width='650',
style={'border-width': '0'})
heatmap = html.Iframe(sandbox='allow-scripts',
id='heatmap_plot',
height='1100',
width='1000',
style={'border-width': '0'})
#TAB OBJECTS AND CALLBACK
#===================================
tab1_selectors = \
[dbc.Row([
dbc.Col(children = [
html.H5("Damage Type"),
html.H6("Plot: Both"),
dropdown_selector_tab1,
html.Br()
])
]),
dbc.Row(children = [
dbc.Col(children = [
html.H5("Date Range Between 1990 - 2002"),
html.H6("Plot: Bird Strike Damage Over Time"),
rangeslider_selector,
html.Br(),
html.Br()]),
dbc.Col([
html.H5("Factor"),
html.H6("Plot: Effect of (factor) on Birdstrikes"),
html.
Li("Qualité douteuse de certaines thématiques => 'meeting', biaise un peu nos résultats."
),
]),
html.Hr()
])
data_exp = pd.read_csv("data/exp_mails.csv")
data_exp = data_exp[["From", "Nombre d'emails envoyés"
]].sort_values(by=['Nombre d\'emails envoyés'],
ascending=False)
figExp = px.bar(data_exp, x="From", y="Nombre d'emails envoyés")
count_mail_exp = html.Div(children=[
html.H5(children='Le nombre d\'emails envoyé pour chaque expéditeur'),
dcc.Graph(figure=figExp)
])
data_exp_thematiques_acp = pd.read_csv("data/extracted_data.csv")
tab_exp_thematiques_acp = html.Div(children=[
dt.DataTable(id='tab',
columns=[{
"name": i,
"id": i
} for i in data_exp_thematiques_acp.iloc[:, 0:5]],
data=data_exp_thematiques_acp.head().to_dict('records'),
sort_action="native",
style_cell={'textAlign': 'left'},
style_data={
'whiteSpace': 'normal',
def annotation_tab(initial):
try:
global FILE_COUNT, LABELS_LIST_DROPDOWN_NEXT, NUMBER_OF_WAVFILES, LABELS_LIST_CHECKLIST_NEXT, LABELS_LIST_DROPDOWN_INITIAL
LABELS_LIST_DROPDOWN_NEXT = []
LABELS_LIST_CHECKLIST_NEXT = []
LABELS_LIST_DROPDOWN_INITIAL = []
if initial:
FILE_COUNT = 0
TOTAL_FOLDER_WAV_FILES = glob.glob(TEXT_PATH + "/*.wav")
if os.path.exists(CSV_FILENAME):
annotated_files = pd.read_csv(CSV_FILENAME,
error_bad_lines=False)
annotated_files = annotated_files['wav_file'].values.tolist()
NUMBER_OF_WAVFILES = []
for i in TOTAL_FOLDER_WAV_FILES:
if i.split("/")[-1] not in annotated_files:
# print(i)
NUMBER_OF_WAVFILES.append(i)
print len(NUMBER_OF_WAVFILES)
else:
NUMBER_OF_WAVFILES = TOTAL_FOLDER_WAV_FILES
print "total wavfiles :", len(NUMBER_OF_WAVFILES)
encoded_image_to_play = base64.b64encode(
open(NUMBER_OF_WAVFILES[FILE_COUNT], 'rb').read())
dataframe = pd.DataFrame()
dataframe["Labels Name"] = CHECKLIST_DISPLAY
return html.Div([
html.Div([
html.Br(),
html.H2(NUMBER_OF_WAVFILES[FILE_COUNT].split("/")[-1],
style={
"text-align": "center",
"color": "green",
'text-decoration': 'underline'
}),
html.Audio(id='myaudio',
src='data:audio/WAV;base64,{}'.format(
encoded_image_to_play),
controls=True,
style={
"margin-top": "20px",
"verticalAlign": "middle",
"margin-bottom": "30px"
})
]),
dash_table.DataTable(id='datatable-interactivity-' +
('inside' if initial else 'next'),
columns=[{
"name": i,
"id": i,
"deletable": True
} for i in dataframe.columns],
data=dataframe.to_dict("rows"),
row_selectable="multi",
style_table={
"maxHeight": "300px",
"maxWidth": "300px",
"overflowY": "scroll"
},
selected_rows=[]),
dcc.Dropdown(id="dropdown_data_" +
("initial" if initial else "next"),
options=[{
'label': 'Nature ',
'value': 'Nature'
}, {
'label': 'Birds Chirping ',
'value': 'Bird'
}, {
'label': 'Wind Gushing',
'value': 'Wind'
}, {
'label': 'Vehicle ',
'value': 'Vehicle'
}, {
'label': 'Honking ',
'value': 'Honking'
}, {
'label': 'Conversation ',
'value': 'Conversation'
}, {
'label': 'Dog Barking ',
'value': 'Dog Barking'
}, {
'label': 'Tools ',
'value': 'Tools'
}, {
'label': 'Axe ',
'value': 'Axe'
}],
value="",
placeholder="Search For Label..",
style={
"fontSize": "17",
"margin-top": ("20" if initial else "40") + "px",
"display": "inline-block",
"font": "bold",
"width": "40%"
}),
dcc.Textarea(id="text_area_" + ("inside" if initial else "next"),
placeholder='Selected Annotation',
value="",
style={
"width": "50%",
"margin-left": "25%",
"fontSize": "16",
"text-align": "center"
}),
html.Div([
html.Button("Submit",
id="submit_" + ("initial" if initial else "next"),
style={
"width": "200px",
"margin-top": "10px"
})
],
style={"text-align": "center"}),
html.Footer('\xc2\xa9' + ' Copyright WildlyTech Inc. 2019 ',
style={
"position": "fixed",
"left": "0",
"bottom": "0",
"height": "2%",
"width": "100%",
"background-color": "black",
"color": "white",
"padding": "20px",
"textAlign": "center"
})
])
except ValueError:
return html.Div([
html.H5("Something wrong with Audio: -" +
NUMBER_OF_WAVFILES[FILE_COUNT].split("/")[-1],
style={"display": "center"})
])
},
'backgroundColor':
'rgb(248, 248, 248)'
}])
],
style={'padding-bottom': '50px'})
],
md=12),
]),
#Fleet level overview: Performance when sailing
dbc.Row([dbc.Col(html.H4("Performance when Sailing"))]),
dbc.Row([
dbc.Col(
[
html.Div([
html.H5("AE"),
dcc.Graph(
figure={
'data': [
go.Bar(x=df3['Vessel Name'],
y=df3['AE Consumption'],
marker=dict(color='skyblue')),
go.Bar(x=df3['Vessel Name'],
y=df3['AE baseline'],
opacity=1,
marker=dict(color='rgba(0,0,0,0)',
line=dict(color='black',
width=2)))
],
'layout':
go.Layout(barmode='overlay')
className="row",
children=[
dcc.Location(id="url", refresh=False),
html.Nav(
id="stockargs",
className="col-lg-2 bg-light sidebar",
children=[
html.Div(className="card card-body bg-dark",
children=[
html.H4("Stock Chart",
className="mx-auto text-white")
]),
html.Div(
className="card card-body",
children=[
html.H5(["Settings"]),
html.Div(
className="form-group",
children=[
html.Label("Codes:",
className="col-form-label"),
dcc.Input(className="form-control",
id="codes",
type="text",
value="",
placeholder=
"codes e.g. 1111 2222 3333")
]),
html.Div(
className="form-group",
children=[
'overflowX': 'scroll',
'overflowY': 'scroll',
'maxHeight': '500px'
})
])
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.layout = html.Div(children=[
html.Div(children=[
html.H4(children='データ概要'),
generate_table(df, 5),
html.H5('shape: {} raws × {} columns'.format(df.shape[0], df.shape[1]))
],
style={'background': '#FFFFCC'}),
html.Div(children=[
html.Div(children=[
html.H5('目的変数を選択してください'),
dcc.Dropdown(id='select-target',
options=[{
'label': column,
'value': column
} for column in df.columns]),
html.Button(id="target-button", n_clicks=0, children="決定")
],
style={'width': '40%'}),
html.Div(id='target_distribution',
children=html.H6('カラムが選択されていません'),
figure_duration = plot_histogram(data, 'duration', 'Duration (sec)')
figure_num_words = plot_histogram(data, 'num_words', '#words')
figure_num_chars = plot_histogram(data, 'num_chars', '#chars')
figure_word_rate = plot_histogram(data, 'word_rate', '#words/sec')
figure_char_rate = plot_histogram(data, 'char_rate', '#chars/sec')
if metrics_available:
figure_wer = plot_histogram(data, 'WER', 'WER, %')
figure_cer = plot_histogram(data, 'CER', 'CER, %')
figure_wmr = plot_histogram(data, 'WMR', 'WMR, %')
figure_word_acc = plot_word_accuracy(vocabulary)
stats_layout = [
dbc.Row(dbc.Col(html.H5(children='Global Statistics'), className='text-secondary'), className='mt-3'),
dbc.Row(
[
dbc.Col(html.Div('Number of hours', className='text-secondary'), width=3, className='border-right'),
dbc.Col(html.Div('Number of utterances', className='text-secondary'), width=3, className='border-right'),
dbc.Col(html.Div('Vocabulary size', className='text-secondary'), width=3, className='border-right'),
dbc.Col(html.Div('Alphabet size', className='text-secondary'), width=3),
],
className='bg-light mt-2 rounded-top border-top border-left border-right',
),
dbc.Row(
[
dbc.Col(
html.H5(
'{:.2f} hours'.format(num_hours),
className='text-center p-1',
html.Div([
html.Div(dcc.Markdown(
children=
"""**Fig. 1** relative distances among accessions given cluster profiles selected and analysed.
In fact, loadings plot\n of PCA run on the former."""),
className='six columns'),
html.Div(dcc.Markdown(
children=
"""**Table 1** Passport information on accessions shown in Fig. 1. If cluster cloud is selected
below, then only accessions in red (updated plot) are shown."""),
className='six columns')
],
className="row"),
html.Hr(),
html.Div([
html.H5(id="header1", className="six columns", children="opacity"),
html.H5(id="header2",
className="six columns",
children="Likelihood threshold")
],
className="row"),
html.Div([
dcc.Slider(updatemode="drag",
className='six columns',
id='opacity',
min=.1,
max=1,
value=.8,
step=.1,
marks={.1 * x: str(round(.1 * x, 1))
for x in range(3, 9)}),
dropdown_style = {
'width': '50%',
'display': 'inline-block',
'vertical-align': 'middle',
'padding': '5px'
}
client = bigquery.Client()
click_count = 0
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = "LandingSpot"
app.layout = html.Div([
html.H1("LandingSpot", style={'text-align': 'center'}),
html.H5("Where will you land?", style={'text-align': 'center'}),
html.Br(),
# Preferences
html.Div(
[
html.H6("Tell me what you need...",
style={
'text-align': 'left',
'padding': '5px'
}),
# Bedrooms Button
html.Div([
html.Label([
"Bedrooms:",
"display": "flex",
"justify-content": "center"
}), )
],
style={"margin-left": "15rem"})
# ----------------------------------------------------------------------- Content 2
content2 = html.Div([
html.H1(["Segmentación"], style=CONTENT_STYLE),
html.
P("Aca se muestra una clasificación para 25 mil clientes por restricciones de velocidad,\
la base completa se uso para la hoja 'recomendaciones'."),
html.Div([
dbc.Row(
dbc.Col(
html.H5("Seleccione un clúster para ver sus estadísticas:"))),
perfilamiento_header,
html.Div(id="tabla_resumen_clu")
],
style={}), graphs2
],
style={"margin-left": "10rem"})
def content_us(app, visible):
return html.Div(
[
dbc.Row(
dbc.Col(
dbc.Button("< Back",
color="primary",
html.A(['Print PDF'],
className="button no-print",
style={'position': "absolute", 'top': '-40', 'right': '0'}),
html.Div([ # subpage 1
# Row 1 (Header)
html.Div([
html.Div([
#html.H5(
##
# dict["title"] + " 4-D Report lo"),
html.H5(first_dict["title_output"], id='title'),
html.H6(first_dict["location_output"], id='location',
style={'color': '#7F90AC'}),
html.Div([
html.Div([
dcc.Dropdown(
id='locas',
options=first_dict["locas_output"],
value="All",
clearable=False,
className="dropper",
placeholder="Type Location",
html.H2('Appendix - Common HTML Components'),
html.Hr(),
reusable_components.Markdown('html.H1("H1 Element")',
style={'borderLeft': 'thin solid lightgrey'}),
html.H1('H1 Element'),
html.Hr(),
reusable_components.Markdown('html.H2("H2 Element")',
style={'borderLeft': 'thin solid lightgrey'}),
html.H2('H2 Element'),
html.Hr(),
reusable_components.Markdown('html.H3("H3 Element")',
style={'borderLeft': 'thin solid lightgrey'}),
html.H3('H3 Element'),
html.Hr(),
reusable_components.Markdown('html.H4("H4 Element")',
style={'borderLeft': 'thin solid lightgrey'}),
html.H4('H4 Element'),
html.Hr(),
reusable_components.Markdown('html.H5("H5 Element")',
style={'borderLeft': 'thin solid lightgrey'}),
html.H5('H5 Element'),
html.Hr(),
reusable_components.Markdown('html.H6("H6 Element")',
style={'borderLeft': 'thin solid lightgrey'}),
html.H6('H6 Element'),
html.Hr(),
reusable_components.Markdown('html.Div("Generic Div Element")',
style={'borderLeft': 'thin solid lightgrey'}),
html.Div('Generic Div Element')
])
def choose_assist_line(episode, network_graph):
return html.Div(id="choose_assist_line", className="lineBlock card", children=[
html.H4("Choose or Assist"),
html.Div(className="card-body row", children=[
html.Div(className="col-7", children=[
html.H5("Network at time step t"),
dcc.Graph(id="network_graph_choose", figure=network_graph,),
]),
html.Div(className="col-5", children=[
dbc.Tabs(children=[
dbc.Tab(label='Choose', labelClassName="fas fa-user", children=[
dbc.Tabs(children=[
dbc.Tab(label="Dropdowns", children=[
dbc.Tabs(children=[
dbc.Tab(label='Lines', children=[
html.P("Choose a line to act on:", className="mt-1"),
dac.Select(
id='select_lines_simulation',
options=[{'label': line_name,
'value': line_name}
for line_name in episode.line_names],
mode='default',
value=episode.line_names[0]
),
html.P("Choose an action:", className="mt-1"),
dac.Radio(options=[
{"label": "Set", "value": "Set"},
{"label": "Change", "value": "Change"},
], value="Set", id="select_action_lines", buttonStyle="solid"),
]),
dbc.Tab(label='Subs', children=[
html.P("Choose a substation to act on:"),
dac.Select(
id='select_subs_simulation',
options=[{'label': name,
'value': name}
for name in episode.name_sub],
mode='default',
value=episode.name_sub[0]
),
html.P("Choose an action:"),
dac.Radio(options=[
{"label": "Set", "value": "Set"},
{"label": "Change", "value": "Change"},
], value="Set", id="select_action_subs", buttonStyle="solid"),
]),
dbc.Tab(label='Gens', children=[
html.P("Choose a generator to act on:"),
dac.Select(
id='select_gens_simulation',
options=[{'label': prod_name,
'value': prod_name}
for prod_name in episode.prod_names],
mode='default',
value=episode.prod_names[0]
),
html.P("Choose an action:"),
dac.Radio(options=[
{"label": "Redispatch", "value": "Redispatch"},
], value="Redispatch", id="select_action_gens", buttonStyle="solid"),
])
])
]),
dbc.Tab(label="Dict", children=[
html.P("Enter the action dictionary:"),
dbc.Textarea(className="mb-3", placeholder="{set_line_status: []}"),
])
]),
]),
dbc.Tab(label='Assist', labelClassName="fas fa-robot", children=[
"content"
]),
]),
dbc.Button("Simulate", id="simulate_action", color="danger", className="mt-3 mb-3"),
html.P("Action dictionary:"),
html.Div(id="action_dictionary")
]),
]),
])
def server_layout(mode=None):
#return the layout of the GUI
session_id = str(uuid.uuid4())
#variable, distribution selection panel
selection_panel = html.Div(id='selection-panel',children=[
html.Div(id='selected-column-panel',children=[
html.Div(id='selected-column-sec1-panel',children=[
drc.NamedDropdown(
name='Select series',
id='selected-series',
searchable=False,
clearable=False
),
dcc.Checklist(
id='apply-log-transform',
options=[{'label':' Apply log transform','value':'logtransform'}
]),
]),
drc.NamedTextarea(
id='series-characteristics',
name='Series characteristics',
cols='50',
rows='2',
readOnly='readOnly'
),
]),
html.Div(id='apply-panel',children=[
html.Div(id='apply-sec1-panel',children=[
drc.NamedDropdown(
id='fitted-distributions',
name='Fitted distribution',
options=[
{'label': ' '+ v[0], 'value': k}
for k,v in dist_par_template.items() if k != 'native' and v[2]
],
value='normal',
searchable=False,
clearable=False
),
]),
# html.Div(id='apply-sec1b-panel',children=[
# drc.NamedDropdown(
# id='plotting-distributions',
# name='Probability scale',
# options=[
# {'label': ' '+ v[0], 'value': k}
# for k,v in dist_par_template.items() if v[2]
# ],
# value='native',
# searchable=False,
# clearable=False
# ),
# ]),
html.Div(id='apply-sec2a-panel',children=[
html.Button('Fit',id='fit-button',n_clicks=0),
]),
#dcc.Checklist(
# id='show-y-log',
# options=[{'label':' Show y-axis in log scale','value':'true'}
#]),
]),
html.Label(id='graph-refresh'),
dcc.Store(
id='graph-refresh-hidden',
data=0
),
html.Div(id='message-panel',children=[""]),
])
#variable, distribution selection panel
par_panel = html.Div(id='fitted-panel',children=[
html.Div(id='fitted-sec1-panel',children=[
drc.NamedTextarea(
id='fitted-par',
name='Estimated distribution parameters',
readOnly='readOnly',
cols= 40,
rows= 3
),
drc.NamedTextarea(
id='estimated-quantiles',
name='Estimated quantiles',
readOnly='readOnly',
cols= 40,
rows= 3
),
])
])
upload_panel = html.Div(id='last-card',children=[
dcc.Upload(
id='upload-data',
children=html.Div([
'Drag and Drop or ',
html.A('Select File')
]),
style={
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
}
),
html.Label('''File upload limited to < %0.0f Kb, containing no more than %d rows and %d columns'''%(configs['max_file_size']/1024,configs['max_file_rows'],configs['max_file_cols'])),
html.Label('',id='data-filename'),
html.Label('',id='data-description'),
html.Div(id='table-panel',children=[
dash_table.DataTable(
id='attribute-table',
columns=[],
row_selectable='multi',
editable=False,
style_header={
'textAlign': 'center',
'fontWeight': 'bold',
'color': 'black',
},
style_cell={
'padding': '2px',
'--selected-background': 'grey',
'color': 'black',
},
style_data_conditional=[
{
'if': {'row_index': 'odd'},
'backgroundColor': 'rgb(248, 248, 248)'
}
],
#locale_format= '0.3',
)
],style={'visibility':'none','display':'none'}),
])
chart_panel = html.Div(id='chart-panel',children=[
dcc.Loading(id = "loading-icon", children=[
dcc.Graph(
id='graph',
figure={
},
responsive=True,
config=graph_config,
style={'display':'none'}
)
]),
],
style={'visibility':'none'})
layout = html.Div(
id="body",
className="container scalable",
children=[
visdcc.Run_js(id = 'chart-updated-js'),
visdcc.Run_js(id = 'chart-unupdated-js'),
visdcc.Run_js(id = 'error-display-js'),
dcc.Store(id='error-display',data=''),
html.Title(title=configs['title']),
#hidden session id
#row 1 for title
html.Div(id='top-panel',children=[
html.H5(id='app-title',children=
'''Distribution Fitting and Analysis Tool.
This tool is only for demostration porpuses''',
style={"margin-bottom": "0px"},
),
],className="row flex-display"),
#second row layout
html.Div(id='main-panel',children=[
html.Div(id='left-panel',children=[
upload_panel
]),
html.Div(id='output-panel',children=[
selection_panel,
par_panel,
chart_panel
],style={'visibility':'none','display':'none'}),
]),
#row 4 is the footer
html.Div(id='footer-panel',children=[
'''
A web application built on top of Dash (v%s) (framework for Python) by
Exequiel Sepúlveda and Dmitri Kavetski.'''%(dash.__version__)
]),
],
)
return layout
y=df_init[selected_col],
name=selected_col,
line=dict(width=2, color='rgb(229, 151, 50)'),
mode='markers')
layout = go.Layout(title='Profit & Loss Plot', hovermode='closest')
fig = go.Figure(data=[trace_1], layout=layout)
fig.update_xaxes(title_text='Year')
fig.update_yaxes(title_text='Profit&Loss ($)')
# Create a Dash layout
app.layout = html.Div([
# Header
html.Div(
[
html.H1("Trading Strategy Back Testing Dashboard"),
html.H5("Final testing version 1.0"),
],
style={
'paddingLeft': '10%',
'paddingBottom': '2.5%',
'paddingTop': '3%',
'backgroundColor': '#74F3FF'
}),
# Dropdowns
html.Div(
[
# Sector
html.Div(
[
html.H5('Select sectors'),
students_hybrid_chart = dcc.Graph(id='students-hybrid-chart',
figure=go.Figure(),
config=modebar_config)
students_sps_chart = dcc.Graph(id='students-sps-chart',
figure=go.Figure(),
config=modebar_config)
students_phd_chart = dcc.Graph(id='students-phd-chart',
figure=go.Figure(),
config=modebar_config)
"""
Main students tab skeleton
"""
students_tab = html.Div([
html.H5('Undergraduate', id='students-ug-header', className='text-info'),
students_ug_chart,
html.Div([
html.H5('Masters', id='students-masters-header',
className='text-info'),
students_masters_chart,
],
id='students-masters-container'),
html.Div([
html.H5('Interdepartmental Masters',
id='students-interdept-header',
className='text-info'),
students_interdept_chart,
],
id='students-interdept-container'),
html.Div([
)
# make a button to run the simulator
run_btn = dbc.Button(children = "Run Simulation", outline=True, size = "lg", color="primary", className="mb-3", id="btn_run", n_clicks = 0)
# make a button for plots
plot_btn = dbc.Button(children = "Add Chart", outline=True, size = "lg", color="primary", className="mb-3", id="btn_plot", n_clicks = 0)
# layout all the components to be displayed
content = html.Div(
[
dbc.Row([
dbc.Col(html.H1(children='Reactor Modeling Sandbox'), width = 9),
]),
dbc.Row([
dbc.Col(html.H5(children='Beep bop'), width = 9),
dbc.Col(
dbc.DropdownMenu(
label = "Select a model",
children = dropdown_models(0),
right=False,
id = 'dd_models'
),
)
]),
dbc.Row([
dbc.Col(html.H1(children=''), width = 12),
]),
dbc.Row([
dbc.Col(dbc.Col([
dbc.Row(dsc.collapse([], 'Manipulated Variables', 'mvars-collapse')),
def init_attacker(server):
app = dash.Dash(
server=server,
url_base_pathname=URL_BASE,
suppress_callback_exceptions=True,
)
app.layout = html.Div(children=[
html.H1(children='Fantasy Premier League Attack/Midfield Dashboard',
style={
'textAlign': 'center',
'color': '#28D0B4',
}),
html.Div(
children=
'This interactive web app can be a toolkit for you to select and optimise your player selection based on their previous year\'s performances',
style={
'color': '#28D0B4',
}),
html.Div(
id='stats-menu',
className='dropdowns',
children=[
dcc.Dropdown(id='yaxis',
className='ydropdown',
options=[{
'label': 'Players',
'value': 'player'
}, {
'label': 'Games',
'value': 'games'
}, {
'label': 'Minutes',
'value': 'minutes'
}, {
'label': 'Total 90s',
'value': 'minutes_90s'
}, {
'label': 'Goals per90',
'value': 'goals_per90'
}, {
'label': 'Assists per90',
'value': 'assists_per90'
}, {
'label': 'Penalties Attempted',
'value': 'pens_att'
}, {
'label': 'Penalty Conversion %',
'value': 'pens_conv'
}, {
'label': 'Yellow Cards',
'value': 'cards_yellow'
}, {
'label': 'Red Cards',
'value': 'cards_red'
}, {
'label': 'Goals & Assists per90',
'value': 'goals_assists_per90'
}, {
'label': 'Goals + Assists - Penalties per90',
'value': 'goals_assists_pen_per90'
}, {
'label': 'Expected Goals per90',
'value': 'xg_per90'
}, {
'label': 'Expected Assists per90',
'value': 'xa_per90'
}, {
'label': 'xG & xA per90',
'value': 'xg_xa_per90'
}, {
'label': 'Non-Penalty Expected goals per90',
'value': 'npxg_per90'
}, {
'label': 'npxG + xA per90',
'value': 'npxg_xa_per90'
}, {
'label': 'xG_net',
'value': 'xg_net'
}, {
'label': 'Cost',
'value': 'cost'
}, {
'label': 'Points earned',
'value': 'points'
}, {
'label': 'Points per game',
'value': 'ppg'
}, {
'label': 'Points per cost',
'value': 'ppc'
}],
placeholder='Choose statistics for Y axis',
searchable=True,
value='points'),
dcc.Dropdown(id='xaxis',
className='xdropdown',
options=[{
'label': 'Players',
'value': 'player'
}, {
'label': 'Games',
'value': 'games'
}, {
'label': 'Minutes',
'value': 'minutes'
}, {
'label': 'Total 90s',
'value': 'minutes_90s'
}, {
'label': 'Goals per90',
'value': 'goals_per90'
}, {
'label': 'Assists per90',
'value': 'assists_per90'
}, {
'label': 'Penalties Attempted',
'value': 'pens_att'
}, {
'label': 'Penalty Conversion %',
'value': 'pens_conv'
}, {
'label': 'Yellow Cards',
'value': 'cards_yellow'
}, {
'label': 'Red Cards',
'value': 'cards_red'
}, {
'label': 'Goals & Assists per90',
'value': 'goals_assists_per90'
}, {
'label': 'Goals + Assists - Penalties per90',
'value': 'goals_assists_pen_per90'
}, {
'label': 'Expected Goals per90',
'value': 'xg_per90'
}, {
'label': 'Expected Assists per90',
'value': 'xa_per90'
}, {
'label': 'xG & xA per90',
'value': 'xg_xa_per90'
}, {
'label': 'Non-Penalty Expected goals per90',
'value': 'npxg_per90'
}, {
'label': 'npxG + xA per90',
'value': 'npxg_xa_per90'
}, {
'label': 'xG_net',
'value': 'xg_net'
}, {
'label': 'Cost',
'value': 'cost'
}, {
'label': 'Points earned',
'value': 'points'
}, {
'label': 'Points per game',
'value': 'ppg'
}, {
'label': 'Points per cost',
'value': 'ppc'
}],
placeholder='Choose statistics for X axis',
searchable=True,
value='player'),
dcc.RadioItems(id='plot',
className='plot-select',
options=[
{
'label': 'Bar Plot',
'value': 'bar'
},
{
'label': 'Scatter Plot',
'value': 'scatter'
},
],
value='scatter'),
]),
dcc.Graph(
id='stats-graph', className='graph', style={
'marginTop': 40,
}),
html.Div(className='info-panel',
children=[
html.H5(children='Some of the stats used',
style={'marginBottom': -2}),
dcc.Markdown('''
'''),
html.
A(' - Statisfy - A collections of Basic Football Analytics',
href='https://github.com/sidthakur08/statisfy'),
html.Br(),
html.A(' - Contact me on Twitter :)',
href='https://twitter.com/sidtweetsnow',
target='_blank'),
html.Br(),
]),
html.Div(className='link-name',
children=[
html.A('Link to the github repository',
href="https://github.com/sidthakur08/fpl_explore",
target='_blank')
]),
html.Br(),
html.Br(),
html.Br(),
])
init_attacker_callbacks(app)
return app.server
children=[
html.H4('''Sales remuneration by units : '''),
generate_table(
comparaison[['marketplace', 'sales/units']]),
]),
html.Div(className="dataframe1",
children=[
html.H4('''Shipment price by units : '''),
generate_table(comparaison[[
'marketplace', 'shipping/units'
]]),
]),
html.Div(className="dataframe1",
children=[
html.H4('''FBA shipping cost per order : '''),
html.H5(fba),
html.H4('''MFN shipping cost per order : '''),
html.H5(mfn),
]),
html.Div(children=[
dcc.Graph(figure=word_count),
dcc.Graph(figure=shipment),
dcc.Graph(figure=sunburst),
dcc.Graph(figure=commands),
dcc.Graph(figure=sell),
]),
])
])
if __name__ == '__main__':
app.run_server(port=8050, host='0.0.0.0', debug=True)
app.layout = html.Div([
Row([
Col([html.H1('Histogram Comparison for bme data',
style = {'margin-bottom':50, 'margin-top':20})],
width={"size": 12, "offset": 1})
]),
Row([
Col([
dcc.Graph(id='hist_1', style={'width':'95%','box-shadow':'2px 2px 2px grey'})
], width={"size": 4, "offset": 1}),
Col([
html.Div([
html.Div([
html.H3('Hist 1'),
dropdown('var_1', var),
Row([Col(html.H5('Q0: '), sm=1), Col(checklist('multi_Q0_1',dropdown_options.Q0))]),
Row([Col(html.H5('Q1: '), sm=1), Col(checklist('multi_Q1_1',dropdown_options.Q1))]),
Row([Col(html.H5('Q2: '), sm=1), Col(checklist('multi_Q2_1',dropdown_options.Q2))]),
Row([Col(html.H5('Q3: '), sm=1), Col(checklist('multi_Q3_1',dropdown_options.Q3))]),
Row([Col(html.H5('Q4: '), sm=1), Col(rgslider('rgslider_Q4_1',df.Q4, 8))]),
Row([Col(html.H5('Q5: '), sm=1), Col(rgslider('rgslider_Q5_1',df.Q5, 5))]),
Row([Col(html.H5('Q6: '), sm=1), Col(rgslider('rgslider_Q6_1',df.Q6, 5))]),
Row([Col(html.H5('Q7: '), sm=1), Col(checklist('multi_Q7_1',dropdown_options.Q7))]),
Row([Col(html.H5('Q8: '), sm=1), Col(rgslider('rgslider_Q8_1',df.Q8, 5))]),
Row([Col(html.H5('Q9: '), sm=1), Col(checklist('multi_Q9_1',dropdown_options.Q9))]),
Row([Col(html.H5('Q10: '), sm=1), Col(rgslider('rgslider_Q10_1',df.Q10, 5))])
], style={'width':'95%'})
], style={'width':'95%', 'box-shadow':'2px 2px 2px grey'})
])
]),
import datetime
import operator
import os
import base64
import io
#app = dash.Dash()
app = dash.Dash(__name__)
server = app.server
app.scripts.config.serve_locally = True
app.config['suppress_callback_exceptions'] = True
app.layout = html.Div([
html.H5("Upload Files"),
dcc.Upload(id='upload-data',
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=False),
html.Br(),
MAX_RECENT_POSTS = 10
app_colors = {
"text": "#0C0F0A",
"background": "#ffffff"
}
app = dash.Dash(__name__)
app.layout = html.Div([
html.Div(
className="container-fluid",
children=[
html.H1("Live reddit sentiment"),
html.H5("Search:"),
dcc.Input(id="keyword", type="text", value="")
], style={'width':'50%','margin-left':10,'margin-right':60,'max-width':50000}
),
html.Div(className="row", children=[
html.Div(id="")
]),
html.Div(className="row", children=[
html.Div(id="recent-reddit-table", className="col s12 m6 l6"),
html.Div(dcc.Graph(id="sentiment-pie", animate=False), className="col s12 m6 l6")
]),
dcc.Interval(
id="recent-reddit-table-update",
colors = {
'background': '#111111',
'text': '#009a00'
}
app.layout = html.Div([
html.Div(
className="banner",
children = [html.H2("WorkspaceC.alpha 0.1",
style={
'color': colors['text'],
}),
html.Img(src='/assets/stock_icon.png')]),
html.Div(
children=html.Div([
html.H5('Log in'),
])),
html.Div(["Input: ",
dcc.Input(id='my-input', value='initial value', type='text')]),
html.Div(["Write: ",
html.Button(id='time-button', children='Gettime')]),
html.Br(),
html.Div(id='my-output'),
])
@app.callback(
Output(component_id='my-output', component_property='children'),
[Input(component_id='time-button', component_property='n_clicks')]
)
style={
'height': '60px',
'width': 'auto',
'margin-bottom': '25px',
},
)
],
className='one-third column'),
html.Div(
[
html.Div([
html.H3(
'The Best Movie Site Ever',
style={'margin-bottom': '0px'},
),
html.H5('Find the right movie for you',
style={'margin-top': '0px'}),
])
],
className='one-half column',
id='title',
),
html.Div(
[
html.A(
html.Button("Like it", id="like-it-button"),
href="https://plot.ly/dash/pricing/",
)
],
className="one-third column",
id="button",
),
[
dbc.NavItem(dbc.NavLink("NP Classifier", href="#")),
dbc.NavItem(dbc.NavLink("Report Feedback", href="https://docs.google.com/forms/d/e/1FAIpQLSf1-sw-P0SQGokyeaOpEmLda0UPJW93qkrI8rfp7D46fHVi6g/viewform?usp=sf_link")),
dbc.NavItem(dbc.NavLink("Preprint Publication", href="https://chemrxiv.org/articles/preprint/NPClassifier_A_Deep_Neural_Network-Based_Structural_Classification_Tool_for_Natural_Products/12885494/1")),
dbc.NavItem(dbc.NavLink("API", href="https://ccms-ucsd.github.io/GNPSDocumentation/api/#structure-np-classifier")),
],
navbar=True)
],
color="light",
dark=False,
sticky="top",
)
DASHBOARD = [
dcc.Location(id='url', refresh=False),
dbc.CardHeader(html.H5("NP Classifier")),
dbc.CardBody(
[
html.Div(id='version', children="Version - 1.5"),
html.Br(),
dbc.Textarea(className="mb-3", id='smiles_string', placeholder="Smiles Structure"),
dcc.Loading(
id="structure",
children=[html.Div([html.Div(id="loading-output-5")])],
type="default",
),
dcc.Loading(
id="classification_table",
children=[html.Div([html.Div(id="loading-output-3")])],
type="default",
),
'videoCount': 'Video',
'voiceParticipantCount': 'Sprecher'}
colors = {
'bg_black': '#818181',
'text_green': '#1dcf9d',
'bg_blue': '#365b6b',
'bg_blue_graph': '#334f65'
}
#########
### DBC Card
### Sum of meeting, listener, participant, and video
card_content_1 = [
dbc.CardBody(
[
html.H5("Meeting", className="card-title"),
html.P("", className="card-text", ),
df.shape[0]
#dfLog['createTime'].nunique(),
]
),
]
card_content_2 = [
dbc.CardBody(
[
html.H5("Zuhörer", className="card-title"),
html.P("", className="card-text", ),
groupListenerCount['listenerCount'].sum(),
]
),
figure={
'data': [
go.Bar(x=cov_data['Country_Region'],
y=cov_data['Recovered'],
marker={'color': 'rgb(51,204,153)'})
],
'layout': {
'title': 'Recovery by contry',
}
},
),
dcc.Graph(
id='example-graph2',
figure={
'data': [
go.Bar(x=cov_data['Country_Region'],
y=cov_data['Deaths'],
marker={'color': 'rgb(200,204,53)'})
],
'layout': {
'title': 'Fatalities by country'
}
},
),
html.H5("Developed by Natalie Andrade", style={'font-family': "Open Sans"
}),
])
if __name__ == '__main__':
app.run_server(debug=True)
def get_evaluation_dash_app(self, dash_=None):
"""
Evaluation dass app.
:return:
"""
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
if dash_ is None:
app = dash.Dash(__name__,
external_stylesheets=external_stylesheets,
suppress_callback_exceptions=True)
else:
app = dash_
# forward_returns_period = [1, 2, 5, 10] # period list
# forward_str = str(forward_returns_period).replace('[', '').replace(']', '')
para_dcc_list = []
for k, v in self.alpha_func_paras.items():
para_dcc_list.append(html.Div(children=k))
para_dcc_list.append(
dcc.Input(id="input_{}".format(k),
placeholder=str(k),
type='number',
value=str(v),
debounce=True))
app.layout = html.Div(
children=[
html.H1(children=self.factor_name + ' evaluation',
style={
'font-weight': 'normal',
'text-align': 'center',
'display': 'block',
'fontFamily': 'helvetica neue',
'margin': '100px auto'
}),
html.Div([
# change forward returns
html.Div(
[
html.Div(id='forward-returns-period'),
# add forward returns
html.Div(
children=
'Enter a value to add or remove forward return value'
),
dcc.Input(id='forwards-periods-input',
type='text',
value='1, 2, 5, 10'),
html.Button('Update', id='UpdateButton'),
],
style={
'margin-left': '6.25em',
'width:': '25em',
'float': 'left'
}),
# change parameter
html.Div([
html.Div(children='Factor Parameter'),
html.Div(para_dcc_list, id='alpha_paras'),
html.Button('Submit', id='AlphaButton'),
html.Div(id="current-parameter"),
],
style={
'margin-left': '20em',
'display': 'inline-block'
}),
]),
html.Div([
dcc.RadioItems(
id='in-out-sample',
options=[{
'label': i,
'value': i
} for i in ['In sample', 'Out ot the sample']],
value='In sample',
labelStyle={'display': 'inline-block'})
],
style={
'display': 'block',
'margin': '0px 100px 50px 100px'
}),
# summary table
html.Div([
html.H5(children='Factor Summary Table',
style={
'text-align': 'center',
'margin-bottom': '20px'
}),
html.Table(id='summary-table',
style={
'width:': '40%',
'float': 'left',
'font-size': '1.25em'
})
],
style={
'margin-left': '6.25rem',
'display': 'inline-block'
}),
# ic_table
html.Div([
html.H5(children='Factor IC Table',
style={
'display': 'block',
'text-align': 'center',
'margin-bottom': '20px'
}),
html.Table(id='ic-table',
style={
'width:': '40%',
'font-size': '1.25em'
}),
],
style={
'margin-left': '18.75em',
'display': 'inline-block'
}),
# beta table
html.Div([
html.H5(children='Factor Beta',
style={
'text-align': 'center',
'margin-bottom': '20px'
}),
html.Table(id='beta-table',
style={
'width': '100%',
'font-size': '1.25em'
})
],
style={
'display': 'block',
'margin': '20px auto 60px'
}),
html.Div(
[
html.H5(children='Factor Distribution',
style={
'text-align': 'center',
'margin': 'auto'
}),
dcc.Graph(id='distribution')
],
style={
'width': '49%',
'display': 'inline-block',
'margin-bottom': '50px'
}),
html.Div(
[
html.H5(children='Q-Q plot ',
style={
'text-align': 'center',
'margin': 'auto'
}),
dcc.Graph(id='qqplot')
],
style={
'width': '49%',
'display': 'inline-block',
'margin-bottom': '50px'
}),
html.Div(
[
html.H5(children='Factor IC',
style={
'text-align': 'center',
'margin': 'auto'
}),
dcc.Graph(id='ic_heatmap')
],
style={
'width': '100%',
'display': 'inline-block',
'margin-bottom': '50px'
}),
html.Div(
[
html.H5(children='Price Factor',
style={
'text-align': 'center',
'margin': 'auto'
}),
dcc.Graph(id='price_factor')
],
style={
'width': '100%',
'display': 'inline-block',
'margin-bottom': '50px'
}),
html.Div(
[
html.H5(children='Factor Return',
style={
'text-align': 'center',
'margin': 'auto'
}),
dcc.Graph(id='factor-returns')
],
style={
'width': '100%',
'display': 'inline-block',
'margin-bottom': '50px'
}),
html.Div(
[
html.H5(children='Factor Backtesting',
style={
'text-align': 'center',
'margin': 'auto'
}),
dcc.Graph(id='factor-backtest')
],
style={
'width': '100%',
'display': 'inline-block',
'margin-bottom': '50px'
}),
html.Div(children=self.factor.to_json(orient='split'),
id='in_sample_factor',
style={'display': 'none'}),
html.Div(id='out_sample_factor', style={'display': 'none'}),
html.Div(children=json.dumps([1, 2, 5, 10]),
id='forward_returns_period_saved',
style={'display': 'none'}),
html.Div(id='forward_str', style={'display': 'none'}),
],
style={'margin': '20px'})
# make input parameter into dict
def _get_alpha_parameter_from_div(alpha_paras):
paras = {}
for child in alpha_paras:
if child['type'] == 'Input':
props = child['props']
k = props['id'].replace('input_', '')
v = props['value']
try:
v = int(v)
except:
v = float(v)
paras[k] = v
return paras
@app.callback(Output('in_sample_factor', 'children'), [
Input('AlphaButton', 'n_clicks'),
Input('alpha_paras', 'children')
])
def update_alpha_insample(n_clicks, alpha_paras):
# some expensive clean data step
# print(alpha_paras)
paras = _get_alpha_parameter_from_div(alpha_paras)
self.calculate_factor(self.alpha_func, **paras)
in_sample_factor = self.factor # type: pd.Series
# more generally, this line would be
# json.dumps(cleaned_df)
return in_sample_factor.to_json(orient='split')
@app.callback(Output('out_sample_factor', 'children'), [
Input('AlphaButton', 'n_clicks'),
Input('alpha_paras', 'children')
])
def update_alpha_out_of_sample(n_clicks, alpha_paras):
paras = _get_alpha_parameter_from_div(alpha_paras)
self.calculate_factor(self.alpha_func, **paras)
out_of_sample_factor = self.alpha_func(self.out_of_sample,
**paras) # type: pd.Series
# more generally, this line would be
# json.dumps(cleaned_df)
return out_of_sample_factor.to_json(orient='split')
@app.callback([
Output('forward_returns_period_saved', 'children'),
Output("forward-returns-period", "children")
], [Input("UpdateButton", "n_clicks")],
[State("forwards-periods-input", "value")])
def update_forward_return(n_clicks, value):
fr = list(set([int(p) for p in value.split(',')]))
fr.sort()
forward_str = str(fr).replace('[', '').replace(']', '')
return json.dumps(fr), 'Forward return list: ' + forward_str
@app.callback([
Output('distribution', 'figure'),
Output('ic_heatmap', 'figure'),
Output('qqplot', 'figure'),
Output('price_factor', 'figure'),
Output('factor-returns', 'figure'),
Output('factor-backtest', 'figure'),
Output('summary-table', 'children'),
Output('ic-table', 'children'),
Output('beta-table', 'children'),
], [
Input("UpdateButton", "n_clicks"),
Input('in-out-sample', 'value'),
Input('forward_returns_period_saved', 'children'),
Input('in_sample_factor', 'children'),
Input('out_sample_factor', 'children'),
])
def update_forward_returns(n_clicks, value, forward_period, alpha_json,
out_alpha_json):
forward_returns_period = json.loads(forward_period)
factor = pd.read_json(alpha_json, orient='split', typ='series')
if value == 'In sample':
update_distribution_figure = factor_distribution_plot(factor)
returns = calculate_forward_returns(self.in_sample,
forward_returns_period)
ic_heatmap = get_monthly_ic(returns, factor,
forward_returns_period)
update_heatmap_figure = monthly_ic_heatmap_plot(ic_heatmap)
update_qqplot_figure = qq_plot(factor)
update_factor_plot_figure = price_factor_plot(
self.in_sample, factor)
factor_returns = calculate_ts_factor_returns(
self.in_sample, factor, forward_returns_period)
update_factor_plot_figure1 = returns_plot(
factor_returns, self.factor_name)
factor_returns = calculate_ts_factor_returns(
self.in_sample, factor, forward_returns_period)
cumulative_returns = calculate_cumulative_returns(
factor_returns, 1)
benchmark = self.in_sample['close'] / self.in_sample['close'][0]
update_factor_plot_figure2 = cumulative_return_plot(
cumulative_returns,
benchmark=benchmark,
factor_name=self.factor_name)
# tables
factor_table = pd_to_dash_table(factor_summary(factor),
'summary')
ic_table = pd_to_dash_table(
pd.DataFrame(calculate_ts_information_coefficient(
factor, returns),
columns=[self.factor_name]), 'ic')
ols_table = pd_to_dash_table(
factor_ols_regression(factor, returns), 'ols')
return update_distribution_figure, update_heatmap_figure, \
update_qqplot_figure, update_factor_plot_figure, \
update_factor_plot_figure1, update_factor_plot_figure2, \
factor_table, ic_table, ols_table
else:
# out of sample的情况还没搞好
out_factor = pd.read_json(out_alpha_json,
orient='split',
typ='series')
# update_distribution_figure = factor_distribution_plot(out_factor)
returns = calculate_forward_returns(self.out_of_sample,
forward_returns_period)
ic_heatmap = get_monthly_ic(returns, out_factor,
forward_returns_period)
update_heatmap_figure = monthly_ic_heatmap_plot(ic_heatmap)
# update_qqplot_figure = qq_plot(out_factor)
update_factor_plot_figure = price_factor_plot(
self.out_of_sample, out_factor)
factor_returns = calculate_ts_factor_returns(
self.out_of_sample, out_factor, forward_returns_period)
update_factor_plot_figure1 = returns_plot(
factor_returns, self.factor_name)
factor_returns = calculate_ts_factor_returns(
self.out_of_sample, out_factor, forward_returns_period)
cumulative_returns = calculate_cumulative_returns(
factor_returns, 1)
benchmark = self.out_of_sample['close'] / self.out_of_sample[
'close'][0]
update_factor_plot_figure2 = cumulative_return_plot(
cumulative_returns,
benchmark=benchmark,
factor_name=self.factor_name)
# for out of sample data onlye
in_out_distplot = overlaid_factor_distribution_plot(
factor, out_factor)
inout_qqplot = observed_qq_plot(factor, out_factor)
# inout_qqplot.show()
factor_table = pd_to_dash_table(factor_summary(out_factor),
'summary')
ic_table = pd_to_dash_table(
pd.DataFrame(calculate_ts_information_coefficient(
out_factor, returns),
columns=[self.factor_name]), 'ic')
ols_table = pd_to_dash_table(
factor_ols_regression(out_factor, returns), 'ols')
return in_out_distplot, update_heatmap_figure, \
inout_qqplot, update_factor_plot_figure, \
update_factor_plot_figure1, update_factor_plot_figure2, \
factor_table, ic_table, ols_table
return app
def tri_community():
return html.Div(
id='tri_community',
style={
'position': 'relative',
'float': 'center',
'border': '3px solid #319997',
'border-radius': '6px',
'margin': '0.25%',
'padding': '1%',
'width': '100%',
'margin-bottom': '2vH'
},
children=[
html.Div(id='comm_filled', style={'display': 'none'}),
html.Div(
children=[dbc.Input(id='data_com', type='text', value='')],
style={'display': 'none'}),
html.Div([
html.H6('COVID Triage - Community',
style={'font-weight': '600'}),
]),
html.Br(),
html.Div(
style={},
children=[
html.Div(
[
html.H5('Pregnancy - Demographics',
style={'font-weight': 'bold'}),
html.Br(),
html.Br(),
html.Br(),
dbc.Row([
# Q1
dbc.Col(surname_qc, width=1),
dbc.Col(surname_rc, width=1),
dbc.Col(html.P(), width=1),
# Q2
dbc.Col(nhs_qc, width=1),
dbc.Col(nhs_rc, width=1),
dbc.Col(html.P(), width=1),
# Q3
dbc.Col(doby_qc, width=1),
dbc.Col(doby_rc, width=1),
dbc.Col(html.P(), width=1),
# Q4
dbc.Col(parity_qc, width=1),
dbc.Col(parity_rc, width=2),
]),
html.Br(),
dbc.Row([
# Q5
dbc.Col(del_qc, width=1),
dbc.Col(del_rc, width=2),
# Q6
dbc.Col(edd_qc, width=1),
dbc.Col(edd_rc, width=1),
dbc.Col(html.P(), width=1),
# Q7
dbc.Col(deldate_qc, width=1),
dbc.Col(deldate_rc, width=2),
# Q7
dbc.Col(tel_qc, width=1),
dbc.Col(tel_rc, width=2),
])
],
style={
'border': '1px solid #319997',
'border-radius': '6px',
'padding': '0.5%',
'padding-bottom': '20px'
}),
html.Br(),
html.Br(),
html.Br(),
html.Div(
[
html.H5('COVID - Test Status',
style={'font-weight': 'bold'}),
html.Br(),
html.Br(),
html.Br(),
dbc.Row([
# Q9
dbc.Col(vacc_qc, width=1),
dbc.Col(vacc_rc, width=2),
# Q10
dbc.Col(vacwhich_qc, width=1),
dbc.Col(vacwhich1_rc, width=1),
dbc.Col(vacwhich2_rc, width=1),
# Q11
dbc.Col(vac1date_qc, width=1),
dbc.Col(vac1date_rc, width=2),
# Q12
dbc.Col(vac2date_qc, width=1),
dbc.Col(vac2date_rc, width=2),
]),
html.Br(),
dbc.Row([
# Q13
dbc.Col(covidtest_qc, width=1),
dbc.Col(covidtest_rc, width=2),
# Q14
dbc.Col(covidstatus_qc, width=1),
dbc.Col(covidstatus_rc, width=2),
# Q15
dbc.Col(covswabdate_qc, width=1),
dbc.Col(covswabdate_rc, width=2),
]),
html.Br(),
dbc.Row([
# Q16
dbc.Col(covadmit_qc, width=1),
dbc.Col(covadmit_rc, width=2),
# Q17
dbc.Col(covadmitdate_qc, width=1),
dbc.Col(covadmitdate_rc, width=2),
# Q18
dbc.Col(covdischargedate_qc, width=1),
dbc.Col(covdischargedate_rc, width=2),
]),
html.Br(),
html.Br(),
],
style={
'border': '1px solid #319997',
'border-radius': '6px',
'padding': '0.5%',
'padding-bottom': '20px'
}),
]),
### Row ###
html.Br(),
html.Br(),
html.Br(),
html.Div(
style={},
children=[
html.Div(
[
html.H5('COVID - Clinical Assessment',
style={'font-weight': 'bold'}),
html.Br(),
html.Br(),
html.Br(),
dbc.Row([
# Q19
dbc.Col(sentences_qc, width=2),
dbc.Col(sentences_rc, width=2),
dbc.Col(rwt_qc, width=1),
dbc.Col(rwt_rc, width=4),
]),
html.Br(),
dbc.Row([
# Q20
dbc.Col(covsymp_qc, width=2),
dbc.Col(tsym1_rc, width=2),
dbc.Col(tsym2_rc, width=2),
# 21
dbc.Col(tsym3_rc, width=2),
# 22
dbc.Col(tsym4_rc, width=2),
]),
### Row ###
html.Br(),
dbc.Row([
dbc.Col(covrisksw_qc, width=2),
dbc.Col(risksw1_rc, width=2),
# Q24
dbc.Col(risksw2_rc, width=2),
# Q25
dbc.Col(risksw3_rc, width=2),
# Q26
dbc.Col(risksw4_rc, width=2),
]),
html.Br(),
### Row ###
dbc.Row([
dbc.Col(covrisksb_qc, width=2),
# Q27
dbc.Col(risksb1_rc, width=2),
# Q28
dbc.Col(risksb2_rc, width=2),
# Q29
dbc.Col(risksb3_rc, width=2),
# Q30
dbc.Col(risksb4_rc, width=2),
]),
html.Br(),
### Row ###
dbc.Row([
# Q31
dbc.Col(covrx_qc, width=2),
dbc.Col(covrxw1_rc, width=2),
# Q32
dbc.Col(covrxw2_rc, width=2),
# Q33
dbc.Col(covrxw3_rc, width=2),
# Q34
dbc.Col(covrxw4_rc, width=2),
]),
html.Br(),
### Row ###
dbc.Row([
dbc.Col(covclinassess_qc, width=2),
# Q27
dbc.Col(clin1assess1_rc, width=2),
# Q28
dbc.Col(clin1assess2_rc, width=2),
# Q29
dbc.Col(clin1assess3_rc, width=2),
# Q30
dbc.Col(clin1assess4_rc, width=2)
]),
html.Br(),
dbc.Row([
dbc.Col(labour_qc, width=2),
# Q27
dbc.Col(labour_rc, width=2),
dbc.Col(fetalconcerns_qc, width=2),
# Q29
dbc.Col(fetalconcerns_rc, width=2),
]),
html.Br(),
dbc.Row([
dbc.Col(fetalheart_qc, width=2),
# Q30
dbc.Col(fhr_rc, width=2),
]),
### Row ###
# dbc.Col(covfreetext_qc, width=2),
# ]),
# dbc.Row([
# dbc.Col(covfreetext_rc, width=10),
# ]),
],
style={
'border': '1px solid #319997',
'border-radius': '6px',
'padding': '0.5%',
'padding-bottom': '20px'
}),
html.Br(),
html.Div(children=[
html.Div([
html.Button(id='communitytriage_btn_confirm',
children='Confirm',
n_clicks=0)
],
style={
'margin-right': '10px',
'display': 'inline-block'
}),
# html.Div([html.A(html.Button(id='communitytriage_btn_confirm', children='Confirm', n_clicks=0),
# href='/experiment')],
# style={'margin-right': '10px', 'display': 'inline-block'}),
html.Div([
html.Button(id='communitytriage_btn_cancel',
children='Cancel',
n_clicks=0)
],
style={'display': 'inline-block'})
])
])
])
import dash
import dash_bootstrap_components as dbc
import dash_html_components as html
app = dash.Dash(external_stylesheets=[dbc.themes.BOOTSTRAP])
card_content_1 = [
dbc.CardHeader("Card header"),
dbc.CardBody([
html.H5("Card title", className="card-title"),
html.P(
"This is some card content that we'll reuse",
className="card-text",
),
]),
]
card_content_2 = dbc.CardBody([
html.Blockquote(
[
html.P("A learning experience is one of those things that says, "
"'You know that thing you just did? Don't do that.'"),
html.Footer(html.Small("Douglas Adams", className="text-muted")),
],
className="blockquote",
)
])
card_content_3 = [
dbc.CardImg(src="./assets/placeholder286x180.png", top=True),
dbc.CardBody([
