update assign_units and add kcal_per_h

[chaoming0625]

This pull request includes several changes to the brainunit library, focusing on improvements to type handling, unit assignment, and documentation corrections.

Type Handling and Unit Assignment Improvements:

• brainunit/_base.py: Added cast import for type casting and introduced CallableAssignUnit and Missing classes to improve the handling of callable units and missing values. Enhanced the assign_units decorator to support partial assignments and improved error messages for type mismatches. [1] [2] [3] [4]

New Unit Constants:

• brainunit/_unit_constants.py: Added a new unit constant kcal_per_h for kilocalories per hour. [1] [2]

Documentation Corrections:

• docs/index.rst: Corrected a typo in the filename from elinstein_operations.ipynb to einstein_operations.ipynb.
• docs/mathematical_functions/einstein_operations.ipynb: Updated the title and links within the notebook to reflect the correct spelling of "Einstein".

Summary by Sourcery

Improve the assign_units decorator to support partial unit assignments and add a new unit constant for kilocalories per hour (kcal_per_h). Also fix a typo in the documentation.

New Features:

• Add kcal_per_h unit constant.

Tests:

• Improve type handling and error messages in assign_units decorator.

[sourcery-ai]

Reviewer's Guide by Sourcery

This pull request enhances the brainunit library by improving type handling within the assign_units decorator, introducing a new unit constant kcal_per_h, and correcting documentation typos.

Sequence diagram for enhanced assign_units decorator flow

sequenceDiagram
    participant C as Client Code
    participant A as assign_units
    participant R as _remove_unit

    C->>A: Call with function and units
    A->>A: Parse function arguments
    loop For each argument
        alt Argument has unit specification
            A->>R: Remove/convert units
            R-->>A: Return processed value
        end
    end
    A->>A: Execute original function
    alt Result units specified
        A->>A: Validate result structure
        A->>A: Assign result units
    end
    A-->>C: Return result

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Class diagram for new unit assignment types

classDiagram
    class CallableAssignUnit {
        +without_result_units: Callable
        +__call__(*args, **kwargs)
    }
    class Missing {
    }
    class Callable
    CallableAssignUnit --|> Callable
    note for CallableAssignUnit "New class for handling
callable units"
    note for Missing "Represents missing
values in assignments"

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Class diagram for unit constants update

classDiagram
    class UnitConstants {
        +watt: Unit
        +horsepower: Unit
        +kcal_per_h: Unit
    }
    note for UnitConstants "Added kcal_per_h
with conversion factor
1.162222 watts"

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File-Level Changes

Change	Details	Files
Improved type handling and unit assignment logic within the assign_units decorator. This includes support for partial assignments and enhanced error messages for type mismatches.	Added CallableAssignUnit and Missing classes to handle callable units and missing values. Improved error messages for type mismatches. Added support for partial unit assignments using the partial function. Refactored the decorator logic to handle various input types and unit assignments more robustly. Added more comprehensive type checking to ensure correct unit assignments and prevent unexpected behavior.	brainunit/_base.py
Introduced a new unit constant kcal_per_h representing kilocalories per hour.	Added kcal_per_h constant. Defined the kcal_per_h unit in terms of watts using the Unit.create method.	brainunit/_unit_constants.py
Corrected a typo in the documentation.	Corrected the filename from elinstein_operations.ipynb to einstein_operations.ipynb in docs/index.rst. Updated the title and links in einstein_operations.ipynb to reflect the correct spelling of "Einstein".	docs/index.rst docs/mathematical_functions/einstein_operations.ipynb

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[sourcery-ai]

Hey @chaoming0625 - I've reviewed your changes and they look great!

Here's what I looked at during the review

• 🟢 General issues: all looks good
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟢 Complexity: all looks good
• 🟢 Documentation: all looks good

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