def figure_deaths(count3):
df2 = df.loc[(df['State/UnionTerritory'] == count3)]
return px.area(df2,
x="Date",
y="Cured",
color="State/UnionTerritory",
line_group="State/UnionTerritory")
def figure_deaths(count3):
# count3 = "World"
df2 = df.loc[(df['location'] == count3)]
return px.area(df2,
x="date",
y="total_deaths",
color="location",
line_group="location")
#
# @app.callback(
# Output("cases_or_deaths_country","figure"),
# [Input("countries","value"), Input("columns","value")]
# )
# def density_fig(ch1, ch2):
# df3 = df.loc[df["location"].isin(ch1)]
# return px.area(
# df3, x="date", y=ch2, color="location", line_group="location"
# )
#
# @app.callback(
# Output("total_cases_or_deaths_country","figure"),
# [Input("countries","value"), Input("columns","value")]
# )
# def line_fig(ch1, ch2):
# df3 = df.loc[df["location"].isin(ch1)]
# return px.line(
# df3, x="date", y=ch2, color="location", line_group="location"
# )
def figure_case(count2):
df2 = df.loc[(df['State/UnionTerritory'] == count2)]
return px.area(df2,
x="Date",
y="Confirmed",
color="State/UnionTerritory",
line_group="State/UnionTerritory")
def figure_case(c2):
# count2 = "World"
df2 = df.loc[(df['location'] == c2)]
return px.area(df2,
x="date",
y="total_cases",
color="location",
line_group="location")
def density_fig(ch1, ch2):
df3 = df.loc[df["State/UnionTerritory"].isin(ch1)]
return px.area(df3,
x="Date",
y=ch2,
color="State/UnionTerritory",
line_group="State/UnionTerritory")
if plot_type == "bar":
fig = px.bar(dfp,
x='Tahun',
y=selected_columns,
color=color_value,
barmode=mode)
st.plotly_chart(fig)
elif plot_type == "line":
fig = px.line(dfp,
x='Tahun',
y=selected_columns,
color=color_value)
st.plotly_chart(fig)
elif plot_type == "area":
fig = px.area(dfp,
x='Tahun',
y=selected_columns,
color=color_value)
elif plot_type == "hist":
fig = px.histogram(dfp, x=selected_columns)
st.plotly_chart(fig)
elif plot_type == "box":
fig = px.box(dfp, y=selected_columns, x=xbox)
st.plotly_chart(fig)
elif plot_type:
cust_plot = dfp[selected_columns].plot(kind=plot_type)
st.write(cust_plot)
st.pyplot()
if st.checkbox("Budget Allocation vs Index Change Analysis"):
# df = pd.read_csv('belanja_apbd.csv',sep=";")
df = pd.read_excel('belanja_apbd_full.xlsx')
), ),
html.Div(
dcc.Graph(figure=px.box(
tmp,
x="amount",
y="Weekday",
orientation="h",
notched=True,
category_orders={
"Weekday": [
"Friday", "Saturday", "Sunday", "Monday",
"Tuesday", "Wednesday", "Thursday"
]
}))),
html.Div(
dcc.Graph(figure=px.area(
tmp, x="Date", y="amount", color="Category")))
],
className="two column",
style={
'width': '100%',
'backgroundColor': 'blue'
},
),
]
# hide/show modal
@app.callback(Output("trans_modal", "style"),
[Input("add-new-btn", "n_clicks")])
def display_trans_modal_callback(n):
if n > 0:
df = px.data.gapminder()
my_text = dcc.Markdown("""
## Dash Boostrap Practice App
This app is a quickstart to developing your own app
- Use this Markdown component for your own text. You can
learn more about how to format text such as adding **bold** and
*italics* [here](https://commonmark.org/help/tutorial/)
- Use the cards to create components and then add them to the layout.
This figure is from the [Plotly Tutorial](https://plotly.com/python/plotly-express/)
""")
fig = px.area(df, x="year", y="pop", color="continent", line_group="country")
text_card = html.Div(
dbc.Card(
dbc.CardBody(
[
html.H4(" Card Title", className="card-title"),
my_text,
],
className="mt-4",
)))
graph_card = html.Div(
dbc.Card(
dbc.CardBody(
[
)
st.write(pc)
if st.checkbox("Show Principal Components", False):
st.write(components)
# PRINCIPAL COMPONENTS EXPLAINED VARIANCE
pcafit = pca.fit(feature_set)
expvar = pca.explained_variance_ratio_
st.subheader("")
st.write("### Principal Components Explained Variance")
exp = px.area(
x = range(0, 35),
y = expvar,
template = 'plotly_dark',
height = 700,
labels = {"x": "Principal Components", "y": "Explained Variance"}
)
st.write(exp)
# 3-DIMENSIONAL PRINCIPAL COMPONENTS EXPLORATION
pca3 = PCA(n_components=3)
components3 = pca3.fit_transform(feature_set)
totalvar = pca3.explained_variance_ratio_.sum() * 100
st.title("")
st.subheader("Visualizing the Principal Components in 3 Dimensions")
pc3 = px.scatter_3d(
def density_fig(ch1, ch2):
df3 = df.loc[df["location"].isin(ch1)]
return px.area(
df3, x="date", y=ch2, color="location", line_group="location"
)
def figure_deaths(count3):
df2 = df.loc[(df['location'] == count3)]
return px.area(
df2, x="date", y="total_deaths", color="location", line_group="location"
)
'dim1': 'year',
'dim2': 'ryr',
'dim3': 'trst',
'dim4': 'lstkw',
'dim5': 'level'
},
inplace=True)
years = cars['year'].unique()
cars = cars.set_index(['ryr', 'trst', 'lstkw', 'year'])
#cars = cars.unstack(level='year')
cars_sum = cars.groupby(['trst', 'year']).sum().reset_index()
fig = px.line(cars_sum, x="year", y="level", color="trst", log_y=True)
fig2 = px.bar(cars_sum, x="year", y="level", color="trst")
fig3 = px.area(cars_sum, x="year", y="level", color="trst")
## Macro economic indicators
## Aggregated results
gdx_file = os.path.join(temp_dir, 'results.gdx')
db = ws.add_database_from_gdx(gdx_file)
results_agg = gdxt.get_parameter(db, 'results_agg')
results_agg.rename(columns={
'dim1': 'year',
'dim2': 'scenario',
'dim3': 'indicator',
'dim4': 'level'
},
inplace=True)
indicators = results_agg['indicator'].unique()
col2.plotly_chart(fig1, use_column_width=True)
col1.subheader('')
col1.subheader('')
col1.subheader('')
col1.subheader('')
col1.subheader('')
col1.subheader('')
col1.subheader('Indicators')
indicators = col1.selectbox('Choose Indicator',('Volume', 'RSI','MACD'))
if indicators == 'Volume':
fig2 = px.area(df, x = df.index, y = 'Volume',title=symbol)
fig2.update_layout(xaxis_rangeslider_visible=True)
col2.plotly_chart(fig2)
if indicators == 'RSI':
fig3 = go.Figure()
fig3.add_trace(go.Scatter(x=RSI.index, y=RSI.values, mode='lines',line=dict(color='gray'), name = 'RSI'))
fig3.update_layout(xaxis_rangeslider_visible=True)
col2.plotly_chart(fig3,use_column_width=True)
if indicators == 'MACD':
fig4 = go.Figure()
fig4.add_trace(go.Scatter(x=df.index, y=df['MACD'], mode='lines',line=dict(color='red'), name = 'MACD'))
fig4.add_trace(go.Scatter(x=df.index, y=df['SIGNAL'], mode='lines',line=dict(color='blue'), name = 'SIGNAL LINE'))
