def render_content(tab):
if tab == 'tab-1':
return html.Div([
dac.TabItem(id='content_value_boxes',
children=[
html.Div([
dac.InfoBox(title="TOTAL CONFIRMED ",
value=sum(report["Confirmed"]),
gradient_color="danger",
icon="bookmark"),
dac.InfoBox(title="TOTAL RECOVERED",
gradient_color="success",
value=sum(Recovered.iloc[:, -1]),
icon="bookmark"),
dac.InfoBox(title="TOTAL DEATHS",
color="black",
value=sum(report["Deaths"]),
icon="bookmark")
],
className='row')
]),
dbc.Container([
dcc.RadioItems(
id='maptype',
options=[{
'label': i,
'value': i
} for i in [
'National_map', 'open-street-map', 'stamen-toner', '3D'
]],
value='National_map',
labelStyle=items_style),
]),
dcc.Graph(id='graph-1-tabs', figure=fig)
])
elif tab == 'tab-2':
return html.Div([
html.H3('Worldwide Evolution of COVID19'),
dcc.Graph(id='graph-2-tabs', figure=fig3),
html.H2('Evolution of COVID19 by country'),
dcc.Dropdown(id='country',
options=[{
'label': i,
'value': i
} for i in factors],
value=['Spain', 'Italy'],
multi=True),
html.Div([
html.Div([dcc.Graph(id='confirmed-evolution')],
className="six columns",
style={'display': 'inline-block'}),
html.Div([dcc.Graph(id='death-evolution')],
className="six columns",
style={'display': 'inline-block'}),
],
className="row",
style={
'width': '100%',
'display': 'inline-block'
})
])
elif tab == 'tab-3':
return html.Div([
dash_table.DataTable(
id='datatable-interactivity',
columns=[{
"name": i,
"id": i,
"deletable": True,
"selectable": True
} for i in globalreport.columns],
data=globalreport.to_dict('records'),
editable=True,
filter_action="native",
sort_action="native",
sort_mode="multi",
column_selectable="single",
row_selectable="multi",
row_deletable=True,
selected_columns=[],
selected_rows=[],
page_action="native",
page_current=0,
page_size=10,
style_table={'textAlign': 'center'},
style_cell={
'padding': '5px',
'fontSize': 12,
'textAlign': 'center'
},
style_header={
'backgroundColor': 'white',
'fontWeight': 'bold',
'fontSize': 12
},
)
])
country_name_list = choropleth_country_map_helpers.get_property_items_list(
country_map_available_property_items, country_map_name_mapper_dict,
country_map_property)
layout = html.Div([
html.Div(firstrow_layout,
className='row',
style={
'margin-top': 20,
'margin-bottom': 20
}),
html.Div(secondrow_layout, className='row', style={'margin-bottom': 20}),
html.Div(
dcc.Graph(id='choropleth-country-map',
config={'displayModeBar': False},
style={'margin-bottom': 8},
hoverData={'points': [{
'customdata': 'Indonesia'
}]}),
className='row',
),
html.Div([
dcc.Graph(id='summary-heatmap',
style={'margin-bottom': 8},
className='six columns',
hoverData={
'points': [{
'x': '2019-08-26 -',
'y': 'CPO Product Price'
}]
},
config={'displayModeBar': False}),
app.layout = html.Div(children=[
# append the visualization to the page
dcc.Graph(
id='example-graph',
figure={
# configure the data
'data': [
go.Scatter(
# setting x as fat, and y as calories (points represent data)
x= df['fat'],
y = df['calories'],
mode = 'markers',
text = df ['name'],
marker = {
'size': 12,
'color': 'rgb(0,255,0)'
}
)
],
# configure the layout of the visualization --
# set the title
'layout': {
'title': 'Fat vs. Calories',
'xaxis': {'title': 'Fat (g)'},
'yaxis': {'title': 'Calories'}
}
}
)
])
if __name__ == '__exercise3__':
dcc.Dropdown(
id='currency',
options=[{'label': 'EUR/USD', 'value': 'eur_usd'}, {'label': 'BTC/USD', 'value': 'btc_usd'}],
value='eur_usd',
placeholder="Currency",
style={'width': '100%', 'display': 'inline-block'}
),
], style={'width': '100%', 'display': 'inline-block'}),
html.Br(),
html.Br(),
dcc.Tabs(id="tabs", children=[
dcc.Tab(label='Recurrent Neural Network ', value='rnn', selected_style={'backgroundColor': '#21252C', 'color': 'white'}),
dcc.Tab(label='Convolutional neural network', value='cnn', selected_style={'backgroundColor': '#21252C', 'color': 'white'}),
], value="rnn", colors={'border': 'gray', 'background': '#21252C'}),
html.Div([
dcc.Graph(id='graph'),
], className='twelve columns eur-usd-graph'),
html.Div([
dcc.Dropdown(id='type', options=[
{"label": "Line", "value": "line_trace"},
{"label": "Mountain", "value": "area_trace"},
{
"label": "Colored bar",
"value": "colored_bar_trace",
},
], value="line_trace", clearable=False, style={'width': '98%'}),
], className='twelve columns', style={'display': 'inline-block', 'width': '40%'}),
html.Div([
dcc.Dropdown(id='metric', options=[
{
"label": "Accumulation/D",
value=dt.date.today()),
dcc.Input(id='enddate_input', type='Date', value=dt.date.today()),
]),
],
style={
'width': 200,
'height': 200
}),
html.Button(id='submit_button',
n_clicks=0,
children='Submit',
style={
'fontSize': 20,
'height': 50
}),
dcc.Graph(id='feature-graphic'),
dcc.Markdown(id='content_text', containerProps={'fontSize': 200}),
])
# 어떤 DB인지에 따라 능동적으로 Dropdown 변경해줄 수 있는 코드 (현재는 하나의 DB라서 의미 없는 코드)
@app.callback(Output('company_type', 'options'),
[Input('column_type', 'value')])
def set_company_type(select_column):
return [{'label': i, 'value': i} for i in all_options[select_column]]
@app.callback(Output('company_type', 'value'),
[Input('company_type', 'options')])
def set_company_value(avail_options):
return avail_options[0]['value']
'101-004-7108173-009',]
app.layout = html.Div(children=[
html.H1(children='MLinc Thunder Dashboard'),
html.Div(children='''
Making billions and billions and billions.
'''),
dcc.Dropdown(
id='dropdown',
options=[{'label': i, 'value': i} for i in account_id],
value=account_id[0]
),
dcc.Graph(id='ML_dashboard', style={'height': '90vh'}),
html.H2(children='Mede mogelijk gemaakt door:'),
html.Img(src='data:image/png;base64,{}'.format(encoded_image.decode()))
])
@app.callback(
dash.dependencies.Output('ML_dashboard', 'figure'),
[dash.dependencies.Input('dropdown', 'value')])
def update_figure(selected_account):
conf_input = from_conf_file('all', r'C:\Data\2_Personal\Python_Projects\MLinc\mlinc\conf.ini')
conf_input['accountid'] = selected_account
html.H1(children='Welcome to a DataViz Project using DASH',
style={
'color': 'blue',
'fontSize': 48
}),
html.
H3(children='Created by: Kunal PATIL (AIS) and Salil MARATH-PONMADOM (DSA)',
style={
'color': 'grey',
'fontSize': 20
}),
html.Div(children='''
Magnitude of Earthquakes in Japan during 2001-2018
'''),
dcc.Graph(
id='JE1',
figure=fig_scatter_mapbox,
),
html.
H6(children=
"Observation and Comments: Using Scatter Mapbox, We can observe geaographical distribution of Earthquake's origin."
"All the earthquakes originated from east side in pacific ocean.",
style={
'color': 'black',
'fontSize': 12
}),
dcc.Graph(id='JE2', figure=fig_line),
html.
H6(children=
"Observation and Comments: As observed from scatter mapbox, we know the count is highest during 2011-12. "
"With Line graph, we can see the highest magnitude of earthquake observed in March 2011 (9.1)",
style={
for json_entry in ['', '']
]),
html.Div(children=str(str(int(round(avgdelaynumber * 100))) + '% of S-Bahns delayed in total on this line. '),
style={
# 'textAlign': 'center',
# 'color': colors['text']
}),
html.Div(children=str(str(((avgdelay))) + ' minutes average delay on this line.'), style={
# 'textAlign': 'center',
# 'color': colors['text']
}),
dcc.Graph(id='graph'),
])
@app.callback(
dash.dependencies.Output('graph', 'figure'),
[dash.dependencies.Input('dropdown', 'value')])
def update_figure(value):
dff = df[df['Endstation'].eq(value)]
# Set order for stations
if value == 'Flughafen München':
order = ['Herrsching', 'Seefeld-Hechendorf', 'Steinebach', 'Weßling', 'Neugilching', 'Gilching-Argelsried',
'Geisenbrunn',
'Germering-Unterpfaffenhofen', 'Harthaus', 'Freiham', 'Neuaubing',
#df2 = pd.DataFrame({'percentage': df.groupby(['company'] ,as_index=False).size() / len(df) }).reset_index()
#df2 = pd.DataFrame({'percentage': df.groupby([ 'site']).size() / len(df)}).reset_index()
#df2 = df.groupby('zone').count().reset_index()
#print(df2.head())
app = dash.Dash()
app.layout = html.Div([
dcc.Graph(id='scatterplot',
figure={
'data': [
go.Box(y=df[df.site == 'pisoscom']['price'],
name="pisos.com"),
go.Box(y=df[df.site == 'habitaclia']['price'],
name="habitaclia")
],
'layout':
go.Layout(title='Price/Surface',
xaxis={'title': 'price distribution'},
yaxis={'title': 'surface'})
})
])
"""
app.layout = html.Div([dcc.Graph(id='scatterplot',
figure = {'data': [
go.Box(
y=df[df.site=='pisoscom']['price'],
name="pisos.com"
options=[{'label': i, 'value': i} for i in available_indicators],
value=available_indicators[0]
)],
style={'width': '50%', 'display': 'inline-block'})],
style={
#'borderBottom': 'thin lightgrey solid',
'backgroundColor': 'rgb(255,255,255)',
#'padding': '10px',
#'width': '100%',
#'margin': '20px, 10px, 30px, 40px',
#'font-family': 'Century Gothic'
}),
dcc.Graph(
id='graph',
style={'height': 600}
)
],
style={'font-family': 'Century Gothic',
'margin': '5px'
})
#1
@app.callback(
Output(component_id='result', component_property='children'),
[Input(component_id='tc', component_property='value'),
Input(component_id='rt', component_property='value')])
def update_three_inputs(tc, rt):
if tc is not None and tc is not '' and rt is not None and rt is not '':
try:
# routes_pathname_prefix='/dash3/'
# )
conn = sqlite3.connect("hospital_data.db")
df = pd.read_sql_query("SELECT * FROM data", conn)
x1_list = ["masks", "ventilators", "faceshields", "gowns"]
y1_list = [df[key].sum() for key in x1_list]
app1.layout = html.Div(
[
dcc.Graph(
id='graph',
figure={
'data': [
{'x': x1_list,
'y': y1_list, 'type': 'bar', 'name': 'Supplies'},
],
'layout': {
'title': 'Supplies Requested by Hospitals'
}
}
),
]
)
@server.route("/")
def root():
return render_template("index.html")
@server.route("/about")
def about():
app.layout = html.Div(children=[
html.H1(children="Private Dash App"),
html.Div(id='placeholder', style={'display': 'none'}),
html.Div(id='welcome'),
dcc.Graph(id='example-graph',
figure={
'data': [
{
'x': [1, 2, 3],
'y': [4, 1, 2],
'type': 'bar',
'name': 'SF'
},
{
'x': [1, 2, 3],
'y': [2, 4, 6],
'type': 'bar',
'name': 'Montreal'
},
],
'layout': {
'title': 'Dash Data Visualization'
}
})
])
@app.callback(Output('welcome', 'children'), [Input('placeholder', 'value')])
def on_load(value):
return "Welcome, {}!".format(session['email'])
},
0.35: {
'label': '0.35'
},
0.4: {
'label': '0.4'
},
0.45: {
'label': '0.45'
},
0.5: {
'label': '0.5'
}
}),
html.Hr(),
dcc.Graph(id='plot_ts'),
# Temporary hack for live dataframe caching
# 'hidden' set to 'loaded' triggers next callback
html.P(hidden='unload',
id='raw_container',
style={'display': 'none'}),
html.P(hidden='unload',
id='filtered_container',
style={'display': 'none'})
])
]),
dcc.Tab(label='tab_2')
#dcc.Tab(label='tab_3'),
#dcc.Tab(label='tab_4'),
#dcc.Tab(label='tab_5')
'display': 'inline-block',
'vertical-align': 'top',
'min-height': '355px',
'position': 'relative',
"box-shadow":
"0 4px 8px 0 rgba(0, 0, 0, 0.05), 0 6px 20px 0 rgba(0, 0, 0, 0.05)",
"font-family": "Helvetica"
},
),
html.Div(
[
html.Div([
dcc.Graph(id="map_graph",
config={
'modeBarButtonsToRemove':
['select2d', 'lasso2d'],
'displaylogo':
False
})
],
id="map_container",
className="map_container",
style={'margin-top': '20px'}),
],
id="map_area",
className="map area",
style={
"margin":
"5px",
"width":
"58%",
),
],
),
html.P(id="total-rides"),
html.P(id="total-rides-selection"),
html.P(id="date-value"),
dcc.Markdown(children=[
"Source: [Los Angeles Police Department](https://catalog.data.gov/dataset/crime-data-from-2010-to-present-c7a76)"
]),
],
),
# Column for app graphs and plots
html.Div(
className="eight columns div-for-charts bg-grey",
children=[
dcc.Graph(id="map-graph"),
html.Div(
className="text-padding",
children=["Hourly Crime Totals On Selected Date"],
),
dcc.Graph(id="histogram"),
],
),
],
)
])
# Gets the amount of days in the specified month
# Index represents month (0 is April, 1 is May, ... etc.)
daysInMonth = [30, 31, 30, 31, 31, 30]
import dash_html_components as html
from dash.dependencies import Input, Output
import os
app = dash.Dash(__name__)
#---------------------------------------------------------------
script_dir = os.path.dirname(__file__) # Script directory
full_path = os.path.join(script_dir,
'../Hotel_Reviews.csv') # Full Directory to the CSV
hotelDf = pd.read_csv(full_path) # Read csv to Dataframe
#---------------------------------------------------------------
app.layout = html.Div([
html.Div([dcc.Graph(id='your_graph')], className=''),
html.P(id='total', className='text-center'),
html.Div(
[
html.Label(['Choose column:'],
style={
'font-weight': 'bold',
"text-align": "center"
}),
dcc.Dropdown(
id='my_dropdown',
options=[
{
'label': 'Positive',
'value': '1'
},
dcc.Graph(
id='map',
animate=True,
figure={
'data': [{
'lat': df['LAT'],
'lon': df['LON'],
'text': df['text'],
'marker': {
'color': df['created_at'],
'size': 8,
'opacity': 0.6
},
'customdata': df['LAT'],
'type': 'scattermapbox'
}],
'layout': {
'mapbox': {
'accesstoken':
'pk.eyJ1IjoiY2hyaWRkeXAiLCJhIjoiY2ozcGI1MTZ3MDBpcTJ3cXR4b3owdDQwaCJ9.8jpMunbKjdq1anXwU5gxIw'
},
'hovermode': 'closest',
'height': 500,
'margin': {
'l': 0,
'r': 0,
'b': 0,
't': 0
}
}
}),
dcc.Graph(
id='crops',
figure={
'data': [
go.Scatter3d(
x=X_embedded[:, 0],
y=X_embedded[:, 1],
z=carbs,
hovertext=names,
mode='markers+text',
opacity=1,
showlegend=True,
marker=dict(
size=8,
color=
carbs, # set color to an array/list of desired values
colorscale='Rainbow', # choose a colorscale
showscale=True,
opacity=1,
colorbar=dict(title='Carbs level'),
))
],
'layout':
go.Layout(
title='Crops temperature VS carb level',
autosize=False,
width=1200,
height=800,
margin=go.Margin(l=50, r=50, b=100, t=100, pad=4),
),
})
import pandas as pd
import os
os.chdir(
r'C:\Users\Franciszek.Grymula\Documents\_Szkoła\CEIDG-data-visualisation\final_app\scripts'
)
df_corr_feat = pd.read_csv('../data/corrs_and_features.csv', index_col=0)
app = dash.Dash()
app.layout = html.Div([
html.Div(
[
dcc.Graph(id='life-exp-vs-gdp'),
html.Label('Wybierz metrykę'),
dcc.Dropdown(id='column',
options=[{
'label': 'Korelacja',
'value': 'correlations'
}, {
'label': 'Feature importance',
'value': 'importance'
}],
value='importance',
clearable=False),
# html.Label('Show top'),
# dcc.Slider(
# id='expectancy-slider',
# min=3,
def layout(self):
radio_options = ["Statistics", "Data"]
data = get_data(self.statistics_file)
key_dropdown_options = [{
"label": i,
"value": i
} for i in list(data["summary_key"].unique())]
xaxis_dropdown_options = [{
"label": i,
"value": i
} for i in list(data["batch" if self.xaxis ==
"date" else "date"].unique())]
xaxis_dropdown_title = ("Batches to plot"
if self.xaxis == "date" else "Dates to plot")
# Keywords dropdown.
keyword_elements = [
html.Label("Keywords to plot:"),
dcc.Dropdown(
id=self.key_dropdown_id,
options=key_dropdown_options,
multi=True,
),
]
# X-axis dropdown.
xaxis_elements = [
html.Label(xaxis_dropdown_title, style={"margin-top": 24}),
dcc.Dropdown(
id=self.xaxis_dropdown_id,
options=xaxis_dropdown_options,
multi=True,
value=[xaxis_dropdown_options[0]["value"]],
),
]
# Radio for switching between data and statistics.
radio_elements = [
dcc.RadioItems(
id=self.radio_id,
options=[{
"label": i,
"value": i
} for i in radio_options],
value=radio_options[0],
labelStyle={"display": "inline-block"},
style={"margin-top": 20},
),
]
return html.Div([
html.Div([
html.Div(
keyword_elements + xaxis_elements + radio_elements,
style={
"width": "29%",
"display": "inline-block",
"vertical-align": "top",
},
),
html.Div(
[
dcc.Graph(
id=self.graph_id,
config={
"modeBarButtonsToRemove": ["toImage"],
"displaylogo": False,
},
)
],
style={
"width": "69%",
"display": "inline-block"
},
),
]),
])
def layout(matrix):
drug1 = matrix.combination.lib1.name
drug2 = matrix.combination.lib2.name
matrix_df = pd.DataFrame([w.to_dict() for w in matrix.well_results])
matrix_df = matrix_df.assign(viability=lambda df: 1 - df.inhibition)
matrix_df = matrix_df[['lib1_conc', 'lib2_conc'] +
list(well_metrics.keys())]
return dbc.Row(
dbc.Col(
width=12,
children=[
html.Div(
className='border p-3 bg-white shadow-sm',
children=[
dbc.Row([
dbc.Col(
width=12,
className='d-flex flex-row',
children=[
dbc.Col(
width='auto',
children=html.H3(
["Drug combination interaction"],
className='pt-1'),
),
dbc.Col(width=3,
children=dcc.Dropdown(
id='combo-heatmap-zvalue',
options=list(
well_metrics.values()),
value='bliss_excess',
searchable=False,
clearable=False)),
]),
dbc.Col(html.Hr(), width=12)
]),
dbc.Row([
dbc.Col(
width=7,
children=[
dcc.Loading(dcc.Graph(id='combo-heatmap'),
className='gdsc-spinner'),
dcc.Loading(dcc.Graph(id='combo-surface'),
className='gdsc-spinner')
]),
dbc.Col(width={
"size": 4,
"offset": 1
},
children=[infoblock_matrix(matrix)])
])
]),
html.Div(id='combo-values',
style={'display': 'none'},
children=matrix_df.to_json(date_format='iso',
orient='split')),
html.Div(id='drug_names',
style={'display': 'none'},
children=f"{drug1}:_:{drug2}")
]))
# Générer le code HTML
app.layout = html.Div(
style={'background-color': '#1C2840'},
children=[
html.H1("Données COVID-19 dans le monde",
style={
'text-align': 'center',
'color': 'white',
'padding-top': '2vh',
'padding-bottom': '2vh'
}),
html.Br(),
#Dessin de l'élément dans le dashboard
#Création de la carte des répartitions des cas confirmés dans le monde
dcc.Graph(id='Carte1', figure=carte1),
#Création de la carte des répartitions des cas morts dans le monde
dcc.Graph(id='Carte2', figure=carte2),
#Graphiques
#Création de l'histogramme représentant les cas du Covid confirmés en fonction de la date
dcc.Graph(id='graph1', figure=figure1),
#Création de l'histogramme représentant les morts du Covid confirmés en fonction de la date
dcc.Graph(id='graph2', figure=figure2),
#Création du graphique représentant le nombre de cas confirmé par pays
dcc.Graph(id='graph3', figure=figure3),
#Création du graphique représentant le nombre de morts par pays
dcc.Graph(id='graph4', figure=figure4),
#Création du graphique représentant le nombre de cas confirmé par pays en fonction du temps
dcc.Graph(id='graph5', figure=figure5),
'textAlign': 'center',
'color': colors['text']
}
),
html.Div(children='Dash: A web application framework for Python.', style={
'textAlign': 'center',
'color': colors['text']
}),
dcc.Graph(
id='example-graph-2',
figure={
'data': [
{'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar', 'name': 'SF'},
{'x': [1, 2, 3], 'y': [2, 4, 5], 'type': 'bar', 'name': u'Montréal'},
],
'layout': {
'plot_bgcolor': colors['background'],
'paper_bgcolor': colors['background'],
'font': {
'color': colors['text']
}
}
}
)
])
if __name__ == '__main__':
app.run_server(debug=True)
id='yaxis_raditem',
options=[
{
'label': 'Time Spent on Site (hours)',
'value': 'Time Spent on Site (hours)'
},
{
'label': 'Amount of Animals',
'value': 'Amount of Animals'
},
],
value='Time Spent on Site (hours)',
#style={"width": "50%"}
),
]),
html.Div([dcc.Graph(id='the_graph')]),
])
#-------------------------------------------------------------------------------------
@app.callback(Output(component_id='the_graph', component_property='figure'), [
Input(component_id='xaxis_raditem', component_property='value'),
Input(component_id='yaxis_raditem', component_property='value')
])
def update_graph(x_axis, y_axis):
dff = df
# print(dff[[x_axis,y_axis]][:1])
barchart = px.bar(
data_frame=dff,
def generate_curves(height=950,
width=800,
bg_color='white',
font_size=10,
tick_font_size=8,
line_width=0.5):
# include one graph for all curves, since they have the same x axis
yvals = 'DEPT'
cols = list(lf.curves.keys())
plots = []
plots.append(['BTVPVS', 'DGRC'])
plots.append(
list(
filter(lambda x: x == 'EWXT' or re.search(r'R[0-9][0-9]P', x),
cols)))
plots.append(['ALCDLC', 'ALDCLC'])
plots.append(['TNPS'])
plots.append(['BTCSS', 'BTCS'])
fig = tools.make_subplots(rows=1,
cols=len(plots),
shared_yaxes=True,
horizontal_spacing=0)
for i in range(len(plots)):
for column in plots[i]:
fig.append_trace(go.Scatter(
x=lf.curves[column].data,
y=lf.curves[yvals].data,
name=column,
line={
'width': line_width,
'dash': 'dashdot' if column in plots[1] else 'solid'
},
),
row=1,
col=i + 1)
fig['layout']['xaxis{}'.format(i + 1)].update(
title=generate_axis_title(lf.curves[plots[i][0]]['descr'],
lf.curves[plots[i][0]]['unit']),
type='log' if column in plots[1] else 'linear')
fig['data'][1]['xaxis'] = 'x6'
fig['data'][6]['xaxis'] = 'x7'
fig['data'][8]['xaxis'] = 'x8'
fig['data'][11]['xaxis'] = 'x9'
# DGRC on graph 1
fig['layout']['xaxis6'] = dict(overlaying='x1',
anchor='y',
side='top',
title=generate_axis_title(
lf.curves['DGRC']['descr'],
lf.curves['DGRC']['unit']))
# EWXT on graph 2
fig['layout']['xaxis7'] = dict(overlaying='x2',
anchor='y',
side='top',
title=generate_axis_title(
lf.curves['EWXT']['descr'],
lf.curves['EWXT']['unit']))
# ALDCLC on graph 3
fig['layout']['xaxis8'] = dict(overlaying='x3',
anchor='y',
side='top',
title=generate_axis_title(
lf.curves['ALDCLC']['descr'],
lf.curves['ALDCLC']['unit']))
# BTCS on graph 5
fig['layout']['xaxis9'] = dict(overlaying='x5',
anchor='y',
side='top',
title=generate_axis_title(
lf.curves['BTCS']['descr'],
lf.curves['BTCS']['unit']))
# y axis title
fig['layout']['yaxis'].update(title=generate_axis_title(
lf.curves[yvals]['descr'], lf.curves[yvals]['unit']),
autorange='reversed')
for axis in fig['layout']:
if re.search(r'[xy]axis[0-9]*', axis):
fig['layout'][axis].update(
mirror='all',
automargin=True,
showline=True,
title=dict(
font=dict(family='Arial, sans-serif', size=font_size)),
tickfont=dict(family='Arial, sans-serif', size=tick_font_size))
fig['layout'].update(height=height,
width=width,
plot_bgcolor=bg_color,
paper_bgcolor=bg_color,
hovermode='y',
legend={'font': {
'size': tick_font_size
}},
margin=go.layout.Margin(r=100))
return dcc.Graph(figure=fig)
def final_UI(tabs, start_date, end_date, group, report_types,device_date,service_date):
print("Data Type of start_date value = " , str(type(start_date)))
print("Data of start_date value = " , str(start_date))
print("Data Type of end_date value = " , str(type(end_date)))
print("Data of end_date value = " , str(end_date))
print("Data Type of group value = " , str(type(group)))
print("Data of group value = " , str(group))
print("Data Type of report_type value = " , str(type(report_types)))
print("Data of report_type value = " , str(report_types))
print("Data Type of device_date value = " , str(type(device_date)))
print("Data of device_date value = " , str(device_date))
print("Data Type of service_date value = " , str(type(service_date)))
print("Data of service_date value = " , str(service_date))
if tabs=="tab-1":
# Filter the data as per the selection of the drop downs
call_analytics_data = calldata[ (calldata["date"]>=start_date) & (calldata["date"]<=end_date) ]
if group == [] or group is None:
pass
else:
call_analytics_data = call_analytics_data[call_analytics_data["Group"].isin(group)]
graph_data = call_analytics_data
# Group the data based on the drop down
if report_types == "Hourly":
graph_data = graph_data.groupby("hourly_range")["Call_Direction"].value_counts().reset_index(name = "count")
x = "hourly_range"
content = call_analytics_data["hourly_range"].value_counts().idxmax()
title = "Busiest Hour"
elif report_types == "Daily":
graph_data = graph_data.groupby("date")["Call_Direction"].value_counts().reset_index(name = "count")
x = "date"
content = call_analytics_data["date"].value_counts().idxmax()
title = "Busiest Day"
else:
graph_data = graph_data.groupby("weekly_range")["Call_Direction"].value_counts().reset_index(name = "count")
x = "weekly_range"
content = call_analytics_data["weekly_range"].value_counts().idxmax()
title = "Busiest WeekDay"
# Graph Section
figure = px.area(graph_data,
x = x,
y = "count",
color = "Call_Direction",
hover_data=[ "Call_Direction", "count"],
template = "plotly_white")
figure.update_traces(mode = "lines+markers")
# Card Section
total_calls = call_analytics_data["Call_Direction"].count()
card_1 = card_creation("Total Calls",total_calls, "success")
incoming_calls = call_analytics_data["Call_Direction"][call_analytics_data["Call_Direction"]=="Incoming"].count()
card_2 = card_creation("Incoming Calls", incoming_calls, "primary")
outgoing_calls = call_analytics_data["Call_Direction"][call_analytics_data["Call_Direction"]=="Outgoing"].count()
card_3 = card_creation("Outgoing Calls", outgoing_calls, "secondary")
missed_calls = call_analytics_data["Missed Calls"][call_analytics_data["Missed Calls"] == 3].count()
card_4 = card_creation("Missed Calls", missed_calls, "warning")
max_duration = call_analytics_data["duration"].max()
card_5 = card_creation("Max Duration", f'{max_duration} min', "dark")
card_6 = card_creation(title, content, "info")
graphRow0 = dbc.Row([dbc.Col(id='card1', children=[card_1], md=3), dbc.Col(id='card2', children=[card_2], md=3)])
graphRow1 = dbc.Row([dbc.Col(id='card3', children=[card_3], md=3), dbc.Col(id='card4', children=[card_4], md=3)])
graphRow2 = dbc.Row([dbc.Col(id='card5', children=[card_5], md=3), dbc.Col(id='card6', children=[card_6], md=3)])
cardDiv = html.Div([graphRow0,html.Br(), graphRow1,html.Br(), graphRow2])
# Data Table Section
datatable_data = call_analytics_data.groupby(["Group", "UserID", "UserDeviceType"])["Call_Direction"].value_counts().unstack(fill_value = 0).reset_index()
if call_analytics_data["Missed Calls"][call_analytics_data["Missed Calls"]==19].count()!=0:
datatable_data["Missed Calls"] = call_analytics_data.groupby(["Group", "UserID", "UserDeviceType"])["Missed Calls"].value_counts().unstack()[3]
else:
datatable_data["Missed Calls"] = 0
datatable_data["Total_call_duration"] = call_analytics_data.groupby(["Group", "UserID", "UserDeviceType"])["duration"].sum().tolist()
datatable = dt.DataTable(
id='table',
columns=[{"name": i, "id": i} for i in datatable_data.columns],
data=datatable_data.to_dict('records'),
page_current=0,
page_size=5,
page_action='native',
style_header={'backgroundColor': 'rgb(40, 40, 40)'},
style_cell={
'backgroundColor': 'rgb(60, 60, 60)',
'color': 'white'
}
)
return [
dcc.Graph(figure = figure),
html.Br() ,
cardDiv,
html.Br(),
datatable
]
#device analytics
elif tabs == "tab-2":
if device_date is None or device_date == []:
device_analytics_data = device_count(devicedata)
else:
device_analytics_data = device_count(devicedata[devicedata["DeviceEventDate"].isin(device_date)])
fig = px.pie(device_analytics_data, names = "Device", values = "Count", color = "Device", hole = .3)
fig.update_layout(autosize=True,
margin=dict(l=0, r=0, t=25, b=20),
)
return dcc.Graph(figure = fig)
#service analytics
elif tabs == "tab-3":
if service_date is None or service_date == []:
service_analytics_data = servicedata["FeatureName"].value_counts().reset_index(name = "Count")
else:
service_analytics_data = servicedata["FeatureName"][servicedata["FeatureEventDate"].isin(service_date)].value_counts().reset_index(name = "Count")
fig = px.pie(service_analytics_data, names = "index", values = "Count",color = "index")
fig.update_layout(autosize=True,
margin=dict(l=0, r=0, t=25, b=20),
)
return dcc.Graph(figure = fig)
#if any other return nothing
else:
return None
fig.update_layout(hovermode='closest',
mapbox=dict(accesstoken=mapbox_access_token,
style='outdoors',
bearing=2,
center=go.layout.mapbox.Center(lat=-13,
lon=-53),
pitch=0,
zoom=3),
height=600)
fig.show()
return fig
def plot_tweets_per_destination():
fig = px.bar(tweets_per_dest, x='destination', y='tweets')
return fig
tweets_on_map = plot_tweets_map()
tweets_per_dest = plot_tweets_per_destination()
layout = html.Div([
html.H1('Travel mentions on Twitter (per destination)',
style={"textAlign": "center"}),
dcc.Graph(id='my-tweets', figure=tweets_on_map),
html.H1('Most travel destinations mentioned',
style={"textAlign": "center"}),
dcc.Graph(id='my-tweets-bar', figure=tweets_per_dest)
])
"±3%",
className="fancyValue",
id="savings-accuracy",
),
]),
html.P(
children=[
"95% confidence interval for Average Savings result. Smaller is better."
],
className="help",
),
html.Div([
html.Div([
dcc.Graph(
id="accuracy-graph",
config={
"displayModeBar": False
},
)
])
]),
html.P(children=[
html.Span(
"Privacy Cost:",
className="fancyLabel",
),
html.Span(
"1.2%",
className="fancyValue",
id="privacy-cost",
),
]),
),
dcc.Dropdown(
id='crossfilter-code-column',
options=[{'label': i, 'value': i} for i in codes],
value='FB'
),
],style={'width': '49%', 'display': 'inline-block'}),
], style={
'borderBottom': 'thin lightgrey solid',
'backgroundColor': 'rgb(250, 250, 250)',
'padding': '10px 5px'
}),
html.Div([
dcc.Graph(
id='crossfilter-indicator-scatter'
)
], style={'width': '49%', 'display': 'inline-block', 'padding': '0 20'}),
html.Div([
dcc.Graph(
id='crossfilter-model-predictor'
)
], style={'width': '49%', 'display': 'inline-block', 'padding': '0 20'}),
html.Br(),
html.Div(id='news')
])
def register_callbacks(app):
@app.callback(
'backgroundColor': '#119DFF',
'color': 'white',
'padding': '6px'
}
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
df = pd.read_csv("https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv")
df_continent = df['location'].value_counts().keys()
df_continent
df_Ind = df[df['location'] == 'India']
print(df_Ind[:5])
layout = html.Div([html.Br(),
dcc.Tabs([
dcc.Tab(label='Global',style=tab_style, children=[
html.H3('Daily Vaccination update in world', style={"textAlign": "center"}),
dcc.Graph(
figure=px.line(df, x="date", y="daily_vaccinations", color="location"),
)
], selected_style=tab_selected_style),
dcc.Tab(label='India', children=[
html.H3('Daily Vaccination update in India', style={"textAlign": "center"}),
dcc.Graph(
figure=px.area(df_Ind, x="date", y="daily_vaccinations", color="location", line_group="location"),
)
], style=tab_style, selected_style=tab_selected_style ),
],style=tabs_styles)
])
