def raceuma_info_render(shap_df_win, shap_df_jiku, shap_df_are):
# 勝ち根拠
fig1 = wp.cp_controlling_text_fontsize_with_uniformtext_win_basis(
shap_df_win)
# 軸根拠
fig2 = wp.cp_controlling_text_fontsize_with_uniformtext_jiku_basis(
shap_df_jiku)
# 荒れ根拠
fig3 = wp.cp_controlling_text_fontsize_with_uniformtext_are_basis(
shap_df_are)
return [
dbc.Row([
wp.dbc_graph("fig1", 4, fig1, "勝ち根拠", 300, bottom=100),
wp.dbc_graph("fig2", 4, fig2, "軸根拠", 300, bottom=100),
wp.dbc_graph("fig3", 4, fig3, "荒れ根拠", 300, bottom=100),
],
className="h-50"),
]
def race_result_render(race_df, raceuma_df, bet_df, haraimodoshi_dict):
race_sr = race_df.iloc[0]
raceuma_df = raceuma_df.sort_values("タイム指数")
raceuma_df.loc[:, "馬名表用"] = raceuma_df["馬名"]
raceuma_df.loc[:, "馬名"] = raceuma_df.apply(
lambda x: x["馬名"] + "<br>(" + str(x["単勝人気"]) + "番人気 " + str(x[
"確定着順"]) + "着 " + str(x["馬券評価順位"]) + "位)",
axis=1)
haraimodoshi_df = pd.DataFrame()
for key, val in haraimodoshi_dict.items():
if len(val.index) != 0:
val.loc[:, "式別"] = key
haraimodoshi_df = haraimodoshi_df.append(val)
# 得点
fig1 = wp.cp_basic_horizontal_bar_chart_score(raceuma_df)
# 得点バブル
#fig3_df = raceuma_df[["確定着順", "デフォルト得点", "SCORE", "得点V3", "馬名"]].sort_values("確定着順")
#fig3 = wp.cp_buble_chart_score(fig3_df)
# テーブル
fig4_df = raceuma_df[[
"枠番", "馬番", "馬名表用", "性別コード", "タイム", "タイム指数", "デフォルト得点", "得点", "馬券評価順位",
"単勝オッズ", "単勝人気", "複勝オッズ1", "複勝オッズ2", "展開コード", "騎手名", "馬齢", "調教師名",
"負担重量", "馬体重", "確定着順", "コーナー順位1", "コーナー順位2", "コーナー順位3", "コーナー順位4",
"上がりタイム", "所属", "得点V3", "WIN_RATE", "JIKU_RATE", "ANA_RATE",
"WIN_RANK", "JIKU_RANK", "ANA_RANK", "SCORE", "SCORE_RANK", "CK1_RATE",
"CK2_RATE", "CK3_RATE", "CK1_RANK", "CK2_RANK", "CK3_RANK"
]].sort_values("タイム指数", ascending=False)
# 払戻・投票
if len(haraimodoshi_df.index) != 0:
fig5_haraimodoshi_df = haraimodoshi_df[["式別", "馬番", "払戻"]]
else:
fig5_haraimodoshi_df = haraimodoshi_df
fig5_bet_df = bet_df[["式別名", "番号", "金額", "結果", "合計"]]
# タイム指数、予想タイム指数
fig6 = wp.cp_basic_dot_plot_time_record(raceuma_df)
# タイム指数、単勝オッズ
fig10 = wp.cp_bar_chart_with_line_plot_time_record_tansho(raceuma_df)
# ラップ
#fig11 = wp.cp_simple_line_rap(race_sr, 500)
# 単勝支持率
fig12 = wp.cp_pie_chart_tansho_approval_rate(raceuma_df)
return dcc.Loading(
id="race-result-render-loading",
children=[
dbc.Row([
wp.dbc_race_info(race_sr, 8),
wp.dbc_race_result(race_sr, haraimodoshi_df, 4),
],
className="h-8",
no_gutters=True),
dbc.Row([
wp.dbc_graph("fig12", 4, fig12, "単勝支持率", 400),
wp.dbc_graph("fig10", 4, fig10, "タイム指数、単勝オッズ", 500),
wp.dbc_haraimodoshi_table("fig5_1", 2, fig5_haraimodoshi_df),
wp.dbc_bet_table("fig5_2", 2, fig5_bet_df),
],
className="h-50",
no_gutters=True),
dbc.Row([
wp.dbc_graph("fig1", 6, fig1, "得点", 400),
wp.dbc_graph("fig6", 6, fig6, "タイム指数、予想タイム指数", 500),
],
className="h-50",
no_gutters=True),
# dbc.Row([
# wp.dbc_graph("fig3", 6, fig3, "得点バブル", 500),
# wp.dbc_graph("fig11", 4, fig11, "ラップ", 400),
# ], className="h-50", no_gutters=True),
wp.dbc_race_result_table("fig4", 16, fig4_df)
])
def toppage_render(race_df, raceuma_df, bet_df, haraimodoshi_dict):
daily_bet_df = cd.get_daily_bet_df(bet_df)
daily_rank1_raceuma_df = cd.get_daily_rank1_raceuma_df(raceuma_df)
summary_bet_df = cd.get_summary_bet_df(bet_df)
place_bet_df = cd.get_place_bet_df(bet_df)
prev_daily_bet_df = daily_bet_df.drop(daily_bet_df.tail(1).index)
prev_daily_rank1_raceuma_df = daily_rank1_raceuma_df.drop(
daily_bet_df.tail(1).index)
place_bet_df = place_bet_df[place_bet_df["金額"] != 0].copy()
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)
# 回収
fig1 = wp.cp_data_cards_return(daily_bet_df, prev_daily_bet_df)
# 回収率
fig2 = wp.cp_showing_information_above_return_rate(daily_bet_df,
prev_daily_bet_df)
# 単勝回収率
fig3 = wp.cp_add_steps_threshold_anda_delta_tansho_return(
daily_rank1_raceuma_df, prev_daily_rank1_raceuma_df)
# 複勝回収率
fig4 = wp.cp_add_steps_threshold_anda_delta_fukusho_return(
daily_rank1_raceuma_df, prev_daily_rank1_raceuma_df)
# 残高推移1
fig5 = wp.cp_time_series_with_range_selector_buttions_balance_change(
daily_bet_df)
# 残高推移2
fig6 = wp.cp_simple_waterfall_chart_balance_change(summary_bet_df)
# 場所別回収率的中率払戻
fig7 = wp.cp_buble_chart_place_return_hit(place_bet_df)
# 券種毎回収率
fig8 = wp.cp_multi_bullet(summary_bet_df)
# 得点１位着順
fig9 = wp.cp_pie_chart_score_1(raceuma_df)
# 場所別単複回収率・的中率
fig10 = wp.cp_multiple_line_and_bar_chart_place_tanpuku_return(
race_df, raceuma_df)
# 場所別馬券回収率・的中率
fig11 = wp.cp_multiple_line_and_bar_chart_place_bet_return(bet_df)
# 馬連的中1
fig12 = wp.cp_basic_funnel_plot_umaren_1(race_df, raceuma_df,
haraimodoshi_dict)
# 馬連的中2
fig13 = wp.cp_basic_funnel_plot_umaren_2(race_df, raceuma_df,
haraimodoshi_dict)
# 馬単的中1
fig14 = wp.cp_basic_funnel_plot_umatan_1(race_df, raceuma_df,
haraimodoshi_dict)
# 馬単的中2
fig15 = wp.cp_basic_funnel_plot_umatan_2(race_df, raceuma_df,
haraimodoshi_dict)
# 馬単的中3
fig16 = wp.cp_basic_funnel_plot_umatan_3(race_df, raceuma_df,
haraimodoshi_dict)
# ワイド的中1
fig17 = wp.cp_basic_funnel_plot_wide_1(race_df, raceuma_df,
haraimodoshi_dict)
# 馬連配当分布
fig18 = wp.cp_choosing_the_algorithm_umaren_dist(race_df,
haraimodoshi_dict)
# 馬単配当分布
fig19 = wp.cp_choosing_the_algorithm_umatan_dist(race_df,
haraimodoshi_dict)
# ワイド配当分布
fig20 = wp.cp_choosing_the_algorithm_wide_dist(race_df, haraimodoshi_dict)
# 三連複配当分布
fig21 = wp.cp_choosing_the_algorithm_sanrenpuku_dist(
race_df, haraimodoshi_dict)
return dcc.Loading(
id="top-detail-loading",
children=[
dbc.Row([
wp.dbc_graph("fig1", 2, fig1, "回収", 130),
wp.dbc_graph("fig2", 4, fig2, "回収率", 130),
wp.dbc_graph("fig9", 2, fig9, "得点１位着順", 130),
wp.dbc_graph("fig3", 2, fig3, "単勝回収率", 130),
wp.dbc_graph("fig4", 2, fig4, "複勝回収率", 130),
],
className="h-20",
no_gutters=True),
dbc.Row([
wp.dbc_graph("fig8", 3, fig8, "券種毎回収率", 450),
dbc.Col([
dbc.Row(wp.dbc_graph("fig5", 12, fig5, "残高推移", 250), ),
dbc.Row(wp.dbc_graph("fig6", 12, fig6, "", 200), )
],
width=4),
wp.dbc_graph("fig7", 5, fig7, "場所別回収率的中率払戻", 450),
],
className="h-50",
no_gutters=True),
dbc.Row([
wp.dbc_graph("fig10", 6, fig10, "場所別単複回収率・的中率", 300),
wp.dbc_graph("fig11", 6, fig11, "場所別馬券回収率・的中率", 300),
],
className="h-50",
no_gutters=True),
dbc.Row([
wp.dbc_graph("fig12", 2, fig12, "馬連的中1", 300),
wp.dbc_graph("fig13", 2, fig13, "馬連的中2", 300),
wp.dbc_graph("fig14", 2, fig14, "馬単的中1", 300),
wp.dbc_graph("fig15", 2, fig15, "馬単的中2", 300),
wp.dbc_graph("fig16", 2, fig16, "馬単的中3", 300),
wp.dbc_graph("fig17", 2, fig17, "ワイド的中1", 300),
],
className="h-50",
no_gutters=True),
dbc.Row([
wp.dbc_graph("fig18", 6, fig18, "馬連配当分布", 250),
wp.dbc_graph("fig19", 6, fig19, "馬単配当分布", 250),
],
className="h-50",
no_gutters=True),
dbc.Row([
wp.dbc_graph("fig20", 6, fig20, "ワイド配当分布", 250),
wp.dbc_graph("fig21", 6, fig21, "三連複配当分布", 250),
],
className="h-50",
no_gutters=True),
])
def race_info_render(race_df, raceuma_df, shap_df_umaren_are,
shap_df_umatan_are, shap_df_sanrenpuku_are):
race_sr = race_df.iloc[0]
term_start_date = "2019-12-01"
term_end_date = race_sr["月日"].strftime("%Y-%m-%d")
ketto_toroku_bango_list = raceuma_df["血統登録番号"].tolist()
raceuma_prev_df = GetData.get_raceuma_prev_df(term_start_date,
term_end_date,
ketto_toroku_bango_list)
raceuma_prev_df = pd.merge(raceuma_df[["血統登録番号", "馬名"]],
raceuma_prev_df,
on="血統登録番号")
raceuma_df = raceuma_df.sort_values("馬番", ascending=False)
raceuma_prev_df = raceuma_prev_df.sort_values("馬名", ascending=False)
# 得点
fig1 = wp.cp_basic_horizontal_bar_chart_score(raceuma_df)
# 最大得点分布
#fig2 = wp.cp_basic_dot_plot_max_score(raceuma_df)
# 得点バブル
#fig3 = wp.cp_buble_chart_score(raceuma_df.sort_values("馬番"))
# レーダーチャート
fig4 = wp.cp_multiple_trace_rader_chart_score(raceuma_df.sort_values("馬番"))
# 先行率
fig5 = wp.cp_basic_horizontal_box_plot_senko_rate(raceuma_prev_df)
# タイム指数
fig6 = wp.cp_basic_horizontal_box_plot_time_score(raceuma_prev_df)
# テーブルデータ
fig7_df = raceuma_df[[
"枠番", "馬番", "馬名", "性別コード", "馬齢", "負担重量", "予想タイム指数順位", "予想タイム指数",
"デフォルト得点", "得点", "馬券評価順位", "先行指数", "予想展開", "騎手名", "調教師名", "所属", "得点V3",
"WIN_RATE", "JIKU_RATE", "ANA_RATE", "WIN_RANK", "JIKU_RANK",
"ANA_RANK", "SCORE", "SCORE_RANK", "CK1_RATE", "CK2_RATE", "CK3_RATE",
"CK1_RANK", "CK2_RANK", "CK3_RANK"
]].sort_values("馬番")
# 馬連荒れ根拠
#fig8 = wp.cp_controlling_text_fontsize_with_uniformtext_umaren_basis(shap_df_umaren_are)
# 馬単荒れ根拠
#fig9 = wp.cp_controlling_text_fontsize_with_uniformtext_umatan_basis(shap_df_umatan_are)
# 三連複荒れ根拠
#fig10 = wp.cp_controlling_text_fontsize_with_uniformtext_sanrenpuku_basis(shap_df_sanrenpuku_are)
raceuma_dropdown_option = raceuma_df[["馬名", "馬番"]].rename(columns={
"馬名": "label",
"馬番": "value"
}).to_dict(orient='record')
return dcc.Loading(
id="race-info-render-loading",
children=[
dbc.Row([
wp.dbc_race_info(race_sr, 12),
], className="h-8"),
# dbc.Row([
# wp.dbc_graph("fig8", 4, fig8, "馬連荒れ根拠", 300, bottom=100),
# wp.dbc_graph("fig9", 4, fig9, "馬単荒れ根拠", 300, bottom=100),
# wp.dbc_graph("fig10", 4, fig10, "三連複荒れ根拠", 300, bottom=100),
# ], className="h-30", no_gutters=True),
dbc.Row([
wp.dbc_graph("fig1", 6, fig1, "得点", 500),
wp.dbc_graph("fig4", 6, fig4, "レーダーチャート", 500),
],
className="h-50",
no_gutters=True),
# dbc.Row([
# wp.dbc_graph("fig2", 6, fig2, "最大得点分布", 500),
# wp.dbc_graph("fig3", 6, fig3, "得点バブル", 500),
# ], className="h-50", no_gutters=True),
dbc.Row([
wp.dbc_graph("fig5", 6, fig5, "先行率", 500),
wp.dbc_graph("fig6", 6, fig6, "タイム指数", 500),
],
className="h-50",
no_gutters=True),
wp.dbc_race_info_table("fig7", 16, fig7_df),
dbc.Row([
wp.dbc_title("得点根拠", 6),
dcc.Dropdown(id="raceinfo-dropdown-raceuma",
options=raceuma_dropdown_option,
style={"width": "300px"}),
],
className="h-8"),
dcc.Loading(id="raceuma-info-loading",
children=[html.Div(id="raceinfo-raceuma-detail")])
])
def return_analytics_render(raceuma_df, bet_df, haraimodoshi_dict):
daily_summary_bet_df = cd.get_daily_summary_bet_df(bet_df)
# 単勝
fig1 = wp.cp_add_steps_threshold_anda_delta_tansho(daily_summary_bet_df)
# 複勝
fig2 = wp.cp_add_steps_threshold_anda_delta_fukusho(daily_summary_bet_df)
# 馬連
fig3 = wp.cp_add_steps_threshold_anda_delta_umaren(daily_summary_bet_df)
# 馬単
fig4 = wp.cp_add_steps_threshold_anda_delta_umatan(daily_summary_bet_df)
# ワイド
fig5 = wp.cp_add_steps_threshold_anda_delta_wide(daily_summary_bet_df)
# 三連複
fig6 = wp.cp_add_steps_threshold_anda_delta_sanrenpuku(
daily_summary_bet_df)
# 月別券種別回収率推移
#fig7 = wp.cp_label_lines_with_annotations_bet_return_balance(bet_df)
# 券種別配当分布
#fig8 = wp.cp_visualizing_the_distribution_haraimodoshi_dist(haraimodoshi_dict)
# 場所別回収的中率
fig9 = wp.cp_bar_chart_with_line_plot_place_bet_return(bet_df)
# 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)
# 券種別配当分布比較
fig16 = wp.cp_choosing_the_algorithm_haraimodoshi_dist(haraimodoshi_dict)
# 券種別配当中央値比較
#fig17 = wp.cp_styled_categorical_dot_plot_haraimodoshi_median(haraimodoshi_dict)
return dcc.Loading(
id="return-analytics-render-loading",
children=[
dbc.Row([
wp.dbc_graph("fig1", 2, fig1, "単勝", 130),
wp.dbc_graph("fig2", 2, fig2, "複勝", 130),
wp.dbc_graph("fig3", 2, fig3, "馬連", 130),
wp.dbc_graph("fig4", 2, fig4, "馬単", 130),
wp.dbc_graph("fig5", 2, fig5, "ワイド", 130),
wp.dbc_graph("fig6", 2, fig6, "三連複", 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("fig9", 5, fig9, "場所別回収的中率", 600),
wp.dbc_graph("fig16", 7, fig16, "券種別配当分布比較", 600),
],
className="h-50",
no_gutters=True),
# dbc.Row([
# wp.dbc_graph("fig7", 4, fig7, "月別券種別回収率推移", 400),
# wp.dbc_graph("fig8", 4, fig8, "券種別配当分布", 400),
# wp.dbc_graph("fig17", 5, fig17, "券種別配当中央値比較", 400),
# ], className="h-50", no_gutters=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 kpi_analytics_render(race_df, raceuma_df, bet_df, haraimodoshi_dict, end_date):
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)
# 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_df_diff = fig2_df[fig2_df["年月日"] != end_date]
fig2 = wp.cp_add_steps_threshold_anda_delta_tansho_return(fig2_df, fig2_df_diff)
# 複勝回収率
fig3_df = raceuma_df[raceuma_df["馬券評価順位"] == 1].copy()
fig3_df_diff = fig3_df[fig3_df["年月日"] != end_date]
fig3 = wp.cp_add_steps_threshold_anda_delta_fukusho_return(fig3_df, fig3_df_diff)
# 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_df_diff = fig5_df[fig5_df["年月日"] != end_date]
fig5 = wp.cp_add_steps_threshold_anda_delta_tansho_return(fig5_df, fig5_df_diff)
# 複勝回収率
fig6_df = raceuma_df[raceuma_df["単勝人気"] == 1].copy()
fig6_df_diff = fig6_df[fig6_df["年月日"] != end_date]
fig6 = wp.cp_add_steps_threshold_anda_delta_fukusho_return(fig6_df, fig6_df_diff)
# 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)
# 場所別馬券回収率・的中率
#fig8 = wp.cp_multiple_line_and_bar_chart_place_bet_return(bet_df)
# 馬連的中1
fig10 = wp.cp_stacked_funnel_plot_umaren_1(race_df, raceuma_df, haraimodoshi_dict)
# 馬連的中2
fig11 = wp.cp_stacked_funnel_plot_umaren_2(race_df, raceuma_df, haraimodoshi_dict)
# 馬単的中1
fig12 = wp.cp_stacked_funnel_plot_umatan_1(race_df, raceuma_df, haraimodoshi_dict)
# 馬単的中2
fig13 = wp.cp_stacked_funnel_plot_umatan_2(race_df, raceuma_df, haraimodoshi_dict)
# 馬単的中3
fig14 = wp.cp_stacked_funnel_plot_umatan_3(race_df, raceuma_df, haraimodoshi_dict)
# ワイド的中1
fig15 = wp.cp_stacked_funnel_plot_wide_1(race_df, raceuma_df, haraimodoshi_dict)
# 馬連的中1
fig20 = wp.cp_parallel_categories_diagram_umaren_1(race_df, raceuma_df, haraimodoshi_dict)
# 馬連的中2
fig21 = wp.cp_parallel_categories_diagram_umaren_2(race_df, raceuma_df, haraimodoshi_dict)
# 馬単的中1
fig22 = wp.cp_parallel_categories_diagram_umatan_1(race_df, raceuma_df, haraimodoshi_dict)
# 馬単的中2
fig23 = wp.cp_parallel_categories_diagram_umatan_2(race_df, raceuma_df, haraimodoshi_dict)
# 馬単的中3
fig24 = wp.cp_parallel_categories_diagram_umatan_3(race_df, raceuma_df, haraimodoshi_dict)
# ワイド的中1
fig25 = wp.cp_parallel_categories_diagram_wide_1(race_df, raceuma_df, haraimodoshi_dict)
return dcc.Loading(id="kpi-analytics-lender-loading", children=[
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("fig7", 6, fig7, "場所別単複回収率・的中率", 400),
# wp.dbc_graph("fig8", 6, fig8, "場所別馬券回収率・的中率", 400),
# ], className="h-50", no_gutters=True),
dbc.Row([
wp.dbc_graph("fig10", 5, fig10, "馬連的中1", 400),
wp.dbc_graph("fig20", 7, fig20, "", 400),
], className="h-50", no_gutters=True),
dbc.Row([
wp.dbc_graph("fig11", 5, fig11, "馬連的中2", 400),
wp.dbc_graph("fig21", 7, fig21, "", 400),
], className="h-50", no_gutters=True),
dbc.Row([
wp.dbc_graph("fig12", 5, fig12, "馬単的中1", 400),
wp.dbc_graph("fig22", 7, fig22, "", 400),
], className="h-50", no_gutters=True),
dbc.Row([
wp.dbc_graph("fig13", 5, fig13, "馬単的中2", 400),
wp.dbc_graph("fig23", 7, fig23, "", 400),
], className="h-50", no_gutters=True),
dbc.Row([
wp.dbc_graph("fig14", 5, fig14, "馬単的中3", 400),
wp.dbc_graph("fig24", 7, fig24, "", 400),
], className="h-50", no_gutters=True),
dbc.Row([
wp.dbc_graph("fig15", 5, fig15, "ワイド的中1", 400),
wp.dbc_graph("fig25", 7, fig25, "", 400),
], className="h-50", no_gutters=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
)])