Keeping Your Code Squeaky Clean: Utilizing assert_no_warnings() in NumPy Tests

• Context Manager
  testing.assert_no_warnings() can also be used as a context manager with a with statement. This is a convenient way to ensure no warnings are raised within a specific code block.

• Functionality
  You pass a callable object (like a function) as an argument to testing.assert_no_warnings(). This function then executes the provided code and checks if any warnings are emitted during the execution.

• Purpose
  This function ensures that the code you're testing doesn't generate any warnings when executed. This helps maintain clean test results and identify potential issues that might arise due to deprecated functions or unexpected behavior.

import numpy.testing as npt

def my_function(x):
  # Code that normally wouldn't raise warnings

npt.assert_no_warnings(my_function, 5)  # Test if my_function with argument 5 raises no warnings

In this example, npt.assert_no_warnings() executes my_function(5) and raises an error if any warnings are generated during the execution.

Key Points

• It can be used as a function or within a with statement for code block testing.
• This function helps ensure your code runs without warnings, indicating potential problems.

---

Example 1: Testing a function with no warnings

import numpy.testing as npt

def my_function(x):
  return x * 2  # Simple function without warnings

# Test if my_function raises no warnings with argument 10
npt.assert_no_warnings(my_function, 10)

This example defines a simple function my_function that doesn't generate any warnings. We use npt.assert_no_warnings to test if calling it with argument 10 executes without warnings.

Example 2: Using a context manager with with statement

import numpy.testing as npt
import warnings

def my_function_with_warning(x):
  warnings.warn("This is a warning message", RuntimeWarning)
  return x * 3

# Test if the code block within the with statement raises no warnings
with npt.assert_no_warnings():
  result = my_function_with_warning(5)
  assert result == 15  # Additional assertion for the function's output

# This line will execute normally as the context manager is closed
print("No warnings raised within the block")

Here, my_function_with_warning deliberately generates a runtime warning. We use a with statement and npt.assert_no_warnings as a context manager. The code inside the with block (including my_function_with_warning) will be executed, and the test will fail if any warnings are emitted. The assert statement after the with block verifies the function's output.

import numpy.testing as npt
import warnings

def deprecated_function(x):
  warnings.warn("This function is deprecated", DeprecationWarning)
  return x**2

# This approach is not recommended for general testing 
with warnings.catch_warnings():
  warnings.simplefilter("ignore", DeprecationWarning)
  npt.assert_no_warnings(deprecated_function, 4)

---

Using warnings.catch_warnings and Manual Checking

• Use warnings.catch_warnings as a context manager:
• Import warnings: import warnings

with warnings.catch_warnings() as record:
  warnings.simplefilter("default")  # Set the filter to capture all warnings by default
  # Your code that might generate warnings
  for item in record:
    print(f"Warning message: {item.message}")

This approach captures all warnings emitted within the with block and stores them in the record list. You can then iterate through the list and manually check the warning messages. This gives you more control over handling specific warnings.

Checking for Specific Warnings

• Use the warnings.warn function with a custom message and category:
• Import warnings: import warnings

def my_function(x):
  if x < 0:
    warnings.warn("Input value cannot be negative", RuntimeWarning)
  return x * 2

# Test if the warning is raised for a negative input
with warnings.catch_warnings() as record:
  warnings.simplefilter("always")  # Ensure all warnings are captured
  my_function(-3)
  assert len(record) == 1  # Assert that one warning was raised
  assert record[0].message.args[0] == "Input value cannot be negative"  # Check the warning message

Here, we use warnings.warn to generate a custom warning within the function. The test captures all warnings and verifies if the specific message and category (RuntimeWarning in this case) match your expectations.

Mocking Warnings (for advanced testing)

• Use libraries like pytest-mock to mock warnings during testing. This allows you to define expected warning messages and behavior in your tests.

• Mocking warnings is an advanced technique for unit testing frameworks like pytest where you have more control over the testing environment.
• If you need more control or want to verify specific warnings, consider warnings.catch_warnings and manual checking or checking for specific warnings.
• testing.assert_no_warnings is a good starting point for checking if your code runs without unexpected warnings.