def create_figure3(dff):
fig3 = px.line_geo(dff, locations="ISO",
#color="Continent",
projection="orthographic",
width=600, height=600)
fig3.update_geos(projection_rotation=dict(lon=-42, lat=69, roll=0),)
fig3.update_layout(margin={"r":0,"t":0,"l":100,"b":10})
return fig3
def UpdateGraph(lap_ids: List[int],
point_values: List[Text]) -> List[dcc.Graph]:
if not isinstance(point_values, list):
point_values = [point_values]
if lap_ids:
graphs = []
laps_data = queries.GetLapsData(lap_ids, point_values)
for point_value in point_values:
figure_data = laps_data
title = point_value
# Copied so that time_delta can redefine without breaking other graphs.
if point_value == 'time_delta':
figure_data = laps_data.copy()
if point_value == 'racing_line':
graph_type = 'map'
fig = px.line_geo(
laps_data,
title=title,
lat='lat',
lon='lon',
color='lap_id',
fitbounds='locations',
)
else:
if point_value == 'time_delta':
if len(lap_ids) < 2:
continue # Need at least two laps to make a comparison.
figure_data = queries.GetTimeDelta(lap_ids)
title = 'time_delta vs first selected lap (lap_id: %s)' % lap_ids[
0]
graph_type = 'graph'
fig = px.line(figure_data,
title=title,
x='elapsed_distance_m',
y=point_value,
color='lap_id',
hover_data=['lap_id', 'lap_number', point_value])
fig.update_xaxes(showspikes=True)
fig.update_yaxes(fixedrange=True)
fig.update_layout(hovermode='x unified')
graph = dcc.Graph(
{
'type': graph_type,
'index': point_values.index(point_value)
},
figure=fig,
style={
'display': 'inline-grid',
'width': '50%'
})
graphs.append(graph)
return graphs
# Empty line when no rows have been selected.
return [dcc.Graph(figure=px.line())]
def plot(self, json_fp: Path):
with open(str(json_fp)) as json_file:
data = json.load(json_file)
trajectory_dict = data["prediction"][1]["trajectory"]
df = pd.DataFrame.from_dict(trajectory_dict)
# Convert to datetime format. Have to remove last 3 decimal places for the parser to work
df['datetime'] = df['datetime'].str.slice(0, -4)
df['datetime_type'] = pd.to_datetime(df['datetime'])
# Hacky section to calculate solar elevation for display.
# TODO: convert this into more pandas/numpy form.
ts_list = df["datetime_type"].dt.to_pydatetime().tolist()
solar_elevation_list = []
for i in range(len(ts_list)):
elevation = get_altitude(
longitude_deg=df["longitude"][i],
latitude_deg=df["latitude"][i],
when=ts_list[i].replace(tzinfo=timezone.utc))
solar_elevation_list.append(elevation)
df["solar_elevation"] = solar_elevation_list
fig = px.line_geo(
df,
lat="latitude",
lon="longitude",
hover_data=["altitude", "datetime", "solar_elevation"],
projection="orthographic",
)
#fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig.update_geos(resolution=110, showcountries=True)
return fig
st.write("Les cas importés, ayant ensuite crée des cas contact, proviennent des pays suivants:")
ch3 = alt.Chart(df.dropna(subset=['Source/Voyage'])).mark_bar().encode(
x = 'Source/Voyage:N',
y=alt.Y('count()', title='Nombre de patients')
).properties(title="Provenance des malades", height=300, width=700)
st.write(ch3)
# Interactive Map
st.write("Visualisation interactive de la provenance des cas de COVID-19:")
df3 = px.data.gapminder().query("year == 2007")
df2 = df3[(df3['country']=="Italy") | (df3['country']=="Senegal") | (df3['country']=="United Kingdom") | (df3['country']=="France") | (df3['country']=="Spain")]
fig = px.line_geo(df2, locations="iso_alpha",
projection="orthographic")
st.plotly_chart(fig)
# V. Population
st.markdown("---")
st.subheader("Population touchée")
st.write("Les chiffres présentés ci-dessous tiennent compte des publication du Ministère de la Santé et de l'Action Sociale. Certaines données sont manquantes, et nous n'affichons que les valeurs connues à ce jour.")
st.write("1. L'age moyen des patients est de ", np.mean(df['Age'].dropna()), " ans")
ch = alt.Chart(df).mark_bar().encode(
x = 'Age:Q',
y=alt.Y('count()', title='Nombre de patients')
).properties(title="atu aji wop gii ", height=300, width=700)
import plotly.express as px
fig = px.line_geo(lat=[51.555051, 55.753215, 28.613506],
lon=[46.013428, 37.622504, 77.207917],
projection="orthographic")
gapminder,
locations="iso_alpha",
color="continent",
hover_name="country",
size="pop",
animation_frame="year",
projection="natural earth",
)
fig.write_html(os.path.join(dir_name, "scatter_geo.html"), auto_play=False)
import plotly.express as px
gapminder = px.data.gapminder()
fig = px.line_geo(
gapminder.query("year==2007"),
locations="iso_alpha",
color="continent",
projection="orthographic",
)
fig.write_html(os.path.join(dir_name, "line_geo.html"))
import plotly.express as px
gapminder = px.data.gapminder()
fig = px.choropleth(
gapminder,
locations="iso_alpha",
color="lifeExp",
hover_name="country",
animation_frame="year",
range_color=[20, 80],
)
fig.show()
'''Map projections can be rotated using the layout.geo.projection.rotation attribute,
and maps can be translated using the layout.geo.center attributed, as well as truncated to
a certain longitude and latitude range using the layout.geo.lataxis.range and layout.geo.lonaxis.range.
'''
fig = go.Figure(go.Scattergeo())
fig.update_geos(center=dict(lon=-30, lat=-30),
projection_rotation=dict(lon=30, lat=30, roll=30),
lataxis_range=[-50, 20],
lonaxis_range=[0, 200])
fig.update_layout(height=300, margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig.show()
'''To automatically set the boundaries of the map according to the data to be plotted...fitbounds'''
import plotly.express as px
fig = px.line_geo(lat=[0, 15, 20, 35], lon=[5, 10, 25, 30])
fig.update_geos(fitbounds="locations")
fig.update_layout(height=300, margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig.show()
'''The available scopes are: 'world', 'usa', 'europe', 'asia', 'africa', 'north america', 'south america'.
'''
fig = go.Figure(go.Scattergeo())
fig.update_geos(visible=False,
resolution=50,
scope="north america",
showcountries=True,
countrycolor="Black",
showsubunits=True,
subunitcolor="Blue")
fig.update_layout(height=300, margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig.show()
import plotly.express as px
gapminder = px.data.gapminder().query("year == 2007")
fig = px.line_geo(
gapminder,
locations="iso_alpha",
color="continent", # "continent" is one of the columns of gapminder
projection="orthographic")
fig.show()
import plotly.graph_objects as go
fig = go.Figure(go.Densitymapbox(lat=quakes.Latitude, lon=quakes.Longitude, z=quakes.Magnitude,
radius=10))
fig.update_layout(mapbox_style="stamen-terrain", mapbox_center_lon=180)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
# In[ ]:
import plotly.express as px
df = px.data.gapminder().query("year == 2007")
fig = px.line_geo(df, locations="iso_alpha",
color="continent", # "continent" is one of the columns of gapminder
projection="orthographic")
fig.show()
# In[ ]:
import plotly.graph_objects as go
import pandas as pd
df_airports = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
df_airports.head()
df_flight_paths = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_aa_flight_paths.csv')
df_flight_paths.head()
px.scatter_geo(gapminder, # 数据集 locations="iso_alpha", # 配和color显示颜色 color="continent", # 颜色的字段显示 hover_name="country", # 悬停数据 size="pop", # 大小 animation_frame="year" # 数据联动变化的选择 #,projection="natural earth" # 去掉projection参数 ) # In[14]: fig = px.line_geo( gapminder, # 数据集 locations="iso_alpha", # 配合和color显示数据 color="continent", # 颜色 projection="orthographic") # 球形的地图 fig.show() # In[15]: iris = px.data.iris() # In[16]: fig = px.scatter(
parser.add_argument('csv_file', help="name of the csv file serving as input")
parser.add_argument('-f',
'--format',
choices=['line', 'scatter', 'animation'],
default='line',
help="format of the map")
parser.add_argument('map_file', help="name of the output html file")
args = parser.parse_args()
df = pd.read_csv(args.csv_file, parse_dates=True)
if args.format == 'line':
fig = px.line_geo(df[df['stadt_latitude'] != None],
lat='stadt_latitude',
lon='stadt_longitude',
scope='europe',
text='Stadt',
hover_data=['Anzahl Stadt'])
elif args.format == 'scatter':
fig = px.scatter_geo(df,
lat='Latitude',
lon='Longitude',
scope='europe',
text='Ort',
hover_data=['Datum', 'Ereignis'])
# elif args.format == 'bubble':
# fig = px.scatter_geo(df, lat='Latitude', lon='Longitude', scope='europe',
# text='Ort', hover_data=['Datum', 'Ereignis'], size='')
elif args.format == 'animation':
fig = px.scatter_geo(df,
lat='Latitude',
def view_edges_network(edges):
""" Method to show edges (roads) of a network
Args:
edges (dataframe): Dataframe from networkx analysis of a graph for edges.
"""
lats = []
lons = []
names = []
thickness = []
print("edges are ", len(edges))
thousands_counter = 0
for feature, name, traff in zip(edges.geometry, edges.name, edges.traffic):
if isinstance(feature, shapely.geometry.linestring.LineString):
linestrings = [feature]
elif isinstance(feature,
shapely.geometry.multilinestring.MultiLineString):
linestrings = feature.geoms
else:
continue
for linestring in linestrings:
x, y = linestring.xy
lats = np.append(lats, y)
lons = np.append(lons, x)
names = np.append(names, [name] * len(y))
thickness = np.append(thickness, [traff] * len(y))
lats = np.append(lats, None)
lons = np.append(lons, None)
names = np.append(names, None)
thickness = np.append(thickness, None)
thousands_counter += 1
if thousands_counter % 1000 == 0:
print(thousands_counter)
colors = thickness #map_traffic_to_color(thickness)
colors = np.array(colors)
colors[colors == None] = 0 # make none values go
colors = colors.astype(int)
colors = colors / 10000000
fig = px.line_geo(lat=lats,
lon=lons) #, color=colors) #, hover_name=names)
fig.add_trace(
go.Scattermapbox(
lon=lons,
lat=lats,
mode='lines+markers',
marker=dict(
size=0,
showscale=True,
colorscale='armyrose', #[[0, 'green'], [1, 'red']],
cmin=0,
cmax=max(colors)),
))
# fig.add_trace(
# go.Scattermapbox(
# lon = lons,
# lat = lats,
# mode='lines',
# line={'color':'green'},
# )
# )
fig.update_layout(margin={
'l': 0,
't': 0,
'b': 0,
'r': 0
},
mapbox={
'center': {
'lon': 10,
'lat': 10
},
'style': "stamen-terrain",
'center': {
'lon': -20,
'lat': -20
},
'zoom': 1
})
fig.show()
pd.set_option('display.max_columns', 150)
pd.set_option('display.max_rows', 150)
#%%
import plotly.express as px
import plotly.io as pio
pio.renderers
#%%
df = px.data.gapminder().query("year == 2007")
df.head()
#%%
fig = px.line_geo(df, locations="iso_alpha", color="continent",
projection="orthographic")
fig.show()
fig.show(renderer = 'firefox')
#%%
#%%
import plotly.graph_objects as go
df_airports = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
df_airports.head()
df_flight_paths = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_aa_flight_paths.csv')
df_flight_paths.head()
#%%
fig = go.Figure()
def new_data(url):
df = pd.read_json(url)
df['lon'] = df.loc['longitude', 'iss_position']
df['lat'] = df.loc['latitude', 'iss_position']
df.reset_index(drop=True, inplace=True)
df.drop(['iss_position', 'message'], axis=1, inplace=True)
df.drop(0, axis=0, inplace=True)
return df
url = 'http://api.open-notify.org/iss-now.json'
df = new_data(url) # starting point
fig = px.line_geo(df,
lat='lat',
lon='lon',
title='Where is ISS Now',
projection='natural earth')
app = dash.Dash(__name__, update_title=None) # remove "Updating..." from title
app.layout = html.Div(
[dcc.Graph(id='graph', figure=fig),
dcc.Interval(id="interval")])
@app.callback(Output('graph', 'extendData'),
[Input('interval', 'n_intervals')])
def add_data(n_intervals):
df = new_data(url)
return {'lon': [df['lon']], 'lat': [df['lat']]}
marker=dict(
size=4,
color=h_sol,
colorscale='Viridis',
),line=dict(
color='darkblue',
width=2
)
))
fig.show()
# %%
import plotly.express as px
fig = px.line_geo(lat=28.5971-theta_sol/pi*180, lon=-80.6833-phi_sol/pi*180, )
# fig.update_geos(fitbounds="locations")
# fig.update_layout(height=300, margin={"r":0,"t":0,"l":0,"b":0})
# fig.update_geos(projection_type="orthographic")
fig.add_scattergeo(lat=[28.5971], lon=[-80.6837], text="Kennedy Space Center Shuttle Landing Facility")
fig.update_geos(projection_type="orthographic",
resolution=50,
showcoastlines=True, coastlinecolor="RebeccaPurple",
showland=True, landcolor="LightGreen",
showocean=True, oceancolor="LightBlue",
showlakes=False, lakecolor="Blue",
showrivers=False, rivercolor="Blue"
)
fig.update_layout(height=300, margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
