Exploring use of Python to formulate NERACOOS ERDDAP data requests and process the responses.
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%matplotlib inline
import pandas as pd
import numpy as np
import seawater as sw
import matplotlib.pyplot as plt
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# Use ERDDAP's built-in relative time functionality to get last 48 hours:
#start='now-7days'
#stop='now'
# or specify a specific period:
start = '2016-09-06T00:00:00Z'
stop = '2016-11-07T00:00:00Z'
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# read instrument data (E01_sbe16)
url='http://www.neracoos.org/erddap/tabledap/E01_sbe16_trans_all.csv?\
station,time,depth,longitude,latitude,attenuation,sigma_t,temperature,salinity\
&time>={}&time<={}'.format(start,stop)
print(url)
df_sb = pd.read_csv(url,index_col='time',parse_dates=True,skiprows=[1]) # skip the units row
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# read met data (E01_met)
url='http://www.neracoos.org/erddap/tabledap/E01_met_all.csv?\
station,time,air_temperature,barometric_pressure,wind_gust,wind_speed,\
wind_direction,visibility\
&time>={}&time<={}'.format(start,stop)
print(url)
df_met = pd.read_csv(url,index_col='time',parse_dates=True,skiprows=[1]) # skip the units row
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# read wave data (E01_accelerometer)
url='http://www.neracoos.org/erddap/tabledap/E01_accelerometer_all.csv?\
station,time,mooring_site_desc,significant_wave_height,dominant_wave_period&\
time>={}&time<={}'.format(start,stop)
print(url)
# Load the CSV data directly into Pandas
df_wave = pd.read_csv(url,index_col='time',parse_dates=True,skiprows=[1]) # skip the units row
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# List last ten records
df_sb.tail(10)
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df_sb['sigma_t'].plot(figsize=(12,4),title=df_sb['station'][0]);plt.legend(loc=2)
df_sb['attenuation'].plot(figsize=(12,4),secondary_y=True,legend=True);
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p1=df_met['wind_speed'].plot(figsize=(12,4),x_compat=True);plt.legend(loc=2)
p2=df_wave['significant_wave_height'].plot(secondary_y=True,legend=True,x_compat=True);
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df_wave.plot(figsize=(12,4));
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plt.scatter(df_sb['sigma_t'],df_sb['attenuation'])
plt.grid()
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def tsplot(sobs,tobs):
smin=sobs.min()
smax=sobs.max()
tmin=tobs.min()
tmax=tobs.max()
s_inc=(smax-smin)/8.
t_inc=(tmax-tmin)/8.
t = np.arange(tmin,tmax+t_inc,t_inc)
s = np.arange(smin,smax+s_inc,s_inc)
S, T = np.meshgrid(s, t)
st = sw.dens0(S, T) - 1000
st_inc=(st.max()-st.min())/8.
levels = np.arange(st.min(),st.max()+st_inc,st_inc)
from matplotlib import rcParams
from matplotlib.ticker import MultipleLocator
rcParams['xtick.direction'] = 'out'
rcParams['ytick.direction'] = 'out'
fig, ax = plt.subplots(figsize=(6, 4))
# ax.xaxis.set_minor_locator(MultipleLocator(0.1))
# ax.yaxis.set_minor_locator(MultipleLocator(1))
ax.set_ylabel(u"Temperature \u00b0C")
ax.set_xlabel(r"Salinity [g kg$^{-1}$]")
ax.axis([smin,smax,tmin,tmax])
cs = ax.contour(s, t, st, colors='black', levels=levels)
ax.clabel(cs, fontsize=9, inline=1, fmt='%3.2f')
#sg = ax.contour(s, t, sigma_theta, linestyle='--', colors='grey', levels=[0, line])
#ax.clabel(sg, fontsize=9, inline=1, fmt='%2.1f')
ax.plot(sobs,tobs,'o')
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tsplot(df_sb['salinity'],df_sb['temperature'])
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