Use ERDDAP's RESTful advanced search to try to find OOI Pioneer glider water temperatures from the OOI ERDDAP. Use case from Stace Beaulieu (sbeaulieu@whoi.edu)
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import pandas as pd
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url = 'http://ooi-data.marine.rutgers.edu/erddap/search/advanced.csv?page=1&itemsPerPage=1000&searchFor=glider'
dft = pd.read_csv(url, usecols=['Title', 'Summary', 'Institution', 'Dataset ID'])
dft.head()
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start = '2000-01-01T00:00:00Z'
stop = '2017-02-22T00:00:00Z'
lat_min = 39.
lat_max = 41.5
lon_min = -72.
lon_max = -69.
standard_name = 'sea_water_temperature'
endpoint = 'http://ooi-data.marine.rutgers.edu/erddap/search/advanced.csv'
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import pandas as pd
base = (
'{}'
'?page=1'
'&itemsPerPage=1000'
'&searchFor='
'&protocol=(ANY)'
'&cdm_data_type=(ANY)'
'&institution=(ANY)'
'&ioos_category=(ANY)'
'&keywords=(ANY)'
'&long_name=(ANY)'
'&standard_name={}'
'&variableName=(ANY)'
'&maxLat={}'
'&minLon={}'
'&maxLon={}'
'&minLat={}'
'&minTime={}'
'&maxTime={}').format
url = base(
endpoint,
standard_name,
lat_max,
lon_min,
lon_max,
lat_min,
start,
stop
)
print(url)
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dft = pd.read_csv(url, usecols=['Title', 'Summary', 'Institution','Dataset ID'])
print('Datasets Found = {}'.format(len(dft)))
print(url)
dft
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Define a function that returns a Pandas DataFrame based on the dataset ID. The ERDDAP request variables (e.g. "ctdpf_ckl_wfp_instrument_ctdpf_ckl_seawater_temperature") are hard-coded here, so this routine should be modified for other ERDDAP endpoints or datasets.
Since we didn't actually find any glider data, we just request the last temperature value from each dataset, using the ERDDAP orderByMax("time") constraint. This way we can see when the data ends, and if the mooring locations look correct
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def download_df(glider_id):
from pandas import DataFrame, read_csv
# from urllib.error import HTTPError
uri = ('http://ooi-data.marine.rutgers.edu/erddap/tabledap/{}.csv'
'?trajectory,'
'time,latitude,longitude,'
'ctdpf_ckl_wfp_instrument_ctdpf_ckl_seawater_temperature'
'&orderByMax("time")'
'&time>={}'
'&time<={}'
'&latitude>={}'
'&latitude<={}'
'&longitude>={}'
'&longitude<={}').format
url = uri(glider_id,start,stop,lat_min,lat_max,lon_min,lon_max)
print(url)
# Not sure if returning an empty df is the best idea.
try:
df = read_csv(url, index_col='time', parse_dates=True, skiprows=[1])
except:
df = pd.DataFrame()
return df
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df = pd.concat(list(map(download_df, dft['Dataset ID'].values)))
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print('Total Data Values Found: {}'.format(len(df)))
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df
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%matplotlib inline
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from cartopy.feature import NaturalEarthFeature
bathym_1000 = NaturalEarthFeature(name='bathymetry_J_1000',
scale='10m', category='physical')
fig, ax = plt.subplots(
figsize=(9, 9),
subplot_kw=dict(projection=ccrs.PlateCarree())
)
ax.coastlines(resolution='10m')
ax.add_feature(bathym_1000, facecolor=[0.9, 0.9, 0.9], edgecolor='none')
dx = dy = 0.5
ax.set_extent([lon_min-dx, lon_max+dx, lat_min-dy, lat_max+dy])
g = df.groupby('trajectory')
for glider in g.groups:
traj = df[df['trajectory'] == glider]
ax.plot(traj['longitude'], traj['latitude'], 'o', label=glider)
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=2, color='gray', alpha=0.5, linestyle='--')
ax.legend();
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