def display_predicted_price(n_clicks, apt, ec, condo, time, radius,
postal_input, property_type, floor_num, floor_area,
lease):
if n_clicks:
##### Current Global Listing Object #####
global curr_listing
curr_listing = Listing(postal_input, property_type, int(floor_num),
float(floor_area), int(lease))
global price_output, price_psm_output
price_output, price_psm_output = curr_listing.pred_price(
"modelling/", cols, postal_code_area, area_df, sch, train,
police_centre, avg_cases)
# For testing
#curr_listing = Listing('597592', 'Condominium', 6, 99, 70)
#curr_listing = Listing('689527', 'Condominium', 6, 99, 70)
##### Parameters of Sample Object #####
time_param = [0, 0]
if (time == 'Past 5 Years'):
time_param[0] = 1
elif (time == 'Past 10 Years'):
time_param[1] = 1
radius_param = [0, 0]
if (radius == 'Within 1km'):
radius_param[0] = 1
elif (radius == 'Within 2km'):
radius_param[1] = 1
ec_param, condo_param, apt_param = 0, 0, 0
# Setting default property_filter to property_type of listing
if ((not apt) and (not condo) and (not ec)):
if (property_type == 'Condominium'):
condo_param = 1
elif (property_type == 'Apartment'):
apt_param = 1
elif (property_type == 'Executive Condominium'):
ec_param = 1
else:
if ec:
ec_param = 1
if condo:
condo_param = 1
if apt:
apt_param = 1
##### Current Global Sample Object #####
global curr_sample
params = {
'radius': radius_param,
'property': [ec_param, condo_param, apt_param],
'time': time_param
}
curr_sample = Sample(params, prelim_ds)
curr_sample.get_filtered_df(prelim_ds,
curr_listing.get_lon(postal_code_area),
curr_listing.get_lat(postal_code_area))
curr_sample.get_map(curr_listing.get_lon(postal_code_area),
curr_listing.get_lat(postal_code_area),
price_psm_output, curr_listing.get_building(),
curr_listing.get_road_name(), 100)
map_component = html.Iframe(srcDoc=open('sample_map.html', 'r').read(),
height='600')
transaction_table = curr_sample.get_transaction_table()
psm_timeseries_plot = html.Div([
html.Div([
'Aggregated resale market conditions for ',
html.B(
curr_listing.get_planning_area(postal_code_area,
area_df).title()),
" planning area together with its 2 closest neighbours in the past "
+ str(curr_sample.get_time()) + ' years'
],
style={'font-size': 'medium'}),
html.Div(
'Only resale transactions of ' + ", ".join([
property + "s" for property in curr_sample.get_property()
]) +
" within each planning area are included within the computation",
style={'font-size': 'medium'}),
curr_sample.plot_psm(
prelim_ds, area_df,
curr_listing.get_planning_area(postal_code_area, area_df), 2),
])
return [
overview_section(curr_listing, price_output, price_psm_output),
curr_listing.get_planning_area(postal_code_area, area_df).title(),
transaction_features(curr_sample), map_component,
transaction_table, psm_timeseries_plot,
[
'All resale transactions of ' + ", ".join([
property + "s" for property in curr_sample.get_property()
]) + " in the past ",
html.B(str(curr_sample.get_time()) + " years"),
" that are within a radius of ",
html.B(str(curr_sample.get_radius()) + "km"),
" from your property"
]
]
#### Default output
# Map
map_component = html.Iframe(srcDoc=open('assets/default_map.html',
'r').read(),
height='600')
# Timeseries
filtered_df = prelim_ds.copy()
filtered_df['Sale Month'] = filtered_df['Sale Date'].apply(
lambda x: x.strftime('%Y-%m')) # to plot based on Year and Month
filtered_df['Sale Year'] = filtered_df['Sale Date'].apply(
lambda x: x.year) # to plot based on Year
grp_df = filtered_df.groupby(['Sale Month',
'Planning Area']).mean().reset_index()
fig = px.line(
grp_df,
x="Sale Month",
y="PPI",
#color='Planning Area',
labels={
"Sale Month": "Year",
"PPI": "Property Price Index"
})
fig.update_layout(plot_bgcolor='#f8f4f0')
# To control white space surrounding the plot
fig.update_layout(margin={'t': 15, 'b': 20, 'l': 20, 'r': 30})
fig.update_layout(height=450)
ts_plot = dcc.Graph(figure=fig)
# Transaction Table
df = prelim_ds[[
'Sale Date', 'Address', 'Floor Number', 'Area (SQFT)',
'Remaining Lease', 'Unit Price ($ PSF)'
]].copy()
df = df.rename(columns={
'Area (SQFT)': 'Floor Area',
'BUILDING': 'Building Name'
})
df = df.sort_values(by=['Sale Date'], ascending=False).head(100)
df['Sale Date'] = df['Sale Date'].apply(lambda x: x.date())
table = dash_table.DataTable(
data=df.to_dict('records'),
columns=[{
'id': c,
'name': c
} for c in df.columns],
# Remove Pagination
page_action='none',
#For sorting by columns
sort_action="native",
# For filtering rows by column values
filter_action="native",
#style_as_list_view=True,
style_table={
'max-height': '400px',
'font-size': '13px'
},
style_cell={
'textAlign': 'center',
'font-family': 'sans-serif',
'width': '{}%'.format(len(df.columns))
#'minWidth': '20px', 'width': '20px', 'maxWidth': '200px'
},
#Controilling width of columns
style_cell_conditional=[
{
'if': {
'column_id': 'Sale Date'
},
'width': '5%'
},
{
'if': {
'column_id': 'Address'
},
'width': '5.5%'
},
],
style_data={'padding-left': 7},
#striped rows
style_data_conditional=[{
'if': {
'row_index': 'even'
},
'backgroundColor': '#f2f2ed'
#'lightgrey'
}],
#Fixed row for when scrolling vertically
fixed_rows={'headers': True},
style_header={
'backgroundColor': 'rgb(255, 255, 255)',
'fontWeight': 'bold',
'padding-left': 7
},
)
transaction_table = html.Div([
html.Div('Past 100 Recent Transactions',
style={
'padding-bottom': 2,
'font-size': 'xx-large'
}), table
])
return [
"", 'Island Wide',
transaction_features(full_sample), map_component, transaction_table,
ts_plot,
"Showing all resale transactions of Apartments, Condominiums, Executive Condominiums within the past 10 years"
]
