html.H6(datetime.datetime.fromtimestamp(date)),
dash_table.DataTable(
data=df.to_dict('records'),
columns=[{'name': i, 'id': i} for i in df.columns],
row_selectable='multi',
selected_rows=[],
virtualization=True,
page_action='none',
export_format='csv'
),
html.Div(id='selected_rows'),
html.Hr() # horizontal line
])
@app.callback(Output('output-data-upload', 'children'),
[Input('upload-data', 'contents')],
[State('upload-data', 'filename'),
State('upload-data', 'last_modified')])
def update_output(list_of_contents, list_of_names, list_of_dates):
if list_of_contents is not None:
children = [
parse_contents(c, n, d) for c, n, d in
zip(list_of_contents, list_of_names, list_of_dates)]
return children
if __name__ == '__main__':
app.run_server(debug=True)
def get_live_app():
app = DjangoDash('live_app')
app.css.append_css(
{'external_url': 'https://codepen.io/amyoshino/pen/jzXypZ.css'})
external_js = [
"https://code.jquery.com/jquery-3.2.1.min.js",
"https://codepen.io/bcd/pen/YaXojL.js"
]
for js in external_js:
app.scripts.append_script({"external_url": js})
app.layout = html.Div([
html.Div([
html.Iframe(
src=
'http://www.flightradar24.com/simple_index.php?lat=49.9&lon=12.3&z=8',
height=500,
width=1200)
]),
html.Div([
html.Pre(id='counter_text', children='Active flights worldwide:'),
dcc.Graph(id='live-update-graph', style={'width': 1200}),
dcc.Interval(id='interval-component',
interval=600000,
n_intervals=0)
])
])
counter_list = []
@app.callback(Output('counter_text', 'children'),
[Input('interval-component', 'n_intervals')])
def update_layout(n):
url = "https://data-live.flightradar24.com/zones/fcgi/feed.js?faa=1&mlat=1&flarm=1&adsb=1&gnd=1&air=1&vehicles=1&estimated=1&stats=1 " # airline limit
# A fake header is necessary to access the site:
res = requests.get(url, headers={'User-Agent': 'Mozilla/5.0'})
data = res.json()
counter = 0
for element in data["stats"]["total"]:
counter += data["stats"]["total"][element]
counter_list.append(counter)
try:
df = pd.read_csv('flight_df.csv', index_col=0)
row_df = pd.DataFrame([[str(datetime.datetime.now()), counter]],
columns=['date', 'count'])
df = df.append(row_df, ignore_index=True)
df.to_csv('flight_df.csv')
except Exception as e:
print(str(e))
# log = open('flight_log.txt', 'a')
# log.write(f'{str(datetime.datetime.now())}|{counter}\n')
# log.close()
return 'Active flights worldwide: {}'.format(counter)
@app.callback(Output('live-update-graph', 'figure'),
[Input('interval-component', 'n_intervals')])
def update_graph(n):
fig = go.Figure(data=[
go.Scatter(x=list(range(len(counter_list))),
y=counter_list,
mode='lines+markers')
])
return fig
if __name__ == '__main__':
app.run_server(debug=True)
return app
return fig
@app.callback(
Output("gpa-graph", "figure"),
[
Input("chapter-data", "data"),
Input("years-slider", "value"),
],
[State("years-slider", "marks")],
)
def gpa_graph(data, years, year_info, **kwargs):
df = pd.DataFrame.from_dict(data)
if year_info is None or "Year Term" not in df:
raise PreventUpdate
start_indx = df.index[df["Year Term"] == year_info[str(years[0])]["label"]]
end_indx = df.index[df["Year Term"] == year_info[str(years[-1])]["label"]]
fig = px.line(
df.iloc[start_indx[0]:end_indx[0] + 1],
x="Year Term",
y="gpa__avg",
title="Average GPA",
hover_data=["gpa__count"],
)
fig.layout.update(showlegend=False, yaxis_title="", xaxis_title="")
return fig
if __name__ == "__main__":
app.run_server(debug=False)
'title': 'Bar Chart Visualization'
}
}),
],
className='six columns'),
# Adding one more app/component
html.Div([
dcc.Graph(id='line-chart',
figure={
'data': [
{
'x': df['year'],
'y': df['pop'],
'type': 'line',
'name': 'SF'
},
],
'layout': {
'title': 'Line Chart Visualization'
}
})
],
className='six columns')
],
className='row')
]))
if __name__ == '__main__':
app.run_server(8052, debug=False)
def get_bfm_rs_app(df):
app = DjangoDash('bfm_rs_app')
app.css.append_css(
{'external_url': 'https://codepen.io/amyoshino/pen/jzXypZ.css'})
external_js = [
"https://code.jquery.com/jquery-3.2.1.min.js",
"https://codepen.io/bcd/pen/YaXojL.js"
]
for js in external_js:
app.scripts.append_script({"external_url": js})
app.layout = html.Div(
html.Div(
[
html.Div([dcc.Graph(id='map-graph')],
className='ten columns offset-by-one'),
html.Div([dcc.Graph(id='time_vs_fare-graph')],
className='ten columns offset-by-one'),
html.Div([dcc.Graph(id='fare_histogram-graph')],
className='four columns offset-by-one'),
html.Div([dcc.Graph(id='fare_histogram-graph2')],
className='four columns offset-by-one'),
html.Div([dcc.Graph(id='time_histogram-graph')],
className='ten columns offset-by-one'),
#html.Div([dcc.Graph(id='heatmap-graph')], className= 'ten columns offset-by-one'),
html.Div([
dt.DataTable(rows=df.to_dict('records'),
row_selectable=True,
filterable=True,
sortable=True,
selected_row_indices=[],
id='datatable')
],
className='ten columns offset-by-one')
],
className='row'))
@app.callback(dash.dependencies.Output('time_vs_fare-graph', 'figure'), [
Input('datatable', 'rows'),
Input('datatable', 'selected_row_indices')
])
def update_image_src(rows, selected_row_indices):
fig = plotly.tools.make_subplots(rows=1,
cols=1,
subplot_titles=('Time vs Fare', ),
shared_xaxes=False,
shared_yaxes=False)
if len(selected_row_indices) != 0:
rows = [rows[x] for x in selected_row_indices]
df = pd.DataFrame(rows)
fig.append_trace(
{
'x': df['price'],
'y': df['travel_time'],
'type': 'scatter',
'opacity': 0.5,
'mode': 'markers',
'marker': {
'color': 'red',
'size': 15,
'line': {
'width': 2
}
},
#'name': str(df['itinerary']),
#'orientation': 'h',
},
1,
1)
return fig
@app.callback(dash.dependencies.Output('fare_histogram-graph', 'figure'), [
Input('datatable', 'rows'),
Input('datatable', 'selected_row_indices')
])
def fare_histogram_graph(rows, selected_row_indices):
if len(selected_row_indices) != 0:
rows = [rows[x] for x in selected_row_indices]
df = pd.DataFrame(rows)
nrows = df.shape[0]
data = Data([go.Histogram(x=df['price'],
nbinsx=10)]) #,nbinsx = nrows // 20
return go.Figure(data=data)
@app.callback(dash.dependencies.Output('fare_histogram-graph2', 'figure'),
[
Input('datatable', 'rows'),
Input('datatable', 'selected_row_indices')
])
def fare_histogram_graph2(rows, selected_row_indices):
if len(selected_row_indices) != 0:
rows = [rows[x] for x in selected_row_indices]
df = pd.DataFrame(rows)
fl_count = df['flight_count'].unique()
x = []
group_labels = []
for fl in fl_count:
x.append(df[df['flight_count'] == fl]['price'].astype(float))
group_labels.append(str(fl))
#fig['layout'].update(title='Fare Distribution by Number of Flights')
data = Data([go.Histogram(x=x, nbinsx=1)])
colors = ['#3A4750', '#F64E8B', '#c7f9f1', 'blue']
fig = ff.create_distplot(x,
group_labels,
bin_size=.8,
curve_type='normal',
colors=colors) #
return fig
@app.callback(dash.dependencies.Output('time_histogram-graph', 'figure'), [
Input('datatable', 'rows'),
Input('datatable', 'selected_row_indices')
])
def time_histogram_graph(rows, selected_row_indices):
if len(selected_row_indices) != 0:
rows = [rows[x] for x in selected_row_indices]
df = pd.DataFrame(rows)
nrows = df.shape[0]
data = Data([go.Histogram(x=df['travel_time'],
nbinsx=15)]) #,nbinsx = nrows // 20
#TODO:
print(df[['travel_time', 'price']].astype(float).describe())
print(df[['travel_time', 'price']].astype(float).var())
return go.Figure(data=data)
@app.callback(dash.dependencies.Output('map-graph', 'figure'), [
Input('datatable', 'rows'),
Input('datatable', 'selected_row_indices')
])
def map(rows, selected_row_indices):
if len(selected_row_indices) != 0:
rows = [rows[x] for x in selected_row_indices]
df = pd.DataFrame(rows)
stopover = len(df['itinerary'][0].split('--')[0].split('-'))
DepartureAirports = df['DepartureAirports'][0].split('-')
#ArrivalAirports=df['DepartureAirports'][0].split('-')
origin_iata, destination_iata = str(DepartureAirports[0]), str(
DepartureAirports[stopover])
airport_details = AS.get_airport_details(
[origin_iata, destination_iata])
lat = [
airport_details[iata]['lat']
for iata in [origin_iata, destination_iata]
]
lon = [
airport_details[iata]['lon']
for iata in [origin_iata, destination_iata]
]
min_lat, max_lat, min_lon, max_lon = min(lat) - 10, max(lat) + 10, min(
lon) - 10, max(lon) + 10
aux = set(
zip(list(df['DepartureAirports']), list(df['ArrivalAirports'])))
cities = []
for onds in aux:
color = 'rgb' + str(
(int(random.random() * 256) % 256, int(random.random() * 256) %
256, int(random.random() * 256) % 256))
(x, y) = onds
segments = (list(zip(x.split('-'), y.split('-'))))
for segment in segments:
(ori, dest) = segment
airports_iata_list = [ori, dest]
airport_details = AS.get_airport_details(airports_iata_list)
lat = [
airport_details[iata]['lat'] for iata in airports_iata_list
]
lon = [
airport_details[iata]['lon'] for iata in airports_iata_list
]
ap_names = [
airport_details[iata]['city'] + '_' +
airport_details[iata]['country']
for iata in airports_iata_list
]
cities.append(
dict(type='scattergeo',
lat=lat,
lon=lon,
hoverinfo='text',
text=ap_names,
mode='lines',
line=dict(
width=2,
color=color,
)))
layout = dict(
title=' '.join(airports_iata_list),
showlegend=False,
geo=dict(
resolution=50,
showland=True,
landcolor='rgb(243, 243, 243)',
countrycolor='rgb(204, 204, 204)',
lakecolor='rgb(255, 255, 255)',
projection=dict(type="equirectangular"),
coastlinewidth=2,
lataxis=dict(
range=[0, 0], # [ min_lat, max_lat ],
showgrid=True,
tickmode="linear",
dtick=10),
lonaxis=dict(
range=[0, 0], #[ min_lon, max_lon ],
showgrid=True,
tickmode="linear",
dtick=20),
))
fig = dict(data=cities, layout=layout)
return fig
#@app.callback(dash.dependencies.Output('heatmap-graph', 'figure'),[Input('datatable', 'rows'), Input('datatable', 'selected_row_indices')])
def heatmap(rows, selected_row_indices):
if len(selected_row_indices) != 0:
rows = [rows[x] for x in selected_row_indices]
df = pd.DataFrame(rows)
its = list(df['itinerary'])
dist_m = AS.get_matrix_distance(its)
print('CALCULATED')
return go.Figure(data=Data([go.Heatmap(z=dist_m)]))
if __name__ == '__main__':
app.run_server(debug=True)
return app
context['se'] = se
context['cou'] = cou
layout = {
"title": "Profession vs Missing Numbers",
"yaxis": {
"title": "Number Missing",
},
"xaxis": {
"title": "Profession"
},
'width': 800,
'height': 600
}
fig = go.Scatter(x=se,
y=cou,
line=dict(color=('rgb(255,236,0)'), width=4))
context['data_pro'] = [fig]
context['layout_pro'] = layout
figg = dict(data=[fig], layout=layout)
div_pro = opy.plot(figg, auto_open=False, output_type='div')
context['graph'] = div
context['geo'] = div_geo
context['age'] = div_age
context['gender'] = div_gender
context['pro'] = div_pro
return context
if __name__ == '__main__':
app.run_server(debug=True, host='192.241.128.56', port=8000)
