# 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
#NECOFS MassBay grid
model='Massbay'
url='http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_FVCOM_OCEAN_MASSBAY_FORECAST.nc'
# GOM3 Grid
#model='GOM3'
#url='http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_GOM3_FORECAST.nc'
def dms2dd(d,m,s):
return d+(m+s/60.)/60.
dms2dd(41,33,15.7)
41.55436111111111
-dms2dd(70,30,20.2)
-70.50561111111111
x = '''
Station, Lat, Lon
Falmouth Harbor, 41.541575, -70.608020
Sage Lot Pond, 41.554361, -70.505611
'''
x = '''
Station, Lat, Lon
Boston, 42.368186, -71.047984
Carolyn Seep Spot, 39.8083, -69.5917
Falmouth Harbor, 41.541575, -70.608020
'''
# 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
'''
# Create a Pandas DataFrame
obs=pd.read_csv(StringIO(x.strip()), sep=",\s*",index_col='Station')
-c:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.
obs
Lat | Lon | |
---|---|---|
Station | ||
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 |
# 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
# open NECOFS remote OPeNDAP dataset
nc=netCDF4.Dataset(url).variables
# find closest NECOFS nodes to station locations
obs['0-Based Index'] = nearxy(nc['lon'][:],nc['lat'][:],obs['Lon'],obs['Lat'])
obs
Lat | Lon | 0-Based Index | |
---|---|---|---|
Station | |||
Boston | 42.368186 | -71.047984 | 90913 |
Scituate Harbor | 42.199447 | -70.720090 | 37964 |
Scituate Beach | 42.209973 | -70.724523 | 28474 |
Falmouth Harbor | 41.541575 | -70.608020 | 47470 |
Marion | 41.689008 | -70.746576 | 49654 |
Marshfield | 42.108480 | -70.648691 | 24272 |
Provincetown | 42.042745 | -70.171180 | 26595 |
Sandwich | 41.767990 | -70.466219 | 38036 |
Hampton Bay | 42.900103 | -70.818510 | 13022 |
Gloucester | 42.610253 | -70.660570 | 22082 |
# get time values and convert to datetime objects
times = nc['time']
jd = netCDF4.num2date(times[:],times.units)
# 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['zeta'][:,obs['0-Based Index'][i]]
# make a DataFrame out of the interpolated time series at each location
zvals=pd.DataFrame(z,index=jd,columns=obs.index)
# list out a few values
zvals.head()
Station | Boston | Scituate Harbor | Scituate Beach | Falmouth Harbor | Marion | Marshfield | Provincetown | Sandwich | Hampton Bay | Gloucester |
---|---|---|---|---|---|---|---|---|---|---|
2017-03-11 00:00:00.000 | 0.260008 | 0.486000 | 0.381463 | 0.392506 | 0.973188 | 0.371882 | 0.293114 | 0.330359 | 0.415981 | 0.436011 |
2017-03-11 01:01:52.500 | 1.052041 | 1.092550 | 1.129166 | 0.276473 | 0.553178 | 1.102932 | 1.018170 | 1.046942 | 1.126426 | 1.117460 |
2017-03-11 01:58:07.500 | 1.647629 | 1.578530 | 1.592741 | 0.046371 | 0.093620 | 1.598297 | 1.614599 | 1.628950 | 1.549906 | 1.555701 |
2017-03-11 03:00:00.000 | 1.702411 | 1.646736 | 1.677559 | -0.070460 | -0.450842 | 1.690268 | 1.803100 | 1.792436 | 1.635421 | 1.627011 |
2017-03-11 04:01:52.500 | 1.414511 | 1.312948 | 1.327706 | -0.087120 | -0.806962 | 1.335137 | 1.458376 | 1.433764 | 1.308162 | 1.252133 |
# plotting at DataFrame is easy!
ax=zvals.plot(figsize=(16,4),grid=True,title=('NECOFS Forecast Water Level from %s Forecast' % model),legend=False);
# read units from dataset for ylabel
plt.ylabel(nc['zeta'].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));
# what is the maximum water level at Scituate over this period?
zvals['Boston'].max()
2.0235898494720459
# make a new DataFrame of maximum water levels at all stations
b=pd.DataFrame(zvals.idxmax(),columns=['time of max water level (UTC)'])
# create heading for new column containing max water level
zmax_heading='zmax (%s)' % nc['zeta'].units
# Add new column to DataFrame
b[zmax_heading]=zvals.max()
b
time of max water level (UTC) | zmax (meters) | |
---|---|---|
Station | ||
Boston | 2017-03-14 18:00:00.000 | 2.023590 |
Scituate Harbor | 2017-03-14 18:00:00.000 | 1.815455 |
Scituate Beach | 2017-03-11 15:00:00.000 | 1.790602 |
Falmouth Harbor | 2017-03-14 15:00:00.000 | 0.659908 |
Marion | 2017-03-15 03:00:00.000 | 1.420215 |
Marshfield | 2017-03-11 15:00:00.000 | 1.804025 |
Provincetown | 2017-03-11 15:00:00.000 | 1.883487 |
Sandwich | 2017-03-11 15:00:00.000 | 1.914992 |
Hampton Bay | 2017-03-14 16:58:07.500 | 1.792463 |
Gloucester | 2017-03-14 16:58:07.500 | 1.740331 |