def convert_by_type(lng, lat, type):
if type == 'g2b':
return gcj02_to_bd09(lng, lat)
elif type == 'b2g':
return bd09_to_gcj02(lng, lat)
elif type == 'w2g':
return wgs84_to_gcj02(lng, lat)
elif type == 'g2w':
return gcj02_to_wgs84(lng, lat)
elif type == 'b2w':
return bd09_to_wgs84(lng, lat)
elif type == 'w2b':
return wgs84_to_bd09(lng, lat)
else:
print('Usage: type must be in one of g2b, b2g, w2g, g2w, b2w, w2b')
sys.exit()
def tPoint(lng, lat, type):
if type == 'g2b':
tcoord = gcj02_to_bd09(lng, lat)
elif type == 'b2g':
tcoord = bd09_to_gcj02(lng, lat)
elif type == 'w2g':
tcoord = wgs84_to_gcj02(lng, lat)
elif type == 'g2w':
tcoord = gcj02_to_wgs84(lng, lat)
elif type == 'b2w':
tcoord = bd09_to_wgs84(lng, lat)
elif type == 'w2b':
tcoord = wgs84_to_bd09(lng, lat)
else:
tcoord = [lng, lat]
return tcoord
reversePointDicts, G)
fixed = minDistanceBetweenNodeInDiffSubGs.values()
G.add_edges_from(fixed)
fixedLine.extend(fixed)
for i in fixedLine:
simplePipes.append(i)
simplePipeInfo.append(219)
# 文件处理
data = []
for k, v in pointDicts.items():
x, y = k
lat, lng = transform(2437, 4326, y, x)
lng, lat = wgs84_to_bd09(float(lng), float(lat))
data.append((v, lng, lat))
pointsSavePath = r"D:\Users\Illusion\Desktop\points_.csv"
saveFile(pointsSavePath, data)
pipeSavePath = r"D:\Users\Illusion\Desktop\pipes_.csv"
pipeInfo = []
for p, info in zip(simplePipes, simplePipeInfo):
s, e = p
sx, sy = reversePointDicts[s]
slat, slng = transform(2437, 4326, sy, sx)
ex, ey = reversePointDicts[e]
elat, elng = transform(2437, 4326, ey, ex)
lens = geodesic((slat, slng), (elat, elng)).m
pipeInfo.append((s, e, lens, info))
return 0.0
s = arr[0][1] * (arr[arr_len - 1][0] - arr[1][0])
for i in range(1, arr_len):
s += arr[i][1] * (arr[i - 1][0] - arr[(i + 1) % arr_len][0])
return round(math.fabs(s / 2) * 10000, 3)
'''
2、坐标转换
input：原坐标，转换前后的坐标类别函数
output：转换后的坐标
'''
from coordTransform_utils import wgs84_to_bd09
import pandas as pd
result1 = wgs84_to_bd09(116.469271, 39.985568)
df = pd.read_csv('E:/Desktop/workf/shdistrict.csv', encoding='utf8')
newlist = pd.DataFrame(list(map(wgs84_to_bd09, df['lon'], df['lat'])))
df['bdlat'] = newlist[1]
df['bdlon'] = newlist[0]
'''
3、画简易热力图
input：有坐标点和该点权重组成的数据集
output：能反应坐标及热点（不明显）分布情况的热量力图
'''
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import glob
import csv
from coordTransform_utils import wgs84_to_bd09
for file in glob.glob("./visual/assets/*.csv"):
if 'bd09' in file:
continue
with open(file, "rt") as csvfile:
output = file.replace(".csv", ".bd09.csv")
with open(output, "wt") as outputfile:
reader = csv.reader(csvfile)
for row in reader:
lon, lat = wgs84_to_bd09(float(row[2]), float(row[3]))
outputfile.write("%s,%s,%s,%s\n" % (row[0], row[1], lon, lat))
