import pandas as pd
df = pd.read_csv('/Users/code/Documents/Code_Immersives/Project/Term1Final/fourStations.csv')
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
features = df.columns
Days = df['DAY'].unique()
app = dash.Dash()
app.layout = html.Div([
html.Div([
html.Label('DAY'),
dcc.Dropdown(
id='DAY',
options=[{'label': i, 'value': i} for i in Days],
value='',
placeholder='Select...',
multi=True
)
],
style={'width': '20%', 'display': 'inline-block', 'margin-bottom': '20px'}),
html.Div([
html.Label('HOUR'),
dcc.Slider(
id='HOUR-slider',
min=df['HOUR'].min(),
def map_view_layout(self) -> html.Div:
return html.Div(
children=[
wcc.FlexBox(
children=[
html.Div(
children=[
html.Label(
style={
"font-weight": "bold",
"textAlign": "center",
},
children="Select surface",
),
dcc.Dropdown(
id=self.ids("map-dropdown"),
options=[
{"label": name, "value": name}
for name in self.surfacenames
],
value=self.surfacenames[0],
clearable=False,
persistence=True,
persistence_type="session",
),
]
),
],
),
html.Div(
style={
"marginTop": "0px",
"height": "800px",
"zIndex": -9999,
},
children=[
# pylint: disable=no-member
webviz_subsurface_components.LeafletMap(
id=self.ids("layered-map"),
layers=[],
unitScale={},
autoScaleMap=True,
minZoom=-5,
drawTools={
"drawMarker": False,
"drawPolygon": False,
"drawPolyline": True,
"position": "topright",
},
switch={
"value": self.state["switch"],
"disabled": False,
"label": "Hillshading",
},
mouseCoords={"position": "bottomright"},
colorBar={"position": "bottomright"},
),
],
),
]
)
# Defining app layout
app.layout = html.Div(
style={"backgroundColor": colors["background"]},
children=[
html.Div(
[
html.H2("Chess App"),
html.Img(src="/assets/chess-app.jpg"),
],
className="banner",
),
html.Div(
[
html.Div(
[
html.Label("Range Slider"),
dcc.RangeSlider(
id="rangeslider",
marks={
i: "label {}".format(i)
for i in range(0, 5)
},
min=0,
max=5,
value=[0, 1],
step=1,
),
],
className="five columns",
),
html.Div(
'textAlign': 'center',
'font-family': 'Dosis',
'font-size': '32x',
'color': colors['text'],
'padding': 0
}
),
html.Div(children='A web application for analyzing comments on Youtube videos', style={
'textAlign': 'center',
'color': colors['subtext'],
'padding': 8
}),
html.Label('URL: ', style={
'font-family': 'Dosis',
'color': colors['text']
}),
#dcc.Textarea(
# id='my-id',
# placeholder='Enter a value...',
# value='Enter Youtube video URL here',
# style={'height': '8px',
# 'width': '800px',
# 'textAlign': 'left', 'padding': 8}
# ),
#dcc.Input(id='my-id', value='initial value', type='text'),
dcc.Input(id='video-input', value='Enter Youtube video URL here', type='text',
style={'width': '600px'}),
html.Div(id='video-input-div'),
def cross_section_widgets_layout(self) -> html.Div:
return html.Div(
children=[
html.Div(
children=[
dbc.Button(
"Surface Settings",
id=self.ids("button-open-graph-settings"),
color="light",
className="mr-1",
),
dbc.Modal(
children=[
dbc.ModalHeader("Surface Settings"),
dbc.ModalBody(
children=[
html.Label(
style={
"font-weight": "bold",
"textAlign": "Left",
},
children="Select Surfaces",
),
dcc.Checklist(
id=self.ids("all-surfaces-checkbox"),
options=[{"label": "all", "value": "True"}],
value=["True"],
persistence=True,
persistence_type="session",
),
dcc.Checklist(
id=self.ids("surfaces-checklist"),
options=[
{"label": name, "value": name}
for name in self.surfacenames
],
value=self.surfacenames,
persistence=True,
persistence_type="session",
),
dcc.Checklist(
id=self.ids("surfaces-de-checklist"),
options=[
{
"label": name + " SD",
"value": name,
"disabled": False,
}
for name in self.surfacenames
],
value=self.surfacenames,
persistence=True,
persistence_type="session",
),
],
),
dbc.ModalFooter(
children=[
dbc.Button(
"Close",
id=self.ids("button-close-graph-settings"),
className="ml-auto",
),
dbc.Button(
"Apply changes",
id=self.ids("button-apply-checklist"),
className="ml-auto",
),
]
),
],
id=self.ids("modal-graph-settings"),
size="sm",
centered=True,
backdrop=False,
fade=False,
),
dbc.Button(
"Well Settings", id=self.ids("button-open-well-settings")
),
dbc.Modal(
children=[
dbc.ModalHeader("Well Settings"),
dbc.ModalBody(
children=[
html.Label(
style={
"font-weight": "bold",
"textAlign": "Left",
},
children="Select Well Attributes",
),
dcc.Checklist(
id=self.ids("all-well-settings-checkbox"),
options=[{"label": "all", "value": "True"}],
value=["True"],
persistence=True,
persistence_type="session",
),
dcc.Checklist(
id=self.ids("well-settings-checklist"),
options=[
{
"label": "Zonelog",
"value": "zonelog",
},
{
"label": "Zonation points",
"value": "zonation_points",
},
{
"label": "Conditional points",
"value": "conditional_points",
},
],
value=[
"zonelog",
"zonation_points",
"conditional_points",
],
persistence=True,
persistence_type="session",
),
],
),
dbc.ModalFooter(
children=[
dbc.Button(
"Close",
id=self.ids("button-close-well-settings"),
className="ml-auto",
),
dbc.Button(
"Apply",
id=self.ids(
"button-apply-well-settings-checklist"
),
className="ml-auto",
),
]
),
],
id=self.ids("modal-well-settings"),
size="sm",
centered=True,
backdrop=False,
fade=False,
),
],
),
wcc.FlexBox(
children=[
html.Div(
children=[
html.Label(
style={
"font-weight": "bold",
"textAlign": "center",
},
children="Select well",
),
dcc.Dropdown(
id=self.ids("well-dropdown"),
options=[
{
"label": self.wells[wf].wellname,
"value": str(wf),
}
for wf in self.wellfiles
]
+ [
{
"label": self.planned_wells[wf].wellname,
"value": str(wf),
}
for wf in self.planned_wellfiles
],
value=str(self.wellfiles[0]),
clearable=False,
disabled=False,
persistence=True,
persistence_type="session",
),
]
),
],
),
html.Div(
children=[
html.Div(
style={
"marginTop": "0px",
"height": "800px",
"zIndex": -9999,
},
children=[self.cross_section_graph_layout],
id=self.ids("cross-section-view"),
)
]
),
]
)
def visualize_noisy_affects_filter():
# Parameters
seed = 2
data_name = 'MNIST'
show_all = False
noise_type = 'gaussian_gray'
# Save indices
if noise_type == 'snp':
save_indices = [118, 175]
else:
save_indices = [333, 1444]
# Download MNIST data set
set_seed(seed)
test_set_n = get_data(data_name,
False,
noise_type=noise_type,
percent_noise=0.1)
test_set = get_data(data_name, False)
# Create models
conv_net = ConvClassificationModel()
# Load models
conv_net.load(
torch.load('models\\pre_trained_models\\mnist_digit_conv.model'))
# Pre-compute values
raw_results = {}
filter_results = {}
for i in range(len(test_set)):
clean_data, original_label = test_set[i]
clean_data_torch = clean_data.view(1, *clean_data.shape).float()
clean_label = conv_net.get_label(
clean_data_torch).detach().numpy()[0][0]
clean_p_dist = np.round(np.exp(
conv_net.classify(clean_data_torch).detach().numpy()[0]),
decimals=2)
clean_data_np = np.flipud(clean_data.detach().numpy()[0, :, :])
noisy_data, _ = test_set_n[i]
noisy_data_torch = noisy_data.view(1, *noisy_data.shape).float()
noisy_label = conv_net.get_label(
noisy_data_torch).detach().numpy()[0][0]
noisy_p_dist = np.round(np.exp(
conv_net.classify(noisy_data_torch).detach().numpy()[0]),
decimals=2)
noisy_data_np = np.flipud(noisy_data.detach().numpy()[0, :, :])
label_mismatch = original_label == clean_label and original_label != noisy_label
if show_all or label_mismatch:
raw_results[i] = (clean_data_np, noisy_data_np, original_label,
clean_label, noisy_label, clean_p_dist,
noisy_p_dist)
filter_results[i] = (get_plots(clean_data_torch, conv_net),
get_plots(noisy_data_torch, conv_net))
if i in save_indices:
# Dump output
filename = 'data\\{}\\noisy_data_np\\{}_{}'.format(
data_name, noise_type, i)
np.save(filename, noisy_data_np)
# Create dash app
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.layout = html.Div([
html.Div([
html.Div([
html.Label('Test Set Image Index:'),
dcc.Dropdown(id='data_set-index',
options=[{
'label': i,
'value': i
} for i in raw_results.keys()],
value=list(raw_results.keys())[0])
],
style={
'width': '33%',
'display': 'inline-block',
'vertical-align': 'top'
}),
html.Div([
html.Label('Network Layer:'),
dcc.Dropdown(id='filter-index',
options=[{
'label': i,
'value': i
} for i in [1, 2]],
value=2),
],
style={
'width': '33%',
'display': 'inline-block',
'vertical-align': 'top'
}),
html.Div([
html.Label('Network Layer Type:'),
dcc.RadioItems(id='display-type',
options=[{
'label': i,
'value': i
} for i in ['Filter', 'Activation']],
value='Filter',
labelStyle={'display': 'inline-block'})
],
style={
'width': '33%',
'display': 'inline-block',
'vertical-align': 'top'
})
]),
html.Div([
html.Div([
html.Div(id='original-label'),
html.Div(id='clean-label'),
html.Div(id='noisy-label'),
dcc.Graph(id='raw-graph')
],
style={
'width': '35%',
'display': 'inline-block',
'vertical-align': 'top'
}),
html.Div([dcc.Graph(id='filter-graph')],
style={
'width': '60%',
'display': 'inline-block',
'vertical-align': 'top'
})
])
])
@app.callback(Output('filter-graph', 'figure'), [
Input('data_set-index', 'value'),
Input('display-type', 'value'),
Input('filter-index', 'value')
])
def update_graph(selected_index, display_type, filter_index):
clean_plot_set, noisy_plot_set = filter_results[selected_index]
clean_plots, labels = clean_plot_set[(filter_index, display_type)]
noisy_plots, _ = noisy_plot_set[(filter_index, display_type)]
num_rows = clean_plots.shape[0]
fig = make_subplots(
rows=num_rows,
cols=3,
subplot_titles=[
'{} on {}'.format(t, l) for l in labels
for t in ['Clean Image', 'Noisy Image', 'Difference']
])
for i in range(num_rows):
clean_plot = np.flipud(clean_plots[i, :, :])
noisy_plot = np.flipud(noisy_plots[i, :, :])
diff_plot = clean_plot - noisy_plot
axis_num = (i * 3) + 1
fig.add_trace(go.Heatmap(z=clean_plot,
type='heatmap',
coloraxis='coloraxis',
showscale=False),
row=i + 1,
col=1)
fig.update_xaxes(showgrid=False,
showticklabels=False,
zeroline=False,
scaleanchor='y{}'.format(axis_num),
row=i + 1,
col=1)
fig.update_yaxes(showgrid=False,
showticklabels=False,
zeroline=False,
row=i + 1,
col=1)
fig.add_trace(go.Heatmap(z=noisy_plot,
type='heatmap',
coloraxis='coloraxis',
showscale=False),
row=i + 1,
col=2)
fig.update_xaxes(showgrid=False,
showticklabels=False,
zeroline=False,
scaleanchor='y{}'.format(axis_num + 1),
row=i + 1,
col=2)
fig.update_yaxes(showgrid=False,
showticklabels=False,
zeroline=False,
row=i + 1,
col=2)
fig.add_trace(go.Heatmap(z=diff_plot,
type='heatmap',
coloraxis='coloraxis',
showscale=False),
row=i + 1,
col=3)
fig.update_xaxes(showgrid=False,
showticklabels=False,
zeroline=False,
scaleanchor='y{}'.format(axis_num + 2),
row=i + 1,
col=3)
fig.update_yaxes(showgrid=False,
showticklabels=False,
zeroline=False,
row=i + 1,
col=3)
fig.update_layout(autosize=False,
height=300 * num_rows,
coloraxis={'colorscale': 'Gray'})
return fig
@app.callback([
Output('raw-graph', 'figure'),
Output('original-label', 'children'),
Output('clean-label', 'children'),
Output('noisy-label', 'children')
], [Input('data_set-index', 'value')])
def update_graph(selected_index):
clean_data_np, noisy_data_np, original_label, clean_label, noisy_label, clean_p_dist, noisy_p_dist = raw_results[
selected_index]
fig = make_subplots(rows=1,
cols=2,
horizontal_spacing=0.1,
vertical_spacing=0.1,
subplot_titles=['Clean Image', 'Noisy Image'])
fig.add_trace(go.Heatmap(z=clean_data_np,
type='heatmap',
coloraxis='coloraxis',
showscale=False),
row=1,
col=1)
fig.update_xaxes(showgrid=False,
showticklabels=False,
zeroline=False,
scaleanchor='y1',
row=1,
col=1)
fig.update_yaxes(showgrid=False,
showticklabels=False,
zeroline=False,
row=1,
col=1)
fig.add_trace(go.Heatmap(z=noisy_data_np,
type='heatmap',
coloraxis='coloraxis',
showscale=False),
row=1,
col=2)
fig.update_xaxes(showgrid=False,
showticklabels=False,
zeroline=False,
scaleanchor='y2',
row=1,
col=2)
fig.update_yaxes(showgrid=False,
showticklabels=False,
zeroline=False,
row=1,
col=2)
fig.update_layout(autosize=False,
coloraxis={
'colorscale': 'Gray',
'showscale': False
})
clean_desc = 'Clean Label: {}, Clean Label Probabilities: {}'.format(
clean_label, clean_p_dist)
noisy_desc = 'Noisy Label: {}, Noisy Label Probabilities: {}'.format(
noisy_label, noisy_p_dist)
return fig, 'Original Label: {}'.format(
original_label), clean_desc, noisy_desc
app.run_server(port=8051, debug=True)
def render_status():
# render content for status tab
global cmd_algorithm, cmd_loading, ldc_signal
return html.Div(
children=[
html.Div(
[
html.H1("Microgrid Status",
style={
'marginTop': '5',
'text-align': 'center',
'float': 'center',
'color': 'white'
}),
],
className='banner',
style={
'width': '100%',
'display': 'inline-block',
"backgroundColor": "#18252E",
}),
html.Div(
[
html.Div(
[
html.Label('Power limit:',
className='column',
style={
'color': 'white',
'display': 'inline-block',
'margin': '3'
}),
html.Div(id='cmd-loading',
children=np.round(
float(cmd_loading) / 1000, 3),
className='row',
style={
'font-size': 'xx-large',
'color': 'white',
'text-align': 'right',
'display': 'inline-block',
'padding': '9',
"position": "relative",
}),
html.Div(id='cmd-loading-unit',
children='kVA',
className='row',
style={
'font-size': 'xx-large',
'color': 'white',
'text-align': 'left',
'display': 'inline-block',
'padding': '9',
"position": "relative",
}),
],
className='column',
),
html.Div(
[
html.Label('Set limit (kVA):',
className='column',
style={
'color': 'white',
'display': 'inline-block',
'margin': '3',
"position": "relative",
}),
dcc.Input(
id='input-cmd-loading',
className='row',
value=np.round(float(cmd_loading) / 1000,
3), # converted to kW
disabled=False,
type='number',
min=0,
max=30, # converted to kW
step=0.10,
inputmode='numeric',
style={
'text-align': 'center',
'display': 'inline-block',
'padding': '9',
"position": "relative",
}),
],
className='column',
),
html.Div(
[
html.Label('LDC Signal:',
className='column',
style={
'color': 'white',
'display': 'inline-block',
'margin': '3'
}),
html.Div(id='cmd-signal',
children=ldc_signal,
className='row',
style={
'font-size': 'xx-large',
'color': 'white',
'text-align': 'right',
'display': 'inline-block',
'padding': '9',
"position": "relative",
}),
html.Div(id='cmd-signal-unit',
children='Hz',
className='row',
style={
'font-size': 'xx-large',
'color': 'white',
'text-align': 'left',
'display': 'inline-block',
'padding': '9',
"position": "relative",
}),
],
className='row',
),
html.Div(
[
html.Label('Algorithm:',
className='column',
style={
'color': 'white',
'display': 'inline-block',
"position": "relative",
}),
dcc.RadioItems(
id='cmd-algorithm',
options=[
{
"label": "No LDC",
"value": "A0"
},
{
"label": "Basic LDC",
"value": "A1"
},
{
"label": "Advance LDC",
"value": "A2"
},
# {"label": "Smart LDC", "value": "A3"},
],
value=cmd_algorithm,
className='column',
style={
'color': 'white',
'margin': '3',
"position": "relative",
}),
],
className='row',
style={
'display': 'inline-block',
"position": "relative",
}),
],
className='row s12 m2 l2',
# style={'padding':'3', 'float':'left'}
style={
"position": "relative",
"float": "left",
# "border": "1px solid",
# "borderColor": "rgba(68,149,209,.9)",
"overflow": "hidden",
"marginBottom": "2px",
"width": "15%"
},
),
html.Div(
[
html.Div(
children=html.Div(id='graphs'),
className='row',
),
# dcc.Interval(id='graph-update', interval=1.5*1000),
],
className='row s12 m8 l8',
# style={'padding':'3', 'float':'left'},
style={
"position": "relative",
"float": "left",
# "border": "1px solid",
# "borderColor": "rgba(68,149,209,.9)",
# "overflow": "hidden",
"marginBottom": "2px",
"width": "80%",
},
),
# hidden div: holder of data
html.Div(
[
html.Div(children=html.Div(id='data'),
className='row',
style={
'opacity': '1.0',
'display': 'none'
}),
dcc.Interval(id='data-update', interval=1 * 1000),
],
className='row',
style={
'display': 'none',
},
),
# hidden update for sending ldc command signal
html.Div(children=html.Div(id='command'),
style={'display': 'none'}),
],
className='row',
style={
'display': 'inline-block',
"backgroundColor": "#18252E",
"width": "100%"
})
def update_range_display(time_val):
return [html.Label(time_val)]
)
],
className="row",
),
html.Div(
[
html.H1(children='CCTV Dash Board', ),
],
className="row",
style={"text-align": "center"},
),
html.Div(
className='row',
children=[
html.Div([
html.Label('Camera'),
dcc.Dropdown(
id='Camera',
options=[{
'label': 'Camera 1 - cars',
'value': 'C1'
}, {
'label': 'Camera 2 - People',
'value': 'C2'
}, {
'label': 'Camera 3 - Total # inside',
'value': 'C3'
}, {
'label': 'Real time',
'value': 'R'
}],
df = pd.concat(dfs)
app.layout = html.Div(children=[
html.H2(
children='Relationship between delivery type and neonatal mortality'),
dcc.Graph(id='relacao-partos', figure={'data': []}),
html.Div(id='grafico-slider',
style={
'width': '60vw',
'margin': '0 auto',
'fontFamily': 'sans-serif',
'textAlign': 'center'
},
children=[
html.Label('Year:'),
dcc.Slider(
id='ano-slider',
min=2000,
max=2016,
marks={i: str(i)
for i in range(2000, 2017)},
value=2000,
included=False,
),
html.Br(),
html.Br(),
html.Label('Transition delay (ms):'),
dcc.Slider(
id='transicao-slider',
min=0,
explaination = """
# Pie chart
India mode of Debt
India has changed its policy more towards bilateral commitments than multilateral in the Modi Government.
"""
side_elements = html.Div(
className='column',
children=[
html.Div(
id='pie-table',
className='table-pane',
children=[
html.Label('Data'),
generate_table(
idsdata_[(idsdata_['Country Code'].isin(['IND', 'CHN']))
& (idsdata_['Indicator Code'] == 'DT.NFL.MOTH.CD')
& (idsdata_['Year'] <= 2018)],
max_rows=np.inf)
]),
html.Div(
className='text-pane',
children=[
dcc.Markdown(explaination)
# generate_table(df, np.inf),
]),
])
world_map = dcc.Graph(id='pie-world-map',
def generate_html():
months_ago = util.AddMonths(util.ajd, -3)
# Layout
layout = html.Div(
[
html.H1('Analyse des ventes par produit'),
html.Div(
[
html.Label('Etudier les ventes entre'),
dcc.DatePickerRange(
id='date_import_orders',
display_format='DD/MM/YY',
# start_date par défaut : il y a 3 mois (1er jour du mois)
start_date=datetime.date(months_ago.year,
months_ago.month, 1),
# end_date par défaut : aujourd'hui
end_date=util.ajd),
html.Button(id='import_button',
n_clicks=0,
children='Réimporter',
style={'margin': '0px 0px 0px 10px'}),
dcc.Checklist(
id='box_rm',
options=[{
'label': 'Ignorer les commandes des teammates',
'value': 'rm_teammates'
}, {
'label': 'Ignorer le product type Frais Livraison',
'value': 'rm_delivery'
}],
values=['rm_delivery'],
style={'margin': '10px 0px 0px 0px'})
],
className='row'),
html.Div([
html.Div([
html.Label('Sélection des collections'),
html.Div(id='collections_clecklist')
],
className='six columns'),
html.Div([
html.Label('Choix de la mesure à afficher'),
dcc.Dropdown(id='dropdown_variable',
clearable=False,
options=[{
'label': 'Ventes HT',
'value': 'sells'
}, {
'label': 'Marge brute',
'value': 'margin'
}, {
'label': 'Nombre de produits vendus',
'value': 'products_sold'
}],
value='sells'),
dcc.Checklist(id='box_pct',
options=[{
'label': 'Afficher en pourcentages',
'value': 'pct'
}],
values=[],
style={'margin': '10px 0px 0px 0px'}),
],
className='two columns'),
html.Div([
html.Label('Regrouper les produits par'),
dcc.RadioItems(id='radio_level',
options=[{
'label': 'Collections',
'value': 'collections'
}, {
'label': 'Product types',
'value': 'product_type'
}],
value='collections')
],
className='two columns'),
html.Div([
html.Label('Afficher les résultats par'),
dcc.RadioItems(id='radio_duration',
options=[{
'label': 'Jours',
'value': 'day'
}, {
'label': 'Semaines',
'value': 'week'
}, {
'label': 'Mois',
'value': 'month'
}],
value='month')
],
className='two columns')
],
className='row',
style={'margin': '10px 0px 0px 0px'}),
dcc.Graph(id='graph_sells_evolution'),
html.Div(id='table_sells_evolution'),
# Divs invisibles qui stockeront les données intermédiaires
html.Div(id='df_orders_init_storage', style={'display': 'none'}),
html.Div(id='table_evolution_storage', style={'display': 'none'}),
html.Div(id='df_orders_storage', style={'display': 'none'})
],
style={'padding-left': '7px'})
return layout
)
]),
html.Div(
style={'textAlign': "center"},
children=[html.H4(children='Data of News'),
generate_table(df_news)]),
#html.Div(style={'textAlign': "center"},
# children=[html.H4(children='Data of News'),
# generate_table(train1)]),
html.Div(children=[
html.H2("Model Evaluations"),
html.
H6('Please wait a moment after changing the values, and ignore the alerts until the table is updated'
),
html.Div(children=[
html.Label('Select Model: '),
dcc.Dropdown(id='model',
options=[{
'label': 'Logistic Regression',
'value': 'logistic'
}, {
'label': 'Random Forest Classifier',
'value': 'rf'
}, {
'label': 'XGBoost Classifier',
'value': 'xgboost'
}, {
'label': 'LightGBM Classifier',
'value': 'lightgbm'
}],
value='lightgbm')
"z-index": 1000,
'display': 'block',
"height": "100%",
"padding": "2rem 1rem",
"background-color": "#f8f9fa"
}
layout1 = html.Div(
className='info',
children=[
dbc.Nav(
[
# Sidebar Title
html.H4("Filter on Districts"),
# District_Dropdown
html.Label(children=['Stadsdeel: ']),
dcc.Dropdown(
id='District',
options=[{
'label': b,
'value': b
} for b in sorted(df['District'].unique())],
value=[b for b in sorted(df['District'].unique())],
multi=True),
# SubDistrict_Dropdown
html.Label(children=['Wijk: ']),
dcc.Dropdown(id='SubDistrict',
options=[],
placeholder='Select a SubDistrict',
multi=True),
# SubSubDistrict_Dropdown
from pandas import DataFrame, Series
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import plotly.graph_objs as go
import numpy as np
df = pd.read_excel("output.xlsx")
#print df["OOM_ADJ.Native"]
app = dash.Dash()
app.layout = html.Div(
[
html.Div([
html.Label("OOM_ADJ_CheckBoxes"),
dcc.Checklist(id="OOM_ADJ_Checklist",
options=[{
'label': "OOM_ADJ.Native",
'value': "native"
}, {
'label': "OOM_ADJ.Persist",
'value': "persist"
}, {
'label': "OOM_ADJ.SystemServer",
'value': "system"
}, {
'label': "OOM_ADJ.Foreground",
'value': "foreground"
}, {
'label': "OOM_ADJ.Visible",
"https://www.medrxiv.org/content/10.1101/2021.01.15.20248217v1",
target="_blank",
style={
"color": "#6211FF",
"font-size": "20px"
}))
]),
dbc.Row([
html.Label([
'Projections on removal of lockdown can be found on this link ---> ',
html.A(
'here',
href=
'https://sars-covid-tracker-india.herokuapp.com/lockdown',
style={
"color": "#E60B1F",
'font-size': '20px'
})
],
style={
"color": "#151516",
'font-size': '20px'
})
]),
dbc.Row([
html.
P("Computing is provided by Chameleon Cloud, sponsored by NSF-USA",
style={"font-size": "10px"})
]),
dbc.Row([
html.
globalvars['single_zz'] = single_zz
return globalvars
globalvars = initialize()
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
server = app.server
app.layout = html.Div([
html.H1('AdaBoost Visualized'),
html.Div([
html.Div([
html.Label('Tree Depth'),
dcc.Dropdown(id='tree-depth-dropdown',
options=[{
'label': '1',
'value': '1'
}, {
'label': '2',
'value': '2'
}, {
'label': '3',
'value': '3'
}],
value='2'),
],
style={
'width': '12%',
'Search for a particular security topic to learn about.',
className='faded')
],
className='header-title')
],
className='page-header')
],
className='container p-4')
],
className='custom-header mb-4 noselect'),
html.Div([
html.Div([
html.Div([
html.Label([
'Date of Start:',
dcc.DatePickerSingle(id='date-picker-single1',
date=datetime(1997, 5, 10)),
],
className='col-sm-4 d-sm-inline-block'),
html.Label([
'Date of End:',
dcc.DatePickerSingle(
id='date-picker-single2',
date=datetime(1997, 5, 10),
),
],
className='col-sm-4 d-sm-inline-block'),
html.Label([
'Select a City:',
dcc.Dropdown(id='demo-dropdown-central',
options=[{
'label': 'New York City',
app = dash.Dash()
beta_slider = daq.Slider(id='beta', value=1.2, min=0, max=4, step=0.01,
marks={0: '0', 2: '2', 4: '4'})
gamma_slider = daq.Slider(id='gamma', value=0.2, min=0, max=1, step=0.01,
marks={0: '0', 1: '1'})
delta_slider = daq.Slider(id='delta', value=0.2, min=0, max=1, step=0.01,
marks={0: '0', 1: '1'})
app.layout = html.Div(
children=[dcc.Graph(id='sir-model'),
html.Div([
html.Label('Beta'),
beta_slider],
),html.Br(),
html.Br(),
html.Div(
[html.Label('Gamma'),
gamma_slider]),
html.Br(),
html.Br(),
html.Div(
[html.Label('Delta'),
delta_slider])]
)
@app.callback(Output('sir-model', 'figure'),
ICD9_dx = dill.load(open('op_lib.pkd', 'rb'))
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
server = flask.Flask(__name__)
app = dash.Dash(__name__, server=server, external_stylesheets=external_stylesheets)
app.config.supress_callback_exceptions=True
app.layout = html.Div([
html.H1(children='National Medicare Provider Lookup'),
dcc.Location(id='url', refresh=False),
html.Div(id='page-content'),
html.Div([
html.Label('Who can help me with: '),
dcc.Dropdown(
options=a,
value='V70',
multi=False,
id='input'
),
html.Div(id='output')
]),
html.Label('I live in: '),
html.Div([
html.Label('City: '),
dcc.Dropdown(
options=cities_list,
value='Oakland',
children=html.Div(
['Drag and Drop or ',
html.A('Select Files')]),
style={
'width': '100%',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'margin': '10px'
},
multiple=True),
html.Div(id='output-data-upload'),
html.Label('Subject ID'),
dcc.Dropdown(id='subject-id',
options=[{
'label': i,
'value': i
} for i in df['patient_id'].values.tolist()]),
dcc.RadioItems(id='editing-choice',
options=[{
'label': i,
'value': i
} for i in ['Edit SNP', 'Optimize SNP']]),
html.Button(id='submit-button', n_clicks=0, children='Submit'),
dcc.Graph(id='population-distribution-graph'),
html.Div(id='branch')
])
# -*- coding: utf-8 -*-
"""
Created on Sun Jun 16 19:52:22 2019
@author: reeff
"""
import dash_html_components as html
References=html.Div([
html.Div([
html.H3('Referencias:'),
html.Label(['-Indian Diabetic Retinopathy Image Dataset (IDRiD) ', html.A('Link', href='https://ieee-dataport.org/open-access/indian-diabetic-retinopathy-image-dataset-idrid', target='_blank')]),
html.H4(''),
html.Label(['-Decencière et al.. Feedback on a publicly distributed database: the Messidor database. Image Analysis & Stereology, v. 33, n. 3, p. 231-234, aug. 2014. ISSN 1854-5165. ', html.A('Link 1', href='http://www.ias-iss.org/ojs/IAS/article/view/1155', target='_blank')]),
html.Label(['', html.A('Link 2', href='http://dx.doi.org/10.5566/ias.1155', target='_blank')]),
html.H4(''),
html.Label(['-Kaggle: Diabetic Retinopathy Detection ', html.A('Link', href='https://www.kaggle.com/c/diabetic-retinopathy-detection', target='_blank')]),
html.H4(''),
html.Label(['-Source Code in GitHub ', html.A('https://fernandoferrer.github.io/ICDR/', href='https://fernandoferrer.github.io/ICDR/', target='_blank')]),
],style = {'background-image':'linear-gradient(to bottom right, rgba(183,183,183,0.7), rgba(111,163,225,0.7))',
'font-size':'medium',
'box-shadow':'5px 1px 10px 1px grey'},
),
])
'width': '5px',
'color': 'white'
}),
html.Br(),
html.Br(),
html.H2("Slicing/Plotting Selectors: "),
html.Div(
id="dropdown-div",
children=[
dbc.Row(
id="dropdown-row",
children=[
dbc.Col(children=[
html.Label(
['Select harmonic'],
style={
'font-weight': 'bold',
"text-align": "left"
}),
dcc.Dropdown(
id="slct_harm",
options=[{
'label': x,
'value': x
} for x in harm_list],
style={
'width': '150px',
'vertical-align': "middle",
'color': 'black'
},
value='1',
multi=False,
def load_main_chart():
return html.Div(children=[
html.Div(children=[
html.H2('Incrementum Store of Value Crypto Index vs Bitcoin')
],
className='main-title'),
html.Div(children=[
html.Details(id='export-data',
children=[
html.Summary('Export Data'),
html.Ul(id='export-list',
className='filedownload',
children=[],
style={
'position': 'absolute',
'display': 'block',
'z-index': 1
})
],
className='export-data-opts'),
html.Div(className='frequency-selector m-l-auto',
children=[
html.Label('Frequency:'),
dcc.Dropdown(id='freq-dropdown-one',
options=[
{
'label': 'Daily',
'value': 'Daily'
},
{
'label': 'Weekly',
'value': 'Weekly'
},
{
'label': 'Monthly',
'value': 'Monthly'
},
{
'label': 'Quaterly',
'value': 'Quaterly'
},
],
value='Daily'),
]),
html.Div(className='quick-filters m-l-auto',
children=[
html.Button('YTD',
id='button5',
n_clicks_timestamp=0,
style={'border': 'none'}),
html.Span('|'),
html.Button('1Y',
id='button3',
n_clicks_timestamp=0,
style={'border': 'none'}),
html.Span('|'),
html.Button('5Y',
id='button2',
n_clicks_timestamp=0,
style={'border': 'none'}),
html.Span('|'),
html.Button('10Y',
id='button4',
n_clicks_timestamp=0,
style={'border': 'none'}),
html.Span('|'),
html.Button('MAX',
id='button1',
n_clicks_timestamp=0,
style={'border': 'none'}),
]),
html.Div([
dcc.DatePickerRange(
id='date-picker-range-one',
start_date=data.index.min(),
end_date=data.index.max(),
max_date_allowed=data.index.max(),
min_date_allowed=data.index.min(),
display_format='DD/MM/YYYY',
),
],
className='m-l-auto'),
],
className='date-and-export'),
html.Div([
dcc.Graph(
id='store-of-val-index',
config={
'displaylogo':
False,
'displayModeBar':
True,
'modeBarButtonsToRemove':
['toggleSpikelines', 'hoverCompareCartesian'],
},
figure={
'data': [
go.Scatter(
x=data.index, y=data['SOV_index'], mode='lines'),
go.Scatter(x=data.index,
y=data['Btc_Close_Price'],
mode='lines',
fillcolor='rgb(24, 128, 56)'),
],
'layout':
main_graph_layout
},
)
],
style={'margin': 0}),
html.Div(
id='graph-info',
children=[
html.Span('Source:', className='source-title'),
html.Span('Coinmarketcap.com, Incrementum AG',
className='sources'),
html.
P('The Incrementum Store of Value Index is a market capitalization \
weighted index comprising of all non-Turing complete cryptocurrencies \
with at least two out of three criteria met including market capitalization \
of $500,000 or higher, market trading for 365 days or greater, and market \
trading on two exchanges or more. Privacy coins are excluded. \
The three coins in the index are Bitcoin, Bitcoin Cash, and Litecoin.',
className='source-description'),
html.Span('Suggested Citation:', className='citation-title'),
html.
P('Incrementum AG, Incrementum Store of Value Crypto Index, retrieved \
from Crypto Research Report; http://data.cryptoresearch.report/graph/storeofvalueindex, \
September, 2019.',
className='citation-info')
],
className='graph-info'),
html.Div(children=social_share_links, className='social-links'),
],
className='detailed-graph')
def well_points_tab_layout(self) -> html.Div:
return html.Div(
[
dbc.Button("Table Settings", id=self.ids("button-open-table-settings")),
dbc.Modal(
children=[
dbc.ModalHeader("Table Settings"),
dbc.ModalBody(
children=[
html.Label(
style={
"font-weight": "bold",
"textAlign": "Left",
},
children="Select Table Columns",
),
dcc.Checklist(
id=self.ids("columns-checklist"),
options=[
{"label": name, "value": column_name}
for name, column_name in zip(
self.df_well_target_points.get_wellpoints_df()
.keys()
.values,
self.df_well_target_points.get_wellpoints_df()
.keys()
.values,
)
],
value=[
"Surface",
"Well",
"TVD",
"MD",
"Outlier",
"Deleted",
"Residual",
],
persistence=True,
persistence_type="session",
),
],
),
dbc.ModalFooter(
children=[
dbc.Button(
"Close",
id=self.ids("button-close-table-settings"),
className="ml-auto",
),
dbc.Button(
"Apply",
id=self.ids("button-apply-columnlist"),
className="ml-auto",
),
]
),
],
id=self.ids("modal-table-settings"),
size="sm",
centered=True,
backdrop=False,
fade=False,
),
html.Div(id=self.ids("well-points-table-container")),
]
)
def test_cblp001_radio_buttons_callbacks_generating_children(dash_duo):
TIMEOUT = 2
with open(os.path.join(os.path.dirname(__file__),
"state_path.json")) as fp:
EXPECTED_PATHS = json.load(fp)
app = Dash(__name__)
app.layout = html.Div([
dcc.RadioItems(
options=[
{
"label": "Chapter 1",
"value": "chapter1"
},
{
"label": "Chapter 2",
"value": "chapter2"
},
{
"label": "Chapter 3",
"value": "chapter3"
},
{
"label": "Chapter 4",
"value": "chapter4"
},
{
"label": "Chapter 5",
"value": "chapter5"
},
],
value="chapter1",
id="toc",
),
html.Div(id="body"),
])
for script in dcc._js_dist:
app.scripts.append_script(script)
chapters = {
"chapter1":
html.Div([
html.H1("Chapter 1", id="chapter1-header"),
dcc.Dropdown(
options=[{
"label": i,
"value": i
} for i in ["NYC", "MTL", "SF"]],
value="NYC",
id="chapter1-controls",
),
html.Label(id="chapter1-label"),
dcc.Graph(id="chapter1-graph"),
]),
# Chapter 2 has the some of the same components in the same order
# as Chapter 1. This means that they won't get remounted
# unless they set their own keys are differently.
# Switching back and forth between 1 and 2 implicitly
# tests how components update when they aren't remounted.
"chapter2":
html.Div([
html.H1("Chapter 2", id="chapter2-header"),
dcc.RadioItems(
options=[{
"label": i,
"value": i
} for i in ["USA", "Canada"]],
value="USA",
id="chapter2-controls",
),
html.Label(id="chapter2-label"),
dcc.Graph(id="chapter2-graph"),
]),
# Chapter 3 has a different layout and so the components
# should get rewritten
"chapter3": [
html.Div(
html.Div([
html.H3("Chapter 3", id="chapter3-header"),
html.Label(id="chapter3-label"),
dcc.Graph(id="chapter3-graph"),
dcc.RadioItems(
options=[{
"label": i,
"value": i
} for i in ["Summer", "Winter"]],
value="Winter",
id="chapter3-controls",
),
]))
],
# Chapter 4 doesn't have an object to recursively traverse
"chapter4":
"Just a string",
}
call_counts = {
"body": Value("i", 0),
"chapter1-graph": Value("i", 0),
"chapter1-label": Value("i", 0),
"chapter2-graph": Value("i", 0),
"chapter2-label": Value("i", 0),
"chapter3-graph": Value("i", 0),
"chapter3-label": Value("i", 0),
}
@app.callback(Output("body", "children"), [Input("toc", "value")])
def display_chapter(toc_value):
call_counts["body"].value += 1
return chapters[toc_value]
app.config.suppress_callback_exceptions = True
def generate_graph_callback(counterId):
def callback(value):
call_counts[counterId].value += 1
return {
"data": [{
"x": ["Call Counter"],
"y": [call_counts[counterId].value],
"type": "bar",
}],
"layout": {
"title": value
},
}
return callback
def generate_label_callback(id_):
def update_label(value):
call_counts[id_].value += 1
return value
return update_label
for chapter in ["chapter1", "chapter2", "chapter3"]:
app.callback(
Output("{}-graph".format(chapter), "figure"),
[Input("{}-controls".format(chapter), "value")],
)(generate_graph_callback("{}-graph".format(chapter)))
app.callback(
Output("{}-label".format(chapter), "children"),
[Input("{}-controls".format(chapter), "value")],
)(generate_label_callback("{}-label".format(chapter)))
dash_duo.start_server(app)
def check_chapter(chapter):
dash_duo.wait_for_element(
'#{}-graph:not(.dash-graph--pending)'.format(chapter))
for key in dash_duo.redux_state_paths:
assert dash_duo.find_elements(
"#{}".format(key)), "each element should exist in the dom"
value = (chapters[chapter][0]["{}-controls".format(chapter)].value
if chapter == "chapter3" else
chapters[chapter]["{}-controls".format(chapter)].value)
# check the actual values
dash_duo.wait_for_text_to_equal("#{}-label".format(chapter), value)
wait.until(
lambda: (dash_duo.driver.execute_script(
"return document."
'querySelector("#{}-graph:not(.dash-graph--pending) .js-plotly-plot").'
.format(chapter) + "layout.title.text") == value),
TIMEOUT,
)
rqs = dash_duo.redux_state_rqs
assert rqs, "request queue is not empty"
assert all((rq["status"] == 200 and not rq["rejected"] for rq in rqs))
def check_call_counts(chapters, count):
for chapter in chapters:
assert call_counts[chapter + "-graph"].value == count
assert call_counts[chapter + "-label"].value == count
wait.until(lambda: call_counts["body"].value == 1, TIMEOUT)
wait.until(lambda: call_counts["chapter1-graph"].value == 1, TIMEOUT)
wait.until(lambda: call_counts["chapter1-label"].value == 1, TIMEOUT)
check_call_counts(("chapter2", "chapter3"), 0)
assert dash_duo.redux_state_paths == EXPECTED_PATHS["chapter1"]
check_chapter("chapter1")
dash_duo.percy_snapshot(name="chapter-1")
dash_duo.find_elements('input[type="radio"]')[1].click() # switch chapters
wait.until(lambda: call_counts["body"].value == 2, TIMEOUT)
wait.until(lambda: call_counts["chapter2-graph"].value == 1, TIMEOUT)
wait.until(lambda: call_counts["chapter2-label"].value == 1, TIMEOUT)
check_call_counts(("chapter1", ), 1)
assert dash_duo.redux_state_paths == EXPECTED_PATHS["chapter2"]
check_chapter("chapter2")
dash_duo.percy_snapshot(name="chapter-2")
# switch to 3
dash_duo.find_elements('input[type="radio"]')[2].click()
wait.until(lambda: call_counts["body"].value == 3, TIMEOUT)
wait.until(lambda: call_counts["chapter3-graph"].value == 1, TIMEOUT)
wait.until(lambda: call_counts["chapter3-label"].value == 1, TIMEOUT)
check_call_counts(("chapter2", "chapter1"), 1)
assert dash_duo.redux_state_paths == EXPECTED_PATHS["chapter3"]
check_chapter("chapter3")
dash_duo.percy_snapshot(name="chapter-3")
dash_duo.find_elements('input[type="radio"]')[3].click() # switch to 4
dash_duo.wait_for_text_to_equal("#body", "Just a string")
dash_duo.percy_snapshot(name="chapter-4")
for key in dash_duo.redux_state_paths:
assert dash_duo.find_elements(
"#{}".format(key)), "each element should exist in the dom"
assert dash_duo.redux_state_paths == {
"toc": ["props", "children", 0],
"body": ["props", "children", 1],
}
dash_duo.find_elements('input[type="radio"]')[0].click()
wait.until(
lambda: dash_duo.redux_state_paths == EXPECTED_PATHS["chapter1"],
TIMEOUT,
)
check_chapter("chapter1")
dash_duo.percy_snapshot(name="chapter-1-again")
import dash_bootstrap_components as dbc
import pandas as pd
import plotly.express as px
df = pd.read_csv('politics.csv')
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
server = app.server
# radioItem list for the layout (long_code.py lines 13-45)
radio_list = []
for s,v in zip(['AZ','FL','GA','IA','ME','MI','NC','NV','OH','PA','TX','WI'],
[11,29,16,6,4,16,15,6,18,20,38,10]):
radio_list.append(
html.Div([
html.Label(f'{s}-{v}: ', style={'display':'inline', 'fontSize':15}),
dcc.RadioItems(
id=f'radiolist-{s}',
options=[
{"label": "Dem", "value": "democrat"},
{"label": "Rep", "value": "republican"},
{"label": "NA", "value": "unsure"},
],
value='unsure',
inputStyle={'margin-left': '10px'},
labelStyle={'display': 'inline-block'},
style={'display':'inline'}
),
], style={'textAlign':'end'})
)
print(radio_list)
'''
app.layout = html.Div([
html.H1('DATA 608 : Final Project',
style={
'backgroundColor': 'black',
'color': 'white'
}),
html.H3('Jose A. Mawyin',
style={
'backgroundColor': 'grey',
'color': 'white'
}),
dcc.Markdown(children=intro),
dcc.Markdown(children=markdown_text1),
html.Label('Select an Acorn Group to Populate Graph: '),
dcc.Dropdown(id='dropdown1',
options=[{
'label': i,
'value': i
} for i in Average_Hour_Day.Acorn_group.unique()],
multi=False,
placeholder='Filter by boroname...'),
html.Div(id='Bar1'),
html.Div(style={'textAlign': 'center'},
children=''' Jose A. Mawyin 2020. ''')
],
style={
'width': '100%',
'padding-left': '15%',
'padding-right': '15%'
mode='lines+markers',
name=query_date + " " + source + " - [km/h]",
xaxis="x1",
yaxis="y2")
return trace_y1, trace_y2
app = dash.Dash('Travel Times')
mapbox_access_token = tokens.get_mapbox_token()
app.layout = html.Div([
# This component generates a <h1></h1> HTML element in your application
html.H1('Tiempos de viaje'),
# The dcc describe higher-level components that are interactive
html.Label('Selecciona la ruta'),
dcc.Dropdown(
id='query-routes',
options=[{
'label': i,
'value': i
} for i in df_tt_w['name'].unique()],
# This is the default value
value=df_tt_w.iloc[0, 0] #This is a string, ok!
),
html.Label('Selecciona la fecha'),
dcc.Dropdown(id='query-dates',
options=[{
'label': i.strftime("%d-%m-%Y"),
'value': i
} for i in df_tt_w['date'].unique()],
import dash
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.Label('Dropdown'),
dcc.Dropdown(
options=[
{'label': '佐藤', 'value': 'sato'},
{'label': '鈴木', 'value': 'suzuki'},
{'label': '石川', 'value': 'ishikawa'},
],
value='suzuki'
),
html.Label('Multi-Select Dropdown'),
dcc.Dropdown(
options=[
{'label': '佐藤', 'value': 'sato'},
{'label': '鈴木', 'value': 'suzuki'},
{'label': '田中', 'value': 'tanaka'},
],
value=['sato','suzuki'],
multi=True
),
