如何在x，y点的2D数组中找到第一个极值？

The following idea can be given:

Read the file and put data into a 2D NumPy array.

arr = []        
with open(str(Path('example.csv'))) as csv_file:
    reader = csv.reader(csv_file, delimiter=',')
    for row in reader:
        arr.append([float(row[0]),float(row[1])])
arr = np.array(arr)

Since the data is unordered then when we connect points subsequently, we observe an ugly graph.

fig0 = go.Figure(go.Scatter(x=arr[:,0],y=arr[:,1], mode='lines'))
fig0.show()

To get an appropriate picture we need to sort points from some starting point in such a way that only the nearest points are connected

arx, x0, xmin, xmax = sort_by_nearest(arr)
x = arx[:,0]
y = arx[:,1]

排序后的数据y看起来像：非双射行为消失了，我们可以y使用find_peaks函数轻松找到仅数据的第一个峰。prominence是从一个山峰到下一个山峰的最小下降和上升高度。因此，小的噪声峰值将被丢弃。height负责仅在以下区域搜索峰的参数y>height

peaks, props = find_peaks(y, prominence=0.01, height=0.4)

这里有几个候选人

print('rmax candidates=', y[peaks])

当然，我们拿第一个

rmax = y[peaks[0]]
x_rmax = x[peaks[0]] - x0

让我们得出结果：

fig0 = go.Figure(go.Scatter(y=arx[:,1], mode='lines'))
fig0.show()

fig0 = go.Figure(go.Scatter(x=arx[:,0]-x0, y=arx[:,1], name='my_line', mode='lines'))
fig0.add_trace(go.Scatter(x=[x_rmax,x_rmax], y=[0,0.5], name='peak coord', mode='lines', line=dict(color='black', width=2, dash='dot')))

fig0.show()

让我们集中讨论按邻居对数据进行排序的算法。

import pandas as pd
import numpy as np
import plotly.graph_objects as go
import csv
from scipy.signal import find_peaks
import scipy.spatial.distance as ds
from pathlib import Path

搜索最接近点的功能很棘手，我们需要以某种方式标记已经观察到的点，以避免无限循环。我们只是通过大修改数组副本的点坐标来做到这一点arr_full

def closest_point(arr_full, xi, yi, inds):
    N = arr_full.shape[0]
    arr_full[inds] = 1e+30
    dist = (arr_full[:, 0] - xi)**2 + (arr_full[:, 1] - yi)**2
    i_min = dist.argmin()
    #returns an index of the nearest point and its coordinate
    return i_min, arr_full[i_min][0], arr_full[i_min][1]

def sort_by_nearest(arr):
    N = arr.shape[0]
    nearest_point = [None]*N
    #find initial point
    xmin = min(arr[:,0])
    xmax = max(arr[:,0])
    # we copy the original array
    arr_cut = np.copy(arr)
    # the area in which we are 100% sure in absence of the starting point we flag by big value
    arr_cut[arr[:,0] > 0.5*(xmin + xmax)] = 10e+30
    iymin = arr_cut[:,1].argmin()
    # the staring points are
    x0, y0 = arr[iymin, 0], arr[iymin, 1]
    # we initialize the sorted value nearest_point
    nearest_point[0] = [arr[iymin,0],arr[iymin,1]]
    print('Starting point:', x0, y0)
    # we put in it the indices of visited points
    observed = [iymin]
    i_min = iymin
    for i in range(1,N):
        xi, yi = arr[i_min]
        i_min, xip, yip = closest_point(arr, xi, yi, i_min)
        nearest_point[i] = [xip, yip]
        observed.append(i_min)
    nearest_point = np.array(nearest_point)
    return np.array(nearest_point), x0, xmin, xmax