def getFVcom(latpt,lonpt,time_roms,depth_roms):
url='http://www.smast.umassd.edu:8080/thredds/dodsC/fvcom/hindcasts/30yr_gom3'
nc = netCDF4.Dataset(url)
lat = nc.variables['lat'][:]
lon = nc.variables['lon'][:]
Depth = nc.variables['h'][:]
siglay=nc.variables['siglay'][:]
time=nc.variables['time'][:]
modtemp=nc.variables['temp']
modtime=[]
for i in range(len(time)):
t=timedelta(days=float(time[i])).total_seconds()
modtime.append(t) #change time days to seconds
depth=(-Depth*siglay).transpose() #each layer`s depth
distance=dist(lonpt,latpt,lon,lat)
node=np.argmin(distance) #get nearest node
layer=[]
for i in depth_roms[0:len(depth_roms)+1]:
layer.append(np.argmin(abs(i-depth[node]))) #get layer of depth
t_diff=(time_roms-datetime(1858,11,17)).total_seconds() #1858,11,17 is FVCOM`s start time
TIME=np.argmin(abs(t_diff-np.array(modtime)))
Temp=[]
for i in range(len(layer)):
t=modtemp[TIME][i][node]
Temp.append(t)
Temp.reverse()
return Temp
def get_roms(lat,lon,depth,time):
url_1='http://tds.marine.rutgers.edu:8080/thredds/dodsC/roms/espresso/hidden/2006_da/his'
nc_1=netCDF4.Dataset(url_1)
time_1=nc_1.variables['ocean_time'][:]
lats=nc_1.variables['lat_rho'][:]
lons=nc_1.variables['lon_rho'][:]
s_rho=nc_1.variables['s_rho'][:]
h=nc_1.variables['h'][:]
temp_1=nc_1.variables['temp']
url_2='http://tds.marine.rutgers.edu:8080/thredds/dodsC/roms/espresso/2013_da/his_Best/ESPRESSO_Real-Time_v2_History_Best_Available_best.ncd'
nc_2=netCDF4.Dataset(url_2)
time_2=nc_2.variables['time'][:]
temp_2=nc_2.variables['temp']
TEMP=[]
for i in depth.index:
Distance=dist(lat[i],lon[i],lats,lons)
near_index=np.argmin(Distance)
index_one,index_two=near_index/len(Distance[0]),near_index%len(Distance[0])
H=h[index_one,index_two]
layer_depth=-s_rho*H
index_D=np.argmin(np.abs(depth[i]-layer_depth))
if (time[i]-datetime(2013,05,18)).total_seconds()/3600>25: #25 hour is the first time ni 2013_da
index_T=np.argmin(np.abs((time[i]-datetime(2013,05,18)).total_seconds()/3600-time_2))
tt=temp_2[index_T,index_D,index_one,index_two]
if type(tt) not float:
tt=-10
TEMP.append(tt)
else:
index_T=np.argmin(np.abs((time[i]-datetime(2006,01,01)).total_seconds()-time_1))
tt=temp_1[index_T,index_D,index_one,index_two]
if type(tt) not float:
tt=-10
TEMP.append(tt)
print i
def get_roms(lat,lon,depth,time):
url_1='http://tds.marine.rutgers.edu:8080/thredds/dodsC/roms/espresso/hidden/2006_da/his'
nc_1=netCDF4.Dataset(url_1)
time_1=nc_1.variables['ocean_time'][:]
lats=nc_1.variables['lat_rho'][:]
lons=nc_1.variables['lon_rho'][:]
s_rho=nc_1.variables['s_rho'][:]
h=nc_1.variables['h'][:]
temp_1=nc_1.variables['temp']
url_2='http://tds.marine.rutgers.edu:8080/thredds/dodsC/roms/espresso/2013_da/his_Best/ESPRESSO_Real-Time_v2_History_Best_Available_best.ncd'
nc_2=netCDF4.Dataset(url_2)
time_2=nc_2.variables['time'][:]
temp_2=nc_2.variables['temp']
TEMP=[]
for i in depth.index:
Distance=dist(lat[i],lon[i],lats,lons)
near_index=np.argmin(Distance)
index_one,index_two=near_index/len(Distance[0]),near_index%len(Distance[0])
H=h[index_one,index_two]
layer_depth=-s_rho*H
index_D=np.argmin(np.abs(depth[i]-layer_depth))
if (time[i]-datetime(2013,05,18)).total_seconds()/3600>25: #25 hour is the first time ni 2013_da
index_T=np.argmin(np.abs((time[i]-datetime(2013,05,18)).total_seconds()/3600-time_2))
tt=temp_2[index_T,index_D,index_one,index_two]
if type(tt) not float:
tt=-10
TEMP.append(tt)
def small_dist_index(
lon, lat, all_lon, all_lat
): # obtain the index according to the exacted positon within 1 km with all other turtle position
for i in range(len(all_lon)):
if all_lon[i] == lon:
start = i + 1
index = [] # find the index within 1 kilometers
for i in range(start, len(all_lon)):
l = dist(lon, lat, all_lon[i], all_lat[i])
if l <= 1:
index.append(i)
return index
def find_range(day,t1,t2,index1,index2,lon1,lat1,lon2,lat2):
one,two=[],[]
for r in np.arange(0,30,2):
ix1,ix2=[],[]
for i in index1:
for j in index2:
l = dist(lon1[i], lat1[i],lon2[j],lat2[j])
if l<r+2 and l>=r:
t=abs(t1[i]-t2[j])
if t<timedelta(days=day):
ix1.append(i)
ix2.append(j)
one.append(ix1) #have 15 value,each value also have many value
two.append(ix2)
return one,two
def getFVcom(latpt,lonpt,time_roms):
url='http://www.smast.umassd.edu:8080/thredds/dodsC/fvcom/hindcasts/30yr_gom3'
nc = netCDF4.Dataset(url)
lats = nc.variables['lat'][:]
lons = nc.variables['lon'][:]
Depth = nc.variables['h'][:]
siglay=nc.variables['siglay'][:]
time=nc.variables['time'][200000:316008]#time between 2006-2013
modtemp=nc.variables['temp']
modtime=[]
for i in range(len(time)):
t=timedelta(days=float(time[i])).total_seconds()
modtime.append(t) #change time days to seconds
depth=(-Depth*siglay).transpose() #each layer`s depth
Temp=[]
for i in latpt.index:
distance=dist(lonpt[i],latpt[i],lons,lats)
node=np.argmin(distance) #get nearest node
t_diff=(time_roms[i]-datetime(1858,11,17)).total_seconds() #1858,11,17 is FVCOM`s start time
TIME=np.argmin(abs(t_diff-np.array(modtime)))
T=modtemp[200000+TIME][44][node]
Temp.append(T)
nc.close()
return Temp
if obsturtle_id[i] == turtle_id:
indx.append(i)
obs=obsData.ix[indx]
goodTime=pd.Series(np_datetime(obs['END_DATE']), index=indx)
goodlons=pd.Series(obs['LON'], index=indx)
goodlats=pd.Series(obs['LAT'], index=indx)
d=pd.DataFrame({'Time':goodTime,'lon':goodlons,'lat':goodlats},index=indx)
d=d.sort(['Time'])
d.index=range(len(d))
diff_dist=[] #collect distance
diff_time=[] #collect time
for i in d.index:
if i+1 == len(d):
break
dis=dist(d['lon'][i],d['lat'][i],d['lon'][i+1],d['lat'][i+1])
diff_dist.append(dis)
t=(d['Time'][i+1]-d['Time'][i]).total_seconds()/3600
diff_time.append(t)
time_ave=round(np.mean(diff_time),2)
dist_ave=round(np.mean(diff_dist),2)
fig=plt.figure()
plt.bar(range(len(diff_dist)),diff_dist)
plt.ylim([0,220])
plt.xlim([0,164])
plt.xlabel('Sort by time')
plt.ylabel('Distance unit:km')
plt.title('Dist,average='+str(dist_ave)+'(km). turtle:'+str(turtle_id))
plt.savefig('between_dive_dist.png')
fig=plt.figure()
'wave_height': buoywave2,
'lat': buoylat2,
'lon': buoylon2
},
index=tf_index2)
data2 = data.sort_index(by='time')
early_2011 = []
after_2011 = []
early_2013 = []
after_2013 = []
early_mod = []
after_mod = []
for i in obsData.index:
for j in indx:
if dist(buoylat[0], buoylon[0], obslat[i],
obslon[i]) < 50: #distance is smaller than 50km
if obstime[i].month == 8 and obstime[i].year == 2011:
if obstime[i].day > 28: #choose day after storm
after_2011.append(i)
if dist(buoylat[0], buoylon[0], obslat[i], obslon[i]) < 50:
if obstime[i].month == 8 and obstime[i].year == 2011:
if obstime[i].day == 27: #choose day before storm
early_2011.append(i)
for j in indx2:
if dist(buoylat2[0], buoylon2[0], obslat[i], obslon[i]) < 50:
if obstime[i].month == 5 and obstime[i].year == 2013:
if obstime[i].day == 26: #choose day after storm
after_2013.append(i)
if dist(buoylat2[0], buoylon2[0], obslat[i], obslon[i]) < 50:
if obstime[i].month == 5 and obstime[i].year == 2013:
if obstime[i].day == 24: #choose day before storm
for q in range(67, 99):
pp.append((modData['lon'][q], modData['lat'][q]))
#range of fvcom
FV_index = []
for i in obslat.index:
'n=point_in_poly(pp,obslon[i],obslat[i]) ' #turtle
n = point_in_poly(pp, obslon[i][0], obslat[i][0]) #ship
if n == 1:
#print i
FV_index.append(i) #ensure turtle data in range of FVcom
Depth = []
node = []
TIME = []
for i in FV_index:
distance = dist(obslon[i], obslat[i], modData['lon'], modData['lat'])
k = np.argmin(distance) #get nearest node
node.append(k)
depth = []
#print i
for j in range(len(obsDepth[i])):
m = Closest_num(obsDepth[i][j], np.array(
modData['depthBYlayer']
[k])) #in the nearest node get which layer it belongs to
depth.append(int(m))
Depth.append(depth)
t_diff = (obsTime[i] - datetime(
1858, 11, 17)).total_seconds() #1858,11,17 is FVCOM`s start time
T = Closest_num(t_diff, np.array(modTime)) #get the nearest time
TIME.append(T)
hour = 3 # the ctd time that has gps time within (hour) hours might be considered as good data.
ctd = pd.read_csv('2014_04_16_rawctd.csv') # original data file
ctdlat = ctd['LAT']
ctdlon = ctd['LON']
ctdtime = np_datetime(ctd['END_DATE'])
gps = pd.read_csv('2014_04_16_rawgps.csv') # orginal data file
gpslat = gps['LAT']
gpslon = gps['LON']
gpstime = np_datetime(gps['D_DATE'])
lonsize = [np.min(ctdlon), np.max(ctdlon)]
latsize = [np.min(ctdlat), np.max(ctdlat)]
index = []
i = 0
for lat, lon, ctdtm in zip(ctdlat, ctdlon, ctdtime):
l = dist(lon, lat, gpslon, gpslat)
p = np.where(l < r)
maxtime = ctdtm + timedelta(hours=hour)
mintime = ctdtm - timedelta(hours=hour)
mx = gpstime[p[0]] < maxtime
mn = gpstime[p[0]] > mintime
TF = mx * mn
if TF.any():
index.append(i)
i += 1
print(i)
ctd_TF = pd.Series([True] * len(index), index=index)
ctd['TF'] = ctd_TF
print(ctd)
print('{0} is OK(including "null" lon and lat values.).'.format(
len(ctd_TF) / 28975.0))
indx2=[]
for i in tf_index2:
buoytime2[i]=datetime.strptime(buoytime2[i], "%Y-%m-%d %H:%M") # change str to datatime
if 17.2<buoyspeed2[i]:
indx2.append(i) #choose which day has storm
data2=pd.DataFrame({'time':buoytime2,'wind_speed':buoyspeed2,'wave_height':buoywave2,
'lat':buoylat2,'lon':buoylon2},index=tf_index2)
data2 = data.sort_index(by='time')
early_2011=[]
after_2011=[]
early_2013=[]
after_2013=[]
for i in obsData.index:
for j in indx:
if dist(buoylat[0],buoylon[0],obslat[i],obslon[i])<50: #distance is smaller than 50km
if obstime[i].month==8 and obstime[i].year==2011:
if obstime[i].day>28: #choose day after storm
after_2011.append(i)
if dist(buoylat[0],buoylon[0],obslat[i],obslon[i])<50:
if obstime[i].month==8 and obstime[i].year==2011:
if obstime[i].day==27: #choose day before storm
early_2011.append(i)
for j in indx2:
if dist(buoylat2[0],buoylon2[0],obslat[i],obslon[i])<50:
if obstime[i].month==5 and obstime[i].year==2013:
if obstime[i].day==26: #choose day after storm
after_2013.append(i)
if dist(buoylat2[0],buoylon2[0],obslat[i],obslon[i])<50:
if obstime[i].month==5 and obstime[i].year==2013:
if obstime[i].day==24: #choose day before storm
for q in range(67,99):
pp.append((modData['lon'][q],modData['lat'][q]))
#range of fvcom
FV_index=[]
for i in obslat.index:
n=point_in_poly(pp,obslon[i],obslat[i]) #turtle
'n=point_in_poly(pp,obslon[i][0],obslat[i][0])' #ship
if n==1:
print i
FV_index.append(i) #ensure turtle data in range of FVcom
Depth=[]
node=[]
TIME=[]
for i in FV_index:
distance=dist(obslon[i],obslat[i],modData['lon'],modData['lat'])
k=np.argmin(distance) #get nearest node
node.append(k)
depth=[]
print i
for j in range(len(obsDepth[i])):
m=Closest_num(obsDepth[i][j],np.array(modData['depthBYlayer'][k])) #in the nearest node get which layer it belongs to
depth.append(int(m))
Depth.append(depth)
t_diff=(obsTime[i]-datetime(1858,11,17)).total_seconds() #1858,11,17 is FVCOM`s start time
T=Closest_num(t_diff,np.array(modTime)) #get the nearest time
TIME.append(T)
ctd_FV = pd.Series([True]*len(FV_index), index=FV_index)
obsData['in FVcom range'] = ctd_FV
obsData['modnode']=pd.Series(node,index=FV_index)
time_aves=[]
dist_aves=[] #collect all average
for i in range(len(ids)):
obs=obsData.ix[ids[i]]
goodTime=pd.Series(np_datetime(obs['END_DATE']), index=ids[i])
goodlons=pd.Series(obs['LON'], index=ids[i])
goodlats=pd.Series(obs['LAT'], index=ids[i])
d=pd.DataFrame({'Time':goodTime,'lon':goodlons,'lat':goodlats},index=ids[i]) #each turtle`s modtemp and obstemp
d=d.sort(['Time'])
d.index=range(len(d))
diff_dist=[]
diff_time=[]
for j in d.index:
if j+1 == len(d):
break
dis=dist(d['lon'][j],d['lat'][j],d['lon'][j+1],d['lat'][j+1])
diff_dist.append(dis)
t=(d['Time'][j+1]-d['Time'][j]).total_seconds()/3600
diff_time.append(t)
time_ave=round(np.mean(diff_time))
dist_ave=round(np.mean(diff_dist)) #each turtle`s average
time_aves.append(time_ave)
dist_aves.append(dist_ave)
ave_time=round(np.mean(np.array(time_aves)),2)
ave_dist=round(np.mean(np.array(dist_aves)),2)
std_time=round(np.std(np.array(time_aves)),2)
std_dist=round(np.std(np.array(dist_aves)),2)
time_aves.sort()
dist_aves.sort()
time_aves=pd.Series(time_aves)
goodlons = pd.Series(obs['LON'], index=ids[i])
goodlats = pd.Series(obs['LAT'], index=ids[i])
d = pd.DataFrame({
'Time': goodTime,
'lon': goodlons,
'lat': goodlats
},
index=ids[i]) #each turtle`s modtemp and obstemp
d = d.sort(['Time'])
d.index = range(len(d))
diff_dist = []
diff_time = []
for j in d.index:
if j + 1 == len(d):
break
dis = dist(d['lon'][j], d['lat'][j], d['lon'][j + 1], d['lat'][j + 1])
diff_dist.append(dis)
t = (d['Time'][j + 1] - d['Time'][j]).total_seconds() / 3600
diff_time.append(t)
time_ave = round(np.mean(diff_time))
dist_ave = round(np.mean(diff_dist)) #each turtle`s average
time_aves.append(time_ave)
dist_aves.append(dist_ave)
ave_time = round(np.mean(np.array(time_aves)), 2)
ave_dist = round(np.mean(np.array(dist_aves)), 2)
std_time = round(np.std(np.array(time_aves)), 2)
std_dist = round(np.std(np.array(dist_aves)), 2)
time_aves.sort()
dist_aves.sort()
time_aves = pd.Series(time_aves)
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime,timedelta
import netCDF4
from matplotlib.mlab import griddata
import sys
sys.path.append('../modules')
import basemap
from basemap import basemap_usgs,basemap_standard
from turtleModule import dist,str2ndlist
lat_AB01=42.0368
lon_AB01=-70.1313
lat=np.arange(41.7797,42.0368,0.025)
lon=np.arange(-70.4873,-70.0,0.035)
time=[datetime(2004,9,17),datetime(2004,9,27)]
distance_AB01=dist(lon[0],lat[0],lon_AB01,lat_AB01)
deepest=[14.6066,27.6068,27.792999,31.504299,34.486198,36.026402,37.494999,38.680599,
40.936199,35.314899,25.784]
fig=plt.figure()
ax=fig.add_subplot(111)
distance=list(np.array([dist(lon[0],lat[0],lon[-1],lat[-1])])/len(lat)*range(1,len(lat)+1))
polygon=[]
for j in range(len(deepest)):
polygon.append([distance[j],deepest[j]])
polygon.append([distance[-1],max(deepest)+1])
polygon.append([distance[0],max(deepest)+1])
plt.ylim([max(deepest)+1,0])
plt.xlim([min(distance),max(distance)])
pg=plt.Polygon(polygon,color='red')
ax.add_patch(pg)
plt.show()
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 20 11:11:18 2015
creat ctdWithdepthofbottom_fvcom.csv with FVCOM depth
@author: zdong
"""
import numpy as np
import pandas as pd
from turtleModule import str2ndlist, np_datetime, dist
import netCDF4
##########################################################
url = "http://www.smast.umassd.edu:8080/thredds/dodsC/fvcom/hindcasts/30yr_gom3"
nc = netCDF4.Dataset(url)
lat = nc.variables['lat'][:]
lon = nc.variables['lon'][:]
h = nc.variables['h'][:]
obsData = pd.read_csv('ctd_FVcom_temp.csv')
tf_index = np.where(obsData['in FVcom range'].notnull())[0]
obsLon, obsLat = obsData['LON'][tf_index], obsData['LAT'][tf_index]
depth = []
for i in tf_index:
nearest_dist = np.argmin(dist(obsLon[i], obsLat[i], lon, lat))
depth.append(h[nearest_dist])
obsData['depth_of_bottom_fvcom'] = pd.Series(depth, index=tf_index)
obsData.to_csv('ctdWithdepthofbottom_fvcom.csv')
for i in np.arange(52,34,-1):
temp_roms=[]
LAT_roms.append(lats_roms[i][50])
LON_roms.append(lons_roms[i][50])
H_roms.append(h_roms[i][50]) #52,34 and 50 is location from shallow to deep
for j in range(36): #this is roms`s layers
temp_roms.append(temps_roms[40000][j][i][50])
Temp_roms.append(temp_roms)
ttt_roms=np.array(Temp_roms).transpose()
hh_roms=[]
for i in H_roms:
hh_roms.append(-i*s_rho)
hh=np.array(hh_roms).transpose()
distance=list(np.array([dist(LON_roms[0],LAT_roms[0],LON_roms[-1],LAT_roms[-1])])/len(LAT_roms)*range(1,len(LAT_roms)+1)) #this is distance between location
distances=[]
for i in range(36):
distances.append(distance)
temp_hycom=[]
for i in range(len(LON_roms)):
t=getHYcom(LAT_roms[i],LON_roms[i],TIME_roms,hh_roms[i])
temp_hycom.append(t)
ttt_hycom=np.array(temp_hycom).transpose()
temp_fvcom=[]
for i in range(len(LON_roms)):
t=getFVcom(LAT_roms[i],LON_roms[i],TIME_roms,hh_roms[i])
temp_fvcom.append(t)
ttt_fvcom=np.array(temp_fvcom).transpose()
obs=obsData.ix[ids[i]]
goodTime=pd.Series(np_datetime(obs['END_DATE']), index=ids[i])
goodlons=pd.Series(obs['LON'], index=ids[i])
goodlats=pd.Series(obs['LAT'], index=ids[i])
d=pd.DataFrame({'Time':goodTime,'lon':goodlons,'lat':goodlats},index=ids[i])
d=d.sort(['Time'])
d.index=range(len(d))
diff_dist=[]
diff_time=[]
for j in d.index:
if j+1 == len(d):
diff_dist.append(0)
diff_time.append(0)
break
if j>0:
dis=dist(d['lon'][0],d['lat'][0],d['lon'][j],d['lat'][j])
diff_dist.append(dis)
t=(d['Time'][j]-d['Time'][0]).total_seconds()/3600/24
diff_time.append(t)
time_ave=int(np.max(diff_time))
dist_ave=int(np.max(diff_dist))
time_aves.append(time_ave)
dist_aves.append(dist_ave)
time_aves.sort()
dist_aves.sort()
time_aves=pd.Series(time_aves)
dist_aves=pd.Series(dist_aves)
y1=time_aves.value_counts()
y1=y1.sort_index()
y2=dist_aves.value_counts()
def nearest_point_index2(lon, lat, lons, lats):
d = dist(lon, lat, lons, lats)
min_dist = np.min(d)
index = np.where(d == min_dist)
return index
goodlons = pd.Series(obs['LON'], index=indx)
goodlats = pd.Series(obs['LAT'], index=indx)
d = pd.DataFrame({
'Time': goodTime,
'lon': goodlons,
'lat': goodlats
},
index=indx)
d = d.sort(['Time'])
d.index = range(len(d))
diff_dist = [] #collect distance
diff_time = [] #collect time
for i in d.index:
if i + 1 == len(d):
break
dis = dist(d['lon'][i], d['lat'][i], d['lon'][i + 1], d['lat'][i + 1])
diff_dist.append(dis)
t = (d['Time'][i + 1] - d['Time'][i]).total_seconds() / 3600
diff_time.append(t)
time_ave = round(np.mean(diff_time), 2)
dist_ave = round(np.mean(diff_dist), 2)
fig = plt.figure()
plt.bar(range(len(diff_dist)), diff_dist)
plt.ylim([0, 220])
plt.xlim([0, 164])
plt.xlabel('Sort by time')
plt.ylabel('Distance unit:km')
plt.title('Dist,average=' + str(dist_ave) + '(km). turtle:' + str(turtle_id))
plt.savefig('between_dive_dist.png')
fig = plt.figure()
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 20 11:11:18 2015
creat ctdWithdepthofbottom_fvcom.csv with FVCOM depth
@author: zdong
"""
import numpy as np
import pandas as pd
from turtleModule import str2ndlist,np_datetime,dist
import netCDF4
##########################################################
url="http://www.smast.umassd.edu:8080/thredds/dodsC/fvcom/hindcasts/30yr_gom3"
nc=netCDF4.Dataset(url)
lat=nc.variables['lat'][:]
lon=nc.variables['lon'][:]
h=nc.variables['h'][:]
obsData = pd.read_csv('ctd_FVcom_temp.csv')
tf_index = np.where(obsData['in FVcom range'].notnull())[0]
obsLon, obsLat = obsData['LON'][tf_index], obsData['LAT'][tf_index]
depth=[]
for i in tf_index:
nearest_dist=np.argmin(dist(obsLon[i],obsLat[i],lon,lat))
depth.append(h[nearest_dist])
obsData['depth_of_bottom_fvcom']=pd.Series(depth,index=tf_index)
obsData.to_csv('ctdWithdepthofbottom_fvcom.csv')
node=np.argmin(distance) #get nearest node
t_diff=(time_roms-datetime(1858,11,17)).total_seconds() #1858,11,17 is FVCOM`s start time
TIME=np.argmin(abs(t_diff-np.array(modtime)))
Temp=[]
for i in range(45):
t=modtemp[TIME][i][node]
Temp.append(t)
return Temp,depth[node],Depth[node]
#########################################################
lat_AB01=42.0368
lon_AB01=-70.1313 #location of AB01
lat=np.arange(42.0368,41.7797,-0.025)
lon=np.arange(-70.1313,-70.4873,-0.035) #location of vertical transect
time=[datetime(2004,9,19),datetime(2004,9,25)] #choose two days
distance_AB01=dist(lon[0],lat[0],lon_AB01,lat_AB01)
fig = plt.figure()
for q in range(len(time)):
Temp,depth,deepest=[],[],[]
for i in range(len(lat)):
t,d,D=getFVcom(lat[i],lon[i],time[q])
depth.append(d)
deepest.append(D)
Temp.append(t)
distance=list(np.array([dist(lon[0],lat[0],lon[-1],lat[-1])])/len(lat)*range(0,len(lat))) #this is distance between location
distances=[]
for i in range(45):
distances.append(distance)
dd=np.array(distances).transpose()
TEMP,DEPTH,DIST=[],[],[]
for i in range(len(Temp)):
turtle_id = obsData['PTT'][tf_index]
shipData = pd.read_csv('ship06-08_MODELtemp.csv', index_col=0)
shipid = shipData['id']
shiplat = shipData['LAT']
shiplon = shipData['LON']
shiptime = pd.Series(
(datetime.strptime(x, "%Y-%m-%d %H:%M:%S") for x in shipData['time']))
shipdepth = pd.Series(str2ndlist(shipData['depth'], bracket=True))
shiptemp = pd.Series(str2ndlist(shipData['temperature'], bracket=True))
index = [] #index of turtle
indx = [] #index of shipboard
for i in tf_index:
for j in shipData.index:
l = dist(obslon[i], obslat[i], shiplon[j], shiplat[j])
if l < r2 and l >= r1:
#print l #distance
maxtime = obstime[i] + timedelta(days=day)
mintime = obstime[i] - timedelta(days=day)
mx = shiptime[j] < maxtime
mn = shiptime[j] > mintime
TF = mx * mn
if TF == 1: #time
index.append(i) #turtle index
indx.append(j) #ship index
INDX = pd.Series(indx).unique()
data = pd.DataFrame(range(len(indx)))
s_id,t_id,s_time,t_time,s_lat,s_lon,t_lat,t_lon=[],[],[],[],[],[],[],[]
for i in INDX:
shipData1=pd.read_csv('ship_FVcom_temp.csv') #this ship`s FVCOM temperature
tf_index_fvcom=np.where(shipData1['modtempBYdepth'].notnull())[0]
modtemp_fvcom=pd.Series(shipData1['modtempBYdepth'],index=tf_index_fvcom)
for i in tf_index_fvcom:
modtemp_fvcom[i]=str2list(modtemp_fvcom[i],bracket=True)
shipData2=pd.read_csv('ship_withHYCOMtemp.csv')
tf_index_hycom=np.where(shipData2['modtemp_HYCOM'].notnull())[0]
modtemp_hycom=pd.Series(shipData2['modtemp_HYCOM'],index=tf_index_hycom)#this ship`s HYCOM temperature
for i in tf_index_hycom:
modtemp_hycom[i]=str2list(modtemp_hycom[i],bracket=True)
index_hycom = [] #index of turtle
indx_hycom=[] #index of shipboard
for i in tf_index_HYCOM:
for j in tf_index_hycom:
l = dist(obslon[i], obslat[i],shiplon[j],shiplat[j])
if l<r:
print l #distance
maxtime = obstime[i]+timedelta(days=day)
mintime = obstime[i]-timedelta(days=day)
mx = shiptime[j]<maxtime
mn = shiptime[j]>mintime
TF = mx*mn
if TF==1: #time
index_hycom.append(i) #turtle index
indx_hycom.append(j) #ship index
#print index_hycom,indx_hycom
index,indx=[],[]
for i in index_hycom:
if i in tf_index_FVCOM:
hour = 3 # the ctd time that has gps time within (hour) hours might be considered as good data.
ctd = pd.read_csv("2014_04_16_rawctd.csv") # original data file
ctdlat = ctd["LAT"]
ctdlon = ctd["LON"]
ctdtime = np_datetime(ctd["END_DATE"])
gps = pd.read_csv("2014_04_16_rawgps.csv") # orginal data file
gpslat = gps["LAT"]
gpslon = gps["LON"]
gpstime = np_datetime(gps["D_DATE"])
lonsize = [np.min(ctdlon), np.max(ctdlon)]
latsize = [np.min(ctdlat), np.max(ctdlat)]
index = []
i = 0
for lat, lon, ctdtm in zip(ctdlat, ctdlon, ctdtime):
l = dist(lon, lat, gpslon, gpslat)
p = np.where(l < r)
maxtime = ctdtm + timedelta(hours=hour)
mintime = ctdtm - timedelta(hours=hour)
mx = gpstime[p[0]] < maxtime
mn = gpstime[p[0]] > mintime
TF = mx * mn
if TF.any():
index.append(i)
i += 1
print(i)
ctd_TF = pd.Series([True] * len(index), index=index)
ctd["TF"] = ctd_TF
print(ctd)
print('{0} is OK(including "null" lon and lat values.).'.format(len(ctd_TF) / 28975.0))
print("{0} is OK.".format(len(ctd_TF) / 15657.0))
def nearest_point_index2(lon, lat, lons, lats):
d = dist(lon, lat, lons ,lats)
min_dist = np.min(d)
index = np.where(d==min_dist)
return index