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# Plot forecast water levels from NECOFS model from list of lon,lat locations
# (uses the nearest point, no interpolation)
import netCDF4
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from StringIO import StringIO
%matplotlib inline
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#model='NECOFS Massbay'
#url='http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_FVCOM_OCEAN_MASSBAY_FORECAST.nc'
# GOM3 Grid
#model='NECOFS GOM3'
#url='http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_GOM3_FORECAST.nc'
model = 'NECOFS GOM3 Wave'
# forecast
#url = 'http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_WAVE_FORECAST.nc'
# archive
url = 'http://www.smast.umassd.edu:8080/thredds/dodsC/fvcom/archives/necofs_gom3_wave'
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# Desired time for snapshot
# ....right now (or some number of hours from now) ...
start = dt.datetime.utcnow() + dt.timedelta(hours=-72)
stop = dt.datetime.utcnow() + dt.timedelta(hours=+72)
# ... or specific time (UTC)
start = dt.datetime(1991,1,1,0,0,0) + dt.timedelta(hours=+0)
start = dt.datetime(1992,7,1,0,0,0) + dt.timedelta(hours=+0)
start = dt.datetime(1992,8,1,0,0,0) + dt.timedelta(hours=+0)
start = dt.datetime(2016,1,1,0,0,0) + dt.timedelta(hours=+0)
stop = dt.datetime(2016,6,1,0,0,0) + dt.timedelta(hours=+0)
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def dms2dd(d,m,s):
return d+(m+s/60.)/60.
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dms2dd(41,33,15.7)
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-dms2dd(70,30,20.2)
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x = '''
Station, Lat, Lon
Falmouth Harbor, 41.541575, -70.608020
Sage Lot Pond, 41.554361, -70.505611
'''
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x = '''
Station, Lat, Lon
Boston, 42.368186, -71.047984
Carolyn Seep Spot, 39.8083, -69.5917
Falmouth Harbor, 41.541575, -70.608020
'''
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# Enter desired (Station, Lat, Lon) values here:
x = '''
Station, Lat, Lon
Boston, 42.368186, -71.047984
Scituate Harbor, 42.199447, -70.720090
Scituate Beach, 42.209973, -70.724523
Falmouth Harbor, 41.541575, -70.608020
Marion, 41.689008, -70.746576
Marshfield, 42.108480, -70.648691
Provincetown, 42.042745, -70.171180
Sandwich, 41.767990, -70.466219
Hampton Bay, 42.900103, -70.818510
Gloucester, 42.610253, -70.660570
'''
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# Create a Pandas DataFrame
obs=pd.read_csv(StringIO(x.strip()), sep=",\s*",index_col='Station')
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obs
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# find the indices of the points in (x,y) closest to the points in (xi,yi)
def nearxy(x,y,xi,yi):
ind = np.ones(len(xi),dtype=int)
for i in np.arange(len(xi)):
dist = np.sqrt((x-xi[i])**2+(y-yi[i])**2)
ind[i] = dist.argmin()
return ind
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# open NECOFS remote OPeNDAP dataset
nc=netCDF4.Dataset(url).variables
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# find closest NECOFS nodes to station locations
obs['0-Based Index'] = nearxy(nc['lon'][:],nc['lat'][:],obs['Lon'],obs['Lat'])
obs
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# Get desired time step
time_var = nc['time']
istart = netCDF4.date2index(start,time_var,select='nearest')
istop = netCDF4.date2index(stop,time_var,select='nearest')
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# get time values and convert to datetime objects
jd = netCDF4.num2date(time_var[istart:istop],time_var.units)
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# get all time steps of water level from each station
nsta = len(obs)
z = np.ones((len(jd),nsta))
for i in range(nsta):
z[:,i] = nc['hs'][istart:istop,obs['0-Based Index'][i]]
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# make a DataFrame out of the interpolated time series at each location
zvals=pd.DataFrame(z,index=jd,columns=obs.index)
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# list out a few values
zvals.head()
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# model blew up producing very high waves on Jan 21, 2016
# eliminate unrealistically high values
mask = zvals>10.
zvals[mask] = np.NaN
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# plotting at DataFrame is easy!
ax=zvals.plot(figsize=(16,4),grid=True,title=('Wave Height from %s Forecast' % model),legend=False);
# read units from dataset for ylabel
plt.ylabel(nc['hs'].units)
# plotting the legend outside the axis is a bit tricky
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5));
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# what is the maximum over the whole record at a specific location
zvals['Boston'].max()
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# make a new DataFrame of maximum water levels at all stations
b=pd.DataFrame(zvals.idxmax(),columns=['time of max value (UTC)'])
# create heading for new column containing max water level
zmax_heading='zmax (%s)' % nc['hs'].units
# Add new column to DataFrame
b[zmax_heading]=zvals.max()
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b
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