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Functional AssessmentVerdict: ✅ Completed
🧠 User Story ID: TDRS-001-A — NumPy Educational Suite📝 Feature CompletenessThe Requirement was.. The system must include eight new Python programs (P01-P08) demonstrating NumPy array creation, attribute inspection, manipulation, and mathematical operations. This is what is built... Implemented eight Python scripts (P01-P08) covering the full range of required NumPy functionalities including array generation, manipulation, and character/math operations. 📊 Implementation Status
✅ Completed Components
🧠 User Story ID: TDRS-001-B — Directory Streamlining and Legacy Removal📝 Feature CompletenessThe Requirement was.. Remove sixteen legacy scripts (P31-P80) from the Test2 directory to focus the scope on numerical processing. This is what is built... Successfully removed 16 unrelated legacy scripts including data structures, multithreading, and utility scripts. 📊 Implementation Status
✅ Completed Components
🎯 Conclusion & Final AssessmentImportant 🟢 Completed Features: Key completed features include the introduction of eight NumPy fundamental programs (P01-P08) covering array creation, attribute inspection, manipulation, and mathematical/string operations. Additionally, sixteen legacy scripts (P31-P80) were successfully removed to streamline the directory. |
⚙️ DevOps and Release Automation🟢 Status: Passed🌟 Excellent work! Your code passed the DevOps review. |
🔍 Technical Quality Assessment📋 SummaryWe are updating our internal technical library to focus on modern data processing tools (NumPy) while removing 16 outdated and unrelated files. This cleanup makes our resources more relevant for current projects, though several of the new examples contain minor logic errors that could cause them to 'crash' or provide incorrect results if used as-is. 💼 Business Impact
🎯 Purpose & Scope
📊 Change AnalysisFiles by Category:
Impact Distribution:
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| File | Status | Description | Impact | Issues Detected |
|---|---|---|---|---|
Test2/P03_NumpyAttributes.py |
Added ( +24/ -0) | Introduced a new script demonstrating fundamental NumPy array attributes such as size, shape, ndim, and itemsize. | Low – This is an educational script with no direct impact on production systems, but it serves as a reference for NumPy usage. | 0 |
Test2/P31_SinglyLinkedList.py |
Deleted ( +0/ -95) | The file P31_SinglyLinkedList.py, which contained a standard implementation of a singly linked list, has been completely removed from the repository. | Low – The removal of this file simplifies the directory structure as part of a cleanup of older scripts, shifting focus toward NumPy-based examples as per the PR description. | 0 |
Test2/P08_NumpyArithmeticOperations.py |
Added ( +25/ -0) | Added a script demonstrating NumPy arithmetic operations including addition, subtraction, multiplication, division, and power functions. | Low – This is a standalone educational script. The logic error in array broadcasting will cause a runtime crash if executed as written. | 1 |
Test2/P04_ArrayFromNumericalRanges.py |
Added ( +43/ -0) | Added a Python script demonstrating NumPy array creation using arange, linspace, and logspace functions. | Low – This is an educational script with no direct impact on production systems, but it contains a typo in a function name comment. | 1 |
Test2/P05_NumpyArrayManipulation.py |
Added ( +83/ -0) | Added a new script demonstrating various NumPy array manipulation techniques including reshaping, flattening, transposing, and resizing. | Low – This is an educational script and does not affect production application logic. | 1 |
Test2/P32_Multithreading_Client.py |
Deleted ( +0/ -24) | The multithreading client script was removed as part of a directory cleanup and streamlining process. | Low – Removing legacy or unrelated scripts reduces technical debt and improves repository organization without affecting the new NumPy-focused functionality. | 0 |
Test2/P06_NumpyStringFunctions.py |
Added ( +43/ -0) | Added a new Python script demonstrating various NumPy string functions from the np.char module. | Low – This is an educational or utility script demonstrating NumPy features; it does not impact core application logic but contains a functional error in the join example. | 1 |
Test2/P01_Introduction.py |
Added ( +83/ -0) | Introduced a new Python script demonstrating fundamental NumPy concepts including array creation, reshaping, and attributes. | Low – This is an educational script and does not affect core application logic, but it contains a potential runtime error in the reshape logic. | 1 |
Test2/P32_Mutithreading_Server.py |
Deleted ( +0/ -38) | The file 'Test2/P32_Mutithreading_Server.py' has been completely removed as part of a directory cleanup and reorganization focusing on NumPy examples. | Low – Removing this file cleans up the repository by deleting an unrelated multithreading example, aligning with the PR's goal of streamlining the directory for NumPy content. | 0 |
Test2/P02_NumpyDataTypes.py |
Added ( +34/ -0) | Added a new Python script demonstrating NumPy data types and explicit type casting during array creation. | Low – This is an educational script with no direct impact on production systems, but it contains a minor implementation inefficiency. | 1 |
Test2/P70_SimpleProgressBar.py |
Deleted ( +0/ -18) | The file P70_SimpleProgressBar.py has been completely removed as part of a directory cleanup and transition to NumPy-focused examples. | Low – Removal of a utility script does not affect the new NumPy functionality being introduced. | 0 |
Test2/P33_DoublyLinkedList.py |
Deleted ( +0/ -108) | The file P33_DoublyLinkedList.py has been completely removed as part of a directory cleanup and transition to NumPy-focused examples. | Low – Removal of an older data structure implementation script. This aligns with the PR's goal of streamlining the directory, but ensures that any dependencies on this specific implementation are now broken. | 0 |
Test2/P34_Stack.py |
Deleted ( +0/ -58) | The file P34_Stack.py, which contained a Python implementation of a Stack data structure, has been completely removed as part of a directory cleanup and transition to NumPy-focused examples. | Low – The removal of this script is part of a planned cleanup of older, unrelated scripts in the Test2 directory. It does not negatively impact the new NumPy-focused functionality being introduced. | 0 |
Test2/P72_PythonLambda.py |
Deleted ( +0/ -33) | The file P72_PythonLambda.py has been deleted as part of a directory cleanup and reorganization focused on NumPy examples. | Low – The removal of this script is part of a planned cleanup of older utility scripts and does not affect the new NumPy-focused functionality. | 0 |
Test2/P71_PythonUnittest.py |
Deleted ( +0/ -44) | The file 'Test2/P71_PythonUnittest.py' has been completely removed as part of a directory cleanup and transition to NumPy-focused content. | Low – Removal of an older utility script does not affect the new NumPy functionality being introduced. | 0 |
Test2/P73_SimplePythonEncryption.py |
Deleted ( +0/ -27) | The file 'Test2/P73_SimplePythonEncryption.py' has been completely removed as part of a directory cleanup and restructuring effort focusing on NumPy examples. | Low – Removing this file eliminates an older RSA encryption example. Since the PR intent is to streamline the directory for NumPy content, this deletion is consistent with the stated goals and does not negatively impact the new functional focus. | 0 |
Test2/P74_PythonGenerators.py |
Deleted ( +0/ -18) | The file P74_PythonGenerators.py has been completely removed as part of a directory cleanup to focus on NumPy examples. | Low – Removal of an educational script on Python generators. This does not affect the new NumPy functionality being introduced. | 0 |
Test2/P75_TicTacToe.py |
Deleted ( +0/ -75) | The file P75_TicTacToe.py has been removed as part of a directory cleanup to focus on NumPy-related examples. | Low – Removing an unrelated script does not affect the core functionality of the new NumPy programs being introduced. | 0 |
Test2/P77_FileSearching.py |
Deleted ( +0/ -51) | The file 'Test2/P77_FileSearching.py' has been completely removed as part of a directory cleanup and reorganization focusing on NumPy examples. | Low – The removal of this utility script streamlines the repository but does not affect the newly added NumPy functionality. | 0 |
Test2/P78_HashTable.py |
Deleted ( +0/ -40) | The file P78_HashTable.py has been removed as part of a directory cleanup to focus on NumPy-related content. | Low – Removal of an older data structure example script does not affect the new NumPy-focused functionality. | 0 |
Test2/P80_SQLAlchemyTutorial.py |
Deleted ( +0/ -61) | The file 'Test2/P80_SQLAlchemyTutorial.py' has been completely removed as part of a directory cleanup to focus on NumPy examples. | Low – Removal of an educational tutorial script does not affect application functionality, as it was a standalone example. | 0 |
Test2/P76_PythonFTP.py |
Deleted ( +0/ -46) | The file P76_PythonFTP.py has been completely removed as part of a directory cleanup to focus on NumPy examples. | Low – The removal of this legacy script is intentional and aligns with the PR's goal of streamlining the directory. | 0 |
Test2/P79_SimplePythonKeylogger.py |
Deleted ( +0/ -49) | The file P79_SimplePythonKeylogger.py has been completely removed from the repository. | Low – The removal of this script reduces the overall codebase size and removes a utility that was deemed unrelated to the new NumPy focus of the directory. | 0 |
Test2/P07_NumpyMathematicalFunctions.py |
Added ( +30/ -0) | Added a script demonstrating NumPy mathematical functions including trigonometry, inverse trigonometry, and rounding. | Low – This is a standalone educational script. The logic errors identified affect the accuracy of the demonstrated mathematical concepts. | 1 |
![appmod-pr-genie[bot]](https://avatars.githubusercontent.com/u/178300120?s=60&v=4){kind=small}
| secondArray = np.arange(4) | ||
| print(secondArray) | ||
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| # adding above two arrays (NOTE: array shapes should be same) |
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Incompatible Array Shapes for Arithmetic
JAS - Just a suggestion
I see you're trying to perform arithmetic between a (3, 4) array and a (4,) array. While NumPy broadcasting usually handles this, your comment on line 11 incorrectly states that shapes should be the same. More importantly, if the intended operation was element-wise addition of two identical shapes, secondArray needs to be reshaped or defined differently to avoid confusion or potential broadcasting errors in more complex logic.
| # adding above two arrays (NOTE: array shapes should be same) | |
| # adding above two arrays using NumPy broadcasting |
| myArray = np.linspace(10, 20, 5, endpoint = False) | ||
| print(myArray) # [ 10. 12. 14. 16. 18.] | ||
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| # ndarray.lopspace returns an ndarray object that contains the numbers that are evenly spaced |
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Typo in Function Name Comment
JAS - Just a suggestion
I noticed a small typo in the comment here. It says ndarray.lopspace, but the actual NumPy function is np.logspace. While it's just a comment, fixing it will help avoid confusion for anyone learning from this script.
| # ndarray.lopspace returns an ndarray object that contains the numbers that are evenly spaced | |
| # ndarray.logspace returns an ndarray object that contains the numbers that are evenly spaced |
| # [[1 2 3] | ||
| # [4 5 6]] | ||
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| print(np.append(myArray, [7, 8, 9])) |
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Inconsistent Axis Handling in np.append
JAS - Just a suggestion
I notice we're using np.append on a 2D array without specifying an axis. By default, this flattens the array, which might not be what we expect when working with matrices. If we want to maintain the 2D structure, we should specify axis=0 for rows or axis=1 for columns.
| print(np.append(myArray, [7, 8, 9])) | |
| print(np.append(myArray, [], axis=0)) |
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| # numpy.char.join(): This method returns a string in which the individual characters are joined by | ||
| # separator character specified. | ||
| print(np.char.join(':','dmy')) # d:m:y |
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Incorrect Argument Order in np.char.join
JAS - Just a suggestion
I noticed a small mix-up in the np.char.join call. In NumPy, the separator comes first and the target string/array comes second, but the current code passes them in an order that might be confusing or lead to unexpected results if used with arrays. While it works for a single string, following the standard sep, seq pattern is safer.
| print(np.char.join(':','dmy')) # d:m:y | |
| print(np.char.join(':', 'dmy')) # d:m:y |
| # [10 11 12 13 14] | ||
| # [15 16 17 18 19]] | ||
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| myArray = myArray.reshape(10, 2) |
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Potential Incompatible Reshape Operation
I noticed that we are reshaping myArray to (10, 2) on line 35. While this works for the current array size of 20, if the preceding arange or reshape logic changes, this hardcoded value will cause a ValueError. It's safer to use -1 for one of the dimensions to let NumPy calculate it automatically based on the total number of elements.
| myArray = myArray.reshape(10, 2) | |
| myArray = myArray.reshape(-1, 2) |
Reasons & Gaps
Reasons
- Hardcoded dimensions in reshape() can cause runtime failures
- Incompatible shapes lead to ValueError exceptions
- Using automatic dimension inference improves code robustness
Gaps
- The script is currently static, so the error only occurs if the array size is modified in earlier lines.
- The developer's intent is educational, where hardcoded values are common for clarity.
| myArray = np.arange(10) | ||
| print(myArray) # [0 1 2 3 4 5 6 7 8 9] | ||
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| myArray = np.array(myArray, dtype = np.float32) |
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Inefficient Array Re-creation for Type Casting
JAS - Just a suggestion
I notice we're using np.array(myArray, dtype=...) to change the data type of an existing array. While this works, it actually creates a full copy of the array in memory. A more efficient and idiomatic way to do this in NumPy is using the .astype() method, which is specifically designed for casting and is generally faster.
| myArray = np.array(myArray, dtype = np.float32) | |
| myArray = myArray.astype(np.float32) |
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| # for computing inverse of trigonometric functions | ||
| sine = np.sin(angles * np.pi/180) | ||
| sineinv = np.arcsin(sine) |
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Inaccurate Inverse Trigonometry Logic
I notice we're calculating the inverse sine of a range that includes 360 degrees. Since sin(360°) is 0, arcsin(0) returns 0°, not the original 360°. This might be confusing for someone learning how these functions map back to angles. We should probably limit the input range to [-90°, 90°] if the goal is to demonstrate a perfect round-trip conversion.
Reasons & Gaps
Reasons
- The np.arcsin() function returns values only within the principal range of [-π/2, π/2]
- Trigonometric functions are periodic, leading to information loss during inverse operations
- Floating point precision errors near boundaries can cause unexpected results in inverse functions
Gaps
- The specific educational goal of the script is not explicitly stated in the PR description
- User might intentionally be showing the periodic nature of trigonometric functions
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| Coding Standard | Violations | Citation |
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Variable naming convention |
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JAS - Just a suggestion
🗂️ Test2/P02_NumpyDataTypes.py
| Coding Standard | Violations | Citation |
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Variable naming convention |
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JAS - Just a suggestion
🗂️ Test2/P03_NumpyAttributes.py
| Coding Standard | Violations | Citation |
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Variable naming convention |
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JAS - Just a suggestion
🗂️ Test2/P04_ArrayFromNumericalRanges.py
| Coding Standard | Violations | Citation |
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Variable naming convention |
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JAS - Just a suggestion
🗂️ Test2/P05_NumpyArrayManipulation.py
| Coding Standard | Violations | Citation |
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Variable naming convention |
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JAS - Just a suggestion
🗂️ Test2/P06_NumpyStringFunctions.py
| Coding Standard | Violations | Citation |
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Variable naming convention |
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JAS - Just a suggestion
🗂️ Test2/P07_NumpyMathematicalFunctions.py
| Coding Standard | Violations | Citation |
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Variable naming convention |
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JAS - Just a suggestion
🗂️ Test2/P08_NumpyArithmeticOperations.py
| Coding Standard | Violations | Citation |
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Variable naming convention |
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JAS - Just a suggestion
🗂️ Test2/P32_Multithreading_Client.py
| Coding Standard | Violations | Citation |
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Variable naming convention |
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JAS - Just a suggestion
🗂️ Test2/P34_Stack.py
| Coding Standard | Violations | Citation |
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Variable naming convention |
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JAS - Just a suggestion
| import numpy as np | ||
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| # we have a function arange() which makes an array of the specified dimension. Example: | ||
| myArray = np.arange(20) |
There was a problem hiding this comment.
JAS - Just a suggestion
Non-Standard Variable Abbreviation
The variable name 'myArray' uses a generic term 'Array'. While functional, it is better to use a more descriptive name like 'numbers_array' or 'sequence_array' to reflect the data it holds.
| myArray = np.arange(20) | |
| numbers_array = np.arange(20) |
| import numpy as np | ||
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| # while creating a numpy array, any data type from above can be explicitly specified. | ||
| myArray = np.arange(10) |
There was a problem hiding this comment.
JAS - Just a suggestion
Non-Descriptive Variable Name
The variable name 'myArray' uses a generic term and camelCase, which is non-standard for Python. A more descriptive snake_case name like 'numbers_array' or 'sequence_array' would improve clarity.
| myArray = np.arange(10) | |
| numbers_array = np.arange(10) |
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| import numpy as np | ||
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| myArray = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) |
There was a problem hiding this comment.
JAS - Just a suggestion
Variable Naming Improvement
The variable name 'myArray' uses a generic 'my' prefix and camelCase, which is non-standard for Python. Renaming it to 'matrix' or 'data_array' in snake_case would improve clarity and consistency.
| myArray = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) | |
| matrix = np.array([,,]) |
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| # ndarray.arange(start, stop, step, dtype) | ||
| # Creates a numpy array from 1 to 20 | ||
| myArray = np.arange(1, 21) |
There was a problem hiding this comment.
JAS - Just a suggestion
Variable Naming Improvement
The variable name 'myArray' uses a generic prefix and camelCase, which is non-standard for Python. Renaming it to something more descriptive like 'numerical_range_array' improves clarity.
| myArray = np.arange(1, 21) | |
| numerical_range_array = np.arange(1, 21) |
| import numpy as np | ||
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| # numpy.reshape(array_to_reshape, tuple_of_new_shape) gives new shape (dimension) to our array | ||
| myArray = np.arange(0, 30, 2) |
There was a problem hiding this comment.
JAS - Just a suggestion
Variable Naming Improvement
The variable name 'myArray' uses a generic 'my' prefix which adds no semantic value. Renaming it to 'even_numbers_sequence' or 'sequence_array' would better describe the data it contains.
| myArray = np.arange(0, 30, 2) | |
| even_numbers_sequence = np.arange(0, 30, 2) |
| import numpy as np | ||
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| abc = ['abc'] | ||
| xyz = ['xyz'] |
There was a problem hiding this comment.
JAS - Just a suggestion
Non-Descriptive Variable Name
The variable name 'xyz' is a generic placeholder. Replacing it with a more descriptive name like 'second_word_list' or 'suffix_array' would improve the readability of the string operations.
| xyz = ['xyz'] | |
| second_word_list = ['xyz'] |
| print(np.tan(angles * np.pi/180)) | ||
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| # for computing inverse of trigonometric functions | ||
| sine = np.sin(angles * np.pi/180) |
There was a problem hiding this comment.
JAS - Just a suggestion
Single-Character Variable Name
The variable name 'sine' is partially descriptive but 'sine_values' would better represent the array of results. However, 'sine' itself is a common mathematical term and is acceptable in this context.
| sine = np.sin(angles * np.pi/180) | |
| sine_values = np.sin(angles * np.pi/180) |
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| # for computing inverse of trigonometric functions | ||
| sine = np.sin(angles * np.pi/180) | ||
| sineinv = np.arcsin(sine) |
There was a problem hiding this comment.
JAS - Just a suggestion
Cryptic Abbreviation in Variable Name
The variable name 'sineinv' uses a cryptic abbreviation 'inv'. Expanding this to 'inverse_sine' or 'sine_inverse' would significantly improve readability and follow standard naming conventions.
| sineinv = np.arcsin(sine) | |
| inverse_sine = np.arcsin(sine) |
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| import numpy as np | ||
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| firstArray = np.arange(12).reshape(3, 4) |
There was a problem hiding this comment.
JAS - Just a suggestion
Variable Naming Improvement
The variable name 'firstArray' is functional but could be more expressive. In Python, 'snake_case' is the standard convention for variable names.
| firstArray = np.arange(12).reshape(3, 4) | |
| first_array = np.arange(12).reshape(3, 4) |
| firstArray = np.arange(12).reshape(3, 4) | ||
| print(firstArray) | ||
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| secondArray = np.arange(4) |
There was a problem hiding this comment.
JAS - Just a suggestion
Variable Naming Improvement
The variable name 'secondArray' uses camelCase. Following Python's PEP 8 style guide, 'snake_case' is preferred for variable names.
| secondArray = np.arange(4) | |
| second_array = np.arange(4) |
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