Implement matrix truncation for latex to enhance the display of large matrices

[mohamedrezk122]

References to other Issues or PRs

Fixes #16251

Brief description of what is fixed or changed

The _print_matrix_contents method now includes options for compactifying and setting maximum visible rows and columns, providing a more readable representation. This improvement prevents printing slowdowns for large matrices and improves overall user experience.
Basically, the method looks if the number of rows or number of columns exceeds certain thresholds, which are predefined in the _settingsdictionary under the names mat_visible_rows and mat_visible_cols respectively, with 15 as a default value. If so, the method avoids iterating over all elements of the matrix and adds dots to imply compaction.

The compaction feature is also predefined in _settings dictionary with default value True to avoid large matrix printing slowdown.

Here is the behavior after enabling the compaction feature:

And here is the behavior with it:

Also, compaction of rows and cols are independent based on the dimension of each. The following two examples illustrates this better:

Finally, if the matrix dimensions are small (below thresholds) in both axes then no compaction takes place like so :

Other comments

Release Notes

• printing
  • Added a truncation feature to latex for large matrices printing.

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• printing
  • Added a truncation feature to latex for large matrices printing. (#26284 by @mohamedrezk122)

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Fixes #16251 


#### Brief description of what is fixed or changed
The `_print_matrix_contents` method now includes options for compactifying and setting maximum visible rows and columns, providing a more readable representation. This improvement prevents printing slowdowns for large matrices and improves overall user experience. 
Basically, the method looks if the number of rows or number of columns exceeds certain thresholds, which are predefined in the `_settings`dictionary under the names `mat_visible_rows` and `mat_visible_cols` respectively, with `15` as a default value. If so, the method avoids iterating over all elements of the matrix and adds dots to imply compaction.

The compaction feature is also predefined in `_settings` dictionary with default value `True` to avoid large matrix printing slowdown.    

**Here is the behavior after enabling the compaction feature:**  
![image](https://github.com/sympy/sympy/assets/59314933/00021ae5-425e-4295-a450-a29fb14242a0)
**And here is the behavior with it:**
![image](https://github.com/sympy/sympy/assets/59314933/79be328d-d597-46f7-9b77-9c528f0d0d31)

**Also, compaction of rows and cols are independent based on the dimension of each. The following two examples illustrates this better:**

![image](https://github.com/sympy/sympy/assets/59314933/3b9fe932-d723-4cd9-870e-a787cef5691d)
![image](https://github.com/sympy/sympy/assets/59314933/b8c1fa3e-2fa0-4ef0-acb1-aa987088d9ef)

**Finally, if the matrix dimensions are small (below thresholds) in both axes then no compaction takes place like so :**
![image](https://github.com/sympy/sympy/assets/59314933/61cbeea8-27a0-4d01-a26c-46179cf01c64)



#### Other comments


#### Release Notes

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* printing
  * Added a truncation feature to latex  for large matrices printing.


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[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)

| Change   | Before [a00718ba]    | After [24792d5f]    |   Ratio | Benchmark (Parameter)                                                |
|----------|----------------------|---------------------|---------|----------------------------------------------------------------------|
| -        | 68.6±0.5ms           | 44.3±0.2ms          |    0.65 | integrate.TimeIntegrationRisch02.time_doit(10)                       |
| -        | 66.5±1ms             | 43.8±0.2ms          |    0.66 | integrate.TimeIntegrationRisch02.time_doit_risch(10)                 |
| +        | 18.5±0.5μs           | 30.0±0.5μs          |    1.62 | integrate.TimeIntegrationRisch03.time_doit(1)                        |
| -        | 5.20±0.02ms          | 2.90±0.03ms         |    0.56 | logic.LogicSuite.time_load_file                                      |
| -        | 72.2±0.6ms           | 28.4±0.2ms          |    0.39 | polys.TimeGCD_GaussInt.time_op(1, 'dense')                           |
| -        | 25.4±0.4ms           | 16.7±0.05ms         |    0.66 | polys.TimeGCD_GaussInt.time_op(1, 'expr')                            |
| -        | 73.0±0.9ms           | 28.6±0.2ms          |    0.39 | polys.TimeGCD_GaussInt.time_op(1, 'sparse')                          |
| -        | 249±2ms              | 123±0.9ms           |    0.49 | polys.TimeGCD_GaussInt.time_op(2, 'dense')                           |
| -        | 252±3ms              | 123±2ms             |    0.49 | polys.TimeGCD_GaussInt.time_op(2, 'sparse')                          |
| -        | 643±9ms              | 364±8ms             |    0.57 | polys.TimeGCD_GaussInt.time_op(3, 'dense')                           |
| -        | 643±9ms              | 368±2ms             |    0.57 | polys.TimeGCD_GaussInt.time_op(3, 'sparse')                          |
| -        | 483±8μs              | 282±2μs             |    0.58 | polys.TimeGCD_LinearDenseQuadraticGCD.time_op(1, 'dense')            |
| -        | 1.75±0ms             | 1.04±0.01ms         |    0.59 | polys.TimeGCD_LinearDenseQuadraticGCD.time_op(2, 'dense')            |
| -        | 5.78±0.03ms          | 3.08±0.01ms         |    0.53 | polys.TimeGCD_LinearDenseQuadraticGCD.time_op(3, 'dense')            |
| -        | 448±2μs              | 225±1μs             |    0.5  | polys.TimeGCD_QuadraticNonMonicGCD.time_op(1, 'dense')               |
| -        | 1.44±0.02ms          | 654±20μs            |    0.46 | polys.TimeGCD_QuadraticNonMonicGCD.time_op(2, 'dense')               |
| -        | 4.80±0.09ms          | 1.63±0.01ms         |    0.34 | polys.TimeGCD_QuadraticNonMonicGCD.time_op(3, 'dense')               |
| -        | 374±2μs              | 206±0.8μs           |    0.55 | polys.TimeGCD_SparseGCDHighDegree.time_op(1, 'dense')                |
| -        | 2.39±0.05ms          | 1.22±0.01ms         |    0.51 | polys.TimeGCD_SparseGCDHighDegree.time_op(3, 'dense')                |
| -        | 9.84±0.1ms           | 4.30±0.03ms         |    0.44 | polys.TimeGCD_SparseGCDHighDegree.time_op(5, 'dense')                |
| -        | 356±1μs              | 169±2μs             |    0.48 | polys.TimeGCD_SparseNonMonicQuadratic.time_op(1, 'dense')            |
| -        | 2.50±0.03ms          | 893±0.4μs           |    0.36 | polys.TimeGCD_SparseNonMonicQuadratic.time_op(3, 'dense')            |
| -        | 9.70±0.06ms          | 2.68±0.01ms         |    0.28 | polys.TimeGCD_SparseNonMonicQuadratic.time_op(5, 'dense')            |
| -        | 1.02±0.01ms          | 424±1μs             |    0.42 | polys.TimePREM_LinearDenseQuadraticGCD.time_op(3, 'dense')           |
| -        | 1.74±0.02ms          | 516±4μs             |    0.3  | polys.TimePREM_LinearDenseQuadraticGCD.time_op(3, 'sparse')          |
| -        | 5.85±0.03ms          | 1.79±0.01ms         |    0.31 | polys.TimePREM_LinearDenseQuadraticGCD.time_op(5, 'dense')           |
| -        | 8.35±0.03ms          | 1.52±0ms            |    0.18 | polys.TimePREM_LinearDenseQuadraticGCD.time_op(5, 'sparse')          |
| -        | 285±0.9μs            | 65.0±0.8μs          |    0.23 | polys.TimePREM_QuadraticNonMonicGCD.time_op(1, 'sparse')             |
| -        | 3.42±0.02ms          | 396±2μs             |    0.12 | polys.TimePREM_QuadraticNonMonicGCD.time_op(3, 'dense')              |
| -        | 3.92±0.03ms          | 282±2μs             |    0.07 | polys.TimePREM_QuadraticNonMonicGCD.time_op(3, 'sparse')             |
| -        | 6.98±0.08ms          | 1.26±0.01ms         |    0.18 | polys.TimePREM_QuadraticNonMonicGCD.time_op(5, 'dense')              |
| -        | 8.65±0.07ms          | 858±3μs             |    0.1  | polys.TimePREM_QuadraticNonMonicGCD.time_op(5, 'sparse')             |
| -        | 5.04±0.03ms          | 2.99±0.01ms         |    0.59 | polys.TimeSUBRESULTANTS_LinearDenseQuadraticGCD.time_op(2, 'sparse') |
| -        | 12.0±0.09ms          | 6.55±0.1ms          |    0.54 | polys.TimeSUBRESULTANTS_LinearDenseQuadraticGCD.time_op(3, 'dense')  |
| -        | 22.4±0.2ms           | 9.12±0.09ms         |    0.41 | polys.TimeSUBRESULTANTS_LinearDenseQuadraticGCD.time_op(3, 'sparse') |
| -        | 5.16±0.02ms          | 869±4μs             |    0.17 | polys.TimeSUBRESULTANTS_QuadraticNonMonicGCD.time_op(1, 'sparse')    |
| -        | 12.7±0.1ms           | 7.10±0.03ms         |    0.56 | polys.TimeSUBRESULTANTS_QuadraticNonMonicGCD.time_op(2, 'sparse')    |
| -        | 101±0.4ms            | 25.5±0.1ms          |    0.25 | polys.TimeSUBRESULTANTS_QuadraticNonMonicGCD.time_op(3, 'dense')     |
| -        | 165±2ms              | 54.3±0.1ms          |    0.33 | polys.TimeSUBRESULTANTS_QuadraticNonMonicGCD.time_op(3, 'sparse')    |
| -        | 174±3μs              | 111±0.9μs           |    0.64 | polys.TimeSUBRESULTANTS_SparseGCDHighDegree.time_op(1, 'dense')      |
| -        | 359±0.9μs            | 217±1μs             |    0.61 | polys.TimeSUBRESULTANTS_SparseGCDHighDegree.time_op(1, 'sparse')     |
| -        | 4.25±0.1ms           | 838±6μs             |    0.2  | polys.TimeSUBRESULTANTS_SparseGCDHighDegree.time_op(3, 'dense')      |
| -        | 5.25±0.08ms          | 384±2μs             |    0.07 | polys.TimeSUBRESULTANTS_SparseGCDHighDegree.time_op(3, 'sparse')     |
| -        | 19.7±0.2ms           | 2.81±0ms            |    0.14 | polys.TimeSUBRESULTANTS_SparseGCDHighDegree.time_op(5, 'dense')      |
| -        | 22.5±0.1ms           | 637±5μs             |    0.03 | polys.TimeSUBRESULTANTS_SparseGCDHighDegree.time_op(5, 'sparse')     |
| -        | 482±2μs              | 136±1μs             |    0.28 | polys.TimeSUBRESULTANTS_SparseNonMonicQuadratic.time_op(1, 'sparse') |
| -        | 4.51±0.1ms           | 623±3μs             |    0.14 | polys.TimeSUBRESULTANTS_SparseNonMonicQuadratic.time_op(3, 'dense')  |
| -        | 5.23±0.03ms          | 140±0.3μs           |    0.03 | polys.TimeSUBRESULTANTS_SparseNonMonicQuadratic.time_op(3, 'sparse') |
| -        | 13.1±0.06ms          | 1.28±0ms            |    0.1  | polys.TimeSUBRESULTANTS_SparseNonMonicQuadratic.time_op(5, 'dense')  |
| -        | 13.5±0.4ms           | 141±1μs             |    0.01 | polys.TimeSUBRESULTANTS_SparseNonMonicQuadratic.time_op(5, 'sparse') |
| -        | 132±3μs              | 75.5±0.3μs          |    0.57 | solve.TimeMatrixOperations.time_rref(3, 0)                           |
| -        | 249±1μs              | 88.4±0.4μs          |    0.35 | solve.TimeMatrixOperations.time_rref(4, 0)                           |
| -        | 24.0±0.06ms          | 10.0±0.06ms         |    0.42 | solve.TimeSolveLinSys189x49.time_solve_lin_sys                       |
| -        | 27.8±0.4ms           | 15.4±0.1ms          |    0.55 | solve.TimeSparseSystem.time_linsolve_Aaug(20)                        |
| -        | 54.4±0.2ms           | 24.4±0.4ms          |    0.45 | solve.TimeSparseSystem.time_linsolve_Aaug(30)                        |
| -        | 27.9±0.1ms           | 15.3±0.09ms         |    0.55 | solve.TimeSparseSystem.time_linsolve_Ab(20)                          |
| -        | 54.7±0.4ms           | 24.6±0.1ms          |    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).