def update_forecast(json_data):
""" update forecast figure"""
df_fcst = pd.read_json(json_data, orient='split')
trace_f = []
selected_stocks = df_fcst.columns.tolist()
selected_stocks = [
stock for stock in selected_stocks if stock != 'type_obs'
]
type_obs = df_fcst['type_obs'].unique().tolist()
for stock in selected_stocks:
# loop over types to differentiate forecast vs observed
for observed_type in type_obs:
x = df_fcst[df_fcst['type_obs'] == observed_type][stock].index
y = df_fcst[df_fcst['type_obs'] == observed_type][stock]
tmp_name = observed_type + ' ' + stock
# append Plotly object
trace_f.append(dashf.go_scatter(x=x, y=y, name=tmp_name))
figure = dashf.scatter_figure(trace=trace_f,
min_range=df_fcst.index.min(),
max_range=df_fcst.index.max(),
text_title='Stock Prices Forecast')
return figure
def update_trend(json_data):
""" update trned component figure"""
df_season = pd.read_json(json_data, orient='split')
trace1 = []
# append plotly object
trace1.append(dashf.go_scatter(x=df_season.index, y=df_season['trend'], name='trend'))
# update figure
figure = dashf.scatter_figure(trace1, min_range=df_season.index.min(), max_range=df_season.index.max(), text_title='Trend Component')
return figure
def update_residual(json_data):
""" update residual figure"""
df_season = pd.read_json(json_data, orient='split')
trace1 = []
# append plotly object
trace1.append(dashf.go_scatter(x=df_season.index, y=df_season['resid'], name='residuals'))
# update figure
figure = dashf.scatter_figure(trace1, min_range=df_season.index.min(), max_range=df_season.index.max(),
text_title='Residuals')
return figure
def update_graph(selected_dropdown_value):
"""update stock price figure """
trace1 = []
df_sub = df
for stock in selected_dropdown_value:
# append Plotly Scatter object
trace1.append(
dashf.go_scatter(x=df_sub.index, y=df_sub[stock], name=stock))
# Update figure
figure = dashf.scatter_figure(trace1,
min_range=df_sub.index.min(),
max_range=df_sub.index.max(),
text_title='Stock Prices')
return figure
