Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fix the _array_ methods to avoid deprecation warnings #26442

Merged
merged 8 commits into from
Apr 4, 2024

Conversation

SamLubelsky
Copy link
Contributor

References to other Issues or PRs

Brief description of what is fixed or changed

Fixes #26302

I finished the work started in PR #26303 by adding test cases for the array methods.

Note: currently the tests with copy=False are commented out because there is an issue
with numpy passing the copy argument into the array function. As a result, array
will not raise an error as it should, so this test must be commented out for now.

see this comment for confirmation:
numpy/numpy#25941 (comment).

Other comments

Release Notes

NO ENTRY

RajSapale04 and others added 3 commits March 4, 2024 21:45
   def __array__(self, dtype=object, copy=None):
        if copy is not None and not copy:
            raise TypeError("Cannot implement copy=False when converting Matrix to ndarray")
Currently copy=False is commented out due to a bug
in numpy. see:
numpy/numpy#25941 (comment)
@sympy-bot
Copy link

Hi, I am the SymPy bot. I'm here to help you write a release notes entry. Please read the guide on how to write release notes.

  • No release notes entry will be added for this pull request.
Click here to see the pull request description that was parsed.
<!-- Your title above should be a short description of what
was changed. Do not include the issue number in the title. -->

#### 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
open. -->


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

Fixes #26302

I finished the work started in PR #26303 by adding test cases for the __array__ methods.

Note: currently the tests with copy=False are commented out because there is an issue 
with numpy passing the copy argument into the __array__ function.  As a result, __array__ 
will not raise an error as it should, so this test must be commented out for now.

see this comment for confirmation:
 https://github.com/numpy/numpy/issues/25941#issuecomment-2010343170.
#### Other comments


#### Release Notes

<!-- Write the release notes for this release below between the BEGIN and END
statements. The basic format is a bulleted list with the name of the subpackage
and the release note for this PR. For example:

* solvers
  * Added a new solver for logarithmic equations.

* functions
  * Fixed a bug with log of integers. Formerly, `log(-x)` incorrectly gave `-log(x)`.

* physics.units
  * Corrected a semantical error in the conversion between volt and statvolt which
    reported the volt as being larger than the statvolt.

or if no release note(s) should be included use:

NO ENTRY

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 -->
NO ENTRY
<!-- END RELEASE NOTES -->

np_array = array([[1,2], [3,4]])
assert array_equal(array(A), np_array)
assert array_equal(array(A, copy=True), np_array)
#raises(TypeError, lambda: array(A, copy=False)) TODO: Uncomment this whenever copy variable properly passes to __array__
Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

This works as intended if numpy 2.0.0rc1 is installed.

We should add a version check

int(importlib.metadata.version('numpy').split('.')[0]) >= 2

also, fixed a little bug in test_matexpr which was
causing the test to fail on master.
Copy link

github-actions bot commented Apr 4, 2024

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)

| Change   | Before [40e55587]    | After [d91b8ad6] <1.12.1rc1>   |   Ratio | Benchmark (Parameter)                                                |
|----------|----------------------|--------------------------------|---------|----------------------------------------------------------------------|
| -        | 68.8±0.9ms           | 44.5±0.5ms                     |    0.65 | integrate.TimeIntegrationRisch02.time_doit(10)                       |
| -        | 66.9±0.7ms           | 44.1±0.5ms                     |    0.66 | integrate.TimeIntegrationRisch02.time_doit_risch(10)                 |
| +        | 18.5±0.07μs          | 30.1±0.1μs                     |    1.63 | integrate.TimeIntegrationRisch03.time_doit(1)                        |
| -        | 5.45±0.02ms          | 2.87±0.01ms                    |    0.53 | logic.LogicSuite.time_load_file                                      |
| -        | 72.8±0.8ms           | 28.8±0.3ms                     |    0.4  | polys.TimeGCD_GaussInt.time_op(1, 'dense')                           |
| -        | 25.5±0.2ms           | 16.9±0.05ms                    |    0.66 | polys.TimeGCD_GaussInt.time_op(1, 'expr')                            |
| -        | 73.2±0.5ms           | 29.0±0.04ms                    |    0.4  | polys.TimeGCD_GaussInt.time_op(1, 'sparse')                          |
| -        | 254±0.8ms            | 127±0.1ms                      |    0.5  | polys.TimeGCD_GaussInt.time_op(2, 'dense')                           |
| -        | 258±3ms              | 126±0.6ms                      |    0.49 | polys.TimeGCD_GaussInt.time_op(2, 'sparse')                          |
| -        | 667±4ms              | 376±2ms                        |    0.56 | polys.TimeGCD_GaussInt.time_op(3, 'dense')                           |
| -        | 662±4ms              | 375±2ms                        |    0.57 | polys.TimeGCD_GaussInt.time_op(3, 'sparse')                          |
| -        | 498±2μs              | 289±2μs                        |    0.58 | polys.TimeGCD_LinearDenseQuadraticGCD.time_op(1, 'dense')            |
| -        | 1.77±0ms             | 1.06±0.02ms                    |    0.6  | polys.TimeGCD_LinearDenseQuadraticGCD.time_op(2, 'dense')            |
| -        | 5.74±0.07ms          | 3.16±0.02ms                    |    0.55 | polys.TimeGCD_LinearDenseQuadraticGCD.time_op(3, 'dense')            |
| -        | 446±2μs              | 236±1μs                        |    0.53 | polys.TimeGCD_QuadraticNonMonicGCD.time_op(1, 'dense')               |
| -        | 1.47±0.01ms          | 687±4μs                        |    0.47 | polys.TimeGCD_QuadraticNonMonicGCD.time_op(2, 'dense')               |
| -        | 4.90±0.04ms          | 1.68±0.01ms                    |    0.34 | polys.TimeGCD_QuadraticNonMonicGCD.time_op(3, 'dense')               |
| -        | 376±1μs              | 206±0.5μs                      |    0.55 | polys.TimeGCD_SparseGCDHighDegree.time_op(1, 'dense')                |
| -        | 2.40±0.01ms          | 1.23±0.01ms                    |    0.51 | polys.TimeGCD_SparseGCDHighDegree.time_op(3, 'dense')                |
| -        | 10.00±0.05ms         | 4.36±0.03ms                    |    0.44 | polys.TimeGCD_SparseGCDHighDegree.time_op(5, 'dense')                |
| -        | 354±0.7μs            | 170±1μs                        |    0.48 | polys.TimeGCD_SparseNonMonicQuadratic.time_op(1, 'dense')            |
| -        | 2.45±0.03ms          | 898±8μs                        |    0.37 | polys.TimeGCD_SparseNonMonicQuadratic.time_op(3, 'dense')            |
| -        | 9.64±0.1ms           | 2.66±0.02ms                    |    0.28 | polys.TimeGCD_SparseNonMonicQuadratic.time_op(5, 'dense')            |
| -        | 1.03±0.01ms          | 441±2μs                        |    0.43 | polys.TimePREM_LinearDenseQuadraticGCD.time_op(3, 'dense')           |
| -        | 1.76±0.01ms          | 514±1μs                        |    0.29 | polys.TimePREM_LinearDenseQuadraticGCD.time_op(3, 'sparse')          |
| -        | 5.87±0.06ms          | 1.82±0.02ms                    |    0.31 | polys.TimePREM_LinearDenseQuadraticGCD.time_op(5, 'dense')           |
| -        | 8.31±0.05ms          | 1.54±0.01ms                    |    0.19 | polys.TimePREM_LinearDenseQuadraticGCD.time_op(5, 'sparse')          |
| -        | 285±0.8μs            | 66.2±0.4μs                     |    0.23 | polys.TimePREM_QuadraticNonMonicGCD.time_op(1, 'sparse')             |
| -        | 3.46±0.02ms          | 399±3μs                        |    0.12 | polys.TimePREM_QuadraticNonMonicGCD.time_op(3, 'dense')              |
| -        | 4.02±0.01ms          | 291±2μs                        |    0.07 | polys.TimePREM_QuadraticNonMonicGCD.time_op(3, 'sparse')             |
| -        | 6.99±0.1ms           | 1.29±0.01ms                    |    0.18 | polys.TimePREM_QuadraticNonMonicGCD.time_op(5, 'dense')              |
| -        | 8.68±0.09ms          | 862±6μs                        |    0.1  | polys.TimePREM_QuadraticNonMonicGCD.time_op(5, 'sparse')             |
| -        | 5.04±0.02ms          | 3.08±0.03ms                    |    0.61 | polys.TimeSUBRESULTANTS_LinearDenseQuadraticGCD.time_op(2, 'sparse') |
| -        | 12.0±0.03ms          | 6.82±0.03ms                    |    0.57 | polys.TimeSUBRESULTANTS_LinearDenseQuadraticGCD.time_op(3, 'dense')  |
| -        | 22.2±0.07ms          | 9.10±0.02ms                    |    0.41 | polys.TimeSUBRESULTANTS_LinearDenseQuadraticGCD.time_op(3, 'sparse') |
| -        | 5.25±0.02ms          | 878±3μs                        |    0.17 | polys.TimeSUBRESULTANTS_QuadraticNonMonicGCD.time_op(1, 'sparse')    |
| -        | 12.6±0.04ms          | 7.14±0.02ms                    |    0.57 | polys.TimeSUBRESULTANTS_QuadraticNonMonicGCD.time_op(2, 'sparse')    |
| -        | 101±0.6ms            | 26.3±0.09ms                    |    0.26 | polys.TimeSUBRESULTANTS_QuadraticNonMonicGCD.time_op(3, 'dense')     |
| -        | 166±0.6ms            | 54.3±0.7ms                     |    0.33 | polys.TimeSUBRESULTANTS_QuadraticNonMonicGCD.time_op(3, 'sparse')    |
| -        | 358±2μs              | 219±2μs                        |    0.61 | polys.TimeSUBRESULTANTS_SparseGCDHighDegree.time_op(1, 'sparse')     |
| -        | 4.24±0.03ms          | 849±6μs                        |    0.2  | polys.TimeSUBRESULTANTS_SparseGCDHighDegree.time_op(3, 'dense')      |
| -        | 5.24±0.05ms          | 388±1μs                        |    0.07 | polys.TimeSUBRESULTANTS_SparseGCDHighDegree.time_op(3, 'sparse')     |
| -        | 19.8±0.06ms          | 2.84±0.01ms                    |    0.14 | polys.TimeSUBRESULTANTS_SparseGCDHighDegree.time_op(5, 'dense')      |
| -        | 22.8±0.1ms           | 643±4μs                        |    0.03 | polys.TimeSUBRESULTANTS_SparseGCDHighDegree.time_op(5, 'sparse')     |
| -        | 479±3μs              | 138±1μs                        |    0.29 | polys.TimeSUBRESULTANTS_SparseNonMonicQuadratic.time_op(1, 'sparse') |
| -        | 4.85±0.03ms          | 622±8μs                        |    0.13 | polys.TimeSUBRESULTANTS_SparseNonMonicQuadratic.time_op(3, 'dense')  |
| -        | 5.30±0.04ms          | 141±2μs                        |    0.03 | polys.TimeSUBRESULTANTS_SparseNonMonicQuadratic.time_op(3, 'sparse') |
| -        | 12.9±0.05ms          | 1.31±0ms                       |    0.1  | polys.TimeSUBRESULTANTS_SparseNonMonicQuadratic.time_op(5, 'dense')  |
| -        | 14.0±0.2ms           | 144±2μs                        |    0.01 | polys.TimeSUBRESULTANTS_SparseNonMonicQuadratic.time_op(5, 'sparse') |
| -        | 135±0.6μs            | 73.7±0.4μs                     |    0.55 | solve.TimeMatrixOperations.time_rref(3, 0)                           |
| -        | 249±0.7μs            | 86.4±0.2μs                     |    0.35 | solve.TimeMatrixOperations.time_rref(4, 0)                           |
| -        | 24.8±0.2ms           | 10.3±0.06ms                    |    0.42 | solve.TimeSolveLinSys189x49.time_solve_lin_sys                       |
| -        | 28.7±0.2ms           | 15.4±0.2ms                     |    0.54 | solve.TimeSparseSystem.time_linsolve_Aaug(20)                        |
| -        | 55.0±0.4ms           | 25.4±0.1ms                     |    0.46 | solve.TimeSparseSystem.time_linsolve_Aaug(30)                        |
| -        | 28.4±0.2ms           | 15.3±0.2ms                     |    0.54 | solve.TimeSparseSystem.time_linsolve_Ab(20)                          |
| -        | 54.5±0.3ms           | 24.6±0.07ms                    |    0.45 | solve.TimeSparseSystem.time_linsolve_Ab(30)                          |

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

@oscarbenjamin
Copy link
Collaborator

Looks good. Thanks

@oscarbenjamin oscarbenjamin merged commit e7fb271 into sympy:master Apr 4, 2024
48 checks passed
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Projects
None yet
Development

Successfully merging this pull request may close these issues.

Add copy=None to __array__ for compatibility with future numpy
4 participants