, -1), contour_points)
# 取距离的最小值
min_distance.append(distances.min())
# 记录最短距离对应的点
for ii in range(distances.size):
if distances[0][ii] == min_distance[-1]:
contours_p.append(contour_points[ii])
# 计算角度（y轴为正北方向）
direct = contours_p[-1] - point
if direct[0] < 0:
angles.append(360-np.arccos(np.dot(direct, np.array(([0, 1]))) / np.linalg.norm(
direct)) / np.pi * 180)
else:
angles.append(np.arccos(np.dot(direct, np.array(([0, 1]))) / np.linalg.norm(
direct)) / np.pi * 180)
self.min_distance = min_distance
self.welltoedge_points = contours_p
self.angle = angles

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data.pos
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data1.pos
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test2.py
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import sys
from PySide6.QtWidgets import QApplication, QMainWindow, QGraphicsView
from PySide6.QtUiTools import QUiLoader
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
from matplotlib.figure import Figure
import matplotlib.pyplot as plt
from PySide6.QtWidgets import QVBoxLayout
import CREAT
import numpy as np
import matplotlib as mpl
class MainWindow(QMainWindow):
def __init__(self):
super().__init__()
self.toolbar = None
self.canvas = None
# 加载UI文件
self.ui = QUiLoader().load('QT/PLT2.ui')
# 更新图窗
self.ui.graphicsView.repaint()
self.ui.graphicsView.update()
self.graphics_layout = QVBoxLayout(self.ui.graphicsView)
# 绘制Matplotlib图形
self.ui.pushButton_4.clicked.connect(lambda: self.plot_density())
def plot_density(self):
fig, ax = self.scene_fig()
data_name = self.ui.lineEdit.text().split(',')
level_nums = self.ui.spinBox_2.value()
gWeight = self.ui.spinBox.value()
data = CREAT.create()
zmin = 1
zmax = 0
for i in range(len(data_name)):
data.data_pre(data_name[i])
data.contours_pre(level_nums, gWeight)
"""画出密度等高线"""
f = data.f
x_min, x_max = data.x_range[0], data.x_range[1]
y_min, y_max = data.y_range[0], data.y_range[1]
levels = data.levels
xx, yy = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
# 填充等高线图中间的区域
cmap = mpl.colormaps.get_cmap('jet')
colors = cmap(np.linspace(0, 1, level_nums))
level = [levels[0], levels[-1]]
ff = ax.contourf(xx, yy, f, levels=level, colors=colors[level_nums - i - 1:level_nums - i], alpha=1,
zorder=len(data_name) - i)
zmin = min(zmin, ff.zmin)
zmax = max(zmax, ff.zmax)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_title('Density Distribution')
# 生成colorbar
sm = mpl.cm.ScalarMappable(cmap=cmap, norm=mpl.colors.Normalize(vmin=zmin, vmax=zmax))
sm.set_array([])
fig.colorbar(sm, ax=ax)
wells_name = self.ui.lineEdit_4.text()
if wells_name == '':
self.canvas_adjust(fig)
else:
wells = CREAT.well_to_edge()
wells.wells_name_and_position(wells_name)
wells.welltoedge_distance(data.contours)
"""画井位信息"""
typee = wells.type
points = wells.position
namee = wells.name
min_distance = wells.min_distance
contours_p = wells.welltoedge_points
for i in range(len(points)):
if typee[i][0] == 0:
ax.scatter(points[i][0], points[i][1], marker='o', edgecolors='black', facecolors='none', s=100)
ax.scatter(points[i][0], points[i][1], marker='o', edgecolors='black', facecolors='none', s=50,
linewidths=1)
ax.scatter(points[i][0], points[i][1], marker='o', edgecolors='black', facecolors='none', s=30)
ax.text(points[i][0] + min_distance[i] / 20, points[i][1] - min_distance[i] / 20, f'{namee[i][0]}',
fontdict={'size': '10', 'color': 'b'}, zorder=len(data_name) + 2) # 井名信息
else:
ax.scatter(points[i][0], points[i][1], marker='o', edgecolors='black', facecolors='black', s=100, zorder=len(data_name)+1)
ax.text(points[i][0] + min_distance[i] / 20, points[i][1] - min_distance[i] / 20, f'{namee[i][0]}',
fontdict={'size': '10', 'color': 'b'}, zorder=len(data_name) + 2) # 井名信息
continue
# 绘制油井边缘最短距离点的箭头并标出距离
ax.quiver(points[i][0], points[i][1], contours_p[i][0] - points[i][0], contours_p[i][1] - points[i][1],
angles='xy', scale=1.03,
scale_units='xy', width=0.002, zorder=len(data_name)+1) # 绘制箭头
ax.text(points[i][0] * 1 / 9 + contours_p[i][0] * 8 / 9 + min_distance[i] / 20,
points[i][1] * 1 / 9 + contours_p[i][1] * 8 / 9 - min_distance[i] / 20, f'{round(min_distance[i], 2)}',
fontdict={'size': '10', 'color': 'g'}) # 标出距离
ax.text(points[i][0] * 1 / 2 + contours_p[i][0] * 1 / 2 + min_distance[i] / 20,
points[i][1] * 1 / 2 + contours_p[i][1] * 1 / 2 - min_distance[i] / 20,
r'$\theta$=' f'{round(wells.angle[i], 2)}', fontdict={'size': '10', 'color': 'y'}) # 标出角度
self.canvas_adjust(fig)
def scene_fig(self):
fig = Figure()
# 在Figure对象中添加子图
ax = fig.add_subplot(111)
return fig, ax
def canvas_adjust(self, fig):
if self.canvas is not None:
# 从布局中删除旧的 canvas 和 toolbar
item = self.graphics_layout.takeAt(0)
while item:
widget = item.widget()
if widget:
widget.setParent(None)
item = self.graphics_layout.takeAt(0)
self.graphics_layout.removeWidget(self.canvas)
self.canvas = FigureCanvas(fig)
self.toolbar = NavigationToolbar(self.canvas, self.ui.graphicsView)
self.toolbar.setParent(self.canvas)
self.graphics_layout.addWidget(self.canvas)
self.adjustSize()
if __name__ == '__main__':
app = QApplication([])
stats = MainWindow()
stats.ui.show()
app.exec()

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well.txt
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0 0 1 L40-5
-184 -243 0 L40-4
-258 178 0 L39-5
188 -169 0 L41-5
242 321 0 L40-6
446 180 0 L41-6
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