new_sanwei
parent
abbef53bc3
commit
3f3c890eea
84
tools/t7.py
84
tools/t7.py
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@ -521,8 +521,8 @@ class AutoLayout:
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self.no_assign_data.extend(no_ori_data.to_dict('records'))
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# 包lane的剔除
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orderlane_mask = ori_library_df['productname'].str.contains('包lane')
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# orderlane_mask = ori_library_df['lanepackcode'].str.contains('包lane')
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orderlane_mask = ori_library_df['lanepackcode'] != ''
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self.order_assign_data = ori_library_df[orderlane_mask].to_dict('records')
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# 使用布尔索引筛选出不是数字和非日期的行,包lane的
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@ -549,46 +549,48 @@ class AutoLayout:
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continue
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# 不平衡文库 大于250G 的数据 先进行拆分
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if is_balance_lib == '否' and size > 250:
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self.return_log.append(f'文库{library} 是不平衡文库, 数据为{size}, 大于250G, 已做拆分处理, 请注意!!! ')
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data_needed = library_df['orderdatavolume'].copy()
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for num in range(int(size), 0, -200):
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addnum = 200
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if num <= 200:
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addnum = num
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library_df['orderdatavolume'] = (addnum / size) * data_needed
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# 取消 20240912
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# if is_balance_lib == '否' and size > 250:
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# self.return_log.append(f'文库{library} 是不平衡文库, 数据为{size}, 大于250G, 已做拆分处理, 请注意!!! ')
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# data_needed = library_df['orderdatavolume'].copy()
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# for num in range(int(size), 0, -200):
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# addnum = 200
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# if num <= 200:
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# addnum = num
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# library_df['orderdatavolume'] = (addnum / size) * data_needed
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#
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# self.ori_lib_data.append(dict(
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# library=library,
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# sample_code=library_df['sampleCode'].values[0],
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# is_balance_lib=library_df['librarybalancedflag'].values[0],
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# size=library_df['orderdatavolume'].sum(),
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# split_method=library_df['cycletype'].values[0],
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# time=library_df['receivedtime'].values[0],
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# level=1950,
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# customer=library_df['companynamea'].values[0],
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# classification=library_df['classification'].values[0],
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# data=library_df.to_dict('records')
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# ))
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# self.split_lib.add(library)
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# continue
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self.ori_lib_data.append(dict(
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library=library,
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sample_code=library_df['sampleCode'].values[0],
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is_balance_lib=library_df['librarybalancedflag'].values[0],
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size=library_df['orderdatavolume'].sum(),
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split_method=library_df['cycletype'].values[0],
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time=library_df['receivedtime'].values[0],
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level=1950,
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customer=library_df['companynamea'].values[0],
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classification=library_df['classification'].values[0],
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data=library_df.to_dict('records')
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))
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self.split_lib.add(library)
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continue
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# 拆分处理 分为了2个大文库
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if size > self.data_limit / 2:
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library_df['orderdatavolume'] = library_df['orderdatavolume'] / 2
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self.return_log.append(f'文库{library} 已做拆分处理, 请注意!!! ')
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self.ori_lib_data.append(dict(
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library=library,
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sample_code=library_df['sampleCode'].values[0],
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is_balance_lib=library_df['librarybalancedflag'].values[0],
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size=library_df['orderdatavolume'].sum(),
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split_method=library_df['cycletype'].values[0],
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time=library_df['receivedtime'].values[0],
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level=library_df['level'].values[0],
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customer=library_df['companynamea'].values[0],
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classification=library_df['classification'].values[0],
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data=library_df.to_dict('records')
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))
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# # 拆分处理 分为了2个大文库
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# 取消 20240912
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# if size > self.data_limit / 2:
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# library_df['orderdatavolume'] = library_df['orderdatavolume'] / 2
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# self.return_log.append(f'文库{library} 已做拆分处理, 请注意!!! ')
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# self.ori_lib_data.append(dict(
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# library=library,
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# sample_code=library_df['sampleCode'].values[0],
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# is_balance_lib=library_df['librarybalancedflag'].values[0],
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# size=library_df['orderdatavolume'].sum(),
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# split_method=library_df['cycletype'].values[0],
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# time=library_df['receivedtime'].values[0],
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# level=library_df['level'].values[0],
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# customer=library_df['companynamea'].values[0],
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# classification=library_df['classification'].values[0],
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# data=library_df.to_dict('records')
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# ))
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self.ori_lib_data.append(dict(
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library=library,
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