marks={
0: '0',
1: '1',
2: '2',
3: '3',
4: '4',
5: '5'
},
value=[0, 6])
]),
html.H3('Neighborhood'),
dcc.Dropdown(
id='dropdown1',
options=[{
'label': i,
'value': i
} for i in df.Neighborhood.unique()],
multi=True,
# value = [i for i in df.Neighborhood.unique()]
),
html.P('\n'),
html.H3('Borough'),
dcc.Dropdown(
id='dropdown2',
options=[{
'label': i,
'value': i
} for i in df.Borough.unique()],
multi=True,
# value = [i for i in df.Neighborhood.unique()]
)
app = dash.Dash()
df = pd.read_csv('https://gist.githubusercontent.com/chriddyp/'
'cb5392c35661370d95f300086accea51/raw/'
'8e0768211f6b747c0db42a9ce9a0937dafcbd8b2/'
'indicators.csv')
available_indicators = df['Indicator Name'].unique()
app.layout = html.Div([
html.Div(
[
html.Div([
dcc.Dropdown(id='crossfilter-xaxis-column',
options=[{
'label': i,
'value': i
} for i in available_indicators],
value='Fertility rate, total (births per woman)'),
dcc.RadioItems(id='crossfilter-xaxis-type',
options=[{
'label': i,
'value': i
} for i in ['Linear', 'Log']],
value='Linear',
labelStyle={'display': 'inline-block'})
],
style={
'width': '49%',
'display': 'inline-block'
}),
html.Div([
def _make_layout(embeddings_list: List[WordEmbeddings]) -> object:
"""Create the layout for the embedding projector app."""
return dbc.Container(fluid=True, children=[
html.H1(children='Embedding Projector'),
# We use a hidden div to faciliate callbacks.
# This 'signal' can be used both ways: to trigger a callback, or to have a callback
# return nothing.
#
# For example, when we want to do some data processing (such as computing the PCA)
# but may not necessarily want to render anything new.
html.Div(id='update-embeddings-signal', style={'display': 'none'}),
html.Div(id='hidden-div', style={'display': 'none'}),
dcc.Loading(id='loading-spinner', fullscreen=True, loading_state={'is_loading': True}),
dbc.Row([
dbc.Col(html.Div([
html.Div([
html.H3('DATA', style={'font-weight': 'bold'}),
html.Label('Embeddings'),
dcc.Dropdown(id='embeddings-dropdown',
options=[
{'label': str(embeddings), 'value': i}
for i, embeddings in enumerate(embeddings_list)
],
clearable=False,
value=0),
html.Div(id='metadata-div'),
dbc.Checklist(id='spherize-data',
options=[{'label': 'Spherize data', 'value': 'yes'}],
value=['yes'],
inline=True,
switch=True)
]),
html.Div([
html.H3('PCA', style={'font-weight': 'bold'}),
dbc.Label('X-axis'),
dcc.Dropdown(id='x-component-dropdown', value=0, clearable=False),
dbc.Label('Y-axis'),
dcc.Dropdown(id='y-component-dropdown', value=1, clearable=False),
dbc.Checklist(id='use-z-component',
options=[{'label': 'Z-Axis', 'value': 'yes'}],
value=['yes'],
inline=True,
switch=True,
className='py-1'),
dcc.Dropdown(id='z-component-dropdown', value=2, clearable=False),
html.Br(),
html.Label(id='pca-variance-label')
])
]), width=2),
dbc.Col(html.Div([
dcc.Loading(id='embedding-graph-loading',
children=[
dcc.Graph(
# Make a defualt empty graph
figure=_make_embedding_scatter([], [], [], []),
id='embedding-graph',
style={'height': '100vh'}
)
],
type='default')
]), width=8),
dbc.Col(html.Div([
html.Div([
html.H3('ANALYSIS', style={'font-weight': 'bold'}),
dbc.Row([
dbc.Col([dbc.Button('Show All Data',
id='show-all-data-button',
outline=True,
color='dark',
className='mr-2 my-4',
disabled=True)]),
dbc.Col([dbc.Button('Isolate Points',
id='isolate-points-button',
outline=True,
color='dark',
className='mr-2 my-4',
disabled=True)]),
dbc.Col([dbc.Button('Clear Selection',
id='clear-selection-button',
outline=True,
color='dark',
className='my-4',
disabled=True)])
], no_gutters=True),
html.Div(id='selected-word-state', style={'display': 'none'}),
dbc.Tabs([
dbc.Tab(tab_id='search-tab', children=[
html.Div([
dbc.Input(id='word-search-input',
type='text',
placeholder='Search'),
dbc.FormText(id='word-search-matches', color='secondary'),
html.Div([
dbc.ListGroup(id='word-search-results', className='pt-3')
], className='overflow-auto', style={
'max-height': '50vh',
'height': '100%'
})
], className='mt-3')
], label='Search'),
dbc.Tab(
id='selected-word-tab',
tab_id='selected-word-tab',
label='Selection',
disabled=True
),
], id='analysis-tabs', active_tab='search-tab')
]),
]), width=2)
])
])
def get_cote_layout():
return html.Div(className='main', children=[
dcc.Location(id='url-cotes', refresh=False), # Pour intercepter un appel à la page
html.P(children=html.A(href="/", children=lang['back_to_main'])),
html.H1(children=lang['title_cotes']),
dcc.Tabs(
id='main-tabs',
value='tab-viz6',
className='main-tabs',
parent_className='parent-main-tabs',
colors={
'border': 'brown', # Couleur des bordures des onglets
'primary': 'brown', # Couleur de ligne au-dessus de l'onglet sélectionné
'background': '#EFE0E0' # Couleur de fond des onglets non sélectionnés
},
children = [
dcc.Tab(
id='tab-viz0',
value='tab-viz0',
label=lang['viz0'],
className='tab',
selected_className='selected-tab',
children=[html.Div(
id='div_tab_viz0',
className='div-tab',
children=[dcc.Markdown(lang['info']), html.Br()]
)]
),
dcc.Tab(
id='tab-viz4',
value='tab-viz4',
label=lang['viz4'],
className='tab',
selected_className='selected-tab',
children=[html.Div(
id='div_tab_viz4',
className='div-tab',
children=[
html.H3(lang['title_viz4']),
html.Table(className='table-center', children=[html.Tbody([
html.Tr(children=[
html.Td(children=html.H6(lang['choose_action'])),
html.Td(children=[dcc.Dropdown(
id='viz4_choice_action',
className='viz-dropdown-large',
multi=False,
options=[
{'label': lang['title_viz1'], 'value': 'viz1'},
{'label': lang['title_viz2'], 'value': 'viz2'},
{'label': lang['title_viz3'], 'value': 'viz3'},
{'label': lang['title_viz3a'], 'value': 'viz3a'}
],
value='viz1'
)])
]),
html.Tr(id='tr_viz1', children=[
html.Td(children=html.H6(lang['choose_wine_alea'])),
html.Td(children=[dcc.Dropdown(
id='viz1_choice',
className='viz-dropdown-large',
multi=False,
)])
]),
html.Tr(id='tr_viz2', children=[
html.Td(children=html.H6(lang['choose_appellation'])),
html.Td(children=[dcc.Dropdown(
id='viz2_choice',
className='viz-dropdown-large',
multi=False,
options=(options := [{'label': app, 'value': app} for app in DF_viz2_choice['appellation'].tolist()]),
value=options[0]['value']
)])
]),
html.Tr(id='tr_viz3', children=[
html.Td(children=html.H6(lang['choose_appellation'])),
html.Td(children=[dcc.Dropdown(
id='viz3_choice',
className='viz-dropdown-large',
multi=False,
options=(options := [{'label': app, 'value': app} for app in DF_viz3_choice['appellation'].tolist()]),
value=options[0]['value']
)])
]),
html.Tr(id='tr_viz3a', children=[
html.Td(children=html.H6(lang['choose_wine'])),
html.Td(children=[dcc.Dropdown(
id='viz3a_choice',
className='viz-dropdown-large',
multi=False,
options = (options := [{'label': label, 'value': value} for (value, label) in zip(
DF_viz3a_choice['index'],
DF_viz3a_choice.apply(lambda S:
S['domaine'] + " : " + S['nom_du_vin'] + " " + str(int(S['millesime'])) + " (" \
+ S['appellation'] + ", " + S['couleur'] + ")",
axis=1
)
)]),
value = options[0]['value']
)])
])
])]),
html.Div(id='viz4_result', className='viz-result')
]
)]
),
get_prediction_tab('viz5'),
get_prediction_tab('viz6', with_tf=True)
]
)
])
html.Div(
[
html.H4('Select Company Stock',
style={
'textAlign': 'left',
'color': colors['text']
}),
dcc.Dropdown(
id='my-tickers',
options=[{
'label': 'MSFT',
'value': 'MSFT'
}, {
'label': 'BABA',
'value': 'BABA'
}, {
'label': 'SPY',
'value': 'SPY'
}, {
'label': 'Coke',
'value': 'COKE'
}],
value='MSFT',
)
],
style={
'width': '100%',
'display': 'flex',
'align-items': 'center',
'justify-content': 'center'
}),
}
app.layout = html.Div([
html.H1(children='Release Deployment History',
style={
'textAlign': 'center',
'color': colors['text']
}),
#html.H1(children='Release Deployment History', style={'textAlign': 'center','color': colors['text'],'backgroundColor':colors['background']}),
html.Div([
html.Div('Application Name', className='app_name'),
html.Div(
dcc.Dropdown(
id='application-dropdown',
options=[{
'label': k,
'value': k
} for k in all_options.keys()],
))
]),
html.Br(),
html.Div([
html.Div('Environment', className='app_name'),
html.Div(dcc.Dropdown(id='environments-dropdown'))
]),
#html.Br(),
#html.Label('CRQ Number'),
#dcc.Input(id='crq-number', value='', type='text'),
html.Br(),
html.Br(),
html.Div(id='display-selected-values')
],
id="imgdiv")
],
id="titlediv")
datadiv = html.Div(
[
# Links
html.Div(
[
### Kanal 1
dcc.Dropdown(id='channel1',
options=[{
'label': i,
'value': i
} for i in sorted(CHANNELS)],
multi=True,
value=default_shop,
placeholder="Kanal auswählen",
clearable=True,
className='dropdown'),
### KPI 1
dcc.Dropdown(
id='metric1',
options=[{
'label': i,
'value': i
} for i in sorted(METRICS)],
multi=False,
value=default_metric,
placeholder="KPI 1 auswählen (Default: Nettoumsatz)",
className='dropdown',
'float': 'right',
'display': 'inline-block'
}),
# Space between text and dropdown
html.H1(id='space', children=' '),
# Dropdown
html.Div([
dcc.Dropdown(options=[{
'label': 'El Pais English',
'value': 'EPE'
}, {
'label': 'The Guardian',
'value': 'THG'
}, {
'label': 'The Mirror',
'value': 'TMI'
}],
value=['EPE', 'THG'],
multi=True,
id='checklist')
],
style={
'width': '40%',
'display': 'inline-block',
'float': 'left'
}),
# Button
html.Div([html.Button('Submit', id='submit', type='submit')],
html.Div([
dcc.Markdown('###### Wind Speed'),
dcc.Slider(id='wind_speed',
min=0,
max=30,
step=5,
value=10,
marks={n: str(n)
for n in range(0, 31, 5)}),
],
style=style),
html.Div([
dcc.Markdown('###### Batter'),
dcc.Dropdown(id='batter',
options=[{
'label': batter,
'value': batter
} for batter in batters],
value=batters[0]),
],
style=style),
html.Div([
dcc.Markdown('###### Pitcher'),
dcc.Dropdown(id='pitcher',
options=[{
'label': pitcher,
'value': pitcher
} for pitcher in pitchers],
value=pitchers[0]),
],
style=style),
html.Div([
def layout(self):
return html.Div([
html.Div(
[
dcc.Dropdown(
id=self.dropwdown_vector_id,
clearable=False,
options=[{
"label": i,
"value": i
} for i in self.vctr_cols_no_hist],
value=self.vctr_cols_no_hist[0],
),
html.Div([
dcc.Checklist(
id=self.chlst,
options=[{
"label": "Delta time series",
"value": "show_delta_series",
}],
labelStyle={"display": "inline-block"},
value=[],
),
html.Div(
id=self.show_ens_selectors,
children=[
dcc.Dropdown(
id=self.dropdown_iorens_id,
placeholder="Base case",
options=[{
"label": i,
"value": i
} for i in self.base_ensembles],
),
dcc.Dropdown(
id=self.dropdown_refens_id,
placeholder="Select ensembles",
options=[{
"label": i,
"value": i
} for i in self.delta_ensembles],
multi=True,
),
],
style={"display": "none"},
),
]),
],
style={
"width": "20%",
"float": "left"
},
),
html.Div(
[
dcc.Tabs(
id=self.tab_id,
value="summary_data",
children=[
dcc.Tab(label="Realizations",
value="summary_data"),
dcc.Tab(label="Statistics", value="summary_stats"),
],
),
html.Div(id="tabs-content"),
html.Div(id=self.chart_id),
],
style={
"width": "80%",
"float": "right"
},
),
])
'width': '5%'
}
),
## dropdown lists for selecting projections
html.Div([
html.Div('Select projections:',
style={'display': 'block'
}
),
dcc.Dropdown(
id='species_dropdown',
placeholder='select species',
multi=True,
style={'display': 'block',
'height': '34px',
'width': '100%',
'marginBottom': '5px',
'borderWidth': '1px'}
),
dcc.Dropdown(
id='orb_dropdown',
placeholder='select orbital',
multi=True,
style={'display': 'block',
'height': '34px',
'width': '100%',
'marginBottom': '5px',
'borderWidth': '1px'}
),
respaldo = pd.read_csv('respaldo.csv')
df = lista_g.copy()
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
available_indicators = lista_g['dependencia_clean']
app.layout = html.Div([
html.Div(
[
html.Div([
dcc.Dropdown(id='dep',
options=[{
'label': i,
'value': i
} for i in available_indicators],
value='cfe'),
dcc.Checklist(id='activaciones',
options=[{
'label': 'Usar Dependencia',
'value': 'uso_dep'
}, {
'label': 'Usar Top',
'value': 'uso_top'
}],
value=[]),
html.Div(dcc.Slider(
id='year--slider',
min=df['año_dependencia'].min(),
max=df['año_dependencia'].max(),
html.H1('SIR Model (Susceptible, Infectious, and Recovered)', style={'text-align': 'center',
'padding': 10,
'background-color': '#f0f0f5',}),
dcc.Markdown(''' The below graphs shows the future trend of the spread of COVID-19(dotted line) using the SIR model.''', style={'text-align': 'center',
'padding': 10,
'background-color': '#f0f0f5',}),
dcc.Markdown('''__Select Country for visualization__ '''),
dcc.Dropdown(
id = 'country_drop_down',
options=[ {'label': each,'value':each} for each in df_analyse['country'].unique()],
value= 'India', # which are pre-selected
multi=False),
dcc.Graph(figure = fig, id = 'SIR_graph')
])
@app.callback(
Output('SIR_graph', 'figure'),
[Input('country_drop_down', 'value')])
def update_SIR_figure(country_drop_down):
traces = []
df_plot = df_analyse[df_analyse['country'] == country_drop_down]
html.Div([
html.H1('SPYN-Starter'),
dcc.Markdown(
children='## `EDDP Web Application Demo using PYNQ`')
],
style={
'text-align': 'left',
'padding': '10px 0px 0px 0px'
}),
html.Div([
dcc.Markdown(children='### `Set Mode`'),
dcc.Dropdown(id='modes-dropdown',
options=[{
'label': 'Speed',
'value': 'Speed'
}, {
'label': 'Current',
'value': 'Current'
}],
value='Speed'),
dcc.Markdown(children='### `Set Decimation`'),
dcc.Dropdown(id='decimate-dropdown',
options=[
{
'label': 'no decimation',
'value': '0'
},
{
'label': '1',
'value': '1'
},
stock_header_dict = {}
start = (date.today() - relativedelta(months=6))
end = date.today()
#### CSS Styling that we can change to have better white space etc
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.config.supress_callback_exceptions = True
#### Layout which holds everything
app.layout = html.Div([
html.H1('Stock Search'),
dcc.Dropdown(id='stock-dropdown',
placeholder='Pick Stocks From Here...',
searchable=True,
options=search_dict),
html.Div(id='stock-search-results'),
html.Br(),
html.H4(id='header-1', style={'color': '#0189aa'}),
html.H6(id='header-2'),
html.H6(id='header-3'),
html.H6(id='header-4'),
html.H6(id='header-5'),
html.H6(id='header-6'),
html.H6(id='header-7'),
dcc.Graph(id='candlestick-main')
],
style={'height': '100vh'})
dcc.Tabs(id="tabs-styled-with-inline",
value='scraper',
children=[
dcc.Tab(label='Scraper',
value='scraper',
style=tab_style,
selected_style=tab_selected_style,
children=[
html.Div([
dcc.Dropdown(
id='platform-input',
options=[{
'label': 'Amazon',
'value': 'amazon'
}, {
'label': 'Facebook',
'value': 'facebook'
}, {
'label': 'Twitter',
'value': 'twitter'
}],
value='amazon',
style={'color': colors['text']}),
html.Button('Submit', id='button'),
html.Div(id='platform-output',
style={'color': colors['text']})
])
]),
dcc.Tab(label='Preprocessing',
value='preprocessing',
style=tab_style,
selected_style=tab_selected_style),
# Div containing everything matrix
html.Div(
[
# Details on matrix goes here
html.Div([
html.P([
'Generate a ',
html.A(['matrix'],
href='https://en.wikipedia.org/wiki/Matrix'),
]),
]),
html.Div([
html.Div([
html.Label('Row: '),
dcc.Dropdown(
options=dropdown_options,
id='row_dropdown',
),
html.Label('Column: '),
dcc.Dropdown(
options=dropdown_options,
id='column_dropdown',
),
# When dealing with matrices of different sizes will need something like this
# dcc.RadioItems(
# options=[
# {'label': 'Two matrices of same size', 'value': 'two_matrices'},
# ],
# value='two_matrices',
# id="two_matrices_checklist"
# ),
html.Button(id='generate_matrix_submit_button',
dcc.Dropdown(id='select-industry',
options=[
{
'label': 'All',
'value': 'all'
},
{
'label': 'Farms',
'value': 'farms'
},
{
'label': 'Oil and Gas',
'value': 'oil_and_gas'
},
{
'label': 'Utilities',
'value': 'utilities'
},
{
'label': 'Construction',
'value': 'construction'
},
{
'label': 'Manufacturing',
'value': 'manufacturing'
},
{
'label': 'Wholesale Trade',
'value': 'wholesale_trade'
},
{
'label': 'Retail Trade',
'value': 'retail_trade'
},
{
'label': 'Air Transportation',
'value': 'air_transportation'
},
{
'label': 'Real Estate',
'value': 'real_estate'
},
],
value='all'),
'layout': {
'title':
'Comparing 2020 Season Average Rebounds, Points & Assist Between Player 1 and Player 2 ',
'xlabel': 'Date',
'ylabel': ' Average Rebounds'
}
},
id="graph1"),
html.H5("Introduction : "),
html.
P("The National Collegiate Athletic Association (NCAA) Women Division 1 Basketball Tournament has 64 teams. This dashboard is comparing players within a team. The graph is comparing three scores of two players. The table summarizes the scores for these two players.",
style={'width': '50%'}),
html.Div([
html.H5("Select Player 1: "),
dcc.Dropdown(options=name_dict,
id='my-dropdown1',
value='Helene Haegerstrand',
style={'width': '64%'})
]),
html.Div([
html.H5("Select Player 2: "),
dcc.Dropdown(options=name_dict,
id='my-dropdown2',
value='Lucia Decortes',
style={'width': '64%'})
]),
html.Div(id="table_div", style={'width': '70%'}),
html.H5("Reference: "),
html.P('https://sports.yahoo.com/ncaaw/teams/albany/roster',
style={'width': '45%'}),
html.H5("Submitted by: "),
html.P('Adwoa Osei-Yeboah', style={'width': '45%'})
dbc.Row([
dbc.Col(html.P("Set Color-Scheme"), width=6),
dbc.Col(html.P(""), width=6)
]),
dbc.Row([
dbc.Col(
dcc.Dropdown(options=[{
'label': 'RdBu',
'value': 'RdBu'
}, {
'label': 'Picnic',
'value': 'Picnic'
}, {
'label': 'Rainbow',
'value': 'Rainbow'
}, {
'label': 'Hot',
'value': 'Hot'
}, {
'label': 'Earth',
'value': 'Earth'
}],
value='Earth',
id='style-dropdown',
style={"width": "120px"})),
dbc.Col(dbc.Button("Submit",
id="submit-button",
color="primary",
className="mr-1"),
width=6)
])
- Dictations are analyzed by IBM Watson Natural Language Understanding.
- Output variables: keywords, entities, concepts and categories.
- Variable sets are clustered, using NMF Non-zero Matrix Factorization
- Dictations and variables are mapped onto the clusters
''',
className = 'nine columns')
], className = "row"),
html.Div(
[
html.Div(
[
html.Label('#Archetypes', style={'font-weight' : 'bold'}),
dcc.Dropdown(
id = 'NoA',
options=[{'label':k,'value':k} for k in range(2,12)],
value = 6,
multi = False
)
],
className = 'one columns offset-by-one',
style={'margin-top': '30'}
),
html.Div(
[
html.Label('Variables', style={'font-weight' : 'bold'}),
dcc.RadioItems(
id = 'Var',
options=[
{'label': 'Keywords' ,'value': 'keywords'},
{'label': 'Entities' ,'value': 'entities'},
{'label': 'Concepts' ,'value': 'concepts'},
center={"lat": 4.655115, "lon": -74.055404}, #Center
color_continuous_scale="matter", #Color Scheme
opacity=0.5,
title='Total crimes in Colombia by Department'
)
Map_fig.update_layout(title='Total de Crímenes por Departamento',paper_bgcolor="#F8F9F9")
values={'Robbery and theft':'robbery','Domestic violence':'dom_viol','Sex offenses':'sex_off'}
#Now we create the dropdown for crimes
dropdown=dcc.Dropdown(
id="crime_dropdown",
options=[{"label":key, "value":values[key]} for key in values.keys()],
value=["sex_off",'robbery'],
placeholder="Select a crime",
multi=True
)
#Now the dropdown for years
ys = [year for year in dff.year.unique()]
dropdown2=dcc.Dropdown(
id="year_dropdown",
options=[{"label":year, "value":year} for year in ys],
value=2018,
placeholder="Select a year",
multi=False
)
def get_prediction_tab(vizname, with_tf=True):
options = [{'label': lang['xgboost'], 'value': 'xg'}]
if with_tf:
options = options + [{'label': lang['tensorflow'], 'value': 'tf'}]
return dcc.Tab(
id='tab-' + vizname,
value='tab-' + vizname,
label=lang[vizname],
className='tab',
selected_className='selected-tab',
children=[
html.Div(
id='div_tab_' + vizname,
className='div-tab',
children=[
html.H3(lang['title_' + vizname]),
html.P(lang[vizname + '_desc']),
html.Table(
className='table-center',
children=[
html.Tbody([
html.Tr(children=[
html.Td(children=html.H6(
lang['choose_model'])),
html.Td(children=[
dcc.Dropdown(
id=vizname +
'_choice_model',
className='viz-dropdown-large',
multi=False,
options=options,
value='xg')
])
]),
html.Tr(children=[
html.Td(children=html.H6(
lang['choose_action'])),
html.Td(children=[
dcc.Dropdown(
id=vizname +
'_choice_action',
className='viz-dropdown-large',
multi=False,
options=[{
'label': lang['see_pred'],
'value': 'viz'
}, {
'label':
lang['get_reco_test'],
'value':
'reco'
}],
value='reco')
])
]),
html.Tr(
id=vizname + '_tr_choose_number',
children=[
html.Td(children=html.H6(
lang['choose_number'])),
html.Td(children=[
dcc.Dropdown(
id=vizname +
'_choice_nbwines',
className='viz-dropdown-small',
multi=False,
options=[{
'label': n,
'value': n
}
for n in np.arange(
5, 55, 5)],
value=20)
])
]),
html.Tr(
id=vizname + '_tr_choose_wine',
children=[
html.Td(children=html.H6(
lang['choose_wine_alea'])),
html.Td(children=[
dcc.Dropdown(
id=vizname +
'_choice_wine',
className='viz-dropdown-large',
multi=False)
])
])
])
]),
html.Div(id=vizname + '_result', className='viz-result')
])
])
'value': 'panPan2',
'label': 'Bonobo (MPI-EVA panpan1.1/panPan2)'
}, {
'value': 'canFam3',
'label': 'Dog (Broad CanFam3.1/canFam3)'
}, {
'value': 'ce11',
'label': 'C. elegans (ce11)'
}]
app.layout = html.Div([
dcc.Loading(id='igv-container'),
html.Hr(),
html.P('Select the genome to display below.'),
dcc.Dropdown(id='igv-genome-select',
options=HOSTED_GENOME_DICT,
value='ce11')
])
# Return the IGV component with the selected genome.
@app.callback(Output('igv-container', 'children'),
Input('igv-genome-select', 'value'))
def return_igv(genome):
return (html.Div(
[dashbio.Igv(
id='default-igv',
genome=genome,
minimumBases=100,
)]))
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
counties = json.load(response)
########### Initiate the app
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
server = app.server
app.title='VA 2016'
####### Layout of the app ########
app.layout = html.Div([
html.H3('2016 Presidential Election: Vote Totals by Jurisdiction'),
dcc.Dropdown(
id='dropdown',
options=[{'label': i, 'value': i} for i in options_list],
value=options_list[10]
),
html.Br(),
dcc.Graph(id='display-map'),
dcc.Graph(id='display-value'),
html.Br(),
html.A('Code on Github', href='https://github.com/austinlasseter/virginia_election_2016'),
html.Br(),
html.A('Data Source', href='https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/LYWX3D')
])
######### Callback #1 #########
@app.callback(dash.dependencies.Output('display-value', 'figure'),
[dash.dependencies.Input('dropdown', 'value')])
app = dash.Dash()
nsdq = pd.read_csv('../data/NASDAQcompanylist.csv')
nsdq.set_index('Symbol', inplace=True)
options = []
for tic in nsdq.index:
options.append({
'label': '{} {}'.format(tic, nsdq.loc[tic]['Name']),
'value': tic
})
app.layout = html.Div([
html.H1('Stock Ticker Dashboard'),
html.Div([
html.H3('Select stock symbols:', style={'paddingRight': '30px'}),
dcc.Dropdown(
id='my_ticker_symbol', options=options, value=['TSLA'], multi=True)
],
style={
'display': 'inline-block',
'verticalAlign': 'top',
'width': '30%'
}),
html.Div([
html.H3('Select start and end dates:'),
dcc.DatePickerRange(id='my_date_picker',
min_date_allowed=datetime(2015, 1, 1),
max_date_allowed=datetime.today(),
start_date=datetime(2018, 1, 1),
end_date=datetime.today())
],
style={'display': 'inline-block'}),
start_date=dash_visits['dt'].min(),
end_date=dash_visits['dt'].max(),
display_format='YYYY-MM-DD',
id='dt_selector',
),
html.Br(),
html.Br(),
# фильтр возраста
html.Label('Выбор возрастной категории'),
# составляем список
dcc.Dropdown(options=[{
'label': x,
'value': x
} for x in dash_visits['age_segment'].unique()],
value=dash_visits['age_segment'].unique(),
multi=True,
id='age-dropdown'),
],
className='six columns'), # конец левого блока
# блок с пикером тем (правый блок)
html.Div(
[
# фильтр тем
html.Label('Выбор темы карточек'),
# составляем список
dcc.Dropdown(options=[{
}),
html.H2(children='Graph 1',
style={
'font-family': 'Arial, Helvetica, sans-serif',
'text-align': 'center'
}),
html.Div([
html.P(children='Select the first indicator:',
style={
'font-size': 15,
'font-family': 'Arial, Helvetica, sans-serif'
}),
dcc.Dropdown(
id='xaxis-column',
options=[{
'label': i,
'value': i
} for i in available_indicators],
value=available_indicators[0],
)
],
style={
'width': '48%',
'display': 'inline-block',
'height': '130px'
}),
html.Div([
html.P(children='Select the second indicator:',
style={
'font-size': 15,
'font-family': 'Arial, Helvetica, sans-serif'
}),
FILTERS = {
c.dash.INPUT_SMOOTHING: (
"Smoothing:",
dbc.Input(id="input_smoothing",
type="number",
value=c.dash.DEFAULT_SMOOTHING),
),
c.dash.INPUT_TIMEWINDOW: (
"Grouping:",
dcc.Dropdown(
id="input_timewindow",
value="M",
options=[{
"label": "Month ",
"value": "M"
}, {
"label": "Year ",
"value": "Y"
}],
),
),
c.dash.INPUT_CATEGORIES: (
"Categories:",
dcc.Dropdown(id="input_categories",
multi=True,
options=get_options(DF[c.cols.CATEGORY].unique())),
),
}
import dash_core_components as dcc
import dash_html_components as html
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.layout = html.Div([
html.Div([
html.Label('Dropdown'),
dcc.Dropdown(options=[{
'label': 'New York City',
'value': 'NYC'
}, {
'label': u'Montréal',
'value': 'MTL'
}, {
'label': 'San Francisco',
'value': 'SF'
}],
value='SF'),
html.Label('Multi-Select Dropdown'),
dcc.Dropdown(options=[{
'label': 'New York City',
'value': 'NYC'
}, {
'label': u'Montréal',
'value': 'MTL'
}, {
'label': 'San Francisco',
'value': 'SF'
