In the past we demonstrated how to perform a CSW catalog search with OWSLib,
and how to obtain near real-time data with pyoos.
In this notebook we will use both to find all observations and model data around the Boston Harbor to access the sea water temperature.
This workflow is part of an example to advise swimmers of the annual Boston lighthouse swim of the Boston Harbor water temperature conditions prior to the race. For more information regarding the workflow presented here see Signell, Richard P.; Fernandes, Filipe; Wilcox, Kyle. 2016. "Dynamic Reusable Workflows for Ocean Science." J. Mar. Sci. Eng. 4, no. 4: 68.
In [1]:
import warnings
# Suppresing warnings for a "pretty output."
warnings.simplefilter('ignore')
This notebook is quite big and complex, so to help us keep things organized we'll define a cell with the most important options and switches.
Below we can define the date,
bounding box, phenomena SOS and CF names and units,
and the catalogs we will search.
In [2]:
%%writefile config.yaml
# Specify a YYYY-MM-DD hh:mm:ss date or integer day offset.
# If both start and stop are offsets they will be computed relative to datetime.today() at midnight.
# Use the dates commented below to reproduce the last Boston Light Swim event forecast.
date:
start: -5 # 2016-8-16 00:00:00
stop: +4 # 2016-8-29 00:00:00
run_name: 'latest'
# Boston harbor.
region:
bbox: [-71.3, 42.03, -70.57, 42.63]
# Try the bounding box below to see how the notebook will behave for a different region.
#bbox: [-74.5, 40, -72., 41.5]
crs: 'urn:ogc:def:crs:OGC:1.3:CRS84'
sos_name: 'sea_water_temperature'
cf_names:
- sea_water_temperature
- sea_surface_temperature
- sea_water_potential_temperature
- equivalent_potential_temperature
- sea_water_conservative_temperature
- pseudo_equivalent_potential_temperature
units: 'celsius'
catalogs:
- https://data.ioos.us/csw
We'll print some of the search configuration options along the way to keep track of them.
In [3]:
import os
import shutil
from datetime import datetime
from ioos_tools.ioos import parse_config
config = parse_config('config.yaml')
# Saves downloaded data into a temporary directory.
save_dir = os.path.abspath(config['run_name'])
if os.path.exists(save_dir):
shutil.rmtree(save_dir)
os.makedirs(save_dir)
fmt = '{:*^64}'.format
print(fmt('Saving data inside directory {}'.format(save_dir)))
print(fmt(' Run information '))
print('Run date: {:%Y-%m-%d %H:%M:%S}'.format(datetime.utcnow()))
print('Start: {:%Y-%m-%d %H:%M:%S}'.format(config['date']['start']))
print('Stop: {:%Y-%m-%d %H:%M:%S}'.format(config['date']['stop']))
print('Bounding box: {0:3.2f}, {1:3.2f},'
'{2:3.2f}, {3:3.2f}'.format(*config['region']['bbox']))
We already created an OWSLib.fes filter before.
The main difference here is that we do not want the atmosphere model data,
so we are filtering out all the GRIB-2 data format.
In [4]:
def make_filter(config):
from owslib import fes
from ioos_tools.ioos import fes_date_filter
kw = dict(wildCard='*', escapeChar='\\',
singleChar='?', propertyname='apiso:AnyText')
or_filt = fes.Or([fes.PropertyIsLike(literal=('*%s*' % val), **kw)
for val in config['cf_names']])
not_filt = fes.Not([fes.PropertyIsLike(literal='GRIB-2', **kw)])
begin, end = fes_date_filter(config['date']['start'],
config['date']['stop'])
bbox_crs = fes.BBox(config['region']['bbox'],
crs=config['region']['crs'])
filter_list = [fes.And([bbox_crs, begin, end, or_filt, not_filt])]
return filter_list
filter_list = make_filter(config)
In the cell below we ask the catalog for all the returns that match the filter and have an OPeNDAP endpoint.
In [5]:
from ioos_tools.ioos import service_urls, get_csw_records
from owslib.csw import CatalogueServiceWeb
dap_urls = []
print(fmt(' Catalog information '))
for endpoint in config['catalogs']:
print('URL: {}'.format(endpoint))
try:
csw = CatalogueServiceWeb(endpoint, timeout=120)
except Exception as e:
print('{}'.format(e))
continue
csw = get_csw_records(csw, filter_list, esn='full')
OPeNDAP = service_urls(csw.records, identifier='OPeNDAP:OPeNDAP')
odp = service_urls(csw.records, identifier='urn:x-esri:specification:ServiceType:odp:url')
dap = OPeNDAP + odp
dap_urls.extend(dap)
print('Number of datasets available: {}'.format(len(csw.records.keys())))
for rec, item in csw.records.items():
print('{}'.format(item.title))
if dap:
print(fmt(' DAP '))
for url in dap:
print('{}.html'.format(url))
print('\n')
# Get only unique endpoints.
dap_urls = list(set(dap_urls))
We found some models, and observations from NERACOOS there. However, we do know that there are some buoys from NDBC and CO-OPS available too. Also, those NERACOOS observations seem to be from a CTD mounted at 65 meters below the sea surface. Rendering them useless from our purpose.
So let's use the catalog only for the models by filtering the observations with is_station below.
And we'll rely CO-OPS and NDBC services for the observations.
In [6]:
from ioos_tools.ioos import is_station
# Filter out some station endpoints.
non_stations = []
for url in dap_urls:
try:
if not is_station(url):
non_stations.append(url)
except (RuntimeError, OSError, IOError) as e:
print('Could not access URL {}. {!r}'.format(url, e))
dap_urls = non_stations
print(fmt(' Filtered DAP '))
for url in dap_urls:
print('{}.html'.format(url))
Now we can use pyoos collectors for NdbcSos,
In [7]:
from pyoos.collectors.ndbc.ndbc_sos import NdbcSos
collector_ndbc = NdbcSos()
collector_ndbc.set_bbox(config['region']['bbox'])
collector_ndbc.end_time = config['date']['stop']
collector_ndbc.start_time = config['date']['start']
collector_ndbc.variables = [config['sos_name']]
ofrs = collector_ndbc.server.offerings
title = collector_ndbc.server.identification.title
print(fmt(' NDBC Collector offerings '))
print('{}: {} offerings'.format(title, len(ofrs)))
In [8]:
import pandas as pd
from ioos_tools.ioos import collector2table
ndbc = collector2table(collector=collector_ndbc,
config=config,
col='sea_water_temperature (C)')
if ndbc:
data = dict(
station_name=[s._metadata.get('station_name') for s in ndbc],
station_code=[s._metadata.get('station_code') for s in ndbc],
sensor=[s._metadata.get('sensor') for s in ndbc],
lon=[s._metadata.get('lon') for s in ndbc],
lat=[s._metadata.get('lat') for s in ndbc],
depth=[s._metadata.get('depth') for s in ndbc],
)
table = pd.DataFrame(data).set_index('station_code')
table
Out[8]:
and CoopsSos.
In [9]:
from pyoos.collectors.coops.coops_sos import CoopsSos
collector_coops = CoopsSos()
collector_coops.set_bbox(config['region']['bbox'])
collector_coops.end_time = config['date']['stop']
collector_coops.start_time = config['date']['start']
collector_coops.variables = [config['sos_name']]
ofrs = collector_coops.server.offerings
title = collector_coops.server.identification.title
print(fmt(' Collector offerings '))
print('{}: {} offerings'.format(title, len(ofrs)))
In [10]:
coops = collector2table(collector=collector_coops,
config=config,
col='sea_water_temperature (C)')
if coops:
data = dict(
station_name=[s._metadata.get('station_name') for s in coops],
station_code=[s._metadata.get('station_code') for s in coops],
sensor=[s._metadata.get('sensor') for s in coops],
lon=[s._metadata.get('lon') for s in coops],
lat=[s._metadata.get('lat') for s in coops],
depth=[s._metadata.get('depth') for s in coops],
)
table = pd.DataFrame(data).set_index('station_code')
table
Out[10]:
We will join all the observations into an uniform series, interpolated to 1-hour interval, for the model-data comparison.
This step is necessary because the observations can be 7 or 10 minutes resolution, while the models can be 30 to 60 minutes.
In [11]:
data = ndbc + coops
index = pd.date_range(start=config['date']['start'].replace(tzinfo=None),
end=config['date']['stop'].replace(tzinfo=None),
freq='1H')
# Preserve metadata with `reindex`.
observations = []
for series in data:
_metadata = series._metadata
obs = series.reindex(index=index, limit=1, method='nearest')
obs._metadata = _metadata
observations.append(obs)
In this next cell we will save the data for quicker access later.
In [12]:
import iris
from ioos_tools.tardis import series2cube
attr = dict(
featureType='timeSeries',
Conventions='CF-1.6',
standard_name_vocabulary='CF-1.6',
cdm_data_type='Station',
comment='Data from http://opendap.co-ops.nos.noaa.gov'
)
cubes = iris.cube.CubeList(
[series2cube(obs, attr=attr) for obs in observations]
)
outfile = os.path.join(save_dir, 'OBS_DATA.nc')
iris.save(cubes, outfile)
Taking a quick look at the observations:
In [13]:
%matplotlib inline
ax = pd.concat(data).plot(figsize=(11, 2.25))
Now it is time to loop the models we found above,
In [14]:
from iris.exceptions import (CoordinateNotFoundError, ConstraintMismatchError,
MergeError)
from ioos_tools.ioos import get_model_name
from ioos_tools.tardis import quick_load_cubes, proc_cube, is_model, get_surface
print(fmt(' Models '))
cubes = dict()
for k, url in enumerate(dap_urls):
print('\n[Reading url {}/{}]: {}'.format(k+1, len(dap_urls), url))
try:
cube = quick_load_cubes(url, config['cf_names'],
callback=None, strict=True)
if is_model(cube):
cube = proc_cube(cube,
bbox=config['region']['bbox'],
time=(config['date']['start'],
config['date']['stop']),
units=config['units'])
else:
print('[Not model data]: {}'.format(url))
continue
cube = get_surface(cube)
mod_name = get_model_name(url)
cubes.update({mod_name: cube})
except (RuntimeError, ValueError,
ConstraintMismatchError, CoordinateNotFoundError,
IndexError) as e:
print('Cannot get cube for: {}\n{}'.format(url, e))
Next, we will match them with the nearest observed time-series. The max_dist=0.08 is in degrees, that is roughly 8 kilometers.
In [15]:
import iris
from iris.pandas import as_series
from ioos_tools.tardis import (make_tree, get_nearest_water,
add_station, ensure_timeseries, remove_ssh)
for mod_name, cube in cubes.items():
fname = '{}.nc'.format(mod_name)
fname = os.path.join(save_dir, fname)
print(fmt(' Downloading to file {} '.format(fname)))
try:
tree, lon, lat = make_tree(cube)
except CoordinateNotFoundError as e:
print('Cannot make KDTree for: {}'.format(mod_name))
continue
# Get model series at observed locations.
raw_series = dict()
for obs in observations:
obs = obs._metadata
station = obs['station_code']
try:
kw = dict(k=10, max_dist=0.08, min_var=0.01)
args = cube, tree, obs['lon'], obs['lat']
try:
series, dist, idx = get_nearest_water(*args, **kw)
except RuntimeError as e:
print('Cannot download {!r}.\n{}'.format(cube, e))
series = None
except ValueError as e:
status = 'No Data'
print('[{}] {}'.format(status, obs['station_name']))
continue
if not series:
status = 'Land '
else:
raw_series.update({station: series})
series = as_series(series)
status = 'Water '
print('[{}] {}'.format(status, obs['station_name']))
if raw_series: # Save cube.
for station, cube in raw_series.items():
cube = add_station(cube, station)
cube = remove_ssh(cube)
try:
cube = iris.cube.CubeList(raw_series.values()).merge_cube()
except MergeError as e:
print(e)
ensure_timeseries(cube)
try:
iris.save(cube, fname)
except AttributeError:
# FIXME: we should patch the bad attribute instead of removing everything.
cube.attributes = {}
iris.save(cube, fname)
del cube
print('Finished processing [{}]'.format(mod_name))
Now it is possible to compute some simple comparison metrics. First we'll calculate the model mean bias:
$$ \text{MB} = \mathbf{\overline{m}} - \mathbf{\overline{o}}$$
In [16]:
from ioos_tools.ioos import stations_keys
def rename_cols(df, config):
cols = stations_keys(config, key='station_name')
return df.rename(columns=cols)
In [17]:
from ioos_tools.ioos import load_ncs
from ioos_tools.skill_score import mean_bias, apply_skill
dfs = load_ncs(config)
df = apply_skill(dfs, mean_bias, remove_mean=False, filter_tides=False)
skill_score = dict(mean_bias=df.to_dict())
# Filter out stations with no valid comparison.
df.dropna(how='all', axis=1, inplace=True)
df = df.applymap('{:.2f}'.format).replace('nan', '--')
And the root mean squared rrror of the deviations from the mean: $$ \text{CRMS} = \sqrt{\left(\mathbf{m'} - \mathbf{o'}\right)^2}$$
where: $\mathbf{m'} = \mathbf{m} - \mathbf{\overline{m}}$ and $\mathbf{o'} = \mathbf{o} - \mathbf{\overline{o}}$
In [18]:
from ioos_tools.skill_score import rmse
dfs = load_ncs(config)
df = apply_skill(dfs, rmse, remove_mean=True, filter_tides=False)
skill_score['rmse'] = df.to_dict()
# Filter out stations with no valid comparison.
df.dropna(how='all', axis=1, inplace=True)
df = df.applymap('{:.2f}'.format).replace('nan', '--')
The next 2 cells make the scores "pretty" for plotting.
In [19]:
import pandas as pd
# Stringfy keys.
for key in skill_score.keys():
skill_score[key] = {str(k): v for k, v in skill_score[key].items()}
mean_bias = pd.DataFrame.from_dict(skill_score['mean_bias'])
mean_bias = mean_bias.applymap('{:.2f}'.format).replace('nan', '--')
skill_score = pd.DataFrame.from_dict(skill_score['rmse'])
skill_score = skill_score.applymap('{:.2f}'.format).replace('nan', '--')
In [20]:
import folium
from ioos_tools.ioos import get_coordinates
def make_map(bbox, **kw):
line = kw.pop('line', True)
layers = kw.pop('layers', True)
zoom_start = kw.pop('zoom_start', 5)
lon = (bbox[0] + bbox[2]) / 2
lat = (bbox[1] + bbox[3]) / 2
m = folium.Map(width='100%', height='100%',
location=[lat, lon], zoom_start=zoom_start)
if layers:
url = 'http://oos.soest.hawaii.edu/thredds/wms/hioos/satellite/dhw_5km'
w = folium.WmsTileLayer(
url,
name='Sea Surface Temperature',
fmt='image/png',
layers='CRW_SST',
attr='PacIOOS TDS',
overlay=True,
transparent=True)
w.add_to(m)
if line:
p = folium.PolyLine(get_coordinates(bbox),
color='#FF0000',
weight=2,
opacity=0.9,
latlon=True)
p.add_to(m)
return m
In [21]:
bbox = config['region']['bbox']
m = make_map(
bbox,
zoom_start=11,
line=True,
layers=True
)
In [22]:
all_obs = stations_keys(config)
from glob import glob
from operator import itemgetter
import iris
from folium.plugins import MarkerCluster
iris.FUTURE.netcdf_promote = True
big_list = []
for fname in glob(os.path.join(save_dir, '*.nc')):
if 'OBS_DATA' in fname:
continue
cube = iris.load_cube(fname)
model = os.path.split(fname)[1].split('-')[-1].split('.')[0]
lons = cube.coord(axis='X').points
lats = cube.coord(axis='Y').points
stations = cube.coord('station_code').points
models = [model]*lons.size
lista = zip(models, lons.tolist(), lats.tolist(), stations.tolist())
big_list.extend(lista)
big_list.sort(key=itemgetter(3))
df = pd.DataFrame(big_list, columns=['name', 'lon', 'lat', 'station'])
df.set_index('station', drop=True, inplace=True)
groups = df.groupby(df.index)
locations, popups = [], []
for station, info in groups:
sta_name = all_obs[station]
for lat, lon, name in zip(info.lat, info.lon, info.name):
locations.append([lat, lon])
popups.append('[{}]: {}'.format(name, sta_name))
MarkerCluster(locations=locations, popups=popups, name='Cluster').add_to(m)
Out[22]:
Here we use a dictionary with some models we expect to find so we can create a better legend for the plots. If any new models are found, we will use its filename in the legend as a default until we can go back and add a short name to our library.
In [23]:
titles = {
'coawst_4_use_best': 'COAWST_4',
'global': 'HYCOM',
'NECOFS_GOM3_FORECAST': 'NECOFS_GOM3',
'NECOFS_FVCOM_OCEAN_MASSBAY_FORECAST': 'NECOFS_MassBay',
'OBS_DATA': 'Observations'
}
In [24]:
from bokeh.resources import CDN
from bokeh.plotting import figure
from bokeh.embed import file_html
from bokeh.models import HoverTool
from itertools import cycle
from bokeh.palettes import Category20
from folium import IFrame
# Plot defaults.
colors = Category20[20]
colorcycler = cycle(colors)
tools = 'pan,box_zoom,reset'
width, height = 750, 250
def make_plot(df, station):
p = figure(
toolbar_location='above',
x_axis_type='datetime',
width=width,
height=height,
tools=tools,
title=str(station)
)
for column, series in df.iteritems():
series.dropna(inplace=True)
if not series.empty:
if 'OBS_DATA' not in column:
bias = mean_bias[str(station)][column]
skill = skill_score[str(station)][column]
line_color = next(colorcycler)
kw = dict(alpha=0.65, line_color=line_color)
else:
skill = bias = 'NA'
kw = dict(alpha=1, color='crimson')
line = p.line(
x=series.index,
y=series.values,
legend='{}'.format(titles.get(column, column)),
line_width=5,
line_cap='round',
line_join='round',
**kw
)
p.add_tools(HoverTool(tooltips=[('Name', '{}'.format(titles.get(column, column))),
('Bias', bias),
('Skill', skill)],
renderers=[line]))
return p
def make_marker(p, station):
lons = stations_keys(config, key='lon')
lats = stations_keys(config, key='lat')
lon, lat = lons[station], lats[station]
html = file_html(p, CDN, station)
iframe = IFrame(html, width=width+40, height=height+80)
popup = folium.Popup(iframe, max_width=2650)
icon = folium.Icon(color='green', icon='stats')
marker = folium.Marker(location=[lat, lon],
popup=popup,
icon=icon)
return marker
In [25]:
dfs = load_ncs(config)
for station in dfs:
sta_name = all_obs[station]
df = dfs[station]
if df.empty:
continue
p = make_plot(df, station)
marker = make_marker(p, station)
marker.add_to(m)
folium.LayerControl().add_to(m)
m
Out[25]:
Now we can navigate the map and click on the markers to explorer our findings.
The green markers locate the observations locations. They pop-up an interactive plot with the time-series and scores for the models (hover over the lines to se the scores). The blue markers indicate the nearest model grid point found for the comparison.