def ajouterMarkers(map, markers, donnees):
couleurChaud = "orange"
couleurFroid = "blue"
iconChaud = "fire"
iconFroid = "cloud"
for marker in markers:
nom, latitude, longitude, station = marker['Nom'], marker['Latitude'], marker['Longitude'], marker['numer_sta']
position = float(marker["Latitude"])
positionLong = float(marker["Longitude"])
if position > 18 and positionLong > -56:
donneesUneStation = csv.filtrerLigne(donnees,'numer_sta', station)
if len(donneesUneStation) > 0:
donneesUneStation = conversion(donneesUneStation)
moyenneStation = moyenne(donneesUneStation)
fichierJson = templateJson(donneesUneStation, nom, moyenneStation )
if moyenneStation >= moyenneGlobale:
couleur = couleurChaud
icon = iconChaud
else:
couleur = couleurFroid
icon = iconFroid
icon = folium.Icon(icon=icon, color = couleur)
popup = folium.Popup().add_child(folium.VegaLite(fichierJson), name = nom)
folium.Marker(location=[latitude,longitude],
popup = popup,
icon = icon
).add_to(map)
else:
print("pas de donées pour la station {}".format(nom))
print("Station: {} - lat, long [{},{}] - temp. moy. : {}".format(nom, latitude, longitude, moyenneStation))
def make_marker(df, country, lat, lon, dataset):
cc = altair.Chart(df).mark_bar().encode(
x='dateRep',
y=dataset,
)
marker = folium.Marker(location=[lat, lon],
popup=folium.Popup(max_width=400).add_child(
folium.VegaLite(cc.to_json(),
width=400,
height=250)))
return marker
def addChartMarker(
chartJson,
CountyState,
map,
opacity=1.0): # 1. Any other parameters? 2. May want to break this up
lati, longi = geoCode(CountyState)
newChart = folium.VegaLite(chartJson, width=600, height=400)
newPopup = folium.Popup(max_width=600)
newChart.add_to(newPopup)
newMarker = folium.Marker(location=[lati, longi],
opacity=opacity,
popup=newPopup,
tooltip=CountyState)
newMarker.add_to(map)
return True # maybe void or something else
def add_places(the_map, places):
"""Add the given places to the given map."""
markers = []
for place in places:
chart = read_json_file(
CHART_DIR.joinpath(CHART_FILE.format(place['name'])))
info = read_json_file(
FORECAST_DIR.joinpath(FORECAST_FILE.format(place['name'])))
color = 'red' if info['will-it-rain'] else 'green'
popup = folium.Popup(max_width=500).add_child(
folium.VegaLite(chart, width=500, height=250), )
marker = folium.Marker(location=[place['lat'], place['lon']],
popup=popup,
icon=folium.Icon(icon='cloud', color=color))
marker.add_to(the_map)
markers.append(marker)
return markers
alt.X("Year:Q",
bin=alt.Bin(
extent=[min(dane['Year'] - 1),
max(dane['Year'] + 1)],
step=1),
axis=alt.Axis(format='.0f')),
y='Value:Q',
color=alt.Color(
'Balance', scale=alt.Scale(
range=['black', 'red', "blue"]))).configure_axisX(
labelAngle=90)
vis1 = chart.to_json()
plots.append(vis1)
#ADD MARKER
data = pd.read_csv('glacier_point.csv')
for i in range(0, len(data)):
for plot in plots:
m.add_child(
CircleMarker(location=[data['x'][i], data['y'][i]],
popup=(folium.Popup(max_width=1000).add_child(
folium.VegaLite(plots[i]))),
fill_opacity=0.3,
color="darkblue",
fill_color=('white'),
marker="o",
markersize=16,
markeredgewidth=1))
m.save('isbre.html')
def create_map(stations: StationsType, tereno_stations: StationsType,
dist: int):
m = folium.Map(location=compute_center_coordinate(stations), tiles=None)
folium.TileLayer("Stamen Toner", name="Stamen Toner").add_to(m)
folium.TileLayer("Stamen Terrain", name="Stamen Terrain").add_to(m)
folium.TileLayer("Stamen Watercolor", name="Stamen Watercolor").add_to(m)
folium.TileLayer("OpenStreetMap", name="OpenStreetMap").add_to(m)
feature_group_tereno = folium.FeatureGroup("TERENO Sites")
feature_group_dwd = folium.FeatureGroup("DWD Sites", control=False)
define_kit_marker().add_to(m)
for station in tereno_stations:
folium.Marker(
(station["geo_lat"], station["geo_lon"]),
tooltip=f"{station['name']} (TERENO)",
icon=folium.Icon(color="green", icon="info-sign"),
).add_to(feature_group_tereno)
# dwd stations
for station in stations:
dummy_df = pd.DataFrame({
"a": range(100),
"b": np.cumsum(np.random.normal(0, 0.1, 100))
})
folium.Marker(
(station["geo_lat"], station["geo_lon"]),
tooltip=f"{station['name']} (id:{station['station_id']})",
# popup = f"{station['name']} (id:{station['station_id']})",
popup=folium.Popup(max_width=300).add_child(
folium.VegaLite(create_chart(dummy_df), width=300,
height=100)),
icon=folium.Icon(color="red", icon="info-sign"),
).add_to(feature_group_dwd)
# distance circle
folium.Circle(
radius=dist * 1000,
location=(ifu["geo_lat"], ifu["geo_lon"]),
dash_array="5",
tooltip=f"{dist} km to IFU",
color="crimson",
fill=False,
).add_to(m)
# fit bounds
bounds = compute_bounds(stations)
m.fit_bounds(bounds)
feature_group_tereno.add_to(m)
feature_group_dwd.add_to(m)
folium.LayerControl(collapsed=True).add_to(m)
plugins.Fullscreen(
position="topright",
title="Expand me",
title_cancel="Exit me",
force_separate_button=True,
).add_to(m)
return m
def to_html(path='./',tag='schism'):
logger.info('create html file for reporting')
m = pyPoseidon.read_model(path + tag +'_model.json')
m.get_data(online=True)
d = m.data.Dataset
vmin = d.elev.min().compute()
vmax = d.elev.max().compute()
x = d.SCHISM_hgrid_node_x.values
y = d.SCHISM_hgrid_node_y.values
tri = d.SCHISM_hgrid_face_nodes.values
nodes = pd.DataFrame({'lon':x,'lat':y})
tria = pd.DataFrame(tri ,columns=['a','b','c'])
tria['ap'] = tria.apply(lambda x : nodes.loc[x.a,['lon','lat']].values,axis=1)
tria['bp'] = tria.apply(lambda x : nodes.loc[x.b,['lon','lat']].values,axis=1)
tria['cp'] = tria.apply(lambda x : nodes.loc[x.c,['lon','lat']].values,axis=1)
tria['geometry'] = tria.apply(
lambda x : shapely.geometry.Polygon([x.ap,x.bp,x.cp]),axis=1)
colormap = branca.colormap.LinearColormap(['green', 'yellow', 'red'], vmin=vmin.values, vmax=vmax.values)
colormap.caption = 'Elevation'
# geopandas
gf_ = gp.GeoDataFrame(tria, crs={'init' :'epsg:4326'})
gf_ = gf_.drop(['a','b','c','ap','bp','cp'], axis=1)
colormap = branca.colormap.LinearColormap(['green', 'yellow', 'red'], vmin=vmin.values, vmax=vmax.values)
colormap.caption = 'Elevation'
# MAXH
mh = d.elev.max('time').values
tria['value'] = tria.apply(lambda x : np.mean([mh[x.a],mh[x.b],mh[x.c]]),axis=1)
gf_ = gp.GeoDataFrame(tria, crs={'init' :'epsg:4326'})
gf_ = gf_.drop(['a','b','c','ap','bp','cp'], axis=1)
maxh = folium.GeoJson(
gf_,
style_function=lambda x: {
"fillColor": colormap(x['properties']['value']),
'weight':0,
"fillOpacity": 1
},
)
maxh.layer_name = 'Maximum Elevation'
# GRID
g = folium.GeoJson(
gf_,
style_function=lambda x: {
"fillColor": "transparent",
"color": "black",
'weight':.2
},
)
g.layer_name = 'Grid'
#VALIDATION
tgs = m.data.obs.locations.loc[m.data.obs.locations.Group=='TD UNESCO']
points = tgs[['lat','lon']].values
mlat,mlon = points.mean(axis=0)
tg = m.data.obs.data
tg.index.levels[1].name = 'date'
tg.index.levels[0].name = 'location'
tg = tg.drop(['Level m','Tide m'],axis=1)
tg.columns=['height']
toponames = tgs.Name.to_list()
toponames = [x.strip(' ') for x in toponames]
st = m.data.time_series.to_dataframe()
st.index.levels[1].name = 'date'
st.index.levels[0].name = 'location'
st.columns=['height']
st.index = st.index.set_levels(m.data.obs.locations.Name.str.strip(' '), level=0)
st = st.loc[toponames]
mc = plugins.MarkerCluster()
for name,latlon in zip(toponames,points):
popup = folium.Popup()
tg_ = tg.loc[name.strip(' ')]
st_ = st.loc[name.strip(' ')]
if tg_.shape[0] <= 1:
tg_ = st_.copy()
tg_.height = 0
s = source(tg_,st_)
gr = graph(s, name)
vis = gr.to_json()
folium.VegaLite(vis, height=350, width=750).add_to(popup)
mc.add_child(folium.Marker(latlon, popup=popup))
mc.layer_name = 'Validation'
#FINAL MAP
Map = folium.Map(location=(mlat, mlon), tiles='OpenStreetMap', date=m.date.__str__(), zoom_start=5)
plugins.Fullscreen(
position='topleft',
title='Expand me',
title_cancel='Exit me',
force_separate_button=True
).add_to(Map)
Map.add_child(maxh)
Map.add_child(g)
Map.add_child(mc)
Map.add_child(folium.map.LayerControl())
Map.add_child(colormap)
Map.save(path + 'report.html')
logger.info('... saved')
return
nitrates['data'] = Data_nitrates[Data_nitrates.code_station == i]
nitrates['title'] = 'Pollution en nitrates'
selenium = {}
selenium['data'] = Data_selenium[Data_selenium.code_station == i]
selenium['title'] = 'Pollution en sélénium'
atrazine = {}
atrazine['data'] = Data_atrazine[Data_atrazine.code_station == i]
atrazine['title'] = 'Pollution en atrazine'
polluants = [nitrates, selenium, atrazine]
charts = []
for j in range(len(polluants)):
charts.append(alt.Chart(polluants[j]['data'])\
.mark_line()\
.encode(alt.X('date_prelevement:T', timeUnit = 'yearmonth', title = 'Date du prélèvement'), alt.Y('resultat:Q', title = 'Quantité en mg/L'), alt.Color('libelle_parametre')).properties(width=200,height=100,title=polluants[j]['title']))
Chart = alt.vconcat(charts[0], charts[1], charts[2])
vis1 = Chart.to_json()
# Etape 3: Création d'un marqueur avec les données en popup
folium.Marker(location=location,
popup=folium.Popup(max_width=750).add_child(
folium.VegaLite(vis1, width=390, height=500)),
tooltip=tooltip,
icon=folium.Icon(icon='tint', color='darkblue')).add_to(m)
# Generate Map
m.save('map_pollution_eau_77.html')
for i in countryoutlines:
if str(i[32:35]) == coloredemissions[country]["ISO"]:
outline = i[0:len(i) - 2]
break
if outline:
#On definit la couche geoJSON et sa couleur
colored_country = folium.Choropleth(
outline,
name=coloredemissions[country]["ISO"],
fill_color=coloredemissions[country]["Color"],
fill_opacity=0.8)
#On definit le graphique Vega qui sera associe au pays
graph = JSONtemplate(bycountryemissions,
coloredemissions[country]["ISO"])
#On definit le popup qui sera ajoute au pays
popup = folium.Popup().add_child(folium.VegaLite(graph))
#On ajoute le popup a la couche geoJSON
popup.add_to(colored_country)
#On ajoute la couche geoJSON sur la carte
colored_country.add_to(m)
print(coloredemissions[country]["Color"])
print(coloredemissions[country]["ISO"] + " ADDED")
print("Choropleth: GENERATED")
#folium.GeoJson(
# countries,
# name = "geojson"
#).add_to(m)
'''
folium.Choropleth(
geo_data=countries,
name="choropleth",
