def getFVcom(latpt, lonpt, time_roms, depth_roms):
url = "http://www.smast.umassd.edu:8080/thredds/dodsC/fvcom/hindcasts/30yr_gom3/mean"
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 getFVcom(latpt, lonpt, time_roms, depth_roms):
url = 'http://www.smast.umassd.edu:8080/thredds/dodsC/fvcom/hindcasts/30yr_gom3/mean'
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
LON_interval, LAT_interval = (
(input_value_1_lon - input_value_0_lon) / 10.0,
(input_value_1_lat - input_value_0_lat) / 10.0,
)
LON, LAT = (
np.arange(input_value_0_lon, input_value_1_lon, LON_interval),
np.arange(input_value_0_lat, input_value_1_lat, LAT_interval),
)
# just ten points between start point and end point
LAT_roms = []
LON_roms = []
H_roms = []
Temp_roms = []
for i in range(len(LON)): # get ten points` temperature of ROMS
temp_roms = []
distance = dist(LON[i], LAT[i], lons_roms, lats_roms)
node = np.argmin(distance) # get nearest node
index_one, index_two = int(node / len(lons_roms[0])), node % len(lons_roms[0]) # change list to list of list
LAT_roms.append(lats_roms[index_one, index_two])
LON_roms.append(lons_roms[index_one, index_two]) # get nearest point`s lat,lon
H_roms.append(h_roms[index_one, index_two])
for j in range(len(s_rho)): # this is roms`s layers
temp_roms.append(temps_roms[TIME][j][index_one, index_two])
Temp_roms.append(temp_roms)
ttt_roms = np.array(Temp_roms).transpose()
"""
change [[layer1,lyaer2,,,last layer],[layer1,lyaer2,,,last layer],,,,[layer1,lyaer2,,,last layer]]
to [[layer1,layer1,,,layer1],[layer2,layer2,,,layer2],,,[last layer,last layer,,,last layer]]
use in griddata later
"""
## example 2.12：Bernoulli distribution
import numpy as np
import matplotlib.pyplot as plt
from math import sqrt
T=1000;repetition=500
y=np.empty(T)
y_hat=np.empty(repetition)
for j in range(repetition):
theta=np.random.uniform(0,1,T)
for i in range(T):
y[i]=np.random.binomial(1,theta[i])
y_hat[j]=sqrt(T)*(np.mean(y-theta)/np.mean(theta*(1-theta)))
from my_modules import distribution as dist
dist(y_hat).hist(disp_norm=1)
input('please input end site lon(for example:-71.72):'))
input_value_1_lat = float(
input('please input end site lon(for example: 40.09):')) #end point
LON_interval, LAT_interval = (input_value_1_lon - input_value_0_lon) / 10.0, (
input_value_1_lat - input_value_0_lat) / 10.0
LON, LAT = np.arange(input_value_0_lon, input_value_1_lon,
LON_interval), np.arange(input_value_0_lat,
input_value_1_lat, LAT_interval)
#just ten points between start point and end point
LAT_roms = []
LON_roms = []
H_roms = []
Temp_roms = []
for i in range(len(LON)): #get ten points` temperature of ROMS
temp_roms = []
distance = dist(LON[i], LAT[i], lons_roms, lats_roms)
node = np.argmin(distance) #get nearest node
index_one, index_two = int(node / len(lons_roms[0])), node % len(
lons_roms[0]) # change list to list of list
LAT_roms.append(lats_roms[index_one, index_two])
LON_roms.append(lons_roms[index_one,
index_two]) #get nearest point`s lat,lon
H_roms.append(h_roms[index_one, index_two])
for j in range(len(s_rho)): #this is roms`s layers
temp_roms.append(temps_roms[TIME][j][index_one, index_two])
Temp_roms.append(temp_roms)
ttt_roms = np.array(Temp_roms).transpose()
'''
change [[layer1,lyaer2,,,last layer],[layer1,lyaer2,,,last layer],,,,[layer1,lyaer2,,,last layer]]
to [[layer1,layer1,,,layer1],[layer2,layer2,,,layer2],,,[last layer,last layer,,,last layer]]