def get_figure(outage_index,
variable_to_show,
outages=None,
towers=None,
discharges=None,
centroid=None):
outage_date = outages.loc[outage_index, "date"]
minutes_animation = 35
frames = get_frames(minutes_animation,
outage_date,
variable_to_show,
discharges=discharges)
df = stats.Discharges_before_outage_by_time(outage_date,
1,
min_before=minutes_animation,
discharges=discharges)
fig = go.Figure(
go.Scattermapbox(
lat=df.latitude,
lon=df.longitude,
mode="markers",
))
fig.add_trace(
go.Scattermapbox(
lat=towers.latitude,
lon=towers.longitude,
mode="markers", # markers+lines
marker=go.scattermapbox.Marker(size=7, color="black", opacity=0.7),
name="Towers",
))
fig.update(layout_showlegend=False)
fig.update_layout(
margin={
"t": 0.2,
"l": 0,
"b": 0
},
height=700,
mapbox=dict( # accesstoken=mapbox_access_token,
bearing=0,
center=dict(lat=centroid[1], lon=centroid[0]),
pitch=0,
zoom=9,
style="carto-positron",
),
)
fig.update(frames=frames)
sliders = [
dict(
steps=[
dict(
method="animate",
args=[
[f"frame{k}"],
dict(
mode="immediate",
frame=dict(duration=100, redraw=True),
transition=dict(duration=0),
label="C",
),
],
label="{:d}".format(k),
) for k in range(len(frames))
],
transition=dict(duration=0),
x=0.1, # slider starting position
y=0,
len=0.9,
ticklen=3,
pad={
"b": 10,
"t": 0
},
font={"size": 10},
currentvalue=dict(font=dict(size=12),
prefix="Minute: ",
visible=True,
xanchor="center"),
)
]
fig["layout"]["updatemenus"] = [{
"buttons": [
{
"args": [
None,
{
"frame": {
"duration": 500,
"redraw": True
},
"fromcurrent": True,
"transition": {
"duration": 0,
"easing": "quadratic-in-out"
},
},
],
"label":
"Play",
"method":
"animate",
},
{
"args": [
[None],
{
"frame": {
"duration": 0,
"redraw": True
},
"mode": "immediate",
"transition": {
"duration": 0
},
},
],
"label":
"Pause",
"method":
"animate",
},
],
"direction":
"left",
"pad": {
"r": 10,
"t": 20
},
"showactive":
False,
"type":
"buttons",
"x":
0.1,
"xanchor":
"right",
"y":
0,
"yanchor":
"top",
}]
fig["layout"]["sliders"] = sliders
return fig
def plot_map(end_year: int) -> None:
"""
Plots a map showing the weather stations in the UK and their projected
increase percentage in E.Coli cases.
"""
total = projection.get_percentage_increase(end_year)
temps = projection.temp_prediction_all(end_year)
print(temps)
mapbox_access_token = 'pk.eyJ1Ijoiam9qb29udGhhdCIsImEiOiJja2lta3Uzbnow' \
'YWRtMzVud3NrNjI3N2JjIn0.kYIFPU3HJbjDsNYyQFaGdA'
df = pd.read_csv('plotly_map_station_locations.csv')
site_lat = df.lat
site_lon = df.lon
locations_name = df.text.tolist()
temp_values = temps['temp'].tolist()
max_temp = max(temp_values)
min_temp = min(temp_values)
temp_diff = max_temp - min_temp
location_colors = []
for i in range(len(locations_name)):
location = locations_name[i]
locations_name[
i] = "{}: Projected to have {:.4f}% of increase until {}".format(
location.title(), total[location], end_year)
temp_value = temps.loc[temps['location'] == location]['temp'][0][0]
ratio = (temp_value - min_temp) / temp_diff
gb_value = 255 - int(ratio * 255)
rgb = 'rgb(255, {}, {})'.format(gb_value, gb_value)
location_colors.append(rgb)
fig = go.Figure()
fig.add_trace(
go.Scattermapbox(lat=site_lat,
lon=site_lon,
name='Weather Station Temperature',
mode='markers',
marker=dict(size=20,
color=location_colors,
colorscale=[[0, "rgb(255, 255, 255)"],
[1, "rgb(255, 0, 0)"]],
showscale=False,
opacity=0.75),
text=locations_name,
hoverinfo='text',
hoverlabel=dict(bgcolor='rgb(255, 217, 255)',
font_size=40,
font_family="Helvetica")))
fig.add_trace(
go.Scattermapbox(lat=site_lat,
lon=site_lon,
name='',
mode='markers',
marker=go.scattermapbox.Marker(
size=0,
color=temps['temp'],
colorscale=[[0, "rgb(255, 255, 255)"],
[1, "rgb(255, 0, 0)"]],
showscale=True,
opacity=0.75),
text=locations_name,
hoverinfo='text',
hoverlabel=dict(bgcolor='rgb(182, 252, 213)',
font_size=40,
font_family="Helvetica")))
fig.add_trace(
go.Scattermapbox(
lat=site_lat,
lon=site_lon,
name='',
mode='markers',
marker=go.scattermapbox.Marker(
size=7,
color='rgb(182, 252, 213)', # percentage
opacity=0.8),
text=locations_name,
hoverinfo='text',
hoverlabel=dict(bgcolor='rgb(182, 252, 213)',
font_size=14,
font_family="Helvetica")))
fig.update_layout(title={
'text':
'Percentage of Increase in E.Coli Cases for Weather Stations in the UK',
'y': 0.9,
'x': 0.5,
'xanchor': 'center',
'yanchor': 'top'
},
autosize=True,
hovermode='closest',
font=dict(family="Helvetica", size=18),
mapbox=dict(accesstoken=mapbox_access_token,
bearing=0,
center=dict(lat=52, lon=0.12),
pitch=10,
zoom=3,
style='dark'))
fig.update_traces(showlegend=True, selector=dict(type='scattermapbox'))
fig.update_layout(legend=dict(
orientation="h", yanchor="top", y=1.00, xanchor="right", x=1.00))
fig.show()
def return_datatable(selected_date, selected_auth, selected_data,
selected_cases):
# print(str(datetime.now()), '[1] start update_map...')
df1 = df[df['date'].isin([selected_date])]
if selected_data:
if selected_cases:
display = 'newCasesByPublishDate'
marker_col = col_1
else:
display = 'newDeaths28DaysByPublishDate'
marker_col = col_2
else:
if selected_cases:
display = 'cumCasesByPublishDate'
marker_col = col_3
else:
display = 'cumDeaths28DaysByPublishDate'
marker_col = col_4
if selected_auth is None or selected_auth == []:
pass
else:
df1 = df1[df1['areaName'].isin(selected_auth)]
df1 = df1.sort_values(by=[display], ascending=False)
df1['Row'] = df1.reset_index().index
df1['Row'] += 1
lat_mean = pd.to_numeric(df1['Latitude']).mean()
lon_mean = pd.to_numeric(df1['Longitude']).mean()
df1.loc[(pd.isna(df1['newDeaths28DaysByPublishDate'])),
'newDeaths28DaysByPublishDate'] = 0
df1.loc[(pd.isna(df1['cumDeaths28DaysByPublishDate'])),
'cumDeaths28DaysByPublishDate'] = 0
fig = go.Figure(
go.Scattermapbox(
lat=df1['Latitude'],
lon=df1['Longitude'],
mode='text+markers',
marker={'size': df1[display] * marker_calc_size / df1[display].max(),
'color': marker_col,
},
name='',
text=df1['areaName'],
textposition='top center',
customdata=np.stack(
(
df1['date'],
df1['newCasesByPublishDate'],
df1['newDeaths28DaysByPublishDate'],
df1['cumCasesByPublishDate'],
df1['cumDeaths28DaysByPublishDate']
),
axis=-1
),
hovertemplate='<br><b>Date</b>: %{customdata[0]}' + \
'<br><b>Local Authority</b>: %{text}' + \
'<br><b>New Cases</b>: %{customdata[1]:,}' + \
'<br><b>New Deaths</b>: %{customdata[2]:,}' + \
'<br><b>Cumulative Cases</b>: %{customdata[3]:,}' + \
'<br><b>Cumulative Deaths</b>: %{customdata[4]:,}'
)
)
fig.update_layout(
hovermode='closest',
mapbox=dict(
accesstoken=mapbox_access_token,
bearing=0,
center=dict(lat=lat_mean, lon=lon_mean),
pitch=0,
zoom=5,
style='light' # satellite, outdoors, streets, dark
),
hoverlabel=dict(bgcolor=bgcol_1, font_size=12, font_family='Rockwell'),
margin=dict(t=0, b=0, l=0, r=0))
# print(str(datetime.now()), '[1] finish update_map...')
return fig # df1.to_dict('records')
def update_MapTotal(TipusMap, TempsSelected, geolocationUser, n):
"""lala"""
mydfTracks=_MyDb.GetTracks(1)
if mydfTracks is None:
fig = go.Figure(go.Scattermapbox(
mode = "markers+text",
marker =dict(size=20, color='red'),
textposition='top right',
name='Basic')
)
fig.update_layout(
margin ={'l':0,'t':0,'b':0,'r':0},
height=750,
mapbox = {
'accesstoken':MapBoxToken,
'style': TipusMap},
title_text='Cercador de Babys')
return "DB error", fig
DateHour=datetime.now()+timedelta(minutes=-TempsSelected*10)
dfSelected=mydfTracks[mydfTracks['LocDate']>=DateHour]
if dfSelected.empty:
dfSelected=mydfTracks.head(MAX_ITEMS_SELECTED)
fig = go.Figure(go.Scattermapbox(
mode = "markers+lines+text",
lon = dfSelected.Longitude,
lat = dfSelected.Latitude,
textposition='top right',
textfont=dict(size=MAX_TEXT_SIZE, color=COLOR_SERIE),
text=dfSelected['LocDate'].dt.strftime('%H:%M:%S'),
marker = {'size': 10, 'color':COLOR_SERIE},
name='Trackers')
)
#si tenim la geocalització de l'usuari, la pintem i posicionem el mapa al centre entre la usr location y els tracks
maxLatitude=mydfTracks['Latitude'].max()
maxLongitude=mydfTracks['Longitude'].max()
minLatitude=mydfTracks['Latitude'].min()
minLongitude=mydfTracks['Longitude'].min()
myloc=geolocationUser.split(",")
if (len(myloc)==2):
fig.add_trace(go.Scattermapbox(
mode = "markers+lines",
lon =[myloc[1]],
lat = [myloc[0]],
textposition='top right',
textfont=dict(size=MAX_TEXT_SIZE, color=COLOR_USR),
text="MyLoc" + str(datetime.now()),
marker =dict(size=20, color='green'),
name='MyLoc')
)
maxLatitude=max(float(myloc[0]),maxLatitude)
maxLongitude=max(float(myloc[1]),maxLongitude)
minLatitude=min(float(myloc[0]),minLatitude)
minLongitude=min(float(myloc[1]),minLongitude)
fig.update_layout(
margin ={'l':0,'t':0,'b':0,'r':0},
height=750,
mapbox = {
'center': {'lon':(minLongitude+maxLongitude)/2, 'lat': (minLatitude+maxLatitude)/2},
'accesstoken':MapBoxToken,
'style': TipusMap,
'zoom':calcZoom(minLongitude,maxLongitude,minLatitude,maxLatitude)}
)
#mydfTracks=_MyDb.GetTracks(1,HourValue)
#fig = go.Figure(go.Scattermapbox(
# mode = "markers+lines",
# lon = mydfTracks.Longitude,
# lat = mydfTracks.Latitude,
# marker = {'size': 10, 'color':'fuchsia'},
# name='DadUpdated'))
#fig.update_layout(
# margin ={'l':0,'t':0,'b':0,'r':0},
# height=750,
# mapbox = {
# 'center': {'lon':(MIN_LONGITUDE+MAX_LONGITUDE)/2, 'lat': (MIN_LATITUDE+MAX_LATITUDE)/2},
# 'style': "stamen-terrain",
# 'zoom': CNST_ZOOM},
# title_text='Cercador de Babys')
return f" --Slider SELECT: {TempsSelected} -- GEOLOCATION: {myloc} --",fig
#Read CSV File
import pandas as pd
import config_p24
mapbox_token = (config_p24.mapbox_token)
sta_df = pd.read_excel(config_p24.dash_ready)
# TO DO refine marker color based on price and add more info in text box (Title, address, size etc.) like here https://docs.mapbox.com/mapbox-gl-js/example/popup-on-hover/
# this data trace shows the listings
from plotly.offline import plot
import plotly.graph_objects as go
scatt = go.Scattermapbox(
lat = list(sta_df["lat"]),
lon = list(sta_df["lon"]),
mode='markers',
hoverinfo='text',
marker=dict(symbol ='marker', size=5, color='white'),
textposition='top right',
textfont=dict(size=16, color='black'),
text = list(sta_df["Address"] + " Location: " + sta_df["Location"] + " Price: " + sta_df["Price"].astype(str))
)
# TO DO add trace with crime stats per region chlorochart
layout = go.Layout(hovermode = "closest",
mapbox = dict(
accesstoken= mapbox_token,
zoom=8,
center=dict(lat = sta_df["lat"].median(),
lon = sta_df["lon"].median()
)
)
def update_cards(n_clicks, dummy_clicks):
sum_conf,sum_deat,sum_rec,countries,df_name,df_lat,df_lon,midpoint,df_country,recent,summary,dff_conf,dff_rec = data_parse()
card1 = do.Figure(do.Indicator(
mode= 'number',
value= sum_conf,
number={'valueformat':'.%f', 'font':{'size':30,'family':'Nunito Sans','color':'#d9534f'}},
title = {"text": 'Confirmed', 'font':{'size':25,'family':'Nunito Sans'}}))
card1.update_layout(margin= do.layout.Margin(t=35,b=0), plot_bgcolor='#ffffff', paper_bgcolor='#ffffff')
card2 = do.Figure(do.Indicator(
mode= 'number',
value= sum_deat,
number={'valueformat':'.%f', 'font':{'size':30,'family':'Nunito Sans','color':'#d9534f'}},
title = {"text": 'Deaths', 'font':{'size':25,'family':'Nunito Sans'}}))
card2.update_layout(margin= do.layout.Margin(t=35,b=0), plot_bgcolor='#ffffff', paper_bgcolor='#ffffff')
card3 = do.Figure(do.Indicator(
mode= 'number',
value= sum_rec,
number={'valueformat':'.%f', 'font':{'size':30,'family':'Nunito Sans','color':'#4bbf73'}},
title = {"text": 'Recovered', 'font':{'size':25,'family':'Nunito Sans'}}))
card3.update_layout(margin= do.layout.Margin(t=35,b=0), plot_bgcolor='#ffffff', paper_bgcolor='#ffffff')
card4 = do.Figure(do.Indicator(
mode= 'number',
value= len(countries),
number={'valueformat':'.%f', 'font':{'size':30,'family':'Nunito Sans','color':'#d9534f'}},
title = {"text": 'Countries with cases', 'font':{'size':20,'family':'Nunito Sans'}}))
card4.update_layout(margin= do.layout.Margin(t=35,b=0), plot_bgcolor='#ffffff', paper_bgcolor='#ffffff')
hover_text = ['Province/State: '+'{}'.format(x) + '<br>Country: '+'{}'.format(y) + '<br>Confirmed: '+'{}'.format(z) + '<br>Deaths: '+'{}'.format(v) + '<br>Recovered: '+'{}'.format(w) for x,y,z,v,w in zip(df_name,df_country,list(summary['Confirmed']),list(summary['Deaths']),list(summary['Recovered']))]
map1 = do.Figure()
map1.add_trace(
do.Scattermapbox(
lat=df_lat,
lon=df_lon,
mode='markers',
marker=do.scattermapbox.Marker(
size=5,
color='#c94631',
opacity=0.7
),
text= hover_text,
hoverinfo='text',
showlegend= False
)
)
map1.update_layout(
mapbox= do.layout.Mapbox(
accesstoken= map_token,
center= do.layout.mapbox.Center(
lat= 30.3753,
lon= 69.3451
),
zoom= 1.3,
style= map_style
),
margin= do.layout.Margin(
l=0,
r=0,
t=0,
b=0
)
)
desc = html.P(["Click 'Refresh' to fetch new data & update charts. Featured data are as of {}.".format(recent)], style={'textAlign':'center'})
table = do.Figure(
do.Table(
header={'values':list(summary.columns),'fill':{'color':'#017cbf'},'font':{'family':'Nunito Sans','color':'#ffffff'}},
cells={'values':[summary.State,summary.Country,summary.Confirmed,summary.Deaths,summary.Recovered],
'fill':{'color':'#f5f4ef'},'font':{'family':'Nunito Sans'}}
)
)
table.update_layout(margin= do.layout.Margin(t=0,b=0,l=0,r=0), plot_bgcolor='#ffffff', paper_bgcolor='#ffffff')
line1 = do.Figure(
do.Scatter(
x= dff_conf.index,
y= dff_conf[0],
marker_color='#d9534f',
)
)
line1.update_layout(
margin= do.layout.Margin(t=35,b=0,l=0,r=0), plot_bgcolor='#ffffff', paper_bgcolor='#ffffff',
xaxis={'showgrid':False,'showticklabels':False},yaxis={'showgrid':False,'showticklabels':False},
title={'text':'Confirmed (previous 15-day range)','font':{'family':'Nunito Sans','size':12}}
)
line2 = do.Figure(
do.Scatter(
x= dff_rec.index,
y= dff_rec[0],
marker_color='#4bbf73',
)
)
line2.update_layout(
margin= do.layout.Margin(t=35,b=0,l=0,r=0), plot_bgcolor='#ffffff', paper_bgcolor='#ffffff',
xaxis={'showgrid':False,'showticklabels':False},yaxis={'showgrid':False,'showticklabels':False},
title={'text':'Recovered (previous 15-day range)','font':{'family':'Nunito Sans','size':12}}
)
return card1,card2,card3,card4,map1,desc,table,line1,line2
def generate_geo_map(df):
lat = df["latitude"].tolist()
lon = df["longitude"].tolist()
state_list = df["State"].tolist()
nation_list = df["Country"].tolist()
confirmed_list = df["Confirmed"].tolist()
deaths_list = df["Deaths"].tolist()
recovered_list = df["Recovered"].tolist()
#average_confirmed = df["Confirmed"]["mean"].tolist()
colors = ["#21c7ef", "#76f2ff", "#ff6969", "#ff1717"]
confirmed_metric_data = {}
confirmed_metric_data["min"] = df["Confirmed"].min()
confirmed_metric_data["max"] = sorted(confirmed_list, reverse=True)[1]
confirmed_metric_data["mid"] = (confirmed_metric_data["min"] +
confirmed_metric_data["max"]) / 2
confirmed_metric_data["low_mid"] = (confirmed_metric_data["min"] +
confirmed_metric_data["mid"]) / 2
confirmed_metric_data["high_mid"] = (confirmed_metric_data["mid"] +
confirmed_metric_data["max"]) / 2
countries = []
for i in range(len(lat)):
#print("CHECK POINT 1 - {}".format(i))
state = state_list[i]
nation = nation_list[i]
confirmed = confirmed_list[i]
deaths = deaths_list[i]
recovered = recovered_list[i]
if confirmed <= confirmed_metric_data["low_mid"]:
color = colors[0]
elif confirmed_metric_data[
"low_mid"] < confirmed <= confirmed_metric_data["mid"]:
color = colors[1]
elif confirmed_metric_data["mid"] < confirmed <= confirmed_metric_data[
"high_mid"]:
color = colors[2]
else:
color = colors[3]
country = go.Scattermapbox(
lat=[lat[i]],
lon=[lon[i]],
mode="markers",
marker=dict(color=color,
showscale=False,
colorscale=[
[0, "#21c7ef"],
[0.33, "#76f2ff"],
[0.66, "#ff6969"],
[1, "#ff1717"],
],
size=math.log(confirmed, 6) * 10,
opacity=0.7),
selected=dict(marker={"color": "#ffff00"}),
hoverinfo="text",
text=nation + "<br>" + state + "<br>" +
"Confirmed : {}".format(confirmed) + "<br>" +
"Deaths : {}".format(deaths) + "<br>" +
"Recoverd : {}".format(recovered),
)
countries.append(country)
layout = go.Layout(
title="Global Map",
margin=dict(l=10, r=10, t=30, b=10, pad=3),
plot_bgcolor="white",
paper_bgcolor="white",
clickmode="event+select",
hovermode="closest",
showlegend=False,
mapbox=go.layout.Mapbox(
accesstoken=mapbox_access_token,
bearing=0,
center=go.layout.mapbox.Center(lat=25, lon=120),
pitch=20,
zoom=2,
#style="mapbox://styles/plotlymapbox/cjvppq1jl1ips1co3j12b9hex",
style="light"),
)
figure = dict(data=countries, layout=layout)
return figure
fig.show()
manh_df = crime[crime.BOROUGH_NAME==2]
manh_m_df = manh_df[manh_df.LEVEL_OF_OFFENSE==-1]
manh_f_df = manh_df[manh_df.LEVEL_OF_OFFENSE==1]
pip install geopandas
import geopandas as gpd
import plotly.offline as py
mapbox_access_token = 'pk.eyJ1IjoiZmlzaGVlcCIsImEiOiJjazgwcXd5amIwMnRtM2ZwNDR5OHRjb2Q1In0.wUN67xk4G_3OYy9-tqoqgA'
d = [ go.Scattermapbox(
lat=manh_m_df.Latitude[m_kmeans.labels_==0],
lon=manh_m_df.Longitude[m_kmeans.labels_==0],
mode='markers',
name='Mis_red',
marker=go.scattermapbox.Marker(size=5,color='Red')),
go.Scattermapbox(
lat=manh_m_df.Latitude[m_kmeans.labels_==1],
lon=manh_m_df.Longitude[m_kmeans.labels_==1],
mode='markers',
name='Mis_green',
marker=go.scattermapbox.Marker(size=5,color='Green')),
go.Scattermapbox(
lat=manh_m_df.Latitude[m_kmeans.labels_==2],
lon=manh_m_df.Longitude[m_kmeans.labels_==2],
mode='markers',
name='Mis_blue',
# this is the token used to generate the plotly map, may have to be regenerated
token = "pk.eyJ1IjoidWdoaXRzc2lkIiwiYSI6ImNrNm54Z3I5cTE1aDIzbW55MjcwdWp4MnEifQ.xylSSPUA0Yt9ly6jOBMg4w"
# the latitutes, longitudes, and names of the respective points. First lat corresponds to first lon and text, second to second, and so on
lats = [rep.codeDay.lat, rep.sb1.lat, rep.chpt1.lat, rep.pnbrd1.lat]
lons = [rep.codeDay.lon, rep.sb1.lon, rep.chpt1.lon, rep.pnbrd1.lon]
texts = [rep.codeDay.text, rep.sb1.text, rep.chpt1.text, rep.pnbrd1.text]
# create a new figure using plotly
# initialize the longitudes, latitudes, and texs as points
fig = go.Figure(
go.Scattermapbox(
lat=lats,
lon=lons,
mode='markers',
marker=go.scattermapbox.Marker(size=14),
text=texts,
))
# create the map with needed visual attributes
fig.update_layout(
autosize=True,
hovermode='closest',
mapbox=dict(
accesstoken=token,
bearing=0,
center=go.layout.mapbox.Center(
# center the map at a calculated average of the points' positions
lat=rep.avgLat,
lon=rep.avgLon),
def animedPlot(self, solution, time_res, costmap, start, goal, region, obstacle,scenario, filename):
#colorscale to be used in the contour plot
colorscale=[[0, '#ffffff'],
[0.25, '#a0ff7d'],
[0.5, '#c5c400'],
[0.75, '#dc8000'],
[1, '#e10000']]
colorscale = 'dense'
#Create the plot Structure
fig = make_subplots(
rows=1, cols=2,
column_widths=[0.45,0.45],
row_heights=[1],
subplot_titles=("Solution Path in a Real Map","2D CostMap"),
specs=[[{"type": "Scattermapbox"},{"type": "contour"}]])
#0
#Trace for solution
fig.append_trace(go.Scattermapbox(
lat=solution['lat'],
lon=solution['lon'],
#lat=[],
#lon=[],
name='Solution Path',
mode='markers+lines',
marker=dict(size=5, color='blue')
),row=1,col=1)
#1
#Trace for area of flight
lat_aux = [reg[0] for reg in region]
lon_aux = [reg[1] for reg in region]
fig.append_trace(go.Scattermapbox(
lon = lon_aux,
lat = lat_aux,
fill = "toself",
name='Area of Flight',
marker = { 'size': 5, 'color': "rgba(123, 239, 178, 1)" }),row=1,col=1)
#2
#Start position
fig.append_trace(go.Scattermapbox(
mode = "markers",
lon = [start[1]],
lat = [start[0]],
marker = {'size':5,'color':"black"},
name='Start'
),row=1,col=1)
#3
#goal position
fig.append_trace(go.Scattermapbox(
mode = "markers",
lon = [goal[1]],
lat = [goal[0]],
marker = {'size':5,'color':"black"},
name='Goal'
),row=1,col=1)
#4
#Solution trace for contour
fig.append_trace(go.Scatter(
x = solution['lon'],
y = solution['lat'],
#x = [],
#y = [],
mode='markers+lines',
name='Solution Path',
marker=dict(size=5, color='red')
),row=1,col=2)
#5
#Contour plot
lat,lon = zip(*region)
lat = list(lat)
lon = list(lon)
costmap_y = np.linspace(min(lat),max(lat), len(costmap.y))
costmap_x = np.linspace(min(lon),max(lon), len(costmap.x))
fig.append_trace(go.Contour(
z=costmap.z_time[0],
x=costmap_x,
y=costmap_y,
ids=costmap.z_time,
name='Cost Time: 0',
line_smoothing=0,
colorscale=colorscale,
contours=dict(
start=costmap.z_time[0].min(),
end=costmap.z_time[0].max(),
size=1,
showlines=False)
), row=1, col=2)
#Fixa o eixo dos plots
fig.update_xaxes(range=[min(lon),max(lon)],showgrid=False,constrain="domain")
fig.update_yaxes(range=[min(lat),max(lat)],showgrid=False,constrain="domain")
#Trace for area of flight
if obstacle != [] :
for idx, polygon in enumerate(obstacle):
lat,lon = zip(*polygon[0])
lat = list(lat)
lon = list(lon)
fig.append_trace(go.Scattermapbox(
lon = lon,
lat = lat,
fill = "toself",
name=polygon[3],
marker = { 'size': 5, 'color': "rgba(255, 0, 0, 0)" }),row=1,col=1)
#Cria o perimetro ao redor dos vertiports
if costmap.verti_perimeters != []:
vertiport_lat, vertiport_lon = zip(*scenario.vertiports_real)
fig.append_trace(go.Scattermapbox(
lon = vertiport_lon,
lat = vertiport_lat,
mode='markers',
name='Vertiport',
marker=dict(size=5, color='yellow')
),row=1,col=1)
fig.append_trace(go.Scatter(
x = vertiport_lon,
y = vertiport_lat,
mode='markers',
name='Vertiports',
marker=dict(size=5, color='yellow')
),row=1,col=2)
verti_r_lat = []
verti_r_lon = []
for idx, verti_region in enumerate(costmap.verti_perimeters):
r_lon = verti_region[1]*scenario.lon_range + min(scenario.lon_region)
r_lat = verti_region[0]*scenario.lat_range + min(scenario.lat_region)
verti_r_lon.extend(r_lon)
verti_r_lat.extend(r_lat)
fig.append_trace(go.Scattermapbox(
mode = "markers",
lon = verti_r_lon,
lat = verti_r_lat,
marker = {'size':5,'color':"green"},
name='Vertiport Region'
),row=1,col=1)
fig.append_trace(go.Scatter(
x = verti_r_lon,
y = verti_r_lat,
mode='markers',
name='Vertiport Region',
marker=dict(size=5, color='green')
),row=1,col=2)
#Atualiza o layout
token = 'pk.eyJ1Ijoiam9zdWVoZmEiLCJhIjoiY2tldnNnODB3MDBtdDJzbXUxMXowMTY5MyJ9.Vwj9BTqB1z9RLKlyh70RHw'
fig.update_layout(
mapbox = {
#'style': "outdoors",
'style': "stamen-terrain",
'center': {'lon': -43.9520, 'lat': -19.8997 },
#'accesstoken': token,
'zoom': 11},
title_text="UAS Path Generation", hovermode="closest",
legend=dict(
orientation="h",
yanchor="top",
#y=1.02,
xanchor="right",
x=1
),
updatemenus=[dict( type="buttons",
showactive= False,
buttons=[
dict(label="Play",
method="animate",
args=[[None],
dict(frame= { "duration": 100},
fromcurrent= True,
mode='immediate',
transition= {"duration":0, "easing": "linear"}
)]),
dict(label='Pause',
method='animate',
args= [ [None],
dict(frame= { "duration": 0},
fromcurrent= True,
mode='immediate',
transition= {"duration": 0, "easing": "linear"}
)]
)]
)])
frames = []
#for idx, t in enumerate(time_res):
# frames.append(go.Frame(data=[go.Scattermapbox(
# lat=solution['lat'][:idx+1],
# lon=solution['lon'][:idx+1],mode='markers+lines')],traces=[0]))
# frames.append(go.Frame(data=[go.Scatter(
# x=solution['lon'][:idx],
# y=solution['lat'][:idx],mode='markers+lines')],traces=[4]))
# frames.append(go.Frame(data=[go.Contour(x=costmap_x, y=costmap_y, z=costmap.z_time[t], line_smoothing=0,
# colorscale=colorscale,
# name='Cost Time: '+str(t),
# contours=dict(
# start=costmap.z_time[t].min(),
# end=costmap.z_time[t].max(),
# size=1,
# showlines=False))],traces=[5]))
#fig.update(frames=frames)
plotly.offline.plot(fig, filename=filename,auto_open=False)
def createFigureWithOptimalPointsAndBubbleFlows(D_res,
D_res_optimal,
latCol,
lonCol,
descrCol='PERIOD'):
'''
generates the map with the optimal location of a network.
Optimal locations for each period are in D_res_optimal. Period are prepresented
as animation frames of the figure
#D_res is a dataframe with flows defined by the function calculateOptimalLocation
#D_res_optimal is a dataframe with optimal locations defined by the function calculateOptimalLocation
#latCol is a string with column name for latitude
#lonCol is a string with column name for longitude
#descrCol is a string with column name for node description
'''
#assign color for the optimal points
#aaa=pd.factorize(D_res['YEAR'])
D_res_optimal['COLOR'] = pd.factorize(D_res_optimal['YEAR'])[0]
########################################
######### DEFINE RAW FIGURE ############
########################################
#define raw figure
fig_dict = {"data": [], "layout": {}, "frames": []}
########################################
######### DEFINE BASE LAYOUT ###########
########################################
# Identify all the years
years = list(set(D_res_optimal["YEAR"]))
years.sort()
# define layout hovermode
fig_dict["layout"]["hovermode"] = "closest"
# define sliders
fig_dict["layout"]["sliders"] = {
"args": ["transition", {
"duration": 400,
"easing": "cubic-in-out"
}],
"initialValue": "1952",
"plotlycommand": "animate",
"values": years,
"visible": True
}
#define menus and buttons
fig_dict["layout"]["updatemenus"] = [{
"buttons": [{
"args": [
None, {
"frame": {
"duration": 500,
"redraw": True
},
"fromcurrent": True,
"transition": {
"duration": 300,
"easing": "quadratic-in-out"
}
}
],
"label":
"Play",
"method":
"animate"
}, {
"args": [[None], {
"frame": {
"duration": 0,
"redraw": True
},
"mode": "immediate",
"transition": {
"duration": 0
}
}],
"label":
"Pause",
"method":
"animate"
}],
"direction":
"left",
"pad": {
"r": 10,
"t": 87
},
"showactive":
False,
"type":
"buttons",
"x":
0.1,
"xanchor":
"right",
"y":
0,
"yanchor":
"top"
}]
#define slider dictionary
sliders_dict = {
"active": 0,
"yanchor": "top",
"xanchor": "left",
"currentvalue": {
"font": {
"size": 20
},
"prefix": "Year:",
"visible": True,
"xanchor": "right"
},
"transition": {
"duration": 300,
"easing": "cubic-in-out"
},
"pad": {
"b": 10,
"t": 50
},
"len": 0.9,
"x": 0.1,
"y": 0,
"steps": []
}
########################################
######### DEFINE DATA ##################
########################################
#start from the first frame and define the figure
year = years[0]
########################################
######### DEFINE FIGURE ################
########################################
#define the trace with optimal point
currentColor = 0
data_dict = go.Scattermapbox(
lat=D_res_optimal[D_res_optimal['YEAR'] == year][latCol],
lon=D_res_optimal[D_res_optimal['YEAR'] == year][lonCol],
mode='markers',
marker=go.scattermapbox.Marker(
size=14,
#color='red',
#color=currentColor,
#color=cm.Reds(colore),
color=[i for i in range(0, len(D_res_optimal['YEAR'] == year))],
opacity=1,
colorscale="Reds"),
text=D_res_optimal[D_res_optimal['YEAR'] == year]['PERIOD'],
name='optimal')
fig_dict["data"].append(data_dict)
#define the trace with bubbles of the other flows
data_dict = go.Scattermapbox(
lat=D_res[D_res['YEAR'] == year][latCol],
lon=D_res[D_res['YEAR'] == year][lonCol],
#color=,
text=D_res[D_res['YEAR'] == year][descrCol],
marker=go.scattermapbox.Marker(
size=D_res[D_res['YEAR'] == year]['FLOW_norm'],
color=D_res[D_res['YEAR'] == year]['COST_TOBE'],
opacity=0.5,
showscale=True,
colorscale='Viridis',
),
name='flow intensity')
#projection="natural earth")
fig_dict["data"].append(data_dict)
########################################
######### DEFINE FRAMES ################
########################################
for year in years:
frame = {"data": [], "name": year}
#count the current color to have a gradient in the optimal point
currentColor = currentColor + 1
#define the trace with optimal point
data_dict = go.Scattermapbox(
lat=D_res_optimal[D_res_optimal['YEAR'] <= year][latCol],
lon=D_res_optimal[D_res_optimal['YEAR'] <= year][lonCol],
mode='markers',
marker=go.scattermapbox.Marker(
size=14,
#color='red',
#color=currentColor,
#color=cm.Reds(colore),
color=D_res_optimal[D_res_optimal['YEAR'] <= year]['COLOR'],
#color=[i for i in range(0,len(D_res_optimal['YEAR']==year))],
opacity=1,
colorscale="Reds"),
text=D_res_optimal[D_res_optimal['YEAR'] <= year]['PERIOD'],
name='optimal')
frame["data"].append(data_dict)
#define the trace with bubbles of the other flows
data_dict = go.Scattermapbox(
lat=D_res[D_res['YEAR'] == year][latCol],
lon=D_res[D_res['YEAR'] == year][lonCol],
#color=,
text=D_res[D_res['YEAR'] == year][descrCol],
marker=go.scattermapbox.Marker(
size=D_res[D_res['YEAR'] == year]['FLOW_norm'],
color=D_res[D_res['YEAR'] == year]['COST_TOBE'],
opacity=0.5,
showscale=True,
colorscale='Viridis',
),
name='flow intensity')
#projection="natural earth")
frame["data"].append(data_dict)
fig_dict["frames"].append(frame)
# update the slider
slider_step = {
"args": [[year], {
"frame": {
"duration": 300,
"redraw": True
},
"mode": "immediate",
"transition": {
"duration": 300
}
}],
"label":
year,
"method":
"animate"
}
sliders_dict["steps"].append(slider_step)
#update the layout
fig_dict["layout"]["sliders"] = [sliders_dict]
#create the figure
fig = go.Figure(fig_dict)
#update with openStreetMap style
fig.update_layout(mapbox_style="open-street-map")
return fig
def simplePlot(self, solution, final_solution, obstacle, costmap, start, goal, region):
#Generate a simple solution plot using plotly
t = np.linspace(-1, 1, 100)
x = t + t ** 2
y = t - t ** 2
xm = np.min(x) - 1.5
xM = np.max(x) + 1.5
ym = np.min(y) - 1.5
yM = np.max(y) + 1.5
N = 200
s = np.linspace(-1, 1, N)
xx = s + s ** 2
yy = s - s ** 2
def get_sliders(n_frames, fr_duration=100, x_pos=0.0, slider_len=1.0):
# n_frames= number of frames
#fr_duration=the duration in milliseconds of each frame
#x_pos x-coordinate where the slider starts
#slider_len is a number in (0,1] giving the slider length as a fraction of x-axis length
return [dict(steps= [dict(method= 'animate',#Sets the Plotly method to be called when the slider value is changed.
args= [ [ 'frame{}'.format(k) ],#Sets the arguments values to be passed to the Plotly,
#method set in method on slide
dict(mode= 'immediate',
frame= dict( duration=fr_duration, redraw= True ),
transition=dict( duration= 0)
)
],
label='fr{}'.format(k)
) for k in range(n_frames)],
transition= { 'duration': 0 },
x=x_pos,
len=slider_len)]
#Create a Plot Structure
fig = make_subplots(
rows=1, cols=2,
column_widths=[0.45,0.45],
row_heights=[1],
subplot_titles=("Solution Path in a Real Map","2D CostMap"),
specs=[[{"type": "Scattermapbox"},{"type": "contour"}]])
#data_fig.append(go.Scattermapbox(
# mode = "markers+lines",
# lon = [],
# lat = [],
# marker = {'size': 5},
# name='test'))
#data_fig.append(go.Scattermapbox(
# mode = "markers+lines",
# lon = [],
# lat = [],
# marker = {'size': 5},
# name='FinalResult'))
z = [[2, 4, 7, 12, 13, 14, 15, 16],
[3, 1, 6, 11, 12, 13, 16, 17],
[4, 2, 7, 7, 11, 14, 17, 18],
[5, 3, 8, 8, 13, 15, 18, 19],
[7, 4, 10, 9, 16, 18, 20, 19],
[9, 10, 5, 27, 23, 21, 21, 21],
[11, 14, 17, 26, 25, 24, 23, 22]]
nrows = 80
ncols = 80
time = 10
z_t = []
for t in range(time):
z = np.zeros((nrows+1, ncols+1), dtype=np.uint8) + 1
xx,yy = disk((0.5*nrows,0.5*nrows),0.5*nrows)
z[xx,yy] = 10
xx,yy = disk((0.5*nrows,0.5*nrows),0.25*nrows)
z[xx,yy] = 20
xx,yy = disk((0.5*nrows,0.5*nrows),0.125*nrows)
z[xx,yy] = 50
#Region Clearece
xx, yy = ellipse(0.5*nrows+t*(0.5*nrows/time), 0.5*ncols-t*(0.5*ncols/time), 0.15*nrows, 0.25*ncols, rotation=np.deg2rad(10))
x_del = np.argwhere( (xx <= 0) | (xx >= nrows) )
y_del = np.argwhere( (yy <= 0) | (yy >= ncols) )
xx = np.delete(xx, np.concatenate((x_del, y_del), axis=0))
yy = np.delete(yy, np.concatenate((x_del, y_del), axis=0))
z[xx,yy] = 50
#Region Clearece
xx, yy = ellipse(0.5*nrows-t*(0.5*nrows/time), 0.5*ncols+t*(0.5*ncols/time), 0.15*nrows, 0.25*ncols, rotation=np.deg2rad(10))
x_del = np.argwhere( (xx <= 0) | (xx >= nrows) )
y_del = np.argwhere( (yy <= 0) | (yy >= ncols) )
xx = np.delete(xx, np.concatenate((x_del, y_del), axis=0))
yy = np.delete(yy, np.concatenate((x_del, y_del), axis=0))
z[xx,yy] = 50
z = np.asarray(z,dtype=np.double)
z_t.append(z)
X=np.linspace(-1,1, 80)
Y=np.linspace(-1,1, 80)
colorscale=[[0, '#e1ebec'],
[0.25, '#b1d3e3'],
[0.5, '#72a5d3'],
[0.75, '#3b6ba5'],
[1, '#193f6e']]
colorscale=[[0, '#ffffff'],
[0.25, '#a0ff7d'],
[0.5, '#c5c400'],
[0.75, '#dc8000'],
[1, '#e10000']]
y=np.arange(100)
#fig.append_trace(go.Scatter(x=[1,2,3,4,5,1,2,3,4,5,1,2,3,4,5], y=[1,1,1,1,1,2,2,2,2,2,3,3,3,3,3], marker_size = 100,mode='markers',marker_symbol='square', marker=dict(
# color=z,
# line_width=1)),row=1,col=1)
#filename = "https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"
#df = pd.read_csv(filename, encoding='utf-8')
#df = df.head(100)
lat=np.arange(50)
lon=np.arange(50)
fig.append_trace(go.Scattermapbox(
lat=lat,
lon=lon,
mode='markers+lines',
marker=dict(size=5, color='red')
),row=1,col=1)
lat_aux = [reg[0] for reg in region]
lon_aux = [reg[1] for reg in region]
fig.append_trace(go.Scattermapbox(
lon = lon_aux,
lat = lat_aux,
fill = "toself",
name='Area of Flight',
marker = { 'size': 5, 'color': "rgba(123, 239, 178, 1)" }),row=1,col=1)
fig.append_trace(go.Scattermapbox(
mode = "markers",
lon = [start[1]],
lat = [start[0]],
marker = {'size':5,'color':"black"},
name='Start'
),row=1,col=1)
fig.append_trace(go.Scattermapbox(
mode = "markers",
lon = [goal[1]],
lat = [goal[0]],
marker = {'size':5,'color':"black"},
name='Goal'
),row=1,col=1)
fig.append_trace(go.Scatter(
x = [],
y = [],
ids=X,
mode='markers+lines',
marker=dict(size=5, color='black')
),row=1,col=2)
fig.append_trace(go.Contour(x=X, y=Y, z=z_t[0], ids=z_t,
name='testcolormap',
line_smoothing=0,
colorscale=colorscale,
contours=dict(
start=0,
end=50,
size=1,
showlines=False)
), row=1, col=2)
fig.update_xaxes(range=[-1,1],showgrid=False,constrain="domain")
fig.update_yaxes(range=[-1,1],showgrid=False,constrain="domain")
fig.update_layout(
mapbox = {
'style': "stamen-terrain",
'center': {'lon': 0, 'lat': 0 },
'zoom': 5},
title_text="UAS Path Generation", hovermode="closest",
legend=dict(
orientation="h",
yanchor="top",
#y=1.02,
xanchor="right",
x=1
),
updatemenus=[dict( #x=0,
#y=0,
#yanchor='top',
#xanchor= 'right',
#pad= dict(r= 10, t=40 ),
type="buttons",
showactive= False,
buttons=[dict(label="Play",
method="animate",
args=[[None], dict(frame= { "duration": 50},
fromcurrent= True,
mode='immediate'#,
#transition= {"duration":10, "easing": "linear"}
)]),
dict(label='Pause',
method='animate',
args= [ [None],
dict(frame= { "duration": 0},
fromcurrent= True,
mode='immediate'#,
#transition= {"duration": 0, "easing": "linear"}
)
]
)])]#,
#sliders=get_sliders(n_frames=100)
)
#frames = [go.Frame(
# data=[go.Scattermapbox(
# lon=solution[k][1],
# lat=solution[k][0],
# mode="markers",
# marker=dict(color="orange", size=10),
# name='test'),
# go.Scattermapbox(
# lon=final_solution[1][:k],
# lat=final_solution[1][:k],
# mode="lines",
# marker=dict(color="red", size=10),
# name='FinalResult')]) for k in range(len(solution))]
move = 0
frames = []
for i in range(1, 50):
frames.append(go.Frame(data=[go.Scattermapbox(
lat=lat[:i],
lon=lon[:i],mode='markers+lines')],traces=[0]))
frames.append(go.Frame(data=[go.Scatter(
x=X[:i],
y=Y[:i],mode='markers+lines')],traces=[4]))
frames.append(go.Frame(data=[go.Contour(x=X, y=Y, z=z_t[move], line_smoothing=0,
colorscale=colorscale,
contours=dict(
start=0,
end=50,
size=1,
showlines=False))],traces=[5]))
if i%10 == 0 and i < 100:
move = move + 1
#frames= [go.Frame(data=[go.Scatter(y=y[:i],name='Testing Points'),go.Contour(z=z, line_smoothing=0, colorscale='dense',name='testcolormap')]) for i in range(1, 100)]
fig.update(frames=frames)
plotly.offline.plot(fig, filename='path.html')
#fig.show()
print()
def get_states_cases_plot(self):
coord_dict = {
'AC': [-8.77, -70.55],
'AL': [-9.71, -35.73],
'AM': [-3.07, -61.66],
'AP': [1.41, -51.77],
'BA': [-12.96, -38.51],
'CE': [-3.71, -38.54],
'DF': [-15.83, -47.86],
'ES': [-19.19, -40.34],
'GO': [-16.64, -49.31],
'MA': [-2.55, -44.30],
'MT': [-12.64, -55.42],
'MS': [-20.51, -54.54],
'MG': [-18.10, -44.38],
'PA': [-5.53, -52.29],
'PB': [-7.06, -35.55],
'PR': [-24.89, -51.55],
'PE': [-8.28, -35.07],
'PI': [-8.28, -43.68],
'RJ': [-22.84, -43.15],
'RN': [-5.22, -36.52],
'RO': [-11.22, -62.80],
'RS': [-30.01, -51.22],
'RR': [1.89, -61.22],
'SC': [-27.33, -49.44],
'SE': [-10.90, -37.07],
'SP': [-23.55, -46.64],
'TO': [-10.25, -48.25]
}
_, siglas_df = self.get_state_cases()
list_states = [state for state in coord_dict.keys()]
lat_coords = [coord[0] for coord in coord_dict.values()]
long_coords = [coord[1] for coord in coord_dict.values()]
coord_df = pd.DataFrame({
'state': list_states,
'lat': lat_coords,
'long': long_coords
})
coord_states_cases = pd.merge(coord_df,
siglas_df,
how='inner',
left_on='state',
right_on='uf')
coord_states_cases['hover_text'] = coord_states_cases.apply(
lambda row: row.state + ': ' + str(row.cases), axis=1)
api_key = 'pk.eyJ1IjoiYWxleG1hZ25vIiwiYSI6ImNrODU5d2pveDA0b28zaW95a2p6cHhydnAifQ.YiOEJwtD28loQ2d4txK6dg'
fig = go.Figure(
go.Scattermapbox(lat=coord_states_cases['lat'],
lon=coord_states_cases['long'],
mode='markers',
marker=go.scattermapbox.Marker(
size=coord_states_cases['cases'] / 30),
text=coord_states_cases['hover_text'],
hoverinfo='text'))
fig.update_layout(
title={
'text':
'<b>Total de casos por estado</b><br>(Explore com o mouse)',
'x': 0.5,
'xanchor': 'center',
'yanchor': 'top'
},
autosize=True,
hovermode='closest',
mapbox=dict(accesstoken=api_key,
bearing=0,
center=dict(lat=-15, lon=-55),
pitch=5,
zoom=2.5),
)
return fig
def update_alarms(tab, n_intervals, type_connection, sta):
# COUNTING THE ALARMS WHATEVER THE TAB
i = 0
try:
with open(f'log/{type_connection}/alarms.xml', 'r', encoding='utf-8') as fp:
content = fp.read()
bs_alarms = BS(content, 'lxml-xml')
bs_ongoing = bs_alarms.find('ongoing')
if bs_ongoing is not None:
i = len(bs_ongoing.find_all('alarm'))
except FileNotFoundError:
pass
if tab == 'map':
fig = go.Figure(data=go.Scattermapbox(lat=LIST_LAT_STA, lon=LIST_LON_STA,
text=LIST_NAME_STA, mode='markers',
hovertemplate='<b>Sta:</b> %{text}<br>' +
'<b>Pos:</b> (%{lat}, %{lon})<extra></extra>',
marker=dict(size=12, color='rgba(17, 119, 51, 0.6)')))
fig.update_layout(height=450, margin={"r": 0, "t": 0, "l": 0, "b": 0},
mapbox=dict(zoom=ZOOM_MAP, style='stamen-terrain', bearing=0,
center=go.layout.mapbox.Center(lat=LAT_MAP, lon=LON_MAP), pitch=0))
return html.Div([
dcc.Graph(figure=fig, config={'displaylogo': False}),
html.Div(id='number-alarms', children=i, hidden=True)
])
elif tab == 'soh':
states_xat = []
states_xat_table = []
states_soh = []
states_soh_table = []
try:
with open(f'log/{type_connection}/states_xat.xml', 'r', encoding='utf-8') as fp:
content = fp.read()
bs_states_xat = BS(content, 'lxml-xml')
bs_station = bs_states_xat.find('station', {'name': sta})
if bs_station is not None:
for state in bs_station.find_all('state'):
state_dt = state.get('datetime')
state_datetime = state_dt[1:5] + '-' + state_dt[5:7] + '-' + \
state_dt[7:9] + ' ' + state_dt[10:12] + ':' + \
state_dt[12:14] + ':' + state_dt[14:]
states_xat_table.append(html.Tr(
[html.Td(state_datetime),
html.Td(state.get('name')),
html.Td(state.get('value'))
]
))
table_body = [html.Tbody(states_xat_table)]
states_xat = dbc.Table(table_body,
bordered=True,
dark=True,
hover=True,
responsive=True,
striped=True
)
except FileNotFoundError:
print(f'states_xat.xml file not found in log/{type_connection}')
try:
with open(f'log/{type_connection}/states_soh.xml', 'r', encoding='utf-8') as fp:
content = fp.read()
bs_states_soh = BS(content, 'lxml-xml')
bs_station = bs_states_soh.find('station', {'name': sta})
if bs_station is not None:
for state in bs_station.find_all('state'):
state_dt = state.get('datetime')
state_datetime = state_dt[1:5] + '-' + state_dt[5:7] + '-' + \
state_dt[7:9] + ' ' + state_dt[10:12] + ':' + \
state_dt[12:14] + ':' + state_dt[14:]
states_soh_table.append(html.Tr(
[html.Td(state_datetime),
html.Td(state.get('name')),
html.Td(state.get('value'))
]
))
table_body = [html.Tbody(states_soh_table)]
states_soh = dbc.Table(table_body,
bordered=True,
dark=True,
hover=True,
responsive=True,
striped=True
)
except FileNotFoundError:
print(f'states_soh.xml file not found in log/{type_connection}')
return [html.Div(id='tabs-content-inline', children=[states_xat, states_soh]),
html.Div(id='number-alarms', children=i, hidden=True)]
elif tab == 'alarms_in_progress':
nc_alarms_list = []
i = 0
try:
with open(f'log/{type_connection}/alarms.xml', 'r', encoding='utf-8') as fp:
content = fp.read()
bs_alarms = BS(content, 'lxml-xml')
bs_ongoing = bs_alarms.find('ongoing')
# display all the ongoing alarms
nc_alarms_inside = []
if bs_ongoing is not None:
for alarm in bs_ongoing.find_all('alarm'):
i = i + 1
alarm_station = alarm.get('station')
alarm_state = alarm.get('state')
alarm_detail = alarm.get('detail')
alarm_id = alarm.get('id')
alarm_problem = int(alarm.get('problem'))
if alarm_problem == 1:
text_badge = "Warning"
color = "warning"
else:
text_badge = "Critic"
color = "danger"
alarm_dt = alarm.get('datetime')
alarm_datetime = alarm_dt[1:5] + '-' + alarm_dt[5:7] + '-' + \
alarm_dt[7:9] + ' ' + alarm_dt[10:12] + ':' + \
alarm_dt[12:14] + ':' + alarm_dt[14:]
nc_alarms_inside.append(html.Tr([html.Td(alarm_datetime),
html.Td(alarm_station),
html.Td(alarm_state),
html.Td(alarm_detail),
html.Td(dbc.Badge(text_badge, color=color, className="mr-1")),
html.Td([dbc.Button('Complete Alarm?',
id={'type': 'btn-alarm',
'id_alarm': alarm_id},
className="mr-1", n_clicks=0)
]),
]))
table_body = [html.Tbody(nc_alarms_inside)]
nc_alarms_list = dbc.Table(table_body,
bordered=True,
dark=True,
hover=True,
responsive=True,
striped=True
)
except FileNotFoundError:
pass
return [html.Div(id='tabs-content-inline', children=nc_alarms_list),
html.Div(id='number-alarms', children=i, hidden=True)]
elif tab == 'alarms_completed':
c_alarms_list = []
try:
with open(f'log/{type_connection}/alarms.xml', 'r', encoding='utf-8') as fp:
content = fp.read()
bs_alarms = BS(content, 'lxml-xml')
bs_completed = bs_alarms.find('completed')
# display all the ongoing alarms
c_alarms_inside = []
if bs_completed is not None:
for alarm in bs_completed.find_all('alarm'):
alarm_station = alarm.get('station')
alarm_state = alarm.get('state')
alarm_detail = alarm.get('detail')
alarm_problem = int(alarm.get('problem'))
if alarm_problem == 1:
text_badge = "Warning"
color = "warning"
else:
text_badge = "Critic"
color = "danger"
alarm_dt = alarm.get('datetime')
alarm_datetime = alarm_dt[1:5] + '-' + alarm_dt[5:7] + '-' + \
alarm_dt[7:9] + ' ' + alarm_dt[10:12] + ':' + \
alarm_dt[12:14] + ':' + alarm_dt[14:]
c_alarms_inside.append(html.Tr([html.Td(alarm_datetime),
html.Td(alarm_station),
html.Td(alarm_state),
html.Td(alarm_detail),
html.Td(dbc.Badge(text_badge, color=color, className="mr-1"))]))
table_body = [html.Tbody(c_alarms_inside)]
c_alarms_list = dbc.Table(table_body,
bordered=True,
dark=True,
hover=True,
responsive=True,
striped=True
)
except FileNotFoundError:
pass
return [html.Div(id='tabs-content-inline', children=c_alarms_list),
html.Div(id='number-alarms', children=i, hidden=True)]
def update_figures(derived_virtual_selected_rows):
# When the table is first rendered, `derived_virtual_data` and
# `derived_virtual_selected_rows` will be `None`. This is due to an
# idiosyncracy in Dash (unsupplied properties are always None and Dash
# calls the dependent callbacks when the component is first rendered).
# So, if `rows` is `None`, then the component was just rendered
# and its value will be the same as the component's dataframe.
# Instead of setting `None` in here, you could also set
# `derived_virtual_data=df.to_rows('dict')` when you initialize
# the component.
if derived_virtual_selected_rows is None:
derived_virtual_selected_rows = []
dff = dfSum
mapbox_access_token = "pk.eyJ1IjoicGxvdGx5bWFwYm94IiwiYSI6ImNqdnBvNDMyaTAxYzkzeW5ubWdpZ2VjbmMifQ.TXcBE-xg9BFdV2ocecc_7g"
# Generate a list for hover text display
textList = []
for area, region in zip(dfs[keyList[0]]['Province/State'],
dfs[keyList[0]]['Country/Region']):
if type(area) is str:
if region == "Hong Kong" or region == "Macau" or region == "Taiwan":
textList.append(area)
else:
textList.append(area + ', ' + region)
else:
textList.append(region)
fig2 = go.Figure(
go.Scattermapbox(
lat=dfs[keyList[0]]['lat'],
lon=dfs[keyList[0]]['lon'],
mode='markers',
marker=go.scattermapbox.Marker(
color='#ca261d',
size=dfs[keyList[0]]['Confirmed'].tolist(),
sizemin=4,
sizemode='area',
sizeref=2. * max(dfs[keyList[0]]['Confirmed'].tolist()) /
(150.**2),
),
text=textList,
hovertext=[
'Comfirmed: {}<br>Recovered: {}<br>Death: {}'.format(i, j, k)
for i, j, k in zip(dfs[keyList[0]]['Confirmed'], dfs[
keyList[0]]['Recovered'], dfs[keyList[0]]['Deaths'])
],
hovertemplate="<b>%{text}</b><br><br>" + "%{hovertext}<br>" +
"<extra></extra>"))
fig2.update_layout(
plot_bgcolor='#151920',
paper_bgcolor='#cbd2d3',
margin=go.layout.Margin(l=10, r=10, b=10, t=0, pad=40),
hovermode='closest',
transition={'duration': 1000},
mapbox=go.layout.Mapbox(
accesstoken=mapbox_access_token,
style="light",
# The direction you're facing, measured clockwise as an angle from true north on a compass
bearing=0,
center=go.layout.mapbox.Center(
lat=3.684188 if len(derived_virtual_selected_rows) == 0 else
dff['lat'][derived_virtual_selected_rows[0]],
lon=148.374024 if len(derived_virtual_selected_rows) == 0 else
dff['lon'][derived_virtual_selected_rows[0]]),
pitch=0,
zoom=1.2 if len(derived_virtual_selected_rows) == 0 else 4))
return fig2
def update_figures(row_ids, selected_row_ids):
# When the table is first rendered, `derived_virtual_data` and
# `derived_virtual_selected_rows` will be `None`. This is due to an
# idiosyncracy in Dash (unsupplied properties are always None and Dash
# calls the dependent callbacks when the component is first rendered).
# So, if `rows` is `None`, then the component was just rendered
# and its value will be the same as the component's dataframe.
# Instead of setting `None` in here, you could also set
# `derived_virtual_data=df.to_rows('dict')` when you initialize
# the component.
if row_ids is None:
row_ids = []
dff = dfSum
mapbox_access_token = "pk.eyJ1IjoicGxvdGx5bWFwYm94IiwiYSI6ImNqdnBvNDMyaTAxYzkzeW5ubWdpZ2VjbmMifQ.TXcBE-xg9BFdV2ocecc_7g"
# Generate a list for hover text display
textList = []
for area, region in zip(dfs[keyList[0]]['Province/State'],
dfs[keyList[0]]['Country/Region']):
if type(area) is str:
if region == "Hong Kong" or region == "Macau" or region == "Taiwan":
textList.append(area)
else:
textList.append(area + ', ' + region)
else:
textList.append(region)
# Generate a list for color gradient display
colorList = []
for comfirmed, recovered, deaths in zip(dfs[keyList[0]]['Confirmed'],
dfs[keyList[0]]['Recovered'],
dfs[keyList[0]]['Deaths']):
remaining = recovered / (comfirmed - deaths)
colorList.append(remaining)
fig2 = go.Figure(
go.Scattermapbox(
lat=dfs[keyList[0]]['lat'],
lon=dfs[keyList[0]]['lon'],
mode='markers',
marker=go.scattermapbox.Marker(
color=['#d7191c' if i < 1 else '#1a9622' for i in colorList],
size=[i**(1 / 3) for i in dfs[keyList[0]]['Confirmed']],
sizemin=1,
sizemode='area',
sizeref=2. *
max([math.sqrt(i)
for i in dfs[keyList[0]]['Confirmed']]) / (100.**2),
),
text=textList,
hovertext=[
'Comfirmed: {}<br>Recovered: {}<br>Death: {}'.format(i, j, k)
for i, j, k in zip(dfs[keyList[0]]['Confirmed'], dfs[
keyList[0]]['Recovered'], dfs[keyList[0]]['Deaths'])
],
hovertemplate="<b>%{text}</b><br><br>" + "%{hovertext}<br>" +
"<extra></extra>"))
fig2.update_layout(
plot_bgcolor='#151920',
paper_bgcolor='#cbd2d3',
margin=go.layout.Margin(l=10, r=10, b=10, t=0, pad=40),
hovermode='closest',
transition={'duration': 50},
annotations=[
dict(
x=.5,
y=-.01,
align='center',
showarrow=False,
text=
"Points are placed based on data geolocation levels.<br><b>Province/State level<b> - China, Australia, United States, and Canada; <b>Country level<b> - other countries.",
xref="paper",
yref="paper",
font=dict(size=10, color='#292929'),
)
],
mapbox=go.layout.Mapbox(
accesstoken=mapbox_access_token,
style="light",
# The direction you're facing, measured clockwise as an angle from true north on a compass
bearing=0,
center=go.layout.mapbox.Center(
lat=14.056159
if len(row_ids) == 0 else dff.loc[selected_row_ids[0]].lat,
lon=22.920039
if len(row_ids) == 0 else dff.loc[selected_row_ids[0]].lon),
pitch=0,
zoom=1.03 if len(row_ids) == 0 else 4))
return fig2
def plot_movement(df, selected_numbers):
data = []
colors = [
'#636EFA', '#EF553B', '#00CC96', '#AB63FA', '#FFA15A', '#19D3F3',
'#FF6692', '#B6E880', '#FF97FF', '#FECB52'
]
color_no = 0
df = pd.merge(df, towers[['lat', 'lon', 'TowerID']], on='TowerID')
for number in selected_numbers:
df['Date'] = pd.to_datetime(df['Date'], format='%d-%m-%Y')
#df=df[df['Date'].isin(pd.date_range('2020/06/05','2020/06/06'))].sort_values(['Date','Time']).reset_index()
filtered_df = df[df['Caller'] == number].sort_values(['Date', 'Time'
]).reset_index()
all_lat = []
all_lon = []
locs = []
duration_hover = []
duration_output = []
l1 = filtered_df['Date'].dt.date.apply(
str) + ' ' + filtered_df['Time'].apply(str)
for i in range(len(l1)):
l1[i] = pd.to_datetime(l1[i], format='%Y-%m-%d %H:%M:%S')
for i in range(len(l1) - 1):
x = l1[i + 1] - l1[i]
duration_hover.append(x)
dura_op = [] # Will hold str(transitduration) to be hovered per edge
for i in range(len(duration_output)):
dura_op.append(str(duration_output[i]))
for (index, row) in filtered_df.iterrows():
locs.append([row['lat'], row['lon']])
for i in range(len(locs) - 1):
all_lat, all_lon, duration_output = addEdgemap(
locs[i], locs[i + 1], duration_hover[i], all_lat, all_lon,
duration_output, 0.7, "end", 0.009, 30, 10)
data.append(
go.Scattermapbox(
lat=all_lat,
lon=all_lon,
mode="lines",
hovertext=duration_output,
marker=go.scattermapbox.Marker(size=17,
color=colors[color_no],
opacity=1),
))
color_no += 1
color_no %= len(colors)
fig = go.Figure(data,
layout={
'mapbox_style':
'open-street-map',
'showlegend':
False,
'margin':
dict(l=0, r=0, t=0, b=0),
'mapbox':
dict(bearing=0,
center=dict(lat=23.2599, lon=77.4126),
pitch=0,
zoom=10)
})
return fig
# mapbox_access_token = open(".mapbox_token").read()
df = pd.read_csv(
'https://raw.githubusercontent.com/plotly/datasets/master/Nuclear%20Waste%20Sites%20on%20American%20Campuses.csv'
)
site_lat = df.lat
site_lon = df.lon
locations_name = df.text
fig = go.Figure()
fig.add_trace(
go.Scattermapbox(lat=site_lat,
lon=site_lon,
mode='markers',
marker=go.scattermapbox.Marker(size=17,
color='rgb(255, 0, 0)',
opacity=0.7),
text=locations_name,
hoverinfo='text'))
fig.add_trace(
go.Scattermapbox(lat=site_lat,
lon=site_lon,
mode='markers',
marker=go.scattermapbox.Marker(size=8,
color='rgb(242, 177, 172)',
opacity=0.7),
hoverinfo='none'))
fig.update_layout(
title='Nuclear Waste Sites on Campus',
years = [
2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2018, 2019, 2020
]
maps = go.Figure()
access = (
"pk.eyJ1Ijoiam5kaXRpZmVpIiwiYSI6ImNrbHVwN2o4NDA5NmgybnBsNzI3Z3c1dWYifQ.971IMEJSB-BBVl575_HH3w"
)
maps.add_trace(
go.Scattermapbox(lat=teamsStats['lat'],
lon=teamsStats['lon'],
mode='markers',
hovertext=teamsStats['short'],
hoverinfo="text",
marker=go.scattermapbox.Marker(size=20,
color='#e83e8c',
opacity=0.7)))
maps.update_layout(autosize=True,
hovermode='closest',
showlegend=False,
mapbox=dict(accesstoken=access,
bearing=0,
center=dict(lat=38, lon=-94),
pitch=0,
zoom=3,
style='light'),
mapbox_style="light",
margin=dict(
mapbox_style = "mapbox://styles/neeharikak/ck3yt0g624vwr1clm0itq8t5r"
listvolcanos = []
lats = []
lons = []
for obj in volc.find():
listvolcanos.append(db["volcanos"].find().sort([("PEI", -1)]))
lats.append(obj["latitude"])
lons.append(obj["longitude"])
fig = go.Figure()
fig.add_trace(
go.Scattermapbox(lat=lats,
lon=lons,
mode="markers",
marker={
"size": [30, 20, 10],
"color": ["Red", "Orange", "Yellow"]
}))
fig.update_layout(autosize=True,
hovermode='closest',
mapbox=go.layout.Mapbox(accesstoken=token,
bearing=0,
style=mapbox_style,
center=go.layout.mapbox.Center(
lat=33.930828, lon=-98.484879),
pitch=0,
zoom=3))
fig.show()
xWidth=maxLong-minLong
yZoom=-1.446*math.log(yWidth)+7.2753
xZoom=-1.415*math.log(yWidth)+8.7068
#yZoom=CNST_ZOOM
#xZoom=CNST_ZOOM
return min(round(yZoom,2),round(xZoom,2))
_MyDb=db.BabyTrackerDB(Params.DB_USER, Params.DB_PASS, Params.DB_SERVER, Params.DB_DATABASE, Params.DB_PORT)
mydfTracks=_MyDb.GetTracks(1)
#mydfTracksA=_MyDb.GetTracks(1,2,datetime.strptime('03/01/2021','%d/%m/%Y'))
#Dibuixem el mapa base amb la posició de l'usuari
if mydfTracks is None:
fig = go.Figure(go.Scattermapbox(
mode = "markers+text",
marker =dict(size=20, color='red'),
textposition='top right',
name='Basic')
)
fig.update_layout(
margin ={'l':0,'t':0,'b':0,'r':0},
height=750,
mapbox = {
'accesstoken':MapBoxToken,
'style': 'stamen-watercolor',
'zoom': CNST_ZOOM},
title_text='Cercador de Babys')
else:
fig = go.Figure(go.Scattermapbox(
mode = "markers+text",
lon =mydfTracks,
def generate_map():
json_data = request.get_json(force=True)
offenders_co_ordinates = json_data[
'offenders'] if 'offenders' in json_data else {
'lats': [],
'lons': []
}
citizens_co_ordinates = json_data[
'citizens'] if 'citizens' in json_data else {
'lats': [],
'lons': []
}
victims_co_ordinates = json_data['victims'] if 'victims' in json_data else {
'lats': [],
'lons': []
}
cameras_co_ordinates = json_data['cameras'] if 'cameras' in json_data else {
'lats': [],
'lons': []
}
campaigns_co_ordinates = json_data[
'campaigns'] if 'campaigns' in json_data else {
'lats': [],
'lons': []
}
stations_co_ordinates = json_data[
'stations'] if 'stations' in json_data else {
'lats': [],
'lons': []
}
parks_co_ordinates = json_data['parks'] if 'parks' in json_data else {
'lats': [],
'lons': []
}
whether_night_time = json_data[
'whether_night_time'] if 'whether_night_time' in json_data else False
style = 'light'
if (whether_night_time):
style = 'dark'
offenders = len(offenders_co_ordinates['lats'])
citizens = len(citizens_co_ordinates['lats'])
victims = len(victims_co_ordinates['lats'])
cameras = len(cameras_co_ordinates['lats'])
campaigns = len(campaigns_co_ordinates['lats'])
stations = len(stations_co_ordinates['lats'])
parks = len(parks_co_ordinates['lats'])
mapbox_access_token = open(".mapbox_token").read()
fig = go.Figure()
fig.add_trace(
go.Scattermapbox(
name="Offenders",
lat=offenders_co_ordinates['lats'],
lon=offenders_co_ordinates['lons'],
mode='markers',
hoverinfo='text',
marker=go.scattermapbox.Marker(
size=size_of_the_markers['offenders'],
color=colors_of_the_markers['offenders'],
opacity=0.7),
text=generate_names("Offender", offenders, 'offender'),
))
fig.add_trace(
go.Scattermapbox(
name="Citizens",
lat=citizens_co_ordinates['lats'],
lon=citizens_co_ordinates['lons'],
mode='markers',
hoverinfo='text',
marker=go.scattermapbox.Marker(
size=size_of_the_markers['citizens'],
color=colors_of_the_markers['citizens'],
opacity=0.7),
text=generate_names("Citizen", offenders),
))
fig.add_trace(
go.Scattermapbox(
name="Victims",
lat=victims_co_ordinates['lats'],
lon=victims_co_ordinates['lons'],
mode='markers',
hoverinfo='text',
marker=go.scattermapbox.Marker(
size=size_of_the_markers['victims'],
color=colors_of_the_markers['victims'],
opacity=0.7),
text=generate_names("Victim", victims, 'victim'),
))
# Cameras to the map
fig.add_trace(
go.Scattermapbox(
name="Cameras",
lat=cameras_co_ordinates['lats'],
lon=cameras_co_ordinates['lons'],
mode='markers',
hoverinfo='text',
marker=go.scattermapbox.Marker(
size=size_of_the_markers['cameras'],
color=colors_of_the_markers['cameras'],
opacity=0.7),
text=generate_names("Camera", cameras),
))
# Campaigns to the map
fig.add_trace(
go.Scattermapbox(
name="Campaigns",
lat=campaigns_co_ordinates['lats'],
lon=campaigns_co_ordinates['lons'],
mode='markers',
hoverinfo='text',
marker=go.scattermapbox.Marker(
size=size_of_the_markers['campaigns'],
color=colors_of_the_markers['campaigns'],
opacity=0.7),
text=generate_names("Campaign", campaigns),
))
# Stations to the map
fig.add_trace(
go.Scattermapbox(
name="Stations",
lat=stations_co_ordinates['lats'],
lon=stations_co_ordinates['lons'],
mode='markers',
hoverinfo='text',
marker=go.scattermapbox.Marker(
size=size_of_the_markers['station'],
color=colors_of_the_markers['station'],
opacity=0.7),
text=generate_names("Station", stations),
))
# Parks to the map
fig.add_trace(
go.Scattermapbox(
name="Parks",
lat=parks_co_ordinates['lats'],
lon=parks_co_ordinates['lons'],
mode='markers',
hoverinfo='text',
marker=go.scattermapbox.Marker(size=size_of_the_markers['park'],
color=colors_of_the_markers['park'],
opacity=0.7),
text=generate_names("Park", parks),
))
fig.update_layout(
title="Westminster Crime Simulator",
autosize=True,
hovermode='closest',
showlegend=True,
mapbox=go.layout.Mapbox(accesstoken=mapbox_access_token,
bearing=0,
center=go.layout.mapbox.Center(lat=lat,
lon=lon),
pitch=0,
zoom=11.5,
style=style),
)
fig.write_html('templates/map.html')
return {'status': True, 'message': "Successfully created Map"}
obj["TotalFatalInjuries"]) >= 100:
Lats_3.append(obj["Latitude"])
Lons_3.append(obj["Longitude"])
else:
Lats_4.append(obj["Latitude"])
Lons_4.append(obj["Longitude"])
else:
Lats_4.append(obj["Latitude"])
Lons_4.append(obj["Longitude"])
L4 = [
go.Scattermapbox(lat=Lats_1,
lon=Lons_1,
mode='markers',
marker=dict(
size=20,
color='red',
opacity=1,
),
name="Greater than 300")
]
L3 = [
go.Scattermapbox(lat=Lats_2,
lon=Lons_2,
mode='markers',
marker=dict(
size=15,
color='orange',
opacity=1,
),
def run_simulation():
json_data = request.get_json(force=True)
max_effective_distance['campaigns'] = int(
json_data['campaigns_effective_distance']
) if 'campaigns_effective_distance' in json_data else max_effective_distance[
'cameras']
max_effective_distance['cameras'] = int(
json_data['cameras_effective_distance']
) if 'cameras_effective_distance' in json_data else max_effective_distance[
'campaigns']
whether_night_time = json_data[
'whether_night_time'] if 'whether_night_time' in json_data else False
day = json_data['day']
print("day: ", day)
style = 'light'
if (whether_night_time):
max_effective_distance['campaigns'] = max_effective_distance[
'campaigns'] * night_effectiveness_reduction_factor
max_effective_distance['cameras'] = max_effective_distance[
'cameras'] * night_effectiveness_reduction_factor
style = 'dark'
print("type", type(max_effective_distance['cameras']))
sim.env.setCameraEffective(max_effective_distance['cameras'])
sim.env.setCampaignEffective(max_effective_distance['campaigns'])
happened_attacks, attackpositions = sim.runStep()
mapbox_access_token = open(".mapbox_token").read()
attackPostion['lat'] = attackPostion['lat'] + happened_attacks['lats']
attackPostion['lon'] = attackPostion['lon'] + happened_attacks['lons']
fig = go.Figure()
fig.add_trace(
go.Scattermapbox(
lat=happened_attacks['lats'],
lon=happened_attacks['lons'],
mode='markers',
hoverinfo='text',
marker=go.scattermapbox.Marker(
size=size_of_the_markers['offenders'],
color=colors_of_the_markers['offenders'],
opacity=0.7),
text=happened_attacks['texts'],
))
fig.update_layout(
title="Westminster Crime Simulator",
autosize=True,
hovermode='closest',
showlegend=False,
mapbox=go.layout.Mapbox(accesstoken=mapbox_access_token,
bearing=0,
center=go.layout.mapbox.Center(lat=lat,
lon=lon),
pitch=0,
zoom=11.5,
style=style),
)
print("day: ", day)
fig.write_html('templates/attacks-day-' + str(day) + '.html')
return {
'status': True,
'message': "Successfully created Attack",
'attacks': attackpositions,
}
marker_line_width=5,
marker_line_color="rgb(0,0,0)",
showscale=False),
go.Choroplethmapbox(
geojson=choropleth_bg.set_index('GEOID').__geo_interface__,
locations=choropleth_bg.GEOID,
z=choropleth_bg.over_60,
text=choropleth_bg.NAMELSAD,
hoverinfo="text+z",
colorscale="Viridis",
marker_opacity=0.3,
marker_line_width=2,
marker_line_color="#C6C6C6"),
go.Scattermapbox(lat=sampled_bgs.Sampled_Point_Latitude,
lon=sampled_bgs.Sampled_Point_Longitude,
mode='markers',
name='Patient Location',
marker=go.scattermapbox.Marker(color='rgb(0,0,0)')),
go.Scattermapbox(lat=comprehensives.Latitude,
lon=comprehensives.Longitude,
name='Comprehensive',
text=comprehensives.OrganizationName,
hoverinfo='text',
mode='markers',
marker=go.scattermapbox.Marker(size=15,
color='rgb(255,0,0)')),
go.Scattermapbox(lat=primaries.Latitude,
lon=primaries.Longitude,
text=primaries.OrganizationName,
hoverinfo='text',
name='Primary',
orangelat200.append(obj["Latitude"])
orangelon200.append(obj["Longitude"])
elif (int(obj["TotalFatalInjuries"]) > 100):
yellowlat100.append(obj["Latitude"])
yellowlon100.append(obj["Longitude"])
else:
bluelat.append(obj["Latitude"])
bluelon.append(obj["Longitude"])
fig = go.Figure()
fig.add_trace(
go.Scattermapbox(lat=redlat300,
lon=redlon300,
mode="markers",
marker={
"size": 10,
"color": "red"
}))
fig.add_trace(
go.Scattermapbox(lat=orangelat200,
lon=orangelon200,
mode="markers",
marker={
"size": 10,
"color": "orange"
}))
fig.add_trace(
go.Scattermapbox(lat=yellowlat100,
def plot_path(lat, long, origin_point, destination_point):
"""
Given a list of latitudes and longitudes, origin
and destination point, plots a path on a map
Parameters
----------
lat, long: list of latitudes and longitudes
origin_point, destination_point: co-ordinates of origin
and destination
Returns
-------
Nothing. Only shows the map.
"""
# adding the lines joining the nodes
fig = go.Figure(
go.Scattermapbox(name="Path",
mode="lines",
lon=long,
lat=lat,
marker={'size': 10},
line=dict(width=4.5, color='blue')))
# adding source marker
fig.add_trace(
go.Scattermapbox(name="Source",
mode="markers",
lon=[origin_point[1]],
lat=[origin_point[0]],
marker={
'size': 12,
'color': "red"
}))
# adding destination marker
fig.add_trace(
go.Scattermapbox(name="Destination",
mode="markers",
lon=[destination_point[1]],
lat=[destination_point[0]],
marker={
'size': 12,
'color': 'green'
}))
# getting center for plots:
lat_center = np.mean(lat)
long_center = np.mean(long)
# defining the layout using mapbox_style
fig.update_layout(mapbox_style="stamen-terrain",
mapbox_center_lat=30,
mapbox_center_lon=-80)
fig.update_layout(margin={
"r": 0,
"t": 0,
"l": 0,
"b": 0
},
mapbox={
'center': {
'lat': lat_center,
'lon': long_center
},
'zoom': 13
})
fig.show()
mapbox_token = ""
# using pymongo to interface with my mongo database image (mongodb) in docker
client = pymongo.MongoClient("mongodb://localhost:27017/")
db = client["armageddon"]
uf = db["ufos"]
# iterate through mongodb collection and create a pandas dataframe with the collection data
cursor_ufos = uf.find()
df = pd.DataFrame(list(cursor_ufos))
# sets the geospatial data up
figure = go.Figure(
go.Scattermapbox(lat=df["latitude"],
lon=df["longitude"],
text=df["city"],
mode="markers",
marker=dict(size=4, color="blue", opacity=.8)))
# sets the layout for plotting
figure.update_layout(autosize=True,
hovermode="closest",
template="plotly_dark",
mapbox=go.layout.Mapbox(accesstoken=mapbox_token,
bearing=0,
center=go.layout.mapbox.Center(
lat=33.9137, lon=-98.4934),
pitch=0,
zoom=5),
title="UFO sightings around the world")
columns=['totalCasesMS', 'deathsMS', 'country', 'URL', 'notConfirmedByMS'])
df_table = df_table.rename(columns={
'id': 'Local',
'totalCases': 'Total',
'deaths': 'Fatais'
})
trace_size = result.Confirmados * 100
fig = go.Figure(
go.Scattermapbox(
lat=result.lat,
lon=result.lon,
mode='markers',
marker=go.scattermapbox.Marker(sizemode='area',
size=result.Confirmados,
sizeref=2),
text=result.id,
customdata=result.Mortes,
hovertemplate="<b>%{text}</b><br><br>" +
"Confirmados: %{marker.size}<br>" + "Óbitos: %{customdata}<br>" +
"<extra></extra>",
))
fig.update_layout(mapbox=dict(
center=go.layout.mapbox.Center(lat=-15, lon=-47),
zoom=3,
))
fig.update_layout(mapbox_style="carto-positron")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
map_layout = html.Div(children=[
def iplot(n,
fig=None,
bus_colors='blue',
bus_colorscale=None,
bus_colorbar=None,
bus_sizes=10,
bus_text=None,
line_colors='green',
line_widths=2,
line_text=None,
layouter=None,
title="",
size=None,
branch_components=['Line', 'Link'],
iplot=True,
jitter=None,
mapbox=False,
mapbox_style='open-street-map',
mapbox_token="",
mapbox_parameters={}):
"""
Plot the network buses and lines interactively using plotly.
Parameters
----------
fig : dict, default None
If not None, figure is built upon this fig.
bus_colors : dict/pandas.Series
Colors for the buses, defaults to "b"
bus_colorscale : string
Name of colorscale if bus_colors are floats, e.g. 'Jet', 'Viridis'
bus_colorbar : dict
Plotly colorbar, e.g. {'title' : 'my colorbar'}
bus_sizes : dict/pandas.Series
Sizes of bus points, defaults to 10
bus_text : dict/pandas.Series
Text for each bus, defaults to bus names
line_colors : dict/pandas.Series
Colors for the lines, defaults to "g" for Lines and "cyan" for
Links. Colors for branches other than Lines can be
specified using a pandas Series with a MultiIndex.
line_widths : dict/pandas.Series
Widths of lines, defaults to 2. Widths for branches other
than Lines can be specified using a pandas Series with a
MultiIndex.
line_text : dict/pandas.Series
Text for lines, defaults to line names. Text for branches other
than Lines can be specified using a pandas Series with a
MultiIndex.
layouter : networkx.drawing.layout function, default None
Layouting function from `networkx <https://networkx.github.io/>`_ which
overrules coordinates given in ``n.buses[['x','y']]``. See
`list <https://networkx.github.io/documentation/stable/reference/drawing.html#module-networkx.drawing.layout>`_
of available options.
title : string
Graph title
size : None|tuple
Tuple specifying width and height of figure; e.g. (width, heigh).
branch_components : list of str
Branch components to be plotted, defaults to Line and Link.
iplot : bool, default True
Automatically do an interactive plot of the figure.
jitter : None|float
Amount of random noise to add to bus positions to distinguish
overlapping buses
mapbox : bool, default False
Switch to use Mapbox.
mapbox_style : str, default 'open-street-map'
Define the mapbox layout style of the interactive plot. If this is set
to a mapbox layout, the argument ``mapbox_token`` must be a valid Mapbox
API access token.
Valid open layouts are:
open-street-map, white-bg, carto-positron, carto-darkmatter,
stamen-terrain, stamen-toner, stamen-watercolor
Valid mapbox layouts are:
basic, streets, outdoors, light, dark, satellite, satellite-streets
mapbox_token : string
Mapbox API access token. Obtain from https://www.mapbox.com.
Can also be included in mapbox_parameters as `accesstoken=mapbox_token`.
mapbox_parameters : dict
Configuration parameters of the Mapbox layout.
E.g. {"bearing": 5, "pitch": 10, "zoom": 1, "style": 'dark'}.
Returns
-------
fig: dictionary for plotly figure
"""
defaults_for_branches = {
'Link': dict(color="cyan", width=2),
'Line': dict(color="blue", width=2),
'Transformer': dict(color='green', width=2)
}
if fig is None:
fig = dict(data=[], layout={})
if bus_text is None:
bus_text = 'Bus ' + n.buses.index
x, y = _get_coordinates(n, layouter=layouter)
if jitter is not None:
x = x + np.random.uniform(low=-jitter, high=jitter, size=len(x))
y = y + np.random.uniform(low=-jitter, high=jitter, size=len(y))
bus_trace = dict(
x=x,
y=y,
text=bus_text,
type="scatter",
mode="markers",
hoverinfo="text",
marker=dict(color=bus_colors, size=bus_sizes),
)
if bus_colorscale is not None:
bus_trace['marker']['colorscale'] = bus_colorscale
if bus_colorbar is not None:
bus_trace['marker']['colorbar'] = bus_colorbar
def as_branch_series(ser):
if isinstance(ser, dict) and set(ser).issubset(branch_components):
return pd.Series(ser)
elif isinstance(ser, pd.Series):
if isinstance(ser.index, pd.MultiIndex):
return ser
index = ser.index
ser = ser.values
else:
index = n.lines.index
return pd.Series(ser,
index=pd.MultiIndex(levels=(["Line"], index),
labels=(np.zeros(len(index)),
np.arange(len(index)))))
line_colors = as_branch_series(line_colors)
line_widths = as_branch_series(line_widths)
if line_text is not None:
line_text = as_branch_series(line_text)
shapes = []
shape_traces = []
for c in n.iterate_components(branch_components):
l_defaults = defaults_for_branches[c.name]
l_widths = line_widths.get(c.name, l_defaults['width'])
l_colors = line_colors.get(c.name, l_defaults['color'])
if line_text is None:
l_text = c.name + ' ' + c.df.index
else:
l_text = line_text.get(c.name)
if isinstance(l_colors, pd.Series):
if issubclass(l_colors.dtype.type, np.number):
l_colors = None
else:
l_colors.fillna(l_defaults['color'], inplace=True)
x0 = c.df.bus0.map(x)
x1 = c.df.bus1.map(x)
y0 = c.df.bus0.map(y)
y1 = c.df.bus1.map(y)
for line in c.df.index:
color = l_colors if isinstance(l_colors,
string_types) else l_colors[line]
width = l_widths if isinstance(l_widths,
(int, float)) else l_widths[line]
shapes.append(
dict(type='line',
x0=x0[line],
y0=y0[line],
x1=x1[line],
y1=y1[line],
opacity=0.7,
line=dict(color=color, width=width)))
shape_traces.append(
dict(x=0.5 * (x0 + x1),
y=0.5 * (y0 + y1),
text=l_text,
type="scatter",
mode="markers",
hoverinfo="text",
marker=dict(opacity=0.)))
if mapbox:
shape_traces_latlon = []
for st in shape_traces:
st['lon'] = st.pop('x')
st['lat'] = st.pop('y')
shape_traces_latlon.append(go.Scattermapbox(st))
shape_traces = shape_traces_latlon
shapes_mapbox = []
for s in shapes:
s['lon'] = [s.pop('x0'), s.pop('x1')]
s['lat'] = [s.pop('y0'), s.pop('y1')]
shapes_mapbox.append(go.Scattermapbox(s, mode='lines'))
shapes = shapes_mapbox
bus_trace['lon'] = bus_trace.pop('x')
bus_trace['lat'] = bus_trace.pop('y')
bus_trace = go.Scattermapbox(bus_trace)
fig['data'].extend(shapes + shape_traces + [bus_trace])
else:
fig['data'].extend([bus_trace] + shape_traces)
fig['layout'].update(
dict(title=title, hovermode='closest', showlegend=False))
if size is not None:
assert len(
size) == 2, "Parameter size must specify a tuple (width, height)."
fig['layout'].update(dict(width=size[0], height=size[1]))
if mapbox:
if mapbox_token != "":
mapbox_parameters['accesstoken'] = mapbox_token
mapbox_parameters.setdefault('style', mapbox_style)
if mapbox_parameters['style'] in _token_required_mb_styles:
assert 'accesstoken' in mapbox_parameters.keys(), (
"Using Mapbox "
"layout styles requires a valid access token from https://www.mapbox.com/, "
f"style which do not require a token are:\n{', '.join(_open__mb_styles)}."
)
if 'center' not in mapbox_parameters.keys():
lon = (n.buses.x.min() + n.buses.x.max()) / 2
lat = (n.buses.y.min() + n.buses.y.max()) / 2
mapbox_parameters['center'] = dict(lat=lat, lon=lon)
if 'zoom' not in mapbox_parameters.keys():
mapbox_parameters['zoom'] = 2
fig['layout']['mapbox'] = mapbox_parameters
else:
fig['layout']['shapes'] = shapes
if iplot:
if not pltly_present:
logger.warning(
"Plotly is not present, so interactive plotting won't work.")
else:
pltly.iplot(fig)
return fig
