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Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

- Low writing fluency that does not affect understanding.

- Incorrect version numbers, including software package names and version numbers on the UI.

Usability

- Usability:

- Incorrect or missing key steps.

- Missing main function descriptions, keyword explanation, necessary prerequisites, or precautions.

- Ambiguous descriptions, unclear reference, or contradictory context.

- Unclear logic, such as missing classifications, items, and steps.

Correctness

- Correctness:

- Technical principles, function descriptions, supported platforms, parameter types, or exceptions inconsistent with that of software implementation.

- Incorrect schematic or architecture diagrams.

- Incorrect commands or command parameters.

- Incorrect code.

- Commands inconsistent with the functions.

- Wrong screenshots.

- Sample code running error, or running results inconsistent with the expectation.

Risk Warnings

- Risk Warnings:

- Lack of risk warnings for operations that may damage the system or important data.

Content Compliance

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- Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions.

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mindspore.ops.trapz

View Source On Gitee
mindspore.ops.trapz(y, x=None, *, dx=1.0, dim=- 1)[source]

Compute the trapezoidal rule along dim.

The spacing between elements is specified by the tensor x or the scalar dx , default 1 .

y(x)dx
Parameters
  • y (Tensor) – The input tensor to integrate.

  • x (Tensor, optional) – If specified, defines spacing between values.

Keyword Arguments
  • dx (float, optional) – The spacing between elements. Default 1.0 . If x is specified, dx does not take effect.

  • dim (int, optional) – The dimension along which to compute the trapezoidal rule. Default -1 .

Returns

Tensor

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> y = mindspore.tensor([[1., 2., 3.], [2., 3., 4.], [3., 2., 1.]])
>>> # case 1: Integrate over a regular grid, with spacing 1.
>>> output = mindspore.ops.trapz(y, dx=1.)
>>> print(output)
[4. 6. 4.]
>>>
>>> # case 2: Integrate over an irregular grid.
>>> x = mindspore.tensor([[1, 2, 3], [1, 3, 5], [1, 4, 7]])
>>> output = mindspore.ops.trapz(y, x)
>>> print(output)
[ 4. 12. 12.]