def create_layout(app):
# Page layouts
return html.Div(
[
html.Div([Header(app)]),
# page 1
html.Div(
[
# Row 3
html.Div(
[
html.Div(
[
html.H5("Data analysis report"),
html.Br([]),
html.P(
"\
In this section is presented some important characteristics that describe the data",
style={"color": "#ffffff"},
className="row",
),
],
className="product",
id='buffer1',
)
],
className="row",
),
# Row 4
html.Div(
[
html.Div(
[
html.H6(["Important-data"],
className="subtitle padded"),
html.Table(make_dash_table(df_fund_facts)),
],
className="seven columns",
),
],
className="row",
style={"margin-bottom": "35px"},
),
html.Div([
html.H6(
"Graph of number of employees per company",
className="subtitle padded",
),
dcc.Graph(id="graph-1", ),
], ),
html.Div([
html.H6(
"Number of employees versus Response",
className="subtitle padded",
),
dcc.Graph(id="graph-1-1", ),
], ),
# Row 5
html.Div(
[
html.Div([
html.H6(
"BOX PLOT responses PART 1: INNOVATION STRATEGY 4, ORGANIZATION 12, INNOVATION PROJECT 16, VALUE NETWORK 16 and RESULTS-27",
className="subtitle padded",
),
dcc.Graph(id="graph-2", ),
],
# className="six columns",
),
html.Div([
html.
H6("BOX PLOT PART2:29: Which areas are you most eager to strengthen in order to reach your future ambition within 3 years:",
className="subtitle padded"),
dcc.Graph(id="graph-3", ),
],
#className="six columns",
),
html.Div([
html.
H6("BOX PLOT PART3:28: Where do you see your authority:",
className="subtitle padded"),
dcc.Graph(id="graph-4A", ),
],
#className="six columns",
),
html.Div([
html.
H6("BOX PLOT NEW-RESULT-EXTERNAL: 25: In our innovation work with a focus on external results (eg new products, services and forms of collaboration), we as a whole succeed in meeting new challenges / opportunities by:",
className="subtitle padded"),
dcc.Graph(id="graph-5", ),
],
#className="six columns",
),
],
# className="row ",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
# Page layouts
return html.Div(
[
html.Div([Header(app)]),
# page 1
html.Div(
[
# Row 3
html.Div(
[
html.Div(
[
html.H5("မြန်မာ့နိုင်ငံတော် သမ္မတများ"),
html.Br([]),
html.P(
"မြန်မာနိုင်ငံ လွတ်လပ်ရေးရပြီးသည်မှ စ၍ နိုင်ငံတော်၏အကြီးအမှူးအဖြစ် \
ပါလီမန်ဒီမိုကရေစီ၊ ဆိုရှယ်လစ်စနစ်နှင့် ပါတီစုံဒီမိုကရေစီ အထိ \
ခေတ်အဆက်ဆက် တာဝန်ထမ်းဆောင်ခဲ့ကြသည့်သမ္မတများ",
style={"color": "#ffffff"},
className="row",
),
],
className="product",
)
],
className="row",
),
# Row 4
html.Div(
[
html.Div(
[
html.H6(
["သမ္မတတာဝန်ထမ်းဆောင်ချိန်"], className="subtitle padded"
),
html.Table(make_dash_table(df_president_tenure)),
],
className="six columns",
),
],
className="row",
style={"margin-bottom": "35px"},
),
# Row 5
html.Div(
[
html.Div(
[
html.H6(
["သမ္မတ၏နောက်ခံပါတီ"], className="subtitle padded"
),
html.Table(make_dash_table(df_president_data)),
],
className="six columns",
),
],
className="row",
style={"margin-bottom": "35px"},
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
return html.Div(
[
Header(app),
# page 5
html.Div(
[
# Row 1
html.Div(
[
html.Div(
[
html.H6(["Distribuciones"],
className="subtitle padded"),
html.P(
[
"Distributions for this fund are scheduled quaterly"
],
style={"color": "#7a7a7a"},
),
],
className="twelve columns",
)
],
className="row ",
),
# Row 2
html.Div(
[
html.Div(
[
html.Br([]),
html.H6(
["Retorno inversiones en Educación"],
className="subtitle tiny-header padded",
),
html.Div(
[
html.Table(
make_dash_table(df_dividend),
className="tiny-header",
)
],
style={"overflow-x": "auto"},
),
],
className="twelve columns",
)
],
className="row ",
),
# Row 3
html.Div(
[
html.Div(
[
html.H6(
[
"Compras realizadas/no realizadas 01/31/2018"
],
className="subtitle tiny-header padded",
)
],
className=" twelve columns",
)
],
className="row ",
),
# Row 4
html.Div(
[
html.Div(
[html.Table(make_dash_table(df_realized))],
className="six columns",
),
html.Div(
[html.Table(make_dash_table(df_unrealized))],
className="six columns",
),
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
# Page layouts
return html.Div(
[
html.Div([Header(app)]),
# page 1
html.Div(
[
# Row 3
html.Div(
[
html.Div(
[
html.H5("Algorithm Summary"),
html.Br([]),
html.P(
"\
Currently, only deep Q learning algorithm has been implemented with many bugs. We will lose millions of money if we use this stupid trading algorithm. As such, being philanthropists instead of traders is our new goal.",
style={"color": "#ffffff"},
className="row",
),
],
className="product",
)
],
className="row",
),
# Row 4
html.Div(
[
html.Div(
[
html.H6(
["Fund Facts"], className="subtitle padded"
),
html.Table(make_dash_table(df_fund_facts)),
],
className="six columns",
),
html.Div(
[
html.H6(
"Average annual performance",
className="subtitle padded",
),
dcc.Graph(
id="graph-1",
figure={
"data": [
go.Bar(
x=[
"1 Year",
"3 Year",
"5 Year",
"10 Year",
"41 Year",
],
y=[
"21.67",
"11.26",
"15.62",
"8.37",
"11.11",
],
marker={
"color": "#97151c",
"line": {
"color": "rgb(255, 255, 255)",
"width": 2,
},
},
name="DQN",
),
go.Bar(
x=[
"1 Year",
"3 Year",
"5 Year",
"10 Year",
"41 Year",
],
y=[
"21.83",
"11.41",
"15.79",
"8.50",
],
marker={
"color": "#dddddd",
"line": {
"color": "rgb(255, 255, 255)",
"width": 2,
},
},
name="S&P 500 Index",
),
],
"layout": go.Layout(
autosize=False,
bargap=0.35,
font={"family": "Raleway", "size": 10},
height=200,
hovermode="closest",
legend={
"x": -0.0228945952895,
"y": -0.189563896463,
"orientation": "h",
"yanchor": "top",
},
margin={
"r": 0,
"t": 20,
"b": 10,
"l": 10,
},
showlegend=True,
title="",
width=330,
xaxis={
"autorange": True,
"range": [-0.5, 4.5],
"showline": True,
"title": "",
"type": "category",
},
yaxis={
"autorange": True,
"range": [0, 22.9789473684],
"showgrid": True,
"showline": True,
"title": "",
"type": "linear",
"zeroline": False,
},
),
},
config={"displayModeBar": False},
),
],
className="six columns",
),
],
className="row",
style={"margin-bottom": "35px"},
),
# Row 5
html.Div(
[
html.Div(
[
html.H6(
"Hypothetical growth of $10,000",
className="subtitle padded",
),
dcc.Graph(
id="graph-2",
figure={
"data": [
go.Scatter(
x=[
"2008",
"2009",
"2010",
"2011",
"2012",
"2013",
"2014",
"2015",
"2016",
"2017",
"2018",
],
y=[
"10000",
"7500",
"9000",
"10000",
"10500",
"11000",
"14000",
"18000",
"19000",
"20500",
"24000",
],
line={"color": "#97151c"},
mode="lines",
name="DQN",
)
],
"layout": go.Layout(
autosize=True,
title="",
font={"family": "Raleway", "size": 10},
height=200,
width=340,
hovermode="closest",
legend={
"x": -0.0277108433735,
"y": -0.142606516291,
"orientation": "h",
},
margin={
"r": 20,
"t": 20,
"b": 20,
"l": 50,
},
showlegend=True,
xaxis={
"autorange": True,
"linecolor": "rgb(0, 0, 0)",
"linewidth": 1,
"range": [2008, 2018],
"showgrid": False,
"showline": True,
"title": "",
"type": "linear",
},
yaxis={
"autorange": False,
"gridcolor": "rgba(127, 127, 127, 0.2)",
"mirror": False,
"nticks": 4,
"range": [0, 30000],
"showgrid": True,
"showline": True,
"ticklen": 10,
"ticks": "outside",
"title": "$",
"type": "linear",
"zeroline": False,
"zerolinewidth": 4,
},
),
},
config={"displayModeBar": False},
),
],
className="six columns",
),
html.Div(
[
html.H6(
"Price & Performance (%)",
className="subtitle padded",
),
html.Table(make_dash_table(df_price_perf)),
],
className="six columns",
),
html.Div(
[
html.H6(
"Risk Potential", className="subtitle padded"
),
html.Img(
src=app.get_asset_url("risk_reward.png"),
className="risk-reward",
),
],
className="six columns",
),
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
return html.Div(
[
Header(app),
# page 5
html.Div(
[
# Row 1
html.Div(
[
html.Div(
[
html.H6(["DATASE TO THE ANALYSIS"],
className="subtitle padded"),
html.P(
["Dataset organized"],
style={"color": "#7a7a7a"},
),
],
className="twelve columns",
)
],
className="row ",
),
# Row 2
html.Div(
[
html.Div(
[
html.Br([]),
html.H6(
[
"Authority,Number of employees,A,B,C,D,E"
],
className="subtitle tiny-header padded",
),
html.Div(
[
html.Table(
make_dash_table(df_graph),
className="tiny-header",
)
],
style={
"overflow-x": "auto",
'height': '350px',
'overflow': 'scroll',
},
),
],
className="twelve columns",
)
],
className="row ",
),
# Row 3
html.Div([html.H6(["Data saved"])])
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
# Page layouts
return html.Div(
[
html.Div([Header(app)]),
# page 1
html.Div(
[
# Row 3
html.Div(
[
html.Div(
[
html.H5("Analysis Summary"),
html.Br([]),
html.P(
"\
The University of Virginia has laid out an ambitious 10-year plan to accelerate the University’s sustainability goals across operations, research, curriculum, accountability and engagement – a framework of stewardship and discovery. The 2020-30 UVA Sustainability Plan includes six goals that UVA’s Board of Visitors approved in December, when the University committed to pursuing carbon neutrality by 2030 in partnership with the College of William & Mary. The full 2030 Plan also outlines strategic actions for success and adds four new goals.",
style={"color": "#ffffff"},
className="row",
),
],
className="product",
)
],
className="row",
),
# Row 4
html.Div(
[
html.Div(
[
html.H6(["Quick Facts"],
className="subtitle padded"),
html.Table(make_dash_table(df_waste)),
],
className="six columns",
),
html.Div(
[
html.H6(
"Bar Graph",
className="subtitle padded",
),
dcc.Graph(
id="graph-1",
figure={
"data": [
go.Bar(
x=[
"Factor 1",
"Factor 2",
"Factor 3",
"Factor 4",
"Factor 5",
],
y=[
"21.67",
"11.26",
"15.62",
"8.37",
"11.11",
],
marker={
"color": "#141E3C",
"line": {
"color":
"rgb(255, 255, 255)",
"width": 2,
},
},
name="Group 1",
),
go.Bar(
x=[
"Factor 1",
"Factor 2",
"Factor 3",
"Factor 4",
"Factor 5",
],
y=[
"21.83",
"11.41",
"15.79",
"8.50",
],
marker={
"color": "#dddddd",
"line": {
"color":
"rgb(255, 255, 255)",
"width": 2,
},
},
name="Group 2",
),
],
"layout":
go.Layout(
autosize=False,
bargap=0.35,
font={
"family": "Raleway",
"size": 10
},
height=200,
hovermode="closest",
legend={
"x": -0.0228945952895,
"y": -0.189563896463,
"orientation": "h",
"yanchor": "top",
},
margin={
"r": 0,
"t": 20,
"b": 10,
"l": 10,
},
showlegend=True,
title="",
width=330,
xaxis={
"autorange": True,
"range": [-0.5, 4.5],
"showline": True,
"title": "",
"type": "category",
},
yaxis={
"autorange": True,
"range":
[0, 22.9789473684],
"showgrid": True,
"showline": True,
"title": "",
"type": "linear",
"zeroline": False,
},
),
},
config={"displayModeBar": False},
),
],
className="six columns",
),
],
className="row",
style={"margin-bottom": "35px"},
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
# Page layouts
return html.Div(
[
html.Div([Header(app)]),
# page 1
html.Div(
[
# Row 3
html.Div(
[
html.Div(
[
html.
H5("Extractive Summary - Supervisor-centred automation"
),
html.Br([]),
html.P(
"At the extreme, there is the “pull” model of data collection, implicitly described in the example I used in the introduction to these remarks - in which regulators would be able to pull data, at any level of granularity, directly from firms’ systems in real time, with no intervention on the part of firms. A more strategic approach, however, is likely to prove necessary to make a reality of a longer-term goal of embedding technology at the heart of how prudential risks are supervised – that is, not simply identifying applications in supervision that would benefit from technology, but fundamentally re-engineering the way we work. Providing answers to the questions I have outlined in the preceding remarks will help us to know how far we might, in time, go in introducing technology into supervision, and provide a road map for the future of how prudential supervision could be done. For example, by more consistently applying meta-data and tabs to not just the rule book, but also the related library of supervisory expectations, it would become easier and quicker for a more or less intelligent search engine to find and collect together all the relevant and related pieces of regulatory and supervisory text. Gradually over time, advances in technology and modelling techniques should – I believe – make more possible the type of flexible desk-top simulations of banks’ balance sheets imagined in my example - just as an earlier generation of technology enabled, some years ago, quick-fire desk-top simulations of the effect of shocks on the macro economy: but there remain significant technical and practical challenges to overcome.",
style={"color": "#ffffff"},
className="row",
),
],
className="product",
)
],
className="row",
),
# Row 4
html.Div(
[
html.Div(
[
html.H6(["Bigrams & Trigrams"],
className="subtitle padded"),
html.Table(
make_dash_table(df_bi_tr_grams)),
],
className="six columns",
),
html.Div(
[
html.H6(
"Key words",
className="subtitle padded",
),
dcc.Graph(
id="graph-1",
figure={
"data": [
go.Bar(
x=[
15,
17,
22,
24,
28,
],
y=[
'way',
'supervision',
'firms',
'technology',
"data",
],
orientation='h',
marker={
"color": "#97151c",
"line": {
"color":
"rgb(255, 255, 255)",
"width": 2,
},
},
name="Word frequency",
)
],
"layout":
go.Layout(
autosize=True,
bargap=0.35,
font={
"family": "Raleway",
"size": 10
},
height=200,
hovermode="closest",
legend={
"orientation": "h",
"yanchor": "top",
},
margin={
"r": 0,
"t": 0,
"b": 10,
"l": 50,
},
showlegend=True,
title="",
width=330,
xaxis={
"autorange": True,
"range": [-0.5, 4.5],
"showline": True,
"title": "",
"type": "linear",
},
yaxis={
"showgrid": True,
"showline": True,
"title": "",
"type": "category",
"zeroline": False,
},
),
},
config={"displayModeBar": False},
),
],
className="six columns",
),
],
className="row",
style={"margin-bottom": "35px"},
),
# Row 5
html.Div(
[
html.Div(
[
html.H6(
"Lexical Dispersion",
className="subtitle padded",
),
dcc.Graph(
id="graph-2",
figure={
"data": [
go.Scatter(
x=lexical_disp['position'],
y=lexical_disp['word'],
line={"color": "#97151c"},
mode="markers",
)
],
"layout":
go.Layout(
autosize=False,
title="",
font={
"family": "Raleway",
"size": 10
},
height=160,
width=340,
hovermode="closest",
margin={
"r": 20,
"t": 0,
"b": 20,
"l": 50,
},
xaxis={
"autorange": True,
"linecolor":
"rgb(0, 0, 0)",
"showgrid": False,
"showline": True,
"title": "Word Offset",
"type": "linear",
'automargin': True,
},
yaxis={
"autorange":
True,
"gridcolor":
"rgba(127, 127, 127, 0.2)",
"showgrid":
True,
"type":
"category",
'categoryorder':
'array',
'categoryarray': [
'way', 'supervision',
'firms', 'technology',
'data'
],
},
),
},
config={"displayModeBar": False},
),
],
className="six columns",
),
html.Div(
[
html.H6(
"Entity Recognition",
className="subtitle padded",
),
html.Table(make_dash_table(entity_rec)),
],
className="six columns",
)
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
# Page layouts
return html.Div(
[
html.Div([Header(app)]),
# page 1
html.Div(
[
html.Div(
[
html.H5("Investment Objective"),
html.P(
"\
The multi-asset portfolio is to combine capital growth and modest income potential for medium term through an equal contribution\
to risk approach. The portfolio invests in diversified ETF instruments with global exposures by avoiding concentration of\
risk through the construction of a risk balanced portfolio. We manage risk and take advantage of traditional asset classes in a non-traditional way.",
style={"color": "#ffffff"},
className="row",
),
],
className="product",
),
# Row 1
html.Div(
[
html.Div(
[
html.H6(["Reasons for Investing"],
className="subtitle padded"),
html.
P("• Invests in a mix of ETF diversified by asset\
class, geographic region, economic sector and\
investment style, aiming to maximize returns while\
managing risk."),
html.
P("• Rigorous portfolio construction using 60/40 strategy to provide reasonable reuturns with bond like volatility, which lead to a high risk-adjusted return."
),
html.
P("• Traditional asset class allocations combined with equal risk to contribution strategy gives a competitive and robust portfolio."
),
],
className="six columns",
style={"color": "#696969"},
),
html.Div(
[
html.H6(
"Average Annual Performance",
className="subtitle padded",
),
dcc.Graph(
id="graph-1",
figure={
"data": [
go.Bar(
x=df_performance["Date"],
y=df_performance[
"Portfolio"],
marker={
"color": "#97151c",
"line": {
"color":
"rgb(255, 255, 255)",
"width": 2,
},
},
name=
"All-weather Portfolio",
),
go.Bar(
x=df_performance["Date"],
y=df_performance[
"Benchmark"],
marker={
"color": "#dddddd",
"line": {
"color":
"rgb(255, 255, 255)",
"width": 2,
},
},
name=
"S&P Risk Parity Benchmark",
),
],
"layout":
go.Layout(
autosize=False,
bargap=0.35,
font={
"family": "Raleway",
"size": 10
},
height=200,
hovermode="closest",
legend={
"x": -0.0228945952895,
"y": -0.189563896463,
"orientation": "h",
"yanchor": "top",
},
margin={
"r": 0,
"t": 20,
"b": 10,
"l": 30,
},
showlegend=True,
title="",
width=330,
xaxis={
"autorange": True,
"range": [-0.5, 4.5],
"showline": True,
"title": "",
"type": "category",
},
yaxis={
"autorange": True,
"range":
[0, 22.9789473684],
"showgrid": True,
"showline": True,
"title": "",
"type": "linear",
"zeroline": False,
},
),
},
config={"displayModeBar": False},
),
],
className="six columns",
),
],
className="row ",
),
# Row 5
html.Div(
[
html.Div(
[
html.H6("Risk Potential",
className="subtitle padded"),
html.Img(
src=app.get_asset_url(
"risk_reward.png"),
className="risk-reward",
),
],
className="six columns",
),
html.Div(
[
html.H6(
"Fund Essentials",
className="subtitle padded",
),
html.Table(make_dash_table(df_essentials)),
],
className="six columns",
),
],
className="row ",
),
# Row 4
html.Div(
[
html.Div(
[
html.H6(["Allocations (%)"],
className="subtitle padded"),
html.Table(
make_dash_table(df_allocations)),
],
className="six columns",
),
html.Div(
[
html.H6(
["Composition"],
className="subtitle padded",
),
dcc.Graph(
figure={
'data': [{
'labels':
df_composition['Class'],
'values':
df_composition['Portfolio'],
'marker': {
'colors': [
'#97140c',
'#ff9900',
'#f8e0b0',
'#93b5cf',
'#1ba784',
'#ad6598',
'#696969',
]
},
'hole':
.3,
'type':
'pie',
'name':
"Portfolio",
'hoverinfo':
'label+percent+name',
# 'sort': false,
}],
'layout': {
'autosize': True,
'width': 350,
'height': 200,
'font': {
"family": "Raleway",
"size": 10
},
'margin': {
"r": 0,
"t": 50,
"b": 0,
"l": 10,
},
}
},
config={"displayModeBar": False},
),
],
className="six columns",
),
],
className="row",
style={"margin-bottom": "15px"},
),
# Row 2
html.Div(
[
html.Div(
[
html.H6("Growth of $100K",
className="subtitle padded"),
dcc.Graph(
id="graph-4",
figure={
"data": [
go.Scatter(
x=df_port["Date"],
y=df_port["accountCAD"],
line={"color": "#ff9900"},
mode="lines",
name="CAD Account",
),
go.Scatter(
x=df_port["Date"],
y=df_port["accountUSD"],
line={"color": "#f8e0b0"},
mode="lines",
name="USD Account",
),
go.Scatter(
x=df_port["Date"],
y=df_port["portfolio"],
line={"color": "#97140c"},
mode="lines",
name=
"All-weather Portfolio",
),
go.Scatter(
x=df_benchmark["Date"],
y=df_benchmark["Benchmark"],
line={"color": "#b5b5b5"},
mode="lines",
name=
"S&P Risk Parity Benchmark",
),
],
"layout":
go.Layout(
autosize=True,
width=700,
height=200,
font={
"family": "Raleway",
"size": 10
},
margin={
"r": 30,
"t": 30,
"b": 30,
"l": 30,
},
showlegend=True,
titlefont={
"family": "Raleway",
"size": 10,
},
xaxis={
"autorange":
True,
"range": [
"2007-12-31",
"2018-03-06",
],
"rangeselector": {
"buttons": [
{
"count":
1,
"label":
"1Y",
"step":
"year",
"stepmode":
"backward",
},
{
"count":
3,
"label":
"3Y",
"step":
"year",
"stepmode":
"backward",
},
{
"count": 5,
"label": "5Y",
"step": "year",
},
{
"label": "All",
"step": "all",
},
]
},
"showline":
True,
"type":
"date",
"zeroline":
False,
},
yaxis={
"autorange":
True,
"range": [
18.6880162434,
278.431996757,
],
"showline":
True,
"type":
"linear",
"zeroline":
False,
},
),
},
config={"displayModeBar": False},
),
],
className="twelve columns",
)
],
className="row ",
),
# Row 4
html.Div(
[
html.Div(
[
html.H6(
["Portfolio Holdings"],
className="subtitle padded",
),
html.Div(
[
html.Table(
make_dash_table(df_holdings),
className="tiny-header",
)
],
style={"overflow-x": "auto"},
),
],
className=" twelve columns",
)
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app, df_head, df_target_count):
return html.Div(
[
html.Div([Header(app)]),
# page 1
html.Div(
[
# Row 3
html.Div(
[
html.Div(
[
html.H5("Basic KPIs"),
html.Br([]),
html.P(
"\
This section provides basic information about the dataset provided.\
The rest of the analysis is available by clicking on the next tabs.\
Note that it's possible to have a look at the entire analysis by clicking\
on the Full View tab.",
style={"color": "#ffffff"},
className="row",
),
],
className="product",
)
],
className="row",
),
# Row 4
html.Div(
[
html.Div([
html.H6(["DataFrame Overview"],
className="subtitle padded"),
html.Table(make_dash_table(df_head)),
],
#className="six columns",
),
],
className="row",
style={"margin-bottom": "35px"},
),
# Row 5
html.Div(
[
html.Div(
[
html.H6(
"Hypothetical growth of $10,000",
className="subtitle padded",
),
dcc.Graph(
id="graph-2",
figure={
"data": [
go.Scatter(
x=[
"2008",
"2009",
"2010",
"2011",
"2012",
"2013",
"2014",
"2015",
"2016",
"2017",
"2018",
],
y=[
"10000",
"7500",
"9000",
"10000",
"10500",
"11000",
"14000",
"18000",
"19000",
"20500",
"24000",
],
line={"color": "#97151c"},
mode="lines",
name=
"Calibre Index Fund Inv",
)
],
"layout":
go.Layout(
autosize=True,
title="",
font={
"family": "Raleway",
"size": 10
},
height=200,
width=340,
hovermode="closest",
legend={
"x": -0.0277108433735,
"y": -0.142606516291,
"orientation": "h",
},
margin={
"r": 20,
"t": 20,
"b": 20,
"l": 50,
},
showlegend=True,
xaxis={
"autorange": True,
"linecolor":
"rgb(0, 0, 0)",
"linewidth": 1,
"range": [2008, 2018],
"showgrid": False,
"showline": True,
"title": "",
"type": "linear",
},
yaxis={
"autorange": False,
"gridcolor":
"rgba(127, 127, 127, 0.2)",
"mirror": False,
"nticks": 4,
"range": [0, 30000],
"showgrid": True,
"showline": True,
"ticklen": 10,
"ticks": "outside",
"title": "$",
"type": "linear",
"zeroline": False,
"zerolinewidth": 4,
},
),
},
config={"displayModeBar": False},
),
],
className="six columns",
),
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
# Page layouts
return html.Div(
[
html.Div([Header(app)]),
# page 1
html.Div(
[
# Row 3
html.Div(
[
html.Div(
[
html.H5("Project summary"),
html.Br([]),
html.P(
"\
Using a mix of quantitative and qualitative methods we are examining the ways \
in which the transport sector can drastically reduce its greenhouse gas \
emissions by 2050. Initially, we will collect data from previous research \
and from discussions with various experts during workshops to look for options \
and measures for reducing greenhouse gases in the transport sector. We will \
then develop a range of scenarios for successfully reducing emissions in the \
transport sector by 2050, and calculate the economic effects for Switzerland \
(change in employment, GDP) with an economic equilibrium model. From these \
results and in dialogue with experts and based on an optimisation concept \
we will create a path that will show over time how a new approach to \
mobility can be economically viable.",
style={"color": "#ffffff"},
className="row",
),
],
className="product",
)
],
className="row",
),
# Row 4
html.Div(
[
html.Div(
[
html.H6(
[
"Macroeconomic and energy indicators"
],
className="subtitle padded",
),
dcc.Graph(id="graph-2", figure=fig2),
dcc.Dropdown(id='yaxis-column',
options=[{
'label': ind,
'value': ind
} for ind in indicators],
value='Consumption (nv)')
],
style={
"width": "50%",
"float": "left"
},
className="six columns",
),
html.Div(
[
html.H6(
"Private car stock",
className="subtitle padded",
),
dcc.Graph(id="graph-1", figure=fig),
],
style={
"width": "50%",
"float": "left"
},
className="six columns", # px
),
],
className="row",
style={"margin-bottom": "35px"},
),
# Row 5
html.Div(
[
html.Div(
[
html.H6(["Scenarios analyzed"],
className="subtitle padded"),
html.Table(make_dash_table(df_fund_facts)),
],
className="six columns",
),
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
],
className="product",
)
],
className="row",
)
layout_graphs = html.Div(
[
html.H5(
["Yearly Revenue (w/ YoY % change)"],
className="subtitle padded",
),
html.Div(
[
html.Table(make_dash_table(df_yearly_revenue)),
],
className="row",
),
dcc.Graph(
id="graph-yearly-performance",
figure=px.bar(
df_yearly_performance,
x="Year",
y="value",
color="variable",
barmode="group",
title="Yearly Revenue and Gross Profit",
labels={"value": "$"},
).update_yaxes(tickprefix="$"),
),
def create_layout(app):
# Page layouts
return html.Div(
[
html.Div([Header(app)]),
# page 1
html.Div(
[
# Row 3
html.Div(
[
html.Div(
[
html.H5("Executive Summary"),
html.Br([]),
html.P(
"\
Over the course of 2019, the Bakar Fitness Center's Indoor Pool was patronized at least 43,067 times over \
324 days; on average, the pool was patronized 133 times per day. \
Lifeguards, who are tasked with tallying pool attendance every 30 minutes on the \
15-minute mark, recorded collectively 5,581 tallies, totaling 167,430 minutes worth \
of data, or an average of 9 operational pool hours per day (out of an estimated maximum of \
12 hours on most days). While Lap Swim is the pool activity which saw the most cumulative \
annual patronage (accounting for nearly a third of all patrons), the busiest days occured during Family Swim \
programming on weekends. The busiest months were January and September respectively, a trend that can \
be reasonably correlated to the start of the University's academic calendar for the Fall & \
Winter Quarters. Included in this report are two analytical case studies: 1) a comparison of \
attendance between weekends with scheduled lessons and those with none, and 2) change in patronage \
during early morning Lap Swim programming on weekdays.",
style={"color": "#ffffff"},
className="row",
),
html.Hr([]),
html.P(
"\
Throughout the report, the term “patronage“ refers to \
the usage of the facility by undefined individuals. That is, we cannot assess whether these \
are unique or reoccuring patrons for any given time.",
),
html.P(
"\
Water Exercise includes tallies for Water Walking and Aqua Fit.",
),
],
className="product",
)
],
className="row",
),
# Row 4
html.Div(
[
html.Div(
[
html.H6(["Fast Facts"],
className="subtitle padded"),
html.Table(
make_dash_table(df_tally2019_facts)),
],
className="six columns",
),
html.Div(
[
html.H6(
"2019 Total Patronage per Activities",
className="subtitle padded",
),
html.Iframe(
src="//plotly.com/~HP-Nunes/243.embed",
width="350",
height="500"),
],
className="six columns",
),
],
className="row",
style={"margin-bottom": "10px"},
),
# Row 5
html.Div(
[
html.H5("Recommendations"),
html.Br([]),
html.P(
"\
The following are a set of recommendations in order to ensure \
that future tallying records improve in accuracy, fidelity, \
and completedness, with the intent to generate greater patronage insights:",
style={"color": "black"},
className="row",
),
html.Li(
"\
A Well-Defined and Consistent Data-Entry Protocol: Consistently digitizing \
the records on a regular basis (weekly preferrably) would allow to identify \
tallying errors or incoherences between programming and attendance. It would \
also decrease the risk of data-entry errors, or data loss (as shown with the \
four weeks' worth of missing tallies in the Fall).",
),
html.Li(
"\
Tally Annotations: Perhaps adding a section on the Tally Sheets to make note of \
'irregularities' either in scheduling or activity would allow for higher accuracy \
during data-entry.", ),
html.Li(
"\
Tallying Accountability: Although tallying is not the most important or \
pressing of tasks, lifeguards are nonetheless liable to tally and should be able \
to do so consistently. Thus, a tally completedness score per scheduling \
cycles would permit to evaluate overall staff performance on the matter, but also ensure that \
enough tallies are collected on a daily basis so that meaningful inferences about \
long-term trends can be made.", ),
html.Li(
"\
Normalizing Patronage to Unique Patrons: either through the use of selective sampling \
or a survey, normalize patronage down to unique patrons. This would allow to derive \
a supplementary and meaningful metric for analysis.",
),
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
return html.Div(
[
#header(app),
html.Div(
[
"证券研究报告"
],
className = "header",
),
html.Div(
[
html.Div(
[
html.Div(["公司研究/公告点评"], id = "title_part1",),
html.Div(["2020年06月07日"], id = "title_part2",),
],
id = "title_left",
),
html.Div(
[
html.Div(
[
html.Div(["交运设备/汽车整车 II"], id = "title_part3",),
],
id = "category",
),
],
id = "title_right",
),
],
className = "title",
),
html.Div(
[
html.Div(
[
html.Div(
[
html.Div(
[
"投资评级：买入（维持评级）"
],
className = "section_title",
),
html.Div(
[
html.Div(
[
html.P(["当前价格(元)："]),
html.P(["合理价格区间(元)： "]),
],
id = "subsect1_left",
),
html.Div(
[
html.P(["18.23"]),
html.P(["24.64~26.40"]),
],
id = "subsect1_right",
),
html.Div([], id = "border_bot"),
html.Div(
[
html.Div(["林志轩"], className = "staff_name"),
html.Div(["研究员"], className = "staff_position"),
html.Div(["刘千琳"], className = "staff_name"),
html.Div(["研究员"], className = "staff_position"),
html.Div(["王涛"], className = "staff_name"),
html.Div(["研究员"], className = "staff_position"),
html.Div(["邢重阳"], className = "staff_name"),
html.Div(["联系人"], className = "staff_position"),
],
id = "staff_info",
),
html.Div(
[
html.Div(
[
"执业证书编号：S0570519060005",
html.Br(),
"021-28972090 ",
html.Br(),
"*****@*****.**",
],
className = "contact_info",
),
html.Div(
[
"执业证书编号：S0570518060004",
html.Br(),
"021-28972076",
html.Br(),
"*****@*****.**",
],
className = "contact_info",
),
html.Div(
[
"执业证书编号：S0570519110001",
html.Br(),
"021-28972053",
html.Br(),
"*****@*****.**",
],
className = "contact_info",
),
html.Div(
[
"021-38476205 ",
html.Br(),
"*****@*****.**"
],
className = "contact_info",
),
],
id = "staff_contact",
),
],
id = "subsect1",
),
],
className = "block1",
),
html.Div(
[
html.Div(
[
"相关研究"
],
className = "section_title",
),
html.Div(
[
"1《上汽集团(600104 SH,买入): 19 盈利下滑 29%，龙头整装再出发》2020.01"
],
className = "study_details",
),
html.Div(
[
"2《上汽集团(600104 SH,买入): 产量同比提 升，批发销量跌幅收窄》2019.09"
],
className = "study_details",
),
html.Div(
[
"3《上汽集团(600104 SH,买入): Q2 终端折扣 大，业绩略低于预期》2019.08"
],
className = "study_details",
),
],
className = "block2",
),
html.Div(
[
html.Div(
[
"一年股价走势图"
],
className = "section_title",
),
html.Div(
[
html.Img(src=app.get_asset_url('stocks.png')),
],
className = "stocks_graph",
),
],
className = "block3",
),
],
className = "col_left",
),
html.Div(
[
html.Div(
[
"5月国内销量转正，上汽大众待改善"
],
className = "cr_title",
),
html.Div(
[
"上汽集团(600104)"
],
className = "cr_title2",
),
html.Div(
[
"5月国内销量增速转正，批发销量增速或弱于行业"
],
className = "para_title",
),
html.Div(
[
"6月5日，公司发布5月销量情况。5月公司实现批发销量47.3万台，同比-1.6%，其中国内销量45.6万台，同比+0.3%。根据中汽协预测，5月汽车 销量同比+11%，上汽销量增速弱于行业，主要原因是受海外疫情影响，出口销量大幅下滑，同时上汽大众销量下滑。我们认为随着疫情结束，汽车需 求有望逐步恢复，Q2 汽车行业批发销量有望转正。随着行业恢复和公司战 略调整进一步深入，公司销量和归母净利润有望逐季恢复，预计 20-22 年 EPS 分别为 1.76、1.98、2.17 元，维持“买入”评级。"
],
className = "para_body",
),
html.P(),
html.Div(
[
"上汽通用五菱销量改善明显，上汽通用、自主跌幅收窄"
],
className = "para_title",
),
html.Div(
[
"5 月，上汽大众批发销量 13 万台，同比-15%，上汽通用批发销量 13.6 万 台，同比-3.6%，上汽自主批发销量 5.2 万台，同比-5%，上汽通用五菱批 发销量 12 万台，同比+11%；上汽大通销量 1.6 万台，同比+61%；上汽红 岩销量 10109 台，同比+110%。上汽集团开展了“五五”购物节促销活动， 商用车销量增长迅速，国内批发销量转正。上汽通用五菱战略转型后推出 了较多新车型，销量情况有明显改善。上汽通用三缸机车型逐步转回四缸 机，销量跌幅收窄。上汽自主新车 RX5 Plus 新车上市，销量跌幅收窄。细 分市场竞争激烈，上汽大众销量仍有下滑。"
],
className = "para_body",
),
html.P(),
html.Div(
[
"上汽通用三缸机逐步转回四缸机，上汽自主 RX5 Plus 开启预售"
],
className = "para_title",
),
html.Div(
[
"2019 年上汽通用批发销量同比-19%，其中一个原因是三缸机车型不受国 内消费者欢迎，上汽通用英朗、威朗等 13 个车型有三缸机版本。从 2020 年 4 月开始，上汽通用逐步将三缸机车型转回四缸，英朗和科鲁泽已经转 为四缸车型。5 月 4 日，RX5 Plus 开启预售，该车型是上汽自主主力车型 rx5 中期改款，新车共推出三个版本，官方预售价 12.28~13.98 万元。我 们认为新车在前脸设计、内饰设计和智能网联配置上有较大改善，RX5 Plus 有望帮助上汽自主提升 10~15 万紧凑型 SUV 细分领域市占率，上汽自主 销量有望逐季改善。"
],
className = "para_body",
),
html.P(),
html.Div(
[
"销量和利润有望逐季改善，维持“买入”评级"
],
className = "para_title",
),
html.Div(
[
"上汽通用和上汽通用五菱销量情况正在逐步改善。2020 年 Q4 上汽大众 MEB 工厂有望投产，上汽大众有望在新能源汽车领域取得突破。2021 年 上汽奥迪投产在望，公司进一步布局豪华车领域，未来发展值得期待。我 们预计公司 2020-22 年分别实现归母净利 205、231、254 亿元，EPS 分 别为 1.76、1.98、2.17 元，同行业可比公司 20 年平均估值 16.8XPE， 考虑到公司业绩弹性略弱于可比公司，维持公司 20 年 14~15XPE 估值， 维持目标价 24.64~26.4 元，维持“买入”评级。 "
],
className = "para_body",
),
html.P(),
html.Div(
[
"风险提示：我国汽车销量增速不及预期，公司海外市场拓展不及预期。"
],
className = "para_body",
),
],
className = "col_right",
),
html.Div(
[
html.Div(
[
html.Div(
[
"公司基本资料"
],
className = "section_title",
),
html.Div(
[
html.Table(make_dash_table(stocks_asset)),
],
id = "table1",
),
],
id = "block4",
),
html.Div(
[
html.Div(
[
"经营预测指标与估值"
],
className = "section_title",
),
html.Table(make_dash_table(accounting)),
],
id = "block5",
),
],
className = "col_bot",
),
],
className = "body",
),
],
className = "page",
)
def create_layout(app):
# Page layouts
return html.Div(
[
html.Div([Header(app)]),
# page 1
html.Div(
[
# Row 3
html.Div(
[
html.Div(
[
html.
H5("Extractive Summary - Cyborg Supervision"
),
html.Br([]),
html.P(
"So – along the same lines pursued by law firms for example – one big win is the ability to produce structured data from a range of sources, the analysis of which traditionally required significant manual effort. For example, while ML models could alter banks’ trading and retail businesses – enabling them to make better decisions more quickly – the opacity, however, of these models may also make them more difficult for humans to understand. At the macroeconomic level, changes in technology, including AI, could, over time, profoundly affect the nature of the financial services consumed and may result inchanges to the structure of the financial services industry. A typical problem faced by supervisors, for example, is the ‘needle-in-a-haystack’ problem: if something is going wrong in a firm, it can be necessary to find out who in the firm made relevant decisions, based on what information, and why the checks and balances of the firm – the board, and second and third lines of defence – did not work. To achieve complex supervisory outcomes – which often require significant, multi-year remediation by firms – boards and senior management of firms have to understand the context and rationale for what we are trying to achieve, as well as what we would deem to be a successful outcome.",
style={"color": "#ffffff"},
className="row",
),
],
className="product",
)
],
className="row",
),
# Row 4
html.Div(
[
html.Div(
[
html.H6(["Bigrams & Trigrams"],
className="subtitle padded"),
html.Table(
make_dash_table(df_bi_tr_grams)),
],
className="six columns",
),
html.Div(
[
html.H6(
"Key words",
className="subtitle padded",
),
dcc.Graph(
id="graph-1",
figure={
"data": [
go.Bar(
x=[
11,
12,
12,
20,
27,
],
y=[
'supervisory',
'ML',
'machine',
'firms',
"data",
],
marker={
"color": "#97151c",
"line": {
"color":
"rgb(255, 255, 255)",
"width": 2,
},
},
name="Word frequency",
orientation='h',
)
],
"layout":
go.Layout(
autosize=False,
bargap=0.35,
font={
"family": "Raleway",
"size": 10
},
height=200,
hovermode="closest",
legend={
"orientation": "h",
"yanchor": "top",
},
margin={
"r": 0,
"t": 0,
"b": 10,
"l": 50,
},
showlegend=True,
title="",
width=330,
xaxis={
"showline": True,
"title": "",
"type": "linear",
},
yaxis={
"showgrid": True,
"showline": True,
"title": "",
"type": "category",
"zeroline": False,
},
),
},
config={"displayModeBar": False},
),
],
className="six columns",
),
],
className="row",
style={"margin-bottom": "35px"},
),
# Row 5
html.Div(
[
html.Div(
[
html.H6(
"Lexical Dispersion",
className="subtitle padded",
),
dcc.Graph(
id="graph-2",
figure={
"data": [
go.Scatter(
x=lexical_disp['position'],
y=lexical_disp['word'],
line={"color": "#97151c"},
mode="markers",
)
],
"layout":
go.Layout(
autosize=False,
title="",
font={
"family": "Raleway",
"size": 10
},
height=160,
width=340,
hovermode="closest",
margin={
"r": 20,
"t": 0,
"b": 20,
"l": 50,
},
xaxis={
"autorange": True,
"linecolor":
"rgb(0, 0, 0)",
"showgrid": False,
"showline": True,
"title": "Word Offset",
"type": "linear",
'automargin': True,
},
yaxis={
"autorange":
True,
"gridcolor":
"rgba(127, 127, 127, 0.2)",
"showgrid":
True,
"type":
"category",
'categoryorder':
'array',
'categoryarray': [
'supervisory', 'ML',
'machine', 'firms',
'data'
]
},
),
},
config={"displayModeBar": False},
),
],
className="six columns",
),
html.Div(
[
html.H6(
"Entity Recognition",
className="subtitle padded",
),
html.Table(make_dash_table(entity_rec)),
],
className="six columns",
)
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
# Page layouts
return html.Div(
[
html.Div([Header(app)]),
# page 1
html.Div(
[
# Row 3
html.Div(
[
html.Div(
[
html.H5("Kim's Corner"),
html.Br([]),
html.P(
"\
Lorem ipsum dolor sit amet, consectetur adipiscing elit, \
sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\
Facilisis sed odio morbi quis commodo odio. Ac turpis egestas integer\
eget aliquet nibh praesent tristique. Quam vulputate dignissim suspendisse \
in est ante in nibh mauris. Volutpat sed cras ornare arcu dui vivamus arcu.",
style={"color": "#ffffff"},
className="row",
),
],
className="product",
)
],
className="row",
),
# Row 3
html.Div(
[
html.Div(
[
html.H6(["Operations Highlights"],
className="subtitle"),
html.Br([]),
html.Div(
[
html.Div(
[
html.Br([]),
html.Strong(
[
"Employee Engagement Index"
],
style={
"color": "#515151"
},
)
],
className=
"three columns right-aligned",
),
html.Div(
[
html.Br([]),
html.Strong(
[
"Highlighting the results:"
],
style={
"color": "#515151"
},
),
html.P(
[
"EEI scores saw year over-year boosts from 62% to 69% in engagement and 53% to 77% in participation. \
Notable improvements: 13% in the belief that your job offers development opportunities, 12% in your \
manager communicates their expectations and, 10% in regularly receiving performance feedback."
],
style={
"color": "#7a7a7a"
},
),
html.Br([]),
html.Strong(
[
"Several areas we are focused on improving:"
],
style={
"color": "#515151"
},
),
html.P(
[
"\
• The rationale behind business decisions is clear to me (50%) \
"
],
style={
"color": "#7a7a7a"
},
),
html.P(
[
"\
• Lumen makes it easy for me to deliver the desired customer experience (59%) \
"
],
style={
"color": "#7a7a7a"
},
),
html.P(
[
"\
• My job offers development opportunities (56%) \
"
],
style={
"color": "#7a7a7a"
},
),
html.Br([]),
html.Strong(
["We're Listening:"],
style={
"color": "#515151"
},
),
html.P(
[
"\
My leadership team and I continue our commitment to you as we head towards 2021, \
seeking to identify opportunities and take action to make further improvements in \
all areas."
],
style={
"color": "#7a7a7a"
},
),
],
className="nine columns",
),
],
className="row",
style={
"background-color": "#f9f9f9",
"padding-bottom": "30px",
},
),
],
className="twelve columns",
)
],
style={"margin-bottom": "35px"},
className="row",
),
html.Div(
[
html.Div(
[
html.H6(["Happy Thanksgiving!"],
className="subtitle"),
html.Br([]),
html.Div(
[
html.Div(
[
html.Br([]),
html.Strong(
["From Our Team"],
style={
"color": "#515151"
},
)
],
className=
"three columns right-aligned",
),
html.Div(
[
html.Br([]),
html.Strong(
[
"If Lumen was a pie, then we are all the ingredients to making it Amazing!"
],
style={
"color": "#515151"
},
),
html.Br([]),
html.Br([]),
html.P(
[
"Thanksgiving is a time to remember that “now is no time to think of what you do not have. Think of what you can do with what there is.” – Ernest Hemingway"
],
style={
"color": "#7a7a7a"
},
),
html.Br([]),
html.Strong(
[
"'No Bake' Pumpkin Cheesecake"
],
style={
"color": "#515151"
},
),
html.P(
[
"We've included a recipe from one of our team members below....."
],
style={
"color": "#7a7a7a"
},
),
html.Br([]),
html.Img(
src=
'https://i.ibb.co/TBzt0Sm/Picture1.jpg'
)
],
className="nine columns",
),
],
className="row",
style={
"background-color": "#f9f9f9",
"padding-bottom": "30px",
},
),
],
className="twelve columns",
)
],
className="row",
),
# Row 4
html.Div(
[
html.Div(
[
html.H6(["Ingredients"],
className="subtitle padded"),
html.Table(make_dash_table(df_fund_facts)),
],
className="six columns",
),
html.Div(
[
html.H6(["Instructions"],
className="subtitle padded"),
html.Table(make_dash_table(df_price_perf)),
],
className="six columns",
),
],
className="row",
style={"margin-bottom": "35px"},
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
return html.Div(
[
Header(app),
# page 2
html.Div(
[
# Row
html.Div(
[
html.Div(
[
html.H6("Data Cleaning and Manipulation",
className="subtitle padded"),
# html.Br([]),
html.Div(
[
html.
P("I filtered the original dataset based on the following criteria:"
),
html.
Li("I only retained tallies for 2019 (January 1st - December 31st);"
),
html.
Li("I excluded days with noted pool closures on: January, Thursday 17th \
(from 5:45 am to 8:15 am); February, Sunday 10th to Thursday 14th (door-breaking incident), \
and May, Sunday 26th at 10:45 am; "
),
html.
Li("I dropped rows if no tallies were taken for any Aquatic Activity \
(i.e. Family Swim, Lap Swim, Water Excercise, and Aquatic Programs), i.e. if \
the row only had NULL for any tally. You can think of those as missing tallies;"
),
html.
Li("IMPORTANT: The tally sheets for the period spanning October 21st to November 24th \
were missing, which are equivalent to four weeks worth of tallies. Thus there is a \
significant gap in the dataset for the Fall period. "
),
html.
P("As a result, approximately 45 % of all rows were retained from the original raw \
dataset, with a count breakdown per Aquatic Activity summarized in the table below:"
),
html.Div(
[
html.Div(
[
html.Table(
make_dash_table(
SampleSizeoftheFiltered30minIncrementalDataset
),
className=
"tiny-header",
)
],
style={
"overflow-x":
"auto"
},
),
],
className="twelve columns",
),
html.Div([
html.Div([
html.
P("The filtered dataset represents a total of 167,430 minutes, or approximately \
over 116 days, for which tallies were recorded. There were 241 recorded instances \
of 0 swimmers, equivalent to 7,230 minutes or approximately 5 days. \
Do note however that, once the dataset is grouped by the day, these 167,430 minutes \
of collected tallies are spread over 314 days, roughly equivalent of 9 hours worth of \
collected tallies per day on average. For added context, if we assume on most weekdays \
that the pool is operational from 5:30 am to 9:30 pm, and closed from 12:00 pm to 4 pm, \
usual operational hours at the Indoor Pool should be amounting close to 12 hours. \
Holidays were defined by the Fitness Facility’s Holiday Schedules, transposed for 2019."
),
], ),
], ),
],
style={"color": "#7a7a7a"},
),
],
className="row",
),
html.Div([
html.Br([]),
html.H6(
"Data Table: Patronage per Day",
className="subtitle padded",
),
html.Iframe(
src="//plotly.com/~HP-Nunes/241.embed",
width="100%",
height="400")
]),
html.Div([
html.Br([]),
html.H6(
"Daily Patronage Timeseries",
className="subtitle padded",
),
html.Iframe(
src="//plotly.com/~HP-Nunes/250.embed",
width="100%",
height="600"),
]),
],
className="row ",
),
# Row 2
html.Div(
[
html.Div(
[
html.Br([]),
html.
H6("Descriptive Statistics of Daily Patronage",
className="subtitle padded"),
# html.Br([]),
html.Div(
[
html.
P("I dropped rows where the number of tallies grouped by the day where below \
two standard deviation from the mean. In other words, from a maximum of 32 tallies \
that can be taken per day, I dropped those which had fewer than 7 entries (given the \
average was close to 18 tallies/days)."
),
html.
P("I ended-up removing about 10 entries (or days) down to a total of 314 days. I deemed \
this to be a necessary step in order to remove tallies with–for the most part–deflated \
values, thereby filtering out outlier values from the dataset."
),
html.Br([]),
html.
P("Statistical Summary of Daily Patronage for 2019:"
),
html.Div(
[
html.Div(
[
html.Table(
make_dash_table(
StatisticalSummaryofDailyPatronageofBakarIndoorPoolfor2019
),
className=
"tiny-header",
)
],
style={
"overflow-x":
"auto"
},
),
],
className="twelve columns",
),
html.Div([
html.Div([
html.
P("The distribution of total daily patronage follows a normal distribution \
skewing to the right, with a daily average of 136 patrons. The right-skewing in \
the histogram can be explained by a many handful of outliers where attendance was \
very high (with a recorded maximum of 679 daily patrons)."
),
html.Img(
src=app.get_asset_url(
"boxplot1.png"),
className="plot",
),
html.
P("From the boxplot above, note that the median is equal to 106 daily patrons, a useful comparative statistic to the mean (shown as the green triangle), as the median is less suceptible to outlier values. That being said, since the distribution is approximately Gaussian (bell-curved), the mean should be approximate to the “real mean” of the population; i.e. the average daily patronage if we had 100% of all tallies recorded."
),
], ),
], ),
],
style={"color": "#7a7a7a"},
),
],
className="row",
),
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app, ExpID, projectname=projectname):
### Read experiment specific learnings
try:
aim = dfrunmaster[dfrunmaster.ExpID == ExpID]['Description'].values[0]
param = dfrunmaster[dfrunmaster.ExpID == ExpID]['Params'].values[0]
param = ast.literal_eval(param)
exppath = param['Artefacts']
dffeatobs = pd.read_csv(f"{exppath}/observations.csv")
if os.path.exists(f"{exppath}/importance.csv"):
imp = pd.read_csv(f"{exppath}/importance.csv")
imp.columns = [i.lower() for i in imp.columns]
imp = imp.groupby('feature',
as_index=False).agg({'importance': 'sum'})
imp['importance'] = imp['importance'] / sum(imp['importance'])
topfeatures = imp.sort_values(by='importance',
ascending=False).head(10)
topfeatures.sort_values(by='importance',
ascending=True,
inplace=True)
except:
return html.Div()
return html.Div(
[
html.Div([Header(app, projectname)]),
html.Div(
[
html.Div(
[
html.Div(
[
html.H6(f"Experiment{ExpID} : {aim}"),
],
className="product",
)
],
className="row",
),
html.Div(
[
html.Div(
[
html.H6(
f"Top 10 Features",
className="subtitle padded",
),
create_feature_imp_plot(
topfeatures, "graph-4",
topfeatures['importance'],
"<b>Importance : %{x:.02f}")
],
className="seven columns",
),
html.Div(
[
html.H6(
"Observations from Features",
className="subtitle padded",
),
html.Table(make_dash_table(dffeatobs)),
],
className="five columns",
),
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
return html.Div(
[
Header(app),
# page 5
html.Div(
[
# Row 1
html.Div(
[
html.Div(
[
html.H6(["DATASE CAD/USD"],
className="subtitle padded"),
html.P(
[
"Datasate available in Yahoo FInance"
],
style={"color": "#7a7a7a"},
),
],
className="twelve columns",
)
],
className="row ",
),
# Row 2
html.Div(
[
html.Div(
[
html.Br([]),
html.H6(
["Date,value, Open"],
className="subtitle tiny-header padded",
),
html.Div(
[
html.Table(
make_dash_table(df_graph),
className="tiny-header",
)
],
style={
"overflow-x": "auto",
'height': '350px',
'overflow': 'scroll',
},
),
],
className="twelve columns",
)
],
className="row ",
),
# Row 3
html.Div([html.H6(["Data saved"])])
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
# Page layouts
return html.Div(
[
html.Div([Header(app)]),
# page 1
html.Div(
[
# Row 1 - Texto
html.Div(
[
html.Div(
[
html.H5("Resumo do App"),
html.Br([]),
html.P(
"\
Por meio dos dados abertos é disponibilizado pelo IBAMA uma base de registros de comunicação de acidentes ambientais. Aqui temos uma análise desses dados afim de encontrar informações relevantes e insights importantes sobre acidentes ambientais no Brasil.",
style={"color": "#ffffff"},
className="row",
),
],
className="product",
)
],
className="row",
),
# Row 2 - Resumo e Tipos Eventos
html.Div(
[
html.Div(
[
html.H6(
["Resumo dos Acidentes"], className="subtitle padded"
),
html.Table(make_dash_table(df_resumo)),
html.P(
[
"Um resumo geral de algumas informações que chamam atenção e podem ser relavantes. Podemos identificar o periodo dos registros, o total de registros, o estado com maior número de acidentes dentre outras informações."
],
style={"color": "#7a7a7a"},
),
],
className="six columns",
),
html.Div(
[
html.H6(
"Top 5 Tipos de Eventos",
className="subtitle padded",
),
dcc.Graph(
id="graph-1",
figure={
"data": [
go.Bar(
y=df_tipo_evento['Quantidade'],
x=df_tipo_evento['Tipo Evento'],
marker={
"color": "#97151c",
"line": {
"color": "rgb(255, 255, 255)",
"width": 2,
},
},
name="Eventos",
),
],
"layout": go.Layout(
autosize=False,
bargap=0.4,
font={"family": "Raleway", "size": 10},
height=200,
hovermode="closest",
legend={
"x": -0.0228945952895,
"y": -0.189563896463,
"orientation": "h",
"yanchor": "top",
},
margin={
"r": 0,
"t": 20,
"b": 10,
"l": 10,
},
showlegend=True,
title="",
width=330,
xaxis={
"autorange": True,
#"range": [-0.5, 2000],
"showline": True,
"title": "",
"type": "category",
},
yaxis={
"autorange": False,
"gridcolor": "rgba(127, 127, 127, 0.2)",
"mirror": False,
"nticks": 4,
"range": [0, tipo_evento_max],
"showgrid": True,
"showline": True,
"ticklen": 10,
"ticks": "outside",
"title": "",
"type": "linear",
"zeroline": False,
"zerolinewidth": 4,
},
),
},
config={"displayModeBar": False},
),
html.P(
[
"Informações de quais são os tipos de acidentes mais comuns. Aqui já podemos ver que o tipo mais comum, não podemos identificar e ele corresponde a mais de 50% dos acidentes."
],
style={"color": "#7a7a7a"},
),
],
className="six columns",
),
],
className="row",
style={"margin-bottom": "35px"},
),
# Row 3 - Instituições e Origens
html.Div(
[
html.Div(
[
html.H6(
"Top 5 Origens Acidentes",
className="subtitle padded",
),
dcc.Graph(
id="graph-1",
figure={
"data": [
go.Bar(
x=df_origem['Origem'],
y=df_origem['Quantidade'],
marker={
"color": "#97151c",
"line": {
"color": "rgb(255, 255, 255)",
"width": 2,
},
},
name="Origens",
),
],
"layout": go.Layout(
autosize=False,
bargap=0.4,
font={"family": "Raleway", "size": 10},
height=200,
hovermode="closest",
legend={
"x": -0.0228945952895,
"y": -0.189563896463,
"orientation": "h",
"yanchor": "top",
},
margin={
"r": 0,
"t": 20,
"b": 10,
"l": 10,
},
showlegend=True,
title="",
width=330,
xaxis={
"autorange": True,
#"range": [-0.5, 2000],
"showline": True,
"title": "",
"type": "category",
},
yaxis={
"autorange": False,
"gridcolor": "rgba(127, 127, 127, 0.2)",
"mirror": False,
"nticks": 4,
"range": [0, origem_max],
"showgrid": True,
"showline": True,
"ticklen": 10,
"ticks": "outside",
"title": "",
"type": "linear",
"zeroline": False,
"zerolinewidth": 4,
},
),
},
config={"displayModeBar": False},
),
html.P(
[
"Na origem dos acidentes, o notável é que são as rodovias a mais comum das origens, revelando outros problemas do Brasil. Problemas de educação no trânsito, problemas nas estradas que são problemas não só para acidentes ambientais."
],
style={"color": "#7a7a7a"},
),
],
className="six columns",
),
html.Div(
[
html.H6(
["Porcentagem Atuação das Principais Instituições"], className="subtitle padded"
),
html.Table(make_dash_table(df_institiuicoes_atuando)),
html.P(
[
"Nas instituições fica um pensamento, por que em mais de 50% dos acidentes ambientais, não tem atuação de orgão oficiais do governo? Logo pensamos, será que a maioria dos acidentes ambientais os culpados não são punidos?"
],
style={"color": "#7a7a7a"},
),
],
className="six columns",
),
],
className="row",
style={"margin-bottom": "35px"},
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
return html.Div(
[
Header(app),
# page 2
html.Div(
[
# Row
html.Div(
[
html.Div(
[
html.H6(
["Precios actuales"], className="subtitle padded"
),
html.Table(make_dash_table(df_current_prices)),
],
className="six columns",
),
html.Div(
[
html.H6(
["Precios históricos"],
className="subtitle padded",
),
html.Table(make_dash_table(df_hist_prices)),
],
className="six columns",
),
],
className="row ",
),
# Row 2
html.Div(
[
html.Div(
[
html.H6("Comportamiento", className="subtitle padded"),
dcc.Graph(
id="graph-4",
figure={
"data": [
go.Scatter(
x=df_graph["Date"],
y=df_graph["Calibre Index Fund"],
line={"color": "#97151c"},
mode="lines",
name="Calibre Index Fund",
),
go.Scatter(
x=df_graph["Date"],
y=df_graph[
"MSCI EAFE Index Fund (ETF)"
],
line={"color": "#b5b5b5"},
mode="lines",
name="MSCI EAFE Index Fund (ETF)",
),
],
"layout": go.Layout(
autosize=True,
width=700,
height=200,
font={"family": "Raleway", "size": 10},
margin={
"r": 30,
"t": 30,
"b": 30,
"l": 30,
},
showlegend=True,
titlefont={
"family": "Raleway",
"size": 10,
},
xaxis={
"autorange": True,
"range": [
"2007-12-31",
"2018-03-06",
],
"rangeselector": {
"buttons": [
{
"count": 1,
"label": "1Y",
"step": "year",
"stepmode": "backward",
},
{
"count": 3,
"label": "3Y",
"step": "year",
"stepmode": "backward",
},
{
"count": 5,
"label": "5Y",
"step": "year",
},
{
"count": 10,
"label": "10Y",
"step": "year",
"stepmode": "backward",
},
{
"label": "All",
"step": "all",
},
]
},
"showline": True,
"type": "date",
"zeroline": False,
},
yaxis={
"autorange": True,
"range": [
18.6880162434,
278.431996757,
],
"showline": True,
"type": "linear",
"zeroline": False,
},
),
},
config={"displayModeBar": False},
),
],
className="twelve columns",
)
],
className="row ",
),
# Row 3
html.Div(
[
html.Div(
[
html.H6(
[
"Retorno promedio anual--actualizado mensualmente al 02/28/2018"
],
className="subtitle padded",
),
html.Div(
[
html.Table(
make_dash_table(df_avg_returns),
className="tiny-header",
)
],
style={"overflow-x": "auto"},
),
],
className="twelve columns",
)
],
className="row ",
),
# Row 4
html.Div(
[
html.Div(
[
html.H6(
[
"Retorno después de impuestos--actualizado trimestral al 12/31/2017"
],
className="subtitle padded",
),
html.Div(
[
html.Table(
make_dash_table(df_after_tax),
className="tiny-header",
)
],
style={"overflow-x": "auto"},
),
],
className=" twelve columns",
)
],
className="row ",
),
# Row 5
html.Div(
[
html.Div(
[
html.H6(
["Retorno de inversiones recientes"],
className="subtitle padded",
),
html.Table(
make_dash_table(df_recent_returns),
className="tiny-header",
),
],
className=" twelve columns",
)
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
fig_battery = []
fig_location = []
fig_device_trans = []
df = pd.read_csv(csv_files[0])
df_fund_facts = pd.DataFrame()
df_fund_facts["label"] = df.columns
df_fund_facts["value"] = df.iloc[1, :].values.tolist()
# fig = get_dataframe_for_plotting_transmission()
selected_device_trans = [0, 1, 2]
for each_fig in selected_device_trans:
df_date = get_dataframe_for_plotting_transmission(each_fig)
device1 = plot_function_bar(df_date, each_fig)
fig_device_trans.append(device1)
selected_battery = [0]
for each_fig in selected_battery:
df_date = get_dataframe_for_plotting_transmission(each_fig)
device1 = plot_function(df_date)
# fig_battery = device1
fig_battery.append(device1)
selected_location = [0]
# for each_fig in selected_location:
fig_location = get_figure(selected_location)
data_location = fig_location['data']
return html.Div(
[
html.Div([Header(app)]),
# page 1
html.Div(
[
# Row 3
html.Div(
[
html.Div(
[
html.H5("Product Summary"),
html.Br([]),
html.P(
"Wildly Listen Product Specifications and Operations in detail",
style={"color": "#ffffff"},
className="row",
),
],
className="product",
)
],
className="row",
),
# Row 4
html.Div(
[
html.Div(
[
html.H6(["Device Details"],
className="subtitle padded"),
html.Table(make_dash_table(df_fund_facts)),
],
className="six columns",
),
html.Div(
[
html.H6(
"Transmission Performance",
className="subtitle padded",
),
dcc.Graph(
id="graph-1",
figure={
"data":
fig_device_trans,
"layout":
dict(
autosize=False,
bargap=0.4,
font={
"family":
"Courier New, monospace",
"size": 10
},
height=500,
hovermode="closest",
legend={
"x": -0.0228945952895,
"y": -0.189563896463,
"orientation": "h",
"yanchor": "top",
},
margin={
"r": 10,
"t": 40,
# "b": 35,
# "l": 45,
},
showlegend=False,
# title="Transmission",
width=350,
xaxis={
'title': 'Device ID',
'titlefont': {
'family':
'Courier New, monospace',
'size': 16,
'color': 'black'
}
},
yaxis={
'title': 'No. Files',
'titlefont': {
'family':
'Courier New, monospace',
'size': 16,
'color': 'black'
}
})
},
config={"displayModeBar": False},
),
],
className="six columns",
),
],
className="row",
style={"margin-bottom": "35px"},
),
# Row 5
html.Div(
[
html.Div(
[
html.H6(
"Battery Performance",
className="subtitle padded",
),
dcc.Graph(
id="graph-2",
figure={
"data":
fig_battery,
"layout":
go.Layout(
autosize=True,
font={
"family":
'Courier New, monospace',
"size": 8
},
height=400,
width=350,
hovermode="closest",
legend={
"x": -0.0277108433735,
"y": -0.142606516291,
"orientation": "h",
},
margin={
"r": 0,
"t": 0,
"b": 35,
"l": 45,
},
showlegend=False,
xaxis={
'title': 'Time (Seconds) ',
'titlefont': {
'family':
'Courier New, monospace',
'size': 16,
'color': 'black'
}
},
yaxis={
'title':
'Battey Level (Percentage) ',
'titlefont': {
'family':
'Courier New, monospace',
'size': 16,
'color': 'black'
}
},
),
},
config={"displayModeBar": False},
style={"margin-bottom": "10px"}),
],
className="six columns",
),
html.Div(
[
html.H6(
"Location",
className="subtitle padded",
),
dcc.Graph(
id="graph-3",
figure={
"data": data_location,
"layout": fig_location["layout"]
},
config={"displayModeBar": False},
),
],
style={
"margin-right": "-20%",
"margin-bottom": "10px"
},
className="six columns",
),
html.Div(
[
html.H6("Device Health Status",
className="subtitle padded"),
html.Img(
src=app.get_asset_url(
"risk_reward.png"),
className="risk-reward",
),
],
className="six columns",
),
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
# Page layouts
return html.Div(
[
html.Div([Header(app)]),
# page 1
html.Div(
[
# Row 3
html.Div(
[
html.Div(
[
html.H6(["Quick Facts"],
className="subtitle padded"),
html.Table(make_dash_table(df_waste)),
],
className="six columns",
),
html.Div(
[
html.H6(
"Bar Graph",
className="subtitle padded",
),
dcc.Graph(
id="graph-1",
figure={
"data": [
go.Bar(
x=[
"Factor 1",
"Factor 2",
"Factor 3",
"Factor 4",
"Factor 5",
],
y=[
"21.67",
"11.26",
"15.62",
"8.37",
"11.11",
],
marker={
"color": "#141E3C",
"line": {
"color":
"rgb(255, 255, 255)",
"width": 2,
},
},
name="Group 1",
),
go.Bar(
x=[
"Factor 1",
"Factor 2",
"Factor 3",
"Factor 4",
"Factor 5",
],
y=[
"21.83",
"11.41",
"15.79",
"8.50",
],
marker={
"color": "#dddddd",
"line": {
"color":
"rgb(255, 255, 255)",
"width": 2,
},
},
name="Group 2",
),
],
"layout":
go.Layout(
autosize=False,
bargap=0.35,
font={
"family": "Raleway",
"size": 10
},
height=200,
hovermode="closest",
legend={
"x": -0.0228945952895,
"y": -0.189563896463,
"orientation": "h",
"yanchor": "top",
},
margin={
"r": 0,
"t": 20,
"b": 10,
"l": 10,
},
showlegend=True,
title="",
width=330,
xaxis={
"autorange": True,
"range": [-0.5, 4.5],
"showline": True,
"title": "",
"type": "category",
},
yaxis={
"autorange": True,
"range":
[0, 22.9789473684],
"showgrid": True,
"showline": True,
"title": "",
"type": "linear",
"zeroline": False,
},
),
},
config={"displayModeBar": False},
),
],
className="six columns",
),
],
className="row",
style={"margin-bottom": "35px"},
),
# Row 5
html.Div(
[
html.Div(
[
html.H6(
"Line Graph",
className="subtitle padded",
),
dcc.Graph(
id="graph-2",
figure={
"data": [
go.Scatter(
x=[
"2008",
"2009",
"2010",
"2011",
"2012",
"2013",
"2014",
"2015",
"2016",
"2017",
"2018",
],
y=[
"10000",
"7500",
"9000",
"10000",
"10500",
"11000",
"14000",
"18000",
"19000",
"20500",
"24000",
],
line={"color": "#E57200"},
mode="lines",
name="Factor 1",
)
],
"layout":
go.Layout(
autosize=True,
title="",
font={
"family": "Raleway",
"size": 10
},
height=200,
width=340,
hovermode="closest",
legend={
"x": -0.0277108433735,
"y": -0.142606516291,
"orientation": "h",
},
margin={
"r": 20,
"t": 20,
"b": 20,
"l": 50,
},
showlegend=True,
xaxis={
"autorange": True,
"linecolor":
"rgb(0, 0, 0)",
"linewidth": 1,
"range": [2008, 2018],
"showgrid": False,
"showline": True,
"title": "",
"type": "linear",
},
yaxis={
"autorange": False,
"gridcolor":
"rgba(127, 127, 127, 0.2)",
"mirror": False,
"nticks": 4,
"range": [0, 30000],
"showgrid": True,
"showline": True,
"ticklen": 10,
"ticks": "outside",
"title": "$",
"type": "linear",
"zeroline": False,
"zerolinewidth": 4,
},
),
},
config={"displayModeBar": False},
),
],
className="six columns",
),
html.Div(
[
html.H6("Image",
className="subtitle padded"),
html.Img(
src=app.get_asset_url(
"landfill_waste.jpeg"),
className="diagram",
),
],
className="six columns",
),
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
return html.Div(
[
Header(app),
# page 3
html.Div(
[
# Row 1
html.Div(
[
html.Div(
[html.H6(["Portfolio"], className="subtitle padded")],
className="twelve columns",
)
],
className="rows",
),
# Row 2
html.Div(
[
html.Div(
[
html.P(["Stock style"], style={"color": "#7a7a7a"}),
dcc.Graph(
id="graph-5",
figure={
"data": [
go.Scatter(
x=["1"],
y=["1"],
hoverinfo="none",
marker={"opacity": 0},
mode="markers",
name="B",
)
],
"layout": go.Layout(
title="",
annotations=[
{
"x": 0.990130093458,
"y": 1.00181709504,
"align": "left",
"font": {
"family": "Raleway, sans-serif",
"size": 7,
"color": "#7a7a7a",
},
"showarrow": False,
"text": "<b>Market<br>Cap</b>",
"xref": "x",
"yref": "y",
},
{
"x": 1.00001816013,
"y": 1.35907755794e-16,
"font": {
"family": "Raleway, sans-serif",
"size": 7,
"color": "#7a7a7a",
},
"showarrow": False,
"text": "<b>Style</b>",
"xref": "x",
"yanchor": "top",
"yref": "y",
},
],
autosize=False,
width=200,
height=150,
hovermode="closest",
margin={
"r": 30,
"t": 20,
"b": 20,
"l": 30,
},
shapes=[
{
"fillcolor": "#f9f9f9",
"line": {
"color": "#ffffff",
"width": 0,
},
"type": "rect",
"x0": 0,
"x1": 0.33,
"xref": "paper",
"y0": 0,
"y1": 0.33,
"yref": "paper",
},
{
"fillcolor": "#f2f2f2",
"line": {
"color": "#ffffff",
"dash": "solid",
"width": 0,
},
"type": "rect",
"x0": 0.33,
"x1": 0.66,
"xref": "paper",
"y0": 0,
"y1": 0.33,
"yref": "paper",
},
{
"fillcolor": "#f9f9f9",
"line": {
"color": "#ffffff",
"width": 0,
},
"type": "rect",
"x0": 0.66,
"x1": 0.99,
"xref": "paper",
"y0": 0,
"y1": 0.33,
"yref": "paper",
},
{
"fillcolor": "#f2f2f2",
"line": {
"color": "#ffffff",
"width": 0,
},
"type": "rect",
"x0": 0,
"x1": 0.33,
"xref": "paper",
"y0": 0.33,
"y1": 0.66,
"yref": "paper",
},
{
"fillcolor": "#f9f9f9",
"line": {
"color": "#ffffff",
"width": 0,
},
"type": "rect",
"x0": 0.33,
"x1": 0.66,
"xref": "paper",
"y0": 0.33,
"y1": 0.66,
"yref": "paper",
},
{
"fillcolor": "#f2f2f2",
"line": {
"color": "#ffffff",
"width": 0,
},
"type": "rect",
"x0": 0.66,
"x1": 0.99,
"xref": "paper",
"y0": 0.33,
"y1": 0.66,
"yref": "paper",
},
{
"fillcolor": "#f9f9f9",
"line": {
"color": "#ffffff",
"width": 0,
},
"type": "rect",
"x0": 0,
"x1": 0.33,
"xref": "paper",
"y0": 0.66,
"y1": 0.99,
"yref": "paper",
},
{
"fillcolor": " #97151c",
"line": {
"color": "#ffffff",
"width": 0,
},
"type": "rect",
"x0": 0.33,
"x1": 0.66,
"xref": "paper",
"y0": 0.66,
"y1": 0.99,
"yref": "paper",
},
{
"fillcolor": "#f9f9f9",
"line": {
"color": "#ffffff",
"width": 0,
},
"type": "rect",
"x0": 0.66,
"x1": 0.99,
"xref": "paper",
"y0": 0.66,
"y1": 0.99,
"yref": "paper",
},
],
xaxis={
"autorange": True,
"range": [
0.989694747864,
1.00064057995,
],
"showgrid": False,
"showline": False,
"showticklabels": False,
"title": "<br>",
"type": "linear",
"zeroline": False,
},
yaxis={
"autorange": True,
"range": [
-0.0358637178721,
1.06395696354,
],
"showgrid": False,
"showline": False,
"showticklabels": False,
"title": "<br>",
"type": "linear",
"zeroline": False,
},
),
},
config={"displayModeBar": False},
),
],
className="four columns",
),
html.Div(
[
html.P(
"Duo Esse Index Fund seeks to track the performance of\
a benchmark index that measures the investment return of large-capitalization stocks."
),
html.P(
"Learn more about this portfolio's investment strategy and policy."
),
],
className="eight columns middle-aligned",
style={"color": "#696969"},
),
],
className="row ",
),
# Row 3
html.Br([]),
html.Div(
[
html.Div(
[
html.H6(
["Equity characteristics as of 01/31/2018"],
className="subtitle padded",
),
html.Table(
make_dash_table(df_equity_char),
className="tiny-header",
),
],
className=" twelve columns",
)
],
className="row ",
),
# Row 4
html.Div(
[
html.Div(
[
html.H6(
["Equity sector diversification"],
className="subtitle padded",
),
html.Table(
make_dash_table(df_equity_diver),
className="tiny-header",
),
],
className=" twelve columns",
)
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
return html.Div(
[
Header(app),
# page 2
html.Div(
[
# Row
html.Div(
[
html.Div(
[
html.H6(
["Current Prices"], className="subtitle padded"
),
html.Table(make_dash_table(df_current_prices)),
],
className="six columns",
),
html.Div(
[
html.H6(
["Historical Prices"],
className="subtitle padded",
),
html.Table(make_dash_table(df_hist_prices)),
],
className="six columns",
),
],
className="row ",
),
# Row 2
html.Div(
[
html.Div(
[
html.H6("Performance", className="subtitle padded"),
dcc.Graph(
id="graph-4",
figure={
"data": [
go.Scatter(
x=df_graph["Date"],
y=df_graph["Duo Esse Index Fund"],
line={"color": "#97151c"},
mode="lines",
name="Duo Esse Index Fund",
),
go.Scatter(
x=df_graph["Date"],
y=df_graph[
"MSCI EAFE Index Fund (ETF)"
],
line={"color": "#b5b5b5"},
mode="lines",
name="MSCI EAFE Index Fund (ETF)",
),
],
"layout": go.Layout(
autosize=True,
width=700,
height=200,
font={"family": "Raleway", "size": 10},
margin={
"r": 30,
"t": 30,
"b": 30,
"l": 30,
},
showlegend=True,
titlefont={
"family": "Raleway",
"size": 10,
},
xaxis={
"autorange": True,
"range": [
"2007-12-31",
"2018-03-06",
],
"rangeselector": {
"buttons": [
{
"count": 1,
"label": "1Y",
"step": "year",
"stepmode": "backward",
},
{
"count": 3,
"label": "3Y",
"step": "year",
"stepmode": "backward",
},
{
"count": 5,
"label": "5Y",
"step": "year",
},
{
"count": 10,
"label": "10Y",
"step": "year",
"stepmode": "backward",
},
{
"label": "All",
"step": "all",
},
]
},
"showline": True,
"type": "date",
"zeroline": False,
},
yaxis={
"autorange": True,
"range": [
18.6880162434,
278.431996757,
],
"showline": True,
"type": "linear",
"zeroline": False,
},
),
},
config={"displayModeBar": False},
),
],
className="twelve columns",
)
],
className="row ",
),
# Row 3
html.Div(
[
html.Div(
[
html.H6(
[
"Average annual returns--updated monthly as of 11/30/2019"
],
className="subtitle padded",
),
html.Div(
[
html.Table(
make_dash_table(df_avg_returns),
className="tiny-header",
)
],
style={"overflow-x": "auto"},
),
],
className="twelve columns",
)
],
className="row ",
),
# Row 4
html.Div(
[
html.Div(
[
html.H6(
[
"After-tax returns--updated quarterly as of 12/31/2018"
],
className="subtitle padded",
),
html.Div(
[
html.Table(
make_dash_table(df_after_tax),
className="tiny-header",
)
],
style={"overflow-x": "auto"},
),
],
className=" twelve columns",
)
],
className="row ",
),
# Row 5
html.Div(
[
html.Div(
[
html.H6(
["Recent investment returns"],
className="subtitle padded",
),
html.Table(
make_dash_table(df_recent_returns),
className="tiny-header",
),
],
className=" twelve columns",
)
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
])
],
className="twelve columns",
)
],
className="row ",
),
html.Div(
[
html.Div(
[
html.H6(
"Confusion Matrix",
className="subtitle padded",
),
html.Table(make_dash_table(confusion)),
],
className="six columns",
),
html.Div(
[
html.H6(
"Classification Report",
className="subtitle padded",
),
html.Table(make_dash_table(class_rep)),
],
className="six columns",
),
],
className="row ",
def create_layout(app):
return html.Div(
[
html.Div([Header(app)]),
html.Div(
[
html.Div([
html.Div(
[
html.
H5("Curah Hujan dan Penderita Demam Berdarah"),
html.Br([]),
html.P(
"\
Sebagai wabah endemik di Indonesia, demam berdarah merupakan salah satu penyakit \
yang kasus penyebarannya selalu terjadi setiap tahun. Demam berdarah ditularkan \
lewat virus dengue dengan perantara nyamuk Aedes Aegypti. Penyakit ini sangat \
berbahaya apabila terlambat ditangani. Di Kabupaten Banyumas sendiri, pernah \
terjadi kasus demam berdarah yang sangat banyak dalam setahun sehingga ditetapkan \
sebagai Kejadian Luar Biasa. Melihat betapa berbahayanya demam berdarah, tentunya \
diperlukan tindakan antisipasi dan pencegahan. Salah satu cara antisipasi demam \
berdarah adalah dengan melakukan prediksi. Berdasarkan penelitian sebelumnya, \
faktor yang dianggap berpengaruh terhadap penyebaran demam berdarah adalah cuaca. \
Dengan demikian, dapat diasumsikan dengan melakukan prediksi terhadap curah hujan, \
jumlah penderita demam berdarah dapat diprediksi juga.",
style={"color": "#ffffff"},
className="row",
),
],
className="product",
),
],
className="row"), # Tambah row di sini
html.Div([
html.Div([
html.H6([
"Tabel Keterangan Data Mentah Curah Hujan Tahun 2010-2019"
],
className="subtitle padded"),
html.Table(make_dash_table(data_RR)),
],
className="six columns"),
html.Div([
html.H6("Deskripsi Data Curah Hujan",
className="subtitle padded"),
html.Br([]),
html.Div(
[
html.
Li("Data dan pengetahuan data didapatkan dari BMKG secara online dan langsung ke kantor Cilacap."
),
html.
Li("Data berkisar dari tahun 2010 hingga pertengahan tahun 2019."
),
html.
Li("Data curah hujan berjumlah 2993 record dari 2994 data."
),
html.
Li("Rata-rata curah hujan dari tahun 2010 hingga pertengahan 2019 adalah 12.668625250040412."
),
html.
Li("Curah hujan terendah adalah 0, dan yang tertinggi adalah 199,5."
),
html.
Li("Curah hujan diprediksi dan dicari keterkaitannya dengan penyebaran demam berdarah."
),
],
id="reviews-bullet-pts",
),
],
className="six columns"),
],
className="row"),
html.Div([
html.H6("Tabel Data Cuaca",
className="subtitle padded"),
tabel1,
],
className="row"),
html.Div([
html.H6("Tabel Keterangan Curah Hujan",
className="subtitle padded"),
html.Table(make_dash_table(df_hujan)),
],
className="row"),
html.Div([
html.H6("Grafik Curah Hujan",
className="subtitle padded"),
grafik1,
],
className="row"),
],
className="sub_page"),
],
className="page",
)
def create_layout(app):
# Page layouts
return html.Div(
[
html.Div([Header(app)]),
# page 1
html.Div(
[
# Row 3
html.Div(
[
html.Div(
[
html.
H5("Compra de pasajes nacionales e internacionales con precios sobre el doble del precio promedio del mercado"
),
html.Br([]),
html.P(
"\
As the industry’s first index fund for individual investors, \
the Calibre Index Fund is a low-cost way to gain diversified exposure \
to the U.S. equity market. The fund offers exposure to 500 of the \
largest U.S. companies, which span many different industries and \
account for about three-fourths of the U.S. stock market’s value. \
The key risk for the fund is the volatility that comes with its full \
exposure to the stock market. Because the Calibre Index Fund is broadly \
diversified within the large-capitalization market, it may be \
considered a core equity holding in a portfolio.",
style={"color": "#ffffff"},
className="row",
),
],
className="product",
)
],
className="row",
),
# Row 4
html.Div(
[
html.Div(
[
html.H6(["Hechos fundamentales"],
className="subtitle padded"),
html.Table(make_dash_table(df_fund_facts)),
],
className="six columns",
),
html.Div(
[
html.H6(
"Comportamiento anual promedio",
className="subtitle padded",
),
dcc.Graph(
id="graph-1",
figure={
"data": [
go.Bar(
x=[
"1 Year",
"3 Year",
"5 Year",
"10 Year",
"41 Year",
],
y=[
"21.67",
"11.26",
"15.62",
"8.37",
"11.11",
],
marker={
"color": "#97151c",
"line": {
"color":
"rgb(255, 255, 255)",
"width": 2,
},
},
name="Calibre Index Fund",
),
go.Bar(
x=[
"1 Year",
"3 Year",
"5 Year",
"10 Year",
"41 Year",
],
y=[
"21.83",
"11.41",
"15.79",
"8.50",
],
marker={
"color": "#dddddd",
"line": {
"color":
"rgb(255, 255, 255)",
"width": 2,
},
},
name="S&P 500 Index",
),
],
"layout":
go.Layout(
autosize=False,
bargap=0.35,
font={
"family": "Raleway",
"size": 10
},
height=200,
hovermode="closest",
legend={
"x": -0.0228945952895,
"y": -0.189563896463,
"orientation": "h",
"yanchor": "top",
},
margin={
"r": 0,
"t": 20,
"b": 10,
"l": 10,
},
showlegend=True,
title="",
width=330,
xaxis={
"autorange": True,
"range": [-0.5, 4.5],
"showline": True,
"title": "",
"type": "category",
},
yaxis={
"autorange": True,
"range":
[0, 22.9789473684],
"showgrid": True,
"showline": True,
"title": "",
"type": "linear",
"zeroline": False,
},
),
},
config={"displayModeBar": False},
),
],
className="six columns",
),
],
className="row",
style={"margin-bottom": "35px"},
),
# Row 5
html.Div(
[
html.Div(
[
html.H6(
"Crecimiento hipotético de $10,000",
className="subtitle padded",
),
dcc.Graph(
id="graph-2",
figure={
"data": [
go.Scatter(
x=[
"2008",
"2009",
"2010",
"2011",
"2012",
"2013",
"2014",
"2015",
"2016",
"2017",
"2018",
],
y=[
"10000",
"7500",
"9000",
"10000",
"10500",
"11000",
"14000",
"18000",
"19000",
"20500",
"24000",
],
line={"color": "#97151c"},
mode="lines",
name=
"Calibre Index Fund Inv",
)
],
"layout":
go.Layout(
autosize=True,
title="",
font={
"family": "Raleway",
"size": 10
},
height=200,
width=340,
hovermode="closest",
legend={
"x": -0.0277108433735,
"y": -0.142606516291,
"orientation": "h",
},
margin={
"r": 20,
"t": 20,
"b": 20,
"l": 50,
},
showlegend=True,
xaxis={
"autorange": True,
"linecolor":
"rgb(0, 0, 0)",
"linewidth": 1,
"range": [2008, 2018],
"showgrid": False,
"showline": True,
"title": "",
"type": "linear",
},
yaxis={
"autorange": False,
"gridcolor":
"rgba(127, 127, 127, 0.2)",
"mirror": False,
"nticks": 4,
"range": [0, 30000],
"showgrid": True,
"showline": True,
"ticklen": 10,
"ticks": "outside",
"title": "$",
"type": "linear",
"zeroline": False,
"zerolinewidth": 4,
},
),
},
config={"displayModeBar": False},
),
],
className="six columns",
),
html.Div(
[
html.H6(
"Precio y comportamiento (%)",
className="subtitle padded",
),
html.Table(make_dash_table(df_price_perf)),
],
className="six columns",
),
html.Div(
[
html.H6("Riesgo potencial",
className="subtitle padded"),
html.Img(
src=app.get_asset_url(
"risk_reward.png"),
className="risk-reward",
),
],
className="six columns",
),
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
return html.Div(
[
Header(app),
# page 2
html.Div(
[
html.Div([
html.H5("Data analysis report"),
html.Br([]),
html.P(
"\
This section shows the results and tools for the variables INNOVATION STRATEGY, ORGANIZATION, INNOVATION PROJECT, VALUE NETWORK and RESULTS.",
style={"color": "#121214"},
className="row",
),
]),
# Row
html.Div(
[
html.Div(
[
html.H6(
["Correlation graph"], className="subtitle padded"
),
html.P(
[
"In this section is presented the correlation between different variables"
],
style={"color": "#7a7a7a"},
),
],
className="twelve columns",
)
],
className="row ",
),
# Row 1.1 ## Matrix correlation
html.Div(
[
html.Div(
[
html.Br([]),
html.H6(
["Correlation matrix"],
className="subtitle tiny-header padded",
),
html.Div([
dcc.Dropdown(
id='correlation-selector',
options=[
{'label': 'Total Correlation', 'value': 'TOT'},
{'label': 'Answer YES', 'value': 'YES'},
{'label': 'Answer NO', 'value': 'NO'}
],
value='TOT'
),
]),
html.Div(
[
dcc.Graph(id="correlation"
),
],
style={"overflow-x": "auto"},
),
],
className="twelve columns",
)
],
className="row ",
),
html.Div(
[
html.Div(
[
html.H6(
["Resume"],
className="subtitle tiny-header padded",
)
],
className=" twelve columns",
)
],
className="row ",
),
# Row 4
html.Div(
[
html.Div(
[html.Table(make_dash_table(df_realized))],
className="six columns",
),
],
className="row ",
),
# New Row
html.Div(
[
html.Div(
[
html.Br([]),
html.H6(
["Correlation between two variables"],
className="subtitle tiny-header padded",
),
html.Div([
dcc.Dropdown(
id='VARIABLE1',
options=[
{'label': 'A INNOVATION STRATEGY ', 'value': 'A'},
{'label': 'B ORGANIZATION', 'value': 'B'},
{'label': 'C INNOVATION PROJECT', 'value': 'C'},
{'label': 'D VALUE NETWORK', 'value': 'D'},
{'label': 'E RESULTS', 'value': 'E'},
],
value='A'
),
dcc.Dropdown(
id='VARIABLE2',
options=[
{'label': 'A INNOVATION STRATEGY ', 'value': 'A'},
{'label': 'B ORGANIZATION', 'value': 'B'},
{'label': 'C INNOVATION PROJECT', 'value': 'C'},
{'label': 'D VALUE NETWORK', 'value': 'D'},
{'label': 'E RESULTS', 'value': 'E'},
],
value='A'
),
]
),
html.Div(
[
dcc.Graph(
id="graph-4",
),
],
),
],
className="twelve columns",
)
],
className="row ",
),
html.Div([
html.H6("Factor analysis", className="subtitle padded"),
html.Div([]),
dcc.Graph(
id="graph-fa",
config={"displayModeBar": False},
),
]),
html.Div(id='buffer'),
# Row 3
html.Div(
[
html.Div(
[
html.H6(
[
"Eigenvalues of Factor analysis"
],
className="subtitle padded",
),
html.Div(
[
html.Table(
make_dash_table(df_avg_returns),
className="tiny-header",
)
],
style={"overflow-x": "auto"},
),
],
className="twelve columns",
)
],
className="row ",
),
html.Div(
[
html.Div(
[
html.H6(
[
"CLUSTERING ANALYSIS "
],
className="subtitle padded",
),
html.H6(
[
"Variable selectors"
],
className="subtitle padded",
),
html.Div([
dcc.Dropdown(
id='varD1',
options=[
{'label': 'A INNOVATION STRATEGY ', 'value': 'A'},
{'label': 'B ORGANIZATION', 'value': 'B'},
{'label': 'C INNOVATION PROJECT', 'value': 'C'},
{'label': 'D VALUE NETWORK', 'value': 'D'},
{'label': 'E RESULTS', 'value': 'E'},
],
value='A'
),
]),
html.Div([
dcc.Dropdown(
id='varD2',
options=[
{'label': 'A INNOVATION STRATEGY ', 'value': 'A'},
{'label': 'B ORGANIZATION', 'value': 'B'},
{'label': 'C INNOVATION PROJECT', 'value': 'C'},
{'label': 'D VALUE NETWORK', 'value': 'D'},
{'label': 'E RESULTS', 'value': 'E'},
],
value='B'
),
]),
html.Div([
dcc.Dropdown(
id='varD3',
options=[
{'label': 'A INNOVATION STRATEGY ', 'value': 'A'},
{'label': 'B ORGANIZATION', 'value': 'B'},
{'label': 'C INNOVATION PROJECT', 'value': 'C'},
{'label': 'D VALUE NETWORK', 'value': 'D'},
{'label': 'E RESULTS', 'value': 'E'},
],
value='C'
),
]),
html.Div([
dcc.Dropdown(
id='Ncluster',
options=[
{'label': '2 ', 'value': '2'},
{'label': '3', 'value': '3'},
{'label': '4', 'value': '4'},
{'label': '5', 'value': '5'},
{'label': '6', 'value': '6'},
],
value='3'
)
]),
html.Div(
[
dcc.Graph(
id="k-means",
config={"displayModeBar": True},
),
],
),
html.Div(
[
html.H6(
[
"Elbow rule"
],
className="subtitle padded",
),
html.Div(
[
dcc.Graph(
id="metric1",
config={"displayModeBar": False},
),
],
),
],
className="twelve columns",
),
html.Div(
[
html.H6(
[
"Rule2 for the clusters"
],
className="six columns",
),
html.Div(
[
dcc.Graph(
id="metric2",
config={"displayModeBar": True},
),
],
),
],
className="six columns",
),
],
className="six columns",
)
],
className="row ",
),
# Row 4 Taska plot regressions
html.Div(
[
html.Div(
[
html.H6(
[
"Graph of the points"
],
className="subtitle padded",
),
html.Div(
[
dcc.Graph(
id="taska",
config={"displayModeBar": False},
),
],
),
],
className="twelve columns",
)
],
className="row ",
),
# Row 5 Task 1 plot regressions
html.Div(
[
html.Div(
[
html.H6(
[
"A affects B, C and D"
],
className="subtitle padded",
),
html.Div(
[
dcc.Graph(
id="task1",
config={"displayModeBar": False},
),
],
),
],
className="twelve columns",
)
],
className="row ",
),
# Row 6 Task 2 plot regressions
html.Div(
[
html.Div(
[
html.H6(
[
"B affects C and D "
],
className="subtitle padded",
),
html.Div(
[
dcc.Graph(
id="task2",
config={"displayModeBar": False},
),
],
),
],
className="twelve columns",
)
],
className="row ",
),
# Row 7 Task 3 plot regressions
html.Div(
[
html.Div(
[
html.H6(
[
"B, C and D as a package affect E "
],
className="subtitle padded",
),
html.Div(
[
dcc.Graph(
id="task3",
config={"displayModeBar": False},
),
],
),
],
className="twelve columns",
)
],
className="row ",
),
# Row 8 Task 5 plot regressions
html.Div(
[
html.Div(
[
html.H6(
[
"Relationship between A and E "
],
className="subtitle padded",
),
html.Div(
[
dcc.Graph(
id="task5",
config={"displayModeBar": False},
),
],
),
html.Div(dcc.Graph(id="task6")),
],
className="twelve columns",
)
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
# Page layouts
return html.Div(
[
html.Div([Header(app)]),
# page 1
html.Div(
[
# Row 1 - Por UF
html.Div(
[
html.Div(
[
html.H6(
"Acidentes por UF",
className="subtitle padded",
),
dcc.Graph(
id="graph-1",
figure={
"data": [
go.Bar(
x=df_uf['UF'],
y=df_uf['Quantidade'],
marker={
"color": "#97151c",
"line": {
"color":
"rgb(255, 255, 255)",
"width": 2,
},
},
name="Estados",
),
],
"layout":
go.Layout(
autosize=False,
bargap=0.4,
font={
"family": "Raleway",
"size": 10
},
height=400,
hovermode="closest",
legend={
"x": -0.0228945952895,
"y": -0.189563896463,
"orientation": "h",
"yanchor": "top",
},
margin={
"r": 0,
"t": 20,
"b": 10,
"l": 10,
},
showlegend=True,
title="",
width=660,
xaxis={
"autorange": True,
#"range": [-0.5, 2000],
"showline": True,
"title": "",
"type": "category",
},
yaxis={
"autorange": False,
"gridcolor":
"rgba(127, 127, 127, 0.2)",
"mirror": False,
"nticks": 4,
"range": [0, uf_max],
"showgrid": True,
"showline": True,
"ticklen": 10,
"ticks": "outside",
"title": "",
"type": "linear",
"zeroline": False,
"zerolinewidth": 4,
},
),
},
config={"displayModeBar": False},
),
html.P(
[
"60% dos acidentes ambientais no Brasil estão concentrados em MG, SP e RJ. Se incluimos o ES nessa lista, temos a redião SUDESTE com aproximadamente 64% dos acidentes."
],
style={"color": "#7a7a7a"},
),
],
className="row ",
),
],
className="row ",
),
# Row 2 - Muinícipios
html.Div(
[
html.Div(
[
html.H6(["Top 5 Municípios"],
className="subtitle padded"),
html.Table(
make_dash_table(df_municipio.head(5))),
html.P(
[
"Dos 5 municípios com mais acidentes, todos eles estão na região SUDESTE. Região que concentra o maior índice de acidentes do país."
],
style={"color": "#7a7a7a"},
),
],
className="six columns",
),
html.Div(
[
html.H6(["Top 5 Munícípios MG"],
className="subtitle padded"),
html.Table(
make_dash_table(
df_municipio.query(
'UF == "MG"').head(5))),
html.P(
[
"Mas se fizermos uma análise com os top 5 do estado com maior número de acidentes (MG), somente 1 (Belo Horizonte) está no top 5 dos municípios do Brasil todo."
],
style={"color": "#7a7a7a"},
),
],
className="six columns",
),
],
className="row",
style={"margin-bottom": "35px"},
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
return html.Div(
[
Header(app),
# page 4
html.Div(
[
# Row 1
html.Div(
[
html.Div(
[html.H6(["Gastos"], className="subtitle padded")],
className="twelve columns",
)
],
className="row ",
),
# Row 2
html.Div(
[
html.Div(
[
html.Strong(),
html.Table(make_dash_table(df_expenses)),
html.H6(["Minimos"], className="subtitle padded"),
html.Table(make_dash_table(df_minimums)),
],
className="six columns",
),
html.Div(
[
html.Br([]),
html.Strong(
"Tarifas de $10,000 invertidos en 10 años",
style={"color": "#3a3a3a"},
),
dcc.Graph(
id="graph-6",
figure={
"data": [
go.Bar(
x=["Category Average", "This fund"],
y=["2242", "329"],
marker={"color": "#97151c"},
name="A",
),
go.Bar(
x=["This fund"],
y=["1913"],
marker={"color": " #dddddd"},
name="B",
),
],
"layout": go.Layout(
annotations=[
{
"x": -0.0111111111111,
"y": 2381.92771084,
"font": {
"color": "#7a7a7a",
"family": "Arial sans serif",
"size": 8,
},
"showarrow": False,
"text": "$2,242",
"xref": "x",
"yref": "y",
},
{
"x": 0.995555555556,
"y": 509.638554217,
"font": {
"color": "#7a7a7a",
"family": "Arial sans serif",
"size": 8,
},
"showarrow": False,
"text": "$329",
"xref": "x",
"yref": "y",
},
{
"x": 0.995551020408,
"y": 1730.32432432,
"font": {
"color": "#7a7a7a",
"family": "Arial sans serif",
"size": 8,
},
"showarrow": False,
"text": "You save<br><b>$1,913</b>",
"xref": "x",
"yref": "y",
},
],
autosize=False,
height=260,
width=320,
bargap=0.4,
barmode="stack",
hovermode="closest",
margin={
"r": 40,
"t": 20,
"b": 20,
"l": 40,
},
showlegend=False,
title="",
xaxis={
"autorange": True,
"range": [-0.5, 1.5],
"showline": True,
"tickfont": {
"family": "Arial sans serif",
"size": 8,
},
"title": "",
"type": "category",
"zeroline": False,
},
yaxis={
"autorange": False,
"mirror": False,
"nticks": 3,
"range": [0, 3000],
"showgrid": True,
"showline": True,
"tickfont": {
"family": "Arial sans serif",
"size": 10,
},
"tickprefix": "$",
"title": "",
"type": "linear",
"zeroline": False,
},
),
},
config={"displayModeBar": False},
),
],
className="six columns",
),
],
className="row ",
),
# Row 3
html.Div(
[
html.Div(
[
html.H6(["Tarifas"], className="subtitle"),
html.Br([]),
html.Div(
[
html.Div(
[
html.Div(
[
html.Strong(
["Purchase fee"],
style={
"color": "#515151"
},
)
],
className="three columns right-aligned",
),
html.Div(
[
html.P(
["None"],
style={
"color": "#7a7a7a"
},
)
],
className="nine columns",
),
],
className="row",
style={
"background-color": "#f9f9f9",
"padding-top": "20px",
},
),
html.Div(
[
html.Div(
[
html.Strong(
["Redemption fee"],
style={
"color": "#515151"
},
)
],
className="three columns right-aligned",
),
html.Div(
[
html.P(
["None"],
style={
"color": "#7a7a7a"
},
)
],
className="nine columns",
),
],
className="row",
style={"background-color": "#f9f9f9"},
),
html.Div(
[
html.Div(
[
html.Strong(
["12b-1 fee"],
style={
"color": "#515151"
},
)
],
className="three columns right-aligned",
),
html.Div(
[
html.P(
["None"],
style={
"color": "#7a7a7a"
},
)
],
className="nine columns",
),
],
className="row",
style={"background-color": "#f9f9f9"},
),
],
className="fees",
),
html.Div(
[
html.Div(
[
html.Strong(
["Account service fee"],
style={"color": "#515151"},
)
],
className="three columns right-aligned",
),
html.Div(
[
html.Strong(
[
"Nonretirement accounts, traditional IRAs, Roth IRAs, UGMAs/UTMAs, SEP-IRAs, and education savings accounts (ESAs)"
],
style={"color": "#515151"},
),
html.P(
[
"We charge a $20 annual account service fee for each Brokerage Account, as well as each individual mutual fund holding with a balance of less than $10,000 in an account. This fee does not apply if you sign up for account and choose electronic delivery of statements, confirmations, and fund reports and prospectuses. This fee also does not apply to members of Flagship Select™, Flagship®, Voyager Select®, and Voyager® Services."
],
style={"color": "#7a7a7a"},
),
html.Br([]),
html.Strong(
["SIMPLE IRAs"],
style={"color": "#515151"},
),
html.P(
[
"We charge participants a $25 annual account service fee for each fund they hold in their SIMPLE IRA. This fee does not apply to members of Flagship Select, Flagship, Voyager Select, and Voyager Services."
],
style={"color": "#7a7a7a"},
),
html.Br([]),
html.Strong(
["403(b)(7) plans"],
style={"color": "#515151"},
),
html.P(
[
"We charge participants a $15 annual account service fee for each fund they hold in their 403(b)(7) account. This fee does not apply to members of Flagship Select, Flagship, Voyager Select, and Voyager Services."
],
style={"color": "#7a7a7a"},
),
html.Br([]),
html.Strong(
["Individual 401(k) plans"],
style={"color": "#515151"},
),
html.P(
[
"We charge participants a $20 annual account service fee for each fund they hold in their Individual 401(k) account. This fee will be waived for all participants in the plan if at least 1 participant qualifies for Flagship Select, Flagship, Voyager Select, and Voyager Services"
],
style={"color": "#7a7a7a"},
),
html.Br([]),
],
className="nine columns",
),
],
className="row",
style={
"background-color": "#f9f9f9",
"padding-bottom": "30px",
},
),
],
className="twelve columns",
)
],
className="row",
),
],
className="sub_page",
),
],
className="page",
)
