def toppage():
return dbc.Container([
dbc.Row([
dcc.DatePickerRange(id='toppage-date-picker-range',
min_date_allowed=dt(2020, 4, 30),
initial_visible_month=dt.today(),
start_date=datetime.date(2020, 4, 30),
end_date=datetime.date(dt.now().year,
dt.now().month,
dt.now().day)),
wp.dbc_top_title("Dashboard top")
],
className="h-30"),
dcc.Loading(id="top-loading", children=[html.Div(id="top_dashoboard")])
],
style={"height": "90vh"},
fluid=True)
def return_analytics():
return dbc.Container([
dbc.Row([
dcc.DatePickerRange(id='return-analytics-date-picker-range',
min_date_allowed=dt(2019, 12, 1),
initial_visible_month=dt.today(),
start_date=datetime.date(2020, 4, 30),
end_date=datetime.date(dt.now().year,
dt.now().month,
dt.now().day)),
wp.dbc_top_title("return analytics")
],
className="h-30"),
dcc.Loading(id="return-analytics-loading",
children=[html.Div(id="return_analytics")])
],
style={"height": "90vh"},
fluid=True)
def race_info():
return dbc.Container([
dbc.Row([
dcc.DatePickerSingle(id='raceinfo-date-picker-single',
min_date_allowed=dt(2019, 12, 1),
initial_visible_month=dt.today(),
date=datetime.date(dt.now().year,
dt.now().month,
dt.now().day)),
dcc.Dropdown(id="raceinfo-dropdown-keibajo", options=[], value=""),
dcc.Dropdown(id="raceinfo-dropdown-raceid", options=[], value=""),
wp.dbc_top_title("race info")
],
className="h-30"),
dcc.Loading(id="race-info-loading",
children=[html.Div(id="raceinfo-race-detail")])
],
style={"height": "90vh"},
fluid=True)
def graph_example():
fig01 = gp.simple_line()
fig02 = gp.line_with_column_encoding_color()
fig03 = gp.label_lines_with_annotations()
fig04 = gp.filled_line()
fig05 = gp.bar_chart()
fig06 = gp.customized_bar_chart()
fig07 = gp.facetted_subplots()
fig08 = gp.basic_bar_chart_with_plotly()
fig09 = gp.grouped_bar_chart()
fig10 = gp.stacked_bar_chart()
fig11 = gp.bar_chart_with_hover_text()
fig12 = gp.bar_chart_with_direct_labels()
fig13 = gp.controlling_text_fontsize_with_uniformtext()
fig14 = gp.pie_chart()
fig15 = gp.buble_chart()
fig16 = gp.basic_dot_plot()
fig17 = gp.styled_categorical_dot_plot()
fig18 = gp.basic_horizontal_bar_chart()
fig19 = gp.color_palette_for_bar_chart()
fig20 = gp.bar_chart_with_line_plot()
fig21 = gp.sunburst_of_a_rectangular()
fig22 = gp.sankey_diagram()
fig23 = gp.treemap()
fig24 = gp.bar_chart_with_error_bars()
fig25 = gp.display_the_underlying_data()
fig26 = gp.choosing_the_algorithm()
fig27 = gp.difference_between_quartile_algorithms()
fig28 = gp.basic_horizontal_box_plot()
fig29 = gp.grouped_box_plot()
fig30 = gp.rainbow_box_plot()
return \
dbc.Container([
dbc.Row([
wp.dbc_top_title("グラフサンプル")
],className="h-30"),
dbc.Row([
wp.dbc_title("simple_line", 4),
wp.dbc_title("line_with_column_encoding_color", 4),
wp.dbc_title("label_lines_with_annotations", 4),
], className="h-8"),
dbc.Row([
wp.dbc_graph("fig01", 4, fig01),
wp.dbc_graph("fig02", 4, fig02),
wp.dbc_graph("fig03", 4, fig03),
], className="h-50"),
dbc.Row([
wp.dbc_title("filled_line", 4),
wp.dbc_title("bar_chart", 4),
wp.dbc_title("customized_bar_chart", 4),
], className="h-8"),
dbc.Row([
wp.dbc_graph("fig04", 4, fig04),
wp.dbc_graph("fig05", 4, fig05),
wp.dbc_graph("fig06", 4, fig06),
], className="h-50"),
dbc.Row([
wp.dbc_title("facetted_subplots", 4),
wp.dbc_title("basic_bar_chart_with_plotly", 4),
wp.dbc_title("grouped_bar_chart", 4),
], className="h-8"),
dbc.Row([
wp.dbc_graph("fig07", 4, fig07),
wp.dbc_graph("fig08", 4, fig08),
wp.dbc_graph("fig09", 4, fig09),
], className="h-50"),
dbc.Row([
wp.dbc_title("stacked_bar_chart", 4),
wp.dbc_title("bar_chart_with_hover_text", 4),
wp.dbc_title("bar_chart_with_direct_labels", 4),
], className="h-8"),
dbc.Row([
wp.dbc_graph("fig10", 4, fig10),
wp.dbc_graph("fig11", 4, fig11),
wp.dbc_graph("fig12", 4, fig12),
], className="h-50"),
dbc.Row([
wp.dbc_title("controlling_text_fontsize_with_uniformtext", 4),
wp.dbc_title("pie_chart", 4),
wp.dbc_title("buble_chart", 4),
], className="h-8"),
dbc.Row([
wp.dbc_graph("fig13", 4, fig13),
wp.dbc_graph("fig14", 4, fig14),
wp.dbc_graph("fig15", 4, fig15),
], className="h-50"),
dbc.Row([
wp.dbc_title("basic_dot_plot", 4),
wp.dbc_title("styled_categorical_dot_plot", 4),
wp.dbc_title("basic_horizontal_bar_chart", 4),
], className="h-8"),
dbc.Row([
wp.dbc_graph("fig16", 4, fig16),
wp.dbc_graph("fig17", 4, fig17),
wp.dbc_graph("fig18", 4, fig18),
], className="h-50"),
dbc.Row([
wp.dbc_title("color_palette_for_bar_chart", 4),
wp.dbc_title("bar_chart_with_line_plot", 4),
wp.dbc_title("sunburst_of_a_rectangular", 4),
], className="h-8"),
dbc.Row([
wp.dbc_graph("fig19", 4, fig19),
wp.dbc_graph("fig20", 4, fig20),
wp.dbc_graph("fig21", 4, fig21),
], className="h-50"),
dbc.Row([
wp.dbc_title("sankey_diagram", 4),
wp.dbc_title("treemap", 4),
wp.dbc_title("bar_chart_with_error_bars", 4),
], className="h-8"),
dbc.Row([
wp.dbc_graph("fig22", 4, fig22),
wp.dbc_graph("fig23", 4, fig23),
wp.dbc_graph("fig24", 4, fig24),
], className="h-50"),
dbc.Row([
wp.dbc_title("display_the_underlying_data", 4),
wp.dbc_title("choosing_the_algorithm", 4),
wp.dbc_title("difference_between_quartile_algorithms", 4),
], className="h-8"),
dbc.Row([
wp.dbc_graph("fig25", 4, fig25),
wp.dbc_graph("fig26", 4, fig26),
wp.dbc_graph("fig27", 4, fig27),
], className="h-50"),
dbc.Row([
wp.dbc_title("basic_horizontal_box_plot", 4),
wp.dbc_title("grouped_box_plot", 4),
wp.dbc_title("rainbow_box_plot", 4),
], className="h-8"),
dbc.Row([
wp.dbc_graph("fig28", 4, fig28),
wp.dbc_graph("fig29", 4, fig29),
wp.dbc_graph("fig30", 4, fig30),
], className="h-50"),
],
style={"height": "90vh"},
fluid=True
)
def race_trend():
race_df = GetData.get_race_data_real().query("データ区分 == '7'")
raceuma_df = GetData.get_raceuma_data_real().query("データ区分 == '7'")
raceuma_df.loc[:, "競走馬コード"] = raceuma_df.apply(lambda x : str(x["競走コード"]) + str(x["馬番"]).zfill(2), axis=1)
bet_df = GetData.get_bet_data_real()
haraimodoshi_dict = GetData.get_haraimodoshi_dict_real()
if len(race_df.index) == 0 or len(raceuma_df.index) == 0:
return html.P("no data")
# 得点１位着順
fig1 = wp.cp_pie_chart_score_1(raceuma_df)
# 単勝回収率
fig2_df = raceuma_df[raceuma_df["馬券評価順位"] == 1].copy()
fig2_value = fig2_df["単勝配当"].mean()
fig2_reference = 0
fig2 = gp.add_steps_threshold_anda_delta(fig2_value, fig2_reference)
# 複勝回収率
fig3_df = raceuma_df[raceuma_df["馬券評価順位"] == 1].copy()
fig3_value = fig3_df["複勝配当"].mean()
fig3_reference = 0
fig3 = gp.add_steps_threshold_anda_delta(fig3_value, fig3_reference)
# １番人気
fig4 = wp.cp_pie_chart_ninki_1(raceuma_df)
# 単勝回収率
fig5_df = raceuma_df[raceuma_df["単勝人気"] == 1].copy()
fig5_value = fig5_df["単勝配当"].mean()
fig5_reference = 0
fig5 = gp.add_steps_threshold_anda_delta(fig5_value, fig5_reference)
# 複勝回収率
fig6_df = raceuma_df[raceuma_df["単勝人気"] == 1].copy()
fig6_value = fig6_df["複勝配当"].mean()
fig6_reference = 0
fig6 = gp.add_steps_threshold_anda_delta(fig6_value, fig6_reference)
# 場所別単複回収率・的中率
fig7 = wp.cp_multiple_line_and_bar_chart_place_tanpuku_return(race_df, raceuma_df)
if len(bet_df.index) != 0:
# 場所別馬券回収率・的中率
fig8 = wp.cp_multiple_line_and_bar_chart_place_bet_return(bet_df)
# 券種別結果
summary_bet_df = cd.get_summary_bet_df(bet_df)
fig9 = wp.cp_simple_waterfall_chart_balance_change(summary_bet_df)
else:
fig8 = ""
fig9 = ""
# 単勝配当分布
#fig10 = wp.cp_pie_chart_tansho_dist(haraimodoshi_dict)
# 複勝配当分布
#fig11 = wp.cp_pie_chart_fukusho_dist(haraimodoshi_dict)
# 馬連配当分布
#fig12 = wp.cp_pie_chart_umaren_dist(haraimodoshi_dict)
# 馬単配当分布
#fig13 = wp.cp_pie_chart_umatan_dist(haraimodoshi_dict)
# ワイド配当分布
#fig14 = wp.cp_pie_chart_wide_dist(haraimodoshi_dict)
# 三連複配当分布
#fig15 = wp.cp_pie_chart_sanrenpuku_dist(haraimodoshi_dict)
# 馬連的中1
fig20 = wp.cp_stacked_funnel_plot_umaren_1(race_df, raceuma_df, haraimodoshi_dict)
# 馬連的中2
fig21 = wp.cp_stacked_funnel_plot_umaren_2(race_df, raceuma_df, haraimodoshi_dict)
# 馬単的中1
fig22 = wp.cp_stacked_funnel_plot_umatan_1(race_df, raceuma_df, haraimodoshi_dict)
# 馬単的中2
fig23 = wp.cp_stacked_funnel_plot_umatan_2(race_df, raceuma_df, haraimodoshi_dict)
# 馬単的中3
fig24 = wp.cp_stacked_funnel_plot_umatan_3(race_df, raceuma_df, haraimodoshi_dict)
# ワイド的中1
fig25 = wp.cp_stacked_funnel_plot_wide_1(race_df, raceuma_df, haraimodoshi_dict)
return dcc.Loading(id="racetrend-loading", children=[
dbc.Container([
dbc.Row([
wp.dbc_top_title("todays race trend")
], className="h-30"),
dbc.Row([
wp.dbc_graph("fig1", 2, fig1, "得点１位着順", 130),
wp.dbc_graph("fig2", 2, fig2, "単勝回収率", 130),
wp.dbc_graph("fig3", 2, fig3, "複勝回収率", 130),
wp.dbc_graph("fig4", 2, fig4, "１番人気", 130),
wp.dbc_graph("fig5", 2, fig5, "単勝回収率", 130),
wp.dbc_graph("fig6", 2, fig6, "複勝回収率", 130),
], className="h-20", no_gutters=True),
# dbc.Row([
# wp.dbc_graph("fig10", 2, fig10, "単勝配当分布", 200),
# wp.dbc_graph("fig11", 2, fig11, "複勝配当分布", 200),
# wp.dbc_graph("fig12", 2, fig12, "馬連配当分布", 200),
# wp.dbc_graph("fig13", 2, fig13, "馬単配当分布", 200),
# wp.dbc_graph("fig14", 2, fig14, "ワイド配当分布", 200),
# wp.dbc_graph("fig15", 2, fig15, "三連複配当分布", 200),
# ], className="h-20", no_gutters=True),
dbc.Row([
wp.dbc_graph("fig7", 4, fig7, "場所別単複回収率・的中率", 300),
wp.dbc_graph("fig8", 4, fig8, "場所別馬券回収率・的中率", 300),
wp.dbc_graph("fig9", 4, fig9, "券種別結果", 300),
], className="h-30", no_gutters=True),
dbc.Row([
wp.dbc_graph("fig20", 2, fig20, "馬連的中1", 400),
wp.dbc_graph("fig21", 2, fig21, "馬連的中2", 400),
wp.dbc_graph("fig22", 2, fig22, "馬単的中1", 400),
wp.dbc_graph("fig23", 2, fig23, "馬単的中2", 400),
wp.dbc_graph("fig24", 2, fig24, "馬単的中3", 400),
wp.dbc_graph("fig25", 2, fig25, "ワイド的中1", 400),
], className="h-30", no_gutters=True),
],
style={"height": "90vh"},
fluid=True
)])