}, { "label": "Circles", "value": "circles", }, ], clearable=False, searchable=False, value="moons", ), drc.NamedSlider( name="Sample Size", id="slider-dataset-sample-size", min=100, max=500, step=100, marks={ str(i): str(i) for i in [100, 200, 300, 400, 500] }, value=300, ), drc.NamedSlider( name="Noise Level", id="slider-dataset-noise-level", min=0, max=1, marks={ i / 10: str(i / 10) for i in range(0, 11, 2) }, step=0.1,
), html.Div(id='output-data-upload'), drc.NamedDropdown( name="Target Variable", id="input-target-variable", placeholder="Choose target variable...", style={ 'width': '100%', } ), drc.NamedSlider( name="Test Size", id="slider-dataset-test-size", min=0.1, max=0.5, step=0.1, marks={ str(i): str(i) for i in [0.1, 0.2, 0.3, 0.4, 0.5] }, value=0.2, ), drc.NamedDropdown( name="Feature Selection/Extraction", id="dropdown-feature-selection", options=[ { "label": "PCA", "value": "pca" }, { "label": "Chi2",
}, { 'label': 'Linearly Separable', 'value': 'linear' }, { 'label': 'Circles', 'value': 'circles' }], clearable=False, searchable=False, value='moons'), drc.NamedSlider( name='Sample Size', id='slider-dataset-sample-size', min=100, max=500, step=100, marks={ i: i for i in [100, 200, 300, 400, 500] }, value=300), drc.NamedSlider( name='Noise Level', id='slider-dataset-noise-level', min=0, max=1, marks={ i / 10: str(i / 10) for i in range(0, 11, 2) }, step=0.1,
}, { "label": "Circles", "value": "circles", }, ], clearable=False, searchable=False, value="moons", ), drc.NamedSlider( name="Sample Size", id="slider-dataset-sample-size", min=100, max=500, step=100, marks={ str(i): str(i) for i in [100, 200, 300, 400, 500] }, value=300, ), drc.NamedSlider( name="Noise Level", id="slider-dataset-noise-level", min=0, max=1, marks={ i / 10: str(i / 10) for i in range(0, 11, 2) }, step=0.1,
term_ranks = nlp.get_topic_term_ranks(docterm, termtopics) print('Building and populating app layout') app = dash.Dash(__name__) app.layout = html.Div( children=[ html.Div( id='div-top-controls', children=[ html.Div( id='slider-term-relevance-lambda-container', children=drc.NamedSlider( name='Lambda', id='slider-term-relevance-lambda', min=0, max=1, step=0.1, value=0.6, ), ), html.Div( id='input-topic-id-container', children=drc.NamedInput( name='Topic ID', id='input-topic-id', type='number', value=0, min=0, max=args.n_topics - 1, step=1, ),
def build_tab_1(): return [ # html.Div( # id="herd-immunity-definition", # children=[ # html.B('Definition of Herd Immunity'), # html.P( # # "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nullam consequat tellus ac magna ullamcorper consequat a eget tellus. Maecenas eleifend vel velit non rhoncus. Ut mollis nec justo quis " # # "pharetra. Donec a bibendum lectus, sed tincidunt urna. Vivamus sem odio, pharetra vel nibh in, gravida finibus nisl. Vestibulum tincidunt et odio non eleifend. Ut non purus rhoncus, convallis risus eu," # # " aliquet magna. Duis varius massa eget massa ultricies venenatis lobortis vel velit. Vivamus sollicitudin ultrices velit sed tristique. Sed luctus lectus at" # # "neque feugiat suscipit in id odio. Aenean commodo purus eu vestibulum lacinia. Morbi dignissim aliquet lacus eu ultricies. Cras nec rutrum orci. Nam sed massa" # # " eu metus lobortis vulputate fermentum at diam. Aliquam tincidunt dolor elementum est tempus feugiat. Curabitur non nunc velit." # "Resistance to the spread of an infectious disease within a population that is based on pre-existing immunity of a high proportion of individuals as a result of previous infection or vaccination." # "Though herd immunity is defined as no new cases for a threshold n days, this seems to be quite impossible in the current scenario. Therefore, our solution allows users to set their own definition of herd immunity. The definition includes, no. of days - n, threshold no. of cases - c. and the herd immunity % is calculated by, No. of days out of n for which the predicted new cases are less then c/n * 100" # ) # ], # style={'margin': '5rem 10rem'} # ), html.Div(children=[ html.Div(children=[ drc.NamedSlider( name="Select % of population to vaccinate", id="slider-vac-perc", min=0, max=15, marks={i: str(i) for i in [0, 1, 3, 5, 9]}, step=1, value=1, ), ], style={'padding': '1rem 10rem'}), html.Div(children=[dcc.Graph(id='vac-graph-herd')], style={ 'padding': '1rem 10rem', 'display': 'inline-block', 'width': '70%' }), ]), html.Div(id="set-specs-intro-container", children=html.H5("Set your parameters for Herd Immunity"), style={'margin': '1.5rem 10rem'}), html.Div(id="herd-immunity-panel", children=[ html.Div( className='six columns', children=[ drc.NamedSlider( name="Set the threshold for number of cases", id="slider-threshold-perc", min=0, max=15, marks={i: str(i) for i in range(0, 16, 5)}, step=5, value=5, ) ]), html.Div( className='six columns', children=[ drc.NamedSlider( name="Select the number of days", id="slider-num-days", min=0, max=15, marks={i: str(i) for i in range(0, 16, 3)}, step=3, value=3, ) ], ) ], style={'margin': '1.5rem 10rem'}), html.Div(id="herd-immunity-results", children=html.H5("Predictions for Herd Immunity"), style={'margin': '0.5rem 10rem'}), html.Div(className='twelve columns', children=[html.H6(id='slider-output')], style={'margin': '0rem 10rem 5rem 10rem'}) ] # def build_tab_4(): # return html.Div( # children=[ # html.Div( # children=[ # drc.NamedSlider( # name="Select % of population to vaccinate", # id="slider-vac-perc", # min=0, # max=15, # marks={ # i: str(i) # for i in [0, 1, 3, 5, 9] # }, # step=1, # value=1, # ), # ], # ), # html.Div( # children=[ # dcc.Graph( # id='vac-graph' # ) # ] # ) # ], # style={'padding': '5rem 10rem'} # )
def card2(): return drc.Card( id="button-card", children=[ div_input_line( ["Periods", "Paths"], ["periods", "paths"], [500, 100], ), drc.NamedSlider( name="Market Liquidity", id="slider-ml", min=0, max=0.1, step=0.001, marks={ i/100: str(i/100) for i in range(0, 11, 2) }, tooltip = {"always_visible": False, "placement": "top"}, value=0.01, ), drc.NamedSlider( name="Switching Strength", id="slider-ss", min=0.1, max=2, marks={ i / 10: str(i / 10) for i in range(0, 20, 4) }, step=0.1, tooltip = {"always_visible": False, "placement": "top"}, value=1 ), drc.CheckboxSlider("Fundamental price window", 'rvmean_cb', enabled = False, id = 'rvmean', min = 1, max = 365, marks={ i: str(i) for i in range(0, 365, 90) }, tooltip = { "always_visible": False, "placement": "top" }, value = 180, ), drc.CheckboxSlider("Effective return window", 'retmean_cb', enabled = False, id = 'retmean', min = 1, max = 21, marks={ i: str(i) for i in range(0, 21, 5) }, tooltip = { "always_visible": False, "placement": "top" }, value = 5, ), html.Button( "Simulate", id="btn-simulate", style = {'color': 'inherit', }, ), dcc.Checklist( id = 'pick_checkbox', options=[ {'label': 'Start from picked point', 'value': 'enable'}, ], value = [] ), html.Button( "Pick Start", id="pick_start", style = {'color': 'inherit', }, ), ], )
'value': 'gaussian' }, { 'label': 'Hole-Effect', 'value': 'hole-effect' }, ], clearable=False, searchable=False, value='spherical'), drc.NamedSlider( name='Sample Size', id='slider-dataset-sample-size', min=100, max=500, step=10, marks={ i: i for i in [100, 200, 300, 400, 500] }, value=200), drc.NamedSlider( name='Noise Level', id='slider-dataset-noise-level', min=0, max=2, marks={ i: str(i) for i in [0, 0.5, 1, 1.5, 2] }, step=0.1,
'label': 'Yes', 'value': 'Yes' }, { 'label': 'No', 'value': 'No' }], value='Yes', style={'display': 'none'}) ]), ]), drc.Card([ drc.NamedSlider(name='Number of clusters', id='n_clusters', min=2, max=20, step=1, marks={ i: str(i) for i in range(2, 21) }, value=2), drc.NamedSlider( name='Number of base partitions', id='n_partitions', min=100, max=1000, step=100, marks={ i: str(i) for i in range(100, 1001, 100) }, value=500),
}, { "label": "Two Gaussians", "value": "gaussians", }, ], clearable=False, searchable=False, value="moons", ), drc.NamedSlider( name="Sample Size", id="slider-dataset-sample-size", min=100, max=500, step=100, marks={ str(i): str(i) for i in [100, 200, 300, 400, 500] }, value=300, ), drc.NamedSlider( name="Noise Level", id="slider-dataset-noise-level", min=0, max=1, marks={ i / 10: str(i / 10) for i in range(0, 11, 2) }, step=0.1,
name="Select feature", id="dropdown-select-col2", options=cols_opts, clearable=False, searchable=False, value="satisfaccion_inicial", ) ], ), drc.Card( id="button-card", children=[ drc.NamedSlider( name="Threshold", id="slider-threshold", min=0, max=1, value=0.5, step=0.01, ), html.Button( "Reset Threshold", id="button-zero-threshold", ), ], ), ], ), html.Div( id="div-graphs", children=dcc.Graph( id="graph-sklearn-svm",