indx=S[d][s] # ship index
index=T[d][s]# turtle index
INDX=pd.Series(indx).unique()
scales.append(str(day[d])+'day '+str(2*s)+'~'+str(2+2*s)+'km')
n1=len(INDX)
n2=len(pd.Series(index).unique())
n3=len(turtle_id[pd.Series(index).unique()].unique())
N1.append(n1)
N2.append(n2)
N3.append(n3)
print 'number of ship profile,turtle profile,turtle: ',str(day[d])+'day '+str(2*s)+'~'+str(2+2*s)+'km',n1,n2,n3
n_ID=[]
num=0
ID_turtle=[]
for i in range(len(INDX)):
wd1=-gettopo(shiplat[INDX[i]],shiplon[INDX[i]])
bm1=shipdepth[INDX[i]][-1]
diff_turVSship=[] # each ship pofile be compared with many turtle profile in the difference of temp
if bm1>=wd1*0.9:
for j in range(len(indx)):
if indx[j]==INDX[i]:
wd2=-gettopo(obslat[index[j]],obslon[index[j]])
bm2=obsDepth[index[j]][-1] # bottom depth of the turtle profile
if bm2>=wd2*0.9:
dif_turVSship=abs(obstemp[index[j]][-1]-shiptemp[INDX[i]][-1])
diff_turVSship.append(dif_turVSship)
ID_turtle.append(turtle_id[index[j]])
if diff_turVSship ==[]:
num+=1
if diff_turVSship!=[]: # some turtle dive on the surface ,but the ship data start from the deeper depth
mean_turVSship=np.mean(np.array(diff_turVSship))
from mpl_toolkits.basemap import Basemap
import pandas as pd
from turtleModule import draw_basemap
from gettopo import gettopo
ship = pd.read_csv('ship06-08_MODELtemp.csv')
lonship, latship = ship['LON'], ship['LAT']
ctd = pd.read_csv('ctd_extract_good.csv', index_col=0)
TF = ctd['TF'] # If True, data is good, if False, data is bad.
latGoodCTD, lonGoodCTD = ctd['LAT'][TF == True], ctd['LON'][TF == True]
contour_lon, contour_lat, contour_depth = [], [], []
for i in ship.index:
contour_lon.append(lonship[i])
contour_lat.append(latship[i])
wd = -gettopo(latship[i], lonship[i])
contour_depth.append(wd)
for i in latGoodCTD.index:
contour_lon.append(lonGoodCTD[i])
contour_lat.append(latGoodCTD[i])
wd = -gettopo(latGoodCTD[i], lonGoodCTD[i])
contour_depth.append(wd)
'''obsData = pd.read_csv('ctdWithdepthofbottom_roms.csv')
obsLon, obsLat = obsData['LON'], obsData['LAT'] #use for plotting depth line
depthBottom = pd.Series(obsData['depth_bottom'],index=obsData.index)
for i in obsData.index:
if depthBottom[i]>200:
depthBottom[i]=200
'''
lonsize = [-79.5, -64]
latsize = [33, 46]
new_index = range(len(inde))
turtle_index.index = new_index
lon.index = new_index
lat.index = new_index
depth.index = new_index
'''turtle_index=pd.Series(data['index'][inde],index=new_index)
lon=pd.Series(data['lon'][inde],index=new_index)
lat=pd.Series(data['lat'][inde],index=new_index)
'''
water_depth = [] # get water depth according to lat and lon
for i in new_index:
if i % 10000 == 0:
print(i)
if i == 0 or i == len(new_index) - 1:
wd1 = -gettopo(
lat[i], lon[i]
) # this is a function to get water depth from a USGS database
elif turtle_index[i - 1] != turtle_index[i + 1]:
wd1 = -gettopo(lat[i], lon[i])
water_depth.append(wd1)
wd = pd.Series(water_depth, index=new_index)
indx = [] #find the start of downcast
for n in new_index:
if depth[n] <= wd[n] * 0.1: # use 2 beacuse some dives don`t go to air
I = find_start(n, depth)
indx.append(I)
if n % 1000 == 0:
print(I, n)
Indx = [list(x)
for x in set(tuple(x) for x in indx)] #get rid of repeated data
inde=pd.Series(secondData['index'],index=tf_index1)
time.index=tf_index2
depth.index=tf_index2
temp.index=tf_index2
inde.index=tf_index2
indx=[]
for i in tf_index:
if obsturtle_id[i]==118905: #this turtle is same turtle with 4-second turtle
indx.append(i)
obsLon, obsLat = obsData['LON'][indx], obsData['LAT'][indx]
obsTime = pd.Series(np_datetime(obsData['END_DATE'][indx]), index=indx)
obsTemp = pd.Series(str2ndlist(obsData['TEMP_VALS'][indx]), index=indx)
obsDepth = pd.Series(str2ndlist(obsData['TEMP_DBAR'][indx]), index=indx)
Waterdepth=[]
for i in indx:
wd=-gettopo(obsLat[i],obsLon[i])
Waterdepth.append(wd)
Waterdepth=pd.Series(Waterdepth, index=indx)
for i in indx[0:2]:
waterdepth=Waterdepth[i]
print 'waterdepth: '+ str(waterdepth)
Index_all=[] #find indices which are in same area
for j in tf_index2:
if i==inde[j]:
Index_all.append(j)
newdepth=pd.Series(depth,index=Index_all)
newdepth.index=range(len(Index_all))
newtime=pd.Series(time,index=Index_all)
newtime.index=range(len(Index_all))
newtime=pd.to_datetime(newtime)
Index=[] # all dives for each profile
i) # obtain the number of the turtle profile more than one
AV = []
mean_STD = []
n = 0
for i in INDX1: #[1727,4011,4801,8038]:#[3274,4557,9106]:##[4801]:#[656,5327,1728,8699]:###
ix = [] #g_index[i]
cd = max(depth[i])
for j in np.arange(len(g_index[i])):
d = max(depth[g_index[i][j]])
if i == 4801:
ix.append(g_index[i][j])
if abs(cd - d) < 5:
wd1 = -gettopo(lat[i], lon[i])
wd2 = -gettopo(lat[g_index[i][j]], lon[g_index[i][j]])
if cd >= wd1 * 0.9 and d >= wd2 * 0.9:
ix.append(g_index[i][j])
if ix != []:
print i
n += 1
A = []
S = []
'''b=np.mean(temp[i])
S.append(b)
for k in range(len(ix)):
c=np.mean(temp[ix[k]])
a=b-c
A.append(a)