def raceresult_set_race_list_option(date):
race_df = GetData.get_race_data(date, date)
bet_df = GetData.get_bet_data(date,
date).groupby("競走コード").sum().reset_index()
if len(bet_df.index) != 0:
race_df = pd.merge(race_df, bet_df, on="競走コード", how="left")
race_df.loc[:, "投票チェック"] = race_df.apply(lambda x: "[的中]"
if x["結果"] > 0 else
("[外れ]"
if x["金額"] > 0 else ""),
axis=1)
else:
race_df["投票チェック"] = ""
ba_list_df = race_df[race_df["月日"] == date][[
"場名", "競走番号", "競走コード", "競走名略称", "投票フラグ", "投票チェック"
]]
ba_list_df.loc[:, "競走名"] = ba_list_df["投票チェック"] + ba_list_df[
"場名"] + "_" + ba_list_df["競走番号"].astype(
str) + "R_" + ba_list_df["競走名略称"]
ba_list_df = ba_list_df[["競走名", "競走コード"]].rename(columns={
"競走名": "label",
"競走コード": "value"
})
ba_list = ba_list_df.drop_duplicates().to_dict(orient='record')
return ba_list
def return_analytics_render_return_analytics(start_date, end_date):
if start_date != None and end_date != None:
raceuma_df = GetData.get_raceuma_data(start_date, end_date)
bet_df = GetData.get_bet_data(start_date, end_date)
haraimodoshi_dict = GetData.get_haraimodoshi_dict(start_date, end_date)
if len(raceuma_df.index) != 0 and len(
bet_df.index) != 0 and len(haraimodoshi_dict):
return p2.return_analytics_render(raceuma_df, bet_df,
haraimodoshi_dict)
else:
return html.P(
f"raceuma_df: {len(raceuma_df.index)} , bet_df: {len(bet_df.index)}, haraimodoshi_dict: {len(haraimodoshi_dict)}"
)
else:
return html.P("not loading data")
def raceinfo_set_keibajo_list_option(date):
race_df = GetData.get_race_data(date, date)
ba_list_df = race_df[race_df["月日"] == date][[
"場名"
]].rename(columns={"場名": "label"})
ba_list_df.loc[:, "value"] = ba_list_df["label"]
ba_list = ba_list_df.drop_duplicates().to_dict(orient='record')
return ba_list
def toppage_render_top_dashboard(start_date, end_date):
if start_date != None and end_date != None:
race_df = GetData.get_race_data(start_date,
end_date).query("データ区分 == '7'")
raceuma_df = GetData.get_raceuma_data(start_date,
end_date).query("データ区分 == '7'")
bet_df = GetData.get_bet_data(start_date, end_date)
haraimodoshi_dict = GetData.get_haraimodoshi_dict(start_date, end_date)
if len(race_df.index) != 0 and len(raceuma_df.index) != 0 and len(
bet_df.index) != 0 and len(haraimodoshi_dict):
return p1.toppage_render(race_df, raceuma_df, bet_df,
haraimodoshi_dict)
else:
return html.P(
f"race_df: {len(race_df.index)} raceuma_df: {len(raceuma_df.index)} , bet_df: {len(bet_df.index)}, haraimodoshi_dict: {len(haraimodoshi_dict)}"
)
else:
return html.P("not loading data")
def kpi_analytics_render_return_analytics(start_date, end_date):
if start_date != None and end_date != None:
race_df = GetData.get_race_data(start_date,
end_date).query("データ区分 == '7'")
raceuma_df = GetData.get_raceuma_data(start_date,
end_date).query("データ区分 == '7'")
bet_df = GetData.get_bet_data(start_date, end_date)
haraimodoshi_dict = GetData.get_haraimodoshi_dict(start_date, end_date)
if len(race_df.index) != 0 and len(raceuma_df.index) != 0 and len(
bet_df.index) != 0 and len(haraimodoshi_dict):
return p3.kpi_analytics_render(race_df, raceuma_df, bet_df,
haraimodoshi_dict, end_date)
else:
return html.P(
f"race_df: {len(race_df.index)} raceuma_df: {len(raceuma_df.index)} , bet_df: {len(bet_df.index)}, haraimodoshi_dict: {len(haraimodoshi_dict)}"
)
else:
return html.P("not loading data")
def raceinfo_render_raceuma_detail(race_id, umaban, date):
if type(umaban) == int:
raceuma_df = GetData.get_raceuma_data(date, date)
this_raceuma_df = raceuma_df[raceuma_df["競走コード"] == race_id]
shap_df_win = cd.get_shap_raceuma_df(race_id, this_raceuma_df, umaban,
date, "WIN_FLAG")
shap_df_jiku = cd.get_shap_raceuma_df(race_id, this_raceuma_df, umaban,
date, "JIKU_FLAG")
shap_df_are = cd.get_shap_raceuma_df(race_id, this_raceuma_df, umaban,
date, "ANA_FLAG")
return p5.raceuma_info_render(shap_df_win, shap_df_jiku, shap_df_are)
def raceinfo_render_race_detail(race_id, date):
if type(race_id) == int:
race_df = GetData.get_race_data(date, date)
raceuma_df = GetData.get_raceuma_data(date, date)
this_race_df = race_df[race_df["競走コード"] == race_id]
this_raceuma_df = raceuma_df[raceuma_df["競走コード"] == race_id]
if len(this_race_df.index) != 0 and len(this_raceuma_df.index) != 0:
shap_df_umaren_are = cd.get_shap_race_df(race_id, this_race_df,
date, "UMAREN_ARE")
shap_df_umatan_are = cd.get_shap_race_df(race_id, this_race_df,
date, "UMATAN_ARE")
shap_df_sanrenpuku_are = cd.get_shap_race_df(
race_id, this_race_df, date, "SANRENPUKU_ARE")
return p5.race_info_render(this_race_df, this_raceuma_df,
shap_df_umaren_are, shap_df_umatan_are,
shap_df_sanrenpuku_are)
else:
return html.P(
f"this_race_df: {len(this_race_df.index)} this_raceuma_df: {len(this_raceuma_df.index)}"
)
else:
return html.P("not loading data")
def raceinfo_set_raceid_list_option(ba_name, date):
if type(ba_name) == str:
race_df = GetData.get_race_data(date, date)
race_list_df = race_df[race_df["場名"] == ba_name][[
"競走コード", "競走番号"
]].rename(columns={
"競走番号": "label",
"競走コード": "value"
})
race_list = race_list_df.drop_duplicates().to_dict(orient='record')
else:
race_list = []
return race_list
def raceresult_render_race_detail(race_id, date):
if type(race_id) == int:
race_df = GetData.get_race_data(date, date)
raceuma_df = GetData.get_raceuma_data(date, date)
haraimodoshi_dict = GetData.get_haraimodoshi_dict(date, date)
bet_df = GetData.get_bet_data(date, date)
this_tansho_df = haraimodoshi_dict["tansho_df"].query(
f"競走コード == {race_id}")
this_fukusho_df = haraimodoshi_dict["fukusho_df"].query(
f"競走コード == {race_id}")
this_umaren_df = haraimodoshi_dict["umaren_df"].query(
f"競走コード == {race_id}")
this_umatan_df = haraimodoshi_dict["umatan_df"].query(
f"競走コード == {race_id}")
this_wide_df = haraimodoshi_dict["wide_df"].query(
f"競走コード == {race_id}")
this_sanrenpuku_df = haraimodoshi_dict["sanrenpuku_df"].query(
f"競走コード == {race_id}")
this_race_df = race_df[race_df["競走コード"] == race_id]
this_raceuma_df = raceuma_df[raceuma_df["競走コード"] == race_id]
this_bet_df = bet_df.query(f"競走コード == {race_id}")
this_haraimodoshi_dict = {
"tansho_df": this_tansho_df,
"fukusho_df": this_fukusho_df,
"umaren_df": this_umaren_df,
"umatan_df": this_umatan_df,
"wide_df": this_wide_df,
"sanrenpuku_df": this_sanrenpuku_df
}
if len(this_race_df.index) != 0 and len(this_raceuma_df.index) != 0:
return p8.race_result_render(this_race_df, this_raceuma_df,
this_bet_df, this_haraimodoshi_dict)
else:
return html.P(
f"this_race_df: {len(this_race_df.index)} this_raceuma_df: {len(this_raceuma_df.index)} this_bet_df: {len(this_bet_df)} this_haraimodoshi_dict: {len(this_haraimodoshi_dict)}"
)
else:
return html.P("not loading data")
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 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
)])