},
{
"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",
