lat = model_data[st]['lat']
lon = model_data[st]['lon']
popupstring = "HF Radar: [" + str(lat) + ":" + str(
lon) + "]" + "<br>for<br>" + ent['name']
map.circle_marker([lat, lon],
popup=popupstring,
radius=500,
line_color='#66FF33',
fill_color='#66FF33',
fill_opacity=0.5)
except:
pass
display(HTML(htmlContent))
## adds the HF radar tile layers
jd_now = dt.datetime.utcnow()
map.add_tile_layer(
tile_name='hfradar 2km',
tile_url=
'http://hfradar.ndbc.noaa.gov/tilesavg.php?s=10&e=100&x={x}&y={y}&z={z}&t='
+ str(jd_now.year) + '-' + str(jd_now.month) + '-' + str(jd_now.day) +
' ' + str(jd_now.hour - 2) + ':00:00&rez=2')
map.add_layers_to_map()
inline_map(map)
# <codecell>
# get the station data from the sos end point
name = stations[n]
longname = obs_df[n].name
lat = obs_lat[n]
lon = obs_lon[n]
popup_string = "<b>Station:</b><br>" + longname
m.simple_marker([lat, lon], popup=popup_string)
popup_string = "<b>Model Grid Point</b>"
m.circle_marker(
[model_lat[n], model_lon[n]], popup=popup_string, fill_color="#ff0000", radius=5000, line_color="#ff0000"
)
m.line(get_coordinates(bounding_box, bounding_box_type), line_color="#FF0000", line_weight=5)
inline_map(m)
# <markdowncell>
# #### Plot Modeled vs Obs Currents
# <codecell>
# for df in model_df:
for n in range(len(obs_df)):
ax = model_df[n].plot(figsize=(14, 6), title=model_df[n].name, legend=False)
plt.setp(ax.lines[0], linewidth=3, color="0.7", zorder=1)
ax.legend()
# ax.set_ylabel('Current speed m/s')
# Overlay the obs data plot the first bin
popupstring = "HF Radar: ["+ str(lat)+":"+str(lon)+"]" + "<br>for<br>" + ent['name']
map.circle_marker([lat,lon], popup=popupstring,
radius=1000,
line_color='#FF00FF',
fill_color='#FF00FF',
fill_opacity=0.5)
display(HTML(htmlContent))
## adds the HF radar tile layers
map.add_tile_layer(tile_name='hfradar 6km',
tile_url='http://hfradar.ndbc.noaa.gov/tilesavg.php?s=10&e=100&x={x}&y={y}&z={z}&t=2014-8-21 17:00:00&rez=6')
map.add_tile_layer(tile_name='hfradar 2km',
tile_url='http://hfradar.ndbc.noaa.gov/tilesavg.php?s=10&e=100&x={x}&y={y}&z={z}&t=2014-8-21 17:00:00&rez=2')
map.add_tile_layer(tile_name='hfradar 1km',
tile_url='http://hfradar.ndbc.noaa.gov/tilesavg.php?s=10&e=100&x={x}&y={y}&z={z}&t=2014-8-21 17:00:00&rez=1')
map.add_tile_layer(tile_name='hfradar 500m',
tile_url='http://hfradar.ndbc.noaa.gov/tilesavg.php?s=10&e=100&x={x}&y={y}&z={z}&t=2014-8-21 17:00:00&rez=0.5')
map.add_layers_to_map()
inline_map(map)
# <codecell>
# <codecell>
if 'SABGOM' in df.columns:
kw = dict(popup=popup, marker_color="green", marker_icon="ok-sign")
else:
kw = dict(popup=popup, marker_color="green", marker_icon="ok")
ssh.simple_marker(location=[obs['lat'], obs['lon']], **kw)
# Bad datum.
if isinstance(bad_datum, DataFrame):
for station, obs in bad_datum.iterrows():
popup = '<b>Station:</b> {}<br><b>Datum:</b> {}<br>'
popup = popup.format(station, obs['datum'])
kw = dict(popup=popup, marker_color="red", marker_icon="question-sign")
ssh.simple_marker(location=[obs['lat'], obs['lon']], **kw)
ssh.create_map(path=os.path.join('ssh.html'))
inline_map('ssh.html')
# In[ ]:
elapsed = time.time() - start_time
log.info(elapsed)
log.info('EOF')
# ### Compute bias
# In[ ]:
import os
import sys
m.simple_marker(location=[s["latitude"], s["longitude"]],
popup=popup_string)
else:
popup_string = ('<b>Not Enough Station Data for Num of years'
'requested</b><br><br>Num requested:' +
str(num_years_required) + '<br>Num Available:' +
str(len(s["data"].keys())) +
'<br><b>Station:</b><br>' + str(s['station_id']) +
"<br><b>Long Name:</b><br>" + str(s["long_name"]))
add_invalid_marker(m, s, popup_string)
m.line(get_coordinates(bounding_box, bounding_box_type),
line_color='#FF0000',
line_weight=5)
inline_map(m)
# <markdowncell>
# ### Creates a time series plot with stations that have enough data
# <codecell>
# Set the random seed for consistency
np.random.seed(12)
fig, ax = plt.subplots()
# Show the whole color range
for s in station_list:
if "data" in s:
for line, name in zip(ax.lines, labels)]
html = 'station_{}.html'.format(station)
save_html(fig, '{}/{}'.format(run_name, html))
plt.close(fig)
popup = "<div align='center'> {} <br><iframe src='{}' alt='image'"
popup += "width='{}px' height='{}px' frameBorder='0'></div>"
popup = popup.format('{}'.format(sta_name), html,
(width*resolution)+75, (height*resolution)+50)
kw = dict(popup=popup, width=(width*resolution)+75)
if (df.columns == 'OBS_DATA').all():
kw.update(dict(marker_color="blue", marker_icon="ok"))
else:
kw.update(dict(marker_color="green", marker_icon="ok"))
obs = all_obs[all_obs['station'].astype(str) == station].squeeze()
mapa.simple_marker(location=[obs['lat'], obs['lon']], **kw)
# ### Map
# In[7]:
from utilities import inline_map
mapa.create_map(path=os.path.join(run_name, 'mapa.html'))
inline_map(os.path.join(run_name, 'mapa.html'))
"<br><b>Long Name:</b><br>" +
str(s["long_name"]))
add_invalid_marker(m, s, popup_string)
else: # If its a model station.
if "latitude" in s:
popup_string = ('<b>Station:</b><br>' + str(s['station_id']) +
"<br><b>Long Name:</b><br>" + str(s["long_name"]))
m.simple_marker([s["latitude"], s["longitude"]],
popup=popup_string)
# Create the map and add the bounding box line
m.line(get_coordinates(bounding_box, bounding_box_type),
line_color='#FF0000', line_weight=5)
# Show map of results.
inline_map(m)
# <markdowncell>
# ### Creates a time series plot only showing stations that have enough data
# <codecell>
# Set the random seed for consistency
np.random.seed(12)
fig, ax = plt.subplots(1)
# Show the whole color range
for s in station_list:
if "data" in s:
if 'SABGOM' in df.columns:
kw = dict(popup=popup, marker_color="green", marker_icon="ok-sign")
else:
kw = dict(popup=popup, marker_color="green", marker_icon="ok")
ssh.simple_marker(location=[obs['lat'], obs['lon']], **kw)
# Bad datum.
if isinstance(bad_datum, DataFrame):
for station, obs in bad_datum.iterrows():
popup = '<b>Station:</b> {}<br><b>Datum:</b> {}<br>'
popup = popup.format(station, obs['datum'])
kw = dict(popup=popup, marker_color="red", marker_icon="question-sign")
ssh.simple_marker(location=[obs['lat'], obs['lon']], **kw)
ssh.create_map(path=os.path.join('ssh.html'))
inline_map('ssh.html')
# In[ ]:
elapsed = time.time() - start_time
log.info(elapsed)
log.info('EOF')
# ### Compute bias
# In[ ]:
import os
import sys
root = os.path.abspath(os.path.join(os.getcwd(), os.pardir))
sys.path.append(root)
