depth=pd.Series(secondData['depth'],index=tf_index1)
temp=pd.Series(secondData['temp'],index=tf_index1)
lon=pd.Series(secondData['lon'],index=tf_index1)
lat=pd.Series(secondData['lat'],index=tf_index1)
inde=pd.Series(secondData['index'],index=tf_index1)
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)
modTemp = pd.Series(obsData['modTempByDepth'],index=indx)
for i in indx:
modTemp[i]=str2list(modTemp[i],bracket=True)
INDX=[]
for j in tf_index1:
if i==inde[j]:
INDX.append(j) #find indices which are in same area
fig=plt.figure()
ax=fig.add_subplot(111)
ax.plot(temp[INDX],depth[INDX],'ro-', label='raw',linewidth=1)
ax.plot(modTemp[i],obsDepth[i],'go--' ,label='roms',linewidth=3)
ax.plot(obsTemp[i],obsDepth[i],'bo--', label='telemetered',linewidth=3)
ax.set_xlim([0, 30])
ax.set_ylim([max(obsDepth[i])+3, -1])
ax.set_xlabel('Temp', fontsize=10)
ax.set_ylabel('Depth', fontsize=10)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
r = 10 # the obs position that has shipboard position within (r) kilometers might be considered as good data.
day = 3 # the obs time that has shipboard time within (day) days might be considered as good data.
obsData=pd.read_csv('ctdWithModTempByDepth.csv',index_col=0)
tf_index=np.where(obsData['TF'].notnull())[0]
obslat = pd.Series(obsData['LAT'][tf_index],index=tf_index)
obslon = pd.Series(obsData['LON'][tf_index],index=tf_index)
obstime = pd.Series(np_datetime(obsData['END_DATE'][tf_index]),index=tf_index)
obsDepth=pd.Series(str2ndlist(obsData['TEMP_DBAR'][tf_index]),index=tf_index)
obstemp=pd.Series(str2ndlist(obsData['TEMP_VALS'][tf_index]),index=tf_index)
modtemp=pd.Series(str2ndlist(obsData['modTempByDepth'][tf_index],bracket=True),index=tf_index)
obsData1=pd.read_csv('ctd_FVcom_temp.csv') #this ctd`s FVCOM temperature
tf_index_FVCOM=np.where(obsData1['modtempBYdepth'].notnull())[0]
modtemp_FVCOM=pd.Series(obsData1['modtempBYdepth'],index=tf_index_FVCOM)
for i in tf_index_FVCOM:
modtemp_FVCOM[i]=str2list(modtemp_FVCOM[i],bracket=True)
obsData2=pd.read_csv('ctd_withHYCOMtemp.csv') #this ctd`s HYCOM temperature
tf_index_HYCOM=np.where(obsData2['modtemp_HYCOM'].notnull())[0]
modtemp_HYCOM=pd.Series(obsData2['modtemp_HYCOM'],index=tf_index_HYCOM)
for i in tf_index_HYCOM:
modtemp_HYCOM[i]=str2list(modtemp_HYCOM[i],bracket=True)
shipData=pd.read_csv('ship06-08_MODELtemp.csv',index_col=0)
shiplat=pd.Series(shipData['LAT'],index=shipData.index)
shiplon=pd.Series(shipData['LON'],index=shipData.index)
shiptime=pd.Series(shipData['time'],index=shipData.index)
shipdepth=pd.Series(str2ndlist(shipData['depth'],bracket=True),index=shipData.index)
shiptemp=pd.Series(str2ndlist(shipData['temperature'],bracket=True),index=shipData.index)
MODtemp=pd.Series(str2ndlist(shipData['modTempByDepth'],bracket=True),index=shipData.index)
for i in range(len(shiptime)):
shiptime[i]=datetime.strptime(shiptime[i], "%Y-%m-%d %H:%M:%S") # change str to datatime
temp = pd.Series(secondData['temp'], index=tf_index1)
lon = pd.Series(secondData['lon'], index=tf_index1)
lat = pd.Series(secondData['lat'], index=tf_index1)
inde = pd.Series(secondData['index'], index=tf_index1)
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)
modTemp = pd.Series(obsData['modTempByDepth'], index=indx)
for i in indx:
modTemp[i] = str2list(modTemp[i], bracket=True)
INDX = []
for j in tf_index1:
if i == inde[j]:
INDX.append(j) #find indices which are in same area
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(temp[INDX], depth[INDX], 'ro-', label='raw', linewidth=1)
ax.plot(modTemp[i], obsDepth[i], 'go--', label='roms', linewidth=3)
ax.plot(obsTemp[i], obsDepth[i], 'bo--', label='telemetered', linewidth=3)
ax.set_xlim([0, 30])
ax.set_ylim([max(obsDepth[i]) + 3, -1])
ax.set_xlabel('Temp', fontsize=10)
ax.set_ylabel('Depth', fontsize=10)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
import matplotlib.pyplot as plt
from matplotlib import path
from turtleModule import mon_alpha2num, np_datetime, str2list
from watertempModule import water_roms
###########################main code#################################################
ctddata = pd.read_csv('ctdWithModTempByDepth.csv')
shallow = ctddata.ix[17] # 17, 19, 22
deep = ctddata.ix[13469] # 18914
shallowtime = np_datetime(shallow['END_DATE'])
shallowtemp = [float(temp) for temp in shallow['TEMP_VALS'].split(',')]
#modelobj = water_roms()
#modelurl = modelobj.get_url(shallowtime, shallowtime+timedelta(hours=1))
depth = [int(dep) for dep in shallow['TEMP_DBAR'].split(',')]
modeltemp = str2list(shallow['modTempByDepth'], bracket=True)
'''
modeltemp = []
for dep in depth:
print dep
modeltemp.append(modelobj.watertemp(shallow['LON'], shallow['LAT'], dep,
shallowtime, modelurl))
'''
deeptime = np_datetime(deep['END_DATE'])
deeptemp = [float(temp) for temp in deep['TEMP_VALS'].split(',')]
#modelobj2 = water_roms()
#modelurl2 = modelobj2.get_url(deeptime, deeptime+timedelta(hours=1))
depth2 = [int(dep) for dep in deep['TEMP_DBAR'].split(',')]
'''
modeltemp2 = []
print 'start deep'
tf_index = np.where(obsData['TF'].notnull())[0]
obslat = pd.Series(obsData['LAT'][tf_index], index=tf_index)
obslon = pd.Series(obsData['LON'][tf_index], index=tf_index)
obstime = pd.Series(np_datetime(obsData['END_DATE'][tf_index]), index=tf_index)
obsDepth = pd.Series(str2ndlist(obsData['TEMP_DBAR'][tf_index]),
index=tf_index)
obstemp = pd.Series(str2ndlist(obsData['TEMP_VALS'][tf_index]), index=tf_index)
modtemp = pd.Series(str2ndlist(obsData['modTempByDepth'][tf_index],
bracket=True),
index=tf_index)
obsData1 = pd.read_csv('ctd_FVcom_temp.csv') #this ctd`s FVCOM temperature
tf_index_FVCOM = np.where(obsData1['modtempBYdepth'].notnull())[0]
modtemp_FVCOM = pd.Series(obsData1['modtempBYdepth'], index=tf_index_FVCOM)
for i in tf_index_FVCOM:
modtemp_FVCOM[i] = str2list(modtemp_FVCOM[i], bracket=True)
obsData2 = pd.read_csv('ctd_withHYCOMtemp.csv') #this ctd`s HYCOM temperature
tf_index_HYCOM = np.where(obsData2['modtemp_HYCOM'].notnull())[0]
modtemp_HYCOM = pd.Series(obsData2['modtemp_HYCOM'], index=tf_index_HYCOM)
for i in tf_index_HYCOM:
modtemp_HYCOM[i] = str2list(modtemp_HYCOM[i], bracket=True)
shipData = pd.read_csv('ship06-08_MODELtemp.csv', index_col=0)
shiplat = pd.Series(shipData['LAT'], index=shipData.index)
shiplon = pd.Series(shipData['LON'], index=shipData.index)
shiptime = pd.Series(shipData['time'], index=shipData.index)
shipdepth = pd.Series(str2ndlist(shipData['depth'], bracket=True),
index=shipData.index)
shiptemp = pd.Series(str2ndlist(shipData['temperature'], bracket=True),
index=shipData.index)
MODtemp = pd.Series(str2ndlist(shipData['modTempByDepth'], bracket=True),
