Run 00-fetch_data.ipynb and 01-skill_score.ipynb first.
In [1]:
import os
try:
import cPickle as pickle
except ImportError:
import pickle
run_name = '2014-07-07'
fname = os.path.join(run_name, 'config.pkl')
with open(fname, 'rb') as f:
config = pickle.load(f)
In [2]:
try:
import cPickle as pickle
except ImportError:
import pickle
fname = os.path.join(run_name, 'skill_score.pkl')
with open(fname, 'rb') as f:
skill_score = pickle.load(f)
In [3]:
from mpld3 import save_html
import matplotlib.pyplot as plt
from mpld3.plugins import LineLabelTooltip, connect
from utilities import make_map
bbox = config['bbox']
units = config['units']
run_name = config['run_name']
secoora_models = config['secoora_models']
kw = dict(line=True, states=False, hf_radar=True, layers=False)
mapa = make_map(bbox, **kw)
In [4]:
import folium
roms_stations = [(-77.7866, 34.2133),
(-78.9183, 33.6550),
(-81.0000, 30.3966),
(-80.1600, 25.7300),
(-81.8116, 24.5550),
(-82.6137, 26.1300),
(-82.8320, 27.9770),
(-86.4987, 30.1520)]
for station in roms_stations:
location = station[::-1]
popup = '[SABGOM/USEAST]\nROMS station file'
kw = dict(radius=700, fill_color='red', popup=popup,
fill_opacity=0.75)
folium.CircleMarker(location=location, **kw).add_to(mapa)
In [5]:
from glob import glob
from operator import itemgetter
import iris
from pandas import DataFrame, read_csv
from folium.plugins import MarkerCluster
fname = '{}-all_obs.csv'.format(run_name)
all_obs = read_csv(os.path.join(run_name, fname), index_col='name')
big_list = []
for fname in glob(os.path.join(run_name, "*.nc")):
if 'OBS_DATA' in fname:
continue
cube = iris.load_cube(fname)
model = fname.split('-')[-1].split('.')[0]
lons = cube.coord(axis='X').points
lats = cube.coord(axis='Y').points
stations = cube.coord('station name').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 = 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'][all_obs['station'] == station].index[0]
for lat, lon, name in zip(info.lat, info.lon, info.name):
locations.append([lat, lon])
popups.append('[{}]: {}'.format(name, sta_name))
mapa.add_children(MarkerCluster(locations=locations, popups=popups))
In [6]:
from folium.element import IFrame
from utilities import load_secoora_ncs
mean_bias = skill_score['mean_bias'].applymap('{:.2f}'.format).replace('nan', '--')
low_pass_r2 = skill_score['low_pass_resampled_3H_r2'].applymap('{:.2f}'.format).replace('nan', '--')
resolution, width, height = 75, 7, 3
def make_plot():
fig, ax = plt.subplots(figsize=(width, height))
ax.set_ylabel('Sea surface salinity ({})'.format(units))
ax.grid(True)
return fig, ax
dfs = load_secoora_ncs(run_name)
# SABGOM dt = 3 hours.
dfs = dfs.swapaxes('items', 'major').resample('3H').swapaxes('items', 'major')
for station in dfs:
df = dfs[station].dropna(axis=1, how='all')
if df.empty:
continue
labels = []
fig, ax = make_plot()
for col in df.columns:
serie = df[col].dropna()
lines = ax.plot(serie.index, serie, label=col,
linewidth=2.5, alpha=0.5)
if 'OBS_DATA' not in col:
text0 = col
text1 = mean_bias.get(station).get(col, '--')
text2 = low_pass_r2.get(station).get(col, '--')
tooltip = '{}:\nbias {}\nskill: {}'.format
labels.append(tooltip(text0, text1, text2))
else:
labels.append('OBS_DATA')
kw = dict(loc='upper center', bbox_to_anchor=(0.5, 1.05), numpoints=1,
ncol=2, framealpha=0)
l = ax.legend(**kw)
l.set_title("") # Workaround str(None).
[connect(fig, LineLabelTooltip(line, name))
for line, name in zip(ax.lines, labels)]
html = 'station_{}.html'.format(station)
figname = '{}/{}'.format(run_name, html)
save_html(fig, figname)
plt.close(fig)
with open(figname, 'r') as f:
html = f.read()
iframe = IFrame(html, width=(width*resolution)+75, height=(height*resolution)+50)
popup = folium.Popup(iframe, max_width=2650)
if (df.columns == 'OBS_DATA').all():
icon = folium.Icon(color='blue', icon_color='white', icon='ok')
else:
conj = set(df.columns)
conj.intersection_update(secoora_models)
if conj:
icon = folium.Icon(color='green', icon_color='white', icon='ok-sign')
else:
icon = folium.Icon(color='green', icon_color='white', icon='ok')
obs = all_obs[all_obs['station'] == station].squeeze()
folium.Marker(location=[obs['lat'], obs['lon']], icon=icon, popup=popup).add_to(mapa)
In [7]:
bad_station = all_obs[all_obs['bad_station']]
if not bad_station.empty:
for station, obs in bad_station.iterrows():
popup = '[Station]: {}'
popup = popup.format(station)
icon = folium.Icon(color='red', icon_color='white', icon='question-sign')
folium.Marker(location=[obs['lat'], obs['lon']], icon=icon, popup=popup).add_to(mapa)
In [8]:
mapa.save(os.path.join(run_name, 'mapa.html'))
mapa
Out[8]: