TimePickerDialog:以编程方式设置不带主题的颜色

时间:2017-06-26 14:23:12

标签: android timepickerdialog

我们的调查是动态加载并从Android应用中显示的调查中填充。正在从服务器下载颜色,字体,样式等。

看起来您可以通过主题(Change theme of TimePickerDialog to use AppTheme)设置TimePickerDialog TimePicker的颜色,但有没有办法设置重音没有主题的颜色?

1 个答案:

答案 0 :(得分:0)

您可以使用自己的布局,使用不同的颜色。

scala> weights.join(input, "sourceId").show
org.apache.spark.sql.AnalysisException: Detected cartesian product for INNER join between logical plans
Project [_1#34 AS sourceId#39, _2#35 AS weight#40]
+- Filter (((1 <=> _1#34) || (2 <=> _1#34)) && (_1#34 = 1))
   +- LocalRelation [_1#34, _2#35]
and
Union
:- Project [_1#0 AS a#5, _2#1 AS b#6]
:  +- LocalRelation [_1#0, _2#1]
+- Project [_1#10 AS a#15, _2#11 AS b#16]
   +- LocalRelation [_1#10, _2#11]
Join condition is missing or trivial.
Use the CROSS JOIN syntax to allow cartesian products between these relations.;
  at org.apache.spark.sql.catalyst.optimizer.CheckCartesianProducts$$anonfun$apply$19.applyOrElse(Optimizer.scala:1011)
  at org.apache.spark.sql.catalyst.optimizer.CheckCartesianProducts$$anonfun$apply$19.applyOrElse(Optimizer.scala:1008)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:288)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:288)
  at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:287)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:293)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:293)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:331)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:329)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:293)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:293)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:293)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:331)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:329)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:293)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:277)
  at org.apache.spark.sql.catalyst.optimizer.CheckCartesianProducts.apply(Optimizer.scala:1008)
  at org.apache.spark.sql.catalyst.optimizer.CheckCartesianProducts.apply(Optimizer.scala:993)
  at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:85)
  at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:82)
  at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:57)
  at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:66)
  at scala.collection.mutable.WrappedArray.foldLeft(WrappedArray.scala:35)
  at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:82)
  at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:74)
  at scala.collection.immutable.List.foreach(List.scala:381)
  at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74)
  at org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:73)
  at org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:73)
  at org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:79)
  at org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:75)
  at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:84)
  at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:84)
  at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2791)
  at org.apache.spark.sql.Dataset.head(Dataset.scala:2112)
  at org.apache.spark.sql.Dataset.take(Dataset.scala:2327)
  at org.apache.spark.sql.Dataset.showString(Dataset.scala:248)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:636)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:595)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:604)
  ... 48 elided