def plotSiteIntensityFrequencyDistribution(self,dictSiteInfo,begYear,endYear,figName='SiteIntensityFrequencyDistribution.png'):
allData = {} # {'site':{'tyNum':{'lat':21,'lon':120,'grade':2}...}...}
for iKey in dictSiteInfo.keys():
# read data
inputFileName="site_data/"+iKey+"_"+str(begYear)+"-"+str(endYear)+".csv"
print("reading data from ",inputFileName)
dataset = pd.read_csv(inputFileName,header=None,sep=',')
dataset = np.array(dataset)
m ,n = np.shape(dataset)
iTyNums = dataset[:,0]
iDate = dataset[:,1]
iLats = dataset[:,2]
iLons = dataset[:,3]
iGrades = dataset[:,5]
allData[iKey] = {}
for i in range(m):
tyNum = iTyNums[i]
#对于某个台风过程影响某一站点，只从整个台风记录中选取最靠近站点的相应记录
if tyNum not in allData[iKey].keys():
#第一次出现直接写入，防止台风记录只有一条时会遗漏
allData[iKey][tyNum] = {'lat':iLats[i],'lon':iLons[i],'grade':iGrades[i],'date':iDate[i]}
else:
latSite = dictSiteInfo[iKey]['lat']
lonSite = dictSiteInfo[iKey]['lon']
latTyPre = allData[iKey][tyNum]['lat']
lonTyPre = allData[iKey][tyNum]['lon']
latTyNow = iLats[i]
lonTyNow = iLons[i]
distSiteTyPre = custfunc.SphereDistance(lonSite,latSite,lonTyPre,latTyPre)
distSiteTyNow = custfunc.SphereDistance(lonSite,latSite,lonTyNow,latTyNow)
if distSiteTyNow < distSiteTyPre:
allData[iKey].update({tyNum:{'lat':iLats[i],'lon':iLons[i], \
'grade':iGrades[i],'date':iDate[i]}})
chinesFontSize = 10
myfont = mpl.font_manager.FontProperties(fname=r'./chinese_font/simhei.ttf',size=chinesFontSize)
fig = plt.gcf()
fontsize = 10
plt.rcParams['savefig.dpi'] = 3000 #像素
plt.rcParams['figure.dpi'] = 3000 #分辨率
xticks = range(1,9)
xtickName = [u'低于热带低压',u'热带低压',u'热带风暴',u'强热带风暴',\
u'台风',u'强台风',u'超强台风',u'变性台风']
countGrade = {}
iCountDict = {0:0,1:1,2:2,3:3,4:4,5:5,6:6,9:7}
iCountArr = np.zeros(8)
maker = ['-o','-D','-s','-*','-d','-v','-+','-x','-^']
mIdx = 0 # makerIndex
for iKey in allData.keys(): # site is key
siteData = allData[iKey]
iCountArr = np.zeros(8)
for jKey in siteData.keys(): # tyNum is key
jGrade = int(siteData[jKey]['grade'])
index= iCountDict[jGrade]
iCountArr[index] += 1
countGrade[iKey] = iCountArr
iStationInfo = iKey+":"+dictSiteInfo[iKey]['name']
plt.plot(xticks,iCountArr,maker[mIdx],label=iStationInfo,linewidth=1.0,ms=5)
mIdx += 1
plt.legend(loc='upper right', frameon=False, prop=myfont,fontsize=fontsize)
plt.ylabel(u'频数(个)', fontproperties=myfont,fontsize=fontsize)
plt.xticks(xticks,xtickName,rotation=23,fontproperties=myfont,fontsize=fontsize)
plt.show()
fig.savefig(figName)
plt.close()
return None
def plotSiteIntensitySpatialDistribution(self,dictSiteInfo,begYear,endYear,minLat=0,maxLat=70, \
minLon=90,maxLon=180,figName='SiteIntensitySpatailDistribution.png'):
allData = {} # {'site':{'tyNum':{'lat':21,'lon':120,'grade':2}...}...}
for iKey in dictSiteInfo.keys():
# read data
inputFileName="site_data/"+iKey+"_"+str(begYear)+"-"+str(endYear)+".csv"
print("reading data from ",inputFileName)
dataset = pd.read_csv(inputFileName,header=None,sep=',')
dataset = np.array(dataset)
m ,n = np.shape(dataset)
iTyNums = dataset[:,0]
iDate = dataset[:,1]
iLats = dataset[:,2]
iLons = dataset[:,3]
iGrades = dataset[:,5]
allData[iKey] = {}
for i in range(m):
tyNum = iTyNums[i]
#对于某个台风过程影响某一站点，只从整个台风记录中选取最靠近站点的相应记录
if tyNum not in allData[iKey].keys():
#第一次出现直接写入，防止台风记录只有一条时会遗漏
allData[iKey][tyNum] = {'lat':iLats[i],'lon':iLons[i],'grade':iGrades[i],'date':iDate[i]}
else:
latSite = dictSiteInfo[iKey]['lat']
lonSite = dictSiteInfo[iKey]['lon']
latTyPre = allData[iKey][tyNum]['lat']
lonTyPre = allData[iKey][tyNum]['lon']
latTyNow = iLats[i]
lonTyNow = iLons[i]
distSiteTyPre = custfunc.SphereDistance(lonSite,latSite,lonTyPre,latTyPre)
distSiteTyNow = custfunc.SphereDistance(lonSite,latSite,lonTyNow,latTyNow)
if distSiteTyNow < distSiteTyPre:
allData[iKey].update({tyNum:{'lat':iLats[i],'lon':iLons[i],'grade':iGrades[i],'date':iDate[i]}})
chinesFontSize = 6
myfont = mpl.font_manager.FontProperties(fname=r'./chinese_font/simhei.ttf',size=chinesFontSize)
fig = plt.gcf()
plt.rcParams['savefig.dpi'] = 3000 #像素
plt.rcParams['figure.dpi'] = 3000 #分辨率
m = Basemap(llcrnrlon=minLon,llcrnrlat=minLat,urcrnrlon=maxLon,urcrnrlat=maxLat,resolution='h')
m.drawcoastlines(linewidth=0.3)
#m.drawcountries(linewidth=0.3),此命令香港会有一个方框
#m.drawrivers(linewidth=0.3)
chn_shp = './GADM_Shapefile/gadm36_CHN_1'
twn_shp = './GADM_Shapefile/gadm36_TWN_1'
hkg_shp = './GADM_Shapefile/gadm36_HKG_1'
mac_shp = './GADM_Shapefile/gadm36_MAC_1'
m.readshapefile(chn_shp,'chn',drawbounds=True)
m.readshapefile(twn_shp,'twn',drawbounds=True)
m.readshapefile(hkg_shp,'hkg',drawbounds=True)
m.readshapefile(mac_shp,'mac',drawbounds=True)
legendIdx = {0:u'低于热带低压',1:u'热带低压(10.8-17.1m/s)',\
2:u'热带风暴(17.2-24.4m/s)',3:u'强热带风暴(24.5-32.6m/s)',\
4:u'台风(32.7-41.4m/s)',5:u'强台风(41.5-50.9m/s)',\
6:u'超强台风(>51.0m/s)',9:u'变性台风'}
colorIdx = {0:'grey',1:'brown',2:'blue', \
3:'green',4:'red',5:'gold',\
6:'magenta',9:'purple'}
firstIdx = {0:1, 1:1, 2:1, 3:1, 4:1, 5:1, 6:1, 9:1}
handlesDict = {} # 收集第一次出现的强度，用于绘制图例
for iKey in allData.keys():
iSiteData = allData[iKey]
for jKey in iSiteData.keys():
jLat = iSiteData[jKey]['lat']
jLon = iSiteData[jKey]['lon']
jGrade = iSiteData[jKey]['grade']
legengColorKey = int(jGrade)
first = firstIdx[legengColorKey]
if first != 1 : #强度等级不是第一次出现，绘制
plt.scatter(jLon,jLat,s=5, marker="o",\
color=colorIdx[legengColorKey])
else: #强度等级第一次出现，保留以便后面绘制顺便加上图例
iHandle = {}
iHandle.update({'lon':jLon})
iHandle.update({'lat':jLat})
iHandle.update({'color':colorIdx[legengColorKey]})
iHandle.update({'label':legendIdx[legengColorKey]})
handlesDict.update({legengColorKey:iHandle})
firstIdx.update({legengColorKey:0})
#统计每个等级发生的次数
# 去重:以发生日期和台风编号为唯一索引去重
deduplicateData = {} # {dateTyNum:{'grade':1,...}
countGrade = {}
for iKey in allData.keys(): # site is key
siteData = allData[iKey]
for jKey in siteData.keys(): # tyNum is key
jDate = siteData[jKey]['date']
dateTyNum = str(int(jDate))+str(int(jKey))
if dateTyNum not in deduplicateData:
jGrade = int(siteData[jKey]['grade'])
deduplicateData[dateTyNum] = {'grade':jGrade}
if jGrade in countGrade.keys():
numGrades = countGrade[jGrade] + 1
countGrade[jGrade] = numGrades
else:
countGrade[jGrade] = 1
#print(countGrade)
for i in range(10):
if i in handlesDict.keys():
iHandle = handlesDict[i]
iLon = iHandle['lon']
iLat = iHandle['lat']
iColor = iHandle['color']
iLabel0 = iHandle['label']
iLabel1 = u' 发生次数:'+str(countGrade[i])
iLabel = iLabel0+iLabel1
plt.scatter(iLon,iLat,color=iColor,\
s=5, marker="o",label=iLabel)
plt.legend(loc='lower right',prop=myfont)
plt.show()
fig.savefig(figName)
plt.close()
return None
# Read data
inputFileName = siteName + str(begYear) + "-" + str(endYear) + ".csv"
print("reading data from ", inputFileName)
dataset = pd.read_csv(inputFileName, header=None, sep=',')
dataset = np.array(dataset)
m, n = np.shape(dataset)
lats = dataset[:, 2]
lons = dataset[:, 3]
tcNum = dataset[:, 0]
print("calculate translation velocity(VT)")
VT = []
for i in range(0, m - 1):
if tcNum[i] == tcNum[i + 1]:
dist = func.SphereDistance(lons[i], lats[i], lons[i + 1],
lats[i + 1]) # unit:KM
VT_temp = dist / 6.0 # unit: km/h
if VT_temp >= 2.0 and VT_temp <= 65:
VT.append(VT_temp)
VT = np.array(VT)
print('Max and Min VT : ', np.max(VT), np.min(VT))
Var = VT
### Normal Distrbution ##########################################
print("fitting the VT with Normal distribution")
# Fitting Data
loc, scale = st.norm.fit(Var)
Var_NormMu = loc # mean
Var_NormStd = scale # standard variation
print('norm Mu and Sigma :', Var_NormMu, Var_NormStd)
lons = dataset[:, 3]
Pres = dataset[:, 4]
tyNum = []
Date = []
VT = []
deltaP = []
Rmax = []
Dmin = []
L_ST = []
Alpha_ST = []
Theta = []
for i in range(0, m - 1):
### VT
dist = func.SphereDistance(lons[i], lats[i], lons[i + 1],
lats[i + 1]) # unit:KM
VT_temp = dist / 6.0 # unit: km/h
### DeltaP
P0 = 1010.0
deltaP_temp = P0 - Pres[i]
### Rmax samples
# obtain Rmax from center pressure with empirical formula
Rmax_temp = 1119 * (1010 - Pres[i])**(-0.805)
### Dmin samples
lon1 = lons[i]
lat1 = lats[i]
lon2 = lons[i + 1]
lat2 = lats[i + 1]
if lon1 == lon2 and lat1 == lat2:
#print(i)
continue
numberTy = numberTy
yyymmddhh = line[0:10]
latRec = float(line[13:16]) * 0.1 #unit 1.0 degree
lonRec = float(line[17:21]) * 0.1
presRec = line[22:26]
gradeRec = line[11:12]
newLine[0] = numberTy
newLine[1] = yyymmddhh
newLine[2] = str(latRec)
newLine[3] = str(lonRec)
newLine[4] = presRec
newLine[5] = gradeRec
#if numberTy == "0000":
if int(numberTy) == 0:
continue # Skip nameless TC
distTy2Site = func.SphereDistance(float(lonRec), float(latRec),
lonSite, latSite)
if distTy2Site > radiusInflu:
# Skip outside the scope of influence radius
continue
else:
# print(distTy2Site)
pass
allNewLine.append(newLine)
fileRead.close
allNewLineArr = np.array(allNewLine)
# output
outFileName = "data_site/" + siteName + "_" + str(begYear) + "-" + str(
endYear) + ".csv"
print("output data : ", outFileName)
np.savetxt(outFileName, allNewLineArr, delimiter=',', fmt='%s')