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%matplotlib inline
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import numpy as np
import xarray as xr
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import datetime as dt
from datetime import datetime, timedelta #
dayFile = datetime.now() - timedelta(days=1)
dayFile = dayFile.strftime("%Y%m%d")
url='http://nomads.ncep.noaa.gov:9090/dods/nam/nam%s/nam1hr_00z' %(dayFile)
print(url)
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ds = xr.open_dataset(url)
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# Specify desired station time series location
# note we add 360 because of the lon convention in this dataset
#lati = 36.605; loni = -121.85899 # west of Pacific Grove, CA
lati = 41.4; loni = -100.8 # Georges Bank
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# extract a dataset closeste to specified point
dsloc = ds.sel(lon=loni, lat=lati, method='nearest')
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# select a variable to plot
dsloc['dswrfsfc'].plot()
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import netCDF4
import pandas as pd
import datetime as dt
import matplotlib.pyplot as plt
from datetime import datetime, timedelta #
dayFile = datetime.now() - timedelta(days=1)
dayFile = dayFile.strftime("%Y%m%d")
url='http://nomads.ncep.noaa.gov:9090/dods/nam/nam%s/nam1hr_00z' %(dayFile)
# NetCDF4-Python can open OPeNDAP dataset just like a local NetCDF file
nc = netCDF4.Dataset(url)
varsInFile = nc.variables.keys()
lat = nc.variables['lat'][:]
lon = nc.variables['lon'][:]
time_var = nc.variables['time']
dtime = netCDF4.num2date(time_var[:],time_var.units)
first = netCDF4.num2date(time_var[0],time_var.units)
last = netCDF4.num2date(time_var[-1],time_var.units)
print first.strftime('%Y-%b-%d %H:%M')
print last.strftime('%Y-%b-%d %H:%M')
# determine what longitude convention is being used
print lon.min(),lon.max()
# Specify desired station time series location
# note we add 360 because of the lon convention in this dataset
#lati = 36.605; loni = -121.85899 + 360. # west of Pacific Grove, CA
lati = 41.4; loni = -100.8 +360.0 # Georges Bank
lati = 41.4; loni = -100.8 # Georges Bank
# Function to find index to nearest point
def near(array,value):
idx=(abs(array-value)).argmin()
return idx
# Find nearest point to desired location (no interpolation)
ix = near(lon, loni)
iy = near(lat, lati)
print ix,iy
# Extract desired times.
# 1. Select -+some days around the current time:
start = netCDF4.num2date(time_var[0],time_var.units)
stop = netCDF4.num2date(time_var[-1],time_var.units)
time_var = nc.variables['time']
datetime = netCDF4.num2date(time_var[:],time_var.units)
istart = netCDF4.date2index(start,time_var,select='nearest')
istop = netCDF4.date2index(stop,time_var,select='nearest')
print istart,istop
# Get all time records of variable [vname] at indices [iy,ix]
vname = 'dswrfsfc'
var = nc.variables[vname]
hs = var[istart:istop,iy,ix]
tim = dtime[istart:istop]
# Create Pandas time series object
ts = pd.Series(hs,index=tim,name=vname)
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ts
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