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我正在编写一个 pyDatalog 程序来分析来自 Weather Underground 的天气数据（目前只是作为我自己和公司其他人的演示）。我编写了一个自定义谓词解析器，它返回开始时间和结束时间之间的读数：

# class for the reading table.
class Reading(Base):
      __table__ = Table('reading', Base.metadata, autoload = True, autoload_with = engine)
      def __repr__(self):
        return str(self.Time)
      # predicate to resolve 'timeBetween(X, Y, Z)' statements
      # matches items as X where the time of day is between Y and Z (inclusive).
      # if Y is later than Z, it returns the items not between Z and Y (exclusive).
      # TODO - make it work where t1 and t2 are not bound.
      # somehow needs to tell the engine to try somewhere else first.
      @classmethod
      def _pyD_timeBetween3(cls, dt, t1, t2):
        if dt.is_const():
          # dt is already known
          if t1.is_const() and t2.is_const():
            if (dt.id.Time.time() >= makeTime(t1.id)) and (dt.id.Time.time() <= makeTime(t2.id)):
              yield (dt.id, t1.id, t2.id)
        else:
          # dt is an unbound variable
          if t1.is_const() and t2.is_const():
            if makeTime(t2.id) > makeTime(t1.id):
              op = 'and'
            else:
              op = 'or'
            sqlWhere = "time(Time) >= '%s' %s time(Time) <= '%s'" % (t1.id, op, t2.id)
            for instance in cls.session.query(cls).filter(sqlWhere):
              yield(instance, t1.id, t2.id)

这适用于 t1 和 t2 绑定到特定值的情况：

:> easterly(X) <= (Reading.WindDirection[X] == 'East')
:> + rideAfter('11:00:00')
:> + rideBefore('15:00:00')
:> goodTime(X) <= rideAfter(Y) & rideBefore(Z) & Reading.timeBetween(X, Y, Z)
:> goodTime(X)
[(2013-02-19 11:25:00,), (2013-02-19 12:45:00,), (2013-02-19 12:50:00,), (2013-02-19  13:25:00,), (2013-02-19 14:30:00,), (2013-02-19 15:00:00,), (2013-02-19 13:35:00,), (2013-02-19 13:50:00,), (2013-02-19 12:20:00,), (2013-02-19 12:35:00,), (2013-02-19 14:05:00,), (2013-02-19 11:20:00,), (2013-02-19 11:50:00,), (2013-02-19 13:15:00,), (2013-02-19 14:55:00,), (2013-02-19 12:00:00,), (2013-02-19 13:00:00,), (2013-02-19 14:20:00,), (2013-02-19 14:15:00,), (2013-02-19 13:10:00,), (2013-02-19 12:10:00,), (2013-02-19 14:45:00,), (2013-02-19 14:35:00,), (2013-02-19 13:20:00,), (2013-02-19 11:10:00,), (2013-02-19 13:05:00,), (2013-02-19 12:55:00,), (2013-02-19 14:10:00,), (2013-02-19 13:45:00,), (2013-02-19 13:55:00,), (2013-02-19 11:05:00,), (2013-02-19 12:25:00,), (2013-02-19 14:00:00,), (2013-02-19 12:05:00,), (2013-02-19 12:40:00,), (2013-02-19 14:40:00,), (2013-02-19 11:00:00,), (2013-02-19 11:15:00,), (2013-02-19 11:30:00,), (2013-02-19 11:45:00,), (2013-02-19 13:40:00,), (2013-02-19 11:55:00,), (2013-02-19 14:25:00,), (2013-02-19 13:30:00,), (2013-02-19 12:30:00,), (2013-02-19 12:15:00,), (2013-02-19 11:40:00,), (2013-02-19 14:50:00,), (2013-02-19 11:35:00,)]

但是，如果我使用其他顺序的条件声明 goodTime 规则（即在它尝试解析 timeBetween 时 Y 和 Z 未绑定），它将返回一个空集：

:> atoms('niceTime')
:> niceTime(X) <= Reading.timeBetween(X, Y, Z) & rideAfter(Y) & rideBefore(Z)
<pyDatalog.pyEngine.Clause object at 0x0adfa510>
:> niceTime(X)
[]

这似乎是错误的——这两个查询应该返回相同的结果集。

我的问题是在 pyDatalog 中是否有处理这种情况的方法？我认为需要发生的是 timeBetween 谓词应该能够告诉引擎以某种方式退出并尝试在尝试此规则之前先解决其他规则，但我在文档中看不到任何对此的引用。

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1 回答 1

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pyDatalog参考说：“尽管 pyDatalog 语句的顺序无关紧要，但正文中文字的顺序很重要” pyDatalog 确实按照陈述的顺序解析了正文中的谓词。

话虽如此，有可能改进 pyDatalog 以首先使用绑定变量解析谓词，但我不确定为什么这很重要。

于 2013-03-04T12:02:40.677 回答