一、ECMWF（方正附近，46，128.75，张志浩）
from netCDF4 import Dataset
from netCDF4 import num2date
nc_obj = Dataset ( "download.nc" )
# 查看nc文件中的变量
print ( nc_obj.variables.keys() )
odict_keys([\'longitude\', \'latitude\', \'time\', \'u10\', \'v10\', \'t2m\', \'sp\'])
# 查看每个变量的单位
for i in nc_obj.variables.keys ():
print ( nc_obj.variables[i].units )
degrees_east
degrees_north
hours since 1900-01-01 00:00:00.0
m s**-1
m s**-1
K
Pa
# 查看每个变量的形状（维度）
for i in nc_obj.variables.keys ():
print ( nc_obj.variables[i].shape )
(21,)
(41,)
(144,)
(144, 41, 21)
(144, 41, 21)
(144, 41, 21)
(144, 41, 21)
#取出时间
time=nc_obj[\'time\'][:]
print(time[:5])
[1051152 1051153 1051154 1051155 1051156]
#将时间转化为人类可读时间 datetime.datetime
time1=num2date(time,units=nc_obj[\'time\'].units)
print(time1[:5])
[datetime.datetime(2019, 12, 1, 0, 0) datetime.datetime(2019, 12, 1, 1, 0)
datetime.datetime(2019, 12, 1, 2, 0) datetime.datetime(2019, 12, 1, 3, 0)
datetime.datetime(2019, 12, 1, 4, 0)]
#经度 128.5 14 128.75 15
longitude=nc_obj[\'longitude\'][:]
print(longitude)
# print(nc_obj[\'longitude\'][14])
[125. 125.25 125.5 125.75 126. 126.25 126.5 126.75 127. 127.25
127.5 127.75 128. 128.25 128.5 128.75 129. 129.25 129.5 129.75
130. ]
#纬度 45.5 18 46 16
latitude=nc_obj[\'latitude\'][:]
print(latitude)
# print(nc_obj[\'latitude\'][18])
# print(nc_obj[\'latitude\'][16])
[50. 49.75 49.5 49.25 49. 48.75 48.5 48.25 48. 47.75 47.5 47.25
47. 46.75 46.5 46.25 46. 45.75 45.5 45.25 45. 44.75 44.5 44.25
44. 43.75 43.5 43.25 43. 42.75 42.5 42.25 42. 41.75 41.5 41.25
41. 40.75 40.5 40.25 40. ]
#取出变量blh 10 metre V wind component(10米V风分量)
v10=nc_obj[\'v10\'][:]
#取出北纬46东经128.75的v10数值
a = [0]*144
for i in range(144):
a[i]=nc_obj.variables[\'v10\'][i][16][15]
#取出变量10 metre U wind component,(10米U风分量)
u10=nc_obj[\'u10\'][:]
#取出北纬46东经128.75的u10数值
b = [0]*144
for i in range(144):
b[i]=nc_obj.variables[\'u10\'][i][16][15]
#对v10和u10平方和开平方
c = [0]*144
for i in range(144):
c[i]=(a[i]**2+b[i]**2)**0.5
import matplotlib.pyplot as plt
import pandas as pd
plt.rc(\'font\', family=\'SimHei\', size=15) #绘图中的中文显示问题，图表字体为SimHei，字号为15
#plt.rcParams[\'font.sans-serif\']=[\'SimHei\'] #用来正常显示中文标签
plt.rcParams[\'axes.unicode_minus\']=False #用来正常显示负号
plt.figure(figsize=(31,8))
plt.title(\'ECMWF风速(v10和u10平方和的开平方)与时间的函数\') #有标题(风速与风向的函数)
plt.xlabel(\'时间\') #横坐标的标题
plt.ylabel(\'风速（m/s）\') #纵坐标的标题
plt.grid(color=\'#95a5a6\',linestyle=\'--\',linewidth=3,axis=\'both\',alpha=0.4) #设置网格
plt.plot(time1,c,marker=\'o\',c=\'r\')
# plt.xticks(pd.date_range(\'2019-12-01\',\'2019-01-31\'))
# plt.xticks(rotation=\'vertical\')
# plt.xticks(rotation=30)
plt.gcf().autofmt_xdate()
# plt.savefig(\'ECMWF.png\') #保存图片
plt.show()
D:\Users\zzh\Anaconda3\lib\site-packages\pandas\plotting\_matplotlib\converter.py:103: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.
To register the converters:
>>> from pandas.plotting import register_matplotlib_converters
>>> register_matplotlib_converters()
warnings.warn(msg, FutureWarning)
t2m=nc_obj[\'t2m\'][:]
# print(t2m)
#取出北纬46东经128.75的t2m数值
t2m1 = [0]*144
for i in range(144):
t2m1[i]=nc_obj.variables[\'t2m\'][i][16][15]
# 将开氏度转化为摄氏度
# 1摄氏度(℃)=274.15开氏度(K)
t2m2=[]
for i in t2m1:
t2m2.append(i/274.15)
plt.figure(figsize=(31,8))
plt.title(\'ECMWF的2米温度（t2m）与时间的函数\') #有标题(风速与风向的函数)
plt.xlabel(\'时间\') #横坐标的标题
plt.ylabel(\'温度（K）\') #纵坐标的标题
plt.grid(color=\'#95a5a6\',linestyle=\'--\',linewidth=3,axis=\'both\',alpha=0.4) #设置网格
plt.plot(time1,t2m2,marker=\'o\',c=\'r\')
# plt.xticks(pd.date_range(\'2019-12-01\',\'2019-01-31\'))
# plt.xticks(rotation=\'vertical\')
# plt.xticks(rotation=30)
plt.gcf().autofmt_xdate()
# plt.savefig(\'ECMWF.png\') #保存图片
plt.show()
sp=nc_obj[\'sp\'][:]
# print(sp)
#取出北纬46东经128.75的sp数值
e = [0]*144
for i in range(144):
e[i]=nc_obj.variables[\'sp\'][i][16][15]
plt.figure(figsize=(31,8))
plt.title(\'ECMWF的表明压力（sp）与时间的函数\') #有标题(风速与风向的函数)
plt.xlabel(\'时间\') #横坐标的标题
plt.ylabel(\'压力（Pa）\') #纵坐标的标题
plt.grid(color=\'#95a5a6\',linestyle=\'--\',linewidth=3,axis=\'both\',alpha=0.4) #设置网格
plt.plot(time1,e,marker=\'o\',c=\'r\')
# plt.xticks(pd.date_range(\'2019-12-01\',\'2019-01-31\'))
# plt.xticks(rotation=\'vertical\')
# plt.xticks(rotation=30)
plt.gcf().autofmt_xdate()
# plt.savefig(\'ECMWF.png\') #保存图片
plt.show()
#将数据存储到csv文件
import pandas as pd
#a和b的长c度必须保持一致，否则报错
#字典中的key值即为csv中列名
dataframe = pd.DataFrame({\'时间\':time1,\'风速（m/s）\':c,\'2米温度(℃)\':t2m2,\'压力(Pa)\':e})
#将DataFrame存储为csv,index表示是否显示行名，default=True
dataframe.to_csv(r"ECMWF(方正)20191201-06.csv",sep=\',\',index=False)
二、NOAA（通河,45.967 ,128.733王阔）
from datetime import datetime
import pandas as pd
noaa=pd.read_csv("C:\\Users\\zzh\\Desktop\\tonghe1201-1208.csv")
print(noaa.shape)
(64, 33)
noaa.head()
| USAF | WBAN | TIME | DIR | SPD | GUS | CLG | SKC | L | M | ... | SLP | ALT | STP | MAX | MIN | PCP01 | PCP06 | PCP24 | PCPXX | SD | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 509630 | ***** | 201912010000 | 261 | 18 | 28 | *** | *** | * | * | ... | 1023.6 | ***** | 1009.2 | *** | *** | ***** | ***** | 0.19 | ***** | 0 |
| 1 | 509630 | ***** | 201912010300 | 272 | 12 | 28 | *** | *** | * | * | ... | 1023.4 | ***** | 1009.0 | *** | *** | ***** | 0.00 | 0.19 | ***** | 0 |
| 2 | 509630 | ***** | 201912010600 | 292 | 7 | 28 | *** | *** | * | * | ... | 1022.7 | ***** | 1008.3 | 20 | 14 | ***** | 0.01 | 0.19 | ***** | 0 |
| 3 | 509630 | ***** | 201912010900 | 300 | 4 | 28 | 722 | CLR | * | * | ... | 1022.5 | ***** | 1007.9 | *** | *** | ***** | 0.00 | 0.16 | 0.01 | ** |
| 4 | 509630 | ***** | 201912011200 | 223 | 1 | 25 | 722 | CLR | * | * | ... | 1021.6 | ***** | 1006.8 | *** | *** | ***** | ***** | 0.05 | ***** | 0 |
5 rows × 33 columns
#取出时间
TIME=noaa[\'TIME\'][:64,]
print(TIME)
0 201912010000
1 201912010300
2 201912010600
3 201912010900
4 201912011200
...
59 201912080900
60 201912081200
61 201912081500
62 201912081800
63 201912082100
Name: TIME, Length: 64, dtype: int64
# 201901041500，
# 每一个时间如上所示，为2019-01-04，15：00
# 我把分钟数截断，因为所有数据分钟数都是00
TIME1=[]
for i in TIME:
# 201901041500
TIME1.append((str.split(str(i)[:10]))[0])
print(TIME1)
[\'2019120100\', \'2019120103\', \'2019120106\', \'2019120109\', \'2019120112\', \'2019120115\', \'2019120118\', \'2019120121\', \'2019120200\', \'2019120203\', \'2019120206\', \'2019120209\', \'2019120212\', \'2019120215\', \'2019120218\', \'2019120221\', \'2019120300\', \'2019120303\', \'2019120306\', \'2019120309\', \'2019120312\', \'2019120315\', \'2019120318\', \'2019120321\', \'2019120400\', \'2019120403\', \'2019120406\', \'2019120409\', \'2019120412\', \'2019120415\', \'2019120418\', \'2019120421\', \'2019120500\', \'2019120503\', \'2019120506\', \'2019120509\', \'2019120512\', \'2019120515\', \'2019120518\', \'2019120521\', \'2019120600\', \'2019120603\', \'2019120606\', \'2019120609\', \'2019120612\', \'2019120615\', \'2019120618\', \'2019120621\', \'2019120700\', \'2019120703\', \'2019120706\', \'2019120709\', \'2019120712\', \'2019120715\', \'2019120718\', \'2019120721\', \'2019120800\', \'2019120803\', \'2019120806\', \'2019120809\', \'2019120812\', \'2019120815\', \'2019120818\', \'2019120821\']
TIME2 =[]
for i in TIME1:
# 2019010415
TIME2.append(datetime.strptime(i,\'%Y%m%d%H\'))
# # 隔行选取数据
# TIME3 =[]
# for i in range(0,247,2):
# TIME3 .append(TIME2[i])
SPD=noaa[\'SPD\'][:64,]
# print(DIR)
#将英里每小时转化为米每秒
# 1英里每小时（mile/h）=0.44704米每秒(m/s)
SPD1=[]
for i in SPD:
SPD1.append(i*0.44704)
# print(spd)
# # 隔行选取数据
# SPD2 =[]
# for i in range(0,247,2):
# SPD2 .append(SPD1[i])
plt.figure(figsize=(31,8))
plt.title(\'NOAA风速与时间的函数\') #有标题(风速与风向的函数)
plt.xlabel(\'时间\') #横坐标的标题
plt.ylabel(\'风速（m/s）\') #纵坐标的标题
plt.grid(color=\'#95a5a6\',linestyle=\'--\',linewidth=3,axis=\'both\',alpha=0.4) #设置网格
plt.plot(TIME2,SPD1,marker=\'o\',c=\'g\')
# plt.xticks(pd.date_range(\'2019-01-01\',\'2019-01-31\'))
# plt.xticks(rotation=\'vertical\')
# plt.xticks(rotation=30)
plt.gcf().autofmt_xdate()
# plt.savefig(\'NOAA.png\') #保存图片
plt.show()
# TEMP 温度 华氏度
TEMP=noaa[\'TEMP\'][:64,]
# 将华氏度转化为摄氏度
# 1摄氏度(℃)=33.8华氏度(℉)
TEMP1=[]
for i in TEMP:
TEMP1.append(i/33.8)
plt.figure(figsize=(31,8))
plt.title(\'NOAA温度与时间的函数\') #有标题(风速与风向的函数)
plt.xlabel(\'时间\') #横坐标的标题
plt.ylabel(\'温度（℃）\') #纵坐标的标题
plt.grid(color=\'#95a5a6\',linestyle=\'--\',linewidth=3,axis=\'both\',alpha=0.4) #设置网格
plt.plot(TIME2,TEMP1,marker=\'o\',c=\'g\')
# plt.xticks(pd.date_range(\'2019-01-01\',\'2019-01-31\'))
# plt.xticks(rotation=\'vertical\')
# plt.xticks(rotation=30)
plt.gcf().autofmt_xdate()
# plt.savefig(\'NOAA.png\') #保存图片
plt.show()
plt.figure(figsize=(31,8))
plt.title(\'NOAA温度与时间的函数\') #有标题(风速与风向的函数)
plt.xlabel(\'时间\') #横坐标的标题
plt.ylabel(\'温度（℃）\') #纵坐标的标题
plt.grid(color=\'#95a5a6\',linestyle=\'--\',linewidth=3,axis=\'both\',alpha=0.4) #设置网格
plt.plot(TIME2,TEMP1,marker=\'o\',c=\'g\')
# plt.xticks(pd.date_range(\'2019-01-01\',\'2019-01-31\'))
# plt.xticks(rotation=\'vertical\')
# plt.xticks(rotation=30)
plt.gcf().autofmt_xdate()
# plt.savefig(\'NOAA.png\') #保存图片
plt.show()
# STP 站压 毫巴
STP=noaa[\'STP\'][:64,]
# 讲毫巴转化成帕
# 1毫巴(mbar)=100帕斯卡(Pa)Pa
STP1=[]
for i in STP:
STP1.append(i*100)
plt.figure(figsize=(31,8))
plt.title(\'NOAA站压与时间的函数\') #有标题(风速与风向的函数)
plt.xlabel(\'时间\') #横坐标的标题
plt.ylabel(\'气压（Pa）\') #纵坐标的标题
plt.grid(color=\'#95a5a6\',linestyle=\'--\',linewidth=3,axis=\'both\',alpha=0.4) #设置网格
plt.plot(TIME2,STP1,marker=\'o\',c=\'g\')
# plt.xticks(pd.date_range(\'2019-01-01\',\'2019-01-31\'))
# plt.xticks(rotation=\'vertical\')
# plt.xticks(rotation=30)
plt.gcf().autofmt_xdate()
# plt.savefig(\'NOAA.png\') #保存图片
plt.show()
三、中国气象网（方正，45.50，128.48，王阔）
china=pd.read_csv("C:\\Users\\zzh\\Desktop\\方正1202-1208.csv")
print(china.head())
Unnamed: 0 Station_Id_C Year Mon Day Hour PRS PRS_Sea PRS_Max \
0 1 50964 2019 12 2 0 998.4 1014.2 998.5
1 2 50964 2019 12 2 1 998.3 1014.1 998.5
2 3 50964 2019 12 2 2 998.5 1014.3 998.6
3 4 50964 2019 12 2 3 997.8 1013.6 998.5
4 5 50964 2019 12 2 4 997.7 1013.5 997.9
PRS_Min ... WIN_S_Avg_2mi WIN_D_Avg_2mi WEP_Now WIN_S_Inst_Max tigan \
0 998.3 ... 3.8 206 0.0 8.0 -12.66
1 998.2 ... 2.9 217 22.0 9.6 -12.96
2 998.2 ... 5.8 200 70.0 11.0 -12.70
3 997.8 ... 4.1 220 0.0 11.3 -11.98
4 997.7 ... 5.4 218 70.0 11.9 -12.68
windpower VIS CLO_Cov CLO_Cov_Low CLO_COV_LM
0 3 7200.0 999999 999999 999999
1 3 10500.0 999999 999999 999999
2 3 11200.0 999999 999999 999999
3 3 16900.0 999999 999999 999999
4 3 5000.0 999999 999999 999999
[5 rows x 30 columns]
china[\'Year\'] = [str(i) for i in china[\'Year\']]
china[\'Mon\'] = [str(i) for i in china[\'Mon\']]
china[\'Day\'] = [\'0\'+str(i) for i in china[\'Day\']]
china[\'Hour\'] = [str(i) for i in china[\'Hour\']]
# china[\'time\'] = china[[\'Year\', \'Mon\',\'Day\',\'Hour\']].apply(lambda x: \'\'.join(x), axis=1)
china[\'time\'] =china[\'Year\']+china[\'Mon\']+china[\'Day\']+china[\'Hour\']
print(china.head())
Unnamed: 0 Station_Id_C Year Mon Day Hour PRS PRS_Sea PRS_Max \
0 1 50964 2019 12 02 0 998.4 1014.2 998.5
1 2 50964 2019 12 02 1 998.3 1014.1 998.5
2 3 50964 2019 12 02 2 998.5 1014.3 998.6
3 4 50964 2019 12 02 3 997.8 1013.6 998.5
4 5 50964 2019 12 02 4 997.7 1013.5 997.9
PRS_Min ... WIN_D_Avg_2mi WEP_Now WIN_S_Inst_Max tigan windpower \
0 998.3 ... 206 0.0 8.0 -12.66 3
1 998.2 ... 217 22.0 9.6 -12.96 3
2 998.2 ... 200 70.0 11.0 -12.70 3
3 997.8 ... 220 0.0 11.3 -11.98 3
4 997.7 ... 218 70.0 11.9 -12.68 3
VIS CLO_Cov CLO_Cov_Low CLO_COV_LM time
0 7200.0 999999 999999 999999 201912020
1 10500.0 999999 999999 999999 201912021
2 11200.0 999999 999999 999999 201912022
3 16900.0 999999 999999 999999 201912023
4 5000.0 999999 999999 999999 201912024
[5 rows x 31 columns]
china_time=china[\'time\']
print(china_time)
0 201912020
1 201912021
2 201912022
3 201912023
4 201912024
...
163 2019120819
164 2019120820
165 2019120821
166 2019120822
167 2019120823
Name: time, Length: 168, dtype: object
china_time1 =[]
for i in china_time:
# 2019010415
china_time1.append(datetime.strptime(i,\'%Y%m%d%H\'))
WIN_S_Avg_2mi=china[\'WIN_S_Avg_2mi\']
plt.figure(figsize=(31,8))
plt.title(\'中国气象网2分钟平均风速与时间的函数\') #有标题(风速与风向的函数)
plt.xlabel(\'时间\') #横坐标的标题
plt.ylabel(\'风速（m/s）\') #纵坐标的标题
plt.grid(color=\'#95a5a6\',linestyle=\'--\',linewidth=3,axis=\'both\',alpha=0.4) #设置网格
plt.plot(china_time1,WIN_S_Avg_2mi,marker=\'o\',c=\'y\')
# plt.xticks(pd.date_range(\'2019-01-01\',\'2019-01-31\'))
# plt.xticks(rotation=\'vertical\')
# plt.xticks(rotation=30)
plt.gcf().autofmt_xdate()
# plt.savefig(\'NOAA.png\') #保存图片
plt.show()
TEM=china[\'TEM\']
# TEM1 = [-i for i in TEM]
plt.figure(figsize=(31,8))
plt.title(\'中国气象网温度与时间的函数\') #有标题(风速与风向的函数)
plt.xlabel(\'时间\') #横坐标的标题
plt.ylabel(\'温度（℃）\') #纵坐标的标题
plt.grid(color=\'#95a5a6\',linestyle=\'--\',linewidth=3,axis=\'both\',alpha=0.4) #设置网格
plt.plot(china_time1,TEM,marker=\'o\',c=\'y\')
# plt.xticks(pd.date_range(\'2019-01-01\',\'2019-01-31\'))
# plt.xticks(rotation=\'vertical\')
# plt.xticks(rotation=30)
plt.gcf().autofmt_xdate()
# plt.savefig(\'NOAA.png\') #保存图片
plt.show()
PRS=china[\'PRS\']
# 将百帕转化成帕
# 1百帕(hpa)=100帕斯卡(Pa)
PRS1=[]
for i in PRS:
PRS1.append(i*100)
plt.figure(figsize=(31,8))
plt.title(\'中国气象网气压与时间的函数\') #有标题(风速与风向的函数)
plt.xlabel(\'时间\') #横坐标的标题
plt.ylabel(\'气压（Pa）\') #纵坐标的标题
plt.grid(color=\'#95a5a6\',linestyle=\'--\',linewidth=3,axis=\'both\',alpha=0.4) #设置网格
plt.plot(china_time1,PRS1,marker=\'o\',c=\'y\')
# plt.xticks(pd.date_range(\'2019-01-01\',\'2019-01-31\'))
# plt.xticks(rotation=\'vertical\')
# plt.xticks(rotation=30)
plt.gcf().autofmt_xdate()
# plt.savefig(\'NOAA.png\') #保存图片
plt.show()
四、GFS（方正附近，46.0、128.75，梁晨）
from datetime import datetime
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
df1 = pd.read_csv("C:\\Users\\zzh\Desktop\\20191130-1209.csv",header=None)
print(df1[:5])
0 1 2 3 4 5 \
0 2019-12-09 06:00:00 2019-12-09 06:00:00 GUST surface 128.00 45.0
1 2019-12-09 06:00:00 2019-12-09 06:00:00 GUST surface 128.25 45.0
2 2019-12-09 06:00:00 2019-12-09 06:00:00 GUST surface 128.50 45.0
3 2019-12-09 06:00:00 2019-12-09 06:00:00 GUST surface 128.75 45.0
4 2019-12-09 06:00:00 2019-12-09 06:00:00 GUST surface 129.00 45.0
6
0 7.1135
1 2.3135
2 3.2135
3 4.1135
4 3.4135
df1.columns=["time1","time2","WindSpeed","level","longitude","latitude","value"]
print(df1[:5])
time1 time2 WindSpeed level longitude \
0 2019-12-09 06:00:00 2019-12-09 06:00:00 GUST surface 128.00
1 2019-12-09 06:00:00 2019-12-09 06:00:00 GUST surface 128.25
2 2019-12-09 06:00:00 2019-12-09 06:00:00 GUST surface 128.50
3 2019-12-09 06:00:00 2019-12-09 06:00:00 GUST surface 128.75
4 2019-12-09 06:00:00 2019-12-09 06:00:00 GUST surface 129.00
latitude value
0 45.0 7.1135
1 45.0 2.3135
2 45.0 3.2135
3 45.0 4.1135
4 45.0 3.4135
#df2=df1[(df1["longitude"]==128.75)&(df1["latitude"]==46.00)]
df1 =df1.sort_values("time1",inplace=False)
df1 = df1.reset_index(drop=True)
n_time=[]
for i in range(0,df1.iloc[:,0].size):
if (df1["longitude"][i]==128.75)&(df1["latitude"][i]==46.00):
n_time.append(datetime.strptime( df1["time1"][i], "%Y-%m-%d %H:%M:%S"))
n_gust=[]
for i in range(0,df1.iloc[:,0].size):
if (df1["longitude"][i]==128.75)&(df1["latitude"][i]==46.00):
n_gust.append(df1["value"][i])
plt.figure(figsize=(30,8))
plt.plot(n_time,n_gust,\'go-\')
plt.title("201903-09 00:00 06:00 12:00 18:00 GUST")
Text(0.5, 1.0, \'201903-09 00:00 06:00 12:00 18:00 GUST\')
五、对比图
plt.figure(figsize=(31,8))
plt.title(\'风速与时间的函数\') #有标题(风速与风向的函数)
plt.xlabel(\'时间\') #横坐标的标题
plt.ylabel(\'风速（m/s）\') #纵坐标的标题
plt.grid(color=\'#95a5a6\',linestyle=\'--\',linewidth=3,axis=\'both\',alpha=0.4) #设置网格
plt.plot(time1,c,marker=\'o\',c=\'r\',label=\'ECMWF\')#ECMWF
plt.plot(TIME2,SPD1,marker=\'o\',c=\'g\',label=\'NOAA\')#NOAA
plt.plot(n_time,n_gust,marker=\'o\',c=\'b\',label=\'GFS\')#GFS
plt.plot(china_time1,WIN_S_Avg_2mi,marker=\'o\',c=\'y\',label=\'中国气象网\')#中国气象网
# plt.xticks(pd.date_range(\'2019-01-01\',\'2019-01-31\'))
plt.xticks(rotation=\'vertical\')
# plt.xticks(rotation=30)
# plt.gcf().autofmt_xdate()
plt.legend(loc=\'best\')
plt.gcf().autofmt_xdate()
plt.savefig(\'风速对比图\') #保存图片
plt.show()
plt.figure(figsize=(31,8))
plt.title(\'温度与时间的函数\') #有标题(风速与风向的函数)
plt.xlabel(\'时间\') #横坐标的标题
plt.ylabel(\'温度（℃）\') #纵坐标的标题
plt.grid(color=\'#95a5a6\',linestyle=\'--\',linewidth=3,axis=\'both\',alpha=0.4) #设置网格
plt.plot(time1,t2m2,marker=\'o\',c=\'r\',label=\'ECMWF\')#ECMWF
plt.plot(TIME2,TEMP1,marker=\'o\',c=\'g\',label=\'NOAA\')#NOAA
# plt.plot(china_time1,TEM,marker=\'o\',c=\'y\',label=\'中国气象网\')#中国气象网
# plt.xticks(pd.date_range(\'2019-01-01\',\'2019-01-31\'))
plt.xticks(rotation=\'vertical\')
# plt.xticks(rotation=30)
# plt.gcf().autofmt_xdate()
plt.legend(loc=\'best\')
plt.gcf().autofmt_xdate()
plt.savefig(\'温度对比图\') #保存图片
plt.show()
plt.figure(figsize=(31,8))
plt.title(\'气压与时间的函数\') #有标题(风速与风向的函数)
plt.xlabel(\'时间\') #横坐标的标题
plt.ylabel(\'气压（Pa）\') #纵坐标的标题
plt.grid(color=\'#95a5a6\',linestyle=\'--\',linewidth=3,axis=\'both\',alpha=0.4) #设置网格
plt.plot(time1,e,marker=\'o\',c=\'r\',label=\'ECMWF\')#ECMWF
plt.plot(TIME2,STP1,marker=\'o\',c=\'g\',label=\'NOAA\',)#NOAA
plt.plot(china_time1,PRS1,marker=\'o\',c=\'y\',label=\'中国气象网\')#中国气象网
# plt.xticks(pd.date_range(\'2019-01-01\',\'2019-01-31\'))
plt.xticks(rotation=\'vertical\')
# plt.xticks(rotation=30)
# plt.gcf().autofmt_xdate()
plt.legend(loc=\'best\')
plt.gcf().autofmt_xdate()
plt.savefig(\'气压对比图\') #保存图片
plt.show()