def widget(self):
Info(
self.frame,
text=f"Version {self.version}",
)._pack()
Header(self.frame, text="General Information")._pack()
general_info = f"PyAdSkipper is a tool used to skip Spotify ads. It functions by detecting when an ad is being played, restarting Spotify, and resuming music. View the README included in the local directory for the most information."
Info(
self.frame,
text=general_info,
)._pack()
Link(
self.frame,
"Developed by Ethanol",
"https://github.com/3than0ls",
)._pack()
Link(
self.frame,
"Visit this project's GitHub repo for more information.",
"https://github.com/3than0ls/PyAdSkipper",
)._pack()
Header(self.frame, text="FAQs and Common Issues")._pack()
Header(
self.frame,
text=
"The tool seems to be having some effect, but doesn't seem to be working correctly.",
)._pack()
not_working = "The script has certain intervals in which it executes commands such as restarting Spotify and playing the music. If a computer is under heavy load or is slow, some timed processes and events may be skipped."
Info(
self.frame,
text=not_working,
)._pack()
Header(
self.frame,
text="The script does not seem to be skipping some ads.",
)._pack()
not_skipping = 'The script relies on the Spotify window name to recognize if an ad is playing, meaning there is no need to use the Spotify API (which woud require additional bothersome credentials.) The downside, however, is that while for most ads, the window name is "Advertisement," some ads (usually those from Spotify artist/songs) are not named this, and so the script does not detect that they are ads. Unfortunately, there is no current way to solve this.'
Info(
self.frame,
text=not_skipping,
)._pack()
Header(
self.frame,
text="The script is skipping my local file songs.",
)._pack()
skipping_local = 'Again, the script relies on the Spotify window name to recognize if an ad is playing. The window name of Spotify when playing a local file is the name of that local file. If you name your local file "Advertisement", it will automatically be considered an advertisement.'
Info(
self.frame,
text=skipping_local,
)._pack()
def create_layout(app, params):
return html.Div(
[
Header(app),
# page 4
html.Div(
[
# Row 1
html.Div(
[
html.Div(
[html.H6('Descriptive statistics of "{}" data set'.format(params["datasetdict"][params["dataname"]]), className="subtitle padded")],
),
# html.Br([]),
dcc.RadioItems(
id='class-dropdown',
options=[
{'label': i, 'value': i}
for i in ["All"] + ["Class " + str(c) for c in list(params["target_classes"])]
],
value="All",
labelStyle={'display': 'inline-block'}
),
# html.Br([]),
# html.Table(make_dash_table(df_fund_facts)),
html.Div(id="datasum-table"),
],
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
profile = '''
Authors: [Newton Greco](https://www.linkedin.com/in/newtongreco/) & [Reyner Akira](https://www.linkedin.com/in/reyner-akira/)
'''
analysis_five = '''
***
### Model Evaluation
In order to evaluate the accuracy of these models we have saved the data from year 2018 and compared with the predicted forecast for 2018.
In the table underneath we can see that the coffee model produced the most accurate prediction with na error of 2.3%. Lemon and Maize both produced less accurate predictions with erros around 10%.
![alt text][id]
[id]: https://github.com/R-Akira/Crop-Productivity-Dashboard/blob/master/Assets/Model_Evaluation.jpg?raw=True "Title"
'''
return html.Div(children=[
html.Div([Header(app)]),
dcc.Markdown(children=profile,
style={
'textAlign': 'left',
'marginLeft': 20
}),
dcc.Markdown(children=analysis_five,
style={
'textAlign': 'justify',
'marginLeft': 20,
'marginRight': 50
}),
])
def create_layout(app):
return html.Div(
[
Header(app),
# page 3
# html.Div(
# [
#
# # Row 3
# html.Br([]),
# dbc.Container(
# [
# dbc.Row([
# dbc.Col([dbc.Jumbotron([html.H6(children="Select", className="display-5")])], md=4, align="center"),
# dbc.Col([dbc.Card( [dbc.CardHeader(html.H6("Test right"))])], md=8),])
#
# ],
# className="row ",
# ),
# ],
# className="sub-page", id="sub-page"
# ),
],
className="page",
)
def create_layout(app,valueSet):
return html.Div(
[
Header(app),
setValue(valueSet),
# page 2
html.Div(
[
html.Div(
children=[
html.Div('인구 1000명당 의사수', className="subtitle padded", style={'font-weight': 'bold','fontSize': 18}),
html.Br(),
html.Div(id='output-state', style={'font-weight': 'bold', 'fontSize': 20}),
dcc.Graph(id='tpd-graph'),
html.Br([]),
html.Div('1) OECD평균(1960~2019) - OECD 가입 국가의 평균 "인구 1000명당 의사수" / 출처-OECD Health Statistics 2020', style={'fontSize': 12}),
html.Div('2) OECD평균(2020~2047) - 1) OECD평균(1960~2019) 데이터를 사용한 선형회귀 도출 값', style={'fontSize': 12}),
html.Div('3) 대한민국 - 본 연구를 통해 도출된 추정 연도별 1000명당 의사수', style={'fontSize': 12}),
html.Br([]), html.Br([]),html.Br([]),html.Br([]),
html.Div('의사 1명당 연간 진료수', className="subtitle padded", style={'font-weight': 'bold','fontSize': 18}),
html.Br(),html.Br(),
dcc.Graph(id='dpd-graph'),
html.Br([]),
html.Div('1) OECD평균(2010~2019) - OECD 가입 국가의 평균 "인구 1명당 연간 진료수" / 출처-OECD Health Statistics 2020', style={'fontSize': 12}),
html.Div('2) OECD평균(2020~2047) - 1) OECD평균(2010~2019) 데이터를 사용한 선형회귀 도출 값', style={'fontSize': 12}),
html.Div('3) 대한민국 - 본 연구를 통해 도출된 추정 연도별 인구 1명당 연간 진료수', style={'fontSize': 12}),
html.Br([]),
]),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app, report_list):
rows = []
for ii in range(len(report_list)): # numero de filas
elems = []
for jj in range(len(report_list[ii])):
if report_list[ii][jj][2] == "product":
aux = html.H5(report_list[ii][jj][0])
else:
aux = html.H6(report_list[ii][jj][0],
className="subtitle padded")
elems.append(
html.Div(
[
aux,
report_list[ii][jj][1],
],
className=report_list[ii][jj][2],
))
rows.append(html.Div(
elems,
className="row",
))
return html.Div(
[
html.Div([Header(app)]),
# page
html.Div(
rows,
className="sub_page",
),
],
className="page",
)
def create_layout(app):
return html.Div(
[
Header(app),
# page 2
# html.Div(
# [
# # Row 1
# html.Div(
# [
# html.Div([
# html.H6("Select Symptom-Cluster of Recovered", className="subtitle padded"),
# dcc.Dropdown(id="symptom-cluster-select", options=[{"label": i, "value": i} for i in symptom_cluster_list], value=symptom_cluster_list[0]),
# html.H6("Select min Level of Symptom of Recovered", className="subtitle padded"),
# dcc.Dropdown(id="symptom-cluster-level", options=[{"label": i, "value": i} for i in symptom_cluster_level],value=symptom_cluster_level[1]),
# dcc.Graph(id='symp-barplot'), ],className="five columns",),
#
# html.Div([
# html.H6(["Surveys' Question of Recovered Patients"], className="subtitle padded",),
# dcc.RadioItems(id="survey-list-select", options=[{'label': 'Likert Diagram', 'value': 'Lik'},],value='Lik'),
# dcc.Graph(id='survey-barplot'),
# ], className="seven columns",),
# ], className="row"),
#
# ],
# className="sub-page", id="sub-page"
# ),
],
className="page",
)
def new_first_page(app, title=projectname):
return html.Div([
html.Div([Header(app, title)]),
html.Div(
[
html.Div(
[
html.H6(
f"Objective : {aim}",
className="row",
),
],
className="product",
),
html.Div(
[
html.H6(
"Success Criteria",
className="row",
),
],
className="product",
)
],
className="sub_page",
)
],
className="page")
def create_layout(app, language):
return html.Div(
[
Header(app, language),
html.Div(
[
html.Div(
[
html.Div(
[
html.H5("Module Summary"),
html.Br([]),
html.P(
'\
This module uses machine learning algorithms to determine the \
probability that he will remain delinquent, assuming that he receives\
no collection actions. ',
style={"color": "#ffffff"},
className="row",
),
],
className="red-box",
)
],
className="row",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
return html.Div(
[
Header(app),
],
className="page",
)
def create_layout(app):
# Page layouts
return html.Div(
[
html.Div([Header(app)]),
# overview
], )
def journey_page(app, title=projectname):
chosenChanges = dfrunmaster[dfrunmaster.Chosen == 1]['Description'].values
return html.Div([
html.Div([Header(app, title)]),
html.Div(
[
html.Div(
[
html.H6(
"Journey Plot",
className="subtitle padded",
),
create_journey_plot_line(dfrunmaster),
],
className="seven columns",
),
html.Div(
[
html.H6(
"Road to best model",
className="subtitle padded",
),
make_unordered_list(chosenChanges),
],
className="five columns",
),
],
className="sub_page",
)
],
className="page")
def create_layout(app):
return html.Div(
[ Header(app),html.Div([
html.Div([
html.Div([
html.H6("Hasil Regresi 1 Variabel dengan Curah Hujan Asli", className="subtitle padded"),reg1_asli[0], ],className="six columns"),
html.Div([html.H6(["Hasil Regresi 1 Variabel dengan Curah Hujan Prediksi"],className = "subtitle padded"),reg1_pred[0], ],className = "six columns"),
],className = "row"),# Tambah row di sini
html.Div([
html.Div([
html.H6("Hasil Regresi 2 Variabel dengan Curah Hujan Asli", className="subtitle padded"),reg2_asli[0], ],className="six columns"),
html.Div([html.H6(["Hasil Regresi 2 Variabel dengan Curah Hujan Prediksi"],className = "subtitle padded"),reg2_pred[0], ],className = "six columns"),
],className = "row"),# Tambah row di sini
html.Div([
html.Div([
html.H6("Score Regresi 1 Variabel", className="subtitle padded"),reg1_score, ],className="six columns"),
html.Div([html.H6(["Score Regresi 2 Variabel"],className = "subtitle padded"),reg2_score, ],className = "six columns"),
],className = "row"),# Tambah row di sini
html.Div([
html.Div([html.H6("Prediksi Penderita Demam Berdarah dari Curah Hujan", className="subtitle padded"),
dcc.Input(id="input-1", type="number", value =1,debounce=True, min=1),html.Div(id="number-output"),
],className = "row"),
],className = "row"),# Tambah row di sini
],className = "sub_page"),],className="page",)
def create_layout():
return html.Div(
[
html.Div([Header(app)], style={"height": "6rem"}),
html.Div(
[
dbc.Row([
dbc.Col(div_year_to_date_metrics(), width=3),
dbc.Col(div_overall_performance()),
]),
],
className="mb-3",
),
html.Div(
[
dbc.Row([
dbc.Col(card_main_volumn_based_measures()),
dbc.Col(card_main_value_based_measures()),
]),
],
className="mb-3",
style={"padding-top": "5rem"},
)
],
style={
"padding-left": "3rem",
"padding-right": "3rem"
},
)
def create_layout(app):
return html.Div(
[
Header(app),
# page 2
html.Div([
dcc.Dropdown(
id='my-dropdown',
options=F,
value=list(F[0].values())[1]),
html.Div(id='output-container'),
dcc.Graph(id='graph-with-slider'),
dcc.Slider(
id='year-slider',
min=min(years),
max=max(years),
value=min(years),
marks={str(year): str(year) for year in years},
step=None
)
])
],
className="page",
)
def create_layout(app):
return html.Div(
[
Header(app),
html.Div(
[
html.Div([
html.Div([
html.H6("Demam Berdarah di Banyumas",
className="subtitle padded"),
html.Br([]),
html.Div(
[
html.
Li("Demam berdarah merupakan ancaman yang nyata di Indonesia."
),
html.
Li("Penyakit demam berdarah banyak ditemukan di daerah tropis dan sub-tropis seperti Indonesia."
),
html.
Li("Di Kabupaten Banyumas sendiri, pada awal Januari 2019, terjadi demam massal di Desa Pandak. "
),
html.
Li("Jumlah penderita terbanyak tercatat pada tahun 2016."
),
html.
Li("Jumlah penderita selama tahun 2012 hingga 2018 adalah 2329 orang."
),
],
id="reviews-bullet-pts",
),
],
className="six columns"),
html.Div([
html.H6(["Grafik Penderita DB Banyumas"],
className="subtitle padded"),
grafik1,
],
className="six columns"),
],
className="row"), # Tambah row di sini
html.Div([
html.H6("Jumlah Korban Demam Berdarah Per Kecamatan",
className="subtitle padded"),
grafik2,
],
className="row"),
html.Div([
html.H6(
"Presentase Korban Demam Berdarah Per Kecamatan",
className="subtitle padded"),
grafik_pie,
],
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.H6("The Ask:", className="subtitle padded"),
html.
P("Compare weekend patronage between periods with and without lessons, between the time of 8am to 1pm. Make note of holidays and if it had a noticeable effect on attendance."
),
html.
P("Note: Periods in which no lessons occured were based-off handwritten annotation on the tally sheets. If there were no notes on the tally sheets indicating otherwise, it was then assumed that lessons did occur.\
Noted periods indicating ‘no lessons’ occured from:"
),
html.Li(
"December 31st, 2018 to January 13th, 2019;"),
html.Li("March 25th to April 7th;"),
html.Li("June 10th to June 16th;"),
html.Li("July 8th to July 14th;"),
html.Li("August 26th to September 1st;"),
html.Li("September 23rd to September 28th;"),
html.Li("November 25th to December 7th;"),
html.Li("December 30th to January 5th, 2020."),
html.Br([]),
html.Div([
html.H6(
"Weekend Morning (8am-1pm) Patronage, between days With & Without Lessons",
className="subtitle padded",
),
html.Iframe(
src="//plotly.com/~HP-Nunes/212.embed",
width="100%",
height="400")
]),
html.Br([]),
html.H6("Key Observations",
className="subtitle padded"),
html.
Li("There were 5,686 patrons over the course of 2019 on weekends with Lessons, compared to 1,056 on those with none. That’s a comparison of approximately 76 swimmers per day to 59 between weekends with and without lessons."
),
html.
Li("There were 484 tallies taken over the course of 75 days in 2019 on weekends with Lessons, compared to 147 tallies over 18 days on those with none. That’s approximately 6 tallies taken compared to approximately 8 between weekends with and without lessons (a maximum of 10 tallies can be taken between 8am - 1pm)."
),
html.
Li("Only one facility holiday fell on a Weekend for 2019: Cesar Chavez Day on Sunday March 31st."
),
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
return html.Div(
[
Header(app),
# page 6
html.Div(
[
# Row 1
html.Div(
[
html.H6("Correlation Observations", className="subtitle padded"),
html.Br([]),
html.Div(
[
html.P(
"According to the graphs daily detections of Sensor 2 & 3 are \
more correlated and in a more granualr\
level like hourly analysis, it seems that there is no\
significant difference between the correlation. However\
when we get more granular like in a minutely manner, sensors\
1 & 2 are more correlated. Hence, for a more accurate \
result we need more information of theses detectors, like\
what are the nature of these detectors and what exactly \
they are doing."
),
],
style={"color": "#7a7a7a"},
),
],
className="row"),
html.Div(
[
html.H6("Lagging Observations", className="subtitle padded"),
html.Br([]),
html.Div(
[
html.P(
"According to the graphs, it seems that by shifting sensor 1 \
for data two days (= 48 hours) , we will get more correlated data."
),
html.P(
"According to the graphs Sensor 2 and 3 do not\
have any lags in terms of days, however \
in terms of hours there might be a lag of 4 \
hours and in terms of minutes a lag of 5 minutes is\
observed. "
),
],
style={"color": "#7a7a7a"},
),
],
className="row"),
],
className="sub_page"),
],
className="page",
)
def create_layout(app):
return html.Div(
[
Header(app),
# page 6
html.Div(
[
# Row 1
html.Div(
[
html.Div(
[
html.H6("COMMENTS",
className="subtitle padded"),
html.Br([]),
html.Div(
[
html.
P("THIS IS A TEST EXAMPLE OF dASHBOARD USING THE PYTHON APPI DASH"
),
html.
P("The data or dataset was extracted from Yahoo finannce"
),
],
style={"color": "#7a7a7a"},
),
],
className="row",
),
html.Div(
[
html.H6("Resume",
className="subtitle padded"),
html.Br([]),
html.Div(
[
html.Li("Divisa JPY."),
html.
Li("According to the movement of the market in recent years it is observed that this currency had variable pivot points, yet the last yeas remains stable with respect to its history.*"
),
html.
Li("It is recommended to review the attached repositories to review the approximation algorithms using Deep QLearning"
),
],
id="reviews-bullet-pts",
),
],
className="row",
),
],
className="row ",
)
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
return html.Div(
[
html.Div([Header(app)]),
# page 1
# html.Div(
# [
# dash_pivottable.PivotTable(
# id="pvtable",
# data=df_finalorders,
# # cols=['OrderId'],
# # colOrder="key_a_to_z",
# # rows=['Division'],
# # rowOrder="key_a_to_z",
# # rendererName="Grouped Column Chart",
# # aggregatorName="Average",
# # vals=["AfterOrderDiscountValue"],
# # valueFilter={"Store": {"ΦΡΟΥΡΙΟ": True}},
#
# # dash_pivottable.PivotTable(
# # id="table",
# # data=data,
# # cols=["Day of Week"],
# # colOrder="key_a_to_z",
# # rows=["Party Size"],
# # rowOrder="key_a_to_z",
# # rendererName="Grouped Column Chart",
# # aggregatorName="Average",
# # vals=["Total Bill"],
# # valueFilter={"Day of Week": {"Thursday": False}},
# ),
#
#
# html.Div(id="pvoutput"),
# ],
#className="sub-page", id="sub-page"
# ),
# html.Div(
# [
# dash_pivottable.PivotTable(
# id="pvtable",
# data=df_finalorders,
# cols=['OrderId'],
# colOrder="key_a_to_z",
# rows=['Division'],
# rowOrder="key_a_to_z",
# rendererName="Grouped Column Chart",
# aggregatorName="Average",
# vals=["AfterOrderDiscountValue"],
# valueFilter={"Store": {"ΦΡΟΥΡΙΟ": True}},
# ),
# html.Div(id="pvoutput"),
# ],
# className="sub-page", id="sub-page"
# ),
],
className="page")
def create_layout(app):
return html.Div(
[
Header(app),
# page 5
html.Div(
[
html.Div(
[
html.H6(["Developer 开发者"],
className="subtitle padded"),
],
className="twelve columns",
),
],
className="row ",
),
html.Div(
children='''Sishuo Chen,Junior Student at Peking University'''
),
html.Div(
[
html.Div(
[
html.H6(["Contact 联系"],
className="subtitle padded"),
],
className="twelve columns",
),
],
className="row ",
),
html.Div(children=dcc.Markdown(
"[[email protected]](mailto:[email protected])"), ),
html.Div(
[
html.Div(
[
html.H6(["Acknowledgement 鸣谢"],
className="subtitle padded"),
],
className="twelve columns",
),
],
className="row ",
),
html.Div(children=dcc.Markdown('''
感谢北京大学校园应用[PKU Helper](https://pkuhelper.pku.edu.cn/)的开发团队，以及2019年秋季计算机网络与Web技术的指导老师 [张岩](https://eecs.pku.edu.cn/info/1342/6102.htm) 教授。
- The Development team of the campus APP [PKU Helper](https://pkuhelper.pku.edu.cn/)
- The Director of the Computer Network and Web Techology course in PKU [Yan Zhang](https://eecs.pku.edu.cn/info/1342/6102.htm)
'''), ),
],
className="page",
)
def create_layout(app, valueSet):
return html.Div(
[
Header(app),
setValue(valueSet),
# page 2
html.Div(
[
html.Div(children=[
html.Div('연도별, 성별 신규 의사수',
className="subtitle padded",
style={
'font-weight': 'bold',
'fontSize': 18
}),
html.Br(),
html.Div(id='output-state',
style={
'font-weight': 'bold',
'fontSize': 18
}),
dcc.Graph(id='nd-graph'),
dcc.Slider(id='nd-year-slider',
min=1952,
max=2047,
value=2018,
marks=sliderMarks,
step=1,
updatemode='drag'),
html.Br([]),
html.Div(
'1) 신규 의사수 - 본 연구를 통해 도출된 추정 연도별/성별/연령별 신규 의사수',
style={'fontSize': 12}),
html.Br([]),
html.Br([]),
html.Br([]),
html.Br([]),
html.Div('연간 신규 의사수',
className="subtitle padded",
style={
'font-weight': 'bold',
'fontSize': 18
}),
html.Br([]),
html.Br([]),
dcc.Graph(id='ndy-graph'),
html.Br([]),
html.Div('1) 신규 의사수 - 본 연구를 통해 도출된 추정 연도별/성별 신규 의사수',
style={'fontSize': 12}),
html.Br([]),
]),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app, language):
return html.Div(
[
Header(app, language),
html.Div(
[
html.Div(
[
html.Div(
[
html.H5("Module Summary"),
html.Br([]),
html.P(
'\
This module receives the data required \
for the credit risk, effectiveness and optimization analysis.',
style={"color": "#ffffff"},
className="row",
),
],
className="red-box",
)
],
className="row",
),
html.Div(
[
html.Div('\
Please insert the Register and Invoice Database ',
style={'textAlign': 'center'}
#className="row",
),
dcc.Upload(
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'
},
)
],
# className="red-box",
)
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
return html.Div(
[
Header(app),
html.Img(src=app.get_asset_url("ohno.gif"),
className='twelve columns'),
],
className="page",
)
def create_layout(app):
return html.Div(
[
Header(app),
# page 3
html.Div(
[
#Row 1
html.Div(
[
html.Div(
[
html.H5("Sensor 1 & Sensor 2", className="subtitle padded"),
dcc.Graph(
id="graph-70",
figure=fig12,
config={"displayModeBar": False},
),
],
className="twelve columns")]
, className='row'),
# Row 2
html.Div(
[
html.Div(
[
html.H5("Sensor 1 & Sensor 3", className="subtitle padded"),
dcc.Graph(
id="graph-71",
figure=fig13,
config={"displayModeBar": False},
),
],
className="twelve columns")]
, className='row'),
html.Div(
[
html.Div(
[
html.H5("Sensor 2 & Sensor 3", className="subtitle padded"),
dcc.Graph(
id="graph-72",
figure=fig23,
config={"displayModeBar": False},
),
],
className="twelve columns")]
, className='row'),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
# Page layouts
return html.Div(
[
html.Div([Header(app)]),
# class "sub_page"
html.Div(
[
# Row 2
html.Div(
[
html.Div(
[
html.H6(
["Distribución por género"], className="subtitle padded"
),
get_donut_chart(
id="donut-graph",
labels=distr_genero_df["Sexo"],
values=distr_genero_df["cantidad"]
)
],
className="six columns",
),
html.Div(
[
html.H6(
["Distribución por Edad"], className="subtitle padded"
),
get_bar_chart(
id="histogram-graph",
data=get_edades(),
x_col="edad",
y_col="cantidad",
)
],
className="six columns",
),
],
className="row",
style={"margin-bottom": "35px"},
),
# Row 3
html.Div(
[
],
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
# Title box
html.Div(
[
html.Div(
[
html.H5("Heat wave impact"),
html.Br([]),
html.P(
"In the 1960s, Major cities experienced, on average, about two heat waves per year. In the 2010s, that number rose to more than six heat waves per year. These heat waves are also lasting longer, on average 47 days longer than in 1960. Even under different climate models and emission scenarios, results indicate that extreme heat events worsen. Heatwaves, or heat and hot weather that can last for several days, can have a significant impact on society, including a rise in heat-related deaths. More than 70 000 people died during the 2003 heatwave in Europe. Workers who are exposed to extreme heat or work in hot environments may be at risk of heat stress. Exposure to extreme heat can result in occupational illnesses and injuries. Heat stress can result in heat stroke, heat exhaustion, heat cramps, or heat rashes. Humidity is an important factor in heat index assessment. When the humidity is high, water does not evaporate as easily and so it becomes difficult for the body to cool off through sweating.",
style={"color": "#ffffff"},
className="row",
),
],
className="product",
)
],
className="row",
),
# Our approach subtitle
html.Div(
[
html.Div(
[html.H6(["Our approach"],
className="subtitle padded")],
className="twelve columns",
)
],
className="rows",
),
html.P("This dashboard was created for investors who want to assess the short-term impact of heat waves on their portfolio. It allows these investors to specify their portfolio, after which the application applies pre-made models to the portfolio data and provides the user with contextual information related to heat waves."),
html.Br([]),
html.P("Although a company's fundamentals are not affected by heat waves in the short term, we believe that a single heat wave can have an impact on a portfolio in the short term. The reasoning goes like this: heat waves are known to have a negative impact on the mental health of people, including investors, and this negative state of mind could in turn affect the stock market in the short term. "),
html.Br([]),
html.P("In order to investigate this we compared the S&P500 daily closing value to sentiment gathered from the 'stocks' and 'investing' subreddits and analyzed using the vader sentiment analysis tool. The results are reproduced below with days corresponding to estimated heatwaves highlighted in red:"),
html.Div(html.Img(src=app.get_asset_url('reddit_results.PNG'),
style={"width": "80%"}),
style={'textAlign': 'center'}),
html.P("During the second heatwave period in 2019, both the invester sentiment and the value of the S&P 500 went down. This effect can of course be random, however, this should be investigated. The dashboard uses a collection of models to determine whether the portfolio specified by the user is exposed to this kind of risk."),
html.Br([]),
html.Br([]),
html.Div(html.A('Click here to move to the next page and pick your portfolio', href="/dash-financial-report/portfolio", id="next-page"),
style={"textAlign": "right", 'font-size': '150%', "text-decoration": "underline"})
],
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.Br([]),
html.H6(
["Modules 1"],
className="subtitle tiny-header padded",
),
html.Div(
[html.Table()],
style={"overflow-x": "auto"},
),
],
className="twelve columns",
)
],
className="row ",
),
# Row 2
html.Div(
[
html.Div(
[
html.H6(
["Modules 2"],
className="subtitle tiny-header padded",
)
],
className=" twelve columns",
)
],
className="row ",
),
# Row 3
html.Div(
[
html.Div(),
html.Div(),
],
className="row ",
),
],
className="sub_page",
),
],
className="page",
)
def create_layout(app):
# Page layouts
return html.Div(
[
html.Div([Header(app)]),
# page 1
# add your UI here, and callbacks go at the bottom of app.py
# assets and .js go in assets folder
# csv or images go in data folder
], )
def create_layout(app):
# Page layouts
return html.Div(
[
html.Div([Header(app)]),
html.Div(
[layout_intro, layout_graphs, layout_howto],
className="sub_page",
),
],
className="page",
)
