column == 0的判断可以去掉
时间: 2024-03-12 13:43:54 浏览: 19
如果`column`的值总是为0或者只有在`column`为0时才需要进行赋值操作,那么的确可以去掉`column == 0`的判断。修改后的代码如下:
```
double temp = setText.toDouble();
if(showtype[row]) {
Uall[row] = setText.toInt(); // 将setText转换成int类型并赋值给Uall[row]
} else {
if(setText.contains("+") || setText.contains("-")) {
temp = qAbs(temp); // 取绝对值
if(setText.contains("-")) {
temp = -temp; // 取相反数
}
}
Uall[row] = temp * 1048576; // 将temp乘以1048576并转换成int类型,然后赋值给Uall[row]
}
```
这样修改后,程序仍然会对`setText`进行类型转换和赋值操作,但是不再需要判断`column`的值是否为0。
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if self.config.deduplicate_order_by_type == "asc": order_by_columns = [F.col(column_name) for column_name in self.config.deduplication_order_columns] ranking_column = "duplicates_rank" is_deleted_column = "IsDeleted" if (self.config.filter_deleted_flag) & ( is_deleted_column in df.columns): # if True removes records that marked deleted filter = (F.col(ranking_column) == 1) & (F.col(is_deleted_column) == "False") else: self.logger.warning(f"Records marked as deleted will be loaded to {self.config.table_full_name} table!") filter = F.col(ranking_column) == 1 return df.withColumn(ranking_column, F.row_number().over(Window.partitionBy(key_columns).orderBy(order_by_columns))).filter( filter).drop(ranking_column)将这个转为sql形式
将这段 Python 代码转换成 SQL 代码的大致形式如下:
```
IF self.config.deduplicate_order_by_type = "asc" THEN
SET @order_by_columns = CONCAT_WS(',', column_name1, column_name2, ...);
ELSEIF self.config.deduplicate_order_by_type = "desc" THEN
SET @order_by_columns = CONCAT_WS(',', CONCAT(column_name1, ' DESC'), CONCAT(column_name2, ' DESC'), ...);
END IF;
IF self.config.filter_deleted_flag AND FIND_IN_SET('IsDeleted', @column_names) THEN
SET @filter = CONCAT('(', duplicates_rank, '= 1 AND IsDeleted = "False")');
ELSE
SET @filter = CONCAT('(', duplicates_rank, '= 1)');
END IF;
SELECT column_name1, column_name2, ...,
ROW_NUMBER() OVER (PARTITION BY @key_columns ORDER BY @order_by_columns) AS duplicates_rank
FROM table_name
WHERE @filter;
```
其中,`self.config.deduplicate_order_by_type` 是用于判断排序方式的变量,`column_name1, column_name2, ...` 是需要去重的列名,`table_name` 是表名,`key_columns` 是用于分组的列名,`duplicates_rank` 是新生成的列名,用于标记数据是否重复,`IsDeleted` 是用于标记数据是否已经删除的列名。
该 SQL 代码使用了 ROW_NUMBER() 函数来生成新列 `duplicates_rank`,并且使用 IF 和 ELSEIF 语句来判断排序方式,使用 CONCAT_WS() 和 CONCAT() 函数来生成排序的列名和筛选条件。如果 `self.config.filter_deleted_flag` 为 True 并且表中包含 `IsDeleted` 列,则会在筛选时去除被标记为删除的记录;否则会提示将标记为删除的记录加载到表中。最后,生成的结果集中包含原来的列和新列 `duplicates_rank`,并且根据 `duplicates_rank` 列进行了筛选。
将这段代码改为按照收缩压和舒张压的分级范围查询,分别取数据中的收缩压和舒张压和分级规则中的收缩压取值范围和舒张压取值范围比较,要有是否需要同时满足舒张压和收缩压范围的选项,不再按照value值查询。- (NSMutableArray *)getBloodPresureDataField:(NSString *)field value:(NSInteger)value startTime:(NSTimeInterval)startTime endTime:(NSTimeInterval)endTime { __block NSMutableArray *array = [NSMutableArray array]; [[HWFitnessMgr sharedInstance] excuteInTransaction:^(FMDatabase *db ,BOOL * rollBack) { NSMutableString *rs = [NSMutableString string]; [rs appendString:@" where"]; //此处逻辑:若搜索字段不为COLUMN_SYNC_CLOUD_STATUS去掉本地已删除未上云的数据 if ([field isEqualToString:COLUMN_SYNC_CLOUD_STATUS]) { [rs appendFormat:@" %@ >= %.0lf and %@ <= %.0lf and %@ = %ld", Column_TimeStamp, startTime, Column_TimeStamp, endTime, field, (long)value]; } else { [rs appendFormat:@" %@ >= %.0lf and %@ <= %.0lf and %@ = %ld and %@ != %ld", Column_TimeStamp, startTime, Column_TimeStamp, endTime, field, (long)value, COLUMN_SYNC_CLOUD_STATUS, (long)HWHealthDataSyncCloudStatusDeleteNotSync]; } [rs appendFormat:@" order by %@ desc", Column_TimeStamp]; NSString *strSql = [@"select * from '%@'" stringByAppendingString:rs]; NSString *bloodPressureTable = [self getTableName:FitnessSamplePointBloodPressureTable]; FMResultSet * detailRs = [[HWFitnessMgr sharedInstance] selectBy:strSql andTable:bloodPressureTable inDatabase:db]; if (detailRs) { while ([detailRs next]) { [array addObject:[self fetchBloodPressureDataWithDetailRs:detailRs]]; } [detailRs close]; } }]; return array; }
修改后的代码如下:
- (NSMutableArray *)getBloodPresureDataWithSystolicRange:(NSRange)systolicRange diastolicRange:(NSRange)diastolicRange requireBoth:(BOOL)requireBoth startTime:(NSTimeInterval)startTime endTime:(NSTimeInterval)endTime {
__block NSMutableArray *array = [NSMutableArray array];
[[HWFitnessMgr sharedInstance] excuteInTransaction:^(FMDatabase *db ,BOOL * rollBack) {
NSMutableString *rs = [NSMutableString string];
[rs appendString:@" where"];
//此处逻辑:若搜索字段不为COLUMN_SYNC_CLOUD_STATUS去掉本地已删除未上云的数据
[rs appendFormat:@" %@ >= %.0lf and %@ <= %.0lf and %@ != %ld", Column_TimeStamp, startTime, Column_TimeStamp, endTime, COLUMN_SYNC_CLOUD_STATUS, (long)HWHealthDataSyncCloudStatusDeleteNotSync];
if (requireBoth) {
[rs appendFormat:@" and %@ BETWEEN %ld AND %ld AND %@ BETWEEN %ld AND %ld", Column_BloodPressureSystolic, (long)systolicRange.location, (long)systolicRange.location + (long)systolicRange.length - 1, Column_BloodPressureDiastolic, (long)diastolicRange.location, (long)diastolicRange.location + (long)diastolicRange.length - 1];
} else {
if (systolicRange.length > 0) {
[rs appendFormat:@" and %@ BETWEEN %ld AND %ld", Column_BloodPressureSystolic, (long)systolicRange.location, (long)systolicRange.location + (long)systolicRange.length - 1];
}
if (diastolicRange.length > 0) {
[rs appendFormat:@" and %@ BETWEEN %ld AND %ld", Column_BloodPressureDiastolic, (long)diastolicRange.location, (long)diastolicRange.location + (long)diastolicRange.length - 1];
}
}
[rs appendFormat:@" order by %@ desc", Column_TimeStamp];
NSString *strSql = [NSString stringWithFormat:@"select * from '%@'%@", [self getTableName:FitnessSamplePointBloodPressureTable], rs];
FMResultSet *detailRs = [[HWFitnessMgr sharedInstance] selectBy:strSql andTable:[self getTableName:FitnessSamplePointBloodPressureTable] inDatabase:db];
if (detailRs) {
while ([detailRs next]) {
[array addObject:[self fetchBloodPressureDataWithDetailRs:detailRs]];
}
[detailRs close];
}
}];
return array;
}
这里修改了方法名,同时增加了两个参数,分别是收缩压和舒张压的分级范围,使用NSRange类型表示,另外增加了一个布尔值参数requireBoth,用于控制是否需要同时满足收缩压和舒张压范围的选项。如果requireBoth为YES,则需要同时满足两个范围;如果为NO,则只需要满足其中一个范围即可。
在方法的实现中,按照收缩压和舒张压范围分别构造查询条件。如果requireBoth为NO,则只需要满足其中一个条件,所以在构造查询条件时需要判断范围值是否大于0。最后拼接查询语句并执行查询,将查询结果转化为模型对象并添加到数组中返回。