This notebook is a follow-up from the Sea Surface Height notebook, here we use the same workflow to compare observations and models for sea waves height.
We start with virtually the same configuration from before, except that now we changed the variable names to "waves" and the corresponding standard_names
for waves.
For more information regarding the workflow presented here see SSH notebook and Signell, Richard P.; Fernandes, Filipe; Wilcox, Kyle. 2016. "Dynamic Reusable Workflows for Ocean Science." J. Mar. Sci. Eng. 4, no. 4: 68.
import warnings
# Suppresing warnings for a "pretty output."
warnings.simplefilter('ignore')
%%writefile wave_config.yaml
date:
start: 2018-2-28 00:00:00
stop: 2018-3-10 00:00:00
run_name: 'latest'
region:
bbox: [-71.20, 41.40, -69.20, 43.74]
crs: 'urn:ogc:def:crs:OGC:1.3:CRS84'
# try: sea_surface_wave_significant_height
sos_name: 'waves'
cf_names:
- sea_surface_wave_significant_height
- sea_surface_wind_wave_significant_height
units: 'm'
catalogs:
- https://data.ioos.us/csw
Overwriting wave_config.yaml
import os
import shutil
from datetime import datetime
from ioos_tools.ioos import parse_config
config = parse_config('wave_config.yaml')
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']))
Saving data inside directory /home/filipe/IOOS/notebooks_demos/notebooks/latest *********************** Run information ************************ Run date: 2018-03-12 13:08:47 Start: 2018-02-28 00:00:00 Stop: 2018-03-10 00:00:00 Bounding box: -71.20, 41.40,-69.20, 43.74
def make_filter(config):
from owslib import fes
from ioos_tools.ioos import fes_date_filter
kw = dict(wildCard='*', escapeChar='\\',
singleChar='?', propertyname='apiso:Subject')
if len(config['cf_names']) > 1:
or_filt = fes.Or([fes.PropertyIsLike(literal=('*%s*' % val), **kw)
for val in config['cf_names']])
else:
or_filt = fes.PropertyIsLike(literal=('*%s*' % config['cf_names'][0]), **kw)
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)
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))
********************* Catalog information ********************** URL: https://data.ioos.us/csw Number of datasets available: 12 Directional wave and sea surface temperature measurements collected in situ by Datawell Mark 3 directional buoy located near LOWER COOK INLET, AK from 2016/12/16 00:00:00 to 2018/03/11 18:09:42. NECOFS GOM3 Wave - Northeast US - Latest Forecast BIO WW III Latest Forecasts/EastCoast.nc BIO WW III Latest Forecasts/GulfOfMaine.nc BIO WW III Latest Forecasts/NorthAtlantic.nc Coupled Northwest Atlantic Prediction System (CNAPS) Directional wave and sea surface temperature measurements collected in situ by Datawell Mark 3 directional buoy located near ASTORIA CANYON, OR from 2017/08/03 18:00:00 to 2018/03/11 17:57:11. Directional wave and sea surface temperature measurements collected in situ by Datawell Mark 3 directional buoy located near CAPE COD BAY, MA from 2017/10/05 20:00:00 to 2018/03/11 18:01:09. Directional wave and sea surface temperature measurements collected in situ by Datawell Mark 3 directional buoy located near CLATSOP SPIT, OR from 2018/02/07 22:00:00 to 2018/03/11 18:00:33. Directional wave and sea surface temperature measurements collected in situ by Datawell Mark 3 directional buoy located near GRAYS HARBOR, WA from 2017/06/29 16:00:00 to 2018/03/11 18:00:29. Directional wave and sea surface temperature measurements collected in situ by Datawell Mark 3 directional buoy located near JEFFREYS LEDGE, NH from 2017/01/31 18:00:00 to 2018/03/11 18:08:17. Directional wave and sea surface temperature measurements collected in situ by Datawell Mark 3 directional buoy located near LAKESIDE, OR from 2017/03/31 23:00:00 to 2018/03/11 17:30:56. ***************************** DAP ****************************** http://thredds.cdip.ucsd.edu/thredds/dodsC/cdip/realtime/036p1_rt.nc.html http://thredds.cdip.ucsd.edu/thredds/dodsC/cdip/realtime/160p1_rt.nc.html http://thredds.cdip.ucsd.edu/thredds/dodsC/cdip/realtime/162p1_rt.nc.html http://thredds.cdip.ucsd.edu/thredds/dodsC/cdip/realtime/179p1_rt.nc.html http://thredds.cdip.ucsd.edu/thredds/dodsC/cdip/realtime/204p1_rt.nc.html http://thredds.cdip.ucsd.edu/thredds/dodsC/cdip/realtime/221p1_rt.nc.html http://thredds.cdip.ucsd.edu/thredds/dodsC/cdip/realtime/231p1_rt.nc.html http://thredds.secoora.org/thredds/dodsC/SECOORA_NCSU_CNAPS.nc.html http://www.neracoos.org/thredds/dodsC/WW3/EastCoast.nc.html http://www.neracoos.org/thredds/dodsC/WW3/GulfOfMaine.nc.html http://www.neracoos.org/thredds/dodsC/WW3/NorthAtlantic.nc.html http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_WAVE_FORECAST.nc.html
from ioos_tools.ioos import is_station
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))
************************* Filtered DAP ************************* http://www.neracoos.org/thredds/dodsC/WW3/GulfOfMaine.nc.html http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_WAVE_FORECAST.nc.html http://www.neracoos.org/thredds/dodsC/WW3/EastCoast.nc.html http://thredds.secoora.org/thredds/dodsC/SECOORA_NCSU_CNAPS.nc.html http://www.neracoos.org/thredds/dodsC/WW3/NorthAtlantic.nc.html
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)))
******************* NDBC Collector offerings ******************* National Data Buoy Center SOS: 1017 offerings
import pandas as pd
from ioos_tools.ioos import collector2table
ndbc = collector2table(
collector=collector_ndbc,
config=config,
col='sea_surface_wave_significant_height (m)'
)
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
depth | lat | lon | sensor | station_name | |
---|---|---|---|---|---|
station_code | |||||
44007 | None | 43.525 | -70.141 | urn:ioos:sensor:wmo:44007::wpm1 | PORTLAND - 12 NM Southeast of Portland,ME |
44013 | None | 42.346 | -70.651 | urn:ioos:sensor:wmo:44013::wpm1 | BOSTON 16 NM East of Boston, MA |
44020 | None | 41.439 | -70.186 | urn:ioos:sensor:wmo:44020::wpm1 | NANTUCKET SOUND |
44029 | None | 42.523 | -70.566 | urn:ioos:sensor:wmo:44029::summarywav1 | Buoy A01 - Massachusetts Bay |
44030 | None | 43.181 | -70.428 | urn:ioos:sensor:wmo:44030::summarywav1 | Buoy B01 - Western Maine Shelf |
44032 | None | 43.716 | -69.355 | urn:ioos:sensor:wmo:44032::summarywav1 | Buoy E01 - Central Maine Shelf |
44090 | None | 41.840 | -70.329 | urn:ioos:sensor:wmo:44090::summarywav1 | Cape Cod Bay, MA (221) |
44098 | None | 42.798 | -70.168 | urn:ioos:sensor:wmo:44098::summarywav1 | Jeffrey's Ledge, NH (160) |
data = ndbc
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
# FIXME: remove this!
obs._metadata['standard_name']='sea_surface_wave_significant_height'
observations.append(obs)
%matplotlib inline
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(13, 2.75))
for series in data:
series.plot(ax=ax, label=series._metadata['station_name'])
plt.legend(bbox_to_anchor=(1.38, 0.95), loc='upper right')
ax.grid(True)
ax.set_ylabel('Wave height (m)')
ax.set_xlabel('')
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)
def check_standard_name(url, standard_names):
from netCDF4 import Dataset
standard_name = lambda v: v in standard_names
with Dataset(url) as nc:
variables = nc.get_variables_by_attributes(standard_name=standard_name)
if variables:
return True
else:
return False
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))
if not check_standard_name(url, config['cf_names']):
print('Could not find {} in {}'.format(config['cf_names'], url))
continue
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
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))
**************************** Models **************************** [Reading url 1/5]: http://www.neracoos.org/thredds/dodsC/WW3/GulfOfMaine.nc [Reading url 2/5]: http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_WAVE_FORECAST.nc [Reading url 3/5]: http://www.neracoos.org/thredds/dodsC/WW3/EastCoast.nc [Reading url 4/5]: http://thredds.secoora.org/thredds/dodsC/SECOORA_NCSU_CNAPS.nc [Reading url 5/5]: http://www.neracoos.org/thredds/dodsC/WW3/NorthAtlantic.nc
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)
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))
Downloading to file /home/filipe/IOOS/notebooks_demos/notebooks/latest/WW3-GulfOfMaine.nc [Water ] PORTLAND - 12 NM Southeast of Portland,ME [Water ] BOSTON 16 NM East of Boston, MA [Water ] NANTUCKET SOUND [Water ] Buoy A01 - Massachusetts Bay [Water ] Buoy B01 - Western Maine Shelf [Water ] Buoy E01 - Central Maine Shelf [Water ] Cape Cod Bay, MA (221) [Water ] Jeffrey's Ledge, NH (160) Finished processing [WW3-GulfOfMaine] Downloading to file /home/filipe/IOOS/notebooks_demos/notebooks/latest/FVCOM_Forecasts-NECOFS_WAVE_FORECAST.nc [Water ] PORTLAND - 12 NM Southeast of Portland,ME [Water ] BOSTON 16 NM East of Boston, MA [Water ] NANTUCKET SOUND [Water ] Buoy A01 - Massachusetts Bay [Water ] Buoy B01 - Western Maine Shelf [Water ] Buoy E01 - Central Maine Shelf [Water ] Cape Cod Bay, MA (221) [Water ] Jeffrey's Ledge, NH (160) Finished processing [FVCOM_Forecasts-NECOFS_WAVE_FORECAST] Downloading to file /home/filipe/IOOS/notebooks_demos/notebooks/latest/WW3-EastCoast.nc [Water ] PORTLAND - 12 NM Southeast of Portland,ME [Water ] BOSTON 16 NM East of Boston, MA [No Data] NANTUCKET SOUND [Water ] Buoy A01 - Massachusetts Bay [No Data] Buoy B01 - Western Maine Shelf [Water ] Buoy E01 - Central Maine Shelf [Water ] Cape Cod Bay, MA (221) [No Data] Jeffrey's Ledge, NH (160) Finished processing [WW3-EastCoast] Downloading to file /home/filipe/IOOS/notebooks_demos/notebooks/latest/SECOORA_NCSU_CNAPS.nc [Water ] PORTLAND - 12 NM Southeast of Portland,ME [Water ] BOSTON 16 NM East of Boston, MA [Water ] NANTUCKET SOUND [Water ] Buoy A01 - Massachusetts Bay [Water ] Buoy B01 - Western Maine Shelf [Water ] Buoy E01 - Central Maine Shelf [Water ] Cape Cod Bay, MA (221) [Water ] Jeffrey's Ledge, NH (160) Finished processing [SECOORA_NCSU_CNAPS] Downloading to file /home/filipe/IOOS/notebooks_demos/notebooks/latest/WW3-NorthAtlanti.nc [No Data] PORTLAND - 12 NM Southeast of Portland,ME [No Data] BOSTON 16 NM East of Boston, MA [No Data] NANTUCKET SOUND [Water ] Buoy A01 - Massachusetts Bay [No Data] Buoy B01 - Western Maine Shelf [No Data] Buoy E01 - Central Maine Shelf [No Data] Cape Cod Bay, MA (221) [No Data] Jeffrey's Ledge, NH (160) Finished processing [WW3-NorthAtlanti]
from ioos_tools.ioos import stations_keys
def rename_cols(df, config):
cols = stations_keys(config, key='station_name')
return df.rename(columns=cols)
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', '--')
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', '--')
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', '--')
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://geoport-dev.whoi.edu/thredds/wms/coawst_4/use/fmrc/coawst_4_use_best.ncd'
w = folium.WmsTileLayer(url, name='COAWST Wave Height', fmt='image/png', layers='Hwave',
style='boxfill/rainbow', COLORSCALERANGE='0,5', 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
bbox = config['region']['bbox']
m = make_map(
bbox,
zoom_start=9,
line=True,
layers=True
)
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].replace("'","")
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);
# Some known models names. Unknonwn models will use the title metadata or the URL.
titles = {
'coawst_4_use_best': 'COAWST_4',
'global': 'HYCOM',
'NECOFS_GOM3_FORECAST': 'NECOFS_GOM3',
'NECOFS_FVCOM_OCEAN_MASSBAY_FORECAST': 'NECOFS_MassBay',
'OBS_DATA': 'Observations'
}
from bokeh.resources import CDN
from bokeh.plotting import figure
from bokeh.embed import file_html
from bokeh.models import HoverTool, Legend
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)
)
leg = []
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,
line_width=5,
line_cap='round',
line_join='round',
**kw
)
leg.append(('{}'.format(titles.get(column, column)), [line]))
p.add_tools(HoverTool(tooltips=[('Name', '{}'.format(titles.get(column, column))),
('Bias', bias),
('Skill', skill)],
renderers=[line]))
legend = Legend(items=leg, location=(0, 60))
legend.click_policy = 'mute'
p.add_layout(legend, 'right')
p.yaxis[0].axis_label = 'Wave Height (m)'
p.xaxis[0].axis_label = 'Date/time'
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
dfs = load_ncs(config)
for station in dfs:
sta_name = all_obs[station].replace("'","")
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
We can observe a noteworthy increase in the significant wave height starting February 2nd and peaking around March 3rd and followed but a second smaller peak after March 8th.