def display_page(pathname):
if pathname is None:
pathname = ''
language = find_language_in_url(pathname)
if match_paths(pathname) == 'introduction':
return p_01_introduction.create_layout(app, language)
if match_paths(pathname) == 'data_input':
return p_02_data_input.create_layout(app, language)
if match_paths(pathname) == 'delinquecy_analysis':
return p_03_delinquecy_analysis.create_layout(app, language)
if match_paths(pathname) == 'covid_impacts':
return p_04_covid_impacts.create_layout(app, language)
if match_paths(pathname) == 'expected_results':
return p_05_expected_results.create_layout(app, language)
if match_paths(pathname) == 'credit_risk':
return p_06_credit_risk.create_layout(app, language)
if match_paths(pathname) == 'effectiveness':
return p_07_effectiveness.create_layout(app, language)
if match_paths(pathname) == 'filtering_module':
return p_08_filtering_module.create_layout(app, language)
if match_paths(pathname) == 'action_optimization':
return p_09_action_optimization.create_layout(app, language)
if match_paths(pathname) == 'routing':
return p_10_routing.create_layout(app, language)
def update_mekkos(url):
language = find_language_in_url(url)
page_texts = pages['effectiveness'][language]
filtered_df = df.copy()
x_bins = np.linspace(0, 1, 6)
mekko_1_pivot = summarize_data_2(filtered_df, y_bins=None, y_col='DSC_CLASSE', x_bins=x_bins, x_col='PD_Default30', value_col='debito_total')
mekko_2_pivot = summarize_data_2(filtered_df, y_bins=None, y_col='DSC_CLASSE', x_bins=x_bins, x_col='PD_Default30', value_col=None)
mekko_1 = draw_mekko_2(mekko_1_pivot)
mekko_2 = draw_mekko_2(mekko_2_pivot)
graphs = [
html.Div([
html.H6([page_texts['graph_titles'][2]], className="subtitle padded"),
dcc.Graph(figure=mekko_1)
],
className= 'center', #"six columns",
),
# html.Div([
# html.H6([page_texts['graph_titles'][5]], className="subtitle padded"),
# dcc.Graph(figure=mekko_2)
# ],
# className="six columns",
# ),
]
return graphs
def overview_mekkos_9(url):
language = find_language_in_url(url)
page_texts = pages['action_optimization'][language]
filtered_df = df.copy()
x_bins = np.linspace(0, 1, 6)
mekko_1_pivot = summarize_data_2(filtered_df, y_bins=None, y_col='DSC_CLASSE', x_bins=x_bins, x_col='PD_Default30', value_col='debito_total')
mekko_2_pivot = summarize_data_2(filtered_df, y_bins=None, y_col='DSC_CLASSE', x_bins=x_bins, x_col='PD_Default30', value_col=None)
mekko_1 = draw_mekko_2(mekko_1_pivot)
mekko_2 = draw_mekko_2(mekko_2_pivot)
graphs = [
html.Div([
html.H6([page_texts['graph_titles'][2]], className="subtitle padded"),
dcc.Graph(figure=mekko_1)
],
className="six columns",
),
html.Div([
html.H6([page_texts['graph_titles'][3]], className="subtitle padded"),
dcc.Graph(figure=mekko_2)
],
className="six columns",
),
]
return graphs
def update_mekkos(selected_class, selected_locality, url):
language = find_language_in_url(url)
page_texts = pages['credit_risk'][language]
if selected_class == 'Select all':
filtered_df = df
else:
filtered_df = df[df.DSC_CLASSE == selected_class]
if selected_locality == 'Select all':
filtered_df = filtered_df
else:
filtered_df = filtered_df[filtered_df.NOME_LOCALIDADE ==
selected_locality]
filtered_df = filtered_df.copy()
x_bins = np.linspace(0, 1, 6)
y_bins = get_same_percentile_total_bins(filtered_df,
'debito_total',
n_bins=6,
max_value=50000)
mekko_1_pivot = summarize_data_2(filtered_df,
y_bins=y_bins,
y_col='debito_total',
x_bins=x_bins,
x_col='PD_Default30',
value_col='debito_total')
mekko_2_pivot = summarize_data_2(filtered_df,
y_bins=y_bins,
y_col='debito_total',
x_bins=x_bins,
x_col='PD_Default30',
value_col=None)
mekko_1 = draw_mekko_2(mekko_1_pivot)
mekko_2 = draw_mekko_2(mekko_2_pivot)
graphs = [
html.Div(
[
html.H6([page_texts['graph_titles'][4]],
className="subtitle padded"),
dcc.Graph(figure=mekko_1)
],
className="six columns",
),
html.Div(
[
html.H6([page_texts['graph_titles'][5]],
className="subtitle padded"),
dcc.Graph(figure=mekko_2)
],
className="six columns",
),
]
return graphs
def update_graphs(selected_class, selected_locality, url):
language = find_language_in_url(url)
page_texts = pages['credit_risk'][language]
if selected_class == 'Select all':
filtered_df = df
else:
filtered_df = df[df.DSC_CLASSE == selected_class]
if selected_locality == 'Select all':
filtered_df = filtered_df
else:
filtered_df = filtered_df[filtered_df.NOME_LOCALIDADE ==
selected_locality]
data = [0] + filtered_df.PD_Default30.values.tolist() + [1]
fig_hist = {
'data': [{
'x': data,
'name': 'Open Date',
'type': 'histogram',
'marker': {
"color": "#cc0000"
},
'nbinsx': 25,
'xbins': {
'start': 0,
'end': 1
}
}],
'layout': {
'xaxis': {
'title': 'Credit risk estimate',
"titlefont": legend_font
},
'yaxis': {
'title': 'Number of customers',
"titlefont": legend_font
},
'legend': {
'title': {
'text': '111',
"font": legend_font
}
}
}
}
x_data = [0] + filtered_df.PD_Default30.values.tolist() + [1]
y_data = [0] + filtered_df.debito_total.clip(
0, filtered_df.debito_total.quantile(0.95)).values.tolist() + [0]
fig_hist_2d = {
'data': [{
'x': x_data,
'y': y_data,
'nbinsx': 25,
'nbinsy': 25,
'name': 'Open Date',
'type': 'histogram2dcontour',
'colorscale': [[0, '#FFFFFF'], [1, '#cc0000']]
}],
'layout': {
'xaxis': {
'title': 'Credit risk estimate',
"titlefont": legend_font
},
'yaxis': {
'title': 'Total amount owed',
"titlefont": legend_font
},
'legend': {
'bgcolor': 'black',
'title': {
'text': '111',
"font": legend_font
}
},
'annotations': [{
'text': '# of<br>customers',
'align': 'left',
'showarrow': False,
'xref': 'paper',
'yref': 'paper',
'x': 1.15,
'y': 1.13,
"font": legend_font
}]
}
}
graphs = [
html.Div(
[
html.H6([page_texts['graph_titles'][2]],
className="subtitle padded"),
dcc.Graph(figure=fig_hist)
],
className="six columns",
),
html.Div(
[
html.H6([page_texts['graph_titles'][3]],
className="subtitle padded"),
dcc.Graph(figure=fig_hist_2d)
],
className="six columns",
),
]
return graphs
def overview_mekkos_9_2(url):
language = find_language_in_url(url)
page_texts = pages['action_optimization'][language]
filtered_df = df.copy()
x_bins = np.linspace(0, 1, 6)
mekko_1_pivot = summarize_data_2(filtered_df, y_bins=None, y_col='DSC_CLASSE', x_bins=x_bins, x_col='PD_Default30', value_col='debito_total')
mekko_2_pivot = summarize_data_2(filtered_df, y_bins=None, y_col='DSC_CLASSE', x_bins=x_bins, x_col='PD_Default30', value_col=None)
mekko_1 = draw_mekko_2(mekko_1_pivot)
mekko_2 = draw_mekko_2(mekko_2_pivot)
graphs = [
html.Div([
html.H6([page_texts['graph_titles'][0]], className="subtitle padded"),
dcc.Graph(figure=mekko_1)
],
className="six columns",
),
html.Div([
html.H6([page_texts['graph_titles'][1]], className="subtitle padded"),
dcc.Graph(figure=mekko_2)
],
className="six columns",
),
]
return graphs
# # @app.callback(
# # Output('efetividade_prevista', component_property='children'),
# # [
# # Input('url', 'pathname')
# # ]
# # )
# # def update_mekkos(url):
# # language = find_language_in_url(url)
# # page_texts = pages['effectiveness'][language]
# # filtered_df = df.copy()
# # x_bins = np.linspace(0, 1, 6)
# # mekko_1_pivot = summarize_data_2(filtered_df, y_bins=None, y_col='DSC_CLASSE', x_bins=x_bins, x_col='PD_Default30', value_col='debito_total')
# # mekko_2_pivot = summarize_data_2(filtered_df, y_bins=None, y_col='DSC_CLASSE', x_bins=x_bins, x_col='PD_Default30', value_col=None)
# # mekko_1 = draw_mekko_2(mekko_1_pivot)
# # mekko_2 = draw_mekko_2(mekko_2_pivot)
# # graphs = [
# # html.Div([
# # html.H6([page_texts['graph_titles'][2]], className="subtitle padded"),
# # dcc.Graph(figure=mekko_1)
# # ],
# # className= 'center', #"six columns",
# # ),
# # # html.Div([
# # # html.H6([page_texts['graph_titles'][5]], className="subtitle padded"),
# # # dcc.Graph(figure=mekko_2)
# # # ],
# # # className="six columns",
# # # ),
# # ]
# # return graphs
# # # @app.callback(
# # # Output('credit_risk_result_graphs', component_property='children'),
# # # [Input('class-dropdown', 'value'),
# # # Input('locality-dropdown', 'value'),
# # # Input('url', 'pathname')]
# # # )
# # # def update_graphs(selected_class, selected_locality, url):
# # # language = find_language_in_url(url)
# # # page_texts = pages['credit_risk'][language]
# # # if selected_class == 'Select all':
# # # filtered_df = df
# # # else:
# # # filtered_df = df[df.DSC_CLASSE == selected_class]
# # # if selected_locality == 'Select all':
# # # filtered_df = filtered_df
# # # else:
# # # filtered_df = filtered_df[filtered_df.NOME_LOCALIDADE == selected_locality]
# # # data = [0] + filtered_df.PD_Default30.values.tolist() + [1]
# # # fig_hist = {
# # # 'data': [
# # # {
# # # 'x': data,
# # # 'name': 'Open Date',
# # # 'type': 'histogram',
# # # 'marker': {"color": "#cc0000"},
# # # 'nbinsx': 25,
# # # 'xbins': {'start': 0, 'end': 1}
# # # }
# # # ],
# # # 'layout': {
# # # 'xaxis': {'title':'Credit risk estimate', "titlefont": legend_font},
# # # 'yaxis': {'title':'Number of customers', "titlefont": legend_font},
# # # 'legend': {'title': {'text': '111', "font": legend_font}}
# # # }
# # # }
# # # x_data = [0] + filtered_df.PD_Default30.values.tolist() + [1]
# # # y_data = [0] + filtered_df.debito_total.clip(0, filtered_df.debito_total.quantile(0.95)).values.tolist() + [0]
# # # fig_hist_2d = {
# # # 'data': [{
# # # 'x': x_data,
# # # 'y': y_data,
# # # 'nbinsx': 25,
# # # 'nbinsy': 25,
# # # 'name': 'Open Date',
# # # 'type': 'histogram2dcontour',
# # # 'colorscale': [[0, '#FFFFFF'], [1, '#cc0000']]
# # # }],
# # # 'layout': {
# # # 'xaxis': {'title':'Credit risk estimate', "titlefont": legend_font},
# # # 'yaxis': {'title':'Total amount owed', "titlefont": legend_font},
# # # 'legend': {'bgcolor': 'black','title': {'text': '111', "font": legend_font}},
# # # 'annotations': [{'text':'# of<br>customers', 'align':'left', 'showarrow':False,'xref':'paper', 'yref':'paper', 'x':1.15, 'y':1.13, "font":legend_font}]
# # # }
# # # }
# # # graphs = [
# # # html.Div([
# # # html.H6([page_texts['graph_titles'][2]], className="subtitle padded"),
# # # dcc.Graph(figure=fig_hist)
# # # ],
# # # className="six columns",
# # # ),
# # # html.Div([
# # # html.H6([page_texts['graph_titles'][3]], className="subtitle padded"),
# # # dcc.Graph(figure=fig_hist_2d)
# # # ],
# # # className="six columns",
# # # ),
# # # ]
# # # return graphs
# # # get relative data folder
# # PATH = pathlib.Path(__file__).parent
# # DATA_PATH = PATH.joinpath("../data").resolve()
# # df = read_csv(DATA_PATH.joinpath("2 - risco_cred_e_cadastro_short.csv"), encoding='utf=8')
# # class_names = ['Select all'] + df.DSC_CLASSE.unique().tolist()
# # localities = ['Select all'] + df.groupby('NOME_LOCALIDADE').NOME_LOCALIDADE.count().sort_values(ascending=False).index.tolist()
# # 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",
# # )