except:
print 'FVCOM is down?'
modt = 'nan' # the FVCOM must be down
else:
modt = 'nan'
# get DOPPIO
if max(df1.datet).to_pydatetime() >= dt(2017, 11, 1, 0, 0, 0):
print 'looking for DOPPIO'
try:
modt_doppio = str(
round(
c2f(
doppio_modules.get_doppio(
lat=df1[8][-1:].values[0],
lon=df1[7][-1:].values[0],
depth='bottom',
time=max(df1.datet).to_pydatetime() -
timedelta(days=4),
fortype='temperature'))[0], 1))
except:
modt_doppio = np.nan
print 'looking for GOMOFS'
try:
modt_gomofs = str(
round(
c2f(
gomofs_modules.get_gomofs(
max(df1.datet).to_pydatetime() -
timedelta(days=0), df1[8][-1:].values[0],
df1[7][-1:].values[0], 'bottom'))[0], 1))
except:
depth = int(sys.argv[3]) #99999 for bottom
datet = dt(int(sys.argv[4]), int(sys.argv[5]), int(sys.argv[6]),
int(sys.argv[7]), int(sys.argv[8]), 0)
clim_files_directory = '/net/data5/jmanning/clim/'
#####################
# get GOMOFS
try:
t = gomofs_modules.get_gomofs(datet, lat, lon, depth)
print('gomofs = ', '%.3f' % t)
except:
print('gomofs not available?')
# get DOPPIO
try:
t = doppio_modules.get_doppio(lat, lon, depth, datet, 'temperature')
print('doppio = ', '%.3f' % t)
except:
print('doppio not available?')
# get FVCOM
try:
url = fvcom_modules.get_FVCOM_url(datet)
t = fvcom_modules.get_FVCOM_temp(url, lat, lon, datet, depth) #
print('fvcom =', '%.3f' % t)
except:
print('fvcom not available?')
# get CLIM
dflat = pd.read_csv(clim_files_directory + 'LatGrid.csv', header=None)
dflon = pd.read_csv(clim_files_directory + 'LonGrid.csv', header=None)
if min(lats[i])<min_lat:
min_lat=min(lats[i])
if min(lons[i])<min_lon:
min_lon=min(lons[i])
if max(lats[i])>max_lat:
max_lat=max(lats[i])
if max(lons[i])>max_lon:
max_lon=max(lons[i])
if (max_lon-min_lon)>(max_lat-min_lat):
max_lat=max_lat+((max_lon-min_lon)-(max_lat-min_lat))/2.0
min_lat=min_lat-((max_lon-min_lon)-(max_lat-min_lat))/2.0
else:
max_lon=max_lon+((max_lat-min_lat)-(max_lon-min_lon))/2.0
min_lon=min_lon-((max_lat-min_lat)-(max_lon-min_lon))/2.0
point_temp,index_1,index_2=dm.get_doppio(lat=input_lat,lon=input_lon,depth=input_depth,time=input_date_time)
if input_depth==99999: #if input_depth=99999, we need output the bottom layer
S_coordinate=1 #1 represent the bottom layer, 0 represent the surface layer
else:
S_coordinate=float(input_depth)/float(doppio_depth[index_1][index_2])
#creat map
#Create a blank canvas
fig=plt.figure(figsize = (20, 20))
fig.suptitle('DOPPIO model bottom temp(deg C) and depth(meter)',fontsize=35, fontweight='bold')
#Draw contour lines and temperature maps in detail
ax1=fig.add_axes([0.07,0.03,0.85,0.95])
ax1.set_title(input_date_time, loc='center')
ax1.axes.title.set_size(24)
#ax12=ax1.twinx()
def classify_by_boat(telemetry_status, start_time, end_time, dict):
try:
tele_dict = dict['tele_dict']
Doppio_dict = dict['Doppio']
GoMOLFs_dict = dict['GoMOLFs']
FVCOM_dict = dict['FVCOM']
start_time = dict[
'end_time'] # if dict['end_time'] is wrong, please comment this code
except:
dict = {}
tele_dict = {}
Doppio_dict = {}
GoMOLFs_dict = {}
FVCOM_dict = {}
telemetrystatus_df = read_telemetrystatus(telemetry_status)
#download the data of telementry
tele_df = read_telemetry(
) #path='/home/jmanning/Desktop/telementry.csv') #tele_df means telemeterd data
#screen out the data of telemetry in interval
valuable_tele_df = pd.DataFrame(
data=None,
columns=['vessel_n', 'esn', 'time', 'lon', 'lat', 'depth',
'temp']) #use to save the data from start time to end time
for i in range(len(tele_df)):
tele_time_str=str(tele_df['year'].iloc[i])+'-'+str(tele_df['month'].iloc[i])+'-'+str(tele_df['day'].iloc[i])+' '+\
str(tele_df['Hours'].iloc[i])+':'+str(tele_df['minates'].iloc[i])+':'+'00'
tele_time = datetime.strptime(tele_time_str, '%Y-%m-%d %H:%M:%S')
if start_time < tele_time <= end_time:
valuable_tele_df=valuable_tele_df.append(pd.DataFrame(data=[[tele_df['vessel_n'][i],tele_df['esn'][i],tele_time,tele_df['lon'][i],\
tele_df['lat'][i],tele_df['depth'][i],tele_df['temp'][i]]],\
columns=['vessel_n','esn','time','lon','lat','depth','temp']))
#clean the index of valuable telementry data
if len(valuable_tele_df) > 0:
valuable_tele_df.index = range(len(valuable_tele_df))
dict['end_time'] = valuable_tele_df['time'][len(valuable_tele_df) - 1]
print(len(valuable_tele_df))
for j in range(len(telemetrystatus_df)):
if telemetrystatus_df['Boat'][j] not in tele_dict.keys():
tele_dict[telemetrystatus_df['Boat'][j]] = pd.DataFrame(
data=None, columns=['time', 'temp', 'depth', 'lat', 'lon'])
if telemetrystatus_df['Boat'][j] not in Doppio_dict.keys():
Doppio_dict[telemetrystatus_df['Boat'][j]] = pd.DataFrame(
data=None, columns=['time', 'temp', 'depth', 'lat', 'lon'])
if telemetrystatus_df['Boat'][j] not in GoMOLFs_dict.keys():
GoMOLFs_dict[telemetrystatus_df['Boat'][j]] = pd.DataFrame(
data=None, columns=['time', 'temp', 'depth', 'lat', 'lon'])
if telemetrystatus_df['Boat'][j] not in FVCOM_dict.keys():
FVCOM_dict[telemetrystatus_df['Boat'][j]] = pd.DataFrame(
data=None, columns=['time', 'temp', 'depth', 'lat', 'lon'])
dop_nu = []
gmf_nu = []
tele_nu = []
fvc_nu = []
for i in valuable_tele_df.index: #valuable_tele_df is the valuable telemetry data during start time and end time
if int(valuable_tele_df['vessel_n'][i].split('_')
[1]) == telemetrystatus_df['Vessel#'][j]:
dtime = valuable_tele_df['time'][i]
latp = float(valuable_tele_df['lat'][i])
lonp = float(valuable_tele_df['lon'][i])
tele_nu.append([
dtime, latp, lonp,
float(valuable_tele_df['temp'][i]),
float(valuable_tele_df['depth'][i])
])
try: #try to get doppio data in the same locattion
# print('dpo:',lat,lon,dtime)
dpo_temp, dpo_depth = dpo.get_doppio(
lat=latp,
lon=lonp,
depth='bottom',
time=dtime.strftime("%Y-%m-%d %H:%M:%S"),
fortype='tempdepth')
except:
dpo_temp, dpo_depth = np.nan, np.nan
dop_nu.append([dtime, latp, lonp, dpo_temp, dpo_depth])
try: #try to get the gomofs data in the same location
gmf_temp, gmf_depth = gmf.get_gomofs(date_time=dtime,
lat=latp,
lon=lonp,
depth='bottom',
fortype='tempdepth')
except:
gmf_temp, gmf_depth = np.nan, np.nan
gmf_nu.append([dtime, latp, lonp, gmf_temp, gmf_depth])
try:
FV_temp, FV_depth = fv.get_FVCOM_bottom_temp(lati=latp,
loni=lonp,
dtime=dtime,
layer=-1)
except:
FV_temp, FV_depth = np.nan, np.nan
fvc_nu.append([dtime, latp, lonp, FV_temp, FV_depth])
valuable_tele_df = valuable_tele_df.drop(
i) #if this line has been classify, delete this line
if len(dop_nu) > 0:
Doppio_dict[telemetrystatus_df['Boat'][j]]=Doppio_dict[telemetrystatus_df['Boat'][j]].append(pd.DataFrame(data=dop_nu,\
columns=['time','lat','lon','temp','depth']),ignore_index=True)
if len(gmf_nu) > 0:
GoMOLFs_dict[telemetrystatus_df['Boat'][j]]=GoMOLFs_dict[telemetrystatus_df['Boat'][j]].append(pd.DataFrame(data=gmf_nu,\
columns=['time','lat','lon','temp','depth']),ignore_index=True)
if len(tele_nu) > 0:
tele_dict[telemetrystatus_df['Boat'][j]]=tele_dict[telemetrystatus_df['Boat'][j]].append(pd.DataFrame(data=tele_nu,\
columns=['time','lat','lon','temp','depth']),ignore_index=True)
if len(fvc_nu) > 0:
FVCOM_dict[telemetrystatus_df['Boat'][j]]=tele_dict[telemetrystatus_df['Boat'][j]].append(pd.DataFrame(data=fvc_nu,\
columns=['time','lat','lon','temp','depth']),ignore_index=True)
dict['tele_dict'] = tele_dict
dict['Doppio'] = Doppio_dict
dict['GoMOLFs'] = GoMOLFs_dict
dict['FVCOM'] = FVCOM_dict
return dict