correct Piecewise simplification of expressions; improve condition handling

[smichr]

References to other Issues or PRs

fixes #22747

Brief description of what is fixed or changed

Other comments

Release Notes

• functions
  • bug fixed for simplification of Piecewise expressions
  • condition of Piecewise which are univariate are made more canonical (redundant conditions removed)

[sympy-bot]

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• functions

  • bug fixed for simplification of Piecewise expressions (#22751 by @smichr)

  • condition of Piecewise which are univariate are made more canonical (redundant conditions removed) (#22751 by @smichr)

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#### References to other Issues or PRs
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fixes #22747

#### Brief description of what is fixed or changed


#### Other comments


#### Release Notes

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  * Added a new solver for logarithmic equations.

* functions
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<!-- BEGIN RELEASE NOTES -->
* functions
    * bug fixed for simplification of Piecewise expressions
    * condition of Piecewise which are univariate are made more canonical (redundant conditions removed)
<!-- END RELEASE NOTES -->

Update

The release notes on the wiki have been updated.

[github-actions]

Benchmark results from GitHub Actions

Lower numbers are good, higher numbers are bad. A ratio less than 1
means a speed up and greater than 1 means a slowdown. Green lines
beginning with + are slowdowns (the PR is slower then master or
master is slower than the previous release). Red lines beginning
with - are speedups.

Significantly changed benchmark results (PR vs master)

Significantly changed benchmark results (master vs previous release)

       before           after         ratio
     [907895ac]       [6632ed8b]
-         230±6ms          136±2ms     0.59  large_exprs.TimeLargeExpressionOperations.time_subs
-      15.8±0.4ms       10.4±0.3ms     0.66  matrices.TimeMatrixExpression.time_MatAdd_doit
-         225±8μs          103±5μs     0.46  matrices.TimeMatrixExpression.time_MatMul
-      14.3±0.3ms      7.42±0.04ms     0.52  matrices.TimeMatrixExpression.time_MatMul_doit
+      6.77±0.1ms       10.2±0.4ms     1.51  matrices.TimeMatrixPower.time_Case1
-      4.60±0.01s          333±7ms     0.07  polygon.PolygonArbitraryPoint.time_bench01
+     3.53±0.07ms       6.21±0.1ms     1.76  solve.TimeMatrixOperations.time_det(4, 2)
+      3.58±0.1ms       5.96±0.3ms     1.66  solve.TimeMatrixOperations.time_det_bareiss(4, 2)
+        488±10ms          747±5ms     1.53  solve.TimeMatrixSolve.time_solve('GE')
+      39.5±0.6ms         77.6±3ms     1.96  solve.TimeMatrixSolvePyDySlow.time_linsolve(1)
+        40.4±3ms         78.9±3ms     1.95  solve.TimeMatrixSolvePyDySlow.time_solve(1)

Full benchmark results can be found as artifacts in GitHub Actions
(click on checks at the top of the PR).