昆明地图
def
kmdt
(
x
)
:
import
matplotlib
.
pyplot as plt
import
json
with
open
(
'km.json'
,
'rb'
)
as f
:
data
=
json
.
load
(
f
)
for
fea in
range
(
len
(
data
[
'features'
]
)
)
:
df
=
data
[
'features'
]
[
fea
]
[
'geometry'
]
[
'coordinates'
]
for
f1 in
range
(
len
(
df
)
)
:
df1
=
df
[
f1
]
for
f2 in
range
(
len
(
df1
)
)
:
d
=
df1
[
f2
]
for
i in
range
(
len
(
d
)
)
:
km
=
plt
.
plot
(
[
d
[
i
-
1
]
[
0
]
,
d
[
i
]
[
0
]
]
,
[
d
[
i
-
1
]
[
1
]
,
d
[
i
]
[
1
]
]
,
'k'
,
linewidth
=
x
)
return
km
插值
def
chazhi
(
x
,
xx
,
cz
)
:
import
numpy as np
from scipy
import
interpolate
if
cz
==
''
:
cz
=
'nearest'
lenx
=
len
(
x
)
weizhi
=
np
.
where
(
x
==
x
)
[
0
]
lenw
=
len
(
weizhi
)
xh
=
np
.
array
(
[
0.0
]
*
lenw
)
for
i in
range
(
0
,
lenw
)
:
xh
[
i
]
=
x
[
weizhi
[
i
]
]
f
=
interpolate
.
interp1d
(
weizhi
,
xh
,
kind
=
cz
)
ynew
=
f
(
xx
)
return
ynew
一元回归
def
huigui
(
x
,
y
)
:
from sklearn
import
linear_model
import
numpy as np
model
=
linear_model
.
LinearRegression
(
)
xx
=
np
.
array
(
x
)
.
reshape
(
-
1
,
1
)
model
.
fit
(
xx
,
y
)
k
=
model
.
coef_
b
=
model
.
intercept_
ff
=
'y = %.2f+%.2f'
%
(
b
,
k
)
return
(
k
,
b
,
ff
)
去空值，求平均
def
offnan
(
x
,
y
)
:
import
numpy as np
a
=
np
.
array
(
x
)
b
=
np
.
array
(
y
)
aa
=
list
(
np
.
where
(
a
==
a
)
[
0
]
)
bb
=
list
(
np
.
where
(
b
==
b
)
[
0
]
)
c
=
list
(
set
(
aa
)
&
set
(
bb
)
)
return
(
a
[
c
]
,
b
[
c
]
)
def
avern
(
x
)
:
import
numpy as np
a
=
np
.
array
(
x
)
le
=
len
(
a
)
su
=
0.0
js
=
0.0
for
i in
range
(
0
,
le
)
:
if
a
[
i
]
==
a
[
i
]
:
su
=
su
+
a
[
i
]
js
=
js
+
1
av
=
su
/
js
return
av
归一化
def
guiyi
(
x
,
Type
=
'oz'
)
:
import
numpy as np
a
=
np
.
array
(
x
)
ma
=
max
(
a
)
mi
=
min
(
a
)
me
=
np
.
mean
(
a
)
b
=
np
.
array
(
[
0.0
]
*
len
(
a
)
)
if
Type
==
'oz'
:
for
i in
range
(
0
,
len
(
a
)
)
:
b
[
i
]
=
(
a
[
i
]
-
mi
)
/
(
ma
-
mi
)
if
Type
==
'0'
:
for
i in
range
(
0
,
len
(
a
)
)
:
b
[
i
]
=
(
a
[
i
]
-
me
)
/
(
ma
-
mi
)
return
b
标准化
def
biaozhun
(
x
)
:
from sklearn
import
preprocessing
a
=
preprocessing
.
scale
(
x
)
return
(
a
)
def
zhongxin
(
x
)
:
import
numpy as np
a
=
np
.
array
(
x
)
b
=
np
.
mean
(
a
)
for
i in
range
(
0
,
len
(
a
)
)
:
a
[
i
]
=
a
[
i
]
-
b
return
a
获取文件列表
def
listfile
(
dirname
,
postfix
=
''
)
:
import
os
filelist
=
[
]
files
=
os
.
listdir
(
dirname
)
for
item in files
:
#filelist
.
append
(
[
dirname
,
item
]
)
if
os
.
path
.
isfile
(
dirname
+
item
)
:
if
item
.
endswith
(
postfix
)
:
filelist
.
append
(
[
dirname
,
item
]
)
else
:
if
os
.
path
.
isdir
(
dirname
+
item
)
:
filelist
.
extend
(
listfile
(
dirname
+
item
+
'/'
,
postfix
)
)
return
filelist
灰色关联
def
huise
(
a
,
b
,
c
,
rho
)
:
import
pandas as pd
import
numpy as np
a
=
np
.
array
(
a
)
length
=
len
(
c
)
lent
=
len
(
a
)
a
=
a
/
a
[
0
]
aa
=
list
(
a
)
data
=
[
]
dataa
=
[
]
szi
=
[
0.0
]
*
lent
zi
=
[
]
for
i in
range
(
0
,
length
)
:
cc
=
c
[
i
]
bb
=
np
.
array
(
b
[
cc
]
)
bb
=
bb
/
bb
[
0
]
data
.
append
(
list
(
bb
)
)
dataa
.
append
(
aa
)
data
=
np
.
array
(
data
)
dataa
=
np
.
array
(
dataa
)
gmin
=
abs
(
data
-
dataa
)
.
min
(
)
gmax
=
abs
(
data
-
dataa
)
.
max
(
)
for
i in
range
(
0
,
length
)
:
cc
=
c
[
i
]
bb
=
np
.
array
(
b
[
cc
]
)
bb
=
bb
/
bb
[
0
]
for
j in
range
(
0
,
lent
)
:
szi
[
j
]
=
(
gmin
+
rho
*
gmax
)
/
(
abs
(
a
[
j
]
-
bb
[
j
]
)
+
rho
*
gmax
)
zi
.
append
(
list
(
szi
)
)
zi
=
np
.
array
(
zi
)
qz
=
np
.
mean
(
zi
,
1
)
return
qz
多元回归
def
dyhuigui
(
y
,
x
)
:
from sklearn
import
linear_model
import
numpy as np
import
pandas as pd
model
=
linear_model
.
LinearRegression
(
)
# xx
=
np
.
array
(
x
)
.
reshape
(
-
1
,
1
)
model
.
fit
(
x
,
y
)
k
=
model
.
coef_
b
=
model
.
intercept_
r
=
model
.
score
(
x
,
y
)
return
(
k
,
b
,
r
)
极射赤面投影地图
def
jiquditu
(
axes
)
:
import
matplotlib
.
pyplot as plt
import
numpy as np
import
matplotlib
.
path as mpath
import
matplotlib
.
ticker as mticker
import
cartopy
.
crs as ccrs
ax
=
axes
ax
.
set_extent
(
[
-
180
,
180
,
0
,
90
]
,
crs
=
ccrs
.
PlateCarree
(
)
)
theta
=
np
.
linspace
(
0
,
2
*
np
.
pi
,
1000
)
center
,
radius
=
[
0.5
,
0.5
]
,
0.5
verts
=
np
.
vstack
(
[
np
.
sin
(
theta
)
,
np
.
cos
(
theta
)
]
)
.
T
circle
=
mpath
.
Path
(
verts
*
radius
+
center
)
ax
.
set_boundary
(
circle
,
transform
=
ax
.
transAxes
)
ax
.
coastlines
(
)
line
=
ax
.
gridlines
(
draw_labels
=
False
)
line
.
ylocator
=
mticker
.
FixedLocator
(
[
0
,
90
]
)
#手动设置x轴刻度
line
.
xlocator
=
mticker
.
FixedLocator
(
np
.
arange
(
-
180
,
181
,
30
)
)
#手动设置x轴刻度
#创建要标注的labels字符串
ticks
=
np
.
arange
(
0
,
181
,
30
)
etick
=
[
'0°'
]
+
[
'%d°E'
%
tick
for
tick in ticks
if
(
tick
!=
0
)
&
(
tick
!=
180
)
]
+
[
'180°'
]
ticks
=
np
.
arange
(
150
,
0
,
-
30
)
wtick
=
[
'%d°W'
%
tick
for
tick in ticks
if
(
tick
!=
0
)
&
(
tick
!=
180
)
]
labels
=
etick
+
wtick
#创建与labels对应的经纬度标注位置
#xticks
=
[
i
for
i in np
.
arange
(
0
,
210
,
30
)
]
+
[
i
for
i in np
.
arange
(
-
32
,
-
180
,
-
30
)
]
for
i in
range
(
0
,
12
)
:
p
=
-
7
if
i
%
6
==
0
:
p
=
-
3
if
i
%
6
==
5
or i
%
6
==
1
:
p
=
-
5
if
i
%
6
==
4
or i
%
6
==
2
:
p
=
-
6
ax
.
text
(
i
*
30
,
p
,
labels
[
i
]
,
transform
=
ccrs
.
PlateCarree
(
)
,
horizontalalignment
=
'center'
,
verticalalignment
=
'center'
)
ax
.
plot
(
np
.
linspace
(
-
180
,
180
,
200
)
,
[
30
]
*
200
,
':'
,
transform
=
ccrs
.
PlateCarree
(
)
,
color
=
'k'
,
linewidth
=
0.4
)
ax
.
plot
(
np
.
linspace
(
-
180
,
180
,
200
)
,
[
60
]
*
200
,
':'
,
transform
=
ccrs
.
PlateCarree
(
)
,
color
=
'k'
,
linewidth
=
0.4
)
return
ax
