def get_doppio(lat=0,lon=0,depth='bottom',time='2018-11-12 12:00:00',fortype='temperature'):
"""
notice:
the format of time is like "%Y-%m-%d %H:%M:%S" this time is utctime or it can also be datetime
the depth is under the bottom depth
the module only output the temperature of point location
if fortype ='temperature',only return temperature, else return temperature and depth
"""
if depth==99999:
depth='bottom'
if not doppio_coordinate(lat,lon):
print('the lat and lon out of range in doppio')
return np.nan,np.nan
if type(time)==str:
date_time=datetime.datetime.strptime(time,'%Y-%m-%d %H:%M:%S') # transform time format
elif type(time)==datetime.datetime:
date_time=time
else:
print('check the type of input time in get_doppio')
for m in range(0,7):
try:
url_time=(date_time-datetime.timedelta(days=m)).strftime('%Y-%m-%d')#
url=zl.get_doppio_url(url_time)
#get the data
nc=netCDF4.Dataset(url)
lons=nc.variables['lon_rho'][:]
lats=nc.variables['lat_rho'][:]
doppio_time=nc.variables['time']
doppio_rho=nc.variables['s_rho']
doppio_temp=nc.variables['temp']
doppio_h=nc.variables['h']
except:
continue
min_diff_time=abs(datetime.datetime(2017,11,1,0,0,0)+datetime.timedelta(hours=int(doppio_time[0]))-date_time)
min_diff_index=0
for i in range(1,len(doppio_time)):
diff_time=abs(datetime.datetime(2017,11,1,0,0,0)+datetime.timedelta(hours=int(doppio_time[i]))-date_time)
if diff_time<min_diff_time:
min_diff_time=diff_time
min_diff_index=i
#calculate the min,second small and third small distance and index
target_distance=zl.dist(lat1=lats[0][0],lon1=lons[0][0],lat2=lats[0][1],lon2=lons[0][1])
index_1,index_2=zl.find_nd(target=target_distance,lat=lat,lon=lon,lats=lats,lons=lons)
#calculate the optimal layer index added this section Feb 2020
doppio_depth=nc['h'][index_1][index_2]
if depth > doppio_depth:# case of bottom
S_coordinate=1
else:
S_coordinate=float(depth)/float(doppio_depth)
if 0<=S_coordinate<1:
layer_index=39-int(S_coordinate/0.025)#doppio_temp=temp[itime,39-int(S_coordinate/0.025),index_1,index_2]# because there are 0.025 between each later
elif S_coordinate==1:
layer_index=0#doppio_temp=temp[itime][0][index_1][index_2]
else:
layer_index=0#doppio_temp=temp[itime][0][index_1][index_2]
#return doppio_temp
#layer_index=0 #specify the initial layer index
'''if depth!='bottom':
h_distance=depth+doppio_rho[0]*doppio_h[index_1,index_2] #specify the initial distanc of high
for i in range(len(doppio_rho)):
if abs(depth+doppio_rho[0]*doppio_h[index_1,index_2])<=h_distance:
h_distance=depth+doppio_rho[i]*doppio_h[index_1,index_2]
layer_index=i
if depth>doppio_h[index_1,index_2]:
print ("the depth is out of the depth of bottom:"+str(doppio_h[index_1,index_2]))
'''
if index_1==0:
index_1=1
if index_1==len(lats)-1:
index_1=len(lats)-2
if index_2==0:
index_2=1
if index_2==len(lats[0])-1:
index_2=len(lats[0])-2
while True:
point=[[lats[index_1][index_2],lons[index_1][index_2],doppio_temp[min_diff_index,layer_index,index_1,index_2]],\
[lats[index_1-1][index_2],lons[index_1-1][index_2],doppio_temp[min_diff_index,layer_index,(index_1-1),index_2]],\
[lats[index_1+1][index_2],lons[index_1+1][index_2],doppio_temp[min_diff_index,layer_index,(index_1+1),index_2]],\
[lats[index_1][index_2-1],lons[index_1][index_2-1],doppio_temp[min_diff_index,layer_index,index_1,(index_2-1)]],\
[lats[index_1][index_2+1],lons[index_1][index_2+1],doppio_temp[min_diff_index,layer_index,index_1,(index_2+1)]]]
break
point_temp=fitting(point,lat,lon)
if np.isnan(point_temp):
continue
if min_diff_time<datetime.timedelta(hours=1):
break
if fortype=='temperature':
return point_temp
else:
return point_temp,doppio_h[index_1,index_2]
def get_doppio_fitting(latp=0,
lonp=0,
depth='bottom',
dtime=datetime.datetime.now(),
fortype='temperature',
hour_allowed=1):
"""
notice:
the format of time is like "%Y-%m-%d %H:%M:%S" this time is utctime or the type of time is datetime.datetime
the depth is under the bottom depth
the module only output the temperature of point location
"""
if not doppio_coordinate(latp, lonp):
print('the lat and lon out of range in doppio')
return np.nan, np.nan
if type(dtime) == str:
date_time = datetime.datetime.strptime(
dtime, '%Y-%m-%d %H:%M:%S') # transform time format
else:
date_time = dtime
for m in range(0, 7):
try:
url_time = (date_time -
datetime.timedelta(days=m)).strftime('%Y-%m-%d')
url = zl.get_doppio_url(url_time)
#get the data
nc = netCDF4.Dataset(url)
lons = nc.variables['lon_rho'][:]
lats = nc.variables['lat_rho'][:]
doppio_time = nc.variables['time']
doppio_rho = nc.variables['s_rho']
doppio_temp = nc.variables['temp']
doppio_h = nc.variables['h']
except:
continue
#calculate the index of the minimum timedelta
parameter = (datetime.datetime(2017, 11, 1, 0, 0, 0) -
date_time).days * 24 + (datetime.datetime(
2017, 11, 1, 0, 0, 0) - date_time).seconds / 3600.
time_delta = abs(doppio_time[:] + parameter)
min_diff_index = np.argmin(time_delta)
#calculate the min distance and index
target_distance = 2 * zl.dist(
lat1=lats[0, 0], lon1=lons[0, 0], lat2=lats[0, 1], lon2=lons[0, 1])
index_1, index_2 = find_nd(target=target_distance,
lat=latp,
lon=lonp,
lats=lats,
lons=lons)
#calculate the optimal layer index
if depth == 'bottom':
layer_index = 0 #specify the initial layer index
else:
h_distance = abs(doppio_rho[:] * doppio_h[index_1, index_2] +
abs(depth))
layer_index = np.argmin(h_distance)
# fitting the data through the 5 points
if index_1 == 0:
index_1 = 1
if index_1 == len(lats) - 1:
index_1 = len(lats) - 2
if index_2 == 0:
index_2 = 1
if index_2 == len(lats[0]) - 1:
index_2 = len(lats[0]) - 2
while True:
point=[[lats[index_1][index_2],lons[index_1][index_2],doppio_temp[min_diff_index,layer_index,index_1,index_2]],\
[lats[index_1-1][index_2],lons[index_1-1][index_2],doppio_temp[min_diff_index,layer_index,(index_1-1),index_2]],\
[lats[index_1+1][index_2],lons[index_1+1][index_2],doppio_temp[min_diff_index,layer_index,(index_1+1),index_2]],\
[lats[index_1][index_2-1],lons[index_1][index_2-1],doppio_temp[min_diff_index,layer_index,index_1,(index_2-1)]],\
[lats[index_1][index_2+1],lons[index_1][index_2+1],doppio_temp[min_diff_index,layer_index,index_1,(index_2+1)]]]
break
point_temp = fitting(point, latp, lonp)
while True:
points_h=[[lats[index_1][index_2],lons[index_1][index_2],doppio_h[index_1,index_2]],\
[lats[index_1-1][index_2],lons[index_1-1][index_2],doppio_h[(index_1-1),index_2]],\
[lats[index_1+1][index_2],lons[index_1+1][index_2],doppio_h[(index_1+1),index_2]],\
[lats[index_1][index_2-1],lons[index_1][index_2-1],doppio_h[index_1,(index_2-1)]],\
[lats[index_1][index_2+1],lons[index_1][index_2+1],doppio_h[index_1,(index_2+1)]]]
break
point_temp = fitting(point, latp, lonp)
point_h = fitting(points_h, latp, lonp)
if np.isnan(point_temp):
continue
if time_delta[min_diff_index] < hour_allowed:
break
if fortype == 'tempdepth':
return point_temp, point_h
else:
return point_temp
import zlconversions as zl
import doppio_modules as dm
import numpy as np
#HARDCODES
input_date_time='2018-11-11 12:00:00'
input_lat=41.784712
input_lon=-69.231081
input_depth=0 #If you enter 99999, the default output bottom temperature,enter 0 will output the temperature of surface
output_path='/home/jmanning/Desktop/testout/doppio/'
#########################
date_time=datetime.datetime.strptime(input_date_time,'%Y-%m-%d %H:%M:%S') # transform time format
#find index of the nearest time about data
for m in range(0,7):
try:
url_time=(date_time-datetime.timedelta(days=m)).strftime('%Y-%m-%d')#
url=zl.get_doppio_url(url_time)
#get the data
nc=netCDF4.Dataset(url)
except:
continue
lons=nc.variables['lon_rho'][:]
lats=nc.variables['lat_rho'][:]
temp=nc.variables['temp']
doppio_time=nc.variables['time']
doppio_depth=nc.variables['h'][:]
min_diff_time=abs(datetime.datetime(2017,11,1,0,0,0)+datetime.timedelta(hours=int(doppio_time[0]))-date_time) #Set initial value
min_diff_index=0 #Set initial value
for i in range(0,157):
diff_time=abs(datetime.datetime(2017,11,1,0,0,0)+datetime.timedelta(hours=int(doppio_time[i]))-date_time)
if diff_time<min_diff_time:
min_diff_time=diff_time
def get_doppio(lat=0, lon=0, depth=99999, time='2018-11-12 12:00:00'):
"""
notice:
the format of time is like "%Y-%m-%d %H:%M:%S"
the depth is under the bottom depth
the module only output the temperature of point location
"""
date_time = datetime.datetime.strptime(
time, '%Y-%m-%d %H:%M:%S') # transform time format
for m in range(0, 7):
try:
url_time = (date_time - datetime.timedelta(days=m)).strftime(
'%Y-%m-%d') #
url = zl.get_doppio_url(url_time)
#get the data
nc = netCDF4.Dataset(url)
except:
continue
lons = nc.variables['lon_rho'][:]
lats = nc.variables['lat_rho'][:]
temp = nc.variables['temp']
doppio_time = nc.variables['time']
doppio_depth = nc.variables['h'][:]
min_diff_time = abs(
datetime.datetime(2017, 11, 1, 0, 0, 0) +
datetime.timedelta(hours=int(doppio_time[0])) - date_time)
min_diff_index = 0
for i in range(1, len(doppio_time)):
diff_time = abs(
datetime.datetime(2017, 11, 1, 0, 0, 0) +
datetime.timedelta(hours=int(doppio_time[i])) - date_time)
if diff_time < min_diff_time:
min_diff_time = diff_time
min_diff_index = i
#calculate the min,second small and third small distance and index
min_distance = zl.dist(lat1=lat,
lon1=lon,
lat2=lats[0][0],
lon2=lons[0][0])
# secondmin_distance=min_distance
# thirdmin_distance=min_distance
#
index_1, index_2 = 0, 0
secondindex_1, secondindex_2 = 0, 0
thirdindex_1, thirdindex_2 = 0, 0
fourthindex_1, fourthindex_2 = 0, 0
fifthindex_1, fifthindex_2 = 0, 0
# sixthindex_1,sixthindex_2=0,0
for i in range(len(lons)):
for j in range(len(lons[i])):
distance = zl.dist(lat1=lat,
lon1=lon,
lat2=lats[i][j],
lon2=lons[i][j])
if min_distance >= distance:
# thirdmin_distance=secondmin_distance
# secondmin_distance=min_distance
min_distance = distance
# sixthindex_1,sixthindex_2=fifthindex_1,fifthindex_2
fifthindex_1, fifthindex_2 = fourthindex_1, fourthindex_2
fourthindex_1, fourthindex_2 = thirdindex_1, thirdindex_2
thirdindex_1, thirdindex_2 = secondindex_1, secondindex_2
secondindex_1, secondindex_2 = index_1, index_2
index_1, index_2 = i, j
if depth == 99999:
S_coordinate = 1
else:
S_coordinate = float(depth) / float(doppio_depth[index_1][index_2])
if 0 <= S_coordinate < 1:
layer_index = 39 - int(S_coordinate / 0.025)
elif S_coordinate == 1:
layer_index = 39
else:
return 9999
point=[[lats[index_1][index_2],lons[index_1][index_2],temp[min_diff_index][layer_index][index_1][index_2]],\
[lats[secondindex_1][secondindex_2],lons[secondindex_1][secondindex_2],temp[min_diff_index][layer_index][secondindex_1][secondindex_2]],\
[lats[thirdindex_1][thirdindex_2],lons[thirdindex_1][thirdindex_2],temp[min_diff_index][layer_index][thirdindex_1][thirdindex_2]],\
[lats[fourthindex_1][fourthindex_2],lons[fourthindex_1][fourthindex_2],temp[min_diff_index][layer_index][fourthindex_1][fourthindex_2]],\
[lats[fifthindex_1][fifthindex_2],lons[fifthindex_1][fifthindex_2],temp[min_diff_index][layer_index][fifthindex_1][fifthindex_2]]]
point_temp = fitting(point, lat, lon)
if np.isnan(point_temp):
continue
if min_diff_time < datetime.timedelta(hours=1):
break
return point_temp, index_1, index_2