问如何从.csv文件中计算x- y坐标

EN

xcoord,ycoord
112000,556000
112000,555500
112000,555000
112000,554500
112000,554000
112000,553500

#example code stack

x = 2000
y = 3000

new_value = x + y + 10


print(x)
print(y)

xcoord,ycoord,new_value
112000,556000,668010
112000,555500,667510
112000,555000,667010
112000,554500,666510
112000,554000,666010
112000,553500,665510

import geopandas as gpd
import itertools
import math
from pyproj import Transformer
import numpy as np
from math import sqrt
import os
import pandas as pd
from pathlib import Path
from matplotlib import pyplot as plt


## data
land = gpd.read_file('land2.shp')
land = land.to_crs("EPSG:28992")


os.chdir('C:\\Users\\###\\sensoren\\data')
os.getcwd()
wind = pd.read_csv('data_totaal_2.csv', usecols= [ 0, 1,2 , 5],skiprows = 33, names = ['STN','DT','HH','FF'])


## closest point
def min_distance(x, y, iterable):
       list_of_distances = list(map(lambda t: sqrt(pow(t[0]-x,2)+pow(t[1]-y,2)),iterable))
       min_res = min(list_of_distances)
       index_of_min = list_of_distances.index(min_res)
       return iterable[index_of_min]

## check if point is inside triangle
def area(x1, y1, x2, y2, x3, y3):
    return abs((x1 * (y2 - y3) +
                x2 * (y3 - y1) +
                x3 * (y1 - y2)) / 2.0)


def is_inside(x1, y1, x2, y2, x3, y3, x, y):
    A = area(x1, y1, x2, y2, x3, y3)
    A1 = area(x, y, x2, y2, x3, y3)
    A2 = area(x1, y1, x, y, x3, y3)
    A3 = area(x1, y1, x2, y2, x, y)
    return A == A1 + A2 + A3

## calculate perimeter
def perimeter(points):
    x1, y1, x2, y2, x3, y3 = *points[0], *points[1], *points[2]
    return (math.hypot(x1-x2, y1-y2) +
            math.hypot(x2-x3, y2-y3) +
            math.hypot(x3-x1, y3-y1))

## barycentric interpolation
def bary_interpol (inter_cor_x1, inter_cor_x2, inter_cor_x3, inter_cor_y1,
                   inter_cor_y2, inter_cor_y3, inter_cor_px, inteR_cor_py,
                   inter_wind_v1, inter_wind_v2, inter_wind_v3):
    W_v1 = ((inter_cor_y2-inter_cor_y3)*(inter_cor_px-inter_cor_x3)+
            (inter_cor_x3-inter_cor_x2)*(inter_cor_py-inter_cor_y3))/((inter_cor_y2-inter_cor_y3)*(inter_cor_x1-inter_cor_x3)+
            (inter_cor_x3-inter_cor_x2)*(inter_cor_y1-inter_cor_y3))
    W_v2 = ((inter_cor_y3-inter_cor_y1)*(inter_cor_px-inter_cor_x3)+
            (inter_cor_x1-inter_cor_x3)*(inter_cor_py-inter_cor_y3))/((inter_cor_y2-inter_cor_y3)*(inter_cor_x1-inter_cor_x3)+
            (inter_cor_x3-inter_cor_x2)*(inter_cor_y1-inter_cor_y3))
    W_v3 = 1 - W_v1 - W_v2
    inter_wind_pv = (W_v1 * inter_wind_v1) + (W_v2 * inter_wind_v2) +  (W_v3 * inter_wind_v3)
    return inter_wind_pv

       
## datum shift
transformer = Transformer.from_crs("epsg:4326", "epsg:28992")

RD_229 = transformer.transform(53.04309, 4.81969) #texelhors_229
RD_235 = transformer.transform(52.91382, 4.79498) #de_kooy_235
RD_242 = transformer.transform(53.25337, 4.94041) #vlieland_242
RD_251 = transformer.transform(53.39335, 5.34623) #terschelling_251
RD_267 = transformer.transform(52.89709, 5.38331) #stavoren_267
RD_277 = transformer.transform(53.40848, 6.19614) #lauwersoog_277

df = pd.read_csv(r"C:\Users\###\OneDrive - NIOZ\NIOZ\Pyth\coordinates.csv")

P =  ( df['xcoord'], df['ycoord'])

STN_229_x = RD_229[0]
STN_235_x = RD_235[0]
STN_242_x = RD_242[0]
STN_251_x = RD_251[0]
STN_267_x = RD_267[0]
STN_277_x = RD_277[0]

points = [(RD_229[0],RD_229[1]),(RD_235[0],RD_235[1]),(RD_242[0],RD_242[1]),(RD_251[0],RD_251[1]),(RD_267[0],RD_267[1]),(RD_277[0],RD_277[1])]


## find best triangle or point
triangle_fit = []
for triangle in itertools.combinations(points, 3):
    p1, p2, p3 = triangle
    if is_inside(*p1, *p2, *p3, *P):
       triangle_fit.append(triangle)

triangle_fit.sort(key=perimeter) 


 # Sort by perimeter.

triangle_fit.append(triangle)
triangle_fit.sort(key=perimeter) 
inter_cor = triangle_fit[0]
inter_cor_x1 = inter_cor[0][0]
inter_cor_x2 = inter_cor[1][0]
inter_cor_x3 = inter_cor[2][0]
inter_cor_y1 = inter_cor[0][1]
inter_cor_y2 = inter_cor[1][1]
inter_cor_y3 = inter_cor[2][1]

inter_cor_px = P[0]
inter_cor_py = P[1]


print(inter_cor_x1)
print(inter_cor_x2)
print(inter_cor_x3)
print(inter_cor_y1)
print(inter_cor_y2)
print(inter_cor_y3)

if inter_cor_x1 == STN_229_x:
    STN_v1 = 229
elif inter_cor_x1  == STN_235_x:
    STN_v1 = 235
elif inter_cor_x1  == STN_242_x:
    STN_v1 = 242
elif inter_cor_x1  == STN_251_x:
    STN_v1 = 251
elif inter_cor_x1  == STN_267_x:
    STN_v1 = 267
elif inter_cor_x1  == STN_277_x:
    STN_v1 = 277
else:
    print("no matching stations in v1")

if inter_cor_x2 == STN_229_x:
    STN_v2 = 229
elif inter_cor_x2  == STN_235_x:
    STN_v2 = 235
elif inter_cor_x2  == STN_242_x:
    STN_v2 = 242
elif inter_cor_x2  == STN_251_x:
    STN_v2 = 251
elif inter_cor_x2  == STN_267_x:
    STN_v2 = 267
elif inter_cor_x2  == STN_277_x:
    STN_v2 = 277
else:
    print("no matching stations in v2")
    
if inter_cor_x3 == STN_229_x:
    STN_v3 = 229
elif inter_cor_x3  == STN_235_x:
    STN_v3 = 235
elif inter_cor_x3  == STN_242_x:
    STN_v3 = 242
elif inter_cor_x3  == STN_251_x:
    STN_v3 = 251
elif inter_cor_x3  == STN_267_x:
    STN_v3 = 267
elif inter_cor_x3  == STN_277_x:
    STN_v3 = 277
else:
    print("no matching stations in v1")
    

inter_wind_v1 = wind[wind['STN'] == STN_v1]



inter_wind_v2 = wind[wind['STN'] == STN_v2]


inter_wind_v3 = wind[wind['STN'] == STN_v3]



df_add = inter_wind_v1.FF.add(inter_wind_v1.FF, fill_value=0)



inter_wind_v1 = wind[wind['STN'] == STN_v1].reset_index()
# =============================================================================
# print(f"inter_wind_v1:\n{inter_wind_v1}")
# =============================================================================


inter_wind_v2 = wind[wind['STN'] == STN_v2].reset_index()
# =============================================================================
# print(f"inter_wind_v2:\n{inter_wind_v2}")
# =============================================================================


inter_wind_v3 = wind[wind['STN'] == STN_v3].reset_index()
# =============================================================================
# print(f"inter_wind_v3:\n{inter_wind_v3}")
# =============================================================================

interp = pd.DataFrame(inter_wind_v1['FF']).rename(columns = {'FF':'inter_wind_v1'})
interp['inter_wind_v2'] = inter_wind_v2['FF']
interp['inter_wind_v3'] = inter_wind_v3['FF']
# =============================================================================
# print(interp)
# =============================================================================

def doInterp(inter_wind_v1, inter_wind_v2, inter_wind_v3):
    return bary_interpol (inter_cor_x1, inter_cor_x2, inter_cor_x3, inter_cor_y1,
                   inter_cor_y2, inter_cor_y3, inter_cor_px, inter_cor_py,
                   inter_wind_v1, inter_wind_v2, inter_wind_v3)
interp['inter_wind_pv'] = interp.apply(lambda x: doInterp(x['inter_wind_v1'], x['inter_wind_v2'], x['inter_wind_v3']), axis=1)
print(interp.inter_wind_pv)

wind_time = pd.date_range(start = '1/1/2021 00:01:00', end = '3/13/2022', freq = 'H')

wind_time = pd.DataFrame(wind_time)

print(wind_time)

result = pd.concat([wind_time, interp.inter_wind_pv], axis=1)

print(result)

filepath = Path('C:/Users/###/sensoren/data/out.csv')  

filepath.parent.mkdir(parents=True, exist_ok=True)  

result.to_csv(filepath)  

os.makedirs('C:/Users/###/sensoren/data/', exist_ok=True)  





data_s = np.array([
    [RD_229],
    [RD_235],
    [RD_242],
    [RD_251],
    [RD_267],
    [RD_277]
])
data_p = np.array([
    [P]
])


data_i = np.array([
    [ inter_cor_x1, inter_cor_y1],
    [ inter_cor_x2, inter_cor_y2],
    [ inter_cor_x3, inter_cor_y3]
])

x_s, y_s = data_s.T
x_p, y_p = data_p. T
x_i, y_i = data_i. T

ax = land.plot()
plt.scatter( x_p, y_p, c="red")
plt.scatter( x_s, y_s, c="yellow")
plt.scatter( x_i, y_i, c="green")
plt.show()