v4.1.6

pure_ocean_breeze/__init__.py

@@ -3,7 +3,7 @@
 """
 
 __updated__ = "2023-07-18 14:49:23"
-__version__ = "4.1.5"
+__version__ = "4.1.6"
 __author__ = "chenzongwei"
 __author_email__ = "winterwinter999@163.com"
 __url__ = "https://github.com/chen-001/pure_ocean_breeze"

pure_ocean_breeze/jason/data/tools.py

@@ -979,6 +979,55 @@ def lu计算连续期数2(
  return continuous_ones.replace(0, nan_value)
 
 
+def lu计算连续期数奇正偶反(s:Union[pd.Series,pd.DataFrame])->Union[pd.Series,pd.DataFrame]:
+ if isinstance(s,pd.DataFrame):
+ return s.apply(lu计算连续期数奇正偶反)
+ else:
+ s=s.reset_index(drop=True)
+ # 用于标记每个连续非NaN片段
+ s_diff = (~s.isna()).astype(int).diff().fillna(1).cumsum()
+
+ # 对每个连续片段进行编号（1开始）
+ segment_nums = s_diff[s_diff.diff().fillna(1) != 0].cumsum()
+
+ # 将连续片段编号映射回原始序列
+ s_mapped = segment_nums.reindex_like(s).ffill().fillna(0)
+
+ # 对每个连续非NaN片段生成顺序序列
+ order = s.groupby(s_mapped).cumcount() + 1
+
+ # 计算每个连续非NaN片段的长度
+ segment_lengths = s.groupby(s_mapped).transform('count')
+
+ # 对偶数编号的片段进行逆序排列
+ s[s.notna()] = np.where(s_mapped % 2 == 1, order, segment_lengths[s_mapped == s_mapped] - order + 1)
+ return s.values
+
+def lu计算连续期数偶正奇反(s:Union[pd.Series,pd.DataFrame])->Union[pd.Series,pd.DataFrame]:
+ if isinstance(s,pd.DataFrame):
+ return s.apply(lu计算连续期数偶正奇反)
+ else:
+ s=s.reset_index(drop=True)
+ # 用于标记每个连续非NaN片段
+ s_diff = (~s.isna()).astype(int).diff().fillna(1).cumsum()
+
+ # 对每个连续片段进行编号（1开始）
+ segment_nums = s_diff[s_diff.diff().fillna(1) != 0].cumsum()
+
+ # 将连续片段编号映射回原始序列
+ s_mapped = segment_nums.reindex_like(s).ffill().fillna(0)
+
+ # 对每个连续非NaN片段生成顺序序列
+ order = s.groupby(s_mapped).cumcount() + 1
+
+ # 计算每个连续非NaN片段的长度
+ segment_lengths = s.groupby(s_mapped).transform('count')
+
+ # 对偶数编号的片段进行逆序排列
+ s[s.notna()] = np.where(s_mapped % 2 == 1, segment_lengths[s_mapped == s_mapped] - order + 1, order)
+ return s.values
+
+
 def lu计算连续期数长度(s:Union[pd.Series,pd.DataFrame],final_mean=1)->Union[float,pd.Series,pd.DataFrame]:
  if isinstance(s,pd.DataFrame):
  return s.apply(lambda x:lu计算连续期数长度(x,final_mean))
@@ -1000,6 +1049,71 @@ def lu计算连续期数长度(s:Union[pd.Series,pd.DataFrame],final_mean=1)->Un
  return segment_lengths.mean()
 
 
+def lu标记连续片段(s:Union[pd.Series,pd.DataFrame],label_nan=0)->Union[pd.Series,pd.DataFrame]:
+ not_nan = ~s.isna()
+ segment_starts = not_nan.diff().fillna(True) # 对序列首个元素填充True，因为diff会产生NaN
+
+ # 为每个连续片段分配一个唯一标识符
+ segments = segment_starts.cumsum()
+
+ # 仅对非NaN片段应用标识符，NaN值保持不变
+ if not label_nan:
+ return segments*np.sign(s.abs()+1)
+ else:
+ return segments
+
+def lu删去连续片段中的最大值(s:Union[pd.Series,pd.DataFrame])->Union[pd.Series,pd.DataFrame]:
+ if isinstance(s,pd.DataFrame):
+ return s.apply(lu删去连续片段中的最大值)
+ else:
+ # 生成连续片段的标识符
+ s_diff = s.isna().astype(int).diff().fillna(0).ne(0).cumsum()
+
+ # 对每个片段使用transform找到最大值
+ max_vals = s.groupby(s_diff).transform('max')
+
+ # 将原始序列中等于最大值的元素替换为NaN
+ s[s == max_vals] = np.nan
+ return s
+
+
+def lu仅保留连续片段中的最大值(s:Union[pd.Series,pd.DataFrame])->Union[pd.Series,pd.DataFrame]:
+ if isinstance(s,pd.DataFrame):
+ return s.apply(lu删去连续片段中的最大值)
+ else:
+ # 生成连续片段的标识符
+ s_diff = s.isna().astype(int).diff().fillna(0).ne(0).cumsum()
+
+ # 对每个片段使用transform找到最大值
+ max_vals = s.groupby(s_diff).transform('max')
+
+ # 将原始序列中等于最大值的元素替换为NaN
+ s[s != max_vals] = np.nan
+ return s
+
+def lu删去连续片段中的最大值及其后面的值(s:Union[pd.Series,pd.DataFrame])->Union[pd.Series,pd.DataFrame]:
+ if isinstance(s,pd.DataFrame):
+ return s.apply(lu删去连续片段中的最大值及其后面的值)
+ else:
+ # 生成连续片段的标识符
+ s_diff = s.isna().astype(int).diff().fillna(0).ne(0).cumsum()
+
+ # 对每个片段使用transform找到最大值
+ max_vals = s.groupby(s_diff).transform('max')
+
+ # 使用cummax标记最大值及其之后的值
+ max_flag = (s.groupby(s_diff).cummax() == max_vals).astype(int)
+
+ # 使用cumsum在每个片段内生成标记，从最大值开始累加
+ max_flag_cum = max_flag.groupby(s_diff).cumsum()
+
+ # 将标记的值替换为NaN
+ s[max_flag_cum > 0] = np.nan
+ return s
+
+def lu删去连续片段中的最大值及其前面的值(s:Union[pd.Series,pd.DataFrame])->Union[pd.Series,pd.DataFrame]:
+ return lu删去连续片段中的最大值及其后面的值(s[::-1])[::-1]
+
 @do_on_dfs
 def is_pos(s: Union[pd.Series, pd.DataFrame],zero_as_pos:bool=1) -> Union[pd.Series, pd.DataFrame]:
  if zero_as_pos:

pure_ocean_breeze/jason/labor/process.py

@@ -1378,7 +1378,7 @@ def plot_net_values(self, y2, filename, iplot=1, ilegend=1, without_breakpoint=0
  )
  here.columns = ["信息系数", "结果", "绩效指标", "结果", "其他指标", "结果","单侧","结果"]
  # here=here.to_numpy().tolist()+[['信息系数','结果','绩效指标','结果']]
- table = FF.create_table(here.iloc[::-1])
+ table = FF.create_table(here.iloc[::-1],xgap=0)
  table.update_yaxes(matches=None)
  pic2 = go.Figure(
  go.Bar(
@@ -1421,7 +1421,7 @@ def plot_net_values(self, y2, filename, iplot=1, ilegend=1, without_breakpoint=0
  shape=(2, 14),
  base_layout=base_layout,
  vertical_spacing=0.15,
- horizontal_spacing=0.03,
+ horizontal_spacing=0.025,
  shared_yaxes=False,
  specs=[
  [
@@ -1466,7 +1466,7 @@ def plot_net_values(self, y2, filename, iplot=1, ilegend=1, without_breakpoint=0
  "绩效指标",
  ],
  )
- sp["layout"].update(showlegend=ilegend,width=1800,height=230)
+ sp["layout"].update(showlegend=ilegend,width=1780,height=230,margin=dict(l=0, r=0, b=0, t=0, pad=0))
  # sp['colors']=['#FF5733', '#33FF57', '#3357FF']
  # los=sp['layout']['annotations']
  # los[0]['font']['color']='#000000'