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Merged
merged 2 commits into from
Aug 14, 2021
Merged

[GSoC] Function for I.L.D. for Moment added #21871

merged 2 commits into from
Aug 14, 2021

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Psycho-Pirate
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References to other Issues or PRs

See #19069

Brief description of what is fixed or changed

Function solve_for_ild_moment() is added which calculates the moment equation under the effect of a moving load.
Function plot_ild_moment() is added which plots these equations.

Other comments

Release Notes

  • physics.continuum_mechanics
    • Added ability to calculate and plot I.L.D. Moment Equations

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sympy-bot commented Aug 14, 2021 •
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Hi, I am the SymPy bot (v161). I'm here to help you write a release notes entry. Please read the guide on how to write release notes.

Your release notes are in good order.

Here is what the release notes will look like:

  • physics.continuum_mechanics

This will be added to https://github.com/sympy/sympy/wiki/Release-Notes-for-1.9.

Click here to see the pull request description that was parsed. 
#### References to other Issues or PRs
<!-- If this pull request fixes an issue, write "Fixes #NNNN" in that exact
format, e.g. "Fixes #1234" (see
https://tinyurl.com/auto-closing for more information). Also, please
write a comment on that issue linking back to this pull request once it is
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See #19069

#### Brief description of what is fixed or changed
Function `solve_for_ild_moment()` is added which calculates the moment equation under the effect of a moving load.
Function `plot_ild_moment()` is added which plots these equations.

#### Other comments


#### Release Notes

<!-- Write the release notes for this release below. See
https://github.com/sympy/sympy/wiki/Writing-Release-Notes for more information
on how to write release notes. The bot will check your release notes
automatically to see if they are formatted correctly. -->

<!-- BEGIN RELEASE NOTES -->
* physics.continuum_mechanics 
   * Added ability to calculate and plot I.L.D. Moment Equations
<!-- END RELEASE NOTES -->

Update

The release notes on the wiki have been updated.

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@ishanaj

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github-actions bot commented Aug 14, 2021 •
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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
     [ed9a550f]       [34015781]
     <sympy-1.8^0>                 
-      1.19±0.01s          161±7ms     0.14  dsolve.TimeDsolve01.time_dsolve
-      10.0±0.01s       5.00±0.07s     0.50  integrate.TimeIntegrationRisch02.time_doit_risch(100)
+         121±1ms          192±5ms     1.59  logic.LogicSuite.time_dpll2
-        88.7±1μs         36.6±1μs     0.41  matrices.TimeDiagonalEigenvals.time_eigenvals
-         111±4μs       68.3±0.7μs     0.62  matrices.TimeMatrixGetItem.time_ImmutableDenseMatrix_getitem
-         104±9μs       68.1±0.8μs     0.65  matrices.TimeMatrixGetItem.time_MutableDenseMatrix_getitem
-         111±7μs         66.8±4μs     0.60  matrices.TimeMatrixGetItem.time_MutableSparseMatrix_getitem
-        94.3±3μs         59.4±3μs     0.63  solve.TimeMatrixArithmetic.time_dense_add(10, 0)
+      13.6±0.7μs       23.0±0.5μs     1.69  solve.TimeMatrixArithmetic.time_dense_add(3, 0)
+      16.3±0.5μs         37.4±2μs     2.29  solve.TimeMatrixArithmetic.time_dense_add(3, 5)
+        23.2±1μs       47.8±0.9μs     2.06  solve.TimeMatrixArithmetic.time_dense_add(4, 5)
+      43.0±0.9μs         74.6±2μs     1.74  solve.TimeMatrixArithmetic.time_dense_add(6, 5)
-     1.40±0.06ms          238±7μs     0.17  solve.TimeMatrixArithmetic.time_dense_multiply(10, 0)
-        54.7±3μs         28.8±1μs     0.53  solve.TimeMatrixArithmetic.time_dense_multiply(3, 0)
+        85.9±5μs          153±4μs     1.78  solve.TimeMatrixArithmetic.time_dense_multiply(3, 5)
-         120±1μs         35.6±1μs     0.30  solve.TimeMatrixArithmetic.time_dense_multiply(4, 0)
-        338±30μs         75.4±2μs     0.22  solve.TimeMatrixArithmetic.time_dense_multiply(6, 0)
-         116±4μs         51.5±4μs     0.44  solve.TimeMatrixOperations.time_det(3, 0)
-         196±6μs          111±2μs     0.57  solve.TimeMatrixOperations.time_det(3, 2)
-         186±6μs          110±2μs     0.59  solve.TimeMatrixOperations.time_det(3, 5)
-         105±4μs         48.0±1μs     0.46  solve.TimeMatrixOperations.time_det_bareiss(3, 0)
-         204±4μs          120±2μs     0.59  solve.TimeMatrixOperations.time_det_bareiss(3, 2)
-         195±6μs          112±3μs     0.57  solve.TimeMatrixOperations.time_det_bareiss(3, 5)
-         108±4μs       49.1±0.8μs     0.45  solve.TimeMatrixOperations.time_det_berkowitz(3, 0)
-         199±7μs          115±6μs     0.58  solve.TimeMatrixOperations.time_det_berkowitz(3, 2)
-         190±9μs          109±3μs     0.57  solve.TimeMatrixOperations.time_det_berkowitz(3, 5)
+        849±40μs       1.44±0.1ms     1.70  solve.TimeMatrixOperations.time_det_berkowitz(4, 0)
+        997±50μs      2.07±0.06ms     2.08  solve.TimeMatrixOperations.time_det_berkowitz(4, 2)
+     1.04±0.01ms      2.14±0.08ms     2.06  solve.TimeMatrixOperations.time_det_berkowitz(4, 5)
+         312±7μs         527±20μs     1.69  solve.TimeMatrixOperations.time_rank(3, 0)
+        550±20μs         832±30μs     1.51  solve.TimeMatrixOperations.time_rank(4, 0)
-      5.90±0.1ms      3.17±0.08ms     0.54  solve.TimeRationalSystem.time_linsolve(10)
-     1.23±0.05ms         794±20μs     0.64  solve.TimeRationalSystem.time_linsolve(5)
+      3.64±0.3ms      5.49±0.09ms     1.51  solve.TimeSparseSystem.time_linear_eq_to_matrix(20)
-     4.22±0.03ms       2.65±0.1ms     0.63  solve.TimeSparseSystem.time_linsolve_eqs(30)



Full benchmark results can be found as artifacts in GitHub Actions

(click on checks at the top of the PR).

      		
    

  





        


            

            
            

              
            

        

      


      

            
  
  

    
  
    
    

  

  

  


    












      

  
            







      

  
        

  

      

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LGTM!

      		
    

  





        


            

            
            

              
            

        

      


      

            
  
  

    
  
    
    

  

  

  


    












      

  
        





      

  
      



  

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Merging!

      		
    

  





        


            

            
            

              
            

        

      


      

            
  
  

    
  
    
    

  

  

  


    












      

  
          
 
  

    
  

  



        @ishanaj
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